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pkruskal/scikit-learn | sklearn/covariance/graph_lasso_.py | 127 | 25626 | """GraphLasso: sparse inverse covariance estimation with an l1-penalized
estimator.
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
# Copyright: INRIA
import warnings
import operator
import sys
import time
import numpy as np
from scipy import linalg
from .empirical_covariance_ import (empirical_covariance, EmpiricalCovariance,
log_likelihood)
from ..utils import ConvergenceWarning
from ..utils.extmath import pinvh
from ..utils.validation import check_random_state, check_array
from ..linear_model import lars_path
from ..linear_model import cd_fast
from ..cross_validation import check_cv, cross_val_score
from ..externals.joblib import Parallel, delayed
import collections
# Helper functions to compute the objective and dual objective functions
# of the l1-penalized estimator
def _objective(mle, precision_, alpha):
"""Evaluation of the graph-lasso objective function
the objective function is made of a shifted scaled version of the
normalized log-likelihood (i.e. its empirical mean over the samples) and a
penalisation term to promote sparsity
"""
p = precision_.shape[0]
cost = - 2. * log_likelihood(mle, precision_) + p * np.log(2 * np.pi)
cost += alpha * (np.abs(precision_).sum()
- np.abs(np.diag(precision_)).sum())
return cost
def _dual_gap(emp_cov, precision_, alpha):
"""Expression of the dual gap convergence criterion
The specific definition is given in Duchi "Projected Subgradient Methods
for Learning Sparse Gaussians".
"""
gap = np.sum(emp_cov * precision_)
gap -= precision_.shape[0]
gap += alpha * (np.abs(precision_).sum()
- np.abs(np.diag(precision_)).sum())
return gap
def alpha_max(emp_cov):
"""Find the maximum alpha for which there are some non-zeros off-diagonal.
Parameters
----------
emp_cov : 2D array, (n_features, n_features)
The sample covariance matrix
Notes
-----
This results from the bound for the all the Lasso that are solved
in GraphLasso: each time, the row of cov corresponds to Xy. As the
bound for alpha is given by `max(abs(Xy))`, the result follows.
"""
A = np.copy(emp_cov)
A.flat[::A.shape[0] + 1] = 0
return np.max(np.abs(A))
# The g-lasso algorithm
def graph_lasso(emp_cov, alpha, cov_init=None, mode='cd', tol=1e-4,
enet_tol=1e-4, max_iter=100, verbose=False,
return_costs=False, eps=np.finfo(np.float64).eps,
return_n_iter=False):
"""l1-penalized covariance estimator
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
Parameters
----------
emp_cov : 2D ndarray, shape (n_features, n_features)
Empirical covariance from which to compute the covariance estimate.
alpha : positive float
The regularization parameter: the higher alpha, the more
regularization, the sparser the inverse covariance.
cov_init : 2D array (n_features, n_features), optional
The initial guess for the covariance.
mode : {'cd', 'lars'}
The Lasso solver to use: coordinate descent or LARS. Use LARS for
very sparse underlying graphs, where p > n. Elsewhere prefer cd
which is more numerically stable.
tol : positive float, optional
The tolerance to declare convergence: if the dual gap goes below
this value, iterations are stopped.
enet_tol : positive float, optional
The tolerance for the elastic net solver used to calculate the descent
direction. This parameter controls the accuracy of the search direction
for a given column update, not of the overall parameter estimate. Only
used for mode='cd'.
max_iter : integer, optional
The maximum number of iterations.
verbose : boolean, optional
If verbose is True, the objective function and dual gap are
printed at each iteration.
return_costs : boolean, optional
If return_costs is True, the objective function and dual gap
at each iteration are returned.
eps : float, optional
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems.
return_n_iter : bool, optional
Whether or not to return the number of iterations.
Returns
-------
covariance : 2D ndarray, shape (n_features, n_features)
The estimated covariance matrix.
precision : 2D ndarray, shape (n_features, n_features)
The estimated (sparse) precision matrix.
costs : list of (objective, dual_gap) pairs
The list of values of the objective function and the dual gap at
each iteration. Returned only if return_costs is True.
n_iter : int
Number of iterations. Returned only if `return_n_iter` is set to True.
See Also
--------
GraphLasso, GraphLassoCV
Notes
-----
The algorithm employed to solve this problem is the GLasso algorithm,
from the Friedman 2008 Biostatistics paper. It is the same algorithm
as in the R `glasso` package.
One possible difference with the `glasso` R package is that the
diagonal coefficients are not penalized.
"""
_, n_features = emp_cov.shape
if alpha == 0:
if return_costs:
precision_ = linalg.inv(emp_cov)
cost = - 2. * log_likelihood(emp_cov, precision_)
cost += n_features * np.log(2 * np.pi)
d_gap = np.sum(emp_cov * precision_) - n_features
if return_n_iter:
return emp_cov, precision_, (cost, d_gap), 0
else:
return emp_cov, precision_, (cost, d_gap)
else:
if return_n_iter:
return emp_cov, linalg.inv(emp_cov), 0
else:
return emp_cov, linalg.inv(emp_cov)
if cov_init is None:
covariance_ = emp_cov.copy()
else:
covariance_ = cov_init.copy()
# As a trivial regularization (Tikhonov like), we scale down the
# off-diagonal coefficients of our starting point: This is needed, as
# in the cross-validation the cov_init can easily be
# ill-conditioned, and the CV loop blows. Beside, this takes
# conservative stand-point on the initial conditions, and it tends to
# make the convergence go faster.
covariance_ *= 0.95
diagonal = emp_cov.flat[::n_features + 1]
covariance_.flat[::n_features + 1] = diagonal
precision_ = pinvh(covariance_)
indices = np.arange(n_features)
costs = list()
# The different l1 regression solver have different numerical errors
if mode == 'cd':
errors = dict(over='raise', invalid='ignore')
else:
errors = dict(invalid='raise')
try:
# be robust to the max_iter=0 edge case, see:
# https://github.com/scikit-learn/scikit-learn/issues/4134
d_gap = np.inf
for i in range(max_iter):
for idx in range(n_features):
sub_covariance = covariance_[indices != idx].T[indices != idx]
row = emp_cov[idx, indices != idx]
with np.errstate(**errors):
if mode == 'cd':
# Use coordinate descent
coefs = -(precision_[indices != idx, idx]
/ (precision_[idx, idx] + 1000 * eps))
coefs, _, _, _ = cd_fast.enet_coordinate_descent_gram(
coefs, alpha, 0, sub_covariance, row, row,
max_iter, enet_tol, check_random_state(None), False)
else:
# Use LARS
_, _, coefs = lars_path(
sub_covariance, row, Xy=row, Gram=sub_covariance,
alpha_min=alpha / (n_features - 1), copy_Gram=True,
method='lars', return_path=False)
# Update the precision matrix
precision_[idx, idx] = (
1. / (covariance_[idx, idx]
- np.dot(covariance_[indices != idx, idx], coefs)))
precision_[indices != idx, idx] = (- precision_[idx, idx]
* coefs)
precision_[idx, indices != idx] = (- precision_[idx, idx]
* coefs)
coefs = np.dot(sub_covariance, coefs)
covariance_[idx, indices != idx] = coefs
covariance_[indices != idx, idx] = coefs
d_gap = _dual_gap(emp_cov, precision_, alpha)
cost = _objective(emp_cov, precision_, alpha)
if verbose:
print(
'[graph_lasso] Iteration % 3i, cost % 3.2e, dual gap %.3e'
% (i, cost, d_gap))
if return_costs:
costs.append((cost, d_gap))
if np.abs(d_gap) < tol:
break
if not np.isfinite(cost) and i > 0:
raise FloatingPointError('Non SPD result: the system is '
'too ill-conditioned for this solver')
else:
warnings.warn('graph_lasso: did not converge after %i iteration:'
' dual gap: %.3e' % (max_iter, d_gap),
ConvergenceWarning)
except FloatingPointError as e:
e.args = (e.args[0]
+ '. The system is too ill-conditioned for this solver',)
raise e
if return_costs:
if return_n_iter:
return covariance_, precision_, costs, i + 1
else:
return covariance_, precision_, costs
else:
if return_n_iter:
return covariance_, precision_, i + 1
else:
return covariance_, precision_
class GraphLasso(EmpiricalCovariance):
"""Sparse inverse covariance estimation with an l1-penalized estimator.
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
Parameters
----------
alpha : positive float, default 0.01
The regularization parameter: the higher alpha, the more
regularization, the sparser the inverse covariance.
mode : {'cd', 'lars'}, default 'cd'
The Lasso solver to use: coordinate descent or LARS. Use LARS for
very sparse underlying graphs, where p > n. Elsewhere prefer cd
which is more numerically stable.
tol : positive float, default 1e-4
The tolerance to declare convergence: if the dual gap goes below
this value, iterations are stopped.
enet_tol : positive float, optional
The tolerance for the elastic net solver used to calculate the descent
direction. This parameter controls the accuracy of the search direction
for a given column update, not of the overall parameter estimate. Only
used for mode='cd'.
max_iter : integer, default 100
The maximum number of iterations.
verbose : boolean, default False
If verbose is True, the objective function and dual gap are
plotted at each iteration.
assume_centered : boolean, default False
If True, data are not centered before computation.
Useful when working with data whose mean is almost, but not exactly
zero.
If False, data are centered before computation.
Attributes
----------
covariance_ : array-like, shape (n_features, n_features)
Estimated covariance matrix
precision_ : array-like, shape (n_features, n_features)
Estimated pseudo inverse matrix.
n_iter_ : int
Number of iterations run.
See Also
--------
graph_lasso, GraphLassoCV
"""
def __init__(self, alpha=.01, mode='cd', tol=1e-4, enet_tol=1e-4,
max_iter=100, verbose=False, assume_centered=False):
self.alpha = alpha
self.mode = mode
self.tol = tol
self.enet_tol = enet_tol
self.max_iter = max_iter
self.verbose = verbose
self.assume_centered = assume_centered
# The base class needs this for the score method
self.store_precision = True
def fit(self, X, y=None):
X = check_array(X)
if self.assume_centered:
self.location_ = np.zeros(X.shape[1])
else:
self.location_ = X.mean(0)
emp_cov = empirical_covariance(
X, assume_centered=self.assume_centered)
self.covariance_, self.precision_, self.n_iter_ = graph_lasso(
emp_cov, alpha=self.alpha, mode=self.mode, tol=self.tol,
enet_tol=self.enet_tol, max_iter=self.max_iter,
verbose=self.verbose, return_n_iter=True)
return self
# Cross-validation with GraphLasso
def graph_lasso_path(X, alphas, cov_init=None, X_test=None, mode='cd',
tol=1e-4, enet_tol=1e-4, max_iter=100, verbose=False):
"""l1-penalized covariance estimator along a path of decreasing alphas
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
Parameters
----------
X : 2D ndarray, shape (n_samples, n_features)
Data from which to compute the covariance estimate.
alphas : list of positive floats
The list of regularization parameters, decreasing order.
X_test : 2D array, shape (n_test_samples, n_features), optional
Optional test matrix to measure generalisation error.
mode : {'cd', 'lars'}
The Lasso solver to use: coordinate descent or LARS. Use LARS for
very sparse underlying graphs, where p > n. Elsewhere prefer cd
which is more numerically stable.
tol : positive float, optional
The tolerance to declare convergence: if the dual gap goes below
this value, iterations are stopped.
enet_tol : positive float, optional
The tolerance for the elastic net solver used to calculate the descent
direction. This parameter controls the accuracy of the search direction
for a given column update, not of the overall parameter estimate. Only
used for mode='cd'.
max_iter : integer, optional
The maximum number of iterations.
verbose : integer, optional
The higher the verbosity flag, the more information is printed
during the fitting.
Returns
-------
covariances_ : List of 2D ndarray, shape (n_features, n_features)
The estimated covariance matrices.
precisions_ : List of 2D ndarray, shape (n_features, n_features)
The estimated (sparse) precision matrices.
scores_ : List of float
The generalisation error (log-likelihood) on the test data.
Returned only if test data is passed.
"""
inner_verbose = max(0, verbose - 1)
emp_cov = empirical_covariance(X)
if cov_init is None:
covariance_ = emp_cov.copy()
else:
covariance_ = cov_init
covariances_ = list()
precisions_ = list()
scores_ = list()
if X_test is not None:
test_emp_cov = empirical_covariance(X_test)
for alpha in alphas:
try:
# Capture the errors, and move on
covariance_, precision_ = graph_lasso(
emp_cov, alpha=alpha, cov_init=covariance_, mode=mode, tol=tol,
enet_tol=enet_tol, max_iter=max_iter, verbose=inner_verbose)
covariances_.append(covariance_)
precisions_.append(precision_)
if X_test is not None:
this_score = log_likelihood(test_emp_cov, precision_)
except FloatingPointError:
this_score = -np.inf
covariances_.append(np.nan)
precisions_.append(np.nan)
if X_test is not None:
if not np.isfinite(this_score):
this_score = -np.inf
scores_.append(this_score)
if verbose == 1:
sys.stderr.write('.')
elif verbose > 1:
if X_test is not None:
print('[graph_lasso_path] alpha: %.2e, score: %.2e'
% (alpha, this_score))
else:
print('[graph_lasso_path] alpha: %.2e' % alpha)
if X_test is not None:
return covariances_, precisions_, scores_
return covariances_, precisions_
class GraphLassoCV(GraphLasso):
"""Sparse inverse covariance w/ cross-validated choice of the l1 penalty
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
Parameters
----------
alphas : integer, or list positive float, optional
If an integer is given, it fixes the number of points on the
grids of alpha to be used. If a list is given, it gives the
grid to be used. See the notes in the class docstring for
more details.
n_refinements: strictly positive integer
The number of times the grid is refined. Not used if explicit
values of alphas are passed.
cv : cross-validation generator, optional
see sklearn.cross_validation module. If None is passed, defaults to
a 3-fold strategy
tol: positive float, optional
The tolerance to declare convergence: if the dual gap goes below
this value, iterations are stopped.
enet_tol : positive float, optional
The tolerance for the elastic net solver used to calculate the descent
direction. This parameter controls the accuracy of the search direction
for a given column update, not of the overall parameter estimate. Only
used for mode='cd'.
max_iter: integer, optional
Maximum number of iterations.
mode: {'cd', 'lars'}
The Lasso solver to use: coordinate descent or LARS. Use LARS for
very sparse underlying graphs, where number of features is greater
than number of samples. Elsewhere prefer cd which is more numerically
stable.
n_jobs: int, optional
number of jobs to run in parallel (default 1).
verbose: boolean, optional
If verbose is True, the objective function and duality gap are
printed at each iteration.
assume_centered : Boolean
If True, data are not centered before computation.
Useful when working with data whose mean is almost, but not exactly
zero.
If False, data are centered before computation.
Attributes
----------
covariance_ : numpy.ndarray, shape (n_features, n_features)
Estimated covariance matrix.
precision_ : numpy.ndarray, shape (n_features, n_features)
Estimated precision matrix (inverse covariance).
alpha_ : float
Penalization parameter selected.
cv_alphas_ : list of float
All penalization parameters explored.
`grid_scores`: 2D numpy.ndarray (n_alphas, n_folds)
Log-likelihood score on left-out data across folds.
n_iter_ : int
Number of iterations run for the optimal alpha.
See Also
--------
graph_lasso, GraphLasso
Notes
-----
The search for the optimal penalization parameter (alpha) is done on an
iteratively refined grid: first the cross-validated scores on a grid are
computed, then a new refined grid is centered around the maximum, and so
on.
One of the challenges which is faced here is that the solvers can
fail to converge to a well-conditioned estimate. The corresponding
values of alpha then come out as missing values, but the optimum may
be close to these missing values.
"""
def __init__(self, alphas=4, n_refinements=4, cv=None, tol=1e-4,
enet_tol=1e-4, max_iter=100, mode='cd', n_jobs=1,
verbose=False, assume_centered=False):
self.alphas = alphas
self.n_refinements = n_refinements
self.mode = mode
self.tol = tol
self.enet_tol = enet_tol
self.max_iter = max_iter
self.verbose = verbose
self.cv = cv
self.n_jobs = n_jobs
self.assume_centered = assume_centered
# The base class needs this for the score method
self.store_precision = True
def fit(self, X, y=None):
"""Fits the GraphLasso covariance model to X.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Data from which to compute the covariance estimate
"""
X = check_array(X)
if self.assume_centered:
self.location_ = np.zeros(X.shape[1])
else:
self.location_ = X.mean(0)
emp_cov = empirical_covariance(
X, assume_centered=self.assume_centered)
cv = check_cv(self.cv, X, y, classifier=False)
# List of (alpha, scores, covs)
path = list()
n_alphas = self.alphas
inner_verbose = max(0, self.verbose - 1)
if isinstance(n_alphas, collections.Sequence):
alphas = self.alphas
n_refinements = 1
else:
n_refinements = self.n_refinements
alpha_1 = alpha_max(emp_cov)
alpha_0 = 1e-2 * alpha_1
alphas = np.logspace(np.log10(alpha_0), np.log10(alpha_1),
n_alphas)[::-1]
t0 = time.time()
for i in range(n_refinements):
with warnings.catch_warnings():
# No need to see the convergence warnings on this grid:
# they will always be points that will not converge
# during the cross-validation
warnings.simplefilter('ignore', ConvergenceWarning)
# Compute the cross-validated loss on the current grid
# NOTE: Warm-restarting graph_lasso_path has been tried, and
# this did not allow to gain anything (same execution time with
# or without).
this_path = Parallel(
n_jobs=self.n_jobs,
verbose=self.verbose
)(
delayed(graph_lasso_path)(
X[train], alphas=alphas,
X_test=X[test], mode=self.mode,
tol=self.tol, enet_tol=self.enet_tol,
max_iter=int(.1 * self.max_iter),
verbose=inner_verbose)
for train, test in cv)
# Little danse to transform the list in what we need
covs, _, scores = zip(*this_path)
covs = zip(*covs)
scores = zip(*scores)
path.extend(zip(alphas, scores, covs))
path = sorted(path, key=operator.itemgetter(0), reverse=True)
# Find the maximum (avoid using built in 'max' function to
# have a fully-reproducible selection of the smallest alpha
# in case of equality)
best_score = -np.inf
last_finite_idx = 0
for index, (alpha, scores, _) in enumerate(path):
this_score = np.mean(scores)
if this_score >= .1 / np.finfo(np.float64).eps:
this_score = np.nan
if np.isfinite(this_score):
last_finite_idx = index
if this_score >= best_score:
best_score = this_score
best_index = index
# Refine the grid
if best_index == 0:
# We do not need to go back: we have chosen
# the highest value of alpha for which there are
# non-zero coefficients
alpha_1 = path[0][0]
alpha_0 = path[1][0]
elif (best_index == last_finite_idx
and not best_index == len(path) - 1):
# We have non-converged models on the upper bound of the
# grid, we need to refine the grid there
alpha_1 = path[best_index][0]
alpha_0 = path[best_index + 1][0]
elif best_index == len(path) - 1:
alpha_1 = path[best_index][0]
alpha_0 = 0.01 * path[best_index][0]
else:
alpha_1 = path[best_index - 1][0]
alpha_0 = path[best_index + 1][0]
if not isinstance(n_alphas, collections.Sequence):
alphas = np.logspace(np.log10(alpha_1), np.log10(alpha_0),
n_alphas + 2)
alphas = alphas[1:-1]
if self.verbose and n_refinements > 1:
print('[GraphLassoCV] Done refinement % 2i out of %i: % 3is'
% (i + 1, n_refinements, time.time() - t0))
path = list(zip(*path))
grid_scores = list(path[1])
alphas = list(path[0])
# Finally, compute the score with alpha = 0
alphas.append(0)
grid_scores.append(cross_val_score(EmpiricalCovariance(), X,
cv=cv, n_jobs=self.n_jobs,
verbose=inner_verbose))
self.grid_scores = np.array(grid_scores)
best_alpha = alphas[best_index]
self.alpha_ = best_alpha
self.cv_alphas_ = alphas
# Finally fit the model with the selected alpha
self.covariance_, self.precision_, self.n_iter_ = graph_lasso(
emp_cov, alpha=best_alpha, mode=self.mode, tol=self.tol,
enet_tol=self.enet_tol, max_iter=self.max_iter,
verbose=inner_verbose, return_n_iter=True)
return self
| bsd-3-clause |
mikekestemont/ruzicka | code/04latin_test_o2.py | 1 | 3340 | from __future__ import print_function
import os
import time
import json
import pickle
import sys
from itertools import product, combinations
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from ruzicka.utilities import binarize
from ruzicka.vectorization import Vectorizer
from ruzicka.utilities import load_pan_dataset, train_dev_split, get_vocab_size
from sklearn.cross_validation import train_test_split
from ruzicka.score_shifting import ScoreShifter
from ruzicka.evaluation import pan_metrics
from ruzicka.Order2Verifier import Order2Verifier as Verifier
import ruzicka.art as art
# run script for top-5 metrics
ngram_type = 'word'
ngram_size = 1
base = 'profile'
vector_space = 'tf_std'
metric = 'cosine'
nb_bootstrap_iter = 100
rnd_prop = 0.5
nb_imposters = 30
mfi = sys.maxint
min_df = 2
# get imposter data:
train_data, _ = load_pan_dataset('../data/latin/dev') # ignore unknown documents
train_labels, train_documents = zip(*train_data)
# get test data:
test_data, _ = load_pan_dataset('../data/latin/test') # ignore unknown documents
test_labels, test_documents = zip(*test_data)
# fit encoder for author labels:
label_encoder = LabelEncoder()
label_encoder.fit(train_labels+test_labels)
train_ints = label_encoder.transform(train_labels)
test_ints = label_encoder.transform(test_labels)
# fit vectorizer:
vectorizer = Vectorizer(mfi = mfi,
vector_space = vector_space,
ngram_type = ngram_type,
ngram_size = ngram_size)
vectorizer.fit(train_documents+test_documents)
train_X = vectorizer.transform(train_documents).toarray()
test_X = vectorizer.transform(test_documents).toarray()
cols = ['label']
for test_author in sorted(set(test_ints)):
auth_label = label_encoder.inverse_transform([test_author])[0]
cols.append(auth_label)
proba_df = pd.DataFrame(columns=cols)
for idx in range(len(test_documents)):
target_auth = test_ints[idx]
target_docu = test_X[idx]
non_target_test_ints = np.array([test_ints[i] for i in range(len(test_ints)) if i != idx])
non_target_test_X = np.array([test_X[i] for i in range(len(test_ints)) if i != idx])
tmp_train_X = np.vstack((train_X, non_target_test_X))
tmp_train_y = np.hstack((train_ints, non_target_test_ints))
tmp_test_X, tmp_test_y = [], []
for t_auth in sorted(set(test_ints)):
tmp_test_X.append(target_docu)
tmp_test_y.append(t_auth)
# fit the verifier:
verifier = Verifier(metric = metric,
base = base,
nb_bootstrap_iter = nb_bootstrap_iter,
rnd_prop = rnd_prop)
verifier.fit(tmp_train_X, tmp_train_y)
probas = verifier.predict_proba(test_X = tmp_test_X,
test_y = tmp_test_y,
nb_imposters = nb_imposters)
row = [label_encoder.inverse_transform([target_auth])[0]] # author label
row += list(probas)
print(row)
proba_df.loc[len(proba_df)] = row
proba_df = proba_df.set_index('label')
# write away score tables:
table_dir = '../output/tables/'
if not os.path.isdir(table_dir):
os.mkdir(table_dir)
proba_df.to_csv(table_dir+'lat_proba_'+metric+'_'+vector_space+'.csv')
| mit |
MartinDelzant/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 |
ipashchenko/emcee-x | document/plots/oned.py | 16 | 2164 | import os
import sys
import time
import numpy as np
import matplotlib.pyplot as pl
import h5py
from multiprocessing import Pool
sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..", "..")))
import emcee
# import acor
def lnprobfn(p, icov):
return -0.5 * np.dot(p, np.dot(icov, p))
def random_cov(ndim, dof=1):
v = np.random.randn(ndim * (ndim + dof)).reshape((ndim + dof, ndim))
return (sum([np.outer(v[i], v[i]) for i in range(ndim + dof)])
/ (ndim + dof))
_rngs = {}
def _worker(args):
i, outfn, nsteps = args
pid = os.getpid()
_random = _rngs.get(pid, np.random.RandomState(int(int(pid)
+ time.time())))
_rngs[pid] = _random
ndim = int(np.ceil(2 ** (7 * _random.rand())))
nwalkers = 2 * ndim + 2
# nwalkers += nwalkers % 2
print ndim, nwalkers
cov = random_cov(ndim)
icov = np.linalg.inv(cov)
ens_samp = emcee.EnsembleSampler(nwalkers, ndim, lnprobfn,
args=[icov])
ens_samp.random_state = _random.get_state()
pos, lnprob, state = ens_samp.run_mcmc(np.random.randn(nwalkers * ndim)
.reshape([nwalkers, ndim]), nsteps)
proposal = np.diag(cov.diagonal())
mh_samp = emcee.MHSampler(proposal, ndim, lnprobfn,
args=[icov])
mh_samp.random_state = state
mh_samp.run_mcmc(np.random.randn(ndim), nsteps)
f = h5py.File(outfn)
f["data"][i, :] = np.array([ndim, np.mean(ens_samp.acor),
np.mean(mh_samp.acor)])
f.close()
def oned():
nsteps = 10000
niter = 10
nthreads = 2
outfn = os.path.join(os.path.split(__file__)[0], "gauss_scaling.h5")
print outfn
f = h5py.File(outfn, "w")
f.create_dataset("data", (niter, 3), "f")
f.close()
pool = Pool(nthreads)
pool.map(_worker, [(i, outfn, nsteps) for i in range(niter)])
f = h5py.File(outfn)
data = f["data"][...]
f.close()
pl.clf()
pl.plot(data[:, 0], data[:, 1], "ks", alpha=0.5)
pl.plot(data[:, 0], data[:, 2], ".k", alpha=0.5)
pl.savefig(os.path.join(os.path.split(__file__)[0], "gauss_scaling.png"))
if __name__ == "__main__":
oned()
| mit |
gdl-civestav-localization/cinvestav_location_fingerprinting | experimentation/__init__.py | 1 | 1691 | import os
import cPickle
import matplotlib.pyplot as plt
from datasets import DatasetManager
def plot_cost(results, data_name, plot_label):
plt.figure(plot_label)
plt.ylabel('Accuracy (m)', fontsize=30)
plt.xlabel('Epoch', fontsize=30)
plt.yscale('symlog')
plt.tick_params(axis='both', which='major', labelsize=20)
plt.grid(True)
for i in range(1, 2, 1):
y, x = zip(*results[i][data_name])
name = results[i]['Name']
plt.plot(x, y, label=name, linewidth=5.0)
plt.legend(fontsize='xx-large')
def get_metrics(test_set_y, predicted_values, model_name):
for i in xrange(len(predicted_values)):
print predicted_values[i][1]
if __name__ == '__main__':
"""
seed = 50
with open(os.path.join('experimentation', 'cinvestav_testbed_experiment_results_' + str(seed)), 'rb') as f:
results = cPickle.load(f)
plot_cost(
results=results,
data_name='cost_train',
plot_label='Cost on train phase')
plot_cost(
results=results,
data_name='cost_valid',
plot_label='Cost on valid phase')
plot_cost(
results=results,
data_name='cost_test',
plot_label='Cost on test phase')
plt.show()
"""
seed = 50
dataset, result = DatasetManager.read_dataset2('test_cleaned_dataset.csv', shared=True, seed=seed)
with open(os.path.join('trained_models', 'Logistic Regressionbrandeis_university.save'), 'rb') as f:
model = cPickle.load(f)
predicted_values = model.predict(dataset)
get_metrics(
test_set_y=result,
predicted_values=predicted_values,
model_name='Logistic Regression'
)
| gpl-3.0 |
bachiraoun/fullrmc | Constraints/StructureFactorConstraints.py | 1 | 64342 | """
StructureFactorConstraints contains classes for all constraints related experimental static structure factor functions.
.. inheritance-diagram:: fullrmc.Constraints.StructureFactorConstraints
:parts: 1
"""
# standard libraries imports
from __future__ import print_function
import itertools, re
# external libraries imports
import numpy as np
from pdbparser.Utilities.Database import is_element_property, get_element_property
from pdbparser.Utilities.Collection import get_normalized_weighting
# fullrmc imports
from ..Globals import INT_TYPE, FLOAT_TYPE, PI, PRECISION, LOGGER
from ..Globals import str, long, unicode, bytes, basestring, range, xrange, maxint
from ..Core.Collection import is_number, is_integer, get_path
from ..Core.Collection import reset_if_collected_out_of_date, get_real_elements_weight
from ..Core.Collection import get_caller_frames
from ..Core.Constraint import Constraint, ExperimentalConstraint
from ..Core.pairs_histograms import multiple_pairs_histograms_coords, full_pairs_histograms_coords
class StructureFactorConstraint(ExperimentalConstraint):
"""
Controls the Structure Factor noted as S(Q) and also called
total-scattering structure function or Static Structure Factor.
S(Q) is a dimensionless quantity and normalized such as the average
value :math:`<S(Q)>=1`.
It is worth mentioning that S(Q) is nothing other than the normalized and
corrected diffraction pattern if all experimental artefacts powder.
The computation of S(Q) is done through an inverse Sine Fourier transform
of the computed pair distribution function G(r).
.. math::
S(Q) = 1+ \\frac{1}{Q} \\int_{0}^{\\infty} G(r) sin(Qr) dr
From an atomistic model and histogram point of view, G(r) is computed as
the following:
.. math::
G(r) = 4 \\pi r (\\rho_{r} - \\rho_{0})
= 4 \\pi \\rho_{0} r (g(r)-1)
= \\frac{R(r)}{r} - 4 \\pi \\rho_{0}
g(r) is calculated after binning all pair atomic distances into a
weighted histograms as the following:
.. math::
g(r) = \\sum \\limits_{i,j}^{N} w_{i,j} \\frac{\\rho_{i,j}(r)}{\\rho_{0}}
= \\sum \\limits_{i,j}^{N} w_{i,j} \\frac{n_{i,j}(r) / v(r)}{N_{i,j} / V}
Where:\n
:math:`Q` is the momentum transfer. \n
:math:`r` is the distance between two atoms. \n
:math:`\\rho_{i,j}(r)` is the pair density function of atoms i and j. \n
:math:`\\rho_{0}` is the average number density of the system. \n
:math:`w_{i,j}` is the relative weighting of atom types i and j. \n
:math:`R(r)` is the radial distribution function (rdf). \n
:math:`N` is the total number of atoms. \n
:math:`V` is the volume of the system. \n
:math:`n_{i,j}(r)` is the number of atoms i neighbouring j at a distance r. \n
:math:`v(r)` is the annulus volume at distance r and of thickness dr. \n
:math:`N_{i,j}` is the total number of atoms i and j in the system. \n
+----------------------------------------------------------------------+
|.. figure:: reduced_structure_factor_constraint_plot_method.png |
| :width: 530px |
| :height: 400px |
| :align: left |
| |
| Reduced structure factor of memory shape Nickel-Titanium alloy. |
+----------------------------------------------------------------------+
:Parameters:
#. experimentalData (numpy.ndarray, string): Experimental data as
numpy.ndarray or string path to load data using numpy.loadtxt
method.
#. dataWeights (None, numpy.ndarray): Weights array of the same number
of points of experimentalData used in the constraint's standard
error computation. Therefore particular fitting emphasis can be
put on different data points that might be considered as more or less
important in order to get a reasonable and plausible modal.\n
If None is given, all data points are considered of the same
importance in the computation of the constraint's standard error.\n
If numpy.ndarray is given, all weights must be positive and all
zeros weighted data points won't contribute to the total
constraint's standard error. At least a single weight point is
required to be non-zeros and the weights array will be automatically
scaled upon setting such as the the sum of all the weights
is equal to the number of data points.
#. weighting (string): The elements weighting scheme. It must be any
atomic attribute (atomicNumber, neutronCohb, neutronIncohb,
neutronCohXs, neutronIncohXs, atomicWeight, covalentRadius) defined
in pdbparser database. In case of xrays or neutrons experimental
weights, one can simply set weighting to 'xrays' or 'neutrons'
and the value will be automatically adjusted to respectively
'atomicNumber' and 'neutronCohb'. If attribute values are
missing in the pdbparser database, atomic weights must be
given in atomsWeight dictionary argument.
#. atomsWeight (None, dict): Atoms weight dictionary where keys are
atoms element and values are custom weights. If None is given
or partially given, missing elements weighting will be fully set
given weighting scheme.
#. rmin (None, number): The minimum distance value to compute G(r)
histogram. If None is given, rmin is computed as
:math:`2 \\pi / Q_{max}`.
#. rmax (None, number): The maximum distance value to compute G(r)
histogram. If None is given, rmax is computed as
:math:`2 \\pi / dQ`.
#. dr (None, number): The distance bin value to compute G(r)
histogram. If None is given, bin is computed as
:math:`2 \\pi / (Q_{max}-Q_{min})`.
#. scaleFactor (number): A normalization scale factor used to normalize
the computed data to the experimental ones.
#. adjustScaleFactor (list, tuple): Used to adjust fit or guess
the best scale factor during stochastic engine runtime.
It must be a list of exactly three entries.\n
#. The frequency in number of generated moves of finding the best
scale factor. If 0 frequency is given, it means that the scale
factor is fixed.
#. The minimum allowed scale factor value.
#. The maximum allowed scale factor value.
#. windowFunction (None, numpy.ndarray): The window function to
convolute with the computed pair distribution function of the
system prior to comparing it with the experimental data. In
general, the experimental pair distribution function G(r) shows
artificial wrinkles, among others the main reason is because
G(r) is computed by applying a sine Fourier transform to the
experimental structure factor S(q). Therefore window function is
used to best imitate the numerical artefacts in the experimental
data.
#. limits (None, tuple, list): The distance limits to compute the
histograms. If None is given, the limits will be automatically
set the the min and max distance of the experimental data.
Otherwise, a tuple of exactly two items where the first is the
minimum distance or None and the second is the maximum distance
or None.
**NB**: If adjustScaleFactor first item (frequency) is 0, the scale factor
will remain untouched and the limits minimum and maximum won't be checked.
.. code-block:: python
# import fullrmc modules
from fullrmc.Engine import Engine
from fullrmc.Constraints.StructureFactorConstraints import StructureFactorConstraint
# create engine
ENGINE = Engine(path='my_engine.rmc')
# set pdb file
ENGINE.set_pdb('system.pdb')
# create and add constraint
SFC = StructureFactorConstraint(experimentalData="sq.dat", weighting="atomicNumber")
ENGINE.add_constraints(SFC)
"""
def __init__(self, experimentalData, dataWeights=None,
weighting="atomicNumber", atomsWeight=None,
rmin=None, rmax=None, dr=None,
scaleFactor=1.0, adjustScaleFactor=(0, 0.8, 1.2),
windowFunction=None, limits=None):
# initialize variables
self.__experimentalQValues = None
self.__experimentalSF = None
self.__rmin = None
self.__rmax = None
self.__dr = None
self.__minimumDistance = None
self.__maximumDistance = None
self.__bin = None
self.__shellCenters = None
self.__histogramSize = None
self.__shellVolumes = None
self.__Gr2SqMatrix = None
# initialize constraint
super(StructureFactorConstraint, self).__init__( experimentalData=experimentalData, dataWeights=dataWeights, scaleFactor=scaleFactor, adjustScaleFactor=adjustScaleFactor)
# set atomsWeight
self.set_atoms_weight(atomsWeight)
# set elements weighting
self.set_weighting(weighting)
self.__set_weighting_scheme()
# set window function
self.set_window_function(windowFunction)
# set r parameters
self.set_rmin(rmin)
self.set_rmax(rmax)
self.set_dr(dr)
# set frame data
FRAME_DATA = [d for d in self.FRAME_DATA]
FRAME_DATA.extend(['_StructureFactorConstraint__experimentalQValues',
'_StructureFactorConstraint__experimentalSF',
'_StructureFactorConstraint__elementsPairs',
'_StructureFactorConstraint__weightingScheme',
'_StructureFactorConstraint__atomsWeight',
'_StructureFactorConstraint__qmin',
'_StructureFactorConstraint__qmax',
'_StructureFactorConstraint__rmin',
'_StructureFactorConstraint__rmax',
'_StructureFactorConstraint__dr',
'_StructureFactorConstraint__minimumDistance',
'_StructureFactorConstraint__maximumDistance',
'_StructureFactorConstraint__bin',
'_StructureFactorConstraint__shellCenters',
'_StructureFactorConstraint__histogramSize',
'_StructureFactorConstraint__shellVolumes',
'_StructureFactorConstraint__Gr2SqMatrix',
'_StructureFactorConstraint__windowFunction',
'_elementsWeight',] )
RUNTIME_DATA = [d for d in self.RUNTIME_DATA]
RUNTIME_DATA.extend( [] )
object.__setattr__(self, 'FRAME_DATA', tuple(FRAME_DATA) )
object.__setattr__(self, 'RUNTIME_DATA', tuple(RUNTIME_DATA) )
def _codify_update__(self, name='constraint', addDependencies=True):
dependencies = []
code = []
if addDependencies:
code.extend(dependencies)
dw = self.dataWeights
if dw is not None:
dw = list(dw)
code.append("dw = {dw}".format(dw=dw))
wf = self.windowFunction
if isinstance(wf, np.ndarray):
code.append("wf = np.array({wf})".format(wf=list(wf)))
else:
code.append("wf = {wf}".format(wf=wf))
code.append("{name}.set_used({val})".format(name=name, val=self.used))
code.append("{name}.set_scale_factor({val})".format(name=name, val=self.scaleFactor))
code.append("{name}.set_adjust_scale_factor({val})".format(name=name, val=self.adjustScaleFactor))
code.append("{name}.set_data_weights(dw)".format(name=name))
code.append("{name}.set_atoms_weight({val})".format(name=name, val=self.atomsWeight))
code.append("{name}.set_window_function(wf)".format(name=name))
code.append("{name}.set_rmin({val})".format(name=name, val=self.rmin))
code.append("{name}.set_rmax({val})".format(name=name, val=self.rmax))
code.append("{name}.set_dr({val})".format(name=name, val=self.dr))
code.append("{name}.set_limits({val})".format(name=name, val=self.limits))
# return
return dependencies, '\n'.join(code)
def _codify__(self, engine, name='constraint', addDependencies=True):
assert isinstance(name, basestring), LOGGER.error("name must be a string")
assert re.match('[a-zA-Z_][a-zA-Z0-9_]*$', name) is not None, LOGGER.error("given name '%s' can't be used as a variable name"%name)
klass = self.__class__.__name__
dependencies = ['import numpy as np','from fullrmc.Constraints import StructureFactorConstraints']
code = []
if addDependencies:
code.extend(dependencies)
x = list(self.experimentalData[:,0])
y = list(self.experimentalData[:,1])
code.append("x = {x}".format(x=x))
code.append("y = {y}".format(y=y))
code.append("d = np.transpose([x,y]).astype(np.float32)")
dw = self.dataWeights
if dw is not None:
dw = list(dw)
code.append("dw = {dw}".format(dw=dw))
wf = self.windowFunction
if isinstance(wf, np.ndarray):
code.append("wf = np.array({wf})".format(wf=list(wf)))
else:
code.append("wf = {wf}".format(wf=wf))
code.append("{name} = {klass}s.{klass}\
(experimentalData=d, dataWeights=dw, weighting='{weighting}', atomsWeight={atomsWeight}, \
rmin={rmin}, rmax={rmax}, dr={dr}, scaleFactor={scaleFactor}, adjustScaleFactor={adjustScaleFactor}, \
shapeFuncParams=sfp, windowFunction=wf, limits={limits})".format(name=name, klass=klass,
weighting=self.weighting, atomsWeight=self.atomsWeight, rmin=self.rmin,
rmax=self.rmax, dr=self.dr, scaleFactor=self.scaleFactor,
adjustScaleFactor=self.adjustScaleFactor, limits=self.limits))
code.append("{engine}.add_constraints([{name}])".format(engine=engine, name=name))
# return
return dependencies, '\n'.join(code)
#def __getstate__(self):
# # make sure that __Gr2SqMatrix is not pickled but saved to the disk as None
# state = super(StructureFactorConstraint, self).__getstate__()
# state["_StructureFactorConstraint__Gr2SqMatrix"] = None
# return state
#
#def __setstate__(self, state):
# # make sure to regenerate G(r) to S(q) matrix at loading time
# self.__dict__.update( state )
# self.__set_Gr_2_Sq_matrix()
#
def __set_Gr_2_Sq_matrix(self):
if self.__experimentalQValues is None or self.__shellCenters is None:
self.__Gr2SqMatrix = None
else:
Qs = self.__experimentalQValues
Rs = self.__shellCenters
dr = self.__shellCenters[1]-self.__shellCenters[0]
qr = Rs.reshape((-1,1))*(np.ones((len(Rs),1), dtype=FLOAT_TYPE)*Qs)
sinqr = np.sin(qr)
sinqr_q = sinqr/Qs
self.__Gr2SqMatrix = dr*sinqr_q
def __set_weighting_scheme(self):
if self.engine is not None:
self.__elementsPairs = sorted(itertools.combinations_with_replacement(self.engine.elements,2))
#elementsWeight = dict([(el,float(get_element_property(el,self.__weighting))) for el in self.engine.elements])
#self._elementsWeight = dict([(el,self.__atomsWeight.get(el, float(get_element_property(el,self.__weighting)))) for el in self.engine.elements])
self._elementsWeight = get_real_elements_weight(elements=self.engine.elements, weightsDict=self.__atomsWeight, weighting=self.__weighting)
self.__weightingScheme = get_normalized_weighting(numbers=self.engine.numberOfAtomsPerElement, weights=self._elementsWeight)
for k in self.__weightingScheme:
self.__weightingScheme[k] = FLOAT_TYPE(self.__weightingScheme[k])
else:
self.__elementsPairs = None
self.__weightingScheme = None
# dump to repository
self._dump_to_repository({'_StructureFactorConstraint__elementsPairs' : self.__elementsPairs,
'_StructureFactorConstraint__weightingScheme': self.__weightingScheme})
def __set_histogram(self):
if self.__minimumDistance is None or self.__maximumDistance is None or self.__bin is None:
self.__shellCenters = None
self.__histogramSize = None
self.__shellVolumes = None
else:
# compute edges
if self.engine is not None and self.rmax is None:
minHalfBox = np.min( [np.linalg.norm(v)/2. for v in self.engine.basisVectors])
self.__edges = np.arange(self.__minimumDistance,minHalfBox, self.__bin).astype(FLOAT_TYPE)
else:
self.__edges = np.arange(self.__minimumDistance, self.__maximumDistance+self.__bin, self.__bin).astype(FLOAT_TYPE)
# adjust rmin and rmax
self.__minimumDistance = self.__edges[0]
self.__maximumDistance = self.__edges[-1]
# compute shellCenters
self.__shellCenters = (self.__edges[0:-1]+self.__edges[1:])/FLOAT_TYPE(2.)
# set histogram size
self.__histogramSize = INT_TYPE( len(self.__edges)-1 )
# set shell centers and volumes
self.__shellVolumes = FLOAT_TYPE(4.0/3.)*PI*((self.__edges[1:])**3 - self.__edges[0:-1]**3)
# dump to repository
self._dump_to_repository({'_StructureFactorConstraint__minimumDistance': self.__minimumDistance,
'_StructureFactorConstraint__maximumDistance': self.__maximumDistance,
'_StructureFactorConstraint__shellCenters' : self.__shellCenters,
'_StructureFactorConstraint__histogramSize' : self.__histogramSize,
'_StructureFactorConstraint__shellVolumes' : self.__shellVolumes})
# reset constraint
self.reset_constraint()
# reset sq matrix
self.__set_Gr_2_Sq_matrix()
def _on_collector_reset(self):
pass
@property
def rmin(self):
""" Histogram minimum distance. """
return self.__rmin
@property
def rmax(self):
""" Histogram maximum distance. """
return self.__rmax
@property
def dr(self):
""" Histogram bin size."""
return self.__dr
@property
def bin(self):
""" Computed histogram distance bin size."""
return self.__bin
@property
def minimumDistance(self):
""" Computed histogram minimum distance. """
return self.__minimumDistance
@property
def maximumDistance(self):
""" Computed histogram maximum distance. """
return self.__maximumDistance
@property
def qmin(self):
""" Experimental data reciprocal distances minimum. """
return self.__qmin
@property
def qmax(self):
""" Experimental data reciprocal distances maximum. """
return self.__qmax
@property
def dq(self):
""" Experimental data reciprocal distances bin size. """
return self.__experimentalQValues[1]-self.__experimentalQValues[0]
@property
def experimentalQValues(self):
""" Experimental data used q values. """
return self.__experimentalQValues
@property
def histogramSize(self):
""" Histogram size"""
return self.__histogramSize
@property
def shellCenters(self):
""" Shells center array"""
return self.__shellCenters
@property
def shellVolumes(self):
""" Shells volume array"""
return self.__shellVolumes
@property
def experimentalSF(self):
""" Experimental Structure Factor or S(q)"""
return self.__experimentalSF
@property
def elementsPairs(self):
""" Elements pairs """
return self.__elementsPairs
@property
def atomsWeight(self):
"""Custom atoms weight"""
return self.__atomsWeight
@property
def weighting(self):
""" Elements weighting definition. """
return self.__weighting
@property
def weightingScheme(self):
""" Elements weighting scheme. """
return self.__weightingScheme
@property
def windowFunction(self):
""" Convolution window function. """
return self.__windowFunction
@property
def Gr2SqMatrix(self):
""" G(r) to S(q) transformation matrix."""
return self.__Gr2SqMatrix
@property
def _experimentalX(self):
"""For internal use only to interface
ExperimentalConstraint.get_constraints_properties"""
return self.__experimentalQValues
@property
def _experimentalY(self):
"""For internal use only to interface
ExperimentalConstraint.get_constraints_properties"""
return self.__experimentalSF
@property
def _modelX(self):
"""For internal use only to interface
ExperimentalConstraint.get_constraints_properties"""
return self.__experimentalQValues
def listen(self, message, argument=None):
"""
Listens to any message sent from the Broadcaster.
:Parameters:
#. message (object): Any python object to send to constraint's
listen method.
#. argument (object): Any type of argument to pass to the
listeners.
"""
if message in ("engine set","update pdb","update molecules indexes","update elements indexes","update names indexes"):
self.__set_weighting_scheme()
# reset histogram
if self.engine is not None:
self.__set_histogram()
self.reset_constraint() # ADDED 2017-JAN-08
elif message in("update boundary conditions",):
self.reset_constraint()
def set_rmin(self, rmin):
"""
Set rmin value.
:parameters:
#. rmin (None, number): The minimum distance value to compute G(r)
histogram. If None is given, rmin is computed as
:math:`2 \\pi / Q_{max}`.
"""
if rmin is None:
minimumDistance = FLOAT_TYPE( 2.*PI/self.__qmax )
else:
assert is_number(rmin), LOGGER.error("rmin must be None or a number")
minimumDistance = FLOAT_TYPE(rmin)
if self.__maximumDistance is not None:
assert minimumDistance<self.__maximumDistance, LOGGER.error("rmin must be smaller than rmax %s"%self.__maximumDistance)
self.__rmin = rmin
self.__minimumDistance = minimumDistance
# dump to repository
self._dump_to_repository({'_StructureFactorConstraint__rmin': self.__rmin,
'_StructureFactorConstraint__minimumDistance': self.__minimumDistance})
# reset histogram
self.__set_histogram()
def set_rmax(self, rmax):
"""
Set rmax value.
:Parameters:
#. rmax (None, number): The maximum distance value to compute G(r)
histogram. If None is given, rmax is computed as
:math:`2 \\pi / dQ`.
"""
if rmax is None:
dq = self.__experimentalQValues[1]-self.__experimentalQValues[0]
maximumDistance = FLOAT_TYPE( 2.*PI/dq )
else:
assert is_number(rmax), LOGGER.error("rmax must be None or a number")
maximumDistance = FLOAT_TYPE(rmax)
if self.__minimumDistance is not None:
assert maximumDistance>self.__minimumDistance, LOGGER.error("rmax must be bigger than rmin %s"%self.__minimumDistance)
self.__rmax = rmax
self.__maximumDistance = maximumDistance
# dump to repository
self._dump_to_repository({'_StructureFactorConstraint__rmax': self.__rmax,
'_StructureFactorConstraint__maximumDistance': self.__maximumDistance})
# reset histogram
self.__set_histogram()
def set_dr(self, dr):
"""
Set dr value.
:Parameters:
#. dr (None, number): The distance bin value to compute G(r)
histogram. If None is given, bin is computed as
:math:`2 \\pi / (Q_{max}-Q_{min})`.
"""
if dr is None:
bin = 2.*PI/self.__qmax
rbin = round(bin,1)
if rbin>bin:
rbin -= 0.1
bin = FLOAT_TYPE( rbin )
else:
assert is_number(dr), LOGGER.error("dr must be None or a number")
bin = FLOAT_TYPE(dr)
self.__dr = dr
self.__bin = bin
# dump to repository
self._dump_to_repository({'_StructureFactorConstraint__dr': self.__dr,
'_StructureFactorConstraint__bin': self.__bin})
# reset histogram
self.__set_histogram()
def set_weighting(self, weighting):
"""
Set elements weighting. It must be a valid entry of pdbparser atom's
database.
:Parameters:
#. weighting (string): The elements weighting scheme. It must be
any atomic attribute (atomicNumber, neutronCohb, neutronIncohb,
neutronCohXs, neutronIncohXs, atomicWeight, covalentRadius)
defined in pdbparser database. In case of xrays or neutrons
experimental weights, one can simply set weighting to 'xrays'
or 'neutrons' and the value will be automatically adjusted to
respectively 'atomicNumber' and 'neutronCohb'. If attribute
values are missing in the pdbparser database, atomic weights
must be given in atomsWeight dictionary argument.
"""
if weighting.lower() in ["xrays","x-rays","xray","x-ray"]:
LOGGER.fixed("'%s' weighting is set to atomicNumber"%weighting)
weighting = "atomicNumber"
elif weighting.lower() in ["neutron","neutrons"]:
LOGGER.fixed("'%s' weighting is set to neutronCohb"%weighting)
weighting = "neutronCohb"
assert is_element_property(weighting),LOGGER.error( "weighting is not a valid pdbparser atoms database entry")
assert weighting != "atomicFormFactor", LOGGER.error("atomicFormFactor weighting is not allowed")
self.__weighting = weighting
# dump to repository
self._dump_to_repository({'_StructureFactorConstraint__weighting': self.__weighting})
def set_atoms_weight(self, atomsWeight):
"""
Custom set atoms weight. This is the way to setting a atoms weights
different than the given weighting scheme.
:Parameters:
#. atomsWeight (None, dict): Atoms weight dictionary where keys are
atoms element and values are custom weights. If None is given
or partially given, missing elements weighting will be fully set
given weighting scheme.
"""
if atomsWeight is None:
AW = {}
else:
assert isinstance(atomsWeight, dict),LOGGER.error("atomsWeight must be None or a dictionary")
AW = {}
for k in atomsWeight:
assert isinstance(k, basestring),LOGGER.error("atomsWeight keys must be strings")
try:
val = float(atomsWeight[k])
except:
raise LOGGER.error( "atomsWeight values must be numerical")
AW[k]=val
# set atomsWeight
self.__atomsWeight = AW
# dump to repository
self._dump_to_repository({'_StructureFactorConstraint__atomsWeight': self.__atomsWeight})
def set_window_function(self, windowFunction):
"""
Set convolution window function.
:Parameters:
#. windowFunction (None, numpy.ndarray): The window function to
convolute with the computed pair distribution function of the
system prior to comparing it with the experimental data. In
general, the experimental pair distribution function G(r) shows
artificial wrinkles, among others the main reason is because
G(r) is computed by applying a sine Fourier transform to the
experimental structure factor S(q). Therefore window function is
used to best imitate the numerical artefacts in the experimental
data.
"""
if windowFunction is not None:
assert isinstance(windowFunction, np.ndarray), LOGGER.error("windowFunction must be a numpy.ndarray")
assert windowFunction.dtype.type is FLOAT_TYPE, LOGGER.error("windowFunction type must be %s"%FLOAT_TYPE)
assert len(windowFunction.shape) == 1, LOGGER.error("windowFunction must be of dimension 1")
assert len(windowFunction) <= self.experimentalData.shape[0], LOGGER.error("windowFunction length must be smaller than experimental data")
# normalize window function
windowFunction /= np.sum(windowFunction)
# check window size
# set windowFunction
self.__windowFunction = windowFunction
# dump to repository
self._dump_to_repository({'_StructureFactorConstraint__windowFunction': self.__windowFunction})
def set_experimental_data(self, experimentalData):
"""
Set constraint's experimental data.
:Parameters:
#. experimentalData (numpy.ndarray, string): The experimental
data as numpy.ndarray or string path to load data using
numpy.loadtxt function.
"""
# get experimental data
super(StructureFactorConstraint, self).set_experimental_data(experimentalData=experimentalData)
# set limits
self.set_limits(self.limits)
def set_limits(self, limits):
"""
Set the reciprocal distance limits (qmin, qmax).
:Parameters:
#. limits (None, tuple, list): Distance limits to bound
experimental data and compute histograms.
If None is given, the limits will be automatically set to
min and max reciprocal distance recorded in experimental data.
If given, a tuple of minimum reciprocal distance (qmin) or None
and maximum reciprocal distance (qmax) or None should be given.
"""
self._ExperimentalConstraint__set_limits(limits)
# set qvalues
self.__experimentalQValues = self.experimentalData[self.limitsIndexStart:self.limitsIndexEnd+1,0].astype(FLOAT_TYPE)
self.__experimentalSF = self.experimentalData[self.limitsIndexStart:self.limitsIndexEnd+1,1].astype(FLOAT_TYPE)
# set qmin and qmax
self.__qmin = self.__experimentalQValues[0]
self.__qmax = self.__experimentalQValues[-1]
assert self.__qmin>0, LOGGER.error("qmin must be bigger than 0. Experimental null q values are ambigous. Try setting limits.")
# dump to repository
self._dump_to_repository({'_StructureFactorConstraint__experimentalQValues': self.__experimentalQValues,
'_StructureFactorConstraint__experimentalSF' : self.__experimentalSF,
'_StructureFactorConstraint__qmin' : self.__qmin,
'_StructureFactorConstraint__qmax' : self.__qmax})
# set used dataWeights
self._set_used_data_weights(limitsIndexStart=self.limitsIndexStart, limitsIndexEnd=self.limitsIndexEnd)
# reset constraint
self.reset_constraint()
# reset sq matrix
self.__set_Gr_2_Sq_matrix()
def update_standard_error(self):
""" Compute and set constraint's standardError."""
# set standardError
totalSQ = self.get_constraint_value()["total_no_window"]
self.set_standard_error(self.compute_standard_error(modelData = totalSQ))
def check_experimental_data(self, experimentalData):
"""
Check whether experimental data is correct.
:Parameters:
#. experimentalData (object): The experimental data to check.
:Returns:
#. result (boolean): Whether it is correct or not.
#. message (str): Checking message that explains whats's wrong
with the given data
"""
if not isinstance(experimentalData, np.ndarray):
return False, "experimentalData must be a numpy.ndarray"
if experimentalData.dtype.type is not FLOAT_TYPE:
return False, "experimentalData type must be %s"%FLOAT_TYPE
if len(experimentalData.shape) !=2:
return False, "experimentalData must be of dimension 2"
if experimentalData.shape[1] !=2:
return False, "experimentalData must have only 2 columns"
# check distances order
inOrder = (np.array(sorted(experimentalData[:,0]), dtype=FLOAT_TYPE)-experimentalData[:,0])<=PRECISION
if not np.all(inOrder):
return False, "experimentalData distances are not sorted in order"
if experimentalData[0][0]<0:
return False, "experimentalData distances min value is found negative"
# data format is correct
return True, ""
def compute_standard_error(self, modelData):
"""
Compute the standard error (StdErr) as the squared deviations
between model computed data and the experimental ones.
.. math::
StdErr = \\sum \\limits_{i}^{N} W_{i}(Y(X_{i})-F(X_{i}))^{2}
Where:\n
:math:`N` is the total number of experimental data points. \n
:math:`W_{i}` is the data point weight. It becomes equivalent to 1 when dataWeights is set to None. \n
:math:`Y(X_{i})` is the experimental data point :math:`X_{i}`. \n
:math:`F(X_{i})` is the computed from the model data :math:`X_{i}`. \n
:Parameters:
#. modelData (numpy.ndarray): The data to compare with the
experimental one and compute the squared deviation.
:Returns:
#. standardError (number): The calculated constraint's
standardError.
"""
# compute difference
diff = self.__experimentalSF-modelData
# return standard error
if self._usedDataWeights is None:
return np.add.reduce((diff)**2)
else:
return np.add.reduce(self._usedDataWeights*((diff)**2))
def _get_Sq_from_Gr(self, Gr):
return np.sum(Gr.reshape((-1,1))*self.__Gr2SqMatrix, axis=0)+1
def _apply_scale_factor(self, Sq, scaleFactor):
if scaleFactor != 1:
Sq = scaleFactor*(Sq-1) + 1
return Sq
def __get_total_Sq(self, data, rho0):
"""This method is created just to speed up the computation of
the total Sq upon fitting."""
Gr = np.zeros(self.__histogramSize, dtype=FLOAT_TYPE)
for pair in self.__elementsPairs:
# get weighting scheme
wij = self.__weightingScheme.get(pair[0]+"-"+pair[1], None)
if wij is None:
wij = self.__weightingScheme[pair[1]+"-"+pair[0]]
# get number of atoms per element
ni = self.engine.numberOfAtomsPerElement[pair[0]]
nj = self.engine.numberOfAtomsPerElement[pair[1]]
# get index of element
idi = self.engine.elements.index(pair[0])
idj = self.engine.elements.index(pair[1])
# get Nij
if idi == idj:
Nij = ni*(ni-1)/2.0
Dij = Nij/self.engine.volume
nij = data["intra"][idi,idj,:]+data["inter"][idi,idj,:]
Gr += wij*nij/Dij
else:
Nij = ni*nj
Dij = Nij/self.engine.volume
nij = data["intra"][idi,idj,:]+data["intra"][idj,idi,:] + data["inter"][idi,idj,:]+data["inter"][idj,idi,:]
Gr += wij*nij/Dij
# Devide by shells volume
Gr /= self.shellVolumes
# compute total G(r)
#rho0 = (self.engine.numberOfAtoms/self.engine.volume).astype(FLOAT_TYPE)
Gr = (FLOAT_TYPE(4.)*PI*self.__shellCenters*rho0)*(Gr-1)
# Compute S(q) from G(r)
Sq = self._get_Sq_from_Gr(Gr)
# Multiply by scale factor
self._fittedScaleFactor = self.get_adjusted_scale_factor(self.__experimentalSF, Sq, self._usedDataWeights)
# apply scale factor
Sq = self._apply_scale_factor(Sq, self._fittedScaleFactor)
# apply multiframe prior and weight
Sq = self._apply_multiframe_prior(Sq)
# convolve total with window function
if self.__windowFunction is not None:
Sq = np.convolve(Sq, self.__windowFunction, 'same')
return Sq
def get_adjusted_scale_factor(self, experimentalData, modelData, dataWeights):
"""Overload to reduce S(q) prior to fitting scale factor.
S(q) -> 1 at high q and this will create a wrong scale factor.
Overloading can be avoided but it's better to for performance reasons
"""
SF = self.scaleFactor
# check to update scaleFactor
if self.adjustScaleFactorFrequency:
if not self.engine.accepted%self.adjustScaleFactorFrequency:
SF = self.fit_scale_factor(experimentalData-1, modelData-1, dataWeights)
return SF
def _get_constraint_value(self, data, applyMultiframePrior=True):
# http://erice2011.docking.org/upload/Other/Billinge_PDF/03-ReadingMaterial/BillingePDF2011.pdf page 6
#import time
#startTime = time.clock()
output = {}
for pair in self.__elementsPairs:
output["sf_intra_%s-%s" % pair] = np.zeros(self.__histogramSize, dtype=FLOAT_TYPE)
output["sf_inter_%s-%s" % pair] = np.zeros(self.__histogramSize, dtype=FLOAT_TYPE)
output["sf_total_%s-%s" % pair] = np.zeros(self.__histogramSize, dtype=FLOAT_TYPE)
gr = np.zeros(self.__histogramSize, dtype=FLOAT_TYPE)
for pair in self.__elementsPairs:
# get weighting scheme
wij = self.__weightingScheme.get(pair[0]+"-"+pair[1], None)
if wij is None:
wij = self.__weightingScheme[pair[1]+"-"+pair[0]]
# get number of atoms per element
ni = self.engine.numberOfAtomsPerElement[pair[0]]
nj = self.engine.numberOfAtomsPerElement[pair[1]]
# get index of element
idi = self.engine.elements.index(pair[0])
idj = self.engine.elements.index(pair[1])
# get Nij
if idi == idj:
Nij = ni*(ni-1)/2.0
output["sf_intra_%s-%s" % pair] += data["intra"][idi,idj,:]
output["sf_inter_%s-%s" % pair] += data["inter"][idi,idj,:]
else:
Nij = ni*nj
output["sf_intra_%s-%s" % pair] += data["intra"][idi,idj,:] + data["intra"][idj,idi,:]
output["sf_inter_%s-%s" % pair] += data["inter"][idi,idj,:] + data["inter"][idj,idi,:]
# compute g(r)
nij = output["sf_intra_%s-%s" % pair] + output["sf_inter_%s-%s" % pair]
dij = nij/self.__shellVolumes
Dij = Nij/self.engine.volume
gr += wij*dij/Dij
# calculate intensityFactor
intensityFactor = (self.engine.volume*wij)/(Nij*self.__shellVolumes)
# divide by factor
output["sf_intra_%s-%s" % pair] *= intensityFactor
output["sf_inter_%s-%s" % pair] *= intensityFactor
output["sf_total_%s-%s" % pair] = output["sf_intra_%s-%s" % pair] + output["sf_inter_%s-%s" % pair]
# Compute S(q) from G(r)
output["sf_intra_%s-%s" % pair] = self._get_Sq_from_Gr(output["sf_intra_%s-%s" % pair])
output["sf_inter_%s-%s" % pair] = self._get_Sq_from_Gr(output["sf_inter_%s-%s" % pair])
output["sf_total_%s-%s" % pair] = self._get_Sq_from_Gr(output["sf_total_%s-%s" % pair])
# compute total G(r)
rho0 = (self.engine.numberOfAtoms/self.engine.volume).astype(FLOAT_TYPE)
Gr = (FLOAT_TYPE(4.)*PI*self.__shellCenters*rho0) * (gr-1)
# Compute S(q) from G(r)
Sq = self._get_Sq_from_Gr(Gr)
# multiply by scale factor
output["total_no_window"] = self._apply_scale_factor(Sq, self._fittedScaleFactor)
# apply multiframe prior and weight
if applyMultiframePrior:
output["total_no_window"] = self._apply_multiframe_prior(output["total_no_window"])
# convolve total with window function
if self.__windowFunction is not None:
output["total"] = np.convolve(output["total_no_window"], self.__windowFunction, 'same').astype(FLOAT_TYPE)
else:
output["total"] = output["total_no_window"]
return output
def get_constraint_value(self, applyMultiframePrior=True):
"""
Compute all partial Structure Factor (SQs).
:Parameters:
#. applyMultiframePrior (boolean): Whether to apply subframe weight
and prior to the total. This will only have an effect when used
frame is a subframe and in case subframe weight and prior is
defined.
:Returns:
#. SQs (dictionary): The SQs dictionnary, where keys are the
element wise intra and inter molecular SQs and values are
the computed SQs.
"""
if self.data is None:
LOGGER.warn("data must be computed first using 'compute_data' method.")
return {}
return self._get_constraint_value(self.data, applyMultiframePrior=applyMultiframePrior)
def get_constraint_original_value(self):
"""
Compute all partial Pair Distribution Functions (PDFs).
:Returns:
#. PDFs (dictionary): The PDFs dictionnary, where keys are the
element wise intra and inter molecular PDFs and values are the
computed PDFs.
"""
if self.originalData is None:
LOGGER.warn("originalData must be computed first using 'compute_data' method.")
return {}
return self._get_constraint_value(self.originalData)
@reset_if_collected_out_of_date
def compute_data(self, update=True):
""" Compute constraint's data.
:Parameters:
#. update (boolean): whether to update constraint data and
standard error with new computation. If data is computed and
updated by another thread or process while the stochastic
engine is running, this might lead to a state alteration of
the constraint which will lead to a no additional accepted
moves in the run
:Returns:
#. data (dict): constraint data dictionary
#. standardError (float): constraint standard error
"""
intra,inter = full_pairs_histograms_coords( boxCoords = self.engine.boxCoordinates,
basis = self.engine.basisVectors,
isPBC = self.engine.isPBC,
moleculeIndex = self.engine.moleculesIndex,
elementIndex = self.engine.elementsIndex,
numberOfElements = self.engine.numberOfElements,
minDistance = self.__minimumDistance,
maxDistance = self.__maximumDistance,
histSize = self.__histogramSize,
bin = self.__bin,
ncores = self.engine._runtime_ncores )
# create data and compute standard error
data = {"intra":intra, "inter":inter}
totalSQ = self.__get_total_Sq(data, rho0=self.engine.numberDensity)
stdError = self.compute_standard_error(modelData = totalSQ)
# update
if update:
self.set_data(data)
self.set_active_atoms_data_before_move(None)
self.set_active_atoms_data_after_move(None)
self.set_standard_error(stdError)
# set original data
if self.originalData is None:
self._set_original_data(self.data)
# return
return data, stdError
def compute_before_move(self, realIndexes, relativeIndexes):
"""
Compute constraint before move is executed
:Parameters:
#. realIndexes (numpy.ndarray): Not used here.
#. relativeIndexes (numpy.ndarray): Group atoms relative index
the move will be applied to.
"""
intraM,interM = multiple_pairs_histograms_coords( indexes = relativeIndexes,
boxCoords = self.engine.boxCoordinates,
basis = self.engine.basisVectors,
isPBC = self.engine.isPBC,
moleculeIndex = self.engine.moleculesIndex,
elementIndex = self.engine.elementsIndex,
numberOfElements = self.engine.numberOfElements,
minDistance = self.__minimumDistance,
maxDistance = self.__maximumDistance,
histSize = self.__histogramSize,
bin = self.__bin,
allAtoms = True,
ncores = self.engine._runtime_ncores )
intraF,interF = full_pairs_histograms_coords( boxCoords = self.engine.boxCoordinates[relativeIndexes],
basis = self.engine.basisVectors,
isPBC = self.engine.isPBC,
moleculeIndex = self.engine.moleculesIndex[relativeIndexes],
elementIndex = self.engine.elementsIndex[relativeIndexes],
numberOfElements = self.engine.numberOfElements,
minDistance = self.__minimumDistance,
maxDistance = self.__maximumDistance,
histSize = self.__histogramSize,
bin = self.__bin,
ncores = self.engine._runtime_ncores )
self.set_active_atoms_data_before_move( {"intra":intraM-intraF, "inter":interM-interF} )
self.set_active_atoms_data_after_move(None)
def compute_after_move(self, realIndexes, relativeIndexes, movedBoxCoordinates):
"""
Compute constraint after move is executed
:Parameters:
#. realIndexes (numpy.ndarray): Not used here.
#. relativeIndexes (numpy.ndarray): Group atoms relative index
the move will be applied to.
#. movedBoxCoordinates (numpy.ndarray): The moved atoms new coordinates.
"""
# change coordinates temporarily
boxData = np.array(self.engine.boxCoordinates[relativeIndexes], dtype=FLOAT_TYPE)
self.engine.boxCoordinates[relativeIndexes] = movedBoxCoordinates
# calculate pair distribution function
intraM,interM = multiple_pairs_histograms_coords( indexes = relativeIndexes,
boxCoords = self.engine.boxCoordinates,
basis = self.engine.basisVectors,
isPBC = self.engine.isPBC,
moleculeIndex = self.engine.moleculesIndex,
elementIndex = self.engine.elementsIndex,
numberOfElements = self.engine.numberOfElements,
minDistance = self.__minimumDistance,
maxDistance = self.__maximumDistance,
histSize = self.__histogramSize,
bin = self.__bin,
allAtoms = True,
ncores = self.engine._runtime_ncores )
intraF,interF = full_pairs_histograms_coords( boxCoords = self.engine.boxCoordinates[relativeIndexes],
basis = self.engine.basisVectors,
isPBC = self.engine.isPBC,
moleculeIndex = self.engine.moleculesIndex[relativeIndexes],
elementIndex = self.engine.elementsIndex[relativeIndexes],
numberOfElements = self.engine.numberOfElements,
minDistance = self.__minimumDistance,
maxDistance = self.__maximumDistance,
histSize = self.__histogramSize,
bin = self.__bin,
ncores = self.engine._runtime_ncores )
# set active atoms data
self.set_active_atoms_data_after_move( {"intra":intraM-intraF, "inter":interM-interF} )
# reset coordinates
self.engine.boxCoordinates[relativeIndexes] = boxData
# compute standardError after move
dataIntra = self.data["intra"]-self.activeAtomsDataBeforeMove["intra"]+self.activeAtomsDataAfterMove["intra"]
dataInter = self.data["inter"]-self.activeAtomsDataBeforeMove["inter"]+self.activeAtomsDataAfterMove["inter"]
totalSQ = self.__get_total_Sq({"intra":dataIntra, "inter":dataInter}, rho0=self.engine.numberDensity)
self.set_after_move_standard_error( self.compute_standard_error(modelData = totalSQ) )
# increment tried
self.increment_tried()
def accept_move(self, realIndexes, relativeIndexes):
"""
Accept move
:Parameters:
#. realIndexes (numpy.ndarray): Not used here.
#. relativeIndexes (numpy.ndarray): Not used here.
"""
dataIntra = self.data["intra"]-self.activeAtomsDataBeforeMove["intra"]+self.activeAtomsDataAfterMove["intra"]
dataInter = self.data["inter"]-self.activeAtomsDataBeforeMove["inter"]+self.activeAtomsDataAfterMove["inter"]
# change permanently _data
self.set_data( {"intra":dataIntra, "inter":dataInter} )
# reset activeAtoms data
self.set_active_atoms_data_before_move(None)
self.set_active_atoms_data_after_move(None)
# update standardError
self.set_standard_error( self.afterMoveStandardError )
self.set_after_move_standard_error( None )
# set new scale factor
self._set_fitted_scale_factor_value(self._fittedScaleFactor)
# increment accepted
self.increment_accepted()
def reject_move(self, realIndexes, relativeIndexes):
"""
Reject move
:Parameters:
#. realIndexes (numpy.ndarray): Not used here.
#. relativeIndexes (numpy.ndarray): Not used here.
"""
# reset activeAtoms data
self.set_active_atoms_data_before_move(None)
self.set_active_atoms_data_after_move(None)
# update standardError
self.set_after_move_standard_error( None )
def compute_as_if_amputated(self, realIndex, relativeIndex):
"""
Compute and return constraint's data and standard error as if
given atom is amputated.
:Parameters:
#. realIndex (numpy.ndarray): Atom's index as a numpy array
of a single element.
#. relativeIndex (numpy.ndarray): Atom's relative index as a
numpy array of a single element.
"""
# compute data
self.compute_before_move(realIndexes=realIndex, relativeIndexes=relativeIndex)
dataIntra = self.data["intra"]-self.activeAtomsDataBeforeMove["intra"]
dataInter = self.data["inter"]-self.activeAtomsDataBeforeMove["inter"]
data = {"intra":dataIntra, "inter":dataInter}
# temporarily adjust self.__weightingScheme
weightingScheme = self.__weightingScheme
relativeIndex = relativeIndex[0]
selectedElement = self.engine.allElements[relativeIndex]
self.engine.numberOfAtomsPerElement[selectedElement] -= 1
self.__weightingScheme = get_normalized_weighting(numbers=self.engine.numberOfAtomsPerElement, weights=self._elementsWeight )
for k in self.__weightingScheme:
self.__weightingScheme[k] = FLOAT_TYPE(self.__weightingScheme[k])
## END OF ADDED 08 FEB 2017
# compute standard error
if not self.engine._RT_moveGenerator.allowFittingScaleFactor:
SF = self.adjustScaleFactorFrequency
self._set_adjust_scale_factor_frequency(0)
rho0 = ((self.engine.numberOfAtoms-1)/self.engine.volume).astype(FLOAT_TYPE)
totalSQ = self.__get_total_Sq(data, rho0=rho0)
standardError = self.compute_standard_error(modelData = totalSQ)
if not self.engine._RT_moveGenerator.allowFittingScaleFactor:
self._set_adjust_scale_factor_frequency(SF)
# reset activeAtoms data
self.set_active_atoms_data_before_move(None)
# set amputation
self.set_amputation_data( {'data':data, 'weightingScheme':self.__weightingScheme} )
# compute standard error
self.set_amputation_standard_error( standardError )
# reset weightingScheme and number of atoms per element
self.__weightingScheme = weightingScheme
self.engine.numberOfAtomsPerElement[selectedElement] += 1
def accept_amputation(self, realIndex, relativeIndex):
"""
Accept amputated atom and sets constraints data and standard error accordingly.
:Parameters:
#. realIndex (numpy.ndarray): Not used here.
#. relativeIndex (numpy.ndarray): Not used here.
"""
#self.set_data( self.amputationData ) ## COMMENTED 08 FEB 2017
self.set_data( self.amputationData['data'] )
self.__weightingScheme = self.amputationData['weightingScheme']
self.set_standard_error( self.amputationStandardError )
self.set_amputation_data( None )
self.set_amputation_standard_error( None )
# set new scale factor
self._set_fitted_scale_factor_value(self._fittedScaleFactor)
def reject_amputation(self, realIndex, relativeIndex):
"""
Reject amputated atom and set constraint's data and standard
error accordingly.
:Parameters:
#. realIndex (numpy.ndarray): Not used here.
#. relativeIndex (numpy.ndarray): Not used here.
"""
self.set_amputation_data( None )
self.set_amputation_standard_error( None )
def _on_collector_collect_atom(self, realIndex):
pass
def _on_collector_release_atom(self, realIndex):
pass
def _constraint_copy_needs_lut(self):
return {'_StructureFactorConstraint__elementsPairs' :'_StructureFactorConstraint__elementsPairs',
'_StructureFactorConstraint__histogramSize' :'_StructureFactorConstraint__histogramSize',
'_StructureFactorConstraint__weightingScheme' :'_StructureFactorConstraint__weightingScheme',
'_StructureFactorConstraint__shellVolumes' :'_StructureFactorConstraint__shellVolumes',
'_StructureFactorConstraint__shellCenters' :'_StructureFactorConstraint__shellCenters',
'_StructureFactorConstraint__windowFunction' :'_StructureFactorConstraint__windowFunction',
'_StructureFactorConstraint__experimentalQValues' :'_StructureFactorConstraint__experimentalQValues',
'_StructureFactorConstraint__experimentalSF' :'_StructureFactorConstraint__experimentalSF',
'_StructureFactorConstraint__Gr2SqMatrix' :'_StructureFactorConstraint__Gr2SqMatrix',
'_StructureFactorConstraint__minimumDistance' :'_StructureFactorConstraint__minimumDistance',
'_StructureFactorConstraint__maximumDistance' :'_StructureFactorConstraint__maximumDistance',
'_StructureFactorConstraint__bin' :'_StructureFactorConstraint__bin',
'_ExperimentalConstraint__scaleFactor' :'_ExperimentalConstraint__scaleFactor',
'_ExperimentalConstraint__dataWeights' :'_ExperimentalConstraint__dataWeights',
'_ExperimentalConstraint__multiframePrior' :'_ExperimentalConstraint__multiframePrior',
'_ExperimentalConstraint__multiframeWeight' :'_ExperimentalConstraint__multiframeWeight',
'_ExperimentalConstraint__limits' :'_ExperimentalConstraint__limits',
'_ExperimentalConstraint__limitsIndexStart' :'_ExperimentalConstraint__limitsIndexStart',
'_ExperimentalConstraint__limitsIndexEnd' :'_ExperimentalConstraint__limitsIndexEnd',
'_Constraint__used' :'_Constraint__used',
'_Constraint__data' :'_Constraint__data',
'_Constraint__state' :'_Constraint__state',
'_Constraint__standardError' :'_Constraint__standardError',
'_fittedScaleFactor' :'_fittedScaleFactor',
'_usedDataWeights' :'_usedDataWeights',
'_Engine__state' :'_Engine__state',
'_Engine__boxCoordinates' :'_Engine__boxCoordinates',
'_Engine__basisVectors' :'_Engine__basisVectors',
'_Engine__isPBC' :'_Engine__isPBC',
'_Engine__moleculesIndex' :'_Engine__moleculesIndex',
'_Engine__elementsIndex' :'_Engine__elementsIndex',
'_Engine__numberOfAtomsPerElement' :'_Engine__numberOfAtomsPerElement',
'_Engine__elements' :'_Engine__elements',
'_Engine__numberDensity' :'_Engine__numberDensity',
'_Engine__volume' :'_Engine__volume',
'_Engine__realCoordinates' :'_Engine__realCoordinates',
'_atomsCollector' :'_atomsCollector',
('engine','_atomsCollector') :'_atomsCollector',
}
def plot(self, xlabelParams={'xlabel':'$Q(\\AA^{-1})$', 'size':10},
ylabelParams={'ylabel':'$S(Q)$', 'size':10},
**kwargs):
"""
Alias to ExperimentalConstraint.plot with additional parameters
:Additional/Adjusted Parameters:
#. xlabelParams (None, dict): modified matplotlib.axes.Axes.set_xlabel
parameters.
#. ylabelParams (None, dict): modified matplotlib.axes.Axes.set_ylabel
parameters.
"""
return super(StructureFactorConstraint, self).plot(xlabelParams= xlabelParams,
ylabelParams= ylabelParams,
**kwargs)
class ReducedStructureFactorConstraint(StructureFactorConstraint):
"""
The Reduced Structure Factor that we will also note S(Q)
is exactly the same quantity as the Structure Factor but with
the slight difference that it is normalized to 0 rather than 1
and therefore :math:`<S(Q)>=0`.
The computation of S(Q) is done through a Sine inverse Fourier transform
of the computed pair distribution function noted as G(r).
.. math::
S(Q) = \\frac{1}{Q} \\int_{0}^{\\infty} G(r) sin(Qr) dr
The only reason why the Reduced Structure Factor is implemented, is because
many experimental data are treated in this form. And it is just convenient
not to manipulate the experimental data every time.
"""
def _get_Sq_from_Gr(self, Gr):
return np.sum(Gr.reshape((-1,1))*self.Gr2SqMatrix, axis=0)
def _apply_scale_factor(self, Sq, scaleFactor):
if scaleFactor != 1:
Sq = scaleFactor*Sq
return Sq
def get_adjusted_scale_factor(self, experimentalData, modelData, dataWeights):
""" dummy overload that does exactly the same thing
"""
SF = self.scaleFactor
# check to update scaleFactor
if self.adjustScaleFactorFrequency:
if not self.engine.accepted%self.adjustScaleFactorFrequency:
SF = self.fit_scale_factor(experimentalData, modelData, dataWeights)
return SF
def plot(self, xlabelParams={'xlabel':'$Q(\\AA^{-1})$', 'size':10},
ylabelParams={'ylabel':'$S(Q)-1$', 'size':10},
**kwargs):
"""
Alias to ExperimentalConstraint.plot with additional parameters
:Additional/Adjusted Parameters:
#. xlabelParams (None, dict): modified matplotlib.axes.Axes.set_xlabel
parameters.
#. ylabelParams (None, dict): modified matplotlib.axes.Axes.set_ylabel
parameters.
"""
return super(StructureFactorConstraint, self).plot(xlabelParams= xlabelParams,
ylabelParams= ylabelParams,
**kwargs)
| agpl-3.0 |
q1ang/scikit-learn | examples/ensemble/plot_forest_importances_faces.py | 403 | 1519 | """
=================================================
Pixel importances with a parallel forest of trees
=================================================
This example shows the use of forests of trees to evaluate the importance
of the pixels in an image classification task (faces). The hotter the pixel,
the more important.
The code below also illustrates how the construction and the computation
of the predictions can be parallelized within multiple jobs.
"""
print(__doc__)
from time import time
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.ensemble import ExtraTreesClassifier
# Number of cores to use to perform parallel fitting of the forest model
n_jobs = 1
# Load the faces dataset
data = fetch_olivetti_faces()
X = data.images.reshape((len(data.images), -1))
y = data.target
mask = y < 5 # Limit to 5 classes
X = X[mask]
y = y[mask]
# Build a forest and compute the pixel importances
print("Fitting ExtraTreesClassifier on faces data with %d cores..." % n_jobs)
t0 = time()
forest = ExtraTreesClassifier(n_estimators=1000,
max_features=128,
n_jobs=n_jobs,
random_state=0)
forest.fit(X, y)
print("done in %0.3fs" % (time() - t0))
importances = forest.feature_importances_
importances = importances.reshape(data.images[0].shape)
# Plot pixel importances
plt.matshow(importances, cmap=plt.cm.hot)
plt.title("Pixel importances with forests of trees")
plt.show()
| bsd-3-clause |
Ttl/scikit-rf | skrf/io/general.py | 3 | 22567 |
'''
.. module:: skrf.io.general
========================================
general (:mod:`skrf.io.general`)
========================================
General io functions for reading and writing skrf objects
.. autosummary::
:toctree: generated/
read
read_all
read_all_networks
write
write_all
save_sesh
Writing output to spreadsheet
.. autosummary::
:toctree: generated/
network_2_spreadsheet
networkset_2_spreadsheet
'''
import sys
import six.moves.cPickle as pickle
from six.moves.cPickle import UnpicklingError
import inspect
import os
import zipfile
import warnings
import sys
from ..util import get_extn, get_fid
from ..network import Network
from ..frequency import Frequency
from ..media import Media
from ..networkSet import NetworkSet
from ..calibration.calibration import Calibration
from copy import copy
dir_ = copy(dir)
# delayed import: from pandas import DataFrame, Series for ntwk_2_spreadsheet
# file extension conventions for skrf objects.
global OBJ_EXTN
OBJ_EXTN = [
[Frequency, 'freq'],
[Network, 'ntwk'],
[NetworkSet, 'ns'],
[Calibration, 'cal'],
[Media, 'med'],
[object, 'p'],
]
def read(file, *args, **kwargs):
'''
Read skrf object[s] from a pickle file
Reads a skrf object that is written with :func:`write`, which uses
the :mod:`pickle` module.
Parameters
------------
file : str or file-object
name of file, or a file-object
\*args, \*\*kwargs : arguments and keyword arguments
passed through to pickle.load
Examples
-------------
>>> n = rf.Network(f=[1,2,3],s=[1,1,1],z0=50)
>>> n.write('my_ntwk.ntwk')
>>> n_2 = rf.read('my_ntwk.ntwk')
See Also
----------
read : read a skrf object
write : write skrf object[s]
read_all : read all skrf objects in a directory
write_all : write dictionary of skrf objects to a directory
Notes
-------
if `file` is a file-object it is left open, if it is a filename then
a file-object is opened and closed. If file is a file-object
and reading fails, then the position is reset back to 0 using seek
if possible.
'''
fid = get_fid(file, mode='rb')
try:
obj = pickle.load(fid, *args, **kwargs)
except (UnpicklingError, UnicodeDecodeError) as e:
# if fid is seekable then reset to beginning of file
fid.seek(0)
if isinstance(file, str):
# we created the fid so close it
fid.close()
raise
if isinstance(file, str):
# we created the fid so close it
fid.close()
return obj
def write(file, obj, overwrite = True):
'''
Write skrf object[s] to a file
This uses the :mod:`pickle` module to write skrf objects to a file.
Note that you can write any pickl-able python object. For example,
you can write a list or dictionary of :class:`~skrf.network.Network`
objects
or :class:`~skrf.calibration.calibration.Calibration` objects. This
will write out a single file. If you would like to write out a
seperate file for each object, use :func:`write_all`.
Parameters
------------
file : file or string
File or filename to which the data is saved. If file is a
file-object, then the filename is unchanged. If file is a
string, an appropriate extension will be appended to the file
name if it does not already have an extension.
obj : an object, or list/dict of objects
object or list/dict of objects to write to disk
overwrite : Boolean
if file exists, should it be overwritten?
Notes
-------
If `file` is a str, but doesnt contain a suffix, one is chosen
automatically. Here are the extensions
==================================================== ===============
skrf object extension
==================================================== ===============
:class:`~skrf.frequency.Frequency` '.freq'
:class:`~skrf.network.Network` '.ntwk'
:class:`~skrf.networkSet.NetworkSet` '.ns'
:class:`~skrf.calibration.calibration.Calibration` '.cal'
:class:`~skrf.media.media.Media` '.med'
other '.p'
==================================================== ===============
To make the file written by this method cross-platform, the pickling
protocol 2 is used. See :mod:`pickle` for more info.
Examples
-------------
Convert a touchstone file to a pickled Network,
>>> n = rf.Network('my_ntwk.s2p')
>>> rf.write('my_ntwk',n)
>>> n_red = rf.read('my_ntwk.ntwk')
Writing a list of different objects
>>> n = rf.Network('my_ntwk.s2p')
>>> ns = rf.NetworkSet([n,n,n])
>>> rf.write('out',[n,ns])
>>> n_red = rf.read('out.p')
See Also
------------
read : read a skrf object
write : write skrf object[s]
read_all : read all skrf objects in a directory
write_all : write dictionary of skrf objects to a directory
skrf.network.Network.write : write method of Network
skrf.calibration.calibration.Calibration.write : write method of Calibration
'''
if isinstance(file, str):
extn = get_extn(file)
if extn is None:
# if there is not extension add one
for obj_extn in OBJ_EXTN:
if isinstance(obj, obj_extn[0]):
extn = obj_extn[1]
break
file = file + '.' + extn
if os.path.exists(file):
if not overwrite:
warnings.warn('file exists, and overwrite option is False. Not writing.')
return
with open(file, 'wb') as fid:
pickle.dump(obj, fid, protocol=2)
else:
fid = file
pickle.dump(obj, fid, protocol=2)
fid.close()
def read_all(dir='.', contains = None, f_unit = None, obj_type=None):
'''
Read all skrf objects in a directory
Attempts to load all files in `dir`, using :func:`read`. Any file
that is not readable by skrf is skipped. Optionally, simple filtering
can be achieved through the use of `contains` argument.
Parameters
--------------
dir : str, optional
the directory to load from, default \'.\'
contains : str, optional
if not None, only files containing this substring will be loaded
f_unit : ['hz','khz','mhz','ghz','thz']
for all :class:`~skrf.network.Network` objects, set their
frequencies's :attr:`~skrf.frequency.Frequency.f_unit`
obj_type : str
Name of skrf object types to read (ie 'Network')
Returns
---------
out : dictionary
dictionary containing all loaded skrf objects. keys are the
filenames without extensions, and the values are the objects
Examples
----------
>>> rf.read_all('skrf/data/')
{'delay_short': 1-Port Network: 'delay_short', 75-110 GHz, 201 pts, z0=[ 50.+0.j],
'line': 2-Port Network: 'line', 75-110 GHz, 201 pts, z0=[ 50.+0.j 50.+0.j],
'ntwk1': 2-Port Network: 'ntwk1', 1-10 GHz, 91 pts, z0=[ 50.+0.j 50.+0.j],
'one_port': one port Calibration: 'one_port', 500-750 GHz, 201 pts, 4-ideals/4-measured,
...
>>> rf.read_all('skrf/data/', obj_type = 'Network')
{'delay_short': 1-Port Network: 'delay_short', 75-110 GHz, 201 pts, z0=[ 50.+0.j],
'line': 2-Port Network: 'line', 75-110 GHz, 201 pts, z0=[ 50.+0.j 50.+0.j],
'ntwk1': 2-Port Network: 'ntwk1', 1-10 GHz, 91 pts, z0=[ 50.+0.j 50.+0.j],
...
See Also
----------
read : read a skrf object
write : write skrf object[s]
read_all : read all skrf objects in a directory
write_all : write dictionary of skrf objects to a directory
'''
out={}
for filename in os.listdir(dir):
if contains is not None and contains not in filename:
continue
fullname = os.path.join(dir,filename)
keyname = os.path.splitext(filename)[0]
try:
out[keyname] = read(fullname)
continue
except:
pass
try:
out[keyname] = Network(fullname)
continue
except:
pass
if f_unit is not None:
for keyname in out:
try:
out[keyname].frequency.unit = f_unit
except:
pass
if obj_type is not None:
out = dict([(k, out[k]) for k in out if
isinstance(out[k],sys.modules[__name__].__dict__[obj_type])])
return out
def read_all_networks(*args, **kwargs):
'''
Read all networks in a directory.
This is a convenience function. It just calls::
read_all(*args,obj_type='Network', **kwargs)
See Also
----------
read_all
'''
if 'f_unit' not in kwargs:
kwargs.update({'f_unit':'ghz'})
return read_all(*args,obj_type='Network', **kwargs)
ran = read_all_networks
def write_all(dict_objs, dir='.', *args, **kwargs):
'''
Write a dictionary of skrf objects individual files in `dir`.
Each object is written to its own file. The filename used for each
object is taken from its key in the dictionary. If no extension
exists in the key, then one is added. See :func:`write` for a list
of extensions. If you would like to write the dictionary to a single
output file use :func:`write`.
Notes
-------
Any object in dict_objs that is pickl-able will be written.
Parameters
------------
dict_objs : dict
dictionary of skrf objects
dir : str
directory to save skrf objects into
\*args, \*\*kwargs :
passed through to :func:`~skrf.io.general.write`. `overwrite`
option may be of use.
See Also
-----------
read : read a skrf object
write : write skrf object[s]
read_all : read all skrf objects in a directory
write_all : write dictionary of skrf objects to a directory
Examples
----------
Writing a diction of different skrf objects
>>> from skrf.data import line, short
>>> d = {'ring_slot':ring_slot, 'one_port_cal':one_port_cal}
>>> rf.write_all(d)
'''
if not os.path.exists('.'):
raise OSError('No such directory: %s'%dir)
for k in dict_objs:
filename = k
obj = dict_objs[k]
extn = get_extn(filename)
if extn is None:
# if there is not extension add one
for obj_extn in OBJ_EXTN:
if isinstance(obj, obj_extn[0]):
extn = obj_extn[1]
break
filename = filename + '.' + extn
try:
with open(os.path.join(dir+'/', filename), 'wb') as fid:
write(fid, obj,*args, **kwargs)
except Exception as inst:
print(inst)
warnings.warn('couldnt write %s: %s'%(k,str(inst)))
pass
def save_sesh(dict_objs, file='skrfSesh.p', module='skrf', exclude_prefix='_'):
'''
Save all `skrf` objects in the local namespace.
This is used to save current workspace in a hurry, by passing it the
output of :func:`locals` (see Examples). Note this can be
used for other modules as well by passing a different `module` name.
Parameters
------------
dict_objs : dict
dictionary containing `skrf` objects. See the Example.
file : str or file-object, optional
the file to save all objects to
module : str, optional
the module name to grep for.
exclude_prefix: str, optional
dont save objects which have this as a prefix.
See Also
----------
read : read a skrf object
write : write skrf object[s]
read_all : read all skrf objects in a directory
write_all : write dictionary of skrf objects to a directory
Examples
---------
Write out all skrf objects in current namespace.
>>> rf.write_all(locals(), 'mysesh.p')
'''
objects = {}
print('pickling: ')
for k in dict_objs:
try:
if module in inspect.getmodule(dict_objs[k]).__name__:
try:
pickle.dumps(dict_objs[k])
if k[0] != '_':
objects[k] = dict_objs[k]
print(k+', ')
finally:
pass
except(AttributeError, TypeError):
pass
if len (objects ) == 0:
print('nothing')
write(file, objects)
def load_all_touchstones(dir = '.', contains=None, f_unit=None):
'''
Loads all touchtone files in a given dir into a dictionary.
Notes
-------
Alternatively you can use the :func:`read_all` function.
Parameters
-----------
dir : string
the path
contains : string
a string the filenames must contain to be loaded.
f_unit : ['hz','mhz','ghz']
the frequency unit to assign all loaded networks. see
:attr:`frequency.Frequency.unit`.
Returns
---------
ntwkDict : a dictonary with keys equal to the file name (without
a suffix), and values equal to the corresponding ntwk types
Examples
----------
>>> ntwk_dict = rf.load_all_touchstones('.', contains ='20v')
See Also
-----------
read_all
'''
ntwkDict = {}
for f in os.listdir (dir):
if contains is not None and contains not in f:
continue
fullname = os.path.join(dir,f)
keyname,extn = os.path.splitext(f)
extn = extn.lower()
try:
if extn[1]== 's' and extn[-1]=='p':
ntwkDict[keyname]=(Network(dir +'/'+f))
if f_unit is not None: ntwkDict[keyname].frequency.unit=f_unit
except:
pass
return ntwkDict
def write_dict_of_networks(ntwkDict, dir='.'):
'''
Saves a dictionary of networks touchstone files in a given directory
The filenames assigned to the touchstone files are taken from
the keys of the dictionary.
Parameters
-----------
ntwkDict : dictionary
dictionary of :class:`Network` objects
dir : string
directory to write touchstone file to
'''
warnings.warn('Deprecated. use write_all.', DeprecationWarning)
for ntwkKey in ntwkDict:
ntwkDict[ntwkKey].write_touchstone(filename = dir+'/'+ntwkKey)
def read_csv(filename):
'''
Read a 2-port s-parameter data from a csv file.
Specifically, this reads a two-port csv file saved from a Rohde Shcwarz
ZVA-40, and possibly other network analyzers. It returns into a
:class:`Network` object.
Parameters
------------
filename : str
name of file
Returns
--------
ntwk : :class:`Network` object
the network representing data in the csv file
'''
ntwk = Network(name=filename[:-4])
try:
data = npy.loadtxt(filename, skiprows=3,delimiter=',',\
usecols=range(9))
s11 = data[:,1] +1j*data[:,2]
s21 = data[:,3] +1j*data[:,4]
s12 = data[:,5] +1j*data[:,6]
s22 = data[:,7] +1j*data[:,8]
ntwk.s = npy.array([[s11, s21],[s12,s22]]).transpose().reshape(-1,2,2)
except(IndexError):
data = npy.loadtxt(filename, skiprows=3,delimiter=',',\
usecols=range(3))
ntwk.s = data[:,1] +1j*data[:,2]
ntwk.frequency.f = data[:,0]
ntwk.frequency.unit='ghz'
return ntwk
## file conversion
def statistical_2_touchstone(file_name, new_file_name=None,\
header_string='# GHz S RI R 50.0'):
'''
Converts Statistical file to a touchstone file.
Converts the file format used by Statistical and other Dylan Williams
software to standard touchstone format.
Parameters
------------
file_name : string
name of file to convert
new_file_name : string
name of new file to write out (including extension)
header_string : string
touchstone header written to first beginning of file
'''
if new_file_name is None:
new_file_name = 'tmp-'+file_name
remove_tmp_file = True
# This breaks compatibility with python 2.6 and older
with file(file_name, 'r') as old_file, open(new_file_name, 'w') as new_file:
new_file.write('%s\n'%header_string)
for line in old_file:
new_file.write(line)
if remove_tmp_file is True:
os.rename(new_file_name,file_name)
def network_2_spreadsheet(ntwk, file_name =None, file_type= 'excel', form='db',
*args, **kwargs):
'''
Write a Network object to a spreadsheet, for your boss
Write the s-parameters of a network to a spreadsheet, in a variety
of forms. This functions makes use of the pandas module, which in
turn makes use of the xlrd module. These are imported during this
function call. For more details about the file-writing functions
see the pandas.DataFrom.to_?? functions.
Notes
------
The frequency unit used in the spreadsheet is take from
`ntwk.frequency.unit`
Parameters
-----------
ntwk : :class:`~skrf.network.Network` object
the network to write
file_name : str, None
the file_name to write. if None, ntwk.name is used.
file_type : ['csv','excel','html']
the type of file to write. See pandas.DataFrame.to_??? functions.
form : 'db','ma','ri'
format to write data,
* db = db, deg
* ma = mag, deg
* ri = real, imag
\*args, \*\*kwargs :
passed to pandas.DataFrame.to_??? functions.
See Also
---------
networkset_2_spreadsheet : writes a spreadsheet for many networks
'''
from pandas import DataFrame, Series # delayed because its not a requirement
file_extns = {'csv':'csv','excel':'xls','html':'html'}
form = form.lower()
if form not in ['db','ri','ma']:
raise ValueError('`form` must be either `db`,`ma`,`ri`')
file_type = file_type.lower()
if file_type not in file_extns.keys():
raise ValueError('file_type must be `csv`,`html`,`excel` ')
if ntwk.name is None and file_name is None:
raise ValueError('Either ntwk must have name or give a file_name')
if file_name is None and 'excel_writer' not in kwargs.keys():
file_name = ntwk.name + '.'+file_extns[file_type]
d = {}
index =ntwk.frequency.f_scaled
if form =='db':
for m,n in ntwk.port_tuples:
d['S%i%i Log Mag(dB)'%(m+1,n+1)] = \
Series(ntwk.s_db[:,m,n], index = index)
d[u'S%i%i Phase(deg)'%(m+1,n+1)] = \
Series(ntwk.s_deg[:,m,n], index = index)
elif form =='ma':
for m,n in ntwk.port_tuples:
d['S%i%i Mag(lin)'%(m+1,n+1)] = \
Series(ntwk.s_mag[:,m,n], index = index)
d[u'S%i%i Phase(deg)'%(m+1,n+1)] = \
Series(ntwk.s_deg[:,m,n], index = index)
elif form =='ri':
for m,n in ntwk.port_tuples:
d['S%i%i Real'%(m+1,n+1)] = \
Series(ntwk.s_re[:,m,n], index = index)
d[u'S%i%i Imag'%(m+1,n+1)] = \
Series(ntwk.s_im[:,m,n], index = index)
df = DataFrame(d)
df.__getattribute__('to_%s'%file_type)(file_name,
index_label='Freq(%s)'%ntwk.frequency.unit, *args, **kwargs)
def network_2_dataframe(ntwk, attrs=['s_db'], ports = None):
'''
Convert one or more attributes of a network to a pandas DataFrame
Parameters
--------------
ntwk : :class:`~skrf.network.Network` object
the network to write
attrs : list Network attributes
like ['s_db','s_deg']
ports : list of tuples
list of port pairs to write. defaults to ntwk.port_tuples
(like [[0,0]])
Returns
----------
df : pandas DataFrame Object
'''
from pandas import DataFrame, Series # delayed because its not a requirement
d = {}
index =ntwk.frequency.f_scaled
if ports is None:
ports = ntwk.port_tuples
for attr in attrs:
for m,n in ports:
d['%s %i%i'%(attr, m+1,n+1)] = \
Series(ntwk.__getattribute__(attr)[:,m,n], index = index)
return DataFrame(d)
def networkset_2_spreadsheet(ntwkset, file_name=None, file_type= 'excel',
*args, **kwargs):
'''
Write a NetworkSet object to a spreadsheet, for your boss
Write the s-parameters of a each network in the networkset to a
spreadsheet. If the `excel` file_type is used, then each network,
is written to its own sheet, with the sheetname taken from the
network `name` attribute.
This functions makes use of the pandas module, which in turn makes
use of the xlrd module. These are imported during this function
Notes
------
The frequency unit used in the spreadsheet is take from
`ntwk.frequency.unit`
Parameters
-----------
ntwkset : :class:`~skrf.networkSet.NetworkSet` object
the network to write
file_name : str, None
the file_name to write. if None, ntwk.name is used.
file_type : ['csv','excel','html']
the type of file to write. See pandas.DataFrame.to_??? functions.
form : 'db','ma','ri'
format to write data,
* db = db, deg
* ma = mag, deg
* ri = real, imag
\*args, \*\*kwargs :
passed to pandas.DataFrame.to_??? functions.
See Also
---------
networkset_2_spreadsheet : writes a spreadsheet for many networks
'''
from pandas import DataFrame, Series, ExcelWriter # delayed because its not a requirement
if ntwkset.name is None and file_name is None:
raise(ValueError('Either ntwkset must have name or give a file_name'))
if file_type == 'excel':
writer = ExcelWriter(file_name)
[network_2_spreadsheet(k, writer, sheet_name =k.name, *args, **kwargs) for k in ntwkset]
writer.save()
else:
[network_2_spreadsheet(k,*args, **kwargs) for k in ntwkset]
# Provide a StringBuffer that let's me work with Python2 strings and Python3 unicode strings without thinking
if sys.version_info < (3, 0):
import StringIO
class StringBuffer(StringIO.StringIO):
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
else:
import io
StringBuffer = io.StringIO
| bsd-3-clause |
MobleyLab/SAMPL6 | host_guest/Analysis/Scripts/analyze_sampling.py | 1 | 116143 | #!/usr/bin/env python
# =============================================================================
# GLOBAL IMPORTS
# =============================================================================
import collections
import copy
import itertools
import json
import math
import os
import numpy as np
import pandas as pd
import scipy as sp
import seaborn as sns
from matplotlib import pyplot as plt
from pkganalysis.stats import mean_confidence_interval
from pkganalysis.sampling import (SamplingSubmission, YankSamplingAnalysis,
YANK_N_ITERATIONS, DG_KEY, DDG_KEY, export_dictionary)
from pkganalysis.submission import (load_submissions)
# =============================================================================
# CONSTANTS
# =============================================================================
YANK_METHOD_PAPER_NAME = 'OpenMM/HREX'
# Paths to input data.
SAMPLING_SUBMISSIONS_DIR_PATH = '../SubmissionsDoNotUpload/975/'
YANK_ANALYSIS_DIR_PATH = 'YankAnalysis/Sampling/'
SAMPLING_ANALYSIS_DIR_PATH = '../SAMPLing/'
SAMPLING_DATA_DIR_PATH = os.path.join(SAMPLING_ANALYSIS_DIR_PATH, 'Data')
SAMPLING_PLOT_DIR_PATH = os.path.join(SAMPLING_ANALYSIS_DIR_PATH, 'Plots')
SAMPLING_PAPER_DIR_PATH = os.path.join(SAMPLING_ANALYSIS_DIR_PATH, 'PaperImages')
# All system ids.
SYSTEM_IDS = [
'CB8-G3-0', 'CB8-G3-1', 'CB8-G3-2', 'CB8-G3-3', 'CB8-G3-4',
'OA-G3-0', 'OA-G3-1', 'OA-G3-2', 'OA-G3-3', 'OA-G3-4',
'OA-G6-0', 'OA-G6-1', 'OA-G6-2', 'OA-G6-3', 'OA-G6-4'
]
# Kelly's colors for maximum contrast.
# "gray95", "gray13", "gold2", "plum4", "darkorange1", "lightskyblue2", "firebrick", "burlywood3", "gray51", "springgreen4", "lightpink2", "deepskyblue4", "lightsalmon2", "mediumpurple4", "orange", "maroon", "yellow3", "brown4", "yellow4", "sienna4", "chocolate", "gray19"
KELLY_COLORS = ['#F2F3F4', '#222222', '#F3C300', '#875692', '#F38400', '#A1CAF1', '#BE0032', '#C2B280', '#848482', '#008856', '#E68FAC', '#0067A5', '#F99379', '#604E97', '#F6A600', '#B3446C', '#DCD300', '#882D17', '#8DB600', '#654522', '#E25822', '#2B3D26']
TAB10_COLORS = sns.color_palette('tab10')
# Index of Kelly's colors associated to each submission.
SUBMISSION_COLORS = {
'AMBER/APR': 'dodgerblue',#KELLY_COLORS[11],
'OpenMM/REVO': 'gold', #KELLY_COLORS[7],
'OpenMM/SOMD': KELLY_COLORS[4],
'GROMACS/EE': 'darkviolet', #KELLY_COLORS[3],
'GROMACS/EE-fullequil': 'hotpink', #KELLY_COLORS[10],
YANK_METHOD_PAPER_NAME: '#4ECC41', #'limegreen', #KELLY_COLORS[9],
'GROMACS/NS-DS/SB-long': KELLY_COLORS[6],
'GROMACS/NS-DS/SB': KELLY_COLORS[1],
'GROMACS/NS-Jarz-F': TAB10_COLORS[0],
'GROMACS/NS-Jarz-R': TAB10_COLORS[1],
'GROMACS/NS-Gauss-F': TAB10_COLORS[2],
'GROMACS/NS-Gauss-R': TAB10_COLORS[4],
'NAMD/BAR': 'saddlebrown'
}
SUBMISSION_LINE_STYLES = {
'AMBER/APR': '--',
'OpenMM/REVO': '-',
'OpenMM/SOMD': '-',
'GROMACS/EE': '-',
'GROMACS/EE-fullequil': '-',
YANK_METHOD_PAPER_NAME: '-',
'GROMACS/NS-DS/SB-long': '-',
'GROMACS/NS-DS/SB': '-',
'GROMACS/NS-Jarz-F': '-',
'GROMACS/NS-Jarz-R': '-',
'GROMACS/NS-Gauss-F': '-',
'GROMACS/NS-Gauss-R': '-',
'NAMD/BAR': '--',
}
N_ENERGY_EVALUATIONS_SCALE = 1e6
# =============================================================================
# UTILITY FUNCTIONS
# =============================================================================
def reduce_to_first_significant_digit(quantity, uncertainty):
"""Truncate a quantity to the first significant digit of its uncertainty."""
first_significant_digit = math.floor(math.log10(abs(uncertainty)))
quantity = round(quantity, -first_significant_digit)
uncertainty = round(uncertainty, -first_significant_digit)
return quantity, uncertainty
def load_yank_analysis():
"""Load the YANK analysis in a single dataframe."""
yank_free_energies = {}
for system_id in SYSTEM_IDS:
file_path = os.path.join(YANK_ANALYSIS_DIR_PATH, 'yank-{}.json'.format(system_id))
with open(file_path, 'r') as f:
yank_free_energies[system_id] = json.load(f)
return yank_free_energies
def fit_efficiency(mean_data, find_best_fit=True):
"""Compute the efficiency by fitting the model and using only the asymptotic data.
We fit using the simulation percentage as the independent value
because it is less prone to overflowing during fitting. We then
return the efficiency in units of (kcal/mol)**2/n_energy_evaluations.
"""
from scipy.optimize import curve_fit
def model(x, log_efficiency):
return np.exp(log_efficiency) / x
vars = mean_data['std'].values**2
cost = mean_data['Simulation percentage'].values
# cost = mean_data['N energy evaluations'].values / 1e7
if find_best_fit:
# Find fit with best error up to discarding 70% of calculation.
max_discarded = math.floor(0.5*len(cost))
else:
# Use all the data.
max_discarded = 1
# Fit.
fits = []
for n_discarded in range(max_discarded):
cost_fit = cost[n_discarded:]
vars_fit = vars[n_discarded:]
fit = curve_fit(model, cost_fit, vars_fit, p0=[0.0])
fits.append((np.exp(fit[0]), fit[1]))
# Find the fit with the minimum error.
n_discarded = fits.index(min(fits, key=lambda x: x[1]))
# Convert efficiency / simulation_percentage to efficiency / n_energy_evaluations
efficiency = fits[n_discarded][0][0] / 100 * mean_data['N energy evaluations'].values[-1]
# efficiency = fits[n_discarded][0][0] * 1e7
return efficiency, n_discarded
def export_submissions(submissions, reference_free_energies):
"""Export the submission data to CSV and JSON format."""
for submission in submissions:
exported_data = {}
# Export data of the 5 independent replicates.
for system_id in sorted(submission.data['System ID'].unique()):
system_id_data = submission.data[submission.data['System ID'] == system_id]
exported_data[system_id] = collections.OrderedDict([
('DG', system_id_data[DG_KEY].values.tolist()),
('dDG', system_id_data[DDG_KEY].values.tolist()),
('cpu_times', system_id_data['CPU time [s]'].values.tolist()),
('n_energy_evaluations', system_id_data['N energy evaluations'].values.tolist()),
])
# Export data of mean trajectory and confidence intervals.
mean_free_energies = submission.mean_free_energies()
for system_name in mean_free_energies['System name'].unique():
system_name_data = mean_free_energies[mean_free_energies['System name'] == system_name]
# Obtain free energies and bias.
free_energies = system_name_data[DG_KEY].values
free_energies_ci = system_name_data['$\Delta$G CI'].values
reference_diff = free_energies - reference_free_energies.loc[system_name, '$\Delta$G [kcal/mol]']
exported_data[system_name + '-mean'] = collections.OrderedDict([
('DG', free_energies.tolist()),
('DG_CI', free_energies_ci.tolist()),
('reference_difference', reference_diff.tolist()),
('n_energy_evaluations', system_name_data['N energy evaluations'].values.tolist()),
])
# Export.
file_base_path = os.path.join(SAMPLING_DATA_DIR_PATH, submission.receipt_id)
export_dictionary(exported_data, file_base_path)
# =============================================================================
# PLOTTING FUNCTIONS
# =============================================================================
def plot_mean_free_energy(mean_data, ax, x='Simulation percentage',
color_mean=None, color_ci=None, zorder=None,
start=None, stride=1, scale_n_energy_evaluations=True,
plot_ci=True, **plot_kwargs):
"""Plot mean trajectory with confidence intervals."""
ci_key = '$\Delta$G CI'
if start is None:
# Discard the first datapoint which are 0.0 (i.e. no estimate).
start = np.nonzero(mean_data[DG_KEY].values)[0][0]
if x == 'N energy evaluations' and scale_n_energy_evaluations:
# Plot in millions of energy evaluations.
scale = N_ENERGY_EVALUATIONS_SCALE
else:
scale = 1
x = mean_data[x].values[start::stride] / scale
mean_dg = mean_data[DG_KEY].values[start::stride]
sem_dg = mean_data[ci_key].values[start::stride]
# Plot mean trajectory confidence intervals.
if plot_ci:
ax.fill_between(x, mean_dg + sem_dg, mean_dg - sem_dg, alpha=0.15, color=color_ci, zorder=zorder)
# Plot the mean free energy trajectory.
if zorder is not None:
# Push the CI shaded area in the background so that the trajectories are always visible.
zorder += 20
ax.plot(x, mean_dg, color=color_mean, alpha=1.0, zorder=zorder, **plot_kwargs)
return ax
def plot_mean_data(mean_data, axes, color=None, ls=None, label=None, x='N energy evaluations',
zorder=None, plot_std=True, plot_bias=True, plot_ci=True):
"""Plot free energy, variance and bias as a function of the cost in three different axes."""
# Do not plot the part of data without index.
first_nonzero_idx = np.nonzero(mean_data[DG_KEY].values)[0][0]
# If the x-axis is the number of energy/force evaluations, plot it in units of millions.
if x == 'N energy evaluations':
scale = N_ENERGY_EVALUATIONS_SCALE
else:
scale = 1
# Plot the submission mean trajectory with CI.
plot_mean_free_energy(mean_data, x=x, ax=axes[0],
color_mean=color, color_ci=color, ls=ls, zorder=zorder,
start=first_nonzero_idx, label=label, plot_ci=plot_ci)
# Plot standard deviation of the trajectories.
if plot_std:
axes[1].plot(mean_data[x].values[first_nonzero_idx:] / scale,
mean_data['std'].values[first_nonzero_idx:], color=color, alpha=0.8,
ls=ls, zorder=zorder, label=label)
if plot_bias:
axes[2].plot(mean_data[x].values[first_nonzero_idx:] / scale,
mean_data['bias'].values[first_nonzero_idx:], color=color, alpha=0.8,
ls=ls, zorder=zorder, label=label)
def align_yaxis(ax1, v1, ax2, v2):
"""Adjust ax2 ylimit so that v2 in in the twin ax2 is aligned to v1 in ax1.
From https://stackoverflow.com/questions/10481990/matplotlib-axis-with-two-scales-shared-origin .
"""
_, y1 = ax1.transData.transform((0, v1))
_, y2 = ax2.transData.transform((0, v2))
inv = ax2.transData.inverted()
_, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2))
miny, maxy = ax2.get_ylim()
ax2.set_ylim(miny+dy, maxy+dy)
# =============================================================================
# FIGURE 1 - SAMPLING CHALLENGE OVERVIEW
# =============================================================================
def plot_example_bias_variance(yank_analysis, type='mixed', cost='generic',
max_n_eval_percentage=1.0,
mixed_proportion=0.5,
model_free_energy=None,
plot_experimental_value=False):
"""Free energy trajectories used to visualize bias and variance on the plots.
This is used to illustrate how bias and uncertainty are intended in the paper.
Parameters
----------
type : str, optional
Can be 'single' (plot only CB8-G3-1), 'all' (plot all system IDs of CB8-G3),
'mean' (plot mean trajectory and uncertainties), and 'mixed (first part is
all system IDs and second part is mean trajectory and uncertainties).
cost : str, optional
Can be 'generic' (no label on x-axis) or 'neval' (x-axis in number of
energy evaluations).
mixed_proportion : float, optional
The proportion of all System IDs and mean trajectories in mixed-type plots.
"""
# sns.set_context('paper', font_scale=1.6)
sns.set_style('white')
sns.set_context('paper', font_scale=1.0)
# Load the data
n_iterations = 40000
cb8_data = yank_analysis.get_free_energies_from_iteration(n_iterations, system_name='CB8-G3', mean_trajectory=False)
cb8_data_mean = yank_analysis.get_free_energies_from_iteration(n_iterations, system_name='CB8-G3', mean_trajectory=True)
max_n_eval = max(cb8_data_mean['N energy evaluations'])
max_n_eval_scaled = int(max_n_eval / N_ENERGY_EVALUATIONS_SCALE)
max_displayed_n_eval = next(x for x in cb8_data_mean['N energy evaluations'] if x >= max_n_eval * max_n_eval_percentage)
max_displayed_n_eval_scaled = int(max_displayed_n_eval / N_ENERGY_EVALUATIONS_SCALE)
# Determine the asymptotic free energy if not given.
if model_free_energy is None:
model_free_energy = cb8_data_mean[DG_KEY].values[-1]
# Scale the number of energy evaluations.
cb8_data.loc[:,'N energy evaluations'] /= N_ENERGY_EVALUATIONS_SCALE
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(2.5, 1.8))
if type == 'single':
# Plot only CB8-G3-1.
cb8_data_1 = cb8_data[cb8_data['System ID'] == 'CB8-G3-1']
sns.lineplot(data=cb8_data_1, x='N energy evaluations', y=DG_KEY,
hue='System ID', palette='bright', ax=ax, alpha=0.6)
elif type == 'all':
# Plot the 5 replicates individual trajectories.
sns.lineplot(data=cb8_data, x='N energy evaluations', y=DG_KEY,
hue='System ID', palette='bright', ax=ax, alpha=0.6)
elif type == 'mean':
# Plot the submission mean trajectory with CI.
plot_mean_free_energy(cb8_data_mean, x='N energy evaluations', ax=ax,
color_mean='black', plot_ci=True,
color_ci='black',
scale_n_energy_evaluations=True)
elif type == 'mixed':
# Plot all System IDs for the first half and mean/uncertainty in second half.
half_n_eval = max_displayed_n_eval_scaled * mixed_proportion
cb8_data_first_half = cb8_data[cb8_data['N energy evaluations'] <= half_n_eval + max_n_eval_scaled / 100]
sns.lineplot(data=cb8_data_first_half, x='N energy evaluations', y=DG_KEY,
hue='System ID', palette='bright', ax=ax, alpha=0.6)
cb8_data_second_half = cb8_data_mean[cb8_data_mean['N energy evaluations'] >= half_n_eval * N_ENERGY_EVALUATIONS_SCALE]
plot_mean_free_energy(cb8_data_second_half, x='N energy evaluations', ax=ax,
color_mean='black', plot_ci=True,
color_ci=(0.3, 0.3, 0.3), scale_n_energy_evaluations=True,
ls='--')
try:
ax.get_legend().remove()
except AttributeError:
pass
# Set limits
x_lim = (0, max_displayed_n_eval_scaled)
ax.set_xlim(x_lim)
y_lim = (-12.5, -10.5)
ax.set_ylim(y_lim)
# Plot model and experiment indication. Both values are not real data, just an example.
model_free_energy = -10.75
final_prediction = cb8_data_mean[cb8_data_mean['N energy evaluations'] == max_displayed_n_eval][DG_KEY].values[0]
ax.plot(x_lim, [model_free_energy]*2, color='gray', ls='--')
ax.text(x_lim[-1]+(max_n_eval_scaled*max_n_eval_percentage)/100, model_free_energy, r'$\Delta$G$_{\theta}$')
ax.text(x_lim[-1]+(max_n_eval_scaled*max_n_eval_percentage)/100, final_prediction - 0.13, r'$\overline{\Delta G}$')
# Plot experimental value horizontal line only for generic plot.
if plot_experimental_value:
experiment_dg = -11.75
plt.plot(x_lim, [experiment_dg]*2, color='black')
if cost == 'neval':
ax.set_xlabel('N force/energy evaluations')
else:
ax.set_xlabel('Computational cost', labelpad=-5)
ax.set_ylabel('$\Delta$G', labelpad=-5)
ax.set_yticklabels([])
ax.set_xticklabels([])
plt.tight_layout(pad=0.1, rect=[0.0, 0.0, 0.90, 1.0])
# Save file.
figure_dir_path = os.path.join(SAMPLING_PAPER_DIR_PATH, 'Figure 1 - host-guest')
os.makedirs(figure_dir_path, exist_ok=True)
output_base_path = os.path.join(figure_dir_path, 'example_trajectories')
plt.savefig(output_base_path + '.pdf')
# =============================================================================
# FIGURE 2 - MEAN ERROR AND RELATIVE EFFICIENCY CARTOON
# =============================================================================
def plot_mean_error_cartoon():
"""Plot the cartoon used to explain mean error and relative efficiency.
This is used as an example to clarify some gotchas with the difinition
of efficiency.
"""
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
sns.set_context('paper')
sns.set_style('white')
def err_decay_func_square(decay_coeff, c):
return decay_coeff / np.sqrt(c)
def mean_error_square(decay_coeff, c_min, c_max):
return 2 * decay_coeff * (np.sqrt(c_max) - np.sqrt(c_min)) / (c_max - c_min)
def err_decay_func_B(decay_coeff, c):
return decay_coeff / c**(5/6)
def mean_error_B(decay_coeff, c_min, c_max):
return 6 * decay_coeff * (c_max**(1/6) - c_min**(1/6)) / (c_max - c_min)
decay_coeffs = {
'A': 1.0,
'B': 2.5,
'Z': 1.5,
}
c_ranges = collections.OrderedDict([
("A'", np.arange(1, 4.5, 0.1)),
("A''", np.arange(3, 6, 0.1)),
("B", np.arange(2, 6.5, 0.1)),
("Z", np.arange(1, 6.5, 0.1)),
])
# Determine colors colors.
colors = {m: 'C'+str(i) for i, m in enumerate(sorted(c_ranges))}
# Plot the error trajectories.
fig, ax = plt.subplots(figsize=(3.5, 2.6))
# method_names = ["B", "Z", "A'", "A''"]
method_names = ["Z", "A'", "A''"]
for method_name in method_names:
color = colors[method_name]
c_range = c_ranges[method_name]
decay_coeff = decay_coeffs[method_name[0]]
if method_name == 'B':
err_decay_func = err_decay_func_B
else:
err_decay_func = err_decay_func_square
err = err_decay_func(decay_coeff, c_range)
# Plot error area.
ax.plot(c_range, err, color=color, label=method_name, zorder=1)
ax.fill_between(c_range, err, 0, color=color, alpha=0.5, zorder=0)
# Add method label.
c_method_label_idx = int(len(c_range) / 8)
ax.text(c_range[c_method_label_idx], err[c_method_label_idx]+0.01, method_name, fontsize=12)
if method_name[0] == 'A':
# Plot mean error.
c_min, c_max = min(c_range), max(c_range)
mean_err = mean_error_square(decay_coeff, c_min, c_max)
# Start mean error horizontal line from the error curve.
c_mean = (decay_coeff / mean_err)**2
ax.plot([0, c_mean], [mean_err, mean_err], color='black', ls='--', alpha=0.8, zorder=1)
# Add label mean error.
# ax.text(1.05, mean_err+0.025, '$\mathbb{E}[RMSE_{' + method_name + '}]$', fontsize=9)
ax.text(-0.3, mean_err+0.025, '$\mathbb{E}[RMSE_{' + method_name + '}]$', fontsize=9)
# Add c_min/max labels.
ax.text(c_min-0.4, -0.1, 'c$_{min,' + method_name + '}$', fontsize=9)
ax.text(c_max-0.4, -0.1, 'c$_{max,' + method_name + '}$', fontsize=9)
# Configure axes.
ax.set_xlim(1, 6.4)
ax.set_ylim(0, 2)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_ylabel('$RMSE(\Delta G)$')
ax.set_xlabel('computational cost')
# Pull axes labels closest to axes.
ax.tick_params(axis='x', which='major', pad=2.0)
ax.yaxis.set_label_coords(0.0, 0.65)
# Plot the relative efficiencies in an inset plot.
ax_ins = inset_axes(ax, width='100%', height='100%', bbox_to_anchor=[145, 115, 90, 50])
# Compute relative efficiencies with respect to Z.
relative_efficiencies = collections.OrderedDict()
for method_name in [name for name in method_names if name != 'Z']:
c_min, c_max = min(c_ranges[method_name]), max(c_ranges[method_name])
if method_name == 'B':
mean_error_func = mean_error_B
else:
mean_error_func = mean_error_square
mean_err_method = mean_error_func(decay_coeffs[method_name[0]], c_min, c_max)
mean_err_Z = mean_error_square(decay_coeffs['Z'], c_min, c_max)
relative_efficiencies[method_name] = -np.log(mean_err_method/mean_err_Z)
# Plot horizontal bar plot with all efficiencies.
labels, rel_effs = zip(*relative_efficiencies.items())
bar_colors = [colors[m] for m in labels]
labels = [l + '/Z' for l in labels]
# labels = ['$e_{err,' + str(l) + '/Z}$' for l in labels]
ax_ins.barh(y=labels, width=rel_effs, color=bar_colors, alpha=0.85)
ax_ins.set_title('relative efficiency', pad=2.5)
# plt.tight_layout(rect=[0.0, 0.0, 1.0, 1.0])
plt.tight_layout(rect=[0.1, 0.0, 1.0, 1.0])
# Pull axes labels closest to axes.
ax_ins.set_xticks([0.0])
ax_ins.grid(axis='x')
ax_ins.tick_params(axis='x', which='major', pad=0.0)
ax_ins.tick_params(axis='y', which='major', pad=0.0)
output_dir_path = os.path.join(SAMPLING_PAPER_DIR_PATH, 'Figure2-efficiency_cartoon')
os.makedirs(output_dir_path, exist_ok=True)
plt.savefig(os.path.join(output_dir_path, 'error_trajectories.pdf'))
# =============================================================================
# FIGURE 3 - FREE ENERGY TRAJECTORIES
# =============================================================================
def plot_submissions_trajectory(submissions, yank_analysis, axes, y_limits=None,
plot_std=True, plot_bias=True, plot_bias_to_reference=False,
system_names=None):
"""Plot free energy trajectories, std, and bias of the given submissions."""
if system_names is None:
system_names = ['CB8-G3', 'OA-G3', 'OA-G6']
n_systems = len(system_names)
max_n_energy_evaluations = {system_name: 0 for system_name in system_names}
min_n_energy_evaluations = {system_name: np.inf for system_name in system_names}
# Handle default arguments.
if y_limits is None:
# 3 by 3 matrix of y limits for the plots.
y_limits = [[None for _ in range(n_systems)] for _ in range(n_systems)]
# We need a 2D array of axes for the code to work even if we're not plotting std or bias.
try:
axes_shape = len(axes.shape)
except AttributeError:
axes = np.array([[axes]])
else:
if axes_shape == 1:
axes = np.array([axes])
# Build a dictionary mapping submissions and system names to their mean data.
all_mean_data = {}
for submission in submissions:
# We always want to print in order
all_mean_data[submission.paper_name] = {}
mean_free_energies = submission.mean_free_energies()
for system_name in system_names:
# CB8-G3 calculations for GROMACS/EE did not converge.
if submission.name == 'Expanded-ensemble/MBAR' and system_name == 'CB8-G3':
continue
# Add mean free energies for this system.
system_mean_data = mean_free_energies[mean_free_energies['System name'] == system_name]
n_energy_evaluations = system_mean_data['N energy evaluations'].values[-1]
all_mean_data[submission.paper_name][system_name] = system_mean_data
# Keep track of the maximum and minimum number of energy evaluations,
# which will be used to determine how to truncate the plotted reference
# data and determine the zorder of the trajectories respectively.
max_n_energy_evaluations[system_name] = max(max_n_energy_evaluations[system_name],
n_energy_evaluations)
min_n_energy_evaluations[system_name] = min(min_n_energy_evaluations[system_name],
n_energy_evaluations)
# Add also reference YANK calculations if provided.
if yank_analysis is not None:
all_mean_data[YANK_METHOD_PAPER_NAME] = {}
for system_name in system_names:
system_mean_data = yank_analysis.get_free_energies_from_energy_evaluations(
max_n_energy_evaluations[system_name], system_name=system_name, mean_trajectory=True)
all_mean_data[YANK_METHOD_PAPER_NAME][system_name] = system_mean_data
# Create a table mapping submissions and system name to the zorder used
# to plot the free energy trajectory so that smaller shaded areas are on
# top of bigger ones.
# First find the average CI for all methods up to min_n_energy_evaluations.
methods_cis = {name: {} for name in system_names}
for method_name, method_mean_data in all_mean_data.items():
for system_name, system_mean_data in method_mean_data.items():
# Find index of all energy evaluations < min_n_energy_evaluations.
n_energy_evaluations = system_mean_data['N energy evaluations'].values
last_idx = np.searchsorted(n_energy_evaluations, min_n_energy_evaluations[system_name], side='right')
cis = system_mean_data['$\Delta$G CI'].values[:last_idx]
methods_cis[system_name][method_name] = np.mean(cis)
# For each system, order methods from smallest CI (plot on top) to greatest CI (background).
zorders = {name: {} for name in system_names}
for system_name, system_cis in methods_cis.items():
ordered_methods = sorted(system_cis.keys(), key=lambda method_name: system_cis[method_name])
for zorder, method_name in enumerate(ordered_methods):
zorders[system_name][method_name] = zorder
# The columns are in order CB8-G3, OA-G3, and OA-G6.
system_columns = {'CB8-G3': 0, 'OA-G3': 1, 'OA-G6': 2}
# Plot submissions in alphabetical order to order he legend labels.
for method_name in sorted(all_mean_data.keys()):
submission_mean_data = all_mean_data[method_name]
submission_color = SUBMISSION_COLORS[method_name]
submission_ls = SUBMISSION_LINE_STYLES[method_name]
# Plot free energy trajectories.
for system_name, mean_data in submission_mean_data.items():
ax_idx = system_columns[system_name]
# The OA prediction of the NS short protocol are the same of the long protocol submission file.
if method_name == 'GROMACS/NS-DS/SB-long' and system_name != 'CB8-G3':
# Just add the label.
axes[0][ax_idx].plot([], color=submission_color, ls=submission_ls, label=method_name)
continue
# Update maximum number of energy evaluations.
n_energy_evaluations = mean_data['N energy evaluations'].values[-1]
max_n_energy_evaluations[system_name] = max(max_n_energy_evaluations[system_name],
n_energy_evaluations)
# Determine zorder and plot.
zorder = zorders[system_name][method_name]
plot_mean_data(mean_data, axes[:,ax_idx], color=submission_color,
ls=submission_ls, zorder=zorder, label=method_name,
plot_std=plot_std, plot_bias=plot_bias)
# Fix labels.
axes[0][0].set_ylabel('$\Delta$G [kcal/mol]')
if plot_std:
axes[1][0].set_ylabel('std($\Delta$G) [kcal/mol]')
if plot_bias:
axes[2][0].set_ylabel('bias [kcal/mol]')
central_column_idx = int(len(axes[0])/2)
axes[-1][central_column_idx].set_xlabel('number of energy/force evaluations [10$^6$]')
# Fix axes limits.
for ax_idx, system_name in enumerate(system_names):
for row_idx in range(len(axes)):
ax = axes[row_idx][ax_idx]
# Set the x-axis limits.
ax.set_xlim((0, max_n_energy_evaluations[system_name]/N_ENERGY_EVALUATIONS_SCALE))
# Keep the x-axis label only at the bottom row.
if row_idx != len(axes)-1:
ax.xaxis.set_ticklabels([])
y_lim = y_limits[row_idx][ax_idx]
if y_lim is not None:
ax.set_ylim(y_lim)
# Set the system name in the title.
axes[0][ax_idx].set_title(system_name)
# Create a bias axis AFTER the ylim has been set.
if yank_analysis is not None and plot_bias_to_reference:
for ax_idx, (system_name, ax) in enumerate(zip(system_names, axes[0])):
yank_full_mean_data = yank_analysis.get_system_free_energies(system_name, mean_trajectory=True)
ref_free_energy = yank_full_mean_data[DG_KEY].values[-1]
with sns.axes_style('white'):
ax2 = ax.twinx()
# Plot a vertical line to fix the scale.
vertical_line = np.linspace(*ax.get_ylim()) - ref_free_energy
ax2.plot([50] * len(vertical_line), vertical_line, alpha=0.0001)
ax2.grid(alpha=0.5, linestyle='dashed', zorder=0)
# We add the bias y-label only on the rightmost Axis.
if ax_idx == n_systems - 1:
ax2.set_ylabel('Bias to reference [kcal/mol]')
# Set the 0 of the twin axis to the YANK reference free energy.
align_yaxis(ax, ref_free_energy, ax2, 0.0)
def plot_all_entries_trajectory(submissions, yank_analysis, zoomed=False):
"""Plot free energy trajectories, std, and bias of the challenge entries."""
sns.set_style('whitegrid')
sns.set_context('paper')
# Create a figure with 3 columns (one for each system) and 2 rows.
# The first row contains the free energy trajectory and CI, the second
# a plot of the estimator variance, and the third the bias to the
# asymptotic value.
if zoomed:
figsize = (7.25, 7.0) # Without REVO
else:
figsize = (7.25, 7.0) # With REVO
fig, axes = plt.subplots(nrows=3, ncols=3, figsize=figsize)
# Optionally, remove REVO.
if zoomed:
submissions = [s for s in submissions if s.name not in ['WExploreRateRatio']]
if zoomed:
# Y-axis limits when REVO calculations are excluded.
y_limits = [
[(-15, -10), (-9, -4), (-9, -4)],
[(0, 2), (0, 0.8), (0, 0.8)],
[(-3, 1), (-0.6, 0.6), (-0.6, 0.6)],
]
else:
# Y-axis limits when REVO calculations are included.
y_limits = [
[(-17, -9), (-13, -5), (-13, -5)],
[(0, 2), (0, 1.75), (0, 1.75)],
[(-4, 4), (-0.6, 0.6), (-0.6, 0.6)],
]
plot_submissions_trajectory(submissions, yank_analysis, axes, y_limits=y_limits)
# Show/save figure.
if zoomed:
plt.tight_layout(h_pad=0.2, rect=[0.0, 0.00, 1.0, 0.92], w_pad=0.0) # Without REVO
else:
plt.tight_layout(h_pad=0.2, rect=[0.0, 0.00, 1.0, 0.92]) # With REVO
# Plot legend.
if zoomed:
# bbox_to_anchor = (2.52, 1.55) # Without REVO.
bbox_to_anchor = (2.4, 1.48)
else:
bbox_to_anchor = (2.4, 1.48) # With REVO.
axes[0][1].legend(loc='upper right', bbox_to_anchor=bbox_to_anchor,
fancybox=True, ncol=4)
plt.subplots_adjust(wspace=0.35)
# plt.show()
if zoomed:
file_name = 'Figure3-free_energy_trajectories_zoomed'
else:
file_name = 'Figure3-free_energy_trajectories'
figure_dir_path = os.path.join(SAMPLING_PAPER_DIR_PATH, 'Figure3-free_energy_trajectories')
os.makedirs(figure_dir_path, exist_ok=True)
output_base_path = os.path.join(figure_dir_path, file_name)
plt.savefig(output_base_path + '.pdf')
# plt.savefig(output_base_path + '.png', dpi=500)
# =============================================================================
# FIGURE 4 - NONEQUILIBRIUM SWITCHING ESTIMATOR COMPARISON
# =============================================================================
def plot_all_nonequilibrium_switching(submissions):
"""Plot free energy trajectories, std, and bias of the nonequilibrium-switching calculations."""
# Create a figure with 3 columns (one for each system) and 2 rows.
# The first row contains the free energy trajectory and CI, the second
# a plot of the estimator variance, and the third the bias to the
# asymptotic value.
figsize = (7.25, 3.5)
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=figsize)
# Select nonequilibrium-switching calculations with estimators.
submissions = [s for s in submissions if 'NS' in s.paper_name]
# Y-axis limits.
y_limits = [
[(-20, 5), (-40, 0), (-40, 0)]
]
plot_submissions_trajectory(submissions, yank_analysis=None, axes=axes,
y_limits=y_limits, plot_std=False, plot_bias=False)
# Show/save figure.
plt.tight_layout(pad=0.0, rect=[0.0, 0.00, 1.0, 0.85])
# Plot legend.
legend = axes[0].legend(loc='upper left', bbox_to_anchor=(0.6, 1.3),
fancybox=True, ncol=3)
# Change legend labels to refer to estimator used rather than overall method ID.
legend_labels_map = {
'GROMACS/NS-DS/SB-long': 'BAR-long',
'GROMACS/NS-DS/SB': 'BAR',
'GROMACS/NS-Jarz-F': 'Jarzynski-Forward',
'GROMACS/NS-Jarz-R': 'Jarzynski-Reverse',
'GROMACS/NS-Gauss-F': 'Gaussian-Forward',
'GROMACS/NS-Gauss-R': 'Gaussian-Reverse',
}
for text in legend.get_texts():
text.set_text(legend_labels_map[text.get_text()])
plt.subplots_adjust(wspace=0.35)
# plt.show()
figure_dir_path = os.path.join(SAMPLING_PAPER_DIR_PATH, 'Figure4-nonequilibrium_comparison')
os.makedirs(figure_dir_path, exist_ok=True)
output_base_path = os.path.join(figure_dir_path, 'Figure4-nonequilibrium_comparison')
plt.savefig(output_base_path + '.pdf')
# plt.savefig(output_base_path + '.png', dpi=500)
# =============================================================================
# FIGURE 5 - BAROSTAT AND RESTRAINT
# =============================================================================
# Directories containing the volume information of YANK and GROMACS/EE.
BAROSTAT_DATA_DIR_PATH = os.path.join('..', 'SAMPLing', 'Data', 'BarostatData')
YANK_VOLUMES_DIR_PATH = os.path.join(BAROSTAT_DATA_DIR_PATH, 'YankVolumes')
EE_VOLUMES_DIR_PATH = os.path.join(BAROSTAT_DATA_DIR_PATH, 'EEVolumes')
def plot_volume_distributions(axes, plot_predicted=False):
"""Plot the volume distributions obtained with Monte Carlo and Berendsen barostat."""
import scipy.stats
import scipy.integrate
from simtk import unit
# Load data.
mc_volumes = collections.OrderedDict([
(1, np.load(os.path.join(YANK_VOLUMES_DIR_PATH, 'volumes_pressure100.npy'))),
(100, np.load(os.path.join(YANK_VOLUMES_DIR_PATH, 'volumes_pressure10000.npy'))),
])
mc_volumes_hrex = collections.OrderedDict([
(1, np.load(os.path.join(YANK_VOLUMES_DIR_PATH, 'hrex_state_volumes_state0.npy'))),
(58, np.load(os.path.join(YANK_VOLUMES_DIR_PATH, 'hrex_state_volumes_state58.npy'))),
])
b_volumes = collections.OrderedDict([
(1, np.load(os.path.join(EE_VOLUMES_DIR_PATH, '1atm_vanilla.npy'))),
(100, np.load(os.path.join(EE_VOLUMES_DIR_PATH, '100atm_vanilla.npy'))),
])
b_volumes_ee = collections.OrderedDict([
(1, np.load(os.path.join(EE_VOLUMES_DIR_PATH, '1atm_expanded.npy'))),
(100, np.load(os.path.join(EE_VOLUMES_DIR_PATH, '100atm_expanded.npy'))),
])
# Print some statistics for each distribution.
for volume_trajectories, label in [(mc_volumes, 'MC-MD '),
(mc_volumes_hrex, 'MC-HREX'),
(b_volumes, 'BB-MD '),
(b_volumes_ee, 'BB-EE ')]:
for pressure, trajectory in volume_trajectories.items():
n = len(trajectory)
t_stat = 2.326 # 98% CI
mean = np.mean(trajectory)
sem = scipy.stats.sem(trajectory)
mean_ci = t_stat * sem
var = np.var(trajectory, ddof=1)
# Standard error of variance if volume is gaussianly distributed
sev = var * np.sqrt(2 / (n-1))
var_ci = t_stat * sev
skew = scipy.stats.skew(trajectory)
# Standard error of skewness if volume is gaussianly distributed
ses = np.sqrt( 6*n*(n-1) / ((n-2)*(n+1)*(n+3)) )
skew_ci = t_stat * ses
print('{}-{} (n={}): mean={:.3f} +- {:.3f}nm^3\t\tvar={:.3f} +- {:.3f}\tskew={:.3f} +- {:.3f}'.format(
pressure, label, n, mean, mean_ci, var, var_ci, skew, skew_ci))
# Plot the 1atm vs 100atm comparison.
barostats = ['B', 'MC']
for ax, volume_trajectories, barostat in zip(axes, [b_volumes, mc_volumes], barostats):
barostat += ',MD'
barostat = 'MD'
for pressure, trajectory in volume_trajectories.items():
label = '$\\rho_{{\mathrm{{{}}}}}$(V|{}atm)'.format(barostat, pressure)
ax = sns.distplot(trajectory, label=label, hist=False, ax=ax)
if plot_predicted:
# Plot predicted distribution.
beta = 1.0 / (unit.BOLTZMANN_CONSTANT_kB * 298.15*unit.kelvin)
p1 = 1.0 * unit.atmosphere
p2 = 100.0 * unit.atmosphere
volumes = np.linspace(78.0, 82.0, num=200)
fit = scipy.stats.norm
# Fit the original distribution.
original_pressure, new_pressure = list(volume_trajectories.keys())
original_trajectory = list(volume_trajectories.values())[0]
fit_parameters = fit.fit(original_trajectory)
# Find normalizing constant predicted distribution.
predicted_distribution = lambda v: np.exp(-beta*(p2 - p1)*v*unit.nanometer**3) * fit.pdf([v], *fit_parameters)
normalizing_factor = scipy.integrate.quad(predicted_distribution, volumes[0], volumes[-1])[0]
predicted = np.array([predicted_distribution(v) / normalizing_factor for v in volumes])
# Set the scale.
label = '$\\rho_{{\mathrm{{{}}}}}$(V|{}atm)$\cdot e^{{\\beta ({}atm - {}atm) V}}$'.format(barostat, original_pressure, new_pressure, original_pressure)
ax.plot(volumes, predicted, ls='--', label=label)
# ax.plot(volumes, [fit.pdf([v], *fit_parameters) for v in volumes], label='original')
# Plot comparison MD vs expanded ensemble and HREX volumes.
for ax_idx, (trajectory, label) in enumerate([
(b_volumes_ee[1], 'B,EE'), (mc_volumes_hrex[1], 'MC,HREX')
]):
label = 'E'
ax = axes[ax_idx]
label = '$\\rho_{{\mathrm{{{}}}}}$(V|1atm)'.format(label)
sns.distplot(trajectory, label=label, hist=False, ax=ax)
# Set titles and configure axes.
axes[0].set_title('Berendsen barostat volume distribution', pad=2.0)
axes[1].set_title('Monte Carlo barostat volume distribution', pad=2.0)
for ax_idx in range(len(axes)):
axes[ax_idx].set_xlim((78.8, 81.2))
axes[ax_idx].set_ylim((0.0, 6.0))
axes[ax_idx].set_ylabel('density')
axes[0].set_xlabel('', labelpad=0.3)
axes[1].set_xlabel('Volume [nm^3]', labelpad=0.3)
# Create single legend for both MC and B barostat axes.
bbox_to_anchor = (-0.1, -0.15)
axes[0].legend(fontsize='xx-small', loc='upper left', bbox_to_anchor=bbox_to_anchor, ncol=4,
fancybox=True, labelspacing=0.7, handletextpad=0.4, columnspacing=1.1,)
# axes[0].get_legend().remove()
axes[1].get_legend().remove()
plt.tight_layout(pad=0, rect=[0.0, 0.0, 1.0, 1.0])
# Directory with the restraint information.
RESTRAINT_DATA_DIR_PATH = os.path.join('YankAnalysis', 'RestraintAnalysis')
# The state index of the discharged state with LJ interactions intact.
DISCHARGED_STATE = {
'CB8-G3': 25,
'OA-G3': 32,
'OA-G6': 29
}
# The final free energy predictions without restraint unbiasing.
BIASED_FREE_ENERGIES = {
'CB8-G3-0': -10.643,
'CB8-G3-1': -10.533,
'CB8-G3-2': -10.463,
'CB8-G3-3': None, # TODO: Run the biased analysis
'CB8-G3-4': -10.324,
'OA-G3-0': -5.476,
'OA-G3-1': -5.588,
'OA-G3-2': -5.486,
'OA-G3-3': -5.510,
'OA-G3-4': -5.497,
'OA-G6-0': -5.669,
'OA-G6-1': -5.665,
'OA-G6-2': -5.767,
'OA-G6-3': -5.737,
'OA-G6-4': -5.788,
}
def plot_restraint_distance_distribution(system_id, ax, kde=True, iteration_set=None):
"""Plot the distribution of restraint distances at bound, discharged, and decoupled states.
