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matthiasplappert/motion_classification | src/toolkit/hmm/impl_hmmlearn.py | 1 | 30715 | import itertools
import logging
import numpy as np
from sklearn.mixture.gmm import log_multivariate_normal_density, sample_gaussian
from sklearn.utils.extmath import logsumexp
import hmmlearn.hmm as impl
import hmmlearn.fhmmc as fhmmc
from hmmlearn.utils import normalize
from .base import (BaseHMM, transition_matrix, start_probabilities, estimate_normal_distribution_params)
def _new_model(n_states, transition_init, means, covars, covar_type, topology, n_iter, thresh, verbose):
# Generate transition matrix
if transition_init == 'uniform':
transitions = transition_matrix(n_states, topology, randomize=False)
pi = start_probabilities(n_states, topology, randomize=False)
elif transition_init == 'random':
transitions = transition_matrix(n_states, topology, randomize=True)
pi = start_probabilities(n_states, topology, randomize=True)
else:
raise ValueError('unknown initialization strategy %s' % transition_init)
# Create a model that trains mean (m), covariance (c), transition probabilities (t).
# Note: the probabilities for transmat and startprob will currently be replaced with a
# very small number for all entries that are exactly zero.
logging.info('creating HMM with n_states=%d, transition_init=%s, topology=%s, n_iter=%d, thresh=%f, covar_type=%s' % (n_states, transition_init, topology, n_iter, thresh, covar_type))
logging.info('transmat:\n' + str(transitions))
logging.info('pi:\n' + str(pi))
logging.info('means:\n' + str(means))
logging.info('covars:\n' + str(covars))
model = impl.GaussianHMM(n_states, transmat=transitions, startprob=pi, covariance_type=covar_type, params='mct',
init_params='', verbose=verbose, n_iter=n_iter, thresh=thresh)
if covar_type == 'diag':
model.covars_ = [covar.diagonal() for covar in covars]
else:
model.covars_ = covars
model.means_ = means
model.verbose = True
return model
def greedy_sample(chains, means, covars, n_samples=1, random_state=None, max_cycle_duration=0):
assert means.shape[0] == covars.shape[0]
# Greedy sample algorithm as described by Takano et al.
n_chains = len(chains)
n_features = means.shape[-1]
states = np.zeros((n_samples, n_chains), dtype=int)
for chain_idx, chain in enumerate(chains):
states[0, chain_idx] = np.argmax(chain._log_startprob)
t = 1
while t < n_samples:
prev_state = states[t - 1, chain_idx]
# Stay in state until duration is over or until we reach the n_samples limit
trans_prob_cycle = np.exp(chain._log_transmat[prev_state, prev_state])
if trans_prob_cycle == 1.0:
trans_prob_cycle -= np.finfo(float).eps
assert 0.0 <= trans_prob_cycle < 1.0
duration = int(np.floor(min(min(1.0 / (1.0 - trans_prob_cycle), n_samples-t), max_cycle_duration)))
for d in xrange(duration):
states[t + d, chain_idx] = prev_state
t += duration
if t >= n_samples:
continue
# Get argmax of transition probability of previous state but ignore transition from prev_state -> prev_state
state = None
for idx, val in enumerate(chain._log_transmat[prev_state]):
if idx != prev_state and (state is None or val > chain._log_transmat[prev_state, state]):
state = idx
assert state is not None
assert state != prev_state
states[t, chain_idx] = state
t += 1
obs = np.zeros((n_samples, n_features))
for t in xrange(n_samples):
state_combination = tuple(states[t])
mean = means[state_combination]
covar = covars[state_combination]
obs[t] = sample_gaussian(mean, covar, 'diag', random_state=random_state)
return obs
class GaussianHMM(BaseHMM):
def __init__(self, n_states=10, n_training_iterations=10, training_threshold=1e-2, topology='left-to-right',
verbose=False, transition_init='uniform', emission_init='k-means', covar_type='diag'):
super(GaussianHMM, self).__init__(n_states, n_training_iterations, training_threshold, topology, verbose,
transition_init, emission_init, covar_type)
self.model_ = None
def _init(self, obs):
randomize = True if self.emission_init == 'random' else False
means, covars = estimate_normal_distribution_params(obs, n_states=self.n_states, covar_type=self.covar_type,
randomize=randomize)
self.model_ = _new_model(self.n_states, self.transition_init, means, covars, self.covar_type, self.topology,
self.n_training_iterations, self.training_threshold, self.verbose)
def _fit(self, obs):
assert self.model_
self.model_.fit(obs)
def _loglikelihood(self, ob, method):
if method != 'exact':
raise ValueError('unknown method "%s"' % method)
assert self.model_
return self.model_.score(ob)
def _sample(self, n_samples, max_cycle_duration):
assert self.model_
return greedy_sample([self.model_], self.model_._means_, self.model_._covars_, n_samples=n_samples,
max_cycle_duration=max_cycle_duration)
# TODO: normalize seems to also do some sort of maximum thingy
# TODO: implement masking!
def _normalize_transmat(transmat):
return normalize(np.maximum(transmat, 1e-20), axis=1)
def _normalize_startprob(startprob):
return normalize(np.maximum(startprob, 1e-20))
class ExactGaussianFHMM(BaseHMM):
def __init__(self, n_states=10, n_chains=2, n_training_iterations=10, training_threshold=1e-2,
topology='left-to-right', verbose=False, transition_init='uniform', emission_init='k-means',
covar_type='diag'):
super(ExactGaussianFHMM, self).__init__(n_states, n_training_iterations, training_threshold, topology, verbose,
transition_init, emission_init, covar_type)
self.n_chains = n_chains
def _init(self, obs):
self.log_transmat = np.zeros((self.n_chains, self.n_states, self.n_states))
self.log_startprob = np.zeros((self.n_chains, self.n_states))
for chain_idx in xrange(self.n_chains):
self.log_transmat[chain_idx] = np.log(_normalize_transmat(transition_matrix(self.n_states, self.topology)))
self.log_startprob[chain_idx] = np.log(_normalize_startprob(start_probabilities(self.n_states, self.topology)))
# Estimate covar
_, covars = estimate_normal_distribution_params(obs, n_states=1, covar_type='full')
self.covar = covars[0]
# Estimate means. We estimate different means for each chain and each state. Each mean is divided by the number
# of chains since means from each chain are summed to form the actual mean.
means, _ = estimate_normal_distribution_params(obs, n_states=self.n_states * self.n_chains)
self.means = (means.reshape((self.n_chains, self.n_states, self.n_features_)) / float(self.n_chains))
def _do_forward_pass(self, framelogprob):
n_observations = framelogprob.shape[0]
state_combinations = [tuple(x) for x in list(itertools.product(np.arange(self.n_states), repeat=self.n_chains))]
fwdlattice = np.zeros((n_observations, self.n_states ** self.n_chains))
fhmmc._forward(n_observations, self.n_chains, self.n_states, state_combinations, self.log_startprob,
self.log_transmat, framelogprob, fwdlattice)
return logsumexp(fwdlattice[-1]), fwdlattice
def _do_backward_pass(self, framelogprob):
n_observations = framelogprob.shape[0]
state_combinations = [tuple(x) for x in list(itertools.product(np.arange(self.n_states), repeat=self.n_chains))]
bwdlattice = np.zeros((n_observations, self.n_states ** self.n_chains))
fhmmc._backward(n_observations, self.n_chains, self.n_states, state_combinations, self.log_startprob,
self.log_transmat, framelogprob, bwdlattice)
return bwdlattice
def _compute_logeta(self, framelogprob, fwdlattice, bwdlattice):
n_observations = framelogprob.shape[0]
state_combinations = [tuple(x) for x in list(itertools.product(np.arange(self.n_states), repeat=self.n_chains))]
logeta = np.zeros((n_observations - 1, self.n_chains, self.n_states, self.n_states))
fhmmc._compute_logeta(n_observations, self.n_chains, self.n_states, state_combinations, self.log_transmat,
framelogprob, fwdlattice, bwdlattice, logeta)
# TODO: remove this validation eventually
for t in xrange(n_observations - 1):
for chain_idx in xrange(self.n_chains):
assert np.allclose(np.sum(np.exp(logeta[t, chain_idx])), 1.0)
return logeta
def _do_mstep(self, stats):
# Startprob and transmat
for chain_idx in xrange(self.n_chains):
self.log_startprob[chain_idx] = np.log(_normalize_startprob(stats['start'][chain_idx]))
self.log_transmat[chain_idx] = np.log(_normalize_transmat(stats['trans'][chain_idx]))
# Means
print 'means equal per chain before', np.allclose(self.means[0], self.means[1])
means = np.dot(stats['means_sum1'], np.linalg.pinv(stats['means_sum2'])).T
means = means.reshape(self.means.shape)
self.means = means
print 'means equal per chain after', np.allclose(means[0], means[1])
# Covariance
covar1 = 1.0 / float(stats['T']) * (stats['covar_sum1'] - stats['covar_sum2'])
# Alternative way of calculating covar
covar2 = (-2.0 * stats['covar_sum']) / float(stats['T'])
print 'covar1 == covar2', np.allclose(covar1, covar2)
#self.covar = covar1
assert np.allclose(self.covar.T, self.covar) # ensure that covar is symmetric
def _accumulate_sufficient_statistics(self, stats, seq, framelogprob, in_posteriors, fwdlattice, bwdlattice):
n_observations, n_features = seq.shape
partial_state_combinations = [list(x) for x in list(itertools.product(np.arange(self.n_states), repeat=self.n_chains - 1))]
posteriors = in_posteriors.view()
state_combination_shape = tuple([self.n_states for _ in xrange(self.n_chains)])
posteriors.shape = (n_observations,) + state_combination_shape
# Calculate posteriors for each time step and each chain (<S_t^(m)>)
chain_posteriors = np.zeros((n_observations, self.n_chains, self.n_states))
for t in xrange(n_observations):
for chain_idx in xrange(self.n_chains):
for state in xrange(self.n_states):
for partial_combination in partial_state_combinations:
state_combination = tuple(partial_combination[:chain_idx] + [state] + partial_combination[chain_idx:])
chain_posteriors[t, chain_idx, state] += posteriors[t][state_combination]
# Ensure that posteriors for each chain sum to 1
assert np.allclose(np.sum(chain_posteriors[t], axis=1), 1.0)
chain_chain_posteriors = np.zeros((n_observations, self.n_chains, self.n_chains, self.n_states, self.n_states))
assert self.n_chains == 2, 'This code currently only works for 2 chains'
for t in xrange(n_observations):
for chain0_idx in xrange(self.n_chains):
for chain1_idx in xrange(self.n_chains):
for state0 in xrange(self.n_states):
for state1 in xrange(self.n_states):
# Now keep state0 and state1 fixed and vary the rest-in the case of only two chains, this
# however means that we cannot vary anything
chain_chain_posteriors[t, chain0_idx, chain1_idx, state0, state1] = posteriors[t, state0, state1]
assert np.allclose(np.sum(chain_chain_posteriors[t, chain0_idx, chain1_idx]), 1.0)
combined_chain_chain_posteriors = np.zeros((n_observations, self.n_chains * self.n_states, self.n_chains * self.n_states))
for t in xrange(n_observations):
for chain0_idx in xrange(self.n_chains):
for chain1_idx in xrange(self.n_chains):
post = chain_chain_posteriors[t, chain0_idx, chain1_idx]
assert post.shape == (self.n_states, self.n_states)
idx0 = chain0_idx * self.n_states
idx1 = chain1_idx * self.n_states
assert combined_chain_chain_posteriors[t, idx0:idx0+self.n_states, idx1:idx1+self.n_states].shape == (self.n_states, self.n_states)
assert np.allclose(combined_chain_chain_posteriors[t, idx0:idx0+self.n_states, idx1:idx1+self.n_states], 0)
combined_chain_chain_posteriors[t, idx0:idx0+self.n_states, idx1:idx1+self.n_states] = post
#combined_chain_chain_posteriors = chain_chain_posteriors.reshape(n_observations, self.n_states * self.n_chains, self.n_states * self.n_chains)
# Calculate posteriors for each time step and each chain combination (<S_t^(m),S_t^(n)'>)
# TODO: this is super sketchy code right here
# start = timeit.default_timer()
# chain_chain_posteriors = np.zeros((n_observations, self.n_chains, self.n_chains, self.n_states, self.n_states))
# steps = 0
# skipped = 0
# for t in xrange(n_observations):
# for chain0_idx in xrange(self.n_chains):
# for chain1_idx in xrange(self.n_chains):
# processed_state_combinations = set() # TODO: is this correct?
# for i in xrange(self.n_states):
# for j in xrange(self.n_states):
# for state_combination in state_combinations:
# actual_state_combination = list(state_combination)
# actual_state_combination[chain0_idx] = i
# actual_state_combination[chain1_idx] = j
# actual_state_combination = tuple(actual_state_combination)
# if actual_state_combination in processed_state_combinations:
# # Skip states that we have already processed.
# # TODO: make this efficient, right now we skip most states
# skipped += 1
# continue
# steps += 1
# processed_state_combinations.add(actual_state_combination)
# chain_chain_posteriors[t, chain0_idx, chain1_idx, i, j] += posteriors[t][actual_state_combination]
# if posteriors[t][actual_state_combination] == 0.0:
# print '0.0!'
# print('took %fs, %d steps (%d)' % ((timeit.default_timer() - start), steps, skipped))
# print np.size(chain_chain_posteriors)
# Update stats for start and trans
stats['start'] += chain_posteriors[0]
logeta = self._compute_logeta(framelogprob, fwdlattice, bwdlattice)
for chain_idx in xrange(self.n_chains):
# No need to normalize here since we'll do that later anyway
stats['trans'][chain_idx] += np.exp(logsumexp(logeta[:, chain_idx], axis=0))
# Update stats for means
for t in xrange(n_observations):
ob = seq[t].reshape(n_features, 1)
post = chain_posteriors[t].flatten().reshape(1, self.n_chains * self.n_states)
val1 = np.dot(ob, post)
assert val1.shape == stats['means_sum1'].shape
stats['means_sum1'] += val1
val2 = combined_chain_chain_posteriors[t]
assert val2.shape == stats['means_sum2'].shape
stats['means_sum2'] += val2
new_means = np.dot(stats['means_sum1'], np.linalg.pinv(stats['means_sum2'])).T.reshape(self.means.shape)
# Alternative way of calculcating the covariance stats (seems to yield same (bad) results)
for t in xrange(n_observations):
ob = seq[t].reshape(n_features, 1)
sum1 = np.zeros((self.n_features_, self.n_features_))
for chain_idx in xrange(self.n_chains):
post = chain_posteriors[t, chain_idx].reshape(self.n_states, 1)
val1 = np.dot(np.dot(ob, post.T), self.means[chain_idx])
assert val1.shape == sum1.shape
sum1 += val1
sum2 = np.zeros((self.n_features_, self.n_features_))
for chain0_idx in xrange(self.n_chains):
for chain1_idx in xrange(self.n_chains):
post = chain_chain_posteriors[t, chain1_idx, chain0_idx]
val2 = np.dot(np.dot(self.means[chain1_idx].T, post), self.means[chain0_idx])
assert val2.shape == sum2.shape
sum2 += val2
covar_sum = sum1 - 0.5 * np.dot(ob, ob.T) - 0.5 * sum2
assert covar_sum.shape == stats['covar_sum'].shape
stats['covar_sum'] += covar_sum
# Update covariance stats
for t in xrange(n_observations):
ob = seq[t].reshape(n_features, 1)
val1 = np.dot(ob, ob.T)
assert val1.shape == stats['covar_sum1'].shape
stats['covar_sum1'] += val1
tmp = np.zeros((n_features, 1))
for chain_idx in xrange(self.n_chains):
tmp += np.dot(new_means[chain_idx].T, chain_posteriors[t, chain_idx]).reshape((n_features, 1))
val2 = np.dot(tmp, ob.T)
assert val2.shape == stats['covar_sum2'].shape
stats['covar_sum2'] += val2
# Update bookkeeping stats
stats['nobs'] += 1
stats['T'] += n_observations
def _initialize_sufficient_statistics(self):
stats = {'nobs': 0,
'T': 0,
'start': np.zeros((self.n_chains, self.n_states)),
'trans': np.zeros((self.n_chains, self.n_states, self.n_states)),
'covar_sum1': np.zeros((self.n_features_, self.n_features_)),
'covar_sum2': np.zeros((self.n_features_, self.n_features_)),
'covar_sum': np.zeros((self.n_features_, self.n_features_)), # TODO: remove eventually
'means_sum1': np.zeros((self.n_features_, self.n_states * self.n_chains)),
'means_sum2': np.zeros((self.n_states * self.n_chains, self.n_states * self.n_chains))}
return stats
def _compute_log_likelihood(self, seq):
state_combinations = [tuple(x) for x in list(itertools.product(np.arange(self.n_states), repeat=self.n_chains))]
n_state_combinations = self.n_states ** self.n_chains
n_observations, n_features = seq.shape
covars = np.array([self.covar for _ in xrange(n_state_combinations)]) # TODO: correct?!
means = np.zeros((n_state_combinations, n_features))
for idx, state_combination in enumerate(state_combinations):
for chain_idx, state in enumerate(state_combination):
means[idx] += self.means[chain_idx, state]
framelogprob = log_multivariate_normal_density(seq, means, covars, covariance_type='full')
return framelogprob
def _fit(self, obs):
prev_loglikelihood = None
for iteration in xrange(self.n_training_iterations):
stats = self._initialize_sufficient_statistics()
curr_loglikelihood = 0
for seq in obs:
# Forward-backward pass and accumulate stats
framelogprob = self._compute_log_likelihood(seq)
lpr, fwdlattice = self._do_forward_pass(framelogprob)
bwdlattice = self._do_backward_pass(framelogprob)
gamma = fwdlattice + bwdlattice
posteriors = np.exp(gamma.T - logsumexp(gamma, axis=1)).T
assert np.allclose(np.sum(posteriors, axis=1), 1.0) # posteriors must sum to 1 for each t
curr_loglikelihood += lpr
self._accumulate_sufficient_statistics(stats, seq, framelogprob, posteriors, fwdlattice, bwdlattice)
# Test for convergence
if prev_loglikelihood is not None:
delta = curr_loglikelihood - prev_loglikelihood
print ('%f (%f)' % (curr_loglikelihood, delta))
assert delta >= -0.01 # Likelihood when training with Baum-Welch should grow monotonically
if delta <= self.training_threshold:
break
self._do_mstep(stats)
prev_loglikelihood = curr_loglikelihood
def _loglikelihood(self, ob, method):
framelogprob = self._compute_log_likelihood(ob)
loglikelihood, _ = self._do_forward_pass(framelogprob)
return loglikelihood
def _sample(self, n_samples, max_cycle_duration):
raise NotImplementedError('not yet implemented')
pass
class SequentialGaussianFHMM(BaseHMM):
def __init__(self, n_states=10, n_chains=2, n_training_iterations=10, training_threshold=1e-2,
topology='left-to-right', verbose=False, transition_init='uniform', emission_init='k-means',
covar_type='diag'):
super(SequentialGaussianFHMM, self).__init__(n_states, n_training_iterations, training_threshold, topology,
verbose, transition_init, emission_init, covar_type)
self.n_chains = n_chains
self.chains_ = None
def _init(self, obs):
# Initialize first chain
randomize = True if self.emission_init == 'random' else False
means, covars = estimate_normal_distribution_params(obs, n_states=self.n_states, randomize=randomize)
chain = _new_model(self.n_states, self.transition_init, np.copy(means), np.copy(covars), self.covar_type,
self.topology, self.n_training_iterations, self.training_threshold, self.verbose)
self.chains_ = [chain]
def _fit(self, obs):
assert self.chains_
assert len(self.chains_) > 0
# Re-use generated observations for later use when iterating over already trained chains. We do not need an
# entry for the last chain since it never generates an observation.
generated_obs_cache = [[] for _ in xrange(self.n_chains - 1)]
# Train first chain
self.chains_[0].fit(obs)
# Train subsequent chains on the error
for curr_chain_idx in xrange(1, self.n_chains):
# Calculate residual error for each observation
weight = 1.0 / float(self.n_chains) # TODO: it's a bit unclear if this is correct
err = []
for ob_idx, ob in enumerate(obs):
# Calculate residual error
combined_generated_ob = self._generate_observation(ob, ob_idx, curr_chain_idx, weight, generated_obs_cache)
curr_err = (1.0 / weight) * (ob - combined_generated_ob)
assert curr_err.shape == ob.shape
err.append(curr_err)
assert len(err) == len(obs)
# Create new chain
randomize = True if self.emission_init == 'random' else False
means, covars = estimate_normal_distribution_params(err, n_states=self.n_states, randomize=randomize)
curr_chain = _new_model(self.n_states, self.transition_init, means, covars, self.covar_type, self.topology,
self.n_training_iterations, self.training_threshold, self.verbose)
self.chains_.append(curr_chain)
# Fit chain on residual error
curr_chain.fit(err)
# Ensure that cache was filled properly and works as expected
assert len(generated_obs_cache) == self.n_chains - 1
for chain_cache in generated_obs_cache:
assert len(chain_cache) == len(obs)
def _loglikelihood_residual_approx(self, ob):
scores = np.zeros(self.n_chains)
# Re-use generated observations for later use when iterating over already trained chains. We do not need an
# entry for the last chain since it never generates an observation.
generated_obs_cache = [[] for _ in xrange(self.n_chains - 1)]
for curr_chain_idx, curr_chain in enumerate(self.chains_):
if curr_chain_idx == 0:
scores[curr_chain_idx] = curr_chain.score(ob)
continue
# Calculate residual error for observation and calculate score on it
weight = 1.0 / float(self.n_chains) # TODO: it's a bit unclear if this is correct
combined_generated_ob = self._generate_observation(ob, 0, curr_chain_idx, weight, generated_obs_cache)
curr_err = (1.0 / weight) * (ob - combined_generated_ob)
scores[curr_chain_idx] = curr_chain.score(curr_err)
return logsumexp(scores)
def _exact_loglikelihood(self, ob):
log_transmat = np.zeros((self.n_chains, self.n_states, self.n_states))
log_startprob = np.zeros((self.n_chains, self.n_states))
for idx, chain in enumerate(self.chains_):
log_transmat[idx] = chain._log_transmat
log_startprob[idx] = chain._log_startprob
n_state_combinations = self.n_states ** self.n_chains
state_combinations = [tuple(x) for x in list(itertools.product(np.arange(self.n_states), repeat=self.n_chains))]
n_observations = ob.shape[0]
n_features = ob.shape[1]
fwdlattice = np.zeros((n_observations, n_state_combinations))
# Calculate means and covariances for all state combinations and calculate emission probabilities
weight = (1.0 / float(self.n_chains))
weight_squared = weight * weight
covars = np.zeros((n_state_combinations, n_features)) # TODO: add support for all covariance types
means = np.zeros((n_state_combinations, n_features))
for idx, state_combination in enumerate(state_combinations):
for chain_idx, state in enumerate(state_combination):
chain = self.chains_[chain_idx]
covars[idx] += chain._covars_[state]
means[idx] += chain._means_[state]
covars[idx] *= weight_squared
means[idx] *= weight
framelogprob = log_multivariate_normal_density(ob, means, covars, covariance_type='diag') # TODO: add support for all covariance types
# Run the forward algorithm
fhmmc._forward(n_observations, self.n_chains, self.n_states, state_combinations, log_startprob, log_transmat,
framelogprob, fwdlattice)
last_column = fwdlattice[-1]
assert np.size(last_column) == n_state_combinations
score = logsumexp(last_column)
return score
def _loglikelihood(self, ob, method):
if method == 'exact':
return self._exact_loglikelihood(ob)
elif method == 'approx':
return self._loglikelihood_residual_approx(ob)
else:
raise ValueError('unknown method "%s"' % method)
def _sample(self, n_samples, max_cycle_duration):
# Calculate means and covariances for all state combinations and calculate emission probabilities
state_combinations = [tuple(x) for x in list(itertools.product(np.arange(self.n_states), repeat=self.n_chains))]
state_combinations_shape = tuple([self.n_states for _ in xrange(self.n_chains)])
weight = (1.0 / float(self.n_chains))
weight_squared = weight * weight
covars = np.zeros(state_combinations_shape + (self.n_features_,)) # TODO: add support for all covariance types
means = np.zeros(state_combinations_shape + (self.n_features_,))
for state_combination in state_combinations:
for chain_idx, state in enumerate(state_combination):
chain = self.chains_[chain_idx]
covars[state_combination] += chain._covars_[state]
means[state_combination] += chain._means_[state]
covars[state_combination] *= weight_squared
means[state_combination] *= weight
obs = greedy_sample(self.chains_, means, covars, n_samples=n_samples, max_cycle_duration=max_cycle_duration)
return obs
def _generate_observation(self, ob, ob_idx, curr_chain_idx, weight, generated_obs_cache):
combined_generated_ob = np.zeros(ob.shape)
# Iterate over all chains up until (but excluding) the current chain and combine their generated
# observations
for prev_chain_idx in xrange(curr_chain_idx):
prev_chain = self.chains_[prev_chain_idx]
generated_ob = np.zeros(ob.shape)
if ob_idx < len(generated_obs_cache[prev_chain_idx]):
# Use cached value
assert generated_obs_cache[prev_chain_idx][ob_idx].shape == ob.shape
generated_ob = generated_obs_cache[prev_chain_idx][ob_idx]
else:
# Option a: gamma
framelogprob = prev_chain._compute_log_likelihood(ob)
logprob, fwdlattice = prev_chain._do_forward_pass(framelogprob)
bwdlattice = prev_chain._do_backward_pass(framelogprob)
fwdbwdlattice = fwdlattice + bwdlattice
gamma = np.exp(fwdbwdlattice.T - logsumexp(fwdbwdlattice, axis=1)).T
# TODO: this can probably be vectorized
for t in xrange(ob.shape[0]):
for state in xrange(self.n_states):
generated_ob[t] += prev_chain.means_[state] * gamma[t][state]
# Option b: Viterbi
# _, state_seq = prev_chain.decode(ob)
# assert np.size(state_seq) == generated_ob.shape[0]
# for idx, state in enumerate(state_seq):
# generated_ob[idx] = prev_chain.means_[state]
# Option c: generated sequence
# generated_ob = prev_chain.sample(ob.shape[0])[0]
# Cache for future iterations
generated_obs_cache[prev_chain_idx].append(generated_ob)
combined_generated_ob += weight * generated_ob
return combined_generated_ob
| mit |
uglyboxer/linear_neuron | net-p3/lib/python3.5/site-packages/matplotlib/backends/backend_webagg.py | 10 | 12184 | """
Displays Agg images in the browser, with interactivity
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
# The WebAgg backend is divided into two modules:
#
# - `backend_webagg_core.py` contains code necessary to embed a WebAgg
# plot inside of a web application, and communicate in an abstract
# way over a web socket.
#
# - `backend_webagg.py` contains a concrete implementation of a basic
# application, implemented with tornado.
import six
import datetime
import errno
import json
import os
import random
import sys
import socket
import threading
try:
import tornado
except ImportError:
raise RuntimeError("The WebAgg backend requires Tornado.")
import tornado.web
import tornado.ioloop
import tornado.websocket
import matplotlib
from matplotlib import rcParams
from matplotlib import backend_bases
from matplotlib.figure import Figure
from matplotlib._pylab_helpers import Gcf
from . import backend_webagg_core as core
from .backend_nbagg import TimerTornado
def new_figure_manager(num, *args, **kwargs):
"""
Create a new figure manager instance
"""
FigureClass = kwargs.pop('FigureClass', Figure)
thisFig = FigureClass(*args, **kwargs)
return new_figure_manager_given_figure(num, thisFig)
def new_figure_manager_given_figure(num, figure):
"""
Create a new figure manager instance for the given figure.
"""
canvas = FigureCanvasWebAgg(figure)
manager = core.FigureManagerWebAgg(canvas, num)
return manager
def draw_if_interactive():
"""
Is called after every pylab drawing command
"""
if matplotlib.is_interactive():
figManager = Gcf.get_active()
if figManager is not None:
figManager.canvas.draw_idle()
class Show(backend_bases.ShowBase):
def mainloop(self):
WebAggApplication.initialize()
url = "http://127.0.0.1:{port}{prefix}".format(
port=WebAggApplication.port,
prefix=WebAggApplication.url_prefix)
if rcParams['webagg.open_in_browser']:
import webbrowser
webbrowser.open(url)
else:
print("To view figure, visit {0}".format(url))
WebAggApplication.start()
show = Show().mainloop
class ServerThread(threading.Thread):
def run(self):
tornado.ioloop.IOLoop.instance().start()
webagg_server_thread = ServerThread()
class FigureCanvasWebAgg(core.FigureCanvasWebAggCore):
def show(self):
# show the figure window
show()
def new_timer(self, *args, **kwargs):
return TimerTornado(*args, **kwargs)
def start_event_loop(self, timeout):
backend_bases.FigureCanvasBase.start_event_loop_default(
self, timeout)
start_event_loop.__doc__ = \
backend_bases.FigureCanvasBase.start_event_loop_default.__doc__
def stop_event_loop(self):
backend_bases.FigureCanvasBase.stop_event_loop_default(self)
stop_event_loop.__doc__ = \
backend_bases.FigureCanvasBase.stop_event_loop_default.__doc__
class WebAggApplication(tornado.web.Application):
initialized = False
started = False
class FavIcon(tornado.web.RequestHandler):
def get(self):
image_path = os.path.join(
os.path.dirname(os.path.dirname(__file__)),
'mpl-data', 'images')
self.set_header('Content-Type', 'image/png')
with open(os.path.join(image_path,
'matplotlib.png'), 'rb') as fd:
self.write(fd.read())
class SingleFigurePage(tornado.web.RequestHandler):
def __init__(self, application, request, **kwargs):
self.url_prefix = kwargs.pop('url_prefix', '')
return tornado.web.RequestHandler.__init__(self, application,
request, **kwargs)
def get(self, fignum):
fignum = int(fignum)
manager = Gcf.get_fig_manager(fignum)
ws_uri = 'ws://{req.host}{prefix}/'.format(req=self.request,
prefix=self.url_prefix)
self.render(
"single_figure.html",
prefix=self.url_prefix,
ws_uri=ws_uri,
fig_id=fignum,
toolitems=core.NavigationToolbar2WebAgg.toolitems,
canvas=manager.canvas)
class AllFiguresPage(tornado.web.RequestHandler):
def __init__(self, application, request, **kwargs):
self.url_prefix = kwargs.pop('url_prefix', '')
return tornado.web.RequestHandler.__init__(self, application,
request, **kwargs)
def get(self):
ws_uri = 'ws://{req.host}{prefix}/'.format(req=self.request,
prefix=self.url_prefix)
self.render(
"all_figures.html",
prefix=self.url_prefix,
ws_uri=ws_uri,
figures=sorted(
list(Gcf.figs.items()), key=lambda item: item[0]),
toolitems=core.NavigationToolbar2WebAgg.toolitems)
class MplJs(tornado.web.RequestHandler):
def get(self):
self.set_header('Content-Type', 'application/javascript')
js_content = core.FigureManagerWebAgg.get_javascript()
self.write(js_content)
class Download(tornado.web.RequestHandler):
def get(self, fignum, fmt):
fignum = int(fignum)
manager = Gcf.get_fig_manager(fignum)
# TODO: Move this to a central location
mimetypes = {
'ps': 'application/postscript',
'eps': 'application/postscript',
'pdf': 'application/pdf',
'svg': 'image/svg+xml',
'png': 'image/png',
'jpeg': 'image/jpeg',
'tif': 'image/tiff',
'emf': 'application/emf'
}
self.set_header('Content-Type', mimetypes.get(fmt, 'binary'))
buff = six.BytesIO()
manager.canvas.print_figure(buff, format=fmt)
self.write(buff.getvalue())
class WebSocket(tornado.websocket.WebSocketHandler):
supports_binary = True
def open(self, fignum):
self.fignum = int(fignum)
self.manager = Gcf.get_fig_manager(self.fignum)
self.manager.add_web_socket(self)
if hasattr(self, 'set_nodelay'):
self.set_nodelay(True)
def on_close(self):
self.manager.remove_web_socket(self)
def on_message(self, message):
message = json.loads(message)
# The 'supports_binary' message is on a client-by-client
# basis. The others affect the (shared) canvas as a
# whole.
if message['type'] == 'supports_binary':
self.supports_binary = message['value']
else:
manager = Gcf.get_fig_manager(self.fignum)
# It is possible for a figure to be closed,
# but a stale figure UI is still sending messages
# from the browser.
if manager is not None:
manager.handle_json(message)
def send_json(self, content):
self.write_message(json.dumps(content))
def send_binary(self, blob):
if self.supports_binary:
self.write_message(blob, binary=True)
else:
data_uri = "data:image/png;base64,{0}".format(
blob.encode('base64').replace('\n', ''))
self.write_message(data_uri)
def __init__(self, url_prefix=''):
if url_prefix:
assert url_prefix[0] == '/' and url_prefix[-1] != '/', \
'url_prefix must start with a "/" and not end with one.'
super(WebAggApplication, self).__init__(
[
# Static files for the CSS and JS
(url_prefix + r'/_static/(.*)',
tornado.web.StaticFileHandler,
{'path': core.FigureManagerWebAgg.get_static_file_path()}),
# An MPL favicon
(url_prefix + r'/favicon.ico', self.FavIcon),
# The page that contains all of the pieces
(url_prefix + r'/([0-9]+)', self.SingleFigurePage,
{'url_prefix': url_prefix}),
# The page that contains all of the figures
(url_prefix + r'/?', self.AllFiguresPage,
{'url_prefix': url_prefix}),
(url_prefix + r'/mpl.js', self.MplJs),
# Sends images and events to the browser, and receives
# events from the browser
(url_prefix + r'/([0-9]+)/ws', self.WebSocket),
# Handles the downloading (i.e., saving) of static images
(url_prefix + r'/([0-9]+)/download.([a-z0-9.]+)',
self.Download),
],
template_path=core.FigureManagerWebAgg.get_static_file_path())
@classmethod
def initialize(cls, url_prefix='', port=None):
if cls.initialized:
return
# Create the class instance
app = cls(url_prefix=url_prefix)
cls.url_prefix = url_prefix
# This port selection algorithm is borrowed, more or less
# verbatim, from IPython.
def random_ports(port, n):
"""
Generate a list of n random ports near the given port.
The first 5 ports will be sequential, and the remaining n-5 will be
randomly selected in the range [port-2*n, port+2*n].
"""
for i in range(min(5, n)):
yield port + i
for i in range(n - 5):
yield port + random.randint(-2 * n, 2 * n)
success = None
cls.port = rcParams['webagg.port']
for port in random_ports(cls.port, rcParams['webagg.port_retries']):
try:
app.listen(port)
except socket.error as e:
if e.errno != errno.EADDRINUSE:
raise
else:
cls.port = port
success = True
break
if not success:
raise SystemExit(
"The webagg server could not be started because an available "
"port could not be found")
cls.initialized = True
@classmethod
def start(cls):
if cls.started:
return
# Set the flag to True *before* blocking on IOLoop.instance().start()
cls.started = True
"""
IOLoop.running() was removed as of Tornado 2.4; see for example
https://groups.google.com/forum/#!topic/python-tornado/QLMzkpQBGOY
Thus there is no correct way to check if the loop has already been
launched. We may end up with two concurrently running loops in that
unlucky case with all the expected consequences.
"""
print("Press Ctrl+C to stop WebAgg server")
sys.stdout.flush()
try:
tornado.ioloop.IOLoop.instance().start()
except KeyboardInterrupt:
print("Server is stopped")
sys.stdout.flush()
finally:
cls.started = False
def ipython_inline_display(figure):
import tornado.template
WebAggApplication.initialize()
if not webagg_server_thread.is_alive():
webagg_server_thread.start()
with open(os.path.join(
core.FigureManagerWebAgg.get_static_file_path(),
'ipython_inline_figure.html')) as fd:
tpl = fd.read()
fignum = figure.number
t = tornado.template.Template(tpl)
return t.generate(
prefix=WebAggApplication.url_prefix,
fig_id=fignum,
toolitems=core.NavigationToolbar2WebAgg.toolitems,
canvas=figure.canvas,
port=WebAggApplication.port).decode('utf-8')
FigureCanvas = FigureCanvasWebAgg
| mit |
jschuecker/nest-simulator | pynest/examples/intrinsic_currents_subthreshold.py | 13 | 7182 | # -*- coding: utf-8 -*-
#
# intrinsic_currents_subthreshold.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
'''
Intrinsic currents subthreshold
-------------------------------
This example illustrates how to record from a model with multiple
intrinsic currents and visualize the results. This is illustrated
using the `ht_neuron` which has four intrinsic currents: I_NaP,
I_KNa, I_T, and I_h. It is a slightly simplified implementation of
neuron model proposed in Hill and Tononi (2005) **Modeling Sleep
and Wakefulness in the Thalamocortical System** *J Neurophysiol* 93:1671
http://dx.doi.org/10.1152/jn.00915.2004 .
The neuron is driven by DC current, which is alternated
between depolarizing and hyperpolarizing. Hyperpolarization
intervals become increasingly longer.
See also: intrinsic_currents_spiking.py
'''
'''
We imported all necessary modules for simulation, analysis and
plotting.
'''
import nest
import numpy as np
import matplotlib.pyplot as plt
'''
Additionally, we set the verbosity using `set_verbosity` to
suppress info messages. We also reset the kernel to be sure to start
with a clean NEST.
'''
nest.set_verbosity("M_WARNING")
nest.ResetKernel()
'''
We define simulation parameters:
- The length of depolarization intervals
- The length of hyperpolarization intervals
- The amplitude for de- and hyperpolarizing currents
- The end of the time window to plot
'''
n_blocks = 5
t_block = 20.
t_dep = [t_block] * n_blocks
t_hyp = [t_block * 2 ** n for n in range(n_blocks)]
I_dep = 10.
I_hyp = -5.
t_end = 500.
'''
We create the one neuron instance and the DC current generator
and store the returned handles.
'''
nrn = nest.Create('ht_neuron')
dc = nest.Create('dc_generator')
'''
We create a multimeter to record
- membrane potential `V_m`
- threshold value `theta`
- intrinsic currents `I_NaP`, `I_KNa`, `I_T`, `I_h`
by passing these names in the `record_from` list.
To find out which quantities can be recorded from a given neuron,
run::
nest.GetDefaults('ht_neuron')['recordables']
The result will contain an entry like::
<SLILiteral: V_m>
for each recordable quantity. You need to pass the value of the `SLILiteral`,
in this case `V_m` in the `record_from` list.
We want to record values with 0.1 ms resolution, so we set the
recording interval as well; the default recording resolution is 1 ms.
'''
# create multimeter and configure it to record all information
# we want at 0.1ms resolution
mm = nest.Create('multimeter',
params={'interval': 0.1,
'record_from': ['V_m', 'theta',
'I_NaP', 'I_KNa', 'I_T', 'I_h']}
)
'''
We connect the DC generator and the multimeter to the neuron.
Note that the multimeter, just like the voltmeter is connected
to the neuron, not the neuron to the multimeter.
'''
nest.Connect(dc, nrn)
nest.Connect(mm, nrn)
'''
We are ready to simulate. We alternate between driving the neuron
with depolarizing and hyperpolarizing currents. Before each simulation
interval, we set the amplitude of the DC generator to the correct value.
'''
for t_sim_dep, t_sim_hyp in zip(t_dep, t_hyp):
nest.SetStatus(dc, {'amplitude': I_dep})
nest.Simulate(t_sim_dep)
nest.SetStatus(dc, {'amplitude': I_hyp})
nest.Simulate(t_sim_hyp)
'''
We now fetch the data recorded by the multimeter. The data are
returned as a dictionary with entry ``'times'`` containing timestamps
for all recorded data, plus one entry per recorded quantity.
All data is contained in the ``'events'`` entry of the status dictionary
returned by the multimeter. Because all NEST function return arrays,
we need to pick out element ``0`` from the result of `GetStatus`.
'''
data = nest.GetStatus(mm)[0]['events']
t = data['times']
'''
The next step is to plot the results. We create a new figure, add a
single subplot and plot at first membrane potential and threshold.
'''
fig = plt.figure()
Vax = fig.add_subplot(111)
Vax.plot(t, data['V_m'], 'b-', lw=2, label=r'$V_m$')
Vax.plot(t, data['theta'], 'g-', lw=2, label=r'$\Theta$')
Vax.set_ylim(-80., 0.)
Vax.set_ylabel('Voltageinf [mV]')
Vax.set_xlabel('Time [ms]')
'''
To plot the input current, we need to create an input
current trace. We construct it from the durations of the de- and
hyperpolarizing inputs and add the delay in the connection between
DC generator and neuron:
1. We find the delay by checking the status of the dc->nrn connection.
1. We find the resolution of the simulation from the kernel status.
1. Each current interval begins one time step after the previous interval,
is delayed by the delay and effective for the given duration.
1. We build the time axis incrementally. We only add the delay when adding
the first time point after t=0. All subsequent points are then automatically
shifted by the delay.
'''
delay = nest.GetStatus(nest.GetConnections(dc, nrn))[0]['delay']
dt = nest.GetKernelStatus('resolution')
t_dc, I_dc = [0], [0]
for td, th in zip(t_dep, t_hyp):
t_prev = t_dc[-1]
t_start_dep = t_prev + dt if t_prev > 0 else t_prev + dt + delay
t_end_dep = t_start_dep + td
t_start_hyp = t_end_dep + dt
t_end_hyp = t_start_hyp + th
t_dc.extend([t_start_dep, t_end_dep, t_start_hyp, t_end_hyp])
I_dc.extend([I_dep, I_dep, I_hyp, I_hyp])
'''
The following function turns a name such as I_NaP into proper TeX code
$I_{\mathrm{NaP}}$ for a pretty label.
'''
def texify_name(name):
return r'${}_{{\mathrm{{{}}}}}$'.format(*name.split('_'))
'''
Next, we add a right vertical axis and plot the currents with respect
to that axis.
'''
Iax = Vax.twinx()
Iax.plot(t_dc, I_dc, 'k-', lw=2, label=texify_name('I_DC'))
for iname, color in (('I_h', 'maroon'), ('I_T', 'orange'),
('I_NaP', 'crimson'), ('I_KNa', 'aqua')):
Iax.plot(t, data[iname], color=color, lw=2, label=texify_name(iname))
Iax.set_xlim(0, t_end)
Iax.set_ylim(-10., 15.)
Iax.set_ylabel('Current [pA]')
Iax.set_title('ht_neuron driven by DC current')
'''
We need to make a little extra effort to combine lines from the two axis
into one legend.
'''
lines_V, labels_V = Vax.get_legend_handles_labels()
lines_I, labels_I = Iax.get_legend_handles_labels()
try:
Iax.legend(lines_V + lines_I, labels_V + labels_I, fontsize='small')
except TypeError:
# work-around for older Matplotlib versions
Iax.legend(lines_V + lines_I, labels_V + labels_I)
'''
Note that I_KNa is not activated in this example because the neuron does
not spike. I_T has only a very small amplitude.
'''
| gpl-2.0 |
michigraber/scikit-learn | sklearn/ensemble/__init__.py | 217 | 1307 | """
The :mod:`sklearn.ensemble` module includes ensemble-based methods for
classification and regression.
"""
from .base import BaseEnsemble
from .forest import RandomForestClassifier
from .forest import RandomForestRegressor
from .forest import RandomTreesEmbedding
from .forest import ExtraTreesClassifier
from .forest import ExtraTreesRegressor
from .bagging import BaggingClassifier
from .bagging import BaggingRegressor
from .weight_boosting import AdaBoostClassifier
from .weight_boosting import AdaBoostRegressor
from .gradient_boosting import GradientBoostingClassifier
from .gradient_boosting import GradientBoostingRegressor
from .voting_classifier import VotingClassifier
from . import bagging
from . import forest
from . import weight_boosting
from . import gradient_boosting
from . import partial_dependence
__all__ = ["BaseEnsemble",
"RandomForestClassifier", "RandomForestRegressor",
"RandomTreesEmbedding", "ExtraTreesClassifier",
"ExtraTreesRegressor", "BaggingClassifier",
"BaggingRegressor", "GradientBoostingClassifier",
"GradientBoostingRegressor", "AdaBoostClassifier",
"AdaBoostRegressor", "VotingClassifier",
"bagging", "forest", "gradient_boosting",
"partial_dependence", "weight_boosting"]
| bsd-3-clause |
Brotcrunsher/BrotboxEngine | Third-Party/portaudio/test/patest_suggested_vs_streaminfo_latency.py | 74 | 5354 | #!/usr/bin/env python
"""
Run and graph the results of patest_suggested_vs_streaminfo_latency.c
Requires matplotlib for plotting: http://matplotlib.sourceforge.net/
"""
import os
from pylab import *
import numpy
from matplotlib.backends.backend_pdf import PdfPages
testExeName = "PATest.exe" # rename to whatever the compiled patest_suggested_vs_streaminfo_latency.c binary is
dataFileName = "patest_suggested_vs_streaminfo_latency.csv" # code below calls the exe to generate this file
inputDeviceIndex = -1 # -1 means default
outputDeviceIndex = -1 # -1 means default
sampleRate = 44100
pdfFilenameSuffix = "_wmme"
pdfFile = PdfPages("patest_suggested_vs_streaminfo_latency_" + str(sampleRate) + pdfFilenameSuffix +".pdf") #output this pdf file
def loadCsvData( dataFileName ):
params= ""
inputDevice = ""
outputDevice = ""
startLines = file(dataFileName).readlines(1024)
for line in startLines:
if "output device" in line:
outputDevice = line.strip(" \t\n\r#")
if "input device" in line:
inputDevice = line.strip(" \t\n\r#")
params = startLines[0].strip(" \t\n\r#")
data = numpy.loadtxt(dataFileName, delimiter=",", skiprows=4).transpose()
class R(object): pass
result = R()
result.params = params
for s in params.split(','):
if "sample rate" in s:
result.sampleRate = s
result.inputDevice = inputDevice
result.outputDevice = outputDevice
result.suggestedLatency = data[0]
result.halfDuplexOutputLatency = data[1]
result.halfDuplexInputLatency = data[2]
result.fullDuplexOutputLatency = data[3]
result.fullDuplexInputLatency = data[4]
return result;
def setFigureTitleAndAxisLabels( framesPerBufferString ):
title("PortAudio suggested (requested) vs. resulting (reported) stream latency\n" + framesPerBufferString)
ylabel("PaStreamInfo::{input,output}Latency (s)")
xlabel("Pa_OpenStream suggestedLatency (s)")
grid(True)
legend(loc="upper left")
def setDisplayRangeSeconds( maxSeconds ):
xlim(0, maxSeconds)
ylim(0, maxSeconds)
# run the test with different frames per buffer values:
compositeTestFramesPerBufferValues = [0]
# powers of two
for i in range (1,11):
compositeTestFramesPerBufferValues.append( pow(2,i) )
# multiples of 50
for i in range (1,20):
compositeTestFramesPerBufferValues.append( i * 50 )
# 10ms buffer sizes
compositeTestFramesPerBufferValues.append( 441 )
compositeTestFramesPerBufferValues.append( 882 )
# large primes
#compositeTestFramesPerBufferValues.append( 39209 )
#compositeTestFramesPerBufferValues.append( 37537 )
#compositeTestFramesPerBufferValues.append( 26437 )
individualPlotFramesPerBufferValues = [0,64,128,256,512] #output separate plots for these
isFirst = True
for framesPerBuffer in compositeTestFramesPerBufferValues:
commandString = testExeName + " " + str(inputDeviceIndex) + " " + str(outputDeviceIndex) + " " + str(sampleRate) + " " + str(framesPerBuffer) + ' > ' + dataFileName
print commandString
os.system(commandString)
d = loadCsvData(dataFileName)
if isFirst:
figure(1) # title sheet
gcf().text(0.1, 0.0,
"patest_suggested_vs_streaminfo_latency\n%s\n%s\n%s\n"%(d.inputDevice,d.outputDevice,d.sampleRate))
pdfFile.savefig()
figure(2) # composite plot, includes all compositeTestFramesPerBufferValues
if isFirst:
plot( d.suggestedLatency, d.suggestedLatency, label="Suggested latency" )
plot( d.suggestedLatency, d.halfDuplexOutputLatency )
plot( d.suggestedLatency, d.halfDuplexInputLatency )
plot( d.suggestedLatency, d.fullDuplexOutputLatency )
plot( d.suggestedLatency, d.fullDuplexInputLatency )
if framesPerBuffer in individualPlotFramesPerBufferValues: # individual plots
figure( 3 + individualPlotFramesPerBufferValues.index(framesPerBuffer) )
plot( d.suggestedLatency, d.suggestedLatency, label="Suggested latency" )
plot( d.suggestedLatency, d.halfDuplexOutputLatency, label="Half-duplex output latency" )
plot( d.suggestedLatency, d.halfDuplexInputLatency, label="Half-duplex input latency" )
plot( d.suggestedLatency, d.fullDuplexOutputLatency, label="Full-duplex output latency" )
plot( d.suggestedLatency, d.fullDuplexInputLatency, label="Full-duplex input latency" )
if framesPerBuffer == 0:
framesPerBufferText = "paFramesPerBufferUnspecified"
else:
framesPerBufferText = str(framesPerBuffer)
setFigureTitleAndAxisLabels( "user frames per buffer: "+str(framesPerBufferText) )
setDisplayRangeSeconds(2.2)
pdfFile.savefig()
setDisplayRangeSeconds(0.1)
setFigureTitleAndAxisLabels( "user frames per buffer: "+str(framesPerBufferText)+" (detail)" )
pdfFile.savefig()
isFirst = False
figure(2)
setFigureTitleAndAxisLabels( "composite of frames per buffer values:\n"+str(compositeTestFramesPerBufferValues) )
setDisplayRangeSeconds(2.2)
pdfFile.savefig()
setDisplayRangeSeconds(0.1)
setFigureTitleAndAxisLabels( "composite of frames per buffer values:\n"+str(compositeTestFramesPerBufferValues)+" (detail)" )
pdfFile.savefig()
pdfFile.close()
#uncomment this to display interactively, otherwise we just output a pdf
#show()
| mit |
yyjiang/scikit-learn | sklearn/learning_curve.py | 110 | 13467 | """Utilities to evaluate models with respect to a variable
"""
# Author: Alexander Fabisch <afabisch@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import warnings
import numpy as np
from .base import is_classifier, clone
from .cross_validation import check_cv
from .externals.joblib import Parallel, delayed
from .cross_validation import _safe_split, _score, _fit_and_score
from .metrics.scorer import check_scoring
from .utils import indexable
from .utils.fixes import astype
__all__ = ['learning_curve', 'validation_curve']
def learning_curve(estimator, X, y, train_sizes=np.linspace(0.1, 1.0, 5),
cv=None, scoring=None, exploit_incremental_learning=False,
n_jobs=1, pre_dispatch="all", verbose=0):
"""Learning curve.
Determines cross-validated training and test scores for different training
set sizes.
A cross-validation generator splits the whole dataset k times in training
and test data. Subsets of the training set with varying sizes will be used
to train the estimator and a score for each training subset size and the
test set will be computed. Afterwards, the scores will be averaged over
all k runs for each training subset size.
Read more in the :ref:`User Guide <learning_curves>`.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
generate the learning curve. If the dtype is float, it is regarded as a
fraction of the maximum size of the training set (that is determined
by the selected validation method), i.e. it has to be within (0, 1].
Otherwise it is interpreted as absolute sizes of the training sets.
Note that for classification the number of samples usually have to
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))
cv : integer, cross-validation generator, optional
If an integer is passed, it is the number of folds (defaults to 3).
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
exploit_incremental_learning : boolean, optional, default: False
If the estimator supports incremental learning, this will be
used to speed up fitting for different training set sizes.
n_jobs : integer, optional
Number of jobs to run in parallel (default 1).
pre_dispatch : integer or string, optional
Number of predispatched jobs for parallel execution (default is
all). The option can reduce the allocated memory. The string can
be an expression like '2*n_jobs'.
verbose : integer, optional
Controls the verbosity: the higher, the more messages.
Returns
-------
train_sizes_abs : array, shape = (n_unique_ticks,), dtype int
Numbers of training examples that has been used to generate the
learning curve. Note that the number of ticks might be less
than n_ticks because duplicate entries will be removed.
train_scores : array, shape (n_ticks, n_cv_folds)
Scores on training sets.
test_scores : array, shape (n_ticks, n_cv_folds)
Scores on test set.
Notes
-----
See :ref:`examples/model_selection/plot_learning_curve.py
<example_model_selection_plot_learning_curve.py>`
"""
if exploit_incremental_learning and not hasattr(estimator, "partial_fit"):
raise ValueError("An estimator must support the partial_fit interface "
"to exploit incremental learning")
X, y = indexable(X, y)
# Make a list since we will be iterating multiple times over the folds
cv = list(check_cv(cv, X, y, classifier=is_classifier(estimator)))
scorer = check_scoring(estimator, scoring=scoring)
# HACK as long as boolean indices are allowed in cv generators
if cv[0][0].dtype == bool:
new_cv = []
for i in range(len(cv)):
new_cv.append((np.nonzero(cv[i][0])[0], np.nonzero(cv[i][1])[0]))
cv = new_cv
n_max_training_samples = len(cv[0][0])
# Because the lengths of folds can be significantly different, it is
# not guaranteed that we use all of the available training data when we
# use the first 'n_max_training_samples' samples.
train_sizes_abs = _translate_train_sizes(train_sizes,
n_max_training_samples)
n_unique_ticks = train_sizes_abs.shape[0]
if verbose > 0:
print("[learning_curve] Training set sizes: " + str(train_sizes_abs))
parallel = Parallel(n_jobs=n_jobs, pre_dispatch=pre_dispatch,
verbose=verbose)
if exploit_incremental_learning:
classes = np.unique(y) if is_classifier(estimator) else None
out = parallel(delayed(_incremental_fit_estimator)(
clone(estimator), X, y, classes, train, test, train_sizes_abs,
scorer, verbose) for train, test in cv)
else:
out = parallel(delayed(_fit_and_score)(
clone(estimator), X, y, scorer, train[:n_train_samples], test,
verbose, parameters=None, fit_params=None, return_train_score=True)
for train, test in cv for n_train_samples in train_sizes_abs)
out = np.array(out)[:, :2]
n_cv_folds = out.shape[0] // n_unique_ticks
out = out.reshape(n_cv_folds, n_unique_ticks, 2)
out = np.asarray(out).transpose((2, 1, 0))
return train_sizes_abs, out[0], out[1]
def _translate_train_sizes(train_sizes, n_max_training_samples):
"""Determine absolute sizes of training subsets and validate 'train_sizes'.
Examples:
_translate_train_sizes([0.5, 1.0], 10) -> [5, 10]
_translate_train_sizes([5, 10], 10) -> [5, 10]
Parameters
----------
train_sizes : array-like, shape (n_ticks,), dtype float or int
Numbers of training examples that will be used to generate the
learning curve. If the dtype is float, it is regarded as a
fraction of 'n_max_training_samples', i.e. it has to be within (0, 1].
n_max_training_samples : int
Maximum number of training samples (upper bound of 'train_sizes').
Returns
-------
train_sizes_abs : array, shape (n_unique_ticks,), dtype int
Numbers of training examples that will be used to generate the
learning curve. Note that the number of ticks might be less
than n_ticks because duplicate entries will be removed.
"""
train_sizes_abs = np.asarray(train_sizes)
n_ticks = train_sizes_abs.shape[0]
n_min_required_samples = np.min(train_sizes_abs)
n_max_required_samples = np.max(train_sizes_abs)
if np.issubdtype(train_sizes_abs.dtype, np.float):
if n_min_required_samples <= 0.0 or n_max_required_samples > 1.0:
raise ValueError("train_sizes has been interpreted as fractions "
"of the maximum number of training samples and "
"must be within (0, 1], but is within [%f, %f]."
% (n_min_required_samples,
n_max_required_samples))
train_sizes_abs = astype(train_sizes_abs * n_max_training_samples,
dtype=np.int, copy=False)
train_sizes_abs = np.clip(train_sizes_abs, 1,
n_max_training_samples)
else:
if (n_min_required_samples <= 0 or
n_max_required_samples > n_max_training_samples):
raise ValueError("train_sizes has been interpreted as absolute "
"numbers of training samples and must be within "
"(0, %d], but is within [%d, %d]."
% (n_max_training_samples,
n_min_required_samples,
n_max_required_samples))
train_sizes_abs = np.unique(train_sizes_abs)
if n_ticks > train_sizes_abs.shape[0]:
warnings.warn("Removed duplicate entries from 'train_sizes'. Number "
"of ticks will be less than than the size of "
"'train_sizes' %d instead of %d)."
% (train_sizes_abs.shape[0], n_ticks), RuntimeWarning)
return train_sizes_abs
def _incremental_fit_estimator(estimator, X, y, classes, train, test,
train_sizes, scorer, verbose):
"""Train estimator on training subsets incrementally and compute scores."""
train_scores, test_scores = [], []
partitions = zip(train_sizes, np.split(train, train_sizes)[:-1])
for n_train_samples, partial_train in partitions:
train_subset = train[:n_train_samples]
X_train, y_train = _safe_split(estimator, X, y, train_subset)
X_partial_train, y_partial_train = _safe_split(estimator, X, y,
partial_train)
X_test, y_test = _safe_split(estimator, X, y, test, train_subset)
if y_partial_train is None:
estimator.partial_fit(X_partial_train, classes=classes)
else:
estimator.partial_fit(X_partial_train, y_partial_train,
classes=classes)
train_scores.append(_score(estimator, X_train, y_train, scorer))
test_scores.append(_score(estimator, X_test, y_test, scorer))
return np.array((train_scores, test_scores)).T
def validation_curve(estimator, X, y, param_name, param_range, cv=None,
scoring=None, n_jobs=1, pre_dispatch="all", verbose=0):
"""Validation curve.
Determine training and test scores for varying parameter values.
Compute scores for an estimator with different values of a specified
parameter. This is similar to grid search with one parameter. However, this
will also compute training scores and is merely a utility for plotting the
results.
Read more in the :ref:`User Guide <validation_curve>`.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
param_name : string
Name of the parameter that will be varied.
param_range : array-like, shape (n_values,)
The values of the parameter that will be evaluated.
cv : integer, cross-validation generator, optional
If an integer is passed, it is the number of folds (defaults to 3).
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
n_jobs : integer, optional
Number of jobs to run in parallel (default 1).
pre_dispatch : integer or string, optional
Number of predispatched jobs for parallel execution (default is
all). The option can reduce the allocated memory. The string can
be an expression like '2*n_jobs'.
verbose : integer, optional
Controls the verbosity: the higher, the more messages.
Returns
-------
train_scores : array, shape (n_ticks, n_cv_folds)
Scores on training sets.
test_scores : array, shape (n_ticks, n_cv_folds)
Scores on test set.
Notes
-----
See
:ref:`examples/model_selection/plot_validation_curve.py
<example_model_selection_plot_validation_curve.py>`
"""
X, y = indexable(X, y)
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
scorer = check_scoring(estimator, scoring=scoring)
parallel = Parallel(n_jobs=n_jobs, pre_dispatch=pre_dispatch,
verbose=verbose)
out = parallel(delayed(_fit_and_score)(
estimator, X, y, scorer, train, test, verbose,
parameters={param_name: v}, fit_params=None, return_train_score=True)
for train, test in cv for v in param_range)
out = np.asarray(out)[:, :2]
n_params = len(param_range)
n_cv_folds = out.shape[0] // n_params
out = out.reshape(n_cv_folds, n_params, 2).transpose((2, 1, 0))
return out[0], out[1]
| bsd-3-clause |
kmacinnis/sympy | sympy/physics/quantum/circuitplot.py | 25 | 12934 | """Matplotlib based plotting of quantum circuits.
Todo:
* Optimize printing of large circuits.
* Get this to work with single gates.
* Do a better job checking the form of circuits to make sure it is a Mul of
Gates.
* Get multi-target gates plotting.
* Get initial and final states to plot.
* Get measurements to plot. Might need to rethink measurement as a gate
issue.
* Get scale and figsize to be handled in a better way.
* Write some tests/examples!
"""
from __future__ import print_function, division
from sympy import Mul
from sympy.core.compatibility import u
from sympy.external import import_module
from sympy.physics.quantum.gate import Gate, OneQubitGate, CGate, CGateS
from sympy.core.core import BasicMeta
from sympy.core.assumptions import ManagedProperties
__all__ = [
'CircuitPlot',
'circuit_plot',
'labeller',
'Mz',
'Mx',
'CreateOneQubitGate',
'CreateCGate',
]
np = import_module('numpy')
matplotlib = import_module(
'matplotlib', __import__kwargs={'fromlist': ['pyplot']},
catch=(RuntimeError,)) # This is raised in environments that have no display.
if not np or not matplotlib:
class CircuitPlot(object):
def __init__(*args, **kwargs):
raise ImportError('numpy or matplotlib not available.')
def circuit_plot(*args, **kwargs):
raise ImportError('numpy or matplotlib not available.')
else:
pyplot = matplotlib.pyplot
Line2D = matplotlib.lines.Line2D
Circle = matplotlib.patches.Circle
#from matplotlib import rc
#rc('text',usetex=True)
class CircuitPlot(object):
"""A class for managing a circuit plot."""
scale = 1.0
fontsize = 20.0
linewidth = 1.0
control_radius = 0.05
not_radius = 0.15
swap_delta = 0.05
labels = []
inits = {}
label_buffer = 0.5
def __init__(self, c, nqubits, **kwargs):
self.circuit = c
self.ngates = len(self.circuit.args)
self.nqubits = nqubits
self.update(kwargs)
self._create_grid()
self._create_figure()
self._plot_wires()
self._plot_gates()
self._finish()
def update(self, kwargs):
"""Load the kwargs into the instance dict."""
self.__dict__.update(kwargs)
def _create_grid(self):
"""Create the grid of wires."""
scale = self.scale
wire_grid = np.arange(0.0, self.nqubits*scale, scale, dtype=float)
gate_grid = np.arange(0.0, self.ngates*scale, scale, dtype=float)
self._wire_grid = wire_grid
self._gate_grid = gate_grid
def _create_figure(self):
"""Create the main matplotlib figure."""
self._figure = pyplot.figure(
figsize=(self.ngates*self.scale, self.nqubits*self.scale),
facecolor='w',
edgecolor='w'
)
ax = self._figure.add_subplot(
1, 1, 1,
frameon=True
)
ax.set_axis_off()
offset = 0.5*self.scale
ax.set_xlim(self._gate_grid[0] - offset, self._gate_grid[-1] + offset)
ax.set_ylim(self._wire_grid[0] - offset, self._wire_grid[-1] + offset)
ax.set_aspect('equal')
self._axes = ax
def _plot_wires(self):
"""Plot the wires of the circuit diagram."""
xstart = self._gate_grid[0]
xstop = self._gate_grid[-1]
xdata = (xstart - self.scale, xstop + self.scale)
for i in range(self.nqubits):
ydata = (self._wire_grid[i], self._wire_grid[i])
line = Line2D(
xdata, ydata,
color='k',
lw=self.linewidth
)
self._axes.add_line(line)
if self.labels:
init_label_buffer = 0
if self.inits.get(self.labels[i]): init_label_buffer = 0.25
self._axes.text(
xdata[0]-self.label_buffer-init_label_buffer,ydata[0],
render_label(self.labels[i],self.inits),
size=self.fontsize,
color='k',ha='center',va='center')
self._plot_measured_wires()
def _plot_measured_wires(self):
ismeasured = self._measurements()
xstop = self._gate_grid[-1]
dy = 0.04 # amount to shift wires when doubled
# Plot doubled wires after they are measured
for im in ismeasured:
xdata = (self._gate_grid[ismeasured[im]],xstop+self.scale)
ydata = (self._wire_grid[im]+dy,self._wire_grid[im]+dy)
line = Line2D(
xdata, ydata,
color='k',
lw=self.linewidth
)
self._axes.add_line(line)
# Also double any controlled lines off these wires
for i,g in enumerate(self._gates()):
if isinstance(g, CGate) or isinstance(g, CGateS):
wires = g.controls + g.targets
for wire in wires:
if wire in ismeasured and \
self._gate_grid[i] > self._gate_grid[ismeasured[wire]]:
ydata = min(wires), max(wires)
xdata = self._gate_grid[i]-dy, self._gate_grid[i]-dy
line = Line2D(
xdata, ydata,
color='k',
lw=self.linewidth
)
self._axes.add_line(line)
def _gates(self):
"""Create a list of all gates in the circuit plot."""
gates = []
if isinstance(self.circuit, Mul):
for g in reversed(self.circuit.args):
if isinstance(g, Gate):
gates.append(g)
elif isinstance(self.circuit, Gate):
gates.append(self.circuit)
return gates
def _plot_gates(self):
"""Iterate through the gates and plot each of them."""
for i, gate in enumerate(self._gates()):
gate.plot_gate(self, i)
def _measurements(self):
"""Return a dict {i:j} where i is the index of the wire that has
been measured, and j is the gate where the wire is measured.
"""
ismeasured = {}
for i,g in enumerate(self._gates()):
if getattr(g,'measurement',False):
for target in g.targets:
if target in ismeasured:
if ismeasured[target] > i:
ismeasured[target] = i
else:
ismeasured[target] = i
return ismeasured
def _finish(self):
# Disable clipping to make panning work well for large circuits.
for o in self._figure.findobj():
o.set_clip_on(False)
def one_qubit_box(self, t, gate_idx, wire_idx):
"""Draw a box for a single qubit gate."""
x = self._gate_grid[gate_idx]
y = self._wire_grid[wire_idx]
self._axes.text(
x, y, t,
color='k',
ha='center',
va='center',
bbox=dict(ec='k', fc='w', fill=True, lw=self.linewidth),
size=self.fontsize
)
def two_qubit_box(self, t, gate_idx, wire_idx):
"""Draw a box for a two qubit gate. Doesn't work yet.
"""
x = self._gate_grid[gate_idx]
y = self._wire_grid[wire_idx]+0.5
print(self._gate_grid)
print(self._wire_grid)
obj = self._axes.text(
x, y, t,
color='k',
ha='center',
va='center',
bbox=dict(ec='k', fc='w', fill=True, lw=self.linewidth),
size=self.fontsize
)
def control_line(self, gate_idx, min_wire, max_wire):
"""Draw a vertical control line."""
xdata = (self._gate_grid[gate_idx], self._gate_grid[gate_idx])
ydata = (self._wire_grid[min_wire], self._wire_grid[max_wire])
line = Line2D(
xdata, ydata,
color='k',
lw=self.linewidth
)
self._axes.add_line(line)
def control_point(self, gate_idx, wire_idx):
"""Draw a control point."""
x = self._gate_grid[gate_idx]
y = self._wire_grid[wire_idx]
radius = self.control_radius
c = Circle(
(x, y),
radius*self.scale,
ec='k',
fc='k',
fill=True,
lw=self.linewidth
)
self._axes.add_patch(c)
def not_point(self, gate_idx, wire_idx):
"""Draw a NOT gates as the circle with plus in the middle."""
x = self._gate_grid[gate_idx]
y = self._wire_grid[wire_idx]
radius = self.not_radius
c = Circle(
(x, y),
radius,
ec='k',
fc='w',
fill=False,
lw=self.linewidth
)
self._axes.add_patch(c)
l = Line2D(
(x, x), (y - radius, y + radius),
color='k',
lw=self.linewidth
)
self._axes.add_line(l)
def swap_point(self, gate_idx, wire_idx):
"""Draw a swap point as a cross."""
x = self._gate_grid[gate_idx]
y = self._wire_grid[wire_idx]
d = self.swap_delta
l1 = Line2D(
(x - d, x + d),
(y - d, y + d),
color='k',
lw=self.linewidth
)
l2 = Line2D(
(x - d, x + d),
(y + d, y - d),
color='k',
lw=self.linewidth
)
self._axes.add_line(l1)
self._axes.add_line(l2)
def circuit_plot(c, nqubits, **kwargs):
"""Draw the circuit diagram for the circuit with nqubits.
Parameters
==========
c : circuit
The circuit to plot. Should be a product of Gate instances.
nqubits : int
The number of qubits to include in the circuit. Must be at least
as big as the largest `min_qubits`` of the gates.
"""
return CircuitPlot(c, nqubits, **kwargs)
def render_label(label, inits={}):
"""Slightly more flexible way to render labels.
>>> from sympy.physics.quantum.circuitplot import render_label
>>> render_label('q0')
'$|q0\\\\rangle$'
>>> render_label('q0', {'q0':'0'})
'$|q0\\\\rangle=|0\\\\rangle$'
"""
init = inits.get(label)
if init:
return r'$|%s\rangle=|%s\rangle$' % (label, init)
return r'$|%s\rangle$' % label
def labeller(n, symbol='q'):
"""Autogenerate labels for wires of quantum circuits.
Parameters
==========
n : int
number of qubits in the circuit
symbol : string
A character string to precede all gate labels. E.g. 'q_0', 'q_1', etc.
>>> from sympy.physics.quantum.circuitplot import labeller
>>> labeller(2)
['q_1', 'q_0']
>>> labeller(3,'j')
['j_2', 'j_1', 'j_0']
"""
return ['%s_%d' % (symbol,n-i-1) for i in range(n)]
class Mz(OneQubitGate):
"""Mock-up of a z measurement gate.
This is in circuitplot rather than gate.py because it's not a real
gate, it just draws one.
"""
measurement = True
gate_name='Mz'
gate_name_latex=u('M_z')
class Mx(OneQubitGate):
"""Mock-up of an x measurement gate.
This is in circuitplot rather than gate.py because it's not a real
gate, it just draws one.
"""
measurement = True
gate_name='Mx'
gate_name_latex=u('M_x')
class CreateOneQubitGate(ManagedProperties):
def __new__(mcl, name, latexname=None):
if not latexname:
latexname = name
return BasicMeta.__new__(mcl, name + "Gate", (OneQubitGate,),
{'gate_name': name, 'gate_name_latex': latexname})
def CreateCGate(name, latexname=None):
"""Use a lexical closure to make a controlled gate.
"""
if not latexname:
latexname = name
onequbitgate = CreateOneQubitGate(name, latexname)
def ControlledGate(ctrls,target):
return CGate(tuple(ctrls),onequbitgate(target))
return ControlledGate
| bsd-3-clause |
camallen/aggregation | experimental/mapreduce/reducer/DBSCAN.py | 2 | 1655 | #!/usr/bin/env python
import sys
from sklearn.cluster import DBSCAN
import numpy as np
#read in the type for each of the parameters - continuous vs. discrete - we will ignore discrete values
additional_param_type = []
configFile = sys.argv[1]
with open(configFile, 'r') as conf:
configuration = conf.read()
exec(configuration)
param_type = ["continuous","continuous"]
param_type.extend(additional_param_type)
curr_subject = None
pts = []
# input comes from STDIN (standard input)
for line in sys.stdin:
subject_id,v = line.split("\t")
v = v[:-1]
if curr_subject != subject_id:
if curr_subject is not None:
X = np.array(pts)
db = DBSCAN(eps=100, min_samples=1).fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
cluster_id = db.labels_
unique_ids = set(cluster_id)
sys.stdout.write(subject_id+","+str(len(pts[0])))
for id in unique_ids:
if id == -1:
continue
in_cluster = [p for i,p in enumerate(pts) if cluster_id[i] == id]
#print in_cluster
center = [np.mean(c) for c in zip(*in_cluster)]
for c in center:
sys.stdout.write(","+ str(c))
print
curr_subject = subject_id
param = v.split(",")
gold_standard = (param[0] == "True")
user_id = param[1]
#treat the rest of the parameters as a multi-dimensional point
pts.append([float(p) for i,p in enumerate(param[2:]) if param_type[i] == "continuous"])
| apache-2.0 |
dwweiss/pmLib | src/flow/initialTurbulenceEnergyDissipation.py | 1 | 3650 | """
Copyright (c) 2016-17 by Dietmar W Weiss
This is free software; you can redistribute it and/or modify it
under the terms of the GNU Lesser General Public License as
published by the Free Software Foundation; either version 3.0 of
the License, or (at your option) any later version.
This software is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this software; if not, write to the Free
Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
02110-1301 USA, or see the FSF site: http://www.fsf.org.
Version:
2017-12-06 DWW
"""
def initialTurbulenceEnergyAndDissipation(v, D, nu):
"""
Turbulence kinetic energy and dissipation rate
Args:
v (float):
axial component of velocity [m/s]
D (float):
inner pipe diameter [m]
nu (float):
kinematic viscosity [m^2/s]
Returns:
k_turb (float):
turbulent kinetic energy [m^2/s^2]
eps_turb (float):
turbulence dissipation rate [m/s^2]
"""
C_u = 0.09 # turbulence model constant [m/s^2]
Re = v * D / nu # Reynolds number [/]
I = 0.16 * Re**-0.125 # turbulence intensity [/]
l = 0.07 * D # turbulence length scale [m]
k_turb = 1.5 * (v * I)**2 # turb. kinetic energy [m^2/s^2]
eps_turb = C_u**0.75 * k_turb**1.5 / l # turb. dissipation rate [m/s^2]
return k_turb, eps_turb
# Example #####################################################################
if __name__ == '__main__':
import numpy as np
import matplotlib.pyplot as plt
D = 25e-3
nu = 1e-6
v_seq = [0.1, 0.2, 0.5, 1, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 7, 8, 9, 10]
nu_seq = [1E-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2]
for nu in nu_seq:
kk, ee = [], []
for v in v_seq:
k, eps = initialTurbulenceEnergyAndDissipation(v, D, nu)
kk.append(k)
ee.append(eps)
plt.plot(v_seq, kk, ls='--', label=r'$k(\nu:'+str(nu*1e6)+')$')
plt.plot(v_seq, ee, label=r'$\varepsilon(\nu:'+str(nu*1e6)+')$')
fontsize = 12
plt.title(r'Axial velocity $v_z(r) = f(n, \nu)$')
plt.rcParams.update({'font.size': fontsize})
plt.rcParams['legend.fontsize'] = fontsize
plt.xlabel('$v$ [m/s]')
plt.ylabel(r'$k, \varepsilon$ [/]')
plt.yscale('log', nonposy='clip')
plt.grid()
plt.legend(bbox_to_anchor=(1.1, 1.03), loc='upper left')
plt.show()
# same as above, y-axis is linear
for nu in nu_seq:
kk, ee = [], []
for v in v_seq:
k, eps = initialTurbulenceEnergyAndDissipation(v, D, nu)
kk.append(k)
ee.append(eps)
plt.plot(v_seq, kk, ls='--', label=r'$k(\nu:'+str(nu*1e6)+')$')
# plt.plot(v_seq, ee, label=r'$\varepsilon(\nu:'+str(nu*1e6)+')$')
fontsize = 12
plt.title(r'Axial velocity $v_z(r) = f(n, \nu)$')
plt.rcParams.update({'font.size': fontsize})
plt.rcParams['legend.fontsize'] = fontsize
plt.xlabel('$v$ [m/s]')
plt.ylabel(r'$k, \varepsilon$ [/]')
plt.grid()
plt.legend(bbox_to_anchor=(1.1, 1.03), loc='upper left')
plt.show()
| lgpl-3.0 |
SANDAG/orca | orca/server/tests/test_server.py | 2 | 10586 | import json
import orca
import pandas as pd
import pandas.util.testing as pdt
import pytest
from .. import server
@pytest.fixture
def tapp():
server.app.config['TESTING'] = True
return server.app.test_client()
@pytest.fixture(scope='module')
def dfa():
return pd.DataFrame(
{'a': [100, 200, 300, 200, 100]},
index=['v', 'w', 'x', 'y', 'z'])
@pytest.fixture(scope='module')
def dfb():
return pd.DataFrame(
{'b': [70, 80, 90],
'a_id': ['w', 'v', 'z']},
index=['a', 'b', 'b'])
@pytest.fixture(scope='module')
def dfa_col(dfa):
return pd.Series([2, 4, 6, 8, 10], index=dfa.index)
@pytest.fixture(scope='module')
def dfb_col(dfb):
return pd.Series([10, 20, 30], index=dfb.index)
@pytest.fixture(scope='module')
def dfa_factor():
return 0.5
@pytest.fixture(scope='module')
def dfb_factor():
return 2
@pytest.fixture(scope='module', autouse=True)
def setup_orca(dfa, dfb, dfa_col, dfb_col, dfa_factor, dfb_factor):
orca.add_injectable('a_factor', dfa_factor)
@orca.injectable()
def b_factor():
return dfb_factor
orca.add_table('dfa', dfa)
@orca.table('dfb')
def dfb_table():
return dfb
orca.add_column('dfa', 'acol', dfa_col)
orca.add_column('dfb', 'bcol', dfb_col)
@orca.column('dfa')
def extra_acol(a_factor):
return dfa_col * a_factor
@orca.column('dfb')
def extra_bcol(b_factor):
return dfb_col * b_factor
orca.broadcast('dfb', 'dfa', cast_on='a_id', onto_index=True)
@orca.step()
def test_step(dfa, dfb):
pass
def test_schema(tapp):
rv = tapp.get('/schema')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert set(data['tables']) == {'dfa', 'dfb'}
assert set(data['columns']['dfa']) == {'extra_acol', 'acol', 'a'}
assert set(data['columns']['dfb']) == {'bcol', 'extra_bcol', 'a_id', 'b'}
assert data['steps'] == ['test_step']
assert set(data['injectables']) == {'a_factor', 'b_factor'}
assert data['broadcasts'] == [['dfb', 'dfa']]
def test_list_tables(tapp):
rv = tapp.get('/tables')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert set(data['tables']) == {'dfa', 'dfb'}
def test_table_info(tapp):
rv = tapp.get('/tables/dfa/info')
assert rv.status_code == 200
data = rv.data.decode('utf-8')
assert 'extra_acol' in data
def test_table_preview(tapp):
rv = tapp.get('/tables/dfa/preview')
assert rv.status_code == 200
data = rv.data.decode('utf-8')
assert data == orca.get_table('dfa').to_frame().to_json(orient='split')
def test_table_preview_404(tapp):
rv = tapp.get('/tables/not_a_table/preview')
assert rv.status_code == 404
def test_table_describe(tapp):
rv = tapp.get('/tables/dfa/describe')
assert rv.status_code == 200
data = rv.data.decode('utf-8')
assert data == \
orca.get_table('dfa').to_frame().describe().to_json(orient='split')
def test_table_definition_frame(tapp):
rv = tapp.get('/tables/dfa/definition')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data == {'type': 'dataframe'}
def test_table_definition_func(tapp):
rv = tapp.get('/tables/dfb/definition')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data['type'] == 'function'
assert data['filename'].endswith('test_server.py')
assert isinstance(data['lineno'], int)
assert data['text'] == (
" @orca.table('dfb')\n"
" def dfb_table():\n"
" return dfb\n")
assert 'dfb_table' in data['html']
def test_table_csv(tapp):
rv = tapp.get('/tables/dfb/csv')
assert rv.status_code == 200
data = rv.data.decode('utf-8')
assert rv.mimetype == 'text/csv'
assert data == orca.get_table('dfb').to_frame().to_csv()
def test_list_table_columns(tapp):
rv = tapp.get('/tables/dfb/columns')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert set(data['columns']) == {'a_id', 'b', 'bcol', 'extra_bcol'}
def test_column_definition_local(tapp):
rv = tapp.get('/tables/dfa/columns/a/definition')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data == {'type': 'local'}
def test_column_definition_series(tapp):
rv = tapp.get('/tables/dfa/columns/acol/definition')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data == {'type': 'series'}
def test_column_definition_func(tapp):
rv = tapp.get('/tables/dfa/columns/extra_acol/definition')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data['type'] == 'function'
assert data['filename'].endswith('test_server.py')
assert isinstance(data['lineno'], int)
assert data['text'] == (
" @orca.column('dfa')\n"
" def extra_acol(a_factor):\n"
" return dfa_col * a_factor\n")
assert 'extra_acol' in data['html']
def test_column_describe(tapp):
rv = tapp.get('/tables/dfa/columns/extra_acol/describe')
assert rv.status_code == 200
data = rv.data.decode('utf-8')
assert data == \
orca.get_table('dfa').extra_acol.describe().to_json(orient='split')
def test_column_csv(tapp, dfa):
rv = tapp.get('/tables/dfa/columns/a/csv')
assert rv.status_code == 200
data = rv.data.decode('utf-8')
assert data == dfa.a.to_csv(path=None)
def test_no_column_404(tapp):
rv = tapp.get('/tables/dfa/columns/not-a-column/csv')
assert rv.status_code == 404
def test_list_injectables(tapp):
rv = tapp.get('/injectables')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert set(data['injectables']) == {'a_factor', 'b_factor'}
def test_injectable_repr(tapp, dfb_factor):
rv = tapp.get('/injectables/b_factor/repr')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data == {'type': str(type(42)), 'repr': '2'}
def test_no_injectable_404(tapp):
rv = tapp.get('/injectables/nope/repr')
assert rv.status_code == 404
def test_injectable_definition_var(tapp):
rv = tapp.get('/injectables/a_factor/definition')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data == {'type': 'variable'}
def test_injectable_definition_func(tapp):
rv = tapp.get('/injectables/b_factor/definition')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data['type'] == 'function'
assert data['filename'].endswith('test_server.py')
assert isinstance(data['lineno'], int)
assert data['text'] == (
" @orca.injectable()\n"
" def b_factor():\n"
" return dfb_factor\n")
assert 'b_factor' in data['html']
def test_list_broadcasts(tapp):
rv = tapp.get('/broadcasts')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data == {'broadcasts': [{'cast': 'dfb', 'onto': 'dfa'}]}
def test_broadcast_definition(tapp):
rv = tapp.get('/broadcasts/dfb/dfa/definition')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data == {
'cast': 'dfb',
'onto': 'dfa',
'cast_on': 'a_id',
'onto_on': None,
'cast_index': False,
'onto_index': True}
def test_no_broadcast_404(tapp):
rv = tapp.get('/broadcasts/table1/table2/definition')
assert rv.status_code == 404
def test_list_steps(tapp):
rv = tapp.get('/steps')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data == {'steps': ['test_step']}
def test_no_step_404(tapp):
rv = tapp.get('/steps/not_a_step/definition')
assert rv.status_code == 404
def test_step_definition(tapp):
rv = tapp.get('/steps/test_step/definition')
assert rv.status_code == 200
data = json.loads(rv.data.decode('utf-8'))
assert data['filename'].endswith('test_server.py')
assert isinstance(data['lineno'], int)
assert data['text'] == (
" @orca.step()\n"
" def test_step(dfa, dfb):\n"
" pass\n")
assert 'test_step' in data['html']
def test_table_groupbyagg_errors(tapp):
# non-existant column
rv = tapp.get('/tables/dfa/groupbyagg?column=notacolumn')
assert rv.status_code == 400
# both by and level missing
rv = tapp.get('/tables/dfa/groupbyagg?column=a')
assert rv.status_code == 400
# bad or missing agg type
rv = tapp.get('/tables/dfa/groupbyagg?column=a&level=0&agg=notanagg')
assert rv.status_code == 400
def test_table_groupbyagg_by_size(tapp):
rv = tapp.get('/tables/dfa/groupbyagg?by=a&column=a&agg=size')
assert rv.status_code == 200
data = rv.data.decode('utf-8')
test = pd.read_json(data, orient='split', typ='series')
pdt.assert_series_equal(
test,
pd.Series([2, 2, 1], index=[100, 200, 300]))
def test_table_groupbyagg_level_mean(tapp):
rv = tapp.get('/tables/dfb/groupbyagg?level=0&column=b&agg=mean')
assert rv.status_code == 200
data = rv.data.decode('utf-8')
test = pd.read_json(data, orient='split', typ='series')
pdt.assert_series_equal(
test,
pd.Series([70, 85], index=['a', 'b'], name='b'))
def test_table_groupbyagg_level_median(tapp):
rv = tapp.get('/tables/dfb/groupbyagg?level=0&column=b&agg=median')
assert rv.status_code == 200
data = rv.data.decode('utf-8')
test = pd.read_json(data, orient='split', typ='series')
pdt.assert_series_equal(
test,
pd.Series([70, 85], index=['a', 'b'], name='b'))
def test_table_groupbyagg_level_sum(tapp):
rv = tapp.get('/tables/dfb/groupbyagg?level=0&column=b&agg=sum')
assert rv.status_code == 200
data = rv.data.decode('utf-8')
test = pd.read_json(data, orient='split', typ='series')
pdt.assert_series_equal(
test,
pd.Series([70, 170], index=['a', 'b'], name='b'))
def test_table_groupbyagg_level_std(tapp):
rv = tapp.get('/tables/dfb/groupbyagg?level=0&column=b&agg=std')
assert rv.status_code == 200
data = rv.data.decode('utf-8')
test = pd.read_json(data, orient='split', typ='series')
pdt.assert_series_equal(
test,
pd.Series(
[pd.np.nan, pd.Series([80, 90]).std()],
index=['a', 'b'], name='b'))
| bsd-3-clause |
hansomesong/TracesAnalyzer | 20160218Tasks/dissimilarity_Benoit.py | 1 | 131080 | # -*- coding: utf-8 -*-
__author__ = 'yueli'
import matplotlib.pyplot as plt
from config.config import *
import numpy as np
from collections import Counter
import math
plt.rc('xtick', labelsize='45')
plt.rc('ytick', labelsize='45')
import matplotlib as mpl
mpl.rcParams['text.usetex'] = True
mpl.rcParams.update({'figure.autolayout': True})
# From the obtained original path file in column B of statistic_all.csv to compute the dissimilarity of its
# corresponding <EID,MR,VP>, and then return the result
# input = original file path
# e.g.: "/Users/yueli/Documents/Codes/Luigi_Codes/PlanetLab_20140716/ucl/mappings/onelab1-EID-0.0.0.0-MR-149.20.48.61.log"
# output = dissimilarity value for this file
def dissimilarity_calculator(origin_file_path):
# build a .csv file by using the original file path
probe = origin_file_path.split("/")[7]
file_name = "{0}.csv".format(origin_file_path.split("/")[9])
file = os.path.join(PLANET_CSV_DIR, probe, file_name)
rloc_list_temp = []
rloc_list_current =[]
dis_list = []
with open(file) as f_handler:
next(f_handler)
for line in f_handler:
lines = line.split(";")
if lines[0] == 'RoundNoReply':
continue
else:
for rloc in lines[14:]:
if not rloc:
continue
else:
rloc_list_current.append(rloc.split(",")[1])
if not rloc_list_temp:
rloc_list_temp = rloc_list_current
rloc_list_current = []
continue
# rloc_list_temp has been filled, begin to compare
if rloc_list_current == rloc_list_temp:
rloc_list_temp = rloc_list_current
rloc_list_current = []
else:
# Begin to compute the dissimilarity
print "temp & current =", set(rloc_list_temp) & set(rloc_list_current)
print "temp | current =", set(rloc_list_temp) | set(rloc_list_current)
dis_list.append(1 - float(len(set(rloc_list_temp) & set(rloc_list_current)))/float(len(set(rloc_list_temp) | set(rloc_list_current))))
print "dis_list =", dis_list
if not dis_list:
dissimilarity_value = 0
else:
dissimilarity_value = np.mean(dis_list)
return dissimilarity_value
# pdf list ==> cdf list
# input = pdf_lidt[pdf1, pdf2, ...]
# output = cdf_list[cdf1, cdf2, ...]
def cdf_from_pdf_list(pdf_list):
cdf_list = []
for value in pdf_list:
if not cdf_list:
cdf_list.append(value)
else:
cdf_list.append(value + cdf_list[-1])
return cdf_list
if __name__ == '__main__':
# with open(os.path.join(CSV_FILE_DESTDIR, 'statistic_all.csv')) as statistic_file:
# dissimilarity_list = []
#
# next(statistic_file)
# for line in statistic_file:
# lines = line.split(";")
# if lines[5] == 'True':
# dissimilarity_list.append(0)
# elif 'NegativeReply' in lines[9].split(",") and 'RoundNormal' in lines[9].split(","):
# dissimilarity_list.append(1)
# else:
# dissimilarity_list.append(dissimilarity_calculator(lines[1]))
#
# print "dissimilarity_list =", dissimilarity_list
# To save time, once the above codes hava been executed once, we can record its result in dissimilarity_list,
# so that the figure can be directly plotted from this list
dissimilarity_list = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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# After getting the dissimilarity of all <EID, MR, VP>, we compute the PDF & CDF of dissimilarity_list
dissimilarity_int_list = [math.floor(i*100) for i in dissimilarity_list]
dissimilarity_dict = dict(Counter(dissimilarity_int_list))
dissimilarity_dict = sorted(dissimilarity_dict.items(), key=lambda e:e[0])
print "dissimilarity_dict =", dissimilarity_dict
x_values = [ i[0] for i in dissimilarity_dict[1:]] # Damien doesn't let plot the stable value, i.e., the first value
y_values = [ i[1] for i in dissimilarity_dict[1:]] # Damien doesn't let plot the stable value, i.e., the first value
y_pdf = [float(i)/float(sum(y_values))*100 for i in y_values]
y_cdf = cdf_from_pdf_list(y_pdf)
print "X:", x_values
print "Y:", y_values
print "Y pdf =", y_pdf
print "Y cdf =", y_cdf
# # ############### Plot Part
# # Modify the size and dpi of picture, default size is (8,6), default dpi is 80
# or this command to set the figure size:
# plt.figure(figsize=(15, 12))
# plt.gcf().set_size_inches(11,9)
fig = plt.figure()
ax = fig.add_axes([0.23, 0.23, 0.85, 0.83])
# plt.tight_layout()
plt.plot(x_values, y_cdf, c='black', linewidth=5)
plt.axis([x_values[0], 100, 0, 100], font)
plt.xlabel(r'\mathrm{dissimilarity}', font)
plt.ylabel(r'\mathrm{cdf (\%)}', font)
locs = [ 40, 50, 60, 70, 80, 90, 100]
n_labels = ['$0.4$', '$0.5$', '$0.6$', '$0.7$', '$0.8$', '$0.9$', '$1.0$']
plt.xticks(locs, n_labels)
plt.grid(True, color='gray', linestyle='dashed')
plt.gca().xaxis.grid(True, color='gray', linestyle='dashed', which='minor')
plt.savefig(os.path.join(PLOT_DIR, 'Plot_newSize', 'toto.png')) | gpl-2.0 |
astocko/statsmodels | statsmodels/genmod/cov_struct.py | 6 | 40352 | from statsmodels.compat.python import iterkeys, itervalues, zip, range
from statsmodels.stats.correlation_tools import cov_nearest
import numpy as np
import pandas as pd
from scipy import linalg as spl
from collections import defaultdict
from statsmodels.tools.sm_exceptions import (ConvergenceWarning,
IterationLimitWarning)
import warnings
"""
Some details for the covariance calculations can be found in the Stata
docs:
http://www.stata.com/manuals13/xtxtgee.pdf
"""
class CovStruct(object):
"""
A base class for correlation and covariance structures of grouped
data.
Each implementation of this class takes the residuals from a
regression model that has been fitted to grouped data, and uses
them to estimate the within-group dependence structure of the
random errors in the model.
The state of the covariance structure is represented through the
value of the class variable `dep_params`. The default state of a
newly-created instance should correspond to the identity
correlation matrix.
"""
def __init__(self, cov_nearest_method="clipped"):
# Parameters describing the dependency structure
self.dep_params = None
# Keep track of the number of times that the covariance was
# adjusted.
self.cov_adjust = []
# Method for projecting the covariance matrix if it not SPD.
self.cov_nearest_method = cov_nearest_method
def initialize(self, model):
"""
Called by GEE, used by implementations that need additional
setup prior to running `fit`.
Parameters
----------
model : GEE class
A reference to the parent GEE class instance.
"""
self.model = model
def update(self, params):
"""
Updates the association parameter values based on the current
regression coefficients.
Parameters
----------
params : array-like
Working values for the regression parameters.
"""
raise NotImplementedError
def covariance_matrix(self, endog_expval, index):
"""
Returns the working covariance or correlation matrix for a
given cluster of data.
Parameters
----------
endog_expval: array-like
The expected values of endog for the cluster for which the
covariance or correlation matrix will be returned
index: integer
The index of the cluster for which the covariane or
correlation matrix will be returned
Returns
-------
M: matrix
The covariance or correlation matrix of endog
is_cor: bool
True if M is a correlation matrix, False if M is a
covariance matrix
"""
raise NotImplementedError
def covariance_matrix_solve(self, expval, index, stdev, rhs):
"""
Solves matrix equations of the form `covmat * soln = rhs` and
returns the values of `soln`, where `covmat` is the covariance
matrix represented by this class.
Parameters
----------
expval: array-like
The expected value of endog for each observed value in the
group.
index: integer
The group index.
stdev : array-like
The standard deviation of endog for each observation in
the group.
rhs : list/tuple of array-like
A set of right-hand sides; each defines a matrix equation
to be solved.
Returns
-------
soln : list/tuple of array-like
The solutions to the matrix equations.
Notes
-----
Returns None if the solver fails.
Some dependence structures do not use `expval` and/or `index`
to determine the correlation matrix. Some families
(e.g. binomial) do not use the `stdev` parameter when forming
the covariance matrix.
If the covariance matrix is singular or not SPD, it is
projected to the nearest such matrix. These projection events
are recorded in the fit_history member of the GEE model.
Systems of linear equations with the covariance matrix as the
left hand side (LHS) are solved for different right hand sides
(RHS); the LHS is only factorized once to save time.
This is a default implementation, it can be reimplemented in
subclasses to optimize the linear algebra according to the
struture of the covariance matrix.
"""
vmat, is_cor = self.covariance_matrix(expval, index)
if is_cor:
vmat *= np.outer(stdev, stdev)
# Factor the covariance matrix. If the factorization fails,
# attempt to condition it into a factorizable matrix.
threshold = 1e-2
success = False
cov_adjust = 0
for itr in range(20):
try:
vco = spl.cho_factor(vmat)
success = True
break
except np.linalg.LinAlgError:
vmat = cov_nearest(vmat, method=self.cov_nearest_method,
threshold=threshold)
threshold *= 2
cov_adjust += 1
self.cov_adjust.append(cov_adjust)
# Last resort if we still can't factor the covariance matrix.
if success == False:
warnings.warn("Unable to condition covariance matrix to an SPD matrix using cov_nearest",
ConvergenceWarning)
vmat = np.diag(np.diag(vmat))
vco = spl.cho_factor(vmat)
soln = [spl.cho_solve(vco, x) for x in rhs]
return soln
def summary(self):
"""
Returns a text summary of the current estimate of the
dependence structure.
"""
raise NotImplementedError
class Independence(CovStruct):
"""
An independence working dependence structure.
"""
# Nothing to update
def update(self, params):
return
def covariance_matrix(self, expval, index):
dim = len(expval)
return np.eye(dim, dtype=np.float64), True
def covariance_matrix_solve(self, expval, index, stdev, rhs):
v = stdev**2
rslt = []
for x in rhs:
if x.ndim == 1:
rslt.append(x / v)
else:
rslt.append(x / v[:, None])
return rslt
update.__doc__ = CovStruct.update.__doc__
covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__
def summary(self):
return "Observations within a cluster are modeled as being independent."
class Exchangeable(CovStruct):
"""
An exchangeable working dependence structure.
"""
def __init__(self):
super(Exchangeable, self).__init__()
# The correlation between any two values in the same cluster
self.dep_params = 0.
def update(self, params):
endog = self.model.endog_li
nobs = self.model.nobs
varfunc = self.model.family.variance
cached_means = self.model.cached_means
has_weights = self.model.weights is not None
weights_li = self.model.weights
residsq_sum, scale = 0, 0
fsum1, fsum2, n_pairs = 0., 0., 0.
for i in range(self.model.num_group):
expval, _ = cached_means[i]
stdev = np.sqrt(varfunc(expval))
resid = (endog[i] - expval) / stdev
f = weights_li[i] if has_weights else 1.
ngrp = len(resid)
residsq = np.outer(resid, resid)
scale += f * np.trace(residsq)
fsum1 += f * len(endog[i])
residsq = np.tril(residsq, -1)
residsq_sum += f * residsq.sum()
npr = 0.5 * ngrp * (ngrp - 1)
fsum2 += f * npr
n_pairs += npr
ddof = self.model.ddof_scale
scale /= (fsum1 * (nobs - ddof) / float(nobs))
residsq_sum /= scale
self.dep_params = residsq_sum / (fsum2 * (n_pairs - ddof) / float(n_pairs))
def covariance_matrix(self, expval, index):
dim = len(expval)
dp = self.dep_params * np.ones((dim, dim), dtype=np.float64)
np.fill_diagonal(dp, 1)
return dp, True
def covariance_matrix_solve(self, expval, index, stdev, rhs):
k = len(expval)
c = self.dep_params / (1. - self.dep_params)
c /= 1. + self.dep_params * (k - 1)
rslt = []
for x in rhs:
if x.ndim == 1:
x1 = x / stdev
y = x1 / (1. - self.dep_params)
y -= c * sum(x1)
y /= stdev
else:
x1 = x / stdev[:, None]
y = x1 / (1. - self.dep_params)
y -= c * x1.sum(0)
y /= stdev[:, None]
rslt.append(y)
return rslt
update.__doc__ = CovStruct.update.__doc__
covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__
def summary(self):
return ("The correlation between two observations in the " +
"same cluster is %.3f" % self.dep_params)
class Nested(CovStruct):
"""
A nested working dependence structure.
A working dependence structure that captures a nested hierarchy of
groups, each level of which contributes to the random error term
of the model.
When using this working covariance structure, `dep_data` of the
GEE instance should contain a n_obs x k matrix of 0/1 indicators,
corresponding to the k subgroups nested under the top-level
`groups` of the GEE instance. These subgroups should be nested
from left to right, so that two observations with the same value
for column j of `dep_data` should also have the same value for all
columns j' < j (this only applies to observations in the same
top-level cluster given by the `groups` argument to GEE).
Examples
--------
Suppose our data are student test scores, and the students are in
classrooms, nested in schools, nested in school districts. The
school district is the highest level of grouping, so the school
district id would be provided to GEE as `groups`, and the school
and classroom id's would be provided to the Nested class as the
`dep_data` argument, e.g.
0 0 # School 0, classroom 0, student 0
0 0 # School 0, classroom 0, student 1
0 1 # School 0, classroom 1, student 0
0 1 # School 0, classroom 1, student 1
1 0 # School 1, classroom 0, student 0
1 0 # School 1, classroom 0, student 1
1 1 # School 1, classroom 1, student 0
1 1 # School 1, classroom 1, student 1
Labels lower in the hierarchy are recycled, so that student 0 in
classroom 0 is different fro student 0 in classroom 1, etc.
Notes
-----
The calculations for this dependence structure involve all pairs
of observations within a group (that is, within the top level
`group` structure passed to GEE). Large group sizes will result
in slow iterations.
The variance components are estimated using least squares
regression of the products r*r', for standardized residuals r and
r' in the same group, on a vector of indicators defining which
variance components are shared by r and r'.
"""
def initialize(self, model):
"""
Called on the first call to update
`ilabels` is a list of n_i x n_i matrices containing integer
labels that correspond to specific correlation parameters.
Two elements of ilabels[i] with the same label share identical
variance components.
`designx` is a matrix, with each row containing dummy
variables indicating which variance components are associated
with the corresponding element of QY.
"""
super(Nested, self).initialize(model)
if self.model.weights is not None:
warnings.warn("weights not implemented for nested cov_struct, using unweighted covariance estimate")
# A bit of processing of the nest data
id_matrix = np.asarray(self.model.dep_data)
if id_matrix.ndim == 1:
id_matrix = id_matrix[:,None]
self.id_matrix = id_matrix
endog = self.model.endog_li
designx, ilabels = [], []
# The number of layers of nesting
n_nest = self.id_matrix.shape[1]
for i in range(self.model.num_group):
ngrp = len(endog[i])
glab = self.model.group_labels[i]
rix = self.model.group_indices[glab]
# Determine the number of common variance components
# shared by each pair of observations.
ix1, ix2 = np.tril_indices(ngrp, -1)
ncm = (self.id_matrix[rix[ix1], :] ==
self.id_matrix[rix[ix2], :]).sum(1)
# This is used to construct the working correlation
# matrix.
ilabel = np.zeros((ngrp, ngrp), dtype=np.int32)
ilabel[[ix1, ix2]] = ncm + 1
ilabel[[ix2, ix1]] = ncm + 1
ilabels.append(ilabel)
# This is used to estimate the variance components.
dsx = np.zeros((len(ix1), n_nest+1), dtype=np.float64)
dsx[:,0] = 1
for k in np.unique(ncm):
ii = np.flatnonzero(ncm == k)
dsx[ii, 1:k+1] = 1
designx.append(dsx)
self.designx = np.concatenate(designx, axis=0)
self.ilabels = ilabels
svd = np.linalg.svd(self.designx, 0)
self.designx_u = svd[0]
self.designx_s = svd[1]
self.designx_v = svd[2].T
def update(self, params):
endog = self.model.endog_li
nobs = self.model.nobs
dim = len(params)
if self.designx is None:
self._compute_design(self.model)
cached_means = self.model.cached_means
varfunc = self.model.family.variance
dvmat = []
scale = 0.
for i in range(self.model.num_group):
expval, _ = cached_means[i]
stdev = np.sqrt(varfunc(expval))
resid = (endog[i] - expval) / stdev
ix1, ix2 = np.tril_indices(len(resid), -1)
dvmat.append(resid[ix1] * resid[ix2])
scale += np.sum(resid**2)
dvmat = np.concatenate(dvmat)
scale /= (nobs - dim)
# Use least squares regression to estimate the variance
# components
vcomp_coeff = np.dot(self.designx_v, np.dot(self.designx_u.T,
dvmat) / self.designx_s)
self.vcomp_coeff = np.clip(vcomp_coeff, 0, np.inf)
self.scale = scale
self.dep_params = self.vcomp_coeff.copy()
def covariance_matrix(self, expval, index):
dim = len(expval)
# First iteration
if self.dep_params is None:
return np.eye(dim, dtype=np.float64), True
ilabel = self.ilabels[index]
c = np.r_[self.scale, np.cumsum(self.vcomp_coeff)]
vmat = c[ilabel]
vmat /= self.scale
return vmat, True
update.__doc__ = CovStruct.update.__doc__
covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
def summary(self):
"""
Returns a summary string describing the state of the
dependence structure.
"""
msg = "Variance estimates\n------------------\n"
for k in range(len(self.vcomp_coeff)):
msg += "Component %d: %.3f\n" % (k+1, self.vcomp_coeff[k])
msg += "Residual: %.3f\n" % (self.scale -
np.sum(self.vcomp_coeff))
return msg
class Autoregressive(CovStruct):
"""
An autoregressive working dependence structure.
The dependence is defined in terms of the `time` component of the
parent GEE class. Time represents a potentially multidimensional
index from which distances between pairs of observations can be
determined. The correlation between two observations in the same
cluster is dep_params^distance, where `dep_params` is the
autocorrelation parameter to be estimated, and `distance` is the
distance between the two observations, calculated from their
corresponding time values. `time` is stored as an n_obs x k
matrix, where `k` represents the number of dimensions in the time
index.
The autocorrelation parameter is estimated using weighted
nonlinear least squares, regressing each value within a cluster on
each preceeding value in the same cluster.
Parameters
----------
dist_func: function from R^k x R^k to R^+, optional
A function that computes the distance between the two
observations based on their `time` values.
References
----------
B Rosner, A Munoz. Autoregressive modeling for the analysis of
longitudinal data with unequally spaced examinations. Statistics
in medicine. Vol 7, 59-71, 1988.
"""
def __init__(self, dist_func=None):
super(Autoregressive, self).__init__()
# The function for determining distances based on time
if dist_func is None:
self.dist_func = lambda x, y: np.abs(x - y).sum()
else:
self.dist_func = dist_func
self.designx = None
# The autocorrelation parameter
self.dep_params = 0.
def update(self, params):
if self.model.weights is not None:
warnings.warn("weights not implemented for autoregressive cov_struct, using unweighted covariance estimate")
endog = self.model.endog_li
time = self.model.time_li
# Only need to compute this once
if self.designx is not None:
designx = self.designx
else:
designx = []
for i in range(self.model.num_group):
ngrp = len(endog[i])
if ngrp == 0:
continue
# Loop over pairs of observations within a cluster
for j1 in range(ngrp):
for j2 in range(j1):
designx.append(self.dist_func(time[i][j1, :],
time[i][j2, :]))
designx = np.array(designx)
self.designx = designx
scale = self.model.estimate_scale()
varfunc = self.model.family.variance
cached_means = self.model.cached_means
# Weights
var = 1. - self.dep_params**(2*designx)
var /= 1. - self.dep_params**2
wts = 1. / var
wts /= wts.sum()
residmat = []
for i in range(self.model.num_group):
expval, _ = cached_means[i]
stdev = np.sqrt(scale * varfunc(expval))
resid = (endog[i] - expval) / stdev
ngrp = len(resid)
for j1 in range(ngrp):
for j2 in range(j1):
residmat.append([resid[j1], resid[j2]])
residmat = np.array(residmat)
# Need to minimize this
def fitfunc(a):
dif = residmat[:, 0] - (a**designx)*residmat[:, 1]
return np.dot(dif**2, wts)
# Left bracket point
b_lft, f_lft = 0., fitfunc(0.)
# Center bracket point
b_ctr, f_ctr = 0.5, fitfunc(0.5)
while f_ctr > f_lft:
b_ctr /= 2
f_ctr = fitfunc(b_ctr)
if b_ctr < 1e-8:
self.dep_params = 0
return
# Right bracket point
b_rgt, f_rgt = 0.75, fitfunc(0.75)
while f_rgt < f_ctr:
b_rgt = b_rgt + (1. - b_rgt) / 2
f_rgt = fitfunc(b_rgt)
if b_rgt > 1. - 1e-6:
raise ValueError(
"Autoregressive: unable to find right bracket")
from scipy.optimize import brent
self.dep_params = brent(fitfunc, brack=[b_lft, b_ctr, b_rgt])
def covariance_matrix(self, endog_expval, index):
ngrp = len(endog_expval)
if self.dep_params == 0:
return np.eye(ngrp, dtype=np.float64), True
idx = np.arange(ngrp)
cmat = self.dep_params**np.abs(idx[:, None] - idx[None, :])
return cmat, True
def covariance_matrix_solve(self, expval, index, stdev, rhs):
# The inverse of an AR(1) covariance matrix is tri-diagonal.
k = len(expval)
soln = []
# LHS has 1 column
if k == 1:
return [x / stdev**2 for x in rhs]
# LHS has 2 columns
if k == 2:
mat = np.array([[1, -self.dep_params], [-self.dep_params, 1]])
mat /= (1. - self.dep_params**2)
for x in rhs:
if x.ndim == 1:
x1 = x / stdev
else:
x1 = x / stdev[:, None]
x1 = np.dot(mat, x1)
if x.ndim == 1:
x1 /= stdev
else:
x1 /= stdev[:, None]
soln.append(x1)
return soln
# LHS has >= 3 columns: values c0, c1, c2 defined below give
# the inverse. c0 is on the diagonal, except for the first
# and last position. c1 is on the first and last position of
# the diagonal. c2 is on the sub/super diagonal.
c0 = (1. + self.dep_params**2) / (1. - self.dep_params**2)
c1 = 1. / (1. - self.dep_params**2)
c2 = -self.dep_params / (1. - self.dep_params**2)
soln = []
for x in rhs:
flatten = False
if x.ndim == 1:
x = x[:, None]
flatten = True
x1 = x / stdev[:, None]
z0 = np.zeros((1, x.shape[1]))
rhs1 = np.concatenate((x[1:,:], z0), axis=0)
rhs2 = np.concatenate((z0, x[0:-1,:]), axis=0)
y = c0*x + c2*rhs1 + c2*rhs2
y[0, :] = c1*x[0, :] + c2*x[1, :]
y[-1, :] = c1*x[-1, :] + c2*x[-2, :]
y /= stdev[:, None]
if flatten:
y = np.squeeze(y)
soln.append(y)
return soln
update.__doc__ = CovStruct.update.__doc__
covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__
def summary(self):
return ("Autoregressive(1) dependence parameter: %.3f\n" %
self.dep_params)
class GlobalOddsRatio(CovStruct):
"""
Estimate the global odds ratio for a GEE with ordinal or nominal
data.
References
----------
PJ Heagerty and S Zeger. "Marginal Regression Models for Clustered
Ordinal Measurements". Journal of the American Statistical
Association Vol. 91, Issue 435 (1996).
Thomas Lumley. Generalized Estimating Equations for Ordinal Data:
A Note on Working Correlation Structures. Biometrics Vol. 52,
No. 1 (Mar., 1996), pp. 354-361
http://www.jstor.org/stable/2533173
Notes
-----
The following data structures are calculated in the class:
'ibd' is a list whose i^th element ibd[i] is a sequence of integer
pairs (a,b), where endog_li[i][a:b] is the subvector of binary
indicators derived from the same ordinal value.
`cpp` is a dictionary where cpp[group] is a map from cut-point
pairs (c,c') to the indices of all between-subject pairs derived
from the given cut points.
"""
def __init__(self, endog_type):
super(GlobalOddsRatio, self).__init__()
self.endog_type = endog_type
self.dep_params = 0.
def initialize(self, model):
super(GlobalOddsRatio, self).initialize(model)
if self.model.weights is not None:
warnings.warn("weights not implemented for GlobalOddsRatio cov_struct, using unweighted covariance estimate")
self.nlevel = len(model.endog_values)
self.ncut = self.nlevel - 1
ibd = []
for v in model.endog_li:
jj = np.arange(0, len(v) + 1, self.ncut)
ibd1 = np.hstack((jj[0:-1][:, None], jj[1:][:, None]))
ibd1 = [(jj[k], jj[k + 1]) for k in range(len(jj) - 1)]
ibd.append(ibd1)
self.ibd = ibd
# Need to restrict to between-subject pairs
cpp = []
for v in model.endog_li:
# Number of subjects in this group
m = int(len(v) / self.ncut)
cpp1 = {}
# Loop over distinct subject pairs
for i1 in range(m):
for i2 in range(i1):
# Loop over cut point pairs
for k1 in range(self.ncut):
for k2 in range(k1+1):
if (k2, k1) not in cpp1:
cpp1[(k2, k1)] = []
j1 = i1*self.ncut + k1
j2 = i2*self.ncut + k2
cpp1[(k2, k1)].append([j2, j1])
for k in cpp1.keys():
cpp1[k] = np.asarray(cpp1[k])
cpp.append(cpp1)
self.cpp = cpp
# Initialize the dependence parameters
self.crude_or = self.observed_crude_oddsratio()
self.dep_params = self.crude_or
def pooled_odds_ratio(self, tables):
"""
Returns the pooled odds ratio for a list of 2x2 tables.
The pooled odds ratio is the inverse variance weighted average
of the sample odds ratios of the tables.
"""
if len(tables) == 0:
return 1.
# Get the sampled odds ratios and variances
log_oddsratio, var = [], []
for table in tables:
lor = np.log(table[1, 1]) + np.log(table[0, 0]) -\
np.log(table[0, 1]) - np.log(table[1, 0])
log_oddsratio.append(lor)
var.append((1 / table.astype(np.float64)).sum())
# Calculate the inverse variance weighted average
wts = [1 / v for v in var]
wtsum = sum(wts)
wts = [w / wtsum for w in wts]
log_pooled_or = sum([w*e for w, e in zip(wts, log_oddsratio)])
return np.exp(log_pooled_or)
def covariance_matrix(self, expected_value, index):
vmat = self.get_eyy(expected_value, index)
vmat -= np.outer(expected_value, expected_value)
return vmat, False
def observed_crude_oddsratio(self):
"""
To obtain the crude (global) odds ratio, first pool all binary
indicators corresponding to a given pair of cut points (c,c'),
then calculate the odds ratio for this 2x2 table. The crude
odds ratio is the inverse variance weighted average of these
odds ratios. Since the covariate effects are ignored, this OR
will generally be greater than the stratified OR.
"""
cpp = self.cpp
endog = self.model.endog_li
# Storage for the contingency tables for each (c,c')
tables = {}
for ii in iterkeys(cpp[0]):
tables[ii] = np.zeros((2, 2), dtype=np.float64)
# Get the observed crude OR
for i in range(len(endog)):
# The observed joint values for the current cluster
yvec = endog[i]
endog_11 = np.outer(yvec, yvec)
endog_10 = np.outer(yvec, 1. - yvec)
endog_01 = np.outer(1. - yvec, yvec)
endog_00 = np.outer(1. - yvec, 1. - yvec)
cpp1 = cpp[i]
for ky in iterkeys(cpp1):
ix = cpp1[ky]
tables[ky][1, 1] += endog_11[ix[:, 0], ix[:, 1]].sum()
tables[ky][1, 0] += endog_10[ix[:, 0], ix[:, 1]].sum()
tables[ky][0, 1] += endog_01[ix[:, 0], ix[:, 1]].sum()
tables[ky][0, 0] += endog_00[ix[:, 0], ix[:, 1]].sum()
return self.pooled_odds_ratio(list(itervalues(tables)))
def get_eyy(self, endog_expval, index):
"""
Returns a matrix V such that V[i,j] is the joint probability
that endog[i] = 1 and endog[j] = 1, based on the marginal
probabilities of endog and the global odds ratio `current_or`.
"""
current_or = self.dep_params
ibd = self.ibd[index]
# The between-observation joint probabilities
if current_or == 1.0:
vmat = np.outer(endog_expval, endog_expval)
else:
psum = endog_expval[:, None] + endog_expval[None, :]
pprod = endog_expval[:, None] * endog_expval[None, :]
pfac = np.sqrt((1. + psum * (current_or - 1.))**2 +
4 * current_or * (1. - current_or) * pprod)
vmat = 1. + psum * (current_or - 1.) - pfac
vmat /= 2. * (current_or - 1)
# Fix E[YY'] for elements that belong to same observation
for bdl in ibd:
evy = endog_expval[bdl[0]:bdl[1]]
if self.endog_type == "ordinal":
eyr = np.outer(evy, np.ones(len(evy)))
eyc = np.outer(np.ones(len(evy)), evy)
vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] = \
np.where(eyr < eyc, eyr, eyc)
else:
vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] = np.diag(evy)
return vmat
def update(self, params):
"""
Update the global odds ratio based on the current value of
params.
"""
endog = self.model.endog_li
cpp = self.cpp
cached_means = self.model.cached_means
# This will happen if all the clusters have only
# one observation
if len(cpp[0]) == 0:
return
tables = {}
for ii in cpp[0]:
tables[ii] = np.zeros((2, 2), dtype=np.float64)
for i in range(self.model.num_group):
endog_expval, _ = cached_means[i]
emat_11 = self.get_eyy(endog_expval, i)
emat_10 = endog_expval[:, None] - emat_11
emat_01 = -emat_11 + endog_expval
emat_00 = 1. - (emat_11 + emat_10 + emat_01)
cpp1 = cpp[i]
for ky in iterkeys(cpp1):
ix = cpp1[ky]
tables[ky][1, 1] += emat_11[ix[:, 0], ix[:, 1]].sum()
tables[ky][1, 0] += emat_10[ix[:, 0], ix[:, 1]].sum()
tables[ky][0, 1] += emat_01[ix[:, 0], ix[:, 1]].sum()
tables[ky][0, 0] += emat_00[ix[:, 0], ix[:, 1]].sum()
cor_expval = self.pooled_odds_ratio(list(itervalues(tables)))
self.dep_params *= self.crude_or / cor_expval
if not np.isfinite(self.dep_params):
self.dep_params = 1.
warnings.warn("dep_params became inf, resetting to 1",
ConvergenceWarning)
update.__doc__ = CovStruct.update.__doc__
covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
def summary(self):
return "Global odds ratio: %.3f\n" % self.dep_params
class Equivalence(CovStruct):
"""
A covariance structure defined in terms of equivalence classes.
An 'equivalence class' is a set of pairs of observations such that
the covariance of every pair within the equivalence class has a
common value.
Parameters
----------
pairs : dict-like
A dictionary of dictionaries, where `pairs[group][label]`
provides the indices of all pairs of observations in the group
that have the same covariance value. Specifically,
`pairs[group][label]` is a tuple `(j1, j2)`, where `j1` and `j2`
are integer arrays of the same length. `j1[i], j2[i]` is one
index pair that belongs to the `label` equivalence class. Only
one triangle of each covariance matrix should be included.
Positions where j1 and j2 have the same value are variance
parameters.
labels : array-like
An array of labels such that every distinct pair of labels
defines an equivalence class. Either `labels` or `pairs` must
be provided. When the two labels in a pair are equal two
equivalence classes are defined: one for the diagonal elements
(corresponding to variances) and one for the off-diagonal
elements (corresponding to covariances).
return_cov : boolean
If True, `covariance_matrix` returns an estimate of the
covariance matrix, otherwise returns an estimate of the
correlation matrix.
Notes
-----
Using `labels` to define the class is much easier than using
`pairs`, but is less general.
Any pair of values not contained in `pairs` will be assigned zero
covariance.
The index values in `pairs` are row indices into the `exog`
matrix. They are not updated if missing data are present. When
using this covariance structure, missing data should be removed
before constructing the model.
If using `labels`, after a model is defined using the covariance
structure it is possible to remove a label pair from the second
level of the `pairs` dictionary to force the corresponding
covariance to be zero.
Examples
--------
The following sets up the `pairs` dictionary for a model with two
groups, equal variance for all observations, and constant
covariance for all pairs of observations within each group.
>> pairs = {0: {}, 1: {}}
>> pairs[0][0] = (np.r_[0, 1, 2], np.r_[0, 1, 2])
>> pairs[0][1] = np.tril_indices(3, -1)
>> pairs[1][0] = (np.r_[3, 4, 5], np.r_[3, 4, 5])
>> pairs[1][2] = 3 + np.tril_indices(3, -1)
"""
def __init__(self, pairs=None, labels=None, return_cov=False):
super(Equivalence, self).__init__()
if (pairs is None) and (labels is None):
raise ValueError("Equivalence cov_struct requires either `pairs` or `labels`")
if (pairs is not None) and (labels is not None):
raise ValueError("Equivalence cov_struct accepts only one of `pairs` and `labels`")
if pairs is not None:
import copy
self.pairs = copy.deepcopy(pairs)
if labels is not None:
self.labels = np.asarray(labels)
self.return_cov = return_cov
def _make_pairs(self, i, j):
"""
Create arrays `i_`, `j_` containing all unique ordered pairs of elements in `i` and `j`.
The arrays `i` and `j` must be one-dimensional containing non-negative integers.
"""
mat = np.zeros((len(i)*len(j), 2), dtype=np.int32)
# Create the pairs and order them
f = np.ones(len(j))
mat[:, 0] = np.kron(f, i).astype(np.int32)
f = np.ones(len(i))
mat[:, 1] = np.kron(j, f).astype(np.int32)
mat.sort(1)
# Remove repeated rows
try:
dtype = np.dtype((np.void, mat.dtype.itemsize * mat.shape[1]))
bmat = np.ascontiguousarray(mat).view(dtype)
_, idx = np.unique(bmat, return_index=True)
except TypeError:
# workaround for old numpy that can't call unique with complex
# dtypes
np.random.seed(4234)
bmat = np.dot(mat, np.random.uniform(size=mat.shape[1]))
_, idx = np.unique(bmat, return_index=True)
mat = mat[idx, :]
return mat[:, 0], mat[:, 1]
def _pairs_from_labels(self):
from collections import defaultdict
pairs = defaultdict(lambda : defaultdict(lambda : None))
model = self.model
df = pd.DataFrame({"labels": self.labels, "groups": model.groups})
gb = df.groupby(["groups", "labels"])
ulabels = np.unique(self.labels)
for g_ix, g_lb in enumerate(model.group_labels):
# Loop over label pairs
for lx1 in range(len(ulabels)):
for lx2 in range(lx1+1):
lb1 = ulabels[lx1]
lb2 = ulabels[lx2]
try:
i1 = gb.groups[(g_lb, lb1)]
i2 = gb.groups[(g_lb, lb2)]
except KeyError:
continue
i1, i2 = self._make_pairs(i1, i2)
clabel = str(lb1) + "/" + str(lb2)
# Variance parameters belong in their own equiv class.
jj = np.flatnonzero(i1 == i2)
if len(jj) > 0:
clabelv = clabel + "/v"
pairs[g_lb][clabelv] = (i1[jj], i2[jj])
# Covariance parameters
jj = np.flatnonzero(i1 != i2)
if len(jj) > 0:
i1 = i1[jj]
i2 = i2[jj]
pairs[g_lb][clabel] = (i1, i2)
self.pairs = pairs
def initialize(self, model):
super(Equivalence, self).initialize(model)
if self.model.weights is not None:
warnings.warn("weights not implemented for equalence cov_struct, using unweighted covariance estimate")
if not hasattr(self, 'pairs'):
self._pairs_from_labels()
# Initialize so that any equivalence class containing a
# variance parameter has value 1.
self.dep_params = defaultdict(lambda : 0.)
self._var_classes = set([])
for gp in self.model.group_labels:
for lb in self.pairs[gp]:
j1, j2 = self.pairs[gp][lb]
if np.any(j1 == j2):
if not np.all(j1 == j2):
warnings.warn("equivalence class contains both variance and covariance parameters")
self._var_classes.add(lb)
self.dep_params[lb] = 1
# Need to start indexing at 0 within each group.
# rx maps olds indices to new indices
rx = -1 * np.ones(len(self.model.endog), dtype=np.int32)
for g_ix, g_lb in enumerate(self.model.group_labels):
ii = self.model.group_indices[g_lb]
rx[ii] = np.arange(len(ii), dtype=np.int32)
# Reindex
for gp in self.model.group_labels:
for lb in self.pairs[gp].keys():
a, b = self.pairs[gp][lb]
self.pairs[gp][lb] = (rx[a], rx[b])
def update(self, params):
endog = self.model.endog_li
varfunc = self.model.family.variance
cached_means = self.model.cached_means
dep_params = defaultdict(lambda : [0., 0., 0.])
n_pairs = defaultdict(lambda : 0)
dim = len(params)
for k, gp in enumerate(self.model.group_labels):
expval, _ = cached_means[k]
stdev = np.sqrt(varfunc(expval))
resid = (endog[k] - expval) / stdev
for lb in self.pairs[gp].keys():
if (not self.return_cov) and lb in self._var_classes:
continue
jj = self.pairs[gp][lb]
dep_params[lb][0] += np.sum(resid[jj[0]] * resid[jj[1]])
if not self.return_cov:
dep_params[lb][1] += np.sum(resid[jj[0]]**2)
dep_params[lb][2] += np.sum(resid[jj[1]]**2)
n_pairs[lb] += len(jj[0])
if self.return_cov:
for lb in dep_params.keys():
dep_params[lb] = dep_params[lb][0] / (n_pairs[lb] - dim)
else:
for lb in dep_params.keys():
den = np.sqrt(dep_params[lb][1] * dep_params[lb][2])
dep_params[lb] = dep_params[lb][0] / den
for lb in self._var_classes:
dep_params[lb] = 1.
self.dep_params = dep_params
self.n_pairs = n_pairs
def covariance_matrix(self, expval, index):
dim = len(expval)
cmat = np.zeros((dim, dim))
g_lb = self.model.group_labels[index]
for lb in self.pairs[g_lb].keys():
j1, j2 = self.pairs[g_lb][lb]
cmat[j1, j2] = self.dep_params[lb]
cmat = cmat + cmat.T
np.fill_diagonal(cmat, cmat.diagonal() / 2)
return cmat, not self.return_cov
update.__doc__ = CovStruct.update.__doc__
covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
| bsd-3-clause |
fabianp/scikit-learn | sklearn/datasets/mldata.py | 309 | 7838 | """Automatically download MLdata datasets."""
# Copyright (c) 2011 Pietro Berkes
# License: BSD 3 clause
import os
from os.path import join, exists
import re
import numbers
try:
# Python 2
from urllib2 import HTTPError
from urllib2 import quote
from urllib2 import urlopen
except ImportError:
# Python 3+
from urllib.error import HTTPError
from urllib.parse import quote
from urllib.request import urlopen
import numpy as np
import scipy as sp
from scipy import io
from shutil import copyfileobj
from .base import get_data_home, Bunch
MLDATA_BASE_URL = "http://mldata.org/repository/data/download/matlab/%s"
def mldata_filename(dataname):
"""Convert a raw name for a data set in a mldata.org filename."""
dataname = dataname.lower().replace(' ', '-')
return re.sub(r'[().]', '', dataname)
def fetch_mldata(dataname, target_name='label', data_name='data',
transpose_data=True, data_home=None):
"""Fetch an mldata.org data set
If the file does not exist yet, it is downloaded from mldata.org .
mldata.org does not have an enforced convention for storing data or
naming the columns in a data set. The default behavior of this function
works well with the most common cases:
1) data values are stored in the column 'data', and target values in the
column 'label'
2) alternatively, the first column stores target values, and the second
data values
3) the data array is stored as `n_features x n_samples` , and thus needs
to be transposed to match the `sklearn` standard
Keyword arguments allow to adapt these defaults to specific data sets
(see parameters `target_name`, `data_name`, `transpose_data`, and
the examples below).
mldata.org data sets may have multiple columns, which are stored in the
Bunch object with their original name.
Parameters
----------
dataname:
Name of the data set on mldata.org,
e.g.: "leukemia", "Whistler Daily Snowfall", etc.
The raw name is automatically converted to a mldata.org URL .
target_name: optional, default: 'label'
Name or index of the column containing the target values.
data_name: optional, default: 'data'
Name or index of the column containing the data.
transpose_data: optional, default: True
If True, transpose the downloaded data array.
data_home: optional, default: None
Specify another download and cache folder for the data sets. By default
all scikit learn data is stored in '~/scikit_learn_data' subfolders.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'target', the classification labels,
'DESCR', the full description of the dataset, and
'COL_NAMES', the original names of the dataset columns.
Examples
--------
Load the 'iris' dataset from mldata.org:
>>> from sklearn.datasets.mldata import fetch_mldata
>>> import tempfile
>>> test_data_home = tempfile.mkdtemp()
>>> iris = fetch_mldata('iris', data_home=test_data_home)
>>> iris.target.shape
(150,)
>>> iris.data.shape
(150, 4)
Load the 'leukemia' dataset from mldata.org, which needs to be transposed
to respects the sklearn axes convention:
>>> leuk = fetch_mldata('leukemia', transpose_data=True,
... data_home=test_data_home)
>>> leuk.data.shape
(72, 7129)
Load an alternative 'iris' dataset, which has different names for the
columns:
>>> iris2 = fetch_mldata('datasets-UCI iris', target_name=1,
... data_name=0, data_home=test_data_home)
>>> iris3 = fetch_mldata('datasets-UCI iris',
... target_name='class', data_name='double0',
... data_home=test_data_home)
>>> import shutil
>>> shutil.rmtree(test_data_home)
"""
# normalize dataset name
dataname = mldata_filename(dataname)
# check if this data set has been already downloaded
data_home = get_data_home(data_home=data_home)
data_home = join(data_home, 'mldata')
if not exists(data_home):
os.makedirs(data_home)
matlab_name = dataname + '.mat'
filename = join(data_home, matlab_name)
# if the file does not exist, download it
if not exists(filename):
urlname = MLDATA_BASE_URL % quote(dataname)
try:
mldata_url = urlopen(urlname)
except HTTPError as e:
if e.code == 404:
e.msg = "Dataset '%s' not found on mldata.org." % dataname
raise
# store Matlab file
try:
with open(filename, 'w+b') as matlab_file:
copyfileobj(mldata_url, matlab_file)
except:
os.remove(filename)
raise
mldata_url.close()
# load dataset matlab file
with open(filename, 'rb') as matlab_file:
matlab_dict = io.loadmat(matlab_file, struct_as_record=True)
# -- extract data from matlab_dict
# flatten column names
col_names = [str(descr[0])
for descr in matlab_dict['mldata_descr_ordering'][0]]
# if target or data names are indices, transform then into names
if isinstance(target_name, numbers.Integral):
target_name = col_names[target_name]
if isinstance(data_name, numbers.Integral):
data_name = col_names[data_name]
# rules for making sense of the mldata.org data format
# (earlier ones have priority):
# 1) there is only one array => it is "data"
# 2) there are multiple arrays
# a) copy all columns in the bunch, using their column name
# b) if there is a column called `target_name`, set "target" to it,
# otherwise set "target" to first column
# c) if there is a column called `data_name`, set "data" to it,
# otherwise set "data" to second column
dataset = {'DESCR': 'mldata.org dataset: %s' % dataname,
'COL_NAMES': col_names}
# 1) there is only one array => it is considered data
if len(col_names) == 1:
data_name = col_names[0]
dataset['data'] = matlab_dict[data_name]
# 2) there are multiple arrays
else:
for name in col_names:
dataset[name] = matlab_dict[name]
if target_name in col_names:
del dataset[target_name]
dataset['target'] = matlab_dict[target_name]
else:
del dataset[col_names[0]]
dataset['target'] = matlab_dict[col_names[0]]
if data_name in col_names:
del dataset[data_name]
dataset['data'] = matlab_dict[data_name]
else:
del dataset[col_names[1]]
dataset['data'] = matlab_dict[col_names[1]]
# set axes to sklearn conventions
if transpose_data:
dataset['data'] = dataset['data'].T
if 'target' in dataset:
if not sp.sparse.issparse(dataset['target']):
dataset['target'] = dataset['target'].squeeze()
return Bunch(**dataset)
# The following is used by nosetests to setup the docstring tests fixture
def setup_module(module):
# setup mock urllib2 module to avoid downloading from mldata.org
from sklearn.utils.testing import install_mldata_mock
install_mldata_mock({
'iris': {
'data': np.empty((150, 4)),
'label': np.empty(150),
},
'datasets-uci-iris': {
'double0': np.empty((150, 4)),
'class': np.empty((150,)),
},
'leukemia': {
'data': np.empty((72, 7129)),
},
})
def teardown_module(module):
from sklearn.utils.testing import uninstall_mldata_mock
uninstall_mldata_mock()
| bsd-3-clause |
zihua/scikit-learn | sklearn/datasets/tests/test_lfw.py | 55 | 7877 | """This test for the LFW require medium-size data downloading and processing
If the data has not been already downloaded by running the examples,
the tests won't run (skipped).
If the test are run, the first execution will be long (typically a bit
more than a couple of minutes) but as the dataset loader is leveraging
joblib, successive runs will be fast (less than 200ms).
"""
import random
import os
import shutil
import tempfile
import numpy as np
from sklearn.externals import six
try:
try:
from scipy.misc import imsave
except ImportError:
from scipy.misc.pilutil import imsave
except ImportError:
imsave = None
from sklearn.datasets import load_lfw_pairs
from sklearn.datasets import load_lfw_people
from sklearn.datasets import fetch_lfw_pairs
from sklearn.datasets import fetch_lfw_people
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import raises
SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
SCIKIT_LEARN_EMPTY_DATA = tempfile.mkdtemp(prefix="scikit_learn_empty_test_")
LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, 'lfw_home')
FAKE_NAMES = [
'Abdelatif_Smith',
'Abhati_Kepler',
'Camara_Alvaro',
'Chen_Dupont',
'John_Lee',
'Lin_Bauman',
'Onur_Lopez',
]
def setup_module():
"""Test fixture run once and common to all tests of this module"""
if imsave is None:
raise SkipTest("PIL not installed.")
if not os.path.exists(LFW_HOME):
os.makedirs(LFW_HOME)
random_state = random.Random(42)
np_rng = np.random.RandomState(42)
# generate some random jpeg files for each person
counts = {}
for name in FAKE_NAMES:
folder_name = os.path.join(LFW_HOME, 'lfw_funneled', name)
if not os.path.exists(folder_name):
os.makedirs(folder_name)
n_faces = np_rng.randint(1, 5)
counts[name] = n_faces
for i in range(n_faces):
file_path = os.path.join(folder_name, name + '_%04d.jpg' % i)
uniface = np_rng.randint(0, 255, size=(250, 250, 3))
try:
imsave(file_path, uniface)
except ImportError:
raise SkipTest("PIL not installed")
# add some random file pollution to test robustness
with open(os.path.join(LFW_HOME, 'lfw_funneled', '.test.swp'), 'wb') as f:
f.write(six.b('Text file to be ignored by the dataset loader.'))
# generate some pairing metadata files using the same format as LFW
with open(os.path.join(LFW_HOME, 'pairsDevTrain.txt'), 'wb') as f:
f.write(six.b("10\n"))
more_than_two = [name for name, count in six.iteritems(counts)
if count >= 2]
for i in range(5):
name = random_state.choice(more_than_two)
first, second = random_state.sample(range(counts[name]), 2)
f.write(six.b('%s\t%d\t%d\n' % (name, first, second)))
for i in range(5):
first_name, second_name = random_state.sample(FAKE_NAMES, 2)
first_index = random_state.choice(np.arange(counts[first_name]))
second_index = random_state.choice(np.arange(counts[second_name]))
f.write(six.b('%s\t%d\t%s\t%d\n' % (first_name, first_index,
second_name, second_index)))
with open(os.path.join(LFW_HOME, 'pairsDevTest.txt'), 'wb') as f:
f.write(six.b("Fake place holder that won't be tested"))
with open(os.path.join(LFW_HOME, 'pairs.txt'), 'wb') as f:
f.write(six.b("Fake place holder that won't be tested"))
def teardown_module():
"""Test fixture (clean up) run once after all tests of this module"""
if os.path.isdir(SCIKIT_LEARN_DATA):
shutil.rmtree(SCIKIT_LEARN_DATA)
if os.path.isdir(SCIKIT_LEARN_EMPTY_DATA):
shutil.rmtree(SCIKIT_LEARN_EMPTY_DATA)
@raises(IOError)
def test_load_empty_lfw_people():
fetch_lfw_people(data_home=SCIKIT_LEARN_EMPTY_DATA, download_if_missing=False)
def test_load_lfw_people_deprecation():
msg = ("Function 'load_lfw_people' has been deprecated in 0.17 and will be "
"removed in 0.19."
"Use fetch_lfw_people(download_if_missing=False) instead.")
assert_warns_message(DeprecationWarning, msg, load_lfw_people,
data_home=SCIKIT_LEARN_DATA)
def test_load_fake_lfw_people():
lfw_people = fetch_lfw_people(data_home=SCIKIT_LEARN_DATA,
min_faces_per_person=3, download_if_missing=False)
# The data is croped around the center as a rectangular bounding box
# around the face. Colors are converted to gray levels:
assert_equal(lfw_people.images.shape, (10, 62, 47))
assert_equal(lfw_people.data.shape, (10, 2914))
# the target is array of person integer ids
assert_array_equal(lfw_people.target, [2, 0, 1, 0, 2, 0, 2, 1, 1, 2])
# names of the persons can be found using the target_names array
expected_classes = ['Abdelatif Smith', 'Abhati Kepler', 'Onur Lopez']
assert_array_equal(lfw_people.target_names, expected_classes)
# It is possible to ask for the original data without any croping or color
# conversion and not limit on the number of picture per person
lfw_people = fetch_lfw_people(data_home=SCIKIT_LEARN_DATA,
resize=None, slice_=None, color=True, download_if_missing=False)
assert_equal(lfw_people.images.shape, (17, 250, 250, 3))
# the ids and class names are the same as previously
assert_array_equal(lfw_people.target,
[0, 0, 1, 6, 5, 6, 3, 6, 0, 3, 6, 1, 2, 4, 5, 1, 2])
assert_array_equal(lfw_people.target_names,
['Abdelatif Smith', 'Abhati Kepler', 'Camara Alvaro',
'Chen Dupont', 'John Lee', 'Lin Bauman', 'Onur Lopez'])
@raises(ValueError)
def test_load_fake_lfw_people_too_restrictive():
fetch_lfw_people(data_home=SCIKIT_LEARN_DATA, min_faces_per_person=100, download_if_missing=False)
@raises(IOError)
def test_load_empty_lfw_pairs():
fetch_lfw_pairs(data_home=SCIKIT_LEARN_EMPTY_DATA, download_if_missing=False)
def test_load_lfw_pairs_deprecation():
msg = ("Function 'load_lfw_pairs' has been deprecated in 0.17 and will be "
"removed in 0.19."
"Use fetch_lfw_pairs(download_if_missing=False) instead.")
assert_warns_message(DeprecationWarning, msg, load_lfw_pairs,
data_home=SCIKIT_LEARN_DATA)
def test_load_fake_lfw_pairs():
lfw_pairs_train = fetch_lfw_pairs(data_home=SCIKIT_LEARN_DATA, download_if_missing=False)
# The data is croped around the center as a rectangular bounding box
# around the face. Colors are converted to gray levels:
assert_equal(lfw_pairs_train.pairs.shape, (10, 2, 62, 47))
# the target is whether the person is the same or not
assert_array_equal(lfw_pairs_train.target, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
# names of the persons can be found using the target_names array
expected_classes = ['Different persons', 'Same person']
assert_array_equal(lfw_pairs_train.target_names, expected_classes)
# It is possible to ask for the original data without any croping or color
# conversion
lfw_pairs_train = fetch_lfw_pairs(data_home=SCIKIT_LEARN_DATA,
resize=None, slice_=None, color=True, download_if_missing=False)
assert_equal(lfw_pairs_train.pairs.shape, (10, 2, 250, 250, 3))
# the ids and class names are the same as previously
assert_array_equal(lfw_pairs_train.target, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
assert_array_equal(lfw_pairs_train.target_names, expected_classes)
| bsd-3-clause |
dotsdl/PyTables | doc/sphinxext/inheritance_diagram.py | 98 | 13648 | """
Defines a docutils directive for inserting inheritance diagrams.
Provide the directive with one or more classes or modules (separated
by whitespace). For modules, all of the classes in that module will
be used.
Example::
Given the following classes:
class A: pass
class B(A): pass
class C(A): pass
class D(B, C): pass
class E(B): pass
.. inheritance-diagram: D E
Produces a graph like the following:
A
/ \
B C
/ \ /
E D
The graph is inserted as a PNG+image map into HTML and a PDF in
LaTeX.
"""
import inspect
import os
import re
import subprocess
try:
from hashlib import md5
except ImportError:
from md5 import md5
from docutils.nodes import Body, Element
from docutils.parsers.rst import directives
from sphinx.roles import xfileref_role
def my_import(name):
"""Module importer - taken from the python documentation.
This function allows importing names with dots in them."""
mod = __import__(name)
components = name.split('.')
for comp in components[1:]:
mod = getattr(mod, comp)
return mod
class DotException(Exception):
pass
class InheritanceGraph(object):
"""
Given a list of classes, determines the set of classes that
they inherit from all the way to the root "object", and then
is able to generate a graphviz dot graph from them.
"""
def __init__(self, class_names, show_builtins=False):
"""
*class_names* is a list of child classes to show bases from.
If *show_builtins* is True, then Python builtins will be shown
in the graph.
"""
self.class_names = class_names
self.classes = self._import_classes(class_names)
self.all_classes = self._all_classes(self.classes)
if len(self.all_classes) == 0:
raise ValueError("No classes found for inheritance diagram")
self.show_builtins = show_builtins
py_sig_re = re.compile(r'''^([\w.]*\.)? # class names
(\w+) \s* $ # optionally arguments
''', re.VERBOSE)
def _import_class_or_module(self, name):
"""
Import a class using its fully-qualified *name*.
"""
try:
path, base = self.py_sig_re.match(name).groups()
except:
raise ValueError(
"Invalid class or module '%s' specified for inheritance diagram" % name)
fullname = (path or '') + base
path = (path and path.rstrip('.'))
if not path:
path = base
try:
module = __import__(path, None, None, [])
# We must do an import of the fully qualified name. Otherwise if a
# subpackage 'a.b' is requested where 'import a' does NOT provide
# 'a.b' automatically, then 'a.b' will not be found below. This
# second call will force the equivalent of 'import a.b' to happen
# after the top-level import above.
my_import(fullname)
except ImportError:
raise ValueError(
"Could not import class or module '%s' specified for inheritance diagram" % name)
try:
todoc = module
for comp in fullname.split('.')[1:]:
todoc = getattr(todoc, comp)
except AttributeError:
raise ValueError(
"Could not find class or module '%s' specified for inheritance diagram" % name)
# If a class, just return it
if inspect.isclass(todoc):
return [todoc]
elif inspect.ismodule(todoc):
classes = []
for cls in todoc.__dict__.values():
if inspect.isclass(cls) and cls.__module__ == todoc.__name__:
classes.append(cls)
return classes
raise ValueError(
"'%s' does not resolve to a class or module" % name)
def _import_classes(self, class_names):
"""
Import a list of classes.
"""
classes = []
for name in class_names:
classes.extend(self._import_class_or_module(name))
return classes
def _all_classes(self, classes):
"""
Return a list of all classes that are ancestors of *classes*.
"""
all_classes = {}
def recurse(cls):
all_classes[cls] = None
for c in cls.__bases__:
if c not in all_classes:
recurse(c)
for cls in classes:
recurse(cls)
return all_classes.keys()
def class_name(self, cls, parts=0):
"""
Given a class object, return a fully-qualified name. This
works for things I've tested in matplotlib so far, but may not
be completely general.
"""
module = cls.__module__
if module == '__builtin__':
fullname = cls.__name__
else:
fullname = "%s.%s" % (module, cls.__name__)
if parts == 0:
return fullname
name_parts = fullname.split('.')
return '.'.join(name_parts[-parts:])
def get_all_class_names(self):
"""
Get all of the class names involved in the graph.
"""
return [self.class_name(x) for x in self.all_classes]
# These are the default options for graphviz
default_graph_options = {
"rankdir": "LR",
"size": '"8.0, 12.0"'
}
default_node_options = {
"shape": "box",
"fontsize": 10,
"height": 0.25,
"fontname": "Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",
"style": '"setlinewidth(0.5)"'
}
default_edge_options = {
"arrowsize": 0.5,
"style": '"setlinewidth(0.5)"'
}
def _format_node_options(self, options):
return ','.join(["%s=%s" % x for x in options.items()])
def _format_graph_options(self, options):
return ''.join(["%s=%s;\n" % x for x in options.items()])
def generate_dot(self, fd, name, parts=0, urls={},
graph_options={}, node_options={},
edge_options={}):
"""
Generate a graphviz dot graph from the classes that
were passed in to __init__.
*fd* is a Python file-like object to write to.
*name* is the name of the graph
*urls* is a dictionary mapping class names to http urls
*graph_options*, *node_options*, *edge_options* are
dictionaries containing key/value pairs to pass on as graphviz
properties.
"""
g_options = self.default_graph_options.copy()
g_options.update(graph_options)
n_options = self.default_node_options.copy()
n_options.update(node_options)
e_options = self.default_edge_options.copy()
e_options.update(edge_options)
fd.write('digraph %s {\n' % name)
fd.write(self._format_graph_options(g_options))
for cls in self.all_classes:
if not self.show_builtins and cls in __builtins__.values():
continue
name = self.class_name(cls, parts)
# Write the node
this_node_options = n_options.copy()
url = urls.get(self.class_name(cls))
if url is not None:
this_node_options['URL'] = '"%s"' % url
fd.write(' "%s" [%s];\n' %
(name, self._format_node_options(this_node_options)))
# Write the edges
for base in cls.__bases__:
if not self.show_builtins and base in __builtins__.values():
continue
base_name = self.class_name(base, parts)
fd.write(' "%s" -> "%s" [%s];\n' %
(base_name, name,
self._format_node_options(e_options)))
fd.write('}\n')
def run_dot(self, args, name, parts=0, urls={},
graph_options={}, node_options={}, edge_options={}):
"""
Run graphviz 'dot' over this graph, returning whatever 'dot'
writes to stdout.
*args* will be passed along as commandline arguments.
*name* is the name of the graph
*urls* is a dictionary mapping class names to http urls
Raises DotException for any of the many os and
installation-related errors that may occur.
"""
try:
dot = subprocess.Popen(['dot'] + list(args),
stdin=subprocess.PIPE, stdout=subprocess.PIPE,
close_fds=True)
except OSError:
raise DotException("Could not execute 'dot'. Are you sure you have 'graphviz' installed?")
except ValueError:
raise DotException("'dot' called with invalid arguments")
except:
raise DotException("Unexpected error calling 'dot'")
self.generate_dot(dot.stdin, name, parts, urls, graph_options,
node_options, edge_options)
dot.stdin.close()
result = dot.stdout.read()
returncode = dot.wait()
if returncode != 0:
raise DotException("'dot' returned the errorcode %d" % returncode)
return result
class inheritance_diagram(Body, Element):
"""
A docutils node to use as a placeholder for the inheritance
diagram.
"""
pass
def inheritance_diagram_directive(name, arguments, options, content, lineno,
content_offset, block_text, state,
state_machine):
"""
Run when the inheritance_diagram directive is first encountered.
"""
node = inheritance_diagram()
class_names = arguments
# Create a graph starting with the list of classes
graph = InheritanceGraph(class_names)
# Create xref nodes for each target of the graph's image map and
# add them to the doc tree so that Sphinx can resolve the
# references to real URLs later. These nodes will eventually be
# removed from the doctree after we're done with them.
for name in graph.get_all_class_names():
refnodes, x = xfileref_role(
'class', ':class:`%s`' % name, name, 0, state)
node.extend(refnodes)
# Store the graph object so we can use it to generate the
# dot file later
node['graph'] = graph
# Store the original content for use as a hash
node['parts'] = options.get('parts', 0)
node['content'] = " ".join(class_names)
return [node]
def get_graph_hash(node):
return md5(node['content'] + str(node['parts'])).hexdigest()[-10:]
def html_output_graph(self, node):
"""
Output the graph for HTML. This will insert a PNG with clickable
image map.
"""
graph = node['graph']
parts = node['parts']
graph_hash = get_graph_hash(node)
name = "inheritance%s" % graph_hash
path = '_images'
dest_path = os.path.join(setup.app.builder.outdir, path)
if not os.path.exists(dest_path):
os.makedirs(dest_path)
png_path = os.path.join(dest_path, name + ".png")
path = setup.app.builder.imgpath
# Create a mapping from fully-qualified class names to URLs.
urls = {}
for child in node:
if child.get('refuri') is not None:
urls[child['reftitle']] = child.get('refuri')
elif child.get('refid') is not None:
urls[child['reftitle']] = '#' + child.get('refid')
# These arguments to dot will save a PNG file to disk and write
# an HTML image map to stdout.
image_map = graph.run_dot(['-Tpng', '-o%s' % png_path, '-Tcmapx'],
name, parts, urls)
return ('<img src="%s/%s.png" usemap="#%s" class="inheritance"/>%s' %
(path, name, name, image_map))
def latex_output_graph(self, node):
"""
Output the graph for LaTeX. This will insert a PDF.
"""
graph = node['graph']
parts = node['parts']
graph_hash = get_graph_hash(node)
name = "inheritance%s" % graph_hash
dest_path = os.path.abspath(os.path.join(setup.app.builder.outdir, '_images'))
if not os.path.exists(dest_path):
os.makedirs(dest_path)
pdf_path = os.path.abspath(os.path.join(dest_path, name + ".pdf"))
graph.run_dot(['-Tpdf', '-o%s' % pdf_path],
name, parts, graph_options={'size': '"6.0,6.0"'})
return '\n\\includegraphics{%s}\n\n' % pdf_path
def visit_inheritance_diagram(inner_func):
"""
This is just a wrapper around html/latex_output_graph to make it
easier to handle errors and insert warnings.
"""
def visitor(self, node):
try:
content = inner_func(self, node)
except DotException, e:
# Insert the exception as a warning in the document
warning = self.document.reporter.warning(str(e), line=node.line)
warning.parent = node
node.children = [warning]
else:
source = self.document.attributes['source']
self.body.append(content)
node.children = []
return visitor
def do_nothing(self, node):
pass
def setup(app):
setup.app = app
setup.confdir = app.confdir
app.add_node(
inheritance_diagram,
latex=(visit_inheritance_diagram(latex_output_graph), do_nothing),
html=(visit_inheritance_diagram(html_output_graph), do_nothing))
app.add_directive(
'inheritance-diagram', inheritance_diagram_directive,
False, (1, 100, 0), parts = directives.nonnegative_int)
| bsd-3-clause |
gfyoung/pandas | pandas/tests/indexes/timedeltas/methods/test_factorize.py | 2 | 1275 | import numpy as np
from pandas import TimedeltaIndex, factorize, timedelta_range
import pandas._testing as tm
class TestTimedeltaIndexFactorize:
def test_factorize(self):
idx1 = TimedeltaIndex(["1 day", "1 day", "2 day", "2 day", "3 day", "3 day"])
exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)
exp_idx = TimedeltaIndex(["1 day", "2 day", "3 day"])
arr, idx = idx1.factorize()
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(idx, exp_idx)
assert idx.freq == exp_idx.freq
arr, idx = idx1.factorize(sort=True)
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(idx, exp_idx)
assert idx.freq == exp_idx.freq
def test_factorize_preserves_freq(self):
# GH#38120 freq should be preserved
idx3 = timedelta_range("1 day", periods=4, freq="s")
exp_arr = np.array([0, 1, 2, 3], dtype=np.intp)
arr, idx = idx3.factorize()
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(idx, idx3)
assert idx.freq == idx3.freq
arr, idx = factorize(idx3)
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(idx, idx3)
assert idx.freq == idx3.freq
| bsd-3-clause |
eramirem/astroML | astroML/density_estimation/gauss_mixture.py | 3 | 1283 | import numpy as np
from sklearn.mixture import GMM
class GaussianMixture1D(object):
"""
Simple class to work with 1D mixtures of Gaussians
Parameters
----------
means : array_like
means of component distributions (default = 0)
sigmas : array_like
standard deviations of component distributions (default = 1)
weights : array_like
weight of component distributions (default = 1)
"""
def __init__(self, means=0, sigmas=1, weights=1):
data = np.array([t for t in np.broadcast(means, sigmas, weights)])
self._gmm = GMM(data.shape[0])
self._gmm.fit = None # disable fit method for safety
self._gmm.means_ = data[:, :1]
self._gmm.covars_ = data[:, 1:2] ** 2
self._gmm.weights = data[:, 2] / data[:, 2].sum()
def sample(self, size):
"""Random sample"""
return self._gmm.sample(size)
def pdf(self, x):
"""Compute probability distribution"""
logprob, responsibilities = self._gmm.eval(x)
return np.exp(logprob)
def pdf_individual(self, x):
"""Compute probability distribution of each component"""
logprob, responsibilities = self._gmm.eval(x)
return responsibilities * np.exp(logprob[:, np.newaxis])
| bsd-2-clause |
raghavrv/scikit-learn | examples/covariance/plot_outlier_detection.py | 15 | 5121 | """
==========================================
Outlier detection with several methods.
==========================================
When the amount of contamination is known, this example illustrates three
different ways of performing :ref:`outlier_detection`:
- based on a robust estimator of covariance, which is assuming that the
data are Gaussian distributed and performs better than the One-Class SVM
in that case.
- using the One-Class SVM and its ability to capture the shape of the
data set, hence performing better when the data is strongly
non-Gaussian, i.e. with two well-separated clusters;
- using the Isolation Forest algorithm, which is based on random forests and
hence more adapted to large-dimensional settings, even if it performs
quite well in the examples below.
- using the Local Outlier Factor to measure the local deviation of a given
data point with respect to its neighbors by comparing their local density.
The ground truth about inliers and outliers is given by the points colors
while the orange-filled area indicates which points are reported as inliers
by each method.
Here, we assume that we know the fraction of outliers in the datasets.
Thus rather than using the 'predict' method of the objects, we set the
threshold on the decision_function to separate out the corresponding
fraction.
"""
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import matplotlib.font_manager
from sklearn import svm
from sklearn.covariance import EllipticEnvelope
from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor
print(__doc__)
rng = np.random.RandomState(42)
# Example settings
n_samples = 200
outliers_fraction = 0.25
clusters_separation = [0, 1, 2]
# define two outlier detection tools to be compared
classifiers = {
"One-Class SVM": svm.OneClassSVM(nu=0.95 * outliers_fraction + 0.05,
kernel="rbf", gamma=0.1),
"Robust covariance": EllipticEnvelope(contamination=outliers_fraction),
"Isolation Forest": IsolationForest(max_samples=n_samples,
contamination=outliers_fraction,
random_state=rng),
"Local Outlier Factor": LocalOutlierFactor(
n_neighbors=35,
contamination=outliers_fraction)}
# Compare given classifiers under given settings
xx, yy = np.meshgrid(np.linspace(-7, 7, 100), np.linspace(-7, 7, 100))
n_inliers = int((1. - outliers_fraction) * n_samples)
n_outliers = int(outliers_fraction * n_samples)
ground_truth = np.ones(n_samples, dtype=int)
ground_truth[-n_outliers:] = -1
# Fit the problem with varying cluster separation
for i, offset in enumerate(clusters_separation):
np.random.seed(42)
# Data generation
X1 = 0.3 * np.random.randn(n_inliers // 2, 2) - offset
X2 = 0.3 * np.random.randn(n_inliers // 2, 2) + offset
X = np.r_[X1, X2]
# Add outliers
X = np.r_[X, np.random.uniform(low=-6, high=6, size=(n_outliers, 2))]
# Fit the model
plt.figure(figsize=(9, 7))
for i, (clf_name, clf) in enumerate(classifiers.items()):
# fit the data and tag outliers
if clf_name == "Local Outlier Factor":
y_pred = clf.fit_predict(X)
scores_pred = clf.negative_outlier_factor_
else:
clf.fit(X)
scores_pred = clf.decision_function(X)
y_pred = clf.predict(X)
threshold = stats.scoreatpercentile(scores_pred,
100 * outliers_fraction)
n_errors = (y_pred != ground_truth).sum()
# plot the levels lines and the points
if clf_name == "Local Outlier Factor":
# decision_function is private for LOF
Z = clf._decision_function(np.c_[xx.ravel(), yy.ravel()])
else:
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
subplot = plt.subplot(2, 2, i + 1)
subplot.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 7),
cmap=plt.cm.Blues_r)
a = subplot.contour(xx, yy, Z, levels=[threshold],
linewidths=2, colors='red')
subplot.contourf(xx, yy, Z, levels=[threshold, Z.max()],
colors='orange')
b = subplot.scatter(X[:-n_outliers, 0], X[:-n_outliers, 1], c='white',
s=20, edgecolor='k')
c = subplot.scatter(X[-n_outliers:, 0], X[-n_outliers:, 1], c='black',
s=20, edgecolor='k')
subplot.axis('tight')
subplot.legend(
[a.collections[0], b, c],
['learned decision function', 'true inliers', 'true outliers'],
prop=matplotlib.font_manager.FontProperties(size=10),
loc='lower right')
subplot.set_xlabel("%d. %s (errors: %d)" % (i + 1, clf_name, n_errors))
subplot.set_xlim((-7, 7))
subplot.set_ylim((-7, 7))
plt.subplots_adjust(0.04, 0.1, 0.96, 0.94, 0.1, 0.26)
plt.suptitle("Outlier detection")
plt.show()
| bsd-3-clause |
JPFrancoia/scikit-learn | examples/feature_selection/plot_feature_selection.py | 95 | 2847 | """
===============================
Univariate Feature Selection
===============================
An example showing univariate feature selection.
Noisy (non informative) features are added to the iris data and
univariate feature selection is applied. For each feature, we plot the
p-values for the univariate feature selection and the corresponding
weights of an SVM. We can see that univariate feature selection
selects the informative features and that these have larger SVM weights.
In the total set of features, only the 4 first ones are significant. We
can see that they have the highest score with univariate feature
selection. The SVM assigns a large weight to one of these features, but also
Selects many of the non-informative features.
Applying univariate feature selection before the SVM
increases the SVM weight attributed to the significant features, and will
thus improve classification.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, svm
from sklearn.feature_selection import SelectPercentile, f_classif
###############################################################################
# import some data to play with
# The iris dataset
iris = datasets.load_iris()
# Some noisy data not correlated
E = np.random.uniform(0, 0.1, size=(len(iris.data), 20))
# Add the noisy data to the informative features
X = np.hstack((iris.data, E))
y = iris.target
###############################################################################
plt.figure(1)
plt.clf()
X_indices = np.arange(X.shape[-1])
###############################################################################
# Univariate feature selection with F-test for feature scoring
# We use the default selection function: the 10% most significant features
selector = SelectPercentile(f_classif, percentile=10)
selector.fit(X, y)
scores = -np.log10(selector.pvalues_)
scores /= scores.max()
plt.bar(X_indices - .45, scores, width=.2,
label=r'Univariate score ($-Log(p_{value})$)', color='darkorange')
###############################################################################
# Compare to the weights of an SVM
clf = svm.SVC(kernel='linear')
clf.fit(X, y)
svm_weights = (clf.coef_ ** 2).sum(axis=0)
svm_weights /= svm_weights.max()
plt.bar(X_indices - .25, svm_weights, width=.2, label='SVM weight',
color='navy')
clf_selected = svm.SVC(kernel='linear')
clf_selected.fit(selector.transform(X), y)
svm_weights_selected = (clf_selected.coef_ ** 2).sum(axis=0)
svm_weights_selected /= svm_weights_selected.max()
plt.bar(X_indices[selector.get_support()] - .05, svm_weights_selected,
width=.2, label='SVM weights after selection', color='c')
plt.title("Comparing feature selection")
plt.xlabel('Feature number')
plt.yticks(())
plt.axis('tight')
plt.legend(loc='upper right')
plt.show()
| bsd-3-clause |
flightgong/scikit-learn | sklearn/datasets/tests/test_lfw.py | 50 | 6849 | """This test for the LFW require medium-size data dowloading and processing
If the data has not been already downloaded by running the examples,
the tests won't run (skipped).
If the test are run, the first execution will be long (typically a bit
more than a couple of minutes) but as the dataset loader is leveraging
joblib, successive runs will be fast (less than 200ms).
"""
import random
import os
import shutil
import tempfile
import numpy as np
from sklearn.externals import six
try:
try:
from scipy.misc import imsave
except ImportError:
from scipy.misc.pilutil import imsave
except ImportError:
imsave = None
from sklearn.datasets import load_lfw_pairs
from sklearn.datasets import load_lfw_people
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import raises
SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
SCIKIT_LEARN_EMPTY_DATA = tempfile.mkdtemp(prefix="scikit_learn_empty_test_")
LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, 'lfw_home')
FAKE_NAMES = [
'Abdelatif_Smith',
'Abhati_Kepler',
'Camara_Alvaro',
'Chen_Dupont',
'John_Lee',
'Lin_Bauman',
'Onur_Lopez',
]
def setup_module():
"""Test fixture run once and common to all tests of this module"""
if imsave is None:
raise SkipTest("PIL not installed.")
if not os.path.exists(LFW_HOME):
os.makedirs(LFW_HOME)
random_state = random.Random(42)
np_rng = np.random.RandomState(42)
# generate some random jpeg files for each person
counts = {}
for name in FAKE_NAMES:
folder_name = os.path.join(LFW_HOME, 'lfw_funneled', name)
if not os.path.exists(folder_name):
os.makedirs(folder_name)
n_faces = np_rng.randint(1, 5)
counts[name] = n_faces
for i in range(n_faces):
file_path = os.path.join(folder_name, name + '_%04d.jpg' % i)
uniface = np_rng.randint(0, 255, size=(250, 250, 3))
try:
imsave(file_path, uniface)
except ImportError:
raise SkipTest("PIL not installed")
# add some random file pollution to test robustness
with open(os.path.join(LFW_HOME, 'lfw_funneled', '.test.swp'), 'wb') as f:
f.write(six.b('Text file to be ignored by the dataset loader.'))
