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plissonf/scikit-learn | sklearn/linear_model/coordinate_descent.py | 59 | 76336 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Gael Varoquaux <gael.varoquaux@inria.fr>
#
# License: BSD 3 clause
import sys
import warnings
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy import sparse
from .base import LinearModel, _pre_fit
from ..base import RegressorMixin
from .base import center_data, sparse_center_data
from ..utils import check_array, check_X_y, deprecated
from ..utils.validation import check_random_state
from ..cross_validation import check_cv
from ..externals.joblib import Parallel, delayed
from ..externals import six
from ..externals.six.moves import xrange
from ..utils.extmath import safe_sparse_dot
from ..utils.validation import check_is_fitted
from ..utils.validation import column_or_1d
from ..utils import ConvergenceWarning
from . import cd_fast
###############################################################################
# Paths functions
def _alpha_grid(X, y, Xy=None, l1_ratio=1.0, fit_intercept=True,
eps=1e-3, n_alphas=100, normalize=False, copy_X=True):
""" Compute the grid of alpha values for elastic net parameter search
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data. Pass directly as Fortran-contiguous data to avoid
unnecessary memory duplication
y : ndarray, shape (n_samples,)
Target values
Xy : array-like, optional
Xy = np.dot(X.T, y) that can be precomputed.
l1_ratio : float
The elastic net mixing parameter, with ``0 <= l1_ratio <= 1``.
For ``l1_ratio = 0`` the penalty is an L2 penalty. ``For
l1_ratio = 1`` it is an L1 penalty. For ``0 < l1_ratio <
1``, the penalty is a combination of L1 and L2.
eps : float, optional
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``
n_alphas : int, optional
Number of alphas along the regularization path
fit_intercept : boolean, default True
Whether to fit an intercept or not
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
"""
n_samples = len(y)
sparse_center = False
if Xy is None:
X_sparse = sparse.isspmatrix(X)
sparse_center = X_sparse and (fit_intercept or normalize)
X = check_array(X, 'csc',
copy=(copy_X and fit_intercept and not X_sparse))
if not X_sparse:
# X can be touched inplace thanks to the above line
X, y, _, _, _ = center_data(X, y, fit_intercept,
normalize, copy=False)
Xy = safe_sparse_dot(X.T, y, dense_output=True)
if sparse_center:
# Workaround to find alpha_max for sparse matrices.
# since we should not destroy the sparsity of such matrices.
_, _, X_mean, _, X_std = sparse_center_data(X, y, fit_intercept,
normalize)
mean_dot = X_mean * np.sum(y)
if Xy.ndim == 1:
Xy = Xy[:, np.newaxis]
if sparse_center:
if fit_intercept:
Xy -= mean_dot[:, np.newaxis]
if normalize:
Xy /= X_std[:, np.newaxis]
alpha_max = (np.sqrt(np.sum(Xy ** 2, axis=1)).max() /
(n_samples * l1_ratio))
if alpha_max <= np.finfo(float).resolution:
alphas = np.empty(n_alphas)
alphas.fill(np.finfo(float).resolution)
return alphas
return np.logspace(np.log10(alpha_max * eps), np.log10(alpha_max),
num=n_alphas)[::-1]
def lasso_path(X, y, eps=1e-3, n_alphas=100, alphas=None,
precompute='auto', Xy=None, copy_X=True, coef_init=None,
verbose=False, return_n_iter=False, positive=False, **params):
"""Compute Lasso path with coordinate descent
The Lasso optimization function varies for mono and multi-outputs.
For mono-output tasks it is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
For multi-output tasks it is::
(1 / (2 * n_samples)) * ||Y - XW||^2_Fro + alpha * ||W||_21
Where::
||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the :ref:`User Guide <lasso>`.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data. Pass directly as Fortran-contiguous data to avoid
unnecessary memory duplication. If ``y`` is mono-output then ``X``
can be sparse.
y : ndarray, shape (n_samples,), or (n_samples, n_outputs)
Target values
eps : float, optional
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``
n_alphas : int, optional
Number of alphas along the regularization path
alphas : ndarray, optional
List of alphas where to compute the models.
If ``None`` alphas are set automatically
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.
Xy : array-like, optional
Xy = np.dot(X.T, y) that can be precomputed. It is useful
only when the Gram matrix is precomputed.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
coef_init : array, shape (n_features, ) | None
The initial values of the coefficients.
verbose : bool or integer
Amount of verbosity.
params : kwargs
keyword arguments passed to the coordinate descent solver.
positive : bool, default False
If set to True, forces coefficients to be positive.
return_n_iter : bool
whether to return the number of iterations or not.
Returns
-------
alphas : array, shape (n_alphas,)
The alphas along the path where models are computed.
coefs : array, shape (n_features, n_alphas) or \
(n_outputs, n_features, n_alphas)
Coefficients along the path.
dual_gaps : array, shape (n_alphas,)
The dual gaps at the end of the optimization for each alpha.
n_iters : array-like, shape (n_alphas,)
The number of iterations taken by the coordinate descent optimizer to
reach the specified tolerance for each alpha.
Notes
-----
See examples/linear_model/plot_lasso_coordinate_descent_path.py
for an example.
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.
Note that in certain cases, the Lars solver may be significantly
faster to implement this functionality. In particular, linear
interpolation can be used to retrieve model coefficients between the
values output by lars_path
Examples
---------
Comparing lasso_path and lars_path with interpolation:
>>> X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
>>> y = np.array([1, 2, 3.1])
>>> # Use lasso_path to compute a coefficient path
>>> _, coef_path, _ = lasso_path(X, y, alphas=[5., 1., .5])
>>> print(coef_path)
[[ 0. 0. 0.46874778]
[ 0.2159048 0.4425765 0.23689075]]
>>> # Now use lars_path and 1D linear interpolation to compute the
>>> # same path
>>> from sklearn.linear_model import lars_path
>>> alphas, active, coef_path_lars = lars_path(X, y, method='lasso')
>>> from scipy import interpolate
>>> coef_path_continuous = interpolate.interp1d(alphas[::-1],
... coef_path_lars[:, ::-1])
>>> print(coef_path_continuous([5., 1., .5]))
[[ 0. 0. 0.46915237]
[ 0.2159048 0.4425765 0.23668876]]
See also
--------
lars_path
Lasso
LassoLars
LassoCV
LassoLarsCV
sklearn.decomposition.sparse_encode
"""
return enet_path(X, y, l1_ratio=1., eps=eps, n_alphas=n_alphas,
alphas=alphas, precompute=precompute, Xy=Xy,
copy_X=copy_X, coef_init=coef_init, verbose=verbose,
positive=positive, **params)
def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
precompute='auto', Xy=None, copy_X=True, coef_init=None,
verbose=False, return_n_iter=False, positive=False, **params):
"""Compute elastic net path with coordinate descent
The elastic net optimization function varies for mono and multi-outputs.
For mono-output tasks it is::
1 / (2 * n_samples) * ||y - Xw||^2_2 +
+ alpha * l1_ratio * ||w||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2
For multi-output tasks it is::
(1 / (2 * n_samples)) * ||Y - XW||^Fro_2
+ alpha * l1_ratio * ||W||_21
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
Where::
||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the :ref:`User Guide <elastic_net>`.
Parameters
----------
X : {array-like}, shape (n_samples, n_features)
Training data. Pass directly as Fortran-contiguous data to avoid
unnecessary memory duplication. If ``y`` is mono-output then ``X``
can be sparse.
y : ndarray, shape (n_samples,) or (n_samples, n_outputs)
Target values
l1_ratio : float, optional
float between 0 and 1 passed to elastic net (scaling between
l1 and l2 penalties). ``l1_ratio=1`` corresponds to the Lasso
eps : float
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``
n_alphas : int, optional
Number of alphas along the regularization path
alphas : ndarray, optional
List of alphas where to compute the models.
If None alphas are set automatically
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.
Xy : array-like, optional
Xy = np.dot(X.T, y) that can be precomputed. It is useful
only when the Gram matrix is precomputed.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
coef_init : array, shape (n_features, ) | None
The initial values of the coefficients.
verbose : bool or integer
Amount of verbosity.
params : kwargs
keyword arguments passed to the coordinate descent solver.
return_n_iter : bool
whether to return the number of iterations or not.
positive : bool, default False
If set to True, forces coefficients to be positive.
Returns
-------
alphas : array, shape (n_alphas,)
The alphas along the path where models are computed.
coefs : array, shape (n_features, n_alphas) or \
(n_outputs, n_features, n_alphas)
Coefficients along the path.
dual_gaps : array, shape (n_alphas,)
The dual gaps at the end of the optimization for each alpha.
n_iters : array-like, shape (n_alphas,)
The number of iterations taken by the coordinate descent optimizer to
reach the specified tolerance for each alpha.
(Is returned when ``return_n_iter`` is set to True).
Notes
-----
See examples/plot_lasso_coordinate_descent_path.py for an example.
See also
--------
MultiTaskElasticNet
MultiTaskElasticNetCV
ElasticNet
ElasticNetCV
"""
# We expect X and y to be already float64 Fortran ordered when bypassing
# checks
check_input = 'check_input' not in params or params['check_input']
pre_fit = 'check_input' not in params or params['pre_fit']
if check_input:
X = check_array(X, 'csc', dtype=np.float64, order='F', copy=copy_X)
y = check_array(y, 'csc', dtype=np.float64, order='F', copy=False,
ensure_2d=False)
if Xy is not None:
Xy = check_array(Xy, 'csc', dtype=np.float64, order='F',
copy=False,
ensure_2d=False)
n_samples, n_features = X.shape
multi_output = False
if y.ndim != 1:
multi_output = True
_, n_outputs = y.shape
# MultiTaskElasticNet does not support sparse matrices
if not multi_output and sparse.isspmatrix(X):
if 'X_mean' in params:
# As sparse matrices are not actually centered we need this
# to be passed to the CD solver.
X_sparse_scaling = params['X_mean'] / params['X_std']
else:
X_sparse_scaling = np.zeros(n_features)
# X should be normalized and fit already if function is called
# from ElasticNet.fit
if pre_fit:
X, y, X_mean, y_mean, X_std, precompute, Xy = \
_pre_fit(X, y, Xy, precompute, normalize=False,
fit_intercept=False,
copy=False, Xy_precompute_order='F')
if alphas is None:
# No need to normalize of fit_intercept: it has been done
# above
alphas = _alpha_grid(X, y, Xy=Xy, l1_ratio=l1_ratio,
fit_intercept=False, eps=eps, n_alphas=n_alphas,
normalize=False, copy_X=False)
else:
alphas = np.sort(alphas)[::-1] # make sure alphas are properly ordered
n_alphas = len(alphas)
tol = params.get('tol', 1e-4)
max_iter = params.get('max_iter', 1000)
dual_gaps = np.empty(n_alphas)
n_iters = []
rng = check_random_state(params.get('random_state', None))
selection = params.get('selection', 'cyclic')
if selection not in ['random', 'cyclic']:
raise ValueError("selection should be either random or cyclic.")
random = (selection == 'random')
if not multi_output:
coefs = np.empty((n_features, n_alphas), dtype=np.float64)
else:
coefs = np.empty((n_outputs, n_features, n_alphas),
dtype=np.float64)
if coef_init is None:
coef_ = np.asfortranarray(np.zeros(coefs.shape[:-1]))
else:
coef_ = np.asfortranarray(coef_init)
for i, alpha in enumerate(alphas):
l1_reg = alpha * l1_ratio * n_samples
l2_reg = alpha * (1.0 - l1_ratio) * n_samples
if not multi_output and sparse.isspmatrix(X):
model = cd_fast.sparse_enet_coordinate_descent(
coef_, l1_reg, l2_reg, X.data, X.indices,
X.indptr, y, X_sparse_scaling,
max_iter, tol, rng, random, positive)
elif multi_output:
model = cd_fast.enet_coordinate_descent_multi_task(
coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random)
elif isinstance(precompute, np.ndarray):
# We expect precompute to be already Fortran ordered when bypassing
# checks
if check_input:
precompute = check_array(precompute, 'csc', dtype=np.float64,
order='F')
model = cd_fast.enet_coordinate_descent_gram(
coef_, l1_reg, l2_reg, precompute, Xy, y, max_iter,
tol, rng, random, positive)
elif precompute is False:
model = cd_fast.enet_coordinate_descent(
coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random,
positive)
else:
raise ValueError("Precompute should be one of True, False, "
"'auto' or array-like")
coef_, dual_gap_, eps_, n_iter_ = model
coefs[..., i] = coef_
dual_gaps[i] = dual_gap_
n_iters.append(n_iter_)
if dual_gap_ > eps_:
warnings.warn('Objective did not converge.' +
' You might want' +
' to increase the number of iterations',
ConvergenceWarning)
if verbose:
if verbose > 2:
print(model)
elif verbose > 1:
print('Path: %03i out of %03i' % (i, n_alphas))
else:
sys.stderr.write('.')
if return_n_iter:
return alphas, coefs, dual_gaps, n_iters
return alphas, coefs, dual_gaps
###############################################################################
# ElasticNet model
class ElasticNet(LinearModel, RegressorMixin):
"""Linear regression with combined L1 and L2 priors as regularizer.
Minimizes the objective function::
1 / (2 * n_samples) * ||y - Xw||^2_2 +
+ alpha * l1_ratio * ||w||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2
If you are interested in controlling the L1 and L2 penalty
separately, keep in mind that this is equivalent to::
a * L1 + b * L2
where::
alpha = a + b and l1_ratio = a / (a + b)
The parameter l1_ratio corresponds to alpha in the glmnet R package while
alpha corresponds to the lambda parameter in glmnet. Specifically, l1_ratio
= 1 is the lasso penalty. Currently, l1_ratio <= 0.01 is not reliable,
unless you supply your own sequence of alpha.
Read more in the :ref:`User Guide <elastic_net>`.
Parameters
----------
alpha : float
Constant that multiplies the penalty terms. Defaults to 1.0
See the notes for the exact mathematical meaning of this
parameter.
``alpha = 0`` is equivalent to an ordinary least square, solved
by the :class:`LinearRegression` object. For numerical
reasons, using ``alpha = 0`` with the Lasso object is not advised
and you should prefer the LinearRegression object.
l1_ratio : float
The ElasticNet mixing parameter, with ``0 <= l1_ratio <= 1``. For
``l1_ratio = 0`` the penalty is an L2 penalty. ``For l1_ratio = 1`` it
is an L1 penalty. For ``0 < l1_ratio < 1``, the penalty is a
combination of L1 and L2.
fit_intercept : bool
Whether the intercept should be estimated or not. If ``False``, the
data is assumed to be already centered.
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument. For sparse input
this option is always ``True`` to preserve sparsity.
WARNING : The ``'auto'`` option is deprecated and will
be removed in 0.18.
max_iter : int, optional
The maximum number of iterations
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
tol : float, optional
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
than ``tol``.
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.
positive : bool, optional
When set to ``True``, forces the coefficients to be positive.
selection : str, default 'cyclic'
If set to 'random', a random coefficient is updated every iteration
rather than looping over features sequentially by default. This
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4.
random_state : int, RandomState instance, or None (default)
The seed of the pseudo random number generator that selects
a random feature to update. Useful only when selection is set to
'random'.
Attributes
----------
coef_ : array, shape (n_features,) | (n_targets, n_features)
parameter vector (w in the cost function formula)
sparse_coef_ : scipy.sparse matrix, shape (n_features, 1) | \
(n_targets, n_features)
``sparse_coef_`` is a readonly property derived from ``coef_``
intercept_ : float | array, shape (n_targets,)
independent term in decision function.
n_iter_ : array-like, shape (n_targets,)
number of iterations run by the coordinate descent solver to reach
the specified tolerance.
Notes
-----
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.
See also
--------
SGDRegressor: implements elastic net regression with incremental training.
SGDClassifier: implements logistic regression with elastic net penalty
(``SGDClassifier(loss="log", penalty="elasticnet")``).
"""
path = staticmethod(enet_path)
def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,
normalize=False, precompute=False, max_iter=1000,
copy_X=True, tol=1e-4, warm_start=False, positive=False,
random_state=None, selection='cyclic'):
self.alpha = alpha
self.l1_ratio = l1_ratio
self.coef_ = None
self.fit_intercept = fit_intercept
self.normalize = normalize
self.precompute = precompute
self.max_iter = max_iter
self.copy_X = copy_X
self.tol = tol
self.warm_start = warm_start
self.positive = positive
self.intercept_ = 0.0
self.random_state = random_state
self.selection = selection
def fit(self, X, y, check_input=True):
"""Fit model with coordinate descent.
Parameters
-----------
X : ndarray or scipy.sparse matrix, (n_samples, n_features)
Data
y : ndarray, shape (n_samples,) or (n_samples, n_targets)
Target
Notes
-----
Coordinate descent is an algorithm that considers each column of
data at a time hence it will automatically convert the X input
as a Fortran-contiguous numpy array if necessary.
To avoid memory re-allocation it is advised to allocate the
initial data in memory directly using that format.
"""
if self.alpha == 0:
warnings.warn("With alpha=0, this algorithm does not converge "
"well. You are advised to use the LinearRegression "
"estimator", stacklevel=2)
if self.precompute == 'auto':
warnings.warn("Setting precompute to 'auto', was found to be "
"slower even when n_samples > n_features. Hence "
"it will be removed in 0.18.",
DeprecationWarning, stacklevel=2)
# We expect X and y to be already float64 Fortran ordered arrays
# when bypassing checks
if check_input:
X, y = check_X_y(X, y, accept_sparse='csc', dtype=np.float64,
order='F',
copy=self.copy_X and self.fit_intercept,
multi_output=True, y_numeric=True)
X, y, X_mean, y_mean, X_std, precompute, Xy = \
_pre_fit(X, y, None, self.precompute, self.normalize,
self.fit_intercept, copy=False, Xy_precompute_order='F')
if y.ndim == 1:
y = y[:, np.newaxis]
if Xy is not None and Xy.ndim == 1:
Xy = Xy[:, np.newaxis]
n_samples, n_features = X.shape
n_targets = y.shape[1]
if self.selection not in ['cyclic', 'random']:
raise ValueError("selection should be either random or cyclic.")
if not self.warm_start or self.coef_ is None:
coef_ = np.zeros((n_targets, n_features), dtype=np.float64,
order='F')
else:
coef_ = self.coef_
if coef_.ndim == 1:
coef_ = coef_[np.newaxis, :]
dual_gaps_ = np.zeros(n_targets, dtype=np.float64)
self.n_iter_ = []
for k in xrange(n_targets):
if Xy is not None:
this_Xy = Xy[:, k]
else:
this_Xy = None
_, this_coef, this_dual_gap, this_iter = \
self.path(X, y[:, k],
l1_ratio=self.l1_ratio, eps=None,
n_alphas=None, alphas=[self.alpha],
precompute=precompute, Xy=this_Xy,
fit_intercept=False, normalize=False, copy_X=True,
verbose=False, tol=self.tol, positive=self.positive,
X_mean=X_mean, X_std=X_std, return_n_iter=True,
coef_init=coef_[k], max_iter=self.max_iter,
random_state=self.random_state,
selection=self.selection,
check_input=False,
pre_fit=False)
coef_[k] = this_coef[:, 0]
dual_gaps_[k] = this_dual_gap[0]
self.n_iter_.append(this_iter[0])
if n_targets == 1:
self.n_iter_ = self.n_iter_[0]
self.coef_, self.dual_gap_ = map(np.squeeze, [coef_, dual_gaps_])
self._set_intercept(X_mean, y_mean, X_std)
# return self for chaining fit and predict calls
return self
@property
def sparse_coef_(self):
""" sparse representation of the fitted coef """
return sparse.csr_matrix(self.coef_)
@deprecated(" and will be removed in 0.19")
def decision_function(self, X):
"""Decision function of the linear model
Parameters
----------
X : numpy array or scipy.sparse matrix of shape (n_samples, n_features)
Returns
-------
T : array, shape (n_samples,)
The predicted decision function
"""
return self._decision_function(X)
def _decision_function(self, X):
"""Decision function of the linear model
Parameters
----------
X : numpy array or scipy.sparse matrix of shape (n_samples, n_features)
Returns
-------
T : array, shape (n_samples,)
The predicted decision function
"""
check_is_fitted(self, 'n_iter_')
if sparse.isspmatrix(X):
return np.ravel(safe_sparse_dot(self.coef_, X.T, dense_output=True)
+ self.intercept_)
else:
return super(ElasticNet, self)._decision_function(X)
###############################################################################
# Lasso model
class Lasso(ElasticNet):
"""Linear Model trained with L1 prior as regularizer (aka the Lasso)
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
Technically the Lasso model is optimizing the same objective function as
the Elastic Net with ``l1_ratio=1.0`` (no L2 penalty).
Read more in the :ref:`User Guide <lasso>`.
Parameters
----------
alpha : float, optional
Constant that multiplies the L1 term. Defaults to 1.0.