Return the 99.99-percentile restraint radius that was used as a cutoff during analysis.
"""
n_iterations = YANK_N_ITERATIONS + 1 # Count also iteration 0.
system_name = system_id[:-2]
discharged_state_idx = DISCHARGED_STATE[system_name]
# Load all distances cached during the analysis.
cache_dir_path = os.path.join('pkganalysis', 'cache', system_id.replace('-', ''))
cached_distances_file_path = os.path.join(cache_dir_path, 'restraint_distances_cache.npz')
distances_kn = np.load(cached_distances_file_path)['arr_0']
# Distances are in nm but we plot in Angstrom.
distances_kn *= 10
n_states = int(len(distances_kn) / n_iterations)
# Use the same colors that are used in the water analysis figures.
color_palette = sns.color_palette('viridis', n_colors=n_states)
color_palette = [color_palette[i] for i in (0, discharged_state_idx, -1)]
# Isolate distances in the bound, discharged (only LJ), and decoupled state.
distances_kn_bound = distances_kn[:n_iterations]
distances_kn_discharged = distances_kn[(discharged_state_idx-1)*n_iterations:discharged_state_idx*n_iterations]
distances_kn_decoupled = distances_kn[(n_states-1)*n_iterations:]
# Filter iterations.
if iteration_set is not None:
distances_kn_bound = distances_kn_bound[iteration_set]
distances_kn_discharged = distances_kn_discharged[iteration_set]
distances_kn_decoupled = distances_kn_decoupled[iteration_set]
assert len(distances_kn_bound) == len(distances_kn_decoupled)
# Plot the distributions.
# sns.distplot(distances_kn, ax=ax, kde=True, label='all states')
sns.distplot(distances_kn_bound, ax=ax, kde=kde, label='bound', color=color_palette[0])
sns.distplot(distances_kn_discharged, ax=ax, kde=kde, label='discharged', color=color_palette[1])
sns.distplot(distances_kn_decoupled, ax=ax, kde=kde, label='decoupled', color=color_palette[2])
# Plot the threshold used for analysis, computed as the
# 99.99-percentile of all distances in the bound state.
distance_cutoff = np.percentile(a=distances_kn_bound, q=99.99)
limits = ax.get_ylim()
ax.plot([distance_cutoff for _ in range(100)],
np.linspace(limits[0], limits[1]/2, num=100), color='black')
return distance_cutoff
def plot_restraint_profile(system_id, ax, restraint_cutoff):
"""Plot the free energy as a function of the restraint cutoff."""
# Load the free energy profile for this system.
restraint_profile_file_path = os.path.join(RESTRAINT_DATA_DIR_PATH,
system_id.replace('-', '') + '.json')
with open(restraint_profile_file_path, 'r') as f:
free_energies_profile = json.load(f)
# Reorder the free energies by increasing cutoff and convert str keys to floats.
free_energies_profile = [(float(d), f) for d, f in free_energies_profile.items()]
free_energies_profile = sorted(free_energies_profile, key=lambda x: x[0])
distance_cutoffs, free_energies = list(zip(*free_energies_profile))
f, df = list(zip(*free_energies))
# Convert string to floats.
distance_cutoffs = [float(c) for c in distance_cutoffs]
# Plot profile.
ax.errorbar(x=distance_cutoffs, y=f, yerr=df, label='after reweighting')
# Plot biased free energy
biased_f = BIASED_FREE_ENERGIES[system_id]
x = np.linspace(*ax.get_xlim())
ax.plot(x, [biased_f for _ in x], label='before reweighting')
# Plot restraint distance cutoff.
limits = ax.get_ylim()
x = [restraint_cutoff for _ in range(100)]
y = np.linspace(limits[0], limits[1], num=100)
ax.plot(x, y, color='black')
def plot_restraint_analysis(system_id, axes):
"""Plot distribution of restraint distances and free energy profile on two axes."""
# Histograms of restraint distances/energies.
ax = axes[0]
kde = True
restraint_cutoff = plot_restraint_distance_distribution(system_id, ax, kde=kde)
# Set restraint distance distribution lables and titles.
ax.set_title('Restrained ligand-receptor distance', pad=2.0)
if kde is False:
ax.set_ylabel('Number of samples')
else:
ax.set_ylabel('density')
ax.legend(loc='upper right', fontsize='x-small')
ax.set_xlabel('Restrained distance [$\mathrm{\AA}$]', labelpad=0.3)
# Free energy as a function of restraint distance.
ax = axes[1]
ax.set_title('$\Delta G$ as a function of restraint radius cutoff', pad=2.0 )
plot_restraint_profile(system_id, ax, restraint_cutoff)
# Labels and legend.
ax.set_xlabel('Restraint radius cutoff [$\mathrm{\AA}$]', labelpad=0.3)
ax.set_ylabel('$\Delta G$ [kcal/mol]')
ax.legend(fontsize='x-small')
def plot_restraint_and_barostat_analysis():
"""Plot the Figure showing info for the restraint and barostat analysis."""
import seaborn as sns
from matplotlib import pyplot as plt
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.0)
# Create two columns, each of them share the x-axis.
fig = plt.figure(figsize=(7.25, 4))
# Restraint distribution axes.
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(223, sharex=ax1)
barostat_axes = [ax1, ax2]
# Volume distribution axes.
ax3 = fig.add_subplot(222)
ax4 = fig.add_subplot(224, sharex=ax3)
restraint_axes = [ax3, ax4]
# Plot barostat analysis.
plot_volume_distributions(barostat_axes, plot_predicted=True)
# Plot restraint analysis.
system_id = 'OA-G3-0'
plot_restraint_analysis(system_id, restraint_axes)
# Configure axes.
restraint_axes[0].set_xlim((0, 10.045))
restraint_axes[1].set_ylim((-7, -3.9))
for ax in restraint_axes + barostat_axes:
ax.tick_params(axis='x', which='major', pad=0.1)
ax.tick_params(axis='y', which='major', pad=0.1)
plt.tight_layout(pad=0.3)
# plt.show()
output_file_path = os.path.join(SAMPLING_PAPER_DIR_PATH, 'Figure5-restraint_barostat',
'restraint_barostat.pdf')
os.makedirs(os.path.dirname(output_file_path), exist_ok=True)
plt.savefig(output_file_path)
# =============================================================================
# FIGURE 6 - HREX INITIAL BIAS
# =============================================================================
def plot_yank_system_bias(system_name, data_dir_paths, axes, shift_to_origin=True, plot_std=True):
"""Plot the YANK free energy trajectoies when discarding initial samples for a single system."""
color_palette = sns.color_palette('viridis', n_colors=len(data_dir_paths)+1)
# Plot trajectories with truncated data.
all_iterations = set()
for data_idx, data_dir_path in enumerate(data_dir_paths):
yank_analysis = YankSamplingAnalysis(data_dir_path)
# In the YankAnalysis folder, each analysis starting from
# iteration N is in the folder "iterN/".
last_dir_name = os.path.basename(os.path.normpath(data_dir_path))
label = last_dir_name[4:]
# First color is for the full data.
color = color_palette[data_idx+1]
# Collect all iterations that we'll plot for the full data.
mean_data = yank_analysis.get_system_free_energies(system_name, mean_trajectory=True)
all_iterations.update(mean_data['HREX iteration'].values.tolist())
# Simulate plotting starting from the origin.
if shift_to_origin:
mean_data['HREX iteration'] -= mean_data['HREX iteration'].values[0]
plot_mean_data(mean_data, axes, x='HREX iteration', color=color,
label=label, plot_std=plot_std, plot_bias=False, plot_ci=False)
# Plot trajectory with full data.
color = color_palette[0]
# Plot an early iteration and all the iterations analyzed for the bias.
yank_analysis = YankSamplingAnalysis(YANK_ANALYSIS_DIR_PATH)
system_ids = [system_name + '-' + str(i) for i in range(5)]
first_iteration = yank_analysis.get_system_iterations(system_ids[0])[2]
iterations = [first_iteration] + sorted(all_iterations)
mean_data = yank_analysis._get_free_energies_from_iterations(
iterations, system_ids, mean_trajectory=True)
# Simulate plotting starting from the origin.
if shift_to_origin:
mean_data['HREX iteration'] -= mean_data['HREX iteration'].values[0]
# Simulate ploatting starting from the origin.
plot_mean_data(mean_data, axes, x='HREX iteration', color=color,
label='0', plot_std=plot_std, plot_bias=False, plot_ci=False)
axes[0].set_title(system_name)
def plot_yank_bias(plot_std=True, figure_dir_path=None):
"""Plot YANK free energy trajectories when discarding initial samples."""
# In the first column, plot the "unshifted" trajectory of CB8-G3,
# with all sub-trajectories shifted to the origin. In the second
# and third columns, plot the trajectories of CB8-G3 and OA-G3
# with all sub-trajectories shifted to the origin.
what_to_plot = [
('CB8-G3', False),
# ('CB8-G3', True),
('OA-G3', False),
# ('OA-G3', False),
('OA-G6', False),
]
if plot_std:
n_rows = 2
else:
n_rows = 1
n_cols = len(what_to_plot)
fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=(7.25, 4.0))
# The loops are based on a two dimensional array of axes.
if n_rows == 1:
axes = np.array([axes])
# Sort paths by how many samples they have.
data_dir_paths = ['YankAnalysis/BiasAnalysis/iter{}/'.format(i) for i in [1000, 2000, 4000, 8000, 16000, 24000]]
for column_idx, (system_name, shift_to_origin) in enumerate(what_to_plot):
plot_yank_system_bias(system_name, data_dir_paths, axes[:,column_idx],
shift_to_origin=shift_to_origin, plot_std=plot_std)
title = system_name + ' (shifted)' if shift_to_origin else system_name
axes[0,column_idx].set_title(title)
# Fix axes limits and labels.
ylimits = {
'CB8-G3': (-12.5, -10.5),
'OA-G3': (-8, -6),
'OA-G6': (-8, -6)
}
for column_idx, (system_name, _) in enumerate(what_to_plot):
axes[0][column_idx].set_ylim(ylimits[system_name])
if plot_std:
axes[1][column_idx].set_ylim((0, 0.6))
for row_idx, ax_idx in itertools.product(range(n_rows), range(n_cols)):
# Control the number of ticks for the x axis.
axes[row_idx][ax_idx].locator_params(axis='x', nbins=4)
# Set x limits for number of iterations.
axes[row_idx][ax_idx].set_xlim((0, YANK_N_ITERATIONS))
# Remove ticks labels that are shared with the last row.
for row_idx, ax_idx in itertools.product(range(n_rows-1), range(n_cols)):
axes[row_idx][ax_idx].set_xticklabels([])
# Set axes labels.
axes[0][0].set_ylabel('$\Delta$G [kcal/mol]')
if plot_std:
axes[1][0].set_ylabel('std($\Delta$G) [kcal/mol]')
# If there is an odd number of columns print x label only on the central one.
if n_cols % 2 == 1:
axes[-1][1].set_xlabel('HREX iteration')
else:
for ax in axes[-1]:
ax.set_xlabel('HREX iteration')
plt.tight_layout(h_pad=0.1, rect=[0.0, 0.00, 1.0, 0.91])
handles, labels = axes[0][0].get_legend_handles_labels()
handles = [handles[-1]] + handles[:-1]
labels = [labels[-1]] + labels[:-1]
bbox_to_anchor = (0.4, 1.53)
axes[0][0].legend(handles, labels, loc='upper left', bbox_to_anchor=bbox_to_anchor,
title='number of discarded initial iterations', ncol=len(data_dir_paths)+1,
fancybox=True, labelspacing=0.8, handletextpad=0.5, columnspacing=1.2,
fontsize='small')
# plt.show()
if figure_dir_path is None:
figure_dir_path = os.path.join(SAMPLING_PAPER_DIR_PATH, 'Figure6-bias_hrex')
os.makedirs(figure_dir_path, exist_ok=True)
output_file_path = os.path.join(figure_dir_path, 'Figure6-bias_hrex')
plt.savefig(output_file_path + '.pdf')
# plt.savefig(output_file_path + '.png', dpi=600)
# =============================================================================
# SUPPORTING INFORMATION - EXAMPLE OF HREX BIAS
# =============================================================================
def simulate_correlation_samples():
"""Simulation of bias from same initial configuration.
There are 3 states as different harmonic oscillators, but all
or almost all the samples come from the first (bound) state to
simulate what happens when they don't decorrelate fast enough.
The hypothesis is that most is that starting from the bound
state causes the initial free energy to be artificially negative
if the correlation times are long.
The second (discharged) state is just a shifted harmonic oscillator
(same free energy as bound state). The third (unbound) is shifted
and has much higher entropy.
"""
from numpy.random import normal
from pymbar import MBAR
def harmonic_oscillator_free_energy(sigma):
"""Analytical expression for the free energy of a harmonic oscillator."""
#return - np.log(2 * np.pi * sigma**2) * 3.0 / 2.0 # 3D oscillator
return - np.log(np.sqrt(2 * np.pi) * sigma)
def harmonic_oscillator_potential(x, loc, std):
"""Compute potential of the given positions given location
and standard deviation of the Gaussian distribution.
Potentials are returned in units of kT.
"""
spring_constant = 1 / std**2
return spring_constant / 2.0 * (x - loc)**2
def print_free_energies(Deltaf_ij, dDeltaf_ij):
mbar_str = ', '.join(['{:.4f} +- {:.4f}'.format(f, df) for f, df in zip(Deltaf_ij[:,0], dDeltaf_ij[:,0])])
print('MBAR :', mbar_str)
analytical_str = ', '.join(['{:.4f} '.format(f) for f in analytical_Deltaf])
print('Analytical:', analytical_str)
def compute_mbar_free_energy(all_samples, shifts, stds, analytical_f):
n_states = len(all_samples)
# u_kn[k,n] is the reduced potential energy n-th sample evaluated at state k.
u_kn = np.empty(shape=(n_states, n_states*n_samples))
# Convert samples to potentials.
for k in range(n_states):
for sampled_k, samples in enumerate(all_samples):
start = sampled_k * n_samples
end = (sampled_k + 1) * n_samples
u_kn[k,start:end] = harmonic_oscillator_potential(samples, loc=shifts[k], std=stds[k])
# Compute MBAR free energy.
N_k = np.array([n_samples] * n_states)
mbar = MBAR(u_kn, N_k=N_k, initial_f_k=analytical_f)
Deltaf_ij, dDeltaf_ij, _ = mbar.getFreeEnergyDifferences()
return Deltaf_ij, dDeltaf_ij
# Determine standard deviation and shift of the harmonic distributions.
n_samples = 5000000
stds = np.array([2.0, 2.0, 5.0])
shifts = np.array([0.0, 2.0, 2.0])
print('\nspring constants:', 1 / stds**2)
# Compute analytical free energy.
analytical_f = np.array([harmonic_oscillator_free_energy(s) for s in stds])
analytical_Deltaf = np.array([analytical_f[0] - analytical_f[i] for i in range(len(stds))])
# FIRST TEST.
# Sample from all states and verify that MBAR free energy is correct.
# -------------------------------------------------------------------
all_samples = [normal(loc=l, scale=s, size=n_samples) for l, s in zip(shifts, stds)]
Deltaf_ij, dDeltaf_ij = compute_mbar_free_energy(all_samples, shifts, stds, analytical_f)
print()
print_free_energies(Deltaf_ij, dDeltaf_ij)
# SECOND TEST.
# Check if the bias is not due to lack of overlap. If we sample only the end states the estimate should be correct.
# -----------------------------------------------------------------------------------------------------------------
for i in range(1, len(all_samples)):
all_samples_bar = [all_samples[0], all_samples[i]]
shifts_bar = [shifts[0], shifts[i]]
stds_bar = [stds[0], stds[i]]
analytical_f_bar = [analytical_f[0], analytical_f[i]]
Deltaf_ij, dDeltaf_ij = compute_mbar_free_energy(all_samples_bar, shifts_bar, stds_bar, analytical_f_bar)
print('\nBAR_{}0'.format(i))
print_free_energies(Deltaf_ij, dDeltaf_ij)
# THIRD TEST.
# Now sample from only the bound state to see how the free energy changes.
# ------------------------------------------------------------------------
all_samples[1:] = [normal(loc=shifts[0], scale=stds[0], size=n_samples) for _ in range(len(stds)-1)]
Deltaf_ij, dDeltaf_ij = compute_mbar_free_energy(all_samples, shifts, stds, analytical_f)
print()
print_free_energies(Deltaf_ij, dDeltaf_ij)
# FOURTH TEST.
# Now let the unbound state decorrelate fast (i.e. sample from its own distribution).
# -----------------------------------------------------------------------------------
all_samples[-1] = normal(loc=shifts[-1], scale=stds[-1], size=n_samples)
Deltaf_ij, dDeltaf_ij = compute_mbar_free_energy(all_samples, shifts, stds, analytical_f)
print()
print_free_energies(Deltaf_ij, dDeltaf_ij)
# RESULT: SUCCESS!!!
# =============================================================================
# SUPPORTING INFORMATION - COMPLEX/SOLVENT and ENTROPY/ENTHALPY DECOMPOSITION
# =============================================================================
def _mean_data_decomposition(data):
# Convert into a numpy array to take the mean.
# Convert None (not supported by numpy) into nans.
try:
# This may fail if we have computed different iterations for each.
data = np.array(data, dtype=np.float)
except ValueError:
data_lengths = [len(x) for x in data]
print('Warning: Truncating data of shape {}'.format(data_lengths))
min_length = min(data_lengths)
data = [x[:min_length] for x in data]
data = np.array(data, dtype=np.float)
# Compute std and mean along the trajectory ignoring NaNs.
return np.nanmean(data, axis=0), np.nanstd(data, axis=0)
def _plot_phase_decomposition(ax, phase_free_energies):
# Shortcuts.
data = phase_free_energies
label = '$\Delta$G'
# Plot each phase data on a separate axis to make the comparison on different order of magnitudes easier.
# Receipt with three axes: https://matplotlib.org/3.1.0/gallery/ticks_and_spines/multiple_yaxis_with_spines.html
phase_axes = {
'complex': ax.twinx(),
'solvent': ax.twinx()
}
phase_colors = {
'complex': 'C1',
'solvent': 'C0',
}
for ax_name in sorted(phase_axes):
phase_axes[ax_name].set_ylabel(label + ' ' + ax_name + ' [kcal/mol]',
color=phase_colors[ax_name])
phase_axes[ax_name].spines["right"].set_position(("axes", 1.2))
# Compute total free energy summing complex and solvent for all replicates.
total_mean = [np.array(data['solvent'][i]) + np.array(data['complex'][i]) for i in range(5)]
total_mean, total_std = _mean_data_decomposition(total_mean)
# Compute and plot the phase free energy.
for phase_name in ['complex', 'solvent']:
color = phase_colors[phase_name]
# Convert into a numpy array to take the mean.
# Convert None (not supported by numpy) into nans.
data[phase_name], std = _mean_data_decomposition(data[phase_name])
# Plot each phase data on a separate axis to make the comparison easier.
phase_axes[phase_name].plot(data[phase_name], ls='-', color=color,
label=label + ' ' + phase_name)
# Plot uncertainties.
phase_axes[phase_name].fill_between(x=list(range(len(std))), y1=data[phase_name]-std,
y2=data[phase_name]+std, color=color, alpha=0.7)
# Plot total free energy.
# total = data['solvent'] + data['complex']
# ax.plot(total, color='black', label=label+' total')
ax.plot(total_mean, color='black', label=label+' total')
ax.fill_between(x=list(range(len(total_std))), y1=total_mean-total_std,
y2=total_mean+total_std, color='black', alpha=0.7)
ax.set_ylabel(label + ' total [kcal/mol]')
ax.set_xlabel('simulation percentage')
# Make the range of all y axes the same.
ax.set_ylim((-21, -18))
phase_axes['complex'].set_ylim((-151.0, -148.0))
phase_axes['solvent'].set_ylim((129.0, 132.0))
def _plot_entropy_enthalpy_decomposition(ax, phase_free_energies, phase_enthalpy):
# Analyze only the complex.
phase_name = 'complex'
# Plot each phase data on a separate axis to make the comparison on different order of magnitudes easier.
# Receipt with three axes: https://matplotlib.org/3.1.0/gallery/ticks_and_spines/multiple_yaxis_with_spines.html
axes = {
'$\Delta$G': ax,
'$\Delta$H': ax.twinx(),
'-T$\Delta$S': ax.twinx(),
}
colors = {
'$\Delta$G': 'black',
'$\Delta$H': 'C1',
'-T$\Delta$S': 'C0',
}
for ax_name in sorted(axes):
axes[ax_name].set_ylabel(ax_name + ' ' + phase_name + ' [kcal/mol]', color=colors[ax_name])
axes[ax_name].spines["right"].set_position(("axes", 1.2))
# Variable used to propagate entropy decomposition.
entropy_std = []
# Plot the total average free energy and enthalpy and for each phase.
for data, label in [(phase_free_energies, '$\Delta$G'),
(phase_enthalpy, '$\Delta$H')]:
color = colors[label]
# Convert into a numpy array to take the mean.
# Convert None (not supported by numpy) into nans.
data[phase_name], std = _mean_data_decomposition(data[phase_name])
ns_replica = np.arange(0.0, 40.0, 40/len(std))
# Plot each phase data on a separate axis to make the comparison easier.
axes[label].plot(ns_replica, data[phase_name], ls='-', color=color, label=label+' '+phase_name)
# Plot uncertainties.
axes[label].fill_between(x=ns_replica, y1=data[phase_name]-std,
y2=data[phase_name]+std, color=color, alpha=0.7)
# Propagate uncertainty.
if len(entropy_std) == 0:
entropy_std = std**2
else:
entropy_std += std**2
entropy_std = np.sqrt(entropy_std)
# Plot also entropies.
label = '-T$\Delta$S'
color = colors[label]
entropy = phase_free_energies[phase_name] - phase_enthalpy[phase_name]
axes[label].plot(ns_replica, entropy, ls='-', color=color, label=label+' '+phase_name)
# Plot uncertainties.
axes[label].fill_between(x=ns_replica, y1=entropy-entropy_std,
y2=entropy+entropy_std, color=color, alpha=0.7)
ax.set_xlabel('ns/replica')
def plot_decomposition(system_name, starting_iteration, type, output_file_path):
"""
Decomposition of the free energy trajectory in complex/solvent phase or entropy/enthalpy.
Parameters
----------
type : str
Can be 'entropy-enthalpy' or 'phase'.
"""
data_file_pattern = 'YankAnalysis/BiasAnalysis/iter{}/fe-decomposition-{}-{{}}.json'.format(
starting_iteration, system_name)
n_replicates = 5
phase_free_energies = {'complex': [[] for _ in range(n_replicates)],
'solvent': [[] for _ in range(n_replicates)]}
phase_enthalpy = copy.deepcopy(phase_free_energies)
for replicate_idx in range(n_replicates):
# Read decomposition data.
decomposition_data_file_path = data_file_pattern.format(replicate_idx)
with open(decomposition_data_file_path, 'r') as f:
decomposition_data = json.load(f)
# Read free energy and enthalpy at each iteration.
sorted_decomposition_data = sorted(decomposition_data, key=lambda x: int(x.split('-')[1]))
for phase_iter in sorted_decomposition_data:
decomposition = decomposition_data[phase_iter]
phase_name, iteration = phase_iter.split('-')
# Correct sign consistent with thermodynamic cycle.
if phase_name == 'complex':
sign = -1
else:
sign = 1
corrected_free_energy = sign * (decomposition['DeltaF'] + decomposition['DeltaF_standard_state_correction'])
phase_free_energies[phase_name][replicate_idx].append(corrected_free_energy)
# Multiplication works only if enthalpy is not None.
if decomposition['DeltaH'] is not None:
decomposition['DeltaH'] *= sign
phase_enthalpy[phase_name][replicate_idx].append(decomposition['DeltaH'])
# Create figure.
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(7.25, 4.6))
if type == 'entropy-enthalpy':
_plot_entropy_enthalpy_decomposition(ax, phase_free_energies, phase_enthalpy)
else:
_plot_phase_decomposition(ax, phase_free_energies)
# # Plot total free energy.
# total = data['solvent'] + data['complex']
# ax.plot(total, color=color, label=label)
# totals.append(total)
# Plot also entropies.
# ax.plot(totals[0] - totals[1], color='blue', label='-T$\Delta$S')
# ax.set_ylim((-20, -18))
# phase_axes['complex'].set_ylim((-153, -148))
# phase_axes['solvent'].set_ylim((128, 133))
# ax.set_ylim((-23, -18))
# phase_axes['complex'].set_ylim((30, 45))
# phase_axes['solvent'].set_ylim((-55, -40))
# ax.legend()
plt.tight_layout()
if output_file_path is not None:
os.makedirs(os.path.dirname(output_file_path), exist_ok=True)
plt.savefig(output_file_path)
else:
plt.show()
# =============================================================================
# RELATIVE EFFICIENCY ANALYSIS
# =============================================================================
def get_relative_efficiency_input(submission, yank_analysis, system_name):
"""Prepare the data to compute the mean relative efficiencies for this system."""
# For GROMACS/EE-fullquil we need to account for the extra equilibration
# cost and shift all energy evaluation to the right.
if submission.paper_name == 'GROMACS/EE-fullequil':
mean_free_energies = submission.mean_free_energies()
mean_data = mean_free_energies[mean_free_energies['System name'] == system_name]
first_shifted = mean_data['N energy evaluations'].values[0]
last_shifted = mean_data['N energy evaluations'].values[-1]
calibration_cost = first_shifted*100/99 - last_shifted/99
else:
calibration_cost = 0
# Isolate the data for the system.
data_sub = submission.data[submission.data['System name'] == system_name]
n_energy_evaluations = max(data_sub['N energy evaluations'])
data_ref = yank_analysis.get_free_energies_from_energy_evaluations(
n_energy_evaluations, system_name=system_name, mean_trajectory=False,
start=calibration_cost)
# Obtain the free energies for the submission.
n_replicates = 5
free_energy_sub = np.empty(shape=(n_replicates, 100))
free_energy_ref = np.empty(shape=(n_replicates, 100))
for data, free_energy in [
(data_sub, free_energy_sub),
(data_ref, free_energy_ref),
]:
for i in range(n_replicates):
system_id = system_name + '-' + str(i)
system_id_data = data[data['System ID'] == system_id]
free_energy[i] = system_id_data[DG_KEY].values
# Discard the initial frames of REVO and GROMACS/EE that don't have predictions.
from pkganalysis.efficiency import discard_initial_zeros
free_energy_ref, free_energy_sub = discard_initial_zeros(free_energy_ref, free_energy_sub)
# Determine the actual asymptotic free energy of YANK.
asymptotic_free_energy_ref = yank_analysis.get_reference_free_energies()[system_name]
return free_energy_ref, free_energy_sub, asymptotic_free_energy_ref
def compute_all_relative_efficiencies(
free_energy_A, free_energy_B, ci, n_bootstrap_samples,
asymptotic_free_energy_A=None, asymptotic_free_energy_B=None
):
from pkganalysis.efficiency import EfficiencyAnalysis
analysis = EfficiencyAnalysis(free_energy_A, free_energy_B,
asymptotic_free_energy_A,
asymptotic_free_energy_B)
std_rel_eff = analysis.compute_std_relative_efficiency(
confidence_interval=ci, n_bootstrap_samples=n_bootstrap_samples)
abs_bias_rel_eff = analysis.compute_abs_bias_relative_efficiency(
confidence_interval=ci, n_bootstrap_samples=n_bootstrap_samples)
rmse_rel_eff = analysis.compute_rmse_relative_efficiency(
confidence_interval=ci, n_bootstrap_samples=n_bootstrap_samples)
if ci is None:
rel_eff = [std_rel_eff, abs_bias_rel_eff, rmse_rel_eff]
return rel_eff
else:
rel_eff = [std_rel_eff[0], abs_bias_rel_eff[0], rmse_rel_eff[0]]
cis = [std_rel_eff[1], abs_bias_rel_eff[1], rmse_rel_eff[1]]
return rel_eff, cis
def plot_relative_efficiencies(submissions, yank_analysis, ci=0.95, n_bootstrap_samples=1000,
same_plot=False, step_cumulative=2):
sns.set_style('whitegrid')
sns.set_context('paper')
statistic_names = ['std', 'absolute bias', 'RMSE']
# Create output directory.
figure_dir_path = os.path.join(SAMPLING_PAPER_DIR_PATH, 'SI_Figure-efficiencies')
os.makedirs(figure_dir_path, exist_ok=True)
# Check if we need all the efficiencies in the same plot or not.
if same_plot:
fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(7.25, 8))
# Keep track of data range by statistic.
statistic_ranges = {name: [np.inf, 0] for name in statistic_names}
# Keep track of n_energy_evaluations by column.
max_n_energy_evaluations = [0 for _ in range(3)]
for submission in submissions:
if submission.paper_name in {'OpenMM/REVO'}:
continue
# if submission.paper_name in {'AMBER/APR', 'GROMACS/NS-DS/SB', 'GROMACS/NS-DS/SB-long',
# 'NAMD/BAR', 'GROMACS/EE', 'GROMACS/EE-fullequil', 'OpenMM/SOMD'}:
# continue
print(submission.paper_name)
system_names = submission.data['System name'].unique()
# Create figure.
if not same_plot:
# For GROMACS/EE, there are no submissions for CB8-G3.
if 'GROMACS/EE' in submission.paper_name:
system_names = system_names[~(system_names == 'CB8-G3')]
fig, axes = plt.subplots(nrows=3, ncols=len(system_names),
figsize=(7.25, 8))
statistic_ranges = {name: [np.inf, 0] for name in statistic_names}
for col_idx, system_name in enumerate(system_names):
color = SUBMISSION_COLORS[submission.paper_name]
# For GROMACS/EE, there are no submissions for CB8-G3.
if 'GROMACS/EE' in submission.paper_name and system_name == 'CB8-G3':
continue
# For GROMACS/NS-DS/SB-long there are no new submissions for OAs.
if 'GROMACS/NS-DS/SB-long' in submission.paper_name and system_name != 'CB8-G3':
# Just add the label.
axes[0][col_idx].plot([], color=color, label=submission.paper_name)
continue
# Get input for EfficiencyAnalysis.
free_energy_ref, free_energy_sub, asymptotic_free_energy_ref = get_relative_efficiency_input(
submission, yank_analysis, system_name)
# Get the relative efficiencies.
rel_eff = compute_all_relative_efficiencies(
free_energy_ref, free_energy_sub, ci, n_bootstrap_samples,
asymptotic_free_energy_A=asymptotic_free_energy_ref
)
if ci is not None:
rel_eff, cis = rel_eff # Unpack confidence intervals.
# Use the same asymptotic free energies to compute the absolute bias
# relative efficiency as a function of the simulation length.
asymptotic_free_energy_sub = free_energy_sub.mean(axis=0)[-1]
# # Print relative efficiencies.
# print(system_name, ci)
# if ci is not None:
# for rel_eff, bounds in zip(rel_eff, cis):
# print('\t', rel_eff, bounds.tolist())
# else:
# for rel_eff in rel_eff:
# print('\t', rel_eff)
# Compute mean efficiencies as a function of the length of the simulation.
n_costs = free_energy_ref.shape[1]
n_rel_eff = int(n_costs / step_cumulative)
relative_efficiencies = np.empty(shape=(3, n_rel_eff))
low_bounds = np.empty(shape=(3, n_rel_eff))
high_bounds = np.empty(shape=(3, n_rel_eff))
for i, c in enumerate(range(step_cumulative-1, n_costs, step_cumulative)):
c1 = c + 1
rel_eff = compute_all_relative_efficiencies(
free_energy_ref[:,:c1], free_energy_sub[:,:c1],
ci, n_bootstrap_samples,
asymptotic_free_energy_A=asymptotic_free_energy_ref,
asymptotic_free_energy_B=asymptotic_free_energy_sub
)
if ci is not None:
rel_eff, cis = rel_eff # Unpack confidence intervals.
# Update CI lower and upper bound.
relative_efficiencies[:,i] = rel_eff
if ci is not None:
low_bounds[:,i] = [x[0] for x in cis]
high_bounds[:,i] = [x[1] for x in cis]
# Get number of energy evaluations.
mean_data = submission.mean_free_energies(system_name=system_name)
# Check how many initial iteration have been discarded.
discarded_iterations = 100 - n_costs
n_energy_evaluations = mean_data['N energy evaluations'].values[
discarded_iterations+1::step_cumulative] / 1e6
for row_idx, rel_eff in enumerate(relative_efficiencies):
ax = axes[row_idx][col_idx]
ax.plot(n_energy_evaluations, rel_eff, color=color, label=submission.paper_name)
# Plot back line at 0.
ax.plot(n_energy_evaluations, [0 for _ in n_energy_evaluations], color='black', ls='--')
# Update data range.
statistic_range = statistic_ranges[statistic_names[row_idx]]
# if ci is None:
# min_rel_eff = min(rel_eff)
# max_rel_eff = max(rel_eff)
# else:
# min_rel_eff = min(*rel_eff, *low_bounds[row_idx])
# max_rel_eff = max(*rel_eff, *high_bounds[row_idx])
statistic_range[0] = min(statistic_range[0], min(rel_eff))
statistic_range[1] = max(statistic_range[1], max(rel_eff))
# Update x-axis range.
if same_plot:
max_n_energy_evaluations[col_idx] = max(max_n_energy_evaluations[col_idx],
n_energy_evaluations[-1])
else:
for row_idx in range(len(statistic_names)):
axes[row_idx][col_idx].set_xlim((0, n_energy_evaluations[-1]))
if ci is not None:
# Plot confidence intervals.
for row_idx, (low_bound_c, high_bound_c) in enumerate(zip(low_bounds, high_bounds)):
ax = axes[row_idx][col_idx]
ax.fill_between(n_energy_evaluations, low_bound_c, high_bound_c,
alpha=0.35, color='gray')
# We do this multiple times unnecessarily if same_plot is True, but the code is simpler.
for col_idx, system_name in enumerate(system_names):
axes[0][col_idx].set_title(system_name)
for row_idx, statistic_name in enumerate(statistic_names):
axes[row_idx][0].set_ylabel(statistic_name + ' rel eff')
for col_idx in range(len(system_names)):
if same_plot:
extra_space = 0.1
else:
# Make space for confidence intervals.
extra_space = 1
ylimits = (statistic_ranges[statistic_name][0] - extra_space,
statistic_ranges[statistic_name][1] + extra_space)
axes[row_idx][col_idx].set_ylim(ylimits)
axes[row_idx][col_idx].tick_params(axis='y', which='major', pad=0.1)
axes[-1][1].set_xlabel('Number of force/energy evaluations [10$^6$]')
# Set labels and axes limits.
if not same_plot:
fig.suptitle(submission.paper_name)
output_file_base_name = 'releff-{}-{}'.format(submission.file_name, submission.receipt_id)
output_file_base_path = os.path.join(figure_dir_path, output_file_base_name)
plt.savefig(output_file_base_path + '.pdf')
# plt.savefig(output_file_base_path + '.png', dpi=600)
# plt.show()
if same_plot:
for row_idx in range(len(statistic_names)):
for col_idx in range(len(system_names)):
axes[row_idx][col_idx].set_xlim((0, max_n_energy_evaluations[col_idx]))
axes[0][1].legend(loc='upper right', bbox_to_anchor=(2.0, 1.48),
fancybox=True, ncol=3)
output_file_base_path = os.path.join(figure_dir_path, 'relative-efficiencies')
plt.savefig(output_file_base_path + '.pdf')
# plt.savefig(output_file_base_path + '.png', dpi=600)
# plt.show()
def plot_absolute_efficiencies(submissions, yank_analysis, ci=0.95, n_bootstrap_samples=1000):
sns.set_style('whitegrid')
sns.set_context('paper')
# Keep track of data range by statistic.
statistic_names = ['std', 'absolute bias', 'RMSE']
# Keep track of maximum number of energy evaluations
# to determine plotting range for YANK.
system_names = ['CB8-G3', 'OA-G3', 'OA-G6']
max_n_energy_eval = {name: 0 for name in system_names}
# Create figure.
fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(7.25, 8))
for submission in submissions + [yank_analysis]:
if 'REVO' in submission.paper_name:
continue
print(submission.paper_name)
# Obtain std, bias, and RMSE of the 5 trajectories.
# If this is a YANK analysis, we get it later specifically for the system.
if not isinstance(submission, YankSamplingAnalysis):
mean_free_energies = submission.mean_free_energies()
color = SUBMISSION_COLORS[submission.paper_name]
for col_idx, system_name in enumerate(system_names):
# GROMACS/EE doesn't have submissions for CB8-G3.
if 'GROMACS/EE' in submission.paper_name and system_name == 'CB8-G3':
continue
# For GROMACS/NS-DS/SB-long there are no new submissions for OAs.
if 'GROMACS/NS-DS/SB-long' in submission.paper_name and 'OA' in system_name:
# Just add the label.
axes[0][col_idx].plot([], color=color, label=submission.paper_name)
continue
# Select the submission data for only this host-guest system.
if isinstance(submission, YankSamplingAnalysis):
line_style = '--'
mean_data = submission.get_free_energies_from_energy_evaluations(
max_n_energy_eval[system_name], system_name=system_name, mean_trajectory=True)
else:
line_style = '-'
mean_data = mean_free_energies[mean_free_energies['System name'] == system_name]
# Update maximum number of energy evaluations.
n_energy_evaluations = mean_data['N energy evaluations'].values
max_n_energy_eval[system_name] = max(max_n_energy_eval[system_name], n_energy_evaluations[-1])
# Discard initial computational costs for which there's no data.
first_nonzero_idx = np.nonzero(mean_data[DG_KEY])[0][0]
n_energy_evaluations = n_energy_evaluations[first_nonzero_idx:]
# Compute cumulative total std, abs_bias, and RMSE.
scale_energy_evaluations = 1e6
norm_factor = (n_energy_evaluations - n_energy_evaluations[0])[1:] / scale_energy_evaluations
avg_std = sp.integrate.cumtrapz(mean_data['std'].values[first_nonzero_idx:]) / norm_factor
avg_abs_bias = sp.integrate.cumtrapz(np.abs(mean_data['bias'].values[first_nonzero_idx:])) / norm_factor
avg_rmse = sp.integrate.cumtrapz(mean_data['RMSE'].values[first_nonzero_idx:]) / norm_factor
# Plot total statistics as a function of the energy evaluations.
# Discard first energy evaluation as cumtrapz doesn't return a result for it.
for row_idx, avg_stats in enumerate([avg_std, avg_abs_bias, avg_rmse]):
ax = axes[row_idx, col_idx]
ax.plot(n_energy_evaluations[1:] / scale_energy_evaluations, avg_stats,
color=color, label=submission.paper_name, ls=line_style)
# Set x axis.
ax.set_xlim((0, n_energy_evaluations[-1] / scale_energy_evaluations))
# Set labels and axes limits.
y_limits = {
'std': (0, 0.4),
'absolute bias': (0, 0.3),
'RMSE': (0, 0.4)
}
for col_idx, system_name in enumerate(system_names):
axes[0][col_idx].set_title(system_name)
# Set y limits (shared for each row).
for row_idx, statistic_name in enumerate(statistic_names):
axes[row_idx][col_idx].set_ylim(y_limits[statistic_name])
axes[row_idx][col_idx].tick_params(axis='y', which='major', pad=0.1)
# # Remove shared ticks.
# for row_idx in range(len(statistic_names)):
# for col_idx in range(len(system_names)):
# if col_idx > 0:
# axes[row_idx][col_idx].set_yticklabels([])
# if row_idx < len(statistic_names)-1:
# axes[row_idx][col_idx].set_xticklabels([])
for row_idx, statistic_name in enumerate(statistic_names):
axes[row_idx][0].set_ylabel('mean ' + statistic_name + ' [kcal/mol]')
axes[-1][1].set_xlabel('N energy evaluations [M]')
axes[0][1].legend(loc='upper right', bbox_to_anchor=(2.0, 1.48),
fancybox=True, ncol=3)
figure_dir_path = os.path.join(SAMPLING_PAPER_DIR_PATH, 'SI_Figure-efficiencies')
os.makedirs(figure_dir_path, exist_ok=True)
output_file_base_path = os.path.join(figure_dir_path, 'absolute-efficiencies')
plt.savefig(output_file_base_path + '.pdf')
# plt.savefig(output_file_base_path + '.png', dpi=600)
# plt.show()
def print_relative_efficiency_table(
submissions, yank_analysis, ci=0.95,
n_bootstrap_samples=100,
print_bias_corrected=False
):
"""Create a table with standard deviation, absolute bias, and RMSE relative efficiency."""
methods = []
# Initialize the table to be converted into a Pandas dataframe.
system_names = ['CB8-G3', 'OA-G3', 'OA-G6']
statistic_names = [r'$e_{\mathrm{std}}$', r'$e_{|\mathrm{bias}|}$', r'$e_{\mathrm{RMSD}}$']
column_names = ['\\makecell{$\Delta$ G \\\\ $[$kcal/mol$]$}', '\\makecell{n eval \\\\ $[$M$]$}'] + statistic_names
# Add columns.
efficiency_table = collections.OrderedDict()
for system_name, column_name in itertools.product(system_names, column_names):
efficiency_table[(system_name, column_name)] = []
for submission in submissions:
# Collect method's names in the given order.
methods.append(submission.paper_name)
mean_free_energies = submission.mean_free_energies()
for system_name in system_names:
# CB8-G3 calculations for GROMACS/EE did not converge yet, and the
# long protocol in CS-NS calculations have been run only on CB8-G3.
if ((submission.name == 'Expanded-ensemble/MBAR' and system_name == 'CB8-G3') or
(submission.paper_name == 'GROMACS/NS-DS/SB-long' and system_name != 'CB8-G3')):
relative_efficiencies, relative_efficiencies_corrected = np.full((2, 3), fill_value=np.nan)
dg = ''
n_force_eval = ''
else:
# Get input for EfficiencyAnalysis.
free_energy_ref, free_energy_sub, asymptotic_free_energy_ref = get_relative_efficiency_input(
submission, yank_analysis, system_name)
# Get the relative efficiencies.
relative_efficiencies, cis = compute_all_relative_efficiencies(
free_energy_ref, free_energy_sub, ci, n_bootstrap_samples,
asymptotic_free_energy_A=asymptotic_free_energy_ref
)
# Recompute relative efficiencies assuming that YANK converged.
if print_bias_corrected:
relative_efficiencies_corrected, cis_corrected = compute_all_relative_efficiencies(
free_energy_ref, free_energy_sub, ci, n_bootstrap_samples)
# Select the data for only this host-guest system.
mean_data_sub = mean_free_energies[mean_free_energies['System name'] == system_name]
# Get the final free energy and number of energy/force evaluations.
dg = mean_data_sub[DG_KEY].values[-1]
dg_CI = mean_data_sub['$\Delta$G CI'].values[-1] # Confidence interval.
dg, dg_CI = reduce_to_first_significant_digit(dg, dg_CI)
n_force_eval = mean_data_sub['N energy evaluations'].values[-1]
# Convert to string format.
dg = '{} $\\pm$ {}'.format(dg, dg_CI)
n_force_eval = str(int(round(n_force_eval / 1e6)))
# Add free energy and cost entries.
efficiency_table[(system_name, column_names[0])].append(dg)
efficiency_table[(system_name, column_names[1])].append(n_force_eval)
# Add efficiency entries for the table.
for statistic_idx, statistic_name in enumerate(statistic_names):
# Gather the format arguments.
rel_effs = [relative_efficiencies[statistic_idx], cis[statistic_idx][0], cis[statistic_idx][1]]
if print_bias_corrected:
rel_effs.append(relative_efficiencies_corrected[statistic_idx])
# Comment this if we don't want to print CIs for the corrected estimate.
rel_effs.extend([cis_corrected[statistic_idx][0], cis_corrected[statistic_idx][1]])
# Print significant digits.
efficiencies_format = []
for e_idx in range(0, len(rel_effs), 3):
rel_eff, low_bound, high_bound = rel_effs[e_idx:e_idx+3]
if high_bound - rel_eff < 0.1 or rel_eff - low_bound < 0.1:
fmt = '{:2.2f}'
else:
fmt = '{:2.1f}'
# Print lower and higher bound as sub and superscripts of the estimate.
efficiencies_format.append(fmt + '$_{{\raisem{{2pt}}{{' + fmt + '}}}}^{{\mathstrut ' + fmt + '}}$')
if np.isnan(rel_effs[0]):
data_entry = ''
# Standard deviation efficiency is not affected by the bias.
elif print_bias_corrected and ('std' not in statistic_name):
data_entry = efficiencies_format[0] + ' (' + efficiencies_format[1] + ')'
data_entry = data_entry.format(*rel_effs)
else:
data_entry = efficiencies_format[0].format(*rel_effs[:3])
# Remove the minus sign from "-0".
data_entry = data_entry.replace('-0.0', '0.0')
data_entry = data_entry.replace('-0.00', '0.00')
efficiency_table[(system_name, statistic_name)].append(data_entry)
# Add row for reference calculation.
methods.append(YANK_METHOD_PAPER_NAME)
# Add free energy and cost entries.
for system_name in system_names:
yank_mean_data = yank_analysis.get_free_energies_from_iteration(
YANK_N_ITERATIONS, system_name=system_name, mean_trajectory=True)
dg = yank_mean_data[DG_KEY].values[-1]
dg_CI = yank_mean_data['$\Delta$G CI'].values[-1] # Confidence interval.
dg, dg_CI = reduce_to_first_significant_digit(dg, dg_CI)
n_force_eval = yank_mean_data['N energy evaluations'].values[-1]
n_force_eval = str(int(round(n_force_eval / 1e6)))
efficiency_table[(system_name, column_names[0])].append('{} $\\pm$ {}'.format(dg, dg_CI))
efficiency_table[(system_name, column_names[1])].append(n_force_eval)
# All efficiencies are relative to YANK so they're all 1.
for system_name, statistic_name in itertools.product(system_names, statistic_names):
efficiency_table[(system_name, statistic_name)].append('0.0')
# Convert to Pandas Dataframe.
efficiency_table = pd.DataFrame(efficiency_table)
# Set the method's names as index column.
efficiency_table = efficiency_table.assign(Method=methods)
efficiency_table.set_index(keys='Method', inplace=True)
# Print table.
column_format = 'lccccc|ccccc|ccccc'
efficiency_table_latex = efficiency_table.to_latex(column_format=column_format, multicolumn_format='c',
escape=False)
# Make header and reference method bold.
textbf = lambda s: '\\textbf{' + s + '}'
efficiency_table_latex = efficiency_table_latex.replace(YANK_METHOD_PAPER_NAME, textbf(YANK_METHOD_PAPER_NAME))
efficiency_table_latex = efficiency_table_latex.replace('Method', textbf('Method'))
for system_name in system_names:
efficiency_table_latex = efficiency_table_latex.replace(system_name, textbf(system_name))
for column_name in column_names:
efficiency_table_latex = efficiency_table_latex.replace(column_name, textbf(column_name))
print(efficiency_table_latex)
def print_nonequilibrium_relative_efficiencies(nonequilibrium_submissions):
"""Print relative efficiencies w.r.t. for the nonequilibrium estimators table."""
system_names = ['CB8-G3', 'OA-G3', 'OA-G6']
def _get_free_energy_array(submission, system_name, step=1, max_c=100, get_asymptotic=False):
n_replicates = 5
system_data = submission.data[submission.data['System name'] == system_name]
free_energy_array = np.empty(shape=(n_replicates, int(max_c/step)))
for i in range(n_replicates):
system_id = system_name + '-' + str(i)
system_id_data = system_data[system_data['System ID'] == system_id]
free_energy_array[i] = system_id_data[DG_KEY].values[:max_c:step]
if get_asymptotic:
mean_free_energies = submission.mean_free_energies()
asymptotic = mean_free_energies[mean_free_energies['System name'] == system_name][DG_KEY].values[-1]
return free_energy_array, asymptotic
return free_energy_array
# Use GROMACS/NS-DS/SB-long as reference method.
reference_submission = [s for s in nonequilibrium_submissions if s.paper_name == 'GROMACS/NS-DS/SB-long'][0]
# Also remove the other BAR submission.
nonequilibrium_submissions = [s for s in nonequilibrium_submissions if 'GROMACS/NS-DS/SB' not in s.paper_name]
# Get only the first 50 as the 1-directional estimators only have half the cost.
free_energy_ref = {}
asymptotic_ref = {}
for system_name in system_names:
DG, asympt = _get_free_energy_array(reference_submission, system_name, max_c=50, get_asymptotic=True)
free_energy_ref[system_name] = DG
asymptotic_ref[system_name] = asympt
for submission in nonequilibrium_submissions:
print(submission.paper_name, end='')
for system_name in system_names:
free_energy_sub = _get_free_energy_array(submission, system_name, step=2)
rel_eff, cis = compute_all_relative_efficiencies(
free_energy_ref[system_name], free_energy_sub, ci=0.95, n_bootstrap_samples=1000,
asymptotic_free_energy_A=asymptotic_ref[system_name],
asymptotic_free_energy_B=asymptotic_ref[system_name]
)
for i, stat_name in enumerate(['std', 'bias', 'RMSE']):
print(r' & {:.1f}$_{{\raisem{{2pt}}{{{:.1f}}}}}^{{\mathstrut {:.1f}}}$'.format(rel_eff[i], cis[i][0], cis[i][1]), end='')
print(r' \\')
def print_final_prediction_table(submissions, yank_analysis):
"""Plot the table containing the fina binding free energy predictions for all replicates."""
for submission in submissions + [yank_analysis]:
# GROMACS/EE-fullequil predictions are identical to GROMACS/EE
if submission.paper_name == 'GROMACS/EE-fullequil':
continue
if isinstance(submission, YankSamplingAnalysis):
submission_data = yank_analysis.get_free_energies_from_iteration(final_iteration=YANK_N_ITERATIONS)
else:
submission_data = submission.data
submission_data = submission_data[submission_data['Simulation percentage'] == 100]
row_str = submission.paper_name + ' & '
submission_final_DGs = []
for system_id in submission_data['System ID'].unique():
# GROMACS/EE doesn't have predictions for CB8-G3, and the
# GROMACS/NS-DS/SB-long protocol was applied only to CB8-G3.
if (('GROMACS/EE' in submission.paper_name and 'CB8-G3' in system_id) or
(submission.paper_name == 'GROMACS/NS-DS/SB-long' and 'OA' in system_id)):
submission_final_DGs.append('')
continue
dg = submission_data.loc[submission_data['System ID'] == system_id, DG_KEY].values[0]
ddg = submission_data.loc[submission_data['System ID'] == system_id, DDG_KEY].values[0]
dg, ddg = reduce_to_first_significant_digit(dg, ddg)
submission_final_DGs.append(r'{} $\pm$ {}'.format(dg, ddg))
row_str += ' & '.join(submission_final_DGs) + r' \\'
print(row_str)
# =============================================================================
# SUPPORTING INFORMATION - SINGLE TRAJECTORIES
# =============================================================================
def plot_single_trajectories_figures(axes, system_data, system_mean_data,
reference_system_mean_data=None,
plot_errors=True, plot_methods_uncertainties=True):
"""Plot individual free energy trajectories and standard deviations for a single method and system."""
system_name = system_data['System name'].unique()[0]
palette_mean = sns.color_palette('pastel')
submission_mean_color = 'black'
reference_mean_color = palette_mean[9]
# Plot the method uncertainties of the single replicate trajectories.
# First scale the number of energy evaluations.
system_data.loc[:,'N energy evaluations'] /= N_ENERGY_EVALUATIONS_SCALE
# Plot the 5 replicates individual trajectories.
# First remove the initial predictions that are 0.0 (i.e. there is no estimate).
ax = axes[0]
system_data = system_data[system_data[DG_KEY] != 0.0]
sns.lineplot(data=system_data, x='N energy evaluations', y=DG_KEY,
hue='System ID', palette='bright', ax=ax, alpha=0.6)
# Plot the submission mean trajectory with CI.
plot_mean_free_energy(system_mean_data, x='N energy evaluations', ax=ax,
color_mean=submission_mean_color, plot_ci=False,
color_ci=submission_mean_color, label='Best estimate',
scale_n_energy_evaluations=True)
# Plot YANK mean trajectory with CI.
if reference_system_mean_data is not None:
plot_mean_free_energy(reference_system_mean_data, x='N energy evaluations', ax=ax,
color_mean=reference_mean_color, plot_ci=False,
color_ci=reference_mean_color, label='Reference estimate',
scale_n_energy_evaluations=True)
ax.set_title(system_name)
# Add the y-label only on the leftmost Axis.
if system_name != 'CB8-G3':
ax.set_ylabel('')
# Remove the legend for now, which will be added at the end after tighting up the plot.
ax.get_legend().remove()
# Create a bias axis.
if reference_system_mean_data is not None:
ref_free_energy = reference_free_energies.loc[system_name, DG_KEY]
with sns.axes_style('white'):
ax2 = ax.twinx()
# Plot a vertical line to make the scale.
vertical_line = np.linspace(*ax.get_ylim()) - ref_free_energy
ax2.plot([50] * len(vertical_line), vertical_line, alpha=0.0001)
ax2.grid(alpha=0.5, linestyle='dashed', zorder=0)
# We add the bias y-label only on the rightmost Axis.
if system_name == 'OA-G6':
ax2.set_ylabel('Bias to reference [kcal/mol]')
# Set the 0 of the twin axis to the YANK reference free energy.
align_yaxis(ax, ref_free_energy, ax2, 0.0)
if plot_errors:
# The x-axis is shared between the 2 rows so we can plot the ticks only in the bottom one.
ax.xaxis.set_ticklabels([])
ax.set_xlabel('')
ax = axes[1]
# REVO uses the mean of the 5 replicates to estimate the
# uncertainty so it doesn't add information.
if plot_methods_uncertainties:
sns.lineplot(data=system_data, x='N energy evaluations', y=DDG_KEY,
hue='System ID', palette='bright', ax=ax, alpha=0.6)
# The legend is added later at the top.
ax.get_legend().remove()
# Plot the standard deviation of the free energy trajectories.
# submission_std = system_mean_data['std']
submission_std = system_mean_data['unbiased_std']
# cost = system_mean_data['Simulation percentage'].values
cost = system_mean_data['N energy evaluations'].values / N_ENERGY_EVALUATIONS_SCALE
ax.plot(cost, submission_std, color=submission_mean_color)
# Plot confidence interval around standard deviation.
submission_std_low_ci = system_mean_data['unbiased_std_low_CI'].values
submission_std_up_ci = system_mean_data['unbiased_std_up_CI'].values
ax.fill_between(cost, submission_std_low_ci, submission_std_up_ci, alpha=0.35, color='gray')
if reference_system_mean_data is not None:
# reference_std = reference_system_mean_data['std']
reference_std = reference_system_mean_data['unbiased_std']
ax.plot(cost, reference_std, color=reference_mean_color)
# Only the central plot shows the x-label.
ax.set_xlabel('')
# Add the y-label only on the leftmost Axis.
if system_name != 'CB8-G3':
ax.set_ylabel('')
else:
ax.set_ylabel('std($\Delta$G) [kcal/mol]')
# Set x limits.
for ax in axes:
ax.set_xlim((0, max(system_data['N energy evaluations'])))
def plot_all_single_trajectories_figures(submissions, yank_analysis, plot_errors=True, output_path_dir=None):
"""Individual plots for each method with the 5 individual free energy and uncertainty trajectories."""
sns.set_style('whitegrid')
sns.set_context('paper')
if output_path_dir is None:
output_path_dir = os.path.join(SAMPLING_PAPER_DIR_PATH, 'SI_Figure-individual-trajectories/')
os.makedirs(output_path_dir, exist_ok=True)
# -------------------- #
# Plot submission data #
# -------------------- #
# Remove nonequilibrium-switching calculations with single-direction estimators.
submissions = [s for s in submissions if ('Jarz' not in s.paper_name and 'Gauss' not in s.paper_name)]
for submission in submissions + [yank_analysis]:
# CB8-G3 calculations for GROMACS/EE did not converge yet.
if submission.name == 'Expanded-ensemble/MBAR':
submission.data = submission.data[submission.data['System name'] != 'CB8-G3']
# REVO uses the mean of the 5 replicates to estimate the
# uncertainty so it doesn't add information.
if 'REVO' in submission.paper_name:
plot_methods_uncertainties = False
else:
plot_methods_uncertainties = True
if not isinstance(submission, YankSamplingAnalysis):
mean_free_energies = submission.mean_free_energies()
unique_system_names = submission.data['System name'].unique()
else:
unique_system_names = sorted(submission.system_names)
# Create a figure with 3 axes (one for each system).
n_systems = len(unique_system_names)
if plot_errors:
# The second row will plot the errors.
fig, axes = plt.subplots(nrows=2, ncols=n_systems, figsize=(7.25, 4.8))
trajectory_axes = axes[0]
else:
fig, axes = plt.subplots(nrows=1, ncols=n_systems, figsize=(7.25, 2.4))
trajectory_axes = axes
# Set figure title.
fig.suptitle(submission.paper_name)
# Determine range of data across systems.
min_DG = np.inf
max_DG = -np.inf
min_dDG = np.inf
max_dDG = -np.inf
# for system_name in unique_system_names:
for ax_idx, system_name in enumerate(unique_system_names):
if isinstance(submission, YankSamplingAnalysis):
data = submission.get_free_energies_from_iteration(final_iteration=YANK_N_ITERATIONS,
system_name=system_name)
mean_data = submission.get_free_energies_from_iteration(final_iteration=YANK_N_ITERATIONS,
system_name=system_name,
mean_trajectory=True)
else:
# Select the data for only this host-guest system.
data = submission.data[submission.data['System name'] == system_name]
mean_data = mean_free_energies[mean_free_energies['System name'] == system_name]
plot_single_trajectories_figures(axes[:,ax_idx], data, mean_data, plot_errors=plot_errors,
reference_system_mean_data=None,
plot_methods_uncertainties=plot_methods_uncertainties)
# Collect max and min data to determine axes range.
min_DG = min(min_DG, min(data[DG_KEY]), min(mean_data[DG_KEY]))
max_DG = max(max_DG, max(data[DG_KEY]), max(mean_data[DG_KEY]))
min_dDG = min(min_dDG, min(data[DDG_KEY]), min(mean_data['std']))
max_dDG = max(max_dDG, max(data[DDG_KEY]), max(mean_data['std']))
# Set limits.
for i in range(len(unique_system_names)):
axes[0][i].set_ylim((min_DG, max_DG))
axes[1][i].set_ylim((min_dDG, max_dDG))
# Keep ticks only in external plots.
axes[0][i].set_xticklabels([])
for i in range(1, len(unique_system_names)):
axes[0][i].set_yticklabels([])
axes[1][i].set_yticklabels([])
# The x-label is shown only in the central plot.
axes[-1][1].set_xlabel('N energy evaluations [10$^6$]')
plt.tight_layout(pad=0.2, rect=[0.0, 0.0, 1.0, 0.85])
# Create legend.
# The first handle/label is the legend title "System ID" so we get rid of it.
handles, labels = trajectory_axes[0].get_legend_handles_labels()
labels = ['replicate ' + str(i) for i in range(5)] + labels[6:]
bbox_to_anchor = (-0.1, 1.35)
trajectory_axes[0].legend(handles=handles[1:], labels=labels, loc='upper left',
bbox_to_anchor=bbox_to_anchor, ncol=6, fancybox=True,
labelspacing=0.8, handletextpad=0.5, columnspacing=1.2)
# Save figure.
output_file_name = 'replicates-{}-{}'.format(submission.file_name, submission.receipt_id)
plt.savefig(os.path.join(output_path_dir, output_file_name + '.pdf'))
# plt.savefig(os.path.join(output_path_dir, output_file_name + '.png'), dpi=300)
# plt.show()
# =============================================================================
# SUPPORTING INFORMATION - HREX/MBAR STATISTICAL INEFFICIENCY ANALYSIS
# =============================================================================
def plot_hrex_stat_ineff_trajectories():
"""Individual plots for HREX with the 5 individual free energy and uncertainty trajectories
as a function of the statistical inefficiency."""
sns.set_context('paper')
# Limits of y-axis (free energies, uncertainties) by system.
y_limits = {
'CB8-G3': [(-14, -10), (0, 2)],
'OA-G3': [(-9, -5), (0, 1.5)],
'OA-G6': [(-9, -5), (0, 1.5)],
}
# Create output dir.
output_path_dir = os.path.join(SAMPLING_PAPER_DIR_PATH, 'SI_Figure-statistical-inefficiency')
os.makedirs(output_path_dir, exist_ok=True)
# Read the data, which is organized by statistical inefficiency.
# We'll then plot by system.
yank_analysis_by_statineff = collections.OrderedDict()
for stat_ineff in ['5', '10', '20', '50', '100', '200']:
data_dir_path = os.path.join('YankAnalysis', 'CorrelationAnalysis', 'statineff-{}'.format(stat_ineff))
yank_analysis = YankSamplingAnalysis(data_dir_path)
yank_analysis_by_statineff[stat_ineff] = yank_analysis
# Plot by system.
for system_name in ['CB8-G3', 'OA-G3', 'OA-G6']:
fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(7.25, 9.8))
# Set figure title.
fig.suptitle('HREX uncertainty predictions as a function of\n'
'statistical inefficiency for {}'.format(system_name))
# for system_name in unique_system_names:
for stat_ineff_idx, stat_ineff in enumerate(yank_analysis_by_statineff):
yank_analysis = yank_analysis_by_statineff[stat_ineff]
data = yank_analysis.get_free_energies_from_iteration(final_iteration=YANK_N_ITERATIONS,
system_name=system_name)
mean_data = yank_analysis.get_free_energies_from_iteration(final_iteration=YANK_N_ITERATIONS,
system_name=system_name,
mean_trajectory=True)
# Plot on the correct axis.
DG_row = 2*int(stat_ineff_idx / 3)
col = stat_ineff_idx % 3
stat_ineff_axes = axes[DG_row:DG_row+2, col]
plot_single_trajectories_figures(stat_ineff_axes, data, mean_data, plot_errors=True,
reference_system_mean_data=None,
plot_methods_uncertainties=True)
# Set titles and limits.
title = 'Statistical inefficiency: {} ps'.format(stat_ineff)
if DG_row > 0:
title = '\n' + title
stat_ineff_axes[0].set_title(title, fontweight='bold')
stat_ineff_axes[0].set_ylim(y_limits[system_name][0])
stat_ineff_axes[1].set_ylim(y_limits[system_name][1])
stat_ineff_axes[0].set_ylabel('$\Delta$G [kcal/mol]')
stat_ineff_axes[1].set_ylabel('std($\Delta$G) [kcal/mol]')
# Keep ticks only in external plots.
for row_idx in range(axes.shape[0]):
for col_idx in range(axes.shape[1]):
if row_idx != len(axes[0]) - 1:
axes[row_idx][col_idx].set_xticklabels([])
if col_idx != 0:
axes[row_idx][col_idx].set_ylabel('')
axes[row_idx][col_idx].set_yticklabels([])
# Set x label.
axes[-1][1].set_xlabel('N energy evaluations [10$^6$]')
plt.tight_layout(pad=0.0, rect=[0.0, 0.0, 1.0, 0.88])
# Create legend.
# The first handle/label is the legend title "System ID" so we get rid of it.
handles, labels = axes[0][0].get_legend_handles_labels()
labels = ['replicate ' + str(i) for i in range(5)] + labels[6:]
bbox_to_anchor = (0.05, 1.35)
axes[0][0].legend(handles=handles[1:], labels=labels, loc='upper left',
bbox_to_anchor=bbox_to_anchor, ncol=6, fancybox=True,
labelspacing=0.8, handletextpad=0.5, columnspacing=1.2)
# Save figure.
output_file_name = 'statineff-{}'.format(system_name)
plt.savefig(os.path.join(output_path_dir, output_file_name + '.pdf'))
# plt.savefig(os.path.join(output_path_dir, output_file_name + '.png'), dpi=300)
# plt.show()
# =============================================================================
# MAIN
# =============================================================================
if __name__ == '__main__':
sns.set_style('whitegrid')
sns.set_context('paper')
# Read reference values.
yank_analysis = YankSamplingAnalysis(YANK_ANALYSIS_DIR_PATH)
# Obtain free energies and final reference values.
mean_reference_free_energies = yank_analysis.get_free_energies_from_iteration(YANK_N_ITERATIONS, mean_trajectory=True)
reference_free_energies = mean_reference_free_energies[mean_reference_free_energies['Simulation percentage'] == 100]
reference_free_energies.set_index('System name', inplace=True)
# Compute efficiency of reference.
reference_efficiencies = {}
for system_name in mean_reference_free_energies['System name'].unique():
mean_data = mean_reference_free_energies[mean_reference_free_energies ['System name'] == system_name]
reference_efficiencies[system_name], n_discarded = fit_efficiency(mean_data)
# Import user map.
with open('../SubmissionsDoNotUpload/SAMPL6_user_map.csv', 'r') as f:
user_map = pd.read_csv(f)
# Load submissions data. We do OA and TEMOA together.
all_submissions = load_submissions(SamplingSubmission, SAMPLING_SUBMISSIONS_DIR_PATH, user_map)
# Remove AMBER/TI.
all_submissions = [s for s in all_submissions if s.name not in ['Langevin/Virtual Bond/TI']]
# Create an extra submission for GROMACS/EE where the full cost of equilibration has been taken into account.
gromacs_ee_submission = copy.deepcopy([s for s in all_submissions if s.paper_name == 'GROMACS/EE'][0])
gromacs_ee_submission.paper_name = 'GROMACS/EE-fullequil'
gromacs_ee_submission.file_name = 'EENVT-fullequil'
data = gromacs_ee_submission.data # Shortcut.
mean_free_energies = gromacs_ee_submission.mean_free_energies()
for system_name in ['OA-G3', 'OA-G6']:
mean_data = mean_free_energies[mean_free_energies['System name'] == system_name]
first_nonzero_idx = np.nonzero(mean_data[DG_KEY].values)[0][0]
full_equilibration_cost = mean_data['N energy evaluations'].values[first_nonzero_idx] * 4
for i in data[data['System name'] == system_name].index:
data.at[i, 'N energy evaluations'] += full_equilibration_cost
all_submissions.append(gromacs_ee_submission)
# Sort the submissions to have all pot and tables in the same order.
all_submissions = sorted(all_submissions, key=lambda s: s.paper_name)
# Separate the main submissions from the data about nonequilibrium estimators.
main_submissions = [s for s in all_submissions if not ('Jarz' in s.paper_name or 'Gauss' in s.paper_name)]
noneq_submissions = [s for s in all_submissions if 'NS' in s.paper_name]
# Export YANK analysis and submissions to CSV/JSON tables.
yank_analysis.export(os.path.join(SAMPLING_DATA_DIR_PATH, 'reference_free_energies'))
for s in main_submissions:
file_base_path = os.path.join(SAMPLING_DATA_DIR_PATH, s.receipt_id + '-reference')
yank_analysis.export_by_submission(file_base_path, s)
export_submissions(all_submissions, reference_free_energies)
# Create example trajectory for the figure describing the challenge process.
plot_example_bias_variance(yank_analysis, max_n_eval_percentage=0.4, mixed_proportion=0.3)
# Cartoon explaining mean error and relative efficiency.
plot_mean_error_cartoon()
# Create figure with free energy, standard deviation, and bias as a function of computational cost.
plot_all_entries_trajectory(main_submissions, yank_analysis, zoomed=False)
plot_all_entries_trajectory(main_submissions, yank_analysis, zoomed=True)
# Create results and efficiency table.
print_relative_efficiency_table(main_submissions, yank_analysis, print_bias_corrected=False)
# Plot nonequilibrium-switching single-direction estimator.
plot_all_nonequilibrium_switching(noneq_submissions)
# Plot sensitivity analysis figure.
plot_restraint_and_barostat_analysis()
# Plot figure for HREX bias analysis.
plot_yank_bias()
# Supporting information
# ----------------------
# Absolute/relative efficiency as a function of the computational cost.
plot_relative_efficiencies(main_submissions, yank_analysis)
plot_relative_efficiencies(main_submissions, yank_analysis, ci=None, same_plot=True)
plot_absolute_efficiencies(main_submissions, yank_analysis)
# Relative efficiency for uni/bi-directional estimators.
print_nonequilibrium_relative_efficiencies(noneq_submissions)
# Plot replicate predictions table.
print_final_prediction_table(all_submissions, yank_analysis)
# Plot individual trajectories.
plot_all_single_trajectories_figures(all_submissions, yank_analysis)
# Plot statistical inefficiency analysis.
plot_hrex_stat_ineff_trajectories()
# Supporting information for bias section.
output_dir_path = os.path.join(SAMPLING_PAPER_DIR_PATH, 'SI_Figure-bias_hrex')
plot_decomposition('CB8-G3', starting_iteration=5, type='phase',
output_file_path=output_dir_path + '/free-energy-phase-decomposition.pdf'))
plot_decomposition('CB8-G3', starting_iteration=5, type='entropy-enthalpy',
output_file_path=output_dir_path + '/free-energy-entropy-decomposition.pdf')
| mit |
felipemontefuscolo/bitme | get_bitmex_candles.py | 1 | 4122 | #!/usr/bin/env python
import sys
import time
import swagger_client
from swagger_client.rest import ApiException
from utils.utils import smart_open
import argparse
import pandas as pd
MAX_NUM_CANDLES_BITMEX = 500
def print_file(file_or_stdout, api_instance, bin_size, partial, symbol, reverse, start_time, end_time):
chunks = split_in_chunks(start_time, end_time, MAX_NUM_CANDLES_BITMEX, bin_size)
with smart_open(file_or_stdout) as fh:
print("time,open,high,low,close,volume", file=fh)
num_pages = len(chunks)
for i in range(num_pages):
chunk = chunks[i]
s = chunk[0]
e = chunk[1]
count = (e - s) / pd.Timedelta(bin_size)
page = api_instance.trade_get_bucketed(
bin_size=bin_size,
partial=partial,
symbol=symbol,
count=count,
start=0.0,
reverse=reverse,
start_time=s,
end_time=e)
print("from {} to {}: {} candles downloaded".format(s, e, len(page)))
# TODO: bitmex has a bug where the high is not the highest value !!!!!
for line in reversed(page):
print(','.join([line.timestamp.strftime('%Y-%m-%dT%H:%M:%S'),
str(line.open),
str(max(line.high, line.open)),
str(min(line.low, line.open)),
str(line.close),
str(line.volume)]), file=fh)
sys.stdout.write(
"progress: completed %d out of %d pages (%.2f%%) \r" %
(i + 1, num_pages, 100 * float(i + 1) / num_pages))
sys.stdout.flush()
time.sleep(1.001)
print("")
def split_in_chunks(start: pd.Timedelta, end: pd.Timedelta, chunk_size: int, bucket_size: str):
i = start
r = []
dt = chunk_size * pd.Timedelta(bucket_size)
while i <= end:
r += [(i, min(end, i + dt))]
i += dt
return r
def get_args(args=None, namespace=None):
parser = argparse.ArgumentParser(description="Get bitmex data")
parser.add_argument('-b', '--begin-time', type=pd.Timestamp, required=True, help="Example: '2018-04-01T00:00:01'")
parser.add_argument('-e', '--end-time', type=pd.Timestamp, required=True, help="Example: '2018-04-02T00:00:01'")
parser.add_argument('-s', '--symbol', type=str, default='XBTUSD',
help='Instrument symbol. Send a bare series (e.g. XBU) to get data for the nearest expiring'
'contract in that series. You can also send a timeframe, e.g. `XBU:monthly`. '
'Timeframes are `daily`, `weekly`, `monthly`, `quarterly`, and `biquarterly`. (optional)')
parser.add_argument('-z', '--bin-size', choices=('1m', '5m', '1h', '1d'), default='1m', type=str,
help='Time interval to bucket by')
parser.add_argument('-o', '--file-or-stdout', type=str, required=True, help='Output filename or "-" for stdout')
parser.add_argument('--partial', action='store_true', default=False, )
args = parser.parse_args(args, namespace)
return args
def main():
args = get_args()
# create an instance of the API class
configuration = swagger_client.Configuration()
configuration.host = 'https://www.bitmex.com/api/v1'
api_instance = swagger_client.TradeApi(swagger_client.ApiClient(configuration))
print("print to file " + (args.file_or_stdout if args.file_or_stdout is not '-' else 'std output'))
try:
print_file(file_or_stdout=args.file_or_stdout,
api_instance=api_instance,
bin_size=args.bin_size, partial=args.partial, symbol=args.symbol,
reverse=False,
start_time=args.begin_time, end_time=args.end_time)
except ApiException as e:
print("Exception when calling TradeApi->trade_get_bucketed: %s\n" % e)
return 0
if __name__ == "__main__":
sys.exit(main())
| mpl-2.0 |
rsignell-usgs/PySeidon | pyseidon/tidegaugeClass/plotsTidegauge.py | 2 | 1096 | #!/usr/bin/python2.7
# encoding: utf-8
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as Tri
import matplotlib.ticker as ticker
import seaborn
class PlotsTidegauge:
"""'Plots' subset of Tidegauge class gathers plotting functions"""
def __init__(self, variable, debug=False):
self._var = variable
def plot_xy(self, x, y, title=' ', xLabel=' ', yLabel=' '):
"""
Simple X vs Y plot
Inputs:
------
- x = 1D array
- y = 1D array
"""
fig = plt.figure(figsize=(18,10))
plt.rc('font',size='22')
self._fig = plt.plot(x, y, label=title)
scale = 1
ticks = ticker.FuncFormatter(lambda lon, pos: '{0:g}'.format(lon/scale))
plt.ylabel(yLabel)
plt.xlabel(xLabel)
#plt.legend()
plt.show()
#TR_comments: templates
# def whatever(self, debug=False):
# if debug or self._debug:
# print 'Start whatever...'
#
# if debug or self._debug:
# print '...Passed'
| agpl-3.0 |
mxjl620/scikit-learn | examples/manifold/plot_mds.py | 261 | 2616 | """
=========================
Multi-dimensional scaling
=========================
An illustration of the metric and non-metric MDS on generated noisy data.
The reconstructed points using the metric MDS and non metric MDS are slightly
shifted to avoid overlapping.
"""
# Author: Nelle Varoquaux <nelle.varoquaux@gmail.com>
# Licence: BSD
print(__doc__)
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
from sklearn import manifold
from sklearn.metrics import euclidean_distances
from sklearn.decomposition import PCA
n_samples = 20
seed = np.random.RandomState(seed=3)
X_true = seed.randint(0, 20, 2 * n_samples).astype(np.float)
X_true = X_true.reshape((n_samples, 2))
# Center the data
X_true -= X_true.mean()
similarities = euclidean_distances(X_true)
# Add noise to the similarities
noise = np.random.rand(n_samples, n_samples)
noise = noise + noise.T
noise[np.arange(noise.shape[0]), np.arange(noise.shape[0])] = 0
similarities += noise
mds = manifold.MDS(n_components=2, max_iter=3000, eps=1e-9, random_state=seed,
dissimilarity="precomputed", n_jobs=1)
pos = mds.fit(similarities).embedding_
nmds = manifold.MDS(n_components=2, metric=False, max_iter=3000, eps=1e-12,
dissimilarity="precomputed", random_state=seed, n_jobs=1,
n_init=1)
npos = nmds.fit_transform(similarities, init=pos)
# Rescale the data
pos *= np.sqrt((X_true ** 2).sum()) / np.sqrt((pos ** 2).sum())
npos *= np.sqrt((X_true ** 2).sum()) / np.sqrt((npos ** 2).sum())
# Rotate the data
clf = PCA(n_components=2)
X_true = clf.fit_transform(X_true)
pos = clf.fit_transform(pos)
npos = clf.fit_transform(npos)
fig = plt.figure(1)
ax = plt.axes([0., 0., 1., 1.])
plt.scatter(X_true[:, 0], X_true[:, 1], c='r', s=20)
plt.scatter(pos[:, 0], pos[:, 1], s=20, c='g')
plt.scatter(npos[:, 0], npos[:, 1], s=20, c='b')
plt.legend(('True position', 'MDS', 'NMDS'), loc='best')
similarities = similarities.max() / similarities * 100
similarities[np.isinf(similarities)] = 0
# Plot the edges
start_idx, end_idx = np.where(pos)
#a sequence of (*line0*, *line1*, *line2*), where::
# linen = (x0, y0), (x1, y1), ... (xm, ym)
segments = [[X_true[i, :], X_true[j, :]]
for i in range(len(pos)) for j in range(len(pos))]
values = np.abs(similarities)
lc = LineCollection(segments,
zorder=0, cmap=plt.cm.hot_r,
norm=plt.Normalize(0, values.max()))
lc.set_array(similarities.flatten())
lc.set_linewidths(0.5 * np.ones(len(segments)))
ax.add_collection(lc)
plt.show()
| bsd-3-clause |
RuthAngus/LSST-max | code/GP_periodogram.py | 1 | 1066 | from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from GProtation import make_plot, lnprob, neglnlike
import emcee
import time
import george
from george.kernels import ExpSquaredKernel, ExpSine2Kernel
import scipy.optimize as spo
def GP_periodogram(x, y, yerr, p_init, plims, N):
"""
This function takes a light curves and attempts to produce a GP periodogram.
It returns the value of the highest peak.
The kernel hyperparameters are optimised over a grid of periods.
This is also a "profile likelihood".
x, y, yerr: the light curve.
p_init: the initial guess for the period.
plims: the (log) boundaries for the grid.
N: the number of grid points.
"""
# create the grid
periods = np.linspace(np.exp(plims[0], np.exp(plims[1], 10)
# initial hyperparameters
if __name__ == "__main__":
# fake data
x = np.arange(0, 10, 100)
p = 2
err = .1
y = np.sin(2*np.pi*(1./p)*x) + np.random.randn(100)*err
yerr = np.ones_like(y) * err
p_init, plims = 2, np.log(.1, 5)
GP_periodogram(x, y, yerr, p_init, plims, 10)
| mit |
equialgo/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 |
sodafree/backend | build/ipython/docs/examples/parallel/dagdeps.py | 6 | 3566 | """Example for generating an arbitrary DAG as a dependency map.
This demo uses networkx to generate the graph.
Authors
-------
* MinRK
"""
import networkx as nx
from random import randint, random
from IPython import parallel
def randomwait():
import time
from random import random
time.sleep(random())
return time.time()
def random_dag(nodes, edges):
"""Generate a random Directed Acyclic Graph (DAG) with a given number of nodes and edges."""
G = nx.DiGraph()
for i in range(nodes):
G.add_node(i)
while edges > 0:
a = randint(0,nodes-1)
b=a
while b==a:
b = randint(0,nodes-1)
G.add_edge(a,b)
if nx.is_directed_acyclic_graph(G):
edges -= 1
else:
# we closed a loop!
G.remove_edge(a,b)
return G
def add_children(G, parent, level, n=2):
"""Add children recursively to a binary tree."""
if level == 0:
return
for i in range(n):
child = parent+str(i)
G.add_node(child)
G.add_edge(parent,child)
add_children(G, child, level-1, n)
def make_bintree(levels):
"""Make a symmetrical binary tree with @levels"""
G = nx.DiGraph()
root = '0'
G.add_node(root)
add_children(G, root, levels, 2)
return G
def submit_jobs(view, G, jobs):
"""Submit jobs via client where G describes the time dependencies."""
results = {}
for node in nx.topological_sort(G):
with view.temp_flags(after=[ results[n] for n in G.predecessors(node) ]):
results[node] = view.apply(jobs[node])
return results
def validate_tree(G, results):
"""Validate that jobs executed after their dependencies."""
for node in G:
started = results[node].metadata.started
for parent in G.predecessors(node):
finished = results[parent].metadata.completed
assert started > finished, "%s should have happened after %s"%(node, parent)
def main(nodes, edges):
"""Generate a random graph, submit jobs, then validate that the
dependency order was enforced.
Finally, plot the graph, with time on the x-axis, and
in-degree on the y (just for spread). All arrows must
point at least slightly to the right if the graph is valid.
"""
from matplotlib import pyplot as plt
from matplotlib.dates import date2num
from matplotlib.cm import gist_rainbow
print("building DAG")
G = random_dag(nodes, edges)
jobs = {}
pos = {}
colors = {}
for node in G:
jobs[node] = randomwait
client = parallel.Client()
view = client.load_balanced_view()
print("submitting %i tasks with %i dependencies"%(nodes,edges))
results = submit_jobs(view, G, jobs)
print("waiting for results")
view.wait()
print("done")
for node in G:
md = results[node].metadata
start = date2num(md.started)
runtime = date2num(md.completed) - start
pos[node] = (start, runtime)
colors[node] = md.engine_id
validate_tree(G, results)
nx.draw(G, pos, node_list=colors.keys(), node_color=colors.values(), cmap=gist_rainbow,
with_labels=False)
x,y = zip(*pos.values())
xmin,ymin = map(min, (x,y))
xmax,ymax = map(max, (x,y))
xscale = xmax-xmin
yscale = ymax-ymin
plt.xlim(xmin-xscale*.1,xmax+xscale*.1)
plt.ylim(ymin-yscale*.1,ymax+yscale*.1)
return G,results
if __name__ == '__main__':
from matplotlib import pyplot as plt
# main(5,10)
main(32,96)
plt.show()
| bsd-3-clause |
dhhagan/PAM | Python/PAM.py | 1 | 5037 | #PAM.py
import re
import glob, os, time
from numpy import *
from pylab import *
def analyzeFile(fileName,delim):
cols = {}
indexToName = {}
lineNum = 0
goodLines = 0
shortLines = 0
FILE = open(fileName,'r')
for line in FILE:
line = line.strip()
if lineNum < 1:
lineNum += 1
continue
elif lineNum == 1:
headings = line.split(delim)
i = 0
for heading in headings:
heading = heading.strip()
cols[heading] = []
indexToName[i] = heading
i += 1
lineNum += 1
lineLength = len(cols)
else:
data = line.split(delim)
if len(data) == lineLength:
goodLines += 1
i = 0
for point in data:
point = point.strip()
cols[indexToName[i]] += [point]
i += 1
lineNum += 1
else:
shortLines += 1
lineNum += 1
continue
FILE.close
return cols, indexToName, lineNum, shortLines
def numericalSort(value):
numbers = re.compile(r'(\d+)')
parts = numbers.split(value)
parts[1::2] = map(int, parts[1::2])
return parts
def popDate(fileName):
run = fileName.split('.')[0]
runNo = run.split('_')[-1]
return runNo
def getFile(date,regex):#Works
files = []
files = sorted((glob.glob('*'+regex+'*')),key=numericalSort,reverse=False)
if date.lower() == 'last':
files = files.pop()
else:
files = [item for item in files if re.search(date,item)]
return files
def plotConc(data,ozone,times):
# This function plots data versus time
import datetime as dt
from matplotlib import pyplot as plt
from matplotlib.dates import date2num
#time = [dt.datetime.strptime(time,"%m/%d/%Y %I:%M:%S %p") for time in times]
time = [dt.datetime.strptime(time,"%m/%d/%Y %I:%M:%S %p") for time in times]
x = date2num(time)
legend1 = []
legend2 = []
fig = plt.figure('Gas Concentration Readings for East St.Louis')
ax1 = fig.add_subplot(111)
ax2 = twinx()
for key,value in data.items():
ax1.plot_date(x,data[key],'-',xdate=True)
legend1.append(key)
for key, value in ozone.items():
ax2.plot_date(x,ozone[key],'-.',xdate=True)
legend2.append(key)
title('Gas Concentrations for East St. Louis', fontsize = 12)
ax1.set_ylabel(r'$Concentration(ppb)$', fontsize = 12)
ax2.set_ylabel(r'$Concentration(ppb)$', fontsize = 12)
xlabel(r"$Time \, Stamp$", fontsize = 12)
ax1.legend(legend1,loc='upper right')
ax2.legend(legend2,loc='lower right')
grid(True)
return
def plotBankRelays(data,relays,times):
# This function plots data versus time
import datetime as dt
from matplotlib import pyplot as plt
from matplotlib.dates import date2num
time = [dt.datetime.strptime(time,"%m/%d/%Y %I:%M:%S %p") for time in times]
x = date2num(time)
#x1 = [date.strftime("%m-%d %H:%M:%S") for date in time]
legend1 = []
legend2 = []
#plt.locator_params(axis='x', nbins=4)
fig = plt.figure('VAPS Thermocouple Readings: Chart 2')
ax1 = fig.add_subplot(111)
ax2 = twinx()
for key,value in data.items():
ax1.plot_date(x,data[key],'-',xdate=True)
legend1.append(key)
for key,value in relays.items():
ax2.plot_date(x,relays[key],'--',xdate=True)
legend2.append(key)
title('VAPS Temperatures: Chart 2', fontsize = 12)
ax1.set_ylabel(r'$Temperature(^oC)$', fontsize = 12)
ax2.set_ylabel(r'$Relay \, States$', fontsize = 12)
ax1.set_xlabel(r"$Time \, Stamp$", fontsize = 12)
#print [num2date(item) for item in ax1.get_xticks()]
#ax1.set_xticks(x)
#ax1.set_xticklabels([date.strftime("%m-%d %H:%M %p") for date in time])
#ax1.legend(bbox_to_anchor=(0.,1.02,1.,.102),loc=3,ncol=2,mode="expand",borderaxespad=0.)
ax1.legend(legend1,loc='upper right')
ax2.legend(legend2,loc='lower right')
#ax1.xaxis.set_major_formatter(FormatStrFormatter(date.strftime("%m-%d %H:%M:%S")))
plt.subplots_adjust(bottom=0.15)
grid(True)
return
def goodFiles(files,goodHeaders,delim): # Good
irregFiles = 0
goodFiles = []
for file in files:
lineNo = 0
falseCount = 0
FILE = open(file,'r')
for line in FILE:
line = line.strip()
if lineNo == 5:
# Check all the headings to make sure the file is good
head = line.split(delim)
for item in head:
if item in goodHeaders:
continue
else:
falseCount += 1
if falseCount == 0:
goodFiles.append(file)
else:
irregFiles += 1
lineNo += 1
else:
lineNo += 1
continue
FILE.close
return goodFiles, irregFiles
| mit |
RachitKansal/scikit-learn | sklearn/neighbors/tests/test_approximate.py | 71 | 18815 | """
Testing for the approximate neighbor search using
Locality Sensitive Hashing Forest module
(sklearn.neighbors.LSHForest).
"""
# Author: Maheshakya Wijewardena, Joel Nothman
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_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_array_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import ignore_warnings
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.neighbors import LSHForest
from sklearn.neighbors import NearestNeighbors
def test_neighbors_accuracy_with_n_candidates():
# Checks whether accuracy increases as `n_candidates` increases.
n_candidates_values = np.array([.1, 50, 500])
n_samples = 100
n_features = 10
n_iter = 10
n_points = 5
rng = np.random.RandomState(42)
accuracies = np.zeros(n_candidates_values.shape[0], dtype=float)
X = rng.rand(n_samples, n_features)
for i, n_candidates in enumerate(n_candidates_values):
lshf = LSHForest(n_candidates=n_candidates)
lshf.fit(X)
for j in range(n_iter):
query = X[rng.randint(0, n_samples)].reshape(1, -1)
neighbors = lshf.kneighbors(query, n_neighbors=n_points,
return_distance=False)
distances = pairwise_distances(query, X, metric='cosine')
ranks = np.argsort(distances)[0, :n_points]
intersection = np.intersect1d(ranks, neighbors).shape[0]
ratio = intersection / float(n_points)
accuracies[i] = accuracies[i] + ratio
accuracies[i] = accuracies[i] / float(n_iter)
# Sorted accuracies should be equal to original accuracies
assert_true(np.all(np.diff(accuracies) >= 0),
msg="Accuracies are not non-decreasing.")
# Highest accuracy should be strictly greater than the lowest
assert_true(np.ptp(accuracies) > 0,
msg="Highest accuracy is not strictly greater than lowest.")
def test_neighbors_accuracy_with_n_estimators():
# Checks whether accuracy increases as `n_estimators` increases.
n_estimators = np.array([1, 10, 100])
n_samples = 100
n_features = 10
n_iter = 10
n_points = 5
rng = np.random.RandomState(42)
accuracies = np.zeros(n_estimators.shape[0], dtype=float)
X = rng.rand(n_samples, n_features)
for i, t in enumerate(n_estimators):
lshf = LSHForest(n_candidates=500, n_estimators=t)
lshf.fit(X)
for j in range(n_iter):
query = X[rng.randint(0, n_samples)].reshape(1, -1)
neighbors = lshf.kneighbors(query, n_neighbors=n_points,
return_distance=False)
distances = pairwise_distances(query, X, metric='cosine')
ranks = np.argsort(distances)[0, :n_points]
intersection = np.intersect1d(ranks, neighbors).shape[0]
ratio = intersection / float(n_points)
accuracies[i] = accuracies[i] + ratio
accuracies[i] = accuracies[i] / float(n_iter)
# Sorted accuracies should be equal to original accuracies
assert_true(np.all(np.diff(accuracies) >= 0),
msg="Accuracies are not non-decreasing.")
# Highest accuracy should be strictly greater than the lowest
assert_true(np.ptp(accuracies) > 0,
msg="Highest accuracy is not strictly greater than lowest.")
@ignore_warnings
def test_kneighbors():
# Checks whether desired number of neighbors are returned.
# It is guaranteed to return the requested number of neighbors
# if `min_hash_match` is set to 0. Returned distances should be
# in ascending order.
n_samples = 12
n_features = 2
n_iter = 10
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
lshf = LSHForest(min_hash_match=0)
# Test unfitted estimator
assert_raises(ValueError, lshf.kneighbors, X[0])
lshf.fit(X)
for i in range(n_iter):
n_neighbors = rng.randint(0, n_samples)
query = X[rng.randint(0, n_samples)].reshape(1, -1)
neighbors = lshf.kneighbors(query, n_neighbors=n_neighbors,
return_distance=False)
# Desired number of neighbors should be returned.
assert_equal(neighbors.shape[1], n_neighbors)
# Multiple points
n_queries = 5
queries = X[rng.randint(0, n_samples, n_queries)]
distances, neighbors = lshf.kneighbors(queries,
n_neighbors=1,
return_distance=True)
assert_equal(neighbors.shape[0], n_queries)
assert_equal(distances.shape[0], n_queries)
# Test only neighbors
neighbors = lshf.kneighbors(queries, n_neighbors=1,
return_distance=False)
assert_equal(neighbors.shape[0], n_queries)
# Test random point(not in the data set)
query = rng.randn(n_features).reshape(1, -1)
lshf.kneighbors(query, n_neighbors=1,
return_distance=False)
# Test n_neighbors at initialization
neighbors = lshf.kneighbors(query, return_distance=False)
assert_equal(neighbors.shape[1], 5)
# Test `neighbors` has an integer dtype
assert_true(neighbors.dtype.kind == 'i',
msg="neighbors are not in integer dtype.")
def test_radius_neighbors():
# Checks whether Returned distances are less than `radius`
# At least one point should be returned when the `radius` is set
# to mean distance from the considering point to other points in
# the database.
# Moreover, this test compares the radius neighbors of LSHForest
# with the `sklearn.neighbors.NearestNeighbors`.
n_samples = 12
n_features = 2
n_iter = 10
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
lshf = LSHForest()
# Test unfitted estimator
assert_raises(ValueError, lshf.radius_neighbors, X[0])
lshf.fit(X)
for i in range(n_iter):
# Select a random point in the dataset as the query
query = X[rng.randint(0, n_samples)].reshape(1, -1)
# At least one neighbor should be returned when the radius is the
# mean distance from the query to the points of the dataset.
mean_dist = np.mean(pairwise_distances(query, X, metric='cosine'))
neighbors = lshf.radius_neighbors(query, radius=mean_dist,
return_distance=False)
assert_equal(neighbors.shape, (1,))
assert_equal(neighbors.dtype, object)
assert_greater(neighbors[0].shape[0], 0)
# All distances to points in the results of the radius query should
# be less than mean_dist
distances, neighbors = lshf.radius_neighbors(query,
radius=mean_dist,
return_distance=True)
assert_array_less(distances[0], mean_dist)
# Multiple points
n_queries = 5
queries = X[rng.randint(0, n_samples, n_queries)]
distances, neighbors = lshf.radius_neighbors(queries,
return_distance=True)
# dists and inds should not be 1D arrays or arrays of variable lengths
# hence the use of the object dtype.
assert_equal(distances.shape, (n_queries,))
assert_equal(distances.dtype, object)
assert_equal(neighbors.shape, (n_queries,))
assert_equal(neighbors.dtype, object)
# Compare with exact neighbor search
query = X[rng.randint(0, n_samples)].reshape(1, -1)
mean_dist = np.mean(pairwise_distances(query, X, metric='cosine'))
nbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X)
distances_exact, _ = nbrs.radius_neighbors(query, radius=mean_dist)
distances_approx, _ = lshf.radius_neighbors(query, radius=mean_dist)
# Radius-based queries do not sort the result points and the order
# depends on the method, the random_state and the dataset order. Therefore
# we need to sort the results ourselves before performing any comparison.
sorted_dists_exact = np.sort(distances_exact[0])
sorted_dists_approx = np.sort(distances_approx[0])
# Distances to exact neighbors are less than or equal to approximate
# counterparts as the approximate radius query might have missed some
# closer neighbors.
assert_true(np.all(np.less_equal(sorted_dists_exact,
sorted_dists_approx)))
def test_radius_neighbors_boundary_handling():
X = [[0.999, 0.001], [0.5, 0.5], [0, 1.], [-1., 0.001]]
n_points = len(X)
# Build an exact nearest neighbors model as reference model to ensure
# consistency between exact and approximate methods
nnbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X)
# Build a LSHForest model with hyperparameter values that always guarantee
# exact results on this toy dataset.
lsfh = LSHForest(min_hash_match=0, n_candidates=n_points).fit(X)
# define a query aligned with the first axis
query = [[1., 0.]]
# Compute the exact cosine distances of the query to the four points of
# the dataset
dists = pairwise_distances(query, X, metric='cosine').ravel()
# The first point is almost aligned with the query (very small angle),
# the cosine distance should therefore be almost null:
assert_almost_equal(dists[0], 0, decimal=5)
# The second point form an angle of 45 degrees to the query vector
assert_almost_equal(dists[1], 1 - np.cos(np.pi / 4))
# The third point is orthogonal from the query vector hence at a distance
# exactly one:
assert_almost_equal(dists[2], 1)
# The last point is almost colinear but with opposite sign to the query
# therefore it has a cosine 'distance' very close to the maximum possible
# value of 2.
assert_almost_equal(dists[3], 2, decimal=5)
# If we query with a radius of one, all the samples except the last sample
# should be included in the results. This means that the third sample
# is lying on the boundary of the radius query:
exact_dists, exact_idx = nnbrs.radius_neighbors(query, radius=1)
approx_dists, approx_idx = lsfh.radius_neighbors(query, radius=1)
assert_array_equal(np.sort(exact_idx[0]), [0, 1, 2])
assert_array_equal(np.sort(approx_idx[0]), [0, 1, 2])
assert_array_almost_equal(np.sort(exact_dists[0]), dists[:-1])
assert_array_almost_equal(np.sort(approx_dists[0]), dists[:-1])
# If we perform the same query with a slighltly lower radius, the third
# point of the dataset that lay on the boundary of the previous query
# is now rejected:
eps = np.finfo(np.float64).eps
exact_dists, exact_idx = nnbrs.radius_neighbors(query, radius=1 - eps)
approx_dists, approx_idx = lsfh.radius_neighbors(query, radius=1 - eps)
assert_array_equal(np.sort(exact_idx[0]), [0, 1])
assert_array_equal(np.sort(approx_idx[0]), [0, 1])
assert_array_almost_equal(np.sort(exact_dists[0]), dists[:-2])
assert_array_almost_equal(np.sort(approx_dists[0]), dists[:-2])
def test_distances():
# Checks whether returned neighbors are from closest to farthest.
n_samples = 12
n_features = 2
n_iter = 10
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
lshf = LSHForest()
lshf.fit(X)
for i in range(n_iter):
n_neighbors = rng.randint(0, n_samples)
query = X[rng.randint(0, n_samples)].reshape(1, -1)
distances, neighbors = lshf.kneighbors(query,
n_neighbors=n_neighbors,
return_distance=True)
# Returned neighbors should be from closest to farthest, that is
# increasing distance values.
assert_true(np.all(np.diff(distances[0]) >= 0))
# Note: the radius_neighbors method does not guarantee the order of
# the results.
def test_fit():
# Checks whether `fit` method sets all attribute values correctly.
n_samples = 12
n_features = 2
n_estimators = 5
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
lshf = LSHForest(n_estimators=n_estimators)
lshf.fit(X)
# _input_array = X
assert_array_equal(X, lshf._fit_X)
# A hash function g(p) for each tree
assert_equal(n_estimators, len(lshf.hash_functions_))
# Hash length = 32
assert_equal(32, lshf.hash_functions_[0].components_.shape[0])
# Number of trees_ in the forest
assert_equal(n_estimators, len(lshf.trees_))
# Each tree has entries for every data point
assert_equal(n_samples, len(lshf.trees_[0]))
# Original indices after sorting the hashes
assert_equal(n_estimators, len(lshf.original_indices_))
# Each set of original indices in a tree has entries for every data point
assert_equal(n_samples, len(lshf.original_indices_[0]))
def test_partial_fit():
# Checks whether inserting array is consitent with fitted data.
# `partial_fit` method should set all attribute values correctly.
n_samples = 12
n_samples_partial_fit = 3
n_features = 2
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
X_partial_fit = rng.rand(n_samples_partial_fit, n_features)
lshf = LSHForest()
# Test unfitted estimator
lshf.partial_fit(X)
assert_array_equal(X, lshf._fit_X)
lshf.fit(X)
# Insert wrong dimension
assert_raises(ValueError, lshf.partial_fit,
np.random.randn(n_samples_partial_fit, n_features - 1))
lshf.partial_fit(X_partial_fit)
# size of _input_array = samples + 1 after insertion
assert_equal(lshf._fit_X.shape[0],
n_samples + n_samples_partial_fit)
# size of original_indices_[1] = samples + 1
assert_equal(len(lshf.original_indices_[0]),
n_samples + n_samples_partial_fit)
# size of trees_[1] = samples + 1
assert_equal(len(lshf.trees_[1]),
n_samples + n_samples_partial_fit)
def test_hash_functions():
# Checks randomness of hash functions.
# Variance and mean of each hash function (projection vector)
# should be different from flattened array of hash functions.
# If hash functions are not randomly built (seeded with
# same value), variances and means of all functions are equal.
n_samples = 12
n_features = 2
n_estimators = 5
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
lshf = LSHForest(n_estimators=n_estimators,
random_state=rng.randint(0, np.iinfo(np.int32).max))
lshf.fit(X)
hash_functions = []
for i in range(n_estimators):
hash_functions.append(lshf.hash_functions_[i].components_)
for i in range(n_estimators):
assert_not_equal(np.var(hash_functions),
np.var(lshf.hash_functions_[i].components_))
for i in range(n_estimators):
assert_not_equal(np.mean(hash_functions),
np.mean(lshf.hash_functions_[i].components_))
def test_candidates():
# Checks whether candidates are sufficient.
# This should handle the cases when number of candidates is 0.
# User should be warned when number of candidates is less than
# requested number of neighbors.
X_train = np.array([[5, 5, 2], [21, 5, 5], [1, 1, 1], [8, 9, 1],
[6, 10, 2]], dtype=np.float32)
X_test = np.array([7, 10, 3], dtype=np.float32).reshape(1, -1)
# For zero candidates
lshf = LSHForest(min_hash_match=32)
lshf.fit(X_train)
message = ("Number of candidates is not sufficient to retrieve"
" %i neighbors with"
" min_hash_match = %i. Candidates are filled up"
" uniformly from unselected"
" indices." % (3, 32))
assert_warns_message(UserWarning, message, lshf.kneighbors,
X_test, n_neighbors=3)
distances, neighbors = lshf.kneighbors(X_test, n_neighbors=3)
assert_equal(distances.shape[1], 3)
# For candidates less than n_neighbors
lshf = LSHForest(min_hash_match=31)
lshf.fit(X_train)
message = ("Number of candidates is not sufficient to retrieve"
" %i neighbors with"
" min_hash_match = %i. Candidates are filled up"
" uniformly from unselected"
" indices." % (5, 31))
assert_warns_message(UserWarning, message, lshf.kneighbors,
X_test, n_neighbors=5)
distances, neighbors = lshf.kneighbors(X_test, n_neighbors=5)
assert_equal(distances.shape[1], 5)
def test_graphs():
# Smoke tests for graph methods.
n_samples_sizes = [5, 10, 20]
n_features = 3
rng = np.random.RandomState(42)
for n_samples in n_samples_sizes:
X = rng.rand(n_samples, n_features)
lshf = LSHForest(min_hash_match=0)
lshf.fit(X)
kneighbors_graph = lshf.kneighbors_graph(X)
radius_neighbors_graph = lshf.radius_neighbors_graph(X)
assert_equal(kneighbors_graph.shape[0], n_samples)
assert_equal(kneighbors_graph.shape[1], n_samples)
assert_equal(radius_neighbors_graph.shape[0], n_samples)
assert_equal(radius_neighbors_graph.shape[1], n_samples)
def test_sparse_input():
# note: Fixed random state in sp.rand is not supported in older scipy.
# The test should succeed regardless.
X1 = sp.rand(50, 100)
X2 = sp.rand(10, 100)
forest_sparse = LSHForest(radius=1, random_state=0).fit(X1)
forest_dense = LSHForest(radius=1, random_state=0).fit(X1.A)
d_sparse, i_sparse = forest_sparse.kneighbors(X2, return_distance=True)
d_dense, i_dense = forest_dense.kneighbors(X2.A, return_distance=True)
assert_almost_equal(d_sparse, d_dense)
assert_almost_equal(i_sparse, i_dense)
d_sparse, i_sparse = forest_sparse.radius_neighbors(X2,
return_distance=True)
d_dense, i_dense = forest_dense.radius_neighbors(X2.A,
return_distance=True)
assert_equal(d_sparse.shape, d_dense.shape)
for a, b in zip(d_sparse, d_dense):
assert_almost_equal(a, b)
for a, b in zip(i_sparse, i_dense):
assert_almost_equal(a, b)
| bsd-3-clause |
ChanChiChoi/scikit-learn | sklearn/linear_model/ransac.py | 191 | 14261 | # coding: utf-8
# Author: Johannes Schönberger
#
# License: BSD 3 clause
import numpy as np
from ..base import BaseEstimator, MetaEstimatorMixin, RegressorMixin, clone
from ..utils import check_random_state, check_array, check_consistent_length
from ..utils.random import sample_without_replacement
from ..utils.validation import check_is_fitted
from .base import LinearRegression
_EPSILON = np.spacing(1)
def _dynamic_max_trials(n_inliers, n_samples, min_samples, probability):
"""Determine number trials such that at least one outlier-free subset is
sampled for the given inlier/outlier ratio.
Parameters
----------
n_inliers : int
Number of inliers in the data.
n_samples : int
Total number of samples in the data.
min_samples : int
Minimum number of samples chosen randomly from original data.
probability : float
Probability (confidence) that one outlier-free sample is generated.
Returns
-------
trials : int
Number of trials.
"""
inlier_ratio = n_inliers / float(n_samples)
nom = max(_EPSILON, 1 - probability)
denom = max(_EPSILON, 1 - inlier_ratio ** min_samples)
if nom == 1:
return 0
if denom == 1:
return float('inf')
return abs(float(np.ceil(np.log(nom) / np.log(denom))))
class RANSACRegressor(BaseEstimator, MetaEstimatorMixin, RegressorMixin):
"""RANSAC (RANdom SAmple Consensus) algorithm.
RANSAC is an iterative algorithm for the robust estimation of parameters
from a subset of inliers from the complete data set. More information can
be found in the general documentation of linear models.
A detailed description of the algorithm can be found in the documentation
of the ``linear_model`` sub-package.
Read more in the :ref:`User Guide <RansacRegression>`.
Parameters
----------
base_estimator : object, optional
Base estimator object which implements the following methods:
* `fit(X, y)`: Fit model to given training data and target values.
* `score(X, y)`: Returns the mean accuracy on the given test data,
which is used for the stop criterion defined by `stop_score`.
Additionally, the score is used to decide which of two equally
large consensus sets is chosen as the better one.
If `base_estimator` is None, then
``base_estimator=sklearn.linear_model.LinearRegression()`` is used for
target values of dtype float.
Note that the current implementation only supports regression
estimators.
min_samples : int (>= 1) or float ([0, 1]), optional
Minimum number of samples chosen randomly from original data. Treated
as an absolute number of samples for `min_samples >= 1`, treated as a
relative number `ceil(min_samples * X.shape[0]`) for
`min_samples < 1`. This is typically chosen as the minimal number of
samples necessary to estimate the given `base_estimator`. By default a
``sklearn.linear_model.LinearRegression()`` estimator is assumed and
`min_samples` is chosen as ``X.shape[1] + 1``.
residual_threshold : float, optional
Maximum residual for a data sample to be classified as an inlier.
By default the threshold is chosen as the MAD (median absolute
deviation) of the target values `y`.
is_data_valid : callable, optional
This function is called with the randomly selected data before the
model is fitted to it: `is_data_valid(X, y)`. If its return value is
False the current randomly chosen sub-sample is skipped.
is_model_valid : callable, optional
This function is called with the estimated model and the randomly
selected data: `is_model_valid(model, X, y)`. If its return value is
False the current randomly chosen sub-sample is skipped.
Rejecting samples with this function is computationally costlier than
with `is_data_valid`. `is_model_valid` should therefore only be used if
the estimated model is needed for making the rejection decision.
max_trials : int, optional
Maximum number of iterations for random sample selection.
stop_n_inliers : int, optional
Stop iteration if at least this number of inliers are found.
stop_score : float, optional
Stop iteration if score is greater equal than this threshold.
stop_probability : float in range [0, 1], optional
RANSAC iteration stops if at least one outlier-free set of the training
data is sampled in RANSAC. This requires to generate at least N
samples (iterations)::
N >= log(1 - probability) / log(1 - e**m)
where the probability (confidence) is typically set to high value such
as 0.99 (the default) and e is the current fraction of inliers w.r.t.
the total number of samples.
residual_metric : callable, optional
Metric to reduce the dimensionality of the residuals to 1 for
multi-dimensional target values ``y.shape[1] > 1``. By default the sum
of absolute differences is used::
lambda dy: np.sum(np.abs(dy), axis=1)
random_state : integer or numpy.RandomState, optional
The generator used to initialize the centers. If an integer is
given, it fixes the seed. Defaults to the global numpy random
number generator.
Attributes
----------
estimator_ : object
Best fitted model (copy of the `base_estimator` object).
n_trials_ : int
Number of random selection trials until one of the stop criteria is
met. It is always ``<= max_trials``.
inlier_mask_ : bool array of shape [n_samples]
Boolean mask of inliers classified as ``True``.
References
----------
.. [1] http://en.wikipedia.org/wiki/RANSAC
.. [2] http://www.cs.columbia.edu/~belhumeur/courses/compPhoto/ransac.pdf
.. [3] http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf
"""
def __init__(self, base_estimator=None, min_samples=None,
residual_threshold=None, is_data_valid=None,
is_model_valid=None, max_trials=100,
stop_n_inliers=np.inf, stop_score=np.inf,
stop_probability=0.99, residual_metric=None,
random_state=None):
self.base_estimator = base_estimator
self.min_samples = min_samples
self.residual_threshold = residual_threshold
self.is_data_valid = is_data_valid
self.is_model_valid = is_model_valid
self.max_trials = max_trials
self.stop_n_inliers = stop_n_inliers
self.stop_score = stop_score
self.stop_probability = stop_probability
self.residual_metric = residual_metric
self.random_state = random_state
def fit(self, X, y):
"""Fit estimator using RANSAC algorithm.
Parameters
----------
X : array-like or sparse matrix, shape [n_samples, n_features]
Training data.
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values.
Raises
------
ValueError
If no valid consensus set could be found. This occurs if
`is_data_valid` and `is_model_valid` return False for all
`max_trials` randomly chosen sub-samples.
"""
X = check_array(X, accept_sparse='csr')
y = check_array(y, ensure_2d=False)
check_consistent_length(X, y)
if self.base_estimator is not None:
base_estimator = clone(self.base_estimator)
else:
base_estimator = LinearRegression()
if self.min_samples is None:
# assume linear model by default
min_samples = X.shape[1] + 1
elif 0 < self.min_samples < 1:
min_samples = np.ceil(self.min_samples * X.shape[0])
elif self.min_samples >= 1:
if self.min_samples % 1 != 0:
raise ValueError("Absolute number of samples must be an "
"integer value.")
min_samples = self.min_samples
else:
raise ValueError("Value for `min_samples` must be scalar and "
"positive.")
if min_samples > X.shape[0]:
raise ValueError("`min_samples` may not be larger than number "
"of samples ``X.shape[0]``.")
if self.stop_probability < 0 or self.stop_probability > 1:
raise ValueError("`stop_probability` must be in range [0, 1].")
if self.residual_threshold is None:
# MAD (median absolute deviation)
residual_threshold = np.median(np.abs(y - np.median(y)))
else:
residual_threshold = self.residual_threshold
if self.residual_metric is None:
residual_metric = lambda dy: np.sum(np.abs(dy), axis=1)
else:
residual_metric = self.residual_metric
random_state = check_random_state(self.random_state)
try: # Not all estimator accept a random_state
base_estimator.set_params(random_state=random_state)
except ValueError:
pass
n_inliers_best = 0
score_best = np.inf
inlier_mask_best = None
X_inlier_best = None
y_inlier_best = None
# number of data samples
n_samples = X.shape[0]
sample_idxs = np.arange(n_samples)
n_samples, _ = X.shape
for self.n_trials_ in range(1, self.max_trials + 1):
# choose random sample set
subset_idxs = sample_without_replacement(n_samples, min_samples,
random_state=random_state)
X_subset = X[subset_idxs]
y_subset = y[subset_idxs]
# check if random sample set is valid
if (self.is_data_valid is not None
and not self.is_data_valid(X_subset, y_subset)):
continue
# fit model for current random sample set
base_estimator.fit(X_subset, y_subset)
# check if estimated model is valid
if (self.is_model_valid is not None and not
self.is_model_valid(base_estimator, X_subset, y_subset)):
continue
# residuals of all data for current random sample model
y_pred = base_estimator.predict(X)
diff = y_pred - y
if diff.ndim == 1:
diff = diff.reshape(-1, 1)
residuals_subset = residual_metric(diff)
# classify data into inliers and outliers
inlier_mask_subset = residuals_subset < residual_threshold
n_inliers_subset = np.sum(inlier_mask_subset)
# less inliers -> skip current random sample
if n_inliers_subset < n_inliers_best:
continue
if n_inliers_subset == 0:
raise ValueError("No inliers found, possible cause is "
"setting residual_threshold ({0}) too low.".format(
self.residual_threshold))
# extract inlier data set
inlier_idxs_subset = sample_idxs[inlier_mask_subset]
X_inlier_subset = X[inlier_idxs_subset]
y_inlier_subset = y[inlier_idxs_subset]
# score of inlier data set
score_subset = base_estimator.score(X_inlier_subset,
y_inlier_subset)
# same number of inliers but worse score -> skip current random
# sample
if (n_inliers_subset == n_inliers_best
and score_subset < score_best):
continue
# save current random sample as best sample
n_inliers_best = n_inliers_subset
score_best = score_subset
inlier_mask_best = inlier_mask_subset
X_inlier_best = X_inlier_subset
y_inlier_best = y_inlier_subset
# break if sufficient number of inliers or score is reached
if (n_inliers_best >= self.stop_n_inliers
or score_best >= self.stop_score
or self.n_trials_
>= _dynamic_max_trials(n_inliers_best, n_samples,
min_samples,
self.stop_probability)):
break
# if none of the iterations met the required criteria
if inlier_mask_best is None:
raise ValueError(
"RANSAC could not find valid consensus set, because"
" either the `residual_threshold` rejected all the samples or"
" `is_data_valid` and `is_model_valid` returned False for all"
" `max_trials` randomly ""chosen sub-samples. Consider "
"relaxing the ""constraints.")
# estimate final model using all inliers
base_estimator.fit(X_inlier_best, y_inlier_best)
self.estimator_ = base_estimator
self.inlier_mask_ = inlier_mask_best
return self
def predict(self, X):
"""Predict using the estimated model.
This is a wrapper for `estimator_.predict(X)`.
Parameters
----------
X : numpy array of shape [n_samples, n_features]
Returns
-------
y : array, shape = [n_samples] or [n_samples, n_targets]
Returns predicted values.
"""
check_is_fitted(self, 'estimator_')
return self.estimator_.predict(X)
def score(self, X, y):
"""Returns the score of the prediction.
This is a wrapper for `estimator_.score(X, y)`.
Parameters
----------
X : numpy array or sparse matrix of shape [n_samples, n_features]
Training data.
y : array, shape = [n_samples] or [n_samples, n_targets]
Target values.
Returns
-------
z : float
Score of the prediction.
"""
check_is_fitted(self, 'estimator_')
return self.estimator_.score(X, y)
| bsd-3-clause |
jakobworldpeace/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py | 104 | 3139 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a movie review dataset.
"""
# Author: Olivier Grisel <olivier.grisel@ensta.org>
# License: Simplified BSD
import sys
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_files
from sklearn.model_selection import train_test_split
from sklearn import metrics
if __name__ == "__main__":
# NOTE: we put the following in a 'if __name__ == "__main__"' protected
# block to be able to use a multi-core grid search that also works under
# Windows, see: http://docs.python.org/library/multiprocessing.html#windows
# The multiprocessing module is used as the backend of joblib.Parallel
# that is used when n_jobs != 1 in GridSearchCV
# the training data folder must be passed as first argument
movie_reviews_data_folder = sys.argv[1]
dataset = load_files(movie_reviews_data_folder, shuffle=False)
print("n_samples: %d" % len(dataset.data))
# split the dataset in training and test set:
docs_train, docs_test, y_train, y_test = train_test_split(
dataset.data, dataset.target, test_size=0.25, random_state=None)
# TASK: Build a vectorizer / classifier pipeline that filters out tokens
# that are too rare or too frequent
pipeline = Pipeline([
('vect', TfidfVectorizer(min_df=3, max_df=0.95)),
('clf', LinearSVC(C=1000)),
])
# TASK: Build a grid search to find out whether unigrams or bigrams are
# more useful.
# Fit the pipeline on the training set using grid search for the parameters
parameters = {
'vect__ngram_range': [(1, 1), (1, 2)],
}
grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1)
grid_search.fit(docs_train, y_train)
# TASK: print the mean and std for each candidate along with the parameter
# settings for all the candidates explored by grid search.
n_candidates = len(grid_search.cv_results_['params'])
for i in range(n_candidates):
print(i, 'params - %s; mean - %0.2f; std - %0.2f'
% (grid_search.cv_results_['params'][i],
grid_search.cv_results_['mean_test_score'][i],
grid_search.cv_results_['std_test_score'][i]))
# TASK: Predict the outcome on the testing set and store it in a variable
# named y_predicted
y_predicted = grid_search.predict(docs_test)
# Print the classification report
print(metrics.classification_report(y_test, y_predicted,
target_names=dataset.target_names))
# Print and plot the confusion matrix
cm = metrics.confusion_matrix(y_test, y_predicted)
print(cm)
# import matplotlib.pyplot as plt
# plt.matshow(cm)
# plt.show()
| bsd-3-clause |
duthchao/kaggle-galaxies | predict_augmented_npy_maxout2048_pysex.py | 7 | 9584 | """
Load an analysis file and redo the predictions on the validation set / test set,
this time with augmented data and averaging. Store them as numpy files.
"""
import numpy as np
# import pandas as pd
import theano
import theano.tensor as T
import layers
import cc_layers
import custom
import load_data
import realtime_augmentation as ra
import time
import csv
import os
import cPickle as pickle
BATCH_SIZE = 32 # 16
NUM_INPUT_FEATURES = 3
CHUNK_SIZE = 8000 # 10000 # this should be a multiple of the batch size
# ANALYSIS_PATH = "analysis/try_convnet_cc_multirot_3x69r45_untied_bias.pkl"
ANALYSIS_PATH = "analysis/final/try_convnet_cc_multirotflip_3x69r45_maxout2048_pysex.pkl"
DO_VALID = True # disable this to not bother with the validation set evaluation
DO_TEST = True # disable this to not generate predictions on the testset
target_filename = os.path.basename(ANALYSIS_PATH).replace(".pkl", ".npy.gz")
target_path_valid = os.path.join("predictions/final/augmented/valid", target_filename)
target_path_test = os.path.join("predictions/final/augmented/test", target_filename)
print "Loading model data etc."
analysis = np.load(ANALYSIS_PATH)
input_sizes = [(69, 69), (69, 69)]
ds_transforms = [
ra.build_ds_transform(3.0, target_size=input_sizes[0]),
ra.build_ds_transform(3.0, target_size=input_sizes[1]) + ra.build_augmentation_transform(rotation=45)]
num_input_representations = len(ds_transforms)
# split training data into training + a small validation set
num_train = load_data.num_train
num_valid = num_train // 10 # integer division
num_train -= num_valid
num_test = load_data.num_test
valid_ids = load_data.train_ids[num_train:]
train_ids = load_data.train_ids[:num_train]
test_ids = load_data.test_ids
train_indices = np.arange(num_train)
valid_indices = np.arange(num_train, num_train+num_valid)
test_indices = np.arange(num_test)
y_valid = np.load("data/solutions_train.npy")[num_train:]
print "Build model"
l0 = layers.Input2DLayer(BATCH_SIZE, NUM_INPUT_FEATURES, input_sizes[0][0], input_sizes[0][1])
l0_45 = layers.Input2DLayer(BATCH_SIZE, NUM_INPUT_FEATURES, input_sizes[1][0], input_sizes[1][1])
l0r = layers.MultiRotSliceLayer([l0, l0_45], part_size=45, include_flip=True)
l0s = cc_layers.ShuffleBC01ToC01BLayer(l0r)
l1a = cc_layers.CudaConvnetConv2DLayer(l0s, n_filters=32, filter_size=6, weights_std=0.01, init_bias_value=0.1, dropout=0.0, partial_sum=1, untie_biases=True)
l1 = cc_layers.CudaConvnetPooling2DLayer(l1a, pool_size=2)
l2a = cc_layers.CudaConvnetConv2DLayer(l1, n_filters=64, filter_size=5, weights_std=0.01, init_bias_value=0.1, dropout=0.0, partial_sum=1, untie_biases=True)
l2 = cc_layers.CudaConvnetPooling2DLayer(l2a, pool_size=2)
l3a = cc_layers.CudaConvnetConv2DLayer(l2, n_filters=128, filter_size=3, weights_std=0.01, init_bias_value=0.1, dropout=0.0, partial_sum=1, untie_biases=True)
l3b = cc_layers.CudaConvnetConv2DLayer(l3a, n_filters=128, filter_size=3, pad=0, weights_std=0.1, init_bias_value=0.1, dropout=0.0, partial_sum=1, untie_biases=True)
l3 = cc_layers.CudaConvnetPooling2DLayer(l3b, pool_size=2)
l3s = cc_layers.ShuffleC01BToBC01Layer(l3)
j3 = layers.MultiRotMergeLayer(l3s, num_views=4) # 2) # merge convolutional parts
# l4 = layers.DenseLayer(j3, n_outputs=4096, weights_std=0.001, init_bias_value=0.01, dropout=0.5)
l4a = layers.DenseLayer(j3, n_outputs=4096, weights_std=0.001, init_bias_value=0.01, dropout=0.5, nonlinearity=layers.identity)
l4 = layers.FeatureMaxPoolingLayer(l4a, pool_size=2, feature_dim=1, implementation='reshape')
# l5 = layers.DenseLayer(l4, n_outputs=37, weights_std=0.01, init_bias_value=0.0, dropout=0.5, nonlinearity=custom.clip_01) # nonlinearity=layers.identity)
l5 = layers.DenseLayer(l4, n_outputs=37, weights_std=0.01, init_bias_value=0.1, dropout=0.5, nonlinearity=layers.identity)
# l6 = layers.OutputLayer(l5, error_measure='mse')
l6 = custom.OptimisedDivGalaxyOutputLayer(l5) # this incorporates the constraints on the output (probabilities sum to one, weighting, etc.)
xs_shared = [theano.shared(np.zeros((1,1,1,1), dtype=theano.config.floatX)) for _ in xrange(num_input_representations)]
idx = T.lscalar('idx')
givens = {
l0.input_var: xs_shared[0][idx*BATCH_SIZE:(idx+1)*BATCH_SIZE],
l0_45.input_var: xs_shared[1][idx*BATCH_SIZE:(idx+1)*BATCH_SIZE],
}
compute_output = theano.function([idx], l6.predictions(dropout_active=False), givens=givens)
print "Load model parameters"
layers.set_param_values(l6, analysis['param_values'])
print "Create generators"
# set here which transforms to use to make predictions
augmentation_transforms = []
for zoom in [1 / 1.2, 1.0, 1.2]:
for angle in np.linspace(0, 360, 10, endpoint=False):
augmentation_transforms.append(ra.build_augmentation_transform(rotation=angle, zoom=zoom))
augmentation_transforms.append(ra.build_augmentation_transform(rotation=(angle + 180), zoom=zoom, shear=180)) # flipped
print " %d augmentation transforms." % len(augmentation_transforms)
augmented_data_gen_valid = ra.realtime_fixed_augmented_data_gen(valid_indices, 'train', augmentation_transforms=augmentation_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes, ds_transforms=ds_transforms, processor_class=ra.LoadAndProcessFixedPysexCenteringRescaling)
valid_gen = load_data.buffered_gen_mp(augmented_data_gen_valid, buffer_size=1)
augmented_data_gen_test = ra.realtime_fixed_augmented_data_gen(test_indices, 'test', augmentation_transforms=augmentation_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes, ds_transforms=ds_transforms, processor_class=ra.LoadAndProcessFixedPysexCenteringRescaling)
test_gen = load_data.buffered_gen_mp(augmented_data_gen_test, buffer_size=1)
approx_num_chunks_valid = int(np.ceil(num_valid * len(augmentation_transforms) / float(CHUNK_SIZE)))
approx_num_chunks_test = int(np.ceil(num_test * len(augmentation_transforms) / float(CHUNK_SIZE)))
print "Approximately %d chunks for the validation set" % approx_num_chunks_valid
print "Approximately %d chunks for the test set" % approx_num_chunks_test
if DO_VALID:
print
print "VALIDATION SET"
print "Compute predictions"
predictions_list = []
start_time = time.time()
for e, (chunk_data, chunk_length) in enumerate(valid_gen):
print "Chunk %d" % (e + 1)
xs_chunk = chunk_data
# need to transpose the chunks to move the 'channels' dimension up
xs_chunk = [x_chunk.transpose(0, 3, 1, 2) for x_chunk in xs_chunk]
print " load data onto GPU"
for x_shared, x_chunk in zip(xs_shared, xs_chunk):
x_shared.set_value(x_chunk)
num_batches_chunk = int(np.ceil(chunk_length / float(BATCH_SIZE)))
# make predictions, don't forget to cute off the zeros at the end
predictions_chunk_list = []
for b in xrange(num_batches_chunk):
if b % 1000 == 0:
print " batch %d/%d" % (b + 1, num_batches_chunk)
predictions = compute_output(b)
predictions_chunk_list.append(predictions)
predictions_chunk = np.vstack(predictions_chunk_list)
predictions_chunk = predictions_chunk[:chunk_length] # cut off zeros / padding
print " compute average over transforms"
predictions_chunk_avg = predictions_chunk.reshape(-1, len(augmentation_transforms), 37).mean(1)
predictions_list.append(predictions_chunk_avg)
time_since_start = time.time() - start_time
print " %s since start" % load_data.hms(time_since_start)
all_predictions = np.vstack(predictions_list)
print "Write predictions to %s" % target_path_valid
load_data.save_gz(target_path_valid, all_predictions)
print "Evaluate"
rmse_valid = analysis['losses_valid'][-1]
rmse_augmented = np.sqrt(np.mean((y_valid - all_predictions)**2))
print " MSE (last iteration):\t%.6f" % rmse_valid
print " MSE (augmented):\t%.6f" % rmse_augmented
if DO_TEST:
print
print "TEST SET"
print "Compute predictions"
predictions_list = []
start_time = time.time()
for e, (chunk_data, chunk_length) in enumerate(test_gen):
print "Chunk %d" % (e + 1)
xs_chunk = chunk_data
# need to transpose the chunks to move the 'channels' dimension up
xs_chunk = [x_chunk.transpose(0, 3, 1, 2) for x_chunk in xs_chunk]
print " load data onto GPU"
for x_shared, x_chunk in zip(xs_shared, xs_chunk):
x_shared.set_value(x_chunk)
num_batches_chunk = int(np.ceil(chunk_length / float(BATCH_SIZE)))
# make predictions, don't forget to cute off the zeros at the end
predictions_chunk_list = []
for b in xrange(num_batches_chunk):
if b % 1000 == 0:
print " batch %d/%d" % (b + 1, num_batches_chunk)
predictions = compute_output(b)
predictions_chunk_list.append(predictions)
predictions_chunk = np.vstack(predictions_chunk_list)
predictions_chunk = predictions_chunk[:chunk_length] # cut off zeros / padding
print " compute average over transforms"
predictions_chunk_avg = predictions_chunk.reshape(-1, len(augmentation_transforms), 37).mean(1)
predictions_list.append(predictions_chunk_avg)
time_since_start = time.time() - start_time
print " %s since start" % load_data.hms(time_since_start)
all_predictions = np.vstack(predictions_list)
print "Write predictions to %s" % target_path_test
load_data.save_gz(target_path_test, all_predictions)
print "Done!"
| bsd-3-clause |
chrismattmann/tika-similarity | sk_kmeans.py | 2 | 4409 | #!/usr/bin/env python2.7
#
# 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 tika import parser
import pandas as pd
from vector import Vector
from sklearn.cluster import KMeans
import argparse, os, json
def filterFiles(inputDir, acceptTypes):
filename_list = []
for root, dirnames, files in os.walk(inputDir):
dirnames[:] = [d for d in dirnames if not d.startswith('.')]
for filename in files:
if not filename.startswith('.'):
filename_list.append(os.path.join(root, filename))
filename_list = (filename for filename in filename_list if "metadata" in parser.from_file(filename))
if acceptTypes:
filename_list = (filename for filename in filename_list if str(parser.from_file(filename)['metadata']['Content-Type'].encode('utf-8').decode('utf-8')).split('/')[-1] in acceptTypes)
else:
print("Accepting all MIME Types.....")
return filename_list
if __name__ == "__main__":
argParser = argparse.ArgumentParser('k-means Clustering of documents based on metadata values')
argParser.add_argument('--inputDir', required=True, help='path to directory containing files')
argParser.add_argument('--outJSON', required=True, help='/path/to/clusters.json containing k-means cluster assignments')
argParser.add_argument('--Kvalue', help='number of clusters to find')
#argParser.add_argument('--findK', action='store_true', help='find the optimal value of K')
argParser.add_argument('--accept', nargs='+', type=str, help='Optional: compute similarity only on specified IANA MIME Type(s)')
args = argParser.parse_args()
# cluster for a particular value of K
# if args.inputDir and args.outJSON and args.findK:
if args.inputDir and args.outJSON and args.Kvalue:
list_of_points = []
for eachFile in filterFiles(args.inputDir, args.accept):
list_of_points.append(Vector(eachFile, parser.from_file(eachFile)["metadata"]))
list_of_Dicts = (point.features for point in list_of_points)
df = pd.DataFrame(list_of_Dicts)
df = df.fillna(0)
print(df.shape)
kmeans = KMeans(n_clusters=int(args.Kvalue),
init='k-means++',
max_iter=300, # k-means convergence
n_init=10, # find global minima
n_jobs=-2, # parallelize
)
labels = kmeans.fit_predict(df) # unsupervised (X, y=None)
print(labels) # kmeans.labels_
clusters = {}
for i in range(0, len(labels)):
node = { "metadata": json.dumps(list_of_points[i].features),
"name": list_of_points[i].filename.split('/')[-1],
"path": list_of_points[i].filename
}
try:
clusters[str(labels[i])].append(node)
except KeyError:
clusters[str(labels[i])] = []
clusters[str(labels[i])].append(node)
# generate clusters.JSON
with open(args.outJSON, "w") as jsonF:
json_data = {"name": "clusters"}
children = []
for key in clusters:
cluster_children = {"name": "cluster"+key, "children": clusters[key]}
children.append(cluster_children)
json_data["children"] = children
json.dump(json_data, jsonF)
# print matplotlib
# user chooses k => generates k
# find elbow
#kmeans.transform()
# String Length Of Course
# df.to_csv("bashhshs.csv", sep=',')
| apache-2.0 |
xiaoxiamii/scikit-learn | examples/preprocessing/plot_function_transformer.py | 161 | 1949 | """
=========================================================
Using FunctionTransformer to select columns
=========================================================
Shows how to use a function transformer in a pipeline. If you know your
dataset's first principle component is irrelevant for a classification task,
you can use the FunctionTransformer to select all but the first column of the
PCA transformed data.
"""
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer
def _generate_vector(shift=0.5, noise=15):
return np.arange(1000) + (np.random.rand(1000) - shift) * noise
def generate_dataset():
"""
This dataset is two lines with a slope ~ 1, where one has
a y offset of ~100
"""
return np.vstack((
np.vstack((
_generate_vector(),
_generate_vector() + 100,
)).T,
np.vstack((
_generate_vector(),
_generate_vector(),
)).T,
)), np.hstack((np.zeros(1000), np.ones(1000)))
def all_but_first_column(X):
return X[:, 1:]
def drop_first_component(X, y):
"""
Create a pipeline with PCA and the column selector and use it to
transform the dataset.
"""
pipeline = make_pipeline(
PCA(), FunctionTransformer(all_but_first_column),
)
X_train, X_test, y_train, y_test = train_test_split(X, y)
pipeline.fit(X_train, y_train)
return pipeline.transform(X_test), y_test
if __name__ == '__main__':
X, y = generate_dataset()
plt.scatter(X[:, 0], X[:, 1], c=y, s=50)
plt.show()
X_transformed, y_transformed = drop_first_component(*generate_dataset())
plt.scatter(
X_transformed[:, 0],
np.zeros(len(X_transformed)),
c=y_transformed,
s=50,
)
plt.show()
| bsd-3-clause |
cl4rke/scikit-learn | sklearn/svm/tests/test_sparse.py | 95 | 12156 | from nose.tools import assert_raises, assert_true, assert_false
import numpy as np
from scipy import sparse
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_equal)
from sklearn import datasets, svm, linear_model, base
from sklearn.datasets import make_classification, load_digits, make_blobs
from sklearn.svm.tests import test_svm
from sklearn.utils import ConvergenceWarning
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.utils.testing import assert_warns, assert_raise_message
# test sample 1
X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
X_sp = sparse.lil_matrix(X)
Y = [1, 1, 1, 2, 2, 2]
T = np.array([[-1, -1], [2, 2], [3, 2]])
true_result = [1, 2, 2]
# test sample 2
X2 = np.array([[0, 0, 0], [1, 1, 1], [2, 0, 0, ],
[0, 0, 2], [3, 3, 3]])
X2_sp = sparse.dok_matrix(X2)
Y2 = [1, 2, 2, 2, 3]
T2 = np.array([[-1, -1, -1], [1, 1, 1], [2, 2, 2]])
true_result2 = [1, 2, 3]
iris = datasets.load_iris()
# permute
rng = np.random.RandomState(0)
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
# sparsify
iris.data = sparse.csr_matrix(iris.data)
def check_svm_model_equal(dense_svm, sparse_svm, X_train, y_train, X_test):
dense_svm.fit(X_train.toarray(), y_train)
if sparse.isspmatrix(X_test):
X_test_dense = X_test.toarray()
else:
X_test_dense = X_test
sparse_svm.fit(X_train, y_train)
assert_true(sparse.issparse(sparse_svm.support_vectors_))
assert_true(sparse.issparse(sparse_svm.dual_coef_))
assert_array_almost_equal(dense_svm.support_vectors_,
sparse_svm.support_vectors_.toarray())
assert_array_almost_equal(dense_svm.dual_coef_, sparse_svm.dual_coef_.toarray())
if dense_svm.kernel == "linear":
assert_true(sparse.issparse(sparse_svm.coef_))
assert_array_almost_equal(dense_svm.coef_, sparse_svm.coef_.toarray())
assert_array_almost_equal(dense_svm.support_, sparse_svm.support_)
assert_array_almost_equal(dense_svm.predict(X_test_dense), sparse_svm.predict(X_test))
assert_array_almost_equal(dense_svm.decision_function(X_test_dense),
sparse_svm.decision_function(X_test))
assert_array_almost_equal(dense_svm.decision_function(X_test_dense),
sparse_svm.decision_function(X_test_dense))
assert_array_almost_equal(dense_svm.predict_proba(X_test_dense),
sparse_svm.predict_proba(X_test), 4)
msg = "cannot use sparse input in 'SVC' trained on dense data"
if sparse.isspmatrix(X_test):
assert_raise_message(ValueError, msg, dense_svm.predict, X_test)
def test_svc():
"""Check that sparse SVC gives the same result as SVC"""
# many class dataset:
X_blobs, y_blobs = make_blobs(n_samples=100, centers=10, random_state=0)
X_blobs = sparse.csr_matrix(X_blobs)
datasets = [[X_sp, Y, T], [X2_sp, Y2, T2],
[X_blobs[:80], y_blobs[:80], X_blobs[80:]],
[iris.data, iris.target, iris.data]]
kernels = ["linear", "poly", "rbf", "sigmoid"]
for dataset in datasets:
for kernel in kernels:
clf = svm.SVC(kernel=kernel, probability=True, random_state=0)
sp_clf = svm.SVC(kernel=kernel, probability=True, random_state=0)
check_svm_model_equal(clf, sp_clf, *dataset)
def test_unsorted_indices():
# test that the result with sorted and unsorted indices in csr is the same
# we use a subset of digits as iris, blobs or make_classification didn't
# show the problem
digits = load_digits()
X, y = digits.data[:50], digits.target[:50]
X_test = sparse.csr_matrix(digits.data[50:100])
X_sparse = sparse.csr_matrix(X)
coef_dense = svm.SVC(kernel='linear', probability=True,
random_state=0).fit(X, y).coef_
sparse_svc = svm.SVC(kernel='linear', probability=True,
random_state=0).fit(X_sparse, y)
coef_sorted = sparse_svc.coef_
# make sure dense and sparse SVM give the same result
assert_array_almost_equal(coef_dense, coef_sorted.toarray())
X_sparse_unsorted = X_sparse[np.arange(X.shape[0])]
X_test_unsorted = X_test[np.arange(X_test.shape[0])]
# make sure we scramble the indices
assert_false(X_sparse_unsorted.has_sorted_indices)
assert_false(X_test_unsorted.has_sorted_indices)
unsorted_svc = svm.SVC(kernel='linear', probability=True,
random_state=0).fit(X_sparse_unsorted, y)
coef_unsorted = unsorted_svc.coef_
# make sure unsorted indices give same result
assert_array_almost_equal(coef_unsorted.toarray(), coef_sorted.toarray())
assert_array_almost_equal(sparse_svc.predict_proba(X_test_unsorted),
sparse_svc.predict_proba(X_test))
def test_svc_with_custom_kernel():
kfunc = lambda x, y: safe_sparse_dot(x, y.T)
clf_lin = svm.SVC(kernel='linear').fit(X_sp, Y)
clf_mylin = svm.SVC(kernel=kfunc).fit(X_sp, Y)
assert_array_equal(clf_lin.predict(X_sp), clf_mylin.predict(X_sp))
def test_svc_iris():
# Test the sparse SVC with the iris dataset
for k in ('linear', 'poly', 'rbf'):
sp_clf = svm.SVC(kernel=k).fit(iris.data, iris.target)
clf = svm.SVC(kernel=k).fit(iris.data.toarray(), iris.target)
assert_array_almost_equal(clf.support_vectors_,
sp_clf.support_vectors_.toarray())
assert_array_almost_equal(clf.dual_coef_, sp_clf.dual_coef_.toarray())
assert_array_almost_equal(
clf.predict(iris.data.toarray()), sp_clf.predict(iris.data))
if k == 'linear':
assert_array_almost_equal(clf.coef_, sp_clf.coef_.toarray())
def test_sparse_decision_function():
#Test decision_function
#Sanity check, test that decision_function implemented in python
#returns the same as the one in libsvm
# multi class:
clf = svm.SVC(kernel='linear', C=0.1).fit(iris.data, iris.target)
dec = safe_sparse_dot(iris.data, clf.coef_.T) + clf.intercept_
assert_array_almost_equal(dec, clf.decision_function(iris.data))
# binary:
clf.fit(X, Y)
dec = np.dot(X, clf.coef_.T) + clf.intercept_
prediction = clf.predict(X)
assert_array_almost_equal(dec.ravel(), clf.decision_function(X))
assert_array_almost_equal(
prediction,
clf.classes_[(clf.decision_function(X) > 0).astype(np.int).ravel()])
expected = np.array([-1., -0.66, -1., 0.66, 1., 1.])
assert_array_almost_equal(clf.decision_function(X), expected, 2)
def test_error():
# Test that it gives proper exception on deficient input
# impossible value of C
assert_raises(ValueError, svm.SVC(C=-1).fit, X, Y)
# impossible value of nu
clf = svm.NuSVC(nu=0.0)
assert_raises(ValueError, clf.fit, X_sp, Y)
Y2 = Y[:-1] # wrong dimensions for labels
assert_raises(ValueError, clf.fit, X_sp, Y2)
clf = svm.SVC()
clf.fit(X_sp, Y)
assert_array_equal(clf.predict(T), true_result)
def test_linearsvc():
# Similar to test_SVC
clf = svm.LinearSVC(random_state=0).fit(X, Y)
sp_clf = svm.LinearSVC(random_state=0).fit(X_sp, Y)
assert_true(sp_clf.fit_intercept)
assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4)
assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=4)
assert_array_almost_equal(clf.predict(X), sp_clf.predict(X_sp))
clf.fit(X2, Y2)
sp_clf.fit(X2_sp, Y2)
assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4)
assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=4)
def test_linearsvc_iris():
# Test the sparse LinearSVC with the iris dataset
sp_clf = svm.LinearSVC(random_state=0).fit(iris.data, iris.target)
clf = svm.LinearSVC(random_state=0).fit(iris.data.toarray(), iris.target)
assert_equal(clf.fit_intercept, sp_clf.fit_intercept)
assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=1)
assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=1)
assert_array_almost_equal(
clf.predict(iris.data.toarray()), sp_clf.predict(iris.data))
# check decision_function
pred = np.argmax(sp_clf.decision_function(iris.data), 1)
assert_array_almost_equal(pred, clf.predict(iris.data.toarray()))
# sparsify the coefficients on both models and check that they still
# produce the same results
clf.sparsify()
assert_array_equal(pred, clf.predict(iris.data))
sp_clf.sparsify()
assert_array_equal(pred, sp_clf.predict(iris.data))
def test_weight():
# Test class weights
X_, y_ = make_classification(n_samples=200, n_features=100,
weights=[0.833, 0.167], random_state=0)
X_ = sparse.csr_matrix(X_)
for clf in (linear_model.LogisticRegression(),
svm.LinearSVC(random_state=0),
svm.SVC()):
clf.set_params(class_weight={0: 5})
clf.fit(X_[:180], y_[:180])
y_pred = clf.predict(X_[180:])
assert_true(np.sum(y_pred == y_[180:]) >= 11)
def test_sample_weights():
# Test weights on individual samples
clf = svm.SVC()
clf.fit(X_sp, Y)
assert_array_equal(clf.predict(X[2]), [1.])
sample_weight = [.1] * 3 + [10] * 3
clf.fit(X_sp, Y, sample_weight=sample_weight)
assert_array_equal(clf.predict(X[2]), [2.])
def test_sparse_liblinear_intercept_handling():
# Test that sparse liblinear honours intercept_scaling param
test_svm.test_dense_liblinear_intercept_handling(svm.LinearSVC)
def test_sparse_realdata():
# Test on a subset from the 20newsgroups dataset.
# This catchs some bugs if input is not correctly converted into
# sparse format or weights are not correctly initialized.
data = np.array([0.03771744, 0.1003567, 0.01174647, 0.027069])
indices = np.array([6, 5, 35, 31])
indptr = np.array(
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4])
X = sparse.csr_matrix((data, indices, indptr))
y = np.array(
[1., 0., 2., 2., 1., 1., 1., 2., 2., 0., 1., 2., 2.,
0., 2., 0., 3., 0., 3., 0., 1., 1., 3., 2., 3., 2.,
0., 3., 1., 0., 2., 1., 2., 0., 1., 0., 2., 3., 1.,
3., 0., 1., 0., 0., 2., 0., 1., 2., 2., 2., 3., 2.,
0., 3., 2., 1., 2., 3., 2., 2., 0., 1., 0., 1., 2.,
3., 0., 0., 2., 2., 1., 3., 1., 1., 0., 1., 2., 1.,
1., 3.])
clf = svm.SVC(kernel='linear').fit(X.toarray(), y)
sp_clf = svm.SVC(kernel='linear').fit(sparse.coo_matrix(X), y)
assert_array_equal(clf.support_vectors_, sp_clf.support_vectors_.toarray())
assert_array_equal(clf.dual_coef_, sp_clf.dual_coef_.toarray())
def test_sparse_svc_clone_with_callable_kernel():
# Test that the "dense_fit" is called even though we use sparse input
# meaning that everything works fine.
a = svm.SVC(C=1, kernel=lambda x, y: x * y.T, probability=True,
random_state=0)
b = base.clone(a)
b.fit(X_sp, Y)
pred = b.predict(X_sp)
b.predict_proba(X_sp)
dense_svm = svm.SVC(C=1, kernel=lambda x, y: np.dot(x, y.T),
probability=True, random_state=0)
pred_dense = dense_svm.fit(X, Y).predict(X)
assert_array_equal(pred_dense, pred)
# b.decision_function(X_sp) # XXX : should be supported
def test_timeout():
sp = svm.SVC(C=1, kernel=lambda x, y: x * y.T, probability=True,
random_state=0, max_iter=1)
assert_warns(ConvergenceWarning, sp.fit, X_sp, Y)
def test_consistent_proba():
a = svm.SVC(probability=True, max_iter=1, random_state=0)
proba_1 = a.fit(X, Y).predict_proba(X)
a = svm.SVC(probability=True, max_iter=1, random_state=0)
proba_2 = a.fit(X, Y).predict_proba(X)
assert_array_almost_equal(proba_1, proba_2)
| bsd-3-clause |
Kongsea/tensorflow | tensorflow/examples/learn/hdf5_classification.py | 75 | 2899 | # Copyright 2016 The TensorFlow 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.