# generate some pairing metadata files using the same format as LFW
with open(os.path.join(LFW_HOME, 'pairsDevTrain.txt'), 'wb') as f:
f.write(six.b("10\n"))
more_than_two = [name for name, count in six.iteritems(counts)
if count >= 2]
for i in range(5):
name = random_state.choice(more_than_two)
first, second = random_state.sample(range(counts[name]), 2)
f.write(six.b('%s\t%d\t%d\n' % (name, first, second)))
for i in range(5):
first_name, second_name = random_state.sample(FAKE_NAMES, 2)
first_index = random_state.choice(np.arange(counts[first_name]))
second_index = random_state.choice(np.arange(counts[second_name]))
f.write(six.b('%s\t%d\t%s\t%d\n' % (first_name, first_index,
second_name, second_index)))
with open(os.path.join(LFW_HOME, 'pairsDevTest.txt'), 'wb') as f:
f.write(six.b("Fake place holder that won't be tested"))
with open(os.path.join(LFW_HOME, 'pairs.txt'), 'wb') as f:
f.write(six.b("Fake place holder that won't be tested"))
def teardown_module():
"""Test fixture (clean up) run once after all tests of this module"""
if os.path.isdir(SCIKIT_LEARN_DATA):
shutil.rmtree(SCIKIT_LEARN_DATA)
if os.path.isdir(SCIKIT_LEARN_EMPTY_DATA):
shutil.rmtree(SCIKIT_LEARN_EMPTY_DATA)
@raises(IOError)
def test_load_empty_lfw_people():
load_lfw_people(data_home=SCIKIT_LEARN_EMPTY_DATA)
def test_load_fake_lfw_people():
lfw_people = load_lfw_people(data_home=SCIKIT_LEARN_DATA,
min_faces_per_person=3)
# The data is croped around the center as a rectangular bounding box
# arounthe the face. Colors are converted to gray levels:
assert_equal(lfw_people.images.shape, (10, 62, 47))
assert_equal(lfw_people.data.shape, (10, 2914))
# the target is array of person integer ids
assert_array_equal(lfw_people.target, [2, 0, 1, 0, 2, 0, 2, 1, 1, 2])
# names of the persons can be found using the target_names array
expected_classes = ['Abdelatif Smith', 'Abhati Kepler', 'Onur Lopez']
assert_array_equal(lfw_people.target_names, expected_classes)
# It is possible to ask for the original data without any croping or color
# conversion and not limit on the number of picture per person
lfw_people = load_lfw_people(data_home=SCIKIT_LEARN_DATA,
resize=None, slice_=None, color=True)
assert_equal(lfw_people.images.shape, (17, 250, 250, 3))
# the ids and class names are the same as previously
assert_array_equal(lfw_people.target,
[0, 0, 1, 6, 5, 6, 3, 6, 0, 3, 6, 1, 2, 4, 5, 1, 2])
assert_array_equal(lfw_people.target_names,
['Abdelatif Smith', 'Abhati Kepler', 'Camara Alvaro',
'Chen Dupont', 'John Lee', 'Lin Bauman', 'Onur Lopez'])
@raises(ValueError)
def test_load_fake_lfw_people_too_restrictive():
load_lfw_people(data_home=SCIKIT_LEARN_DATA, min_faces_per_person=100)
@raises(IOError)
def test_load_empty_lfw_pairs():
load_lfw_pairs(data_home=SCIKIT_LEARN_EMPTY_DATA)
def test_load_fake_lfw_pairs():
lfw_pairs_train = load_lfw_pairs(data_home=SCIKIT_LEARN_DATA)
# The data is croped around the center as a rectangular bounding box
# arounthe the face. Colors are converted to gray levels:
assert_equal(lfw_pairs_train.pairs.shape, (10, 2, 62, 47))
# the target is whether the person is the same or not
assert_array_equal(lfw_pairs_train.target, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
# names of the persons can be found using the target_names array
expected_classes = ['Different persons', 'Same person']
assert_array_equal(lfw_pairs_train.target_names, expected_classes)
# It is possible to ask for the original data without any croping or color
# conversion
lfw_pairs_train = load_lfw_pairs(data_home=SCIKIT_LEARN_DATA,
resize=None, slice_=None, color=True)
assert_equal(lfw_pairs_train.pairs.shape, (10, 2, 250, 250, 3))
# the ids and class names are the same as previously
assert_array_equal(lfw_pairs_train.target, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
assert_array_equal(lfw_pairs_train.target_names, expected_classes)
| bsd-3-clause |
ashhher3/scikit-learn | sklearn/decomposition/tests/test_incremental_pca.py | 23 | 8317 | """Tests for Incremental PCA."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn import datasets
from sklearn.decomposition import PCA, IncrementalPCA
iris = datasets.load_iris()
def test_incremental_pca():
"""Incremental PCA on dense arrays."""
X = iris.data
batch_size = X.shape[0] // 3
ipca = IncrementalPCA(n_components=2, batch_size=batch_size)
pca = PCA(n_components=2)
pca.fit_transform(X)
X_transformed = ipca.fit_transform(X)
np.testing.assert_equal(X_transformed.shape, (X.shape[0], 2))
assert_almost_equal(ipca.explained_variance_ratio_.sum(),
pca.explained_variance_ratio_.sum(), 1)
for n_components in [1, 2, X.shape[1]]:
ipca = IncrementalPCA(n_components, batch_size=batch_size)
ipca.fit(X)
cov = ipca.get_covariance()
precision = ipca.get_precision()
assert_array_almost_equal(np.dot(cov, precision),
np.eye(X.shape[1]))
def test_incremental_pca_check_projection():
"""Test that the projection of data is correct."""
rng = np.random.RandomState(1999)
n, p = 100, 3
X = rng.randn(n, p) * .1
X[:10] += np.array([3, 4, 5])
Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])
# Get the reconstruction of the generated data X
# Note that Xt has the same "components" as X, just separated
# This is what we want to ensure is recreated correctly
Yt = IncrementalPCA(n_components=2).fit(X).transform(Xt)
# Normalize
Yt /= np.sqrt((Yt ** 2).sum())
# Make sure that the first element of Yt is ~1, this means
# the reconstruction worked as expected
assert_almost_equal(np.abs(Yt[0][0]), 1., 1)
def test_incremental_pca_inverse():
"""Test that the projection of data can be inverted."""
rng = np.random.RandomState(1999)
n, p = 50, 3
X = rng.randn(n, p) # spherical data
X[:, 1] *= .00001 # make middle component relatively small
X += [5, 4, 3] # make a large mean
# same check that we can find the original data from the transformed
# signal (since the data is almost of rank n_components)
ipca = IncrementalPCA(n_components=2, batch_size=10).fit(X)
Y = ipca.transform(X)
Y_inverse = ipca.inverse_transform(Y)
assert_almost_equal(X, Y_inverse, decimal=3)
def test_incremental_pca_validation():
"""Test that n_components is >=1 and <= n_features."""
X = [[0, 1], [1, 0]]
for n_components in [-1, 0, .99, 3]:
assert_raises(ValueError, IncrementalPCA(n_components,
batch_size=10).fit, X)
def test_incremental_pca_set_params():
"""Test that components_ sign is stable over batch sizes."""
rng = np.random.RandomState(1999)
n_samples = 100
n_features = 20
X = rng.randn(n_samples, n_features)
X2 = rng.randn(n_samples, n_features)
X3 = rng.randn(n_samples, n_features)
ipca = IncrementalPCA(n_components=20)
ipca.fit(X)
# Decreasing number of components
ipca.set_params(n_components=10)
assert_raises(ValueError, ipca.partial_fit, X2)
# Increasing number of components
ipca.set_params(n_components=15)
assert_raises(ValueError, ipca.partial_fit, X3)
# Returning to original setting
ipca.set_params(n_components=20)
ipca.partial_fit(X)
def test_incremental_pca_num_features_change():
"""Test that changing n_components will raise an error."""
rng = np.random.RandomState(1999)
n_samples = 100
X = rng.randn(n_samples, 20)
X2 = rng.randn(n_samples, 50)
ipca = IncrementalPCA(n_components=None)
ipca.fit(X)
assert_raises(ValueError, ipca.partial_fit, X2)
def test_incremental_pca_batch_signs():
"""Test that components_ sign is stable over batch sizes."""
rng = np.random.RandomState(1999)
n_samples = 100
n_features = 3
X = rng.randn(n_samples, n_features)
all_components = []
batch_sizes = np.arange(10, 20)
for batch_size in batch_sizes:
ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X)
all_components.append(ipca.components_)
for i, j in zip(all_components[:-1], all_components[1:]):
assert_almost_equal(np.sign(i), np.sign(j), decimal=6)
def test_incremental_pca_batch_values():
"""Test that components_ values are stable over batch sizes."""
rng = np.random.RandomState(1999)
n_samples = 100
n_features = 3
X = rng.randn(n_samples, n_features)
all_components = []
batch_sizes = np.arange(20, 40, 3)
for batch_size in batch_sizes:
ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X)
all_components.append(ipca.components_)
for i, j in zip(all_components[:-1], all_components[1:]):
assert_almost_equal(i, j, decimal=1)
def test_incremental_pca_partial_fit():
"""Test that fit and partial_fit get equivalent results."""
rng = np.random.RandomState(1999)
n, p = 50, 3
X = rng.randn(n, p) # spherical data
X[:, 1] *= .00001 # make middle component relatively small
X += [5, 4, 3] # make a large mean
# same check that we can find the original data from the transformed
# signal (since the data is almost of rank n_components)
batch_size = 10
ipca = IncrementalPCA(n_components=2, batch_size=batch_size).fit(X)
pipca = IncrementalPCA(n_components=2, batch_size=batch_size)
# Add one to make sure endpoint is included
batch_itr = np.arange(0, n + 1, batch_size)
for i, j in zip(batch_itr[:-1], batch_itr[1:]):
pipca.partial_fit(X[i:j, :])
assert_almost_equal(ipca.components_, pipca.components_, decimal=3)
def test_incremental_pca_against_pca_iris():
"""Test that IncrementalPCA and PCA are approximate (to a sign flip)."""
X = iris.data
Y_pca = PCA(n_components=2).fit_transform(X)
Y_ipca = IncrementalPCA(n_components=2, batch_size=25).fit_transform(X)
assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1)
def test_incremental_pca_against_pca_random_data():
"""Test that IncrementalPCA and PCA are approximate (to a sign flip)."""
rng = np.random.RandomState(1999)
n_samples = 100
n_features = 3
X = rng.randn(n_samples, n_features) + 5 * rng.rand(1, n_features)
Y_pca = PCA(n_components=3).fit_transform(X)
Y_ipca = IncrementalPCA(n_components=3, batch_size=25).fit_transform(X)
assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1)
def test_explained_variances():
"""Test that PCA and IncrementalPCA calculations match"""
X = datasets.make_low_rank_matrix(1000, 100, tail_strength=0.,
effective_rank=10, random_state=1999)
prec = 3
n_samples, n_features = X.shape
for nc in [None, 99]:
pca = PCA(n_components=nc).fit(X)
ipca = IncrementalPCA(n_components=nc, batch_size=100).fit(X)
assert_almost_equal(pca.explained_variance_, ipca.explained_variance_,
decimal=prec)
assert_almost_equal(pca.explained_variance_ratio_,
ipca.explained_variance_ratio_, decimal=prec)
assert_almost_equal(pca.noise_variance_, ipca.noise_variance_,
decimal=prec)
def test_whitening():
"""Test that PCA and IncrementalPCA transforms match to sign flip."""
X = datasets.make_low_rank_matrix(1000, 10, tail_strength=0.,
effective_rank=2, random_state=1999)
prec = 3
n_samples, n_features = X.shape
for nc in [None, 9]:
pca = PCA(whiten=True, n_components=nc).fit(X)
ipca = IncrementalPCA(whiten=True, n_components=nc,
batch_size=250).fit(X)
Xt_pca = pca.transform(X)
Xt_ipca = ipca.transform(X)
assert_almost_equal(np.abs(Xt_pca), np.abs(Xt_ipca), decimal=prec)
Xinv_ipca = ipca.inverse_transform(Xt_ipca)
Xinv_pca = pca.inverse_transform(Xt_pca)
assert_almost_equal(X, Xinv_ipca, decimal=prec)
assert_almost_equal(X, Xinv_pca, decimal=prec)
assert_almost_equal(Xinv_pca, Xinv_ipca, decimal=prec)
| bsd-3-clause |
chicm/carvana | car-segment/common.py | 1 | 1940 | import os
os.environ['HOME'] = '/root'
#os.environ['PYTHONUNBUFFERED'] = '1'
#numerical libs
import math
import numpy as np
import random
import PIL
import cv2
import matplotlib
matplotlib.use('TkAgg')
#matplotlib.use('Qt4Agg')
#matplotlib.use('Qt5Agg')
# torch libs
import torch
import torchvision.transforms as transforms
from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader
from torch.utils.data.sampler import *
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim
import torch.backends.cudnn as cudnn
# std libs
import collections
import types
import numbers
import inspect
import shutil
#import pickle
import dill
from timeit import default_timer as timer #ubuntu: default_timer = time.time, seconds
from datetime import datetime
import csv
import pandas as pd
import pickle
import glob
import sys
#from time import sleep
from distutils.dir_util import copy_tree
import zipfile
import zlib
import matplotlib.pyplot as plt
import sklearn
import sklearn.metrics
from skimage import io
from sklearn.metrics import fbeta_score
'''
updating pytorch
https://discuss.pytorch.org/t/updating-pytorch/309
./conda config --add channels soumith
conda update pytorch torchvision
conda install pytorch torchvision cuda80 -c soumith
'''
#---------------------------------------------------------------------------------
print('@%s: ' % os.path.basename(__file__))
if 0:
SEED=235202
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
print ('\tset random seed')
print ('\t\tSEED=%d'%SEED)
if 1:
cudnn.benchmark = True ##uses the inbuilt cudnn auto-tuner to find the fastest convolution algorithms. -
print ('\tset cuda environment')
print('')
#--------------------------------------------------------------------------------- | apache-2.0 |
vahndi/scitwi | scitwi/trends/trends.py | 1 | 1635 | from pandas import DataFrame
from twitter.api import TwitterListResponse
from scitwi.places.location import Location
from scitwi.utils.attrs import datetime_attr, list_obj_attr
from scitwi.utils.strs import obj_string, list_obj_string
from .trend import Trend
class Trends(object):
def __init__(self, response: TwitterListResponse):
self._response = response
self.as_of = datetime_attr(response[0], 'as_of')
self.created_at = datetime_attr(response[0], 'created_at')
self.locations = list_obj_attr(response[0], 'locations', Location)
self.trends_list = [
Trend(trend_dict=t, as_of=self.as_of,
created_at=self.created_at, locations=self.locations)
for t in self._response_dict['trends']
]
@property
def _response_dict(self):
return self._response[0]
@property
def trend_names(self):
return [t.name for t in self.trends_list]
@property
def trends_dataframe(self):
return DataFrame(self.trends_list)
@staticmethod
def common_trend_names(trends_1, trends_2):
set_1 = set([trend.name for trend in trends_1.trends_list])
set_2 = set([trend.name for trend in trends_2.trends_list])
return sorted(set_1.intersection(set_2))
def __str__(self):
str_out = ''
str_out += obj_string('As Of', self.as_of)
str_out += obj_string('Created At', self.created_at)
str_out += list_obj_string('Locations', self.locations)
str_out += list_obj_string('Trends', self.trend_names)
return str_out
| mit |
bennlich/scikit-image | doc/examples/plot_hog.py | 17 | 4358 | """
===============================
Histogram of Oriented Gradients
===============================
The `Histogram of Oriented Gradient
<http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients>`__ (HOG) feature
descriptor [1]_ is popular for object detection.
In the following example, we compute the HOG descriptor and display
a visualisation.
Algorithm overview
------------------
Compute a Histogram of Oriented Gradients (HOG) by
1. (optional) global image normalisation
2. computing the gradient image in x and y
3. computing gradient histograms
4. normalising across blocks
5. flattening into a feature vector
The first stage applies an optional global image normalisation
equalisation that is designed to reduce the influence of illumination
effects. In practice we use gamma (power law) compression, either
computing the square root or the log of each colour channel.
Image texture strength is typically proportional to the local surface
illumination so this compression helps to reduce the effects of local
shadowing and illumination variations.
The second stage computes first order image gradients. These capture
contour, silhouette and some texture information, while providing
further resistance to illumination variations. The locally dominant
colour channel is used, which provides colour invariance to a large
extent. Variant methods may also include second order image derivatives,
which act as primitive bar detectors - a useful feature for capturing,
e.g. bar like structures in bicycles and limbs in humans.
The third stage aims to produce an encoding that is sensitive to
local image content while remaining resistant to small changes in
pose or appearance. The adopted method pools gradient orientation
information locally in the same way as the SIFT [2]_
feature. The image window is divided into small spatial regions,
called "cells". For each cell we accumulate a local 1-D histogram
of gradient or edge orientations over all the pixels in the
cell. This combined cell-level 1-D histogram forms the basic
"orientation histogram" representation. Each orientation histogram
divides the gradient angle range into a fixed number of
predetermined bins. The gradient magnitudes of the pixels in the
cell are used to vote into the orientation histogram.
The fourth stage computes normalisation, which takes local groups of
cells and contrast normalises their overall responses before passing
to next stage. Normalisation introduces better invariance to illumination,
shadowing, and edge contrast. It is performed by accumulating a measure
of local histogram "energy" over local groups of cells that we call
"blocks". The result is used to normalise each cell in the block.
Typically each individual cell is shared between several blocks, but
its normalisations are block dependent and thus different. The cell
thus appears several times in the final output vector with different
normalisations. This may seem redundant but it improves the performance.
We refer to the normalised block descriptors as Histogram of Oriented
Gradient (HOG) descriptors.
The final step collects the HOG descriptors from all blocks of a dense
overlapping grid of blocks covering the detection window into a combined
feature vector for use in the window classifier.
References
----------
.. [1] Dalal, N. and Triggs, B., "Histograms of Oriented Gradients for
Human Detection," IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, 2005, San Diego, CA, USA.
.. [2] David G. Lowe, "Distinctive image features from scale-invariant
keypoints," International Journal of Computer Vision, 60, 2 (2004),
pp. 91-110.
"""
import matplotlib.pyplot as plt
from skimage.feature import hog
from skimage import data, color, exposure
image = color.rgb2gray(data.astronaut())
fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualise=True)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.axis('off')
ax1.imshow(image, cmap=plt.cm.gray)
ax1.set_title('Input image')
# Rescale histogram for better display
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))
ax2.axis('off')
ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)
ax2.set_title('Histogram of Oriented Gradients')
plt.show()
| bsd-3-clause |
amozie/amozie | testzie/opencv_helloworld.py | 1 | 15032 | import numpy as np
from matplotlib import pyplot as plt
import cv2
# show
img = cv2.imread('f:/index.jpg', 1)
cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.imshow('img', img)
k = cv2.waitKey(0)
if k == ord('s'):
print(chr(k))
cv2.destroyAllWindows()
# draw
def draw_circle(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDOWN:
param[0] = True
param[1] = (x, y)
return
if event == cv2.EVENT_LBUTTONUP:
param[0] = False
param[1] = None
if param[0] and param[1] and event == cv2.EVENT_MOUSEMOVE:
cv2.line(img, param[1], (x, y), (255, 0, 0), 10)
param[1] = (x, y)
img = np.zeros((512, 512, 3), np.uint8) + 255
cv2.namedWindow('image')
cv2.setMouseCallback('image', draw_circle, [False, None])
while True:
cv2.imshow('image', img)
if cv2.waitKey(20) & 0xFF == 27:
break
cv2.destroyAllWindows()
# basic
img1 = cv2.imread('f:/index.jpg', cv2.IMREAD_COLOR)
img2 = cv2.imread('f:/123.jpg', cv2.IMREAD_COLOR)
# threshold
def nothing(x):
pass
gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
ret, img = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)
cv2.namedWindow('img')
cv2.createTrackbar('num', 'img', 0, 255, nothing)
while True:
cv2.imshow('img',img)
if cv2.waitKey(1) == ord('q'):
break
num = cv2.getTrackbarPos('num', 'img')
ret, img = cv2.threshold(gray, num, 255, cv2.THRESH_BINARY)
cv2.destroyAllWindows()
# bit calculation
rows, cols, _ = img2.shape
roi = img1[0:rows, 0:cols]
ret, mask = cv2.threshold(gray, 160, 255, cv2.THRESH_BINARY)
mask_inv = cv2.bitwise_not(mask)
img1_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)
img2_bg = cv2.bitwise_and(img2, img2, mask=mask)
dst = cv2.add(img1_bg, img2_bg)
cv2.imshow('dst', dst)
img1[0:rows, 0:cols] = dst
cv2.imshow('img', img1)
# inRange
cv2.imshow('img', img1)
hsv = cv2.cvtColor(img1, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, np.array([0, 50, 0]), np.array([255, 255, 255]))
cv2.imshow('mask', mask)
res = cv2.bitwise_and(hsv, hsv, mask=mask)
res = cv2.cvtColor(res, cv2.COLOR_HSV2BGR)
cv2.imshow('res', res)
# resize
res = cv2.resize(img1, None, fx=2, fy=2)
cv2.imshow('img', res)
# Perspective Transform
def mouse_event(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONUP:
cv2.circle(img1, (x, y), 10, (0, 255, 0), 2)
param.append([y, x])
img1 = cv2.imread('f:/index.jpg', cv2.IMREAD_COLOR)
l = []
cv2.namedWindow('img')
cv2.setMouseCallback('img', mouse_event, l)
while True:
cv2.imshow('img', img1)
if cv2.waitKey(1) == ord('q'):
break
if len(l) == 4:
w1 = l[2][1] - l[0][1]
w2 = l[3][1] - l[1][1]
h1 = l[1][0] - l[0][0]
h2 = l[3][0] - l[2][0]
w = int((w1 + w2) / 2)
h = int((h1 + h2) / 2)
p1 = np.float32(l)
p2 = np.float32([[0, 0], [h, 0], [0, w], [h, w]])
M = cv2.getPerspectiveTransform(p1, p2)
dst = cv2.warpPerspective(img1, M, (w, h))
cv2.imshow('dst', dst)
cv2.destroyAllWindows()
# pyramid
img = cv2.imread('f:/index.jpg', cv2.IMREAD_COLOR)
img = cv2.resize(img, (688, 400))
gp = [img]
for i in range(4):
img = cv2.pyrDown(img)
gp.append(img)
for i, v in enumerate(gp):
cv2.imshow(str(i), v)
lp = [gp[4]]
for i in range(4, 0, -1):
pu = cv2.pyrUp(gp[i])
sub = cv2.subtract(gp[i-1], pu)
lp.append(sub)
for i, v in enumerate(lp):
cv2.imshow(str(i), v)
ls = lp[0]
for i, v in enumerate(lp):
if i == 0:
continue
ls = cv2.pyrUp(ls)
ls = cv2.add(ls, v)
# contour
img = cv2.imread('f:/index.jpg', 0)
shape = img.shape
img = cv2.bitwise_not(img)
ret, th = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU)
th = cv2.morphologyEx(img, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)))
ret, th = cv2.threshold(th, 0, 255, cv2.THRESH_OTSU)
cv2.imshow('th', th)
image, contours, hierarchy = cv2.findContours(th, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
img = cv2.imread('f:/index.jpg')
img = cv2.drawContours(img, contours, 7, (0, 0, 255), -1)
cv2.imshow('img', img)
# moment
cnt = contours[7]
m = cv2.moments(cnt)
cx = int(m['m10']/m['m00'])
cy = int(m['m01']/m['m00'])
cv2.circle(img, (cx, cy), 2, (0, 0, 255), 2)
cv2.imshow('img', img)
cv2.contourArea(cnt)
cv2.arcLength(cnt, True)
epsilon = 0.01 * cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, epsilon, True)
img = cv2.polylines(img, approx.swapaxes(0, 1), True, (0, 255, 0), 2)
cv2.imshow('img', img)
hull = cv2.convexHull(cnt)
img = cv2.polylines(img, hull.swapaxes(0, 1), True, (0, 255, 0), 2)
cv2.imshow('img', img)
mask = np.zeros(shape, np.uint8)
cv2.drawContours(mask, [cnt], 0, 255, -1)
cv2.minMaxLoc(img, mask)
cv2.mean(img, mask)
cv2.circle(img, tuple(cnt[cnt[:, :, 0].argmin()][0]), 2, (0, 0, 255), 2)
cv2.circle(img, tuple(cnt[cnt[:, :, 0].argmax()][0]), 2, (0, 0, 255), 2)
cv2.circle(img, tuple(cnt[cnt[:, :, 1].argmin()][0]), 2, (0, 0, 255), 2)
cv2.circle(img, tuple(cnt[cnt[:, :, 1].argmax()][0]), 2, (0, 0, 255), 2)
cv2.imshow('img', img)
# region
hull = cv2.convexHull(cnt, returnPoints=False)
defects = cv2.convexityDefects(cnt, hull)
for i in range(defects.shape[0]):
s, e, f, d = defects[i, 0]
# cv2.circle(img, tuple(cnt[s][0]), 2, (0, 0, 255), 2)
# cv2.circle(img, tuple(cnt[e][0]), 2, (0, 255, 0), 2)
cv2.line(img, tuple(cnt[s][0]), tuple(cnt[e][0]), (0, 0, 255), 1)
cv2.circle(img, tuple(cnt[f][0]), 2, (0, 255, 0), 2)
cv2.imshow('img', img)
dist = cv2.pointPolygonTest(cnt, (50, 50), True)
inner = cv2.pointPolygonTest(cnt, (343, 218), False)
img = cv2.imread('f:/index.jpg')
img = cv2.drawContours(img, contours, 7, (0, 0, 255), 2)
img = cv2.drawContours(img, contours, 9, (0, 0, 255), 2)
cv2.imshow('img', img)
cnt1 = contours[7]
cnt2 = contours[9]
cv2.matchShapes(cnt1, cnt2, cv2.CONTOURS_MATCH_I1, 0.0)
# retrieval
image, contours, hierarchy = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# histogram
img = cv2.imread('f:/a.jpg', 0)
hist = cv2.calcHist([img], [0], None, [256], [0, 256])
cdf = hist.cumsum()
cdf_norm = cdf * hist.max() / cdf.max()
plt.hist(img.ravel(), 256, [0, 256])
plt.plot(cdf_norm, 'r')
cdf_m = np.ma.masked_equal(cdf, 0)
cdf_m = (cdf_m - cdf_m.min()) * 255 / (cdf_m.max() - cdf_m.min())
cdf = np.ma.filled(cdf_m, 0).astype(np.uint8)
img2 = cdf[img]
cv2.imshow('img', img)
cv2.imshow('img2', img2)
equ = cv2.equalizeHist(img)
cv2.imshow('equ', equ)
clahe = cv2.createCLAHE(2, (8, 8))
cl = clahe.apply(img)
cv2.imshow('cl', cl)
# histogram 2D
img = cv2.imread('f:/a.jpg')
cv2.imshow('img', img)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
plt.imshow(hist)
# back project
img = cv2.imread('f:/se.jpg')
p1 = 100
p2 = 200
roi = img[p1:p2, p1:p2].copy()
# cv2.imshow('roi', roi)
imgc = img.copy()
imgc = cv2.rectangle(imgc, (p1, p1), (p2, p2), (255, 255, 255), 2)
cv2.imshow('imgc', imgc)
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
hsvt = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
M = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
I = cv2.calcHist([hsvt], [0, 1], None, [180, 256], [0, 180, 0, 256])
Im = np.ma.masked_equal(I, 0.0)
Rm = M/Im
R = np.ma.filled(Rm, 1.0)
h, s, v = cv2.split(hsvt)
B = R[h.ravel(), s.ravel()]
B = np.minimum(B, 1)
B = B.reshape(hsvt.shape[:2])
cv2.imshow('B', B)
disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
dst = cv2.filter2D(B, -1, disc)
dst = np.uint8(dst)
cv2.normalize(dst, dst, 0, 255, cv2.NORM_MINMAX)
cv2.imshow('dst', dst)
ret, th = cv2.threshold(dst, 5, 255, cv2.THRESH_BINARY)
cv2.imshow('th', th)
imgt = cv2.bitwise_and(img, img, mask=th)
imgt = cv2.rectangle(imgt, (p1, p1), (p2, p2), (255, 255, 255), 2)
cv2.imshow('imgt', imgt)
B = cv2.calcBackProject([hsvt], [0, 1], M, [0, 180, 0, 256], 1)
# FFT
img = cv2.imread('f:/a.jpg', 0)
f = np.fft.fft2(img)
fs = np.fft.fftshift(f)
mag = np.log(np.abs(fs))
mag = cv2.normalize(mag, mag, 0, 255, cv2.NORM_MINMAX)
mag = mag.astype(int)
plt.imshow(mag, 'gray')
laplacian = np.array([[0, 1, 0],[1, -4, 1], [0, 1, 0]])
f = np.fft.fft2(laplacian)
fs = np.fft.fftshift(f)
mag = np.log(np.abs(fs)+1)
plt.imshow(mag, 'gray')
# match
img = cv2.imread('f:/1.jpg', 0)
ret, th = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU)
th = cv2.bitwise_not(th)
image, contours, hierarchy = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
x, y, w, h = cv2.boundingRect(contours[11])
img = cv2.imread('f:/1.jpg')
cv2.rectangle(img, (x-3, y-3), (x+w+3, y+h+3), (0, 255, 0), 2)
cv2.imshow('img', img)
img = cv2.imread('f:/1.jpg', 0)
roi = img[y-3:y+h+4,x-3:x+w+4].copy()
hs, ws = roi.shape
res = cv2.matchTemplate(img, roi, cv2.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
img = cv2.imread('f:/1.jpg')
cv2.rectangle(img, max_loc, (max_loc[0]+ws, max_loc[1]+hs), (255, 0, 255), 2)
cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.imshow('img', img)
loc = np.where(res >= 0.95)
img = cv2.imread('f:/1.jpg')
pts = []
for i, pt in enumerate(zip(*loc[::-1])):
cv2.rectangle(img, pt, (pt[0]+ws, pt[1]+hs), (255, 0, 255), 2)
cv2.putText(img, str(i+1), pt, cv2.FONT_HERSHEY_SIMPLEX, 4, (0, 255, 255), 2)
cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.imshow('img', img)
# hough
img = cv2.imread('f:/pat1.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edge = cv2.Canny(gray, 50, 150, apertureSize=3)
cv2.imshow('edge',edge)
lines = cv2.HoughLines(edge, 1, np.pi/360, 100)
lines = lines.reshape(-1, 2)
for r, theta in lines:
a = np.cos(theta)
b = np.sin(theta)
x0 = r*a
y0 = r*b
x1 = int(x0 + 1000*(-b))
y1 = int(y0 + 1000*(a))
x2 = int(x0 - 1000*(-b))
y2 = int(y0 - 1000*(a))
cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.imshow('img', img)
img = cv2.imread('f:/pat1.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edge = cv2.Canny(gray, 50, 150, apertureSize=3)
lines = cv2.HoughLinesP(edge, 1, np.pi/360, 50, minLineLength=50, maxLineGap=10)
lines = lines.reshape(-1, 4)
for x1, y1, x2, y2 in lines:
cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.imshow('img', img)
# watershed
img = cv2.imread('f:/bi.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, th = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY_INV)
cv2.imshow('th', th)
mp = cv2.morphologyEx(th, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)), iterations=2)
cv2.imshow('mp', mp)
bg = cv2.dilate(mp, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)), iterations=3)
cv2.imshow('bg', bg)
dist = cv2.distanceTransform(mp, cv2.DIST_L1, 5)
cv2.imshow('dist', dist)
ret, fg = cv2.threshold(dist, 0.7*dist.max(), 255, cv2.THRESH_BINARY)
fg = np.uint8(fg)
cv2.imshow('fg', fg)
unknown = cv2.subtract(bg, fg)
cv2.imshow('unknown', unknown)
ret, markers = cv2.connectedComponents(fg)
# markers = (markers - markers.min())*255/(markers.max() - markers.min())
# markers = np.uint8(markers)
# cv2.imshow('markers', markers)
markers = markers + 1
markers[unknown == 255] = 0
markers3 = cv2.watershed(img, markers)
img[markers3 == -1] = [255, 0, 0]
cv2.imshow('img', img)
# grabcut
def grabcut(event, x, y, flags, param):
global imga, point, flag, mask
if event == cv2.EVENT_LBUTTONDOWN:
cv2.circle(imga, (x, y), 5, (255, 0, 0, 64), -1)
cv2.circle(mask, (x, y), 5, cv2.GC_PR_FGD, -1)
point = (x, y)
if event == cv2.EVENT_LBUTTONUP:
point = None
flag = True
if point is not None and event == cv2.EVENT_MOUSEMOVE:
cv2.line(imga, point, (x, y), (255, 0, 0, 64), 10)
cv2.line(mask, point, (x, y), cv2.GC_PR_FGD, 10)
point = (x, y)
img = cv2.imread('f:/photo.jpg')
imga = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# cv2.imshow('img', img)
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
mask = np.zeros(img.shape[:2], np.uint8) + cv2.GC_PR_BGD
point = None
flag = True
cv2.namedWindow('image')
cv2.setMouseCallback('image', grabcut, [False, None])
while True:
cv2.imshow('image', imga)
cv2.imshow('mask', cv2.multiply(mask2, 255))
if flag:
cv2.grabCut(img, mask, None, bgdModel, fgdModel, 5)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype(np.uint8)
mask2_not = cv2.subtract(1, mask2)
img_m = cv2.bitwise_and(img, img, mask=mask2)
img_m = cv2.cvtColor(img_m, cv2.COLOR_BGR2BGRA)
img_n = cv2.bitwise_and(gray, gray, mask=mask2_not)
img_n = cv2.merge([img_n, img_n, img_n, cv2.add(np.zeros(gray.shape, np.uint8), 255)])
imga = cv2.add(img_m, img_n)
flag = False
if cv2.waitKey(20) == ord('q'):
break
cv2.destroyAllWindows()
# harris
img = cv2.imread('f:/1.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv2.cornerHarris(gray, 2, 3, 0.05)
dst = cv2.dilate(dst, None)
img[dst > 0.01*dst.max()] = [0, 0, 255]
cv2.imshow('img', img)
dst = np.uint8(dst)
ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)
corners = cv2.cornerSubPix(gray, np.float32(centroids), (5, 5), (-1, -1), criteria)
res = np.hstack((centroids, corners))
res = np.int0(res)
img[res[:, 1], res[:, 0]] = [0, 0, 255]
img[res[:, 3], res[:, 2]] = [0, 255, 0]
for x1, y1, x2, y2 in res:
cv2.circle(img, (x1, y1), 2, (0, 0, 255), -1)
cv2.circle(img, (x2, y2), 2, (0, 255, 0), -1)
cv2.imshow('img', img)
# shi-tomasi
img = cv2.imread('f:/1.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
corners = cv2.goodFeaturesToTrack(gray, 100, 0.01, 10)
corners = np.int0(corners)
for i in corners:
x, y = i.ravel()
cv2.circle(img, (x, y), 2, (0, 0, 255), -1)
cv2.imshow('img', img)
# SIFT
img = cv2.imread('f:/index.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# SURF
# FAST
img = cv2.imread('f:/1.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
fast = cv2.FastFeatureDetector_create()
kp = fast.detect(img, None)
cv2.drawKeypoints(img, kp, img, color=(0, 0, 255))
cv2.imshow('img', img)
fast.setNonmaxSuppression(False)
kp = fast.detect(img, None)
cv2.drawKeypoints(img, kp, img, color=(0, 0, 255))
cv2.imshow('img', img)
# BRIEF & CenSurE(Star)
img = cv2.imread('f:/a.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# ORB
img = cv2.imread('f:/a.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
orb = cv2.ORB_create()
kp = orb.detect(gray, None)
cv2.drawKeypoints(img, kp, img, color=(0, 0, 255))
cv2.imshow('img', img)
# match
img1 = cv2.imread('f:/m0.jpg', 0)
img2 = cv2.imread('f:/m2.jpg', 0)
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, True)
matches = bf.match(des1, des2)
matches = sorted(matches, key=lambda x: x.distance)
img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, flags=0)
cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.imshow('img', img3)
# meanshift
# camshift
# optical flow
| apache-2.0 |
HyukjinKwon/spark | python/pyspark/pandas/tests/test_sql.py | 15 | 1979 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from pyspark import pandas as ps
from pyspark.sql.utils import ParseException
from pyspark.testing.pandasutils import PandasOnSparkTestCase
from pyspark.testing.sqlutils import SQLTestUtils
class SQLTest(PandasOnSparkTestCase, SQLTestUtils):
def test_error_variable_not_exist(self):
msg = "The key variable_foo in the SQL statement was not found.*"
with self.assertRaisesRegex(ValueError, msg):
ps.sql("select * from {variable_foo}")
def test_error_unsupported_type(self):
msg = "Unsupported variable type dict: {'a': 1}"
with self.assertRaisesRegex(ValueError, msg):
some_dict = {"a": 1}
ps.sql("select * from {some_dict}")
def test_error_bad_sql(self):
with self.assertRaises(ParseException):
ps.sql("this is not valid sql")
if __name__ == "__main__":
import unittest
from pyspark.pandas.tests.test_sql import * # noqa: F401
try:
import xmlrunner # type: ignore[import]
testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
| apache-2.0 |
pinkavaj/gnuradio | gr-fec/python/fec/polar/decoder.py | 24 | 10396 | #!/usr/bin/env python
#
# Copyright 2015 Free Software Foundation, Inc.
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# GNU Radio is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GNU Radio; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
import numpy as np
from common import PolarCommon
# for dev
from encoder import PolarEncoder
from matplotlib import pyplot as plt
class PolarDecoder(PolarCommon):
def __init__(self, n, k, frozen_bit_position, frozenbits=None):
PolarCommon.__init__(self, n, k, frozen_bit_position, frozenbits)
self.error_probability = 0.1 # this is kind of a dummy value. usually chosen individually.
self.lrs = ((1 - self.error_probability) / self.error_probability, self.error_probability / (1 - self.error_probability))
self.llrs = np.log(self.lrs)
def _llr_bit(self, bit):
return self.llrs[bit]
def _llr_odd(self, la, lb):
# this functions uses the min-sum approximation
# exact formula: np.log((np.exp(la + lb) + 1) / (np.exp(la) + np.exp(lb)))
return np.sign(la) * np.sign(lb) * np.minimum(np.abs(la), np.abs(lb))
_f_vals = np.array((1.0, -1.0), dtype=float)
def _llr_even(self, la, lb, f):
return (la * self._f_vals[f]) + lb
def _llr_bit_decision(self, llr):
if llr < 0.0:
ui = int(1)
else:
ui = int(0)
return ui
def _retrieve_bit_from_llr(self, lr, pos):
f_index = np.where(self.frozen_bit_position == pos)[0]
if not f_index.size == 0:
ui = self.frozenbits[f_index][0]
else:
ui = self._llr_bit_decision(lr)
return ui
def _lr_bit(self, bit):
return self.lrs[bit]
def _lr_odd(self, la, lb):
# la is upper branch and lb is lower branch
return (la * lb + 1) / (la + lb)
def _lr_even(self, la, lb, f):
# la is upper branch and lb is lower branch, f is last decoded bit.
return (la ** (1 - (2 * f))) * lb
def _lr_bit_decision(self, lr):
if lr < 1:
return int(1)
return int(0)
def _get_even_indices_values(self, u_hat):
# looks like overkill for some indexing, but zero and one based indexing mix-up gives you haedaches.
return u_hat[1::2]
def _get_odd_indices_values(self, u_hat):
return u_hat[0::2]
def _calculate_lrs(self, y, u):
ue = self._get_even_indices_values(u)
uo = self._get_odd_indices_values(u)
ya = y[0:y.size//2]
yb = y[(y.size//2):]
la = self._lr_decision_element(ya, (ue + uo) % 2)
lb = self._lr_decision_element(yb, ue)
return la, lb
def _lr_decision_element(self, y, u):
if y.size == 1:
return self._llr_bit(y[0])
if u.size % 2 == 0: # use odd branch formula
la, lb = self._calculate_lrs(y, u)
return self._llr_odd(la, lb)
else:
ui = u[-1]
la, lb = self._calculate_lrs(y, u[0:-1])
return self._llr_even(la, lb, ui)
def _retrieve_bit_from_lr(self, lr, pos):
f_index = np.where(self.frozen_bit_position == pos)[0]
if not f_index.size == 0:
ui = self.frozenbits[f_index][0]
else:
ui = self._lr_bit_decision(lr)
return ui
def _lr_sc_decoder(self, y):
# this is the standard SC decoder as derived from the formulas. It sticks to natural bit order.
u = np.array([], dtype=int)
for i in range(y.size):
lr = self._lr_decision_element(y, u)
ui = self._retrieve_bit_from_llr(lr, i)
u = np.append(u, ui)
return u
def _llr_retrieve_bit(self, llr, pos):
f_index = np.where(self.frozen_bit_position == pos)[0]
if not f_index.size == 0:
ui = self.frozenbits[f_index][0]
else:
ui = self._llr_bit_decision(llr)
return ui
def _butterfly_decode_bits(self, pos, graph, u):
bit_num = u.size
llr = graph[pos][0]
ui = self._llr_retrieve_bit(llr, bit_num)
# ui = self._llr_bit_decision(llr)
u = np.append(u, ui)
lower_right = pos + (self.N // 2)
la = graph[pos][1]
lb = graph[lower_right][1]
graph[lower_right][0] = self._llr_even(la, lb, ui)
llr = graph[lower_right][0]
# ui = self._llr_bit_decision(llr)
ui = self._llr_retrieve_bit(llr, u.size)
u = np.append(u, ui)
return graph, u
def _lr_sc_decoder_efficient(self, y):
graph = np.full((self.N, self.power + 1), np.NaN, dtype=float)
for i in range(self.N):
graph[i][self.power] = self._llr_bit(y[i])
decode_order = self._vector_bit_reversed(np.arange(self.N), self.power)
decode_order = np.delete(decode_order, np.where(decode_order >= self.N // 2))
u = np.array([], dtype=int)
for pos in decode_order:
graph = self._butterfly(pos, 0, graph, u)
graph, u = self._butterfly_decode_bits(pos, graph, u)
return u
def _stop_propagation(self, bf_entry_row, stage):
# calculate break condition
modulus = 2 ** (self.power - stage)
# stage_size = self.N // (2 ** stage)
# half_stage_size = stage_size // 2
half_stage_size = self.N // (2 ** (stage + 1))
stage_pos = bf_entry_row % modulus
return stage_pos >= half_stage_size
def _butterfly(self, bf_entry_row, stage, graph, u):
if not self.power > stage:
return graph
if self._stop_propagation(bf_entry_row, stage):
upper_right = bf_entry_row - self.N // (2 ** (stage + 1))
la = graph[upper_right][stage + 1]
lb = graph[bf_entry_row][stage + 1]
ui = u[-1]
graph[bf_entry_row][stage] = self._llr_even(la, lb, ui)
return graph
# activate right side butterflies
u_even = self._get_even_indices_values(u)
u_odd = self._get_odd_indices_values(u)
graph = self._butterfly(bf_entry_row, stage + 1, graph, (u_even + u_odd) % 2)
lower_right = bf_entry_row + self.N // (2 ** (stage + 1))
graph = self._butterfly(lower_right, stage + 1, graph, u_even)
la = graph[bf_entry_row][stage + 1]
lb = graph[lower_right][stage + 1]
graph[bf_entry_row][stage] = self._llr_odd(la, lb)
return graph
def decode(self, data, is_packed=False):
if not len(data) == self.N:
raise ValueError("len(data)={0} is not equal to n={1}!".format(len(data), self.N))
if is_packed:
data = np.unpackbits(data)
data = self._lr_sc_decoder_efficient(data)
data = self._extract_info_bits(data)
if is_packed:
data = np.packbits(data)
return data
def _extract_info_bits_reversed(self, y):
info_bit_positions_reversed = self._vector_bit_reversed(self.info_bit_position, self.power)
return y[info_bit_positions_reversed]
def decode_systematic(self, data):
if not len(data) == self.N:
raise ValueError("len(data)={0} is not equal to n={1}!".format(len(data), self.N))
# data = self._reverse_bits(data)
data = self._lr_sc_decoder_efficient(data)
data = self._encode_natural_order(data)
data = self._extract_info_bits_reversed(data)
return data
def test_systematic_decoder():
ntests = 1000
n = 16
k = 8
frozenbitposition = np.array((0, 1, 2, 3, 4, 5, 8, 9), dtype=int)
encoder = PolarEncoder(n, k, frozenbitposition)
decoder = PolarDecoder(n, k, frozenbitposition)
for i in range(ntests):
bits = np.random.randint(2, size=k)
y = encoder.encode_systematic(bits)
u_hat = decoder.decode_systematic(y)
assert (bits == u_hat).all()
def test_reverse_enc_dec():
n = 16
k = 8
frozenbits = np.zeros(n - k)
frozenbitposition = np.array((0, 1, 2, 3, 4, 5, 8, 9), dtype=int)
bits = np.random.randint(2, size=k)
encoder = PolarEncoder(n, k, frozenbitposition, frozenbits)
decoder = PolarDecoder(n, k, frozenbitposition, frozenbits)
encoded = encoder.encode(bits)
print 'encoded:', encoded
rx = decoder.decode(encoded)
print 'bits:', bits
print 'rx :', rx
print (bits == rx).all()
def compare_decoder_impls():
print '\nthis is decoder test'
n = 8
k = 4
frozenbits = np.zeros(n - k)
# frozenbitposition16 = np.array((0, 1, 2, 3, 4, 5, 8, 9), dtype=int)
frozenbitposition = np.array((0, 1, 2, 4), dtype=int)
bits = np.random.randint(2, size=k)
print 'bits:', bits
encoder = PolarEncoder(n, k, frozenbitposition, frozenbits)
decoder = PolarDecoder(n, k, frozenbitposition, frozenbits)
encoded = encoder.encode(bits)
print 'encoded:', encoded
rx_st = decoder._lr_sc_decoder(encoded)
rx_eff = decoder._lr_sc_decoder_efficient(encoded)
print 'standard :', rx_st
print 'efficient:', rx_eff
print (rx_st == rx_eff).all()
def main():
# power = 3
# n = 2 ** power
# k = 4
# frozenbits = np.zeros(n - k, dtype=int)
# frozenbitposition = np.array((0, 1, 2, 4), dtype=int)
# frozenbitposition4 = np.array((0, 1), dtype=int)
#
#
# encoder = PolarEncoder(n, k, frozenbitposition, frozenbits)
# decoder = PolarDecoder(n, k, frozenbitposition, frozenbits)
#
# bits = np.ones(k, dtype=int)
# print "bits: ", bits
# evec = encoder.encode(bits)
# print "froz: ", encoder._insert_frozen_bits(bits)
# print "evec: ", evec
#
# evec[1] = 0
# deced = decoder._lr_sc_decoder(evec)
# print 'SC decoded:', deced
#
# test_reverse_enc_dec()
# compare_decoder_impls()
test_systematic_decoder()
if __name__ == '__main__':
main()
| gpl-3.0 |
kevin-intel/scikit-learn | sklearn/utils/tests/test_pprint.py | 11 | 27137 | import re
from pprint import PrettyPrinter
import numpy as np
from sklearn.utils._pprint import _EstimatorPrettyPrinter
from sklearn.linear_model import LogisticRegressionCV
from sklearn.pipeline import make_pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.feature_selection import SelectKBest, chi2
from sklearn import set_config, config_context
# Ignore flake8 (lots of line too long issues)
# flake8: noqa
# Constructors excerpted to test pprinting
class LogisticRegression(BaseEstimator):
def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0,
fit_intercept=True, intercept_scaling=1, class_weight=None,
random_state=None, solver='warn', max_iter=100,
multi_class='warn', verbose=0, warm_start=False, n_jobs=None,
l1_ratio=None):
self.penalty = penalty
self.dual = dual
self.tol = tol
self.C = C
self.fit_intercept = fit_intercept
self.intercept_scaling = intercept_scaling
self.class_weight = class_weight
self.random_state = random_state
self.solver = solver
self.max_iter = max_iter
self.multi_class = multi_class
self.verbose = verbose
self.warm_start = warm_start
self.n_jobs = n_jobs
self.l1_ratio = l1_ratio
def fit(self, X, y):
return self
class StandardScaler(TransformerMixin, BaseEstimator):
def __init__(self, copy=True, with_mean=True, with_std=True):
self.with_mean = with_mean
self.with_std = with_std
self.copy = copy
def transform(self, X, copy=None):
return self
class RFE(BaseEstimator):
def __init__(self, estimator, n_features_to_select=None, step=1,
verbose=0):
self.estimator = estimator
self.n_features_to_select = n_features_to_select
self.step = step
self.verbose = verbose
class GridSearchCV(BaseEstimator):
def __init__(self, estimator, param_grid, scoring=None,
n_jobs=None, iid='warn', refit=True, cv='warn', verbose=0,
pre_dispatch='2*n_jobs', error_score='raise-deprecating',
return_train_score=False):
self.estimator = estimator
self.param_grid = param_grid
self.scoring = scoring
self.n_jobs = n_jobs
self.iid = iid
self.refit = refit
self.cv = cv
self.verbose = verbose
self.pre_dispatch = pre_dispatch
self.error_score = error_score
self.return_train_score = return_train_score
class CountVectorizer(BaseEstimator):
def __init__(self, input='content', encoding='utf-8',
decode_error='strict', strip_accents=None,
lowercase=True, preprocessor=None, tokenizer=None,
stop_words=None, token_pattern=r"(?u)\b\w\w+\b",
ngram_range=(1, 1), analyzer='word',
max_df=1.0, min_df=1, max_features=None,
vocabulary=None, binary=False, dtype=np.int64):
self.input = input
self.encoding = encoding
self.decode_error = decode_error
self.strip_accents = strip_accents
self.preprocessor = preprocessor
self.tokenizer = tokenizer
self.analyzer = analyzer
self.lowercase = lowercase
self.token_pattern = token_pattern
self.stop_words = stop_words
self.max_df = max_df
self.min_df = min_df
self.max_features = max_features
self.ngram_range = ngram_range
self.vocabulary = vocabulary
self.binary = binary
self.dtype = dtype
class Pipeline(BaseEstimator):
def __init__(self, steps, memory=None):
self.steps = steps
self.memory = memory
class SVC(BaseEstimator):
def __init__(self, C=1.0, kernel='rbf', degree=3, gamma='auto_deprecated',
coef0=0.0, shrinking=True, probability=False,
tol=1e-3, cache_size=200, class_weight=None,
verbose=False, max_iter=-1, decision_function_shape='ovr',
random_state=None):
self.kernel = kernel
self.degree = degree
self.gamma = gamma
self.coef0 = coef0
self.tol = tol
self.C = C
self.shrinking = shrinking
self.probability = probability
self.cache_size = cache_size
self.class_weight = class_weight
self.verbose = verbose
self.max_iter = max_iter
self.decision_function_shape = decision_function_shape
self.random_state = random_state
class PCA(BaseEstimator):
def __init__(self, n_components=None, copy=True, whiten=False,
svd_solver='auto', tol=0.0, iterated_power='auto',
random_state=None):
self.n_components = n_components
self.copy = copy
self.whiten = whiten
self.svd_solver = svd_solver
self.tol = tol
self.iterated_power = iterated_power
self.random_state = random_state
class NMF(BaseEstimator):
def __init__(self, n_components=None, init=None, solver='cd',
beta_loss='frobenius', tol=1e-4, max_iter=200,
random_state=None, alpha=0., l1_ratio=0., verbose=0,
shuffle=False):
self.n_components = n_components
self.init = init
self.solver = solver
self.beta_loss = beta_loss
self.tol = tol
self.max_iter = max_iter
self.random_state = random_state
self.alpha = alpha
self.l1_ratio = l1_ratio
self.verbose = verbose
self.shuffle = shuffle
class SimpleImputer(BaseEstimator):
def __init__(self, missing_values=np.nan, strategy="mean",
fill_value=None, verbose=0, copy=True):
self.missing_values = missing_values
self.strategy = strategy
self.fill_value = fill_value
self.verbose = verbose
self.copy = copy
def test_basic(print_changed_only_false):
# Basic pprint test
lr = LogisticRegression()
expected = """
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='warn', n_jobs=None, penalty='l2',
random_state=None, solver='warn', tol=0.0001, verbose=0,
warm_start=False)"""
expected = expected[1:] # remove first \n
assert lr.__repr__() == expected
def test_changed_only():
# Make sure the changed_only param is correctly used when True (default)
lr = LogisticRegression(C=99)
expected = """LogisticRegression(C=99)"""
assert lr.__repr__() == expected
# Check with a repr that doesn't fit on a single line
lr = LogisticRegression(C=99, class_weight=.4, fit_intercept=False,
tol=1234, verbose=True)
expected = """
LogisticRegression(C=99, class_weight=0.4, fit_intercept=False, tol=1234,
verbose=True)"""
expected = expected[1:] # remove first \n
assert lr.__repr__() == expected
imputer = SimpleImputer(missing_values=0)
expected = """SimpleImputer(missing_values=0)"""
assert imputer.__repr__() == expected
# Defaults to np.NaN, trying with float('NaN')
imputer = SimpleImputer(missing_values=float('NaN'))
expected = """SimpleImputer()"""
assert imputer.__repr__() == expected
# make sure array parameters don't throw error (see #13583)
repr(LogisticRegressionCV(Cs=np.array([0.1, 1])))
def test_pipeline(print_changed_only_false):
# Render a pipeline object
pipeline = make_pipeline(StandardScaler(), LogisticRegression(C=999))
expected = """
Pipeline(memory=None,
steps=[('standardscaler',
StandardScaler(copy=True, with_mean=True, with_std=True)),
('logisticregression',
LogisticRegression(C=999, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1,
l1_ratio=None, max_iter=100,
multi_class='warn', n_jobs=None,
penalty='l2', random_state=None,
solver='warn', tol=0.0001, verbose=0,
warm_start=False))],
verbose=False)"""
expected = expected[1:] # remove first \n
assert pipeline.__repr__() == expected
def test_deeply_nested(print_changed_only_false):
# Render a deeply nested estimator
rfe = RFE(RFE(RFE(RFE(RFE(RFE(RFE(LogisticRegression())))))))
expected = """
RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=LogisticRegression(C=1.0,
class_weight=None,
dual=False,
fit_intercept=True,
intercept_scaling=1,
l1_ratio=None,
max_iter=100,
multi_class='warn',
n_jobs=None,
penalty='l2',
random_state=None,
solver='warn',
tol=0.0001,
verbose=0,
warm_start=False),
n_features_to_select=None,
step=1,
verbose=0),
n_features_to_select=None,
step=1,
verbose=0),
n_features_to_select=None,
step=1, verbose=0),
n_features_to_select=None, step=1,
verbose=0),
n_features_to_select=None, step=1, verbose=0),
n_features_to_select=None, step=1, verbose=0),
n_features_to_select=None, step=1, verbose=0)"""
expected = expected[1:] # remove first \n
assert rfe.__repr__() == expected
def test_gridsearch(print_changed_only_false):
# render a gridsearch
param_grid = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
'C': [1, 10, 100, 1000]},
{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
gs = GridSearchCV(SVC(), param_grid, cv=5)
expected = """
GridSearchCV(cv=5, error_score='raise-deprecating',
estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='auto_deprecated', kernel='rbf', max_iter=-1,
probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
iid='warn', n_jobs=None,
param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],
'kernel': ['rbf']},
{'C': [1, 10, 100, 1000], 'kernel': ['linear']}],
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)"""
expected = expected[1:] # remove first \n
assert gs.__repr__() == expected
def test_gridsearch_pipeline(print_changed_only_false):
# render a pipeline inside a gridsearch
pp = _EstimatorPrettyPrinter(compact=True, indent=1, indent_at_name=True)
pipeline = Pipeline([
('reduce_dim', PCA()),
('classify', SVC())
])
N_FEATURES_OPTIONS = [2, 4, 8]
C_OPTIONS = [1, 10, 100, 1000]
param_grid = [
{
'reduce_dim': [PCA(iterated_power=7), NMF()],
'reduce_dim__n_components': N_FEATURES_OPTIONS,
'classify__C': C_OPTIONS
},
{
'reduce_dim': [SelectKBest(chi2)],
'reduce_dim__k': N_FEATURES_OPTIONS,
'classify__C': C_OPTIONS
}
]
gspipline = GridSearchCV(pipeline, cv=3, n_jobs=1, param_grid=param_grid)
expected = """
GridSearchCV(cv=3, error_score='raise-deprecating',
estimator=Pipeline(memory=None,
steps=[('reduce_dim',
PCA(copy=True, iterated_power='auto',
n_components=None,
random_state=None,
svd_solver='auto', tol=0.0,
whiten=False)),
('classify',
SVC(C=1.0, cache_size=200,
class_weight=None, coef0=0.0,
decision_function_shape='ovr',
degree=3, gamma='auto_deprecated',
kernel='rbf', max_iter=-1,
probability=False,
random_state=None, shrinking=True,
tol=0.001, verbose=False))]),
iid='warn', n_jobs=1,
param_grid=[{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [PCA(copy=True, iterated_power=7,
n_components=None,
random_state=None,
svd_solver='auto', tol=0.0,
whiten=False),
NMF(alpha=0.0, beta_loss='frobenius',
init=None, l1_ratio=0.0,
max_iter=200, n_components=None,
random_state=None, shuffle=False,
solver='cd', tol=0.0001,
verbose=0)],
'reduce_dim__n_components': [2, 4, 8]},
{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [SelectKBest(k=10,
score_func=<function chi2 at some_address>)],
'reduce_dim__k': [2, 4, 8]}],
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)"""
expected = expected[1:] # remove first \n
repr_ = pp.pformat(gspipline)
# Remove address of '<function chi2 at 0x.....>' for reproducibility
repr_ = re.sub('function chi2 at 0x.*>',
'function chi2 at some_address>', repr_)
assert repr_ == expected
def test_n_max_elements_to_show(print_changed_only_false):
n_max_elements_to_show = 30
pp = _EstimatorPrettyPrinter(
compact=True, indent=1, indent_at_name=True,
n_max_elements_to_show=n_max_elements_to_show
)
# No ellipsis
vocabulary = {i: i for i in range(n_max_elements_to_show)}
vectorizer = CountVectorizer(vocabulary=vocabulary)
expected = r"""
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
tokenizer=None,
vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
27: 27, 28: 28, 29: 29})"""
expected = expected[1:] # remove first \n
assert pp.pformat(vectorizer) == expected
# Now with ellipsis
vocabulary = {i: i for i in range(n_max_elements_to_show + 1)}
vectorizer = CountVectorizer(vocabulary=vocabulary)
expected = r"""
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
tokenizer=None,
vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
27: 27, 28: 28, 29: 29, ...})"""
expected = expected[1:] # remove first \n
assert pp.pformat(vectorizer) == expected
# Also test with lists
param_grid = {'C': list(range(n_max_elements_to_show))}
gs = GridSearchCV(SVC(), param_grid)
expected = """
GridSearchCV(cv='warn', error_score='raise-deprecating',
estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='auto_deprecated', kernel='rbf', max_iter=-1,
probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
iid='warn', n_jobs=None,
param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 29]},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)"""
expected = expected[1:] # remove first \n
assert pp.pformat(gs) == expected
# Now with ellipsis
param_grid = {'C': list(range(n_max_elements_to_show + 1))}
gs = GridSearchCV(SVC(), param_grid)
expected = """
GridSearchCV(cv='warn', error_score='raise-deprecating',
estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='auto_deprecated', kernel='rbf', max_iter=-1,
probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
iid='warn', n_jobs=None,
param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 29, ...]},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)"""
expected = expected[1:] # remove first \n
assert pp.pformat(gs) == expected
def test_bruteforce_ellipsis(print_changed_only_false):
# Check that the bruteforce ellipsis (used when the number of non-blank
# characters exceeds N_CHAR_MAX) renders correctly.
lr = LogisticRegression()
# test when the left and right side of the ellipsis aren't on the same
# line.
expected = """
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
in...
multi_class='warn', n_jobs=None, penalty='l2',
random_state=None, solver='warn', tol=0.0001, verbose=0,
warm_start=False)"""
expected = expected[1:] # remove first \n
assert expected == lr.__repr__(N_CHAR_MAX=150)
# test with very small N_CHAR_MAX
# Note that N_CHAR_MAX is not strictly enforced, but it's normal: to avoid
# weird reprs we still keep the whole line of the right part (after the
# ellipsis).
expected = """
Lo...
warm_start=False)"""
expected = expected[1:] # remove first \n
assert expected == lr.__repr__(N_CHAR_MAX=4)
# test with N_CHAR_MAX == number of non-blank characters: In this case we
# don't want ellipsis
full_repr = lr.__repr__(N_CHAR_MAX=float('inf'))
n_nonblank = len(''.join(full_repr.split()))
assert lr.__repr__(N_CHAR_MAX=n_nonblank) == full_repr
assert '...' not in full_repr
# test with N_CHAR_MAX == number of non-blank characters - 10: the left and
# right side of the ellispsis are on different lines. In this case we
# want to expend the whole line of the right side
expected = """
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_i...
multi_class='warn', n_jobs=None, penalty='l2',
random_state=None, solver='warn', tol=0.0001, verbose=0,
warm_start=False)"""
expected = expected[1:] # remove first \n
assert expected == lr.__repr__(N_CHAR_MAX=n_nonblank - 10)
# test with N_CHAR_MAX == number of non-blank characters - 10: the left and
# right side of the ellispsis are on the same line. In this case we don't
# want to expend the whole line of the right side, just add the ellispsis
# between the 2 sides.
expected = """
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter...,
multi_class='warn', n_jobs=None, penalty='l2',
random_state=None, solver='warn', tol=0.0001, verbose=0,
warm_start=False)"""
expected = expected[1:] # remove first \n
assert expected == lr.__repr__(N_CHAR_MAX=n_nonblank - 4)
# test with N_CHAR_MAX == number of non-blank characters - 2: the left and
# right side of the ellispsis are on the same line, but adding the ellipsis
# would actually make the repr longer. So we don't add the ellipsis.
expected = """
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='warn', n_jobs=None, penalty='l2',
random_state=None, solver='warn', tol=0.0001, verbose=0,
warm_start=False)"""
expected = expected[1:] # remove first \n
assert expected == lr.__repr__(N_CHAR_MAX=n_nonblank - 2)
def test_builtin_prettyprinter():
# non regression test than ensures we can still use the builtin
# PrettyPrinter class for estimators (as done e.g. by joblib).
# Used to be a bug
PrettyPrinter().pprint(LogisticRegression())
def test_kwargs_in_init():
# Make sure the changed_only=True mode is OK when an argument is passed as
# kwargs.
# Non-regression test for
# https://github.com/scikit-learn/scikit-learn/issues/17206
class WithKWargs(BaseEstimator):
# Estimator with a kwargs argument. These need to hack around
# set_params and get_params. Here we mimic what LightGBM does.
def __init__(self, a='willchange', b='unchanged', **kwargs):
self.a = a
self.b = b
self._other_params = {}
self.set_params(**kwargs)
def get_params(self, deep=True):
params = super().get_params(deep=deep)
params.update(self._other_params)
return params
def set_params(self, **params):
for key, value in params.items():
setattr(self, key, value)
self._other_params[key] = value
return self
est = WithKWargs(a='something', c='abcd', d=None)
expected = "WithKWargs(a='something', c='abcd', d=None)"
assert expected == est.__repr__()
with config_context(print_changed_only=False):
expected = "WithKWargs(a='something', b='unchanged', c='abcd', d=None)"
assert expected == est.__repr__()
def test_complexity_print_changed_only():
# Make sure `__repr__` is called the same amount of times
# whether `print_changed_only` is True or False
# Non-regression test for
# https://github.com/scikit-learn/scikit-learn/issues/18490
class DummyEstimator(TransformerMixin, BaseEstimator):
nb_times_repr_called = 0
def __init__(self, estimator=None):
self.estimator = estimator
def __repr__(self):
DummyEstimator.nb_times_repr_called += 1
return super().__repr__()
def transform(self, X, copy=None): # pragma: no cover
return X
estimator = DummyEstimator(make_pipeline(DummyEstimator(DummyEstimator()),
DummyEstimator(),
'passthrough'))
with config_context(print_changed_only=False):
repr(estimator)
nb_repr_print_changed_only_false = DummyEstimator.nb_times_repr_called
DummyEstimator.nb_times_repr_called = 0
with config_context(print_changed_only=True):
repr(estimator)
nb_repr_print_changed_only_true = DummyEstimator.nb_times_repr_called
assert nb_repr_print_changed_only_false == nb_repr_print_changed_only_true
| bsd-3-clause |
Eric89GXL/mne-python | mne/decoding/mixin.py | 14 | 2851 |
class TransformerMixin(object):
"""Mixin class for all transformers in scikit-learn."""
def fit_transform(self, X, y=None, **fit_params):
"""Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
Parameters
----------
X : array, shape (n_samples, n_features)
Training set.
y : array, shape (n_samples,)
Target values.
**fit_params : dict
Additional fitting parameters passed to ``self.fit``.
Returns
-------
X_new : array, shape (n_samples, n_features_new)
Transformed array.
"""
# non-optimized default implementation; override when a better
# method is possible for a given clustering algorithm
if y is None:
# fit method of arity 1 (unsupervised transformation)
return self.fit(X, **fit_params).transform(X)
else:
# fit method of arity 2 (supervised transformation)
return self.fit(X, y, **fit_params).transform(X)
class EstimatorMixin(object):
"""Mixin class for estimators."""
def get_params(self, deep=True):
"""Get the estimator params.
Parameters
----------
deep : bool
Deep.
"""
return
def set_params(self, **params):
"""Set parameters (mimics sklearn API).
Parameters
----------
**params : dict
Extra parameters.
Returns
-------
inst : object
The instance.
"""
if not params:
return self
valid_params = self.get_params(deep=True)
for key, value in params.items():
split = key.split('__', 1)
if len(split) > 1:
# nested objects case
name, sub_name = split
if name not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(name, self))
sub_object = valid_params[name]
sub_object.set_params(**{sub_name: value})
else:
# simple objects case
if key not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(key, self.__class__.__name__))
setattr(self, key, value)
return self
| bsd-3-clause |
JoeJimFlood/RugbyPredictifier | 2017SuperRugby/Validation/matchup_w_distance.py | 1 | 14552 | import os
import sys
import pandas as pd
import numpy as np
from numpy.random import poisson, uniform
from numpy import mean
import time
import math
po = True
team_homes = pd.read_csv(os.path.join(os.path.split(__file__)[0], 'TeamHomes.csv'), header = None, index_col = 0)
stadium_locs = pd.read_csv(os.path.join(os.path.split(__file__)[0], 'StadiumLocs.csv'), index_col = 0)
teamsheetpath = os.path.join(os.path.split(__file__)[0], 'teamcsvs')
compstat = {'TF': 'TA', 'TA': 'TF', #Dictionary to use to compare team stats with opponent stats
'CF': 'CA', 'CA': 'CF',
'CON%F': 'CON%A', 'CON%A': 'CON%F',
'PF': 'PA', 'PA': 'PF',
'DGF': 'DGA', 'DGA': 'DGF'}
def get_opponent_stats(opponent, venue): #Gets summaries of statistics for opponent each week
opponent_stats = {}
global teamsheetpath, stadium_locs, team_homes
opp_stats = pd.DataFrame.from_csv(os.path.join(teamsheetpath, opponent + '.csv'))
opponent_home = team_homes[1][opponent]
(venue_lat, venue_lng) = stadium_locs.loc[venue, ['Lat', 'Long']]
(opponent_home_lat, opponent_home_lng) = stadium_locs.loc[opponent_home, ['Lat', 'Long']]
opponent_reference_distance = geodesic_distance(opponent_home_lat, opponent_home_lng, venue_lat, venue_lng)
def get_opponent_weight(location):
return get_travel_weight(location, opponent_home_lat, opponent_home_lng, opponent_reference_distance)
opp_stats['Weight'] = opp_stats['VENUE'].apply(get_opponent_weight)
for stat in opp_stats.columns:
if stat != 'VENUE':
if stat != 'OPP':
opponent_stats.update({stat: np.average(opp_stats[stat], weights = opp_stats['Weight'])})
opponent_stats.update({'CON%F': float((opp_stats['CF']*opp_stats['Weight']).sum())/(opp_stats['TF']*opp_stats['Weight']).sum()})
opponent_stats.update({'CON%A': float((opp_stats['CA']*opp_stats['Weight']).sum())/(opp_stats['TA']*opp_stats['Weight']).sum()})
return opponent_stats
def get_residual_performance(score_df): #Get how each team has done compared to the average performance of their opponents
global teamsheetpath, team_homes, stadium_locs
#score_df = pd.DataFrame.from_csv(os.path.join(teamsheetpath, team + '.csv'))
residual_stats = {}
score_df['CON%F'] = np.nan
score_df['CON%A'] = np.nan
for week in score_df.index:
opponent_stats = get_opponent_stats(score_df['OPP'][week], score_df['VENUE'][week])
for stat in opponent_stats:
if week == score_df.index.tolist()[0]:
score_df['OPP_' + stat] = np.nan
score_df['OPP_' + stat][week] = opponent_stats[stat]
score_df['CON%F'][week] = float(score_df['CF'][week]) / score_df['TF'][week]
score_df['CON%A'][week] = float(score_df['CA'][week]) / score_df['TA'][week]
#print opponent_stats
for stat in opponent_stats:
if stat == 'Weight':
continue
score_df['R_' + stat] = score_df[stat] - score_df['OPP_' + compstat[stat]]
if stat in ['TF', 'PF', 'DGF', 'TA', 'PA', 'DGA']:
residual_stats.update({stat: np.average(score_df['R_' + stat], weights = score_df['Weight'])})
elif stat == 'CON%F':
residual_stats.update({stat: (score_df['R_CON%F'].multiply(score_df['TF'])*score_df['Weight']).sum() / (score_df['TF']*score_df['Weight']).sum()})
elif stat == 'CON%A':
residual_stats.update({stat: (score_df['R_CON%A'].multiply(score_df['TA'])*score_df['Weight']).sum() / (score_df['TA']*score_df['Weight']).sum()})
return residual_stats
def get_score(expected_scores): #Get the score for a team based on expected scores
score = 0
if expected_scores['T'] > 0:
tries = poisson(expected_scores['T'])
else:
tries = poisson(0.01)
score = score + 6 * tries
if expected_scores['P'] > 0:
fgs = poisson(expected_scores['P'])
else:
fgs = poisson(0.01)
score = score + 3 * fgs
if expected_scores['DG'] > 0:
sfs = poisson(expected_scores['DG'])
else:
sfs = poisson(0.01)
score = score + 2 * sfs
for t in range(tries):
successful_con_determinant = uniform(0, 1)
if successful_con_determinant <= expected_scores['CONPROB']:
score += 2
else:
continue
#if tries >= 4:
# bp = True
#else:
# bp = False
return (score, tries)
def game(team_1, team_2,
expected_scores_1, expected_scores_2,
playoff = False): #Get two scores and determine a winner
(score_1, tries_1) = get_score(expected_scores_1)
(score_2, tries_2) = get_score(expected_scores_2)
if tries_1 - tries_2 >= 3:
bp1 = True
bp2 = False
elif tries_2 - tries_1 >= 3:
bp1 = False
bp2 = True
else:
bp1 = False
bp2 = False
if score_1 > score_2:
win_1 = 1
win_2 = 0
draw_1 = 0
draw_2 = 0
if bp1:
bpw1 = 1
else:
bpw1 = 0
if bp2:
bpl2 = 1
else:
bpl2 = 0
bpl1 = 0
bpw2 = 0
bpd1 = 0
bpd2 = 0
lbp1 = 0
if score_1 - score_2 <= 7:
lbp2 = 1
else:
lbp2 = 0
elif score_2 > score_1:
win_1 = 0
win_2 = 1
draw_1 = 0
draw_2 = 0
if bp1:
bpl1 = 1
else:
bpl1 = 0
if bp2:
bpw2 = 1
else:
bpw2 = 0
bpw1 = 0
bpl2 = 0
bpd1 = 0
bpd2 = 0
lbp2 = 0
if score_2 - score_1 <= 7:
lbp1 = 1
else:
lbp1 = 0
else:
if playoff:
win_1 = 0.5
win_2 = 0.5
draw_1 = 0
draw_2 = 0
bpw1 = 0
bpw2 = 0
bpd1 = 0
bpd2 = 0
bpl1 = 0
bpl2 = 0
lbp1 = 0
lbp2 = 0
else:
win_1 = 0
win_2 = 0
draw_1 = 1
draw_2 = 1
bpw1 = 0
bpw2 = 0
bpl1 = 0
bpl2 = 0
lbp1 = 0
lbp2 = 0
if bp1:
bpd1 = 1
else:
bpd1 = 0
if bp2:
bpd2 = 1
else:
bpd2 = 0
summary = {team_1: [win_1, draw_1, score_1, bpw1, bpd1, bpl1, lbp1]}
summary.update({team_2: [win_2, draw_2, score_2, bpw2, bpd2, bpl2, lbp2]})
return summary
def get_expected_scores(team_1_stats, team_2_stats, team_1_df, team_2_df): #Get the expected scores for a matchup based on the previous teams' performances
expected_scores = {}
for stat in team_1_stats:
expected_scores.update({'T': mean([team_1_stats['TF'] + np.average(team_2_df['TA'], weights = team_2_df['Weight']),
team_2_stats['TA'] + np.average(team_1_df['TF'], weights = team_1_df['Weight'])])})
expected_scores.update({'P': mean([team_1_stats['PF'] + np.average(team_2_df['PA'], weights = team_2_df['Weight']),
team_2_stats['PA'] + np.average(team_1_df['PF'], weights = team_1_df['Weight'])])})
expected_scores.update({'DG': mean([team_1_stats['DGF'] + np.average(team_2_df['DGA'], weights = team_2_df['Weight']),
team_2_stats['DGA'] + np.average(team_1_df['DGF'], weights = team_1_df['Weight'])])})
#print mean([team_1_stats['PAT1%F'] + team_2_df['PAT1AS'].astype('float').sum() / team_2_df['PAT1AA'].sum(),
# team_2_stats['PAT1%A'] + team_1_df['PAT1FS'].astype('float').sum() / team_1_df['PAT1FA'].sum()])
conprob = mean([team_1_stats['CON%F'] + (team_2_df['CA']*team_2_df['Weight']).sum() / (team_2_df['TA']*team_2_df['Weight']).sum(),
team_2_stats['CON%A'] + (team_1_df['CF']*team_1_df['Weight']).sum() / (team_1_df['TF']*team_1_df['Weight']).sum()])
if not math.isnan(conprob):
expected_scores.update({'CONPROB': conprob})
else:
expected_scores.update({'CONPROB': 0.75})
#print(expected_scores['PAT1PROB'])
#print(expected_scores)
return expected_scores
def geodesic_distance(olat, olng, dlat, dlng):
'''
Returns geodesic distance in percentage of half the earth's circumference between two points on the earth's surface
'''
scale = math.tau/360
olat *= scale
olng *= scale
dlat *= scale
dlng *= scale
delta_lat = (dlat - olat)
delta_lng = (dlng - olng)
a = math.sin(delta_lat/2)**2 + math.cos(olat)*math.cos(dlat)*math.sin(delta_lng/2)**2
return 4*math.atan2(math.sqrt(a), math.sqrt(1-a))/math.tau
def get_travel_weight(venue, home_lat, home_lng, reference_distance):
'''
Gets the travel weight based on a venue, a team's home lat/long coordinates, and a reference distance
'''
global stadium_locs
(venue_lat, venue_lng) = stadium_locs.loc[venue, ['Lat', 'Long']]
travel_distance = geodesic_distance(home_lat, home_lng, venue_lat, venue_lng)
return 1 - abs(travel_distance - reference_distance)
def matchup(team_1, team_2, venue = None):
ts = time.time()
global team_homes, stadium_locs
team_1_home = team_homes[1][team_1]
team_2_home = team_homes[1][team_2]
if venue is None:
venue = team_homes[1][team_1]
(venue_lat, venue_lng) = stadium_locs.loc[venue, ['Lat', 'Long']]
(team_1_home_lat, team_1_home_lng) = stadium_locs.loc[team_1_home, ['Lat', 'Long']]
(team_2_home_lat, team_2_home_lng) = stadium_locs.loc[team_2_home, ['Lat', 'Long']]
team_1_reference_distance = geodesic_distance(team_1_home_lat, team_1_home_lng, venue_lat, venue_lng)
team_2_reference_distance = geodesic_distance(team_2_home_lat, team_2_home_lng, venue_lat, venue_lng)
def get_team_1_weight(location):
return get_travel_weight(location, team_1_home_lat, team_1_home_lng, team_1_reference_distance)
def get_team_2_weight(location):
return get_travel_weight(location, team_2_home_lat, team_2_home_lng, team_2_reference_distance)
team_1_season = pd.DataFrame.from_csv(os.path.join(teamsheetpath, team_1 + '.csv'))
team_2_season = pd.DataFrame.from_csv(os.path.join(teamsheetpath, team_2 + '.csv'))
team_1_season['Weight'] = team_1_season['VENUE'].apply(get_team_1_weight)
team_2_season['Weight'] = team_2_season['VENUE'].apply(get_team_2_weight)
stats_1 = get_residual_performance(team_1_season)
stats_2 = get_residual_performance(team_2_season)
expected_scores_1 = get_expected_scores(stats_1, stats_2, team_1_season, team_2_season)
expected_scores_2 = get_expected_scores(stats_2, stats_1, team_2_season, team_1_season)
team_1_wins = 0
team_2_wins = 0
team_1_draws = 0
team_2_draws = 0
team_1_bpw = 0
team_2_bpw = 0
team_1_bpd = 0
team_2_bpd = 0
team_1_bpl = 0
team_2_bpl = 0
team_1_lbp = 0
team_2_lbp = 0
team_1_scores = []
team_2_scores = []
i = 0
error = 1
while error > 0.000001 or i < 5000000: #Run until convergence after 5 million iterations
summary = game(team_1, team_2,
expected_scores_1, expected_scores_2,
playoff = po)
team_1_prev_wins = team_1_wins
team_1_wins += summary[team_1][0]
team_2_wins += summary[team_2][0]
team_1_draws += summary[team_1][1]
team_2_draws += summary[team_2][1]
team_1_scores.append(summary[team_1][2])
team_2_scores.append(summary[team_2][2])
team_1_bpw += summary[team_1][3]
team_2_bpw += summary[team_2][3]
team_1_bpd += summary[team_1][4]
team_2_bpd += summary[team_2][4]
team_1_bpl += summary[team_1][5]
team_2_bpl += summary[team_2][5]
team_1_lbp += summary[team_1][6]
team_2_lbp += summary[team_2][6]
team_1_prob = float(team_1_wins) / len(team_1_scores)
team_2_prob = float(team_2_wins) / len(team_2_scores)
team_1_bpw_prob = float(team_1_bpw) / len(team_1_scores)
team_2_bpw_prob = float(team_2_bpw) / len(team_2_scores)
team_1_bpd_prob = float(team_1_bpd) / len(team_1_scores)
team_2_bpd_prob = float(team_2_bpd) / len(team_2_scores)
team_1_bpl_prob = float(team_1_bpl) / len(team_1_scores)
team_2_bpl_prob = float(team_2_bpl) / len(team_2_scores)
team_1_lbp_prob = float(team_1_lbp) / len(team_1_scores)
team_2_lbp_prob = float(team_2_lbp) / len(team_2_scores)
if i > 0:
team_1_prev_prob = float(team_1_prev_wins) / i
error = team_1_prob - team_1_prev_prob
i = i + 1
if i == 5000000:
print('Probability converged within 5 million iterations')
else:
print('Probability converged after ' + str(i) + ' iterations')
games = pd.DataFrame.from_items([(team_1, team_1_scores), (team_2, team_2_scores)])
pre_summaries = games.describe(percentiles = list(np.linspace(0.05, 0.95, 19)))
summaries = pd.DataFrame(columns = pre_summaries.columns)
summaries.loc['mean'] = pre_summaries.loc['mean']
for i in pre_summaries.index:
try:
percentile = int(round(float(i[:-1])))
summaries.loc['{}%'.format(percentile)] = pre_summaries.loc[i]
except ValueError:
continue
summaries = summaries.reset_index()
for item in summaries.index:
try:
summaries['index'][item] = str(int(float(summaries['index'][item][:-1]))) + '%'
except ValueError:
continue
bonus_points = pd.DataFrame(index = ['4-Try Bonus Point with Win',
'4-Try Bonus Point with Draw',
'4-Try Bonus Point with Loss',
'Losing Bonus Point'])
bonus_points[team_1] = [team_1_bpw_prob, team_1_bpd_prob, team_1_bpl_prob, team_1_lbp_prob]
bonus_points[team_2] = [team_2_bpw_prob, team_2_bpd_prob, team_2_bpl_prob, team_2_lbp_prob]
summaries = summaries.set_index('index')
summaries = summaries.groupby(level = 0).last()
output = {'ProbWin': {team_1: team_1_prob, team_2: team_2_prob}, 'Scores': summaries, 'Bonus Points': bonus_points}
print(team_1 + '/' + team_2 + ' score distributions computed in ' + str(round(time.time() - ts, 1)) + ' seconds')
return output | mit |
wlamond/scikit-learn | examples/svm/plot_svm_nonlinear.py | 62 | 1119 | """
==============
Non-linear SVM
==============
Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.
The color map illustrates the decision function learned by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
xx, yy = np.meshgrid(np.linspace(-3, 3, 500),
np.linspace(-3, 3, 500))
np.random.seed(0)
X = np.random.randn(300, 2)
Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
# fit the model
clf = svm.NuSVC()
clf.fit(X, Y)
# plot the decision function for each datapoint on the grid
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()), aspect='auto',
origin='lower', cmap=plt.cm.PuOr_r)
contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2,
linetypes='--')
plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired,
edgecolors='k')
plt.xticks(())
plt.yticks(())
plt.axis([-3, 3, -3, 3])
plt.show()
| bsd-3-clause |
JPFrancoia/scikit-learn | sklearn/metrics/pairwise.py | 5 | 46491 | # -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Robert Layton <robertlayton@gmail.com>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Philippe Gervais <philippe.gervais@inria.fr>
# Lars Buitinck
# Joel Nothman <joel.nothman@gmail.com>
# License: BSD 3 clause
import itertools
import numpy as np
from scipy.spatial import distance
from scipy.sparse import csr_matrix
from scipy.sparse import issparse
from ..utils import check_array
from ..utils import gen_even_slices
from ..utils import gen_batches
from ..utils.fixes import partial
from ..utils.extmath import row_norms, safe_sparse_dot
from ..preprocessing import normalize
from ..externals.joblib import Parallel
from ..externals.joblib import delayed
from ..externals.joblib.parallel import cpu_count
from .pairwise_fast import _chi2_kernel_fast, _sparse_manhattan
# Utility Functions
def _return_float_dtype(X, Y):
"""
1. If dtype of X and Y is float32, then dtype float32 is returned.
2. Else dtype float is returned.
"""
if not issparse(X) and not isinstance(X, np.ndarray):
X = np.asarray(X)
if Y is None:
Y_dtype = X.dtype
elif not issparse(Y) and not isinstance(Y, np.ndarray):
Y = np.asarray(Y)
Y_dtype = Y.dtype
else:
Y_dtype = Y.dtype
if X.dtype == Y_dtype == np.float32:
dtype = np.float32
else:
dtype = np.float
return X, Y, dtype
def check_pairwise_arrays(X, Y, precomputed=False, dtype=None):
""" Set X and Y appropriately and checks inputs
If Y is None, it is set as a pointer to X (i.e. not a copy).
If Y is given, this does not happen.
All distance metrics should use this function first to assert that the
given parameters are correct and safe to use.
Specifically, this function first ensures that both X and Y are arrays,
then checks that they are at least two dimensional while ensuring that
their elements are floats (or dtype if provided). Finally, the function
checks that the size of the second dimension of the two arrays is equal, or
the equivalent check for a precomputed distance matrix.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples_a, n_features)
Y : {array-like, sparse matrix}, shape (n_samples_b, n_features)
precomputed : bool
True if X is to be treated as precomputed distances to the samples in
Y.
dtype : string, type, list of types or None (default=None)
Data type required for X and Y. If None, the dtype will be an
appropriate float type selected by _return_float_dtype.
.. versionadded:: 0.18
Returns
-------
safe_X : {array-like, sparse matrix}, shape (n_samples_a, n_features)
An array equal to X, guaranteed to be a numpy array.
safe_Y : {array-like, sparse matrix}, shape (n_samples_b, n_features)
An array equal to Y if Y was not None, guaranteed to be a numpy array.
If Y was None, safe_Y will be a pointer to X.
"""
X, Y, dtype_float = _return_float_dtype(X, Y)
warn_on_dtype = dtype is not None
estimator = 'check_pairwise_arrays'
if dtype is None:
dtype = dtype_float
if Y is X or Y is None:
X = Y = check_array(X, accept_sparse='csr', dtype=dtype,
warn_on_dtype=warn_on_dtype, estimator=estimator)
else:
X = check_array(X, accept_sparse='csr', dtype=dtype,
warn_on_dtype=warn_on_dtype, estimator=estimator)
Y = check_array(Y, accept_sparse='csr', dtype=dtype,
warn_on_dtype=warn_on_dtype, estimator=estimator)
if precomputed:
if X.shape[1] != Y.shape[0]:
raise ValueError("Precomputed metric requires shape "
"(n_queries, n_indexed). Got (%d, %d) "
"for %d indexed." %
(X.shape[0], X.shape[1], Y.shape[0]))
elif X.shape[1] != Y.shape[1]:
raise ValueError("Incompatible dimension for X and Y matrices: "
"X.shape[1] == %d while Y.shape[1] == %d" % (
X.shape[1], Y.shape[1]))
return X, Y
def check_paired_arrays(X, Y):
""" Set X and Y appropriately and checks inputs for paired distances
All paired distance metrics should use this function first to assert that
the given parameters are correct and safe to use.
Specifically, this function first ensures that both X and Y are arrays,
then checks that they are at least two dimensional while ensuring that
their elements are floats. Finally, the function checks that the size
of the dimensions of the two arrays are equal.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples_a, n_features)
Y : {array-like, sparse matrix}, shape (n_samples_b, n_features)
Returns
-------
safe_X : {array-like, sparse matrix}, shape (n_samples_a, n_features)
An array equal to X, guaranteed to be a numpy array.
safe_Y : {array-like, sparse matrix}, shape (n_samples_b, n_features)
An array equal to Y if Y was not None, guaranteed to be a numpy array.
If Y was None, safe_Y will be a pointer to X.
"""
X, Y = check_pairwise_arrays(X, Y)
if X.shape != Y.shape:
raise ValueError("X and Y should be of same shape. They were "
"respectively %r and %r long." % (X.shape, Y.shape))
return X, Y
# Pairwise distances
def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False,
X_norm_squared=None):
"""
Considering the rows of X (and Y=X) as vectors, compute the
distance matrix between each pair of vectors.
For efficiency reasons, the euclidean distance between a pair of row
vector x and y is computed as::
dist(x, y) = sqrt(dot(x, x) - 2 * dot(x, y) + dot(y, y))
This formulation has two advantages over other ways of computing distances.
First, it is computationally efficient when dealing with sparse data.
Second, if one argument varies but the other remains unchanged, then
`dot(x, x)` and/or `dot(y, y)` can be pre-computed.
However, this is not the most precise way of doing this computation, and
the distance matrix returned by this function may not be exactly
symmetric as required by, e.g., ``scipy.spatial.distance`` functions.
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples_1, n_features)
Y : {array-like, sparse matrix}, shape (n_samples_2, n_features)
Y_norm_squared : array-like, shape (n_samples_2, ), optional
Pre-computed dot-products of vectors in Y (e.g.,
``(Y**2).sum(axis=1)``)
squared : boolean, optional
Return squared Euclidean distances.
X_norm_squared : array-like, shape = [n_samples_1], optional
Pre-computed dot-products of vectors in X (e.g.,
``(X**2).sum(axis=1)``)
Returns
-------
distances : {array, sparse matrix}, shape (n_samples_1, n_samples_2)
Examples
--------
>>> from sklearn.metrics.pairwise import euclidean_distances
>>> X = [[0, 1], [1, 1]]
>>> # distance between rows of X
>>> euclidean_distances(X, X)
array([[ 0., 1.],
[ 1., 0.]])
>>> # get distance to origin
>>> euclidean_distances(X, [[0, 0]])
array([[ 1. ],
[ 1.41421356]])
See also
--------
paired_distances : distances betweens pairs of elements of X and Y.
"""
X, Y = check_pairwise_arrays(X, Y)
if X_norm_squared is not None:
XX = check_array(X_norm_squared)
if XX.shape == (1, X.shape[0]):
XX = XX.T
elif XX.shape != (X.shape[0], 1):
raise ValueError(
"Incompatible dimensions for X and X_norm_squared")
else:
XX = row_norms(X, squared=True)[:, np.newaxis]
if X is Y: # shortcut in the common case euclidean_distances(X, X)
YY = XX.T
elif Y_norm_squared is not None:
YY = np.atleast_2d(Y_norm_squared)
if YY.shape != (1, Y.shape[0]):
raise ValueError(
"Incompatible dimensions for Y and Y_norm_squared")
else:
YY = row_norms(Y, squared=True)[np.newaxis, :]
distances = safe_sparse_dot(X, Y.T, dense_output=True)
distances *= -2
distances += XX
distances += YY
np.maximum(distances, 0, out=distances)
if X is Y:
# Ensure that distances between vectors and themselves are set to 0.0.
# This may not be the case due to floating point rounding errors.
distances.flat[::distances.shape[0] + 1] = 0.0
return distances if squared else np.sqrt(distances, out=distances)
def pairwise_distances_argmin_min(X, Y, axis=1, metric="euclidean",
batch_size=500, metric_kwargs=None):
"""Compute minimum distances between one point and a set of points.
This function computes for each row in X, the index of the row of Y which
is closest (according to the specified distance). The minimal distances are
also returned.
This is mostly equivalent to calling:
(pairwise_distances(X, Y=Y, metric=metric).argmin(axis=axis),
pairwise_distances(X, Y=Y, metric=metric).min(axis=axis))
but uses much less memory, and is faster for large arrays.
Parameters
----------
X, Y : {array-like, sparse matrix}
Arrays containing points. Respective shapes (n_samples1, n_features)
and (n_samples2, n_features)
batch_size : integer
To reduce memory consumption over the naive solution, data are
processed in batches, comprising batch_size rows of X and
batch_size rows of Y. The default value is quite conservative, but
can be changed for fine-tuning. The larger the number, the larger the
memory usage.
metric : string or callable, default 'euclidean'
metric to use for distance computation. Any metric from scikit-learn
or scipy.spatial.distance can be used.
If metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays as input and return one value indicating the
distance between them. This works for Scipy's metrics, but is less
efficient than passing the metric name as a string.
Distance matrices are not supported.
Valid values for metric are:
- from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
'manhattan']
- from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto',
'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
'sqeuclidean', 'yule']
See the documentation for scipy.spatial.distance for details on these
metrics.
metric_kwargs : dict, optional
Keyword arguments to pass to specified metric function.
axis : int, optional, default 1
Axis along which the argmin and distances are to be computed.
Returns
-------
argmin : numpy.ndarray
Y[argmin[i], :] is the row in Y that is closest to X[i, :].
distances : numpy.ndarray
distances[i] is the distance between the i-th row in X and the
argmin[i]-th row in Y.
See also
--------
sklearn.metrics.pairwise_distances
sklearn.metrics.pairwise_distances_argmin
"""
dist_func = None
if metric in PAIRWISE_DISTANCE_FUNCTIONS:
dist_func = PAIRWISE_DISTANCE_FUNCTIONS[metric]
elif not callable(metric) and not isinstance(metric, str):
raise ValueError("'metric' must be a string or a callable")
X, Y = check_pairwise_arrays(X, Y)
if metric_kwargs is None:
metric_kwargs = {}
if axis == 0:
X, Y = Y, X
# Allocate output arrays
indices = np.empty(X.shape[0], dtype=np.intp)
values = np.empty(X.shape[0])
values.fill(np.infty)
for chunk_x in gen_batches(X.shape[0], batch_size):
X_chunk = X[chunk_x, :]
for chunk_y in gen_batches(Y.shape[0], batch_size):
Y_chunk = Y[chunk_y, :]
if dist_func is not None:
if metric == 'euclidean': # special case, for speed
d_chunk = safe_sparse_dot(X_chunk, Y_chunk.T,
dense_output=True)
d_chunk *= -2
d_chunk += row_norms(X_chunk, squared=True)[:, np.newaxis]
d_chunk += row_norms(Y_chunk, squared=True)[np.newaxis, :]
np.maximum(d_chunk, 0, d_chunk)
else:
d_chunk = dist_func(X_chunk, Y_chunk, **metric_kwargs)
else:
d_chunk = pairwise_distances(X_chunk, Y_chunk,
metric=metric, **metric_kwargs)
# Update indices and minimum values using chunk
min_indices = d_chunk.argmin(axis=1)
min_values = d_chunk[np.arange(chunk_x.stop - chunk_x.start),
min_indices]
flags = values[chunk_x] > min_values
indices[chunk_x][flags] = min_indices[flags] + chunk_y.start
values[chunk_x][flags] = min_values[flags]
if metric == "euclidean" and not metric_kwargs.get("squared", False):
np.sqrt(values, values)
return indices, values
def pairwise_distances_argmin(X, Y, axis=1, metric="euclidean",
batch_size=500, metric_kwargs=None):
"""Compute minimum distances between one point and a set of points.
This function computes for each row in X, the index of the row of Y which
is closest (according to the specified distance).
This is mostly equivalent to calling:
pairwise_distances(X, Y=Y, metric=metric).argmin(axis=axis)
but uses much less memory, and is faster for large arrays.
This function works with dense 2D arrays only.
Parameters
----------
X : array-like
Arrays containing points. Respective shapes (n_samples1, n_features)
and (n_samples2, n_features)
Y : array-like
Arrays containing points. Respective shapes (n_samples1, n_features)
and (n_samples2, n_features)
batch_size : integer
To reduce memory consumption over the naive solution, data are
processed in batches, comprising batch_size rows of X and
batch_size rows of Y. The default value is quite conservative, but
can be changed for fine-tuning. The larger the number, the larger the
memory usage.
metric : string or callable
metric to use for distance computation. Any metric from scikit-learn
or scipy.spatial.distance can be used.
If metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays as input and return one value indicating the
distance between them. This works for Scipy's metrics, but is less
efficient than passing the metric name as a string.
Distance matrices are not supported.
Valid values for metric are:
- from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
'manhattan']
- from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto',
'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
'sqeuclidean', 'yule']
See the documentation for scipy.spatial.distance for details on these
metrics.
metric_kwargs : dict
keyword arguments to pass to specified metric function.
axis : int, optional, default 1
Axis along which the argmin and distances are to be computed.
Returns
-------
argmin : numpy.ndarray
Y[argmin[i], :] is the row in Y that is closest to X[i, :].
See also
--------
sklearn.metrics.pairwise_distances
sklearn.metrics.pairwise_distances_argmin_min
"""
if metric_kwargs is None:
metric_kwargs = {}
return pairwise_distances_argmin_min(X, Y, axis, metric, batch_size,
metric_kwargs)[0]
def manhattan_distances(X, Y=None, sum_over_features=True,
size_threshold=5e8):
""" Compute the L1 distances between the vectors in X and Y.
With sum_over_features equal to False it returns the componentwise
distances.
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array_like
An array with shape (n_samples_X, n_features).
Y : array_like, optional
An array with shape (n_samples_Y, n_features).
sum_over_features : bool, default=True
If True the function returns the pairwise distance matrix
else it returns the componentwise L1 pairwise-distances.
Not supported for sparse matrix inputs.
size_threshold : int, default=5e8
Unused parameter.
Returns
-------
D : array
If sum_over_features is False shape is
(n_samples_X * n_samples_Y, n_features) and D contains the
componentwise L1 pairwise-distances (ie. absolute difference),
else shape is (n_samples_X, n_samples_Y) and D contains
the pairwise L1 distances.
Examples
--------
>>> from sklearn.metrics.pairwise import manhattan_distances
>>> manhattan_distances([[3]], [[3]])#doctest:+ELLIPSIS
array([[ 0.]])
>>> manhattan_distances([[3]], [[2]])#doctest:+ELLIPSIS
array([[ 1.]])
>>> manhattan_distances([[2]], [[3]])#doctest:+ELLIPSIS
array([[ 1.]])
>>> manhattan_distances([[1, 2], [3, 4]],\
[[1, 2], [0, 3]])#doctest:+ELLIPSIS
array([[ 0., 2.],
[ 4., 4.]])
>>> import numpy as np
>>> X = np.ones((1, 2))
>>> y = 2 * np.ones((2, 2))
>>> manhattan_distances(X, y, sum_over_features=False)#doctest:+ELLIPSIS
array([[ 1., 1.],
[ 1., 1.]]...)
"""
X, Y = check_pairwise_arrays(X, Y)
if issparse(X) or issparse(Y):
if not sum_over_features:
raise TypeError("sum_over_features=%r not supported"
" for sparse matrices" % sum_over_features)
X = csr_matrix(X, copy=False)
Y = csr_matrix(Y, copy=False)
D = np.zeros((X.shape[0], Y.shape[0]))
_sparse_manhattan(X.data, X.indices, X.indptr,
Y.data, Y.indices, Y.indptr,
X.shape[1], D)
return D
if sum_over_features:
return distance.cdist(X, Y, 'cityblock')
D = X[:, np.newaxis, :] - Y[np.newaxis, :, :]
D = np.abs(D, D)
return D.reshape((-1, X.shape[1]))
def cosine_distances(X, Y=None):
"""Compute cosine distance between samples in X and Y.
Cosine distance is defined as 1.0 minus the cosine similarity.
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array_like, sparse matrix
with shape (n_samples_X, n_features).
Y : array_like, sparse matrix (optional)
with shape (n_samples_Y, n_features).
Returns
-------
distance matrix : array
An array with shape (n_samples_X, n_samples_Y).
See also
--------
sklearn.metrics.pairwise.cosine_similarity
scipy.spatial.distance.cosine (dense matrices only)
"""
# 1.0 - cosine_similarity(X, Y) without copy
S = cosine_similarity(X, Y)
S *= -1
S += 1
return S
# Paired distances
def paired_euclidean_distances(X, Y):
"""
Computes the paired euclidean distances between X and Y
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Y : array-like, shape (n_samples, n_features)
Returns
-------
distances : ndarray (n_samples, )
"""
X, Y = check_paired_arrays(X, Y)
return row_norms(X - Y)
def paired_manhattan_distances(X, Y):
"""Compute the L1 distances between the vectors in X and Y.
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Y : array-like, shape (n_samples, n_features)
Returns
-------
distances : ndarray (n_samples, )
"""
X, Y = check_paired_arrays(X, Y)
diff = X - Y
if issparse(diff):
diff.data = np.abs(diff.data)
return np.squeeze(np.array(diff.sum(axis=1)))
else:
return np.abs(diff).sum(axis=-1)
def paired_cosine_distances(X, Y):
"""
Computes the paired cosine distances between X and Y
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Y : array-like, shape (n_samples, n_features)
Returns
-------
distances : ndarray, shape (n_samples, )
Notes
------
The cosine distance is equivalent to the half the squared
euclidean distance if each sample is normalized to unit norm
"""
X, Y = check_paired_arrays(X, Y)
return .5 * row_norms(normalize(X) - normalize(Y), squared=True)
PAIRED_DISTANCES = {
'cosine': paired_cosine_distances,
'euclidean': paired_euclidean_distances,
'l2': paired_euclidean_distances,
'l1': paired_manhattan_distances,
'manhattan': paired_manhattan_distances,
'cityblock': paired_manhattan_distances}
def paired_distances(X, Y, metric="euclidean", **kwds):
"""
Computes the paired distances between X and Y.
Computes the distances between (X[0], Y[0]), (X[1], Y[1]), etc...
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : ndarray (n_samples, n_features)
Array 1 for distance computation.
Y : ndarray (n_samples, n_features)
Array 2 for distance computation.
metric : string or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string, it must be one of the options
specified in PAIRED_DISTANCES, including "euclidean",
"manhattan", or "cosine".
Alternatively, if metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays from X as input and return a value indicating
the distance between them.
Returns
-------
distances : ndarray (n_samples, )
Examples
--------
>>> from sklearn.metrics.pairwise import paired_distances
>>> X = [[0, 1], [1, 1]]
>>> Y = [[0, 1], [2, 1]]
>>> paired_distances(X, Y)
array([ 0., 1.])
See also
--------
pairwise_distances : pairwise distances.
"""
if metric in PAIRED_DISTANCES:
func = PAIRED_DISTANCES[metric]
return func(X, Y)
elif callable(metric):
# Check the matrix first (it is usually done by the metric)
X, Y = check_paired_arrays(X, Y)
distances = np.zeros(len(X))
for i in range(len(X)):
distances[i] = metric(X[i], Y[i])
return distances
else:
raise ValueError('Unknown distance %s' % metric)
# Kernels
def linear_kernel(X, Y=None):
"""
Compute the linear kernel between X and Y.
Read more in the :ref:`User Guide <linear_kernel>`.
Parameters
----------
X : array of shape (n_samples_1, n_features)
Y : array of shape (n_samples_2, n_features)
Returns
-------
Gram matrix : array of shape (n_samples_1, n_samples_2)
"""
X, Y = check_pairwise_arrays(X, Y)
return safe_sparse_dot(X, Y.T, dense_output=True)
def polynomial_kernel(X, Y=None, degree=3, gamma=None, coef0=1):
"""
Compute the polynomial kernel between X and Y::
K(X, Y) = (gamma <X, Y> + coef0)^degree
Read more in the :ref:`User Guide <polynomial_kernel>`.
Parameters
----------
X : ndarray of shape (n_samples_1, n_features)
Y : ndarray of shape (n_samples_2, n_features)
degree : int, default 3
gamma : float, default None
if None, defaults to 1.0 / n_samples_1
coef0 : int, default 1
Returns
-------
Gram matrix : array of shape (n_samples_1, n_samples_2)
"""
X, Y = check_pairwise_arrays(X, Y)
if gamma is None:
gamma = 1.0 / X.shape[1]
K = safe_sparse_dot(X, Y.T, dense_output=True)
K *= gamma
K += coef0
K **= degree
return K
def sigmoid_kernel(X, Y=None, gamma=None, coef0=1):
"""
Compute the sigmoid kernel between X and Y::
K(X, Y) = tanh(gamma <X, Y> + coef0)
Read more in the :ref:`User Guide <sigmoid_kernel>`.
Parameters
----------
X : ndarray of shape (n_samples_1, n_features)
Y : ndarray of shape (n_samples_2, n_features)
gamma : float, default None
If None, defaults to 1.0 / n_samples_1
coef0 : int, default 1
Returns
-------
Gram matrix : array of shape (n_samples_1, n_samples_2)
"""
X, Y = check_pairwise_arrays(X, Y)
if gamma is None:
gamma = 1.0 / X.shape[1]
K = safe_sparse_dot(X, Y.T, dense_output=True)
K *= gamma
K += coef0
np.tanh(K, K) # compute tanh in-place
return K
def rbf_kernel(X, Y=None, gamma=None):
"""
Compute the rbf (gaussian) kernel between X and Y::
K(x, y) = exp(-gamma ||x-y||^2)
for each pair of rows x in X and y in Y.
Read more in the :ref:`User Guide <rbf_kernel>`.
Parameters
----------
X : array of shape (n_samples_X, n_features)
Y : array of shape (n_samples_Y, n_features)
gamma : float, default None
If None, defaults to 1.0 / n_samples_X
Returns
-------
kernel_matrix : array of shape (n_samples_X, n_samples_Y)
"""
X, Y = check_pairwise_arrays(X, Y)
if gamma is None:
gamma = 1.0 / X.shape[1]
K = euclidean_distances(X, Y, squared=True)
K *= -gamma
np.exp(K, K) # exponentiate K in-place
return K
def laplacian_kernel(X, Y=None, gamma=None):
"""Compute the laplacian kernel between X and Y.
The laplacian kernel is defined as::
K(x, y) = exp(-gamma ||x-y||_1)
for each pair of rows x in X and y in Y.
Read more in the :ref:`User Guide <laplacian_kernel>`.
.. versionadded:: 0.17
Parameters
----------
X : array of shape (n_samples_X, n_features)
Y : array of shape (n_samples_Y, n_features)
gamma : float, default None
If None, defaults to 1.0 / n_samples_X
Returns
-------
kernel_matrix : array of shape (n_samples_X, n_samples_Y)
"""
X, Y = check_pairwise_arrays(X, Y)
if gamma is None:
gamma = 1.0 / X.shape[1]
K = -gamma * manhattan_distances(X, Y)
np.exp(K, K) # exponentiate K in-place
return K
def cosine_similarity(X, Y=None, dense_output=True):
"""Compute cosine similarity between samples in X and Y.
Cosine similarity, or the cosine kernel, computes similarity as the
normalized dot product of X and Y:
K(X, Y) = <X, Y> / (||X||*||Y||)
On L2-normalized data, this function is equivalent to linear_kernel.
Read more in the :ref:`User Guide <cosine_similarity>`.
Parameters
----------
X : ndarray or sparse array, shape: (n_samples_X, n_features)
Input data.
Y : ndarray or sparse array, shape: (n_samples_Y, n_features)
Input data. If ``None``, the output will be the pairwise
similarities between all samples in ``X``.
dense_output : boolean (optional), default True
Whether to return dense output even when the input is sparse. If
``False``, the output is sparse if both input arrays are sparse.
.. versionadded:: 0.17
parameter ``dense_output`` for dense output.
Returns
-------
kernel matrix : array
An array with shape (n_samples_X, n_samples_Y).
"""
# to avoid recursive import
X, Y = check_pairwise_arrays(X, Y)
X_normalized = normalize(X, copy=True)
if X is Y:
Y_normalized = X_normalized
else:
Y_normalized = normalize(Y, copy=True)
K = safe_sparse_dot(X_normalized, Y_normalized.T, dense_output=dense_output)
return K
def additive_chi2_kernel(X, Y=None):
"""Computes the additive chi-squared kernel between observations in X and Y
The chi-squared kernel is computed between each pair of rows in X and Y. X
and Y have to be non-negative. This kernel is most commonly applied to
histograms.
The chi-squared kernel is given by::
k(x, y) = -Sum [(x - y)^2 / (x + y)]
It can be interpreted as a weighted difference per entry.
Read more in the :ref:`User Guide <chi2_kernel>`.
Notes
-----
As the negative of a distance, this kernel is only conditionally positive
definite.
Parameters
----------
X : array-like of shape (n_samples_X, n_features)
Y : array of shape (n_samples_Y, n_features)
Returns
-------
kernel_matrix : array of shape (n_samples_X, n_samples_Y)
References
----------
* Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C.
Local features and kernels for classification of texture and object
categories: A comprehensive study
International Journal of Computer Vision 2007
http://research.microsoft.com/en-us/um/people/manik/projects/trade-off/papers/ZhangIJCV06.pdf
See also
--------
chi2_kernel : The exponentiated version of the kernel, which is usually
preferable.
sklearn.kernel_approximation.AdditiveChi2Sampler : A Fourier approximation
to this kernel.
"""
if issparse(X) or issparse(Y):
raise ValueError("additive_chi2 does not support sparse matrices.")
X, Y = check_pairwise_arrays(X, Y)
if (X < 0).any():
raise ValueError("X contains negative values.")
if Y is not X and (Y < 0).any():
raise ValueError("Y contains negative values.")
result = np.zeros((X.shape[0], Y.shape[0]), dtype=X.dtype)
_chi2_kernel_fast(X, Y, result)
return result
def chi2_kernel(X, Y=None, gamma=1.):
"""Computes the exponential chi-squared kernel X and Y.
The chi-squared kernel is computed between each pair of rows in X and Y. X
and Y have to be non-negative. This kernel is most commonly applied to
histograms.
The chi-squared kernel is given by::
k(x, y) = exp(-gamma Sum [(x - y)^2 / (x + y)])
It can be interpreted as a weighted difference per entry.
Read more in the :ref:`User Guide <chi2_kernel>`.
Parameters
----------
X : array-like of shape (n_samples_X, n_features)
Y : array of shape (n_samples_Y, n_features)
gamma : float, default=1.
Scaling parameter of the chi2 kernel.
Returns
-------
kernel_matrix : array of shape (n_samples_X, n_samples_Y)
References
----------
* Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C.
Local features and kernels for classification of texture and object
categories: A comprehensive study
International Journal of Computer Vision 2007
http://research.microsoft.com/en-us/um/people/manik/projects/trade-off/papers/ZhangIJCV06.pdf
See also
--------
additive_chi2_kernel : The additive version of this kernel
sklearn.kernel_approximation.AdditiveChi2Sampler : A Fourier approximation
to the additive version of this kernel.
"""
K = additive_chi2_kernel(X, Y)
K *= gamma
return np.exp(K, K)
# Helper functions - distance
PAIRWISE_DISTANCE_FUNCTIONS = {
# If updating this dictionary, update the doc in both distance_metrics()
# and also in pairwise_distances()!
'cityblock': manhattan_distances,
'cosine': cosine_distances,
'euclidean': euclidean_distances,
'l2': euclidean_distances,
'l1': manhattan_distances,
'manhattan': manhattan_distances,
'precomputed': None, # HACK: precomputed is always allowed, never called
}
def distance_metrics():
"""Valid metrics for pairwise_distances.
This function simply returns the valid pairwise distance metrics.
It exists to allow for a description of the mapping for
each of the valid strings.
The valid distance metrics, and the function they map to, are:
============ ====================================
metric Function
============ ====================================
'cityblock' metrics.pairwise.manhattan_distances
'cosine' metrics.pairwise.cosine_distances
'euclidean' metrics.pairwise.euclidean_distances
'l1' metrics.pairwise.manhattan_distances
'l2' metrics.pairwise.euclidean_distances
'manhattan' metrics.pairwise.manhattan_distances
============ ====================================
Read more in the :ref:`User Guide <metrics>`.
"""
return PAIRWISE_DISTANCE_FUNCTIONS
def _parallel_pairwise(X, Y, func, n_jobs, **kwds):
"""Break the pairwise matrix in n_jobs even slices
and compute them in parallel"""
if n_jobs < 0:
n_jobs = max(cpu_count() + 1 + n_jobs, 1)
if Y is None:
Y = X
if n_jobs == 1:
# Special case to avoid picklability checks in delayed
return func(X, Y, **kwds)
# TODO: in some cases, backend='threading' may be appropriate
fd = delayed(func)
ret = Parallel(n_jobs=n_jobs, verbose=0)(
fd(X, Y[s], **kwds)
for s in gen_even_slices(Y.shape[0], n_jobs))
return np.hstack(ret)
def _pairwise_callable(X, Y, metric, **kwds):
"""Handle the callable case for pairwise_{distances,kernels}
"""
X, Y = check_pairwise_arrays(X, Y)
if X is Y:
# Only calculate metric for upper triangle
out = np.zeros((X.shape[0], Y.shape[0]), dtype='float')
iterator = itertools.combinations(range(X.shape[0]), 2)
for i, j in iterator:
out[i, j] = metric(X[i], Y[j], **kwds)
# Make symmetric
# NB: out += out.T will produce incorrect results
out = out + out.T
# Calculate diagonal
# NB: nonzero diagonals are allowed for both metrics and kernels
for i in range(X.shape[0]):
x = X[i]
out[i, i] = metric(x, x, **kwds)
else:
# Calculate all cells
out = np.empty((X.shape[0], Y.shape[0]), dtype='float')
iterator = itertools.product(range(X.shape[0]), range(Y.shape[0]))
for i, j in iterator:
out[i, j] = metric(X[i], Y[j], **kwds)
return out
_VALID_METRICS = ['euclidean', 'l2', 'l1', 'manhattan', 'cityblock',
'braycurtis', 'canberra', 'chebyshev', 'correlation',
'cosine', 'dice', 'hamming', 'jaccard', 'kulsinski',
'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto',
'russellrao', 'seuclidean', 'sokalmichener',
'sokalsneath', 'sqeuclidean', 'yule', "wminkowski"]
def pairwise_distances(X, Y=None, metric="euclidean", n_jobs=1, **kwds):
""" Compute the distance matrix from a vector array X and optional Y.
This method takes either a vector array or a distance matrix, and returns
a distance matrix. If the input is a vector array, the distances are
computed. If the input is a distances matrix, it is returned instead.
This method provides a safe way to take a distance matrix as input, while
preserving compatibility with many other algorithms that take a vector
array.
If Y is given (default is None), then the returned matrix is the pairwise
distance between the arrays from both X and Y.
Valid values for metric are:
- From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
'manhattan']. These metrics support sparse matrix inputs.
- From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis',
'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule']
See the documentation for scipy.spatial.distance for details on these
metrics. These metrics do not support sparse matrix inputs.
Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are
valid scipy.spatial.distance metrics), the scikit-learn implementation
will be used, which is faster and has support for sparse matrices (except
for 'cityblock'). For a verbose description of the metrics from
scikit-learn, see the __doc__ of the sklearn.pairwise.distance_metrics
function.
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \
[n_samples_a, n_features] otherwise
Array of pairwise distances between samples, or a feature array.
Y : array [n_samples_b, n_features], optional
An optional second feature array. Only allowed if metric != "precomputed".
metric : string, or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string, it must be one of the options
allowed by scipy.spatial.distance.pdist for its metric parameter, or
a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
If metric is "precomputed", X is assumed to be a distance matrix.
Alternatively, if metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays from X as input and return a value indicating
the distance between them.
n_jobs : int
The number of jobs to use for the computation. This works by breaking
down the pairwise matrix into n_jobs even slices and computing them in
parallel.
If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
`**kwds` : optional keyword parameters
Any further parameters are passed directly to the distance function.
If using a scipy.spatial.distance metric, the parameters are still
metric dependent. See the scipy docs for usage examples.
Returns
-------
D : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b]
A distance matrix D such that D_{i, j} is the distance between the
ith and jth vectors of the given matrix X, if Y is None.
If Y is not None, then D_{i, j} is the distance between the ith array
from X and the jth array from Y.
"""
if (metric not in _VALID_METRICS and
not callable(metric) and metric != "precomputed"):
raise ValueError("Unknown metric %s. "
"Valid metrics are %s, or 'precomputed', or a "
"callable" % (metric, _VALID_METRICS))
if metric == "precomputed":
X, _ = check_pairwise_arrays(X, Y, precomputed=True)
return X
elif metric in PAIRWISE_DISTANCE_FUNCTIONS:
func = PAIRWISE_DISTANCE_FUNCTIONS[metric]
elif callable(metric):
func = partial(_pairwise_callable, metric=metric, **kwds)
else:
if issparse(X) or issparse(Y):
raise TypeError("scipy distance metrics do not"
" support sparse matrices.")
dtype = bool if metric in PAIRWISE_BOOLEAN_FUNCTIONS else None
X, Y = check_pairwise_arrays(X, Y, dtype=dtype)
if n_jobs == 1 and X is Y:
return distance.squareform(distance.pdist(X, metric=metric,
**kwds))
func = partial(distance.cdist, metric=metric, **kwds)
return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
# These distances recquire boolean arrays, when using scipy.spatial.distance
PAIRWISE_BOOLEAN_FUNCTIONS = [
'dice',
'jaccard',
'kulsinski',
'matching',
'rogerstanimoto',
'russellrao',
'sokalmichener',
'sokalsneath',
'yule',
]
# Helper functions - distance
PAIRWISE_KERNEL_FUNCTIONS = {
# If updating this dictionary, update the doc in both distance_metrics()
# and also in pairwise_distances()!
'additive_chi2': additive_chi2_kernel,
'chi2': chi2_kernel,
'linear': linear_kernel,
'polynomial': polynomial_kernel,
'poly': polynomial_kernel,
'rbf': rbf_kernel,
'laplacian': laplacian_kernel,
'sigmoid': sigmoid_kernel,
'cosine': cosine_similarity, }
def kernel_metrics():
""" Valid metrics for pairwise_kernels
This function simply returns the valid pairwise distance metrics.
It exists, however, to allow for a verbose description of the mapping for
each of the valid strings.
The valid distance metrics, and the function they map to, are:
=============== ========================================
metric Function
=============== ========================================
'additive_chi2' sklearn.pairwise.additive_chi2_kernel
'chi2' sklearn.pairwise.chi2_kernel
'linear' sklearn.pairwise.linear_kernel
'poly' sklearn.pairwise.polynomial_kernel
'polynomial' sklearn.pairwise.polynomial_kernel
'rbf' sklearn.pairwise.rbf_kernel
'laplacian' sklearn.pairwise.laplacian_kernel
'sigmoid' sklearn.pairwise.sigmoid_kernel
'cosine' sklearn.pairwise.cosine_similarity
=============== ========================================
Read more in the :ref:`User Guide <metrics>`.
"""
return PAIRWISE_KERNEL_FUNCTIONS
KERNEL_PARAMS = {
"additive_chi2": (),
"chi2": (),
"cosine": (),
"exp_chi2": frozenset(["gamma"]),
"linear": (),
"poly": frozenset(["gamma", "degree", "coef0"]),
"polynomial": frozenset(["gamma", "degree", "coef0"]),
"rbf": frozenset(["gamma"]),
"laplacian": frozenset(["gamma"]),
"sigmoid": frozenset(["gamma", "coef0"]),
}
def pairwise_kernels(X, Y=None, metric="linear", filter_params=False,
n_jobs=1, **kwds):
"""Compute the kernel between arrays X and optional array Y.
This method takes either a vector array or a kernel matrix, and returns
a kernel matrix. If the input is a vector array, the kernels are
computed. If the input is a kernel matrix, it is returned instead.
This method provides a safe way to take a kernel matrix as input, while
preserving compatibility with many other algorithms that take a vector
array.
If Y is given (default is None), then the returned matrix is the pairwise
kernel between the arrays from both X and Y.
Valid values for metric are::
['rbf', 'sigmoid', 'polynomial', 'poly', 'linear', 'cosine']
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \
[n_samples_a, n_features] otherwise
Array of pairwise kernels between samples, or a feature array.
Y : array [n_samples_b, n_features]
A second feature array only if X has shape [n_samples_a, n_features].
metric : string, or callable
The metric to use when calculating kernel between instances in a
feature array. If metric is a string, it must be one of the metrics
in pairwise.PAIRWISE_KERNEL_FUNCTIONS.
If metric is "precomputed", X is assumed to be a kernel matrix.
Alternatively, if metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays from X as input and return a value indicating
the distance between them.
n_jobs : int
The number of jobs to use for the computation. This works by breaking
down the pairwise matrix into n_jobs even slices and computing them in
parallel.
If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
filter_params : boolean
Whether to filter invalid parameters or not.
`**kwds` : optional keyword parameters
Any further parameters are passed directly to the kernel function.
Returns
-------
K : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b]
A kernel matrix K such that K_{i, j} is the kernel between the
ith and jth vectors of the given matrix X, if Y is None.
If Y is not None, then K_{i, j} is the kernel between the ith array
from X and the jth array from Y.
Notes
-----
If metric is 'precomputed', Y is ignored and X is returned.
"""
# import GPKernel locally to prevent circular imports
from ..gaussian_process.kernels import Kernel as GPKernel
if metric == "precomputed":
X, _ = check_pairwise_arrays(X, Y, precomputed=True)
return X
elif isinstance(metric, GPKernel):
func = metric.__call__
elif metric in PAIRWISE_KERNEL_FUNCTIONS:
if filter_params:
kwds = dict((k, kwds[k]) for k in kwds
if k in KERNEL_PARAMS[metric])
func = PAIRWISE_KERNEL_FUNCTIONS[metric]
elif callable(metric):
func = partial(_pairwise_callable, metric=metric, **kwds)
else:
raise ValueError("Unknown kernel %r" % metric)
return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
| bsd-3-clause |
alxio/gmmreg | Python/_plotting.py | 14 | 2435 | #!/usr/bin/env python
#coding=utf-8
##====================================================
## $Author$
## $Date$
## $Revision$
##====================================================
from pylab import *
from configobj import ConfigObj
import matplotlib.pyplot as plt
def display2Dpointset(A):
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.grid(True)
ax.plot(A[:,0],A[:,1],'yo',markersize=8,mew=1)
labels = plt.getp(plt.gca(), 'xticklabels')
plt.setp(labels, color='k', fontweight='bold')
labels = plt.getp(plt.gca(), 'yticklabels')
plt.setp(labels, color='k', fontweight='bold')
for i,x in enumerate(A):
ax.annotate('%d'%(i+1), xy = x, xytext = x + 0)
ax.set_axis_off()
#fig.show()
def display2Dpointsets(A, B, ax = None):
""" display a pair of 2D point sets """
if not ax:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(A[:,0],A[:,1],'yo',markersize=8,mew=1)
ax.plot(B[:,0],B[:,1],'b+',markersize=8,mew=1)
#pylab.setp(pylab.gca(), 'xlim', [-0.15,0.6])
labels = plt.getp(plt.gca(), 'xticklabels')
plt.setp(labels, color='k', fontweight='bold')
labels = plt.getp(plt.gca(), 'yticklabels')
plt.setp(labels, color='k', fontweight='bold')
def display3Dpointsets(A,B,ax):
#ax.plot3d(A[:,0],A[:,1],A[:,2],'yo',markersize=10,mew=1)
#ax.plot3d(B[:,0],B[:,1],B[:,2],'b+',markersize=10,mew=1)
ax.scatter(A[:,0],A[:,1],A[:,2], c = 'y', marker = 'o')
ax.scatter(B[:,0],B[:,1],B[:,2], c = 'b', marker = '+')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
from mpl_toolkits.mplot3d import Axes3D
def displayABC(A,B,C):
fig = plt.figure()
dim = A.shape[1]
if dim==2:
ax = plt.subplot(121)
display2Dpointsets(A, B, ax)
ax = plt.subplot(122)
display2Dpointsets(C, B, ax)
if dim==3:
plot1 = plt.subplot(1,2,1)
ax = Axes3D(fig, rect = plot1.get_position())
display3Dpointsets(A,B,ax)
plot2 = plt.subplot(1,2,2)
ax = Axes3D(fig, rect = plot2.get_position())
display3Dpointsets(C,B,ax)
plt.show()
def display_pts(f_config):
config = ConfigObj(f_config)
file_section = config['FILES']
mf = file_section['model']
sf = file_section['scene']
tf = file_section['transformed_model']
m = np.loadtxt(mf)
s = np.loadtxt(sf)
t = np.loadtxt(tf)
displayABC(m,s,t)
| gpl-3.0 |
Haunter17/MIR_SU17 | exp8/exp8f_none.py | 1 | 7466 | import numpy as np
import tensorflow as tf
import h5py
from sklearn.preprocessing import OneHotEncoder
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import time
import scipy.io
# Functions for initializing neural nets parameters
def weight_variable(shape, var_name):
initial = tf.truncated_normal(shape, stddev=0.1, dtype=tf.float64)
return tf.Variable(initial, name=var_name)
def bias_variable(shape, var_name):
initial = tf.constant(0.1, shape=shape, dtype=tf.float64)
return tf.Variable(initial, name=var_name)
def conv2d(x, W):
return tf.nn.conv2d(x, W, [1, 1, 1, 1], 'VALID')
def batch_nm(x, eps=1e-5):
# batch normalization to have zero mean and unit variance
mu, var = tf.nn.moments(x, [0])
return tf.nn.batch_normalization(x, mu, var, None, None, eps)
# Download data from .mat file into numpy array
print('==> Experiment 8f')
filepath = '/scratch/ttanpras/exp8a_d7_1s.mat'
print('==> Loading data from {}'.format(filepath))
f = h5py.File(filepath)
data_train = np.array(f.get('trainingFeatures'))
data_val = np.array(f.get('validationFeatures'))
del f
print('==> Data sizes:',data_train.shape, data_val.shape)
# Transform labels into on-hot encoding form
enc = OneHotEncoder(n_values = 71)
'''
NN config parameters
'''
sub_window_size = 32
num_features = 169*sub_window_size
num_frames = 32
hidden_layer_size = 64
num_bits = 64
num_classes = 71
print("Number of features:", num_features)
print("Number of songs:",num_classes)
# Reshape input features
X_train = np.reshape(data_train,(-1, num_features))
X_val = np.reshape(data_val,(-1, num_features))
print("Input sizes:", X_train.shape, X_val.shape)
y_train = []
y_val = []
# Add Labels
for label in range(num_classes):
for sampleCount in range(X_train.shape[0]//num_classes):
y_train.append([label])
for sampleCount in range(X_val.shape[0]//num_classes):
y_val.append([label])
X_train = np.concatenate((X_train, y_train), axis=1)
X_val = np.concatenate((X_val, y_val), axis=1)
# Shuffle
np.random.shuffle(X_train)
np.random.shuffle(X_val)
# Separate coefficients and labels
y_train = X_train[:, -1].reshape(-1, 1)
X_train = X_train[:, :-1]
y_val = X_val[:, -1].reshape(-1, 1)
X_val = X_val[:, :-1]
print('==> Data sizes:',X_train.shape, y_train.shape,X_val.shape, y_val.shape)
y_train = enc.fit_transform(y_train.copy()).astype(int).toarray()
y_val = enc.fit_transform(y_val.copy()).astype(int).toarray()
plotx = []
ploty_train = []
ploty_val = []
# Set-up NN layers
x = tf.placeholder(tf.float64, [None, num_features])
W1 = weight_variable([num_features, hidden_layer_size], "W1")
b1 = bias_variable([hidden_layer_size], "b1")
OpW1 = tf.placeholder(tf.float64, [num_features, hidden_layer_size])
Opb1 = tf.placeholder(tf.float64, [hidden_layer_size])
# Hidden layer activation function: ReLU
h1 = tf.nn.relu(tf.matmul(x, W1) + b1)
W2 = weight_variable([hidden_layer_size, num_bits], "W2")
b2 = bias_variable([num_bits], "b2")
OpW2 = tf.placeholder(tf.float64, [hidden_layer_size, num_bits])
Opb2 = tf.placeholder(tf.float64, [num_bits])
# Pre-activation value for bit representation
h = tf.matmul(h1, W2) + b2
h2 = tf.nn.relu(tf.matmul(h1, W2) + b2)
W3 = weight_variable([num_bits, num_classes], "W3")
b3 = bias_variable([num_classes], "b3")
OpW3 = tf.placeholder(tf.float64, [num_bits, num_classes])
Opb3 = tf.placeholder(tf.float64, [num_classes])
# Softmax layer (Output), dtype = float64
y = tf.matmul(h2, W3) + b3
# NN desired value (labels)
y_ = tf.placeholder(tf.float64, [None, num_classes])
# Loss function
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess = tf.InteractiveSession()
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))
sess.run(tf.initialize_all_variables())
# Training
numTrainingVec = len(X_train)
batchSize = 500
numEpochs = 1000
bestValErr = 10000
bestValEpoch = 0
startTime = time.time()
for epoch in range(numEpochs):
for i in range(0,numTrainingVec,batchSize):
# Batch Data
batchEndPoint = min(i+batchSize, numTrainingVec)
trainBatchData = X_train[i:batchEndPoint]
trainBatchLabel = y_train[i:batchEndPoint]
train_step.run(feed_dict={x: trainBatchData, y_: trainBatchLabel})
# Print accuracy
if epoch % 5 == 0 or epoch == numEpochs-1:
plotx.append(epoch)
train_error = cross_entropy.eval(feed_dict={x:trainBatchData, y_: trainBatchLabel})
train_acc = accuracy.eval(feed_dict={x:trainBatchData, y_: trainBatchLabel})
val_error = cross_entropy.eval(feed_dict={x:X_val, y_: y_val})
val_acc = accuracy.eval(feed_dict={x:X_val, y_: y_val})
ploty_train.append(train_error)
ploty_val.append(val_error)
print("epoch: %d, val error %g, train error %g"%(epoch, val_error, train_error))
if val_error < bestValErr:
bestValErr = val_error
bestValEpoch = epoch
OpW1 = W1
Opb1 = b1
OpW2 = W2
Opb2 = b2
OpW3 = W3
Opb3 = b3
endTime = time.time()
print("Elapse Time:", endTime - startTime)
print("Best validation error: %g at epoch %d"%(bestValErr, bestValEpoch))
# Restore best model for early stopping
W1 = OpW1
b1 = Opb1
W2 = OpW2
b2 = Opb2
W3 = OpW3
b3 = Opb3
print('==> Generating error plot...')
errfig = plt.figure()
trainErrPlot = errfig.add_subplot(111)
trainErrPlot.set_xlabel('Number of Epochs')
trainErrPlot.set_ylabel('Cross-Entropy Error')
trainErrPlot.set_title('Error vs Number of Epochs')
trainErrPlot.scatter(plotx, ploty_train)
valErrPlot = errfig.add_subplot(111)
valErrPlot.scatter(plotx, ploty_val)
errfig.savefig('exp8f_none.png')
'''
GENERATING REPRESENTATION OF NOISY FILES
'''
namelist = ['orig','comp5','comp10','str5','str10','ampSat_(-15)','ampSat_(-10)','ampSat_(-5)', \
'ampSat_(5)','ampSat_(10)','ampSat_(15)','pitchShift_(-1)','pitchShift_(-0.5)', \
'pitchShift_(0.5)','pitchShift_(1)','rev_dkw','rev_gal','rev_shan0','rev_shan1', \
'rev_gen','crowd-15','crowd-10','crowd-5','crowd0','crowd5','crowd10','crowd15', \
'crowd100','rest-15','rest-10','rest-5','rest0','rest5','rest10','rest15', \
'rest100','AWGN-15','AWGN-10','AWGN-5','AWGN0','AWGN5','AWGN10','AWGN15', 'AWGN100']
outdir = '/scratch/ttanpras/taylorswift_noisy_processed/'
repDict = {}
# Loop over each CQT files, not shuffled
for count in range(len(namelist)):
name = namelist[count]
filename = outdir + name + '.mat'
cqt = scipy.io.loadmat(filename)['Q']
cqt = np.transpose(np.array(cqt))
# Group into windows of 32 without overlapping
# Discard any leftover frames
num_windows = cqt.shape[0] // 32
cqt = cqt[:32*num_windows]
X = np.reshape(cqt,(num_windows, num_features))
# Feed window through model (Only 1 layer of weight w/o non-linearity)
rep = h.eval(feed_dict={x:X})
# Put the output representation into a dictionary
repDict['n'+str(count)] = rep
scipy.io.savemat('exp8f_none_repNon.mat',repDict) | mit |
johnarban/arban | myhelpers.py | 1 | 1202 | import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as colors
def clean_color(color, reverse=False):
if isinstance(color, str):
if color[-2:] == '_r':
return color[:-2], True
elif reverse is True:
return color, True
else:
return color, False
else:
return color, reverse
def color_cmap(c, alpha=1, to_white=True, reverse=False):
if to_white:
end = (1, 1, 1, alpha)
else:
end = (0, 0, 0, alpha)
color, reverse = clean_color(c, reverse=reverse)
cmap = mpl.colors.LinearSegmentedColormap.from_list(
"density_cmap", [color, end])
if reverse:
return cmap.reversed()
else:
return cmap
def contour_level_colors(cmap, levels, vmin=None, vmax=None):
vmin = vmin or 0
vmax = vmax or max(levels)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
#offset = np.diff(levels)[0] * .5
#colors = mpl.cm.get_cmap(cmap)(norm(levels-offset))
levels = np.r_[0, levels]
center_levels = 0.5 * (levels[1:] + levels[:-1])
return mpl.cm.get_cmap(cmap)(norm(center_levels))
| mit |
ptonner/GPy | GPy/models/gplvm.py | 8 | 3049 | # Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from .. import kern
from ..core import GP, Param
from ..likelihoods import Gaussian
from .. import util
class GPLVM(GP):
"""
Gaussian Process Latent Variable Model
"""
def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, name="gplvm"):
"""
:param Y: observed data
:type Y: np.ndarray
:param input_dim: latent dimensionality
:type input_dim: int
:param init: initialisation method for the latent space
:type init: 'PCA'|'random'
"""
if X is None:
from ..util.initialization import initialize_latent
X, fracs = initialize_latent(init, input_dim, Y)
else:
fracs = np.ones(input_dim)
if kernel is None:
kernel = kern.RBF(input_dim, lengthscale=fracs, ARD=input_dim > 1) + kern.Bias(input_dim, np.exp(-2))
likelihood = Gaussian()
super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM')
self.X = Param('latent_mean', X)
self.link_parameter(self.X, index=0)
def parameters_changed(self):
super(GPLVM, self).parameters_changed()
self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)
#def jacobian(self,X):
# J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
# for i in range(self.output_dim):
# J[:,:,i] = self.kern.gradients_X(self.posterior.woodbury_vector[:,i:i+1], X, self.X)
# return J
#def magnification(self,X):
# target=np.zeros(X.shape[0])
# #J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
## J = self.jacobian(X)
# for i in range(X.shape[0]):
# target[i]=np.sqrt(np.linalg.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
# return target
def plot(self):
assert self.Y.shape[1] == 2, "too high dimensional to plot. Try plot_latent"
from matplotlib import pyplot as plt
plt.scatter(self.Y[:, 0],
self.Y[:, 1],
40, self.X[:, 0].copy(),
linewidth=0, cmap=plt.cm.jet)
Xnew = np.linspace(self.X.min(), self.X.max(), 200)[:, None]
mu, _ = self.predict(Xnew)
plt.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
def plot_latent(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, legend=True,
plot_limits=None,
aspect='auto', updates=False, **kwargs):
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import dim_reduction_plots
return dim_reduction_plots.plot_latent(self, labels, which_indices,
resolution, ax, marker, s,
fignum, False, legend,
plot_limits, aspect, updates, **kwargs)
| bsd-3-clause |
victor-prado/broker-manager | environment/lib/python3.5/site-packages/pandas/core/generic.py | 7 | 209040 | # pylint: disable=W0231,E1101
import collections
import warnings
import operator
import weakref
import gc
import numpy as np
import pandas.lib as lib
import pandas as pd
from pandas.types.common import (_coerce_to_dtype,
_ensure_int64,
needs_i8_conversion,
is_scalar,
is_integer, is_bool,
is_bool_dtype,
is_numeric_dtype,
is_datetime64_dtype,
is_timedelta64_dtype,
is_datetime64tz_dtype,
is_list_like,
is_dict_like,
is_re_compilable)
from pandas.types.cast import _maybe_promote, _maybe_upcast_putmask
from pandas.types.missing import isnull, notnull
from pandas.types.generic import ABCSeries, ABCPanel
from pandas.core.common import (_values_from_object,
_maybe_box_datetimelike,
SettingWithCopyError, SettingWithCopyWarning,
AbstractMethodError)
from pandas.core.base import PandasObject
from pandas.core.index import (Index, MultiIndex, _ensure_index,
InvalidIndexError)
import pandas.core.indexing as indexing
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex, Period
from pandas.core.internals import BlockManager
import pandas.core.algorithms as algos
import pandas.core.common as com
import pandas.core.missing as missing
from pandas.formats.printing import pprint_thing
from pandas.formats.format import format_percentiles
from pandas.tseries.frequencies import to_offset
from pandas import compat
from pandas.compat.numpy import function as nv
from pandas.compat import (map, zip, lrange, string_types,
isidentifier, set_function_name)
import pandas.core.nanops as nanops
from pandas.util.decorators import Appender, Substitution, deprecate_kwarg
from pandas.core import config
# goal is to be able to define the docs close to function, while still being
# able to share
_shared_docs = dict()
_shared_doc_kwargs = dict(
axes='keywords for axes', klass='NDFrame',
axes_single_arg='int or labels for object',
args_transpose='axes to permute (int or label for object)',
optional_by="""
by : str or list of str
Name or list of names which refer to the axis items.""")
def _single_replace(self, to_replace, method, inplace, limit):
if self.ndim != 1:
raise TypeError('cannot replace {0} with method {1} on a {2}'
.format(to_replace, method, type(self).__name__))
orig_dtype = self.dtype
result = self if inplace else self.copy()
fill_f = missing.get_fill_func(method)
mask = missing.mask_missing(result.values, to_replace)
values = fill_f(result.values, limit=limit, mask=mask)
if values.dtype == orig_dtype and inplace:
return
result = pd.Series(values, index=self.index,
dtype=self.dtype).__finalize__(self)
if inplace:
self._update_inplace(result._data)
return
return result
class NDFrame(PandasObject):
"""
N-dimensional analogue of DataFrame. Store multi-dimensional in a
size-mutable, labeled data structure
Parameters
----------
data : BlockManager
axes : list
copy : boolean, default False
"""
_internal_names = ['_data', '_cacher', '_item_cache', '_cache', 'is_copy',
'_subtyp', '_name', '_index', '_default_kind',
'_default_fill_value', '_metadata', '__array_struct__',
'__array_interface__']
_internal_names_set = set(_internal_names)
_accessors = frozenset([])
_metadata = []
is_copy = None
def __init__(self, data, axes=None, copy=False, dtype=None,
fastpath=False):
if not fastpath:
if dtype is not None:
data = data.astype(dtype)
elif copy:
data = data.copy()
if axes is not None:
for i, ax in enumerate(axes):
data = data.reindex_axis(ax, axis=i)
object.__setattr__(self, 'is_copy', None)
object.__setattr__(self, '_data', data)
object.__setattr__(self, '_item_cache', {})
def _validate_dtype(self, dtype):
""" validate the passed dtype """
if dtype is not None:
dtype = _coerce_to_dtype(dtype)
# a compound dtype
if dtype.kind == 'V':
raise NotImplementedError("compound dtypes are not implemented"
"in the {0} constructor"
.format(self.__class__.__name__))
return dtype
def _init_mgr(self, mgr, axes=None, dtype=None, copy=False):
""" passed a manager and a axes dict """
for a, axe in axes.items():
if axe is not None:
mgr = mgr.reindex_axis(axe,
axis=self._get_block_manager_axis(a),
copy=False)
# make a copy if explicitly requested
if copy:
mgr = mgr.copy()
if dtype is not None:
# avoid further copies if we can
if len(mgr.blocks) > 1 or mgr.blocks[0].values.dtype != dtype:
mgr = mgr.astype(dtype=dtype)
return mgr
# ----------------------------------------------------------------------
# Construction
@property
def _constructor(self):
"""Used when a manipulation result has the same dimensions as the
original.
"""
raise AbstractMethodError(self)
def __unicode__(self):
# unicode representation based upon iterating over self
# (since, by definition, `PandasContainers` are iterable)
prepr = '[%s]' % ','.join(map(pprint_thing, self))
return '%s(%s)' % (self.__class__.__name__, prepr)
def _dir_additions(self):
""" add the string-like attributes from the info_axis """
return set([c for c in self._info_axis
if isinstance(c, string_types) and isidentifier(c)])
@property
def _constructor_sliced(self):
"""Used when a manipulation result has one lower dimension(s) as the
original, such as DataFrame single columns slicing.
"""
raise AbstractMethodError(self)
@property
def _constructor_expanddim(self):
"""Used when a manipulation result has one higher dimension as the
original, such as Series.to_frame() and DataFrame.to_panel()
"""
raise NotImplementedError
# ----------------------------------------------------------------------
# Axis
@classmethod
def _setup_axes(cls, axes, info_axis=None, stat_axis=None, aliases=None,
slicers=None, axes_are_reversed=False, build_axes=True,
ns=None):
"""Provide axes setup for the major PandasObjects.
Parameters
----------
axes : the names of the axes in order (lowest to highest)
info_axis_num : the axis of the selector dimension (int)
stat_axis_num : the number of axis for the default stats (int)
aliases : other names for a single axis (dict)
slicers : how axes slice to others (dict)
axes_are_reversed : boolean whether to treat passed axes as
reversed (DataFrame)
build_axes : setup the axis properties (default True)
"""
cls._AXIS_ORDERS = axes
cls._AXIS_NUMBERS = dict((a, i) for i, a in enumerate(axes))
cls._AXIS_LEN = len(axes)
cls._AXIS_ALIASES = aliases or dict()
cls._AXIS_IALIASES = dict((v, k) for k, v in cls._AXIS_ALIASES.items())
cls._AXIS_NAMES = dict(enumerate(axes))
cls._AXIS_SLICEMAP = slicers or None
cls._AXIS_REVERSED = axes_are_reversed
# typ
setattr(cls, '_typ', cls.__name__.lower())
# indexing support
cls._ix = None
if info_axis is not None:
cls._info_axis_number = info_axis
cls._info_axis_name = axes[info_axis]
if stat_axis is not None:
cls._stat_axis_number = stat_axis
cls._stat_axis_name = axes[stat_axis]
# setup the actual axis
if build_axes:
def set_axis(a, i):
setattr(cls, a, lib.AxisProperty(i))
cls._internal_names_set.add(a)
if axes_are_reversed:
m = cls._AXIS_LEN - 1
for i, a in cls._AXIS_NAMES.items():
set_axis(a, m - i)
else:
for i, a in cls._AXIS_NAMES.items():
set_axis(a, i)
# addtl parms
if isinstance(ns, dict):
for k, v in ns.items():
setattr(cls, k, v)
def _construct_axes_dict(self, axes=None, **kwargs):
"""Return an axes dictionary for myself."""
d = dict([(a, self._get_axis(a)) for a in (axes or self._AXIS_ORDERS)])
d.update(kwargs)
return d
@staticmethod
def _construct_axes_dict_from(self, axes, **kwargs):
"""Return an axes dictionary for the passed axes."""
d = dict([(a, ax) for a, ax in zip(self._AXIS_ORDERS, axes)])
d.update(kwargs)
return d
def _construct_axes_dict_for_slice(self, axes=None, **kwargs):
"""Return an axes dictionary for myself."""
d = dict([(self._AXIS_SLICEMAP[a], self._get_axis(a))
for a in (axes or self._AXIS_ORDERS)])
d.update(kwargs)
return d
def _construct_axes_from_arguments(self, args, kwargs, require_all=False):
"""Construct and returns axes if supplied in args/kwargs.
If require_all, raise if all axis arguments are not supplied
return a tuple of (axes, kwargs).
"""
# construct the args
args = list(args)
for a in self._AXIS_ORDERS:
# if we have an alias for this axis
alias = self._AXIS_IALIASES.get(a)
if alias is not None:
if a in kwargs:
if alias in kwargs:
raise TypeError("arguments are mutually exclusive "
"for [%s,%s]" % (a, alias))
continue
if alias in kwargs:
kwargs[a] = kwargs.pop(alias)
continue
# look for a argument by position
if a not in kwargs:
try:
kwargs[a] = args.pop(0)
except IndexError:
if require_all:
raise TypeError("not enough/duplicate arguments "
"specified!")
axes = dict([(a, kwargs.pop(a, None)) for a in self._AXIS_ORDERS])
return axes, kwargs
@classmethod
def _from_axes(cls, data, axes, **kwargs):
# for construction from BlockManager
if isinstance(data, BlockManager):
return cls(data, **kwargs)
else:
if cls._AXIS_REVERSED:
axes = axes[::-1]
d = cls._construct_axes_dict_from(cls, axes, copy=False)
d.update(kwargs)
return cls(data, **d)
def _get_axis_number(self, axis):
axis = self._AXIS_ALIASES.get(axis, axis)
if is_integer(axis):
if axis in self._AXIS_NAMES:
return axis
else:
try:
return self._AXIS_NUMBERS[axis]
except:
pass
raise ValueError('No axis named {0} for object type {1}'
.format(axis, type(self)))
def _get_axis_name(self, axis):
axis = self._AXIS_ALIASES.get(axis, axis)
if isinstance(axis, string_types):
if axis in self._AXIS_NUMBERS:
return axis
else:
try:
return self._AXIS_NAMES[axis]
except:
pass
raise ValueError('No axis named {0} for object type {1}'
.format(axis, type(self)))
def _get_axis(self, axis):
name = self._get_axis_name(axis)
return getattr(self, name)
def _get_block_manager_axis(self, axis):
"""Map the axis to the block_manager axis."""
axis = self._get_axis_number(axis)
if self._AXIS_REVERSED:
m = self._AXIS_LEN - 1
return m - axis
return axis
def _get_axis_resolvers(self, axis):
# index or columns
axis_index = getattr(self, axis)
d = dict()
prefix = axis[0]
for i, name in enumerate(axis_index.names):
if name is not None:
key = level = name
else:
# prefix with 'i' or 'c' depending on the input axis
# e.g., you must do ilevel_0 for the 0th level of an unnamed
# multiiindex
key = '{prefix}level_{i}'.format(prefix=prefix, i=i)
level = i
level_values = axis_index.get_level_values(level)
s = level_values.to_series()
s.index = axis_index
d[key] = s
# put the index/columns itself in the dict
if isinstance(axis_index, MultiIndex):
dindex = axis_index
else:
dindex = axis_index.to_series()
d[axis] = dindex
return d
def _get_index_resolvers(self):
d = {}
for axis_name in self._AXIS_ORDERS:
d.update(self._get_axis_resolvers(axis_name))
return d
@property
def _info_axis(self):
return getattr(self, self._info_axis_name)
@property
def _stat_axis(self):
return getattr(self, self._stat_axis_name)
@property
def shape(self):
"""Return a tuple of axis dimensions"""
return tuple(len(self._get_axis(a)) for a in self._AXIS_ORDERS)
@property
def axes(self):
"""Return index label(s) of the internal NDFrame"""
# we do it this way because if we have reversed axes, then
# the block manager shows then reversed
return [self._get_axis(a) for a in self._AXIS_ORDERS]
@property
def ndim(self):
"""Number of axes / array dimensions"""
return self._data.ndim
@property
def size(self):
"""number of elements in the NDFrame"""
return np.prod(self.shape)
def _expand_axes(self, key):
new_axes = []
for k, ax in zip(key, self.axes):
if k not in ax:
if type(k) != ax.dtype.type:
ax = ax.astype('O')
new_axes.append(ax.insert(len(ax), k))
else:
new_axes.append(ax)
return new_axes
def set_axis(self, axis, labels):
""" public verson of axis assignment """
setattr(self, self._get_axis_name(axis), labels)
def _set_axis(self, axis, labels):
self._data.set_axis(axis, labels)
self._clear_item_cache()
_shared_docs['transpose'] = """
Permute the dimensions of the %(klass)s
Parameters
----------
args : %(args_transpose)s
copy : boolean, default False
Make a copy of the underlying data. Mixed-dtype data will
always result in a copy
Examples
--------
>>> p.transpose(2, 0, 1)
>>> p.transpose(2, 0, 1, copy=True)
Returns
-------
y : same as input
"""
@Appender(_shared_docs['transpose'] % _shared_doc_kwargs)
def transpose(self, *args, **kwargs):
# construct the args
axes, kwargs = self._construct_axes_from_arguments(args, kwargs,
require_all=True)
axes_names = tuple([self._get_axis_name(axes[a])
for a in self._AXIS_ORDERS])
axes_numbers = tuple([self._get_axis_number(axes[a])
for a in self._AXIS_ORDERS])
# we must have unique axes
if len(axes) != len(set(axes)):
raise ValueError('Must specify %s unique axes' % self._AXIS_LEN)
new_axes = self._construct_axes_dict_from(self, [self._get_axis(x)
for x in axes_names])
new_values = self.values.transpose(axes_numbers)
if kwargs.pop('copy', None) or (len(args) and args[-1]):
new_values = new_values.copy()
nv.validate_transpose_for_generic(self, kwargs)
return self._constructor(new_values, **new_axes).__finalize__(self)
def swapaxes(self, axis1, axis2, copy=True):
"""
Interchange axes and swap values axes appropriately
Returns
-------
y : same as input
"""
i = self._get_axis_number(axis1)
j = self._get_axis_number(axis2)
if i == j:
if copy:
return self.copy()
return self
mapping = {i: j, j: i}
new_axes = (self._get_axis(mapping.get(k, k))
for k in range(self._AXIS_LEN))
new_values = self.values.swapaxes(i, j)
if copy:
new_values = new_values.copy()
return self._constructor(new_values, *new_axes).__finalize__(self)
def pop(self, item):
"""
Return item and drop from frame. Raise KeyError if not found.
"""
result = self[item]
del self[item]
try:
result._reset_cacher()
except AttributeError:
pass
return result
def squeeze(self, **kwargs):
"""Squeeze length 1 dimensions."""
nv.validate_squeeze(tuple(), kwargs)
try:
return self.iloc[tuple([0 if len(a) == 1 else slice(None)
for a in self.axes])]
except:
return self
def swaplevel(self, i=-2, j=-1, axis=0):
"""
Swap levels i and j in a MultiIndex on a particular axis
Parameters
----------
i, j : int, string (can be mixed)
Level of index to be swapped. Can pass level name as string.
Returns
-------
swapped : type of caller (new object)
.. versionchanged:: 0.18.1
The indexes ``i`` and ``j`` are now optional, and default to
the two innermost levels of the index.
"""
axis = self._get_axis_number(axis)
result = self.copy()
labels = result._data.axes[axis]
result._data.set_axis(axis, labels.swaplevel(i, j))
return result
# ----------------------------------------------------------------------
# Rename
# TODO: define separate funcs for DataFrame, Series and Panel so you can
# get completion on keyword arguments.
_shared_docs['rename'] = """
Alter axes input function or functions. Function / dict values must be
unique (1-to-1). Labels not contained in a dict / Series will be left
as-is. Extra labels listed don't throw an error. Alternatively, change
``Series.name`` with a scalar value (Series only).
Parameters
----------
%(axes)s : scalar, list-like, dict-like or function, optional
Scalar or list-like will alter the ``Series.name`` attribute,
and raise on DataFrame or Panel.
dict-like or functions are transformations to apply to
that axis' values
copy : boolean, default True
Also copy underlying data
inplace : boolean, default False
Whether to return a new %(klass)s. If True then value of copy is
ignored.
Returns
-------
renamed : %(klass)s (new object)
See Also
--------
pandas.NDFrame.rename_axis
Examples
--------
>>> s = pd.Series([1, 2, 3])
>>> s
0 1
1 2
2 3
dtype: int64
>>> s.rename("my_name") # scalar, changes Series.name
0 1
1 2
2 3
Name: my_name, dtype: int64
>>> s.rename(lambda x: x ** 2) # function, changes labels
0 1
1 2
4 3
dtype: int64
>>> s.rename({1: 3, 2: 5}) # mapping, changes labels
0 1
3 2
5 3
dtype: int64
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(2)
...
TypeError: 'int' object is not callable
>>> df.rename(index=str, columns={"A": "a", "B": "c"})
a c
0 1 4
1 2 5
2 3 6
>>> df.rename(index=str, columns={"A": "a", "C": "c"})
a B
0 1 4
1 2 5
2 3 6
"""
@Appender(_shared_docs['rename'] % dict(axes='axes keywords for this'
' object', klass='NDFrame'))
def rename(self, *args, **kwargs):
axes, kwargs = self._construct_axes_from_arguments(args, kwargs)
copy = kwargs.pop('copy', True)
inplace = kwargs.pop('inplace', False)
if kwargs:
raise TypeError('rename() got an unexpected keyword '
'argument "{0}"'.format(list(kwargs.keys())[0]))
if com._count_not_none(*axes.values()) == 0:
raise TypeError('must pass an index to rename')
# renamer function if passed a dict
def _get_rename_function(mapper):
if isinstance(mapper, (dict, ABCSeries)):
def f(x):
if x in mapper:
return mapper[x]
else:
return x
else:
f = mapper
return f
self._consolidate_inplace()
result = self if inplace else self.copy(deep=copy)
# start in the axis order to eliminate too many copies
for axis in lrange(self._AXIS_LEN):
v = axes.get(self._AXIS_NAMES[axis])
if v is None:
continue
f = _get_rename_function(v)
baxis = self._get_block_manager_axis(axis)
result._data = result._data.rename_axis(f, axis=baxis, copy=copy)
result._clear_item_cache()
if inplace:
self._update_inplace(result._data)
else:
return result.__finalize__(self)
rename.__doc__ = _shared_docs['rename']
def rename_axis(self, mapper, axis=0, copy=True, inplace=False):
"""
Alter index and / or columns using input function or functions.
A scaler or list-like for ``mapper`` will alter the ``Index.name``
or ``MultiIndex.names`` attribute.
A function or dict for ``mapper`` will alter the labels.
Function / dict values must be unique (1-to-1). Labels not contained in
a dict / Series will be left as-is.
Parameters
----------
mapper : scalar, list-like, dict-like or function, optional
axis : int or string, default 0
copy : boolean, default True
Also copy underlying data
inplace : boolean, default False
Returns
-------
renamed : type of caller
See Also
--------
pandas.NDFrame.rename
pandas.Index.rename
Examples
--------
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename_axis("foo") # scalar, alters df.index.name
A B
foo
0 1 4
1 2 5
2 3 6
>>> df.rename_axis(lambda x: 2 * x) # function: alters labels
A B
0 1 4
2 2 5
4 3 6
>>> df.rename_axis({"A": "ehh", "C": "see"}, axis="columns") # mapping
ehh B
0 1 4
1 2 5
2 3 6
"""
non_mapper = is_scalar(mapper) or (is_list_like(mapper) and not
is_dict_like(mapper))
if non_mapper:
return self._set_axis_name(mapper, axis=axis)
else:
axis = self._get_axis_name(axis)
d = {'copy': copy, 'inplace': inplace}
d[axis] = mapper
return self.rename(**d)
def _set_axis_name(self, name, axis=0):
"""
Alter the name or names of the axis, returning self.
Parameters
----------
name : str or list of str
Name for the Index, or list of names for the MultiIndex
axis : int or str
0 or 'index' for the index; 1 or 'columns' for the columns
Returns
-------
renamed : type of caller
See Also
--------
pandas.DataFrame.rename
pandas.Series.rename
pandas.Index.rename
Examples
--------
>>> df._set_axis_name("foo")
A
foo
0 1
1 2
2 3
>>> df.index = pd.MultiIndex.from_product([['A'], ['a', 'b', 'c']])
>>> df._set_axis_name(["bar", "baz"])
A
bar baz
A a 1
b 2
c 3
"""
axis = self._get_axis_number(axis)
idx = self._get_axis(axis).set_names(name)
renamed = self.copy(deep=True)
renamed.set_axis(axis, idx)
return renamed
# ----------------------------------------------------------------------
# Comparisons
def _indexed_same(self, other):
return all([self._get_axis(a).equals(other._get_axis(a))
for a in self._AXIS_ORDERS])
def __neg__(self):
values = _values_from_object(self)
if values.dtype == np.bool_:
arr = operator.inv(values)
else:
arr = operator.neg(values)
return self.__array_wrap__(arr)
def __invert__(self):
try:
arr = operator.inv(_values_from_object(self))
return self.__array_wrap__(arr)
except:
# inv fails with 0 len
if not np.prod(self.shape):
return self
raise
def equals(self, other):
"""
Determines if two NDFrame objects contain the same elements. NaNs in
the same location are considered equal.
"""
if not isinstance(other, self._constructor):
return False
return self._data.equals(other._data)
# ----------------------------------------------------------------------
# Iteration
def __hash__(self):
raise TypeError('{0!r} objects are mutable, thus they cannot be'
' hashed'.format(self.__class__.__name__))
def __iter__(self):
"""Iterate over infor axis"""
return iter(self._info_axis)
# can we get a better explanation of this?
def keys(self):
"""Get the 'info axis' (see Indexing for more)
This is index for Series, columns for DataFrame and major_axis for
Panel.
"""
return self._info_axis
def iteritems(self):
"""Iterate over (label, values) on info axis
This is index for Series, columns for DataFrame, major_axis for Panel,
and so on.
"""
for h in self._info_axis:
yield h, self[h]
# originally used to get around 2to3's changes to iteritems.
# Now unnecessary. Sidenote: don't want to deprecate this for a while,
# otherwise libraries that use 2to3 will have issues.
def iterkv(self, *args, **kwargs):
"iteritems alias used to get around 2to3. Deprecated"
warnings.warn("iterkv is deprecated and will be removed in a future "
"release, use ``iteritems`` instead.", FutureWarning,
stacklevel=2)
return self.iteritems(*args, **kwargs)
def __len__(self):
"""Returns length of info axis"""
return len(self._info_axis)
def __contains__(self, key):
"""True if the key is in the info axis"""
return key in self._info_axis
@property
def empty(self):
"""True if NDFrame is entirely empty [no items], meaning any of the
axes are of length 0.
Notes
-----
If NDFrame contains only NaNs, it is still not considered empty. See
the example below.
Examples
--------
An example of an actual empty DataFrame. Notice the index is empty:
>>> df_empty = pd.DataFrame({'A' : []})
>>> df_empty
Empty DataFrame
Columns: [A]
Index: []
>>> df_empty.empty
True
If we only have NaNs in our DataFrame, it is not considered empty! We
will need to drop the NaNs to make the DataFrame empty:
>>> df = pd.DataFrame({'A' : [np.nan]})
>>> df
A
0 NaN
>>> df.empty
False
>>> df.dropna().empty
True
See also
--------
pandas.Series.dropna
pandas.DataFrame.dropna
"""
return not all(len(self._get_axis(a)) > 0 for a in self._AXIS_ORDERS)
def __nonzero__(self):
raise ValueError("The truth value of a {0} is ambiguous. "
"Use a.empty, a.bool(), a.item(), a.any() or a.all()."
.format(self.__class__.__name__))
__bool__ = __nonzero__
def bool(self):
"""Return the bool of a single element PandasObject.
This must be a boolean scalar value, either True or False. Raise a
ValueError if the PandasObject does not have exactly 1 element, or that
element is not boolean
"""
v = self.squeeze()
if isinstance(v, (bool, np.bool_)):
return bool(v)
elif is_scalar(v):
raise ValueError("bool cannot act on a non-boolean single element "
"{0}".format(self.__class__.__name__))
self.__nonzero__()
def __abs__(self):
return self.abs()
def __round__(self, decimals=0):
return self.round(decimals)
# ----------------------------------------------------------------------
# Array Interface
def __array__(self, dtype=None):
return _values_from_object(self)
def __array_wrap__(self, result, context=None):
d = self._construct_axes_dict(self._AXIS_ORDERS, copy=False)
return self._constructor(result, **d).__finalize__(self)
# ideally we would define this to avoid the getattr checks, but
# is slower
# @property
# def __array_interface__(self):
# """ provide numpy array interface method """
# values = self.values
# return dict(typestr=values.dtype.str,shape=values.shape,data=values)
def to_dense(self):
"""Return dense representation of NDFrame (as opposed to sparse)"""
# compat
return self
# ----------------------------------------------------------------------
# Picklability
def __getstate__(self):
meta = dict((k, getattr(self, k, None)) for k in self._metadata)
return dict(_data=self._data, _typ=self._typ, _metadata=self._metadata,
**meta)
def __setstate__(self, state):
if isinstance(state, BlockManager):
self._data = state
elif isinstance(state, dict):
typ = state.get('_typ')
if typ is not None:
# set in the order of internal names
# to avoid definitional recursion
# e.g. say fill_value needing _data to be
# defined
meta = set(self._internal_names + self._metadata)
for k in list(meta):
if k in state:
v = state[k]
object.__setattr__(self, k, v)
for k, v in state.items():
if k not in meta:
object.__setattr__(self, k, v)
else:
self._unpickle_series_compat(state)
elif isinstance(state[0], dict):
if len(state) == 5:
self._unpickle_sparse_frame_compat(state)
else:
self._unpickle_frame_compat(state)
elif len(state) == 4:
self._unpickle_panel_compat(state)
elif len(state) == 2:
self._unpickle_series_compat(state)
else: # pragma: no cover
# old pickling format, for compatibility
self._unpickle_matrix_compat(state)
self._item_cache = {}
# ----------------------------------------------------------------------
# IO
# ----------------------------------------------------------------------
# I/O Methods
def to_json(self, path_or_buf=None, orient=None, date_format='epoch',
double_precision=10, force_ascii=True, date_unit='ms',
default_handler=None, lines=False):
"""
Convert the object to a JSON string.
Note NaN's and None will be converted to null and datetime objects
will be converted to UNIX timestamps.
Parameters
----------
path_or_buf : the path or buffer to write the result string
if this is None, return a StringIO of the converted string
orient : string
* Series
- default is 'index'
- allowed values are: {'split','records','index'}
* DataFrame
- default is 'columns'
- allowed values are:
{'split','records','index','columns','values'}
* The format of the JSON string
- split : dict like
{index -> [index], columns -> [columns], data -> [values]}
- records : list like
[{column -> value}, ... , {column -> value}]
- index : dict like {index -> {column -> value}}
- columns : dict like {column -> {index -> value}}
- values : just the values array
date_format : {'epoch', 'iso'}
Type of date conversion. `epoch` = epoch milliseconds,
`iso`` = ISO8601, default is epoch.
double_precision : The number of decimal places to use when encoding
floating point values, default 10.
force_ascii : force encoded string to be ASCII, default True.
date_unit : string, default 'ms' (milliseconds)
The time unit to encode to, governs timestamp and ISO8601
precision. One of 's', 'ms', 'us', 'ns' for second, millisecond,
microsecond, and nanosecond respectively.
default_handler : callable, default None
Handler to call if object cannot otherwise be converted to a
suitable format for JSON. Should receive a single argument which is
the object to convert and return a serialisable object.
lines : boolean, defalut False
If 'orient' is 'records' write out line delimited json format. Will
throw ValueError if incorrect 'orient' since others are not list
like.
.. versionadded:: 0.19.0
Returns
-------
same type as input object with filtered info axis
"""
from pandas.io import json
return json.to_json(path_or_buf=path_or_buf, obj=self, orient=orient,
date_format=date_format,
double_precision=double_precision,
force_ascii=force_ascii, date_unit=date_unit,
default_handler=default_handler,
lines=lines)
def to_hdf(self, path_or_buf, key, **kwargs):
"""Write the contained data to an HDF5 file using HDFStore.
Parameters
----------
path_or_buf : the path (string) or HDFStore object
key : string
indentifier for the group in the store
mode : optional, {'a', 'w', 'r+'}, default 'a'
``'w'``
Write; a new file is created (an existing file with the same
name would be deleted).
``'a'``
Append; an existing file is opened for reading and writing,
and if the file does not exist it is created.
``'r+'``
It is similar to ``'a'``, but the file must already exist.
format : 'fixed(f)|table(t)', default is 'fixed'
fixed(f) : Fixed format
Fast writing/reading. Not-appendable, nor searchable
table(t) : Table format
Write as a PyTables Table structure which may perform
worse but allow more flexible operations like searching
/ selecting subsets of the data
append : boolean, default False
For Table formats, append the input data to the existing
data_columns : list of columns, or True, default None
List of columns to create as indexed data columns for on-disk
queries, or True to use all columns. By default only the axes
of the object are indexed. See `here
<http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns>`__.