``alpha = 0`` is equivalent to an ordinary least square, solved
by the :class:`LinearRegression` object. For numerical
reasons, using ``alpha = 0`` is with the Lasso object is not advised
and you should prefer the LinearRegression object.
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument. For sparse input
this option is always ``True`` to preserve sparsity.
WARNING : The ``'auto'`` option is deprecated and will
be removed in 0.18.
max_iter : int, optional
The maximum number of iterations
tol : float, optional
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
than ``tol``.
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.
positive : bool, optional
When set to ``True``, forces the coefficients to be positive.
selection : str, default 'cyclic'
If set to 'random', a random coefficient is updated every iteration
rather than looping over features sequentially by default. This
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4.
random_state : int, RandomState instance, or None (default)
The seed of the pseudo random number generator that selects
a random feature to update. Useful only when selection is set to
'random'.
Attributes
----------
coef_ : array, shape (n_features,) | (n_targets, n_features)
parameter vector (w in the cost function formula)
sparse_coef_ : scipy.sparse matrix, shape (n_features, 1) | \
(n_targets, n_features)
``sparse_coef_`` is a readonly property derived from ``coef_``
intercept_ : float | array, shape (n_targets,)
independent term in decision function.
n_iter_ : int | array-like, shape (n_targets,)
number of iterations run by the coordinate descent solver to reach
the specified tolerance.
Examples
--------
>>> from sklearn import linear_model
>>> clf = linear_model.Lasso(alpha=0.1)
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
>>> print(clf.coef_)
[ 0.85 0. ]
>>> print(clf.intercept_)
0.15
See also
--------
lars_path
lasso_path
LassoLars
LassoCV
LassoLarsCV
sklearn.decomposition.sparse_encode
Notes
-----
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.
"""
path = staticmethod(enet_path)
def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
precompute=False, copy_X=True, max_iter=1000,
tol=1e-4, warm_start=False, positive=False,
random_state=None, selection='cyclic'):
super(Lasso, self).__init__(
alpha=alpha, l1_ratio=1.0, fit_intercept=fit_intercept,
normalize=normalize, precompute=precompute, copy_X=copy_X,
max_iter=max_iter, tol=tol, warm_start=warm_start,
positive=positive, random_state=random_state,
selection=selection)
###############################################################################
# Functions for CV with paths functions
def _path_residuals(X, y, train, test, path, path_params, alphas=None,
l1_ratio=1, X_order=None, dtype=None):
"""Returns the MSE for the models computed by 'path'
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
train : list of indices
The indices of the train set
test : list of indices
The indices of the test set
path : callable
function returning a list of models on the path. See
enet_path for an example of signature
path_params : dictionary
Parameters passed to the path function
alphas : array-like, optional
Array of float that is used for cross-validation. If not
provided, computed using 'path'
l1_ratio : float, optional
float between 0 and 1 passed to ElasticNet (scaling between
l1 and l2 penalties). For ``l1_ratio = 0`` the penalty is an
L2 penalty. For ``l1_ratio = 1`` it is an L1 penalty. For ``0
< l1_ratio < 1``, the penalty is a combination of L1 and L2
X_order : {'F', 'C', or None}, optional
The order of the arrays expected by the path function to
avoid memory copies
dtype : a numpy dtype or None
The dtype of the arrays expected by the path function to
avoid memory copies
"""
X_train = X[train]
y_train = y[train]
X_test = X[test]
y_test = y[test]
fit_intercept = path_params['fit_intercept']
normalize = path_params['normalize']
if y.ndim == 1:
precompute = path_params['precompute']
else:
# No Gram variant of multi-task exists right now.
# Fall back to default enet_multitask
precompute = False
X_train, y_train, X_mean, y_mean, X_std, precompute, Xy = \
_pre_fit(X_train, y_train, None, precompute, normalize, fit_intercept,
copy=False)
path_params = path_params.copy()
path_params['Xy'] = Xy
path_params['X_mean'] = X_mean
path_params['X_std'] = X_std
path_params['precompute'] = precompute
path_params['copy_X'] = False
path_params['alphas'] = alphas
if 'l1_ratio' in path_params:
path_params['l1_ratio'] = l1_ratio
# Do the ordering and type casting here, as if it is done in the path,
# X is copied and a reference is kept here
X_train = check_array(X_train, 'csc', dtype=dtype, order=X_order)
alphas, coefs, _ = path(X_train, y_train, **path_params)
del X_train, y_train
if y.ndim == 1:
# Doing this so that it becomes coherent with multioutput.
coefs = coefs[np.newaxis, :, :]
y_mean = np.atleast_1d(y_mean)
y_test = y_test[:, np.newaxis]
if normalize:
nonzeros = np.flatnonzero(X_std)
coefs[:, nonzeros] /= X_std[nonzeros][:, np.newaxis]
intercepts = y_mean[:, np.newaxis] - np.dot(X_mean, coefs)
if sparse.issparse(X_test):
n_order, n_features, n_alphas = coefs.shape
# Work around for sparse matices since coefs is a 3-D numpy array.
coefs_feature_major = np.rollaxis(coefs, 1)
feature_2d = np.reshape(coefs_feature_major, (n_features, -1))
X_test_coefs = safe_sparse_dot(X_test, feature_2d)
X_test_coefs = X_test_coefs.reshape(X_test.shape[0], n_order, -1)
else:
X_test_coefs = safe_sparse_dot(X_test, coefs)
residues = X_test_coefs - y_test[:, :, np.newaxis]
residues += intercepts
this_mses = ((residues ** 2).mean(axis=0)).mean(axis=0)
return this_mses
class LinearModelCV(six.with_metaclass(ABCMeta, LinearModel)):
"""Base class for iterative model fitting along a regularization path"""
@abstractmethod
def __init__(self, eps=1e-3, n_alphas=100, alphas=None, fit_intercept=True,
normalize=False, precompute='auto', max_iter=1000, tol=1e-4,
copy_X=True, cv=None, verbose=False, n_jobs=1,
positive=False, random_state=None, selection='cyclic'):
self.eps = eps
self.n_alphas = n_alphas
self.alphas = alphas
self.fit_intercept = fit_intercept
self.normalize = normalize
self.precompute = precompute
self.max_iter = max_iter
self.tol = tol
self.copy_X = copy_X
self.cv = cv
self.verbose = verbose
self.n_jobs = n_jobs
self.positive = positive
self.random_state = random_state
self.selection = selection
def fit(self, X, y):
"""Fit linear model with coordinate descent
Fit is on grid of alphas and best alpha estimated by cross-validation.
Parameters
----------
X : {array-like}, shape (n_samples, n_features)
Training data. Pass directly as float64, Fortran-contiguous data
to avoid unnecessary memory duplication. If y is mono-output,
X can be sparse.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
Target values
"""
y = np.asarray(y, dtype=np.float64)
if y.shape[0] == 0:
raise ValueError("y has 0 samples: %r" % y)
if hasattr(self, 'l1_ratio'):
model_str = 'ElasticNet'
else:
model_str = 'Lasso'
if isinstance(self, ElasticNetCV) or isinstance(self, LassoCV):
if model_str == 'ElasticNet':
model = ElasticNet()
else:
model = Lasso()
if y.ndim > 1 and y.shape[1] > 1:
raise ValueError("For multi-task outputs, use "
"MultiTask%sCV" % (model_str))
y = column_or_1d(y, warn=True)
else:
if sparse.isspmatrix(X):
raise TypeError("X should be dense but a sparse matrix was"
"passed")
elif y.ndim == 1:
raise ValueError("For mono-task outputs, use "
"%sCV" % (model_str))
if model_str == 'ElasticNet':
model = MultiTaskElasticNet()
else:
model = MultiTaskLasso()
if self.selection not in ["random", "cyclic"]:
raise ValueError("selection should be either random or cyclic.")
# This makes sure that there is no duplication in memory.
# Dealing right with copy_X is important in the following:
# Multiple functions touch X and subsamples of X and can induce a
# lot of duplication of memory
copy_X = self.copy_X and self.fit_intercept
if isinstance(X, np.ndarray) or sparse.isspmatrix(X):
# Keep a reference to X
reference_to_old_X = X
# Let us not impose fortran ordering or float64 so far: it is
# not useful for the cross-validation loop and will be done
# by the model fitting itself
X = check_array(X, 'csc', copy=False)
if sparse.isspmatrix(X):
if (hasattr(reference_to_old_X, "data") and
not np.may_share_memory(reference_to_old_X.data, X.data)):
# X is a sparse matrix and has been copied
copy_X = False
elif not np.may_share_memory(reference_to_old_X, X):
# X has been copied
copy_X = False
del reference_to_old_X
else:
X = check_array(X, 'csc', dtype=np.float64, order='F', copy=copy_X)
copy_X = False
if X.shape[0] != y.shape[0]:
raise ValueError("X and y have inconsistent dimensions (%d != %d)"
% (X.shape[0], y.shape[0]))
# All LinearModelCV parameters except 'cv' are acceptable
path_params = self.get_params()
if 'l1_ratio' in path_params:
l1_ratios = np.atleast_1d(path_params['l1_ratio'])
# For the first path, we need to set l1_ratio
path_params['l1_ratio'] = l1_ratios[0]
else:
l1_ratios = [1, ]
path_params.pop('cv', None)
path_params.pop('n_jobs', None)
alphas = self.alphas
n_l1_ratio = len(l1_ratios)
if alphas is None:
alphas = []
for l1_ratio in l1_ratios:
alphas.append(_alpha_grid(
X, y, l1_ratio=l1_ratio,
fit_intercept=self.fit_intercept,
eps=self.eps, n_alphas=self.n_alphas,
normalize=self.normalize,
copy_X=self.copy_X))
else:
# Making sure alphas is properly ordered.
alphas = np.tile(np.sort(alphas)[::-1], (n_l1_ratio, 1))
# We want n_alphas to be the number of alphas used for each l1_ratio.
n_alphas = len(alphas[0])
path_params.update({'n_alphas': n_alphas})
path_params['copy_X'] = copy_X
# We are not computing in parallel, we can modify X
# inplace in the folds
if not (self.n_jobs == 1 or self.n_jobs is None):
path_params['copy_X'] = False
# init cross-validation generator
cv = check_cv(self.cv, X)
# Compute path for all folds and compute MSE to get the best alpha
folds = list(cv)
best_mse = np.inf
# We do a double for loop folded in one, in order to be able to
# iterate in parallel on l1_ratio and folds
jobs = (delayed(_path_residuals)(X, y, train, test, self.path,
path_params, alphas=this_alphas,
l1_ratio=this_l1_ratio, X_order='F',
dtype=np.float64)
for this_l1_ratio, this_alphas in zip(l1_ratios, alphas)
for train, test in folds)
mse_paths = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
backend="threading")(jobs)
mse_paths = np.reshape(mse_paths, (n_l1_ratio, len(folds), -1))
mean_mse = np.mean(mse_paths, axis=1)
self.mse_path_ = np.squeeze(np.rollaxis(mse_paths, 2, 1))
for l1_ratio, l1_alphas, mse_alphas in zip(l1_ratios, alphas,
mean_mse):
i_best_alpha = np.argmin(mse_alphas)
this_best_mse = mse_alphas[i_best_alpha]
if this_best_mse < best_mse:
best_alpha = l1_alphas[i_best_alpha]
best_l1_ratio = l1_ratio
best_mse = this_best_mse
self.l1_ratio_ = best_l1_ratio
self.alpha_ = best_alpha
if self.alphas is None:
self.alphas_ = np.asarray(alphas)
if n_l1_ratio == 1:
self.alphas_ = self.alphas_[0]
# Remove duplicate alphas in case alphas is provided.
else:
self.alphas_ = np.asarray(alphas[0])
# Refit the model with the parameters selected
common_params = dict((name, value)
for name, value in self.get_params().items()
if name in model.get_params())
model.set_params(**common_params)
model.alpha = best_alpha
model.l1_ratio = best_l1_ratio
model.copy_X = copy_X
model.precompute = False
model.fit(X, y)
if not hasattr(self, 'l1_ratio'):
del self.l1_ratio_
self.coef_ = model.coef_
self.intercept_ = model.intercept_
self.dual_gap_ = model.dual_gap_
self.n_iter_ = model.n_iter_
return self
class LassoCV(LinearModelCV, RegressorMixin):
"""Lasso linear model with iterative fitting along a regularization path
The best model is selected by cross-validation.
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
Read more in the :ref:`User Guide <lasso>`.
Parameters
----------
eps : float, optional
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``.
n_alphas : int, optional
Number of alphas along the regularization path
alphas : numpy array, optional
List of alphas where to compute the models.
If ``None`` alphas are set automatically
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.
max_iter : int, optional
The maximum number of iterations
tol : float, optional
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
than ``tol``.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
verbose : bool or integer
Amount of verbosity.
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If ``-1``, use
all the CPUs.
positive : bool, optional
If positive, restrict regression coefficients to be positive
selection : str, default 'cyclic'
If set to 'random', a random coefficient is updated every iteration
rather than looping over features sequentially by default. This
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4.
random_state : int, RandomState instance, or None (default)
The seed of the pseudo random number generator that selects
a random feature to update. Useful only when selection is set to
'random'.
fit_intercept : boolean, default True
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
Attributes
----------
alpha_ : float
The amount of penalization chosen by cross validation
coef_ : array, shape (n_features,) | (n_targets, n_features)
parameter vector (w in the cost function formula)
intercept_ : float | array, shape (n_targets,)
independent term in decision function.
mse_path_ : array, shape (n_alphas, n_folds)
mean square error for the test set on each fold, varying alpha
alphas_ : numpy array, shape (n_alphas,)
The grid of alphas used for fitting
dual_gap_ : ndarray, shape ()
The dual gap at the end of the optimization for the optimal alpha
(``alpha_``).
n_iter_ : int
number of iterations run by the coordinate descent solver to reach
the specified tolerance for the optimal alpha.
Notes
-----
See examples/linear_model/lasso_path_with_crossvalidation.py
for an example.
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.
See also
--------
lars_path
lasso_path
LassoLars
Lasso
LassoLarsCV
"""
path = staticmethod(lasso_path)
def __init__(self, eps=1e-3, n_alphas=100, alphas=None, fit_intercept=True,
normalize=False, precompute='auto', max_iter=1000, tol=1e-4,
copy_X=True, cv=None, verbose=False, n_jobs=1,
positive=False, random_state=None, selection='cyclic'):
super(LassoCV, self).__init__(
eps=eps, n_alphas=n_alphas, alphas=alphas,
fit_intercept=fit_intercept, normalize=normalize,
precompute=precompute, max_iter=max_iter, tol=tol, copy_X=copy_X,
cv=cv, verbose=verbose, n_jobs=n_jobs, positive=positive,
random_state=random_state, selection=selection)
class ElasticNetCV(LinearModelCV, RegressorMixin):
"""Elastic Net model with iterative fitting along a regularization path
The best model is selected by cross-validation.
Read more in the :ref:`User Guide <elastic_net>`.
Parameters
----------
l1_ratio : float, optional
float between 0 and 1 passed to ElasticNet (scaling between
l1 and l2 penalties). For ``l1_ratio = 0``
the penalty is an L2 penalty. For ``l1_ratio = 1`` it is an L1 penalty.
For ``0 < l1_ratio < 1``, the penalty is a combination of L1 and L2
This parameter can be a list, in which case the different
values are tested by cross-validation and the one giving the best
prediction score is used. Note that a good choice of list of
values for l1_ratio is often to put more values close to 1
(i.e. Lasso) and less close to 0 (i.e. Ridge), as in ``[.1, .5, .7,
.9, .95, .99, 1]``
eps : float, optional
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``.
n_alphas : int, optional
Number of alphas along the regularization path, used for each l1_ratio.
alphas : numpy array, optional
List of alphas where to compute the models.
If None alphas are set automatically
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.
max_iter : int, optional
The maximum number of iterations
tol : float, optional
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
than ``tol``.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
verbose : bool or integer
Amount of verbosity.
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If ``-1``, use
all the CPUs.
positive : bool, optional
When set to ``True``, forces the coefficients to be positive.
selection : str, default 'cyclic'
If set to 'random', a random coefficient is updated every iteration
rather than looping over features sequentially by default. This
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4.
random_state : int, RandomState instance, or None (default)
The seed of the pseudo random number generator that selects
a random feature to update. Useful only when selection is set to
'random'.
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
Attributes
----------
alpha_ : float
The amount of penalization chosen by cross validation
l1_ratio_ : float
The compromise between l1 and l2 penalization chosen by
cross validation
coef_ : array, shape (n_features,) | (n_targets, n_features)
Parameter vector (w in the cost function formula),
intercept_ : float | array, shape (n_targets, n_features)
Independent term in the decision function.
mse_path_ : array, shape (n_l1_ratio, n_alpha, n_folds)
Mean square error for the test set on each fold, varying l1_ratio and
alpha.
alphas_ : numpy array, shape (n_alphas,) or (n_l1_ratio, n_alphas)
The grid of alphas used for fitting, for each l1_ratio.
n_iter_ : int
number of iterations run by the coordinate descent solver to reach
the specified tolerance for the optimal alpha.
Notes
-----
See examples/linear_model/lasso_path_with_crossvalidation.py
for an example.
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.
The parameter l1_ratio corresponds to alpha in the glmnet R package
while alpha corresponds to the lambda parameter in glmnet.
More specifically, the optimization objective is::
1 / (2 * n_samples) * ||y - Xw||^2_2 +
+ alpha * l1_ratio * ||w||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2
If you are interested in controlling the L1 and L2 penalty
separately, keep in mind that this is equivalent to::
a * L1 + b * L2
for::
alpha = a + b and l1_ratio = a / (a + b).
See also
--------
enet_path
ElasticNet
"""
path = staticmethod(enet_path)
def __init__(self, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
fit_intercept=True, normalize=False, precompute='auto',
max_iter=1000, tol=1e-4, cv=None, copy_X=True,
verbose=0, n_jobs=1, positive=False, random_state=None,
selection='cyclic'):
self.l1_ratio = l1_ratio
self.eps = eps
self.n_alphas = n_alphas
self.alphas = alphas
self.fit_intercept = fit_intercept
self.normalize = normalize
self.precompute = precompute
self.max_iter = max_iter
self.tol = tol
self.cv = cv
self.copy_X = copy_X
self.verbose = verbose
self.n_jobs = n_jobs
self.positive = positive
self.random_state = random_state
self.selection = selection
###############################################################################
# Multi Task ElasticNet and Lasso models (with joint feature selection)
class MultiTaskElasticNet(Lasso):
"""Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer
The optimization objective for MultiTaskElasticNet is::
(1 / (2 * n_samples)) * ||Y - XW||^Fro_2
+ alpha * l1_ratio * ||W||_21
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
Where::
||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the :ref:`User Guide <multi_task_lasso>`.
Parameters
----------
alpha : float, optional
Constant that multiplies the L1/L2 term. Defaults to 1.0
l1_ratio : float
The ElasticNet mixing parameter, with 0 < l1_ratio <= 1.
For l1_ratio = 0 the penalty is an L1/L2 penalty. For l1_ratio = 1 it
is an L1 penalty.
For ``0 < l1_ratio < 1``, the penalty is a combination of L1/L2 and L2.
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
max_iter : int, optional
The maximum number of iterations
tol : float, optional
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
than ``tol``.
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.
selection : str, default 'cyclic'
If set to 'random', a random coefficient is updated every iteration
rather than looping over features sequentially by default. This
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4.
random_state : int, RandomState instance, or None (default)
The seed of the pseudo random number generator that selects
a random feature to update. Useful only when selection is set to
'random'.
Attributes
----------
intercept_ : array, shape (n_tasks,)
Independent term in decision function.
coef_ : array, shape (n_tasks, n_features)
Parameter vector (W in the cost function formula). If a 1D y is \
passed in at fit (non multi-task usage), ``coef_`` is then a 1D array
n_iter_ : int
number of iterations run by the coordinate descent solver to reach
the specified tolerance.
Examples
--------
>>> from sklearn import linear_model
>>> clf = linear_model.MultiTaskElasticNet(alpha=0.1)
>>> clf.fit([[0,0], [1, 1], [2, 2]], [[0, 0], [1, 1], [2, 2]])
... #doctest: +NORMALIZE_WHITESPACE
MultiTaskElasticNet(alpha=0.1, copy_X=True, fit_intercept=True,
l1_ratio=0.5, max_iter=1000, normalize=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
>>> print(clf.coef_)
[[ 0.45663524 0.45612256]
[ 0.45663524 0.45612256]]
>>> print(clf.intercept_)
[ 0.0872422 0.0872422]
See also
--------
ElasticNet, MultiTaskLasso
Notes
-----
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.