"""Example of DNNClassifier for Iris plant dataset, hdf5 format."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
import tensorflow as tf
import h5py # pylint: disable=g-bad-import-order
X_FEATURE = 'x' # Name of the input feature.
def main(unused_argv):
# Load dataset.
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = model_selection.train_test_split(
iris.data, iris.target, test_size=0.2, random_state=42)
# Note that we are saving and load iris data as h5 format as a simple
# demonstration here.
h5f = h5py.File('/tmp/test_hdf5.h5', 'w')
h5f.create_dataset('X_train', data=x_train)
h5f.create_dataset('X_test', data=x_test)
h5f.create_dataset('y_train', data=y_train)
h5f.create_dataset('y_test', data=y_test)
h5f.close()
h5f = h5py.File('/tmp/test_hdf5.h5', 'r')
x_train = np.array(h5f['X_train'])
x_test = np.array(h5f['X_test'])
y_train = np.array(h5f['y_train'])
y_test = np.array(h5f['y_test'])
# Build 3 layer DNN with 10, 20, 10 units respectively.
feature_columns = [
tf.feature_column.numeric_column(
X_FEATURE, shape=np.array(x_train).shape[1:])]
classifier = tf.estimator.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
# Train.
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
classifier.train(input_fn=train_input_fn, steps=200)
# Predict.
test_input_fn = tf.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
predictions = classifier.predict(input_fn=test_input_fn)
y_predicted = np.array(list(p['class_ids'] for p in predictions))
y_predicted = y_predicted.reshape(np.array(y_test).shape)
# Score with sklearn.
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy (sklearn): {0:f}'.format(score))
# Score with tensorflow.
scores = classifier.evaluate(input_fn=test_input_fn)
print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
if __name__ == '__main__':
tf.app.run()
| apache-2.0 |
pmediano/ComputationalNeurodynamics | Fall2016/Exercise_1/Solutions/IzNeuronRK4.py | 1 | 1897 | """
Computational Neurodynamics
Exercise 1
Simulates Izhikevich's neuron model using the Runge-Kutta 4 method.
Parameters for regular spiking, fast spiking and bursting
neurons extracted from:
http://www.izhikevich.org/publications/spikes.htm
(C) Murray Shanahan et al, 2016
"""
import numpy as np
import matplotlib.pyplot as plt
# Create time points
Tmin = 0
Tmax = 200 # Simulation time
dt = 0.01 # Step size
T = np.arange(Tmin, Tmax+dt, dt)
# Base current
I = 10
## Parameters of Izhikevich's model (regular spiking)
a = 0.02
b = 0.2
c = -65
d = 8
## Parameters of Izhikevich's model (fast spiking)
# a = 0.02
# b = 0.25
# c = -65
# d = 2
## Parameters of Izhikevich's model (bursting)
# a = 0.02
# b = 0.2
# c = -50
# d = 2
## Make a state vector that has a (v, u) pair for each timestep
s = np.zeros((len(T), 2))
## Initial values
s[0, 0] = -65
s[0, 1] = -1
# Note that s1[0] is v, s1[1] is u. This is Izhikevich equation in vector form
def s_dt(s1, I):
v_dt = 0.04*(s1[0]**2) + 5*s1[0] + 140 - s1[1] + I
u_dt = a*(b*s1[0] - s1[1])
return np.array([v_dt, u_dt])
## SIMULATE
for t in range(len(T)-1):
# Calculate the four constants of Runge-Kutta method
k_1 = s_dt(s[t], I)
k_2 = s_dt(s[t] + 0.5*dt*k_1, I)
k_3 = s_dt(s[t] + 0.5*dt*k_2, I)
k_4 = s_dt(s[t] + dt*k_3, I)
s[t+1] = s[t] + (1.0/6)*dt*(k_1 + 2*k_2 + 2*k_3 + k_4)
# Reset the neuron if it has spiked
if s[t+1, 0] >= 30:
s[t, 0] = 30 # Add a Dirac pulse for visualisation
s[t+1, 0] = c # Reset to resting potential
s[t+1, 1] += d # Update recovery variable
v = s[:, 0]
u = s[:, 1]
## Plot the membrane potential
plt.subplot(211)
plt.plot(T, v)
plt.xlabel('Time (ms)')
plt.ylabel('Membrane potential v (mV)')
plt.title('Izhikevich Neuron')
# Plot the reset variable
plt.subplot(212)
plt.plot(T, u)
plt.xlabel('Time (ms)')
plt.ylabel('Reset variable u')
plt.show()
| gpl-3.0 |
craigcitro/pydatalab | tests/bigquery/schema_tests.py | 6 | 4284 | # Copyright 2015 Google Inc. 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.
from __future__ import absolute_import
from __future__ import unicode_literals
import collections
import pandas
import sys
import unittest
import google.datalab.bigquery
import google.datalab.utils
class TestCases(unittest.TestCase):
def test_schema_from_dataframe(self):
df = TestCases._create_data_frame()
result = google.datalab.bigquery.Schema.from_data(df)
self.assertEqual(google.datalab.bigquery.Schema.from_data(TestCases._create_inferred_schema()),
result)
def test_schema_from_data(self):
variant1 = [
3,
2.0,
True,
['cow', 'horse', [0, []]]
]
variant2 = collections.OrderedDict()
variant2['Column1'] = 3
variant2['Column2'] = 2.0
variant2['Column3'] = True
variant2['Column4'] = collections.OrderedDict()
variant2['Column4']['Column1'] = 'cow'
variant2['Column4']['Column2'] = 'horse'
variant2['Column4']['Column3'] = collections.OrderedDict()
variant2['Column4']['Column3']['Column1'] = 0
variant2['Column4']['Column3']['Column2'] = collections.OrderedDict()
master = [
{'name': 'Column1', 'type': 'INTEGER'},
{'name': 'Column2', 'type': 'FLOAT'},
{'name': 'Column3', 'type': 'BOOLEAN'},
{'name': 'Column4', 'type': 'RECORD', 'fields': [
{'name': 'Column1', 'type': 'STRING'},
{'name': 'Column2', 'type': 'STRING'},
{'name': 'Column3', 'type': 'RECORD', 'fields': [
{'name': 'Column1', 'type': 'INTEGER'},
{'name': 'Column2', 'type': 'RECORD', 'fields': []}
]}
]}
]
schema_master = google.datalab.bigquery.Schema(master)
with self.assertRaises(Exception) as error1:
google.datalab.bigquery.Schema.from_data(variant1)
if sys.version_info[0] == 3:
self.assertEquals('Cannot create a schema from heterogeneous list [3, 2.0, True, ' +
'[\'cow\', \'horse\', [0, []]]]; perhaps you meant to use ' +
'Schema.from_record?', str(error1.exception))
else:
self.assertEquals('Cannot create a schema from heterogeneous list [3, 2.0, True, ' +
'[u\'cow\', u\'horse\', [0, []]]]; perhaps you meant to use ' +
'Schema.from_record?', str(error1.exception))
schema3 = google.datalab.bigquery.Schema.from_data([variant1])
schema4 = google.datalab.bigquery.Schema.from_data([variant2])
schema5 = google.datalab.bigquery.Schema.from_data(master)
schema6 = google.datalab.bigquery.Schema.from_record(variant1)
schema7 = google.datalab.bigquery.Schema.from_record(variant2)
self.assertEquals(schema_master, schema3, 'schema inferred from list of lists with from_data')
self.assertEquals(schema_master, schema4, 'schema inferred from list of dicts with from_data')
self.assertEquals(schema_master, schema5, 'schema inferred from BQ schema list with from_data')
self.assertEquals(schema_master, schema6, 'schema inferred from list with from_record')
self.assertEquals(schema_master, schema7, 'schema inferred from dict with from_record')
@staticmethod
def _create_data_frame():
data = {
'some': [
0, 1, 2, 3
],
'column': [
'r0', 'r1', 'r2', 'r3'
],
'headers': [
10.0, 10.0, 10.0, 10.0
]
}
return pandas.DataFrame(data)
@staticmethod
def _create_inferred_schema(extra_field=None):
schema = [
{'name': 'some', 'type': 'INTEGER'},
{'name': 'column', 'type': 'STRING'},
{'name': 'headers', 'type': 'FLOAT'},
]
if extra_field:
schema.append({'name': extra_field, 'type': 'INTEGER'})
return schema
| apache-2.0 |
joelfrederico/SciSalt | scisalt/qt/mplwidget.py | 1 | 13557 | from PyQt4 import QtGui
from PyQt4 import QtCore
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as _FigureCanvas
from matplotlib.backends.backend_qt4 import NavigationToolbar2QT as _NavigationToolbar
import matplotlib as _mpl
import numpy as _np
from .Rectangle import Rectangle
import pdb
import traceback
import logging
loggerlevel = logging.DEBUG
logger = logging.getLogger(__name__)
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
def _fromUtf8(s):
return s
try:
_encoding = QtGui.QApplication.UnicodeUTF8
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig, _encoding)
except AttributeError:
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig)
class Slider_and_Text(QtGui.QWidget):
valueChanged = QtCore.pyqtSignal(int)
sliderReleased = QtCore.pyqtSignal(int)
def __init__(self, parent=None):
QtGui.QWidget.__init__(self)
self.setMaximumHeight(40)
# Enable tracking by default
self._tracking = True
self.hLayout = QtGui.QHBoxLayout()
self.slider = QtGui.QSlider()
self.leftbutton = QtGui.QPushButton()
self.leftbutton.setText("<")
sizePolicy = QtGui.QSizePolicy(QtGui.QSizePolicy.Minimum, QtGui.QSizePolicy.Minimum)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.leftbutton.sizePolicy().hasHeightForWidth())
# self.leftbutton.setSizePolicy(sizePolicy)
self.leftbutton.clicked.connect(self._subone)
self.rightbutton = QtGui.QPushButton()
self.rightbutton.setText(">")
sizePolicy = QtGui.QSizePolicy(QtGui.QSizePolicy.Minimum, QtGui.QSizePolicy.Minimum)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.rightbutton.sizePolicy().hasHeightForWidth())
# self.rightbutton.setSizePolicy(sizePolicy)
self.rightbutton.clicked.connect(self._addone)
self.v = QtGui.QIntValidator()
self.box = QtGui.QLineEdit()
self.box.setValidator(self.v)
sizePolicy = QtGui.QSizePolicy(QtGui.QSizePolicy.Minimum, QtGui.QSizePolicy.Minimum)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.box.sizePolicy().hasHeightForWidth())
# self.box.setSizePolicy(sizePolicy)
self.hLayout.addWidget(self.leftbutton)
self.hLayout.addWidget(self.slider)
self.hLayout.addWidget(self.box)
self.hLayout.addWidget(self.rightbutton)
self.setLayout(self.hLayout)
self.slider.valueChanged.connect(self._sliderChanged)
self.box.editingFinished.connect(self._textChanged)
self.setOrientation(QtCore.Qt.Horizontal)
# Connect release so tracking works as expected
self.slider.sliderReleased.connect(self._sliderReleased)
def _addone(self):
self.value = self.value + 1
self.valueChanged.emit(self.value)
def _subone(self):
self.value = self.value - 1
self.valueChanged.emit(self.value)
def _sliderReleased(self):
print('Released')
self.sliderReleased.emit(self.slider.value)
def setTracking(self, val):
print('Tracking set to {}'.format(val))
self._tracking = val
def setMaximum(self, val):
self.slider.setMaximum(val)
self.v.setRange(self.slider.minimum(), self.slider.maximum())
self.box.setValidator(self.v)
def setMinimum(self, val):
self.slider.setMinimum(val)
self.v.setRange(self.slider.minimum(), self.slider.maximum())
self.box.setValidator(self.v)
def _sliderChanged(self, val):
self.box.setText(str(val))
if self._tracking:
try:
self.slider.sliderReleased.disconnect()
except:
pass
self.valueChanged.emit(val)
else:
try:
self.slider.sliderReleased.disconnect()
except:
pass
self.slider.sliderReleased.connect(self._sliderChanged_notracking)
def _sliderChanged_notracking(self):
val = self.slider.value()
# print('Value to be emitted is {}'.format(val))
self.valueChanged.emit(val)
def _textChanged(self):
val = self.box.text()
self.slider.setValue(int(val))
self._sliderChanged_notracking()
def setOrientation(self, *args, **kwargs):
self.slider.setOrientation(*args, **kwargs)
def _getValue(self):
return self.slider.value()
def _setValue(self, val):
self.slider.setValue(val)
self.box.setText(str(val))
value = property(_getValue, _setValue)
def setValue(self, val):
self.slider.setValue(val)
self.box.setText(str(val))
# self.valueChanged.emit(val)
class Mpl_Plot(_FigureCanvas):
def __init__(self, parent=None):
# Initialize things
self.fig = _mpl.figure.Figure()
_FigureCanvas.__init__(self, self.fig)
_FigureCanvas.setSizePolicy(self, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Expanding)
_FigureCanvas.updateGeometry(self)
# Create axes
self.ax = self.fig.add_subplot(111)
def plot(self, *args, **kwargs):
self.ax.clear()
self.ax.plot(*args, **kwargs)
self.ax.ticklabel_format(style='sci', scilimits=(0, 0), axis='y')
self.ax.figure.canvas.draw()
class Mpl_Image(QtGui.QWidget):
# Signal for when the rectangle is changed
rectChanged = QtCore.pyqtSignal(Rectangle)
def __init__(self, parent=None, rectbool = True, toolbarbool=False, image=None):
# Initialize things
QtGui.QWidget.__init__(self)
self.rectbool = rectbool
self._clim_min = 0
self._clim_max = 3600
self._pressed = False
# Add a vertical layout
self.vLayout = QtGui.QVBoxLayout()
# Add a figure
self.fig = _mpl.figure.Figure()
# Add a canvas containing the fig
self.canvas = _FigureCanvas(self.fig)
_FigureCanvas.setSizePolicy(self.canvas, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Expanding)
_FigureCanvas.updateGeometry(self.canvas)
# Setup the layout
if toolbarbool:
self.toolbar = _NavigationToolbar(self.canvas, self)
self.toolbar.setMaximumHeight(20)
self.vLayout.addWidget(self.toolbar)
self.vLayout.addWidget(self.canvas)
self.setLayout(self.vLayout)
# Create axes
self.ax = self.fig.add_subplot(111)
# Include rectangle functionality
if rectbool:
self.fig.canvas.mpl_connect('button_press_event', self.on_press)
self.fig.canvas.mpl_connect('button_release_event', self.on_release)
self.Rectangle = Rectangle(
x = -10 ,
y = 0 ,
width = 0 ,
height = 3 ,
axes = self.ax
)
# Add image
self.image = image
def _get_img(self):
return self._image
def _set_img(self, image):
self.ax.clear()
self._image = image
if image is not None:
self._imgplot = self.ax.imshow(image, interpolation='none')
if self.rectbool:
self.ax.add_patch(self.Rectangle.get_rect())
# imagemax = _np.max(_np.max(image))
self.set_clim(self._clim_min, self._clim_max)
image = property(_get_img, _set_img)
def set_clim(self, clim_min, clim_max):
if self.image is not None:
self._clim_min = clim_min
self._clim_max = clim_max
self._imgplot.set_clim(clim_min, clim_max)
self.ax.figure.canvas.draw()
def on_press(self, event):
if self.toolbar._active is None:
self._pressed = True
self.x0 = event.xdata
self.y0 = event.ydata
logger.log(level=loggerlevel, msg='Pressed: x0: {}, y0: {}'.format(self.x0, self.y0))
def on_release(self, event):
if self._pressed:
self._pressed = False
print('release')
self.x1 = event.xdata
self.y1 = event.ydata
width = self.x1 - self.x0
height = self.y1 - self.y0
logger.log(level=loggerlevel, msg='Released: x0: {}, y0: {}, x1: {}, y1: {}, width: {}, height: {}'.format(
self.x0 ,
self.y0 ,
self.x1 ,
self.y1 ,
width ,
height
)
)
self.Rectangle.set_xy((self.x0, self.y0))
self.Rectangle.set_width(width)
self.Rectangle.set_height(height)
self.ax.figure.canvas.draw()
self.rectChanged.emit(self.Rectangle)
# print(self.rect)
def zoom_rect(self, border=None, border_px=None):
# ======================================
# Get x coordinates
# ======================================
x0 = self.Rectangle.get_x()
width = self.Rectangle.get_width()
x1 = x0+width
# ======================================
# Get y coordinates
# ======================================
y0 = self.Rectangle.get_y()
height = self.Rectangle.get_height()
y1 = y0+height
# ======================================
# Validate borders
# ======================================
if (border_px is None) and (border is not None):
xborder = border[0]*width
yborder = border[1]*height
elif (border_px is not None) and (border is None):
xborder = border_px[0]
yborder = border_px[1]
elif (border_px is None) and (border is None):
raise IOError('No border info specified!')
elif (border_px is not None) and (border is not None):
raise IOError('Too much border info specified, both border_px and border!')
else:
raise IOError('End of the line!')
# ======================================
# Add borders
# ======================================
x0 = x0 - xborder
x1 = x1 + xborder
y0 = y0 - yborder
y1 = y1 + yborder
# ======================================
# Validate coordinates to prevent
# unPythonic crash
# ======================================
if not ((0 <= x0 and x0 <= self.image.shape[1]) and (0 <= x1 and x1 <= self.image.shape[1])):
print('X issue')
print('Requested: x=({}, {})'.format(x0, x1))
x0 = 0
x1 = self.image.shape[1]
if not ((0 <= y0 and y0 <= self.image.shape[0]) and (0 <= y1 and y1 <= self.image.shape[0])):
print('y issue')
print('Requested: y=({}, {})'.format(y0, y1))
y0 = 0
y1 = self.image.shape[0]
# ======================================
# Set viewable area
# ======================================
self.ax.set_xlim(x0, x1)
self.ax.set_ylim(y0, y1)
# ======================================
# Redraw canvas to show updates
# ======================================
self.ax.figure.canvas.draw()
class Mpl_Image_Plus_Slider(QtGui.QWidget):
# def __init__(self, parent=None, **kwargs):
def __init__(self, parent=None, **kwargs):
# Initialize self as a widget
QtGui.QWidget.__init__(self, parent)
# Add a vertical layout with parent self
self.vLayout = QtGui.QVBoxLayout(self)
self.vLayout.setObjectName(_fromUtf8("vLayout"))
# Add an Mpl_Image widget to vLayout,
# save it to self._img
# Pass arguments through to Mpl_Image.
self._img = Mpl_Image(parent=parent, toolbarbool=True, **kwargs)
self._img.setObjectName(_fromUtf8("_img"))
self.vLayout.addWidget(self._img)
# Add a slider to vLayout,
# save it to self.max_slider
# self.max_slider = QtGui.QSlider(self)
self.max_slider = Slider_and_Text(self)
self.max_slider.setObjectName(_fromUtf8("max_slider"))
self.max_slider.setOrientation(QtCore.Qt.Horizontal)
self.vLayout.addWidget(self.max_slider)
# Setup slider to work with _img's clims
self.max_slider.valueChanged.connect(lambda val: self.set_clim(0, val))
def _get_image(self):
return self._img.image
def _set_image(self, image):
self._img.image = image
maximage = _np.max(_np.max(image))
self.max_slider.setMaximum(maximage)
image = property(_get_image, _set_image)
def _get_ax(self):
return self._img.ax
ax = property(_get_ax)
def _get_Rectangle(self):
return self._img.Rectangle
# def _set_rect(self, rect):
# self._img.rect(rect)
Rectangle = property(_get_Rectangle)
def zoom_rect(self, border=None, border_px=None):
self._img.zoom_rect(border, border_px)
def set_clim(self, *args, **kwargs):
self._img.set_clim(*args, **kwargs)
def setSliderValue(self, val):
self.max_slider.setValue(val)
| mit |
jonyroda97/redbot-amigosprovaveis | lib/matplotlib/units.py | 2 | 6084 | """
The classes here provide support for using custom classes with
matplotlib, e.g., those that do not expose the array interface but know
how to convert themselves to arrays. It also supports classes with
units and units conversion. Use cases include converters for custom
objects, e.g., a list of datetime objects, as well as for objects that
are unit aware. We don't assume any particular units implementation;
rather a units implementation must provide the register with the Registry
converter dictionary and a ConversionInterface. For example,
here is a complete implementation which supports plotting with native
datetime objects::
import matplotlib.units as units
import matplotlib.dates as dates
import matplotlib.ticker as ticker
import datetime
class DateConverter(units.ConversionInterface):
@staticmethod
def convert(value, unit, axis):
'convert value to a scalar or array'
return dates.date2num(value)
@staticmethod
def axisinfo(unit, axis):
'return major and minor tick locators and formatters'
if unit!='date': return None
majloc = dates.AutoDateLocator()
majfmt = dates.AutoDateFormatter(majloc)
return AxisInfo(majloc=majloc,
majfmt=majfmt,
label='date')
@staticmethod
def default_units(x, axis):
'return the default unit for x or None'
return 'date'
# finally we register our object type with a converter
units.registry[datetime.date] = DateConverter()
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from matplotlib.cbook import iterable, is_numlike, safe_first_element
import numpy as np
class AxisInfo(object):
"""information to support default axis labeling and tick labeling, and
default limits"""
def __init__(self, majloc=None, minloc=None,
majfmt=None, minfmt=None, label=None,
default_limits=None):
"""
majloc and minloc: TickLocators for the major and minor ticks
majfmt and minfmt: TickFormatters for the major and minor ticks
label: the default axis label
default_limits: the default min, max of the axis if no data is present
If any of the above are None, the axis will simply use the default
"""
self.majloc = majloc
self.minloc = minloc
self.majfmt = majfmt
self.minfmt = minfmt
self.label = label
self.default_limits = default_limits
class ConversionInterface(object):
"""
The minimal interface for a converter to take custom instances (or
sequences) and convert them to values mpl can use
"""
@staticmethod
def axisinfo(unit, axis):
'return an units.AxisInfo instance for axis with the specified units'
return None
@staticmethod
def default_units(x, axis):
'return the default unit for x or None for the given axis'
return None
@staticmethod
def convert(obj, unit, axis):
"""
convert obj using unit for the specified axis. If obj is a sequence,
return the converted sequence. The output must be a sequence of
scalars that can be used by the numpy array layer
"""
return obj
@staticmethod
def is_numlike(x):
"""
The matplotlib datalim, autoscaling, locators etc work with
scalars which are the units converted to floats given the
current unit. The converter may be passed these floats, or
arrays of them, even when units are set. Derived conversion
interfaces may opt to pass plain-ol unitless numbers through
the conversion interface and this is a helper function for
them.
"""
if iterable(x):
for thisx in x:
return is_numlike(thisx)
else:
return is_numlike(x)
class Registry(dict):
"""
register types with conversion interface
"""
def __init__(self):
dict.__init__(self)
self._cached = {}
def get_converter(self, x):
'get the converter interface instance for x, or None'
if not len(self):
return None # nothing registered
# DISABLED idx = id(x)
# DISABLED cached = self._cached.get(idx)
# DISABLED if cached is not None: return cached
converter = None
classx = getattr(x, '__class__', None)
if classx is not None:
converter = self.get(classx)
if isinstance(x, np.ndarray) and x.size:
xravel = x.ravel()
try:
# pass the first value of x that is not masked back to
# get_converter
if not np.all(xravel.mask):
# some elements are not masked
converter = self.get_converter(
xravel[np.argmin(xravel.mask)])
return converter
except AttributeError:
# not a masked_array
# Make sure we don't recurse forever -- it's possible for
# ndarray subclasses to continue to return subclasses and
# not ever return a non-subclass for a single element.
next_item = xravel[0]
if (not isinstance(next_item, np.ndarray) or
next_item.shape != x.shape):
converter = self.get_converter(next_item)
return converter
if converter is None:
try:
thisx = safe_first_element(x)
except (TypeError, StopIteration):
pass
else:
if classx and classx != getattr(thisx, '__class__', None):
converter = self.get_converter(thisx)
return converter
# DISABLED self._cached[idx] = converter
return converter
registry = Registry()
| gpl-3.0 |
preprocessed-connectomes-project/quality-assessment-protocol | scripts/qap_check_output_csv.py | 1 | 1302 | #!/usr/bin/env python
def main():
import os
import argparse
from qap.script_utils import check_csv_missing_subs, csv_to_pandas_df, \
write_inputs_dict_to_yaml_file, read_yml_file
from qap.qap_utils import raise_smart_exception
parser = argparse.ArgumentParser()
parser.add_argument("output_csv", type=str,
help="the main output directory of the QAP run "
"which contains the participant directories")
parser.add_argument("data_config", type=str,
help="the main output directory of the QAP run "
"which contains the participant directories")
parser.add_argument("data_type", type=str,
help="the main output directory of the QAP run "
"which contains the participant directories")
args = parser.parse_args()
csv_df = csv_to_pandas_df(args.output_csv)
data_dict = read_yml_file(args.data_config)
new_dict = check_csv_missing_subs(csv_df, data_dict, args.data_type)
if new_dict:
out_file = os.path.join(os.getcwd(),
"missing_%s_data.yml" % args.data_type)
write_inputs_dict_to_yaml_file(new_dict, out_file)
if __name__ == "__main__":
main() | bsd-3-clause |
xyguo/scikit-learn | examples/cross_decomposition/plot_compare_cross_decomposition.py | 55 | 4761 | """
===================================
Compare cross decomposition methods
===================================
Simple usage of various cross decomposition algorithms:
- PLSCanonical
- PLSRegression, with multivariate response, a.k.a. PLS2
- PLSRegression, with univariate response, a.k.a. PLS1
- CCA
Given 2 multivariate covarying two-dimensional datasets, X, and Y,
PLS extracts the 'directions of covariance', i.e. the components of each
datasets that explain the most shared variance between both datasets.
This is apparent on the **scatterplot matrix** display: components 1 in
dataset X and dataset Y are maximally correlated (points lie around the
first diagonal). This is also true for components 2 in both dataset,
however, the correlation across datasets for different components is
weak: the point cloud is very spherical.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cross_decomposition import PLSCanonical, PLSRegression, CCA
###############################################################################
# Dataset based latent variables model
n = 500
# 2 latents vars:
l1 = np.random.normal(size=n)
l2 = np.random.normal(size=n)
latents = np.array([l1, l1, l2, l2]).T
X = latents + np.random.normal(size=4 * n).reshape((n, 4))
Y = latents + np.random.normal(size=4 * n).reshape((n, 4))
X_train = X[:n / 2]
Y_train = Y[:n / 2]
X_test = X[n / 2:]
Y_test = Y[n / 2:]
print("Corr(X)")
print(np.round(np.corrcoef(X.T), 2))
print("Corr(Y)")
print(np.round(np.corrcoef(Y.T), 2))
###############################################################################
# Canonical (symmetric) PLS
# Transform data
# ~~~~~~~~~~~~~~
plsca = PLSCanonical(n_components=2)
plsca.fit(X_train, Y_train)
X_train_r, Y_train_r = plsca.transform(X_train, Y_train)
X_test_r, Y_test_r = plsca.transform(X_test, Y_test)
# Scatter plot of scores
# ~~~~~~~~~~~~~~~~~~~~~~
# 1) On diagonal plot X vs Y scores on each components
plt.figure(figsize=(12, 8))
plt.subplot(221)
plt.plot(X_train_r[:, 0], Y_train_r[:, 0], "ob", label="train")
plt.plot(X_test_r[:, 0], Y_test_r[:, 0], "or", label="test")
plt.xlabel("x scores")
plt.ylabel("y scores")
plt.title('Comp. 1: X vs Y (test corr = %.2f)' %
np.corrcoef(X_test_r[:, 0], Y_test_r[:, 0])[0, 1])
plt.xticks(())
plt.yticks(())
plt.legend(loc="best")
plt.subplot(224)
plt.plot(X_train_r[:, 1], Y_train_r[:, 1], "ob", label="train")
plt.plot(X_test_r[:, 1], Y_test_r[:, 1], "or", label="test")
plt.xlabel("x scores")
plt.ylabel("y scores")
plt.title('Comp. 2: X vs Y (test corr = %.2f)' %
np.corrcoef(X_test_r[:, 1], Y_test_r[:, 1])[0, 1])
plt.xticks(())
plt.yticks(())
plt.legend(loc="best")
# 2) Off diagonal plot components 1 vs 2 for X and Y
plt.subplot(222)
plt.plot(X_train_r[:, 0], X_train_r[:, 1], "*b", label="train")
plt.plot(X_test_r[:, 0], X_test_r[:, 1], "*r", label="test")
plt.xlabel("X comp. 1")
plt.ylabel("X comp. 2")
plt.title('X comp. 1 vs X comp. 2 (test corr = %.2f)'
% np.corrcoef(X_test_r[:, 0], X_test_r[:, 1])[0, 1])
plt.legend(loc="best")
plt.xticks(())
plt.yticks(())
plt.subplot(223)
plt.plot(Y_train_r[:, 0], Y_train_r[:, 1], "*b", label="train")
plt.plot(Y_test_r[:, 0], Y_test_r[:, 1], "*r", label="test")
plt.xlabel("Y comp. 1")
plt.ylabel("Y comp. 2")
plt.title('Y comp. 1 vs Y comp. 2 , (test corr = %.2f)'
% np.corrcoef(Y_test_r[:, 0], Y_test_r[:, 1])[0, 1])
plt.legend(loc="best")
plt.xticks(())
plt.yticks(())
plt.show()
###############################################################################
# PLS regression, with multivariate response, a.k.a. PLS2
n = 1000
q = 3
p = 10
X = np.random.normal(size=n * p).reshape((n, p))
B = np.array([[1, 2] + [0] * (p - 2)] * q).T
# each Yj = 1*X1 + 2*X2 + noize
Y = np.dot(X, B) + np.random.normal(size=n * q).reshape((n, q)) + 5
pls2 = PLSRegression(n_components=3)
pls2.fit(X, Y)
print("True B (such that: Y = XB + Err)")
print(B)
# compare pls2.coef_ with B
print("Estimated B")
print(np.round(pls2.coef_, 1))
pls2.predict(X)
###############################################################################
# PLS regression, with univariate response, a.k.a. PLS1
n = 1000
p = 10
X = np.random.normal(size=n * p).reshape((n, p))
y = X[:, 0] + 2 * X[:, 1] + np.random.normal(size=n * 1) + 5
pls1 = PLSRegression(n_components=3)
pls1.fit(X, y)
# note that the number of components exceeds 1 (the dimension of y)
print("Estimated betas")
print(np.round(pls1.coef_, 1))
###############################################################################
# CCA (PLS mode B with symmetric deflation)
cca = CCA(n_components=2)
cca.fit(X_train, Y_train)
X_train_r, Y_train_r = plsca.transform(X_train, Y_train)
X_test_r, Y_test_r = plsca.transform(X_test, Y_test)
| bsd-3-clause |
colin2328/asciiclass | lectures/lec6/match-loop.py | 3 | 2094 | import csv
from sklearn import tree
import editdist
import re
def string_match_score(p1,p2,field):
s1 = p1[field]
s2 = p2[field]
return editdist.distance(s1.lower(),s2.lower())/float(len(s1))
def jaccard_score(p1,p2,field):
name1 = p1[field]
name2 = p2[field]
set1 = set(name1.lower().split())
set2 = set(name2.lower().split())
c = set1.intersection(set2)
return float(len(c)) / (len(set1) + len(set2) - len(c))
def price_score(p1,p2,field):
price1 = p1[field]
if (len(price1) == 0): return 10000
price2 = p2[field]
if (len(price2) == 0): return 10000
price1 = re.sub('[\$,]', '', price1)
price2 = re.sub('[\$,]', '', price2)
price1 = float(price1)
price2 = float(price2)
return abs(price1 - price2)
print "Loading Data"
abtReader = csv.DictReader(open("Abt.csv","rU"))
buyReader = csv.DictReader(open("Buy.csv","rU"))
gtLines = csv.DictReader(open("abt_buy_perfectMapping.csv","rU"))
gtBuyMap = {}
gtAbtMap = {}
abtAr = []
buyAr = []
for r in abtReader:
abtAr.append(r)
for r in buyReader:
buyAr.append(r)
for r in gtLines:
gtAbtMap[r["idAbt"]] = r["idBuy"]
gtBuyMap[r["idBuy"]] = r["idAbt"]
for loop in range(0,10,1):
falsePos = 0
truePos = 0
falseNeg = 0
trueNeg = 0
thresh = float(loop)/10.0
for r1 in buyAr:
bestMatch = 0
bestVal = []
j = 0
for r2 in abtAr:
s = jaccard_score(r1,r2,"name")
if (s > bestMatch):
bestMatch = s
bestVal = r2
if (bestMatch > thresh):
# print "Best match: ",r1["name"],bestVal["name"],"score=",bestMatch
if (gtBuyMap[r1["id"]] == bestVal["id"]):
truePos = truePos + 1
else:
falsePos = falsePos + 1
precision = truePos / float(truePos + falsePos)
recall = truePos / float(len(buyAr))
fmeas = (2.0 * precision * recall) / (precision + recall)
print "THRESH = ",thresh,"TP = ",truePos,"FP = ",falsePos,"PREC = ",precision,"RECALL = ",recall,"F = ",fmeas
| mit |
Obus/scikit-learn | doc/datasets/mldata_fixture.py | 367 | 1183 | """Fixture module to skip the datasets loading when offline
Mock urllib2 access to mldata.org and create a temporary data folder.
"""
from os import makedirs
from os.path import join
import numpy as np
import tempfile
import shutil
from sklearn import datasets
from sklearn.utils.testing import install_mldata_mock
from sklearn.utils.testing import uninstall_mldata_mock
def globs(globs):
# Create a temporary folder for the data fetcher
global custom_data_home
custom_data_home = tempfile.mkdtemp()
makedirs(join(custom_data_home, 'mldata'))
globs['custom_data_home'] = custom_data_home
return globs
def setup_module():
# setup mock urllib2 module to avoid downloading from mldata.org
install_mldata_mock({
'mnist-original': {
'data': np.empty((70000, 784)),
'label': np.repeat(np.arange(10, dtype='d'), 7000),
},
'iris': {
'data': np.empty((150, 4)),
},
'datasets-uci-iris': {
'double0': np.empty((150, 4)),
'class': np.empty((150,)),
},
})
def teardown_module():
uninstall_mldata_mock()
shutil.rmtree(custom_data_home)
| bsd-3-clause |
amozie/amozie | studzie/keras_gym/mountain_car_v0.py | 1 | 2577 | import numpy as np
import matplotlib.pyplot as plt
import gym
import time
import copy
from keras.models import Sequential, Model
from keras.layers import Dense, Activation, Flatten, Lambda, Input, Reshape, concatenate, Merge
from keras.optimizers import Adam, RMSprop
from keras.callbacks import History
from keras import backend as K
import tensorflow as tf
from gym import Env, Space, spaces
from gym.utils import seeding
from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy, EpsGreedyQPolicy
from rl.memory import SequentialMemory, EpisodeParameterMemory
from rl.agents.cem import CEMAgent
from rl.agents import SARSAAgent
from rl.callbacks import TrainEpisodeLogger, CallbackList
class MountainCarEnv(Env):
metadata = {'render.modes': ['human', 'rgb_array']}
def __init__(self) -> None:
self.env = gym.make('MountainCar-v0')
self.action_space = self.env.action_space
self.observation_space = self.env.observation_space
def _step(self, action):
step = self.env.step(action)
step = list(step)
step[1] = np.abs(step[0][1]) - 0.05
return tuple(step)
def _reset(self):
return self.env.reset()
def _seed(self, seed=None):
return self.env.seed(seed)
def _render(self, mode='human', close=False):
return self.env.render(mode, close)
def _close(self):
return self.env.close()
env = MountainCarEnv()
env.seed()
nb_actions = env.action_space.n
x = Input((1,) + env.observation_space.shape)
y = Flatten()(x)
y = Dense(16)(y)
y = Activation('relu')(y)
y = Dense(16)(y)
y = Activation('relu')(y)
y = Dense(16)(y)
y = Activation('relu')(y)
y = Dense(nb_actions)(y)
y = Activation('linear')(y)
model = Model(x, y)
memory = SequentialMemory(limit=10000, window_length=1)
# policy = BoltzmannQPolicy()
policy = EpsGreedyQPolicy()
dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=1000, gamma=.9, batch_size=32,
enable_dueling_network=False, dueling_type='avg', target_model_update=.1, policy=policy)
dqn.compile(Adam(), metrics=['mae'])
hist = dqn.fit(env, nb_steps=10000, visualize=False, verbose=2, callbacks=None)
state = env.reset()
action = env.action_space.sample()
print(action)
state_list= []
for i in range(500):
action = np.argmax(dqn.model.predict(np.expand_dims(np.expand_dims(state, 0), 0))[0])
state, reward, done, _ = env.step(2)
state_list.append(reward)
env.render()
env.render(close=True)
dqn.test(env, nb_episodes=5, visualize=True)
env.render(close=True) | apache-2.0 |
nolanliou/tensorflow | tensorflow/examples/get_started/regression/imports85.py | 24 | 6638 | # Copyright 2016 The TensorFlow 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.
# ==============================================================================
"""A dataset loader for imports85.data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import tensorflow as tf
try:
import pandas as pd # pylint: disable=g-import-not-at-top
except ImportError:
pass
URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data"
# Order is important for the csv-readers, so we use an OrderedDict here.
defaults = collections.OrderedDict([
("symboling", [0]),
("normalized-losses", [0.0]),
("make", [""]),
("fuel-type", [""]),
("aspiration", [""]),
("num-of-doors", [""]),
("body-style", [""]),
("drive-wheels", [""]),
("engine-location", [""]),
("wheel-base", [0.0]),
("length", [0.0]),
("width", [0.0]),
("height", [0.0]),
("curb-weight", [0.0]),
("engine-type", [""]),
("num-of-cylinders", [""]),
("engine-size", [0.0]),
("fuel-system", [""]),
("bore", [0.0]),
("stroke", [0.0]),
("compression-ratio", [0.0]),
("horsepower", [0.0]),
("peak-rpm", [0.0]),
("city-mpg", [0.0]),
("highway-mpg", [0.0]),
("price", [0.0])
]) # pyformat: disable
types = collections.OrderedDict((key, type(value[0]))
for key, value in defaults.items())
def _get_imports85():
path = tf.contrib.keras.utils.get_file(URL.split("/")[-1], URL)
return path
def dataset(y_name="price", train_fraction=0.7):
"""Load the imports85 data as a (train,test) pair of `Dataset`.
Each dataset generates (features_dict, label) pairs.
Args:
y_name: The name of the column to use as the label.
train_fraction: A float, the fraction of data to use for training. The
remainder will be used for evaluation.
Returns:
A (train,test) pair of `Datasets`
"""
# Download and cache the data
path = _get_imports85()
# Define how the lines of the file should be parsed
def decode_line(line):
"""Convert a csv line into a (features_dict,label) pair."""
# Decode the line to a tuple of items based on the types of
# csv_header.values().
items = tf.decode_csv(line, list(defaults.values()))
# Convert the keys and items to a dict.
pairs = zip(defaults.keys(), items)
features_dict = dict(pairs)
# Remove the label from the features_dict
label = features_dict.pop(y_name)
return features_dict, label
def has_no_question_marks(line):
"""Returns True if the line of text has no question marks."""
# split the line into an array of characters
chars = tf.string_split(line[tf.newaxis], "").values
# for each character check if it is a question mark
is_question = tf.equal(chars, "?")
any_question = tf.reduce_any(is_question)
no_question = ~any_question
return no_question
def in_training_set(line):
"""Returns a boolean tensor, true if the line is in the training set."""
# If you randomly split the dataset you won't get the same split in both
# sessions if you stop and restart training later. Also a simple
# random split won't work with a dataset that's too big to `.cache()` as
# we are doing here.
num_buckets = 1000000
bucket_id = tf.string_to_hash_bucket_fast(line, num_buckets)
# Use the hash bucket id as a random number that's deterministic per example
return bucket_id < int(train_fraction * num_buckets)
def in_test_set(line):
"""Returns a boolean tensor, true if the line is in the training set."""
# Items not in the training set are in the test set.
# This line must use `~` instead of `not` because `not` only works on python
# booleans but we are dealing with symbolic tensors.
return ~in_training_set(line)
base_dataset = (tf.contrib.data
# Get the lines from the file.
.TextLineDataset(path)
# drop lines with question marks.
.filter(has_no_question_marks))
train = (base_dataset
# Take only the training-set lines.
.filter(in_training_set)
# Decode each line into a (features_dict, label) pair.
.map(decode_line)
# Cache data so you only decode the file once.
.cache())
# Do the same for the test-set.
test = (base_dataset.filter(in_test_set).cache().map(decode_line))
return train, test
def raw_dataframe():
"""Load the imports85 data as a pd.DataFrame."""
# Download and cache the data
path = _get_imports85()
# Load it into a pandas dataframe
df = pd.read_csv(path, names=types.keys(), dtype=types, na_values="?")
return df
def load_data(y_name="price", train_fraction=0.7, seed=None):
"""Get the imports85 data set.
A description of the data is available at:
https://archive.ics.uci.edu/ml/datasets/automobile
The data itself can be found at:
https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data
Args:
y_name: the column to return as the label.
train_fraction: the fraction of the dataset to use for training.
seed: The random seed to use when shuffling the data. `None` generates a
unique shuffle every run.
Returns:
a pair of pairs where the first pair is the training data, and the second
is the test data:
`(x_train, y_train), (x_test, y_test) = get_imports85_dataset(...)`
`x` contains a pandas DataFrame of features, while `y` contains the label
array.
"""
# Load the raw data columns.
data = raw_dataframe()
# Delete rows with unknowns
data = data.dropna()
# Shuffle the data
np.random.seed(seed)
# Split the data into train/test subsets.
x_train = data.sample(frac=train_fraction, random_state=seed)
x_test = data.drop(x_train.index)
# Extract the label from the features dataframe.
y_train = x_train.pop(y_name)
y_test = x_test.pop(y_name)
return (x_train, y_train), (x_test, y_test)
| apache-2.0 |
waynenilsen/statsmodels | statsmodels/sandbox/examples/ex_kaplan_meier.py | 33 | 2838 | #An example for the Kaplan-Meier estimator
from __future__ import print_function
from statsmodels.compat.python import lrange
import statsmodels.api as sm
import matplotlib.pyplot as plt
import numpy as np
from statsmodels.sandbox.survival2 import KaplanMeier
#Getting the strike data as an array
dta = sm.datasets.strikes.load()
print('basic data')
print('\n')
dta = list(dta.values()[-1])
print(dta[lrange(5),:])
print('\n')
#Create the KaplanMeier object and fit the model
km = KaplanMeier(dta,0)
km.fit()
#show the results
km.plot()
print('basic model')
print('\n')
km.summary()
print('\n')
#Mutiple survival curves
km2 = KaplanMeier(dta,0,exog=1)
km2.fit()
print('more than one curve')
print('\n')
km2.summary()
print('\n')
km2.plot()
#with censoring
censoring = np.ones_like(dta[:,0])
censoring[dta[:,0] > 80] = 0
dta = np.c_[dta,censoring]
print('with censoring')
print('\n')
print(dta[lrange(5),:])
print('\n')
km3 = KaplanMeier(dta,0,exog=1,censoring=2)
km3.fit()
km3.summary()
print('\n')
km3.plot()
#Test for difference of survival curves
log_rank = km3.test_diff([0.0645,-0.03957])
print('log rank test')
print('\n')
print(log_rank)
print('\n')
#The zeroth element of log_rank is the chi-square test statistic
#for the difference between the survival curves for exog = 0.0645
#and exog = -0.03957, the index one element is the degrees of freedom for
#the test, and the index two element is the p-value for the test
wilcoxon = km3.test_diff([0.0645,-0.03957], rho=1)
print('Wilcoxon')
print('\n')
print(wilcoxon)
print('\n')
#Same info as log_rank, but for Peto and Peto modification to the
#Gehan-Wilcoxon test
#User specified functions for tests
#A wider range of rates can be accessed by using the 'weight' parameter
#for the test_diff method
#For example, if the desire weights are S(t)*(1-S(t)), where S(t) is a pooled
#estimate for the survival function, this could be computed by doing
def weights(t):
#must accept one arguement, even though it is not used here
s = KaplanMeier(dta,0,censoring=2)
s.fit()
s = s.results[0][0]
s = s * (1 - s)
return s
#KaplanMeier provides an array of times to the weighting function
#internally, so the weighting function must accept one arguement
test = km3.test_diff([0.0645,-0.03957], weight=weights)
print('user specified weights')
print('\n')
print(test)
print('\n')
#Groups with nan names
#These can be handled by passing the data to KaplanMeier as an array of strings
groups = np.ones_like(dta[:,1])
groups = groups.astype('S4')
groups[dta[:,1] > 0] = 'high'
groups[dta[:,1] <= 0] = 'low'
dta = dta.astype('S4')
dta[:,1] = groups
print('with nan group names')
print('\n')
print(dta[lrange(5),:])
print('\n')
km4 = KaplanMeier(dta,0,exog=1,censoring=2)
km4.fit()
km4.summary()
print('\n')
km4.plot()
#show all the plots
plt.show()
| bsd-3-clause |
INCF/BIDS2ISATab | setup.py | 1 | 2176 | from setuptools import setup
import os
here = os.path.abspath(os.path.dirname(__file__))
setup(
name="BIDS2ISATab",
# Versions should comply with PEP440. For a discussion on single-sourcing
# the version across setup.py and the project code, see
# http://packaging.python.org/en/latest/tutorial.html#version
version='0.1.0',
description="Command line tool generating ISA-Tab compatible description from a Brain Imaging Data Structure "
"compatible dataset.",
long_description="Command line tool generating ISA-Tab compatible description from a Brain Imaging Data Structure "
"compatible dataset.",
# The project URL.
url='https://github.com/INCF/BIDS2ISATab',
# Choose your license
license='BSD',
classifiers=[
# How mature is this project? Common values are
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 4 - Beta',
# Pick your license as you wish (should match "license" above)
'License :: OSI Approved :: BSD License',
# Specify the Python versions you support here. In particular, ensure
# that you indicate whether you support Python 2, Python 3 or both.
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.5',
],
# What does your project relate to?
keywords='bids isatab',
# You can just specify the packages manually here if your project is
# simple. Or you can use find_packages.
packages=["bids2isatab"],
# List run-time dependencies here. These will be installed by pip when your
# project is installed.
install_requires = ["future",
"pandas",
'nibabel'],
include_package_data=True,
# To provide executable scripts, use entry points in preference to the
# "scripts" keyword. Entry points provide cross-platform support and allow
# pip to create the appropriate form of executable for the target platform.
entry_points={
'console_scripts': [
'bids2isatab=bids2isatab.main:main',
],
},
)
| apache-2.0 |
MichaelAquilina/numpy | numpy/lib/npyio.py | 42 | 71218 | from __future__ import division, absolute_import, print_function
import sys
import os
import re
import itertools
import warnings
import weakref
from operator import itemgetter
import numpy as np
from . import format
from ._datasource import DataSource
from numpy.core.multiarray import packbits, unpackbits
from ._iotools import (
LineSplitter, NameValidator, StringConverter, ConverterError,
ConverterLockError, ConversionWarning, _is_string_like, has_nested_fields,
flatten_dtype, easy_dtype, _bytes_to_name
)
from numpy.compat import (
asbytes, asstr, asbytes_nested, bytes, basestring, unicode
)
if sys.version_info[0] >= 3:
import pickle
else:
import cPickle as pickle
from future_builtins import map
loads = pickle.loads
__all__ = [
'savetxt', 'loadtxt', 'genfromtxt', 'ndfromtxt', 'mafromtxt',
'recfromtxt', 'recfromcsv', 'load', 'loads', 'save', 'savez',
'savez_compressed', 'packbits', 'unpackbits', 'fromregex', 'DataSource'
]
class BagObj(object):
"""
BagObj(obj)
Convert attribute look-ups to getitems on the object passed in.
Parameters
----------
obj : class instance
Object on which attribute look-up is performed.
Examples
--------
>>> from numpy.lib.npyio import BagObj as BO
>>> class BagDemo(object):
... def __getitem__(self, key): # An instance of BagObj(BagDemo)
... # will call this method when any
... # attribute look-up is required
... result = "Doesn't matter what you want, "
... return result + "you're gonna get this"
...
>>> demo_obj = BagDemo()
>>> bagobj = BO(demo_obj)
>>> bagobj.hello_there
"Doesn't matter what you want, you're gonna get this"
>>> bagobj.I_can_be_anything
"Doesn't matter what you want, you're gonna get this"
"""
def __init__(self, obj):
# Use weakref to make NpzFile objects collectable by refcount
self._obj = weakref.proxy(obj)
def __getattribute__(self, key):
try:
return object.__getattribute__(self, '_obj')[key]
except KeyError:
raise AttributeError(key)
def __dir__(self):
"""
Enables dir(bagobj) to list the files in an NpzFile.
This also enables tab-completion in an interpreter or IPython.
"""
return object.__getattribute__(self, '_obj').keys()
def zipfile_factory(*args, **kwargs):
import zipfile
kwargs['allowZip64'] = True
return zipfile.ZipFile(*args, **kwargs)
class NpzFile(object):
"""
NpzFile(fid)
A dictionary-like object with lazy-loading of files in the zipped
archive provided on construction.
`NpzFile` is used to load files in the NumPy ``.npz`` data archive
format. It assumes that files in the archive have a ``.npy`` extension,
other files are ignored.
The arrays and file strings are lazily loaded on either
getitem access using ``obj['key']`` or attribute lookup using
``obj.f.key``. A list of all files (without ``.npy`` extensions) can
be obtained with ``obj.files`` and the ZipFile object itself using
``obj.zip``.
Attributes
----------
files : list of str
List of all files in the archive with a ``.npy`` extension.
zip : ZipFile instance
The ZipFile object initialized with the zipped archive.
f : BagObj instance
An object on which attribute can be performed as an alternative
to getitem access on the `NpzFile` instance itself.
allow_pickle : bool, optional
Allow loading pickled data. Default: True
pickle_kwargs : dict, optional
Additional keyword arguments to pass on to pickle.load.
These are only useful when loading object arrays saved on
Python 2 when using Python 3.
Parameters
----------
fid : file or str
The zipped archive to open. This is either a file-like object
or a string containing the path to the archive.
own_fid : bool, optional
Whether NpzFile should close the file handle.
Requires that `fid` is a file-like object.
Examples
--------
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> x = np.arange(10)
>>> y = np.sin(x)
>>> np.savez(outfile, x=x, y=y)
>>> outfile.seek(0)
>>> npz = np.load(outfile)
>>> isinstance(npz, np.lib.io.NpzFile)
True
>>> npz.files
['y', 'x']
>>> npz['x'] # getitem access
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> npz.f.x # attribute lookup
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""
def __init__(self, fid, own_fid=False, allow_pickle=True,
pickle_kwargs=None):
# Import is postponed to here since zipfile depends on gzip, an
# optional component of the so-called standard library.
_zip = zipfile_factory(fid)
self._files = _zip.namelist()
self.files = []
self.allow_pickle = allow_pickle
self.pickle_kwargs = pickle_kwargs
for x in self._files:
if x.endswith('.npy'):
self.files.append(x[:-4])
else:
self.files.append(x)
self.zip = _zip
self.f = BagObj(self)
if own_fid:
self.fid = fid
else:
self.fid = None
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def close(self):
"""
Close the file.
"""
if self.zip is not None:
self.zip.close()
self.zip = None
if self.fid is not None:
self.fid.close()
self.fid = None
self.f = None # break reference cycle
def __del__(self):
self.close()
def __getitem__(self, key):
# FIXME: This seems like it will copy strings around
# more than is strictly necessary. The zipfile
# will read the string and then
# the format.read_array will copy the string
# to another place in memory.
# It would be better if the zipfile could read
# (or at least uncompress) the data
# directly into the array memory.
member = 0
if key in self._files:
member = 1
elif key in self.files:
member = 1
key += '.npy'
if member:
bytes = self.zip.open(key)
magic = bytes.read(len(format.MAGIC_PREFIX))
bytes.close()
if magic == format.MAGIC_PREFIX:
bytes = self.zip.open(key)
return format.read_array(bytes,
allow_pickle=self.allow_pickle,
pickle_kwargs=self.pickle_kwargs)
else:
return self.zip.read(key)
else:
raise KeyError("%s is not a file in the archive" % key)
def __iter__(self):
return iter(self.files)
def items(self):
"""
Return a list of tuples, with each tuple (filename, array in file).
"""
return [(f, self[f]) for f in self.files]
def iteritems(self):
"""Generator that returns tuples (filename, array in file)."""
for f in self.files:
yield (f, self[f])
def keys(self):
"""Return files in the archive with a ``.npy`` extension."""
return self.files
def iterkeys(self):
"""Return an iterator over the files in the archive."""
return self.__iter__()
def __contains__(self, key):
return self.files.__contains__(key)
def load(file, mmap_mode=None, allow_pickle=True, fix_imports=True,
encoding='ASCII'):
"""
Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.
Parameters
----------
file : file-like object or string
The file to read. File-like objects must support the
``seek()`` and ``read()`` methods. Pickled files require that the
file-like object support the ``readline()`` method as well.
mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
If not None, then memory-map the file, using the given mode (see
`numpy.memmap` for a detailed description of the modes). A
memory-mapped array is kept on disk. However, it can be accessed
and sliced like any ndarray. Memory mapping is especially useful
for accessing small fragments of large files without reading the
entire file into memory.
allow_pickle : bool, optional
Allow loading pickled object arrays stored in npy files. Reasons for
disallowing pickles include security, as loading pickled data can
execute arbitrary code. If pickles are disallowed, loading object
arrays will fail.
Default: True
fix_imports : bool, optional
Only useful when loading Python 2 generated pickled files on Python 3,
which includes npy/npz files containing object arrays. If `fix_imports`
is True, pickle will try to map the old Python 2 names to the new names
used in Python 3.
encoding : str, optional
What encoding to use when reading Python 2 strings. Only useful when
loading Python 2 generated pickled files on Python 3, which includes
npy/npz files containing object arrays. Values other than 'latin1',
'ASCII', and 'bytes' are not allowed, as they can corrupt numerical
data. Default: 'ASCII'
Returns
-------
result : array, tuple, dict, etc.
Data stored in the file. For ``.npz`` files, the returned instance
of NpzFile class must be closed to avoid leaking file descriptors.
Raises
------
IOError
If the input file does not exist or cannot be read.
ValueError
The file contains an object array, but allow_pickle=False given.
See Also
--------
save, savez, savez_compressed, loadtxt
memmap : Create a memory-map to an array stored in a file on disk.
Notes
-----
- If the file contains pickle data, then whatever object is stored
in the pickle is returned.
- If the file is a ``.npy`` file, then a single array is returned.
- If the file is a ``.npz`` file, then a dictionary-like object is
returned, containing ``{filename: array}`` key-value pairs, one for
each file in the archive.
- If the file is a ``.npz`` file, the returned value supports the
context manager protocol in a similar fashion to the open function::
with load('foo.npz') as data:
a = data['a']
The underlying file descriptor is closed when exiting the 'with'
block.
Examples
--------
Store data to disk, and load it again:
>>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
>>> np.load('/tmp/123.npy')
array([[1, 2, 3],
[4, 5, 6]])
Store compressed data to disk, and load it again:
>>> a=np.array([[1, 2, 3], [4, 5, 6]])
>>> b=np.array([1, 2])
>>> np.savez('/tmp/123.npz', a=a, b=b)
>>> data = np.load('/tmp/123.npz')
>>> data['a']
array([[1, 2, 3],
[4, 5, 6]])
>>> data['b']
array([1, 2])
>>> data.close()
Mem-map the stored array, and then access the second row
directly from disk:
>>> X = np.load('/tmp/123.npy', mmap_mode='r')
>>> X[1, :]
memmap([4, 5, 6])
"""
import gzip
own_fid = False
if isinstance(file, basestring):
fid = open(file, "rb")
own_fid = True
else:
fid = file
if encoding not in ('ASCII', 'latin1', 'bytes'):
# The 'encoding' value for pickle also affects what encoding
# the serialized binary data of Numpy arrays is loaded
# in. Pickle does not pass on the encoding information to
# Numpy. The unpickling code in numpy.core.multiarray is
# written to assume that unicode data appearing where binary
# should be is in 'latin1'. 'bytes' is also safe, as is 'ASCII'.
#
# Other encoding values can corrupt binary data, and we
# purposefully disallow them. For the same reason, the errors=
# argument is not exposed, as values other than 'strict'
# result can similarly silently corrupt numerical data.
raise ValueError("encoding must be 'ASCII', 'latin1', or 'bytes'")
if sys.version_info[0] >= 3:
pickle_kwargs = dict(encoding=encoding, fix_imports=fix_imports)
else:
# Nothing to do on Python 2
pickle_kwargs = {}
try:
# Code to distinguish from NumPy binary files and pickles.
_ZIP_PREFIX = asbytes('PK\x03\x04')
N = len(format.MAGIC_PREFIX)
magic = fid.read(N)
fid.seek(-N, 1) # back-up
if magic.startswith(_ZIP_PREFIX):
# zip-file (assume .npz)
# Transfer file ownership to NpzFile
tmp = own_fid
own_fid = False
return NpzFile(fid, own_fid=tmp, allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
elif magic == format.MAGIC_PREFIX:
# .npy file
if mmap_mode:
return format.open_memmap(file, mode=mmap_mode)
else:
return format.read_array(fid, allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
else:
# Try a pickle
if not allow_pickle:
raise ValueError("allow_pickle=False, but file does not contain "
"non-pickled data")
try:
return pickle.load(fid, **pickle_kwargs)
except:
raise IOError(
"Failed to interpret file %s as a pickle" % repr(file))
finally:
if own_fid:
fid.close()
def save(file, arr, allow_pickle=True, fix_imports=True):
"""
Save an array to a binary file in NumPy ``.npy`` format.
Parameters
----------
file : file or str
File or filename to which the data is saved. If file is a file-object,
then the filename is unchanged. If file is a string, a ``.npy``
extension will be appended to the file name if it does not already
have one.
allow_pickle : bool, optional
Allow saving object arrays using Python pickles. Reasons for disallowing
pickles include security (loading pickled data can execute arbitrary
code) and portability (pickled objects may not be loadable on different
Python installations, for example if the stored objects require libraries
that are not available, and not all pickled data is compatible between
Python 2 and Python 3).
Default: True
fix_imports : bool, optional
Only useful in forcing objects in object arrays on Python 3 to be
pickled in a Python 2 compatible way. If `fix_imports` is True, pickle
will try to map the new Python 3 names to the old module names used in
Python 2, so that the pickle data stream is readable with Python 2.
arr : array_like
Array data to be saved.
See Also
--------
savez : Save several arrays into a ``.npz`` archive
savetxt, load
Notes
-----
For a description of the ``.npy`` format, see the module docstring
of `numpy.lib.format` or the Numpy Enhancement Proposal
http://docs.scipy.org/doc/numpy/neps/npy-format.html
Examples
--------
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> x = np.arange(10)
>>> np.save(outfile, x)
>>> outfile.seek(0) # Only needed here to simulate closing & reopening file
>>> np.load(outfile)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""
own_fid = False
if isinstance(file, basestring):
if not file.endswith('.npy'):
file = file + '.npy'
fid = open(file, "wb")
own_fid = True
else:
fid = file
if sys.version_info[0] >= 3:
pickle_kwargs = dict(fix_imports=fix_imports)
else:
# Nothing to do on Python 2
pickle_kwargs = None
try:
arr = np.asanyarray(arr)
format.write_array(fid, arr, allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
finally:
if own_fid:
fid.close()
def savez(file, *args, **kwds):
"""
Save several arrays into a single file in uncompressed ``.npz`` format.
If arguments are passed in with no keywords, the corresponding variable
names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword
arguments are given, the corresponding variable names, in the ``.npz``
file will match the keyword names.
Parameters
----------
file : str or file
Either the file name (string) or an open file (file-like object)
where the data will be saved. If file is a string, the ``.npz``
extension will be appended to the file name if it is not already there.
args : Arguments, optional
Arrays to save to the file. Since it is not possible for Python to
know the names of the arrays outside `savez`, the arrays will be saved
with names "arr_0", "arr_1", and so on. These arguments can be any
expression.
kwds : Keyword arguments, optional
Arrays to save to the file. Arrays will be saved in the file with the
keyword names.
Returns
-------
None
See Also
--------
save : Save a single array to a binary file in NumPy format.
savetxt : Save an array to a file as plain text.
savez_compressed : Save several arrays into a compressed ``.npz`` archive
Notes
-----
The ``.npz`` file format is a zipped archive of files named after the
variables they contain. The archive is not compressed and each file
in the archive contains one variable in ``.npy`` format. For a
description of the ``.npy`` format, see `numpy.lib.format` or the
Numpy Enhancement Proposal
http://docs.scipy.org/doc/numpy/neps/npy-format.html
When opening the saved ``.npz`` file with `load` a `NpzFile` object is
returned. This is a dictionary-like object which can be queried for
its list of arrays (with the ``.files`` attribute), and for the arrays
themselves.
Examples
--------
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> x = np.arange(10)
>>> y = np.sin(x)
Using `savez` with \\*args, the arrays are saved with default names.
>>> np.savez(outfile, x, y)
>>> outfile.seek(0) # Only needed here to simulate closing & reopening file
>>> npzfile = np.load(outfile)
>>> npzfile.files
['arr_1', 'arr_0']
>>> npzfile['arr_0']
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Using `savez` with \\**kwds, the arrays are saved with the keyword names.
>>> outfile = TemporaryFile()
>>> np.savez(outfile, x=x, y=y)
>>> outfile.seek(0)
>>> npzfile = np.load(outfile)
>>> npzfile.files
['y', 'x']
>>> npzfile['x']
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""
_savez(file, args, kwds, False)
def savez_compressed(file, *args, **kwds):
"""
Save several arrays into a single file in compressed ``.npz`` format.
If keyword arguments are given, then filenames are taken from the keywords.
If arguments are passed in with no keywords, then stored file names are
arr_0, arr_1, etc.
Parameters
----------
file : str
File name of ``.npz`` file.
args : Arguments
Function arguments.
kwds : Keyword arguments
Keywords.
See Also
--------
numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
numpy.load : Load the files created by savez_compressed.
"""
_savez(file, args, kwds, True)
def _savez(file, args, kwds, compress, allow_pickle=True, pickle_kwargs=None):
# Import is postponed to here since zipfile depends on gzip, an optional
# component of the so-called standard library.
import zipfile
# Import deferred for startup time improvement
import tempfile
if isinstance(file, basestring):
if not file.endswith('.npz'):
file = file + '.npz'
namedict = kwds
for i, val in enumerate(args):
key = 'arr_%d' % i
if key in namedict.keys():
raise ValueError(
"Cannot use un-named variables and keyword %s" % key)
namedict[key] = val
if compress:
compression = zipfile.ZIP_DEFLATED
else:
compression = zipfile.ZIP_STORED
zipf = zipfile_factory(file, mode="w", compression=compression)
# Stage arrays in a temporary file on disk, before writing to zip.
fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')
os.close(fd)
try:
for key, val in namedict.items():
fname = key + '.npy'
fid = open(tmpfile, 'wb')
try:
format.write_array(fid, np.asanyarray(val),
allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
fid.close()
fid = None
zipf.write(tmpfile, arcname=fname)
finally:
if fid:
fid.close()
finally:
os.remove(tmpfile)
zipf.close()
def _getconv(dtype):
""" Find the correct dtype converter. Adapted from matplotlib """
def floatconv(x):
x.lower()
if b'0x' in x:
return float.fromhex(asstr(x))
return float(x)
typ = dtype.type
if issubclass(typ, np.bool_):
return lambda x: bool(int(x))
if issubclass(typ, np.uint64):
return np.uint64
if issubclass(typ, np.int64):
return np.int64
if issubclass(typ, np.integer):
return lambda x: int(float(x))
elif issubclass(typ, np.floating):
return floatconv
elif issubclass(typ, np.complex):
return lambda x: complex(asstr(x))
elif issubclass(typ, np.bytes_):
return bytes
else:
return str
def loadtxt(fname, dtype=float, comments='#', delimiter=None,
converters=None, skiprows=0, usecols=None, unpack=False,
ndmin=0):
"""
Load data from a text file.
Each row in the text file must have the same number of values.
Parameters
----------
fname : file or str
File, filename, or generator to read. If the filename extension is
``.gz`` or ``.bz2``, the file is first decompressed. Note that
generators should return byte strings for Python 3k.
dtype : data-type, optional
Data-type of the resulting array; default: float. If this is a
structured data-type, the resulting array will be 1-dimensional, and
each row will be interpreted as an element of the array. In this
case, the number of columns used must match the number of fields in
the data-type.
comments : str or sequence, optional
The characters or list of characters used to indicate the start of a
comment;
default: '#'.
delimiter : str, optional
The string used to separate values. By default, this is any
whitespace.
converters : dict, optional
A dictionary mapping column number to a function that will convert
that column to a float. E.g., if column 0 is a date string:
``converters = {0: datestr2num}``. Converters can also be used to
provide a default value for missing data (but see also `genfromtxt`):
``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None.
skiprows : int, optional
Skip the first `skiprows` lines; default: 0.
usecols : sequence, optional
Which columns to read, with 0 being the first. For example,
``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
The default, None, results in all columns being read.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = loadtxt(...)``. When used with a structured
data-type, arrays are returned for each field. Default is False.
ndmin : int, optional
The returned array will have at least `ndmin` dimensions.
Otherwise mono-dimensional axes will be squeezed.
Legal values: 0 (default), 1 or 2.
.. versionadded:: 1.6.0
Returns
-------
out : ndarray
Data read from the text file.
See Also
--------
load, fromstring, fromregex
genfromtxt : Load data with missing values handled as specified.
scipy.io.loadmat : reads MATLAB data files
Notes
-----
This function aims to be a fast reader for simply formatted files. The
`genfromtxt` function provides more sophisticated handling of, e.g.,
lines with missing values.
.. versionadded:: 1.10.0
The strings produced by the Python float.hex method can be used as
input for floats.
Examples
--------
>>> from io import StringIO # StringIO behaves like a file object
>>> c = StringIO("0 1\\n2 3")
>>> np.loadtxt(c)
array([[ 0., 1.],
[ 2., 3.]])
>>> d = StringIO("M 21 72\\nF 35 58")
>>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
... 'formats': ('S1', 'i4', 'f4')})
array([('M', 21, 72.0), ('F', 35, 58.0)],
dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])
>>> c = StringIO("1,0,2\\n3,0,4")
>>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)
>>> x
array([ 1., 3.])
>>> y
array([ 2., 4.])
"""
# Type conversions for Py3 convenience
if comments is not None:
if isinstance(comments, (basestring, bytes)):
comments = [asbytes(comments)]
else:
comments = [asbytes(comment) for comment in comments]
# Compile regex for comments beforehand
comments = (re.escape(comment) for comment in comments)
regex_comments = re.compile(asbytes('|').join(comments))
user_converters = converters
if delimiter is not None:
delimiter = asbytes(delimiter)
if usecols is not None:
usecols = list(usecols)
fown = False
try:
if _is_string_like(fname):
fown = True
if fname.endswith('.gz'):
import gzip
fh = iter(gzip.GzipFile(fname))
elif fname.endswith('.bz2'):
import bz2
fh = iter(bz2.BZ2File(fname))
elif sys.version_info[0] == 2:
fh = iter(open(fname, 'U'))
else:
fh = iter(open(fname))
else:
fh = iter(fname)
except TypeError:
raise ValueError('fname must be a string, file handle, or generator')
X = []
def flatten_dtype(dt):
"""Unpack a structured data-type, and produce re-packing info."""
if dt.names is None:
# If the dtype is flattened, return.
# If the dtype has a shape, the dtype occurs
# in the list more than once.
shape = dt.shape
if len(shape) == 0:
return ([dt.base], None)
else:
packing = [(shape[-1], list)]
if len(shape) > 1:
for dim in dt.shape[-2::-1]:
packing = [(dim*packing[0][0], packing*dim)]
return ([dt.base] * int(np.prod(dt.shape)), packing)
else:
types = []
packing = []
for field in dt.names:
tp, bytes = dt.fields[field]
flat_dt, flat_packing = flatten_dtype(tp)
types.extend(flat_dt)
# Avoid extra nesting for subarrays
if len(tp.shape) > 0:
packing.extend(flat_packing)
else:
packing.append((len(flat_dt), flat_packing))
return (types, packing)
def pack_items(items, packing):
"""Pack items into nested lists based on re-packing info."""
if packing is None:
return items[0]
elif packing is tuple:
return tuple(items)
elif packing is list:
return list(items)
else:
start = 0
ret = []
for length, subpacking in packing:
ret.append(pack_items(items[start:start+length], subpacking))
start += length
return tuple(ret)
def split_line(line):
"""Chop off comments, strip, and split at delimiter.
Note that although the file is opened as text, this function
returns bytes.
"""
line = asbytes(line)
if comments is not None:
line = regex_comments.split(asbytes(line), maxsplit=1)[0]
line = line.strip(asbytes('\r\n'))
if line:
return line.split(delimiter)
else:
return []
try:
# Make sure we're dealing with a proper dtype
dtype = np.dtype(dtype)
defconv = _getconv(dtype)
# Skip the first `skiprows` lines
for i in range(skiprows):
next(fh)
# Read until we find a line with some values, and use
# it to estimate the number of columns, N.
first_vals = None
try:
while not first_vals:
first_line = next(fh)
first_vals = split_line(first_line)
except StopIteration:
# End of lines reached
first_line = ''
first_vals = []
warnings.warn('loadtxt: Empty input file: "%s"' % fname)
N = len(usecols or first_vals)
dtype_types, packing = flatten_dtype(dtype)
if len(dtype_types) > 1:
# We're dealing with a structured array, each field of
# the dtype matches a column
converters = [_getconv(dt) for dt in dtype_types]
else:
# All fields have the same dtype
converters = [defconv for i in range(N)]
if N > 1:
packing = [(N, tuple)]
# By preference, use the converters specified by the user
for i, conv in (user_converters or {}).items():
if usecols:
try:
i = usecols.index(i)
except ValueError:
# Unused converter specified
continue
converters[i] = conv
# Parse each line, including the first
for i, line in enumerate(itertools.chain([first_line], fh)):
vals = split_line(line)
if len(vals) == 0:
continue
if usecols:
vals = [vals[i] for i in usecols]
if len(vals) != N:
line_num = i + skiprows + 1
raise ValueError("Wrong number of columns at line %d"
% line_num)
# Convert each value according to its column and store
items = [conv(val) for (conv, val) in zip(converters, vals)]
# Then pack it according to the dtype's nesting
items = pack_items(items, packing)
X.append(items)
finally:
if fown:
fh.close()
X = np.array(X, dtype)
# Multicolumn data are returned with shape (1, N, M), i.e.
# (1, 1, M) for a single row - remove the singleton dimension there
if X.ndim == 3 and X.shape[:2] == (1, 1):
X.shape = (1, -1)
# Verify that the array has at least dimensions `ndmin`.
# Check correctness of the values of `ndmin`
if ndmin not in [0, 1, 2]:
raise ValueError('Illegal value of ndmin keyword: %s' % ndmin)
# Tweak the size and shape of the arrays - remove extraneous dimensions
if X.ndim > ndmin:
X = np.squeeze(X)
# and ensure we have the minimum number of dimensions asked for
# - has to be in this order for the odd case ndmin=1, X.squeeze().ndim=0
if X.ndim < ndmin:
if ndmin == 1:
X = np.atleast_1d(X)
elif ndmin == 2:
X = np.atleast_2d(X).T
if unpack:
if len(dtype_types) > 1:
# For structured arrays, return an array for each field.
return [X[field] for field in dtype.names]
else:
return X.T
else:
return X
def savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='',
footer='', comments='# '):
"""
Save an array to a text file.
Parameters
----------
fname : filename or file handle
If the filename ends in ``.gz``, the file is automatically saved in
compressed gzip format. `loadtxt` understands gzipped files
transparently.
X : array_like
Data to be saved to a text file.
fmt : str or sequence of strs, optional
A single format (%10.5f), a sequence of formats, or a
multi-format string, e.g. 'Iteration %d -- %10.5f', in which
case `delimiter` is ignored. For complex `X`, the legal options
for `fmt` are:
a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted
like `' (%s+%sj)' % (fmt, fmt)`
b) a full string specifying every real and imaginary part, e.g.
`' %.4e %+.4j %.4e %+.4j %.4e %+.4j'` for 3 columns
c) a list of specifiers, one per column - in this case, the real
and imaginary part must have separate specifiers,
e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns
delimiter : str, optional
String or character separating columns.
newline : str, optional
String or character separating lines.
.. versionadded:: 1.5.0
header : str, optional
String that will be written at the beginning of the file.
.. versionadded:: 1.7.0
footer : str, optional
String that will be written at the end of the file.
.. versionadded:: 1.7.0
comments : str, optional
String that will be prepended to the ``header`` and ``footer`` strings,
to mark them as comments. Default: '# ', as expected by e.g.
``numpy.loadtxt``.
.. versionadded:: 1.7.0
See Also
--------
save : Save an array to a binary file in NumPy ``.npy`` format
savez : Save several arrays into an uncompressed ``.npz`` archive
savez_compressed : Save several arrays into a compressed ``.npz`` archive
Notes
-----
Further explanation of the `fmt` parameter
(``%[flag]width[.precision]specifier``):
flags:
``-`` : left justify
``+`` : Forces to precede result with + or -.
``0`` : Left pad the number with zeros instead of space (see width).
width:
Minimum number of characters to be printed. The value is not truncated
if it has more characters.
precision:
- For integer specifiers (eg. ``d,i,o,x``), the minimum number of
digits.
- For ``e, E`` and ``f`` specifiers, the number of digits to print
after the decimal point.
- For ``g`` and ``G``, the maximum number of significant digits.
- For ``s``, the maximum number of characters.
specifiers:
``c`` : character
``d`` or ``i`` : signed decimal integer
``e`` or ``E`` : scientific notation with ``e`` or ``E``.
``f`` : decimal floating point
``g,G`` : use the shorter of ``e,E`` or ``f``
``o`` : signed octal
``s`` : string of characters
``u`` : unsigned decimal integer
``x,X`` : unsigned hexadecimal integer
This explanation of ``fmt`` is not complete, for an exhaustive
specification see [1]_.
References
----------
.. [1] `Format Specification Mini-Language
<http://docs.python.org/library/string.html#
format-specification-mini-language>`_, Python Documentation.
Examples
--------
>>> x = y = z = np.arange(0.0,5.0,1.0)
>>> np.savetxt('test.out', x, delimiter=',') # X is an array
>>> np.savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays
>>> np.savetxt('test.out', x, fmt='%1.4e') # use exponential notation
"""
# Py3 conversions first
if isinstance(fmt, bytes):
fmt = asstr(fmt)
delimiter = asstr(delimiter)
own_fh = False
if _is_string_like(fname):
own_fh = True
if fname.endswith('.gz'):
import gzip
fh = gzip.open(fname, 'wb')
else:
if sys.version_info[0] >= 3:
fh = open(fname, 'wb')
else:
fh = open(fname, 'w')
elif hasattr(fname, 'write'):
fh = fname
else:
raise ValueError('fname must be a string or file handle')
try:
X = np.asarray(X)
# Handle 1-dimensional arrays
if X.ndim == 1:
# Common case -- 1d array of numbers
if X.dtype.names is None:
X = np.atleast_2d(X).T
ncol = 1
# Complex dtype -- each field indicates a separate column
else:
ncol = len(X.dtype.descr)
else:
ncol = X.shape[1]
iscomplex_X = np.iscomplexobj(X)
# `fmt` can be a string with multiple insertion points or a
# list of formats. E.g. '%10.5f\t%10d' or ('%10.5f', '$10d')
if type(fmt) in (list, tuple):
if len(fmt) != ncol:
raise AttributeError('fmt has wrong shape. %s' % str(fmt))
format = asstr(delimiter).join(map(asstr, fmt))
elif isinstance(fmt, str):
n_fmt_chars = fmt.count('%')
error = ValueError('fmt has wrong number of %% formats: %s' % fmt)
if n_fmt_chars == 1:
if iscomplex_X:
fmt = [' (%s+%sj)' % (fmt, fmt), ] * ncol
else:
fmt = [fmt, ] * ncol
format = delimiter.join(fmt)
elif iscomplex_X and n_fmt_chars != (2 * ncol):
raise error
elif ((not iscomplex_X) and n_fmt_chars != ncol):
raise error
else:
format = fmt
else:
raise ValueError('invalid fmt: %r' % (fmt,))
if len(header) > 0:
header = header.replace('\n', '\n' + comments)
fh.write(asbytes(comments + header + newline))
if iscomplex_X:
for row in X:
row2 = []
for number in row:
row2.append(number.real)
row2.append(number.imag)
fh.write(asbytes(format % tuple(row2) + newline))
else:
for row in X:
try:
fh.write(asbytes(format % tuple(row) + newline))
except TypeError:
raise TypeError("Mismatch between array dtype ('%s') and "
"format specifier ('%s')"
% (str(X.dtype), format))
if len(footer) > 0:
footer = footer.replace('\n', '\n' + comments)
fh.write(asbytes(comments + footer + newline))
finally:
if own_fh:
fh.close()
def fromregex(file, regexp, dtype):
"""
Construct an array from a text file, using regular expression parsing.
The returned array is always a structured array, and is constructed from
all matches of the regular expression in the file. Groups in the regular
expression are converted to fields of the structured array.
Parameters
----------
file : str or file
File name or file object to read.
regexp : str or regexp
Regular expression used to parse the file.
Groups in the regular expression correspond to fields in the dtype.
dtype : dtype or list of dtypes
Dtype for the structured array.
Returns
-------
output : ndarray
The output array, containing the part of the content of `file` that
was matched by `regexp`. `output` is always a structured array.
Raises
------
TypeError
When `dtype` is not a valid dtype for a structured array.
See Also
--------
fromstring, loadtxt
Notes
-----
Dtypes for structured arrays can be specified in several forms, but all
forms specify at least the data type and field name. For details see
`doc.structured_arrays`.
Examples
--------
>>> f = open('test.dat', 'w')
>>> f.write("1312 foo\\n1534 bar\\n444 qux")
>>> f.close()
>>> regexp = r"(\\d+)\\s+(...)" # match [digits, whitespace, anything]
>>> output = np.fromregex('test.dat', regexp,
... [('num', np.int64), ('key', 'S3')])
>>> output
array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],
dtype=[('num', '<i8'), ('key', '|S3')])
>>> output['num']
array([1312, 1534, 444], dtype=int64)
"""
own_fh = False
if not hasattr(file, "read"):
file = open(file, 'rb')
own_fh = True
try:
if not hasattr(regexp, 'match'):
regexp = re.compile(asbytes(regexp))
if not isinstance(dtype, np.dtype):
dtype = np.dtype(dtype)
seq = regexp.findall(file.read())
if seq and not isinstance(seq[0], tuple):
# Only one group is in the regexp.
# Create the new array as a single data-type and then
# re-interpret as a single-field structured array.
newdtype = np.dtype(dtype[dtype.names[0]])
output = np.array(seq, dtype=newdtype)
output.dtype = dtype
else:
output = np.array(seq, dtype=dtype)
return output
finally:
if own_fh:
file.close()
#####--------------------------------------------------------------------------
#---- --- ASCII functions ---
#####--------------------------------------------------------------------------
def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
skip_header=0, skip_footer=0, converters=None,
missing_values=None, filling_values=None, usecols=None,
names=None, excludelist=None, deletechars=None,
replace_space='_', autostrip=False, case_sensitive=True,
defaultfmt="f%i", unpack=None, usemask=False, loose=True,
invalid_raise=True, max_rows=None):
"""
Load data from a text file, with missing values handled as specified.
Each line past the first `skip_header` lines is split at the `delimiter`
character, and characters following the `comments` character are discarded.
Parameters
----------
fname : file or str
File, filename, or generator to read. If the filename extension is
`.gz` or `.bz2`, the file is first decompressed. Note that
generators must return byte strings in Python 3k.
dtype : dtype, optional
Data type of the resulting array.
If None, the dtypes will be determined by the contents of each
column, individually.
comments : str, optional
The character used to indicate the start of a comment.
All the characters occurring on a line after a comment are discarded
delimiter : str, int, or sequence, optional
The string used to separate values. By default, any consecutive
whitespaces act as delimiter. An integer or sequence of integers
can also be provided as width(s) of each field.
skiprows : int, optional
`skiprows` was removed in numpy 1.10. Please use `skip_header` instead.
skip_header : int, optional
The number of lines to skip at the beginning of the file.
skip_footer : int, optional
The number of lines to skip at the end of the file.
converters : variable, optional
The set of functions that convert the data of a column to a value.
The converters can also be used to provide a default value
for missing data: ``converters = {3: lambda s: float(s or 0)}``.
missing : variable, optional
`missing` was removed in numpy 1.10. Please use `missing_values`
instead.
missing_values : variable, optional
The set of strings corresponding to missing data.
filling_values : variable, optional
The set of values to be used as default when the data are missing.
usecols : sequence, optional
Which columns to read, with 0 being the first. For example,
``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns.
names : {None, True, str, sequence}, optional
If `names` is True, the field names are read from the first valid line
after the first `skip_header` lines.
If `names` is a sequence or a single-string of comma-separated names,
the names will be used to define the field names in a structured dtype.
If `names` is None, the names of the dtype fields will be used, if any.
excludelist : sequence, optional
A list of names to exclude. This list is appended to the default list
['return','file','print']. Excluded names are appended an underscore:
for example, `file` would become `file_`.
deletechars : str, optional
A string combining invalid characters that must be deleted from the
names.
defaultfmt : str, optional
A format used to define default field names, such as "f%i" or "f_%02i".
autostrip : bool, optional
Whether to automatically strip white spaces from the variables.
replace_space : char, optional
Character(s) used in replacement of white spaces in the variables
names. By default, use a '_'.
case_sensitive : {True, False, 'upper', 'lower'}, optional
If True, field names are case sensitive.
If False or 'upper', field names are converted to upper case.
If 'lower', field names are converted to lower case.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = loadtxt(...)``
usemask : bool, optional
If True, return a masked array.
If False, return a regular array.
loose : bool, optional
If True, do not raise errors for invalid values.
invalid_raise : bool, optional
If True, an exception is raised if an inconsistency is detected in the
number of columns.
If False, a warning is emitted and the offending lines are skipped.
max_rows : int, optional
The maximum number of rows to read. Must not be used with skip_footer
at the same time. If given, the value must be at least 1. Default is
to read the entire file.
.. versionadded:: 1.10.0
Returns
-------
out : ndarray
Data read from the text file. If `usemask` is True, this is a
masked array.
See Also
--------
numpy.loadtxt : equivalent function when no data is missing.
Notes
-----
* When spaces are used as delimiters, or when no delimiter has been given
as input, there should not be any missing data between two fields.
* When the variables are named (either by a flexible dtype or with `names`,
there must not be any header in the file (else a ValueError
exception is raised).
* Individual values are not stripped of spaces by default.
When using a custom converter, make sure the function does remove spaces.
References
----------
.. [1] Numpy User Guide, section `I/O with Numpy
<http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.
Examples
---------
>>> from io import StringIO
>>> import numpy as np
Comma delimited file with mixed dtype
>>> s = StringIO("1,1.3,abcde")
>>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
... ('mystring','S5')], delimiter=",")
>>> data
array((1, 1.3, 'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
Using dtype = None
>>> s.seek(0) # needed for StringIO example only
>>> data = np.genfromtxt(s, dtype=None,
... names = ['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, 'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
Specifying dtype and names
>>> s.seek(0)
>>> data = np.genfromtxt(s, dtype="i8,f8,S5",
... names=['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, 'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
An example with fixed-width columns
>>> s = StringIO("11.3abcde")
>>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
... delimiter=[1,3,5])
>>> data
array((1, 1.3, 'abcde'),
dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '|S5')])
"""
if max_rows is not None:
if skip_footer:
raise ValueError(
"The keywords 'skip_footer' and 'max_rows' can not be "
"specified at the same time.")
if max_rows < 1:
raise ValueError("'max_rows' must be at least 1.")
# Py3 data conversions to bytes, for convenience
if comments is not None:
comments = asbytes(comments)
if isinstance(delimiter, unicode):
delimiter = asbytes(delimiter)
if isinstance(missing_values, (unicode, list, tuple)):
missing_values = asbytes_nested(missing_values)
#
if usemask:
from numpy.ma import MaskedArray, make_mask_descr
# Check the input dictionary of converters
user_converters = converters or {}
if not isinstance(user_converters, dict):
raise TypeError(
"The input argument 'converter' should be a valid dictionary "
"(got '%s' instead)" % type(user_converters))
# Initialize the filehandle, the LineSplitter and the NameValidator
own_fhd = False
try:
if isinstance(fname, basestring):
if sys.version_info[0] == 2:
fhd = iter(np.lib._datasource.open(fname, 'rbU'))
else:
fhd = iter(np.lib._datasource.open(fname, 'rb'))
own_fhd = True
else:
fhd = iter(fname)
except TypeError:
raise TypeError(
"fname must be a string, filehandle, or generator. "
"(got %s instead)" % type(fname))
split_line = LineSplitter(delimiter=delimiter, comments=comments,
autostrip=autostrip)._handyman
validate_names = NameValidator(excludelist=excludelist,
deletechars=deletechars,
case_sensitive=case_sensitive,
replace_space=replace_space)
# Skip the first `skip_header` rows
for i in range(skip_header):
next(fhd)
# Keep on until we find the first valid values
first_values = None
try:
while not first_values:
first_line = next(fhd)
if names is True:
if comments in first_line:
first_line = (
asbytes('').join(first_line.split(comments)[1:]))
first_values = split_line(first_line)
except StopIteration:
# return an empty array if the datafile is empty
first_line = asbytes('')
first_values = []
warnings.warn('genfromtxt: Empty input file: "%s"' % fname)
# Should we take the first values as names ?
if names is True:
fval = first_values[0].strip()
if fval in comments:
del first_values[0]
# Check the columns to use: make sure `usecols` is a list
if usecols is not None:
try:
usecols = [_.strip() for _ in usecols.split(",")]
except AttributeError:
try:
usecols = list(usecols)
except TypeError:
usecols = [usecols, ]
nbcols = len(usecols or first_values)
# Check the names and overwrite the dtype.names if needed
if names is True:
names = validate_names([_bytes_to_name(_.strip())
for _ in first_values])
first_line = asbytes('')
elif _is_string_like(names):
names = validate_names([_.strip() for _ in names.split(',')])
elif names:
names = validate_names(names)
# Get the dtype
if dtype is not None:
dtype = easy_dtype(dtype, defaultfmt=defaultfmt, names=names,
excludelist=excludelist,
deletechars=deletechars,
case_sensitive=case_sensitive,
replace_space=replace_space)
# Make sure the names is a list (for 2.5)
if names is not None:
names = list(names)
if usecols:
for (i, current) in enumerate(usecols):
# if usecols is a list of names, convert to a list of indices
if _is_string_like(current):
usecols[i] = names.index(current)
elif current < 0:
usecols[i] = current + len(first_values)
# If the dtype is not None, make sure we update it
if (dtype is not None) and (len(dtype) > nbcols):
descr = dtype.descr
dtype = np.dtype([descr[_] for _ in usecols])
names = list(dtype.names)
# If `names` is not None, update the names
elif (names is not None) and (len(names) > nbcols):
names = [names[_] for _ in usecols]
elif (names is not None) and (dtype is not None):
names = list(dtype.names)
# Process the missing values ...............................
# Rename missing_values for convenience
user_missing_values = missing_values or ()
# Define the list of missing_values (one column: one list)
missing_values = [list([asbytes('')]) for _ in range(nbcols)]
# We have a dictionary: process it field by field
if isinstance(user_missing_values, dict):
# Loop on the items
for (key, val) in user_missing_values.items():
# Is the key a string ?
if _is_string_like(key):
try:
# Transform it into an integer
key = names.index(key)
except ValueError:
# We couldn't find it: the name must have been dropped
continue
# Redefine the key as needed if it's a column number
if usecols:
try:
key = usecols.index(key)
except ValueError:
pass
# Transform the value as a list of string
if isinstance(val, (list, tuple)):
val = [str(_) for _ in val]
else:
val = [str(val), ]
# Add the value(s) to the current list of missing
if key is None:
# None acts as default
for miss in missing_values:
miss.extend(val)
else:
missing_values[key].extend(val)
# We have a sequence : each item matches a column
elif isinstance(user_missing_values, (list, tuple)):
for (value, entry) in zip(user_missing_values, missing_values):
value = str(value)
if value not in entry:
entry.append(value)
# We have a string : apply it to all entries
elif isinstance(user_missing_values, bytes):
user_value = user_missing_values.split(asbytes(","))
for entry in missing_values:
entry.extend(user_value)
# We have something else: apply it to all entries
else:
for entry in missing_values:
entry.extend([str(user_missing_values)])
# Process the filling_values ...............................
# Rename the input for convenience
user_filling_values = filling_values
if user_filling_values is None:
user_filling_values = []
# Define the default
filling_values = [None] * nbcols
# We have a dictionary : update each entry individually
if isinstance(user_filling_values, dict):
for (key, val) in user_filling_values.items():
if _is_string_like(key):
try:
# Transform it into an integer
key = names.index(key)
except ValueError:
# We couldn't find it: the name must have been dropped,
continue
# Redefine the key if it's a column number and usecols is defined
if usecols:
try:
key = usecols.index(key)
except ValueError:
pass
# Add the value to the list
filling_values[key] = val
# We have a sequence : update on a one-to-one basis
elif isinstance(user_filling_values, (list, tuple)):
n = len(user_filling_values)
if (n <= nbcols):
filling_values[:n] = user_filling_values
else:
filling_values = user_filling_values[:nbcols]
# We have something else : use it for all entries
else:
filling_values = [user_filling_values] * nbcols
# Initialize the converters ................................
if dtype is None:
# Note: we can't use a [...]*nbcols, as we would have 3 times the same
# ... converter, instead of 3 different converters.
converters = [StringConverter(None, missing_values=miss, default=fill)
for (miss, fill) in zip(missing_values, filling_values)]
else:
dtype_flat = flatten_dtype(dtype, flatten_base=True)
# Initialize the converters
if len(dtype_flat) > 1:
# Flexible type : get a converter from each dtype
zipit = zip(dtype_flat, missing_values, filling_values)
converters = [StringConverter(dt, locked=True,
missing_values=miss, default=fill)
for (dt, miss, fill) in zipit]
else:
# Set to a default converter (but w/ different missing values)
zipit = zip(missing_values, filling_values)
converters = [StringConverter(dtype, locked=True,
missing_values=miss, default=fill)
for (miss, fill) in zipit]
# Update the converters to use the user-defined ones
uc_update = []
for (j, conv) in user_converters.items():
# If the converter is specified by column names, use the index instead
if _is_string_like(j):
try:
j = names.index(j)
i = j
except ValueError:
continue
elif usecols:
try:
i = usecols.index(j)
except ValueError:
# Unused converter specified
continue
else:
i = j
# Find the value to test - first_line is not filtered by usecols:
if len(first_line):
testing_value = first_values[j]
else:
testing_value = None
converters[i].update(conv, locked=True,
testing_value=testing_value,
default=filling_values[i],
missing_values=missing_values[i],)
uc_update.append((i, conv))
# Make sure we have the corrected keys in user_converters...
user_converters.update(uc_update)
# Fixme: possible error as following variable never used.
#miss_chars = [_.missing_values for _ in converters]
# Initialize the output lists ...
# ... rows
rows = []
append_to_rows = rows.append
# ... masks
if usemask:
masks = []
append_to_masks = masks.append
# ... invalid
invalid = []
append_to_invalid = invalid.append
# Parse each line
for (i, line) in enumerate(itertools.chain([first_line, ], fhd)):
values = split_line(line)
nbvalues = len(values)
# Skip an empty line
if nbvalues == 0:
continue
if usecols:
# Select only the columns we need
try:
values = [values[_] for _ in usecols]
except IndexError:
append_to_invalid((i + skip_header + 1, nbvalues))
continue
elif nbvalues != nbcols:
append_to_invalid((i + skip_header + 1, nbvalues))
continue
# Store the values
append_to_rows(tuple(values))
if usemask:
append_to_masks(tuple([v.strip() in m
for (v, m) in zip(values,
missing_values)]))
if len(rows) == max_rows:
break
if own_fhd:
fhd.close()
# Upgrade the converters (if needed)
if dtype is None:
for (i, converter) in enumerate(converters):
current_column = [itemgetter(i)(_m) for _m in rows]
try:
converter.iterupgrade(current_column)
except ConverterLockError:
errmsg = "Converter #%i is locked and cannot be upgraded: " % i
current_column = map(itemgetter(i), rows)
for (j, value) in enumerate(current_column):
try:
converter.upgrade(value)
except (ConverterError, ValueError):
errmsg += "(occurred line #%i for value '%s')"
errmsg %= (j + 1 + skip_header, value)
raise ConverterError(errmsg)
# Check that we don't have invalid values
nbinvalid = len(invalid)
if nbinvalid > 0:
nbrows = len(rows) + nbinvalid - skip_footer
# Construct the error message
template = " Line #%%i (got %%i columns instead of %i)" % nbcols
if skip_footer > 0:
nbinvalid_skipped = len([_ for _ in invalid
if _[0] > nbrows + skip_header])
invalid = invalid[:nbinvalid - nbinvalid_skipped]
skip_footer -= nbinvalid_skipped
#
# nbrows -= skip_footer
# errmsg = [template % (i, nb)
# for (i, nb) in invalid if i < nbrows]
# else:
errmsg = [template % (i, nb)
for (i, nb) in invalid]
if len(errmsg):
errmsg.insert(0, "Some errors were detected !")
errmsg = "\n".join(errmsg)
# Raise an exception ?
if invalid_raise:
raise ValueError(errmsg)
# Issue a warning ?
else:
warnings.warn(errmsg, ConversionWarning)
# Strip the last skip_footer data
if skip_footer > 0:
rows = rows[:-skip_footer]
if usemask:
masks = masks[:-skip_footer]
# Convert each value according to the converter:
# We want to modify the list in place to avoid creating a new one...
if loose:
rows = list(
zip(*[[conv._loose_call(_r) for _r in map(itemgetter(i), rows)]
for (i, conv) in enumerate(converters)]))
else:
rows = list(
zip(*[[conv._strict_call(_r) for _r in map(itemgetter(i), rows)]
for (i, conv) in enumerate(converters)]))
# Reset the dtype
data = rows
if dtype is None:
# Get the dtypes from the types of the converters
column_types = [conv.type for conv in converters]
# Find the columns with strings...
strcolidx = [i for (i, v) in enumerate(column_types)
if v in (type('S'), np.string_)]
# ... and take the largest number of chars.
for i in strcolidx:
column_types[i] = "|S%i" % max(len(row[i]) for row in data)
#
if names is None:
# If the dtype is uniform, don't define names, else use ''
base = set([c.type for c in converters if c._checked])
if len(base) == 1:
(ddtype, mdtype) = (list(base)[0], np.bool)
else:
ddtype = [(defaultfmt % i, dt)
for (i, dt) in enumerate(column_types)]
if usemask:
mdtype = [(defaultfmt % i, np.bool)
for (i, dt) in enumerate(column_types)]
else:
ddtype = list(zip(names, column_types))
mdtype = list(zip(names, [np.bool] * len(column_types)))
output = np.array(data, dtype=ddtype)
if usemask:
outputmask = np.array(masks, dtype=mdtype)
else:
# Overwrite the initial dtype names if needed
if names and dtype.names:
dtype.names = names
# Case 1. We have a structured type
if len(dtype_flat) > 1:
# Nested dtype, eg [('a', int), ('b', [('b0', int), ('b1', 'f4')])]
# First, create the array using a flattened dtype:
# [('a', int), ('b1', int), ('b2', float)]
# Then, view the array using the specified dtype.
if 'O' in (_.char for _ in dtype_flat):
if has_nested_fields(dtype):
raise NotImplementedError(
"Nested fields involving objects are not supported...")
else:
output = np.array(data, dtype=dtype)
else:
rows = np.array(data, dtype=[('', _) for _ in dtype_flat])
output = rows.view(dtype)
# Now, process the rowmasks the same way
if usemask:
rowmasks = np.array(
masks, dtype=np.dtype([('', np.bool) for t in dtype_flat]))
# Construct the new dtype
mdtype = make_mask_descr(dtype)
outputmask = rowmasks.view(mdtype)
# Case #2. We have a basic dtype
else:
# We used some user-defined converters
if user_converters:
ishomogeneous = True
descr = []
for i, ttype in enumerate([conv.type for conv in converters]):
# Keep the dtype of the current converter
if i in user_converters:
ishomogeneous &= (ttype == dtype.type)
if ttype == np.string_:
ttype = "|S%i" % max(len(row[i]) for row in data)
descr.append(('', ttype))
else:
descr.append(('', dtype))
# So we changed the dtype ?
if not ishomogeneous:
# We have more than one field
if len(descr) > 1:
dtype = np.dtype(descr)
# We have only one field: drop the name if not needed.
else:
dtype = np.dtype(ttype)
#
output = np.array(data, dtype)
if usemask:
if dtype.names:
mdtype = [(_, np.bool) for _ in dtype.names]
else:
mdtype = np.bool
outputmask = np.array(masks, dtype=mdtype)
# Try to take care of the missing data we missed
names = output.dtype.names
if usemask and names:
for (name, conv) in zip(names or (), converters):
missing_values = [conv(_) for _ in conv.missing_values
if _ != asbytes('')]
for mval in missing_values:
outputmask[name] |= (output[name] == mval)
# Construct the final array
if usemask:
output = output.view(MaskedArray)
output._mask = outputmask
if unpack:
return output.squeeze().T
return output.squeeze()
def ndfromtxt(fname, **kwargs):
"""
Load ASCII data stored in a file and return it as a single array.
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function.
"""
kwargs['usemask'] = False
return genfromtxt(fname, **kwargs)
def mafromtxt(fname, **kwargs):
"""
Load ASCII data stored in a text file and return a masked array.
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function to load ASCII data.
"""
kwargs['usemask'] = True
return genfromtxt(fname, **kwargs)
def recfromtxt(fname, **kwargs):
"""
Load ASCII data from a file and return it in a record array.
If ``usemask=False`` a standard `recarray` is returned,
if ``usemask=True`` a MaskedRecords array is returned.
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function
Notes
-----
By default, `dtype` is None, which means that the data-type of the output
array will be determined from the data.
"""
kwargs.setdefault("dtype", None)
usemask = kwargs.get('usemask', False)
output = genfromtxt(fname, **kwargs)
if usemask:
from numpy.ma.mrecords import MaskedRecords
output = output.view(MaskedRecords)
else:
output = output.view(np.recarray)
return output
def recfromcsv(fname, **kwargs):
"""
Load ASCII data stored in a comma-separated file.
The returned array is a record array (if ``usemask=False``, see
`recarray`) or a masked record array (if ``usemask=True``,
see `ma.mrecords.MaskedRecords`).
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function to load ASCII data.
Notes
-----
By default, `dtype` is None, which means that the data-type of the output
array will be determined from the data.
"""
# Set default kwargs for genfromtxt as relevant to csv import.
kwargs.setdefault("case_sensitive", "lower")
kwargs.setdefault("names", True)
kwargs.setdefault("delimiter", ",")
kwargs.setdefault("dtype", None)
output = genfromtxt(fname, **kwargs)
usemask = kwargs.get("usemask", False)
if usemask:
from numpy.ma.mrecords import MaskedRecords
output = output.view(MaskedRecords)
else:
output = output.view(np.recarray)
return output
| bsd-3-clause |
banesullivan/ParaViewGeophysics | PVGeo/ubc/tensor.py | 1 | 21910 | __all__ = [
'TensorMeshReader',
'TensorMeshAppender',
'TopoMeshAppender',
]
__displayname__ = 'Tensor Mesh'
import os
import sys
import numpy as np
import pandas as pd
import vtk
from .. import _helpers, interface
from ..base import AlgorithmBase
from .two_file_base import ModelAppenderBase, ubcMeshReaderBase
if sys.version_info < (3,):
from StringIO import StringIO
else:
from io import StringIO
class TensorMeshReader(ubcMeshReaderBase):
"""UBC Mesh 2D/3D models are defined using a 2-file format. The "mesh" file
describes how the data is discretized. The "model" file lists the physical
property values for all cells in a mesh. A model file is meaningless without
an associated mesh file. The reader will automatically detect if the mesh is
2D or 3D and read the remainder of the data with that dimensionality
assumption. If the mesh file is 2D, then then model file must also be in the
2D format (same for 3D).
Note:
Model File is optional. Reader will still construct
``vtkRectilinearGrid`` safely.
"""
__displayname__ = 'UBC Tensor Mesh Reader'
__category__ = 'reader'
description = 'PVGeo: UBC Mesh 2D/3D Two-File Format'
def __init__(self, nOutputPorts=1, outputType='vtkRectilinearGrid', **kwargs):
ubcMeshReaderBase.__init__(
self, nOutputPorts=nOutputPorts, outputType=outputType, **kwargs
)
self.__mesh = vtk.vtkRectilinearGrid()
self.__models = []
@staticmethod
def place_model_on_mesh(mesh, model, data_name='Data'):
"""Places model data onto a mesh. This is for the UBC Grid data reaers
to associate model data with the mesh grid.
Args:
mesh (vtkRectilinearGrid): The ``vtkRectilinearGrid`` that is the
mesh to place the model data upon.
model (np.array): A NumPy float array that holds all of the data to
place inside of the mesh's cells.
data_name (str) : The name of the model data array once placed on the
``vtkRectilinearGrid``.
Return:
vtkRectilinearGrid :
Returns the input ``vtkRectilinearGrid`` with model data appended.
"""
if isinstance(model, dict):
for key in model.keys():
TensorMeshReader.place_model_on_mesh(mesh, model[key], data_name=key)
return mesh
# model.GetNumberOfValues() if model is vtkDataArray
# Make sure this model file fits the dimensions of the mesh
ext = mesh.GetExtent()
n1, n2, n3 = ext[1], ext[3], ext[5]
if n1 * n2 * n3 < len(model):
raise _helpers.PVGeoError(
'Model `%s` has more data than the given mesh has cells to hold.'
% data_name
)
elif n1 * n2 * n3 > len(model):
raise _helpers.PVGeoError(
'Model `%s` does not have enough data to fill the given mesh\'s cells.'
% data_name
)
# Swap axes because VTK structures the coordinates a bit differently
# - This is absolutely crucial!
# - Do not play with unless you know what you are doing!
if model.ndim > 1 and model.ndim < 3:
ncomp = model.shape[1]
model = np.reshape(model, (n1, n2, n3, ncomp))
model = np.swapaxes(model, 0, 1)
model = np.swapaxes(model, 0, 2)
# Now reverse Z axis
model = model[::-1, :, :, :] # Note it is in Fortran ordering
model = np.reshape(model, (n1 * n2 * n3, ncomp))
else:
model = np.reshape(model, (n1, n2, n3))
model = np.swapaxes(model, 0, 1)
model = np.swapaxes(model, 0, 2)
# Now reverse Z axis
model = model[::-1, :, :] # Note it is in Fortran ordering
model = model.flatten()
# Convert data to VTK data structure and append to output
c = interface.convert_array(model, name=data_name, deep=True)
# THIS IS CELL DATA! Add the model data to CELL data:
mesh.GetCellData().AddArray(c)
return mesh
# ------------------------------------------------------------------#
# ---------------------- UBC MESH 2D ------------------------#
# ------------------------------------------------------------------#
@staticmethod
def ubc_mesh_2d(FileName, output):
"""This method reads a UBC 2D Mesh file and builds an empty
``vtkRectilinearGrid`` for data to be inserted into. `Format Specs`_.
.. _Format Specs: http://giftoolscookbook.readthedocs.io/en/latest/content/fileFormats/mesh2Dfile.html
Args:
FileName (str) : The mesh filename as an absolute path for the input
mesh file in UBC 3D Mesh Format.
output (vtkRectilinearGrid) : The output data object
Return:
vtkRectilinearGrid :
a ``vtkRectilinearGrid`` generated from the UBC 3D Mesh grid.
Mesh is defined by the input mesh file.
No data attributes here, simply an empty mesh. Use the
``place_model_on_mesh()`` method to associate with model data.
"""
# Read in data from file
xpts, xdisc, zpts, zdisc = ubcMeshReaderBase._ubc_mesh_2d_part(FileName)
nx = np.sum(np.array(xdisc, dtype=int)) + 1
nz = np.sum(np.array(zdisc, dtype=int)) + 1
# Now generate the vtkRectilinear Grid
def _genCoords(pts, disc, z=False):
c = [float(pts[0])]
for i in range(len(pts) - 1):
start = float(pts[i])
stop = float(pts[i + 1])
num = int(disc[i])
w = (stop - start) / num
for j in range(1, num):
c.append(start + (j) * w)
c.append(stop)
c = np.array(c, dtype=float)
if z:
c = -c[::-1]
return interface.convert_array(c, deep=True)
xcoords = _genCoords(xpts, xdisc)
zcoords = _genCoords(zpts, zdisc, z=True)
ycoords = interface.convert_array(np.zeros(1), deep=True)
output.SetDimensions(nx, 2, nz) # note this subtracts 1
output.SetXCoordinates(xcoords)
output.SetYCoordinates(ycoords)
output.SetZCoordinates(zcoords)
return output
@staticmethod
def ubc_model_2d(FileName):
"""Reads a 2D model file and returns a 1D NumPy float array. Use the
``place_model_on_mesh()`` method to associate with a grid.
Note:
Only supports single component data
Args:
FileName (str) : The model filename as an absolute path for the
input model file in UBCMesh Model Format. Also accepts a list of
string file names.
Return:
np.array :
a NumPy float array that holds the model data read from
the file. Use the ``place_model_on_mesh()`` method to associate
with a grid. If a list of file names is given then it will
return a dictionary of NumPy float array with keys as the
basenames of the files.
"""
if isinstance(FileName, (list, tuple)):
out = {}
for f in FileName:
out[os.path.basename(f)] = TensorMeshReader.ubc_model_2d(f)
return out
dim = np.genfromtxt(
FileName, dtype=int, delimiter=None, comments='!', max_rows=1
)
names = ['col%d' % i for i in range(dim[0])]
df = pd.read_csv(
FileName, names=names, delim_whitespace=True, skiprows=1, comment='!'
)
data = df.values
if np.shape(data)[0] != dim[1] and np.shape(data)[1] != dim[0]:
raise _helpers.PVGeoError('Mode file `%s` improperly formatted.' % FileName)
return data.flatten(order='F')
def __ubc_mesh_data_2d(self, filename_mesh, filename_models, output):
"""Helper method to read a 2D mesh"""
# Construct/read the mesh
if self.need_to_readMesh():
TensorMeshReader.ubc_mesh_2d(filename_mesh, self.__mesh)
self.need_to_readMesh(flag=False)
output.DeepCopy(self.__mesh)
if self.need_to_readModels() and self.this_has_models():
self.__models = []
for f in filename_models:
# Read the model data
self.__models.append(TensorMeshReader.ubc_model_2d(f))
self.need_to_readModels(flag=False)
return output
# ------------------------------------------------------------------#
# ---------------------- UBC MESH 3D ------------------------#
# ------------------------------------------------------------------#
@staticmethod
def ubc_mesh_3d(FileName, output):
"""This method reads a UBC 3D Mesh file and builds an empty
``vtkRectilinearGrid`` for data to be inserted into.
Args:
FileName (str) : The mesh filename as an absolute path for the input
mesh file in UBC 3D Mesh Format.
output (vtkRectilinearGrid) : The output data object
Return:
vtkRectilinearGrid :
a ``vtkRectilinearGrid`` generated from the UBC 3D Mesh grid.
Mesh is defined by the input mesh file.
No data attributes here, simply an empty mesh. Use the
``place_model_on_mesh()`` method to associate with model data.
"""
# --- Read in the mesh ---#
fileLines = np.genfromtxt(FileName, dtype=str, delimiter='\n', comments='!')
# Get mesh dimensions
dim = np.array(fileLines[0].split('!')[0].split(), dtype=int)
dim = (dim[0] + 1, dim[1] + 1, dim[2] + 1)
# The origin corner (Southwest-top)
# - Remember UBC format specifies down as the positive Z
# - Easting, Northing, Altitude
oo = np.array(fileLines[1].split('!')[0].split(), dtype=float)
ox, oy, oz = oo[0], oo[1], oo[2]
# Read cell sizes for each line in the UBC mesh files
def _readCellLine(line):
line_list = []
for seg in line.split():
if '*' in seg:
sp = seg.split('*')
seg_arr = np.ones((int(sp[0]),), dtype=float) * float(sp[1])
else:
seg_arr = np.array([float(seg)], dtype=float)
line_list.append(seg_arr)
return np.concatenate(line_list)
# Read the cell sizes
cx = _readCellLine(fileLines[2].split('!')[0])
cy = _readCellLine(fileLines[3].split('!')[0])
cz = _readCellLine(fileLines[4].split('!')[0])
# Invert the indexing of the vector to start from the bottom.
cz = cz[::-1]
# Adjust the reference point to the bottom south west corner
oz = oz - np.sum(cz)
# Now generate the coordinates for from cell width and origin
cox = ox + np.cumsum(cx)
cox = np.insert(cox, 0, ox)
coy = oy + np.cumsum(cy)
coy = np.insert(coy, 0, oy)
coz = oz + np.cumsum(cz)
coz = np.insert(coz, 0, oz)
# Set the dims and coordinates for the output
output.SetDimensions(dim[0], dim[1], dim[2])
# Convert to VTK array for setting coordinates
output.SetXCoordinates(interface.convert_array(cox, deep=True))
output.SetYCoordinates(interface.convert_array(coy, deep=True))
output.SetZCoordinates(interface.convert_array(coz, deep=True))
return output
def __ubc_mesh_data_3d(self, filename_mesh, filename_models, output):
"""Helper method to read a 3D mesh"""
# Construct/read the mesh
if self.need_to_readMesh():
TensorMeshReader.ubc_mesh_3d(filename_mesh, self.__mesh)
self.need_to_readMesh(flag=False)
output.DeepCopy(self.__mesh)
if self.need_to_readModels() and self.this_has_models():
self.__models = []
for f in filename_models:
# Read the model data
self.__models.append(TensorMeshReader.ubc_model_3d(f))
self.need_to_readModels(flag=False)
return output
def __ubc_tensor_mesh(self, filename_mesh, filename_models, output):
"""Wrapper to Read UBC GIF 2D and 3D meshes. UBC Mesh 2D/3D models are
defined using a 2-file format. The "mesh" file describes how the data is
descritized. The "model" file lists the physical property values for all
cells in a mesh. A model file is meaningless without an associated mesh
file. If the mesh file is 2D, then then model file must also be in the
2D format (same for 3D).
Args:
filename_mesh (str) : The mesh filename as an absolute path for the
input mesh file in UBC 2D/3D Mesh Format
filename_models (str or list(str)) : The model filename(s) as an
absolute path for the input model file in UBC 2D/3D Model Format.
output (vtkRectilinearGrid) : The output data object
Return:
vtkRectilinearGrid :
a ``vtkRectilinearGrid`` generated from the UBC 2D/3D Mesh grid.
Mesh is defined by the input mesh file.
Cell data is defined by the input model file.
"""
# Check if the mesh is a UBC 2D mesh
if self.is_2d():
self.__ubc_mesh_data_2d(filename_mesh, filename_models, output)
# Check if the mesh is a UBC 3D mesh
elif self.is_3d():
self.__ubc_mesh_data_3d(filename_mesh, filename_models, output)
else:
raise _helpers.PVGeoError('File format not recognized')
return output
def RequestData(self, request, inInfo, outInfo):
"""Handles data request by the pipeline."""
# Get output:
output = self.GetOutputData(outInfo, 0)
# Get requested time index
i = _helpers.get_requested_time(self, outInfo)
self.__ubc_tensor_mesh(
self.get_mesh_filename(), self.get_model_filenames(), output
)
# Place the model data for given timestep onto the mesh
if len(self.__models) > i:
TensorMeshReader.place_model_on_mesh(
output, self.__models[i], self.get_data_name()
)
return 1
def RequestInformation(self, request, inInfo, outInfo):
"""Handles info request by pipeline about timesteps and grid extents."""
# Call parent to handle time stuff
ubcMeshReaderBase.RequestInformation(self, request, inInfo, outInfo)
# Now set whole output extent
if self.need_to_readMesh():
ext = self._read_extent()
info = outInfo.GetInformationObject(0)
# Set WHOLE_EXTENT: This is absolutely necessary
info.Set(vtk.vtkStreamingDemandDrivenPipeline.WHOLE_EXTENT(), ext, 6)
return 1
def clear_mesh(self):
"""Use to clean/rebuild the mesh"""
self.__mesh = vtk.vtkRectilinearGrid()
ubcMeshReaderBase.clear_models(self)
def clear_models(self):
"""Use to clean the models and reread"""
self.__models = []
ubcMeshReaderBase.clear_models(self)
###############################################################################
class TensorMeshAppender(ModelAppenderBase):
"""This filter reads a timeseries of models and appends it to an input
``vtkRectilinearGrid``
"""
__displayname__ = 'UBC Tensor Mesh Appender'
__category__ = 'filter'
def __init__(self, **kwargs):
ModelAppenderBase.__init__(
self,
inputType='vtkRectilinearGrid',
outputType='vtkRectilinearGrid',
**kwargs
)
def _read_up_front(self):
"""Internal helepr to read data at start"""
reader = ubcMeshReaderBase.ubc_model_3d
if not self._is_3D:
# Note how in UBC format, 2D grids are specified on an XZ plane (no Y component)
# This will only work prior to rotations to account for real spatial reference
reader = TensorMeshReader.ubc_model_2d
self._models = []
for f in self._model_filenames:
# Read the model data
self._models.append(reader(f))
self.need_to_read(flag=False)
return
def _place_on_mesh(self, output, idx=0):
"""Internal helepr to place a model on the mesh for a given index"""
TensorMeshReader.place_model_on_mesh(
output, self._models[idx], self.get_data_name()
)
return
###############################################################################
class TopoMeshAppender(AlgorithmBase):
"""This filter reads a single discrete topography file and appends it as a
boolean data array.
"""
__displayname__ = 'Append UBC Discrete Topography'
__category__ = 'filter'
def __init__(
self, inputType='vtkRectilinearGrid', outputType='vtkRectilinearGrid', **kwargs
):
AlgorithmBase.__init__(
self,
nInputPorts=1,
inputType=inputType,
nOutputPorts=1,
outputType=outputType,
)
self._topoFileName = kwargs.get('filename', None)
self.__indices = None
self.__need_to_read = True
self.__ne, self.__nn = None, None
def need_to_read(self, flag=None):
"""Ask self if the reader needs to read the files again
Args:
flag (bool): if the flag is set then this method will set the read
status
Return:
bool:
The status of the reader aspect of the filter.
"""
if flag is not None and isinstance(flag, (bool, int)):
self.__need_to_read = flag
return self.__need_to_read
def Modified(self, read_again=True):
"""Call modified if the files needs to be read again again."""
if read_again:
self.__need_to_read = read_again
AlgorithmBase.Modified(self)
def modified(self, read_again=True):
"""Call modified if the files needs to be read again again."""
return self.Modified(read_again=read_again)
def _read_up_front(self):
"""Internal helepr to read data at start"""
# Read the file
content = np.genfromtxt(
self._topoFileName, dtype=str, delimiter='\n', comments='!'
)
dim = content[0].split()
self.__ne, self.__nn = int(dim[0]), int(dim[1])
self.__indices = pd.read_csv(
StringIO("\n".join(content[1::])),
names=['i', 'j', 'k'],
delim_whitespace=True,
)
# NOTE: K indices are inverted
self.need_to_read(flag=False)
return
def _place_on_mesh(self, output):
"""Internal helepr to place an active cells model on the mesh"""
# Check mesh extents to math topography
nx, ny, nz = output.GetDimensions()
nx, ny, nz = nx - 1, ny - 1, nz - 1 # because GetDimensions counts the nodes
topz = np.max(self.__indices['k']) + 1
if nx != self.__nn or ny != self.__ne or topz > nz:
raise _helpers.PVGeoError(
'Dimension mismatch between input grid and topo file.'
)
# # Adjust the k indices to be in caarteian system
# self.__indices['k'] = nz - self.__indices['k']
# Fill out the topo and add it as model as it will be in UBC format
# Create a 3D array of 1s and zeros (1 means beneath topo or active)
topo = np.empty((ny, nx, nz), dtype=float)
topo[:] = np.nan
for row in self.__indices.values:
i, j, k = row
topo[i, j, k + 1 :] = 0
topo[i, j, : k + 1] = 1
# Add as model... ``place_model_on_mesh`` handles the rest
TensorMeshReader.place_model_on_mesh(
output, topo.flatten(), 'Active Topography'
)
return
def RequestData(self, request, inInfo, outInfo):
"""Used by pipeline to generate output"""
# Get input/output of Proxy
pdi = self.GetInputData(inInfo, 0, 0)
output = self.GetOutputData(outInfo, 0)
output.DeepCopy(pdi) # ShallowCopy if you want changes to propagate upstream
# Perfrom task:
if self.__need_to_read:
self._read_up_front()
# Place the model data for given timestep onto the mesh
self._place_on_mesh(output)
return 1
#### Setters and Getters ####
def clear_topo_file(self):
"""Use to clear data file name."""
self._topoFileName = None
self.Modified(read_again=True)
def set_topo_filename(self, filename):
"""Use to set the file names for the reader. Handles single strings only"""
if filename is None:
return # do nothing if None is passed by a constructor on accident
elif isinstance(filename, str) and self._topoFileName != filename:
self._topoFileName = filename
self.Modified()
return 1
###############################################################################
#
# import numpy as np
# indices = np.array([[0,0,1],
# [0,1,1],
# [0,2,1],
# [1,0,1],
# [1,1,1],
# [1,2,1],
# [2,0,1],
# [2,1,1],
# [2,2,1],
# ])
#
# topo = np.empty((3,3,3), dtype=float)
# topo[:] = np.nan
#
# for row in indices:
# i, j, k = row
# topo[i, j, k:] = 0
# topo[i, j, :k] = 1
# topo
| bsd-3-clause |
Healthcast/RSV | python/all_year_predict/methods.py | 2 | 3879 | #!/usr/bin/pyhton
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, neighbors, linear_model
from sklearn import svm
from sklearn import metrics
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
def apply_algorithm(paras, X, y):
if paras['clf'] == 'svm':
clf = svm.SVC(kernel=paras['svm'][1], C=paras['svm'][0], probability=True)
elif paras['clf'] == 'knn':
clf = neighbors.KNeighborsClassifier(paras['knn'][0],\
weights=paras['knn'][1])
elif paras['clf'] == 'rf':
clf = RandomForestClassifier(max_depth=paras['rf'][0], \
n_estimators=paras['rf'][1],\
max_features=paras['rf'][2])
else:
print str("unknown classifier")
sys.exit(2)
return clf
def apply_evaluation(paras, X, y, clf, data):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, \
random_state=0)
clf.fit(X_train, y_train)
r = clf.predict(X_test)
d = clf.decision_function(X)
p = clf.predict_proba(X).T[1]*3
h = data["hospital"].T[data["city"].index(paras["city"])]
h1 = h.astype(float)
m = max(h1)
h1=h1/m*4
plt.figure()
# plt.plot(d)
plt.plot(y)
plt.plot(h1)
plt.plot(p)
# height = 4
# bottom = -2
# ss = data["season_start"]
# date=data["date1"]
# c_id = data["city"].index(paras["city"])
# ylabel = data["ylabels"]
# for m in ss:
# plt.plot([m, m],[bottom, height], 'y--', linewidth=1)
#
# for m in range(1, len(ss)-1):
# a = ss[m]
# plt.text(a-5,height, date[a].split('-')[0])
#
# #plot the start week
# up=1
# for j in range(len(ylabel.T[c_id])-1):
# if ylabel.T[c_id,j] == 1 :
# plt.plot([j, j],[bottom, height], 'k-', linewidth=2)
# if up==1:
# plt.text(j-10, height-1, date[j])
# up=0
# else:
# plt.text(j-10, height-2, date[j])
# up=1
#
plt.show()
#plot the results
# x_min, x_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1
# y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1
#
# xx, yy = np.meshgrid(np.arange(x_min, x_max, 1), np.arange(y_min, y_max, 1))
# Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Z = Z.reshape(xx.shape)
#
# plt.figure()
# plt.pcolormesh(xx, yy, Z)
# plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train)
# plt.xlim(xx.min(), xx.max())
# plt.ylim(yy.min(), yy.max())
# plt.title("binary classification classification")
# plt.show()
#
if paras['eva'] == 'accuracy':
print "The accuracy:"
print metrics.accuracy_score(y_test, r)
elif paras['eva'] == 'precision':
print "The precision:"
print metrics.precision_score(y_test, r)
elif paras['eva'] == 'recall':
print "The recall:"
print metrics.recall_score(y_test, r)
elif paras['eva'] == 'confusion':
print "The confusion matrix:"
print metrics.confusion_matrix(y_test, r)
elif paras['eva'] == 'report':
print "The report:"
print metrics.classification_report(y_test, r)
elif paras['eva'] == 'roc' and paras['clf'] == 'svm':
scores = clf.decision_function(X_test)
print "The auc:"
fpr, tpr, thresholds = metrics.roc_curve(y_test, scores)
roc_auc = metrics.auc(fpr, tpr)
print str(roc_auc)
plt.figure()
plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.show()
| gpl-2.0 |
CIFASIS/pylearn2 | pylearn2/packaged_dependencies/theano_linear/unshared_conv/localdot.py | 39 | 5044 | """
WRITEME
"""
import logging
from ..linear import LinearTransform
from .unshared_conv import FilterActs, ImgActs
from theano.compat.six.moves import xrange
from theano.sandbox import cuda
if cuda.cuda_available:
import gpu_unshared_conv # register optimizations
import numpy as np
import warnings
try:
import matplotlib.pyplot as plt
except (RuntimeError, ImportError, TypeError) as matplotlib_exception:
warnings.warn("Unable to import matplotlib. Some features unavailable. "
"Original exception: " + str(matplotlib_exception))
logger = logging.getLogger(__name__)
class LocalDot(LinearTransform):
"""
LocalDot is an linear operation computationally similar to
convolution in the spatial domain, except that whereas convolution
applying a single filter or set of filters across an image, the
LocalDot has different filterbanks for different points in the image.
Mathematically, this is a general linear transform except for a
restriction that filters are 0 outside of a spatially localized patch
within the image.
Image shape is 5-tuple:
color_groups
colors_per_group
rows
cols
images
Filterbank shape is 7-tuple (!)
0 row_positions
1 col_positions
2 colors_per_group
3 height
4 width
5 color_groups
6 filters_per_group
The result of left-multiplication a 5-tuple with shape:
filter_groups
filters_per_group
row_positions
col_positions
images
Parameters
----------
filters : WRITEME
irows : WRITEME
Image rows
icols : WRITEME
Image columns
subsample : WRITEME
padding_start : WRITEME
filters_shape : WRITEME
message : WRITEME
"""
def __init__(self, filters, irows, icols=None,
subsample=(1, 1),
padding_start=None,
filters_shape=None,
message=""):
LinearTransform.__init__(self, [filters])
self._filters = filters
if filters_shape is None:
self._filters_shape = tuple(filters.get_value(borrow=True).shape)
else:
self._filters_shape = tuple(filters_shape)
self._irows = irows
if icols is None:
self._icols = irows
else:
self._icols = icols
if self._icols != self._irows:
raise NotImplementedError('GPU code at least needs square imgs')
self._subsample = tuple(subsample)
self._padding_start = padding_start
if len(self._filters_shape) != 7:
raise TypeError('need 7-tuple filter shape', self._filters_shape)
if self._subsample[0] != self._subsample[1]:
raise ValueError('subsampling must be same in rows and cols')
self._filter_acts = FilterActs(self._subsample[0])
self._img_acts = ImgActs(module_stride=self._subsample[0])
if message:
self._message = message
else:
self._message = filters.name
def rmul(self, x):
"""
.. todo::
WRITEME
"""
assert x.ndim == 5
return self._filter_acts(x, self._filters)
def rmul_T(self, x):
"""
.. todo::
WRITEME
"""
return self._img_acts(self._filters, x, self._irows, self._icols)
def col_shape(self):
"""
.. todo::
WRITEME
"""
ishape = self.row_shape() + (-99,)
fshape = self._filters_shape
hshape, = self._filter_acts.infer_shape(None, (ishape, fshape))
assert hshape[-1] == -99
return hshape[:-1]
def row_shape(self):
"""
.. todo::
WRITEME
"""
fshape = self._filters_shape
fmodulesR, fmodulesC, fcolors, frows, fcols = fshape[:-2]
fgroups, filters_per_group = fshape[-2:]
return fgroups, fcolors, self._irows, self._icols
def print_status(self):
"""
.. todo::
WRITEME
"""
raise NotImplementedError("TODO: fix dependence on non-existent "
"ndarray_status function")
"""print ndarray_status(
self._filters.get_value(borrow=True),
msg='%s{%s}'% (self.__class__.__name__,
self._message))
"""
def imshow_gray(self):
"""
.. todo::
WRITEME
"""
filters = self._filters.get_value()
modR, modC, colors, rows, cols, grps, fs_per_grp = filters.shape
logger.info(filters.shape)
rval = np.zeros((
modR * (rows + 1) - 1,
modC * (cols + 1) - 1,
))
for rr, modr in enumerate(xrange(0, rval.shape[0], rows + 1)):
for cc, modc in enumerate(xrange(0, rval.shape[1], cols + 1)):
rval[modr:modr + rows, modc:modc + cols] = filters[rr, cc, 0, :, :, 0, 0]
plt.imshow(rval, cmap='gray')
return rval
| bsd-3-clause |
kcompher/thunder | thunder/extraction/source.py | 6 | 31847 | from numpy import asarray, mean, sqrt, ndarray, amin, amax, concatenate, sum, zeros, maximum, \
argmin, newaxis, ones, delete, NaN, inf, isnan, clip, logical_or, unique, where, all
from thunder.utils.serializable import Serializable
from thunder.utils.common import checkParams, aslist
from thunder.rdds.images import Images
from thunder.rdds.series import Series
class Source(Serializable, object):
"""
A single source, represented as a list of coordinates and other optional specifications.
A source also has a set of lazily computed attributes useful for representing and comparing
its geometry, such as center, bounding box, and bounding polygon. These properties
will be computed lazily and made available as attributes when requested.
Parameters
----------
coordinates : array-like
List of 2D or 3D coordinates, can be a list of lists or array of shape (n,2) or (n,3)
values : list or array-like
Value (or weight) associated with each coordiante
id : int or string
Arbitrary specification per source, typically an index or string label
Attributes
----------
center : list or array-like
The coordinates of the center of the source
polygon : list or array-like
The coordinates of a polygon bounding the region (a convex hull)
bbox : list or array-like
Boundaries of the source (with the lowest values for all axes followed by the highest values)
area : scalar
The area of the region
"""
from zope.cachedescriptors import property
def __init__(self, coordinates, values=None, id=None):
self.coordinates = asarray(coordinates)
if self.coordinates.ndim == 1 and len(self.coordinates) > 0:
self.coordinates = asarray([self.coordinates])
if values is not None:
self.values = asarray(values)
if self.values.ndim == 0:
self.values = asarray([self.values])
if not (len(self.coordinates) == len(self.values)):
raise ValueError("Lengths of coordinates %g and values %g do not match"
% (len(self.coordinates), len(self.values)))
if id is not None:
self.id = id
@property.Lazy
def center(self):
"""
Find the region center using a mean.
"""
# TODO Add option to use weights
return mean(self.coordinates, axis=0)
@property.Lazy
def polygon(self):
"""
Find the bounding polygon as a convex hull
"""
# TODO Add option for simplification
from scipy.spatial import ConvexHull
if len(self.coordinates) >= 4:
inds = ConvexHull(self.coordinates).vertices
return self.coordinates[inds]
else:
return self.coordinates
@property.Lazy
def bbox(self):
"""
Find the bounding box.
"""
mn = amin(self.coordinates, axis=0)
mx = amax(self.coordinates, axis=0)
return concatenate((mn, mx))
@property.Lazy
def area(self):
"""
Find the region area.
"""
return len(self.coordinates)
def restore(self, skip=None):
"""
Remove all lazy properties, will force recomputation
"""
if skip is None:
skip = []
elif isinstance(skip, str):
skip = [skip]
for prop in LAZY_ATTRIBUTES:
if prop in self.__dict__.keys() and prop not in skip:
del self.__dict__[prop]
return self
def distance(self, other, method='euclidean'):
"""
Distance between the center of this source and another.
Parameters
----------
other : Source, or array-like
Either another source, or the center coordinates of another source
method : str
Specify a distance measure to used for spatial distance between source
centers. Current options include Euclidean distance ('euclidean') and
L1-norm ('l1').
"""
from numpy.linalg import norm
checkParams(method, ['euclidean', 'l1'])
if method == 'l1':
order = 1
else:
order = 2
if isinstance(other, Source):
return norm(self.center - other.center, ord=order)
elif isinstance(other, list) or isinstance(other, ndarray):
return norm(self.center - asarray(other), ord=order)
def overlap(self, other, method='fraction'):
"""
Compute the overlap between this source and other.
Options are a symmetric measure of overlap based on the fraction
of intersecting pixels relative to the union ('fraction'), an assymmetric
measure of overlap that expresses detected intersecting pixels
(relative to this source) using precision and recall rates ('rates'), or
a correlation coefficient of the weights within the intersection
(not defined for binary weights) ('correlation')
Parameters
----------
other : Source
The source to compute overlap with.
method : str
Which estimate of overlap to compute, options are
'fraction' (symmetric) 'rates' (asymmetric) or 'correlation'
"""
checkParams(method, ['fraction', 'rates', 'correlation'])
coordsSelf = aslist(self.coordinates)
coordsOther = aslist(other.coordinates)
intersection = [a for a in coordsSelf if a in coordsOther]
nhit = float(len(intersection))
ntotal = float(len(set([tuple(x) for x in coordsSelf] + [tuple(x) for x in coordsOther])))
if method == 'rates':
recall = nhit / len(coordsSelf)
precision = nhit / len(coordsOther)
return recall, precision
if method == 'fraction':
return nhit / float(ntotal)
if method == 'correlation':
from scipy.stats import spearmanr
if not (hasattr(self, 'values') and hasattr(other, 'values')):
raise ValueError('Sources must have values to compute correlation')
else:
valuesSelf = aslist(self.values)
valuesOther = aslist(other.values)
if len(intersection) > 0:
left = [v for v, c in zip(valuesSelf, coordsSelf) if c in coordsOther]
right = [v for v, c in zip(valuesOther, coordsOther) if c in coordsSelf]
rho, _ = spearmanr(left, right)
else:
rho = 0.0
return rho
def merge(self, other):
"""
Combine this source with other
"""
self.coordinates = concatenate((self.coordinates, other.coordinates))
if hasattr(self, 'values'):
self.values = concatenate((self.values, other.values))
return self
def tolist(self):
"""
Convert array-like attributes to list
"""
import copy
new = copy.copy(self)
for prop in ["coordinates", "values", "center", "bbox", "polygon"]:
if prop in self.__dict__.keys():
val = new.__getattribute__(prop)
if val is not None and not isinstance(val, list):
setattr(new, prop, val.tolist())
return new
def toarray(self):
"""
Convert array-like attributes to ndarray
"""
import copy
new = copy.copy(self)
for prop in ["coordinates", "values", "center", "bbox", "polygon"]:
if prop in self.__dict__.keys():
val = new.__getattribute__(prop)
if val is not None and not isinstance(val, ndarray):
setattr(new, prop, asarray(val))
return new
def crop(self, minBound, maxBound):
"""
Crop a source by removing coordinates outside bounds.
Follows normal slice indexing conventions.
Parameters
----------
minBound : tuple
Minimum or starting bounds for each axis
maxBound : tuple
Maximum or ending bounds for each axis
"""
coords = self.coordinates
newid = self.id if hasattr(self, 'id') else None
if hasattr(self, 'values') and self.values is not None:
values = self.values
inside = [(c, v) for c, v in zip(coords, values) if c not in coords]
newcoords, newvalues = zip(*inside)
return Source(coordinates=newcoords, values=newvalues, id=newid)
else:
newcoords = [c for c in coords if all(c >= minBound) and all(c < maxBound)]
return Source(coordinates=newcoords, id=newid)
def dilate(self, size):
"""
Dilate a source using morphological operators.
Parameters
----------
size : int
Size of dilation in pixels
"""
if size == 0:
newcoords = self.coordinates
else:
size = (size * 2) + 1
if hasattr(self, 'values') and self.values is not None:
raise AttributeError('Cannot dilate sources with values')
from skimage.morphology import binary_dilation
coords = self.coordinates
extent = self.bbox[len(self.center):] - self.bbox[0:len(self.center)] + 1 + size * 2
m = zeros(extent)
coords = (coords - self.bbox[0:len(self.center)] + size)
m[coords.T.tolist()] = 1
m = binary_dilation(m, ones((size, size)))
newcoords = asarray(where(m)).T + self.bbox[0:len(self.center)] - size
newcoords = [c for c in newcoords if all(c >= 0)]
newid = self.id if hasattr(self, 'id') else None
return Source(coordinates=newcoords, id=newid)
def exclude(self, other):
"""
Remove coordinates derived from another Source or an array.
If other is an array, will remove coordinates of all
non-zero elements from this source. If other is a source,
will remove any matching coordinates.