Applicable only to format='table'.
complevel : int, 1-9, default 0
If a complib is specified compression will be applied
where possible
complib : {'zlib', 'bzip2', 'lzo', 'blosc', None}, default None
If complevel is > 0 apply compression to objects written
in the store wherever possible
fletcher32 : bool, default False
If applying compression use the fletcher32 checksum
dropna : boolean, default False.
If true, ALL nan rows will not be written to store.
"""
from pandas.io import pytables
return pytables.to_hdf(path_or_buf, key, self, **kwargs)
def to_msgpack(self, path_or_buf=None, encoding='utf-8', **kwargs):
"""
msgpack (serialize) object to input file path
THIS IS AN EXPERIMENTAL LIBRARY and the storage format
may not be stable until a future release.
Parameters
----------
path : string File path, buffer-like, or None
if None, return generated string
append : boolean whether to append to an existing msgpack
(default is False)
compress : type of compressor (zlib or blosc), default to None (no
compression)
"""
from pandas.io import packers
return packers.to_msgpack(path_or_buf, self, encoding=encoding,
**kwargs)
def to_sql(self, name, con, flavor=None, schema=None, if_exists='fail',
index=True, index_label=None, chunksize=None, dtype=None):
"""
Write records stored in a DataFrame to a SQL database.
Parameters
----------
name : string
Name of SQL table
con : SQLAlchemy engine or DBAPI2 connection (legacy mode)
Using SQLAlchemy makes it possible to use any DB supported by that
library. If a DBAPI2 object, only sqlite3 is supported.
flavor : 'sqlite', default None
DEPRECATED: this parameter will be removed in a future version,
as 'sqlite' is the only supported option if SQLAlchemy is not
installed.
schema : string, default None
Specify the schema (if database flavor supports this). If None, use
default schema.
if_exists : {'fail', 'replace', 'append'}, default 'fail'
- fail: If table exists, do nothing.
- replace: If table exists, drop it, recreate it, and insert data.
- append: If table exists, insert data. Create if does not exist.
index : boolean, default True
Write DataFrame index as a column.
index_label : string or sequence, default None
Column label for index column(s). If None is given (default) and
`index` is True, then the index names are used.
A sequence should be given if the DataFrame uses MultiIndex.
chunksize : int, default None
If not None, then rows will be written in batches of this size at a
time. If None, all rows will be written at once.
dtype : dict of column name to SQL type, default None
Optional specifying the datatype for columns. The SQL type should
be a SQLAlchemy type, or a string for sqlite3 fallback connection.
"""
from pandas.io import sql
sql.to_sql(self, name, con, flavor=flavor, schema=schema,
if_exists=if_exists, index=index, index_label=index_label,
chunksize=chunksize, dtype=dtype)
def to_pickle(self, path):
"""
Pickle (serialize) object to input file path.
Parameters
----------
path : string
File path
"""
from pandas.io.pickle import to_pickle
return to_pickle(self, path)
def to_clipboard(self, excel=None, sep=None, **kwargs):
"""
Attempt to write text representation of object to the system clipboard
This can be pasted into Excel, for example.
Parameters
----------
excel : boolean, defaults to True
if True, use the provided separator, writing in a csv
format for allowing easy pasting into excel.
if False, write a string representation of the object
to the clipboard
sep : optional, defaults to tab
other keywords are passed to to_csv
Notes
-----
Requirements for your platform
- Linux: xclip, or xsel (with gtk or PyQt4 modules)
- Windows: none
- OS X: none
"""
from pandas.io import clipboard
clipboard.to_clipboard(self, excel=excel, sep=sep, **kwargs)
def to_xarray(self):
"""
Return an xarray object from the pandas object.
Returns
-------
a DataArray for a Series
a Dataset for a DataFrame
a DataArray for higher dims
Examples
--------
>>> df = pd.DataFrame({'A' : [1, 1, 2],
'B' : ['foo', 'bar', 'foo'],
'C' : np.arange(4.,7)})
>>> df
A B C
0 1 foo 4.0
1 1 bar 5.0
2 2 foo 6.0
>>> df.to_xarray()
<xarray.Dataset>
Dimensions: (index: 3)
Coordinates:
* index (index) int64 0 1 2
Data variables:
A (index) int64 1 1 2
B (index) object 'foo' 'bar' 'foo'
C (index) float64 4.0 5.0 6.0
>>> df = pd.DataFrame({'A' : [1, 1, 2],
'B' : ['foo', 'bar', 'foo'],
'C' : np.arange(4.,7)}
).set_index(['B','A'])
>>> df
C
B A
foo 1 4.0
bar 1 5.0
foo 2 6.0
>>> df.to_xarray()
<xarray.Dataset>
Dimensions: (A: 2, B: 2)
Coordinates:
* B (B) object 'bar' 'foo'
* A (A) int64 1 2
Data variables:
C (B, A) float64 5.0 nan 4.0 6.0
>>> p = pd.Panel(np.arange(24).reshape(4,3,2),
items=list('ABCD'),
major_axis=pd.date_range('20130101', periods=3),
minor_axis=['first', 'second'])
>>> p
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: A to D
Major_axis axis: 2013-01-01 00:00:00 to 2013-01-03 00:00:00
Minor_axis axis: first to second
>>> p.to_xarray()
<xarray.DataArray (items: 4, major_axis: 3, minor_axis: 2)>
array([[[ 0, 1],
[ 2, 3],
[ 4, 5]],
[[ 6, 7],
[ 8, 9],
[10, 11]],
[[12, 13],
[14, 15],
[16, 17]],
[[18, 19],
[20, 21],
[22, 23]]])
Coordinates:
* items (items) object 'A' 'B' 'C' 'D'
* major_axis (major_axis) datetime64[ns] 2013-01-01 2013-01-02 2013-01-03 # noqa
* minor_axis (minor_axis) object 'first' 'second'
Notes
-----
See the `xarray docs <http://xarray.pydata.org/en/stable/>`__
"""
import xarray
if self.ndim == 1:
return xarray.DataArray.from_series(self)
elif self.ndim == 2:
return xarray.Dataset.from_dataframe(self)
# > 2 dims
coords = [(a, self._get_axis(a)) for a in self._AXIS_ORDERS]
return xarray.DataArray(self,
coords=coords,
)
# ----------------------------------------------------------------------
# Fancy Indexing
@classmethod
def _create_indexer(cls, name, indexer):
"""Create an indexer like _name in the class."""
if getattr(cls, name, None) is None:
iname = '_%s' % name
setattr(cls, iname, None)
def _indexer(self):
i = getattr(self, iname)
if i is None:
i = indexer(self, name)
setattr(self, iname, i)
return i
setattr(cls, name, property(_indexer, doc=indexer.__doc__))
# add to our internal names set
cls._internal_names_set.add(iname)
def get(self, key, default=None):
"""
Get item from object for given key (DataFrame column, Panel slice,
etc.). Returns default value if not found.
Parameters
----------
key : object
Returns
-------
value : type of items contained in object
"""
try:
return self[key]
except (KeyError, ValueError, IndexError):
return default
def __getitem__(self, item):
return self._get_item_cache(item)
def _get_item_cache(self, item):
"""Return the cached item, item represents a label indexer."""
cache = self._item_cache
res = cache.get(item)
if res is None:
values = self._data.get(item)
res = self._box_item_values(item, values)
cache[item] = res
res._set_as_cached(item, self)
# for a chain
res.is_copy = self.is_copy
return res
def _set_as_cached(self, item, cacher):
"""Set the _cacher attribute on the calling object with a weakref to
cacher.
"""
self._cacher = (item, weakref.ref(cacher))
def _reset_cacher(self):
"""Reset the cacher."""
if hasattr(self, '_cacher'):
del self._cacher
def _iget_item_cache(self, item):
"""Return the cached item, item represents a positional indexer."""
ax = self._info_axis
if ax.is_unique:
lower = self._get_item_cache(ax[item])
else:
lower = self.take(item, axis=self._info_axis_number, convert=True)
return lower
def _box_item_values(self, key, values):
raise AbstractMethodError(self)
def _maybe_cache_changed(self, item, value):
"""The object has called back to us saying maybe it has changed.
numpy < 1.8 has an issue with object arrays and aliasing
GH6026
"""
self._data.set(item, value, check=pd._np_version_under1p8)
@property
def _is_cached(self):
"""Return boolean indicating if self is cached or not."""
return getattr(self, '_cacher', None) is not None
def _get_cacher(self):
"""return my cacher or None"""
cacher = getattr(self, '_cacher', None)
if cacher is not None:
cacher = cacher[1]()
return cacher
@property
def _is_view(self):
"""Return boolean indicating if self is view of another array """
return self._data.is_view
def _maybe_update_cacher(self, clear=False, verify_is_copy=True):
"""
See if we need to update our parent cacher if clear, then clear our
cache.
Parameters
----------
clear : boolean, default False
clear the item cache
verify_is_copy : boolean, default True
provide is_copy checks
"""
cacher = getattr(self, '_cacher', None)
if cacher is not None:
ref = cacher[1]()
# we are trying to reference a dead referant, hence
# a copy
if ref is None:
del self._cacher
else:
try:
ref._maybe_cache_changed(cacher[0], self)
except:
pass
if verify_is_copy:
self._check_setitem_copy(stacklevel=5, t='referant')
if clear:
self._clear_item_cache()
def _clear_item_cache(self, i=None):
if i is not None:
self._item_cache.pop(i, None)
else:
self._item_cache.clear()
def _slice(self, slobj, axis=0, kind=None):
"""
Construct a slice of this container.
kind parameter is maintained for compatibility with Series slicing.
"""
axis = self._get_block_manager_axis(axis)
result = self._constructor(self._data.get_slice(slobj, axis=axis))
result = result.__finalize__(self)
# this could be a view
# but only in a single-dtyped view slicable case
is_copy = axis != 0 or result._is_view
result._set_is_copy(self, copy=is_copy)
return result
def _set_item(self, key, value):
self._data.set(key, value)
self._clear_item_cache()
def _set_is_copy(self, ref=None, copy=True):
if not copy:
self.is_copy = None
else:
if ref is not None:
self.is_copy = weakref.ref(ref)
else:
self.is_copy = None
def _check_is_chained_assignment_possible(self):
"""
Check if we are a view, have a cacher, and are of mixed type.
If so, then force a setitem_copy check.
Should be called just near setting a value
Will return a boolean if it we are a view and are cached, but a
single-dtype meaning that the cacher should be updated following
setting.
"""
if self._is_view and self._is_cached:
ref = self._get_cacher()
if ref is not None and ref._is_mixed_type:
self._check_setitem_copy(stacklevel=4, t='referant',
force=True)
return True
elif self.is_copy:
self._check_setitem_copy(stacklevel=4, t='referant')
return False
def _check_setitem_copy(self, stacklevel=4, t='setting', force=False):
"""
Parameters
----------
stacklevel : integer, default 4
the level to show of the stack when the error is output
t : string, the type of setting error
force : boolean, default False
if True, then force showing an error
validate if we are doing a settitem on a chained copy.
If you call this function, be sure to set the stacklevel such that the
user will see the error *at the level of setting*
It is technically possible to figure out that we are setting on
a copy even WITH a multi-dtyped pandas object. In other words, some
blocks may be views while other are not. Currently _is_view will ALWAYS
return False for multi-blocks to avoid having to handle this case.
df = DataFrame(np.arange(0,9), columns=['count'])
df['group'] = 'b'
# This technically need not raise SettingWithCopy if both are view
# (which is not # generally guaranteed but is usually True. However,
# this is in general not a good practice and we recommend using .loc.
df.iloc[0:5]['group'] = 'a'
"""
if force or self.is_copy:
value = config.get_option('mode.chained_assignment')
if value is None:
return
# see if the copy is not actually refererd; if so, then disolve
# the copy weakref
try:
gc.collect(2)
if not gc.get_referents(self.is_copy()):
self.is_copy = None
return
except:
pass
# we might be a false positive
try:
if self.is_copy().shape == self.shape:
self.is_copy = None
return
except:
pass
# a custom message
if isinstance(self.is_copy, string_types):
t = self.is_copy
elif t == 'referant':
t = ("\n"
"A value is trying to be set on a copy of a slice from a "
"DataFrame\n\n"
"See the caveats in the documentation: "
"http://pandas.pydata.org/pandas-docs/stable/"
"indexing.html#indexing-view-versus-copy"
)
else:
t = ("\n"
"A value is trying to be set on a copy of a slice from a "
"DataFrame.\n"
"Try using .loc[row_indexer,col_indexer] = value "
"instead\n\nSee the caveats in the documentation: "
"http://pandas.pydata.org/pandas-docs/stable/"
"indexing.html#indexing-view-versus-copy"
)
if value == 'raise':
raise SettingWithCopyError(t)
elif value == 'warn':
warnings.warn(t, SettingWithCopyWarning, stacklevel=stacklevel)
def __delitem__(self, key):
"""
Delete item
"""
deleted = False
maybe_shortcut = False
if hasattr(self, 'columns') and isinstance(self.columns, MultiIndex):
try:
maybe_shortcut = key not in self.columns._engine
except TypeError:
pass
if maybe_shortcut:
# Allow shorthand to delete all columns whose first len(key)
# elements match key:
if not isinstance(key, tuple):
key = (key, )
for col in self.columns:
if isinstance(col, tuple) and col[:len(key)] == key:
del self[col]
deleted = True
if not deleted:
# If the above loop ran and didn't delete anything because
# there was no match, this call should raise the appropriate
# exception:
self._data.delete(key)
# delete from the caches
try:
del self._item_cache[key]
except KeyError:
pass
def take(self, indices, axis=0, convert=True, is_copy=True, **kwargs):
"""
Analogous to ndarray.take
Parameters
----------
indices : list / array of ints
axis : int, default 0
convert : translate neg to pos indices (default)
is_copy : mark the returned frame as a copy
Returns
-------
taken : type of caller
"""
nv.validate_take(tuple(), kwargs)
self._consolidate_inplace()
new_data = self._data.take(indices,
axis=self._get_block_manager_axis(axis),
convert=True, verify=True)
result = self._constructor(new_data).__finalize__(self)
# maybe set copy if we didn't actually change the index
if is_copy:
if not result._get_axis(axis).equals(self._get_axis(axis)):
result._set_is_copy(self)
return result
def xs(self, key, axis=0, level=None, drop_level=True):
"""
Returns a cross-section (row(s) or column(s)) from the
Series/DataFrame. Defaults to cross-section on the rows (axis=0).
Parameters
----------
key : object
Some label contained in the index, or partially in a MultiIndex
axis : int, default 0
Axis to retrieve cross-section on
level : object, defaults to first n levels (n=1 or len(key))
In case of a key partially contained in a MultiIndex, indicate
which levels are used. Levels can be referred by label or position.
drop_level : boolean, default True
If False, returns object with same levels as self.
Examples
--------
>>> df
A B C
a 4 5 2
b 4 0 9
c 9 7 3
>>> df.xs('a')
A 4
B 5
C 2
Name: a
>>> df.xs('C', axis=1)
a 2
b 9
c 3
Name: C
>>> df
A B C D
first second third
bar one 1 4 1 8 9
two 1 7 5 5 0
baz one 1 6 6 8 0
three 2 5 3 5 3
>>> df.xs(('baz', 'three'))
A B C D
third
2 5 3 5 3
>>> df.xs('one', level=1)
A B C D
first third
bar 1 4 1 8 9
baz 1 6 6 8 0
>>> df.xs(('baz', 2), level=[0, 'third'])
A B C D
second
three 5 3 5 3
Returns
-------
xs : Series or DataFrame
Notes
-----
xs is only for getting, not setting values.
MultiIndex Slicers is a generic way to get/set values on any level or
levels. It is a superset of xs functionality, see
:ref:`MultiIndex Slicers <advanced.mi_slicers>`
"""
axis = self._get_axis_number(axis)
labels = self._get_axis(axis)
if level is not None:
loc, new_ax = labels.get_loc_level(key, level=level,
drop_level=drop_level)
# convert to a label indexer if needed
if isinstance(loc, slice):
lev_num = labels._get_level_number(level)
if labels.levels[lev_num].inferred_type == 'integer':
loc = labels[loc]
# create the tuple of the indexer
indexer = [slice(None)] * self.ndim
indexer[axis] = loc
indexer = tuple(indexer)
result = self.ix[indexer]
setattr(result, result._get_axis_name(axis), new_ax)
return result
if axis == 1:
return self[key]
self._consolidate_inplace()
index = self.index
if isinstance(index, MultiIndex):
loc, new_index = self.index.get_loc_level(key,
drop_level=drop_level)
else:
loc = self.index.get_loc(key)
if isinstance(loc, np.ndarray):
if loc.dtype == np.bool_:
inds, = loc.nonzero()
return self.take(inds, axis=axis, convert=False)
else:
return self.take(loc, axis=axis, convert=True)
if not is_scalar(loc):
new_index = self.index[loc]
if is_scalar(loc):
new_values = self._data.fast_xs(loc)
# may need to box a datelike-scalar
#
# if we encounter an array-like and we only have 1 dim
# that means that their are list/ndarrays inside the Series!
# so just return them (GH 6394)
if not is_list_like(new_values) or self.ndim == 1:
return _maybe_box_datetimelike(new_values)
result = self._constructor_sliced(
new_values, index=self.columns,
name=self.index[loc], dtype=new_values.dtype)
else:
result = self.iloc[loc]
result.index = new_index
# this could be a view
# but only in a single-dtyped view slicable case
result._set_is_copy(self, copy=not result._is_view)
return result
_xs = xs
# TODO: Check if this was clearer in 0.12
def select(self, crit, axis=0):
"""
Return data corresponding to axis labels matching criteria
Parameters
----------
crit : function
To be called on each index (label). Should return True or False
axis : int
Returns
-------
selection : type of caller
"""
axis = self._get_axis_number(axis)
axis_name = self._get_axis_name(axis)
axis_values = self._get_axis(axis)
if len(axis_values) > 0:
new_axis = axis_values[
np.asarray([bool(crit(label)) for label in axis_values])]
else:
new_axis = axis_values
return self.reindex(**{axis_name: new_axis})
def reindex_like(self, other, method=None, copy=True, limit=None,
tolerance=None):
"""Return an object with matching indices to myself.
Parameters
----------
other : Object
method : string or None
copy : boolean, default True
limit : int, default None
Maximum number of consecutive labels to fill for inexact matches.
tolerance : optional
Maximum distance between labels of the other object and this
object for inexact matches.
.. versionadded:: 0.17.0
Notes
-----
Like calling s.reindex(index=other.index, columns=other.columns,
method=...)
Returns
-------
reindexed : same as input
"""
d = other._construct_axes_dict(axes=self._AXIS_ORDERS, method=method,
copy=copy, limit=limit,
tolerance=tolerance)
return self.reindex(**d)
def drop(self, labels, axis=0, level=None, inplace=False, errors='raise'):
"""
Return new object with labels in requested axis removed.
Parameters
----------
labels : single label or list-like
axis : int or axis name
level : int or level name, default None
For MultiIndex
inplace : bool, default False
If True, do operation inplace and return None.
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
.. versionadded:: 0.16.1
Returns
-------
dropped : type of caller
"""
axis = self._get_axis_number(axis)
axis_name = self._get_axis_name(axis)
axis, axis_ = self._get_axis(axis), axis
if axis.is_unique:
if level is not None:
if not isinstance(axis, MultiIndex):
raise AssertionError('axis must be a MultiIndex')
new_axis = axis.drop(labels, level=level, errors=errors)
else:
new_axis = axis.drop(labels, errors=errors)
dropped = self.reindex(**{axis_name: new_axis})
try:
dropped.axes[axis_].set_names(axis.names, inplace=True)
except AttributeError:
pass
result = dropped
else:
labels = com._index_labels_to_array(labels)
if level is not None:
if not isinstance(axis, MultiIndex):
raise AssertionError('axis must be a MultiIndex')
indexer = ~axis.get_level_values(level).isin(labels)
else:
indexer = ~axis.isin(labels)
slicer = [slice(None)] * self.ndim
slicer[self._get_axis_number(axis_name)] = indexer
result = self.ix[tuple(slicer)]
if inplace:
self._update_inplace(result)
else:
return result
def _update_inplace(self, result, verify_is_copy=True):
"""
Replace self internals with result.
Parameters
----------
verify_is_copy : boolean, default True
provide is_copy checks
"""
# NOTE: This does *not* call __finalize__ and that's an explicit
# decision that we may revisit in the future.
self._reset_cache()
self._clear_item_cache()
self._data = getattr(result, '_data', result)
self._maybe_update_cacher(verify_is_copy=verify_is_copy)
def add_prefix(self, prefix):
"""
Concatenate prefix string with panel items names.
Parameters
----------
prefix : string
Returns
-------
with_prefix : type of caller
"""
new_data = self._data.add_prefix(prefix)
return self._constructor(new_data).__finalize__(self)
def add_suffix(self, suffix):
"""
Concatenate suffix string with panel items names.
Parameters
----------
suffix : string
Returns
-------
with_suffix : type of caller
"""
new_data = self._data.add_suffix(suffix)
return self._constructor(new_data).__finalize__(self)
_shared_docs['sort_values'] = """
Sort by the values along either axis
.. versionadded:: 0.17.0
Parameters
----------%(optional_by)s
axis : %(axes_single_arg)s, default 0
Axis to direct sorting
ascending : bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
inplace : bool, default False
if True, perform operation in-place
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
`first` puts NaNs at the beginning, `last` puts NaNs at the end
Returns
-------
sorted_obj : %(klass)s
"""
def sort_values(self, by, axis=0, ascending=True, inplace=False,
kind='quicksort', na_position='last'):
raise AbstractMethodError(self)
_shared_docs['sort_index'] = """
Sort object by labels (along an axis)
Parameters
----------
axis : %(axes)s to direct sorting
level : int or level name or list of ints or list of level names
if not None, sort on values in specified index level(s)
ascending : boolean, default True
Sort ascending vs. descending
inplace : bool, default False
if True, perform operation in-place
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
`first` puts NaNs at the beginning, `last` puts NaNs at the end
sort_remaining : bool, default True
if true and sorting by level and index is multilevel, sort by other
levels too (in order) after sorting by specified level
Returns
-------
sorted_obj : %(klass)s
"""
@Appender(_shared_docs['sort_index'] % dict(axes="axes", klass="NDFrame"))
def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
kind='quicksort', na_position='last', sort_remaining=True):
axis = self._get_axis_number(axis)
axis_name = self._get_axis_name(axis)
labels = self._get_axis(axis)
if level is not None:
raise NotImplementedError("level is not implemented")
if inplace:
raise NotImplementedError("inplace is not implemented")
sort_index = labels.argsort()
if not ascending:
sort_index = sort_index[::-1]
new_axis = labels.take(sort_index)
return self.reindex(**{axis_name: new_axis})
_shared_docs['reindex'] = """
Conform %(klass)s to new index with optional filling logic, placing
NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
copy=False
Parameters
----------
%(axes)s : array-like, optional (can be specified in order, or as
keywords)
New labels / index to conform to. Preferably an Index object to
avoid duplicating data
method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}, optional
method to use for filling holes in reindexed DataFrame.
Please note: this is only applicable to DataFrames/Series with a
monotonically increasing/decreasing index.
* default: don't fill gaps
* pad / ffill: propagate last valid observation forward to next
valid
* backfill / bfill: use next valid observation to fill gap
* nearest: use nearest valid observations to fill gap
copy : boolean, default True
Return a new object, even if the passed indexes are the same
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
fill_value : scalar, default np.NaN
Value to use for missing values. Defaults to NaN, but can be any
"compatible" value
limit : int, default None
Maximum number of consecutive elements to forward or backward fill
tolerance : optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations most
satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
.. versionadded:: 0.17.0
Examples
--------
Create a dataframe with some fictional data.
>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> df = pd.DataFrame({
... 'http_status': [200,200,404,404,301],
... 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
... index=index)
>>> df
http_status response_time
Firefox 200 0.04
Chrome 200 0.02
Safari 404 0.07
IE10 404 0.08
Konqueror 301 1.00
Create a new index and reindex the dataframe. By default
values in the new index that do not have corresponding
records in the dataframe are assigned ``NaN``.
>>> new_index= ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
... 'Chrome']
>>> df.reindex(new_index)
http_status response_time
Safari 404 0.07
Iceweasel NaN NaN
Comodo Dragon NaN NaN
IE10 404 0.08
Chrome 200 0.02
We can fill in the missing values by passing a value to
the keyword ``fill_value``. Because the index is not monotonically
increasing or decreasing, we cannot use arguments to the keyword
``method`` to fill the ``NaN`` values.
>>> df.reindex(new_index, fill_value=0)
http_status response_time
Safari 404 0.07
Iceweasel 0 0.00
Comodo Dragon 0 0.00
IE10 404 0.08
Chrome 200 0.02
>>> df.reindex(new_index, fill_value='missing')
http_status response_time
Safari 404 0.07
Iceweasel missing missing
Comodo Dragon missing missing
IE10 404 0.08
Chrome 200 0.02
To further illustrate the filling functionality in
``reindex``, we will create a dataframe with a
monotonically increasing index (for example, a sequence
of dates).
>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
... index=date_index)
>>> df2
prices
2010-01-01 100
2010-01-02 101
2010-01-03 NaN
2010-01-04 100
2010-01-05 89
2010-01-06 88
Suppose we decide to expand the dataframe to cover a wider
date range.
>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2)
prices
2009-12-29 NaN
2009-12-30 NaN
2009-12-31 NaN
2010-01-01 100
2010-01-02 101
2010-01-03 NaN
2010-01-04 100
2010-01-05 89
2010-01-06 88
2010-01-07 NaN
The index entries that did not have a value in the original data frame
(for example, '2009-12-29') are by default filled with ``NaN``.
If desired, we can fill in the missing values using one of several
options.
For example, to backpropagate the last valid value to fill the ``NaN``
values, pass ``bfill`` as an argument to the ``method`` keyword.
>>> df2.reindex(date_index2, method='bfill')
prices
2009-12-29 100
2009-12-30 100
2009-12-31 100
2010-01-01 100
2010-01-02 101
2010-01-03 NaN
2010-01-04 100
2010-01-05 89
2010-01-06 88
2010-01-07 NaN
Please note that the ``NaN`` value present in the original dataframe
(at index value 2010-01-03) will not be filled by any of the
value propagation schemes. This is because filling while reindexing
does not look at dataframe values, but only compares the original and
desired indexes. If you do want to fill in the ``NaN`` values present
in the original dataframe, use the ``fillna()`` method.
Returns
-------
reindexed : %(klass)s
"""
# TODO: Decide if we care about having different examples for different
# kinds
@Appender(_shared_docs['reindex'] % dict(axes="axes", klass="NDFrame"))
def reindex(self, *args, **kwargs):
# construct the args
axes, kwargs = self._construct_axes_from_arguments(args, kwargs)
method = missing.clean_reindex_fill_method(kwargs.pop('method', None))
level = kwargs.pop('level', None)
copy = kwargs.pop('copy', True)
limit = kwargs.pop('limit', None)
tolerance = kwargs.pop('tolerance', None)
fill_value = kwargs.pop('fill_value', np.nan)
if kwargs:
raise TypeError('reindex() got an unexpected keyword '
'argument "{0}"'.format(list(kwargs.keys())[0]))
self._consolidate_inplace()
# if all axes that are requested to reindex are equal, then only copy
# if indicated must have index names equal here as well as values
if all([self._get_axis(axis).identical(ax)
for axis, ax in axes.items() if ax is not None]):
if copy:
return self.copy()
return self
# check if we are a multi reindex
if self._needs_reindex_multi(axes, method, level):
try:
return self._reindex_multi(axes, copy, fill_value)
except:
pass
# perform the reindex on the axes
return self._reindex_axes(axes, level, limit, tolerance, method,
fill_value, copy).__finalize__(self)
def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,
copy):
"""Perform the reindex for all the axes."""
obj = self
for a in self._AXIS_ORDERS:
labels = axes[a]
if labels is None:
continue
ax = self._get_axis(a)
new_index, indexer = ax.reindex(labels, level=level, limit=limit,
tolerance=tolerance, method=method)
axis = self._get_axis_number(a)
obj = obj._reindex_with_indexers({axis: [new_index, indexer]},
fill_value=fill_value,
copy=copy, allow_dups=False)
return obj
def _needs_reindex_multi(self, axes, method, level):
"""Check if we do need a multi reindex."""
return ((com._count_not_none(*axes.values()) == self._AXIS_LEN) and
method is None and level is None and not self._is_mixed_type)
def _reindex_multi(self, axes, copy, fill_value):
return NotImplemented
_shared_docs[
'reindex_axis'] = ("""Conform input object to new index with optional
filling logic, placing NA/NaN in locations having no value in the
previous index. A new object is produced unless the new index is
equivalent to the current one and copy=False
Parameters
----------
labels : array-like
New labels / index to conform to. Preferably an Index object to
avoid duplicating data
axis : %(axes_single_arg)s
method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}, optional
Method to use for filling holes in reindexed DataFrame:
* default: don't fill gaps
* pad / ffill: propagate last valid observation forward to next
valid
* backfill / bfill: use next valid observation to fill gap
* nearest: use nearest valid observations to fill gap
copy : boolean, default True
Return a new object, even if the passed indexes are the same
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
limit : int, default None
Maximum number of consecutive elements to forward or backward fill
tolerance : optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations most
satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
.. versionadded:: 0.17.0
Examples
--------
>>> df.reindex_axis(['A', 'B', 'C'], axis=1)
See Also
--------
reindex, reindex_like
Returns
-------
reindexed : %(klass)s
""")
@Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs)
def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True,
limit=None, fill_value=np.nan):
self._consolidate_inplace()
axis_name = self._get_axis_name(axis)
axis_values = self._get_axis(axis_name)
method = missing.clean_reindex_fill_method(method)
new_index, indexer = axis_values.reindex(labels, method, level,
limit=limit)
return self._reindex_with_indexers({axis: [new_index, indexer]},
fill_value=fill_value, copy=copy)
def _reindex_with_indexers(self, reindexers, fill_value=np.nan, copy=False,
allow_dups=False):
"""allow_dups indicates an internal call here """
# reindex doing multiple operations on different axes if indicated
new_data = self._data
for axis in sorted(reindexers.keys()):
index, indexer = reindexers[axis]
baxis = self._get_block_manager_axis(axis)
if index is None:
continue
index = _ensure_index(index)
if indexer is not None:
indexer = _ensure_int64(indexer)
# TODO: speed up on homogeneous DataFrame objects
new_data = new_data.reindex_indexer(index, indexer, axis=baxis,
fill_value=fill_value,
allow_dups=allow_dups,
copy=copy)
if copy and new_data is self._data:
new_data = new_data.copy()
return self._constructor(new_data).__finalize__(self)
def _reindex_axis(self, new_index, fill_method, axis, copy):
new_data = self._data.reindex_axis(new_index, axis=axis,
method=fill_method, copy=copy)
if new_data is self._data and not copy:
return self
else:
return self._constructor(new_data).__finalize__(self)
def filter(self, items=None, like=None, regex=None, axis=None):
"""
Subset rows or columns of dataframe according to labels in
the specified index.
Note that this routine does not filter a dataframe on its
contents. The filter is applied to the labels of the index.
Parameters
----------
items : list-like
List of info axis to restrict to (must not all be present)
like : string
Keep info axis where "arg in col == True"
regex : string (regular expression)
Keep info axis with re.search(regex, col) == True
axis : int or string axis name
The axis to filter on. By default this is the info axis,
'index' for Series, 'columns' for DataFrame
Returns
-------
same type as input object
Examples
--------
>>> df
one two three
mouse 1 2 3
rabbit 4 5 6
>>> # select columns by name
>>> df.filter(items=['one', 'three'])
one three
mouse 1 3
rabbit 4 6
>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
one three
mouse 1 3
rabbit 4 6
>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
one two three
rabbit 4 5 6
See Also
--------
pandas.DataFrame.select
Notes
-----
The ``items``, ``like``, and ``regex`` parameters are
enforced to be mutually exclusive.
``axis`` defaults to the info axis that is used when indexing
with ``[]``.
"""
import re
nkw = sum([x is not None for x in [items, like, regex]])
if nkw > 1:
raise TypeError('Keyword arguments `items`, `like`, or `regex` '
'are mutually exclusive')
if axis is None:
axis = self._info_axis_name
axis_name = self._get_axis_name(axis)
axis_values = self._get_axis(axis_name)
if items is not None:
return self.reindex(**{axis_name:
[r for r in items if r in axis_values]})
elif like:
matchf = lambda x: (like in x if isinstance(x, string_types) else
like in str(x))
return self.select(matchf, axis=axis_name)
elif regex:
matcher = re.compile(regex)
return self.select(lambda x: matcher.search(str(x)) is not None,
axis=axis_name)
else:
raise TypeError('Must pass either `items`, `like`, or `regex`')
def head(self, n=5):
"""
Returns first n rows
"""
return self.iloc[:n]
def tail(self, n=5):
"""
Returns last n rows
"""
if n == 0:
return self.iloc[0:0]
return self.iloc[-n:]
def sample(self, n=None, frac=None, replace=False, weights=None,
random_state=None, axis=None):
"""
Returns a random sample of items from an axis of object.
.. versionadded:: 0.16.1
Parameters
----------
n : int, optional
Number of items from axis to return. Cannot be used with `frac`.
Default = 1 if `frac` = None.
frac : float, optional
Fraction of axis items to return. Cannot be used with `n`.
replace : boolean, optional
Sample with or without replacement. Default = False.
weights : str or ndarray-like, optional
Default 'None' results in equal probability weighting.
If passed a Series, will align with target object on index. Index
values in weights not found in sampled object will be ignored and
index values in sampled object not in weights will be assigned
weights of zero.
If called on a DataFrame, will accept the name of a column
when axis = 0.
Unless weights are a Series, weights must be same length as axis
being sampled.
If weights do not sum to 1, they will be normalized to sum to 1.
Missing values in the weights column will be treated as zero.
inf and -inf values not allowed.
random_state : int or numpy.random.RandomState, optional
Seed for the random number generator (if int), or numpy RandomState
object.
axis : int or string, optional
Axis to sample. Accepts axis number or name. Default is stat axis
for given data type (0 for Series and DataFrames, 1 for Panels).
Returns
-------
A new object of same type as caller.
Examples
--------
Generate an example ``Series`` and ``DataFrame``:
>>> s = pd.Series(np.random.randn(50))
>>> s.head()
0 -0.038497
1 1.820773
2 -0.972766
3 -1.598270
4 -1.095526
dtype: float64
>>> df = pd.DataFrame(np.random.randn(50, 4), columns=list('ABCD'))
>>> df.head()
A B C D
0 0.016443 -2.318952 -0.566372 -1.028078
1 -1.051921 0.438836 0.658280 -0.175797
2 -1.243569 -0.364626 -0.215065 0.057736
3 1.768216 0.404512 -0.385604 -1.457834
4 1.072446 -1.137172 0.314194 -0.046661
Next extract a random sample from both of these objects...
3 random elements from the ``Series``:
>>> s.sample(n=3)
27 -0.994689
55 -1.049016
67 -0.224565
dtype: float64
And a random 10% of the ``DataFrame`` with replacement:
>>> df.sample(frac=0.1, replace=True)
A B C D
35 1.981780 0.142106 1.817165 -0.290805
49 -1.336199 -0.448634 -0.789640 0.217116
40 0.823173 -0.078816 1.009536 1.015108
15 1.421154 -0.055301 -1.922594 -0.019696
6 -0.148339 0.832938 1.787600 -1.383767
"""
if axis is None:
axis = self._stat_axis_number
axis = self._get_axis_number(axis)
axis_length = self.shape[axis]
# Process random_state argument
rs = com._random_state(random_state)
# Check weights for compliance
if weights is not None:
# If a series, align with frame
if isinstance(weights, pd.Series):
weights = weights.reindex(self.axes[axis])
# Strings acceptable if a dataframe and axis = 0
if isinstance(weights, string_types):
if isinstance(self, pd.DataFrame):
if axis == 0:
try:
weights = self[weights]
except KeyError:
raise KeyError("String passed to weights not a "
"valid column")
else:
raise ValueError("Strings can only be passed to "
"weights when sampling from rows on "
"a DataFrame")
else:
raise ValueError("Strings cannot be passed as weights "
"when sampling from a Series or Panel.")
weights = pd.Series(weights, dtype='float64')
if len(weights) != axis_length:
raise ValueError("Weights and axis to be sampled must be of "
"same length")
if (weights == np.inf).any() or (weights == -np.inf).any():
raise ValueError("weight vector may not include `inf` values")
if (weights < 0).any():
raise ValueError("weight vector many not include negative "
"values")
# If has nan, set to zero.
weights = weights.fillna(0)
# Renormalize if don't sum to 1
if weights.sum() != 1:
if weights.sum() != 0:
weights = weights / weights.sum()
else:
raise ValueError("Invalid weights: weights sum to zero")
weights = weights.values
# If no frac or n, default to n=1.
if n is None and frac is None:
n = 1
elif n is not None and frac is None and n % 1 != 0:
raise ValueError("Only integers accepted as `n` values")
elif n is None and frac is not None:
n = int(round(frac * axis_length))
elif n is not None and frac is not None:
raise ValueError('Please enter a value for `frac` OR `n`, not '
'both')
# Check for negative sizes
if n < 0:
raise ValueError("A negative number of rows requested. Please "
"provide positive value.")
locs = rs.choice(axis_length, size=n, replace=replace, p=weights)
return self.take(locs, axis=axis, is_copy=False)
_shared_docs['pipe'] = ("""
Apply func(self, \*args, \*\*kwargs)
.. versionadded:: 0.16.2
Parameters
----------
func : function
function to apply to the %(klass)s.
``args``, and ``kwargs`` are passed into ``func``.
Alternatively a ``(callable, data_keyword)`` tuple where
``data_keyword`` is a string indicating the keyword of
``callable`` that expects the %(klass)s.
args : positional arguments passed into ``func``.
kwargs : a dictionary of keyword arguments passed into ``func``.
Returns
-------
object : the return type of ``func``.
Notes
-----
Use ``.pipe`` when chaining together functions that expect
on Series or DataFrames. Instead of writing
>>> f(g(h(df), arg1=a), arg2=b, arg3=c)
You can write
>>> (df.pipe(h)
... .pipe(g, arg1=a)
... .pipe(f, arg2=b, arg3=c)
... )
If you have a function that takes the data as (say) the second
argument, pass a tuple indicating which keyword expects the
data. For example, suppose ``f`` takes its data as ``arg2``:
>>> (df.pipe(h)
... .pipe(g, arg1=a)
... .pipe((f, 'arg2'), arg1=a, arg3=c)
... )
See Also
--------
pandas.DataFrame.apply
pandas.DataFrame.applymap
pandas.Series.map
""")
@Appender(_shared_docs['pipe'] % _shared_doc_kwargs)
def pipe(self, func, *args, **kwargs):
if isinstance(func, tuple):
func, target = func
if target in kwargs:
raise ValueError('%s is both the pipe target and a keyword '
'argument' % target)
kwargs[target] = self
return func(*args, **kwargs)
else:
return func(self, *args, **kwargs)
# ----------------------------------------------------------------------
# Attribute access
def __finalize__(self, other, method=None, **kwargs):
"""
Propagate metadata from other to self.
Parameters
----------
other : the object from which to get the attributes that we are going
to propagate
method : optional, a passed method name ; possibly to take different
types of propagation actions based on this
"""
if isinstance(other, NDFrame):
for name in self._metadata:
object.__setattr__(self, name, getattr(other, name, None))
return self
def __getattr__(self, name):
"""After regular attribute access, try looking up the name
This allows simpler access to columns for interactive use.
"""
# Note: obj.x will always call obj.__getattribute__('x') prior to
# calling obj.__getattr__('x').
if (name in self._internal_names_set or name in self._metadata or
name in self._accessors):
return object.__getattribute__(self, name)
else:
if name in self._info_axis:
return self[name]
return object.__getattribute__(self, name)
def __setattr__(self, name, value):
"""After regular attribute access, try setting the name
This allows simpler access to columns for interactive use.
"""
# first try regular attribute access via __getattribute__, so that
# e.g. ``obj.x`` and ``obj.x = 4`` will always reference/modify
# the same attribute.
try:
object.__getattribute__(self, name)
return object.__setattr__(self, name, value)
except AttributeError:
pass
# if this fails, go on to more involved attribute setting
# (note that this matches __getattr__, above).
if name in self._internal_names_set:
object.__setattr__(self, name, value)
elif name in self._metadata:
object.__setattr__(self, name, value)
else:
try:
existing = getattr(self, name)
if isinstance(existing, Index):
object.__setattr__(self, name, value)
elif name in self._info_axis:
self[name] = value
else:
object.__setattr__(self, name, value)
except (AttributeError, TypeError):
object.__setattr__(self, name, value)
# ----------------------------------------------------------------------
# Getting and setting elements
# ----------------------------------------------------------------------
# Consolidation of internals
def _protect_consolidate(self, f):
"""Consolidate _data -- if the blocks have changed, then clear the
cache
"""
blocks_before = len(self._data.blocks)
result = f()
if len(self._data.blocks) != blocks_before:
self._clear_item_cache()
return result
def _consolidate_inplace(self):
"""Consolidate data in place and return None"""
def f():
self._data = self._data.consolidate()
self._protect_consolidate(f)
def consolidate(self, inplace=False):
"""
Compute NDFrame with "consolidated" internals (data of each dtype
grouped together in a single ndarray). Mainly an internal API function,
but available here to the savvy user
Parameters
----------
inplace : boolean, default False
If False return new object, otherwise modify existing object
Returns
-------
consolidated : type of caller
"""
if inplace:
self._consolidate_inplace()
else:
f = lambda: self._data.consolidate()
cons_data = self._protect_consolidate(f)
return self._constructor(cons_data).__finalize__(self)
@property
def _is_mixed_type(self):
f = lambda: self._data.is_mixed_type
return self._protect_consolidate(f)
@property
def _is_numeric_mixed_type(self):
f = lambda: self._data.is_numeric_mixed_type
return self._protect_consolidate(f)
@property
def _is_datelike_mixed_type(self):
f = lambda: self._data.is_datelike_mixed_type
return self._protect_consolidate(f)
def _check_inplace_setting(self, value):
""" check whether we allow in-place setting with this type of value """
if self._is_mixed_type:
if not self._is_numeric_mixed_type:
# allow an actual np.nan thru
try:
if np.isnan(value):
return True
except:
pass
raise TypeError('Cannot do inplace boolean setting on '
'mixed-types with a non np.nan value')
return True
def _get_numeric_data(self):
return self._constructor(
self._data.get_numeric_data()).__finalize__(self)
def _get_bool_data(self):
return self._constructor(self._data.get_bool_data()).__finalize__(self)
# ----------------------------------------------------------------------
# Internal Interface Methods
def as_matrix(self, columns=None):
"""
Convert the frame to its Numpy-array representation.
Parameters
----------
columns: list, optional, default:None
If None, return all columns, otherwise, returns specified columns.
Returns
-------
values : ndarray
If the caller is heterogeneous and contains booleans or objects,
the result will be of dtype=object. See Notes.
Notes
-----
Return is NOT a Numpy-matrix, rather, a Numpy-array.
The dtype will be a lower-common-denominator dtype (implicit
upcasting); that is to say if the dtypes (even of numeric types)
are mixed, the one that accommodates all will be chosen. Use this
with care if you are not dealing with the blocks.
e.g. If the dtypes are float16 and float32, dtype will be upcast to
float32. If dtypes are int32 and uint8, dtype will be upcase to
int32. By numpy.find_common_type convention, mixing int64 and uint64
will result in a flot64 dtype.
This method is provided for backwards compatibility. Generally,
it is recommended to use '.values'.
See Also
--------
pandas.DataFrame.values
"""
self._consolidate_inplace()
if self._AXIS_REVERSED:
return self._data.as_matrix(columns).T
return self._data.as_matrix(columns)
@property
def values(self):
"""Numpy representation of NDFrame
Notes
-----
The dtype will be a lower-common-denominator dtype (implicit
upcasting); that is to say if the dtypes (even of numeric types)
are mixed, the one that accommodates all will be chosen. Use this
with care if you are not dealing with the blocks.
e.g. If the dtypes are float16 and float32, dtype will be upcast to
float32. If dtypes are int32 and uint8, dtype will be upcast to
int32. By numpy.find_common_type convention, mixing int64 and uint64
will result in a flot64 dtype.
"""
return self.as_matrix()
@property
def _values(self):
"""internal implementation"""
return self.values
@property
def _get_values(self):
# compat
return self.as_matrix()
def get_values(self):
"""same as values (but handles sparseness conversions)"""
return self.as_matrix()
def get_dtype_counts(self):
"""Return the counts of dtypes in this object."""
from pandas import Series
return Series(self._data.get_dtype_counts())
def get_ftype_counts(self):
"""Return the counts of ftypes in this object."""
from pandas import Series
return Series(self._data.get_ftype_counts())
@property
def dtypes(self):
"""Return the dtypes in this object."""
from pandas import Series
return Series(self._data.get_dtypes(), index=self._info_axis,
dtype=np.object_)
@property
def ftypes(self):
"""
Return the ftypes (indication of sparse/dense and dtype)
in this object.
"""
from pandas import Series
return Series(self._data.get_ftypes(), index=self._info_axis,
dtype=np.object_)
def as_blocks(self, copy=True):
"""
Convert the frame to a dict of dtype -> Constructor Types that each has
a homogeneous dtype.
NOTE: the dtypes of the blocks WILL BE PRESERVED HERE (unlike in
as_matrix)
Parameters
----------
copy : boolean, default True
.. versionadded: 0.16.1
Returns
-------
values : a dict of dtype -> Constructor Types
"""
self._consolidate_inplace()
bd = {}
for b in self._data.blocks:
bd.setdefault(str(b.dtype), []).append(b)
result = {}
for dtype, blocks in bd.items():
# Must combine even after consolidation, because there may be
# sparse items which are never consolidated into one block.
combined = self._data.combine(blocks, copy=copy)
result[dtype] = self._constructor(combined).__finalize__(self)
return result
@property
def blocks(self):
"""Internal property, property synonym for as_blocks()"""
return self.as_blocks()
def astype(self, dtype, copy=True, raise_on_error=True, **kwargs):
"""
Cast object to input numpy.dtype
Return a copy when copy = True (be really careful with this!)
Parameters
----------
dtype : data type, or dict of column name -> data type
Use a numpy.dtype or Python type to cast entire pandas object to
the same type. Alternatively, use {col: dtype, ...}, where col is a
column label and dtype is a numpy.dtype or Python type to cast one
or more of the DataFrame's columns to column-specific types.
raise_on_error : raise on invalid input
kwargs : keyword arguments to pass on to the constructor
Returns
-------
casted : type of caller
"""
if isinstance(dtype, collections.Mapping):
if self.ndim == 1: # i.e. Series
if len(dtype) > 1 or list(dtype.keys())[0] != self.name:
raise KeyError('Only the Series name can be used for '
'the key in Series dtype mappings.')
new_type = list(dtype.values())[0]
return self.astype(new_type, copy, raise_on_error, **kwargs)
elif self.ndim > 2:
raise NotImplementedError(
'astype() only accepts a dtype arg of type dict when '
'invoked on Series and DataFrames. A single dtype must be '
'specified when invoked on a Panel.'
)
for col_name in dtype.keys():
if col_name not in self:
raise KeyError('Only a column name can be used for the '
'key in a dtype mappings argument.')
from pandas import concat
results = []
for col_name, col in self.iteritems():
if col_name in dtype:
results.append(col.astype(dtype[col_name], copy=copy))
else:
results.append(results.append(col.copy() if copy else col))
return concat(results, axis=1, copy=False)
# else, only a single dtype is given
new_data = self._data.astype(dtype=dtype, copy=copy,
raise_on_error=raise_on_error, **kwargs)
return self._constructor(new_data).__finalize__(self)
def copy(self, deep=True):
"""
Make a copy of this objects data.
Parameters
----------
deep : boolean or string, default True
Make a deep copy, including a copy of the data and the indices.
With ``deep=False`` neither the indices or the data are copied.
Note that when ``deep=True`` data is copied, actual python objects
will not be copied recursively, only the reference to the object.
This is in contrast to ``copy.deepcopy`` in the Standard Library,
which recursively copies object data.
Returns
-------
copy : type of caller
"""
data = self._data.copy(deep=deep)
return self._constructor(data).__finalize__(self)
def _convert(self, datetime=False, numeric=False, timedelta=False,
coerce=False, copy=True):
"""
Attempt to infer better dtype for object columns
Parameters
----------
datetime : boolean, default False
If True, convert to date where possible.
numeric : boolean, default False
If True, attempt to convert to numbers (including strings), with
unconvertible values becoming NaN.
timedelta : boolean, default False
If True, convert to timedelta where possible.
coerce : boolean, default False
If True, force conversion with unconvertible values converted to
nulls (NaN or NaT)
copy : boolean, default True
If True, return a copy even if no copy is necessary (e.g. no
conversion was done). Note: This is meant for internal use, and
should not be confused with inplace.
Returns
-------
converted : same as input object
"""
return self._constructor(
self._data.convert(datetime=datetime, numeric=numeric,
timedelta=timedelta, coerce=coerce,
copy=copy)).__finalize__(self)
# TODO: Remove in 0.18 or 2017, which ever is sooner
def convert_objects(self, convert_dates=True, convert_numeric=False,
convert_timedeltas=True, copy=True):
"""
Deprecated.
Attempt to infer better dtype for object columns
Parameters
----------
convert_dates : boolean, default True
If True, convert to date where possible. If 'coerce', force
conversion, with unconvertible values becoming NaT.
convert_numeric : boolean, default False
If True, attempt to coerce to numbers (including strings), with
unconvertible values becoming NaN.
convert_timedeltas : boolean, default True
If True, convert to timedelta where possible. If 'coerce', force
conversion, with unconvertible values becoming NaT.
copy : boolean, default True
If True, return a copy even if no copy is necessary (e.g. no
conversion was done). Note: This is meant for internal use, and
should not be confused with inplace.
See Also
--------
pandas.to_datetime : Convert argument to datetime.
pandas.to_timedelta : Convert argument to timedelta.
pandas.to_numeric : Return a fixed frequency timedelta index,
with day as the default.
Returns
-------
converted : same as input object
"""
from warnings import warn
warn("convert_objects is deprecated. Use the data-type specific "
"converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.",
FutureWarning, stacklevel=2)
return self._constructor(
self._data.convert(convert_dates=convert_dates,
convert_numeric=convert_numeric,
convert_timedeltas=convert_timedeltas,
copy=copy)).__finalize__(self)
# ----------------------------------------------------------------------
# Filling NA's
_shared_docs['fillna'] = ("""
Fill NA/NaN values using the specified method
Parameters
----------
value : scalar, dict, Series, or DataFrame
Value to use to fill holes (e.g. 0), alternately a
dict/Series/DataFrame of values specifying which value to use for
each index (for a Series) or column (for a DataFrame). (values not
in the dict/Series/DataFrame will not be filled). This value cannot
be a list.
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
Method to use for filling holes in reindexed Series
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use NEXT valid observation to fill gap
axis : %(axes_single_arg)s
inplace : boolean, default False
If True, fill in place. Note: this will modify any
other views on this object, (e.g. a no-copy slice for a column in a
DataFrame).
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled.
downcast : dict, default is None
a dict of item->dtype of what to downcast if possible,
or the string 'infer' which will try to downcast to an appropriate
equal type (e.g. float64 to int64 if possible)
See Also
--------
reindex, asfreq
Returns
-------
filled : %(klass)s
""")
@Appender(_shared_docs['fillna'] % _shared_doc_kwargs)
def fillna(self, value=None, method=None, axis=None, inplace=False,
limit=None, downcast=None):
if isinstance(value, (list, tuple)):
raise TypeError('"value" parameter must be a scalar or dict, but '
'you passed a "{0}"'.format(type(value).__name__))
self._consolidate_inplace()
# set the default here, so functions examining the signaure
# can detect if something was set (e.g. in groupby) (GH9221)
if axis is None:
axis = 0
axis = self._get_axis_number(axis)
method = missing.clean_fill_method(method)
from pandas import DataFrame
if value is None:
if method is None:
raise ValueError('must specify a fill method or value')
if self._is_mixed_type and axis == 1:
if inplace:
raise NotImplementedError()
result = self.T.fillna(method=method, limit=limit).T
# need to downcast here because of all of the transposes
result._data = result._data.downcast()
return result
# > 3d
if self.ndim > 3:
raise NotImplementedError('Cannot fillna with a method for > '
'3dims')
# 3d
elif self.ndim == 3:
# fill in 2d chunks
result = dict([(col, s.fillna(method=method, value=value))
for col, s in self.iteritems()])
new_obj = self._constructor.\
from_dict(result).__finalize__(self)
new_data = new_obj._data
else:
# 2d or less
method = missing.clean_fill_method(method)
new_data = self._data.interpolate(method=method, axis=axis,
limit=limit, inplace=inplace,
coerce=True,
downcast=downcast)
else:
if method is not None:
raise ValueError('cannot specify both a fill method and value')
if len(self._get_axis(axis)) == 0:
return self
if self.ndim == 1:
if isinstance(value, (dict, ABCSeries)):
from pandas import Series
value = Series(value)
elif not is_list_like(value):
pass
else:
raise ValueError("invalid fill value with a %s" %
type(value))
new_data = self._data.fillna(value=value, limit=limit,
inplace=inplace,
downcast=downcast)
elif isinstance(value, (dict, ABCSeries)):
if axis == 1:
raise NotImplementedError('Currently only can fill '
'with dict/Series column '
'by column')
result = self if inplace else self.copy()
for k, v in compat.iteritems(value):
if k not in result:
continue
obj = result[k]
obj.fillna(v, limit=limit, inplace=True)
return result
elif not is_list_like(value):
new_data = self._data.fillna(value=value, limit=limit,
inplace=inplace,
downcast=downcast)
elif isinstance(value, DataFrame) and self.ndim == 2:
new_data = self.where(self.notnull(), value)
else:
raise ValueError("invalid fill value with a %s" % type(value))
if inplace:
self._update_inplace(new_data)
else:
return self._constructor(new_data).__finalize__(self)
def ffill(self, axis=None, inplace=False, limit=None, downcast=None):
"""Synonym for NDFrame.fillna(method='ffill')"""
return self.fillna(method='ffill', axis=axis, inplace=inplace,
limit=limit, downcast=downcast)
def bfill(self, axis=None, inplace=False, limit=None, downcast=None):
"""Synonym for NDFrame.fillna(method='bfill')"""
return self.fillna(method='bfill', axis=axis, inplace=inplace,
limit=limit, downcast=downcast)
def replace(self, to_replace=None, value=None, inplace=False, limit=None,
regex=False, method='pad', axis=None):
"""
Replace values given in 'to_replace' with 'value'.
Parameters
----------
to_replace : str, regex, list, dict, Series, numeric, or None
* str or regex:
- str: string exactly matching `to_replace` will be replaced
with `value`
- regex: regexs matching `to_replace` will be replaced with
`value`
* list of str, regex, or numeric:
- First, if `to_replace` and `value` are both lists, they
**must** be the same length.
- Second, if ``regex=True`` then all of the strings in **both**
lists will be interpreted as regexs otherwise they will match
directly. This doesn't matter much for `value` since there
are only a few possible substitution regexes you can use.
- str and regex rules apply as above.
* dict:
- Nested dictionaries, e.g., {'a': {'b': nan}}, are read as
follows: look in column 'a' for the value 'b' and replace it
with nan. You can nest regular expressions as well. Note that
column names (the top-level dictionary keys in a nested
dictionary) **cannot** be regular expressions.
- Keys map to column names and values map to substitution
values. You can treat this as a special case of passing two
lists except that you are specifying the column to search in.
* None:
- This means that the ``regex`` argument must be a string,
compiled regular expression, or list, dict, ndarray or Series
of such elements. If `value` is also ``None`` then this
**must** be a nested dictionary or ``Series``.
See the examples section for examples of each of these.
value : scalar, dict, list, str, regex, default None
Value to use to fill holes (e.g. 0), alternately a dict of values
specifying which value to use for each column (columns not in the
dict will not be filled). Regular expressions, strings and lists or
dicts of such objects are also allowed.
inplace : boolean, default False
If True, in place. Note: this will modify any
other views on this object (e.g. a column form a DataFrame).
Returns the caller if this is True.
limit : int, default None
Maximum size gap to forward or backward fill
regex : bool or same types as `to_replace`, default False
Whether to interpret `to_replace` and/or `value` as regular
expressions. If this is ``True`` then `to_replace` *must* be a
string. Otherwise, `to_replace` must be ``None`` because this
parameter will be interpreted as a regular expression or a list,
dict, or array of regular expressions.
method : string, optional, {'pad', 'ffill', 'bfill'}
The method to use when for replacement, when ``to_replace`` is a
``list``.
See Also
--------
NDFrame.reindex
NDFrame.asfreq
NDFrame.fillna
Returns
-------
filled : NDFrame
Raises
------
AssertionError
* If `regex` is not a ``bool`` and `to_replace` is not ``None``.
TypeError
* If `to_replace` is a ``dict`` and `value` is not a ``list``,
``dict``, ``ndarray``, or ``Series``
* If `to_replace` is ``None`` and `regex` is not compilable into a
regular expression or is a list, dict, ndarray, or Series.
ValueError
* If `to_replace` and `value` are ``list`` s or ``ndarray`` s, but
they are not the same length.
Notes
-----
* Regex substitution is performed under the hood with ``re.sub``. The
rules for substitution for ``re.sub`` are the same.
* Regular expressions will only substitute on strings, meaning you
cannot provide, for example, a regular expression matching floating
point numbers and expect the columns in your frame that have a
numeric dtype to be matched. However, if those floating point numbers
*are* strings, then you can do this.
* This method has *a lot* of options. You are encouraged to experiment
and play with this method to gain intuition about how it works.
"""
if not is_bool(regex) and to_replace is not None:
raise AssertionError("'to_replace' must be 'None' if 'regex' is "
"not a bool")
if axis is not None:
from warnings import warn
warn('the "axis" argument is deprecated and will be removed in'
'v0.13; this argument has no effect')
self._consolidate_inplace()
if value is None:
# passing a single value that is scalar like
# when value is None (GH5319), for compat
if not is_dict_like(to_replace) and not is_dict_like(regex):
to_replace = [to_replace]
if isinstance(to_replace, (tuple, list)):
return _single_replace(self, to_replace, method, inplace,
limit)
if not is_dict_like(to_replace):
if not is_dict_like(regex):
raise TypeError('If "to_replace" and "value" are both None'
' and "to_replace" is not a list, then '
'regex must be a mapping')
to_replace = regex
regex = True
items = list(compat.iteritems(to_replace))
keys, values = zip(*items)
are_mappings = [is_dict_like(v) for v in values]
if any(are_mappings):
if not all(are_mappings):
raise TypeError("If a nested mapping is passed, all values"
" of the top level mapping must be "
"mappings")
# passed a nested dict/Series
to_rep_dict = {}
value_dict = {}
for k, v in items:
keys, values = zip(*v.items())
if set(keys) & set(values):
raise ValueError("Replacement not allowed with "
"overlapping keys and values")
to_rep_dict[k] = list(keys)
value_dict[k] = list(values)
to_replace, value = to_rep_dict, value_dict
else:
to_replace, value = keys, values
return self.replace(to_replace, value, inplace=inplace,
limit=limit, regex=regex)
else:
# need a non-zero len on all axes
for a in self._AXIS_ORDERS:
if not len(self._get_axis(a)):
return self
new_data = self._data
if is_dict_like(to_replace):
if is_dict_like(value): # {'A' : NA} -> {'A' : 0}
res = self if inplace else self.copy()
for c, src in compat.iteritems(to_replace):
if c in value and c in self:
# object conversion is handled in
# series.replace which is called recursivelly
res[c] = res[c].replace(to_replace=src,
value=value[c],
inplace=False,
regex=regex)
return None if inplace else res
# {'A': NA} -> 0
elif not is_list_like(value):
keys = [(k, src) for k, src in compat.iteritems(to_replace)
if k in self]
keys_len = len(keys) - 1
for i, (k, src) in enumerate(keys):
convert = i == keys_len
new_data = new_data.replace(to_replace=src,
value=value,
filter=[k],
inplace=inplace,
regex=regex,
convert=convert)
else:
raise TypeError('value argument must be scalar, dict, or '
'Series')
elif is_list_like(to_replace): # [NA, ''] -> [0, 'missing']
if is_list_like(value):
if len(to_replace) != len(value):
raise ValueError('Replacement lists must match '
'in length. Expecting %d got %d ' %
(len(to_replace), len(value)))
new_data = self._data.replace_list(src_list=to_replace,
dest_list=value,
inplace=inplace,
regex=regex)
else: # [NA, ''] -> 0
new_data = self._data.replace(to_replace=to_replace,
value=value, inplace=inplace,
regex=regex)
elif to_replace is None:
if not (is_re_compilable(regex) or
is_list_like(regex) or is_dict_like(regex)):
raise TypeError("'regex' must be a string or a compiled "
"regular expression or a list or dict of "
"strings or regular expressions, you "
"passed a"
" {0!r}".format(type(regex).__name__))
return self.replace(regex, value, inplace=inplace, limit=limit,
regex=True)
else:
# dest iterable dict-like
if is_dict_like(value): # NA -> {'A' : 0, 'B' : -1}
new_data = self._data
for k, v in compat.iteritems(value):
if k in self:
new_data = new_data.replace(to_replace=to_replace,
value=v, filter=[k],
inplace=inplace,
regex=regex)
elif not is_list_like(value): # NA -> 0
new_data = self._data.replace(to_replace=to_replace,
value=value, inplace=inplace,
regex=regex)
else:
msg = ('Invalid "to_replace" type: '
'{0!r}').format(type(to_replace).__name__)
raise TypeError(msg) # pragma: no cover
if inplace:
self._update_inplace(new_data)
else:
return self._constructor(new_data).__finalize__(self)
_shared_docs['interpolate'] = """
Please note that only ``method='linear'`` is supported for
DataFrames/Series with a MultiIndex.
Parameters
----------
method : {'linear', 'time', 'index', 'values', 'nearest', 'zero',
'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh',
'polynomial', 'spline', 'piecewise_polynomial',
'from_derivatives', 'pchip', 'akima'}
* 'linear': ignore the index and treat the values as equally
spaced. This is the only method supported on MultiIndexes.
default
* 'time': interpolation works on daily and higher resolution
data to interpolate given length of interval
* 'index', 'values': use the actual numerical values of the index
* 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
'barycentric', 'polynomial' is passed to
``scipy.interpolate.interp1d``. Both 'polynomial' and 'spline'
require that you also specify an `order` (int),
e.g. df.interpolate(method='polynomial', order=4).
These use the actual numerical values of the index.
* 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima' are all
wrappers around the scipy interpolation methods of similar
names. These use the actual numerical values of the index. See
the scipy documentation for more on their behavior
`here <http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__ # noqa
`and here <http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__ # noqa
* 'from_derivatives' refers to BPoly.from_derivatives which
replaces 'piecewise_polynomial' interpolation method in scipy 0.18
.. versionadded:: 0.18.1
Added support for the 'akima' method
Added interpolate method 'from_derivatives' which replaces
'piecewise_polynomial' in scipy 0.18; backwards-compatible with
scipy < 0.18
axis : {0, 1}, default 0
* 0: fill column-by-column
* 1: fill row-by-row
limit : int, default None.
Maximum number of consecutive NaNs to fill.
limit_direction : {'forward', 'backward', 'both'}, defaults to 'forward'
If limit is specified, consecutive NaNs will be filled in this
direction.
.. versionadded:: 0.17.0
inplace : bool, default False
Update the NDFrame in place if possible.
downcast : optional, 'infer' or None, defaults to None
Downcast dtypes if possible.
kwargs : keyword arguments to pass on to the interpolating function.
Returns
-------
Series or DataFrame of same shape interpolated at the NaNs
See Also
--------
reindex, replace, fillna
Examples
--------
Filling in NaNs
>>> s = pd.Series([0, 1, np.nan, 3])
>>> s.interpolate()
0 0
1 1
2 2
3 3
dtype: float64
"""
@Appender(_shared_docs['interpolate'] % _shared_doc_kwargs)
def interpolate(self, method='linear', axis=0, limit=None, inplace=False,
limit_direction='forward', downcast=None, **kwargs):
"""
Interpolate values according to different methods.
"""
if self.ndim > 2:
raise NotImplementedError("Interpolate has not been implemented "
"on Panel and Panel 4D objects.")
if axis == 0:
ax = self._info_axis_name
_maybe_transposed_self = self
elif axis == 1:
_maybe_transposed_self = self.T
ax = 1
else:
_maybe_transposed_self = self
ax = _maybe_transposed_self._get_axis_number(ax)
if _maybe_transposed_self.ndim == 2:
alt_ax = 1 - ax
else:
alt_ax = ax
if (isinstance(_maybe_transposed_self.index, MultiIndex) and
method != 'linear'):
raise ValueError("Only `method=linear` interpolation is supported "
"on MultiIndexes.")
if _maybe_transposed_self._data.get_dtype_counts().get(
'object') == len(_maybe_transposed_self.T):
raise TypeError("Cannot interpolate with all NaNs.")
# create/use the index
if method == 'linear':
# prior default
index = np.arange(len(_maybe_transposed_self._get_axis(alt_ax)))
else:
index = _maybe_transposed_self._get_axis(alt_ax)
if pd.isnull(index).any():
raise NotImplementedError("Interpolation with NaNs in the index "
"has not been implemented. Try filling "
"those NaNs before interpolating.")
data = _maybe_transposed_self._data
new_data = data.interpolate(method=method, axis=ax, index=index,
values=_maybe_transposed_self, limit=limit,
limit_direction=limit_direction,
inplace=inplace, downcast=downcast,
**kwargs)
if inplace:
if axis == 1:
new_data = self._constructor(new_data).T._data
self._update_inplace(new_data)
else:
res = self._constructor(new_data).__finalize__(self)
if axis == 1:
res = res.T
return res
# ----------------------------------------------------------------------
# Timeseries methods Methods
def asof(self, where, subset=None):
"""
The last row without any NaN is taken (or the last row without
NaN considering only the subset of columns in the case of a DataFrame)
.. versionadded:: 0.19.0 For DataFrame
If there is no good value, NaN is returned.
Parameters
----------
where : date or array of dates
subset : string or list of strings, default None
if not None use these columns for NaN propagation
Notes
-----
Dates are assumed to be sorted
Raises if this is not the case
Returns
-------
where is scalar
- value or NaN if input is Series
- Series if input is DataFrame
where is Index: same shape object as input
See Also
--------
merge_asof
"""
if isinstance(where, compat.string_types):
from pandas import to_datetime
where = to_datetime(where)
if not self.index.is_monotonic:
raise ValueError("asof requires a sorted index")
is_series = isinstance(self, ABCSeries)
if is_series:
if subset is not None:
raise ValueError("subset is not valid for Series")
elif self.ndim > 2:
raise NotImplementedError("asof is not implemented "
"for {type}".format(type(self)))
else:
if subset is None:
subset = self.columns
if not is_list_like(subset):
subset = [subset]
is_list = is_list_like(where)
if not is_list:
start = self.index[0]
if isinstance(self.index, PeriodIndex):
where = Period(where, freq=self.index.freq).ordinal
start = start.ordinal
if where < start:
return np.nan
# It's always much faster to use a *while* loop here for
# Series than pre-computing all the NAs. However a
# *while* loop is extremely expensive for DataFrame
# so we later pre-compute all the NAs and use the same
# code path whether *where* is a scalar or list.
# See PR: https://github.com/pandas-dev/pandas/pull/14476
if is_series:
loc = self.index.searchsorted(where, side='right')
if loc > 0:
loc -= 1
values = self._values
while loc > 0 and isnull(values[loc]):
loc -= 1
return values[loc]
if not isinstance(where, Index):
where = Index(where) if is_list else Index([where])
nulls = self.isnull() if is_series else self[subset].isnull().any(1)
locs = self.index.asof_locs(where, ~(nulls.values))
# mask the missing
missing = locs == -1
data = self.take(locs, is_copy=False)
data.index = where
data.loc[missing] = np.nan
return data if is_list else data.iloc[-1]
# ----------------------------------------------------------------------
# Action Methods
_shared_docs['isnull'] = """
Return a boolean same-sized object indicating if the values are null.
See Also
--------
notnull : boolean inverse of isnull
"""
@Appender(_shared_docs['isnull'])
def isnull(self):
return isnull(self).__finalize__(self)
_shared_docs['isnotnull'] = """
Return a boolean same-sized object indicating if the values are
not null.
See Also
--------
isnull : boolean inverse of notnull
"""
@Appender(_shared_docs['isnotnull'])
def notnull(self):
return notnull(self).__finalize__(self)
def clip(self, lower=None, upper=None, axis=None, *args, **kwargs):
"""
Trim values at input threshold(s).
Parameters
----------
lower : float or array_like, default None
upper : float or array_like, default None
axis : int or string axis name, optional
Align object with lower and upper along the given axis.
Returns
-------
clipped : Series
Examples
--------
>>> df
0 1
0 0.335232 -1.256177
1 -1.367855 0.746646
2 0.027753 -1.176076
3 0.230930 -0.679613
4 1.261967 0.570967
>>> df.clip(-1.0, 0.5)
0 1
0 0.335232 -1.000000
1 -1.000000 0.500000
2 0.027753 -1.000000
3 0.230930 -0.679613
4 0.500000 0.500000
>>> t
0 -0.3
1 -0.2
2 -0.1
3 0.0
4 0.1
dtype: float64
>>> df.clip(t, t + 1, axis=0)
0 1
0 0.335232 -0.300000
1 -0.200000 0.746646
2 0.027753 -0.100000
3 0.230930 0.000000
4 1.100000 0.570967
"""
if isinstance(self, ABCPanel):
raise NotImplementedError("clip is not supported yet for panels")
axis = nv.validate_clip_with_axis(axis, args, kwargs)
# GH 2747 (arguments were reversed)
if lower is not None and upper is not None:
if is_scalar(lower) and is_scalar(upper):
lower, upper = min(lower, upper), max(lower, upper)
result = self
if lower is not None:
result = result.clip_lower(lower, axis)
if upper is not None:
result = result.clip_upper(upper, axis)
return result
def clip_upper(self, threshold, axis=None):
"""
Return copy of input with values above given value(s) truncated.
Parameters
----------
threshold : float or array_like
axis : int or string axis name, optional
Align object with threshold along the given axis.
See Also
--------
clip
Returns
-------
clipped : same type as input
"""
if np.any(isnull(threshold)):
raise ValueError("Cannot use an NA value as a clip threshold")
subset = self.le(threshold, axis=axis) | isnull(self)
return self.where(subset, threshold, axis=axis)
def clip_lower(self, threshold, axis=None):
"""
Return copy of the input with values below given value(s) truncated.
Parameters
----------
threshold : float or array_like
axis : int or string axis name, optional
Align object with threshold along the given axis.
See Also
--------
clip
Returns
-------
clipped : same type as input
"""
if np.any(isnull(threshold)):
raise ValueError("Cannot use an NA value as a clip threshold")
subset = self.ge(threshold, axis=axis) | isnull(self)
return self.where(subset, threshold, axis=axis)
def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True,
group_keys=True, squeeze=False, **kwargs):
"""
Group series using mapper (dict or key function, apply given function
to group, return result as series) or by a series of columns.
Parameters
----------
by : mapping function / list of functions, dict, Series, or tuple /
list of column names.
Called on each element of the object index to determine the groups.
If a dict or Series is passed, the Series or dict VALUES will be
used to determine the groups
axis : int, default 0
level : int, level name, or sequence of such, default None
If the axis is a MultiIndex (hierarchical), group by a particular
level or levels
as_index : boolean, default True
For aggregated output, return object with group labels as the
index. Only relevant for DataFrame input. as_index=False is
effectively "SQL-style" grouped output
sort : boolean, default True
Sort group keys. Get better performance by turning this off.
Note this does not influence the order of observations within each
group. groupby preserves the order of rows within each group.
group_keys : boolean, default True
When calling apply, add group keys to index to identify pieces
squeeze : boolean, default False
reduce the dimensionality of the return type if possible,
otherwise return a consistent type
Examples
--------
DataFrame results
>>> data.groupby(func, axis=0).mean()
>>> data.groupby(['col1', 'col2'])['col3'].mean()
DataFrame with hierarchical index
>>> data.groupby(['col1', 'col2']).mean()
Returns
-------
GroupBy object
"""
from pandas.core.groupby import groupby
if level is None and by is None:
raise TypeError("You have to supply one of 'by' and 'level'")
axis = self._get_axis_number(axis)
return groupby(self, by=by, axis=axis, level=level, as_index=as_index,
sort=sort, group_keys=group_keys, squeeze=squeeze,
**kwargs)
def asfreq(self, freq, method=None, how=None, normalize=False):
"""
Convert TimeSeries to specified frequency.
Optionally provide filling method to pad/backfill missing values.
Parameters
----------
freq : DateOffset object, or string
method : {'backfill'/'bfill', 'pad'/'ffill'}, default None
Method to use for filling holes in reindexed Series (note this
does not fill NaNs that already were present):
* 'pad' / 'ffill': propagate last valid observation forward to next
valid
* 'backfill' / 'bfill': use NEXT valid observation to fill
how : {'start', 'end'}, default end
For PeriodIndex only, see PeriodIndex.asfreq
normalize : bool, default False
Whether to reset output index to midnight
Returns
-------
converted : type of caller
Notes
-----
To learn more about the frequency strings, please see `this link
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
"""
from pandas.tseries.resample import asfreq
return asfreq(self, freq, method=method, how=how, normalize=normalize)
def at_time(self, time, asof=False):
"""
Select values at particular time of day (e.g. 9:30AM).
Parameters
----------
time : datetime.time or string
Returns
-------
values_at_time : type of caller
"""
try:
indexer = self.index.indexer_at_time(time, asof=asof)
return self.take(indexer, convert=False)
except AttributeError:
raise TypeError('Index must be DatetimeIndex')
def between_time(self, start_time, end_time, include_start=True,
include_end=True):
"""
Select values between particular times of the day (e.g., 9:00-9:30 AM).
Parameters
----------
start_time : datetime.time or string
end_time : datetime.time or string
include_start : boolean, default True
include_end : boolean, default True
Returns
-------
values_between_time : type of caller
"""
try:
indexer = self.index.indexer_between_time(
start_time, end_time, include_start=include_start,
include_end=include_end)
return self.take(indexer, convert=False)
except AttributeError:
raise TypeError('Index must be DatetimeIndex')
def resample(self, rule, how=None, axis=0, fill_method=None, closed=None,
label=None, convention='start', kind=None, loffset=None,
limit=None, base=0, on=None, level=None):
"""
Convenience method for frequency conversion and resampling of time
series. Object must have a datetime-like index (DatetimeIndex,
PeriodIndex, or TimedeltaIndex), or pass datetime-like values
to the on or level keyword.
Parameters
----------
rule : string
the offset string or object representing target conversion
axis : int, optional, default 0
closed : {'right', 'left'}
Which side of bin interval is closed
label : {'right', 'left'}
Which bin edge label to label bucket with
convention : {'start', 'end', 's', 'e'}
loffset : timedelta
Adjust the resampled time labels
base : int, default 0
For frequencies that evenly subdivide 1 day, the "origin" of the
aggregated intervals. For example, for '5min' frequency, base could
range from 0 through 4. Defaults to 0
on : string, optional
For a DataFrame, column to use instead of index for resampling.
Column must be datetime-like.
.. versionadded:: 0.19.0
level : string or int, optional
For a MultiIndex, level (name or number) to use for
resampling. Level must be datetime-like.
.. versionadded:: 0.19.0
To learn more about the offset strings, please see `this link
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
Examples
--------
Start by creating a series with 9 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=9, freq='T')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00 0
2000-01-01 00:01:00 1
2000-01-01 00:02:00 2
2000-01-01 00:03:00 3
2000-01-01 00:04:00 4
2000-01-01 00:05:00 5
2000-01-01 00:06:00 6
2000-01-01 00:07:00 7
2000-01-01 00:08:00 8
Freq: T, dtype: int64
Downsample the series into 3 minute bins and sum the values
of the timestamps falling into a bin.
>>> series.resample('3T').sum()
2000-01-01 00:00:00 3
2000-01-01 00:03:00 12
2000-01-01 00:06:00 21
Freq: 3T, dtype: int64
Downsample the series into 3 minute bins as above, but label each
bin using the right edge instead of the left. Please note that the
value in the bucket used as the label is not included in the bucket,
which it labels. For example, in the original series the
bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed
value in the resampled bucket with the label``2000-01-01 00:03:00``
does not include 3 (if it did, the summed value would be 6, not 3).
To include this value close the right side of the bin interval as
illustrated in the example below this one.
>>> series.resample('3T', label='right').sum()
2000-01-01 00:03:00 3
2000-01-01 00:06:00 12
2000-01-01 00:09:00 21
Freq: 3T, dtype: int64
Downsample the series into 3 minute bins as above, but close the right
side of the bin interval.
>>> series.resample('3T', label='right', closed='right').sum()
2000-01-01 00:00:00 0
2000-01-01 00:03:00 6
2000-01-01 00:06:00 15
2000-01-01 00:09:00 15
Freq: 3T, dtype: int64
Upsample the series into 30 second bins.
>>> series.resample('30S').asfreq()[0:5] #select first 5 rows
2000-01-01 00:00:00 0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 1
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2
Freq: 30S, dtype: float64
Upsample the series into 30 second bins and fill the ``NaN``
values using the ``pad`` method.
>>> series.resample('30S').pad()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 0
2000-01-01 00:01:00 1
2000-01-01 00:01:30 1
2000-01-01 00:02:00 2
Freq: 30S, dtype: int64
Upsample the series into 30 second bins and fill the
``NaN`` values using the ``bfill`` method.
>>> series.resample('30S').bfill()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 1
2000-01-01 00:01:00 1
2000-01-01 00:01:30 2
2000-01-01 00:02:00 2
Freq: 30S, dtype: int64
Pass a custom function via ``apply``
>>> def custom_resampler(array_like):
... return np.sum(array_like)+5
>>> series.resample('3T').apply(custom_resampler)
2000-01-01 00:00:00 8
2000-01-01 00:03:00 17
2000-01-01 00:06:00 26
Freq: 3T, dtype: int64
"""
from pandas.tseries.resample import (resample,
_maybe_process_deprecations)
axis = self._get_axis_number(axis)
r = resample(self, freq=rule, label=label, closed=closed,
axis=axis, kind=kind, loffset=loffset,
convention=convention,
base=base, key=on, level=level)
return _maybe_process_deprecations(r,
how=how,
fill_method=fill_method,
limit=limit)
def first(self, offset):
"""
Convenience method for subsetting initial periods of time series data
based on a date offset.
Parameters
----------
offset : string, DateOffset, dateutil.relativedelta
Examples
--------
ts.first('10D') -> First 10 days
Returns
-------
subset : type of caller
"""
from pandas.tseries.frequencies import to_offset
if not isinstance(self.index, DatetimeIndex):
raise NotImplementedError("'first' only supports a DatetimeIndex "
"index")
if len(self.index) == 0:
return self
offset = to_offset(offset)
end_date = end = self.index[0] + offset
# Tick-like, e.g. 3 weeks
if not offset.isAnchored() and hasattr(offset, '_inc'):
if end_date in self.index:
end = self.index.searchsorted(end_date, side='left')
return self.ix[:end]
def last(self, offset):
"""
Convenience method for subsetting final periods of time series data
based on a date offset.
Parameters
----------
offset : string, DateOffset, dateutil.relativedelta
Examples
--------
ts.last('5M') -> Last 5 months
Returns
-------
subset : type of caller
"""
from pandas.tseries.frequencies import to_offset
if not isinstance(self.index, DatetimeIndex):
raise NotImplementedError("'last' only supports a DatetimeIndex "
"index")
if len(self.index) == 0:
return self
offset = to_offset(offset)
start_date = start = self.index[-1] - offset
start = self.index.searchsorted(start_date, side='right')
return self.ix[start:]
def rank(self, axis=0, method='average', numeric_only=None,
na_option='keep', ascending=True, pct=False):
"""
Compute numerical data ranks (1 through n) along axis. Equal values are
assigned a rank that is the average of the ranks of those values
Parameters
----------
axis: {0 or 'index', 1 or 'columns'}, default 0
index to direct ranking
method : {'average', 'min', 'max', 'first', 'dense'}
* average: average rank of group
* min: lowest rank in group
* max: highest rank in group
* first: ranks assigned in order they appear in the array
* dense: like 'min', but rank always increases by 1 between groups
numeric_only : boolean, default None
Include only float, int, boolean data. Valid only for DataFrame or
Panel objects
na_option : {'keep', 'top', 'bottom'}
* keep: leave NA values where they are
* top: smallest rank if ascending
* bottom: smallest rank if descending
ascending : boolean, default True
False for ranks by high (1) to low (N)
pct : boolean, default False
Computes percentage rank of data
Returns
-------
ranks : same type as caller
"""
axis = self._get_axis_number(axis)
if self.ndim > 2:
msg = "rank does not make sense when ndim > 2"
raise NotImplementedError(msg)
def ranker(data):
ranks = algos.rank(data.values, axis=axis, method=method,
ascending=ascending, na_option=na_option,
pct=pct)
ranks = self._constructor(ranks, **data._construct_axes_dict())
return ranks.__finalize__(self)
# if numeric_only is None, and we can't get anything, we try with
# numeric_only=True
if numeric_only is None:
try:
return ranker(self)
except TypeError:
numeric_only = True
if numeric_only:
data = self._get_numeric_data()
else:
data = self
return ranker(data)
_shared_docs['align'] = ("""
Align two object on their axes with the
specified join method for each axis Index
Parameters
----------
other : DataFrame or Series
join : {'outer', 'inner', 'left', 'right'}, default 'outer'
axis : allowed axis of the other object, default None
Align on index (0), columns (1), or both (None)
level : int or level name, default None
Broadcast across a level, matching Index values on the
passed MultiIndex level
copy : boolean, default True
Always returns new objects. If copy=False and no reindexing is
required then original objects are returned.
fill_value : scalar, default np.NaN
Value to use for missing values. Defaults to NaN, but can be any
"compatible" value
method : str, default None
limit : int, default None
fill_axis : %(axes_single_arg)s, default 0
Filling axis, method and limit
broadcast_axis : %(axes_single_arg)s, default None
Broadcast values along this axis, if aligning two objects of
different dimensions
.. versionadded:: 0.17.0
Returns
-------
(left, right) : (%(klass)s, type of other)
Aligned objects
""")
@Appender(_shared_docs['align'] % _shared_doc_kwargs)
def align(self, other, join='outer', axis=None, level=None, copy=True,
fill_value=None, method=None, limit=None, fill_axis=0,
broadcast_axis=None):
from pandas import DataFrame, Series
method = missing.clean_fill_method(method)
if broadcast_axis == 1 and self.ndim != other.ndim:
if isinstance(self, Series):
# this means other is a DataFrame, and we need to broadcast
# self
cons = self._constructor_expanddim
df = cons(dict((c, self) for c in other.columns),
**other._construct_axes_dict())
return df._align_frame(other, join=join, axis=axis,
level=level, copy=copy,
fill_value=fill_value, method=method,
limit=limit, fill_axis=fill_axis)
elif isinstance(other, Series):
# this means self is a DataFrame, and we need to broadcast
# other
cons = other._constructor_expanddim
df = cons(dict((c, other) for c in self.columns),
**self._construct_axes_dict())
return self._align_frame(df, join=join, axis=axis, level=level,
copy=copy, fill_value=fill_value,
method=method, limit=limit,
fill_axis=fill_axis)
if axis is not None:
axis = self._get_axis_number(axis)
if isinstance(other, DataFrame):
return self._align_frame(other, join=join, axis=axis, level=level,
copy=copy, fill_value=fill_value,
method=method, limit=limit,
fill_axis=fill_axis)
elif isinstance(other, Series):
return self._align_series(other, join=join, axis=axis, level=level,
copy=copy, fill_value=fill_value,
method=method, limit=limit,
fill_axis=fill_axis)
else: # pragma: no cover
raise TypeError('unsupported type: %s' % type(other))
def _align_frame(self, other, join='outer', axis=None, level=None,
copy=True, fill_value=np.nan, method=None, limit=None,
fill_axis=0):
# defaults
join_index, join_columns = None, None
ilidx, iridx = None, None
clidx, cridx = None, None
is_series = isinstance(self, ABCSeries)
if axis is None or axis == 0:
if not self.index.equals(other.index):
join_index, ilidx, iridx = self.index.join(
other.index, how=join, level=level, return_indexers=True)
if axis is None or axis == 1:
if not is_series and not self.columns.equals(other.columns):
join_columns, clidx, cridx = self.columns.join(
other.columns, how=join, level=level, return_indexers=True)
if is_series:
reindexers = {0: [join_index, ilidx]}
else:
reindexers = {0: [join_index, ilidx], 1: [join_columns, clidx]}
left = self._reindex_with_indexers(reindexers, copy=copy,
fill_value=fill_value,
allow_dups=True)
# other must be always DataFrame
right = other._reindex_with_indexers({0: [join_index, iridx],
1: [join_columns, cridx]},
copy=copy, fill_value=fill_value,
allow_dups=True)
if method is not None:
left = left.fillna(axis=fill_axis, method=method, limit=limit)
right = right.fillna(axis=fill_axis, method=method, limit=limit)
# if DatetimeIndex have different tz, convert to UTC
if is_datetime64tz_dtype(left.index):
if left.index.tz != right.index.tz:
if join_index is not None:
left.index = join_index
right.index = join_index
return left.__finalize__(self), right.__finalize__(other)
def _align_series(self, other, join='outer', axis=None, level=None,
copy=True, fill_value=None, method=None, limit=None,
fill_axis=0):
is_series = isinstance(self, ABCSeries)
# series/series compat, other must always be a Series
if is_series:
if axis:
raise ValueError('cannot align series to a series other than '
'axis 0')
# equal
if self.index.equals(other.index):
join_index, lidx, ridx = None, None, None
else:
join_index, lidx, ridx = self.index.join(other.index, how=join,
level=level,
return_indexers=True)
left = self._reindex_indexer(join_index, lidx, copy)
right = other._reindex_indexer(join_index, ridx, copy)
else:
# one has > 1 ndim
fdata = self._data
if axis == 0:
join_index = self.index
lidx, ridx = None, None
if not self.index.equals(other.index):
join_index, lidx, ridx = self.index.join(
other.index, how=join, level=level,
return_indexers=True)
if lidx is not None:
fdata = fdata.reindex_indexer(join_index, lidx, axis=1)
elif axis == 1:
join_index = self.columns
lidx, ridx = None, None
if not self.columns.equals(other.index):
join_index, lidx, ridx = self.columns.join(
other.index, how=join, level=level,
return_indexers=True)
if lidx is not None:
fdata = fdata.reindex_indexer(join_index, lidx, axis=0)
else:
raise ValueError('Must specify axis=0 or 1')
if copy and fdata is self._data:
fdata = fdata.copy()
left = self._constructor(fdata)
if ridx is None:
right = other
else:
right = other.reindex(join_index, level=level)
# fill
fill_na = notnull(fill_value) or (method is not None)
if fill_na:
left = left.fillna(fill_value, method=method, limit=limit,
axis=fill_axis)
right = right.fillna(fill_value, method=method, limit=limit)
# if DatetimeIndex have different tz, convert to UTC
if is_series or (not is_series and axis == 0):
if is_datetime64tz_dtype(left.index):
if left.index.tz != right.index.tz:
if join_index is not None:
left.index = join_index
right.index = join_index
return left.__finalize__(self), right.__finalize__(other)
def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None,
try_cast=False, raise_on_error=True):
"""
Equivalent to public method `where`, except that `other` is not
applied as a function even if callable. Used in __setitem__.
"""
cond = com._apply_if_callable(cond, self)
if isinstance(cond, NDFrame):
cond, _ = cond.align(self, join='right', broadcast_axis=1)
else:
if not hasattr(cond, 'shape'):
raise ValueError('where requires an ndarray like object for '
'its condition')
if cond.shape != self.shape:
raise ValueError('Array conditional must be same shape as '
'self')
cond = self._constructor(cond, **self._construct_axes_dict())
if inplace:
cond = -(cond.fillna(True).astype(bool))
else:
cond = cond.fillna(False).astype(bool)
# try to align
try_quick = True
if hasattr(other, 'align'):
# align with me
if other.ndim <= self.ndim:
_, other = self.align(other, join='left', axis=axis,
level=level, fill_value=np.nan)
# if we are NOT aligned, raise as we cannot where index
if (axis is None and
not all([other._get_axis(i).equals(ax)
for i, ax in enumerate(self.axes)])):
raise InvalidIndexError
# slice me out of the other
else:
raise NotImplemented("cannot align with a higher dimensional "
"NDFrame")
elif is_list_like(other):
if self.ndim == 1:
# try to set the same dtype as ourselves
try:
new_other = np.array(other, dtype=self.dtype)
except ValueError:
new_other = np.array(other)
except TypeError:
new_other = other
# we can end up comparing integers and m8[ns]
# which is a numpy no no
is_i8 = needs_i8_conversion(self.dtype)
if is_i8:
matches = False
else:
matches = (new_other == np.array(other))
if matches is False or not matches.all():
# coerce other to a common dtype if we can
if needs_i8_conversion(self.dtype):
try:
other = np.array(other, dtype=self.dtype)
except:
other = np.array(other)
else:
other = np.asarray(other)
other = np.asarray(other,
dtype=np.common_type(other,
new_other))
# we need to use the new dtype
try_quick = False
else:
other = new_other
else:
other = np.array(other)
if isinstance(other, np.ndarray):
if other.shape != self.shape:
if self.ndim == 1:
icond = cond.values
# GH 2745 / GH 4192
# treat like a scalar
if len(other) == 1:
other = np.array(other[0])
# GH 3235
# match True cond to other
elif len(cond[icond]) == len(other):
# try to not change dtype at first (if try_quick)
if try_quick:
try:
new_other = _values_from_object(self).copy()
new_other[icond] = other
other = new_other
except:
try_quick = False
# let's create a new (if we failed at the above
# or not try_quick
if not try_quick:
dtype, fill_value = _maybe_promote(other.dtype)
new_other = np.empty(len(icond), dtype=dtype)
new_other.fill(fill_value)
_maybe_upcast_putmask(new_other, icond, other)
other = new_other
else:
raise ValueError('Length of replacements must equal '
'series length')
else:
raise ValueError('other must be the same shape as self '
'when an ndarray')
# we are the same shape, so create an actual object for alignment
else:
other = self._constructor(other, **self._construct_axes_dict())
if axis is None:
axis = 0
if self.ndim == getattr(other, 'ndim', 0):
align = True
else:
align = (self._get_axis_number(axis) == 1)
block_axis = self._get_block_manager_axis(axis)
if inplace:
# we may have different type blocks come out of putmask, so
# reconstruct the block manager
self._check_inplace_setting(other)
new_data = self._data.putmask(mask=cond, new=other, align=align,
inplace=True, axis=block_axis,
transpose=self._AXIS_REVERSED)
self._update_inplace(new_data)
else:
new_data = self._data.where(other=other, cond=cond, align=align,
raise_on_error=raise_on_error,
try_cast=try_cast, axis=block_axis,
transpose=self._AXIS_REVERSED)
return self._constructor(new_data).__finalize__(self)
_shared_docs['where'] = ("""
Return an object of same shape as self and whose corresponding
entries are from self where cond is %(cond)s and otherwise are from
other.
Parameters
----------
cond : boolean %(klass)s, array or callable
If cond is callable, it is computed on the %(klass)s and
should return boolean %(klass)s or array.
The callable must not change input %(klass)s
(though pandas doesn't check it).
.. versionadded:: 0.18.1
A callable can be used as cond.
other : scalar, %(klass)s, or callable
If other is callable, it is computed on the %(klass)s and
should return scalar or %(klass)s.
The callable must not change input %(klass)s
(though pandas doesn't check it).
.. versionadded:: 0.18.1
A callable can be used as other.
inplace : boolean, default False
Whether to perform the operation in place on the data
axis : alignment axis if needed, default None
level : alignment level if needed, default None
try_cast : boolean, default False
try to cast the result back to the input type (if possible),
raise_on_error : boolean, default True
Whether to raise on invalid data types (e.g. trying to where on
strings)
Returns
-------
wh : same type as caller
Notes
-----
The %(name)s method is an application of the if-then idiom. For each
element in the calling DataFrame, if ``cond`` is ``%(cond)s`` the
element is used; otherwise the corresponding element from the DataFrame
``other`` is used.
The signature for :func:`DataFrame.where` differs from
:func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to
``np.where(m, df1, df2)``.
For further details and examples see the ``%(name)s`` documentation in
:ref:`indexing <indexing.where_mask>`.
Examples
--------
>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0 NaN
1 1.0
2 2.0
3 3.0
4 4.0
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> m = df %% 3 == 0
>>> df.where(m, -df)
A B
0 0 -1
1 -2 3
2 -4 -5
3 6 -7
4 -8 9
>>> df.where(m, -df) == np.where(m, df, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
>>> df.where(m, -df) == df.mask(~m, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
See Also
--------
:func:`DataFrame.%(name_other)s`
""")
@Appender(_shared_docs['where'] % dict(_shared_doc_kwargs, cond="True",
name='where', name_other='mask'))
def where(self, cond, other=np.nan, inplace=False, axis=None, level=None,
try_cast=False, raise_on_error=True):
other = com._apply_if_callable(other, self)
return self._where(cond, other, inplace, axis, level, try_cast,
raise_on_error)
@Appender(_shared_docs['where'] % dict(_shared_doc_kwargs, cond="False",
name='mask', name_other='where'))
def mask(self, cond, other=np.nan, inplace=False, axis=None, level=None,
try_cast=False, raise_on_error=True):
cond = com._apply_if_callable(cond, self)
return self.where(~cond, other=other, inplace=inplace, axis=axis,
level=level, try_cast=try_cast,
raise_on_error=raise_on_error)
_shared_docs['shift'] = ("""
Shift index by desired number of periods with an optional time freq
Parameters
----------
periods : int
Number of periods to move, can be positive or negative
freq : DateOffset, timedelta, or time rule string, optional
Increment to use from the tseries module or time rule (e.g. 'EOM').
See Notes.
axis : %(axes_single_arg)s
Notes
-----
If freq is specified then the index values are shifted but the data
is not realigned. That is, use freq if you would like to extend the
index when shifting and preserve the original data.
Returns
-------
shifted : %(klass)s
""")
@Appender(_shared_docs['shift'] % _shared_doc_kwargs)
def shift(self, periods=1, freq=None, axis=0):
if periods == 0:
return self
block_axis = self._get_block_manager_axis(axis)
if freq is None:
new_data = self._data.shift(periods=periods, axis=block_axis)
else:
return self.tshift(periods, freq)
return self._constructor(new_data).__finalize__(self)
def slice_shift(self, periods=1, axis=0):
"""
Equivalent to `shift` without copying data. The shifted data will
not include the dropped periods and the shifted axis will be smaller
than the original.
Parameters
----------
periods : int
Number of periods to move, can be positive or negative
Notes
-----
While the `slice_shift` is faster than `shift`, you may pay for it
later during alignment.
Returns
-------
shifted : same type as caller
"""
if periods == 0:
return self
if periods > 0:
vslicer = slice(None, -periods)
islicer = slice(periods, None)
else:
vslicer = slice(-periods, None)
islicer = slice(None, periods)
new_obj = self._slice(vslicer, axis=axis)
shifted_axis = self._get_axis(axis)[islicer]
new_obj.set_axis(axis, shifted_axis)
return new_obj.__finalize__(self)
def tshift(self, periods=1, freq=None, axis=0):
"""
Shift the time index, using the index's frequency if available.
Parameters
----------
periods : int
Number of periods to move, can be positive or negative
freq : DateOffset, timedelta, or time rule string, default None
Increment to use from the tseries module or time rule (e.g. 'EOM')
axis : int or basestring
Corresponds to the axis that contains the Index
Notes
-----
If freq is not specified then tries to use the freq or inferred_freq
attributes of the index. If neither of those attributes exist, a
ValueError is thrown
Returns
-------
shifted : NDFrame
"""
index = self._get_axis(axis)
if freq is None:
freq = getattr(index, 'freq', None)
if freq is None:
freq = getattr(index, 'inferred_freq', None)
if freq is None:
msg = 'Freq was not given and was not set in the index'
raise ValueError(msg)
if periods == 0:
return self
if isinstance(freq, string_types):
freq = to_offset(freq)
block_axis = self._get_block_manager_axis(axis)
if isinstance(index, PeriodIndex):
orig_freq = to_offset(index.freq)
if freq == orig_freq:
new_data = self._data.copy()
new_data.axes[block_axis] = index.shift(periods)
else:
msg = ('Given freq %s does not match PeriodIndex freq %s' %
(freq.rule_code, orig_freq.rule_code))
raise ValueError(msg)
else:
new_data = self._data.copy()
new_data.axes[block_axis] = index.shift(periods, freq)
return self._constructor(new_data).__finalize__(self)
def truncate(self, before=None, after=None, axis=None, copy=True):
"""Truncates a sorted NDFrame before and/or after some particular
index value. If the axis contains only datetime values, before/after
parameters are converted to datetime values.
Parameters
----------
before : date
Truncate before index value
after : date
Truncate after index value
axis : the truncation axis, defaults to the stat axis
copy : boolean, default is True,
return a copy of the truncated section
Returns
-------
truncated : type of caller
"""
if axis is None:
axis = self._stat_axis_number
axis = self._get_axis_number(axis)
ax = self._get_axis(axis)
# if we have a date index, convert to dates, otherwise
# treat like a slice
if ax.is_all_dates:
from pandas.tseries.tools import to_datetime
before = to_datetime(before)
after = to_datetime(after)
if before is not None and after is not None:
if before > after:
raise ValueError('Truncate: %s must be after %s' %
(after, before))
slicer = [slice(None, None)] * self._AXIS_LEN
slicer[axis] = slice(before, after)
result = self.ix[tuple(slicer)]
if isinstance(ax, MultiIndex):
setattr(result, self._get_axis_name(axis),
ax.truncate(before, after))
if copy:
result = result.copy()
return result
def tz_convert(self, tz, axis=0, level=None, copy=True):
"""
Convert tz-aware axis to target time zone.
Parameters
----------
tz : string or pytz.timezone object
axis : the axis to convert
level : int, str, default None
If axis ia a MultiIndex, convert a specific level. Otherwise
must be None
copy : boolean, default True
Also make a copy of the underlying data
Returns
-------
Raises
------
TypeError
If the axis is tz-naive.
"""
axis = self._get_axis_number(axis)
ax = self._get_axis(axis)
def _tz_convert(ax, tz):
if not hasattr(ax, 'tz_convert'):
if len(ax) > 0:
ax_name = self._get_axis_name(axis)
raise TypeError('%s is not a valid DatetimeIndex or '
'PeriodIndex' % ax_name)
else:
ax = DatetimeIndex([], tz=tz)
else:
ax = ax.tz_convert(tz)
return ax
# if a level is given it must be a MultiIndex level or
# equivalent to the axis name
if isinstance(ax, MultiIndex):
level = ax._get_level_number(level)
new_level = _tz_convert(ax.levels[level], tz)
ax = ax.set_levels(new_level, level=level)
else:
if level not in (None, 0, ax.name):
raise ValueError("The level {0} is not valid".format(level))
ax = _tz_convert(ax, tz)
result = self._constructor(self._data, copy=copy)
result.set_axis(axis, ax)
return result.__finalize__(self)
@deprecate_kwarg(old_arg_name='infer_dst', new_arg_name='ambiguous',
mapping={True: 'infer',
False: 'raise'})
def tz_localize(self, tz, axis=0, level=None, copy=True,
ambiguous='raise'):
"""
Localize tz-naive TimeSeries to target time zone.
Parameters
----------
tz : string or pytz.timezone object
axis : the axis to localize
level : int, str, default None
If axis ia a MultiIndex, localize a specific level. Otherwise
must be None
copy : boolean, default True
Also make a copy of the underlying data
ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
- 'infer' will attempt to infer fall dst-transition hours based on
order
- bool-ndarray where True signifies a DST time, False designates
a non-DST time (note that this flag is only applicable for
ambiguous times)
- 'NaT' will return NaT where there are ambiguous times
- 'raise' will raise an AmbiguousTimeError if there are ambiguous
times
infer_dst : boolean, default False (DEPRECATED)
Attempt to infer fall dst-transition hours based on order
Returns
-------
Raises
------
TypeError
If the TimeSeries is tz-aware and tz is not None.
"""
axis = self._get_axis_number(axis)
ax = self._get_axis(axis)
def _tz_localize(ax, tz, ambiguous):
if not hasattr(ax, 'tz_localize'):
if len(ax) > 0:
ax_name = self._get_axis_name(axis)
raise TypeError('%s is not a valid DatetimeIndex or '
'PeriodIndex' % ax_name)
else:
ax = DatetimeIndex([], tz=tz)
else:
ax = ax.tz_localize(tz, ambiguous=ambiguous)
return ax
# if a level is given it must be a MultiIndex level or
# equivalent to the axis name
if isinstance(ax, MultiIndex):
level = ax._get_level_number(level)
new_level = _tz_localize(ax.levels[level], tz, ambiguous)
ax = ax.set_levels(new_level, level=level)
else:
if level not in (None, 0, ax.name):
raise ValueError("The level {0} is not valid".format(level))
ax = _tz_localize(ax, tz, ambiguous)
result = self._constructor(self._data, copy=copy)
result.set_axis(axis, ax)
return result.__finalize__(self)
# ----------------------------------------------------------------------
# Numeric Methods
def abs(self):
"""
Return an object with absolute value taken--only applicable to objects
that are all numeric.
Returns
-------
abs: type of caller
"""
return np.abs(self)
_shared_docs['describe'] = """
Generate various summary statistics, excluding NaN values.
Parameters
----------
percentiles : array-like, optional
The percentiles to include in the output. Should all
be in the interval [0, 1]. By default `percentiles` is
[.25, .5, .75], returning the 25th, 50th, and 75th percentiles.
include, exclude : list-like, 'all', or None (default)
Specify the form of the returned result. Either:
- None to both (default). The result will include only
numeric-typed columns or, if none are, only categorical columns.
- A list of dtypes or strings to be included/excluded.
To select all numeric types use numpy numpy.number. To select
categorical objects use type object. See also the select_dtypes
documentation. eg. df.describe(include=['O'])
- If include is the string 'all', the output column-set will
match the input one.
Returns
-------
summary: %(klass)s of summary statistics
Notes
-----
The output DataFrame index depends on the requested dtypes:
For numeric dtypes, it will include: count, mean, std, min,
max, and lower, 50, and upper percentiles.
For object dtypes (e.g. timestamps or strings), the index
will include the count, unique, most common, and frequency of the
most common. Timestamps also include the first and last items.
For mixed dtypes, the index will be the union of the corresponding
output types. Non-applicable entries will be filled with NaN.
Note that mixed-dtype outputs can only be returned from mixed-dtype
inputs and appropriate use of the include/exclude arguments.
If multiple values have the highest count, then the
`count` and `most common` pair will be arbitrarily chosen from
among those with the highest count.
The include, exclude arguments are ignored for Series.
See Also
--------
DataFrame.select_dtypes
"""
@Appender(_shared_docs['describe'] % _shared_doc_kwargs)
def describe(self, percentiles=None, include=None, exclude=None):
if self.ndim >= 3:
msg = "describe is not implemented on Panel or PanelND objects."
raise NotImplementedError(msg)
elif self.ndim == 2 and self.columns.size == 0:
raise ValueError("Cannot describe a DataFrame without columns")
if percentiles is not None:
# get them all to be in [0, 1]
self._check_percentile(percentiles)
# median should always be included
if 0.5 not in percentiles:
percentiles.append(0.5)
percentiles = np.asarray(percentiles)
else:
percentiles = np.array([0.25, 0.5, 0.75])
# sort and check for duplicates
unique_pcts = np.unique(percentiles)
if len(unique_pcts) < len(percentiles):
raise ValueError("percentiles cannot contain duplicates")
percentiles = unique_pcts
formatted_percentiles = format_percentiles(percentiles)
def describe_numeric_1d(series):
stat_index = (['count', 'mean', 'std', 'min'] +
formatted_percentiles + ['max'])
d = ([series.count(), series.mean(), series.std(), series.min()] +
[series.quantile(x) for x in percentiles] + [series.max()])
return pd.Series(d, index=stat_index, name=series.name)
def describe_categorical_1d(data):
names = ['count', 'unique']
objcounts = data.value_counts()
count_unique = len(objcounts[objcounts != 0])
result = [data.count(), count_unique]
if result[1] > 0:
top, freq = objcounts.index[0], objcounts.iloc[0]
if is_datetime64_dtype(data):
asint = data.dropna().values.view('i8')
names += ['top', 'freq', 'first', 'last']
result += [lib.Timestamp(top), freq,
lib.Timestamp(asint.min()),
lib.Timestamp(asint.max())]
else:
names += ['top', 'freq']
result += [top, freq]
return pd.Series(result, index=names, name=data.name)
def describe_1d(data):
if is_bool_dtype(data):
return describe_categorical_1d(data)
elif is_numeric_dtype(data):
return describe_numeric_1d(data)
elif is_timedelta64_dtype(data):
return describe_numeric_1d(data)
else:
return describe_categorical_1d(data)
if self.ndim == 1:
return describe_1d(self)
elif (include is None) and (exclude is None):
# when some numerics are found, keep only numerics
data = self.select_dtypes(include=[np.number])
if len(data.columns) == 0:
data = self
elif include == 'all':
if exclude is not None:
msg = "exclude must be None when include is 'all'"
raise ValueError(msg)
data = self
else:
data = self.select_dtypes(include=include, exclude=exclude)
ldesc = [describe_1d(s) for _, s in data.iteritems()]
# set a convenient order for rows
names = []
ldesc_indexes = sorted([x.index for x in ldesc], key=len)
for idxnames in ldesc_indexes:
for name in idxnames:
if name not in names:
names.append(name)
d = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1)
d.columns = data.columns.copy()
return d
def _check_percentile(self, q):
"""Validate percentiles (used by describe and quantile)."""
msg = ("percentiles should all be in the interval [0, 1]. "
"Try {0} instead.")
q = np.asarray(q)
if q.ndim == 0:
if not 0 <= q <= 1:
raise ValueError(msg.format(q / 100.0))
else:
if not all(0 <= qs <= 1 for qs in q):
raise ValueError(msg.format(q / 100.0))
return q
_shared_docs['pct_change'] = """
Percent change over given number of periods.
Parameters
----------
periods : int, default 1
Periods to shift for forming percent change
fill_method : str, default 'pad'
How to handle NAs before computing percent changes
limit : int, default None
The number of consecutive NAs to fill before stopping
freq : DateOffset, timedelta, or offset alias string, optional
Increment to use from time series API (e.g. 'M' or BDay())
Returns
-------
chg : %(klass)s
Notes
-----
By default, the percentage change is calculated along the stat
axis: 0, or ``Index``, for ``DataFrame`` and 1, or ``minor`` for
``Panel``. You can change this with the ``axis`` keyword argument.
"""
@Appender(_shared_docs['pct_change'] % _shared_doc_kwargs)
def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None,
**kwargs):
# TODO: Not sure if above is correct - need someone to confirm.
axis = self._get_axis_number(kwargs.pop('axis', self._stat_axis_name))
if fill_method is None:
data = self
else:
data = self.fillna(method=fill_method, limit=limit, axis=axis)
rs = (data.div(data.shift(periods=periods, freq=freq, axis=axis,
**kwargs)) - 1)
if freq is None:
mask = isnull(_values_from_object(self))
np.putmask(rs.values, mask, np.nan)
return rs
def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs):
grouped = self.groupby(level=level, axis=axis)
if hasattr(grouped, name) and skipna:
return getattr(grouped, name)(**kwargs)
axis = self._get_axis_number(axis)
method = getattr(type(self), name)
applyf = lambda x: method(x, axis=axis, skipna=skipna, **kwargs)
return grouped.aggregate(applyf)
@classmethod
def _add_numeric_operations(cls):
"""Add the operations to the cls; evaluate the doc strings again"""
axis_descr, name, name2 = _doc_parms(cls)
cls.any = _make_logical_function(
cls, 'any', name, name2, axis_descr,
'Return whether any element is True over requested axis',
nanops.nanany)
cls.all = _make_logical_function(
cls, 'all', name, name2, axis_descr,
'Return whether all elements are True over requested axis',
nanops.nanall)
@Substitution(outname='mad',
desc="Return the mean absolute deviation of the values "
"for the requested axis",
name1=name, name2=name2, axis_descr=axis_descr)
@Appender(_num_doc)
def mad(self, axis=None, skipna=None, level=None):
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level('mad', axis=axis, level=level,
skipna=skipna)
data = self._get_numeric_data()
if axis == 0:
demeaned = data - data.mean(axis=0)
else:
demeaned = data.sub(data.mean(axis=1), axis=0)
return np.abs(demeaned).mean(axis=axis, skipna=skipna)
cls.mad = mad
cls.sem = _make_stat_function_ddof(
cls, 'sem', name, name2, axis_descr,
"Return unbiased standard error of the mean over requested "
"axis.\n\nNormalized by N-1 by default. This can be changed "
"using the ddof argument",
nanops.nansem)
cls.var = _make_stat_function_ddof(
cls, 'var', name, name2, axis_descr,
"Return unbiased variance over requested axis.\n\nNormalized by "
"N-1 by default. This can be changed using the ddof argument",
nanops.nanvar)
cls.std = _make_stat_function_ddof(
cls, 'std', name, name2, axis_descr,
"Return sample standard deviation over requested axis."
"\n\nNormalized by N-1 by default. This can be changed using the "
"ddof argument",
nanops.nanstd)
@Substitution(outname='compounded',
desc="Return the compound percentage of the values for "
"the requested axis", name1=name, name2=name2,
axis_descr=axis_descr)
@Appender(_num_doc)
def compound(self, axis=None, skipna=None, level=None):
if skipna is None:
skipna = True
return (1 + self).prod(axis=axis, skipna=skipna, level=level) - 1
cls.compound = compound
cls.cummin = _make_cum_function(
cls, 'cummin', name, name2, axis_descr, "cumulative minimum",
lambda y, axis: np.minimum.accumulate(y, axis), np.inf, np.nan)
cls.cumsum = _make_cum_function(
cls, 'cumsum', name, name2, axis_descr, "cumulative sum",
lambda y, axis: y.cumsum(axis), 0., np.nan)
cls.cumprod = _make_cum_function(
cls, 'cumprod', name, name2, axis_descr, "cumulative product",
lambda y, axis: y.cumprod(axis), 1., np.nan)
cls.cummax = _make_cum_function(
cls, 'cummax', name, name2, axis_descr, "cumulative max",
lambda y, axis: np.maximum.accumulate(y, axis), -np.inf, np.nan)
cls.sum = _make_stat_function(
cls, 'sum', name, name2, axis_descr,
'Return the sum of the values for the requested axis',
nanops.nansum)
cls.mean = _make_stat_function(
cls, 'mean', name, name2, axis_descr,
'Return the mean of the values for the requested axis',
nanops.nanmean)
cls.skew = _make_stat_function(
cls, 'skew', name, name2, axis_descr,
'Return unbiased skew over requested axis\nNormalized by N-1',
nanops.nanskew)
cls.kurt = _make_stat_function(
cls, 'kurt', name, name2, axis_descr,
"Return unbiased kurtosis over requested axis using Fisher's "
"definition of\nkurtosis (kurtosis of normal == 0.0). Normalized "
"by N-1\n",
nanops.nankurt)
cls.kurtosis = cls.kurt
cls.prod = _make_stat_function(
cls, 'prod', name, name2, axis_descr,
'Return the product of the values for the requested axis',
nanops.nanprod)
cls.product = cls.prod
cls.median = _make_stat_function(
cls, 'median', name, name2, axis_descr,
'Return the median of the values for the requested axis',
nanops.nanmedian)
cls.max = _make_stat_function(
cls, 'max', name, name2, axis_descr,
"""This method returns the maximum of the values in the object.
If you want the *index* of the maximum, use ``idxmax``. This is
the equivalent of the ``numpy.ndarray`` method ``argmax``.""",
nanops.nanmax)
cls.min = _make_stat_function(
cls, 'min', name, name2, axis_descr,
"""This method returns the minimum of the values in the object.
If you want the *index* of the minimum, use ``idxmin``. This is
the equivalent of the ``numpy.ndarray`` method ``argmin``.""",
nanops.nanmin)
@classmethod
def _add_series_only_operations(cls):
"""Add the series only operations to the cls; evaluate the doc
strings again.
"""
axis_descr, name, name2 = _doc_parms(cls)
def nanptp(values, axis=0, skipna=True):
nmax = nanops.nanmax(values, axis, skipna)
nmin = nanops.nanmin(values, axis, skipna)
return nmax - nmin
cls.ptp = _make_stat_function(
cls, 'ptp', name, name2, axis_descr,
"""Returns the difference between the maximum value and the
minimum value in the object. This is the equivalent of the
``numpy.ndarray`` method ``ptp``.""",
nanptp)
@classmethod
def _add_series_or_dataframe_operations(cls):
"""Add the series or dataframe only operations to the cls; evaluate
the doc strings again.
"""
from pandas.core import window as rwindow
@Appender(rwindow.rolling.__doc__)
def rolling(self, window, min_periods=None, freq=None, center=False,
win_type=None, on=None, axis=0):
axis = self._get_axis_number(axis)
return rwindow.rolling(self, window=window,
min_periods=min_periods, freq=freq,
center=center, win_type=win_type,
on=on, axis=axis)
cls.rolling = rolling
@Appender(rwindow.expanding.__doc__)
def expanding(self, min_periods=1, freq=None, center=False, axis=0):
axis = self._get_axis_number(axis)
return rwindow.expanding(self, min_periods=min_periods, freq=freq,
center=center, axis=axis)
cls.expanding = expanding
@Appender(rwindow.ewm.__doc__)
def ewm(self, com=None, span=None, halflife=None, alpha=None,
min_periods=0, freq=None, adjust=True, ignore_na=False,
axis=0):
axis = self._get_axis_number(axis)
return rwindow.ewm(self, com=com, span=span, halflife=halflife,
alpha=alpha, min_periods=min_periods, freq=freq,
adjust=adjust, ignore_na=ignore_na, axis=axis)
cls.ewm = ewm
def _doc_parms(cls):
"""Return a tuple of the doc parms."""
axis_descr = "{%s}" % ', '.join(["{0} ({1})".format(a, i)
for i, a in enumerate(cls._AXIS_ORDERS)])
name = (cls._constructor_sliced.__name__
if cls._AXIS_LEN > 1 else 'scalar')
name2 = cls.__name__
return axis_descr, name, name2
_num_doc = """
%(desc)s
Parameters
----------
axis : %(axis_descr)s
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a %(name1)s
numeric_only : boolean, default None
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data. Not implemented for Series.
Returns
-------
%(outname)s : %(name1)s or %(name2)s (if level specified)\n"""
_num_ddof_doc = """
%(desc)s
Parameters
----------
axis : %(axis_descr)s
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a %(name1)s
ddof : int, default 1
degrees of freedom
numeric_only : boolean, default None
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data. Not implemented for Series.
Returns
-------
%(outname)s : %(name1)s or %(name2)s (if level specified)\n"""
_bool_doc = """
%(desc)s
Parameters
----------
axis : %(axis_descr)s
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a %(name1)s
bool_only : boolean, default None
Include only boolean columns. If None, will attempt to use everything,
then use only boolean data. Not implemented for Series.
Returns
-------
%(outname)s : %(name1)s or %(name2)s (if level specified)\n"""
_cnum_doc = """
Parameters
----------
axis : %(axis_descr)s
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
Returns
-------
%(outname)s : %(name1)s\n"""
def _make_stat_function(cls, name, name1, name2, axis_descr, desc, f):
@Substitution(outname=name, desc=desc, name1=name1, name2=name2,
axis_descr=axis_descr)
@Appender(_num_doc)
def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None,
**kwargs):
nv.validate_stat_func(tuple(), kwargs, fname=name)
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level(name, axis=axis, level=level,
skipna=skipna)
return self._reduce(f, name, axis=axis, skipna=skipna,
numeric_only=numeric_only)
return set_function_name(stat_func, name, cls)
def _make_stat_function_ddof(cls, name, name1, name2, axis_descr, desc, f):
@Substitution(outname=name, desc=desc, name1=name1, name2=name2,
axis_descr=axis_descr)
@Appender(_num_ddof_doc)
def stat_func(self, axis=None, skipna=None, level=None, ddof=1,
numeric_only=None, **kwargs):
nv.validate_stat_ddof_func(tuple(), kwargs, fname=name)
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level(name, axis=axis, level=level,
skipna=skipna, ddof=ddof)
return self._reduce(f, name, axis=axis, numeric_only=numeric_only,
skipna=skipna, ddof=ddof)
return set_function_name(stat_func, name, cls)
def _make_cum_function(cls, name, name1, name2, axis_descr, desc, accum_func,
mask_a, mask_b):
@Substitution(outname=name, desc=desc, name1=name1, name2=name2,
axis_descr=axis_descr)
@Appender("Return {0} over requested axis.".format(desc) +
_cnum_doc)
def cum_func(self, axis=None, skipna=True, *args, **kwargs):
skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name)
if axis is None:
axis = self._stat_axis_number
else:
axis = self._get_axis_number(axis)
y = _values_from_object(self).copy()
if (skipna and
issubclass(y.dtype.type, (np.datetime64, np.timedelta64))):
result = accum_func(y, axis)
mask = isnull(self)
np.putmask(result, mask, pd.tslib.iNaT)
elif skipna and not issubclass(y.dtype.type, (np.integer, np.bool_)):
mask = isnull(self)
np.putmask(y, mask, mask_a)
result = accum_func(y, axis)
np.putmask(result, mask, mask_b)
else:
result = accum_func(y, axis)
d = self._construct_axes_dict()
d['copy'] = False
return self._constructor(result, **d).__finalize__(self)
return set_function_name(cum_func, name, cls)
def _make_logical_function(cls, name, name1, name2, axis_descr, desc, f):
@Substitution(outname=name, desc=desc, name1=name1, name2=name2,
axis_descr=axis_descr)
@Appender(_bool_doc)
def logical_func(self, axis=None, bool_only=None, skipna=None, level=None,
**kwargs):
nv.validate_logical_func(tuple(), kwargs, fname=name)
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
if bool_only is not None:
raise NotImplementedError("Option bool_only is not "
"implemented with option level.")
return self._agg_by_level(name, axis=axis, level=level,
skipna=skipna)
return self._reduce(f, axis=axis, skipna=skipna,
numeric_only=bool_only, filter_type='bool',
name=name)
return set_function_name(logical_func, name, cls)
# install the indexes
for _name, _indexer in indexing.get_indexers_list():
NDFrame._create_indexer(_name, _indexer)
| mit |
lenovor/scikit-learn | examples/applications/topics_extraction_with_nmf_lda.py | 133 | 3517 | """
========================================================================================
Topics extraction with Non-Negative Matrix Factorization And Latent Dirichlet Allocation
========================================================================================
This is an example of applying Non Negative Matrix Factorization
and Latent Dirichlet Allocation on a corpus of documents and
extract additive models of the topic structure of the corpus.
The output is a list of topics, each represented as a list of terms
(weights are not shown).
The default parameters (n_samples / n_features / n_topics) should make
the example runnable in a couple of tens of seconds. You can try to
increase the dimensions of the problem, but be aware that the time
complexity is polynomial in NMF. In LDA, the time complexity is
proportional to (n_samples * iterations).
"""
# Author: Olivier Grisel <olivier.grisel@ensta.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Chyi-Kwei Yau <chyikwei.yau@gmail.com>
# License: BSD 3 clause
from __future__ import print_function
from time import time
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import NMF, LatentDirichletAllocation
from sklearn.datasets import fetch_20newsgroups
n_samples = 2000
n_features = 1000
n_topics = 10
n_top_words = 20
def print_top_words(model, feature_names, n_top_words):
for topic_idx, topic in enumerate(model.components_):
print("Topic #%d:" % topic_idx)
print(" ".join([feature_names[i] for i in topic.argsort()[:-n_top_words - 1:-1]]))
print()
# Load the 20 newsgroups dataset and vectorize it. We use a few heuristics
# to filter out useless terms early on: the posts are stripped of headers,
# footers and quoted replies, and common English words, words occurring in
# only one document or in at least 95% of the documents are removed.
t0 = time()
print("Loading dataset and extracting features...")
dataset = fetch_20newsgroups(shuffle=True, random_state=1,
remove=('headers', 'footers', 'quotes'))
data_samples = dataset.data[:n_samples]
# use tf-idf feature for NMF model
tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2, max_features=n_features,
stop_words='english')
tfidf = tfidf_vectorizer.fit_transform(data_samples)
# use tf feature for LDA model
tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=n_features,
stop_words='english')
tf = tf_vectorizer.fit_transform(data_samples)
print("done in %0.3fs." % (time() - t0))
# Fit the NMF model
print("Fitting the NMF model with tf-idf feature, n_samples=%d and n_features=%d..."
% (n_samples, n_features))
nmf = NMF(n_components=n_topics, random_state=1).fit(tfidf)
print("done in %0.3fs." % (time() - t0))
print("\nTopics in NMF model:")
tfidf_feature_names = tfidf_vectorizer.get_feature_names()
print_top_words(nmf, tfidf_feature_names, n_top_words)
print("\nFitting LDA models with tf feature, n_samples=%d and n_features=%d..."
% (n_samples, n_features))
lda = LatentDirichletAllocation(n_topics=n_topics, max_iter=5,
learning_method='online', learning_offset=50.,
random_state=0)
lda.fit(tf)
print("done in %0.3fs." % (time() - t0))
print("\nTopics in LDA model:")
tf_feature_names = tf_vectorizer.get_feature_names()
print_top_words(lda, tf_feature_names, n_top_words)
| bsd-3-clause |
shnizzedy/SM_openSMILE | openSMILE_analysis/pca.py | 1 | 3971 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
pca.py
Run PCA analyses on
[n_participants × n_files × n_features × n_Dxs]
*.csv file from openSMILE_csv.py
Authors:
– Jon Clucas, 2017 (jon.clucas@childmind.org)
@author: jon.clucas
"""
from sklearn.decomposition import FactorAnalysis, PCA
from sklearn.preprocessing import LabelEncoder
import ast, csv, numpy as np, os, sys
if os.path.abspath('../../') not in sys.path:
if os.path.isdir(os.path.join(os.path.abspath('../..'), 'SM_openSMILE')):
sys.path.append(os.path.abspath('../..'))
elif os.path.isdir(os.path.join(os.path.abspath('..'), 'SM_openSMILE')):
sys.path.append(os.path.abspath('..'))
elif os.path.isdir('SM_openSMILE'):
sys.path.append(os.path.abspath('.'))
from itertools import zip_longest
from SM_openSMILE.cfg import conditions, configs, oSdir, replacements
def main():
for cfg in configs:
labels = []
with open(os.path.abspath(os.path.join(os.path.dirname(os.path.dirname(
oSdir)), 'configs', cfg, ''.join([cfg, '_features.csv']))),
'r') as lp:
lreader = csv.reader(lp)
for row in lreader:
labels.append(row[1])
np_labels = np.array(labels)
for replacement in replacements:
for condition in conditions:
print(cfg, end=" : \n\t")
print(replacement, end=" : ")
print(condition.strip('_'))
pca = PCA(n_components=12)
fa = FactorAnalysis(n_components=12)
features = []
dx = []
n_samples = 0
n_features = 0
rpath = os.path.join(oSdir, cfg, ''.join([replacement,
condition, 'feature_data.csv']))
opath = os.path.join(oSdir, cfg, ''.join(['original',
condition, 'feature_data.csv']))
if(os.path.exists(rpath)):
# fill in unaltered rows when no altered row exists
with open(rpath, 'r') as rf, open(opath, 'r') as of:
rreader = csv.reader(rf)
oreader = csv.reader(of)
for rrow, orow in zip_longest(rreader, oreader):
if rrow:
if len(rrow) > 0:
row = rrow
else:
row = orow
for column in row:
column = ast.literal_eval(column)
if (column[0] != "['unknown']"):
enc = LabelEncoder()
enc.fit(np.array(column[0]))
for feature in list(column[0]):
try:
feature = float(feature)
except:
feature = enc.transform(np.array([
feature]))[0]
features.append(feature)
if n_samples == 0:
n_features = n_features + 1
dx.append(column[1])
n_samples = n_samples + 1
np_features = np.array(features).reshape(n_samples, n_features)
np_dx = np.array(dx).reshape(n_samples)
pca.fit(np_features, np_dx)
fa.fit(np_features, np_dx)
print(pca.components_)
print(pca.explained_variance_ratio_)
print(fa.components_)
# ============================================================================
if __name__ == '__main__':
main() | apache-2.0 |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/pandas/tests/types/test_inference.py | 7 | 31375 | # -*- coding: utf-8 -*-
"""
These the test the public routines exposed in types/common.py
related to inference and not otherwise tested in types/test_common.py
"""
import nose
import collections
import re
from datetime import datetime, date, timedelta, time
import numpy as np
import pandas as pd
from pandas import lib, tslib
from pandas import (Series, Index, DataFrame, Timedelta,
DatetimeIndex, TimedeltaIndex, Timestamp,
Panel, Period, Categorical)
from pandas.compat import u, PY2, lrange
from pandas.types import inference
from pandas.types.common import (is_timedelta64_dtype,
is_timedelta64_ns_dtype,
is_number,
is_integer,
is_float,
is_bool,
is_scalar,
_ensure_int32,
_ensure_categorical)
from pandas.types.missing import isnull
from pandas.util import testing as tm
_multiprocess_can_split_ = True
def test_is_sequence():
is_seq = inference.is_sequence
assert (is_seq((1, 2)))
assert (is_seq([1, 2]))
assert (not is_seq("abcd"))
assert (not is_seq(u("abcd")))
assert (not is_seq(np.int64))
class A(object):
def __getitem__(self):
return 1
assert (not is_seq(A()))
def test_is_list_like():
passes = ([], [1], (1, ), (1, 2), {'a': 1}, set([1, 'a']), Series([1]),
Series([]), Series(['a']).str)
fails = (1, '2', object())
for p in passes:
assert inference.is_list_like(p)
for f in fails:
assert not inference.is_list_like(f)
def test_is_dict_like():
passes = [{}, {'A': 1}, Series([1])]
fails = ['1', 1, [1, 2], (1, 2), range(2), Index([1])]
for p in passes:
assert inference.is_dict_like(p)
for f in fails:
assert not inference.is_dict_like(f)
def test_is_named_tuple():
passes = (collections.namedtuple('Test', list('abc'))(1, 2, 3), )
fails = ((1, 2, 3), 'a', Series({'pi': 3.14}))
for p in passes:
assert inference.is_named_tuple(p)
for f in fails:
assert not inference.is_named_tuple(f)
def test_is_hashable():
# all new-style classes are hashable by default
class HashableClass(object):
pass
class UnhashableClass1(object):
__hash__ = None
class UnhashableClass2(object):
def __hash__(self):
raise TypeError("Not hashable")
hashable = (1,
3.14,
np.float64(3.14),
'a',
tuple(),
(1, ),
HashableClass(), )
not_hashable = ([], UnhashableClass1(), )
abc_hashable_not_really_hashable = (([], ), UnhashableClass2(), )
for i in hashable:
assert inference.is_hashable(i)
for i in not_hashable:
assert not inference.is_hashable(i)
for i in abc_hashable_not_really_hashable:
assert not inference.is_hashable(i)
# numpy.array is no longer collections.Hashable as of
# https://github.com/numpy/numpy/pull/5326, just test
# is_hashable()
assert not inference.is_hashable(np.array([]))
# old-style classes in Python 2 don't appear hashable to
# collections.Hashable but also seem to support hash() by default
if PY2:
class OldStyleClass():
pass
c = OldStyleClass()
assert not isinstance(c, collections.Hashable)
assert inference.is_hashable(c)
hash(c) # this will not raise
def test_is_re():
passes = re.compile('ad'),
fails = 'x', 2, 3, object()
for p in passes:
assert inference.is_re(p)
for f in fails:
assert not inference.is_re(f)
def test_is_recompilable():
passes = (r'a', u('x'), r'asdf', re.compile('adsf'), u(r'\u2233\s*'),
re.compile(r''))
fails = 1, [], object()
for p in passes:
assert inference.is_re_compilable(p)
for f in fails:
assert not inference.is_re_compilable(f)
class TestInference(tm.TestCase):
def test_infer_dtype_bytes(self):
compare = 'string' if PY2 else 'bytes'
# string array of bytes
arr = np.array(list('abc'), dtype='S1')
self.assertEqual(lib.infer_dtype(arr), compare)
# object array of bytes
arr = arr.astype(object)
self.assertEqual(lib.infer_dtype(arr), compare)
def test_isinf_scalar(self):
# GH 11352
self.assertTrue(lib.isposinf_scalar(float('inf')))
self.assertTrue(lib.isposinf_scalar(np.inf))
self.assertFalse(lib.isposinf_scalar(-np.inf))
self.assertFalse(lib.isposinf_scalar(1))
self.assertFalse(lib.isposinf_scalar('a'))
self.assertTrue(lib.isneginf_scalar(float('-inf')))
self.assertTrue(lib.isneginf_scalar(-np.inf))
self.assertFalse(lib.isneginf_scalar(np.inf))
self.assertFalse(lib.isneginf_scalar(1))
self.assertFalse(lib.isneginf_scalar('a'))
def test_maybe_convert_numeric_infinities(self):
# see gh-13274
infinities = ['inf', 'inF', 'iNf', 'Inf',
'iNF', 'InF', 'INf', 'INF']
na_values = set(['', 'NULL', 'nan'])
pos = np.array(['inf'], dtype=np.float64)
neg = np.array(['-inf'], dtype=np.float64)
msg = "Unable to parse string"
for infinity in infinities:
for maybe_int in (True, False):
out = lib.maybe_convert_numeric(
np.array([infinity], dtype=object),
na_values, maybe_int)
tm.assert_numpy_array_equal(out, pos)
out = lib.maybe_convert_numeric(
np.array(['-' + infinity], dtype=object),
na_values, maybe_int)
tm.assert_numpy_array_equal(out, neg)
out = lib.maybe_convert_numeric(
np.array([u(infinity)], dtype=object),
na_values, maybe_int)
tm.assert_numpy_array_equal(out, pos)
out = lib.maybe_convert_numeric(
np.array(['+' + infinity], dtype=object),
na_values, maybe_int)
tm.assert_numpy_array_equal(out, pos)
# too many characters
with tm.assertRaisesRegexp(ValueError, msg):
lib.maybe_convert_numeric(
np.array(['foo_' + infinity], dtype=object),
na_values, maybe_int)
def test_maybe_convert_numeric_post_floatify_nan(self):
# see gh-13314
data = np.array(['1.200', '-999.000', '4.500'], dtype=object)
expected = np.array([1.2, np.nan, 4.5], dtype=np.float64)
nan_values = set([-999, -999.0])
for coerce_type in (True, False):
out = lib.maybe_convert_numeric(data, nan_values, coerce_type)
tm.assert_numpy_array_equal(out, expected)
def test_convert_infs(self):
arr = np.array(['inf', 'inf', 'inf'], dtype='O')
result = lib.maybe_convert_numeric(arr, set(), False)
self.assertTrue(result.dtype == np.float64)
arr = np.array(['-inf', '-inf', '-inf'], dtype='O')
result = lib.maybe_convert_numeric(arr, set(), False)
self.assertTrue(result.dtype == np.float64)
def test_scientific_no_exponent(self):
# See PR 12215
arr = np.array(['42E', '2E', '99e', '6e'], dtype='O')
result = lib.maybe_convert_numeric(arr, set(), False, True)
self.assertTrue(np.all(np.isnan(result)))
def test_convert_non_hashable(self):
# GH13324
# make sure that we are handing non-hashables
arr = np.array([[10.0, 2], 1.0, 'apple'])
result = lib.maybe_convert_numeric(arr, set(), False, True)
tm.assert_numpy_array_equal(result, np.array([np.nan, 1.0, np.nan]))
class TestTypeInference(tm.TestCase):
_multiprocess_can_split_ = True
def test_length_zero(self):
result = lib.infer_dtype(np.array([], dtype='i4'))
self.assertEqual(result, 'integer')
result = lib.infer_dtype([])
self.assertEqual(result, 'empty')
def test_integers(self):
arr = np.array([1, 2, 3, np.int64(4), np.int32(5)], dtype='O')
result = lib.infer_dtype(arr)
self.assertEqual(result, 'integer')
arr = np.array([1, 2, 3, np.int64(4), np.int32(5), 'foo'], dtype='O')
result = lib.infer_dtype(arr)
self.assertEqual(result, 'mixed-integer')
arr = np.array([1, 2, 3, 4, 5], dtype='i4')
result = lib.infer_dtype(arr)
self.assertEqual(result, 'integer')
def test_bools(self):
arr = np.array([True, False, True, True, True], dtype='O')
result = lib.infer_dtype(arr)
self.assertEqual(result, 'boolean')
arr = np.array([np.bool_(True), np.bool_(False)], dtype='O')
result = lib.infer_dtype(arr)
self.assertEqual(result, 'boolean')
arr = np.array([True, False, True, 'foo'], dtype='O')
result = lib.infer_dtype(arr)
self.assertEqual(result, 'mixed')
arr = np.array([True, False, True], dtype=bool)
result = lib.infer_dtype(arr)
self.assertEqual(result, 'boolean')
def test_floats(self):
arr = np.array([1., 2., 3., np.float64(4), np.float32(5)], dtype='O')
result = lib.infer_dtype(arr)
self.assertEqual(result, 'floating')
arr = np.array([1, 2, 3, np.float64(4), np.float32(5), 'foo'],
dtype='O')
result = lib.infer_dtype(arr)
self.assertEqual(result, 'mixed-integer')
arr = np.array([1, 2, 3, 4, 5], dtype='f4')
result = lib.infer_dtype(arr)
self.assertEqual(result, 'floating')
arr = np.array([1, 2, 3, 4, 5], dtype='f8')
result = lib.infer_dtype(arr)
self.assertEqual(result, 'floating')
def test_string(self):
pass
def test_unicode(self):
pass
def test_datetime(self):
dates = [datetime(2012, 1, x) for x in range(1, 20)]
index = Index(dates)
self.assertEqual(index.inferred_type, 'datetime64')
def test_infer_dtype_datetime(self):
arr = np.array([Timestamp('2011-01-01'),
Timestamp('2011-01-02')])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
arr = np.array([np.datetime64('2011-01-01'),
np.datetime64('2011-01-01')], dtype=object)
self.assertEqual(lib.infer_dtype(arr), 'datetime64')
arr = np.array([datetime(2011, 1, 1), datetime(2012, 2, 1)])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
# starts with nan
for n in [pd.NaT, np.nan]:
arr = np.array([n, pd.Timestamp('2011-01-02')])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
arr = np.array([n, np.datetime64('2011-01-02')])
self.assertEqual(lib.infer_dtype(arr), 'datetime64')
arr = np.array([n, datetime(2011, 1, 1)])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
arr = np.array([n, pd.Timestamp('2011-01-02'), n])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
arr = np.array([n, np.datetime64('2011-01-02'), n])
self.assertEqual(lib.infer_dtype(arr), 'datetime64')
arr = np.array([n, datetime(2011, 1, 1), n])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
# different type of nat
arr = np.array([np.timedelta64('nat'),
np.datetime64('2011-01-02')], dtype=object)
self.assertEqual(lib.infer_dtype(arr), 'mixed')
arr = np.array([np.datetime64('2011-01-02'),
np.timedelta64('nat')], dtype=object)
self.assertEqual(lib.infer_dtype(arr), 'mixed')
# mixed datetime
arr = np.array([datetime(2011, 1, 1),
pd.Timestamp('2011-01-02')])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
# should be datetime?
arr = np.array([np.datetime64('2011-01-01'),
pd.Timestamp('2011-01-02')])
self.assertEqual(lib.infer_dtype(arr), 'mixed')
arr = np.array([pd.Timestamp('2011-01-02'),
np.datetime64('2011-01-01')])
self.assertEqual(lib.infer_dtype(arr), 'mixed')
arr = np.array([np.nan, pd.Timestamp('2011-01-02'), 1])
self.assertEqual(lib.infer_dtype(arr), 'mixed-integer')
arr = np.array([np.nan, pd.Timestamp('2011-01-02'), 1.1])
self.assertEqual(lib.infer_dtype(arr), 'mixed')
arr = np.array([np.nan, '2011-01-01', pd.Timestamp('2011-01-02')])
self.assertEqual(lib.infer_dtype(arr), 'mixed')
def test_infer_dtype_timedelta(self):
arr = np.array([pd.Timedelta('1 days'),
pd.Timedelta('2 days')])
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
arr = np.array([np.timedelta64(1, 'D'),
np.timedelta64(2, 'D')], dtype=object)
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
arr = np.array([timedelta(1), timedelta(2)])
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
# starts with nan
for n in [pd.NaT, np.nan]:
arr = np.array([n, Timedelta('1 days')])
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
arr = np.array([n, np.timedelta64(1, 'D')])
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
arr = np.array([n, timedelta(1)])
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
arr = np.array([n, pd.Timedelta('1 days'), n])
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
arr = np.array([n, np.timedelta64(1, 'D'), n])
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
arr = np.array([n, timedelta(1), n])
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
# different type of nat
arr = np.array([np.datetime64('nat'), np.timedelta64(1, 'D')],
dtype=object)
self.assertEqual(lib.infer_dtype(arr), 'mixed')
arr = np.array([np.timedelta64(1, 'D'), np.datetime64('nat')],
dtype=object)
self.assertEqual(lib.infer_dtype(arr), 'mixed')
def test_infer_dtype_period(self):
# GH 13664
arr = np.array([pd.Period('2011-01', freq='D'),
pd.Period('2011-02', freq='D')])
self.assertEqual(pd.lib.infer_dtype(arr), 'period')
arr = np.array([pd.Period('2011-01', freq='D'),
pd.Period('2011-02', freq='M')])
self.assertEqual(pd.lib.infer_dtype(arr), 'period')
# starts with nan
for n in [pd.NaT, np.nan]:
arr = np.array([n, pd.Period('2011-01', freq='D')])
self.assertEqual(pd.lib.infer_dtype(arr), 'period')
arr = np.array([n, pd.Period('2011-01', freq='D'), n])
self.assertEqual(pd.lib.infer_dtype(arr), 'period')
# different type of nat
arr = np.array([np.datetime64('nat'), pd.Period('2011-01', freq='M')],
dtype=object)
self.assertEqual(pd.lib.infer_dtype(arr), 'mixed')
arr = np.array([pd.Period('2011-01', freq='M'), np.datetime64('nat')],
dtype=object)
self.assertEqual(pd.lib.infer_dtype(arr), 'mixed')
def test_infer_dtype_all_nan_nat_like(self):
arr = np.array([np.nan, np.nan])
self.assertEqual(lib.infer_dtype(arr), 'floating')
# nan and None mix are result in mixed
arr = np.array([np.nan, np.nan, None])
self.assertEqual(lib.infer_dtype(arr), 'mixed')
arr = np.array([None, np.nan, np.nan])
self.assertEqual(lib.infer_dtype(arr), 'mixed')
# pd.NaT
arr = np.array([pd.NaT])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
arr = np.array([pd.NaT, np.nan])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
arr = np.array([np.nan, pd.NaT])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
arr = np.array([np.nan, pd.NaT, np.nan])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
arr = np.array([None, pd.NaT, None])
self.assertEqual(lib.infer_dtype(arr), 'datetime')
# np.datetime64(nat)
arr = np.array([np.datetime64('nat')])
self.assertEqual(lib.infer_dtype(arr), 'datetime64')
for n in [np.nan, pd.NaT, None]:
arr = np.array([n, np.datetime64('nat'), n])
self.assertEqual(lib.infer_dtype(arr), 'datetime64')
arr = np.array([pd.NaT, n, np.datetime64('nat'), n])
self.assertEqual(lib.infer_dtype(arr), 'datetime64')
arr = np.array([np.timedelta64('nat')], dtype=object)
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
for n in [np.nan, pd.NaT, None]:
arr = np.array([n, np.timedelta64('nat'), n])
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
arr = np.array([pd.NaT, n, np.timedelta64('nat'), n])
self.assertEqual(lib.infer_dtype(arr), 'timedelta')
# datetime / timedelta mixed
arr = np.array([pd.NaT, np.datetime64('nat'),
np.timedelta64('nat'), np.nan])
self.assertEqual(lib.infer_dtype(arr), 'mixed')
arr = np.array([np.timedelta64('nat'), np.datetime64('nat')],
dtype=object)
self.assertEqual(lib.infer_dtype(arr), 'mixed')
def test_is_datetimelike_array_all_nan_nat_like(self):
arr = np.array([np.nan, pd.NaT, np.datetime64('nat')])
self.assertTrue(lib.is_datetime_array(arr))
self.assertTrue(lib.is_datetime64_array(arr))
self.assertFalse(lib.is_timedelta_array(arr))
self.assertFalse(lib.is_timedelta64_array(arr))
self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr))
arr = np.array([np.nan, pd.NaT, np.timedelta64('nat')])
self.assertFalse(lib.is_datetime_array(arr))
self.assertFalse(lib.is_datetime64_array(arr))
self.assertTrue(lib.is_timedelta_array(arr))
self.assertTrue(lib.is_timedelta64_array(arr))
self.assertTrue(lib.is_timedelta_or_timedelta64_array(arr))
arr = np.array([np.nan, pd.NaT, np.datetime64('nat'),
np.timedelta64('nat')])
self.assertFalse(lib.is_datetime_array(arr))
self.assertFalse(lib.is_datetime64_array(arr))
self.assertFalse(lib.is_timedelta_array(arr))
self.assertFalse(lib.is_timedelta64_array(arr))
self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr))
arr = np.array([np.nan, pd.NaT])
self.assertTrue(lib.is_datetime_array(arr))
self.assertTrue(lib.is_datetime64_array(arr))
self.assertTrue(lib.is_timedelta_array(arr))
self.assertTrue(lib.is_timedelta64_array(arr))
self.assertTrue(lib.is_timedelta_or_timedelta64_array(arr))
arr = np.array([np.nan, np.nan], dtype=object)
self.assertFalse(lib.is_datetime_array(arr))
self.assertFalse(lib.is_datetime64_array(arr))
self.assertFalse(lib.is_timedelta_array(arr))
self.assertFalse(lib.is_timedelta64_array(arr))
self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr))
def test_date(self):
dates = [date(2012, 1, x) for x in range(1, 20)]
index = Index(dates)
self.assertEqual(index.inferred_type, 'date')
def test_to_object_array_tuples(self):
r = (5, 6)
values = [r]
result = lib.to_object_array_tuples(values)
try:
# make sure record array works
from collections import namedtuple
record = namedtuple('record', 'x y')
r = record(5, 6)
values = [r]
result = lib.to_object_array_tuples(values) # noqa
except ImportError:
pass
def test_object(self):
# GH 7431
# cannot infer more than this as only a single element
arr = np.array([None], dtype='O')
result = lib.infer_dtype(arr)
self.assertEqual(result, 'mixed')
def test_to_object_array_width(self):
# see gh-13320
rows = [[1, 2, 3], [4, 5, 6]]
expected = np.array(rows, dtype=object)
out = lib.to_object_array(rows)
tm.assert_numpy_array_equal(out, expected)
expected = np.array(rows, dtype=object)
out = lib.to_object_array(rows, min_width=1)
tm.assert_numpy_array_equal(out, expected)
expected = np.array([[1, 2, 3, None, None],
[4, 5, 6, None, None]], dtype=object)
out = lib.to_object_array(rows, min_width=5)
tm.assert_numpy_array_equal(out, expected)
def test_is_period(self):
self.assertTrue(lib.is_period(pd.Period('2011-01', freq='M')))
self.assertFalse(lib.is_period(pd.PeriodIndex(['2011-01'], freq='M')))
self.assertFalse(lib.is_period(pd.Timestamp('2011-01')))
self.assertFalse(lib.is_period(1))
self.assertFalse(lib.is_period(np.nan))
def test_categorical(self):
# GH 8974
from pandas import Categorical, Series
arr = Categorical(list('abc'))
result = lib.infer_dtype(arr)
self.assertEqual(result, 'categorical')
result = lib.infer_dtype(Series(arr))
self.assertEqual(result, 'categorical')
arr = Categorical(list('abc'), categories=['cegfab'], ordered=True)
result = lib.infer_dtype(arr)
self.assertEqual(result, 'categorical')
result = lib.infer_dtype(Series(arr))
self.assertEqual(result, 'categorical')
class TestNumberScalar(tm.TestCase):
def test_is_number(self):
self.assertTrue(is_number(True))
self.assertTrue(is_number(1))
self.assertTrue(is_number(1.1))
self.assertTrue(is_number(1 + 3j))
self.assertTrue(is_number(np.bool(False)))
self.assertTrue(is_number(np.int64(1)))
self.assertTrue(is_number(np.float64(1.1)))
self.assertTrue(is_number(np.complex128(1 + 3j)))
self.assertTrue(is_number(np.nan))
self.assertFalse(is_number(None))
self.assertFalse(is_number('x'))
self.assertFalse(is_number(datetime(2011, 1, 1)))
self.assertFalse(is_number(np.datetime64('2011-01-01')))
self.assertFalse(is_number(Timestamp('2011-01-01')))
self.assertFalse(is_number(Timestamp('2011-01-01',
tz='US/Eastern')))
self.assertFalse(is_number(timedelta(1000)))
self.assertFalse(is_number(Timedelta('1 days')))
# questionable
self.assertFalse(is_number(np.bool_(False)))
self.assertTrue(is_number(np.timedelta64(1, 'D')))
def test_is_bool(self):
self.assertTrue(is_bool(True))
self.assertTrue(is_bool(np.bool(False)))
self.assertTrue(is_bool(np.bool_(False)))
self.assertFalse(is_bool(1))
self.assertFalse(is_bool(1.1))
self.assertFalse(is_bool(1 + 3j))
self.assertFalse(is_bool(np.int64(1)))
self.assertFalse(is_bool(np.float64(1.1)))
self.assertFalse(is_bool(np.complex128(1 + 3j)))
self.assertFalse(is_bool(np.nan))
self.assertFalse(is_bool(None))
self.assertFalse(is_bool('x'))
self.assertFalse(is_bool(datetime(2011, 1, 1)))
self.assertFalse(is_bool(np.datetime64('2011-01-01')))
self.assertFalse(is_bool(Timestamp('2011-01-01')))
self.assertFalse(is_bool(Timestamp('2011-01-01',
tz='US/Eastern')))
self.assertFalse(is_bool(timedelta(1000)))
self.assertFalse(is_bool(np.timedelta64(1, 'D')))
self.assertFalse(is_bool(Timedelta('1 days')))
def test_is_integer(self):
self.assertTrue(is_integer(1))
self.assertTrue(is_integer(np.int64(1)))
self.assertFalse(is_integer(True))
self.assertFalse(is_integer(1.1))
self.assertFalse(is_integer(1 + 3j))
self.assertFalse(is_integer(np.bool(False)))
self.assertFalse(is_integer(np.bool_(False)))
self.assertFalse(is_integer(np.float64(1.1)))
self.assertFalse(is_integer(np.complex128(1 + 3j)))
self.assertFalse(is_integer(np.nan))
self.assertFalse(is_integer(None))
self.assertFalse(is_integer('x'))
self.assertFalse(is_integer(datetime(2011, 1, 1)))
self.assertFalse(is_integer(np.datetime64('2011-01-01')))
self.assertFalse(is_integer(Timestamp('2011-01-01')))
self.assertFalse(is_integer(Timestamp('2011-01-01',
tz='US/Eastern')))
self.assertFalse(is_integer(timedelta(1000)))
self.assertFalse(is_integer(Timedelta('1 days')))
# questionable
self.assertTrue(is_integer(np.timedelta64(1, 'D')))
def test_is_float(self):
self.assertTrue(is_float(1.1))
self.assertTrue(is_float(np.float64(1.1)))
self.assertTrue(is_float(np.nan))
self.assertFalse(is_float(True))
self.assertFalse(is_float(1))
self.assertFalse(is_float(1 + 3j))
self.assertFalse(is_float(np.bool(False)))
self.assertFalse(is_float(np.bool_(False)))
self.assertFalse(is_float(np.int64(1)))
self.assertFalse(is_float(np.complex128(1 + 3j)))
self.assertFalse(is_float(None))
self.assertFalse(is_float('x'))
self.assertFalse(is_float(datetime(2011, 1, 1)))
self.assertFalse(is_float(np.datetime64('2011-01-01')))
self.assertFalse(is_float(Timestamp('2011-01-01')))
self.assertFalse(is_float(Timestamp('2011-01-01',
tz='US/Eastern')))
self.assertFalse(is_float(timedelta(1000)))
self.assertFalse(is_float(np.timedelta64(1, 'D')))
self.assertFalse(is_float(Timedelta('1 days')))
def test_is_timedelta(self):
self.assertTrue(is_timedelta64_dtype('timedelta64'))
self.assertTrue(is_timedelta64_dtype('timedelta64[ns]'))
self.assertFalse(is_timedelta64_ns_dtype('timedelta64'))
self.assertTrue(is_timedelta64_ns_dtype('timedelta64[ns]'))
tdi = TimedeltaIndex([1e14, 2e14], dtype='timedelta64')
self.assertTrue(is_timedelta64_dtype(tdi))
self.assertTrue(is_timedelta64_ns_dtype(tdi))
self.assertTrue(is_timedelta64_ns_dtype(tdi.astype('timedelta64[ns]')))
# Conversion to Int64Index:
self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64')))
self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64[h]')))
class Testisscalar(tm.TestCase):
def test_isscalar_builtin_scalars(self):
self.assertTrue(is_scalar(None))
self.assertTrue(is_scalar(True))
self.assertTrue(is_scalar(False))
self.assertTrue(is_scalar(0.))
self.assertTrue(is_scalar(np.nan))
self.assertTrue(is_scalar('foobar'))
self.assertTrue(is_scalar(b'foobar'))
self.assertTrue(is_scalar(u('efoobar')))
self.assertTrue(is_scalar(datetime(2014, 1, 1)))
self.assertTrue(is_scalar(date(2014, 1, 1)))
self.assertTrue(is_scalar(time(12, 0)))
self.assertTrue(is_scalar(timedelta(hours=1)))
self.assertTrue(is_scalar(pd.NaT))
def test_isscalar_builtin_nonscalars(self):
self.assertFalse(is_scalar({}))
self.assertFalse(is_scalar([]))
self.assertFalse(is_scalar([1]))
self.assertFalse(is_scalar(()))
self.assertFalse(is_scalar((1, )))
self.assertFalse(is_scalar(slice(None)))
self.assertFalse(is_scalar(Ellipsis))
def test_isscalar_numpy_array_scalars(self):
self.assertTrue(is_scalar(np.int64(1)))
self.assertTrue(is_scalar(np.float64(1.)))
self.assertTrue(is_scalar(np.int32(1)))
self.assertTrue(is_scalar(np.object_('foobar')))
self.assertTrue(is_scalar(np.str_('foobar')))
self.assertTrue(is_scalar(np.unicode_(u('foobar'))))
self.assertTrue(is_scalar(np.bytes_(b'foobar')))
self.assertTrue(is_scalar(np.datetime64('2014-01-01')))
self.assertTrue(is_scalar(np.timedelta64(1, 'h')))
def test_isscalar_numpy_zerodim_arrays(self):
for zerodim in [np.array(1), np.array('foobar'),
np.array(np.datetime64('2014-01-01')),
np.array(np.timedelta64(1, 'h')),
np.array(np.datetime64('NaT'))]:
self.assertFalse(is_scalar(zerodim))
self.assertTrue(is_scalar(lib.item_from_zerodim(zerodim)))
def test_isscalar_numpy_arrays(self):
self.assertFalse(is_scalar(np.array([])))
self.assertFalse(is_scalar(np.array([[]])))
self.assertFalse(is_scalar(np.matrix('1; 2')))
def test_isscalar_pandas_scalars(self):
self.assertTrue(is_scalar(Timestamp('2014-01-01')))
self.assertTrue(is_scalar(Timedelta(hours=1)))
self.assertTrue(is_scalar(Period('2014-01-01')))
def test_lisscalar_pandas_containers(self):
self.assertFalse(is_scalar(Series()))
self.assertFalse(is_scalar(Series([1])))
self.assertFalse(is_scalar(DataFrame()))
self.assertFalse(is_scalar(DataFrame([[1]])))
self.assertFalse(is_scalar(Panel()))
self.assertFalse(is_scalar(Panel([[[1]]])))
self.assertFalse(is_scalar(Index([])))
self.assertFalse(is_scalar(Index([1])))
def test_datetimeindex_from_empty_datetime64_array():
for unit in ['ms', 'us', 'ns']:
idx = DatetimeIndex(np.array([], dtype='datetime64[%s]' % unit))
assert (len(idx) == 0)
def test_nan_to_nat_conversions():
df = DataFrame(dict({
'A': np.asarray(
lrange(10), dtype='float64'),
'B': Timestamp('20010101')
}))
df.iloc[3:6, :] = np.nan
result = df.loc[4, 'B'].value
assert (result == tslib.iNaT)
s = df['B'].copy()
s._data = s._data.setitem(indexer=tuple([slice(8, 9)]), value=np.nan)
assert (isnull(s[8]))
# numpy < 1.7.0 is wrong
from distutils.version import LooseVersion
if LooseVersion(np.__version__) >= '1.7.0':
assert (s[8].value == np.datetime64('NaT').astype(np.int64))
def test_ensure_int32():
values = np.arange(10, dtype=np.int32)
result = _ensure_int32(values)
assert (result.dtype == np.int32)
values = np.arange(10, dtype=np.int64)
result = _ensure_int32(values)
assert (result.dtype == np.int32)
def test_ensure_categorical():
values = np.arange(10, dtype=np.int32)
result = _ensure_categorical(values)
assert (result.dtype == 'category')
values = Categorical(values)
result = _ensure_categorical(values)
tm.assert_categorical_equal(result, values)
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
| mit |
fdft/ml | ch01/webstats.py | 1 | 1029 | import scipy as sp
import matplotlib.pyplot as plt
data = sp.genfromtxt("data/web_traffic.tsv", delimiter="\t")
print(data[:10])
x = data[:,0]
y = data[:,1]
x = x[~sp.isnan(y)];
y = y[~sp.isnan(y)];
plt.scatter(x,y)
plt.title("Web traffic over the last month")
plt.xlabel("Time")
plt.ylabel("Hits/hour")
plt.xticks([w*7*24 for w in range(10)],['week %i'%w for w in range(10)])
plt.autoscale(tight=True)
plt.grid()
#plt.show()
fp1, residuals, rank, sv, rcond = sp.polyfit(x, y, 8, full=True)
f1 = sp.poly1d(fp1)
fx = sp.linspace(0,x[-1], 1000) # generate X-values for plotting
plt.plot(fx, f1(fx), linewidth=4)
plt.legend(["d=%i" % f1.order], loc="upper left")
inflection = 3.5*7*24 # calculate the inflection point in hours
xa = x[:inflection] # data before the inflection point
ya = y[:inflection]
xb = x[inflection:] # data after
yb = y[inflection:]
fa = sp.poly1d(sp.polyfit(xa, ya, 1))
fb = sp.poly1d(sp.polyfit(xb, yb, 1))
fa_error = error(fa, xa, ya)
fb_error = error(fb, xb, yb)
plt.plot(fx, f1(fx), linewidth=4) | mit |
ahuang11/ahh | sketches/my_sleep/my_sleep.py | 1 | 8823 | from ahh import vis, ext, sci
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime
from scipy.stats import pearsonr
sleep_df = pd.read_pickle('sleep_data_fmt.pkl')
sleep_hours = sleep_df['minutes'] / 60
sleep_quality = sleep_df['quality'] * 100
sleep_df_interp = sleep_df
sleep_df_interp['minutes'][sleep_df_interp.index[0]] = 9
sleep_hours_interp = sleep_df_interp['minutes'].interpolate() / 60
wx_df = pd.read_csv('kcmi_wx.csv') # https://www.wunderground.com/history/airport/KCMI/2014/1/1/
tmp = wx_df['Mean TemperatureF']
x = mdates.date2num(sleep_df_interp.index)
xx = np.linspace(x.min(), x.max(), len(sleep_df_interp.index))
z4 = np.polyfit(x, sleep_hours_interp, 4)
p4 = np.poly1d(z4)
sleep_fit = p4(xx)
fig, ax = vis.plot(sleep_df.index, sleep_hours, y2=sleep_fit,
bar=True, bar_dates=True, save='andrew_sleep', sharex=True,
figsize=(70, 20), major='months', interval=3, width=0.65,
title="Andrew's Daily Sleep (2014 - 2016)", ylabel='Hours',
titlescale=4, fontscale=3.5, labelscale=3.5, linewidth2=5,
minor='years')
years = range(2014,2017)
yearly_sleep_avg_list = []
yearly_sleep_std_list = []
months = range(1, 13)
monthly_sleep_avg_list = []
monthly_sleep_std_list = []
sleep_quality_avg_list = []
yr_monthly_sleep_avg_list = []
yr_monthly_quality_avg_list = []
sleep_masked = np.ma.masked_array(sleep_hours, np.isnan(sleep_hours))
quality_masked = np.ma.masked_array(sleep_quality, np.isnan(sleep_quality))
for year in years:
year_idc = np.where(pd.DatetimeIndex(sleep_df.index).year == year)[0]
yearly_sleep_avg_list.append(np.ma.average(sleep_masked[year_idc]))
yearly_sleep_std_list.append(np.std(sleep_hours[year_idc]))
for month in months:
month_idc = np.where(pd.DatetimeIndex(sleep_df.index).month == month)[0]
monthly_sleep_avg_list.append(np.ma.average(sleep_masked[month_idc]))
monthly_sleep_std_list.append(np.std(sleep_hours[month_idc]))
sleep_quality_avg_list.append(np.ma.average(quality_masked[month_idc]))
months_avg = np.ones(len(months)) * np.average(monthly_sleep_avg_list)
quality_months_avg = np.ones(len(months)) * np.average(sleep_quality_avg_list)
caption = """
Yearly Avg: 2014:{avg2014:02.2f}, 2015:{avg2015:02.2f}, 2016:{avg2016:02.2f} Yearly Std: 2014:{std2014:02.2f}, 2015:{std2015:02.2f}, 2016:{std2016:02.2f}
Monthly Avg: Jan:{jan:02.2f}, Feb:{feb:02.2f}, Mar:{mar:02.2f}, Apr:{apr:02.2f}, May:{may:02.2f}, Jun:{jun:02.2f}, Jul:{jul:02.2f}, Aug:{aug:02.2f}, Sep:{sep:02.2f}, Oct:{oct:02.2f}, Nov:{nov:02.2f}, Dec:{dec:02.2f}
Monthly Std: Jan:{jan_std:02.2f}, Feb:{feb_std:02.2f}, Mar:{mar_std:02.2f}, Apr:{apr_std:02.2f}, May:{may_std:02.2f}, Jun:{jun_std:02.2f}, Jul:{jul_std:02.2f}, Aug:{aug_std:02.2f}, Sep:{sep_std:02.2f}, Oct:{oct_std:02.2f}, Nov:{nov_std:02.2f}, Dec:{dec_std:02.2f}
"""
plt.figtext(0.5, 0.005, caption.format(
avg2014=yearly_sleep_avg_list[0],
std2014=yearly_sleep_std_list[0],
avg2015=yearly_sleep_avg_list[1],
std2015=yearly_sleep_std_list[1],
avg2016=yearly_sleep_avg_list[2],
std2016=yearly_sleep_std_list[2],
jan=monthly_sleep_avg_list[0],
feb=monthly_sleep_avg_list[1],
mar=monthly_sleep_avg_list[2],
apr=monthly_sleep_avg_list[3],
may=monthly_sleep_avg_list[4],
jun=monthly_sleep_avg_list[5],
jul=monthly_sleep_avg_list[6],
aug=monthly_sleep_avg_list[7],
sep=monthly_sleep_avg_list[8],
oct=monthly_sleep_avg_list[9],
nov=monthly_sleep_avg_list[10],
dec=monthly_sleep_avg_list[11],
jan_std=monthly_sleep_std_list[0],
feb_std=monthly_sleep_std_list[1],
mar_std=monthly_sleep_std_list[2],
apr_std=monthly_sleep_std_list[3],
may_std=monthly_sleep_std_list[4],
jun_std=monthly_sleep_std_list[5],
jul_std=monthly_sleep_std_list[6],
aug_std=monthly_sleep_std_list[7],
sep_std=monthly_sleep_std_list[8],
oct_std=monthly_sleep_std_list[9],
nov_std=monthly_sleep_std_list[10],
dec_std=monthly_sleep_std_list[11],
),
ha='center', size=40, color='.5',
)
plt.savefig("andrew_sleep")
for year in years:
for month in months:
yr_month_idc = np.where((pd.DatetimeIndex(sleep_df.index).month == month) & (pd.DatetimeIndex(sleep_df.index).year == year))[0]
yr_monthly_sleep_avg_list.append(np.ma.average(sleep_masked[yr_month_idc]))
yr_monthly_quality_avg_list.append(np.ma.average(quality_masked[yr_month_idc]))
start = datetime.datetime(2013, 12, 31)
dates = pd.date_range(start, periods=len(yr_monthly_sleep_avg_list), freq='m')
x = mdates.date2num(dates[:-2])
xx = np.linspace(x.min(), x.max(), len(dates))
z4 = np.polyfit(x, np.array(yr_monthly_sleep_avg_list[:-2]), 4)
p4 = np.poly1d(z4)
yearly_monthly_sleep_fit = p4(xx)
monthly_qual_norm = sci.get_norm_anom(np.array(yr_monthly_sleep_avg_list[:-2]))
monthly_hour_norm = sci.get_norm_anom(np.array(yr_monthly_quality_avg_list[:-2]))
coeff, pval = pearsonr(monthly_qual_norm, monthly_hour_norm)
plt.figure()
title_fmt = 'Monthly Average Hours of Sleep'
fig, ax = vis.plot(dates, yr_monthly_sleep_avg_list, y2=yearly_monthly_sleep_fit,
ylabel='Hours', sharex=True, extra=True, xlabel='Month', bar_dates=True, linewidth2=2,
title=title_fmt.format(coeff), ylabel2='Quality', bar=True, ylim=(7, 9.5), width=15,
figsize=(20,15), major='months', interval=3, fontscale=1.5, labelscale=1.5, minor='years')
plt.savefig('yr_monthly_andrew_quality_hour.png')
plt.figure()
vis.plot(months, monthly_sleep_avg_list, y2=months_avg,
ylabel='Hours', sharex=True, extra=True,
title='Monthly Average Hours of Sleep (2014 - 2016)', xlabel='Month',
save='monthly_andrew_sleep', figsize=(20,15), xlim=(1, 12))
plt.figure()
vis.plot(months, sleep_quality_avg_list, y2=quality_months_avg,
ylabel='%', sharex=True, extra=True, xlabel='Month',
title='Monthly Average Quality of Sleep (2014 - 2016)',
save='monthly_andrew_quality', figsize=(20,15), xlim=(1, 12))
plt.figure()
hist, bins = np.histogram(sleep_hours, bins=20, range=(6, 11))
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
vis.plot(center, hist, width=width, ylabel='Count',
title='Hours of Sleep Histogram (2014 - 2016)', xlabel='Hours',
save='histogram_andrew_sleep', figsize=(20,15), bar=True,
xlim=(6, 11))
monthly_qual_norm = sci.get_norm_anom(np.array(monthly_sleep_avg_list))
monthly_hour_norm = sci.get_norm_anom(np.array(sleep_quality_avg_list))
coeff, pval = pearsonr(monthly_qual_norm, monthly_hour_norm)
plt.figure()
title_fmt = 'Monthly Quality of Sleep vs Hours of Sleep Correlation = {:.2f}'
vis.plot(months, monthly_sleep_avg_list, y2=sleep_quality_avg_list,
ylabel='Hours', sharex=True, extra=True, xlabel='Month', extray=True,
title=title_fmt.format(coeff), ylabel2='Quality',
save='monthly_andrew_quality_hour', figsize=(20,15), xlim=(1, 12))
qual_norm = sci.get_norm_anom(quality_masked)
tmp_norm = sci.get_norm_anom(tmp)
qual_norm_cut = qual_norm[~qual_norm.mask]
tmp_norm_cut = tmp_norm[~qual_norm.mask]
coeff, pval = pearsonr(qual_norm_cut, tmp_norm_cut)
fig, ax = vis.plot(sleep_df.index, quality_masked, y2=tmp,
dates=True, save='qual_vs_tmp', sharex=True,
figsize=(70, 20), major='months', interval=3, extray=True,
title="Sleep Quality vs Temperature Correlation: {}".format(coeff), ylabel='Sleep Quality',
titlescale=4, fontscale=3.5, labelscale=3.5, linewidth2=5,
minor='years') | mit |
chengguo-clemson/chengguo-clemson.github.io | markdown_generator/talks.py | 199 | 4000 |
# coding: utf-8
# # Talks markdown generator for academicpages
#
# Takes a TSV of talks with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html)). The core python code is also in `talks.py`. Run either from the `markdown_generator` folder after replacing `talks.tsv` with one containing your data.