"""
def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,
normalize=False, copy_X=True, max_iter=1000, tol=1e-4,
warm_start=False, random_state=None, selection='cyclic'):
self.l1_ratio = l1_ratio
self.alpha = alpha
self.coef_ = None
self.fit_intercept = fit_intercept
self.normalize = normalize
self.max_iter = max_iter
self.copy_X = copy_X
self.tol = tol
self.warm_start = warm_start
self.random_state = random_state
self.selection = selection
def fit(self, X, y):
"""Fit MultiTaskLasso model with coordinate descent
Parameters
-----------
X : ndarray, shape (n_samples, n_features)
Data
y : ndarray, shape (n_samples, n_tasks)
Target
Notes
-----
Coordinate descent is an algorithm that considers each column of
data at a time hence it will automatically convert the X input
as a Fortran-contiguous numpy array if necessary.
To avoid memory re-allocation it is advised to allocate the
initial data in memory directly using that format.
"""
# X and y must be of type float64
X = check_array(X, dtype=np.float64, order='F',
copy=self.copy_X and self.fit_intercept)
y = np.asarray(y, dtype=np.float64)
if hasattr(self, 'l1_ratio'):
model_str = 'ElasticNet'
else:
model_str = 'Lasso'
if y.ndim == 1:
raise ValueError("For mono-task outputs, use %s" % model_str)
n_samples, n_features = X.shape
_, n_tasks = y.shape
if n_samples != y.shape[0]:
raise ValueError("X and y have inconsistent dimensions (%d != %d)"
% (n_samples, y.shape[0]))
X, y, X_mean, y_mean, X_std = center_data(
X, y, self.fit_intercept, self.normalize, copy=False)
if not self.warm_start or self.coef_ is None:
self.coef_ = np.zeros((n_tasks, n_features), dtype=np.float64,
order='F')
l1_reg = self.alpha * self.l1_ratio * n_samples
l2_reg = self.alpha * (1.0 - self.l1_ratio) * n_samples
self.coef_ = np.asfortranarray(self.coef_) # coef contiguous in memory
if self.selection not in ['random', 'cyclic']:
raise ValueError("selection should be either random or cyclic.")
random = (self.selection == 'random')
self.coef_, self.dual_gap_, self.eps_, self.n_iter_ = \
cd_fast.enet_coordinate_descent_multi_task(
self.coef_, l1_reg, l2_reg, X, y, self.max_iter, self.tol,
check_random_state(self.random_state), random)
self._set_intercept(X_mean, y_mean, X_std)
if self.dual_gap_ > self.eps_:
warnings.warn('Objective did not converge, you might want'
' to increase the number of iterations')
# return self for chaining fit and predict calls
return self
class MultiTaskLasso(MultiTaskElasticNet):
"""Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||Y - XW||^2_Fro + alpha * ||W||_21
Where::
||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of earch row.
Read more in the :ref:`User Guide <multi_task_lasso>`.
Parameters
----------
alpha : float, optional
Constant that multiplies the L1/L2 term. Defaults to 1.0
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
max_iter : int, optional
The maximum number of iterations
tol : float, optional
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
than ``tol``.
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.
selection : str, default 'cyclic'
If set to 'random', a random coefficient is updated every iteration
rather than looping over features sequentially by default. This
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4
random_state : int, RandomState instance, or None (default)
The seed of the pseudo random number generator that selects
a random feature to update. Useful only when selection is set to
'random'.
Attributes
----------
coef_ : array, shape (n_tasks, n_features)
parameter vector (W in the cost function formula)
intercept_ : array, shape (n_tasks,)
independent term in decision function.
n_iter_ : int
number of iterations run by the coordinate descent solver to reach
the specified tolerance.
Examples
--------
>>> from sklearn import linear_model
>>> clf = linear_model.MultiTaskLasso(alpha=0.1)
>>> clf.fit([[0,0], [1, 1], [2, 2]], [[0, 0], [1, 1], [2, 2]])
MultiTaskLasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, random_state=None, selection='cyclic', tol=0.0001,
warm_start=False)
>>> print(clf.coef_)
[[ 0.89393398 0. ]
[ 0.89393398 0. ]]
>>> print(clf.intercept_)
[ 0.10606602 0.10606602]
See also
--------
Lasso, MultiTaskElasticNet
Notes
-----
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.
"""
def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
copy_X=True, max_iter=1000, tol=1e-4, warm_start=False,
random_state=None, selection='cyclic'):
self.alpha = alpha
self.coef_ = None
self.fit_intercept = fit_intercept
self.normalize = normalize
self.max_iter = max_iter
self.copy_X = copy_X
self.tol = tol
self.warm_start = warm_start
self.l1_ratio = 1.0
self.random_state = random_state
self.selection = selection
class MultiTaskElasticNetCV(LinearModelCV, RegressorMixin):
"""Multi-task L1/L2 ElasticNet with built-in cross-validation.
The optimization objective for MultiTaskElasticNet is::
(1 / (2 * n_samples)) * ||Y - XW||^Fro_2
+ alpha * l1_ratio * ||W||_21
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
Where::
||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the :ref:`User Guide <multi_task_lasso>`.
Parameters
----------
eps : float, optional
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``.
alphas : array-like, optional
List of alphas where to compute the models.
If not provided, set automatically.
n_alphas : int, optional
Number of alphas along the regularization path
l1_ratio : float or array of floats
The ElasticNet mixing parameter, with 0 < l1_ratio <= 1.
For l1_ratio = 0 the penalty is an L1/L2 penalty. For l1_ratio = 1 it
is an L1 penalty.
For ``0 < l1_ratio < 1``, the penalty is a combination of L1/L2 and L2.
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
max_iter : int, optional
The maximum number of iterations
tol : float, optional
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
than ``tol``.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
verbose : bool or integer
Amount of verbosity.
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If ``-1``, use
all the CPUs. Note that this is used only if multiple values for
l1_ratio are given.
selection : str, default 'cyclic'
If set to 'random', a random coefficient is updated every iteration
rather than looping over features sequentially by default. This
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4.
random_state : int, RandomState instance, or None (default)
The seed of the pseudo random number generator that selects
a random feature to update. Useful only when selection is set to
'random'.
Attributes
----------
intercept_ : array, shape (n_tasks,)
Independent term in decision function.
coef_ : array, shape (n_tasks, n_features)
Parameter vector (W in the cost function formula).
alpha_ : float
The amount of penalization chosen by cross validation
mse_path_ : array, shape (n_alphas, n_folds) or \
(n_l1_ratio, n_alphas, n_folds)
mean square error for the test set on each fold, varying alpha
alphas_ : numpy array, shape (n_alphas,) or (n_l1_ratio, n_alphas)
The grid of alphas used for fitting, for each l1_ratio
l1_ratio_ : float
best l1_ratio obtained by cross-validation.
n_iter_ : int
number of iterations run by the coordinate descent solver to reach
the specified tolerance for the optimal alpha.
Examples
--------
>>> from sklearn import linear_model
>>> clf = linear_model.MultiTaskElasticNetCV()
>>> clf.fit([[0,0], [1, 1], [2, 2]],
... [[0, 0], [1, 1], [2, 2]])
... #doctest: +NORMALIZE_WHITESPACE
MultiTaskElasticNetCV(alphas=None, copy_X=True, cv=None, eps=0.001,
fit_intercept=True, l1_ratio=0.5, max_iter=1000, n_alphas=100,
n_jobs=1, normalize=False, random_state=None, selection='cyclic',
tol=0.0001, verbose=0)
>>> print(clf.coef_)
[[ 0.52875032 0.46958558]
[ 0.52875032 0.46958558]]
>>> print(clf.intercept_)
[ 0.00166409 0.00166409]
See also
--------
MultiTaskElasticNet
ElasticNetCV
MultiTaskLassoCV
Notes
-----
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.
"""
path = staticmethod(enet_path)
def __init__(self, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
fit_intercept=True, normalize=False,
max_iter=1000, tol=1e-4, cv=None, copy_X=True,
verbose=0, n_jobs=1, random_state=None, selection='cyclic'):
self.l1_ratio = l1_ratio
self.eps = eps
self.n_alphas = n_alphas
self.alphas = alphas
self.fit_intercept = fit_intercept
self.normalize = normalize
self.max_iter = max_iter
self.tol = tol
self.cv = cv
self.copy_X = copy_X
self.verbose = verbose
self.n_jobs = n_jobs
self.random_state = random_state
self.selection = selection
class MultiTaskLassoCV(LinearModelCV, RegressorMixin):
"""Multi-task L1/L2 Lasso with built-in cross-validation.
The optimization objective for MultiTaskLasso is::
(1 / (2 * n_samples)) * ||Y - XW||^Fro_2 + alpha * ||W||_21
Where::
||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the :ref:`User Guide <multi_task_lasso>`.
Parameters
----------
eps : float, optional
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``.
alphas : array-like, optional
List of alphas where to compute the models.
If not provided, set automaticlly.
n_alphas : int, optional
Number of alphas along the regularization path
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
max_iter : int, optional
The maximum number of iterations.
tol : float, optional
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
than ``tol``.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
verbose : bool or integer
Amount of verbosity.
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If ``-1``, use
all the CPUs. Note that this is used only if multiple values for
l1_ratio are given.
selection : str, default 'cyclic'
If set to 'random', a random coefficient is updated every iteration
rather than looping over features sequentially by default. This
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4.
random_state : int, RandomState instance, or None (default)
The seed of the pseudo random number generator that selects
a random feature to update. Useful only when selection is set to
'random'.
Attributes
----------
intercept_ : array, shape (n_tasks,)
Independent term in decision function.
coef_ : array, shape (n_tasks, n_features)
Parameter vector (W in the cost function formula).
alpha_ : float
The amount of penalization chosen by cross validation
mse_path_ : array, shape (n_alphas, n_folds)
mean square error for the test set on each fold, varying alpha
alphas_ : numpy array, shape (n_alphas,)
The grid of alphas used for fitting.
n_iter_ : int
number of iterations run by the coordinate descent solver to reach
the specified tolerance for the optimal alpha.
See also
--------
MultiTaskElasticNet
ElasticNetCV
MultiTaskElasticNetCV
Notes
-----
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.
"""
path = staticmethod(lasso_path)
def __init__(self, eps=1e-3, n_alphas=100, alphas=None, fit_intercept=True,
normalize=False, max_iter=1000, tol=1e-4, copy_X=True,
cv=None, verbose=False, n_jobs=1, random_state=None,
selection='cyclic'):
super(MultiTaskLassoCV, self).__init__(
eps=eps, n_alphas=n_alphas, alphas=alphas,
fit_intercept=fit_intercept, normalize=normalize,
max_iter=max_iter, tol=tol, copy_X=copy_X,
cv=cv, verbose=verbose, n_jobs=n_jobs, random_state=random_state,
selection=selection)
| bsd-3-clause |
DonBeo/scikit-learn | sklearn/utils/tests/test_class_weight.py | 14 | 6559 | import numpy as np
from sklearn.utils.class_weight import compute_class_weight
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
def test_compute_class_weight():
# Test (and demo) compute_class_weight.
y = np.asarray([2, 2, 2, 3, 3, 4])
classes = np.unique(y)
cw = compute_class_weight("auto", classes, y)
assert_almost_equal(cw.sum(), classes.shape)
assert_true(cw[0] < cw[1] < cw[2])
def test_compute_class_weight_not_present():
# Raise error when y does not contain all class labels
classes = np.arange(4)
y = np.asarray([0, 0, 0, 1, 1, 2])
assert_raises(ValueError, compute_class_weight, "auto", classes, y)
def test_compute_class_weight_auto_negative():
# Test compute_class_weight when labels are negative
# Test with balanced class labels.
classes = np.array([-2, -1, 0])
y = np.asarray([-1, -1, 0, 0, -2, -2])
cw = compute_class_weight("auto", classes, y)
assert_almost_equal(cw.sum(), classes.shape)
assert_equal(len(cw), len(classes))
assert_array_almost_equal(cw, np.array([1., 1., 1.]))
# Test with unbalanced class labels.
y = np.asarray([-1, 0, 0, -2, -2, -2])
cw = compute_class_weight("auto", classes, y)
assert_almost_equal(cw.sum(), classes.shape)
assert_equal(len(cw), len(classes))
assert_array_almost_equal(cw, np.array([0.545, 1.636, 0.818]), decimal=3)
def test_compute_class_weight_auto_unordered():
# Test compute_class_weight when classes are unordered
classes = np.array([1, 0, 3])
y = np.asarray([1, 0, 0, 3, 3, 3])
cw = compute_class_weight("auto", classes, y)
assert_almost_equal(cw.sum(), classes.shape)
assert_equal(len(cw), len(classes))
assert_array_almost_equal(cw, np.array([1.636, 0.818, 0.545]), decimal=3)
def test_compute_sample_weight():
# Test (and demo) compute_sample_weight.
# Test with balanced classes
y = np.asarray([1, 1, 1, 2, 2, 2])
sample_weight = compute_sample_weight("auto", y)
assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.])
# Test with user-defined weights
sample_weight = compute_sample_weight({1: 2, 2: 1}, y)
assert_array_almost_equal(sample_weight, [2., 2., 2., 1., 1., 1.])
# Test with column vector of balanced classes
y = np.asarray([[1], [1], [1], [2], [2], [2]])
sample_weight = compute_sample_weight("auto", y)
assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.])
# Test with unbalanced classes
y = np.asarray([1, 1, 1, 2, 2, 2, 3])
sample_weight = compute_sample_weight("auto", y)
expected = np.asarray([.6, .6, .6, .6, .6, .6, 1.8])
assert_array_almost_equal(sample_weight, expected)
# Test with `None` weights
sample_weight = compute_sample_weight(None, y)
assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1., 1.])
# Test with multi-output of balanced classes
y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
sample_weight = compute_sample_weight("auto", y)
assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.])
# Test with multi-output with user-defined weights
y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
sample_weight = compute_sample_weight([{1: 2, 2: 1}, {0: 1, 1: 2}], y)
assert_array_almost_equal(sample_weight, [2., 2., 2., 2., 2., 2.])
# Test with multi-output of unbalanced classes
y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1], [3, -1]])
sample_weight = compute_sample_weight("auto", y)
assert_array_almost_equal(sample_weight, expected ** 2)
def test_compute_sample_weight_with_subsample():
# Test compute_sample_weight with subsamples specified.
# Test with balanced classes and all samples present
y = np.asarray([1, 1, 1, 2, 2, 2])
sample_weight = compute_sample_weight("auto", y, range(6))
assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.])
# Test with column vector of balanced classes and all samples present
y = np.asarray([[1], [1], [1], [2], [2], [2]])
sample_weight = compute_sample_weight("auto", y, range(6))
assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.])
# Test with a subsample
y = np.asarray([1, 1, 1, 2, 2, 2])
sample_weight = compute_sample_weight("auto", y, range(4))
assert_array_almost_equal(sample_weight, [.5, .5, .5, 1.5, 1.5, 1.5])
# Test with a bootstrap subsample
y = np.asarray([1, 1, 1, 2, 2, 2])
sample_weight = compute_sample_weight("auto", y, [0, 1, 1, 2, 2, 3])
expected = np.asarray([1/3., 1/3., 1/3., 5/3., 5/3., 5/3.])
assert_array_almost_equal(sample_weight, expected)
# Test with a bootstrap subsample for multi-output
y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
sample_weight = compute_sample_weight("auto", y, [0, 1, 1, 2, 2, 3])
assert_array_almost_equal(sample_weight, expected ** 2)
# Test with a missing class
y = np.asarray([1, 1, 1, 2, 2, 2, 3])
sample_weight = compute_sample_weight("auto", y, range(6))
assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1., 0.])
# Test with a missing class for multi-output
y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1], [2, 2]])
sample_weight = compute_sample_weight("auto", y, range(6))
assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1., 0.])
def test_compute_sample_weight_errors():
# Test compute_sample_weight raises errors expected.
# Invalid preset string
y = np.asarray([1, 1, 1, 2, 2, 2])
y_ = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
assert_raises(ValueError, compute_sample_weight, "ni", y)
assert_raises(ValueError, compute_sample_weight, "ni", y, range(4))
assert_raises(ValueError, compute_sample_weight, "ni", y_)
assert_raises(ValueError, compute_sample_weight, "ni", y_, range(4))
# Not "auto" for subsample
assert_raises(ValueError,
compute_sample_weight, {1: 2, 2: 1}, y, range(4))
# Not a list or preset for multi-output
assert_raises(ValueError, compute_sample_weight, {1: 2, 2: 1}, y_)
# Incorrect length list for multi-output
assert_raises(ValueError, compute_sample_weight, [{1: 2, 2: 1}], y_)
| bsd-3-clause |
Phil9l/cosmos | code/artificial_intelligence/src/naive_bayes/gaussian_naive_bayes.py | 3 | 1370 | # example using iris dataset
# Part of Cosmos by OpenGenus
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import classification_report, confusion_matrix
dataset = pd.read_csv("iris1.csv", header=0)
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 4].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y)
classifier = GaussianNB()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
# labeled confusion matrix
print(
pd.crosstab(y_test, y_pred, rownames=["True"], colnames=["Predicted"], margins=True)
)
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score
skf = StratifiedKFold(n_splits=10)
skf.get_n_splits(X, y)
StratifiedKFold(n_splits=10, random_state=None, shuffle=False)
a = 0
for train_index, test_index in skf.split(X, y):
# print("TRAIN:", train_index, "TEST:", test_index) #These are the mutually exclusive sets from the 10 folds
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
y_pred = classifier.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
a += accuracy
print("\nK-fold cross validation (10 folds): " + str(a / 10))
| gpl-3.0 |
xyguo/scikit-learn | sklearn/decomposition/nmf.py | 6 | 46993 | """ Non-negative matrix factorization
"""
# Author: Vlad Niculae
# Lars Buitinck
# Mathieu Blondel <mathieu@mblondel.org>
# Tom Dupre la Tour
# Author: Chih-Jen Lin, National Taiwan University (original projected gradient
# NMF implementation)
# Author: Anthony Di Franco (Projected gradient, Python and NumPy port)
# License: BSD 3 clause
from __future__ import division, print_function
from math import sqrt
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from ..externals import six
from ..base import BaseEstimator, TransformerMixin
from ..utils import check_random_state, check_array
from ..utils.extmath import randomized_svd, safe_sparse_dot, squared_norm
from ..utils.extmath import fast_dot
from ..utils.validation import check_is_fitted, check_non_negative
from ..utils import deprecated
from ..exceptions import ConvergenceWarning
from .cdnmf_fast import _update_cdnmf_fast
def safe_vstack(Xs):
if any(sp.issparse(X) for X in Xs):
return sp.vstack(Xs)
else:
return np.vstack(Xs)
def norm(x):
"""Dot product-based Euclidean norm implementation
See: http://fseoane.net/blog/2011/computing-the-vector-norm/
"""
return sqrt(squared_norm(x))
def trace_dot(X, Y):
"""Trace of np.dot(X, Y.T)."""
return np.dot(X.ravel(), Y.ravel())
def _sparseness(x):
"""Hoyer's measure of sparsity for a vector"""
sqrt_n = np.sqrt(len(x))
return (sqrt_n - np.linalg.norm(x, 1) / norm(x)) / (sqrt_n - 1)
def _check_init(A, shape, whom):
A = check_array(A)
if np.shape(A) != shape:
raise ValueError('Array with wrong shape passed to %s. Expected %s, '
'but got %s ' % (whom, shape, np.shape(A)))
check_non_negative(A, whom)
if np.max(A) == 0:
raise ValueError('Array passed to %s is full of zeros.' % whom)
def _safe_compute_error(X, W, H):
"""Frobenius norm between X and WH, safe for sparse array"""
if not sp.issparse(X):
error = norm(X - np.dot(W, H))
else:
norm_X = np.dot(X.data, X.data)
norm_WH = trace_dot(np.dot(np.dot(W.T, W), H), H)
cross_prod = trace_dot((X * H.T), W)
error = sqrt(norm_X + norm_WH - 2. * cross_prod)
return error
def _check_string_param(sparseness, solver):
allowed_sparseness = (None, 'data', 'components')
if sparseness not in allowed_sparseness:
raise ValueError(
'Invalid sparseness parameter: got %r instead of one of %r' %
(sparseness, allowed_sparseness))
allowed_solver = ('pg', 'cd')
if solver not in allowed_solver:
raise ValueError(
'Invalid solver parameter: got %r instead of one of %r' %
(solver, allowed_solver))
def _initialize_nmf(X, n_components, init=None, eps=1e-6,
random_state=None):
"""Algorithms for NMF initialization.
Computes an initial guess for the non-negative
rank k matrix approximation for X: X = WH
Parameters
----------
X : array-like, shape (n_samples, n_features)
The data matrix to be decomposed.
n_components : integer
The number of components desired in the approximation.
init : None | 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar'
Method used to initialize the procedure.
Default: 'nndsvdar' if n_components < n_features, otherwise 'random'.
Valid options:
- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)
- 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
- 'nndsvda': NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
- 'nndsvdar': NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa
for when sparsity is not desired)
- 'custom': use custom matrices W and H
eps : float
Truncate all values less then this in output to zero.
random_state : int seed, RandomState instance, or None (default)
Random number generator seed control, used in 'nndsvdar' and
'random' modes.