Parameters
----------
other : ndarray or Source
Source to remove
"""
if isinstance(other, ndarray):
coordsOther = asarray(where(other)).T
else:
coordsOther = aslist(other.coordinates)
coordsSelf = aslist(self.coordinates)
newid = self.id if hasattr(self, 'id') else None
if hasattr(self, 'values') and self.values is not None:
valuesSelf = self.values
complement = [(c, v) for c, v in zip(coordsSelf, valuesSelf) if c not in coordsOther]
newcoords, newvalues = zip(*complement)
return Source(coordinates=newcoords, values=newvalues, id=newid)
else:
complement = [a for a in coordsSelf if a not in coordsOther]
return Source(coordinates=complement, id=newid)
def outline(self, inner, outer):
"""
Compute source outline by differencing two dilations
Parameters
----------
inner : int
Size of inner outline boundary (in pixels)
outer : int
Size of outer outline boundary (in pixels)
"""
return self.dilate(outer).exclude(self.dilate(inner))
def transform(self, data, collect=True):
"""
Extract series from data using a list of sources.
Currently only supports averaging over coordinates.
Params
------
data : Images or Series object
The data from which to extract
collect : boolean, optional, default = True
Whether to collect to local array or keep as a Series
"""
if not (isinstance(data, Images) or isinstance(data, Series)):
raise Exception("Input must either be Images or Series (or a subclass)")
# TODO add support for weighting
if isinstance(data, Images):
output = data.meanByRegions([self.coordinates]).toSeries()
else:
output = data.meanOfRegion(self.coordinates)
if collect:
return output.collectValuesAsArray()
else:
return output
def mask(self, dims=None, binary=True, outline=False, color=None):
"""
Construct a mask from a source, either locally or within a larger image.
Parameters
----------
dims : list or tuple, optional, default = None
Dimensions of large image in which to draw mask. If none, will restrict
to the bounding box of the region.
binary : boolean, optional, deafult = True
Whether to incoporate values or only show a binary mask
outline : boolean, optional, deafult = False
Whether to only show outlines (derived using binary dilation)
color : str or array-like
RGB triplet (from 0 to 1) or named color (e.g. 'red', 'blue')
"""
from thunder import Colorize
coords = self.coordinates
if dims is None:
extent = self.bbox[len(self.center):] - self.bbox[0:len(self.center)] + 1
m = zeros(extent)
coords = (coords - self.bbox[0:len(self.center)])
else:
m = zeros(dims)
if hasattr(self, 'values') and self.values is not None and binary is False:
m[coords.T.tolist()] = self.values
else:
m[coords.T.tolist()] = 1
if outline:
from skimage.morphology import binary_dilation
m = binary_dilation(m, ones((3, 3))) - m
if color is not None:
m = Colorize(cmap='indexed', colors=[color]).transform([m])
return m
def inbounds(self, minBound, maxBound):
"""
Check what fraction of coordinates are inside given bounds
Parameters
----------
minBound : list or tuple
Minimum bounds
maxBounds : list or tuple
Maximum bounds
"""
minCheck = sum(self.coordinates < minBound, axis=1) > 0
maxCheck = sum(self.coordinates > maxBound, axis=1) > 0
fraction = 1 - sum(logical_or(minCheck, maxCheck)) / float(len(self.coordinates))
return fraction
@staticmethod
def fromMask(mask, id=None):
"""
Genearte a source from a mask.
Assumes that the mask is an image where all non-zero
elements are part of the source. If all non-zero
elements are 1, then values will be ignored
as the source is assumed to be binary.
Parameters
----------
mask : array-like
An array (typically 2D or 3D) containing the image mask
id : int or string
Arbitrary identifier for the source, typically an int or string
"""
mask = asarray(mask)
u = unique(mask)
if len(u) == 2 and u[0] == 0 and u[1] == 1:
inds = where(mask)
return Source(coordinates=asarray(zip(*inds)), id=id)
else:
inds = where(mask)
values = mask[inds]
coords = asarray(zip(*inds))
return Source(coordinates=coords, values=values, id=id)
@staticmethod
def fromCoordinates(coordinates, values=None, id=None):
"""
Generate a source from a list of coordinates and values.
Parameters
----------
coordinates : array-like
List coordinates as a list of lists or array of shape (n,2) or (n,3)
values : list or array-like
Value (or weight) associated with each coordiante
id : int or string
Arbitrary specification per source, typically an index or string label
"""
return Source(coordinates, values, id)
def __repr__(self):
s = self.__class__.__name__
for opt in ["id", "center", "bbox"]:
if hasattr(self, opt):
o = self.__getattribute__(opt)
os = o.tolist() if isinstance(o, ndarray) else o
s += '\n%s: %s' % (opt, repr(os))
return s
class SourceModel(Serializable, object):
"""
A source model as a collection of extracted sources.
Parameters
----------
sources : list or Sources or a single Source
The identified sources
See also
--------
Source
"""
def __init__(self, sources):
if isinstance(sources, Source):
self.sources = [sources]
elif isinstance(sources, list) and isinstance(sources[0], Source):
self.sources = sources
elif isinstance(sources, list):
self.sources = []
for ss in sources:
self.sources.append(Source(ss))
else:
raise Exception("Input type not recognized, must be Source, list of Sources, "
"or list of coordinates, got %s" % type(sources))
def __getitem__(self, entry):
if not isinstance(entry, int):
raise IndexError("Selection not recognized, must be Int, got %s" % type(entry))
return self.sources[entry]
def combiner(self, prop, tolist=True):
combined = []
for s in self.sources:
p = getattr(s, prop)
if tolist:
p = p.tolist()
combined.append(p)
return combined
@property
def coordinates(self):
"""
List of coordinates combined across sources
"""
return self.combiner('coordinates')
@property
def values(self):
"""
List of coordinates combined across sources
"""
return self.combiner('values')
@property
def centers(self):
"""
Array of centers combined across sources
"""
return asarray(self.combiner('center'))
@property
def polygons(self):
"""
List of polygons combined across sources
"""
return self.combiner('polygon')
@property
def areas(self):
"""
List of areas combined across sources
"""
return self.combiner('area', tolist=False)
@property
def count(self):
"""
Number of sources
"""
return len(self.sources)
def masks(self, dims=None, binary=True, outline=False, base=None, color=None, inds=None):
"""
Composite masks combined across sources as an image.
Parameters
----------
dims : list or tuple, optional, default = None
Dimensions of image in which to create masks, must either provide
these or provide a base image
binary : boolean, optional, deafult = True
Whether to incoporate values or only show a binary mask
outline : boolean, optional, deafult = False
Whether to only show outlines (derived using binary dilation)
base : SourceModel or array-like, optional, deafult = None
Base background image on which to put masks,
or another set of sources (usually for comparisons).
color : str, optional, deafult = None
Color to assign regions, will assign randomly if 'random'
inds : array-like, optional, deafult = None
List of indices if only showing a subset
"""
from thunder import Colorize
from matplotlib.cm import get_cmap
if inds is None:
inds = range(0, self.count)
if dims is None and base is None:
raise Exception("Must provide image dimensions for composite masks "
"or provide a base image.")
if base is not None and isinstance(base, SourceModel):
outline = True
if dims is None and base is not None:
dims = asarray(base).shape
if isinstance(base, SourceModel):
base = base.masks(dims, color='silver')
elif isinstance(base, ndarray):
base = Colorize(cmap='indexed', colors=['white']).transform([base])
if base is not None and color is None:
color = 'deeppink'
if color == 'random':
combined = zeros(list(dims) + [3])
ncolors = min(self.count, 20)
colors = get_cmap('rainbow', ncolors)(range(0, ncolors, 1))[:, 0:3]
for i in inds:
combined = maximum(self.sources[i].mask(dims, binary, outline, colors[i % len(colors)]), combined)
else:
combined = zeros(dims)
for i in inds:
combined = maximum(self.sources[i].mask(dims, binary, outline), combined)
if color is not None and color != 'random':
combined = Colorize(cmap='indexed', colors=[color]).transform([combined])
if base is not None:
combined = maximum(base, combined)
return combined
def match(self, other, unique=False, minDistance=inf):
"""
For each source in self, find the index of the closest source in other.
Uses euclidean distances between centers to determine distances.
Can select nearest matches with or without enforcing uniqueness;
if unique is False, will return the closest source in other for
each source in self, possibly repeating sources multiple times
if unique is True, will only allow each source in other to be matched
with a single source in self, as determined by a greedy selection procedure.
The minDistance parameter can be used to prevent far-away sources from being
chosen during greedy selection.
Params
------
other : SourceModel
The source model to match sources to
unique : boolean, optional, deafult = True
Whether to only return unique matches
minDistance : scalar, optiona, default = inf
Minimum distance to use when selecting matches
"""
from scipy.spatial.distance import cdist
targets = other.centers
targetInds = range(0, len(targets))
matches = []
for s in self.sources:
update = 1
# skip if no targets left, otherwise update
if len(targets) == 0:
update = 0
else:
dists = cdist(targets, s.center[newaxis])
if dists.min() < minDistance:
ind = argmin(dists)
else:
update = 0
# apply updates, otherwise add a nan
if update == 1:
matches.append(targetInds[ind])
if unique is True:
targets = delete(targets, ind, axis=0)
targetInds = delete(targetInds, ind)
else:
matches.append(NaN)
return matches
def distance(self, other, minDistance=inf):
"""
Compute the distance between each source in self and other.
First estimates a matching source from other for each source
in self, then computes the distance between the two sources.
The matches are unique, using a greedy procedure,
and minDistance can be used to prevent outliers during matching.
Parameters
----------
other : SourceModel
The sources to compute distances to
minDistance : scalar, optiona, default = inf
Minimum distance to use when matching indices
"""
inds = self.match(other, unique=True, minDistance=minDistance)
d = []
for jj, ii in enumerate(inds):
if ii is not NaN:
d.append(self[jj].distance(other[ii]))
else:
d.append(NaN)
return asarray(d)
def overlap(self, other, method='fraction', minDistance=inf):
"""
Estimate overlap between sources in self and other.
Will compute the similarity of sources in self that are found
in other, based on either source pixel overlap or correlation.
Parameters
----------
other : SourceModel
The sources to compare to
method : str, optional, default = 'fraction"
Method to use when computing overlap between sources
('fraction', 'rates', or 'correlation')
minDistance : scalar, optional, default = inf
Minimum distance to use when matching indices
"""
inds = self.match(other, unique=True, minDistance=minDistance)
d = []
for jj, ii in enumerate(inds):
if ii is not NaN:
d.append(self[jj].overlap(other[ii], method=method))
else:
if method == 'rates':
d.append((NaN, NaN))
else:
d.append(NaN)
return asarray(d)
def similarity(self, other, metric='distance', thresh=5, minDistance=inf):
"""
Estimate similarity to another set of sources using recall and precision.
Will compute the number of sources in self that are also
in other, based on a given distance metric and a threshold.
The recall rate is the number of matches divided by the number in self,
and the precision rate is the number of matches divided by the number in other.
Typically self is ground truth and other is an estimate.
The F score is defined as 2 * (recall * precision) / (recall + precision)
Before computing metrics, all sources in self are matched to other,
and a minimum distance can be set to control matching.
Parameters
----------
other : SourceModel
The sources to compare to.
metric : str, optional, default = 'distance'
Metric to use when computing distances,
options include 'distance' and 'overlap'
thresh : scalar, optional, default = 5
The distance below which a source is considered found.
minDistance : scalar, optional, default = inf
Minimum distance to use when matching indices.
"""
checkParams(metric, ['distance', 'overlap'])
if metric == 'distance':
# when evaluating distances,
# minimum distance should be the threshold
if minDistance == inf:
minDistance = thresh
vals = self.distance(other, minDistance=minDistance)
vals[isnan(vals)] = inf
compare = lambda x: x < thresh
elif metric == 'overlap':
vals = self.overlap(other, method='fraction', minDistance=minDistance)
vals[isnan(vals)] = 0
compare = lambda x: x > thresh
else:
raise Exception("Metric not recognized")
recall = sum(map(compare, vals)) / float(self.count)
precision = sum(map(compare, vals)) / float(other.count)
score = 2 * (recall * precision) / (recall + precision)
return recall, precision, score
def transform(self, data, collect=True):
"""
Extract series from data using a list of sources.
Currently only supports simple averaging over coordinates.
Params
------
data : Images or Series object
The data from which to extract signals
collect : boolean, optional, default = True
Whether to collect to local array or keep as a Series
"""
if not (isinstance(data, Images) or isinstance(data, Series)):
raise Exception("Input must either be Images or Series (or a subclass)")
# TODO add support for weighting
if isinstance(data, Images):
output = data.meanByRegions(self.coordinates).toSeries()
else:
output = data.meanByRegions(self.coordinates)
if collect:
return output.collectValuesAsArray()
else:
return output
def clean(self, cleaners=None):
"""
Apply one or more cleaners to sources, returning filtered sources
Parameters
----------
cleaners : Cleaner or list of Cleaners, optional, default = None
Which cleaners to apply, if None, will apply BasicCleaner with defaults
"""
from thunder.extraction.cleaners import Cleaner, BasicCleaner
from copy import copy
if isinstance(cleaners, list):
for c in cleaners:
if not isinstance(c, Cleaner):
raise Exception("List must only contain Cleaners")
elif isinstance(cleaners, Cleaner):
cleaners = [cleaners]
elif cleaners is None:
cleaners = [BasicCleaner()]
else:
raise Exception("Must provide Cleaner or list of Cleaners, got %s" % type(cleaners))
newmodel = copy(self)
for c in cleaners:
newmodel = c.clean(newmodel)
return newmodel
def dilate(self, size):
"""
Dilate all sources using morphological operators
Parameters
----------
size : int
Size of dilation in pixels
"""
return SourceModel([s.dilate(size) for s in self.sources])
def outline(self, inner, outer):
"""
Outline all sources
inner : int
Size of inner outline boundary (in pixels)
outer : int
Size of outer outline boundary (in pixels)
"""
return SourceModel([s.outline(inner, outer) for s in self.sources])
def crop(self, minBound, maxBound):
"""
Crop all sources by removing coordinates outside of bounds
Parameters
----------
minBound : tuple
Minimum or starting bounds for each axis
maxBound : tuple
Maximum or ending bounds for each axis
"""
return SourceModel([s.crop(minBound, maxBound) for s in self.sources])
def save(self, f, include=None, overwrite=False, **kwargs):
"""
Custom save to file with simplified, human-readable output, and selection of lazy attributes.
"""
import copy
output = copy.deepcopy(self)
if isinstance(include, str):
include = [include]
if include is not None:
for prop in include:
map(lambda s: getattr(s, prop), output.sources)
output.sources = map(lambda s: s.restore(include).tolist(), output.sources)
simplify = lambda d: d['sources']['py/homogeneousList']['data']
super(SourceModel, output).save(f, simplify=simplify, overwrite=overwrite, **kwargs)
@classmethod
def load(cls, f, **kwargs):
"""
Custom load from file to handle simplified, human-readable output
"""
unsimplify = lambda d: {'sources': {
'py/homogeneousList': {'data': d, 'module': 'thunder.extraction.source', 'type': 'Source'}}}
output = super(SourceModel, cls).load(f, unsimplify=unsimplify)
output.sources = map(lambda s: s.toarray(), output.sources)
return output
@classmethod
def deserialize(cls, d, **kwargs):
"""
Custom load from JSON to handle simplified, human-readable output
"""
unsimplify = lambda d: {'sources': {
'py/homogeneousList': {'data': d, 'module': 'thunder.extraction.source', 'type': 'Source'}}}
output = super(SourceModel, cls).deserialize(d, unsimplify=unsimplify)
output.sources = map(lambda s: s.toarray(), output.sources)
return output
def __repr__(self):
s = self.__class__.__name__
s += '\n%g sources' % (len(self.sources))
return s
LAZY_ATTRIBUTES = ["center", "polygon", "bbox", "area"]
| apache-2.0 |
danviv/trading-with-python | cookbook/reconstructVXX/reconstructVXX.py | 77 | 3574 | # -*- coding: utf-8 -*-
"""
Reconstructing VXX from futures data
author: Jev Kuznetsov
License : BSD
"""
from __future__ import division
from pandas import *
import numpy as np
import os
class Future(object):
""" vix future class, used to keep data structures simple """
def __init__(self,series,code=None):
""" code is optional, example '2010_01' """
self.series = series.dropna() # price data
self.settleDate = self.series.index[-1]
self.dt = len(self.series) # roll period (this is default, should be recalculated)
self.code = code # string code 'YYYY_MM'
def monthNr(self):
""" get month nr from the future code """
return int(self.code.split('_')[1])
def dr(self,date):
""" days remaining before settlement, on a given date """
return(sum(self.series.index>date))
def price(self,date):
""" price on a date """
return self.series.get_value(date)
def returns(df):
""" daily return """
return (df/df.shift(1)-1)
def recounstructVXX():
"""
calculate VXX returns
needs a previously preprocessed file vix_futures.csv
"""
dataDir = os.path.expanduser('~')+'/twpData'
X = DataFrame.from_csv(dataDir+'/vix_futures.csv') # raw data table
# build end dates list & futures classes
futures = []
codes = X.columns
endDates = []
for code in codes:
f = Future(X[code],code=code)
print code,':', f.settleDate
endDates.append(f.settleDate)
futures.append(f)
endDates = np.array(endDates)
# set roll period of each future
for i in range(1,len(futures)):
futures[i].dt = futures[i].dr(futures[i-1].settleDate)
# Y is the result table
idx = X.index
Y = DataFrame(index=idx, columns=['first','second','days_left','w1','w2',
'ret','30days_avg'])
# W is the weight matrix
W = DataFrame(data = np.zeros(X.values.shape),index=idx,columns = X.columns)
# for VXX calculation see http://www.ipathetn.com/static/pdf/vix-prospectus.pdf
# page PS-20
for date in idx:
i =np.nonzero(endDates>=date)[0][0] # find first not exprired future
first = futures[i] # first month futures class
second = futures[i+1] # second month futures class
dr = first.dr(date) # number of remaining dates in the first futures contract
dt = first.dt #number of business days in roll period
W.set_value(date,codes[i],100*dr/dt)
W.set_value(date,codes[i+1],100*(dt-dr)/dt)
# this is all just debug info
p1 = first.price(date)
p2 = second.price(date)
w1 = 100*dr/dt
w2 = 100*(dt-dr)/dt
Y.set_value(date,'first',p1)
Y.set_value(date,'second',p2)
Y.set_value(date,'days_left',first.dr(date))
Y.set_value(date,'w1',w1)
Y.set_value(date,'w2',w2)
Y.set_value(date,'30days_avg',(p1*w1+p2*w2)/100)
valCurr = (X*W.shift(1)).sum(axis=1) # value on day N
valYest = (X.shift(1)*W.shift(1)).sum(axis=1) # value on day N-1
Y['ret'] = valCurr/valYest-1 # index return on day N
return Y
##-------------------Main script---------------------------
if __name__=="__main__":
Y = recounstructVXX()
print Y.head(30)#
Y.to_csv('reconstructedVXX.csv')
| bsd-3-clause |
einarhuseby/arctic | arctic/_util.py | 3 | 1846 | from pandas import DataFrame
from pandas.util.testing import assert_frame_equal
from pymongo.errors import OperationFailure
import string
import logging
logger = logging.getLogger(__name__)
def indent(s, num_spaces):
s = string.split(s, '\n')
s = [(num_spaces * ' ') + line for line in s]
s = string.join(s, '\n')
return s
def are_equals(o1, o2, **kwargs):
try:
if isinstance(o1, DataFrame):
assert_frame_equal(o1, o2, kwargs)
return True
return o1 == o2
except Exception:
return False
def enable_sharding(arctic, library_name, hashed=False):
c = arctic._conn
lib = arctic[library_name]._arctic_lib
dbname = lib._db.name
library_name = lib.get_top_level_collection().name
try:
c.admin.command('enablesharding', dbname)
except OperationFailure, e:
if not 'failed: already enabled' in str(e):
raise
if not hashed:
logger.info("Range sharding 'symbol' on: " + dbname + '.' + library_name)
c.admin.command('shardCollection', dbname + '.' + library_name, key={'symbol': 1})
else:
logger.info("Hash sharding 'symbol' on: " + dbname + '.' + library_name)
c.admin.command('shardCollection', dbname + '.' + library_name, key={'symbol': 'hashed'})
def enable_powerof2sizes(arctic, library_name):
lib = arctic[library_name]._arctic_lib
collection = lib.get_top_level_collection()
lib._db.command({"collMod": collection.name, 'usePowerOf2Sizes': "true"})
logger.info("usePowerOf2Sizes enabled for %s", collection.name)
for coll in collection.database.collection_names():
if coll.startswith("%s." % collection.name):
lib._db.command({"collMod": coll, 'usePowerOf2Sizes': "true"})
logger.info("usePowerOf2Sizes enabled for %s", coll)
| lgpl-2.1 |
BillyLiggins/fitting | first.py | 1 | 7031 | import copy
import echidna
import echidna.output.plot as plot
import echidna.core.spectra as spectra
from echidna.output import store
import matplotlib.pyplot as plt
import argparse
import glob
import numpy as np
import os
def convertor(path):
flist=np.array(glob.glob(path))
for ntuple in flist:
os.system("python ~/echidna/echidna/scripts/dump_spectra_ntuple.py -c ~/workspace/PhD/fitting/config.yml -f "+ str(ntuple)+" -s hdf5/")
def combinerNtuple(path,filename):
flist=np.array(glob.glob(path))
print flist
first = True
for hdf5 in flist:
print hdf5
if first:
spectrum1 = store.fill_from_ntuple(hdf5)
first = False
else:
spectrum2 = store.fill_from_ntuple(hdf5)
spectrum1.add(spectrum2)
store.dump(filename, spectrum1)
def combiner(path,filename):
flist=np.array(glob.glob(path))
print flist
first = True
for hdf5 in flist:
print hdf5
if first:
spectrum1 = store.load(hdf5)
first = False
else:
spectrum2 = store.load(hdf5)
spectrum1.add(spectrum2)
store.dump(filename, spectrum1)
"""The way you should do it is to define a lot of spectra and then plot them.
You don't really know how to normlise the histrogram or indeed weather that is of any uses in the first
place.
"""
def slicer(spectrumPath,filler,nslice):
for i in range(nslice):
spectrum=store.load(spectrumPath)
print spectrum.sum()
shrink_dict = {"energy_reco_low": 0.,
"energy_reco_high": 0.6,
"radial_reco_low": i*6000.0/nslice,
"radial_reco_high": (i+1)*6000/nslice}
spectrum.cut(**shrink_dict)
spectrum.scale(1)
spec2=copy.copy(spectrum)
spec2._name=str(i*1000)+"mm to "+str((i+1)*1000)+"mm"
print type(spec2)
filler.append(spec2)
def slicerMC(spectrumPath,filler,nslice):
for i in range(nslice):
spectrum=store.load(spectrumPath)
print spectrum.sum()
shrink_dict = {"energy_mc_low": 0.,
"energy_mc_high": 1,
"radial_mc_low": i*6000.0/nslice,
"radial_mc_high": (i+1)*6000/nslice}
spectrum.cut(**shrink_dict)
spectrum.scale(1)
spec2=copy.copy(spectrum)
spec2._name="MC"
print type(spec2)
print "This gives the number os events in each window:"
print "mc : "+str(i*6000.0/nslice)+"mm to "+str((i+1)*6000.0/nslice)+"mm : "+str(spec2.sum())
filler.append(spec2)
def slicerReco(spectrumPath,filler,nslice):
for i in range(nslice):
spectrum=store.load(spectrumPath)
print spectrum.sum()
shrink_dict = {"energy_reco_low": 0.,
"energy_reco_high": 1.,
"radial_reco_low": i*6000.0/nslice,
"radial_reco_high": (i+1)*6000/nslice}
spectrum.cut(**shrink_dict)
spectrum.scale(1)
spec2=copy.copy(spectrum)
spec2._name="Reco"
print type(spec2)
print "This gives the number os events in each window:"
print "reco : "+str(i*6000.0/nslice)+"mm to "+str((i+1)*6000.0/nslice)+"mm : "+str(spec2.sum())
filler.append(spec2)
def signalPlotter(spectra,dim,name):
i=0
for spec in spectra:
fig = plt.figure()
ax= fig.add_subplot(1,1,1)
par = spec.get_config().get_par(dim)
width = par.get_width()
bins = np.linspace(par._low,par._high, par._bins+1)
x = bins[:-1] + 0.5*width
plt.xlabel(str(dim)+ " [" + par.get_unit() + "]")
plt.ylabel("Events per " + str(width) + " " + par.get_unit() + " bin")
ax.set(title="Normalised energy spectrum in "+str(i*1000)+"mm to "+str((i+1)*1000)+"mm ",ylabel="Events per " + str(width) + " " + par.get_unit() + " bin", xlabel=str(dim)+" [" + par.get_unit() + "]")
ax.hist(x,bins,weights=spec.project(dim),histtype="stepfilled", color="RoyalBlue",label=spec._name)
fig.savefig("slice_"+str(name)+"_"+str(i*1000)+"_"+str((i+1)*1000)+".png")
i=1+i
def combiPlotter(spectra,dim,name):
i=0
fig = plt.figure()
ax= fig.add_subplot(1,1,1)
for spec in spectra:
par = spec.get_config().get_par(dim)
width = par.get_width()
bins = np.linspace(par._low,par._high, par._bins+1)
x = bins[:-1] + 0.5*width
plt.xlabel(str(dim)+ " [" + par.get_unit() + "]")
plt.ylabel("Events per " + str(width) + " " + par.get_unit() + " bin")
ax.set(title="Normalised energy spectrum in 1000mm slices",ylabel="Events per " + str(width) + " " + par.get_unit() + " bin", xlabel="energy_reco"+ " [" + par.get_unit() + "]")
ax.hist(x,bins,weights=spec.project("energy_reco"),label=spec._name,histtype='step')
ax.set_ylim([0,0.03])
ax.set_xlim([0.2,0.7])
ax.legend(loc="best")
fig.savefig("combined_"+str(name)+".png")
def func(path,nslice,name):
spectra=[]
slicer(path,spectra,nslice)
signalPlotter(spectra,"energy_reco",name)
combiPlotter(spectra,"energy_reco",name)
def po210():
convertor("po210_ntuple/*")
combiner("hdf5/SolarPo**ntuple*","hdf5/SolarPo210_combined.hdf5")
plotpath="plots/"
func("hdf5/SolarPo210_combined.hdf5",6,"po210")
def bi210():
convertor("bi210_ntuple/*")
combiner("hdf5/SolarBi**ntuple*","hdf5/SolarBi210_combined.hdf5")
plotpath="plots/"
func("hdf5/SolarBi210_combined.hdf5",6,"bi210")
def compair(spectrumPathReco,spectrumPathMC,name):
spectraReco=[]
spectraMC=[]
slicerReco(spectrumPathReco,spectraReco,6)
slicerMC(spectrumPathMC,spectraMC,6)
for i in range(0,len(spectraReco)):
fig = plt.figure()
ax= fig.add_subplot(1,1,1)
par = spectraReco[i].get_config().get_par("energy_reco")
width = par.get_width()
bins = np.linspace(par._low,par._high, par._bins+1)
x = bins[:-1] + 0.5*width
ax.set(title="Normalised energy spectrum in "+str(i*1000)+"mm to "+str((i+1)*1000)+"mm ",ylabel="Events per " + str(width) + " " + par.get_unit() + " bin", xlabel="Energy [" + par.get_unit() + "]")
ax.hist(x,bins,weights=spectraReco[i].project("energy_reco"),histtype="stepfilled",label=spectraReco[i]._name)
par = spectraMC[i].get_config().get_par("energy_mc")
width = par.get_width()
bins = np.linspace(par._low,par._high, par._bins+1)
x = bins[:-1] + 0.5*width
ax.hist(x,bins,weights=spectraMC[i].project("energy_mc"),histtype="stepfilled",label=spectraMC[i]._name,alpha=0.75)
ax.legend(loc=2)
fig.savefig("compare_"+str(name)+"_"+str(i*1000)+"_"+str((i+1)*1000)+".png")
if __name__=="__main__":
print "You need to compare the recon against the mc"
print "You should bin in bigger bins becuase you could then bin in 4d"
"""You need to plot the standard spectra"""
| mit |
rexshihaoren/scikit-learn | sklearn/feature_extraction/tests/test_feature_hasher.py | 258 | 2861 | from __future__ import unicode_literals
import numpy as np
from sklearn.feature_extraction import FeatureHasher
from nose.tools import assert_raises, assert_true
from numpy.testing import assert_array_equal, assert_equal
def test_feature_hasher_dicts():
h = FeatureHasher(n_features=16)
assert_equal("dict", h.input_type)
raw_X = [{"dada": 42, "tzara": 37}, {"gaga": 17}]
X1 = FeatureHasher(n_features=16).transform(raw_X)
gen = (iter(d.items()) for d in raw_X)
X2 = FeatureHasher(n_features=16, input_type="pair").transform(gen)
assert_array_equal(X1.toarray(), X2.toarray())
def test_feature_hasher_strings():
# mix byte and Unicode strings; note that "foo" is a duplicate in row 0
raw_X = [["foo", "bar", "baz", "foo".encode("ascii")],
["bar".encode("ascii"), "baz", "quux"]]
for lg_n_features in (7, 9, 11, 16, 22):
n_features = 2 ** lg_n_features
it = (x for x in raw_X) # iterable
h = FeatureHasher(n_features, non_negative=True, input_type="string")
X = h.transform(it)
assert_equal(X.shape[0], len(raw_X))
assert_equal(X.shape[1], n_features)
assert_true(np.all(X.data > 0))
assert_equal(X[0].sum(), 4)
assert_equal(X[1].sum(), 3)
assert_equal(X.nnz, 6)
def test_feature_hasher_pairs():
raw_X = (iter(d.items()) for d in [{"foo": 1, "bar": 2},
{"baz": 3, "quux": 4, "foo": -1}])
h = FeatureHasher(n_features=16, input_type="pair")
x1, x2 = h.transform(raw_X).toarray()
x1_nz = sorted(np.abs(x1[x1 != 0]))
x2_nz = sorted(np.abs(x2[x2 != 0]))
assert_equal([1, 2], x1_nz)
assert_equal([1, 3, 4], x2_nz)
def test_hash_empty_input():
n_features = 16
raw_X = [[], (), iter(range(0))]
h = FeatureHasher(n_features=n_features, input_type="string")
X = h.transform(raw_X)
assert_array_equal(X.A, np.zeros((len(raw_X), n_features)))
def test_hasher_invalid_input():
assert_raises(ValueError, FeatureHasher, input_type="gobbledygook")
assert_raises(ValueError, FeatureHasher, n_features=-1)
assert_raises(ValueError, FeatureHasher, n_features=0)
assert_raises(TypeError, FeatureHasher, n_features='ham')
h = FeatureHasher(n_features=np.uint16(2 ** 6))
assert_raises(ValueError, h.transform, [])
assert_raises(Exception, h.transform, [[5.5]])
assert_raises(Exception, h.transform, [[None]])
def test_hasher_set_params():
# Test delayed input validation in fit (useful for grid search).
hasher = FeatureHasher()
hasher.set_params(n_features=np.inf)
assert_raises(TypeError, hasher.fit)
def test_hasher_zeros():
# Assert that no zeros are materialized in the output.
X = FeatureHasher().transform([{'foo': 0}])
assert_equal(X.data.shape, (0,))
| bsd-3-clause |
tmhm/scikit-learn | examples/plot_kernel_approximation.py | 262 | 8004 | """
==================================================
Explicit feature map approximation for RBF kernels
==================================================
An example illustrating the approximation of the feature map
of an RBF kernel.
.. currentmodule:: sklearn.kernel_approximation
It shows how to use :class:`RBFSampler` and :class:`Nystroem` to
approximate the feature map of an RBF kernel for classification with an SVM on
the digits dataset. Results using a linear SVM in the original space, a linear
SVM using the approximate mappings and using a kernelized SVM are compared.
Timings and accuracy for varying amounts of Monte Carlo samplings (in the case
of :class:`RBFSampler`, which uses random Fourier features) and different sized
subsets of the training set (for :class:`Nystroem`) for the approximate mapping
are shown.
Please note that the dataset here is not large enough to show the benefits
of kernel approximation, as the exact SVM is still reasonably fast.
Sampling more dimensions clearly leads to better classification results, but
comes at a greater cost. This means there is a tradeoff between runtime and
accuracy, given by the parameter n_components. Note that solving the Linear
SVM and also the approximate kernel SVM could be greatly accelerated by using
stochastic gradient descent via :class:`sklearn.linear_model.SGDClassifier`.
This is not easily possible for the case of the kernelized SVM.
The second plot visualized the decision surfaces of the RBF kernel SVM and
the linear SVM with approximate kernel maps.
The plot shows decision surfaces of the classifiers projected onto
the first two principal components of the data. This visualization should
be taken with a grain of salt since it is just an interesting slice through
the decision surface in 64 dimensions. In particular note that
a datapoint (represented as a dot) does not necessarily be classified
into the region it is lying in, since it will not lie on the plane
that the first two principal components span.
The usage of :class:`RBFSampler` and :class:`Nystroem` is described in detail
in :ref:`kernel_approximation`.
"""
print(__doc__)
# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# License: BSD 3 clause
# Standard scientific Python imports
import matplotlib.pyplot as plt
import numpy as np
from time import time
# Import datasets, classifiers and performance metrics
from sklearn import datasets, svm, pipeline
from sklearn.kernel_approximation import (RBFSampler,
Nystroem)
from sklearn.decomposition import PCA
# The digits dataset
digits = datasets.load_digits(n_class=9)
# To apply an classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.data)
data = digits.data / 16.
data -= data.mean(axis=0)
# We learn the digits on the first half of the digits
data_train, targets_train = data[:n_samples / 2], digits.target[:n_samples / 2]
# Now predict the value of the digit on the second half:
data_test, targets_test = data[n_samples / 2:], digits.target[n_samples / 2:]
#data_test = scaler.transform(data_test)
# Create a classifier: a support vector classifier
kernel_svm = svm.SVC(gamma=.2)
linear_svm = svm.LinearSVC()
# create pipeline from kernel approximation
# and linear svm
feature_map_fourier = RBFSampler(gamma=.2, random_state=1)
feature_map_nystroem = Nystroem(gamma=.2, random_state=1)
fourier_approx_svm = pipeline.Pipeline([("feature_map", feature_map_fourier),
("svm", svm.LinearSVC())])
nystroem_approx_svm = pipeline.Pipeline([("feature_map", feature_map_nystroem),
("svm", svm.LinearSVC())])
# fit and predict using linear and kernel svm:
kernel_svm_time = time()
kernel_svm.fit(data_train, targets_train)
kernel_svm_score = kernel_svm.score(data_test, targets_test)
kernel_svm_time = time() - kernel_svm_time
linear_svm_time = time()
linear_svm.fit(data_train, targets_train)
linear_svm_score = linear_svm.score(data_test, targets_test)
linear_svm_time = time() - linear_svm_time
sample_sizes = 30 * np.arange(1, 10)
fourier_scores = []
nystroem_scores = []
fourier_times = []
nystroem_times = []
for D in sample_sizes:
fourier_approx_svm.set_params(feature_map__n_components=D)
nystroem_approx_svm.set_params(feature_map__n_components=D)
start = time()
nystroem_approx_svm.fit(data_train, targets_train)
nystroem_times.append(time() - start)
start = time()
fourier_approx_svm.fit(data_train, targets_train)
fourier_times.append(time() - start)
fourier_score = fourier_approx_svm.score(data_test, targets_test)
nystroem_score = nystroem_approx_svm.score(data_test, targets_test)
nystroem_scores.append(nystroem_score)
fourier_scores.append(fourier_score)
# plot the results:
plt.figure(figsize=(8, 8))
accuracy = plt.subplot(211)
# second y axis for timeings
timescale = plt.subplot(212)
accuracy.plot(sample_sizes, nystroem_scores, label="Nystroem approx. kernel")
timescale.plot(sample_sizes, nystroem_times, '--',
label='Nystroem approx. kernel')
accuracy.plot(sample_sizes, fourier_scores, label="Fourier approx. kernel")
timescale.plot(sample_sizes, fourier_times, '--',
label='Fourier approx. kernel')
# horizontal lines for exact rbf and linear kernels:
accuracy.plot([sample_sizes[0], sample_sizes[-1]],
[linear_svm_score, linear_svm_score], label="linear svm")
timescale.plot([sample_sizes[0], sample_sizes[-1]],
[linear_svm_time, linear_svm_time], '--', label='linear svm')
accuracy.plot([sample_sizes[0], sample_sizes[-1]],
[kernel_svm_score, kernel_svm_score], label="rbf svm")
timescale.plot([sample_sizes[0], sample_sizes[-1]],
[kernel_svm_time, kernel_svm_time], '--', label='rbf svm')
# vertical line for dataset dimensionality = 64
accuracy.plot([64, 64], [0.7, 1], label="n_features")
# legends and labels
accuracy.set_title("Classification accuracy")
timescale.set_title("Training times")
accuracy.set_xlim(sample_sizes[0], sample_sizes[-1])
accuracy.set_xticks(())
accuracy.set_ylim(np.min(fourier_scores), 1)
timescale.set_xlabel("Sampling steps = transformed feature dimension")
accuracy.set_ylabel("Classification accuracy")
timescale.set_ylabel("Training time in seconds")
accuracy.legend(loc='best')
timescale.legend(loc='best')
# visualize the decision surface, projected down to the first
# two principal components of the dataset
pca = PCA(n_components=8).fit(data_train)
X = pca.transform(data_train)
# Gemerate grid along first two principal components
multiples = np.arange(-2, 2, 0.1)
# steps along first component
first = multiples[:, np.newaxis] * pca.components_[0, :]
# steps along second component
second = multiples[:, np.newaxis] * pca.components_[1, :]
# combine
grid = first[np.newaxis, :, :] + second[:, np.newaxis, :]
flat_grid = grid.reshape(-1, data.shape[1])
# title for the plots
titles = ['SVC with rbf kernel',
'SVC (linear kernel)\n with Fourier rbf feature map\n'
'n_components=100',
'SVC (linear kernel)\n with Nystroem rbf feature map\n'
'n_components=100']
plt.tight_layout()
plt.figure(figsize=(12, 5))
# predict and plot
for i, clf in enumerate((kernel_svm, nystroem_approx_svm,
fourier_approx_svm)):
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
plt.subplot(1, 3, i + 1)
Z = clf.predict(flat_grid)
# Put the result into a color plot
Z = Z.reshape(grid.shape[:-1])
plt.contourf(multiples, multiples, Z, cmap=plt.cm.Paired)
plt.axis('off')
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=targets_train, cmap=plt.cm.Paired)
plt.title(titles[i])
plt.tight_layout()
plt.show()
| bsd-3-clause |
harisbal/pandas | pandas/core/tools/datetimes.py | 4 | 30680 | from functools import partial
from datetime import datetime, time
from collections import MutableMapping
import numpy as np
from pandas._libs import tslib, tslibs
from pandas._libs.tslibs.strptime import array_strptime
from pandas._libs.tslibs import parsing, conversion, Timestamp
from pandas._libs.tslibs.parsing import ( # noqa
parse_time_string,
DateParseError,
_format_is_iso,
_guess_datetime_format)
from pandas.core.dtypes.common import (
ensure_object,
is_datetime64_ns_dtype,
is_datetime64_dtype,
is_datetime64tz_dtype,
is_integer_dtype,
is_integer,
is_float,
is_list_like,
is_scalar,
is_numeric_dtype,
is_object_dtype)
from pandas.core.dtypes.generic import (
ABCIndexClass, ABCSeries,
ABCDataFrame)
from pandas.core.dtypes.missing import notna
from pandas.core import algorithms
from pandas.compat import zip
def _guess_datetime_format_for_array(arr, **kwargs):
# Try to guess the format based on the first non-NaN element
non_nan_elements = notna(arr).nonzero()[0]
if len(non_nan_elements):
return _guess_datetime_format(arr[non_nan_elements[0]], **kwargs)
def _maybe_cache(arg, format, cache, convert_listlike):
"""
Create a cache of unique dates from an array of dates
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
format : string
Strftime format to parse time
cache : boolean
True attempts to create a cache of converted values
convert_listlike : function
Conversion function to apply on dates
Returns
-------
cache_array : Series
Cache of converted, unique dates. Can be empty
"""
from pandas import Series
cache_array = Series()
if cache:
# Perform a quicker unique check
from pandas import Index
if not Index(arg).is_unique:
unique_dates = algorithms.unique(arg)
cache_dates = convert_listlike(unique_dates, True, format)
cache_array = Series(cache_dates, index=unique_dates)
return cache_array
def _convert_and_box_cache(arg, cache_array, box, errors, name=None):
"""
Convert array of dates with a cache and box the result
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
cache_array : Series
Cache of converted, unique dates
box : boolean
True boxes result as an Index-like, False returns an ndarray
errors : string
'ignore' plus box=True will convert result to Index
name : string, default None
Name for a DatetimeIndex
Returns
-------
result : datetime of converted dates
Returns:
- Index-like if box=True
- ndarray if box=False
"""
from pandas import Series, DatetimeIndex, Index
result = Series(arg).map(cache_array)
if box:
if errors == 'ignore':
return Index(result, name=name)
else:
return DatetimeIndex(result, name=name)
return result.values
def _return_parsed_timezone_results(result, timezones, box, tz, name):
"""
Return results from array_strptime if a %z or %Z directive was passed.
Parameters
----------
result : ndarray
int64 date representations of the dates
timezones : ndarray
pytz timezone objects
box : boolean
True boxes result as an Index-like, False returns an ndarray
tz : object
None or pytz timezone object
name : string, default None
Name for a DatetimeIndex
Returns
-------
tz_result : ndarray of parsed dates with timezone
Returns:
- Index-like if box=True
- ndarray of Timestamps if box=False
"""
if tz is not None:
raise ValueError("Cannot pass a tz argument when "
"parsing strings with timezone "
"information.")
tz_results = np.array([Timestamp(res).tz_localize(zone) for res, zone
in zip(result, timezones)])
if box:
from pandas import Index
return Index(tz_results, name=name)
return tz_results
def _convert_listlike_datetimes(arg, box, format, name=None, tz=None,
unit=None, errors=None,
infer_datetime_format=None, dayfirst=None,
yearfirst=None, exact=None):
"""
Helper function for to_datetime. Performs the conversions of 1D listlike
of dates
Parameters
----------
arg : list, tuple, ndarray, Series, Index
date to be parced
box : boolean
True boxes result as an Index-like, False returns an ndarray
name : object
None or string for the Index name
tz : object
None or 'utc'
unit : string
None or string of the frequency of the passed data
errors : string
error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
infer_datetime_format : boolean
inferring format behavior from to_datetime
dayfirst : boolean
dayfirst parsing behavior from to_datetime
yearfirst : boolean
yearfirst parsing behavior from to_datetime
exact : boolean
exact format matching behavior from to_datetime
Returns
-------
ndarray of parsed dates
Returns:
- Index-like if box=True
- ndarray of Timestamps if box=False
"""
from pandas import DatetimeIndex
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
# these are shortcutable
if is_datetime64tz_dtype(arg):
if not isinstance(arg, DatetimeIndex):
return DatetimeIndex(arg, tz=tz, name=name)
if tz == 'utc':
arg = arg.tz_convert(None).tz_localize(tz)
return arg
elif is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz=tz, name=name)
except ValueError:
pass
return arg
elif unit is not None:
if format is not None:
raise ValueError("cannot specify both format and unit")
arg = getattr(arg, 'values', arg)
result = tslib.array_with_unit_to_datetime(arg, unit,
errors=errors)
if box:
if errors == 'ignore':
from pandas import Index
return Index(result, name=name)
return DatetimeIndex(result, tz=tz, name=name)
return result
elif getattr(arg, 'ndim', 1) > 1:
raise TypeError('arg must be a string, datetime, list, tuple, '
'1-d array, or Series')
arg = ensure_object(arg)
require_iso8601 = False
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = _format_is_iso(format)
if format_is_iso8601:
require_iso8601 = not infer_datetime_format
format = None
try:
result = None
if format is not None:
# shortcut formatting here
if format == '%Y%m%d':
try:
result = _attempt_YYYYMMDD(arg, errors=errors)
except (ValueError, TypeError, tslibs.OutOfBoundsDatetime):
raise ValueError("cannot convert the input to "
"'%Y%m%d' date format")
# fallback
if result is None:
try:
result, timezones = array_strptime(
arg, format, exact=exact, errors=errors)
if '%Z' in format or '%z' in format:
return _return_parsed_timezone_results(
result, timezones, box, tz, name)
except tslibs.OutOfBoundsDatetime:
if errors == 'raise':
raise
result = arg
except ValueError:
# if format was inferred, try falling back
# to array_to_datetime - terminate here
# for specified formats
if not infer_datetime_format:
if errors == 'raise':
raise
result = arg
if result is None and (format is None or infer_datetime_format):
result, tz_parsed = tslib.array_to_datetime(
arg,
errors=errors,
utc=tz == 'utc',
dayfirst=dayfirst,
yearfirst=yearfirst,
require_iso8601=require_iso8601
)
if tz_parsed is not None:
if box:
# We can take a shortcut since the datetime64 numpy array
# is in UTC
return DatetimeIndex._simple_new(result, name=name,
tz=tz_parsed)
else:
# Convert the datetime64 numpy array to an numpy array
# of datetime objects
result = [Timestamp(ts, tz=tz_parsed).to_pydatetime()
for ts in result]
return np.array(result, dtype=object)
if box:
# Ensure we return an Index in all cases where box=True
if is_datetime64_dtype(result):
return DatetimeIndex(result, tz=tz, name=name)
elif is_object_dtype(result):
# e.g. an Index of datetime objects
from pandas import Index
return Index(result, name=name)
return result
except ValueError as e:
try:
values, tz = conversion.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, name=name, tz=tz)
except (ValueError, TypeError):
raise e
def _adjust_to_origin(arg, origin, unit):
"""
Helper function for to_datetime.
Adjust input argument to the specified origin
Parameters
----------
arg : list, tuple, ndarray, Series, Index
date to be adjusted
origin : 'julian' or Timestamp
origin offset for the arg
unit : string
passed unit from to_datetime, must be 'D'
Returns
-------
ndarray or scalar of adjusted date(s)
"""
if origin == 'julian':
original = arg
j0 = Timestamp(0).to_julian_date()
if unit != 'D':
raise ValueError("unit must be 'D' for origin='julian'")
try:
arg = arg - j0
except TypeError:
raise ValueError("incompatible 'arg' type for given "
"'origin'='julian'")
# premptively check this for a nice range
j_max = Timestamp.max.to_julian_date() - j0
j_min = Timestamp.min.to_julian_date() - j0
if np.any(arg > j_max) or np.any(arg < j_min):
raise tslibs.OutOfBoundsDatetime(
"{original} is Out of Bounds for "
"origin='julian'".format(original=original))
else:
# arg must be numeric
if not ((is_scalar(arg) and (is_integer(arg) or is_float(arg))) or
is_numeric_dtype(np.asarray(arg))):
raise ValueError(
"'{arg}' is not compatible with origin='{origin}'; "
"it must be numeric with a unit specified ".format(
arg=arg,
origin=origin))
# we are going to offset back to unix / epoch time
try:
offset = Timestamp(origin)
except tslibs.OutOfBoundsDatetime:
raise tslibs.OutOfBoundsDatetime(
"origin {origin} is Out of Bounds".format(origin=origin))
except ValueError:
raise ValueError("origin {origin} cannot be converted "
"to a Timestamp".format(origin=origin))
if offset.tz is not None:
raise ValueError(
"origin offset {} must be tz-naive".format(offset))
offset -= Timestamp(0)
# convert the offset to the unit of the arg
# this should be lossless in terms of precision
offset = offset // tslibs.Timedelta(1, unit=unit)
# scalars & ndarray-like can handle the addition
if is_list_like(arg) and not isinstance(
arg, (ABCSeries, ABCIndexClass, np.ndarray)):
arg = np.asarray(arg)
arg = arg + offset
return arg
def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
utc=None, box=True, format=None, exact=True,
unit=None, infer_datetime_format=False, origin='unix',
cache=False):
"""
Convert argument to datetime.
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
.. versionadded:: 0.18.1
or DataFrame/dict-like
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as NaT
- If 'ignore', then invalid parsing will return the input
dayfirst : boolean, default False
Specify a date parse order if `arg` is str or its list-likes.
If True, parses dates with the day first, eg 10/11/12 is parsed as
2012-11-10.
Warning: dayfirst=True is not strict, but will prefer to parse
with day first (this is a known bug, based on dateutil behavior).
yearfirst : boolean, default False
Specify a date parse order if `arg` is str or its list-likes.
- If True parses dates with the year first, eg 10/11/12 is parsed as
2010-11-12.
- If both dayfirst and yearfirst are True, yearfirst is preceded (same
as dateutil).
Warning: yearfirst=True is not strict, but will prefer to parse
with year first (this is a known bug, based on dateutil behavior).
.. versionadded:: 0.16.1
utc : boolean, default None
Return UTC DatetimeIndex if True (converting any tz-aware
datetime.datetime objects as well).
box : boolean, default True
- If True returns a DatetimeIndex or Index-like object
- If False returns ndarray of values.
format : string, default None
strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
all the way up to nanoseconds.
exact : boolean, True by default
- If True, require an exact format match.
- If False, allow the format to match anywhere in the target string.
unit : string, default 'ns'
unit of the arg (D,s,ms,us,ns) denote the unit, which is an
integer or float number. This will be based off the origin.
Example, with unit='ms' and origin='unix' (the default), this
would calculate the number of milliseconds to the unix epoch start.
infer_datetime_format : boolean, default False
If True and no `format` is given, attempt to infer the format of the
datetime strings, and if it can be inferred, switch to a faster
method of parsing them. In some cases this can increase the parsing
speed by ~5-10x.
origin : scalar, default is 'unix'
Define the reference date. The numeric values would be parsed as number
of units (defined by `unit`) since this reference date.
- If 'unix' (or POSIX) time; origin is set to 1970-01-01.
- If 'julian', unit must be 'D', and origin is set to beginning of
Julian Calendar. Julian day number 0 is assigned to the day starting
at noon on January 1, 4713 BC.
- If Timestamp convertible, origin is set to Timestamp identified by
origin.
.. versionadded:: 0.20.0
cache : boolean, default False
If True, use a cache of unique, converted dates to apply the datetime
conversion. May produce significant speed-up when parsing duplicate
date strings, especially ones with timezone offsets.
.. versionadded:: 0.23.0
Returns
-------
ret : datetime if parsing succeeded.
Return type depends on input:
- list-like: DatetimeIndex
- Series: Series of datetime64 dtype
- scalar: Timestamp
In case when it is not possible to return designated types (e.g. when
any element of input is before Timestamp.min or after Timestamp.max)
return will have datetime.datetime type (or corresponding
array/Series).
Examples
--------
Assembling a datetime from multiple columns of a DataFrame. The keys can be
common abbreviations like ['year', 'month', 'day', 'minute', 'second',
'ms', 'us', 'ns']) or plurals of the same
>>> df = pd.DataFrame({'year': [2015, 2016],
'month': [2, 3],
'day': [4, 5]})
>>> pd.to_datetime(df)
0 2015-02-04
1 2016-03-05
dtype: datetime64[ns]
If a date does not meet the `timestamp limitations
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html
#timeseries-timestamp-limits>`_, passing errors='ignore'
will return the original input instead of raising any exception.
Passing errors='coerce' will force an out-of-bounds date to NaT,
in addition to forcing non-dates (or non-parseable dates) to NaT.
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore')
datetime.datetime(1300, 1, 1, 0, 0)
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
NaT
Passing infer_datetime_format=True can often-times speedup a parsing
if its not an ISO8601 format exactly, but in a regular format.
>>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000']*1000)
>>> s.head()
0 3/11/2000
1 3/12/2000
2 3/13/2000
3 3/11/2000
4 3/12/2000
dtype: object
>>> %timeit pd.to_datetime(s,infer_datetime_format=True)
100 loops, best of 3: 10.4 ms per loop
>>> %timeit pd.to_datetime(s,infer_datetime_format=False)
1 loop, best of 3: 471 ms per loop
Using a unix epoch time
>>> pd.to_datetime(1490195805, unit='s')
Timestamp('2017-03-22 15:16:45')
>>> pd.to_datetime(1490195805433502912, unit='ns')
Timestamp('2017-03-22 15:16:45.433502912')
.. warning:: For float arg, precision rounding might happen. To prevent
unexpected behavior use a fixed-width exact type.
Using a non-unix epoch origin
>>> pd.to_datetime([1, 2, 3], unit='D',
origin=pd.Timestamp('1960-01-01'))
0 1960-01-02
1 1960-01-03
2 1960-01-04
See also
--------
pandas.DataFrame.astype : Cast argument to a specified dtype.
pandas.to_timedelta : Convert argument to timedelta.
"""
if arg is None:
return None
if origin != 'unix':
arg = _adjust_to_origin(arg, origin, unit)
tz = 'utc' if utc else None
convert_listlike = partial(_convert_listlike_datetimes, tz=tz, unit=unit,
dayfirst=dayfirst, yearfirst=yearfirst,
errors=errors, exact=exact,
infer_datetime_format=infer_datetime_format)
if isinstance(arg, Timestamp):
result = arg
elif isinstance(arg, ABCSeries):
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
if not cache_array.empty:
result = arg.map(cache_array)
else:
from pandas import Series
values = convert_listlike(arg._values, True, format)
result = Series(values, index=arg.index, name=arg.name)
elif isinstance(arg, (ABCDataFrame, MutableMapping)):
result = _assemble_from_unit_mappings(arg, errors=errors)
elif isinstance(arg, ABCIndexClass):
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
if not cache_array.empty:
result = _convert_and_box_cache(arg, cache_array, box, errors,
name=arg.name)
else:
convert_listlike = partial(convert_listlike, name=arg.name)
result = convert_listlike(arg, box, format)
elif is_list_like(arg):
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
if not cache_array.empty:
result = _convert_and_box_cache(arg, cache_array, box, errors)
else:
result = convert_listlike(arg, box, format)
else:
result = convert_listlike(np.array([arg]), box, format)[0]
return result
# mappings for assembling units
_unit_map = {'year': 'year',
'years': 'year',
'month': 'month',
'months': 'month',
'day': 'day',
'days': 'day',
'hour': 'h',
'hours': 'h',
'minute': 'm',
'minutes': 'm',
'second': 's',
'seconds': 's',
'ms': 'ms',
'millisecond': 'ms',
'milliseconds': 'ms',
'us': 'us',
'microsecond': 'us',
'microseconds': 'us',
'ns': 'ns',
'nanosecond': 'ns',
'nanoseconds': 'ns'
}
def _assemble_from_unit_mappings(arg, errors):
"""
assemble the unit specified fields from the arg (DataFrame)
Return a Series for actual parsing
Parameters
----------
arg : DataFrame
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as NaT
- If 'ignore', then invalid parsing will return the input
Returns
-------
Series
"""
from pandas import to_timedelta, to_numeric, DataFrame
arg = DataFrame(arg)
if not arg.columns.is_unique:
raise ValueError("cannot assemble with duplicate keys")
# replace passed unit with _unit_map
def f(value):
if value in _unit_map:
return _unit_map[value]
# m is case significant
if value.lower() in _unit_map:
return _unit_map[value.lower()]
return value
unit = {k: f(k) for k in arg.keys()}
unit_rev = {v: k for k, v in unit.items()}
# we require at least Ymd
required = ['year', 'month', 'day']
req = sorted(list(set(required) - set(unit_rev.keys())))
if len(req):
raise ValueError("to assemble mappings requires at least that "
"[year, month, day] be specified: [{required}] "
"is missing".format(required=','.join(req)))
# keys we don't recognize
excess = sorted(list(set(unit_rev.keys()) - set(_unit_map.values())))
if len(excess):
raise ValueError("extra keys have been passed "
"to the datetime assemblage: "
"[{excess}]".format(excess=','.join(excess)))
def coerce(values):
# we allow coercion to if errors allows
values = to_numeric(values, errors=errors)
# prevent overflow in case of int8 or int16
if is_integer_dtype(values):
values = values.astype('int64', copy=False)
return values
values = (coerce(arg[unit_rev['year']]) * 10000 +
coerce(arg[unit_rev['month']]) * 100 +
coerce(arg[unit_rev['day']]))
try:
values = to_datetime(values, format='%Y%m%d', errors=errors)
except (TypeError, ValueError) as e:
raise ValueError("cannot assemble the "
"datetimes: {error}".format(error=e))
for u in ['h', 'm', 's', 'ms', 'us', 'ns']:
value = unit_rev.get(u)
if value is not None and value in arg:
try:
values += to_timedelta(coerce(arg[value]),
unit=u,
errors=errors)
except (TypeError, ValueError) as e:
raise ValueError("cannot assemble the datetimes [{value}]: "
"{error}".format(value=value, error=e))
return values
def _attempt_YYYYMMDD(arg, errors):
""" try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like,
arg is a passed in as an object dtype, but could really be ints/strings
with nan-like/or floats (e.g. with nan)
Parameters
----------
arg : passed value
errors : 'raise','ignore','coerce'
"""
def calc(carg):
# calculate the actual result
carg = carg.astype(object)
parsed = parsing.try_parse_year_month_day(carg / 10000,
carg / 100 % 100,
carg % 100)
return tslib.array_to_datetime(parsed, errors=errors)[0]
def calc_with_mask(carg, mask):
result = np.empty(carg.shape, dtype='M8[ns]')
iresult = result.view('i8')
iresult[~mask] = tslibs.iNaT
masked_result = calc(carg[mask].astype(np.float64).astype(np.int64))
result[mask] = masked_result.astype('M8[ns]')
return result
# try intlike / strings that are ints
try:
return calc(arg.astype(np.int64))
except ValueError:
pass
# a float with actual np.nan
try:
carg = arg.astype(np.float64)
return calc_with_mask(carg, notna(carg))
except ValueError:
pass
# string with NaN-like
try:
mask = ~algorithms.isin(arg, list(tslib.nat_strings))
return calc_with_mask(arg, mask)
except ValueError:
pass
return None
# Fixed time formats for time parsing
_time_formats = ["%H:%M", "%H%M", "%I:%M%p", "%I%M%p",
"%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p"]
def _guess_time_format_for_array(arr):
# Try to guess the format based on the first non-NaN element
non_nan_elements = notna(arr).nonzero()[0]
if len(non_nan_elements):
element = arr[non_nan_elements[0]]
for time_format in _time_formats:
try:
datetime.strptime(element, time_format)
return time_format
except ValueError:
pass
return None
def to_time(arg, format=None, infer_time_format=False, errors='raise'):
"""
Parse time strings to time objects using fixed strptime formats ("%H:%M",
"%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p",
"%I%M%S%p")
Use infer_time_format if all the strings are in the same format to speed
up conversion.
Parameters
----------
arg : string in time format, datetime.time, list, tuple, 1-d array, Series
format : str, default None
Format used to convert arg into a time object. If None, fixed formats
are used.
infer_time_format: bool, default False
Infer the time format based on the first non-NaN element. If all
strings are in the same format, this will speed up conversion.
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as None
- If 'ignore', then invalid parsing will return the input
Returns
-------
datetime.time
"""
from pandas.core.series import Series
def _convert_listlike(arg, format):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
elif getattr(arg, 'ndim', 1) > 1:
raise TypeError('arg must be a string, datetime, list, tuple, '
'1-d array, or Series')
arg = ensure_object(arg)
if infer_time_format and format is None:
format = _guess_time_format_for_array(arg)
times = []
if format is not None:
for element in arg:
try:
times.append(datetime.strptime(element, format).time())
except (ValueError, TypeError):
if errors == 'raise':
msg = ("Cannot convert {element} to a time with given "
"format {format}").format(element=element,
format=format)
raise ValueError(msg)
elif errors == 'ignore':
return arg
else:
times.append(None)
else:
formats = _time_formats[:]
format_found = False
for element in arg:
time_object = None
for time_format in formats:
try:
time_object = datetime.strptime(element,
time_format).time()
if not format_found:
# Put the found format in front
fmt = formats.pop(formats.index(time_format))
formats.insert(0, fmt)
format_found = True
break
except (ValueError, TypeError):
continue
if time_object is not None:
times.append(time_object)
elif errors == 'raise':
raise ValueError("Cannot convert arg {arg} to "
"a time".format(arg=arg))
elif errors == 'ignore':
return arg
else:
times.append(None)
return times
if arg is None:
return arg
elif isinstance(arg, time):
return arg
elif isinstance(arg, Series):
values = _convert_listlike(arg._values, format)
return Series(values, index=arg.index, name=arg.name)
elif isinstance(arg, ABCIndexClass):
return _convert_listlike(arg, format)
elif is_list_like(arg):
return _convert_listlike(arg, format)
return _convert_listlike(np.array([arg]), format)[0]
| bsd-3-clause |
guziy/basemap | setup.py | 1 | 6013 | from __future__ import (absolute_import, division, print_function)
import glob
import io
import os
import sys
from setuptools.dist import Distribution
if sys.version_info < (2, 6):
raise SystemExit("""matplotlib and the basemap toolkit require Python 2.6 or later.""")
# Do not require numpy for just querying the package
# Taken from the netcdf-python setup file (which took it from h5py setup file).
inc_dirs = []
if any('--' + opt in sys.argv for opt in Distribution.display_option_names +
['help-commands', 'help']) or sys.argv[1] == 'egg_info':
from setuptools import setup, Extension
else:
import numpy
# Use numpy versions if they are available.
from numpy.distutils.core import setup, Extension
# append numpy include dir.
inc_dirs.append(numpy.get_include())
def get_install_requirements(path):
path = os.path.join(os.path.dirname(__file__), path)
with io.open(path, encoding='utf-8') as fp:
content = fp.read()
return [req for req in content.split("\n")
if req != '' and not req.startswith('#')]
def checkversion(GEOS_dir):
"""check geos C-API header file (geos_c.h)"""
try:
f = open(os.path.join(GEOS_dir, 'include', 'geos_c.h'))
except IOError:
return None
geos_version = None
for line in f:
if line.startswith('#define GEOS_VERSION'):
geos_version = line.split()[2]
return geos_version
# get location of geos lib from environment variable if it is set.
if 'GEOS_DIR' in os.environ:
GEOS_dir = os.environ.get('GEOS_DIR')
else:
# set GEOS_dir manually here if automatic detection fails.
GEOS_dir = None
user_home = os.path.expanduser('~')
geos_search_locations = [user_home, os.path.join(user_home, 'local'),
'/usr', '/usr/local', '/sw', '/opt', '/opt/local']
if GEOS_dir is None:
# if GEOS_dir not set, check a few standard locations.
GEOS_dirs = geos_search_locations
for direc in GEOS_dirs:
geos_version = checkversion(direc)
sys.stdout.write('checking for GEOS lib in %s ....\n' % direc)
if geos_version is None or geos_version < '"3.1.1"':
continue
else:
sys.stdout.write('GEOS lib (version %s) found in %s\n' %\
(geos_version[1:-1],direc))
GEOS_dir = direc
break
else:
geos_version = checkversion(GEOS_dir)
if GEOS_dir is None:
raise SystemExit("""
Can't find geos library in standard locations ('%s').
Please install the corresponding packages using your
systems software management system (e.g. for Debian Linux do:
'apt-get install libgeos-3.3.3 libgeos-c1 libgeos-dev' and/or
set the environment variable GEOS_DIR to point to the location
where geos is installed (for example, if geos_c.h
is in /usr/local/include, and libgeos_c is in /usr/local/lib,
set GEOS_DIR to /usr/local), or edit the setup.py script
manually and set the variable GEOS_dir (right after the line
that says "set GEOS_dir manually here".""" % "', '".join(geos_search_locations))
else:
geos_include_dirs=[os.path.join(GEOS_dir,'include')] + inc_dirs
geos_library_dirs=[os.path.join(GEOS_dir,'lib'),os.path.join(GEOS_dir,'lib64')]
packages = ['mpl_toolkits','mpl_toolkits.basemap']
namespace_packages = ['mpl_toolkits']
package_dirs = {'':'lib'}
# can't install _geoslib in mpl_toolkits.basemap namespace,
# or Basemap objects won't be pickleable.
# don't use runtime_library_dirs on windows (workaround
# for a distutils bug - http://bugs.python.org/issue2437).
if sys.platform == 'win32':
runtime_lib_dirs = []
else:
runtime_lib_dirs = geos_library_dirs
extensions = [ Extension("_geoslib",['src/_geoslib.c'],
library_dirs=geos_library_dirs,
runtime_library_dirs=runtime_lib_dirs,
include_dirs=geos_include_dirs,
libraries=['geos_c']) ]
# Specify all the required mpl data
pathout =\
os.path.join('lib',os.path.join('mpl_toolkits',os.path.join('basemap','data')))
datafiles = glob.glob(os.path.join(pathout,'*'))
datafiles = [os.path.join('data',os.path.basename(f)) for f in datafiles]
package_data = {'mpl_toolkits.basemap':datafiles}
install_requires = get_install_requirements("requirements.txt")
__version__ = "1.2.1"
setup(
name = "basemap",
version = __version__,
description = "Plot data on map projections with matplotlib",
long_description = """
An add-on toolkit for matplotlib that lets you plot data
on map projections with coastlines, lakes, rivers and political boundaries.
See http://matplotlib.org/basemap/users/examples.html for
examples of what it can do.""",
url = "https://matplotlib.org/basemap/",
download_url = "https://github.com/matplotlib/basemap/archive/v{0}rel.tar.gz".format(__version__),
author = "Jeff Whitaker",
author_email = "jeffrey.s.whitaker@noaa.gov",
maintainer = "Ben Root",
maintainer_email = "ben.v.root@gmail.com",
install_requires = install_requires,
platforms = ["any"],
license = "OSI Approved",
keywords = ["python","plotting","plots","graphs","charts","GIS","mapping","map projections","maps"],
classifiers = ["Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"License :: OSI Approved",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Topic :: Scientific/Engineering :: Visualization",
"Topic :: Software Development :: Libraries :: Python Modules",
"Operating System :: OS Independent"],
packages = packages,
namespace_packages = namespace_packages,
package_dir = package_dirs,
ext_modules = extensions,
package_data = package_data
)
| gpl-2.0 |
jmetzen/scikit-learn | examples/svm/plot_oneclass.py | 80 | 2338 | """
==========================================
One-class SVM with non-linear kernel (RBF)
==========================================
An example using a one-class SVM for novelty detection.
:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
algorithm that learns a decision function for novelty detection:
classifying new data as similar or different to the training set.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager
from sklearn import svm
xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))
# Generate train data
X = 0.3 * np.random.randn(100, 2)
X_train = np.r_[X + 2, X - 2]
# Generate some regular novel observations
X = 0.3 * np.random.randn(20, 2)
X_test = np.r_[X + 2, X - 2]
# Generate some abnormal novel observations
X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
# fit the model
clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)
clf.fit(X_train)
y_pred_train = clf.predict(X_train)
y_pred_test = clf.predict(X_test)
y_pred_outliers = clf.predict(X_outliers)
n_error_train = y_pred_train[y_pred_train == -1].size
n_error_test = y_pred_test[y_pred_test == -1].size
n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size
# plot the line, the points, and the nearest vectors to the plane
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.title("Novelty Detection")
plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)
a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='darkred')
plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='palevioletred')
s = 40
b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white', s=s)
b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='blueviolet', s=s)
c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='gold', s=s)
plt.axis('tight')
plt.xlim((-5, 5))
plt.ylim((-5, 5))
plt.legend([a.collections[0], b1, b2, c],
["learned frontier", "training observations",
"new regular observations", "new abnormal observations"],
loc="upper left",
prop=matplotlib.font_manager.FontProperties(size=11))
plt.xlabel(
"error train: %d/200 ; errors novel regular: %d/40 ; "
"errors novel abnormal: %d/40"
% (n_error_train, n_error_test, n_error_outliers))
plt.show()
| bsd-3-clause |
olologin/scikit-learn | examples/svm/plot_iris.py | 225 | 3252 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:
- Sepal length
- Sepal width
This example shows how to plot the decision surface for four SVM classifiers
with different kernels.
The linear models ``LinearSVC()`` and ``SVC(kernel='linear')`` yield slightly
different decision boundaries. This can be a consequence of the following
differences:
- ``LinearSVC`` minimizes the squared hinge loss while ``SVC`` minimizes the
regular hinge loss.
- ``LinearSVC`` uses the One-vs-All (also known as One-vs-Rest) multiclass
reduction while ``SVC`` uses the One-vs-One multiclass reduction.
Both linear models have linear decision boundaries (intersecting hyperplanes)
while the non-linear kernel models (polynomial or Gaussian RBF) have more
flexible non-linear decision boundaries with shapes that depend on the kind of
kernel and its parameters.
.. NOTE:: while plotting the decision function of classifiers for toy 2D
datasets can help get an intuitive understanding of their respective
expressive power, be aware that those intuitions don't always generalize to
more realistic high-dimensional problems.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
y = iris.target
h = .02 # step size in the mesh
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0 # SVM regularization parameter
svc = svm.SVC(kernel='linear', C=C).fit(X, y)
rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)
poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, y)
lin_svc = svm.LinearSVC(C=C).fit(X, y)
# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# title for the plots
titles = ['SVC with linear kernel',
'LinearSVC (linear kernel)',
'SVC with RBF kernel',
'SVC with polynomial (degree 3) kernel']
for i, clf in enumerate((svc, lin_svc, rbf_svc, poly_svc)):
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
plt.subplot(2, 2, i + 1)
plt.subplots_adjust(wspace=0.4, hspace=0.4)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.title(titles[i])
plt.show()
| bsd-3-clause |
nlhepler/freetype-py3 | examples/glyph-vector-2.py | 1 | 3414 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
#
# FreeType high-level python API - Copyright 2011 Nicolas P. Rougier
# Distributed under the terms of the new BSD license.
#
# -----------------------------------------------------------------------------
'''
Show how to access glyph outline description.
'''
from freetype import *
if __name__ == '__main__':
import numpy
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
face = Face(b'./Vera.ttf')
face.set_char_size( 32*64 )
face.load_char('g')
slot = face.glyph
bitmap = face.glyph.bitmap
width = face.glyph.bitmap.width
rows = face.glyph.bitmap.rows
pitch = face.glyph.bitmap.pitch
data = []
for i in range(rows):
data.extend(bitmap.buffer[i*pitch:i*pitch+width])
Z = numpy.array(data,dtype=numpy.ubyte).reshape(rows, width)
outline = slot.outline
points = numpy.array(outline.points, dtype=[('x',float), ('y',float)])
x, y = points['x'], points['y']
figure = plt.figure(figsize=(8,10))
axis = figure.add_subplot(111)
#axis.scatter(points['x'], points['y'], alpha=.25)
start, end = 0, 0
VERTS, CODES = [], []
# Iterate over each contour
for i in range(len(outline.contours)):
end = outline.contours[i]
points = outline.points[start:end+1]
points.append(points[0])
tags = outline.tags[start:end+1]
tags.append(tags[0])
segments = [ [points[0],], ]
for j in range(1, len(points) ):
segments[-1].append(points[j])
if tags[j] & (1 << 0) and j < (len(points)-1):
segments.append( [points[j],] )
verts = [points[0], ]
codes = [Path.MOVETO,]
for segment in segments:
if len(segment) == 2:
verts.extend(segment[1:])
codes.extend([Path.LINETO])
elif len(segment) == 3:
verts.extend(segment[1:])
codes.extend([Path.CURVE3, Path.CURVE3])
else:
verts.append(segment[1])
codes.append(Path.CURVE3)
for i in range(1,len(segment)-2):
A,B = segment[i], segment[i+1]
C = ((A[0]+B[0])/2.0, (A[1]+B[1])/2.0)
verts.extend([ C, B ])
codes.extend([ Path.CURVE3, Path.CURVE3])
verts.append(segment[-1])
codes.append(Path.CURVE3)
VERTS.extend(verts)
CODES.extend(codes)
start = end+1
# Draw glyph
path = Path(VERTS, CODES)
glyph = patches.PathPatch(path, fill = True, facecolor=(0.8,0.5,0.8), alpha=.25, lw=0)
glyph_outline = patches.PathPatch(path, fill = False, edgecolor='black', lw=3)
plt.imshow(Z, extent=[x.min(), x.max(),y.min(), y.max()],
interpolation='nearest', cmap = plt.cm.gray_r, vmin=0, vmax=400)
plt.xticks(numpy.linspace(x.min(), x.max(), Z.shape[1]+1), ())
plt.yticks(numpy.linspace(y.min(), y.max(), Z.shape[0]+1), ())
plt.grid(color='k', linewidth=1, linestyle='-')
axis.add_patch(glyph)
axis.add_patch(glyph_outline)
axis.set_xlim(x.min(), x.max())
axis.set_ylim(y.min(), y.max())
plt.savefig('test.pdf')
plt.show()
| bsd-3-clause |
ssaeger/scikit-learn | sklearn/feature_selection/tests/test_base.py | 143 | 3670 | import numpy as np
from scipy import sparse as sp
from nose.tools import assert_raises, assert_equal
from numpy.testing import assert_array_equal
from sklearn.base import BaseEstimator
from sklearn.feature_selection.base import SelectorMixin
from sklearn.utils import check_array
class StepSelector(SelectorMixin, BaseEstimator):
"""Retain every `step` features (beginning with 0)"""
def __init__(self, step=2):
self.step = step
def fit(self, X, y=None):
X = check_array(X, 'csc')
self.n_input_feats = X.shape[1]
return self
def _get_support_mask(self):
mask = np.zeros(self.n_input_feats, dtype=bool)
mask[::self.step] = True
return mask
support = [True, False] * 5
support_inds = [0, 2, 4, 6, 8]
X = np.arange(20).reshape(2, 10)
Xt = np.arange(0, 20, 2).reshape(2, 5)
Xinv = X.copy()
Xinv[:, 1::2] = 0
y = [0, 1]
feature_names = list('ABCDEFGHIJ')
feature_names_t = feature_names[::2]
feature_names_inv = np.array(feature_names)
feature_names_inv[1::2] = ''
def test_transform_dense():
sel = StepSelector()
Xt_actual = sel.fit(X, y).transform(X)
Xt_actual2 = StepSelector().fit_transform(X, y)
assert_array_equal(Xt, Xt_actual)
assert_array_equal(Xt, Xt_actual2)
# Check dtype matches
assert_equal(np.int32, sel.transform(X.astype(np.int32)).dtype)
assert_equal(np.float32, sel.transform(X.astype(np.float32)).dtype)
# Check 1d list and other dtype:
names_t_actual = sel.transform([feature_names])
assert_array_equal(feature_names_t, names_t_actual.ravel())
# Check wrong shape raises error
assert_raises(ValueError, sel.transform, np.array([[1], [2]]))
def test_transform_sparse():
sparse = sp.csc_matrix
sel = StepSelector()
Xt_actual = sel.fit(sparse(X)).transform(sparse(X))
Xt_actual2 = sel.fit_transform(sparse(X))
assert_array_equal(Xt, Xt_actual.toarray())
assert_array_equal(Xt, Xt_actual2.toarray())
# Check dtype matches
assert_equal(np.int32, sel.transform(sparse(X).astype(np.int32)).dtype)
assert_equal(np.float32, sel.transform(sparse(X).astype(np.float32)).dtype)
# Check wrong shape raises error
assert_raises(ValueError, sel.transform, np.array([[1], [2]]))
def test_inverse_transform_dense():
sel = StepSelector()
Xinv_actual = sel.fit(X, y).inverse_transform(Xt)
assert_array_equal(Xinv, Xinv_actual)
# Check dtype matches
assert_equal(np.int32,
sel.inverse_transform(Xt.astype(np.int32)).dtype)
assert_equal(np.float32,
sel.inverse_transform(Xt.astype(np.float32)).dtype)
# Check 1d list and other dtype:
names_inv_actual = sel.inverse_transform([feature_names_t])
assert_array_equal(feature_names_inv, names_inv_actual.ravel())
# Check wrong shape raises error
assert_raises(ValueError, sel.inverse_transform, np.array([[1], [2]]))
def test_inverse_transform_sparse():
sparse = sp.csc_matrix
sel = StepSelector()
Xinv_actual = sel.fit(sparse(X)).inverse_transform(sparse(Xt))
assert_array_equal(Xinv, Xinv_actual.toarray())
# Check dtype matches
assert_equal(np.int32,
sel.inverse_transform(sparse(Xt).astype(np.int32)).dtype)
assert_equal(np.float32,
sel.inverse_transform(sparse(Xt).astype(np.float32)).dtype)
# Check wrong shape raises error
assert_raises(ValueError, sel.inverse_transform, np.array([[1], [2]]))
def test_get_support():
sel = StepSelector()
sel.fit(X, y)
assert_array_equal(support, sel.get_support())
assert_array_equal(support_inds, sel.get_support(indices=True))
| bsd-3-clause |
costypetrisor/scikit-learn | sklearn/gaussian_process/tests/test_gaussian_process.py | 267 | 6813 | """
Testing for Gaussian Process module (sklearn.gaussian_process)
"""
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# Licence: BSD 3 clause
from nose.tools import raises
from nose.tools import assert_true
import numpy as np
from sklearn.gaussian_process import GaussianProcess
from sklearn.gaussian_process import regression_models as regression
from sklearn.gaussian_process import correlation_models as correlation
from sklearn.datasets import make_regression
from sklearn.utils.testing import assert_greater
f = lambda x: x * np.sin(x)
X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T
X2 = np.atleast_2d([2., 4., 5.5, 6.5, 7.5]).T
y = f(X).ravel()
def test_1d(regr=regression.constant, corr=correlation.squared_exponential,
random_start=10, beta0=None):
# MLE estimation of a one-dimensional Gaussian Process model.
# Check random start optimization.
# Test the interpolating property.
gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0,
theta0=1e-2, thetaL=1e-4, thetaU=1e-1,
random_start=random_start, verbose=False).fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
y2_pred, MSE2 = gp.predict(X2, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.)
and np.allclose(MSE2, 0., atol=10))
def test_2d(regr=regression.constant, corr=correlation.squared_exponential,
random_start=10, beta0=None):
# MLE estimation of a two-dimensional Gaussian Process model accounting for
# anisotropy. Check random start optimization.
# Test the interpolating property.
b, kappa, e = 5., .5, .1
g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e) ** 2.
X = np.array([[-4.61611719, -6.00099547],
[4.10469096, 5.32782448],
[0.00000000, -0.50000000],
[-6.17289014, -4.6984743],
[1.3109306, -6.93271427],
[-5.03823144, 3.10584743],
[-2.87600388, 6.74310541],
[5.21301203, 4.26386883]])
y = g(X).ravel()
thetaL = [1e-4] * 2
thetaU = [1e-1] * 2
gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0,
theta0=[1e-2] * 2, thetaL=thetaL,
thetaU=thetaU,
random_start=random_start, verbose=False)
gp.fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.))
eps = np.finfo(gp.theta_.dtype).eps
assert_true(np.all(gp.theta_ >= thetaL - eps)) # Lower bounds of hyperparameters
assert_true(np.all(gp.theta_ <= thetaU + eps)) # Upper bounds of hyperparameters
def test_2d_2d(regr=regression.constant, corr=correlation.squared_exponential,
random_start=10, beta0=None):
# MLE estimation of a two-dimensional Gaussian Process model accounting for
# anisotropy. Check random start optimization.
# Test the GP interpolation for 2D output
b, kappa, e = 5., .5, .1
g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e) ** 2.
f = lambda x: np.vstack((g(x), g(x))).T
X = np.array([[-4.61611719, -6.00099547],
[4.10469096, 5.32782448],
[0.00000000, -0.50000000],
[-6.17289014, -4.6984743],
[1.3109306, -6.93271427],
[-5.03823144, 3.10584743],
[-2.87600388, 6.74310541],
[5.21301203, 4.26386883]])
y = f(X)
gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0,
theta0=[1e-2] * 2, thetaL=[1e-4] * 2,
thetaU=[1e-1] * 2,
random_start=random_start, verbose=False)
gp.fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.))
@raises(ValueError)
def test_wrong_number_of_outputs():
gp = GaussianProcess()
gp.fit([[1, 2, 3], [4, 5, 6]], [1, 2, 3])
def test_more_builtin_correlation_models(random_start=1):
# Repeat test_1d and test_2d for several built-in correlation
# models specified as strings.
all_corr = ['absolute_exponential', 'squared_exponential', 'cubic',
'linear']
for corr in all_corr:
test_1d(regr='constant', corr=corr, random_start=random_start)
test_2d(regr='constant', corr=corr, random_start=random_start)
test_2d_2d(regr='constant', corr=corr, random_start=random_start)
def test_ordinary_kriging():
# Repeat test_1d and test_2d with given regression weights (beta0) for
# different regression models (Ordinary Kriging).
test_1d(regr='linear', beta0=[0., 0.5])
test_1d(regr='quadratic', beta0=[0., 0.5, 0.5])
test_2d(regr='linear', beta0=[0., 0.5, 0.5])
test_2d(regr='quadratic', beta0=[0., 0.5, 0.5, 0.5, 0.5, 0.5])
test_2d_2d(regr='linear', beta0=[0., 0.5, 0.5])
test_2d_2d(regr='quadratic', beta0=[0., 0.5, 0.5, 0.5, 0.5, 0.5])
def test_no_normalize():
gp = GaussianProcess(normalize=False).fit(X, y)
y_pred = gp.predict(X)
assert_true(np.allclose(y_pred, y))
def test_random_starts():
# Test that an increasing number of random-starts of GP fitting only
# increases the reduced likelihood function of the optimal theta.
n_samples, n_features = 50, 3
np.random.seed(0)
rng = np.random.RandomState(0)
X = rng.randn(n_samples, n_features) * 2 - 1
y = np.sin(X).sum(axis=1) + np.sin(3 * X).sum(axis=1)
best_likelihood = -np.inf
for random_start in range(1, 5):
gp = GaussianProcess(regr="constant", corr="squared_exponential",
theta0=[1e-0] * n_features,
thetaL=[1e-4] * n_features,
thetaU=[1e+1] * n_features,
random_start=random_start, random_state=0,
verbose=False).fit(X, y)
rlf = gp.reduced_likelihood_function()[0]
assert_greater(rlf, best_likelihood - np.finfo(np.float32).eps)
best_likelihood = rlf
def test_mse_solving():
# test the MSE estimate to be sane.
# non-regression test for ignoring off-diagonals of feature covariance,
# testing with nugget that renders covariance useless, only
# using the mean function, with low effective rank of data
gp = GaussianProcess(corr='absolute_exponential', theta0=1e-4,
thetaL=1e-12, thetaU=1e-2, nugget=1e-2,
optimizer='Welch', regr="linear", random_state=0)
X, y = make_regression(n_informative=3, n_features=60, noise=50,
random_state=0, effective_rank=1)
gp.fit(X, y)
assert_greater(1000, gp.predict(X, eval_MSE=True)[1].mean())
| bsd-3-clause |
alvarofierroclavero/scikit-learn | sklearn/kernel_ridge.py | 155 | 6545 | """Module :mod:`sklearn.kernel_ridge` implements kernel ridge regression."""
# Authors: Mathieu Blondel <mathieu@mblondel.org>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause
import numpy as np
from .base import BaseEstimator, RegressorMixin
from .metrics.pairwise import pairwise_kernels
from .linear_model.ridge import _solve_cholesky_kernel
from .utils import check_X_y
from .utils.validation import check_is_fitted
class KernelRidge(BaseEstimator, RegressorMixin):
"""Kernel ridge regression.
Kernel ridge regression (KRR) combines ridge regression (linear least
squares with l2-norm regularization) with the kernel trick. It thus
learns a linear function in the space induced by the respective kernel and
the data. For non-linear kernels, this corresponds to a non-linear
function in the original space.
The form of the model learned by KRR is identical to support vector
regression (SVR). However, different loss functions are used: KRR uses
squared error loss while support vector regression uses epsilon-insensitive
loss, both combined with l2 regularization. In contrast to SVR, fitting a
KRR model can be done in closed-form and is typically faster for
medium-sized datasets. On the other hand, the learned model is non-sparse
and thus slower than SVR, which learns a sparse model for epsilon > 0, at
prediction-time.
This estimator has built-in support for multi-variate regression
(i.e., when y is a 2d-array of shape [n_samples, n_targets]).
Read more in the :ref:`User Guide <kernel_ridge>`.
Parameters
----------
alpha : {float, array-like}, shape = [n_targets]
Small positive values of alpha improve the conditioning of the problem
and reduce the variance of the estimates. Alpha corresponds to
``(2*C)^-1`` in other linear models such as LogisticRegression or
LinearSVC. If an array is passed, penalties are assumed to be specific
to the targets. Hence they must correspond in number.
kernel : string or callable, default="linear"
Kernel mapping used internally. A callable should accept two arguments
and the keyword arguments passed to this object as kernel_params, and
should return a floating point number.
gamma : float, default=None
Gamma parameter for the RBF, polynomial, exponential chi2 and
sigmoid kernels. Interpretation of the default value is left to
the kernel; see the documentation for sklearn.metrics.pairwise.
Ignored by other kernels.
degree : float, default=3
Degree of the polynomial kernel. Ignored by other kernels.
coef0 : float, default=1
Zero coefficient for polynomial and sigmoid kernels.
Ignored by other kernels.
kernel_params : mapping of string to any, optional
Additional parameters (keyword arguments) for kernel function passed
as callable object.
Attributes
----------
dual_coef_ : array, shape = [n_features] or [n_targets, n_features]
Weight vector(s) in kernel space
X_fit_ : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data, which is also required for prediction
References
----------
* Kevin P. Murphy
"Machine Learning: A Probabilistic Perspective", The MIT Press
chapter 14.4.3, pp. 492-493
See also
--------
Ridge
Linear ridge regression.
SVR
Support Vector Regression implemented using libsvm.
Examples
--------
>>> from sklearn.kernel_ridge import KernelRidge
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> rng = np.random.RandomState(0)
>>> y = rng.randn(n_samples)
>>> X = rng.randn(n_samples, n_features)
>>> clf = KernelRidge(alpha=1.0)
>>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
KernelRidge(alpha=1.0, coef0=1, degree=3, gamma=None, kernel='linear',
kernel_params=None)
"""
def __init__(self, alpha=1, kernel="linear", gamma=None, degree=3, coef0=1,
kernel_params=None):
self.alpha = alpha
self.kernel = kernel
self.gamma = gamma
self.degree = degree
self.coef0 = coef0
self.kernel_params = kernel_params
def _get_kernel(self, X, Y=None):
if callable(self.kernel):
params = self.kernel_params or {}
else:
params = {"gamma": self.gamma,
"degree": self.degree,
"coef0": self.coef0}
return pairwise_kernels(X, Y, metric=self.kernel,
filter_params=True, **params)
@property
def _pairwise(self):
return self.kernel == "precomputed"
def fit(self, X, y=None, sample_weight=None):
"""Fit Kernel Ridge regression model
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
sample_weight : float or numpy array of shape [n_samples]
Individual weights for each sample, ignored if None is passed.
Returns
-------
self : returns an instance of self.
"""
# Convert data
X, y = check_X_y(X, y, accept_sparse=("csr", "csc"), multi_output=True,
y_numeric=True)
K = self._get_kernel(X)
alpha = np.atleast_1d(self.alpha)
ravel = False
if len(y.shape) == 1:
y = y.reshape(-1, 1)
ravel = True
copy = self.kernel == "precomputed"
self.dual_coef_ = _solve_cholesky_kernel(K, y, alpha,
sample_weight,
copy)
if ravel:
self.dual_coef_ = self.dual_coef_.ravel()
self.X_fit_ = X
return self
def predict(self, X):
"""Predict using the the kernel ridge model
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Samples.
Returns
-------
C : array, shape = [n_samples] or [n_samples, n_targets]
Returns predicted values.
"""
check_is_fitted(self, ["X_fit_", "dual_coef_"])
K = self._get_kernel(X, self.X_fit_)
return np.dot(K, self.dual_coef_)
| bsd-3-clause |
jcasner/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/path.py | 69 | 20263 | """
Contains a class for managing paths (polylines).
"""
import math
from weakref import WeakValueDictionary
import numpy as np
from numpy import ma
from matplotlib._path import point_in_path, get_path_extents, \
point_in_path_collection, get_path_collection_extents, \
path_in_path, path_intersects_path, convert_path_to_polygons
from matplotlib.cbook import simple_linear_interpolation
class Path(object):
"""
:class:`Path` represents a series of possibly disconnected,
possibly closed, line and curve segments.
The underlying storage is made up of two parallel numpy arrays:
- *vertices*: an Nx2 float array of vertices
- *codes*: an N-length uint8 array of vertex types
These two arrays always have the same length in the first
dimension. For example, to represent a cubic curve, you must
provide three vertices as well as three codes ``CURVE3``.
The code types are:
- ``STOP`` : 1 vertex (ignored)
A marker for the end of the entire path (currently not
required and ignored)
- ``MOVETO`` : 1 vertex
Pick up the pen and move to the given vertex.
- ``LINETO`` : 1 vertex
Draw a line from the current position to the given vertex.
- ``CURVE3`` : 1 control point, 1 endpoint
Draw a quadratic Bezier curve from the current position,
with the given control point, to the given end point.
- ``CURVE4`` : 2 control points, 1 endpoint
Draw a cubic Bezier curve from the current position, with
the given control points, to the given end point.
- ``CLOSEPOLY`` : 1 vertex (ignored)
Draw a line segment to the start point of the current
polyline.
Users of Path objects should not access the vertices and codes
arrays directly. Instead, they should use :meth:`iter_segments`
to get the vertex/code pairs. This is important, since many
:class:`Path` objects, as an optimization, do not store a *codes*
at all, but have a default one provided for them by
:meth:`iter_segments`.
Note also that the vertices and codes arrays should be treated as
immutable -- there are a number of optimizations and assumptions
made up front in the constructor that will not change when the
data changes.
"""
# Path codes
STOP = 0 # 1 vertex
MOVETO = 1 # 1 vertex
LINETO = 2 # 1 vertex
CURVE3 = 3 # 2 vertices
CURVE4 = 4 # 3 vertices
CLOSEPOLY = 5 # 1 vertex
NUM_VERTICES = [1, 1, 1, 2, 3, 1]
code_type = np.uint8
def __init__(self, vertices, codes=None):
"""
Create a new path with the given vertices and codes.
*vertices* is an Nx2 numpy float array, masked array or Python
sequence.
*codes* is an N-length numpy array or Python sequence of type
:attr:`matplotlib.path.Path.code_type`.
These two arrays must have the same length in the first
dimension.
If *codes* is None, *vertices* will be treated as a series of
line segments.
If *vertices* contains masked values, they will be converted
to NaNs which are then handled correctly by the Agg
PathIterator and other consumers of path data, such as
:meth:`iter_segments`.
"""
if ma.isMaskedArray(vertices):
vertices = vertices.astype(np.float_).filled(np.nan)
else:
vertices = np.asarray(vertices, np.float_)
if codes is not None:
codes = np.asarray(codes, self.code_type)
assert codes.ndim == 1
assert len(codes) == len(vertices)
assert vertices.ndim == 2
assert vertices.shape[1] == 2
self.should_simplify = (len(vertices) >= 128 and
(codes is None or np.all(codes <= Path.LINETO)))
self.has_nonfinite = not np.isfinite(vertices).all()
self.codes = codes
self.vertices = vertices
#@staticmethod
def make_compound_path(*args):
"""
(staticmethod) Make a compound path from a list of Path
objects. Only polygons (not curves) are supported.
"""
for p in args:
assert p.codes is None
lengths = [len(x) for x in args]
total_length = sum(lengths)
vertices = np.vstack([x.vertices for x in args])
vertices.reshape((total_length, 2))
codes = Path.LINETO * np.ones(total_length)
i = 0
for length in lengths:
codes[i] = Path.MOVETO
i += length
return Path(vertices, codes)
make_compound_path = staticmethod(make_compound_path)
def __repr__(self):
return "Path(%s, %s)" % (self.vertices, self.codes)
def __len__(self):
return len(self.vertices)
def iter_segments(self, simplify=None):
"""
Iterates over all of the curve segments in the path. Each
iteration returns a 2-tuple (*vertices*, *code*), where
*vertices* is a sequence of 1 - 3 coordinate pairs, and *code* is
one of the :class:`Path` codes.
If *simplify* is provided, it must be a tuple (*width*,
*height*) defining the size of the figure, in native units
(e.g. pixels or points). Simplification implies both removing
adjacent line segments that are very close to parallel, and
removing line segments outside of the figure. The path will
be simplified *only* if :attr:`should_simplify` is True, which
is determined in the constructor by this criteria:
- No curves
- More than 128 vertices
"""
vertices = self.vertices
if not len(vertices):
return
codes = self.codes
len_vertices = len(vertices)
isfinite = np.isfinite
NUM_VERTICES = self.NUM_VERTICES
MOVETO = self.MOVETO
LINETO = self.LINETO
CLOSEPOLY = self.CLOSEPOLY
STOP = self.STOP
if simplify is not None and self.should_simplify:
polygons = self.to_polygons(None, *simplify)
for vertices in polygons:
yield vertices[0], MOVETO
for v in vertices[1:]:
yield v, LINETO
elif codes is None:
if self.has_nonfinite:
next_code = MOVETO
for v in vertices:
if np.isfinite(v).all():
yield v, next_code
next_code = LINETO
else:
next_code = MOVETO
else:
yield vertices[0], MOVETO
for v in vertices[1:]:
yield v, LINETO
else:
i = 0
was_nan = False
while i < len_vertices:
code = codes[i]
if code == CLOSEPOLY:
yield [], code
i += 1
elif code == STOP:
return
else:
num_vertices = NUM_VERTICES[int(code)]
curr_vertices = vertices[i:i+num_vertices].flatten()
if not isfinite(curr_vertices).all():
was_nan = True
elif was_nan:
yield curr_vertices[-2:], MOVETO
was_nan = False
else:
yield curr_vertices, code
i += num_vertices
def transformed(self, transform):
"""
Return a transformed copy of the path.
.. seealso::
:class:`matplotlib.transforms.TransformedPath`:
A specialized path class that will cache the
transformed result and automatically update when the
transform changes.
"""
return Path(transform.transform(self.vertices), self.codes)
def contains_point(self, point, transform=None):
"""
Returns *True* if the path contains the given point.
If *transform* is not *None*, the path will be transformed
before performing the test.
"""
if transform is not None:
transform = transform.frozen()
return point_in_path(point[0], point[1], self, transform)
def contains_path(self, path, transform=None):
"""
Returns *True* if this path completely contains the given path.
If *transform* is not *None*, the path will be transformed
before performing the test.
"""
if transform is not None:
transform = transform.frozen()
return path_in_path(self, None, path, transform)
def get_extents(self, transform=None):
"""
Returns the extents (*xmin*, *ymin*, *xmax*, *ymax*) of the
path.
Unlike computing the extents on the *vertices* alone, this
algorithm will take into account the curves and deal with
control points appropriately.
"""
from transforms import Bbox
if transform is not None:
transform = transform.frozen()
return Bbox(get_path_extents(self, transform))
def intersects_path(self, other, filled=True):
"""
Returns *True* if this path intersects another given path.
*filled*, when True, treats the paths as if they were filled.
That is, if one path completely encloses the other,
:meth:`intersects_path` will return True.
"""
return path_intersects_path(self, other, filled)
def intersects_bbox(self, bbox, filled=True):
"""
Returns *True* if this path intersects a given
:class:`~matplotlib.transforms.Bbox`.
*filled*, when True, treats the path as if it was filled.
That is, if one path completely encloses the other,
:meth:`intersects_path` will return True.
"""
from transforms import BboxTransformTo
rectangle = self.unit_rectangle().transformed(
BboxTransformTo(bbox))
result = self.intersects_path(rectangle, filled)
return result
def interpolated(self, steps):
"""
Returns a new path resampled to length N x steps. Does not
currently handle interpolating curves.
"""
vertices = simple_linear_interpolation(self.vertices, steps)
codes = self.codes
if codes is not None:
new_codes = Path.LINETO * np.ones(((len(codes) - 1) * steps + 1, ))
new_codes[0::steps] = codes
else:
new_codes = None
return Path(vertices, new_codes)
def to_polygons(self, transform=None, width=0, height=0):
"""
Convert this path to a list of polygons. Each polygon is an
Nx2 array of vertices. In other words, each polygon has no
``MOVETO`` instructions or curves. This is useful for
displaying in backends that do not support compound paths or
Bezier curves, such as GDK.
If *width* and *height* are both non-zero then the lines will
be simplified so that vertices outside of (0, 0), (width,
height) will be clipped.
"""
if len(self.vertices) == 0:
return []
if transform is not None:
transform = transform.frozen()
if self.codes is None and (width == 0 or height == 0):
if transform is None:
return [self.vertices]
else:
return [transform.transform(self.vertices)]
# Deal with the case where there are curves and/or multiple
# subpaths (using extension code)
return convert_path_to_polygons(self, transform, width, height)
_unit_rectangle = None
#@classmethod
def unit_rectangle(cls):
"""
(staticmethod) Returns a :class:`Path` of the unit rectangle
from (0, 0) to (1, 1).
"""
if cls._unit_rectangle is None:
cls._unit_rectangle = \
Path([[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0]])
return cls._unit_rectangle
unit_rectangle = classmethod(unit_rectangle)
_unit_regular_polygons = WeakValueDictionary()
#@classmethod
def unit_regular_polygon(cls, numVertices):
"""
(staticmethod) Returns a :class:`Path` for a unit regular
polygon with the given *numVertices* and radius of 1.0,
centered at (0, 0).
"""
if numVertices <= 16:
path = cls._unit_regular_polygons.get(numVertices)
else:
path = None
if path is None:
theta = (2*np.pi/numVertices *
np.arange(numVertices + 1).reshape((numVertices + 1, 1)))
# This initial rotation is to make sure the polygon always
# "points-up"
theta += np.pi / 2.0
verts = np.concatenate((np.cos(theta), np.sin(theta)), 1)
path = Path(verts)
cls._unit_regular_polygons[numVertices] = path
return path
unit_regular_polygon = classmethod(unit_regular_polygon)
_unit_regular_stars = WeakValueDictionary()
#@classmethod
def unit_regular_star(cls, numVertices, innerCircle=0.5):
"""
(staticmethod) Returns a :class:`Path` for a unit regular star
with the given numVertices and radius of 1.0, centered at (0,
0).
"""
if numVertices <= 16:
path = cls._unit_regular_stars.get((numVertices, innerCircle))
else:
path = None
if path is None:
ns2 = numVertices * 2
theta = (2*np.pi/ns2 * np.arange(ns2 + 1))
# This initial rotation is to make sure the polygon always
# "points-up"
theta += np.pi / 2.0
r = np.ones(ns2 + 1)
r[1::2] = innerCircle
verts = np.vstack((r*np.cos(theta), r*np.sin(theta))).transpose()
path = Path(verts)
cls._unit_regular_polygons[(numVertices, innerCircle)] = path
return path
unit_regular_star = classmethod(unit_regular_star)
#@classmethod
def unit_regular_asterisk(cls, numVertices):
"""
(staticmethod) Returns a :class:`Path` for a unit regular
asterisk with the given numVertices and radius of 1.0,
centered at (0, 0).
"""
return cls.unit_regular_star(numVertices, 0.0)
unit_regular_asterisk = classmethod(unit_regular_asterisk)
_unit_circle = None
#@classmethod
def unit_circle(cls):
"""
(staticmethod) Returns a :class:`Path` of the unit circle.
The circle is approximated using cubic Bezier curves. This
uses 8 splines around the circle using the approach presented
here:
Lancaster, Don. `Approximating a Circle or an Ellipse Using Four
Bezier Cubic Splines <http://www.tinaja.com/glib/ellipse4.pdf>`_.
"""
if cls._unit_circle is None:
MAGIC = 0.2652031
SQRTHALF = np.sqrt(0.5)
MAGIC45 = np.sqrt((MAGIC*MAGIC) / 2.0)
vertices = np.array(
[[0.0, -1.0],
[MAGIC, -1.0],
[SQRTHALF-MAGIC45, -SQRTHALF-MAGIC45],
[SQRTHALF, -SQRTHALF],
[SQRTHALF+MAGIC45, -SQRTHALF+MAGIC45],
[1.0, -MAGIC],
[1.0, 0.0],
[1.0, MAGIC],
[SQRTHALF+MAGIC45, SQRTHALF-MAGIC45],
[SQRTHALF, SQRTHALF],
[SQRTHALF-MAGIC45, SQRTHALF+MAGIC45],
[MAGIC, 1.0],
[0.0, 1.0],
[-MAGIC, 1.0],
[-SQRTHALF+MAGIC45, SQRTHALF+MAGIC45],
[-SQRTHALF, SQRTHALF],
[-SQRTHALF-MAGIC45, SQRTHALF-MAGIC45],
[-1.0, MAGIC],
[-1.0, 0.0],
[-1.0, -MAGIC],
[-SQRTHALF-MAGIC45, -SQRTHALF+MAGIC45],
[-SQRTHALF, -SQRTHALF],
[-SQRTHALF+MAGIC45, -SQRTHALF-MAGIC45],
[-MAGIC, -1.0],
[0.0, -1.0],
[0.0, -1.0]],
np.float_)
codes = cls.CURVE4 * np.ones(26)
codes[0] = cls.MOVETO
codes[-1] = cls.CLOSEPOLY
cls._unit_circle = Path(vertices, codes)
return cls._unit_circle
unit_circle = classmethod(unit_circle)
#@classmethod
def arc(cls, theta1, theta2, n=None, is_wedge=False):
"""
(staticmethod) Returns an arc on the unit circle from angle
*theta1* to angle *theta2* (in degrees).
If *n* is provided, it is the number of spline segments to make.
If *n* is not provided, the number of spline segments is
determined based on the delta between *theta1* and *theta2*.
Masionobe, L. 2003. `Drawing an elliptical arc using
polylines, quadratic or cubic Bezier curves
<http://www.spaceroots.org/documents/ellipse/index.html>`_.
"""
# degrees to radians
theta1 *= np.pi / 180.0
theta2 *= np.pi / 180.0
twopi = np.pi * 2.0
halfpi = np.pi * 0.5
eta1 = np.arctan2(np.sin(theta1), np.cos(theta1))
eta2 = np.arctan2(np.sin(theta2), np.cos(theta2))
eta2 -= twopi * np.floor((eta2 - eta1) / twopi)
if (theta2 - theta1 > np.pi) and (eta2 - eta1 < np.pi):
eta2 += twopi
# number of curve segments to make
if n is None:
n = int(2 ** np.ceil((eta2 - eta1) / halfpi))
if n < 1:
raise ValueError("n must be >= 1 or None")
deta = (eta2 - eta1) / n
t = np.tan(0.5 * deta)
alpha = np.sin(deta) * (np.sqrt(4.0 + 3.0 * t * t) - 1) / 3.0
steps = np.linspace(eta1, eta2, n + 1, True)
cos_eta = np.cos(steps)
sin_eta = np.sin(steps)
xA = cos_eta[:-1]
yA = sin_eta[:-1]
xA_dot = -yA
yA_dot = xA
xB = cos_eta[1:]
yB = sin_eta[1:]
xB_dot = -yB
yB_dot = xB
if is_wedge:
length = n * 3 + 4
vertices = np.zeros((length, 2), np.float_)
codes = Path.CURVE4 * np.ones((length, ), Path.code_type)
vertices[1] = [xA[0], yA[0]]
codes[0:2] = [Path.MOVETO, Path.LINETO]
codes[-2:] = [Path.LINETO, Path.CLOSEPOLY]
vertex_offset = 2
end = length - 2
else:
length = n * 3 + 1
vertices = np.zeros((length, 2), np.float_)
codes = Path.CURVE4 * np.ones((length, ), Path.code_type)
vertices[0] = [xA[0], yA[0]]
codes[0] = Path.MOVETO
vertex_offset = 1
end = length
vertices[vertex_offset :end:3, 0] = xA + alpha * xA_dot
vertices[vertex_offset :end:3, 1] = yA + alpha * yA_dot
vertices[vertex_offset+1:end:3, 0] = xB - alpha * xB_dot
vertices[vertex_offset+1:end:3, 1] = yB - alpha * yB_dot
vertices[vertex_offset+2:end:3, 0] = xB
vertices[vertex_offset+2:end:3, 1] = yB
return Path(vertices, codes)
arc = classmethod(arc)
#@classmethod
def wedge(cls, theta1, theta2, n=None):
"""
(staticmethod) Returns a wedge of the unit circle from angle
*theta1* to angle *theta2* (in degrees).
If *n* is provided, it is the number of spline segments to make.
If *n* is not provided, the number of spline segments is
determined based on the delta between *theta1* and *theta2*.
"""
return cls.arc(theta1, theta2, n, True)
wedge = classmethod(wedge)
_get_path_collection_extents = get_path_collection_extents
def get_path_collection_extents(*args):
"""
Given a sequence of :class:`Path` objects, returns the bounding
box that encapsulates all of them.
"""
from transforms import Bbox
if len(args[1]) == 0:
raise ValueError("No paths provided")
return Bbox.from_extents(*_get_path_collection_extents(*args))
| agpl-3.0 |
Loisel/tmr3 | tmr.py | 1 | 15096 | #!/usr/bin/python
"""
A module to calculate the current, the conductance and the TMR from
a set of rate arrays.
The rate arrays are supposed to be stored in a h5 file in the job directory.
The result is stored in a h5 file. The name of the dataset contains all
parameters. They are also stored as attributes in the dataset.
The conductance in the two lead configurations (parallel/anti-parallel)
are stored in arrays in the dataset.
Usage:
./tmr.py <jobname>
"""
import numpy as np
from numpy import linalg
import time
import sys
import getopt
import h5py
import os
# We are picky about possible floating point overflows
# to avoid calculating NaNs
np.seterr(divide="raise")
np.seterr(invalid="raise")
# A helper module to calculate the populations.
import pop
# The configuration module
import cfg
# path to the dat directory
datpath = "dat/"
# name of the temporary file where the rates are stored
ratefile = "running_calc.h5"
# name of the h5 file to store the conductance for the two configuration
# and the configuraion parameters.
hdffile = "simdata_new.h5"
def save_hdf5(fname,G_P,G_AP):
"""
Store the conductance and the configuration to the h5 file.
Args:
fname: filename of the h5 file
G_P: the conductance for leads with parallel magnetization
G_AP: the conductance for leads with anti-parallel magnetization
"""
print "Shape of GP {}".format(G_P.shape)
fileh = h5py.File(fname,"a")
# Note that the selection of parameters to construct the name of the
# dataset should be chosen such that this string is unique!
# That is, it should contain all running parameters.
dset_name = "G={}_kbT={}_Ec={}_E0={}_Pol={}_PolOrb={}_SO={}_tau={}_DS={}_B_P={}_B_AP={}_B_ORB_P={}_B_ORB_AP={}_W_e={}_W_0={}".format(cfg.conf['G_scale'],cfg.conf['kBT'],cfg.conf['E_C'],cfg.conf['E_0'],cfg.conf['Pol'],cfg.conf['OrbPol'],cfg.conf['SO'],cfg.conf['tau_r'],cfg.conf['D_S_factor'],cfg.conf['B_P'],cfg.conf['B_AP'],cfg.conf['B_ORB_P'],cfg.conf['B_ORB_AP'],cfg.conf['W_E'],cfg.conf['W_0'])
try:
# we create the dataset
dset = fileh.create_dataset(dset_name,data=np.vstack((G_P,G_AP)))
# and store the config attributes
dset.attrs['alpha'] = cfg.conf['ALPHA']
dset.attrs['temperature'] = cfg.conf['kBT']
dset.attrs['coupling'] = cfg.conf['G_scale']
dset.attrs['electron_number'] = cfg.conf['N_0']
dset.attrs['charging_energy'] = cfg.conf['E_C']
dset.attrs['level_spacing'] = cfg.conf['E_0']
dset.attrs['polarization_spin'] = cfg.conf['Pol']
dset.attrs['polarization_orbit'] = cfg.conf['OrbPol']
dset.attrs['spinorbit'] = cfg.conf['SO']
dset.attrs['stonershift'] = cfg.conf['D_S_factor']
dset.attrs['tau_r'] = cfg.conf['tau_r']
dset.attrs['vg_min'] = cfg.conf['V_g_min']
dset.attrs['vg_max'] = cfg.conf['V_g_max']
dset.attrs['b_p'] = cfg.conf['B_P']
dset.attrs['b_ap'] = cfg.conf['B_AP']
dset.attrs['b_orb_p'] = cfg.conf['B_ORB_P']
dset.attrs['b_orb_ap'] = cfg.conf['B_ORB_AP']
dset.attrs['w_0'] = cfg.conf['W_0']
dset.attrs['w_e'] = cfg.conf['W_E']
dset.attrs['timestamp'] = time.time()
except KeyError:
# If the choice was not unique we complain but continue.
print "Dataset exists."
fileh.close()
def eval_DENKER(GM,GP,configuration):
"""
Evaluate the density matrix kernel using the in- and out-tunneling rates.
Args:
GM,GP: numpy arrays containing in- and out-tunneling rates
in the order of cfg.TLIST.
configuration: integer determining parallel (0) or anti-parallel(1)
configuration
Returns:
the density matrix as a square 2-d numpy array that is NP**2 in size,
where NP is the number of states in the groundstatespace.
"""
# we get a view on the transition list and, for simplicity, its transpose
TLIST = cfg.TLIST[configuration]
TLIST_T = np.transpose(TLIST)
# from all transitions we extract all groundstates in the statespace
# this is probably a complicated way to do it
PLIST = list(set(TLIST_T[0]).union(TLIST_T[1]))
# ... and sort it by index
PLIST.sort()
# the number of groundstates
NP = len(PLIST)
# let's create an empty density matrix
ME = np.zeros((NP,NP))
# we create a version of the transition list that does not contain
# the indices in terms of the energy array (see cfg.py), but
# in terms of the number in the state list (plist)
# (the transition list can then be used to denote non-zero matrix elements)
TMP = np.copy(TLIST)
for idx,val in enumerate(PLIST):
TMP[TLIST == val] = idx
# We calculate diagonal elements of the density matrix:
# TLIST_T[1] == num selects the correct in-tunneling rates for the
# state with label num
# have a look at numpy.where to understand this line
for idx,num in enumerate(PLIST):
ME[idx,idx] = -np.sum(np.where(TLIST_T[1] == num,GP,0.)) - np.sum(np.where(TLIST_T[0] == num,GM,0.))
# for the off diagonal elements we can directly use the generated TMP
# transition list
for k,tup in enumerate(TMP):
ME[tup[0],tup[1]] = GP[k]
ME[tup[1],tup[0]] = GM[k]
# print "tup: {} and matrix element {}".format(tup,ME[tuple(tup)])
return ME
def eval_CURKER(GM,GP,configuration):
"""
Evaluate the current kernel using the in- and out-tunneling rates.
Args:
GM,GP: numpy arrays containing in- and out-tunneling rates
in the order of cfg.TLIST.
configuration: integer determining parallel (0) or anti-parallel(1)
configuration
Returns:
the current kernel as a 1-d numpy array.
"""
# We get a view on the transition list and its transpose
TLIST = cfg.TLIST[configuration]
TLIST_T = np.transpose(TLIST)
# ... and extract the list of groundstates (see also eval_DENKER)
PLIST = list(set(TLIST_T[0]).union(TLIST_T[1]))
PLIST.sort()
# this determines the size of the statespace
NP = len(PLIST)
CUR = np.zeros(NP)
# Note that the current kernel can be calculated by summing the diagonal elements
# of the density matrix with opposite sign
# compare eval_DENKER
for idx,num in enumerate(PLIST):
CUR[idx] = np.sum(np.where(TLIST_T[1] == num,GP,0.)) - np.sum(np.where(TLIST_T[0] == num,GM,0.))
return CUR
def current(GP,GM,POP,configuration):
"""
Calculate the current using the rates and populations.
Args:
GP, GM: np-arrays containing in- and out-tunneling rates.
POP: np-array for the populations
configuration: integer determining parallel (0) or anti-parallel(1)
configuration
Returns:
current as a float.
"""
# We calculate the current kernel
CURKER = eval_CURKER(GM,GP,configuration)
# and vector-multiply it with the population vector
I = -np.sum(cfg.conf["ELE"]*np.dot( CURKER, POP))
return I
def eval_tmr(fname,plotname,pop):
"""
Calculates the TMR by evaluating conductance through
parallel and anti-parallel polarized contacts.
Args:
fname: the h5 file to load the rates from.
plotname: A name for the pdf output to produce.
pop: If True, we plot the populations, too.
"""
# We prepare the current and conductance vectors for different
# values of gate and bias voltage
C_p = np.zeros((cfg.conf['NV'],cfg.conf['NVb']))
C_ap = np.zeros((cfg.conf['NV'],cfg.conf['NVb']))
G_p = np.zeros((cfg.conf['NV'],cfg.conf['NVb']-1))
G_ap = np.zeros((cfg.conf['NV'],cfg.conf['NVb']-1))
dVb = cfg.conf['Vb_range'][1]- cfg.conf['Vb_range'][0]
# the population vectors, for all values of gate and bias
POP_p = np.zeros((cfg.conf['NVb'],cfg.conf['NV'],cfg.N_GS[0]))
POP_ap = np.zeros((cfg.conf['NVb'],cfg.conf['NV'],cfg.N_GS[1]))
# We iterate over two bias values first
for nV,Vb in enumerate(cfg.conf["Vb_range"]):
# now the rates are loaded from the h5 file
# note that the label of the specific rate arrays are fixed
with h5py.File(fname) as file:
GP0_p = np.array(file['par_P0_V{}'.format(Vb)])
GP0_ap = np.array(file['apa_P0_V{}'.format(Vb)])
GP1_p = np.array(file['par_P1_V{}'.format(Vb)])
GP1_ap = np.array(file['apa_P1_V{}'.format(Vb)])
GM0_p = np.array(file['par_M0_V{}'.format(Vb)])
GM0_ap = np.array(file['apa_M0_V{}'.format(Vb)])
GM1_p = np.array(file['par_M1_V{}'.format(Vb)])
GM1_ap = np.array(file['apa_M1_V{}'.format(Vb)])
# for the density kernel, we sum all rates over both leads
DENKER_p = np.array([eval_DENKER(GM0_p[n]+GM1_p[n],GP0_p[n]+GP1_p[n],0)for n in range(cfg.conf["NV"])])
DENKER_ap = np.array([eval_DENKER(GM0_ap[n]+GM1_ap[n],GP0_ap[n]+GP1_ap[n],1)for n in range(cfg.conf["NV"])])
# the populations are calculated from the density kernel by an asymptotic
# approximation scheme
POP_ap[nV] = np.array([pop.asymptotic_ssp(DENKER_ap[n]) for n in range(cfg.conf["NV"])])
POP_p[nV] = np.array([pop.asymptotic_ssp(DENKER_p[n]) for n in range(cfg.conf["NV"])])
# note that the current is calculated from the rates in one of the leads only
C_p[:,nV] = np.array([ current(GP0_p[n],GM0_p[n],POP_p[nV,n],0) for n in np.arange(cfg.conf["NV"]) ])
C_ap[:,nV] = np.array([ current(GP0_ap[n],GM0_ap[n],POP_ap[nV,n],1) for n in np.arange(cfg.conf["NV"]) ])
# the numerical derivative gives the conductance
G_p = np.diff(C_p).flatten()/dVb
G_ap = np.diff(C_ap).flatten()/dVb
# we save the conductance traces to a h5 file specified as a global variable
# hdffile in the path datpath
# It is possible that the dataset already exists. In this case, we issue a warning.
try:
save_hdf5("{}{}".format(datpath,hdffile),G_p,G_ap)
except RuntimeError:
print "Unable to save to {}, maybe there is already a dataset with similar parameters...".format(hdffile)
# the tmr and conductance graphs are plotted to a pdf file for review.
plot_tmr_pdf(G_p,G_ap,plotname)
# if the pop flag is set, we also plot the population for one bias value
if pop:
plot_population([POP_p[0],POP_ap[0]],os.path.splitext(plotname)[0]+"_POP.pdf")
def plot_tmr_pdf(C_p,C_ap,fname):
"""
A helper routine to plot the conductance and TMR to a pdf file in the datpath.
Args:
C_p, C_ap: the parallel and anti-parallel conductance.
fname: the filename to plot to
"""
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
# we plot the conductance graph on top, p and ap with different colors
Axes1 = plt.subplot(2,1,1)
Axes1.set_xticklabels([])
plt.ylabel("Conductance (e^2/h)")
plt.title("Conductance at zero bias")
# parallel is plotted in red, and anti-parallel as blue dashed line
plt.plot( cfg.conf["V_g"],C_p,'r',cfg.conf["V_g"], C_ap, 'b--')
# on the second panel, the TMR is plotted
Axes2 = plt.subplot(2,1,2)
plt.xlabel("gate voltage (V)")
plt.ylabel("TMR")
plt.title("TMR")
plt.ylim((-0.3,1.5))
TMR = np.zeros(cfg.conf["NV"])
for i in range(cfg.conf["NV"]):
try:
TMR[i] = C_p[i]/C_ap[i]-1.
except ZeroDivisionError:
print "Zero Division, returning null."
TMR[i] = 0.
plt.plot( cfg.conf["V_g"], TMR)
plt.savefig(fname, bbox_inches='tight')
def plot_population(POP, fname):
"""
Calculates and plots selected populations of the quantum dot
with gate voltage. The edge states N=-1 and 5 are neglected.
Args:
POP: a list with the two population vectors
for parallel and anti-parallel configurations
fname: the filename to plot to
"""
import matplotlib.pyplot as plt
NV = cfg.conf["NV"]
print "Calculating populations..."
# We plot the populations for both configurations
# the parallel populations on top
# the anti-parallel on bottom
Ax = [plt.subplot(2,1,1),plt.subplot(2,1,2)]
cm = plt.get_cmap('gist_rainbow')
PopPlots = [1,4,8,12,17,18]
NP = len(PopPlots)
for gamidx in range(2):
TLIST = cfg.TLIST[gamidx]
TLIST_T = np.transpose(TLIST)
PLIST = list(set(TLIST_T[0]).union(TLIST_T[1]))
PLIST.sort()
# we cycle through the linecolors to distinguish the different
# groundstates
Ax[gamidx].set_color_cycle([cm(1.*k/NP) for k in range(NP)])
for i in PopPlots:
color = cm(1.*i/NP)
LABEL = "P_{}".format(cfg.int_to_state(PLIST[i]))
Ax[gamidx].plot( cfg.conf["V_g"], POP[gamidx][:,i],label=LABEL)
lines =Ax[gamidx].get_lines()
labels = [l.get_label() for l in lines]
leg = plt.figlegend(lines,labels,loc='upper right')
plt.savefig(fname)
plt.show()
class Usage(Exception):
def __init__(self, msg):
self.msg = msg
def main(argv=None):
"""
Interface routine to call the tmr module.
Example:
./tmr.py <jobname>
In principle, there were routines to plot rates, populations,
conductances etc. but apart from the population plotting,
none of the use cases was needed anymore.
"""
POP = False
# The default config file is called cnt.conf
cfile = "cnt.conf"
rlist = [0.,]
if argv is None:
argv = sys.argv
try:
try:
opts, args = getopt.getopt(argv[1:], "hc:P", ["help","config=","pop"])
except getopt.error, msg:
raise Usage(msg)
for o,a in opts:
if o in ('-h','--help'):
usage()
exit()
elif o in ('-c','--config'):
cfile = a
elif o in ('-P','--pop'):
POP = True
else:
raise Usage('Invalid argument.')
# we parse the config and initialize it
cfg.parse_conf("dat/{0}/{1}".format(args[0],cfile))
cfg.init()
h5file = "{}{}/{}".format(datpath,args[0],ratefile)
pdffile = "{}{}.pdf".format(datpath,args[0])
print "Try to open {}".format(h5file)
eval_tmr(h5file,pdffile,POP)
except Usage, err:
print >>sys.stderr, err.msg
print >>sys.stderr, "for help use --help"
return 2
def usage():
print "This is a tool to process rate files.\n\
\n\
usage: tmr.py [-hP] [--pop] jobname\n\
\n\
--pop or -P: Plot the populations.\n\
\n\
jobname: The name of the directory for the rate files.\n\
\n\
The script searches for files dat/jobname/running_calc.h5\n\
and dat/jobname/cnt.conf"
if __name__ == "__main__":
sys.exit(main())
| gpl-3.0 |
mlperf/training_results_v0.7 | NVIDIA/benchmarks/minigo/implementations/tensorflow/minigo/oneoffs/training_curve.py | 8 | 5964 | # Copyright 2018 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.
"""
Used to plot the accuracy of the policy and value networks in
predicting professional game moves and results over the course
of training. Check FLAGS for default values for what models to
load and what sgf files to parse.
Usage:
python training_curve.py
Sample 3 positions from each game
python training_curve.py --num_positions=3
Only grab games after 2005 (default is 2000)
python training_curve.py --min_year=2005
"""
import sys
sys.path.insert(0, '.')
import os.path
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from absl import app, flags
from tqdm import tqdm
import coords
from rl_loop import fsdb
import oneoff_utils
flags.DEFINE_string("sgf_dir", None, "sgf database")
flags.DEFINE_string("plot_dir", "data", "Where to save the plots.")
flags.DEFINE_integer("min_year", "2000",
"Only take sgf games with date >= min_year")
flags.DEFINE_string("komi", "7.5", "Only take sgf games with given komi")
flags.DEFINE_integer("idx_start", 150, "Only take models after given idx")
flags.DEFINE_integer("num_positions", 1,
"How many positions from each game to sample from.")
flags.DEFINE_integer("eval_every", 5,
"Eval every k models to generate the curve")
flags.mark_flag_as_required('sgf_dir')
FLAGS = flags.FLAGS
def batch_run_many(player, positions, batch_size=100):
"""Used to avoid a memory oveflow issue when running the network
on too many positions. TODO: This should be a member function of
player.network?"""
prob_list = []
value_list = []
for idx in range(0, len(positions), batch_size):
probs, values = player.network.run_many(positions[idx:idx + batch_size])
prob_list.append(probs)
value_list.append(values)
return np.concatenate(prob_list, axis=0), np.concatenate(value_list, axis=0)
def eval_player(player, positions, moves, results):
probs, values = batch_run_many(player, positions)
policy_moves = [coords.from_flat(c) for c in np.argmax(probs, axis=1)]
top_move_agree = [moves[idx] == policy_moves[idx]
for idx in range(len(moves))]
square_err = (values - results) ** 2 / 4
return top_move_agree, square_err
def sample_positions_from_games(sgf_files, num_positions=1):
pos_data = []
move_data = []
result_data = []
move_idxs = []
fail_count = 0
for path in tqdm(sgf_files, desc="loading sgfs", unit="games"):
try:
positions, moves, results = oneoff_utils.parse_sgf_to_examples(path)
except KeyboardInterrupt:
raise
except Exception as e:
print("Parse exception:", e)
fail_count += 1
continue
# add entire game
if num_positions == -1:
pos_data.extend(positions)
move_data.extend(moves)
move_idxs.extend(range(len(positions)))
result_data.extend(results)
else:
for idx in np.random.choice(len(positions), num_positions):
pos_data.append(positions[idx])
move_data.append(moves[idx])
result_data.append(results[idx])
move_idxs.append(idx)
print("Sampled {} positions, failed to parse {} files".format(
len(pos_data), fail_count))
return pos_data, move_data, result_data, move_idxs
def get_training_curve_data(
model_dir, pos_data, move_data, result_data, idx_start, eval_every):
model_paths = oneoff_utils.get_model_paths(model_dir)
df = pd.DataFrame()
player = None
print("Evaluating models {}-{}, eval_every={}".format(
idx_start, len(model_paths), eval_every))
for idx in tqdm(range(idx_start, len(model_paths), eval_every)):
if player:
oneoff_utils.restore_params(model_paths[idx], player)
else:
player = oneoff_utils.load_player(model_paths[idx])
correct, squared_errors = eval_player(
player=player, positions=pos_data,
moves=move_data, results=result_data)
avg_acc = np.mean(correct)
avg_mse = np.mean(squared_errors)
print("Model: {}, acc: {:.4f}, mse: {:.4f}".format(
model_paths[idx], avg_acc, avg_mse))
df = df.append({"num": idx, "acc": avg_acc,
"mse": avg_mse}, ignore_index=True)
return df
def save_plots(data_dir, df):
plt.plot(df["num"], df["acc"])
plt.xlabel("Model idx")
plt.ylabel("Accuracy")
plt.title("Accuracy in Predicting Professional Moves")
plot_path = os.path.join(data_dir, "move_acc.pdf")
plt.savefig(plot_path)
plt.figure()
plt.plot(df["num"], df["mse"])
plt.xlabel("Model idx")
plt.ylabel("MSE/4")
plt.title("MSE in predicting outcome")
plot_path = os.path.join(data_dir, "value_mse.pdf")
plt.savefig(plot_path)
def main(unusedargv):
sgf_files = oneoff_utils.find_and_filter_sgf_files(
FLAGS.sgf_dir, FLAGS.min_year, FLAGS.komi)
pos_data, move_data, result_data, move_idxs = sample_positions_from_games(
sgf_files=sgf_files, num_positions=FLAGS.num_positions)
df = get_training_curve_data(fsdb.models_dir(), pos_data, move_data,
result_data, FLAGS.idx_start, FLAGS.eval_every)
save_plots(FLAGS.plot_dir, df)
if __name__ == "__main__":
app.run(main)
| apache-2.0 |
rhattersley/cartopy | lib/cartopy/tests/mpl/test_ticker.py | 3 | 8574 | # (C) British Crown Copyright 2014 - 2017, Met Office
#
# This file is part of cartopy.
#
# cartopy 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 of the License, or
# (at your option) any later version.
#
# cartopy 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 cartopy. If not, see <https://www.gnu.org/licenses/>.
from __future__ import (absolute_import, division, print_function)
try:
from unittest.mock import Mock
except ImportError:
from mock import Mock
from matplotlib.axes import Axes
import pytest
import cartopy.crs as ccrs
from cartopy.mpl.geoaxes import GeoAxes
from cartopy.mpl.ticker import LatitudeFormatter, LongitudeFormatter
def test_LatitudeFormatter_bad_axes():
formatter = LatitudeFormatter()
formatter.axis = Mock(axes=Mock(Axes, projection=ccrs.PlateCarree()))
message = 'This formatter can only be used with cartopy axes.'
with pytest.raises(TypeError, message=message):
formatter(0)
def test_LatitudeFormatter_bad_projection():
formatter = LatitudeFormatter()
formatter.axis = Mock(axes=Mock(GeoAxes, projection=ccrs.Orthographic()))
message = 'This formatter cannot be used with non-rectangular projections.'
with pytest.raises(TypeError, message=message):
formatter(0)
def test_LongitudeFormatter_bad_axes():
formatter = LongitudeFormatter()
formatter.axis = Mock(axes=Mock(Axes, projection=ccrs.PlateCarree()))
message = 'This formatter can only be used with cartopy axes.'
with pytest.raises(TypeError, message=message):
formatter(0)
def test_LongitudeFormatter_bad_projection():
formatter = LongitudeFormatter()
formatter.axis = Mock(axes=Mock(GeoAxes, projection=ccrs.Orthographic()))
message = 'This formatter cannot be used with non-rectangular projections.'
with pytest.raises(TypeError, message=message):
formatter(0)
def test_LatitudeFormatter():
formatter = LatitudeFormatter()
p = ccrs.PlateCarree()
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-90, -60, -30, 0, 30, 60, 90]
result = [formatter(tick) for tick in test_ticks]
expected = [u'90\u00B0S', u'60\u00B0S', u'30\u00B0S', u'0\u00B0',
u'30\u00B0N', u'60\u00B0N', u'90\u00B0N']
assert result == expected
def test_LatitudeFormatter_degree_symbol():
formatter = LatitudeFormatter(degree_symbol='')
p = ccrs.PlateCarree()
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-90, -60, -30, 0, 30, 60, 90]
result = [formatter(tick) for tick in test_ticks]
expected = [u'90S', u'60S', u'30S', u'0',
u'30N', u'60N', u'90N']
assert result == expected
def test_LatitudeFormatter_number_format():
formatter = LatitudeFormatter(number_format='.2f')
p = ccrs.PlateCarree()
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-90, -60, -30, 0, 30, 60, 90]
result = [formatter(tick) for tick in test_ticks]
expected = [u'90.00\u00B0S', u'60.00\u00B0S', u'30.00\u00B0S',
u'0.00\u00B0', u'30.00\u00B0N', u'60.00\u00B0N',
u'90.00\u00B0N']
assert result == expected
def test_LatitudeFormatter_mercator():
formatter = LatitudeFormatter()
p = ccrs.Mercator()
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-15496570.739707904, -8362698.548496634,
-3482189.085407435, 0.0, 3482189.085407435,
8362698.548496634, 15496570.739707898]
result = [formatter(tick) for tick in test_ticks]
expected = [u'80\u00B0S', u'60\u00B0S', u'30\u00B0S', u'0\u00B0',
u'30\u00B0N', u'60\u00B0N', u'80\u00B0N']
assert result == expected
def test_LatitudeFormatter_small_numbers():
formatter = LatitudeFormatter(number_format='.7f')
p = ccrs.PlateCarree()
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [40.1275150, 40.1275152, 40.1275154]
result = [formatter(tick) for tick in test_ticks]
expected = [u'40.1275150\u00B0N', u'40.1275152\u00B0N',
u'40.1275154\u00B0N']
assert result == expected
def test_LongitudeFormatter_central_longitude_0():
formatter = LongitudeFormatter(dateline_direction_label=True)
p = ccrs.PlateCarree()
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-180, -120, -60, 0, 60, 120, 180]
result = [formatter(tick) for tick in test_ticks]
expected = [u'180\u00B0W', u'120\u00B0W', u'60\u00B0W', u'0\u00B0',
u'60\u00B0E', u'120\u00B0E', u'180\u00B0E']
assert result == expected
def test_LongitudeFormatter_central_longitude_180():
formatter = LongitudeFormatter(zero_direction_label=True)
p = ccrs.PlateCarree(central_longitude=180)
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-180, -120, -60, 0, 60, 120, 180]
result = [formatter(tick) for tick in test_ticks]
expected = [u'0\u00B0E', u'60\u00B0E', u'120\u00B0E', u'180\u00B0',
u'120\u00B0W', u'60\u00B0W', u'0\u00B0W']
assert result == expected
def test_LongitudeFormatter_central_longitude_120():
formatter = LongitudeFormatter()
p = ccrs.PlateCarree(central_longitude=120)
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-180, -120, -60, 0, 60, 120, 180]
result = [formatter(tick) for tick in test_ticks]
expected = [u'60\u00B0W', u'0\u00B0', u'60\u00B0E', u'120\u00B0E',
u'180\u00B0', u'120\u00B0W', u'60\u00B0W']
assert result == expected
def test_LongitudeFormatter_degree_symbol():
formatter = LongitudeFormatter(degree_symbol='',
dateline_direction_label=True)
p = ccrs.PlateCarree()
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-180, -120, -60, 0, 60, 120, 180]
result = [formatter(tick) for tick in test_ticks]
expected = [u'180W', u'120W', u'60W', u'0', u'60E', u'120E', u'180E']
assert result == expected
def test_LongitudeFormatter_number_format():
formatter = LongitudeFormatter(number_format='.2f',
dateline_direction_label=True)
p = ccrs.PlateCarree()
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-180, -120, -60, 0, 60, 120, 180]
result = [formatter(tick) for tick in test_ticks]
expected = [u'180.00\u00B0W', u'120.00\u00B0W', u'60.00\u00B0W',
u'0.00\u00B0', u'60.00\u00B0E', u'120.00\u00B0E',
u'180.00\u00B0E']
assert result == expected
def test_LongitudeFormatter_mercator():
formatter = LongitudeFormatter(dateline_direction_label=True)
p = ccrs.Mercator()
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-20037508.342783064, -13358338.895188706,
-6679169.447594353, 0.0, 6679169.447594353,
13358338.895188706, 20037508.342783064]
result = [formatter(tick) for tick in test_ticks]
expected = [u'180\u00B0W', u'120\u00B0W', u'60\u00B0W', u'0\u00B0',
u'60\u00B0E', u'120\u00B0E', u'180\u00B0E']
assert result == expected
def test_LongitudeFormatter_small_numbers_0():
formatter = LongitudeFormatter(number_format='.7f')
p = ccrs.PlateCarree(central_longitude=0)
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-17.1142343, -17.1142340, -17.1142337]
result = [formatter(tick) for tick in test_ticks]
expected = [u'17.1142343\u00B0W', u'17.1142340\u00B0W',
u'17.1142337\u00B0W']
assert result == expected
def test_LongitudeFormatter_small_numbers_180():
formatter = LongitudeFormatter(zero_direction_label=True,
number_format='.7f')
p = ccrs.PlateCarree(central_longitude=180)
formatter.axis = Mock(axes=Mock(GeoAxes, projection=p))
test_ticks = [-17.1142343, -17.1142340, -17.1142337]
result = [formatter(tick) for tick in test_ticks]
expected = [u'162.8857657\u00B0E', u'162.8857660\u00B0E',
u'162.8857663\u00B0E']
assert result == expected
| lgpl-3.0 |
nolanliou/tensorflow | tensorflow/contrib/losses/python/metric_learning/metric_loss_ops_test.py | 41 | 20535 | # Copyright 2017 The TensorFlow 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.
# ==============================================================================
"""Tests for triplet_semihard_loss."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.losses.python import metric_learning as metric_loss_ops
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.platform import test
try:
# pylint: disable=g-import-not-at-top
from sklearn import datasets
from sklearn import metrics
HAS_SKLEARN = True
except ImportError:
HAS_SKLEARN = False
def pairwise_distance_np(feature, squared=False):
"""Computes the pairwise distance matrix in numpy.
Args:
feature: 2-D numpy array of size [number of data, feature dimension]
squared: Boolean. If true, output is the pairwise squared euclidean
distance matrix; else, output is the pairwise euclidean distance matrix.
Returns:
pairwise_distances: 2-D numpy array of size
[number of data, number of data].