#
# TODO: Make this work with BibTex and other databases, rather than Stuart's non-standard TSV format and citation style.
# In[1]:
import pandas as pd
import os
# ## Data format
#
# The TSV needs to have the following columns: title, type, url_slug, venue, date, location, talk_url, description, with a header at the top. Many of these fields can be blank, but the columns must be in the TSV.
#
# - Fields that cannot be blank: `title`, `url_slug`, `date`. All else can be blank. `type` defaults to "Talk"
# - `date` must be formatted as YYYY-MM-DD.
# - `url_slug` will be the descriptive part of the .md file and the permalink URL for the page about the paper.
# - The .md file will be `YYYY-MM-DD-[url_slug].md` and the permalink will be `https://[yourdomain]/talks/YYYY-MM-DD-[url_slug]`
# - The combination of `url_slug` and `date` must be unique, as it will be the basis for your filenames
#
# ## Import TSV
#
# Pandas makes this easy with the read_csv function. We are using a TSV, so we specify the separator as a tab, or `\t`.
#
# I found it important to put this data in a tab-separated values format, because there are a lot of commas in this kind of data and comma-separated values can get messed up. However, you can modify the import statement, as pandas also has read_excel(), read_json(), and others.
# In[3]:
talks = pd.read_csv("talks.tsv", sep="\t", header=0)
talks
# ## Escape special characters
#
# YAML is very picky about how it takes a valid string, so we are replacing single and double quotes (and ampersands) with their HTML encoded equivilents. This makes them look not so readable in raw format, but they are parsed and rendered nicely.
# In[4]:
html_escape_table = {
"&": "&",
'"': """,
"'": "'"
}
def html_escape(text):
if type(text) is str:
return "".join(html_escape_table.get(c,c) for c in text)
else:
return "False"
# ## Creating the markdown files
#
# This is where the heavy lifting is done. This loops through all the rows in the TSV dataframe, then starts to concatentate a big string (```md```) that contains the markdown for each type. It does the YAML metadata first, then does the description for the individual page.
# In[5]:
loc_dict = {}
for row, item in talks.iterrows():
md_filename = str(item.date) + "-" + item.url_slug + ".md"
html_filename = str(item.date) + "-" + item.url_slug
year = item.date[:4]
md = "---\ntitle: \"" + item.title + '"\n'
md += "collection: talks" + "\n"
if len(str(item.type)) > 3:
md += 'type: "' + item.type + '"\n'
else:
md += 'type: "Talk"\n'
md += "permalink: /talks/" + html_filename + "\n"
if len(str(item.venue)) > 3:
md += 'venue: "' + item.venue + '"\n'
if len(str(item.location)) > 3:
md += "date: " + str(item.date) + "\n"
if len(str(item.location)) > 3:
md += 'location: "' + str(item.location) + '"\n'
md += "---\n"
if len(str(item.talk_url)) > 3:
md += "\n[More information here](" + item.talk_url + ")\n"
if len(str(item.description)) > 3:
md += "\n" + html_escape(item.description) + "\n"
md_filename = os.path.basename(md_filename)
#print(md)
with open("../_talks/" + md_filename, 'w') as f:
f.write(md)
# These files are in the talks directory, one directory below where we're working from.
| mit |
nicaogr/Style-Transfer | Filter_Rep.py | 1 | 24343 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 02 2017
The goal of this script is to vizualised the reponse of the filter of the
different convolution of the network
@author: nicolas
"""
import scipy
import numpy as np
import tensorflow as tf
import Style_Transfer as st
from Arg_Parser import get_parser_args
import seaborn as sns
import matplotlib.pyplot as plt
import os
import pandas as pd
import time
import pickle
from matplotlib.backends.backend_pdf import PdfPages
import scipy.stats as stats
from tensorflow.python.framework import dtypes
import matplotlib.gridspec as gridspec
import math
from skimage import exposure
from PIL import Image
# Name of the 19 first layers of the VGG19
VGG19_LAYERS = (
'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
'relu3_3', 'conv3_4', 'relu3_4', 'pool3',
'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
'relu5_3', 'conv5_4', 'relu5_4'
)
VGG19_LAYERS_INDICES = {'conv1_1' : 0,'conv1_2' : 2,'conv2_1' : 5,'conv2_2' : 7,
'conv3_1' : 10,'conv3_2' : 12,'conv3_3' : 14,'conv3_4' : 16,'conv4_1' : 19,
'conv4_2' : 21,'conv4_3' : 23,'conv4_4' : 25,'conv5_1' : 28,'conv5_2' : 30,
'conv5_3' : 32,'conv5_4' : 34}
VGG19_LAYERS_INTEREST = (
'conv1_1','conv2_1', 'conv3_1'
)
#VGG19_LAYERS_INTEREST = ('conv1_1' ,'conv1_2','conv2_1' ,'conv2_2' ,
#'conv3_1','conv3_2','conv3_3' ,'conv3_4','conv4_1' ,'conv4_2')
VGG19_LAYERS_INTEREST = {'conv1_1'}
def hist(values,value_range,nbins=100,dtype=dtypes.float32):
nbins_float = float(nbins)
# Map tensor values that fall within value_range to [0, 1].
# scaled_values = math_ops.truediv(values - value_range[0],
# value_range[1] - value_range[0],
# name='scaled_values') # values - value_range[0] / value_range[1] - value_range[0]
scaled_values = tf.truediv(values - value_range[0],value_range[1] - value_range[0])
scaled_values =tf.multiply(nbins_float,scaled_values)
# map tensor values within the open interval value_range to {0,.., nbins-1},
# values outside the open interval will be zero or less, or nbins or more.
# indices = math_ops.floor(nbins_float * scaled_values, name='indices')
indices = tf.floor(scaled_values)
print(indices)
print(type(indices))
histo = indices
# Clip edge cases (e.g. value = value_range[1]) or "outliers."
#indices = math_ops.cast(
# clip_ops.clip_by_value(indices, 0, nbins_float- 1), dtypes.int32)
# TODO(langmore) This creates an array of ones to add up and place in the
# bins. This is inefficient, so replace when a better Op is available.
#histo= math_ops.unsorted_segment_sum(array_ops.ones_like(indices, dtype=dtype),indices,nbins)
return(histo)
def is_square(apositiveint):
x = apositiveint // 2
seen = set([x])
while x * x != apositiveint:
x = (x + (apositiveint // x)) // 2
if x in seen: return False
seen.add(x)
return True
def plot_and_save(Matrix,path,name=''):
Matrix = Matrix[0] # Remove first dim
h,w,channels = Matrix.shape
df_Matrix = pd.DataFrame(np.reshape(Matrix,(h*w,channels)))
len_columns = len(df_Matrix.columns)
if(len_columns<6):
fig, axes = plt.subplots(1,len_columns)
else:
if(len_columns%4==0):
fig, axes = plt.subplots(len_columns//4, 4)
elif(len_columns%3==0):
fig, axes = plt.subplots(len_columns//3, 3)
elif(len_columns%5==0):
fig, axes = plt.subplots(len_columns//5, 5)
elif(len_columns%2==0):
fig, axes = plt.subplots(len_columns//2, 2)
else:
j=6
while(not(len_columns%j==0)):
j += 1
fig, axes = plt.subplots(len_columns//j, j)
i = 0
axes = axes.flatten()
for axis in zip(axes):
df_Matrix.hist(column = i, bins = 64, ax=axis)
i += 1
pltname = path+name+'.png'
# TODO avoid to Plot some ligne on the screen
fig.savefig(pltname, dpi = 1000)
def plot_and_save_pdf(Matrix,path,name=''):
pltname = path+name+'_hist.pdf'
pltname_rep = path+name+'_img.pdf'
pp = PdfPages(pltname)
Matrix = Matrix[0] # Remove first dim
h,w,channels = Matrix.shape
df_Matrix = pd.DataFrame(np.reshape(Matrix,(h*w,channels)))
len_columns = len(df_Matrix.columns)
for i in range(len_columns):
df_Matrix.hist(column = i, bins = 128)
plt.savefig(pp, format='pdf')
plt.close()
pp.close()
plt.clf()
# Result of the convolution
pp_img = PdfPages(pltname_rep)
for i in range(len_columns):
plt.imshow(Matrix[:,:,i], cmap='gray')
plt.savefig(pp_img, format='pdf')
plt.close()
pp_img.close()
def plot_Rep(args):
"""
Plot the reponse to the filters/kernels and the histogram
"""
directory_path = 'Results/Filter_Rep/'+args.style_img_name+'/'
if not os.path.exists(directory_path):
os.makedirs(directory_path)
sns.set()
image_style_path = args.img_folder + args.style_img_name + args.img_ext
image_style = st.preprocess(scipy.misc.imread(image_style_path).astype('float32'))
_,image_h_art, image_w_art, _ = image_style.shape
plot_and_save_pdf(image_style,directory_path,'ProcessIm')
print("Plot initial image")
vgg_layers = st.get_vgg_layers()
net = st.net_preloaded(vgg_layers, image_style) # net for the style image
sess = tf.Session()
sess.run(net['input'].assign(image_style))
for layer in VGG19_LAYERS_INTEREST:
a = net[layer].eval(session=sess)
print(layer,a.shape)
plot_and_save_pdf(a,directory_path,layer)
def estimate_gennorm(args):
sns.set_style("white")
image_style_path = args.img_folder + args.style_img_name + args.img_ext
image_style = st.preprocess(scipy.misc.imread(image_style_path).astype('float32'))
vgg_layers = st.get_vgg_layers()
net = st.net_preloaded(vgg_layers, image_style) # net for the style image
sess = tf.Session()
sess.run(net['input'].assign(image_style))
Distrib_Estimation = {}
dict_pvalue = {}
alpha = 0.1
for layer in VGG19_LAYERS_INTEREST:
print(layer)
a = net[layer].eval(session=sess)
a = a[0]
h,w,number_of_features = a.shape
a_reshaped = np.reshape(a,(h*w,number_of_features))
print(h*w)
Distrib_Estimation[layer] = np.array([])
dict_pvalue[layer] = []
for i in range(number_of_features):
print(i)
samples = a_reshaped[:,i]
# This fit is computed by maximizing a log-likelihood function, with
# penalty applied for samples outside of range of the distribution. The
# returned answer is not guaranteed to be the globally optimal MLE, it
# may only be locally optimal, or the optimization may fail altogether.
beta, loc, scale = stats.gennorm.fit(samples)
if(len(Distrib_Estimation[layer])==0):
print("Number of points",len(samples))
Distrib_Estimation[layer] = np.array([beta,loc,scale])
else:
Distrib_Estimation[layer] = np.vstack((Distrib_Estimation[layer],np.array([beta,loc,scale])))
# The KS test is only valid for continuous distributions. and with a theoritical distribution
D,pvalue = stats.kstest(samples, 'gennorm',(beta, loc, scale ))
dict_pvalue[layer] += [pvalue]
if(pvalue > alpha ): #p-value> α
print(layer,i,pvalue)
pass
#print(Distrib_Estimation[layer])
#print(dict_pvalue[layer])
return(Distrib_Estimation)
def unpool(value, name='unpool'):
"""N-dimensional version of the unpooling operation from
https://www.robots.ox.ac.uk/~vgg/rg/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf
:param value: A Tensor of shape [b, d0, d1, ..., dn, ch]
:return: A Tensor of shape [b, 2*d0, 2*d1, ..., 2*dn, ch]
"""
with tf.name_scope(name) as scope:
sh = value.get_shape().as_list()
dim = len(sh[1:-1])
out = (tf.reshape(value, [-1] + sh[-dim:]))
for i in range(dim, 0, -1):
out = tf.concat(i, [out, tf.zeros_like(out)])
out_size = [-1] + [s * 2 for s in sh[1:-1]] + [sh[-1]]
out = tf.reshape(out, out_size, name=scope)
return(out)
def calculate_output_shape(in_layer, n_kernel, kernel_size, border_mode='same'):
"""
Always assumes stride=1
"""
in_shape = in_layer.get_shape() # assumes in_shape[0] = None or batch_size
out_shape = [s for s in in_shape] # copy
out_shape[-1] = n_kernel # always true
if border_mode=='same':
out_shape[1] = in_shape[1]
out_shape[2] = in_shape[2]
elif border_mode == 'valid':
out_shape[1] = tf.to_int32(in_shape[1]+kernel_size - 1)
out_shape[2] = tf.to_int32(in_shape[2]+kernel_size - 1)
return(out_shape)
def genTexture(args):
image_style_path = args.img_folder + args.style_img_name + args.img_ext
image_style = st.preprocess(scipy.misc.imread(image_style_path).astype('float32'))
output_image_path = args.img_folder + args.output_img_name + args.img_ext
vgg_layers = st.get_vgg_layers()
net = st.net_preloaded(vgg_layers, image_style) # net for the style image
sess = tf.Session()
sess.run(net['input'].assign(image_style))
Distrib_Estimation = {}
dict_pvalue = {}
data = 'data.pkl'
try:
Distrib_Estimation = pickle.load(open(data, 'rb'))
except(FileNotFoundError):
for layer in VGG19_LAYERS_INTEREST:
print(layer)
a = net[layer].eval(session=sess)
a = a[0]
h,w,number_of_features = a.shape
a_reshaped = np.reshape(a,(h*w,number_of_features))
Distrib_Estimation[layer] = np.array([])
dict_pvalue[layer] = []
for i in range(number_of_features):
samples = a_reshaped[:,i]
# This fit is computed by maximizing a log-likelihood function, with
# penalty applied for samples outside of range of the distribution. The
# returned answer is not guaranteed to be the globally optimal MLE, it
# may only be locally optimal, or the optimization may fail altogether.
beta, loc, scale = stats.gennorm.fit(samples)
if(len(Distrib_Estimation[layer])==0):
print("Number of points",len(samples))
Distrib_Estimation[layer] = np.array([beta,loc,scale])
else:
Distrib_Estimation[layer] = np.vstack((Distrib_Estimation[layer],np.array([beta,loc,scale])))
# chaque ligne est un channel
# The KS test is only valid for continuous distributions. and with a theoritical distribution
D,pvalue = stats.kstest(samples, 'gennorm',(beta, loc, scale ))
dict_pvalue[layer] += [pvalue]
with open(data, 'wb') as output:
pickle.dump(Distrib_Estimation,output)
print('End Computation of marginal distrib')
generative_net = {}
generative_net['conv3_1_input'] = tf.Variable(np.zeros(net['conv3_1'].shape, dtype=np.float32))
weights = tf.constant(np.transpose(vgg_layers[10][0][0][2][0][0], (1, 0, 2, 3)))
bias = -tf.constant(vgg_layers[10][0][0][2][0][1].reshape(-1))
#print(weights.get_shape()[0],weights.get_shape()[1],weights.get_shape()[2],weights.get_shape()[3])
#print(calculate_output_shape(generative_net['conv3_1_input'],tf.to_int32(tf.shape(weights)[3]),tf.to_int32(tf.shape(weights)[1])))
generative_net['conv3_1'] = tf.nn.conv2d_transpose(value=tf.nn.bias_add(generative_net['conv3_1_input'],bias),filter=weights,
output_shape=calculate_output_shape(generative_net['conv3_1_input'],256,3),
strides=(1, 1, 1, 1), padding='SAME')
generative_net['pool2'] = unpool(generative_net['conv3_1'])
# RELU ???????
weights = tf.constant(np.transpose(vgg_layers[7][0][0][2][0][0], (1, 0, 2, 3)))
bias = -tf.constant(vgg_layers[7][0][0][2][0][1].reshape(-1))
generative_net['conv2_2'] = tf.nn.conv2d_transpose(tf.nn.bias_add(generative_net['pool2'],bias),
tf.shape(generative_net['pool2']), weights, strides=(1, 1, 1, 1), padding='SAME')
weights = tf.constant(np.transpose(vgg_layers[5][0][0][2][0][0], (1, 0, 2, 3)))
bias = -tf.constant(vgg_layers[5][0][0][2][0][1].reshape(-1))
generative_net['conv2_1'] = tf.nn.conv2d_transpose(tf.nn.bias_add(generative_net['conv2_2'],bias),
tf.shape(generative_net['conv2_2']),weights, strides=(1, 1, 1, 1), padding='SAME')
generative_net['pool1'] = unpool(generative_net['conv2_1'])
weights = tf.constant(np.transpose(vgg_layers[2][0][0][2][0][0], (1, 0, 2, 3)))
bias = -tf.constant(vgg_layers[2][0][0][2][0][1].reshape(-1))
generative_net['conv1_2'] = tf.nn.conv2d_transpose(tf.nn.bias_add(generative_net['pool2'],bias),
tf.shape(generative_net['pool1']),weights, strides=(1, 1, 1, 1), padding='SAME')
weights = tf.constant(np.transpose(vgg_layers[0][0][0][2][0][0], (1, 0, 2, 3)))
bias = -tf.constant(vgg_layers[0][0][0][2][0][1].reshape(-1))
generative_net['conv1_1'] = tf.nn.conv2d_transpose(tf.nn.bias_add(generative_net['conv2_2'],bias),
tf.shape(generative_net['conv1_2']).shape,weights, strides=(1, 1, 1, 1), padding='SAME')
generative_net['output'] = tf.Variable(np.zeros(image_style.shape).astype('float32'))
# Random draw marginal distribution
for layer in VGG19_LAYERS_INTEREST:
print(layer)
a = net[layer].eval(session=sess)
a = a[0]
h,w,number_of_features = a.shape
#number_samples = h*w
#a_reshaped = np.reshape(a,(h*w,number_of_features))
distribs = Distrib_Estimation[layer]
generative_filters_response = np.zeros(net[layer].shape, dtype=np.float32)
for i in range(number_of_features):
print(i)
beta, loc, scale = distribs[i,:]
r = stats.gennorm.rvs(beta,loc=loc,scale=scale, size=(h,w))
generative_filters_response[1,:,:,i] = r
sess.run(net['conv3_1_input'].assign(generative_filters_response))
print('End generative initialisation')
result_img = sess.run(net['input'])
result_img_postproc = st.postprocess(result_img)
scipy.misc.toimage(result_img_postproc).save(output_image_path)
def generateArt(args):
if args.verbose:
print("verbosity turned on")
output_image_path = args.img_folder + args.output_img_name +args.img_ext
image_style_path = args.img_folder + args.style_img_name + args.img_ext
image_style = st.preprocess(scipy.misc.imread(image_style_path).astype('float32'))
_,image_h_art, image_w_art, _ = image_style.shape
t1 = time.time()
vgg_layers = st.get_vgg_layers()
net = st.net_preloaded(vgg_layers, image_style) # The output image as the same size as the content one
t2 = time.time()
if(args.verbose): print("net loaded and gram computation after ",t2-t1," s")
try:
sess = tf.Session()
init_img = st.get_init_noise_img(image_style,1)
loss_total = hist_style_loss(sess,net,image_style)
if(args.verbose): print("init loss total")
print(tf.trainable_variables())
#optimizer = tf.train.AdamOptimizer(args.learning_rate) # Gradient Descent
#train = optimizer.minimize(loss_total)
bnds = st.get_lbfgs_bnds(init_img)
optimizer_kwargs = {'maxiter': args.max_iter,'iprint': args.print_iter}
optimizer = tf.contrib.opt.ScipyOptimizerInterface(loss_total,bounds=bnds, method='L-BFGS-B',options=optimizer_kwargs)
sess.run(tf.global_variables_initializer())
sess.run(net['input'].assign(init_img)) # This line must be after variables initialization !
optimizer.minimize(sess)
t3 = time.time()
if(args.verbose): print("sess Adam initialized after ",t3-t2," s")
if(args.verbose): print("loss before optimization")
if(args.verbose): print(sess.run(loss_total))
# for i in range(args.max_iter):
# if(i%args.print_iter==0):
# t3 = time.time()
# sess.run(train)
# t4 = time.time()
# if(args.verbose): print("Iteration ",i, "after ",t4-t3," s")
# if(args.verbose): print(sess.run(loss_total))
# result_img = sess.run(net['input'])
# result_img_postproc = st.postprocess(result_img)
# scipy.misc.toimage(result_img_postproc).save(output_image_path)
# else:
# sess.run(train)
except:
print("Error")
result_img = sess.run(net['input'])
result_img_postproc = st.postprocess(result_img)
output_image_path_error = args.img_folder + args.output_img_name+'_error' +args.img_ext
scipy.misc.toimage(result_img_postproc).save(output_image_path_error)
raise
finally:
if(args.verbose): print("Close Sess")
sess.close()
def hist_style_loss(sess,net,style_img):
#value_range = [-2000.0,2000.0] # TODO change according to the layer
value_range = [-2000.0,2000.0]
style_value_range = {'conv1_1' : [-200.0,200.0],'conv2_1': [-500.0,500.0],'conv3_1' : [-2000.0,2000.0] }
nbins = 2048
style_layers = [('conv1_1',1.),('conv2_1',1.),('conv3_1',1.)]
#style_layers = [('conv1_1',1.)]
#style_layers_size = {'conv1' : 64,'conv2' : 128,'conv3' : 256,'conv4': 512,'conv5' : 512}
length_style_layers = float(len(style_layers))
sess.run(net['input'].assign(style_img))
style_loss = 0.0
weight_help_convergence = 10**9
for layer, weight in style_layers:
value_range = style_value_range[layer]
style_loss_layer = 0.0
a = sess.run(net[layer])
_,h,w,N = a.shape
M =h*w
tf_M = tf.to_int32(M)
tf_N = tf.to_int32(N)
a_reshaped = tf.reshape(a,[tf_M,tf_N])
a_split = tf.unstack(a_reshaped,axis=1)
x = net[layer]
#print("x.op",x.op)
x_reshaped = tf.reshape(x,[tf_M,tf_N])
x_split = tf.unstack(x_reshaped,axis=1)
for a_slide,x_slide in zip(a_split,x_split): # N iteration
# Descripteur des representations des histogrammes moment d'ordre 1 a N
#hist_a = hist(a_slide,value_range, nbins=nbins,dtype=tf.float32)
#hist_x = hist(x_slide,value_range, nbins=nbins,dtype=tf.float32)
hist_a = tf.histogram_fixed_width(a_slide, value_range, nbins=nbins,dtype=tf.float32)
hist_x = tf.histogram_fixed_width(x_slide, value_range, nbins=nbins,dtype=tf.float32)
#hist_a = tf.floor(a_slide)
#hist_x = tf.floor(x_slide)
# TODO normalized les histogrammes avant le calcul plutot qu'apres
#style_loss_layer += tf.to_float(tf.reduce_mean(tf.abs(hist_a- hist_x))) # norm L1
#style_loss_layer += tf.reduce_mean(tf.pow(hist_a- hist_x,2)) # Norm L2
style_loss_layer += tf.sqrt(1-tf.reduce_sum(tf.multiply(tf.sqrt(hist_a),tf.sqrt(hist_x))))
# TODO use bhattacharyya distance
style_loss_layer *= weight * weight_help_convergence / (2.*tf.to_float(N**2)*tf.to_float(M**2)*length_style_layers)
style_loss += style_loss_layer
return(style_loss)
def do_pdf_comparison(args):
directory_path = 'Results/Rep/'+args.style_img_name+'/'
if not os.path.exists(directory_path):
os.makedirs(directory_path)
sns.set_style("white")
image_style_path = args.img_folder + args.style_img_name + args.img_ext
image_style = st.preprocess(scipy.misc.imread(image_style_path).astype('float32'))
_,image_h_art, image_w_art, _ = image_style.shape
vgg_layers = st.get_vgg_layers()
net = st.net_preloaded(vgg_layers, image_style) # net for the style image
placeholder = tf.placeholder(tf.float32, shape=image_style.shape)
assign_op = net['input'].assign(placeholder)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(assign_op, {placeholder: image_style})
sess.graph.finalize()
for layer in VGG19_LAYERS_INTEREST:
a = net[layer].eval(session=sess)
print(layer,a.shape)
plot_compare_pdf(vgg_layers,a,directory_path,layer)
def plot_compare_pdf(vgg_layers,Matrix,path,name):
number_img_large_tab = {'conv1_1' : 1,'conv1_2' : 4,'conv2_1' : 4,'conv2_2' : 8,
'conv3_1' : 8,'conv3_2' : 8,'conv3_3' : 16,'conv3_4' : 16,'conv4_1' : 16,
'conv4_2' : 16,'conv4_3' : 16,'conv4_4' : 16,'conv5_1' : 16,'conv5_2' : 16,
'conv5_3' : 16,'conv5_4' : 16}
pltname = path+name+'_comp.pdf'
pp = PdfPages(pltname)
Matrix = Matrix[0] # Remove first dim
h,w,channels = Matrix.shape
Matrix_reshaped = np.reshape(Matrix,(h*w,channels))
df_Matrix = pd.DataFrame(Matrix_reshaped)
len_columns = len(df_Matrix.columns)
index_in_vgg = VGG19_LAYERS_INDICES[name]
kernels = vgg_layers[index_in_vgg][0][0][2][0][0]
# A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
print(kernels.shape)
bias = vgg_layers[index_in_vgg][0][0][2][0][1]
print(bias.shape)
#len_columns = 1
input_kernel = kernels.shape[2]
alpha=0.75
#cmkernel = 'gray'
cmImg = 'jet'
cmkernel = plt.get_cmap('hot')
for i in range(len_columns):
#print("Features",i)
# For each feature
f = plt.figure()
gs0 = gridspec.GridSpec(1,3, width_ratios=[0.05,4,4]) # 2 columns
axcm = plt.subplot(gs0[0])
number_img_large = number_img_large_tab[name]
if(not(name=='conv1_1')):
gs00 = gridspec.GridSpecFromSubplotSpec(input_kernel//number_img_large, number_img_large, subplot_spec=gs0[1])
axes = []
for j in range(input_kernel):
ax = plt.subplot(gs00[j])
axes += [ax]
kernel = kernels[:,:,:,i]
mean_kernel = np.mean(kernel)
bias_i = bias[i,0]
j = 0
vmin = np.min(kernel)
vmax = np.max(kernel)
for ax in axes:
im = ax.matshow(kernel[:,:,j],cmap=cmkernel,alpha=alpha,vmin=vmin, vmax=vmax)
ax.axis('off')
j += 1
else:
gs00 = gridspec.GridSpecFromSubplotSpec(4, 1, subplot_spec=gs0[1])
axes = []
for j in range(input_kernel):
ax = plt.subplot(gs00[j])
axes += [ax]
kernel = kernels[:,:,:,i]
mean_kernel = np.mean(kernel)
bias_i = bias[i,0]
j = 0
vmin = np.min(kernel)
vmax = np.max(kernel)
for ax in axes:
im = ax.matshow(kernel[:,:,j],cmap=cmkernel,alpha=alpha,vmin=vmin, vmax=vmax)
ax.axis('off')
j += 1
ax0 = plt.subplot(gs00[3])
# bgr to rgb
img = kernel[...,::-1]
#img = Image.fromarray(img, 'RGB')
#img = exposure.rescale_intensity(img, in_range='uint8')
img -= np.min(img)
img /= (np.max(img)/255.)
img = np.floor(img).astype('uint8')
ax0.imshow(img)
ax0.axis('off')
ax0.set_title('Color Kernel')
plt.colorbar(im, cax=axcm)
#plt.colorbar(im, cax=axes[-1])
#f.subplots_adjust(right=0.8)
#cbar_ax = f.add_axes([0.85, 0.15, 0.05, 0.7])
#f.colorbar(im, cax=cbar_ax)
#plt.colorbar(im, cax=axes)
gs01 = gridspec.GridSpecFromSubplotSpec(2, 1, subplot_spec=gs0[2],height_ratios=[4,3])
ax1 = plt.subplot(gs01[1])
ax2 = plt.subplot(gs01[0])
samples = Matrix_reshaped[:,i]
beta, loc, scale = stats.gennorm.fit(samples)
D,pvalue = stats.kstest(samples, 'gennorm',(beta, loc, scale )) # 1-alph = 0.9
#D,pvalue = stats.stats.ks_2samp(samples,stats.gennorm.rvs(beta,loc=loc,scale=scale,size=len(samples) ))
Dcritial = 1.224/math.sqrt(len(samples))
#print("pvalue",pvalue)
df_Matrix.hist(column = i, bins = 128,ax=ax1,normed=True, histtype='stepfilled', alpha=1)
x = np.linspace(stats.gennorm.ppf(0.005, beta, loc, scale),
stats.gennorm.ppf(0.995, beta, loc, scale), 128)
ax1.plot(x, stats.gennorm.pdf(x, beta, loc, scale ),'r-',alpha=0.4, label='gennorm pdf')
#ax1.legend(loc='best', frameon=False)
#textstr = '$\mu=%.2f$\n$\mathrm{scale}=%.2f$\n$beta=%.2f$ \n $\mathrm{D}=%.4f$ \n $\mathrm{Dcri}=%.4f$ '%(loc,scale,beta,D,Dcritial)
textstr = '$\mu=%.2f$\n$\mathrm{scale}=%.2f$\n$beta=%.2f$\n$\mathrm{pvalue}=%.4f$'%(loc,scale,beta,pvalue)
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
ax1.text(0.05, 0.95, textstr, transform=ax1.transAxes, fontsize=6,
verticalalignment='top', bbox=props)
ax2.matshow(Matrix[:,:,i], cmap=cmImg)
ax2.axis('off')
titre = 'Kernel {} with mean = {:.2e} in range [{:.2e},{:.2e}] and bias = {:.2e}'.format(i,mean_kernel,vmin,vmax,bias_i)
plt.suptitle(titre)
#gs0.tight_layout(f)
plt.savefig(pp, format='pdf')
plt.close()
pp.close()
plt.clf()
def main_plot(name=None):
"""
Plot the reponse to the filters/kernels and histogram in differents pdfs
"""
parser = get_parser_args()
if(name==None):
style_img_name = "StarryNight"
else:
style_img_name = name
#style_img_name = "Louvre_Big"
parser.set_defaults(style_img_name=style_img_name)
args = parser.parse_args()
plot_Rep(args)
def main_distrib(name=None):
"""
Estimate the distribution of the distribution
"""
parser = get_parser_args()
if(name==None):
style_img_name = "StarryNight"
else:
style_img_name = name
parser.set_defaults(style_img_name=style_img_name)
args = parser.parse_args()
estimate_gennorm(args)
def main_plot_commun(name=None):
"""
Plot for each layer in VGG Interest the kernels, the response of the
kernel but also the histogram fitted
"""
parser = get_parser_args()
if(name==None):
style_img_name = "StarryNight"
else:
style_img_name = name
parser.set_defaults(style_img_name=style_img_name)
args = parser.parse_args()
do_pdf_comparison(args)
if __name__ == '__main__':
main_plot_commun('grad_Uniform')
| gpl-3.0 |
htygithub/bokeh | examples/glyphs/trail.py | 2 | 4262 | # -*- coding: utf-8 -*-
from __future__ import print_function
from math import sin, cos, atan2, sqrt, radians
import numpy as np
import scipy.ndimage as im
from bokeh.document import Document
from bokeh.embed import file_html
from bokeh.resources import INLINE
from bokeh.browserlib import view
from bokeh.models.glyphs import Line, Patches
from bokeh.models.widgets import VBox
from bokeh.models import (
Plot, GMapPlot, GMapOptions,
DataRange1d, ColumnDataSource,
LinearAxis, Grid,
PanTool, WheelZoomTool, ResetTool)
from bokeh.sampledata.mtb import obiszow_mtb_xcm
def haversin(theta):
return sin(0.5 * theta) ** 2
def distance(p1, p2):
"""Distance between (lat1, lon1) and (lat2, lon2). """
R = 6371
lat1, lon1 = p1
lat2, lon2 = p2
phi1 = radians(lat1)
phi2 = radians(lat2)
delta_lat = radians(lat2 - lat1)
delta_lon = radians(lon2 - lon1)
a = haversin(delta_lat) + cos(phi1) * cos(phi2) * haversin(delta_lon)
return 2 * R * atan2(sqrt(a), sqrt(1 - a))
def prep_data(dataset):
df = dataset.copy()
latlon = list(zip(df.lat, df.lon))
dist = np.array([distance(latlon[i + 1], latlon[i]) for i in range(len((latlon[:-1])))])
df["dist"] = np.concatenate(([0], np.cumsum(dist)))
slope = np.abs(100 * np.diff(df.alt) / (1000 * dist))
slope[np.where( slope < 4) ] = 0 # "green"
slope[np.where((slope >= 4) & (slope < 6))] = 1 # "yellow"
slope[np.where((slope >= 6) & (slope < 10))] = 2 # "pink"
slope[np.where((slope >= 10) & (slope < 15))] = 3 # "orange"
slope[np.where( slope >= 15 )] = 4 # "red"
slope = im.median_filter(slope, 6)
colors = np.empty_like(slope, dtype=object)
colors[np.where(slope == 0)] = "green"
colors[np.where(slope == 1)] = "yellow"
colors[np.where(slope == 2)] = "pink"
colors[np.where(slope == 3)] = "orange"
colors[np.where(slope == 4)] = "red"
df["colors"] = list(colors) + [None] # NOTE: add [None] just make pandas happy
return df
title = "Obiszów MTB XCM"
def trail_map(data):
lon = (min(data.lon) + max(data.lon)) / 2
lat = (min(data.lat) + max(data.lat)) / 2
map_options = GMapOptions(lng=lon, lat=lat, zoom=13)
plot = GMapPlot(title="%s - Trail Map" % title, map_options=map_options, plot_width=800, plot_height=800)
plot.x_range = DataRange1d()
plot.y_range = DataRange1d()
plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
line_source = ColumnDataSource(dict(x=data.lon, y=data.lat, dist=data.dist))
line = Line(x="x", y="y", line_color="blue", line_width=2)
plot.add_glyph(line_source, line)
return plot
def altitude_profile(data):
plot = Plot(title="%s - Altitude Profile" % title, plot_width=800, plot_height=400)
plot.x_range = DataRange1d()
plot.y_range = DataRange1d()
xaxis = LinearAxis(axis_label="Distance (km)")
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis(axis_label="Altitude (m)")
plot.add_layout(yaxis, 'left')
xgrid = Grid(plot=plot, dimension=0, ticker=xaxis.ticker)
ygrid = Grid(plot=plot, dimension=1, ticker=yaxis.ticker)
plot.renderers.extend([xgrid, ygrid])
plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
X, Y = data.dist, data.alt
y0 = min(Y)
patches_source = ColumnDataSource(dict(
xs=[[X[i], X[i+1], X[i+1], X[i]] for i in range(len(X[:-1])) ],
ys=[[y0, y0, Y[i+1], Y[i]] for i in range(len(Y[:-1])) ],
color=data.colors[:-1]
))
patches = Patches(xs="xs", ys="ys", fill_color="color", line_color="color")
plot.add_glyph(patches_source, patches)
line_source = ColumnDataSource(dict(x=data.dist, y=data.alt))
line = Line(x='x', y='y', line_color="black", line_width=1)
plot.add_glyph(line_source, line)
return plot
data = prep_data(obiszow_mtb_xcm)
trail = trail_map(data)
altitude = altitude_profile(data)
layout = VBox(children=[altitude, trail])
doc = Document()
doc.add_root(layout)
if __name__ == "__main__":
filename = "trail.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Trail map and altitude profile"))
print("Wrote %s" % filename)
view(filename)
| bsd-3-clause |
fspaolo/scikit-learn | sklearn/datasets/tests/test_base.py | 8 | 5607 | import os
import shutil
import tempfile
import warnings
import nose
import numpy
from sklearn.datasets import get_data_home
from sklearn.datasets import clear_data_home
from sklearn.datasets import load_files
from sklearn.datasets import load_sample_images
from sklearn.datasets import load_sample_image
from sklearn.datasets import load_digits
from sklearn.datasets import load_diabetes
from sklearn.datasets import load_linnerud
from sklearn.datasets import load_iris
from sklearn.datasets import load_boston
from sklearn.externals.six import b, u
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_raises
DATA_HOME = tempfile.mkdtemp(prefix="scikit_learn_data_home_test_")
LOAD_FILES_ROOT = tempfile.mkdtemp(prefix="scikit_learn_load_files_test_")
TEST_CATEGORY_DIR1 = ""
TEST_CATEGORY_DIR2 = ""
def _remove_dir(path):
if os.path.isdir(path):
shutil.rmtree(path)
def teardown_module():
"""Test fixture (clean up) run once after all tests of this module"""
for path in [DATA_HOME, LOAD_FILES_ROOT]:
_remove_dir(path)
def setup_load_files():
global TEST_CATEGORY_DIR1
global TEST_CATEGORY_DIR2
TEST_CATEGORY_DIR1 = tempfile.mkdtemp(dir=LOAD_FILES_ROOT)
TEST_CATEGORY_DIR2 = tempfile.mkdtemp(dir=LOAD_FILES_ROOT)
sample_file = tempfile.NamedTemporaryFile(dir=TEST_CATEGORY_DIR1,
delete=False)
sample_file.write(b("Hello World!\n"))
sample_file.close()
def teardown_load_files():
_remove_dir(TEST_CATEGORY_DIR1)
_remove_dir(TEST_CATEGORY_DIR2)
def test_data_home():
# get_data_home will point to a pre-existing folder
data_home = get_data_home(data_home=DATA_HOME)
assert_equal(data_home, DATA_HOME)
assert_true(os.path.exists(data_home))
# clear_data_home will delete both the content and the folder it-self
clear_data_home(data_home=data_home)
assert_false(os.path.exists(data_home))
# if the folder is missing it will be created again
data_home = get_data_home(data_home=DATA_HOME)
assert_true(os.path.exists(data_home))
def test_default_empty_load_files():
res = load_files(LOAD_FILES_ROOT)
assert_equal(len(res.filenames), 0)
assert_equal(len(res.target_names), 0)
assert_equal(res.DESCR, None)
@nose.tools.with_setup(setup_load_files, teardown_load_files)
def test_default_load_files():
res = load_files(LOAD_FILES_ROOT)
assert_equal(len(res.filenames), 1)
assert_equal(len(res.target_names), 2)
assert_equal(res.DESCR, None)
assert_equal(res.data, [b("Hello World!\n")])
@nose.tools.with_setup(setup_load_files, teardown_load_files)
def test_load_files_w_categories_desc_and_encoding():
category = os.path.abspath(TEST_CATEGORY_DIR1).split('/').pop()
res = load_files(LOAD_FILES_ROOT, description="test",
categories=category, encoding="utf-8")
assert_equal(len(res.filenames), 1)
assert_equal(len(res.target_names), 1)
assert_equal(res.DESCR, "test")
assert_equal(res.data, [u("Hello World!\n")])
@nose.tools.with_setup(setup_load_files, teardown_load_files)
def test_load_files_wo_load_content():
res = load_files(LOAD_FILES_ROOT, load_content=False)
assert_equal(len(res.filenames), 1)
assert_equal(len(res.target_names), 2)
assert_equal(res.DESCR, None)
assert_equal(res.get('data'), None)
def test_load_sample_images():
try:
res = load_sample_images()
assert_equal(len(res.images), 2)
assert_equal(len(res.filenames), 2)
assert_true(res.DESCR)
except ImportError:
warnings.warn("Could not load sample images, PIL is not available.")
def test_load_digits():
digits = load_digits()
assert_equal(digits.data.shape, (1797, 64))
assert_equal(numpy.unique(digits.target).size, 10)
def test_load_digits_n_class_lt_10():
digits = load_digits(9)
assert_equal(digits.data.shape, (1617, 64))
assert_equal(numpy.unique(digits.target).size, 9)
def test_load_sample_image():
try:
china = load_sample_image('china.jpg')
assert_equal(china.dtype, 'uint8')
assert_equal(china.shape, (427, 640, 3))
except ImportError:
warnings.warn("Could not load sample images, PIL is not available.")
def test_load_missing_sample_image_error():
have_PIL = True
try:
try:
from scipy.misc import imread
except ImportError:
from scipy.misc.pilutil import imread
except ImportError:
have_PIL = False
if have_PIL:
assert_raises(AttributeError, load_sample_image,
'blop.jpg')
else:
warnings.warn("Could not load sample images, PIL is not available.")
def test_load_diabetes():
res = load_diabetes()
assert_equal(res.data.shape, (442, 10))
assert_true(res.target.size, 442)
def test_load_linnerud():
res = load_linnerud()
assert_equal(res.data.shape, (20, 3))
assert_equal(res.target.shape, (20, 3))
assert_equal(len(res.target_names), 3)
assert_true(res.DESCR)
def test_load_iris():
res = load_iris()
assert_equal(res.data.shape, (150, 4))
assert_equal(res.target.size, 150)
assert_equal(res.target_names.size, 3)
assert_true(res.DESCR)
def test_load_boston():
res = load_boston()
assert_equal(res.data.shape, (506, 13))
assert_equal(res.target.size, 506)
assert_equal(res.feature_names.size, 14)
assert_true(res.DESCR)
| bsd-3-clause |
glouppe/scikit-learn | sklearn/linear_model/tests/test_randomized_l1.py | 35 | 4726 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.linear_model.randomized_l1 import (lasso_stability_path,
RandomizedLasso,
RandomizedLogisticRegression)
from sklearn.datasets import load_diabetes, load_iris
from sklearn.feature_selection import f_regression, f_classif
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model.base import center_data
diabetes = load_diabetes()
X = diabetes.data
y = diabetes.target
X = StandardScaler().fit_transform(X)
X = X[:, [2, 3, 6, 7, 8]]
# test that the feature score of the best features
F, _ = f_regression(X, y)
def test_lasso_stability_path():
# Check lasso stability path
# Load diabetes data and add noisy features
scaling = 0.3
coef_grid, scores_path = lasso_stability_path(X, y, scaling=scaling,
random_state=42,
n_resampling=30)
assert_array_equal(np.argsort(F)[-3:],
np.argsort(np.sum(scores_path, axis=1))[-3:])
def test_randomized_lasso():
# Check randomized lasso
scaling = 0.3
selection_threshold = 0.5
# or with 1 alpha
clf = RandomizedLasso(verbose=False, alpha=1, random_state=42,
scaling=scaling,
selection_threshold=selection_threshold)
feature_scores = clf.fit(X, y).scores_
assert_array_equal(np.argsort(F)[-3:], np.argsort(feature_scores)[-3:])
# or with many alphas
clf = RandomizedLasso(verbose=False, alpha=[1, 0.8], random_state=42,
scaling=scaling,
selection_threshold=selection_threshold)
feature_scores = clf.fit(X, y).scores_
assert_equal(clf.all_scores_.shape, (X.shape[1], 2))
assert_array_equal(np.argsort(F)[-3:], np.argsort(feature_scores)[-3:])
X_r = clf.transform(X)
X_full = clf.inverse_transform(X_r)
assert_equal(X_r.shape[1], np.sum(feature_scores > selection_threshold))
assert_equal(X_full.shape, X.shape)
clf = RandomizedLasso(verbose=False, alpha='aic', random_state=42,
scaling=scaling)
feature_scores = clf.fit(X, y).scores_
assert_array_equal(feature_scores, X.shape[1] * [1.])
clf = RandomizedLasso(verbose=False, scaling=-0.1)
assert_raises(ValueError, clf.fit, X, y)
clf = RandomizedLasso(verbose=False, scaling=1.1)
assert_raises(ValueError, clf.fit, X, y)
def test_randomized_logistic():
# Check randomized sparse logistic regression
iris = load_iris()
X = iris.data[:, [0, 2]]
y = iris.target
X = X[y != 2]
y = y[y != 2]
F, _ = f_classif(X, y)
scaling = 0.3
clf = RandomizedLogisticRegression(verbose=False, C=1., random_state=42,
scaling=scaling, n_resampling=50,
tol=1e-3)
X_orig = X.copy()
feature_scores = clf.fit(X, y).scores_
assert_array_equal(X, X_orig) # fit does not modify X
assert_array_equal(np.argsort(F), np.argsort(feature_scores))
clf = RandomizedLogisticRegression(verbose=False, C=[1., 0.5],
random_state=42, scaling=scaling,
n_resampling=50, tol=1e-3)
feature_scores = clf.fit(X, y).scores_
assert_array_equal(np.argsort(F), np.argsort(feature_scores))
def test_randomized_logistic_sparse():
# Check randomized sparse logistic regression on sparse data
iris = load_iris()
X = iris.data[:, [0, 2]]
y = iris.target
X = X[y != 2]
y = y[y != 2]
# center here because sparse matrices are usually not centered
# labels should not be centered
X, _, _, _, _ = center_data(X, y, True, True)
X_sp = sparse.csr_matrix(X)
F, _ = f_classif(X, y)
scaling = 0.3
clf = RandomizedLogisticRegression(verbose=False, C=1., random_state=42,
scaling=scaling, n_resampling=50,
tol=1e-3)
feature_scores = clf.fit(X, y).scores_
clf = RandomizedLogisticRegression(verbose=False, C=1., random_state=42,
scaling=scaling, n_resampling=50,
tol=1e-3)
feature_scores_sp = clf.fit(X_sp, y).scores_
assert_array_equal(feature_scores, feature_scores_sp)
| bsd-3-clause |
ClimbsRocks/scikit-learn | sklearn/cluster/bicluster.py | 66 | 19850 | """Spectral biclustering algorithms.
Authors : Kemal Eren
License: BSD 3 clause
"""
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy.sparse import dia_matrix
from scipy.sparse import issparse
from . import KMeans, MiniBatchKMeans
from ..base import BaseEstimator, BiclusterMixin
from ..externals import six
from ..utils import check_random_state
from ..utils.arpack import eigsh, svds
from ..utils.extmath import (make_nonnegative, norm, randomized_svd,
safe_sparse_dot)
from ..utils.validation import assert_all_finite, check_array
__all__ = ['SpectralCoclustering',
'SpectralBiclustering']
def _scale_normalize(X):
"""Normalize ``X`` by scaling rows and columns independently.
Returns the normalized matrix and the row and column scaling
factors.
"""
X = make_nonnegative(X)
row_diag = np.asarray(1.0 / np.sqrt(X.sum(axis=1))).squeeze()
col_diag = np.asarray(1.0 / np.sqrt(X.sum(axis=0))).squeeze()
row_diag = np.where(np.isnan(row_diag), 0, row_diag)
col_diag = np.where(np.isnan(col_diag), 0, col_diag)
if issparse(X):
n_rows, n_cols = X.shape
r = dia_matrix((row_diag, [0]), shape=(n_rows, n_rows))
c = dia_matrix((col_diag, [0]), shape=(n_cols, n_cols))
an = r * X * c
else:
an = row_diag[:, np.newaxis] * X * col_diag
return an, row_diag, col_diag
def _bistochastic_normalize(X, max_iter=1000, tol=1e-5):
"""Normalize rows and columns of ``X`` simultaneously so that all
rows sum to one constant and all columns sum to a different
constant.
"""
# According to paper, this can also be done more efficiently with
# deviation reduction and balancing algorithms.
X = make_nonnegative(X)
X_scaled = X
dist = None
for _ in range(max_iter):
X_new, _, _ = _scale_normalize(X_scaled)
if issparse(X):
dist = norm(X_scaled.data - X.data)
else:
dist = norm(X_scaled - X_new)
X_scaled = X_new
if dist is not None and dist < tol:
break
return X_scaled
def _log_normalize(X):
"""Normalize ``X`` according to Kluger's log-interactions scheme."""
X = make_nonnegative(X, min_value=1)
if issparse(X):
raise ValueError("Cannot compute log of a sparse matrix,"
" because log(x) diverges to -infinity as x"
" goes to 0.")
L = np.log(X)
row_avg = L.mean(axis=1)[:, np.newaxis]
col_avg = L.mean(axis=0)
avg = L.mean()
return L - row_avg - col_avg + avg
class BaseSpectral(six.with_metaclass(ABCMeta, BaseEstimator,
BiclusterMixin)):
"""Base class for spectral biclustering."""
@abstractmethod
def __init__(self, n_clusters=3, svd_method="randomized",
n_svd_vecs=None, mini_batch=False, init="k-means++",
n_init=10, n_jobs=1, random_state=None):
self.n_clusters = n_clusters
self.svd_method = svd_method
self.n_svd_vecs = n_svd_vecs
self.mini_batch = mini_batch
self.init = init
self.n_init = n_init
self.n_jobs = n_jobs
self.random_state = random_state
def _check_parameters(self):
legal_svd_methods = ('randomized', 'arpack')
if self.svd_method not in legal_svd_methods:
raise ValueError("Unknown SVD method: '{0}'. svd_method must be"
" one of {1}.".format(self.svd_method,
legal_svd_methods))
def fit(self, X):
"""Creates a biclustering for X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
"""
X = check_array(X, accept_sparse='csr', dtype=np.float64)
self._check_parameters()
self._fit(X)
def _svd(self, array, n_components, n_discard):
"""Returns first `n_components` left and right singular
vectors u and v, discarding the first `n_discard`.
"""
if self.svd_method == 'randomized':
kwargs = {}
if self.n_svd_vecs is not None:
kwargs['n_oversamples'] = self.n_svd_vecs
u, _, vt = randomized_svd(array, n_components,
random_state=self.random_state,
**kwargs)
elif self.svd_method == 'arpack':
u, _, vt = svds(array, k=n_components, ncv=self.n_svd_vecs)
if np.any(np.isnan(vt)):
# some eigenvalues of A * A.T are negative, causing
# sqrt() to be np.nan. This causes some vectors in vt
# to be np.nan.
A = safe_sparse_dot(array.T, array)
random_state = check_random_state(self.random_state)
# initialize with [-1,1] as in ARPACK
v0 = random_state.uniform(-1, 1, A.shape[0])
_, v = eigsh(A, ncv=self.n_svd_vecs, v0=v0)
vt = v.T
if np.any(np.isnan(u)):
A = safe_sparse_dot(array, array.T)
random_state = check_random_state(self.random_state)
# initialize with [-1,1] as in ARPACK
v0 = random_state.uniform(-1, 1, A.shape[0])
_, u = eigsh(A, ncv=self.n_svd_vecs, v0=v0)
assert_all_finite(u)
assert_all_finite(vt)
u = u[:, n_discard:]
vt = vt[n_discard:]
return u, vt.T
def _k_means(self, data, n_clusters):
if self.mini_batch:
model = MiniBatchKMeans(n_clusters,
init=self.init,
n_init=self.n_init,
random_state=self.random_state)
else:
model = KMeans(n_clusters, init=self.init,
n_init=self.n_init, n_jobs=self.n_jobs,
random_state=self.random_state)
model.fit(data)
centroid = model.cluster_centers_
labels = model.labels_
return centroid, labels
class SpectralCoclustering(BaseSpectral):
"""Spectral Co-Clustering algorithm (Dhillon, 2001).
Clusters rows and columns of an array `X` to solve the relaxed
normalized cut of the bipartite graph created from `X` as follows:
the edge between row vertex `i` and column vertex `j` has weight
`X[i, j]`.
The resulting bicluster structure is block-diagonal, since each
row and each column belongs to exactly one bicluster.
Supports sparse matrices, as long as they are nonnegative.
Read more in the :ref:`User Guide <spectral_coclustering>`.
Parameters
----------
n_clusters : integer, optional, default: 3
The number of biclusters to find.
svd_method : string, optional, default: 'randomized'
Selects the algorithm for finding singular vectors. May be
'randomized' or 'arpack'. If 'randomized', use
:func:`sklearn.utils.extmath.randomized_svd`, which may be faster
for large matrices. If 'arpack', use
:func:`sklearn.utils.arpack.svds`, which is more accurate, but
possibly slower in some cases.
n_svd_vecs : int, optional, default: None
Number of vectors to use in calculating the SVD. Corresponds
to `ncv` when `svd_method=arpack` and `n_oversamples` when
`svd_method` is 'randomized`.
mini_batch : bool, optional, default: False
Whether to use mini-batch k-means, which is faster but may get
different results.
init : {'k-means++', 'random' or an ndarray}
Method for initialization of k-means algorithm; defaults to
'k-means++'.
n_init : int, optional, default: 10
Number of random initializations that are tried with the
k-means algorithm.
If mini-batch k-means is used, the best initialization is
chosen and the algorithm runs once. Otherwise, the algorithm
is run for each initialization and the best solution chosen.
n_jobs : int, optional, default: 1
The number of jobs to use for the computation. This works by breaking
down the pairwise matrix into n_jobs even slices and computing them in
parallel.
If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
random_state : int seed, RandomState instance, or None (default)
A pseudo random number generator used by the K-Means
initialization.
Attributes
----------
rows_ : array-like, shape (n_row_clusters, n_rows)
Results of the clustering. `rows[i, r]` is True if
cluster `i` contains row `r`. Available only after calling ``fit``.
columns_ : array-like, shape (n_column_clusters, n_columns)
Results of the clustering, like `rows`.
row_labels_ : array-like, shape (n_rows,)
The bicluster label of each row.
column_labels_ : array-like, shape (n_cols,)
The bicluster label of each column.
References
----------
* Dhillon, Inderjit S, 2001. `Co-clustering documents and words using
bipartite spectral graph partitioning
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.3011>`__.
"""
def __init__(self, n_clusters=3, svd_method='randomized',
n_svd_vecs=None, mini_batch=False, init='k-means++',
n_init=10, n_jobs=1, random_state=None):
super(SpectralCoclustering, self).__init__(n_clusters,
svd_method,
n_svd_vecs,
mini_batch,
init,
n_init,
n_jobs,
random_state)
def _fit(self, X):
normalized_data, row_diag, col_diag = _scale_normalize(X)
n_sv = 1 + int(np.ceil(np.log2(self.n_clusters)))
u, v = self._svd(normalized_data, n_sv, n_discard=1)
z = np.vstack((row_diag[:, np.newaxis] * u,
col_diag[:, np.newaxis] * v))
_, labels = self._k_means(z, self.n_clusters)
n_rows = X.shape[0]
self.row_labels_ = labels[:n_rows]
self.column_labels_ = labels[n_rows:]
self.rows_ = np.vstack(self.row_labels_ == c
for c in range(self.n_clusters))
self.columns_ = np.vstack(self.column_labels_ == c
for c in range(self.n_clusters))
class SpectralBiclustering(BaseSpectral):
"""Spectral biclustering (Kluger, 2003).
Partitions rows and columns under the assumption that the data has
an underlying checkerboard structure. For instance, if there are
two row partitions and three column partitions, each row will
belong to three biclusters, and each column will belong to two
biclusters. The outer product of the corresponding row and column
label vectors gives this checkerboard structure.
Read more in the :ref:`User Guide <spectral_biclustering>`.
Parameters
----------
n_clusters : integer or tuple (n_row_clusters, n_column_clusters)
The number of row and column clusters in the checkerboard
structure.
method : string, optional, default: 'bistochastic'
Method of normalizing and converting singular vectors into
biclusters. May be one of 'scale', 'bistochastic', or 'log'.
The authors recommend using 'log'. If the data is sparse,
however, log normalization will not work, which is why the
default is 'bistochastic'. CAUTION: if `method='log'`, the
data must not be sparse.
n_components : integer, optional, default: 6
Number of singular vectors to check.
n_best : integer, optional, default: 3
Number of best singular vectors to which to project the data
for clustering.
svd_method : string, optional, default: 'randomized'
Selects the algorithm for finding singular vectors. May be
'randomized' or 'arpack'. If 'randomized', uses
`sklearn.utils.extmath.randomized_svd`, which may be faster
for large matrices. If 'arpack', uses
`sklearn.utils.arpack.svds`, which is more accurate, but
possibly slower in some cases.
n_svd_vecs : int, optional, default: None
Number of vectors to use in calculating the SVD. Corresponds
to `ncv` when `svd_method=arpack` and `n_oversamples` when
`svd_method` is 'randomized`.
mini_batch : bool, optional, default: False
Whether to use mini-batch k-means, which is faster but may get
different results.
init : {'k-means++', 'random' or an ndarray}
Method for initialization of k-means algorithm; defaults to
'k-means++'.
n_init : int, optional, default: 10
Number of random initializations that are tried with the
k-means algorithm.
If mini-batch k-means is used, the best initialization is
chosen and the algorithm runs once. Otherwise, the algorithm
is run for each initialization and the best solution chosen.
n_jobs : int, optional, default: 1
The number of jobs to use for the computation. This works by breaking
down the pairwise matrix into n_jobs even slices and computing them in
parallel.
If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
random_state : int seed, RandomState instance, or None (default)
A pseudo random number generator used by the K-Means
initialization.
Attributes
----------
rows_ : array-like, shape (n_row_clusters, n_rows)
Results of the clustering. `rows[i, r]` is True if
cluster `i` contains row `r`. Available only after calling ``fit``.
columns_ : array-like, shape (n_column_clusters, n_columns)
Results of the clustering, like `rows`.
row_labels_ : array-like, shape (n_rows,)
Row partition labels.
column_labels_ : array-like, shape (n_cols,)
Column partition labels.
References
----------
* Kluger, Yuval, et. al., 2003. `Spectral biclustering of microarray
data: coclustering genes and conditions
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.1608>`__.
"""
def __init__(self, n_clusters=3, method='bistochastic',
n_components=6, n_best=3, svd_method='randomized',
n_svd_vecs=None, mini_batch=False, init='k-means++',
n_init=10, n_jobs=1, random_state=None):
super(SpectralBiclustering, self).__init__(n_clusters,
svd_method,
n_svd_vecs,
mini_batch,
init,
n_init,
n_jobs,
random_state)
self.method = method
self.n_components = n_components
self.n_best = n_best
def _check_parameters(self):
super(SpectralBiclustering, self)._check_parameters()
legal_methods = ('bistochastic', 'scale', 'log')
if self.method not in legal_methods:
raise ValueError("Unknown method: '{0}'. method must be"
" one of {1}.".format(self.method, legal_methods))
try:
int(self.n_clusters)
except TypeError:
try:
r, c = self.n_clusters
int(r)
int(c)
except (ValueError, TypeError):
raise ValueError("Incorrect parameter n_clusters has value:"
" {}. It should either be a single integer"
" or an iterable with two integers:"
" (n_row_clusters, n_column_clusters)")
if self.n_components < 1:
raise ValueError("Parameter n_components must be greater than 0,"
" but its value is {}".format(self.n_components))
if self.n_best < 1:
raise ValueError("Parameter n_best must be greater than 0,"
" but its value is {}".format(self.n_best))
if self.n_best > self.n_components:
raise ValueError("n_best cannot be larger than"
" n_components, but {} > {}"
"".format(self.n_best, self.n_components))
def _fit(self, X):
n_sv = self.n_components
if self.method == 'bistochastic':
normalized_data = _bistochastic_normalize(X)
n_sv += 1
elif self.method == 'scale':
normalized_data, _, _ = _scale_normalize(X)
n_sv += 1
elif self.method == 'log':
normalized_data = _log_normalize(X)
n_discard = 0 if self.method == 'log' else 1
u, v = self._svd(normalized_data, n_sv, n_discard)
ut = u.T
vt = v.T
try:
n_row_clusters, n_col_clusters = self.n_clusters
except TypeError:
n_row_clusters = n_col_clusters = self.n_clusters
best_ut = self._fit_best_piecewise(ut, self.n_best,
n_row_clusters)
best_vt = self._fit_best_piecewise(vt, self.n_best,
n_col_clusters)
self.row_labels_ = self._project_and_cluster(X, best_vt.T,
n_row_clusters)
self.column_labels_ = self._project_and_cluster(X.T, best_ut.T,
n_col_clusters)
self.rows_ = np.vstack(self.row_labels_ == label
for label in range(n_row_clusters)
for _ in range(n_col_clusters))
self.columns_ = np.vstack(self.column_labels_ == label
for _ in range(n_row_clusters)
for label in range(n_col_clusters))
def _fit_best_piecewise(self, vectors, n_best, n_clusters):
"""Find the ``n_best`` vectors that are best approximated by piecewise
constant vectors.
The piecewise vectors are found by k-means; the best is chosen
according to Euclidean distance.
"""
def make_piecewise(v):
centroid, labels = self._k_means(v.reshape(-1, 1), n_clusters)
return centroid[labels].ravel()
piecewise_vectors = np.apply_along_axis(make_piecewise,
axis=1, arr=vectors)
dists = np.apply_along_axis(norm, axis=1,
arr=(vectors - piecewise_vectors))
result = vectors[np.argsort(dists)[:n_best]]
return result
def _project_and_cluster(self, data, vectors, n_clusters):
"""Project ``data`` to ``vectors`` and cluster the result."""
projected = safe_sparse_dot(data, vectors)
_, labels = self._k_means(projected, n_clusters)
return labels
| bsd-3-clause |
JoeJimFlood/RugbyPredictifier | 2018SuperRugby/round.py | 1 | 9347 | import os
os.chdir(os.path.dirname(__file__))
import pandas as pd
import matchup
import ranking
import xlsxwriter
import xlrd
import sys
import time
import collections
import matplotlib.pyplot as plt
from math import ceil, sqrt, log2
def rgb2hex(r, g, b):
r_hex = hex(r)[-2:].replace('x', '0')
g_hex = hex(g)[-2:].replace('x', '0')
b_hex = hex(b)[-2:].replace('x', '0')
return '#' + r_hex + g_hex + b_hex
plot_shape = {1: (1, 1),
2: (1, 2),
3: (2, 2),
4: (2, 2),
5: (2, 3),
6: (2, 3),
7: (3, 3)}
round_timer = time.time()
round_number = 'Postseason'
matchups = collections.OrderedDict()
matchups['Saturday'] = []
location = os.getcwd().replace('\\', '/')
stadium_file = location + '/StadiumLocs.csv'
teamloc_file = location + '/TeamHomes.csv'
output_file = location + '/Weekly Forecasts/Round_' + str(round_number) + '.xlsx'
output_fig = location + '/Weekly Forecasts/Round_' + str(round_number) + '.png'
rankings = ranking.rank(os.path.join(location, 'teamcsvs'), round_number)
n_games = 0
for day in matchups:
n_games += len(matchups[day])
colours = {}
team_formats = {}
colour_df = pd.DataFrame.from_csv(location + '/colours.csv')
teams = list(colour_df.index)
for team in teams:
primary = rgb2hex(int(colour_df.loc[team, 'R1']), int(colour_df.loc[team, 'G1']), int(colour_df.loc[team, 'B1']))
secondary = rgb2hex(int(colour_df.loc[team, 'R2']), int(colour_df.loc[team, 'G2']), int(colour_df.loc[team, 'B2']))
colours[team] = (primary, secondary)
plt.figure(figsize = (15, 15), dpi = 96)
plt.title('Round ' + str(round_number))
counter = 0
stadiums = pd.read_csv(stadium_file, index_col = 0)
teamlocs = pd.read_csv(teamloc_file, header = None, index_col = 0)[1]
for read_data in range(1):
week_book = xlsxwriter.Workbook(output_file)
header_format = week_book.add_format({'align': 'center', 'bold': True, 'bottom': True})
index_format = week_book.add_format({'align': 'right', 'bold': True})
score_format = week_book.add_format({'num_format': '#0', 'align': 'right'})
percent_format = week_book.add_format({'num_format': '#0%', 'align': 'right'})
merged_format = week_book.add_format({'num_format': '#0.00', 'align': 'center'})
merged_format2 = week_book.add_format({'num_format': '0.000', 'align': 'center'})
for team in teams:
team_formats[team] = week_book.add_format({'align': 'center', 'bold': True, 'border': True,
'bg_color': colours[team][0], 'font_color': colours[team][1]})
for game_time in matchups:
if read_data:
data_book = xlrd.open_workbook(output_file)
data_sheet = data_book.sheet_by_name(game_time)
sheet = week_book.add_worksheet(game_time)
sheet.write_string(1, 0, 'City', index_format)
sheet.write_string(2, 0, 'Quality', index_format)
sheet.write_string(3, 0, 'Entropy', index_format)
sheet.write_string(4, 0, 'Hype', index_format)
sheet.write_string(5, 0, 'Chance of Winning', index_format)
sheet.write_string(6, 0, 'Expected Score', index_format)
for i in range(1, 20):
sheet.write_string(6+i, 0, str(5*i) + 'th Percentile Score', index_format)
sheet.write_string(26, 0, 'Chance of Bonus Point Win', index_format)
#sheet.write_string(23, 0, 'Chance of 4-Try Bonus Point with Draw', index_format)
#sheet.write_string(24, 0, 'Chance of 4-Try Bonus Point with Loss', index_format)
sheet.write_string(27, 0, 'Chance of Losing Bonus Point', index_format)
sheet.freeze_panes(0, 1)
games = matchups[game_time]
for i in range(len(games)):
home = games[i][0]
away = games[i][1]
try:
venue = games[i][2]
except IndexError:
venue = teamlocs.loc[home]
stadium = stadiums.loc[venue, 'Venue']
city = stadiums.loc[venue, 'City']
country = stadiums.loc[venue, 'Country']
homecol = 3 * i + 1
awaycol = 3 * i + 2
sheet.write_string(0, homecol, home, team_formats[home])
sheet.write_string(0, awaycol, away, team_formats[away])
sheet.write_string(0, awaycol + 1, ' ')
if read_data: #Get rid of this as I never use this option anymore
sheet.write_number(5, homecol, data_sheet.cell(1, homecol).value, percent_format)
sheet.write_number(5, awaycol, data_sheet.cell(1, awaycol).value, percent_format)
for rownum in range(6, 26):
sheet.write_number(rownum, homecol, data_sheet.cell(rownum, homecol).value, score_format)
sheet.write_number(rownum, awaycol, data_sheet.cell(rownum, awaycol).value, score_format)
for rownum in range(26, 30):
sheet.write_number(rownum, homecol, data_sheet.cell(rownum, homecol).value, percent_format)
sheet.write_number(rownum, awaycol, data_sheet.cell(rownum, awaycol).value, percent_format)
else:
results = matchup.matchup(home, away)
probwin = results['ProbWin']
hwin = probwin[home]
awin = probwin[away]
draw = 1 - hwin - awin
#Calculate hype
home_ranking = rankings.loc[home, 'Quantile']
away_ranking = rankings.loc[away, 'Quantile']
ranking_factor = (home_ranking + away_ranking)/2
#uncertainty_factor = 1 - (hwin - awin)**2
hp = hwin/(1-draw)
ap = awin/(1-draw)
entropy = -hp*log2(hp) - ap*log2(ap)
hype = 100*ranking_factor*entropy
sheet.write_number(5, homecol, probwin[home], percent_format)
sheet.write_number(5, awaycol, probwin[away], percent_format)
home_dist = results['Scores'][home]
away_dist = results['Scores'][away]
home_bp = results['Bonus Points'][home]
away_bp = results['Bonus Points'][away]
sheet.write_number(6, homecol, home_dist['mean'], score_format)
sheet.write_number(6, awaycol, away_dist['mean'], score_format)
for i in range(1, 20):
#print(type(home_dist))
#print(home_dist[str(5*i)+'%'])
sheet.merge_range(1, homecol, 1, awaycol, city, merged_format)
sheet.merge_range(2, homecol, 2, awaycol, ranking_factor, merged_format2)
sheet.merge_range(3, homecol, 3, awaycol, entropy, merged_format2)
sheet.merge_range(4, homecol, 4, awaycol, hype, merged_format)
sheet.write_number(6+i, homecol, home_dist[str(5*i)+'%'], score_format)
sheet.write_number(6+i, awaycol, away_dist[str(5*i)+'%'], score_format)
sheet.write_number(26, homecol, home_bp['4-Try Bonus Point with Win'], percent_format)
#sheet.write_number(23, homecol, home_bp['Try-Scoring Bonus Point with Draw'], percent_format)
#sheet.write_number(24, homecol, home_bp['Try-Scoring Bonus Point with Loss'], percent_format)
sheet.write_number(27, homecol, home_bp['Losing Bonus Point'], percent_format)
sheet.write_number(26, awaycol, away_bp['4-Try Bonus Point with Win'], percent_format)
#sheet.write_number(23, awaycol, away_bp['Try-Scoring Bonus Point with Draw'], percent_format)
#sheet.write_number(24, awaycol, away_bp['Try-Scoring Bonus Point with Loss'], percent_format)
sheet.write_number(27, awaycol, away_bp['Losing Bonus Point'], percent_format)
if i != len(games) - 1:
sheet.write_string(0, 3 * i + 3, ' ')
counter += 1
if n_games == 5 and counter == 5:
plot_pos = 6
elif n_games == 7 and counter == 7:
plot_pos = 8
elif n_games == 8 and counter == 8:
plot_pos = 9
else:
plot_pos = counter
plt.subplot(plot_shape[n_games][0], plot_shape[n_games][1], plot_pos)
labels = [home[:3], away[:3]]#, 'DRAW']
values = [hp, ap]#, 1 - hwin - awin]
colors = [colours[home][0], colours[away][0]]#, '#808080']
ex = 0.05
explode = [ex, ex]#, ex]
plt.pie(values,
colors = colors,
labels = labels,
explode = explode,
autopct='%.0f%%',
startangle = 90,
labeldistance = 1,
textprops = {'backgroundcolor': '#ffffff', 'ha': 'center', 'va': 'center', 'fontsize': 24})
plt.title(home + ' vs ' + away + '\n' + stadium + '\n' + city + ', ' + country + '\nHype: ' + str(round(hype, 2)), size = 24)
plt.axis('equal')
week_book.close()
plt.savefig(output_fig)
print('Round ' + str(round_number) + ' predictions calculated in ' + str(round((time.time() - round_timer) / 60, 2)) + ' minutes') | mit |
zymsys/sms-tools | lectures/06-Harmonic-model/plots-code/sines-partials-harmonics.py | 24 | 2020 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, triang, blackmanharris
import sys, os, functools, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import dftModel as DFT
import utilFunctions as UF
(fs, x) = UF.wavread('../../../sounds/sine-440-490.wav')
w = np.hamming(3529)
N = 32768
hN = N/2
t = -20
pin = 4850
x1 = x[pin:pin+w.size]
mX1, pX1 = DFT.dftAnal(x1, w, N)
ploc = UF.peakDetection(mX1, t)
pmag = mX1[ploc]
iploc, ipmag, ipphase = UF.peakInterp(mX1, pX1, ploc)
plt.figure(1, figsize=(9, 6))
plt.subplot(311)
plt.plot(fs*np.arange(mX1.size)/float(N), mX1-max(mX1), 'r', lw=1.5)
plt.plot(fs * iploc/N, ipmag-max(mX1), marker='x', color='b', alpha=1, linestyle='', markeredgewidth=1.5)
plt.axis([200, 1000, -80, 4])
plt.title('mX + peaks (sine-440-490.wav)')
(fs, x) = UF.wavread('../../../sounds/vibraphone-C6.wav')
w = np.blackman(401)
N = 1024
hN = N/2
t = -80
pin = 200
x2 = x[pin:pin+w.size]
mX2, pX2 = DFT.dftAnal(x2, w, N)
ploc = UF.peakDetection(mX2, t)
pmag = mX2[ploc]
iploc, ipmag, ipphase = UF.peakInterp(mX2, pX2, ploc)
plt.subplot(3,1,2)
plt.plot(fs*np.arange(mX2.size)/float(N), mX2-max(mX2), 'r', lw=1.5)
plt.plot(fs * iploc/N, ipmag-max(mX2), marker='x', color='b', alpha=1, linestyle='', markeredgewidth=1.5)
plt.axis([500,10000,-100,4])
plt.title('mX + peaks (vibraphone-C6.wav)')
(fs, x) = UF.wavread('../../../sounds/oboe-A4.wav')
w = np.blackman(651)
N = 2048
hN = N/2
t = -80
pin = 10000
x3 = x[pin:pin+w.size]
mX3, pX3 = DFT.dftAnal(x3, w, N)
ploc = UF.peakDetection(mX3, t)
pmag = mX3[ploc]
iploc, ipmag, ipphase = UF.peakInterp(mX3, pX3, ploc)
plt.subplot(3,1,3)
plt.plot(fs*np.arange(mX3.size)/float(N), mX3-max(mX3), 'r', lw=1.5)
plt.plot(fs * iploc/N, ipmag-max(mX3), marker='x', color='b', alpha=1, linestyle='', markeredgewidth=1.5)
plt.axis([0,6000,-70,2])
plt.title('mX + peaks (oboe-A4.wav)')
plt.tight_layout()
plt.savefig('sines-partials-harmonics.png')
plt.show()
| agpl-3.0 |
mattilyra/scikit-learn | sklearn/linear_model/tests/test_sgd.py | 21 | 47783 | import pickle
import unittest
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import raises
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_false, assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_raises_regexp
from sklearn.utils.testing import ignore_warnings
from sklearn import linear_model, datasets, metrics
from sklearn.base import clone
from sklearn.linear_model import SGDClassifier, SGDRegressor
from sklearn.preprocessing import LabelEncoder, scale, MinMaxScaler
from sklearn.linear_model import sgd_fast
class SparseSGDClassifier(SGDClassifier):
def fit(self, X, y, *args, **kw):
X = sp.csr_matrix(X)
return super(SparseSGDClassifier, self).fit(X, y, *args, **kw)
def partial_fit(self, X, y, *args, **kw):
X = sp.csr_matrix(X)
return super(SparseSGDClassifier, self).partial_fit(X, y, *args, **kw)
def decision_function(self, X):
X = sp.csr_matrix(X)
return super(SparseSGDClassifier, self).decision_function(X)
def predict_proba(self, X):
X = sp.csr_matrix(X)
return super(SparseSGDClassifier, self).predict_proba(X)
class SparseSGDRegressor(SGDRegressor):
def fit(self, X, y, *args, **kw):
X = sp.csr_matrix(X)
return SGDRegressor.fit(self, X, y, *args, **kw)
def partial_fit(self, X, y, *args, **kw):
X = sp.csr_matrix(X)
return SGDRegressor.partial_fit(self, X, y, *args, **kw)
def decision_function(self, X, *args, **kw):
X = sp.csr_matrix(X)
return SGDRegressor.decision_function(self, X, *args, **kw)
# Test Data
# test sample 1
X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
Y = [1, 1, 1, 2, 2, 2]
T = np.array([[-1, -1], [2, 2], [3, 2]])
true_result = [1, 2, 2]
# test sample 2; string class labels
X2 = np.array([[-1, 1], [-0.75, 0.5], [-1.5, 1.5],
[1, 1], [0.75, 0.5], [1.5, 1.5],
[-1, -1], [0, -0.5], [1, -1]])
Y2 = ["one"] * 3 + ["two"] * 3 + ["three"] * 3
T2 = np.array([[-1.5, 0.5], [1, 2], [0, -2]])
true_result2 = ["one", "two", "three"]
# test sample 3
X3 = np.array([[1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 1, 1],
[0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0]])
Y3 = np.array([1, 1, 1, 1, 2, 2, 2, 2])
# test sample 4 - two more or less redundant feature groups
X4 = np.array([[1, 0.9, 0.8, 0, 0, 0], [1, .84, .98, 0, 0, 0],
[1, .96, .88, 0, 0, 0], [1, .91, .99, 0, 0, 0],
[0, 0, 0, .89, .91, 1], [0, 0, 0, .79, .84, 1],
[0, 0, 0, .91, .95, 1], [0, 0, 0, .93, 1, 1]])
Y4 = np.array([1, 1, 1, 1, 2, 2, 2, 2])
iris = datasets.load_iris()
# test sample 5 - test sample 1 as binary classification problem
X5 = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
Y5 = [1, 1, 1, 2, 2, 2]
true_result5 = [0, 1, 1]
# Classification Test Case
class CommonTest(object):
def factory(self, **kwargs):
if "random_state" not in kwargs:
kwargs["random_state"] = 42
return self.factory_class(**kwargs)
# a simple implementation of ASGD to use for testing
# uses squared loss to find the gradient
def asgd(self, X, y, eta, alpha, weight_init=None, intercept_init=0.0):
if weight_init is None:
weights = np.zeros(X.shape[1])
else:
weights = weight_init
average_weights = np.zeros(X.shape[1])
intercept = intercept_init
average_intercept = 0.0
decay = 1.0
# sparse data has a fixed decay of .01
if (isinstance(self, SparseSGDClassifierTestCase) or
isinstance(self, SparseSGDRegressorTestCase)):
decay = .01
for i, entry in enumerate(X):
p = np.dot(entry, weights)
p += intercept
gradient = p - y[i]
weights *= 1.0 - (eta * alpha)
weights += -(eta * gradient * entry)
intercept += -(eta * gradient) * decay
average_weights *= i
average_weights += weights
average_weights /= i + 1.0
average_intercept *= i
average_intercept += intercept
average_intercept /= i + 1.0
return average_weights, average_intercept
def _test_warm_start(self, X, Y, lr):
# Test that explicit warm restart...
clf = self.factory(alpha=0.01, eta0=0.01, n_iter=5, shuffle=False,
learning_rate=lr)
clf.fit(X, Y)
clf2 = self.factory(alpha=0.001, eta0=0.01, n_iter=5, shuffle=False,
learning_rate=lr)
clf2.fit(X, Y,
coef_init=clf.coef_.copy(),
intercept_init=clf.intercept_.copy())
# ... and implicit warm restart are equivalent.
clf3 = self.factory(alpha=0.01, eta0=0.01, n_iter=5, shuffle=False,
warm_start=True, learning_rate=lr)
clf3.fit(X, Y)
assert_equal(clf3.t_, clf.t_)
assert_array_almost_equal(clf3.coef_, clf.coef_)
clf3.set_params(alpha=0.001)
clf3.fit(X, Y)
assert_equal(clf3.t_, clf2.t_)
assert_array_almost_equal(clf3.coef_, clf2.coef_)
def test_warm_start_constant(self):
self._test_warm_start(X, Y, "constant")
def test_warm_start_invscaling(self):
self._test_warm_start(X, Y, "invscaling")
def test_warm_start_optimal(self):
self._test_warm_start(X, Y, "optimal")
def test_input_format(self):
# Input format tests.
clf = self.factory(alpha=0.01, n_iter=5,
shuffle=False)
clf.fit(X, Y)
Y_ = np.array(Y)[:, np.newaxis]
Y_ = np.c_[Y_, Y_]
assert_raises(ValueError, clf.fit, X, Y_)
def test_clone(self):
# Test whether clone works ok.
clf = self.factory(alpha=0.01, n_iter=5, penalty='l1')
clf = clone(clf)
clf.set_params(penalty='l2')
clf.fit(X, Y)
clf2 = self.factory(alpha=0.01, n_iter=5, penalty='l2')
clf2.fit(X, Y)
assert_array_equal(clf.coef_, clf2.coef_)
def test_plain_has_no_average_attr(self):
clf = self.factory(average=True, eta0=.01)
clf.fit(X, Y)
assert_true(hasattr(clf, 'average_coef_'))
assert_true(hasattr(clf, 'average_intercept_'))
assert_true(hasattr(clf, 'standard_intercept_'))
assert_true(hasattr(clf, 'standard_coef_'))
clf = self.factory()
clf.fit(X, Y)
assert_false(hasattr(clf, 'average_coef_'))
assert_false(hasattr(clf, 'average_intercept_'))
assert_false(hasattr(clf, 'standard_intercept_'))
assert_false(hasattr(clf, 'standard_coef_'))
def test_late_onset_averaging_not_reached(self):
clf1 = self.factory(average=600)
clf2 = self.factory()
for _ in range(100):
if isinstance(clf1, SGDClassifier):
clf1.partial_fit(X, Y, classes=np.unique(Y))
clf2.partial_fit(X, Y, classes=np.unique(Y))
else:
clf1.partial_fit(X, Y)
clf2.partial_fit(X, Y)
assert_array_almost_equal(clf1.coef_, clf2.coef_, decimal=16)
assert_almost_equal(clf1.intercept_, clf2.intercept_, decimal=16)
def test_late_onset_averaging_reached(self):
eta0 = .001
alpha = .0001
Y_encode = np.array(Y)
Y_encode[Y_encode == 1] = -1.0
Y_encode[Y_encode == 2] = 1.0
clf1 = self.factory(average=7, learning_rate="constant",
loss='squared_loss', eta0=eta0,
alpha=alpha, n_iter=2, shuffle=False)
clf2 = self.factory(average=0, learning_rate="constant",
loss='squared_loss', eta0=eta0,
alpha=alpha, n_iter=1, shuffle=False)
clf1.fit(X, Y_encode)
clf2.fit(X, Y_encode)
average_weights, average_intercept = \
self.asgd(X, Y_encode, eta0, alpha,
weight_init=clf2.coef_.ravel(),
intercept_init=clf2.intercept_)
assert_array_almost_equal(clf1.coef_.ravel(),
average_weights.ravel(),
decimal=16)
assert_almost_equal(clf1.intercept_, average_intercept, decimal=16)
@raises(ValueError)
def test_sgd_bad_alpha_for_optimal_learning_rate(self):
# Check whether expected ValueError on bad alpha, i.e. 0
# since alpha is used to compute the optimal learning rate
self.factory(alpha=0, learning_rate="optimal")
class DenseSGDClassifierTestCase(unittest.TestCase, CommonTest):
"""Test suite for the dense representation variant of SGD"""
factory_class = SGDClassifier
def test_sgd(self):
# Check that SGD gives any results :-)
for loss in ("hinge", "squared_hinge", "log", "modified_huber"):
clf = self.factory(penalty='l2', alpha=0.01, fit_intercept=True,
loss=loss, n_iter=10, shuffle=True)
clf.fit(X, Y)
# assert_almost_equal(clf.coef_[0], clf.coef_[1], decimal=7)
assert_array_equal(clf.predict(T), true_result)
@raises(ValueError)
def test_sgd_bad_l1_ratio(self):
# Check whether expected ValueError on bad l1_ratio
self.factory(l1_ratio=1.1)
@raises(ValueError)
def test_sgd_bad_learning_rate_schedule(self):
# Check whether expected ValueError on bad learning_rate
self.factory(learning_rate="<unknown>")
@raises(ValueError)
def test_sgd_bad_eta0(self):
# Check whether expected ValueError on bad eta0
self.factory(eta0=0, learning_rate="constant")
@raises(ValueError)
def test_sgd_bad_alpha(self):
# Check whether expected ValueError on bad alpha
self.factory(alpha=-.1)
@raises(ValueError)
def test_sgd_bad_penalty(self):
# Check whether expected ValueError on bad penalty
self.factory(penalty='foobar', l1_ratio=0.85)
@raises(ValueError)
def test_sgd_bad_loss(self):
# Check whether expected ValueError on bad loss
self.factory(loss="foobar")
@raises(ValueError)
def test_sgd_n_iter_param(self):
# Test parameter validity check
self.factory(n_iter=-10000)
@raises(ValueError)
def test_sgd_shuffle_param(self):
# Test parameter validity check
self.factory(shuffle="false")
@raises(TypeError)
def test_argument_coef(self):
# Checks coef_init not allowed as model argument (only fit)
# Provided coef_ does not match dataset.
self.factory(coef_init=np.zeros((3,))).fit(X, Y)
@raises(ValueError)
def test_provide_coef(self):
# Checks coef_init shape for the warm starts
# Provided coef_ does not match dataset.
self.factory().fit(X, Y, coef_init=np.zeros((3,)))
@raises(ValueError)
def test_set_intercept(self):
# Checks intercept_ shape for the warm starts
# Provided intercept_ does not match dataset.
self.factory().fit(X, Y, intercept_init=np.zeros((3,)))
def test_set_intercept_binary(self):
# Checks intercept_ shape for the warm starts in binary case
self.factory().fit(X5, Y5, intercept_init=0)
def test_average_binary_computed_correctly(self):
# Checks the SGDClassifier correctly computes the average weights
eta = .1
alpha = 2.
n_samples = 20
n_features = 10
rng = np.random.RandomState(0)
X = rng.normal(size=(n_samples, n_features))
w = rng.normal(size=n_features)
clf = self.factory(loss='squared_loss',
learning_rate='constant',
eta0=eta, alpha=alpha,
fit_intercept=True,
n_iter=1, average=True, shuffle=False)
# simple linear function without noise
y = np.dot(X, w)
y = np.sign(y)
clf.fit(X, y)
average_weights, average_intercept = self.asgd(X, y, eta, alpha)
average_weights = average_weights.reshape(1, -1)
assert_array_almost_equal(clf.coef_,
average_weights,
decimal=14)
assert_almost_equal(clf.intercept_, average_intercept, decimal=14)
def test_set_intercept_to_intercept(self):
# Checks intercept_ shape consistency for the warm starts
# Inconsistent intercept_ shape.
clf = self.factory().fit(X5, Y5)
self.factory().fit(X5, Y5, intercept_init=clf.intercept_)
clf = self.factory().fit(X, Y)
self.factory().fit(X, Y, intercept_init=clf.intercept_)
@raises(ValueError)
def test_sgd_at_least_two_labels(self):
# Target must have at least two labels
self.factory(alpha=0.01, n_iter=20).fit(X2, np.ones(9))
def test_partial_fit_weight_class_balanced(self):
# partial_fit with class_weight='balanced' not supported"""
assert_raises_regexp(ValueError,
"class_weight 'balanced' is not supported for "
"partial_fit. In order to use 'balanced' weights, "
"use compute_class_weight\('balanced', classes, y\). "
"In place of y you can us a large enough sample "
"of the full training set target to properly "
"estimate the class frequency distributions. "
"Pass the resulting weights as the class_weight "
"parameter.",
self.factory(class_weight='balanced').partial_fit,
X, Y, classes=np.unique(Y))
def test_sgd_multiclass(self):
# Multi-class test case
clf = self.factory(alpha=0.01, n_iter=20).fit(X2, Y2)
assert_equal(clf.coef_.shape, (3, 2))
assert_equal(clf.intercept_.shape, (3,))
assert_equal(clf.decision_function([[0, 0]]).shape, (1, 3))
pred = clf.predict(T2)
assert_array_equal(pred, true_result2)
def test_sgd_multiclass_average(self):
eta = .001
alpha = .01
# Multi-class average test case
clf = self.factory(loss='squared_loss',
learning_rate='constant',
eta0=eta, alpha=alpha,
fit_intercept=True,
n_iter=1, average=True, shuffle=False)
np_Y2 = np.array(Y2)
clf.fit(X2, np_Y2)
classes = np.unique(np_Y2)
for i, cl in enumerate(classes):
y_i = np.ones(np_Y2.shape[0])
y_i[np_Y2 != cl] = -1
average_coef, average_intercept = self.asgd(X2, y_i, eta, alpha)
assert_array_almost_equal(average_coef, clf.coef_[i], decimal=16)
assert_almost_equal(average_intercept,
clf.intercept_[i],
decimal=16)
def test_sgd_multiclass_with_init_coef(self):
# Multi-class test case
clf = self.factory(alpha=0.01, n_iter=20)
clf.fit(X2, Y2, coef_init=np.zeros((3, 2)),
intercept_init=np.zeros(3))
assert_equal(clf.coef_.shape, (3, 2))
assert_true(clf.intercept_.shape, (3,))
pred = clf.predict(T2)
assert_array_equal(pred, true_result2)
def test_sgd_multiclass_njobs(self):
# Multi-class test case with multi-core support
clf = self.factory(alpha=0.01, n_iter=20, n_jobs=2).fit(X2, Y2)
assert_equal(clf.coef_.shape, (3, 2))
assert_equal(clf.intercept_.shape, (3,))
assert_equal(clf.decision_function([[0, 0]]).shape, (1, 3))
pred = clf.predict(T2)
assert_array_equal(pred, true_result2)
def test_set_coef_multiclass(self):
# Checks coef_init and intercept_init shape for multi-class
# problems
# Provided coef_ does not match dataset
clf = self.factory()
assert_raises(ValueError, clf.fit, X2, Y2, coef_init=np.zeros((2, 2)))
# Provided coef_ does match dataset
clf = self.factory().fit(X2, Y2, coef_init=np.zeros((3, 2)))
# Provided intercept_ does not match dataset
clf = self.factory()
assert_raises(ValueError, clf.fit, X2, Y2,
intercept_init=np.zeros((1,)))
# Provided intercept_ does match dataset.
clf = self.factory().fit(X2, Y2, intercept_init=np.zeros((3,)))
def test_sgd_proba(self):
# Check SGD.predict_proba
# Hinge loss does not allow for conditional prob estimate.
# We cannot use the factory here, because it defines predict_proba
# anyway.
clf = SGDClassifier(loss="hinge", alpha=0.01, n_iter=10).fit(X, Y)
assert_false(hasattr(clf, "predict_proba"))
assert_false(hasattr(clf, "predict_log_proba"))
# log and modified_huber losses can output probability estimates
# binary case
for loss in ["log", "modified_huber"]:
clf = self.factory(loss="modified_huber", alpha=0.01, n_iter=10)
clf.fit(X, Y)
p = clf.predict_proba([[3, 2]])
assert_true(p[0, 1] > 0.5)
p = clf.predict_proba([[-1, -1]])
assert_true(p[0, 1] < 0.5)
p = clf.predict_log_proba([[3, 2]])
assert_true(p[0, 1] > p[0, 0])
p = clf.predict_log_proba([[-1, -1]])
assert_true(p[0, 1] < p[0, 0])
# log loss multiclass probability estimates
clf = self.factory(loss="log", alpha=0.01, n_iter=10).fit(X2, Y2)
d = clf.decision_function([[.1, -.1], [.3, .2]])
p = clf.predict_proba([[.1, -.1], [.3, .2]])
assert_array_equal(np.argmax(p, axis=1), np.argmax(d, axis=1))
assert_almost_equal(p[0].sum(), 1)
assert_true(np.all(p[0] >= 0))
p = clf.predict_proba([[-1, -1]])
d = clf.decision_function([[-1, -1]])
assert_array_equal(np.argsort(p[0]), np.argsort(d[0]))
l = clf.predict_log_proba([[3, 2]])
p = clf.predict_proba([[3, 2]])
assert_array_almost_equal(np.log(p), l)
l = clf.predict_log_proba([[-1, -1]])
p = clf.predict_proba([[-1, -1]])
assert_array_almost_equal(np.log(p), l)
# Modified Huber multiclass probability estimates; requires a separate
# test because the hard zero/one probabilities may destroy the
# ordering present in decision_function output.
clf = self.factory(loss="modified_huber", alpha=0.01, n_iter=10)
clf.fit(X2, Y2)
d = clf.decision_function([[3, 2]])
p = clf.predict_proba([[3, 2]])
if not isinstance(self, SparseSGDClassifierTestCase):
assert_equal(np.argmax(d, axis=1), np.argmax(p, axis=1))
else: # XXX the sparse test gets a different X2 (?)
assert_equal(np.argmin(d, axis=1), np.argmin(p, axis=1))
# the following sample produces decision_function values < -1,
# which would cause naive normalization to fail (see comment
# in SGDClassifier.predict_proba)
x = X.mean(axis=0)
d = clf.decision_function([x])
if np.all(d < -1): # XXX not true in sparse test case (why?)
p = clf.predict_proba([x])
assert_array_almost_equal(p[0], [1 / 3.] * 3)
def test_sgd_l1(self):
# Test L1 regularization
n = len(X4)
rng = np.random.RandomState(13)
idx = np.arange(n)
rng.shuffle(idx)
X = X4[idx, :]
Y = Y4[idx]
clf = self.factory(penalty='l1', alpha=.2, fit_intercept=False,
n_iter=2000, shuffle=False)
clf.fit(X, Y)
assert_array_equal(clf.coef_[0, 1:-1], np.zeros((4,)))
pred = clf.predict(X)
assert_array_equal(pred, Y)
# test sparsify with dense inputs
clf.sparsify()
assert_true(sp.issparse(clf.coef_))
pred = clf.predict(X)
assert_array_equal(pred, Y)
# pickle and unpickle with sparse coef_
clf = pickle.loads(pickle.dumps(clf))
assert_true(sp.issparse(clf.coef_))
pred = clf.predict(X)
assert_array_equal(pred, Y)
def test_class_weights(self):
# Test class weights.
X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y = [1, 1, 1, -1, -1]
clf = self.factory(alpha=0.1, n_iter=1000, fit_intercept=False,
class_weight=None)
clf.fit(X, y)
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))
# we give a small weights to class 1
clf = self.factory(alpha=0.1, n_iter=1000, fit_intercept=False,
class_weight={1: 0.001})
clf.fit(X, y)
# now the hyperplane should rotate clock-wise and
# the prediction on this point should shift
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([-1]))
def test_equal_class_weight(self):
# Test if equal class weights approx. equals no class weights.
X = [[1, 0], [1, 0], [0, 1], [0, 1]]
y = [0, 0, 1, 1]
clf = self.factory(alpha=0.1, n_iter=1000, class_weight=None)
clf.fit(X, y)
X = [[1, 0], [0, 1]]
y = [0, 1]
clf_weighted = self.factory(alpha=0.1, n_iter=1000,
class_weight={0: 0.5, 1: 0.5})
clf_weighted.fit(X, y)
# should be similar up to some epsilon due to learning rate schedule
assert_almost_equal(clf.coef_, clf_weighted.coef_, decimal=2)
@raises(ValueError)
def test_wrong_class_weight_label(self):
# ValueError due to not existing class label.
clf = self.factory(alpha=0.1, n_iter=1000, class_weight={0: 0.5})
clf.fit(X, Y)
@raises(ValueError)
def test_wrong_class_weight_format(self):
# ValueError due to wrong class_weight argument type.
clf = self.factory(alpha=0.1, n_iter=1000, class_weight=[0.5])
clf.fit(X, Y)
def test_weights_multiplied(self):
# Tests that class_weight and sample_weight are multiplicative
class_weights = {1: .6, 2: .3}
sample_weights = np.random.random(Y4.shape[0])
multiplied_together = np.copy(sample_weights)
multiplied_together[Y4 == 1] *= class_weights[1]
multiplied_together[Y4 == 2] *= class_weights[2]
clf1 = self.factory(alpha=0.1, n_iter=20, class_weight=class_weights)
clf2 = self.factory(alpha=0.1, n_iter=20)
clf1.fit(X4, Y4, sample_weight=sample_weights)
clf2.fit(X4, Y4, sample_weight=multiplied_together)
assert_almost_equal(clf1.coef_, clf2.coef_)
def test_balanced_weight(self):
# Test class weights for imbalanced data"""
# compute reference metrics on iris dataset that is quite balanced by
# default
X, y = iris.data, iris.target
X = scale(X)
idx = np.arange(X.shape[0])
rng = np.random.RandomState(6)
rng.shuffle(idx)
X = X[idx]
y = y[idx]
clf = self.factory(alpha=0.0001, n_iter=1000,
class_weight=None, shuffle=False).fit(X, y)
assert_almost_equal(metrics.f1_score(y, clf.predict(X), average='weighted'), 0.96,
decimal=1)
# make the same prediction using balanced class_weight
clf_balanced = self.factory(alpha=0.0001, n_iter=1000,
class_weight="balanced",
shuffle=False).fit(X, y)
assert_almost_equal(metrics.f1_score(y, clf_balanced.predict(X), average='weighted'), 0.96,
decimal=1)
# Make sure that in the balanced case it does not change anything
# to use "balanced"
assert_array_almost_equal(clf.coef_, clf_balanced.coef_, 6)
# build an very very imbalanced dataset out of iris data
X_0 = X[y == 0, :]
y_0 = y[y == 0]
X_imbalanced = np.vstack([X] + [X_0] * 10)
y_imbalanced = np.concatenate([y] + [y_0] * 10)
# fit a model on the imbalanced data without class weight info
clf = self.factory(n_iter=1000, class_weight=None, shuffle=False)
clf.fit(X_imbalanced, y_imbalanced)
y_pred = clf.predict(X)
assert_less(metrics.f1_score(y, y_pred, average='weighted'), 0.96)
# fit a model with balanced class_weight enabled
clf = self.factory(n_iter=1000, class_weight="balanced", shuffle=False)
clf.fit(X_imbalanced, y_imbalanced)
y_pred = clf.predict(X)
assert_greater(metrics.f1_score(y, y_pred, average='weighted'), 0.96)
# fit another using a fit parameter override
clf = self.factory(n_iter=1000, class_weight="balanced", shuffle=False)
clf.fit(X_imbalanced, y_imbalanced)
y_pred = clf.predict(X)
assert_greater(metrics.f1_score(y, y_pred, average='weighted'), 0.96)
def test_sample_weights(self):
# Test weights on individual samples
X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y = [1, 1, 1, -1, -1]
clf = self.factory(alpha=0.1, n_iter=1000, fit_intercept=False)
clf.fit(X, y)
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))
# we give a small weights to class 1
clf.fit(X, y, sample_weight=[0.001] * 3 + [1] * 2)
# now the hyperplane should rotate clock-wise and
# the prediction on this point should shift
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([-1]))
@raises(ValueError)
def test_wrong_sample_weights(self):
# Test if ValueError is raised if sample_weight has wrong shape
clf = self.factory(alpha=0.1, n_iter=1000, fit_intercept=False)
# provided sample_weight too long
clf.fit(X, Y, sample_weight=np.arange(7))
@raises(ValueError)
def test_partial_fit_exception(self):
clf = self.factory(alpha=0.01)
# classes was not specified
clf.partial_fit(X3, Y3)
def test_partial_fit_binary(self):
third = X.shape[0] // 3
clf = self.factory(alpha=0.01)
classes = np.unique(Y)
clf.partial_fit(X[:third], Y[:third], classes=classes)
assert_equal(clf.coef_.shape, (1, X.shape[1]))
assert_equal(clf.intercept_.shape, (1,))
assert_equal(clf.decision_function([[0, 0]]).shape, (1, ))
id1 = id(clf.coef_.data)
clf.partial_fit(X[third:], Y[third:])
id2 = id(clf.coef_.data)
# check that coef_ haven't been re-allocated
assert_true(id1, id2)
y_pred = clf.predict(T)
assert_array_equal(y_pred, true_result)
def test_partial_fit_multiclass(self):
third = X2.shape[0] // 3
clf = self.factory(alpha=0.01)
classes = np.unique(Y2)
clf.partial_fit(X2[:third], Y2[:third], classes=classes)
assert_equal(clf.coef_.shape, (3, X2.shape[1]))
assert_equal(clf.intercept_.shape, (3,))
assert_equal(clf.decision_function([[0, 0]]).shape, (1, 3))
id1 = id(clf.coef_.data)
clf.partial_fit(X2[third:], Y2[third:])
id2 = id(clf.coef_.data)
# check that coef_ haven't been re-allocated
assert_true(id1, id2)
def test_partial_fit_multiclass_average(self):
third = X2.shape[0] // 3
clf = self.factory(alpha=0.01, average=X2.shape[0])
classes = np.unique(Y2)
clf.partial_fit(X2[:third], Y2[:third], classes=classes)
assert_equal(clf.coef_.shape, (3, X2.shape[1]))
assert_equal(clf.intercept_.shape, (3,))
clf.partial_fit(X2[third:], Y2[third:])
assert_equal(clf.coef_.shape, (3, X2.shape[1]))
assert_equal(clf.intercept_.shape, (3,))
def test_fit_then_partial_fit(self):
# Partial_fit should work after initial fit in the multiclass case.
# Non-regression test for #2496; fit would previously produce a
# Fortran-ordered coef_ that subsequent partial_fit couldn't handle.
clf = self.factory()
clf.fit(X2, Y2)
clf.partial_fit(X2, Y2) # no exception here
def _test_partial_fit_equal_fit(self, lr):
for X_, Y_, T_ in ((X, Y, T), (X2, Y2, T2)):
clf = self.factory(alpha=0.01, eta0=0.01, n_iter=2,
learning_rate=lr, shuffle=False)
clf.fit(X_, Y_)
y_pred = clf.decision_function(T_)
t = clf.t_
classes = np.unique(Y_)
clf = self.factory(alpha=0.01, eta0=0.01, learning_rate=lr,
shuffle=False)
for i in range(2):
clf.partial_fit(X_, Y_, classes=classes)
y_pred2 = clf.decision_function(T_)
assert_equal(clf.t_, t)
assert_array_almost_equal(y_pred, y_pred2, decimal=2)
def test_partial_fit_equal_fit_constant(self):
self._test_partial_fit_equal_fit("constant")
def test_partial_fit_equal_fit_optimal(self):
self._test_partial_fit_equal_fit("optimal")
def test_partial_fit_equal_fit_invscaling(self):
self._test_partial_fit_equal_fit("invscaling")
def test_regression_losses(self):
clf = self.factory(alpha=0.01, learning_rate="constant",
eta0=0.1, loss="epsilon_insensitive")
clf.fit(X, Y)
assert_equal(1.0, np.mean(clf.predict(X) == Y))
clf = self.factory(alpha=0.01, learning_rate="constant",
eta0=0.1, loss="squared_epsilon_insensitive")
clf.fit(X, Y)
assert_equal(1.0, np.mean(clf.predict(X) == Y))
clf = self.factory(alpha=0.01, loss="huber")
clf.fit(X, Y)
assert_equal(1.0, np.mean(clf.predict(X) == Y))
clf = self.factory(alpha=0.01, learning_rate="constant", eta0=0.01,
loss="squared_loss")
clf.fit(X, Y)
assert_equal(1.0, np.mean(clf.predict(X) == Y))
def test_warm_start_multiclass(self):
self._test_warm_start(X2, Y2, "optimal")
def test_multiple_fit(self):
# Test multiple calls of fit w/ different shaped inputs.
clf = self.factory(alpha=0.01, n_iter=5,
shuffle=False)
clf.fit(X, Y)
assert_true(hasattr(clf, "coef_"))
# Non-regression test: try fitting with a different label set.
y = [["ham", "spam"][i] for i in LabelEncoder().fit_transform(Y)]
clf.fit(X[:, :-1], y)
class SparseSGDClassifierTestCase(DenseSGDClassifierTestCase):
"""Run exactly the same tests using the sparse representation variant"""
factory_class = SparseSGDClassifier
###############################################################################
# Regression Test Case
class DenseSGDRegressorTestCase(unittest.TestCase, CommonTest):
"""Test suite for the dense representation variant of SGD"""
factory_class = SGDRegressor
def test_sgd(self):
# Check that SGD gives any results.
clf = self.factory(alpha=0.1, n_iter=2,
fit_intercept=False)
clf.fit([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
assert_equal(clf.coef_[0], clf.coef_[1])
@raises(ValueError)
def test_sgd_bad_penalty(self):
# Check whether expected ValueError on bad penalty
self.factory(penalty='foobar', l1_ratio=0.85)
@raises(ValueError)
def test_sgd_bad_loss(self):
# Check whether expected ValueError on bad loss
self.factory(loss="foobar")
def test_sgd_averaged_computed_correctly(self):
# Tests the average regressor matches the naive implementation
eta = .001
alpha = .01
n_samples = 20
n_features = 10
rng = np.random.RandomState(0)
X = rng.normal(size=(n_samples, n_features))
w = rng.normal(size=n_features)
# simple linear function without noise
y = np.dot(X, w)
clf = self.factory(loss='squared_loss',
learning_rate='constant',
eta0=eta, alpha=alpha,
fit_intercept=True,
n_iter=1, average=True, shuffle=False)
clf.fit(X, y)
average_weights, average_intercept = self.asgd(X, y, eta, alpha)
assert_array_almost_equal(clf.coef_,
average_weights,
decimal=16)
assert_almost_equal(clf.intercept_, average_intercept, decimal=16)
def test_sgd_averaged_partial_fit(self):
# Tests whether the partial fit yields the same average as the fit
eta = .001
alpha = .01
n_samples = 20
n_features = 10
rng = np.random.RandomState(0)
X = rng.normal(size=(n_samples, n_features))
w = rng.normal(size=n_features)
# simple linear function without noise
y = np.dot(X, w)
clf = self.factory(loss='squared_loss',
learning_rate='constant',
eta0=eta, alpha=alpha,
fit_intercept=True,
n_iter=1, average=True, shuffle=False)
clf.partial_fit(X[:int(n_samples / 2)][:], y[:int(n_samples / 2)])
clf.partial_fit(X[int(n_samples / 2):][:], y[int(n_samples / 2):])
average_weights, average_intercept = self.asgd(X, y, eta, alpha)
assert_array_almost_equal(clf.coef_,
average_weights,
decimal=16)
assert_almost_equal(clf.intercept_[0], average_intercept, decimal=16)
def test_average_sparse(self):
# Checks the average weights on data with 0s
eta = .001
alpha = .01
clf = self.factory(loss='squared_loss',
learning_rate='constant',
eta0=eta, alpha=alpha,
fit_intercept=True,
n_iter=1, average=True, shuffle=False)
n_samples = Y3.shape[0]
clf.partial_fit(X3[:int(n_samples / 2)][:], Y3[:int(n_samples / 2)])
clf.partial_fit(X3[int(n_samples / 2):][:], Y3[int(n_samples / 2):])
average_weights, average_intercept = self.asgd(X3, Y3, eta, alpha)
assert_array_almost_equal(clf.coef_,
average_weights,
decimal=16)
assert_almost_equal(clf.intercept_, average_intercept, decimal=16)
def test_sgd_least_squares_fit(self):
xmin, xmax = -5, 5
n_samples = 100
rng = np.random.RandomState(0)
X = np.linspace(xmin, xmax, n_samples).reshape(n_samples, 1)
# simple linear function without noise
y = 0.5 * X.ravel()
clf = self.factory(loss='squared_loss', alpha=0.1, n_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert_greater(score, 0.99)
# simple linear function with noise
y = 0.5 * X.ravel() + rng.randn(n_samples, 1).ravel()
clf = self.factory(loss='squared_loss', alpha=0.1, n_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert_greater(score, 0.5)
def test_sgd_epsilon_insensitive(self):
xmin, xmax = -5, 5
n_samples = 100
X = np.linspace(xmin, xmax, n_samples).reshape(n_samples, 1)
# simple linear function without noise
y = 0.5 * X.ravel()
clf = self.factory(loss='epsilon_insensitive', epsilon=0.01,
alpha=0.1, n_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert_true(score > 0.99)
# simple linear function with noise
y = 0.5 * X.ravel() \
+ np.random.randn(n_samples, 1).ravel()
clf = self.factory(loss='epsilon_insensitive', epsilon=0.01,
alpha=0.1, n_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert_true(score > 0.5)
def test_sgd_huber_fit(self):
xmin, xmax = -5, 5
n_samples = 100
rng = np.random.RandomState(0)
X = np.linspace(xmin, xmax, n_samples).reshape(n_samples, 1)
# simple linear function without noise
y = 0.5 * X.ravel()
clf = self.factory(loss="huber", epsilon=0.1, alpha=0.1, n_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert_greater(score, 0.99)
# simple linear function with noise
y = 0.5 * X.ravel() + rng.randn(n_samples, 1).ravel()
clf = self.factory(loss="huber", epsilon=0.1, alpha=0.1, n_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert_greater(score, 0.5)
def test_elasticnet_convergence(self):
# Check that the SGD output is consistent with coordinate descent
n_samples, n_features = 1000, 5
rng = np.random.RandomState(0)
X = np.random.randn(n_samples, n_features)
# ground_truth linear model that generate y from X and to which the
# models should converge if the regularizer would be set to 0.0
ground_truth_coef = rng.randn(n_features)
y = np.dot(X, ground_truth_coef)
# XXX: alpha = 0.1 seems to cause convergence problems
for alpha in [0.01, 0.001]:
for l1_ratio in [0.5, 0.8, 1.0]:
cd = linear_model.ElasticNet(alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=False)
cd.fit(X, y)
sgd = self.factory(penalty='elasticnet', n_iter=50,
alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=False)
sgd.fit(X, y)
err_msg = ("cd and sgd did not converge to comparable "
"results for alpha=%f and l1_ratio=%f"
% (alpha, l1_ratio))
assert_almost_equal(cd.coef_, sgd.coef_, decimal=2,
err_msg=err_msg)
@ignore_warnings
def test_partial_fit(self):
third = X.shape[0] // 3
clf = self.factory(alpha=0.01)
clf.partial_fit(X[:third], Y[:third])
assert_equal(clf.coef_.shape, (X.shape[1], ))
assert_equal(clf.intercept_.shape, (1,))
assert_equal(clf.predict([[0, 0]]).shape, (1, ))
id1 = id(clf.coef_.data)
clf.partial_fit(X[third:], Y[third:])
id2 = id(clf.coef_.data)
# check that coef_ haven't been re-allocated
assert_true(id1, id2)
def _test_partial_fit_equal_fit(self, lr):
clf = self.factory(alpha=0.01, n_iter=2, eta0=0.01,
learning_rate=lr, shuffle=False)
clf.fit(X, Y)
y_pred = clf.predict(T)
t = clf.t_
clf = self.factory(alpha=0.01, eta0=0.01,
learning_rate=lr, shuffle=False)
for i in range(2):
clf.partial_fit(X, Y)
y_pred2 = clf.predict(T)
assert_equal(clf.t_, t)
assert_array_almost_equal(y_pred, y_pred2, decimal=2)
def test_partial_fit_equal_fit_constant(self):
self._test_partial_fit_equal_fit("constant")
def test_partial_fit_equal_fit_optimal(self):
self._test_partial_fit_equal_fit("optimal")
def test_partial_fit_equal_fit_invscaling(self):
self._test_partial_fit_equal_fit("invscaling")
def test_loss_function_epsilon(self):
clf = self.factory(epsilon=0.9)
clf.set_params(epsilon=0.1)
assert clf.loss_functions['huber'][1] == 0.1
class SparseSGDRegressorTestCase(DenseSGDRegressorTestCase):
# Run exactly the same tests using the sparse representation variant
factory_class = SparseSGDRegressor
def test_l1_ratio():
# Test if l1 ratio extremes match L1 and L2 penalty settings.
X, y = datasets.make_classification(n_samples=1000,
n_features=100, n_informative=20,
random_state=1234)
# test if elasticnet with l1_ratio near 1 gives same result as pure l1
est_en = SGDClassifier(alpha=0.001, penalty='elasticnet',
l1_ratio=0.9999999999, random_state=42).fit(X, y)
est_l1 = SGDClassifier(alpha=0.001, penalty='l1', random_state=42).fit(X, y)
assert_array_almost_equal(est_en.coef_, est_l1.coef_)
# test if elasticnet with l1_ratio near 0 gives same result as pure l2
est_en = SGDClassifier(alpha=0.001, penalty='elasticnet',
l1_ratio=0.0000000001, random_state=42).fit(X, y)
est_l2 = SGDClassifier(alpha=0.001, penalty='l2', random_state=42).fit(X, y)
assert_array_almost_equal(est_en.coef_, est_l2.coef_)
def test_underflow_or_overlow():
with np.errstate(all='raise'):
# Generate some weird data with hugely unscaled features
rng = np.random.RandomState(0)
n_samples = 100
n_features = 10
X = rng.normal(size=(n_samples, n_features))
X[:, :2] *= 1e300
assert_true(np.isfinite(X).all())
# Use MinMaxScaler to scale the data without introducing a numerical
# instability (computing the standard deviation naively is not possible
# on this data)
X_scaled = MinMaxScaler().fit_transform(X)
assert_true(np.isfinite(X_scaled).all())
# Define a ground truth on the scaled data
ground_truth = rng.normal(size=n_features)
y = (np.dot(X_scaled, ground_truth) > 0.).astype(np.int32)
assert_array_equal(np.unique(y), [0, 1])
model = SGDClassifier(alpha=0.1, loss='squared_hinge', n_iter=500)
# smoke test: model is stable on scaled data
model.fit(X_scaled, y)
assert_true(np.isfinite(model.coef_).all())
# model is numerically unstable on unscaled data
msg_regxp = (r"Floating-point under-/overflow occurred at epoch #.*"
" Scaling input data with StandardScaler or MinMaxScaler"
" might help.")
assert_raises_regexp(ValueError, msg_regxp, model.fit, X, y)
def test_numerical_stability_large_gradient():
# Non regression test case for numerical stability on scaled problems
# where the gradient can still explode with some losses
model = SGDClassifier(loss='squared_hinge', n_iter=10, shuffle=True,
penalty='elasticnet', l1_ratio=0.3, alpha=0.01,
eta0=0.001, random_state=0)
with np.errstate(all='raise'):
model.fit(iris.data, iris.target)
assert_true(np.isfinite(model.coef_).all())
def test_large_regularization():
# Non regression tests for numerical stability issues caused by large
# regularization parameters
for penalty in ['l2', 'l1', 'elasticnet']:
model = SGDClassifier(alpha=1e5, learning_rate='constant', eta0=0.1,
n_iter=5, penalty=penalty, shuffle=False)
with np.errstate(all='raise'):
model.fit(iris.data, iris.target)
assert_array_almost_equal(model.coef_, np.zeros_like(model.coef_))
def _test_gradient_common(loss_function, cases):
# Test gradient of different loss functions
# cases is a list of (p, y, expected)
for p, y, expected in cases:
assert_almost_equal(loss_function.dloss(p, y), expected)
def test_gradient_hinge():
# Test Hinge (hinge / perceptron)
# hinge
loss = sgd_fast.Hinge(1.0)
cases = [
# (p, y, expected)
(1.1, 1.0, 0.0), (-2.0, -1.0, 0.0),
(1.0, 1.0, -1.0), (-1.0, -1.0, 1.0), (0.5, 1.0, -1.0),
(2.0, -1.0, 1.0), (-0.5, -1.0, 1.0), (0.0, 1.0, -1.0)
]
_test_gradient_common(loss, cases)
# perceptron
loss = sgd_fast.Hinge(0.0)
cases = [
# (p, y, expected)
(1.0, 1.0, 0.0), (-0.1, -1.0, 0.0),
(0.0, 1.0, -1.0), (0.0, -1.0, 1.0), (0.5, -1.0, 1.0),
(2.0, -1.0, 1.0), (-0.5, 1.0, -1.0), (-1.0, 1.0, -1.0),
]
_test_gradient_common(loss, cases)
def test_gradient_squared_hinge():
# Test SquaredHinge
loss = sgd_fast.SquaredHinge(1.0)
cases = [
# (p, y, expected)
(1.0, 1.0, 0.0), (-2.0, -1.0, 0.0), (1.0, -1.0, 4.0),
(-1.0, 1.0, -4.0), (0.5, 1.0, -1.0), (0.5, -1.0, 3.0)
]
_test_gradient_common(loss, cases)
def test_gradient_log():
# Test Log (logistic loss)
loss = sgd_fast.Log()
cases = [
# (p, y, expected)
(1.0, 1.0, -1.0 / (np.exp(1.0) + 1.0)),
(1.0, -1.0, 1.0 / (np.exp(-1.0) + 1.0)),
(-1.0, -1.0, 1.0 / (np.exp(1.0) + 1.0)),
(-1.0, 1.0, -1.0 / (np.exp(-1.0) + 1.0)),
(0.0, 1.0, -0.5), (0.0, -1.0, 0.5),
(17.9, -1.0, 1.0), (-17.9, 1.0, -1.0),
]
_test_gradient_common(loss, cases)
assert_almost_equal(loss.dloss(18.1, 1.0), np.exp(-18.1) * -1.0, 16)
assert_almost_equal(loss.dloss(-18.1, -1.0), np.exp(-18.1) * 1.0, 16)
def test_gradient_squared_loss():
# Test SquaredLoss
loss = sgd_fast.SquaredLoss()
cases = [
# (p, y, expected)
(0.0, 0.0, 0.0), (1.0, 1.0, 0.0), (1.0, 0.0, 1.0),
(0.5, -1.0, 1.5), (-2.5, 2.0, -4.5)
]
_test_gradient_common(loss, cases)
def test_gradient_huber():
# Test Huber
loss = sgd_fast.Huber(0.1)
cases = [
# (p, y, expected)
(0.0, 0.0, 0.0), (0.1, 0.0, 0.1), (0.0, 0.1, -0.1),
(3.95, 4.0, -0.05), (5.0, 2.0, 0.1), (-1.0, 5.0, -0.1)
]
_test_gradient_common(loss, cases)
def test_gradient_modified_huber():
# Test ModifiedHuber
loss = sgd_fast.ModifiedHuber()
cases = [
# (p, y, expected)
(1.0, 1.0, 0.0), (-1.0, -1.0, 0.0), (2.0, 1.0, 0.0),
(0.0, 1.0, -2.0), (-1.0, 1.0, -4.0), (0.5, -1.0, 3.0),
(0.5, -1.0, 3.0), (-2.0, 1.0, -4.0), (-3.0, 1.0, -4.0)
]
_test_gradient_common(loss, cases)
def test_gradient_epsilon_insensitive():
# Test EpsilonInsensitive
loss = sgd_fast.EpsilonInsensitive(0.1)
cases = [
(0.0, 0.0, 0.0), (0.1, 0.0, 0.0), (-2.05, -2.0, 0.0),
(3.05, 3.0, 0.0), (2.2, 2.0, 1.0), (2.0, -1.0, 1.0),
(2.0, 2.2, -1.0), (-2.0, 1.0, -1.0)
]
_test_gradient_common(loss, cases)
def test_gradient_squared_epsilon_insensitive():
# Test SquaredEpsilonInsensitive
loss = sgd_fast.SquaredEpsilonInsensitive(0.1)
cases = [
(0.0, 0.0, 0.0), (0.1, 0.0, 0.0), (-2.05, -2.0, 0.0),
(3.05, 3.0, 0.0), (2.2, 2.0, 0.2), (2.0, -1.0, 5.8),
(2.0, 2.2, -0.2), (-2.0, 1.0, -5.8)
]
_test_gradient_common(loss, cases)
| bsd-3-clause |
jaidevd/scikit-learn | examples/covariance/plot_outlier_detection.py | 36 | 5023 | """
==========================================
Outlier detection with several methods.
==========================================
When the amount of contamination is known, this example illustrates three
different ways of performing :ref:`outlier_detection`:
- based on a robust estimator of covariance, which is assuming that the
data are Gaussian distributed and performs better than the One-Class SVM
in that case.
- using the One-Class SVM and its ability to capture the shape of the
data set, hence performing better when the data is strongly
non-Gaussian, i.e. with two well-separated clusters;
- using the Isolation Forest algorithm, which is based on random forests and
hence more adapted to large-dimensional settings, even if it performs
quite well in the examples below.
- using the Local Outlier Factor to measure the local deviation of a given
data point with respect to its neighbors by comparing their local density.
The ground truth about inliers and outliers is given by the points colors
while the orange-filled area indicates which points are reported as inliers
by each method.
Here, we assume that we know the fraction of outliers in the datasets.
Thus rather than using the 'predict' method of the objects, we set the
threshold on the decision_function to separate out the corresponding
fraction.
"""
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import matplotlib.font_manager
from sklearn import svm
from sklearn.covariance import EllipticEnvelope
from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor
print(__doc__)
rng = np.random.RandomState(42)
# Example settings
n_samples = 200
outliers_fraction = 0.25
clusters_separation = [0, 1, 2]
# define two outlier detection tools to be compared
classifiers = {
"One-Class SVM": svm.OneClassSVM(nu=0.95 * outliers_fraction + 0.05,
kernel="rbf", gamma=0.1),
"Robust covariance": EllipticEnvelope(contamination=outliers_fraction),
"Isolation Forest": IsolationForest(max_samples=n_samples,
contamination=outliers_fraction,
random_state=rng),
"Local Outlier Factor": LocalOutlierFactor(
n_neighbors=35,
contamination=outliers_fraction)}
# Compare given classifiers under given settings
xx, yy = np.meshgrid(np.linspace(-7, 7, 100), np.linspace(-7, 7, 100))
n_inliers = int((1. - outliers_fraction) * n_samples)
n_outliers = int(outliers_fraction * n_samples)
ground_truth = np.ones(n_samples, dtype=int)
ground_truth[-n_outliers:] = -1
# Fit the problem with varying cluster separation
for i, offset in enumerate(clusters_separation):
np.random.seed(42)
# Data generation
X1 = 0.3 * np.random.randn(n_inliers // 2, 2) - offset
X2 = 0.3 * np.random.randn(n_inliers // 2, 2) + offset
X = np.r_[X1, X2]
# Add outliers
X = np.r_[X, np.random.uniform(low=-6, high=6, size=(n_outliers, 2))]
# Fit the model
plt.figure(figsize=(9, 7))
for i, (clf_name, clf) in enumerate(classifiers.items()):
# fit the data and tag outliers
if clf_name == "Local Outlier Factor":
y_pred = clf.fit_predict(X)
scores_pred = clf.negative_outlier_factor_
else:
clf.fit(X)
scores_pred = clf.decision_function(X)
y_pred = clf.predict(X)
threshold = stats.scoreatpercentile(scores_pred,
100 * outliers_fraction)
n_errors = (y_pred != ground_truth).sum()
# plot the levels lines and the points
if clf_name == "Local Outlier Factor":
# decision_function is private for LOF
Z = clf._decision_function(np.c_[xx.ravel(), yy.ravel()])
else:
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
subplot = plt.subplot(2, 2, i + 1)
subplot.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 7),
cmap=plt.cm.Blues_r)
a = subplot.contour(xx, yy, Z, levels=[threshold],
linewidths=2, colors='red')
subplot.contourf(xx, yy, Z, levels=[threshold, Z.max()],
colors='orange')
b = subplot.scatter(X[:-n_outliers, 0], X[:-n_outliers, 1], c='white')
c = subplot.scatter(X[-n_outliers:, 0], X[-n_outliers:, 1], c='black')
subplot.axis('tight')
subplot.legend(
[a.collections[0], b, c],
['learned decision function', 'true inliers', 'true outliers'],
prop=matplotlib.font_manager.FontProperties(size=10),
loc='lower right')
subplot.set_xlabel("%d. %s (errors: %d)" % (i + 1, clf_name, n_errors))
subplot.set_xlim((-7, 7))
subplot.set_ylim((-7, 7))
plt.subplots_adjust(0.04, 0.1, 0.96, 0.94, 0.1, 0.26)
plt.suptitle("Outlier detection")
plt.show()
| bsd-3-clause |
jbbskinny/sympy | sympy/utilities/runtests.py | 34 | 81153 | """
This is our testing framework.
Goals:
* it should be compatible with py.test and operate very similarly
(or identically)
* doesn't require any external dependencies
* preferably all the functionality should be in this file only
* no magic, just import the test file and execute the test functions, that's it
* portable
"""
from __future__ import print_function, division
import os
import sys
import platform
import inspect
import traceback
import pdb
import re
import linecache
import time
from fnmatch import fnmatch
from timeit import default_timer as clock
import doctest as pdoctest # avoid clashing with our doctest() function
from doctest import DocTestFinder, DocTestRunner
import random
import subprocess
import signal
import stat
from inspect import isgeneratorfunction
from sympy.core.cache import clear_cache
from sympy.core.compatibility import exec_, PY3, string_types, range
from sympy.utilities.misc import find_executable
from sympy.external import import_module
from sympy.utilities.exceptions import SymPyDeprecationWarning
IS_WINDOWS = (os.name == 'nt')
class Skipped(Exception):
pass
import __future__
# add more flags ??
future_flags = __future__.division.compiler_flag
def _indent(s, indent=4):
"""
Add the given number of space characters to the beginning of
every non-blank line in ``s``, and return the result.
If the string ``s`` is Unicode, it is encoded using the stdout
encoding and the ``backslashreplace`` error handler.
"""
# After a 2to3 run the below code is bogus, so wrap it with a version check
if not PY3:
if isinstance(s, unicode):
s = s.encode(pdoctest._encoding, 'backslashreplace')
# This regexp matches the start of non-blank lines:
return re.sub('(?m)^(?!$)', indent*' ', s)
pdoctest._indent = _indent
# ovverride reporter to maintain windows and python3
def _report_failure(self, out, test, example, got):
"""
Report that the given example failed.
"""
s = self._checker.output_difference(example, got, self.optionflags)
s = s.encode('raw_unicode_escape').decode('utf8', 'ignore')
out(self._failure_header(test, example) + s)
if PY3 and IS_WINDOWS:
DocTestRunner.report_failure = _report_failure
def convert_to_native_paths(lst):
"""
Converts a list of '/' separated paths into a list of
native (os.sep separated) paths and converts to lowercase
if the system is case insensitive.
"""
newlst = []
for i, rv in enumerate(lst):
rv = os.path.join(*rv.split("/"))
# on windows the slash after the colon is dropped
if sys.platform == "win32":
pos = rv.find(':')
if pos != -1:
if rv[pos + 1] != '\\':
rv = rv[:pos + 1] + '\\' + rv[pos + 1:]
newlst.append(sys_normcase(rv))
return newlst
def get_sympy_dir():
"""
Returns the root sympy directory and set the global value
indicating whether the system is case sensitive or not.
"""
global sys_case_insensitive
this_file = os.path.abspath(__file__)
sympy_dir = os.path.join(os.path.dirname(this_file), "..", "..")
sympy_dir = os.path.normpath(sympy_dir)
sys_case_insensitive = (os.path.isdir(sympy_dir) and
os.path.isdir(sympy_dir.lower()) and
os.path.isdir(sympy_dir.upper()))
return sys_normcase(sympy_dir)
def sys_normcase(f):
if sys_case_insensitive: # global defined after call to get_sympy_dir()
return f.lower()
return f
def setup_pprint():
from sympy import pprint_use_unicode, init_printing
# force pprint to be in ascii mode in doctests
pprint_use_unicode(False)
# hook our nice, hash-stable strprinter
init_printing(pretty_print=False)
def run_in_subprocess_with_hash_randomization(function, function_args=(),
function_kwargs={}, command=sys.executable,
module='sympy.utilities.runtests', force=False):
"""
Run a function in a Python subprocess with hash randomization enabled.
If hash randomization is not supported by the version of Python given, it
returns False. Otherwise, it returns the exit value of the command. The
function is passed to sys.exit(), so the return value of the function will
be the return value.
The environment variable PYTHONHASHSEED is used to seed Python's hash
randomization. If it is set, this function will return False, because
starting a new subprocess is unnecessary in that case. If it is not set,
one is set at random, and the tests are run. Note that if this
environment variable is set when Python starts, hash randomization is
automatically enabled. To force a subprocess to be created even if
PYTHONHASHSEED is set, pass ``force=True``. This flag will not force a
subprocess in Python versions that do not support hash randomization (see
below), because those versions of Python do not support the ``-R`` flag.
``function`` should be a string name of a function that is importable from
the module ``module``, like "_test". The default for ``module`` is
"sympy.utilities.runtests". ``function_args`` and ``function_kwargs``
should be a repr-able tuple and dict, respectively. The default Python
command is sys.executable, which is the currently running Python command.
This function is necessary because the seed for hash randomization must be
set by the environment variable before Python starts. Hence, in order to
use a predetermined seed for tests, we must start Python in a separate
subprocess.
Hash randomization was added in the minor Python versions 2.6.8, 2.7.3,
3.1.5, and 3.2.3, and is enabled by default in all Python versions after
and including 3.3.0.
Examples
========
>>> from sympy.utilities.runtests import (
... run_in_subprocess_with_hash_randomization)
>>> # run the core tests in verbose mode
>>> run_in_subprocess_with_hash_randomization("_test",
... function_args=("core",),
... function_kwargs={'verbose': True}) # doctest: +SKIP
# Will return 0 if sys.executable supports hash randomization and tests
# pass, 1 if they fail, and False if it does not support hash
# randomization.
"""
# Note, we must return False everywhere, not None, as subprocess.call will
# sometimes return None.
# First check if the Python version supports hash randomization
# If it doesn't have this support, it won't reconize the -R flag
p = subprocess.Popen([command, "-RV"], stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
p.communicate()
if p.returncode != 0:
return False
hash_seed = os.getenv("PYTHONHASHSEED")
if not hash_seed:
os.environ["PYTHONHASHSEED"] = str(random.randrange(2**32))
else:
if not force:
return False
# Now run the command
commandstring = ("import sys; from %s import %s;sys.exit(%s(*%s, **%s))" %
(module, function, function, repr(function_args),
repr(function_kwargs)))
try:
p = subprocess.Popen([command, "-R", "-c", commandstring])
p.communicate()
except KeyboardInterrupt:
p.wait()
finally:
# Put the environment variable back, so that it reads correctly for
# the current Python process.
if hash_seed is None:
del os.environ["PYTHONHASHSEED"]
else:
os.environ["PYTHONHASHSEED"] = hash_seed
return p.returncode
def run_all_tests(test_args=(), test_kwargs={}, doctest_args=(),
doctest_kwargs={}, examples_args=(), examples_kwargs={'quiet': True}):
"""
Run all tests.
Right now, this runs the regular tests (bin/test), the doctests
(bin/doctest), the examples (examples/all.py), and the sage tests (see
sympy/external/tests/test_sage.py).
This is what ``setup.py test`` uses.
You can pass arguments and keyword arguments to the test functions that
support them (for now, test, doctest, and the examples). See the
docstrings of those functions for a description of the available options.
For example, to run the solvers tests with colors turned off:
>>> from sympy.utilities.runtests import run_all_tests
>>> run_all_tests(test_args=("solvers",),
... test_kwargs={"colors:False"}) # doctest: +SKIP
"""
tests_successful = True
try:
# Regular tests
if not test(*test_args, **test_kwargs):
# some regular test fails, so set the tests_successful
# flag to false and continue running the doctests
tests_successful = False
# Doctests
print()
if not doctest(*doctest_args, **doctest_kwargs):
tests_successful = False
# Examples
print()
sys.path.append("examples")
from all import run_examples # examples/all.py
if not run_examples(*examples_args, **examples_kwargs):
tests_successful = False
# Sage tests
if not (sys.platform == "win32" or PY3):
# run Sage tests; Sage currently doesn't support Windows or Python 3
dev_null = open(os.devnull, 'w')
if subprocess.call("sage -v", shell=True, stdout=dev_null,
stderr=dev_null) == 0:
if subprocess.call("sage -python bin/test "
"sympy/external/tests/test_sage.py", shell=True) != 0:
tests_successful = False
if tests_successful:
return
else:
# Return nonzero exit code
sys.exit(1)
except KeyboardInterrupt:
print()
print("DO *NOT* COMMIT!")
sys.exit(1)
def test(*paths, **kwargs):
"""
Run tests in the specified test_*.py files.
Tests in a particular test_*.py file are run if any of the given strings
in ``paths`` matches a part of the test file's path. If ``paths=[]``,
tests in all test_*.py files are run.
Notes:
- If sort=False, tests are run in random order (not default).
- Paths can be entered in native system format or in unix,
forward-slash format.
- Files that are on the blacklist can be tested by providing
their path; they are only excluded if no paths are given.
**Explanation of test results**
====== ===============================================================
Output Meaning
====== ===============================================================
. passed
F failed
X XPassed (expected to fail but passed)
f XFAILed (expected to fail and indeed failed)
s skipped
w slow
T timeout (e.g., when ``--timeout`` is used)
K KeyboardInterrupt (when running the slow tests with ``--slow``,
you can interrupt one of them without killing the test runner)
====== ===============================================================
Colors have no additional meaning and are used just to facilitate
interpreting the output.
Examples
========
>>> import sympy
Run all tests:
>>> sympy.test() # doctest: +SKIP
Run one file:
>>> sympy.test("sympy/core/tests/test_basic.py") # doctest: +SKIP
>>> sympy.test("_basic") # doctest: +SKIP
Run all tests in sympy/functions/ and some particular file:
>>> sympy.test("sympy/core/tests/test_basic.py",
... "sympy/functions") # doctest: +SKIP
Run all tests in sympy/core and sympy/utilities:
>>> sympy.test("/core", "/util") # doctest: +SKIP
Run specific test from a file:
>>> sympy.test("sympy/core/tests/test_basic.py",
... kw="test_equality") # doctest: +SKIP
Run specific test from any file:
>>> sympy.test(kw="subs") # doctest: +SKIP
Run the tests with verbose mode on:
>>> sympy.test(verbose=True) # doctest: +SKIP
Don't sort the test output:
>>> sympy.test(sort=False) # doctest: +SKIP
Turn on post-mortem pdb:
>>> sympy.test(pdb=True) # doctest: +SKIP
Turn off colors:
>>> sympy.test(colors=False) # doctest: +SKIP
Force colors, even when the output is not to a terminal (this is useful,
e.g., if you are piping to ``less -r`` and you still want colors)
>>> sympy.test(force_colors=False) # doctest: +SKIP
The traceback verboseness can be set to "short" or "no" (default is
"short")
>>> sympy.test(tb='no') # doctest: +SKIP
The ``split`` option can be passed to split the test run into parts. The
split currently only splits the test files, though this may change in the
future. ``split`` should be a string of the form 'a/b', which will run
part ``a`` of ``b``. For instance, to run the first half of the test suite:
>>> sympy.test(split='1/2') # doctest: +SKIP
You can disable running the tests in a separate subprocess using
``subprocess=False``. This is done to support seeding hash randomization,
which is enabled by default in the Python versions where it is supported.
If subprocess=False, hash randomization is enabled/disabled according to
whether it has been enabled or not in the calling Python process.
However, even if it is enabled, the seed cannot be printed unless it is
called from a new Python process.
Hash randomization was added in the minor Python versions 2.6.8, 2.7.3,
3.1.5, and 3.2.3, and is enabled by default in all Python versions after
and including 3.3.0.
If hash randomization is not supported ``subprocess=False`` is used
automatically.
>>> sympy.test(subprocess=False) # doctest: +SKIP
To set the hash randomization seed, set the environment variable
``PYTHONHASHSEED`` before running the tests. This can be done from within
Python using
>>> import os
>>> os.environ['PYTHONHASHSEED'] = '42' # doctest: +SKIP
Or from the command line using
$ PYTHONHASHSEED=42 ./bin/test
If the seed is not set, a random seed will be chosen.
Note that to reproduce the same hash values, you must use both the same seed
as well as the same architecture (32-bit vs. 64-bit).
"""
subprocess = kwargs.pop("subprocess", True)
rerun = kwargs.pop("rerun", 0)
# count up from 0, do not print 0
print_counter = lambda i : (print("rerun %d" % (rerun-i))
if rerun-i else None)
if subprocess:
# loop backwards so last i is 0
for i in range(rerun, -1, -1):
print_counter(i)
ret = run_in_subprocess_with_hash_randomization("_test",
function_args=paths, function_kwargs=kwargs)
if ret is False:
break
val = not bool(ret)
# exit on the first failure or if done
if not val or i == 0:
return val
# rerun even if hash randomization is not supported
for i in range(rerun, -1, -1):
print_counter(i)
val = not bool(_test(*paths, **kwargs))
if not val or i == 0:
return val
def _test(*paths, **kwargs):
"""
Internal function that actually runs the tests.
All keyword arguments from ``test()`` are passed to this function except for
``subprocess``.
Returns 0 if tests passed and 1 if they failed. See the docstring of
``test()`` for more information.
"""
verbose = kwargs.get("verbose", False)
tb = kwargs.get("tb", "short")
kw = kwargs.get("kw", None) or ()
# ensure that kw is a tuple
if isinstance(kw, str):
kw = (kw, )
post_mortem = kwargs.get("pdb", False)
colors = kwargs.get("colors", True)
force_colors = kwargs.get("force_colors", False)
sort = kwargs.get("sort", True)
seed = kwargs.get("seed", None)
if seed is None:
seed = random.randrange(100000000)
timeout = kwargs.get("timeout", False)
slow = kwargs.get("slow", False)
enhance_asserts = kwargs.get("enhance_asserts", False)
split = kwargs.get('split', None)
blacklist = kwargs.get('blacklist', [])
blacklist = convert_to_native_paths(blacklist)
fast_threshold = kwargs.get('fast_threshold', None)
slow_threshold = kwargs.get('slow_threshold', None)
r = PyTestReporter(verbose=verbose, tb=tb, colors=colors,
force_colors=force_colors, split=split)
t = SymPyTests(r, kw, post_mortem, seed,
fast_threshold=fast_threshold,
slow_threshold=slow_threshold)
# Disable warnings for external modules
import sympy.external
sympy.external.importtools.WARN_OLD_VERSION = False
sympy.external.importtools.WARN_NOT_INSTALLED = False
# Show deprecation warnings
import warnings
warnings.simplefilter("error", SymPyDeprecationWarning)
test_files = t.get_test_files('sympy')
not_blacklisted = [f for f in test_files
if not any(b in f for b in blacklist)]
if len(paths) == 0:
matched = not_blacklisted
else:
paths = convert_to_native_paths(paths)
matched = []
for f in not_blacklisted:
basename = os.path.basename(f)
for p in paths:
if p in f or fnmatch(basename, p):
matched.append(f)
break
if slow:
# Seed to evenly shuffle slow tests among splits
random.seed(41992450)
random.shuffle(matched)
if split:
matched = split_list(matched, split)
t._testfiles.extend(matched)
return int(not t.test(sort=sort, timeout=timeout,
slow=slow, enhance_asserts=enhance_asserts))
def doctest(*paths, **kwargs):
"""
Runs doctests in all \*.py files in the sympy directory which match
any of the given strings in ``paths`` or all tests if paths=[].
Notes:
- Paths can be entered in native system format or in unix,
forward-slash format.
- Files that are on the blacklist can be tested by providing
their path; they are only excluded if no paths are given.
Examples
========
>>> import sympy
Run all tests:
>>> sympy.doctest() # doctest: +SKIP
Run one file:
>>> sympy.doctest("sympy/core/basic.py") # doctest: +SKIP
>>> sympy.doctest("polynomial.rst") # doctest: +SKIP
Run all tests in sympy/functions/ and some particular file:
>>> sympy.doctest("/functions", "basic.py") # doctest: +SKIP
Run any file having polynomial in its name, doc/src/modules/polynomial.rst,
sympy/functions/special/polynomials.py, and sympy/polys/polynomial.py:
>>> sympy.doctest("polynomial") # doctest: +SKIP
The ``split`` option can be passed to split the test run into parts. The
split currently only splits the test files, though this may change in the
future. ``split`` should be a string of the form 'a/b', which will run
part ``a`` of ``b``. Note that the regular doctests and the Sphinx
doctests are split independently. For instance, to run the first half of
the test suite:
>>> sympy.doctest(split='1/2') # doctest: +SKIP
The ``subprocess`` and ``verbose`` options are the same as with the function
``test()``. See the docstring of that function for more information.
"""
subprocess = kwargs.pop("subprocess", True)
rerun = kwargs.pop("rerun", 0)
# count up from 0, do not print 0
print_counter = lambda i : (print("rerun %d" % (rerun-i))
if rerun-i else None)
if subprocess:
# loop backwards so last i is 0
for i in range(rerun, -1, -1):
print_counter(i)
ret = run_in_subprocess_with_hash_randomization("_doctest",
function_args=paths, function_kwargs=kwargs)
if ret is False:
break
val = not bool(ret)
# exit on the first failure or if done
if not val or i == 0:
return val
# rerun even if hash randomization is not supported
for i in range(rerun, -1, -1):
print_counter(i)
val = not bool(_doctest(*paths, **kwargs))
if not val or i == 0:
return val
def _doctest(*paths, **kwargs):
"""
Internal function that actually runs the doctests.
All keyword arguments from ``doctest()`` are passed to this function
except for ``subprocess``.
Returns 0 if tests passed and 1 if they failed. See the docstrings of
``doctest()`` and ``test()`` for more information.
"""
normal = kwargs.get("normal", False)
verbose = kwargs.get("verbose", False)
colors = kwargs.get("colors", True)
force_colors = kwargs.get("force_colors", False)
blacklist = kwargs.get("blacklist", [])
split = kwargs.get('split', None)
blacklist.extend([
"doc/src/modules/plotting.rst", # generates live plots
"sympy/utilities/compilef.py", # needs tcc
"sympy/physics/gaussopt.py", # raises deprecation warning
])
if import_module('numpy') is None:
blacklist.extend([
"sympy/plotting/experimental_lambdify.py",
"sympy/plotting/plot_implicit.py",
"examples/advanced/autowrap_integrators.py",
"examples/advanced/autowrap_ufuncify.py",
"examples/intermediate/sample.py",
"examples/intermediate/mplot2d.py",
"examples/intermediate/mplot3d.py",
"doc/src/modules/numeric-computation.rst"
])
else:
if import_module('matplotlib') is None:
blacklist.extend([
"examples/intermediate/mplot2d.py",
"examples/intermediate/mplot3d.py"
])
else:
# don't display matplotlib windows
from sympy.plotting.plot import unset_show
unset_show()
if import_module('pyglet') is None:
blacklist.extend(["sympy/plotting/pygletplot"])
if import_module('theano') is None:
blacklist.extend(["doc/src/modules/numeric-computation.rst"])
# disabled because of doctest failures in asmeurer's bot
blacklist.extend([
"sympy/utilities/autowrap.py",
"examples/advanced/autowrap_integrators.py",
"examples/advanced/autowrap_ufuncify.py"
])
# blacklist these modules until issue 4840 is resolved
blacklist.extend([
"sympy/conftest.py",
"sympy/utilities/benchmarking.py"
])
blacklist = convert_to_native_paths(blacklist)
# Disable warnings for external modules
import sympy.external
sympy.external.importtools.WARN_OLD_VERSION = False
sympy.external.importtools.WARN_NOT_INSTALLED = False
# Show deprecation warnings
import warnings
warnings.simplefilter("error", SymPyDeprecationWarning)
r = PyTestReporter(verbose, split=split, colors=colors,\
force_colors=force_colors)
t = SymPyDocTests(r, normal)
test_files = t.get_test_files('sympy')
test_files.extend(t.get_test_files('examples', init_only=False))
not_blacklisted = [f for f in test_files
if not any(b in f for b in blacklist)]
if len(paths) == 0:
matched = not_blacklisted
else:
# take only what was requested...but not blacklisted items
# and allow for partial match anywhere or fnmatch of name
paths = convert_to_native_paths(paths)
matched = []
for f in not_blacklisted:
basename = os.path.basename(f)
for p in paths:
if p in f or fnmatch(basename, p):
matched.append(f)
break
if split:
matched = split_list(matched, split)
t._testfiles.extend(matched)
# run the tests and record the result for this *py portion of the tests
if t._testfiles:
failed = not t.test()
else:
failed = False
# N.B.
# --------------------------------------------------------------------
# Here we test *.rst files at or below doc/src. Code from these must
# be self supporting in terms of imports since there is no importing
# of necessary modules by doctest.testfile. If you try to pass *.py
# files through this they might fail because they will lack the needed
# imports and smarter parsing that can be done with source code.
#
test_files = t.get_test_files('doc/src', '*.rst', init_only=False)
test_files.sort()
not_blacklisted = [f for f in test_files
if not any(b in f for b in blacklist)]
if len(paths) == 0:
matched = not_blacklisted
else:
# Take only what was requested as long as it's not on the blacklist.
# Paths were already made native in *py tests so don't repeat here.
# There's no chance of having a *py file slip through since we
# only have *rst files in test_files.
matched = []
for f in not_blacklisted:
basename = os.path.basename(f)
for p in paths:
if p in f or fnmatch(basename, p):
matched.append(f)
break
if split:
matched = split_list(matched, split)
setup_pprint()
first_report = True
for rst_file in matched:
if not os.path.isfile(rst_file):
continue
old_displayhook = sys.displayhook
try:
out = sympytestfile(
rst_file, module_relative=False, encoding='utf-8',
optionflags=pdoctest.ELLIPSIS | pdoctest.NORMALIZE_WHITESPACE |
pdoctest.IGNORE_EXCEPTION_DETAIL)
finally:
# make sure we return to the original displayhook in case some
# doctest has changed that
sys.displayhook = old_displayhook
rstfailed, tested = out
if tested:
failed = rstfailed or failed
if first_report:
first_report = False
msg = 'rst doctests start'
if not t._testfiles:
r.start(msg=msg)
else:
r.write_center(msg)
print()
# use as the id, everything past the first 'sympy'
file_id = rst_file[rst_file.find('sympy') + len('sympy') + 1:]
print(file_id, end=" ")
# get at least the name out so it is know who is being tested
wid = r.terminal_width - len(file_id) - 1 # update width
test_file = '[%s]' % (tested)
report = '[%s]' % (rstfailed or 'OK')
print(''.join(
[test_file, ' '*(wid - len(test_file) - len(report)), report])
)
# the doctests for *py will have printed this message already if there was
# a failure, so now only print it if there was intervening reporting by
# testing the *rst as evidenced by first_report no longer being True.
if not first_report and failed:
print()
print("DO *NOT* COMMIT!")
return int(failed)
sp = re.compile(r'([0-9]+)/([1-9][0-9]*)')
def split_list(l, split):
"""
Splits a list into part a of b
split should be a string of the form 'a/b'. For instance, '1/3' would give
the split one of three.
If the length of the list is not divisible by the number of splits, the
last split will have more items.
>>> from sympy.utilities.runtests import split_list
>>> a = list(range(10))
>>> split_list(a, '1/3')
[0, 1, 2]
>>> split_list(a, '2/3')
[3, 4, 5]
>>> split_list(a, '3/3')
[6, 7, 8, 9]
"""
m = sp.match(split)
if not m:
raise ValueError("split must be a string of the form a/b where a and b are ints")
i, t = map(int, m.groups())
return l[(i - 1)*len(l)//t:i*len(l)//t]
from collections import namedtuple
SymPyTestResults = namedtuple('TestResults', 'failed attempted')
def sympytestfile(filename, module_relative=True, name=None, package=None,
globs=None, verbose=None, report=True, optionflags=0,
extraglobs=None, raise_on_error=False,
parser=pdoctest.DocTestParser(), encoding=None):
"""
Test examples in the given file. Return (#failures, #tests).
Optional keyword arg ``module_relative`` specifies how filenames
should be interpreted:
- If ``module_relative`` is True (the default), then ``filename``
specifies a module-relative path. By default, this path is
relative to the calling module's directory; but if the
``package`` argument is specified, then it is relative to that
package. To ensure os-independence, ``filename`` should use
"/" characters to separate path segments, and should not
be an absolute path (i.e., it may not begin with "/").
- If ``module_relative`` is False, then ``filename`` specifies an
os-specific path. The path may be absolute or relative (to
the current working directory).
Optional keyword arg ``name`` gives the name of the test; by default
use the file's basename.
Optional keyword argument ``package`` is a Python package or the
name of a Python package whose directory should be used as the
base directory for a module relative filename. If no package is
specified, then the calling module's directory is used as the base
directory for module relative filenames. It is an error to
specify ``package`` if ``module_relative`` is False.
Optional keyword arg ``globs`` gives a dict to be used as the globals
when executing examples; by default, use {}. A copy of this dict
is actually used for each docstring, so that each docstring's
examples start with a clean slate.
Optional keyword arg ``extraglobs`` gives a dictionary that should be
merged into the globals that are used to execute examples. By
default, no extra globals are used.
Optional keyword arg ``verbose`` prints lots of stuff if true, prints
only failures if false; by default, it's true iff "-v" is in sys.argv.
Optional keyword arg ``report`` prints a summary at the end when true,
else prints nothing at the end. In verbose mode, the summary is
detailed, else very brief (in fact, empty if all tests passed).
Optional keyword arg ``optionflags`` or's together module constants,
and defaults to 0. Possible values (see the docs for details):
- DONT_ACCEPT_TRUE_FOR_1
- DONT_ACCEPT_BLANKLINE
- NORMALIZE_WHITESPACE
- ELLIPSIS
- SKIP
- IGNORE_EXCEPTION_DETAIL
- REPORT_UDIFF
- REPORT_CDIFF
- REPORT_NDIFF
- REPORT_ONLY_FIRST_FAILURE
Optional keyword arg ``raise_on_error`` raises an exception on the
first unexpected exception or failure. This allows failures to be
post-mortem debugged.
Optional keyword arg ``parser`` specifies a DocTestParser (or
subclass) that should be used to extract tests from the files.
Optional keyword arg ``encoding`` specifies an encoding that should
be used to convert the file to unicode.
Advanced tomfoolery: testmod runs methods of a local instance of
class doctest.Tester, then merges the results into (or creates)
global Tester instance doctest.master. Methods of doctest.master
can be called directly too, if you want to do something unusual.
Passing report=0 to testmod is especially useful then, to delay
displaying a summary. Invoke doctest.master.summarize(verbose)
when you're done fiddling.
"""
if package and not module_relative:
raise ValueError("Package may only be specified for module-"
"relative paths.")
# Relativize the path
if not PY3:
text, filename = pdoctest._load_testfile(
filename, package, module_relative)
if encoding is not None:
text = text.decode(encoding)
else:
text, filename = pdoctest._load_testfile(
filename, package, module_relative, encoding)
# If no name was given, then use the file's name.
if name is None:
name = os.path.basename(filename)
# Assemble the globals.
if globs is None:
globs = {}
else:
globs = globs.copy()
if extraglobs is not None:
globs.update(extraglobs)
if '__name__' not in globs:
globs['__name__'] = '__main__'
if raise_on_error:
runner = pdoctest.DebugRunner(verbose=verbose, optionflags=optionflags)
else:
runner = SymPyDocTestRunner(verbose=verbose, optionflags=optionflags)
runner._checker = SymPyOutputChecker()
# Read the file, convert it to a test, and run it.
test = parser.get_doctest(text, globs, name, filename, 0)
runner.run(test, compileflags=future_flags)
if report:
runner.summarize()
if pdoctest.master is None:
pdoctest.master = runner
else:
pdoctest.master.merge(runner)
return SymPyTestResults(runner.failures, runner.tries)
class SymPyTests(object):
def __init__(self, reporter, kw="", post_mortem=False,
seed=None, fast_threshold=None, slow_threshold=None):
self._post_mortem = post_mortem
self._kw = kw
self._count = 0
self._root_dir = sympy_dir
self._reporter = reporter
self._reporter.root_dir(self._root_dir)
self._testfiles = []
self._seed = seed if seed is not None else random.random()
# Defaults in seconds, from human / UX design limits
# http://www.nngroup.com/articles/response-times-3-important-limits/
#
# These defaults are *NOT* set in stone as we are measuring different
# things, so others feel free to come up with a better yardstick :)
if fast_threshold:
self._fast_threshold = float(fast_threshold)
else:
self._fast_threshold = 0.1
if slow_threshold:
self._slow_threshold = float(slow_threshold)
else:
self._slow_threshold = 10
def test(self, sort=False, timeout=False, slow=False, enhance_asserts=False):
"""
Runs the tests returning True if all tests pass, otherwise False.
If sort=False run tests in random order.
"""
if sort:
self._testfiles.sort()
elif slow:
pass
else:
random.seed(self._seed)
random.shuffle(self._testfiles)
self._reporter.start(self._seed)
for f in self._testfiles:
try:
self.test_file(f, sort, timeout, slow, enhance_asserts)
except KeyboardInterrupt:
print(" interrupted by user")
self._reporter.finish()
raise
return self._reporter.finish()
def _enhance_asserts(self, source):
from ast import (NodeTransformer, Compare, Name, Store, Load, Tuple,
Assign, BinOp, Str, Mod, Assert, parse, fix_missing_locations)
ops = {"Eq": '==', "NotEq": '!=', "Lt": '<', "LtE": '<=',
"Gt": '>', "GtE": '>=', "Is": 'is', "IsNot": 'is not',
"In": 'in', "NotIn": 'not in'}
class Transform(NodeTransformer):
def visit_Assert(self, stmt):
if isinstance(stmt.test, Compare):
compare = stmt.test
values = [compare.left] + compare.comparators
names = [ "_%s" % i for i, _ in enumerate(values) ]
names_store = [ Name(n, Store()) for n in names ]
names_load = [ Name(n, Load()) for n in names ]
target = Tuple(names_store, Store())
value = Tuple(values, Load())
assign = Assign([target], value)
new_compare = Compare(names_load[0], compare.ops, names_load[1:])
msg_format = "\n%s " + "\n%s ".join([ ops[op.__class__.__name__] for op in compare.ops ]) + "\n%s"
msg = BinOp(Str(msg_format), Mod(), Tuple(names_load, Load()))
test = Assert(new_compare, msg, lineno=stmt.lineno, col_offset=stmt.col_offset)
return [assign, test]
else:
return stmt
tree = parse(source)
new_tree = Transform().visit(tree)
return fix_missing_locations(new_tree)
def test_file(self, filename, sort=True, timeout=False, slow=False, enhance_asserts=False):
reporter = self._reporter
funcs = []
try:
gl = {'__file__': filename}
try:
if PY3:
open_file = lambda: open(filename, encoding="utf8")
else:
open_file = lambda: open(filename)
with open_file() as f:
source = f.read()
if self._kw:
for l in source.splitlines():
if l.lstrip().startswith('def '):
if any(l.find(k) != -1 for k in self._kw):
break
else:
return
if enhance_asserts:
try:
source = self._enhance_asserts(source)
except ImportError:
pass
code = compile(source, filename, "exec")
exec_(code, gl)
except (SystemExit, KeyboardInterrupt):
raise
except ImportError:
reporter.import_error(filename, sys.exc_info())
return
clear_cache()
self._count += 1
random.seed(self._seed)
pytestfile = ""
if "XFAIL" in gl:
pytestfile = inspect.getsourcefile(gl["XFAIL"])
pytestfile2 = ""
if "slow" in gl:
pytestfile2 = inspect.getsourcefile(gl["slow"])
disabled = gl.get("disabled", False)
if not disabled:
# we need to filter only those functions that begin with 'test_'
# that are defined in the testing file or in the file where
# is defined the XFAIL decorator
funcs = [gl[f] for f in gl.keys() if f.startswith("test_") and
(inspect.isfunction(gl[f]) or inspect.ismethod(gl[f])) and
(inspect.getsourcefile(gl[f]) == filename or
inspect.getsourcefile(gl[f]) == pytestfile or
inspect.getsourcefile(gl[f]) == pytestfile2)]
if slow:
funcs = [f for f in funcs if getattr(f, '_slow', False)]
# Sorting of XFAILed functions isn't fixed yet :-(
funcs.sort(key=lambda x: inspect.getsourcelines(x)[1])
i = 0
while i < len(funcs):
if isgeneratorfunction(funcs[i]):
# some tests can be generators, that return the actual
# test functions. We unpack it below:
f = funcs.pop(i)
for fg in f():
func = fg[0]
args = fg[1:]
fgw = lambda: func(*args)
funcs.insert(i, fgw)
i += 1
else:
i += 1
# drop functions that are not selected with the keyword expression:
funcs = [x for x in funcs if self.matches(x)]
if not funcs:
return
except Exception:
reporter.entering_filename(filename, len(funcs))
raise
reporter.entering_filename(filename, len(funcs))
if not sort:
random.shuffle(funcs)
for f in funcs:
start = time.time()
reporter.entering_test(f)
try:
if getattr(f, '_slow', False) and not slow:
raise Skipped("Slow")
if timeout:
self._timeout(f, timeout)
else:
random.seed(self._seed)
f()
except KeyboardInterrupt:
if getattr(f, '_slow', False):
reporter.test_skip("KeyboardInterrupt")
else:
raise
except Exception:
if timeout:
signal.alarm(0) # Disable the alarm. It could not be handled before.
t, v, tr = sys.exc_info()
if t is AssertionError:
reporter.test_fail((t, v, tr))
if self._post_mortem:
pdb.post_mortem(tr)
elif t.__name__ == "Skipped":
reporter.test_skip(v)
elif t.__name__ == "XFail":
reporter.test_xfail()
elif t.__name__ == "XPass":
reporter.test_xpass(v)
else:
reporter.test_exception((t, v, tr))
if self._post_mortem:
pdb.post_mortem(tr)
else:
reporter.test_pass()
taken = time.time() - start
if taken > self._slow_threshold:
reporter.slow_test_functions.append((f.__name__, taken))
if getattr(f, '_slow', False) and slow:
if taken < self._fast_threshold:
reporter.fast_test_functions.append((f.__name__, taken))
reporter.leaving_filename()
def _timeout(self, function, timeout):
def callback(x, y):
signal.alarm(0)
raise Skipped("Timeout")
signal.signal(signal.SIGALRM, callback)
signal.alarm(timeout) # Set an alarm with a given timeout
function()
signal.alarm(0) # Disable the alarm
def matches(self, x):
"""
Does the keyword expression self._kw match "x"? Returns True/False.
Always returns True if self._kw is "".
"""
if not self._kw:
return True
for kw in self._kw:
if x.__name__.find(kw) != -1:
return True
return False
def get_test_files(self, dir, pat='test_*.py'):
"""
Returns the list of test_*.py (default) files at or below directory
``dir`` relative to the sympy home directory.
"""
dir = os.path.join(self._root_dir, convert_to_native_paths([dir])[0])
g = []
for path, folders, files in os.walk(dir):
g.extend([os.path.join(path, f) for f in files if fnmatch(f, pat)])
return sorted([sys_normcase(gi) for gi in g])
class SymPyDocTests(object):
def __init__(self, reporter, normal):
self._count = 0
self._root_dir = sympy_dir
self._reporter = reporter
self._reporter.root_dir(self._root_dir)
self._normal = normal
self._testfiles = []
def test(self):
"""
Runs the tests and returns True if all tests pass, otherwise False.