Returns
-------
W : array-like, shape (n_samples, n_components)
Initial guesses for solving X ~= WH
H : array-like, shape (n_components, n_features)
Initial guesses for solving X ~= WH
References
----------
C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for
nonnegative matrix factorization - Pattern Recognition, 2008
http://tinyurl.com/nndsvd
"""
check_non_negative(X, "NMF initialization")
n_samples, n_features = X.shape
if init is None:
if n_components < n_features:
init = 'nndsvd'
else:
init = 'random'
# Random initialization
if init == 'random':
avg = np.sqrt(X.mean() / n_components)
rng = check_random_state(random_state)
H = avg * rng.randn(n_components, n_features)
W = avg * rng.randn(n_samples, n_components)
# we do not write np.abs(H, out=H) to stay compatible with
# numpy 1.5 and earlier where the 'out' keyword is not
# supported as a kwarg on ufuncs
np.abs(H, H)
np.abs(W, W)
return W, H
# NNDSVD initialization
U, S, V = randomized_svd(X, n_components, random_state=random_state)
W, H = np.zeros(U.shape), np.zeros(V.shape)
# The leading singular triplet is non-negative
# so it can be used as is for initialization.
W[:, 0] = np.sqrt(S[0]) * np.abs(U[:, 0])
H[0, :] = np.sqrt(S[0]) * np.abs(V[0, :])
for j in range(1, n_components):
x, y = U[:, j], V[j, :]
# extract positive and negative parts of column vectors
x_p, y_p = np.maximum(x, 0), np.maximum(y, 0)
x_n, y_n = np.abs(np.minimum(x, 0)), np.abs(np.minimum(y, 0))
# and their norms
x_p_nrm, y_p_nrm = norm(x_p), norm(y_p)
x_n_nrm, y_n_nrm = norm(x_n), norm(y_n)
m_p, m_n = x_p_nrm * y_p_nrm, x_n_nrm * y_n_nrm
# choose update
if m_p > m_n:
u = x_p / x_p_nrm
v = y_p / y_p_nrm
sigma = m_p
else:
u = x_n / x_n_nrm
v = y_n / y_n_nrm
sigma = m_n
lbd = np.sqrt(S[j] * sigma)
W[:, j] = lbd * u
H[j, :] = lbd * v
W[W < eps] = 0
H[H < eps] = 0
if init == "nndsvd":
pass
elif init == "nndsvda":
avg = X.mean()
W[W == 0] = avg
H[H == 0] = avg
elif init == "nndsvdar":
rng = check_random_state(random_state)
avg = X.mean()
W[W == 0] = abs(avg * rng.randn(len(W[W == 0])) / 100)
H[H == 0] = abs(avg * rng.randn(len(H[H == 0])) / 100)
else:
raise ValueError(
'Invalid init parameter: got %r instead of one of %r' %
(init, (None, 'random', 'nndsvd', 'nndsvda', 'nndsvdar')))
return W, H
def _nls_subproblem(V, W, H, tol, max_iter, alpha=0., l1_ratio=0.,
sigma=0.01, beta=0.1):
"""Non-negative least square solver
Solves a non-negative least squares subproblem using the projected
gradient descent algorithm.
Parameters
----------
V : array-like, shape (n_samples, n_features)
Constant matrix.
W : array-like, shape (n_samples, n_components)
Constant matrix.
H : array-like, shape (n_components, n_features)
Initial guess for the solution.
tol : float
Tolerance of the stopping condition.
max_iter : int
Maximum number of iterations before timing out.
alpha : double, default: 0.
Constant that multiplies the regularization terms. Set it to zero to
have no regularization.
l1_ratio : double, default: 0.
The regularization mixing parameter, with 0 <= l1_ratio <= 1.
For l1_ratio = 0 the penalty is an L2 penalty.
For l1_ratio = 1 it is an L1 penalty.
For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
sigma : float
Constant used in the sufficient decrease condition checked by the line
search. Smaller values lead to a looser sufficient decrease condition,
thus reducing the time taken by the line search, but potentially
increasing the number of iterations of the projected gradient
procedure. 0.01 is a commonly used value in the optimization
literature.
beta : float
Factor by which the step size is decreased (resp. increased) until
(resp. as long as) the sufficient decrease condition is satisfied.
Larger values allow to find a better step size but lead to longer line
search. 0.1 is a commonly used value in the optimization literature.
Returns
-------
H : array-like, shape (n_components, n_features)
Solution to the non-negative least squares problem.
grad : array-like, shape (n_components, n_features)
The gradient.
n_iter : int
The number of iterations done by the algorithm.
References
----------
C.-J. Lin. Projected gradient methods for non-negative matrix
factorization. Neural Computation, 19(2007), 2756-2779.
http://www.csie.ntu.edu.tw/~cjlin/nmf/
"""
WtV = safe_sparse_dot(W.T, V)
WtW = fast_dot(W.T, W)
# values justified in the paper (alpha is renamed gamma)
gamma = 1
for n_iter in range(1, max_iter + 1):
grad = np.dot(WtW, H) - WtV
if alpha > 0 and l1_ratio == 1.:
grad += alpha
elif alpha > 0:
grad += alpha * (l1_ratio + (1 - l1_ratio) * H)
# The following multiplication with a boolean array is more than twice
# as fast as indexing into grad.
if norm(grad * np.logical_or(grad < 0, H > 0)) < tol:
break
Hp = H
for inner_iter in range(20):
# Gradient step.
Hn = H - gamma * grad
# Projection step.
Hn *= Hn > 0
d = Hn - H
gradd = np.dot(grad.ravel(), d.ravel())
dQd = np.dot(np.dot(WtW, d).ravel(), d.ravel())
suff_decr = (1 - sigma) * gradd + 0.5 * dQd < 0
if inner_iter == 0:
decr_gamma = not suff_decr
if decr_gamma:
if suff_decr:
H = Hn
break
else:
gamma *= beta
elif not suff_decr or (Hp == Hn).all():
H = Hp
break
else:
gamma /= beta
Hp = Hn
if n_iter == max_iter:
warnings.warn("Iteration limit reached in nls subproblem.")
return H, grad, n_iter
def _update_projected_gradient_w(X, W, H, tolW, nls_max_iter, alpha, l1_ratio,
sparseness, beta, eta):
"""Helper function for _fit_projected_gradient"""
n_samples, n_features = X.shape
n_components_ = H.shape[0]
if sparseness is None:
Wt, gradW, iterW = _nls_subproblem(X.T, H.T, W.T, tolW, nls_max_iter,
alpha=alpha, l1_ratio=l1_ratio)
elif sparseness == 'data':
Wt, gradW, iterW = _nls_subproblem(
safe_vstack([X.T, np.zeros((1, n_samples))]),
safe_vstack([H.T, np.sqrt(beta) * np.ones((1,
n_components_))]),
W.T, tolW, nls_max_iter, alpha=alpha, l1_ratio=l1_ratio)
elif sparseness == 'components':
Wt, gradW, iterW = _nls_subproblem(
safe_vstack([X.T,
np.zeros((n_components_, n_samples))]),
safe_vstack([H.T,
np.sqrt(eta) * np.eye(n_components_)]),
W.T, tolW, nls_max_iter, alpha=alpha, l1_ratio=l1_ratio)
return Wt.T, gradW.T, iterW
def _update_projected_gradient_h(X, W, H, tolH, nls_max_iter, alpha, l1_ratio,
sparseness, beta, eta):
"""Helper function for _fit_projected_gradient"""
n_samples, n_features = X.shape
n_components_ = W.shape[1]
if sparseness is None:
H, gradH, iterH = _nls_subproblem(X, W, H, tolH, nls_max_iter,
alpha=alpha, l1_ratio=l1_ratio)
elif sparseness == 'data':
H, gradH, iterH = _nls_subproblem(
safe_vstack([X, np.zeros((n_components_, n_features))]),
safe_vstack([W,
np.sqrt(eta) * np.eye(n_components_)]),
H, tolH, nls_max_iter, alpha=alpha, l1_ratio=l1_ratio)
elif sparseness == 'components':
H, gradH, iterH = _nls_subproblem(
safe_vstack([X, np.zeros((1, n_features))]),
safe_vstack([W, np.sqrt(beta) * np.ones((1, n_components_))]),
H, tolH, nls_max_iter, alpha=alpha, l1_ratio=l1_ratio)
return H, gradH, iterH
def _fit_projected_gradient(X, W, H, tol, max_iter,
nls_max_iter, alpha, l1_ratio,
sparseness, beta, eta):
"""Compute Non-negative Matrix Factorization (NMF) with Projected Gradient
References
----------
C.-J. Lin. Projected gradient methods for non-negative matrix
factorization. Neural Computation, 19(2007), 2756-2779.
http://www.csie.ntu.edu.tw/~cjlin/nmf/
P. Hoyer. Non-negative Matrix Factorization with Sparseness Constraints.
Journal of Machine Learning Research 2004.
"""
gradW = (np.dot(W, np.dot(H, H.T)) -
safe_sparse_dot(X, H.T, dense_output=True))
gradH = (np.dot(np.dot(W.T, W), H) -
safe_sparse_dot(W.T, X, dense_output=True))
init_grad = squared_norm(gradW) + squared_norm(gradH.T)
# max(0.001, tol) to force alternating minimizations of W and H
tolW = max(0.001, tol) * np.sqrt(init_grad)
tolH = tolW
for n_iter in range(1, max_iter + 1):
# stopping condition
# as discussed in paper
proj_grad_W = squared_norm(gradW * np.logical_or(gradW < 0, W > 0))
proj_grad_H = squared_norm(gradH * np.logical_or(gradH < 0, H > 0))
if (proj_grad_W + proj_grad_H) / init_grad < tol ** 2:
break
# update W
W, gradW, iterW = _update_projected_gradient_w(X, W, H, tolW,
nls_max_iter,
alpha, l1_ratio,
sparseness, beta, eta)
if iterW == 1:
tolW = 0.1 * tolW
# update H
H, gradH, iterH = _update_projected_gradient_h(X, W, H, tolH,
nls_max_iter,
alpha, l1_ratio,
sparseness, beta, eta)
if iterH == 1:
tolH = 0.1 * tolH
H[H == 0] = 0 # fix up negative zeros
if n_iter == max_iter:
W, _, _ = _update_projected_gradient_w(X, W, H, tol, nls_max_iter,
alpha, l1_ratio, sparseness,
beta, eta)
return W, H, n_iter
def _update_coordinate_descent(X, W, Ht, l1_reg, l2_reg, shuffle,
random_state):
"""Helper function for _fit_coordinate_descent
Update W to minimize the objective function, iterating once over all
coordinates. By symmetry, to update H, one can call
_update_coordinate_descent(X.T, Ht, W, ...)
"""
n_components = Ht.shape[1]
HHt = fast_dot(Ht.T, Ht)
XHt = safe_sparse_dot(X, Ht)
# L2 regularization corresponds to increase of the diagonal of HHt
if l2_reg != 0.:
# adds l2_reg only on the diagonal
HHt.flat[::n_components + 1] += l2_reg
# L1 regularization corresponds to decrease of each element of XHt
if l1_reg != 0.:
XHt -= l1_reg
if shuffle:
permutation = random_state.permutation(n_components)
else:
permutation = np.arange(n_components)
# The following seems to be required on 64-bit Windows w/ Python 3.5.
permutation = np.asarray(permutation, dtype=np.intp)
return _update_cdnmf_fast(W, HHt, XHt, permutation)
def _fit_coordinate_descent(X, W, H, tol=1e-4, max_iter=200, alpha=0.001,
l1_ratio=0., regularization=None, update_H=True,
verbose=0, shuffle=False, random_state=None):
"""Compute Non-negative Matrix Factorization (NMF) with Coordinate Descent
The objective function is minimized with an alternating minimization of W
and H. Each minimization is done with a cyclic (up to a permutation of the
features) Coordinate Descent.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Constant matrix.
W : array-like, shape (n_samples, n_components)
Initial guess for the solution.
H : array-like, shape (n_components, n_features)
Initial guess for the solution.
tol : float, default: 1e-4
Tolerance of the stopping condition.
max_iter : integer, default: 200
Maximum number of iterations before timing out.
alpha : double, default: 0.
Constant that multiplies the regularization terms.
l1_ratio : double, default: 0.
The regularization mixing parameter, with 0 <= l1_ratio <= 1.
For l1_ratio = 0 the penalty is an L2 penalty.
For l1_ratio = 1 it is an L1 penalty.
For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
regularization : 'both' | 'components' | 'transformation' | None
Select whether the regularization affects the components (H), the
transformation (W), both or none of them.
update_H : boolean, default: True
Set to True, both W and H will be estimated from initial guesses.
Set to False, only W will be estimated.
verbose : integer, default: 0
The verbosity level.
shuffle : boolean, default: False
If true, randomize the order of coordinates in the CD solver.
random_state : integer seed, RandomState instance, or None (default)
Random number generator seed control.
Returns
-------
W : array-like, shape (n_samples, n_components)
Solution to the non-negative least squares problem.
H : array-like, shape (n_components, n_features)
Solution to the non-negative least squares problem.
n_iter : int
The number of iterations done by the algorithm.
References
----------
Cichocki, Andrzej, and P. H. A. N. Anh-Huy. "Fast local algorithms for
large scale nonnegative matrix and tensor factorizations."
IEICE transactions on fundamentals of electronics, communications and
computer sciences 92.3: 708-721, 2009.
"""
# so W and Ht are both in C order in memory
Ht = check_array(H.T, order='C')
X = check_array(X, accept_sparse='csr')
# L1 and L2 regularization
l1_H, l2_H, l1_W, l2_W = 0, 0, 0, 0
if regularization in ('both', 'components'):
alpha = float(alpha)
l1_H = l1_ratio * alpha
l2_H = (1. - l1_ratio) * alpha
if regularization in ('both', 'transformation'):
alpha = float(alpha)
l1_W = l1_ratio * alpha
l2_W = (1. - l1_ratio) * alpha
rng = check_random_state(random_state)
for n_iter in range(max_iter):
violation = 0.
# Update W
violation += _update_coordinate_descent(X, W, Ht, l1_W, l2_W,
shuffle, rng)
# Update H
if update_H:
violation += _update_coordinate_descent(X.T, Ht, W, l1_H, l2_H,
shuffle, rng)
if n_iter == 0:
violation_init = violation
if violation_init == 0:
break
if verbose:
print("violation:", violation / violation_init)
if violation / violation_init <= tol:
if verbose:
print("Converged at iteration", n_iter + 1)
break
return W, Ht.T, n_iter
def non_negative_factorization(X, W=None, H=None, n_components=None,
init='random', update_H=True, solver='cd',
tol=1e-4, max_iter=200, alpha=0., l1_ratio=0.,
regularization=None, random_state=None,
verbose=0, shuffle=False, nls_max_iter=2000,
sparseness=None, beta=1, eta=0.1):
"""Compute Non-negative Matrix Factorization (NMF)
Find two non-negative matrices (W, H) whose product approximates the non-
negative matrix X. This factorization can be used for example for
dimensionality reduction, source separation or topic extraction.
The objective function is::
0.5 * ||X - WH||_Fro^2
+ alpha * l1_ratio * ||vec(W)||_1
+ alpha * l1_ratio * ||vec(H)||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
+ 0.5 * alpha * (1 - l1_ratio) * ||H||_Fro^2
Where::
||A||_Fro^2 = \sum_{i,j} A_{ij}^2 (Frobenius norm)
||vec(A)||_1 = \sum_{i,j} abs(A_{ij}) (Elementwise L1 norm)
The objective function is minimized with an alternating minimization of W
and H. If H is given and update_H=False, it solves for W only.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Constant matrix.
W : array-like, shape (n_samples, n_components)
If init='custom', it is used as initial guess for the solution.
H : array-like, shape (n_components, n_features)
If init='custom', it is used as initial guess for the solution.
If update_H=False, it is used as a constant, to solve for W only.
n_components : integer
Number of components, if n_components is not set all features
are kept.
init : None | 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar' | 'custom'
Method used to initialize the procedure.
Default: 'nndsvd' if n_components < n_features, otherwise random.
Valid options:
- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)
- 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
- 'nndsvda': NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
- 'nndsvdar': NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa
for when sparsity is not desired)
- 'custom': use custom matrices W and H
update_H : boolean, default: True
Set to True, both W and H will be estimated from initial guesses.
Set to False, only W will be estimated.
solver : 'pg' | 'cd'
Numerical solver to use:
'pg' is a (deprecated) Projected Gradient solver.
'cd' is a Coordinate Descent solver.
tol : float, default: 1e-4
Tolerance of the stopping condition.
max_iter : integer, default: 200
Maximum number of iterations before timing out.
alpha : double, default: 0.
Constant that multiplies the regularization terms.
l1_ratio : double, default: 0.
The regularization mixing parameter, with 0 <= l1_ratio <= 1.
For l1_ratio = 0 the penalty is an elementwise L2 penalty
(aka Frobenius Norm).
For l1_ratio = 1 it is an elementwise L1 penalty.
For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
regularization : 'both' | 'components' | 'transformation' | None
Select whether the regularization affects the components (H), the
transformation (W), both or none of them.
random_state : integer seed, RandomState instance, or None (default)
Random number generator seed control.
verbose : integer, default: 0
The verbosity level.
shuffle : boolean, default: False
If true, randomize the order of coordinates in the CD solver.
nls_max_iter : integer, default: 2000
Number of iterations in NLS subproblem.
Used only in the deprecated 'pg' solver.
sparseness : 'data' | 'components' | None, default: None
Where to enforce sparsity in the model.
Used only in the deprecated 'pg' solver.
beta : double, default: 1
Degree of sparseness, if sparseness is not None. Larger values mean
more sparseness. Used only in the deprecated 'pg' solver.
eta : double, default: 0.1
Degree of correctness to maintain, if sparsity is not None. Smaller
values mean larger error. Used only in the deprecated 'pg' solver.
Returns
-------
W : array-like, shape (n_samples, n_components)
Solution to the non-negative least squares problem.
H : array-like, shape (n_components, n_features)
Solution to the non-negative least squares problem.
n_iter : int
Actual number of iterations.
References
----------
C.-J. Lin. Projected gradient methods for non-negative matrix
factorization. Neural Computation, 19(2007), 2756-2779.
http://www.csie.ntu.edu.tw/~cjlin/nmf/
Cichocki, Andrzej, and P. H. A. N. Anh-Huy. "Fast local algorithms for
large scale nonnegative matrix and tensor factorizations."
IEICE transactions on fundamentals of electronics, communications and
computer sciences 92.3: 708-721, 2009.
"""
X = check_array(X, accept_sparse=('csr', 'csc'))
check_non_negative(X, "NMF (input X)")
_check_string_param(sparseness, solver)
n_samples, n_features = X.shape
if n_components is None:
n_components = n_features
if not isinstance(n_components, six.integer_types) or n_components <= 0:
raise ValueError("Number of components must be positive;"
" got (n_components=%r)" % n_components)
if not isinstance(max_iter, numbers.Number) or max_iter < 0:
raise ValueError("Maximum number of iteration must be positive;"
" got (max_iter=%r)" % max_iter)
if not isinstance(tol, numbers.Number) or tol < 0:
raise ValueError("Tolerance for stopping criteria must be "
"positive; got (tol=%r)" % tol)
# check W and H, or initialize them
if init == 'custom' and update_H:
_check_init(H, (n_components, n_features), "NMF (input H)")
_check_init(W, (n_samples, n_components), "NMF (input W)")
elif not update_H:
_check_init(H, (n_components, n_features), "NMF (input H)")
W = np.zeros((n_samples, n_components))
else:
W, H = _initialize_nmf(X, n_components, init=init,
random_state=random_state)
if solver == 'pg':
warnings.warn("'pg' solver will be removed in release 0.19."
" Use 'cd' solver instead.", DeprecationWarning)
if update_H: # fit_transform
W, H, n_iter = _fit_projected_gradient(X, W, H, tol,
max_iter,
nls_max_iter,
alpha, l1_ratio,
sparseness,
beta, eta)
else: # transform
W, H, n_iter = _update_projected_gradient_w(X, W, H,
tol, nls_max_iter,
alpha, l1_ratio,
sparseness, beta,
eta)
elif solver == 'cd':
W, H, n_iter = _fit_coordinate_descent(X, W, H, tol,
max_iter,
alpha, l1_ratio,
regularization,
update_H=update_H,
verbose=verbose,
shuffle=shuffle,
random_state=random_state)
else:
raise ValueError("Invalid solver parameter '%s'." % solver)
if n_iter == max_iter:
warnings.warn("Maximum number of iteration %d reached. Increase it to"
" improve convergence." % max_iter, ConvergenceWarning)
return W, H, n_iter
class NMF(BaseEstimator, TransformerMixin):
"""Non-Negative Matrix Factorization (NMF)
Find two non-negative matrices (W, H) whose product approximates the non-
negative matrix X. This factorization can be used for example for
dimensionality reduction, source separation or topic extraction.