"""
triu = np.triu_indices(feature.shape[0], 1)
upper_tri_pdists = np.linalg.norm(feature[triu[1]] - feature[triu[0]], axis=1)
if squared:
upper_tri_pdists **= 2.
num_data = feature.shape[0]
pairwise_distances = np.zeros((num_data, num_data))
pairwise_distances[np.triu_indices(num_data, 1)] = upper_tri_pdists
# Make symmetrical.
pairwise_distances = pairwise_distances + pairwise_distances.T - np.diag(
pairwise_distances.diagonal())
return pairwise_distances
class ContrastiveLossTest(test.TestCase):
def testContrastive(self):
with self.test_session():
num_data = 10
feat_dim = 6
margin = 1.0
embeddings_anchor = np.random.rand(num_data, feat_dim).astype(np.float32)
embeddings_positive = np.random.rand(num_data, feat_dim).astype(
np.float32)
labels = np.random.randint(0, 2, size=(num_data,)).astype(np.float32)
# Compute the loss in NP
dist = np.sqrt(
np.sum(np.square(embeddings_anchor - embeddings_positive), axis=1))
loss_np = np.mean(
labels * np.square(dist) +
(1.0 - labels) * np.square(np.maximum(margin - dist, 0.0)))
# Compute the loss with TF
loss_tf = metric_loss_ops.contrastive_loss(
labels=ops.convert_to_tensor(labels),
embeddings_anchor=ops.convert_to_tensor(embeddings_anchor),
embeddings_positive=ops.convert_to_tensor(embeddings_positive),
margin=margin)
loss_tf = loss_tf.eval()
self.assertAllClose(loss_np, loss_tf)
class TripletSemiHardLossTest(test.TestCase):
def testTripletSemiHard(self):
with self.test_session():
num_data = 10
feat_dim = 6
margin = 1.0
num_classes = 4
embedding = np.random.rand(num_data, feat_dim).astype(np.float32)
labels = np.random.randint(
0, num_classes, size=(num_data)).astype(np.float32)
# Reshape labels to compute adjacency matrix.
labels_reshaped = np.reshape(labels, (labels.shape[0], 1))
# Compute the loss in NP.
adjacency = np.equal(labels_reshaped, labels_reshaped.T)
pdist_matrix = pairwise_distance_np(embedding, squared=True)
loss_np = 0.0
num_positives = 0.0
for i in range(num_data):
for j in range(num_data):
if adjacency[i][j] > 0.0 and i != j:
num_positives += 1.0
pos_distance = pdist_matrix[i][j]
neg_distances = []
for k in range(num_data):
if adjacency[i][k] == 0:
neg_distances.append(pdist_matrix[i][k])
# Sort by distance.
neg_distances.sort()
chosen_neg_distance = neg_distances[0]
for l in range(len(neg_distances)):
chosen_neg_distance = neg_distances[l]
if chosen_neg_distance > pos_distance:
break
loss_np += np.maximum(
0.0, margin - chosen_neg_distance + pos_distance)
loss_np /= num_positives
# Compute the loss in TF.
loss_tf = metric_loss_ops.triplet_semihard_loss(
labels=ops.convert_to_tensor(labels),
embeddings=ops.convert_to_tensor(embedding),
margin=margin)
loss_tf = loss_tf.eval()
self.assertAllClose(loss_np, loss_tf)
class LiftedStructLossTest(test.TestCase):
def testLiftedStruct(self):
with self.test_session():
num_data = 10
feat_dim = 6
margin = 1.0
num_classes = 4
embedding = np.random.rand(num_data, feat_dim).astype(np.float32)
labels = np.random.randint(
0, num_classes, size=(num_data)).astype(np.float32)
# Reshape labels to compute adjacency matrix.
labels_reshaped = np.reshape(labels, (labels.shape[0], 1))
# Compute the loss in NP
adjacency = np.equal(labels_reshaped, labels_reshaped.T)
pdist_matrix = pairwise_distance_np(embedding)
loss_np = 0.0
num_constraints = 0.0
for i in range(num_data):
for j in range(num_data):
if adjacency[i][j] > 0.0 and i != j:
d_pos = pdist_matrix[i][j]
negs = []
for k in range(num_data):
if not adjacency[i][k]:
negs.append(margin - pdist_matrix[i][k])
for l in range(num_data):
if not adjacency[j][l]:
negs.append(margin - pdist_matrix[j][l])
negs = np.array(negs)
max_elem = np.max(negs)
negs -= max_elem
negs = np.exp(negs)
soft_maximum = np.log(np.sum(negs)) + max_elem
num_constraints += 1.0
this_loss = max(soft_maximum + d_pos, 0)
loss_np += this_loss * this_loss
loss_np = loss_np / num_constraints / 2.0
# Compute the loss in TF
loss_tf = metric_loss_ops.lifted_struct_loss(
labels=ops.convert_to_tensor(labels),
embeddings=ops.convert_to_tensor(embedding),
margin=margin)
loss_tf = loss_tf.eval()
self.assertAllClose(loss_np, loss_tf)
def convert_to_list_of_sparse_tensor(np_matrix):
list_of_sparse_tensors = []
nrows, ncols = np_matrix.shape
for i in range(nrows):
sp_indices = []
for j in range(ncols):
if np_matrix[i][j] == 1:
sp_indices.append([j])
num_non_zeros = len(sp_indices)
list_of_sparse_tensors.append(sparse_tensor.SparseTensor(
indices=np.array(sp_indices),
values=np.ones((num_non_zeros,)),
dense_shape=np.array([ncols,])))
return list_of_sparse_tensors
class NpairsLossTest(test.TestCase):
def testNpairs(self):
with self.test_session():
num_data = 15
feat_dim = 6
num_classes = 5
reg_lambda = 0.02
embeddings_anchor = np.random.rand(num_data, feat_dim).astype(np.float32)
embeddings_positive = np.random.rand(num_data, feat_dim).astype(
np.float32)
labels = np.random.randint(
0, num_classes, size=(num_data)).astype(np.float32)
# Reshape labels to compute adjacency matrix.
labels_reshaped = np.reshape(labels, (labels.shape[0], 1))
# Compute the loss in NP
reg_term = np.mean(np.sum(np.square(embeddings_anchor), 1))
reg_term += np.mean(np.sum(np.square(embeddings_positive), 1))
reg_term *= 0.25 * reg_lambda
similarity_matrix = np.matmul(embeddings_anchor, embeddings_positive.T)
labels_remapped = np.equal(
labels_reshaped, labels_reshaped.T).astype(np.float32)
labels_remapped /= np.sum(labels_remapped, axis=1, keepdims=True)
xent_loss = math_ops.reduce_mean(nn.softmax_cross_entropy_with_logits(
logits=ops.convert_to_tensor(similarity_matrix),
labels=ops.convert_to_tensor(labels_remapped))).eval()
loss_np = xent_loss + reg_term
# Compute the loss in TF
loss_tf = metric_loss_ops.npairs_loss(
labels=ops.convert_to_tensor(labels),
embeddings_anchor=ops.convert_to_tensor(embeddings_anchor),
embeddings_positive=ops.convert_to_tensor(embeddings_positive),
reg_lambda=reg_lambda)
loss_tf = loss_tf.eval()
self.assertAllClose(loss_np, loss_tf)
class NpairsLossMultiLabelTest(test.TestCase):
def testNpairsMultiLabelLossWithSingleLabelEqualsNpairsLoss(self):
with self.test_session():
num_data = 15
feat_dim = 6
reg_lambda = 0.02
embeddings_anchor = np.random.rand(num_data, feat_dim).astype(np.float32)
embeddings_positive = np.random.rand(num_data, feat_dim).astype(
np.float32)
labels = np.arange(num_data)
labels = np.reshape(labels, -1)
# Compute vanila npairs loss.
loss_npairs = metric_loss_ops.npairs_loss(
labels=ops.convert_to_tensor(labels),
embeddings_anchor=ops.convert_to_tensor(embeddings_anchor),
embeddings_positive=ops.convert_to_tensor(embeddings_positive),
reg_lambda=reg_lambda).eval()
# Compute npairs multilabel loss.
labels_one_hot = np.identity(num_data)
loss_npairs_multilabel = metric_loss_ops.npairs_loss_multilabel(
sparse_labels=convert_to_list_of_sparse_tensor(labels_one_hot),
embeddings_anchor=ops.convert_to_tensor(embeddings_anchor),
embeddings_positive=ops.convert_to_tensor(embeddings_positive),
reg_lambda=reg_lambda).eval()
self.assertAllClose(loss_npairs, loss_npairs_multilabel)
def testNpairsMultiLabel(self):
with self.test_session():
num_data = 15
feat_dim = 6
num_classes = 10
reg_lambda = 0.02
embeddings_anchor = np.random.rand(num_data, feat_dim).astype(np.float32)
embeddings_positive = np.random.rand(num_data, feat_dim).astype(
np.float32)
labels = np.random.randint(0, 2, (num_data, num_classes))
# set entire column to one so that each row has at least one bit set.
labels[:, -1] = 1
# Compute the loss in NP
reg_term = np.mean(np.sum(np.square(embeddings_anchor), 1))
reg_term += np.mean(np.sum(np.square(embeddings_positive), 1))
reg_term *= 0.25 * reg_lambda
similarity_matrix = np.matmul(embeddings_anchor, embeddings_positive.T)
labels_remapped = np.dot(labels, labels.T).astype(np.float)
labels_remapped /= np.sum(labels_remapped, 1, keepdims=True)
xent_loss = math_ops.reduce_mean(nn.softmax_cross_entropy_with_logits(
logits=ops.convert_to_tensor(similarity_matrix),
labels=ops.convert_to_tensor(labels_remapped))).eval()
loss_np = xent_loss + reg_term
# Compute the loss in TF
loss_tf = metric_loss_ops.npairs_loss_multilabel(
sparse_labels=convert_to_list_of_sparse_tensor(labels),
embeddings_anchor=ops.convert_to_tensor(embeddings_anchor),
embeddings_positive=ops.convert_to_tensor(embeddings_positive),
reg_lambda=reg_lambda)
loss_tf = loss_tf.eval()
self.assertAllClose(loss_np, loss_tf)
def compute_ground_truth_cluster_score(feat, y):
y_unique = np.unique(y)
score_gt_np = 0.0
for c in y_unique:
feat_subset = feat[y == c, :]
pdist_subset = pairwise_distance_np(feat_subset)
score_gt_np += -1.0 * np.min(np.sum(pdist_subset, axis=0))
score_gt_np = score_gt_np.astype(np.float32)
return score_gt_np
def compute_cluster_loss_numpy(feat,
y,
margin_multiplier=1.0,
enable_pam_finetuning=True):
if enable_pam_finetuning:
facility = ForwardGreedyFacility(
n_clusters=np.unique(y).size).pam_augmented_fit(feat, y,
margin_multiplier)
else:
facility = ForwardGreedyFacility(
n_clusters=np.unique(y).size).loss_augmented_fit(feat, y,
margin_multiplier)
score_augmented = facility.score_aug_
score_gt = compute_ground_truth_cluster_score(feat, y)
return np.maximum(np.float32(0.0), score_augmented - score_gt)
class ForwardGreedyFacility(object):
def __init__(self, n_clusters=8):
self.n_clusters = n_clusters
self.center_ics_ = None
def _check_init_args(self):
# Check n_clusters.
if (self.n_clusters is None or self.n_clusters <= 0 or
not isinstance(self.n_clusters, int)):
raise ValueError('n_clusters has to be nonnegative integer.')
def loss_augmented_fit(self, feat, y, loss_mult):
"""Fit K-Medoids to the provided data."""
self._check_init_args()
# Check that the array is good and attempt to convert it to
# Numpy array if possible.
feat = self._check_array(feat)
# Apply distance metric to get the distance matrix.
pdists = pairwise_distance_np(feat)
num_data = feat.shape[0]
candidate_ids = list(range(num_data))
candidate_scores = np.zeros(num_data,)
subset = []
k = 0
while k < self.n_clusters:
candidate_scores = []
for i in candidate_ids:
# push i to subset.
subset.append(i)
marginal_cost = -1.0 * np.sum(np.min(pdists[:, subset], axis=1))
loss = 1.0 - metrics.normalized_mutual_info_score(
y, self._get_cluster_ics(pdists, subset))
candidate_scores.append(marginal_cost + loss_mult * loss)
# remove i from subset.
subset.pop()
# push i_star to subset.
i_star = candidate_ids[np.argmax(candidate_scores)]
subset.append(i_star)
# remove i_star from candidate indices.
candidate_ids.remove(i_star)
k += 1
# Expose labels_ which are the assignments of
# the training data to clusters.
self.labels_ = self._get_cluster_ics(pdists, subset)
# Expose cluster centers, i.e. medoids.
self.cluster_centers_ = feat.take(subset, axis=0)
# Expose indices of chosen cluster centers.
self.center_ics_ = subset
# Expose the score = -\sum_{i \in V} min_{j \in S} || x_i - x_j ||
self.score_ = np.float32(-1.0) * self._get_facility_distance(pdists, subset)
self.score_aug_ = self.score_ + loss_mult * (
1.0 - metrics.normalized_mutual_info_score(
y, self._get_cluster_ics(pdists, subset)))
self.score_aug_ = self.score_aug_.astype(np.float32)
# Expose the chosen cluster indices.
self.subset_ = subset
return self
def _augmented_update_medoid_ics_in_place(self, pdists, y_gt, cluster_ics,
medoid_ics, loss_mult):
for cluster_idx in range(self.n_clusters):
# y_pred = self._get_cluster_ics(D, medoid_ics)
# Don't prematurely do the assignment step.
# Do this after we've updated all cluster medoids.
y_pred = cluster_ics
if sum(y_pred == cluster_idx) == 0:
# Cluster is empty.
continue
curr_score = (
-1.0 * np.sum(
pdists[medoid_ics[cluster_idx], y_pred == cluster_idx]) +
loss_mult * (1.0 - metrics.normalized_mutual_info_score(
y_gt, y_pred)))
pdist_in = pdists[y_pred == cluster_idx, :]
pdist_in = pdist_in[:, y_pred == cluster_idx]
all_scores_fac = np.sum(-1.0 * pdist_in, axis=1)
all_scores_loss = []
for i in range(y_pred.size):
if y_pred[i] != cluster_idx:
continue
# remove this cluster's current centroid
medoid_ics_i = medoid_ics[:cluster_idx] + medoid_ics[cluster_idx + 1:]
# add this new candidate to the centroid list
medoid_ics_i += [i]
y_pred_i = self._get_cluster_ics(pdists, medoid_ics_i)
all_scores_loss.append(loss_mult * (
1.0 - metrics.normalized_mutual_info_score(y_gt, y_pred_i)))
all_scores = all_scores_fac + all_scores_loss
max_score_idx = np.argmax(all_scores)
max_score = all_scores[max_score_idx]
if max_score > curr_score:
medoid_ics[cluster_idx] = np.where(
y_pred == cluster_idx)[0][max_score_idx]
def pam_augmented_fit(self, feat, y, loss_mult):
pam_max_iter = 5
self._check_init_args()
feat = self._check_array(feat)
pdists = pairwise_distance_np(feat)
self.loss_augmented_fit(feat, y, loss_mult)
print('PAM -1 (before PAM): score: %f, score_aug: %f' % (
self.score_, self.score_aug_))
# Initialize from loss augmented facility location
subset = self.center_ics_
for iter_ in range(pam_max_iter):
# update the cluster assignment
cluster_ics = self._get_cluster_ics(pdists, subset)
# update the medoid for each clusters
self._augmented_update_medoid_ics_in_place(pdists, y, cluster_ics, subset,
loss_mult)
self.score_ = np.float32(-1.0) * self._get_facility_distance(
pdists, subset)
self.score_aug_ = self.score_ + loss_mult * (
1.0 - metrics.normalized_mutual_info_score(
y, self._get_cluster_ics(pdists, subset)))
self.score_aug_ = self.score_aug_.astype(np.float32)
print('PAM iter: %d, score: %f, score_aug: %f' % (iter_, self.score_,
self.score_aug_))
self.center_ics_ = subset
self.labels_ = cluster_ics
return self
def _check_array(self, feat):
# Check that the number of clusters is less than or equal to
# the number of samples
if self.n_clusters > feat.shape[0]:
raise ValueError('The number of medoids ' + '({}) '.format(
self.n_clusters) + 'must be larger than the number ' +
'of samples ({})'.format(feat.shape[0]))
return feat
def _get_cluster_ics(self, pdists, subset):
"""Returns cluster indices for pdist and current medoid indices."""
# Assign data points to clusters based on
# which cluster assignment yields
# the smallest distance`
cluster_ics = np.argmin(pdists[subset, :], axis=0)
return cluster_ics
def _get_facility_distance(self, pdists, subset):
return np.sum(np.min(pdists[subset, :], axis=0))
class ClusterLossTest(test.TestCase):
def _genClusters(self, n_samples, n_clusters):
blobs = datasets.make_blobs(
n_samples=n_samples, centers=n_clusters)
embedding, labels = blobs
embedding = (embedding - embedding.mean(axis=0)) / embedding.std(axis=0)
embedding = embedding.astype(np.float32)
return embedding, labels
def testClusteringLossPAMOff(self):
if not HAS_SKLEARN:
return
with self.test_session():
margin_multiplier = 10.0
embeddings, labels = self._genClusters(n_samples=128, n_clusters=64)
loss_np = compute_cluster_loss_numpy(
embeddings, labels, margin_multiplier, enable_pam_finetuning=False)
loss_tf = metric_loss_ops.cluster_loss(
labels=ops.convert_to_tensor(labels),
embeddings=ops.convert_to_tensor(embeddings),
margin_multiplier=margin_multiplier,
enable_pam_finetuning=False)
loss_tf = loss_tf.eval()
self.assertAllClose(loss_np, loss_tf)
def testClusteringLossPAMOn(self):
if not HAS_SKLEARN:
return
with self.test_session():
margin_multiplier = 10.0
embeddings, labels = self._genClusters(n_samples=128, n_clusters=64)
loss_np = compute_cluster_loss_numpy(
embeddings, labels, margin_multiplier, enable_pam_finetuning=True)
loss_tf = metric_loss_ops.cluster_loss(
labels=ops.convert_to_tensor(labels),
embeddings=ops.convert_to_tensor(embeddings),
margin_multiplier=margin_multiplier,
enable_pam_finetuning=True)
loss_tf = loss_tf.eval()
self.assertAllClose(loss_np, loss_tf)
if __name__ == '__main__':
test.main()
| apache-2.0 |
Jay-Jay-D/LeanSTP | Algorithm.Framework/Portfolio/MinimumVariancePortfolioOptimizer.py | 3 | 4622 | # QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# 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
import pandas as pd
from scipy.optimize import minimize
### <summary>
### Provides an implementation of a portfolio optimizer that calculate the optimal weights
### with the weight range from -1 to 1 and minimize the portfolio variance with a target return of 2%
### </summary>
class MinimumVariancePortfolioOptimizer:
'''Provides an implementation of a portfolio optimizer that calculate the optimal weights
with the weight range from -1 to 1 and minimize the portfolio variance with a target return of 2%'''
def __init__(self,
minimum_weight = -1,
maximum_weight = 1,
target_return = 0.02):
'''Initialize the MinimumVariancePortfolioOptimizer
Args:
minimum_weight(float): The lower bounds on portfolio weights
maximum_weight(float): The upper bounds on portfolio weights
target_return(float): The target portfolio return'''
self.minimum_weight = minimum_weight
self.maximum_weight = maximum_weight
self.target_return = target_return
def Optimize(self, historicalReturns, expectedReturns = None, covariance = None):
'''
Perform portfolio optimization for a provided matrix of historical returns and an array of expected returns
args:
historicalReturns: Matrix of annualized historical returns where each column represents a security and each row returns for the given date/time (size: K x N).
expectedReturns: Array of double with the portfolio annualized expected returns (size: K x 1).
covariance: Multi-dimensional array of double with the portfolio covariance of annualized returns (size: K x K).
Returns:
Array of double with the portfolio weights (size: K x 1)
'''
if covariance is None:
covariance = historicalReturns.cov()
if expectedReturns is None:
expectedReturns = historicalReturns.mean()
size = historicalReturns.columns.size # K x 1
x0 = np.array(size * [1. / size])
constraints = [
{'type': 'eq', 'fun': lambda weights: self.get_budget_constraint(weights)},
{'type': 'eq', 'fun': lambda weights: self.get_target_constraint(weights, expectedReturns)}]
opt = minimize(lambda weights: self.portfolio_variance(weights, covariance), # Objective function
x0, # Initial guess
bounds = self.get_boundary_conditions(size), # Bounds for variables
constraints = constraints, # Constraints definition
method='SLSQP') # Optimization method: Sequential Least SQuares Programming
return opt['x']
def portfolio_variance(self, weights, covariance):
'''Computes the portfolio variance
Args:
weighs: Portfolio weights
covariance: Covariance matrix of historical returns'''
variance = np.dot(weights.T, np.dot(covariance, weights))
if variance == 0:
raise ValueError(f'MinimumVariancePortfolioOptimizer.portfolio_variance: Volatility cannot be zero. Weights: {weights}')
return variance
def get_boundary_conditions(self, size):
'''Creates the boundary condition for the portfolio weights'''
return tuple((self.minimum_weight, self.maximum_weight) for x in range(size))
def get_budget_constraint(self, weights):
'''Defines a budget constraint: the sum of the weights equals unity'''
return np.sum(weights) - 1
def get_target_constraint(self, weights, expectedReturns):
'''Ensure that the portfolio return target a given return'''
return np.dot(np.matrix(expectedReturns), np.matrix(weights).T).item() - self.target_return | apache-2.0 |
bgris/ODL_bgris | lib/python3.5/site-packages/matplotlib/patches.py | 6 | 148732 | # -*- coding: utf-8 -*-
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import map, zip
import math
import matplotlib as mpl
import numpy as np
import matplotlib.cbook as cbook
import matplotlib.artist as artist
from matplotlib.artist import allow_rasterization
import matplotlib.colors as colors
from matplotlib import docstring
import matplotlib.transforms as transforms
from matplotlib.path import Path
import matplotlib.lines as mlines
from matplotlib.bezier import split_bezier_intersecting_with_closedpath
from matplotlib.bezier import get_intersection, inside_circle, get_parallels
from matplotlib.bezier import make_wedged_bezier2
from matplotlib.bezier import split_path_inout, get_cos_sin
from matplotlib.bezier import make_path_regular, concatenate_paths
# these are not available for the object inspector until after the
# class is built so we define an initial set here for the init
# function and they will be overridden after object definition
docstring.interpd.update(Patch="""
================= ==============================================
Property Description
================= ==============================================
alpha float
animated [True | False]
antialiased or aa [True | False]
capstyle ['butt' | 'round' | 'projecting']
clip_box a matplotlib.transform.Bbox instance
clip_on [True | False]
edgecolor or ec any matplotlib color
facecolor or fc any matplotlib color
figure a matplotlib.figure.Figure instance
fill [True | False]
hatch unknown
joinstyle ['miter' | 'round' | 'bevel']
label any string
linewidth or lw float
lod [True | False]
transform a matplotlib.transform transformation instance
visible [True | False]
zorder any number
================= ==============================================
""")
_patch_alias_map = {
'antialiased': ['aa'],
'edgecolor': ['ec'],
'facecolor': ['fc'],
'linewidth': ['lw'],
'linestyle': ['ls']
}
class Patch(artist.Artist):
"""
A patch is a 2D artist with a face color and an edge color.
If any of *edgecolor*, *facecolor*, *linewidth*, or *antialiased*
are *None*, they default to their rc params setting.
"""
zorder = 1
validCap = ('butt', 'round', 'projecting')
validJoin = ('miter', 'round', 'bevel')
# Whether to draw an edge by default. Set on a
# subclass-by-subclass basis.
_edge_default = False
def __str__(self):
return str(self.__class__).split('.')[-1]
def __init__(self,
edgecolor=None,
facecolor=None,
color=None,
linewidth=None,
linestyle=None,
antialiased=None,
hatch=None,
fill=True,
capstyle=None,
joinstyle=None,
**kwargs):
"""
The following kwarg properties are supported
%(Patch)s
"""
artist.Artist.__init__(self)
if linewidth is None:
linewidth = mpl.rcParams['patch.linewidth']
if linestyle is None:
linestyle = "solid"
if capstyle is None:
capstyle = 'butt'
if joinstyle is None:
joinstyle = 'miter'
if antialiased is None:
antialiased = mpl.rcParams['patch.antialiased']
self._fill = True # needed for set_facecolor call
if color is not None:
if (edgecolor is not None or facecolor is not None):
import warnings
warnings.warn("Setting the 'color' property will override"
"the edgecolor or facecolor properties. ")
self.set_color(color)
else:
self.set_edgecolor(edgecolor)
self.set_facecolor(facecolor)
# unscaled dashes. Needed to scale dash patterns by lw
self._us_dashes = None
self._linewidth = 0
self.set_fill(fill)
self.set_linestyle(linestyle)
self.set_linewidth(linewidth)
self.set_antialiased(antialiased)
self.set_hatch(hatch)
self.set_capstyle(capstyle)
self.set_joinstyle(joinstyle)
self._combined_transform = transforms.IdentityTransform()
if len(kwargs):
self.update(kwargs)
def get_verts(self):
"""
Return a copy of the vertices used in this patch
If the patch contains Bezier curves, the curves will be
interpolated by line segments. To access the curves as
curves, use :meth:`get_path`.
"""
trans = self.get_transform()
path = self.get_path()
polygons = path.to_polygons(trans)
if len(polygons):
return polygons[0]
return []
def _process_radius(self, radius):
if radius is not None:
return radius
if cbook.is_numlike(self._picker):
_radius = self._picker
else:
if self.get_edgecolor()[3] == 0:
_radius = 0
else:
_radius = self.get_linewidth()
return _radius
def contains(self, mouseevent, radius=None):
"""Test whether the mouse event occurred in the patch.
Returns T/F, {}
"""
if six.callable(self._contains):
return self._contains(self, mouseevent)
radius = self._process_radius(radius)
inside = self.get_path().contains_point(
(mouseevent.x, mouseevent.y), self.get_transform(), radius)
return inside, {}
def contains_point(self, point, radius=None):
"""
Returns *True* if the given point is inside the path
(transformed with its transform attribute).
"""
radius = self._process_radius(radius)
return self.get_path().contains_point(point,
self.get_transform(),
radius)
def update_from(self, other):
"""
Updates this :class:`Patch` from the properties of *other*.
"""
artist.Artist.update_from(self, other)
# For some properties we don't need or don't want to go through the
# getters/setters, so we just copy them directly.
self._edgecolor = other._edgecolor
self._facecolor = other._facecolor
self._fill = other._fill
self._hatch = other._hatch
# copy the unscaled dash pattern
self._us_dashes = other._us_dashes
self.set_linewidth(other._linewidth) # also sets dash properties
self.set_transform(other.get_data_transform())
def get_extents(self):
"""
Return a :class:`~matplotlib.transforms.Bbox` object defining
the axis-aligned extents of the :class:`Patch`.
"""
return self.get_path().get_extents(self.get_transform())
def get_transform(self):
"""
Return the :class:`~matplotlib.transforms.Transform` applied
to the :class:`Patch`.
"""
return self.get_patch_transform() + artist.Artist.get_transform(self)
def get_data_transform(self):
"""
Return the :class:`~matplotlib.transforms.Transform` instance which
maps data coordinates to physical coordinates.
"""
return artist.Artist.get_transform(self)
def get_patch_transform(self):
"""
Return the :class:`~matplotlib.transforms.Transform` instance which
takes patch coordinates to data coordinates.
For example, one may define a patch of a circle which represents a
radius of 5 by providing coordinates for a unit circle, and a
transform which scales the coordinates (the patch coordinate) by 5.
"""
return transforms.IdentityTransform()
def get_antialiased(self):
"""
Returns True if the :class:`Patch` is to be drawn with antialiasing.
"""
return self._antialiased
get_aa = get_antialiased
def get_edgecolor(self):
"""
Return the edge color of the :class:`Patch`.
"""
return self._edgecolor
get_ec = get_edgecolor
def get_facecolor(self):
"""
Return the face color of the :class:`Patch`.
"""
return self._facecolor
get_fc = get_facecolor
def get_linewidth(self):
"""
Return the line width in points.
"""
return self._linewidth
get_lw = get_linewidth
def get_linestyle(self):
"""
Return the linestyle. Will be one of ['solid' | 'dashed' |
'dashdot' | 'dotted']
"""
return self._linestyle
get_ls = get_linestyle
def set_antialiased(self, aa):
"""
Set whether to use antialiased rendering
ACCEPTS: [True | False] or None for default
"""
if aa is None:
aa = mpl.rcParams['patch.antialiased']
self._antialiased = aa
self.stale = True
def set_aa(self, aa):
"""alias for set_antialiased"""
return self.set_antialiased(aa)
def _set_edgecolor(self, color):
if color is None:
if (mpl.rcParams['patch.force_edgecolor'] or
not self._fill or self._edge_default):
color = mpl.rcParams['patch.edgecolor']
else:
color = 'none'
self._edgecolor = colors.to_rgba(color, self._alpha)
self.stale = True
def set_edgecolor(self, color):
"""
Set the patch edge color
ACCEPTS: mpl color spec, None, 'none', or 'auto'
"""
self._original_edgecolor = color
self._set_edgecolor(color)
def set_ec(self, color):
"""alias for set_edgecolor"""
return self.set_edgecolor(color)
def _set_facecolor(self, color):
if color is None:
color = mpl.rcParams['patch.facecolor']
alpha = self._alpha if self._fill else 0
self._facecolor = colors.to_rgba(color, alpha)
self.stale = True
def set_facecolor(self, color):
"""
Set the patch face color
ACCEPTS: mpl color spec, or None for default, or 'none' for no color
"""
self._original_facecolor = color
self._set_facecolor(color)
def set_fc(self, color):
"""alias for set_facecolor"""
return self.set_facecolor(color)
def set_color(self, c):
"""
Set both the edgecolor and the facecolor.
ACCEPTS: matplotlib color spec
.. seealso::
:meth:`set_facecolor`, :meth:`set_edgecolor`
For setting the edge or face color individually.
"""
self.set_facecolor(c)
self.set_edgecolor(c)
def set_alpha(self, alpha):
"""
Set the alpha tranparency of the patch.
ACCEPTS: float or None
"""
if alpha is not None:
try:
float(alpha)
except TypeError:
raise TypeError('alpha must be a float or None')
artist.Artist.set_alpha(self, alpha)
self._set_facecolor(self._original_facecolor)
self._set_edgecolor(self._original_edgecolor)
# stale is already True
def set_linewidth(self, w):
"""
Set the patch linewidth in points
ACCEPTS: float or None for default
"""
if w is None:
w = mpl.rcParams['patch.linewidth']
if w is None:
w = mpl.rcParams['axes.linewidth']
self._linewidth = float(w)
# scale the dash pattern by the linewidth
offset, ls = self._us_dashes
self._dashoffset, self._dashes = mlines._scale_dashes(
offset, ls, self._linewidth)
self.stale = True
def set_lw(self, lw):
"""alias for set_linewidth"""
return self.set_linewidth(lw)
def set_linestyle(self, ls):
"""
Set the patch linestyle
=========================== =================
linestyle description
=========================== =================
``'-'`` or ``'solid'`` solid line
``'--'`` or ``'dashed'`` dashed line
``'-.'`` or ``'dashdot'`` dash-dotted line
``':'`` or ``'dotted'`` dotted line
=========================== =================
Alternatively a dash tuple of the following form can be provided::
(offset, onoffseq),
where ``onoffseq`` is an even length tuple of on and off ink
in points.
ACCEPTS: ['solid' | 'dashed', 'dashdot', 'dotted' |
(offset, on-off-dash-seq) |
``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` |
``' '`` | ``''``]
Parameters
----------
ls : { '-', '--', '-.', ':'} and more see description
The line style.
"""
if ls is None:
ls = "solid"
self._linestyle = ls
# get the unscalled dash pattern
offset, ls = self._us_dashes = mlines._get_dash_pattern(ls)
# scale the dash pattern by the linewidth
self._dashoffset, self._dashes = mlines._scale_dashes(
offset, ls, self._linewidth)
self.stale = True
def set_ls(self, ls):
"""alias for set_linestyle"""
return self.set_linestyle(ls)
def set_fill(self, b):
"""
Set whether to fill the patch
ACCEPTS: [True | False]
"""
self._fill = bool(b)
self._set_facecolor(self._original_facecolor)
self._set_edgecolor(self._original_edgecolor)
self.stale = True
def get_fill(self):
'return whether fill is set'
return self._fill
# Make fill a property so as to preserve the long-standing
# but somewhat inconsistent behavior in which fill was an
# attribute.
fill = property(get_fill, set_fill)
def set_capstyle(self, s):
"""
Set the patch capstyle
ACCEPTS: ['butt' | 'round' | 'projecting']
"""
s = s.lower()
if s not in self.validCap:
raise ValueError('set_capstyle passed "%s";\n' % (s,) +
'valid capstyles are %s' % (self.validCap,))
self._capstyle = s
self.stale = True
def get_capstyle(self):
"Return the current capstyle"
return self._capstyle
def set_joinstyle(self, s):
"""
Set the patch joinstyle
ACCEPTS: ['miter' | 'round' | 'bevel']
"""
s = s.lower()
if s not in self.validJoin:
raise ValueError('set_joinstyle passed "%s";\n' % (s,) +
'valid joinstyles are %s' % (self.validJoin,))
self._joinstyle = s
self.stale = True
def get_joinstyle(self):
"Return the current joinstyle"
return self._joinstyle
def set_hatch(self, hatch):
"""
Set the hatching pattern
*hatch* can be one of::
/ - diagonal hatching
\ - back diagonal
| - vertical
- - horizontal
+ - crossed
x - crossed diagonal
o - small circle
O - large circle
. - dots
* - stars
Letters can be combined, in which case all the specified
hatchings are done. If same letter repeats, it increases the
density of hatching of that pattern.
Hatching is supported in the PostScript, PDF, SVG and Agg
backends only.
ACCEPTS: ['/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*']
"""
self._hatch = hatch
self.stale = True
def get_hatch(self):
'Return the current hatching pattern'
return self._hatch
@allow_rasterization
def draw(self, renderer):
'Draw the :class:`Patch` to the given *renderer*.'
if not self.get_visible():
return
renderer.open_group('patch', self.get_gid())
gc = renderer.new_gc()
gc.set_foreground(self._edgecolor, isRGBA=True)
lw = self._linewidth
if self._edgecolor[3] == 0:
lw = 0
gc.set_linewidth(lw)
gc.set_dashes(0, self._dashes)
gc.set_capstyle(self._capstyle)
gc.set_joinstyle(self._joinstyle)
gc.set_antialiased(self._antialiased)
self._set_gc_clip(gc)
gc.set_url(self._url)
gc.set_snap(self.get_snap())
rgbFace = self._facecolor
if rgbFace[3] == 0:
rgbFace = None # (some?) renderers expect this as no-fill signal
gc.set_alpha(self._alpha)
if self._hatch:
gc.set_hatch(self._hatch)
if self.get_sketch_params() is not None:
gc.set_sketch_params(*self.get_sketch_params())
path = self.get_path()
transform = self.get_transform()
tpath = transform.transform_path_non_affine(path)
affine = transform.get_affine()
if self.get_path_effects():
from matplotlib.patheffects import PathEffectRenderer
renderer = PathEffectRenderer(self.get_path_effects(), renderer)
renderer.draw_path(gc, tpath, affine, rgbFace)
gc.restore()
renderer.close_group('patch')
self.stale = False
def get_path(self):
"""
Return the path of this patch
"""
raise NotImplementedError('Derived must override')
def get_window_extent(self, renderer=None):
return self.get_path().get_extents(self.get_transform())
patchdoc = artist.kwdoc(Patch)
for k in ('Rectangle', 'Circle', 'RegularPolygon', 'Polygon', 'Wedge', 'Arrow',
'FancyArrow', 'YAArrow', 'CirclePolygon', 'Ellipse', 'Arc',
'FancyBboxPatch', 'Patch'):
docstring.interpd.update({k: patchdoc})
# define Patch.__init__ docstring after the class has been added to interpd
docstring.dedent_interpd(Patch.__init__)
class Shadow(Patch):
def __str__(self):
return "Shadow(%s)" % (str(self.patch))
@docstring.dedent_interpd
def __init__(self, patch, ox, oy, props=None, **kwargs):
"""
Create a shadow of the given *patch* offset by *ox*, *oy*.
*props*, if not *None*, is a patch property update dictionary.
If *None*, the shadow will have have the same color as the face,
but darkened.
kwargs are
%(Patch)s
"""
Patch.__init__(self)
self.patch = patch
self.props = props
self._ox, self._oy = ox, oy
self._shadow_transform = transforms.Affine2D()
self._update()
def _update(self):
self.update_from(self.patch)
if self.props is not None:
self.update(self.props)
else:
r, g, b, a = colors.to_rgba(self.patch.get_facecolor())
rho = 0.3
r = rho * r
g = rho * g
b = rho * b
self.set_facecolor((r, g, b, 0.5))
self.set_edgecolor((r, g, b, 0.5))
self.set_alpha(0.5)
def _update_transform(self, renderer):
ox = renderer.points_to_pixels(self._ox)
oy = renderer.points_to_pixels(self._oy)
self._shadow_transform.clear().translate(ox, oy)
def _get_ox(self):
return self._ox
def _set_ox(self, ox):
self._ox = ox
def _get_oy(self):
return self._oy
def _set_oy(self, oy):
self._oy = oy
def get_path(self):
return self.patch.get_path()
def get_patch_transform(self):
return self.patch.get_patch_transform() + self._shadow_transform
def draw(self, renderer):
self._update_transform(renderer)
Patch.draw(self, renderer)
class Rectangle(Patch):
"""
Draw a rectangle with lower left at *xy* = (*x*, *y*) with
specified *width* and *height*.
"""
def __str__(self):
return self.__class__.__name__ \
+ "(%g,%g;%gx%g)" % (self._x, self._y, self._width, self._height)
@docstring.dedent_interpd
def __init__(self, xy, width, height, angle=0.0, **kwargs):
"""
*angle*
rotation in degrees (anti-clockwise)
*fill* is a boolean indicating whether to fill the rectangle
Valid kwargs are:
%(Patch)s
"""
Patch.__init__(self, **kwargs)
self._x = float(xy[0])
self._y = float(xy[1])
self._width = float(width)
self._height = float(height)
self._angle = float(angle)
# Note: This cannot be calculated until this is added to an Axes
self._rect_transform = transforms.IdentityTransform()
def get_path(self):
"""
Return the vertices of the rectangle
"""
return Path.unit_rectangle()
def _update_patch_transform(self):
"""NOTE: This cannot be called until after this has been added
to an Axes, otherwise unit conversion will fail. This
maxes it very important to call the accessor method and
not directly access the transformation member variable.
"""
x = self.convert_xunits(self._x)
y = self.convert_yunits(self._y)
width = self.convert_xunits(self._width)
height = self.convert_yunits(self._height)
bbox = transforms.Bbox.from_bounds(x, y, width, height)
rot_trans = transforms.Affine2D()
rot_trans.rotate_deg_around(x, y, self._angle)
self._rect_transform = transforms.BboxTransformTo(bbox)
self._rect_transform += rot_trans
def get_patch_transform(self):
self._update_patch_transform()
return self._rect_transform
def get_x(self):
"Return the left coord of the rectangle"
return self._x
def get_y(self):
"Return the bottom coord of the rectangle"
return self._y
def get_xy(self):
"Return the left and bottom coords of the rectangle"
return self._x, self._y
def get_width(self):
"Return the width of the rectangle"
return self._width
def get_height(self):
"Return the height of the rectangle"
return self._height
def set_x(self, x):
"""
Set the left coord of the rectangle
ACCEPTS: float
"""
self._x = x
self.stale = True
def set_y(self, y):
"""
Set the bottom coord of the rectangle
ACCEPTS: float
"""
self._y = y
self.stale = True
def set_xy(self, xy):
"""
Set the left and bottom coords of the rectangle
ACCEPTS: 2-item sequence
"""
self._x, self._y = xy
self.stale = True
def set_width(self, w):
"""
Set the width rectangle
ACCEPTS: float
"""
self._width = w
self.stale = True
def set_height(self, h):
"""
Set the width rectangle
ACCEPTS: float
"""
self._height = h
self.stale = True
def set_bounds(self, *args):
"""
Set the bounds of the rectangle: l,b,w,h
ACCEPTS: (left, bottom, width, height)
"""
if len(args) == 0:
l, b, w, h = args[0]
else:
l, b, w, h = args
self._x = l
self._y = b
self._width = w
self._height = h
self.stale = True
def get_bbox(self):
return transforms.Bbox.from_bounds(self._x, self._y,
self._width, self._height)
xy = property(get_xy, set_xy)
class RegularPolygon(Patch):
"""
A regular polygon patch.
"""
def __str__(self):
return "Poly%d(%g,%g)" % (self._numVertices, self._xy[0], self._xy[1])
@docstring.dedent_interpd
def __init__(self, xy, numVertices, radius=5, orientation=0,
**kwargs):
"""
Constructor arguments:
*xy*
A length 2 tuple (*x*, *y*) of the center.
*numVertices*
the number of vertices.
*radius*
The distance from the center to each of the vertices.
*orientation*
rotates the polygon (in radians).
Valid kwargs are:
%(Patch)s
"""
self._xy = xy
self._numVertices = numVertices
self._orientation = orientation
self._radius = radius
self._path = Path.unit_regular_polygon(numVertices)
self._poly_transform = transforms.Affine2D()
self._update_transform()
Patch.__init__(self, **kwargs)
def _update_transform(self):
self._poly_transform.clear() \
.scale(self.radius) \
.rotate(self.orientation) \
.translate(*self.xy)
def _get_xy(self):
return self._xy
def _set_xy(self, xy):
self._xy = xy
self._update_transform()
xy = property(_get_xy, _set_xy)
def _get_orientation(self):
return self._orientation
def _set_orientation(self, orientation):
self._orientation = orientation
self._update_transform()
orientation = property(_get_orientation, _set_orientation)
def _get_radius(self):
return self._radius
def _set_radius(self, radius):
self._radius = radius
self._update_transform()
radius = property(_get_radius, _set_radius)
def _get_numvertices(self):
return self._numVertices
def _set_numvertices(self, numVertices):
self._numVertices = numVertices
numvertices = property(_get_numvertices, _set_numvertices)
def get_path(self):
return self._path
def get_patch_transform(self):
self._update_transform()
return self._poly_transform
class PathPatch(Patch):
"""
A general polycurve path patch.
"""
_edge_default = True
def __str__(self):
return "Poly((%g, %g) ...)" % tuple(self._path.vertices[0])
@docstring.dedent_interpd
def __init__(self, path, **kwargs):
"""
*path* is a :class:`matplotlib.path.Path` object.
Valid kwargs are:
%(Patch)s
.. seealso::
:class:`Patch`
For additional kwargs
"""
Patch.__init__(self, **kwargs)
self._path = path
def get_path(self):
return self._path
class Polygon(Patch):
"""
A general polygon patch.
"""
def __str__(self):
return "Poly((%g, %g) ...)" % tuple(self._path.vertices[0])
@docstring.dedent_interpd
def __init__(self, xy, closed=True, **kwargs):
"""
*xy* is a numpy array with shape Nx2.
If *closed* is *True*, the polygon will be closed so the
starting and ending points are the same.
Valid kwargs are:
%(Patch)s
.. seealso::
:class:`Patch`
For additional kwargs
"""
Patch.__init__(self, **kwargs)
self._closed = closed
self.set_xy(xy)
def get_path(self):
"""
Get the path of the polygon
Returns
-------
path : Path
The :class:`~matplotlib.path.Path` object for
the polygon
"""
return self._path
def get_closed(self):
"""
Returns if the polygon is closed
Returns
-------
closed : bool
If the path is closed
"""
return self._closed
def set_closed(self, closed):
"""
Set if the polygon is closed
Parameters
----------
closed : bool
True if the polygon is closed
"""
if self._closed == bool(closed):
return
self._closed = bool(closed)
self.set_xy(self.get_xy())
self.stale = True
def get_xy(self):
"""
Get the vertices of the path
Returns
-------
vertices : numpy array
The coordinates of the vertices as a Nx2
ndarray.
"""
return self._path.vertices
def set_xy(self, xy):
"""
Set the vertices of the polygon
Parameters
----------
xy : numpy array or iterable of pairs
The coordinates of the vertices as a Nx2
ndarray or iterable of pairs.
"""
xy = np.asarray(xy)
if self._closed:
if len(xy) and (xy[0] != xy[-1]).any():
xy = np.concatenate([xy, [xy[0]]])
else:
if len(xy) > 2 and (xy[0] == xy[-1]).all():
xy = xy[:-1]
self._path = Path(xy, closed=self._closed)
self.stale = True
_get_xy = get_xy
_set_xy = set_xy
xy = property(
get_xy, set_xy, None,
"""Set/get the vertices of the polygon. This property is
provided for backward compatibility with matplotlib 0.91.x
only. New code should use
:meth:`~matplotlib.patches.Polygon.get_xy` and
:meth:`~matplotlib.patches.Polygon.set_xy` instead.""")
class Wedge(Patch):
"""
Wedge shaped patch.
"""
def __str__(self):
return "Wedge(%g,%g)" % (self.theta1, self.theta2)
@docstring.dedent_interpd
def __init__(self, center, r, theta1, theta2, width=None, **kwargs):
"""
Draw a wedge centered at *x*, *y* center with radius *r* that
sweeps *theta1* to *theta2* (in degrees). If *width* is given,
then a partial wedge is drawn from inner radius *r* - *width*
to outer radius *r*.
Valid kwargs are:
%(Patch)s
"""
Patch.__init__(self, **kwargs)
self.center = center
self.r, self.width = r, width
self.theta1, self.theta2 = theta1, theta2
self._patch_transform = transforms.IdentityTransform()
self._recompute_path()
def _recompute_path(self):
# Inner and outer rings are connected unless the annulus is complete
if abs((self.theta2 - self.theta1) - 360) <= 1e-12:
theta1, theta2 = 0, 360
connector = Path.MOVETO
else:
theta1, theta2 = self.theta1, self.theta2
connector = Path.LINETO
# Form the outer ring
arc = Path.arc(theta1, theta2)
if self.width is not None:
# Partial annulus needs to draw the outer ring
# followed by a reversed and scaled inner ring
v1 = arc.vertices
v2 = arc.vertices[::-1] * float(self.r - self.width) / self.r
v = np.vstack([v1, v2, v1[0, :], (0, 0)])
c = np.hstack([arc.codes, arc.codes, connector, Path.CLOSEPOLY])
c[len(arc.codes)] = connector
else:
# Wedge doesn't need an inner ring
v = np.vstack([arc.vertices, [(0, 0), arc.vertices[0, :], (0, 0)]])
c = np.hstack([arc.codes, [connector, connector, Path.CLOSEPOLY]])
# Shift and scale the wedge to the final location.
v *= self.r
v += np.asarray(self.center)
self._path = Path(v, c)
def set_center(self, center):
self._path = None
self.center = center
self.stale = True
def set_radius(self, radius):
self._path = None
self.r = radius
self.stale = True
def set_theta1(self, theta1):
self._path = None
self.theta1 = theta1
self.stale = True
def set_theta2(self, theta2):
self._path = None
self.theta2 = theta2
self.stale = True
def set_width(self, width):
self._path = None
self.width = width
self.stale = True
def get_path(self):
if self._path is None:
self._recompute_path()
return self._path
# COVERAGE NOTE: Not used internally or from examples
class Arrow(Patch):
"""
An arrow patch.
"""
def __str__(self):
return "Arrow()"
_path = Path([
[0.0, 0.1], [0.0, -0.1],
[0.8, -0.1], [0.8, -0.3],
[1.0, 0.0], [0.8, 0.3],
[0.8, 0.1], [0.0, 0.1]],
closed=True)
@docstring.dedent_interpd
def __init__(self, x, y, dx, dy, width=1.0, **kwargs):
"""
Draws an arrow, starting at (*x*, *y*), direction and length
given by (*dx*, *dy*) the width of the arrow is scaled by *width*.
Valid kwargs are:
%(Patch)s
"""
Patch.__init__(self, **kwargs)
L = np.hypot(dx, dy)
if L != 0:
cx = float(dx) / L
sx = float(dy) / L
else:
# Account for division by zero
cx, sx = 0, 1
trans1 = transforms.Affine2D().scale(L, width)
trans2 = transforms.Affine2D.from_values(cx, sx, -sx, cx, 0.0, 0.0)
trans3 = transforms.Affine2D().translate(x, y)
trans = trans1 + trans2 + trans3
self._patch_transform = trans.frozen()
def get_path(self):
return self._path
def get_patch_transform(self):
return self._patch_transform
class FancyArrow(Polygon):
"""
Like Arrow, but lets you set head width and head height independently.
"""
_edge_default = True
def __str__(self):
return "FancyArrow()"
@docstring.dedent_interpd
def __init__(self, x, y, dx, dy, width=0.001, length_includes_head=False,
head_width=None, head_length=None, shape='full', overhang=0,
head_starts_at_zero=False, **kwargs):
"""
Constructor arguments
*width*: float (default: 0.001)
width of full arrow tail
*length_includes_head*: [True | False] (default: False)
True if head is to be counted in calculating the length.
*head_width*: float or None (default: 3*width)
total width of the full arrow head
*head_length*: float or None (default: 1.5 * head_width)
length of arrow head
*shape*: ['full', 'left', 'right'] (default: 'full')
draw the left-half, right-half, or full arrow
*overhang*: float (default: 0)
fraction that the arrow is swept back (0 overhang means
triangular shape). Can be negative or greater than one.
*head_starts_at_zero*: [True | False] (default: False)
if True, the head starts being drawn at coordinate 0
instead of ending at coordinate 0.
Other valid kwargs (inherited from :class:`Patch`) are:
%(Patch)s
"""
if head_width is None:
head_width = 3 * width
if head_length is None:
head_length = 1.5 * head_width
distance = np.hypot(dx, dy)
if length_includes_head:
length = distance
else:
length = distance + head_length
if not length:
verts = [] # display nothing if empty
else:
# start by drawing horizontal arrow, point at (0,0)
hw, hl, hs, lw = head_width, head_length, overhang, width
left_half_arrow = np.array([
[0.0, 0.0], # tip
[-hl, -hw / 2.0], # leftmost
[-hl * (1 - hs), -lw / 2.0], # meets stem
[-length, -lw / 2.0], # bottom left
[-length, 0],
])
# if we're not including the head, shift up by head length
if not length_includes_head:
left_half_arrow += [head_length, 0]
# if the head starts at 0, shift up by another head length
if head_starts_at_zero:
left_half_arrow += [head_length / 2.0, 0]
# figure out the shape, and complete accordingly
if shape == 'left':
coords = left_half_arrow
else:
right_half_arrow = left_half_arrow * [1, -1]
if shape == 'right':
coords = right_half_arrow
elif shape == 'full':
# The half-arrows contain the midpoint of the stem,
# which we can omit from the full arrow. Including it
# twice caused a problem with xpdf.
coords = np.concatenate([left_half_arrow[:-2],
right_half_arrow[-2::-1]])
else:
raise ValueError("Got unknown shape: %s" % shape)
if distance != 0:
cx = float(dx) / distance
sx = float(dy) / distance
else:
#Account for division by zero
cx, sx = 0, 1
M = np.array([[cx, sx], [-sx, cx]])
verts = np.dot(coords, M) + (x + dx, y + dy)
Polygon.__init__(self, list(map(tuple, verts)), closed=True, **kwargs)
docstring.interpd.update({"FancyArrow": FancyArrow.__init__.__doc__})
docstring.interpd.update({"FancyArrow": FancyArrow.__init__.__doc__})
class YAArrow(Patch):
"""
Yet another arrow class.
This is an arrow that is defined in display space and has a tip at
*x1*, *y1* and a base at *x2*, *y2*.
"""
def __str__(self):
return "YAArrow()"
@docstring.dedent_interpd
def __init__(self, figure, xytip, xybase,
width=4, frac=0.1, headwidth=12, **kwargs):
"""
Constructor arguments:
*xytip*
(*x*, *y*) location of arrow tip
*xybase*
(*x*, *y*) location the arrow base mid point
*figure*
The :class:`~matplotlib.figure.Figure` instance
(fig.dpi)
*width*
The width of the arrow in points
*frac*
The fraction of the arrow length occupied by the head
*headwidth*
The width of the base of the arrow head in points
Valid kwargs are:
%(Patch)s
"""
self.xytip = xytip
self.xybase = xybase
self.width = width
self.frac = frac
self.headwidth = headwidth
Patch.__init__(self, **kwargs)
# Set self.figure after Patch.__init__, since it sets self.figure to
# None
self.figure = figure
def get_path(self):
# Since this is dpi dependent, we need to recompute the path
# every time.
# the base vertices
x1, y1 = self.xytip
x2, y2 = self.xybase
k1 = self.width * self.figure.dpi / 72. / 2.
k2 = self.headwidth * self.figure.dpi / 72. / 2.
xb1, yb1, xb2, yb2 = self.getpoints(x1, y1, x2, y2, k1)
# a point on the segment 20% of the distance from the tip to the base
theta = math.atan2(y2 - y1, x2 - x1)
r = math.sqrt((y2 - y1) ** 2. + (x2 - x1) ** 2.)
xm = x1 + self.frac * r * math.cos(theta)
ym = y1 + self.frac * r * math.sin(theta)
xc1, yc1, xc2, yc2 = self.getpoints(x1, y1, xm, ym, k1)
xd1, yd1, xd2, yd2 = self.getpoints(x1, y1, xm, ym, k2)
xs = self.convert_xunits([xb1, xb2, xc2, xd2, x1, xd1, xc1, xb1])
ys = self.convert_yunits([yb1, yb2, yc2, yd2, y1, yd1, yc1, yb1])
return Path(list(zip(xs, ys)), closed=True)
def get_patch_transform(self):
return transforms.IdentityTransform()
def getpoints(self, x1, y1, x2, y2, k):
"""
For line segment defined by (*x1*, *y1*) and (*x2*, *y2*)
return the points on the line that is perpendicular to the
line and intersects (*x2*, *y2*) and the distance from (*x2*,
*y2*) of the returned points is *k*.
"""
x1, y1, x2, y2, k = list(map(float, (x1, y1, x2, y2, k)))
if y2 - y1 == 0:
return x2, y2 + k, x2, y2 - k
elif x2 - x1 == 0:
return x2 + k, y2, x2 - k, y2
m = (y2 - y1) / (x2 - x1)
pm = -1. / m
a = 1
b = -2 * y2
c = y2 ** 2. - k ** 2. * pm ** 2. / (1. + pm ** 2.)
y3a = (-b + math.sqrt(b ** 2. - 4 * a * c)) / (2. * a)
x3a = (y3a - y2) / pm + x2
y3b = (-b - math.sqrt(b ** 2. - 4 * a * c)) / (2. * a)
x3b = (y3b - y2) / pm + x2
return x3a, y3a, x3b, y3b
class CirclePolygon(RegularPolygon):
"""
A polygon-approximation of a circle patch.
"""
def __str__(self):
return "CirclePolygon(%d,%d)" % self.center
@docstring.dedent_interpd
def __init__(self, xy, radius=5,
resolution=20, # the number of vertices
** kwargs):
"""
Create a circle at *xy* = (*x*, *y*) with given *radius*.
This circle is approximated by a regular polygon with
*resolution* sides. For a smoother circle drawn with splines,
see :class:`~matplotlib.patches.Circle`.
Valid kwargs are:
%(Patch)s
"""
RegularPolygon.__init__(self, xy,
resolution,
radius,
orientation=0,
**kwargs)
class Ellipse(Patch):
"""
A scale-free ellipse.
"""
def __str__(self):
return "Ellipse(%s,%s;%sx%s)" % (self.center[0], self.center[1],
self.width, self.height)
@docstring.dedent_interpd
def __init__(self, xy, width, height, angle=0.0, **kwargs):
"""
*xy*
center of ellipse
*width*
total length (diameter) of horizontal axis
*height*
total length (diameter) of vertical axis
*angle*
rotation in degrees (anti-clockwise)
Valid kwargs are:
%(Patch)s
"""
Patch.__init__(self, **kwargs)
self.center = xy
self.width, self.height = width, height
self.angle = angle
self._path = Path.unit_circle()
# Note: This cannot be calculated until this is added to an Axes
self._patch_transform = transforms.IdentityTransform()
def _recompute_transform(self):
"""NOTE: This cannot be called until after this has been added
to an Axes, otherwise unit conversion will fail. This
maxes it very important to call the accessor method and
not directly access the transformation member variable.
"""
center = (self.convert_xunits(self.center[0]),
self.convert_yunits(self.center[1]))
width = self.convert_xunits(self.width)
height = self.convert_yunits(self.height)
self._patch_transform = transforms.Affine2D() \
.scale(width * 0.5, height * 0.5) \
.rotate_deg(self.angle) \
.translate(*center)
def get_path(self):
"""
Return the vertices of the rectangle
"""
return self._path
def get_patch_transform(self):
self._recompute_transform()
return self._patch_transform
class Circle(Ellipse):
"""
A circle patch.
"""
def __str__(self):
return "Circle((%g,%g),r=%g)" % (self.center[0],
self.center[1],
self.radius)
@docstring.dedent_interpd
def __init__(self, xy, radius=5, **kwargs):
"""
Create true circle at center *xy* = (*x*, *y*) with given
*radius*. Unlike :class:`~matplotlib.patches.CirclePolygon`
which is a polygonal approximation, this uses Bézier splines
and is much closer to a scale-free circle.
Valid kwargs are:
%(Patch)s
"""
Ellipse.__init__(self, xy, radius * 2, radius * 2, **kwargs)
self.radius = radius
def set_radius(self, radius):
"""
Set the radius of the circle
ACCEPTS: float
"""
self.width = self.height = 2 * radius
self.stale = True
def get_radius(self):
'return the radius of the circle'
return self.width / 2.
radius = property(get_radius, set_radius)
class Arc(Ellipse):
"""
An elliptical arc. Because it performs various optimizations, it
can not be filled.
The arc must be used in an :class:`~matplotlib.axes.Axes`
instance---it can not be added directly to a
:class:`~matplotlib.figure.Figure`---because it is optimized to
only render the segments that are inside the axes bounding box
with high resolution.
"""
def __str__(self):
return "Arc(%s,%s;%sx%s)" % (self.center[0], self.center[1],
self.width, self.height)
@docstring.dedent_interpd
def __init__(self, xy, width, height, angle=0.0,
theta1=0.0, theta2=360.0, **kwargs):
"""
The following args are supported:
*xy*
center of ellipse
*width*
length of horizontal axis
*height*
length of vertical axis
*angle*
rotation in degrees (anti-clockwise)
*theta1*
starting angle of the arc in degrees
*theta2*
ending angle of the arc in degrees
If *theta1* and *theta2* are not provided, the arc will form a
complete ellipse.
Valid kwargs are:
%(Patch)s
"""
fill = kwargs.setdefault('fill', False)
if fill:
raise ValueError("Arc objects can not be filled")
Ellipse.__init__(self, xy, width, height, angle, **kwargs)
self.theta1 = theta1
self.theta2 = theta2
self._path = Path.arc(self.theta1, self.theta2)
@allow_rasterization
def draw(self, renderer):
"""
Ellipses are normally drawn using an approximation that uses
eight cubic bezier splines. The error of this approximation
is 1.89818e-6, according to this unverified source:
Lancaster, Don. Approximating a Circle or an Ellipse Using
Four Bezier Cubic Splines.
http://www.tinaja.com/glib/ellipse4.pdf
There is a use case where very large ellipses must be drawn
with very high accuracy, and it is too expensive to render the
entire ellipse with enough segments (either splines or line
segments). Therefore, in the case where either radius of the
ellipse is large enough that the error of the spline
approximation will be visible (greater than one pixel offset
from the ideal), a different technique is used.
In that case, only the visible parts of the ellipse are drawn,
with each visible arc using a fixed number of spline segments
(8). The algorithm proceeds as follows:
1. The points where the ellipse intersects the axes bounding
box are located. (This is done be performing an inverse
transformation on the axes bbox such that it is relative
to the unit circle -- this makes the intersection
calculation much easier than doing rotated ellipse
intersection directly).
This uses the "line intersecting a circle" algorithm
from:
Vince, John. Geometry for Computer Graphics: Formulae,
Examples & Proofs. London: Springer-Verlag, 2005.
2. The angles of each of the intersection points are
calculated.
3. Proceeding counterclockwise starting in the positive
x-direction, each of the visible arc-segments between the
pairs of vertices are drawn using the bezier arc
approximation technique implemented in
:meth:`matplotlib.path.Path.arc`.
"""
if not hasattr(self, 'axes'):
raise RuntimeError('Arcs can only be used in Axes instances')
self._recompute_transform()
# Get the width and height in pixels
width = self.convert_xunits(self.width)
height = self.convert_yunits(self.height)
width, height = self.get_transform().transform_point(
(width, height))
inv_error = (1.0 / 1.89818e-6) * 0.5
if width < inv_error and height < inv_error:
# self._path = Path.arc(self.theta1, self.theta2)
return Patch.draw(self, renderer)
def iter_circle_intersect_on_line(x0, y0, x1, y1):
dx = x1 - x0
dy = y1 - y0
dr2 = dx * dx + dy * dy
D = x0 * y1 - x1 * y0
D2 = D * D
discrim = dr2 - D2
# Single (tangential) intersection
if discrim == 0.0:
x = (D * dy) / dr2
y = (-D * dx) / dr2
yield x, y
elif discrim > 0.0:
# The definition of "sign" here is different from
# np.sign: we never want to get 0.0
if dy < 0.0:
sign_dy = -1.0
else:
sign_dy = 1.0
sqrt_discrim = np.sqrt(discrim)
for sign in (1., -1.):
x = (D * dy + sign * sign_dy * dx * sqrt_discrim) / dr2
y = (-D * dx + sign * np.abs(dy) * sqrt_discrim) / dr2
yield x, y
def iter_circle_intersect_on_line_seg(x0, y0, x1, y1):
epsilon = 1e-9
if x1 < x0:
x0e, x1e = x1, x0
else:
x0e, x1e = x0, x1
if y1 < y0:
y0e, y1e = y1, y0
else:
y0e, y1e = y0, y1
x0e -= epsilon
y0e -= epsilon
x1e += epsilon
y1e += epsilon
for x, y in iter_circle_intersect_on_line(x0, y0, x1, y1):
if x >= x0e and x <= x1e and y >= y0e and y <= y1e:
yield x, y
# Transforms the axes box_path so that it is relative to the unit
# circle in the same way that it is relative to the desired
# ellipse.
box_path = Path.unit_rectangle()
box_path_transform = transforms.BboxTransformTo(self.axes.bbox) + \
self.get_transform().inverted()
box_path = box_path.transformed(box_path_transform)
PI = np.pi
TWOPI = PI * 2.0
RAD2DEG = 180.0 / PI
DEG2RAD = PI / 180.0
theta1 = self.theta1
theta2 = self.theta2
thetas = {}
# For each of the point pairs, there is a line segment
for p0, p1 in zip(box_path.vertices[:-1], box_path.vertices[1:]):
x0, y0 = p0
x1, y1 = p1
for x, y in iter_circle_intersect_on_line_seg(x0, y0, x1, y1):
theta = np.arccos(x)
if y < 0:
theta = TWOPI - theta
# Convert radians to angles
theta *= RAD2DEG
if theta > theta1 and theta < theta2:
thetas[theta] = None
thetas = list(six.iterkeys(thetas))
thetas.sort()
thetas.append(theta2)
last_theta = theta1
theta1_rad = theta1 * DEG2RAD
inside = box_path.contains_point((np.cos(theta1_rad),
np.sin(theta1_rad)))
# save original path
path_original = self._path
for theta in thetas:
if inside:
Path.arc(last_theta, theta, 8)
Patch.draw(self, renderer)
inside = False
else:
inside = True
last_theta = theta
# restore original path
self._path = path_original
def bbox_artist(artist, renderer, props=None, fill=True):
"""
This is a debug function to draw a rectangle around the bounding
box returned by
:meth:`~matplotlib.artist.Artist.get_window_extent` of an artist,
to test whether the artist is returning the correct bbox.
*props* is a dict of rectangle props with the additional property
'pad' that sets the padding around the bbox in points.
"""
if props is None:
props = {}
props = props.copy() # don't want to alter the pad externally
pad = props.pop('pad', 4)
pad = renderer.points_to_pixels(pad)
bbox = artist.get_window_extent(renderer)
l, b, w, h = bbox.bounds
l -= pad / 2.
b -= pad / 2.
w += pad
h += pad
r = Rectangle(xy=(l, b),
width=w,
height=h,
fill=fill,
)
r.set_transform(transforms.IdentityTransform())
r.set_clip_on(False)
r.update(props)
r.draw(renderer)
def draw_bbox(bbox, renderer, color='k', trans=None):
"""
This is a debug function to draw a rectangle around the bounding
box returned by
:meth:`~matplotlib.artist.Artist.get_window_extent` of an artist,
to test whether the artist is returning the correct bbox.
"""
l, b, w, h = bbox.bounds
r = Rectangle(xy=(l, b),
width=w,
height=h,
edgecolor=color,
fill=False,
)
if trans is not None:
r.set_transform(trans)
r.set_clip_on(False)
r.draw(renderer)
def _pprint_table(_table, leadingspace=2):
"""
Given the list of list of strings, return a string of REST table format.
"""
if leadingspace:
pad = ' ' * leadingspace
else:
pad = ''
columns = [[] for cell in _table[0]]
for row in _table:
for column, cell in zip(columns, row):
column.append(cell)
col_len = [max([len(cell) for cell in column]) for column in columns]
lines = []
table_formatstr = pad + ' '.join([('=' * cl) for cl in col_len])
lines.append('')
lines.append(table_formatstr)
lines.append(pad + ' '.join([cell.ljust(cl)
for cell, cl
in zip(_table[0], col_len)]))
lines.append(table_formatstr)
lines.extend([(pad + ' '.join([cell.ljust(cl)
for cell, cl
in zip(row, col_len)]))
for row in _table[1:]])
lines.append(table_formatstr)
lines.append('')
return "\n".join(lines)
def _pprint_styles(_styles):
"""
A helper function for the _Style class. Given the dictionary of
(stylename : styleclass), return a formatted string listing all the
styles. Used to update the documentation.
"""
names, attrss, clss = [], [], []
import inspect
_table = [["Class", "Name", "Attrs"]]
for name, cls in sorted(_styles.items()):
if six.PY2:
args, varargs, varkw, defaults = inspect.getargspec(cls.__init__)
else:
(args, varargs, varkw, defaults, kwonlyargs, kwonlydefs,
annotations) = inspect.getfullargspec(cls.__init__)
if defaults:
args = [(argname, argdefault)
for argname, argdefault in zip(args[1:], defaults)]
else:
args = None
if args is None:
argstr = 'None'
else:
argstr = ",".join([("%s=%s" % (an, av))
for an, av
in args])
# adding ``quotes`` since - and | have special meaning in reST
_table.append([cls.__name__, "``%s``" % name, argstr])
return _pprint_table(_table)
def _simpleprint_styles(_styles):
"""
A helper function for the _Style class. Given the dictionary of
(stylename : styleclass), return a string rep of the list of keys.
Used to update the documentation.
"""
styles = "[ \'"
styles += "\' | \'".join(str(i) for i in sorted(_styles.keys()))
styles += "\' ]"
return styles
class _Style(object):
"""
A base class for the Styles. It is meant to be a container class,
where actual styles are declared as subclass of it, and it
provides some helper functions.
"""
def __new__(self, stylename, **kw):
"""
return the instance of the subclass with the given style name.
"""
# the "class" should have the _style_list attribute, which is
# a dictionary of stylname, style class paie.
_list = stylename.replace(" ", "").split(",")
_name = _list[0].lower()
try:
_cls = self._style_list[_name]
except KeyError:
raise ValueError("Unknown style : %s" % stylename)
try:
_args_pair = [cs.split("=") for cs in _list[1:]]
_args = dict([(k, float(v)) for k, v in _args_pair])
except ValueError:
raise ValueError("Incorrect style argument : %s" % stylename)
_args.update(kw)
return _cls(**_args)
@classmethod
def get_styles(klass):
"""
A class method which returns a dictionary of available styles.
"""
return klass._style_list
@classmethod
def pprint_styles(klass):
"""
A class method which returns a string of the available styles.
"""
return _pprint_styles(klass._style_list)
@classmethod
def register(klass, name, style):
"""
Register a new style.
"""
if not issubclass(style, klass._Base):
raise ValueError("%s must be a subclass of %s" % (style,
klass._Base))
klass._style_list[name] = style
class BoxStyle(_Style):
"""
:class:`BoxStyle` is a container class which defines several
boxstyle classes, which are used for :class:`FancyBboxPatch`.
A style object can be created as::
BoxStyle.Round(pad=0.2)
or::
BoxStyle("Round", pad=0.2)
or::
BoxStyle("Round, pad=0.2")
Following boxstyle classes are defined.
%(AvailableBoxstyles)s
An instance of any boxstyle class is an callable object,
whose call signature is::
__call__(self, x0, y0, width, height, mutation_size, aspect_ratio=1.)
and returns a :class:`Path` instance. *x0*, *y0*, *width* and
*height* specify the location and size of the box to be
drawn. *mutation_scale* determines the overall size of the
mutation (by which I mean the transformation of the rectangle to
the fancy box). *mutation_aspect* determines the aspect-ratio of
the mutation.
.. plot:: mpl_examples/pylab_examples/fancybox_demo2.py
"""
_style_list = {}
class _Base(object):
"""
:class:`BBoxTransmuterBase` and its derivatives are used to make a
fancy box around a given rectangle. The :meth:`__call__` method
returns the :class:`~matplotlib.path.Path` of the fancy box. This
class is not an artist and actual drawing of the fancy box is done
by the :class:`FancyBboxPatch` class.
"""
# The derived classes are required to be able to be initialized
# w/o arguments, i.e., all its argument (except self) must have
# the default values.
def __init__(self):
"""
initializtion.
"""
super(BoxStyle._Base, self).__init__()
def transmute(self, x0, y0, width, height, mutation_size):
"""
The transmute method is a very core of the
:class:`BboxTransmuter` class and must be overriden in the
subclasses. It receives the location and size of the
rectangle, and the mutation_size, with which the amount of
padding and etc. will be scaled. It returns a
:class:`~matplotlib.path.Path` instance.
"""
raise NotImplementedError('Derived must override')
def __call__(self, x0, y0, width, height, mutation_size,
aspect_ratio=1.):
"""
Given the location and size of the box, return the path of
the box around it.
- *x0*, *y0*, *width*, *height* : location and size of the box
- *mutation_size* : a reference scale for the mutation.
- *aspect_ratio* : aspect-ration for the mutation.
"""
# The __call__ method is a thin wrapper around the transmute method
# and take care of the aspect.
if aspect_ratio is not None:
# Squeeze the given height by the aspect_ratio
y0, height = y0 / aspect_ratio, height / aspect_ratio
# call transmute method with squeezed height.
path = self.transmute(x0, y0, width, height, mutation_size)
vertices, codes = path.vertices, path.codes
# Restore the height
vertices[:, 1] = vertices[:, 1] * aspect_ratio
return Path(vertices, codes)
else:
return self.transmute(x0, y0, width, height, mutation_size)
def __reduce__(self):
# because we have decided to nest thes classes, we need to
# add some more information to allow instance pickling.
import matplotlib.cbook as cbook
return (cbook._NestedClassGetter(),
(BoxStyle, self.__class__.__name__),
self.__dict__
)
class Square(_Base):
"""
A simple square box.
"""
def __init__(self, pad=0.3):
"""
*pad*
amount of padding
"""
self.pad = pad
super(BoxStyle.Square, self).__init__()
def transmute(self, x0, y0, width, height, mutation_size):
pad = mutation_size * self.pad
# width and height with padding added.
width, height = width + 2*pad, height + 2*pad
# boundary of the padded box
x0, y0 = x0 - pad, y0 - pad,
x1, y1 = x0 + width, y0 + height
vertices = [(x0, y0), (x1, y0), (x1, y1), (x0, y1), (x0, y0)]
codes = [Path.MOVETO] + [Path.LINETO] * 3 + [Path.CLOSEPOLY]
return Path(vertices, codes)
_style_list["square"] = Square
class Circle(_Base):
"""A simple circle box."""
def __init__(self, pad=0.3):
"""
Parameters
----------
pad : float
The amount of padding around the original box.
"""
self.pad = pad
super(BoxStyle.Circle, self).__init__()
def transmute(self, x0, y0, width, height, mutation_size):
pad = mutation_size * self.pad
width, height = width + 2 * pad, height + 2 * pad
# boundary of the padded box
x0, y0 = x0 - pad, y0 - pad,
return Path.circle((x0 + width/2., y0 + height/2.),
(max([width, height]) / 2.))
_style_list["circle"] = Circle
class LArrow(_Base):
"""
(left) Arrow Box
"""
def __init__(self, pad=0.3):
self.pad = pad
super(BoxStyle.LArrow, self).__init__()
def transmute(self, x0, y0, width, height, mutation_size):
# padding
pad = mutation_size * self.pad
# width and height with padding added.
width, height = width + 2. * pad, height + 2. * pad
# boundary of the padded box
x0, y0 = x0 - pad, y0 - pad,
x1, y1 = x0 + width, y0 + height
dx = (y1 - y0) / 2.
dxx = dx * .5
# adjust x0. 1.4 <- sqrt(2)
x0 = x0 + pad / 1.4
cp = [(x0 + dxx, y0), (x1, y0), (x1, y1), (x0 + dxx, y1),
(x0 + dxx, y1 + dxx), (x0 - dx, y0 + dx),
(x0 + dxx, y0 - dxx), # arrow
(x0 + dxx, y0), (x0 + dxx, y0)]
com = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO,
Path.LINETO, Path.LINETO, Path.LINETO,
Path.LINETO, Path.CLOSEPOLY]
path = Path(cp, com)
return path
_style_list["larrow"] = LArrow
class RArrow(LArrow):
"""
(right) Arrow Box
"""
def __init__(self, pad=0.3):
super(BoxStyle.RArrow, self).__init__(pad)
def transmute(self, x0, y0, width, height, mutation_size):
p = BoxStyle.LArrow.transmute(self, x0, y0,
width, height, mutation_size)
p.vertices[:, 0] = 2 * x0 + width - p.vertices[:, 0]
return p
_style_list["rarrow"] = RArrow
class DArrow(_Base):
"""
(Double) Arrow Box
"""
# This source is copied from LArrow,
# modified to add a right arrow to the bbox.
def __init__(self, pad=0.3):
self.pad = pad
super(BoxStyle.DArrow, self).__init__()
def transmute(self, x0, y0, width, height, mutation_size):
# padding
pad = mutation_size * self.pad
# width and height with padding added.
# The width is padded by the arrows, so we don't need to pad it.
height = height + 2. * pad
# boundary of the padded box
x0, y0 = x0 - pad, y0 - pad
x1, y1 = x0 + width, y0 + height
dx = (y1 - y0)/2.
dxx = dx * .5
# adjust x0. 1.4 <- sqrt(2)
x0 = x0 + pad / 1.4
cp = [(x0 + dxx, y0), (x1, y0), # bot-segment
(x1, y0 - dxx), (x1 + dx + dxx, y0 + dx),
(x1, y1 + dxx), # right-arrow
(x1, y1), (x0 + dxx, y1), # top-segment
(x0 + dxx, y1 + dxx), (x0 - dx, y0 + dx),
(x0 + dxx, y0 - dxx), # left-arrow
(x0 + dxx, y0), (x0 + dxx, y0)] # close-poly
com = [Path.MOVETO, Path.LINETO,
Path.LINETO, Path.LINETO,
Path.LINETO,
Path.LINETO, Path.LINETO,
Path.LINETO, Path.LINETO,
Path.LINETO,
Path.LINETO, Path.CLOSEPOLY]
path = Path(cp, com)
return path
_style_list['darrow'] = DArrow
class Round(_Base):
"""
A box with round corners.
"""
def __init__(self, pad=0.3, rounding_size=None):
"""
*pad*
amount of padding
*rounding_size*
rounding radius of corners. *pad* if None
"""
self.pad = pad
self.rounding_size = rounding_size
super(BoxStyle.Round, self).__init__()
def transmute(self, x0, y0, width, height, mutation_size):
# padding
pad = mutation_size * self.pad
# size of the roudning corner
if self.rounding_size:
dr = mutation_size * self.rounding_size
else:
dr = pad
width, height = width + 2. * pad, height + 2. * pad
x0, y0 = x0 - pad, y0 - pad,
x1, y1 = x0 + width, y0 + height
# Round corners are implemented as quadratic bezier. e.g.,
# [(x0, y0-dr), (x0, y0), (x0+dr, y0)] for lower left corner.
cp = [(x0 + dr, y0),
(x1 - dr, y0),
(x1, y0), (x1, y0 + dr),
(x1, y1 - dr),
(x1, y1), (x1 - dr, y1),
(x0 + dr, y1),
(x0, y1), (x0, y1 - dr),
(x0, y0 + dr),
(x0, y0), (x0 + dr, y0),
(x0 + dr, y0)]
com = [Path.MOVETO,
Path.LINETO,
Path.CURVE3, Path.CURVE3,
Path.LINETO,
Path.CURVE3, Path.CURVE3,
Path.LINETO,
Path.CURVE3, Path.CURVE3,
Path.LINETO,
Path.CURVE3, Path.CURVE3,
Path.CLOSEPOLY]
path = Path(cp, com)
return path
_style_list["round"] = Round
class Round4(_Base):
"""
Another box with round edges.
"""
def __init__(self, pad=0.3, rounding_size=None):
"""
*pad*
amount of padding
*rounding_size*
rounding size of edges. *pad* if None
"""
self.pad = pad
self.rounding_size = rounding_size
super(BoxStyle.Round4, self).__init__()
def transmute(self, x0, y0, width, height, mutation_size):
# padding
pad = mutation_size * self.pad
# roudning size. Use a half of the pad if not set.
if self.rounding_size:
dr = mutation_size * self.rounding_size
else:
dr = pad / 2.
width, height = (width + 2. * pad - 2 * dr,
height + 2. * pad - 2 * dr)
x0, y0 = x0 - pad + dr, y0 - pad + dr,
x1, y1 = x0 + width, y0 + height
cp = [(x0, y0),
(x0 + dr, y0 - dr), (x1 - dr, y0 - dr), (x1, y0),
(x1 + dr, y0 + dr), (x1 + dr, y1 - dr), (x1, y1),
(x1 - dr, y1 + dr), (x0 + dr, y1 + dr), (x0, y1),
(x0 - dr, y1 - dr), (x0 - dr, y0 + dr), (x0, y0),
(x0, y0)]
com = [Path.MOVETO,
Path.CURVE4, Path.CURVE4, Path.CURVE4,
Path.CURVE4, Path.CURVE4, Path.CURVE4,
Path.CURVE4, Path.CURVE4, Path.CURVE4,
Path.CURVE4, Path.CURVE4, Path.CURVE4,
Path.CLOSEPOLY]
path = Path(cp, com)
return path
_style_list["round4"] = Round4
class Sawtooth(_Base):
"""
A sawtooth box.
"""
def __init__(self, pad=0.3, tooth_size=None):
"""
*pad*
amount of padding
*tooth_size*
size of the sawtooth. pad* if None
"""
self.pad = pad
self.tooth_size = tooth_size
super(BoxStyle.Sawtooth, self).__init__()
def _get_sawtooth_vertices(self, x0, y0, width, height, mutation_size):
# padding
pad = mutation_size * self.pad
# size of sawtooth
if self.tooth_size is None:
tooth_size = self.pad * .5 * mutation_size
else:
tooth_size = self.tooth_size * mutation_size
tooth_size2 = tooth_size / 2.
width, height = (width + 2. * pad - tooth_size,
height + 2. * pad - tooth_size)
# the sizes of the vertical and horizontal sawtooth are
# separately adjusted to fit the given box size.
dsx_n = int(np.round((width - tooth_size) / (tooth_size * 2))) * 2
dsx = (width - tooth_size) / dsx_n
dsy_n = int(np.round((height - tooth_size) / (tooth_size * 2))) * 2
dsy = (height - tooth_size) / dsy_n
x0, y0 = x0 - pad + tooth_size2, y0 - pad + tooth_size2
x1, y1 = x0 + width, y0 + height
bottom_saw_x = [x0] + \
[x0 + tooth_size2 + dsx * .5 * i
for i
in range(dsx_n * 2)] + \
[x1 - tooth_size2]
bottom_saw_y = [y0] + \
[y0 - tooth_size2, y0,
y0 + tooth_size2, y0] * dsx_n + \
[y0 - tooth_size2]
right_saw_x = [x1] + \
[x1 + tooth_size2,
x1,
x1 - tooth_size2,
x1] * dsx_n + \
[x1 + tooth_size2]
right_saw_y = [y0] + \
[y0 + tooth_size2 + dsy * .5 * i
for i
in range(dsy_n * 2)] + \
[y1 - tooth_size2]
top_saw_x = [x1] + \
[x1 - tooth_size2 - dsx * .5 * i
for i
in range(dsx_n * 2)] + \
[x0 + tooth_size2]
top_saw_y = [y1] + \
[y1 + tooth_size2,
y1,
y1 - tooth_size2,
y1] * dsx_n + \
[y1 + tooth_size2]
left_saw_x = [x0] + \
[x0 - tooth_size2,
x0,
x0 + tooth_size2,
x0] * dsy_n + \
[x0 - tooth_size2]
left_saw_y = [y1] + \
[y1 - tooth_size2 - dsy * .5 * i
for i
in range(dsy_n * 2)] + \
[y0 + tooth_size2]
saw_vertices = (list(zip(bottom_saw_x, bottom_saw_y)) +
list(zip(right_saw_x, right_saw_y)) +
list(zip(top_saw_x, top_saw_y)) +
list(zip(left_saw_x, left_saw_y)) +
[(bottom_saw_x[0], bottom_saw_y[0])])
return saw_vertices
def transmute(self, x0, y0, width, height, mutation_size):
saw_vertices = self._get_sawtooth_vertices(x0, y0, width,
height, mutation_size)
path = Path(saw_vertices, closed=True)
return path
_style_list["sawtooth"] = Sawtooth
class Roundtooth(Sawtooth):
"""A rounded tooth box."""
def __init__(self, pad=0.3, tooth_size=None):
"""
*pad*
amount of padding
*tooth_size*
size of the sawtooth. pad* if None
"""
super(BoxStyle.Roundtooth, self).__init__(pad, tooth_size)
def transmute(self, x0, y0, width, height, mutation_size):
saw_vertices = self._get_sawtooth_vertices(x0, y0,
width, height,
mutation_size)
# Add a trailing vertex to allow us to close the polygon correctly
saw_vertices = np.concatenate([np.array(saw_vertices),
[saw_vertices[0]]], axis=0)
codes = ([Path.MOVETO] +
[Path.CURVE3, Path.CURVE3] * ((len(saw_vertices)-1)//2) +
[Path.CLOSEPOLY])
return Path(saw_vertices, codes)
_style_list["roundtooth"] = Roundtooth
if __doc__: # __doc__ could be None if -OO optimization is enabled
__doc__ = cbook.dedent(__doc__) % \
{"AvailableBoxstyles": _pprint_styles(_style_list)}
docstring.interpd.update(
AvailableBoxstyles=_pprint_styles(BoxStyle._style_list),
ListBoxstyles=_simpleprint_styles(BoxStyle._style_list))
class FancyBboxPatch(Patch):
"""
Draw a fancy box around a rectangle with lower left at *xy*=(*x*,
*y*) with specified width and height.
:class:`FancyBboxPatch` class is similar to :class:`Rectangle`
class, but it draws a fancy box around the rectangle. The
transformation of the rectangle box to the fancy box is delegated
to the :class:`BoxTransmuterBase` and its derived classes.
"""
_edge_default = True
def __str__(self):
return self.__class__.__name__ \
+ "(%g,%g;%gx%g)" % (self._x, self._y,
self._width, self._height)
@docstring.dedent_interpd
def __init__(self, xy, width, height,
boxstyle="round",
bbox_transmuter=None,
mutation_scale=1.,
mutation_aspect=None,
**kwargs):
"""
*xy* = lower left corner
*width*, *height*
*boxstyle* determines what kind of fancy box will be drawn. It
can be a string of the style name with a comma separated
attribute, or an instance of :class:`BoxStyle`. Following box
styles are available.
%(AvailableBoxstyles)s
*mutation_scale* : a value with which attributes of boxstyle
(e.g., pad) will be scaled. default=1.
*mutation_aspect* : The height of the rectangle will be
squeezed by this value before the mutation and the mutated
box will be stretched by the inverse of it. default=None.
Valid kwargs are:
%(Patch)s
"""
Patch.__init__(self, **kwargs)
self._x = xy[0]
self._y = xy[1]
self._width = width
self._height = height
if boxstyle == "custom":
if bbox_transmuter is None:
raise ValueError("bbox_transmuter argument is needed with "
"custom boxstyle")
self._bbox_transmuter = bbox_transmuter
else:
self.set_boxstyle(boxstyle)
self._mutation_scale = mutation_scale
self._mutation_aspect = mutation_aspect
self.stale = True
@docstring.dedent_interpd
def set_boxstyle(self, boxstyle=None, **kw):
"""
Set the box style.
*boxstyle* can be a string with boxstyle name with optional
comma-separated attributes. Alternatively, the attrs can
be provided as keywords::
set_boxstyle("round,pad=0.2")
set_boxstyle("round", pad=0.2)
Old attrs simply are forgotten.
Without argument (or with *boxstyle* = None), it returns
available box styles.
The following boxstyles are available:
%(AvailableBoxstyles)s
ACCEPTS: %(ListBoxstyles)s
"""
if boxstyle is None:
return BoxStyle.pprint_styles()
if isinstance(boxstyle, BoxStyle._Base):
self._bbox_transmuter = boxstyle
elif six.callable(boxstyle):
self._bbox_transmuter = boxstyle
else:
self._bbox_transmuter = BoxStyle(boxstyle, **kw)
self.stale = True
def set_mutation_scale(self, scale):
"""
Set the mutation scale.
ACCEPTS: float
"""
self._mutation_scale = scale
self.stale = True
def get_mutation_scale(self):
"""
Return the mutation scale.
"""
return self._mutation_scale
def set_mutation_aspect(self, aspect):
"""
Set the aspect ratio of the bbox mutation.
ACCEPTS: float
"""
self._mutation_aspect = aspect
self.stale = True
def get_mutation_aspect(self):
"""
Return the aspect ratio of the bbox mutation.
"""
return self._mutation_aspect
def get_boxstyle(self):
"Return the boxstyle object"
return self._bbox_transmuter
def get_path(self):
"""
Return the mutated path of the rectangle
"""
_path = self.get_boxstyle()(self._x, self._y,
self._width, self._height,
self.get_mutation_scale(),
self.get_mutation_aspect())
return _path
# Following methods are borrowed from the Rectangle class.
def get_x(self):
"Return the left coord of the rectangle"
return self._x
def get_y(self):
"Return the bottom coord of the rectangle"
return self._y
def get_width(self):
"Return the width of the rectangle"
return self._width
def get_height(self):
"Return the height of the rectangle"
return self._height
def set_x(self, x):
"""
Set the left coord of the rectangle
ACCEPTS: float
"""
self._x = x
self.stale = True
def set_y(self, y):
"""
Set the bottom coord of the rectangle
ACCEPTS: float
"""
self._y = y
self.stale = True
def set_width(self, w):
"""
Set the width rectangle
ACCEPTS: float
"""
self._width = w
self.stale = True
def set_height(self, h):
"""
Set the width rectangle
ACCEPTS: float
"""
self._height = h
self.stale = True
def set_bounds(self, *args):
"""
Set the bounds of the rectangle: l,b,w,h
ACCEPTS: (left, bottom, width, height)
"""
if len(args) == 0:
l, b, w, h = args[0]
else:
l, b, w, h = args
self._x = l
self._y = b
self._width = w
self._height = h
self.stale = True
def get_bbox(self):
return transforms.Bbox.from_bounds(self._x, self._y,
self._width, self._height)
class ConnectionStyle(_Style):
"""
:class:`ConnectionStyle` is a container class which defines
several connectionstyle classes, which is used to create a path
between two points. These are mainly used with
:class:`FancyArrowPatch`.
A connectionstyle object can be either created as::
ConnectionStyle.Arc3(rad=0.2)
or::
ConnectionStyle("Arc3", rad=0.2)
or::
ConnectionStyle("Arc3, rad=0.2")
The following classes are defined
%(AvailableConnectorstyles)s
An instance of any connection style class is an callable object,
whose call signature is::
__call__(self, posA, posB,
patchA=None, patchB=None,
shrinkA=2., shrinkB=2.)
and it returns a :class:`Path` instance. *posA* and *posB* are
tuples of x,y coordinates of the two points to be
connected. *patchA* (or *patchB*) is given, the returned path is
clipped so that it start (or end) from the boundary of the
patch. The path is further shrunk by *shrinkA* (or *shrinkB*)
which is given in points.
"""
_style_list = {}
class _Base(object):
"""
A base class for connectionstyle classes. The subclass needs
to implement a *connect* method whose call signature is::
connect(posA, posB)
where posA and posB are tuples of x, y coordinates to be
connected. The method needs to return a path connecting two
points. This base class defines a __call__ method, and a few
helper methods.
"""
class SimpleEvent:
def __init__(self, xy):
self.x, self.y = xy
def _clip(self, path, patchA, patchB):
"""
Clip the path to the boundary of the patchA and patchB.
The starting point of the path needed to be inside of the
patchA and the end point inside the patch B. The *contains*
methods of each patch object is utilized to test if the point
is inside the path.
"""
if patchA:
def insideA(xy_display):
xy_event = ConnectionStyle._Base.SimpleEvent(xy_display)
return patchA.contains(xy_event)[0]
try:
left, right = split_path_inout(path, insideA)
except ValueError:
right = path
path = right
if patchB:
def insideB(xy_display):
xy_event = ConnectionStyle._Base.SimpleEvent(xy_display)
return patchB.contains(xy_event)[0]
try:
left, right = split_path_inout(path, insideB)
except ValueError:
left = path
path = left
return path
def _shrink(self, path, shrinkA, shrinkB):
"""
Shrink the path by fixed size (in points) with shrinkA and shrinkB
"""
if shrinkA:
x, y = path.vertices[0]
insideA = inside_circle(x, y, shrinkA)
try:
left, right = split_path_inout(path, insideA)
path = right
except ValueError:
pass
if shrinkB:
x, y = path.vertices[-1]
insideB = inside_circle(x, y, shrinkB)
try:
left, right = split_path_inout(path, insideB)
path = left
except ValueError:
pass
return path
def __call__(self, posA, posB,
shrinkA=2., shrinkB=2., patchA=None, patchB=None):
"""
Calls the *connect* method to create a path between *posA*
and *posB*. The path is clipped and shrunken.
"""
path = self.connect(posA, posB)
clipped_path = self._clip(path, patchA, patchB)
shrunk_path = self._shrink(clipped_path, shrinkA, shrinkB)
return shrunk_path
def __reduce__(self):
# because we have decided to nest these classes, we need to
# add some more information to allow instance pickling.
import matplotlib.cbook as cbook
return (cbook._NestedClassGetter(),
(ConnectionStyle, self.__class__.__name__),
self.__dict__
)
class Arc3(_Base):
"""
Creates a simple quadratic bezier curve between two
points. The curve is created so that the middle contol points
(C1) is located at the same distance from the start (C0) and
end points(C2) and the distance of the C1 to the line
connecting C0-C2 is *rad* times the distance of C0-C2.
"""
def __init__(self, rad=0.):
"""
*rad*
curvature of the curve.
"""
self.rad = rad
def connect(self, posA, posB):
x1, y1 = posA
x2, y2 = posB
x12, y12 = (x1 + x2) / 2., (y1 + y2) / 2.
dx, dy = x2 - x1, y2 - y1
f = self.rad
cx, cy = x12 + f * dy, y12 - f * dx
vertices = [(x1, y1),
(cx, cy),
(x2, y2)]
codes = [Path.MOVETO,
Path.CURVE3,
Path.CURVE3]
return Path(vertices, codes)
_style_list["arc3"] = Arc3
class Angle3(_Base):
"""
Creates a simple quadratic bezier curve between two
points. The middle control points is placed at the
intersecting point of two lines which crosses the start (or
end) point and has a angle of angleA (or angleB).
"""
def __init__(self, angleA=90, angleB=0):
"""
*angleA*
starting angle of the path
*angleB*
ending angle of the path
"""
self.angleA = angleA
self.angleB = angleB
def connect(self, posA, posB):
x1, y1 = posA
x2, y2 = posB
cosA, sinA = (math.cos(self.angleA / 180. * math.pi),
math.sin(self.angleA / 180. * math.pi))
cosB, sinB = (math.cos(self.angleB / 180. * math.pi),
math.sin(self.angleB / 180. * math.pi))
cx, cy = get_intersection(x1, y1, cosA, sinA,
x2, y2, cosB, sinB)
vertices = [(x1, y1), (cx, cy), (x2, y2)]
codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3]
return Path(vertices, codes)
_style_list["angle3"] = Angle3
class Angle(_Base):
"""
Creates a picewise continuous quadratic bezier path between
two points. The path has a one passing-through point placed at
the intersecting point of two lines which crosses the start
(or end) point and has a angle of angleA (or angleB). The
connecting edges are rounded with *rad*.
"""
def __init__(self, angleA=90, angleB=0, rad=0.):
"""
*angleA*
starting angle of the path
*angleB*
ending angle of the path
*rad*
rounding radius of the edge
"""
self.angleA = angleA
self.angleB = angleB
self.rad = rad
def connect(self, posA, posB):
x1, y1 = posA
x2, y2 = posB
cosA, sinA = (math.cos(self.angleA / 180. * math.pi),
math.sin(self.angleA / 180. * math.pi))
cosB, sinB = (math.cos(self.angleB / 180. * math.pi),
math.sin(self.angleB / 180. * math.pi))
cx, cy = get_intersection(x1, y1, cosA, sinA,
x2, y2, cosB, sinB)
vertices = [(x1, y1)]
codes = [Path.MOVETO]
if self.rad == 0.:
vertices.append((cx, cy))
codes.append(Path.LINETO)
else:
dx1, dy1 = x1 - cx, y1 - cy
d1 = (dx1 ** 2 + dy1 ** 2) ** .5
f1 = self.rad / d1
dx2, dy2 = x2 - cx, y2 - cy
d2 = (dx2 ** 2 + dy2 ** 2) ** .5
f2 = self.rad / d2
vertices.extend([(cx + dx1 * f1, cy + dy1 * f1),
(cx, cy),
(cx + dx2 * f2, cy + dy2 * f2)])
codes.extend([Path.LINETO, Path.CURVE3, Path.CURVE3])
vertices.append((x2, y2))
codes.append(Path.LINETO)
return Path(vertices, codes)
_style_list["angle"] = Angle
class Arc(_Base):
"""
Creates a picewise continuous quadratic bezier path between
two points. The path can have two passing-through points, a
point placed at the distance of armA and angle of angleA from
point A, another point with respect to point B. The edges are
rounded with *rad*.
"""
def __init__(self, angleA=0, angleB=0, armA=None, armB=None, rad=0.):
"""
*angleA* :
starting angle of the path
*angleB* :
ending angle of the path
*armA* :
length of the starting arm
*armB* :
length of the ending arm
*rad* :
rounding radius of the edges
"""
self.angleA = angleA
self.angleB = angleB
self.armA = armA
self.armB = armB
self.rad = rad
def connect(self, posA, posB):
x1, y1 = posA
x2, y2 = posB
vertices = [(x1, y1)]
rounded = []
codes = [Path.MOVETO]
if self.armA:
cosA = math.cos(self.angleA / 180. * math.pi)
sinA = math.sin(self.angleA / 180. * math.pi)
# x_armA, y_armB
d = self.armA - self.rad
rounded.append((x1 + d * cosA, y1 + d * sinA))
d = self.armA
rounded.append((x1 + d * cosA, y1 + d * sinA))
if self.armB:
cosB = math.cos(self.angleB / 180. * math.pi)
sinB = math.sin(self.angleB / 180. * math.pi)
x_armB, y_armB = x2 + self.armB * cosB, y2 + self.armB * sinB
if rounded:
xp, yp = rounded[-1]
dx, dy = x_armB - xp, y_armB - yp
dd = (dx * dx + dy * dy) ** .5
rounded.append((xp + self.rad * dx / dd,
yp + self.rad * dy / dd))
vertices.extend(rounded)
codes.extend([Path.LINETO,
Path.CURVE3,
Path.CURVE3])
else:
xp, yp = vertices[-1]
dx, dy = x_armB - xp, y_armB - yp
dd = (dx * dx + dy * dy) ** .5
d = dd - self.rad
rounded = [(xp + d * dx / dd, yp + d * dy / dd),
(x_armB, y_armB)]
if rounded:
xp, yp = rounded[-1]
dx, dy = x2 - xp, y2 - yp
dd = (dx * dx + dy * dy) ** .5
rounded.append((xp + self.rad * dx / dd,
yp + self.rad * dy / dd))
vertices.extend(rounded)
codes.extend([Path.LINETO,
Path.CURVE3,
Path.CURVE3])
vertices.append((x2, y2))
codes.append(Path.LINETO)
return Path(vertices, codes)
_style_list["arc"] = Arc
class Bar(_Base):
"""
A line with *angle* between A and B with *armA* and
*armB*. One of the arms is extended so that they are connected in
a right angle. The length of armA is determined by (*armA*
+ *fraction* x AB distance). Same for armB.
"""
def __init__(self, armA=0., armB=0., fraction=0.3, angle=None):
"""
Parameters
----------
armA : float
minimum length of armA
armB : float
minimum length of armB
fraction : float
a fraction of the distance between two points that
will be added to armA and armB.
angle : float or None
angle of the connecting line (if None, parallel
to A and B)
"""
self.armA = armA
self.armB = armB
self.fraction = fraction
self.angle = angle
def connect(self, posA, posB):
x1, y1 = posA
x20, y20 = x2, y2 = posB
x12, y12 = (x1 + x2) / 2., (y1 + y2) / 2.
theta1 = math.atan2(y2 - y1, x2 - x1)
dx, dy = x2 - x1, y2 - y1
dd = (dx * dx + dy * dy) ** .5
ddx, ddy = dx / dd, dy / dd
armA, armB = self.armA, self.armB
if self.angle is not None:
#angle = self.angle % 180.
#if angle < 0. or angle > 180.:
# angle
#theta0 = (self.angle%180.)/180.*math.pi
theta0 = self.angle / 180. * math.pi
#theta0 = (((self.angle+90)%180.) - 90.)/180.*math.pi
dtheta = theta1 - theta0
dl = dd * math.sin(dtheta)
dL = dd * math.cos(dtheta)
#x2, y2 = x2 + dl*ddy, y2 - dl*ddx
x2, y2 = x1 + dL * math.cos(theta0), y1 + dL * math.sin(theta0)
armB = armB - dl
# update
dx, dy = x2 - x1, y2 - y1
dd2 = (dx * dx + dy * dy) ** .5
ddx, ddy = dx / dd2, dy / dd2
else:
dl = 0.
#if armA > armB:
# armB = armA + dl
#else:
# armA = armB - dl
arm = max(armA, armB)
f = self.fraction * dd + arm
#fB = self.fraction*dd + armB
cx1, cy1 = x1 + f * ddy, y1 - f * ddx
cx2, cy2 = x2 + f * ddy, y2 - f * ddx
vertices = [(x1, y1),
(cx1, cy1),
(cx2, cy2),
(x20, y20)]
codes = [Path.MOVETO,
Path.LINETO,
Path.LINETO,
Path.LINETO]
return Path(vertices, codes)
_style_list["bar"] = Bar
if __doc__:
__doc__ = cbook.dedent(__doc__) % \
{"AvailableConnectorstyles": _pprint_styles(_style_list)}
def _point_along_a_line(x0, y0, x1, y1, d):
"""
find a point along a line connecting (x0, y0) -- (x1, y1) whose
distance from (x0, y0) is d.
"""
dx, dy = x0 - x1, y0 - y1
ff = d / (dx * dx + dy * dy) ** .5
x2, y2 = x0 - ff * dx, y0 - ff * dy
return x2, y2
class ArrowStyle(_Style):
"""
:class:`ArrowStyle` is a container class which defines several
arrowstyle classes, which is used to create an arrow path along a
given path. These are mainly used with :class:`FancyArrowPatch`.
A arrowstyle object can be either created as::
ArrowStyle.Fancy(head_length=.4, head_width=.4, tail_width=.4)
or::
ArrowStyle("Fancy", head_length=.4, head_width=.4, tail_width=.4)
or::
ArrowStyle("Fancy, head_length=.4, head_width=.4, tail_width=.4")
The following classes are defined
%(AvailableArrowstyles)s
An instance of any arrow style class is a callable object,
whose call signature is::
__call__(self, path, mutation_size, linewidth, aspect_ratio=1.)
and it returns a tuple of a :class:`Path` instance and a boolean
value. *path* is a :class:`Path` instance along which the arrow
will be drawn. *mutation_size* and *aspect_ratio* have the same
meaning as in :class:`BoxStyle`. *linewidth* is a line width to be
stroked. This is meant to be used to correct the location of the
head so that it does not overshoot the destination point, but not all
classes support it.
.. plot:: mpl_examples/pylab_examples/fancyarrow_demo.py
"""
_style_list = {}
class _Base(object):
"""
Arrow Transmuter Base class
ArrowTransmuterBase and its derivatives are used to make a fancy
arrow around a given path. The __call__ method returns a path
(which will be used to create a PathPatch instance) and a boolean
value indicating the path is open therefore is not fillable. This
class is not an artist and actual drawing of the fancy arrow is
done by the FancyArrowPatch class.
"""
# The derived classes are required to be able to be initialized
# w/o arguments, i.e., all its argument (except self) must have
# the default values.
def __init__(self):
super(ArrowStyle._Base, self).__init__()
@staticmethod
def ensure_quadratic_bezier(path):
""" Some ArrowStyle class only wokrs with a simple
quaratic bezier curve (created with Arc3Connetion or
Angle3Connector). This static method is to check if the
provided path is a simple quadratic bezier curve and returns
its control points if true.
"""
segments = list(path.iter_segments())
if ((len(segments) != 2) or (segments[0][1] != Path.MOVETO) or
(segments[1][1] != Path.CURVE3)):
msg = "'path' it's not a valid quadratic bezier curve"
raise ValueError(msg)
return list(segments[0][0]) + list(segments[1][0])
def transmute(self, path, mutation_size, linewidth):
"""
The transmute method is the very core of the ArrowStyle
class and must be overriden in the subclasses. It receives
the path object along which the arrow will be drawn, and
the mutation_size, with which the arrow head etc.
will be scaled. The linewidth may be used to adjust
the path so that it does not pass beyond the given
points. It returns a tuple of a Path instance and a
boolean. The boolean value indicate whether the path can
be filled or not. The return value can also be a list of paths
and list of booleans of a same length.
"""
raise NotImplementedError('Derived must override')
def __call__(self, path, mutation_size, linewidth,
aspect_ratio=1.):
"""
The __call__ method is a thin wrapper around the transmute method
and take care of the aspect ratio.
"""
path = make_path_regular(path)
if aspect_ratio is not None:
# Squeeze the given height by the aspect_ratio
vertices, codes = path.vertices[:], path.codes[:]
# Squeeze the height
vertices[:, 1] = vertices[:, 1] / aspect_ratio
path_shrunk = Path(vertices, codes)
# call transmute method with squeezed height.
path_mutated, fillable = self.transmute(path_shrunk,
linewidth,
mutation_size)
if cbook.iterable(fillable):
path_list = []
for p in zip(path_mutated):
v, c = p.vertices, p.codes
# Restore the height
v[:, 1] = v[:, 1] * aspect_ratio
path_list.append(Path(v, c))
return path_list, fillable
else:
return path_mutated, fillable
else:
return self.transmute(path, mutation_size, linewidth)
def __reduce__(self):
# because we have decided to nest thes classes, we need to
# add some more information to allow instance pickling.
import matplotlib.cbook as cbook
return (cbook._NestedClassGetter(),
(ArrowStyle, self.__class__.__name__),
self.__dict__
)
class _Curve(_Base):
"""
A simple arrow which will work with any path instance. The
returned path is simply concatenation of the original path + at
most two paths representing the arrow head at the begin point and the
at the end point. The arrow heads can be either open or closed.
"""
def __init__(self, beginarrow=None, endarrow=None,
fillbegin=False, fillend=False,
head_length=.2, head_width=.1):
"""
The arrows are drawn if *beginarrow* and/or *endarrow* are
true. *head_length* and *head_width* determines the size
of the arrow relative to the *mutation scale*. The
arrowhead at the begin (or end) is closed if fillbegin (or
fillend) is True.
"""
self.beginarrow, self.endarrow = beginarrow, endarrow
self.head_length, self.head_width = head_length, head_width
self.fillbegin, self.fillend = fillbegin, fillend
super(ArrowStyle._Curve, self).__init__()
def _get_arrow_wedge(self, x0, y0, x1, y1,
head_dist, cos_t, sin_t, linewidth
):
"""
Return the paths for arrow heads. Since arrow lines are
drawn with capstyle=projected, The arrow goes beyond the
desired point. This method also returns the amount of the path
to be shrunken so that it does not overshoot.
"""
# arrow from x0, y0 to x1, y1
dx, dy = x0 - x1, y0 - y1
cp_distance = np.hypot(dx, dy)
# pad_projected : amount of pad to account the
# overshooting of the projection of the wedge
pad_projected = (.5 * linewidth / sin_t)
# Account for division by zero
if cp_distance == 0:
cp_distance = 1
# apply pad for projected edge
ddx = pad_projected * dx / cp_distance
ddy = pad_projected * dy / cp_distance
# offset for arrow wedge
dx = dx / cp_distance * head_dist
dy = dy / cp_distance * head_dist
dx1, dy1 = cos_t * dx + sin_t * dy, -sin_t * dx + cos_t * dy
dx2, dy2 = cos_t * dx - sin_t * dy, sin_t * dx + cos_t * dy
vertices_arrow = [(x1 + ddx + dx1, y1 + ddy + dy1),
(x1 + ddx, y1 + ddy),
(x1 + ddx + dx2, y1 + ddy + dy2)]
codes_arrow = [Path.MOVETO,
Path.LINETO,
Path.LINETO]
return vertices_arrow, codes_arrow, ddx, ddy
def transmute(self, path, mutation_size, linewidth):
head_length, head_width = self.head_length * mutation_size, \
self.head_width * mutation_size
head_dist = math.sqrt(head_length ** 2 + head_width ** 2)
cos_t, sin_t = head_length / head_dist, head_width / head_dist
# begin arrow
x0, y0 = path.vertices[0]
x1, y1 = path.vertices[1]
# If there is no room for an arrow and a line, then skip the arrow
has_begin_arrow = (self.beginarrow and
not ((x0 == x1) and (y0 == y1)))
if has_begin_arrow:
verticesA, codesA, ddxA, ddyA = \
self._get_arrow_wedge(x1, y1, x0, y0,
head_dist, cos_t, sin_t,
linewidth)
else:
verticesA, codesA = [], []
ddxA, ddyA = 0., 0.
# end arrow
x2, y2 = path.vertices[-2]
x3, y3 = path.vertices[-1]
# If there is no room for an arrow and a line, then skip the arrow
has_end_arrow = (self.endarrow and not ((x2 == x3) and (y2 == y3)))
if has_end_arrow:
verticesB, codesB, ddxB, ddyB = \
self._get_arrow_wedge(x2, y2, x3, y3,
head_dist, cos_t, sin_t,
linewidth)
else:
verticesB, codesB = [], []
ddxB, ddyB = 0., 0.
# this simple code will not work if ddx, ddy is greater than
# separation bettern vertices.
_path = [Path(np.concatenate([[(x0 + ddxA, y0 + ddyA)],
path.vertices[1:-1],
[(x3 + ddxB, y3 + ddyB)]]),
path.codes)]
_fillable = [False]
if has_begin_arrow:
if self.fillbegin:
p = np.concatenate([verticesA, [verticesA[0],
verticesA[0]], ])
c = np.concatenate([codesA, [Path.LINETO, Path.CLOSEPOLY]])
_path.append(Path(p, c))
_fillable.append(True)
else:
_path.append(Path(verticesA, codesA))
_fillable.append(False)
if has_end_arrow:
if self.fillend:
_fillable.append(True)
p = np.concatenate([verticesB, [verticesB[0],
verticesB[0]], ])
c = np.concatenate([codesB, [Path.LINETO, Path.CLOSEPOLY]])
_path.append(Path(p, c))
else:
_fillable.append(False)
_path.append(Path(verticesB, codesB))
return _path, _fillable
class Curve(_Curve):
"""
A simple curve without any arrow head.
"""
def __init__(self):
super(ArrowStyle.Curve, self).__init__(
beginarrow=False, endarrow=False)
_style_list["-"] = Curve
class CurveA(_Curve):
"""
An arrow with a head at its begin point.
"""
def __init__(self, head_length=.4, head_width=.2):
"""
*head_length*
length of the arrow head
*head_width*
width of the arrow head
"""
super(ArrowStyle.CurveA, self).__init__(
beginarrow=True, endarrow=False,
head_length=head_length, head_width=head_width)
_style_list["<-"] = CurveA
class CurveB(_Curve):
"""
An arrow with a head at its end point.
"""
def __init__(self, head_length=.4, head_width=.2):
"""
*head_length*
length of the arrow head
*head_width*
width of the arrow head
"""
super(ArrowStyle.CurveB, self).__init__(
beginarrow=False, endarrow=True,
head_length=head_length, head_width=head_width)
_style_list["->"] = CurveB
class CurveAB(_Curve):
"""
An arrow with heads both at the begin and the end point.
"""
def __init__(self, head_length=.4, head_width=.2):
"""
*head_length*
length of the arrow head
*head_width*
width of the arrow head
"""
super(ArrowStyle.CurveAB, self).__init__(
beginarrow=True, endarrow=True,
head_length=head_length, head_width=head_width)
_style_list["<->"] = CurveAB
class CurveFilledA(_Curve):
"""
An arrow with filled triangle head at the begin.
"""
def __init__(self, head_length=.4, head_width=.2):
"""
*head_length*
length of the arrow head
*head_width*
width of the arrow head
"""
super(ArrowStyle.CurveFilledA, self).__init__(
beginarrow=True, endarrow=False,
fillbegin=True, fillend=False,
head_length=head_length, head_width=head_width)
_style_list["<|-"] = CurveFilledA
class CurveFilledB(_Curve):
"""
An arrow with filled triangle head at the end.
"""
def __init__(self, head_length=.4, head_width=.2):
"""
*head_length*
length of the arrow head
*head_width*
width of the arrow head
"""
super(ArrowStyle.CurveFilledB, self).__init__(
beginarrow=False, endarrow=True,
fillbegin=False, fillend=True,
head_length=head_length, head_width=head_width)
_style_list["-|>"] = CurveFilledB
class CurveFilledAB(_Curve):
"""
An arrow with filled triangle heads both at the begin and the end
point.
"""
def __init__(self, head_length=.4, head_width=.2):
"""
*head_length*
length of the arrow head
*head_width*
width of the arrow head
"""
super(ArrowStyle.CurveFilledAB, self).__init__(
beginarrow=True, endarrow=True,
fillbegin=True, fillend=True,
head_length=head_length, head_width=head_width)
_style_list["<|-|>"] = CurveFilledAB
class _Bracket(_Base):
def __init__(self, bracketA=None, bracketB=None,
widthA=1., widthB=1.,
lengthA=0.2, lengthB=0.2,
angleA=None, angleB=None,
scaleA=None, scaleB=None):
self.bracketA, self.bracketB = bracketA, bracketB
self.widthA, self.widthB = widthA, widthB
self.lengthA, self.lengthB = lengthA, lengthB
self.angleA, self.angleB = angleA, angleB
self.scaleA, self.scaleB = scaleA, scaleB
def _get_bracket(self, x0, y0,
cos_t, sin_t, width, length):
# arrow from x0, y0 to x1, y1
from matplotlib.bezier import get_normal_points
x1, y1, x2, y2 = get_normal_points(x0, y0, cos_t, sin_t, width)
dx, dy = length * cos_t, length * sin_t
vertices_arrow = [(x1 + dx, y1 + dy),
(x1, y1),
(x2, y2),
(x2 + dx, y2 + dy)]
codes_arrow = [Path.MOVETO,
Path.LINETO,
Path.LINETO,
Path.LINETO]
return vertices_arrow, codes_arrow
def transmute(self, path, mutation_size, linewidth):
if self.scaleA is None:
scaleA = mutation_size
else:
scaleA = self.scaleA
if self.scaleB is None:
scaleB = mutation_size
else:
scaleB = self.scaleB
vertices_list, codes_list = [], []
if self.bracketA:
x0, y0 = path.vertices[0]
x1, y1 = path.vertices[1]
cos_t, sin_t = get_cos_sin(x1, y1, x0, y0)
verticesA, codesA = self._get_bracket(x0, y0, cos_t, sin_t,
self.widthA * scaleA,
self.lengthA * scaleA)
vertices_list.append(verticesA)
codes_list.append(codesA)
vertices_list.append(path.vertices)
codes_list.append(path.codes)
if self.bracketB:
x0, y0 = path.vertices[-1]
x1, y1 = path.vertices[-2]
cos_t, sin_t = get_cos_sin(x1, y1, x0, y0)
verticesB, codesB = self._get_bracket(x0, y0, cos_t, sin_t,
self.widthB * scaleB,
self.lengthB * scaleB)
vertices_list.append(verticesB)
codes_list.append(codesB)
vertices = np.concatenate(vertices_list)
codes = np.concatenate(codes_list)
p = Path(vertices, codes)
return p, False
class BracketAB(_Bracket):
"""
An arrow with a bracket(]) at both ends.
"""
def __init__(self,
widthA=1., lengthA=0.2, angleA=None,
widthB=1., lengthB=0.2, angleB=None):
"""
*widthA*
width of the bracket
*lengthA*
length of the bracket
*angleA*
angle between the bracket and the line
*widthB*
width of the bracket
*lengthB*
length of the bracket
*angleB*
angle between the bracket and the line
"""
super(ArrowStyle.BracketAB, self).__init__(
True, True, widthA=widthA, lengthA=lengthA,
angleA=angleA, widthB=widthB, lengthB=lengthB,
angleB=angleB)
_style_list["]-["] = BracketAB
class BracketA(_Bracket):
"""
An arrow with a bracket(]) at its end.
"""
def __init__(self, widthA=1., lengthA=0.2, angleA=None):
"""
*widthA*
width of the bracket
*lengthA*
length of the bracket
*angleA*
angle between the bracket and the line
"""
super(ArrowStyle.BracketA, self).__init__(True, None,
widthA=widthA,
lengthA=lengthA,
angleA=angleA)
_style_list["]-"] = BracketA
class BracketB(_Bracket):
"""
An arrow with a bracket([) at its end.
"""
def __init__(self, widthB=1., lengthB=0.2, angleB=None):
"""
*widthB*
width of the bracket
*lengthB*
length of the bracket
*angleB*
angle between the bracket and the line
"""
super(ArrowStyle.BracketB, self).__init__(None, True,
widthB=widthB,
lengthB=lengthB,
angleB=angleB)
_style_list["-["] = BracketB
class BarAB(_Bracket):
"""
An arrow with a bar(|) at both ends.
"""
def __init__(self,
widthA=1., angleA=None,
widthB=1., angleB=None):
"""
*widthA*
width of the bracket
*lengthA*
length of the bracket
*angleA*
angle between the bracket and the line
*widthB*
width of the bracket
*lengthB*
length of the bracket
*angleB*
angle between the bracket and the line
"""
super(ArrowStyle.BarAB, self).__init__(
True, True, widthA=widthA, lengthA=0, angleA=angleA,
widthB=widthB, lengthB=0, angleB=angleB)
_style_list["|-|"] = BarAB
class Simple(_Base):
"""
A simple arrow. Only works with a quadratic bezier curve.
"""
def __init__(self, head_length=.5, head_width=.5, tail_width=.2):
"""
*head_length*
length of the arrow head
*head_with*
width of the arrow head
*tail_width*
width of the arrow tail
"""
self.head_length, self.head_width, self.tail_width = \
head_length, head_width, tail_width
super(ArrowStyle.Simple, self).__init__()
def transmute(self, path, mutation_size, linewidth):
x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path)
# divide the path into a head and a tail
head_length = self.head_length * mutation_size
in_f = inside_circle(x2, y2, head_length)
arrow_path = [(x0, y0), (x1, y1), (x2, y2)]
from .bezier import NonIntersectingPathException
try:
arrow_out, arrow_in = \
split_bezier_intersecting_with_closedpath(arrow_path,
in_f,
tolerence=0.01)
except NonIntersectingPathException:
# if this happens, make a straight line of the head_length
# long.
x0, y0 = _point_along_a_line(x2, y2, x1, y1, head_length)
x1n, y1n = 0.5 * (x0 + x2), 0.5 * (y0 + y2)
arrow_in = [(x0, y0), (x1n, y1n), (x2, y2)]
arrow_out = None
# head
head_width = self.head_width * mutation_size
head_left, head_right = make_wedged_bezier2(arrow_in,
head_width / 2., wm=.5)
# tail
if arrow_out is not None:
tail_width = self.tail_width * mutation_size
tail_left, tail_right = get_parallels(arrow_out,
tail_width / 2.)
patch_path = [(Path.MOVETO, tail_right[0]),
(Path.CURVE3, tail_right[1]),
(Path.CURVE3, tail_right[2]),
(Path.LINETO, head_right[0]),
(Path.CURVE3, head_right[1]),
(Path.CURVE3, head_right[2]),
(Path.CURVE3, head_left[1]),
(Path.CURVE3, head_left[0]),
(Path.LINETO, tail_left[2]),
(Path.CURVE3, tail_left[1]),
(Path.CURVE3, tail_left[0]),
(Path.LINETO, tail_right[0]),
(Path.CLOSEPOLY, tail_right[0]),
]
else:
patch_path = [(Path.MOVETO, head_right[0]),
(Path.CURVE3, head_right[1]),
(Path.CURVE3, head_right[2]),
(Path.CURVE3, head_left[1]),
(Path.CURVE3, head_left[0]),
(Path.CLOSEPOLY, head_left[0]),
]
path = Path([p for c, p in patch_path], [c for c, p in patch_path])
return path, True
_style_list["simple"] = Simple
class Fancy(_Base):
"""
A fancy arrow. Only works with a quadratic bezier curve.
"""
def __init__(self, head_length=.4, head_width=.4, tail_width=.4):
"""
*head_length*
length of the arrow head
*head_with*
width of the arrow head
*tail_width*
width of the arrow tail
"""
self.head_length, self.head_width, self.tail_width = \
head_length, head_width, tail_width
super(ArrowStyle.Fancy, self).__init__()
def transmute(self, path, mutation_size, linewidth):
x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path)
# divide the path into a head and a tail
head_length = self.head_length * mutation_size
arrow_path = [(x0, y0), (x1, y1), (x2, y2)]
from .bezier import NonIntersectingPathException
# path for head
in_f = inside_circle(x2, y2, head_length)
try:
path_out, path_in = \
split_bezier_intersecting_with_closedpath(
arrow_path,
in_f,
tolerence=0.01)
except NonIntersectingPathException:
# if this happens, make a straight line of the head_length
# long.
x0, y0 = _point_along_a_line(x2, y2, x1, y1, head_length)
x1n, y1n = 0.5 * (x0 + x2), 0.5 * (y0 + y2)
arrow_path = [(x0, y0), (x1n, y1n), (x2, y2)]
path_head = arrow_path
else:
path_head = path_in
# path for head
in_f = inside_circle(x2, y2, head_length * .8)
path_out, path_in = split_bezier_intersecting_with_closedpath(
arrow_path,
in_f,
tolerence=0.01
)
path_tail = path_out
# head
head_width = self.head_width * mutation_size
head_l, head_r = make_wedged_bezier2(path_head,
head_width / 2.,
wm=.6)
# tail
tail_width = self.tail_width * mutation_size
tail_left, tail_right = make_wedged_bezier2(path_tail,
tail_width * .5,
w1=1., wm=0.6, w2=0.3)
# path for head
in_f = inside_circle(x0, y0, tail_width * .3)
path_in, path_out = split_bezier_intersecting_with_closedpath(
arrow_path,
in_f,
tolerence=0.01
)
tail_start = path_in[-1]
head_right, head_left = head_r, head_l
patch_path = [(Path.MOVETO, tail_start),
(Path.LINETO, tail_right[0]),
(Path.CURVE3, tail_right[1]),
(Path.CURVE3, tail_right[2]),
(Path.LINETO, head_right[0]),
(Path.CURVE3, head_right[1]),
(Path.CURVE3, head_right[2]),
(Path.CURVE3, head_left[1]),
(Path.CURVE3, head_left[0]),
(Path.LINETO, tail_left[2]),
(Path.CURVE3, tail_left[1]),
(Path.CURVE3, tail_left[0]),
(Path.LINETO, tail_start),
(Path.CLOSEPOLY, tail_start),
]
path = Path([p for c, p in patch_path], [c for c, p in patch_path])
return path, True
_style_list["fancy"] = Fancy
class Wedge(_Base):
"""
Wedge(?) shape. Only works with a quadratic bezier curve. The
begin point has a width of the tail_width and the end point has a
width of 0. At the middle, the width is shrink_factor*tail_width.
"""
def __init__(self, tail_width=.3, shrink_factor=0.5):
"""
*tail_width*
width of the tail
*shrink_factor*
fraction of the arrow width at the middle point
"""
self.tail_width = tail_width
self.shrink_factor = shrink_factor
super(ArrowStyle.Wedge, self).__init__()
def transmute(self, path, mutation_size, linewidth):
x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path)
arrow_path = [(x0, y0), (x1, y1), (x2, y2)]
b_plus, b_minus = make_wedged_bezier2(
arrow_path,
self.tail_width * mutation_size / 2.,
wm=self.shrink_factor)
patch_path = [(Path.MOVETO, b_plus[0]),
(Path.CURVE3, b_plus[1]),
(Path.CURVE3, b_plus[2]),
(Path.LINETO, b_minus[2]),
(Path.CURVE3, b_minus[1]),
(Path.CURVE3, b_minus[0]),
(Path.CLOSEPOLY, b_minus[0]),
]
path = Path([p for c, p in patch_path], [c for c, p in patch_path])
return path, True
_style_list["wedge"] = Wedge
if __doc__:
__doc__ = cbook.dedent(__doc__) % \
{"AvailableArrowstyles": _pprint_styles(_style_list)}
docstring.interpd.update(
AvailableArrowstyles=_pprint_styles(ArrowStyle._style_list),
AvailableConnectorstyles=_pprint_styles(ConnectionStyle._style_list),
)
class FancyArrowPatch(Patch):
"""
A fancy arrow patch. It draws an arrow using the :class:ArrowStyle.
"""
_edge_default = True
def __str__(self):
if self._posA_posB is not None:
(x1, y1), (x2, y2) = self._posA_posB
return self.__class__.__name__ \
+ "(%g,%g->%g,%g)" % (x1, y1, x2, y2)
else:
return self.__class__.__name__ \
+ "(%s)" % (str(self._path_original),)
@docstring.dedent_interpd
def __init__(self, posA=None, posB=None,
path=None,
arrowstyle="simple",
arrow_transmuter=None,
connectionstyle="arc3",
connector=None,
patchA=None,
patchB=None,
shrinkA=2.,
shrinkB=2.,
mutation_scale=1.,
mutation_aspect=None,
dpi_cor=1.,
**kwargs):
"""
If *posA* and *posB* is given, a path connecting two point are
created according to the connectionstyle. The path will be
clipped with *patchA* and *patchB* and further shrunken by
*shrinkA* and *shrinkB*. An arrow is drawn along this
resulting path using the *arrowstyle* parameter. If *path*
provided, an arrow is drawn along this path and *patchA*,
*patchB*, *shrinkA*, and *shrinkB* are ignored.
The *connectionstyle* describes how *posA* and *posB* are
connected. It can be an instance of the ConnectionStyle class
(matplotlib.patches.ConnectionStlye) or a string of the
connectionstyle name, with optional comma-separated
attributes. The following connection styles are available.
%(AvailableConnectorstyles)s
The *arrowstyle* describes how the fancy arrow will be
drawn. It can be string of the available arrowstyle names,
with optional comma-separated attributes, or one of the
ArrowStyle instance. The optional attributes are meant to be
scaled with the *mutation_scale*. The following arrow styles are
available.
%(AvailableArrowstyles)s
*mutation_scale* : a value with which attributes of arrowstyle
(e.g., head_length) will be scaled. default=1.
*mutation_aspect* : The height of the rectangle will be
squeezed by this value before the mutation and the mutated
box will be stretched by the inverse of it. default=None.
Valid kwargs are:
%(Patch)s
"""
Patch.__init__(self, **kwargs)
if posA is not None and posB is not None and path is None:
self._posA_posB = [posA, posB]
if connectionstyle is None:
connectionstyle = "arc3"
self.set_connectionstyle(connectionstyle)
elif posA is None and posB is None and path is not None:
self._posA_posB = None
self._connetors = None
else:
raise ValueError("either posA and posB, or path need to provided")
self.patchA = patchA
self.patchB = patchB
self.shrinkA = shrinkA
self.shrinkB = shrinkB
self._path_original = path
self.set_arrowstyle(arrowstyle)
self._mutation_scale = mutation_scale
self._mutation_aspect = mutation_aspect
self.set_dpi_cor(dpi_cor)
#self._draw_in_display_coordinate = True
def set_dpi_cor(self, dpi_cor):
"""
dpi_cor is currently used for linewidth-related things and
shrink factor. Mutation scale is affected by this.
"""
self._dpi_cor = dpi_cor
self.stale = True
def get_dpi_cor(self):
"""
dpi_cor is currently used for linewidth-related things and
shrink factor. Mutation scale is affected by this.
"""
return self._dpi_cor
def set_positions(self, posA, posB):
""" set the begin and end positions of the connecting
path. Use current value if None.
"""
if posA is not None:
self._posA_posB[0] = posA
if posB is not None:
self._posA_posB[1] = posB
self.stale = True
def set_patchA(self, patchA):
""" set the begin patch.
"""
self.patchA = patchA
self.stale = True
def set_patchB(self, patchB):
""" set the begin patch
"""
self.patchB = patchB
self.stale = True
def set_connectionstyle(self, connectionstyle, **kw):
"""
Set the connection style.
*connectionstyle* can be a string with connectionstyle name with
optional comma-separated attributes. Alternatively, the attrs can be
provided as keywords.
set_connectionstyle("arc,angleA=0,armA=30,rad=10")
set_connectionstyle("arc", angleA=0,armA=30,rad=10)
Old attrs simply are forgotten.
Without argument (or with connectionstyle=None), return
available styles as a list of strings.
"""
if connectionstyle is None:
return ConnectionStyle.pprint_styles()
if isinstance(connectionstyle, ConnectionStyle._Base):
self._connector = connectionstyle
elif six.callable(connectionstyle):
# we may need check the calling convention of the given function
self._connector = connectionstyle
else:
self._connector = ConnectionStyle(connectionstyle, **kw)
self.stale = True
def get_connectionstyle(self):
"""
Return the ConnectionStyle instance
"""
return self._connector
def set_arrowstyle(self, arrowstyle=None, **kw):
"""
Set the arrow style.
*arrowstyle* can be a string with arrowstyle name with optional
comma-separated attributes. Alternatively, the attrs can
be provided as keywords.
set_arrowstyle("Fancy,head_length=0.2")
set_arrowstyle("fancy", head_length=0.2)
Old attrs simply are forgotten.
Without argument (or with arrowstyle=None), return
available box styles as a list of strings.
"""
if arrowstyle is None:
return ArrowStyle.pprint_styles()
if isinstance(arrowstyle, ArrowStyle._Base):
self._arrow_transmuter = arrowstyle
else:
self._arrow_transmuter = ArrowStyle(arrowstyle, **kw)
self.stale = True
def get_arrowstyle(self):
"""
Return the arrowstyle object
"""
return self._arrow_transmuter
def set_mutation_scale(self, scale):
"""
Set the mutation scale.
ACCEPTS: float
"""
self._mutation_scale = scale
self.stale = True
def get_mutation_scale(self):
"""
Return the mutation scale.
"""
return self._mutation_scale
def set_mutation_aspect(self, aspect):
"""
Set the aspect ratio of the bbox mutation.
ACCEPTS: float
"""
self._mutation_aspect = aspect
self.stale = True
def get_mutation_aspect(self):
"""
Return the aspect ratio of the bbox mutation.
"""
return self._mutation_aspect
def get_path(self):
"""
return the path of the arrow in the data coordinate. Use
get_path_in_displaycoord() method to retrieve the arrow path
in the display coord.
"""
_path, fillable = self.get_path_in_displaycoord()
if cbook.iterable(fillable):
_path = concatenate_paths(_path)
return self.get_transform().inverted().transform_path(_path)
def get_path_in_displaycoord(self):
"""
Return the mutated path of the arrow in the display coord
"""
dpi_cor = self.get_dpi_cor()
if self._posA_posB is not None:
posA = self.get_transform().transform_point(self._posA_posB[0])
posB = self.get_transform().transform_point(self._posA_posB[1])
_path = self.get_connectionstyle()(posA, posB,
patchA=self.patchA,
patchB=self.patchB,
shrinkA=self.shrinkA * dpi_cor,
shrinkB=self.shrinkB * dpi_cor
)
else:
_path = self.get_transform().transform_path(self._path_original)
_path, fillable = self.get_arrowstyle()(
_path,
self.get_mutation_scale() * dpi_cor,
self.get_linewidth() * dpi_cor,
self.get_mutation_aspect()
)
#if not fillable:
# self._fill = False
return _path, fillable
def draw(self, renderer):
if not self.get_visible():
return
renderer.open_group('patch', self.get_gid())
gc = renderer.new_gc()
gc.set_foreground(self._edgecolor, isRGBA=True)
lw = self._linewidth
if self._edgecolor[3] == 0:
lw = 0
gc.set_linewidth(lw)
gc.set_dashes(self._dashoffset, self._dashes)
gc.set_antialiased(self._antialiased)
self._set_gc_clip(gc)
gc.set_capstyle('round')
gc.set_snap(self.get_snap())
rgbFace = self._facecolor
if rgbFace[3] == 0:
rgbFace = None # (some?) renderers expect this as no-fill signal
gc.set_alpha(self._alpha)
if self._hatch:
gc.set_hatch(self._hatch)
if self.get_sketch_params() is not None:
gc.set_sketch_params(*self.get_sketch_params())
# FIXME : dpi_cor is for the dpi-dependecy of the
# linewidth. There could be room for improvement.
#
#dpi_cor = renderer.points_to_pixels(1.)
self.set_dpi_cor(renderer.points_to_pixels(1.))
path, fillable = self.get_path_in_displaycoord()
if not cbook.iterable(fillable):
path = [path]
fillable = [fillable]
affine = transforms.IdentityTransform()
if self.get_path_effects():
from matplotlib.patheffects import PathEffectRenderer
renderer = PathEffectRenderer(self.get_path_effects(), renderer)
for p, f in zip(path, fillable):
if f:
renderer.draw_path(gc, p, affine, rgbFace)
else:
renderer.draw_path(gc, p, affine, None)
gc.restore()
renderer.close_group('patch')
self.stale = False
class ConnectionPatch(FancyArrowPatch):
"""
A :class:`~matplotlib.patches.ConnectionPatch` class is to make
connecting lines between two points (possibly in different axes).
"""
def __str__(self):
return "ConnectionPatch((%g,%g),(%g,%g))" % \
(self.xy1[0], self.xy1[1], self.xy2[0], self.xy2[1])
@docstring.dedent_interpd
def __init__(self, xyA, xyB, coordsA, coordsB=None,
axesA=None, axesB=None,
arrowstyle="-",
arrow_transmuter=None,
connectionstyle="arc3",
connector=None,
patchA=None,
patchB=None,
shrinkA=0.,
shrinkB=0.,
mutation_scale=10.,
mutation_aspect=None,
clip_on=False,
dpi_cor=1.,
**kwargs):
"""
Connect point *xyA* in *coordsA* with point *xyB* in *coordsB*
Valid keys are
=============== ======================================================
Key Description
=============== ======================================================
arrowstyle the arrow style
connectionstyle the connection style
relpos default is (0.5, 0.5)
patchA default is bounding box of the text
patchB default is None
shrinkA default is 2 points
shrinkB default is 2 points
mutation_scale default is text size (in points)
mutation_aspect default is 1.
? any key for :class:`matplotlib.patches.PathPatch`
=============== ======================================================
*coordsA* and *coordsB* are strings that indicate the
coordinates of *xyA* and *xyB*.
================= ===================================================
Property Description
================= ===================================================
'figure points' points from the lower left corner of the figure
'figure pixels' pixels from the lower left corner of the figure
'figure fraction' 0,0 is lower left of figure and 1,1 is upper, right
'axes points' points from lower left corner of axes
'axes pixels' pixels from lower left corner of axes
'axes fraction' 0,1 is lower left of axes and 1,1 is upper right
'data' use the coordinate system of the object being
annotated (default)
'offset points' Specify an offset (in points) from the *xy* value
'polar' you can specify *theta*, *r* for the annotation,
even in cartesian plots. Note that if you
are using a polar axes, you do not need
to specify polar for the coordinate
system since that is the native "data" coordinate
system.
================= ===================================================
"""
if coordsB is None:
coordsB = coordsA
# we'll draw ourself after the artist we annotate by default
self.xy1 = xyA
self.xy2 = xyB
self.coords1 = coordsA
self.coords2 = coordsB
self.axesA = axesA
self.axesB = axesB
FancyArrowPatch.__init__(self,
posA=(0, 0), posB=(1, 1),
arrowstyle=arrowstyle,
arrow_transmuter=arrow_transmuter,
connectionstyle=connectionstyle,
connector=connector,
patchA=patchA,
patchB=patchB,
shrinkA=shrinkA,
shrinkB=shrinkB,
mutation_scale=mutation_scale,
mutation_aspect=mutation_aspect,
clip_on=clip_on,
dpi_cor=dpi_cor,
**kwargs)
# if True, draw annotation only if self.xy is inside the axes
self._annotation_clip = None
def _get_xy(self, x, y, s, axes=None):
"""
caculate the pixel position of given point
"""
if axes is None:
axes = self.axes
if s == 'data':
trans = axes.transData
x = float(self.convert_xunits(x))
y = float(self.convert_yunits(y))
return trans.transform_point((x, y))
elif s == 'offset points':
# convert the data point
dx, dy = self.xy
# prevent recursion
if self.xycoords == 'offset points':
return self._get_xy(dx, dy, 'data')
dx, dy = self._get_xy(dx, dy, self.xycoords)
# convert the offset
dpi = self.figure.get_dpi()
x *= dpi / 72.
y *= dpi / 72.
# add the offset to the data point
x += dx
y += dy
return x, y
elif s == 'polar':
theta, r = x, y
x = r * np.cos(theta)
y = r * np.sin(theta)
trans = axes.transData
return trans.transform_point((x, y))
elif s == 'figure points':
# points from the lower left corner of the figure
dpi = self.figure.dpi
l, b, w, h = self.figure.bbox.bounds
r = l + w
t = b + h
x *= dpi / 72.
y *= dpi / 72.
if x < 0:
x = r + x
if y < 0:
y = t + y
return x, y
elif s == 'figure pixels':
# pixels from the lower left corner of the figure
l, b, w, h = self.figure.bbox.bounds
r = l + w
t = b + h
if x < 0:
x = r + x
if y < 0:
y = t + y
return x, y
elif s == 'figure fraction':
# (0,0) is lower left, (1,1) is upper right of figure
trans = self.figure.transFigure
return trans.transform_point((x, y))
elif s == 'axes points':
# points from the lower left corner of the axes
dpi = self.figure.dpi
l, b, w, h = axes.bbox.bounds
r = l + w
t = b + h
if x < 0:
x = r + x * dpi / 72.
else:
x = l + x * dpi / 72.
if y < 0:
y = t + y * dpi / 72.
else:
y = b + y * dpi / 72.
return x, y
elif s == 'axes pixels':
#pixels from the lower left corner of the axes
l, b, w, h = axes.bbox.bounds
r = l + w
t = b + h
if x < 0:
x = r + x
else:
x = l + x
if y < 0:
y = t + y
else:
y = b + y
return x, y
elif s == 'axes fraction':
#(0,0) is lower left, (1,1) is upper right of axes
trans = axes.transAxes
return trans.transform_point((x, y))
def set_annotation_clip(self, b):
"""
set *annotation_clip* attribute.
* True: the annotation will only be drawn when self.xy is inside the
axes.
* False: the annotation will always be drawn regardless of its
position.
* None: the self.xy will be checked only if *xycoords* is "data"
"""
self._annotation_clip = b
self.stale = True
def get_annotation_clip(self):
"""
Return *annotation_clip* attribute.
See :meth:`set_annotation_clip` for the meaning of return values.
"""
return self._annotation_clip
def get_path_in_displaycoord(self):
"""
Return the mutated path of the arrow in the display coord
"""
dpi_cor = self.get_dpi_cor()
x, y = self.xy1
posA = self._get_xy(x, y, self.coords1, self.axesA)
x, y = self.xy2
posB = self._get_xy(x, y, self.coords2, self.axesB)
_path = self.get_connectionstyle()(posA, posB,
patchA=self.patchA,
patchB=self.patchB,
shrinkA=self.shrinkA * dpi_cor,
shrinkB=self.shrinkB * dpi_cor
)
_path, fillable = self.get_arrowstyle()(
_path,
self.get_mutation_scale() * dpi_cor,
self.get_linewidth() * dpi_cor,
self.get_mutation_aspect()
)
return _path, fillable
def _check_xy(self, renderer):
"""
check if the annotation need to
be drawn.
"""
b = self.get_annotation_clip()
if b or (b is None and self.coords1 == "data"):
x, y = self.xy1
xy_pixel = self._get_xy(x, y, self.coords1, self.axesA)
if not self.axes.contains_point(xy_pixel):
return False
if b or (b is None and self.coords2 == "data"):
x, y = self.xy2
xy_pixel = self._get_xy(x, y, self.coords2, self.axesB)
if self.axesB is None:
axes = self.axes
else:
axes = self.axesB
if not axes.contains_point(xy_pixel):
return False
return True
def draw(self, renderer):
"""
Draw.
"""
if renderer is not None:
self._renderer = renderer
if not self.get_visible():
return
if not self._check_xy(renderer):
return
FancyArrowPatch.draw(self, renderer)
| gpl-3.0 |
emdodds/LCAversions | timing.py | 1 | 3230 | #This file will time various versions of LCA
from __future__ import division
import numpy as np
import sklearn.preprocessing as skp
from timeit import default_timer as timer
from LCAnumpy import lca as lcan
from LCAfortran import lca as lcaf
from LCAnumbaprog import lca as lcag
def main():
"""Profiles various versions of LCA."""
nshort = 6
tshort = 2
nmed = 3
tmed = 6
nlong = 1
#Setup variables for inference
numDict = int(2048)
numBatch = int(128)
dataSize = int(256)
dictsIn = np.random.randn(numDict,dataSize)
# LCA requires that dictionary be unit norm
dictsIn = skp.normalize(dictsIn, axis=1)
stimuli = np.random.randn(numBatch,dataSize)
batchCoeffs = np.random.randn(numBatch,numDict)
coeffs = np.zeros((numBatch, numDict))
eta = .01
lamb = .05
nIter = 300
adapt = .99
softThresh = 0
thresh = np.random.randn(numBatch)
#LCA
params = """Parameters:
numDict: """+str(numDict)+"""
numBatch: """+str(numBatch)+"""
dataSize: """+str(dataSize)+"""
nIter: """+str(nIter)+"""\n"""
print params
start = timer()
lcan.infer(dictsIn,stimuli,eta,lamb,nIter,adapt)
dt = timer()-start
if dt < tshort:
n_times = nshort
elif dt < tmed:
n_times = nmed
else:
n_times = nlong
for ii in xrange(n_times-1):
start = timer()
lcan.infer(dictsIn,stimuli,eta,lamb,nIter,adapt)
dt = dt+timer()-start
dt = dt/(n_times)
print '---------------Numpy based LCA----------------'
print 'Average time over '+str(n_times)+' trials:'
print '%f s' % dt
dictsIn = np.array(dictsIn,order='F')
stimuli = np.array(stimuli,order='F')
coeffs = np.array(coeffs,order='F')
batchCoeffs = np.array(batchCoeffs,order='F')
thresh = np.array(thresh,order='F')
start = timer()
lcaf.lca(dictsIn,stimuli,eta,lamb,nIter,softThresh,adapt,coeffs,batchCoeffs,thresh,numDict,numBatch,dataSize)
dt = timer()-start
if dt < tshort:
n_times = nshort
elif dt < tmed:
n_times = nmed
else:
n_times = nlong
for ii in xrange(n_times-1):
start = timer()
lcaf.lca(dictsIn,stimuli,eta,lamb,nIter,softThresh,adapt,coeffs,batchCoeffs,thresh,numDict,numBatch,dataSize)
dt = dt+timer()-start
dt = dt/(n_times)
print '---------------Fortran based LCA--------------'
print 'Average time over '+str(n_times)+' trials:'
print '%f s' % dt
dictsIn = np.array(dictsIn,dtype=np.float32,order='F')
stimuli = np.array(stimuli,dtype=np.float32,order='F')
start = timer()
lcag.infer(dictsIn,stimuli,eta,lamb,nIter,adapt)
dt = timer()-start
if dt < tshort:
n_times = nshort
elif dt < tmed:
n_times = nmed
else:
n_times = nlong
for ii in xrange(n_times-1):
start = timer()
lcag.infer(dictsIn,stimuli,eta,lamb,nIter,adapt)
dt = dt+timer()-start
dt = dt/(n_times)
print '----------------GPU based LCA-----------------'
print 'Average time over '+str(n_times)+' trials:'
print '%f s' % dt
if __name__ == '__main__':
main()
| mit |
otmaneJai/Zipline | zipline/sources/data_frame_source.py | 26 | 5253 | #
# Copyright 2015 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.
"""
Tools to generate data sources.
"""
import numpy as np
import pandas as pd
from zipline.gens.utils import hash_args
from zipline.sources.data_source import DataSource
class DataFrameSource(DataSource):
"""
Data source that yields from a pandas DataFrame.
:Axis layout:
* columns : sids
* index : datetime
:Note:
Bars where the price is nan are filtered out.
"""
def __init__(self, data, **kwargs):
assert isinstance(data.index, pd.tseries.index.DatetimeIndex)
# Only accept integer SIDs as the items of the DataFrame
assert isinstance(data.columns, pd.Int64Index)
# TODO is ffilling correct/necessary?
# Forward fill prices
self.data = data.fillna(method='ffill')
# Unpack config dictionary with default values.
self.start = kwargs.get('start', self.data.index[0])
self.end = kwargs.get('end', self.data.index[-1])
self.sids = self.data.columns
# Hash_value for downstream sorting.
self.arg_string = hash_args(data, **kwargs)
self._raw_data = None
self.started_sids = set()
@property
def mapping(self):
return {
'dt': (lambda x: x, 'dt'),
'sid': (lambda x: x, 'sid'),
'price': (float, 'price'),
'volume': (int, 'volume'),
}
@property
def instance_hash(self):
return self.arg_string
def raw_data_gen(self):
for dt, series in self.data.iterrows():
for sid, price in series.iteritems():
# Skip SIDs that can not be forward filled
if np.isnan(price) and \
sid not in self.started_sids:
continue
self.started_sids.add(sid)
event = {
'dt': dt,
'sid': sid,
'price': price,
# Just chose something large
# if no volume available.
'volume': 1e9,
}
yield event
@property
def raw_data(self):
if not self._raw_data:
self._raw_data = self.raw_data_gen()
return self._raw_data
class DataPanelSource(DataSource):
"""
Data source that yields from a pandas Panel.
:Axis layout:
* items : sids
* major_axis : datetime
* minor_axis : price, volume, ...
:Note:
Bars where the price is nan are filtered out.