"""
self._reporter.start()
for f in self._testfiles:
try:
self.test_file(f)
except KeyboardInterrupt:
print(" interrupted by user")
self._reporter.finish()
raise
return self._reporter.finish()
def test_file(self, filename):
clear_cache()
from sympy.core.compatibility import StringIO
rel_name = filename[len(self._root_dir) + 1:]
dirname, file = os.path.split(filename)
module = rel_name.replace(os.sep, '.')[:-3]
if rel_name.startswith("examples"):
# Examples files do not have __init__.py files,
# So we have to temporarily extend sys.path to import them
sys.path.insert(0, dirname)
module = file[:-3] # remove ".py"
setup_pprint()
try:
module = pdoctest._normalize_module(module)
tests = SymPyDocTestFinder().find(module)
except (SystemExit, KeyboardInterrupt):
raise
except ImportError:
self._reporter.import_error(filename, sys.exc_info())
return
finally:
if rel_name.startswith("examples"):
del sys.path[0]
tests = [test for test in tests if len(test.examples) > 0]
# By default tests are sorted by alphabetical order by function name.
# We sort by line number so one can edit the file sequentially from
# bottom to top. However, if there are decorated functions, their line
# numbers will be too large and for now one must just search for these
# by text and function name.
tests.sort(key=lambda x: -x.lineno)
if not tests:
return
self._reporter.entering_filename(filename, len(tests))
for test in tests:
assert len(test.examples) != 0
# check if there are external dependencies which need to be met
if '_doctest_depends_on' in test.globs:
if not self._process_dependencies(test.globs['_doctest_depends_on']):
self._reporter.test_skip()
continue
runner = SymPyDocTestRunner(optionflags=pdoctest.ELLIPSIS |
pdoctest.NORMALIZE_WHITESPACE |
pdoctest.IGNORE_EXCEPTION_DETAIL)
runner._checker = SymPyOutputChecker()
old = sys.stdout
new = StringIO()
sys.stdout = new
# If the testing is normal, the doctests get importing magic to
# provide the global namespace. If not normal (the default) then
# then must run on their own; all imports must be explicit within
# a function's docstring. Once imported that import will be
# available to the rest of the tests in a given function's
# docstring (unless clear_globs=True below).
if not self._normal:
test.globs = {}
# if this is uncommented then all the test would get is what
# comes by default with a "from sympy import *"
#exec('from sympy import *') in test.globs
test.globs['print_function'] = print_function
try:
f, t = runner.run(test, compileflags=future_flags,
out=new.write, clear_globs=False)
except KeyboardInterrupt:
raise
finally:
sys.stdout = old
if f > 0:
self._reporter.doctest_fail(test.name, new.getvalue())
else:
self._reporter.test_pass()
self._reporter.leaving_filename()
def get_test_files(self, dir, pat='*.py', init_only=True):
"""
Returns the list of \*.py files (default) from which docstrings
will be tested which are at or below directory ``dir``. By default,
only those that have an __init__.py in their parent directory
and do not start with ``test_`` will be included.
"""
def importable(x):
"""
Checks if given pathname x is an importable module by checking for
__init__.py file.
Returns True/False.
Currently we only test if the __init__.py file exists in the
directory with the file "x" (in theory we should also test all the
parent dirs).
"""
init_py = os.path.join(os.path.dirname(x), "__init__.py")
return os.path.exists(init_py)
dir = os.path.join(self._root_dir, convert_to_native_paths([dir])[0])
g = []
for path, folders, files in os.walk(dir):
g.extend([os.path.join(path, f) for f in files
if not f.startswith('test_') and fnmatch(f, pat)])
if init_only:
# skip files that are not importable (i.e. missing __init__.py)
g = [x for x in g if importable(x)]
return [sys_normcase(gi) for gi in g]
def _process_dependencies(self, deps):
"""
Returns ``False`` if some dependencies are not met and the test should be
skipped otherwise returns ``True``.
"""
executables = deps.get('exe', None)
moduledeps = deps.get('modules', None)
viewers = deps.get('disable_viewers', None)
pyglet = deps.get('pyglet', None)
# print deps
if executables is not None:
for ex in executables:
found = find_executable(ex)
if found is None:
return False
if moduledeps is not None:
for extmod in moduledeps:
if extmod == 'matplotlib':
matplotlib = import_module(
'matplotlib',
__import__kwargs={'fromlist':
['pyplot', 'cm', 'collections']},
min_module_version='1.0.0', catch=(RuntimeError,))
if matplotlib is not None:
pass
else:
return False
else:
# TODO min version support
mod = import_module(extmod)
if mod is not None:
version = "unknown"
if hasattr(mod, '__version__'):
version = mod.__version__
else:
return False
if viewers is not None:
import tempfile
tempdir = tempfile.mkdtemp()
os.environ['PATH'] = '%s:%s' % (tempdir, os.environ['PATH'])
if PY3:
vw = '#!/usr/bin/env python3\n' \
'import sys\n' \
'if len(sys.argv) <= 1:\n' \
' exit("wrong number of args")\n'
else:
vw = '#!/usr/bin/env python\n' \
'import sys\n' \
'if len(sys.argv) <= 1:\n' \
' exit("wrong number of args")\n'
for viewer in viewers:
with open(os.path.join(tempdir, viewer), 'w') as fh:
fh.write(vw)
# make the file executable
os.chmod(os.path.join(tempdir, viewer),
stat.S_IREAD | stat.S_IWRITE | stat.S_IXUSR)
if pyglet:
# monkey-patch pyglet s.t. it does not open a window during
# doctesting
import pyglet
class DummyWindow(object):
def __init__(self, *args, **kwargs):
self.has_exit=True
self.width = 600
self.height = 400
def set_vsync(self, x):
pass
def switch_to(self):
pass
def push_handlers(self, x):
pass
def close(self):
pass
pyglet.window.Window = DummyWindow
return True
class SymPyDocTestFinder(DocTestFinder):
"""
A class used to extract the DocTests that are relevant to a given
object, from its docstring and the docstrings of its contained
objects. Doctests can currently be extracted from the following
object types: modules, functions, classes, methods, staticmethods,
classmethods, and properties.
Modified from doctest's version by looking harder for code in the
case that it looks like the the code comes from a different module.
In the case of decorated functions (e.g. @vectorize) they appear
to come from a different module (e.g. multidemensional) even though
their code is not there.
"""
def _find(self, tests, obj, name, module, source_lines, globs, seen):
"""
Find tests for the given object and any contained objects, and
add them to ``tests``.
"""
if self._verbose:
print('Finding tests in %s' % name)
# If we've already processed this object, then ignore it.
if id(obj) in seen:
return
seen[id(obj)] = 1
# Make sure we don't run doctests for classes outside of sympy, such
# as in numpy or scipy.
if inspect.isclass(obj):
if obj.__module__.split('.')[0] != 'sympy':
return
# Find a test for this object, and add it to the list of tests.
test = self._get_test(obj, name, module, globs, source_lines)
if test is not None:
tests.append(test)
if not self._recurse:
return
# Look for tests in a module's contained objects.
if inspect.ismodule(obj):
for rawname, val in obj.__dict__.items():
# Recurse to functions & classes.
if inspect.isfunction(val) or inspect.isclass(val):
# Make sure we don't run doctests functions or classes
# from different modules
if val.__module__ != module.__name__:
continue
assert self._from_module(module, val), \
"%s is not in module %s (rawname %s)" % (val, module, rawname)
try:
valname = '%s.%s' % (name, rawname)
self._find(tests, val, valname, module,
source_lines, globs, seen)
except KeyboardInterrupt:
raise
# Look for tests in a module's __test__ dictionary.
for valname, val in getattr(obj, '__test__', {}).items():
if not isinstance(valname, string_types):
raise ValueError("SymPyDocTestFinder.find: __test__ keys "
"must be strings: %r" %
(type(valname),))
if not (inspect.isfunction(val) or inspect.isclass(val) or
inspect.ismethod(val) or inspect.ismodule(val) or
isinstance(val, string_types)):
raise ValueError("SymPyDocTestFinder.find: __test__ values "
"must be strings, functions, methods, "
"classes, or modules: %r" %
(type(val),))
valname = '%s.__test__.%s' % (name, valname)
self._find(tests, val, valname, module, source_lines,
globs, seen)
# Look for tests in a class's contained objects.
if inspect.isclass(obj):
for valname, val in obj.__dict__.items():
# Special handling for staticmethod/classmethod.
if isinstance(val, staticmethod):
val = getattr(obj, valname)
if isinstance(val, classmethod):
val = getattr(obj, valname).__func__
# Recurse to methods, properties, and nested classes.
if (inspect.isfunction(val) or
inspect.isclass(val) or
isinstance(val, property)):
# Make sure we don't run doctests functions or classes
# from different modules
if isinstance(val, property):
if hasattr(val.fget, '__module__'):
if val.fget.__module__ != module.__name__:
continue
else:
if val.__module__ != module.__name__:
continue
assert self._from_module(module, val), \
"%s is not in module %s (valname %s)" % (
val, module, valname)
valname = '%s.%s' % (name, valname)
self._find(tests, val, valname, module, source_lines,
globs, seen)
def _get_test(self, obj, name, module, globs, source_lines):
"""
Return a DocTest for the given object, if it defines a docstring;
otherwise, return None.
"""
lineno = None
# Extract the object's docstring. If it doesn't have one,
# then return None (no test for this object).
if isinstance(obj, string_types):
# obj is a string in the case for objects in the polys package.
# Note that source_lines is a binary string (compiled polys
# modules), which can't be handled by _find_lineno so determine
# the line number here.
docstring = obj
matches = re.findall("line \d+", name)
assert len(matches) == 1, \
"string '%s' does not contain lineno " % name
# NOTE: this is not the exact linenumber but its better than no
# lineno ;)
lineno = int(matches[0][5:])
else:
try:
if obj.__doc__ is None:
docstring = ''
else:
docstring = obj.__doc__
if not isinstance(docstring, string_types):
docstring = str(docstring)
except (TypeError, AttributeError):
docstring = ''
# Don't bother if the docstring is empty.
if self._exclude_empty and not docstring:
return None
# check that properties have a docstring because _find_lineno
# assumes it
if isinstance(obj, property):
if obj.fget.__doc__ is None:
return None
# Find the docstring's location in the file.
if lineno is None:
# handling of properties is not implemented in _find_lineno so do
# it here
if hasattr(obj, 'func_closure') and obj.func_closure is not None:
tobj = obj.func_closure[0].cell_contents
elif isinstance(obj, property):
tobj = obj.fget
else:
tobj = obj
lineno = self._find_lineno(tobj, source_lines)
if lineno is None:
return None
# Return a DocTest for this object.
if module is None:
filename = None
else:
filename = getattr(module, '__file__', module.__name__)
if filename[-4:] in (".pyc", ".pyo"):
filename = filename[:-1]
if hasattr(obj, '_doctest_depends_on'):
globs['_doctest_depends_on'] = obj._doctest_depends_on
else:
globs['_doctest_depends_on'] = {}
return self._parser.get_doctest(docstring, globs, name,
filename, lineno)
class SymPyDocTestRunner(DocTestRunner):
"""
A class used to run DocTest test cases, and accumulate statistics.
The ``run`` method is used to process a single DocTest case. It
returns a tuple ``(f, t)``, where ``t`` is the number of test cases
tried, and ``f`` is the number of test cases that failed.
Modified from the doctest version to not reset the sys.displayhook (see
issue 5140).
See the docstring of the original DocTestRunner for more information.
"""
def run(self, test, compileflags=None, out=None, clear_globs=True):
"""
Run the examples in ``test``, and display the results using the
writer function ``out``.
The examples are run in the namespace ``test.globs``. If
``clear_globs`` is true (the default), then this namespace will
be cleared after the test runs, to help with garbage
collection. If you would like to examine the namespace after
the test completes, then use ``clear_globs=False``.
``compileflags`` gives the set of flags that should be used by
the Python compiler when running the examples. If not
specified, then it will default to the set of future-import
flags that apply to ``globs``.
The output of each example is checked using
``SymPyDocTestRunner.check_output``, and the results are
formatted by the ``SymPyDocTestRunner.report_*`` methods.
"""
self.test = test
if compileflags is None:
compileflags = pdoctest._extract_future_flags(test.globs)
save_stdout = sys.stdout
if out is None:
out = save_stdout.write
sys.stdout = self._fakeout
# Patch pdb.set_trace to restore sys.stdout during interactive
# debugging (so it's not still redirected to self._fakeout).
# Note that the interactive output will go to *our*
# save_stdout, even if that's not the real sys.stdout; this
# allows us to write test cases for the set_trace behavior.
save_set_trace = pdb.set_trace
self.debugger = pdoctest._OutputRedirectingPdb(save_stdout)
self.debugger.reset()
pdb.set_trace = self.debugger.set_trace
# Patch linecache.getlines, so we can see the example's source
# when we're inside the debugger.
self.save_linecache_getlines = pdoctest.linecache.getlines
linecache.getlines = self.__patched_linecache_getlines
try:
test.globs['print_function'] = print_function
return self.__run(test, compileflags, out)
finally:
sys.stdout = save_stdout
pdb.set_trace = save_set_trace
linecache.getlines = self.save_linecache_getlines
if clear_globs:
test.globs.clear()
# We have to override the name mangled methods.
SymPyDocTestRunner._SymPyDocTestRunner__patched_linecache_getlines = \
DocTestRunner._DocTestRunner__patched_linecache_getlines
SymPyDocTestRunner._SymPyDocTestRunner__run = DocTestRunner._DocTestRunner__run
SymPyDocTestRunner._SymPyDocTestRunner__record_outcome = \
DocTestRunner._DocTestRunner__record_outcome
class SymPyOutputChecker(pdoctest.OutputChecker):
"""
Compared to the OutputChecker from the stdlib our OutputChecker class
supports numerical comparison of floats occuring in the output of the
doctest examples
"""
def __init__(self):
# NOTE OutputChecker is an old-style class with no __init__ method,
# so we can't call the base class version of __init__ here
got_floats = r'(\d+\.\d*|\.\d+)'
# floats in the 'want' string may contain ellipses
want_floats = got_floats + r'(\.{3})?'
front_sep = r'\s|\+|\-|\*|,'
back_sep = front_sep + r'|j|e'
fbeg = r'^%s(?=%s|$)' % (got_floats, back_sep)
fmidend = r'(?<=%s)%s(?=%s|$)' % (front_sep, got_floats, back_sep)
self.num_got_rgx = re.compile(r'(%s|%s)' %(fbeg, fmidend))
fbeg = r'^%s(?=%s|$)' % (want_floats, back_sep)
fmidend = r'(?<=%s)%s(?=%s|$)' % (front_sep, want_floats, back_sep)
self.num_want_rgx = re.compile(r'(%s|%s)' %(fbeg, fmidend))
def check_output(self, want, got, optionflags):
"""
Return True iff the actual output from an example (`got`)
matches the expected output (`want`). These strings are
always considered to match if they are identical; but
depending on what option flags the test runner is using,
several non-exact match types are also possible. See the
documentation for `TestRunner` for more information about
option flags.
"""
# Handle the common case first, for efficiency:
# if they're string-identical, always return true.
if got == want:
return True
# TODO parse integers as well ?
# Parse floats and compare them. If some of the parsed floats contain
# ellipses, skip the comparison.
matches = self.num_got_rgx.finditer(got)
numbers_got = [match.group(1) for match in matches] # list of strs
matches = self.num_want_rgx.finditer(want)
numbers_want = [match.group(1) for match in matches] # list of strs
if len(numbers_got) != len(numbers_want):
return False
if len(numbers_got) > 0:
nw_ = []
for ng, nw in zip(numbers_got, numbers_want):
if '...' in nw:
nw_.append(ng)
continue
else:
nw_.append(nw)
if abs(float(ng)-float(nw)) > 1e-5:
return False
got = self.num_got_rgx.sub(r'%s', got)
got = got % tuple(nw_)
# <BLANKLINE> can be used as a special sequence to signify a
# blank line, unless the DONT_ACCEPT_BLANKLINE flag is used.
if not (optionflags & pdoctest.DONT_ACCEPT_BLANKLINE):
# Replace <BLANKLINE> in want with a blank line.
want = re.sub('(?m)^%s\s*?$' % re.escape(pdoctest.BLANKLINE_MARKER),
'', want)
# If a line in got contains only spaces, then remove the
# spaces.
got = re.sub('(?m)^\s*?$', '', got)
if got == want:
return True
# This flag causes doctest to ignore any differences in the
# contents of whitespace strings. Note that this can be used
# in conjunction with the ELLIPSIS flag.
if optionflags & pdoctest.NORMALIZE_WHITESPACE:
got = ' '.join(got.split())
want = ' '.join(want.split())
if got == want:
return True
# The ELLIPSIS flag says to let the sequence "..." in `want`
# match any substring in `got`.
if optionflags & pdoctest.ELLIPSIS:
if pdoctest._ellipsis_match(want, got):
return True
# We didn't find any match; return false.
return False
class Reporter(object):
"""
Parent class for all reporters.
"""
pass
class PyTestReporter(Reporter):
"""
Py.test like reporter. Should produce output identical to py.test.
"""
def __init__(self, verbose=False, tb="short", colors=True,
force_colors=False, split=None):
self._verbose = verbose
self._tb_style = tb
self._colors = colors
self._force_colors = force_colors
self._xfailed = 0
self._xpassed = []
self._failed = []
self._failed_doctest = []
self._passed = 0
self._skipped = 0
self._exceptions = []
self._terminal_width = None
self._default_width = 80
self._split = split
# TODO: Should these be protected?
self.slow_test_functions = []
self.fast_test_functions = []
# this tracks the x-position of the cursor (useful for positioning
# things on the screen), without the need for any readline library:
self._write_pos = 0
self._line_wrap = False
def root_dir(self, dir):
self._root_dir = dir
@property
def terminal_width(self):
if self._terminal_width is not None:
return self._terminal_width
def findout_terminal_width():
if sys.platform == "win32":
# Windows support is based on:
#
# http://code.activestate.com/recipes/
# 440694-determine-size-of-console-window-on-windows/
from ctypes import windll, create_string_buffer
h = windll.kernel32.GetStdHandle(-12)
csbi = create_string_buffer(22)
res = windll.kernel32.GetConsoleScreenBufferInfo(h, csbi)
if res:
import struct
(_, _, _, _, _, left, _, right, _, _, _) = \
struct.unpack("hhhhHhhhhhh", csbi.raw)
return right - left
else:
return self._default_width
if hasattr(sys.stdout, 'isatty') and not sys.stdout.isatty():
return self._default_width # leave PIPEs alone
try:
process = subprocess.Popen(['stty', '-a'],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
stdout = process.stdout.read()
if PY3:
stdout = stdout.decode("utf-8")
except (OSError, IOError):
pass
else:
# We support the following output formats from stty:
#
# 1) Linux -> columns 80
# 2) OS X -> 80 columns
# 3) Solaris -> columns = 80
re_linux = r"columns\s+(?P<columns>\d+);"
re_osx = r"(?P<columns>\d+)\s*columns;"
re_solaris = r"columns\s+=\s+(?P<columns>\d+);"
for regex in (re_linux, re_osx, re_solaris):
match = re.search(regex, stdout)
if match is not None:
columns = match.group('columns')
try:
width = int(columns)
except ValueError:
pass
if width != 0:
return width
return self._default_width
width = findout_terminal_width()
self._terminal_width = width
return width
def write(self, text, color="", align="left", width=None,
force_colors=False):
"""
Prints a text on the screen.
It uses sys.stdout.write(), so no readline library is necessary.
Parameters
==========
color : choose from the colors below, "" means default color
align : "left"/"right", "left" is a normal print, "right" is aligned on
the right-hand side of the screen, filled with spaces if
necessary
width : the screen width
"""
color_templates = (
("Black", "0;30"),
("Red", "0;31"),
("Green", "0;32"),
("Brown", "0;33"),
("Blue", "0;34"),
("Purple", "0;35"),
("Cyan", "0;36"),
("LightGray", "0;37"),
("DarkGray", "1;30"),
("LightRed", "1;31"),
("LightGreen", "1;32"),
("Yellow", "1;33"),
("LightBlue", "1;34"),
("LightPurple", "1;35"),
("LightCyan", "1;36"),
("White", "1;37"),
)
colors = {}
for name, value in color_templates:
colors[name] = value
c_normal = '\033[0m'
c_color = '\033[%sm'
if width is None:
width = self.terminal_width
if align == "right":
if self._write_pos + len(text) > width:
# we don't fit on the current line, create a new line
self.write("\n")
self.write(" "*(width - self._write_pos - len(text)))
if not self._force_colors and hasattr(sys.stdout, 'isatty') and not \
sys.stdout.isatty():
# the stdout is not a terminal, this for example happens if the
# output is piped to less, e.g. "bin/test | less". In this case,
# the terminal control sequences would be printed verbatim, so
# don't use any colors.
color = ""
elif sys.platform == "win32":
# Windows consoles don't support ANSI escape sequences
color = ""
elif not self._colors:
color = ""
if self._line_wrap:
if text[0] != "\n":
sys.stdout.write("\n")
# Avoid UnicodeEncodeError when printing out test failures
if PY3 and IS_WINDOWS:
text = text.encode('raw_unicode_escape').decode('utf8', 'ignore')
elif PY3 and not sys.stdout.encoding.lower().startswith('utf'):
text = text.encode(sys.stdout.encoding, 'backslashreplace'
).decode(sys.stdout.encoding)
if color == "":
sys.stdout.write(text)
else:
sys.stdout.write("%s%s%s" %
(c_color % colors[color], text, c_normal))
sys.stdout.flush()
l = text.rfind("\n")
if l == -1:
self._write_pos += len(text)
else:
self._write_pos = len(text) - l - 1
self._line_wrap = self._write_pos >= width
self._write_pos %= width
def write_center(self, text, delim="="):
width = self.terminal_width
if text != "":
text = " %s " % text
idx = (width - len(text)) // 2
t = delim*idx + text + delim*(width - idx - len(text))
self.write(t + "\n")
def write_exception(self, e, val, tb):
t = traceback.extract_tb(tb)
# remove the first item, as that is always runtests.py
t = t[1:]
t = traceback.format_list(t)
self.write("".join(t))
t = traceback.format_exception_only(e, val)
self.write("".join(t))
def start(self, seed=None, msg="test process starts"):
self.write_center(msg)
executable = sys.executable
v = tuple(sys.version_info)
python_version = "%s.%s.%s-%s-%s" % v
implementation = platform.python_implementation()
if implementation == 'PyPy':
implementation += " %s.%s.%s-%s-%s" % sys.pypy_version_info
self.write("executable: %s (%s) [%s]\n" %
(executable, python_version, implementation))
from .misc import ARCH
self.write("architecture: %s\n" % ARCH)
from sympy.core.cache import USE_CACHE
self.write("cache: %s\n" % USE_CACHE)
from sympy.core.compatibility import GROUND_TYPES, HAS_GMPY
version = ''
if GROUND_TYPES =='gmpy':
if HAS_GMPY == 1:
import gmpy
elif HAS_GMPY == 2:
import gmpy2 as gmpy
version = gmpy.version()
self.write("ground types: %s %s\n" % (GROUND_TYPES, version))
if seed is not None:
self.write("random seed: %d\n" % seed)
from .misc import HASH_RANDOMIZATION
self.write("hash randomization: ")
hash_seed = os.getenv("PYTHONHASHSEED") or '0'
if HASH_RANDOMIZATION and (hash_seed == "random" or int(hash_seed)):
self.write("on (PYTHONHASHSEED=%s)\n" % hash_seed)
else:
self.write("off\n")
if self._split:
self.write("split: %s\n" % self._split)
self.write('\n')
self._t_start = clock()
def finish(self):
self._t_end = clock()
self.write("\n")
global text, linelen
text = "tests finished: %d passed, " % self._passed
linelen = len(text)
def add_text(mytext):
global text, linelen
"""Break new text if too long."""
if linelen + len(mytext) > self.terminal_width:
text += '\n'
linelen = 0
text += mytext
linelen += len(mytext)
if len(self._failed) > 0:
add_text("%d failed, " % len(self._failed))
if len(self._failed_doctest) > 0:
add_text("%d failed, " % len(self._failed_doctest))
if self._skipped > 0:
add_text("%d skipped, " % self._skipped)
if self._xfailed > 0:
add_text("%d expected to fail, " % self._xfailed)
if len(self._xpassed) > 0:
add_text("%d expected to fail but passed, " % len(self._xpassed))
if len(self._exceptions) > 0:
add_text("%d exceptions, " % len(self._exceptions))
add_text("in %.2f seconds" % (self._t_end - self._t_start))
if self.slow_test_functions:
self.write_center('slowest tests', '_')
sorted_slow = sorted(self.slow_test_functions, key=lambda r: r[1])
for slow_func_name, taken in sorted_slow:
print('%s - Took %.3f seconds' % (slow_func_name, taken))
if self.fast_test_functions:
self.write_center('unexpectedly fast tests', '_')
sorted_fast = sorted(self.fast_test_functions,
key=lambda r: r[1])
for fast_func_name, taken in sorted_fast:
print('%s - Took %.3f seconds' % (fast_func_name, taken))
if len(self._xpassed) > 0:
self.write_center("xpassed tests", "_")
for e in self._xpassed:
self.write("%s: %s\n" % (e[0], e[1]))
self.write("\n")
if self._tb_style != "no" and len(self._exceptions) > 0:
for e in self._exceptions:
filename, f, (t, val, tb) = e
self.write_center("", "_")
if f is None:
s = "%s" % filename
else:
s = "%s:%s" % (filename, f.__name__)
self.write_center(s, "_")
self.write_exception(t, val, tb)
self.write("\n")
if self._tb_style != "no" and len(self._failed) > 0:
for e in self._failed:
filename, f, (t, val, tb) = e
self.write_center("", "_")
self.write_center("%s:%s" % (filename, f.__name__), "_")
self.write_exception(t, val, tb)
self.write("\n")
if self._tb_style != "no" and len(self._failed_doctest) > 0:
for e in self._failed_doctest:
filename, msg = e
self.write_center("", "_")
self.write_center("%s" % filename, "_")
self.write(msg)
self.write("\n")
self.write_center(text)
ok = len(self._failed) == 0 and len(self._exceptions) == 0 and \
len(self._failed_doctest) == 0
if not ok:
self.write("DO *NOT* COMMIT!\n")
return ok
def entering_filename(self, filename, n):
rel_name = filename[len(self._root_dir) + 1:]
self._active_file = rel_name
self._active_file_error = False
self.write(rel_name)
self.write("[%d] " % n)
def leaving_filename(self):
self.write(" ")
if self._active_file_error:
self.write("[FAIL]", "Red", align="right")
else:
self.write("[OK]", "Green", align="right")
self.write("\n")
if self._verbose:
self.write("\n")
def entering_test(self, f):
self._active_f = f
if self._verbose:
self.write("\n" + f.__name__ + " ")
def test_xfail(self):
self._xfailed += 1
self.write("f", "Green")
def test_xpass(self, v):
message = str(v)
self._xpassed.append((self._active_file, message))
self.write("X", "Green")
def test_fail(self, exc_info):
self._failed.append((self._active_file, self._active_f, exc_info))
self.write("F", "Red")
self._active_file_error = True
def doctest_fail(self, name, error_msg):
# the first line contains "******", remove it:
error_msg = "\n".join(error_msg.split("\n")[1:])
self._failed_doctest.append((name, error_msg))
self.write("F", "Red")
self._active_file_error = True
def test_pass(self, char="."):
self._passed += 1
if self._verbose:
self.write("ok", "Green")
else:
self.write(char, "Green")
def test_skip(self, v=None):
char = "s"
self._skipped += 1
if v is not None:
message = str(v)
if message == "KeyboardInterrupt":
char = "K"
elif message == "Timeout":
char = "T"
elif message == "Slow":
char = "w"
self.write(char, "Blue")
if self._verbose:
self.write(" - ", "Blue")
if v is not None:
self.write(message, "Blue")
def test_exception(self, exc_info):
self._exceptions.append((self._active_file, self._active_f, exc_info))
self.write("E", "Red")
self._active_file_error = True
def import_error(self, filename, exc_info):
self._exceptions.append((filename, None, exc_info))
rel_name = filename[len(self._root_dir) + 1:]
self.write(rel_name)
self.write("[?] Failed to import", "Red")
self.write(" ")
self.write("[FAIL]", "Red", align="right")
self.write("\n")
sympy_dir = get_sympy_dir()
| bsd-3-clause |
heli522/scikit-learn | sklearn/metrics/__init__.py | 214 | 3440 | """
The :mod:`sklearn.metrics` module includes score functions, performance metrics
and pairwise metrics and distance computations.
"""
from .ranking import auc
from .ranking import average_precision_score
from .ranking import coverage_error
from .ranking import label_ranking_average_precision_score
from .ranking import label_ranking_loss
from .ranking import precision_recall_curve
from .ranking import roc_auc_score
from .ranking import roc_curve
from .classification import accuracy_score
from .classification import classification_report
from .classification import cohen_kappa_score
from .classification import confusion_matrix
from .classification import f1_score
from .classification import fbeta_score
from .classification import hamming_loss
from .classification import hinge_loss
from .classification import jaccard_similarity_score
from .classification import log_loss
from .classification import matthews_corrcoef
from .classification import precision_recall_fscore_support
from .classification import precision_score
from .classification import recall_score
from .classification import zero_one_loss
from .classification import brier_score_loss
from . import cluster
from .cluster import adjusted_mutual_info_score
from .cluster import adjusted_rand_score
from .cluster import completeness_score
from .cluster import consensus_score
from .cluster import homogeneity_completeness_v_measure
from .cluster import homogeneity_score
from .cluster import mutual_info_score
from .cluster import normalized_mutual_info_score
from .cluster import silhouette_samples
from .cluster import silhouette_score
from .cluster import v_measure_score
from .pairwise import euclidean_distances
from .pairwise import pairwise_distances
from .pairwise import pairwise_distances_argmin
from .pairwise import pairwise_distances_argmin_min
from .pairwise import pairwise_kernels
from .regression import explained_variance_score
from .regression import mean_absolute_error
from .regression import mean_squared_error
from .regression import median_absolute_error
from .regression import r2_score
from .scorer import make_scorer
from .scorer import SCORERS
from .scorer import get_scorer
__all__ = [
'accuracy_score',
'adjusted_mutual_info_score',
'adjusted_rand_score',
'auc',
'average_precision_score',
'classification_report',
'cluster',
'completeness_score',
'confusion_matrix',
'consensus_score',
'coverage_error',
'euclidean_distances',
'explained_variance_score',
'f1_score',
'fbeta_score',
'get_scorer',
'hamming_loss',
'hinge_loss',
'homogeneity_completeness_v_measure',
'homogeneity_score',
'jaccard_similarity_score',
'label_ranking_average_precision_score',
'label_ranking_loss',
'log_loss',
'make_scorer',
'matthews_corrcoef',
'mean_absolute_error',
'mean_squared_error',
'median_absolute_error',
'mutual_info_score',
'normalized_mutual_info_score',
'pairwise_distances',
'pairwise_distances_argmin',
'pairwise_distances_argmin_min',
'pairwise_distances_argmin_min',
'pairwise_kernels',
'precision_recall_curve',
'precision_recall_fscore_support',
'precision_score',
'r2_score',
'recall_score',
'roc_auc_score',
'roc_curve',
'SCORERS',
'silhouette_samples',
'silhouette_score',
'v_measure_score',
'zero_one_loss',
'brier_score_loss',
]
| bsd-3-clause |
helgako/cms-dqm | notebooks/evaluation.py | 1 | 2680 | from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, average_precision_score
import numpy as np
import matplotlib.pyplot as plt
#fixed recall values
rec_values = [0.8, 0.9, 0.95, 0.99]
# precision at 10 or P@10 measures classification performance,
# being the fraction of the top 10 scored instances which are actually anomalous.
def Pat10(y_test, y_pred, n=10):
ind = np.argpartition(y_pred, -n)[-n:]
return np.mean(y_test[ind])
def perfomance(y_test, y_pred, sample_weight=None, n=10):
print ("P@"+str(n), Pat10(y_test, y_pred, n))
fig, axes = plt.subplots(nrows=3, figsize=(6, 15))
ax = axes[0]
ax.grid(True)
precision, recall, _ = precision_recall_curve(y_test,y_pred)
print ("recalls_values",rec_values)
prec_values = []
for v in rec_values:
prec_values.append(max(precision[recall > v]))
print ("precision_values", prec_values)
print ("average_precision_score", average_precision_score(y_test, y_pred, sample_weight=sample_weight))
print ("roc_auc_score", roc_auc_score(y_test, y_pred))
ax.step(recall, precision, color='b', alpha=0.2,
where='post')
ax.fill_between(recall, precision, step='post', alpha=0.2,
color='b')
ax.set_xlabel('Recall', fontsize=10)
ax.set_ylabel('Precision', fontsize=10)
ax.set_ylim([0.0, 1.05])
ax.set_xlim([0.0, 1.0])
ax.tick_params(axis='x', labelsize=15)
ax.tick_params(axis='y', labelsize=15)
ax.set_title('2-class Precision-Recall curve: AP={0:0.3f}'.format(
average_precision_score(y_test, y_pred, sample_weight=sample_weight)), fontsize=25)
ax = axes[1]
ax.grid(True)
fpr, tpr, _ = roc_curve(y_test, y_pred, sample_weight=sample_weight)
ax.plot(fpr, tpr)
ax.set_title('ROC curve: roc_auc ={0:0.3f}'.format(
roc_auc_score(y_test, y_pred)), fontsize=25)
ax.tick_params(axis='x', labelsize=15)
ax.tick_params(axis='y', labelsize=15)
ax.set_xlabel('FPR', fontsize=10)
ax.set_ylabel('TPR', fontsize=10)
ax = axes[2]
bad_test = np.sum(y_test)
good_test = len(y_test)-np.sum(y_test)
ax.plot(sorted(y_pred[np.where( y_test == 0.)[0]], reverse=True), np.arange(good_test)/good_test*100, label = "good")
ax.plot(sorted(y_pred[np.where( y_test == 1.)[0]]), np.arange(bad_test)/bad_test*100, label = "bad")
ax.set_title('Predicted proba', fontsize=25)
ax.tick_params(axis='x', labelsize=15)
ax.tick_params(axis='y', labelsize=15)
fig.subplots_adjust(hspace=0.5)
plt.legend()
plt.grid(True)
plt.show()
return precision, recall | mit |
go-bears/nupic | src/nupic/research/monitor_mixin/plot.py | 19 | 5187 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2014-2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero Public License version 3 as
# published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU Affero Public License for more details.
#
# You should have received a copy of the GNU Affero Public License
# along with this program. If not, see http://www.gnu.org/licenses.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------
"""
Plot class used in monitor mixin framework.
"""
import logging
import os
try:
# We import in here to avoid creating a matplotlib dependency in nupic.
import matplotlib.pyplot as plt
import matplotlib.cm as cm
except ImportError:
# Suppress; we log it at debug level to avoid polluting the logs of apps
# and services that don't care about plotting
logging.debug("Cannot import matplotlib. Plot class will not work.",
exc_info=True)
class Plot(object):
def __init__(self, monitor, title, show=True):
"""
@param monitor (MonitorMixinBase) Monitor Mixin instance that generated
this plot
@param title (string) Plot title
"""
self._monitor = monitor
self._title = title
self._fig = self._initFigure()
self._show = show
if self._show:
plt.ion()
plt.show()
def _initFigure(self):
fig = plt.figure()
fig.suptitle(self._prettyPrintTitle())
return fig
def _prettyPrintTitle(self):
if self._monitor.mmName is not None:
return "[{0}] {1}".format(self._monitor.mmName, self._title)
return self._title
def addGraph(self, data, position=111, xlabel=None, ylabel=None):
""" Adds a graph to the plot's figure.
@param data See matplotlib.Axes.plot documentation.
@param position A 3-digit number. The first two digits define a 2D grid
where subplots may be added. The final digit specifies the nth grid
location for the added subplot
@param xlabel text to be displayed on the x-axis
@param ylabel text to be displayed on the y-axis
"""
ax = self._addBase(position, xlabel=xlabel, ylabel=ylabel)
ax.plot(data)
plt.draw()
def addHistogram(self, data, position=111, xlabel=None, ylabel=None,
bins=None):
""" Adds a histogram to the plot's figure.
@param data See matplotlib.Axes.hist documentation.
@param position A 3-digit number. The first two digits define a 2D grid
where subplots may be added. The final digit specifies the nth grid
location for the added subplot
@param xlabel text to be displayed on the x-axis
@param ylabel text to be displayed on the y-axis
"""
ax = self._addBase(position, xlabel=xlabel, ylabel=ylabel)
ax.hist(data, bins=bins, color="green", alpha=0.8)
plt.draw()
def add2DArray(self, data, position=111, xlabel=None, ylabel=None, cmap=None,
aspect="auto", interpolation="nearest", name=None):
""" Adds an image to the plot's figure.
@param data a 2D array. See matplotlib.Axes.imshow documentation.
@param position A 3-digit number. The first two digits define a 2D grid
where subplots may be added. The final digit specifies the nth grid
location for the added subplot
@param xlabel text to be displayed on the x-axis
@param ylabel text to be displayed on the y-axis
@param cmap color map used in the rendering
@param aspect how aspect ratio is handled during resize
@param interpolation interpolation method
"""
if cmap is None:
# The default colormodel is an ugly blue-red model.
cmap = cm.Greys
ax = self._addBase(position, xlabel=xlabel, ylabel=ylabel)
ax.imshow(data, cmap=cmap, aspect=aspect, interpolation=interpolation)
if self._show:
plt.draw()
if name is not None:
if not os.path.exists("log"):
os.mkdir("log")
plt.savefig("log/{name}.png".format(name=name), bbox_inches="tight",
figsize=(8, 6), dpi=400)
def _addBase(self, position, xlabel=None, ylabel=None):
""" Adds a subplot to the plot's figure at specified position.
@param position A 3-digit number. The first two digits define a 2D grid
where subplots may be added. The final digit specifies the nth grid
location for the added subplot
@param xlabel text to be displayed on the x-axis
@param ylabel text to be displayed on the y-axis
@returns (matplotlib.Axes) Axes instance
"""
ax = self._fig.add_subplot(position)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
return ax
| agpl-3.0 |
pythonvietnam/scikit-learn | sklearn/metrics/cluster/tests/test_bicluster.py | 394 | 1770 | """Testing for bicluster metrics module"""
import numpy as np
from sklearn.utils.testing import assert_equal, assert_almost_equal
from sklearn.metrics.cluster.bicluster import _jaccard
from sklearn.metrics import consensus_score
def test_jaccard():
a1 = np.array([True, True, False, False])
a2 = np.array([True, True, True, True])
a3 = np.array([False, True, True, False])
a4 = np.array([False, False, True, True])
assert_equal(_jaccard(a1, a1, a1, a1), 1)
assert_equal(_jaccard(a1, a1, a2, a2), 0.25)
assert_equal(_jaccard(a1, a1, a3, a3), 1.0 / 7)
assert_equal(_jaccard(a1, a1, a4, a4), 0)
def test_consensus_score():
a = [[True, True, False, False],
[False, False, True, True]]
b = a[::-1]
assert_equal(consensus_score((a, a), (a, a)), 1)
assert_equal(consensus_score((a, a), (b, b)), 1)
assert_equal(consensus_score((a, b), (a, b)), 1)
assert_equal(consensus_score((a, b), (b, a)), 1)
assert_equal(consensus_score((a, a), (b, a)), 0)
assert_equal(consensus_score((a, a), (a, b)), 0)
assert_equal(consensus_score((b, b), (a, b)), 0)
assert_equal(consensus_score((b, b), (b, a)), 0)
def test_consensus_score_issue2445():
''' Different number of biclusters in A and B'''
a_rows = np.array([[True, True, False, False],
[False, False, True, True],
[False, False, False, True]])
a_cols = np.array([[True, True, False, False],
[False, False, True, True],
[False, False, False, True]])
idx = [0, 2]
s = consensus_score((a_rows, a_cols), (a_rows[idx], a_cols[idx]))
# B contains 2 of the 3 biclusters in A, so score should be 2/3
assert_almost_equal(s, 2.0/3.0)
| bsd-3-clause |
DSSG2017/florence | src/output/cdr_network.py | 1 | 1732 | import pandas as pd
import igraph as ig
from ..utils.database import dbutils
# TODO: Cleanup
def hourly_graph():
connection = dbutils.connect()
foreigners = pd.read_sql("""
SELECT
prev_tower_id,
tower_id,
count(*) AS weight
FROM optourism.foreigners_path_records_joined
WHERE tower_id != prev_tower_id
AND EXTRACT(HOUR FROM date_time_m) = 22
AND delta < (INTERVAL '30 minutes')
GROUP BY tower_id, prev_tower_id
""", con=connection)
tower_vertices = pd.read_sql("""
SELECT DISTINCT tower_id, lat, lon
FROM optourism.foreigners_path_records_joined
""", con=connection)
connection.close()
foreigners['tower_id'] = foreigners['tower_id'].apply(
lambda x: 'tower-%s' % x)
foreigners['prev_tower_id'] = foreigners['prev_tower_id'].apply(
lambda x: 'tower-%s' % x)
tower_vertices['tower_id'] = tower_vertices['tower_id'].apply(
lambda x: 'tower-%s' % x)
graph = ig.Graph()
graph.add_vertices(tower_vertices.shape[0])
graph.vs['name'] = tower_vertices['tower_id']
graph.vs['x'] = tower_vertices['lat']
graph.vs['y'] = tower_vertices['lon']
edges = zip(foreigners['prev_tower_id'], foreigners['tower_id'])
graph.add_edges(edges)
graph.es['weight'] = foreigners['weight']
graph.es.select(weight_lt=5).delete()
visual_style = {'vertex_color': 'black',
'vertex_size': 3,
'edge_width': [.01 * i for i in
graph.es["weight"]]}
ig.plot(graph, bbox=(800, 800), **visual_style)
def dwell_time_graph():
pass
if __name__ == '__main__':
pass
| mit |
asnorkin/sentiment_analysis | site/lib/python2.7/site-packages/scipy/stats/stats.py | 7 | 186886 | # Copyright 2002 Gary Strangman. All rights reserved
# Copyright 2002-2016 The SciPy Developers
#
# The original code from Gary Strangman was heavily adapted for
# use in SciPy by Travis Oliphant. The original code came with the
# following disclaimer:
#
# This software is provided "as-is". There are no expressed or implied
# warranties of any kind, including, but not limited to, the warranties
# of merchantability and fitness for a given application. In no event
# shall Gary Strangman be liable for any direct, indirect, incidental,
# special, exemplary or consequential damages (including, but not limited
# to, loss of use, data or profits, or business interruption) however
# caused and on any theory of liability, whether in contract, strict
# liability or tort (including negligence or otherwise) arising in any way
# out of the use of this software, even if advised of the possibility of
# such damage.
"""
A collection of basic statistical functions for python. The function
names appear below.
Some scalar functions defined here are also available in the scipy.special
package where they work on arbitrary sized arrays.
Disclaimers: The function list is obviously incomplete and, worse, the
functions are not optimized. All functions have been tested (some more
so than others), but they are far from bulletproof. Thus, as with any
free software, no warranty or guarantee is expressed or implied. :-) A
few extra functions that don't appear in the list below can be found by
interested treasure-hunters. These functions don't necessarily have
both list and array versions but were deemed useful.
Central Tendency
----------------
.. autosummary::
:toctree: generated/
gmean
hmean
mode
Moments
-------
.. autosummary::
:toctree: generated/
moment
variation
skew
kurtosis
normaltest
Altered Versions
----------------
.. autosummary::
:toctree: generated/
tmean
tvar
tstd
tsem
describe
Frequency Stats
---------------
.. autosummary::
:toctree: generated/
itemfreq
scoreatpercentile
percentileofscore
histogram
cumfreq
relfreq
Variability
-----------
.. autosummary::
:toctree: generated/
obrientransform
signaltonoise
sem
zmap
zscore
iqr
Trimming Functions
------------------
.. autosummary::
:toctree: generated/
threshold
trimboth
trim1
Correlation Functions
---------------------
.. autosummary::
:toctree: generated/
pearsonr
fisher_exact
spearmanr
pointbiserialr
kendalltau
weightedtau
linregress
theilslopes
Inferential Stats
-----------------
.. autosummary::
:toctree: generated/
ttest_1samp
ttest_ind
ttest_ind_from_stats
ttest_rel
chisquare
power_divergence
ks_2samp
mannwhitneyu
ranksums
wilcoxon
kruskal
friedmanchisquare
combine_pvalues
Probability Calculations
------------------------
.. autosummary::
:toctree: generated/
chisqprob
betai
ANOVA Functions
---------------
.. autosummary::
:toctree: generated/
f_oneway
f_value
Support Functions
-----------------
.. autosummary::
:toctree: generated/
ss
square_of_sums
rankdata
References
----------
.. [CRCProbStat2000] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
Probability and Statistics Tables and Formulae. Chapman & Hall: New
York. 2000.
"""
from __future__ import division, print_function, absolute_import
import warnings
import math
from collections import namedtuple
import numpy as np
from numpy import array, asarray, ma, zeros
from scipy._lib.six import callable, string_types
from scipy._lib._version import NumpyVersion
import scipy.special as special
import scipy.linalg as linalg
from . import distributions
from . import mstats_basic
from ._distn_infrastructure import _lazywhere
from ._stats_mstats_common import _find_repeats, linregress, theilslopes
from ._stats import _kendall_dis, _toint64, _weightedrankedtau
__all__ = ['find_repeats', 'gmean', 'hmean', 'mode', 'tmean', 'tvar',
'tmin', 'tmax', 'tstd', 'tsem', 'moment', 'variation',
'skew', 'kurtosis', 'describe', 'skewtest', 'kurtosistest',
'normaltest', 'jarque_bera', 'itemfreq',
'scoreatpercentile', 'percentileofscore', 'histogram',
'histogram2', 'cumfreq', 'relfreq', 'obrientransform',
'signaltonoise', 'sem', 'zmap', 'zscore', 'iqr', 'threshold',
'sigmaclip', 'trimboth', 'trim1', 'trim_mean', 'f_oneway',
'pearsonr', 'fisher_exact', 'spearmanr', 'pointbiserialr',
'kendalltau', 'weightedtau',
'linregress', 'theilslopes', 'ttest_1samp',
'ttest_ind', 'ttest_ind_from_stats', 'ttest_rel', 'kstest',
'chisquare', 'power_divergence', 'ks_2samp', 'mannwhitneyu',
'tiecorrect', 'ranksums', 'kruskal', 'friedmanchisquare',
'chisqprob', 'betai',
'f_value_wilks_lambda', 'f_value', 'f_value_multivariate',
'ss', 'square_of_sums', 'fastsort', 'rankdata',
'combine_pvalues', ]
def _chk_asarray(a, axis):
if axis is None:
a = np.ravel(a)
outaxis = 0
else:
a = np.asarray(a)
outaxis = axis
if a.ndim == 0:
a = np.atleast_1d(a)
return a, outaxis
def _chk2_asarray(a, b, axis):
if axis is None:
a = np.ravel(a)
b = np.ravel(b)
outaxis = 0
else:
a = np.asarray(a)
b = np.asarray(b)
outaxis = axis
if a.ndim == 0:
a = np.atleast_1d(a)
if b.ndim == 0:
b = np.atleast_1d(b)
return a, b, outaxis
def _contains_nan(a, nan_policy='propagate'):
policies = ['propagate', 'raise', 'omit']
if nan_policy not in policies:
raise ValueError("nan_policy must be one of {%s}" %
', '.join("'%s'" % s for s in policies))
try:
# Calling np.sum to avoid creating a huge array into memory
# e.g. np.isnan(a).any()
with np.errstate(invalid='ignore'):
contains_nan = np.isnan(np.sum(a))
except TypeError:
# If the check cannot be properly performed we fallback to omiting
# nan values and raising a warning. This can happen when attempting to
# sum things that are not numbers (e.g. as in the function `mode`).
contains_nan = False
nan_policy = 'omit'
warnings.warn("The input array could not be properly checked for nan "
"values. nan values will be ignored.", RuntimeWarning)
if contains_nan and nan_policy == 'raise':
raise ValueError("The input contains nan values")
return (contains_nan, nan_policy)
def gmean(a, axis=0, dtype=None):
"""
Compute the geometric mean along the specified axis.
Returns the geometric average of the array elements.
That is: n-th root of (x1 * x2 * ... * xn)
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : int or None, optional
Axis along which the geometric mean is computed. Default is 0.
If None, compute over the whole array `a`.
dtype : dtype, optional
Type of the returned array and of the accumulator in which the
elements are summed. If dtype is not specified, it defaults to the
dtype of a, unless a has an integer dtype with a precision less than
that of the default platform integer. In that case, the default
platform integer is used.
Returns
-------
gmean : ndarray
see dtype parameter above
See Also
--------
numpy.mean : Arithmetic average
numpy.average : Weighted average
hmean : Harmonic mean
Notes
-----
The geometric average is computed over a single dimension of the input
array, axis=0 by default, or all values in the array if axis=None.
float64 intermediate and return values are used for integer inputs.
Use masked arrays to ignore any non-finite values in the input or that
arise in the calculations such as Not a Number and infinity because masked
arrays automatically mask any non-finite values.
"""
if not isinstance(a, np.ndarray):
# if not an ndarray object attempt to convert it
log_a = np.log(np.array(a, dtype=dtype))
elif dtype:
# Must change the default dtype allowing array type
if isinstance(a, np.ma.MaskedArray):
log_a = np.log(np.ma.asarray(a, dtype=dtype))
else:
log_a = np.log(np.asarray(a, dtype=dtype))
else:
log_a = np.log(a)
return np.exp(log_a.mean(axis=axis))
def hmean(a, axis=0, dtype=None):
"""
Calculates the harmonic mean along the specified axis.
That is: n / (1/x1 + 1/x2 + ... + 1/xn)
Parameters
----------
a : array_like
Input array, masked array or object that can be converted to an array.
axis : int or None, optional
Axis along which the harmonic mean is computed. Default is 0.
If None, compute over the whole array `a`.
dtype : dtype, optional
Type of the returned array and of the accumulator in which the
elements are summed. If `dtype` is not specified, it defaults to the
dtype of `a`, unless `a` has an integer `dtype` with a precision less
than that of the default platform integer. In that case, the default
platform integer is used.
Returns
-------
hmean : ndarray
see `dtype` parameter above
See Also
--------
numpy.mean : Arithmetic average
numpy.average : Weighted average
gmean : Geometric mean
Notes
-----
The harmonic mean is computed over a single dimension of the input
array, axis=0 by default, or all values in the array if axis=None.
float64 intermediate and return values are used for integer inputs.
Use masked arrays to ignore any non-finite values in the input or that
arise in the calculations such as Not a Number and infinity.
"""
if not isinstance(a, np.ndarray):
a = np.array(a, dtype=dtype)
if np.all(a > 0):
# Harmonic mean only defined if greater than zero
if isinstance(a, np.ma.MaskedArray):
size = a.count(axis)
else:
if axis is None:
a = a.ravel()
size = a.shape[0]
else:
size = a.shape[axis]
return size / np.sum(1.0/a, axis=axis, dtype=dtype)
else:
raise ValueError("Harmonic mean only defined if all elements greater than zero")
ModeResult = namedtuple('ModeResult', ('mode', 'count'))
def mode(a, axis=0, nan_policy='propagate'):
"""
Returns an array of the modal (most common) value in the passed array.
If there is more than one such value, only the smallest is returned.
The bin-count for the modal bins is also returned.
Parameters
----------
a : array_like
n-dimensional array of which to find mode(s).
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over
the whole array `a`.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
mode : ndarray
Array of modal values.
count : ndarray
Array of counts for each mode.
Examples
--------
>>> a = np.array([[6, 8, 3, 0],
... [3, 2, 1, 7],
... [8, 1, 8, 4],
... [5, 3, 0, 5],
... [4, 7, 5, 9]])
>>> from scipy import stats
>>> stats.mode(a)
(array([[3, 1, 0, 0]]), array([[1, 1, 1, 1]]))
To get mode of whole array, specify ``axis=None``:
>>> stats.mode(a, axis=None)
(array([3]), array([3]))
"""
a, axis = _chk_asarray(a, axis)
if a.size == 0:
return ModeResult(np.array([]), np.array([]))
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.mode(a, axis)
scores = np.unique(np.ravel(a)) # get ALL unique values
testshape = list(a.shape)
testshape[axis] = 1
oldmostfreq = np.zeros(testshape, dtype=a.dtype)
oldcounts = np.zeros(testshape, dtype=int)
for score in scores:
template = (a == score)
counts = np.expand_dims(np.sum(template, axis), axis)
mostfrequent = np.where(counts > oldcounts, score, oldmostfreq)
oldcounts = np.maximum(counts, oldcounts)
oldmostfreq = mostfrequent
return ModeResult(mostfrequent, oldcounts)
def _mask_to_limits(a, limits, inclusive):
"""Mask an array for values outside of given limits.
This is primarily a utility function.
Parameters
----------
a : array
limits : (float or None, float or None)
A tuple consisting of the (lower limit, upper limit). Values in the
input array less than the lower limit or greater than the upper limit
will be masked out. None implies no limit.
inclusive : (bool, bool)
A tuple consisting of the (lower flag, upper flag). These flags
determine whether values exactly equal to lower or upper are allowed.
Returns
-------
A MaskedArray.
Raises
------
A ValueError if there are no values within the given limits.
"""
lower_limit, upper_limit = limits
lower_include, upper_include = inclusive
am = ma.MaskedArray(a)
if lower_limit is not None:
if lower_include:
am = ma.masked_less(am, lower_limit)
else:
am = ma.masked_less_equal(am, lower_limit)
if upper_limit is not None:
if upper_include:
am = ma.masked_greater(am, upper_limit)
else:
am = ma.masked_greater_equal(am, upper_limit)
if am.count() == 0:
raise ValueError("No array values within given limits")
return am
def tmean(a, limits=None, inclusive=(True, True), axis=None):
"""
Compute the trimmed mean.
This function finds the arithmetic mean of given values, ignoring values
outside the given `limits`.
Parameters
----------
a : array_like
Array of values.
limits : None or (lower limit, upper limit), optional
Values in the input array less than the lower limit or greater than the
upper limit will be ignored. When limits is None (default), then all
values are used. Either of the limit values in the tuple can also be
None representing a half-open interval.
inclusive : (bool, bool), optional
A tuple consisting of the (lower flag, upper flag). These flags
determine whether values exactly equal to the lower or upper limits
are included. The default value is (True, True).
axis : int or None, optional
Axis along which to compute test. Default is None.
Returns
-------
tmean : float
See also
--------
trim_mean : returns mean after trimming a proportion from both tails.
Examples
--------
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.tmean(x)
9.5
>>> stats.tmean(x, (3,17))
10.0
"""
a = asarray(a)
if limits is None:
return np.mean(a, None)
am = _mask_to_limits(a.ravel(), limits, inclusive)
return am.mean(axis=axis)
def tvar(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
"""
Compute the trimmed variance
This function computes the sample variance of an array of values,
while ignoring values which are outside of given `limits`.
Parameters
----------
a : array_like
Array of values.
limits : None or (lower limit, upper limit), optional
Values in the input array less than the lower limit or greater than the
upper limit will be ignored. When limits is None, then all values are
used. Either of the limit values in the tuple can also be None
representing a half-open interval. The default value is None.
inclusive : (bool, bool), optional
A tuple consisting of the (lower flag, upper flag). These flags
determine whether values exactly equal to the lower or upper limits
are included. The default value is (True, True).
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over the
whole array `a`.
ddof : int, optional
Delta degrees of freedom. Default is 1.
Returns
-------
tvar : float
Trimmed variance.
Notes
-----
`tvar` computes the unbiased sample variance, i.e. it uses a correction
factor ``n / (n - 1)``.
Examples
--------
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.tvar(x)
35.0
>>> stats.tvar(x, (3,17))
20.0
"""
a = asarray(a)
a = a.astype(float).ravel()
if limits is None:
n = len(a)
return a.var() * n/(n-1.)
am = _mask_to_limits(a, limits, inclusive)
return np.ma.var(am, ddof=ddof, axis=axis)
def tmin(a, lowerlimit=None, axis=0, inclusive=True, nan_policy='propagate'):
"""
Compute the trimmed minimum
This function finds the miminum value of an array `a` along the
specified axis, but only considering values greater than a specified
lower limit.
Parameters
----------
a : array_like
array of values
lowerlimit : None or float, optional
Values in the input array less than the given limit will be ignored.
When lowerlimit is None, then all values are used. The default value
is None.
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over the
whole array `a`.
inclusive : {True, False}, optional
This flag determines whether values exactly equal to the lower limit
are included. The default value is True.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
tmin : float, int or ndarray
Examples
--------
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.tmin(x)
0
>>> stats.tmin(x, 13)
13
>>> stats.tmin(x, 13, inclusive=False)
14
"""
a, axis = _chk_asarray(a, axis)
am = _mask_to_limits(a, (lowerlimit, None), (inclusive, False))
contains_nan, nan_policy = _contains_nan(am, nan_policy)
if contains_nan and nan_policy == 'omit':
am = ma.masked_invalid(am)
res = ma.minimum.reduce(am, axis).data
if res.ndim == 0:
return res[()]
return res
def tmax(a, upperlimit=None, axis=0, inclusive=True, nan_policy='propagate'):
"""
Compute the trimmed maximum
This function computes the maximum value of an array along a given axis,
while ignoring values larger than a specified upper limit.
Parameters
----------
a : array_like
array of values
upperlimit : None or float, optional
Values in the input array greater than the given limit will be ignored.
When upperlimit is None, then all values are used. The default value
is None.
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over the
whole array `a`.
inclusive : {True, False}, optional
This flag determines whether values exactly equal to the upper limit
are included. The default value is True.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
tmax : float, int or ndarray
Examples
--------
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.tmax(x)
19
>>> stats.tmax(x, 13)
13
>>> stats.tmax(x, 13, inclusive=False)
12
"""
a, axis = _chk_asarray(a, axis)
am = _mask_to_limits(a, (None, upperlimit), (False, inclusive))
contains_nan, nan_policy = _contains_nan(am, nan_policy)
if contains_nan and nan_policy == 'omit':
am = ma.masked_invalid(am)
res = ma.maximum.reduce(am, axis).data
if res.ndim == 0:
return res[()]
return res
def tstd(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
"""
Compute the trimmed sample standard deviation
This function finds the sample standard deviation of given values,
ignoring values outside the given `limits`.
Parameters
----------
a : array_like
array of values
limits : None or (lower limit, upper limit), optional
Values in the input array less than the lower limit or greater than the
upper limit will be ignored. When limits is None, then all values are
used. Either of the limit values in the tuple can also be None
representing a half-open interval. The default value is None.
inclusive : (bool, bool), optional
A tuple consisting of the (lower flag, upper flag). These flags
determine whether values exactly equal to the lower or upper limits
are included. The default value is (True, True).
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over the
whole array `a`.
ddof : int, optional
Delta degrees of freedom. Default is 1.
Returns
-------
tstd : float
Notes
-----
`tstd` computes the unbiased sample standard deviation, i.e. it uses a
correction factor ``n / (n - 1)``.
Examples
--------
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.tstd(x)
5.9160797830996161
>>> stats.tstd(x, (3,17))
4.4721359549995796
"""
return np.sqrt(tvar(a, limits, inclusive, axis, ddof))
def tsem(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
"""
Compute the trimmed standard error of the mean.
This function finds the standard error of the mean for given
values, ignoring values outside the given `limits`.
Parameters
----------
a : array_like
array of values
limits : None or (lower limit, upper limit), optional
Values in the input array less than the lower limit or greater than the
upper limit will be ignored. When limits is None, then all values are
used. Either of the limit values in the tuple can also be None
representing a half-open interval. The default value is None.
inclusive : (bool, bool), optional
A tuple consisting of the (lower flag, upper flag). These flags
determine whether values exactly equal to the lower or upper limits
are included. The default value is (True, True).
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over the
whole array `a`.
ddof : int, optional
Delta degrees of freedom. Default is 1.
Returns
-------
tsem : float
Notes
-----
`tsem` uses unbiased sample standard deviation, i.e. it uses a
correction factor ``n / (n - 1)``.
Examples
--------
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.tsem(x)
1.3228756555322954
>>> stats.tsem(x, (3,17))
1.1547005383792515
"""
a = np.asarray(a).ravel()
if limits is None:
return a.std(ddof=ddof) / np.sqrt(a.size)
am = _mask_to_limits(a, limits, inclusive)
sd = np.sqrt(np.ma.var(am, ddof=ddof, axis=axis))
return sd / np.sqrt(am.count())
#####################################
# MOMENTS #
#####################################
def moment(a, moment=1, axis=0, nan_policy='propagate'):
r"""
Calculates the nth moment about the mean for a sample.
A moment is a specific quantitative measure of the shape of a set of points.
It is often used to calculate coefficients of skewness and kurtosis due
to its close relationship with them.
Parameters
----------
a : array_like
data
moment : int or array_like of ints, optional
order of central moment that is returned. Default is 1.
axis : int or None, optional
Axis along which the central moment is computed. Default is 0.
If None, compute over the whole array `a`.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
n-th central moment : ndarray or float
The appropriate moment along the given axis or over all values if axis
is None. The denominator for the moment calculation is the number of
observations, no degrees of freedom correction is done.
See also
--------
kurtosis, skew, describe
Notes
-----
The k-th central moment of a data sample is:
.. math::
m_k = \frac{1}{n} \sum_{i = 1}^n (x_i - \bar{x})^k
Where n is the number of samples and x-bar is the mean. This function uses
exponentiation by squares [1]_ for efficiency.
References
----------
.. [1] http://eli.thegreenplace.net/2009/03/21/efficient-integer-exponentiation-algorithms
"""
a, axis = _chk_asarray(a, axis)
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.moment(a, moment, axis)
if a.size == 0:
# empty array, return nan(s) with shape matching `moment`
if np.isscalar(moment):
return np.nan
else:
return np.ones(np.asarray(moment).shape, dtype=np.float64) * np.nan
# for array_like moment input, return a value for each.
if not np.isscalar(moment):
mmnt = [_moment(a, i, axis) for i in moment]
return np.array(mmnt)
else:
return _moment(a, moment, axis)
def _moment(a, moment, axis):
if np.abs(moment - np.round(moment)) > 0:
raise ValueError("All moment parameters must be integers")
if moment == 0:
# When moment equals 0, the result is 1, by definition.
shape = list(a.shape)
del shape[axis]
if shape:
# return an actual array of the appropriate shape
return np.ones(shape, dtype=float)
else:
# the input was 1D, so return a scalar instead of a rank-0 array
return 1.0
elif moment == 1:
# By definition the first moment about the mean is 0.
shape = list(a.shape)
del shape[axis]
if shape:
# return an actual array of the appropriate shape
return np.zeros(shape, dtype=float)
else:
# the input was 1D, so return a scalar instead of a rank-0 array
return np.float64(0.0)
else:
# Exponentiation by squares: form exponent sequence
n_list = [moment]
current_n = moment
while current_n > 2:
if current_n % 2:
current_n = (current_n-1)/2
else:
current_n /= 2
n_list.append(current_n)
# Starting point for exponentiation by squares
a_zero_mean = a - np.expand_dims(np.mean(a, axis), axis)
if n_list[-1] == 1:
s = a_zero_mean.copy()
else:
s = a_zero_mean**2
# Perform multiplications
for n in n_list[-2::-1]:
s = s**2
if n % 2:
s *= a_zero_mean
return np.mean(s, axis)
def variation(a, axis=0, nan_policy='propagate'):
"""
Computes the coefficient of variation, the ratio of the biased standard
deviation to the mean.
Parameters
----------
a : array_like
Input array.
axis : int or None, optional
Axis along which to calculate the coefficient of variation. Default
is 0. If None, compute over the whole array `a`.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
variation : ndarray
The calculated variation along the requested axis.
References
----------
.. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
Probability and Statistics Tables and Formulae. Chapman & Hall: New
York. 2000.
"""
a, axis = _chk_asarray(a, axis)
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.variation(a, axis)
return a.std(axis) / a.mean(axis)
def skew(a, axis=0, bias=True, nan_policy='propagate'):
"""
Computes the skewness of a data set.
For normally distributed data, the skewness should be about 0. A skewness
value > 0 means that there is more weight in the left tail of the
distribution. The function `skewtest` can be used to determine if the
skewness value is close enough to 0, statistically speaking.
Parameters
----------
a : ndarray
data
axis : int or None, optional
Axis along which skewness is calculated. Default is 0.
If None, compute over the whole array `a`.
bias : bool, optional
If False, then the calculations are corrected for statistical bias.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
skewness : ndarray
The skewness of values along an axis, returning 0 where all values are
equal.
References
----------
.. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
Probability and Statistics Tables and Formulae. Chapman & Hall: New
York. 2000.
Section 2.2.24.1
"""
a, axis = _chk_asarray(a, axis)
n = a.shape[axis]
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.skew(a, axis, bias)
m2 = moment(a, 2, axis)
m3 = moment(a, 3, axis)
zero = (m2 == 0)
vals = _lazywhere(~zero, (m2, m3),
lambda m2, m3: m3 / m2**1.5,
0.)
if not bias:
can_correct = (n > 2) & (m2 > 0)
if can_correct.any():
m2 = np.extract(can_correct, m2)
m3 = np.extract(can_correct, m3)
nval = np.sqrt((n-1.0)*n) / (n-2.0) * m3/m2**1.5
np.place(vals, can_correct, nval)
if vals.ndim == 0:
return vals.item()
return vals
def kurtosis(a, axis=0, fisher=True, bias=True, nan_policy='propagate'):
"""
Computes the kurtosis (Fisher or Pearson) of a dataset.
Kurtosis is the fourth central moment divided by the square of the
variance. If Fisher's definition is used, then 3.0 is subtracted from
the result to give 0.0 for a normal distribution.
If bias is False then the kurtosis is calculated using k statistics to
eliminate bias coming from biased moment estimators
Use `kurtosistest` to see if result is close enough to normal.
Parameters
----------
a : array
data for which the kurtosis is calculated
axis : int or None, optional
Axis along which the kurtosis is calculated. Default is 0.
If None, compute over the whole array `a`.
fisher : bool, optional
If True, Fisher's definition is used (normal ==> 0.0). If False,
Pearson's definition is used (normal ==> 3.0).
bias : bool, optional
If False, then the calculations are corrected for statistical bias.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
kurtosis : array
The kurtosis of values along an axis. If all values are equal,
return -3 for Fisher's definition and 0 for Pearson's definition.
References
----------
.. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
Probability and Statistics Tables and Formulae. Chapman & Hall: New
York. 2000.
"""
a, axis = _chk_asarray(a, axis)
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.kurtosis(a, axis, fisher, bias)
n = a.shape[axis]
m2 = moment(a, 2, axis)
m4 = moment(a, 4, axis)
zero = (m2 == 0)
olderr = np.seterr(all='ignore')
try:
vals = np.where(zero, 0, m4 / m2**2.0)
finally:
np.seterr(**olderr)
if not bias:
can_correct = (n > 3) & (m2 > 0)
if can_correct.any():
m2 = np.extract(can_correct, m2)
m4 = np.extract(can_correct, m4)
nval = 1.0/(n-2)/(n-3) * ((n**2-1.0)*m4/m2**2.0 - 3*(n-1)**2.0)
np.place(vals, can_correct, nval + 3.0)
if vals.ndim == 0:
vals = vals.item() # array scalar
if fisher:
return vals - 3
else:
return vals
DescribeResult = namedtuple('DescribeResult',
('nobs', 'minmax', 'mean', 'variance', 'skewness',
'kurtosis'))
def describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate'):
"""
Computes several descriptive statistics of the passed array.
Parameters
----------
a : array_like
Input data.
axis : int or None, optional
Axis along which statistics are calculated. Default is 0.
If None, compute over the whole array `a`.
ddof : int, optional
Delta degrees of freedom (only for variance). Default is 1.
bias : bool, optional
If False, then the skewness and kurtosis calculations are corrected for
statistical bias.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
nobs : int
Number of observations (length of data along `axis`).
minmax: tuple of ndarrays or floats
Minimum and maximum value of data array.
mean : ndarray or float
Arithmetic mean of data along axis.
variance : ndarray or float
Unbiased variance of the data along axis, denominator is number of
observations minus one.
skewness : ndarray or float
Skewness, based on moment calculations with denominator equal to
the number of observations, i.e. no degrees of freedom correction.
kurtosis : ndarray or float
Kurtosis (Fisher). The kurtosis is normalized so that it is
zero for the normal distribution. No degrees of freedom are used.
See Also
--------
skew, kurtosis
Examples
--------
>>> from scipy import stats
>>> a = np.arange(10)
>>> stats.describe(a)
DescribeResult(nobs=10, minmax=(0, 9), mean=4.5, variance=9.1666666666666661,
skewness=0.0, kurtosis=-1.2242424242424244)
>>> b = [[1, 2], [3, 4]]
>>> stats.describe(b)
DescribeResult(nobs=2, minmax=(array([1, 2]), array([3, 4])),
mean=array([ 2., 3.]), variance=array([ 2., 2.]),
skewness=array([ 0., 0.]), kurtosis=array([-2., -2.]))
"""
a, axis = _chk_asarray(a, axis)
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.describe(a, axis, ddof, bias)
if a.size == 0:
raise ValueError("The input must not be empty.")
n = a.shape[axis]
mm = (np.min(a, axis=axis), np.max(a, axis=axis))
m = np.mean(a, axis=axis)
v = np.var(a, axis=axis, ddof=ddof)
sk = skew(a, axis, bias=bias)
kurt = kurtosis(a, axis, bias=bias)
return DescribeResult(n, mm, m, v, sk, kurt)
#####################################
# NORMALITY TESTS #
#####################################
SkewtestResult = namedtuple('SkewtestResult', ('statistic', 'pvalue'))
def skewtest(a, axis=0, nan_policy='propagate'):
"""
Tests whether the skew is different from the normal distribution.
This function tests the null hypothesis that the skewness of
the population that the sample was drawn from is the same
as that of a corresponding normal distribution.
Parameters
----------
a : array
The data to be tested
axis : int or None, optional
Axis along which statistics are calculated. Default is 0.
If None, compute over the whole array `a`.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
statistic : float
The computed z-score for this test.
pvalue : float
a 2-sided p-value for the hypothesis test
Notes
-----
The sample size must be at least 8.
References
----------
.. [1] R. B. D'Agostino, A. J. Belanger and R. B. D'Agostino Jr.,
"A suggestion for using powerful and informative tests of
normality", American Statistician 44, pp. 316-321, 1990.
"""
a, axis = _chk_asarray(a, axis)
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.skewtest(a, axis)
if axis is None:
a = np.ravel(a)
axis = 0
b2 = skew(a, axis)
n = float(a.shape[axis])
if n < 8:
raise ValueError(
"skewtest is not valid with less than 8 samples; %i samples"
" were given." % int(n))
y = b2 * math.sqrt(((n + 1) * (n + 3)) / (6.0 * (n - 2)))
beta2 = (3.0 * (n**2 + 27*n - 70) * (n+1) * (n+3) /
((n-2.0) * (n+5) * (n+7) * (n+9)))
W2 = -1 + math.sqrt(2 * (beta2 - 1))
delta = 1 / math.sqrt(0.5 * math.log(W2))
alpha = math.sqrt(2.0 / (W2 - 1))
y = np.where(y == 0, 1, y)
Z = delta * np.log(y / alpha + np.sqrt((y / alpha)**2 + 1))
return SkewtestResult(Z, 2 * distributions.norm.sf(np.abs(Z)))
KurtosistestResult = namedtuple('KurtosistestResult', ('statistic', 'pvalue'))
def kurtosistest(a, axis=0, nan_policy='propagate'):
"""
Tests whether a dataset has normal kurtosis
This function tests the null hypothesis that the kurtosis
of the population from which the sample was drawn is that
of the normal distribution: ``kurtosis = 3(n-1)/(n+1)``.
Parameters
----------
a : array
array of the sample data
axis : int or None, optional
Axis along which to compute test. Default is 0. If None,
compute over the whole array `a`.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
statistic : float
The computed z-score for this test.
pvalue : float
The 2-sided p-value for the hypothesis test
Notes
-----
Valid only for n>20. The Z-score is set to 0 for bad entries.
This function uses the method described in [1]_.
References
----------
.. [1] see e.g. F. J. Anscombe, W. J. Glynn, "Distribution of the kurtosis
statistic b2 for normal samples", Biometrika, vol. 70, pp. 227-234, 1983.
"""
a, axis = _chk_asarray(a, axis)
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.kurtosistest(a, axis)
n = float(a.shape[axis])
if n < 5:
raise ValueError(
"kurtosistest requires at least 5 observations; %i observations"
" were given." % int(n))
if n < 20:
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
"anyway, n=%i" % int(n))
b2 = kurtosis(a, axis, fisher=False)
E = 3.0*(n-1) / (n+1)
varb2 = 24.0*n*(n-2)*(n-3) / ((n+1)*(n+1.)*(n+3)*(n+5)) # [1]_ Eq. 1
x = (b2-E) / np.sqrt(varb2) # [1]_ Eq. 4
# [1]_ Eq. 2:
sqrtbeta1 = 6.0*(n*n-5*n+2)/((n+7)*(n+9)) * np.sqrt((6.0*(n+3)*(n+5)) /
(n*(n-2)*(n-3)))
# [1]_ Eq. 3:
A = 6.0 + 8.0/sqrtbeta1 * (2.0/sqrtbeta1 + np.sqrt(1+4.0/(sqrtbeta1**2)))
term1 = 1 - 2/(9.0*A)
denom = 1 + x*np.sqrt(2/(A-4.0))
denom = np.where(denom < 0, 99, denom)
term2 = np.where(denom < 0, term1, np.power((1-2.0/A)/denom, 1/3.0))
Z = (term1 - term2) / np.sqrt(2/(9.0*A)) # [1]_ Eq. 5
Z = np.where(denom == 99, 0, Z)
if Z.ndim == 0:
Z = Z[()]
# zprob uses upper tail, so Z needs to be positive
return KurtosistestResult(Z, 2 * distributions.norm.sf(np.abs(Z)))
NormaltestResult = namedtuple('NormaltestResult', ('statistic', 'pvalue'))
def normaltest(a, axis=0, nan_policy='propagate'):
"""
Tests whether a sample differs from a normal distribution.
This function tests the null hypothesis that a sample comes
from a normal distribution. It is based on D'Agostino and
Pearson's [1]_, [2]_ test that combines skew and kurtosis to
produce an omnibus test of normality.
Parameters
----------
a : array_like
The array containing the data to be tested.
axis : int or None, optional
Axis along which to compute test. Default is 0. If None,
compute over the whole array `a`.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
statistic : float or array
``s^2 + k^2``, where ``s`` is the z-score returned by `skewtest` and
``k`` is the z-score returned by `kurtosistest`.
pvalue : float or array
A 2-sided chi squared probability for the hypothesis test.
References
----------
.. [1] D'Agostino, R. B. (1971), "An omnibus test of normality for
moderate and large sample size", Biometrika, 58, 341-348
.. [2] D'Agostino, R. and Pearson, E. S. (1973), "Tests for departure from
normality", Biometrika, 60, 613-622
"""
a, axis = _chk_asarray(a, axis)
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.normaltest(a, axis)
s, _ = skewtest(a, axis)
k, _ = kurtosistest(a, axis)
k2 = s*s + k*k
return NormaltestResult(k2, distributions.chi2.sf(k2, 2))
def jarque_bera(x):
"""
Perform the Jarque-Bera goodness of fit test on sample data.
The Jarque-Bera test tests whether the sample data has the skewness and
kurtosis matching a normal distribution.
Note that this test only works for a large enough number of data samples
(>2000) as the test statistic asymptotically has a Chi-squared distribution
with 2 degrees of freedom.
Parameters
----------
x : array_like
Observations of a random variable.
Returns
-------
jb_value : float
The test statistic.
p : float
The p-value for the hypothesis test.
References
----------
.. [1] Jarque, C. and Bera, A. (1980) "Efficient tests for normality,
homoscedasticity and serial independence of regression residuals",
6 Econometric Letters 255-259.
Examples
--------
>>> from scipy import stats
>>> np.random.seed(987654321)
>>> x = np.random.normal(0, 1, 100000)
>>> y = np.random.rayleigh(1, 100000)
>>> stats.jarque_bera(x)
(4.7165707989581342, 0.09458225503041906)
>>> stats.jarque_bera(y)
(6713.7098548143422, 0.0)
"""
x = np.asarray(x)
n = float(x.size)
if n == 0:
raise ValueError('At least one observation is required.')
mu = x.mean()
diffx = x - mu
skewness = (1 / n * np.sum(diffx**3)) / (1 / n * np.sum(diffx**2))**(3 / 2.)
kurtosis = (1 / n * np.sum(diffx**4)) / (1 / n * np.sum(diffx**2))**2
jb_value = n / 6 * (skewness**2 + (kurtosis - 3)**2 / 4)
p = 1 - distributions.chi2.cdf(jb_value, 2)
return jb_value, p
#####################################
# FREQUENCY FUNCTIONS #
#####################################
def itemfreq(a):
"""
Returns a 2-D array of item frequencies.
Parameters
----------
a : (N,) array_like
Input array.
Returns
-------
itemfreq : (K, 2) ndarray
A 2-D frequency table. Column 1 contains sorted, unique values from
`a`, column 2 contains their respective counts.
Examples
--------
>>> from scipy import stats
>>> a = np.array([1, 1, 5, 0, 1, 2, 2, 0, 1, 4])
>>> stats.itemfreq(a)
array([[ 0., 2.],
[ 1., 4.],
[ 2., 2.],
[ 4., 1.],
[ 5., 1.]])
>>> np.bincount(a)
array([2, 4, 2, 0, 1, 1])
>>> stats.itemfreq(a/10.)
array([[ 0. , 2. ],
[ 0.1, 4. ],
[ 0.2, 2. ],
[ 0.4, 1. ],
[ 0.5, 1. ]])
"""
items, inv = np.unique(a, return_inverse=True)
freq = np.bincount(inv)
return np.array([items, freq]).T
def scoreatpercentile(a, per, limit=(), interpolation_method='fraction',
axis=None):
"""
Calculate the score at a given percentile of the input sequence.
For example, the score at `per=50` is the median. If the desired quantile
lies between two data points, we interpolate between them, according to
the value of `interpolation`. If the parameter `limit` is provided, it
should be a tuple (lower, upper) of two values.
Parameters
----------
a : array_like
A 1-D array of values from which to extract score.
per : array_like
Percentile(s) at which to extract score. Values should be in range
[0,100].
limit : tuple, optional
Tuple of two scalars, the lower and upper limits within which to
compute the percentile. Values of `a` outside
this (closed) interval will be ignored.
interpolation_method : {'fraction', 'lower', 'higher'}, optional
This optional parameter specifies the interpolation method to use,
when the desired quantile lies between two data points `i` and `j`
- fraction: ``i + (j - i) * fraction`` where ``fraction`` is the
fractional part of the index surrounded by ``i`` and ``j``.
- lower: ``i``.
- higher: ``j``.
axis : int, optional
Axis along which the percentiles are computed. Default is None. If
None, compute over the whole array `a`.
Returns
-------
score : float or ndarray
Score at percentile(s).
See Also
--------
percentileofscore, numpy.percentile
Notes
-----
This function will become obsolete in the future.
For Numpy 1.9 and higher, `numpy.percentile` provides all the functionality
that `scoreatpercentile` provides. And it's significantly faster.
Therefore it's recommended to use `numpy.percentile` for users that have
numpy >= 1.9.
Examples
--------
>>> from scipy import stats
>>> a = np.arange(100)
>>> stats.scoreatpercentile(a, 50)
49.5
"""
# adapted from NumPy's percentile function. When we require numpy >= 1.8,
# the implementation of this function can be replaced by np.percentile.
a = np.asarray(a)
if a.size == 0:
# empty array, return nan(s) with shape matching `per`
if np.isscalar(per):
return np.nan
else:
return np.ones(np.asarray(per).shape, dtype=np.float64) * np.nan
if limit:
a = a[(limit[0] <= a) & (a <= limit[1])]
sorted = np.sort(a, axis=axis)
if axis is None:
axis = 0
return _compute_qth_percentile(sorted, per, interpolation_method, axis)
# handle sequence of per's without calling sort multiple times
def _compute_qth_percentile(sorted, per, interpolation_method, axis):
if not np.isscalar(per):
score = [_compute_qth_percentile(sorted, i, interpolation_method, axis)
for i in per]
return np.array(score)
if (per < 0) or (per > 100):
raise ValueError("percentile must be in the range [0, 100]")
indexer = [slice(None)] * sorted.ndim
idx = per / 100. * (sorted.shape[axis] - 1)
if int(idx) != idx:
# round fractional indices according to interpolation method
if interpolation_method == 'lower':
idx = int(np.floor(idx))
elif interpolation_method == 'higher':
idx = int(np.ceil(idx))
elif interpolation_method == 'fraction':
pass # keep idx as fraction and interpolate
else:
raise ValueError("interpolation_method can only be 'fraction', "
"'lower' or 'higher'")
i = int(idx)
if i == idx:
indexer[axis] = slice(i, i + 1)
weights = array(1)
sumval = 1.0
else:
indexer[axis] = slice(i, i + 2)
j = i + 1
weights = array([(j - idx), (idx - i)], float)
wshape = [1] * sorted.ndim
wshape[axis] = 2
weights.shape = wshape
sumval = weights.sum()
# Use np.add.reduce (== np.sum but a little faster) to coerce data type
return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
def percentileofscore(a, score, kind='rank'):
"""
The percentile rank of a score relative to a list of scores.
A `percentileofscore` of, for example, 80% means that 80% of the
scores in `a` are below the given score. In the case of gaps or
ties, the exact definition depends on the optional keyword, `kind`.
Parameters
----------
a : array_like
Array of scores to which `score` is compared.
score : int or float
Score that is compared to the elements in `a`.
kind : {'rank', 'weak', 'strict', 'mean'}, optional
This optional parameter specifies the interpretation of the
resulting score:
- "rank": Average percentage ranking of score. In case of
multiple matches, average the percentage rankings of
all matching scores.
- "weak": This kind corresponds to the definition of a cumulative
distribution function. A percentileofscore of 80%
means that 80% of values are less than or equal
to the provided score.
- "strict": Similar to "weak", except that only values that are
strictly less than the given score are counted.
- "mean": The average of the "weak" and "strict" scores, often used in
testing. See
http://en.wikipedia.org/wiki/Percentile_rank
Returns
-------
pcos : float
Percentile-position of score (0-100) relative to `a`.
See Also
--------
numpy.percentile
Examples
--------
Three-quarters of the given values lie below a given score:
>>> from scipy import stats
>>> stats.percentileofscore([1, 2, 3, 4], 3)
75.0
With multiple matches, note how the scores of the two matches, 0.6
and 0.8 respectively, are averaged:
>>> stats.percentileofscore([1, 2, 3, 3, 4], 3)
70.0
Only 2/5 values are strictly less than 3:
>>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='strict')
40.0
But 4/5 values are less than or equal to 3:
>>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='weak')
80.0
The average between the weak and the strict scores is
>>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='mean')
60.0
"""
a = np.array(a)
n = len(a)
if kind == 'rank':
if not np.any(a == score):
a = np.append(a, score)
a_len = np.array(list(range(len(a))))
else:
a_len = np.array(list(range(len(a)))) + 1.0
a = np.sort(a)
idx = [a == score]
pct = (np.mean(a_len[idx]) / n) * 100.0
return pct
elif kind == 'strict':
return np.sum(a < score) / float(n) * 100
elif kind == 'weak':
return np.sum(a <= score) / float(n) * 100
elif kind == 'mean':
return (np.sum(a < score) + np.sum(a <= score)) * 50 / float(n)
else:
raise ValueError("kind can only be 'rank', 'strict', 'weak' or 'mean'")
@np.deprecate(message=("scipy.stats.histogram2 is deprecated in scipy 0.16.0; "
"use np.histogram2d instead"))
def histogram2(a, bins):
"""
Compute histogram using divisions in bins.
Count the number of times values from array `a` fall into
numerical ranges defined by `bins`. Range x is given by
bins[x] <= range_x < bins[x+1] where x =0,N and N is the
length of the `bins` array. The last range is given by
bins[N] <= range_N < infinity. Values less than bins[0] are
not included in the histogram.
Parameters
----------
a : array_like of rank 1
The array of values to be assigned into bins
bins : array_like of rank 1
Defines the ranges of values to use during histogramming.
Returns
-------
histogram2 : ndarray of rank 1
Each value represents the occurrences for a given bin (range) of
values.
"""
# comment: probably obsoleted by numpy.histogram()
n = np.searchsorted(np.sort(a), bins)
n = np.concatenate([n, [len(a)]])
return n[1:] - n[:-1]
HistogramResult = namedtuple('HistogramResult',
('count', 'lowerlimit', 'binsize', 'extrapoints'))
@np.deprecate(message=("scipy.stats.histogram is deprecated in scipy 0.17.0; "
"use np.histogram instead"))
def histogram(a, numbins=10, defaultlimits=None, weights=None, printextras=False):
# _histogram is used in relfreq/cumfreq, so need to keep it
res = _histogram(a, numbins=numbins, defaultlimits=defaultlimits,
weights=weights, printextras=printextras)
return res
def _histogram(a, numbins=10, defaultlimits=None, weights=None, printextras=False):
"""
Separates the range into several bins and returns the number of instances
in each bin.
Parameters
----------
a : array_like
Array of scores which will be put into bins.
numbins : int, optional
The number of bins to use for the histogram. Default is 10.
defaultlimits : tuple (lower, upper), optional
The lower and upper values for the range of the histogram.
If no value is given, a range slightly larger than the range of the
values in a is used. Specifically ``(a.min() - s, a.max() + s)``,
where ``s = (1/2)(a.max() - a.min()) / (numbins - 1)``.
weights : array_like, optional
The weights for each value in `a`. Default is None, which gives each
value a weight of 1.0
printextras : bool, optional
If True, if there are extra points (i.e. the points that fall outside
the bin limits) a warning is raised saying how many of those points
there are. Default is False.
Returns
-------
count : ndarray
Number of points (or sum of weights) in each bin.
lowerlimit : float
Lowest value of histogram, the lower limit of the first bin.
binsize : float
The size of the bins (all bins have the same size).
extrapoints : int
The number of points outside the range of the histogram.
See Also
--------
numpy.histogram
Notes
-----
This histogram is based on numpy's histogram but has a larger range by
default if default limits is not set.
"""
a = np.ravel(a)
if defaultlimits is None:
if a.size == 0:
# handle empty arrays. Undetermined range, so use 0-1.
defaultlimits = (0, 1)
else:
# no range given, so use values in `a`
data_min = a.min()
data_max = a.max()
# Have bins extend past min and max values slightly
s = (data_max - data_min) / (2. * (numbins - 1.))
defaultlimits = (data_min - s, data_max + s)
# use numpy's histogram method to compute bins
hist, bin_edges = np.histogram(a, bins=numbins, range=defaultlimits,
weights=weights)
# hist are not always floats, convert to keep with old output
hist = np.array(hist, dtype=float)
# fixed width for bins is assumed, as numpy's histogram gives
# fixed width bins for int values for 'bins'
binsize = bin_edges[1] - bin_edges[0]
# calculate number of extra points
extrapoints = len([v for v in a
if defaultlimits[0] > v or v > defaultlimits[1]])
if extrapoints > 0 and printextras:
warnings.warn("Points outside given histogram range = %s"
% extrapoints)
return HistogramResult(hist, defaultlimits[0], binsize, extrapoints)
CumfreqResult = namedtuple('CumfreqResult',
('cumcount', 'lowerlimit', 'binsize',
'extrapoints'))
def cumfreq(a, numbins=10, defaultreallimits=None, weights=None):
"""
Returns a cumulative frequency histogram, using the histogram function.
A cumulative histogram is a mapping that counts the cumulative number of
observations in all of the bins up to the specified bin.
Parameters
----------
a : array_like
Input array.
numbins : int, optional
The number of bins to use for the histogram. Default is 10.
defaultreallimits : tuple (lower, upper), optional
The lower and upper values for the range of the histogram.
If no value is given, a range slightly larger than the range of the
values in `a` is used. Specifically ``(a.min() - s, a.max() + s)``,
where ``s = (1/2)(a.max() - a.min()) / (numbins - 1)``.
weights : array_like, optional
The weights for each value in `a`. Default is None, which gives each
value a weight of 1.0
Returns
-------
cumcount : ndarray
Binned values of cumulative frequency.
lowerlimit : float
Lower real limit
binsize : float
Width of each bin.
extrapoints : int
Extra points.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from scipy import stats
>>> x = [1, 4, 2, 1, 3, 1]
>>> res = stats.cumfreq(x, numbins=4, defaultreallimits=(1.5, 5))
>>> res.cumcount
array([ 1., 2., 3., 3.])
>>> res.extrapoints
3
Create a normal distribution with 1000 random values
>>> rng = np.random.RandomState(seed=12345)
>>> samples = stats.norm.rvs(size=1000, random_state=rng)
Calculate cumulative frequencies
>>> res = stats.cumfreq(samples, numbins=25)
Calculate space of values for x
>>> x = res.lowerlimit + np.linspace(0, res.binsize*res.cumcount.size,
... res.cumcount.size)
Plot histogram and cumulative histogram
>>> fig = plt.figure(figsize=(10, 4))
>>> ax1 = fig.add_subplot(1, 2, 1)
>>> ax2 = fig.add_subplot(1, 2, 2)
>>> ax1.hist(samples, bins=25)
>>> ax1.set_title('Histogram')
>>> ax2.bar(x, res.cumcount, width=res.binsize)
>>> ax2.set_title('Cumulative histogram')
>>> ax2.set_xlim([x.min(), x.max()])
>>> plt.show()
"""
h, l, b, e = _histogram(a, numbins, defaultreallimits, weights=weights)
cumhist = np.cumsum(h * 1, axis=0)
return CumfreqResult(cumhist, l, b, e)
RelfreqResult = namedtuple('RelfreqResult',
('frequency', 'lowerlimit', 'binsize',
'extrapoints'))
def relfreq(a, numbins=10, defaultreallimits=None, weights=None):
"""
Returns a relative frequency histogram, using the histogram function.
A relative frequency histogram is a mapping of the number of
observations in each of the bins relative to the total of observations.
Parameters
----------
a : array_like
Input array.
numbins : int, optional
The number of bins to use for the histogram. Default is 10.
defaultreallimits : tuple (lower, upper), optional
The lower and upper values for the range of the histogram.
If no value is given, a range slightly larger than the range of the
values in a is used. Specifically ``(a.min() - s, a.max() + s)``,
where ``s = (1/2)(a.max() - a.min()) / (numbins - 1)``.
weights : array_like, optional
The weights for each value in `a`. Default is None, which gives each
value a weight of 1.0
Returns
-------
frequency : ndarray
Binned values of relative frequency.
lowerlimit : float
Lower real limit
binsize : float
Width of each bin.
extrapoints : int
Extra points.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from scipy import stats
>>> a = np.array([2, 4, 1, 2, 3, 2])
>>> res = stats.relfreq(a, numbins=4)
>>> res.frequency
array([ 0.16666667, 0.5 , 0.16666667, 0.16666667])
>>> np.sum(res.frequency) # relative frequencies should add up to 1
1.0
Create a normal distribution with 1000 random values
>>> rng = np.random.RandomState(seed=12345)
>>> samples = stats.norm.rvs(size=1000, random_state=rng)
Calculate relative frequencies
>>> res = stats.relfreq(samples, numbins=25)
Calculate space of values for x
>>> x = res.lowerlimit + np.linspace(0, res.binsize*res.frequency.size,
... res.frequency.size)
Plot relative frequency histogram
>>> fig = plt.figure(figsize=(5, 4))
>>> ax = fig.add_subplot(1, 1, 1)
>>> ax.bar(x, res.frequency, width=res.binsize)
>>> ax.set_title('Relative frequency histogram')
>>> ax.set_xlim([x.min(), x.max()])
>>> plt.show()
"""
a = np.asanyarray(a)
h, l, b, e = _histogram(a, numbins, defaultreallimits, weights=weights)
h = h / float(a.shape[0])
return RelfreqResult(h, l, b, e)
#####################################
# VARIABILITY FUNCTIONS #
#####################################
def obrientransform(*args):
"""
Computes the O'Brien transform on input data (any number of arrays).
Used to test for homogeneity of variance prior to running one-way stats.
Each array in ``*args`` is one level of a factor.
If `f_oneway` is run on the transformed data and found significant,
the variances are unequal. From Maxwell and Delaney [1]_, p.112.
Parameters
----------
args : tuple of array_like
Any number of arrays.
Returns
-------
obrientransform : ndarray
Transformed data for use in an ANOVA. The first dimension
of the result corresponds to the sequence of transformed
arrays. If the arrays given are all 1-D of the same length,
the return value is a 2-D array; otherwise it is a 1-D array
of type object, with each element being an ndarray.
References
----------
.. [1] S. E. Maxwell and H. D. Delaney, "Designing Experiments and
Analyzing Data: A Model Comparison Perspective", Wadsworth, 1990.
Examples
--------
We'll test the following data sets for differences in their variance.
>>> x = [10, 11, 13, 9, 7, 12, 12, 9, 10]
>>> y = [13, 21, 5, 10, 8, 14, 10, 12, 7, 15]
Apply the O'Brien transform to the data.
>>> from scipy.stats import obrientransform
>>> tx, ty = obrientransform(x, y)
Use `scipy.stats.f_oneway` to apply a one-way ANOVA test to the
transformed data.
>>> from scipy.stats import f_oneway
>>> F, p = f_oneway(tx, ty)
>>> p
0.1314139477040335
If we require that ``p < 0.05`` for significance, we cannot conclude
that the variances are different.
"""
TINY = np.sqrt(np.finfo(float).eps)
# `arrays` will hold the transformed arguments.
arrays = []
for arg in args:
a = np.asarray(arg)
n = len(a)
mu = np.mean(a)
sq = (a - mu)**2
sumsq = sq.sum()
# The O'Brien transform.
t = ((n - 1.5) * n * sq - 0.5 * sumsq) / ((n - 1) * (n - 2))
# Check that the mean of the transformed data is equal to the
# original variance.
var = sumsq / (n - 1)
if abs(var - np.mean(t)) > TINY:
raise ValueError('Lack of convergence in obrientransform.')
arrays.append(t)
return np.array(arrays)
@np.deprecate(message="scipy.stats.signaltonoise is deprecated in scipy 0.16.0")
def signaltonoise(a, axis=0, ddof=0):
"""
The signal-to-noise ratio of the input data.
Returns the signal-to-noise ratio of `a`, here defined as the mean
divided by the standard deviation.
Parameters
----------
a : array_like
An array_like object containing the sample data.
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over
the whole array `a`.
ddof : int, optional
Degrees of freedom correction for standard deviation. Default is 0.
Returns
-------
s2n : ndarray
The mean to standard deviation ratio(s) along `axis`, or 0 where the
standard deviation is 0.
"""
a = np.asanyarray(a)
m = a.mean(axis)
sd = a.std(axis=axis, ddof=ddof)
return np.where(sd == 0, 0, m/sd)
def sem(a, axis=0, ddof=1, nan_policy='propagate'):
"""
Calculates the standard error of the mean (or standard error of
measurement) of the values in the input array.
Parameters
----------
a : array_like
An array containing the values for which the standard error is
returned.
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over
the whole array `a`.
ddof : int, optional
Delta degrees-of-freedom. How many degrees of freedom to adjust
for bias in limited samples relative to the population estimate
of variance. Defaults to 1.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
s : ndarray or float
The standard error of the mean in the sample(s), along the input axis.
Notes
-----
The default value for `ddof` is different to the default (0) used by other
ddof containing routines, such as np.std and np.nanstd.
Examples
--------
Find standard error along the first axis:
>>> from scipy import stats
>>> a = np.arange(20).reshape(5,4)
>>> stats.sem(a)
array([ 2.8284, 2.8284, 2.8284, 2.8284])
Find standard error across the whole array, using n degrees of freedom:
>>> stats.sem(a, axis=None, ddof=0)
1.2893796958227628
"""
a, axis = _chk_asarray(a, axis)
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.sem(a, axis, ddof)
n = a.shape[axis]
s = np.std(a, axis=axis, ddof=ddof) / np.sqrt(n)
return s
def zscore(a, axis=0, ddof=0):
"""
Calculates the z score of each value in the sample, relative to the
sample mean and standard deviation.
Parameters
----------
a : array_like
An array like object containing the sample data.
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over
the whole array `a`.
ddof : int, optional
Degrees of freedom correction in the calculation of the
standard deviation. Default is 0.
Returns
-------
zscore : array_like
The z-scores, standardized by mean and standard deviation of
input array `a`.
Notes
-----
This function preserves ndarray subclasses, and works also with
matrices and masked arrays (it uses `asanyarray` instead of
`asarray` for parameters).
Examples
--------
>>> a = np.array([ 0.7972, 0.0767, 0.4383, 0.7866, 0.8091,
... 0.1954, 0.6307, 0.6599, 0.1065, 0.0508])
>>> from scipy import stats
>>> stats.zscore(a)
array([ 1.1273, -1.247 , -0.0552, 1.0923, 1.1664, -0.8559, 0.5786,
0.6748, -1.1488, -1.3324])
Computing along a specified axis, using n-1 degrees of freedom
(``ddof=1``) to calculate the standard deviation:
>>> b = np.array([[ 0.3148, 0.0478, 0.6243, 0.4608],
... [ 0.7149, 0.0775, 0.6072, 0.9656],
... [ 0.6341, 0.1403, 0.9759, 0.4064],
... [ 0.5918, 0.6948, 0.904 , 0.3721],
... [ 0.0921, 0.2481, 0.1188, 0.1366]])
>>> stats.zscore(b, axis=1, ddof=1)
array([[-0.19264823, -1.28415119, 1.07259584, 0.40420358],
[ 0.33048416, -1.37380874, 0.04251374, 1.00081084],
[ 0.26796377, -1.12598418, 1.23283094, -0.37481053],
[-0.22095197, 0.24468594, 1.19042819, -1.21416216],
[-0.82780366, 1.4457416 , -0.43867764, -0.1792603 ]])
"""
a = np.asanyarray(a)
mns = a.mean(axis=axis)
sstd = a.std(axis=axis, ddof=ddof)
if axis and mns.ndim < a.ndim:
return ((a - np.expand_dims(mns, axis=axis)) /
np.expand_dims(sstd, axis=axis))
else:
return (a - mns) / sstd
def zmap(scores, compare, axis=0, ddof=0):
"""
Calculates the relative z-scores.
Returns an array of z-scores, i.e., scores that are standardized to
zero mean and unit variance, where mean and variance are calculated
from the comparison array.
Parameters
----------
scores : array_like
The input for which z-scores are calculated.
compare : array_like
The input from which the mean and standard deviation of the
normalization are taken; assumed to have the same dimension as
`scores`.
axis : int or None, optional
Axis over which mean and variance of `compare` are calculated.
Default is 0. If None, compute over the whole array `scores`.
ddof : int, optional
Degrees of freedom correction in the calculation of the
standard deviation. Default is 0.
Returns
-------
zscore : array_like
Z-scores, in the same shape as `scores`.
Notes
-----
This function preserves ndarray subclasses, and works also with
matrices and masked arrays (it uses `asanyarray` instead of
`asarray` for parameters).
Examples
--------
>>> from scipy.stats import zmap
>>> a = [0.5, 2.0, 2.5, 3]
>>> b = [0, 1, 2, 3, 4]
>>> zmap(a, b)
array([-1.06066017, 0. , 0.35355339, 0.70710678])
"""
scores, compare = map(np.asanyarray, [scores, compare])
mns = compare.mean(axis=axis)
sstd = compare.std(axis=axis, ddof=ddof)
if axis and mns.ndim < compare.ndim:
return ((scores - np.expand_dims(mns, axis=axis)) /
np.expand_dims(sstd, axis=axis))
else:
return (scores - mns) / sstd
# Private dictionary initialized only once at module level
# See https://en.wikipedia.org/wiki/Robust_measures_of_scale
_scale_conversions = {'raw': 1.0,
'normal': special.erfinv(0.5) * 2.0 * math.sqrt(2.0)}
def iqr(x, axis=None, rng=(25, 75), scale='raw', nan_policy='propagate',
interpolation='linear', keepdims=False):
"""
Compute the interquartile range of the data along the specified
axis.
The interquartile range (IQR) is the difference between the 75th and
25th percentile of the data. It is a measure of the dispersion
similar to standard deviation or variance, but is much more robust
against outliers [2]_.
The ``rng`` parameter allows this function to compute other
percentile ranges than the actual IQR. For example, setting
``rng=(0, 100)`` is equivalent to `numpy.ptp`.
The IQR of an empty array is `np.nan`.
.. versionadded:: 0.18.0
Parameters
----------
x : array_like
Input array or object that can be converted to an array.
axis : int or sequence of int, optional
Axis along which the range is computed. The default is to
compute the IQR for the entire array.
rng : Two-element sequence containing floats in range of [0,100] optional
Percentiles over which to compute the range. Each must be
between 0 and 100, inclusive. The default is the true IQR:
`(25, 75)`. The order of the elements is not important.
scale : scalar or str, optional
The numerical value of scale will be divided out of the final
result. The following string values are recognized:
'raw' : No scaling, just return the raw IQR.
'normal' : Scale by :math:`2 \\sqrt{2} erf^{-1}(\\frac{1}{2}) \\approx 1.349`.
The default is 'raw'. Array-like scale is also allowed, as long
as it broadcasts correctly to the output such that
``out / scale`` is a valid operation. The output dimensions
depend on the input array, `x`, the `axis` argument, and the
`keepdims` flag.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate'
returns nan, 'raise' throws an error, 'omit' performs the
calculations ignoring nan values. Default is 'propagate'.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}, optional
Specifies the interpolation method to use when the percentile
boundaries lie between two data points `i` and `j`:
* 'linear' : `i + (j - i) * fraction`, where `fraction` is the
fractional part of the index surrounded by `i` and `j`.
* 'lower' : `i`.
* 'higher' : `j`.
* 'nearest' : `i` or `j` whichever is nearest.
* 'midpoint' : `(i + j) / 2`.
Default is 'linear'.
keepdims : bool, optional
If this is set to `True`, the reduced axes are left in the
result as dimensions with size one. With this option, the result
will broadcast correctly against the original array `x`.
Returns
-------
iqr : scalar or ndarray
If ``axis=None``, a scalar is returned. If the input contains
integers or floats of smaller precision than ``np.float64``, then the
output data-type is ``np.float64``. Otherwise, the output data-type is
the same as that of the input.
See Also
--------
numpy.std, numpy.var
Examples
--------
>>> from scipy.stats import iqr
>>> x = np.array([[10, 7, 4], [3, 2, 1]])
>>> x
array([[10, 7, 4],
[ 3, 2, 1]])
>>> iqr(x)
4.0
>>> iqr(x, axis=0)
array([ 3.5, 2.5, 1.5])
>>> iqr(x, axis=1)
array([ 3., 1.])
>>> iqr(x, axis=1, keepdims=True)
array([[ 3.],
[ 1.]])
Notes
-----
This function is heavily dependent on the version of `numpy` that is
installed. Versions greater than 1.11.0b3 are highly recommended, as they
include a number of enhancements and fixes to `numpy.percentile` and
`numpy.nanpercentile` that affect the operation of this function. The
following modifications apply:
Below 1.10.0 : `nan_policy` is poorly defined.
The default behavior of `numpy.percentile` is used for 'propagate'. This
is a hybrid of 'omit' and 'propagate' that mostly yields a skewed
version of 'omit' since NaNs are sorted to the end of the data. A
warning is raised if there are NaNs in the data.
Below 1.9.0: `numpy.nanpercentile` does not exist.
This means that `numpy.percentile` is used regardless of `nan_policy`
and a warning is issued. See previous item for a description of the
behavior.
Below 1.9.0: `keepdims` and `interpolation` are not supported.
The keywords get ignored with a warning if supplied with non-default
values. However, multiple axes are still supported.
References
----------
.. [1] "Interquartile range" https://en.wikipedia.org/wiki/Interquartile_range
.. [2] "Robust measures of scale" https://en.wikipedia.org/wiki/Robust_measures_of_scale
.. [3] "Quantile" https://en.wikipedia.org/wiki/Quantile
"""
x = asarray(x)
# This check prevents percentile from raising an error later. Also, it is
# consistent with `np.var` and `np.std`.
if not x.size:
return np.nan
# An error may be raised here, so fail-fast, before doing lengthy
# computations, even though `scale` is not used until later
if isinstance(scale, string_types):
scale_key = scale.lower()
if scale_key not in _scale_conversions:
raise ValueError("{0} not a valid scale for `iqr`".format(scale))
scale = _scale_conversions[scale_key]
# Select the percentile function to use based on nans and policy
contains_nan, nan_policy = _contains_nan(x, nan_policy)
if contains_nan and nan_policy == 'omit':
percentile_func = _iqr_nanpercentile
else:
percentile_func = _iqr_percentile
if len(rng) != 2:
raise TypeError("quantile range must be two element sequence")
rng = sorted(rng)
pct = percentile_func(x, rng, axis=axis, interpolation=interpolation,
keepdims=keepdims, contains_nan=contains_nan)
out = np.subtract(pct[1], pct[0])
if scale != 1.0:
out /= scale
return out
def _iqr_percentile(x, q, axis=None, interpolation='linear', keepdims=False, contains_nan=False):
"""
Private wrapper that works around older versions of `numpy`.
While this function is pretty much necessary for the moment, it
should be removed as soon as the minimum supported numpy version
allows.
"""
if contains_nan and NumpyVersion(np.__version__) < '1.10.0a':
# I see no way to avoid the version check to ensure that the corrected
# NaN behavior has been implemented except to call `percentile` on a
# small array.
msg = "Keyword nan_policy='propagate' not correctly supported for " \
"numpy versions < 1.10.x. The default behavior of " \
"`numpy.percentile` will be used."
warnings.warn(msg, RuntimeWarning)
try:
# For older versions of numpy, there are two things that can cause a
# problem here: missing keywords and non-scalar axis. The former can be
# partially handled with a warning, the latter can be handled fully by
# hacking in an implementation similar to numpy's function for
# providing multi-axis functionality
# (`numpy.lib.function_base._ureduce` for the curious).
result = np.percentile(x, q, axis=axis, keepdims=keepdims,
interpolation=interpolation)
except TypeError:
if interpolation != 'linear' or keepdims:
# At time or writing, this means np.__version__ < 1.9.0
warnings.warn("Keywords interpolation and keepdims not supported "
"for your version of numpy", RuntimeWarning)
try:
# Special processing if axis is an iterable
original_size = len(axis)
except TypeError:
# Axis is a scalar at this point
pass
else:
axis = np.unique(np.asarray(axis) % x.ndim)
if original_size > axis.size:
# mimic numpy if axes are duplicated
raise ValueError("duplicate value in axis")
if axis.size == x.ndim:
# axis includes all axes: revert to None
axis = None
elif axis.size == 1:
# no rolling necessary
axis = axis[0]
else:
# roll multiple axes to the end and flatten that part out
for ax in axis[::-1]:
x = np.rollaxis(x, ax, x.ndim)
x = x.reshape(x.shape[:-axis.size] +
(np.prod(x.shape[-axis.size:]),))
axis = -1
result = np.percentile(x, q, axis=axis)
return result
def _iqr_nanpercentile(x, q, axis=None, interpolation='linear', keepdims=False, contains_nan=False):
"""
Private wrapper that works around the following:
1. A bug in `np.nanpercentile` that was around until numpy version
1.11.0.
2. A bug in `np.percentile` NaN handling that was fixed in numpy
version 1.10.0.
3. The non-existence of `np.nanpercentile` before numpy version
1.9.0.
While this function is pretty much necessary for the moment, it
should be removed as soon as the minimum supported numpy version
allows.
"""
if hasattr(np, 'nanpercentile'):
# At time or writing, this means np.__version__ < 1.9.0
result = np.nanpercentile(x, q, axis=axis,
interpolation=interpolation, keepdims=keepdims)
# If non-scalar result and nanpercentile does not do proper axis roll.
# I see no way of avoiding the version test since dimensions may just
# happen to match in the data.
if result.ndim > 1 and NumpyVersion(np.__version__) < '1.11.0a':
axis = np.asarray(axis)
if axis.size == 1:
# If only one axis specified, reduction happens along that dimension
if axis.ndim == 0:
axis = axis[None]
result = np.rollaxis(result, axis[0])
else:
# If multiple axes, reduced dimeision is last
result = np.rollaxis(result, -1)
else:
msg = "Keyword nan_policy='omit' not correctly supported for numpy " \
"versions < 1.9.x. The default behavior of numpy.percentile " \
"will be used."
warnings.warn(msg, RuntimeWarning)
result = _iqr_percentile(x, q, axis=axis)
return result
#####################################
# TRIMMING FUNCTIONS #
#####################################
@np.deprecate(message="stats.threshold is deprecated in scipy 0.17.0")
def threshold(a, threshmin=None, threshmax=None, newval=0):
"""
Clip array to a given value.
Similar to numpy.clip(), except that values less than `threshmin` or
greater than `threshmax` are replaced by `newval`, instead of by
`threshmin` and `threshmax` respectively.
Parameters
----------
a : array_like
Data to threshold.
threshmin : float, int or None, optional
Minimum threshold, defaults to None.
threshmax : float, int or None, optional
Maximum threshold, defaults to None.
newval : float or int, optional
Value to put in place of values in `a` outside of bounds.
Defaults to 0.
Returns
-------
out : ndarray
The clipped input array, with values less than `threshmin` or
greater than `threshmax` replaced with `newval`.
Examples
--------
>>> a = np.array([9, 9, 6, 3, 1, 6, 1, 0, 0, 8])
>>> from scipy import stats
>>> stats.threshold(a, threshmin=2, threshmax=8, newval=-1)
array([-1, -1, 6, 3, -1, 6, -1, -1, -1, 8])
"""
a = asarray(a).copy()
mask = zeros(a.shape, dtype=bool)
if threshmin is not None:
mask |= (a < threshmin)
if threshmax is not None:
mask |= (a > threshmax)
a[mask] = newval
return a
SigmaclipResult = namedtuple('SigmaclipResult', ('clipped', 'lower', 'upper'))
def sigmaclip(a, low=4., high=4.):
"""
Iterative sigma-clipping of array elements.
The output array contains only those elements of the input array `c`
that satisfy the conditions ::
mean(c) - std(c)*low < c < mean(c) + std(c)*high
Starting from the full sample, all elements outside the critical range are
removed. The iteration continues with a new critical range until no
elements are outside the range.
Parameters
----------
a : array_like
Data array, will be raveled if not 1-D.
low : float, optional
Lower bound factor of sigma clipping. Default is 4.
high : float, optional
Upper bound factor of sigma clipping. Default is 4.
Returns
-------
clipped : ndarray
Input array with clipped elements removed.
lower : float
Lower threshold value use for clipping.
upper : float
Upper threshold value use for clipping.
Examples
--------
>>> from scipy.stats import sigmaclip
>>> a = np.concatenate((np.linspace(9.5, 10.5, 31),
... np.linspace(0, 20, 5)))
>>> fact = 1.5
>>> c, low, upp = sigmaclip(a, fact, fact)
>>> c
array([ 9.96666667, 10. , 10.03333333, 10. ])
>>> c.var(), c.std()
(0.00055555555555555165, 0.023570226039551501)
>>> low, c.mean() - fact*c.std(), c.min()
(9.9646446609406727, 9.9646446609406727, 9.9666666666666668)
>>> upp, c.mean() + fact*c.std(), c.max()
(10.035355339059327, 10.035355339059327, 10.033333333333333)
>>> a = np.concatenate((np.linspace(9.5, 10.5, 11),
... np.linspace(-100, -50, 3)))
>>> c, low, upp = sigmaclip(a, 1.8, 1.8)
>>> (c == np.linspace(9.5, 10.5, 11)).all()
True
"""
c = np.asarray(a).ravel()
delta = 1
while delta:
c_std = c.std()
c_mean = c.mean()
size = c.size
critlower = c_mean - c_std*low
critupper = c_mean + c_std*high
c = c[(c > critlower) & (c < critupper)]
delta = size - c.size
return SigmaclipResult(c, critlower, critupper)
def trimboth(a, proportiontocut, axis=0):
"""
Slices off a proportion of items from both ends of an array.
Slices off the passed proportion of items from both ends of the passed
array (i.e., with `proportiontocut` = 0.1, slices leftmost 10% **and**
rightmost 10% of scores). The trimmed values are the lowest and
highest ones.
Slices off less if proportion results in a non-integer slice index (i.e.,
conservatively slices off`proportiontocut`).
Parameters
----------
a : array_like
Data to trim.
proportiontocut : float
Proportion (in range 0-1) of total data set to trim of each end.
axis : int or None, optional
Axis along which to trim data. Default is 0. If None, compute over
the whole array `a`.
Returns
-------
out : ndarray
Trimmed version of array `a`. The order of the trimmed content
is undefined.
See Also
--------
trim_mean
Examples
--------
>>> from scipy import stats
>>> a = np.arange(20)
>>> b = stats.trimboth(a, 0.1)
>>> b.shape
(16,)
"""
a = np.asarray(a)
if a.size == 0:
return a
if axis is None:
a = a.ravel()
axis = 0
nobs = a.shape[axis]
lowercut = int(proportiontocut * nobs)
uppercut = nobs - lowercut
if (lowercut >= uppercut):
raise ValueError("Proportion too big.")
atmp = np.partition(a, (lowercut, uppercut - 1), axis)
sl = [slice(None)] * atmp.ndim
sl[axis] = slice(lowercut, uppercut)
return atmp[sl]
def trim1(a, proportiontocut, tail='right', axis=0):
"""
Slices off a proportion from ONE end of the passed array distribution.
If `proportiontocut` = 0.1, slices off 'leftmost' or 'rightmost'
10% of scores. The lowest or highest values are trimmed (depending on
the tail).
Slices off less if proportion results in a non-integer slice index
(i.e., conservatively slices off `proportiontocut` ).
Parameters
----------
a : array_like
Input array
proportiontocut : float
Fraction to cut off of 'left' or 'right' of distribution
tail : {'left', 'right'}, optional
Defaults to 'right'.
axis : int or None, optional
Axis along which to trim data. Default is 0. If None, compute over
the whole array `a`.
Returns
-------
trim1 : ndarray
Trimmed version of array `a`. The order of the trimmed content is
undefined.
"""
a = np.asarray(a)
if axis is None:
a = a.ravel()
axis = 0
nobs = a.shape[axis]
# avoid possible corner case
if proportiontocut >= 1:
return []
if tail.lower() == 'right':
lowercut = 0
uppercut = nobs - int(proportiontocut * nobs)
elif tail.lower() == 'left':
lowercut = int(proportiontocut * nobs)
uppercut = nobs
atmp = np.partition(a, (lowercut, uppercut - 1), axis)
return atmp[lowercut:uppercut]
def trim_mean(a, proportiontocut, axis=0):
"""
Return mean of array after trimming distribution from both tails.
If `proportiontocut` = 0.1, slices off 'leftmost' and 'rightmost' 10% of
scores. The input is sorted before slicing. Slices off less if proportion
results in a non-integer slice index (i.e., conservatively slices off
`proportiontocut` ).
Parameters
----------
a : array_like
Input array
proportiontocut : float
Fraction to cut off of both tails of the distribution
axis : int or None, optional
Axis along which the trimmed means are computed. Default is 0.
If None, compute over the whole array `a`.
Returns
-------
trim_mean : ndarray
Mean of trimmed array.
See Also
--------
trimboth
tmean : compute the trimmed mean ignoring values outside given `limits`.
Examples
--------
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.trim_mean(x, 0.1)
9.5
>>> x2 = x.reshape(5, 4)
>>> x2
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15],
[16, 17, 18, 19]])
>>> stats.trim_mean(x2, 0.25)
array([ 8., 9., 10., 11.])
>>> stats.trim_mean(x2, 0.25, axis=1)
array([ 1.5, 5.5, 9.5, 13.5, 17.5])
"""
a = np.asarray(a)
if a.size == 0:
return np.nan
if axis is None:
a = a.ravel()
axis = 0
nobs = a.shape[axis]
lowercut = int(proportiontocut * nobs)
uppercut = nobs - lowercut
if (lowercut > uppercut):
raise ValueError("Proportion too big.")
atmp = np.partition(a, (lowercut, uppercut - 1), axis)
sl = [slice(None)] * atmp.ndim
sl[axis] = slice(lowercut, uppercut)
return np.mean(atmp[sl], axis=axis)
F_onewayResult = namedtuple('F_onewayResult', ('statistic', 'pvalue'))
def f_oneway(*args):
"""
Performs a 1-way ANOVA.
The one-way ANOVA tests the null hypothesis that two or more groups have
the same population mean. The test is applied to samples from two or
more groups, possibly with differing sizes.
Parameters
----------
sample1, sample2, ... : array_like
The sample measurements for each group.
Returns
-------
statistic : float
The computed F-value of the test.
pvalue : float
The associated p-value from the F-distribution.
Notes
-----
The ANOVA test has important assumptions that must be satisfied in order
for the associated p-value to be valid.
1. The samples are independent.
2. Each sample is from a normally distributed population.
3. The population standard deviations of the groups are all equal. This
property is known as homoscedasticity.
If these assumptions are not true for a given set of data, it may still be
possible to use the Kruskal-Wallis H-test (`scipy.stats.kruskal`) although
with some loss of power.
The algorithm is from Heiman[2], pp.394-7.
References
----------
.. [1] Lowry, Richard. "Concepts and Applications of Inferential
Statistics". Chapter 14.
http://faculty.vassar.edu/lowry/ch14pt1.html
.. [2] Heiman, G.W. Research Methods in Statistics. 2002.
.. [3] McDonald, G. H. "Handbook of Biological Statistics", One-way ANOVA.
http://www.biostathandbook.com/onewayanova.html
Examples
--------
>>> import scipy.stats as stats
[3]_ Here are some data on a shell measurement (the length of the anterior
adductor muscle scar, standardized by dividing by length) in the mussel
Mytilus trossulus from five locations: Tillamook, Oregon; Newport, Oregon;
Petersburg, Alaska; Magadan, Russia; and Tvarminne, Finland, taken from a
much larger data set used in McDonald et al. (1991).
>>> tillamook = [0.0571, 0.0813, 0.0831, 0.0976, 0.0817, 0.0859, 0.0735,
... 0.0659, 0.0923, 0.0836]
>>> newport = [0.0873, 0.0662, 0.0672, 0.0819, 0.0749, 0.0649, 0.0835,
... 0.0725]
>>> petersburg = [0.0974, 0.1352, 0.0817, 0.1016, 0.0968, 0.1064, 0.105]
>>> magadan = [0.1033, 0.0915, 0.0781, 0.0685, 0.0677, 0.0697, 0.0764,
... 0.0689]
>>> tvarminne = [0.0703, 0.1026, 0.0956, 0.0973, 0.1039, 0.1045]
>>> stats.f_oneway(tillamook, newport, petersburg, magadan, tvarminne)
(7.1210194716424473, 0.00028122423145345439)
"""
args = [np.asarray(arg, dtype=float) for arg in args]
# ANOVA on N groups, each in its own array
num_groups = len(args)
alldata = np.concatenate(args)
bign = len(alldata)
# Determine the mean of the data, and subtract that from all inputs to a
# variance (via sum_of_sq / sq_of_sum) calculation. Variance is invariance
# to a shift in location, and centering all data around zero vastly
# improves numerical stability.
offset = alldata.mean()
alldata -= offset
sstot = _sum_of_squares(alldata) - (_square_of_sums(alldata) / float(bign))
ssbn = 0
for a in args:
ssbn += _square_of_sums(a - offset) / float(len(a))
# Naming: variables ending in bn/b are for "between treatments", wn/w are
# for "within treatments"
ssbn -= (_square_of_sums(alldata) / float(bign))
sswn = sstot - ssbn
dfbn = num_groups - 1
dfwn = bign - num_groups
msb = ssbn / float(dfbn)
msw = sswn / float(dfwn)
f = msb / msw
prob = special.fdtrc(dfbn, dfwn, f) # equivalent to stats.f.sf
return F_onewayResult(f, prob)
def pearsonr(x, y):
"""
Calculates a Pearson correlation coefficient and the p-value for testing
non-correlation.
The Pearson correlation coefficient measures the linear relationship
between two datasets. Strictly speaking, Pearson's correlation requires
that each dataset be normally distributed, and not necessarily zero-mean.
Like other correlation coefficients, this one varies between -1 and +1
with 0 implying no correlation. Correlations of -1 or +1 imply an exact
linear relationship. Positive correlations imply that as x increases, so
does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Pearson correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
Parameters
----------
x : (N,) array_like
Input
y : (N,) array_like
Input
Returns
-------
r : float
Pearson's correlation coefficient
p-value : float
2-tailed p-value
References
----------
http://www.statsoft.com/textbook/glosp.html#Pearson%20Correlation
"""
# x and y should have same length.
x = np.asarray(x)
y = np.asarray(y)
n = len(x)
mx = x.mean()
my = y.mean()
xm, ym = x - mx, y - my
r_num = np.add.reduce(xm * ym)
r_den = np.sqrt(_sum_of_squares(xm) * _sum_of_squares(ym))
r = r_num / r_den
# Presumably, if abs(r) > 1, then it is only some small artifact of floating
# point arithmetic.
r = max(min(r, 1.0), -1.0)
df = n - 2
if abs(r) == 1.0:
prob = 0.0
else:
t_squared = r**2 * (df / ((1.0 - r) * (1.0 + r)))
prob = _betai(0.5*df, 0.5, df/(df+t_squared))
return r, prob
def fisher_exact(table, alternative='two-sided'):
"""Performs a Fisher exact test on a 2x2 contingency table.
Parameters
----------
table : array_like of ints
A 2x2 contingency table. Elements should be non-negative integers.
alternative : {'two-sided', 'less', 'greater'}, optional
Which alternative hypothesis to the null hypothesis the test uses.
Default is 'two-sided'.
Returns
-------
oddsratio : float
This is prior odds ratio and not a posterior estimate.
p_value : float
P-value, the probability of obtaining a distribution at least as
extreme as the one that was actually observed, assuming that the
null hypothesis is true.
See Also
--------
chi2_contingency : Chi-square test of independence of variables in a
contingency table.
Notes
-----
The calculated odds ratio is different from the one R uses. This scipy
implementation returns the (more common) "unconditional Maximum
Likelihood Estimate", while R uses the "conditional Maximum Likelihood
Estimate".
For tables with large numbers, the (inexact) chi-square test implemented
in the function `chi2_contingency` can also be used.
Examples
--------
Say we spend a few days counting whales and sharks in the Atlantic and
Indian oceans. In the Atlantic ocean we find 8 whales and 1 shark, in the
Indian ocean 2 whales and 5 sharks. Then our contingency table is::
Atlantic Indian
whales 8 2
sharks 1 5
We use this table to find the p-value:
>>> import scipy.stats as stats
>>> oddsratio, pvalue = stats.fisher_exact([[8, 2], [1, 5]])
>>> pvalue
0.0349...
The probability that we would observe this or an even more imbalanced ratio
by chance is about 3.5%. A commonly used significance level is 5%--if we
adopt that, we can therefore conclude that our observed imbalance is
statistically significant; whales prefer the Atlantic while sharks prefer
the Indian ocean.
"""
hypergeom = distributions.hypergeom
c = np.asarray(table, dtype=np.int64) # int32 is not enough for the algorithm
if not c.shape == (2, 2):
raise ValueError("The input `table` must be of shape (2, 2).")
if np.any(c < 0):
raise ValueError("All values in `table` must be nonnegative.")
if 0 in c.sum(axis=0) or 0 in c.sum(axis=1):
# If both values in a row or column are zero, the p-value is 1 and
# the odds ratio is NaN.
return np.nan, 1.0
if c[1,0] > 0 and c[0,1] > 0:
oddsratio = c[0,0] * c[1,1] / float(c[1,0] * c[0,1])
else:
oddsratio = np.inf
n1 = c[0,0] + c[0,1]
n2 = c[1,0] + c[1,1]
n = c[0,0] + c[1,0]
def binary_search(n, n1, n2, side):
"""Binary search for where to begin lower/upper halves in two-sided
test.
"""
if side == "upper":
minval = mode
maxval = n
else:
minval = 0
maxval = mode
guess = -1
while maxval - minval > 1:
if maxval == minval + 1 and guess == minval:
guess = maxval
else:
guess = (maxval + minval) // 2
pguess = hypergeom.pmf(guess, n1 + n2, n1, n)
if side == "upper":
ng = guess - 1
else:
ng = guess + 1
if pguess <= pexact < hypergeom.pmf(ng, n1 + n2, n1, n):
break
elif pguess < pexact:
maxval = guess
else:
minval = guess
if guess == -1:
guess = minval
if side == "upper":
while guess > 0 and hypergeom.pmf(guess, n1 + n2, n1, n) < pexact * epsilon:
guess -= 1
while hypergeom.pmf(guess, n1 + n2, n1, n) > pexact / epsilon:
guess += 1
else:
while hypergeom.pmf(guess, n1 + n2, n1, n) < pexact * epsilon:
guess += 1
while guess > 0 and hypergeom.pmf(guess, n1 + n2, n1, n) > pexact / epsilon:
guess -= 1
return guess
if alternative == 'less':
pvalue = hypergeom.cdf(c[0,0], n1 + n2, n1, n)
elif alternative == 'greater':
# Same formula as the 'less' case, but with the second column.
pvalue = hypergeom.cdf(c[0,1], n1 + n2, n1, c[0,1] + c[1,1])
elif alternative == 'two-sided':
mode = int(float((n + 1) * (n1 + 1)) / (n1 + n2 + 2))
pexact = hypergeom.pmf(c[0,0], n1 + n2, n1, n)
pmode = hypergeom.pmf(mode, n1 + n2, n1, n)
epsilon = 1 - 1e-4
if np.abs(pexact - pmode) / np.maximum(pexact, pmode) <= 1 - epsilon:
return oddsratio, 1.
elif c[0,0] < mode:
plower = hypergeom.cdf(c[0,0], n1 + n2, n1, n)
if hypergeom.pmf(n, n1 + n2, n1, n) > pexact / epsilon:
return oddsratio, plower
guess = binary_search(n, n1, n2, "upper")
pvalue = plower + hypergeom.sf(guess - 1, n1 + n2, n1, n)
else:
pupper = hypergeom.sf(c[0,0] - 1, n1 + n2, n1, n)
if hypergeom.pmf(0, n1 + n2, n1, n) > pexact / epsilon:
return oddsratio, pupper
guess = binary_search(n, n1, n2, "lower")
pvalue = pupper + hypergeom.cdf(guess, n1 + n2, n1, n)
else:
msg = "`alternative` should be one of {'two-sided', 'less', 'greater'}"
raise ValueError(msg)
if pvalue > 1.0:
pvalue = 1.0
return oddsratio, pvalue
SpearmanrResult = namedtuple('SpearmanrResult', ('correlation', 'pvalue'))
def spearmanr(a, b=None, axis=0, nan_policy='propagate'):
"""
Calculates a Spearman rank-order correlation coefficient and the p-value
to test for non-correlation.
The Spearman correlation is a nonparametric measure of the monotonicity
of the relationship between two datasets. Unlike the Pearson correlation,
the Spearman correlation does not assume that both datasets are normally
distributed. Like other correlation coefficients, this one varies
between -1 and +1 with 0 implying no correlation. Correlations of -1 or
+1 imply an exact monotonic relationship. Positive correlations imply that
as x increases, so does y. Negative correlations imply that as x
increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
Parameters
----------
a, b : 1D or 2D array_like, b is optional
One or two 1-D or 2-D arrays containing multiple variables and
observations. When these are 1-D, each represents a vector of
observations of a single variable. For the behavior in the 2-D case,
see under ``axis``, below.
Both arrays need to have the same length in the ``axis`` dimension.
axis : int or None, optional
If axis=0 (default), then each column represents a variable, with
observations in the rows. If axis=1, the relationship is transposed:
each row represents a variable, while the columns contain observations.
If axis=None, then both arrays will be raveled.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
correlation : float or ndarray (2-D square)
Spearman correlation matrix or correlation coefficient (if only 2
variables are given as parameters. Correlation matrix is square with
length equal to total number of variables (columns or rows) in a and b
combined.
pvalue : float
The two-sided p-value for a hypothesis test whose null hypothesis is
that two sets of data are uncorrelated, has same dimension as rho.
Notes
-----
Changes in scipy 0.8.0: rewrite to add tie-handling, and axis.
References
----------
.. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
Probability and Statistics Tables and Formulae. Chapman & Hall: New
York. 2000.
Section 14.7
Examples
--------
>>> from scipy import stats
>>> stats.spearmanr([1,2,3,4,5], [5,6,7,8,7])
(0.82078268166812329, 0.088587005313543798)
>>> np.random.seed(1234321)
>>> x2n = np.random.randn(100, 2)
>>> y2n = np.random.randn(100, 2)
>>> stats.spearmanr(x2n)
(0.059969996999699973, 0.55338590803773591)
>>> stats.spearmanr(x2n[:,0], x2n[:,1])
(0.059969996999699973, 0.55338590803773591)
>>> rho, pval = stats.spearmanr(x2n, y2n)
>>> rho
array([[ 1. , 0.05997 , 0.18569457, 0.06258626],
[ 0.05997 , 1. , 0.110003 , 0.02534653],
[ 0.18569457, 0.110003 , 1. , 0.03488749],
[ 0.06258626, 0.02534653, 0.03488749, 1. ]])
>>> pval
array([[ 0. , 0.55338591, 0.06435364, 0.53617935],
[ 0.55338591, 0. , 0.27592895, 0.80234077],
[ 0.06435364, 0.27592895, 0. , 0.73039992],
[ 0.53617935, 0.80234077, 0.73039992, 0. ]])
>>> rho, pval = stats.spearmanr(x2n.T, y2n.T, axis=1)
>>> rho
array([[ 1. , 0.05997 , 0.18569457, 0.06258626],
[ 0.05997 , 1. , 0.110003 , 0.02534653],
[ 0.18569457, 0.110003 , 1. , 0.03488749],
[ 0.06258626, 0.02534653, 0.03488749, 1. ]])
>>> stats.spearmanr(x2n, y2n, axis=None)
(0.10816770419260482, 0.1273562188027364)
>>> stats.spearmanr(x2n.ravel(), y2n.ravel())
(0.10816770419260482, 0.1273562188027364)
>>> xint = np.random.randint(10, size=(100, 2))
>>> stats.spearmanr(xint)
(0.052760927029710199, 0.60213045837062351)
"""
a, axisout = _chk_asarray(a, axis)
a_contains_nan, nan_policy = _contains_nan(a, nan_policy)
if a_contains_nan:
a = ma.masked_invalid(a)
if a.size <= 1:
return SpearmanrResult(np.nan, np.nan)
ar = np.apply_along_axis(rankdata, axisout, a)
br = None
if b is not None:
b, axisout = _chk_asarray(b, axis)
b_contains_nan, nan_policy = _contains_nan(b, nan_policy)
if a_contains_nan or b_contains_nan:
b = ma.masked_invalid(b)
if nan_policy == 'propagate':
rho, pval = mstats_basic.spearmanr(a, b, axis)
return SpearmanrResult(rho * np.nan, pval * np.nan)
if nan_policy == 'omit':
return mstats_basic.spearmanr(a, b, axis)
br = np.apply_along_axis(rankdata, axisout, b)
n = a.shape[axisout]
rs = np.corrcoef(ar, br, rowvar=axisout)
olderr = np.seterr(divide='ignore') # rs can have elements equal to 1
try:
# clip the small negative values possibly caused by rounding
# errors before taking the square root
t = rs * np.sqrt(((n-2)/((rs+1.0)*(1.0-rs))).clip(0))
finally:
np.seterr(**olderr)
prob = 2 * distributions.t.sf(np.abs(t), n-2)
if rs.shape == (2, 2):
return SpearmanrResult(rs[1, 0], prob[1, 0])
else:
return SpearmanrResult(rs, prob)
PointbiserialrResult = namedtuple('PointbiserialrResult',
('correlation', 'pvalue'))
def pointbiserialr(x, y):
r"""
Calculates a point biserial correlation coefficient and its p-value.
The point biserial correlation is used to measure the relationship
between a binary variable, x, and a continuous variable, y. Like other
correlation coefficients, this one varies between -1 and +1 with 0
implying no correlation. Correlations of -1 or +1 imply a determinative
relationship.
This function uses a shortcut formula but produces the same result as
`pearsonr`.
Parameters
----------
x : array_like of bools
Input array.
y : array_like
Input array.
Returns
-------
correlation : float
R value
pvalue : float
2-tailed p-value
Notes
-----
`pointbiserialr` uses a t-test with ``n-1`` degrees of freedom.
It is equivalent to `pearsonr.`
The value of the point-biserial correlation can be calculated from:
.. math::
r_{pb} = \frac{\overline{Y_{1}} -
\overline{Y_{0}}}{s_{y}}\sqrt{\frac{N_{1} N_{2}}{N (N - 1))}}
Where :math:`Y_{0}` and :math:`Y_{1}` are means of the metric
observations coded 0 and 1 respectively; :math:`N_{0}` and :math:`N_{1}`
are number of observations coded 0 and 1 respectively; :math:`N` is the
total number of observations and :math:`s_{y}` is the standard
deviation of all the metric observations.
A value of :math:`r_{pb}` that is significantly different from zero is
completely equivalent to a significant difference in means between the two
groups. Thus, an independent groups t Test with :math:`N-2` degrees of
freedom may be used to test whether :math:`r_{pb}` is nonzero. The
relation between the t-statistic for comparing two independent groups and
:math:`r_{pb}` is given by:
.. math::
t = \sqrt{N - 2}\frac{r_{pb}}{\sqrt{1 - r^{2}_{pb}}}
References
----------
.. [1] J. Lev, "The Point Biserial Coefficient of Correlation", Ann. Math.
Statist., Vol. 20, no.1, pp. 125-126, 1949.
.. [2] R.F. Tate, "Correlation Between a Discrete and a Continuous
Variable. Point-Biserial Correlation.", Ann. Math. Statist., Vol. 25,
np. 3, pp. 603-607, 1954.
.. [3] http://onlinelibrary.wiley.com/doi/10.1002/9781118445112.stat06227/full
Examples
--------
>>> from scipy import stats
>>> a = np.array([0, 0, 0, 1, 1, 1, 1])
>>> b = np.arange(7)
>>> stats.pointbiserialr(a, b)
(0.8660254037844386, 0.011724811003954652)
>>> stats.pearsonr(a, b)
(0.86602540378443871, 0.011724811003954626)
>>> np.corrcoef(a, b)
array([[ 1. , 0.8660254],
[ 0.8660254, 1. ]])
"""
rpb, prob = pearsonr(x, y)
return PointbiserialrResult(rpb, prob)
KendalltauResult = namedtuple('KendalltauResult', ('correlation', 'pvalue'))
def kendalltau(x, y, initial_lexsort=None, nan_policy='propagate'):
"""
Calculates Kendall's tau, a correlation measure for ordinal data.
Kendall's tau is a measure of the correspondence between two rankings.
Values close to 1 indicate strong agreement, values close to -1 indicate
strong disagreement. This is the 1945 "tau-b" version of Kendall's
tau [2]_, which can account for ties and which reduces to the 1938 "tau-a"
version [1]_ in absence of ties.
Parameters
----------
x, y : array_like
Arrays of rankings, of the same shape. If arrays are not 1-D, they will
be flattened to 1-D.
initial_lexsort : bool, optional
Unused (deprecated).
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'. Note that if the input contains nan
'omit' delegates to mstats_basic.kendalltau(), which has a different
implementation.
Returns
-------
correlation : float
The tau statistic.
pvalue : float
The two-sided p-value for a hypothesis test whose null hypothesis is
an absence of association, tau = 0.
See also
--------
spearmanr : Calculates a Spearman rank-order correlation coefficient.
theilslopes : Computes the Theil-Sen estimator for a set of points (x, y).
weightedtau : Computes a weighted version of Kendall's tau.
Notes
-----
The definition of Kendall's tau that is used is [2]_::
tau = (P - Q) / sqrt((P + Q + T) * (P + Q + U))
where P is the number of concordant pairs, Q the number of discordant
pairs, T the number of ties only in `x`, and U the number of ties only in
`y`. If a tie occurs for the same pair in both `x` and `y`, it is not
added to either T or U.
References
----------
.. [1] Maurice G. Kendall, "A New Measure of Rank Correlation", Biometrika
Vol. 30, No. 1/2, pp. 81-93, 1938.
.. [2] Maurice G. Kendall, "The treatment of ties in ranking problems",
Biometrika Vol. 33, No. 3, pp. 239-251. 1945.
.. [3] Gottfried E. Noether, "Elements of Nonparametric Statistics", John
Wiley & Sons, 1967.
.. [4] Peter M. Fenwick, "A new data structure for cumulative frequency
tables", Software: Practice and Experience, Vol. 24, No. 3,
pp. 327-336, 1994.
Examples
--------
>>> from scipy import stats
>>> x1 = [12, 2, 1, 12, 2]
>>> x2 = [1, 4, 7, 1, 0]
>>> tau, p_value = stats.kendalltau(x1, x2)
>>> tau
-0.47140452079103173
>>> p_value
0.2827454599327748
"""
x = np.asarray(x).ravel()
y = np.asarray(y).ravel()
if x.size != y.size:
raise ValueError("All inputs to `kendalltau` must be of the same size, "
"found x-size %s and y-size %s" % (x.size, y.size))
elif not x.size or not y.size:
return KendalltauResult(np.nan, np.nan) # Return NaN if arrays are empty
# check both x and y
cnx, npx = _contains_nan(x, nan_policy)
cny, npy = _contains_nan(y, nan_policy)
contains_nan = cnx or cny
if npx == 'omit' or npy == 'omit':
nan_policy = 'omit'
if contains_nan and nan_policy == 'propagate':
return KendalltauResult(np.nan, np.nan)
elif contains_nan and nan_policy == 'omit':
x = ma.masked_invalid(x)
y = ma.masked_invalid(y)
return mstats_basic.kendalltau(x, y)
if initial_lexsort is not None: # deprecate to drop!
warnings.warn('"initial_lexsort" is gone!')
def count_rank_tie(ranks):
cnt = np.bincount(ranks).astype('int64', copy=False)
cnt = cnt[cnt > 1]
return ((cnt * (cnt - 1) // 2).sum(),
(cnt * (cnt - 1.) * (cnt - 2)).sum(),
(cnt * (cnt - 1.) * (2*cnt + 5)).sum())
size = x.size
perm = np.argsort(y) # sort on y and convert y to dense ranks
x, y = x[perm], y[perm]
y = np.r_[True, y[1:] != y[:-1]].cumsum(dtype=np.intp)
# stable sort on x and convert x to dense ranks
perm = np.argsort(x, kind='mergesort')
x, y = x[perm], y[perm]
x = np.r_[True, x[1:] != x[:-1]].cumsum(dtype=np.intp)
dis = _kendall_dis(x, y) # discordant pairs
obs = np.r_[True, (x[1:] != x[:-1]) | (y[1:] != y[:-1]), True]
cnt = np.diff(np.where(obs)[0]).astype('int64', copy=False)
ntie = (cnt * (cnt - 1) // 2).sum() # joint ties
xtie, x0, x1 = count_rank_tie(x) # ties in x, stats
ytie, y0, y1 = count_rank_tie(y) # ties in y, stats
tot = (size * (size - 1)) // 2
if xtie == tot or ytie == tot:
return KendalltauResult(np.nan, np.nan)
# Note that tot = con + dis + (xtie - ntie) + (ytie - ntie) + ntie
# = con + dis + xtie + ytie - ntie
con_minus_dis = tot - xtie - ytie + ntie - 2 * dis
tau = con_minus_dis / np.sqrt(tot - xtie) / np.sqrt(tot - ytie)
# Limit range to fix computational errors
tau = min(1., max(-1., tau))
# con_minus_dis is approx normally distributed with this variance [3]_
var = (size * (size - 1) * (2.*size + 5) - x1 - y1) / 18. + (
2. * xtie * ytie) / (size * (size - 1)) + x0 * y0 / (9. *
size * (size - 1) * (size - 2))
pvalue = special.erfc(np.abs(con_minus_dis) / np.sqrt(var) / np.sqrt(2))
# Limit range to fix computational errors
return KendalltauResult(min(1., max(-1., tau)), pvalue)
WeightedTauResult = namedtuple('WeightedTauResult', ('correlation', 'pvalue'))
def weightedtau(x, y, rank=True, weigher=None, additive=True):
r"""
Computes a weighted version of Kendall's :math:`\tau`.
The weighted :math:`\tau` is a weighted version of Kendall's
:math:`\tau` in which exchanges of high weight are more influential than
exchanges of low weight. The default parameters compute the additive
hyperbolic version of the index, :math:`\tau_\mathrm h`, which has
been shown to provide the best balance between important and
unimportant elements [1]_.
The weighting is defined by means of a rank array, which assigns a
nonnegative rank to each element, and a weigher function, which
assigns a weight based from the rank to each element. The weight of an
exchange is then the sum or the product of the weights of the ranks of
the exchanged elements. The default parameters compute
:math:`\tau_\mathrm h`: an exchange between elements with rank
:math:`r` and :math:`s` (starting from zero) has weight
:math:`1/(r+1) + 1/(s+1)`.
Specifying a rank array is meaningful only if you have in mind an
external criterion of importance. If, as it usually happens, you do
not have in mind a specific rank, the weighted :math:`\tau` is
defined by averaging the values obtained using the decreasing
lexicographical rank by (`x`, `y`) and by (`y`, `x`). This is the
behavior with default parameters.
Note that if you are computing the weighted :math:`\tau` on arrays of
ranks, rather than of scores (i.e., a larger value implies a lower
rank) you must negate the ranks, so that elements of higher rank are
associated with a larger value.
Parameters
----------
x, y : array_like
Arrays of scores, of the same shape. If arrays are not 1-D, they will
be flattened to 1-D.
rank: array_like of ints or bool, optional
A nonnegative rank assigned to each element. If it is None, the
decreasing lexicographical rank by (`x`, `y`) will be used: elements of
higher rank will be those with larger `x`-values, using `y`-values to
break ties (in particular, swapping `x` and `y` will give a different
result). If it is False, the element indices will be used
directly as ranks. The default is True, in which case this
function returns the average of the values obtained using the
decreasing lexicographical rank by (`x`, `y`) and by (`y`, `x`).
weigher : callable, optional
The weigher function. Must map nonnegative integers (zero
representing the most important element) to a nonnegative weight.
The default, None, provides hyperbolic weighing, that is,
rank :math:`r` is mapped to weight :math:`1/(r+1)`.
additive : bool, optional
If True, the weight of an exchange is computed by adding the
weights of the ranks of the exchanged elements; otherwise, the weights
are multiplied. The default is True.
Returns
-------
correlation : float
The weighted :math:`\tau` correlation index.
pvalue : float
Presently ``np.nan``, as the null statistics is unknown (even in the
additive hyperbolic case).
See also
--------
kendalltau : Calculates Kendall's tau.
spearmanr : Calculates a Spearman rank-order correlation coefficient.
theilslopes : Computes the Theil-Sen estimator for a set of points (x, y).
Notes
-----
This function uses an :math:`O(n \log n)`, mergesort-based algorithm
[1]_ that is a weighted extension of Knight's algorithm for Kendall's
:math:`\tau` [2]_. It can compute Shieh's weighted :math:`\tau` [3]_
between rankings without ties (i.e., permutations) by setting
`additive` and `rank` to False, as the definition given in [1]_ is a
generalization of Shieh's.
NaNs are considered the smallest possible score.
.. versionadded:: 0.19.0
References
----------
.. [1] Sebastiano Vigna, "A weighted correlation index for rankings with
ties", Proceedings of the 24th international conference on World
Wide Web, pp. 1166-1176, ACM, 2015.
.. [2] W.R. Knight, "A Computer Method for Calculating Kendall's Tau with
Ungrouped Data", Journal of the American Statistical Association,
Vol. 61, No. 314, Part 1, pp. 436-439, 1966.
.. [3] Grace S. Shieh. "A weighted Kendall's tau statistic", Statistics &
Probability Letters, Vol. 39, No. 1, pp. 17-24, 1998.
Examples
--------
>>> from scipy import stats
>>> x = [12, 2, 1, 12, 2]
>>> y = [1, 4, 7, 1, 0]
>>> tau, p_value = stats.weightedtau(x, y)
>>> tau
-0.56694968153682723
>>> p_value
nan
>>> tau, p_value = stats.weightedtau(x, y, additive=False)
>>> tau
-0.62205716951801038
NaNs are considered the smallest possible score:
>>> x = [12, 2, 1, 12, 2]
>>> y = [1, 4, 7, 1, np.nan]
>>> tau, _ = stats.weightedtau(x, y)
>>> tau
-0.56694968153682723
This is exactly Kendall's tau:
>>> x = [12, 2, 1, 12, 2]
>>> y = [1, 4, 7, 1, 0]
>>> tau, _ = stats.weightedtau(x, y, weigher=lambda x: 1)
>>> tau
-0.47140452079103173
>>> x = [12, 2, 1, 12, 2]
>>> y = [1, 4, 7, 1, 0]
>>> stats.weightedtau(x, y, rank=None)
WeightedTauResult(correlation=-0.4157652301037516, pvalue=nan)
>>> stats.weightedtau(y, x, rank=None)
WeightedTauResult(correlation=-0.71813413296990281, pvalue=nan)
"""
x = np.asarray(x).ravel()
y = np.asarray(y).ravel()
if x.size != y.size:
raise ValueError("All inputs to `weightedtau` must be of the same size, "
"found x-size %s and y-size %s" % (x.size, y.size))
if not x.size:
return WeightedTauResult(np.nan, np.nan) # Return NaN if arrays are empty
# If there are NaNs we apply _toint64()
if np.isnan(np.min(x)):
x = _toint64(x)
if np.isnan(np.min(y)):
y = _toint64(y)
# Reduce to ranks unsupported types
if x.dtype != y.dtype:
if x.dtype != np.int64:
x = _toint64(x)
if y.dtype != np.int64:
y = _toint64(y)
else:
if x.dtype not in (np.int32, np.int64, np.float32, np.float64):
x = _toint64(x)
y = _toint64(y)
if rank is True:
return WeightedTauResult((
_weightedrankedtau(x, y, None, weigher, additive) +
_weightedrankedtau(y, x, None, weigher, additive)
) / 2, np.nan)
if rank is False:
rank = np.arange(x.size, dtype=np.intp)
elif rank is not None:
rank = np.asarray(rank).ravel()
if rank.size != x.size:
raise ValueError("All inputs to `weightedtau` must be of the same size, "
"found x-size %s and rank-size %s" % (x.size, rank.size))
return WeightedTauResult(_weightedrankedtau(x, y, rank, weigher, additive), np.nan)
#####################################
# INFERENTIAL STATISTICS #
#####################################
Ttest_1sampResult = namedtuple('Ttest_1sampResult', ('statistic', 'pvalue'))
def ttest_1samp(a, popmean, axis=0, nan_policy='propagate'):
"""
Calculates the T-test for the mean of ONE group of scores.
This is a two-sided test for the null hypothesis that the expected value
(mean) of a sample of independent observations `a` is equal to the given
population mean, `popmean`.
Parameters
----------
a : array_like
sample observation
popmean : float or array_like
expected value in null hypothesis, if array_like than it must have the
same shape as `a` excluding the axis dimension
axis : int or None, optional
Axis along which to compute test. If None, compute over the whole
array `a`.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
statistic : float or array
t-statistic
pvalue : float or array
two-tailed p-value
Examples
--------
>>> from scipy import stats
>>> np.random.seed(7654567) # fix seed to get the same result
>>> rvs = stats.norm.rvs(loc=5, scale=10, size=(50,2))
Test if mean of random sample is equal to true mean, and different mean.
We reject the null hypothesis in the second case and don't reject it in
the first case.
>>> stats.ttest_1samp(rvs,5.0)
(array([-0.68014479, -0.04323899]), array([ 0.49961383, 0.96568674]))
>>> stats.ttest_1samp(rvs,0.0)
(array([ 2.77025808, 4.11038784]), array([ 0.00789095, 0.00014999]))
Examples using axis and non-scalar dimension for population mean.
>>> stats.ttest_1samp(rvs,[5.0,0.0])
(array([-0.68014479, 4.11038784]), array([ 4.99613833e-01, 1.49986458e-04]))
>>> stats.ttest_1samp(rvs.T,[5.0,0.0],axis=1)
(array([-0.68014479, 4.11038784]), array([ 4.99613833e-01, 1.49986458e-04]))
>>> stats.ttest_1samp(rvs,[[5.0],[0.0]])
(array([[-0.68014479, -0.04323899],
[ 2.77025808, 4.11038784]]), array([[ 4.99613833e-01, 9.65686743e-01],
[ 7.89094663e-03, 1.49986458e-04]]))
"""
a, axis = _chk_asarray(a, axis)
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.ttest_1samp(a, popmean, axis)
n = a.shape[axis]
df = n - 1
d = np.mean(a, axis) - popmean
v = np.var(a, axis, ddof=1)
denom = np.sqrt(v / float(n))
with np.errstate(divide='ignore', invalid='ignore'):
t = np.divide(d, denom)
t, prob = _ttest_finish(df, t)
return Ttest_1sampResult(t, prob)
def _ttest_finish(df, t):
"""Common code between all 3 t-test functions."""
prob = distributions.t.sf(np.abs(t), df) * 2 # use np.abs to get upper tail
if t.ndim == 0:
t = t[()]
return t, prob
def _ttest_ind_from_stats(mean1, mean2, denom, df):
d = mean1 - mean2
with np.errstate(divide='ignore', invalid='ignore'):
t = np.divide(d, denom)
t, prob = _ttest_finish(df, t)
return (t, prob)
def _unequal_var_ttest_denom(v1, n1, v2, n2):
vn1 = v1 / n1
vn2 = v2 / n2
with np.errstate(divide='ignore', invalid='ignore'):
df = (vn1 + vn2)**2 / (vn1**2 / (n1 - 1) + vn2**2 / (n2 - 1))
# If df is undefined, variances are zero (assumes n1 > 0 & n2 > 0).
# Hence it doesn't matter what df is as long as it's not NaN.
df = np.where(np.isnan(df), 1, df)
denom = np.sqrt(vn1 + vn2)
return df, denom
def _equal_var_ttest_denom(v1, n1, v2, n2):
df = n1 + n2 - 2.0
svar = ((n1 - 1) * v1 + (n2 - 1) * v2) / df
denom = np.sqrt(svar * (1.0 / n1 + 1.0 / n2))
return df, denom
Ttest_indResult = namedtuple('Ttest_indResult', ('statistic', 'pvalue'))
def ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2,
equal_var=True):
"""
T-test for means of two independent samples from descriptive statistics.
This is a two-sided test for the null hypothesis that 2 independent samples
have identical average (expected) values.
Parameters
----------
mean1 : array_like
The mean(s) of sample 1.
std1 : array_like
The standard deviation(s) of sample 1.
nobs1 : array_like
The number(s) of observations of sample 1.
mean2 : array_like
The mean(s) of sample 2
std2 : array_like
The standard deviations(s) of sample 2.
nobs2 : array_like
The number(s) of observations of sample 2.
equal_var : bool, optional
If True (default), perform a standard independent 2 sample test
that assumes equal population variances [1]_.
If False, perform Welch's t-test, which does not assume equal
population variance [2]_.
Returns
-------
statistic : float or array
The calculated t-statistics
pvalue : float or array
The two-tailed p-value.
See also
--------
scipy.stats.ttest_ind
Notes
-----
.. versionadded:: 0.16.0
References
----------
.. [1] http://en.wikipedia.org/wiki/T-test#Independent_two-sample_t-test
.. [2] http://en.wikipedia.org/wiki/Welch%27s_t_test
"""
if equal_var:
df, denom = _equal_var_ttest_denom(std1**2, nobs1, std2**2, nobs2)
else:
df, denom = _unequal_var_ttest_denom(std1**2, nobs1,
std2**2, nobs2)
res = _ttest_ind_from_stats(mean1, mean2, denom, df)
return Ttest_indResult(*res)
def ttest_ind(a, b, axis=0, equal_var=True, nan_policy='propagate'):
"""
Calculates the T-test for the means of *two independent* samples of scores.
This is a two-sided test for the null hypothesis that 2 independent samples
have identical average (expected) values. This test assumes that the
populations have identical variances by default.
Parameters
----------
a, b : array_like
The arrays must have the same shape, except in the dimension
corresponding to `axis` (the first, by default).
axis : int or None, optional
Axis along which to compute test. If None, compute over the whole
arrays, `a`, and `b`.
equal_var : bool, optional
If True (default), perform a standard independent 2 sample test
that assumes equal population variances [1]_.
If False, perform Welch's t-test, which does not assume equal
population variance [2]_.
.. versionadded:: 0.11.0
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
statistic : float or array
The calculated t-statistic.
pvalue : float or array
The two-tailed p-value.
Notes
-----
We can use this test, if we observe two independent samples from
the same or different population, e.g. exam scores of boys and
girls or of two ethnic groups. The test measures whether the
average (expected) value differs significantly across samples. If
we observe a large p-value, for example larger than 0.05 or 0.1,
then we cannot reject the null hypothesis of identical average scores.
If the p-value is smaller than the threshold, e.g. 1%, 5% or 10%,
then we reject the null hypothesis of equal averages.
References
----------
.. [1] http://en.wikipedia.org/wiki/T-test#Independent_two-sample_t-test
.. [2] http://en.wikipedia.org/wiki/Welch%27s_t_test
Examples
--------
>>> from scipy import stats
>>> np.random.seed(12345678)
Test with sample with identical means:
>>> rvs1 = stats.norm.rvs(loc=5,scale=10,size=500)
>>> rvs2 = stats.norm.rvs(loc=5,scale=10,size=500)
>>> stats.ttest_ind(rvs1,rvs2)
(0.26833823296239279, 0.78849443369564776)
>>> stats.ttest_ind(rvs1,rvs2, equal_var = False)
(0.26833823296239279, 0.78849452749500748)
`ttest_ind` underestimates p for unequal variances:
>>> rvs3 = stats.norm.rvs(loc=5, scale=20, size=500)
>>> stats.ttest_ind(rvs1, rvs3)
(-0.46580283298287162, 0.64145827413436174)
>>> stats.ttest_ind(rvs1, rvs3, equal_var = False)
(-0.46580283298287162, 0.64149646246569292)
When n1 != n2, the equal variance t-statistic is no longer equal to the
unequal variance t-statistic:
>>> rvs4 = stats.norm.rvs(loc=5, scale=20, size=100)
>>> stats.ttest_ind(rvs1, rvs4)
(-0.99882539442782481, 0.3182832709103896)
>>> stats.ttest_ind(rvs1, rvs4, equal_var = False)
(-0.69712570584654099, 0.48716927725402048)
T-test with different means, variance, and n:
>>> rvs5 = stats.norm.rvs(loc=8, scale=20, size=100)
>>> stats.ttest_ind(rvs1, rvs5)
(-1.4679669854490653, 0.14263895620529152)
>>> stats.ttest_ind(rvs1, rvs5, equal_var = False)
(-0.94365973617132992, 0.34744170334794122)
"""
a, b, axis = _chk2_asarray(a, b, axis)
# check both a and b
cna, npa = _contains_nan(a, nan_policy)
cnb, npb = _contains_nan(b, nan_policy)
contains_nan = cna or cnb
if npa == 'omit' or npb == 'omit':
nan_policy = 'omit'
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
b = ma.masked_invalid(b)
return mstats_basic.ttest_ind(a, b, axis, equal_var)
if a.size == 0 or b.size == 0:
return Ttest_indResult(np.nan, np.nan)
v1 = np.var(a, axis, ddof=1)
v2 = np.var(b, axis, ddof=1)
n1 = a.shape[axis]
n2 = b.shape[axis]
if equal_var:
df, denom = _equal_var_ttest_denom(v1, n1, v2, n2)
else:
df, denom = _unequal_var_ttest_denom(v1, n1, v2, n2)
res = _ttest_ind_from_stats(np.mean(a, axis), np.mean(b, axis), denom, df)
return Ttest_indResult(*res)
Ttest_relResult = namedtuple('Ttest_relResult', ('statistic', 'pvalue'))
def ttest_rel(a, b, axis=0, nan_policy='propagate'):
"""
Calculates the T-test on TWO RELATED samples of scores, a and b.
This is a two-sided test for the null hypothesis that 2 related or
repeated samples have identical average (expected) values.
Parameters
----------
a, b : array_like
The arrays must have the same shape.
axis : int or None, optional
Axis along which to compute test. If None, compute over the whole
arrays, `a`, and `b`.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
statistic : float or array
t-statistic
pvalue : float or array
two-tailed p-value
Notes
-----
Examples for the use are scores of the same set of student in
different exams, or repeated sampling from the same units. The
test measures whether the average score differs significantly
across samples (e.g. exams). If we observe a large p-value, for
example greater than 0.05 or 0.1 then we cannot reject the null
hypothesis of identical average scores. If the p-value is smaller
than the threshold, e.g. 1%, 5% or 10%, then we reject the null
hypothesis of equal averages. Small p-values are associated with
large t-statistics.
References
----------
http://en.wikipedia.org/wiki/T-test#Dependent_t-test
Examples
--------
>>> from scipy import stats
>>> np.random.seed(12345678) # fix random seed to get same numbers
>>> rvs1 = stats.norm.rvs(loc=5,scale=10,size=500)
>>> rvs2 = (stats.norm.rvs(loc=5,scale=10,size=500) +
... stats.norm.rvs(scale=0.2,size=500))
>>> stats.ttest_rel(rvs1,rvs2)
(0.24101764965300962, 0.80964043445811562)
>>> rvs3 = (stats.norm.rvs(loc=8,scale=10,size=500) +
... stats.norm.rvs(scale=0.2,size=500))
>>> stats.ttest_rel(rvs1,rvs3)
(-3.9995108708727933, 7.3082402191726459e-005)
"""
a, b, axis = _chk2_asarray(a, b, axis)
cna, npa = _contains_nan(a, nan_policy)
cnb, npb = _contains_nan(b, nan_policy)
contains_nan = cna or cnb
if npa == 'omit' or npb == 'omit':
nan_policy = 'omit'
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
b = ma.masked_invalid(b)
m = ma.mask_or(ma.getmask(a), ma.getmask(b))
aa = ma.array(a, mask=m, copy=True)
bb = ma.array(b, mask=m, copy=True)
return mstats_basic.ttest_rel(aa, bb, axis)
if a.shape[axis] != b.shape[axis]:
raise ValueError('unequal length arrays')
if a.size == 0 or b.size == 0:
return np.nan, np.nan
n = a.shape[axis]
df = float(n - 1)
d = (a - b).astype(np.float64)
v = np.var(d, axis, ddof=1)
dm = np.mean(d, axis)
denom = np.sqrt(v / float(n))
with np.errstate(divide='ignore', invalid='ignore'):
t = np.divide(dm, denom)
t, prob = _ttest_finish(df, t)
return Ttest_relResult(t, prob)
KstestResult = namedtuple('KstestResult', ('statistic', 'pvalue'))
def kstest(rvs, cdf, args=(), N=20, alternative='two-sided', mode='approx'):
"""
Perform the Kolmogorov-Smirnov test for goodness of fit.
This performs a test of the distribution G(x) of an observed
random variable against a given distribution F(x). Under the null
hypothesis the two distributions are identical, G(x)=F(x). The
alternative hypothesis can be either 'two-sided' (default), 'less'
or 'greater'. The KS test is only valid for continuous distributions.
Parameters
----------
rvs : str, array or callable
If a string, it should be the name of a distribution in `scipy.stats`.
If an array, it should be a 1-D array of observations of random
variables.
If a callable, it should be a function to generate random variables;
it is required to have a keyword argument `size`.
cdf : str or callable
If a string, it should be the name of a distribution in `scipy.stats`.
If `rvs` is a string then `cdf` can be False or the same as `rvs`.
If a callable, that callable is used to calculate the cdf.
args : tuple, sequence, optional
Distribution parameters, used if `rvs` or `cdf` are strings.
N : int, optional
Sample size if `rvs` is string or callable. Default is 20.
alternative : {'two-sided', 'less','greater'}, optional
Defines the alternative hypothesis (see explanation above).
Default is 'two-sided'.
mode : 'approx' (default) or 'asymp', optional
Defines the distribution used for calculating the p-value.
- 'approx' : use approximation to exact distribution of test statistic
- 'asymp' : use asymptotic distribution of test statistic
Returns
-------
statistic : float
KS test statistic, either D, D+ or D-.
pvalue : float
One-tailed or two-tailed p-value.
Notes
-----
In the one-sided test, the alternative is that the empirical
cumulative distribution function of the random variable is "less"
or "greater" than the cumulative distribution function F(x) of the
hypothesis, ``G(x)<=F(x)``, resp. ``G(x)>=F(x)``.