The objective function is::
0.5 * ||X - WH||_Fro^2
+ alpha * l1_ratio * ||vec(W)||_1
+ alpha * l1_ratio * ||vec(H)||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
+ 0.5 * alpha * (1 - l1_ratio) * ||H||_Fro^2
Where::
||A||_Fro^2 = \sum_{i,j} A_{ij}^2 (Frobenius norm)
||vec(A)||_1 = \sum_{i,j} abs(A_{ij}) (Elementwise L1 norm)
The objective function is minimized with an alternating minimization of W
and H.
Read more in the :ref:`User Guide <NMF>`.
Parameters
----------
n_components : int or None
Number of components, if n_components is not set all features
are kept.
init : 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar' | 'custom'
Method used to initialize the procedure.
Default: 'nndsvdar' if n_components < n_features, otherwise random.
Valid options:
- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)
- 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
- 'nndsvda': NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
- 'nndsvdar': NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa
for when sparsity is not desired)
- 'custom': use custom matrices W and H
solver : 'pg' | 'cd'
Numerical solver to use:
'pg' is a Projected Gradient solver (deprecated).
'cd' is a Coordinate Descent solver (recommended).
.. versionadded:: 0.17
Coordinate Descent solver.
.. versionchanged:: 0.17
Deprecated Projected Gradient solver.
tol : double, default: 1e-4
Tolerance value used in stopping conditions.
max_iter : integer, default: 200
Number of iterations to compute.
random_state : integer seed, RandomState instance, or None (default)
Random number generator seed control.
alpha : double, default: 0.
Constant that multiplies the regularization terms. Set it to zero to
have no regularization.
.. versionadded:: 0.17
*alpha* used in the Coordinate Descent solver.
l1_ratio : double, default: 0.
The regularization mixing parameter, with 0 <= l1_ratio <= 1.
For l1_ratio = 0 the penalty is an elementwise L2 penalty
(aka Frobenius Norm).
For l1_ratio = 1 it is an elementwise L1 penalty.
For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
.. versionadded:: 0.17
Regularization parameter *l1_ratio* used in the Coordinate Descent
solver.
shuffle : boolean, default: False
If true, randomize the order of coordinates in the CD solver.
.. versionadded:: 0.17
*shuffle* parameter used in the Coordinate Descent solver.
nls_max_iter : integer, default: 2000
Number of iterations in NLS subproblem.
Used only in the deprecated 'pg' solver.
.. versionchanged:: 0.17
Deprecated Projected Gradient solver. Use Coordinate Descent solver
instead.
sparseness : 'data' | 'components' | None, default: None
Where to enforce sparsity in the model.
Used only in the deprecated 'pg' solver.
.. versionchanged:: 0.17
Deprecated Projected Gradient solver. Use Coordinate Descent solver
instead.
beta : double, default: 1
Degree of sparseness, if sparseness is not None. Larger values mean
more sparseness. Used only in the deprecated 'pg' solver.
.. versionchanged:: 0.17
Deprecated Projected Gradient solver. Use Coordinate Descent solver
instead.
eta : double, default: 0.1
Degree of correctness to maintain, if sparsity is not None. Smaller
values mean larger error. Used only in the deprecated 'pg' solver.
.. versionchanged:: 0.17
Deprecated Projected Gradient solver. Use Coordinate Descent solver
instead.
Attributes
----------
components_ : array, [n_components, n_features]
Non-negative components of the data.
reconstruction_err_ : number
Frobenius norm of the matrix difference between
the training data and the reconstructed data from
the fit produced by the model. ``|| X - WH ||_2``
n_iter_ : int
Actual number of iterations.
Examples
--------
>>> import numpy as np
>>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
>>> from sklearn.decomposition import NMF
>>> model = NMF(n_components=2, init='random', random_state=0)
>>> model.fit(X) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
NMF(alpha=0.0, beta=1, eta=0.1, init='random', l1_ratio=0.0, max_iter=200,
n_components=2, nls_max_iter=2000, random_state=0, shuffle=False,
solver='cd', sparseness=None, tol=0.0001, verbose=0)
>>> model.components_
array([[ 2.09783018, 0.30560234],
[ 2.13443044, 2.13171694]])
>>> model.reconstruction_err_ #doctest: +ELLIPSIS
0.00115993...
References
----------
C.-J. Lin. Projected gradient methods for non-negative matrix
factorization. Neural Computation, 19(2007), 2756-2779.
http://www.csie.ntu.edu.tw/~cjlin/nmf/
Cichocki, Andrzej, and P. H. A. N. Anh-Huy. "Fast local algorithms for
large scale nonnegative matrix and tensor factorizations."
IEICE transactions on fundamentals of electronics, communications and
computer sciences 92.3: 708-721, 2009.
"""
def __init__(self, n_components=None, init=None, solver='cd',
tol=1e-4, max_iter=200, random_state=None,
alpha=0., l1_ratio=0., verbose=0, shuffle=False,
nls_max_iter=2000, sparseness=None, beta=1, eta=0.1):
self.n_components = n_components
self.init = init
self.solver = solver
self.tol = tol
self.max_iter = max_iter
self.random_state = random_state
self.alpha = alpha
self.l1_ratio = l1_ratio
self.verbose = verbose
self.shuffle = shuffle
if sparseness is not None:
warnings.warn("Controlling regularization through the sparseness,"
" beta and eta arguments is only available"
" for 'pg' solver, which will be removed"
" in release 0.19. Use another solver with L1 or L2"
" regularization instead.", DeprecationWarning)
self.nls_max_iter = nls_max_iter
self.sparseness = sparseness
self.beta = beta
self.eta = eta
def fit_transform(self, X, y=None, W=None, H=None):
"""Learn a NMF model for the data X and returns the transformed data.
This is more efficient than calling fit followed by transform.
Parameters
----------
X: {array-like, sparse matrix}, shape (n_samples, n_features)
Data matrix to be decomposed
W : array-like, shape (n_samples, n_components)
If init='custom', it is used as initial guess for the solution.
H : array-like, shape (n_components, n_features)
If init='custom', it is used as initial guess for the solution.
Attributes
----------
components_ : array-like, shape (n_components, n_features)
Factorization matrix, sometimes called 'dictionary'.
n_iter_ : int
Actual number of iterations for the transform.
Returns
-------
W: array, shape (n_samples, n_components)
Transformed data.
"""
X = check_array(X, accept_sparse=('csr', 'csc'))
W, H, n_iter_ = non_negative_factorization(
X=X, W=W, H=H, n_components=self.n_components,
init=self.init, update_H=True, solver=self.solver,
tol=self.tol, max_iter=self.max_iter, alpha=self.alpha,
l1_ratio=self.l1_ratio, regularization='both',
random_state=self.random_state, verbose=self.verbose,
shuffle=self.shuffle,
nls_max_iter=self.nls_max_iter, sparseness=self.sparseness,
beta=self.beta, eta=self.eta)
if self.solver == 'pg':
self.comp_sparseness_ = _sparseness(H.ravel())
self.data_sparseness_ = _sparseness(W.ravel())
self.reconstruction_err_ = _safe_compute_error(X, W, H)
self.n_components_ = H.shape[0]
self.components_ = H
self.n_iter_ = n_iter_
return W
def fit(self, X, y=None, **params):
"""Learn a NMF model for the data X.
Parameters
----------
X: {array-like, sparse matrix}, shape (n_samples, n_features)
Data matrix to be decomposed
Attributes
----------
components_ : array-like, shape (n_components, n_features)
Factorization matrix, sometimes called 'dictionary'.
n_iter_ : int
Actual number of iterations for the transform.
Returns
-------
self
"""
self.fit_transform(X, **params)
return self
def transform(self, X):
"""Transform the data X according to the fitted NMF model
Parameters
----------
X: {array-like, sparse matrix}, shape (n_samples, n_features)
Data matrix to be transformed by the model
Attributes
----------
n_iter_ : int
Actual number of iterations for the transform.
Returns
-------
W: array, shape (n_samples, n_components)
Transformed data
"""
check_is_fitted(self, 'n_components_')
W, _, n_iter_ = non_negative_factorization(
X=X, W=None, H=self.components_, n_components=self.n_components_,
init=self.init, update_H=False, solver=self.solver,
tol=self.tol, max_iter=self.max_iter, alpha=self.alpha,
l1_ratio=self.l1_ratio, regularization='both',
random_state=self.random_state, verbose=self.verbose,
shuffle=self.shuffle,
nls_max_iter=self.nls_max_iter, sparseness=self.sparseness,
beta=self.beta, eta=self.eta)
self.n_iter_ = n_iter_
return W
def inverse_transform(self, W):
"""
Parameters
----------
W: {array-like, sparse matrix}, shape (n_samples, n_components)
Transformed Data matrix
Returns
-------
X: {array-like, sparse matrix}, shape (n_samples, n_features)
Data matrix of original shape
.. versionadded:: 0.18
"""
check_is_fitted(self, 'n_components_')
return np.dot(W, self.components_)
@deprecated("It will be removed in release 0.19. Use NMF instead."
"'pg' solver is still available until release 0.19.")
class ProjectedGradientNMF(NMF):
"""Non-Negative Matrix Factorization (NMF)
Find two non-negative matrices (W, H) whose product approximates the non-
negative matrix X. This factorization can be used for example for
dimensionality reduction, source separation or topic extraction.
The objective function is::
0.5 * ||X - WH||_Fro^2
+ alpha * l1_ratio * ||vec(W)||_1
+ alpha * l1_ratio * ||vec(H)||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
+ 0.5 * alpha * (1 - l1_ratio) * ||H||_Fro^2
Where::
||A||_Fro^2 = \sum_{i,j} A_{ij}^2 (Frobenius norm)
||vec(A)||_1 = \sum_{i,j} abs(A_{ij}) (Elementwise L1 norm)
The objective function is minimized with an alternating minimization of W
and H.
Read more in the :ref:`User Guide <NMF>`.
Parameters
----------
n_components : int or None
Number of components, if n_components is not set all features
are kept.
init : 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar' | 'custom'
Method used to initialize the procedure.
Default: 'nndsvdar' if n_components < n_features, otherwise random.
Valid options:
- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)
- 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
- 'nndsvda': NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
- 'nndsvdar': NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa
for when sparsity is not desired)
- 'custom': use custom matrices W and H
solver : 'pg' | 'cd'
Numerical solver to use:
'pg' is a Projected Gradient solver (deprecated).
'cd' is a Coordinate Descent solver (recommended).
.. versionadded:: 0.17
Coordinate Descent solver.
.. versionchanged:: 0.17
Deprecated Projected Gradient solver.
tol : double, default: 1e-4
Tolerance value used in stopping conditions.
max_iter : integer, default: 200
Number of iterations to compute.
random_state : integer seed, RandomState instance, or None (default)
Random number generator seed control.
alpha : double, default: 0.
Constant that multiplies the regularization terms. Set it to zero to
have no regularization.
.. versionadded:: 0.17
*alpha* used in the Coordinate Descent solver.
l1_ratio : double, default: 0.
The regularization mixing parameter, with 0 <= l1_ratio <= 1.
For l1_ratio = 0 the penalty is an elementwise L2 penalty
(aka Frobenius Norm).
For l1_ratio = 1 it is an elementwise L1 penalty.
For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
.. versionadded:: 0.17
Regularization parameter *l1_ratio* used in the Coordinate Descent
solver.
shuffle : boolean, default: False
If true, randomize the order of coordinates in the CD solver.
.. versionadded:: 0.17
*shuffle* parameter used in the Coordinate Descent solver.
nls_max_iter : integer, default: 2000
Number of iterations in NLS subproblem.
Used only in the deprecated 'pg' solver.
.. versionchanged:: 0.17
Deprecated Projected Gradient solver. Use Coordinate Descent solver
instead.
sparseness : 'data' | 'components' | None, default: None
Where to enforce sparsity in the model.
Used only in the deprecated 'pg' solver.
.. versionchanged:: 0.17
Deprecated Projected Gradient solver. Use Coordinate Descent solver
instead.
beta : double, default: 1
Degree of sparseness, if sparseness is not None. Larger values mean
more sparseness. Used only in the deprecated 'pg' solver.
.. versionchanged:: 0.17
Deprecated Projected Gradient solver. Use Coordinate Descent solver
instead.
eta : double, default: 0.1
Degree of correctness to maintain, if sparsity is not None. Smaller
values mean larger error. Used only in the deprecated 'pg' solver.
.. versionchanged:: 0.17
Deprecated Projected Gradient solver. Use Coordinate Descent solver
instead.
Attributes
----------
components_ : array, [n_components, n_features]
Non-negative components of the data.
reconstruction_err_ : number
Frobenius norm of the matrix difference between
the training data and the reconstructed data from
the fit produced by the model. ``|| X - WH ||_2``
n_iter_ : int
Actual number of iterations.
Examples
--------
>>> import numpy as np
>>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
>>> from sklearn.decomposition import NMF
>>> model = NMF(n_components=2, init='random', random_state=0)
>>> model.fit(X) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
NMF(alpha=0.0, beta=1, eta=0.1, init='random', l1_ratio=0.0, max_iter=200,
n_components=2, nls_max_iter=2000, random_state=0, shuffle=False,
solver='cd', sparseness=None, tol=0.0001, verbose=0)
>>> model.components_
array([[ 2.09783018, 0.30560234],
[ 2.13443044, 2.13171694]])
>>> model.reconstruction_err_ #doctest: +ELLIPSIS
0.00115993...
References
----------
C.-J. Lin. Projected gradient methods for non-negative matrix
factorization. Neural Computation, 19(2007), 2756-2779.
http://www.csie.ntu.edu.tw/~cjlin/nmf/
Cichocki, Andrzej, and P. H. A. N. Anh-Huy. "Fast local algorithms for
large scale nonnegative matrix and tensor factorizations."
IEICE transactions on fundamentals of electronics, communications and
computer sciences 92.3: 708-721, 2009.
"""
def __init__(self, n_components=None, solver='pg', init=None,
tol=1e-4, max_iter=200, random_state=None,
alpha=0., l1_ratio=0., verbose=0,
nls_max_iter=2000, sparseness=None, beta=1, eta=0.1):
super(ProjectedGradientNMF, self).__init__(
n_components=n_components, init=init, solver='pg', tol=tol,
max_iter=max_iter, random_state=random_state, alpha=alpha,
l1_ratio=l1_ratio, verbose=verbose, nls_max_iter=nls_max_iter,
sparseness=sparseness, beta=beta, eta=eta)
| bsd-3-clause |
embray/numpy | numpy/lib/npyio.py | 1 | 66490 | 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 ._compiled_base 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']
def seek_gzip_factory(f):
"""Use this factory to produce the class so that we can do a lazy
import on gzip.
"""
import gzip
class GzipFile(gzip.GzipFile):
def seek(self, offset, whence=0):
# figure out new position (we can only seek forwards)
if whence == 1:
offset = self.offset + offset
if whence not in [0, 1]:
raise IOError("Illegal argument")
if offset < self.offset:
# for negative seek, rewind and do positive seek
self.rewind()
count = offset - self.offset
for i in range(count // 1024):
self.read(1024)
self.read(count % 1024)
def tell(self):
return self.offset
if isinstance(f, str):
f = GzipFile(f)
elif isinstance(f, gzip.GzipFile):
# cast to our GzipFile if its already a gzip.GzipFile
try:
name = f.name
except AttributeError:
# Backward compatibility for <= 2.5
name = f.filename
mode = f.mode
f = GzipFile(fileobj=f.fileobj, filename=name)
f.mode = mode
return f
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 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.
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):
# 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 = []
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)
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):
"""
Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.
Parameters
----------
file : file-like object or string
The file to read. Compressed files with the filename extension
``.gz`` are acceptable. 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.
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.
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
elif isinstance(file, gzip.GzipFile):
fid = seek_gzip_factory(file)
else:
fid = file
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)
elif magic == format.MAGIC_PREFIX:
# .npy file
if mmap_mode:
return format.open_memmap(file, mode=mmap_mode)
else:
return format.read_array(fid)
else:
# Try a pickle
try:
return pickle.load(fid)
except:
raise IOError(
"Failed to interpret file %s as a pickle" % repr(file))
finally:
if own_fid:
fid.close()
def save(file, arr):
"""
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.
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 `format`.
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
try:
arr = np.asanyarray(arr)
format.write_array(fid, arr)
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 `format`.
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):
# 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))
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 """
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 float
elif issubclass(typ, np.complex):
return complex
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
record 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, optional
The character 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 record
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.
Examples
--------
>>> from StringIO 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
comments = asbytes(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'):
fh = iter(seek_gzip_factory(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."""
line = asbytes(line).split(comments)[0].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]
# 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 not ndmin 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
Character separating columns.
newline : str, optional
.. 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
Character separating lines.
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:
fh.write(asbytes(format % tuple(row) + newline))
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,
skiprows=0, skip_header=0, skip_footer=0, converters=None,
missing='', 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):
"""
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.
skip_rows : int, optional
`skip_rows` was deprecated in numpy 1.5, and will be removed in
numpy 2.0. 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 deprecated in numpy 1.5, and will be removed in
numpy 2.0. 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.
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 StringIO 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')])
"""
# 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, unicode):
missing = asbytes(missing)
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)
# Get the first valid lines after the first skiprows ones ..
if skiprows:
warnings.warn(
"The use of `skiprows` is deprecated, it will be removed in "
"numpy 2.0.\nPlease use `skip_header` instead.",
DeprecationWarning)
skip_header = skiprows
# 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)
# 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 deprecated `missing`
if missing != asbytes(''):
warnings.warn(
"The use of `missing` is deprecated, it will be removed in "
"Numpy 2.0.\nPlease use `missing_values` instead.",
DeprecationWarning)
values = [str(_) for _ in missing.split(asbytes(","))]
for entry in missing_values:
entry.extend(values)
# Process the filling_values ...............................
# Rename the input for convenience
user_filling_values = filling_values or []
# 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 (i, conv) in user_converters.items():
# If the converter is specified by column names, use the index instead
if _is_string_like(i):
try:
i = names.index(i)
except ValueError:
continue
elif usecols:
try:
i = usecols.index(i)
except ValueError:
# Unused converter specified
continue
# Find the value to test:
if len(first_line):
testing_value = first_values[i]
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)
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
# Select only the columns we need
if usecols:
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 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:
# conversionfuncs = [conv._loose_call for conv in converters]
# else:
# conversionfuncs = [conv._strict_call for conv in converters]
# for (i, vals) in enumerate(rows):
# rows[i] = tuple([convert(val)
# for (convert, val) in zip(conversionfuncs, vals)])
if loose:
rows = list(zip(*[[converter._loose_call(_r) for _r in map(itemgetter(i), rows)]
for (i, converter) in enumerate(converters)]))
else:
rows = list(zip(*[[converter._strict_call(_r) for _r in map(itemgetter(i), rows)]
for (i, converter) 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.update(dtype=kwargs.get('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.
"""
case_sensitive = kwargs.get('case_sensitive', "lower") or "lower"
names = kwargs.get('names', True)
if names is None:
names = True
kwargs.update(dtype=kwargs.get('update', None),
delimiter=kwargs.get('delimiter', ",") or ",",
names=names,
case_sensitive=case_sensitive)
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
| 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 |
nmayorov/scikit-learn | examples/plot_multilabel.py | 236 | 4157 | # Authors: Vlad Niculae, Mathieu Blondel
# License: BSD 3 clause
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the number of labels: n ~ Poisson(n_labels)
- n times, choose a class c: c ~ Multinomial(theta)
- pick the document length: k ~ Poisson(length)
- k times, choose a word: w ~ Multinomial(theta_c)
In the above process, rejection sampling is used to make sure that n is more
than 2, and that the document length is never zero. Likewise, we reject classes
which have already been chosen. The documents that are assigned to both
classes are plotted surrounded by two colored circles.
The classification is performed by projecting to the first two principal
components found by PCA and CCA for visualisation purposes, followed by using
the :class:`sklearn.multiclass.OneVsRestClassifier` metaclassifier using two
SVCs with linear kernels to learn a discriminative model for each class.
Note that PCA is used to perform an unsupervised dimensionality reduction,
while CCA is used to perform a supervised one.