"""
def __init__(self, data, **kwargs):
assert isinstance(data.major_axis, pd.tseries.index.DatetimeIndex)
# Only accept integer SIDs as the items of the Panel
assert isinstance(data.items, pd.Int64Index)
# TODO is ffilling correct/necessary?
# forward fill with volumes of 0
self.data = data.fillna(value={'volume': 0})
self.data = self.data.fillna(method='ffill')
# Unpack config dictionary with default values.
self.start = kwargs.get('start', self.data.major_axis[0])
self.end = kwargs.get('end', self.data.major_axis[-1])
self.sids = self.data.items
# Hash_value for downstream sorting.
self.arg_string = hash_args(data, **kwargs)
self._raw_data = None
self.started_sids = set()
@property
def mapping(self):
mapping = {
'dt': (lambda x: x, 'dt'),
'sid': (lambda x: x, 'sid'),
'price': (float, 'price'),
'volume': (int, 'volume'),
}
# Add additional fields.
for field_name in self.data.minor_axis:
if field_name in ['price', 'volume', 'dt', 'sid']:
continue
mapping[field_name] = (lambda x: x, field_name)
return mapping
@property
def instance_hash(self):
return self.arg_string
def raw_data_gen(self):
for dt in self.data.major_axis:
df = self.data.major_xs(dt)
for sid, series in df.iteritems():
# Skip SIDs that can not be forward filled
if np.isnan(series['price']) and \
sid not in self.started_sids:
continue
self.started_sids.add(sid)
event = {
'dt': dt,
'sid': sid,
}
for field_name, value in series.iteritems():
event[field_name] = value
yield event
@property
def raw_data(self):
if not self._raw_data:
self._raw_data = self.raw_data_gen()
return self._raw_data
| apache-2.0 |
GuessWhoSamFoo/pandas | pandas/tests/tslibs/test_parsing.py | 2 | 5799 | # -*- coding: utf-8 -*-
"""
Tests for Timestamp parsing, aimed at pandas/_libs/tslibs/parsing.pyx
"""
from datetime import datetime
from dateutil.parser import parse
import numpy as np
import pytest
from pandas._libs.tslibs import parsing
from pandas._libs.tslibs.parsing import parse_time_string
import pandas.util._test_decorators as td
from pandas.util import testing as tm
def test_parse_time_string():
(date, parsed, reso) = parse_time_string("4Q1984")
(date_lower, parsed_lower, reso_lower) = parse_time_string("4q1984")
assert date == date_lower
assert reso == reso_lower
assert parsed == parsed_lower
@pytest.mark.parametrize("dashed,normal", [
("1988-Q2", "1988Q2"),
("2Q-1988", "2Q1988")
])
def test_parse_time_quarter_with_dash(dashed, normal):
# see gh-9688
(date_dash, parsed_dash, reso_dash) = parse_time_string(dashed)
(date, parsed, reso) = parse_time_string(normal)
assert date_dash == date
assert parsed_dash == parsed
assert reso_dash == reso
@pytest.mark.parametrize("dashed", [
"-2Q1992", "2-Q1992", "4-4Q1992"
])
def test_parse_time_quarter_with_dash_error(dashed):
msg = ("Unknown datetime string format, "
"unable to parse: {dashed}".format(dashed=dashed))
with pytest.raises(parsing.DateParseError, match=msg):
parse_time_string(dashed)
@pytest.mark.parametrize("date_string,expected", [
("123.1234", False),
("-50000", False),
("999", False),
("m", False),
("T", False),
("Mon Sep 16, 2013", True),
("2012-01-01", True),
("01/01/2012", True),
("01012012", True),
("0101", True),
("1-1", True)
])
def test_does_not_convert_mixed_integer(date_string, expected):
assert parsing._does_string_look_like_datetime(date_string) is expected
@pytest.mark.parametrize("date_str,kwargs,msg", [
("2013Q5", dict(),
("Incorrect quarterly string is given, "
"quarter must be between 1 and 4: 2013Q5")),
# see gh-5418
("2013Q1", dict(freq="INVLD-L-DEC-SAT"),
("Unable to retrieve month information "
"from given freq: INVLD-L-DEC-SAT"))
])
def test_parsers_quarterly_with_freq_error(date_str, kwargs, msg):
with pytest.raises(parsing.DateParseError, match=msg):
parsing.parse_time_string(date_str, **kwargs)
@pytest.mark.parametrize("date_str,freq,expected", [
("2013Q2", None, datetime(2013, 4, 1)),
("2013Q2", "A-APR", datetime(2012, 8, 1)),
("2013-Q2", "A-DEC", datetime(2013, 4, 1))
])
def test_parsers_quarterly_with_freq(date_str, freq, expected):
result, _, _ = parsing.parse_time_string(date_str, freq=freq)
assert result == expected
@pytest.mark.parametrize("date_str", [
"2Q 2005", "2Q-200A", "2Q-200",
"22Q2005", "2Q200.", "6Q-20"
])
def test_parsers_quarter_invalid(date_str):
if date_str == "6Q-20":
msg = ("Incorrect quarterly string is given, quarter "
"must be between 1 and 4: {date_str}".format(date_str=date_str))
else:
msg = ("Unknown datetime string format, unable "
"to parse: {date_str}".format(date_str=date_str))
with pytest.raises(ValueError, match=msg):
parsing.parse_time_string(date_str)
@pytest.mark.parametrize("date_str,expected", [
("201101", datetime(2011, 1, 1, 0, 0)),
("200005", datetime(2000, 5, 1, 0, 0))
])
def test_parsers_month_freq(date_str, expected):
result, _, _ = parsing.parse_time_string(date_str, freq="M")
assert result == expected
@td.skip_if_not_us_locale
@pytest.mark.parametrize("string,fmt", [
("20111230", "%Y%m%d"),
("2011-12-30", "%Y-%m-%d"),
("30-12-2011", "%d-%m-%Y"),
("2011-12-30 00:00:00", "%Y-%m-%d %H:%M:%S"),
("2011-12-30T00:00:00", "%Y-%m-%dT%H:%M:%S"),
("2011-12-30 00:00:00.000000", "%Y-%m-%d %H:%M:%S.%f")
])
def test_guess_datetime_format_with_parseable_formats(string, fmt):
result = parsing._guess_datetime_format(string)
assert result == fmt
@pytest.mark.parametrize("dayfirst,expected", [
(True, "%d/%m/%Y"),
(False, "%m/%d/%Y")
])
def test_guess_datetime_format_with_dayfirst(dayfirst, expected):
ambiguous_string = "01/01/2011"
result = parsing._guess_datetime_format(ambiguous_string,
dayfirst=dayfirst)
assert result == expected
@td.skip_if_has_locale
@pytest.mark.parametrize("string,fmt", [
("30/Dec/2011", "%d/%b/%Y"),
("30/December/2011", "%d/%B/%Y"),
("30/Dec/2011 00:00:00", "%d/%b/%Y %H:%M:%S")
])
def test_guess_datetime_format_with_locale_specific_formats(string, fmt):
result = parsing._guess_datetime_format(string)
assert result == fmt
@pytest.mark.parametrize("invalid_dt", [
"2013", "01/2013", "12:00:00", "1/1/1/1",
"this_is_not_a_datetime", "51a", 9,
datetime(2011, 1, 1)
])
def test_guess_datetime_format_invalid_inputs(invalid_dt):
# A datetime string must include a year, month and a day for it to be
# guessable, in addition to being a string that looks like a datetime.
assert parsing._guess_datetime_format(invalid_dt) is None
@pytest.mark.parametrize("string,fmt", [
("2011-1-1", "%Y-%m-%d"),
("1/1/2011", "%m/%d/%Y"),
("30-1-2011", "%d-%m-%Y"),
("2011-1-1 0:0:0", "%Y-%m-%d %H:%M:%S"),
("2011-1-3T00:00:0", "%Y-%m-%dT%H:%M:%S"),
("2011-1-1 00:00:00", "%Y-%m-%d %H:%M:%S")
])
def test_guess_datetime_format_no_padding(string, fmt):
# see gh-11142
result = parsing._guess_datetime_format(string)
assert result == fmt
def test_try_parse_dates():
arr = np.array(["5/1/2000", "6/1/2000", "7/1/2000"], dtype=object)
result = parsing.try_parse_dates(arr, dayfirst=True)
expected = np.array([parse(d, dayfirst=True) for d in arr])
tm.assert_numpy_array_equal(result, expected)
| bsd-3-clause |
xubenben/data-science-from-scratch | code/clustering.py | 60 | 6438 | from __future__ import division
from linear_algebra import squared_distance, vector_mean, distance
import math, random
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
class KMeans:
"""performs k-means clustering"""
def __init__(self, k):
self.k = k # number of clusters
self.means = None # means of clusters
def classify(self, input):
"""return the index of the cluster closest to the input"""
return min(range(self.k),
key=lambda i: squared_distance(input, self.means[i]))
def train(self, inputs):
self.means = random.sample(inputs, self.k)
assignments = None
while True:
# Find new assignments
new_assignments = map(self.classify, inputs)
# If no assignments have changed, we're done.
if assignments == new_assignments:
return
# Otherwise keep the new assignments,
assignments = new_assignments
for i in range(self.k):
i_points = [p for p, a in zip(inputs, assignments) if a == i]
# avoid divide-by-zero if i_points is empty
if i_points:
self.means[i] = vector_mean(i_points)
def squared_clustering_errors(inputs, k):
"""finds the total squared error from k-means clustering the inputs"""
clusterer = KMeans(k)
clusterer.train(inputs)
means = clusterer.means
assignments = map(clusterer.classify, inputs)
return sum(squared_distance(input,means[cluster])
for input, cluster in zip(inputs, assignments))
def plot_squared_clustering_errors(plt):
ks = range(1, len(inputs) + 1)
errors = [squared_clustering_errors(inputs, k) for k in ks]
plt.plot(ks, errors)
plt.xticks(ks)
plt.xlabel("k")
plt.ylabel("total squared error")
plt.show()
#
# using clustering to recolor an image
#
def recolor_image(input_file, k=5):
img = mpimg.imread(path_to_png_file)
pixels = [pixel for row in img for pixel in row]
clusterer = KMeans(k)
clusterer.train(pixels) # this might take a while
def recolor(pixel):
cluster = clusterer.classify(pixel) # index of the closest cluster
return clusterer.means[cluster] # mean of the closest cluster
new_img = [[recolor(pixel) for pixel in row]
for row in img]
plt.imshow(new_img)
plt.axis('off')
plt.show()
#
# hierarchical clustering
#
def is_leaf(cluster):
"""a cluster is a leaf if it has length 1"""
return len(cluster) == 1
def get_children(cluster):
"""returns the two children of this cluster if it's a merged cluster;
raises an exception if this is a leaf cluster"""
if is_leaf(cluster):
raise TypeError("a leaf cluster has no children")
else:
return cluster[1]
def get_values(cluster):
"""returns the value in this cluster (if it's a leaf cluster)
or all the values in the leaf clusters below it (if it's not)"""
if is_leaf(cluster):
return cluster # is already a 1-tuple containing value
else:
return [value
for child in get_children(cluster)
for value in get_values(child)]
def cluster_distance(cluster1, cluster2, distance_agg=min):
"""finds the aggregate distance between elements of cluster1
and elements of cluster2"""
return distance_agg([distance(input1, input2)
for input1 in get_values(cluster1)
for input2 in get_values(cluster2)])
def get_merge_order(cluster):
if is_leaf(cluster):
return float('inf')
else:
return cluster[0] # merge_order is first element of 2-tuple
def bottom_up_cluster(inputs, distance_agg=min):
# start with every input a leaf cluster / 1-tuple
clusters = [(input,) for input in inputs]
# as long as we have more than one cluster left...
while len(clusters) > 1:
# find the two closest clusters
c1, c2 = min([(cluster1, cluster2)
for i, cluster1 in enumerate(clusters)
for cluster2 in clusters[:i]],
key=lambda (x, y): cluster_distance(x, y, distance_agg))
# remove them from the list of clusters
clusters = [c for c in clusters if c != c1 and c != c2]
# merge them, using merge_order = # of clusters left
merged_cluster = (len(clusters), [c1, c2])
# and add their merge
clusters.append(merged_cluster)
# when there's only one cluster left, return it
return clusters[0]
def generate_clusters(base_cluster, num_clusters):
# start with a list with just the base cluster
clusters = [base_cluster]
# as long as we don't have enough clusters yet...
while len(clusters) < num_clusters:
# choose the last-merged of our clusters
next_cluster = min(clusters, key=get_merge_order)
# remove it from the list
clusters = [c for c in clusters if c != next_cluster]
# and add its children to the list (i.e., unmerge it)
clusters.extend(get_children(next_cluster))
# once we have enough clusters...
return clusters
if __name__ == "__main__":
inputs = [[-14,-5],[13,13],[20,23],[-19,-11],[-9,-16],[21,27],[-49,15],[26,13],[-46,5],[-34,-1],[11,15],[-49,0],[-22,-16],[19,28],[-12,-8],[-13,-19],[-41,8],[-11,-6],[-25,-9],[-18,-3]]
random.seed(0) # so you get the same results as me
clusterer = KMeans(3)
clusterer.train(inputs)
print "3-means:"
print clusterer.means
print
random.seed(0)
clusterer = KMeans(2)
clusterer.train(inputs)
print "2-means:"
print clusterer.means
print
print "errors as a function of k"
for k in range(1, len(inputs) + 1):
print k, squared_clustering_errors(inputs, k)
print
print "bottom up hierarchical clustering"
base_cluster = bottom_up_cluster(inputs)
print base_cluster
print
print "three clusters, min:"
for cluster in generate_clusters(base_cluster, 3):
print get_values(cluster)
print
print "three clusters, max:"
base_cluster = bottom_up_cluster(inputs, max)
for cluster in generate_clusters(base_cluster, 3):
print get_values(cluster)
| unlicense |
clairetang6/bokeh | bokeh/charts/builders/bar_builder.py | 5 | 12416 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the Bar class which lets you build your Bar charts just passing
the arguments to the Chart class and calling the proper functions.
It also add a new chained stacked method.
"""
# -----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, 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, print_function, division
from ..builder import Builder, create_and_build
from ...models import FactorRange, Range1d
from ..glyphs import BarGlyph
from ...core.properties import Float, Enum, Bool, Override
from ..properties import Dimension
from ..attributes import ColorAttr, CatAttr
from ..operations import Stack, Dodge
from ...core.enums import Aggregation
from ..stats import stats
from ...models.sources import ColumnDataSource
from ..utils import help
# -----------------------------------------------------------------------------
# Classes and functions
# -----------------------------------------------------------------------------
class BarBuilder(Builder):
"""This is the Bar builder and it is in charge of plotting
Bar chart (grouped and stacked) in an easy and intuitive way.
Essentially, it utilizes a standardized way to ingest the data,
make the proper calculations and generate renderers. The renderers
reference the transformed data, which represent the groups of data
that were derived from the inputs. We additionally make calculations
for the ranges.
The x_range is categorical, and is made either from the label argument
or from the `pandas.DataFrame.index`. The y_range can be supplied as the
parameter continuous_range, or will be calculated as a linear range
(Range1d) based on the supplied values.
The bar builder is and can be further used as a base class for other
builders that might also be performing some aggregation across
derived groups of data.
"""
# ToDo: add label back as a discrete dimension
values = Dimension('values')
dimensions = ['values']
# req_dimensions = [['values']]
default_attributes = {'label': CatAttr(),
'color': ColorAttr(),
'line_color': ColorAttr(default='white'),
'stack': CatAttr(),
'group': CatAttr()}
agg = Enum(Aggregation, default='sum')
max_height = Float(1.0)
min_height = Float(0.0)
bar_width = Float(default=0.8)
fill_alpha = Float(default=0.8)
glyph = BarGlyph
comp_glyph_types = Override(default=[BarGlyph])
label_attributes = ['stack', 'group']
label_only = Bool(False)
values_only = Bool(False)
_perform_stack = False
_perform_group = False
def setup(self):
if self.attributes['color'].columns is None:
if self.attributes['stack'].columns is not None:
self.attributes['color'].setup(columns=self.attributes['stack'].columns)
if self.attributes['group'].columns is not None:
self.attributes['color'].setup(columns=self.attributes['group'].columns)
if self.attributes['stack'].columns is not None:
self._perform_stack = True
if self.attributes['group'].columns is not None:
self._perform_group = True
# ToDo: perform aggregation validation
# Not given values kw, so using only categorical data
if self.values.dtype.name == 'object' and len(self.attribute_columns) == 0:
# agg must be count
self.agg = 'count'
self.attributes['label'].set_columns(self.values.selection)
else:
pass
self._apply_inferred_index()
if self.xlabel is None:
if self.attributes['label'].columns is not None:
self.xlabel = str(
', '.join(self.attributes['label'].columns).title()).title()
else:
self.xlabel = self.values.selection
if self.ylabel is None:
if not self.label_only:
self.ylabel = '%s( %s )' % (
self.agg.title(), str(self.values.selection).title())
else:
self.ylabel = '%s( %s )' % (
self.agg.title(), ', '.join(self.attributes['label'].columns).title())
def _apply_inferred_index(self):
"""Configure chart when labels are provided as index instead of as kwarg."""
# try to infer grouping vs stacking labels
if (self.attributes['label'].columns is None and
self.values.selection is not None):
if self.attributes['stack'].columns is not None:
special_column = 'unity'
else:
special_column = 'index'
self._data['label'] = special_column
self.attributes['label'].setup(data=ColumnDataSource(self._data.df),
columns=special_column)
self.xlabel = ''
def set_ranges(self):
"""Push the Bar data into the ColumnDataSource and calculate
the proper ranges.
"""
x_items = self.attributes['label'].items
if x_items is None:
x_items = ''
x_labels = []
# Items are identified by tuples. If the tuple has a single value,
# we unpack it
for item in x_items:
item = self._get_label(item)
x_labels.append(str(item))
self.x_range = FactorRange(factors=x_labels)
y_shift = abs(0.1 * ((self.min_height + self.max_height) / 2))
if self.min_height < 0:
start = self.min_height - y_shift
else:
start = 0.0
if self.max_height > 0:
end = self.max_height + y_shift
else:
end = 0.0
self.y_range = Range1d(start=start, end=end)
def get_extra_args(self):
if self.__class__ is not BarBuilder:
attrs = self.properties(with_bases=False)
return {attr: getattr(self, attr) for attr in attrs}
else:
return {}
def yield_renderers(self):
"""Use the rect glyphs to display the bars.
Takes reference points from data loaded at the ColumnDataSource.
"""
kwargs = self.get_extra_args()
attrs = self.collect_attr_kwargs()
for group in self._data.groupby(**self.attributes):
glyph_kwargs = self.get_group_kwargs(group, attrs)
group_kwargs = kwargs.copy()
group_kwargs.update(glyph_kwargs)
props = self.glyph.properties().difference(set(['label']))
# make sure we always pass the color and line color
for k in ['color', 'line_color']:
group_kwargs[k] = group[k]
# TODO(fpliger): we shouldn't need to do this to ensure we don't
# have extra kwargs... this is needed now because
# of label, group and stack being "special"
for k in set(group_kwargs):
if k not in props:
group_kwargs.pop(k)
bg = self.glyph(label=group.label,
x_label=self._get_label(group['label']),
values=group.data[self.values.selection].values,
agg=stats[self.agg](),
width=self.bar_width,
fill_alpha=self.fill_alpha,
stack_label=self._get_label(group['stack']),
dodge_label=self._get_label(group['group']),
**group_kwargs)
self.add_glyph(group, bg)
if self._perform_stack:
Stack().apply(self.comp_glyphs)
if self._perform_group:
Dodge().apply(self.comp_glyphs)
# a higher level function of bar chart is to keep track of max height of all bars
self.max_height = max([renderer.y_max for renderer in self.comp_glyphs])
self.min_height = min([renderer.y_min for renderer in self.comp_glyphs])
for renderer in self.comp_glyphs:
for sub_renderer in renderer.renderers:
yield sub_renderer
@help(BarBuilder)
def Bar(data, label=None, values=None, color=None, stack=None, group=None, agg="sum",
xscale="categorical", yscale="linear", xgrid=False, ygrid=True,
continuous_range=None, **kw):
""" Create a Bar chart using :class:`BarBuilder <bokeh.charts.builders.bar_builder.BarBuilder>`
render the geometry from values, cat and stacked.
Args:
data (:ref:`userguide_charts_data_types`): the data
source for the chart.
label (list(str) or str, optional): list of string representing the categories.
(Defaults to None)
values (str, optional): iterable 2d representing the data series
values matrix.
color (str or list(str) or `~bokeh.charts._attributes.ColorAttr`): string color,
string column name, list of string columns or a custom `ColorAttr`,
which replaces the default `ColorAttr` for the builder.
stack (list(str) or str, optional): columns to use for stacking.
(Defaults to False, so grouping is assumed)
group (list(str) or str, optional): columns to use for grouping.
agg (str): how to aggregate the `values`. (Defaults to 'sum', or only label is
provided, then performs a `count`)
continuous_range(Range1d, optional): Custom continuous_range to be
used. (Defaults to None)
In addition to the parameters specific to this chart,
:ref:`userguide_charts_defaults` are also accepted as keyword parameters.
Returns:
:class:`Chart`: includes glyph renderers that generate bars
Examples:
.. bokeh-plot::
:source-position: above
from bokeh.charts import Bar, output_file, show, hplot
# best support is with data in a format that is table-like
data = {
'sample': ['1st', '2nd', '1st', '2nd', '1st', '2nd'],
'interpreter': ['python', 'python', 'pypy', 'pypy', 'jython', 'jython'],
'timing': [-2, 5, 12, 40, 22, 30]
}
# x-axis labels pulled from the interpreter column, stacking labels from sample column
bar = Bar(data, values='timing', label='interpreter', stack='sample', agg='mean',
title="Python Interpreter Sampling", legend='top_right', plot_width=400)
# table-like data results in reconfiguration of the chart with no data manipulation
bar2 = Bar(data, values='timing', label=['interpreter', 'sample'],
agg='mean', title="Python Interpreters", plot_width=400)
output_file("stacked_bar.html")
show(hplot(bar, bar2))
"""
if continuous_range and not isinstance(continuous_range, Range1d):
raise ValueError(
"continuous_range must be an instance of bokeh.models.ranges.Range1d"
)
if label is not None and values is None:
kw['label_only'] = True
if (agg == 'sum') or (agg == 'mean'):
agg = 'count'
values = label
# The continuous_range is the y_range (until we implement HBar charts)
y_range = continuous_range
kw['label'] = label
kw['values'] = values
kw['color'] = color
kw['stack'] = stack
kw['group'] = group
kw['agg'] = agg
kw['xscale'] = xscale
kw['yscale'] = yscale
kw['xgrid'] = xgrid
kw['ygrid'] = ygrid
kw['y_range'] = y_range
chart = create_and_build(BarBuilder, data, **kw)
# hide x labels if there is a single value, implying stacking only
if len(chart.x_range.factors) == 1 and not label:
chart.below[0].visible = False
return chart
| bsd-3-clause |
steven-murray/pydftools | setup.py | 1 | 2408 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""The setup script."""
from setuptools import setup, find_packages
import io
import os
import re
with open("README.rst") as readme_file:
readme = readme_file.read()
with open("HISTORY.rst") as history_file:
history = history_file.read()
requirements = [
"scipy",
"numpy>=1.6.2",
"Click>=6.0",
"attrs>=17.0",
"cached_property",
"chainconsumer",
"matplotlib"
# TODO: put package requirements here
]
def read(*names, **kwargs):
with io.open(
os.path.join(os.path.dirname(__file__), *names),
encoding=kwargs.get("encoding", "utf8"),
) as fp:
return fp.read()
def find_version(*file_paths):
version_file = read(*file_paths)
version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M)
if version_match:
return version_match.group(1)
raise RuntimeError("Unable to find version string.")
setup_requirements = [
"pytest-runner",
# TODO(steven-murray): put setup requirements (distutils extensions, etc.) here
]
test_requirements = [
"pytest",
# TODO: put package test requirements here
]
setup(
name="pydftools",
version=find_version("pydftools", "__init__.py"),
description="A pure-python port of the dftools R package.",
long_description=readme + "\n\n" + history,
author="Steven Murray",
author_email="steven.murray@curtin.edu.au",
url="https://github.com/steven-murray/pydftools",
packages=find_packages(include=["pydftools"]),
entry_points={"console_scripts": ["pydftools=pydftools.cli:main"]},
include_package_data=True,
install_requires=requirements,
license="MIT license",
zip_safe=False,
keywords="pydftools",
classifiers=[
"Development Status :: 2 - Pre-Alpha",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Natural Language :: English",
"Programming Language :: Python :: 2",
"Programming Language :: Python :: 2.6",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.3",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5",
],
test_suite="tests",
tests_require=test_requirements,
setup_requires=setup_requirements,
)
| mit |
jakobworldpeace/scikit-learn | sklearn/ensemble/forest.py | 8 | 67993 | """Forest of trees-based ensemble methods
Those methods include random forests and extremely randomized trees.
The module structure is the following:
- The ``BaseForest`` base class implements a common ``fit`` method for all
the estimators in the module. The ``fit`` method of the base ``Forest``
class calls the ``fit`` method of each sub-estimator on random samples
(with replacement, a.k.a. bootstrap) of the training set.
The init of the sub-estimator is further delegated to the
``BaseEnsemble`` constructor.
- The ``ForestClassifier`` and ``ForestRegressor`` base classes further
implement the prediction logic by computing an average of the predicted
outcomes of the sub-estimators.
- The ``RandomForestClassifier`` and ``RandomForestRegressor`` derived
classes provide the user with concrete implementations of
the forest ensemble method using classical, deterministic
``DecisionTreeClassifier`` and ``DecisionTreeRegressor`` as
sub-estimator implementations.
- The ``ExtraTreesClassifier`` and ``ExtraTreesRegressor`` derived
classes provide the user with concrete implementations of the
forest ensemble method using the extremely randomized trees
``ExtraTreeClassifier`` and ``ExtraTreeRegressor`` as
sub-estimator implementations.
Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Joly Arnaud <arnaud.v.joly@gmail.com>
# Fares Hedayati <fares.hedayati@gmail.com>
#
# License: BSD 3 clause
from __future__ import division
import warnings
from warnings import warn
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy.sparse import issparse
from scipy.sparse import hstack as sparse_hstack
from ..base import ClassifierMixin, RegressorMixin
from ..externals.joblib import Parallel, delayed
from ..externals import six
from ..metrics import r2_score
from ..preprocessing import OneHotEncoder
from ..tree import (DecisionTreeClassifier, DecisionTreeRegressor,
ExtraTreeClassifier, ExtraTreeRegressor)
from ..tree._tree import DTYPE, DOUBLE
from ..utils import check_random_state, check_array, compute_sample_weight
from ..exceptions import DataConversionWarning, NotFittedError
from .base import BaseEnsemble, _partition_estimators
from ..utils.fixes import bincount, parallel_helper
from ..utils.multiclass import check_classification_targets
from ..utils.validation import check_is_fitted
__all__ = ["RandomForestClassifier",
"RandomForestRegressor",
"ExtraTreesClassifier",
"ExtraTreesRegressor",
"RandomTreesEmbedding"]
MAX_INT = np.iinfo(np.int32).max
def _generate_sample_indices(random_state, n_samples):
"""Private function used to _parallel_build_trees function."""
random_instance = check_random_state(random_state)
sample_indices = random_instance.randint(0, n_samples, n_samples)
return sample_indices
def _generate_unsampled_indices(random_state, n_samples):
"""Private function used to forest._set_oob_score function."""
sample_indices = _generate_sample_indices(random_state, n_samples)
sample_counts = bincount(sample_indices, minlength=n_samples)
unsampled_mask = sample_counts == 0
indices_range = np.arange(n_samples)
unsampled_indices = indices_range[unsampled_mask]
return unsampled_indices
def _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees,
verbose=0, class_weight=None):
"""Private function used to fit a single tree in parallel."""
if verbose > 1:
print("building tree %d of %d" % (tree_idx + 1, n_trees))
if forest.bootstrap:
n_samples = X.shape[0]
if sample_weight is None:
curr_sample_weight = np.ones((n_samples,), dtype=np.float64)
else:
curr_sample_weight = sample_weight.copy()
indices = _generate_sample_indices(tree.random_state, n_samples)
sample_counts = bincount(indices, minlength=n_samples)
curr_sample_weight *= sample_counts
if class_weight == 'subsample':
with warnings.catch_warnings():
warnings.simplefilter('ignore', DeprecationWarning)
curr_sample_weight *= compute_sample_weight('auto', y, indices)
elif class_weight == 'balanced_subsample':
curr_sample_weight *= compute_sample_weight('balanced', y, indices)
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
else:
tree.fit(X, y, sample_weight=sample_weight, check_input=False)
return tree
class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble)):
"""Base class for forests of trees.
Warning: This class should not be used directly. Use derived classes
instead.
"""
@abstractmethod
def __init__(self,
base_estimator,
n_estimators=10,
estimator_params=tuple(),
bootstrap=False,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None):
super(BaseForest, self).__init__(
base_estimator=base_estimator,
n_estimators=n_estimators,
estimator_params=estimator_params)
self.bootstrap = bootstrap
self.oob_score = oob_score
self.n_jobs = n_jobs
self.random_state = random_state
self.verbose = verbose
self.warm_start = warm_start
self.class_weight = class_weight
def apply(self, X):
"""Apply trees in the forest to X, return leaf indices.
Parameters
----------
X : array-like or sparse matrix, shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
X_leaves : array_like, shape = [n_samples, n_estimators]
For each datapoint x in X and for each tree in the forest,
return the index of the leaf x ends up in.
"""
X = self._validate_X_predict(X)
results = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
backend="threading")(
delayed(parallel_helper)(tree, 'apply', X, check_input=False)
for tree in self.estimators_)
return np.array(results).T
def decision_path(self, X):
"""Return the decision path in the forest
.. versionadded:: 0.18
Parameters
----------
X : array-like or sparse matrix, shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
indicator : sparse csr array, shape = [n_samples, n_nodes]
Return a node indicator matrix where non zero elements
indicates that the samples goes through the nodes.
n_nodes_ptr : array of size (n_estimators + 1, )
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
gives the indicator value for the i-th estimator.
"""
X = self._validate_X_predict(X)
indicators = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
backend="threading")(
delayed(parallel_helper)(tree, 'decision_path', X,
check_input=False)
for tree in self.estimators_)
n_nodes = [0]
n_nodes.extend([i.shape[1] for i in indicators])
n_nodes_ptr = np.array(n_nodes).cumsum()
return sparse_hstack(indicators).tocsr(), n_nodes_ptr
def fit(self, X, y, sample_weight=None):
"""Build a forest of trees from the training set (X, y).
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The training input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csc_matrix``.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The target values (class labels in classification, real numbers in
regression).
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted. Splits
that would create child nodes with net zero or negative weight are
ignored while searching for a split in each node. In the case of
classification, splits are also ignored if they would result in any
single class carrying a negative weight in either child node.
Returns
-------
self : object
Returns self.
"""
# Validate or convert input data
X = check_array(X, accept_sparse="csc", dtype=DTYPE)
y = check_array(y, accept_sparse='csc', ensure_2d=False, dtype=None)
if sample_weight is not None:
sample_weight = check_array(sample_weight, ensure_2d=False)
if issparse(X):
# Pre-sort indices to avoid that each individual tree of the
# ensemble sorts the indices.
X.sort_indices()
# Remap output
n_samples, self.n_features_ = X.shape
y = np.atleast_1d(y)
if y.ndim == 2 and y.shape[1] == 1:
warn("A column-vector y was passed when a 1d array was"
" expected. Please change the shape of y to "
"(n_samples,), for example using ravel().",
DataConversionWarning, stacklevel=2)
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
self.n_outputs_ = y.shape[1]
y, expanded_class_weight = self._validate_y_class_weight(y)
if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)
if expanded_class_weight is not None:
if sample_weight is not None:
sample_weight = sample_weight * expanded_class_weight
else:
sample_weight = expanded_class_weight
# Check parameters
self._validate_estimator()
if not self.bootstrap and self.oob_score:
raise ValueError("Out of bag estimation only available"
" if bootstrap=True")
random_state = check_random_state(self.random_state)
if not self.warm_start or not hasattr(self, "estimators_"):
# Free allocated memory, if any
self.estimators_ = []
n_more_estimators = self.n_estimators - len(self.estimators_)
if n_more_estimators < 0:
raise ValueError('n_estimators=%d must be larger or equal to '
'len(estimators_)=%d when warm_start==True'
% (self.n_estimators, len(self.estimators_)))
elif n_more_estimators == 0:
warn("Warm-start fitting without increasing n_estimators does not "
"fit new trees.")
else:
if self.warm_start and len(self.estimators_) > 0:
# We draw from the random state to get the random state we
# would have got if we hadn't used a warm_start.
random_state.randint(MAX_INT, size=len(self.estimators_))
trees = []
for i in range(n_more_estimators):
tree = self._make_estimator(append=False,
random_state=random_state)
trees.append(tree)
# Parallel loop: we use the threading backend as the Cython code
# for fitting the trees is internally releasing the Python GIL
# making threading always more efficient than multiprocessing in
# that case.
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
backend="threading")(
delayed(_parallel_build_trees)(
t, self, X, y, sample_weight, i, len(trees),
verbose=self.verbose, class_weight=self.class_weight)
for i, t in enumerate(trees))
# Collect newly grown trees
self.estimators_.extend(trees)
if self.oob_score:
self._set_oob_score(X, y)
# Decapsulate classes_ attributes
if hasattr(self, "classes_") and self.n_outputs_ == 1:
self.n_classes_ = self.n_classes_[0]
self.classes_ = self.classes_[0]
return self
@abstractmethod
def _set_oob_score(self, X, y):
"""Calculate out of bag predictions and score."""
def _validate_y_class_weight(self, y):
# Default implementation
return y, None
def _validate_X_predict(self, X):
"""Validate X whenever one tries to predict, apply, predict_proba"""
if self.estimators_ is None or len(self.estimators_) == 0:
raise NotFittedError("Estimator not fitted, "
"call `fit` before exploiting the model.")
return self.estimators_[0]._validate_X_predict(X, check_input=True)
@property
def feature_importances_(self):
"""Return the feature importances (the higher, the more important the
feature).
Returns
-------
feature_importances_ : array, shape = [n_features]
"""
check_is_fitted(self, 'estimators_')
all_importances = Parallel(n_jobs=self.n_jobs,
backend="threading")(
delayed(getattr)(tree, 'feature_importances_')
for tree in self.estimators_)
return sum(all_importances) / len(self.estimators_)
class ForestClassifier(six.with_metaclass(ABCMeta, BaseForest,
ClassifierMixin)):
"""Base class for forest of trees-based classifiers.
Warning: This class should not be used directly. Use derived classes
instead.
"""
@abstractmethod
def __init__(self,
base_estimator,
n_estimators=10,
estimator_params=tuple(),
bootstrap=False,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None):
super(ForestClassifier, self).__init__(
base_estimator,
n_estimators=n_estimators,
estimator_params=estimator_params,
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
class_weight=class_weight)
def _set_oob_score(self, X, y):
"""Compute out-of-bag score"""
X = check_array(X, dtype=DTYPE, accept_sparse='csr')
n_classes_ = self.n_classes_
n_samples = y.shape[0]
oob_decision_function = []
oob_score = 0.0
predictions = []
for k in range(self.n_outputs_):
predictions.append(np.zeros((n_samples, n_classes_[k])))
for estimator in self.estimators_:
unsampled_indices = _generate_unsampled_indices(
estimator.random_state, n_samples)
p_estimator = estimator.predict_proba(X[unsampled_indices, :],
check_input=False)
if self.n_outputs_ == 1:
p_estimator = [p_estimator]
for k in range(self.n_outputs_):
predictions[k][unsampled_indices, :] += p_estimator[k]
for k in range(self.n_outputs_):
if (predictions[k].sum(axis=1) == 0).any():
warn("Some inputs do not have OOB scores. "
"This probably means too few trees were used "
"to compute any reliable oob estimates.")
decision = (predictions[k] /
predictions[k].sum(axis=1)[:, np.newaxis])
oob_decision_function.append(decision)
oob_score += np.mean(y[:, k] ==
np.argmax(predictions[k], axis=1), axis=0)
if self.n_outputs_ == 1:
self.oob_decision_function_ = oob_decision_function[0]
else:
self.oob_decision_function_ = oob_decision_function
self.oob_score_ = oob_score / self.n_outputs_
def _validate_y_class_weight(self, y):
check_classification_targets(y)
y = np.copy(y)
expanded_class_weight = None
if self.class_weight is not None:
y_original = np.copy(y)
self.classes_ = []
self.n_classes_ = []
y_store_unique_indices = np.zeros(y.shape, dtype=np.int)
for k in range(self.n_outputs_):
classes_k, y_store_unique_indices[:, k] = np.unique(y[:, k], return_inverse=True)
self.classes_.append(classes_k)
self.n_classes_.append(classes_k.shape[0])
y = y_store_unique_indices
if self.class_weight is not None:
valid_presets = ('balanced', 'balanced_subsample')
if isinstance(self.class_weight, six.string_types):
if self.class_weight not in valid_presets:
raise ValueError('Valid presets for class_weight include '
'"balanced" and "balanced_subsample". Given "%s".'
% self.class_weight)
if self.warm_start:
warn('class_weight presets "balanced" or "balanced_subsample" are '
'not recommended for warm_start if the fitted data '
'differs from the full dataset. In order to use '
'"balanced" weights, use compute_class_weight("balanced", '
'classes, y). In place of y you can use 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.')
if (self.class_weight != 'balanced_subsample' or
not self.bootstrap):
if self.class_weight == "balanced_subsample":
class_weight = "balanced"
else:
class_weight = self.class_weight
expanded_class_weight = compute_sample_weight(class_weight,
y_original)
return y, expanded_class_weight
def predict(self, X):
"""Predict class for X.
The predicted class of an input sample is a vote by the trees in
the forest, weighted by their probability estimates. That is,
the predicted class is the one with highest mean probability
estimate across the trees.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted classes.
"""
proba = self.predict_proba(X)
if self.n_outputs_ == 1:
return self.classes_.take(np.argmax(proba, axis=1), axis=0)
else:
n_samples = proba[0].shape[0]
predictions = np.zeros((n_samples, self.n_outputs_))
for k in range(self.n_outputs_):
predictions[:, k] = self.classes_[k].take(np.argmax(proba[k],
axis=1),
axis=0)
return predictions
def predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample are computed as
the mean predicted class probabilities of the trees in the forest. The
class probability of a single tree is the fraction of samples of the same
class in a leaf.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
p : array of shape = [n_samples, n_classes], or a list of n_outputs
such arrays if n_outputs > 1.
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute `classes_`.
"""
check_is_fitted(self, 'estimators_')
# Check data
X = self._validate_X_predict(X)
# Assign chunk of trees to jobs
n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
# Parallel loop
all_proba = Parallel(n_jobs=n_jobs, verbose=self.verbose,
backend="threading")(
delayed(parallel_helper)(e, 'predict_proba', X,
check_input=False)
for e in self.estimators_)
# Reduce
proba = all_proba[0]
if self.n_outputs_ == 1:
for j in range(1, len(all_proba)):
proba += all_proba[j]
proba /= len(self.estimators_)
else:
for j in range(1, len(all_proba)):
for k in range(self.n_outputs_):
proba[k] += all_proba[j][k]
for k in range(self.n_outputs_):
proba[k] /= self.n_estimators
return proba
def predict_log_proba(self, X):
"""Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the trees in the
forest.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
p : array of shape = [n_samples, n_classes], or a list of n_outputs
such arrays if n_outputs > 1.
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute `classes_`.
"""
proba = self.predict_proba(X)
if self.n_outputs_ == 1:
return np.log(proba)
else:
for k in range(self.n_outputs_):
proba[k] = np.log(proba[k])
return proba
class ForestRegressor(six.with_metaclass(ABCMeta, BaseForest, RegressorMixin)):
"""Base class for forest of trees-based regressors.
Warning: This class should not be used directly. Use derived classes
instead.
"""
@abstractmethod
def __init__(self,
base_estimator,
n_estimators=10,
estimator_params=tuple(),
bootstrap=False,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False):
super(ForestRegressor, self).__init__(
base_estimator,
n_estimators=n_estimators,
estimator_params=estimator_params,
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start)
def predict(self, X):
"""Predict regression target for X.
The predicted regression target of an input sample is computed as the
mean predicted regression targets of the trees in the forest.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted values.
"""
check_is_fitted(self, 'estimators_')
# Check data
X = self._validate_X_predict(X)
# Assign chunk of trees to jobs
n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
# Parallel loop
all_y_hat = Parallel(n_jobs=n_jobs, verbose=self.verbose,
backend="threading")(
delayed(parallel_helper)(e, 'predict', X, check_input=False)
for e in self.estimators_)
# Reduce
y_hat = sum(all_y_hat) / len(self.estimators_)
return y_hat
def _set_oob_score(self, X, y):
"""Compute out-of-bag scores"""
X = check_array(X, dtype=DTYPE, accept_sparse='csr')
n_samples = y.shape[0]
predictions = np.zeros((n_samples, self.n_outputs_))
n_predictions = np.zeros((n_samples, self.n_outputs_))
for estimator in self.estimators_:
unsampled_indices = _generate_unsampled_indices(
estimator.random_state, n_samples)
p_estimator = estimator.predict(
X[unsampled_indices, :], check_input=False)
if self.n_outputs_ == 1:
p_estimator = p_estimator[:, np.newaxis]
predictions[unsampled_indices, :] += p_estimator
n_predictions[unsampled_indices, :] += 1
if (n_predictions == 0).any():
warn("Some inputs do not have OOB scores. "
"This probably means too few trees were used "
"to compute any reliable oob estimates.")
n_predictions[n_predictions == 0] = 1
predictions /= n_predictions
self.oob_prediction_ = predictions
if self.n_outputs_ == 1:
self.oob_prediction_ = \
self.oob_prediction_.reshape((n_samples, ))
self.oob_score_ = 0.0
for k in range(self.n_outputs_):
self.oob_score_ += r2_score(y[:, k],
predictions[:, k])
self.oob_score_ /= self.n_outputs_
class RandomForestClassifier(ForestClassifier):
"""A random forest classifier.
A random forest is a meta estimator that fits a number of decision tree
classifiers on various sub-samples of the dataset and use averaging to
improve the predictive accuracy and control over-fitting.
The sub-sample size is always the same as the original
input sample size but the samples are drawn with replacement if
`bootstrap=True` (default).
Read more in the :ref:`User Guide <forest>`.
Parameters
----------
n_estimators : integer, optional (default=10)
The number of trees in the forest.
criterion : string, optional (default="gini")
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "entropy" for the information gain.
Note: this parameter is tree-specific.
max_features : int, float, string or None, optional (default="auto")
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a percentage and
`int(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=sqrt(n_features)`.
- If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_depth : integer or None, optional (default=None)
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int, float, optional (default=2)
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a percentage and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for percentages.
min_samples_leaf : int, float, optional (default=1)
The minimum number of samples required to be at a leaf node:
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a percentage and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for percentages.
min_weight_fraction_leaf : float, optional (default=0.)
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_leaf_nodes : int or None, optional (default=None)
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_split : float, optional (default=1e-7)
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
.. versionadded:: 0.18
bootstrap : boolean, optional (default=True)
Whether bootstrap samples are used when building trees.
oob_score : bool (default=False)
Whether to use out-of-bag samples to estimate
the generalization accuracy.
n_jobs : integer, optional (default=1)
The number of jobs to run in parallel for both `fit` and `predict`.
If -1, then the number of jobs is set to the number of cores.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
verbose : int, optional (default=0)
Controls the verbosity of the tree building process.
warm_start : bool, optional (default=False)
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest.
class_weight : dict, list of dicts, "balanced",
"balanced_subsample" or None, optional (default=None)
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
The "balanced_subsample" mode is the same as "balanced" except that
weights are computed based on the bootstrap sample for every tree
grown.
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
Attributes
----------
estimators_ : list of DecisionTreeClassifier
The collection of fitted sub-estimators.
classes_ : array of shape = [n_classes] or a list of such arrays
The classes labels (single output problem), or a list of arrays of
class labels (multi-output problem).
n_classes_ : int or list
The number of classes (single output problem), or a list containing the
number of classes for each output (multi-output problem).
n_features_ : int
The number of features when ``fit`` is performed.
n_outputs_ : int
The number of outputs when ``fit`` is performed.
feature_importances_ : array of shape = [n_features]
The feature importances (the higher, the more important the feature).
oob_score_ : float
Score of the training dataset obtained using an out-of-bag estimate.
oob_decision_function_ : array of shape = [n_samples, n_classes]
Decision function computed with out-of-bag estimate on the training
set. If n_estimators is small it might be possible that a data point
was never left out during the bootstrap. In this case,
`oob_decision_function_` might contain NaN.
Notes
-----
The features are always randomly permuted at each split. Therefore,
the best found split may vary, even with the same training data,
``max_features=n_features`` and ``bootstrap=False``, if the improvement
of the criterion is identical for several splits enumerated during the
search of the best split. To obtain a deterministic behaviour during
fitting, ``random_state`` has to be fixed.
References
----------
.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
See also
--------
DecisionTreeClassifier, ExtraTreesClassifier
"""
def __init__(self,
n_estimators=10,
criterion="gini",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features="auto",
max_leaf_nodes=None,
min_impurity_split=1e-7,
bootstrap=True,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None):
super(RandomForestClassifier, self).__init__(
base_estimator=DecisionTreeClassifier(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
"min_samples_leaf", "min_weight_fraction_leaf",
"max_features", "max_leaf_nodes", "min_impurity_split",
"random_state"),
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
class_weight=class_weight)
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_split = min_impurity_split
class RandomForestRegressor(ForestRegressor):
"""A random forest regressor.
A random forest is a meta estimator that fits a number of classifying
decision trees on various sub-samples of the dataset and use averaging
to improve the predictive accuracy and control over-fitting.
The sub-sample size is always the same as the original
input sample size but the samples are drawn with replacement if
`bootstrap=True` (default).
Read more in the :ref:`User Guide <forest>`.
Parameters
----------
n_estimators : integer, optional (default=10)
The number of trees in the forest.
criterion : string, optional (default="mse")
The function to measure the quality of a split. Supported criteria
are "mse" for the mean squared error, which is equal to variance
reduction as feature selection criterion, and "mae" for the mean
absolute error.
.. versionadded:: 0.18
Mean Absolute Error (MAE) criterion.
max_features : int, float, string or None, optional (default="auto")
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a percentage and
`int(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=n_features`.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_depth : integer or None, optional (default=None)
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int, float, optional (default=2)
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a percentage and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for percentages.
min_samples_leaf : int, float, optional (default=1)
The minimum number of samples required to be at a leaf node:
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a percentage and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for percentages.
min_weight_fraction_leaf : float, optional (default=0.)
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_leaf_nodes : int or None, optional (default=None)
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_split : float, optional (default=1e-7)
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
.. versionadded:: 0.18
bootstrap : boolean, optional (default=True)
Whether bootstrap samples are used when building trees.
oob_score : bool, optional (default=False)
whether to use out-of-bag samples to estimate
the R^2 on unseen data.
n_jobs : integer, optional (default=1)
The number of jobs to run in parallel for both `fit` and `predict`.
If -1, then the number of jobs is set to the number of cores.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
verbose : int, optional (default=0)
Controls the verbosity of the tree building process.
warm_start : bool, optional (default=False)
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest.
Attributes
----------
estimators_ : list of DecisionTreeRegressor
The collection of fitted sub-estimators.
feature_importances_ : array of shape = [n_features]
The feature importances (the higher, the more important the feature).
n_features_ : int
The number of features when ``fit`` is performed.
n_outputs_ : int
The number of outputs when ``fit`` is performed.
oob_score_ : float
Score of the training dataset obtained using an out-of-bag estimate.
oob_prediction_ : array of shape = [n_samples]
Prediction computed with out-of-bag estimate on the training set.
Notes
-----
The features are always randomly permuted at each split. Therefore,
the best found split may vary, even with the same training data,
``max_features=n_features`` and ``bootstrap=False``, if the improvement
of the criterion is identical for several splits enumerated during the
search of the best split. To obtain a deterministic behaviour during
fitting, ``random_state`` has to be fixed.
References
----------
.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
See also
--------
DecisionTreeRegressor, ExtraTreesRegressor
"""
def __init__(self,
n_estimators=10,
criterion="mse",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features="auto",
max_leaf_nodes=None,
min_impurity_split=1e-7,
bootstrap=True,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False):
super(RandomForestRegressor, self).__init__(
base_estimator=DecisionTreeRegressor(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
"min_samples_leaf", "min_weight_fraction_leaf",
"max_features", "max_leaf_nodes", "min_impurity_split",
"random_state"),
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start)
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_split = min_impurity_split
class ExtraTreesClassifier(ForestClassifier):
"""An extra-trees classifier.
This class implements a meta estimator that fits a number of
randomized decision trees (a.k.a. extra-trees) on various sub-samples
of the dataset and use averaging to improve the predictive accuracy
and control over-fitting.
Read more in the :ref:`User Guide <forest>`.
Parameters
----------
n_estimators : integer, optional (default=10)
The number of trees in the forest.
criterion : string, optional (default="gini")
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "entropy" for the information gain.
max_features : int, float, string or None, optional (default="auto")
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a percentage and
`int(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=sqrt(n_features)`.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_depth : integer or None, optional (default=None)
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int, float, optional (default=2)
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a percentage and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for percentages.
min_samples_leaf : int, float, optional (default=1)
The minimum number of samples required to be at a leaf node:
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a percentage and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for percentages.
min_weight_fraction_leaf : float, optional (default=0.)
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_leaf_nodes : int or None, optional (default=None)
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_split : float, optional (default=1e-7)
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
.. versionadded:: 0.18
bootstrap : boolean, optional (default=False)
Whether bootstrap samples are used when building trees.
oob_score : bool, optional (default=False)
Whether to use out-of-bag samples to estimate
the generalization accuracy.
n_jobs : integer, optional (default=1)
The number of jobs to run in parallel for both `fit` and `predict`.
If -1, then the number of jobs is set to the number of cores.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
verbose : int, optional (default=0)
Controls the verbosity of the tree building process.
warm_start : bool, optional (default=False)
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest.
class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional (default=None)
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
The "balanced_subsample" mode is the same as "balanced" except that weights are
computed based on the bootstrap sample for every tree grown.
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
Attributes
----------
estimators_ : list of DecisionTreeClassifier
The collection of fitted sub-estimators.
classes_ : array of shape = [n_classes] or a list of such arrays
The classes labels (single output problem), or a list of arrays of
class labels (multi-output problem).
n_classes_ : int or list
The number of classes (single output problem), or a list containing the
number of classes for each output (multi-output problem).
feature_importances_ : array of shape = [n_features]
The feature importances (the higher, the more important the feature).
n_features_ : int
The number of features when ``fit`` is performed.
n_outputs_ : int
The number of outputs when ``fit`` is performed.
oob_score_ : float
Score of the training dataset obtained using an out-of-bag estimate.
oob_decision_function_ : array of shape = [n_samples, n_classes]
Decision function computed with out-of-bag estimate on the training
set. If n_estimators is small it might be possible that a data point
was never left out during the bootstrap. In this case,
`oob_decision_function_` might contain NaN.
References
----------
.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
Machine Learning, 63(1), 3-42, 2006.
See also
--------
sklearn.tree.ExtraTreeClassifier : Base classifier for this ensemble.
RandomForestClassifier : Ensemble Classifier based on trees with optimal
splits.
"""
def __init__(self,
n_estimators=10,
criterion="gini",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features="auto",
max_leaf_nodes=None,
min_impurity_split=1e-7,
bootstrap=False,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None):
super(ExtraTreesClassifier, self).__init__(
base_estimator=ExtraTreeClassifier(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
"min_samples_leaf", "min_weight_fraction_leaf",
"max_features", "max_leaf_nodes", "min_impurity_split",
"random_state"),
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
class_weight=class_weight)
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_split = min_impurity_split
class ExtraTreesRegressor(ForestRegressor):
"""An extra-trees regressor.
This class implements a meta estimator that fits a number of
randomized decision trees (a.k.a. extra-trees) on various sub-samples
of the dataset and use averaging to improve the predictive accuracy
and control over-fitting.
Read more in the :ref:`User Guide <forest>`.
Parameters
----------
n_estimators : integer, optional (default=10)
The number of trees in the forest.
criterion : string, optional (default="mse")
The function to measure the quality of a split. Supported criteria
are "mse" for the mean squared error, which is equal to variance
reduction as feature selection criterion, and "mae" for the mean
absolute error.
.. versionadded:: 0.18
Mean Absolute Error (MAE) criterion.
max_features : int, float, string or None, optional (default="auto")
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a percentage and
`int(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=n_features`.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_depth : integer or None, optional (default=None)
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int, float, optional (default=2)
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a percentage and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for percentages.
min_samples_leaf : int, float, optional (default=1)
The minimum number of samples required to be at a leaf node:
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a percentage and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for percentages.
min_weight_fraction_leaf : float, optional (default=0.)
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_leaf_nodes : int or None, optional (default=None)
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_split : float, optional (default=1e-7)
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
.. versionadded:: 0.18
bootstrap : boolean, optional (default=False)
Whether bootstrap samples are used when building trees.
oob_score : bool, optional (default=False)
Whether to use out-of-bag samples to estimate the R^2 on unseen data.
n_jobs : integer, optional (default=1)
The number of jobs to run in parallel for both `fit` and `predict`.
If -1, then the number of jobs is set to the number of cores.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
verbose : int, optional (default=0)
Controls the verbosity of the tree building process.
warm_start : bool, optional (default=False)
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest.
Attributes
----------
estimators_ : list of DecisionTreeRegressor
The collection of fitted sub-estimators.
feature_importances_ : array of shape = [n_features]
The feature importances (the higher, the more important the feature).
n_features_ : int
The number of features.
n_outputs_ : int
The number of outputs.
oob_score_ : float
Score of the training dataset obtained using an out-of-bag estimate.
oob_prediction_ : array of shape = [n_samples]
Prediction computed with out-of-bag estimate on the training set.
References
----------
.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
Machine Learning, 63(1), 3-42, 2006.
See also
--------
sklearn.tree.ExtraTreeRegressor: Base estimator for this ensemble.
RandomForestRegressor: Ensemble regressor using trees with optimal splits.
"""
def __init__(self,
n_estimators=10,
criterion="mse",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features="auto",
max_leaf_nodes=None,
min_impurity_split=1e-7,
bootstrap=False,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False):
super(ExtraTreesRegressor, self).__init__(
base_estimator=ExtraTreeRegressor(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
"min_samples_leaf", "min_weight_fraction_leaf",
"max_features", "max_leaf_nodes", "min_impurity_split",
"random_state"),
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start)
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_split = min_impurity_split
class RandomTreesEmbedding(BaseForest):
"""An ensemble of totally random trees.
An unsupervised transformation of a dataset to a high-dimensional
sparse representation. A datapoint is coded according to which leaf of
each tree it is sorted into. Using a one-hot encoding of the leaves,
this leads to a binary coding with as many ones as there are trees in
the forest.
The dimensionality of the resulting representation is
``n_out <= n_estimators * max_leaf_nodes``. If ``max_leaf_nodes == None``,
the number of leaf nodes is at most ``n_estimators * 2 ** max_depth``.
Read more in the :ref:`User Guide <random_trees_embedding>`.
Parameters
----------
n_estimators : integer, optional (default=10)
Number of trees in the forest.
max_depth : integer, optional (default=5)
The maximum depth of each tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int, float, optional (default=2)
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a percentage and
`ceil(min_samples_split * n_samples)` is the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for percentages.
min_samples_leaf : int, float, optional (default=1)
The minimum number of samples required to be at a leaf node:
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a percentage and
`ceil(min_samples_leaf * n_samples)` is the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for percentages.
min_weight_fraction_leaf : float, optional (default=0.)
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_leaf_nodes : int or None, optional (default=None)
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_split : float, optional (default=1e-7)
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
.. versionadded:: 0.18
sparse_output : bool, optional (default=True)
Whether or not to return a sparse CSR matrix, as default behavior,
or to return a dense array compatible with dense pipeline operators.
n_jobs : integer, optional (default=1)
The number of jobs to run in parallel for both `fit` and `predict`.
If -1, then the number of jobs is set to the number of cores.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
verbose : int, optional (default=0)
Controls the verbosity of the tree building process.
warm_start : bool, optional (default=False)
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest.
Attributes
----------
estimators_ : list of DecisionTreeClassifier
The collection of fitted sub-estimators.
References
----------
.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
Machine Learning, 63(1), 3-42, 2006.
.. [2] Moosmann, F. and Triggs, B. and Jurie, F. "Fast discriminative
visual codebooks using randomized clustering forests"
NIPS 2007
"""
def __init__(self,
n_estimators=10,
max_depth=5,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_leaf_nodes=None,
min_impurity_split=1e-7,
sparse_output=True,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False):
super(RandomTreesEmbedding, self).__init__(
base_estimator=ExtraTreeRegressor(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
"min_samples_leaf", "min_weight_fraction_leaf",
"max_features", "max_leaf_nodes", "min_impurity_split",
"random_state"),
bootstrap=False,
oob_score=False,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start)
self.criterion = 'mse'
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = 1
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_split = min_impurity_split
self.sparse_output = sparse_output
def _set_oob_score(self, X, y):
raise NotImplementedError("OOB score not supported by tree embedding")
def fit(self, X, y=None, sample_weight=None):
"""Fit estimator.
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
The input samples. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported, use sparse
``csc_matrix`` for maximum efficiency.
Returns
-------
self : object
Returns self.
"""
self.fit_transform(X, y, sample_weight=sample_weight)
return self
def fit_transform(self, X, y=None, sample_weight=None):
"""Fit estimator and transform dataset.
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
Input data used to build forests. Use ``dtype=np.float32`` for
maximum efficiency.
Returns
-------
X_transformed : sparse matrix, shape=(n_samples, n_out)
Transformed dataset.
"""
X = check_array(X, accept_sparse=['csc'])
if issparse(X):
# Pre-sort indices to avoid that each individual tree of the
# ensemble sorts the indices.
X.sort_indices()
rnd = check_random_state(self.random_state)
y = rnd.uniform(size=X.shape[0])
super(RandomTreesEmbedding, self).fit(X, y,
sample_weight=sample_weight)
self.one_hot_encoder_ = OneHotEncoder(sparse=self.sparse_output)
return self.one_hot_encoder_.fit_transform(self.apply(X))
def transform(self, X):
"""Transform dataset.
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
Input data to be transformed. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported, use sparse
``csr_matrix`` for maximum efficiency.
Returns
-------
X_transformed : sparse matrix, shape=(n_samples, n_out)
Transformed dataset.
"""
return self.one_hot_encoder_.transform(self.apply(X))
| bsd-3-clause |
BigDataforYou/movie_recommendation_workshop_1 | big_data_4_you_demo_1/venv/lib/python2.7/site-packages/pandas/stats/tests/test_math.py | 9 | 1836 | import nose
from datetime import datetime
from numpy.random import randn
import numpy as np
from pandas.core.api import Series, DataFrame, date_range
import pandas.util.testing as tm
import pandas.stats.math as pmath
from pandas import ols
N, K = 100, 10
_have_statsmodels = True
try:
import statsmodels.api as sm
except ImportError:
try:
import scikits.statsmodels.api as sm # noqa
except ImportError:
_have_statsmodels = False
class TestMath(tm.TestCase):
_nan_locs = np.arange(20, 40)
_inf_locs = np.array([])
def setUp(self):
arr = randn(N)
arr[self._nan_locs] = np.NaN
self.arr = arr
self.rng = date_range(datetime(2009, 1, 1), periods=N)
self.series = Series(arr.copy(), index=self.rng)
self.frame = DataFrame(randn(N, K), index=self.rng,
columns=np.arange(K))
def test_rank_1d(self):
self.assertEqual(1, pmath.rank(self.series))
self.assertEqual(0, pmath.rank(Series(0, self.series.index)))
def test_solve_rect(self):
if not _have_statsmodels:
raise nose.SkipTest("no statsmodels")
b = Series(np.random.randn(N), self.frame.index)
result = pmath.solve(self.frame, b)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
expected = ols(y=b, x=self.frame, intercept=False).beta
self.assertTrue(np.allclose(result, expected))
def test_inv_illformed(self):
singular = DataFrame(np.array([[1, 1], [2, 2]]))
rs = pmath.inv(singular)
expected = np.array([[0.1, 0.2], [0.1, 0.2]])
self.assertTrue(np.allclose(rs, expected))
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
| mit |
Insight-book/data-science-from-scratch | first-edition/code/gradient_descent.py | 53 | 5895 | from __future__ import division
from collections import Counter
from linear_algebra import distance, vector_subtract, scalar_multiply
import math, random
def sum_of_squares(v):
"""computes the sum of squared elements in v"""
return sum(v_i ** 2 for v_i in v)
def difference_quotient(f, x, h):
return (f(x + h) - f(x)) / h
def plot_estimated_derivative():
def square(x):
return x * x
def derivative(x):
return 2 * x
derivative_estimate = lambda x: difference_quotient(square, x, h=0.00001)
# plot to show they're basically the same
import matplotlib.pyplot as plt
x = range(-10,10)
plt.plot(x, map(derivative, x), 'rx') # red x
plt.plot(x, map(derivative_estimate, x), 'b+') # blue +
plt.show() # purple *, hopefully
def partial_difference_quotient(f, v, i, h):
# add h to just the i-th element of v
w = [v_j + (h if j == i else 0)
for j, v_j in enumerate(v)]
return (f(w) - f(v)) / h
def estimate_gradient(f, v, h=0.00001):
return [partial_difference_quotient(f, v, i, h)
for i, _ in enumerate(v)]
def step(v, direction, step_size):
"""move step_size in the direction from v"""
return [v_i + step_size * direction_i
for v_i, direction_i in zip(v, direction)]
def sum_of_squares_gradient(v):
return [2 * v_i for v_i in v]
def safe(f):
"""define a new function that wraps f and return it"""
def safe_f(*args, **kwargs):
try:
return f(*args, **kwargs)
except:
return float('inf') # this means "infinity" in Python
return safe_f
#
#
# minimize / maximize batch
#
#
def minimize_batch(target_fn, gradient_fn, theta_0, tolerance=0.000001):
"""use gradient descent to find theta that minimizes target function"""
step_sizes = [100, 10, 1, 0.1, 0.01, 0.001, 0.0001, 0.00001]
theta = theta_0 # set theta to initial value
target_fn = safe(target_fn) # safe version of target_fn
value = target_fn(theta) # value we're minimizing
while True:
gradient = gradient_fn(theta)
next_thetas = [step(theta, gradient, -step_size)
for step_size in step_sizes]
# choose the one that minimizes the error function
next_theta = min(next_thetas, key=target_fn)
next_value = target_fn(next_theta)
# stop if we're "converging"
if abs(value - next_value) < tolerance:
return theta
else:
theta, value = next_theta, next_value
def negate(f):
"""return a function that for any input x returns -f(x)"""
return lambda *args, **kwargs: -f(*args, **kwargs)
def negate_all(f):
"""the same when f returns a list of numbers"""
return lambda *args, **kwargs: [-y for y in f(*args, **kwargs)]
def maximize_batch(target_fn, gradient_fn, theta_0, tolerance=0.000001):
return minimize_batch(negate(target_fn),
negate_all(gradient_fn),
theta_0,
tolerance)
#
# minimize / maximize stochastic
#
def in_random_order(data):
"""generator that returns the elements of data in random order"""
indexes = [i for i, _ in enumerate(data)] # create a list of indexes
random.shuffle(indexes) # shuffle them
for i in indexes: # return the data in that order
yield data[i]
def minimize_stochastic(target_fn, gradient_fn, x, y, theta_0, alpha_0=0.01):
data = zip(x, y)
theta = theta_0 # initial guess
alpha = alpha_0 # initial step size
min_theta, min_value = None, float("inf") # the minimum so far
iterations_with_no_improvement = 0
# if we ever go 100 iterations with no improvement, stop
while iterations_with_no_improvement < 100:
value = sum( target_fn(x_i, y_i, theta) for x_i, y_i in data )
if value < min_value:
# if we've found a new minimum, remember it
# and go back to the original step size
min_theta, min_value = theta, value
iterations_with_no_improvement = 0
alpha = alpha_0
else:
# otherwise we're not improving, so try shrinking the step size
iterations_with_no_improvement += 1
alpha *= 0.9
# and take a gradient step for each of the data points
for x_i, y_i in in_random_order(data):
gradient_i = gradient_fn(x_i, y_i, theta)
theta = vector_subtract(theta, scalar_multiply(alpha, gradient_i))
return min_theta
def maximize_stochastic(target_fn, gradient_fn, x, y, theta_0, alpha_0=0.01):
return minimize_stochastic(negate(target_fn),
negate_all(gradient_fn),
x, y, theta_0, alpha_0)
if __name__ == "__main__":
print "using the gradient"
v = [random.randint(-10,10) for i in range(3)]
tolerance = 0.0000001
while True:
#print v, sum_of_squares(v)
gradient = sum_of_squares_gradient(v) # compute the gradient at v
next_v = step(v, gradient, -0.01) # take a negative gradient step
if distance(next_v, v) < tolerance: # stop if we're converging
break
v = next_v # continue if we're not
print "minimum v", v
print "minimum value", sum_of_squares(v)
print
print "using minimize_batch"
v = [random.randint(-10,10) for i in range(3)]
v = minimize_batch(sum_of_squares, sum_of_squares_gradient, v)
print "minimum v", v
print "minimum value", sum_of_squares(v)
| unlicense |
macioosch/dynamo-hard-spheres-sim | convergence-plot.py | 1 | 6346 | #!/usr/bin/env python2
# encoding=utf-8
from __future__ import division, print_function
from glob import glob
from itertools import izip
from matplotlib import pyplot as plt
import numpy as np
input_files = glob("csv/convergence-256000-0.*.csv")
#input_files = glob("csv/convergence-500000-0.*.csv")
#input_files = glob("csv/convergence-1000188-0.*.csv")
#plotted_parameter = "msds_diffusion"
plotted_parameter = "pressures_collision"
#plotted_parameter = "pressures_virial"
#plotted_parameter = "msds_val"
#plotted_parameter = "times"
legend_names = []
tight_layout = False
show_legend = False
for file_number, file_name in enumerate(sorted(input_files)):
data = np.genfromtxt(file_name, delimiter='\t', names=[
"packings","densities","collisions","n_atoms","pressures_virial",
"pressures_collision","msds_val","msds_diffusion","times",
"std_pressures_virial","std_pressures_collision","std_msds_val",
"std_msds_diffusion","std_times"])
n_atoms = data["n_atoms"][0]
density = data["densities"][0]
equilibrated_collisions = data["collisions"] - 2*data["collisions"][0] \
+ data["collisions"][1]
"""
### 5 graphs: D(CPS) ###
tight_layout = True
skip_points = 0
ax = plt.subplot(3, 2, file_number+1)
plt.fill_between((equilibrated_collisions / n_atoms)[skip_points:],
data[plotted_parameter][skip_points:]
- data["std_" + plotted_parameter][skip_points:],
data[plotted_parameter][skip_points:]
+ data["std_" + plotted_parameter][skip_points:], alpha=0.3)
plt.plot((equilibrated_collisions / n_atoms)[skip_points:],
data[plotted_parameter][skip_points:], lw=2)
if plotted_parameter == "msds_diffusion":
plt.ylim(0.990*data[plotted_parameter][-1],
1.005*data[plotted_parameter][-1])
plt.xlim([0, 1e5])
plt.legend(["Density {}".format(data["densities"][0])], loc="lower right")
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.4f'))
plt.xlabel("Collisions per sphere")
plt.ylabel("D")
"""
### 5 graphs: relative D(CPS) ###
tight_layout = True
skip_points = 0
ax = plt.subplot(3, 2, file_number+1)
plt.fill_between((equilibrated_collisions / n_atoms)[skip_points:],
-1 + (data[plotted_parameter][skip_points:]
- data["std_" + plotted_parameter][skip_points:])/data[plotted_parameter][-1],
-1 + (data[plotted_parameter][skip_points:]
+ data["std_" + plotted_parameter][skip_points:])/data[plotted_parameter][-1], alpha=0.3)
plt.plot((equilibrated_collisions / n_atoms)[skip_points:],
-1 + data[plotted_parameter][skip_points:]/data[plotted_parameter][-1], lw=2)
plt.ylim(data["std_" + plotted_parameter][-1]*20*np.array([-1, 1])/data[plotted_parameter][-1])
#plt.xscale("log")
plt.xlim([0, 1e5])
plt.legend(["$\\rho\\sigma^3=\\ {}$".format(data["densities"][0])], loc="lower right")
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2e'))
plt.xlabel("$C/N$")
plt.ylabel("$[Z_{MD}(C) / Z_{MD}(C=10^5 N)] - 1$")
"""
### 1 graph: D(t) ###
show_legend = True
skip_points = 0
plt.title("D(t) for 5 densities")
plt.loglog(data["times"][skip_points:],
data[plotted_parameter][skip_points:])
legend_names.append(data["densities"][0])
plt.xlabel("Time")
plt.ylabel("D")
"""
"""
### 1 graph: D(t) / Dinf ###
show_legend = True
skip_points = 0
#plt.fill_between(data["times"][skip_points:],
# (data[plotted_parameter] - data["std_" + plotted_parameter])
# / data[plotted_parameter][-1] - 1,
# (data[plotted_parameter] + data["std_" + plotted_parameter])
# / data[plotted_parameter][-1] - 1, color="grey", alpha=0.4)
plt.plot(data["times"][skip_points:],
data[plotted_parameter] / data[plotted_parameter][-1] - 1, lw=1)
legend_names.append(data["densities"][0])
#plt.xscale("log")
plt.xlabel("Time")
plt.ylabel("D / D(t --> inf)")
"""
"""
### 5 graphs: D(1/CPS) ###
tight_layout = True
skip_points = 40
ax = plt.subplot(3, 2, file_number+1)
plt.fill_between((n_atoms / equilibrated_collisions)[skip_points:],
data[plotted_parameter][skip_points:]
- data["std_" + plotted_parameter][skip_points:],
data[plotted_parameter][skip_points:]
+ data["std_" + plotted_parameter][skip_points:], alpha=0.3)
plt.plot((n_atoms / equilibrated_collisions)[skip_points:],
data[plotted_parameter][skip_points:], lw=2)
plt.title("Density {}:".format(data["densities"][0]))
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.7f'))
plt.xlim(xmin=0)
plt.xlabel("1 / Collisions per sphere")
plt.ylabel("D")
"""
"""
### 1 graph: D(CPS) / Dinf ###
show_legend = True
plt.fill_between(equilibrated_collisions / n_atoms,
(data[plotted_parameter] - data["std_" + plotted_parameter])
/ data[plotted_parameter][-1] - 1,
(data[plotted_parameter] + data["std_" + plotted_parameter])
/ data[plotted_parameter][-1] - 1, color="grey", alpha=0.4)
plt.plot(equilibrated_collisions / n_atoms,
data[plotted_parameter] / data[plotted_parameter][-1] - 1, lw=2)
legend_names.append(data["densities"][0])
plt.xlabel("Collisions per sphere")
plt.ylabel("D / D(t --> inf)")
"""
"""
### 1 graph: D(1/CPS) / Dinf ###
show_legend = True
plt.fill_between(n_atoms / equilibrated_collisions,
(data[plotted_parameter] - data["std_" + plotted_parameter])
/ data[plotted_parameter][-1] - 1,
(data[plotted_parameter] + data["std_" + plotted_parameter])
/ data[plotted_parameter][-1] - 1, color="grey", alpha=0.4)
plt.plot( n_atoms / equilibrated_collisions,
data[plotted_parameter] / data[plotted_parameter][-1] - 1)
legend_names.append(data["densities"][0])
plt.xlabel(" 1 / Collisions per sphere")
plt.ylabel(plotted_parameter)
"""
#if tight_layout:
# plt.tight_layout(pad=0.0, w_pad=0.0, h_pad=0.0)
if show_legend:
plt.legend(legend_names, title="Density:", loc="lower right")
plt.show()
| gpl-3.0 |
webmasterraj/FogOrNot | flask/lib/python2.7/site-packages/pandas/stats/tests/test_moments.py | 3 | 89255 | import nose
import sys
import functools
import warnings
from datetime import datetime
from numpy.random import randn
from numpy.testing.decorators import slow
import numpy as np
from distutils.version import LooseVersion
from pandas import Series, DataFrame, Panel, bdate_range, isnull, notnull, concat
from pandas.util.testing import (
assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, assert_index_equal
)
import pandas.core.datetools as datetools
import pandas.stats.moments as mom
import pandas.util.testing as tm
from pandas.compat import range, zip, PY3, StringIO
N, K = 100, 10
class Base(tm.TestCase):
_multiprocess_can_split_ = True
_nan_locs = np.arange(20, 40)
_inf_locs = np.array([])
def _create_data(self):
arr = randn(N)
arr[self._nan_locs] = np.NaN
self.arr = arr
self.rng = bdate_range(datetime(2009, 1, 1), periods=N)
self.series = Series(arr.copy(), index=self.rng)
self.frame = DataFrame(randn(N, K), index=self.rng,
columns=np.arange(K))
class TestMoments(Base):
def setUp(self):
self._create_data()
warnings.simplefilter("ignore", category=FutureWarning)
def test_centered_axis_validation(self):
# ok
mom.rolling_mean(Series(np.ones(10)),3,center=True ,axis=0)
# bad axis
self.assertRaises(ValueError, mom.rolling_mean,Series(np.ones(10)),3,center=True ,axis=1)
# ok ok
mom.rolling_mean(DataFrame(np.ones((10,10))),3,center=True ,axis=0)
mom.rolling_mean(DataFrame(np.ones((10,10))),3,center=True ,axis=1)
# bad axis
self.assertRaises(ValueError, mom.rolling_mean,DataFrame(np.ones((10,10))),3,center=True ,axis=2)
def test_rolling_sum(self):
self._check_moment_func(mom.rolling_sum, np.sum)
def test_rolling_count(self):
counter = lambda x: np.isfinite(x).astype(float).sum()
self._check_moment_func(mom.rolling_count, counter,
has_min_periods=False,
preserve_nan=False,
fill_value=0)
def test_rolling_mean(self):
self._check_moment_func(mom.rolling_mean, np.mean)
def test_cmov_mean(self):
# GH 8238
tm._skip_if_no_scipy()
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49,
16.68, 9.48, 10.63, 14.48])
xp = np.array([np.nan, np.nan, 9.962, 11.27 , 11.564, 12.516,
12.818, 12.952, np.nan, np.nan])
rs = mom.rolling_mean(vals, 5, center=True)
assert_almost_equal(xp, rs)
xp = Series(rs)
rs = mom.rolling_mean(Series(vals), 5, center=True)
assert_series_equal(xp, rs)
def test_cmov_window(self):
# GH 8238
tm._skip_if_no_scipy()
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81,
13.49, 16.68, 9.48, 10.63, 14.48])
xp = np.array([np.nan, np.nan, 9.962, 11.27 , 11.564, 12.516,
12.818, 12.952, np.nan, np.nan])
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
assert_almost_equal(xp, rs)
xp = Series(rs)
rs = mom.rolling_window(Series(vals), 5, 'boxcar', center=True)
assert_series_equal(xp, rs)
def test_cmov_window_corner(self):
# GH 8238
tm._skip_if_no_scipy()
# all nan
vals = np.empty(10, dtype=float)
vals.fill(np.nan)
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
self.assertTrue(np.isnan(rs).all())
# empty
vals = np.array([])
rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
self.assertEqual(len(rs), 0)
# shorter than window
vals = np.random.randn(5)
rs = mom.rolling_window(vals, 10, 'boxcar')
self.assertTrue(np.isnan(rs).all())
self.assertEqual(len(rs), 5)
def test_cmov_window_frame(self):
# Gh 8238
tm._skip_if_no_scipy()
vals = np.array([[ 12.18, 3.64],
[ 10.18, 9.16],
[ 13.24, 14.61],
[ 4.51, 8.11],
[ 6.15, 11.44],
[ 9.14, 6.21],
[ 11.31, 10.67],
[ 2.94, 6.51],
[ 9.42, 8.39],
[ 12.44, 7.34 ]])
xp = np.array([[ np.nan, np.nan],
[ np.nan, np.nan],
[ 9.252, 9.392],
[ 8.644, 9.906],
[ 8.87 , 10.208],
[ 6.81 , 8.588],
[ 7.792, 8.644],
[ 9.05 , 7.824],
[ np.nan, np.nan],
[ np.nan, np.nan]])
# DataFrame
rs = mom.rolling_window(DataFrame(vals), 5, 'boxcar', center=True)
assert_frame_equal(DataFrame(xp), rs)
def test_cmov_window_na_min_periods(self):
tm._skip_if_no_scipy()
# min_periods
vals = Series(np.random.randn(10))
vals[4] = np.nan
vals[8] = np.nan
xp = mom.rolling_mean(vals, 5, min_periods=4, center=True)
rs = mom.rolling_window(vals, 5, 'boxcar', min_periods=4, center=True)
assert_series_equal(xp, rs)
def test_cmov_window_regular(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
'blackmanharris', 'nuttall', 'barthann']
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81,
13.49, 16.68, 9.48, 10.63, 14.48])
xps = {
'hamming': [np.nan, np.nan, 8.71384, 9.56348, 12.38009,
14.03687, 13.8567, 11.81473, np.nan, np.nan],
'triang': [np.nan, np.nan, 9.28667, 10.34667, 12.00556,
13.33889, 13.38, 12.33667, np.nan, np.nan],
'barthann': [np.nan, np.nan, 8.4425, 9.1925, 12.5575,
14.3675, 14.0825, 11.5675, np.nan, np.nan],
'bohman': [np.nan, np.nan, 7.61599, 9.1764, 12.83559,
14.17267, 14.65923, 11.10401, np.nan, np.nan],
'blackmanharris': [np.nan, np.nan, 6.97691, 9.16438, 13.05052,
14.02156, 15.10512, 10.74574, np.nan, np.nan],
'nuttall': [np.nan, np.nan, 7.04618, 9.16786, 13.02671,
14.03559, 15.05657, 10.78514, np.nan, np.nan],
'blackman': [np.nan, np.nan, 7.73345, 9.17869, 12.79607,
14.20036, 14.57726, 11.16988, np.nan, np.nan],
'bartlett': [np.nan, np.nan, 8.4425, 9.1925, 12.5575,
14.3675, 14.0825, 11.5675, np.nan, np.nan]}
for wt in win_types:
xp = Series(xps[wt])
rs = mom.rolling_window(Series(vals), 5, wt, center=True)
assert_series_equal(xp, rs)
def test_cmov_window_regular_linear_range(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
'blackmanharris', 'nuttall', 'barthann']
vals = np.array(range(10), dtype=np.float)
xp = vals.copy()
xp[:2] = np.nan
xp[-2:] = np.nan
xp = Series(xp)
for wt in win_types:
rs = mom.rolling_window(Series(vals), 5, wt, center=True)
assert_series_equal(xp, rs)
def test_cmov_window_regular_missing_data(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
'blackmanharris', 'nuttall', 'barthann']
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81,
13.49, 16.68, np.nan, 10.63, 14.48])
xps = {
'bartlett': [np.nan, np.nan, 9.70333, 10.5225, 8.4425,
9.1925, 12.5575, 14.3675, 15.61667, 13.655],
'blackman': [np.nan, np.nan, 9.04582, 11.41536, 7.73345,
9.17869, 12.79607, 14.20036, 15.8706, 13.655],
'barthann': [np.nan, np.nan, 9.70333, 10.5225, 8.4425,
9.1925, 12.5575, 14.3675, 15.61667, 13.655],
'bohman': [np.nan, np.nan, 8.9444, 11.56327, 7.61599,
9.1764, 12.83559, 14.17267, 15.90976, 13.655],
'hamming': [np.nan, np.nan, 9.59321, 10.29694, 8.71384,
9.56348, 12.38009, 14.20565, 15.24694, 13.69758],
'nuttall': [np.nan, np.nan, 8.47693, 12.2821, 7.04618,
9.16786, 13.02671, 14.03673, 16.08759, 13.65553],
'triang': [np.nan, np.nan, 9.33167, 9.76125, 9.28667,
10.34667, 12.00556, 13.82125, 14.49429, 13.765],
'blackmanharris': [np.nan, np.nan, 8.42526, 12.36824, 6.97691,
9.16438, 13.05052, 14.02175, 16.1098,
13.65509]
}
for wt in win_types:
xp = Series(xps[wt])
rs = mom.rolling_window(Series(vals), 5, wt, min_periods=3)
assert_series_equal(xp, rs)
def test_cmov_window_special(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['kaiser', 'gaussian', 'general_gaussian', 'slepian']
kwds = [{'beta': 1.}, {'std': 1.}, {'power': 2., 'width': 2.},
{'width': 0.5}]
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81,
13.49, 16.68, 9.48, 10.63, 14.48])
xps = {
'gaussian': [np.nan, np.nan, 8.97297, 9.76077, 12.24763,
13.89053, 13.65671, 12.01002, np.nan, np.nan],
'general_gaussian': [np.nan, np.nan, 9.85011, 10.71589,
11.73161, 13.08516, 12.95111, 12.74577,
np.nan, np.nan],
'slepian': [np.nan, np.nan, 9.81073, 10.89359, 11.70284,
12.88331, 12.96079, 12.77008, np.nan, np.nan],
'kaiser': [np.nan, np.nan, 9.86851, 11.02969, 11.65161,
12.75129, 12.90702, 12.83757, np.nan, np.nan]
}
for wt, k in zip(win_types, kwds):
xp = Series(xps[wt])
rs = mom.rolling_window(Series(vals), 5, wt, center=True,
**k)
assert_series_equal(xp, rs)
def test_cmov_window_special_linear_range(self):
# GH 8238
tm._skip_if_no_scipy()
win_types = ['kaiser', 'gaussian', 'general_gaussian', 'slepian']
kwds = [{'beta': 1.}, {'std': 1.}, {'power': 2., 'width': 2.},
{'width': 0.5}]
vals = np.array(range(10), dtype=np.float)
xp = vals.copy()
xp[:2] = np.nan
xp[-2:] = np.nan
xp = Series(xp)
for wt, k in zip(win_types, kwds):
rs = mom.rolling_window(Series(vals), 5, wt, center=True,
**k)
assert_series_equal(xp, rs)
def test_rolling_median(self):
self._check_moment_func(mom.rolling_median, np.median)
def test_rolling_min(self):
self._check_moment_func(mom.rolling_min, np.min)
a = np.array([1, 2, 3, 4, 5])
b = mom.rolling_min(a, window=100, min_periods=1)
assert_almost_equal(b, np.ones(len(a)))
self.assertRaises(ValueError, mom.rolling_min, np.array([1,
2, 3]), window=3, min_periods=5)
def test_rolling_max(self):
self._check_moment_func(mom.rolling_max, np.max)
a = np.array([1, 2, 3, 4, 5])
b = mom.rolling_max(a, window=100, min_periods=1)
assert_almost_equal(a, b)
self.assertRaises(ValueError, mom.rolling_max, np.array([1,
2, 3]), window=3, min_periods=5)
def test_rolling_quantile(self):
qs = [.1, .5, .9]
def scoreatpercentile(a, per):
values = np.sort(a, axis=0)
idx = per / 1. * (values.shape[0] - 1)
return values[int(idx)]
for q in qs:
def f(x, window, min_periods=None, freq=None, center=False):
return mom.rolling_quantile(x, window, q,
min_periods=min_periods,
freq=freq,
center=center)
def alt(x):
return scoreatpercentile(x, q)
self._check_moment_func(f, alt)
def test_rolling_apply(self):
# suppress warnings about empty slices, as we are deliberately testing with a 0-length Series
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message=".*(empty slice|0 for slice).*", category=RuntimeWarning)
ser = Series([])
assert_series_equal(ser, mom.rolling_apply(ser, 10, lambda x: x.mean()))
def roll_mean(x, window, min_periods=None, freq=None, center=False):
return mom.rolling_apply(x, window,
lambda x: x[np.isfinite(x)].mean(),
min_periods=min_periods,
freq=freq,
center=center)
self._check_moment_func(roll_mean, np.mean)
# GH 8080
s = Series([None, None, None])
result = mom.rolling_apply(s, 2, lambda x: len(x), min_periods=0)
expected = Series([1., 2., 2.])
assert_series_equal(result, expected)
def test_rolling_apply_out_of_bounds(self):
# #1850
arr = np.arange(4)
# it works!
result = mom.rolling_apply(arr, 10, np.sum)
self.assertTrue(isnull(result).all())
result = mom.rolling_apply(arr, 10, np.sum, min_periods=1)
assert_almost_equal(result, result)
def test_rolling_std(self):
self._check_moment_func(mom.rolling_std,
lambda x: np.std(x, ddof=1))
self._check_moment_func(functools.partial(mom.rolling_std, ddof=0),
lambda x: np.std(x, ddof=0))
def test_rolling_std_1obs(self):
result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]),
1, min_periods=1)
expected = np.array([np.nan] * 5)
assert_almost_equal(result, expected)
result = mom.rolling_std(np.array([1., 2., 3., 4., 5.]),
1, min_periods=1, ddof=0)
expected = np.zeros(5)
assert_almost_equal(result, expected)
result = mom.rolling_std(np.array([np.nan, np.nan, 3., 4., 5.]),
3, min_periods=2)
self.assertTrue(np.isnan(result[2]))
def test_rolling_std_neg_sqrt(self):
# unit test from Bottleneck
# Test move_nanstd for neg sqrt.
a = np.array([0.0011448196318903589,
0.00028718669878572767,
0.00028718669878572767,
0.00028718669878572767,
0.00028718669878572767])
b = mom.rolling_std(a, window=3)
self.assertTrue(np.isfinite(b[2:]).all())
b = mom.ewmstd(a, span=3)
self.assertTrue(np.isfinite(b[2:]).all())
def test_rolling_var(self):
self._check_moment_func(mom.rolling_var,
lambda x: np.var(x, ddof=1),
test_stable=True)
self._check_moment_func(functools.partial(mom.rolling_var, ddof=0),
lambda x: np.var(x, ddof=0))
def test_rolling_skew(self):
try:
from scipy.stats import skew
except ImportError:
raise nose.SkipTest('no scipy')
self._check_moment_func(mom.rolling_skew,
lambda x: skew(x, bias=False))
def test_rolling_kurt(self):
try:
from scipy.stats import kurtosis
except ImportError:
raise nose.SkipTest('no scipy')
self._check_moment_func(mom.rolling_kurt,
lambda x: kurtosis(x, bias=False))
def test_fperr_robustness(self):
# TODO: remove this once python 2.5 out of picture
if PY3:
raise nose.SkipTest("doesn't work on python 3")
# #2114
data = '\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x1a@\xaa\xaa\xaa\xaa\xaa\xaa\x02@8\x8e\xe38\x8e\xe3\xe8?z\t\xed%\xb4\x97\xd0?\xa2\x0c<\xdd\x9a\x1f\xb6?\x82\xbb\xfa&y\x7f\x9d?\xac\'\xa7\xc4P\xaa\x83?\x90\xdf\xde\xb0k8j?`\xea\xe9u\xf2zQ?*\xe37\x9d\x98N7?\xe2.\xf5&v\x13\x1f?\xec\xc9\xf8\x19\xa4\xb7\x04?\x90b\xf6w\x85\x9f\xeb>\xb5A\xa4\xfaXj\xd2>F\x02\xdb\xf8\xcb\x8d\xb8>.\xac<\xfb\x87^\xa0>\xe8:\xa6\xf9_\xd3\x85>\xfb?\xe2cUU\xfd?\xfc\x7fA\xed8\x8e\xe3?\xa5\xaa\xac\x91\xf6\x12\xca?n\x1cs\xb6\xf9a\xb1?\xe8%D\xf3L-\x97?5\xddZD\x11\xe7~?#>\xe7\x82\x0b\x9ad?\xd9R4Y\x0fxK?;7x;\nP2?N\xf4JO\xb8j\x18?4\xf81\x8a%G\x00?\x9a\xf5\x97\r2\xb4\xe5>\xcd\x9c\xca\xbcB\xf0\xcc>3\x13\x87(\xd7J\xb3>\x99\x19\xb4\xe0\x1e\xb9\x99>ff\xcd\x95\x14&\x81>\x88\x88\xbc\xc7p\xddf>`\x0b\xa6_\x96|N>@\xb2n\xea\x0eS4>U\x98\x938i\x19\x1b>\x8eeb\xd0\xf0\x10\x02>\xbd\xdc-k\x96\x16\xe8=(\x93\x1e\xf2\x0e\x0f\xd0=\xe0n\xd3Bii\xb5=*\xe9\x19Y\x8c\x8c\x9c=\xc6\xf0\xbb\x90]\x08\x83=]\x96\xfa\xc0|`i=>d\xfc\xd5\xfd\xeaP=R0\xfb\xc7\xa7\x8e6=\xc2\x95\xf9_\x8a\x13\x1e=\xd6c\xa6\xea\x06\r\x04=r\xda\xdd8\t\xbc\xea<\xf6\xe6\x93\xd0\xb0\xd2\xd1<\x9d\xdeok\x96\xc3\xb7<&~\xea9s\xaf\x9f<UUUUUU\x13@q\x1c\xc7q\x1c\xc7\xf9?\xf6\x12\xdaKh/\xe1?\xf2\xc3"e\xe0\xe9\xc6?\xed\xaf\x831+\x8d\xae?\xf3\x1f\xad\xcb\x1c^\x94?\x15\x1e\xdd\xbd>\xb8\x02@\xc6\xd2&\xfd\xa8\xf5\xe8?\xd9\xe1\x19\xfe\xc5\xa3\xd0?v\x82"\xa8\xb2/\xb6?\x9dX\x835\xee\x94\x9d?h\x90W\xce\x9e\xb8\x83?\x8a\xc0th~Kj?\\\x80\xf8\x9a\xa9\x87Q?%\xab\xa0\xce\x8c_7?1\xe4\x80\x13\x11*\x1f? \x98\x00\r\xb6\xc6\x04?\x80u\xabf\x9d\xb3\xeb>UNrD\xbew\xd2>\x1c\x13C[\xa8\x9f\xb8>\x12b\xd7<pj\xa0>m-\x1fQ@\xe3\x85>\xe6\x91)l\x00/m>Da\xc6\xf2\xaatS>\x05\xd7]\xee\xe3\xf09>'
arr = np.frombuffer(data, dtype='<f8')
if sys.byteorder != "little":
arr = arr.byteswap().newbyteorder()
result = mom.rolling_sum(arr, 2)
self.assertTrue((result[1:] >= 0).all())
result = mom.rolling_mean(arr, 2)
self.assertTrue((result[1:] >= 0).all())
result = mom.rolling_var(arr, 2)
self.assertTrue((result[1:] >= 0).all())
# #2527, ugh
arr = np.array([0.00012456, 0.0003, 0])
result = mom.rolling_mean(arr, 1)
self.assertTrue(result[-1] >= 0)
result = mom.rolling_mean(-arr, 1)
self.assertTrue(result[-1] <= 0)
def _check_moment_func(self, func, static_comp, window=50,
has_min_periods=True,
has_center=True,
has_time_rule=True,
preserve_nan=True,
fill_value=None,
test_stable=False):
self._check_ndarray(func, static_comp, window=window,
has_min_periods=has_min_periods,
preserve_nan=preserve_nan,
has_center=has_center,
fill_value=fill_value,
test_stable=test_stable)
self._check_structures(func, static_comp,
has_min_periods=has_min_periods,
has_time_rule=has_time_rule,
fill_value=fill_value,
has_center=has_center)
def _check_ndarray(self, func, static_comp, window=50,
has_min_periods=True,
preserve_nan=True,
has_center=True,
fill_value=None,
test_stable=False,
test_window=True):
result = func(self.arr, window)
assert_almost_equal(result[-1],
static_comp(self.arr[-50:]))
if preserve_nan:
assert(np.isnan(result[self._nan_locs]).all())
# excluding NaNs correctly
arr = randn(50)
arr[:10] = np.NaN
arr[-10:] = np.NaN
if has_min_periods:
result = func(arr, 50, min_periods=30)
assert_almost_equal(result[-1], static_comp(arr[10:-10]))
# min_periods is working correctly
result = func(arr, 20, min_periods=15)
self.assertTrue(np.isnan(result[23]))
self.assertFalse(np.isnan(result[24]))
self.assertFalse(np.isnan(result[-6]))
self.assertTrue(np.isnan(result[-5]))
arr2 = randn(20)
result = func(arr2, 10, min_periods=5)
self.assertTrue(isnull(result[3]))
self.assertTrue(notnull(result[4]))
# min_periods=0
result0 = func(arr, 20, min_periods=0)
result1 = func(arr, 20, min_periods=1)
assert_almost_equal(result0, result1)
else:
result = func(arr, 50)
assert_almost_equal(result[-1], static_comp(arr[10:-10]))
# GH 7925
if has_center:
if has_min_periods:
result = func(arr, 20, min_periods=15, center=True)
expected = func(np.concatenate((arr, np.array([np.NaN] * 9))), 20, min_periods=15)[9:]
else:
result = func(arr, 20, center=True)
expected = func(np.concatenate((arr, np.array([np.NaN] * 9))), 20)[9:]
self.assert_numpy_array_equivalent(result, expected)
if test_stable:
result = func(self.arr + 1e9, window)
assert_almost_equal(result[-1],
static_comp(self.arr[-50:] + 1e9))
# Test window larger than array, #7297
if test_window:
if has_min_periods:
for minp in (0, len(self.arr)-1, len(self.arr)):
result = func(self.arr, len(self.arr)+1, min_periods=minp)
expected = func(self.arr, len(self.arr), min_periods=minp)
nan_mask = np.isnan(result)
self.assertTrue(np.array_equal(nan_mask,
np.isnan(expected)))
nan_mask = ~nan_mask
assert_almost_equal(result[nan_mask], expected[nan_mask])
else:
result = func(self.arr, len(self.arr)+1)
expected = func(self.arr, len(self.arr))
nan_mask = np.isnan(result)
self.assertTrue(np.array_equal(nan_mask, np.isnan(expected)))
nan_mask = ~nan_mask
assert_almost_equal(result[nan_mask], expected[nan_mask])
def _check_structures(self, func, static_comp,
has_min_periods=True, has_time_rule=True,
has_center=True,
fill_value=None):
series_result = func(self.series, 50)
tm.assert_isinstance(series_result, Series)
frame_result = func(self.frame, 50)
self.assertEqual(type(frame_result), DataFrame)
# check time_rule works
if has_time_rule:
win = 25
minp = 10
if has_min_periods:
series_result = func(self.series[::2], win, min_periods=minp,
freq='B')
frame_result = func(self.frame[::2], win, min_periods=minp,
freq='B')
else:
series_result = func(self.series[::2], win, freq='B')
frame_result = func(self.frame[::2], win, freq='B')
last_date = series_result.index[-1]
prev_date = last_date - 24 * datetools.bday
trunc_series = self.series[::2].truncate(prev_date, last_date)
trunc_frame = self.frame[::2].truncate(prev_date, last_date)
assert_almost_equal(series_result[-1], static_comp(trunc_series))
assert_almost_equal(frame_result.xs(last_date),
trunc_frame.apply(static_comp))
# GH 7925
if has_center:
if has_min_periods:
minp = 10
series_xp = func(self.series.reindex(list(self.series.index)+['x%d'%x for x in range(12)]), 25, min_periods=minp).shift(-12).reindex(self.series.index)
frame_xp = func(self.frame.reindex(list(self.frame.index)+['x%d'%x for x in range(12)]), 25, min_periods=minp).shift(-12).reindex(self.frame.index)
series_rs = func(self.series, 25, min_periods=minp,
center=True)
frame_rs = func(self.frame, 25, min_periods=minp,
center=True)
else:
series_xp = func(self.series.reindex(list(self.series.index)+['x%d'%x for x in range(12)]), 25).shift(-12).reindex(self.series.index)
frame_xp = func(self.frame.reindex(list(self.frame.index)+['x%d'%x for x in range(12)]), 25).shift(-12).reindex(self.frame.index)
series_rs = func(self.series, 25, center=True)
frame_rs = func(self.frame, 25, center=True)
if fill_value is not None:
series_xp = series_xp.fillna(fill_value)
frame_xp = frame_xp.fillna(fill_value)
assert_series_equal(series_xp, series_rs)
assert_frame_equal(frame_xp, frame_rs)
def test_ewma(self):
self._check_ew(mom.ewma)
arr = np.zeros(1000)
arr[5] = 1
result = mom.ewma(arr, span=100, adjust=False).sum()
self.assertTrue(np.abs(result - 1) < 1e-2)
s = Series([1.0, 2.0, 4.0, 8.0])
expected = Series([1.0, 1.6, 2.736842, 4.923077])
for f in [lambda s: mom.ewma(s, com=2.0, adjust=True),
lambda s: mom.ewma(s, com=2.0, adjust=True, ignore_na=False),
lambda s: mom.ewma(s, com=2.0, adjust=True, ignore_na=True),
]:
result = f(s)
assert_series_equal(result, expected)
expected = Series([1.0, 1.333333, 2.222222, 4.148148])
for f in [lambda s: mom.ewma(s, com=2.0, adjust=False),
lambda s: mom.ewma(s, com=2.0, adjust=False, ignore_na=False),
lambda s: mom.ewma(s, com=2.0, adjust=False, ignore_na=True),
]:
result = f(s)
assert_series_equal(result, expected)
def test_ewma_nan_handling(self):
s = Series([1.] + [np.nan] * 5 + [1.])
result = mom.ewma(s, com=5)
assert_almost_equal(result, [1.] * len(s))
s = Series([np.nan] * 2 + [1.] + [np.nan] * 2 + [1.])
result = mom.ewma(s, com=5)
assert_almost_equal(result, [np.nan] * 2 + [1.] * 4)
# GH 7603
s0 = Series([np.nan, 1., 101.])
s1 = Series([1., np.nan, 101.])
s2 = Series([np.nan, 1., np.nan, np.nan, 101., np.nan])
s3 = Series([1., np.nan, 101., 50.])
com = 2.
alpha = 1. / (1. + com)
def simple_wma(s, w):
return (s.multiply(w).cumsum() / w.cumsum()).fillna(method='ffill')
for (s, adjust, ignore_na, w) in [
(s0, True, False, [np.nan, (1. - alpha), 1.]),
(s0, True, True, [np.nan, (1. - alpha), 1.]),
(s0, False, False, [np.nan, (1. - alpha), alpha]),
(s0, False, True, [np.nan, (1. - alpha), alpha]),
(s1, True, False, [(1. - alpha)**2, np.nan, 1.]),
(s1, True, True, [(1. - alpha), np.nan, 1.]),
(s1, False, False, [(1. - alpha)**2, np.nan, alpha]),
(s1, False, True, [(1. - alpha), np.nan, alpha]),
(s2, True, False, [np.nan, (1. - alpha)**3, np.nan, np.nan, 1., np.nan]),
(s2, True, True, [np.nan, (1. - alpha), np.nan, np.nan, 1., np.nan]),
(s2, False, False, [np.nan, (1. - alpha)**3, np.nan, np.nan, alpha, np.nan]),
(s2, False, True, [np.nan, (1. - alpha), np.nan, np.nan, alpha, np.nan]),
(s3, True, False, [(1. - alpha)**3, np.nan, (1. - alpha), 1.]),
(s3, True, True, [(1. - alpha)**2, np.nan, (1. - alpha), 1.]),
(s3, False, False, [(1. - alpha)**3, np.nan, (1. - alpha) * alpha, alpha * ((1. - alpha)**2 + alpha)]),
(s3, False, True, [(1. - alpha)**2, np.nan, (1. - alpha) * alpha, alpha]),
]:
expected = simple_wma(s, Series(w))
result = mom.ewma(s, com=com, adjust=adjust, ignore_na=ignore_na)
assert_series_equal(result, expected)
if ignore_na is False:
# check that ignore_na defaults to False
result = mom.ewma(s, com=com, adjust=adjust)
assert_series_equal(result, expected)
def test_ewmvar(self):
self._check_ew(mom.ewmvar)
def test_ewmvol(self):
self._check_ew(mom.ewmvol)
def test_ewma_span_com_args(self):
A = mom.ewma(self.arr, com=9.5)
B = mom.ewma(self.arr, span=20)
assert_almost_equal(A, B)
self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20)
self.assertRaises(Exception, mom.ewma, self.arr)
def test_ewma_halflife_arg(self):
A = mom.ewma(self.arr, com=13.932726172912965)
B = mom.ewma(self.arr, halflife=10.0)
assert_almost_equal(A, B)
self.assertRaises(Exception, mom.ewma, self.arr, span=20, halflife=50)
self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, halflife=50)
self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20, halflife=50)
self.assertRaises(Exception, mom.ewma, self.arr)
def test_ew_empty_arrays(self):
arr = np.array([], dtype=np.float64)
funcs = [mom.ewma, mom.ewmvol, mom.ewmvar]
for f in funcs:
result = f(arr, 3)
assert_almost_equal(result, arr)
def _check_ew(self, func):
self._check_ew_ndarray(func)
self._check_ew_structures(func)
def _check_ew_ndarray(self, func, preserve_nan=False):
result = func(self.arr, com=10)
if preserve_nan:
assert(np.isnan(result[self._nan_locs]).all())
# excluding NaNs correctly
arr = randn(50)
arr[:10] = np.NaN
arr[-10:] = np.NaN
s = Series(arr)
# check min_periods
# GH 7898
result = func(s, 50, min_periods=2)
self.assertTrue(np.isnan(result.values[:11]).all())
self.assertFalse(np.isnan(result.values[11:]).any())
for min_periods in (0, 1):
result = func(s, 50, min_periods=min_periods)
if func == mom.ewma:
self.assertTrue(np.isnan(result.values[:10]).all())
self.assertFalse(np.isnan(result.values[10:]).any())
else:
# ewmstd, ewmvol, ewmvar (with bias=False) require at least two values
self.assertTrue(np.isnan(result.values[:11]).all())
self.assertFalse(np.isnan(result.values[11:]).any())
# check series of length 0
result = func(Series([]), 50, min_periods=min_periods)
assert_series_equal(result, Series([]))
# check series of length 1
result = func(Series([1.]), 50, min_periods=min_periods)
if func == mom.ewma:
assert_series_equal(result, Series([1.]))
else:
# ewmstd, ewmvol, ewmvar with bias=False require at least two values
assert_series_equal(result, Series([np.NaN]))
# pass in ints
result2 = func(np.arange(50), span=10)
self.assertEqual(result2.dtype, np.float_)
def _check_ew_structures(self, func):
series_result = func(self.series, com=10)
tm.assert_isinstance(series_result, Series)
frame_result = func(self.frame, com=10)
self.assertEqual(type(frame_result), DataFrame)
# create the data only once as we are not setting it
def _create_consistency_data():
def create_series():
return [Series(),
Series([np.nan]),
Series([np.nan, np.nan]),
Series([3.]),
Series([np.nan, 3.]),
Series([3., np.nan]),
Series([1., 3.]),
Series([2., 2.]),
Series([3., 1.]),
Series([5., 5., 5., 5., np.nan, np.nan, np.nan, 5., 5., np.nan, np.nan]),
Series([np.nan, 5., 5., 5., np.nan, np.nan, np.nan, 5., 5., np.nan, np.nan]),
Series([np.nan, np.nan, 5., 5., np.nan, np.nan, np.nan, 5., 5., np.nan, np.nan]),
Series([np.nan, 3., np.nan, 3., 4., 5., 6., np.nan, np.nan, 7., 12., 13., 14., 15.]),
Series([np.nan, 5., np.nan, 2., 4., 0., 9., np.nan, np.nan, 3., 12., 13., 14., 15.]),
Series([2., 3., np.nan, 3., 4., 5., 6., np.nan, np.nan, 7., 12., 13., 14., 15.]),
Series([2., 5., np.nan, 2., 4., 0., 9., np.nan, np.nan, 3., 12., 13., 14., 15.]),
Series(range(10)),
Series(range(20, 0, -2)),
]
def create_dataframes():
return [DataFrame(),
DataFrame(columns=['a']),
DataFrame(columns=['a', 'a']),
DataFrame(columns=['a', 'b']),
DataFrame(np.arange(10).reshape((5, 2))),
DataFrame(np.arange(25).reshape((5, 5))),
DataFrame(np.arange(25).reshape((5, 5)), columns=['a', 'b', 99, 'd', 'd']),
] + [DataFrame(s) for s in create_series()]
def is_constant(x):
values = x.values.ravel()
return len(set(values[notnull(values)])) == 1
def no_nans(x):
return x.notnull().all().all()
# data is a tuple(object, is_contant, no_nans)
data = create_series() + create_dataframes()
return [ (x, is_constant(x), no_nans(x)) for x in data ]
_consistency_data = _create_consistency_data()
class TestMomentsConsistency(Base):
def _create_data(self):
super(TestMomentsConsistency, self)._create_data()
self.data = _consistency_data
def setUp(self):
self._create_data()
warnings.simplefilter("ignore", category=FutureWarning)
def _test_moments_consistency(self,
min_periods,
count, mean, mock_mean, corr,
var_unbiased=None, std_unbiased=None, cov_unbiased=None,
var_biased=None, std_biased=None, cov_biased=None,
var_debiasing_factors=None):
def _non_null_values(x):
values = x.values.ravel()
return set(values[notnull(values)].tolist())
for (x, is_constant, no_nans) in self.data:
assert_equal = assert_series_equal if isinstance(x, Series) else assert_frame_equal
count_x = count(x)
mean_x = mean(x)
if mock_mean:
# check that mean equals mock_mean
expected = mock_mean(x)
assert_equal(mean_x, expected)
# check that correlation of a series with itself is either 1 or NaN
corr_x_x = corr(x, x)
# self.assertTrue(_non_null_values(corr_x_x).issubset(set([1.]))) # restore once rolling_cov(x, x) is identically equal to var(x)
if is_constant:
# check mean of constant series
expected = x * np.nan
expected[count_x >= max(min_periods, 1)] = x.max().max()
assert_equal(mean_x, expected)
# check correlation of constant series with itself is NaN
expected[:] = np.nan
assert_equal(corr_x_x, expected)
if var_unbiased and var_biased and var_debiasing_factors:
# check variance debiasing factors
var_unbiased_x = var_unbiased(x)
var_biased_x = var_biased(x)
var_debiasing_factors_x = var_debiasing_factors(x)
assert_equal(var_unbiased_x, var_biased_x * var_debiasing_factors_x)
for (std, var, cov) in [(std_biased, var_biased, cov_biased),
(std_unbiased, var_unbiased, cov_unbiased)]:
# check that var(x), std(x), and cov(x) are all >= 0
var_x = var(x)
std_x = std(x)
self.assertFalse((var_x < 0).any().any())
self.assertFalse((std_x < 0).any().any())
if cov:
cov_x_x = cov(x, x)
self.assertFalse((cov_x_x < 0).any().any())
# check that var(x) == cov(x, x)
assert_equal(var_x, cov_x_x)
# check that var(x) == std(x)^2
assert_equal(var_x, std_x * std_x)
if var is var_biased:
# check that biased var(x) == mean(x^2) - mean(x)^2
mean_x2 = mean(x * x)
assert_equal(var_x, mean_x2 - (mean_x * mean_x))
if is_constant:
# check that variance of constant series is identically 0
self.assertFalse((var_x > 0).any().any())
expected = x * np.nan
expected[count_x >= max(min_periods, 1)] = 0.
if var is var_unbiased:
expected[count_x < 2] = np.nan
assert_equal(var_x, expected)
if isinstance(x, Series):
for (y, is_constant, no_nans) in self.data:
if not x.isnull().equals(y.isnull()):
# can only easily test two Series with similar structure
continue
# check that cor(x, y) is symmetric
corr_x_y = corr(x, y)
corr_y_x = corr(y, x)
assert_equal(corr_x_y, corr_y_x)
if cov:
# check that cov(x, y) is symmetric
cov_x_y = cov(x, y)
cov_y_x = cov(y, x)
assert_equal(cov_x_y, cov_y_x)
# check that cov(x, y) == (var(x+y) - var(x) - var(y)) / 2
var_x_plus_y = var(x + y)
var_y = var(y)
assert_equal(cov_x_y, 0.5 * (var_x_plus_y - var_x - var_y))
# check that corr(x, y) == cov(x, y) / (std(x) * std(y))
std_y = std(y)
assert_equal(corr_x_y, cov_x_y / (std_x * std_y))
if cov is cov_biased:
# check that biased cov(x, y) == mean(x*y) - mean(x)*mean(y)
mean_y = mean(y)
mean_x_times_y = mean(x * y)
assert_equal(cov_x_y, mean_x_times_y - (mean_x * mean_y))
@slow
def test_ewm_consistency(self):
def _weights(s, com, adjust, ignore_na):
if isinstance(s, DataFrame):
if not len(s.columns):
return DataFrame(index=s.index, columns=s.columns)
w = concat([ _weights(s.iloc[:, i],
com=com,
adjust=adjust,
ignore_na=ignore_na) for i, _ in enumerate(s.columns) ],
axis=1)
w.index=s.index
w.columns=s.columns
return w
w = Series(np.nan, index=s.index)
alpha = 1. / (1. + com)
if ignore_na:
w[s.notnull()] = _weights(s[s.notnull()], com=com, adjust=adjust, ignore_na=False)
elif adjust:
for i in range(len(s)):
if s.iat[i] == s.iat[i]:
w.iat[i] = pow(1. / (1. - alpha), i)
else:
sum_wts = 0.
prev_i = -1
for i in range(len(s)):
if s.iat[i] == s.iat[i]:
if prev_i == -1:
w.iat[i] = 1.
else:
w.iat[i] = alpha * sum_wts / pow(1. - alpha, i - prev_i)
sum_wts += w.iat[i]
prev_i = i
return w
def _variance_debiasing_factors(s, com, adjust, ignore_na):
weights = _weights(s, com=com, adjust=adjust, ignore_na=ignore_na)
cum_sum = weights.cumsum().fillna(method='ffill')
cum_sum_sq = (weights * weights).cumsum().fillna(method='ffill')
numerator = cum_sum * cum_sum
denominator = numerator - cum_sum_sq
denominator[denominator <= 0.] = np.nan
return numerator / denominator
def _ewma(s, com, min_periods, adjust, ignore_na):
weights = _weights(s, com=com, adjust=adjust, ignore_na=ignore_na)
result = s.multiply(weights).cumsum().divide(weights.cumsum()).fillna(method='ffill')
result[mom.expanding_count(s) < (max(min_periods, 1) if min_periods else 1)] = np.nan
return result
com = 3.
for min_periods in [0, 1, 2, 3, 4]:
for adjust in [True, False]:
for ignore_na in [False, True]:
# test consistency between different ewm* moments
self._test_moments_consistency(
min_periods=min_periods,
count=mom.expanding_count,
mean=lambda x: mom.ewma(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na),
mock_mean=lambda x: _ewma(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na),
corr=lambda x, y: mom.ewmcorr(x, y, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na),
var_unbiased=lambda x: mom.ewmvar(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=False),
std_unbiased=lambda x: mom.ewmstd(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=False),
cov_unbiased=lambda x, y: mom.ewmcov(x, y, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=False),
var_biased=lambda x: mom.ewmvar(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=True),
std_biased=lambda x: mom.ewmstd(x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=True),
cov_biased=lambda x, y: mom.ewmcov(x, y, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, bias=True),
var_debiasing_factors=lambda x: _variance_debiasing_factors(x, com=com, adjust=adjust, ignore_na=ignore_na))
@slow
def test_expanding_consistency(self):
base_functions = [
(mom.expanding_count, lambda v: Series(v).count(), None),
(mom.expanding_max, lambda v: Series(v).max(), None),
(mom.expanding_min, lambda v: Series(v).min(), None),
(mom.expanding_sum, lambda v: Series(v).sum(), None),
(mom.expanding_mean, lambda v: Series(v).mean(), None),
(mom.expanding_std, lambda v: Series(v).std(), 1),
(mom.expanding_cov, lambda v: Series(v).cov(Series(v)), None),
(mom.expanding_corr, lambda v: Series(v).corr(Series(v)), None),
(mom.expanding_var, lambda v: Series(v).var(), 1),
#(mom.expanding_skew, lambda v: Series(v).skew(), 3), # restore once GH 8086 is fixed
#(mom.expanding_kurt, lambda v: Series(v).kurt(), 4), # restore once GH 8086 is fixed
#(lambda x, min_periods: mom.expanding_quantile(x, 0.3, min_periods=min_periods),
# lambda v: Series(v).quantile(0.3), None), # restore once GH 8084 is fixed
(mom.expanding_median, lambda v: Series(v).median(), None),
(mom.expanding_max, np.nanmax, 1),
(mom.expanding_min, np.nanmin, 1),
(mom.expanding_sum, np.nansum, 1),
]
if np.__version__ >= LooseVersion('1.8.0'):
base_functions += [
(mom.expanding_mean, np.nanmean, 1),
(mom.expanding_std, lambda v: np.nanstd(v, ddof=1), 1),
(mom.expanding_var, lambda v: np.nanvar(v, ddof=1), 1),
]
if np.__version__ >= LooseVersion('1.9.0'):
base_functions += [
(mom.expanding_median, np.nanmedian, 1),
]
no_nan_functions = [
(mom.expanding_max, np.max, None),
(mom.expanding_min, np.min, None),
(mom.expanding_sum, np.sum, None),
(mom.expanding_mean, np.mean, None),
(mom.expanding_std, lambda v: np.std(v, ddof=1), 1),
(mom.expanding_var, lambda v: np.var(v, ddof=1), 1),
(mom.expanding_median, np.median, None),
]
# suppress warnings about empty slices, as we are deliberately testing with empty/0-length Series/DataFrames
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message=".*(empty slice|0 for slice).*", category=RuntimeWarning)
for min_periods in [0, 1, 2, 3, 4]:
# test consistency between different expanding_* moments
self._test_moments_consistency(
min_periods=min_periods,
count=mom.expanding_count,
mean=lambda x: mom.expanding_mean(x, min_periods=min_periods),
mock_mean=lambda x: mom.expanding_sum(x, min_periods=min_periods) / mom.expanding_count(x),
corr=lambda x, y: mom.expanding_corr(x, y, min_periods=min_periods),
var_unbiased=lambda x: mom.expanding_var(x, min_periods=min_periods),
std_unbiased=lambda x: mom.expanding_std(x, min_periods=min_periods),
cov_unbiased=lambda x, y: mom.expanding_cov(x, y, min_periods=min_periods),
var_biased=lambda x: mom.expanding_var(x, min_periods=min_periods, ddof=0),
std_biased=lambda x: mom.expanding_std(x, min_periods=min_periods, ddof=0),
cov_biased=lambda x, y: mom.expanding_cov(x, y, min_periods=min_periods, ddof=0),
var_debiasing_factors=lambda x: mom.expanding_count(x) / (mom.expanding_count(x) - 1.).replace(0., np.nan)
)
# test consistency between expanding_xyz() and either (a) expanding_apply of Series.xyz(),
# or (b) expanding_apply of np.nanxyz()
for (x, is_constant, no_nans) in self.data:
assert_equal = assert_series_equal if isinstance(x, Series) else assert_frame_equal
functions = base_functions
# GH 8269
if no_nans:
functions = base_functions + no_nan_functions
for (expanding_f, f, require_min_periods) in functions:
if require_min_periods and (min_periods is not None) and (min_periods < require_min_periods):
continue
if expanding_f is mom.expanding_count:
expanding_f_result = expanding_f(x)
expanding_apply_f_result = mom.expanding_apply(x, func=f, min_periods=0)
else:
if expanding_f in [mom.expanding_cov, mom.expanding_corr]:
expanding_f_result = expanding_f(x, min_periods=min_periods, pairwise=False)
else:
expanding_f_result = expanding_f(x, min_periods=min_periods)
expanding_apply_f_result = mom.expanding_apply(x, func=f, min_periods=min_periods)
assert_equal(expanding_f_result, expanding_apply_f_result)
if (expanding_f in [mom.expanding_cov, mom.expanding_corr]) and isinstance(x, DataFrame):
# test pairwise=True
expanding_f_result = expanding_f(x, x, min_periods=min_periods, pairwise=True)
expected = Panel(items=x.index, major_axis=x.columns, minor_axis=x.columns)
for i, _ in enumerate(x.columns):
for j, _ in enumerate(x.columns):
expected.iloc[:, i, j] = expanding_f(x.iloc[:, i], x.iloc[:, j], min_periods=min_periods)
assert_panel_equal(expanding_f_result, expected)
@slow
def test_rolling_consistency(self):
base_functions = [
(mom.rolling_count, lambda v: Series(v).count(), None),
(mom.rolling_max, lambda v: Series(v).max(), None),
(mom.rolling_min, lambda v: Series(v).min(), None),
(mom.rolling_sum, lambda v: Series(v).sum(), None),
(mom.rolling_mean, lambda v: Series(v).mean(), None),
(mom.rolling_std, lambda v: Series(v).std(), 1),
(mom.rolling_cov, lambda v: Series(v).cov(Series(v)), None),
(mom.rolling_corr, lambda v: Series(v).corr(Series(v)), None),
(mom.rolling_var, lambda v: Series(v).var(), 1),
#(mom.rolling_skew, lambda v: Series(v).skew(), 3), # restore once GH 8086 is fixed
#(mom.rolling_kurt, lambda v: Series(v).kurt(), 4), # restore once GH 8086 is fixed
#(lambda x, window, min_periods, center: mom.rolling_quantile(x, window, 0.3, min_periods=min_periods, center=center),
# lambda v: Series(v).quantile(0.3), None), # restore once GH 8084 is fixed
(mom.rolling_median, lambda v: Series(v).median(), None),
(mom.rolling_max, np.nanmax, 1),
(mom.rolling_min, np.nanmin, 1),
(mom.rolling_sum, np.nansum, 1),
]
if np.__version__ >= LooseVersion('1.8.0'):
base_functions += [
(mom.rolling_mean, np.nanmean, 1),
(mom.rolling_std, lambda v: np.nanstd(v, ddof=1), 1),
(mom.rolling_var, lambda v: np.nanvar(v, ddof=1), 1),
]
if np.__version__ >= LooseVersion('1.9.0'):
base_functions += [
(mom.rolling_median, np.nanmedian, 1),
]
no_nan_functions = [
(mom.rolling_max, np.max, None),
(mom.rolling_min, np.min, None),
(mom.rolling_sum, np.sum, None),
(mom.rolling_mean, np.mean, None),
(mom.rolling_std, lambda v: np.std(v, ddof=1), 1),
(mom.rolling_var, lambda v: np.var(v, ddof=1), 1),
(mom.rolling_median, np.median, None),
]
for window in [1, 2, 3, 10, 20]:
for min_periods in set([0, 1, 2, 3, 4, window]):
if min_periods and (min_periods > window):
continue
for center in [False, True]:
# test consistency between different rolling_* moments
self._test_moments_consistency(
min_periods=min_periods,
count=lambda x: mom.rolling_count(x, window=window, center=center),
mean=lambda x: mom.rolling_mean(x, window=window, min_periods=min_periods, center=center),
mock_mean=lambda x: mom.rolling_sum(x, window=window, min_periods=min_periods, center=center).divide(
mom.rolling_count(x, window=window, center=center)),
corr=lambda x, y: mom.rolling_corr(x, y, window=window, min_periods=min_periods, center=center),
var_unbiased=lambda x: mom.rolling_var(x, window=window, min_periods=min_periods, center=center),
std_unbiased=lambda x: mom.rolling_std(x, window=window, min_periods=min_periods, center=center),
cov_unbiased=lambda x, y: mom.rolling_cov(x, y, window=window, min_periods=min_periods, center=center),
var_biased=lambda x: mom.rolling_var(x, window=window, min_periods=min_periods, center=center, ddof=0),
std_biased=lambda x: mom.rolling_std(x, window=window, min_periods=min_periods, center=center, ddof=0),
cov_biased=lambda x, y: mom.rolling_cov(x, y, window=window, min_periods=min_periods, center=center, ddof=0),
var_debiasing_factors=lambda x: mom.rolling_count(x, window=window, center=center).divide(
(mom.rolling_count(x, window=window, center=center) - 1.).replace(0., np.nan)),
)
# test consistency between rolling_xyz() and either (a) rolling_apply of Series.xyz(),
# or (b) rolling_apply of np.nanxyz()
for (x, is_constant, no_nans) in self.data:
assert_equal = assert_series_equal if isinstance(x, Series) else assert_frame_equal
functions = base_functions
# GH 8269
if no_nans:
functions = base_functions + no_nan_functions
for (rolling_f, f, require_min_periods) in functions:
if require_min_periods and (min_periods is not None) and (min_periods < require_min_periods):
continue
if rolling_f is mom.rolling_count:
rolling_f_result = rolling_f(x, window=window, center=center)
rolling_apply_f_result = mom.rolling_apply(x, window=window, func=f,
min_periods=0, center=center)
else:
if rolling_f in [mom.rolling_cov, mom.rolling_corr]:
rolling_f_result = rolling_f(x, window=window, min_periods=min_periods, center=center, pairwise=False)
else:
rolling_f_result = rolling_f(x, window=window, min_periods=min_periods, center=center)
rolling_apply_f_result = mom.rolling_apply(x, window=window, func=f,
min_periods=min_periods, center=center)
assert_equal(rolling_f_result, rolling_apply_f_result)
if (rolling_f in [mom.rolling_cov, mom.rolling_corr]) and isinstance(x, DataFrame):
# test pairwise=True
rolling_f_result = rolling_f(x, x, window=window, min_periods=min_periods,
center=center, pairwise=True)
expected = Panel(items=x.index, major_axis=x.columns, minor_axis=x.columns)
for i, _ in enumerate(x.columns):
for j, _ in enumerate(x.columns):
expected.iloc[:, i, j] = rolling_f(x.iloc[:, i], x.iloc[:, j],
window=window, min_periods=min_periods, center=center)
assert_panel_equal(rolling_f_result, expected)
# binary moments
def test_rolling_cov(self):
A = self.series
B = A + randn(len(A))
result = mom.rolling_cov(A, B, 50, min_periods=25)
assert_almost_equal(result[-1], np.cov(A[-50:], B[-50:])[0, 1])
def test_rolling_cov_pairwise(self):
self._check_pairwise_moment(mom.rolling_cov, 10, min_periods=5)
def test_rolling_corr(self):
A = self.series
B = A + randn(len(A))
result = mom.rolling_corr(A, B, 50, min_periods=25)
assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])
# test for correct bias correction
a = tm.makeTimeSeries()
b = tm.makeTimeSeries()
a[:5] = np.nan
b[:10] = np.nan
result = mom.rolling_corr(a, b, len(a), min_periods=1)
assert_almost_equal(result[-1], a.corr(b))
def test_rolling_corr_pairwise(self):
self._check_pairwise_moment(mom.rolling_corr, 10, min_periods=5)
def _check_pairwise_moment(self, func, *args, **kwargs):
panel = func(self.frame, *args, **kwargs)
actual = panel.ix[:, 1, 5]
expected = func(self.frame[1], self.frame[5], *args, **kwargs)
tm.assert_series_equal(actual, expected)
def test_flex_binary_moment(self):
# GH3155
# don't blow the stack
self.assertRaises(TypeError, mom._flex_binary_moment,5,6,None)
def test_corr_sanity(self):
#GH 3155
df = DataFrame(
np.array(
[[ 0.87024726, 0.18505595],
[ 0.64355431, 0.3091617 ],
[ 0.92372966, 0.50552513],
[ 0.00203756, 0.04520709],
[ 0.84780328, 0.33394331],
[ 0.78369152, 0.63919667]])
)
res = mom.rolling_corr(df[0],df[1],5,center=True)
self.assertTrue(all([np.abs(np.nan_to_num(x)) <=1 for x in res]))
# and some fuzzing
for i in range(10):
df = DataFrame(np.random.rand(30,2))
res = mom.rolling_corr(df[0],df[1],5,center=True)
try:
self.assertTrue(all([np.abs(np.nan_to_num(x)) <=1 for x in res]))
except:
print(res)
def test_flex_binary_frame(self):
def _check(method):
series = self.frame[1]
res = method(series, self.frame, 10)
res2 = method(self.frame, series, 10)
exp = self.frame.apply(lambda x: method(series, x, 10))
tm.assert_frame_equal(res, exp)
tm.assert_frame_equal(res2, exp)
frame2 = self.frame.copy()
frame2.values[:] = np.random.randn(*frame2.shape)
res3 = method(self.frame, frame2, 10)
exp = DataFrame(dict((k, method(self.frame[k], frame2[k], 10))
for k in self.frame))
tm.assert_frame_equal(res3, exp)
methods = [mom.rolling_corr, mom.rolling_cov]
for meth in methods:
_check(meth)
def test_ewmcov(self):
self._check_binary_ew(mom.ewmcov)
def test_ewmcov_pairwise(self):
self._check_pairwise_moment(mom.ewmcov, span=10, min_periods=5)
def test_ewmcorr(self):
self._check_binary_ew(mom.ewmcorr)
def test_ewmcorr_pairwise(self):
self._check_pairwise_moment(mom.ewmcorr, span=10, min_periods=5)
def _check_binary_ew(self, func):
A = Series(randn(50), index=np.arange(50))
B = A[2:] + randn(48)
A[:10] = np.NaN
B[-10:] = np.NaN
result = func(A, B, 20, min_periods=5)
self.assertTrue(np.isnan(result.values[:14]).all())
self.assertFalse(np.isnan(result.values[14:]).any())
# GH 7898
for min_periods in (0, 1, 2):
result = func(A, B, 20, min_periods=min_periods)
# binary functions (ewmcov, ewmcorr) with bias=False require at least two values
self.assertTrue(np.isnan(result.values[:11]).all())
self.assertFalse(np.isnan(result.values[11:]).any())
# check series of length 0
result = func(Series([]), Series([]), 50, min_periods=min_periods)
assert_series_equal(result, Series([]))
# check series of length 1
result = func(Series([1.]), Series([1.]), 50, min_periods=min_periods)
assert_series_equal(result, Series([np.NaN]))
self.assertRaises(Exception, func, A, randn(50), 20, min_periods=5)
def test_expanding_apply(self):
ser = Series([])
assert_series_equal(ser, mom.expanding_apply(ser, lambda x: x.mean()))
def expanding_mean(x, min_periods=1, freq=None):
return mom.expanding_apply(x,
lambda x: x.mean(),
min_periods=min_periods,
freq=freq)
self._check_expanding(expanding_mean, np.mean)
# GH 8080
s = Series([None, None, None])
result = mom.expanding_apply(s, lambda x: len(x), min_periods=0)
expected = Series([1., 2., 3.])
assert_series_equal(result, expected)
def test_expanding_apply_args_kwargs(self):
def mean_w_arg(x, const):
return np.mean(x) + const
df = DataFrame(np.random.rand(20, 3))
expected = mom.expanding_apply(df, np.mean) + 20.
assert_frame_equal(mom.expanding_apply(df, mean_w_arg, args=(20,)),
expected)
assert_frame_equal(mom.expanding_apply(df, mean_w_arg,
kwargs={'const' : 20}),
expected)
def test_expanding_corr(self):
A = self.series.dropna()
B = (A + randn(len(A)))[:-5]
result = mom.expanding_corr(A, B)
rolling_result = mom.rolling_corr(A, B, len(A), min_periods=1)
assert_almost_equal(rolling_result, result)
def test_expanding_count(self):
result = mom.expanding_count(self.series)
assert_almost_equal(result, mom.rolling_count(self.series,
len(self.series)))
def test_expanding_quantile(self):
result = mom.expanding_quantile(self.series, 0.5)
rolling_result = mom.rolling_quantile(self.series,
len(self.series),
0.5, min_periods=1)
assert_almost_equal(result, rolling_result)
def test_expanding_cov(self):
A = self.series
B = (A + randn(len(A)))[:-5]
result = mom.expanding_cov(A, B)
rolling_result = mom.rolling_cov(A, B, len(A), min_periods=1)
assert_almost_equal(rolling_result, result)
def test_expanding_max(self):
self._check_expanding(mom.expanding_max, np.max, preserve_nan=False)
def test_expanding_cov_pairwise(self):
result = mom.expanding_cov(self.frame)
rolling_result = mom.rolling_cov(self.frame, len(self.frame),
min_periods=1)
for i in result.items:
assert_almost_equal(result[i], rolling_result[i])
def test_expanding_corr_pairwise(self):
result = mom.expanding_corr(self.frame)
rolling_result = mom.rolling_corr(self.frame, len(self.frame),
min_periods=1)
for i in result.items:
assert_almost_equal(result[i], rolling_result[i])
def test_expanding_cov_diff_index(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
result = mom.expanding_cov(s1, s2)
expected = Series([None, None, 2.0])
assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
result = mom.expanding_cov(s1, s2a)
assert_series_equal(result, expected)
s1 = Series([7, 8, 10], index=[0, 1, 3])
s2 = Series([7, 9, 10], index=[0, 2, 3])
result = mom.expanding_cov(s1, s2)
expected = Series([None, None, None, 4.5])
assert_series_equal(result, expected)
def test_expanding_corr_diff_index(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
result = mom.expanding_corr(s1, s2)
expected = Series([None, None, 1.0])
assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
result = mom.expanding_corr(s1, s2a)
assert_series_equal(result, expected)
s1 = Series([7, 8, 10], index=[0, 1, 3])
s2 = Series([7, 9, 10], index=[0, 2, 3])
result = mom.expanding_corr(s1, s2)
expected = Series([None, None, None, 1.])
assert_series_equal(result, expected)
def test_rolling_cov_diff_length(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
result = mom.rolling_cov(s1, s2, window=3, min_periods=2)
expected = Series([None, None, 2.0])
assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
result = mom.rolling_cov(s1, s2a, window=3, min_periods=2)
assert_series_equal(result, expected)
def test_rolling_corr_diff_length(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
result = mom.rolling_corr(s1, s2, window=3, min_periods=2)
expected = Series([None, None, 1.0])
assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
result = mom.rolling_corr(s1, s2a, window=3, min_periods=2)
assert_series_equal(result, expected)
def test_rolling_functions_window_non_shrinkage(self):
# GH 7764
s = Series(range(4))
s_expected = Series(np.nan, index=s.index)
df = DataFrame([[1,5], [3, 2], [3,9], [-1,0]], columns=['A','B'])
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
df_expected_panel = Panel(items=df.index, major_axis=df.columns, minor_axis=df.columns)
functions = [lambda x: mom.rolling_cov(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_max(x, window=10, min_periods=5),
lambda x: mom.rolling_min(x, window=10, min_periods=5),
lambda x: mom.rolling_sum(x, window=10, min_periods=5),
lambda x: mom.rolling_mean(x, window=10, min_periods=5),
lambda x: mom.rolling_std(x, window=10, min_periods=5),
lambda x: mom.rolling_var(x, window=10, min_periods=5),
lambda x: mom.rolling_skew(x, window=10, min_periods=5),
lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
lambda x: mom.rolling_quantile(x, quantile=0.5, window=10, min_periods=5),
lambda x: mom.rolling_median(x, window=10, min_periods=5),
lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
lambda x: mom.rolling_window(x, win_type='boxcar', window=10, min_periods=5),
]
for f in functions:
try:
s_result = f(s)
assert_series_equal(s_result, s_expected)
df_result = f(df)
assert_frame_equal(df_result, df_expected)
except (ImportError):
# scipy needed for rolling_window
continue
functions = [lambda x: mom.rolling_cov(x, x, pairwise=True, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=True, window=10, min_periods=5),
# rolling_corr_pairwise is depracated, so the following line should be deleted
# when rolling_corr_pairwise is removed.
lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5),
]
for f in functions:
df_result_panel = f(df)
assert_panel_equal(df_result_panel, df_expected_panel)
def test_moment_functions_zero_length(self):
# GH 8056
s = Series()
s_expected = s
df1 = DataFrame()
df1_expected = df1
df1_expected_panel = Panel(items=df1.index, major_axis=df1.columns, minor_axis=df1.columns)
df2 = DataFrame(columns=['a'])
df2_expected = df2
df2_expected_panel = Panel(items=df2.index, major_axis=df2.columns, minor_axis=df2.columns)
functions = [lambda x: mom.expanding_count(x),
lambda x: mom.expanding_cov(x, x, pairwise=False, min_periods=5),
lambda x: mom.expanding_corr(x, x, pairwise=False, min_periods=5),
lambda x: mom.expanding_max(x, min_periods=5),
lambda x: mom.expanding_min(x, min_periods=5),
lambda x: mom.expanding_sum(x, min_periods=5),
lambda x: mom.expanding_mean(x, min_periods=5),
lambda x: mom.expanding_std(x, min_periods=5),
lambda x: mom.expanding_var(x, min_periods=5),
lambda x: mom.expanding_skew(x, min_periods=5),
lambda x: mom.expanding_kurt(x, min_periods=5),
lambda x: mom.expanding_quantile(x, quantile=0.5, min_periods=5),
lambda x: mom.expanding_median(x, min_periods=5),
lambda x: mom.expanding_apply(x, func=sum, min_periods=5),
lambda x: mom.rolling_count(x, window=10),
lambda x: mom.rolling_cov(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_max(x, window=10, min_periods=5),
lambda x: mom.rolling_min(x, window=10, min_periods=5),
lambda x: mom.rolling_sum(x, window=10, min_periods=5),
lambda x: mom.rolling_mean(x, window=10, min_periods=5),
lambda x: mom.rolling_std(x, window=10, min_periods=5),
lambda x: mom.rolling_var(x, window=10, min_periods=5),
lambda x: mom.rolling_skew(x, window=10, min_periods=5),
lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
lambda x: mom.rolling_quantile(x, quantile=0.5, window=10, min_periods=5),
lambda x: mom.rolling_median(x, window=10, min_periods=5),
lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
lambda x: mom.rolling_window(x, win_type='boxcar', window=10, min_periods=5),
]
for f in functions:
try:
s_result = f(s)
assert_series_equal(s_result, s_expected)
df1_result = f(df1)
assert_frame_equal(df1_result, df1_expected)
df2_result = f(df2)
assert_frame_equal(df2_result, df2_expected)
except (ImportError):
# scipy needed for rolling_window
continue
functions = [lambda x: mom.expanding_cov(x, x, pairwise=True, min_periods=5),
lambda x: mom.expanding_corr(x, x, pairwise=True, min_periods=5),
lambda x: mom.rolling_cov(x, x, pairwise=True, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=True, window=10, min_periods=5),
# rolling_corr_pairwise is depracated, so the following line should be deleted
# when rolling_corr_pairwise is removed.
lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5),
]
for f in functions:
df1_result_panel = f(df1)
assert_panel_equal(df1_result_panel, df1_expected_panel)
df2_result_panel = f(df2)
assert_panel_equal(df2_result_panel, df2_expected_panel)
def test_expanding_cov_pairwise_diff_length(self):
# GH 7512
df1 = DataFrame([[1,5], [3, 2], [3,9]], columns=['A','B'])
df1a = DataFrame([[1,5], [3,9]], index=[0,2], columns=['A','B'])
df2 = DataFrame([[5,6], [None,None], [2,1]], columns=['X','Y'])
df2a = DataFrame([[5,6], [2,1]], index=[0,2], columns=['X','Y'])
result1 = mom.expanding_cov(df1, df2, pairwise=True)[2]
result2 = mom.expanding_cov(df1, df2a, pairwise=True)[2]
result3 = mom.expanding_cov(df1a, df2, pairwise=True)[2]
result4 = mom.expanding_cov(df1a, df2a, pairwise=True)[2]
expected = DataFrame([[-3., -5.], [-6., -10.]], index=['A','B'], columns=['X','Y'])
assert_frame_equal(result1, expected)
assert_frame_equal(result2, expected)
assert_frame_equal(result3, expected)
assert_frame_equal(result4, expected)
def test_expanding_corr_pairwise_diff_length(self):
# GH 7512
df1 = DataFrame([[1,2], [3, 2], [3,4]], columns=['A','B'])
df1a = DataFrame([[1,2], [3,4]], index=[0,2], columns=['A','B'])
df2 = DataFrame([[5,6], [None,None], [2,1]], columns=['X','Y'])
df2a = DataFrame([[5,6], [2,1]], index=[0,2], columns=['X','Y'])
result1 = mom.expanding_corr(df1, df2, pairwise=True)[2]
result2 = mom.expanding_corr(df1, df2a, pairwise=True)[2]
result3 = mom.expanding_corr(df1a, df2, pairwise=True)[2]
result4 = mom.expanding_corr(df1a, df2a, pairwise=True)[2]
expected = DataFrame([[-1.0, -1.0], [-1.0, -1.0]], index=['A','B'], columns=['X','Y'])
assert_frame_equal(result1, expected)
assert_frame_equal(result2, expected)
assert_frame_equal(result3, expected)
assert_frame_equal(result4, expected)
def test_pairwise_stats_column_names_order(self):
# GH 7738
df1s = [DataFrame([[2,4],[1,2],[5,2],[8,1]], columns=[0,1]),
DataFrame([[2,4],[1,2],[5,2],[8,1]], columns=[1,0]),
DataFrame([[2,4],[1,2],[5,2],[8,1]], columns=[1,1]),
DataFrame([[2,4],[1,2],[5,2],[8,1]], columns=['C','C']),
DataFrame([[2,4],[1,2],[5,2],[8,1]], columns=[1.,0]),
DataFrame([[2,4],[1,2],[5,2],[8,1]], columns=[0.,1]),
DataFrame([[2,4],[1,2],[5,2],[8,1]], columns=['C',1]),
DataFrame([[2.,4.],[1.,2.],[5.,2.],[8.,1.]], columns=[1,0.]),
DataFrame([[2,4.],[1,2.],[5,2.],[8,1.]], columns=[0,1.]),
DataFrame([[2,4],[1,2],[5,2],[8,1.]], columns=[1.,'X']),
]
df2 = DataFrame([[None,1,1],[None,1,2],[None,3,2],[None,8,1]], columns=['Y','Z','X'])
s = Series([1,1,3,8])
# suppress warnings about incomparable objects, as we are deliberately testing with such column labels
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message=".*incomparable objects.*", category=RuntimeWarning)
# DataFrame methods (which do not call _flex_binary_moment())
for f in [lambda x: x.cov(),
lambda x: x.corr(),
]:
results = [f(df) for df in df1s]
for (df, result) in zip(df1s, results):
assert_index_equal(result.index, df.columns)
assert_index_equal(result.columns, df.columns)
for i, result in enumerate(results):
if i > 0:
self.assert_numpy_array_equivalent(result, results[0])
# DataFrame with itself, pairwise=True
for f in [lambda x: mom.expanding_cov(x, pairwise=True),
lambda x: mom.expanding_corr(x, pairwise=True),
lambda x: mom.rolling_cov(x, window=3, pairwise=True),
lambda x: mom.rolling_corr(x, window=3, pairwise=True),
lambda x: mom.ewmcov(x, com=3, pairwise=True),
lambda x: mom.ewmcorr(x, com=3, pairwise=True),
]:
results = [f(df) for df in df1s]
for (df, result) in zip(df1s, results):
assert_index_equal(result.items, df.index)
assert_index_equal(result.major_axis, df.columns)
assert_index_equal(result.minor_axis, df.columns)
for i, result in enumerate(results):
if i > 0:
self.assert_numpy_array_equivalent(result, results[0])
# DataFrame with itself, pairwise=False
for f in [lambda x: mom.expanding_cov(x, pairwise=False),
lambda x: mom.expanding_corr(x, pairwise=False),
lambda x: mom.rolling_cov(x, window=3, pairwise=False),
lambda x: mom.rolling_corr(x, window=3, pairwise=False),
lambda x: mom.ewmcov(x, com=3, pairwise=False),
lambda x: mom.ewmcorr(x, com=3, pairwise=False),
]:
results = [f(df) for df in df1s]
for (df, result) in zip(df1s, results):
assert_index_equal(result.index, df.index)
assert_index_equal(result.columns, df.columns)
for i, result in enumerate(results):
if i > 0:
self.assert_numpy_array_equivalent(result, results[0])
# DataFrame with another DataFrame, pairwise=True
for f in [lambda x, y: mom.expanding_cov(x, y, pairwise=True),
lambda x, y: mom.expanding_corr(x, y, pairwise=True),
lambda x, y: mom.rolling_cov(x, y, window=3, pairwise=True),
lambda x, y: mom.rolling_corr(x, y, window=3, pairwise=True),
lambda x, y: mom.ewmcov(x, y, com=3, pairwise=True),
lambda x, y: mom.ewmcorr(x, y, com=3, pairwise=True),
]:
results = [f(df, df2) for df in df1s]
for (df, result) in zip(df1s, results):
assert_index_equal(result.items, df.index)
assert_index_equal(result.major_axis, df.columns)
assert_index_equal(result.minor_axis, df2.columns)
for i, result in enumerate(results):
if i > 0:
self.assert_numpy_array_equivalent(result, results[0])
# DataFrame with another DataFrame, pairwise=False
for f in [lambda x, y: mom.expanding_cov(x, y, pairwise=False),
lambda x, y: mom.expanding_corr(x, y, pairwise=False),
lambda x, y: mom.rolling_cov(x, y, window=3, pairwise=False),
lambda x, y: mom.rolling_corr(x, y, window=3, pairwise=False),
lambda x, y: mom.ewmcov(x, y, com=3, pairwise=False),
lambda x, y: mom.ewmcorr(x, y, com=3, pairwise=False),
]:
results = [f(df, df2) if df.columns.is_unique else None for df in df1s]
for (df, result) in zip(df1s, results):
if result is not None:
expected_index = df.index.union(df2.index)
expected_columns = df.columns.union(df2.columns)
assert_index_equal(result.index, expected_index)
assert_index_equal(result.columns, expected_columns)
else:
tm.assertRaisesRegexp(ValueError, "'arg1' columns are not unique", f, df, df2)
tm.assertRaisesRegexp(ValueError, "'arg2' columns are not unique", f, df2, df)
# DataFrame with a Series
for f in [lambda x, y: mom.expanding_cov(x, y),
lambda x, y: mom.expanding_corr(x, y),
lambda x, y: mom.rolling_cov(x, y, window=3),
lambda x, y: mom.rolling_corr(x, y, window=3),
lambda x, y: mom.ewmcov(x, y, com=3),
lambda x, y: mom.ewmcorr(x, y, com=3),
]:
results = [f(df, s) for df in df1s] + [f(s, df) for df in df1s]
for (df, result) in zip(df1s, results):
assert_index_equal(result.index, df.index)
assert_index_equal(result.columns, df.columns)
for i, result in enumerate(results):
if i > 0:
self.assert_numpy_array_equivalent(result, results[0])
def test_rolling_skew_edge_cases(self):
all_nan = Series([np.NaN] * 5)
# yields all NaN (0 variance)
d = Series([1] * 5)
x = mom.rolling_skew(d, window=5)
assert_series_equal(all_nan, x)
# yields all NaN (window too small)
d = Series(np.random.randn(5))
x = mom.rolling_skew(d, window=2)
assert_series_equal(all_nan, x)
# yields [NaN, NaN, NaN, 0.177994, 1.548824]
d = Series([-1.50837035, -0.1297039 , 0.19501095,
1.73508164, 0.41941401])
expected = Series([np.NaN, np.NaN, np.NaN,
0.177994, 1.548824])
x = mom.rolling_skew(d, window=4)
assert_series_equal(expected, x)
def test_rolling_kurt_edge_cases(self):
all_nan = Series([np.NaN] * 5)
# yields all NaN (0 variance)
d = Series([1] * 5)
x = mom.rolling_kurt(d, window=5)
assert_series_equal(all_nan, x)
# yields all NaN (window too small)
d = Series(np.random.randn(5))
x = mom.rolling_kurt(d, window=3)
assert_series_equal(all_nan, x)
# yields [NaN, NaN, NaN, 1.224307, 2.671499]
d = Series([-1.50837035, -0.1297039 , 0.19501095,
1.73508164, 0.41941401])
expected = Series([np.NaN, np.NaN, np.NaN,
1.224307, 2.671499])
x = mom.rolling_kurt(d, window=4)
assert_series_equal(expected, x)
def _check_expanding_ndarray(self, func, static_comp, has_min_periods=True,
has_time_rule=True, preserve_nan=True):
result = func(self.arr)
assert_almost_equal(result[10],
static_comp(self.arr[:11]))
if preserve_nan:
assert(np.isnan(result[self._nan_locs]).all())
arr = randn(50)
if has_min_periods:
result = func(arr, min_periods=30)
assert(np.isnan(result[:29]).all())
assert_almost_equal(result[-1], static_comp(arr[:50]))
# min_periods is working correctly
result = func(arr, min_periods=15)
self.assertTrue(np.isnan(result[13]))
self.assertFalse(np.isnan(result[14]))
arr2 = randn(20)
result = func(arr2, min_periods=5)
self.assertTrue(isnull(result[3]))
self.assertTrue(notnull(result[4]))
# min_periods=0
result0 = func(arr, min_periods=0)
result1 = func(arr, min_periods=1)
assert_almost_equal(result0, result1)
else:
result = func(arr)
assert_almost_equal(result[-1], static_comp(arr[:50]))
def _check_expanding_structures(self, func):
series_result = func(self.series)
tm.assert_isinstance(series_result, Series)
frame_result = func(self.frame)
self.assertEqual(type(frame_result), DataFrame)
def _check_expanding(self, func, static_comp, has_min_periods=True,
has_time_rule=True,
preserve_nan=True):
self._check_expanding_ndarray(func, static_comp,
has_min_periods=has_min_periods,
has_time_rule=has_time_rule,
preserve_nan=preserve_nan)
self._check_expanding_structures(func)
def test_rolling_max_gh6297(self):
"""Replicate result expected in GH #6297"""
indices = [datetime(1975, 1, i) for i in range(1, 6)]
# So that we can have 2 datapoints on one of the days
indices.append(datetime(1975, 1, 3, 6, 0))
series = Series(range(1, 7), index=indices)
# Use floats instead of ints as values
series = series.map(lambda x: float(x))
# Sort chronologically
series = series.sort_index()
expected = Series([1.0, 2.0, 6.0, 4.0, 5.0],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
x = mom.rolling_max(series, window=1, freq='D')
assert_series_equal(expected, x)
def test_rolling_max_how_resample(self):
indices = [datetime(1975, 1, i) for i in range(1, 6)]
# So that we can have 3 datapoints on last day (4, 10, and 20)
indices.append(datetime(1975, 1, 5, 1))
indices.append(datetime(1975, 1, 5, 2))
series = Series(list(range(0, 5)) + [10, 20], index=indices)
# Use floats instead of ints as values
series = series.map(lambda x: float(x))
# Sort chronologically
series = series.sort_index()
# Default how should be max
expected = Series([0.0, 1.0, 2.0, 3.0, 20.0],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
x = mom.rolling_max(series, window=1, freq='D')
assert_series_equal(expected, x)
# Now specify median (10.0)
expected = Series([0.0, 1.0, 2.0, 3.0, 10.0],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
x = mom.rolling_max(series, window=1, freq='D', how='median')
assert_series_equal(expected, x)
# Now specify mean (4+10+20)/3
v = (4.0+10.0+20.0)/3.0
expected = Series([0.0, 1.0, 2.0, 3.0, v],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
x = mom.rolling_max(series, window=1, freq='D', how='mean')
assert_series_equal(expected, x)
def test_rolling_min_how_resample(self):
indices = [datetime(1975, 1, i) for i in range(1, 6)]
# So that we can have 3 datapoints on last day (4, 10, and 20)
indices.append(datetime(1975, 1, 5, 1))
indices.append(datetime(1975, 1, 5, 2))
series = Series(list(range(0, 5)) + [10, 20], index=indices)
# Use floats instead of ints as values
series = series.map(lambda x: float(x))
# Sort chronologically
series = series.sort_index()
# Default how should be min
expected = Series([0.0, 1.0, 2.0, 3.0, 4.0],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
x = mom.rolling_min(series, window=1, freq='D')
assert_series_equal(expected, x)
def test_rolling_median_how_resample(self):
indices = [datetime(1975, 1, i) for i in range(1, 6)]
# So that we can have 3 datapoints on last day (4, 10, and 20)
indices.append(datetime(1975, 1, 5, 1))
indices.append(datetime(1975, 1, 5, 2))
series = Series(list(range(0, 5)) + [10, 20], index=indices)
# Use floats instead of ints as values
series = series.map(lambda x: float(x))
# Sort chronologically
series = series.sort_index()
# Default how should be median
expected = Series([0.0, 1.0, 2.0, 3.0, 10],
index=[datetime(1975, 1, i, 0)
for i in range(1, 6)])
x = mom.rolling_median(series, window=1, freq='D')
assert_series_equal(expected, x)
if __name__ == '__main__':
import nose
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
| gpl-2.0 |
annahs/atmos_research | LEO_2D_histos_from_db.py | 1 | 3992 | import sys
import os
import datetime
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as colors
from pprint import pprint
import sqlite3
import calendar
from datetime import datetime
#id INTEGER PRIMARY KEY AUTOINCREMENT,
#sp2b_file TEXT,
#file_index INT,
#instr TEXT,
#instr_locn TEXT,
#particle_type TEXT,
#particle_dia FLOAT,
#unix_ts_utc FLOAT,
#actual_scat_amp FLOAT,
#actual_peak_pos INT,
#FF_scat_amp FLOAT,
#FF_peak_pos INT,
#FF_gauss_width FLOAT,
#zeroX_to_peak FLOAT,
#LF_scat_amp FLOAT,
#incand_amp FLOAT,
#lag_time_fit_to_incand FLOAT,
#LF_baseline_pct_diff FLOAT,
#rBC_mass_fg FLOAT,
#coat_thickness_nm FLOAT,
#zero_crossing_posn FLOAT,
#UNIQUE (sp2b_file, file_index, instr)
#connect to database
conn = sqlite3.connect('C:/projects/dbs/SP2_data.db')
c = conn.cursor()
instrument = 'UBCSP2'
instrument_locn = 'WHI'
type_particle = 'incand'
start_date = datetime.strptime('20120401','%Y%m%d')
end_date = datetime.strptime('20120531','%Y%m%d')
lookup_file = 'C:/Users/Sarah Hanna/Documents/Data/WHI long term record/coatings/lookup_tables/coating_lookup_table_WHI_2012_UBCSP2.lupckl'
rBC_density = 1.8
incand_sat = 3750
LF_max = 45000 #above this is unreasonable
lookup = open(lookup_file, 'r')
lookup_table = pickle.load(lookup)
lookup.close()
min_rBC_mass = 1.63#120 2.6-#140 3.86-#160nm 0.25
max_rBC_mass = 2.6#140 3.86-160 5.5-#180nm 10.05
VED_min = 65
VED_max = 220
scat_lim = 100
begin_data = calendar.timegm(start_date.timetuple())
end_data = calendar.timegm(end_date.timetuple())
data = []
particles=0
no_scat=0
no_scat_110 =0
fit_failure=0
early_evap=0
early_evap_110=0
flat_fit=0
LF_high=0
for row in c.execute('''SELECT rBC_mass_fg, coat_thickness_nm, unix_ts_utc, LF_scat_amp, LF_baseline_pct_diff, sp2b_file, file_index, instr,actual_scat_amp
FROM SP2_coating_analysis
WHERE instr=? and instr_locn=? and particle_type=? and rBC_mass_fg>=? and rBC_mass_fg<? and unix_ts_utc>=? and unix_ts_utc<?''',
(instrument,instrument_locn,type_particle, min_rBC_mass, max_rBC_mass, begin_data,end_data)):
particles+=1
rBC_mass = row[0]
coat_thickness = row[1]
event_time = datetime.utcfromtimestamp(row[2])
LEO_amp = row[3]
LF_baseline_pctdiff = row[4]
file = row[5]
index = row[6]
instrt = row[7]
meas_scat_amp = row[8]
rBC_VED = (((rBC_mass/(10**15*rBC_density))*6/3.14159)**(1/3.0))*10**7 #VED in nm with 10^15fg/g and 10^7nm/cm
if meas_scat_amp < 6:
no_scat +=1
if rBC_VED > scat_lim:
no_scat_110+=1
data.append([rBC_VED,coat_thickness])
if LEO_amp == 0.0 and LF_baseline_pctdiff == None and meas_scat_amp >= 6:
early_evap +=1
if rBC_VED > scat_lim:
early_evap_110 +=1
if LEO_amp == -2:
early_evap +=1
if rBC_VED > scat_lim:
early_evap_110 +=1
if LEO_amp == -1:
fit_failure +=1
if LEO_amp == 0.0 and LF_baseline_pctdiff != None:
flat_fit +=1
if LEO_amp > LF_max:
LF_high +=1
if LEO_amp > 0:
data.append([rBC_VED,coat_thickness])
print '# of particles', particles
print 'no_scat', no_scat
print 'no_scat_110', no_scat_110
print 'fit_failure', fit_failure
print 'early_evap', early_evap
print 'early_evap_110', early_evap_110
print 'flat_fit', flat_fit
print 'LF_high', LF_high
evap_pct = (early_evap)*100.0/particles
evap_pct_110 = (early_evap_110)*100.0/particles
no_scat_pct = (no_scat)*100.0/particles
no_scat_pct_110 = no_scat_110*100./particles
print evap_pct, evap_pct_110, no_scat_pct,no_scat_pct_110
rBC_VEDs = [row[0] for row in data]
coatings = [row[1] for row in data]
median_coat = np.median (coatings)
print 'median coating',median_coat
#####hexbin coat vs core###
fig = plt.figure()
ax = fig.add_subplot(111)
#x_limits = [0,250]
#y_limits = [0,250]
#h = plt.hexbin(rBC_VEDs, coatings, cmap=cm.jet,gridsize = 50, mincnt=1)
hist = plt.hist(coatings, bins=50)
plt.xlabel('frequency')
plt.xlabel('Coating Thickness (nm)')
#cb = plt.colorbar()
#cb.set_label('frequency')
plt.show()
| mit |
harshaneelhg/scikit-learn | sklearn/naive_bayes.py | 128 | 28358 | # -*- coding: utf-8 -*-
"""
The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These
are supervised learning methods based on applying Bayes' theorem with strong
(naive) feature independence assumptions.