Examples
--------
>>> from scipy import stats
>>> x = np.linspace(-15, 15, 9)
>>> stats.kstest(x, 'norm')
(0.44435602715924361, 0.038850142705171065)
>>> np.random.seed(987654321) # set random seed to get the same result
>>> stats.kstest('norm', False, N=100)
(0.058352892479417884, 0.88531190944151261)
The above lines are equivalent to:
>>> np.random.seed(987654321)
>>> stats.kstest(stats.norm.rvs(size=100), 'norm')
(0.058352892479417884, 0.88531190944151261)
*Test against one-sided alternative hypothesis*
Shift distribution to larger values, so that ``cdf_dgp(x) < norm.cdf(x)``:
>>> np.random.seed(987654321)
>>> x = stats.norm.rvs(loc=0.2, size=100)
>>> stats.kstest(x,'norm', alternative = 'less')
(0.12464329735846891, 0.040989164077641749)
Reject equal distribution against alternative hypothesis: less
>>> stats.kstest(x,'norm', alternative = 'greater')
(0.0072115233216311081, 0.98531158590396395)
Don't reject equal distribution against alternative hypothesis: greater
>>> stats.kstest(x,'norm', mode='asymp')
(0.12464329735846891, 0.08944488871182088)
*Testing t distributed random variables against normal distribution*
With 100 degrees of freedom the t distribution looks close to the normal
distribution, and the K-S test does not reject the hypothesis that the
sample came from the normal distribution:
>>> np.random.seed(987654321)
>>> stats.kstest(stats.t.rvs(100,size=100),'norm')
(0.072018929165471257, 0.67630062862479168)
With 3 degrees of freedom the t distribution looks sufficiently different
from the normal distribution, that we can reject the hypothesis that the
sample came from the normal distribution at the 10% level:
>>> np.random.seed(987654321)
>>> stats.kstest(stats.t.rvs(3,size=100),'norm')
(0.131016895759829, 0.058826222555312224)
"""
if isinstance(rvs, string_types):
if (not cdf) or (cdf == rvs):
cdf = getattr(distributions, rvs).cdf
rvs = getattr(distributions, rvs).rvs
else:
raise AttributeError("if rvs is string, cdf has to be the "
"same distribution")
if isinstance(cdf, string_types):
cdf = getattr(distributions, cdf).cdf
if callable(rvs):
kwds = {'size': N}
vals = np.sort(rvs(*args, **kwds))
else:
vals = np.sort(rvs)
N = len(vals)
cdfvals = cdf(vals, *args)
# to not break compatibility with existing code
if alternative == 'two_sided':
alternative = 'two-sided'
if alternative in ['two-sided', 'greater']:
Dplus = (np.arange(1.0, N + 1)/N - cdfvals).max()
if alternative == 'greater':
return KstestResult(Dplus, distributions.ksone.sf(Dplus, N))
if alternative in ['two-sided', 'less']:
Dmin = (cdfvals - np.arange(0.0, N)/N).max()
if alternative == 'less':
return KstestResult(Dmin, distributions.ksone.sf(Dmin, N))
if alternative == 'two-sided':
D = np.max([Dplus, Dmin])
if mode == 'asymp':
return KstestResult(D, distributions.kstwobign.sf(D * np.sqrt(N)))
if mode == 'approx':
pval_two = distributions.kstwobign.sf(D * np.sqrt(N))
if N > 2666 or pval_two > 0.80 - N*0.3/1000:
return KstestResult(D, pval_two)
else:
return KstestResult(D, 2 * distributions.ksone.sf(D, N))
# Map from names to lambda_ values used in power_divergence().
_power_div_lambda_names = {
"pearson": 1,
"log-likelihood": 0,
"freeman-tukey": -0.5,
"mod-log-likelihood": -1,
"neyman": -2,
"cressie-read": 2/3,
}
def _count(a, axis=None):
"""
Count the number of non-masked elements of an array.
This function behaves like np.ma.count(), but is much faster
for ndarrays.
"""
if hasattr(a, 'count'):
num = a.count(axis=axis)
if isinstance(num, np.ndarray) and num.ndim == 0:
# In some cases, the `count` method returns a scalar array (e.g.
# np.array(3)), but we want a plain integer.
num = int(num)
else:
if axis is None:
num = a.size
else:
num = a.shape[axis]
return num
Power_divergenceResult = namedtuple('Power_divergenceResult',
('statistic', 'pvalue'))
def power_divergence(f_obs, f_exp=None, ddof=0, axis=0, lambda_=None):
"""
Cressie-Read power divergence statistic and goodness of fit test.
This function tests the null hypothesis that the categorical data
has the given frequencies, using the Cressie-Read power divergence
statistic.
Parameters
----------
f_obs : array_like
Observed frequencies in each category.
f_exp : array_like, optional
Expected frequencies in each category. By default the categories are
assumed to be equally likely.
ddof : int, optional
"Delta degrees of freedom": adjustment to the degrees of freedom
for the p-value. The p-value is computed using a chi-squared
distribution with ``k - 1 - ddof`` degrees of freedom, where `k`
is the number of observed frequencies. The default value of `ddof`
is 0.
axis : int or None, optional
The axis of the broadcast result of `f_obs` and `f_exp` along which to
apply the test. If axis is None, all values in `f_obs` are treated
as a single data set. Default is 0.
lambda_ : float or str, optional
`lambda_` gives the power in the Cressie-Read power divergence
statistic. The default is 1. For convenience, `lambda_` may be
assigned one of the following strings, in which case the
corresponding numerical value is used::
String Value Description
"pearson" 1 Pearson's chi-squared statistic.
In this case, the function is
equivalent to `stats.chisquare`.
"log-likelihood" 0 Log-likelihood ratio. Also known as
the G-test [3]_.
"freeman-tukey" -1/2 Freeman-Tukey statistic.
"mod-log-likelihood" -1 Modified log-likelihood ratio.
"neyman" -2 Neyman's statistic.
"cressie-read" 2/3 The power recommended in [5]_.
Returns
-------
statistic : float or ndarray
The Cressie-Read power divergence test statistic. The value is
a float if `axis` is None or if` `f_obs` and `f_exp` are 1-D.
pvalue : float or ndarray
The p-value of the test. The value is a float if `ddof` and the
return value `stat` are scalars.
See Also
--------
chisquare
Notes
-----
This test is invalid when the observed or expected frequencies in each
category are too small. A typical rule is that all of the observed
and expected frequencies should be at least 5.
When `lambda_` is less than zero, the formula for the statistic involves
dividing by `f_obs`, so a warning or error may be generated if any value
in `f_obs` is 0.
Similarly, a warning or error may be generated if any value in `f_exp` is
zero when `lambda_` >= 0.
The default degrees of freedom, k-1, are for the case when no parameters
of the distribution are estimated. If p parameters are estimated by
efficient maximum likelihood then the correct degrees of freedom are
k-1-p. If the parameters are estimated in a different way, then the
dof can be between k-1-p and k-1. However, it is also possible that
the asymptotic distribution is not a chisquare, in which case this
test is not appropriate.
This function handles masked arrays. If an element of `f_obs` or `f_exp`
is masked, then data at that position is ignored, and does not count
towards the size of the data set.
.. versionadded:: 0.13.0
References
----------
.. [1] Lowry, Richard. "Concepts and Applications of Inferential
Statistics". Chapter 8. http://faculty.vassar.edu/lowry/ch8pt1.html
.. [2] "Chi-squared test", http://en.wikipedia.org/wiki/Chi-squared_test
.. [3] "G-test", http://en.wikipedia.org/wiki/G-test
.. [4] Sokal, R. R. and Rohlf, F. J. "Biometry: the principles and
practice of statistics in biological research", New York: Freeman
(1981)
.. [5] Cressie, N. and Read, T. R. C., "Multinomial Goodness-of-Fit
Tests", J. Royal Stat. Soc. Series B, Vol. 46, No. 3 (1984),
pp. 440-464.
Examples
--------
(See `chisquare` for more examples.)
When just `f_obs` is given, it is assumed that the expected frequencies
are uniform and given by the mean of the observed frequencies. Here we
perform a G-test (i.e. use the log-likelihood ratio statistic):
>>> from scipy.stats import power_divergence
>>> power_divergence([16, 18, 16, 14, 12, 12], lambda_='log-likelihood')
(2.006573162632538, 0.84823476779463769)
The expected frequencies can be given with the `f_exp` argument:
>>> power_divergence([16, 18, 16, 14, 12, 12],
... f_exp=[16, 16, 16, 16, 16, 8],
... lambda_='log-likelihood')
(3.3281031458963746, 0.6495419288047497)
When `f_obs` is 2-D, by default the test is applied to each column.
>>> obs = np.array([[16, 18, 16, 14, 12, 12], [32, 24, 16, 28, 20, 24]]).T
>>> obs.shape
(6, 2)
>>> power_divergence(obs, lambda_="log-likelihood")
(array([ 2.00657316, 6.77634498]), array([ 0.84823477, 0.23781225]))
By setting ``axis=None``, the test is applied to all data in the array,
which is equivalent to applying the test to the flattened array.
>>> power_divergence(obs, axis=None)
(23.31034482758621, 0.015975692534127565)
>>> power_divergence(obs.ravel())
(23.31034482758621, 0.015975692534127565)
`ddof` is the change to make to the default degrees of freedom.
>>> power_divergence([16, 18, 16, 14, 12, 12], ddof=1)
(2.0, 0.73575888234288467)
The calculation of the p-values is done by broadcasting the
test statistic with `ddof`.
>>> power_divergence([16, 18, 16, 14, 12, 12], ddof=[0,1,2])
(2.0, array([ 0.84914504, 0.73575888, 0.5724067 ]))
`f_obs` and `f_exp` are also broadcast. In the following, `f_obs` has
shape (6,) and `f_exp` has shape (2, 6), so the result of broadcasting
`f_obs` and `f_exp` has shape (2, 6). To compute the desired chi-squared
statistics, we must use ``axis=1``:
>>> power_divergence([16, 18, 16, 14, 12, 12],
... f_exp=[[16, 16, 16, 16, 16, 8],
... [8, 20, 20, 16, 12, 12]],
... axis=1)
(array([ 3.5 , 9.25]), array([ 0.62338763, 0.09949846]))
"""
# Convert the input argument `lambda_` to a numerical value.
if isinstance(lambda_, string_types):
if lambda_ not in _power_div_lambda_names:
names = repr(list(_power_div_lambda_names.keys()))[1:-1]
raise ValueError("invalid string for lambda_: {0!r}. Valid strings "
"are {1}".format(lambda_, names))
lambda_ = _power_div_lambda_names[lambda_]
elif lambda_ is None:
lambda_ = 1
f_obs = np.asanyarray(f_obs)
if f_exp is not None:
f_exp = np.atleast_1d(np.asanyarray(f_exp))
else:
# Compute the equivalent of
# f_exp = f_obs.mean(axis=axis, keepdims=True)
# Older versions of numpy do not have the 'keepdims' argument, so
# we have to do a little work to achieve the same result.
# Ignore 'invalid' errors so the edge case of a data set with length 0
# is handled without spurious warnings.
with np.errstate(invalid='ignore'):
f_exp = np.atleast_1d(f_obs.mean(axis=axis))
if axis is not None:
reduced_shape = list(f_obs.shape)
reduced_shape[axis] = 1
f_exp.shape = reduced_shape
# `terms` is the array of terms that are summed along `axis` to create
# the test statistic. We use some specialized code for a few special
# cases of lambda_.
if lambda_ == 1:
# Pearson's chi-squared statistic
terms = (f_obs - f_exp)**2 / f_exp
elif lambda_ == 0:
# Log-likelihood ratio (i.e. G-test)
terms = 2.0 * special.xlogy(f_obs, f_obs / f_exp)
elif lambda_ == -1:
# Modified log-likelihood ratio
terms = 2.0 * special.xlogy(f_exp, f_exp / f_obs)
else:
# General Cressie-Read power divergence.
terms = f_obs * ((f_obs / f_exp)**lambda_ - 1)
terms /= 0.5 * lambda_ * (lambda_ + 1)
stat = terms.sum(axis=axis)
num_obs = _count(terms, axis=axis)
ddof = asarray(ddof)
p = distributions.chi2.sf(stat, num_obs - 1 - ddof)
return Power_divergenceResult(stat, p)
def chisquare(f_obs, f_exp=None, ddof=0, axis=0):
"""
Calculates a one-way chi square test.
The chi square test tests the null hypothesis that the categorical data
has the given frequencies.
Parameters
----------
f_obs : array_like
Observed frequencies in each category.
f_exp : array_like, optional
Expected frequencies in each category. By default the categories are
assumed to be equally likely.
ddof : int, optional
"Delta degrees of freedom": adjustment to the degrees of freedom
for the p-value. The p-value is computed using a chi-squared
distribution with ``k - 1 - ddof`` degrees of freedom, where `k`
is the number of observed frequencies. The default value of `ddof`
is 0.
axis : int or None, optional
The axis of the broadcast result of `f_obs` and `f_exp` along which to
apply the test. If axis is None, all values in `f_obs` are treated
as a single data set. Default is 0.
Returns
-------
chisq : float or ndarray
The chi-squared test statistic. The value is a float if `axis` is
None or `f_obs` and `f_exp` are 1-D.
p : float or ndarray
The p-value of the test. The value is a float if `ddof` and the
return value `chisq` are scalars.
See Also
--------
power_divergence
mstats.chisquare
Notes
-----
This test is invalid when the observed or expected frequencies in each
category are too small. A typical rule is that all of the observed
and expected frequencies should be at least 5.
The default degrees of freedom, k-1, are for the case when no parameters
of the distribution are estimated. If p parameters are estimated by
efficient maximum likelihood then the correct degrees of freedom are
k-1-p. If the parameters are estimated in a different way, then the
dof can be between k-1-p and k-1. However, it is also possible that
the asymptotic distribution is not a chisquare, in which case this
test is not appropriate.
References
----------
.. [1] Lowry, Richard. "Concepts and Applications of Inferential
Statistics". Chapter 8. http://faculty.vassar.edu/lowry/ch8pt1.html
.. [2] "Chi-squared test", http://en.wikipedia.org/wiki/Chi-squared_test
Examples
--------
When just `f_obs` is given, it is assumed that the expected frequencies
are uniform and given by the mean of the observed frequencies.
>>> from scipy.stats import chisquare
>>> chisquare([16, 18, 16, 14, 12, 12])
(2.0, 0.84914503608460956)
With `f_exp` the expected frequencies can be given.
>>> chisquare([16, 18, 16, 14, 12, 12], f_exp=[16, 16, 16, 16, 16, 8])
(3.5, 0.62338762774958223)
When `f_obs` is 2-D, by default the test is applied to each column.
>>> obs = np.array([[16, 18, 16, 14, 12, 12], [32, 24, 16, 28, 20, 24]]).T
>>> obs.shape
(6, 2)
>>> chisquare(obs)
(array([ 2. , 6.66666667]), array([ 0.84914504, 0.24663415]))
By setting ``axis=None``, the test is applied to all data in the array,
which is equivalent to applying the test to the flattened array.
>>> chisquare(obs, axis=None)
(23.31034482758621, 0.015975692534127565)
>>> chisquare(obs.ravel())
(23.31034482758621, 0.015975692534127565)
`ddof` is the change to make to the default degrees of freedom.
>>> chisquare([16, 18, 16, 14, 12, 12], ddof=1)
(2.0, 0.73575888234288467)
The calculation of the p-values is done by broadcasting the
chi-squared statistic with `ddof`.
>>> chisquare([16, 18, 16, 14, 12, 12], ddof=[0,1,2])
(2.0, array([ 0.84914504, 0.73575888, 0.5724067 ]))
`f_obs` and `f_exp` are also broadcast. In the following, `f_obs` has
shape (6,) and `f_exp` has shape (2, 6), so the result of broadcasting
`f_obs` and `f_exp` has shape (2, 6). To compute the desired chi-squared
statistics, we use ``axis=1``:
>>> chisquare([16, 18, 16, 14, 12, 12],
... f_exp=[[16, 16, 16, 16, 16, 8], [8, 20, 20, 16, 12, 12]],
... axis=1)
(array([ 3.5 , 9.25]), array([ 0.62338763, 0.09949846]))
"""
return power_divergence(f_obs, f_exp=f_exp, ddof=ddof, axis=axis,
lambda_="pearson")
Ks_2sampResult = namedtuple('Ks_2sampResult', ('statistic', 'pvalue'))
def ks_2samp(data1, data2):
"""
Computes the Kolmogorov-Smirnov statistic on 2 samples.
This is a two-sided test for the null hypothesis that 2 independent samples
are drawn from the same continuous distribution.
Parameters
----------
data1, data2 : sequence of 1-D ndarrays
two arrays of sample observations assumed to be drawn from a continuous
distribution, sample sizes can be different
Returns
-------
statistic : float
KS statistic
pvalue : float
two-tailed p-value
Notes
-----
This tests whether 2 samples are drawn from the same distribution. Note
that, like in the case of the one-sample K-S test, the distribution is
assumed to be continuous.
This is the two-sided test, one-sided tests are not implemented.
The test uses the two-sided asymptotic Kolmogorov-Smirnov distribution.
If the K-S statistic is small or the p-value is high, then we cannot
reject the hypothesis that the distributions of the two samples
are the same.
Examples
--------
>>> from scipy import stats
>>> np.random.seed(12345678) #fix random seed to get the same result
>>> n1 = 200 # size of first sample
>>> n2 = 300 # size of second sample
For a different distribution, we can reject the null hypothesis since the
pvalue is below 1%:
>>> rvs1 = stats.norm.rvs(size=n1, loc=0., scale=1)
>>> rvs2 = stats.norm.rvs(size=n2, loc=0.5, scale=1.5)
>>> stats.ks_2samp(rvs1, rvs2)
(0.20833333333333337, 4.6674975515806989e-005)
For a slightly different distribution, we cannot reject the null hypothesis
at a 10% or lower alpha since the p-value at 0.144 is higher than 10%
>>> rvs3 = stats.norm.rvs(size=n2, loc=0.01, scale=1.0)
>>> stats.ks_2samp(rvs1, rvs3)
(0.10333333333333333, 0.14498781825751686)
For an identical distribution, we cannot reject the null hypothesis since
the p-value is high, 41%:
>>> rvs4 = stats.norm.rvs(size=n2, loc=0.0, scale=1.0)
>>> stats.ks_2samp(rvs1, rvs4)
(0.07999999999999996, 0.41126949729859719)
"""
data1 = np.sort(data1)
data2 = np.sort(data2)
n1 = data1.shape[0]
n2 = data2.shape[0]
data_all = np.concatenate([data1, data2])
cdf1 = np.searchsorted(data1, data_all, side='right') / (1.0*n1)
cdf2 = np.searchsorted(data2, data_all, side='right') / (1.0*n2)
d = np.max(np.absolute(cdf1 - cdf2))
# Note: d absolute not signed distance
en = np.sqrt(n1 * n2 / float(n1 + n2))
try:
prob = distributions.kstwobign.sf((en + 0.12 + 0.11 / en) * d)
except:
prob = 1.0
return Ks_2sampResult(d, prob)
def tiecorrect(rankvals):
"""
Tie correction factor for ties in the Mann-Whitney U and
Kruskal-Wallis H tests.
Parameters
----------
rankvals : array_like
A 1-D sequence of ranks. Typically this will be the array
returned by `stats.rankdata`.
Returns
-------
factor : float
Correction factor for U or H.
See Also
--------
rankdata : Assign ranks to the data
mannwhitneyu : Mann-Whitney rank test
kruskal : Kruskal-Wallis H test
References
----------
.. [1] Siegel, S. (1956) Nonparametric Statistics for the Behavioral
Sciences. New York: McGraw-Hill.
Examples
--------
>>> from scipy.stats import tiecorrect, rankdata
>>> tiecorrect([1, 2.5, 2.5, 4])
0.9
>>> ranks = rankdata([1, 3, 2, 4, 5, 7, 2, 8, 4])
>>> ranks
array([ 1. , 4. , 2.5, 5.5, 7. , 8. , 2.5, 9. , 5.5])
>>> tiecorrect(ranks)
0.9833333333333333
"""
arr = np.sort(rankvals)
idx = np.nonzero(np.r_[True, arr[1:] != arr[:-1], True])[0]
cnt = np.diff(idx).astype(np.float64)
size = np.float64(arr.size)
return 1.0 if size < 2 else 1.0 - (cnt**3 - cnt).sum() / (size**3 - size)
MannwhitneyuResult = namedtuple('MannwhitneyuResult', ('statistic', 'pvalue'))
def mannwhitneyu(x, y, use_continuity=True, alternative=None):
"""
Computes the Mann-Whitney rank test on samples x and y.
Parameters
----------
x, y : array_like
Array of samples, should be one-dimensional.
use_continuity : bool, optional
Whether a continuity correction (1/2.) should be taken into
account. Default is True.
alternative : None (deprecated), 'less', 'two-sided', or 'greater'
Whether to get the p-value for the one-sided hypothesis ('less'
or 'greater') or for the two-sided hypothesis ('two-sided').
Defaults to None, which results in a p-value half the size of
the 'two-sided' p-value and a different U statistic. The
default behavior is not the same as using 'less' or 'greater':
it only exists for backward compatibility and is deprecated.
Returns
-------
statistic : float
The Mann-Whitney U statistic, equal to min(U for x, U for y) if
`alternative` is equal to None (deprecated; exists for backward
compatibility), and U for y otherwise.
pvalue : float
p-value assuming an asymptotic normal distribution. One-sided or
two-sided, depending on the choice of `alternative`.
Notes
-----
Use only when the number of observation in each sample is > 20 and
you have 2 independent samples of ranks. Mann-Whitney U is
significant if the u-obtained is LESS THAN or equal to the critical
value of U.
This test corrects for ties and by default uses a continuity correction.
"""
if alternative is None:
warnings.warn("Calling `mannwhitneyu` without specifying "
"`alternative` is deprecated.", DeprecationWarning)
x = np.asarray(x)
y = np.asarray(y)
n1 = len(x)
n2 = len(y)
ranked = rankdata(np.concatenate((x, y)))
rankx = ranked[0:n1] # get the x-ranks
u1 = n1*n2 + (n1*(n1+1))/2.0 - np.sum(rankx, axis=0) # calc U for x
u2 = n1*n2 - u1 # remainder is U for y
T = tiecorrect(ranked)
if T == 0:
raise ValueError('All numbers are identical in mannwhitneyu')
sd = np.sqrt(T * n1 * n2 * (n1+n2+1) / 12.0)
meanrank = n1*n2/2.0 + 0.5 * use_continuity
if alternative is None or alternative == 'two-sided':
bigu = max(u1, u2)
elif alternative == 'less':
bigu = u1
elif alternative == 'greater':
bigu = u2
else:
raise ValueError("alternative should be None, 'less', 'greater' "
"or 'two-sided'")
z = (bigu - meanrank) / sd
if alternative is None:
# This behavior, equal to half the size of the two-sided
# p-value, is deprecated.
p = distributions.norm.sf(abs(z))
elif alternative == 'two-sided':
p = 2 * distributions.norm.sf(abs(z))
else:
p = distributions.norm.sf(z)
u = u2
# This behavior is deprecated.
if alternative is None:
u = min(u1, u2)
return MannwhitneyuResult(u, p)
RanksumsResult = namedtuple('RanksumsResult', ('statistic', 'pvalue'))
def ranksums(x, y):
"""
Compute the Wilcoxon rank-sum statistic for two samples.
The Wilcoxon rank-sum test tests the null hypothesis that two sets
of measurements are drawn from the same distribution. The alternative
hypothesis is that values in one sample are more likely to be
larger than the values in the other sample.
This test should be used to compare two samples from continuous
distributions. It does not handle ties between measurements
in x and y. For tie-handling and an optional continuity correction
see `scipy.stats.mannwhitneyu`.
Parameters
----------
x,y : array_like
The data from the two samples
Returns
-------
statistic : float
The test statistic under the large-sample approximation that the
rank sum statistic is normally distributed
pvalue : float
The two-sided p-value of the test
References
----------
.. [1] http://en.wikipedia.org/wiki/Wilcoxon_rank-sum_test
"""
x, y = map(np.asarray, (x, y))
n1 = len(x)
n2 = len(y)
alldata = np.concatenate((x, y))
ranked = rankdata(alldata)
x = ranked[:n1]
s = np.sum(x, axis=0)
expected = n1 * (n1+n2+1) / 2.0
z = (s - expected) / np.sqrt(n1*n2*(n1+n2+1)/12.0)
prob = 2 * distributions.norm.sf(abs(z))
return RanksumsResult(z, prob)
KruskalResult = namedtuple('KruskalResult', ('statistic', 'pvalue'))
def kruskal(*args, **kwargs):
"""
Compute the Kruskal-Wallis H-test for independent samples
The Kruskal-Wallis H-test tests the null hypothesis that the population
median of all of the groups are equal. It is a non-parametric version of
ANOVA. The test works on 2 or more independent samples, which may have
different sizes. Note that rejecting the null hypothesis does not
indicate which of the groups differs. Post-hoc comparisons between
groups are required to determine which groups are different.
Parameters
----------
sample1, sample2, ... : array_like
Two or more arrays with the sample measurements can be given as
arguments.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
statistic : float
The Kruskal-Wallis H statistic, corrected for ties
pvalue : float
The p-value for the test using the assumption that H has a chi
square distribution
See Also
--------
f_oneway : 1-way ANOVA
mannwhitneyu : Mann-Whitney rank test on two samples.
friedmanchisquare : Friedman test for repeated measurements
Notes
-----
Due to the assumption that H has a chi square distribution, the number
of samples in each group must not be too small. A typical rule is
that each sample must have at least 5 measurements.
References
----------
.. [1] W. H. Kruskal & W. W. Wallis, "Use of Ranks in
One-Criterion Variance Analysis", Journal of the American Statistical
Association, Vol. 47, Issue 260, pp. 583-621, 1952.
.. [2] http://en.wikipedia.org/wiki/Kruskal-Wallis_one-way_analysis_of_variance
Examples
--------
>>> from scipy import stats
>>> x = [1, 3, 5, 7, 9]
>>> y = [2, 4, 6, 8, 10]
>>> stats.kruskal(x, y)
KruskalResult(statistic=0.27272727272727337, pvalue=0.60150813444058948)
>>> x = [1, 1, 1]
>>> y = [2, 2, 2]
>>> z = [2, 2]
>>> stats.kruskal(x, y, z)
KruskalResult(statistic=7.0, pvalue=0.030197383422318501)
"""
args = list(map(np.asarray, args))
num_groups = len(args)
if num_groups < 2:
raise ValueError("Need at least two groups in stats.kruskal()")
for arg in args:
if arg.size == 0:
return KruskalResult(np.nan, np.nan)
n = np.asarray(list(map(len, args)))
if 'nan_policy' in kwargs.keys():
if kwargs['nan_policy'] not in ('propagate', 'raise', 'omit'):
raise ValueError("nan_policy must be 'propagate', "
"'raise' or'omit'")
else:
nan_policy = kwargs['nan_policy']
else:
nan_policy = 'propagate'
contains_nan = False
for arg in args:
cn = _contains_nan(arg, nan_policy)
if cn[0]:
contains_nan = True
break
if contains_nan and nan_policy == 'omit':
for a in args:
a = ma.masked_invalid(a)
return mstats_basic.kruskal(*args)
if contains_nan and nan_policy == 'propagate':
return KruskalResult(np.nan, np.nan)
alldata = np.concatenate(args)
ranked = rankdata(alldata)
ties = tiecorrect(ranked)
if ties == 0:
raise ValueError('All numbers are identical in kruskal')
# Compute sum^2/n for each group and sum
j = np.insert(np.cumsum(n), 0, 0)
ssbn = 0
for i in range(num_groups):
ssbn += _square_of_sums(ranked[j[i]:j[i+1]]) / float(n[i])
totaln = np.sum(n)
h = 12.0 / (totaln * (totaln + 1)) * ssbn - 3 * (totaln + 1)
df = num_groups - 1
h /= ties
return KruskalResult(h, distributions.chi2.sf(h, df))
FriedmanchisquareResult = namedtuple('FriedmanchisquareResult',
('statistic', 'pvalue'))
def friedmanchisquare(*args):
"""
Computes the Friedman test for repeated measurements
The Friedman test tests the null hypothesis that repeated measurements of
the same individuals have the same distribution. It is often used
to test for consistency among measurements obtained in different ways.
For example, if two measurement techniques are used on the same set of
individuals, the Friedman test can be used to determine if the two
measurement techniques are consistent.
Parameters
----------
measurements1, measurements2, measurements3... : array_like
Arrays of measurements. All of the arrays must have the same number
of elements. At least 3 sets of measurements must be given.
Returns
-------
statistic : float
the test statistic, correcting for ties
pvalue : float
the associated p-value assuming that the test statistic has a chi
squared distribution
Notes
-----
Due to the assumption that the test statistic has a chi squared
distribution, the p-value is only reliable for n > 10 and more than
6 repeated measurements.
References
----------
.. [1] http://en.wikipedia.org/wiki/Friedman_test
"""
k = len(args)
if k < 3:
raise ValueError('Less than 3 levels. Friedman test not appropriate.')
n = len(args[0])
for i in range(1, k):
if len(args[i]) != n:
raise ValueError('Unequal N in friedmanchisquare. Aborting.')
# Rank data
data = np.vstack(args).T
data = data.astype(float)
for i in range(len(data)):
data[i] = rankdata(data[i])
# Handle ties
ties = 0
for i in range(len(data)):
replist, repnum = find_repeats(array(data[i]))
for t in repnum:
ties += t * (t*t - 1)
c = 1 - ties / float(k*(k*k - 1)*n)
ssbn = np.sum(data.sum(axis=0)**2)
chisq = (12.0 / (k*n*(k+1)) * ssbn - 3*n*(k+1)) / c
return FriedmanchisquareResult(chisq, distributions.chi2.sf(chisq, k - 1))
def combine_pvalues(pvalues, method='fisher', weights=None):
"""
Methods for combining the p-values of independent tests bearing upon the
same hypothesis.
Parameters
----------
pvalues : array_like, 1-D
Array of p-values assumed to come from independent tests.
method : {'fisher', 'stouffer'}, optional
Name of method to use to combine p-values. The following methods are
available:
- "fisher": Fisher's method (Fisher's combined probability test),
the default.
- "stouffer": Stouffer's Z-score method.
weights : array_like, 1-D, optional
Optional array of weights used only for Stouffer's Z-score method.
Returns
-------
statistic: float
The statistic calculated by the specified method:
- "fisher": The chi-squared statistic
- "stouffer": The Z-score
pval: float
The combined p-value.
Notes
-----
Fisher's method (also known as Fisher's combined probability test) [1]_ uses
a chi-squared statistic to compute a combined p-value. The closely related
Stouffer's Z-score method [2]_ uses Z-scores rather than p-values. The
advantage of Stouffer's method is that it is straightforward to introduce
weights, which can make Stouffer's method more powerful than Fisher's
method when the p-values are from studies of different size [3]_ [4]_.
Fisher's method may be extended to combine p-values from dependent tests
[5]_. Extensions such as Brown's method and Kost's method are not currently
implemented.
.. versionadded:: 0.15.0
References
----------
.. [1] https://en.wikipedia.org/wiki/Fisher%27s_method
.. [2] http://en.wikipedia.org/wiki/Fisher's_method#Relation_to_Stouffer.27s_Z-score_method
.. [3] Whitlock, M. C. "Combining probability from independent tests: the
weighted Z-method is superior to Fisher's approach." Journal of
Evolutionary Biology 18, no. 5 (2005): 1368-1373.
.. [4] Zaykin, Dmitri V. "Optimally weighted Z-test is a powerful method
for combining probabilities in meta-analysis." Journal of
Evolutionary Biology 24, no. 8 (2011): 1836-1841.
.. [5] https://en.wikipedia.org/wiki/Extensions_of_Fisher%27s_method
"""
pvalues = np.asarray(pvalues)
if pvalues.ndim != 1:
raise ValueError("pvalues is not 1-D")
if method == 'fisher':
Xsq = -2 * np.sum(np.log(pvalues))
pval = distributions.chi2.sf(Xsq, 2 * len(pvalues))
return (Xsq, pval)
elif method == 'stouffer':
if weights is None:
weights = np.ones_like(pvalues)
elif len(weights) != len(pvalues):
raise ValueError("pvalues and weights must be of the same size.")
weights = np.asarray(weights)
if weights.ndim != 1:
raise ValueError("weights is not 1-D")
Zi = distributions.norm.isf(pvalues)
Z = np.dot(weights, Zi) / np.linalg.norm(weights)
pval = distributions.norm.sf(Z)
return (Z, pval)
else:
raise ValueError(
"Invalid method '%s'. Options are 'fisher' or 'stouffer'", method)
#####################################
# PROBABILITY CALCULATIONS #
#####################################
@np.deprecate(message="stats.chisqprob is deprecated in scipy 0.17.0; "
"use stats.distributions.chi2.sf instead.")
def chisqprob(chisq, df):
"""
Probability value (1-tail) for the Chi^2 probability distribution.
Broadcasting rules apply.
Parameters
----------
chisq : array_like or float > 0
df : array_like or float, probably int >= 1
Returns
-------
chisqprob : ndarray
The area from `chisq` to infinity under the Chi^2 probability
distribution with degrees of freedom `df`.
"""
return distributions.chi2.sf(chisq, df)
@np.deprecate(message="stats.betai is deprecated in scipy 0.17.0; "
"use special.betainc instead")
def betai(a, b, x):
"""
Returns the incomplete beta function.
I_x(a,b) = 1/B(a,b)*(Integral(0,x) of t^(a-1)(1-t)^(b-1) dt)
where a,b>0 and B(a,b) = G(a)*G(b)/(G(a+b)) where G(a) is the gamma
function of a.
The standard broadcasting rules apply to a, b, and x.
Parameters
----------
a : array_like or float > 0
b : array_like or float > 0
x : array_like or float
x will be clipped to be no greater than 1.0 .
Returns
-------
betai : ndarray
Incomplete beta function.
"""
return _betai(a, b, x)
def _betai(a, b, x):
x = np.asarray(x)
x = np.where(x < 1.0, x, 1.0) # if x > 1 then return 1.0
return special.betainc(a, b, x)
#####################################
# ANOVA CALCULATIONS #
#####################################
@np.deprecate(message="stats.f_value_wilks_lambda deprecated in scipy 0.17.0")
def f_value_wilks_lambda(ER, EF, dfnum, dfden, a, b):
"""Calculation of Wilks lambda F-statistic for multivarite data, per
Maxwell & Delaney p.657.
"""
if isinstance(ER, (int, float)):
ER = array([[ER]])
if isinstance(EF, (int, float)):
EF = array([[EF]])
lmbda = linalg.det(EF) / linalg.det(ER)
if (a-1)**2 + (b-1)**2 == 5:
q = 1
else:
q = np.sqrt(((a-1)**2*(b-1)**2 - 2) / ((a-1)**2 + (b-1)**2 - 5))
n_um = (1 - lmbda**(1.0/q))*(a-1)*(b-1)
d_en = lmbda**(1.0/q) / (n_um*q - 0.5*(a-1)*(b-1) + 1)
return n_um / d_en
@np.deprecate(message="stats.f_value deprecated in scipy 0.17.0")
def f_value(ER, EF, dfR, dfF):
"""
Returns an F-statistic for a restricted vs. unrestricted model.
Parameters
----------
ER : float
`ER` is the sum of squared residuals for the restricted model
or null hypothesis
EF : float
`EF` is the sum of squared residuals for the unrestricted model
or alternate hypothesis
dfR : int
`dfR` is the degrees of freedom in the restricted model
dfF : int
`dfF` is the degrees of freedom in the unrestricted model
Returns
-------
F-statistic : float
"""
return (ER - EF) / float(dfR - dfF) / (EF / float(dfF))
@np.deprecate(message="stats.f_value_multivariate deprecated in scipy 0.17.0")
def f_value_multivariate(ER, EF, dfnum, dfden):
"""
Returns a multivariate F-statistic.
Parameters
----------
ER : ndarray
Error associated with the null hypothesis (the Restricted model).
From a multivariate F calculation.
EF : ndarray
Error associated with the alternate hypothesis (the Full model)
From a multivariate F calculation.
dfnum : int
Degrees of freedom the Restricted model.
dfden : int
Degrees of freedom associated with the Restricted model.
Returns
-------
fstat : float
The computed F-statistic.
"""
if isinstance(ER, (int, float)):
ER = array([[ER]])
if isinstance(EF, (int, float)):
EF = array([[EF]])
n_um = (linalg.det(ER) - linalg.det(EF)) / float(dfnum)
d_en = linalg.det(EF) / float(dfden)
return n_um / d_en
#####################################
# SUPPORT FUNCTIONS #
#####################################
RepeatedResults = namedtuple('RepeatedResults', ('values', 'counts'))
def find_repeats(arr):
"""
Find repeats and repeat counts.
Parameters
----------
arr : array_like
Input array. This is cast to float64.
Returns
-------
values : ndarray
The unique values from the (flattened) input that are repeated.
counts : ndarray
Number of times the corresponding 'value' is repeated.
Notes
-----
In numpy >= 1.9 `numpy.unique` provides similar functionality. The main
difference is that `find_repeats` only returns repeated values.
Examples
--------
>>> from scipy import stats
>>> stats.find_repeats([2, 1, 2, 3, 2, 2, 5])
RepeatedResults(values=array([ 2.]), counts=array([4]))
>>> stats.find_repeats([[10, 20, 1, 2], [5, 5, 4, 4]])
RepeatedResults(values=array([ 4., 5.]), counts=array([2, 2]))
"""
# Note: always copies.
return RepeatedResults(*_find_repeats(np.array(arr, dtype=np.float64)))
@np.deprecate(message="scipy.stats.ss is deprecated in scipy 0.17.0")
def ss(a, axis=0):
return _sum_of_squares(a, axis)
def _sum_of_squares(a, axis=0):
"""
Squares each element of the input array, and returns the sum(s) of that.
Parameters
----------
a : array_like
Input array.
axis : int or None, optional
Axis along which to calculate. Default is 0. If None, compute over
the whole array `a`.
Returns
-------
sum_of_squares : ndarray
The sum along the given axis for (a**2).
See also
--------
_square_of_sums : The square(s) of the sum(s) (the opposite of
`_sum_of_squares`).
"""
a, axis = _chk_asarray(a, axis)
return np.sum(a*a, axis)
@np.deprecate(message="scipy.stats.square_of_sums is deprecated "
"in scipy 0.17.0")
def square_of_sums(a, axis=0):
return _square_of_sums(a, axis)
def _square_of_sums(a, axis=0):
"""
Sums elements of the input array, and returns the square(s) of that sum.
Parameters
----------
a : array_like
Input array.
axis : int or None, optional
Axis along which to calculate. Default is 0. If None, compute over
the whole array `a`.
Returns
-------
square_of_sums : float or ndarray
The square of the sum over `axis`.
See also
--------
_sum_of_squares : The sum of squares (the opposite of `square_of_sums`).
"""
a, axis = _chk_asarray(a, axis)
s = np.sum(a, axis)
if not np.isscalar(s):
return s.astype(float) * s
else:
return float(s) * s
@np.deprecate(message="scipy.stats.fastsort is deprecated in scipy 0.16.0")
def fastsort(a):
"""
Sort an array and provide the argsort.
Parameters
----------
a : array_like
Input array.
Returns
-------
fastsort : ndarray of type int
sorted indices into the original array
"""
# TODO: the wording in the docstring is nonsense.
it = np.argsort(a)
as_ = a[it]
return as_, it
def rankdata(a, method='average'):
"""
rankdata(a, method='average')
Assign ranks to data, dealing with ties appropriately.
Ranks begin at 1. The `method` argument controls how ranks are assigned
to equal values. See [1]_ for further discussion of ranking methods.
Parameters
----------
a : array_like
The array of values to be ranked. The array is first flattened.
method : str, optional
The method used to assign ranks to tied elements.
The options are 'average', 'min', 'max', 'dense' and 'ordinal'.
'average':
The average of the ranks that would have been assigned to
all the tied values is assigned to each value.
'min':
The minimum of the ranks that would have been assigned to all
the tied values is assigned to each value. (This is also
referred to as "competition" ranking.)
'max':
The maximum of the ranks that would have been assigned to all
the tied values is assigned to each value.
'dense':
Like 'min', but the rank of the next highest element is assigned
the rank immediately after those assigned to the tied elements.
'ordinal':
All values are given a distinct rank, corresponding to the order
that the values occur in `a`.
The default is 'average'.
Returns
-------
ranks : ndarray
An array of length equal to the size of `a`, containing rank
scores.
References
----------
.. [1] "Ranking", http://en.wikipedia.org/wiki/Ranking
Examples
--------
>>> from scipy.stats import rankdata
>>> rankdata([0, 2, 3, 2])
array([ 1. , 2.5, 4. , 2.5])
>>> rankdata([0, 2, 3, 2], method='min')
array([ 1, 2, 4, 2])
>>> rankdata([0, 2, 3, 2], method='max')
array([ 1, 3, 4, 3])
>>> rankdata([0, 2, 3, 2], method='dense')
array([ 1, 2, 3, 2])
>>> rankdata([0, 2, 3, 2], method='ordinal')
array([ 1, 2, 4, 3])
"""
if method not in ('average', 'min', 'max', 'dense', 'ordinal'):
raise ValueError('unknown method "{0}"'.format(method))
arr = np.ravel(np.asarray(a))
algo = 'mergesort' if method == 'ordinal' else 'quicksort'
sorter = np.argsort(arr, kind=algo)
inv = np.empty(sorter.size, dtype=np.intp)
inv[sorter] = np.arange(sorter.size, dtype=np.intp)
if method == 'ordinal':
return inv + 1
arr = arr[sorter]
obs = np.r_[True, arr[1:] != arr[:-1]]
dense = obs.cumsum()[inv]
if method == 'dense':
return dense
# cumulative counts of each unique value
count = np.r_[np.nonzero(obs)[0], len(obs)]
if method == 'max':
return count[dense]
if method == 'min':
return count[dense - 1] + 1
# average method
return .5 * (count[dense] + count[dense - 1] + 1)
| mit |
hainm/scikit-learn | sklearn/svm/setup.py | 321 | 3157 | import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('svm', parent_package, top_path)
config.add_subpackage('tests')
# Section LibSVM
# we compile both libsvm and libsvm_sparse
config.add_library('libsvm-skl',
sources=[join('src', 'libsvm', 'libsvm_template.cpp')],
depends=[join('src', 'libsvm', 'svm.cpp'),
join('src', 'libsvm', 'svm.h')],
# Force C++ linking in case gcc is picked up instead
# of g++ under windows with some versions of MinGW
extra_link_args=['-lstdc++'],
)
libsvm_sources = ['libsvm.c']
libsvm_depends = [join('src', 'libsvm', 'libsvm_helper.c'),
join('src', 'libsvm', 'libsvm_template.cpp'),
join('src', 'libsvm', 'svm.cpp'),
join('src', 'libsvm', 'svm.h')]
config.add_extension('libsvm',
sources=libsvm_sources,
include_dirs=[numpy.get_include(),
join('src', 'libsvm')],
libraries=['libsvm-skl'],
depends=libsvm_depends,
)
### liblinear module
cblas_libs, blas_info = get_blas_info()
if os.name == 'posix':
cblas_libs.append('m')
liblinear_sources = ['liblinear.c',
join('src', 'liblinear', '*.cpp')]
liblinear_depends = [join('src', 'liblinear', '*.h'),
join('src', 'liblinear', 'liblinear_helper.c')]
config.add_extension('liblinear',
sources=liblinear_sources,
libraries=cblas_libs,
include_dirs=[join('..', 'src', 'cblas'),
numpy.get_include(),
blas_info.pop('include_dirs', [])],
extra_compile_args=blas_info.pop('extra_compile_args',
[]),
depends=liblinear_depends,
# extra_compile_args=['-O0 -fno-inline'],
** blas_info)
## end liblinear module
# this should go *after* libsvm-skl
libsvm_sparse_sources = ['libsvm_sparse.c']
config.add_extension('libsvm_sparse', libraries=['libsvm-skl'],
sources=libsvm_sparse_sources,
include_dirs=[numpy.get_include(),
join("src", "libsvm")],
depends=[join("src", "libsvm", "svm.h"),
join("src", "libsvm",
"libsvm_sparse_helper.c")])
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(**configuration(top_path='').todict())
| bsd-3-clause |
rahul-c1/scikit-learn | sklearn/utils/extmath.py | 14 | 20521 | """
Extended math utilities.
"""
# Authors: Gael Varoquaux
# Alexandre Gramfort
# Alexandre T. Passos
# Olivier Grisel
# Lars Buitinck
# Stefan van der Walt
# Kyle Kastner
# License: BSD 3 clause
from __future__ import division
from functools import partial
import warnings
import numpy as np
from scipy import linalg
from scipy.sparse import issparse
from . import check_random_state, deprecated
from .fixes import np_version
from ._logistic_sigmoid import _log_logistic_sigmoid
from ..externals.six.moves import xrange
from .sparsefuncs_fast import csr_row_norms
from .validation import check_array, NonBLASDotWarning
def norm(x):
"""Compute the Euclidean or Frobenius norm of x.
Returns the Euclidean norm when x is a vector, the Frobenius norm when x
is a matrix (2-d array). More precise than sqrt(squared_norm(x)).
"""
x = np.asarray(x)
nrm2, = linalg.get_blas_funcs(['nrm2'], [x])
return nrm2(x)
# Newer NumPy has a ravel that needs less copying.
if np_version < (1, 7, 1):
_ravel = np.ravel
else:
_ravel = partial(np.ravel, order='K')
def squared_norm(x):
"""Squared Euclidean or Frobenius norm of x.
Returns the Euclidean norm when x is a vector, the Frobenius norm when x
is a matrix (2-d array). Faster than norm(x) ** 2.
"""
x = _ravel(x)
return np.dot(x, x)
def row_norms(X, squared=False):
"""Row-wise (squared) Euclidean norm of X.
Equivalent to np.sqrt((X * X).sum(axis=1)), but also supports CSR sparse
matrices and does not create an X.shape-sized temporary.
Performs no input validation.
"""
if issparse(X):
norms = csr_row_norms(X)
else:
norms = np.einsum('ij,ij->i', X, X)
if not squared:
np.sqrt(norms, norms)
return norms
def fast_logdet(A):
"""Compute log(det(A)) for A symmetric
Equivalent to : np.log(nl.det(A)) but more robust.
It returns -Inf if det(A) is non positive or is not defined.
"""
sign, ld = np.linalg.slogdet(A)
if not sign > 0:
return -np.inf
return ld
def _impose_f_order(X):
"""Helper Function"""
# important to access flags instead of calling np.isfortran,
# this catches corner cases.
if X.flags.c_contiguous:
return check_array(X.T, copy=False, order='F'), True
else:
return check_array(X, copy=False, order='F'), False
def _fast_dot(A, B):
if B.shape[0] != A.shape[A.ndim - 1]: # check adopted from '_dotblas.c'
raise ValueError
if A.dtype != B.dtype or any(x.dtype not in (np.float32, np.float64)
for x in [A, B]):
warnings.warn('Data must be of same type. Supported types '
'are 32 and 64 bit float. '
'Falling back to np.dot.', NonBLASDotWarning)
raise ValueError
if min(A.shape) == 1 or min(B.shape) == 1 or A.ndim != 2 or B.ndim != 2:
raise ValueError
# scipy 0.9 compliant API
dot = linalg.get_blas_funcs(['gemm'], (A, B))[0]
A, trans_a = _impose_f_order(A)
B, trans_b = _impose_f_order(B)
return dot(alpha=1.0, a=A, b=B, trans_a=trans_a, trans_b=trans_b)
def _have_blas_gemm():
try:
linalg.get_blas_funcs(['gemm'])
return True
except (AttributeError, ValueError):
warnings.warn('Could not import BLAS, falling back to np.dot')
return False
# Only use fast_dot for older NumPy; newer ones have tackled the speed issue.
if np_version < (1, 7, 2) and _have_blas_gemm():
def fast_dot(A, B):
"""Compute fast dot products directly calling BLAS.
This function calls BLAS directly while warranting Fortran contiguity.
This helps avoiding extra copies `np.dot` would have created.
For details see section `Linear Algebra on large Arrays`:
http://wiki.scipy.org/PerformanceTips
Parameters
----------
A, B: instance of np.ndarray
Input arrays. Arrays are supposed to be of the same dtype and to
have exactly 2 dimensions. Currently only floats are supported.
In case these requirements aren't met np.dot(A, B) is returned
instead. To activate the related warning issued in this case
execute the following lines of code:
>> import warnings
>> from sklearn.utils.validation import NonBLASDotWarning
>> warnings.simplefilter('always', NonBLASDotWarning)
"""
try:
return _fast_dot(A, B)
except ValueError:
# Maltyped or malformed data.
return np.dot(A, B)
else:
fast_dot = np.dot
def density(w, **kwargs):
"""Compute density of a sparse vector
Return a value between 0 and 1
"""
if hasattr(w, "toarray"):
d = float(w.nnz) / (w.shape[0] * w.shape[1])
else:
d = 0 if w is None else float((w != 0).sum()) / w.size
return d
def safe_sparse_dot(a, b, dense_output=False):
"""Dot product that handle the sparse matrix case correctly
Uses BLAS GEMM as replacement for numpy.dot where possible
to avoid unnecessary copies.
"""
if issparse(a) or issparse(b):
ret = a * b
if dense_output and hasattr(ret, "toarray"):
ret = ret.toarray()
return ret
else:
return fast_dot(a, b)
def randomized_range_finder(A, size, n_iter, random_state=None):
"""Computes an orthonormal matrix whose range approximates the range of A.
Parameters
----------
A: 2D array
The input data matrix
size: integer
Size of the return array
n_iter: integer
Number of power iterations used to stabilize the result
random_state: RandomState or an int seed (0 by default)
A random number generator instance
Returns
-------
Q: 2D array
A (size x size) projection matrix, the range of which
approximates well the range of the input matrix A.
Notes
-----
Follows Algorithm 4.3 of
Finding structure with randomness: Stochastic algorithms for constructing
approximate matrix decompositions
Halko, et al., 2009 (arXiv:909) http://arxiv.org/pdf/0909.4061
"""
random_state = check_random_state(random_state)
# generating random gaussian vectors r with shape: (A.shape[1], size)
R = random_state.normal(size=(A.shape[1], size))
# sampling the range of A using by linear projection of r
Y = safe_sparse_dot(A, R)
del R
# perform power iterations with Y to further 'imprint' the top
# singular vectors of A in Y
for i in xrange(n_iter):
Y = safe_sparse_dot(A, safe_sparse_dot(A.T, Y))
# extracting an orthonormal basis of the A range samples
Q, R = linalg.qr(Y, mode='economic')
return Q
def randomized_svd(M, n_components, n_oversamples=10, n_iter=0,
transpose='auto', flip_sign=True, random_state=0):
"""Computes a truncated randomized SVD
Parameters
----------
M: ndarray or sparse matrix
Matrix to decompose
n_components: int
Number of singular values and vectors to extract.
n_oversamples: int (default is 10)
Additional number of random vectors to sample the range of M so as
to ensure proper conditioning. The total number of random vectors
used to find the range of M is n_components + n_oversamples.
n_iter: int (default is 0)
Number of power iterations (can be used to deal with very noisy
problems).
transpose: True, False or 'auto' (default)
Whether the algorithm should be applied to M.T instead of M. The
result should approximately be the same. The 'auto' mode will
trigger the transposition if M.shape[1] > M.shape[0] since this
implementation of randomized SVD tend to be a little faster in that
case).
flip_sign: boolean, (True by default)
The output of a singular value decomposition is only unique up to a
permutation of the signs of the singular vectors. If `flip_sign` is
set to `True`, the sign ambiguity is resolved by making the largest
loadings for each component in the left singular vectors positive.
random_state: RandomState or an int seed (0 by default)
A random number generator instance to make behavior
Notes
-----
This algorithm finds a (usually very good) approximate truncated
singular value decomposition using randomization to speed up the
computations. It is particularly fast on large matrices on which
you wish to extract only a small number of components.
References
----------
* Finding structure with randomness: Stochastic algorithms for constructing
approximate matrix decompositions
Halko, et al., 2009 http://arxiv.org/abs/arXiv:0909.4061
* A randomized algorithm for the decomposition of matrices
Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert
"""
random_state = check_random_state(random_state)
n_random = n_components + n_oversamples
n_samples, n_features = M.shape
if transpose == 'auto' and n_samples > n_features:
transpose = True
if transpose:
# this implementation is a bit faster with smaller shape[1]
M = M.T
Q = randomized_range_finder(M, n_random, n_iter, random_state)
# project M to the (k + p) dimensional space using the basis vectors
B = safe_sparse_dot(Q.T, M)
# compute the SVD on the thin matrix: (k + p) wide
Uhat, s, V = linalg.svd(B, full_matrices=False)
del B
U = np.dot(Q, Uhat)
if flip_sign:
U, V = svd_flip(U, V)
if transpose:
# transpose back the results according to the input convention
return V[:n_components, :].T, s[:n_components], U[:, :n_components].T
else:
return U[:, :n_components], s[:n_components], V[:n_components, :]
def logsumexp(arr, axis=0):
"""Computes the sum of arr assuming arr is in the log domain.
Returns log(sum(exp(arr))) while minimizing the possibility of
over/underflow.
Examples
--------
>>> import numpy as np
>>> from sklearn.utils.extmath import logsumexp
>>> a = np.arange(10)
>>> np.log(np.sum(np.exp(a)))
9.4586297444267107
>>> logsumexp(a)
9.4586297444267107
"""
arr = np.rollaxis(arr, axis)
# Use the max to normalize, as with the log this is what accumulates
# the less errors
vmax = arr.max(axis=0)
out = np.log(np.sum(np.exp(arr - vmax), axis=0))
out += vmax
return out
def weighted_mode(a, w, axis=0):
"""Returns an array of the weighted modal (most common) value in a
If there is more than one such value, only the first is returned.
The bin-count for the modal bins is also returned.
This is an extension of the algorithm in scipy.stats.mode.
Parameters
----------
a : array_like
n-dimensional array of which to find mode(s).
w : array_like
n-dimensional array of weights for each value
axis : int, optional
Axis along which to operate. Default is 0, i.e. the first axis.
Returns
-------
vals : ndarray
Array of modal values.
score : ndarray
Array of weighted counts for each mode.
Examples
--------
>>> from sklearn.utils.extmath import weighted_mode
>>> x = [4, 1, 4, 2, 4, 2]
>>> weights = [1, 1, 1, 1, 1, 1]
>>> weighted_mode(x, weights)
(array([ 4.]), array([ 3.]))
The value 4 appears three times: with uniform weights, the result is
simply the mode of the distribution.
>>> weights = [1, 3, 0.5, 1.5, 1, 2] # deweight the 4's
>>> weighted_mode(x, weights)
(array([ 2.]), array([ 3.5]))
The value 2 has the highest score: it appears twice with weights of
1.5 and 2: the sum of these is 3.
See Also
--------
scipy.stats.mode
"""
if axis is None:
a = np.ravel(a)
w = np.ravel(w)
axis = 0
else:
a = np.asarray(a)
w = np.asarray(w)
axis = axis
if a.shape != w.shape:
w = np.zeros(a.shape, dtype=w.dtype) + w
scores = np.unique(np.ravel(a)) # get ALL unique values
testshape = list(a.shape)
testshape[axis] = 1
oldmostfreq = np.zeros(testshape)
oldcounts = np.zeros(testshape)
for score in scores:
template = np.zeros(a.shape)
ind = (a == score)
template[ind] = w[ind]
counts = np.expand_dims(np.sum(template, axis), axis)
mostfrequent = np.where(counts > oldcounts, score, oldmostfreq)
oldcounts = np.maximum(counts, oldcounts)
oldmostfreq = mostfrequent
return mostfrequent, oldcounts
def pinvh(a, cond=None, rcond=None, lower=True):
"""Compute the (Moore-Penrose) pseudo-inverse of a hermetian matrix.
Calculate a generalized inverse of a symmetric matrix using its
eigenvalue decomposition and including all 'large' eigenvalues.
Parameters
----------
a : array, shape (N, N)
Real symmetric or complex hermetian matrix to be pseudo-inverted
cond, rcond : float or None
Cutoff for 'small' eigenvalues.
Singular values smaller than rcond * largest_eigenvalue are considered
zero.
If None or -1, suitable machine precision is used.
lower : boolean
Whether the pertinent array data is taken from the lower or upper
triangle of a. (Default: lower)
Returns
-------
B : array, shape (N, N)
Raises
------
LinAlgError
If eigenvalue does not converge
Examples
--------
>>> import numpy as np
>>> a = np.random.randn(9, 6)
>>> a = np.dot(a, a.T)
>>> B = pinvh(a)
>>> np.allclose(a, np.dot(a, np.dot(B, a)))
True
>>> np.allclose(B, np.dot(B, np.dot(a, B)))
True
"""
a = np.asarray_chkfinite(a)
s, u = linalg.eigh(a, lower=lower)
if rcond is not None:
cond = rcond
if cond in [None, -1]:
t = u.dtype.char.lower()
factor = {'f': 1E3, 'd': 1E6}
cond = factor[t] * np.finfo(t).eps
# unlike svd case, eigh can lead to negative eigenvalues
above_cutoff = (abs(s) > cond * np.max(abs(s)))
psigma_diag = np.zeros_like(s)
psigma_diag[above_cutoff] = 1.0 / s[above_cutoff]
return np.dot(u * psigma_diag, np.conjugate(u).T)
def cartesian(arrays, out=None):
"""Generate a cartesian product of input arrays.
Parameters
----------
arrays : list of array-like
1-D arrays to form the cartesian product of.
out : ndarray
Array to place the cartesian product in.
Returns
-------
out : ndarray
2-D array of shape (M, len(arrays)) containing cartesian products
formed of input arrays.
Examples
--------
>>> cartesian(([1, 2, 3], [4, 5], [6, 7]))
array([[1, 4, 6],
[1, 4, 7],
[1, 5, 6],
[1, 5, 7],
[2, 4, 6],
[2, 4, 7],
[2, 5, 6],
[2, 5, 7],
[3, 4, 6],
[3, 4, 7],
[3, 5, 6],
[3, 5, 7]])
"""
arrays = [np.asarray(x) for x in arrays]
shape = (len(x) for x in arrays)
dtype = arrays[0].dtype
ix = np.indices(shape)
ix = ix.reshape(len(arrays), -1).T
if out is None:
out = np.empty_like(ix, dtype=dtype)
for n, arr in enumerate(arrays):
out[:, n] = arrays[n][ix[:, n]]
return out
def svd_flip(u, v, u_based_decision=True):
"""Sign correction to ensure deterministic output from SVD.
Adjusts the columns of u and the rows of v such that the loadings in the
columns in u that are largest in absolute value are always positive.
Parameters
----------
u, v : arrays
The output of `linalg.svd` or `sklearn.utils.extmath.randomized_svd`,
with matching inner dimensions so one can compute `np.dot(u * s, v)`.
u_based_decision : boolean, (default=True)
If True, use the columns of u as the basis for sign flipping. Otherwise,
use the rows of v. The choice of which variable to base the decision on
is generally algorithm dependent.
Returns
-------
u_adjusted, v_adjusted : arrays with the same dimensions as the input.
"""
if u_based_decision:
# columns of u, rows of v
max_abs_cols = np.argmax(np.abs(u), axis=0)
signs = np.sign(u[max_abs_cols, xrange(u.shape[1])])
u *= signs
v *= signs[:, np.newaxis]
else:
# rows of v, columns of u
max_abs_rows = np.argmax(np.abs(v), axis=1)
signs = np.sign(v[xrange(v.shape[0]), max_abs_rows])
u *= signs
v *= signs[:, np.newaxis]
return u, v
@deprecated('to be removed in 0.17; use scipy.special.expit or log_logistic')
def logistic_sigmoid(X, log=False, out=None):
"""Logistic function, ``1 / (1 + e ** (-x))``, or its log."""
from .fixes import expit
fn = log_logistic if log else expit
return fn(X, out)
def log_logistic(X, out=None):
"""Compute the log of the logistic function, ``log(1 / (1 + e ** -x))``.
This implementation is numerically stable because it splits positive and
negative values::
-log(1 + exp(-x_i)) if x_i > 0
x_i - log(1 + exp(x_i)) if x_i <= 0
For the ordinary logistic function, use ``sklearn.utils.fixes.expit``.
Parameters
----------
X: array-like, shape (M, N)
Argument to the logistic function
out: array-like, shape: (M, N), optional:
Preallocated output array.
Returns
-------
out: array, shape (M, N)
Log of the logistic function evaluated at every point in x
Notes
-----
See the blog post describing this implementation:
http://fa.bianp.net/blog/2013/numerical-optimizers-for-logistic-regression/
"""
is_1d = X.ndim == 1
X = check_array(X, dtype=np.float)
n_samples, n_features = X.shape
if out is None:
out = np.empty_like(X)
_log_logistic_sigmoid(n_samples, n_features, X, out)
if is_1d:
return np.squeeze(out)
return out
def safe_min(X):
"""Returns the minimum value of a dense or a CSR/CSC matrix.
Adapated from http://stackoverflow.com/q/13426580
"""
if issparse(X):
if len(X.data) == 0:
return 0
m = X.data.min()
return m if X.getnnz() == X.size else min(m, 0)
else:
return X.min()
def make_nonnegative(X, min_value=0):
"""Ensure `X.min()` >= `min_value`."""
min_ = safe_min(X)
if min_ < min_value:
if issparse(X):
raise ValueError("Cannot make the data matrix"
" nonnegative because it is sparse."
" Adding a value to every entry would"
" make it no longer sparse.")
X = X + (min_value - min_)
return X
def _batch_mean_variance_update(X, old_mean, old_variance, old_sample_count):
"""Calculate an average mean update and a Youngs and Cramer variance update.
From the paper "Algorithms for computing the sample variance: analysis and
recommendations", by Chan, Golub, and LeVeque.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Data to use for variance update
old_mean : array-like, shape: (n_features,)
old_variance : array-like, shape: (n_features,)
old_sample_count : int
Returns
-------
updated_mean : array, shape (n_features,)
updated_variance : array, shape (n_features,)
updated_sample_count : int
References
----------
T. Chan, G. Golub, R. LeVeque. Algorithms for computing the sample variance:
recommendations, The American Statistician, Vol. 37, No. 3, pp. 242-247
"""
new_sum = X.sum(axis=0)
new_variance = X.var(axis=0) * X.shape[0]
old_sum = old_mean * old_sample_count
n_samples = X.shape[0]
updated_sample_count = old_sample_count + n_samples
partial_variance = old_sample_count / (n_samples * updated_sample_count) * (
n_samples / old_sample_count * old_sum - new_sum) ** 2
unnormalized_variance = old_variance * old_sample_count + new_variance + \
partial_variance
return ((old_sum + new_sum) / updated_sample_count,
unnormalized_variance / updated_sample_count,
updated_sample_count)
| bsd-3-clause |
PanDAWMS/panda-server | pandaserver/test/testG4sim.py | 1 | 2574 | import sys
import time
import uuid
import pandaserver.userinterface.Client as Client
from pandaserver.taskbuffer.JobSpec import JobSpec
from pandaserver.taskbuffer.FileSpec import FileSpec
if len(sys.argv)>1:
site = sys.argv[1]
else:
site = None
datasetName = 'panda.destDB.%s' % str(uuid.uuid4())
destName = 'BNL_ATLAS_2'
#destName = 'BU_ATLAS_Tier2'
files = {
'mc11.007204.singlepart_mu4.evgen.EVNT.v11000302._00037.pool.root.1':None,
'mc11.007204.singlepart_mu4.evgen.EVNT.v11000302._00038.pool.root.1':None,
}
jobList = []
for lfn in files:
job = JobSpec()
job.jobDefinitionID = int(time.time()) % 10000
job.jobName = str(uuid.uuid4())
job.AtlasRelease = 'Atlas-11.0.3'
job.homepackage = 'JobTransforms-11-00-03-02'
job.transformation = 'share/csc.simul.trf'
job.destinationDBlock = datasetName
job.destinationSE = destName
job.computingSite = site
job.prodDBlock = 'mc11.007204.singlepart_mu4.evgen.EVNT.v11000302'
job.cmtConfig = 'i686-slc4-gcc34-opt'
job.prodSourceLabel = 'test'
job.currentPriority = 1000
fileI = FileSpec()
fileI.dataset = job.prodDBlock
fileI.prodDBlock = job.prodDBlock
fileI.lfn = lfn
fileI.type = 'input'
job.addFile(fileI)
fileOE = FileSpec()
fileOE.lfn = "%s.HITS.pool.root" % str(uuid.uuid4())
fileOE.destinationDBlock = job.destinationDBlock
fileOE.destinationSE = job.destinationSE
fileOE.dataset = job.destinationDBlock
fileOE.destinationDBlockToken = 'ATLASDATADISK'
fileOE.type = 'output'
job.addFile(fileOE)
fileOA = FileSpec()
fileOA.lfn = "%s.RDO.pool.root" % str(uuid.uuid4())
fileOA.destinationDBlock = job.destinationDBlock
fileOA.destinationSE = job.destinationSE
fileOA.dataset = job.destinationDBlock
fileOA.destinationDBlockToken = 'ATLASDATADISK'
fileOA.type = 'output'
job.addFile(fileOA)
fileOL = FileSpec()
fileOL.lfn = "%s.job.log.tgz" % str(uuid.uuid4())
fileOL.destinationDBlock = job.destinationDBlock
fileOL.destinationSE = job.destinationSE
fileOL.dataset = job.destinationDBlock
fileOL.destinationDBlockToken = 'ATLASDATADISK'
fileOL.type = 'log'
job.addFile(fileOL)
job.jobParameters="%s %s %s 100 700 2158" % (fileI.lfn,fileOE.lfn,fileOA.lfn)
jobList.append(job)
s,o = Client.submitJobs(jobList)
print("---------------------")
print(s)
for x in o:
print("PandaID=%s" % x[0])
| apache-2.0 |
RayMick/scikit-learn | examples/model_selection/plot_precision_recall.py | 249 | 6150 | """
================
Precision-Recall
================
Example of Precision-Recall metric to evaluate classifier output quality.
In information retrieval, precision is a measure of result relevancy, while
recall is a measure of how many truly relevant results are returned. A high
area under the curve represents both high recall and high precision, where high
precision relates to a low false positive rate, and high recall relates to a
low false negative rate. High scores for both show that the classifier is
returning accurate results (high precision), as well as returning a majority of
all positive results (high recall).
A system with high recall but low precision returns many results, but most of
its predicted labels are incorrect when compared to the training labels. A
system with high precision but low recall is just the opposite, returning very
few results, but most of its predicted labels are correct when compared to the
training labels. An ideal system with high precision and high recall will
return many results, with all results labeled correctly.
Precision (:math:`P`) is defined as the number of true positives (:math:`T_p`)
over the number of true positives plus the number of false positives
(:math:`F_p`).
:math:`P = \\frac{T_p}{T_p+F_p}`
Recall (:math:`R`) is defined as the number of true positives (:math:`T_p`)
over the number of true positives plus the number of false negatives
(:math:`F_n`).
:math:`R = \\frac{T_p}{T_p + F_n}`
These quantities are also related to the (:math:`F_1`) score, which is defined
as the harmonic mean of precision and recall.
:math:`F1 = 2\\frac{P \\times R}{P+R}`
It is important to note that the precision may not decrease with recall. The
definition of precision (:math:`\\frac{T_p}{T_p + F_p}`) shows that lowering
the threshold of a classifier may increase the denominator, by increasing the
number of results returned. If the threshold was previously set too high, the
new results may all be true positives, which will increase precision. If the
previous threshold was about right or too low, further lowering the threshold
will introduce false positives, decreasing precision.
Recall is defined as :math:`\\frac{T_p}{T_p+F_n}`, where :math:`T_p+F_n` does
not depend on the classifier threshold. This means that lowering the classifier
threshold may increase recall, by increasing the number of true positive
results. It is also possible that lowering the threshold may leave recall
unchanged, while the precision fluctuates.
The relationship between recall and precision can be observed in the
stairstep area of the plot - at the edges of these steps a small change
in the threshold considerably reduces precision, with only a minor gain in
recall. See the corner at recall = .59, precision = .8 for an example of this
phenomenon.
Precision-recall curves are typically used in binary classification to study
the output of a classifier. In order to extend Precision-recall curve and
average precision to multi-class or multi-label classification, it is necessary
to binarize the output. One curve can be drawn per label, but one can also draw
a precision-recall curve by considering each element of the label indicator
matrix as a binary prediction (micro-averaging).
.. note::
See also :func:`sklearn.metrics.average_precision_score`,
:func:`sklearn.metrics.recall_score`,
:func:`sklearn.metrics.precision_score`,
:func:`sklearn.metrics.f1_score`
"""
print(__doc__)
import matplotlib.pyplot as plt
import numpy as np
from sklearn import svm, datasets
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Binarize the output
y = label_binarize(y, classes=[0, 1, 2])
n_classes = y.shape[1]
# Add noisy features
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
# Split into training and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
random_state=random_state)
# Run classifier
classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
random_state=random_state))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)
# Compute Precision-Recall and plot curve
precision = dict()
recall = dict()
average_precision = dict()
for i in range(n_classes):
precision[i], recall[i], _ = precision_recall_curve(y_test[:, i],
y_score[:, i])
average_precision[i] = average_precision_score(y_test[:, i], y_score[:, i])
# Compute micro-average ROC curve and ROC area
precision["micro"], recall["micro"], _ = precision_recall_curve(y_test.ravel(),
y_score.ravel())
average_precision["micro"] = average_precision_score(y_test, y_score,
average="micro")
# Plot Precision-Recall curve
plt.clf()
plt.plot(recall[0], precision[0], label='Precision-Recall curve')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall example: AUC={0:0.2f}'.format(average_precision[0]))
plt.legend(loc="lower left")
plt.show()
# Plot Precision-Recall curve for each class
plt.clf()
plt.plot(recall["micro"], precision["micro"],
label='micro-average Precision-recall curve (area = {0:0.2f})'
''.format(average_precision["micro"]))
for i in range(n_classes):
plt.plot(recall[i], precision[i],
label='Precision-recall curve of class {0} (area = {1:0.2f})'
''.format(i, average_precision[i]))
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Extension of Precision-Recall curve to multi-class')
plt.legend(loc="lower right")
plt.show()
| bsd-3-clause |
zihangdai/xlnet | run_race.py | 1 | 19028 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from os.path import join
from absl import flags
import os
import csv
import collections
import numpy as np
import time
import math
import json
import random
from copy import copy
from collections import defaultdict as dd
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score
import absl.logging as _logging # pylint: disable=unused-import
import tensorflow as tf
import sentencepiece as spm
from data_utils import SEP_ID, VOCAB_SIZE, CLS_ID
import model_utils
import function_builder
from classifier_utils import PaddingInputExample
from classifier_utils import convert_single_example
from prepro_utils import preprocess_text, encode_ids
# Model
flags.DEFINE_string("model_config_path", default=None,
help="Model config path.")
flags.DEFINE_float("dropout", default=0.1,
help="Dropout rate.")
flags.DEFINE_float("dropatt", default=0.1,
help="Attention dropout rate.")
flags.DEFINE_integer("clamp_len", default=-1,
help="Clamp length")
flags.DEFINE_string("summary_type", default="last",
help="Method used to summarize a sequence into a compact vector.")
flags.DEFINE_bool("use_summ_proj", default=True,
help="Whether to use projection for summarizing sequences.")
flags.DEFINE_bool("use_bfloat16", default=False,
help="Whether to use bfloat16.")
# Parameter initialization
flags.DEFINE_enum("init", default="normal",
enum_values=["normal", "uniform"],
help="Initialization method.")
flags.DEFINE_float("init_std", default=0.02,
help="Initialization std when init is normal.")
flags.DEFINE_float("init_range", default=0.1,
help="Initialization std when init is uniform.")
# I/O paths
flags.DEFINE_bool("overwrite_data", default=False,
help="If False, will use cached data if available.")
flags.DEFINE_string("init_checkpoint", default=None,
help="checkpoint path for initializing the model. "
"Could be a pretrained model or a finetuned model.")
flags.DEFINE_string("output_dir", default="",
help="Output dir for TF records.")
flags.DEFINE_string("spiece_model_file", default="",
help="Sentence Piece model path.")
flags.DEFINE_string("model_dir", default="",
help="Directory for saving the finetuned model.")
flags.DEFINE_string("data_dir", default="",
help="Directory for input data.")
# TPUs and machines
flags.DEFINE_bool("use_tpu", default=False, help="whether to use TPU.")
flags.DEFINE_integer("num_hosts", default=1, help="How many TPU hosts.")
flags.DEFINE_integer("num_core_per_host", default=8,
help="8 for TPU v2 and v3-8, 16 for larger TPU v3 pod. In the context "
"of GPU training, it refers to the number of GPUs used.")
flags.DEFINE_string("tpu_job_name", default=None, help="TPU worker job name.")
flags.DEFINE_string("tpu", default=None, help="TPU name.")
flags.DEFINE_string("tpu_zone", default=None, help="TPU zone.")
flags.DEFINE_string("gcp_project", default=None, help="gcp project.")
flags.DEFINE_string("master", default=None, help="master")
flags.DEFINE_integer("iterations", default=1000,
help="number of iterations per TPU training loop.")
# Training
flags.DEFINE_bool("do_train", default=False, help="whether to do training")
flags.DEFINE_integer("train_steps", default=12000,
help="Number of training steps")
flags.DEFINE_integer("warmup_steps", default=0, help="number of warmup steps")
flags.DEFINE_float("learning_rate", default=2e-5, help="initial learning rate")
flags.DEFINE_float("lr_layer_decay_rate", 1.0,
"Top layer: lr[L] = FLAGS.learning_rate."
"Low layer: lr[l-1] = lr[l] * lr_layer_decay_rate.")
flags.DEFINE_float("min_lr_ratio", default=0.0,
help="min lr ratio for cos decay.")
flags.DEFINE_float("clip", default=1.0, help="Gradient clipping")
flags.DEFINE_integer("max_save", default=0,
help="Max number of checkpoints to save. Use 0 to save all.")
flags.DEFINE_integer("save_steps", default=None,
help="Save the model for every save_steps. "
"If None, not to save any model.")
flags.DEFINE_integer("train_batch_size", default=8,
help="Batch size for training. Note that batch size 1 corresponds to "
"4 sequences: one paragraph + one quesetion + 4 candidate answers.")
flags.DEFINE_float("weight_decay", default=0.00, help="weight decay rate")
flags.DEFINE_float("adam_epsilon", default=1e-6, help="adam epsilon")
flags.DEFINE_string("decay_method", default="poly", help="poly or cos")
# Evaluation
flags.DEFINE_bool("do_eval", default=False, help="whether to do eval")
flags.DEFINE_string("eval_split", default="dev",
help="could be dev or test")
flags.DEFINE_integer("eval_batch_size", default=32,
help="Batch size for evaluation.")
# Data config
flags.DEFINE_integer("max_seq_length", default=512,
help="Max length for the paragraph.")
flags.DEFINE_integer("max_qa_length", default=128,
help="Max length for the concatenated question and answer.")
flags.DEFINE_integer("shuffle_buffer", default=2048,
help="Buffer size used for shuffle.")
flags.DEFINE_bool("uncased", default=False,
help="Use uncased.")
flags.DEFINE_bool("high_only", default=False,
help="Evaluate on high school only.")
flags.DEFINE_bool("middle_only", default=False,
help="Evaluate on middle school only.")
FLAGS = flags.FLAGS
SEG_ID_A = 0
SEG_ID_B = 1
SEG_ID_CLS = 2
SEG_ID_SEP = 3
SEG_ID_PAD = 4
class PaddingInputExample(object):
"""Fake example so the num input examples is a multiple of the batch size.
When running eval/predict on the TPU, we need to pad the number of examples
to be a multiple of the batch size, because the TPU requires a fixed batch
size. The alternative is to drop the last batch, which is bad because it means
the entire output data won't be generated.
We use this class instead of `None` because treating `None` as padding
battches could cause silent errors.
"""
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
input_ids,
input_mask,
segment_ids,
label_id,
is_real_example=True):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
self.is_real_example = is_real_example
def convert_single_example(example, tokenize_fn):
"""Converts a single `InputExample` into a single `InputFeatures`."""
if isinstance(example, PaddingInputExample):
return InputFeatures(
input_ids=[0] * FLAGS.max_seq_length * 4,
input_mask=[1] * FLAGS.max_seq_length * 4,
segment_ids=[0] * FLAGS.max_seq_length * 4,
label_id=0,
is_real_example=False)
input_ids, input_mask, all_seg_ids = [], [], []
tokens_context = tokenize_fn(example.context)
for i in range(len(example.qa_list)):
tokens_qa = tokenize_fn(example.qa_list[i])
if len(tokens_qa) > FLAGS.max_qa_length:
tokens_qa = tokens_qa[- FLAGS.max_qa_length:]
if len(tokens_context) + len(tokens_qa) > FLAGS.max_seq_length - 3:
tokens = tokens_context[: FLAGS.max_seq_length - 3 - len(tokens_qa)]
else:
tokens = tokens_context
segment_ids = [SEG_ID_A] * len(tokens)
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_A)
tokens.extend(tokens_qa)
segment_ids.extend([SEG_ID_B] * len(tokens_qa))
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_B)
tokens.append(CLS_ID)
segment_ids.append(SEG_ID_CLS)
cur_input_ids = tokens
cur_input_mask = [0] * len(cur_input_ids)
if len(cur_input_ids) < FLAGS.max_seq_length:
delta_len = FLAGS.max_seq_length - len(cur_input_ids)
cur_input_ids = [0] * delta_len + cur_input_ids
cur_input_mask = [1] * delta_len + cur_input_mask
segment_ids = [SEG_ID_PAD] * delta_len + segment_ids
assert len(cur_input_ids) == FLAGS.max_seq_length
assert len(cur_input_mask) == FLAGS.max_seq_length
assert len(segment_ids) == FLAGS.max_seq_length
input_ids.extend(cur_input_ids)
input_mask.extend(cur_input_mask)
all_seg_ids.extend(segment_ids)
label_id = example.label
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=all_seg_ids,
label_id=label_id)
return feature
class InputExample(object):
def __init__(self, context, qa_list, label, level):
self.context = context
self.qa_list = qa_list
self.label = label
self.level = level
def get_examples(data_dir, set_type):
examples = []
for level in ["middle", "high"]:
if level == "middle" and FLAGS.high_only: continue
if level == "high" and FLAGS.middle_only: continue
cur_dir = os.path.join(data_dir, set_type, level)
for filename in tf.gfile.ListDirectory(cur_dir):
cur_path = os.path.join(cur_dir, filename)
with tf.gfile.Open(cur_path) as f:
cur_data = json.load(f)
answers = cur_data["answers"]
options = cur_data["options"]
questions = cur_data["questions"]
context = cur_data["article"]
for i in range(len(answers)):
label = ord(answers[i]) - ord("A")
qa_list = []
question = questions[i]
for j in range(4):
option = options[i][j]
if "_" in question:
qa_cat = question.replace("_", option)
else:
qa_cat = " ".join([question, option])
qa_list.append(qa_cat)
examples.append(InputExample(context, qa_list, label, level))
return examples
def file_based_convert_examples_to_features(examples, tokenize_fn, output_file):
if tf.gfile.Exists(output_file) and not FLAGS.overwrite_data:
return
tf.logging.info("Start writing tfrecord %s.", output_file)
writer = tf.python_io.TFRecordWriter(output_file)
for ex_index, example in enumerate(examples):
if ex_index % 10000 == 0:
tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
feature = convert_single_example(example, tokenize_fn)
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
def create_float_feature(values):
f = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
return f
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_float_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
features["label_ids"] = create_int_feature([feature.label_id])
features["is_real_example"] = create_int_feature(
[int(feature.is_real_example)])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
writer.close()
def file_based_input_fn_builder(input_file, seq_length, is_training,
drop_remainder):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
name_to_features = {
"input_ids": tf.FixedLenFeature([seq_length * 4], tf.int64),
"input_mask": tf.FixedLenFeature([seq_length * 4], tf.float32),
"segment_ids": tf.FixedLenFeature([seq_length * 4], tf.int64),
"label_ids": tf.FixedLenFeature([], tf.int64),
"is_real_example": tf.FixedLenFeature([], tf.int64),
}
tf.logging.info("Input tfrecord file {}".format(input_file))
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
example[name] = t
return example
def input_fn(params):
"""The actual input function."""
if FLAGS.use_tpu:
batch_size = params["batch_size"]
elif is_training:
batch_size = FLAGS.train_batch_size
elif FLAGS.do_eval:
batch_size = FLAGS.eval_batch_size
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if is_training:
d = d.shuffle(buffer_size=FLAGS.shuffle_buffer)
d = d.repeat()
# d = d.shuffle(buffer_size=100)
d = d.apply(
tf.contrib.data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
return input_fn
def get_model_fn():
def model_fn(features, labels, mode, params):
#### Training or Evaluation
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
total_loss, per_example_loss, logits = function_builder.get_race_loss(
FLAGS, features, is_training)
#### Check model parameters
num_params = sum([np.prod(v.shape) for v in tf.trainable_variables()])
tf.logging.info('#params: {}'.format(num_params))
#### load pretrained models
scaffold_fn = model_utils.init_from_checkpoint(FLAGS)
#### Evaluation mode
if mode == tf.estimator.ModeKeys.EVAL:
assert FLAGS.num_hosts == 1
def metric_fn(per_example_loss, label_ids, logits, is_real_example):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
eval_input_dict = {
'labels': label_ids,
'predictions': predictions,
'weights': is_real_example
}
accuracy = tf.metrics.accuracy(**eval_input_dict)
loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
return {
'eval_accuracy': accuracy,
'eval_loss': loss}
is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
#### Constucting evaluation TPUEstimatorSpec with new cache.
label_ids = tf.reshape(features['label_ids'], [-1])
metric_args = [per_example_loss, label_ids, logits, is_real_example]
if FLAGS.use_tpu:
eval_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=(metric_fn, metric_args),
scaffold_fn=scaffold_fn)
else:
eval_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
eval_metric_ops=metric_fn(*metric_args))
return eval_spec
#### Configuring the optimizer
train_op, learning_rate, _ = model_utils.get_train_op(FLAGS, total_loss)
monitor_dict = {}
monitor_dict["lr"] = learning_rate
#### Constucting training TPUEstimatorSpec with new cache.
if FLAGS.use_tpu:
#### Creating host calls
host_call = None
train_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, loss=total_loss, train_op=train_op, host_call=host_call,
scaffold_fn=scaffold_fn)
else:
train_spec = tf.estimator.EstimatorSpec(
mode=mode, loss=total_loss, train_op=train_op)
return train_spec
return model_fn
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
#### Validate flags
if FLAGS.save_steps is not None:
FLAGS.iterations = min(FLAGS.iterations, FLAGS.save_steps)
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if not tf.gfile.Exists(FLAGS.output_dir):
tf.gfile.MakeDirs(FLAGS.output_dir)
sp = spm.SentencePieceProcessor()
sp.Load(FLAGS.spiece_model_file)
def tokenize_fn(text):
text = preprocess_text(text, lower=FLAGS.uncased)
return encode_ids(sp, text)
# TPU Configuration
run_config = model_utils.configure_tpu(FLAGS)
model_fn = get_model_fn()
spm_basename = os.path.basename(FLAGS.spiece_model_file)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
if FLAGS.use_tpu:
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size)
else:
estimator = tf.estimator.Estimator(
model_fn=model_fn,
config=run_config)
if FLAGS.do_train:
train_file_base = "{}.len-{}.train.tf_record".format(
spm_basename, FLAGS.max_seq_length)
train_file = os.path.join(FLAGS.output_dir, train_file_base)
if not tf.gfile.Exists(train_file) or FLAGS.overwrite_data:
train_examples = get_examples(FLAGS.data_dir, "train")
random.shuffle(train_examples)
file_based_convert_examples_to_features(
train_examples, tokenize_fn, train_file)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.train_steps)
if FLAGS.do_eval:
eval_examples = get_examples(FLAGS.data_dir, FLAGS.eval_split)
tf.logging.info("Num of eval samples: {}".format(len(eval_examples)))
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
#
# Modified in XL: We also adopt the same mechanism for GPUs.
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(PaddingInputExample())
eval_file_base = "{}.len-{}.{}.tf_record".format(
spm_basename, FLAGS.max_seq_length, FLAGS.eval_split)
if FLAGS.high_only:
eval_file_base = "high." + eval_file_base
elif FLAGS.middle_only:
eval_file_base = "middle." + eval_file_base
eval_file = os.path.join(FLAGS.output_dir, eval_file_base)
file_based_convert_examples_to_features(
eval_examples, tokenize_fn, eval_file)
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_input_fn = file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=True)
ret = estimator.evaluate(
input_fn=eval_input_fn,
steps=eval_steps)
# Log current result
tf.logging.info("=" * 80)
log_str = "Eval | "
for key, val in ret.items():
log_str += "{} {} | ".format(key, val)
tf.logging.info(log_str)
tf.logging.info("=" * 80)
if __name__ == "__main__":
tf.app.run()
| apache-2.0 |
mugizico/scikit-learn | benchmarks/bench_tree.py | 297 | 3617 | """
To run this, you'll need to have installed.
* scikit-learn
Does two benchmarks
First, we fix a training set, increase the number of
samples to classify and plot number of classified samples as a
function of time.
In the second benchmark, we increase the number of dimensions of the
training set, classify a sample and plot the time taken as a function
of the number of dimensions.
"""
import numpy as np
import pylab as pl
import gc
from datetime import datetime
# to store the results
scikit_classifier_results = []
scikit_regressor_results = []
mu_second = 0.0 + 10 ** 6 # number of microseconds in a second
def bench_scikit_tree_classifier(X, Y):
"""Benchmark with scikit-learn decision tree classifier"""
from sklearn.tree import DecisionTreeClassifier
gc.collect()
# start time
tstart = datetime.now()
clf = DecisionTreeClassifier()
clf.fit(X, Y).predict(X)
delta = (datetime.now() - tstart)
# stop time
scikit_classifier_results.append(
delta.seconds + delta.microseconds / mu_second)
def bench_scikit_tree_regressor(X, Y):
"""Benchmark with scikit-learn decision tree regressor"""
from sklearn.tree import DecisionTreeRegressor
gc.collect()
# start time
tstart = datetime.now()
clf = DecisionTreeRegressor()
clf.fit(X, Y).predict(X)
delta = (datetime.now() - tstart)
# stop time
scikit_regressor_results.append(
delta.seconds + delta.microseconds / mu_second)
if __name__ == '__main__':
print('============================================')
print('Warning: this is going to take a looong time')
print('============================================')
n = 10
step = 10000
n_samples = 10000
dim = 10
n_classes = 10
for i in range(n):
print('============================================')
print('Entering iteration %s of %s' % (i, n))
print('============================================')
n_samples += step
X = np.random.randn(n_samples, dim)
Y = np.random.randint(0, n_classes, (n_samples,))
bench_scikit_tree_classifier(X, Y)
Y = np.random.randn(n_samples)
bench_scikit_tree_regressor(X, Y)
xx = range(0, n * step, step)
pl.figure('scikit-learn tree benchmark results')
pl.subplot(211)
pl.title('Learning with varying number of samples')
pl.plot(xx, scikit_classifier_results, 'g-', label='classification')
pl.plot(xx, scikit_regressor_results, 'r-', label='regression')
pl.legend(loc='upper left')
pl.xlabel('number of samples')
pl.ylabel('Time (s)')
scikit_classifier_results = []
scikit_regressor_results = []
n = 10
step = 500
start_dim = 500
n_classes = 10
dim = start_dim
for i in range(0, n):
print('============================================')
print('Entering iteration %s of %s' % (i, n))
print('============================================')
dim += step
X = np.random.randn(100, dim)
Y = np.random.randint(0, n_classes, (100,))
bench_scikit_tree_classifier(X, Y)
Y = np.random.randn(100)
bench_scikit_tree_regressor(X, Y)
xx = np.arange(start_dim, start_dim + n * step, step)
pl.subplot(212)
pl.title('Learning in high dimensional spaces')
pl.plot(xx, scikit_classifier_results, 'g-', label='classification')
pl.plot(xx, scikit_regressor_results, 'r-', label='regression')
pl.legend(loc='upper left')
pl.xlabel('number of dimensions')
pl.ylabel('Time (s)')
pl.axis('tight')
pl.show()
| bsd-3-clause |
jljones/berkeley | python_email/simulation_example/chirikov.py | 10 | 1970 | import matplotlib.pyplot as plt
import numpy as np
import math
def generateChirikovMaps(ks, numInits, numParticles):
'''Given four values for the "kick" intensity, this function plots
a Chirikov-Taylor map for each KI value in the same window for
easy comparison.
Recommended Values:
ks = (.5,.75,.95,1.0)
numInits = 20
numParticles = 10000
For more information on the standard map, see:
http://en.wikipedia.org/wiki/Standard_map '''
f, ax = plt.subplots(2, 2)
kin = 0
# Check to make sure the input length is 4, as expected
if len(ks) != 4:
print "Must have 4 and only 4 k values. Exiting..."
return 1
# The outer 2 loops (j,l) handle the plotting for each axis
for j in range( 0,len(ax) ):
for l in range( 0,len(ax) ):
# Run the simulation for numInits # of initial points
print 'Simulating k = %s with %s particles...' %(k[kin], numInits)
for i in range(1,numInits):
temp = np.zeros( (2, numParticles) )
x = temp[0,:]
p = temp[1,:]
# Pick a starting point at random in p-phase space
x[0] = np.random.rand()*2*math.pi
p[0] = np.random.rand()*2*math.pi
# Follow the trajectory of the random point in phase space according
# to the chirikov-taylor relation
for n in range(1,numParticles):
p[n] = ( p[n-1] + ks[kin]*math.sin( x[n-1] ) ) % (2*math.pi)
x[n] = ( x[n-1] + p[n] ) % (2*math.pi)
# Plot each of the numInits trajectories on the same plot
ax[j][l].plot( x/(2*math.pi), p/(2*math.pi), 'b.', ms=1)
ax[j][l].set_title('Chirikov Map, K = %s' %(ks[kin]) )
ax[j][l].set_xlabel(r'$\frac{x}{2\pi}$')
ax[j][l].set_ylabel(r'$\frac{p}{2\pi}$')
kin += 1
plt.savefig('chirikov_results.png')
return ax
if __name__ == '__main__':
k = [0.5, 0.75, 0.9, 1.0]
generateChirikovMaps(k, 20, 50000)
| bsd-3-clause |
kou/arrow | python/examples/flight/client.py | 6 | 6791 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""An example Flight CLI client."""
import argparse
import sys
import pyarrow
import pyarrow.flight
import pyarrow.csv as csv
def list_flights(args, client, connection_args={}):
print('Flights\n=======')
for flight in client.list_flights():
descriptor = flight.descriptor
if descriptor.descriptor_type == pyarrow.flight.DescriptorType.PATH:
print("Path:", descriptor.path)
elif descriptor.descriptor_type == pyarrow.flight.DescriptorType.CMD:
print("Command:", descriptor.command)
else:
print("Unknown descriptor type")
print("Total records:", end=" ")
if flight.total_records >= 0:
print(flight.total_records)
else:
print("Unknown")
print("Total bytes:", end=" ")
if flight.total_bytes >= 0:
print(flight.total_bytes)
else:
print("Unknown")
print("Number of endpoints:", len(flight.endpoints))
print("Schema:")
print(flight.schema)
print('---')
print('\nActions\n=======')
for action in client.list_actions():
print("Type:", action.type)
print("Description:", action.description)
print('---')
def do_action(args, client, connection_args={}):
try:
buf = pyarrow.allocate_buffer(0)
action = pyarrow.flight.Action(args.action_type, buf)
print('Running action', args.action_type)
for result in client.do_action(action):
print("Got result", result.body.to_pybytes())
except pyarrow.lib.ArrowIOError as e:
print("Error calling action:", e)
def push_data(args, client, connection_args={}):
print('File Name:', args.file)
my_table = csv.read_csv(args.file)
print('Table rows=', str(len(my_table)))
df = my_table.to_pandas()
print(df.head())
writer, _ = client.do_put(
pyarrow.flight.FlightDescriptor.for_path(args.file), my_table.schema)
writer.write_table(my_table)
writer.close()
def get_flight(args, client, connection_args={}):
if args.path:
descriptor = pyarrow.flight.FlightDescriptor.for_path(*args.path)
else:
descriptor = pyarrow.flight.FlightDescriptor.for_command(args.command)
info = client.get_flight_info(descriptor)
for endpoint in info.endpoints:
print('Ticket:', endpoint.ticket)
for location in endpoint.locations:
print(location)
get_client = pyarrow.flight.FlightClient(location,
**connection_args)
reader = get_client.do_get(endpoint.ticket)
df = reader.read_pandas()
print(df)
def _add_common_arguments(parser):
parser.add_argument('--tls', action='store_true',
help='Enable transport-level security')
parser.add_argument('--tls-roots', default=None,
help='Path to trusted TLS certificate(s)')
parser.add_argument("--mtls", nargs=2, default=None,
metavar=('CERTFILE', 'KEYFILE'),
help="Enable transport-level security")
parser.add_argument('host', type=str,
help="Address or hostname to connect to")
def main():
parser = argparse.ArgumentParser()
subcommands = parser.add_subparsers()
cmd_list = subcommands.add_parser('list')
cmd_list.set_defaults(action='list')
_add_common_arguments(cmd_list)
cmd_list.add_argument('-l', '--list', action='store_true',
help="Print more details.")
cmd_do = subcommands.add_parser('do')
cmd_do.set_defaults(action='do')
_add_common_arguments(cmd_do)
cmd_do.add_argument('action_type', type=str,
help="The action type to run.")
cmd_put = subcommands.add_parser('put')
cmd_put.set_defaults(action='put')
_add_common_arguments(cmd_put)
cmd_put.add_argument('file', type=str,
help="CSV file to upload.")
cmd_get = subcommands.add_parser('get')
cmd_get.set_defaults(action='get')
_add_common_arguments(cmd_get)
cmd_get_descriptor = cmd_get.add_mutually_exclusive_group(required=True)
cmd_get_descriptor.add_argument('-p', '--path', type=str, action='append',
help="The path for the descriptor.")
cmd_get_descriptor.add_argument('-c', '--command', type=str,
help="The command for the descriptor.")
args = parser.parse_args()
if not hasattr(args, 'action'):
parser.print_help()
sys.exit(1)
commands = {
'list': list_flights,
'do': do_action,
'get': get_flight,
'put': push_data,
}
host, port = args.host.split(':')
port = int(port)
scheme = "grpc+tcp"
connection_args = {}
if args.tls:
scheme = "grpc+tls"
if args.tls_roots:
with open(args.tls_roots, "rb") as root_certs:
connection_args["tls_root_certs"] = root_certs.read()
if args.mtls:
with open(args.mtls[0], "rb") as cert_file:
tls_cert_chain = cert_file.read()
with open(args.mtls[1], "rb") as key_file:
tls_private_key = key_file.read()
connection_args["cert_chain"] = tls_cert_chain
connection_args["private_key"] = tls_private_key
client = pyarrow.flight.FlightClient(f"{scheme}://{host}:{port}",
**connection_args)
while True:
try:
action = pyarrow.flight.Action("healthcheck", b"")
options = pyarrow.flight.FlightCallOptions(timeout=1)
list(client.do_action(action, options=options))
break
except pyarrow.ArrowIOError as e:
if "Deadline" in str(e):
print("Server is not ready, waiting...")
commands[args.action](args, client, connection_args)
if __name__ == '__main__':
main()
| apache-2.0 |
zaxliu/scipy | scipy/integrate/quadrature.py | 26 | 27908 | from __future__ import division, print_function, absolute_import
__all__ = ['fixed_quad','quadrature','romberg','trapz','simps','romb',
'cumtrapz','newton_cotes']
from scipy.special.orthogonal import p_roots
from scipy.special import gammaln
from numpy import sum, ones, add, diff, isinf, isscalar, \
asarray, real, trapz, arange, empty
import numpy as np
import math
import warnings
from scipy._lib.six import xrange
class AccuracyWarning(Warning):
pass
def _cached_p_roots(n):
"""
Cache p_roots results for speeding up multiple calls of the fixed_quad function.
"""
if n in _cached_p_roots.cache:
return _cached_p_roots.cache[n]
_cached_p_roots.cache[n] = p_roots(n)
return _cached_p_roots.cache[n]
_cached_p_roots.cache = dict()
def fixed_quad(func,a,b,args=(),n=5):
"""
Compute a definite integral using fixed-order Gaussian quadrature.
Integrate `func` from `a` to `b` using Gaussian quadrature of
order `n`.
Parameters
----------
func : callable
A Python function or method to integrate (must accept vector inputs).
a : float
Lower limit of integration.
b : float
Upper limit of integration.
args : tuple, optional
Extra arguments to pass to function, if any.
n : int, optional
Order of quadrature integration. Default is 5.
Returns
-------
val : float
Gaussian quadrature approximation to the integral
none : None
Statically returned value of None
See Also
--------
quad : adaptive quadrature using QUADPACK
dblquad : double integrals
tplquad : triple integrals
romberg : adaptive Romberg quadrature
quadrature : adaptive Gaussian quadrature
romb : integrators for sampled data
simps : integrators for sampled data
cumtrapz : cumulative integration for sampled data
ode : ODE integrator
odeint : ODE integrator
"""
[x,w] = _cached_p_roots(n)
x = real(x)
ainf, binf = map(isinf,(a,b))
if ainf or binf:
raise ValueError("Gaussian quadrature is only available for "
"finite limits.")
y = (b-a)*(x+1)/2.0 + a
return (b-a)/2.0*sum(w*func(y,*args),0), None
def vectorize1(func, args=(), vec_func=False):
"""Vectorize the call to a function.
This is an internal utility function used by `romberg` and
`quadrature` to create a vectorized version of a function.
If `vec_func` is True, the function `func` is assumed to take vector
arguments.
Parameters
----------
func : callable
User defined function.
args : tuple, optional
Extra arguments for the function.
vec_func : bool, optional
True if the function func takes vector arguments.
Returns
-------
vfunc : callable
A function that will take a vector argument and return the
result.
"""
if vec_func:
def vfunc(x):
return func(x, *args)
else:
def vfunc(x):
if isscalar(x):
return func(x, *args)
x = asarray(x)
# call with first point to get output type
y0 = func(x[0], *args)
n = len(x)
if hasattr(y0, 'dtype'):
output = empty((n,), dtype=y0.dtype)
else:
output = empty((n,), dtype=type(y0))
output[0] = y0
for i in xrange(1, n):
output[i] = func(x[i], *args)
return output
return vfunc
def quadrature(func, a, b, args=(), tol=1.49e-8, rtol=1.49e-8, maxiter=50,
vec_func=True, miniter=1):
"""
Compute a definite integral using fixed-tolerance Gaussian quadrature.
Integrate `func` from `a` to `b` using Gaussian quadrature
with absolute tolerance `tol`.
Parameters
----------
func : function
A Python function or method to integrate.
a : float
Lower limit of integration.
b : float
Upper limit of integration.
args : tuple, optional
Extra arguments to pass to function.
tol, rtol : float, optional
Iteration stops when error between last two iterates is less than
`tol` OR the relative change is less than `rtol`.
maxiter : int, optional
Maximum order of Gaussian quadrature.
vec_func : bool, optional
True or False if func handles arrays as arguments (is
a "vector" function). Default is True.
miniter : int, optional
Minimum order of Gaussian quadrature.
Returns
-------
val : float
Gaussian quadrature approximation (within tolerance) to integral.
err : float
Difference between last two estimates of the integral.
See also
--------
romberg: adaptive Romberg quadrature
fixed_quad: fixed-order Gaussian quadrature
quad: adaptive quadrature using QUADPACK
dblquad: double integrals
tplquad: triple integrals
romb: integrator for sampled data
simps: integrator for sampled data
cumtrapz: cumulative integration for sampled data
ode: ODE integrator
odeint: ODE integrator
"""
if not isinstance(args, tuple):
args = (args,)
vfunc = vectorize1(func, args, vec_func=vec_func)
val = np.inf
err = np.inf
maxiter = max(miniter+1, maxiter)
for n in xrange(miniter, maxiter+1):
newval = fixed_quad(vfunc, a, b, (), n)[0]
err = abs(newval-val)
val = newval
if err < tol or err < rtol*abs(val):
break
else:
warnings.warn(
"maxiter (%d) exceeded. Latest difference = %e" % (maxiter, err),
AccuracyWarning)
return val, err
def tupleset(t, i, value):
l = list(t)
l[i] = value
return tuple(l)
def cumtrapz(y, x=None, dx=1.0, axis=-1, initial=None):
"""
Cumulatively integrate y(x) using the composite trapezoidal rule.
Parameters
----------
y : array_like
Values to integrate.
x : array_like, optional
The coordinate to integrate along. If None (default), use spacing `dx`
between consecutive elements in `y`.
dx : int, optional
Spacing between elements of `y`. Only used if `x` is None.
axis : int, optional
Specifies the axis to cumulate. Default is -1 (last axis).
initial : scalar, optional
If given, uses this value as the first value in the returned result.
Typically this value should be 0. Default is None, which means no
value at ``x[0]`` is returned and `res` has one element less than `y`
along the axis of integration.
Returns
-------
res : ndarray
The result of cumulative integration of `y` along `axis`.
If `initial` is None, the shape is such that the axis of integration
has one less value than `y`. If `initial` is given, the shape is equal
to that of `y`.
See Also
--------
numpy.cumsum, numpy.cumprod
quad: adaptive quadrature using QUADPACK
romberg: adaptive Romberg quadrature
quadrature: adaptive Gaussian quadrature
fixed_quad: fixed-order Gaussian quadrature
dblquad: double integrals
tplquad: triple integrals
romb: integrators for sampled data
ode: ODE integrators
odeint: ODE integrators
Examples
--------
>>> from scipy import integrate
>>> import matplotlib.pyplot as plt
>>> x = np.linspace(-2, 2, num=20)
>>> y = x
>>> y_int = integrate.cumtrapz(y, x, initial=0)
>>> plt.plot(x, y_int, 'ro', x, y[0] + 0.5 * x**2, 'b-')
>>> plt.show()
"""
y = asarray(y)
if x is None:
d = dx
else:
x = asarray(x)
if x.ndim == 1:
d = diff(x)
# reshape to correct shape
shape = [1] * y.ndim
shape[axis] = -1
d = d.reshape(shape)
elif len(x.shape) != len(y.shape):
raise ValueError("If given, shape of x must be 1-d or the "
"same as y.")
else:
d = diff(x, axis=axis)
if d.shape[axis] != y.shape[axis] - 1:
raise ValueError("If given, length of x along axis must be the "
"same as y.")
nd = len(y.shape)
slice1 = tupleset((slice(None),)*nd, axis, slice(1, None))
slice2 = tupleset((slice(None),)*nd, axis, slice(None, -1))
res = add.accumulate(d * (y[slice1] + y[slice2]) / 2.0, axis)
if initial is not None:
if not np.isscalar(initial):
raise ValueError("`initial` parameter should be a scalar.")
shape = list(res.shape)
shape[axis] = 1
res = np.concatenate([np.ones(shape, dtype=res.dtype) * initial, res],
axis=axis)
return res
def _basic_simps(y,start,stop,x,dx,axis):
nd = len(y.shape)
if start is None:
start = 0
step = 2
all = (slice(None),)*nd
slice0 = tupleset(all, axis, slice(start, stop, step))
slice1 = tupleset(all, axis, slice(start+1, stop+1, step))
slice2 = tupleset(all, axis, slice(start+2, stop+2, step))
if x is None: # Even spaced Simpson's rule.
result = add.reduce(dx/3.0 * (y[slice0]+4*y[slice1]+y[slice2]),
axis)
else:
# Account for possibly different spacings.
# Simpson's rule changes a bit.
h = diff(x,axis=axis)
sl0 = tupleset(all, axis, slice(start, stop, step))
sl1 = tupleset(all, axis, slice(start+1, stop+1, step))
h0 = h[sl0]
h1 = h[sl1]
hsum = h0 + h1
hprod = h0 * h1
h0divh1 = h0 / h1
result = add.reduce(hsum/6.0*(y[slice0]*(2-1.0/h0divh1) +
y[slice1]*hsum*hsum/hprod +
y[slice2]*(2-h0divh1)),axis)
return result
def simps(y, x=None, dx=1, axis=-1, even='avg'):
"""
Integrate y(x) using samples along the given axis and the composite
Simpson's rule. If x is None, spacing of dx is assumed.
If there are an even number of samples, N, then there are an odd
number of intervals (N-1), but Simpson's rule requires an even number
of intervals. The parameter 'even' controls how this is handled.
Parameters
----------
y : array_like
Array to be integrated.
x : array_like, optional
If given, the points at which `y` is sampled.
dx : int, optional
Spacing of integration points along axis of `y`. Only used when
`x` is None. Default is 1.
axis : int, optional
Axis along which to integrate. Default is the last axis.
even : {'avg', 'first', 'str'}, optional
'avg' : Average two results:1) use the first N-2 intervals with
a trapezoidal rule on the last interval and 2) use the last
N-2 intervals with a trapezoidal rule on the first interval.
'first' : Use Simpson's rule for the first N-2 intervals with
a trapezoidal rule on the last interval.
'last' : Use Simpson's rule for the last N-2 intervals with a
trapezoidal rule on the first interval.
See Also
--------
quad: adaptive quadrature using QUADPACK
romberg: adaptive Romberg quadrature
quadrature: adaptive Gaussian quadrature
fixed_quad: fixed-order Gaussian quadrature
dblquad: double integrals
tplquad: triple integrals
romb: integrators for sampled data
cumtrapz: cumulative integration for sampled data
ode: ODE integrators
odeint: ODE integrators
Notes
-----
For an odd number of samples that are equally spaced the result is
exact if the function is a polynomial of order 3 or less. If
the samples are not equally spaced, then the result is exact only
if the function is a polynomial of order 2 or less.
"""
y = asarray(y)
nd = len(y.shape)
N = y.shape[axis]
last_dx = dx
first_dx = dx
returnshape = 0
if x is not None:
x = asarray(x)
if len(x.shape) == 1:
shapex = ones(nd)
shapex[axis] = x.shape[0]
saveshape = x.shape
returnshape = 1
x = x.reshape(tuple(shapex))
elif len(x.shape) != len(y.shape):
raise ValueError("If given, shape of x must be 1-d or the "
"same as y.")
if x.shape[axis] != N:
raise ValueError("If given, length of x along axis must be the "
"same as y.")
if N % 2 == 0:
val = 0.0
result = 0.0
slice1 = (slice(None),)*nd
slice2 = (slice(None),)*nd
if even not in ['avg', 'last', 'first']:
raise ValueError("Parameter 'even' must be 'avg', 'last', or 'first'.")
# Compute using Simpson's rule on first intervals
if even in ['avg', 'first']:
slice1 = tupleset(slice1, axis, -1)
slice2 = tupleset(slice2, axis, -2)
if x is not None:
last_dx = x[slice1] - x[slice2]
val += 0.5*last_dx*(y[slice1]+y[slice2])
result = _basic_simps(y,0,N-3,x,dx,axis)
# Compute using Simpson's rule on last set of intervals
if even in ['avg', 'last']:
slice1 = tupleset(slice1, axis, 0)
slice2 = tupleset(slice2, axis, 1)
if x is not None:
first_dx = x[tuple(slice2)] - x[tuple(slice1)]
val += 0.5*first_dx*(y[slice2]+y[slice1])
result += _basic_simps(y,1,N-2,x,dx,axis)
if even == 'avg':
val /= 2.0
result /= 2.0
result = result + val
else:
result = _basic_simps(y,0,N-2,x,dx,axis)
if returnshape:
x = x.reshape(saveshape)
return result
def romb(y, dx=1.0, axis=-1, show=False):
"""
Romberg integration using samples of a function.
Parameters
----------
y : array_like
A vector of ``2**k + 1`` equally-spaced samples of a function.
dx : float, optional
The sample spacing. Default is 1.
axis : int, optional
The axis along which to integrate. Default is -1 (last axis).
show : bool, optional
When `y` is a single 1-D array, then if this argument is True
print the table showing Richardson extrapolation from the
samples. Default is False.
Returns
-------
romb : ndarray
The integrated result for `axis`.
See also
--------
quad : adaptive quadrature using QUADPACK
romberg : adaptive Romberg quadrature
quadrature : adaptive Gaussian quadrature
fixed_quad : fixed-order Gaussian quadrature
dblquad : double integrals
tplquad : triple integrals
simps : integrators for sampled data
cumtrapz : cumulative integration for sampled data
ode : ODE integrators
odeint : ODE integrators
"""
y = asarray(y)
nd = len(y.shape)
Nsamps = y.shape[axis]
Ninterv = Nsamps-1
n = 1
k = 0
while n < Ninterv:
n <<= 1
k += 1
if n != Ninterv:
raise ValueError("Number of samples must be one plus a "
"non-negative power of 2.")
R = {}
all = (slice(None),) * nd
slice0 = tupleset(all, axis, 0)
slicem1 = tupleset(all, axis, -1)
h = Ninterv*asarray(dx)*1.0
R[(0,0)] = (y[slice0] + y[slicem1])/2.0*h
slice_R = all
start = stop = step = Ninterv
for i in range(1,k+1):
start >>= 1
slice_R = tupleset(slice_R, axis, slice(start,stop,step))
step >>= 1
R[(i,0)] = 0.5*(R[(i-1,0)] + h*add.reduce(y[slice_R],axis))
for j in range(1,i+1):
R[(i,j)] = R[(i,j-1)] + \
(R[(i,j-1)]-R[(i-1,j-1)]) / ((1 << (2*j))-1)
h = h / 2.0
if show:
if not isscalar(R[(0,0)]):
print("*** Printing table only supported for integrals" +
" of a single data set.")
else:
try:
precis = show[0]
except (TypeError, IndexError):
precis = 5
try:
width = show[1]
except (TypeError, IndexError):
width = 8
formstr = "%" + str(width) + '.' + str(precis)+'f'
print("\n Richardson Extrapolation Table for Romberg Integration ")
print("====================================================================")
for i in range(0,k+1):
for j in range(0,i+1):
print(formstr % R[(i,j)], end=' ')
print()
print("====================================================================\n")
return R[(k,k)]
# Romberg quadratures for numeric integration.
#
# Written by Scott M. Ransom <ransom@cfa.harvard.edu>
# last revision: 14 Nov 98
#
# Cosmetic changes by Konrad Hinsen <hinsen@cnrs-orleans.fr>
# last revision: 1999-7-21
#
# Adapted to scipy by Travis Oliphant <oliphant.travis@ieee.org>
# last revision: Dec 2001
def _difftrap(function, interval, numtraps):
"""
Perform part of the trapezoidal rule to integrate a function.
Assume that we had called difftrap with all lower powers-of-2
starting with 1. Calling difftrap only returns the summation
of the new ordinates. It does _not_ multiply by the width
of the trapezoids. This must be performed by the caller.
'function' is the function to evaluate (must accept vector arguments).
'interval' is a sequence with lower and upper limits
of integration.
'numtraps' is the number of trapezoids to use (must be a
power-of-2).
"""
if numtraps <= 0:
raise ValueError("numtraps must be > 0 in difftrap().")
elif numtraps == 1:
return 0.5*(function(interval[0])+function(interval[1]))
else:
numtosum = numtraps/2
h = float(interval[1]-interval[0])/numtosum
lox = interval[0] + 0.5 * h
points = lox + h * arange(0, numtosum)
s = sum(function(points),0)
return s
def _romberg_diff(b, c, k):
"""
Compute the differences for the Romberg quadrature corrections.
See Forman Acton's "Real Computing Made Real," p 143.
"""
tmp = 4.0**k
return (tmp * c - b)/(tmp - 1.0)
def _printresmat(function, interval, resmat):
# Print the Romberg result matrix.
i = j = 0
print('Romberg integration of', repr(function), end=' ')
print('from', interval)
print('')
print('%6s %9s %9s' % ('Steps', 'StepSize', 'Results'))
for i in range(len(resmat)):
print('%6d %9f' % (2**i, (interval[1]-interval[0])/(2.**i)), end=' ')
for j in range(i+1):
print('%9f' % (resmat[i][j]), end=' ')
print('')
print('')
print('The final result is', resmat[i][j], end=' ')
print('after', 2**(len(resmat)-1)+1, 'function evaluations.')
def romberg(function, a, b, args=(), tol=1.48e-8, rtol=1.48e-8, show=False,
divmax=10, vec_func=False):
"""
Romberg integration of a callable function or method.
Returns the integral of `function` (a function of one variable)
over the interval (`a`, `b`).
If `show` is 1, the triangular array of the intermediate results
will be printed. If `vec_func` is True (default is False), then
`function` is assumed to support vector arguments.
Parameters
----------
function : callable
Function to be integrated.
a : float
Lower limit of integration.
b : float
Upper limit of integration.
Returns
-------
results : float
Result of the integration.
Other Parameters
----------------
args : tuple, optional
Extra arguments to pass to function. Each element of `args` will
be passed as a single argument to `func`. Default is to pass no
extra arguments.
tol, rtol : float, optional
The desired absolute and relative tolerances. Defaults are 1.48e-8.
show : bool, optional
Whether to print the results. Default is False.
divmax : int, optional
Maximum order of extrapolation. Default is 10.
vec_func : bool, optional
Whether `func` handles arrays as arguments (i.e whether it is a
"vector" function). Default is False.
See Also
--------
fixed_quad : Fixed-order Gaussian quadrature.
quad : Adaptive quadrature using QUADPACK.
dblquad : Double integrals.
tplquad : Triple integrals.
romb : Integrators for sampled data.
simps : Integrators for sampled data.
cumtrapz : Cumulative integration for sampled data.
ode : ODE integrator.
odeint : ODE integrator.
References
----------
.. [1] 'Romberg's method' http://en.wikipedia.org/wiki/Romberg%27s_method
Examples
--------
Integrate a gaussian from 0 to 1 and compare to the error function.
>>> from scipy import integrate
>>> from scipy.special import erf
>>> gaussian = lambda x: 1/np.sqrt(np.pi) * np.exp(-x**2)
>>> result = integrate.romberg(gaussian, 0, 1, show=True)
Romberg integration of <function vfunc at ...> from [0, 1]
::
Steps StepSize Results
1 1.000000 0.385872
2 0.500000 0.412631 0.421551
4 0.250000 0.419184 0.421368 0.421356
8 0.125000 0.420810 0.421352 0.421350 0.421350
16 0.062500 0.421215 0.421350 0.421350 0.421350 0.421350
32 0.031250 0.421317 0.421350 0.421350 0.421350 0.421350 0.421350
The final result is 0.421350396475 after 33 function evaluations.
>>> print("%g %g" % (2*result, erf(1)))
0.842701 0.842701
"""
if isinf(a) or isinf(b):
raise ValueError("Romberg integration only available for finite limits.")
vfunc = vectorize1(function, args, vec_func=vec_func)
n = 1
interval = [a,b]
intrange = b-a
ordsum = _difftrap(vfunc, interval, n)
result = intrange * ordsum
resmat = [[result]]
err = np.inf
for i in xrange(1, divmax+1):
n = n * 2
ordsum = ordsum + _difftrap(vfunc, interval, n)
resmat.append([])
resmat[i].append(intrange * ordsum / n)
for k in range(i):
resmat[i].append(_romberg_diff(resmat[i-1][k], resmat[i][k], k+1))
result = resmat[i][i]
lastresult = resmat[i-1][i-1]
err = abs(result - lastresult)
if err < tol or err < rtol*abs(result):
break
else:
warnings.warn(
"divmax (%d) exceeded. Latest difference = %e" % (divmax, err),
AccuracyWarning)
if show:
_printresmat(vfunc, interval, resmat)
return result
# Coefficients for Netwon-Cotes quadrature
#
# These are the points being used
# to construct the local interpolating polynomial
# a are the weights for Newton-Cotes integration
# B is the error coefficient.
# error in these coefficients grows as N gets larger.
# or as samples are closer and closer together
# You can use maxima to find these rational coefficients
# for equally spaced data using the commands
# a(i,N) := integrate(product(r-j,j,0,i-1) * product(r-j,j,i+1,N),r,0,N) / ((N-i)! * i!) * (-1)^(N-i);
# Be(N) := N^(N+2)/(N+2)! * (N/(N+3) - sum((i/N)^(N+2)*a(i,N),i,0,N));
# Bo(N) := N^(N+1)/(N+1)! * (N/(N+2) - sum((i/N)^(N+1)*a(i,N),i,0,N));
# B(N) := (if (mod(N,2)=0) then Be(N) else Bo(N));
#
# pre-computed for equally-spaced weights
#
# num_a, den_a, int_a, num_B, den_B = _builtincoeffs[N]
#
# a = num_a*array(int_a)/den_a
# B = num_B*1.0 / den_B
#
# integrate(f(x),x,x_0,x_N) = dx*sum(a*f(x_i)) + B*(dx)^(2k+3) f^(2k+2)(x*)
# where k = N // 2
#
_builtincoeffs = {
1:(1,2,[1,1],-1,12),
2:(1,3,[1,4,1],-1,90),
3:(3,8,[1,3,3,1],-3,80),
4:(2,45,[7,32,12,32,7],-8,945),
5:(5,288,[19,75,50,50,75,19],-275,12096),
6:(1,140,[41,216,27,272,27,216,41],-9,1400),
7:(7,17280,[751,3577,1323,2989,2989,1323,3577,751],-8183,518400),
8:(4,14175,[989,5888,-928,10496,-4540,10496,-928,5888,989],
-2368,467775),
9:(9,89600,[2857,15741,1080,19344,5778,5778,19344,1080,
15741,2857], -4671, 394240),
10:(5,299376,[16067,106300,-48525,272400,-260550,427368,
-260550,272400,-48525,106300,16067],
-673175, 163459296),
11:(11,87091200,[2171465,13486539,-3237113, 25226685,-9595542,
15493566,15493566,-9595542,25226685,-3237113,
13486539,2171465], -2224234463, 237758976000),
12:(1, 5255250, [1364651,9903168,-7587864,35725120,-51491295,
87516288,-87797136,87516288,-51491295,35725120,
-7587864,9903168,1364651], -3012, 875875),
13:(13, 402361344000,[8181904909, 56280729661, -31268252574,
156074417954,-151659573325,206683437987,
-43111992612,-43111992612,206683437987,
-151659573325,156074417954,-31268252574,
56280729661,8181904909], -2639651053,
344881152000),
14:(7, 2501928000, [90241897,710986864,-770720657,3501442784,
-6625093363,12630121616,-16802270373,19534438464,
-16802270373,12630121616,-6625093363,3501442784,
-770720657,710986864,90241897], -3740727473,
1275983280000)
}
def newton_cotes(rn, equal=0):
"""
Return weights and error coefficient for Newton-Cotes integration.
Suppose we have (N+1) samples of f at the positions
x_0, x_1, ..., x_N. Then an N-point Newton-Cotes formula for the
integral between x_0 and x_N is:
:math:`\\int_{x_0}^{x_N} f(x)dx = \\Delta x \\sum_{i=0}^{N} a_i f(x_i)
+ B_N (\\Delta x)^{N+2} f^{N+1} (\\xi)`
where :math:`\\xi \\in [x_0,x_N]` and :math:`\\Delta x = \\frac{x_N-x_0}{N}`
is the averages samples spacing.
If the samples are equally-spaced and N is even, then the error
term is :math:`B_N (\\Delta x)^{N+3} f^{N+2}(\\xi)`.
Parameters
----------
rn : int
The integer order for equally-spaced data or the relative positions of
the samples with the first sample at 0 and the last at N, where N+1 is
the length of `rn`. N is the order of the Newton-Cotes integration.
equal : int, optional
Set to 1 to enforce equally spaced data.
Returns
-------
an : ndarray
1-D array of weights to apply to the function at the provided sample
positions.
B : float
Error coefficient.
Notes
-----
Normally, the Newton-Cotes rules are used on smaller integration
regions and a composite rule is used to return the total integral.
"""
try:
N = len(rn)-1
if equal:
rn = np.arange(N+1)
elif np.all(np.diff(rn) == 1):
equal = 1
except:
N = rn
rn = np.arange(N+1)
equal = 1
if equal and N in _builtincoeffs:
na, da, vi, nb, db = _builtincoeffs[N]
return na*np.array(vi,float)/da, float(nb)/db
if (rn[0] != 0) or (rn[-1] != N):
raise ValueError("The sample positions must start at 0"
" and end at N")
yi = rn / float(N)
ti = 2.0*yi - 1
nvec = np.arange(0,N+1)
C = ti**nvec[:,np.newaxis]
Cinv = np.linalg.inv(C)
# improve precision of result
for i in range(2):
Cinv = 2*Cinv - Cinv.dot(C).dot(Cinv)
vec = 2.0 / (nvec[::2]+1)
ai = np.dot(Cinv[:,::2],vec) * N/2
if (N % 2 == 0) and equal:
BN = N/(N+3.)
power = N+2
else:
BN = N/(N+2.)
power = N+1
BN = BN - np.dot(yi**power, ai)
p1 = power+1
fac = power*math.log(N) - gammaln(p1)
fac = math.exp(fac)
return ai, BN*fac
| bsd-3-clause |
evanbiederstedt/RRBSfun | epiphen/total_chr11.py | 2 | 32998 | import glob
import pandas as pd
import numpy as np
pd.set_option('display.max_columns', 50) # print all rows
import os
os.chdir("/gpfs/commons/home/biederstedte-934/evan_projects/correct_phylo_files")
normalB = glob.glob("binary_position_RRBS_normal_B_cell*")
mcell = glob.glob("binary_position_RRBS_NormalBCD19pCD27mcell*")
pcell = glob.glob("binary_position_RRBS_NormalBCD19pCD27pcell*")
cd19cell = glob.glob("binary_position_RRBS_NormalBCD19pcell*")
cw154 = glob.glob("binary_position_RRBS_cw154*")
trito = glob.glob("binary_position_RRBS_trito_pool*")
print(len(normalB))
print(len(mcell))
print(len(pcell))
print(len(cd19cell))
print(len(cw154))
print(len(trito))
totalfiles = normalB + mcell + pcell + cd19cell + cw154 + trito
print(len(totalfiles))
df_list = []
for file in totalfiles:
df = pd.read_csv(file)
df = df.drop("Unnamed: 0", axis=1)
df["chromosome"] = df["position"].map(lambda x: str(x)[:5])
df = df[df["chromosome"] == "chr11"]
df = df.drop("chromosome", axis=1)
df_list.append(df)
print(len(df_list))
total_matrix = pd.concat([df.set_index("position") for df in df_list], axis=1).reset_index().astype(object)
total_matrix = total_matrix.drop("index", axis=1)
len(total_matrix.columns)
total_matrix.columns = ["RRBS_normal_B_cell_A1_24_TAAGGCGA.ACAACC",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.ACCGCG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.ACGTGG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.AGGATG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.ATAGCG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.ATCGAC",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.CAAGAG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.CATGAC",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.CGGTAG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.CTATTG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.CTCAGC",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.GACACG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.GCTGCC",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.GGCATC",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.GTGAGG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.GTTGAG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.TAGCGG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.TATCTC",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.TCTCTG",
"RRBS_normal_B_cell_A1_24_TAAGGCGA.TGACAG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.ACAACC",
"RRBS_normal_B_cell_B1_24_CGTACTAG.ACCGCG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.ACTCAC",
"RRBS_normal_B_cell_B1_24_CGTACTAG.ATAGCG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.CAAGAG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.CATGAC",
"RRBS_normal_B_cell_B1_24_CGTACTAG.CCTTCG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.CGGTAG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.CTATTG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.CTCAGC",
"RRBS_normal_B_cell_B1_24_CGTACTAG.GACACG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.GCATTC",
"RRBS_normal_B_cell_B1_24_CGTACTAG.GGCATC",
"RRBS_normal_B_cell_B1_24_CGTACTAG.GTGAGG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.GTTGAG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.TAGCGG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.TATCTC",
"RRBS_normal_B_cell_B1_24_CGTACTAG.TCTCTG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.TGACAG",
"RRBS_normal_B_cell_B1_24_CGTACTAG.TGCTGC",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.ACAACC",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.ACCGCG",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.ACGTGG",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.ACTCAC",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.AGGATG",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.ATAGCG",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.ATCGAC",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.CAAGAG",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.CATGAC",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.CGGTAG",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.CTATTG",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.GACACG",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.GCATTC",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.GCTGCC",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.GGCATC",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.GTGAGG",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.GTTGAG",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.TAGCGG",
"RRBS_normal_B_cell_C1_24_AGGCAGAA.TATCTC",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.ACAACC",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.ACCGCG",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.ACGTGG",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.ACTCAC",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.AGGATG",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.ATCGAC",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.CAAGAG",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.CATGAC",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.CCTTCG",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.CGGTAG",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.CTATTG",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.CTCAGC",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.GACACG",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.GCATTC",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.GCTGCC",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.GGCATC",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.GTTGAG",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.TAGCGG",
"RRBS_normal_B_cell_D1_24_TCCTGAGC.TATCTC",
"RRBS_normal_B_cell_G1_22_GGACTCCT.ACAACC",
"RRBS_normal_B_cell_G1_22_GGACTCCT.ACCGCG",
"RRBS_normal_B_cell_G1_22_GGACTCCT.ACGTGG",
"RRBS_normal_B_cell_G1_22_GGACTCCT.ACTCAC",
"RRBS_normal_B_cell_G1_22_GGACTCCT.AGGATG",
"RRBS_normal_B_cell_G1_22_GGACTCCT.ATAGCG",
"RRBS_normal_B_cell_G1_22_GGACTCCT.ATCGAC",
"RRBS_normal_B_cell_G1_22_GGACTCCT.CAAGAG",
"RRBS_normal_B_cell_G1_22_GGACTCCT.CATGAC",
"RRBS_normal_B_cell_G1_22_GGACTCCT.CGGTAG",
"RRBS_normal_B_cell_G1_22_GGACTCCT.CTATTG",
"RRBS_normal_B_cell_G1_22_GGACTCCT.CTCAGC",
"RRBS_normal_B_cell_G1_22_GGACTCCT.GACACG",
"RRBS_normal_B_cell_G1_22_GGACTCCT.GCATTC",
"RRBS_normal_B_cell_G1_22_GGACTCCT.GCTGCC",
"RRBS_normal_B_cell_G1_22_GGACTCCT.GGCATC",
"RRBS_normal_B_cell_G1_22_GGACTCCT.GTGAGG",
"RRBS_normal_B_cell_G1_22_GGACTCCT.TAGCGG",
"RRBS_normal_B_cell_G1_22_GGACTCCT.TATCTC",
"RRBS_normal_B_cell_H1_22_TAGGCATG.ACCGCG",
"RRBS_normal_B_cell_H1_22_TAGGCATG.ACGTGG",
"RRBS_normal_B_cell_H1_22_TAGGCATG.ACTCAC",
"RRBS_normal_B_cell_H1_22_TAGGCATG.AGGATG",
"RRBS_normal_B_cell_H1_22_TAGGCATG.ATCGAC",
"RRBS_normal_B_cell_H1_22_TAGGCATG.CAAGAG",
"RRBS_normal_B_cell_H1_22_TAGGCATG.CATGAC",
"RRBS_normal_B_cell_H1_22_TAGGCATG.CCTTCG",
"RRBS_normal_B_cell_H1_22_TAGGCATG.CTATTG",
"RRBS_normal_B_cell_H1_22_TAGGCATG.CTCAGC",
"RRBS_normal_B_cell_H1_22_TAGGCATG.GCATTC",
"RRBS_normal_B_cell_H1_22_TAGGCATG.GCTGCC",
"RRBS_normal_B_cell_H1_22_TAGGCATG.GGCATC",
"RRBS_normal_B_cell_H1_22_TAGGCATG.GTGAGG",
"RRBS_normal_B_cell_H1_22_TAGGCATG.GTTGAG",
"RRBS_normal_B_cell_H1_22_TAGGCATG.TCTCTG",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.ACCGCG",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.ACGTGG",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.ACTCAC",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.ATAGCG",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.ATCGAC",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.CAAGAG",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.CATGAC",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.CCTTCG",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.CTATTG",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.CTCAGC",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.GACACG",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.GCATTC",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.GCTGCC",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.GGCATC",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.GTGAGG",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.GTTGAG",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.TAGCGG",
"RRBS_NormalBCD19pCD27mcell1_22_CGAGGCTG.TATCTC",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACAACC",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACCGCG",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACGTGG",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACTCAC",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.AGGATG",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ATAGCG",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ATCGAC",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.CAAGAG",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.CATGAC",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.CCTTCG",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.CGGTAG",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.CTATTG",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.CTCAGC",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.GACACG",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.GCATTC",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.GTGAGG",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.GTTGAG",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.TATCTC",
"RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.TCTCTG",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.ACAACC",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.ACGTGG",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.ACTCAC",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.AGGATG",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.ATAGCG",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.ATCGAC",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.CAAGAG",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.CATGAC",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.CCTTCG",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.CGGTAG",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.CTATTG",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.CTCAGC",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.GACACG",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.GTGAGG",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.TAGCGG",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.TATCTC",
"RRBS_NormalBCD19pCD27mcell45_66_TAAGGCGA.TCTCTG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.ACAACC",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.ACCGCG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.ACGTGG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.ACTCAC",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.AGGATG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.ATAGCG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.ATCGAC",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.CAAGAG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.CATGAC",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.CCTTCG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.CGGTAG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.CTATTG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.CTCAGC",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.GACACG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.GCATTC",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.GGCATC",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.GTGAGG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.GTTGAG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TAGCGG",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TATCTC",
"RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TCTCTG",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.ACAACC",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.ACCGCG",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.ACTCAC",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.AGGATG",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.ATAGCG",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.ATCGAC",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.CAAGAG",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.CATGAC",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.CCTTCG",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.CGGTAG",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.CTATTG",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.CTCAGC",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.GCATTC",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.GCTGCC",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.GGCATC",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.GTGAGG",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.GTTGAG",
"RRBS_NormalBCD19pCD27pcell1_22_TAGGCATG.TAGCGG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.ACAACC",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.ACCGCG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.ACGTGG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.ACTCAC",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.AGGATG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.ATAGCG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.ATCGAC",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.CAAGAG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.CATGAC",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.CCTTCG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.CGGTAG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.CTATTG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.CTCAGC",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.GACACG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.GCATTC",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.GCTGCC",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.GGCATC",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.GTGAGG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.GTTGAG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.TAGCGG",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.TATCTC",
"RRBS_NormalBCD19pCD27pcell23_44_CTCTCTAC.TCTCTG",
"RRBS_NormalBCD19pCD27pcell45_66_CAGAGAGG.ACCGCG",
"RRBS_NormalBCD19pCD27pcell45_66_CAGAGAGG.ACTCAC",
"RRBS_NormalBCD19pCD27pcell45_66_CAGAGAGG.ATAGCG",
"RRBS_NormalBCD19pCD27pcell45_66_CAGAGAGG.CAAGAG",
"RRBS_NormalBCD19pCD27pcell45_66_CAGAGAGG.CCTTCG",
"RRBS_NormalBCD19pCD27pcell45_66_CAGAGAGG.CTATTG",
"RRBS_NormalBCD19pCD27pcell45_66_CAGAGAGG.GACACG",
"RRBS_NormalBCD19pCD27pcell45_66_CAGAGAGG.GTGAGG",
"RRBS_NormalBCD19pCD27pcell45_66_CAGAGAGG.TAGCGG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.ACAACC",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.ACCGCG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.ACGTGG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.ACTCAC",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.AGGATG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.ATAGCG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.ATCGAC",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.CATGAC",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.CCTTCG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.CGGTAG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.CTATTG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.CTCAGC",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.GACACG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.GCATTC",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.GCTGCC",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.GGCATC",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.GTGAGG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.GTTGAG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TAGCGG",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TATCTC",
"RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TCTCTG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.ACAACC",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.ACCGCG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.ACGTGG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.ACTCAC",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.AGGATG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.ATAGCG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.ATCGAC",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.CAAGAG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.CATGAC",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.CCTTCG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.CGGTAG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.CTATTG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.CTCAGC",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.GACACG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.GCATTC",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.GCTGCC",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.GGCATC",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.GTTGAG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.TAGCGG",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.TATCTC",
"RRBS_NormalBCD19pcell1_22_TAAGGCGA.TCTCTG",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.ACAACC",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.ACCGCG",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.ACGTGG",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.ACTCAC",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.AGGATG",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.ATAGCG",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.ATCGAC",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.CATGAC",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.CCTTCG",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.CGGTAG",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.CTATTG",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.CTCAGC",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.GACACG",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.GCATTC",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.GCTGCC",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.GGCATC",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.GTGAGG",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.TAGCGG",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.TATCTC",
"RRBS_NormalBCD19pcell23_44_CGTACTAG.TCTCTG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.ACAACC",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.ACCGCG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.ACGTGG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.ACTCAC",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.AGGATG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.ATAGCG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.ATCGAC",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.CAAGAG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.CATGAC",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.CCTTCG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.CGGTAG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.CTATTG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.CTCAGC",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.GACACG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.GCATTC",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.GCTGCC",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.GGCATC",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.GTGAGG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.GTTGAG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.TAGCGG",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.TATCTC",
"RRBS_NormalBCD19pcell45_66_AGGCAGAA.TCTCTG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.ACAACC",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.ACCGCG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.ACGTGG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.ACTCAC",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.AGGATG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.ATAGCG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.ATCGAC",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.CAAGAG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.CATGAC",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.CCTTCG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.CGGTAG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.CTATTG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.CTCAGC",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.GCATTC",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.GCTGCC",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.GGCATC",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.GTGAGG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.GTTGAG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.TAGCGG",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.TATCTC",
"RRBS_NormalBCD19pcell67_88_TCCTGAGC.TCTCTG",
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACAACC',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACCGCG',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACGTGG',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACTCAC',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.AGGATG',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ATAGCG',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ATCGAC',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.CAAGAG',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.CATGAC',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.CCTTCG',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.CGGTAG',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.CTCAGC',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.GACACG',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.GCATTC',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.GCTGCC',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.GGCATC',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.GTGAGG',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.TAGCGG',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.TATCTC',
'RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.TCTCTG',
'RRBS_cw154_Tris_protease_CTCTCTAC.ACAACC',
'RRBS_cw154_Tris_protease_CTCTCTAC.ACCGCG',
'RRBS_cw154_Tris_protease_CTCTCTAC.ACGTGG',
'RRBS_cw154_Tris_protease_CTCTCTAC.ACTCAC',
'RRBS_cw154_Tris_protease_CTCTCTAC.AGGATG',
'RRBS_cw154_Tris_protease_CTCTCTAC.ATAGCG',
'RRBS_cw154_Tris_protease_CTCTCTAC.ATCGAC',
'RRBS_cw154_Tris_protease_CTCTCTAC.CATGAC',
'RRBS_cw154_Tris_protease_CTCTCTAC.CCTTCG',
'RRBS_cw154_Tris_protease_CTCTCTAC.CGGTAG',
'RRBS_cw154_Tris_protease_CTCTCTAC.CTATTG',
'RRBS_cw154_Tris_protease_CTCTCTAC.CTCAGC',
'RRBS_cw154_Tris_protease_CTCTCTAC.GACACG',
'RRBS_cw154_Tris_protease_CTCTCTAC.GCATTC',
'RRBS_cw154_Tris_protease_CTCTCTAC.GCTGCC',
'RRBS_cw154_Tris_protease_CTCTCTAC.GGCATC',
'RRBS_cw154_Tris_protease_CTCTCTAC.GTGAGG',
'RRBS_cw154_Tris_protease_CTCTCTAC.GTTGAG',
'RRBS_cw154_Tris_protease_CTCTCTAC.TAGCGG',
'RRBS_cw154_Tris_protease_CTCTCTAC.TATCTC',
'RRBS_cw154_Tris_protease_CTCTCTAC.TCTCTG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.ACAACC',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.ACCGCG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.ACGTGG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.ACTCAC',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.AGGATG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.ATAGCG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.ATCGAC',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.CATGAC',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.CCTTCG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.CGGTAG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.CTATTG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.CTCAGC',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.GACACG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.GCATTC',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.GCTGCC',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.GGCATC',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.GTGAGG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.GTTGAG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.TAGCGG',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.TATCTC',
'RRBS_cw154_Tris_protease_GR_CAGAGAGG.TCTCTG',
'RRBS_trito_pool_1_TAAGGCGA.ACAACC',
'RRBS_trito_pool_1_TAAGGCGA.ACGTGG',
'RRBS_trito_pool_1_TAAGGCGA.ACTCAC',
'RRBS_trito_pool_1_TAAGGCGA.ATAGCG',
'RRBS_trito_pool_1_TAAGGCGA.ATCGAC',
'RRBS_trito_pool_1_TAAGGCGA.CAAGAG',
'RRBS_trito_pool_1_TAAGGCGA.CATGAC',
'RRBS_trito_pool_1_TAAGGCGA.CCTTCG',
'RRBS_trito_pool_1_TAAGGCGA.CGGTAG',
'RRBS_trito_pool_1_TAAGGCGA.CTATTG',
'RRBS_trito_pool_1_TAAGGCGA.GACACG',
'RRBS_trito_pool_1_TAAGGCGA.GCATTC',
'RRBS_trito_pool_1_TAAGGCGA.GCTGCC',
'RRBS_trito_pool_1_TAAGGCGA.GGCATC',
'RRBS_trito_pool_1_TAAGGCGA.GTGAGG',
'RRBS_trito_pool_1_TAAGGCGA.GTTGAG',
'RRBS_trito_pool_1_TAAGGCGA.TAGCGG',
'RRBS_trito_pool_1_TAAGGCGA.TATCTC',
'RRBS_trito_pool_1_TAAGGCGA.TCTCTG',
'RRBS_trito_pool_1_TAAGGCGA.TGACAG',
'RRBS_trito_pool_1_TAAGGCGA.TGCTGC',
'RRBS_trito_pool_2_CGTACTAG.ACAACC',
'RRBS_trito_pool_2_CGTACTAG.ACGTGG',
'RRBS_trito_pool_2_CGTACTAG.ACTCAC',
'RRBS_trito_pool_2_CGTACTAG.AGGATG',
'RRBS_trito_pool_2_CGTACTAG.ATAGCG',
'RRBS_trito_pool_2_CGTACTAG.ATCGAC',
'RRBS_trito_pool_2_CGTACTAG.CAAGAG',
'RRBS_trito_pool_2_CGTACTAG.CATGAC',
'RRBS_trito_pool_2_CGTACTAG.CCTTCG',
'RRBS_trito_pool_2_CGTACTAG.CGGTAG',
'RRBS_trito_pool_2_CGTACTAG.CTATTG',
'RRBS_trito_pool_2_CGTACTAG.GACACG',
'RRBS_trito_pool_2_CGTACTAG.GCATTC',
'RRBS_trito_pool_2_CGTACTAG.GCTGCC',
'RRBS_trito_pool_2_CGTACTAG.GGCATC',
'RRBS_trito_pool_2_CGTACTAG.GTGAGG',
'RRBS_trito_pool_2_CGTACTAG.GTTGAG',
'RRBS_trito_pool_2_CGTACTAG.TAGCGG',
'RRBS_trito_pool_2_CGTACTAG.TATCTC',
'RRBS_trito_pool_2_CGTACTAG.TCTCTG',
'RRBS_trito_pool_2_CGTACTAG.TGACAG']
print(total_matrix.shape)
total_matrix = total_matrix.applymap(lambda x: int(x) if pd.notnull(x) else str("?"))
total_matrix = total_matrix.astype(str).apply(''.join)
tott = pd.Series(total_matrix.index.astype(str).str.cat(total_matrix.astype(str),' '))
tott.to_csv("total_chrom11.phy", header=None, index=None)
print(tott.shape)
| mit |
annayqho/TheCannon | code/lamost/mass_age/paper_plots/plot_survey_coverage.py | 1 | 5792 | #!/usr/bin/env python
import numpy as np
import healpy as hp
import astropy.table as Table
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib import rc
from matplotlib import rcParams
from matplotlib.colors import LogNorm
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
import pyfits
print("Import data")
# import the data
hdulist = pyfits.open(
"/Users/annaho/Data/LAMOST/Mass_And_Age/catalog_paper.fits")
tbdata = hdulist[1].data
# # cols = hdulist[1].columns
# # cols.names
in_martig_range = tbdata.field("in_martig_range")
snr = tbdata.field("snr")
#choose = np.logical_and(in_martig_range, snr > 80)
choose = in_martig_range
print(sum(choose))
chisq = tbdata.field("chisq")
ra_lamost = tbdata.field('ra')[choose]
dec_lamost = tbdata.field('dec')[choose]
val_lamost = 10**(tbdata.field("cannon_age")[choose])
hdulist.close()
print("Getting APOGEE data")
hdulist = pyfits.open(
"/Users/annaho/Data/APOGEE/Ness2016_Catalog_Full_DR12_Info.fits")
tbdata = hdulist[1].data
ra_apogee_all = tbdata['RA']
dec_apogee_all = tbdata['DEC']
val_apogee_all = np.exp(tbdata['lnAge'])
good_coords = np.logical_and(ra_apogee_all > -90, dec_apogee_all > -90)
good = np.logical_and(good_coords, val_apogee_all > -90)
ra_apogee = ra_apogee_all[good]
dec_apogee = dec_apogee_all[good]
val_apogee = val_apogee_all[good]
hdulist.close()
ra_both = np.hstack((ra_apogee, ra_lamost))
dec_both = np.hstack((dec_apogee, dec_lamost))
val_all = np.hstack((val_apogee, val_lamost))
print("create grid")
# create a RA and Dec grid
ra_all = []
dec_all = []
for ra in np.arange(0, 360, 0.5):
for dec in np.arange(-90, 90, 0.5):
ra_all.append(ra)
dec_all.append(dec)
ra = np.array(ra_all)
dec = np.array(dec_all)
# convert RA and Dec to phi and theta coordinates
def toPhiTheta(ra, dec):
phi = ra * np.pi/180.
theta = (90.0 - dec) * np.pi / 180.
return phi, theta
phi, theta = toPhiTheta(ra, dec)
phi_lamost, theta_lamost = toPhiTheta(ra_lamost, dec_lamost)
phi_apogee, theta_apogee = toPhiTheta(ra_apogee, dec_apogee)
phi_all, theta_all = toPhiTheta(ra_both, dec_both)
# to just plot all points, do
#hp.visufunc.projplot(theta, phi, 'bo')
#hp.visufunc.projplot(theta_lamost, phi_lamost, 'bo')
#hp.visufunc.graticule() # just the bare background w/ lines
# more examples are here
# https://healpy.readthedocs.org/en/latest/generated/healpy.visufunc.projplot.html#healpy.visufunc.projplot
## to plot a 2D histogram in the Mollweide projection
# define the HEALPIX level
# NSIDE = 32 # defines the resolution of the map
# NSIDE = 128 # from paper 1
NSIDE = 64
# find the pixel ID for each point
# pix = hp.pixelfunc.ang2pix(NSIDE, theta, phi)
pix_lamost = hp.pixelfunc.ang2pix(NSIDE, theta_lamost, phi_lamost)
pix_apogee = hp.pixelfunc.ang2pix(NSIDE, theta_apogee, phi_apogee)
pix_all = hp.pixelfunc.ang2pix(NSIDE, theta_all, phi_all)
# pix is in the order of ra and dec
# prepare the map array
m_lamost = hp.ma(np.zeros(hp.nside2npix(NSIDE), dtype='float'))
mask_lamost = np.zeros(hp.nside2npix(NSIDE), dtype='bool')
for pix_val in np.unique(pix_lamost):
choose = np.where(pix_lamost==pix_val)[0]
if len(choose) == 1:
# #m_lamost[pix_val] = rmag_lamost[choose[0]]
m_lamost[pix_val] = val_lamost[choose[0]]
else:
#m_lamost[pix_val] = np.median(rmag_lamost[choose])
m_lamost[pix_val] = np.median(val_lamost[choose])
mask_lamost[np.setdiff1d(np.arange(len(m_lamost)), pix_lamost)] = 1
m_lamost.mask = mask_lamost
m_apogee= hp.ma(np.zeros(hp.nside2npix(NSIDE), dtype='float'))
mask_apogee= np.zeros(hp.nside2npix(NSIDE), dtype='bool')
for pix_val in np.unique(pix_apogee):
choose = np.where(pix_apogee==pix_val)[0]
if len(choose) == 1:
m_apogee[pix_val] = val_apogee[choose[0]]
else:
m_apogee[pix_val] = np.median(val_apogee[choose])
mask_apogee[np.setdiff1d(np.arange(len(m_apogee)), pix_apogee)] = 1
m_apogee.mask = mask_apogee
m_all = hp.ma(np.zeros(hp.nside2npix(NSIDE), dtype='float'))
mask_all= np.zeros(hp.nside2npix(NSIDE), dtype='bool')
for pix_val in np.unique(pix_all):
choose = np.where(pix_all==pix_val)[0]
if len(choose) == 1:
m_all[pix_val] = val_all[choose[0]]
else:
m_all[pix_val] = np.median(val_all[choose])
mask_all[np.setdiff1d(np.arange(len(m_all)), pix_all)] = 1
m_all.mask = mask_all
# perceptually uniform: inferno, viridis, plasma, magma
#cmap=cm.magma
cmap = cm.RdYlBu_r
cmap.set_under('w')
# composite map
# plot map ('C' means the input coordinates were in the equatorial system)
# rcParams.update({'font.size':16})
hp.visufunc.mollview(m_apogee, coord=['C','G'], rot=(150, 0, 0), flip='astro',
notext=False, title=r'Ages from Ness et al. 2016 (APOGEE)', cbar=True,
norm=None, min=0, max=12, cmap=cmap, unit = 'Gyr')
#hp.visufunc.mollview(m_lamost, coord=['C','G'], rot=(150, 0, 0), flip='astro',
# notext=True, title=r'$\alpha$/M for 500,000 LAMOST giants', cbar=True,
# norm=None, min=-0.07, max=0.3, cmap=cmap, unit = r'$\alpha$/M [dex]')
#notext=True, title="r-band magnitude for 500,000 LAMOST giants", cbar=True,
#norm=None, min=11, max=17, cmap=cmap, unit = r"r-band magnitude [mag]")
# hp.visufunc.mollview(m_all, coord=['C','G'], rot=(150, 0, 0), flip='astro',
# notext=True, title='Ages from Ness et al. 2016 + LAMOST giants',
# cbar=True, norm=None, min=0.00, max=12, cmap=cmap, unit = 'Gyr')
hp.visufunc.graticule()
plt.show()
#plt.savefig("full_age_map.png")
#plt.savefig("apogee_age_map.png")
#plt.savefig("lamost_am_map_magma.png")
#plt.savefig("lamost_rmag_map.png")
| mit |
QUANTAXIS/QUANTAXIS | QUANTAXIS/QAApplication/OldBacktest.py | 2 | 3257 | # @Hakase
import QUANTAXIS as QA
import numpy as np
import pandas as pd
import datetime
import sys
import random
class backtest():
"""依据回测场景的建模
"""
def __init__(self, start_time='2015-01-01', end_time='2018-09-24', init_cash=500000, code='RBL8', frequence=QA.FREQUENCE.FIFTEEN_MIN):
self.start_time = start_time
self.end_time = end_time
self.frequence = frequence
self.code = code
self.init_cach = init_cash
self.time_ = None
self.market_data_ = None
self.res = False
@property
def position(self):
return self.account.sell_available.get(self.code, 0)
@property
def time(self):
return self.time_
@property
def market_data(self):
return self.market_data_
# 自定义函数-------------------------------------------------------------------
@property
def hold_judge(self):
"""仓位判断器
Returns:
[type] -- [description]
"""
if self.account.cash/self.account.init_cash < 0.3:
return False
else:
return True
def before_backtest(self):
raise NotImplementedError
def before(self, *args, **kwargs):
self.before_backtest()
self.data_min = QA.QA_fetch_future_min_adv(
self.code, self.start_time, self.end_time, frequence=self.frequence)
self.data_day = QA.QA_fetch_future_day_adv(
self.code, self.start_time, self.end_time)
self.Broker = QA.QA_BacktestBroker()
def model(self, *arg, **kwargs):
raise NotImplementedError
def load_strategy(self, *arg, **kwargs):
# self.load_model(func1)
raise NotImplementedError
def run(self, *arg, **kwargs):
raise NotImplementedError
def buy(self, pos, towards):
self.account.receive_simpledeal(code=self.code,
trade_price=self.market_data.open, trade_amount=pos,
trade_towards=towards, trade_time=self.time,
message=towards)
def sell(self, pos, towards):
self.account.receive_simpledeal(code=self.code,
trade_price=self.market_data.open, trade_amount=pos,
trade_towards=towards, trade_time=self.time,
message=towards)
def main(self, *arg, **kwargs):
print(vars(self))
self.identity_code = '_'.join([str(x) for x in list(kwargs.values())])
self.backtest_cookie = 'future_{}_{}'.format(
datetime.datetime.now().time().__str__()[:8], self.identity_code)
self.account = QA.QA_Account(allow_sellopen=True, allow_t0=True, account_cookie=self.backtest_cookie,
market_type=QA.MARKET_TYPE.FUTURE_CN, frequence=self.frequence, init_cash=self.init_cash)
self.gen = self.data_min.reindex(
self.res) if self.res else self.data_min
for ind, item in self.gen.iterrows:
self.time_ = ind[0]
self.code = ind[1]
self.market_data_ = item
self.run()
| mit |
bartosh/zipline | tests/data/test_resample.py | 4 | 33653 | # Copyright 2016 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict
from numbers import Real
from nose_parameterized import parameterized
from numpy.testing import assert_almost_equal
from numpy import nan, array, full, isnan
import pandas as pd
from pandas import DataFrame
from six import iteritems
from zipline.data.resample import (
minute_frame_to_session_frame,
DailyHistoryAggregator,
MinuteResampleSessionBarReader,
ReindexMinuteBarReader,
ReindexSessionBarReader,
)
from zipline.testing import parameter_space
from zipline.testing.fixtures import (
WithEquityMinuteBarData,
WithBcolzEquityMinuteBarReader,
WithBcolzEquityDailyBarReader,
WithBcolzFutureMinuteBarReader,
ZiplineTestCase,
)
OHLC = ['open', 'high', 'low', 'close']
OHLCV = OHLC + ['volume']
NYSE_MINUTES = OrderedDict((
('day_0_front', pd.date_range('2016-03-15 9:31',
'2016-03-15 9:33',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
('day_0_back', pd.date_range('2016-03-15 15:58',
'2016-03-15 16:00',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
('day_1_front', pd.date_range('2016-03-16 9:31',
'2016-03-16 9:33',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
('day_1_back', pd.date_range('2016-03-16 15:58',
'2016-03-16 16:00',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
))
FUT_MINUTES = OrderedDict((
('day_0_front', pd.date_range('2016-03-15 18:01',
'2016-03-15 18:03',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
('day_0_back', pd.date_range('2016-03-16 17:58',
'2016-03-16 18:00',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
('day_1_front', pd.date_range('2016-03-16 18:01',
'2016-03-16 18:03',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
('day_1_back', pd.date_range('2016-03-17 17:58',
'2016-03-17 18:00',
freq='min',
tz='US/Eastern').tz_convert('UTC')),
))
SCENARIOS = OrderedDict((
('none_missing', array([
[101.5, 101.9, 101.1, 101.3, 1001],
[103.5, 103.9, 103.1, 103.3, 1003],
[102.5, 102.9, 102.1, 102.3, 1002],
])),
('all_missing', array([
[nan, nan, nan, nan, 0],
[nan, nan, nan, nan, 0],
[nan, nan, nan, nan, 0],
])),
('missing_first', array([
[nan, nan, nan, nan, 0],
[103.5, 103.9, 103.1, 103.3, 1003],
[102.5, 102.9, 102.1, 102.3, 1002],
])),
('missing_last', array([
[107.5, 107.9, 107.1, 107.3, 1007],
[108.5, 108.9, 108.1, 108.3, 1008],
[nan, nan, nan, nan, 0],
])),
('missing_middle', array([
[103.5, 103.9, 103.1, 103.3, 1003],
[nan, nan, nan, nan, 0],
[102.5, 102.5, 102.1, 102.3, 1002],
])),
))
OHLCV = ('open', 'high', 'low', 'close', 'volume')
_EQUITY_CASES = (
(1, (('none_missing', 'day_0_front'),
('missing_last', 'day_0_back'))),
(2, (('missing_first', 'day_0_front'),
('none_missing', 'day_0_back'))),
(3, (('missing_last', 'day_0_back'),
('missing_first', 'day_1_front'))),
# Asset 4 has a start date on day 1
(4, (('all_missing', 'day_0_back'),
('none_missing', 'day_1_front'))),
# Asset 5 has a start date before day_0, but does not have data on that
# day.
(5, (('all_missing', 'day_0_back'),
('none_missing', 'day_1_front'))),
)
EQUITY_CASES = OrderedDict()
for sid, combos in _EQUITY_CASES:
frames = [DataFrame(SCENARIOS[s], columns=OHLCV).
set_index(NYSE_MINUTES[m])
for s, m in combos]
EQUITY_CASES[sid] = pd.concat(frames)
_FUTURE_CASES = (
(1001, (('none_missing', 'day_0_front'),
('none_missing', 'day_0_back'))),
(1002, (('missing_first', 'day_0_front'),
('none_missing', 'day_0_back'))),
(1003, (('missing_last', 'day_0_back'),
('missing_first', 'day_1_front'))),
(1004, (('all_missing', 'day_0_back'),
('none_missing', 'day_1_front'))),
)
FUTURE_CASES = OrderedDict()
for sid, combos in _FUTURE_CASES:
frames = [DataFrame(SCENARIOS[s], columns=OHLCV).
set_index(FUT_MINUTES[m])
for s, m in combos]
FUTURE_CASES[sid] = pd.concat(frames)
EXPECTED_AGGREGATION = {
1: DataFrame({
'open': [101.5, 101.5, 101.5, 101.5, 101.5, 101.5],
'high': [101.9, 103.9, 103.9, 107.9, 108.9, 108.9],
'low': [101.1, 101.1, 101.1, 101.1, 101.1, 101.1],
'close': [101.3, 103.3, 102.3, 107.3, 108.3, 108.3],
'volume': [1001, 2004, 3006, 4013, 5021, 5021],
}, columns=OHLCV),
2: DataFrame({
'open': [nan, 103.5, 103.5, 103.5, 103.5, 103.5],
'high': [nan, 103.9, 103.9, 103.9, 103.9, 103.9],
'low': [nan, 103.1, 102.1, 101.1, 101.1, 101.1],
'close': [nan, 103.3, 102.3, 101.3, 103.3, 102.3],
'volume': [0, 1003, 2005, 3006, 4009, 5011],
}, columns=OHLCV),
# Equity 3 straddles two days.
3: DataFrame({
'open': [107.5, 107.5, 107.5, nan, 103.5, 103.5],
'high': [107.9, 108.9, 108.9, nan, 103.9, 103.9],
'low': [107.1, 107.1, 107.1, nan, 103.1, 102.1],
'close': [107.3, 108.3, 108.3, nan, 103.3, 102.3],
'volume': [1007, 2015, 2015, 0, 1003, 2005],
}, columns=OHLCV),
# Equity 4 straddles two days and is not active the first day.
4: DataFrame({
'open': [nan, nan, nan, 101.5, 101.5, 101.5],
'high': [nan, nan, nan, 101.9, 103.9, 103.9],
'low': [nan, nan, nan, 101.1, 101.1, 101.1],
'close': [nan, nan, nan, 101.3, 103.3, 102.3],
'volume': [0, 0, 0, 1001, 2004, 3006],
}, columns=OHLCV),
# Equity 5 straddles two days and does not have data the first day.
5: DataFrame({
'open': [nan, nan, nan, 101.5, 101.5, 101.5],
'high': [nan, nan, nan, 101.9, 103.9, 103.9],
'low': [nan, nan, nan, 101.1, 101.1, 101.1],
'close': [nan, nan, nan, 101.3, 103.3, 102.3],
'volume': [0, 0, 0, 1001, 2004, 3006],
}, columns=OHLCV),
1001: DataFrame({
'open': [101.5, 101.5, 101.5, 101.5, 101.5, 101.5],
'high': [101.9, 103.9, 103.9, 103.9, 103.9, 103.9],
'low': [101.1, 101.1, 101.1, 101.1, 101.1, 101.1],
'close': [101.3, 103.3, 102.3, 101.3, 103.3, 102.3],
'volume': [1001, 2004, 3006, 4007, 5010, 6012],
}, columns=OHLCV),
1002: DataFrame({
'open': [nan, 103.5, 103.5, 103.5, 103.5, 103.5],
'high': [nan, 103.9, 103.9, 103.9, 103.9, 103.9],
'low': [nan, 103.1, 102.1, 101.1, 101.1, 101.1],
'close': [nan, 103.3, 102.3, 101.3, 103.3, 102.3],
'volume': [0, 1003, 2005, 3006, 4009, 5011],
}, columns=OHLCV),
1003: DataFrame({
'open': [107.5, 107.5, 107.5, nan, 103.5, 103.5],
'high': [107.9, 108.9, 108.9, nan, 103.9, 103.9],
'low': [107.1, 107.1, 107.1, nan, 103.1, 102.1],
'close': [107.3, 108.3, 108.3, nan, 103.3, 102.3],
'volume': [1007, 2015, 2015, 0, 1003, 2005],
}, columns=OHLCV),
1004: DataFrame({
'open': [nan, nan, nan, 101.5, 101.5, 101.5],
'high': [nan, nan, nan, 101.9, 103.9, 103.9],
'low': [nan, nan, nan, 101.1, 101.1, 101.1],
'close': [nan, nan, nan, 101.3, 103.3, 102.3],
'volume': [0, 0, 0, 1001, 2004, 3006],
}, columns=OHLCV),
}
EXPECTED_SESSIONS = {
1: DataFrame([EXPECTED_AGGREGATION[1].iloc[-1].values],
columns=OHLCV,
index=pd.to_datetime(['2016-03-15'], utc=True)),
2: DataFrame([EXPECTED_AGGREGATION[2].iloc[-1].values],
columns=OHLCV,
index=pd.to_datetime(['2016-03-15'], utc=True)),
3: DataFrame(EXPECTED_AGGREGATION[3].iloc[[2, 5]].values,
columns=OHLCV,
index=pd.to_datetime(['2016-03-15', '2016-03-16'], utc=True)),
1001: DataFrame([EXPECTED_AGGREGATION[1001].iloc[-1].values],
columns=OHLCV,
index=pd.to_datetime(['2016-03-16'], utc=True)),
1002: DataFrame([EXPECTED_AGGREGATION[1002].iloc[-1].values],
columns=OHLCV,
index=pd.to_datetime(['2016-03-16'], utc=True)),
1003: DataFrame(EXPECTED_AGGREGATION[1003].iloc[[2, 5]].values,
columns=OHLCV,
index=pd.to_datetime(['2016-03-16', '2016-03-17'],
utc=True)),
1004: DataFrame(EXPECTED_AGGREGATION[1004].iloc[[2, 5]].values,
columns=OHLCV,
index=pd.to_datetime(['2016-03-16', '2016-03-17'],
utc=True)),
}
class MinuteToDailyAggregationTestCase(WithBcolzEquityMinuteBarReader,
WithBcolzFutureMinuteBarReader,
ZiplineTestCase):
# March 2016
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5
# 6 7 8 9 10 11 12
# 13 14 15 16 17 18 19
# 20 21 22 23 24 25 26
# 27 28 29 30 31
TRADING_ENV_MIN_DATE = START_DATE = pd.Timestamp(
'2016-03-01', tz='UTC',
)
TRADING_ENV_MAX_DATE = END_DATE = pd.Timestamp(
'2016-03-31', tz='UTC',
)
TRADING_CALENDAR_STRS = ('NYSE', 'us_futures')
ASSET_FINDER_EQUITY_SIDS = 1, 2, 3, 4, 5
ASSET_FINDER_FUTURE_SIDS = 1001, 1002, 1003, 1004
@classmethod
def make_equity_info(cls):
frame = super(MinuteToDailyAggregationTestCase, cls).make_equity_info()
# Make equity 4 start a day behind the data start to exercise assets
# which not alive for the session.
frame.loc[[4], 'start_date'] = pd.Timestamp('2016-03-16', tz='UTC')
return frame
@classmethod
def make_equity_minute_bar_data(cls):
for sid in cls.ASSET_FINDER_EQUITY_SIDS:
frame = EQUITY_CASES[sid]
yield sid, frame
@classmethod
def make_futures_info(cls):
future_dict = {}
for future_sid in cls.ASSET_FINDER_FUTURE_SIDS:
future_dict[future_sid] = {
'multiplier': 1000,
'exchange': 'CME',
'root_symbol': "ABC"
}
return pd.DataFrame.from_dict(future_dict, orient='index')
@classmethod
def make_future_minute_bar_data(cls):
for sid in cls.ASSET_FINDER_FUTURE_SIDS:
frame = FUTURE_CASES[sid]
yield sid, frame
def init_instance_fixtures(self):
super(MinuteToDailyAggregationTestCase, self).init_instance_fixtures()
# Set up a fresh data portal for each test, since order of calling
# needs to be tested.
self.equity_daily_aggregator = DailyHistoryAggregator(
self.nyse_calendar.schedule.market_open,
self.bcolz_equity_minute_bar_reader,
self.nyse_calendar,
)
self.future_daily_aggregator = DailyHistoryAggregator(
self.us_futures_calendar.schedule.market_open,
self.bcolz_future_minute_bar_reader,
self.us_futures_calendar
)
@parameter_space(
field=OHLCV,
sid=ASSET_FINDER_EQUITY_SIDS,
__fail_fast=True,
)
def test_equity_contiguous_minutes_individual(self, field, sid):
asset = self.asset_finder.retrieve_asset(sid)
minutes = EQUITY_CASES[asset].index
self._test_contiguous_minutes_individual(
field,
asset,
minutes,
self.equity_daily_aggregator,
)
@parameter_space(
field=OHLCV,
sid=ASSET_FINDER_FUTURE_SIDS,
__fail_fast=True,
)
def test_future_contiguous_minutes_individual(self, field, sid):
asset = self.asset_finder.retrieve_asset(sid)
minutes = FUTURE_CASES[asset].index
self._test_contiguous_minutes_individual(
field,
asset,
minutes,
self.future_daily_aggregator,
)
def _test_contiguous_minutes_individual(
self,
field,
asset,
minutes,
aggregator,
):
# First test each minute in order.
method_name = field + 's'
results = []
repeat_results = []
for minute in minutes:
value = getattr(aggregator, method_name)(
[asset], minute)[0]
# Prevent regression on building an array when scalar is intended.
self.assertIsInstance(value, Real)
results.append(value)
# Call a second time with the same dt, to prevent regression
# against case where crossed start and end dts caused a crash
# instead of the last value.
value = getattr(aggregator, method_name)(
[asset], minute)[0]
# Prevent regression on building an array when scalar is intended.
self.assertIsInstance(value, Real)
repeat_results.append(value)
assert_almost_equal(results, EXPECTED_AGGREGATION[asset][field],
err_msg='sid={0} field={1}'.format(asset, field))
assert_almost_equal(repeat_results, EXPECTED_AGGREGATION[asset][field],
err_msg='sid={0} field={1}'.format(asset, field))
@parameterized.expand([
('open_sid_1', 'open', 1),
('high_1', 'high', 1),
('low_1', 'low', 1),
('close_1', 'close', 1),
('volume_1', 'volume', 1),
('open_2', 'open', 2),
('high_2', 'high', 2),
('low_2', 'low', 2),
('close_2', 'close', 2),
('volume_2', 'volume', 2),
('open_3', 'open', 3),
('high_3', 'high', 3),
('low_3', 'low', 3),
('close_3', 'close', 3),
('volume_3', 'volume', 3),
('open_4', 'open', 4),
('high_4', 'high', 4),
('low_4', 'low', 4),
('close_4', 'close', 4),
('volume_4', 'volume', 4),
('open_5', 'open', 5),
('high_5', 'high', 5),
('low_5', 'low', 5),
('close_5', 'close', 5),
('volume_5', 'volume', 5),
])
def test_skip_minutes_individual(self, name, field, sid):
# Test skipping minutes, to exercise backfills.
# Tests initial backfill and mid day backfill.
method_name = field + 's'
asset = self.asset_finder.retrieve_asset(sid)
minutes = EQUITY_CASES[asset].index
for i in [0, 2, 3, 5]:
minute = minutes[i]
value = getattr(self.equity_daily_aggregator, method_name)(
[asset], minute)[0]
# Prevent regression on building an array when scalar is intended.
self.assertIsInstance(value, Real)
assert_almost_equal(value,
EXPECTED_AGGREGATION[sid][field][i],
err_msg='sid={0} field={1} dt={2}'.format(
sid, field, minute))
# Call a second time with the same dt, to prevent regression
# against case where crossed start and end dts caused a crash
# instead of the last value.
value = getattr(self.equity_daily_aggregator, method_name)(
[asset], minute)[0]
# Prevent regression on building an array when scalar is intended.
self.assertIsInstance(value, Real)
assert_almost_equal(value,
EXPECTED_AGGREGATION[sid][field][i],
err_msg='sid={0} field={1} dt={2}'.format(
sid, field, minute))
@parameterized.expand(OHLCV)
def test_contiguous_minutes_multiple(self, field):
# First test each minute in order.
method_name = field + 's'
assets = self.asset_finder.retrieve_all([1, 2])
results = {asset: [] for asset in assets}
repeat_results = {asset: [] for asset in assets}
minutes = EQUITY_CASES[1].index
for minute in minutes:
values = getattr(self.equity_daily_aggregator, method_name)(
assets, minute)
for j, asset in enumerate(assets):
value = values[j]
# Prevent regression on building an array when scalar is
# intended.
self.assertIsInstance(value, Real)
results[asset].append(value)
# Call a second time with the same dt, to prevent regression
# against case where crossed start and end dts caused a crash
# instead of the last value.
values = getattr(self.equity_daily_aggregator, method_name)(
assets, minute)
for j, asset in enumerate(assets):
value = values[j]
# Prevent regression on building an array when scalar is
# intended.
self.assertIsInstance(value, Real)
repeat_results[asset].append(value)
for asset in assets:
assert_almost_equal(results[asset],
EXPECTED_AGGREGATION[asset][field],
err_msg='sid={0} field={1}'.format(
asset, field))
assert_almost_equal(repeat_results[asset],
EXPECTED_AGGREGATION[asset][field],
err_msg='sid={0} field={1}'.format(
asset, field))
@parameterized.expand(OHLCV)
def test_skip_minutes_multiple(self, field):
# Test skipping minutes, to exercise backfills.
# Tests initial backfill and mid day backfill.
method_name = field + 's'
assets = self.asset_finder.retrieve_all([1, 2])
minutes = EQUITY_CASES[1].index
for i in [1, 5]:
minute = minutes[i]
values = getattr(self.equity_daily_aggregator, method_name)(
assets, minute)
for j, asset in enumerate(assets):
value = values[j]
# Prevent regression on building an array when scalar is
# intended.
self.assertIsInstance(value, Real)
assert_almost_equal(
value,
EXPECTED_AGGREGATION[asset][field][i],
err_msg='sid={0} field={1} dt={2}'.format(
asset, field, minute))
# Call a second time with the same dt, to prevent regression
# against case where crossed start and end dts caused a crash
# instead of the last value.
values = getattr(self.equity_daily_aggregator, method_name)(
assets, minute)
for j, asset in enumerate(assets):
value = values[j]
# Prevent regression on building an array when scalar is
# intended.
self.assertIsInstance(value, Real)
assert_almost_equal(
value,
EXPECTED_AGGREGATION[asset][field][i],
err_msg='sid={0} field={1} dt={2}'.format(
asset, field, minute))
class TestMinuteToSession(WithEquityMinuteBarData,
ZiplineTestCase):
# March 2016
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5
# 6 7 8 9 10 11 12
# 13 14 15 16 17 18 19
# 20 21 22 23 24 25 26
# 27 28 29 30 31
START_DATE = pd.Timestamp(
'2016-03-15', tz='UTC',
)
END_DATE = pd.Timestamp(
'2016-03-15', tz='UTC',
)
ASSET_FINDER_EQUITY_SIDS = 1, 2, 3
@classmethod
def make_equity_minute_bar_data(cls):
for sid, frame in iteritems(EQUITY_CASES):
yield sid, frame
@classmethod
def init_class_fixtures(cls):
super(TestMinuteToSession, cls).init_class_fixtures()
cls.equity_frames = {
sid: frame for sid, frame in cls.make_equity_minute_bar_data()}
def test_minute_to_session(self):
for sid in self.ASSET_FINDER_EQUITY_SIDS:
frame = self.equity_frames[sid]
expected = EXPECTED_SESSIONS[sid]
result = minute_frame_to_session_frame(frame, self.nyse_calendar)
assert_almost_equal(expected.values,
result.values,
err_msg='sid={0}'.format(sid))
class TestResampleSessionBars(WithBcolzFutureMinuteBarReader,
ZiplineTestCase):
TRADING_CALENDAR_STRS = ('us_futures',)
TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'
ASSET_FINDER_FUTURE_SIDS = 1001, 1002, 1003, 1004
START_DATE = pd.Timestamp('2016-03-16', tz='UTC')
END_DATE = pd.Timestamp('2016-03-17', tz='UTC')
NUM_SESSIONS = 2
@classmethod
def make_futures_info(cls):
future_dict = {}
for future_sid in cls.ASSET_FINDER_FUTURE_SIDS:
future_dict[future_sid] = {
'multiplier': 1000,
'exchange': 'CME',
'root_symbol': "ABC"
}
return pd.DataFrame.from_dict(future_dict, orient='index')
@classmethod
def make_future_minute_bar_data(cls):
for sid in cls.ASSET_FINDER_FUTURE_SIDS:
frame = FUTURE_CASES[sid]
yield sid, frame
def init_instance_fixtures(self):
super(TestResampleSessionBars, self).init_instance_fixtures()
self.session_bar_reader = MinuteResampleSessionBarReader(
self.trading_calendar,
self.bcolz_future_minute_bar_reader
)
def test_resample(self):
calendar = self.trading_calendar
for sid in self.ASSET_FINDER_FUTURE_SIDS:
case_frame = FUTURE_CASES[sid]
first = calendar.minute_to_session_label(
case_frame.index[0])
last = calendar.minute_to_session_label(
case_frame.index[-1])
result = self.session_bar_reader.load_raw_arrays(
OHLCV, first, last, [sid])
for i, field in enumerate(OHLCV):
assert_almost_equal(
EXPECTED_SESSIONS[sid][[field]],
result[i],
err_msg="sid={0} field={1}".format(sid, field))
def test_sessions(self):
sessions = self.session_bar_reader.sessions
self.assertEqual(self.NUM_SESSIONS, len(sessions))
self.assertEqual(self.START_DATE, sessions[0])
self.assertEqual(self.END_DATE, sessions[-1])
def test_last_available_dt(self):
calendar = self.trading_calendar
session_bar_reader = MinuteResampleSessionBarReader(
calendar,
self.bcolz_future_minute_bar_reader
)
self.assertEqual(self.END_DATE, session_bar_reader.last_available_dt)
def test_get_value(self):
calendar = self.trading_calendar
session_bar_reader = MinuteResampleSessionBarReader(
calendar,
self.bcolz_future_minute_bar_reader
)
for sid in self.ASSET_FINDER_FUTURE_SIDS:
expected = EXPECTED_SESSIONS[sid]
for dt_str, values in expected.iterrows():
dt = pd.Timestamp(dt_str, tz='UTC')
for col in OHLCV:
result = session_bar_reader.get_value(sid, dt, col)
assert_almost_equal(result,
values[col],
err_msg="sid={0} col={1} dt={2}".
format(sid, col, dt))
def test_first_trading_day(self):
self.assertEqual(self.START_DATE,
self.session_bar_reader.first_trading_day)
def test_get_last_traded_dt(self):
future = self.asset_finder.retrieve_asset(
self.ASSET_FINDER_FUTURE_SIDS[0]
)
self.assertEqual(
self.trading_calendar.previous_session_label(self.END_DATE),
self.session_bar_reader.get_last_traded_dt(future, self.END_DATE)
)
class TestReindexMinuteBars(WithBcolzEquityMinuteBarReader,
ZiplineTestCase):
TRADING_CALENDAR_STRS = ('us_futures', 'NYSE')
TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'
ASSET_FINDER_EQUITY_SIDS = 1, 2, 3
START_DATE = pd.Timestamp('2015-12-01', tz='UTC')
END_DATE = pd.Timestamp('2015-12-31', tz='UTC')
def test_load_raw_arrays(self):
reindex_reader = ReindexMinuteBarReader(
self.trading_calendar,
self.bcolz_equity_minute_bar_reader,
self.START_DATE,
self.END_DATE,
)
m_open, m_close = self.trading_calendar.open_and_close_for_session(
self.START_DATE)
outer_minutes = self.trading_calendar.minutes_in_range(m_open, m_close)
result = reindex_reader.load_raw_arrays(
OHLCV, m_open, m_close, [1, 2])
opens = DataFrame(data=result[0], index=outer_minutes,
columns=[1, 2])
opens_with_price = opens.dropna()
self.assertEqual(
1440,
len(opens),
"The result should have 1440 bars, the number of minutes in a "
"trading session on the target calendar."