Note: in the plot, "unlabeled samples" does not mean that we don't know the
labels (as in semi-supervised learning) but that the samples simply do *not*
have a label.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_multilabel_classification
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
from sklearn.preprocessing import LabelBinarizer
from sklearn.decomposition import PCA
from sklearn.cross_decomposition import CCA
def plot_hyperplane(clf, min_x, max_x, linestyle, label):
# get the separating hyperplane
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(min_x - 5, max_x + 5) # make sure the line is long enough
yy = a * xx - (clf.intercept_[0]) / w[1]
plt.plot(xx, yy, linestyle, label=label)
def plot_subfigure(X, Y, subplot, title, transform):
if transform == "pca":
X = PCA(n_components=2).fit_transform(X)
elif transform == "cca":
X = CCA(n_components=2).fit(X, Y).transform(X)
else:
raise ValueError
min_x = np.min(X[:, 0])
max_x = np.max(X[:, 0])
min_y = np.min(X[:, 1])
max_y = np.max(X[:, 1])
classif = OneVsRestClassifier(SVC(kernel='linear'))
classif.fit(X, Y)
plt.subplot(2, 2, subplot)
plt.title(title)
zero_class = np.where(Y[:, 0])
one_class = np.where(Y[:, 1])
plt.scatter(X[:, 0], X[:, 1], s=40, c='gray')
plt.scatter(X[zero_class, 0], X[zero_class, 1], s=160, edgecolors='b',
facecolors='none', linewidths=2, label='Class 1')
plt.scatter(X[one_class, 0], X[one_class, 1], s=80, edgecolors='orange',
facecolors='none', linewidths=2, label='Class 2')
plot_hyperplane(classif.estimators_[0], min_x, max_x, 'k--',
'Boundary\nfor class 1')
plot_hyperplane(classif.estimators_[1], min_x, max_x, 'k-.',
'Boundary\nfor class 2')
plt.xticks(())
plt.yticks(())
plt.xlim(min_x - .5 * max_x, max_x + .5 * max_x)
plt.ylim(min_y - .5 * max_y, max_y + .5 * max_y)
if subplot == 2:
plt.xlabel('First principal component')
plt.ylabel('Second principal component')
plt.legend(loc="upper left")
plt.figure(figsize=(8, 6))
X, Y = make_multilabel_classification(n_classes=2, n_labels=1,
allow_unlabeled=True,
random_state=1)
plot_subfigure(X, Y, 1, "With unlabeled samples + CCA", "cca")
plot_subfigure(X, Y, 2, "With unlabeled samples + PCA", "pca")
X, Y = make_multilabel_classification(n_classes=2, n_labels=1,
allow_unlabeled=False,
random_state=1)
plot_subfigure(X, Y, 3, "Without unlabeled samples + CCA", "cca")
plot_subfigure(X, Y, 4, "Without unlabeled samples + PCA", "pca")
plt.subplots_adjust(.04, .02, .97, .94, .09, .2)
plt.show()
| bsd-3-clause |
rs2/pandas | pandas/tests/io/parser/test_na_values.py | 2 | 15082 | """
Tests that NA values are properly handled during
parsing for all of the parsers defined in parsers.py
"""
from io import StringIO
import numpy as np
import pytest
from pandas._libs.parsers import STR_NA_VALUES
from pandas import DataFrame, Index, MultiIndex
import pandas._testing as tm
def test_string_nas(all_parsers):
parser = all_parsers
data = """A,B,C
a,b,c
d,,f
,g,h
"""
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[["a", "b", "c"], ["d", np.nan, "f"], [np.nan, "g", "h"]],
columns=["A", "B", "C"],
)
tm.assert_frame_equal(result, expected)
def test_detect_string_na(all_parsers):
parser = all_parsers
data = """A,B
foo,bar
NA,baz
NaN,nan
"""
expected = DataFrame(
[["foo", "bar"], [np.nan, "baz"], [np.nan, np.nan]], columns=["A", "B"]
)
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"na_values",
[
["-999.0", "-999"],
[-999, -999.0],
[-999.0, -999],
["-999.0"],
["-999"],
[-999.0],
[-999],
],
)
@pytest.mark.parametrize(
"data",
[
"""A,B
-999,1.2
2,-999
3,4.5
""",
"""A,B
-999,1.200
2,-999.000
3,4.500
""",
],
)
def test_non_string_na_values(all_parsers, data, na_values):
# see gh-3611: with an odd float format, we can't match
# the string "999.0" exactly but still need float matching
parser = all_parsers
expected = DataFrame([[np.nan, 1.2], [2.0, np.nan], [3.0, 4.5]], columns=["A", "B"])
result = parser.read_csv(StringIO(data), na_values=na_values)
tm.assert_frame_equal(result, expected)
def test_default_na_values(all_parsers):
_NA_VALUES = {
"-1.#IND",
"1.#QNAN",
"1.#IND",
"-1.#QNAN",
"#N/A",
"N/A",
"n/a",
"NA",
"<NA>",
"#NA",
"NULL",
"null",
"NaN",
"nan",
"-NaN",
"-nan",
"#N/A N/A",
"",
}
assert _NA_VALUES == STR_NA_VALUES
parser = all_parsers
nv = len(_NA_VALUES)
def f(i, v):
if i == 0:
buf = ""
elif i > 0:
buf = "".join([","] * i)
buf = f"{buf}{v}"
if i < nv - 1:
joined = "".join([","] * (nv - i - 1))
buf = f"{buf}{joined}"
return buf
data = StringIO("\n".join(f(i, v) for i, v in enumerate(_NA_VALUES)))
expected = DataFrame(np.nan, columns=range(nv), index=range(nv))
result = parser.read_csv(data, header=None)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("na_values", ["baz", ["baz"]])
def test_custom_na_values(all_parsers, na_values):
parser = all_parsers
data = """A,B,C
ignore,this,row
1,NA,3
-1.#IND,5,baz
7,8,NaN
"""
expected = DataFrame(
[[1.0, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]], columns=["A", "B", "C"]
)
result = parser.read_csv(StringIO(data), na_values=na_values, skiprows=[1])
tm.assert_frame_equal(result, expected)
def test_bool_na_values(all_parsers):
data = """A,B,C
True,False,True
NA,True,False
False,NA,True"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame(
{
"A": np.array([True, np.nan, False], dtype=object),
"B": np.array([False, True, np.nan], dtype=object),
"C": [True, False, True],
}
)
tm.assert_frame_equal(result, expected)
def test_na_value_dict(all_parsers):
data = """A,B,C
foo,bar,NA
bar,foo,foo
foo,bar,NA
bar,foo,foo"""
parser = all_parsers
df = parser.read_csv(StringIO(data), na_values={"A": ["foo"], "B": ["bar"]})
expected = DataFrame(
{
"A": [np.nan, "bar", np.nan, "bar"],
"B": [np.nan, "foo", np.nan, "foo"],
"C": [np.nan, "foo", np.nan, "foo"],
}
)
tm.assert_frame_equal(df, expected)
@pytest.mark.parametrize(
"index_col,expected",
[
(
[0],
DataFrame({"b": [np.nan], "c": [1], "d": [5]}, index=Index([0], name="a")),
),
(
[0, 2],
DataFrame(
{"b": [np.nan], "d": [5]},
index=MultiIndex.from_tuples([(0, 1)], names=["a", "c"]),
),
),
(
["a", "c"],
DataFrame(
{"b": [np.nan], "d": [5]},
index=MultiIndex.from_tuples([(0, 1)], names=["a", "c"]),
),
),
],
)
def test_na_value_dict_multi_index(all_parsers, index_col, expected):
data = """\
a,b,c,d
0,NA,1,5
"""
parser = all_parsers
result = parser.read_csv(StringIO(data), na_values=set(), index_col=index_col)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"kwargs,expected",
[
(
dict(),
DataFrame(
{
"A": ["a", "b", np.nan, "d", "e", np.nan, "g"],
"B": [1, 2, 3, 4, 5, 6, 7],
"C": ["one", "two", "three", np.nan, "five", np.nan, "seven"],
}
),
),
(
dict(na_values={"A": [], "C": []}, keep_default_na=False),
DataFrame(
{
"A": ["a", "b", "", "d", "e", "nan", "g"],
"B": [1, 2, 3, 4, 5, 6, 7],
"C": ["one", "two", "three", "nan", "five", "", "seven"],
}
),
),
(
dict(na_values=["a"], keep_default_na=False),
DataFrame(
{
"A": [np.nan, "b", "", "d", "e", "nan", "g"],
"B": [1, 2, 3, 4, 5, 6, 7],
"C": ["one", "two", "three", "nan", "five", "", "seven"],
}
),
),
(
dict(na_values={"A": [], "C": []}),
DataFrame(
{
"A": ["a", "b", np.nan, "d", "e", np.nan, "g"],
"B": [1, 2, 3, 4, 5, 6, 7],
"C": ["one", "two", "three", np.nan, "five", np.nan, "seven"],
}
),
),
],
)
def test_na_values_keep_default(all_parsers, kwargs, expected):
data = """\
A,B,C
a,1,one
b,2,two
,3,three
d,4,nan
e,5,five
nan,6,
g,7,seven
"""
parser = all_parsers
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
def test_no_na_values_no_keep_default(all_parsers):
# see gh-4318: passing na_values=None and
# keep_default_na=False yields 'None" as a na_value
data = """\
A,B,C
a,1,None
b,2,two
,3,None
d,4,nan
e,5,five
nan,6,
g,7,seven
"""
parser = all_parsers
result = parser.read_csv(StringIO(data), keep_default_na=False)
expected = DataFrame(
{
"A": ["a", "b", "", "d", "e", "nan", "g"],
"B": [1, 2, 3, 4, 5, 6, 7],
"C": ["None", "two", "None", "nan", "five", "", "seven"],
}
)
tm.assert_frame_equal(result, expected)
def test_no_keep_default_na_dict_na_values(all_parsers):
# see gh-19227
data = "a,b\n,2"
parser = all_parsers
result = parser.read_csv(
StringIO(data), na_values={"b": ["2"]}, keep_default_na=False
)
expected = DataFrame({"a": [""], "b": [np.nan]})
tm.assert_frame_equal(result, expected)
def test_no_keep_default_na_dict_na_scalar_values(all_parsers):
# see gh-19227
#
# Scalar values shouldn't cause the parsing to crash or fail.
data = "a,b\n1,2"
parser = all_parsers
df = parser.read_csv(StringIO(data), na_values={"b": 2}, keep_default_na=False)
expected = DataFrame({"a": [1], "b": [np.nan]})
tm.assert_frame_equal(df, expected)
@pytest.mark.parametrize("col_zero_na_values", [113125, "113125"])
def test_no_keep_default_na_dict_na_values_diff_reprs(all_parsers, col_zero_na_values):
# see gh-19227
data = """\
113125,"blah","/blaha",kjsdkj,412.166,225.874,214.008
729639,"qwer","",asdfkj,466.681,,252.373
"""
parser = all_parsers
expected = DataFrame(
{
0: [np.nan, 729639.0],
1: [np.nan, "qwer"],
2: ["/blaha", np.nan],
3: ["kjsdkj", "asdfkj"],
4: [412.166, 466.681],
5: ["225.874", ""],
6: [np.nan, 252.373],
}
)
result = parser.read_csv(
StringIO(data),
header=None,
keep_default_na=False,
na_values={2: "", 6: "214.008", 1: "blah", 0: col_zero_na_values},
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"na_filter,row_data",
[
(True, [[1, "A"], [np.nan, np.nan], [3, "C"]]),
(False, [["1", "A"], ["nan", "B"], ["3", "C"]]),
],
)
def test_na_values_na_filter_override(all_parsers, na_filter, row_data):
data = """\
A,B
1,A
nan,B
3,C
"""
parser = all_parsers
result = parser.read_csv(StringIO(data), na_values=["B"], na_filter=na_filter)
expected = DataFrame(row_data, columns=["A", "B"])
tm.assert_frame_equal(result, expected)
def test_na_trailing_columns(all_parsers):
parser = all_parsers
data = """Date,Currency,Symbol,Type,Units,UnitPrice,Cost,Tax
2012-03-14,USD,AAPL,BUY,1000
2012-05-12,USD,SBUX,SELL,500"""
# Trailing columns should be all NaN.
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
["2012-03-14", "USD", "AAPL", "BUY", 1000, np.nan, np.nan, np.nan],
["2012-05-12", "USD", "SBUX", "SELL", 500, np.nan, np.nan, np.nan],
],
columns=[
"Date",
"Currency",
"Symbol",
"Type",
"Units",
"UnitPrice",
"Cost",
"Tax",
],
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"na_values,row_data",
[
(1, [[np.nan, 2.0], [2.0, np.nan]]),
({"a": 2, "b": 1}, [[1.0, 2.0], [np.nan, np.nan]]),
],
)
def test_na_values_scalar(all_parsers, na_values, row_data):
# see gh-12224
parser = all_parsers
names = ["a", "b"]
data = "1,2\n2,1"
result = parser.read_csv(StringIO(data), names=names, na_values=na_values)
expected = DataFrame(row_data, columns=names)
tm.assert_frame_equal(result, expected)
def test_na_values_dict_aliasing(all_parsers):
parser = all_parsers
na_values = {"a": 2, "b": 1}
na_values_copy = na_values.copy()
names = ["a", "b"]
data = "1,2\n2,1"
expected = DataFrame([[1.0, 2.0], [np.nan, np.nan]], columns=names)
result = parser.read_csv(StringIO(data), names=names, na_values=na_values)
tm.assert_frame_equal(result, expected)
tm.assert_dict_equal(na_values, na_values_copy)
def test_na_values_dict_col_index(all_parsers):
# see gh-14203
data = "a\nfoo\n1"
parser = all_parsers
na_values = {0: "foo"}
result = parser.read_csv(StringIO(data), na_values=na_values)
expected = DataFrame({"a": [np.nan, 1]})
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
(
str(2 ** 63) + "\n" + str(2 ** 63 + 1),
dict(na_values=[2 ** 63]),
DataFrame([str(2 ** 63), str(2 ** 63 + 1)]),
),
(str(2 ** 63) + ",1" + "\n,2", dict(), DataFrame([[str(2 ** 63), 1], ["", 2]])),
(str(2 ** 63) + "\n1", dict(na_values=[2 ** 63]), DataFrame([np.nan, 1])),
],
)
def test_na_values_uint64(all_parsers, data, kwargs, expected):
# see gh-14983
parser = all_parsers
result = parser.read_csv(StringIO(data), header=None, **kwargs)
tm.assert_frame_equal(result, expected)
def test_empty_na_values_no_default_with_index(all_parsers):
# see gh-15835
data = "a,1\nb,2"
parser = all_parsers
expected = DataFrame({"1": [2]}, index=Index(["b"], name="a"))
result = parser.read_csv(StringIO(data), index_col=0, keep_default_na=False)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"na_filter,index_data", [(False, ["", "5"]), (True, [np.nan, 5.0])]
)
def test_no_na_filter_on_index(all_parsers, na_filter, index_data):
# see gh-5239
#
# Don't parse NA-values in index unless na_filter=True
parser = all_parsers
data = "a,b,c\n1,,3\n4,5,6"
expected = DataFrame({"a": [1, 4], "c": [3, 6]}, index=Index(index_data, name="b"))
result = parser.read_csv(StringIO(data), index_col=[1], na_filter=na_filter)
tm.assert_frame_equal(result, expected)
def test_inf_na_values_with_int_index(all_parsers):
# see gh-17128
parser = all_parsers
data = "idx,col1,col2\n1,3,4\n2,inf,-inf"
# Don't fail with OverflowError with inf's and integer index column.
out = parser.read_csv(StringIO(data), index_col=[0], na_values=["inf", "-inf"])
expected = DataFrame(
{"col1": [3, np.nan], "col2": [4, np.nan]}, index=Index([1, 2], name="idx")
)
tm.assert_frame_equal(out, expected)
@pytest.mark.parametrize("na_filter", [True, False])
def test_na_values_with_dtype_str_and_na_filter(all_parsers, na_filter):
# see gh-20377
parser = all_parsers
data = "a,b,c\n1,,3\n4,5,6"
# na_filter=True --> missing value becomes NaN.
# na_filter=False --> missing value remains empty string.
empty = np.nan if na_filter else ""
expected = DataFrame({"a": ["1", "4"], "b": [empty, "5"], "c": ["3", "6"]})
result = parser.read_csv(StringIO(data), na_filter=na_filter, dtype=str)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data, na_values",
[
("false,1\n,1\ntrue", None),
("false,1\nnull,1\ntrue", None),
("false,1\nnan,1\ntrue", None),
("false,1\nfoo,1\ntrue", "foo"),
("false,1\nfoo,1\ntrue", ["foo"]),
("false,1\nfoo,1\ntrue", {"a": "foo"}),
],
)
def test_cast_NA_to_bool_raises_error(all_parsers, data, na_values):
parser = all_parsers
msg = (
"(Bool column has NA values in column [0a])|"
"(cannot safely convert passed user dtype of "
"bool for object dtyped data in column 0)"
)
with pytest.raises(ValueError, match=msg):
parser.read_csv(
StringIO(data),
header=None,
names=["a", "b"],
dtype={"a": "bool"},
na_values=na_values,
)
def test_str_nan_dropped(all_parsers):
# see gh-21131
parser = all_parsers
data = """File: small.csv,,
10010010233,0123,654
foo,,bar
01001000155,4530,898"""
result = parser.read_csv(
StringIO(data),
header=None,
names=["col1", "col2", "col3"],
dtype={"col1": str, "col2": str, "col3": str},
).dropna()
expected = DataFrame(
{
"col1": ["10010010233", "01001000155"],
"col2": ["0123", "4530"],
"col3": ["654", "898"],
},
index=[1, 3],
)
tm.assert_frame_equal(result, expected)
| bsd-3-clause |
kazemakase/scikit-learn | sklearn/feature_extraction/text.py | 24 | 50103 | # -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Robert Layton <robertlayton@gmail.com>
# Jochen Wersdörfer <jochen@wersdoerfer.de>
# Roman Sinayev <roman.sinayev@gmail.com>
#
# License: BSD 3 clause
"""
The :mod:`sklearn.feature_extraction.text` submodule gathers utilities to
build feature vectors from text documents.
"""
from __future__ import unicode_literals
import array
from collections import Mapping, defaultdict
import numbers
from operator import itemgetter
import re
import unicodedata
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..externals.six.moves import xrange
from ..preprocessing import normalize
from .hashing import FeatureHasher
from .stop_words import ENGLISH_STOP_WORDS
from ..utils import deprecated
from ..utils.fixes import frombuffer_empty, bincount
from ..utils.validation import check_is_fitted
__all__ = ['CountVectorizer',
'ENGLISH_STOP_WORDS',
'TfidfTransformer',
'TfidfVectorizer',
'strip_accents_ascii',
'strip_accents_unicode',
'strip_tags']
def strip_accents_unicode(s):
"""Transform accentuated unicode symbols into their simple counterpart
Warning: the python-level loop and join operations make this
implementation 20 times slower than the strip_accents_ascii basic
normalization.
See also
--------
strip_accents_ascii
Remove accentuated char for any unicode symbol that has a direct
ASCII equivalent.
"""
return ''.join([c for c in unicodedata.normalize('NFKD', s)
if not unicodedata.combining(c)])
def strip_accents_ascii(s):
"""Transform accentuated unicode symbols into ascii or nothing
Warning: this solution is only suited for languages that have a direct
transliteration to ASCII symbols.
See also
--------
strip_accents_unicode
Remove accentuated char for any unicode symbol.
"""
nkfd_form = unicodedata.normalize('NFKD', s)
return nkfd_form.encode('ASCII', 'ignore').decode('ASCII')
def strip_tags(s):
"""Basic regexp based HTML / XML tag stripper function
For serious HTML/XML preprocessing you should rather use an external
library such as lxml or BeautifulSoup.
"""
return re.compile(r"<([^>]+)>", flags=re.UNICODE).sub(" ", s)
def _check_stop_list(stop):
if stop == "english":
return ENGLISH_STOP_WORDS
elif isinstance(stop, six.string_types):
raise ValueError("not a built-in stop list: %s" % stop)
else: # assume it's a collection
return stop
class VectorizerMixin(object):
"""Provides common code for text vectorizers (tokenization logic)."""
_white_spaces = re.compile(r"\s\s+")
def decode(self, doc):
"""Decode the input into a string of unicode symbols
The decoding strategy depends on the vectorizer parameters.
"""
if self.input == 'filename':
with open(doc, 'rb') as fh:
doc = fh.read()
elif self.input == 'file':
doc = doc.read()
if isinstance(doc, bytes):
doc = doc.decode(self.encoding, self.decode_error)
if doc is np.nan:
raise ValueError("np.nan is an invalid document, expected byte or "
"unicode string.")
return doc
def _word_ngrams(self, tokens, stop_words=None):
"""Turn tokens into a sequence of n-grams after stop words filtering"""
# handle stop words
if stop_words is not None:
tokens = [w for w in tokens if w not in stop_words]
# handle token n-grams
min_n, max_n = self.ngram_range
if max_n != 1:
original_tokens = tokens
tokens = []
n_original_tokens = len(original_tokens)
for n in xrange(min_n,
min(max_n + 1, n_original_tokens + 1)):
for i in xrange(n_original_tokens - n + 1):
tokens.append(" ".join(original_tokens[i: i + n]))
return tokens
def _char_ngrams(self, text_document):
"""Tokenize text_document into a sequence of character n-grams"""
# normalize white spaces
text_document = self._white_spaces.sub(" ", text_document)
text_len = len(text_document)
ngrams = []
min_n, max_n = self.ngram_range
for n in xrange(min_n, min(max_n + 1, text_len + 1)):
for i in xrange(text_len - n + 1):
ngrams.append(text_document[i: i + n])
return ngrams
def _char_wb_ngrams(self, text_document):
"""Whitespace sensitive char-n-gram tokenization.