"""
# Author: Vincent Michel <vincent.michel@inria.fr>
# Minor fixes by Fabian Pedregosa
# Amit Aides <amitibo@tx.technion.ac.il>
# Yehuda Finkelstein <yehudaf@tx.technion.ac.il>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# (parts based on earlier work by Mathieu Blondel)
#
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy.sparse import issparse
from .base import BaseEstimator, ClassifierMixin
from .preprocessing import binarize
from .preprocessing import LabelBinarizer
from .preprocessing import label_binarize
from .utils import check_X_y, check_array
from .utils.extmath import safe_sparse_dot, logsumexp
from .utils.multiclass import _check_partial_fit_first_call
from .utils.fixes import in1d
from .utils.validation import check_is_fitted
from .externals import six
__all__ = ['BernoulliNB', 'GaussianNB', 'MultinomialNB']
class BaseNB(six.with_metaclass(ABCMeta, BaseEstimator, ClassifierMixin)):
"""Abstract base class for naive Bayes estimators"""
@abstractmethod
def _joint_log_likelihood(self, X):
"""Compute the unnormalized posterior log probability of X
I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an array-like of
shape [n_classes, n_samples].
Input is passed to _joint_log_likelihood as-is by predict,
predict_proba and predict_log_proba.
"""
def predict(self, X):
"""
Perform classification on an array of test vectors X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array, shape = [n_samples]
Predicted target values for X
"""
jll = self._joint_log_likelihood(X)
return self.classes_[np.argmax(jll, axis=1)]
def predict_log_proba(self, X):
"""
Return log-probability estimates for the test vector X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array-like, shape = [n_samples, n_classes]
Returns the log-probability of the samples for each class in
the model. The columns correspond to the classes in sorted
order, as they appear in the attribute `classes_`.
"""
jll = self._joint_log_likelihood(X)
# normalize by P(x) = P(f_1, ..., f_n)
log_prob_x = logsumexp(jll, axis=1)
return jll - np.atleast_2d(log_prob_x).T
def predict_proba(self, X):
"""
Return probability estimates for the test vector X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array-like, shape = [n_samples, n_classes]
Returns the probability of the samples for each class in
the model. The columns correspond to the classes in sorted
order, as they appear in the attribute `classes_`.
"""
return np.exp(self.predict_log_proba(X))
class GaussianNB(BaseNB):
"""
Gaussian Naive Bayes (GaussianNB)
Can perform online updates to model parameters via `partial_fit` method.
For details on algorithm used to update feature means and variance online,
see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:
http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf
Read more in the :ref:`User Guide <gaussian_naive_bayes>`.
Attributes
----------
class_prior_ : array, shape (n_classes,)
probability of each class.
class_count_ : array, shape (n_classes,)
number of training samples observed in each class.
theta_ : array, shape (n_classes, n_features)
mean of each feature per class
sigma_ : array, shape (n_classes, n_features)
variance of each feature per class
Examples
--------
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> Y = np.array([1, 1, 1, 2, 2, 2])
>>> from sklearn.naive_bayes import GaussianNB
>>> clf = GaussianNB()
>>> clf.fit(X, Y)
GaussianNB()
>>> print(clf.predict([[-0.8, -1]]))
[1]
>>> clf_pf = GaussianNB()
>>> clf_pf.partial_fit(X, Y, np.unique(Y))
GaussianNB()
>>> print(clf_pf.predict([[-0.8, -1]]))
[1]
"""
def fit(self, X, y, sample_weight=None):
"""Fit Gaussian Naive Bayes according to X, y
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape (n_samples,)
Target values.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : object
Returns self.
"""
X, y = check_X_y(X, y)
return self._partial_fit(X, y, np.unique(y), _refit=True,
sample_weight=sample_weight)
@staticmethod
def _update_mean_variance(n_past, mu, var, X, sample_weight=None):
"""Compute online update of Gaussian mean and variance.
Given starting sample count, mean, and variance, a new set of
points X, and optionally sample weights, return the updated mean and
variance. (NB - each dimension (column) in X is treated as independent
-- you get variance, not covariance).
Can take scalar mean and variance, or vector mean and variance to
simultaneously update a number of independent Gaussians.
See Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:
http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf
Parameters
----------
n_past : int
Number of samples represented in old mean and variance. If sample
weights were given, this should contain the sum of sample
weights represented in old mean and variance.
mu : array-like, shape (number of Gaussians,)
Means for Gaussians in original set.
var : array-like, shape (number of Gaussians,)
Variances for Gaussians in original set.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
total_mu : array-like, shape (number of Gaussians,)
Updated mean for each Gaussian over the combined set.
total_var : array-like, shape (number of Gaussians,)
Updated variance for each Gaussian over the combined set.
"""
if X.shape[0] == 0:
return mu, var
# Compute (potentially weighted) mean and variance of new datapoints
if sample_weight is not None:
n_new = float(sample_weight.sum())
new_mu = np.average(X, axis=0, weights=sample_weight / n_new)
new_var = np.average((X - new_mu) ** 2, axis=0,
weights=sample_weight / n_new)
else:
n_new = X.shape[0]
new_var = np.var(X, axis=0)
new_mu = np.mean(X, axis=0)
if n_past == 0:
return new_mu, new_var
n_total = float(n_past + n_new)
# Combine mean of old and new data, taking into consideration
# (weighted) number of observations
total_mu = (n_new * new_mu + n_past * mu) / n_total
# Combine variance of old and new data, taking into consideration
# (weighted) number of observations. This is achieved by combining
# the sum-of-squared-differences (ssd)
old_ssd = n_past * var
new_ssd = n_new * new_var
total_ssd = (old_ssd + new_ssd +
(n_past / float(n_new * n_total)) *
(n_new * mu - n_new * new_mu) ** 2)
total_var = total_ssd / n_total
return total_mu, total_var
def partial_fit(self, X, y, classes=None, sample_weight=None):
"""Incremental fit on a batch of samples.
This method is expected to be called several times consecutively
on different chunks of a dataset so as to implement out-of-core
or online learning.
This is especially useful when the whole dataset is too big to fit in
memory at once.
This method has some performance and numerical stability overhead,
hence it is better to call partial_fit on chunks of data that are
as large as possible (as long as fitting in the memory budget) to
hide the overhead.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples,)
Target values.
classes : array-like, shape (n_classes,)
List of all the classes that can possibly appear in the y vector.
Must be provided at the first call to partial_fit, can be omitted
in subsequent calls.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : object
Returns self.
"""
return self._partial_fit(X, y, classes, _refit=False,
sample_weight=sample_weight)
def _partial_fit(self, X, y, classes=None, _refit=False,
sample_weight=None):
"""Actual implementation of Gaussian NB fitting.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples,)
Target values.
classes : array-like, shape (n_classes,)
List of all the classes that can possibly appear in the y vector.
Must be provided at the first call to partial_fit, can be omitted
in subsequent calls.
_refit: bool
If true, act as though this were the first time we called
_partial_fit (ie, throw away any past fitting and start over).
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : object
Returns self.
"""
X, y = check_X_y(X, y)
epsilon = 1e-9
if _refit:
self.classes_ = None
if _check_partial_fit_first_call(self, classes):
# This is the first call to partial_fit:
# initialize various cumulative counters
n_features = X.shape[1]
n_classes = len(self.classes_)
self.theta_ = np.zeros((n_classes, n_features))
self.sigma_ = np.zeros((n_classes, n_features))
self.class_prior_ = np.zeros(n_classes)
self.class_count_ = np.zeros(n_classes)
else:
if X.shape[1] != self.theta_.shape[1]:
msg = "Number of features %d does not match previous data %d."
raise ValueError(msg % (X.shape[1], self.theta_.shape[1]))
# Put epsilon back in each time
self.sigma_[:, :] -= epsilon
classes = self.classes_
unique_y = np.unique(y)
unique_y_in_classes = in1d(unique_y, classes)
if not np.all(unique_y_in_classes):
raise ValueError("The target label(s) %s in y do not exist in the "
"initial classes %s" %
(y[~unique_y_in_classes], classes))
for y_i in unique_y:
i = classes.searchsorted(y_i)
X_i = X[y == y_i, :]
if sample_weight is not None:
sw_i = sample_weight[y == y_i]
N_i = sw_i.sum()
else:
sw_i = None
N_i = X_i.shape[0]
new_theta, new_sigma = self._update_mean_variance(
self.class_count_[i], self.theta_[i, :], self.sigma_[i, :],
X_i, sw_i)
self.theta_[i, :] = new_theta
self.sigma_[i, :] = new_sigma
self.class_count_[i] += N_i
self.sigma_[:, :] += epsilon
self.class_prior_[:] = self.class_count_ / np.sum(self.class_count_)
return self
def _joint_log_likelihood(self, X):
check_is_fitted(self, "classes_")
X = check_array(X)
joint_log_likelihood = []
for i in range(np.size(self.classes_)):
jointi = np.log(self.class_prior_[i])
n_ij = - 0.5 * np.sum(np.log(2. * np.pi * self.sigma_[i, :]))
n_ij -= 0.5 * np.sum(((X - self.theta_[i, :]) ** 2) /
(self.sigma_[i, :]), 1)
joint_log_likelihood.append(jointi + n_ij)
joint_log_likelihood = np.array(joint_log_likelihood).T
return joint_log_likelihood
class BaseDiscreteNB(BaseNB):
"""Abstract base class for naive Bayes on discrete/categorical data
Any estimator based on this class should provide:
__init__
_joint_log_likelihood(X) as per BaseNB
"""
def _update_class_log_prior(self, class_prior=None):
n_classes = len(self.classes_)
if class_prior is not None:
if len(class_prior) != n_classes:
raise ValueError("Number of priors must match number of"
" classes.")
self.class_log_prior_ = np.log(class_prior)
elif self.fit_prior:
# empirical prior, with sample_weight taken into account
self.class_log_prior_ = (np.log(self.class_count_)
- np.log(self.class_count_.sum()))
else:
self.class_log_prior_ = np.zeros(n_classes) - np.log(n_classes)
def partial_fit(self, X, y, classes=None, sample_weight=None):
"""Incremental fit on a batch of samples.
This method is expected to be called several times consecutively
on different chunks of a dataset so as to implement out-of-core
or online learning.
This is especially useful when the whole dataset is too big to fit in
memory at once.
This method has some performance overhead hence it is better to call
partial_fit on chunks of data that are as large as possible
(as long as fitting in the memory budget) to hide the overhead.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
classes : array-like, shape = [n_classes]
List of all the classes that can possibly appear in the y vector.
Must be provided at the first call to partial_fit, can be omitted
in subsequent calls.
sample_weight : array-like, shape = [n_samples], optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : object
Returns self.
"""
X = check_array(X, accept_sparse='csr', dtype=np.float64)
_, n_features = X.shape
if _check_partial_fit_first_call(self, classes):
# This is the first call to partial_fit:
# initialize various cumulative counters
n_effective_classes = len(classes) if len(classes) > 1 else 2
self.class_count_ = np.zeros(n_effective_classes, dtype=np.float64)
self.feature_count_ = np.zeros((n_effective_classes, n_features),
dtype=np.float64)
elif n_features != self.coef_.shape[1]:
msg = "Number of features %d does not match previous data %d."
raise ValueError(msg % (n_features, self.coef_.shape[-1]))
Y = label_binarize(y, classes=self.classes_)
if Y.shape[1] == 1:
Y = np.concatenate((1 - Y, Y), axis=1)
n_samples, n_classes = Y.shape
if X.shape[0] != Y.shape[0]:
msg = "X.shape[0]=%d and y.shape[0]=%d are incompatible."
raise ValueError(msg % (X.shape[0], y.shape[0]))
# label_binarize() returns arrays with dtype=np.int64.
# We convert it to np.float64 to support sample_weight consistently
Y = Y.astype(np.float64)
if sample_weight is not None:
Y *= check_array(sample_weight).T
class_prior = self.class_prior
# Count raw events from data before updating the class log prior
# and feature log probas
self._count(X, Y)
# XXX: OPTIM: we could introduce a public finalization method to
# be called by the user explicitly just once after several consecutive
# calls to partial_fit and prior any call to predict[_[log_]proba]
# to avoid computing the smooth log probas at each call to partial fit
self._update_feature_log_prob()
self._update_class_log_prior(class_prior=class_prior)
return self
def fit(self, X, y, sample_weight=None):
"""Fit Naive Bayes classifier according to X, y
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
sample_weight : array-like, shape = [n_samples], optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : object
Returns self.
"""
X, y = check_X_y(X, y, 'csr')
_, n_features = X.shape
labelbin = LabelBinarizer()
Y = labelbin.fit_transform(y)
self.classes_ = labelbin.classes_
if Y.shape[1] == 1:
Y = np.concatenate((1 - Y, Y), axis=1)
# LabelBinarizer().fit_transform() returns arrays with dtype=np.int64.
# We convert it to np.float64 to support sample_weight consistently;
# this means we also don't have to cast X to floating point
Y = Y.astype(np.float64)
if sample_weight is not None:
Y *= check_array(sample_weight).T
class_prior = self.class_prior
# Count raw events from data before updating the class log prior
# and feature log probas
n_effective_classes = Y.shape[1]
self.class_count_ = np.zeros(n_effective_classes, dtype=np.float64)
self.feature_count_ = np.zeros((n_effective_classes, n_features),
dtype=np.float64)
self._count(X, Y)
self._update_feature_log_prob()
self._update_class_log_prior(class_prior=class_prior)
return self
# XXX The following is a stopgap measure; we need to set the dimensions
# of class_log_prior_ and feature_log_prob_ correctly.
def _get_coef(self):
return (self.feature_log_prob_[1:]
if len(self.classes_) == 2 else self.feature_log_prob_)
def _get_intercept(self):
return (self.class_log_prior_[1:]
if len(self.classes_) == 2 else self.class_log_prior_)
coef_ = property(_get_coef)
intercept_ = property(_get_intercept)
class MultinomialNB(BaseDiscreteNB):
"""
Naive Bayes classifier for multinomial models
The multinomial Naive Bayes classifier is suitable for classification with
discrete features (e.g., word counts for text classification). The
multinomial distribution normally requires integer feature counts. However,
in practice, fractional counts such as tf-idf may also work.
Read more in the :ref:`User Guide <multinomial_naive_bayes>`.
Parameters
----------
alpha : float, optional (default=1.0)
Additive (Laplace/Lidstone) smoothing parameter
(0 for no smoothing).
fit_prior : boolean
Whether to learn class prior probabilities or not.
If false, a uniform prior will be used.
class_prior : array-like, size (n_classes,)
Prior probabilities of the classes. If specified the priors are not
adjusted according to the data.
Attributes
----------
class_log_prior_ : array, shape (n_classes, )
Smoothed empirical log probability for each class.
intercept_ : property
Mirrors ``class_log_prior_`` for interpreting MultinomialNB
as a linear model.
feature_log_prob_ : array, shape (n_classes, n_features)
Empirical log probability of features
given a class, ``P(x_i|y)``.
coef_ : property
Mirrors ``feature_log_prob_`` for interpreting MultinomialNB
as a linear model.
class_count_ : array, shape (n_classes,)
Number of samples encountered for each class during fitting. This
value is weighted by the sample weight when provided.
feature_count_ : array, shape (n_classes, n_features)
Number of samples encountered for each (class, feature)
during fitting. This value is weighted by the sample weight when
provided.
Examples
--------
>>> import numpy as np
>>> X = np.random.randint(5, size=(6, 100))
>>> y = np.array([1, 2, 3, 4, 5, 6])
>>> from sklearn.naive_bayes import MultinomialNB
>>> clf = MultinomialNB()
>>> clf.fit(X, y)
MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)
>>> print(clf.predict(X[2]))
[3]
Notes
-----
For the rationale behind the names `coef_` and `intercept_`, i.e.
naive Bayes as a linear classifier, see J. Rennie et al. (2003),
Tackling the poor assumptions of naive Bayes text classifiers, ICML.
References
----------
C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
Information Retrieval. Cambridge University Press, pp. 234-265.
http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html
"""
def __init__(self, alpha=1.0, fit_prior=True, class_prior=None):
self.alpha = alpha
self.fit_prior = fit_prior
self.class_prior = class_prior
def _count(self, X, Y):
"""Count and smooth feature occurrences."""
if np.any((X.data if issparse(X) else X) < 0):
raise ValueError("Input X must be non-negative")
self.feature_count_ += safe_sparse_dot(Y.T, X)
self.class_count_ += Y.sum(axis=0)
def _update_feature_log_prob(self):
"""Apply smoothing to raw counts and recompute log probabilities"""
smoothed_fc = self.feature_count_ + self.alpha
smoothed_cc = smoothed_fc.sum(axis=1)
self.feature_log_prob_ = (np.log(smoothed_fc)
- np.log(smoothed_cc.reshape(-1, 1)))
def _joint_log_likelihood(self, X):
"""Calculate the posterior log probability of the samples X"""
check_is_fitted(self, "classes_")
X = check_array(X, accept_sparse='csr')
return (safe_sparse_dot(X, self.feature_log_prob_.T)
+ self.class_log_prior_)
class BernoulliNB(BaseDiscreteNB):
"""Naive Bayes classifier for multivariate Bernoulli models.
Like MultinomialNB, this classifier is suitable for discrete data. The
difference is that while MultinomialNB works with occurrence counts,
BernoulliNB is designed for binary/boolean features.
Read more in the :ref:`User Guide <bernoulli_naive_bayes>`.
Parameters
----------
alpha : float, optional (default=1.0)
Additive (Laplace/Lidstone) smoothing parameter
(0 for no smoothing).
binarize : float or None, optional
Threshold for binarizing (mapping to booleans) of sample features.
If None, input is presumed to already consist of binary vectors.
fit_prior : boolean
Whether to learn class prior probabilities or not.
If false, a uniform prior will be used.
class_prior : array-like, size=[n_classes,]
Prior probabilities of the classes. If specified the priors are not
adjusted according to the data.
Attributes
----------
class_log_prior_ : array, shape = [n_classes]
Log probability of each class (smoothed).
feature_log_prob_ : array, shape = [n_classes, n_features]
Empirical log probability of features given a class, P(x_i|y).
class_count_ : array, shape = [n_classes]
Number of samples encountered for each class during fitting. This
value is weighted by the sample weight when provided.
feature_count_ : array, shape = [n_classes, n_features]
Number of samples encountered for each (class, feature)
during fitting. This value is weighted by the sample weight when
provided.
Examples
--------
>>> import numpy as np
>>> X = np.random.randint(2, size=(6, 100))
>>> Y = np.array([1, 2, 3, 4, 4, 5])
>>> from sklearn.naive_bayes import BernoulliNB
>>> clf = BernoulliNB()
>>> clf.fit(X, Y)
BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)
>>> print(clf.predict(X[2]))
[3]
References
----------
C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
Information Retrieval. Cambridge University Press, pp. 234-265.
http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
A. McCallum and K. Nigam (1998). A comparison of event models for naive
Bayes text classification. Proc. AAAI/ICML-98 Workshop on Learning for
Text Categorization, pp. 41-48.
V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with
naive Bayes -- Which naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS).
"""
def __init__(self, alpha=1.0, binarize=.0, fit_prior=True,
class_prior=None):
self.alpha = alpha
self.binarize = binarize
self.fit_prior = fit_prior
self.class_prior = class_prior
def _count(self, X, Y):
"""Count and smooth feature occurrences."""
if self.binarize is not None:
X = binarize(X, threshold=self.binarize)
self.feature_count_ += safe_sparse_dot(Y.T, X)
self.class_count_ += Y.sum(axis=0)
def _update_feature_log_prob(self):
"""Apply smoothing to raw counts and recompute log probabilities"""
smoothed_fc = self.feature_count_ + self.alpha
smoothed_cc = self.class_count_ + self.alpha * 2
self.feature_log_prob_ = (np.log(smoothed_fc)
- np.log(smoothed_cc.reshape(-1, 1)))
def _joint_log_likelihood(self, X):
"""Calculate the posterior log probability of the samples X"""
check_is_fitted(self, "classes_")
X = check_array(X, accept_sparse='csr')
if self.binarize is not None:
X = binarize(X, threshold=self.binarize)
n_classes, n_features = self.feature_log_prob_.shape
n_samples, n_features_X = X.shape
if n_features_X != n_features:
raise ValueError("Expected input with %d features, got %d instead"
% (n_features, n_features_X))
neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
# Compute neg_prob · (1 - X).T as ∑neg_prob - X · neg_prob
jll = safe_sparse_dot(X, (self.feature_log_prob_ - neg_prob).T)
jll += self.class_log_prior_ + neg_prob.sum(axis=1)
return jll
| bsd-3-clause |
aavanian/bokeh | bokeh/sampledata/tests/test_world_cities.py | 2 | 1963 | #-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2017, Anaconda, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Boilerplate
#-----------------------------------------------------------------------------
from __future__ import absolute_import, division, print_function, unicode_literals
import pytest ; pytest
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
# Standard library imports
# External imports
import pandas as pd
# Bokeh imports
from bokeh.util.testing import verify_all
# Module under test
#import bokeh.sampledata.world_cities as bsw
#-----------------------------------------------------------------------------
# Setup
#-----------------------------------------------------------------------------
ALL = (
'data',
)
#-----------------------------------------------------------------------------
# General API
#-----------------------------------------------------------------------------
Test___all__ = pytest.mark.sampledata(verify_all("bokeh.sampledata.world_cities", ALL))
@pytest.mark.sampledata
def test_data():
import bokeh.sampledata.world_cities as bsw
assert isinstance(bsw.data, pd.DataFrame)
# don't check detail for external data
#-----------------------------------------------------------------------------
# Dev API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Private API
#-----------------------------------------------------------------------------
| bsd-3-clause |
thp44/delphin_6_automation | data_process/2d_1d/archieve/temperature.py | 1 | 18075 | __author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import matplotlib.pyplot as plt
import numpy as np
import os
import datetime
import matplotlib.dates as mdates
import pandas as pd
# RiBuild Modules
from delphin_6_automation.file_parsing import delphin_parser
# -------------------------------------------------------------------------------------------------------------------- #
# RIBuild
# Application
colors = {'top': '#FBBA00', 'mid': '#B81A5D', 'bottom': '#79C6C0', '1d_brick': '#000000', '1d_mortar': '#BDCCD4'}
project_dict = {'dresden_zp_high_ratio_uninsulated_4a':
{'map':
{'5ad9e0352e2cb22f2c4f15b4': 'brick_1d',
'5ad9e3bf2e2cb22f2c4f166b': 'mortar_1d',
'5adb0a102e2cb22f2c4f17e9': '2d'}
},
'dresden_zd_high_ratio_uninsulated_4a':
{'map':
{'5ad9e0ba2e2cb22f2c4f15f1': 'brick_1d',
'5ad9e3bf2e2cb22f2c4f166b': 'mortar_1d',
'5adb2dc02e2cb22f2c4f1873': '2d'}
},
'potsdam_high_ratio_uninsulated_4a':
{'map':
{'5ad9e3462e2cb22f2c4f162e': 'brick_1d',
'5ad9e3bf2e2cb22f2c4f166b': 'mortar_1d',
'5adcc9702e2cb22f2c4f18fd': '2d'}
},
'dresden_zp_low_ratio_uninsulated_4a':
{'map':
{'5ad9e6192e2cb22f2c4f175f': 'brick_1d',
'5ad9e5812e2cb22f2c4f1722': 'mortar_1d',
'5adda7172e2cb20baca57c6e': '2d'}
},
'dresden_zd_low_ratio_uninsulated_4a':
{'map':
{'5ad9e44f2e2cb22f2c4f16a8': 'brick_1d',
'5ad9e5812e2cb22f2c4f1722': 'mortar_1d',
'5adcd4402e2cb22f2c4f1987': '2d'}
},
'potsdam_low_ratio_uninsulated_4a':
{'map': {'5ad9e4f22e2cb22f2c4f16e5': 'brick_1d',
'5ad9e5812e2cb22f2c4f1722': 'mortar_1d',
'5add9b902e2cb20baca57be4': '2d'}
},
'dresden_zp_high_ratio_insulated_4a':
{'map': {'5ae824252e2cb22d48db5955': 'brick_1d',
'5ae82c222e2cb2156000902b': 'mortar_1d',
'5ae355cf2e2cb2201055c1a4': '2d'}
},
'dresden_zd_high_ratio_insulated_4a':
{'map': {'5ae824d82e2cb22d48db5998': 'brick_1d',
'5ae82c222e2cb2156000902b': 'mortar_1d',
'5ae398f12e2cb2201055c263': '2d'}
},
'potsdam_high_ratio_insulated_4a':
{'map':
{'5ae82bac2e2cb21560008fe8': 'brick_1d',
'5ae82c222e2cb2156000902b': 'mortar_1d',
'5ae6ca982e2cb2201055c322': '2d'}
},
'dresden_zp_low_ratio_insulated_4a':
{'map':
{'5ae82e5d2e2cb21560009137': 'brick_1d',
'5ae82dc02e2cb215600090f4': 'mortar_1d',
'5ae6fdbf2e2cb20d5891272f': '2d'}
},
'dresden_zd_low_ratio_insulated_4a':
{'map':
{'5ae82cb12e2cb2156000906e': 'brick_1d',
'5ae82dc02e2cb215600090f4': 'mortar_1d',
'5ae6d9bf2e2cb2201055c3e1': '2d'}
},
'potsdam_low_ratio_insulated_4a':
{'map':
{'5ae82d3b2e2cb215600090b1': 'brick_1d',
'5ae82dc02e2cb215600090f4': 'mortar_1d',
'5ae6edaf2e2cb20d58912670': '2d'}
},
}
result_folder = r'U:\RIBuild\2D_1D\Results'
files = ['temperature profile.d6o']
# Functions
def get_points(result: dict, geo: dict):
points = []
for index_ in result['indices']:
x_ = geo['element_geometry'][index_][1]
y_ = geo['element_geometry'][index_][2]
points.append({'cell': index_, 'x': x_, 'y': y_})
return points
def add_data_to_points(points: list, results: dict, result_name: str):
for cell_ in results['result'].keys():
cell_index = int(cell_.split('_')[1])
for point in points:
if point['cell'] == cell_index:
point[result_name] = np.array(results['result'][cell_][8760:])
break
def main(project_):
projects = list(project_dict[project_]['map'].keys())
parsed_dicts = {'brick_1d': {'temp': {}, 'geo': {}},
'mortar_1d': {'temp': {}, 'geo': {}},
'2d': {'temp': {}, 'geo': {}}, }
for p_ in projects:
for mp_key in project_dict[project_]['map'].keys():
if p_ == mp_key:
key = project_dict[project_]['map'][mp_key]
folder = result_folder + f'/{p_}/results'
geo_file = [file
for file in os.listdir(folder)
if file.endswith('.g6a')][0]
parsed_dicts[key]['temp'], _ = delphin_parser.d6o_to_dict(folder, files[0])
parsed_dicts[key]['geo'] = delphin_parser.g6a_to_dict(folder, geo_file)
x_date = [datetime.datetime(2020, 1, 1) + datetime.timedelta(hours=i)
for i in range(len(parsed_dicts['brick_1d']['temp']['result']['cell_0'][8760:]))]
# Brick 1D
brick_1d = get_points(parsed_dicts['brick_1d']['temp'], parsed_dicts['brick_1d']['geo'])
brick_1d.sort(key=lambda point: point['x'])
add_data_to_points(brick_1d, parsed_dicts['brick_1d']['temp'], 'temperature')
# Mortar 1D
mortar_1d = get_points(parsed_dicts['mortar_1d']['temp'], parsed_dicts['mortar_1d']['geo'])
mortar_1d.sort(key=lambda point: point['x'])
add_data_to_points(mortar_1d, parsed_dicts['mortar_1d']['temp'], 'temperature')
# 2D
sim_2d = get_points(parsed_dicts['2d']['temp'], parsed_dicts['2d']['geo'])
sim_2d.sort(key=lambda point: (point['x'], point['y']))
add_data_to_points(sim_2d, parsed_dicts['2d']['temp'], 'temperature')
# Plots
def plot_locations(quantity):
# Axes 00
plt.figure()
plt.title(f"{quantity}\n1D-Location: {brick_1d[0]['x']:.4f} and 2D-Location: {sim_2d[0]['x']:.4f}")
plt.plot(x_date, brick_1d[0][quantity], color=colors['1d_brick'], label=f"1D Brick")
plt.plot(x_date, mortar_1d[0][quantity], color=colors['1d_mortar'], label=f"1D Mortar")
plt.plot(x_date, sim_2d[0][quantity], color=colors['bottom'], label=f"2D Bottom")
plt.plot(x_date, sim_2d[1][quantity], color=colors['mid'], label=f"2D Mid")
plt.plot(x_date, sim_2d[2][quantity], color=colors['top'], label=f"2D Top")
plt.legend()
plt.gcf().autofmt_xdate()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%B'))
plt.ylabel(f'{quantity}')
# Axes 01
plt.figure()
plt.title(f"{quantity}\n1D-Location: {brick_1d[1]['x']:.4f} and 2D-Location: {sim_2d[3]['x']:.4f}")
plt.plot(x_date, brick_1d[1][quantity], color=colors['1d_brick'], label=f"1D Brick")
plt.plot(x_date, mortar_1d[1][quantity], color=colors['1d_mortar'], label=f"1D Mortar")
plt.plot(x_date, sim_2d[3][quantity], color=colors['bottom'], label=f"2D Bottom")
plt.plot(x_date, sim_2d[4][quantity], color=colors['mid'], label=f"2D Mid")
plt.plot(x_date, sim_2d[5][quantity], color=colors['top'], label=f"2D Top")
plt.legend()
plt.gcf().autofmt_xdate()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%B'))
plt.ylabel(f'{quantity}')
# Axes 10
plt.figure()
plt.title(f"{quantity}\n1D-Location: {brick_1d[2]['x']:.4f} and 2D-Location: {sim_2d[6]['x']:.4f}")
plt.plot(x_date, brick_1d[2][quantity], color=colors['1d_brick'], label=f"1D Brick")
plt.plot(x_date, mortar_1d[2][quantity], color=colors['1d_mortar'], label=f"1D Mortar")
plt.plot(x_date, sim_2d[6][quantity], color=colors['bottom'], label=f"2D Bottom")
plt.plot(x_date, sim_2d[7][quantity], color=colors['mid'], label=f"2D Mid")
plt.plot(x_date, sim_2d[8][quantity], color=colors['top'], label=f"2D Top")
plt.legend()
plt.gcf().autofmt_xdate()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%B'))
plt.ylabel(f'{quantity}')
# Axes 11
plt.figure()
plt.title(f"{quantity}\n1D-Location: {brick_1d[3]['x']:.4f} and 2D-Location: {sim_2d[9]['x']:.4f}")
plt.plot(x_date, brick_1d[3][quantity], color=colors['1d_brick'], label=f"1D Brick")
plt.plot(x_date, mortar_1d[3][quantity], color=colors['1d_mortar'], label=f"1D Mortar")
plt.plot(x_date, sim_2d[9][quantity], color=colors['bottom'], label=f"2D Bottom")
plt.plot(x_date, sim_2d[10][quantity], color=colors['mid'], label=f"2D Mid")
plt.plot(x_date, sim_2d[11][quantity], color=colors['top'], label=f"2D Top")
plt.legend()
plt.gcf().autofmt_xdate()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%B'))
plt.ylabel(f'{quantity}')
# Axes 20
plt.figure()
plt.title(f"{quantity}\n1D-Location: {brick_1d[4]['x']:.4f} and 2D-Location: {sim_2d[12]['x']:.4f}")
plt.plot(x_date, brick_1d[4][quantity], color=colors['1d_brick'], label=f"1D Brick")
plt.plot(x_date, mortar_1d[4][quantity], color=colors['1d_mortar'], label=f"1D Mortar")
plt.plot(x_date, sim_2d[12][quantity], color=colors['bottom'], label=f"2D Bottom")
plt.plot(x_date, sim_2d[13][quantity], color=colors['mid'], label=f"2D Mid")
plt.plot(x_date, sim_2d[14][quantity], color=colors['top'], label=f"2D Top")
plt.legend()
plt.gcf().autofmt_xdate()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%B'))
plt.ylabel(f'{quantity}')
# Axes 21
plt.figure()
plt.title(f"{quantity}\n1D-Location: {brick_1d[5]['x']:.4f} and 2D-Location: {sim_2d[15]['x']:.4f}")
plt.plot(x_date, brick_1d[5][quantity], color=colors['1d_brick'], label=f"1D Brick")
plt.plot(x_date, mortar_1d[5][quantity], color=colors['1d_mortar'], label=f"1D Mortar")
plt.plot(x_date, sim_2d[15][quantity], color=colors['bottom'], label=f"2D Bottom")
plt.plot(x_date, sim_2d[16][quantity], color=colors['mid'], label=f"2D Mid")
plt.plot(x_date, sim_2d[17][quantity], color=colors['top'], label=f"2D Top")
plt.legend()
plt.gcf().autofmt_xdate()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%B'))
plt.ylabel(f'{quantity}')
#plot_locations(quantity='temperature')
#plt.show()
def abs_diff(x1, x2):
return x2 - x1
def rel_diff(x1, x2):
return (abs(x2 - x1))/abs(x2) * 100
def differences(i, plots=False):
avg_2d = np.mean([sim_2d[i]['temperature'], sim_2d[i+2]['temperature'], sim_2d[i+2]['temperature']], axis=0)
brick_abs = abs_diff(brick_1d[i]['temperature'], avg_2d)
mortar_abs = abs_diff(mortar_1d[i]['temperature'], avg_2d)
brick_rel = rel_diff(brick_1d[i]['temperature'], avg_2d)
mortar_rel = rel_diff(mortar_1d[i]['temperature'], avg_2d)
if plots:
# Plot
plt.figure()
plt.title(f"Temperature - Absolute Difference\n"
f"1D-Location: {brick_1d[i]['x']:.4f} and 2D-Location: {sim_2d[i*3]['x']:.4f}")
plt.plot(x_date, brick_abs, color=colors['1d_brick'], label=f"1D Brick")
plt.plot(x_date, mortar_abs, color=colors['1d_mortar'], label=f"1D Mortar")
plt.legend()
plt.gcf().autofmt_xdate()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%B'))
plt.ylabel('C')
plt.figure()
plt.title(f"Temperature - Relative Difference\n"
f"1D-Location: {brick_1d[i]['x']:.4f} and 2D-Location: {sim_2d[i*3]['x']:.4f}")
plt.plot(x_date, brick_rel, color=colors['1d_brick'], label=f"1D Brick")
plt.plot(x_date, mortar_rel, color=colors['1d_mortar'], label=f"1D Mortar")
plt.legend()
plt.gcf().autofmt_xdate()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%B'))
plt.ylabel('%')
local_df = pd.DataFrame(columns=[f"{brick_1d[i]['x']:.04f}", f"{brick_1d[i]['x']:.04f}",
f"{brick_1d[i]['x']:.04f}", f"{brick_1d[i]['x']:.04f}"],
index=pd.DatetimeIndex(start=datetime.datetime(2020, 1, 1),
freq='h', periods=len(brick_rel)),
data=np.vstack([brick_rel, brick_abs, mortar_rel, mortar_abs]).T)
local_df.columns = pd.MultiIndex.from_arrays([local_df.columns, ['brick', 'brick', 'mortar', 'mortar'],
['relative', 'absolute', 'relative', 'absolute']],
names=['location', 'material', 'type'])
return local_df
def differences_weighted(i, plots=False):
avg_2d = np.average(a=[sim_2d[i]['temperature'],
sim_2d[i+2]['temperature'],
sim_2d[i+2]['temperature']],
axis=0,
weights=[56, 24., 56])
brick_abs = abs_diff(brick_1d[i]['temperature'], avg_2d)
mortar_abs = abs_diff(mortar_1d[i]['temperature'], avg_2d)
brick_rel = rel_diff(brick_1d[i]['temperature'], avg_2d)
mortar_rel = rel_diff(mortar_1d[i]['temperature'], avg_2d)
if plots:
# Plot
plt.figure()
plt.title(f"Temperature - Weighted Absolute Difference\n"
f"1D-Location: {brick_1d[i]['x']:.4f} and 2D-Location: {sim_2d[i*3]['x']:.4f}")
plt.plot(x_date, brick_abs, color=colors['1d_brick'], label=f"1D Brick")
plt.plot(x_date, mortar_abs, color=colors['1d_mortar'], label=f"1D Mortar")
plt.legend()
plt.gcf().autofmt_xdate()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%B'))
plt.ylabel('%')
plt.figure()
plt.title(f"Temperature - Weighted Relative Difference\n"
f"1D-Location: {brick_1d[i]['x']:.4f} and 2D-Location: {sim_2d[i*3]['x']:.4f}")
plt.plot(x_date, brick_rel, color=colors['1d_brick'], label=f"1D Brick")
plt.plot(x_date, mortar_rel, color=colors['1d_mortar'], label=f"1D Mortar")
plt.legend()
plt.gcf().autofmt_xdate()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%B'))
plt.ylabel('%')
local_df = pd.DataFrame(columns=[f"{brick_1d[i]['x']:.04f}", f"{brick_1d[i]['x']:.04f}",
f"{brick_1d[i]['x']:.04f}", f"{brick_1d[i]['x']:.04f}"],
index=pd.DatetimeIndex(start=datetime.datetime(2020, 1, 1),
freq='h', periods=len(brick_rel)),
data=np.vstack([brick_rel, brick_abs, mortar_rel, mortar_abs]).T)
local_df.columns = pd.MultiIndex.from_arrays([local_df.columns, ['brick', 'brick', 'mortar', 'mortar'],
['relative', 'absolute', 'relative', 'absolute']],
names=['location', 'material', 'type'])
return local_df
dataframes = []
weighted_dataframes = []
for index in range(len(brick_1d)):
dataframes.append(differences(index))
weighted_dataframes.append(differences_weighted(index))
#plt.show()
result_dataframe = pd.concat(dataframes, axis=1)
w_result_dataframe = pd.concat(weighted_dataframes, axis=1)
absolute_df = result_dataframe.loc[:, pd.IndexSlice[:, :, 'absolute']]
absolute_df.columns = absolute_df.columns.droplevel(level=2)
relative_df = result_dataframe.loc[:, pd.IndexSlice[:, :, 'relative']]
relative_df.columns = relative_df.columns.droplevel(level=2)
w_absolute_df = w_result_dataframe.loc[:, pd.IndexSlice[:, :, 'absolute']]
w_absolute_df.columns = w_absolute_df.columns.droplevel(level=2)
w_relative_df = w_result_dataframe.loc[:, pd.IndexSlice[:, :, 'relative']]
w_relative_df.columns = w_relative_df.columns.droplevel(level=2)
plt.figure()
ax = absolute_df.boxplot()
ax.set_ylim(-20, 20)
ax.set_ylabel('Temperature - C')
ax.set_title('Absolute Differences')
#plt.show()
out_folder = r'C:\Users\ocni\PycharmProjects\delphin_6_automation\data_process\2d_1d\processed_data'
def excel():
writer = pd.ExcelWriter(out_folder + '/temperature.xlsx')
relative_df.describe().to_excel(writer, 'relative')
absolute_df.describe().to_excel(writer, 'absolute')
writer.save()
#excel()
def save_relative():
hdf_file = out_folder + '/relative_temperature.h5'
w_relative_df.to_hdf(hdf_file, project_, append=True)
save_relative()
for project_key in project_dict.keys():
print(f'Processing {project_key}')
main(project_key) | mit |
lenovor/scikit-learn | examples/mixture/plot_gmm_selection.py | 248 | 3223 | """
=================================
Gaussian Mixture Model Selection
=================================
This example shows that model selection can be performed with
Gaussian Mixture Models using information-theoretic criteria (BIC).
Model selection concerns both the covariance type
and the number of components in the model.
In that case, AIC also provides the right result (not shown to save time),
but BIC is better suited if the problem is to identify the right model.
Unlike Bayesian procedures, such inferences are prior-free.
In that case, the model with 2 components and full covariance
(which corresponds to the true generative model) is selected.
"""
print(__doc__)
import itertools
import numpy as np
from scipy import linalg
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import mixture
# Number of samples per component
n_samples = 500
# Generate random sample, two components
np.random.seed(0)
C = np.array([[0., -0.1], [1.7, .4]])
X = np.r_[np.dot(np.random.randn(n_samples, 2), C),
.7 * np.random.randn(n_samples, 2) + np.array([-6, 3])]
lowest_bic = np.infty
bic = []
n_components_range = range(1, 7)
cv_types = ['spherical', 'tied', 'diag', 'full']
for cv_type in cv_types:
for n_components in n_components_range:
# Fit a mixture of Gaussians with EM
gmm = mixture.GMM(n_components=n_components, covariance_type=cv_type)
gmm.fit(X)
bic.append(gmm.bic(X))
if bic[-1] < lowest_bic:
lowest_bic = bic[-1]
best_gmm = gmm
bic = np.array(bic)
color_iter = itertools.cycle(['k', 'r', 'g', 'b', 'c', 'm', 'y'])
clf = best_gmm
bars = []
# Plot the BIC scores
spl = plt.subplot(2, 1, 1)
for i, (cv_type, color) in enumerate(zip(cv_types, color_iter)):
xpos = np.array(n_components_range) + .2 * (i - 2)
bars.append(plt.bar(xpos, bic[i * len(n_components_range):
(i + 1) * len(n_components_range)],
width=.2, color=color))
plt.xticks(n_components_range)
plt.ylim([bic.min() * 1.01 - .01 * bic.max(), bic.max()])
plt.title('BIC score per model')
xpos = np.mod(bic.argmin(), len(n_components_range)) + .65 +\
.2 * np.floor(bic.argmin() / len(n_components_range))
plt.text(xpos, bic.min() * 0.97 + .03 * bic.max(), '*', fontsize=14)
spl.set_xlabel('Number of components')
spl.legend([b[0] for b in bars], cv_types)
# Plot the winner
splot = plt.subplot(2, 1, 2)
Y_ = clf.predict(X)
for i, (mean, covar, color) in enumerate(zip(clf.means_, clf.covars_,
color_iter)):
v, w = linalg.eigh(covar)
if not np.any(Y_ == i):
continue
plt.scatter(X[Y_ == i, 0], X[Y_ == i, 1], .8, color=color)
# Plot an ellipse to show the Gaussian component
angle = np.arctan2(w[0][1], w[0][0])
angle = 180 * angle / np.pi # convert to degrees
v *= 4
ell = mpl.patches.Ellipse(mean, v[0], v[1], 180 + angle, color=color)
ell.set_clip_box(splot.bbox)
ell.set_alpha(.5)
splot.add_artist(ell)
plt.xlim(-10, 10)
plt.ylim(-3, 6)
plt.xticks(())
plt.yticks(())
plt.title('Selected GMM: full model, 2 components')
plt.subplots_adjust(hspace=.35, bottom=.02)
plt.show()
| bsd-3-clause |
notkarol/banjin | experiment/python_word_matching_speed.py | 1 | 4650 | #!/usr/bin/python
# Takes in a dictionary of words
# Verifies that all functions return the same answers
# Generates random hands from the probability of getting tiles from the bunch
# Then prints out how long each function takes to find all matching words
# Generates various hand sizes to see if there's any scaling
import matplotlib.pyplot as plt
import numpy as np
import pickle
import os
import sys
import timeit
# Naive list way of matching wordbank
def f0_list(hand, wordbank):
results = []
for w_i in range(len(wordbank)):
match = True
for i in range(26):
if hand[i] < wordbank[w_i][i]:
match = False
break
if match:
results.append(w_i)
return results
# A for loop and some numpy
def f1_list(hand, wordbank):
results = []
for w_i in range(len(wordbank)):
if min(list(map(lambda x: x[1] - x[0], zip(wordbank[w_i], hand)))) >= 0:
results.append(w_i)
return results
# Naive way using numpy
def f0_np(hand, wordbank):
results = []
for w_i in range(len(wordbank)):
match = True
for i in range(26):
if hand[i] < wordbank[w_i,i]:
match = False
break
if match:
results.append(w_i)
return results
# A for loop and some numpy
def f1_np(hand, wordbank):
results = []
for w_i in range(len(wordbank)):
if not np.any((hand - wordbank[w_i]) < 0):
results.append(w_i)
return results
# A for loop and some numpy
def f2_np(hand, wordbank):
results = []
for w_i in range(len(wordbank)):
if np.min(hand - wordbank[w_i]) >= 0:
results.append(w_i)
return results
# Vectorized sum and difference
def f3_np(hand, wordbank):
return np.where(np.sum((wordbank - hand) > 0, axis=1) == 0)[0]
# vectorized just using any
def f4_np(hand, wordbank):
return np.where(np.any(wordbank > hand, axis=1) == 0)[0]
# Prepare a 2D list and a 2D np array of letter frequencies
with open(sys.argv[1]) as f:
words = [x.split()[0] for x in f.readlines()]
wordbank_list = [[0] * 26 for _ in range(len(words))]
wordbank_np = np.zeros((len(words), 26))
for w_i in range(len(words)):
for letter in sorted(words[w_i]):
pos = ord(letter) - 65
wordbank_list[w_i][pos] += 1
wordbank_np[w_i][pos] += 1
# Arrays for keeping track of functions and data-specific wordbanks
hand_sizes = list(range(2, 9))
functions = {'list' : [f0_list, f1_list],
'numpy': [f0_np, f1_np, f2_np, f3_np, f4_np]}
wordbanks = {'list' : wordbank_list,
'numpy': wordbank_np}
n_iter = 10 if len(sys.argv) < 3 else int(sys.argv[2])
timings = {}
for datatype in functions:
timings[datatype] = np.zeros((max(hand_sizes) + 1, n_iter, len(functions[datatype])))
# Verify that our functions give the same answers
for datatype in functions:
for func in functions[datatype]:
print(datatype, func(wordbanks[datatype][len(wordbank_list) // 2], wordbanks[datatype]))
# Time each word
imports = 'from __main__ import functions, wordbanks'
for counter in range(n_iter):
for hand_size in hand_sizes:
# Get a specific hand size
hand = [13,3,3,6,18,3,4,3,12,2,2,5,3,8,11,3,2,9,6,9,6,3,3,2,3,2]
while sum(hand) > hand_size:
pos = np.random.randint(sum(hand))
for i in range(len(hand)):
pos -= hand[i]
if pos < 0:
hand[i] -= 1
break
hand = str(hand)
# For this hand go wild
for datatype in functions:
for f_i in range(len(functions[datatype])):
cmd = 'functions["%s"][%i](%s, wordbanks["%s"])' % (datatype, f_i, hand, datatype)
timings[datatype][hand_size, counter, f_i] += timeit.timeit(cmd, imports, number=8)
print("\rCompleted %.1f%%" % (100 * (counter + 1) / n_iter), end='')
print()
# Save words and timings in case we're doing a long-lasting operation
filename = 'word_matching_timings_%s.pkl' % os.path.basename(sys.argv[1])
with open(filename, 'wb') as f:
print("Saving", filename)
pickle.dump((words, wordbanks, timings), f)
# Show Results
for datatype in functions:
means = np.mean(timings[datatype], axis=1)
for f_i in range(means.shape[1]):
plt.semilogy(hand_sizes, means[:, f_i][min(hand_sizes):], label='%s F%i' % (datatype, f_i))
plt.legend(loc='center left', bbox_to_anchor=(0.85, 0.5))
plt.xlabel("Hand Size")
plt.ylabel("Execution Time")
plt.title("Word Matching")
plt.show()
| mit |
ishanic/scikit-learn | sklearn/feature_extraction/hashing.py | 183 | 6155 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause
import numbers
import numpy as np
import scipy.sparse as sp
from . import _hashing
from ..base import BaseEstimator, TransformerMixin
def _iteritems(d):
"""Like d.iteritems, but accepts any collections.Mapping."""
return d.iteritems() if hasattr(d, "iteritems") else d.items()
class FeatureHasher(BaseEstimator, TransformerMixin):
"""Implements feature hashing, aka the hashing trick.
This class turns sequences of symbolic feature names (strings) into
scipy.sparse matrices, using a hash function to compute the matrix column
corresponding to a name. The hash function employed is the signed 32-bit
version of Murmurhash3.
Feature names of type byte string are used as-is. Unicode strings are
converted to UTF-8 first, but no Unicode normalization is done.
Feature values must be (finite) numbers.
This class is a low-memory alternative to DictVectorizer and
CountVectorizer, intended for large-scale (online) learning and situations
where memory is tight, e.g. when running prediction code on embedded
devices.
Read more in the :ref:`User Guide <feature_hashing>`.
Parameters
----------
n_features : integer, optional
The number of features (columns) in the output matrices. Small numbers
of features are likely to cause hash collisions, but large numbers
will cause larger coefficient dimensions in linear learners.
dtype : numpy type, optional
The type of feature values. Passed to scipy.sparse matrix constructors
as the dtype argument. Do not set this to bool, np.boolean or any
unsigned integer type.
input_type : string, optional
Either "dict" (the default) to accept dictionaries over
(feature_name, value); "pair" to accept pairs of (feature_name, value);
or "string" to accept single strings.
feature_name should be a string, while value should be a number.
In the case of "string", a value of 1 is implied.
The feature_name is hashed to find the appropriate column for the
feature. The value's sign might be flipped in the output (but see
non_negative, below).
non_negative : boolean, optional, default np.float64
Whether output matrices should contain non-negative values only;
effectively calls abs on the matrix prior to returning it.
When True, output values can be interpreted as frequencies.
When False, output values will have expected value zero.
Examples
--------
>>> from sklearn.feature_extraction import FeatureHasher
>>> h = FeatureHasher(n_features=10)
>>> D = [{'dog': 1, 'cat':2, 'elephant':4},{'dog': 2, 'run': 5}]
>>> f = h.transform(D)
>>> f.toarray()
array([[ 0., 0., -4., -1., 0., 0., 0., 0., 0., 2.],
[ 0., 0., 0., -2., -5., 0., 0., 0., 0., 0.]])
See also
--------
DictVectorizer : vectorizes string-valued features using a hash table.
sklearn.preprocessing.OneHotEncoder : handles nominal/categorical features
encoded as columns of integers.
"""
def __init__(self, n_features=(2 ** 20), input_type="dict",
dtype=np.float64, non_negative=False):
self._validate_params(n_features, input_type)
self.dtype = dtype
self.input_type = input_type
self.n_features = n_features
self.non_negative = non_negative
@staticmethod
def _validate_params(n_features, input_type):
# strangely, np.int16 instances are not instances of Integral,
# while np.int64 instances are...
if not isinstance(n_features, (numbers.Integral, np.integer)):
raise TypeError("n_features must be integral, got %r (%s)."
% (n_features, type(n_features)))
elif n_features < 1 or n_features >= 2 ** 31:
raise ValueError("Invalid number of features (%d)." % n_features)
if input_type not in ("dict", "pair", "string"):
raise ValueError("input_type must be 'dict', 'pair' or 'string',"
" got %r." % input_type)
def fit(self, X=None, y=None):
"""No-op.
This method doesn't do anything. It exists purely for compatibility
with the scikit-learn transformer API.
Returns
-------
self : FeatureHasher
"""
# repeat input validation for grid search (which calls set_params)
self._validate_params(self.n_features, self.input_type)
return self
def transform(self, raw_X, y=None):
"""Transform a sequence of instances to a scipy.sparse matrix.
Parameters
----------
raw_X : iterable over iterable over raw features, length = n_samples
Samples. Each sample must be iterable an (e.g., a list or tuple)
containing/generating feature names (and optionally values, see
the input_type constructor argument) which will be hashed.
raw_X need not support the len function, so it can be the result
of a generator; n_samples is determined on the fly.
y : (ignored)
Returns
-------
X : scipy.sparse matrix, shape = (n_samples, self.n_features)
Feature matrix, for use with estimators or further transformers.
"""
raw_X = iter(raw_X)
if self.input_type == "dict":
raw_X = (_iteritems(d) for d in raw_X)
elif self.input_type == "string":
raw_X = (((f, 1) for f in x) for x in raw_X)
indices, indptr, values = \
_hashing.transform(raw_X, self.n_features, self.dtype)
n_samples = indptr.shape[0] - 1
if n_samples == 0:
raise ValueError("Cannot vectorize empty sequence.")
X = sp.csr_matrix((values, indices, indptr), dtype=self.dtype,
shape=(n_samples, self.n_features))
X.sum_duplicates() # also sorts the indices
if self.non_negative:
np.abs(X.data, X.data)
return X
| bsd-3-clause |
moonbury/notebooks | github/MatplotlibCookbook/Chapter 8/wx-supershape-1.py | 3 | 1121 | import wx, numpy
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg
from matplotlib.figure import Figure
def supershape_radius(phi, a, b, m, n1, n2, n3):
theta = .25 * m * phi
cos = numpy.fabs(numpy.cos(theta) / a) ** n2
sin = numpy.fabs(numpy.sin(theta) / b) ** n3
r = (cos + sin) ** (-1. / n1)
r /= numpy.max(r)
return r
class SuperShapeFrame(wx.Frame):
def __init__(self, parent, id, title):
wx.Frame.__init__(self, parent, id, title,
style = wx.DEFAULT_FRAME_STYLE ^ wx.RESIZE_BORDER,
size = (480, 480))
self.fig = Figure((6, 6), dpi = 80)
self.panel = wx.Panel(self, -1)
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(FigureCanvasWxAgg(self.panel, -1, self.fig), 1)
self.panel.SetSizer(sizer)
self.draw_figure()
def draw_figure(self):
phi = numpy.linspace(0, 2 * numpy.pi, 1024)
r = supershape_radius(phi, 1, 1, 3, 2, 18, 18)
ax = self.fig.add_subplot(111, polar = True)
ax.plot(phi, r, lw = 3.)
self.fig.canvas.draw()
app = wx.App(redirect = True)
top = SuperShapeFrame(None, -1, 'SuperShape')
top.Show()
app.MainLoop()
| gpl-3.0 |
mlee92/Programming | Econ/supply_demand_elasticity/demand_elasticity.py | 2 | 1413 | # Elasticity of demand is a measure of how strongly consumers respond to a change in the price of a good
# Formally, % change in demand / % change in price
# Problem: Graph the histogram of average-elasticity for a linear-demand good with random coefficients (a, b)
import random
import matplotlib.pyplot as plt
import numpy as np
SIM = 1000;
UNIT_RANGE = range(1, 50)
AVGS = list()
COEF = [0, 0]
def generate_coefficients():
global COEF
a = random.randint(1, 25)
b = random.randint(a*50, 25*50)
COEF = [a, b]
def price(unit):
return COEF[1] - COEF[0]*unit
def graph_price():
x = np.linspace(1,50,50)
y = price(x)
plt.plot(x, y)
plt.show()
def elasticity(d1, d2):
cPrice = price(d2) - price(d1)
cDemand = d2 - d1
pPrice = cPrice / price(d1)
pDemand = cDemand / d1
return abs(pDemand / pPrice)
def simulate():
global AVGS, COEF, UNIT_RANGE
generate_coefficients()
elast_list = list()
for i in UNIT_RANGE:
for j in UNIT_RANGE:
if(i != j):
elast_list.append(elasticity(i, j))
mu = np.mean(elast_list)
print(COEF, mu)
AVGS.append(mu)
def init():
for i in range(0, SIM):
simulate()
init()
print(SIM)
plt.hist(AVGS)
plt.show()
| gpl-2.0 |
cython-testbed/pandas | pandas/core/apply.py | 4 | 12744 | import warnings
import numpy as np
from pandas import compat
from pandas._libs import reduction
from pandas.core.dtypes.generic import ABCSeries
from pandas.core.dtypes.common import (
is_extension_type,
is_dict_like,
is_list_like,
is_sequence)
from pandas.util._decorators import cache_readonly
from pandas.io.formats.printing import pprint_thing
def frame_apply(obj, func, axis=0, broadcast=None,
raw=False, reduce=None, result_type=None,
ignore_failures=False,
args=None, kwds=None):
""" construct and return a row or column based frame apply object """
axis = obj._get_axis_number(axis)
if axis == 0:
klass = FrameRowApply
elif axis == 1:
klass = FrameColumnApply
return klass(obj, func, broadcast=broadcast,
raw=raw, reduce=reduce, result_type=result_type,
ignore_failures=ignore_failures,
args=args, kwds=kwds)
class FrameApply(object):
def __init__(self, obj, func, broadcast, raw, reduce, result_type,
ignore_failures, args, kwds):
self.obj = obj
self.raw = raw
self.ignore_failures = ignore_failures
self.args = args or ()
self.kwds = kwds or {}
if result_type not in [None, 'reduce', 'broadcast', 'expand']:
raise ValueError("invalid value for result_type, must be one "
"of {None, 'reduce', 'broadcast', 'expand'}")
if broadcast is not None:
warnings.warn("The broadcast argument is deprecated and will "
"be removed in a future version. You can specify "
"result_type='broadcast' to broadcast the result "
"to the original dimensions",
FutureWarning, stacklevel=4)
if broadcast:
result_type = 'broadcast'
if reduce is not None:
warnings.warn("The reduce argument is deprecated and will "
"be removed in a future version. You can specify "
"result_type='reduce' to try to reduce the result "
"to the original dimensions",
FutureWarning, stacklevel=4)
if reduce:
if result_type is not None:
raise ValueError(
"cannot pass both reduce=True and result_type")
result_type = 'reduce'
self.result_type = result_type
# curry if needed
if ((kwds or args) and
not isinstance(func, (np.ufunc, compat.string_types))):
def f(x):
return func(x, *args, **kwds)
else:
f = func
self.f = f
# results
self.result = None
self.res_index = None
self.res_columns = None
@property
def columns(self):
return self.obj.columns
@property
def index(self):
return self.obj.index
@cache_readonly
def values(self):
return self.obj.values
@cache_readonly
def dtypes(self):
return self.obj.dtypes
@property
def agg_axis(self):
return self.obj._get_agg_axis(self.axis)
def get_result(self):
""" compute the results """
# dispatch to agg
if is_list_like(self.f) or is_dict_like(self.f):
return self.obj.aggregate(self.f, axis=self.axis,
*self.args, **self.kwds)
# all empty
if len(self.columns) == 0 and len(self.index) == 0:
return self.apply_empty_result()
# string dispatch
if isinstance(self.f, compat.string_types):
# Support for `frame.transform('method')`
# Some methods (shift, etc.) require the axis argument, others
# don't, so inspect and insert if necessary.
func = getattr(self.obj, self.f)
sig = compat.signature(func)
if 'axis' in sig.args:
self.kwds['axis'] = self.axis
return func(*self.args, **self.kwds)
# ufunc
elif isinstance(self.f, np.ufunc):
with np.errstate(all='ignore'):
results = self.f(self.values)
return self.obj._constructor(data=results, index=self.index,
columns=self.columns, copy=False)
# broadcasting
if self.result_type == 'broadcast':
return self.apply_broadcast()
# one axis empty
elif not all(self.obj.shape):
return self.apply_empty_result()
# raw
elif self.raw and not self.obj._is_mixed_type:
return self.apply_raw()
return self.apply_standard()
def apply_empty_result(self):
"""
we have an empty result; at least 1 axis is 0
we will try to apply the function to an empty
series in order to see if this is a reduction function
"""
# we are not asked to reduce or infer reduction
# so just return a copy of the existing object
if self.result_type not in ['reduce', None]:
return self.obj.copy()
# we may need to infer
reduce = self.result_type == 'reduce'
from pandas import Series
if not reduce:
EMPTY_SERIES = Series([])
try:
r = self.f(EMPTY_SERIES, *self.args, **self.kwds)
reduce = not isinstance(r, Series)
except Exception:
pass
if reduce:
return self.obj._constructor_sliced(np.nan, index=self.agg_axis)
else:
return self.obj.copy()
def apply_raw(self):
""" apply to the values as a numpy array """
try:
result = reduction.reduce(self.values, self.f, axis=self.axis)
except Exception:
result = np.apply_along_axis(self.f, self.axis, self.values)
# TODO: mixed type case
if result.ndim == 2:
return self.obj._constructor(result,
index=self.index,
columns=self.columns)
else:
return self.obj._constructor_sliced(result,
index=self.agg_axis)
def apply_broadcast(self, target):
result_values = np.empty_like(target.values)
# axis which we want to compare compliance
result_compare = target.shape[0]
for i, col in enumerate(target.columns):
res = self.f(target[col])
ares = np.asarray(res).ndim
# must be a scalar or 1d
if ares > 1:
raise ValueError("too many dims to broadcast")
elif ares == 1:
# must match return dim
if result_compare != len(res):
raise ValueError("cannot broadcast result")
result_values[:, i] = res
# we *always* preserve the original index / columns
result = self.obj._constructor(result_values,
index=target.index,
columns=target.columns)
return result
def apply_standard(self):
# try to reduce first (by default)
# this only matters if the reduction in values is of different dtype
# e.g. if we want to apply to a SparseFrame, then can't directly reduce
# we cannot reduce using non-numpy dtypes,
# as demonstrated in gh-12244
if (self.result_type in ['reduce', None] and
not self.dtypes.apply(is_extension_type).any()):
# Create a dummy Series from an empty array
from pandas import Series
values = self.values
index = self.obj._get_axis(self.axis)
labels = self.agg_axis
empty_arr = np.empty(len(index), dtype=values.dtype)
dummy = Series(empty_arr, index=index, dtype=values.dtype)
try:
result = reduction.reduce(values, self.f,
axis=self.axis,
dummy=dummy,
labels=labels)
return self.obj._constructor_sliced(result, index=labels)
except Exception:
pass
# compute the result using the series generator
self.apply_series_generator()
# wrap results
return self.wrap_results()
def apply_series_generator(self):
series_gen = self.series_generator
res_index = self.result_index
i = None
keys = []
results = {}
if self.ignore_failures:
successes = []
for i, v in enumerate(series_gen):
try:
results[i] = self.f(v)
keys.append(v.name)
successes.append(i)
except Exception:
pass
# so will work with MultiIndex
if len(successes) < len(res_index):
res_index = res_index.take(successes)
else:
try:
for i, v in enumerate(series_gen):
results[i] = self.f(v)
keys.append(v.name)
except Exception as e:
if hasattr(e, 'args'):
# make sure i is defined
if i is not None:
k = res_index[i]
e.args = e.args + ('occurred at index %s' %
pprint_thing(k), )
raise
self.results = results
self.res_index = res_index
self.res_columns = self.result_columns
def wrap_results(self):
results = self.results
# see if we can infer the results
if len(results) > 0 and is_sequence(results[0]):
return self.wrap_results_for_axis()
# dict of scalars
result = self.obj._constructor_sliced(results)
result.index = self.res_index
return result
class FrameRowApply(FrameApply):
axis = 0
def apply_broadcast(self):
return super(FrameRowApply, self).apply_broadcast(self.obj)
@property
def series_generator(self):
return (self.obj._ixs(i, axis=1)
for i in range(len(self.columns)))
@property
def result_index(self):
return self.columns
@property
def result_columns(self):
return self.index
def wrap_results_for_axis(self):
""" return the results for the rows """
results = self.results
result = self.obj._constructor(data=results)
if not isinstance(results[0], ABCSeries):
try:
result.index = self.res_columns
except ValueError:
pass
try:
result.columns = self.res_index
except ValueError:
pass
return result
class FrameColumnApply(FrameApply):
axis = 1
def apply_broadcast(self):
result = super(FrameColumnApply, self).apply_broadcast(self.obj.T)
return result.T
@property
def series_generator(self):
constructor = self.obj._constructor_sliced
return (constructor(arr, index=self.columns, name=name)
for i, (arr, name) in enumerate(zip(self.values,
self.index)))
@property
def result_index(self):
return self.index
@property
def result_columns(self):
return self.columns
def wrap_results_for_axis(self):
""" return the results for the columns """
results = self.results
# we have requested to expand
if self.result_type == 'expand':
result = self.infer_to_same_shape()
# we have a non-series and don't want inference
elif not isinstance(results[0], ABCSeries):
from pandas import Series
result = Series(results)
result.index = self.res_index
# we may want to infer results
else:
result = self.infer_to_same_shape()
return result
def infer_to_same_shape(self):
""" infer the results to the same shape as the input object """
results = self.results
result = self.obj._constructor(data=results)
result = result.T
# set the index
result.index = self.res_index
# infer dtypes
result = result.infer_objects()
return result
| bsd-3-clause |
jeffery-do/Vizdoombot | doom/lib/python3.5/site-packages/skimage/viewer/utils/core.py | 19 | 6555 | import numpy as np
from ..qt import QtWidgets, has_qt, FigureManagerQT, FigureCanvasQTAgg
from ..._shared.utils import warn
import matplotlib as mpl
from matplotlib.figure import Figure
from matplotlib import _pylab_helpers
from matplotlib.colors import LinearSegmentedColormap
if has_qt and 'agg' not in mpl.get_backend().lower():
warn("Recommended matplotlib backend is `Agg` for full "
"skimage.viewer functionality.")
__all__ = ['init_qtapp', 'start_qtapp', 'RequiredAttr', 'figimage',
'LinearColormap', 'ClearColormap', 'FigureCanvas', 'new_plot',
'update_axes_image']
QApp = None
def init_qtapp():
"""Initialize QAppliction.
The QApplication needs to be initialized before creating any QWidgets
"""
global QApp
QApp = QtWidgets.QApplication.instance()
if QApp is None:
QApp = QtWidgets.QApplication([])
return QApp
def is_event_loop_running(app=None):
"""Return True if event loop is running."""
if app is None:
app = init_qtapp()
if hasattr(app, '_in_event_loop'):
return app._in_event_loop
else:
return False
def start_qtapp(app=None):
"""Start Qt mainloop"""
if app is None:
app = init_qtapp()
if not is_event_loop_running(app):
app._in_event_loop = True
app.exec_()
app._in_event_loop = False
else:
app._in_event_loop = True
class RequiredAttr(object):
"""A class attribute that must be set before use."""
instances = dict()
def __init__(self, init_val=None):
self.instances[self, None] = init_val
def __get__(self, obj, objtype):
value = self.instances[self, obj]
if value is None:
raise AttributeError('Required attribute not set')
return value
def __set__(self, obj, value):
self.instances[self, obj] = value
class LinearColormap(LinearSegmentedColormap):
"""LinearSegmentedColormap in which color varies smoothly.
This class is a simplification of LinearSegmentedColormap, which doesn't
support jumps in color intensities.
Parameters
----------
name : str
Name of colormap.
segmented_data : dict
Dictionary of 'red', 'green', 'blue', and (optionally) 'alpha' values.
Each color key contains a list of `x`, `y` tuples. `x` must increase
monotonically from 0 to 1 and corresponds to input values for a
mappable object (e.g. an image). `y` corresponds to the color
intensity.
"""
def __init__(self, name, segmented_data, **kwargs):
segmented_data = dict((key, [(x, y, y) for x, y in value])
for key, value in segmented_data.items())
LinearSegmentedColormap.__init__(self, name, segmented_data, **kwargs)
class ClearColormap(LinearColormap):
"""Color map that varies linearly from alpha = 0 to 1
"""
def __init__(self, rgb, max_alpha=1, name='clear_color'):
r, g, b = rgb
cg_speq = {'blue': [(0.0, b), (1.0, b)],
'green': [(0.0, g), (1.0, g)],
'red': [(0.0, r), (1.0, r)],
'alpha': [(0.0, 0.0), (1.0, max_alpha)]}
LinearColormap.__init__(self, name, cg_speq)
class FigureCanvas(FigureCanvasQTAgg):
"""Canvas for displaying images."""
def __init__(self, figure, **kwargs):
self.fig = figure
FigureCanvasQTAgg.__init__(self, self.fig)
FigureCanvasQTAgg.setSizePolicy(self,
QtWidgets.QSizePolicy.Expanding,
QtWidgets.QSizePolicy.Expanding)
FigureCanvasQTAgg.updateGeometry(self)
def resizeEvent(self, event):
FigureCanvasQTAgg.resizeEvent(self, event)
# Call to `resize_event` missing in FigureManagerQT.
# See https://github.com/matplotlib/matplotlib/pull/1585
self.resize_event()
def new_canvas(*args, **kwargs):
"""Return a new figure canvas."""
allnums = _pylab_helpers.Gcf.figs.keys()
num = max(allnums) + 1 if allnums else 1
FigureClass = kwargs.pop('FigureClass', Figure)
figure = FigureClass(*args, **kwargs)
canvas = FigureCanvas(figure)
fig_manager = FigureManagerQT(canvas, num)
return fig_manager.canvas
def new_plot(parent=None, subplot_kw=None, **fig_kw):
"""Return new figure and axes.
Parameters
----------
parent : QtWidget
Qt widget that displays the plot objects. If None, you must manually
call ``canvas.setParent`` and pass the parent widget.
subplot_kw : dict
Keyword arguments passed ``matplotlib.figure.Figure.add_subplot``.
fig_kw : dict
Keyword arguments passed ``matplotlib.figure.Figure``.
"""
if subplot_kw is None:
subplot_kw = {}
canvas = new_canvas(**fig_kw)
canvas.setParent(parent)
fig = canvas.figure
ax = fig.add_subplot(1, 1, 1, **subplot_kw)
return fig, ax
def figimage(image, scale=1, dpi=None, **kwargs):
"""Return figure and axes with figure tightly surrounding image.
Unlike pyplot.figimage, this actually plots onto an axes object, which
fills the figure. Plotting the image onto an axes allows for subsequent
overlays of axes artists.
Parameters
----------
image : array
image to plot
scale : float
If scale is 1, the figure and axes have the same dimension as the
image. Smaller values of `scale` will shrink the figure.
dpi : int
Dots per inch for figure. If None, use the default rcParam.
"""
dpi = dpi if dpi is not None else mpl.rcParams['figure.dpi']
kwargs.setdefault('interpolation', 'nearest')
kwargs.setdefault('cmap', 'gray')
h, w, d = np.atleast_3d(image).shape
figsize = np.array((w, h), dtype=float) / dpi * scale
fig, ax = new_plot(figsize=figsize, dpi=dpi)
fig.subplots_adjust(left=0, bottom=0, right=1, top=1)
ax.set_axis_off()
ax.imshow(image, **kwargs)
ax.figure.canvas.draw()
return fig, ax
def update_axes_image(image_axes, image):
"""Update the image displayed by an image plot.
This sets the image plot's array and updates its shape appropriately
Parameters
----------
image_axes : `matplotlib.image.AxesImage`
Image axes to update.
image : array
Image array.
"""
image_axes.set_array(image)
# Adjust size if new image shape doesn't match the original
h, w = image.shape[:2]
image_axes.set_extent((0, w, h, 0))
| mit |
hlin117/scikit-learn | examples/ensemble/plot_forest_iris.py | 18 | 6190 | """
====================================================================
Plot the decision surfaces of ensembles of trees on the iris dataset
====================================================================
Plot the decision surfaces of forests of randomized trees trained on pairs of
features of the iris dataset.
This plot compares the decision surfaces learned by a decision tree classifier
(first column), by a random forest classifier (second column), by an extra-
trees classifier (third column) and by an AdaBoost classifier (fourth column).
In the first row, the classifiers are built using the sepal width and the sepal
length features only, on the second row using the petal length and sepal length
only, and on the third row using the petal width and the petal length only.
In descending order of quality, when trained (outside of this example) on all
4 features using 30 estimators and scored using 10 fold cross validation, we see::
ExtraTreesClassifier() # 0.95 score
RandomForestClassifier() # 0.94 score
AdaBoost(DecisionTree(max_depth=3)) # 0.94 score
DecisionTree(max_depth=None) # 0.94 score
Increasing `max_depth` for AdaBoost lowers the standard deviation of the scores (but
the average score does not improve).
See the console's output for further details about each model.
In this example you might try to:
1) vary the ``max_depth`` for the ``DecisionTreeClassifier`` and
``AdaBoostClassifier``, perhaps try ``max_depth=3`` for the
``DecisionTreeClassifier`` or ``max_depth=None`` for ``AdaBoostClassifier``
2) vary ``n_estimators``
It is worth noting that RandomForests and ExtraTrees can be fitted in parallel
on many cores as each tree is built independently of the others. AdaBoost's
samples are built sequentially and so do not use multiple cores.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import clone
from sklearn.datasets import load_iris
from sklearn.ensemble import (RandomForestClassifier, ExtraTreesClassifier,
AdaBoostClassifier)
from sklearn.externals.six.moves import xrange
from sklearn.tree import DecisionTreeClassifier
# Parameters
n_classes = 3
n_estimators = 30
cmap = plt.cm.RdYlBu
plot_step = 0.02 # fine step width for decision surface contours
plot_step_coarser = 0.5 # step widths for coarse classifier guesses
RANDOM_SEED = 13 # fix the seed on each iteration
# Load data
iris = load_iris()
plot_idx = 1
models = [DecisionTreeClassifier(max_depth=None),
RandomForestClassifier(n_estimators=n_estimators),
ExtraTreesClassifier(n_estimators=n_estimators),
AdaBoostClassifier(DecisionTreeClassifier(max_depth=3),
n_estimators=n_estimators)]
for pair in ([0, 1], [0, 2], [2, 3]):
for model in models:
# We only take the two corresponding features
X = iris.data[:, pair]
y = iris.target
# Shuffle
idx = np.arange(X.shape[0])
np.random.seed(RANDOM_SEED)
np.random.shuffle(idx)
X = X[idx]
y = y[idx]
# Standardize
mean = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mean) / std
# Train
clf = clone(model)
clf = model.fit(X, y)
scores = clf.score(X, y)
# Create a title for each column and the console by using str() and
# slicing away useless parts of the string
model_title = str(type(model)).split(".")[-1][:-2][:-len("Classifier")]
model_details = model_title
if hasattr(model, "estimators_"):
model_details += " with {} estimators".format(len(model.estimators_))
print( model_details + " with features", pair, "has a score of", scores )
plt.subplot(3, 4, plot_idx)
if plot_idx <= len(models):
# Add a title at the top of each column
plt.title(model_title)
# Now plot the decision boundary using a fine mesh as input to a
# filled contour plot
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
np.arange(y_min, y_max, plot_step))
# Plot either a single DecisionTreeClassifier or alpha blend the
# decision surfaces of the ensemble of classifiers
if isinstance(model, DecisionTreeClassifier):
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=cmap)
else:
# Choose alpha blend level with respect to the number of estimators
# that are in use (noting that AdaBoost can use fewer estimators
# than its maximum if it achieves a good enough fit early on)
estimator_alpha = 1.0 / len(model.estimators_)
for tree in model.estimators_:
Z = tree.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, alpha=estimator_alpha, cmap=cmap)
# Build a coarser grid to plot a set of ensemble classifications
# to show how these are different to what we see in the decision
# surfaces. These points are regularly space and do not have a black outline
xx_coarser, yy_coarser = np.meshgrid(np.arange(x_min, x_max, plot_step_coarser),
np.arange(y_min, y_max, plot_step_coarser))
Z_points_coarser = model.predict(np.c_[xx_coarser.ravel(), yy_coarser.ravel()]).reshape(xx_coarser.shape)
cs_points = plt.scatter(xx_coarser, yy_coarser, s=15, c=Z_points_coarser, cmap=cmap, edgecolors="none")
# Plot the training points, these are clustered together and have a
# black outline
plt.scatter(X[:, 0], X[:, 1], c=y,
cmap=ListedColormap(['r', 'y', 'b']))
plot_idx += 1 # move on to the next plot in sequence
plt.suptitle("Classifiers on feature subsets of the Iris dataset")
plt.axis("tight")
plt.show()
| bsd-3-clause |
linsalrob/EdwardsLab | phage_protein_blast_genera/tax_violin_plots.py | 1 | 2239 | """
"""
import os
import sys
import argparse
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="")
parser.add_argument('-f', help='Genome average output file (from genera_per_phage_protein.py', default='/home/redwards/Desktop/gav_all_host.out')
parser.add_argument('-n', help='taxonomy name one of: kingdom / phylum / genus / species', default='genus')
parser.add_argument('-v', help='verbose output', action="store_true")
args = parser.parse_args()
ynames = {'kingdom' : 'kingdoms', 'phylum' : 'phyla', 'genus' : 'genera', 'species' : 'species'}
col = None
colkey = {'kingdom' : 3, 'phylum' : 4, 'genus' : 5, 'species' : 6}
if args.n not in colkey:
sys.stderr.write("Sorry, taxonomy name must be one of {}\n".format("|".join(list(colkey.keys()))))
sys.exit(-1)
col = colkey[args.n]
want = {'Gut', 'Mouth', 'Nose', 'Skin', 'Lungs'}
data = {}
with open(args.f, 'r') as fin:
for l in fin:
p=l.strip().split("\t")
if p[2] not in want:
p[2] = 'All phages'
#continue ## comment or uncomment this to include/exclude all data
if p[2] not in data:
data[p[2]] = []
data[p[2]].append(float(p[col]))
labels = sorted(data.keys())
scores = []
count = 1
ticks = []
for l in labels:
scores.append(data[l])
ticks.append(count)
count += 1
fig = plt.figure()
ax = fig.add_subplot(111)
# ax.boxplot(alldata)
vp = ax.violinplot(scores, showmeans=True)
for i, j in enumerate(vp['bodies']):
if i == 0:
j.set_color('gray')
elif i == 1:
j.set_color('sandybrown')
else:
j.set_color('lightpink')
ax.set_xlabel("Body Site")
ax.set_ylabel("Average number of {}".format(ynames[args.n]))
ax.set_xticks(ticks)
ax.set_xticklabels(labels, rotation='vertical')
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
fig.set_facecolor('white')
plt.tight_layout()
#plt.show()
fig.savefig("/home/redwards/Desktop/bodysites.png")
| mit |
rabrahm/ceres | utils/FastRotators/spfr.py | 1 | 18831 | from pylab import *
import pyfits
from PyAstronomy import pyasl
import scipy
from scipy import interpolate
from scipy import ndimage
from scipy import signal
import pickle
from matplotlib.backends.backend_pdf import PdfPages
import os
#from pyevolve import G1DList
#from pyevolve import GSimpleGA
from multiprocessing import Pool
import time
def download_models(webpage='http://svo2.cab.inta-csic.es/theory/models/coelho/high/data/',dest='../../data/'):
os.system('mkdir '+dest+'/COELHO2014')
cwd = os.getcwd()
os.chdir(dest+'/COELHO2014')
tf = np.arange(6000,10001,250)
gf = np.arange(2.5,4.6,0.5)
#gf = np.array([2.5])
zf = np.array([-1.,-0.5,0.0,0.2])
for t in tf:
for g in gf:
for z in zf:
modname = get_modname(t,g,z)
if z<0:
sz = 'm'
else:
sz = 'p'
sz = sz+str(float(np.absolute(z))).replace('.','')+'p00/'
os.system('wget ' + webpage+sz+modname+'.fits')
os.system('wget ' + webpage+sz+modname+'plc.fits')
os.chdir(cwd)
return True
def n_Edlen(l):
sigma = 1e4 / l
sigma2 = sigma*sigma
n = 1 + 1e-8 * (8342.13 + 2406030 / (130-sigma2) + 15997/(38.9-sigma2))
return n
def n_Morton(l):
sigma = 1e4 / l
sigma2 = sigma*sigma
n = 1 + 6.4328e-5 + 2.94981e-2 / (146.-sigma2) + 2.5540e-4/(41.-sigma2)
return n
def ToAir(l):
return (l / n_Edlen(l))
def ToVacuum(l):
cond = 1
l_prev = l.copy()
while(cond):
l_new = n_Edlen(l_prev) * l
if (max(np.absolute(l_new - l_prev)) < 1e-10): cond = 0
l_prev = l_new
return l_prev
def get_modname(t,g,z):
st = str(int(t))
if t<10000:
st = '0'+st
sg = '+'+str(np.around(g,1))
if z < 0:
sz = 'm'
else:
sz = 'p'
z=float(z)
sz = sz + str(np.around(np.absolute(z),1))
sz = sz.replace('.','')
return 't'+st+'_g'+sg+'_'+sz+'p00_hr'
def get_model(t,g,z,model_path='../../data/COELHO2014/'):
modname = model_path + get_modname(t,g,z)
try:
out = pyfits.getdata(modname+'.fits')
except:
out = pyfits.getdata(modname+'plc.fits')
return out
def get_near(x,vec):
if x == vec[0]:
mmin = vec[0]
mmax = vec[1]
elif x == vec[-1]:
mmin = vec[-2]
mmax = vec[-1]
else:
tvec = vec - x
In = np.where(tvec < 0)[0]
mmin = tvec[In].max() + x
Ix = np.where(tvec >= 0)[0]
mmax = tvec[Ix].min() + x
return mmin,mmax
def trilinear_interpolation(t,g,z,model_path='../../data/COELHO2014/'):
teffs = np.arange(6000,10001,250)
loggs = np.arange(2.5,4.6,0.5)
fehs = np.array([-1.,-0.5,0.0,0.2])
x0,x1 = get_near(t,teffs)
y0,y1 = get_near(g,loggs)
z0,z1 = get_near(z,fehs)
xd = (t-x0)/(x1-x0)
yd = (g-y0)/(y1-y0)
zd = (z-z0)/(z1-z0)
try:
hd = pyfits.getheader(model_path+get_modname(x0,y0,z0)+'.fits')
except:
hd = pyfits.getheader(model_path+get_modname(x0,y0,z0)+'plc.fits')
c000 = get_model(x0,y0,z0,model_path)
c001 = get_model(x0,y0,z1,model_path)
c010 = get_model(x0,y1,z0,model_path)
c100 = get_model(x1,y0,z0,model_path)
c110 = get_model(x1,y1,z0,model_path)
c101 = get_model(x1,y0,z1,model_path)
c011 = get_model(x0,y1,z1,model_path)
c111 = get_model(x1,y1,z1,model_path)
wav = np.arange(len(c111))*hd['CDELT1'] + hd['CRVAL1']
c00 = c000*(1-xd) + c100*xd
c01 = c001*(1-xd) + c101*xd
c10 = c010*(1-xd) + c110*xd
c11 = c011*(1-xd) + c111*xd
c0 = c00*(1-yd) + c10*yd
c1 = c01*(1-yd) + c11*yd
c = c0*(1-zd) + c1*zd
return wav,c
def normalize_model(w,f):
ow = w.copy()
of = f.copy()
#plot(w,f)
while True:
#medflts = scipy.signal.medfilt(f,1001)
coef = np.polyfit(w,f,6)
fited = np.polyval(coef,w)
res = f - fited
I = np.where(res > -np.sqrt(np.var(res)))[0]
w,f = w[I],f[I]
if len(w) < 0.3* len(ow):
break
#plot(ow,np.polyval(coef,ow))
#show()
return coef
def spec_ccf(sw,sf,mw,mf,vi,vf,dv):
mf = mf -1
mf = -mf
#plot(mw,mf)
tck = interpolate.splrep(mw,mf,k=1)
v = vi
retccf = []
vels = []
while v<=vf:
swt = sw * (1 + v/299792.458)
mft = interpolate.splev(swt,tck)
#if v == 0:
# plot(swt,mft)
# plot(swt,sft)
# show()
mft -= np.mean(mft)
sft = sf - np.mean(sf)
#sft = sf.copy()
#print np.sum(mft**2),np.sum(sft**2)
retccf.append(np.sum(mft*sft)/np.sqrt(np.sum(mft**2)*np.sum(sft**2)))
vels.append(v)
v+=dv
return np.array(vels),np.array(retccf)
def ccf_fft(swt,sft,mwt,mft):
mf = mft -1
mf = -mf
#plot(mw,mf)
tck = interpolate.splrep(np.log(mwt),mf,k=1)
sw = np.log(swt)
tck2 = interpolate.splrep(sw,sft,k=1)
nsw = np.linspace(sw[0], sw[-1], 5000)
sf = interpolate.splev(nsw,tck2)
mf = interpolate.splev(nsw,tck)
sf -= np.mean(sf)
mf -= np.mean(mf)
plot(nsw,sf)
plot(nsw,mf)
show()
retccf = np.fft.ifft(np.conj(np.fft.fft(sf))*np.fft.fft(mf))
retccf = np.hstack((retccf[2500:],retccf[:2500]))
retvels = np.arange(len(retccf)) - 0.5*len(retccf)
retvels *= (nsw[1]-nsw[0])
retvels = 299792.458*(np.exp(retvels)-1.)
return retvels, retccf
def ccf_simple(sw,sf,mw,mf,rv):
mf = mf -1
mf = -mf
#plot(mw,mf)
tck = interpolate.splrep(mw,mf,k=1)
swt = sw * (1 + rv/299792.458)
mft = interpolate.splev(swt,tck)
mft -= np.mean(mft)
sft = sf - np.mean(sf)
return np.sum(mft*sft)/np.sqrt(np.sum(mft**2)*np.sum(sft**2))
def clean_strong_lines(mw,sc,mode=1):
if mode==1:
#""""
I = np.where((mw>6520)&(mw<6600))[0]
sc[I] = 1.
I = np.where((mw>5888)&(mw<5897))[0]
sc[I] = 1.
I = np.where((mw>4310)&(mw<4360))[0]
sc[I] = 1.
I = np.where((mw>4840)&(mw<4880))[0]
sc[I] = 1.
I = np.where((mw>4070)&(mw<4130))[0]
sc[I] = 1.
I = np.where((mw>3875)&(mw<3900))[0]
sc[I] = 1.
I = np.where((mw>3920)&(mw<3945))[0]
sc[I] = 1.
I = np.where((mw>3955)&(mw<3980))[0]
sc[I] = 1.
I = np.where(mw<3850)[0]
sc[I] = 1.
#"""
if mode==2:
#""""
I = np.where((mw>6550)&(mw<6570))[0]
sc[I] = 1.
I = np.where((mw>5888)&(mw<5897))[0]
sc[I] = 1.
I = np.where((mw>4320)&(mw<4350))[0]
sc[I] = 1.
I = np.where((mw>4850)&(mw<4870))[0]
sc[I] = 1.
I = np.where((mw>4090)&(mw<4110))[0]
sc[I] = 1.
I = np.where((mw>3875)&(mw<3900))[0]
sc[I] = 1.
I = np.where((mw>3920)&(mw<3945))[0]
sc[I] = 1.
I = np.where((mw>3955)&(mw<3980))[0]
sc[I] = 1.
I = np.where(mw<3850)[0]
sc[I] = 1.
#"""
return sc
def RVforFR(wavs,flxs,teff=6700,logg=4.0,feh=-1.0,vsini=100.,model_path='../../data/COELHO2014/',vmin=-1000.,vmax=1000.,vstep=10.):
def fitfunc(p,x):
ret = p[3] + p[0] * np.exp(-.5*((x-p[1])/p[2])**2)
return ret
errfunc = lambda p,x,y: np.ravel( (fitfunc(p,x)-y) )
#sc = get_model(teff,logg,feh)
#hd = pyfits.getheader(model_path+get_modname(7000,4.5,0.0)+'.fits')
#wav = np.arange(len(sc))*hd['CDELT1'] + hd['CRVAL1']
teff = float(teff)
try:
sc = get_model(teff,logg,feh)
hd = pyfits.getheader(model_path+get_modname(7000,4.5,0.0)+'.fits')
mw = np.arange(len(sc))*hd['CDELT1'] + hd['CRVAL1']
except:
mw,sc = trilinear_interpolation(teff,logg,feh,model_path)
for order in range(len(flxs)):
flxs[order] = clean_strong_lines(wavs[order],flxs[order])
sc = clean_strong_lines(mw,sc)
II = np.where(sc != 1)[0]
JJ = np.where(sc == 1)[0]
coef = normalize_model(mw[II],sc[II])
sc /= np.polyval(coef,mw)
sc[JJ] = 1.
mw = ToVacuum(mw)
weis1 = []
ccftot = []
for i in range(wavs.shape[0]):
#plot(wavs[i],flxs[i])
scf = flxs[i]
scw = wavs[i]
J = np.where(scf!=0)[0]
scw,scf = scw[J],scf[J]
I = np.where((mw>scw[0]-100) & (mw<scw[-1]+100))
tmf = pyasl.fastRotBroad(mw[I], sc[I], 0.5, vsini)
#plot(mw[I],tmf)
J = np.where(scf!=1)[0]
if len(J)>100:
ccv,ccf = spec_ccf(scw,scf,mw[I],tmf,vmin,vmax,vstep)
#plot(ccv,ccf)
#show()
#ccf = np.array(ccf)
wei1 = len(np.where(scf!=1)[0])**2
weis1.append(wei1)
if len(ccftot)==0:
ccftot = ccf.copy()*wei1
else:
ccftot = np.vstack((ccftot,ccf.copy()*wei1))
#show()
weis1 = np.array(weis1)
ccftot = np.sum(ccftot,axis=0)/ np.sum(weis1)
p0 = [ccftot.min(),ccv[np.argmin(ccftot)],vsini,ccftot[0]]
p1, success = scipy.optimize.leastsq(errfunc,p0, args=(ccv,ccftot))
return p1,ccv,ccftot,fitfunc(p1,ccv)
def calc_bss2(vels,xc,coef, bot_i=0.15, bot_f=0.4, top_i=0.6, top_f=0.9, dt=0.01):
try:
I1 = np.where((vels>coef[1]-3*coef[2]) & (vels<coef[1]) )[0]
I2 = np.where((vels<coef[1]+3*coef[2]) & (vels>coef[1]) )[0]
I3 = np.where(vels<coef[1]-4*coef[2])[0]
I4 = np.where(vels>coef[1]+4*coef[2])[0]
I = np.hstack((I3,I4))
base = np.median(xc[I])
xc = base - xc
xc /= xc.max()
v1,x1 = vels[I1],xc[I1]
v2,x2 = vels[I2],xc[I2]
#plot(v1,x1)
#plot(v2,x2)
#show()
dp = top_f
vect = []
while dp >= top_i:
lb = np.where(x1>dp)[0][0]
m = (v1[lb] - v1[lb-1])/(x1[lb]-x1[lb-1])
n = v1[lb] - m*x1[lb]
bs1 = m*dp+n
lb = np.where(x2>dp)[0][-1]
m = (v2[lb] - v2[lb+1])/(x2[lb]-x2[lb+1])
n = v2[lb] - m*x2[lb]
bs2 = m*dp+n
vect.append(0.5*(bs2+bs1))
dp-=dt
vect = np.array(vect)
dp = bot_f
vecb = []
while dp >= bot_i:
lb = np.where(x1>dp)[0][0]
m = (v1[lb] - v1[lb-1])/(x1[lb]-x1[lb-1])
n = v1[lb] - m*x1[lb]
bs1 = m*dp+n
lb = np.where(x2>dp)[0][-1]
m = (v2[lb] - v2[lb+1])/(x2[lb]-x2[lb+1])
n = v2[lb] - m*x2[lb]
bs2 = m*dp+n
vecb.append(0.5*(bs2+bs1))
dp-=dt
vecb = np.array(vecb)
return np.median(vecb) - np.median(vect)
except:
return -999.0
"""
def lnlike(theta, W, F, Ferr):
mw,sc = trilinear_interpolation(int(theta[0]),theta[1],theta[2])
sct = clean_strong_lines(mw,sc.copy())
#plot(mw,sc)
#show()
coef = normalize_model(mw,sct)
sc /= np.polyval(coef,mw)
#print gfd
mw = ToVacuum(mw)
mw *= 1 + theta[3]/299792.458
totD,totM,totE = np.array([]),np.array([]),np.array([])
for i in range(W.shape[0]):
scf = F[i]
scw = W[i]
scfe = Ferr[i]
J = np.where(scf!=0)[0]
scw,scf,scfe = scw[J],scf[J],scfe[J]
I = np.where((mw>scw[0]-10) & (mw<scw[-1]+10))
tmf = pyasl.fastRotBroad(mw[I], sc[I], 0.5, theta[4])
tck = interpolate.splrep(mw[I],tmf,k=1)
tmf = interpolate.splev(scw,tck)
tmf = clean_strong_lines(scw,tmf.copy())
I = np.where(tmf!=1)[0]
#plot(scw,tmf)
#plot(scw[I],tmf[I])
#plot(scw[I],scf[I])
#show()
#print gfd
tmf = tmf[I]
scf = scf[I]
scfe = scfe[I]
tmf /= np.sum(tmf)
tsf = scf/np.sum(scf)
tse = scfe*(np.sum(scf)**2)
totD = np.hstack((totD,tsf))
totM = np.hstack((totM,tmf))
totE = np.hstack((totE,tse))
#plot(scw[I],tsf)
#plot(scw[I],tmf)
#plot(scw[I],tsf + 1./np.sqrt(tse))
#show()
#print fds
#print theta
#show()
#print gvfd
#ret = -np.log(2*np.pi) + np.log(np.sum(np.exp(-0.5*((y-model)/yerr)**2)/yerr))
#ret = -0.5*(np.sum(inv_sigma2*(F-model)**2 - np.log(inv_sigma2)))
ret = -0.5*(np.sum(totE*(totD-totM)**2 - np.log(totE)))
#for i in range(len(F)):
# errorbar(Y,F[i],yerr=Ferr[i],fmt='b')
#for j in model:
# plot(Y,j,'r')
#show()
#print theta, ret
if np.isnan(ret):
return -np.inf
else:
return ret
def lnprior(theta):
if 6000 < theta[0] < 9000 and 3.0 < theta[1] < 4.5 and -1 < theta[2] < 0.2 and -500 < theta[3] < 500 and 1. < theta[4] < 500.:
return 0.0
return -np.inf
def lnprob(theta, W,F,Ferr):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return lp + lnlike(theta,W,F,Ferr)
"""
def multiccf(pars):
teff,logg,feh,vsini=pars[0],pars[1],pars[2],pars[3]
vmin=-500
vmax=500.
vstep=20.
sc = get_model(teff,logg,feh)
hd = pyfits.getheader(model_path+get_modname(7000,4.5,0.0)+'.fits')
wav = np.arange(len(sc))*hd['CDELT1'] + hd['CRVAL1']
try:
sc = get_model(teff,logg,feh)
hd = pyfits.getheader(model_path+get_modname(7000,4.5,0.0)+'.fits')
mw = np.arange(len(sc))*hd['CDELT1'] + hd['CRVAL1']
except:
mw,sc = trilinear_interpolation(teff,logg,feh,model_path)
sc = clean_strong_lines(mw,sc)
II = np.where(sc != 1)[0]
JJ = np.where(sc == 1)[0]
coef = normalize_model(mw[II],sc[II])
sc /= np.polyval(coef,mw)
sc[JJ] = 1.
mw = ToVacuum(mw)
weis1 = []
ccftot = []
for i in range(wavs.shape[0]):
scf = flxs[i].copy()
scw = wavs[i].copy()
J = np.where(scf!=0)[0]
scw,scf = scw[J],scf[J]
I = np.where((mw>scw[0]-100) & (mw<scw[-1]+100))
tmf = pyasl.fastRotBroad(mw[I], sc[I], 0.5, vsini)
#plot(mw[I],tmf)
J = np.where(scf!=1)[0]
if len(J)>100:
ccv,ccf = spec_ccf(scw,scf,mw[I],tmf,vmin,vmax,vstep)
#ccv,ccf = ccf_fft(scw,scf,mw[I],tmf)
#plot(ccv,ccf)
#show()
wei1 = len(np.where(scf!=1)[0])**2
weis1.append(wei1)
if len(ccftot)==0:
ccftot = ccf.copy()*wei1
else:
ccftot = np.vstack((ccftot,ccf.copy()*wei1))
weis1 = np.array(weis1)
ccftot = np.sum(ccftot,axis=0)/ np.sum(weis1)
#print gfds
#ccftot = np.mean(ccftot,axis=0)
#print pars, ccftot.min()
return ccftot.min()
def get_pars_fr(wavst,flxst,model_patht='../../data/COELHO2014/',npools=4,fixG=1.0):
for order in range(len(flxst)):
flxst[order] = clean_strong_lines(wavst[order],flxst[order],mode=1)
t0 = time.time()
global wavs,flxs
global model_path
wavs,flxs=wavst.copy(),flxst.copy()
model_path=model_patht
gt = np.array([6000,7000,8000,9000,10000])
gg = np.array([2.5,3.0,3.5,4.0,4.5])
if fixG != -1:
gg = np.array([fixG])
gz = np.array([-1,-0.5,0.0,0.2])
gr = np.array([10.,50.,100.,150.,200.,250.,300.])
#"""
tr = np.tile(gr,len(gt)*len(gg)*len(gz))
tg = np.repeat(np.tile(gg,len(gt)),len(gr)*len(gz))
tz = np.repeat(np.tile(gz,len(gt)*len(gg)),len(gr))
tt = np.repeat(gt,len(gg)*len(gr)*len(gz))
tot = np.vstack((tt,tg,tz,tr)).T
#for pars in tot:
# pars = [8000,4.0,-0.5,40.0]
# print pars, multiccf(pars)
p = Pool(npools)
vals = np.array((p.map(multiccf, list(tot))))
p.terminate()
I = np.argmin(vals)
best_vals = tot[I]
bt,bg,bz,br = best_vals[0],best_vals[1],best_vals[2],best_vals[3]
#"""
t1 = time.time()
print bt,bg,bz,br, (t1-t0)/60.,'mins'
#bt,bg,bz,br = 7000.,4.5, 0.2, 100.0
gt = np.arange(bt-1000,bt+1001,250)
I = np.where((gt>=6000) & (gt<=10000))[0]
gt = gt[I]
gr = np.arange(br-60.,br+61.,20.)
I = np.where(gr>=10)[0]
gr = gr[I]
tr = np.tile(gr,len(gt)*len(gg)*len(gz))
tg = np.repeat(np.tile(gg,len(gt)),len(gr)*len(gz))
tz = np.repeat(np.tile(gz,len(gt)*len(gg)),len(gr))
tt = np.repeat(gt,len(gg)*len(gr)*len(gz))
tot = np.vstack((tt,tg,tz,tr)).T
p = Pool(npools)
vals = np.array((p.map(multiccf, list(tot))))
p.terminate()
I = np.argmin(vals)
best_vals = tot[I]
bt,bg,bz,br = best_vals[0],best_vals[1],best_vals[2],best_vals[3]
t2 = time.time()
print bt,bg,bz,br, (t2-t1)/60.,'mins'
#np.savetxt('temp_grid.txt',vals)
if fixG==-1:
grid = np.reshape(vals,(len(gt),len(gg),len(gz),len(gr)))
tckt = interpolate.splrep(gt,np.arange(len(gt)),k=1)
tckg = interpolate.splrep(gg,np.arange(len(gg)),k=1)
tckz = interpolate.splrep(gz,np.arange(len(gz)),k=1)
tckr = interpolate.splrep(gr,np.arange(len(gr)),k=1)
itckt = interpolate.splrep(np.arange(len(gt)),gt,k=1)
itckg = interpolate.splrep(np.arange(len(gg)),gg,k=1)
itckz = interpolate.splrep(np.arange(len(gz)),gz,k=1)
itckr = interpolate.splrep(np.arange(len(gr)),gr,k=1)
st = np.arange(gt[0],gt[-1]+1,10.)
sg = np.arange(gg[0],gg[-1]+0.01,0.1)
sz = np.arange(gz[0],gz[-1]+0.01,0.1)
sr = np.arange(gr[0],gr[-1]+1.,5.)
st = interpolate.splev(st,tckt)
sg = interpolate.splev(sg,tckg)
sz = interpolate.splev(sz,tckz)
sr = interpolate.splev(sr,tckr)
tr2 = np.tile(sr,len(st)*len(sg)*len(sz))
tg2 = np.repeat(np.tile(sg,len(st)),len(sr)*len(sz))
tz2 = np.repeat(np.tile(sz,len(st)*len(sg)),len(sr))
tt2 = np.repeat(st,len(sg)*len(sr)*len(sz))
tot2 = np.vstack((tt2,tg2,tz2,tr2))
zi = ndimage.map_coordinates(grid, tot2, order=3, mode='nearest')
I = np.argmin(zi)
minval = tot2[:,I]
mint = interpolate.splev(minval[0],itckt)
ming = interpolate.splev(minval[1],itckg)
minz = interpolate.splev(minval[2],itckz)
minr = interpolate.splev(minval[3],itckr)
else:
grid = np.reshape(vals,(len(gt),len(gz),len(gr)))
tckt = interpolate.splrep(gt,np.arange(len(gt)),k=1)
tckz = interpolate.splrep(gz,np.arange(len(gz)),k=1)
tckr = interpolate.splrep(gr,np.arange(len(gr)),k=1)
itckt = interpolate.splrep(np.arange(len(gt)),gt,k=1)
itckz = interpolate.splrep(np.arange(len(gz)),gz,k=1)
itckr = interpolate.splrep(np.arange(len(gr)),gr,k=1)
st = np.arange(gt[0],gt[-1]+1,10.)
sz = np.arange(gz[0],gz[-1]+0.01,0.1)
sr = np.arange(gr[0],gr[-1]+1.,5.)
st = interpolate.splev(st,tckt)
sz = interpolate.splev(sz,tckz)
sr = interpolate.splev(sr,tckr)
tr2 = np.tile(sr,len(st)*len(sz))
tz2 = np.repeat(np.tile(sz,len(st)),len(sr))
tt2 = np.repeat(st,len(sr)*len(sz))
tot2 = np.vstack((tt2,tz2,tr2))
zi = ndimage.map_coordinates(grid, tot2, order=3, mode='nearest')
I = np.argmin(zi)
minval = tot2[:,I]
mint = interpolate.splev(minval[0],itckt)
ming = fixG
minz = interpolate.splev(minval[1],itckz)
minr = interpolate.splev(minval[2],itckr)
#d = {'grid':grid, 'zi':zi, 'tot2':tot2, 'gt':gt, 'gg':gg, 'gz':gz, 'gr':gr}
#pickle.dump(d,open('temp_dict.pkl'))
return float(mint),float(ming),float(minz),float(minr)
def plot_CCF_FR(xc_dict,path='XC.pdf'):
vels = xc_dict['vels']
xc_av = xc_dict['xc_av']
XCmodelgau = xc_dict['XCmodelgau']
#refvel = xc_dict['refvel']
p1gau = xc_dict['p1gau']
f1 = figure()
pp = PdfPages(path)
ax1 = f1.add_subplot(111)
ax1.plot(vels, xc_av,'b.', label='CCF')
ax1.plot(vels, XCmodelgau,'r-',label='Gaussian fit')
xlabel('Velocity (km/s)')
ylabel('XC')
ax1.axvline(p1gau[1],linestyle=':',color='r')
ax1.axhline(0.0,linestyle='-')
title('Average Cross-Correlation Function + Fit')
handles, labels = ax1.get_legend_handles_labels()
ax1.legend(handles[::-1], labels[::-1],prop={'size':6})
pp.savefig()
pp.close()
clf()
pass
"""
def trans_chromosome(chromosome):
teff = chromosome[0]*100.+chromosome[1]*10.+chromosome[2]
m = (10000.- 6000.)/999.
n = 6000.
teff = teff*m + n
logg = chromosome[3] + chromosome[4]*0.1
m = (4.5 - 3.0)/9.9
n = 3.
logg = logg*m + n
feh = chromosome[5] + chromosome[6]*0.1
m = (0.2 - -1.)/9.9
n = -1.
feh = feh*m + n
vsini = chromosome[7]*10. + chromosome[8]
m = (300. - 10.)/99.
n = 10.
vsini = vsini*m + n
return teff, logg, feh, vsini
global wavs, flxs
def find_pars_GA(wavs,flxs,model_path='../../data/COELHO2014/'):
def eval_func(chromosome):
print list(chromosome)
teff, logg, feh, vsini = trans_chromosome(chromosome)
print teff, logg, feh, vsini
pt,vels,ccf,mod = RVforFR(wavs,flxs,teff=teff,logg=logg,feh=feh,vsini=vsini,model_path=model_path)
score = -ccf.min()
return score
genome = G1DList.G1DList(9)
genome.evaluator.set(eval_func)
ga = GSimpleGA.GSimpleGA(genome, interactiveMode=True)
ga.setGenerations(40)
ga.setMutationRate(0.2)
ga.setPopulationSize(20)
#ga.setCrossoverRate(1.0)
genome.setParams(rangemin=0, rangemax=9)
#ga.setMultiProcessing(True)
ga.evolve(freq_stats=10)
print ga.bestIndividual()
print trans_chromosome(ga.bestIndividual())
"""
| mit |
HeraclesHX/scikit-learn | sklearn/cluster/tests/test_dbscan.py | 114 | 11393 | """
Tests for DBSCAN clustering algorithm
"""
import pickle
import numpy as np
from scipy.spatial import distance
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.utils.testing import assert_in
from sklearn.utils.testing import assert_not_in
from sklearn.cluster.dbscan_ import DBSCAN
from sklearn.cluster.dbscan_ import dbscan
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn.metrics.pairwise import pairwise_distances
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters)
def test_dbscan_similarity():
# Tests the DBSCAN algorithm with a similarity array.
# Parameters chosen specifically for this task.
eps = 0.15
min_samples = 10
# Compute similarities
D = distance.squareform(distance.pdist(X))
D /= np.max(D)
# Compute DBSCAN
core_samples, labels = dbscan(D, metric="precomputed", eps=eps,
min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - (1 if -1 in labels else 0)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric="precomputed", eps=eps, min_samples=min_samples)
labels = db.fit(D).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
def test_dbscan_feature():
# Tests the DBSCAN algorithm with a feature vector array.
# Parameters chosen specifically for this task.
# Different eps to other test, because distance is not normalised.
eps = 0.8
min_samples = 10
metric = 'euclidean'
# Compute DBSCAN
# parameters chosen for task
core_samples, labels = dbscan(X, metric=metric, eps=eps,
min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples)
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
def test_dbscan_sparse():
core_sparse, labels_sparse = dbscan(sparse.lil_matrix(X), eps=.8,
min_samples=10)
core_dense, labels_dense = dbscan(X, eps=.8, min_samples=10)
assert_array_equal(core_dense, core_sparse)
assert_array_equal(labels_dense, labels_sparse)
def test_dbscan_no_core_samples():
rng = np.random.RandomState(0)
X = rng.rand(40, 10)
X[X < .8] = 0
for X_ in [X, sparse.csr_matrix(X)]:
db = DBSCAN(min_samples=6).fit(X_)
assert_array_equal(db.components_, np.empty((0, X_.shape[1])))
assert_array_equal(db.labels_, -1)
assert_equal(db.core_sample_indices_.shape, (0,))
def test_dbscan_callable():
# Tests the DBSCAN algorithm with a callable metric.
# Parameters chosen specifically for this task.
# Different eps to other test, because distance is not normalised.
eps = 0.8
min_samples = 10
# metric is the function reference, not the string key.
metric = distance.euclidean
# Compute DBSCAN
# parameters chosen for task
core_samples, labels = dbscan(X, metric=metric, eps=eps,
min_samples=min_samples,
algorithm='ball_tree')
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples,
algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
def test_dbscan_balltree():
# Tests the DBSCAN algorithm with balltree for neighbor calculation.
eps = 0.8
min_samples = 10
D = pairwise_distances(X)
core_samples, labels = dbscan(D, metric="precomputed", eps=eps,
min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='kd_tree')
labels = db.fit(X).labels_
n_clusters_3 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_3, n_clusters)
db = DBSCAN(p=1.0, eps=eps, min_samples=min_samples, algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_4 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_4, n_clusters)
db = DBSCAN(leaf_size=20, eps=eps, min_samples=min_samples,
algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_5 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_5, n_clusters)
def test_input_validation():
# DBSCAN.fit should accept a list of lists.
X = [[1., 2.], [3., 4.]]
DBSCAN().fit(X) # must not raise exception
def test_dbscan_badargs():
# Test bad argument values: these should all raise ValueErrors
assert_raises(ValueError,
dbscan,
X, eps=-1.0)
assert_raises(ValueError,
dbscan,
X, algorithm='blah')
assert_raises(ValueError,
dbscan,
X, metric='blah')
assert_raises(ValueError,
dbscan,
X, leaf_size=-1)
assert_raises(ValueError,
dbscan,
X, p=-1)
def test_pickle():
obj = DBSCAN()
s = pickle.dumps(obj)
assert_equal(type(pickle.loads(s)), obj.__class__)
def test_boundaries():
# ensure min_samples is inclusive of core point
core, _ = dbscan([[0], [1]], eps=2, min_samples=2)
assert_in(0, core)
# ensure eps is inclusive of circumference
core, _ = dbscan([[0], [1], [1]], eps=1, min_samples=2)
assert_in(0, core)
core, _ = dbscan([[0], [1], [1]], eps=.99, min_samples=2)
assert_not_in(0, core)
def test_weighted_dbscan():
# ensure sample_weight is validated
assert_raises(ValueError, dbscan, [[0], [1]], sample_weight=[2])
assert_raises(ValueError, dbscan, [[0], [1]], sample_weight=[2, 3, 4])
# ensure sample_weight has an effect
assert_array_equal([], dbscan([[0], [1]], sample_weight=None,
min_samples=6)[0])
assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 5],
min_samples=6)[0])
assert_array_equal([0], dbscan([[0], [1]], sample_weight=[6, 5],
min_samples=6)[0])
assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[6, 6],
min_samples=6)[0])
# points within eps of each other:
assert_array_equal([0, 1], dbscan([[0], [1]], eps=1.5,
sample_weight=[5, 1], min_samples=6)[0])
# and effect of non-positive and non-integer sample_weight:
assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 0],
eps=1.5, min_samples=6)[0])
assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[5.9, 0.1],
eps=1.5, min_samples=6)[0])
assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[6, 0],
eps=1.5, min_samples=6)[0])
assert_array_equal([], dbscan([[0], [1]], sample_weight=[6, -1],
eps=1.5, min_samples=6)[0])
# for non-negative sample_weight, cores should be identical to repetition
rng = np.random.RandomState(42)
sample_weight = rng.randint(0, 5, X.shape[0])
core1, label1 = dbscan(X, sample_weight=sample_weight)
assert_equal(len(label1), len(X))
X_repeated = np.repeat(X, sample_weight, axis=0)
core_repeated, label_repeated = dbscan(X_repeated)
core_repeated_mask = np.zeros(X_repeated.shape[0], dtype=bool)
core_repeated_mask[core_repeated] = True
core_mask = np.zeros(X.shape[0], dtype=bool)
core_mask[core1] = True
assert_array_equal(np.repeat(core_mask, sample_weight), core_repeated_mask)
# sample_weight should work with precomputed distance matrix
D = pairwise_distances(X)
core3, label3 = dbscan(D, sample_weight=sample_weight,
metric='precomputed')
assert_array_equal(core1, core3)
assert_array_equal(label1, label3)
# sample_weight should work with estimator
est = DBSCAN().fit(X, sample_weight=sample_weight)
core4 = est.core_sample_indices_
label4 = est.labels_
assert_array_equal(core1, core4)
assert_array_equal(label1, label4)
est = DBSCAN()
label5 = est.fit_predict(X, sample_weight=sample_weight)
core5 = est.core_sample_indices_
assert_array_equal(core1, core5)
assert_array_equal(label1, label5)
assert_array_equal(label1, est.labels_)
def test_dbscan_core_samples_toy():
X = [[0], [2], [3], [4], [6], [8], [10]]
n_samples = len(X)
for algorithm in ['brute', 'kd_tree', 'ball_tree']:
# Degenerate case: every sample is a core sample, either with its own
# cluster or including other close core samples.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1,
min_samples=1)
assert_array_equal(core_samples, np.arange(n_samples))
assert_array_equal(labels, [0, 1, 1, 1, 2, 3, 4])
# With eps=1 and min_samples=2 only the 3 samples from the denser area
# are core samples. All other points are isolated and considered noise.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1,
min_samples=2)
assert_array_equal(core_samples, [1, 2, 3])
assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1])
# Only the sample in the middle of the dense area is core. Its two
# neighbors are edge samples. Remaining samples are noise.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1,
min_samples=3)
assert_array_equal(core_samples, [2])
assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1])
# It's no longer possible to extract core samples with eps=1:
# everything is noise.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1,
min_samples=4)
assert_array_equal(core_samples, [])
assert_array_equal(labels, -np.ones(n_samples))
def test_dbscan_precomputed_metric_with_degenerate_input_arrays():
# see https://github.com/scikit-learn/scikit-learn/issues/4641 for
# more details
X = np.ones((10, 2))
labels = DBSCAN(eps=0.5, metric='precomputed').fit(X).labels_
assert_equal(len(set(labels)), 1)
X = np.zeros((10, 2))
labels = DBSCAN(eps=0.5, metric='precomputed').fit(X).labels_
assert_equal(len(set(labels)), 1)
| bsd-3-clause |
micahcochran/geopandas | geopandas/tools/tests/test_sjoin.py | 1 | 10287 | from __future__ import absolute_import
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
from shapely.geometry import Point, Polygon
import geopandas
from geopandas import GeoDataFrame, GeoSeries, read_file, base
from geopandas import sjoin
import pytest
from pandas.util.testing import assert_frame_equal
pandas_0_18_problem = 'fails under pandas < 0.19 due to pandas issue 15692,'\
'not problem with sjoin.'
@pytest.fixture()
def dfs(request):
polys1 = GeoSeries(
[Polygon([(0, 0), (5, 0), (5, 5), (0, 5)]),
Polygon([(5, 5), (6, 5), (6, 6), (5, 6)]),
Polygon([(6, 0), (9, 0), (9, 3), (6, 3)])])
polys2 = GeoSeries(
[Polygon([(1, 1), (4, 1), (4, 4), (1, 4)]),
Polygon([(4, 4), (7, 4), (7, 7), (4, 7)]),
Polygon([(7, 7), (10, 7), (10, 10), (7, 10)])])
df1 = GeoDataFrame({'geometry': polys1, 'df1': [0, 1, 2]})
df2 = GeoDataFrame({'geometry': polys2, 'df2': [3, 4, 5]})
if request.param == 'string-index':
df1.index = ['a', 'b', 'c']
df2.index = ['d', 'e', 'f']
# construction expected frames
expected = {}
part1 = df1.copy().reset_index().rename(
columns={'index': 'index_left'})
part2 = df2.copy().iloc[[0, 1, 1, 2]].reset_index().rename(
columns={'index': 'index_right'})
part1['_merge'] = [0, 1, 2]
part2['_merge'] = [0, 0, 1, 3]
exp = pd.merge(part1, part2, on='_merge', how='outer')
expected['intersects'] = exp.drop('_merge', axis=1).copy()
part1 = df1.copy().reset_index().rename(
columns={'index': 'index_left'})
part2 = df2.copy().reset_index().rename(
columns={'index': 'index_right'})
part1['_merge'] = [0, 1, 2]
part2['_merge'] = [0, 3, 3]
exp = pd.merge(part1, part2, on='_merge', how='outer')
expected['contains'] = exp.drop('_merge', axis=1).copy()
part1['_merge'] = [0, 1, 2]
part2['_merge'] = [3, 1, 3]
exp = pd.merge(part1, part2, on='_merge', how='outer')
expected['within'] = exp.drop('_merge', axis=1).copy()
return [request.param, df1, df2, expected]
@pytest.mark.skipif(not base.HAS_SINDEX, reason='Rtree absent, skipping')
class TestSpatialJoin:
@pytest.mark.parametrize('dfs', ['default-index', 'string-index'],
indirect=True)
@pytest.mark.parametrize('op', ['intersects', 'contains', 'within'])
def test_inner(self, op, dfs):
index, df1, df2, expected = dfs
res = sjoin(df1, df2, how='inner', op=op)
exp = expected[op].dropna().copy()
exp = exp.drop('geometry_y', axis=1).rename(
columns={'geometry_x': 'geometry'})
exp[['df1', 'df2']] = exp[['df1', 'df2']].astype('int64')
if index == 'default-index':
exp[['index_left', 'index_right']] = \
exp[['index_left', 'index_right']].astype('int64')
exp = exp.set_index('index_left')
exp.index.name = None
assert_frame_equal(res, exp)
@pytest.mark.parametrize('dfs', ['default-index', 'string-index'],
indirect=True)
@pytest.mark.parametrize('op', ['intersects', 'contains', 'within'])
def test_left(self, op, dfs):
index, df1, df2, expected = dfs
res = sjoin(df1, df2, how='left', op=op)
exp = expected[op].dropna(subset=['index_left']).copy()
exp = exp.drop('geometry_y', axis=1).rename(
columns={'geometry_x': 'geometry'})
exp['df1'] = exp['df1'].astype('int64')
if index == 'default-index':
exp['index_left'] = exp['index_left'].astype('int64')
# TODO: in result the dtype is object
res['index_right'] = res['index_right'].astype(float)
exp = exp.set_index('index_left')
exp.index.name = None
assert_frame_equal(res, exp)
@pytest.mark.parametrize('dfs', ['default-index', 'string-index'],
indirect=True)
@pytest.mark.parametrize('op', ['intersects', 'contains', 'within'])
def test_right(self, op, dfs):
index, df1, df2, expected = dfs
res = sjoin(df1, df2, how='right', op=op)
exp = expected[op].dropna(subset=['index_right']).copy()
exp = exp.drop('geometry_x', axis=1).rename(
columns={'geometry_y': 'geometry'})
exp['df2'] = exp['df2'].astype('int64')
if index == 'default-index':
exp['index_right'] = exp['index_right'].astype('int64')
res['index_left'] = res['index_left'].astype(float)
exp = exp.set_index('index_right')
exp = exp.reindex(columns=res.columns)
assert_frame_equal(res, exp, check_index_type=False)
@pytest.mark.skipif(not base.HAS_SINDEX, reason='Rtree absent, skipping')
class TestSpatialJoinNYBB:
def setup_method(self):
nybb_filename = geopandas.datasets.get_path('nybb')
self.polydf = read_file(nybb_filename)
self.crs = self.polydf.crs
N = 20
b = [int(x) for x in self.polydf.total_bounds]
self.pointdf = GeoDataFrame(
[{'geometry': Point(x, y),
'pointattr1': x + y, 'pointattr2': x - y}
for x, y in zip(range(b[0], b[2], int((b[2]-b[0])/N)),
range(b[1], b[3], int((b[3]-b[1])/N)))],
crs=self.crs)
def test_geometry_name(self):
# test sjoin is working with other geometry name
polydf_original_geom_name = self.polydf.geometry.name
self.polydf = (self.polydf.rename(columns={'geometry': 'new_geom'})
.set_geometry('new_geom'))
assert polydf_original_geom_name != self.polydf.geometry.name
res = sjoin(self.polydf, self.pointdf, how="left")
assert self.polydf.geometry.name == res.geometry.name
def test_sjoin_left(self):
df = sjoin(self.pointdf, self.polydf, how='left')
assert df.shape == (21, 8)
for i, row in df.iterrows():
assert row.geometry.type == 'Point'
assert 'pointattr1' in df.columns
assert 'BoroCode' in df.columns
def test_sjoin_right(self):
# the inverse of left
df = sjoin(self.pointdf, self.polydf, how="right")
df2 = sjoin(self.polydf, self.pointdf, how="left")
assert df.shape == (12, 8)
assert df.shape == df2.shape
for i, row in df.iterrows():
assert row.geometry.type == 'MultiPolygon'
for i, row in df2.iterrows():
assert row.geometry.type == 'MultiPolygon'
def test_sjoin_inner(self):
df = sjoin(self.pointdf, self.polydf, how="inner")
assert df.shape == (11, 8)
def test_sjoin_op(self):
# points within polygons
df = sjoin(self.pointdf, self.polydf, how="left", op="within")
assert df.shape == (21, 8)
assert df.ix[1]['BoroName'] == 'Staten Island'
# points contain polygons? never happens so we should have nulls
df = sjoin(self.pointdf, self.polydf, how="left", op="contains")
assert df.shape == (21, 8)
assert np.isnan(df.ix[1]['Shape_Area'])
def test_sjoin_bad_op(self):
# AttributeError: 'Point' object has no attribute 'spandex'
with pytest.raises(ValueError):
sjoin(self.pointdf, self.polydf, how="left", op="spandex")
def test_sjoin_duplicate_column_name(self):
pointdf2 = self.pointdf.rename(columns={'pointattr1': 'Shape_Area'})
df = sjoin(pointdf2, self.polydf, how="left")
assert 'Shape_Area_left' in df.columns
assert 'Shape_Area_right' in df.columns
def test_sjoin_values(self):
# GH190
self.polydf.index = [1, 3, 4, 5, 6]
df = sjoin(self.pointdf, self.polydf, how='left')
assert df.shape == (21, 8)
df = sjoin(self.polydf, self.pointdf, how='left')
assert df.shape == (12, 8)
@pytest.mark.skipif(str(pd.__version__) < LooseVersion('0.19'),
reason=pandas_0_18_problem)
@pytest.mark.xfail
def test_no_overlapping_geometry(self):
# Note: these tests are for correctly returning GeoDataFrame
# when result of the join is empty
df_inner = sjoin(self.pointdf.iloc[17:], self.polydf, how='inner')
df_left = sjoin(self.pointdf.iloc[17:], self.polydf, how='left')
df_right = sjoin(self.pointdf.iloc[17:], self.polydf, how='right')
# Recent Pandas development has introduced a new way of handling merges
# this change has altered the output when no overlapping geometries
if str(pd.__version__) > LooseVersion('0.18.1'):
right_idxs = pd.Series(range(0, 5), name='index_right',
dtype='int64')
else:
right_idxs = pd.Series(name='index_right', dtype='int64')
expected_inner_df = pd.concat(
[self.pointdf.iloc[:0],
pd.Series(name='index_right', dtype='int64'),
self.polydf.drop('geometry', axis=1).iloc[:0]],
axis=1)
expected_inner = GeoDataFrame(
expected_inner_df, crs={'init': 'epsg:4326', 'no_defs': True})
expected_right_df = pd.concat(
[self.pointdf.drop('geometry', axis=1).iloc[:0],
pd.concat([pd.Series(name='index_left', dtype='int64'),
right_idxs],
axis=1),
self.polydf],
axis=1)
expected_right = GeoDataFrame(
expected_right_df, crs={'init': 'epsg:4326', 'no_defs': True})\
.set_index('index_right')
expected_left_df = pd.concat(
[self.pointdf.iloc[17:],
pd.Series(name='index_right', dtype='int64'),
self.polydf.iloc[:0].drop('geometry', axis=1)],
axis=1)
expected_left = GeoDataFrame(
expected_left_df, crs={'init': 'epsg:4326', 'no_defs': True})
assert expected_inner.equals(df_inner)
assert expected_right.equals(df_right)
assert expected_left.equals(df_left)
@pytest.mark.skip("Not implemented")
def test_sjoin_outer(self):
df = sjoin(self.pointdf, self.polydf, how="outer")
assert df.shape == (21, 8)
| bsd-3-clause |
jrleja/bsfh | misc/timings_pyfsps.py | 3 | 4274 | #compare a lookup table of spectra at ages and metallicities to
#calls to fsps.sps.get_spectrum() for different metallicities
import time, os, subprocess, re, sys
import numpy as np
#import matplotlib.pyplot as pl
import fsps
from prospect import sources as sps_basis
from prospect.models import sedmodel
def run_command(cmd):
"""
Open a child process, and return its exit status and stdout.
"""
child = subprocess.Popen(cmd, shell=True, stderr=subprocess.PIPE,
stdin=subprocess.PIPE, stdout=subprocess.PIPE)
out = [s for s in child.stdout]
w = child.wait()
return os.WEXITSTATUS(w), out
# Check to make sure that the required environment variable is present.
try:
ev = os.environ["SPS_HOME"]
except KeyError:
raise ImportError("You need to have the SPS_HOME environment variable")
# Check the SVN revision number.
cmd = ["svnversion", ev]
stat, out = run_command(" ".join(cmd))
fsps_vers = int(re.match("^([0-9])+", out[0]).group(0))
sps = fsps.StellarPopulation(zcontinuous=True)
print('FSPS version = {}'.format(fsps_vers))
print('Zs={0}, N_lambda={1}'.format(sps.zlegend, len(sps.wavelengths)))
print('single age')
def spec_from_fsps(z, t, s):
t0 = time.time()
sps.params['logzsol'] = z
sps.params['sigma_smooth'] = s
sps.params['tage'] = t
wave, spec = sps.get_spectrum(peraa=True, tage = sps.params['tage'])
#print(spec.shape)
return time.time()-t0
def mags_from_fsps(z, t, s):
t0 = time.time()
sps.params['zred']=t
sps.params['logzsol'] = z
sps.params['sigma_smooth'] = s
sps.params['tage'] = t
mags = sps.get_mags(tage = sps.params['tage'], redshift=0.0)
#print(spec.shape)
return time.time()-t0
def spec_from_ztinterp(z, t, s):
t0 = time.time()
sps.params['logzsol'] = z
sps.params['sigma_smooth'] = s
sps.params['tage'] = t
sps.params['imf3'] = s
spec, m, l = sps.ztinterp(sps.params['logzsol'], sps.params['tage'], peraa=True)
#print(spec.shape)
return time.time()-t0
if sys.argv[1] == 'mags':
from_fsps = mags_from_fsps
print('timing get_mags')
print('nbands = {}'.format(len(sps.get_mags(tage=1.0))))
elif sys.argv[1] == 'spec':
from_fsps = spec_from_fsps
print('timing get_spectrum')
elif sys.argv[1] == 'ztinterp':
from_fsps = spec_from_ztinterp
print('timing get_spectrum')
elif sys.argv[1] == 'sedpy':
from sedpy import observate
nbands = len(sps.get_mags(tage=1.0))
fnames = nbands * ['sdss_r0']
filters = observate.load_filters(fnames)
def mags_from_sedpy(z, t, s):
t0 = time.time()
sps.params['logzsol'] = z
sps.params['sigma_smooth'] = s
sps.params['tage'] = t
wave, spec = sps.get_spectrum(peraa=True,
tage = sps.params['tage'])
mags = observate.getSED(wave, spec, filters)
return time.time()-t0
from_fsps = mags_from_sedpy
sps.params['add_neb_emission'] = False
sps.params['smooth_velocity'] = True
sps.params['sfh'] = 0
ntry = 30
zz = np.random.uniform(-1,0,ntry)
tt = np.random.uniform(0.1,4,ntry)
ss = np.random.uniform(1,2.5,ntry)
#make sure all z's already compiled
_ =[from_fsps(z, 1.0, 0.0) for z in [-1, -0.8, -0.6, -0.4, -0.2, 0.0]]
all_dur = []
print('no neb emission:')
dur_many = np.zeros(ntry)
for i in xrange(ntry):
dur_many[i] = from_fsps(zz[i], tt[i], ss[i])
print('<t/call>={0}s, sigma_t={1}s'.format(dur_many.mean(), dur_many.std()))
all_dur += [dur_many]
print('no neb emission, no smooth:')
dur_many = np.zeros(ntry)
for i in xrange(ntry):
dur_many[i] = from_fsps(zz[i], tt[i], 0.0)
print('<t/call>={0}s, sigma_t={1}s'.format(dur_many.mean(), dur_many.std()))
all_dur += [dur_many]
sps.params['add_neb_emission'] = True
print('neb emission:')
dur_many = np.zeros(ntry)
for i in xrange(ntry):
dur_many[i] = from_fsps(zz[i], tt[i], ss[i])
print('<t/call>={0}s, sigma_t={1}s'.format(dur_many.mean(), dur_many.std()))
all_dur += [dur_many]
print('neb emission, no smooth:')
dur_many = np.zeros(ntry)
for i in xrange(ntry):
dur_many[i] = from_fsps(zz[i], tt[i], 0.0)
print('<t/call>={0}s, sigma_t={1}s'.format(dur_many.mean(), dur_many.std()))
all_dur += [dur_many]
| mit |
imaculate/scikit-learn | examples/preprocessing/plot_function_transformer.py | 158 | 1993 | """
=========================================================
Using FunctionTransformer to select columns
=========================================================
Shows how to use a function transformer in a pipeline. If you know your
dataset's first principle component is irrelevant for a classification task,
you can use the FunctionTransformer to select all but the first column of the
PCA transformed data.