)
self.assertEqual(
390,
len(opens_with_price),
"The result, after dropping nans, should have 390 bars, the "
" number of bars in a trading session in the reader's calendar."
)
slicer = outer_minutes.slice_indexer(
end=pd.Timestamp('2015-12-01 14:30', tz='UTC'))
assert_almost_equal(
opens[1][slicer],
full(slicer.stop, nan),
err_msg="All values before the NYSE market open should be nan.")
slicer = outer_minutes.slice_indexer(
start=pd.Timestamp('2015-12-01 21:01', tz='UTC'))
assert_almost_equal(
opens[1][slicer],
full(slicer.stop - slicer.start, nan),
err_msg="All values after the NYSE market close should be nan.")
first_minute_loc = outer_minutes.get_loc(pd.Timestamp(
'2015-12-01 14:31', tz='UTC'))
# Spot check a value.
# The value is the autogenerated value from test fixtures.
assert_almost_equal(
10.0,
opens[1][first_minute_loc],
err_msg="The value for Equity 1, should be 10.0, at NYSE open.")
class TestReindexSessionBars(WithBcolzEquityDailyBarReader,
ZiplineTestCase):
TRADING_CALENDAR_STRS = ('us_futures', 'NYSE')
TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'
ASSET_FINDER_EQUITY_SIDS = 1, 2, 3
# Dates are chosen to span Thanksgiving, which is not a Holiday on
# us_futures.
START_DATE = pd.Timestamp('2015-11-02', tz='UTC')
END_DATE = pd.Timestamp('2015-11-30', tz='UTC')
# November 2015
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6 7
# 8 9 10 11 12 13 14
# 15 16 17 18 19 20 21
# 22 23 24 25 26 27 28
# 29 30
def init_instance_fixtures(self):
super(TestReindexSessionBars, self).init_instance_fixtures()
self.reader = ReindexSessionBarReader(
self.trading_calendar,
self.bcolz_equity_daily_bar_reader,
self.START_DATE,
self.END_DATE,
)
def test_load_raw_arrays(self):
outer_sessions = self.trading_calendar.sessions_in_range(
self.START_DATE, self.END_DATE)
result = self.reader.load_raw_arrays(
OHLCV, self.START_DATE, self.END_DATE, [1, 2])
opens = DataFrame(data=result[0], index=outer_sessions,
columns=[1, 2])
opens_with_price = opens.dropna()
self.assertEqual(
21,
len(opens),
"The reindexed result should have 21 days, which is the number of "
"business days in 2015-11")
self.assertEqual(
20,
len(opens_with_price),
"The reindexed result after dropping nans should have 20 days, "
"because Thanksgiving is a NYSE holiday.")
tday = pd.Timestamp('2015-11-26', tz='UTC')
# Thanksgiving, 2015-11-26.
# Is a holiday in NYSE, but not in us_futures.
tday_loc = outer_sessions.get_loc(tday)
assert_almost_equal(
nan,
opens[1][tday_loc],
err_msg="2015-11-26 should be `nan`, since Thanksgiving is a "
"holiday in the reader's calendar.")
# Thanksgiving, 2015-11-26.
# Is a holiday in NYSE, but not in us_futures.
tday_loc = outer_sessions.get_loc(pd.Timestamp('2015-11-26', tz='UTC'))
assert_almost_equal(
nan,
opens[1][tday_loc],
err_msg="2015-11-26 should be `nan`, since Thanksgiving is a "
"holiday in the reader's calendar.")
def test_load_raw_arrays_holiday_start(self):
tday = pd.Timestamp('2015-11-26', tz='UTC')
outer_sessions = self.trading_calendar.sessions_in_range(
tday, self.END_DATE)
result = self.reader.load_raw_arrays(
OHLCV, tday, self.END_DATE, [1, 2])
opens = DataFrame(data=result[0], index=outer_sessions,
columns=[1, 2])
opens_with_price = opens.dropna()
self.assertEqual(
3,
len(opens),
"The reindexed result should have 3 days, which is the number of "
"business days in from Thanksgiving to end of 2015-11.")
self.assertEqual(
2,
len(opens_with_price),
"The reindexed result after dropping nans should have 2 days, "
"because Thanksgiving is a NYSE holiday.")
def test_load_raw_arrays_holiday_end(self):
tday = pd.Timestamp('2015-11-26', tz='UTC')
outer_sessions = self.trading_calendar.sessions_in_range(
self.START_DATE, tday)
result = self.reader.load_raw_arrays(
OHLCV, self.START_DATE, tday, [1, 2])
opens = DataFrame(data=result[0], index=outer_sessions,
columns=[1, 2])
opens_with_price = opens.dropna()
self.assertEqual(
19,
len(opens),
"The reindexed result should have 19 days, which is the number of "
"business days in from start of 2015-11 up to Thanksgiving.")
self.assertEqual(
18,
len(opens_with_price),
"The reindexed result after dropping nans should have 18 days, "
"because Thanksgiving is a NYSE holiday.")
def test_get_value(self):
assert_almost_equal(self.reader.get_value(1, self.START_DATE, 'open'),
10.0,
err_msg="The open of the fixture data on the "
"first session should be 10.")
tday = pd.Timestamp('2015-11-26', tz='UTC')
self.assertTrue(isnan(self.reader.get_value(1, tday, 'close')))
self.assertEqual(self.reader.get_value(1, tday, 'volume'), 0)
def test_last_availabe_dt(self):
self.assertEqual(self.reader.last_available_dt, self.END_DATE)
def test_get_last_traded_dt(self):
asset = self.asset_finder.retrieve_asset(1)
self.assertEqual(self.reader.get_last_traded_dt(asset,
self.END_DATE),
self.END_DATE)
def test_sessions(self):
sessions = self.reader.sessions
self.assertEqual(21, len(sessions),
"There should be 21 sessions in 2015-11.")
self.assertEqual(pd.Timestamp('2015-11-02', tz='UTC'),
sessions[0])
self.assertEqual(pd.Timestamp('2015-11-30', tz='UTC'),
sessions[-1])
def test_first_trading_day(self):
self.assertEqual(self.reader.first_trading_day, self.START_DATE)
def test_trading_calendar(self):
self.assertEqual('us_futures',
self.reader.trading_calendar.name,
"The calendar for the reindex reader should be the "
"specified futures calendar.")
| apache-2.0 |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/SkFlow_DEPRECATED/text_classification_cnn.py | 6 | 3587 | # Copyright 2015-present Scikit Flow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from sklearn import metrics
import pandas
import tensorflow as tf
import skflow
### Training data
# Download dbpedia_csv.tar.gz from
# https://drive.google.com/folderview?id=0Bz8a_Dbh9Qhbfll6bVpmNUtUcFdjYmF2SEpmZUZUcVNiMUw1TWN6RDV3a0JHT3kxLVhVR2M
# Unpack: tar -xvf dbpedia_csv.tar.gz
train = pandas.read_csv('dbpedia_csv/train.csv', header=None)
X_train, y_train = train[2], train[0]
test = pandas.read_csv('dbpedia_csv/test.csv', header=None)
X_test, y_test = test[2], test[0]
### Process vocabulary
MAX_DOCUMENT_LENGTH = 100
vocab_processor = skflow.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)
X_train = np.array(list(vocab_processor.fit_transform(X_train)))
X_test = np.array(list(vocab_processor.transform(X_test)))
n_words = len(vocab_processor.vocabulary_)
print('Total words: %d' % n_words)
### Models
EMBEDDING_SIZE = 20
N_FILTERS = 10
WINDOW_SIZE = 20
FILTER_SHAPE1 = [WINDOW_SIZE, EMBEDDING_SIZE]
FILTER_SHAPE2 = [WINDOW_SIZE, N_FILTERS]
POOLING_WINDOW = 4
POOLING_STRIDE = 2
def cnn_model(X, y):
"""2 layer Convolutional network to predict from sequence of words
to a class."""
# Convert indexes of words into embeddings.
# This creates embeddings matrix of [n_words, EMBEDDING_SIZE] and then
# maps word indexes of the sequence into [batch_size, sequence_length,
# EMBEDDING_SIZE].
word_vectors = skflow.ops.categorical_variable(X, n_classes=n_words,
embedding_size=EMBEDDING_SIZE, name='words')
word_vectors = tf.expand_dims(word_vectors, 3)
with tf.variable_scope('CNN_Layer1'):
# Apply Convolution filtering on input sequence.
conv1 = skflow.ops.conv2d(word_vectors, N_FILTERS, FILTER_SHAPE1, padding='VALID')
# Add a RELU for non linearity.
conv1 = tf.nn.relu(conv1)
# Max pooling across output of Convlution+Relu.
pool1 = tf.nn.max_pool(conv1, ksize=[1, POOLING_WINDOW, 1, 1],
strides=[1, POOLING_STRIDE, 1, 1], padding='SAME')
# Transpose matrix so that n_filters from convolution becomes width.
pool1 = tf.transpose(pool1, [0, 1, 3, 2])
with tf.variable_scope('CNN_Layer2'):
# Second level of convolution filtering.
conv2 = skflow.ops.conv2d(pool1, N_FILTERS, FILTER_SHAPE2,
padding='VALID')
# Max across each filter to get useful features for classification.
pool2 = tf.squeeze(tf.reduce_max(conv2, 1), squeeze_dims=[1])
# Apply regular WX + B and classification.
return skflow.models.logistic_regression(pool2, y)
classifier = skflow.TensorFlowEstimator(model_fn=cnn_model, n_classes=15,
steps=100, optimizer='Adam', learning_rate=0.01, continue_training=True)
# Continuesly train for 1000 steps & predict on test set.
while True:
classifier.fit(X_train, y_train, logdir='/tmp/tf_examples/word_cnn')
score = metrics.accuracy_score(y_test, classifier.predict(X_test))
print('Accuracy: {0:f}'.format(score))
| apache-2.0 |
IntelLabs/hpat | examples/series/str/series_str_len.py | 1 | 1782 | # *****************************************************************************
# Copyright (c) 2020, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# *****************************************************************************
import pandas as pd
from numba import njit
@njit
def series_str_len():
series = pd.Series(['foo', 'bar', 'foobar']) # Series of 'foo', 'bar', 'foobar'
out_series = series.str.len()
return out_series # Expect series of 3, 3, 6
print(series_str_len())
| bsd-2-clause |
DougBurke/astropy | astropy/modeling/powerlaws.py | 2 | 16066 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Power law model variants
"""
from collections import OrderedDict
import numpy as np
from .core import Fittable1DModel
from .parameters import Parameter, InputParameterError
from ..units import Quantity
__all__ = ['PowerLaw1D', 'BrokenPowerLaw1D', 'SmoothlyBrokenPowerLaw1D',
'ExponentialCutoffPowerLaw1D', 'LogParabola1D']
class PowerLaw1D(Fittable1DModel):
"""
One dimensional power law model.
Parameters
----------
amplitude : float
Model amplitude at the reference point
x_0 : float
Reference point
alpha : float
Power law index
See Also
--------
BrokenPowerLaw1D, ExponentialCutoffPowerLaw1D, LogParabola1D
Notes
-----
Model formula (with :math:`A` for ``amplitude`` and :math:`\\alpha` for ``alpha``):
.. math:: f(x) = A (x / x_0) ^ {-\\alpha}
"""
amplitude = Parameter(default=1)
x_0 = Parameter(default=1)
alpha = Parameter(default=1)
@staticmethod
def evaluate(x, amplitude, x_0, alpha):
"""One dimensional power law model function"""
xx = x / x_0
return amplitude * xx ** (-alpha)
@staticmethod
def fit_deriv(x, amplitude, x_0, alpha):
"""One dimensional power law derivative with respect to parameters"""
xx = x / x_0
d_amplitude = xx ** (-alpha)
d_x_0 = amplitude * alpha * d_amplitude / x_0
d_alpha = -amplitude * d_amplitude * np.log(xx)
return [d_amplitude, d_x_0, d_alpha]
@property
def input_units(self):
if self.x_0.unit is None:
return None
else:
return {'x': self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return OrderedDict([('x_0', inputs_unit['x']),
('amplitude', outputs_unit['y'])])
class BrokenPowerLaw1D(Fittable1DModel):
"""
One dimensional power law model with a break.
Parameters
----------
amplitude : float
Model amplitude at the break point.
x_break : float
Break point.
alpha_1 : float
Power law index for x < x_break.
alpha_2 : float
Power law index for x > x_break.
See Also
--------
PowerLaw1D, ExponentialCutoffPowerLaw1D, LogParabola1D
Notes
-----
Model formula (with :math:`A` for ``amplitude`` and :math:`\\alpha_1`
for ``alpha_1`` and :math:`\\alpha_2` for ``alpha_2``):
.. math::
f(x) = \\left \\{
\\begin{array}{ll}
A (x / x_{break}) ^ {-\\alpha_1} & : x < x_{break} \\\\
A (x / x_{break}) ^ {-\\alpha_2} & : x > x_{break} \\\\
\\end{array}
\\right.
"""
amplitude = Parameter(default=1)
x_break = Parameter(default=1)
alpha_1 = Parameter(default=1)
alpha_2 = Parameter(default=1)
@staticmethod
def evaluate(x, amplitude, x_break, alpha_1, alpha_2):
"""One dimensional broken power law model function"""
alpha = np.where(x < x_break, alpha_1, alpha_2)
xx = x / x_break
return amplitude * xx ** (-alpha)
@staticmethod
def fit_deriv(x, amplitude, x_break, alpha_1, alpha_2):
"""One dimensional broken power law derivative with respect to parameters"""
alpha = np.where(x < x_break, alpha_1, alpha_2)
xx = x / x_break
d_amplitude = xx ** (-alpha)
d_x_break = amplitude * alpha * d_amplitude / x_break
d_alpha = -amplitude * d_amplitude * np.log(xx)
d_alpha_1 = np.where(x < x_break, d_alpha, 0)
d_alpha_2 = np.where(x >= x_break, d_alpha, 0)
return [d_amplitude, d_x_break, d_alpha_1, d_alpha_2]
@property
def input_units(self):
if self.x_break.unit is None:
return None
else:
return {'x': self.x_break.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return OrderedDict([('x_break', inputs_unit['x']),
('amplitude', outputs_unit['y'])])
class SmoothlyBrokenPowerLaw1D(Fittable1DModel):
"""One dimensional smoothly broken power law model.
Parameters
----------
amplitude : float
Model amplitude at the break point.
x_break : float
Break point.
alpha_1 : float
Power law index for ``x << x_break``.
alpha_2 : float
Power law index for ``x >> x_break``.
delta : float
Smoothness parameter.
See Also
--------
BrokenPowerLaw1D
Notes
-----
Model formula (with :math:`A` for ``amplitude``, :math:`x_b` for
``x_break``, :math:`\\alpha_1` for ``alpha_1``,
:math:`\\alpha_2` for ``alpha_2`` and :math:`\\Delta` for
``delta``):
.. math::
f(x) = A \\left( \\frac{x}{x_b} \\right) ^ {-\\alpha_1}
\\left\\{
\\frac{1}{2}
\\left[
1 + \\left( \\frac{x}{x_b}\\right)^{1 / \\Delta}
\\right]
\\right\\}^{(\\alpha_1 - \\alpha_2) \\Delta}
The change of slope occurs between the values :math:`x_1`
and :math:`x_2` such that:
.. math::
\\log_{10} \\frac{x_2}{x_b} = \\log_{10} \\frac{x_b}{x_1}
\\sim \\Delta
At values :math:`x \\lesssim x_1` and :math:`x \\gtrsim x_2` the
model is approximately a simple power law with index
:math:`\\alpha_1` and :math:`\\alpha_2` respectively. The two
power laws are smoothly joined at values :math:`x_1 < x < x_2`,
hence the :math:`\\Delta` parameter sets the "smoothness" of the
slope change.
The ``delta`` parameter is bounded to values greater than 1e-3
(corresponding to :math:`x_2 / x_1 \\gtrsim 1.002`) to avoid
overflow errors.
The ``amplitude`` parameter is bounded to positive values since
this model is typically used to represent positive quantities.
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling import models
x = np.logspace(0.7, 2.3, 500)
f = models.SmoothlyBrokenPowerLaw1D(amplitude=1, x_break=20,
alpha_1=-2, alpha_2=2)
plt.figure()
plt.title("amplitude=1, x_break=20, alpha_1=-2, alpha_2=2")
f.delta = 0.5
plt.loglog(x, f(x), '--', label='delta=0.5')
f.delta = 0.3
plt.loglog(x, f(x), '-.', label='delta=0.3')
f.delta = 0.1
plt.loglog(x, f(x), label='delta=0.1')
plt.axis([x.min(), x.max(), 0.1, 1.1])
plt.legend(loc='lower center')
plt.grid(True)
plt.show()
"""
amplitude = Parameter(default=1, min=0)
x_break = Parameter(default=1)
alpha_1 = Parameter(default=-2)
alpha_2 = Parameter(default=2)
delta = Parameter(default=1, min=1.e-3)
@amplitude.validator
def amplitude(self, value):
if np.any(value <= 0):
raise InputParameterError(
"amplitude parameter must be > 0")
@delta.validator
def delta(self, value):
if np.any(value < 0.001):
raise InputParameterError(
"delta parameter must be >= 0.001")
@staticmethod
def evaluate(x, amplitude, x_break, alpha_1, alpha_2, delta):
"""One dimensional smoothly broken power law model function"""
# Pre-calculate `x/x_b`
xx = x / x_break
# Initialize the return value
f = np.zeros_like(xx, subok=False)
if isinstance(amplitude, Quantity):
return_unit = amplitude.unit
amplitude = amplitude.value
else:
return_unit = None
# The quantity `t = (x / x_b)^(1 / delta)` can become quite
# large. To avoid overflow errors we will start by calculating
# its natural logarithm:
logt = np.log(xx) / delta
# When `t >> 1` or `t << 1` we don't actually need to compute
# the `t` value since the main formula (see docstring) can be
# significantly simplified by neglecting `1` or `t`
# respectively. In the following we will check whether `t` is
# much greater, much smaller, or comparable to 1 by comparing
# the `logt` value with an appropriate threshold.
threshold = 30 # corresponding to exp(30) ~ 1e13
i = logt > threshold
if (i.max()):
# In this case the main formula reduces to a simple power
# law with index `alpha_2`.
f[i] = amplitude * xx[i] ** (-alpha_2) \
/ (2. ** ((alpha_1 - alpha_2) * delta))
i = logt < -threshold
if (i.max()):
# In this case the main formula reduces to a simple power
# law with index `alpha_1`.
f[i] = amplitude * xx[i] ** (-alpha_1) \
/ (2. ** ((alpha_1 - alpha_2) * delta))
i = np.abs(logt) <= threshold
if (i.max()):
# In this case the `t` value is "comparable" to 1, hence we
# we will evaluate the whole formula.
t = np.exp(logt[i])
r = (1. + t) / 2.
f[i] = amplitude * xx[i] ** (-alpha_1) \
* r ** ((alpha_1 - alpha_2) * delta)
if return_unit:
return Quantity(f, unit=return_unit, copy=False)
else:
return f
@staticmethod
def fit_deriv(x, amplitude, x_break, alpha_1, alpha_2, delta):
"""One dimensional smoothly broken power law derivative with respect
to parameters"""
# Pre-calculate `x_b` and `x/x_b` and `logt` (see comments in
# SmoothlyBrokenPowerLaw1D.evaluate)
xx = x / x_break
logt = np.log(xx) / delta
# Initialize the return values
f = np.zeros_like(xx)
d_amplitude = np.zeros_like(xx)
d_x_break = np.zeros_like(xx)
d_alpha_1 = np.zeros_like(xx)
d_alpha_2 = np.zeros_like(xx)
d_delta = np.zeros_like(xx)
threshold = 30 # (see comments in SmoothlyBrokenPowerLaw1D.evaluate)
i = logt > threshold
if (i.max()):
f[i] = amplitude * xx[i] ** (-alpha_2) \
/ (2. ** ((alpha_1 - alpha_2) * delta))
d_amplitude[i] = f[i] / amplitude
d_x_break[i] = f[i] * alpha_2 / x_break
d_alpha_1[i] = f[i] * (-delta * np.log(2))
d_alpha_2[i] = f[i] * (-np.log(xx[i]) + delta * np.log(2))
d_delta[i] = f[i] * (-(alpha_1 - alpha_2) * np.log(2))
i = logt < -threshold
if (i.max()):
f[i] = amplitude * xx[i] ** (-alpha_1) \
/ (2. ** ((alpha_1 - alpha_2) * delta))
d_amplitude[i] = f[i] / amplitude
d_x_break[i] = f[i] * alpha_1 / x_break
d_alpha_1[i] = f[i] * (-np.log(xx[i]) - delta * np.log(2))
d_alpha_2[i] = f[i] * delta * np.log(2)
d_delta[i] = f[i] * (-(alpha_1 - alpha_2) * np.log(2))
i = np.abs(logt) <= threshold
if (i.max()):
t = np.exp(logt[i])
r = (1. + t) / 2.
f[i] = amplitude * xx[i] ** (-alpha_1) \
* r ** ((alpha_1 - alpha_2) * delta)
d_amplitude[i] = f[i] / amplitude
d_x_break[i] = f[i] * (alpha_1 - (alpha_1 - alpha_2) * t / 2. / r) / x_break
d_alpha_1[i] = f[i] * (-np.log(xx[i]) + delta * np.log(r))
d_alpha_2[i] = f[i] * (-delta * np.log(r))
d_delta[i] = f[i] * (alpha_1 - alpha_2) \
* (np.log(r) - t / (1. + t) / delta * np.log(xx[i]))
return [d_amplitude, d_x_break, d_alpha_1, d_alpha_2, d_delta]
@property
def input_units(self):
if self.x_break.unit is None:
return None
else:
return {'x': self.x_break.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return OrderedDict([('x_break', inputs_unit['x']),
('amplitude', outputs_unit['y'])])
class ExponentialCutoffPowerLaw1D(Fittable1DModel):
"""
One dimensional power law model with an exponential cutoff.
Parameters
----------
amplitude : float
Model amplitude
x_0 : float
Reference point
alpha : float
Power law index
x_cutoff : float
Cutoff point
See Also
--------
PowerLaw1D, BrokenPowerLaw1D, LogParabola1D
Notes
-----
Model formula (with :math:`A` for ``amplitude`` and :math:`\\alpha` for ``alpha``):
.. math:: f(x) = A (x / x_0) ^ {-\\alpha} \\exp (-x / x_{cutoff})
"""
amplitude = Parameter(default=1)
x_0 = Parameter(default=1)
alpha = Parameter(default=1)
x_cutoff = Parameter(default=1)
@staticmethod
def evaluate(x, amplitude, x_0, alpha, x_cutoff):
"""One dimensional exponential cutoff power law model function"""
xx = x / x_0
return amplitude * xx ** (-alpha) * np.exp(-x / x_cutoff)
@staticmethod
def fit_deriv(x, amplitude, x_0, alpha, x_cutoff):
"""One dimensional exponential cutoff power law derivative with respect to parameters"""
xx = x / x_0
xc = x / x_cutoff
d_amplitude = xx ** (-alpha) * np.exp(-xc)
d_x_0 = alpha * amplitude * d_amplitude / x_0
d_alpha = -amplitude * d_amplitude * np.log(xx)
d_x_cutoff = amplitude * x * d_amplitude / x_cutoff ** 2
return [d_amplitude, d_x_0, d_alpha, d_x_cutoff]
@property
def input_units(self):
if self.x_0.unit is None:
return None
else:
return {'x': self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return OrderedDict([('x_0', inputs_unit['x']),
('x_cutoff', inputs_unit['x']),
('amplitude', outputs_unit['y'])])
class LogParabola1D(Fittable1DModel):
"""
One dimensional log parabola model (sometimes called curved power law).
Parameters
----------
amplitude : float
Model amplitude
x_0 : float
Reference point
alpha : float
Power law index
beta : float
Power law curvature
See Also
--------
PowerLaw1D, BrokenPowerLaw1D, ExponentialCutoffPowerLaw1D
Notes
-----
Model formula (with :math:`A` for ``amplitude`` and :math:`\\alpha` for ``alpha`` and :math:`\\beta` for ``beta``):
.. math:: f(x) = A \\left(\\frac{x}{x_{0}}\\right)^{- \\alpha - \\beta \\log{\\left (\\frac{x}{x_{0}} \\right )}}
"""
amplitude = Parameter(default=1)
x_0 = Parameter(default=1)
alpha = Parameter(default=1)
beta = Parameter(default=0)
@staticmethod
def evaluate(x, amplitude, x_0, alpha, beta):
"""One dimensional log parabola model function"""
xx = x / x_0
exponent = -alpha - beta * np.log(xx)
return amplitude * xx ** exponent
@staticmethod
def fit_deriv(x, amplitude, x_0, alpha, beta):
"""One dimensional log parabola derivative with respect to parameters"""
xx = x / x_0
log_xx = np.log(xx)
exponent = -alpha - beta * log_xx
d_amplitude = xx ** exponent
d_beta = -amplitude * d_amplitude * log_xx ** 2
d_x_0 = amplitude * d_amplitude * (beta * log_xx / x_0 - exponent / x_0)
d_alpha = -amplitude * d_amplitude * log_xx
return [d_amplitude, d_x_0, d_alpha, d_beta]
@property
def input_units(self):
if self.x_0.unit is None:
return None
else:
return {'x': self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return OrderedDict([('x_0', inputs_unit['x']),
('amplitude', outputs_unit['y'])])
| bsd-3-clause |
tensorflow/lattice | tensorflow_lattice/python/premade_test.py | 1 | 34444 | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for Tensorflow Lattice premade."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import json
import tempfile
from absl import logging
from absl.testing import parameterized
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow_lattice.python import configs
from tensorflow_lattice.python import premade
from tensorflow_lattice.python import premade_lib
fake_data = {
'train_xs': [np.array([1]), np.array([3]), np.array([0])],
'train_ys': np.array([1]),
'eval_xs': [np.array([2]), np.array([30]), np.array([-3])]
}
unspecified_feature_configs = [
configs.FeatureConfig(
name='numerical_1',
lattice_size=2,
pwl_calibration_input_keypoints=np.linspace(0.0, 1.0, num=10),
),
configs.FeatureConfig(
name='numerical_2',
lattice_size=2,
pwl_calibration_input_keypoints=np.linspace(0.0, 1.0, num=10),
),
configs.FeatureConfig(
name='categorical',
lattice_size=2,
num_buckets=2,
monotonicity=[('0.0', '1.0')],
vocabulary_list=['0.0', '1.0'],
),
]
specified_feature_configs = [
configs.FeatureConfig(
name='numerical_1',
lattice_size=2,
pwl_calibration_input_keypoints=np.linspace(0.0, 1.0, num=10),
),
configs.FeatureConfig(
name='numerical_2',
lattice_size=2,
pwl_calibration_input_keypoints=np.linspace(0.0, 1.0, num=10),
),
configs.FeatureConfig(
name='categorical',
lattice_size=2,
num_buckets=2,
monotonicity=[(0, 1)],
),
]
feature_configs = [
configs.FeatureConfig(
name='numerical_1',
lattice_size=2,
pwl_calibration_input_keypoints=np.linspace(0.0, 1.0, num=10),
),
configs.FeatureConfig(
name='numerical_2',
lattice_size=2,
pwl_calibration_input_keypoints=np.linspace(0.0, 1.0, num=10),
),
configs.FeatureConfig(
name='categorical',
lattice_size=2,
num_buckets=2,
monotonicity=[(0, 1)],
),
]
class PremadeTest(parameterized.TestCase, tf.test.TestCase):
"""Tests for TFL premade."""
def setUp(self):
super(PremadeTest, self).setUp()
# UCI Statlog (Heart) dataset.
heart_csv_file = tf.keras.utils.get_file(
'heart.csv',
'http://storage.googleapis.com/download.tensorflow.org/data/heart.csv')
heart_df = pd.read_csv(heart_csv_file)
heart_train_size = int(len(heart_df) * 0.8)
heart_train_dataframe = heart_df[:heart_train_size]
heart_test_dataframe = heart_df[heart_train_size:]
# Features:
# - age
# - sex
# - cp chest pain type (4 values)
# - trestbps resting blood pressure
# - chol serum cholestoral in mg/dl
# - fbs fasting blood sugar > 120 mg/dl
# - restecg resting electrocardiographic results (values 0,1,2)
# - thalach maximum heart rate achieved
# - exang exercise induced angina
# - oldpeak ST depression induced by exercise relative to rest
# - slope the slope of the peak exercise ST segment
# - ca number of major vessels (0-3) colored by flourosopy
# - thal 3 = normal; 6 = fixed defect; 7 = reversable defect
#
# This ordering of feature names will be the exact same order that we
# construct our model to expect.
self.heart_feature_names = [
'age', 'sex', 'cp', 'chol', 'fbs', 'trestbps', 'thalach', 'restecg',
'exang', 'oldpeak', 'slope', 'ca', 'thal'
]
feature_name_indices = {
name: index for index, name in enumerate(self.heart_feature_names)
}
# This is the vocab list and mapping we will use for the 'thal' categorical
# feature.
thal_vocab_list = ['normal', 'fixed', 'reversible']
thal_map = {category: i for i, category in enumerate(thal_vocab_list)}
# Custom function for converting thal categories to buckets
def convert_thal_features(thal_features):
# Note that two examples in the test set are already converted.
return np.array([
thal_map[feature] if feature in thal_vocab_list else feature
for feature in thal_features
])
# Custom function for extracting each feature.
def extract_features(dataframe, label_name='target'):
features = []
for feature_name in self.heart_feature_names:
if feature_name == 'thal':
features.append(
convert_thal_features(
dataframe[feature_name].values).astype(float))
else:
features.append(dataframe[feature_name].values.astype(float))
labels = dataframe[label_name].values.astype(float)
return features, labels
self.heart_train_x, self.heart_train_y = extract_features(
heart_train_dataframe)
self.heart_test_x, self.heart_test_y = extract_features(
heart_test_dataframe)
# Let's define our label minimum and maximum.
self.heart_min_label = float(np.min(self.heart_train_y))
self.heart_max_label = float(np.max(self.heart_train_y))
# Our lattice models may have predictions above 1.0 due to numerical errors.
# We can subtract this small epsilon value from our output_max to make sure
# we do not predict values outside of our label bound.
self.numerical_error_epsilon = 1e-5
def compute_quantiles(features,
num_keypoints=10,
clip_min=None,
clip_max=None,
missing_value=None):
# Clip min and max if desired.
if clip_min is not None:
features = np.maximum(features, clip_min)
features = np.append(features, clip_min)
if clip_max is not None:
features = np.minimum(features, clip_max)
features = np.append(features, clip_max)
# Make features unique.
unique_features = np.unique(features)
# Remove missing values if specified.
if missing_value is not None:
unique_features = np.delete(unique_features,
np.where(unique_features == missing_value))
# Compute and return quantiles over unique non-missing feature values.
return np.quantile(
unique_features,
np.linspace(0., 1., num=num_keypoints),
interpolation='nearest').astype(float)
self.heart_feature_configs = [
configs.FeatureConfig(
name='age',
lattice_size=3,
monotonicity='increasing',
# We must set the keypoints manually.
pwl_calibration_num_keypoints=5,
pwl_calibration_input_keypoints=compute_quantiles(
self.heart_train_x[feature_name_indices['age']],
num_keypoints=5,
clip_max=100),
# Per feature regularization.
regularizer_configs=[
configs.RegularizerConfig(name='calib_wrinkle', l2=0.1),
],
),
configs.FeatureConfig(
name='sex',
num_buckets=2,
),
configs.FeatureConfig(
name='cp',
monotonicity='increasing',
# Keypoints that are uniformly spaced.
pwl_calibration_num_keypoints=4,
pwl_calibration_input_keypoints=np.linspace(
np.min(self.heart_train_x[feature_name_indices['cp']]),
np.max(self.heart_train_x[feature_name_indices['cp']]),
num=4),
),
configs.FeatureConfig(
name='chol',
monotonicity='increasing',
# Explicit input keypoints initialization.
pwl_calibration_input_keypoints=[126.0, 210.0, 247.0, 286.0, 564.0],
# Calibration can be forced to span the full output range
# by clamping.
pwl_calibration_clamp_min=True,
pwl_calibration_clamp_max=True,
# Per feature regularization.
regularizer_configs=[
configs.RegularizerConfig(name='calib_hessian', l2=1e-4),
],
),
configs.FeatureConfig(
name='fbs',
# Partial monotonicity: output(0) <= output(1)
monotonicity=[(0, 1)],
num_buckets=2,
),
configs.FeatureConfig(
name='trestbps',
monotonicity='decreasing',
pwl_calibration_num_keypoints=5,
pwl_calibration_input_keypoints=compute_quantiles(
self.heart_train_x[feature_name_indices['trestbps']],
num_keypoints=5),
),
configs.FeatureConfig(
name='thalach',
monotonicity='decreasing',
pwl_calibration_num_keypoints=5,
pwl_calibration_input_keypoints=compute_quantiles(
self.heart_train_x[feature_name_indices['thalach']],
num_keypoints=5),
),
configs.FeatureConfig(
name='restecg',
# Partial monotonicity:
# output(0) <= output(1), output(0) <= output(2)
monotonicity=[(0, 1), (0, 2)],
num_buckets=3,
),
configs.FeatureConfig(
name='exang',
# Partial monotonicity: output(0) <= output(1)
monotonicity=[(0, 1)],
num_buckets=2,
),
configs.FeatureConfig(
name='oldpeak',
monotonicity='increasing',
pwl_calibration_num_keypoints=5,
pwl_calibration_input_keypoints=compute_quantiles(
self.heart_train_x[feature_name_indices['oldpeak']],
num_keypoints=5),
),
configs.FeatureConfig(
name='slope',
# Partial monotonicity:
# output(0) <= output(1), output(1) <= output(2)
monotonicity=[(0, 1), (1, 2)],
num_buckets=3,
),
configs.FeatureConfig(
name='ca',
monotonicity='increasing',
pwl_calibration_num_keypoints=4,
pwl_calibration_input_keypoints=compute_quantiles(
self.heart_train_x[feature_name_indices['ca']],
num_keypoints=4),
),
configs.FeatureConfig(
name='thal',
# Partial monotonicity:
# output(normal) <= output(fixed)
# output(normal) <= output(reversible)
monotonicity=[('normal', 'fixed'), ('normal', 'reversible')],
num_buckets=3,
# We must specify the vocabulary list in order to later set the
# monotonicities since we used names and not indices.
vocabulary_list=thal_vocab_list,
),
]
premade_lib.set_categorical_monotonicities(self.heart_feature_configs)
def _ResetAllBackends(self):
tf.keras.backend.clear_session()
tf.compat.v1.reset_default_graph()
class Encoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.int32):
return int(obj)
if isinstance(obj, np.ndarray):
return obj.tolist()
return json.JSONEncoder.default(self, obj)
def testSetRandomLattices(self):
random_model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=copy.deepcopy(unspecified_feature_configs),
lattices='random',
num_lattices=3,
lattice_rank=2,
separate_calibrators=True,
output_initialization=[-1.0, 1.0])
premade_lib.set_random_lattice_ensemble(random_model_config)
self.assertLen(random_model_config.lattices, 3)
self.assertListEqual(
[2, 2, 2], [len(lattice) for lattice in random_model_config.lattices])
specified_model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=copy.deepcopy(specified_feature_configs),
lattices=[['numerical_1', 'categorical'],
['numerical_2', 'categorical']],
num_lattices=2,
lattice_rank=2,
separate_calibrators=True,
output_initialization=[-1.0, 1.0])
with self.assertRaisesRegex(
ValueError, 'model_config.lattices must be set to \'random\'.'):
premade_lib.set_random_lattice_ensemble(specified_model_config)
def testSetCategoricalMonotonicities(self):
set_feature_configs = copy.deepcopy(unspecified_feature_configs)
premade_lib.set_categorical_monotonicities(set_feature_configs)
expectation = [(0, 1)]
self.assertListEqual(expectation, set_feature_configs[2].monotonicity)
def testVerifyConfig(self):
unspecified_model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=copy.deepcopy(unspecified_feature_configs),
lattices='random',
num_lattices=3,
lattice_rank=2,
separate_calibrators=True,
output_initialization=[-1.0, 1.0])
with self.assertRaisesRegex(
ValueError, 'Lattices are not fully specified for ensemble config.'):
premade_lib.verify_config(unspecified_model_config)
premade_lib.set_random_lattice_ensemble(unspecified_model_config)
with self.assertRaisesRegex(
ValueError,
'Element 0 for list/tuple 0 for feature categorical monotonicity is '
'not an index: 0.0'):
premade_lib.verify_config(unspecified_model_config)
fixed_feature_configs = copy.deepcopy(unspecified_feature_configs)
premade_lib.set_categorical_monotonicities(fixed_feature_configs)
unspecified_model_config.feature_configs = fixed_feature_configs
premade_lib.verify_config(unspecified_model_config)
specified_model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=copy.deepcopy(specified_feature_configs),
lattices=[['numerical_1', 'categorical'],
['numerical_2', 'categorical']],
num_lattices=2,
lattice_rank=2,
separate_calibrators=True,
output_initialization=[-1.0, 1.0])
premade_lib.verify_config(specified_model_config)
def testLatticeEnsembleFromConfig(self):
model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=copy.deepcopy(feature_configs),
lattices=[['numerical_1', 'categorical'],
['numerical_2', 'categorical']],
num_lattices=2,
lattice_rank=2,
separate_calibrators=True,
regularizer_configs=[
configs.RegularizerConfig('calib_hessian', l2=1e-3),
configs.RegularizerConfig('torsion', l2=1e-4),
],
output_min=-1.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=5,
output_initialization=[-1.0, 1.0])
model = premade.CalibratedLatticeEnsemble(model_config)
loaded_model = premade.CalibratedLatticeEnsemble.from_config(
model.get_config(), custom_objects=premade.get_custom_objects())
self.assertEqual(
json.dumps(model.get_config(), sort_keys=True, cls=self.Encoder),
json.dumps(loaded_model.get_config(), sort_keys=True, cls=self.Encoder))
def testLatticeFromConfig(self):
model_config = configs.CalibratedLatticeConfig(
feature_configs=copy.deepcopy(feature_configs),
regularizer_configs=[
configs.RegularizerConfig('calib_wrinkle', l2=1e-3),
configs.RegularizerConfig('torsion', l2=1e-3),
],
output_min=0.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=6,
output_initialization=[0.0, 1.0])
model = premade.CalibratedLattice(model_config)
loaded_model = premade.CalibratedLattice.from_config(
model.get_config(), custom_objects=premade.get_custom_objects())
self.assertEqual(
json.dumps(model.get_config(), sort_keys=True, cls=self.Encoder),
json.dumps(loaded_model.get_config(), sort_keys=True, cls=self.Encoder))
def testLatticeSimplexFromConfig(self):
model_config = configs.CalibratedLatticeConfig(
feature_configs=copy.deepcopy(feature_configs),
regularizer_configs=[
configs.RegularizerConfig('calib_wrinkle', l2=1e-3),
configs.RegularizerConfig('torsion', l2=1e-3),
],
output_min=0.0,
output_max=1.0,
interpolation='simplex',
output_calibration=True,
output_calibration_num_keypoints=6,
output_initialization=[0.0, 1.0])
model = premade.CalibratedLattice(model_config)
loaded_model = premade.CalibratedLattice.from_config(
model.get_config(), custom_objects=premade.get_custom_objects())
self.assertEqual(
json.dumps(model.get_config(), sort_keys=True, cls=self.Encoder),
json.dumps(loaded_model.get_config(), sort_keys=True, cls=self.Encoder))
def testLinearFromConfig(self):
model_config = configs.CalibratedLinearConfig(
feature_configs=copy.deepcopy(feature_configs),
regularizer_configs=[
configs.RegularizerConfig('calib_hessian', l2=1e-4),
configs.RegularizerConfig('torsion', l2=1e-3),
],
use_bias=True,
output_min=0.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=6,
output_initialization=[0.0, 1.0])
model = premade.CalibratedLinear(model_config)
loaded_model = premade.CalibratedLinear.from_config(
model.get_config(), custom_objects=premade.get_custom_objects())
self.assertEqual(
json.dumps(model.get_config(), sort_keys=True, cls=self.Encoder),
json.dumps(loaded_model.get_config(), sort_keys=True, cls=self.Encoder))
def testAggregateFromConfig(self):
model_config = configs.AggregateFunctionConfig(
feature_configs=feature_configs,
regularizer_configs=[
configs.RegularizerConfig('calib_hessian', l2=1e-4),
configs.RegularizerConfig('torsion', l2=1e-3),
],
middle_calibration=True,
middle_monotonicity='increasing',
output_min=0.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=8,
output_initialization=[0.0, 1.0])
model = premade.AggregateFunction(model_config)
loaded_model = premade.AggregateFunction.from_config(
model.get_config(), custom_objects=premade.get_custom_objects())
self.assertEqual(
json.dumps(model.get_config(), sort_keys=True, cls=self.Encoder),
json.dumps(loaded_model.get_config(), sort_keys=True, cls=self.Encoder))
@parameterized.parameters(
('hypercube', 'all_vertices', 0, 0.85),
('simplex', 'all_vertices', 0, 0.89),
('hypercube', 'kronecker_factored', 2, 0.82),
('hypercube', 'kronecker_factored', 4, 0.82),
)
def testCalibratedLatticeEnsembleCrystals(self, interpolation,
parameterization, num_terms,
expected_minimum_auc):
# Construct model.
self._ResetAllBackends()
crystals_feature_configs = copy.deepcopy(self.heart_feature_configs)
model_config = configs.CalibratedLatticeEnsembleConfig(
regularizer_configs=[
configs.RegularizerConfig(name='torsion', l2=1e-4),
configs.RegularizerConfig(name='output_calib_hessian', l2=1e-4),
],
feature_configs=crystals_feature_configs,
lattices='crystals',
num_lattices=6,
lattice_rank=5,
interpolation=interpolation,
parameterization=parameterization,
num_terms=num_terms,
separate_calibrators=True,
output_calibration=False,
output_min=self.heart_min_label,
output_max=self.heart_max_label - self.numerical_error_epsilon,
output_initialization=[self.heart_min_label, self.heart_max_label],
)
if parameterization == 'kronecker_factored':
model_config.regularizer_configs = None
for feature_config in model_config.feature_configs:
feature_config.lattice_size = 2
feature_config.unimodality = 'none'
feature_config.reflects_trust_in = None
feature_config.dominates = None
feature_config.regularizer_configs = None
# Perform prefitting steps.
prefitting_model_config = premade_lib.construct_prefitting_model_config(
model_config)
prefitting_model = premade.CalibratedLatticeEnsemble(
prefitting_model_config)
prefitting_model.compile(
loss=tf.keras.losses.BinaryCrossentropy(),
optimizer=tf.keras.optimizers.Adam(0.01))
prefitting_model.fit(
self.heart_train_x,
self.heart_train_y,
batch_size=100,
epochs=50,
verbose=False)
premade_lib.set_crystals_lattice_ensemble(model_config,
prefitting_model_config,
prefitting_model)
# Construct and train final model
model = premade.CalibratedLatticeEnsemble(model_config)
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=tf.keras.metrics.AUC(),
optimizer=tf.keras.optimizers.Adam(0.01))
model.fit(
self.heart_train_x,
self.heart_train_y,
batch_size=100,
epochs=200,
verbose=False)
results = model.evaluate(
self.heart_test_x, self.heart_test_y, verbose=False)
logging.info('Calibrated lattice ensemble crystals classifier results:')
logging.info(results)
self.assertGreater(results[1], expected_minimum_auc)
@parameterized.parameters(
('hypercube', 'all_vertices', 0, 0.85),
('simplex', 'all_vertices', 0, 0.88),
('hypercube', 'kronecker_factored', 2, 0.86),
('hypercube', 'kronecker_factored', 4, 0.9),
)
def testCalibratedLatticeEnsembleRTL(self, interpolation, parameterization,
num_terms, expected_minimum_auc):
# Construct model.
self._ResetAllBackends()
rtl_feature_configs = copy.deepcopy(self.heart_feature_configs)
for feature_config in rtl_feature_configs:
feature_config.lattice_size = 2
feature_config.unimodality = 'none'
feature_config.reflects_trust_in = None
feature_config.dominates = None
feature_config.regularizer_configs = None
model_config = configs.CalibratedLatticeEnsembleConfig(
regularizer_configs=[
configs.RegularizerConfig(name='torsion', l2=1e-4),
configs.RegularizerConfig(name='output_calib_hessian', l2=1e-4),
],
feature_configs=rtl_feature_configs,
lattices='rtl_layer',
num_lattices=6,
lattice_rank=5,
interpolation=interpolation,
parameterization=parameterization,
num_terms=num_terms,
separate_calibrators=True,
output_calibration=False,
output_min=self.heart_min_label,
output_max=self.heart_max_label - self.numerical_error_epsilon,
output_initialization=[self.heart_min_label, self.heart_max_label],
)
# We must remove all regularization if using 'kronecker_factored'.
if parameterization == 'kronecker_factored':
model_config.regularizer_configs = None
# Construct and train final model
model = premade.CalibratedLatticeEnsemble(model_config)
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=tf.keras.metrics.AUC(),
optimizer=tf.keras.optimizers.Adam(0.01))
model.fit(
self.heart_train_x,
self.heart_train_y,
batch_size=100,
epochs=200,
verbose=False)
results = model.evaluate(
self.heart_test_x, self.heart_test_y, verbose=False)
logging.info('Calibrated lattice ensemble rtl classifier results:')
logging.info(results)
self.assertGreater(results[1], expected_minimum_auc)
@parameterized.parameters(
('hypercube', 'all_vertices', 0, 0.81),
('simplex', 'all_vertices', 0, 0.81),
('hypercube', 'kronecker_factored', 2, 0.79),
('hypercube', 'kronecker_factored', 4, 0.8),
)
def testCalibratedLattice(self, interpolation, parameterization, num_terms,
expected_minimum_auc):
# Construct model configuration.
self._ResetAllBackends()
lattice_feature_configs = copy.deepcopy(self.heart_feature_configs[:5])
model_config = configs.CalibratedLatticeConfig(
feature_configs=lattice_feature_configs,
interpolation=interpolation,
parameterization=parameterization,
num_terms=num_terms,
regularizer_configs=[
configs.RegularizerConfig(name='torsion', l2=1e-4),
configs.RegularizerConfig(name='output_calib_hessian', l2=1e-4),
],
output_min=self.heart_min_label,
output_max=self.heart_max_label,
output_calibration=False,
output_initialization=[self.heart_min_label, self.heart_max_label],
)
if parameterization == 'kronecker_factored':
model_config.regularizer_configs = None
for feature_config in model_config.feature_configs:
feature_config.lattice_size = 2
feature_config.unimodality = 'none'
feature_config.reflects_trust_in = None
feature_config.dominates = None
feature_config.regularizer_configs = None
# Construct and train final model
model = premade.CalibratedLattice(model_config)
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=tf.keras.metrics.AUC(),
optimizer=tf.keras.optimizers.Adam(0.01))
model.fit(
self.heart_train_x[:5],
self.heart_train_y,
batch_size=100,
epochs=200,
verbose=False)
results = model.evaluate(
self.heart_test_x[:5], self.heart_test_y, verbose=False)
logging.info('Calibrated lattice classifier results:')
logging.info(results)
self.assertGreater(results[1], expected_minimum_auc)
@parameterized.parameters(
('all_vertices', 0),
('kronecker_factored', 2),
)
def testLatticeEnsembleH5FormatSaveLoad(self, parameterization, num_terms):
model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=copy.deepcopy(feature_configs),
lattices=[['numerical_1', 'categorical'],
['numerical_2', 'categorical']],
num_lattices=2,
lattice_rank=2,
parameterization=parameterization,
num_terms=num_terms,
separate_calibrators=True,
regularizer_configs=[
configs.RegularizerConfig('calib_hessian', l2=1e-3),
configs.RegularizerConfig('torsion', l2=1e-4),
],
output_min=-1.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=5,
output_initialization=[-1.0, 1.0])
if parameterization == 'kronecker_factored':
model_config.regularizer_configs = None
for feature_config in model_config.feature_configs:
feature_config.lattice_size = 2
feature_config.unimodality = 'none'
feature_config.reflects_trust_in = None
feature_config.dominates = None
feature_config.regularizer_configs = None
model = premade.CalibratedLatticeEnsemble(model_config)
# Compile and fit model.
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(0.1))
model.fit(fake_data['train_xs'], fake_data['train_ys'])
# Save model using H5 format.
with tempfile.NamedTemporaryFile(suffix='.h5') as f:
tf.keras.models.save_model(model, f.name)
loaded_model = tf.keras.models.load_model(
f.name, custom_objects=premade.get_custom_objects())
self.assertAllClose(
model.predict(fake_data['eval_xs']),
loaded_model.predict(fake_data['eval_xs']))
@parameterized.parameters(
('all_vertices', 0),
('kronecker_factored', 2),
)
def testLatticeEnsembleRTLH5FormatSaveLoad(self, parameterization, num_terms):
rtl_feature_configs = copy.deepcopy(feature_configs)
for feature_config in rtl_feature_configs:
feature_config.lattice_size = 2
feature_config.unimodality = 'none'
feature_config.reflects_trust_in = None
feature_config.dominates = None
feature_config.regularizer_configs = None
model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=copy.deepcopy(rtl_feature_configs),
lattices='rtl_layer',
num_lattices=2,
lattice_rank=2,
parameterization=parameterization,
num_terms=num_terms,
separate_calibrators=True,
regularizer_configs=[
configs.RegularizerConfig('calib_hessian', l2=1e-3),
configs.RegularizerConfig('torsion', l2=1e-4),
],
output_min=-1.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=5,
output_initialization=[-1.0, 1.0])
if parameterization == 'kronecker_factored':
model_config.regularizer_configs = None
model = premade.CalibratedLatticeEnsemble(model_config)
# Compile and fit model.
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(0.1))
model.fit(fake_data['train_xs'], fake_data['train_ys'])
# Save model using H5 format.
with tempfile.NamedTemporaryFile(suffix='.h5') as f:
tf.keras.models.save_model(model, f.name)
loaded_model = tf.keras.models.load_model(
f.name, custom_objects=premade.get_custom_objects())
self.assertAllClose(
model.predict(fake_data['eval_xs']),
loaded_model.predict(fake_data['eval_xs']))
@parameterized.parameters(
('all_vertices', 0),
('kronecker_factored', 2),
)
def testLatticeH5FormatSaveLoad(self, parameterization, num_terms):
model_config = configs.CalibratedLatticeConfig(
feature_configs=copy.deepcopy(feature_configs),
parameterization=parameterization,
num_terms=num_terms,
regularizer_configs=[
configs.RegularizerConfig('calib_wrinkle', l2=1e-3),
configs.RegularizerConfig('torsion', l2=1e-3),
],
output_min=0.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=6,
output_initialization=[0.0, 1.0])
if parameterization == 'kronecker_factored':
model_config.regularizer_configs = None
for feature_config in model_config.feature_configs:
feature_config.lattice_size = 2
feature_config.unimodality = 'none'
feature_config.reflects_trust_in = None
feature_config.dominates = None
feature_config.regularizer_configs = None
model = premade.CalibratedLattice(model_config)
# Compile and fit model.
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(0.1))
model.fit(fake_data['train_xs'], fake_data['train_ys'])
# Save model using H5 format.
with tempfile.NamedTemporaryFile(suffix='.h5') as f:
tf.keras.models.save_model(model, f.name)
loaded_model = tf.keras.models.load_model(
f.name, custom_objects=premade.get_custom_objects())
self.assertAllClose(
model.predict(fake_data['eval_xs']),
loaded_model.predict(fake_data['eval_xs']))
def testLinearH5FormatSaveLoad(self):
model_config = configs.CalibratedLinearConfig(
feature_configs=copy.deepcopy(feature_configs),
regularizer_configs=[
configs.RegularizerConfig('calib_hessian', l2=1e-4),
configs.RegularizerConfig('torsion', l2=1e-3),
],
use_bias=True,
output_min=0.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=6,
output_initialization=[0.0, 1.0])
model = premade.CalibratedLinear(model_config)
# Compile and fit model.
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(0.1))
model.fit(fake_data['train_xs'], fake_data['train_ys'])
# Save model using H5 format.
with tempfile.NamedTemporaryFile(suffix='.h5') as f:
tf.keras.models.save_model(model, f.name)
loaded_model = tf.keras.models.load_model(
f.name, custom_objects=premade.get_custom_objects())
self.assertAllClose(
model.predict(fake_data['eval_xs']),
loaded_model.predict(fake_data['eval_xs']))
def testAggregateH5FormatSaveLoad(self):
model_config = configs.AggregateFunctionConfig(
feature_configs=feature_configs,
regularizer_configs=[
configs.RegularizerConfig('calib_hessian', l2=1e-4),
configs.RegularizerConfig('torsion', l2=1e-3),
],
middle_calibration=True,
middle_monotonicity='increasing',
output_min=0.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=8,
output_initialization=[0.0, 1.0])
model = premade.AggregateFunction(model_config)
# Compile and fit model.
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(0.1))
model.fit(fake_data['train_xs'], fake_data['train_ys'])
# Save model using H5 format.
with tempfile.NamedTemporaryFile(suffix='.h5') as f:
# Note: because of naming clashes in the optimizer, we cannot include it
# when saving in HDF5. The keras team has informed us that we should not
# push to support this since SavedModel format is the new default and no
# new HDF5 functionality is desired.
tf.keras.models.save_model(model, f.name, include_optimizer=False)
loaded_model = tf.keras.models.load_model(
f.name, custom_objects=premade.get_custom_objects())
self.assertAllClose(
model.predict(fake_data['eval_xs']),
loaded_model.predict(fake_data['eval_xs']))
if __name__ == '__main__':
tf.test.main()
| apache-2.0 |
heli522/scikit-learn | examples/cluster/plot_dict_face_patches.py | 337 | 2747 | """
Online learning of a dictionary of parts of faces
==================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the scikit-learn to process a very large
dataset by chunks. The way we proceed is that we load an image at a time
and extract randomly 50 patches from this image. Once we have accumulated
500 of these patches (using 10 images), we run the `partial_fit` method
of the online KMeans object, MiniBatchKMeans.
The verbose setting on the MiniBatchKMeans enables us to see that some
clusters are reassigned during the successive calls to
partial-fit. This is because the number of patches that they represent
has become too low, and it is better to choose a random new
cluster.
"""
print(__doc__)
import time
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.image import extract_patches_2d
faces = datasets.fetch_olivetti_faces()
###############################################################################
# Learn the dictionary of images
print('Learning the dictionary... ')
rng = np.random.RandomState(0)
kmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True)
patch_size = (20, 20)
buffer = []
index = 1
t0 = time.time()
# The online learning part: cycle over the whole dataset 6 times
index = 0
for _ in range(6):
for img in faces.images:
data = extract_patches_2d(img, patch_size, max_patches=50,
random_state=rng)
data = np.reshape(data, (len(data), -1))
buffer.append(data)
index += 1
if index % 10 == 0:
data = np.concatenate(buffer, axis=0)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)
kmeans.partial_fit(data)
buffer = []
if index % 100 == 0:
print('Partial fit of %4i out of %i'
% (index, 6 * len(faces.images)))
dt = time.time() - t0
print('done in %.2fs.' % dt)
###############################################################################
# Plot the results
plt.figure(figsize=(4.2, 4))
for i, patch in enumerate(kmeans.cluster_centers_):
plt.subplot(9, 9, i + 1)
plt.imshow(patch.reshape(patch_size), cmap=plt.cm.gray,
interpolation='nearest')
plt.xticks(())
plt.yticks(())
plt.suptitle('Patches of faces\nTrain time %.1fs on %d patches' %
(dt, 8 * len(faces.images)), fontsize=16)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
plt.show()
| bsd-3-clause |
rishikksh20/scikit-learn | sklearn/datasets/tests/test_kddcup99.py | 42 | 1278 | """Test kddcup99 loader. Only 'percent10' mode is tested, as the full data
is too big to use in unit-testing.
The test is skipped if the data wasn't previously fetched and saved to
scikit-learn data folder.
"""
from sklearn.datasets import fetch_kddcup99
from sklearn.utils.testing import assert_equal, SkipTest
def test_percent10():
try:
data = fetch_kddcup99(download_if_missing=False)
except IOError:
raise SkipTest("kddcup99 dataset can not be loaded.")
assert_equal(data.data.shape, (494021, 41))
assert_equal(data.target.shape, (494021,))
data_shuffled = fetch_kddcup99(shuffle=True, random_state=0)
assert_equal(data.data.shape, data_shuffled.data.shape)
assert_equal(data.target.shape, data_shuffled.target.shape)
data = fetch_kddcup99('SA')
assert_equal(data.data.shape, (100655, 41))
assert_equal(data.target.shape, (100655,))
data = fetch_kddcup99('SF')
assert_equal(data.data.shape, (73237, 4))
assert_equal(data.target.shape, (73237,))
data = fetch_kddcup99('http')
assert_equal(data.data.shape, (58725, 3))
assert_equal(data.target.shape, (58725,))
data = fetch_kddcup99('smtp')
assert_equal(data.data.shape, (9571, 3))
assert_equal(data.target.shape, (9571,))
| bsd-3-clause |
benglard/ConvNetPy | examples/faces.py | 1 | 2118 | # Requires scikit-learn
from vol_util import augment
from vol import Vol
from net import Net
from trainers import Trainer
from sklearn.cross_validation import train_test_split
from sklearn.datasets import fetch_lfw_people
training_data = None
testing_data = None
network = None
t = None
def load_data():
global training_data, testing_data
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
xs = lfw_people.data
ys = lfw_people.target
inputs = []
labels = list(ys)
for face in xs:
V = Vol(50, 37, 1, 0.0)
V.w = list(face)
inputs.append(augment(V, 30))
x_tr, x_te, y_tr, y_te = train_test_split(inputs, labels, test_size=0.25)
training_data = zip(x_tr, y_tr)
testing_data = zip(x_te, y_te)
print 'Dataset made...'
def start():
global network, t
layers = []
layers.append({'type': 'input', 'out_sx': 30, 'out_sy': 30, 'out_depth': 1})
layers.append({'type': 'fc', 'num_neurons': 100, 'activation': 'sigmoid'})
layers.append({'type': 'softmax', 'num_classes': 7})
print 'Layers made...'
network = Net(layers)
print 'Net made...'
print network
t = Trainer(network, {'method': 'adadelta', 'batch_size': 20, 'l2_decay': 0.001})
print 'Trainer made...'
print t
def train():
global training_data, network, t
print 'In training...'
print 'k', 'time\t\t ', 'loss\t ', 'training accuracy'
print '----------------------------------------------------'
try:
for x, y in training_data:
stats = t.train(x, y)
print stats['k'], stats['time'], stats['loss'], stats['accuracy']
except: #hit control-c or other
return
def test():
global training_data, testing_data, network, t
print 'In testing'
right = 0
count = 0
try:
for x, y in testing_data:
network.forward(x)
right += network.getPrediction() == y
print count
count += 1
except:
pass
finally:
accuracy = float(right) / count * 100
print accuracy | mit |
jmuhlich/rasmodel | kras_gtp_hydrolysis.py | 6 | 1613 | from rasmodel.scenarios.default import model
import numpy as np
from matplotlib import pyplot as plt
from pysb.integrate import Solver
from pysb import *
from tbidbaxlipo.util import fitting
KRAS = model.monomers['KRAS']
GTP = model.monomers['GTP']
total_pi = 50000
for mutant in KRAS.site_states['mutant']:
Initial(KRAS(gtp=1, gap=None, gef=None, p_loop=None, s1s2='open', CAAX=None,
mutant=mutant) % GTP(p=1, label='n'),
Parameter('KRAS_%s_GTP_0' % mutant, 0))
plt.ion()
plt.figure()
t = np.linspace(0, 1000, 1000) # 1000 seconds
for mutant in KRAS.site_states['mutant']:
# Zero out all initial conditions
for ic in model.initial_conditions:
ic[1].value = 0
model.parameters['KRAS_%s_GTP_0' % mutant].value = total_pi
sol = Solver(model, t)
sol.run()
plt.plot(t, sol.yobs['Pi_'] / total_pi, label=mutant)
plt.ylabel('GTP hydrolyzed (%)')
plt.ylim(top=1)
plt.xlabel('Time (s)')
plt.title('Intrinsic hydrolysis')
plt.legend(loc='upper left', fontsize=11, frameon=False)
plt.figure()
for mutant in KRAS.site_states['mutant']:
# Zero out all initial conditions
for ic in model.initial_conditions:
ic[1].value = 0
model.parameters['RASA1_0'].value = 50000
model.parameters['KRAS_%s_GTP_0' % mutant].value = total_pi
sol = Solver(model, t)
sol.run()
plt.plot(t, sol.yobs['Pi_'] / total_pi, label=mutant)
plt.ylabel('GTP hydrolyzed (%)')
plt.ylim(top=1)
plt.xlabel('Time (s)')
plt.title('GAP-mediated hydrolysis')
plt.legend(loc='upper right', fontsize=11, frameon=False)
| mit |
pombredanne/numba | examples/mandel/mandel_vectorize.py | 3 | 1448 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function, division, absolute_import
import sys
from numba import vectorize
import numpy as np
from timeit import default_timer as timer
from matplotlib.pylab import imshow, jet, show, ion
sig = 'uint8(uint32, f4, f4, f4, f4, uint32, uint32, uint32)'
@vectorize([sig], target='cuda')
def mandel(tid, min_x, max_x, min_y, max_y, width, height, iters):
pixel_size_x = (max_x - min_x) / width
pixel_size_y = (max_y - min_y) / height
x = tid % width
y = tid / width
real = min_x + x * pixel_size_x
imag = min_y + y * pixel_size_y
c = complex(real, imag)
z = 0.0j
for i in range(iters):
z = z * z + c
if (z.real * z.real + z.imag * z.imag) >= 4:
return i
return 255
def create_fractal(min_x, max_x, min_y, max_y, width, height, iters):
tids = np.arange(width * height, dtype=np.uint32)
return mandel(tids, np.float32(min_x), np.float32(max_x), np.float32(min_y),
np.float32(max_y), np.uint32(height), np.uint32(width),
np.uint32(iters))
def main():
width = 500 * 10
height = 750 * 10
ts = timer()
pixels = create_fractal(-2.0, 1.0, -1.0, 1.0, width, height, 20)
te = timer()
print('time: %f' % (te - ts))
image = pixels.reshape(width, height)
#print(image)
imshow(image)
show()
if __name__ == '__main__':
main()
| bsd-2-clause |
anhaidgroup/py_entitymatching | py_entitymatching/feature/attributeutils.py | 1 | 8443 | """
This module contains some utility functions for attributes in the DataFrame.
"""
import logging
import pandas as pd
import numpy as np
import six
from py_entitymatching.utils.validation_helper import validate_object_type
logger = logging.getLogger(__name__)
def get_attr_types(data_frame):
"""
This function gets the attribute types for a DataFrame.
Specifically this function gets the attribute types based on the
statistics of the attributes. These attribute types can be str_eq_1w,
str_bt_1w_5w, str_bt_5w_10w, str_gt_10w, boolean or numeric.
The types roughly capture whether the attribute is of type string,
boolean or numeric. Further, with in the string type the subtypes are
capture the average number of tokens in the column values. For example,
str_bt_1w_5w means the average number of tokens in that column is
greater than one word but less than 5 words.
Args:
data_frame (DataFrame): The input DataFrame for which types of
attributes must be determined.
Returns:
A Python dictionary is returned containing the attribute types.
Specifically, in the dictionary key is an attribute name, value
is the type of that attribute.
Further, the dictionary will have a key _table, and the value of
that should be a pointer to the input DataFrame.
Raises:
AssertionError: If `data_frame` is not of type
pandas DataFrame.
Examples:
>>> import py_entitymatching as em
>>> A = em.read_csv_metadata('path_to_csv_dir/table_A.csv', key='ID')
>>> B = em.read_csv_metadata('path_to_csv_dir/table_B.csv', key='ID')
>>> atypes1 = em.get_attr_types(A)
>>> atypes2 = em.get_attr_types(B)
"""
# Validate input paramaters
# # We expect the input object (data_frame) to be of type pandas DataFrame.
if not isinstance(data_frame, pd.DataFrame):
logger.error('Input table is not of type pandas dataframe')
raise AssertionError('Input table is not of type pandas dataframe')
# Now get type for each column
type_list = [_get_type(data_frame[col]) for col in data_frame.columns]
# Create a dictionary containing attribute types
attribute_type_dict = dict(zip(data_frame.columns, type_list))
# Update the dictionary with the _table key and value set to the input
# DataFrame
attribute_type_dict['_table'] = data_frame
# Return the attribute type dictionary
return attribute_type_dict
def get_attr_corres(ltable, rtable):
"""
This function gets the attribute correspondences between the attributes
of ltable and rtable.
The user may need to get the correspondences so
that he/she can generate features based those correspondences.
Args:
ltable,rtable (DataFrame): Input DataFrames for which
the attribute correspondences must be obtained.
Returns:
A Python dictionary is returned containing the attribute
correspondences.
Specifically, this returns a dictionary with the following key-value
pairs:
corres: points to the list correspondences as tuples. Each
correspondence is a tuple with two attributes: one from ltable
and the other from rtable.
ltable: points to ltable.
rtable: points to rtable.
Currently, 'corres' contains only pairs of attributes with exact
names in ltable and rtable.
Raises:
AssertionError: If `ltable` is not of type
pandas DataFrame.
AssertionError: If `rtable` is not of type
pandas DataFrame.
Examples:
>>> import py_entitymatching as em
>>> A = em.read_csv_metadata('path_to_csv_dir/table_A.csv', key='ID')
>>> B = em.read_csv_metadata('path_to_csv_dir/table_B.csv', key='ID')
>>> match_c = em.get_attr_corres(A, B)
"""
# Validate input parameters
# # We expect the input object (ltable) to be of type pandas
# DataFrame
validate_object_type(ltable, pd.DataFrame, error_prefix='Input ltable')
# # We expect the input object (rtable) to be of type pandas
# DataFrame
validate_object_type(rtable, pd.DataFrame, error_prefix='Input rtable')
# Initialize the correspondence list
correspondence_list = []
# Check for each column in ltable, if column exists in rtable,
# If so, add it to the correspondence list.
# Note: This may not be the fastest way to implement this. We could
# refactor this later.
for column in ltable.columns:
if column in rtable.columns:
correspondence_list.append((column, column))
# Initialize a correspondence dictionary.
correspondence_dict = dict()
# Fill the corres, ltable and rtable.
correspondence_dict['corres'] = correspondence_list
correspondence_dict['ltable'] = ltable
correspondence_dict['rtable'] = rtable
# Finally, return the correspondence dictionary
return correspondence_dict
def _get_type(column):
"""
Given a pandas Series (i.e column in pandas DataFrame) obtain its type
"""
# Validate input parameters
# # We expect the input column to be of type pandas Series
if not isinstance(column, pd.Series):
raise AssertionError('Input (column) is not of type pandas series')
# To get the type first drop all NaNa
column = column.dropna()
# Get type for each element and convert it into a set (and for
# convenience convert the resulting set into a list)
type_list = list(set(column.map(type).tolist()))
# If the list is empty, then we cannot decide anything about the column.
# We will raise a warning and return the type to be numeric.
# Note: The reason numeric is returned instead of a special type because,
# we want to keep the types minimal. Further, explicitly recommend the
# user to update the returned types later.
if len(type_list) == 0:
logger.warning("Column {0} does not seem to qualify as any atomic type. "
"It may contain all NaNs. Please update the values of column {0}".format(column.name))
return 'un_determined'
# If the column qualifies to be of more than one type (for instance,
# in a numeric column, some values may be inferred as strings), then we
# will raise an error for the user to fix this case.
if len(type_list) > 1:
logger.warning('Column %s qualifies to be more than one type. \n'
'Please explicitly set the column type like this:\n'
'A["address"] = A["address"].astype(str) \n'
'Similarly use int, float, boolean types.' % column.name)
raise AssertionError('Column %s qualifies to be more than one type. \n'
'Please explicitly set the column type like this:\n'
'A["address"] = A["address"].astype(str) \n'
'Similarly use int, float, boolean types.' % column.name)
else:
# the number of types is 1.
returned_type = type_list[0]
# Check if the type is boolean, if so return boolean
if returned_type == bool or returned_type == np.bool_:
return 'boolean'
# Check if the type is string, if so identify the subtype under it.
# We use average token length to identify the subtypes
# Consider string and unicode as same
elif returned_type == str or returned_type == six.unichr or returned_type == six.text_type:
# get average token length
average_token_len = \
pd.Series.mean(column.str.split().apply(_len_handle_nan))
if average_token_len == 1:
return "str_eq_1w"
elif average_token_len <= 5:
return "str_bt_1w_5w"
elif average_token_len <= 10:
return "str_bt_5w_10w"
else:
return "str_gt_10w"
else:
# Finally, return numeric if it does not qualify for any of the
# types above.
return "numeric"
def _len_handle_nan(input_list):
"""
Get the length of list, handling NaN
"""
# Check if the input is of type list, if so return the len else return NaN
if isinstance(input_list, list):
return len(input_list)
else:
return np.NaN
| bsd-3-clause |
mne-tools/mne-tools.github.io | 0.21/_downloads/823e809619804fe332c02f86cbc25654/plot_find_eog_artifacts.py | 5 | 1216 | """
==================
Find EOG artifacts
==================
Locate peaks of EOG to spot blinks and general EOG artifacts.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
###############################################################################
# Set parameters
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
event_id = 998
eog_events = mne.preprocessing.find_eog_events(raw, event_id)
# Read epochs
picks = mne.pick_types(raw.info, meg=False, eeg=False, stim=False, eog=True,
exclude='bads')
tmin, tmax = -0.2, 0.2
epochs = mne.Epochs(raw, eog_events, event_id, tmin, tmax, picks=picks)
data = epochs.get_data()
print("Number of detected EOG artifacts : %d" % len(data))
###############################################################################
# Plot EOG artifacts
plt.plot(1e3 * epochs.times, np.squeeze(data).T)
plt.xlabel('Times (ms)')
plt.ylabel('EOG (µV)')
plt.show()
| bsd-3-clause |
paulsbrookes/cqed_sims_qutip | spectroscopy/spec_multi_4.py | 1 | 16074 | import numpy as np
import yaml
from qutip import *
from pylab import *
from scipy.fftpack import fft
import matplotlib.pyplot as plt
import yaml
from scipy.interpolate import interp1d
from scipy.optimize import fsolve
from qutip.ui.progressbar import TextProgressBar
class Parameters:
def __init__(self, wc, wq, eps, g, chi, kappa, gamma, t_levels, c_levels):
self.wc = wc
self.wq = wq
self.eps = eps
self.g = g
self.chi = chi
self.gamma = gamma
self.kappa = kappa
self.t_levels = t_levels
self.c_levels = c_levels
def copy(self):
params = Parameters(self.wc, self.wq, self.eps, self.g, self.chi, self.kappa, self.gamma, self.t_levels, self.c_levels)
return params
class Results:
def __init__(self, params=np.array([]), wd_points=np.array([]),
transmissions=np.array([]), edge_occupations_c=np.array([]), edge_occupations_t=np.array([])):
self.params = params
self.wd_points = wd_points
self.transmissions = transmissions
self.edge_occupations_c = edge_occupations_c
self.edge_occupations_t = edge_occupations_t
self.abs_transmissions = np.absolute(self.transmissions)
self.size = self.wd_points.size
def concatenate(self, results):
combined_params = np.concatenate([self.params, results.params])
combined_wd_points = np.concatenate([self.wd_points, results.wd_points])
combined_transmissions = np.concatenate([self.transmissions, results.transmissions])
combined_edge_occupations_c = np.concatenate([self.edge_occupations_c, results.edge_occupations_c])
combined_edge_occupations_t = np.concatenate([self.edge_occupations_t, results.edge_occupations_t])
sort_indices = np.argsort(combined_wd_points)
combined_params = combined_params[sort_indices]
combined_wd_points = combined_wd_points[sort_indices]
combined_transmissions = combined_transmissions[sort_indices]
combined_edge_occupations_c = combined_edge_occupations_c[sort_indices]
combined_edge_occupations_t = combined_edge_occupations_t[sort_indices]
combined_results = Results(combined_params, combined_wd_points,
combined_transmissions, combined_edge_occupations_c, combined_edge_occupations_t)
return combined_results
def delete(self, indices):
reduced_params = np.delete(self.params, indices)
reduced_wd_points = np.delete(self.wd_points, indices)
reduced_transmissions = np.delete(self.transmissions, indices)
reduced_edge_occupations_c = np.delete(self.edge_occupations_c, indices)
reduced_edge_occupations_t = np.delete(self.edge_occupations_t, indices)
reduced_results = Results(reduced_params, reduced_wd_points,
reduced_transmissions, reduced_edge_occupations_c, reduced_edge_occupations_t)
params_change = (reduced_params == self.params)
wd_points_change = (reduced_wd_points == self.wd_points)
transmissions_change = (reduced_transmissions == self.transmissions)
edge_occupations_c_change = (reduced_edge_occupations_c == self.edge_occupations_c)
edge_occupations_t_change = (reduced_edge_occupations_t == self.edge_occupations_t)
print np.all([params_change, wd_points_change, transmissions_change, edge_occupations_c_change, edge_occupations_t_change])
return reduced_results
def queue(self):
queue = Queue(self.params, self.wd_points)
return queue
class Queue:
def __init__(self, params = np.array([]), wd_points = np.array([])):
self.params = params
self.wd_points = wd_points
self.size = self.wd_points.size
sort_indices = np.argsort(self.wd_points)
self.wd_points = self.wd_points[sort_indices]
self.params = self.params[sort_indices]
def curvature_generate(self, results, threshold = 0.05):
curvature_info = CurvatureInfo(results, threshold)
self.wd_points = curvature_info.new_points()
self.params = hilbert_interpolation(self.wd_points, results)
self.size = self.wd_points.size
sort_indices = np.argsort(self.wd_points)
self.wd_points = self.wd_points[sort_indices]
self.params = self.params[sort_indices]
def hilbert_generate(self, results, threshold_c, threshold_t):
suggested_c_levels = []
suggested_t_levels = []
overload_occurred = False
for index, params_instance in enumerate(results.params):
threshold_c_weighted = threshold_c / params_instance.c_levels
threshold_t_weighted = threshold_t / params_instance.t_levels
overload_c = (results.edge_occupations_c[index] > threshold_c_weighted)
overload_t = (results.edge_occupations_t[index] > threshold_t_weighted)
if overload_c:
overload_occurred = True
suggestion = size_correction(
results.edge_occupations_c[index], params_instance.c_levels, threshold_c_weighted / 2)
else:
suggestion = params_instance.c_levels
suggested_c_levels.append(suggestion)
if overload_t:
overload_occurred = True
suggestion = size_correction(
results.edge_occupations_t[index], params_instance.t_levels, threshold_t_weighted / 2)
else:
suggestion = params_instance.t_levels
suggested_t_levels.append(suggestion)
if overload_occurred:
c_levels_new = np.max(suggested_c_levels)
t_levels_new = np.max(suggested_t_levels)
self.wd_points = results.wd_points
for index, params_instance in enumerate(results.params):
results.params[index].t_levels = t_levels_new
results.params[index].c_levels = c_levels_new
self.params = results.params
self.size = results.size
return Results()
else:
self.wd_points = np.array([])
self.params = np.array([])
self.size = 0
return results
def hilbert_generate_alternate(self, results, threshold_c, threshold_t):
old_c_levels = np.zeros(results.size)
suggested_c_levels = np.zeros(results.size)
old_t_levels = np.zeros(results.size)
suggested_t_levels = np.zeros(results.size)
for index, params_instance in enumerate(results.params):
suggested_c_levels[index] = \
size_suggestion(results.edge_occupations_c[index], params_instance.c_levels, threshold_c)
old_c_levels[index] = params_instance.c_levels
suggested_t_levels[index] = \
size_suggestion(results.edge_occupations_t[index], params_instance.t_levels, threshold_t)
old_t_levels[index] = params_instance.t_levels
if np.any(suggested_c_levels > old_c_levels) or np.any(suggested_t_levels > old_t_levels):
c_levels_new = np.max(suggested_c_levels)
t_levels_new = np.max(suggested_t_levels)
self.wd_points = results.wd_points
for index, params_instance in enumerate(results.params):
results.params[index].t_levels = t_levels_new
results.params[index].c_levels = c_levels_new
self.params = results.params
self.size = results.size
return Results()
else:
self.wd_points = np.array([])
self.params = np.array([])
self.size = 0
return results
class CurvatureInfo:
def __init__(self, results, threshold = 0.05):
self.threshold = threshold
self.wd_points = results.wd_points
self.new_wd_points_unique = None
self.abs_transmissions = results.abs_transmissions
self.n_points = self.abs_transmissions.size
def new_points(self):
self.curvature_positions, self.curvatures = derivative(self.wd_points, self.abs_transmissions, 2)
self.abs_curvatures = np.absolute(self.curvatures)
self.mean_curvatures = moving_average(self.abs_curvatures, 2)
self.midpoint_curvatures = \
np.concatenate((np.array([self.abs_curvatures[0]]), self.mean_curvatures))
self.midpoint_curvatures = \
np.concatenate((self.midpoint_curvatures, np.array([self.abs_curvatures[self.n_points - 3]])))
self.midpoint_transmissions = moving_average(self.abs_transmissions, 2)
self.midpoint_curvatures_normed = self.midpoint_curvatures / self.midpoint_transmissions
self.midpoints = moving_average(self.wd_points, 2)
self.intervals = np.diff(self.wd_points)
self.num_of_sections_required = \
np.ceil(self.intervals * np.sqrt(self.midpoint_curvatures_normed / threshold))
new_wd_points = np.array([])
for index in np.arange(self.n_points - 1):
multi_section = \
np.linspace(self.wd_points[index], self.wd_points[index + 1], self.num_of_sections_required[index] + 1)
new_wd_points = np.concatenate((new_wd_points, multi_section))
unique_set = set(new_wd_points) - set(self.wd_points)
self.new_wd_points_unique = np.array(list(unique_set))
return self.new_wd_points_unique
def size_suggestion(edge_occupation, size, threshold):
beta = fsolve(zero_func, 1, args=(edge_occupation, size - 1, size))
new_size = - np.log(threshold) / beta
new_size = int(np.ceil(new_size))
return new_size
def size_correction(edge_occupation, size, threshold):
beta_estimate = np.log(1 + 1 / edge_occupation) / size
beta = fsolve(zero_func, beta_estimate, args=(edge_occupation, size - 1, size))
new_size = 1 + np.log((1 - np.exp(-beta)) / threshold) / beta
new_size = int(np.ceil(new_size))
return new_size
def exponential_occupation(n, beta, size):
factor = np.exp(-beta)
f = np.power(factor, n) * (1 - factor) / (1 - np.power(factor, size))
return f
def zero_func(beta, p, level, size):
f = exponential_occupation(level, beta, size)
f = f - p
return f
def hilbert_interpolation(new_wd_points, results):
c_levels_array = np.array([params.c_levels for params in results.params])
t_levels_array = np.array([params.t_levels for params in results.params])
wd_points = results.wd_points
c_interp = interp1d(wd_points, c_levels_array)
t_interp = interp1d(wd_points, t_levels_array)
base_params = results.params[0]
params_list = []
for wd in new_wd_points:
new_params = base_params.copy()
new_params.c_levels = int(round(c_interp(wd)))
new_params.t_levels = int(round(t_interp(wd)))
params_list.append(new_params)
params_array = np.array(params_list)
return params_array
def moving_average(interval, window_size):
window = np.ones(int(window_size)) / float(window_size)
averages = np.convolve(interval, window, 'same')
return averages[window_size - 1 : averages.size]
def derivative(x, y, n_derivative = 1):
derivatives = np.zeros(y.size - 1)
positions = np.zeros(x.size - 1)
for index in np.arange(y.size - 1):
grad = (y[index + 1] - y[index]) / (x[index + 1] - x[index])
position = np.mean([x[index], x[index + 1]])
derivatives[index] = grad
positions[index] = position
if n_derivative > 1:
positions, derivatives = derivative(positions, derivatives, n_derivative - 1)
return positions, derivatives
def hamiltonian(params, wd):
a = tensor(destroy(params.c_levels), qeye(params.t_levels))
sm = tensor(qeye(params.c_levels), destroy(params.t_levels))
H = (params.wc - wd) * a.dag() * a + (params.wq - wd) * sm.dag() * sm \
+ params.chi * sm.dag() * sm * (sm.dag() * sm - 1) + params.g * (a.dag() * sm + a * sm.dag()) \
+ params.eps * (a + a.dag())
return H
def transmission_calc_array(queue):
args = []
for index, value in enumerate(queue.wd_points):
args.append([value, queue.params[index]])
steady_states = parallel_map(transmission_calc, args, num_cpus=10, progress_bar=TextProgressBar())
transmissions = np.array([steady_state[0] for steady_state in steady_states])
edge_occupations_c = np.array([steady_state[1] for steady_state in steady_states])
edge_occupations_c = np.absolute(edge_occupations_c)
edge_occupations_t = np.array([steady_state[2] for steady_state in steady_states])
edge_occupations_t = np.absolute(edge_occupations_t)
results = Results(queue.params, queue.wd_points, transmissions, edge_occupations_c, edge_occupations_t)
abs_transmissions = np.absolute(transmissions)
return results
def transmission_calc(args):
wd = args[0]
params = args[1]
a = tensor(destroy(params.c_levels), qeye(params.t_levels))
sm = tensor(qeye(params.c_levels), destroy(params.t_levels))
c_ops = []
c_ops.append(np.sqrt(params.kappa) * a)
c_ops.append(np.sqrt(params.gamma) * sm)
H = hamiltonian(params, wd)
rho_ss = steadystate(H, c_ops)
rho_c_ss = rho_ss.ptrace(0)
rho_t_ss = rho_ss.ptrace(1)
c_occupations = rho_c_ss.diag()
t_occupations = rho_t_ss.diag()
edge_occupation_c = c_occupations[params.c_levels - 1]
edge_occupation_t = t_occupations[params.t_levels - 1]
transmission = expect(a, rho_ss)
return np.array([transmission, edge_occupation_c, edge_occupation_t])
def sweep(eps, wd_lower, wd_upper, params, threshold):
hilbert_adjustment = False
threshold_c = 0.001
threshold_t = 0.001
params.eps = eps
wd_points = np.linspace(wd_lower, wd_upper, 10)
params_array = np.array([params.copy() for wd in wd_points])
queue = Queue(params_array, wd_points)
curvature_iterations = 0
results = Results()
while (queue.size > 0) and (curvature_iterations < 3):
print curvature_iterations
curvature_iterations = curvature_iterations + 1
new_results = transmission_calc_array(queue)
results = results.concatenate(new_results)
if hilbert_adjustment == True:
results = queue.hilbert_generate(results, threshold_c, threshold_t)
hilbert_iterations = 0
while (queue.size > 0) and (hilbert_iterations < 3) and hilbert_adjustment:
hilbert_iterations = hilbert_iterations + 1
results = transmission_calc_array(queue)
results = queue.hilbert_generate(results, threshold_c, threshold_t)
queue.curvature_generate(results)
c_levels = [params_instance.c_levels for params_instance in results.params]
t_levels = [params_instance.t_levels for params_instance in results.params]
return results
def multi_sweep(eps_array, wd_lower, wd_upper, params, threshold):
multi_results_dict = dict()
for eps in eps_array:
multi_results_dict[eps] = sweep(eps, wd_lower, wd_upper, params, threshold)
params = multi_results_dict[eps].params[0]
print params.c_levels
print params.t_levels
return multi_results_dict
if __name__ == '__main__':
#wc, wq, eps, g, chi, kappa, gamma, t_levels, c_levels
t_levels = 2
c_levels = 10
params = Parameters(10.4267, 9.39128, 0.0002, 0.3096, -0.097, 0.00146, 0.000833, t_levels, c_levels)
threshold = 0.01
wd_lower = 10.495
wd_upper = 10.520
eps = 0.008
eps_array = np.array([eps])
multi_results = multi_sweep(eps_array, wd_lower, wd_upper, params, threshold)
#with open('data.yml', 'w') as outfile:
# yaml.dump(multi_results, outfile, default_flow_style=False)
#multi_results = []
#multi_results = yaml.load(open('data.yml'))
results = multi_results[eps]
print results.params[0].t_levels
print results.params[0].c_levels
plt.scatter(results.wd_points, results.abs_transmissions)
plt.title('txc: ' + str(t_levels) + 'x' + str(c_levels))
plt.show()
| apache-2.0 |
pratapvardhan/pandas | asv_bench/benchmarks/frame_methods.py | 2 | 14286 | import string
import warnings
import numpy as np
import pandas.util.testing as tm
from pandas import (DataFrame, Series, MultiIndex, date_range, period_range,
isnull, NaT)
from .pandas_vb_common import setup # noqa
class GetNumericData(object):
goal_time = 0.2
def setup(self):
self.df = DataFrame(np.random.randn(10000, 25))
self.df['foo'] = 'bar'
self.df['bar'] = 'baz'
with warnings.catch_warnings(record=True):
self.df = self.df.consolidate()
def time_frame_get_numeric_data(self):
self.df._get_numeric_data()
class Lookup(object):
goal_time = 0.2
def setup(self):
self.df = DataFrame(np.random.randn(10000, 8),
columns=list('abcdefgh'))
self.df['foo'] = 'bar'
self.row_labels = list(self.df.index[::10])[:900]
self.col_labels = list(self.df.columns) * 100
self.row_labels_all = np.array(
list(self.df.index) * len(self.df.columns), dtype='object')
self.col_labels_all = np.array(
list(self.df.columns) * len(self.df.index), dtype='object')
def time_frame_fancy_lookup(self):
self.df.lookup(self.row_labels, self.col_labels)
def time_frame_fancy_lookup_all(self):
self.df.lookup(self.row_labels_all, self.col_labels_all)
class Reindex(object):
goal_time = 0.2
def setup(self):
N = 10**3
self.df = DataFrame(np.random.randn(N * 10, N))
self.idx = np.arange(4 * N, 7 * N)
self.df2 = DataFrame(
{c: {0: np.random.randint(0, 2, N).astype(np.bool_),
1: np.random.randint(0, N, N).astype(np.int16),
2: np.random.randint(0, N, N).astype(np.int32),
3: np.random.randint(0, N, N).astype(np.int64)}
[np.random.randint(0, 4)] for c in range(N)})
def time_reindex_axis0(self):
self.df.reindex(self.idx)
def time_reindex_axis1(self):
self.df.reindex(columns=self.idx)
def time_reindex_both_axes(self):
self.df.reindex(index=self.idx, columns=self.idx)
def time_reindex_both_axes_ix(self):
self.df.ix[self.idx, self.idx]
def time_reindex_upcast(self):
self.df2.reindex(np.random.permutation(range(1200)))
class Iteration(object):
goal_time = 0.2
def setup(self):
N = 1000
self.df = DataFrame(np.random.randn(N * 10, N))
self.df2 = DataFrame(np.random.randn(N * 50, 10))
self.df3 = DataFrame(np.random.randn(N, 5 * N),
columns=['C' + str(c) for c in range(N * 5)])
def time_iteritems(self):
# (monitor no-copying behaviour)
if hasattr(self.df, '_item_cache'):
self.df._item_cache.clear()
for name, col in self.df.iteritems():
pass
def time_iteritems_cached(self):
for name, col in self.df.iteritems():
pass
def time_iteritems_indexing(self):
for col in self.df3:
self.df3[col]
def time_itertuples(self):
for row in self.df2.itertuples():
pass
def time_iterrows(self):
for row in self.df.iterrows():
pass
class ToString(object):
goal_time = 0.2
def setup(self):
self.df = DataFrame(np.random.randn(100, 10))
def time_to_string_floats(self):
self.df.to_string()
class ToHTML(object):
goal_time = 0.2
def setup(self):
nrows = 500
self.df2 = DataFrame(np.random.randn(nrows, 10))
self.df2[0] = period_range('2000', periods=nrows)
self.df2[1] = range(nrows)
def time_to_html_mixed(self):
self.df2.to_html()
class Repr(object):
goal_time = 0.2
def setup(self):
nrows = 10000
data = np.random.randn(nrows, 10)
arrays = np.tile(np.random.randn(3, int(nrows / 100)), 100)
idx = MultiIndex.from_arrays(arrays)
self.df3 = DataFrame(data, index=idx)
self.df4 = DataFrame(data, index=np.random.randn(nrows))
self.df_tall = DataFrame(np.random.randn(nrows, 10))
self.df_wide = DataFrame(np.random.randn(10, nrows))
def time_html_repr_trunc_mi(self):
self.df3._repr_html_()
def time_html_repr_trunc_si(self):
self.df4._repr_html_()
def time_repr_tall(self):
repr(self.df_tall)
def time_frame_repr_wide(self):
repr(self.df_wide)
class MaskBool(object):
goal_time = 0.2
def setup(self):
data = np.random.randn(1000, 500)
df = DataFrame(data)
df = df.where(df > 0)
self.bools = df > 0
self.mask = isnull(df)
def time_frame_mask_bools(self):
self.bools.mask(self.mask)
def time_frame_mask_floats(self):
self.bools.astype(float).mask(self.mask)
class Isnull(object):
goal_time = 0.2
def setup(self):
N = 10**3
self.df_no_null = DataFrame(np.random.randn(N, N))
sample = np.array([np.nan, 1.0])
data = np.random.choice(sample, (N, N))
self.df = DataFrame(data)
sample = np.array(list(string.ascii_letters + string.whitespace))
data = np.random.choice(sample, (N, N))
self.df_strings = DataFrame(data)
sample = np.array([NaT, np.nan, None, np.datetime64('NaT'),
np.timedelta64('NaT'), 0, 1, 2.0, '', 'abcd'])
data = np.random.choice(sample, (N, N))
self.df_obj = DataFrame(data)
def time_isnull_floats_no_null(self):
isnull(self.df_no_null)
def time_isnull(self):
isnull(self.df)
def time_isnull_strngs(self):
isnull(self.df_strings)
def time_isnull_obj(self):
isnull(self.df_obj)
class Fillna(object):
goal_time = 0.2
params = ([True, False], ['pad', 'bfill'])
param_names = ['inplace', 'method']
def setup(self, inplace, method):
values = np.random.randn(10000, 100)
values[::2] = np.nan
self.df = DataFrame(values)
def time_frame_fillna(self, inplace, method):
self.df.fillna(inplace=inplace, method=method)
class Dropna(object):
goal_time = 0.2
params = (['all', 'any'], [0, 1])
param_names = ['how', 'axis']
def setup(self, how, axis):
self.df = DataFrame(np.random.randn(10000, 1000))
self.df.ix[50:1000, 20:50] = np.nan
self.df.ix[2000:3000] = np.nan
self.df.ix[:, 60:70] = np.nan
self.df_mixed = self.df.copy()
self.df_mixed['foo'] = 'bar'
def time_dropna(self, how, axis):
self.df.dropna(how=how, axis=axis)
def time_dropna_axis_mixed_dtypes(self, how, axis):
self.df_mixed.dropna(how=how, axis=axis)
class Count(object):
goal_time = 0.2
params = [0, 1]
param_names = ['axis']
def setup(self, axis):
self.df = DataFrame(np.random.randn(10000, 1000))
self.df.ix[50:1000, 20:50] = np.nan
self.df.ix[2000:3000] = np.nan
self.df.ix[:, 60:70] = np.nan
self.df_mixed = self.df.copy()
self.df_mixed['foo'] = 'bar'
self.df.index = MultiIndex.from_arrays([self.df.index, self.df.index])
self.df.columns = MultiIndex.from_arrays([self.df.columns,
self.df.columns])
self.df_mixed.index = MultiIndex.from_arrays([self.df_mixed.index,
self.df_mixed.index])
self.df_mixed.columns = MultiIndex.from_arrays([self.df_mixed.columns,
self.df_mixed.columns])
def time_count_level_multi(self, axis):
self.df.count(axis=axis, level=1)
def time_count_level_mixed_dtypes_multi(self, axis):
self.df_mixed.count(axis=axis, level=1)
class Apply(object):
goal_time = 0.2
def setup(self):
self.df = DataFrame(np.random.randn(1000, 100))
self.s = Series(np.arange(1028.0))
self.df2 = DataFrame({i: self.s for i in range(1028)})
self.df3 = DataFrame(np.random.randn(1000, 3), columns=list('ABC'))
def time_apply_user_func(self):
self.df2.apply(lambda x: np.corrcoef(x, self.s)[(0, 1)])
def time_apply_axis_1(self):
self.df.apply(lambda x: x + 1, axis=1)
def time_apply_lambda_mean(self):
self.df.apply(lambda x: x.mean())
def time_apply_np_mean(self):
self.df.apply(np.mean)
def time_apply_pass_thru(self):
self.df.apply(lambda x: x)
def time_apply_ref_by_name(self):
self.df3.apply(lambda x: x['A'] + x['B'], axis=1)
class Dtypes(object):
goal_time = 0.2
def setup(self):
self.df = DataFrame(np.random.randn(1000, 1000))
def time_frame_dtypes(self):
self.df.dtypes
class Equals(object):
goal_time = 0.2
def setup(self):
N = 10**3
self.float_df = DataFrame(np.random.randn(N, N))
self.float_df_nan = self.float_df.copy()
self.float_df_nan.iloc[-1, -1] = np.nan
self.object_df = DataFrame('foo', index=range(N), columns=range(N))
self.object_df_nan = self.object_df.copy()
self.object_df_nan.iloc[-1, -1] = np.nan
self.nonunique_cols = self.object_df.copy()
self.nonunique_cols.columns = ['A'] * len(self.nonunique_cols.columns)
self.nonunique_cols_nan = self.nonunique_cols.copy()
self.nonunique_cols_nan.iloc[-1, -1] = np.nan
def time_frame_float_equal(self):
self.float_df.equals(self.float_df)
def time_frame_float_unequal(self):
self.float_df.equals(self.float_df_nan)
def time_frame_nonunique_equal(self):
self.nonunique_cols.equals(self.nonunique_cols)
def time_frame_nonunique_unequal(self):
self.nonunique_cols.equals(self.nonunique_cols_nan)
def time_frame_object_equal(self):
self.object_df.equals(self.object_df)
def time_frame_object_unequal(self):
self.object_df.equals(self.object_df_nan)
class Interpolate(object):
goal_time = 0.2
params = [None, 'infer']
param_names = ['downcast']
def setup(self, downcast):
N = 10000
# this is the worst case, where every column has NaNs.
self.df = DataFrame(np.random.randn(N, 100))
self.df.values[::2] = np.nan
self.df2 = DataFrame({'A': np.arange(0, N),
'B': np.random.randint(0, 100, N),
'C': np.random.randn(N),
'D': np.random.randn(N)})
self.df2.loc[1::5, 'A'] = np.nan
self.df2.loc[1::5, 'C'] = np.nan
def time_interpolate(self, downcast):
self.df.interpolate(downcast=downcast)
def time_interpolate_some_good(self, downcast):
self.df2.interpolate(downcast=downcast)
class Shift(object):
# frame shift speedup issue-5609
goal_time = 0.2
params = [0, 1]
param_names = ['axis']
def setup(self, axis):
self.df = DataFrame(np.random.rand(10000, 500))
def time_shift(self, axis):
self.df.shift(1, axis=axis)
class Nunique(object):
def setup(self):
self.df = DataFrame(np.random.randn(10000, 1000))
def time_frame_nunique(self):
self.df.nunique()
class Duplicated(object):
goal_time = 0.2
def setup(self):
n = (1 << 20)
t = date_range('2015-01-01', freq='S', periods=(n // 64))
xs = np.random.randn(n // 64).round(2)
self.df = DataFrame({'a': np.random.randint(-1 << 8, 1 << 8, n),
'b': np.random.choice(t, n),
'c': np.random.choice(xs, n)})
self.df2 = DataFrame(np.random.randn(1000, 100).astype(str)).T
def time_frame_duplicated(self):
self.df.duplicated()
def time_frame_duplicated_wide(self):
self.df2.duplicated()
class XS(object):
goal_time = 0.2
params = [0, 1]
param_names = ['axis']
def setup(self, axis):
self.N = 10**4
self.df = DataFrame(np.random.randn(self.N, self.N))
def time_frame_xs(self, axis):
self.df.xs(self.N / 2, axis=axis)
class SortValues(object):
goal_time = 0.2
params = [True, False]
param_names = ['ascending']
def setup(self, ascending):
self.df = DataFrame(np.random.randn(1000000, 2), columns=list('AB'))
def time_frame_sort_values(self, ascending):
self.df.sort_values(by='A', ascending=ascending)
class SortIndexByColumns(object):
goal_time = 0.2
def setup(self):
N = 10000
K = 10
self.df = DataFrame({'key1': tm.makeStringIndex(N).values.repeat(K),
'key2': tm.makeStringIndex(N).values.repeat(K),
'value': np.random.randn(N * K)})
def time_frame_sort_values_by_columns(self):
self.df.sort_values(by=['key1', 'key2'])
class Quantile(object):
goal_time = 0.2
params = [0, 1]
param_names = ['axis']
def setup(self, axis):
self.df = DataFrame(np.random.randn(1000, 3), columns=list('ABC'))
def time_frame_quantile(self, axis):
self.df.quantile([0.1, 0.5], axis=axis)
class GetDtypeCounts(object):
# 2807
goal_time = 0.2
def setup(self):
self.df = DataFrame(np.random.randn(10, 10000))
def time_frame_get_dtype_counts(self):
self.df.get_dtype_counts()
def time_info(self):
self.df.info()
class NSort(object):
goal_time = 0.2
params = ['first', 'last', 'all']
param_names = ['keep']
def setup(self, keep):
self.df = DataFrame(np.random.randn(1000, 3), columns=list('ABC'))
def time_nlargest(self, keep):
self.df.nlargest(100, 'A', keep=keep)
def time_nsmallest(self, keep):
self.df.nsmallest(100, 'A', keep=keep)
class Describe(object):
goal_time = 0.2
def setup(self):
self.df = DataFrame({
'a': np.random.randint(0, 100, int(1e6)),
'b': np.random.randint(0, 100, int(1e6)),
'c': np.random.randint(0, 100, int(1e6))
})
def time_series_describe(self):
self.df['a'].describe()
def time_dataframe_describe(self):
self.df.describe()
| bsd-3-clause |
draperjames/bokeh | bokeh/charts/builders/chord_builder.py | 7 | 12304 | """This is the Bokeh charts interface. It gives you a high level API
to build complex plot is a simple way.
This is the Chord class which lets you build your Chord charts
just passing the arguments to the Chart class and calling the proper
functions.
"""
# -----------------------------------------------------------------------------
# Copyright (c) 2012 - 2016, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with this software.
# -----------------------------------------------------------------------------
# -----------------------------------------------------------------------------
# Imports
# -----------------------------------------------------------------------------
from __future__ import absolute_import, division
import numpy as np
import pandas as pd
from math import cos, sin, pi
from bokeh.charts.properties import Dimension
from bokeh.charts.builder import create_and_build, Builder
from bokeh.charts.attributes import MarkerAttr, ColorAttr, CatAttr
from bokeh.charts.utils import color_in_equal_space, help
from bokeh.models import Range1d
from bokeh.models.glyphs import Arc, Bezier, Text
from bokeh.models.renderers import GlyphRenderer
from bokeh.models.sources import ColumnDataSource
from bokeh.core.properties import Instance, Bool, String, Array, Float, Any, Seq, Either, Int
# -----------------------------------------------------------------------------
# Classes and functions
# -----------------------------------------------------------------------------
class Area:
""" It represents an arc area. It will create a list of available points through the arc representing that area and
then those points will be used as start and end for the beziers lines.
"""
def __init__(self, n_conn, start_point, end_point):
# Number of connections in that arc area
self.n_conn = n_conn
# The start point of the arc representing the area
self.start_point = start_point
self.end_point = end_point
# Equally spaced points between start point and end point
free_points_angles = np.linspace(start_point, end_point, n_conn)
# A list of available X,Y in the chart to consume by each bezier's start and end point
self.free_points = [[cos(angle), sin(angle)] for angle in free_points_angles]
assert self.n_conn == len(self.free_points)
class ChordBuilder(Builder):
""" This is the Chord builder and it is in charge of plotting
Chord graphs in an easy and intuitive way.
Essentially, we provide a way to ingest the data, make the proper
calculations and push the references into a source object.
We additionally make calculations for the ranges. And finally add
the needed glyphs (markers) taking the references from the source.
"""
default_attributes = {'color': ColorAttr(),
'marker': MarkerAttr(),
'stack': CatAttr()}
dimensions = ['values']
values = Dimension('values')
arcs_data = Instance(ColumnDataSource)
text_data = Instance(ColumnDataSource)
connection_data = Instance(ColumnDataSource)
origin = String()
destination = String()
value = Any()
square_matrix = Bool()
label = Seq(Any())
matrix = Array(Array(Either(Float(), Int())))
def set_ranges(self):
rng = 1.1 if not self.label else 1.8
self.x_range = Range1d(-rng, rng)
self.y_range = Range1d(-rng, rng)
def setup(self):
# Process only if not a square_matrix
if not self.square_matrix:
source = self.values._data[self.origin]
target = self.values._data[self.destination]
union = source.append(target).unique()
N = union.shape[0]
m = pd.DataFrame(np.zeros((N, N)), columns=union, index=union)
if not self.label:
self.label = list(union)
if self.value is None:
for _, row in self.values._data.iterrows():
m[row[self.origin]][row[self.destination]] += 1
self.matrix = m.get_values()
if self.value is not None:
if isinstance(self.value, int) or isinstance(self.value, float):
for _, row in self.values._data.iterrows():
m[row[self.origin]][row[self.destination]] = self.value
self.matrix = m.get_values()
elif isinstance(self.value, str):
for _, row in self.values._data.iterrows():
m[row[self.origin]][row[self.destination]] = row[self.value]
self.matrix = m.get_values().T
else:
# It's already a square matrix
self.matrix = self._data.df.get_values()
if self.label:
assert len(self.label) == self.matrix.shape[0]
def process_data(self):
weights_of_areas = (self.matrix.sum(axis=0) + self.matrix.sum(axis=1)) - self.matrix.diagonal()
areas_in_radians = (weights_of_areas / weights_of_areas.sum()) * (2 * pi)
# We add a zero in the begging for the cumulative sum
points = np.zeros((areas_in_radians.shape[0] + 1))
points[1:] = areas_in_radians
points = points.cumsum()
colors = [color_in_equal_space(area / areas_in_radians.shape[0]) for area in range(areas_in_radians.shape[0])]
arcs_data = pd.DataFrame({
'start_angle': points[:-1],
'end_angle': points[1:],
'line_color': colors
})
self.arcs_data = ColumnDataSource(arcs_data)
# Text
if self.label:
text_radius = 1.1
angles = (points[:-1]+points[1:])/2.0
text_positions = pd.DataFrame({
'angles': angles,
'text_x': np.cos(angles) * text_radius,
'text_y': np.sin(angles) * text_radius,
'text': list(self.label)
})
self.text_data = ColumnDataSource(text_positions)
# Lines
all_areas = []
for i in range(areas_in_radians.shape[0]):
all_areas.append(Area(weights_of_areas[i], points[:-1][i], points[1:][i]))
all_connections = []
for j, region1 in enumerate(self.matrix):
# Get the connections origin region
source = all_areas[j]
color = colors[j]
weight = weights_of_areas[j]
for k, region2 in enumerate(region1):
# Get the connection destination region
target = all_areas[k]
for _ in range(int(region2)):
p1 = source.free_points.pop()
p2 = target.free_points.pop()
# Get both regions free points and create a connection with the data
all_connections.append(p1 + p2 + [color, weight])
connections_df = pd.DataFrame(all_connections, dtype=str)
connections_df.columns = ["start_x", "start_y", "end_x", "end_y", "colors", "weight"]
connections_df["cx0"] = connections_df.start_x.astype("float64")/2
connections_df["cy0"] = connections_df.start_y.astype("float64")/2
connections_df["cx1"] = connections_df.end_x.astype("float64")/2
connections_df["cy1"] = connections_df.end_y.astype("float64")/2
connections_df.weight = (connections_df.weight.astype("float64")/connections_df.weight.astype("float64").sum()) * 3000
self.connection_data = ColumnDataSource(connections_df)
def yield_renderers(self):
"""Use the marker glyphs to display the arcs and beziers.
Takes reference points from data loaded at the ColumnDataSource.
"""
beziers = Bezier(x0='start_x',
y0='start_y',
x1='end_x',
y1='end_y',
cx0='cx0',
cy0='cy0',
cx1='cx1',
cy1='cy1',
line_alpha='weight',
line_color='colors')
yield GlyphRenderer(data_source=self.connection_data, glyph=beziers)
arcs = Arc(x=0,
y=0,
radius=1,
line_width=10,
start_angle='start_angle',
end_angle='end_angle',
line_color='line_color')
yield GlyphRenderer(data_source=self.arcs_data, glyph=arcs)
if self.label:
text_props = {
"text_color": "#000000",
"text_font_size": "8pt",
"text_align": "left",
"text_baseline": "middle"
}
labels = Text(x='text_x',
y='text_y',
text='text',
angle='angles',
**text_props
)
yield GlyphRenderer(data_source=self.text_data, glyph=labels)
@help(ChordBuilder)
def Chord(data, source=None, target=None, value=None, square_matrix=False, label=None, xgrid=False, ygrid=False, **kw):
"""
Create a chord chart using :class:`ChordBuilder <bokeh.charts.builders.chord_builder.ChordBuilder>`
to render a chord graph from a variety of value forms.
This chart displays the inter-relationships between data in a matrix.
The data can be generated by the chart interface. Given a :class:`DataFrame <pandas.DataFrame>`,
select two columns to be used as arcs with `source` and `target` attributes, passing by the name of those columns.
The :class:`Chord <bokeh.charts.builders.chord_builder.Chord>` chart will then deduce the
relationship between the arcs.
The value of the connections can be inferred automatically by counting `source` and `target`. If you prefer
you can assign a fixed value for all the connections with `value` simply passing by a number. A third option is to
pass a reference to a third column in the :class:`DataFrame <pandas.DataFrame>` with the values for the connections.
If you want to plot the relationships in a squared matrix, simply pass the matrix and set `square_matrix` attribute
to `True`.
Reference: `Chord diagram on Wikipedia <https://en.wikipedia.org/wiki/Chord_diagram>`_
Args:
data (:ref:`userguide_charts_data_types`): the data source for the chart.
source (list(str) or str, optional): Data source to use as origin of the connection to a destination.
target (list(str) or str, optional): Data source to use as destination of a connection.
value (list(num) or num, optional): The value the connection should have.
square_matrix (bool, optional): If square matrix, avoid any calculations during the setup.
label (list(str), optional): The labels to be put in the areas.
Returns:
:class:`Chart`: includes glyph renderers that generate the chord
Examples:
.. bokeh-plot::
:source-position: above
import pandas as pd
from bokeh.charts import Chord
from bokeh.io import show, output_file
from bokeh.sampledata.les_mis import data
nodes = data['nodes']
links = data['links']
nodes_df = pd.DataFrame(nodes)
links_df = pd.DataFrame(links)
source_data = links_df.merge(nodes_df, how='left', left_on='source', right_index=True)
source_data = source_data.merge(nodes_df, how='left', left_on='target', right_index=True)
source_data = source_data[source_data["value"] > 5] # Select those with 5 or more connections
chord_from_df = Chord(source_data, source="name_x", target="name_y", value="value")
output_file('chord_from_df.html')
show(chord_from_df)
"""
kw["origin"] = source
kw["destination"] = target
kw["value"] = value
kw["square_matrix"] = square_matrix
kw["label"] = label
kw['xgrid'] = xgrid
kw['ygrid'] = ygrid
chart = create_and_build(ChordBuilder, data, **kw)
chart.left[0].visible = False
chart.below[0].visible = False
chart.outline_line_color = None
return chart
| bsd-3-clause |
matthieudumont/dipy | dipy/viz/tests/test_fvtk.py | 4 | 3224 | """Testing visualization with fvtk."""
import os
import numpy as np
from dipy.viz import fvtk
from dipy import data
import numpy.testing as npt
from dipy.testing.decorators import xvfb_it
use_xvfb = os.environ.get('TEST_WITH_XVFB', False)
if use_xvfb == 'skip':
skip_it = True
else:
skip_it = False
@npt.dec.skipif(not fvtk.have_vtk or not fvtk.have_vtk_colors or skip_it)
@xvfb_it
def test_fvtk_functions():
# This tests will fail if any of the given actors changed inputs or do
# not exist
# Create a renderer
r = fvtk.ren()
# Create 2 lines with 2 different colors
lines = [np.random.rand(10, 3), np.random.rand(20, 3)]
colors = np.random.rand(2, 3)
c = fvtk.line(lines, colors)
fvtk.add(r, c)
# create streamtubes of the same lines and shift them a bit
c2 = fvtk.streamtube(lines, colors)
c2.SetPosition(2, 0, 0)
fvtk.add(r, c2)
# Create a volume and return a volumetric actor using volumetric rendering
vol = 100 * np.random.rand(100, 100, 100)
vol = vol.astype('uint8')
r = fvtk.ren()
v = fvtk.volume(vol)
fvtk.add(r, v)
# Remove all objects
fvtk.rm_all(r)
# Put some text
l = fvtk.label(r, text='Yes Men')
fvtk.add(r, l)
# Slice the volume
slicer = fvtk.slicer(vol)
slicer.display(50, None, None)
fvtk.add(r, slicer)
# Change the position of the active camera
fvtk.camera(r, pos=(0.6, 0, 0), verbose=False)
fvtk.clear(r)
# Peak directions
p = fvtk.peaks(np.random.rand(3, 3, 3, 5, 3))
fvtk.add(r, p)
p2 = fvtk.peaks(np.random.rand(3, 3, 3, 5, 3),
np.random.rand(3, 3, 3, 5),
colors=(0, 1, 0))
fvtk.add(r, p2)
@npt.dec.skipif(not fvtk.have_vtk or not fvtk.have_vtk_colors or skip_it)
@xvfb_it
def test_fvtk_ellipsoid():
evals = np.array([1.4, .35, .35]) * 10 ** (-3)
evecs = np.eye(3)
mevals = np.zeros((3, 2, 4, 3))
mevecs = np.zeros((3, 2, 4, 3, 3))
mevals[..., :] = evals
mevecs[..., :, :] = evecs
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
ren = fvtk.ren()
fvtk.add(ren, fvtk.tensor(mevals, mevecs, sphere=sphere))
fvtk.add(ren, fvtk.tensor(mevals, mevecs, np.ones(mevals.shape),
sphere=sphere))
npt.assert_equal(ren.GetActors().GetNumberOfItems(), 2)
def test_colormap():
v = np.linspace(0., .5)
map1 = fvtk.create_colormap(v, 'bone', auto=True)
map2 = fvtk.create_colormap(v, 'bone', auto=False)
npt.assert_(not np.allclose(map1, map2))
npt.assert_raises(ValueError, fvtk.create_colormap, np.ones((2, 3)))
npt.assert_raises(ValueError, fvtk.create_colormap, v, 'no such map')
@npt.dec.skipif(not fvtk.have_matplotlib)
def test_colormaps_matplotlib():
v = np.random.random(1000)
for name in 'jet', 'Blues', 'Accent', 'bone':
# Matplotlib version of get_cmap
rgba1 = fvtk.get_cmap(name)(v)
# Dipy version of get_cmap
rgba2 = data.get_cmap(name)(v)
# dipy's colormaps are close to matplotlibs colormaps, but not perfect
npt.assert_array_almost_equal(rgba1, rgba2, 1)
if __name__ == "__main__":
npt.run_module_suite()
| bsd-3-clause |
fyffyt/scikit-learn | sklearn/mixture/tests/test_gmm.py | 200 | 17427 | import unittest
import copy
import sys
from nose.tools import assert_true
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_raises)
from scipy import stats
from sklearn import mixture
from sklearn.datasets.samples_generator import make_spd_matrix
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raise_message
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.externals.six.moves import cStringIO as StringIO
rng = np.random.RandomState(0)
def test_sample_gaussian():
# Test sample generation from mixture.sample_gaussian where covariance
# is diagonal, spherical and full
n_features, n_samples = 2, 300
axis = 1
mu = rng.randint(10) * rng.rand(n_features)
cv = (rng.rand(n_features) + 1.0) ** 2
samples = mixture.sample_gaussian(
mu, cv, covariance_type='diag', n_samples=n_samples)
assert_true(np.allclose(samples.mean(axis), mu, atol=1.3))
assert_true(np.allclose(samples.var(axis), cv, atol=1.5))
# the same for spherical covariances
cv = (rng.rand() + 1.0) ** 2
samples = mixture.sample_gaussian(
mu, cv, covariance_type='spherical', n_samples=n_samples)
assert_true(np.allclose(samples.mean(axis), mu, atol=1.5))
assert_true(np.allclose(
samples.var(axis), np.repeat(cv, n_features), atol=1.5))
# and for full covariances
A = rng.randn(n_features, n_features)
cv = np.dot(A.T, A) + np.eye(n_features)
samples = mixture.sample_gaussian(
mu, cv, covariance_type='full', n_samples=n_samples)
assert_true(np.allclose(samples.mean(axis), mu, atol=1.3))
assert_true(np.allclose(np.cov(samples), cv, atol=2.5))
# Numerical stability check: in SciPy 0.12.0 at least, eigh may return
# tiny negative values in its second return value.
from sklearn.mixture import sample_gaussian
x = sample_gaussian([0, 0], [[4, 3], [1, .1]],
covariance_type='full', random_state=42)
print(x)
assert_true(np.isfinite(x).all())
def _naive_lmvnpdf_diag(X, mu, cv):
# slow and naive implementation of lmvnpdf
ref = np.empty((len(X), len(mu)))
stds = np.sqrt(cv)
for i, (m, std) in enumerate(zip(mu, stds)):
ref[:, i] = np.log(stats.norm.pdf(X, m, std)).sum(axis=1)
return ref
def test_lmvnpdf_diag():
# test a slow and naive implementation of lmvnpdf and
# compare it to the vectorized version (mixture.lmvnpdf) to test
# for correctness
n_features, n_components, n_samples = 2, 3, 10
mu = rng.randint(10) * rng.rand(n_components, n_features)
cv = (rng.rand(n_components, n_features) + 1.0) ** 2
X = rng.randint(10) * rng.rand(n_samples, n_features)
ref = _naive_lmvnpdf_diag(X, mu, cv)
lpr = mixture.log_multivariate_normal_density(X, mu, cv, 'diag')
assert_array_almost_equal(lpr, ref)
def test_lmvnpdf_spherical():
n_features, n_components, n_samples = 2, 3, 10
mu = rng.randint(10) * rng.rand(n_components, n_features)
spherecv = rng.rand(n_components, 1) ** 2 + 1
X = rng.randint(10) * rng.rand(n_samples, n_features)
cv = np.tile(spherecv, (n_features, 1))
reference = _naive_lmvnpdf_diag(X, mu, cv)
lpr = mixture.log_multivariate_normal_density(X, mu, spherecv,
'spherical')
assert_array_almost_equal(lpr, reference)
def test_lmvnpdf_full():
n_features, n_components, n_samples = 2, 3, 10
mu = rng.randint(10) * rng.rand(n_components, n_features)
cv = (rng.rand(n_components, n_features) + 1.0) ** 2
X = rng.randint(10) * rng.rand(n_samples, n_features)
fullcv = np.array([np.diag(x) for x in cv])
reference = _naive_lmvnpdf_diag(X, mu, cv)
lpr = mixture.log_multivariate_normal_density(X, mu, fullcv, 'full')
assert_array_almost_equal(lpr, reference)
def test_lvmpdf_full_cv_non_positive_definite():
n_features, n_samples = 2, 10
rng = np.random.RandomState(0)
X = rng.randint(10) * rng.rand(n_samples, n_features)
mu = np.mean(X, 0)
cv = np.array([[[-1, 0], [0, 1]]])
expected_message = "'covars' must be symmetric, positive-definite"
assert_raise_message(ValueError, expected_message,
mixture.log_multivariate_normal_density,
X, mu, cv, 'full')
def test_GMM_attributes():
n_components, n_features = 10, 4
covariance_type = 'diag'
g = mixture.GMM(n_components, covariance_type, random_state=rng)
weights = rng.rand(n_components)
weights = weights / weights.sum()
means = rng.randint(-20, 20, (n_components, n_features))
assert_true(g.n_components == n_components)
assert_true(g.covariance_type == covariance_type)
g.weights_ = weights
assert_array_almost_equal(g.weights_, weights)
g.means_ = means
assert_array_almost_equal(g.means_, means)
covars = (0.1 + 2 * rng.rand(n_components, n_features)) ** 2
g.covars_ = covars
assert_array_almost_equal(g.covars_, covars)
assert_raises(ValueError, g._set_covars, [])
assert_raises(ValueError, g._set_covars,
np.zeros((n_components - 2, n_features)))
assert_raises(ValueError, mixture.GMM, n_components=20,
covariance_type='badcovariance_type')
class GMMTester():
do_test_eval = True
def _setUp(self):
self.n_components = 10
self.n_features = 4
self.weights = rng.rand(self.n_components)
self.weights = self.weights / self.weights.sum()
self.means = rng.randint(-20, 20, (self.n_components, self.n_features))
self.threshold = -0.5
self.I = np.eye(self.n_features)
self.covars = {
'spherical': (0.1 + 2 * rng.rand(self.n_components,
self.n_features)) ** 2,
'tied': (make_spd_matrix(self.n_features, random_state=0)
+ 5 * self.I),
'diag': (0.1 + 2 * rng.rand(self.n_components,
self.n_features)) ** 2,
'full': np.array([make_spd_matrix(self.n_features, random_state=0)
+ 5 * self.I for x in range(self.n_components)])}
def test_eval(self):
if not self.do_test_eval:
return # DPGMM does not support setting the means and
# covariances before fitting There is no way of fixing this
# due to the variational parameters being more expressive than
# covariance matrices
g = self.model(n_components=self.n_components,
covariance_type=self.covariance_type, random_state=rng)
# Make sure the means are far apart so responsibilities.argmax()
# picks the actual component used to generate the observations.
g.means_ = 20 * self.means
g.covars_ = self.covars[self.covariance_type]
g.weights_ = self.weights
gaussidx = np.repeat(np.arange(self.n_components), 5)
n_samples = len(gaussidx)
X = rng.randn(n_samples, self.n_features) + g.means_[gaussidx]
ll, responsibilities = g.score_samples(X)
self.assertEqual(len(ll), n_samples)
self.assertEqual(responsibilities.shape,
(n_samples, self.n_components))
assert_array_almost_equal(responsibilities.sum(axis=1),
np.ones(n_samples))
assert_array_equal(responsibilities.argmax(axis=1), gaussidx)
def test_sample(self, n=100):
g = self.model(n_components=self.n_components,
covariance_type=self.covariance_type, random_state=rng)
# Make sure the means are far apart so responsibilities.argmax()
# picks the actual component used to generate the observations.
g.means_ = 20 * self.means
g.covars_ = np.maximum(self.covars[self.covariance_type], 0.1)
g.weights_ = self.weights
samples = g.sample(n)
self.assertEqual(samples.shape, (n, self.n_features))
def test_train(self, params='wmc'):
g = mixture.GMM(n_components=self.n_components,
covariance_type=self.covariance_type)
g.weights_ = self.weights
g.means_ = self.means
g.covars_ = 20 * self.covars[self.covariance_type]
# Create a training set by sampling from the predefined distribution.
X = g.sample(n_samples=100)
g = self.model(n_components=self.n_components,
covariance_type=self.covariance_type,
random_state=rng, min_covar=1e-1,
n_iter=1, init_params=params)
g.fit(X)
# Do one training iteration at a time so we can keep track of
# the log likelihood to make sure that it increases after each
# iteration.
trainll = []
for _ in range(5):
g.params = params
g.init_params = ''
g.fit(X)
trainll.append(self.score(g, X))
g.n_iter = 10
g.init_params = ''
g.params = params
g.fit(X) # finish fitting
# Note that the log likelihood will sometimes decrease by a
# very small amount after it has more or less converged due to
# the addition of min_covar to the covariance (to prevent
# underflow). This is why the threshold is set to -0.5
# instead of 0.
delta_min = np.diff(trainll).min()
self.assertTrue(
delta_min > self.threshold,
"The min nll increase is %f which is lower than the admissible"
" threshold of %f, for model %s. The likelihoods are %s."
% (delta_min, self.threshold, self.covariance_type, trainll))
def test_train_degenerate(self, params='wmc'):
# Train on degenerate data with 0 in some dimensions
# Create a training set by sampling from the predefined distribution.
X = rng.randn(100, self.n_features)
X.T[1:] = 0
g = self.model(n_components=2, covariance_type=self.covariance_type,
random_state=rng, min_covar=1e-3, n_iter=5,
init_params=params)
g.fit(X)
trainll = g.score(X)
self.assertTrue(np.sum(np.abs(trainll / 100 / X.shape[1])) < 5)
def test_train_1d(self, params='wmc'):
# Train on 1-D data
# Create a training set by sampling from the predefined distribution.
X = rng.randn(100, 1)
# X.T[1:] = 0
g = self.model(n_components=2, covariance_type=self.covariance_type,
random_state=rng, min_covar=1e-7, n_iter=5,
init_params=params)
g.fit(X)
trainll = g.score(X)
if isinstance(g, mixture.DPGMM):
self.assertTrue(np.sum(np.abs(trainll / 100)) < 5)
else:
self.assertTrue(np.sum(np.abs(trainll / 100)) < 2)
def score(self, g, X):
return g.score(X).sum()
class TestGMMWithSphericalCovars(unittest.TestCase, GMMTester):
covariance_type = 'spherical'
model = mixture.GMM
setUp = GMMTester._setUp
class TestGMMWithDiagonalCovars(unittest.TestCase, GMMTester):
covariance_type = 'diag'
model = mixture.GMM
setUp = GMMTester._setUp
class TestGMMWithTiedCovars(unittest.TestCase, GMMTester):
covariance_type = 'tied'
model = mixture.GMM
setUp = GMMTester._setUp
class TestGMMWithFullCovars(unittest.TestCase, GMMTester):
covariance_type = 'full'
model = mixture.GMM
setUp = GMMTester._setUp
def test_multiple_init():
# Test that multiple inits does not much worse than a single one
X = rng.randn(30, 5)
X[:10] += 2
g = mixture.GMM(n_components=2, covariance_type='spherical',
random_state=rng, min_covar=1e-7, n_iter=5)
train1 = g.fit(X).score(X).sum()
g.n_init = 5
train2 = g.fit(X).score(X).sum()
assert_true(train2 >= train1 - 1.e-2)
def test_n_parameters():
# Test that the right number of parameters is estimated
n_samples, n_dim, n_components = 7, 5, 2
X = rng.randn(n_samples, n_dim)
n_params = {'spherical': 13, 'diag': 21, 'tied': 26, 'full': 41}
for cv_type in ['full', 'tied', 'diag', 'spherical']:
g = mixture.GMM(n_components=n_components, covariance_type=cv_type,
random_state=rng, min_covar=1e-7, n_iter=1)
g.fit(X)
assert_true(g._n_parameters() == n_params[cv_type])
def test_1d_1component():
# Test all of the covariance_types return the same BIC score for
# 1-dimensional, 1 component fits.
n_samples, n_dim, n_components = 100, 1, 1
X = rng.randn(n_samples, n_dim)
g_full = mixture.GMM(n_components=n_components, covariance_type='full',
random_state=rng, min_covar=1e-7, n_iter=1)
g_full.fit(X)
g_full_bic = g_full.bic(X)
for cv_type in ['tied', 'diag', 'spherical']:
g = mixture.GMM(n_components=n_components, covariance_type=cv_type,
random_state=rng, min_covar=1e-7, n_iter=1)
g.fit(X)
assert_array_almost_equal(g.bic(X), g_full_bic)
def assert_fit_predict_correct(model, X):
model2 = copy.deepcopy(model)
predictions_1 = model.fit(X).predict(X)
predictions_2 = model2.fit_predict(X)
assert adjusted_rand_score(predictions_1, predictions_2) == 1.0
def test_fit_predict():
"""
test that gmm.fit_predict is equivalent to gmm.fit + gmm.predict
"""
lrng = np.random.RandomState(101)
n_samples, n_dim, n_comps = 100, 2, 2
mu = np.array([[8, 8]])
component_0 = lrng.randn(n_samples, n_dim)
component_1 = lrng.randn(n_samples, n_dim) + mu
X = np.vstack((component_0, component_1))
for m_constructor in (mixture.GMM, mixture.VBGMM, mixture.DPGMM):
model = m_constructor(n_components=n_comps, covariance_type='full',
min_covar=1e-7, n_iter=5,
random_state=np.random.RandomState(0))
assert_fit_predict_correct(model, X)
model = mixture.GMM(n_components=n_comps, n_iter=0)
z = model.fit_predict(X)
assert np.all(z == 0), "Quick Initialization Failed!"
def test_aic():
# Test the aic and bic criteria
n_samples, n_dim, n_components = 50, 3, 2
X = rng.randn(n_samples, n_dim)
SGH = 0.5 * (X.var() + np.log(2 * np.pi)) # standard gaussian entropy
for cv_type in ['full', 'tied', 'diag', 'spherical']:
g = mixture.GMM(n_components=n_components, covariance_type=cv_type,
random_state=rng, min_covar=1e-7)
g.fit(X)
aic = 2 * n_samples * SGH * n_dim + 2 * g._n_parameters()
bic = (2 * n_samples * SGH * n_dim +
np.log(n_samples) * g._n_parameters())
bound = n_dim * 3. / np.sqrt(n_samples)
assert_true(np.abs(g.aic(X) - aic) / n_samples < bound)
assert_true(np.abs(g.bic(X) - bic) / n_samples < bound)
def check_positive_definite_covars(covariance_type):
r"""Test that covariance matrices do not become non positive definite
Due to the accumulation of round-off errors, the computation of the
covariance matrices during the learning phase could lead to non-positive
definite covariance matrices. Namely the use of the formula:
.. math:: C = (\sum_i w_i x_i x_i^T) - \mu \mu^T
instead of:
.. math:: C = \sum_i w_i (x_i - \mu)(x_i - \mu)^T
while mathematically equivalent, was observed a ``LinAlgError`` exception,
when computing a ``GMM`` with full covariance matrices and fixed mean.
This function ensures that some later optimization will not introduce the
problem again.
"""
rng = np.random.RandomState(1)
# we build a dataset with 2 2d component. The components are unbalanced
# (respective weights 0.9 and 0.1)
X = rng.randn(100, 2)
X[-10:] += (3, 3) # Shift the 10 last points
gmm = mixture.GMM(2, params="wc", covariance_type=covariance_type,
min_covar=1e-3)
# This is a non-regression test for issue #2640. The following call used
# to trigger:
# numpy.linalg.linalg.LinAlgError: 2-th leading minor not positive definite
gmm.fit(X)
if covariance_type == "diag" or covariance_type == "spherical":
assert_greater(gmm.covars_.min(), 0)
else:
if covariance_type == "tied":
covs = [gmm.covars_]
else:
covs = gmm.covars_
for c in covs:
assert_greater(np.linalg.det(c), 0)
def test_positive_definite_covars():
# Check positive definiteness for all covariance types
for covariance_type in ["full", "tied", "diag", "spherical"]:
yield check_positive_definite_covars, covariance_type
def test_verbose_first_level():
# Create sample data
X = rng.randn(30, 5)
X[:10] += 2
g = mixture.GMM(n_components=2, n_init=2, verbose=1)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
g.fit(X)
finally:
sys.stdout = old_stdout
def test_verbose_second_level():
# Create sample data
X = rng.randn(30, 5)
X[:10] += 2
g = mixture.GMM(n_components=2, n_init=2, verbose=2)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
g.fit(X)
finally:
sys.stdout = old_stdout
| bsd-3-clause |
Pold87/paparazzi_clean | sw/airborne/test/stabilization/compare_ref_quat.py | 48 | 1123 | #! /usr/bin/env python
from __future__ import division, print_function, absolute_import
import numpy as np
import matplotlib.pyplot as plt
from ref_quat_float import RefQuatFloat
from ref_quat_int import RefQuatInt
steps = 512 * 2
ref_float_res = np.zeros((steps, 3))
ref_int_res = np.zeros((steps, 3))
ref_float = RefQuatFloat()
ref_int = RefQuatInt()
q_sp = np.array([0.92387956, 0.38268346, 0., 0.])
ref_float.setpoint = q_sp
ref_int.setpoint = q_sp
#print(ref_int.setpoint)
dt = 1/512
for i in range(0, steps):
ref_float.update(dt)
ref_float_res[i, :] = ref_float.eulers
ref_int.update(dt)
ref_int_res[i, :] = ref_int.eulers
plt.figure(1)
plt.subplot(311)
plt.title("reference in euler angles")
plt.plot(np.degrees(ref_float_res[:, 0]), 'g')
plt.plot(np.degrees(ref_int_res[:, 0]), 'r')
plt.ylabel("phi [deg]")
plt.subplot(312)
plt.plot(np.degrees(ref_float_res[:, 1]), 'g')
plt.plot(np.degrees(ref_int_res[:, 1]), 'r')
plt.ylabel("theta [deg]")
plt.subplot(313)
plt.plot(np.degrees(ref_float_res[:, 2]), 'g')
plt.plot(np.degrees(ref_int_res[:, 2]), 'r')
plt.ylabel("psi [deg]")
plt.show()
| gpl-2.0 |
balazssimon/ml-playground | udemy/Machine Learning A-Z/Part 3 - Classification/Section 20 - Random Forest Classification/random_forest_classification.py | 6 | 2748 | # Random Forest Classification
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting Random Forest Classification to the Training set
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Random Forest Classification (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Random Forest Classification (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show() | apache-2.0 |
anbasile/pmu | bot/bus.py | 2 | 3488 | import numpy as np
import scipy.spatial as spatial
import pandas as pd
import time
import datetime
stops = pd.read_csv("data/stops.txt")
stop_times = pd.read_csv("data/stop_times.txt")
routes = pd.read_csv("data/routes.txt")
trips = pd.read_csv("data/trips.txt")
calendar = pd.read_csv("data/calendar.txt",names=['service_id','monday','tuesday','wednesday','thursday','friday','saturday','sunday','start_date','end_date'],header=0)
def dow(date):
days=["monday","tuesday","wednesday","thursday","friday","saturday","sunday"]
dayNumber=date.weekday()
return days[dayNumber]
def prettyprint(df):
"""Take a pandas dataframe in input and return a string containing one row per line"""
temp = []
for i,row in df.iterrows():
temp.append(str(row.values))
response = '\n'.join(temp)
if not temp:
response = "No, mi spiace, ma non ci sono bus adesso..."
return response
def getstop(location):
"""Take a location in input and return the name of the nearest
bus stop the ids of one or two (one per direction, if available) stop_id
"""
pt = location.latitude,location.longitude
pt = np.asarray(pt)
points = stops[['stop_lat','stop_lon']].values
nn = points[spatial.KDTree(points).query(pt)[1]] #find nearest stop given a tree and a point (pt)
stop_name = stops['stop_name'][(stops['stop_lat'] ==nn[0]) & (stops['stop_lon'] ==nn[1])].iloc[0]
stop_id = stops['stop_id'][stops['stop_name'] == stop_name].iloc[0]
stop_code = stops['stop_code'][stops['stop_id'] == stop_id].iloc[0]
stop_id2 = None #check if there is the bus stop for opposite direction
if stop_code[-1] == 'x':
try:
stop_code2 = stop_code
temp = list(stop_code2)
temp[-1] = 'z'
stop_code2 = ''.join(temp)
stop_id2 = stops['stop_id'][stops['stop_code'] == stop_code2].iloc[0]
except (KeyError,IndexError):
return stop_name,stop_id
elif stop_code[-1] == 'z':
try:
stop_code2 = stop_code
temp = list(stop_code2)
temp[-1] = 'x'
stop_code2 = ''.join(temp)
stop_id2 = stops['stop_id'][stops['stop_code'] == stop_code2].iloc[0]
except (KeyError,IndexError):
return stop_name,stop_id
return stop_name,stop_id,stop_id2
def gettrip(location):
"""Take in input a location and return a list containing info about the
6 next buses (3 per direction). Info means: time,route short name and route headsign"""
stop_id = getstop(location)[1]
time = datetime.datetime.now()
now = time.strftime('%H:%M:%S')
a = pd.merge(stop_times,trips,on=['trip_id']) #merge the dfs in order to simplify extraction process
b = pd.merge(a,calendar,on=['service_id'])
c = pd.merge(b,routes,on=['route_id'])
c = c.sort_values('arrival_time')
nextbuses = c[(c['stop_id'] == stop_id) & (c['arrival_time'] >= now) & (c[dow(time)] != 0)].iloc[0:3]
try:
getstop(location)[2]
stop_id2 = getstop(location)[2]
nextbuses2 = c[(c['stop_id'] == stop_id2) & (c['arrival_time'] >= now) & (c[dow(time)] != 0)].iloc[0:3]
dfs = [nextbuses,nextbuses2]
response = prettyprint(pd.concat(dfs)[['arrival_time','route_short_name','trip_headsign']])
return response
except IndexError:
response = prettyprint(nextbuses[['arrival_time','route_short_name','trip_headsign']])
return response
| gpl-3.0 |
michigraber/scikit-learn | sklearn/mixture/tests/test_dpgmm.py | 261 | 4490 | import unittest
import sys
import numpy as np
from sklearn.mixture import DPGMM, VBGMM
from sklearn.mixture.dpgmm import log_normalize
from sklearn.datasets import make_blobs
from sklearn.utils.testing import assert_array_less, assert_equal
from sklearn.mixture.tests.test_gmm import GMMTester
from sklearn.externals.six.moves import cStringIO as StringIO
np.seterr(all='warn')
def test_class_weights():
# check that the class weights are updated
# simple 3 cluster dataset
X, y = make_blobs(random_state=1)
for Model in [DPGMM, VBGMM]:
dpgmm = Model(n_components=10, random_state=1, alpha=20, n_iter=50)
dpgmm.fit(X)
# get indices of components that are used:
indices = np.unique(dpgmm.predict(X))
active = np.zeros(10, dtype=np.bool)
active[indices] = True
# used components are important
assert_array_less(.1, dpgmm.weights_[active])
# others are not
assert_array_less(dpgmm.weights_[~active], .05)
def test_verbose_boolean():
# checks that the output for the verbose output is the same
# for the flag values '1' and 'True'
# simple 3 cluster dataset
X, y = make_blobs(random_state=1)
for Model in [DPGMM, VBGMM]:
dpgmm_bool = Model(n_components=10, random_state=1, alpha=20,
n_iter=50, verbose=True)
dpgmm_int = Model(n_components=10, random_state=1, alpha=20,
n_iter=50, verbose=1)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
# generate output with the boolean flag
dpgmm_bool.fit(X)
verbose_output = sys.stdout
verbose_output.seek(0)
bool_output = verbose_output.readline()
# generate output with the int flag
dpgmm_int.fit(X)
verbose_output = sys.stdout
verbose_output.seek(0)
int_output = verbose_output.readline()
assert_equal(bool_output, int_output)
finally:
sys.stdout = old_stdout
def test_verbose_first_level():
# simple 3 cluster dataset
X, y = make_blobs(random_state=1)
for Model in [DPGMM, VBGMM]:
dpgmm = Model(n_components=10, random_state=1, alpha=20, n_iter=50,
verbose=1)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
dpgmm.fit(X)
finally:
sys.stdout = old_stdout
def test_verbose_second_level():
# simple 3 cluster dataset
X, y = make_blobs(random_state=1)
for Model in [DPGMM, VBGMM]:
dpgmm = Model(n_components=10, random_state=1, alpha=20, n_iter=50,
verbose=2)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
dpgmm.fit(X)
finally:
sys.stdout = old_stdout
def test_log_normalize():
v = np.array([0.1, 0.8, 0.01, 0.09])
a = np.log(2 * v)
assert np.allclose(v, log_normalize(a), rtol=0.01)
def do_model(self, **kwds):
return VBGMM(verbose=False, **kwds)
class DPGMMTester(GMMTester):
model = DPGMM
do_test_eval = False
def score(self, g, train_obs):
_, z = g.score_samples(train_obs)
return g.lower_bound(train_obs, z)
class TestDPGMMWithSphericalCovars(unittest.TestCase, DPGMMTester):
covariance_type = 'spherical'
setUp = GMMTester._setUp
class TestDPGMMWithDiagCovars(unittest.TestCase, DPGMMTester):
covariance_type = 'diag'
setUp = GMMTester._setUp
class TestDPGMMWithTiedCovars(unittest.TestCase, DPGMMTester):
covariance_type = 'tied'
setUp = GMMTester._setUp
class TestDPGMMWithFullCovars(unittest.TestCase, DPGMMTester):
covariance_type = 'full'
setUp = GMMTester._setUp
class VBGMMTester(GMMTester):
model = do_model
do_test_eval = False
def score(self, g, train_obs):
_, z = g.score_samples(train_obs)
return g.lower_bound(train_obs, z)
class TestVBGMMWithSphericalCovars(unittest.TestCase, VBGMMTester):
covariance_type = 'spherical'
setUp = GMMTester._setUp
class TestVBGMMWithDiagCovars(unittest.TestCase, VBGMMTester):
covariance_type = 'diag'
setUp = GMMTester._setUp
class TestVBGMMWithTiedCovars(unittest.TestCase, VBGMMTester):
covariance_type = 'tied'
setUp = GMMTester._setUp
class TestVBGMMWithFullCovars(unittest.TestCase, VBGMMTester):
covariance_type = 'full'
setUp = GMMTester._setUp
| bsd-3-clause |
PYPIT/PYPIT | pypeit/wavemodel.py | 1 | 32569 | # Module to create models of arc lines.
from __future__ import absolute_import, division, print_function
import astropy
import re
import scipy
import numpy as np
import matplotlib.pyplot as plt
from pkg_resources import resource_filename
from astropy.io import fits
from astropy.convolution import convolve, Gaussian1DKernel
from astropy.table import Table
from pypeit import msgs
from pypeit.core import arc
from pypeit import utils
def blackbody(wavelength, T_BB=250., debug=False):
""" Given wavelength [in microns] and Temperature in Kelvin
it returns the black body emission.
Parameters
----------
wavelength : np.array
wavelength vector in microns
T_BB : float
black body temperature in Kelvin. Default is set to:
T_BB = 250.
Returns
-------
blackbody : np.array
spectral radiance of the black body in cgs units:
B_lambda = 2.*h*c^2/lambda^5.*(1./(exp(h*c/(lambda*k_b*T_BB))-1.)
blackbody_counts : np.array
Same as above but in flux density
"""
# Define constants in cgs
PLANCK = astropy.constants.h.cgs.value # erg*s
C_LIGHT = astropy.constants.c.cgs.value # cm/s
K_BOLTZ = astropy.constants.k_B.cgs.value # erg/K
RADIAN_PER_ARCSEC = 1./3600.*np.pi/180.
msgs.info("Creating BB spectrum at T={}K".format(T_BB))
lam = wavelength / 1e4 # convert wave in cm.
blackbody_pol = 2.*PLANCK*np.power(C_LIGHT,2) / np.power(lam,5)
blackbody_exp = np.exp(PLANCK*C_LIGHT/(lam*K_BOLTZ*T_BB)) - 1.
blackbody = blackbody_pol / blackbody_exp
blackbody_counts = blackbody / (PLANCK * C_LIGHT / lam) * 1e-4 \
* np.power(RADIAN_PER_ARCSEC, 2.)
if debug:
utils.pyplot_rcparams()
msgs.info("Plot of the blackbody spectrum.")
plt.figure()
plt.plot(wavelength, blackbody,
color='navy', linestyle='-', alpha=0.8,
label=r'T_BB={}'.format(T_BB))
plt.legend()
plt.xlabel(r"Wavelength [micron]")
plt.ylabel(r"Spectral Radiance")
plt.title(r"Planck's law")
msgs.info("Close the Figure to continue.")
plt.show(block=True)
plt.close()
utils.pyplot_rcparams_default()
return blackbody, blackbody_counts
def addlines2spec(wavelength, wl_line, fl_line, resolution,
scale_spec=1., debug=False):
""" Create a spectrum with a set of (gaussian) emission lines.
Parameters
----------
wavelength : np.array
wavelength vector of the input spectrum
wl_line, fl_line : np.arrays
wavelength and flux of each individual line
resolution : np.float
resolution of the spectrograph. In other words, the lines
will have a FWHM equal to:
fwhm_line = wl_line / resolution
scale_spec : np.float
rescale all the normalization of the final spectrum.
Default scale_spec=1.
debug : boolean
If True will show debug plots
Returns
-------
line_spec : np.array
Spectrum with lines
"""
line_spec = np.zeros_like(wavelength)
wl_line_min, wl_line_max = np.min(wavelength), np.max(wavelength)
good_lines = (wl_line>wl_line_min) & (wl_line<wl_line_max)
wl_line_good = wl_line[good_lines]
fl_line_good = fl_line[good_lines]
# define sigma of the gaussians
sigma = wl_line_good / resolution / 2.355
msgs.info("Creating line spectrum")
for ii in np.arange(len(wl_line_good)):
line_spec += scale_spec*fl_line_good[ii]*\
np.exp(-np.power((wl_line_good[ii]-wavelength),2.)/(2.*np.power(sigma[ii],2.)))
if debug:
utils.pyplot_rcparams()
msgs.info("Plot of the line spectrum.")
plt.figure()
plt.plot(wavelength, line_spec,
color='navy', linestyle='-', alpha=0.8,
label=r'Spectrum with lines included')
plt.legend()
plt.xlabel(r'Wavelength')
plt.ylabel(r'Flux')
msgs.info("Close the Figure to continue.")
plt.show(block=True)
plt.close()
utils.pyplot_rcparams_default()
return line_spec
def oh_lines():
""" Reads in the Rousselot (2000) OH line list"
Returns
-------
wavelength, amplitude : np.arrays
Wavelength [in microns] and amplitude of the OH lines.
"""
msgs.info("Reading in the Rousselot (2000) OH line list")
skisim_dir = resource_filename('pypeit', 'data/skisim/')
oh = np.loadtxt(skisim_dir+"rousselot2000.dat", usecols=(0, 1))
return oh[:,0]/10000., oh[:,1] # wave converted to microns
def transparency(wavelength, debug=False):
""" Interpolate the atmospheric transmission model in the IR over
a given wavelength (in microns) range.
Parameters
----------
wavelength : np.array
wavelength vector in microns
debug : boolean
If True will show debug plots
Returns
-------
transparency : np.array
Transmission of the sky over the considered wavelength rage.
1. means fully transparent and 0. fully opaque
"""
msgs.info("Reading in the atmospheric transmission model")
skisim_dir = resource_filename('pypeit', 'data/skisim/')
transparency = np.loadtxt(skisim_dir+'atm_transmission_secz1.5_1.6mm.dat')
wave_mod = transparency[:,0]
tran_mod = transparency[:,1]
# Limit model between 0.8 and np.max(wavelength) microns
filt_wave_mod = (wave_mod>0.8) & (wave_mod<np.max(wavelength))
wave_mod = wave_mod[filt_wave_mod]
tran_mod = tran_mod[filt_wave_mod]
# Interpolate over input wavelengths
interp_tran = scipy.interpolate.interp1d(wave_mod, tran_mod,
kind='cubic',
fill_value='extrapolate')
transmission = interp_tran(wavelength)
transmission[wavelength<0.9] = 1.
# Clean for spourious values due to interpolation
transmission[transmission<0.] = 0.
transmission[transmission>1.] = 1.
if debug:
utils.pyplot_rcparams()
msgs.info("Plot of the sky transmission template")
plt.figure()
plt.plot(wave_mod, tran_mod,
color='navy', linestyle='-', alpha=0.8,
label=r'Original')
plt.plot(wavelength, transmission,
color='crimson', linestyle='-', alpha=0.8,
label=r'Resampled')
plt.legend()
plt.xlabel(r'Wavelength [microns]')
plt.ylabel(r'Transmission')
plt.title(r' IR Transmission Spectra ')
msgs.info("Close the Figure to continue.")
plt.show(block=True)
plt.close()
utils.pyplot_rcparams_default()
# Returns
return transmission
def h2o_lines():
""" Reads in the H2O atmospheric spectrum"
Returns
-------
wavelength, flux : np.arrays
Wavelength [in microns] and flux of the H2O atmospheric
spectrum.
"""
msgs.info("Reading in the water atmsopheric spectrum")
skisim_dir = resource_filename('pypeit', 'data/skisim/')
h2o = np.loadtxt(skisim_dir+"HITRAN.txt", usecols=(0, 1))
h2o_wv = 1./ h2o[:,0] * 1e4 # microns
h2o_rad = h2o[:,1] * 5e11 # added to match XIDL
return h2o_wv, h2o_rad
def thar_lines():
""" Reads in the H2O atmospheric spectrum"
Detailed information are here: http://astronomy.swin.edu.au/~mmurphy/thar/index.html
Returns
-------
wavelength, flux : np.arrays
Wavelength [in angstrom] and flux of the ThAr lamp
spectrum.
"""
msgs.info("Reading in the ThAr spectrum")
arclines_dir = resource_filename('pypeit', 'data/arc_lines/')
thar = fits.open(arclines_dir+'thar_spec_MM201006.fits')
# create pixel array
thar_pix = np.arange(thar[0].header['CRPIX1'],len(thar[0].data[0,:])+1)
# convert pixels to wavelength in Angstrom
thar_wv = thar[0].header['UP_WLSRT']*10**((thar_pix-thar[0].header['CRPIX1'])*thar[0].header['CD1_1'])
# read in spectrum
thar_spec = thar[0].data[0,:]
return thar_wv, thar_spec
def nearIR_modelsky(resolution, waveminmax=(0.8,2.6), dlam=40.0,
flgd=True, nirsky_outfile=None, T_BB=250.,
SCL_BB=1., SCL_OH=1., SCL_H2O=10.,
WAVE_WATER=2.3, debug=False):
""" Generate a model sky in the near-IR. This includes a continuum model
to match to gemini broadband level, a black body at T_BB, OH lines, and
H2O lines (but only at lambda>WAVE_WATER). Everythins is smoothed at the
given resolution.
Parameters
----------
resolution : np.float
resolution of the spectrograph. The OH and H2O lines will have a
FWHM equal to:
fwhm_line = wl_line / resolution
waveminmax : tuple
wavelength range in microns to be covered by the model.
Default is: (0.8, 2.6)
dlam :
bin to be used to create the wavelength grid of the model.
If flgd='True' it is a bin in velocity (km/s). If flgd='False'
it is a bin in linear space (microns).
Default is: 40.0 (with flgd='True')
flgd : boolean
if flgd='True' (default) wavelengths are created with
equal steps in log space. If 'False', wavelengths will be
created wit equal steps in linear space.
nirsky_outfile : str
name of the fits file where the model sky spectrum will be stored.
default is 'None' (i.e., no file will be written).
T_BB : float
black body temperature in Kelvin. Default is set to:
T_BB = 250.
SCL_BB : float
scale factor for modelling the sky black body emssion.
Default: SCL_BB=1.
SCL_OH : float
scale factor for modelling the OH emssion.
Default: SCL_OH=1.
SCL_H2O : float
scale factor for modelling the H2O emssion.
Default: SCL_H2O=10.
WAVE_WATER : float
wavelength (in microns) at which the H2O are inclued.
Default: WAVE_WATER = 2.3
debug : boolean
If True will show debug plots
Returns
-------
wave, sky_model : np.arrays
wavelength (in Ang.) and flux of the final model of the sky.