Tokenize text_document into a sequence of character n-grams
excluding any whitespace (operating only inside word boundaries)"""
# normalize white spaces
text_document = self._white_spaces.sub(" ", text_document)
min_n, max_n = self.ngram_range
ngrams = []
for w in text_document.split():
w = ' ' + w + ' '
w_len = len(w)
for n in xrange(min_n, max_n + 1):
offset = 0
ngrams.append(w[offset:offset + n])
while offset + n < w_len:
offset += 1
ngrams.append(w[offset:offset + n])
if offset == 0: # count a short word (w_len < n) only once
break
return ngrams
def build_preprocessor(self):
"""Return a function to preprocess the text before tokenization"""
if self.preprocessor is not None:
return self.preprocessor
# unfortunately python functools package does not have an efficient
# `compose` function that would have allowed us to chain a dynamic
# number of functions. However the cost of a lambda call is a few
# hundreds of nanoseconds which is negligible when compared to the
# cost of tokenizing a string of 1000 chars for instance.
noop = lambda x: x
# accent stripping
if not self.strip_accents:
strip_accents = noop
elif callable(self.strip_accents):
strip_accents = self.strip_accents
elif self.strip_accents == 'ascii':
strip_accents = strip_accents_ascii
elif self.strip_accents == 'unicode':
strip_accents = strip_accents_unicode
else:
raise ValueError('Invalid value for "strip_accents": %s' %
self.strip_accents)
if self.lowercase:
return lambda x: strip_accents(x.lower())
else:
return strip_accents
def build_tokenizer(self):
"""Return a function that splits a string into a sequence of tokens"""
if self.tokenizer is not None:
return self.tokenizer
token_pattern = re.compile(self.token_pattern)
return lambda doc: token_pattern.findall(doc)
def get_stop_words(self):
"""Build or fetch the effective stop words list"""
return _check_stop_list(self.stop_words)
def build_analyzer(self):
"""Return a callable that handles preprocessing and tokenization"""
if callable(self.analyzer):
return self.analyzer
preprocess = self.build_preprocessor()
if self.analyzer == 'char':
return lambda doc: self._char_ngrams(preprocess(self.decode(doc)))
elif self.analyzer == 'char_wb':
return lambda doc: self._char_wb_ngrams(
preprocess(self.decode(doc)))
elif self.analyzer == 'word':
stop_words = self.get_stop_words()
tokenize = self.build_tokenizer()
return lambda doc: self._word_ngrams(
tokenize(preprocess(self.decode(doc))), stop_words)
else:
raise ValueError('%s is not a valid tokenization scheme/analyzer' %
self.analyzer)
def _validate_vocabulary(self):
vocabulary = self.vocabulary
if vocabulary is not None:
if not isinstance(vocabulary, Mapping):
vocab = {}
for i, t in enumerate(vocabulary):
if vocab.setdefault(t, i) != i:
msg = "Duplicate term in vocabulary: %r" % t
raise ValueError(msg)
vocabulary = vocab
else:
indices = set(six.itervalues(vocabulary))
if len(indices) != len(vocabulary):
raise ValueError("Vocabulary contains repeated indices.")
for i in xrange(len(vocabulary)):
if i not in indices:
msg = ("Vocabulary of size %d doesn't contain index "
"%d." % (len(vocabulary), i))
raise ValueError(msg)
if not vocabulary:
raise ValueError("empty vocabulary passed to fit")
self.fixed_vocabulary_ = True
self.vocabulary_ = dict(vocabulary)
else:
self.fixed_vocabulary_ = False
def _check_vocabulary(self):
"""Check if vocabulary is empty or missing (not fit-ed)"""
msg = "%(name)s - Vocabulary wasn't fitted."
check_is_fitted(self, 'vocabulary_', msg=msg),
if len(self.vocabulary_) == 0:
raise ValueError("Vocabulary is empty")
@property
@deprecated("The `fixed_vocabulary` attribute is deprecated and will be "
"removed in 0.18. Please use `fixed_vocabulary_` instead.")
def fixed_vocabulary(self):
return self.fixed_vocabulary_
class HashingVectorizer(BaseEstimator, VectorizerMixin):
"""Convert a collection of text documents to a matrix of token occurrences
It turns a collection of text documents into a scipy.sparse matrix holding
token occurrence counts (or binary occurrence information), possibly
normalized as token frequencies if norm='l1' or projected on the euclidean
unit sphere if norm='l2'.
This text vectorizer implementation uses the hashing trick to find the
token string name to feature integer index mapping.
This strategy has several advantages:
- it is very low memory scalable to large datasets as there is no need to
store a vocabulary dictionary in memory
- it is fast to pickle and un-pickle as it holds no state besides the
constructor parameters
- it can be used in a streaming (partial fit) or parallel pipeline as there
is no state computed during fit.
There are also a couple of cons (vs using a CountVectorizer with an
in-memory vocabulary):
- there is no way to compute the inverse transform (from feature indices to
string feature names) which can be a problem when trying to introspect
which features are most important to a model.
- there can be collisions: distinct tokens can be mapped to the same
feature index. However in practice this is rarely an issue if n_features
is large enough (e.g. 2 ** 18 for text classification problems).
- no IDF weighting as this would render the transformer stateful.
The hash function employed is the signed 32-bit version of Murmurhash3.
Read more in the :ref:`User Guide <text_feature_extraction>`.
Parameters
----------
input : string {'filename', 'file', 'content'}
If 'filename', the sequence passed as an argument to fit is
expected to be a list of filenames that need reading to fetch
the raw content to analyze.
If 'file', the sequence items must have a 'read' method (file-like
object) that is called to fetch the bytes in memory.
Otherwise the input is expected to be the sequence strings or
bytes items are expected to be analyzed directly.
encoding : string, default='utf-8'
If bytes or files are given to analyze, this encoding is used to
decode.
decode_error : {'strict', 'ignore', 'replace'}
Instruction on what to do if a byte sequence is given to analyze that
contains characters not of the given `encoding`. By default, it is
'strict', meaning that a UnicodeDecodeError will be raised. Other
values are 'ignore' and 'replace'.
strip_accents : {'ascii', 'unicode', None}
Remove accents during the preprocessing step.
'ascii' is a fast method that only works on characters that have
an direct ASCII mapping.
'unicode' is a slightly slower method that works on any characters.
None (default) does nothing.
analyzer : string, {'word', 'char', 'char_wb'} or callable
Whether the feature should be made of word or character n-grams.
Option 'char_wb' creates character n-grams only from text inside
word boundaries.
If a callable is passed it is used to extract the sequence of features
out of the raw, unprocessed input.
preprocessor : callable or None (default)
Override the preprocessing (string transformation) stage while
preserving the tokenizing and n-grams generation steps.
tokenizer : callable or None (default)
Override the string tokenization step while preserving the
preprocessing and n-grams generation steps.
Only applies if ``analyzer == 'word'``.
ngram_range : tuple (min_n, max_n), default=(1, 1)
The lower and upper boundary of the range of n-values for different
n-grams to be extracted. All values of n such that min_n <= n <= max_n
will be used.
stop_words : string {'english'}, list, or None (default)
If 'english', a built-in stop word list for English is used.
If a list, that list is assumed to contain stop words, all of which
will be removed from the resulting tokens.
Only applies if ``analyzer == 'word'``.
lowercase : boolean, default=True
Convert all characters to lowercase before tokenizing.
token_pattern : string
Regular expression denoting what constitutes a "token", only used
if ``analyzer == 'word'``. The default regexp selects tokens of 2
or more alphanumeric characters (punctuation is completely ignored
and always treated as a token separator).
n_features : integer, default=(2 ** 20)
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.
norm : 'l1', 'l2' or None, optional
Norm used to normalize term vectors. None for no normalization.
binary: boolean, default=False.
If True, all non zero counts are set to 1. This is useful for discrete
probabilistic models that model binary events rather than integer
counts.
dtype: type, optional
Type of the matrix returned by fit_transform() or transform().
non_negative : boolean, default=False
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.
See also
--------
CountVectorizer, TfidfVectorizer
"""
def __init__(self, input='content', encoding='utf-8',
decode_error='strict', strip_accents=None,
lowercase=True, preprocessor=None, tokenizer=None,
stop_words=None, token_pattern=r"(?u)\b\w\w+\b",
ngram_range=(1, 1), analyzer='word', n_features=(2 ** 20),
binary=False, norm='l2', non_negative=False,
dtype=np.float64):
self.input = input
self.encoding = encoding
self.decode_error = decode_error
self.strip_accents = strip_accents
self.preprocessor = preprocessor
self.tokenizer = tokenizer
self.analyzer = analyzer
self.lowercase = lowercase
self.token_pattern = token_pattern
self.stop_words = stop_words
self.n_features = n_features
self.ngram_range = ngram_range
self.binary = binary
self.norm = norm
self.non_negative = non_negative
self.dtype = dtype
def partial_fit(self, X, y=None):
"""Does nothing: this transformer is stateless.
This method is just there to mark the fact that this transformer
can work in a streaming setup.
"""
return self
def fit(self, X, y=None):
"""Does nothing: this transformer is stateless."""
# triggers a parameter validation
self._get_hasher().fit(X, y=y)
return self
def transform(self, X, y=None):
"""Transform a sequence of documents to a document-term matrix.
Parameters
----------
X : iterable over raw text documents, length = n_samples
Samples. Each sample must be a text document (either bytes or
unicode strings, file name or file object depending on the
constructor argument) which will be tokenized and hashed.
y : (ignored)
Returns
-------
X : scipy.sparse matrix, shape = (n_samples, self.n_features)
Document-term matrix.
"""
analyzer = self.build_analyzer()
X = self._get_hasher().transform(analyzer(doc) for doc in X)
if self.binary:
X.data.fill(1)
if self.norm is not None:
X = normalize(X, norm=self.norm, copy=False)
return X
# Alias transform to fit_transform for convenience
fit_transform = transform
def _get_hasher(self):
return FeatureHasher(n_features=self.n_features,
input_type='string', dtype=self.dtype,
non_negative=self.non_negative)
def _document_frequency(X):
"""Count the number of non-zero values for each feature in sparse X."""
if sp.isspmatrix_csr(X):
return bincount(X.indices, minlength=X.shape[1])
else:
return np.diff(sp.csc_matrix(X, copy=False).indptr)
class CountVectorizer(BaseEstimator, VectorizerMixin):
"""Convert a collection of text documents to a matrix of token counts
This implementation produces a sparse representation of the counts using
scipy.sparse.coo_matrix.
If you do not provide an a-priori dictionary and you do not use an analyzer
that does some kind of feature selection then the number of features will
be equal to the vocabulary size found by analyzing the data.
Read more in the :ref:`User Guide <text_feature_extraction>`.
Parameters
----------
input : string {'filename', 'file', 'content'}
If 'filename', the sequence passed as an argument to fit is
expected to be a list of filenames that need reading to fetch
the raw content to analyze.
If 'file', the sequence items must have a 'read' method (file-like
object) that is called to fetch the bytes in memory.
Otherwise the input is expected to be the sequence strings or
bytes items are expected to be analyzed directly.
encoding : string, 'utf-8' by default.
If bytes or files are given to analyze, this encoding is used to
decode.
decode_error : {'strict', 'ignore', 'replace'}
Instruction on what to do if a byte sequence is given to analyze that
contains characters not of the given `encoding`. By default, it is
'strict', meaning that a UnicodeDecodeError will be raised. Other
values are 'ignore' and 'replace'.
strip_accents : {'ascii', 'unicode', None}
Remove accents during the preprocessing step.
'ascii' is a fast method that only works on characters that have
an direct ASCII mapping.
'unicode' is a slightly slower method that works on any characters.
None (default) does nothing.
analyzer : string, {'word', 'char', 'char_wb'} or callable
Whether the feature should be made of word or character n-grams.
Option 'char_wb' creates character n-grams only from text inside
word boundaries.
If a callable is passed it is used to extract the sequence of features
out of the raw, unprocessed input.
Only applies if ``analyzer == 'word'``.
preprocessor : callable or None (default)
Override the preprocessing (string transformation) stage while
preserving the tokenizing and n-grams generation steps.
tokenizer : callable or None (default)
Override the string tokenization step while preserving the
preprocessing and n-grams generation steps.
Only applies if ``analyzer == 'word'``.
ngram_range : tuple (min_n, max_n)
The lower and upper boundary of the range of n-values for different
n-grams to be extracted. All values of n such that min_n <= n <= max_n
will be used.
stop_words : string {'english'}, list, or None (default)
If 'english', a built-in stop word list for English is used.
If a list, that list is assumed to contain stop words, all of which
will be removed from the resulting tokens.
Only applies if ``analyzer == 'word'``.
If None, no stop words will be used. max_df can be set to a value
in the range [0.7, 1.0) to automatically detect and filter stop
words based on intra corpus document frequency of terms.
lowercase : boolean, True by default
Convert all characters to lowercase before tokenizing.
token_pattern : string
Regular expression denoting what constitutes a "token", only used
if ``analyzer == 'word'``. The default regexp select tokens of 2
or more alphanumeric characters (punctuation is completely ignored
and always treated as a token separator).
max_df : float in range [0.0, 1.0] or int, default=1.0
When building the vocabulary ignore terms that have a document
frequency strictly higher than the given threshold (corpus-specific
stop words).
If float, the parameter represents a proportion of documents, integer
absolute counts.
This parameter is ignored if vocabulary is not None.
min_df : float in range [0.0, 1.0] or int, default=1
When building the vocabulary ignore terms that have a document
frequency strictly lower than the given threshold. This value is also
called cut-off in the literature.
If float, the parameter represents a proportion of documents, integer
absolute counts.
This parameter is ignored if vocabulary is not None.
max_features : int or None, default=None
If not None, build a vocabulary that only consider the top
max_features ordered by term frequency across the corpus.
This parameter is ignored if vocabulary is not None.
vocabulary : Mapping or iterable, optional
Either a Mapping (e.g., a dict) where keys are terms and values are
indices in the feature matrix, or an iterable over terms. If not
given, a vocabulary is determined from the input documents. Indices
in the mapping should not be repeated and should not have any gap
between 0 and the largest index.
binary : boolean, default=False
If True, all non zero counts are set to 1. This is useful for discrete
probabilistic models that model binary events rather than integer
counts.
dtype : type, optional
Type of the matrix returned by fit_transform() or transform().
Attributes
----------
vocabulary_ : dict
A mapping of terms to feature indices.
stop_words_ : set
Terms that were ignored because they either:
- occurred in too many documents (`max_df`)
- occurred in too few documents (`min_df`)
- were cut off by feature selection (`max_features`).
This is only available if no vocabulary was given.
See also
--------
HashingVectorizer, TfidfVectorizer
Notes
-----
The ``stop_words_`` attribute can get large and increase the model size
when pickling. This attribute is provided only for introspection and can
be safely removed using delattr or set to None before pickling.
"""
def __init__(self, input='content', encoding='utf-8',
decode_error='strict', strip_accents=None,
lowercase=True, preprocessor=None, tokenizer=None,
stop_words=None, token_pattern=r"(?u)\b\w\w+\b",
ngram_range=(1, 1), analyzer='word',
max_df=1.0, min_df=1, max_features=None,
vocabulary=None, binary=False, dtype=np.int64):
self.input = input
self.encoding = encoding
self.decode_error = decode_error
self.strip_accents = strip_accents
self.preprocessor = preprocessor
self.tokenizer = tokenizer
self.analyzer = analyzer
self.lowercase = lowercase
self.token_pattern = token_pattern
self.stop_words = stop_words
self.max_df = max_df
self.min_df = min_df
if max_df < 0 or min_df < 0:
raise ValueError("negative value for max_df of min_df")
self.max_features = max_features
if max_features is not None:
if (not isinstance(max_features, numbers.Integral) or
max_features <= 0):
raise ValueError(
"max_features=%r, neither a positive integer nor None"
% max_features)
self.ngram_range = ngram_range
self.vocabulary = vocabulary
self.binary = binary
self.dtype = dtype
def _sort_features(self, X, vocabulary):
"""Sort features by name
Returns a reordered matrix and modifies the vocabulary in place
"""
sorted_features = sorted(six.iteritems(vocabulary))
map_index = np.empty(len(sorted_features), dtype=np.int32)
for new_val, (term, old_val) in enumerate(sorted_features):
map_index[new_val] = old_val
vocabulary[term] = new_val
return X[:, map_index]
def _limit_features(self, X, vocabulary, high=None, low=None,
limit=None):
"""Remove too rare or too common features.
Prune features that are non zero in more samples than high or less
documents than low, modifying the vocabulary, and restricting it to
at most the limit most frequent.
This does not prune samples with zero features.
"""
if high is None and low is None and limit is None:
return X, set()
# Calculate a mask based on document frequencies
dfs = _document_frequency(X)
tfs = np.asarray(X.sum(axis=0)).ravel()
mask = np.ones(len(dfs), dtype=bool)
if high is not None:
mask &= dfs <= high
if low is not None:
mask &= dfs >= low
if limit is not None and mask.sum() > limit:
mask_inds = (-tfs[mask]).argsort()[:limit]
new_mask = np.zeros(len(dfs), dtype=bool)
new_mask[np.where(mask)[0][mask_inds]] = True
mask = new_mask
new_indices = np.cumsum(mask) - 1 # maps old indices to new
removed_terms = set()
for term, old_index in list(six.iteritems(vocabulary)):
if mask[old_index]:
vocabulary[term] = new_indices[old_index]
else:
del vocabulary[term]
removed_terms.add(term)
kept_indices = np.where(mask)[0]
if len(kept_indices) == 0:
raise ValueError("After pruning, no terms remain. Try a lower"
" min_df or a higher max_df.")
return X[:, kept_indices], removed_terms
def _count_vocab(self, raw_documents, fixed_vocab):
"""Create sparse feature matrix, and vocabulary where fixed_vocab=False
"""
if fixed_vocab:
vocabulary = self.vocabulary_
else:
# Add a new value when a new vocabulary item is seen
vocabulary = defaultdict()
vocabulary.default_factory = vocabulary.__len__
analyze = self.build_analyzer()
j_indices = _make_int_array()
indptr = _make_int_array()
indptr.append(0)
for doc in raw_documents:
for feature in analyze(doc):
try:
j_indices.append(vocabulary[feature])
except KeyError:
# Ignore out-of-vocabulary items for fixed_vocab=True
continue
indptr.append(len(j_indices))
if not fixed_vocab:
# disable defaultdict behaviour
vocabulary = dict(vocabulary)
if not vocabulary:
raise ValueError("empty vocabulary; perhaps the documents only"
" contain stop words")
j_indices = frombuffer_empty(j_indices, dtype=np.intc)
indptr = np.frombuffer(indptr, dtype=np.intc)
values = np.ones(len(j_indices))
X = sp.csr_matrix((values, j_indices, indptr),
shape=(len(indptr) - 1, len(vocabulary)),
dtype=self.dtype)
X.sum_duplicates()
return vocabulary, X
def fit(self, raw_documents, y=None):
"""Learn a vocabulary dictionary of all tokens in the raw documents.
Parameters
----------
raw_documents : iterable
An iterable which yields either str, unicode or file objects.
Returns
-------
self
"""
self.fit_transform(raw_documents)
return self
def fit_transform(self, raw_documents, y=None):
"""Learn the vocabulary dictionary and return term-document matrix.
This is equivalent to fit followed by transform, but more efficiently
implemented.
Parameters
----------
raw_documents : iterable
An iterable which yields either str, unicode or file objects.
Returns
-------
X : array, [n_samples, n_features]
Document-term matrix.
"""
# We intentionally don't call the transform method to make
# fit_transform overridable without unwanted side effects in
# TfidfVectorizer.
self._validate_vocabulary()
max_df = self.max_df
min_df = self.min_df
max_features = self.max_features
vocabulary, X = self._count_vocab(raw_documents,
self.fixed_vocabulary_)
if self.binary:
X.data.fill(1)
if not self.fixed_vocabulary_:
X = self._sort_features(X, vocabulary)
n_doc = X.shape[0]
max_doc_count = (max_df
if isinstance(max_df, numbers.Integral)
else max_df * n_doc)
min_doc_count = (min_df
if isinstance(min_df, numbers.Integral)
else min_df * n_doc)
if max_doc_count < min_doc_count:
raise ValueError(
"max_df corresponds to < documents than min_df")
X, self.stop_words_ = self._limit_features(X, vocabulary,
max_doc_count,
min_doc_count,
max_features)
self.vocabulary_ = vocabulary
return X
def transform(self, raw_documents):
"""Transform documents to document-term matrix.
Extract token counts out of raw text documents using the vocabulary
fitted with fit or the one provided to the constructor.
Parameters
----------
raw_documents : iterable
An iterable which yields either str, unicode or file objects.
Returns
-------
X : sparse matrix, [n_samples, n_features]
Document-term matrix.