"""
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer
def _generate_vector(shift=0.5, noise=15):
return np.arange(1000) + (np.random.rand(1000) - shift) * noise
def generate_dataset():
"""
This dataset is two lines with a slope ~ 1, where one has
a y offset of ~100
"""
return np.vstack((
np.vstack((
_generate_vector(),
_generate_vector() + 100,
)).T,
np.vstack((
_generate_vector(),
_generate_vector(),
)).T,
)), np.hstack((np.zeros(1000), np.ones(1000)))
def all_but_first_column(X):
return X[:, 1:]
def drop_first_component(X, y):
"""
Create a pipeline with PCA and the column selector and use it to
transform the dataset.
"""
pipeline = make_pipeline(
PCA(), FunctionTransformer(all_but_first_column),
)
X_train, X_test, y_train, y_test = train_test_split(X, y)
pipeline.fit(X_train, y_train)
return pipeline.transform(X_test), y_test
if __name__ == '__main__':
X, y = generate_dataset()
lw = 0
plt.figure()
plt.scatter(X[:, 0], X[:, 1], c=y, lw=lw)
plt.figure()
X_transformed, y_transformed = drop_first_component(*generate_dataset())
plt.scatter(
X_transformed[:, 0],
np.zeros(len(X_transformed)),
c=y_transformed,
lw=lw,
s=60
)
plt.show()
| bsd-3-clause |
rohanp/scikit-learn | sklearn/model_selection/tests/test_validation.py | 20 | 27961 | """Test the validation module"""
from __future__ import division
import sys
import warnings
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_warns
from sklearn.utils.mocking import CheckingClassifier, MockDataFrame
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import permutation_test_score
from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import LeaveOneOut
from sklearn.model_selection import LeaveOneLabelOut
from sklearn.model_selection import LeavePLabelOut
from sklearn.model_selection import LabelKFold
from sklearn.model_selection import LabelShuffleSplit
from sklearn.model_selection import learning_curve
from sklearn.model_selection import validation_curve
from sklearn.model_selection._validation import _check_is_permutation
from sklearn.datasets import make_regression
from sklearn.datasets import load_boston
from sklearn.datasets import load_iris
from sklearn.metrics import explained_variance_score
from sklearn.metrics import make_scorer
from sklearn.metrics import precision_score
from sklearn.linear_model import Ridge
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.cluster import KMeans
from sklearn.preprocessing import Imputer
from sklearn.pipeline import Pipeline
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.base import BaseEstimator
from sklearn.multiclass import OneVsRestClassifier
from sklearn.datasets import make_classification
from sklearn.datasets import make_multilabel_classification
from test_split import MockClassifier
class MockImprovingEstimator(BaseEstimator):
"""Dummy classifier to test the learning curve"""
def __init__(self, n_max_train_sizes):
self.n_max_train_sizes = n_max_train_sizes
self.train_sizes = 0
self.X_subset = None
def fit(self, X_subset, y_subset=None):
self.X_subset = X_subset
self.train_sizes = X_subset.shape[0]
return self
def predict(self, X):
raise NotImplementedError
def score(self, X=None, Y=None):
# training score becomes worse (2 -> 1), test error better (0 -> 1)
if self._is_training_data(X):
return 2. - float(self.train_sizes) / self.n_max_train_sizes
else:
return float(self.train_sizes) / self.n_max_train_sizes
def _is_training_data(self, X):
return X is self.X_subset
class MockIncrementalImprovingEstimator(MockImprovingEstimator):
"""Dummy classifier that provides partial_fit"""
def __init__(self, n_max_train_sizes):
super(MockIncrementalImprovingEstimator,
self).__init__(n_max_train_sizes)
self.x = None
def _is_training_data(self, X):
return self.x in X
def partial_fit(self, X, y=None, **params):
self.train_sizes += X.shape[0]
self.x = X[0]
class MockEstimatorWithParameter(BaseEstimator):
"""Dummy classifier to test the validation curve"""
def __init__(self, param=0.5):
self.X_subset = None
self.param = param
def fit(self, X_subset, y_subset):
self.X_subset = X_subset
self.train_sizes = X_subset.shape[0]
return self
def predict(self, X):
raise NotImplementedError
def score(self, X=None, y=None):
return self.param if self._is_training_data(X) else 1 - self.param
def _is_training_data(self, X):
return X is self.X_subset
# XXX: use 2D array, since 1D X is being detected as a single sample in
# check_consistent_length
X = np.ones((10, 2))
X_sparse = coo_matrix(X)
y = np.arange(10) // 2
def test_cross_val_score():
clf = MockClassifier()
for a in range(-10, 10):
clf.a = a
# Smoke test
scores = cross_val_score(clf, X, y)
assert_array_equal(scores, clf.score(X, y))
# test with multioutput y
scores = cross_val_score(clf, X_sparse, X)
assert_array_equal(scores, clf.score(X_sparse, X))
scores = cross_val_score(clf, X_sparse, y)
assert_array_equal(scores, clf.score(X_sparse, y))
# test with multioutput y
scores = cross_val_score(clf, X_sparse, X)
assert_array_equal(scores, clf.score(X_sparse, X))
# test with X and y as list
list_check = lambda x: isinstance(x, list)
clf = CheckingClassifier(check_X=list_check)
scores = cross_val_score(clf, X.tolist(), y.tolist())
clf = CheckingClassifier(check_y=list_check)
scores = cross_val_score(clf, X, y.tolist())
assert_raises(ValueError, cross_val_score, clf, X, y,
scoring="sklearn")
# test with 3d X and
X_3d = X[:, :, np.newaxis]
clf = MockClassifier(allow_nd=True)
scores = cross_val_score(clf, X_3d, y)
clf = MockClassifier(allow_nd=False)
assert_raises(ValueError, cross_val_score, clf, X_3d, y)
def test_cross_val_score_predict_labels():
# Check if ValueError (when labels is None) propagates to cross_val_score
# and cross_val_predict
# And also check if labels is correctly passed to the cv object
X, y = make_classification(n_samples=20, n_classes=2, random_state=0)
clf = SVC(kernel="linear")
label_cvs = [LeaveOneLabelOut(), LeavePLabelOut(2), LabelKFold(),
LabelShuffleSplit()]
for cv in label_cvs:
assert_raise_message(ValueError,
"The labels parameter should not be None",
cross_val_score, estimator=clf, X=X, y=y, cv=cv)
assert_raise_message(ValueError,
"The labels parameter should not be None",
cross_val_predict, estimator=clf, X=X, y=y, cv=cv)
def test_cross_val_score_pandas():
# check cross_val_score doesn't destroy pandas dataframe
types = [(MockDataFrame, MockDataFrame)]
try:
from pandas import Series, DataFrame
types.append((Series, DataFrame))
except ImportError:
pass
for TargetType, InputFeatureType in types:
# X dataframe, y series
X_df, y_ser = InputFeatureType(X), TargetType(y)
check_df = lambda x: isinstance(x, InputFeatureType)
check_series = lambda x: isinstance(x, TargetType)
clf = CheckingClassifier(check_X=check_df, check_y=check_series)
cross_val_score(clf, X_df, y_ser)
def test_cross_val_score_mask():
# test that cross_val_score works with boolean masks
svm = SVC(kernel="linear")
iris = load_iris()
X, y = iris.data, iris.target
kfold = KFold(5)
scores_indices = cross_val_score(svm, X, y, cv=kfold)
kfold = KFold(5)
cv_masks = []
for train, test in kfold.split(X, y):
mask_train = np.zeros(len(y), dtype=np.bool)
mask_test = np.zeros(len(y), dtype=np.bool)
mask_train[train] = 1
mask_test[test] = 1
cv_masks.append((train, test))
scores_masks = cross_val_score(svm, X, y, cv=cv_masks)
assert_array_equal(scores_indices, scores_masks)
def test_cross_val_score_precomputed():
# test for svm with precomputed kernel
svm = SVC(kernel="precomputed")
iris = load_iris()
X, y = iris.data, iris.target
linear_kernel = np.dot(X, X.T)
score_precomputed = cross_val_score(svm, linear_kernel, y)
svm = SVC(kernel="linear")
score_linear = cross_val_score(svm, X, y)
assert_array_equal(score_precomputed, score_linear)
# Error raised for non-square X
svm = SVC(kernel="precomputed")
assert_raises(ValueError, cross_val_score, svm, X, y)
# test error is raised when the precomputed kernel is not array-like
# or sparse
assert_raises(ValueError, cross_val_score, svm,
linear_kernel.tolist(), y)
def test_cross_val_score_fit_params():
clf = MockClassifier()
n_samples = X.shape[0]
n_classes = len(np.unique(y))
W_sparse = coo_matrix((np.array([1]), (np.array([1]), np.array([0]))),
shape=(10, 1))
P_sparse = coo_matrix(np.eye(5))
DUMMY_INT = 42
DUMMY_STR = '42'
DUMMY_OBJ = object()
def assert_fit_params(clf):
# Function to test that the values are passed correctly to the
# classifier arguments for non-array type
assert_equal(clf.dummy_int, DUMMY_INT)
assert_equal(clf.dummy_str, DUMMY_STR)
assert_equal(clf.dummy_obj, DUMMY_OBJ)
fit_params = {'sample_weight': np.ones(n_samples),
'class_prior': np.ones(n_classes) / n_classes,
'sparse_sample_weight': W_sparse,
'sparse_param': P_sparse,
'dummy_int': DUMMY_INT,
'dummy_str': DUMMY_STR,
'dummy_obj': DUMMY_OBJ,
'callback': assert_fit_params}
cross_val_score(clf, X, y, fit_params=fit_params)
def test_cross_val_score_score_func():
clf = MockClassifier()
_score_func_args = []
def score_func(y_test, y_predict):
_score_func_args.append((y_test, y_predict))
return 1.0
with warnings.catch_warnings(record=True):
scoring = make_scorer(score_func)
score = cross_val_score(clf, X, y, scoring=scoring)
assert_array_equal(score, [1.0, 1.0, 1.0])
assert len(_score_func_args) == 3
def test_cross_val_score_errors():
class BrokenEstimator:
pass
assert_raises(TypeError, cross_val_score, BrokenEstimator(), X)
def test_cross_val_score_with_score_func_classification():
iris = load_iris()
clf = SVC(kernel='linear')
# Default score (should be the accuracy score)
scores = cross_val_score(clf, iris.data, iris.target, cv=5)
assert_array_almost_equal(scores, [0.97, 1., 0.97, 0.97, 1.], 2)
# Correct classification score (aka. zero / one score) - should be the
# same as the default estimator score
zo_scores = cross_val_score(clf, iris.data, iris.target,
scoring="accuracy", cv=5)
assert_array_almost_equal(zo_scores, [0.97, 1., 0.97, 0.97, 1.], 2)
# F1 score (class are balanced so f1_score should be equal to zero/one
# score
f1_scores = cross_val_score(clf, iris.data, iris.target,
scoring="f1_weighted", cv=5)
assert_array_almost_equal(f1_scores, [0.97, 1., 0.97, 0.97, 1.], 2)
def test_cross_val_score_with_score_func_regression():
X, y = make_regression(n_samples=30, n_features=20, n_informative=5,
random_state=0)
reg = Ridge()
# Default score of the Ridge regression estimator
scores = cross_val_score(reg, X, y, cv=5)
assert_array_almost_equal(scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)
# R2 score (aka. determination coefficient) - should be the
# same as the default estimator score
r2_scores = cross_val_score(reg, X, y, scoring="r2", cv=5)
assert_array_almost_equal(r2_scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)
# Mean squared error; this is a loss function, so "scores" are negative
mse_scores = cross_val_score(reg, X, y, cv=5, scoring="mean_squared_error")
expected_mse = np.array([-763.07, -553.16, -274.38, -273.26, -1681.99])
assert_array_almost_equal(mse_scores, expected_mse, 2)
# Explained variance
scoring = make_scorer(explained_variance_score)
ev_scores = cross_val_score(reg, X, y, cv=5, scoring=scoring)
assert_array_almost_equal(ev_scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)
def test_permutation_score():
iris = load_iris()
X = iris.data
X_sparse = coo_matrix(X)
y = iris.target
svm = SVC(kernel='linear')
cv = StratifiedKFold(2)
score, scores, pvalue = permutation_test_score(
svm, X, y, n_permutations=30, cv=cv, scoring="accuracy")
assert_greater(score, 0.9)
assert_almost_equal(pvalue, 0.0, 1)
score_label, _, pvalue_label = permutation_test_score(
svm, X, y, n_permutations=30, cv=cv, scoring="accuracy",
labels=np.ones(y.size), random_state=0)
assert_true(score_label == score)
assert_true(pvalue_label == pvalue)
# check that we obtain the same results with a sparse representation
svm_sparse = SVC(kernel='linear')
cv_sparse = StratifiedKFold(2)
score_label, _, pvalue_label = permutation_test_score(
svm_sparse, X_sparse, y, n_permutations=30, cv=cv_sparse,
scoring="accuracy", labels=np.ones(y.size), random_state=0)
assert_true(score_label == score)
assert_true(pvalue_label == pvalue)
# test with custom scoring object
def custom_score(y_true, y_pred):
return (((y_true == y_pred).sum() - (y_true != y_pred).sum())
/ y_true.shape[0])
scorer = make_scorer(custom_score)
score, _, pvalue = permutation_test_score(
svm, X, y, n_permutations=100, scoring=scorer, cv=cv, random_state=0)
assert_almost_equal(score, .93, 2)
assert_almost_equal(pvalue, 0.01, 3)
# set random y
y = np.mod(np.arange(len(y)), 3)
score, scores, pvalue = permutation_test_score(
svm, X, y, n_permutations=30, cv=cv, scoring="accuracy")
assert_less(score, 0.5)
assert_greater(pvalue, 0.2)
def test_permutation_test_score_allow_nans():
# Check that permutation_test_score allows input data with NaNs
X = np.arange(200, dtype=np.float64).reshape(10, -1)
X[2, :] = np.nan
y = np.repeat([0, 1], X.shape[0] / 2)
p = Pipeline([
('imputer', Imputer(strategy='mean', missing_values='NaN')),
('classifier', MockClassifier()),
])
permutation_test_score(p, X, y, cv=5)
def test_cross_val_score_allow_nans():
# Check that cross_val_score allows input data with NaNs
X = np.arange(200, dtype=np.float64).reshape(10, -1)
X[2, :] = np.nan
y = np.repeat([0, 1], X.shape[0] / 2)
p = Pipeline([
('imputer', Imputer(strategy='mean', missing_values='NaN')),
('classifier', MockClassifier()),
])
cross_val_score(p, X, y, cv=5)
def test_cross_val_score_multilabel():
X = np.array([[-3, 4], [2, 4], [3, 3], [0, 2], [-3, 1],
[-2, 1], [0, 0], [-2, -1], [-1, -2], [1, -2]])
y = np.array([[1, 1], [0, 1], [0, 1], [0, 1], [1, 1],
[0, 1], [1, 0], [1, 1], [1, 0], [0, 0]])
clf = KNeighborsClassifier(n_neighbors=1)
scoring_micro = make_scorer(precision_score, average='micro')
scoring_macro = make_scorer(precision_score, average='macro')
scoring_samples = make_scorer(precision_score, average='samples')
score_micro = cross_val_score(clf, X, y, scoring=scoring_micro, cv=5)
score_macro = cross_val_score(clf, X, y, scoring=scoring_macro, cv=5)
score_samples = cross_val_score(clf, X, y, scoring=scoring_samples, cv=5)
assert_almost_equal(score_micro, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 3])
assert_almost_equal(score_macro, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 4])
assert_almost_equal(score_samples, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 4])
def test_cross_val_predict():
boston = load_boston()
X, y = boston.data, boston.target
cv = KFold()
est = Ridge()
# Naive loop (should be same as cross_val_predict):
preds2 = np.zeros_like(y)
for train, test in cv.split(X, y):
est.fit(X[train], y[train])
preds2[test] = est.predict(X[test])
preds = cross_val_predict(est, X, y, cv=cv)
assert_array_almost_equal(preds, preds2)
preds = cross_val_predict(est, X, y)
assert_equal(len(preds), len(y))
cv = LeaveOneOut()
preds = cross_val_predict(est, X, y, cv=cv)
assert_equal(len(preds), len(y))
Xsp = X.copy()
Xsp *= (Xsp > np.median(Xsp))
Xsp = coo_matrix(Xsp)
preds = cross_val_predict(est, Xsp, y)
assert_array_almost_equal(len(preds), len(y))
preds = cross_val_predict(KMeans(), X)
assert_equal(len(preds), len(y))
class BadCV():
def split(self, X, y=None, labels=None):
for i in range(4):
yield np.array([0, 1, 2, 3]), np.array([4, 5, 6, 7, 8])
assert_raises(ValueError, cross_val_predict, est, X, y, cv=BadCV())
def test_cross_val_predict_input_types():
clf = Ridge()
# Smoke test
predictions = cross_val_predict(clf, X, y)
assert_equal(predictions.shape, (10,))
# test with multioutput y
predictions = cross_val_predict(clf, X_sparse, X)
assert_equal(predictions.shape, (10, 2))
predictions = cross_val_predict(clf, X_sparse, y)
assert_array_equal(predictions.shape, (10,))
# test with multioutput y
predictions = cross_val_predict(clf, X_sparse, X)
assert_array_equal(predictions.shape, (10, 2))
# test with X and y as list
list_check = lambda x: isinstance(x, list)
clf = CheckingClassifier(check_X=list_check)
predictions = cross_val_predict(clf, X.tolist(), y.tolist())
clf = CheckingClassifier(check_y=list_check)
predictions = cross_val_predict(clf, X, y.tolist())
# test with 3d X and
X_3d = X[:, :, np.newaxis]
check_3d = lambda x: x.ndim == 3
clf = CheckingClassifier(check_X=check_3d)
predictions = cross_val_predict(clf, X_3d, y)
assert_array_equal(predictions.shape, (10,))
def test_cross_val_predict_pandas():
# check cross_val_score doesn't destroy pandas dataframe
types = [(MockDataFrame, MockDataFrame)]
try:
from pandas import Series, DataFrame
types.append((Series, DataFrame))
except ImportError:
pass
for TargetType, InputFeatureType in types:
# X dataframe, y series
X_df, y_ser = InputFeatureType(X), TargetType(y)
check_df = lambda x: isinstance(x, InputFeatureType)
check_series = lambda x: isinstance(x, TargetType)
clf = CheckingClassifier(check_X=check_df, check_y=check_series)
cross_val_predict(clf, X_df, y_ser)
def test_cross_val_score_sparse_fit_params():
iris = load_iris()
X, y = iris.data, iris.target
clf = MockClassifier()
fit_params = {'sparse_sample_weight': coo_matrix(np.eye(X.shape[0]))}
a = cross_val_score(clf, X, y, fit_params=fit_params)
assert_array_equal(a, np.ones(3))
def test_learning_curve():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
with warnings.catch_warnings(record=True) as w:
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=3, train_sizes=np.linspace(0.1, 1.0, 10))
if len(w) > 0:
raise RuntimeError("Unexpected warning: %r" % w[0].message)
assert_equal(train_scores.shape, (10, 3))
assert_equal(test_scores.shape, (10, 3))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_unsupervised():
X, _ = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y=None, cv=3, train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_verbose():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
train_sizes, train_scores, test_scores = \
learning_curve(estimator, X, y, cv=3, verbose=1)
finally:
out = sys.stdout.getvalue()
sys.stdout.close()
sys.stdout = old_stdout
assert("[learning_curve]" in out)
def test_learning_curve_incremental_learning_not_possible():
X, y = make_classification(n_samples=2, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
# The mockup does not have partial_fit()
estimator = MockImprovingEstimator(1)
assert_raises(ValueError, learning_curve, estimator, X, y,
exploit_incremental_learning=True)
def test_learning_curve_incremental_learning():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockIncrementalImprovingEstimator(20)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=3, exploit_incremental_learning=True,
train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_incremental_learning_unsupervised():
X, _ = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockIncrementalImprovingEstimator(20)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y=None, cv=3, exploit_incremental_learning=True,
train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_batch_and_incremental_learning_are_equal():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
train_sizes = np.linspace(0.2, 1.0, 5)
estimator = PassiveAggressiveClassifier(n_iter=1, shuffle=False)
train_sizes_inc, train_scores_inc, test_scores_inc = \
learning_curve(
estimator, X, y, train_sizes=train_sizes,
cv=3, exploit_incremental_learning=True)
train_sizes_batch, train_scores_batch, test_scores_batch = \
learning_curve(
estimator, X, y, cv=3, train_sizes=train_sizes,
exploit_incremental_learning=False)
assert_array_equal(train_sizes_inc, train_sizes_batch)
assert_array_almost_equal(train_scores_inc.mean(axis=1),
train_scores_batch.mean(axis=1))
assert_array_almost_equal(test_scores_inc.mean(axis=1),
test_scores_batch.mean(axis=1))
def test_learning_curve_n_sample_range_out_of_bounds():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0, 1])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0.0, 1.0])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0.1, 1.1])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0, 20])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[1, 21])
def test_learning_curve_remove_duplicate_sample_sizes():
X, y = make_classification(n_samples=3, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(2)
train_sizes, _, _ = assert_warns(
RuntimeWarning, learning_curve, estimator, X, y, cv=3,
train_sizes=np.linspace(0.33, 1.0, 3))
assert_array_equal(train_sizes, [1, 2])
def test_learning_curve_with_boolean_indices():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
cv = KFold(n_folds=3)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_validation_curve():
X, y = make_classification(n_samples=2, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
param_range = np.linspace(0, 1, 10)
with warnings.catch_warnings(record=True) as w:
train_scores, test_scores = validation_curve(
MockEstimatorWithParameter(), X, y, param_name="param",
param_range=param_range, cv=2
)
if len(w) > 0:
raise RuntimeError("Unexpected warning: %r" % w[0].message)
assert_array_almost_equal(train_scores.mean(axis=1), param_range)
assert_array_almost_equal(test_scores.mean(axis=1), 1 - param_range)
def test_check_is_permutation():
p = np.arange(100)
assert_true(_check_is_permutation(p, 100))
assert_false(_check_is_permutation(np.delete(p, 23), 100))
p[0] = 23
assert_false(_check_is_permutation(p, 100))
def test_cross_val_predict_sparse_prediction():
# check that cross_val_predict gives same result for sparse and dense input
X, y = make_multilabel_classification(n_classes=2, n_labels=1,
allow_unlabeled=False,
return_indicator=True,
random_state=1)
X_sparse = csr_matrix(X)
y_sparse = csr_matrix(y)
classif = OneVsRestClassifier(SVC(kernel='linear'))
preds = cross_val_predict(classif, X, y, cv=10)
preds_sparse = cross_val_predict(classif, X_sparse, y_sparse, cv=10)
preds_sparse = preds_sparse.toarray()
assert_array_almost_equal(preds_sparse, preds)
| bsd-3-clause |
abbeymiles/aima-python | submissions/Blue/myNN.py | 10 | 3071 | from sklearn import datasets
from sklearn.neural_network import MLPClassifier
import traceback
from submissions.Blue import music
class DataFrame:
data = []
feature_names = []
target = []
target_names = []
musicATRB = DataFrame()
musicATRB.data = []
targetData = []
'''
Extract data from the CORGIS Music Library.
Most 'hit' songs average 48-52 bars and no more than ~3 minutes (180 seconds)...
'''
allSongs = music.get_songs()
for song in allSongs:
try:
length = float(song['song']["duration"])
targetData.append(length)
genre = song['artist']['terms'] #String
title = song['song']['title'] #String
# release = float(song['song']['Release'])
musicATRB.data.append([genre, title])
except:
traceback.print_exc()
musicATRB.feature_names = [
'Genre',
'Title',
'Release',
'Length',
]
musicATRB.target = []
def musicTarget(release):
if (song['song']['duration'] <= 210
): #if the song is less that 3.5 minutes (210 seconds) long
return 1
return 0
for i in targetData:
tt = musicTarget(i)
musicATRB.target.append(tt)
musicATRB.target_names = [
'Not a hit song',
'Could be a hit song',
]
Examples = {
'Music': musicATRB,
}
'''
Make a customn classifier,
'''
mlpc = MLPClassifier(
hidden_layer_sizes = (100,),
activation = 'relu',
solver='sgd', # 'adam',
alpha = 0.0001,
# batch_size='auto',
learning_rate = 'adaptive', # 'constant',
# power_t = 0.5,
max_iter = 1000, # 200,
shuffle = True,
# random_state = None,
# tol = 1e-4,
# verbose = False,
# warm_start = False,
# momentum = 0.9,
# nesterovs_momentum = True,
# early_stopping = False,
# validation_fraction = 0.1,
# beta_1 = 0.9,
# beta_2 = 0.999,
# epsilon = 1e-8,
)
'''
Try scaling the data.
'''
musicScaled = DataFrame()
def setupScales(grid):
global min, max
min = list(grid[0])
max = list(grid[0])
for row in range(1, len(grid)):
for col in range(len(grid[row])):
cell = grid[row][col]
if cell < min[col]:
min[col] = cell
if cell > max[col]:
max[col] = cell
def scaleGrid(grid):
newGrid = []
for row in range(len(grid)):
newRow = []
for col in range(len(grid[row])):
try:
cell = grid[row][col]
scaled = (cell - min[col]) \
/ (max[col] - min[col])
newRow.append(scaled)
except:
pass
newGrid.append(newRow)
return newGrid
setupScales(musicATRB.data)
musicScaled.data = scaleGrid(musicATRB.data)
musicScaled.feature_names = musicATRB.feature_names
musicScaled.target = musicATRB.target
musicScaled.target_names = musicATRB.target_names
Examples = {
'musicDefault': {
'frame': musicATRB,
},
'MusicSGD': {
'frame': musicATRB,
'mlpc': mlpc
},
'MusisScaled': {
'frame': musicScaled,
},
} | mit |
manuelli/director | src/python/director/planplayback.py | 1 | 7857 | import os
import vtkAll as vtk
import math
import time
import re
import numpy as np
from director.timercallback import TimerCallback
from director import objectmodel as om
from director.simpletimer import SimpleTimer
from director.utime import getUtime
from director import robotstate
import copy
import pickle
import scipy.interpolate
def asRobotPlan(msg):
'''
If the given message is a robot_plan_with_supports_t then this function returns
the plan message contained within it. For any other message type, this function
just returns its input argument.
'''
try:
import drc as lcmdrc
except ImportError:
pass
else:
if isinstance(msg, lcmdrc.robot_plan_with_supports_t):
return msg.plan
return msg
class PlanPlayback(object):
def __init__(self):
self.animationCallback = None
self.animationTimer = None
self.interpolationMethod = 'slinear'
self.playbackSpeed = 1.0
self.jointNameRegex = ''
@staticmethod
def getPlanPoses(msgOrList):
if isinstance(msgOrList, list):
messages = msgOrList
allPoseTimes, allPoses = PlanPlayback.getPlanPoses(messages[0])
for msg in messages[1:]:
poseTimes, poses = PlanPlayback.getPlanPoses(msg)
poseTimes += allPoseTimes[-1]
allPoseTimes = np.hstack((allPoseTimes, poseTimes[1:]))
allPoses += poses[1:]
return allPoseTimes, allPoses
else:
msg = asRobotPlan(msgOrList)
poses = []
poseTimes = []
for plan in msg.plan:
pose = robotstate.convertStateMessageToDrakePose(plan)
poseTimes.append(plan.utime / 1e6)
poses.append(pose)
return np.array(poseTimes), poses
@staticmethod
def getPlanElapsedTime(msg):
msg = asRobotPlan(msg)
startTime = msg.plan[0].utime
endTime = msg.plan[-1].utime
return (endTime - startTime) / 1e6
@staticmethod
def mergePlanMessages(plans):
msg = copy.deepcopy(plans[0])
for plan in plans[1:]:
plan = copy.deepcopy(plan)
lastTime = msg.plan[-1].utime
for state in plan.plan:
state.utime += lastTime
msg.plan_info += plan.plan_info
msg.plan += plan.plan
msg.num_states = len(msg.plan)
return msg
@staticmethod
def isPlanInfoFeasible(info):
return 0 <= info < 10
@staticmethod
def isPlanFeasible(plan):
plan = asRobotPlan(plan)
return plan is not None and (max(plan.plan_info) < 10 and min(plan.plan_info) >= 0)
def stopAnimation(self):
if self.animationTimer:
self.animationTimer.stop()
def setInterpolationMethod(method):
self.interpolationMethod = method
def playPlan(self, msg, jointController):
self.playPlans([msg], jointController)
def playPlans(self, messages, jointController):
assert len(messages)
poseTimes, poses = self.getPlanPoses(messages)
self.playPoses(poseTimes, poses, jointController)
def getPoseInterpolatorFromPlan(self, message):
poseTimes, poses = self.getPlanPoses(message)
return self.getPoseInterpolator(poseTimes, poses)
def getPoseInterpolator(self, poseTimes, poses, unwrap_rpy=True):
if unwrap_rpy:
poses = np.array(poses, copy=True)
poses[:,3:6] = np.unwrap(poses[:,3:6],axis=0)
if self.interpolationMethod in ['slinear', 'quadratic', 'cubic']:
f = scipy.interpolate.interp1d(poseTimes, poses, axis=0, kind=self.interpolationMethod)
elif self.interpolationMethod == 'pchip':
f = scipy.interpolate.PchipInterpolator(poseTimes, poses, axis=0)
return f
def getPlanPoseMeshes(self, messages, jointController, robotModel, numberOfSamples):
poseTimes, poses = self.getPlanPoses(messages)
f = self.getPoseInterpolator(poseTimes, poses)
sampleTimes = np.linspace(poseTimes[0], poseTimes[-1], numberOfSamples)
meshes = []
for sampleTime in sampleTimes:
pose = f(sampleTime)
jointController.setPose('plan_playback', pose)
polyData = vtk.vtkPolyData()
robotModel.model.getModelMesh(polyData)
meshes.append(polyData)
return meshes
def showPoseAtTime(self, time, jointController, poseInterpolator):
pose = poseInterpolator(time)
jointController.setPose('plan_playback', pose)
def playPoses(self, poseTimes, poses, jointController):
f = self.getPoseInterpolator(poseTimes, poses)
timer = SimpleTimer()
def updateAnimation():
tNow = timer.elapsed() * self.playbackSpeed
if tNow > poseTimes[-1]:
pose = poses[-1]
jointController.setPose('plan_playback', pose)
if self.animationCallback:
self.animationCallback()
return False
pose = f(tNow)
jointController.setPose('plan_playback', pose)
if self.animationCallback:
self.animationCallback()
self.animationTimer = TimerCallback()
self.animationTimer.targetFps = 60
self.animationTimer.callback = updateAnimation
self.animationTimer.start()
updateAnimation()
def picklePlan(self, filename, msg):
poseTimes, poses = self.getPlanPoses(msg)
pickle.dump((poseTimes, poses), open(filename, 'w'))
def getMovingJointNames(self, msg):
poseTimes, poses = self.getPlanPoses(msg)
diffs = np.diff(poses, axis=0)
jointIds = np.unique(np.where(diffs != 0.0)[1])
jointNames = [robotstate.getDrakePoseJointNames()[jointId] for jointId in jointIds]
return jointNames
def plotPlan(self, msg):
poseTimes, poses = self.getPlanPoses(msg)
self.plotPoses(poseTimes, poses)
def plotPoses(self, poseTimes, poses):
import matplotlib.pyplot as plt
poses = np.array(poses)
if self.jointNameRegex:
jointIds = range(poses.shape[1])
else:
diffs = np.diff(poses, axis=0)
jointIds = np.unique(np.where(diffs != 0.0)[1])
jointNames = [robotstate.getDrakePoseJointNames()[jointId] for jointId in jointIds]
jointTrajectories = [poses[:,jointId] for jointId in jointIds]
seriesNames = []
sampleResolutionInSeconds = 0.01
numberOfSamples = (poseTimes[-1] - poseTimes[0]) / sampleResolutionInSeconds
xnew = np.linspace(poseTimes[0], poseTimes[-1], numberOfSamples)
fig = plt.figure()
ax = fig.add_subplot(111)
for jointId, jointName, jointTrajectory in zip(jointIds, jointNames, jointTrajectories):
if self.jointNameRegex and not re.match(self.jointNameRegex, jointName):
continue
x = poseTimes
y = jointTrajectory
y = np.rad2deg(y)
if self.interpolationMethod in ['slinear', 'quadratic', 'cubic']:
f = scipy.interpolate.interp1d(x, y, kind=self.interpolationMethod)
elif self.interpolationMethod == 'pchip':
f = scipy.interpolate.PchipInterpolator(x, y)
ax.plot(x, y, 'ko')
seriesNames.append(jointName + ' points')
ax.plot(xnew, f(xnew), '-')
seriesNames.append(jointName + ' ' + self.interpolationMethod)
ax.legend(seriesNames, loc='upper right').draggable()
ax.set_xlabel('time (s)')
ax.set_ylabel('joint angle (deg)')
ax.set_title('joint trajectories')
plt.show()
| bsd-3-clause |
pianomania/scikit-learn | sklearn/linear_model/stochastic_gradient.py | 16 | 50617 | # Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)
# Mathieu Blondel (partial_fit support)
#
# License: BSD 3 clause
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
from abc import ABCMeta, abstractmethod
from ..externals.joblib import Parallel, delayed
from .base import LinearClassifierMixin, SparseCoefMixin
from .base import make_dataset
from ..base import BaseEstimator, RegressorMixin
from ..utils import check_array, check_random_state, check_X_y
from ..utils.extmath import safe_sparse_dot
from ..utils.multiclass import _check_partial_fit_first_call
from ..utils.validation import check_is_fitted
from ..externals import six
from .sgd_fast import plain_sgd, average_sgd
from ..utils.fixes import astype
from ..utils import compute_class_weight
from ..utils import deprecated
from .sgd_fast import Hinge
from .sgd_fast import SquaredHinge
from .sgd_fast import Log
from .sgd_fast import ModifiedHuber
from .sgd_fast import SquaredLoss
from .sgd_fast import Huber
from .sgd_fast import EpsilonInsensitive
from .sgd_fast import SquaredEpsilonInsensitive
LEARNING_RATE_TYPES = {"constant": 1, "optimal": 2, "invscaling": 3,
"pa1": 4, "pa2": 5}
PENALTY_TYPES = {"none": 0, "l2": 2, "l1": 1, "elasticnet": 3}
DEFAULT_EPSILON = 0.1
# Default value of ``epsilon`` parameter.
class BaseSGD(six.with_metaclass(ABCMeta, BaseEstimator, SparseCoefMixin)):
"""Base class for SGD classification and regression."""
def __init__(self, loss, penalty='l2', alpha=0.0001, C=1.0,
l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True,
verbose=0, epsilon=0.1, random_state=None,
learning_rate="optimal", eta0=0.0, power_t=0.5,
warm_start=False, average=False):
self.loss = loss
self.penalty = penalty
self.learning_rate = learning_rate
self.epsilon = epsilon
self.alpha = alpha
self.C = C
self.l1_ratio = l1_ratio
self.fit_intercept = fit_intercept
self.n_iter = n_iter
self.shuffle = shuffle
self.random_state = random_state
self.verbose = verbose
self.eta0 = eta0
self.power_t = power_t
self.warm_start = warm_start
self.average = average
self._validate_params()
def set_params(self, *args, **kwargs):
super(BaseSGD, self).set_params(*args, **kwargs)
self._validate_params()
return self
@abstractmethod
def fit(self, X, y):
"""Fit model."""
def _validate_params(self):
"""Validate input params. """
if not isinstance(self.shuffle, bool):
raise ValueError("shuffle must be either True or False")
if self.n_iter <= 0:
raise ValueError("n_iter must be > zero")
if not (0.0 <= self.l1_ratio <= 1.0):
raise ValueError("l1_ratio must be in [0, 1]")
if self.alpha < 0.0:
raise ValueError("alpha must be >= 0")
if self.learning_rate in ("constant", "invscaling"):
if self.eta0 <= 0.0:
raise ValueError("eta0 must be > 0")
if self.learning_rate == "optimal" and self.alpha == 0:
raise ValueError("alpha must be > 0 since "
"learning_rate is 'optimal'. alpha is used "
"to compute the optimal learning rate.")
# raises ValueError if not registered
self._get_penalty_type(self.penalty)
self._get_learning_rate_type(self.learning_rate)
if self.loss not in self.loss_functions:
raise ValueError("The loss %s is not supported. " % self.loss)
def _get_loss_function(self, loss):
"""Get concrete ``LossFunction`` object for str ``loss``. """
try:
loss_ = self.loss_functions[loss]
loss_class, args = loss_[0], loss_[1:]
if loss in ('huber', 'epsilon_insensitive',
'squared_epsilon_insensitive'):
args = (self.epsilon, )
return loss_class(*args)
except KeyError:
raise ValueError("The loss %s is not supported. " % loss)
def _get_learning_rate_type(self, learning_rate):
try:
return LEARNING_RATE_TYPES[learning_rate]
except KeyError:
raise ValueError("learning rate %s "
"is not supported. " % learning_rate)
def _get_penalty_type(self, penalty):
penalty = str(penalty).lower()
try:
return PENALTY_TYPES[penalty]
except KeyError:
raise ValueError("Penalty %s is not supported. " % penalty)
def _validate_sample_weight(self, sample_weight, n_samples):
"""Set the sample weight array."""
if sample_weight is None:
# uniform sample weights
sample_weight = np.ones(n_samples, dtype=np.float64, order='C')
else:
# user-provided array
sample_weight = np.asarray(sample_weight, dtype=np.float64,
order="C")
if sample_weight.shape[0] != n_samples:
raise ValueError("Shapes of X and sample_weight do not match.")
return sample_weight
def _allocate_parameter_mem(self, n_classes, n_features, coef_init=None,
intercept_init=None):
"""Allocate mem for parameters; initialize if provided."""
if n_classes > 2:
# allocate coef_ for multi-class
if coef_init is not None:
coef_init = np.asarray(coef_init, order="C")
if coef_init.shape != (n_classes, n_features):
raise ValueError("Provided ``coef_`` does not match "
"dataset. ")
self.coef_ = coef_init
else:
self.coef_ = np.zeros((n_classes, n_features),
dtype=np.float64, order="C")
# allocate intercept_ for multi-class
if intercept_init is not None:
intercept_init = np.asarray(intercept_init, order="C")
if intercept_init.shape != (n_classes, ):
raise ValueError("Provided intercept_init "
"does not match dataset.")
self.intercept_ = intercept_init
else:
self.intercept_ = np.zeros(n_classes, dtype=np.float64,
order="C")
else:
# allocate coef_ for binary problem
if coef_init is not None:
coef_init = np.asarray(coef_init, dtype=np.float64,
order="C")
coef_init = coef_init.ravel()
if coef_init.shape != (n_features,):
raise ValueError("Provided coef_init does not "
"match dataset.")
self.coef_ = coef_init
else:
self.coef_ = np.zeros(n_features,
dtype=np.float64,
order="C")
# allocate intercept_ for binary problem
if intercept_init is not None:
intercept_init = np.asarray(intercept_init, dtype=np.float64)
if intercept_init.shape != (1,) and intercept_init.shape != ():
raise ValueError("Provided intercept_init "
"does not match dataset.")
self.intercept_ = intercept_init.reshape(1,)
else:
self.intercept_ = np.zeros(1, dtype=np.float64, order="C")
# initialize average parameters
if self.average > 0:
self.standard_coef_ = self.coef_
self.standard_intercept_ = self.intercept_
self.average_coef_ = np.zeros(self.coef_.shape,
dtype=np.float64,
order="C")
self.average_intercept_ = np.zeros(self.standard_intercept_.shape,
dtype=np.float64,
order="C")
def _prepare_fit_binary(est, y, i):
"""Initialization for fit_binary.
Returns y, coef, intercept.
"""
y_i = np.ones(y.shape, dtype=np.float64, order="C")
y_i[y != est.classes_[i]] = -1.0
average_intercept = 0
average_coef = None
if len(est.classes_) == 2:
if not est.average:
coef = est.coef_.ravel()
intercept = est.intercept_[0]
else:
coef = est.standard_coef_.ravel()
intercept = est.standard_intercept_[0]
average_coef = est.average_coef_.ravel()
average_intercept = est.average_intercept_[0]
else:
if not est.average:
coef = est.coef_[i]
intercept = est.intercept_[i]
else:
coef = est.standard_coef_[i]
intercept = est.standard_intercept_[i]
average_coef = est.average_coef_[i]
average_intercept = est.average_intercept_[i]
return y_i, coef, intercept, average_coef, average_intercept
def fit_binary(est, i, X, y, alpha, C, learning_rate, n_iter,
pos_weight, neg_weight, sample_weight):
"""Fit a single binary classifier.
The i'th class is considered the "positive" class.
"""
# if average is not true, average_coef, and average_intercept will be
# unused
y_i, coef, intercept, average_coef, average_intercept = \
_prepare_fit_binary(est, y, i)
assert y_i.shape[0] == y.shape[0] == sample_weight.shape[0]
dataset, intercept_decay = make_dataset(X, y_i, sample_weight)
penalty_type = est._get_penalty_type(est.penalty)
learning_rate_type = est._get_learning_rate_type(learning_rate)
# XXX should have random_state_!
random_state = check_random_state(est.random_state)
# numpy mtrand expects a C long which is a signed 32 bit integer under
# Windows
seed = random_state.randint(0, np.iinfo(np.int32).max)
if not est.average:
return plain_sgd(coef, intercept, est.loss_function_,
penalty_type, alpha, C, est.l1_ratio,
dataset, n_iter, int(est.fit_intercept),
int(est.verbose), int(est.shuffle), seed,
pos_weight, neg_weight,
learning_rate_type, est.eta0,
est.power_t, est.t_, intercept_decay)
else:
standard_coef, standard_intercept, average_coef, \
average_intercept = average_sgd(coef, intercept, average_coef,
average_intercept,
est.loss_function_, penalty_type,
alpha, C, est.l1_ratio, dataset,
n_iter, int(est.fit_intercept),
int(est.verbose), int(est.shuffle),
seed, pos_weight, neg_weight,
learning_rate_type, est.eta0,
est.power_t, est.t_,
intercept_decay,
est.average)
if len(est.classes_) == 2:
est.average_intercept_[0] = average_intercept
else:
est.average_intercept_[i] = average_intercept
return standard_coef, standard_intercept
class BaseSGDClassifier(six.with_metaclass(ABCMeta, BaseSGD,
LinearClassifierMixin)):
loss_functions = {
"hinge": (Hinge, 1.0),
"squared_hinge": (SquaredHinge, 1.0),
"perceptron": (Hinge, 0.0),
"log": (Log, ),
"modified_huber": (ModifiedHuber, ),
"squared_loss": (SquaredLoss, ),
"huber": (Huber, DEFAULT_EPSILON),
"epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
"squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
DEFAULT_EPSILON),
}
@abstractmethod
def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15,
fit_intercept=True, n_iter=5, shuffle=True, verbose=0,
epsilon=DEFAULT_EPSILON, n_jobs=1, random_state=None,
learning_rate="optimal", eta0=0.0, power_t=0.5,
class_weight=None, warm_start=False, average=False):
super(BaseSGDClassifier, self).__init__(loss=loss, penalty=penalty,
alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept,
n_iter=n_iter, shuffle=shuffle,
verbose=verbose,
epsilon=epsilon,
random_state=random_state,
learning_rate=learning_rate,
eta0=eta0, power_t=power_t,
warm_start=warm_start,
average=average)
self.class_weight = class_weight
self.n_jobs = int(n_jobs)
@property
@deprecated("Attribute loss_function was deprecated in version 0.19 and "
"will be removed in 0.21. Use 'loss_function_' instead")
def loss_function(self):
return self.loss_function_
def _partial_fit(self, X, y, alpha, C,
loss, learning_rate, n_iter,
classes, sample_weight,
coef_init, intercept_init):
X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C")
n_samples, n_features = X.shape
self._validate_params()
_check_partial_fit_first_call(self, classes)
n_classes = self.classes_.shape[0]
# Allocate datastructures from input arguments
self._expanded_class_weight = compute_class_weight(self.class_weight,
self.classes_, y)
sample_weight = self._validate_sample_weight(sample_weight, n_samples)
if getattr(self, "coef_", None) is None or coef_init is not None:
self._allocate_parameter_mem(n_classes, n_features,
coef_init, intercept_init)
elif n_features != self.coef_.shape[-1]:
raise ValueError("Number of features %d does not match previous "
"data %d." % (n_features, self.coef_.shape[-1]))
self.loss_function_ = self._get_loss_function(loss)
if not hasattr(self, "t_"):
self.t_ = 1.0
# delegate to concrete training procedure
if n_classes > 2:
self._fit_multiclass(X, y, alpha=alpha, C=C,
learning_rate=learning_rate,
sample_weight=sample_weight, n_iter=n_iter)
elif n_classes == 2:
self._fit_binary(X, y, alpha=alpha, C=C,
learning_rate=learning_rate,
sample_weight=sample_weight, n_iter=n_iter)
else:
raise ValueError("The number of class labels must be "
"greater than one.")
return self
def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None,
intercept_init=None, sample_weight=None):
if hasattr(self, "classes_"):
self.classes_ = None
X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C")
n_samples, n_features = X.shape
# labels can be encoded as float, int, or string literals
# np.unique sorts in asc order; largest class id is positive class
classes = np.unique(y)
if self.warm_start and hasattr(self, "coef_"):
if coef_init is None:
coef_init = self.coef_
if intercept_init is None:
intercept_init = self.intercept_
else:
self.coef_ = None
self.intercept_ = None
if self.average > 0:
self.standard_coef_ = self.coef_
self.standard_intercept_ = self.intercept_
self.average_coef_ = None
self.average_intercept_ = None
# Clear iteration count for multiple call to fit.
self.t_ = 1.0
self._partial_fit(X, y, alpha, C, loss, learning_rate, self.n_iter,
classes, sample_weight, coef_init, intercept_init)
return self
def _fit_binary(self, X, y, alpha, C, sample_weight,
learning_rate, n_iter):
"""Fit a binary classifier on X and y. """
coef, intercept = fit_binary(self, 1, X, y, alpha, C,
learning_rate, n_iter,
self._expanded_class_weight[1],
self._expanded_class_weight[0],
sample_weight)
self.t_ += n_iter * X.shape[0]
# need to be 2d
if self.average > 0:
if self.average <= self.t_ - 1:
self.coef_ = self.average_coef_.reshape(1, -1)
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_.reshape(1, -1)
self.standard_intercept_ = np.atleast_1d(intercept)
self.intercept_ = self.standard_intercept_
else:
self.coef_ = coef.reshape(1, -1)
# intercept is a float, need to convert it to an array of length 1
self.intercept_ = np.atleast_1d(intercept)
def _fit_multiclass(self, X, y, alpha, C, learning_rate,
sample_weight, n_iter):
"""Fit a multi-class classifier by combining binary classifiers
Each binary classifier predicts one class versus all others. This
strategy is called OVA: One Versus All.
"""
# Use joblib to fit OvA in parallel.
result = Parallel(n_jobs=self.n_jobs, backend="threading",
verbose=self.verbose)(
delayed(fit_binary)(self, i, X, y, alpha, C, learning_rate,
n_iter, self._expanded_class_weight[i], 1.,
sample_weight)
for i in range(len(self.classes_)))
for i, (_, intercept) in enumerate(result):
self.intercept_[i] = intercept
self.t_ += n_iter * X.shape[0]
if self.average > 0:
if self.average <= self.t_ - 1.0:
self.coef_ = self.average_coef_
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_
self.standard_intercept_ = np.atleast_1d(self.intercept_)
self.intercept_ = self.standard_intercept_
def partial_fit(self, X, y, classes=None, sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of the training data
y : numpy array, shape (n_samples,)
Subset of the target values
classes : array, shape (n_classes,)
Classes across all calls to partial_fit.
Can be obtained by via `np.unique(y_all)`, where y_all is the
target vector of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that y doesn't need to contain all labels in `classes`.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed.
Returns
-------
self : returns an instance of self.
"""
if self.class_weight in ['balanced']:
raise ValueError("class_weight '{0}' is not supported for "
"partial_fit. In order to use 'balanced' weights,"
" use compute_class_weight('{0}', 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.".format(self.class_weight))
return self._partial_fit(X, y, alpha=self.alpha, C=1.0, loss=self.loss,
learning_rate=self.learning_rate, n_iter=1,
classes=classes, sample_weight=sample_weight,
coef_init=None, intercept_init=None)
def fit(self, X, y, coef_init=None, intercept_init=None,
sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data
y : numpy array, shape (n_samples,)
Target values
coef_init : array, shape (n_classes, n_features)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (n_classes,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed. These weights will
be multiplied with class_weight (passed through the
constructor) if class_weight is specified
Returns
-------
self : returns an instance of self.
"""
return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init, intercept_init=intercept_init,
sample_weight=sample_weight)
class SGDClassifier(BaseSGDClassifier):
"""Linear classifiers (SVM, logistic regression, a.o.) with SGD training.
This estimator implements regularized linear models with stochastic
gradient descent (SGD) learning: the gradient of the loss is estimated
each sample at a time and the model is updated along the way with a
decreasing strength schedule (aka learning rate). SGD allows minibatch
(online/out-of-core) learning, see the partial_fit method.
For best results using the default learning rate schedule, the data should
have zero mean and unit variance.
This implementation works with data represented as dense or sparse arrays
of floating point values for the features. The model it fits can be
controlled with the loss parameter; by default, it fits a linear support
vector machine (SVM).
The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value because of the regularizer, the
update is truncated to 0.0 to allow for learning sparse models and achieve
online feature selection.
Read more in the :ref:`User Guide <sgd>`.
Parameters
----------
loss : str, 'hinge', 'log', 'modified_huber', 'squared_hinge',\
'perceptron', or a regression loss: 'squared_loss', 'huber',\
'epsilon_insensitive', or 'squared_epsilon_insensitive'
The loss function to be used. Defaults to 'hinge', which gives a
linear SVM.
The 'log' loss gives logistic regression, a probabilistic classifier.
'modified_huber' is another smooth loss that brings tolerance to
outliers as well as probability estimates.
'squared_hinge' is like hinge but is quadratically penalized.
'perceptron' is the linear loss used by the perceptron algorithm.
The other losses are designed for regression but can be useful in
classification as well; see SGDRegressor for a description.
penalty : str, 'none', 'l2', 'l1', or 'elasticnet'
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achievable with 'l2'.
alpha : float
Constant that multiplies the regularization term. Defaults to 0.0001
Also used to compute learning_rate when set to 'optimal'.
l1_ratio : float
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
Defaults to 0.15.
fit_intercept : bool
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.
n_iter : int, optional
The number of passes over the training data (aka epochs). The number
of iterations is set to 1 if using partial_fit.
Defaults to 5.
shuffle : bool, optional
Whether or not the training data should be shuffled after each epoch.
Defaults to True.
random_state : int seed, RandomState instance, or None (default)
The seed of the pseudo random number generator to use when
shuffling the data.
verbose : integer, optional
The verbosity level
epsilon : float
Epsilon in the epsilon-insensitive loss functions; only if `loss` is
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'.
For 'huber', determines the threshold at which it becomes less
important to get the prediction exactly right.
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold.
n_jobs : integer, optional
The number of CPUs to use to do the OVA (One Versus All, for
multi-class problems) computation. -1 means 'all CPUs'. Defaults
to 1.
learning_rate : string, optional
The learning rate schedule:
- 'constant': eta = eta0
- 'optimal': eta = 1.0 / (alpha * (t + t0)) [default]
- 'invscaling': eta = eta0 / pow(t, power_t)
where t0 is chosen by a heuristic proposed by Leon Bottou.
eta0 : double
The initial learning rate for the 'constant' or 'invscaling'
schedules. The default value is 0.0 as eta0 is not used by the
default schedule 'optimal'.
power_t : double
The exponent for inverse scaling learning rate [default 0.5].
class_weight : dict, {class_label: weight} or "balanced" or None, optional
Preset for the class_weight fit parameter.
Weights associated with classes. If not given, all classes
are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
warm_start : bool, optional
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
average : bool or int, optional
When set to True, computes the averaged SGD weights and stores the
result in the ``coef_`` attribute. If set to an int greater than 1,
averaging will begin once the total number of samples seen reaches
average. So ``average=10`` will begin averaging after seeing 10
samples.
Attributes
----------
coef_ : array, shape (1, n_features) if n_classes == 2 else (n_classes,\
n_features)
Weights assigned to the features.
intercept_ : array, shape (1,) if n_classes == 2 else (n_classes,)
Constants in decision function.
loss_function_ : concrete ``LossFunction``
Examples
--------
>>> import numpy as np
>>> from sklearn import linear_model
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> Y = np.array([1, 1, 2, 2])
>>> clf = linear_model.SGDClassifier()
>>> clf.fit(X, Y)
... #doctest: +NORMALIZE_WHITESPACE
SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,
eta0=0.0, fit_intercept=True, l1_ratio=0.15,
learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1,
penalty='l2', power_t=0.5, random_state=None, shuffle=True,
verbose=0, warm_start=False)
>>> print(clf.predict([[-0.8, -1]]))
[1]
See also
--------
LinearSVC, LogisticRegression, Perceptron
"""
def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15,
fit_intercept=True, n_iter=5, shuffle=True, verbose=0,
epsilon=DEFAULT_EPSILON, n_jobs=1, random_state=None,
learning_rate="optimal", eta0=0.0, power_t=0.5,
class_weight=None, warm_start=False, average=False):
super(SGDClassifier, self).__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, n_iter=n_iter, shuffle=shuffle,
verbose=verbose, epsilon=epsilon, n_jobs=n_jobs,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, class_weight=class_weight, warm_start=warm_start,
average=average)
def _check_proba(self):
check_is_fitted(self, "t_")
if self.loss not in ("log", "modified_huber"):
raise AttributeError("probability estimates are not available for"
" loss=%r" % self.loss)
@property
def predict_proba(self):
"""Probability estimates.
This method is only available for log loss and modified Huber loss.
Multiclass probability estimates are derived from binary (one-vs.-rest)
estimates by simple normalization, as recommended by Zadrozny and
Elkan.
Binary probability estimates for loss="modified_huber" are given by
(clip(decision_function(X), -1, 1) + 1) / 2. For other loss functions
it is necessary to perform proper probability calibration by wrapping
the classifier with
:class:`sklearn.calibration.CalibratedClassifierCV` instead.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples, n_classes)
Returns the probability of the sample for each class in the model,
where classes are ordered as they are in `self.classes_`.
References
----------
Zadrozny and Elkan, "Transforming classifier scores into multiclass
probability estimates", SIGKDD'02,
http://www.research.ibm.com/people/z/zadrozny/kdd2002-Transf.pdf
The justification for the formula in the loss="modified_huber"
case is in the appendix B in:
http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
"""
self._check_proba()
return self._predict_proba
def _predict_proba(self, X):
if self.loss == "log":
return self._predict_proba_lr(X)
elif self.loss == "modified_huber":
binary = (len(self.classes_) == 2)
scores = self.decision_function(X)
if binary:
prob2 = np.ones((scores.shape[0], 2))
prob = prob2[:, 1]
else:
prob = scores
np.clip(scores, -1, 1, prob)
prob += 1.
prob /= 2.
if binary:
prob2[:, 0] -= prob
prob = prob2
else:
# the above might assign zero to all classes, which doesn't
# normalize neatly; work around this to produce uniform
# probabilities
prob_sum = prob.sum(axis=1)
all_zero = (prob_sum == 0)
if np.any(all_zero):
prob[all_zero, :] = 1
prob_sum[all_zero] = len(self.classes_)
# normalize
prob /= prob_sum.reshape((prob.shape[0], -1))
return prob
else:
raise NotImplementedError("predict_(log_)proba only supported when"
" loss='log' or loss='modified_huber' "
"(%r given)" % self.loss)
@property
def predict_log_proba(self):
"""Log of probability estimates.
This method is only available for log loss and modified Huber loss.
When loss="modified_huber", probability estimates may be hard zeros
and ones, so taking the logarithm is not possible.
See ``predict_proba`` for details.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Returns
-------
T : array-like, shape (n_samples, n_classes)
Returns the log-probability of the sample for each class in the
model, where classes are ordered as they are in
`self.classes_`.
"""
self._check_proba()
return self._predict_log_proba
def _predict_log_proba(self, X):
return np.log(self.predict_proba(X))
class BaseSGDRegressor(BaseSGD, RegressorMixin):
loss_functions = {
"squared_loss": (SquaredLoss, ),
"huber": (Huber, DEFAULT_EPSILON),
"epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
"squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
DEFAULT_EPSILON),
}
@abstractmethod
def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True,
verbose=0, epsilon=DEFAULT_EPSILON, random_state=None,
learning_rate="invscaling", eta0=0.01, power_t=0.25,
warm_start=False, average=False):
super(BaseSGDRegressor, self).__init__(loss=loss, penalty=penalty,
alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept,
n_iter=n_iter, shuffle=shuffle,
verbose=verbose,
epsilon=epsilon,
random_state=random_state,
learning_rate=learning_rate,
eta0=eta0, power_t=power_t,
warm_start=warm_start,
average=average)
def _partial_fit(self, X, y, alpha, C, loss, learning_rate,
n_iter, sample_weight,
coef_init, intercept_init):
X, y = check_X_y(X, y, "csr", copy=False, order='C', dtype=np.float64)
y = astype(y, np.float64, copy=False)
n_samples, n_features = X.shape
self._validate_params()
# Allocate datastructures from input arguments
sample_weight = self._validate_sample_weight(sample_weight, n_samples)
if getattr(self, "coef_", None) is None:
self._allocate_parameter_mem(1, n_features,
coef_init, intercept_init)
elif n_features != self.coef_.shape[-1]:
raise ValueError("Number of features %d does not match previous "
"data %d." % (n_features, self.coef_.shape[-1]))
if self.average > 0 and getattr(self, "average_coef_", None) is None:
self.average_coef_ = np.zeros(n_features,
dtype=np.float64,
order="C")
self.average_intercept_ = np.zeros(1,
dtype=np.float64,
order="C")
self._fit_regressor(X, y, alpha, C, loss, learning_rate,
sample_weight, n_iter)
return self
def partial_fit(self, X, y, sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of training data
y : numpy array of shape (n_samples,)
Subset of target values
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed.
Returns
-------
self : returns an instance of self.
"""
return self._partial_fit(X, y, self.alpha, C=1.0,
loss=self.loss,
learning_rate=self.learning_rate, n_iter=1,
sample_weight=sample_weight,
coef_init=None, intercept_init=None)
def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None,
intercept_init=None, sample_weight=None):
if self.warm_start and getattr(self, "coef_", None) is not None:
if coef_init is None:
coef_init = self.coef_
if intercept_init is None:
intercept_init = self.intercept_
else:
self.coef_ = None
self.intercept_ = None
if self.average > 0:
self.standard_intercept_ = self.intercept_
self.standard_coef_ = self.coef_
self.average_coef_ = None
self.average_intercept_ = None
# Clear iteration count for multiple call to fit.
self.t_ = 1.0
return self._partial_fit(X, y, alpha, C, loss, learning_rate,
self.n_iter, sample_weight,
coef_init, intercept_init)
def fit(self, X, y, coef_init=None, intercept_init=None,
sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data
y : numpy array, shape (n_samples,)
Target values
coef_init : array, shape (n_features,)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (1,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : returns an instance of self.
"""
return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init,
intercept_init=intercept_init,
sample_weight=sample_weight)
def _decision_function(self, X):
"""Predict using the linear model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples,)
Predicted target values per element in X.
"""
check_is_fitted(self, ["t_", "coef_", "intercept_"], all_or_any=all)
X = check_array(X, accept_sparse='csr')
scores = safe_sparse_dot(X, self.coef_.T,
dense_output=True) + self.intercept_
return scores.ravel()
def predict(self, X):
"""Predict using the linear model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples,)
Predicted target values per element in X.
"""
return self._decision_function(X)
def _fit_regressor(self, X, y, alpha, C, loss, learning_rate,
sample_weight, n_iter):
dataset, intercept_decay = make_dataset(X, y, sample_weight)
loss_function = self._get_loss_function(loss)
penalty_type = self._get_penalty_type(self.penalty)
learning_rate_type = self._get_learning_rate_type(learning_rate)
if not hasattr(self, "t_"):
self.t_ = 1.0
random_state = check_random_state(self.random_state)
# numpy mtrand expects a C long which is a signed 32 bit integer under
# Windows
seed = random_state.randint(0, np.iinfo(np.int32).max)
if self.average > 0:
self.standard_coef_, self.standard_intercept_, \
self.average_coef_, self.average_intercept_ =\
average_sgd(self.standard_coef_,
self.standard_intercept_[0],
self.average_coef_,
self.average_intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
n_iter,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay, self.average)
self.average_intercept_ = np.atleast_1d(self.average_intercept_)
self.standard_intercept_ = np.atleast_1d(self.standard_intercept_)
self.t_ += n_iter * X.shape[0]
if self.average <= self.t_ - 1.0:
self.coef_ = self.average_coef_
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_
self.intercept_ = self.standard_intercept_
else:
self.coef_, self.intercept_ = \
plain_sgd(self.coef_,
self.intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
n_iter,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay)
self.t_ += n_iter * X.shape[0]
self.intercept_ = np.atleast_1d(self.intercept_)
class SGDRegressor(BaseSGDRegressor):
"""Linear model fitted by minimizing a regularized empirical loss with SGD
SGD stands for Stochastic Gradient Descent: the gradient of the loss is
estimated each sample at a time and the model is updated along the way with
a decreasing strength schedule (aka learning rate).
The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value because of the regularizer, the
update is truncated to 0.0 to allow for learning sparse models and achieve
online feature selection.
This implementation works with data represented as dense numpy arrays of
floating point values for the features.
Read more in the :ref:`User Guide <sgd>`.
Parameters
----------
loss : str, 'squared_loss', 'huber', 'epsilon_insensitive', \
or 'squared_epsilon_insensitive'
The loss function to be used. Defaults to 'squared_loss' which refers
to the ordinary least squares fit. 'huber' modifies 'squared_loss' to
focus less on getting outliers correct by switching from squared to
linear loss past a distance of epsilon. 'epsilon_insensitive' ignores
errors less than epsilon and is linear past that; this is the loss
function used in SVR. 'squared_epsilon_insensitive' is the same but
becomes squared loss past a tolerance of epsilon.
penalty : str, 'none', 'l2', 'l1', or 'elasticnet'
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achievable with 'l2'.
alpha : float
Constant that multiplies the regularization term. Defaults to 0.0001
Also used to compute learning_rate when set to 'optimal'.
l1_ratio : float
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
Defaults to 0.15.
fit_intercept : bool
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.
n_iter : int, optional
The number of passes over the training data (aka epochs). The number
of iterations is set to 1 if using partial_fit.
Defaults to 5.
shuffle : bool, optional
Whether or not the training data should be shuffled after each epoch.
Defaults to True.
random_state : int seed, RandomState instance, or None (default)
The seed of the pseudo random number generator to use when
shuffling the data.
verbose : integer, optional
The verbosity level.
epsilon : float
Epsilon in the epsilon-insensitive loss functions; only if `loss` is
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'.
For 'huber', determines the threshold at which it becomes less
important to get the prediction exactly right.
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold.
learning_rate : string, optional
The learning rate schedule:
- 'constant': eta = eta0
- 'optimal': eta = 1.0 / (alpha * (t + t0)) [default]
- 'invscaling': eta = eta0 / pow(t, power_t)
where t0 is chosen by a heuristic proposed by Leon Bottou.
eta0 : double, optional
The initial learning rate [default 0.01].
power_t : double, optional
The exponent for inverse scaling learning rate [default 0.25].
warm_start : bool, optional
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
average : bool or int, optional
When set to True, computes the averaged SGD weights and stores the
result in the ``coef_`` attribute. If set to an int greater than 1,
averaging will begin once the total number of samples seen reaches
average. So ``average=10`` will begin averaging after seeing 10
samples.
Attributes
----------
coef_ : array, shape (n_features,)
Weights assigned to the features.
intercept_ : array, shape (1,)
The intercept term.
average_coef_ : array, shape (n_features,)
Averaged weights assigned to the features.
average_intercept_ : array, shape (1,)
The averaged intercept term.
Examples
--------
>>> import numpy as np
>>> from sklearn import linear_model
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = linear_model.SGDRegressor()
>>> clf.fit(X, y)
... #doctest: +NORMALIZE_WHITESPACE
SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01,
fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',
loss='squared_loss', n_iter=5, penalty='l2', power_t=0.25,
random_state=None, shuffle=True, verbose=0, warm_start=False)
See also
--------
Ridge, ElasticNet, Lasso, SVR
"""
def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True,
verbose=0, epsilon=DEFAULT_EPSILON, random_state=None,
learning_rate="invscaling", eta0=0.01, power_t=0.25,
warm_start=False, average=False):
super(SGDRegressor, self).__init__(loss=loss, penalty=penalty,
alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept,
n_iter=n_iter, shuffle=shuffle,
verbose=verbose,
epsilon=epsilon,
random_state=random_state,
learning_rate=learning_rate,
eta0=eta0, power_t=power_t,
warm_start=warm_start,
average=average)
| bsd-3-clause |
brodeau/aerobulk | python/plot_tests/plot_station_asf.py | 1 | 9926 | #!/usr/bin/env python
# -*- Mode: Python; coding: utf-8; indent-tabs-mode: nil; tab-width: 4 -*-
# Post-diagnostic of STATION_ASF / L. Brodeau, 2019
import sys
from os import path as path
#from string import replace
import math
import numpy as nmp
from netCDF4 import Dataset,num2date
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
reload(sys)
sys.setdefaultencoding('utf8')
cy1 = '2016' ; # First year
cy2 = '2018' ; # Last year
jt0 = 0
jt0 = 17519
dir_figs='.'
size_fig=(13,7)
fig_ext='png'
clr_red = '#AD0000'
clr_blu = '#3749A3'
clr_gre = '#548F64'
clr_sat = '#ffed00'
clr_mod = '#008ab8'
rDPI=200.
L_ALGOS = [ 'COARE3p6' , 'ECMWF' , 'NCAR' ]
l_xtrns = [ '-noskin' , '-noskin' , '' ] ; # string to add to algo name (L_ALGOS) to get version without skin params turned on
l_color = [ '#ffed00' , '#008ab8' , '0.4' ] ; # colors to differentiate algos on the plot
l_width = [ 3 , 2 , 1 ] ; # line-width to differentiate algos on the plot
l_style = [ '-' , '-' , '--' ] ; # line-style
L_VNEM = [ 'qla' , 'qsb' , 'qt' , 'qlw' , 'taum' , 'dt_skin' ]
L_VARO = [ 'Qlat' , 'Qsen' , 'Qnet' , 'Qlw' , 'Tau' , 'dT_skin' ] ; # name of variable on figure
L_VARL = [ r'$Q_{lat}$', r'$Q_{sens}$' , r'$Q_{net}$' , r'$Q_{lw}$' , r'$|\tau|$' , r'$\Delta T_{skin}$' ] ; # name of variable in latex mode
L_VUNT = [ r'$W/m^2$' , r'$W/m^2$' , r'$W/m^2$' , r'$W/m^2$' , r'$N/m^2$' , 'K' ]
L_VMAX = [ 75. , 75. , 800. , 25. , 1.2 , -0.7 ]
L_VMIN = [ -250. , -125. , -400. , -150. , 0. , 0.7 ]
L_ANOM = [ True , True , True , True , True , False ]
#L_VNEM = [ 'qlw' ]
#L_VARO = [ 'Qlw' ] ; # name of variable on figure
#L_VARL = [ r'$Q_{lw}$' ] ; # name of variable in latex mode
#L_VUNT = [ r'$W/m^2$' ]
#L_VMAX = [ 25. ]
#L_VMIN = [ -150. ]
#L_ANOM = [ True ]
nb_algos = len(L_ALGOS) ; print(nb_algos)
# Getting arguments:
narg = len(sys.argv)
if narg != 2:
print 'Usage: '+sys.argv[0]+' <DIR_OUT_SASF>'; sys.exit(0)
cdir_data = sys.argv[1]
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# Populating and checking existence of files to be read
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
def chck4f(cf):
cmesg = 'ERROR: File '+cf+' does not exist !!!'
if not path.exists(cf): print cmesg ; sys.exit(0)
###cf_in = nmp.empty((), dtype="S10")
cf_in = [] ; cf_in_ns = []
for ja in range(nb_algos):
cfi = cdir_data+'/output/'+'STATION_ASF-'+L_ALGOS[ja]+'_1h_'+cy1+'0101_'+cy2+'1231_gridT.nc'
chck4f(cfi)
cf_in.append(cfi)
# Same but without skin params:
for ja in range(nb_algos):
cfi = cdir_data+'/output/'+'STATION_ASF-'+L_ALGOS[ja]+l_xtrns[ja]+'_1h_'+cy1+'0101_'+cy2+'1231_gridT.nc'
chck4f(cfi)
cf_in_ns.append(cfi)
print('Files we are goin to use:')
for ja in range(nb_algos): print(cf_in[ja])
print(' --- same without cool-skin/warm-layer:')
for ja in range(nb_algos): print(cf_in_ns[ja])
#-----------------------------------------------------------------
# Getting time array from the first file:
id_in = Dataset(cf_in[0])
vt = id_in.variables['time_counter'][jt0:]
cunit_t = id_in.variables['time_counter'].units
clndr_t = id_in.variables['time_counter'].calendar
id_in.close()
Nt = len(vt)
print(' "time" => units = '+cunit_t+', calendar = "'+clndr_t+'"')
vtime = num2date(vt, units=cunit_t) ; # something understandable!
ii=Nt/300
ib=max(ii-ii%10,1)
xticks_d=int(30*ib)
font_inf = { 'fontname':'Open Sans', 'fontweight':'normal', 'fontsize':14 }
nb_var = len(L_VNEM)
xF = nmp.zeros((Nt,nb_algos))
xFa = nmp.zeros((Nt,nb_algos))
for ctest in ['skin','noskin']:
for jv in range(nb_var):
print('\n *** Treating variable: '+L_VARO[jv]+' ('+ctest+') !')
for ja in range(nb_algos):
#
if ctest == 'skin': id_in = Dataset(cf_in[ja])
if ctest == 'noskin': id_in = Dataset(cf_in_ns[ja])
xF[:,ja] = id_in.variables[L_VNEM[jv]][jt0:,1,1] # only the center point of the 3x3 spatial domain!
if ja == 0: cvar_lnm = id_in.variables[L_VNEM[jv]].long_name
id_in.close()
fig = plt.figure(num = jv, figsize=size_fig, facecolor='w', edgecolor='k')
ax1 = plt.axes([0.07, 0.22, 0.9, 0.75])
ax1.set_xticks(vtime[::xticks_d])
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))
plt.xticks(rotation='60')
for ja in range(nb_algos):
plt.plot(vtime, xF[:,ja], '-', color=l_color[ja], linestyle=l_style[ja], linewidth=l_width[ja], label=L_ALGOS[ja], zorder=10+ja)
ax1.set_ylim(L_VMIN[jv], L_VMAX[jv]) ; ax1.set_xlim(vtime[0],vtime[Nt-1])
plt.ylabel(L_VARL[jv]+' ['+L_VUNT[jv]+']')
ax1.grid(color='k', linestyle='-', linewidth=0.3)
plt.legend(bbox_to_anchor=(0.45, 0.2), ncol=1, shadow=True, fancybox=True)
ax1.annotate(cvar_lnm+' ('+ctest+')', xy=(0.3, 0.97), xycoords='axes fraction', bbox={'facecolor':'w', 'alpha':1., 'pad':10}, zorder=50, **font_inf)
plt.savefig(L_VARO[jv]+'_'+ctest+'.'+fig_ext, dpi=int(rDPI), transparent=False)
plt.close(jv)
if L_ANOM[jv]:
for ja in range(nb_algos): xFa[:,ja] = xF[:,ja] - nmp.mean(xF,axis=1)
if nmp.sum(xFa[:,:]) == 0.0:
print(' Well! Seems that for variable '+L_VARO[jv]+', choice of algo has no impact a all!')
print(' ==> skipping anomaly plot...')
else:
# Want a symetric y-range that makes sense for the anomaly we're looking at:
rmax = nmp.max(xFa) ; rmin = nmp.min(xFa)
rmax = max( abs(rmax) , abs(rmin) )
romagn = math.floor(math.log(rmax, 10)) ; # order of magnitude of the anomaly we're dealing with
rmlt = 10.**(int(romagn)) / 2.
yrng = math.copysign( math.ceil(abs(rmax)/rmlt)*rmlt , rmax)
#print 'yrng = ', yrng ; #sys.exit(0)
fig = plt.figure(num = 10+jv, figsize=size_fig, facecolor='w', edgecolor='k')
ax1 = plt.axes([0.07, 0.22, 0.9, 0.75])
ax1.set_xticks(vtime[::xticks_d])
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))
plt.xticks(rotation='60')
for ja in range(nb_algos):
plt.plot(vtime, xFa[:,ja], '-', color=l_color[ja], linewidth=l_width[ja], label=L_ALGOS[ja], zorder=10+ja)
ax1.set_ylim(-yrng,yrng) ; ax1.set_xlim(vtime[0],vtime[Nt-1])
plt.ylabel(L_VARL[jv]+' ['+L_VUNT[jv]+']')
ax1.grid(color='k', linestyle='-', linewidth=0.3)
plt.legend(bbox_to_anchor=(0.45, 0.2), ncol=1, shadow=True, fancybox=True)
ax1.annotate('Anomaly of '+cvar_lnm+' ('+ctest+')', xy=(0.3, 0.97), xycoords='axes fraction', bbox={'facecolor':'w', 'alpha':1., 'pad':10}, zorder=50, **font_inf)
plt.savefig(L_VARO[jv]+'_'+ctest+'_anomaly.'+fig_ext, dpi=int(rDPI), transparent=False)
plt.close(10+jv)
# Difference skin vs noskin:
xFns = nmp.zeros((Nt,nb_algos))
for jv in range(nb_var-1):
print('\n *** Treating variable: '+L_VARO[jv]+' ('+ctest+') !')
for ja in range(nb_algos-1):
id_in = Dataset(cf_in[ja])
xF[:,ja] = id_in.variables[L_VNEM[jv]][jt0:,1,1] # only the center point of the 3x3 spatial domain!
if ja == 0: cvar_lnm = id_in.variables[L_VNEM[jv]].long_name
id_in.close()
#
id_in = Dataset(cf_in_ns[ja])
xFns[:,ja] = id_in.variables[L_VNEM[jv]][jt0:,1,1] # only the center point of the 3x3 spatial domain!
if ja == 0: cvar_lnm = id_in.variables[L_VNEM[jv]].long_name
id_in.close()
xFa[:,ja] = xF[:,ja] - xFns[:,ja] ; # difference!
# Want a symetric y-range that makes sense for the anomaly we're looking at:
rmax = nmp.max(xFa) ; rmin = nmp.min(xFa)
rmax = max( abs(rmax) , abs(rmin) )
romagn = math.floor(math.log(rmax, 10)) ; # order of magnitude of the anomaly we're dealing with
rmlt = 10.**(int(romagn)) / 2.
yrng = math.copysign( math.ceil(abs(rmax)/rmlt)*rmlt , rmax)
print 'yrng = ', yrng ; #sys.exit(0)
for ja in range(nb_algos-1):
calgo = L_ALGOS[ja]
if nmp.sum(xFa[:,ja]) == 0.0:
print(' Well! Seems that for variable '+L_VARO[jv]+', and algo '+calgo+', skin param has no impact')
print(' ==> skipping difference plot...')
else:
fig = plt.figure(num = jv, figsize=size_fig, facecolor='w', edgecolor='k')
ax1 = plt.axes([0.07, 0.22, 0.9, 0.75])
ax1.set_xticks(vtime[::xticks_d])
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))
plt.xticks(rotation='60')
plt.plot(vtime, xFa[:,ja], '-', color=l_color[ja], linestyle=l_style[ja], linewidth=l_width[ja], label=None, zorder=10+ja)
ax1.set_ylim(-yrng,yrng) ; ax1.set_xlim(vtime[0],vtime[Nt-1])
plt.ylabel(L_VARL[jv]+' ['+L_VUNT[jv]+']')
ax1.grid(color='k', linestyle='-', linewidth=0.3)
#plt.legend(bbox_to_anchor=(0.45, 0.2), ncol=1, shadow=True, fancybox=True)
ax1.annotate(cvar_lnm+' ('+ctest+')', xy=(0.3, 0.97), xycoords='axes fraction', bbox={'facecolor':'w', 'alpha':1., 'pad':10}, zorder=50, **font_inf)
plt.savefig('diff_skin-noskin_'+L_VARO[jv]+'_'+calgo+'_'+ctest+'.'+fig_ext, dpi=int(rDPI), transparent=False)
plt.close(jv)
| gpl-3.0 |
ZENGXH/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`` function, then
shuffled and passed to the Spectral Biclustering algorithm. The rows
and columns of the shuffled matrix are rearranged to show the
biclusters found by the algorithm.
The outer product of the row and column label vectors shows a
representation of the checkerboard structure.
"""
print(__doc__)
# Author: Kemal Eren <kemal@kemaleren.com>
# License: BSD 3 clause
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import make_checkerboard
from sklearn.datasets import samples_generator as sg
from sklearn.cluster.bicluster import SpectralBiclustering
from sklearn.metrics import consensus_score
n_clusters = (4, 3)
data, rows, columns = make_checkerboard(
shape=(300, 300), n_clusters=n_clusters, noise=10,
shuffle=False, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Original dataset")
data, row_idx, col_idx = sg._shuffle(data, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Shuffled dataset")
model = SpectralBiclustering(n_clusters=n_clusters, method='log',
random_state=0)
model.fit(data)
score = consensus_score(model.biclusters_,
(rows[:, row_idx], columns[:, col_idx]))
print("consensus score: {:.1f}".format(score))
fit_data = data[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.title("After biclustering; rearranged to show biclusters")
plt.matshow(np.outer(np.sort(model.row_labels_) + 1,
np.sort(model.column_labels_) + 1),
cmap=plt.cm.Blues)
plt.title("Checkerboard structure of rearranged data")
plt.show()
| bsd-3-clause |
cwu2011/scikit-learn | sklearn/preprocessing/__init__.py | 14 | 1184 | """
The :mod:`sklearn.preprocessing` module includes scaling, centering,
normalization, binarization and imputation methods.
"""
from .data import Binarizer
from .data import KernelCenterer
from .data import MinMaxScaler
from .data import MaxAbsScaler
from .data import Normalizer
from .data import RobustScaler
from .data import StandardScaler
from .data import add_dummy_feature
from .data import binarize
from .data import normalize
from .data import scale
from .data import robust_scale
from .data import maxabs_scale
from .data import OneHotEncoder
from .data import PolynomialFeatures
from .label import label_binarize
from .label import LabelBinarizer
from .label import LabelEncoder
from .label import MultiLabelBinarizer
from .imputation import Imputer
__all__ = [
'Binarizer',
'Imputer',
'KernelCenterer',
'LabelBinarizer',
'LabelEncoder',
'MultiLabelBinarizer',
'MinMaxScaler',
'MaxAbsScaler',
'Normalizer',
'OneHotEncoder',
'RobustScaler',
'StandardScaler',
'add_dummy_feature',
'PolynomialFeatures',
'binarize',
'normalize',
'scale',
'robust_scale',
'maxabs_scale',
'label_binarize',
]
| bsd-3-clause |
cuttlefishh/emp | code/01-metadata/metadata_template_generator.py | 1 | 4911 | #!/usr/bin/env python
import click
import pandas as pd
import re
# Hard-coded variables
investigation_type = 'metagenome'
# Function: return dataframe of environmental package-specific metadata items
# A single environmental package (soil) or list can be provided (soil,water).
def show_items_of_env_pkg(df_env_pkg, list_of_env_pkg):
"""Return dataframe of environmental package-specific metadata items"""
df_items = df_env_pkg[df_env_pkg['Environmental package'].isin(list_of_env_pkg)]
return df_items
# Function: return dataframe of metadata template
def create_template_for_env_pkg(df_QiitaEBI, df_MIMS, df_env_pkg, list_of_env_pkg, number_of_samples, sample_prefix):
"""Return dataframe of metadata template"""
# get headers/requirement/example of Qiita-EBI/MIMS/env_pkg columns
pkg_items = show_items_of_env_pkg(df_env_pkg, list_of_env_pkg)
headers_env_pkg = pkg_items['Structured comment name'].values
require_env_pkg = pkg_items['Requirement']
example_env_pkg = pkg_items['Value syntax']
headers_all = list(df_QiitaEBI.iloc[0]) + list(df_MIMS.iloc[0]) + list(headers_env_pkg)
require_all = pd.concat([df_QiitaEBI.iloc[1], df_MIMS.iloc[1], require_env_pkg])
example_all = pd.concat([df_QiitaEBI.iloc[2], df_MIMS.iloc[2], example_env_pkg])
# populate template dataframe
df_template = pd.DataFrame(columns=headers_all, dtype=object)
df_template.loc['Requirement'] = require_all.values
df_template.loc['Format'] = example_all.values
string_of_env_pkg = re.sub(r'\W', '.', '.'.join(list_of_env_pkg))
for i in range(0, number_of_samples):
df_template.loc[i+1] = ['' for x in range(len(df_template.columns))]
df_template.loc[i+1]['sample_name'] = '%s.%s.%s' % (sample_prefix, string_of_env_pkg, i+1)
df_template.loc[i+1]['investigation_type'] = investigation_type
df_template.loc[i+1]['env_package'] = ' or '.join(list_of_env_pkg)
return df_template
@click.command()
@click.option('--qiita_ebi_mims_path', required=True, type=click.Path(resolve_path=True, readable=True, exists=True), help='Excel file with Qiita/EBI and MIMS required fields. Example: Qiita_EBI_MIMS_v1.xlsx')
@click.option('--migs_mims_path', required=True, type=click.Path(resolve_path=True, readable=True, exists=True), help='Excel file with MIxS standards. Example: MIGS_MIMS_v4.xls')
@click.option('--list_of_env_pkg', required=True, type=click.STRING, help="One (recommended) or more (separated by commas) environmental package. Choose from: air, built environment, host-associated, human-associated, human-skin, human-oral, human-gut, human-vaginal, microbial mat/biofilm, misc environment, plant-associated, sediment, soil, wastewater/sludge, water")
@click.option('--number_of_samples', required=True, type=click.INT, help='Number of samples (per environmental package) to create rows for in the template')
@click.option('--sample_prefix', required=True, type=click.STRING, help='Prefix string to prepend to sample numbers in row indexes. Example: Metcalf40 (EMP500 PI and study number)')
# Main function: generate metadata template and readme csv files
def generate_metadata_template(qiita_ebi_mims_path, migs_mims_path, list_of_env_pkg, number_of_samples, sample_prefix):
"""Generate metadata template and readme csv files"""
# Qiita/EBI/MIMS Excel file to DataFrames
df_QiitaEBI = pd.read_excel(qiita_ebi_mims_path, sheetname='QiitaEBI', header=None)
df_MIMS = pd.read_excel(qiita_ebi_mims_path, sheetname='MIMS', header=None)
list_of_env_pkg = list_of_env_pkg.split(",")
# MIGS/MIMS Excel file to DataFrames
df_README = pd.read_excel(migs_mims_path, sheetname='README', header=None)
df_MIGS_MIMS = pd.read_excel(migs_mims_path, sheetname='MIGS_MIMS', header=0, index_col=0)
df_env_pkg = pd.read_excel(migs_mims_path, sheetname='environmental_packages', header=0)
# generate template file
df_template = create_template_for_env_pkg(df_QiitaEBI, df_MIMS, df_env_pkg, list_of_env_pkg, number_of_samples, sample_prefix)
string_of_env_pkg = re.sub(r'\W', '_', '_'.join(list_of_env_pkg))
df_template.to_csv('%s_%s_%s_samples.csv' % (sample_prefix, string_of_env_pkg, number_of_samples), index_label='index')
# generate info file
df_MIMS_select = df_MIGS_MIMS[df_MIGS_MIMS.Section.isin(['investigation', 'environment', 'migs/mims/mimarks extension'])]
df_MIMS_select.to_csv('README_MIMS_metadata.csv')
df_env_pkg_select = show_items_of_env_pkg(df_env_pkg, list_of_env_pkg)
del df_env_pkg_select['Environmental package']
df_env_pkg_select.set_index('Structured comment name', inplace=True)
string_of_env_pkg = re.sub(r'\W', '_', '_'.join(list_of_env_pkg))
df_env_pkg_select.to_csv('README_%s_metadata.csv' % string_of_env_pkg)
# Execute main function
if __name__ == '__main__':
generate_metadata_template()
| bsd-3-clause |
NixaSoftware/CVis | venv/lib/python2.7/site-packages/pandas/tests/frame/test_replace.py | 15 | 43479 | # -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
from datetime import datetime
import re
from pandas.compat import (zip, range, lrange, StringIO)
from pandas import (DataFrame, Series, Index, date_range, compat,
Timestamp)
import pandas as pd
from numpy import nan
import numpy as np
from pandas.util.testing import (assert_series_equal,
assert_frame_equal)
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData
class TestDataFrameReplace(TestData):
def test_replace_inplace(self):
self.tsframe['A'][:5] = nan
self.tsframe['A'][-5:] = nan
tsframe = self.tsframe.copy()
tsframe.replace(nan, 0, inplace=True)
assert_frame_equal(tsframe, self.tsframe.fillna(0))
pytest.raises(TypeError, self.tsframe.replace, nan, inplace=True)
pytest.raises(TypeError, self.tsframe.replace, nan)
# mixed type
mf = self.mixed_frame
mf.iloc[5:20, mf.columns.get_loc('foo')] = nan
mf.iloc[-10:, mf.columns.get_loc('A')] = nan
result = self.mixed_frame.replace(np.nan, 0)
expected = self.mixed_frame.fillna(value=0)
assert_frame_equal(result, expected)
tsframe = self.tsframe.copy()
tsframe.replace([nan], [0], inplace=True)
assert_frame_equal(tsframe, self.tsframe.fillna(0))
def test_regex_replace_scalar(self):
obj = {'a': list('ab..'), 'b': list('efgh')}
dfobj = DataFrame(obj)
mix = {'a': lrange(4), 'b': list('ab..')}
dfmix = DataFrame(mix)
# simplest cases
# regex -> value
# obj frame
res = dfobj.replace(r'\s*\.\s*', nan, regex=True)
assert_frame_equal(dfobj, res.fillna('.'))
# mixed
res = dfmix.replace(r'\s*\.\s*', nan, regex=True)
assert_frame_equal(dfmix, res.fillna('.'))
# regex -> regex
# obj frame
res = dfobj.replace(r'\s*(\.)\s*', r'\1\1\1', regex=True)
objc = obj.copy()
objc['a'] = ['a', 'b', '...', '...']
expec = DataFrame(objc)
assert_frame_equal(res, expec)
# with mixed
res = dfmix.replace(r'\s*(\.)\s*', r'\1\1\1', regex=True)
mixc = mix.copy()
mixc['b'] = ['a', 'b', '...', '...']
expec = DataFrame(mixc)
assert_frame_equal(res, expec)
# everything with compiled regexs as well
res = dfobj.replace(re.compile(r'\s*\.\s*'), nan, regex=True)
assert_frame_equal(dfobj, res.fillna('.'))
# mixed
res = dfmix.replace(re.compile(r'\s*\.\s*'), nan, regex=True)
assert_frame_equal(dfmix, res.fillna('.'))
# regex -> regex
# obj frame
res = dfobj.replace(re.compile(r'\s*(\.)\s*'), r'\1\1\1')
objc = obj.copy()
objc['a'] = ['a', 'b', '...', '...']
expec = DataFrame(objc)
assert_frame_equal(res, expec)
# with mixed
res = dfmix.replace(re.compile(r'\s*(\.)\s*'), r'\1\1\1')
mixc = mix.copy()
mixc['b'] = ['a', 'b', '...', '...']
expec = DataFrame(mixc)
assert_frame_equal(res, expec)
res = dfmix.replace(regex=re.compile(r'\s*(\.)\s*'), value=r'\1\1\1')
mixc = mix.copy()
mixc['b'] = ['a', 'b', '...', '...']
expec = DataFrame(mixc)
assert_frame_equal(res, expec)
res = dfmix.replace(regex=r'\s*(\.)\s*', value=r'\1\1\1')
mixc = mix.copy()
mixc['b'] = ['a', 'b', '...', '...']
expec = DataFrame(mixc)
assert_frame_equal(res, expec)
def test_regex_replace_scalar_inplace(self):
obj = {'a': list('ab..'), 'b': list('efgh')}
dfobj = DataFrame(obj)
mix = {'a': lrange(4), 'b': list('ab..')}
dfmix = DataFrame(mix)
# simplest cases
# regex -> value
# obj frame
res = dfobj.copy()
res.replace(r'\s*\.\s*', nan, regex=True, inplace=True)
assert_frame_equal(dfobj, res.fillna('.'))
# mixed
res = dfmix.copy()
res.replace(r'\s*\.\s*', nan, regex=True, inplace=True)
assert_frame_equal(dfmix, res.fillna('.'))
# regex -> regex
# obj frame
res = dfobj.copy()
res.replace(r'\s*(\.)\s*', r'\1\1\1', regex=True, inplace=True)
objc = obj.copy()
objc['a'] = ['a', 'b', '...', '...']
expec = DataFrame(objc)
assert_frame_equal(res, expec)
# with mixed
res = dfmix.copy()
res.replace(r'\s*(\.)\s*', r'\1\1\1', regex=True, inplace=True)
mixc = mix.copy()
mixc['b'] = ['a', 'b', '...', '...']
expec = DataFrame(mixc)
assert_frame_equal(res, expec)
# everything with compiled regexs as well
res = dfobj.copy()
res.replace(re.compile(r'\s*\.\s*'), nan, regex=True, inplace=True)
assert_frame_equal(dfobj, res.fillna('.'))
# mixed
res = dfmix.copy()
res.replace(re.compile(r'\s*\.\s*'), nan, regex=True, inplace=True)
assert_frame_equal(dfmix, res.fillna('.'))
# regex -> regex
# obj frame
res = dfobj.copy()
res.replace(re.compile(r'\s*(\.)\s*'), r'\1\1\1', regex=True,
inplace=True)
objc = obj.copy()
objc['a'] = ['a', 'b', '...', '...']
expec = DataFrame(objc)
assert_frame_equal(res, expec)
# with mixed
res = dfmix.copy()
res.replace(re.compile(r'\s*(\.)\s*'), r'\1\1\1', regex=True,
inplace=True)
mixc = mix.copy()
mixc['b'] = ['a', 'b', '...', '...']
expec = DataFrame(mixc)
assert_frame_equal(res, expec)
res = dfobj.copy()
res.replace(regex=r'\s*\.\s*', value=nan, inplace=True)
assert_frame_equal(dfobj, res.fillna('.'))
# mixed
res = dfmix.copy()
res.replace(regex=r'\s*\.\s*', value=nan, inplace=True)
assert_frame_equal(dfmix, res.fillna('.'))
# regex -> regex
# obj frame
res = dfobj.copy()
res.replace(regex=r'\s*(\.)\s*', value=r'\1\1\1', inplace=True)
objc = obj.copy()
objc['a'] = ['a', 'b', '...', '...']
expec = DataFrame(objc)
assert_frame_equal(res, expec)
# with mixed
res = dfmix.copy()
res.replace(regex=r'\s*(\.)\s*', value=r'\1\1\1', inplace=True)
mixc = mix.copy()
mixc['b'] = ['a', 'b', '...', '...']
expec = DataFrame(mixc)
assert_frame_equal(res, expec)
# everything with compiled regexs as well
res = dfobj.copy()
res.replace(regex=re.compile(r'\s*\.\s*'), value=nan, inplace=True)
assert_frame_equal(dfobj, res.fillna('.'))
# mixed
res = dfmix.copy()
res.replace(regex=re.compile(r'\s*\.\s*'), value=nan, inplace=True)
assert_frame_equal(dfmix, res.fillna('.'))
# regex -> regex
# obj frame
res = dfobj.copy()
res.replace(regex=re.compile(r'\s*(\.)\s*'), value=r'\1\1\1',
inplace=True)
objc = obj.copy()
objc['a'] = ['a', 'b', '...', '...']
expec = DataFrame(objc)
assert_frame_equal(res, expec)
# with mixed
res = dfmix.copy()
res.replace(regex=re.compile(r'\s*(\.)\s*'), value=r'\1\1\1',
inplace=True)
mixc = mix.copy()
mixc['b'] = ['a', 'b', '...', '...']
expec = DataFrame(mixc)
assert_frame_equal(res, expec)
def test_regex_replace_list_obj(self):
obj = {'a': list('ab..'), 'b': list('efgh'), 'c': list('helo')}
dfobj = DataFrame(obj)
# lists of regexes and values
# list of [re1, re2, ..., reN] -> [v1, v2, ..., vN]
to_replace_res = [r'\s*\.\s*', r'e|f|g']
values = [nan, 'crap']
res = dfobj.replace(to_replace_res, values, regex=True)
expec = DataFrame({'a': ['a', 'b', nan, nan], 'b': ['crap'] * 3 +
['h'], 'c': ['h', 'crap', 'l', 'o']})
assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [re1, re2, .., reN]
to_replace_res = [r'\s*(\.)\s*', r'(e|f|g)']
values = [r'\1\1', r'\1_crap']
res = dfobj.replace(to_replace_res, values, regex=True)
expec = DataFrame({'a': ['a', 'b', '..', '..'], 'b': ['e_crap',
'f_crap',
'g_crap', 'h'],
'c': ['h', 'e_crap', 'l', 'o']})
assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN
# or vN)]
to_replace_res = [r'\s*(\.)\s*', r'e']
values = [r'\1\1', r'crap']
res = dfobj.replace(to_replace_res, values, regex=True)
expec = DataFrame({'a': ['a', 'b', '..', '..'], 'b': ['crap', 'f', 'g',
'h'],
'c': ['h', 'crap', 'l', 'o']})
assert_frame_equal(res, expec)
to_replace_res = [r'\s*(\.)\s*', r'e']
values = [r'\1\1', r'crap']
res = dfobj.replace(value=values, regex=to_replace_res)
expec = DataFrame({'a': ['a', 'b', '..', '..'], 'b': ['crap', 'f', 'g',
'h'],
'c': ['h', 'crap', 'l', 'o']})
assert_frame_equal(res, expec)
def test_regex_replace_list_obj_inplace(self):
# same as above with inplace=True
# lists of regexes and values
obj = {'a': list('ab..'), 'b': list('efgh'), 'c': list('helo')}
dfobj = DataFrame(obj)
# lists of regexes and values
# list of [re1, re2, ..., reN] -> [v1, v2, ..., vN]
to_replace_res = [r'\s*\.\s*', r'e|f|g']
values = [nan, 'crap']
res = dfobj.copy()
res.replace(to_replace_res, values, inplace=True, regex=True)
expec = DataFrame({'a': ['a', 'b', nan, nan], 'b': ['crap'] * 3 +
['h'], 'c': ['h', 'crap', 'l', 'o']})
assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [re1, re2, .., reN]
to_replace_res = [r'\s*(\.)\s*', r'(e|f|g)']
values = [r'\1\1', r'\1_crap']
res = dfobj.copy()
res.replace(to_replace_res, values, inplace=True, regex=True)
expec = DataFrame({'a': ['a', 'b', '..', '..'], 'b': ['e_crap',
'f_crap',
'g_crap', 'h'],
'c': ['h', 'e_crap', 'l', 'o']})
assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN
# or vN)]
to_replace_res = [r'\s*(\.)\s*', r'e']
values = [r'\1\1', r'crap']
res = dfobj.copy()
res.replace(to_replace_res, values, inplace=True, regex=True)
expec = DataFrame({'a': ['a', 'b', '..', '..'], 'b': ['crap', 'f', 'g',
'h'],
'c': ['h', 'crap', 'l', 'o']})
assert_frame_equal(res, expec)
to_replace_res = [r'\s*(\.)\s*', r'e']
values = [r'\1\1', r'crap']
res = dfobj.copy()
res.replace(value=values, regex=to_replace_res, inplace=True)
expec = DataFrame({'a': ['a', 'b', '..', '..'], 'b': ['crap', 'f', 'g',
'h'],
'c': ['h', 'crap', 'l', 'o']})
assert_frame_equal(res, expec)
def test_regex_replace_list_mixed(self):
# mixed frame to make sure this doesn't break things
mix = {'a': lrange(4), 'b': list('ab..')}
dfmix = DataFrame(mix)
# lists of regexes and values
# list of [re1, re2, ..., reN] -> [v1, v2, ..., vN]
to_replace_res = [r'\s*\.\s*', r'a']
values = [nan, 'crap']
mix2 = {'a': lrange(4), 'b': list('ab..'), 'c': list('halo')}
dfmix2 = DataFrame(mix2)
res = dfmix2.replace(to_replace_res, values, regex=True)
expec = DataFrame({'a': mix2['a'], 'b': ['crap', 'b', nan, nan],
'c': ['h', 'crap', 'l', 'o']})
assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [re1, re2, .., reN]
to_replace_res = [r'\s*(\.)\s*', r'(a|b)']
values = [r'\1\1', r'\1_crap']
res = dfmix.replace(to_replace_res, values, regex=True)
expec = DataFrame({'a': mix['a'], 'b': ['a_crap', 'b_crap', '..',
'..']})
assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN
# or vN)]
to_replace_res = [r'\s*(\.)\s*', r'a', r'(b)']
values = [r'\1\1', r'crap', r'\1_crap']
res = dfmix.replace(to_replace_res, values, regex=True)
expec = DataFrame({'a': mix['a'], 'b': ['crap', 'b_crap', '..', '..']})
assert_frame_equal(res, expec)
to_replace_res = [r'\s*(\.)\s*', r'a', r'(b)']
values = [r'\1\1', r'crap', r'\1_crap']
res = dfmix.replace(regex=to_replace_res, value=values)
expec = DataFrame({'a': mix['a'], 'b': ['crap', 'b_crap', '..', '..']})
assert_frame_equal(res, expec)
def test_regex_replace_list_mixed_inplace(self):
mix = {'a': lrange(4), 'b': list('ab..')}
dfmix = DataFrame(mix)
# the same inplace
# lists of regexes and values
# list of [re1, re2, ..., reN] -> [v1, v2, ..., vN]
to_replace_res = [r'\s*\.\s*', r'a']
values = [nan, 'crap']
res = dfmix.copy()
res.replace(to_replace_res, values, inplace=True, regex=True)
expec = DataFrame({'a': mix['a'], 'b': ['crap', 'b', nan, nan]})
assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [re1, re2, .., reN]
to_replace_res = [r'\s*(\.)\s*', r'(a|b)']
values = [r'\1\1', r'\1_crap']
res = dfmix.copy()
res.replace(to_replace_res, values, inplace=True, regex=True)
expec = DataFrame({'a': mix['a'], 'b': ['a_crap', 'b_crap', '..',
'..']})
assert_frame_equal(res, expec)
# list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN
# or vN)]
to_replace_res = [r'\s*(\.)\s*', r'a', r'(b)']
values = [r'\1\1', r'crap', r'\1_crap']
res = dfmix.copy()
res.replace(to_replace_res, values, inplace=True, regex=True)
expec = DataFrame({'a': mix['a'], 'b': ['crap', 'b_crap', '..', '..']})
assert_frame_equal(res, expec)
to_replace_res = [r'\s*(\.)\s*', r'a', r'(b)']
values = [r'\1\1', r'crap', r'\1_crap']
res = dfmix.copy()
res.replace(regex=to_replace_res, value=values, inplace=True)
expec = DataFrame({'a': mix['a'], 'b': ['crap', 'b_crap', '..', '..']})
assert_frame_equal(res, expec)
def test_regex_replace_dict_mixed(self):
mix = {'a': lrange(4), 'b': list('ab..'), 'c': ['a', 'b', nan, 'd']}
dfmix = DataFrame(mix)
# dicts
# single dict {re1: v1}, search the whole frame
# need test for this...
# list of dicts {re1: v1, re2: v2, ..., re3: v3}, search the whole
# frame
res = dfmix.replace({'b': r'\s*\.\s*'}, {'b': nan}, regex=True)
res2 = dfmix.copy()
res2.replace({'b': r'\s*\.\s*'}, {'b': nan}, inplace=True, regex=True)
expec = DataFrame({'a': mix['a'], 'b': ['a', 'b', nan, nan], 'c':
mix['c']})
assert_frame_equal(res, expec)
assert_frame_equal(res2, expec)
# list of dicts {re1: re11, re2: re12, ..., reN: re1N}, search the
# whole frame
res = dfmix.replace({'b': r'\s*(\.)\s*'}, {'b': r'\1ty'}, regex=True)
res2 = dfmix.copy()
res2.replace({'b': r'\s*(\.)\s*'}, {'b': r'\1ty'}, inplace=True,
regex=True)
expec = DataFrame({'a': mix['a'], 'b': ['a', 'b', '.ty', '.ty'], 'c':
mix['c']})
assert_frame_equal(res, expec)
assert_frame_equal(res2, expec)
res = dfmix.replace(regex={'b': r'\s*(\.)\s*'}, value={'b': r'\1ty'})
res2 = dfmix.copy()
res2.replace(regex={'b': r'\s*(\.)\s*'}, value={'b': r'\1ty'},
inplace=True)
expec = DataFrame({'a': mix['a'], 'b': ['a', 'b', '.ty', '.ty'], 'c':
mix['c']})
assert_frame_equal(res, expec)
assert_frame_equal(res2, expec)
# scalar -> dict
# to_replace regex, {value: value}
expec = DataFrame({'a': mix['a'], 'b': [nan, 'b', '.', '.'], 'c':
mix['c']})
res = dfmix.replace('a', {'b': nan}, regex=True)
res2 = dfmix.copy()
res2.replace('a', {'b': nan}, regex=True, inplace=True)
assert_frame_equal(res, expec)
assert_frame_equal(res2, expec)
res = dfmix.replace('a', {'b': nan}, regex=True)
res2 = dfmix.copy()
res2.replace(regex='a', value={'b': nan}, inplace=True)
expec = DataFrame({'a': mix['a'], 'b': [nan, 'b', '.', '.'], 'c':
mix['c']})
assert_frame_equal(res, expec)
assert_frame_equal(res2, expec)
def test_regex_replace_dict_nested(self):
# nested dicts will not work until this is implemented for Series
mix = {'a': lrange(4), 'b': list('ab..'), 'c': ['a', 'b', nan, 'd']}
dfmix = DataFrame(mix)
res = dfmix.replace({'b': {r'\s*\.\s*': nan}}, regex=True)
res2 = dfmix.copy()
res4 = dfmix.copy()
res2.replace({'b': {r'\s*\.\s*': nan}}, inplace=True, regex=True)
res3 = dfmix.replace(regex={'b': {r'\s*\.\s*': nan}})
res4.replace(regex={'b': {r'\s*\.\s*': nan}}, inplace=True)
expec = DataFrame({'a': mix['a'], 'b': ['a', 'b', nan, nan], 'c':
mix['c']})
assert_frame_equal(res, expec)
assert_frame_equal(res2, expec)
assert_frame_equal(res3, expec)
assert_frame_equal(res4, expec)
def test_regex_replace_dict_nested_gh4115(self):
df = pd.DataFrame({'Type': ['Q', 'T', 'Q', 'Q', 'T'], 'tmp': 2})
expected = DataFrame({'Type': [0, 1, 0, 0, 1], 'tmp': 2})
result = df.replace({'Type': {'Q': 0, 'T': 1}})
assert_frame_equal(result, expected)
def test_regex_replace_list_to_scalar(self):
mix = {'a': lrange(4), 'b': list('ab..'), 'c': ['a', 'b', nan, 'd']}
df = DataFrame(mix)
expec = DataFrame({'a': mix['a'], 'b': np.array([nan] * 4),
'c': [nan, nan, nan, 'd']})
res = df.replace([r'\s*\.\s*', 'a|b'], nan, regex=True)
res2 = df.copy()
res3 = df.copy()
res2.replace([r'\s*\.\s*', 'a|b'], nan, regex=True, inplace=True)
res3.replace(regex=[r'\s*\.\s*', 'a|b'], value=nan, inplace=True)
assert_frame_equal(res, expec)
assert_frame_equal(res2, expec)
assert_frame_equal(res3, expec)
def test_regex_replace_str_to_numeric(self):
# what happens when you try to replace a numeric value with a regex?
mix = {'a': lrange(4), 'b': list('ab..'), 'c': ['a', 'b', nan, 'd']}
df = DataFrame(mix)
res = df.replace(r'\s*\.\s*', 0, regex=True)
res2 = df.copy()
res2.replace(r'\s*\.\s*', 0, inplace=True, regex=True)
res3 = df.copy()
res3.replace(regex=r'\s*\.\s*', value=0, inplace=True)
expec = DataFrame({'a': mix['a'], 'b': ['a', 'b', 0, 0], 'c':
mix['c']})
assert_frame_equal(res, expec)
assert_frame_equal(res2, expec)
assert_frame_equal(res3, expec)
def test_regex_replace_regex_list_to_numeric(self):
mix = {'a': lrange(4), 'b': list('ab..'), 'c': ['a', 'b', nan, 'd']}
df = DataFrame(mix)
res = df.replace([r'\s*\.\s*', 'b'], 0, regex=True)
res2 = df.copy()
res2.replace([r'\s*\.\s*', 'b'], 0, regex=True, inplace=True)
res3 = df.copy()
res3.replace(regex=[r'\s*\.\s*', 'b'], value=0, inplace=True)
expec = DataFrame({'a': mix['a'], 'b': ['a', 0, 0, 0], 'c': ['a', 0,
nan,
'd']})
assert_frame_equal(res, expec)
assert_frame_equal(res2, expec)
assert_frame_equal(res3, expec)
def test_regex_replace_series_of_regexes(self):
mix = {'a': lrange(4), 'b': list('ab..'), 'c': ['a', 'b', nan, 'd']}
df = DataFrame(mix)
s1 = Series({'b': r'\s*\.\s*'})
s2 = Series({'b': nan})
res = df.replace(s1, s2, regex=True)
res2 = df.copy()
res2.replace(s1, s2, inplace=True, regex=True)
res3 = df.copy()
res3.replace(regex=s1, value=s2, inplace=True)
expec = DataFrame({'a': mix['a'], 'b': ['a', 'b', nan, nan], 'c':
mix['c']})
assert_frame_equal(res, expec)
assert_frame_equal(res2, expec)
assert_frame_equal(res3, expec)
def test_regex_replace_numeric_to_object_conversion(self):
mix = {'a': lrange(4), 'b': list('ab..'), 'c': ['a', 'b', nan, 'd']}
df = DataFrame(mix)
expec = DataFrame({'a': ['a', 1, 2, 3], 'b': mix['b'], 'c': mix['c']})
res = df.replace(0, 'a')
assert_frame_equal(res, expec)
assert res.a.dtype == np.object_
def test_replace_regex_metachar(self):
metachars = '[]', '()', r'\d', r'\w', r'\s'
for metachar in metachars:
df = DataFrame({'a': [metachar, 'else']})
result = df.replace({'a': {metachar: 'paren'}})
expected = DataFrame({'a': ['paren', 'else']})
assert_frame_equal(result, expected)
def test_replace(self):
self.tsframe['A'][:5] = nan
self.tsframe['A'][-5:] = nan
zero_filled = self.tsframe.replace(nan, -1e8)
assert_frame_equal(zero_filled, self.tsframe.fillna(-1e8))
assert_frame_equal(zero_filled.replace(-1e8, nan), self.tsframe)
self.tsframe['A'][:5] = nan
self.tsframe['A'][-5:] = nan
self.tsframe['B'][:5] = -1e8
# empty
df = DataFrame(index=['a', 'b'])
assert_frame_equal(df, df.replace(5, 7))
# GH 11698
# test for mixed data types.
df = pd.DataFrame([('-', pd.to_datetime('20150101')),
('a', pd.to_datetime('20150102'))])
df1 = df.replace('-', np.nan)
expected_df = pd.DataFrame([(np.nan, pd.to_datetime('20150101')),
('a', pd.to_datetime('20150102'))])
assert_frame_equal(df1, expected_df)
def test_replace_list(self):
obj = {'a': list('ab..'), 'b': list('efgh'), 'c': list('helo')}
dfobj = DataFrame(obj)
# lists of regexes and values
# list of [v1, v2, ..., vN] -> [v1, v2, ..., vN]
to_replace_res = [r'.', r'e']
values = [nan, 'crap']
res = dfobj.replace(to_replace_res, values)
expec = DataFrame({'a': ['a', 'b', nan, nan],
'b': ['crap', 'f', 'g', 'h'], 'c': ['h', 'crap',
'l', 'o']})
assert_frame_equal(res, expec)
# list of [v1, v2, ..., vN] -> [v1, v2, .., vN]
to_replace_res = [r'.', r'f']
values = [r'..', r'crap']
res = dfobj.replace(to_replace_res, values)
expec = DataFrame({'a': ['a', 'b', '..', '..'], 'b': ['e', 'crap', 'g',
'h'],
'c': ['h', 'e', 'l', 'o']})
assert_frame_equal(res, expec)
def test_replace_series_dict(self):
# from GH 3064
df = DataFrame({'zero': {'a': 0.0, 'b': 1}, 'one': {'a': 2.0, 'b': 0}})
result = df.replace(0, {'zero': 0.5, 'one': 1.0})
expected = DataFrame(
{'zero': {'a': 0.5, 'b': 1}, 'one': {'a': 2.0, 'b': 1.0}})
assert_frame_equal(result, expected)
result = df.replace(0, df.mean())
assert_frame_equal(result, expected)
# series to series/dict
df = DataFrame({'zero': {'a': 0.0, 'b': 1}, 'one': {'a': 2.0, 'b': 0}})
s = Series({'zero': 0.0, 'one': 2.0})
result = df.replace(s, {'zero': 0.5, 'one': 1.0})
expected = DataFrame(
{'zero': {'a': 0.5, 'b': 1}, 'one': {'a': 1.0, 'b': 0.0}})
assert_frame_equal(result, expected)
result = df.replace(s, df.mean())
assert_frame_equal(result, expected)
def test_replace_convert(self):
# gh 3907
df = DataFrame([['foo', 'bar', 'bah'], ['bar', 'foo', 'bah']])
m = {'foo': 1, 'bar': 2, 'bah': 3}
rep = df.replace(m)
expec = Series([np.int64] * 3)
res = rep.dtypes
assert_series_equal(expec, res)
def test_replace_mixed(self):
mf = self.mixed_frame
mf.iloc[5:20, mf.columns.get_loc('foo')] = nan
mf.iloc[-10:, mf.columns.get_loc('A')] = nan
result = self.mixed_frame.replace(np.nan, -18)
expected = self.mixed_frame.fillna(value=-18)
assert_frame_equal(result, expected)
assert_frame_equal(result.replace(-18, nan), self.mixed_frame)
result = self.mixed_frame.replace(np.nan, -1e8)
expected = self.mixed_frame.fillna(value=-1e8)
assert_frame_equal(result, expected)
assert_frame_equal(result.replace(-1e8, nan), self.mixed_frame)
# int block upcasting
df = DataFrame({'A': Series([1.0, 2.0], dtype='float64'),
'B': Series([0, 1], dtype='int64')})
expected = DataFrame({'A': Series([1.0, 2.0], dtype='float64'),
'B': Series([0.5, 1], dtype='float64')})
result = df.replace(0, 0.5)
assert_frame_equal(result, expected)
df.replace(0, 0.5, inplace=True)
assert_frame_equal(df, expected)
# int block splitting
df = DataFrame({'A': Series([1.0, 2.0], dtype='float64'),
'B': Series([0, 1], dtype='int64'),
'C': Series([1, 2], dtype='int64')})
expected = DataFrame({'A': Series([1.0, 2.0], dtype='float64'),
'B': Series([0.5, 1], dtype='float64'),
'C': Series([1, 2], dtype='int64')})
result = df.replace(0, 0.5)
assert_frame_equal(result, expected)
# to object block upcasting
df = DataFrame({'A': Series([1.0, 2.0], dtype='float64'),
'B': Series([0, 1], dtype='int64')})
expected = DataFrame({'A': Series([1, 'foo'], dtype='object'),
'B': Series([0, 1], dtype='int64')})
result = df.replace(2, 'foo')
assert_frame_equal(result, expected)
expected = DataFrame({'A': Series(['foo', 'bar'], dtype='object'),
'B': Series([0, 'foo'], dtype='object')})
result = df.replace([1, 2], ['foo', 'bar'])
assert_frame_equal(result, expected)
# test case from
df = DataFrame({'A': Series([3, 0], dtype='int64'),
'B': Series([0, 3], dtype='int64')})
result = df.replace(3, df.mean().to_dict())
expected = df.copy().astype('float64')
m = df.mean()
expected.iloc[0, 0] = m[0]
expected.iloc[1, 1] = m[1]
assert_frame_equal(result, expected)
def test_replace_simple_nested_dict(self):
df = DataFrame({'col': range(1, 5)})
expected = DataFrame({'col': ['a', 2, 3, 'b']})
result = df.replace({'col': {1: 'a', 4: 'b'}})
assert_frame_equal(expected, result)
# in this case, should be the same as the not nested version
result = df.replace({1: 'a', 4: 'b'})
assert_frame_equal(expected, result)
def test_replace_simple_nested_dict_with_nonexistent_value(self):
df = DataFrame({'col': range(1, 5)})
expected = DataFrame({'col': ['a', 2, 3, 'b']})
result = df.replace({-1: '-', 1: 'a', 4: 'b'})
assert_frame_equal(expected, result)
result = df.replace({'col': {-1: '-', 1: 'a', 4: 'b'}})
assert_frame_equal(expected, result)
def test_replace_value_is_none(self):
pytest.raises(TypeError, self.tsframe.replace, nan)
orig_value = self.tsframe.iloc[0, 0]
orig2 = self.tsframe.iloc[1, 0]
self.tsframe.iloc[0, 0] = nan
self.tsframe.iloc[1, 0] = 1
result = self.tsframe.replace(to_replace={nan: 0})
expected = self.tsframe.T.replace(to_replace={nan: 0}).T
assert_frame_equal(result, expected)
result = self.tsframe.replace(to_replace={nan: 0, 1: -1e8})
tsframe = self.tsframe.copy()
tsframe.iloc[0, 0] = 0
tsframe.iloc[1, 0] = -1e8
expected = tsframe
assert_frame_equal(expected, result)
self.tsframe.iloc[0, 0] = orig_value
self.tsframe.iloc[1, 0] = orig2
def test_replace_for_new_dtypes(self):
# dtypes
tsframe = self.tsframe.copy().astype(np.float32)
tsframe['A'][:5] = nan
tsframe['A'][-5:] = nan
zero_filled = tsframe.replace(nan, -1e8)
assert_frame_equal(zero_filled, tsframe.fillna(-1e8))
assert_frame_equal(zero_filled.replace(-1e8, nan), tsframe)
tsframe['A'][:5] = nan
tsframe['A'][-5:] = nan
tsframe['B'][:5] = -1e8
b = tsframe['B']
b[b == -1e8] = nan
tsframe['B'] = b
result = tsframe.fillna(method='bfill')
assert_frame_equal(result, tsframe.fillna(method='bfill'))
def test_replace_dtypes(self):
# int
df = DataFrame({'ints': [1, 2, 3]})
result = df.replace(1, 0)
expected = DataFrame({'ints': [0, 2, 3]})
assert_frame_equal(result, expected)
df = DataFrame({'ints': [1, 2, 3]}, dtype=np.int32)
result = df.replace(1, 0)
expected = DataFrame({'ints': [0, 2, 3]}, dtype=np.int32)
assert_frame_equal(result, expected)
df = DataFrame({'ints': [1, 2, 3]}, dtype=np.int16)
result = df.replace(1, 0)
expected = DataFrame({'ints': [0, 2, 3]}, dtype=np.int16)
assert_frame_equal(result, expected)
# bools
df = DataFrame({'bools': [True, False, True]})
result = df.replace(False, True)
assert result.values.all()
# complex blocks
df = DataFrame({'complex': [1j, 2j, 3j]})
result = df.replace(1j, 0j)
expected = DataFrame({'complex': [0j, 2j, 3j]})
assert_frame_equal(result, expected)
# datetime blocks
prev = datetime.today()
now = datetime.today()
df = DataFrame({'datetime64': Index([prev, now, prev])})
result = df.replace(prev, now)
expected = DataFrame({'datetime64': Index([now] * 3)})
assert_frame_equal(result, expected)
def test_replace_input_formats_listlike(self):
# both dicts
to_rep = {'A': np.nan, 'B': 0, 'C': ''}
values = {'A': 0, 'B': -1, 'C': 'missing'}
df = DataFrame({'A': [np.nan, 0, np.inf], 'B': [0, 2, 5],
'C': ['', 'asdf', 'fd']})
filled = df.replace(to_rep, values)
expected = {}
for k, v in compat.iteritems(df):
expected[k] = v.replace(to_rep[k], values[k])
assert_frame_equal(filled, DataFrame(expected))
result = df.replace([0, 2, 5], [5, 2, 0])
expected = DataFrame({'A': [np.nan, 5, np.inf], 'B': [5, 2, 0],
'C': ['', 'asdf', 'fd']})
assert_frame_equal(result, expected)
# scalar to dict
values = {'A': 0, 'B': -1, 'C': 'missing'}
df = DataFrame({'A': [np.nan, 0, np.nan], 'B': [0, 2, 5],
'C': ['', 'asdf', 'fd']})
filled = df.replace(np.nan, values)
expected = {}
for k, v in compat.iteritems(df):
expected[k] = v.replace(np.nan, values[k])
assert_frame_equal(filled, DataFrame(expected))
# list to list
to_rep = [np.nan, 0, '']
values = [-2, -1, 'missing']
result = df.replace(to_rep, values)
expected = df.copy()
for i in range(len(to_rep)):
expected.replace(to_rep[i], values[i], inplace=True)
assert_frame_equal(result, expected)
pytest.raises(ValueError, df.replace, to_rep, values[1:])
def test_replace_input_formats_scalar(self):
df = DataFrame({'A': [np.nan, 0, np.inf], 'B': [0, 2, 5],
'C': ['', 'asdf', 'fd']})
# dict to scalar
to_rep = {'A': np.nan, 'B': 0, 'C': ''}
filled = df.replace(to_rep, 0)
expected = {}
for k, v in compat.iteritems(df):
expected[k] = v.replace(to_rep[k], 0)
assert_frame_equal(filled, DataFrame(expected))
pytest.raises(TypeError, df.replace, to_rep, [np.nan, 0, ''])
# list to scalar
to_rep = [np.nan, 0, '']
result = df.replace(to_rep, -1)
expected = df.copy()
for i in range(len(to_rep)):
expected.replace(to_rep[i], -1, inplace=True)
assert_frame_equal(result, expected)
def test_replace_limit(self):
pass
def test_replace_dict_no_regex(self):
answer = Series({0: 'Strongly Agree', 1: 'Agree', 2: 'Neutral', 3:
'Disagree', 4: 'Strongly Disagree'})
weights = {'Agree': 4, 'Disagree': 2, 'Neutral': 3, 'Strongly Agree':
5, 'Strongly Disagree': 1}
expected = Series({0: 5, 1: 4, 2: 3, 3: 2, 4: 1})
result = answer.replace(weights)
assert_series_equal(result, expected)
def test_replace_series_no_regex(self):
answer = Series({0: 'Strongly Agree', 1: 'Agree', 2: 'Neutral', 3:
'Disagree', 4: 'Strongly Disagree'})
weights = Series({'Agree': 4, 'Disagree': 2, 'Neutral': 3,
'Strongly Agree': 5, 'Strongly Disagree': 1})
expected = Series({0: 5, 1: 4, 2: 3, 3: 2, 4: 1})
result = answer.replace(weights)
assert_series_equal(result, expected)
def test_replace_dict_tuple_list_ordering_remains_the_same(self):
df = DataFrame(dict(A=[nan, 1]))
res1 = df.replace(to_replace={nan: 0, 1: -1e8})
res2 = df.replace(to_replace=(1, nan), value=[-1e8, 0])
res3 = df.replace(to_replace=[1, nan], value=[-1e8, 0])
expected = DataFrame({'A': [0, -1e8]})
assert_frame_equal(res1, res2)
assert_frame_equal(res2, res3)
assert_frame_equal(res3, expected)
def test_replace_doesnt_replace_without_regex(self):
raw = """fol T_opp T_Dir T_Enh
0 1 0 0 vo
1 2 vr 0 0
2 2 0 0 0
3 3 0 bt 0"""
df = pd.read_csv(StringIO(raw), sep=r'\s+')
res = df.replace({r'\D': 1})
assert_frame_equal(df, res)
def test_replace_bool_with_string(self):
df = DataFrame({'a': [True, False], 'b': list('ab')})
result = df.replace(True, 'a')
expected = DataFrame({'a': ['a', False], 'b': df.b})
assert_frame_equal(result, expected)
def test_replace_pure_bool_with_string_no_op(self):
df = DataFrame(np.random.rand(2, 2) > 0.5)
result = df.replace('asdf', 'fdsa')
assert_frame_equal(df, result)
def test_replace_bool_with_bool(self):
df = DataFrame(np.random.rand(2, 2) > 0.5)
result = df.replace(False, True)
expected = DataFrame(np.ones((2, 2), dtype=bool))
assert_frame_equal(result, expected)
def test_replace_with_dict_with_bool_keys(self):
df = DataFrame({0: [True, False], 1: [False, True]})
with tm.assert_raises_regex(TypeError, 'Cannot compare types .+'):
df.replace({'asdf': 'asdb', True: 'yes'})
def test_replace_truthy(self):
df = DataFrame({'a': [True, True]})
r = df.replace([np.inf, -np.inf], np.nan)
e = df
assert_frame_equal(r, e)
def test_replace_int_to_int_chain(self):
df = DataFrame({'a': lrange(1, 5)})
with tm.assert_raises_regex(ValueError,
"Replacement not allowed .+"):
df.replace({'a': dict(zip(range(1, 5), range(2, 6)))})
def test_replace_str_to_str_chain(self):
a = np.arange(1, 5)
astr = a.astype(str)
bstr = np.arange(2, 6).astype(str)
df = DataFrame({'a': astr})
with tm.assert_raises_regex(ValueError,
"Replacement not allowed .+"):
df.replace({'a': dict(zip(astr, bstr))})
def test_replace_swapping_bug(self):
df = pd.DataFrame({'a': [True, False, True]})
res = df.replace({'a': {True: 'Y', False: 'N'}})
expect = pd.DataFrame({'a': ['Y', 'N', 'Y']})
assert_frame_equal(res, expect)
df = pd.DataFrame({'a': [0, 1, 0]})
res = df.replace({'a': {0: 'Y', 1: 'N'}})
expect = pd.DataFrame({'a': ['Y', 'N', 'Y']})
assert_frame_equal(res, expect)
def test_replace_period(self):
d = {
'fname': {
'out_augmented_AUG_2011.json':
pd.Period(year=2011, month=8, freq='M'),
'out_augmented_JAN_2011.json':
pd.Period(year=2011, month=1, freq='M'),
'out_augmented_MAY_2012.json':
pd.Period(year=2012, month=5, freq='M'),
'out_augmented_SUBSIDY_WEEK.json':
pd.Period(year=2011, month=4, freq='M'),
'out_augmented_AUG_2012.json':
pd.Period(year=2012, month=8, freq='M'),
'out_augmented_MAY_2011.json':
pd.Period(year=2011, month=5, freq='M'),
'out_augmented_SEP_2013.json':
pd.Period(year=2013, month=9, freq='M')}}
df = pd.DataFrame(['out_augmented_AUG_2012.json',
'out_augmented_SEP_2013.json',
'out_augmented_SUBSIDY_WEEK.json',
'out_augmented_MAY_2012.json',
'out_augmented_MAY_2011.json',
'out_augmented_AUG_2011.json',
'out_augmented_JAN_2011.json'], columns=['fname'])
assert set(df.fname.values) == set(d['fname'].keys())
expected = DataFrame({'fname': [d['fname'][k]
for k in df.fname.values]})
result = df.replace(d)
assert_frame_equal(result, expected)
def test_replace_datetime(self):
d = {'fname':
{'out_augmented_AUG_2011.json': pd.Timestamp('2011-08'),
'out_augmented_JAN_2011.json': pd.Timestamp('2011-01'),
'out_augmented_MAY_2012.json': pd.Timestamp('2012-05'),
'out_augmented_SUBSIDY_WEEK.json': pd.Timestamp('2011-04'),
'out_augmented_AUG_2012.json': pd.Timestamp('2012-08'),
'out_augmented_MAY_2011.json': pd.Timestamp('2011-05'),
'out_augmented_SEP_2013.json': pd.Timestamp('2013-09')}}
df = pd.DataFrame(['out_augmented_AUG_2012.json',
'out_augmented_SEP_2013.json',
'out_augmented_SUBSIDY_WEEK.json',
'out_augmented_MAY_2012.json',
'out_augmented_MAY_2011.json',
'out_augmented_AUG_2011.json',
'out_augmented_JAN_2011.json'], columns=['fname'])
assert set(df.fname.values) == set(d['fname'].keys())
expected = DataFrame({'fname': [d['fname'][k]
for k in df.fname.values]})
result = df.replace(d)
assert_frame_equal(result, expected)
def test_replace_datetimetz(self):
# GH 11326
# behaving poorly when presented with a datetime64[ns, tz]
df = DataFrame({'A': date_range('20130101', periods=3,
tz='US/Eastern'),
'B': [0, np.nan, 2]})
result = df.replace(np.nan, 1)
expected = DataFrame({'A': date_range('20130101', periods=3,
tz='US/Eastern'),
'B': Series([0, 1, 2], dtype='float64')})
assert_frame_equal(result, expected)
result = df.fillna(1)
assert_frame_equal(result, expected)
result = df.replace(0, np.nan)
expected = DataFrame({'A': date_range('20130101', periods=3,
tz='US/Eastern'),
'B': [np.nan, np.nan, 2]})
assert_frame_equal(result, expected)
result = df.replace(Timestamp('20130102', tz='US/Eastern'),
Timestamp('20130104', tz='US/Eastern'))
expected = DataFrame({'A': [Timestamp('20130101', tz='US/Eastern'),
Timestamp('20130104', tz='US/Eastern'),
Timestamp('20130103', tz='US/Eastern')],
'B': [0, np.nan, 2]})
assert_frame_equal(result, expected)
result = df.copy()
result.iloc[1, 0] = np.nan
result = result.replace(
{'A': pd.NaT}, Timestamp('20130104', tz='US/Eastern'))
assert_frame_equal(result, expected)
# coerce to object
result = df.copy()
result.iloc[1, 0] = np.nan
result = result.replace(
{'A': pd.NaT}, Timestamp('20130104', tz='US/Pacific'))
expected = DataFrame({'A': [Timestamp('20130101', tz='US/Eastern'),
Timestamp('20130104', tz='US/Pacific'),
Timestamp('20130103', tz='US/Eastern')],
'B': [0, np.nan, 2]})
assert_frame_equal(result, expected)
result = df.copy()
result.iloc[1, 0] = np.nan
result = result.replace({'A': np.nan}, Timestamp('20130104'))
expected = DataFrame({'A': [Timestamp('20130101', tz='US/Eastern'),
Timestamp('20130104'),
Timestamp('20130103', tz='US/Eastern')],
'B': [0, np.nan, 2]})
assert_frame_equal(result, expected)
def test_replace_with_empty_dictlike(self):
# GH 15289
mix = {'a': lrange(4), 'b': list('ab..'), 'c': ['a', 'b', nan, 'd']}
df = DataFrame(mix)
assert_frame_equal(df, df.replace({}))
assert_frame_equal(df, df.replace(Series([])))
assert_frame_equal(df, df.replace({'b': {}}))
assert_frame_equal(df, df.replace(Series({'b': {}})))
| apache-2.0 |
datapythonista/pandas | pandas/core/arrays/sparse/accessor.py | 2 | 11479 | """Sparse accessor"""
import numpy as np
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.cast import find_common_type
from pandas.core.accessor import (
PandasDelegate,
delegate_names,
)
from pandas.core.arrays.sparse.array import SparseArray
from pandas.core.arrays.sparse.dtype import SparseDtype
class BaseAccessor:
_validation_msg = "Can only use the '.sparse' accessor with Sparse data."
def __init__(self, data=None):
self._parent = data
self._validate(data)
def _validate(self, data):
raise NotImplementedError
@delegate_names(
SparseArray, ["npoints", "density", "fill_value", "sp_values"], typ="property"
)
class SparseAccessor(BaseAccessor, PandasDelegate):
"""
Accessor for SparseSparse from other sparse matrix data types.
"""
def _validate(self, data):
if not isinstance(data.dtype, SparseDtype):
raise AttributeError(self._validation_msg)
def _delegate_property_get(self, name, *args, **kwargs):
return getattr(self._parent.array, name)
def _delegate_method(self, name, *args, **kwargs):
if name == "from_coo":
return self.from_coo(*args, **kwargs)
elif name == "to_coo":
return self.to_coo(*args, **kwargs)
else:
raise ValueError
@classmethod
def from_coo(cls, A, dense_index=False):
"""
Create a Series with sparse values from a scipy.sparse.coo_matrix.
Parameters
----------
A : scipy.sparse.coo_matrix
dense_index : bool, default False
If False (default), the SparseSeries index consists of only the
coords of the non-null entries of the original coo_matrix.
If True, the SparseSeries index consists of the full sorted
(row, col) coordinates of the coo_matrix.
Returns
-------
s : Series
A Series with sparse values.
Examples
--------
>>> from scipy import sparse
>>> A = sparse.coo_matrix(
... ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)
... )
>>> A
<3x4 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in COOrdinate format>
>>> A.todense()
matrix([[0., 0., 1., 2.],
[3., 0., 0., 0.],
[0., 0., 0., 0.]])
>>> ss = pd.Series.sparse.from_coo(A)
>>> ss
0 2 1.0
3 2.0
1 0 3.0
dtype: Sparse[float64, nan]
"""
from pandas import Series
from pandas.core.arrays.sparse.scipy_sparse import coo_to_sparse_series
result = coo_to_sparse_series(A, dense_index=dense_index)
result = Series(result.array, index=result.index, copy=False)
return result
def to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels=False):
"""
Create a scipy.sparse.coo_matrix from a Series with MultiIndex.
Use row_levels and column_levels to determine the row and column
coordinates respectively. row_levels and column_levels are the names
(labels) or numbers of the levels. {row_levels, column_levels} must be
a partition of the MultiIndex level names (or numbers).
Parameters
----------
row_levels : tuple/list
column_levels : tuple/list
sort_labels : bool, default False
Sort the row and column labels before forming the sparse matrix.
Returns
-------
y : scipy.sparse.coo_matrix
rows : list (row labels)
columns : list (column labels)
Examples
--------
>>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
>>> s.index = pd.MultiIndex.from_tuples(
... [
... (1, 2, "a", 0),
... (1, 2, "a", 1),
... (1, 1, "b", 0),
... (1, 1, "b", 1),
... (2, 1, "b", 0),
... (2, 1, "b", 1)
... ],
... names=["A", "B", "C", "D"],
... )
>>> s
A B C D
1 2 a 0 3.0
1 NaN
1 b 0 1.0
1 3.0
2 1 b 0 NaN
1 NaN
dtype: float64
>>> ss = s.astype("Sparse")
>>> ss
A B C D
1 2 a 0 3.0
1 NaN
1 b 0 1.0
1 3.0
2 1 b 0 NaN
1 NaN
dtype: Sparse[float64, nan]
>>> A, rows, columns = ss.sparse.to_coo(
... row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
... )
>>> A
<3x4 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in COOrdinate format>
>>> A.todense()
matrix([[0., 0., 1., 3.],
[3., 0., 0., 0.],
[0., 0., 0., 0.]])
>>> rows
[(1, 1), (1, 2), (2, 1)]
>>> columns
[('a', 0), ('a', 1), ('b', 0), ('b', 1)]
"""
from pandas.core.arrays.sparse.scipy_sparse import sparse_series_to_coo
A, rows, columns = sparse_series_to_coo(
self._parent, row_levels, column_levels, sort_labels=sort_labels
)
return A, rows, columns
def to_dense(self):
"""
Convert a Series from sparse values to dense.
.. versionadded:: 0.25.0
Returns
-------
Series:
A Series with the same values, stored as a dense array.
Examples
--------
>>> series = pd.Series(pd.arrays.SparseArray([0, 1, 0]))
>>> series
0 0
1 1
2 0
dtype: Sparse[int64, 0]
>>> series.sparse.to_dense()
0 0
1 1
2 0
dtype: int64
"""
from pandas import Series
return Series(
self._parent.array.to_dense(),
index=self._parent.index,
name=self._parent.name,
)
class SparseFrameAccessor(BaseAccessor, PandasDelegate):
"""
DataFrame accessor for sparse data.
.. versionadded:: 0.25.0
"""
def _validate(self, data):
dtypes = data.dtypes
if not all(isinstance(t, SparseDtype) for t in dtypes):
raise AttributeError(self._validation_msg)
@classmethod
def from_spmatrix(cls, data, index=None, columns=None):
"""
Create a new DataFrame from a scipy sparse matrix.
.. versionadded:: 0.25.0
Parameters
----------
data : scipy.sparse.spmatrix
Must be convertible to csc format.
index, columns : Index, optional
Row and column labels to use for the resulting DataFrame.
Defaults to a RangeIndex.
Returns
-------
DataFrame
Each column of the DataFrame is stored as a
:class:`arrays.SparseArray`.
Examples
--------
>>> import scipy.sparse
>>> mat = scipy.sparse.eye(3)
>>> pd.DataFrame.sparse.from_spmatrix(mat)
0 1 2
0 1.0 0.0 0.0
1 0.0 1.0 0.0
2 0.0 0.0 1.0
"""
from pandas._libs.sparse import IntIndex
from pandas import DataFrame
data = data.tocsc()
index, columns = cls._prep_index(data, index, columns)
n_rows, n_columns = data.shape
# We need to make sure indices are sorted, as we create
# IntIndex with no input validation (i.e. check_integrity=False ).
# Indices may already be sorted in scipy in which case this adds
# a small overhead.
data.sort_indices()
indices = data.indices
indptr = data.indptr
array_data = data.data
dtype = SparseDtype(array_data.dtype, 0)
arrays = []
for i in range(n_columns):
sl = slice(indptr[i], indptr[i + 1])
idx = IntIndex(n_rows, indices[sl], check_integrity=False)
arr = SparseArray._simple_new(array_data[sl], idx, dtype)
arrays.append(arr)
return DataFrame._from_arrays(
arrays, columns=columns, index=index, verify_integrity=False
)
def to_dense(self):
"""
Convert a DataFrame with sparse values to dense.
.. versionadded:: 0.25.0
Returns
-------
DataFrame
A DataFrame with the same values stored as dense arrays.
Examples
--------
>>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])})
>>> df.sparse.to_dense()
A
0 0
1 1
2 0
"""
from pandas import DataFrame
data = {k: v.array.to_dense() for k, v in self._parent.items()}
return DataFrame(data, index=self._parent.index, columns=self._parent.columns)
def to_coo(self):
"""
Return the contents of the frame as a sparse SciPy COO matrix.
.. versionadded:: 0.25.0
Returns
-------
coo_matrix : scipy.sparse.spmatrix
If the caller is heterogeneous and contains booleans or objects,
the result will be of dtype=object. See Notes.
Notes
-----
The dtype will be the lowest-common-denominator type (implicit
upcasting); that is to say if the dtypes (even of numeric types)
are mixed, the one that accommodates all will be chosen.
e.g. If the dtypes are float16 and float32, dtype will be upcast to
float32. By numpy.find_common_type convention, mixing int64 and
and uint64 will result in a float64 dtype.
"""
import_optional_dependency("scipy")
from scipy.sparse import coo_matrix
dtype = find_common_type(self._parent.dtypes.to_list())
if isinstance(dtype, SparseDtype):
dtype = dtype.subtype
cols, rows, data = [], [], []
for col, name in enumerate(self._parent):
s = self._parent[name]
row = s.array.sp_index.to_int_index().indices
cols.append(np.repeat(col, len(row)))
rows.append(row)
data.append(s.array.sp_values.astype(dtype, copy=False))
cols = np.concatenate(cols)
rows = np.concatenate(rows)
data = np.concatenate(data)
return coo_matrix((data, (rows, cols)), shape=self._parent.shape)
@property
def density(self) -> float:
"""
Ratio of non-sparse points to total (dense) data points.
"""
tmp = np.mean([column.array.density for _, column in self._parent.items()])
return tmp
@staticmethod
def _prep_index(data, index, columns):
from pandas.core.indexes.api import ensure_index
import pandas.core.indexes.base as ibase
N, K = data.shape
if index is None:
index = ibase.default_index(N)
else:
index = ensure_index(index)
if columns is None:
columns = ibase.default_index(K)
else:
columns = ensure_index(columns)
if len(columns) != K:
raise ValueError(f"Column length mismatch: {len(columns)} vs. {K}")
if len(index) != N:
raise ValueError(f"Index length mismatch: {len(index)} vs. {N}")
return index, columns
| bsd-3-clause |