"""
# Create the wavelength array:
wv_min = waveminmax[0]
wv_max = waveminmax[1]
if flgd :
msgs.info("Creating wavelength vector in velocity space.")
velpix = dlam # km/s
loglam = np.log10(1.0 + velpix/299792.458)
wave = np.power(10.,np.arange(np.log10(wv_min), np.log10(wv_max), loglam))
else :
msgs.info("Creating wavelength vector in linear space.")
wave = np.arange(wv_min, wv_max, dlam)
# Calculate transparency
# trans = transparency(wave, debug=False)
# Empirical match to gemini broadband continuum level
logy = - 0.55 - 0.55 * (wave-1.0)
y = np.power(10.,logy)
msgs.info("Add in a blackbody for the atmosphere.")
bb, bb_counts = blackbody(wave, T_BB=T_BB, debug=debug)
bb_counts = bb_counts
msgs.info("Add in OH lines")
oh_wv, oh_fx = oh_lines()
# produces better wavelength solutions with 1.0 threshold
msgs.info("Selecting stronger OH lines")
filt_oh = oh_fx > 1.
oh_wv, oh_fx = oh_wv[filt_oh], oh_fx[filt_oh]
# scale_spec was added to match the XIDL code
ohspec = addlines2spec(wave, oh_wv, oh_fx, resolution=resolution,
scale_spec=((resolution/1000.)/40.),
debug=debug)
if wv_max > WAVE_WATER :
msgs.info("Add in H2O lines")
h2o_wv, h2o_rad = h2o_lines()
filt_h2o = (h2o_wv>wv_min-0.1) & (h2o_wv<wv_max+0.1)
h2o_wv = h2o_wv[filt_h2o]
h2o_rad = h2o_rad[filt_h2o]
# calculate sigma at the mean wavelenght of the H2O spectrum
filt_h2o_med = h2o_wv>WAVE_WATER
mn_wv = np.mean(h2o_wv[filt_h2o_med])
# Convolve to the instrument resolution. This is only
# approximate.
smooth_fx, dwv, h2o_dwv = conv2res(h2o_wv, h2o_rad,
resolution,
central_wl = mn_wv,
debug=debug)
# Interpolate over input wavelengths
interp_h2o = scipy.interpolate.interp1d(h2o_wv, smooth_fx,
kind='cubic',
fill_value='extrapolate')
h2ospec = interp_h2o(wave)
# Zero out below WAVE_WATER microns (reconsider)
h2ospec[wave<WAVE_WATER] = 0.
h2ospec[wave>np.max(h2o_wv)] = 0.
else:
h2ospec = np.zeros(len(wave),dtype='float')
sky_model = y+bb_counts*SCL_BB+ohspec*SCL_OH+h2ospec*SCL_H2O
if nirsky_outfile is not None:
msgs.info("Saving the sky model in: {}".format(nirsky_outfile))
hdu = fits.PrimaryHDU(np.array(sky_model))
header = hdu.header
if flgd :
header['CRVAL1'] = np.log10(wv_min)
header['CDELT1'] = loglam
header['DC-FLAG'] = 1
else :
header['CRVAL1'] = wv_min
header['CDELT1'] = dlam
header['DC-FLAG'] = 0
hdu.writeto(nirsky_outfile, overwrite = True)
if debug:
utils.pyplot_rcparams()
msgs.info("Plot of the sky emission at R={}".format(resolution))
plt.figure()
plt.plot(wave, sky_model,
color='black', linestyle='-', alpha=0.8,
label=r'Sky Model')
plt.plot(wave, y,
color='darkorange', linestyle='-', alpha=0.6,
label=r'Continuum')
plt.plot(wave, bb_counts*SCL_BB,
color='green', linestyle='-', alpha=0.6,
label=r'Black Body at T={}K'.format(T_BB))
plt.plot(wave, ohspec*SCL_OH,
color='darkviolet', linestyle='-', alpha=0.6,
label=r'OH')
plt.plot(wave, h2ospec*SCL_H2O,
color='dodgerblue', linestyle='-', alpha=0.6,
label=r'H2O')
plt.legend()
plt.xlabel(r'Wavelength [microns]')
plt.ylabel(r'Emission')
plt.title(r'Sky Emission Spectrum at R={}'.format(resolution))
msgs.info("Close the Figure to continue.")
plt.show(block=True)
plt.close()
utils.pyplot_rcparams_default()
return np.array(wave*10000.), np.array(sky_model)
def optical_modelThAr(resolution, waveminmax=(3000.,10500.), dlam=40.0,
flgd=True, thar_outfile=None, debug=False):
""" Generate a model of a ThAr lamp in the uvb/optical. This is based on the
Murphy et al. ThAr spectrum. Detailed information are here:
http://astronomy.swin.edu.au/~mmurphy/thar/index.html
Everythins is smoothed at the given resolution.
Parameters
----------
resolution : np.float
resolution of the spectrograph. The ThAr lines will have a
FWHM equal to:
fwhm_line = wl_line / resolution
waveminmax : tuple
wavelength range in angstrom to be covered by the model.
Default is: (3000.,10500.)
dlam :
bin to be used to create the wavelength grid of the model.
If flgd='True' it is a bin in velocity (km/s). If flgd='False'
it is a bin in linear space (microns).
Default is: 40.0 (with flgd='True')
flgd : boolean
if flgd='True' (default) wavelengths are created with
equal steps in log space. If 'False', wavelengths will be
created wit equal steps in linear space.
thar_outfile : str
name of the fits file where the model sky spectrum will be stored.
default is 'None' (i.e., no file will be written).
debug : boolean
If True will show debug plots
Returns
-------
wave, thar_model : np.arrays
wavelength (in Ang.) and flux of the final model of the ThAr lamp emission.
"""
# Create the wavelength array:
wv_min = waveminmax[0]
wv_max = waveminmax[1]
if flgd :
msgs.info("Creating wavelength vector in velocity space.")
velpix = dlam # km/s
loglam = np.log10(1.0 + velpix/299792.458)
wave = np.power(10.,np.arange(np.log10(wv_min), np.log10(wv_max), loglam))
else :
msgs.info("Creating wavelength vector in linear space.")
wave = np.arange(wv_min, wv_max, dlam)
msgs.info("Add in ThAr lines")
th_wv, th_fx = thar_lines()
# select spectral region
filt_wl = (th_wv>=wv_min) & (th_wv<=wv_max)
# calculate sigma at the mean wavelenght of the ThAr spectrum
mn_wv = np.mean(th_wv[filt_wl])
# Convolve to the instrument resolution. This is only
# approximate.
smooth_fx, dwv, thar_dwv = conv2res(th_wv, th_fx,
resolution,
central_wl = mn_wv,
debug=debug)
# Interpolate over input wavelengths
interp_thar = scipy.interpolate.interp1d(th_wv, smooth_fx,
kind='cubic',
fill_value='extrapolate')
thar_spec = interp_thar(wave)
# remove negative artifacts
thar_spec[thar_spec<0.] = 0.
# Remove regions of the spectrum outside the wavelength covered by the ThAr model
if wv_min<np.min(th_wv):
msgs.warn("Model of the ThAr spectrum outside the template coverage.")
thar_spec[wave<np.min(th_wv)] = 0.
if wv_max<np.max(th_wv):
msgs.warn("Model of the ThAr spectrum outside the template coverage.")
thar_spec[wave>np.max(th_wv)] = 0.
if thar_outfile is not None:
msgs.info("Saving the ThAr model in: {}".format(thar_outfile))
hdu = fits.PrimaryHDU(np.array(thar_spec))
header = hdu.header
if flgd :
header['CRVAL1'] = np.log10(wv_min)
header['CDELT1'] = loglam
header['DC-FLAG'] = 1
else :
header['CRVAL1'] = wv_min
header['CDELT1'] = dlam
header['DC-FLAG'] = 0
hdu.writeto(thar_outfile, overwrite = True)
if debug:
utils.pyplot_rcparams()
msgs.info("Plot of the Murphy et al. template at R={}".format(resolution))
plt.figure()
plt.plot(th_wv, th_fx,
color='navy', linestyle='-', alpha=0.3,
label=r'Original')
plt.plot(th_wv, smooth_fx,
color='crimson', linestyle='-', alpha=0.6,
label=r'Convolved at R={}'.format(resolution))
plt.plot(wave, thar_spec,
color='maroon', linestyle='-', alpha=1.0,
label=r'Convolved at R={} and resampled'.format(resolution))
plt.legend()
plt.xlabel(r'Wavelength [Ang.]')
plt.ylabel(r'Emission')
plt.title(r'Murphy et al. ThAr spectrum at R={}'.format(resolution))
msgs.info("Close the Figure to continue.")
plt.show(block=True)
plt.close()
utils.pyplot_rcparams_default()
return np.array(wave), np.array(thar_spec)
def conv2res(wavelength, flux, resolution, central_wl='midpt',
debug=False):
"""Convolve an imput spectrum to a specific resolution. This is only
approximate. It takes a fix FWHM for the entire spectrum given by:
fwhm = wl_cent / resolution
Parameters
----------
wavelength : np.array
wavelength
flux : np.array
flux
resolution : np.float
resolution of the spectrograph
central_wl
if 'midpt' the central pixel of wavelength is used, otherwise
the central_wl will be used.
debug : boolean
If True will show debug plots
Returns
-------
flux_convolved :np.array
Resulting flux after convolution
px_sigma : float
Size of the sigma in pixels at central_wl
px_bin : float
Size of one pixel at central_wl
"""
if central_wl is 'midpt':
wl_cent = np.median(wavelength)
else:
wl_cent = np.float(central_wl)
wl_sigma = wl_cent / resolution / 2.355
wl_bin = np.abs((wavelength - np.roll(wavelength,1))[np.where( np.abs(wavelength-wl_cent) == np.min(np.abs(wavelength-wl_cent)) )])
msgs.info("The binning of the wavelength array at {} is: {}".format(wl_cent, wl_bin[0]))
px_bin = wl_bin[0]
px_sigma = wl_sigma / px_bin
msgs.info("Covolving with a Gaussian kernel with sigma = {} pixels".format(px_sigma))
gauss_kernel = Gaussian1DKernel(px_sigma)
flux_convolved = convolve(flux, gauss_kernel)
if debug:
utils.pyplot_rcparams()
msgs.info("Spectrum Convolved at R = {}".format(resolution))
plt.figure()
plt.plot(wavelength, flux,
color='navy', linestyle='-', alpha=0.8,
label=r'Original')
plt.plot(wavelength, flux_convolved,
color='crimson', linestyle='-', alpha=0.8,
label=r'Convolved')
plt.legend()
plt.xlabel(r'Wavelength')
plt.ylabel(r'Flux')
plt.title(r'Spectrum Convolved at R = {}'.format(resolution))
msgs.info("Close the Figure to continue.")
plt.show(block=True)
plt.close()
utils.pyplot_rcparams_default()
return flux_convolved, px_sigma, px_bin
def iraf_datareader(database_dir, id_file):
"""Reads in a line identification database created with IRAF
identify. These are usually locate in a directory called 'database'.
This read pixel location and wavelength of the lines that
have been id with IRAF. Note that the first pixel in IRAF
is '1', while is '0' in python. The pixel location is thus
shifted of one pixel while reading the database.
Parameters
----------
database_dir : string
directory where the id files are located.
id_file : string
filename that is going to be read.
Returns
-------
pixel, line_id : np.arrays
Position of the line in pixel and ID of the line.
For IRAF output, these are usually in Ang.
"""
lines_database = []
# Open file for reading of text data.
with open (database_dir+id_file, 'rt') as id_file_iraf:
for line in id_file_iraf:
lines_database.append(line.split())
feat_line = re.search(r'features\t(\d+)', line)
if feat_line is not None:
N_lines = np.int(feat_line.group(1))
msgs.info("The number of IDs in the IRAF database {} is {}".format(id_file, N_lines))
pixel = np.zeros(N_lines)
line_id = np.zeros(N_lines)
for iii in range(0,N_lines):
pixel[iii] = lines_database[10:N_lines+10][iii][0]
line_id[iii] = lines_database[10:N_lines+10][iii][2]
# Moving from IRAF 1-based to Python 0-based convention.
pixel = pixel - 1.
return pixel, line_id
def create_linelist(wavelength, spec, fwhm, sigdetec=2.,
cont_samp=10., line_name=None, file_root_name=None,
iraf_frmt=False, debug=False):
""" Create list of lines detected in a spectrum in a PypeIt
compatible format. The name of the output file is
file_root_name+'_lines.dat'.
Parameters
----------
wavelength : np.array
wavelength
spec : np.array
spectrum
fwhm : float
fwhm in pixels used for filtering out arc lines that are too
wide and not considered in fits. Parameter of arc.detect_lines().
sigdetec : float
sigma threshold above fluctuations for line detection. Parameter
of arc.detect_lines(). Default = 2.
cont_samp : float
the number of samples across the spectrum used for continuum
subtraction. Parameter of arc.detect_lines(). Default = 10.
line_name : str
name of the lines to listed in the file.
file_root_name : str
name of the file where the identified lines will be stored.
The code automatically add '_lines.dat' at the end of the
root name.
iraf_frmt : bool
if True, the file is written in the IRAF format (i.e. wavelength,
ion name, amplitude).
"""
msgs.info("Searching for peaks {} sigma above background".format(sigdetec))
tampl_true, tampl, tcent, twid, centerr, ww, arcnorm, nsig = arc.detect_lines(spec, sigdetect=sigdetec,
fwhm=fwhm, cont_samp=cont_samp,
debug=debug)
peaks_good = tcent[ww]
ampl_good = tampl[ww]
# convert from pixel location to wavelength
pixvec = np.arange(spec.size)
wave_peak = scipy.interpolate.interp1d(pixvec, wavelength, bounds_error=False, fill_value='extrapolate')(peaks_good)
npeak = len(wave_peak)
ion = npeak*[str(line_name)]
NIST = npeak*[1]
Instr = npeak*[32]
Source = npeak*['wavemodel.py']
if iraf_frmt:
msgs.info("Printing file in IRAF format: {}".format(file_root_name+'_iraf_lines.dat'))
ion = np.array(ion)
id_lines_iraf = np.vstack( (np.round(wave_peak,5), ion, np.round(ampl_good,5)) ).T
np.savetxt(file_root_name+'_iraf_lines.dat', id_lines_iraf, fmt="%15s %6s %15s", delimiter=" ")
else:
msgs.info("Printing file: {}".format(file_root_name+'_lines.dat'))
dat = Table([wave_peak, ion, NIST, Instr, ampl_good, Source], names=('wave', 'ion','NIST','Instr','amplitude','Source'))
dat.write(file_root_name+'_lines.dat',format='ascii.fixed_width')
def create_OHlinelist(resolution, waveminmax=(0.8,2.6), dlam=40.0, flgd=True, nirsky_outfile=None,
fwhm=None, sigdetec=3., line_name='OH', file_root_name=None, iraf_frmt=False,
debug=False):
"""Create a syntetic sky spectrum at a given resolution, extract significant lines, and
store them in a PypeIt compatibile file. The skymodel is built from nearIR_modelsky and
includes black body at 250K, OH lines, and H2O lines (but only at lambda>2.3microns).
Parameters
----------
resolution : np.float
resolution of the spectrograph
waveminmax : tuple
wavelength range in microns to be covered by the model.
Default is: (0.8, 2.6)
dlam :
bin to be used to create the wavelength grid of the model.
If flgd='True' it is a bin in velocity (km/s). If flgd='False'
it is a bin in linear space (microns).
Default is: 40.0 (with flgd='True')
flgd : boolean
if flgd='True' (default) wavelengths are created with
equal steps in log space. If 'False', wavelengths will be
created wit equal steps in linear space.
nirsky_outfile : str
name of the fits file where the model sky spectrum will be stored.
default is 'None' (i.e., no file will be written).
fwhm : float
fwhm in pixels used for filtering out arc lines that are too
wide and not considered in fits. Parameter of arc.detect_lines().
If set to 'None' the fwhm will be derived from the resolution as:
2. * central_wavelength / resolution
sigdetec : float
sigma threshold above fluctuations for line detection. Parameter
of arc.detect_lines(). Default = 2.
line_name : str
name of the lines to listed in the file. Default is 'OH'.
file_root_name : str
name of the file where the identified lines will be stored.
The code automatically add '_lines.dat' at the end of the
root name.
iraf_frmt : bool
if True, the file is written in the IRAF format (i.e. wavelength,
ion name, amplitude).
debug : boolean
If True will show debug plots
"""
wavelength, spec = nearIR_modelsky(resolution, waveminmax=waveminmax, dlam=dlam,
flgd=flgd, nirsky_outfile=nirsky_outfile, debug=debug)
if fwhm is None:
msgs.warn("No min FWHM for the line detection set. Derived from the resolution at the center of the spectrum")
wl_cent = np.average(wavelength)
wl_fwhm = wl_cent / resolution
wl_bin = np.abs((wavelength-np.roll(wavelength,1))[np.where(np.abs(wavelength-wl_cent)==np.min(np.abs(wavelength-wl_cent)))])
# In order not to exclude all the lines, fwhm is set to 5 times
# the minimum fwhm of the spectrum
fwhm = 1.1 * wl_fwhm / wl_bin[0]
if fwhm < 1.:
msgs.warn("Lines are unresolved. Setting FWHM=2.pixels")
fwhm = 2.
if line_name is None:
msgs.warn("No line_name as been set. The file will contain XXX as ion")
line_name = 'XXX'
if file_root_name is None:
msgs.warn("No file_root_name as been set. The file will called OH_SKY_lines.dat")
file_root_name = 'OH_SKY'
create_linelist(wavelength, spec, fwhm=fwhm, sigdetec=sigdetec, line_name=line_name,
file_root_name=file_root_name, iraf_frmt=iraf_frmt, debug=debug)
def create_ThArlinelist(resolution, waveminmax=(3000.,10500.), dlam=40.0, flgd=True, thar_outfile=None,
fwhm=None, sigdetec=3., line_name='ThAr', file_root_name=None, iraf_frmt=False,
debug=False):
"""Create a syntetic ThAr spectrum at a given resolution, extract significant lines, and
store them in a PypeIt compatibile file. This is based on the Murphy et al. ThAr spectrum.
Detailed information are here: http://astronomy.swin.edu.au/~mmurphy/thar/index.html
Parameters
----------
resolution : np.float
resolution of the spectrograph
waveminmax : tuple
wavelength range in ang. to be covered by the model.
Default is: (3000., 10500.)
dlam :
bin to be used to create the wavelength grid of the model.
If flgd='True' it is a bin in velocity (km/s). If flgd='False'
it is a bin in linear space (angstrom).
Default is: 40.0 (with flgd='True')
flgd : boolean
if flgd='True' (default) wavelengths are created with
equal steps in log space. If 'False', wavelengths will be
created wit equal steps in linear space.
thar_outfile : str
name of the fits file where the model sky spectrum will be stored.
default is 'None' (i.e., no file will be written).
fwhm : float
fwhm in pixels used for filtering out arc lines that are too
wide and not considered in fits. Parameter of arc.detect_lines().
If set to 'None' the fwhm will be derived from the resolution as:
2. * central_wavelength / resolution
sigdetec : float
sigma threshold above fluctuations for line detection. Parameter
of arc.detect_lines(). Default = 2.
line_name : str
name of the lines to listed in the file.
file_root_name : str
name of the file where the identified lines will be stored.
The code automatically add '_lines.dat' at the end of the
root name.
iraf_frmt : bool
if True, the file is written in the IRAF format (i.e. wavelength,
ion name, amplitude).
debug : boolean
If True will show debug plots
"""
wavelength, spec = optical_modelThAr(resolution, waveminmax=waveminmax, dlam=dlam,
flgd=flgd, thar_outfile=thar_outfile, debug=debug)
if fwhm is None:
msgs.warn("No min FWHM for the line detection set. Derived from the resolution at the center of the spectrum")
wl_cent = np.average(wavelength)
wl_fwhm = wl_cent / resolution
wl_bin = np.abs((wavelength-np.roll(wavelength,1))[np.where(np.abs(wavelength-wl_cent)==np.min(np.abs(wavelength-wl_cent)))])
# In order not to exclude all the lines, fwhm is set to 5 times
# the minimum fwhm of the spectrum
fwhm = 1.1 * wl_fwhm / wl_bin[0]
if fwhm < 1.:
msgs.warn("Lines are unresolved. Setting FWHM=2.*pixels")
fwhm = 2.
if line_name is None:
msgs.warn("No line_name as been set. The file will contain XXX as ion")
line_name = 'XXX'
if file_root_name is None:
msgs.warn("No file_root_name as been set. The file will called ThAr_lines.dat")
file_root_name = 'ThAr'
create_linelist(wavelength, spec, fwhm=fwhm, sigdetec=sigdetec, line_name=line_name,
file_root_name=file_root_name, iraf_frmt=iraf_frmt, debug=debug)
| gpl-3.0 |
tmhm/scikit-learn | sklearn/gaussian_process/gaussian_process.py | 83 | 34544 | # -*- coding: utf-8 -*-
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# (mostly translation, see implementation details)
# Licence: BSD 3 clause
from __future__ import print_function
import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics.pairwise import manhattan_distances
from ..utils import check_random_state, check_array, check_X_y
from ..utils.validation import check_is_fitted
from . import regression_models as regression
from . import correlation_models as correlation
MACHINE_EPSILON = np.finfo(np.double).eps
def l1_cross_distances(X):
"""
Computes the nonzero componentwise L1 cross-distances between the vectors
in X.
Parameters
----------
X: array_like
An array with shape (n_samples, n_features)
Returns
-------
D: array with shape (n_samples * (n_samples - 1) / 2, n_features)
The array of componentwise L1 cross-distances.
ij: arrays with shape (n_samples * (n_samples - 1) / 2, 2)
The indices i and j of the vectors in X associated to the cross-
distances in D: D[k] = np.abs(X[ij[k, 0]] - Y[ij[k, 1]]).
"""
X = check_array(X)
n_samples, n_features = X.shape
n_nonzero_cross_dist = n_samples * (n_samples - 1) // 2
ij = np.zeros((n_nonzero_cross_dist, 2), dtype=np.int)
D = np.zeros((n_nonzero_cross_dist, n_features))
ll_1 = 0
for k in range(n_samples - 1):
ll_0 = ll_1
ll_1 = ll_0 + n_samples - k - 1
ij[ll_0:ll_1, 0] = k
ij[ll_0:ll_1, 1] = np.arange(k + 1, n_samples)
D[ll_0:ll_1] = np.abs(X[k] - X[(k + 1):n_samples])
return D, ij
class GaussianProcess(BaseEstimator, RegressorMixin):
"""The Gaussian Process model class.
Read more in the :ref:`User Guide <gaussian_process>`.
Parameters
----------
regr : string or callable, optional
A regression function returning an array of outputs of the linear
regression functional basis. The number of observations n_samples
should be greater than the size p of this basis.
Default assumes a simple constant regression trend.
Available built-in regression models are::
'constant', 'linear', 'quadratic'
corr : string or callable, optional
A stationary autocorrelation function returning the autocorrelation
between two points x and x'.
Default assumes a squared-exponential autocorrelation model.
Built-in correlation models are::
'absolute_exponential', 'squared_exponential',
'generalized_exponential', 'cubic', 'linear'
beta0 : double array_like, optional
The regression weight vector to perform Ordinary Kriging (OK).
Default assumes Universal Kriging (UK) so that the vector beta of
regression weights is estimated using the maximum likelihood
principle.
storage_mode : string, optional
A string specifying whether the Cholesky decomposition of the
correlation matrix should be stored in the class (storage_mode =
'full') or not (storage_mode = 'light').
Default assumes storage_mode = 'full', so that the
Cholesky decomposition of the correlation matrix is stored.
This might be a useful parameter when one is not interested in the
MSE and only plan to estimate the BLUP, for which the correlation
matrix is not required.
verbose : boolean, optional
A boolean specifying the verbose level.
Default is verbose = False.
theta0 : double array_like, optional
An array with shape (n_features, ) or (1, ).
The parameters in the autocorrelation model.
If thetaL and thetaU are also specified, theta0 is considered as
the starting point for the maximum likelihood estimation of the
best set of parameters.
Default assumes isotropic autocorrelation model with theta0 = 1e-1.
thetaL : double array_like, optional
An array with shape matching theta0's.
Lower bound on the autocorrelation parameters for maximum
likelihood estimation.
Default is None, so that it skips maximum likelihood estimation and
it uses theta0.
thetaU : double array_like, optional
An array with shape matching theta0's.
Upper bound on the autocorrelation parameters for maximum
likelihood estimation.
Default is None, so that it skips maximum likelihood estimation and
it uses theta0.
normalize : boolean, optional
Input X and observations y are centered and reduced wrt
means and standard deviations estimated from the n_samples
observations provided.
Default is normalize = True so that data is normalized to ease
maximum likelihood estimation.
nugget : double or ndarray, optional
Introduce a nugget effect to allow smooth predictions from noisy
data. If nugget is an ndarray, it must be the same length as the
number of data points used for the fit.
The nugget is added to the diagonal of the assumed training covariance;
in this way it acts as a Tikhonov regularization in the problem. In
the special case of the squared exponential correlation function, the
nugget mathematically represents the variance of the input values.
Default assumes a nugget close to machine precision for the sake of
robustness (nugget = 10. * MACHINE_EPSILON).
optimizer : string, optional
A string specifying the optimization algorithm to be used.
Default uses 'fmin_cobyla' algorithm from scipy.optimize.
Available optimizers are::
'fmin_cobyla', 'Welch'
'Welch' optimizer is dued to Welch et al., see reference [WBSWM1992]_.
It consists in iterating over several one-dimensional optimizations
instead of running one single multi-dimensional optimization.
random_start : int, optional
The number of times the Maximum Likelihood Estimation should be
performed from a random starting point.
The first MLE always uses the specified starting point (theta0),
the next starting points are picked at random according to an
exponential distribution (log-uniform on [thetaL, thetaU]).
Default does not use random starting point (random_start = 1).
random_state: integer or numpy.RandomState, optional
The generator used to shuffle the sequence of coordinates of theta in
the Welch optimizer. If an integer is given, it fixes the seed.
Defaults to the global numpy random number generator.
Attributes
----------
theta_ : array
Specified theta OR the best set of autocorrelation parameters (the \
sought maximizer of the reduced likelihood function).
reduced_likelihood_function_value_ : array
The optimal reduced likelihood function value.
Examples
--------
>>> import numpy as np
>>> from sklearn.gaussian_process import GaussianProcess
>>> X = np.array([[1., 3., 5., 6., 7., 8.]]).T
>>> y = (X * np.sin(X)).ravel()
>>> gp = GaussianProcess(theta0=0.1, thetaL=.001, thetaU=1.)
>>> gp.fit(X, y) # doctest: +ELLIPSIS
GaussianProcess(beta0=None...
...
Notes
-----
The presentation implementation is based on a translation of the DACE
Matlab toolbox, see reference [NLNS2002]_.
References
----------
.. [NLNS2002] `H.B. Nielsen, S.N. Lophaven, H. B. Nielsen and J.
Sondergaard. DACE - A MATLAB Kriging Toolbox.` (2002)
http://www2.imm.dtu.dk/~hbn/dace/dace.pdf
.. [WBSWM1992] `W.J. Welch, R.J. Buck, J. Sacks, H.P. Wynn, T.J. Mitchell,
and M.D. Morris (1992). Screening, predicting, and computer
experiments. Technometrics, 34(1) 15--25.`
http://www.jstor.org/pss/1269548
"""
_regression_types = {
'constant': regression.constant,
'linear': regression.linear,
'quadratic': regression.quadratic}
_correlation_types = {
'absolute_exponential': correlation.absolute_exponential,
'squared_exponential': correlation.squared_exponential,
'generalized_exponential': correlation.generalized_exponential,
'cubic': correlation.cubic,
'linear': correlation.linear}
_optimizer_types = [
'fmin_cobyla',
'Welch']
def __init__(self, regr='constant', corr='squared_exponential', beta0=None,
storage_mode='full', verbose=False, theta0=1e-1,
thetaL=None, thetaU=None, optimizer='fmin_cobyla',
random_start=1, normalize=True,
nugget=10. * MACHINE_EPSILON, random_state=None):
self.regr = regr
self.corr = corr
self.beta0 = beta0
self.storage_mode = storage_mode
self.verbose = verbose
self.theta0 = theta0
self.thetaL = thetaL
self.thetaU = thetaU
self.normalize = normalize
self.nugget = nugget
self.optimizer = optimizer
self.random_start = random_start
self.random_state = random_state
def fit(self, X, y):
"""
The Gaussian Process model fitting method.
Parameters
----------
X : double array_like
An array with shape (n_samples, n_features) with the input at which
observations were made.
y : double array_like
An array with shape (n_samples, ) or shape (n_samples, n_targets)
with the observations of the output to be predicted.
Returns
-------
gp : self
A fitted Gaussian Process model object awaiting data to perform
predictions.
"""
# Run input checks
self._check_params()
self.random_state = check_random_state(self.random_state)
# Force data to 2D numpy.array
X, y = check_X_y(X, y, multi_output=True, y_numeric=True)
self.y_ndim_ = y.ndim
if y.ndim == 1:
y = y[:, np.newaxis]
# Check shapes of DOE & observations
n_samples, n_features = X.shape
_, n_targets = y.shape
# Run input checks
self._check_params(n_samples)
# Normalize data or don't
if self.normalize:
X_mean = np.mean(X, axis=0)
X_std = np.std(X, axis=0)
y_mean = np.mean(y, axis=0)
y_std = np.std(y, axis=0)
X_std[X_std == 0.] = 1.
y_std[y_std == 0.] = 1.
# center and scale X if necessary
X = (X - X_mean) / X_std
y = (y - y_mean) / y_std
else:
X_mean = np.zeros(1)
X_std = np.ones(1)
y_mean = np.zeros(1)
y_std = np.ones(1)
# Calculate matrix of distances D between samples
D, ij = l1_cross_distances(X)
if (np.min(np.sum(D, axis=1)) == 0.
and self.corr != correlation.pure_nugget):
raise Exception("Multiple input features cannot have the same"
" target value.")
# Regression matrix and parameters
F = self.regr(X)
n_samples_F = F.shape[0]
if F.ndim > 1:
p = F.shape[1]
else:
p = 1
if n_samples_F != n_samples:
raise Exception("Number of rows in F and X do not match. Most "
"likely something is going wrong with the "
"regression model.")
if p > n_samples_F:
raise Exception(("Ordinary least squares problem is undetermined "
"n_samples=%d must be greater than the "
"regression model size p=%d.") % (n_samples, p))
if self.beta0 is not None:
if self.beta0.shape[0] != p:
raise Exception("Shapes of beta0 and F do not match.")
# Set attributes
self.X = X
self.y = y
self.D = D
self.ij = ij
self.F = F
self.X_mean, self.X_std = X_mean, X_std
self.y_mean, self.y_std = y_mean, y_std
# Determine Gaussian Process model parameters
if self.thetaL is not None and self.thetaU is not None:
# Maximum Likelihood Estimation of the parameters
if self.verbose:
print("Performing Maximum Likelihood Estimation of the "
"autocorrelation parameters...")
self.theta_, self.reduced_likelihood_function_value_, par = \
self._arg_max_reduced_likelihood_function()
if np.isinf(self.reduced_likelihood_function_value_):
raise Exception("Bad parameter region. "
"Try increasing upper bound")
else:
# Given parameters
if self.verbose:
print("Given autocorrelation parameters. "
"Computing Gaussian Process model parameters...")
self.theta_ = self.theta0
self.reduced_likelihood_function_value_, par = \
self.reduced_likelihood_function()
if np.isinf(self.reduced_likelihood_function_value_):
raise Exception("Bad point. Try increasing theta0.")
self.beta = par['beta']
self.gamma = par['gamma']
self.sigma2 = par['sigma2']
self.C = par['C']
self.Ft = par['Ft']
self.G = par['G']
if self.storage_mode == 'light':
# Delete heavy data (it will be computed again if required)
# (it is required only when MSE is wanted in self.predict)
if self.verbose:
print("Light storage mode specified. "
"Flushing autocorrelation matrix...")
self.D = None
self.ij = None
self.F = None
self.C = None
self.Ft = None
self.G = None
return self
def predict(self, X, eval_MSE=False, batch_size=None):
"""
This function evaluates the Gaussian Process model at x.
Parameters
----------
X : array_like
An array with shape (n_eval, n_features) giving the point(s) at
which the prediction(s) should be made.
eval_MSE : boolean, optional
A boolean specifying whether the Mean Squared Error should be
evaluated or not.
Default assumes evalMSE = False and evaluates only the BLUP (mean
prediction).
batch_size : integer, optional
An integer giving the maximum number of points that can be
evaluated simultaneously (depending on the available memory).
Default is None so that all given points are evaluated at the same
time.
Returns
-------
y : array_like, shape (n_samples, ) or (n_samples, n_targets)
An array with shape (n_eval, ) if the Gaussian Process was trained
on an array of shape (n_samples, ) or an array with shape
(n_eval, n_targets) if the Gaussian Process was trained on an array
of shape (n_samples, n_targets) with the Best Linear Unbiased
Prediction at x.
MSE : array_like, optional (if eval_MSE == True)
An array with shape (n_eval, ) or (n_eval, n_targets) as with y,
with the Mean Squared Error at x.
"""
check_is_fitted(self, "X")
# Check input shapes
X = check_array(X)
n_eval, _ = X.shape
n_samples, n_features = self.X.shape
n_samples_y, n_targets = self.y.shape
# Run input checks
self._check_params(n_samples)
if X.shape[1] != n_features:
raise ValueError(("The number of features in X (X.shape[1] = %d) "
"should match the number of features used "
"for fit() "
"which is %d.") % (X.shape[1], n_features))
if batch_size is None:
# No memory management
# (evaluates all given points in a single batch run)
# Normalize input
X = (X - self.X_mean) / self.X_std
# Initialize output
y = np.zeros(n_eval)
if eval_MSE:
MSE = np.zeros(n_eval)
# Get pairwise componentwise L1-distances to the input training set
dx = manhattan_distances(X, Y=self.X, sum_over_features=False)
# Get regression function and correlation
f = self.regr(X)
r = self.corr(self.theta_, dx).reshape(n_eval, n_samples)
# Scaled predictor
y_ = np.dot(f, self.beta) + np.dot(r, self.gamma)
# Predictor
y = (self.y_mean + self.y_std * y_).reshape(n_eval, n_targets)
if self.y_ndim_ == 1:
y = y.ravel()
# Mean Squared Error
if eval_MSE:
C = self.C
if C is None:
# Light storage mode (need to recompute C, F, Ft and G)
if self.verbose:
print("This GaussianProcess used 'light' storage mode "
"at instantiation. Need to recompute "
"autocorrelation matrix...")
reduced_likelihood_function_value, par = \
self.reduced_likelihood_function()
self.C = par['C']
self.Ft = par['Ft']
self.G = par['G']
rt = linalg.solve_triangular(self.C, r.T, lower=True)
if self.beta0 is None:
# Universal Kriging
u = linalg.solve_triangular(self.G.T,
np.dot(self.Ft.T, rt) - f.T,
lower=True)
else:
# Ordinary Kriging
u = np.zeros((n_targets, n_eval))
MSE = np.dot(self.sigma2.reshape(n_targets, 1),
(1. - (rt ** 2.).sum(axis=0)
+ (u ** 2.).sum(axis=0))[np.newaxis, :])
MSE = np.sqrt((MSE ** 2.).sum(axis=0) / n_targets)
# Mean Squared Error might be slightly negative depending on
# machine precision: force to zero!
MSE[MSE < 0.] = 0.
if self.y_ndim_ == 1:
MSE = MSE.ravel()
return y, MSE
else:
return y
else:
# Memory management
if type(batch_size) is not int or batch_size <= 0:
raise Exception("batch_size must be a positive integer")
if eval_MSE:
y, MSE = np.zeros(n_eval), np.zeros(n_eval)
for k in range(max(1, n_eval / batch_size)):
batch_from = k * batch_size
batch_to = min([(k + 1) * batch_size + 1, n_eval + 1])
y[batch_from:batch_to], MSE[batch_from:batch_to] = \
self.predict(X[batch_from:batch_to],
eval_MSE=eval_MSE, batch_size=None)
return y, MSE
else:
y = np.zeros(n_eval)
for k in range(max(1, n_eval / batch_size)):
batch_from = k * batch_size
batch_to = min([(k + 1) * batch_size + 1, n_eval + 1])
y[batch_from:batch_to] = \
self.predict(X[batch_from:batch_to],
eval_MSE=eval_MSE, batch_size=None)
return y
def reduced_likelihood_function(self, theta=None):
"""
This function determines the BLUP parameters and evaluates the reduced
likelihood function for the given autocorrelation parameters theta.
Maximizing this function wrt the autocorrelation parameters theta is
equivalent to maximizing the likelihood of the assumed joint Gaussian
distribution of the observations y evaluated onto the design of
experiments X.
Parameters
----------
theta : array_like, optional
An array containing the autocorrelation parameters at which the
Gaussian Process model parameters should be determined.
Default uses the built-in autocorrelation parameters
(ie ``theta = self.theta_``).
Returns
-------
reduced_likelihood_function_value : double
The value of the reduced likelihood function associated to the
given autocorrelation parameters theta.
par : dict
A dictionary containing the requested Gaussian Process model
parameters:
sigma2
Gaussian Process variance.
beta
Generalized least-squares regression weights for
Universal Kriging or given beta0 for Ordinary
Kriging.
gamma
Gaussian Process weights.
C
Cholesky decomposition of the correlation matrix [R].
Ft
Solution of the linear equation system : [R] x Ft = F
G
QR decomposition of the matrix Ft.
"""
check_is_fitted(self, "X")
if theta is None:
# Use built-in autocorrelation parameters
theta = self.theta_
# Initialize output
reduced_likelihood_function_value = - np.inf
par = {}
# Retrieve data
n_samples = self.X.shape[0]
D = self.D
ij = self.ij
F = self.F
if D is None:
# Light storage mode (need to recompute D, ij and F)
D, ij = l1_cross_distances(self.X)
if (np.min(np.sum(D, axis=1)) == 0.
and self.corr != correlation.pure_nugget):
raise Exception("Multiple X are not allowed")
F = self.regr(self.X)
# Set up R
r = self.corr(theta, D)
R = np.eye(n_samples) * (1. + self.nugget)
R[ij[:, 0], ij[:, 1]] = r
R[ij[:, 1], ij[:, 0]] = r
# Cholesky decomposition of R
try:
C = linalg.cholesky(R, lower=True)
except linalg.LinAlgError:
return reduced_likelihood_function_value, par
# Get generalized least squares solution
Ft = linalg.solve_triangular(C, F, lower=True)
try:
Q, G = linalg.qr(Ft, econ=True)
except:
#/usr/lib/python2.6/dist-packages/scipy/linalg/decomp.py:1177:
# DeprecationWarning: qr econ argument will be removed after scipy
# 0.7. The economy transform will then be available through the
# mode='economic' argument.
Q, G = linalg.qr(Ft, mode='economic')
pass
sv = linalg.svd(G, compute_uv=False)
rcondG = sv[-1] / sv[0]
if rcondG < 1e-10:
# Check F
sv = linalg.svd(F, compute_uv=False)
condF = sv[0] / sv[-1]
if condF > 1e15:
raise Exception("F is too ill conditioned. Poor combination "
"of regression model and observations.")
else:
# Ft is too ill conditioned, get out (try different theta)
return reduced_likelihood_function_value, par
Yt = linalg.solve_triangular(C, self.y, lower=True)
if self.beta0 is None:
# Universal Kriging
beta = linalg.solve_triangular(G, np.dot(Q.T, Yt))
else:
# Ordinary Kriging
beta = np.array(self.beta0)
rho = Yt - np.dot(Ft, beta)
sigma2 = (rho ** 2.).sum(axis=0) / n_samples
# The determinant of R is equal to the squared product of the diagonal
# elements of its Cholesky decomposition C
detR = (np.diag(C) ** (2. / n_samples)).prod()
# Compute/Organize output
reduced_likelihood_function_value = - sigma2.sum() * detR
par['sigma2'] = sigma2 * self.y_std ** 2.
par['beta'] = beta
par['gamma'] = linalg.solve_triangular(C.T, rho)
par['C'] = C
par['Ft'] = Ft
par['G'] = G
return reduced_likelihood_function_value, par
def _arg_max_reduced_likelihood_function(self):
"""
This function estimates the autocorrelation parameters theta as the
maximizer of the reduced likelihood function.
(Minimization of the opposite reduced likelihood function is used for
convenience)
Parameters
----------
self : All parameters are stored in the Gaussian Process model object.
Returns
-------
optimal_theta : array_like
The best set of autocorrelation parameters (the sought maximizer of
the reduced likelihood function).
optimal_reduced_likelihood_function_value : double
The optimal reduced likelihood function value.
optimal_par : dict
The BLUP parameters associated to thetaOpt.
"""
# Initialize output
best_optimal_theta = []
best_optimal_rlf_value = []
best_optimal_par = []
if self.verbose:
print("The chosen optimizer is: " + str(self.optimizer))
if self.random_start > 1:
print(str(self.random_start) + " random starts are required.")
percent_completed = 0.
# Force optimizer to fmin_cobyla if the model is meant to be isotropic
if self.optimizer == 'Welch' and self.theta0.size == 1:
self.optimizer = 'fmin_cobyla'
if self.optimizer == 'fmin_cobyla':
def minus_reduced_likelihood_function(log10t):
return - self.reduced_likelihood_function(
theta=10. ** log10t)[0]
constraints = []
for i in range(self.theta0.size):
constraints.append(lambda log10t, i=i:
log10t[i] - np.log10(self.thetaL[0, i]))
constraints.append(lambda log10t, i=i:
np.log10(self.thetaU[0, i]) - log10t[i])
for k in range(self.random_start):
if k == 0:
# Use specified starting point as first guess
theta0 = self.theta0
else:
# Generate a random starting point log10-uniformly
# distributed between bounds
log10theta0 = (np.log10(self.thetaL)
+ self.random_state.rand(*self.theta0.shape)
* np.log10(self.thetaU / self.thetaL))
theta0 = 10. ** log10theta0
# Run Cobyla
try:
log10_optimal_theta = \
optimize.fmin_cobyla(minus_reduced_likelihood_function,
np.log10(theta0).ravel(), constraints,
iprint=0)
except ValueError as ve:
print("Optimization failed. Try increasing the ``nugget``")
raise ve
optimal_theta = 10. ** log10_optimal_theta
optimal_rlf_value, optimal_par = \
self.reduced_likelihood_function(theta=optimal_theta)
# Compare the new optimizer to the best previous one
if k > 0:
if optimal_rlf_value > best_optimal_rlf_value:
best_optimal_rlf_value = optimal_rlf_value
best_optimal_par = optimal_par
best_optimal_theta = optimal_theta
else:
best_optimal_rlf_value = optimal_rlf_value
best_optimal_par = optimal_par
best_optimal_theta = optimal_theta
if self.verbose and self.random_start > 1:
if (20 * k) / self.random_start > percent_completed:
percent_completed = (20 * k) / self.random_start
print("%s completed" % (5 * percent_completed))
optimal_rlf_value = best_optimal_rlf_value
optimal_par = best_optimal_par
optimal_theta = best_optimal_theta
elif self.optimizer == 'Welch':
# Backup of the given atrributes
theta0, thetaL, thetaU = self.theta0, self.thetaL, self.thetaU
corr = self.corr
verbose = self.verbose
# This will iterate over fmin_cobyla optimizer
self.optimizer = 'fmin_cobyla'
self.verbose = False
# Initialize under isotropy assumption
if verbose:
print("Initialize under isotropy assumption...")
self.theta0 = check_array(self.theta0.min())
self.thetaL = check_array(self.thetaL.min())
self.thetaU = check_array(self.thetaU.max())
theta_iso, optimal_rlf_value_iso, par_iso = \
self._arg_max_reduced_likelihood_function()
optimal_theta = theta_iso + np.zeros(theta0.shape)
# Iterate over all dimensions of theta allowing for anisotropy
if verbose:
print("Now improving allowing for anisotropy...")
for i in self.random_state.permutation(theta0.size):
if verbose:
print("Proceeding along dimension %d..." % (i + 1))
self.theta0 = check_array(theta_iso)
self.thetaL = check_array(thetaL[0, i])
self.thetaU = check_array(thetaU[0, i])
def corr_cut(t, d):
return corr(check_array(np.hstack([optimal_theta[0][0:i],
t[0],
optimal_theta[0][(i +
1)::]])),
d)
self.corr = corr_cut
optimal_theta[0, i], optimal_rlf_value, optimal_par = \
self._arg_max_reduced_likelihood_function()
# Restore the given atrributes
self.theta0, self.thetaL, self.thetaU = theta0, thetaL, thetaU
self.corr = corr
self.optimizer = 'Welch'
self.verbose = verbose
else:
raise NotImplementedError("This optimizer ('%s') is not "
"implemented yet. Please contribute!"
% self.optimizer)
return optimal_theta, optimal_rlf_value, optimal_par
def _check_params(self, n_samples=None):
# Check regression model
if not callable(self.regr):
if self.regr in self._regression_types:
self.regr = self._regression_types[self.regr]
else:
raise ValueError("regr should be one of %s or callable, "
"%s was given."
% (self._regression_types.keys(), self.regr))
# Check regression weights if given (Ordinary Kriging)
if self.beta0 is not None:
self.beta0 = check_array(self.beta0)
if self.beta0.shape[1] != 1:
# Force to column vector
self.beta0 = self.beta0.T
# Check correlation model
if not callable(self.corr):
if self.corr in self._correlation_types:
self.corr = self._correlation_types[self.corr]
else:
raise ValueError("corr should be one of %s or callable, "
"%s was given."
% (self._correlation_types.keys(), self.corr))
# Check storage mode
if self.storage_mode != 'full' and self.storage_mode != 'light':
raise ValueError("Storage mode should either be 'full' or "
"'light', %s was given." % self.storage_mode)
# Check correlation parameters
self.theta0 = check_array(self.theta0)
lth = self.theta0.size
if self.thetaL is not None and self.thetaU is not None:
self.thetaL = check_array(self.thetaL)
self.thetaU = check_array(self.thetaU)
if self.thetaL.size != lth or self.thetaU.size != lth:
raise ValueError("theta0, thetaL and thetaU must have the "
"same length.")
if np.any(self.thetaL <= 0) or np.any(self.thetaU < self.thetaL):
raise ValueError("The bounds must satisfy O < thetaL <= "
"thetaU.")
elif self.thetaL is None and self.thetaU is None:
if np.any(self.theta0 <= 0):
raise ValueError("theta0 must be strictly positive.")
elif self.thetaL is None or self.thetaU is None:
raise ValueError("thetaL and thetaU should either be both or "
"neither specified.")
# Force verbose type to bool
self.verbose = bool(self.verbose)
# Force normalize type to bool
self.normalize = bool(self.normalize)
# Check nugget value
self.nugget = np.asarray(self.nugget)
if np.any(self.nugget) < 0.:
raise ValueError("nugget must be positive or zero.")
if (n_samples is not None
and self.nugget.shape not in [(), (n_samples,)]):
raise ValueError("nugget must be either a scalar "
"or array of length n_samples.")
# Check optimizer
if self.optimizer not in self._optimizer_types:
raise ValueError("optimizer should be one of %s"
% self._optimizer_types)
# Force random_start type to int
self.random_start = int(self.random_start)
| bsd-3-clause |
bajibabu/merlin | src/frontend/label_normalisation.py | 1 | 40966 |
import os
import numpy, re, sys
from multiprocessing import Pool
from io_funcs.binary_io import BinaryIOCollection
from .linguistic_base import LinguisticBase
import matplotlib.mlab as mlab
import math
import logging
# from logplot.logging_plotting import LoggerPlotter #, MultipleTimeSeriesPlot, SingleWeightMatrixPlot
class LabelNormalisation(LinguisticBase):
# this class only knows how to deal with a single style of labels (XML or HTS)
# (to deal with composite labels, use LabelComposer instead)
def __init__(self, question_file_name=None,xpath_file_name=None):
pass
def extract_linguistic_features(self, in_file_name, out_file_name=None, label_type="state_align", dur_file_name=None):
if label_type=="phone_align":
A = self.load_labels_with_phone_alignment(in_file_name, dur_file_name)
elif label_type=="state_align":
A = self.load_labels_with_state_alignment(in_file_name)
else:
logger.critical("we don't support %s labels as of now!!" % (label_type))
if out_file_name:
io_funcs = BinaryIOCollection()
io_funcs.array_to_binary_file(A, out_file_name)
else:
return A
# -----------------------------
class HTSLabelNormalisation(LabelNormalisation):
"""This class is to convert HTS format labels into continous or binary values, and store as binary format with float32 precision.
The class supports two kinds of questions: QS and CQS.
**QS**: is the same as that used in HTS
**CQS**: is the new defined question in the system. Here is an example of the question: CQS C-Syl-Tone {_(\d+)+}. regular expression is used for continous values.
Time alignments are expected in the HTS labels. Here is an example of the HTS labels:
3050000 3100000 xx~#-p+l=i:1_4/A/0_0_0/B/1-1-4:1-1&1-4#1-3$1-4>0-1<0-1|i/C/1+1+3/D/0_0/E/content+1:1+3&1+2#0+1/F/content_1/G/0_0/H/4=3:1=1&L-L%/I/0_0/J/4+3-1[2]
3100000 3150000 xx~#-p+l=i:1_4/A/0_0_0/B/1-1-4:1-1&1-4#1-3$1-4>0-1<0-1|i/C/1+1+3/D/0_0/E/content+1:1+3&1+2#0+1/F/content_1/G/0_0/H/4=3:1=1&L-L%/I/0_0/J/4+3-1[3]
3150000 3250000 xx~#-p+l=i:1_4/A/0_0_0/B/1-1-4:1-1&1-4#1-3$1-4>0-1<0-1|i/C/1+1+3/D/0_0/E/content+1:1+3&1+2#0+1/F/content_1/G/0_0/H/4=3:1=1&L-L%/I/0_0/J/4+3-1[4]
3250000 3350000 xx~#-p+l=i:1_4/A/0_0_0/B/1-1-4:1-1&1-4#1-3$1-4>0-1<0-1|i/C/1+1+3/D/0_0/E/content+1:1+3&1+2#0+1/F/content_1/G/0_0/H/4=3:1=1&L-L%/I/0_0/J/4+3-1[5]
3350000 3900000 xx~#-p+l=i:1_4/A/0_0_0/B/1-1-4:1-1&1-4#1-3$1-4>0-1<0-1|i/C/1+1+3/D/0_0/E/content+1:1+3&1+2#0+1/F/content_1/G/0_0/H/4=3:1=1&L-L%/I/0_0/J/4+3-1[6]
305000 310000 are the starting and ending time.
[2], [3], [4], [5], [6] mean the HMM state index.
"""
# this subclass support HTS labels, which include time alignments
def __init__(self, question_file_name=None, add_frame_features=True, subphone_feats='full', continuous_flag=True):
logger = logging.getLogger("labels")
self.question_dict = {}
self.ori_question_dict = {}
self.dict_size = 0
self.continuous_flag = continuous_flag
try:
# self.question_dict, self.ori_question_dict = self.load_question_set(question_file_name)
self.discrete_dict, self.continuous_dict = self.load_question_set_continous(question_file_name)
except:
logger.critical('error whilst loading HTS question set')
raise
###self.dict_size = len(self.question_dict)
self.dict_size = len(self.discrete_dict) + len(self.continuous_dict)
self.add_frame_features = add_frame_features
self.subphone_feats = subphone_feats
if self.subphone_feats == 'full':
self.frame_feature_size = 9 ## zhizheng's original 5 state features + 4 phoneme features
elif self.subphone_feats == 'minimal_frame':
self.frame_feature_size = 2 ## the minimal features necessary to go from a state-level to frame-level model
elif self.subphone_feats == 'state_only':
self.frame_feature_size = 1 ## this is equivalent to a state-based system
elif self.subphone_feats == 'none':
self.frame_feature_size = 0 ## the phoneme level features only
elif self.subphone_feats == 'frame_only':
self.frame_feature_size = 1 ## this is equivalent to a frame-based system without relying on state-features
elif self.subphone_feats == 'uniform_state':
self.frame_feature_size = 2 ## this is equivalent to a frame-based system with uniform state-features
elif self.subphone_feats == 'minimal_phoneme':
self.frame_feature_size = 3 ## this is equivalent to a frame-based system with minimal features
elif self.subphone_feats == 'coarse_coding':
self.frame_feature_size = 4 ## this is equivalent to a frame-based positioning system reported in Heiga Zen's work
self.cc_features = self.compute_coarse_coding_features(3)
else:
sys.exit('Unknown value for subphone_feats: %s'%(subphone_feats))
self.dimension = self.dict_size + self.frame_feature_size
### if user wants to define their own input, simply set the question set to empty.
if self.dict_size == 0:
self.dimension = 0
logger.debug('HTS-derived input feature dimension is %d + %d = %d' % (self.dict_size, self.frame_feature_size, self.dimension) )
def prepare_dur_data(self, ori_file_list, output_file_list, label_type="state_align", feature_type=None, unit_size=None, feat_size=None):
'''
extracting duration binary features or numerical features.
'''
logger = logging.getLogger("dur")
utt_number = len(ori_file_list)
if utt_number != len(output_file_list):
print("the number of input and output files should be the same!\n");
sys.exit(1)
### set default feature type to numerical, if not assigned ###
if not feature_type:
feature_type = "numerical"
### set default unit size to state, if not assigned ###
if not unit_size:
unit_size = "state"
if label_type=="phone_align":
unit_size = "phoneme"
### set default feat size to frame or phoneme, if not assigned ###
if feature_type=="binary":
if not feat_size:
feat_size = "frame"
elif feature_type=="numerical":
if not feat_size:
feat_size = "phoneme"
else:
logger.critical("Unknown feature type: %s \n Please use one of the following: binary, numerical\n" %(feature_type))
sys.exit(1)
for i in range(utt_number):
self.extract_dur_features(ori_file_list[i], output_file_list[i], label_type, feature_type, unit_size, feat_size)
def extract_dur_features(self, in_file_name, out_file_name=None, label_type="state_align", feature_type=None, unit_size=None, feat_size=None):
logger = logging.getLogger("dur")
if label_type=="phone_align":
A = self.extract_dur_from_phone_alignment_labels(in_file_name, feature_type, unit_size, feat_size)
elif label_type=="state_align":
A = self.extract_dur_from_state_alignment_labels(in_file_name, feature_type, unit_size, feat_size)
else:
logger.critical("we don't support %s labels as of now!!" % (label_type))
sys.exit(1)
if out_file_name:
io_funcs = BinaryIOCollection()
io_funcs.array_to_binary_file(A, out_file_name)
else:
return A
def extract_dur_from_state_alignment_labels(self, file_name, feature_type, unit_size, feat_size):
logger = logging.getLogger("dur")
state_number = 5
dur_dim = state_number
if feature_type=="binary":
dur_feature_matrix = numpy.empty((100000, 1))
elif feature_type=="numerical":
if unit_size=="state":
dur_feature_matrix = numpy.empty((100000, dur_dim))
current_dur_array = numpy.zeros((dur_dim, 1))
elif unit_size=="phoneme":
dur_feature_matrix = numpy.empty((100000, 1))
fid = open(file_name)
utt_labels = fid.readlines()
fid.close()
label_number = len(utt_labels)
logger.info('loaded %s, %3d labels' % (file_name, label_number) )
current_index = 0
dur_feature_index = 0
for line in utt_labels:
line = line.strip()
if len(line) < 1:
continue
temp_list = re.split('\s+', line)
start_time = int(temp_list[0])
end_time = int(temp_list[1])
full_label = temp_list[2]
full_label_length = len(full_label) - 3 # remove state information [k]
state_index = full_label[full_label_length + 1]
state_index = int(state_index) - 1
frame_number = int((end_time - start_time)/50000)
if state_index == 1:
phone_duration = frame_number
for i in range(state_number - 1):
line = utt_labels[current_index + i + 1].strip()
temp_list = re.split('\s+', line)
phone_duration += int((int(temp_list[1]) - int(temp_list[0]))/50000)
if feature_type == "binary":
current_block_array = numpy.zeros((frame_number, 1))
if unit_size == "state":
current_block_array[-1] = 1
elif unit_size == "phoneme":
if state_index == state_number:
current_block_array[-1] = 1
else:
logger.critical("Unknown unit size: %s \n Please use one of the following: state, phoneme\n" %(unit_size))
sys.exit(1)
elif feature_type == "numerical":
if unit_size == "state":
current_dur_array[current_index%5] = frame_number
if feat_size == "phoneme" and state_index == state_number:
current_block_array = current_dur_array.transpose()
if feat_size == "frame":
current_block_array = numpy.tile(current_dur_array.transpose(), (frame_number, 1))
elif unit_size == "phoneme":
current_block_array = numpy.array([phone_duration])
### writing into dur_feature_matrix ###
if feat_size == "frame":
dur_feature_matrix[dur_feature_index:dur_feature_index+frame_number,] = current_block_array
dur_feature_index = dur_feature_index + frame_number
elif feat_size == "phoneme" and state_index == state_number:
dur_feature_matrix[dur_feature_index:dur_feature_index+1,] = current_block_array
dur_feature_index = dur_feature_index + 1
current_index += 1
dur_feature_matrix = dur_feature_matrix[0:dur_feature_index,]
logger.debug('made duration matrix of %d frames x %d features' % dur_feature_matrix.shape )
return dur_feature_matrix
def extract_dur_from_phone_alignment_labels(self, file_name, feature_type, unit_size, feat_size):
logger = logging.getLogger("dur")
dur_dim = 1
if feature_type=="binary":
dur_feature_matrix = numpy.empty((100000, 1))
elif feature_type=="numerical":
if unit_size=="phoneme":
dur_feature_matrix = numpy.empty((100000, 1))
fid = open(file_name)
utt_labels = fid.readlines()
fid.close()
label_number = len(utt_labels)
logger.info('loaded %s, %3d labels' % (file_name, label_number) )
current_index = 0
dur_feature_index = 0
for line in utt_labels:
line = line.strip()
if len(line) < 1:
continue
temp_list = re.split('\s+', line)
start_time = int(temp_list[0])
end_time = int(temp_list[1])
full_label = temp_list[2]
frame_number = int((end_time - start_time)/50000)
phone_duration = frame_number
if feature_type == "binary":
current_block_array = numpy.zeros((frame_number, 1))
if unit_size == "phoneme":
current_block_array[-1] = 1
else:
logger.critical("Unknown unit size: %s \n Please use one of the following: phoneme\n" %(unit_size))
sys.exit(1)
elif feature_type == "numerical":
if unit_size == "phoneme":
current_block_array = numpy.array([phone_duration])
### writing into dur_feature_matrix ###
if feat_size == "frame":
dur_feature_matrix[dur_feature_index:dur_feature_index+frame_number,] = current_block_array
dur_feature_index = dur_feature_index + frame_number
elif feat_size == "phoneme":
dur_feature_matrix[dur_feature_index:dur_feature_index+1,] = current_block_array
dur_feature_index = dur_feature_index + 1
current_index += 1
dur_feature_matrix = dur_feature_matrix[0:dur_feature_index,]
logger.debug('made duration matrix of %d frames x %d features' % dur_feature_matrix.shape )
return dur_feature_matrix
def load_labels_with_phone_alignment(self, file_name, dur_file_name):
# this is not currently used ??? -- it works now :D
logger = logging.getLogger("labels")
#logger.critical('unused function ???')
#raise Exception
if dur_file_name:
io_funcs = BinaryIOCollection()
dur_dim = 1 ## hard coded for now
manual_dur_data = io_funcs.load_binary_file(dur_file_name, dur_dim)
if self.add_frame_features:
assert self.dimension == self.dict_size+self.frame_feature_size
elif self.subphone_feats != 'none':
assert self.dimension == self.dict_size+self.frame_feature_size
else:
assert self.dimension == self.dict_size
label_feature_matrix = numpy.empty((100000, self.dimension))
ph_count=0
label_feature_index = 0
fid = open(file_name)
for line in fid.readlines():
line = line.strip()
if len(line) < 1:
continue
temp_list = re.split('\s+', line)
if len(temp_list)==1:
frame_number = 0
full_label = temp_list[0]
else:
start_time = int(temp_list[0])
end_time = int(temp_list[1])
full_label = temp_list[2]
# to do - support different frame shift - currently hardwired to 5msec
# currently under beta testing: support different frame shift
if dur_file_name:
frame_number = manual_dur_data[ph_count]
else:
frame_number = int((end_time - start_time)/50000)
if self.subphone_feats == "coarse_coding":
cc_feat_matrix = self.extract_coarse_coding_features_relative(frame_number)
ph_count = ph_count+1
#label_binary_vector = self.pattern_matching(full_label)
label_binary_vector = self.pattern_matching_binary(full_label)
# if there is no CQS question, the label_continuous_vector will become to empty
label_continuous_vector = self.pattern_matching_continous_position(full_label)
label_vector = numpy.concatenate([label_binary_vector, label_continuous_vector], axis = 1)
if self.add_frame_features:
current_block_binary_array = numpy.zeros((frame_number, self.dict_size+self.frame_feature_size))
for i in range(frame_number):
current_block_binary_array[i, 0:self.dict_size] = label_vector
if self.subphone_feats == 'minimal_phoneme':
## features which distinguish frame position in phoneme
current_block_binary_array[i, self.dict_size] = float(i+1)/float(frame_number) # fraction through phone forwards
current_block_binary_array[i, self.dict_size+1] = float(frame_number - i)/float(frame_number) # fraction through phone backwards
current_block_binary_array[i, self.dict_size+2] = float(frame_number) # phone duration
elif self.subphone_feats == 'coarse_coding':
## features which distinguish frame position in phoneme using three continous numerical features
current_block_binary_array[i, self.dict_size+0] = cc_feat_matrix[i, 0]
current_block_binary_array[i, self.dict_size+1] = cc_feat_matrix[i, 1]
current_block_binary_array[i, self.dict_size+2] = cc_feat_matrix[i, 2]
current_block_binary_array[i, self.dict_size+3] = float(frame_number)
elif self.subphone_feats == 'none':
pass
else:
sys.exit('unknown subphone_feats type')
label_feature_matrix[label_feature_index:label_feature_index+frame_number,] = current_block_binary_array
label_feature_index = label_feature_index + frame_number
elif self.subphone_feats == 'none':
current_block_binary_array = label_vector
label_feature_matrix[label_feature_index:label_feature_index+1,] = current_block_binary_array
label_feature_index = label_feature_index + 1
fid.close()
label_feature_matrix = label_feature_matrix[0:label_feature_index,]
logger.info('loaded %s, %3d labels' % (file_name, ph_count) )
logger.debug('made label matrix of %d frames x %d labels' % label_feature_matrix.shape )
return label_feature_matrix
def load_labels_with_state_alignment(self, file_name):
## setting add_frame_features to False performs either state/phoneme level normalisation
logger = logging.getLogger("labels")
if self.add_frame_features:
assert self.dimension == self.dict_size+self.frame_feature_size
elif self.subphone_feats != 'none':
assert self.dimension == self.dict_size+self.frame_feature_size
else:
assert self.dimension == self.dict_size
# label_feature_matrix = numpy.empty((100000, self.dict_size+self.frame_feature_size))
label_feature_matrix = numpy.empty((100000, self.dimension))
label_feature_index = 0
state_number = 5
lab_binary_vector = numpy.zeros((1, self.dict_size))
fid = open(file_name)
utt_labels = fid.readlines()
fid.close()
current_index = 0
label_number = len(utt_labels)
logger.info('loaded %s, %3d labels' % (file_name, label_number) )
phone_duration = 0
state_duration_base = 0
for line in utt_labels:
line = line.strip()
if len(line) < 1:
continue
temp_list = re.split('\s+', line)
if len(temp_list)==1:
frame_number = 0
state_index = 1
full_label = temp_list[0]
else:
start_time = int(temp_list[0])
end_time = int(temp_list[1])
frame_number = int((end_time - start_time)/50000)
full_label = temp_list[2]
full_label_length = len(full_label) - 3 # remove state information [k]
state_index = full_label[full_label_length + 1]
state_index = int(state_index) - 1
state_index_backward = 6 - state_index
full_label = full_label[0:full_label_length]
if state_index == 1:
current_frame_number = 0
phone_duration = frame_number
state_duration_base = 0
# label_binary_vector = self.pattern_matching(full_label)
label_binary_vector = self.pattern_matching_binary(full_label)
# if there is no CQS question, the label_continuous_vector will become to empty
label_continuous_vector = self.pattern_matching_continous_position(full_label)
label_vector = numpy.concatenate([label_binary_vector, label_continuous_vector], axis = 1)
if len(temp_list)==1:
state_index = state_number
else:
for i in range(state_number - 1):
line = utt_labels[current_index + i + 1].strip()
temp_list = re.split('\s+', line)
phone_duration += int((int(temp_list[1]) - int(temp_list[0]))/50000)
if self.subphone_feats == "coarse_coding":
cc_feat_matrix = self.extract_coarse_coding_features_relative(phone_duration)
if self.add_frame_features:
current_block_binary_array = numpy.zeros((frame_number, self.dict_size+self.frame_feature_size))
for i in range(frame_number):
current_block_binary_array[i, 0:self.dict_size] = label_vector
if self.subphone_feats == 'full':
## Zhizheng's original 9 subphone features:
current_block_binary_array[i, self.dict_size] = float(i+1) / float(frame_number) ## fraction through state (forwards)
current_block_binary_array[i, self.dict_size+1] = float(frame_number - i) / float(frame_number) ## fraction through state (backwards)
current_block_binary_array[i, self.dict_size+2] = float(frame_number) ## length of state in frames
current_block_binary_array[i, self.dict_size+3] = float(state_index) ## state index (counting forwards)
current_block_binary_array[i, self.dict_size+4] = float(state_index_backward) ## state index (counting backwards)
current_block_binary_array[i, self.dict_size+5] = float(phone_duration) ## length of phone in frames
current_block_binary_array[i, self.dict_size+6] = float(frame_number) / float(phone_duration) ## fraction of the phone made up by current state
current_block_binary_array[i, self.dict_size+7] = float(phone_duration - i - state_duration_base) / float(phone_duration) ## fraction through phone (backwards)
current_block_binary_array[i, self.dict_size+8] = float(state_duration_base + i + 1) / float(phone_duration) ## fraction through phone (forwards)
elif self.subphone_feats == 'state_only':
## features which only distinguish state:
current_block_binary_array[i, self.dict_size] = float(state_index) ## state index (counting forwards)
elif self.subphone_feats == 'frame_only':
## features which distinguish frame position in phoneme:
current_frame_number += 1
current_block_binary_array[i, self.dict_size] = float(current_frame_number) / float(phone_duration) ## fraction through phone (counting forwards)
elif self.subphone_feats == 'uniform_state':
## features which distinguish frame position in phoneme:
current_frame_number += 1
current_block_binary_array[i, self.dict_size] = float(current_frame_number) / float(phone_duration) ## fraction through phone (counting forwards)
new_state_index = max(1, round(float(current_frame_number)/float(phone_duration)*5))
current_block_binary_array[i, self.dict_size+1] = float(new_state_index) ## state index (counting forwards)
elif self.subphone_feats == "coarse_coding":
## features which distinguish frame position in phoneme using three continous numerical features
current_block_binary_array[i, self.dict_size+0] = cc_feat_matrix[current_frame_number, 0]
current_block_binary_array[i, self.dict_size+1] = cc_feat_matrix[current_frame_number, 1]
current_block_binary_array[i, self.dict_size+2] = cc_feat_matrix[current_frame_number, 2]
current_block_binary_array[i, self.dict_size+3] = float(phone_duration)
current_frame_number += 1
elif self.subphone_feats == 'minimal_frame':
## features which distinguish state and minimally frame position in state:
current_block_binary_array[i, self.dict_size] = float(i+1) / float(frame_number) ## fraction through state (forwards)
current_block_binary_array[i, self.dict_size+1] = float(state_index) ## state index (counting forwards)
elif self.subphone_feats == 'none':
pass
else:
sys.exit('unknown subphone_feats type')
label_feature_matrix[label_feature_index:label_feature_index+frame_number,] = current_block_binary_array
label_feature_index = label_feature_index + frame_number
elif self.subphone_feats == 'state_only' and state_index == state_number:
current_block_binary_array = numpy.zeros((state_number, self.dict_size+self.frame_feature_size))
for i in range(state_number):
current_block_binary_array[i, 0:self.dict_size] = label_vector
current_block_binary_array[i, self.dict_size] = float(i+1) ## state index (counting forwards)
label_feature_matrix[label_feature_index:label_feature_index+state_number,] = current_block_binary_array
label_feature_index = label_feature_index + state_number
elif self.subphone_feats == 'none' and state_index == state_number:
current_block_binary_array = label_vector
label_feature_matrix[label_feature_index:label_feature_index+1,] = current_block_binary_array
label_feature_index = label_feature_index + 1
state_duration_base += frame_number
current_index += 1
label_feature_matrix = label_feature_matrix[0:label_feature_index,]
logger.debug('made label matrix of %d frames x %d labels' % label_feature_matrix.shape )
return label_feature_matrix
def extract_durational_features(self, dur_file_name=None, dur_data=None):
if dur_file_name:
io_funcs = BinaryIOCollection()
dur_dim = 1 ## hard coded for now
dur_data = io_funcs.load_binary_file(dur_file_name, dur_dim)
ph_count = len(dur_data)
total_num_of_frames = int(sum(dur_data))
duration_feature_array = numpy.zeros((total_num_of_frames, self.frame_feature_size))
frame_index=0
for i in range(ph_count):
frame_number = int(dur_data[i])
if self.subphone_feats == "coarse_coding":
cc_feat_matrix = self.extract_coarse_coding_features_relative(frame_number)
for j in range(frame_number):
duration_feature_array[frame_index, 0] = cc_feat_matrix[j, 0]
duration_feature_array[frame_index, 1] = cc_feat_matrix[j, 1]
duration_feature_array[frame_index, 2] = cc_feat_matrix[j, 2]
duration_feature_array[frame_index, 3] = float(frame_number)
frame_index+=1
return duration_feature_array
def compute_coarse_coding_features(self, num_states):
assert num_states == 3
npoints = 600
cc_features = numpy.zeros((num_states, npoints))
x1 = numpy.linspace(-1.5, 1.5, npoints)
x2 = numpy.linspace(-1.0, 2.0, npoints)
x3 = numpy.linspace(-0.5, 2.5, npoints)
mu1 = 0.0
mu2 = 0.5
mu3 = 1.0
sigma = 0.4
cc_features[0, :] = mlab.normpdf(x1, mu1, sigma)
cc_features[1, :] = mlab.normpdf(x2, mu2, sigma)
cc_features[2, :] = mlab.normpdf(x3, mu3, sigma)
return cc_features
def extract_coarse_coding_features_relative(self, phone_duration):
dur = int(phone_duration)
cc_feat_matrix = numpy.zeros((dur, 3))
for i in range(dur):
rel_indx = int((200/float(dur))*i)
cc_feat_matrix[i,0] = self.cc_features[0, 300+rel_indx]
cc_feat_matrix[i,1] = self.cc_features[1, 200+rel_indx]
cc_feat_matrix[i,2] = self.cc_features[2, 100+rel_indx]
return cc_feat_matrix
### this function is not used now
def extract_coarse_coding_features_absolute(self, phone_duration):
dur = int(phone_duration)
cc_feat_matrix = numpy.zeros((dur, 3))
npoints1 = (dur*2)*10+1
npoints2 = (dur-1)*10+1
npoints3 = (2*dur-1)*10+1
x1 = numpy.linspace(-dur, dur, npoints1)
x2 = numpy.linspace(1, dur, npoints2)
x3 = numpy.linspace(1, 2*dur-1, npoints3)
mu1 = 0
mu2 = (1+dur)/2
mu3 = dur
variance = 1
sigma = variance*((dur/10)+2)
sigma1 = sigma
sigma2 = sigma-1
sigma3 = sigma
y1 = mlab.normpdf(x1, mu1, sigma1)
y2 = mlab.normpdf(x2, mu2, sigma2)
y3 = mlab.normpdf(x3, mu3, sigma3)
for i in range(dur):
cc_feat_matrix[i,0] = y1[(dur+1+i)*10]
cc_feat_matrix[i,1] = y2[i*10]
cc_feat_matrix[i,2] = y3[i*10]
for i in range(3):
cc_feat_matrix[:,i] = cc_feat_matrix[:,i]/max(cc_feat_matrix[:,i])
return cc_feat_matrix
### this function is not used now
def pattern_matching(self, label):
# this function is where most time is spent during label preparation
#
# it might be possible to speed it up by using pre-compiled regular expressions?
# (not trying this now, since we may change to to XML tree format for input instead of HTS labels)
#
label_size = len(label)
lab_binary_vector = numpy.zeros((1, self.dict_size))
for i in range(self.dict_size):
current_question_list = self.question_dict[str(i)]
binary_flag = 0
for iq in range(len(current_question_list)):
current_question = current_question_list[iq]
current_size = len(current_question)
if current_question[0] == '*' and current_question[current_size-1] == '*':
temp_question = current_question[1:current_size-1]
for il in range(1, label_size-current_size+2):
if temp_question == label[il:il+current_size-2]:
binary_flag = 1
elif current_question[current_size-1] != '*':
temp_question = current_question[1:current_size]
if temp_question == label[label_size-current_size+1:label_size]:
binary_flag = 1
elif current_question[0] != '*':
temp_question = current_question[0:current_size-1]
if temp_question == label[0:current_size-1]:
binary_flag = 1
if binary_flag == 1:
break
lab_binary_vector[0, i] = binary_flag
return lab_binary_vector
def pattern_matching_binary(self, label):
dict_size = len(self.discrete_dict)
lab_binary_vector = numpy.zeros((1, dict_size))
for i in range(dict_size):
current_question_list = self.discrete_dict[str(i)]
binary_flag = 0
for iq in range(len(current_question_list)):
current_compiled = current_question_list[iq]
ms = current_compiled.search(label)
if ms is not None:
binary_flag = 1
break
lab_binary_vector[0, i] = binary_flag
return lab_binary_vector
def pattern_matching_continous_position(self, label):
dict_size = len(self.continuous_dict)
lab_continuous_vector = numpy.zeros((1, dict_size))
for i in range(dict_size):
continuous_value = -1.0
current_compiled = self.continuous_dict[str(i)]
ms = current_compiled.search(label)
if ms is not None:
# assert len(ms.group()) == 1
continuous_value = ms.group(1)
lab_continuous_vector[0, i] = continuous_value
return lab_continuous_vector
def load_question_set(self, qs_file_name):
fid = open(qs_file_name)
question_index = 0
question_dict = {}
ori_question_dict = {}
for line in fid.readlines():
line = line.replace('\n', '')
if len(line) > 5:
temp_list = line.split('{')
temp_line = temp_list[1]
temp_list = temp_line.split('}')
temp_line = temp_list[0]
question_list = temp_line.split(',')
question_dict[str(question_index)] = question_list
ori_question_dict[str(question_index)] = line
question_index += 1
fid.close()
logger = logging.getLogger("labels")
logger.debug('loaded question set with %d questions' % len(question_dict))
return question_dict, ori_question_dict
def load_question_set_continous(self, qs_file_name):
logger = logging.getLogger("labels")
fid = open(qs_file_name)
binary_qs_index = 0
continuous_qs_index = 0
binary_dict = {}
continuous_dict = {}
LL=re.compile(re.escape('LL-'))
for line in fid.readlines():
line = line.replace('\n', '')
if len(line) > 5:
temp_list = line.split('{')
temp_line = temp_list[1]
temp_list = temp_line.split('}')
temp_line = temp_list[0]
temp_line = temp_line.strip()
question_list = temp_line.split(',')
temp_list = line.split(' ')
question_key = temp_list[1]
# print line
if temp_list[0] == 'CQS':
assert len(question_list) == 1
processed_question = self.wildcards2regex(question_list[0], convert_number_pattern=True)
continuous_dict[str(continuous_qs_index)] = re.compile(processed_question) #save pre-compiled regular expression
continuous_qs_index = continuous_qs_index + 1
elif temp_list[0] == 'QS':
re_list = []
for temp_question in question_list:
processed_question = self.wildcards2regex(temp_question)
if LL.search(question_key):
processed_question = '^'+processed_question
re_list.append(re.compile(processed_question))
binary_dict[str(binary_qs_index)] = re_list
binary_qs_index = binary_qs_index + 1
else:
logger.critical('The question set is not defined correctly: %s' %(line))
raise Exception
# question_index = question_index + 1
return binary_dict, continuous_dict
def wildcards2regex(self, question, convert_number_pattern=False):
"""
Convert HTK-style question into regular expression for searching labels.
If convert_number_pattern, keep the following sequences unescaped for
extracting continuous values):
(\d+) -- handles digit without decimal point
([\d\.]+) -- handles digits with and without decimal point
"""
## handle HTK wildcards (and lack of them) at ends of label:
prefix = ""
postfix = ""
if '*' in question:
if not question.startswith('*'):
prefix = "\A"
if not question.endswith('*'):
postfix = "\Z"
question = question.strip('*')
question = re.escape(question)
## convert remaining HTK wildcards * and ? to equivalent regex:
question = question.replace('\\*', '.*')
question = prefix + question + postfix
if convert_number_pattern:
question = question.replace('\\(\\\\d\\+\\)', '(\d+)')
question = question.replace('\\(\\[\\\\d\\\\\\.\\]\\+\\)', '([\d\.]+)')
return question
class HTSDurationLabelNormalisation(HTSLabelNormalisation):
"""
Unlike HTSLabelNormalisation, HTSDurationLabelNormalisation does not accept timings.
One line of labels is converted into 1 datapoint, that is, the label is not 'unpacked'
into frames. HTK state index [\d] is not handled in any special way.
"""
def __init__(self, question_file_name=None, subphone_feats='full', continuous_flag=True):
super(HTSDurationLabelNormalisation, self).__init__(question_file_name=question_file_name, \
subphone_feats=subphone_feats, continuous_flag=continuous_flag)
## don't use extra features beyond those in questions for duration labels:
self.dimension = self.dict_size
def load_labels_with_state_alignment(self, file_name, add_frame_features=False):
## add_frame_features not used in HTSLabelNormalisation -- only in XML version
logger = logging.getLogger("labels")
assert self.dimension == self.dict_size
label_feature_matrix = numpy.empty((100000, self.dimension))
label_feature_index = 0
lab_binary_vector = numpy.zeros((1, self.dict_size))
fid = open(file_name)
utt_labels = fid.readlines()
fid.close()
current_index = 0
label_number = len(utt_labels)
logger.info('loaded %s, %3d labels' % (file_name, label_number) )
## remove empty lines
utt_labels = [line for line in utt_labels if line != '']
for (line_number, line) in enumerate(utt_labels):
temp_list = re.split('\s+', line.strip())
full_label = temp_list[-1] ## take last entry -- ignore timings if present
label_binary_vector = self.pattern_matching_binary(full_label)
# if there is no CQS question, the label_continuous_vector will become to empty
label_continuous_vector = self.pattern_matching_continous_position(full_label)
label_vector = numpy.concatenate([label_binary_vector, label_continuous_vector], axis = 1)
label_feature_matrix[line_number, :] = label_vector[:]
label_feature_matrix = label_feature_matrix[:line_number+1,:]
logger.debug('made label matrix of %d frames x %d labels' % label_feature_matrix.shape )
return label_feature_matrix
# -----------------------------
if __name__ == '__main__':
qs_file_name = '/afs/inf.ed.ac.uk/group/cstr/projects/blizzard_entries/blizzard2016/straight_voice/Hybrid_duration_experiments/dnn_tts_release/lstm_rnn/data/questions.hed'
print(qs_file_name)
ori_file_list = ['/afs/inf.ed.ac.uk/group/cstr/projects/blizzard_entries/blizzard2016/straight_voice/Hybrid_duration_experiments/dnn_tts_release/lstm_rnn/data/label_state_align/AMidsummerNightsDream_000_000.lab']
output_file_list = ['/afs/inf.ed.ac.uk/group/cstr/projects/blizzard_entries/blizzard2016/straight_voice/Hybrid_duration_experiments/dnn_tts_release/lstm_rnn/data/binary_label_601/AMidsummerNightsDream_000_000.lab']
#output_file_list = ['/afs/inf.ed.ac.uk/group/cstr/projects/blizzard_entries/blizzard2016/straight_voice/Hybrid_duration_experiments/dnn_tts_release/lstm_rnn/data/dur/AMidsummerNightsDream_000_000.dur']
label_operater = HTSLabelNormalisation(qs_file_name)
label_operater.perform_normalisation(ori_file_list, output_file_list)
#feature_type="binary"
#unit_size = "phoneme"
#feat_size = "phoneme"
#label_operater.prepare_dur_data(ori_file_list, output_file_list, feature_type, unit_size, feat_size)
#label_operater.prepare_dur_data(ori_file_list, output_file_list, feature_type)
print(label_operater.dimension)
| apache-2.0 |
biocore/qiita | qiita_db/test/test_processing_job.py | 3 | 50090 | # -----------------------------------------------------------------------------
# Copyright (c) 2014--, The Qiita Development Team.
#
# Distributed under the terms of the BSD 3-clause License.
#
# The full license is in the file LICENSE, distributed with this software.
# -----------------------------------------------------------------------------
from unittest import TestCase, main
from datetime import datetime
from os import close
from tempfile import mkstemp
from json import dumps, loads
from time import sleep
import networkx as nx
import pandas as pd
import qiita_db as qdb
from qiita_core.util import qiita_test_checker
def _create_job(force=True):
job = qdb.processing_job.ProcessingJob.create(
qdb.user.User('test@foo.bar'),
qdb.software.Parameters.load(
qdb.software.Command(2),
values_dict={"min_seq_len": 100, "max_seq_len": 1000,
"trim_seq_length": False, "min_qual_score": 25,
"max_ambig": 6, "max_homopolymer": 6,
"max_primer_mismatch": 0,
"barcode_type": "golay_12",
"max_barcode_errors": 1.5,
"disable_bc_correction": False,
"qual_score_window": 0, "disable_primers": False,
"reverse_primers": "disable",
"reverse_primer_mismatches": 0,
"truncate_ambi_bases": False, "input_data": 1}),
force)
return job
@qiita_test_checker()
class ProcessingJobUtilTest(TestCase):
def test_system_call(self):
obs_out, obs_err, obs_status = qdb.processing_job._system_call(
'echo "Test system call stdout"')
self.assertEqual(obs_out, "Test system call stdout\n")
self.assertEqual(obs_err, "")
self.assertEqual(obs_status, 0)
def test_system_call_error(self):
obs_out, obs_err, obs_status = qdb.processing_job._system_call(
'>&2 echo "Test system call stderr"; exit 1')
self.assertEqual(obs_out, "")
self.assertEqual(obs_err, "Test system call stderr\n")
self.assertEqual(obs_status, 1)
@qiita_test_checker()
class ProcessingJobTest(TestCase):
def setUp(self):
self.tester1 = qdb.processing_job.ProcessingJob(
"063e553b-327c-4818-ab4a-adfe58e49860")
self.tester2 = qdb.processing_job.ProcessingJob(
"bcc7ebcd-39c1-43e4-af2d-822e3589f14d")
self.tester3 = qdb.processing_job.ProcessingJob(
"b72369f9-a886-4193-8d3d-f7b504168e75")
self.tester4 = qdb.processing_job.ProcessingJob(
"d19f76ee-274e-4c1b-b3a2-a12d73507c55")
self._clean_up_files = []
def _get_all_job_ids(self):
sql = "SELECT processing_job_id FROM qiita.processing_job"
with qdb.sql_connection.TRN:
qdb.sql_connection.TRN.add(sql)
return qdb.sql_connection.TRN.execute_fetchflatten()
def _wait_for_job(self, job):
while job.status not in ('error', 'success'):
sleep(0.5)
def test_exists(self):
self.assertTrue(qdb.processing_job.ProcessingJob.exists(
"063e553b-327c-4818-ab4a-adfe58e49860"))
self.assertTrue(qdb.processing_job.ProcessingJob.exists(
"bcc7ebcd-39c1-43e4-af2d-822e3589f14d"))
self.assertTrue(qdb.processing_job.ProcessingJob.exists(
"b72369f9-a886-4193-8d3d-f7b504168e75"))
self.assertTrue(qdb.processing_job.ProcessingJob.exists(
"d19f76ee-274e-4c1b-b3a2-a12d73507c55"))
self.assertFalse(qdb.processing_job.ProcessingJob.exists(
"d19f76ee-274e-4c1b-b3a2-b12d73507c55"))
self.assertFalse(qdb.processing_job.ProcessingJob.exists(
"some-other-string"))
def test_user(self):
exp_user = qdb.user.User('test@foo.bar')
self.assertEqual(self.tester1.user, exp_user)
self.assertEqual(self.tester2.user, exp_user)
exp_user = qdb.user.User('shared@foo.bar')
self.assertEqual(self.tester3.user, exp_user)
self.assertEqual(self.tester4.user, exp_user)
def test_command(self):
cmd1 = qdb.software.Command(1)
cmd2 = qdb.software.Command(2)
cmd3 = qdb.software.Command(3)
self.assertEqual(self.tester1.command, cmd1)
self.assertEqual(self.tester2.command, cmd2)
self.assertEqual(self.tester3.command, cmd1)
self.assertEqual(self.tester4.command, cmd3)
def test_parameters(self):
json_str = (
'{"max_bad_run_length":3,"min_per_read_length_fraction":0.75,'
'"sequence_max_n":0,"rev_comp_barcode":false,'
'"rev_comp_mapping_barcodes":false,"rev_comp":false,'
'"phred_quality_threshold":3,"barcode_type":"golay_12",'
'"max_barcode_errors":1.5,"input_data":1,"phred_offset":"auto"}')
exp_params = qdb.software.Parameters.load(qdb.software.Command(1),
json_str=json_str)
self.assertEqual(self.tester1.parameters, exp_params)
json_str = (
'{"min_seq_len":100,"max_seq_len":1000,"trim_seq_length":false,'
'"min_qual_score":25,"max_ambig":6,"max_homopolymer":6,'
'"max_primer_mismatch":0,"barcode_type":"golay_12",'
'"max_barcode_errors":1.5,"disable_bc_correction":false,'
'"qual_score_window":0,"disable_primers":false,'
'"reverse_primers":"disable","reverse_primer_mismatches":0,'
'"truncate_ambi_bases":false,"input_data":1}')
exp_params = qdb.software.Parameters.load(qdb.software.Command(2),
json_str=json_str)
self.assertEqual(self.tester2.parameters, exp_params)
json_str = (
'{"max_bad_run_length":3,"min_per_read_length_fraction":0.75,'
'"sequence_max_n":0,"rev_comp_barcode":false,'
'"rev_comp_mapping_barcodes":true,"rev_comp":false,'
'"phred_quality_threshold":3,"barcode_type":"golay_12",'
'"max_barcode_errors":1.5,"input_data":1,"phred_offset":"auto"}')
exp_params = qdb.software.Parameters.load(qdb.software.Command(1),
json_str=json_str)
self.assertEqual(self.tester3.parameters, exp_params)
json_str = (
'{"reference":1,"sortmerna_e_value":1,"sortmerna_max_pos":10000,'
'"similarity":0.97,"sortmerna_coverage":0.97,"threads":1,'
'"input_data":2}')
exp_params = qdb.software.Parameters.load(qdb.software.Command(3),
json_str=json_str)
self.assertEqual(self.tester4.parameters, exp_params)
def test_input_artifacts(self):
exp = [qdb.artifact.Artifact(1)]
self.assertEqual(self.tester1.input_artifacts, exp)
self.assertEqual(self.tester2.input_artifacts, exp)
self.assertEqual(self.tester3.input_artifacts, exp)
exp = [qdb.artifact.Artifact(2)]
self.assertEqual(self.tester4.input_artifacts, exp)
def test_status(self):
self.assertEqual(self.tester1.status, 'queued')
self.assertEqual(self.tester2.status, 'running')
self.assertEqual(self.tester3.status, 'success')
self.assertEqual(self.tester4.status, 'error')
def test_submit(self):
# In order to test a success, we need to actually run the job, which
# will mean to run split libraries, for example.
# TODO: rewrite this test
pass
def test_log(self):
self.assertIsNone(self.tester1.log)
self.assertIsNone(self.tester2.log)
self.assertIsNone(self.tester3.log)
self.assertEqual(self.tester4.log, qdb.logger.LogEntry(1))
def test_heartbeat(self):
self.assertIsNone(self.tester1.heartbeat)
self.assertEqual(self.tester2.heartbeat,
datetime(2015, 11, 22, 21, 00, 00))
self.assertEqual(self.tester3.heartbeat,
datetime(2015, 11, 22, 21, 15, 00))
self.assertEqual(self.tester4.heartbeat,
datetime(2015, 11, 22, 21, 30, 00))
def test_step(self):
self.assertIsNone(self.tester1.step)
self.assertEqual(self.tester2.step, 'demultiplexing')
self.assertIsNone(self.tester3.step)
self.assertEqual(self.tester4.step, 'generating demux file')
def test_children(self):
self.assertEqual(list(self.tester1.children), [])
self.assertEqual(list(self.tester3.children), [self.tester4])
def test_update_and_launch_children(self):
# In order to test a success, we need to actually run the children
# jobs, which will mean to run split libraries, for example.
pass
def test_create(self):
exp_command = qdb.software.Command(1)
json_str = (
'{"input_data": 1, "max_barcode_errors": 1.5, '
'"barcode_type": "golay_12", "max_bad_run_length": 3, '
'"rev_comp": false, "phred_quality_threshold": 3, '
'"rev_comp_barcode": false, "rev_comp_mapping_barcodes": false, '
'"min_per_read_length_fraction": 0.75, "sequence_max_n": 0, '
'"phred_offset": "auto"}')
exp_params = qdb.software.Parameters.load(exp_command,
json_str=json_str)
exp_user = qdb.user.User('test@foo.bar')
obs = qdb.processing_job.ProcessingJob.create(
exp_user, exp_params, True)
self.assertEqual(obs.user, exp_user)
self.assertEqual(obs.command, exp_command)
self.assertEqual(obs.parameters, exp_params)
self.assertEqual(obs.status, 'in_construction')
self.assertEqual(obs.log, None)
self.assertEqual(obs.heartbeat, None)
self.assertEqual(obs.step, None)
self.assertTrue(obs in qdb.artifact.Artifact(1).jobs())
# test with paramters with '
exp_command = qdb.software.Command(1)
exp_params.values["a tests with '"] = 'this is a tests with "'
exp_params.values['a tests with "'] = "this is a tests with '"
obs = qdb.processing_job.ProcessingJob.create(
exp_user, exp_params)
self.assertEqual(obs.user, exp_user)
self.assertEqual(obs.command, exp_command)
self.assertEqual(obs.status, 'in_construction')
self.assertEqual(obs.log, None)
self.assertEqual(obs.heartbeat, None)
self.assertEqual(obs.step, None)
self.assertTrue(obs in qdb.artifact.Artifact(1).jobs())
def test_set_status(self):
job = _create_job()
self.assertEqual(job.status, 'in_construction')
job._set_status('queued')
self.assertEqual(job.status, 'queued')
job._set_status('running')
self.assertEqual(job.status, 'running')
with self.assertRaises(qdb.exceptions.QiitaDBStatusError):
job._set_status('queued')
job._set_status('error')
self.assertEqual(job.status, 'error')
job._set_status('running')
self.assertEqual(job.status, 'running')
job._set_status('success')
self.assertEqual(job.status, 'success')
with self.assertRaises(qdb.exceptions.QiitaDBStatusError):
job._set_status('running')
def test_submit_error(self):
job = _create_job()
job._set_status('queued')
with self.assertRaises(
qdb.exceptions.QiitaDBOperationNotPermittedError):
job.submit()
def test_submit_environment(self):
job = _create_job()
software = job.command.software
current = software.environment_script
# temporal update and then rollback to not commit change
with qdb.sql_connection.TRN:
sql = """UPDATE qiita.software SET environment_script = %s
WHERE software_id = %s"""
qdb.sql_connection.TRN.add(sql, [
f'{current} ENVIRONMENT', software.id])
job.submit()
self.assertEqual(job.status, 'error')
qdb.sql_connection.TRN.rollback()
def test_complete_multiple_outputs(self):
# This test performs the test of multiple functions at the same
# time. "release", "release_validators" and
# "_set_validator_jobs" are tested here for correct execution.
# Those functions are designed to work together, so it becomes
# really hard to test each of the functions individually for
# successfull execution.
# We need to create a new command with multiple outputs, since
# in the test DB there is no command with such characteristics
cmd = qdb.software.Command.create(
qdb.software.Software(1),
"TestCommand", "Test command",
{'input': ['artifact:["Demultiplexed"]', None]},
{'out1': 'BIOM', 'out2': 'BIOM'})
job = qdb.processing_job.ProcessingJob.create(
qdb.user.User('test@foo.bar'),
qdb.software.Parameters.load(
cmd,
values_dict={"input": 1}))
job._set_status("running")
fd, fp1 = mkstemp(suffix="_table.biom")
self._clean_up_files.append(fp1)
close(fd)
with open(fp1, 'w') as f:
f.write('\n')
fd, fp2 = mkstemp(suffix="_table.biom")
self._clean_up_files.append(fp2)
close(fd)
with open(fp2, 'w') as f:
f.write('\n')
# `job` has 2 output artifacts. Each of these artifacts needs to be
# validated by 2 different validation jobs. We are creating those jobs
# here, and add in the 'procenance' parameter that links the original
# jobs with the validator jobs.
params = qdb.software.Parameters.load(
qdb.software.Command(4),
values_dict={'template': 1, 'files': fp1,
'artifact_type': 'BIOM',
'provenance': dumps(
{'job': job.id,
'cmd_out_id': qdb.util.convert_to_id(
'out1', "command_output", "name"),
'name': 'out1'})})
user = qdb.user.User('test@foo.bar')
obs1 = qdb.processing_job.ProcessingJob.create(user, params, True)
obs1._set_status('running')
params = qdb.software.Parameters.load(
qdb.software.Command(4),
values_dict={'template': 1, 'files': fp2,
'artifact_type': 'BIOM',
'provenance': dumps(
{'job': job.id,
'cmd_out_id': qdb.util.convert_to_id(
'out1', "command_output", "name"),
'name': 'out1'})})
obs2 = qdb.processing_job.ProcessingJob.create(user, params, True)
obs2._set_status('running')
# Make sure that we link the original job with its validator jobs
job._set_validator_jobs([obs1, obs2])
artifact_data_1 = {'filepaths': [(fp1, 'biom')],
'artifact_type': 'BIOM'}
# Complete one of the validator jobs. This jobs should store all the
# information about the new artifact, but it does not create it. The
# job then goes to a "waiting" state, where it waits until all the
# validator jobs are completed.
obs1._complete_artifact_definition(artifact_data_1)
self.assertEqual(obs1.status, 'waiting')
self.assertEqual(job.status, 'running')
# When we complete the second validation job, the previous validation
# job is realeaed from its waiting state. All jobs then create the
# artifacts in a single transaction, so either all of them successfully
# complete, or all of them fail.
artifact_data_2 = {'filepaths': [(fp2, 'biom')],
'artifact_type': 'BIOM'}
obs2._complete_artifact_definition(artifact_data_2)
self.assertEqual(obs1.status, 'waiting')
self.assertEqual(obs2.status, 'waiting')
self.assertEqual(job.status, 'running')
job.release_validators()
self.assertEqual(obs1.status, 'success')
self.assertEqual(obs2.status, 'success')
self.assertEqual(job.status, 'success')
def test_complete_artifact_definition(self):
job = _create_job()
job._set_status('running')
fd, fp = mkstemp(suffix="_table.biom")
self._clean_up_files.append(fp)
close(fd)
with open(fp, 'w') as f:
f.write('\n')
artifact_data = {'filepaths': [(fp, 'biom')],
'artifact_type': 'BIOM'}
params = qdb.software.Parameters.load(
qdb.software.Command(4),
values_dict={'template': 1, 'files': fp,
'artifact_type': 'BIOM',
'provenance': dumps(
{'job': job.id,
'cmd_out_id': 3})}
)
obs = qdb.processing_job.ProcessingJob.create(
qdb.user.User('test@foo.bar'), params)
job._set_validator_jobs([obs])
obs._complete_artifact_definition(artifact_data)
self.assertEqual(obs.status, 'waiting')
self.assertEqual(job.status, 'running')
# Upload case implicitly tested by "test_complete_type"
def test_complete_artifact_transformation(self):
# Implicitly tested by "test_complete"
pass
def test_complete_no_artifact_data(self):
job = qdb.processing_job.ProcessingJob.create(
qdb.user.User('test@foo.bar'),
qdb.software.Parameters.load(
qdb.software.Command(5),
values_dict={"input_data": 1}))
job._set_status('running')
job.complete(True)
self.assertEqual(job.status, 'success')
job = qdb.processing_job.ProcessingJob.create(
qdb.user.User('test@foo.bar'),
qdb.software.Parameters.load(
qdb.software.Command(5),
values_dict={"input_data": 1}),
True)
job._set_status('running')
job.complete(False, error='Some Error')
self.assertEqual(job.status, 'error')
def test_complete_type(self):
fd, fp = mkstemp(suffix="_table.biom")
self._clean_up_files.append(fp)
close(fd)
with open(fp, 'w') as f:
f.write('\n')
exp_artifact_count = qdb.util.get_count('qiita.artifact') + 1
artifacts_data = {'ignored': {'filepaths': [(fp, 'biom')],
'artifact_type': 'BIOM'}}
metadata_dict = {
'SKB8.640193': {'center_name': 'ANL',
'primer': 'GTGCCAGCMGCCGCGGTAA',
'barcode': 'GTCCGCAAGTTA',
'run_prefix': "s_G1_L001_sequences",
'platform': 'Illumina',
'instrument_model': 'Illumina MiSeq',
'library_construction_protocol': 'AAAA',
'experiment_design_description': 'BBBB'}}
metadata = pd.DataFrame.from_dict(metadata_dict, orient='index',
dtype=str)
pt = qdb.metadata_template.prep_template.PrepTemplate.create(
metadata, qdb.study.Study(1), "16S")
self._clean_up_files.extend([ptfp for _, ptfp in pt.get_filepaths()])
params = qdb.software.Parameters.load(
qdb.software.Command(4),
values_dict={'template': pt.id, 'files': fp,
'artifact_type': 'BIOM'})
obs = qdb.processing_job.ProcessingJob.create(
qdb.user.User('test@foo.bar'), params, True)
obs._set_status('running')
obs.complete(True, artifacts_data=artifacts_data)
self.assertEqual(obs.status, 'success')
self.assertEqual(qdb.util.get_count('qiita.artifact'),
exp_artifact_count)
self._clean_up_files.extend(
[x['fp'] for x in
qdb.artifact.Artifact(exp_artifact_count).filepaths])
def test_complete_success(self):
# Note that here we are submitting and creating other multiple jobs;
# thus here is the best place to test any intermediary steps/functions
# of the job creation, submission, exectution, and completion.
#
# This first part of the test is just to test that by default the
# naming of the output artifact will be the name of the output
fd, fp = mkstemp(suffix='_table.biom')
self._clean_up_files.append(fp)
close(fd)
with open(fp, 'w') as f:
f.write('\n')
artifacts_data = {'demultiplexed': {'filepaths': [(fp, 'biom')],
'artifact_type': 'BIOM'}}
job = _create_job()
job._set_status('running')
# here we can test that job.release_validator_job hasn't been created
# yet so it has to be None
self.assertIsNone(job.release_validator_job)
job.complete(True, artifacts_data=artifacts_data)
self._wait_for_job(job)
# let's check for the job that released the validators
self.assertIsNotNone(job.release_validator_job)
self.assertEqual(job.release_validator_job.parameters.values['job'],
job.id)
# Retrieve the job that is performing the validation:
validators = list(job.validator_jobs)
self.assertEqual(len(validators), 1)
# the validator actually runs on the system so it gets an external_id
# assigned, let's test that is not None
self.assertFalse(validators[0].external_id == 'Not Available')
# Test the output artifact is going to be named based on the
# input parameters
self.assertEqual(
loads(validators[0].parameters.values['provenance'])['name'],
"demultiplexed")
# To test that the naming of the output artifact is based on the
# parameters that the command is indicating, we need to update the
# parameter information of the command - since the ones existing
# in the database currently do not require using any input parameter
# to name the output artifact
with qdb.sql_connection.TRN:
sql = """UPDATE qiita.command_parameter
SET name_order = %s
WHERE command_parameter_id = %s"""
# Hard-coded values; 19 -> barcode_type, 20 -> max_barcode_errors
qdb.sql_connection.TRN.add(sql, [[1, 19], [2, 20]], many=True)
qdb.sql_connection.TRN.execute()
fd, fp = mkstemp(suffix='_table.biom')
self._clean_up_files.append(fp)
close(fd)
with open(fp, 'w') as f:
f.write('\n')
artifacts_data = {'demultiplexed': {'filepaths': [(fp, 'biom')],
'artifact_type': 'BIOM'}}
job = _create_job()
job._set_status('running')
alljobs = set(self._get_all_job_ids())
job.complete(True, artifacts_data=artifacts_data)
# When completing the previous job, it creates a new job that needs
# to validate the BIOM table that is being added as new artifact.
# Hence, this job is still in running state until the validation job
# is completed. Note that this is tested by making sure that the status
# of this job is running, and that we have one more job than before
# (see assertEqual with len of all jobs)
self.assertEqual(job.status, 'running')
self.assertTrue(job.step.startswith(
'Validating outputs (1 remaining) via job(s)'))
obsjobs = set(self._get_all_job_ids())
# The complete call above submits 2 new jobs: the validator job and
# the release validators job. Hence the +2
self.assertEqual(len(obsjobs), len(alljobs) + 2)
self._wait_for_job(job)
# Retrieve the job that is performing the validation:
validators = list(job.validator_jobs)
self.assertEqual(len(validators), 1)
# here we can test that the validator shape and allocation is correct
validator = validators[0]
self.assertEqual(validator.parameters.values['artifact_type'], 'BIOM')
self.assertEqual(validator.get_resource_allocation_info(), '-q qiita '
'-l nodes=1:ppn=1 -l mem=90gb -l walltime=150:00:00')
self.assertEqual(validator.shape, (27, 31, None))
# Test the output artifact is going to be named based on the
# input parameters
self.assertEqual(
loads(validator.parameters.values['provenance'])['name'],
"demultiplexed golay_12 1.5")
def test_complete_failure(self):
job = _create_job()
job.complete(False, error="Job failure")
self.assertEqual(job.status, 'error')
self.assertEqual(job.log,
qdb.logger.LogEntry.newest_records(numrecords=1)[0])
self.assertEqual(job.log.msg, 'Job failure')
# Test the artifact definition case
job = _create_job()
job._set_status('running')
params = qdb.software.Parameters.load(
qdb.software.Command(4),
values_dict={'template': 1, 'files': 'ignored',
'artifact_type': 'BIOM',
'provenance': dumps(
{'job': job.id,
'cmd_out_id': 3})}
)
obs = qdb.processing_job.ProcessingJob.create(
qdb.user.User('test@foo.bar'), params, True)
job._set_validator_jobs([obs])
obs.complete(False, error="Validation failure")
self.assertEqual(obs.status, 'error')
self.assertEqual(obs.log.msg, 'Validation failure')
self.assertEqual(job.status, 'running')
job.release_validators()
self.assertEqual(job.status, 'error')
self.assertEqual(
job.log.msg, '1 validator jobs failed: Validator %s '
'error message: Validation failure' % obs.id)
def test_complete_error(self):
with self.assertRaises(
qdb.exceptions.QiitaDBOperationNotPermittedError):
self.tester1.complete(True, artifacts_data={})
def test_set_error(self):
job1 = _create_job()
job1._set_status('queued')
job2 = _create_job()
job2._set_status('running')
for t in [job1, job2]:
t._set_error('Job failure')
self.assertEqual(t.status, 'error')
self.assertEqual(
t.log, qdb.logger.LogEntry.newest_records(numrecords=1)[0])
with self.assertRaises(
qdb.exceptions.QiitaDBOperationNotPermittedError):
self.tester3._set_error("Job failure")
def test_update_heartbeat_state(self):
job = _create_job()
job._set_status('running')
before = datetime.now()
job.update_heartbeat_state()
self.assertTrue(before < job.heartbeat < datetime.now())
job = _create_job()
job._set_status('queued')
before = datetime.now()
job.update_heartbeat_state()
self.assertTrue(before < job.heartbeat < datetime.now())
self.assertEqual(job.status, 'running')
with self.assertRaises(
qdb.exceptions.QiitaDBOperationNotPermittedError):
self.tester3.update_heartbeat_state()
def test_step_setter(self):
job = _create_job()
job._set_status('running')
job.step = 'demultiplexing'
self.assertEqual(job.step, 'demultiplexing')
job.step = 'generating demux file'
self.assertEqual(job.step, 'generating demux file')
with self.assertRaises(
qdb.exceptions.QiitaDBOperationNotPermittedError):
self.tester1.step = 'demultiplexing'
with self.assertRaises(
qdb.exceptions.QiitaDBOperationNotPermittedError):
self.tester3.step = 'demultiplexing'
with self.assertRaises(
qdb.exceptions.QiitaDBOperationNotPermittedError):
self.tester4.step = 'demultiplexing'
def test_update_children(self):
# Create a workflow so we can test this functionality
exp_command = qdb.software.Command(1)
json_str = (
'{"input_data": 1, "max_barcode_errors": 1.5, '
'"barcode_type": "golay_12", "max_bad_run_length": 3, '
'"rev_comp": false, "phred_quality_threshold": 3, '
'"rev_comp_barcode": false, "rev_comp_mapping_barcodes": false, '
'"min_per_read_length_fraction": 0.75, "sequence_max_n": 0, '
'"phred_offset": "auto"}')
exp_params = qdb.software.Parameters.load(exp_command,
json_str=json_str)
exp_user = qdb.user.User('test@foo.bar')
name = "Test processing workflow"
tester = qdb.processing_job.ProcessingWorkflow.from_scratch(
exp_user, exp_params, name=name, force=True)
parent = list(tester.graph.nodes())[0]
connections = {parent: {'demultiplexed': 'input_data'}}
dflt_params = qdb.software.DefaultParameters(10)
tester.add(dflt_params, connections=connections)
# we could get the child using tester.graph.nodes()[1] but networkx
# doesn't assure order so using the actual graph to get the child
child = list(nx.topological_sort(tester.graph))[1]
mapping = {1: 3}
obs = parent._update_children(mapping)
exp = [child]
self.assertTrue(obs, exp)
self.assertEqual(child.input_artifacts,
[qdb.artifact.Artifact(3)])
def test_outputs(self):
job = _create_job()
job._set_status('running')
QE = qdb.exceptions
with self.assertRaises(QE.QiitaDBOperationNotPermittedError):
job.outputs
fd, fp = mkstemp(suffix="_table.biom")
self._clean_up_files.append(fp)
close(fd)
with open(fp, 'w') as f:
f.write('\n')
artifact_data = {'filepaths': [(fp, 'biom')], 'artifact_type': 'BIOM'}
params = qdb.software.Parameters.load(
qdb.software.Command(4),
values_dict={'template': 1, 'files': fp,
'artifact_type': 'BIOM',
'provenance': dumps(
{'job': job.id,
'cmd_out_id': 3,
'name': 'outArtifact'})}
)
obs = qdb.processing_job.ProcessingJob.create(
qdb.user.User('test@foo.bar'), params, True)
job._set_validator_jobs([obs])
exp_artifact_count = qdb.util.get_count('qiita.artifact') + 1
obs._complete_artifact_definition(artifact_data)
job.release_validators()
self.assertEqual(job.status, 'success')
artifact = qdb.artifact.Artifact(exp_artifact_count)
obs = job.outputs
self.assertEqual(obs, {'OTU table': artifact})
self._clean_up_files.extend([x['fp'] for x in artifact.filepaths])
self.assertEqual(artifact.name, 'outArtifact')
def test_processing_job_workflow(self):
# testing None
job = qdb.processing_job.ProcessingJob(
"063e553b-327c-4818-ab4a-adfe58e49860")
self.assertIsNone(job.processing_job_workflow)
# testing actual workflow
job = qdb.processing_job.ProcessingJob(
"b72369f9-a886-4193-8d3d-f7b504168e75")
self.assertEqual(job.processing_job_workflow,
qdb.processing_job.ProcessingWorkflow(1))
# testing child job from workflow
job = qdb.processing_job.ProcessingJob(
'd19f76ee-274e-4c1b-b3a2-a12d73507c55')
self.assertEqual(job.processing_job_workflow,
qdb.processing_job.ProcessingWorkflow(1))
def test_hidden(self):
self.assertTrue(self.tester1.hidden)
self.assertTrue(self.tester2.hidden)
self.assertFalse(self.tester3.hidden)
self.assertTrue(self.tester4.hidden)
def test_hide(self):
QE = qdb.exceptions
# It's in a queued state
with self.assertRaises(QE.QiitaDBOperationNotPermittedError):
self.tester1.hide()
# It's in a running state
with self.assertRaises(QE.QiitaDBOperationNotPermittedError):
self.tester2.hide()
# It's in a success state
with self.assertRaises(QE.QiitaDBOperationNotPermittedError):
self.tester3.hide()
job = _create_job()
job._set_error('Setting to error for testing')
self.assertFalse(job.hidden)
job.hide()
self.assertTrue(job.hidden)
def test_shape(self):
jids = {
# Split libraries FASTQ
'6d368e16-2242-4cf8-87b4-a5dc40bb890b': (27, 31, 116),
# Pick closed-reference OTUs
'80bf25f3-5f1d-4e10-9369-315e4244f6d5': (27, 31, 0),
# Single Rarefaction / Analysis
'8a7a8461-e8a1-4b4e-a428-1bc2f4d3ebd0': (5, 56, 3770436),
# Split libraries
'bcc7ebcd-39c1-43e4-af2d-822e3589f14d': (27, 31, 116)}
for jid, shape in jids.items():
job = qdb.processing_job.ProcessingJob(jid)
self.assertEqual(job.shape, shape)
def test_get_resource_allocation_info(self):
jids = {
# Split libraries FASTQ
'6d368e16-2242-4cf8-87b4-a5dc40bb890b':
'-q qiita -l nodes=1:ppn=1 -l mem=120gb -l walltime=80:00:00',
# Pick closed-reference OTUs
'80bf25f3-5f1d-4e10-9369-315e4244f6d5':
'-q qiita -l nodes=1:ppn=5 -l mem=120gb -l walltime=130:00:00',
# Single Rarefaction / Analysis
'8a7a8461-e8a1-4b4e-a428-1bc2f4d3ebd0':
'-q qiita -l nodes=1:ppn=5 -l pmem=8gb -l walltime=168:00:00',
# Split libraries
'bcc7ebcd-39c1-43e4-af2d-822e3589f14d':
'-q qiita -l nodes=1:ppn=1 -l mem=60gb -l walltime=25:00:00'}
for jid, allocation in jids.items():
job = qdb.processing_job.ProcessingJob(jid)
self.assertEqual(job.get_resource_allocation_info(), allocation)
# now let's test get_resource_allocation_info formulas, fun!!
job_changed = qdb.processing_job.ProcessingJob(
'6d368e16-2242-4cf8-87b4-a5dc40bb890b')
job_not_changed = qdb.processing_job.ProcessingJob(
'80bf25f3-5f1d-4e10-9369-315e4244f6d5')
# helper to set memory allocations easier
def _set_allocation(memory):
sql = """UPDATE qiita.processing_job_resource_allocation
SET allocation = '{0}'
WHERE name = 'Split libraries FASTQ'""".format(
'-q qiita -l mem=%s' % memory)
qdb.sql_connection.perform_as_transaction(sql)
# let's start with something simple, samples*1000
# 27*1000 ~ 27000
_set_allocation('{samples}*1000')
self.assertEqual(
job_not_changed.get_resource_allocation_info(),
'-q qiita -l nodes=1:ppn=5 -l mem=120gb -l walltime=130:00:00')
self.assertEqual(job_changed.get_resource_allocation_info(),
'-q qiita -l mem=26K')
# a little more complex ((samples+columns)*1000000)+4000000
# (( 27 + 31 )*1000000)+4000000 ~ 62000000
_set_allocation('(({samples}+{columns})*1000000)+4000000')
self.assertEqual(
job_not_changed.get_resource_allocation_info(),
'-q qiita -l nodes=1:ppn=5 -l mem=120gb -l walltime=130:00:00')
self.assertEqual(job_changed.get_resource_allocation_info(),
'-q qiita -l mem=59M')
# now something real input_size+(2*1e+9)
# 116 +(2*1e+9) ~ 2000000116
_set_allocation('{input_size}+(2*1e+9)')
self.assertEqual(
job_not_changed.get_resource_allocation_info(),
'-q qiita -l nodes=1:ppn=5 -l mem=120gb -l walltime=130:00:00')
self.assertEqual(job_changed.get_resource_allocation_info(),
'-q qiita -l mem=2G')
@qiita_test_checker()
class ProcessingWorkflowTests(TestCase):
def test_name(self):
self.assertEqual(qdb.processing_job.ProcessingWorkflow(1).name,
'Testing processing workflow')
def test_user(self):
self.assertEqual(qdb.processing_job.ProcessingWorkflow(1).user,
qdb.user.User('shared@foo.bar'))
def test_graph(self):
obs = qdb.processing_job.ProcessingWorkflow(1).graph
self.assertTrue(isinstance(obs, nx.DiGraph))
exp_nodes = [
qdb.processing_job.ProcessingJob(
'b72369f9-a886-4193-8d3d-f7b504168e75'),
qdb.processing_job.ProcessingJob(
'd19f76ee-274e-4c1b-b3a2-a12d73507c55')]
self.assertCountEqual(obs.nodes(), exp_nodes)
self.assertEqual(list(obs.edges()), [(exp_nodes[0], exp_nodes[1])])
def test_graph_only_root(self):
obs = qdb.processing_job.ProcessingWorkflow(2).graph
self.assertTrue(isinstance(obs, nx.DiGraph))
exp_nodes = [
qdb.processing_job.ProcessingJob(
'ac653cb5-76a6-4a45-929e-eb9b2dee6b63')]
self.assertCountEqual(obs.nodes(), exp_nodes)
self.assertEqual(list(obs.edges()), [])
def test_raise_if_not_in_construction(self):
# We just need to test that the execution continues (i.e. no raise)
tester = qdb.processing_job.ProcessingWorkflow(2)
tester._raise_if_not_in_construction()
def test_raise_if_not_in_construction_error(self):
tester = qdb.processing_job.ProcessingWorkflow(1)
with self.assertRaises(
qdb.exceptions.QiitaDBOperationNotPermittedError):
tester._raise_if_not_in_construction()
def test_submit(self):
# The submit method is being tested in test_complete_success via
# a job, its release validators and validators submissions.
# Leaving this note here in case it's helpful for future development
pass
def test_from_default_workflow(self):
exp_user = qdb.user.User('test@foo.bar')
dflt_wf = qdb.software.DefaultWorkflow(1)
req_params = {qdb.software.Command(1): {'input_data': 1}}
name = "Test processing workflow"
obs = qdb.processing_job.ProcessingWorkflow.from_default_workflow(
exp_user, dflt_wf, req_params, name=name, force=True)
self.assertEqual(obs.name, name)
self.assertEqual(obs.user, exp_user)
obs_graph = obs.graph
self.assertTrue(isinstance(obs_graph, nx.DiGraph))
self.assertEqual(len(obs_graph.nodes()), 2)
obs_edges = obs_graph.edges()
self.assertEqual(len(obs_edges), 1)
obs_edges = list(obs_edges)[0]
obs_src, obs_dst = list(obs_edges)
self.assertTrue(isinstance(obs_src, qdb.processing_job.ProcessingJob))
self.assertTrue(isinstance(obs_dst, qdb.processing_job.ProcessingJob))
self.assertTrue(obs_src.command, qdb.software.Command(1))
self.assertTrue(obs_dst.command, qdb.software.Command(1))
obs_params = obs_dst.parameters.values
exp_params = {
'input_data': [obs_src.id, u'demultiplexed'],
'reference': 1,
'similarity': 0.97,
'sortmerna_coverage': 0.97,
'sortmerna_e_value': 1,
'sortmerna_max_pos': 10000,
'threads': 1}
self.assertEqual(obs_params, exp_params)
exp_pending = {obs_src.id: {'input_data': 'demultiplexed'}}
self.assertEqual(obs_dst.pending, exp_pending)
def test_from_default_workflow_error(self):
with self.assertRaises(qdb.exceptions.QiitaDBError) as err:
qdb.processing_job.ProcessingWorkflow.from_default_workflow(
qdb.user.User('test@foo.bar'), qdb.software.DefaultWorkflow(1),
{}, name="Test name")
exp = ('Provided required parameters do not match the initial set of '
'commands for the workflow. Command(s) "Split libraries FASTQ"'
' are missing the required parameter set.')
self.assertEqual(str(err.exception), exp)
req_params = {qdb.software.Command(1): {'input_data': 1},
qdb.software.Command(2): {'input_data': 2}}
with self.assertRaises(qdb.exceptions.QiitaDBError) as err:
qdb.processing_job.ProcessingWorkflow.from_default_workflow(
qdb.user.User('test@foo.bar'), qdb.software.DefaultWorkflow(1),
req_params, name="Test name")
exp = ('Provided required parameters do not match the initial set of '
'commands for the workflow. Paramters for command(s) '
'"Split libraries" have been provided, but they are not the '
'initial commands for the workflow.')
self.assertEqual(str(err.exception), exp)
def test_from_scratch(self):
exp_command = qdb.software.Command(1)
json_str = (
'{"input_data": 1, "max_barcode_errors": 1.5, '
'"barcode_type": "golay_12", "max_bad_run_length": 3, '
'"rev_comp": false, "phred_quality_threshold": 3, '
'"rev_comp_barcode": false, "rev_comp_mapping_barcodes": false, '
'"min_per_read_length_fraction": 0.75, "sequence_max_n": 0, '
'"phred_offset": "auto"}')
exp_params = qdb.software.Parameters.load(exp_command,
json_str=json_str)
exp_user = qdb.user.User('test@foo.bar')
name = "Test processing workflow"
obs = qdb.processing_job.ProcessingWorkflow.from_scratch(
exp_user, exp_params, name=name, force=True)
self.assertEqual(obs.name, name)
self.assertEqual(obs.user, exp_user)
obs_graph = obs.graph
self.assertTrue(isinstance(obs_graph, nx.DiGraph))
nodes = obs_graph.nodes()
self.assertEqual(len(nodes), 1)
self.assertEqual(list(nodes)[0].parameters, exp_params)
self.assertEqual(list(obs_graph.edges()), [])
def test_add(self):
exp_command = qdb.software.Command(1)
json_str = (
'{"input_data": 1, "max_barcode_errors": 1.5, '
'"barcode_type": "golay_12", "max_bad_run_length": 3, '
'"rev_comp": false, "phred_quality_threshold": 3, '
'"rev_comp_barcode": false, "rev_comp_mapping_barcodes": false, '
'"min_per_read_length_fraction": 0.75, "sequence_max_n": 0, '
'"phred_offset": "auto"}')
exp_params = qdb.software.Parameters.load(exp_command,
json_str=json_str)
exp_user = qdb.user.User('test@foo.bar')
name = "Test processing workflow"
obs = qdb.processing_job.ProcessingWorkflow.from_scratch(
exp_user, exp_params, name=name, force=True)
parent = list(obs.graph.nodes())[0]
connections = {parent: {'demultiplexed': 'input_data'}}
dflt_params = qdb.software.DefaultParameters(10)
obs.add(dflt_params, connections=connections, force=True)
obs_graph = obs.graph
self.assertTrue(isinstance(obs_graph, nx.DiGraph))
obs_nodes = obs_graph.nodes()
self.assertEqual(len(obs_nodes), 2)
obs_edges = obs_graph.edges()
self.assertEqual(len(obs_edges), 1)
obs_edges = list(obs_edges)[0]
obs_src, obs_dst = list(obs_edges)
self.assertEqual(obs_src, parent)
self.assertTrue(isinstance(obs_dst, qdb.processing_job.ProcessingJob))
obs_params = obs_dst.parameters.values
exp_params = {
'input_data': [parent.id, u'demultiplexed'],
'reference': 1,
'similarity': 0.97,
'sortmerna_coverage': 0.97,
'sortmerna_e_value': 1,
'sortmerna_max_pos': 10000,
'threads': 1}
self.assertEqual(obs_params, exp_params)
# Adding a new root job
# This also tests that the `graph` property returns the graph correctly
# when there are root nodes that don't have any children
dflt_params = qdb.software.DefaultParameters(1)
obs.add(dflt_params, req_params={'input_data': 1}, force=True)
obs_graph = obs.graph
self.assertTrue(isinstance(obs_graph, nx.DiGraph))
root_obs_nodes = obs_graph.nodes()
self.assertEqual(len(root_obs_nodes), 3)
obs_edges = obs_graph.edges()
self.assertEqual(len(obs_edges), 1)
obs_new_jobs = set(root_obs_nodes) - set(obs_nodes)
self.assertEqual(len(obs_new_jobs), 1)
obs_job = obs_new_jobs.pop()
exp_params = {'barcode_type': u'golay_12',
'input_data': 1,
'max_bad_run_length': 3,
'max_barcode_errors': 1.5,
'min_per_read_length_fraction': 0.75,
'phred_quality_threshold': 3,
'rev_comp': False,
'rev_comp_barcode': False,
'rev_comp_mapping_barcodes': False,
'sequence_max_n': 0,
'phred_offset': 'auto'}
self.assertEqual(obs_job.parameters.values, exp_params)
def test_add_error(self):
with self.assertRaises(
qdb.exceptions.QiitaDBOperationNotPermittedError):
qdb.processing_job.ProcessingWorkflow(1).add({}, None)
def test_remove(self):
exp_command = qdb.software.Command(1)
json_str = (
'{"input_data": 1, "max_barcode_errors": 1.5, '
'"barcode_type": "golay_12", "max_bad_run_length": 3, '
'"rev_comp": false, "phred_quality_threshold": 3, '
'"rev_comp_barcode": false, "rev_comp_mapping_barcodes": false, '
'"min_per_read_length_fraction": 0.75, "sequence_max_n": 0,'
'"phred_offset": "auto"}')
exp_params = qdb.software.Parameters.load(exp_command,
json_str=json_str)
exp_user = qdb.user.User('test@foo.bar')
name = "Test processing workflow"
tester = qdb.processing_job.ProcessingWorkflow.from_scratch(
exp_user, exp_params, name=name, force=True)
parent = list(tester.graph.nodes())[0]
connections = {parent: {'demultiplexed': 'input_data'}}
dflt_params = qdb.software.DefaultParameters(10)
tester.add(dflt_params, connections=connections)
self.assertEqual(len(tester.graph.nodes()), 2)
element = list(tester.graph.edges())[0]
tester.remove(element[1])
g = tester.graph
obs_nodes = g.nodes()
self.assertEqual(len(obs_nodes), 1)
self.assertEqual(list(obs_nodes)[0], parent)
self.assertEqual(list(g.edges()), [])
# Test with cascade = true
exp_user = qdb.user.User('test@foo.bar')
dflt_wf = qdb.software.DefaultWorkflow(1)
req_params = {qdb.software.Command(1): {'input_data': 1}}
name = "Test processing workflow"
tester = qdb.processing_job.ProcessingWorkflow.from_default_workflow(
exp_user, dflt_wf, req_params, name=name, force=True)
element = list(tester.graph.edges())[0]
tester.remove(element[0], cascade=True)
self.assertEqual(list(tester.graph.nodes()), [])
def test_remove_error(self):
with self.assertRaises(
qdb.exceptions.QiitaDBOperationNotPermittedError):
qdb.processing_job.ProcessingWorkflow(1).remove(
qdb.processing_job.ProcessingJob(
'b72369f9-a886-4193-8d3d-f7b504168e75'))
exp_user = qdb.user.User('test@foo.bar')
dflt_wf = qdb.software.DefaultWorkflow(1)
req_params = {qdb.software.Command(1): {'input_data': 1}}
name = "Test processing workflow"
tester = qdb.processing_job.ProcessingWorkflow.from_default_workflow(
exp_user, dflt_wf, req_params, name=name, force=True)
with self.assertRaises(
qdb.exceptions.QiitaDBOperationNotPermittedError):
element = list(tester.graph.edges())[0]
tester.remove(element[0])
@qiita_test_checker()
class ProcessingJobDuplicated(TestCase):
def test_create_duplicated(self):
job = _create_job()
job._set_status('success')
with self.assertRaisesRegex(ValueError, 'Cannot create job because '
'the parameters are the same as jobs '
'that are queued, running or already '
'have succeeded:') as context:
_create_job(False)
# If it failed it's because we have jobs in non finished status so
# setting them as error. This is basically testing that the duplicated
# job creation allows to create if all jobs are error and if success
# that the job doesn't have children
for jobs in str(context.exception).split('\n')[1:]:
jid, status = jobs.split(': ')
if status != 'success':
qdb.processing_job.ProcessingJob(jid)._set_status('error')
_create_job(False)
if __name__ == '__main__':
main()
| bsd-3-clause |
joshbohde/scikit-learn | examples/plot_lda_vs_qda.py | 2 | 2680 | """
====================================================================
Linear and Quadratic Discriminant Analysis with confidence ellipsoid
====================================================================
Plot the confidence ellipsoids of each class and decision boundary
"""
print __doc__
from scipy import linalg
import numpy as np
import pylab as pl
import matplotlib as mpl
from sklearn.lda import LDA
from sklearn.qda import QDA
################################################################################
# load sample dataset
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data[:,:2] # Take only 2 dimensions
y = iris.target
X = X[y > 0]
y = y[y > 0]
y -= 1
target_names = iris.target_names[1:]
################################################################################
# LDA
lda = LDA()
y_pred = lda.fit(X, y, store_covariance=True).predict(X)
# QDA
qda = QDA()
y_pred = qda.fit(X, y, store_covariances=True).predict(X)
###############################################################################
# Plot results
def plot_ellipse(splot, mean, cov, color):
v, w = linalg.eigh(cov)
u = w[0] / linalg.norm(w[0])
angle = np.arctan(u[1]/u[0])
angle = 180 * angle / np.pi # convert to degrees
# filled gaussian at 2 standard deviation
ell = mpl.patches.Ellipse(mean, 2 * v[0] ** 0.5, 2 * v[1] ** 0.5,
180 + angle, color=color)
ell.set_clip_box(splot.bbox)
ell.set_alpha(0.5)
splot.add_artist(ell)
xx, yy = np.meshgrid(np.linspace(4, 8.5, 200), np.linspace(1.5, 4.5, 200))
X_grid = np.c_[xx.ravel(), yy.ravel()]
zz_lda = lda.predict_proba(X_grid)[:,1].reshape(xx.shape)
zz_qda = qda.predict_proba(X_grid)[:,1].reshape(xx.shape)
pl.figure()
splot = pl.subplot(1, 2, 1)
pl.contourf(xx, yy, zz_lda > 0.5, alpha=0.5)
pl.scatter(X[y==0,0], X[y==0,1], c='b', label=target_names[0])
pl.scatter(X[y==1,0], X[y==1,1], c='r', label=target_names[1])
pl.contour(xx, yy, zz_lda, [0.5], linewidths=2., colors='k')
plot_ellipse(splot, lda.means_[0], lda.covariance_, 'b')
plot_ellipse(splot, lda.means_[1], lda.covariance_, 'r')
pl.legend()
pl.axis('tight')
pl.title('Linear Discriminant Analysis')
splot = pl.subplot(1, 2, 2)
pl.contourf(xx, yy, zz_qda > 0.5, alpha=0.5)
pl.scatter(X[y==0,0], X[y==0,1], c='b', label=target_names[0])
pl.scatter(X[y==1,0], X[y==1,1], c='r', label=target_names[1])
pl.contour(xx, yy, zz_qda, [0.5], linewidths=2., colors='k')
plot_ellipse(splot, qda.means_[0], qda.covariances_[0], 'b')
plot_ellipse(splot, qda.means_[1], qda.covariances_[1], 'r')
pl.legend()
pl.axis('tight')
pl.title('Quadratic Discriminant Analysis')
pl.show()
| bsd-3-clause |
aflaxman/scikit-learn | examples/hetero_feature_union.py | 81 | 6241 | """
=============================================
Feature Union with Heterogeneous Data Sources
=============================================
Datasets can often contain components of that require different feature
extraction and processing pipelines. This scenario might occur when:
1. Your dataset consists of heterogeneous data types (e.g. raster images and
text captions)
2. Your dataset is stored in a Pandas DataFrame and different columns
require different processing pipelines.
This example demonstrates how to use
:class:`sklearn.feature_extraction.FeatureUnion` on a dataset containing
different types of features. We use the 20-newsgroups dataset and compute
standard bag-of-words features for the subject line and body in separate
pipelines as well as ad hoc features on the body. We combine them (with
weights) using a FeatureUnion and finally train a classifier on the combined
set of features.
The choice of features is not particularly helpful, but serves to illustrate
the technique.
"""
# Author: Matt Terry <matt.terry@gmail.com>
#
# License: BSD 3 clause
from __future__ import print_function
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.datasets import fetch_20newsgroups
from sklearn.datasets.twenty_newsgroups import strip_newsgroup_footer
from sklearn.datasets.twenty_newsgroups import strip_newsgroup_quoting
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report
from sklearn.pipeline import FeatureUnion
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
class ItemSelector(BaseEstimator, TransformerMixin):
"""For data grouped by feature, select subset of data at a provided key.
The data is expected to be stored in a 2D data structure, where the first
index is over features and the second is over samples. i.e.
>> len(data[key]) == n_samples
Please note that this is the opposite convention to scikit-learn feature
matrixes (where the first index corresponds to sample).
ItemSelector only requires that the collection implement getitem
(data[key]). Examples include: a dict of lists, 2D numpy array, Pandas
DataFrame, numpy record array, etc.
>> data = {'a': [1, 5, 2, 5, 2, 8],
'b': [9, 4, 1, 4, 1, 3]}
>> ds = ItemSelector(key='a')
>> data['a'] == ds.transform(data)
ItemSelector is not designed to handle data grouped by sample. (e.g. a
list of dicts). If your data is structured this way, consider a
transformer along the lines of `sklearn.feature_extraction.DictVectorizer`.
Parameters
----------
key : hashable, required
The key corresponding to the desired value in a mappable.
"""
def __init__(self, key):
self.key = key
def fit(self, x, y=None):
return self
def transform(self, data_dict):
return data_dict[self.key]
class TextStats(BaseEstimator, TransformerMixin):
"""Extract features from each document for DictVectorizer"""
def fit(self, x, y=None):
return self
def transform(self, posts):
return [{'length': len(text),
'num_sentences': text.count('.')}
for text in posts]
class SubjectBodyExtractor(BaseEstimator, TransformerMixin):
"""Extract the subject & body from a usenet post in a single pass.
Takes a sequence of strings and produces a dict of sequences. Keys are
`subject` and `body`.
"""
def fit(self, x, y=None):
return self
def transform(self, posts):
features = np.recarray(shape=(len(posts),),
dtype=[('subject', object), ('body', object)])
for i, text in enumerate(posts):
headers, _, bod = text.partition('\n\n')
bod = strip_newsgroup_footer(bod)
bod = strip_newsgroup_quoting(bod)
features['body'][i] = bod
prefix = 'Subject:'
sub = ''
for line in headers.split('\n'):
if line.startswith(prefix):
sub = line[len(prefix):]
break
features['subject'][i] = sub
return features
pipeline = Pipeline([
# Extract the subject & body
('subjectbody', SubjectBodyExtractor()),
# Use FeatureUnion to combine the features from subject and body
('union', FeatureUnion(
transformer_list=[
# Pipeline for pulling features from the post's subject line
('subject', Pipeline([
('selector', ItemSelector(key='subject')),
('tfidf', TfidfVectorizer(min_df=50)),
])),
# Pipeline for standard bag-of-words model for body
('body_bow', Pipeline([
('selector', ItemSelector(key='body')),
('tfidf', TfidfVectorizer()),
('best', TruncatedSVD(n_components=50)),
])),
# Pipeline for pulling ad hoc features from post's body
('body_stats', Pipeline([
('selector', ItemSelector(key='body')),
('stats', TextStats()), # returns a list of dicts
('vect', DictVectorizer()), # list of dicts -> feature matrix
])),
],
# weight components in FeatureUnion
transformer_weights={
'subject': 0.8,
'body_bow': 0.5,
'body_stats': 1.0,
},
)),
# Use a SVC classifier on the combined features
('svc', SVC(kernel='linear')),
])
# limit the list of categories to make running this example faster.
categories = ['alt.atheism', 'talk.religion.misc']
train = fetch_20newsgroups(random_state=1,
subset='train',
categories=categories,
)
test = fetch_20newsgroups(random_state=1,
subset='test',
categories=categories,
)
pipeline.fit(train.data, train.target)
y = pipeline.predict(test.data)
print(classification_report(y, test.target))
| bsd-3-clause |
theoryno3/scikit-learn | sklearn/manifold/tests/test_isomap.py | 28 | 4007 | from itertools import product
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from sklearn import datasets
from sklearn import manifold
from sklearn import neighbors
from sklearn import pipeline
from sklearn import preprocessing
from sklearn.utils.testing import assert_less
eigen_solvers = ['auto', 'dense', 'arpack']
path_methods = ['auto', 'FW', 'D']
def test_isomap_simple_grid():
# Isomap should preserve distances when all neighbors are used
N_per_side = 5
Npts = N_per_side ** 2
n_neighbors = Npts - 1
# grid of equidistant points in 2D, n_components = n_dim
X = np.array(list(product(range(N_per_side), repeat=2)))
# distances from each point to all others
G = neighbors.kneighbors_graph(X, n_neighbors,
mode='distance').toarray()
for eigen_solver in eigen_solvers:
for path_method in path_methods:
clf = manifold.Isomap(n_neighbors=n_neighbors, n_components=2,
eigen_solver=eigen_solver,
path_method=path_method)
clf.fit(X)
G_iso = neighbors.kneighbors_graph(clf.embedding_,
n_neighbors,
mode='distance').toarray()
assert_array_almost_equal(G, G_iso)
def test_isomap_reconstruction_error():
# Same setup as in test_isomap_simple_grid, with an added dimension
N_per_side = 5
Npts = N_per_side ** 2
n_neighbors = Npts - 1
# grid of equidistant points in 2D, n_components = n_dim
X = np.array(list(product(range(N_per_side), repeat=2)))
# add noise in a third dimension
rng = np.random.RandomState(0)
noise = 0.1 * rng.randn(Npts, 1)
X = np.concatenate((X, noise), 1)
# compute input kernel
G = neighbors.kneighbors_graph(X, n_neighbors,
mode='distance').toarray()
centerer = preprocessing.KernelCenterer()
K = centerer.fit_transform(-0.5 * G ** 2)
for eigen_solver in eigen_solvers:
for path_method in path_methods:
clf = manifold.Isomap(n_neighbors=n_neighbors, n_components=2,
eigen_solver=eigen_solver,
path_method=path_method)
clf.fit(X)
# compute output kernel
G_iso = neighbors.kneighbors_graph(clf.embedding_,
n_neighbors,
mode='distance').toarray()
K_iso = centerer.fit_transform(-0.5 * G_iso ** 2)
# make sure error agrees
reconstruction_error = np.linalg.norm(K - K_iso) / Npts
assert_almost_equal(reconstruction_error,
clf.reconstruction_error())
def test_transform():
n_samples = 200
n_components = 10
noise_scale = 0.01
# Create S-curve dataset
X, y = datasets.samples_generator.make_s_curve(n_samples, random_state=0)
# Compute isomap embedding
iso = manifold.Isomap(n_components, 2)
X_iso = iso.fit_transform(X)
# Re-embed a noisy version of the points
rng = np.random.RandomState(0)
noise = noise_scale * rng.randn(*X.shape)
X_iso2 = iso.transform(X + noise)
# Make sure the rms error on re-embedding is comparable to noise_scale
assert_less(np.sqrt(np.mean((X_iso - X_iso2) ** 2)), 2 * noise_scale)
def test_pipeline():
# check that Isomap works fine as a transformer in a Pipeline
# only checks that no error is raised.
# TODO check that it actually does something useful
X, y = datasets.make_blobs(random_state=0)
clf = pipeline.Pipeline(
[('isomap', manifold.Isomap()),
('clf', neighbors.KNeighborsClassifier())])
clf.fit(X, y)
assert_less(.9, clf.score(X, y))
if __name__ == '__main__':
import nose
nose.runmodule()
| bsd-3-clause |
Zubair-Marediya/project-zeta | code/linear_model_scripts_sub4.py | 3 | 25730 | # Goal for this scripts:
#
# Perform linear regression and analyze the similarity in terms of the activated brain area when recognizing different
# objects in odd and even runs of subject 1
# Load required function and modules:
from __future__ import print_function, division
import numpy as np
import numpy.linalg as npl
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import colors
from matplotlib import gridspec
import os
import re
import json
import nibabel as nib
from utils import subject_class as sc
from utils import outlier
from utils import diagnostics as diagnos
from utils import get_object_neural as neural
from utils import stimuli
from utils import convolution as convol
from utils import smooth as sm
from utils import linear_model as lm
from utils import maskfunc as msk
from utils import affine
import copy
# important path:
base_path = os.path.abspath(os.path.dirname(__file__))
base_path = os.path.join(base_path, "..")
# where to store figures
figure_path = os.path.join(base_path, "code", "images", "")
# where to store txt files
file_path = os.path.join(base_path, "code", "txt", "")
# help to make directory to save figures and txt files
# if figure folder doesn't exist -> make it
if not os.path.exists(figure_path):
os.makedirs(figure_path)
# if txt folder doesn't exist -> make it
if not os.path.exists(file_path):
os.makedirs(file_path)
# color display:
# list of all objects in this study in alphabetical order
object_list = ["bottle", "cat", "chair", "face", "house", "scissors", "scrambledpix", "shoe"]
# assign color for task time course for each object
color_list_s = ["b", "g", "r", "c", "m", "y", "k", "sienna"]
match_color_s = dict(zip(object_list, color_list_s))
# assign color for convolved result for each object
color_list_c = ["royalblue", "darksage", "tomato", "cadetblue", "orchid", "goldenrod", "dimgrey", "sandybrown"]
match_color_c = dict(zip(object_list, color_list_c))
# color for showing beta values
nice_cmap_values = np.loadtxt(file_path + 'actc.txt')
nice_cmap = colors.ListedColormap(nice_cmap_values, 'actc')
# assign object parameter number: each object has a iterable number
match_para = dict(zip(object_list, range(8)))
# check slice number:
# this is the specific slice we use to run 2D correlation
slice_number = 39
# separator for better report display
sec_separator = '#' * 80
separator = "-" * 80
# which subject to work on?
subid = "sub004"
# work on results from this subject:
################################### START #####################################
print (sec_separator)
print ("Project-Zeta: use linear regression to study ds105 dataset")
print (separator)
print ("Focus on %s for the analysis" % subid)
print (sec_separator)
print ("Progress: Clean up data")
print (separator)
# load important data for this subject by using subject_class
sub = sc.subject(subid)
# get image files of this subject:
sub_img = sub.run_img_result
# get run numbers of this subject:
run_num = len(sub.run_keys)
# report keys of all images:
print ("Import %s images" % subid)
print (separator)
print ("These images are imported:")
img_key = sub_img.keys()
img_key = sorted(img_key)
for i in img_key:
print (i)
# report how many runs in this subject
print ("There are %d runs for %s" % (run_num, subid))
print (separator)
# get data for those figures
print ("Get data from images...")
sub_data = {}
for key, img in sub_img.items():
sub_data[key] = img.get_data()
print ("Complete!")
print (separator)
# use rms_diff to check outlier for all runs of this subject
print ("Analyze outliers in these runs:")
for key, data in sub_data.items():
rms_diff = diagnos.vol_rms_diff(data)
# get outlier indices and the threshold for the outlier
rms_outlier_indices, rms_thresh = diagnos.iqr_outliers(rms_diff)
y_value2 = [rms_diff[i] for i in rms_outlier_indices]
# create figures to show the outlier
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.55, 0.75])
ax.plot(rms_diff, label='rms')
ax.plot([0, len(rms_diff)], [rms_thresh[1], rms_thresh[1]], "k--",\
label='high threshold', color='m')
ax.plot([0, len(rms_diff)], [rms_thresh[0], rms_thresh[0]], "k--",\
label='low threshold', color='c')
ax.plot(rms_outlier_indices, y_value2, 'o', color='g', label='outlier')
# label the figure
ax.set_xlabel('Scan time course')
ax.set_ylabel('Volumne RMS difference')
ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., numpoints=1)
fig.text(0.05, 0.9, 'Volume RMS Difference with Outliers for %s' % key, weight='bold')
# save figure
fig.savefig(figure_path + 'Volume_RMS_Difference_Outliers_%s.png' % key)
# clear figure
fig.clf()
# close pyplot window
plt.close()
# report
print ("Outlier analysis results are saved as figures!")
print (separator)
# remove outlier from images
sub_clean_img, outlier_index = outlier.remove_data_outlier(sub_img)
print ("Remove outlier:")
print ("outliers are removed from each run!")
print (sec_separator)
# run generate predicted bold signals:
print ("Progress: create predicted BOLD signals based on condition files")
# get general all tr times == 121*2.5 = about 300 s
# this is the x-axis to plot hemodynamic prediction
all_tr_times = np.arange(sub.BOLD_shape[-1]) * sub.TR
# the y-axis to plot hemodynamic prediction is the neural value from condition (on-off)
sub_neural = neural.get_object_neural(sub.sub_id, sub.conditions, sub.TR, sub.BOLD_shape[-1])
# report info for all run details
print ("The detailed run info for %s:" % subid)
neural_key = sub_neural.keys()
neural_key = sorted(neural_key)
for i in neural_key:
print (i)
print (separator)
# get task time course for all runs -> save as images
print ("generate task time course images")
print (separator)
for run in range(1, run_num):
# make plots to display task time course
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.55, 0.75])
for item in object_list:
check_key = "run0%02d-%s" % (run, item)
ax.plot(all_tr_times, sub_neural[check_key][0], label="%s" % item, c=match_color_s[item])
# make labels:
ax.set_title("Task time course for %s-run0%02d" % (subid, run), weight='bold')
ax.set_xlabel("Time course (second)")
ax.set_ylabel("Task (Off = 0, On = 1)")
ax.set_yticks([0, 1])
ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., numpoints=1)
# save figure
fig.savefig(figure_path + "Task_time_course_%s_run0%02d" % (subid, run))
# clear figure
fig.clf()
# close pyplot window
plt.close()
# report
print ("task time course images are saved!")
print (separator)
# assume true HRF starts at zero, and gets to zero sometime before 35 seconds.
tr_times = np.arange(0, 30, sub.TR)
hrf_at_trs = convol.hrf(tr_times)
# get convolution data for each objects in this run -> show figure for run001
print ("Work on convolution based on condition files:")
print (separator)
sub_convolved = convol.get_all_convolved(sub_neural, hrf_at_trs, file_path)
print ("convolution analysis for all runs is complete")
print (separator)
# save convolved data
for key, data in sub_convolved.items():
np.savetxt(file_path + "convolved_%s.txt" % key, data)
print ("convolved results are saved as txt files")
# show relationship between task time course and bold signals
print ("Show relationship between task time course and predicted BOLD signals")
# get keys for each neural conditions
sub_neural_key = sub_neural.keys()
# sort key for better display
sub_neural_key = sorted(sub_neural_key)
# create figures
fig = plt.figure()
for run in range(1, run_num):
ax = plt.subplot(111)
ax2 = ax.twinx()
figures = {}
count = 0
for item in object_list:
# focus on one at a time:
check_key = "run0%02d-%s" % (run, item)
# perform convolution to generate estimated BOLD signals
convolved = convol.convolution(sub_neural[check_key][0], hrf_at_trs)
# plot the task time course and the estimated BOLD signals in same plot
# plot the estimated BOLD signals
# plot the task time course
figures["fig" + "%s" % str(count)] = ax.plot(all_tr_times, sub_neural[check_key][0], c=match_color_s[item], label="%s-task" % item)
count += 1
# plot estimated BOLD signal
figures["fig" + "%s" % str(count)] = ax2.plot(all_tr_times, convolved, c=match_color_c[item], label="%s-BOLD" % item)
count += 1
# label this plot
plt.subplots_adjust(left=0.1, right=0.6, bottom=0.1, top=0.85)
plt.text(0.25, 1.05, "Hemodynamic prediction of %s-run0%02d" % (subid, run), weight='bold')
ax.set_xlabel("Time course (second)")
ax.set_ylabel("Task (Off = 0, On = 1)")
ax.set_yticks([-0.2, 0, 0.2, 0.4, 0.6, 0.8, 1.0])
ax2.set_ylabel("Estimated BOLD signal")
ax2.set_yticks([-0.2, 0, 0.2, 0.4, 0.6, 0.8, 1.0])
# label legend
total_figures = figures["fig0"]
for i in range(1, len(figures)):
total_figures += figures["fig" + "%s" % str(i)]
labs = [fig.get_label() for fig in total_figures]
ax.legend(total_figures, labs, bbox_to_anchor=(1.2, 1.0), loc=0, borderaxespad=0., fontsize=11)
# save plot
plt.savefig(figure_path + "%s_run0%02d_bold_prediction.png" % (subid, run))
# clear plot
plt.clf()
# close pyplot window
plt.close()
print (sec_separator)
# remove outlier from convolved results
print("Progress: clean up convolved results")
sub_convolved = convol.remove_outlier(sub.sub_id, sub_convolved, outlier_index)
print ("Outliers are removed from convolved results")
print (sec_separator)
# smooth the images:
print ("Progress: Smooth images")
# subject clean and smooth img == sub_cs_img
sub_cs_img = sm.smooth(sub_clean_img)
print ("Smooth images: Complete!")
print (sec_separator)
# get shape info of the images
print ("Progress: record shape information")
shape = {}
for key, img in sub_cs_img.items():
shape[key] = img.shape
print ("shape of %s = %s" % (key, shape[key]))
with open(file_path+'new_shape.json', 'a') as fp:
json.dump(shape, fp)
print ("New shape info of images is recorded and saved as file")
print (sec_separator)
############################## Linear regression ##############################
print ("Let's run Linear regression")
print (separator)
# generate design matrix
print ("Progress: generate design matrix")
# generate design matrix for each runs
design_matrix = lm.batch_make_design(sub_cs_img, sub_convolved)
# check parameter numbers
parameters = design_matrix["%s_run001" % subid].shape[-1]
print ("parameter number: %d" % parameters)
print ("Design matrix generated")
print (separator)
# rescale design matrix
print ("Progress: rescale design matrix")
design_matrix = lm.batch_scale_matrix(design_matrix)
print ("Rescale design matrix: complete!")
# save scaled design matrix as figure
# plot scaled design matrix
fig = plt.figure(figsize=(8.0, 8.0))
for key, matrix in design_matrix.items():
ax = plt.subplot(111)
ax.imshow(matrix, aspect=0.1, interpolation="nearest", cmap="gray")
# label this plot
fig.text(0.15, 0.95, "scaled design matrix for %s" % key, weight='bold', fontsize=18)
ax.set_xlabel("Parameters", fontsize=16)
ax.set_xticklabels([])
ax.set_ylabel("Scan time course", fontsize=16)
# save plot
plt.savefig(figure_path + "design_matrix_%s" % key)
# clean plot
plt.clf()
# close pyplot window
plt.close()
print ("Design matrices are saved as figures")
print (separator)
# use maskfunc to generate mask
print ("Progress: generate mask for brain images")
mask, mean_data = msk.generateMaskedBrain(sub_clean_img)
print ("Generate mask for brain images: complete!")
# save mask as figure
for key, each in mask.items():
for i in range(1, 90):
plt.subplot(9, 10, i)
plt.imshow(each[:, :, i], interpolation="nearest", cmap="gray", alpha=0.5)
# label plot
ax = plt.gca()
ax.set_xticklabels([])
ax.set_yticklabels([])
# save plot
plt.savefig(figure_path + "all_masks_for_%s.png" % key)
# clear plot
plt.clf()
# close pyplot window
plt.close()
print (separator)
# run linear regression to generate betas
# first step: use mask to get data and reshape to 2D
print ("Progress: Use mask to subset brain images")
sub_cs_mask_img = lm.apply_mask(sub_cs_img, mask)
sub_cs_mask_img_2d = lm.batch_convert_2d_based(sub_cs_mask_img, shape)
# sub1_cs_mask_img_2d = lm.batch_convert_2d(sub1_cs_mask_img)
print ("Use mask to subset brain images: complete!")
print (separator)
# second step: run linear regression to get betas:
print ("Progress: Run linear regression to get beta hat")
all_betas = {}
for key, img in sub_cs_mask_img_2d.items():
#img_2d = np.reshape(img, (-1, img.shape[-1]))
Y = img.T
all_betas[key] = npl.pinv(design_matrix[key]).dot(Y)
print ("Getting betas from linear regression: complete!")
print (separator)
# third step: put betas back into it's original place:
print ("Save beta figures:")
beta_vols = {}
raw_beta_vols = {}
for key, betas in all_betas.items():
# create 3D zeros to hold betas
beta_vols[key] = np.zeros(shape[key][:-1] + (parameters,))
# get the mask info
check_mask = (mask[key] == 1)
# fit betas back to 3D
beta_vols[key][check_mask] = betas.T
print ("betas of %s is fitted back to 3D!" % key)
# save 3D betas in dictionary
raw_beta_vols[key] = beta_vols[key]
# get min and max of figure
vmin = beta_vols[key][:, :, 21:70].min()
vmax = beta_vols[key][:, :, 21:70].max()
# clear the background
beta_vols[key][~check_mask] = np.nan
mean_data[key][~check_mask] = np.nan
# plot betas
fig = plt.figure(figsize=(8.0, 8.0))
for item in object_list:
# plot 50 pictures
fig, axes = plt.subplots(nrows=5, ncols=10)
lookat = 20
for ax in axes.flat:
# show plot from z= 21~70
ax.imshow(mean_data[key][:, :, lookat], interpolation="nearest", cmap="gray", alpha=0.5)
im = ax.imshow(beta_vols[key][:, :, lookat, match_para[item]], cmap=nice_cmap, alpha=0.5)
# label the plot
ax.set_xticks([])
ax.set_yticks([])
ax.set_xticklabels([])
ax.set_yticklabels([])
lookat += 1
# label the plot
fig.subplots_adjust(bottom=0.2, hspace=0)
fig.text(0.28, 0.9, "Brain area responding to %s in %s" % (item, subid), weight='bold')
# color bar
cbar_ax = fig.add_axes([0.15, 0.08, 0.7, 0.04])
fig.colorbar(im, cax=cbar_ax, ticks=[], orientation='horizontal')
fig.text(0.35, 0.15, "Relative responding intensity")
fig.text(0.095, 0.09, "Low")
fig.text(0.87, 0.09, "High")
# save plot
plt.savefig(figure_path + "betas_for_%s_%s.png" % (key, item))
# clear plot
plt.clf()
# close pyplot window
plt.close()
# report
print ("beta figures are generated!!")
print (sec_separator)
# analyze based on odd runs even runs using affine
print ("Progress: Use affine matrix to check brain position")
print ("print affine matrix for each images:")
affine_matrix = {}
for key, img in sub_img.items():
affine_matrix[key] = img.affine
print("%s :\n %s" % (key, img.affine))
# check if they all have same affine matrix
same_affine = True
check_matrix = affine_matrix["%s_run001" % subid]
for key, aff in affine_matrix.items():
if aff.all() != check_matrix.all():
same_affine = False
if same_affine:
print ("They have the same affine matrix!")
else:
print ("They don't have same affine matrix -> be careful about the brain position")
print (sec_separator)
############################## 2D correlation #################################
# Focus on 2D slice to run the analysis:
print ("Progress: Try 2D correlation")
print ("Focus on one slice: k = %d" % slice_number)
print (separator)
print ("Run correlation between run1_house, run2_house and run2_face")
# get 2D slice for run1 house
run1_house = raw_beta_vols["%s_run001" % subid][:, 25:50, slice_number, 5]
# save as plot
plt.imshow(run1_house, interpolation="nearest", cmap=nice_cmap, alpha=0.5)
plt.title("%s_Run1_House" % subid)
plt.savefig(figure_path + "%s_run1_house.png" % subid)
plt.clf()
# get 2D slice of run2 house
run2_house = raw_beta_vols["%s_run002" % subid][:, 25:50, slice_number, 5]
# save as plot
plt.imshow(run2_house, interpolation="nearest", cmap=nice_cmap, alpha=0.5)
plt.title("%s_Run2_House" % subid)
plt.savefig(figure_path + "%s_run2_house.png" % subid)
plt.clf()
# get 2D slice for run2 face
run2_face = raw_beta_vols["%s_run002" % subid][:, 25:50, slice_number, 4]
# save as plot
plt.imshow(run2_face, interpolation="nearest", cmap=nice_cmap, alpha=0.5)
plt.title("%s_Run2_Face" % subid)
plt.savefig(figure_path + "%s_run2_face.png" % subid)
plt.close()
# put those 2D plots together
fig = plt.figure()
plt.subplot(1, 3, 1, xticks=[], yticks=[], xticklabels=[], yticklabels=[])
plt.imshow(run1_house, interpolation="nearest", cmap=nice_cmap, alpha=0.5)
plt.title("Sub%s_Run1_House" % subid[-1], weight='bold', fontsize=10)
plt.subplot(1, 3, 2, xticks=[], yticks=[])
plt.imshow(run2_house, interpolation="nearest", cmap=nice_cmap, alpha=0.5)
plt.title("Sub%s_Run2_House" % subid[-1], weight='bold', fontsize=10)
plt.subplot(1, 3, 3, xticks=[], yticks=[])
plt.imshow(run2_face, interpolation="nearest", cmap=nice_cmap, alpha=0.5)
plt.title("Sub%s_Run2_Face" % subid[-1], weight='bold', fontsize=10)
# label plot
fig.subplots_adjust(bottom=0.2, hspace=0)
cbar_ax = fig.add_axes([0.15, 0.08, 0.7, 0.04])
plt.colorbar(cax=cbar_ax, ticks=[], orientation='horizontal')
fig.text(0.35, 0.15, "Relative responding intensity")
fig.text(0.095, 0.09, "Low")
fig.text(0.87, 0.09, "High")
# save plot
plt.savefig(figure_path + "%s_run_figure_compile.png" % subid)
# close pyplot window
plt.close()
print ("plots for analysis are saved as figures")
print ("Progress: Run correlation coefficient")
# create a deepcopy of raw_beta_vols for correlation analysis:
raw_beta_vols_corr = copy.deepcopy(raw_beta_vols)
# flatten the 2D matrix
house1 = np.ravel(raw_beta_vols_corr["%s_run001" % subid][:, 25:50, slice_number, match_para["house"]])
house2 = np.ravel(raw_beta_vols_corr["%s_run001" % subid][:, 25:50, slice_number, match_para["house"]])
face2 = np.ravel(raw_beta_vols_corr["%s_run001" % subid][:, 25:50, slice_number, match_para["face"]])
# save flatten results for further analysis
np.savetxt(file_path + "%s_house1.txt" % subid, house1)
np.savetxt(file_path + "%s_house2.txt" % subid, house2)
np.savetxt(file_path + "%s_face2.txt" % subid, face2)
# change nan to 0 in the array
house1[np.isnan(house1)] = 0
house2[np.isnan(house2)] = 0
face2[np.isnan(face2)] = 0
# correlation coefficient study:
house1_house2 = np.corrcoef(house1, house2)
house1_face2 = np.corrcoef(house1, face2)
print ("%s run1 house vs run2 house: %s" % (subid, house1_house2))
print ("%s run1 house vs run2 face : %s" % (subid, house1_face2))
print (sec_separator)
# save individual 2D slice as txt for further analysis
print ("save 2D result for each object and each run individually as txt file")
for i in range(1, run_num+1):
for item in object_list:
temp = raw_beta_vols_corr["%s_run0%02d" % (subid, i)][:, 25:50, slice_number, match_para[item]]
np.savetxt(file_path + "%s_run0%02d_%s.txt" % (subid, i, item), np.ravel(temp))
print ("Complete!!")
print (sec_separator)
# analyze based on odd runs even runs
print ("Progress: prepare data to run correlation based on odd runs and even runs:")
print ("Take average of odd run / even run results to deal with impacts of variations between runs")
even_run = {}
odd_run = {}
even_count = 0
odd_count = 0
# add up even run results / odd run results and take mean for each groups
for item in object_list:
even_run[item] = np.zeros_like(raw_beta_vols_corr["%s_run001" % subid][:, 25:50, slice_number, 5])
odd_run[item] = np.zeros_like(raw_beta_vols_corr["%s_run001" % subid][:, 25:50, slice_number, 5])
print ("make average of odd run results:")
# add up odd run results
for i in range(1, run_num+1, 2):
temp = raw_beta_vols_corr["%s_run0%02d" % (subid, i)][:, 25:50, slice_number, match_para[item]]
temp[np.isnan(temp)] = 0
odd_run[item] += temp
odd_count += 1
print("odd runs: %d-%s" % (i, item))
print ("make average od even run results:")
# take mean
odd_run[item] = odd_run[item]/odd_count
# add up even run results
for i in range(2, run_num+1, 2):
temp = raw_beta_vols_corr["%s_run0%02d" % (subid, i)][:, 25:50, slice_number, match_para[item]]
temp[np.isnan(temp)] = 0
even_run[item] += temp
even_count += 1
print("even: %d, %s" % (i, item))
# take mean
even_run[item] = even_run[item]/even_count
print (separator)
# save odd run and even run results as txt file
print ("Progress: save flatten mean odd / even run results as txt files")
for key, fig in even_run.items():
np.savetxt(file_path + "%s_even_%s.txt" % (subid, key), np.ravel(fig))
for key, fig in odd_run.items():
np.savetxt(file_path + "%s_odd_%s.txt" % (subid, key), np.ravel(fig))
print ("odd run and even run results are saved as txt files!!!!!")
print (separator)
############################ 3D correlation ###################################
# check 3D:
print ("Focus on one 3D analysis, shape = [:, 25:50, 31:36]")
# put 3D slice of run1 house, run2 face, run2 house together
fig = plt.figure()
i = 1
run1_house = raw_beta_vols["%s_run001" % subid][:, 25:50, 37:42, match_para["house"]]
for z in range(5):
plt.subplot(3, 5, i, xticks=[], yticks=[])
plt.imshow(run1_house[:, :, z], interpolation="nearest", cmap=nice_cmap, alpha=0.5)
i += 1
if z == 2:
plt.title("%s_run1_house" % subid)
run2_house = raw_beta_vols["%s_run002" % subid][:, 25:50, 37:42, match_para["house"]]
for z in range(5):
plt.subplot(3, 5, i, xticks=[], yticks=[])
plt.imshow(run2_house[:, :, z], interpolation="nearest", cmap=nice_cmap, alpha=0.5)
i += 1
if z == 2:
plt.title("%s_run2_house" % subid)
run2_face = raw_beta_vols["%s_run002" % subid][:, 25:50, 37:42, match_para["face"]]
for z in range(5):
plt.subplot(3, 5, i, xticks=[], yticks=[])
plt.imshow(run2_face[:, :, z], interpolation="nearest", cmap=nice_cmap, alpha=0.5)
i += 1
if z == 2:
plt.title("%s_run2_face" % subid)
# label plot
fig.subplots_adjust(bottom=0.2, hspace=0.5)
cbar_ax = fig.add_axes([0.15, 0.06, 0.7, 0.02])
plt.colorbar(cax=cbar_ax, ticks=[], orientation='horizontal')
fig.text(0.35, 0.1, "Relative responding intensity")
fig.text(0.095, 0.07, "Low")
fig.text(0.87, 0.07, "High")
plt.savefig(figure_path + "Try_3D_correlation_%s.png" % subid)
plt.close()
# try to run 3D correlation study:
print ("Progress: Run correlation coefficient with 3D data")
# make a deepcopy of the raw_beta_vols for correlation study:
raw_beta_vols_3d_corr = copy.deepcopy(raw_beta_vols)
# get flatten 3D slice:
house1_3d = np.ravel(raw_beta_vols_3d_corr["%s_run001" % subid][:, 25:50, 37:42, match_para["house"]])
house2_3d = np.ravel(raw_beta_vols_3d_corr["%s_run002" % subid][:, 25:50, 37:42, match_para["house"]])
face2_3d = np.ravel(raw_beta_vols_3d_corr["%s_run002" % subid][:, 25:50, 37:42, match_para["face"]])
# change nan to 0 in the array
house1_3d[np.isnan(house1_3d)] = 0
house2_3d[np.isnan(house2_3d)] = 0
face2_3d[np.isnan(face2_3d)] = 0
# correlation coefficient study:
threeD_house1_house2 = np.corrcoef(house1_3d, house2_3d)
threeD_house1_face2 = np.corrcoef(house1_3d, face2_3d)
print ("%s run1 house vs run2 house in 3D: %s" % (subid, threeD_house1_house2))
print ("%s run1 house vs run2 face in 3D: %s" % (subid, threeD_house1_face2))
print (separator)
# prepare data to analyze 3D brain based on odd runs even runs
print ("Prepare data to analyze \"3D\" brain based on odd runs and even runs:")
print ("Take average of \"3D\" odd runs / even runs to deal with impacts of variations between runs")
even_run_3d = {}
odd_run_3d = {}
# add up even run results / odd run results and take mean for each groups
for item in object_list:
even_run_3d[item] = np.zeros_like(raw_beta_vols_3d_corr["%s_run001" % subid][:, 25:50, 37:42, match_para[item]])
odd_run_3d[item] = np.zeros_like(raw_beta_vols_3d_corr["%s_run001" % subid][:, 25:50, 37:42, match_para[item]])
print ("make average of \"3D\" odd run results:")
# add up odd runs results
for i in range(1, run_num+1, 2):
temp = raw_beta_vols_3d_corr["%s_run0%02d" % (subid, i)][:, 25:50, 37:42, match_para[item]]
temp[np.isnan(temp)] = 0
odd_run_3d[item] += temp
print("odd runs 3D: %d-%s" % (i, item))
# take mean
odd_run_3d[item] = odd_run_3d[item]/odd_count
print ("make average of \"3D\" even run results:")
# add up even runs results
for i in range(2, run_num+1, 2):
temp = raw_beta_vols_3d_corr["%s_run0%02d" % (subid, i)][:, 25:50, 37:42, match_para[item]]
temp[np.isnan(temp)] = 0
even_run_3d[item] += temp
print("even runs 3D: %d-%s" % (i, item))
# take mean
even_run_3d[item] = even_run_3d[item]/even_count
# save odd run and even run results as txt file
for key, fig in even_run_3d.items():
np.savetxt(file_path + "%s_even_%s_3d.txt" % (subid, key), np.ravel(fig))
for key, fig in odd_run_3d.items():
np.savetxt(file_path + "%s_odd_%s_3d.txt" % (subid, key), np.ravel(fig))
print ("\"3D\" odd run and even run results are saved as txt files!!!!!")
print (separator)
print ("Analysis and Data Pre-processing for %s : Complete!!!" % subid) | bsd-3-clause |