"""
if not hasattr(self, 'vocabulary_'):
self._validate_vocabulary()
self._check_vocabulary()
# use the same matrix-building strategy as fit_transform
_, X = self._count_vocab(raw_documents, fixed_vocab=True)
if self.binary:
X.data.fill(1)
return X
def inverse_transform(self, X):
"""Return terms per document with nonzero entries in X.
Parameters
----------
X : {array, sparse matrix}, shape = [n_samples, n_features]
Returns
-------
X_inv : list of arrays, len = n_samples
List of arrays of terms.
"""
self._check_vocabulary()
if sp.issparse(X):
# We need CSR format for fast row manipulations.
X = X.tocsr()
else:
# We need to convert X to a matrix, so that the indexing
# returns 2D objects
X = np.asmatrix(X)
n_samples = X.shape[0]
terms = np.array(list(self.vocabulary_.keys()))
indices = np.array(list(self.vocabulary_.values()))
inverse_vocabulary = terms[np.argsort(indices)]
return [inverse_vocabulary[X[i, :].nonzero()[1]].ravel()
for i in range(n_samples)]
def get_feature_names(self):
"""Array mapping from feature integer indices to feature name"""
self._check_vocabulary()
return [t for t, i in sorted(six.iteritems(self.vocabulary_),
key=itemgetter(1))]
def _make_int_array():
"""Construct an array.array of a type suitable for scipy.sparse indices."""
return array.array(str("i"))
class TfidfTransformer(BaseEstimator, TransformerMixin):
"""Transform a count matrix to a normalized tf or tf-idf representation
Tf means term-frequency while tf-idf means term-frequency times inverse
document-frequency. This is a common term weighting scheme in information
retrieval, that has also found good use in document classification.
The goal of using tf-idf instead of the raw frequencies of occurrence of a
token in a given document is to scale down the impact of tokens that occur
very frequently in a given corpus and that are hence empirically less
informative than features that occur in a small fraction of the training
corpus.
The actual formula used for tf-idf is tf * (idf + 1) = tf + tf * idf,
instead of tf * idf. The effect of this is that terms with zero idf, i.e.
that occur in all documents of a training set, will not be entirely
ignored. The formulas used to compute tf and idf depend on parameter
settings that correspond to the SMART notation used in IR, as follows:
Tf is "n" (natural) by default, "l" (logarithmic) when sublinear_tf=True.
Idf is "t" when use_idf is given, "n" (none) otherwise.
Normalization is "c" (cosine) when norm='l2', "n" (none) when norm=None.
Read more in the :ref:`User Guide <text_feature_extraction>`.
Parameters
----------
norm : 'l1', 'l2' or None, optional
Norm used to normalize term vectors. None for no normalization.
use_idf : boolean, default=True
Enable inverse-document-frequency reweighting.
smooth_idf : boolean, default=True
Smooth idf weights by adding one to document frequencies, as if an
extra document was seen containing every term in the collection
exactly once. Prevents zero divisions.
sublinear_tf : boolean, default=False
Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
References
----------
.. [Yates2011] `R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern
Information Retrieval. Addison Wesley, pp. 68-74.`
.. [MRS2008] `C.D. Manning, P. Raghavan and H. Schuetze (2008).
Introduction to Information Retrieval. Cambridge University
Press, pp. 118-120.`
"""
def __init__(self, norm='l2', use_idf=True, smooth_idf=True,
sublinear_tf=False):
self.norm = norm
self.use_idf = use_idf
self.smooth_idf = smooth_idf
self.sublinear_tf = sublinear_tf
def fit(self, X, y=None):
"""Learn the idf vector (global term weights)
Parameters
----------
X : sparse matrix, [n_samples, n_features]
a matrix of term/token counts
"""
if not sp.issparse(X):
X = sp.csc_matrix(X)
if self.use_idf:
n_samples, n_features = X.shape
df = _document_frequency(X)
# perform idf smoothing if required
df += int(self.smooth_idf)
n_samples += int(self.smooth_idf)
# log+1 instead of log makes sure terms with zero idf don't get
# suppressed entirely.
idf = np.log(float(n_samples) / df) + 1.0
self._idf_diag = sp.spdiags(idf,
diags=0, m=n_features, n=n_features)
return self
def transform(self, X, copy=True):
"""Transform a count matrix to a tf or tf-idf representation
Parameters
----------
X : sparse matrix, [n_samples, n_features]
a matrix of term/token counts
copy : boolean, default True
Whether to copy X and operate on the copy or perform in-place
operations.
Returns
-------
vectors : sparse matrix, [n_samples, n_features]
"""
if hasattr(X, 'dtype') and np.issubdtype(X.dtype, np.float):
# preserve float family dtype
X = sp.csr_matrix(X, copy=copy)
else:
# convert counts or binary occurrences to floats
X = sp.csr_matrix(X, dtype=np.float64, copy=copy)
n_samples, n_features = X.shape
if self.sublinear_tf:
np.log(X.data, X.data)
X.data += 1
if self.use_idf:
check_is_fitted(self, '_idf_diag', 'idf vector is not fitted')
expected_n_features = self._idf_diag.shape[0]
if n_features != expected_n_features:
raise ValueError("Input has n_features=%d while the model"
" has been trained with n_features=%d" % (
n_features, expected_n_features))
# *= doesn't work
X = X * self._idf_diag
if self.norm:
X = normalize(X, norm=self.norm, copy=False)
return X
@property
def idf_(self):
if hasattr(self, "_idf_diag"):
return np.ravel(self._idf_diag.sum(axis=0))
else:
return None
class TfidfVectorizer(CountVectorizer):
"""Convert a collection of raw documents to a matrix of TF-IDF features.
Equivalent to CountVectorizer followed by TfidfTransformer.
Read more in the :ref:`User Guide <text_feature_extraction>`.
Parameters
----------
input : string {'filename', 'file', 'content'}
If 'filename', the sequence passed as an argument to fit is
expected to be a list of filenames that need reading to fetch
the raw content to analyze.
If 'file', the sequence items must have a 'read' method (file-like
object) that is called to fetch the bytes in memory.
Otherwise the input is expected to be the sequence strings or
bytes items are expected to be analyzed directly.
encoding : string, 'utf-8' by default.
If bytes or files are given to analyze, this encoding is used to
decode.
decode_error : {'strict', 'ignore', 'replace'}
Instruction on what to do if a byte sequence is given to analyze that
contains characters not of the given `encoding`. By default, it is
'strict', meaning that a UnicodeDecodeError will be raised. Other
values are 'ignore' and 'replace'.
strip_accents : {'ascii', 'unicode', None}
Remove accents during the preprocessing step.
'ascii' is a fast method that only works on characters that have
an direct ASCII mapping.
'unicode' is a slightly slower method that works on any characters.
None (default) does nothing.
analyzer : string, {'word', 'char'} or callable
Whether the feature should be made of word or character n-grams.
If a callable is passed it is used to extract the sequence of features
out of the raw, unprocessed input.
preprocessor : callable or None (default)
Override the preprocessing (string transformation) stage while
preserving the tokenizing and n-grams generation steps.
tokenizer : callable or None (default)
Override the string tokenization step while preserving the
preprocessing and n-grams generation steps.
Only applies if ``analyzer == 'word'``.
ngram_range : tuple (min_n, max_n)
The lower and upper boundary of the range of n-values for different
n-grams to be extracted. All values of n such that min_n <= n <= max_n
will be used.
stop_words : string {'english'}, list, or None (default)
If a string, it is passed to _check_stop_list and the appropriate stop
list is returned. 'english' is currently the only supported string
value.
If a list, that list is assumed to contain stop words, all of which
will be removed from the resulting tokens.
Only applies if ``analyzer == 'word'``.
If None, no stop words will be used. max_df can be set to a value
in the range [0.7, 1.0) to automatically detect and filter stop
words based on intra corpus document frequency of terms.
lowercase : boolean, default True
Convert all characters to lowercase before tokenizing.
token_pattern : string
Regular expression denoting what constitutes a "token", only used
if ``analyzer == 'word'``. The default regexp selects tokens of 2
or more alphanumeric characters (punctuation is completely ignored
and always treated as a token separator).
max_df : float in range [0.0, 1.0] or int, default=1.0
When building the vocabulary ignore terms that have a document
frequency strictly higher than the given threshold (corpus-specific
stop words).
If float, the parameter represents a proportion of documents, integer
absolute counts.
This parameter is ignored if vocabulary is not None.
min_df : float in range [0.0, 1.0] or int, default=1
When building the vocabulary ignore terms that have a document
frequency strictly lower than the given threshold. This value is also
called cut-off in the literature.
If float, the parameter represents a proportion of documents, integer
absolute counts.
This parameter is ignored if vocabulary is not None.
max_features : int or None, default=None
If not None, build a vocabulary that only consider the top
max_features ordered by term frequency across the corpus.
This parameter is ignored if vocabulary is not None.
vocabulary : Mapping or iterable, optional
Either a Mapping (e.g., a dict) where keys are terms and values are
indices in the feature matrix, or an iterable over terms. If not
given, a vocabulary is determined from the input documents.
binary : boolean, default=False
If True, all non-zero term counts are set to 1. This does not mean
outputs will have only 0/1 values, only that the tf term in tf-idf
is binary. (Set idf and normalization to False to get 0/1 outputs.)
dtype : type, optional
Type of the matrix returned by fit_transform() or transform().
norm : 'l1', 'l2' or None, optional
Norm used to normalize term vectors. None for no normalization.
use_idf : boolean, default=True
Enable inverse-document-frequency reweighting.
smooth_idf : boolean, default=True
Smooth idf weights by adding one to document frequencies, as if an
extra document was seen containing every term in the collection
exactly once. Prevents zero divisions.
sublinear_tf : boolean, default=False
Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
Attributes
----------
idf_ : array, shape = [n_features], or None
The learned idf vector (global term weights)
when ``use_idf`` is set to True, None otherwise.
stop_words_ : set
Terms that were ignored because they either:
- occurred in too many documents (`max_df`)
- occurred in too few documents (`min_df`)
- were cut off by feature selection (`max_features`).
This is only available if no vocabulary was given.
See also
--------
CountVectorizer
Tokenize the documents and count the occurrences of token and return
them as a sparse matrix
TfidfTransformer
Apply Term Frequency Inverse Document Frequency normalization to a
sparse matrix of occurrence counts.
Notes
-----
The ``stop_words_`` attribute can get large and increase the model size
when pickling. This attribute is provided only for introspection and can
be safely removed using delattr or set to None before pickling.
"""
def __init__(self, input='content', encoding='utf-8',
decode_error='strict', strip_accents=None, lowercase=True,
preprocessor=None, tokenizer=None, analyzer='word',
stop_words=None, token_pattern=r"(?u)\b\w\w+\b",
ngram_range=(1, 1), max_df=1.0, min_df=1,
max_features=None, vocabulary=None, binary=False,
dtype=np.int64, norm='l2', use_idf=True, smooth_idf=True,
sublinear_tf=False):
super(TfidfVectorizer, self).__init__(
input=input, encoding=encoding, decode_error=decode_error,
strip_accents=strip_accents, lowercase=lowercase,
preprocessor=preprocessor, tokenizer=tokenizer, analyzer=analyzer,
stop_words=stop_words, token_pattern=token_pattern,
ngram_range=ngram_range, max_df=max_df, min_df=min_df,
max_features=max_features, vocabulary=vocabulary, binary=binary,
dtype=dtype)
self._tfidf = TfidfTransformer(norm=norm, use_idf=use_idf,
smooth_idf=smooth_idf,
sublinear_tf=sublinear_tf)
# Broadcast the TF-IDF parameters to the underlying transformer instance
# for easy grid search and repr
@property
def norm(self):
return self._tfidf.norm
@norm.setter
def norm(self, value):
self._tfidf.norm = value
@property
def use_idf(self):
return self._tfidf.use_idf
@use_idf.setter
def use_idf(self, value):
self._tfidf.use_idf = value
@property
def smooth_idf(self):
return self._tfidf.smooth_idf
@smooth_idf.setter
def smooth_idf(self, value):
self._tfidf.smooth_idf = value
@property
def sublinear_tf(self):
return self._tfidf.sublinear_tf
@sublinear_tf.setter
def sublinear_tf(self, value):
self._tfidf.sublinear_tf = value
@property
def idf_(self):
return self._tfidf.idf_
def fit(self, raw_documents, y=None):
"""Learn vocabulary and idf from training set.
Parameters
----------
raw_documents : iterable
an iterable which yields either str, unicode or file objects
Returns
-------
self : TfidfVectorizer
"""
X = super(TfidfVectorizer, self).fit_transform(raw_documents)
self._tfidf.fit(X)
return self
def fit_transform(self, raw_documents, y=None):
"""Learn vocabulary and idf, return term-document matrix.
This is equivalent to fit followed by transform, but more efficiently
implemented.
Parameters
----------
raw_documents : iterable
an iterable which yields either str, unicode or file objects
Returns
-------
X : sparse matrix, [n_samples, n_features]
Tf-idf-weighted document-term matrix.
"""
X = super(TfidfVectorizer, self).fit_transform(raw_documents)
self._tfidf.fit(X)
# X is already a transformed view of raw_documents so
# we set copy to False
return self._tfidf.transform(X, copy=False)
def transform(self, raw_documents, copy=True):
"""Transform documents to document-term matrix.
Uses the vocabulary and document frequencies (df) learned by fit (or
fit_transform).
Parameters
----------
raw_documents : iterable
an iterable which yields either str, unicode or file objects
copy : boolean, default True
Whether to copy X and operate on the copy or perform in-place
operations.
Returns
-------
X : sparse matrix, [n_samples, n_features]
Tf-idf-weighted document-term matrix.
"""
check_is_fitted(self, '_tfidf', 'The tfidf vector is not fitted')
X = super(TfidfVectorizer, self).transform(raw_documents)
return self._tfidf.transform(X, copy=False)
| bsd-3-clause |
rjenc29/numerical | course/matplotlib/examples/fill_example.py | 1 | 2229 | """
Illustrate different ways of using the various fill functions.
"""
import numpy as np
import matplotlib.pyplot as plt
import example_utils
def main():
fig, axes = example_utils.setup_axes()
fill_example(axes[0])
fill_between_example(axes[1])
stackplot_example(axes[2])
example_utils.title(fig, 'fill/fill_between/stackplot: Filled polygons',
y=0.95)
fig.savefig('fill_example.png', facecolor='none')
plt.show()
def fill_example(ax):
# Use fill when you want a simple filled polygon between vertices
x, y = fill_data()
ax.fill(x, y, color='lightblue')
ax.margins(0.1)
example_utils.label(ax, 'fill')
def fill_between_example(ax):
# Fill between fills between two curves or a curve and a constant value
# It can be used in several ways. We'll illustrate a few below.
x, y1, y2 = sin_data()
# The most basic (and common) use of fill_between
err = np.random.rand(x.size)**2 + 0.1
y = 0.7 * x + 2
ax.fill_between(x, y + err, y - err, color='orange')
# Filling between two curves with different colors when they cross in
# different directions
ax.fill_between(x, y1, y2, where=y1>y2, color='lightblue')
ax.fill_between(x, y1, y2, where=y1<y2, color='forestgreen')
# Note that this is fillbetween*x*!
ax.fill_betweenx(x, -y1, where=y1>0, color='red', alpha=0.5)
ax.fill_betweenx(x, -y1, where=y1<0, color='blue', alpha=0.5)
ax.margins(0.15)
example_utils.label(ax, 'fill_between/x')
def stackplot_example(ax):
# Stackplot is equivalent to a series of ax.fill_between calls
x, y = stackplot_data()
ax.stackplot(x, y.cumsum(axis=0), alpha=0.5)
example_utils.label(ax, 'stackplot')
#-- Data generation ----------------------
def stackplot_data():
x = np.linspace(0, 10, 100)
y = np.random.normal(0, 1, (5, 100))
y = y.cumsum(axis=1)
y -= y.min(axis=0, keepdims=True)
return x, y
def sin_data():
x = np.linspace(0, 10, 100)
y = np.sin(x)
y2 = np.cos(x)
return x, y, y2
def fill_data():
t = np.linspace(0, 2*np.pi, 100)
r = np.random.normal(0, 1, 100).cumsum()
r -= r.min()
return r * np.cos(t), r * np.sin(t)
main()
| mit |
tjhunter/phd-thesis-tjhunter | python/kdd/plot_network.py | 1 | 1065 |
__author__ = 'tjhunter'
import build
import json
import pylab as pl
from matplotlib.collections import LineCollection
# Draws the network as a pdf and SVG file.
def draw_network(ax, fd, link_style):
def decode_line(l):
#print l
dct = json.loads(l)
lats = dct['lats']
lons = dct['lons']
return zip(lons, lats)
lines = [decode_line(l) for l in fd]
#print lines
xmin = min([x for l in lines for x,y in l])
xmax = max([x for l in lines for x,y in l])
ymin = min([y for l in lines for x,y in l])
ymax = max([y for l in lines for x,y in l])
lc = LineCollection(lines, **link_style)
ax.add_collection(lc, autolim=True)
return ((xmin,xmax),(ymin,ymax))
fname = build.data_name('kdd/net_export_6.json')
fig = pl.figure("fig1",figsize=(10,10))
ax = fig.gca()
ax.set_axis_off()
style = {'colors':'k','linewidths':0.5}
with open(fname) as f:
(xlims, ylims) = draw_network(ax, f, style)
ax.set_xlim(*xlims)
ax.set_ylim(*ylims)
# Saving in pdf is a bit slow
build.save_figure(fig, 'figures-kdd/network_export_6',save_svg=True)
| apache-2.0 |
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 |
amanzi/ats-dev | tools/utils/transect_data.py | 2 | 7741 | """Loads and/or plots 2D, topologlically structured data on quadrilaterals using matplotlib.
"""
import sys,os
import numpy as np
import h5py
import mesh
import colors
def fullname(varname):
fullname = varname
if not '.cell.' in fullname:
fullname = fullname+'.cell.0'
return fullname
def transect_data(varnames, keys='all', directory=".", filename="visdump_data.h5",
mesh_filename="visdump_mesh.h5", coord_order=None, deformable=False, return_map=False):
"""Pulls simulation output into structured 2D arrays for transect-based, (i,j) indexing.
Input:
varnames | A list of variable names to pull, e.g.
| ['saturation_liquid', 'saturation_ice'], or a single variable
| name, e.g. 'saturation_liquid'
keys | Indices of timesteps to pull. Either an int (i.e. 0, -1, etc)
| for the kth timestep, or a list of ints, or 'all'.
directory | Directory of the run. Defaults to '.'
filename | Filename of the run. Defaults to 'visdump_data.h5'
mesh_filename | Filename of the mesh. Defaults to 'visdump_mesh.h5'
coord_order | Order of the transect coordinates. Defaults to ['x','z']. The
| mesh is sorted in this order.
deformable | Is the mesh deforming?
return_map | See return value below.
Output:
Output is an array of shape:
( len(varnames+2), len(keys), n_cells_coord_order[0], n_cells_coord_order[1] )
data[0,0,:,:] is the coord_order[0] centroid
data[1,0,:,:] is the coord_order[1] centroid
data[i+2,k,:,:] is the ith varname data at the kth requested timestep, sorted in
the same way as the centroids.
Note that the data is re-ordered in INCREASING coordinate, i.e. bottom to top in z.
If return_map is True, then returns a tuple, (data, map) where
map is a (NX,NZ) array of integers specifying which global id
corresponds to the (i,j) cell. This is useful for mapping input
data back INTO the unstructured mesh.
Example usage:
Calculate and plot the thaw depth at step 5.
// Pull saturation ice -- TD is where sat ice = 0."
data = transect_data(['saturation_ice', 5)
// x coordinate for plotting
x = data[0,0,:,0]
// for each column, find highest z where sat_ice > 0.
td_i = np.array([np.where(data[2,0,i,:] > 0.)[0][-1] for i in range(data.shape[2])])
// now that we have an index into the highest cell with ice, determine td as the
// mean of the highest cell with ice and the one above that. Note this assumes
// all columns have some thawing.
td_z = np.array( [ (dat[1,0,i,td_i[i]] + dat[1,0,i,td_i[i+1]]) / 2.
for i in range(len(td_i)) ] )
plt.plot(x, td_z)
"""
if coord_order is None:
coord_order = ['x','z']
if type(varnames) is str:
varnames = [varnames,]
# get centroids
xyz = mesh.meshElemCentroids(mesh_filename, directory)
# round to avoid issues
xyz = np.round(xyz, decimals=5)
# get ordering of centroids
dtype = [(coord_order[0], float), (coord_order[1], float)]
num_order = []
for i in coord_order:
if i == 'x':
num_order.append(0)
elif i == 'y':
num_order.append(1)
elif i == 'z':
num_order.append(2)
xyz_sort_order = np.array([tuple([xyz[i,x] for x in num_order]) for i in range(len(xyz))], dtype=dtype)
xyz_sorting = xyz_sort_order.argsort(order=coord_order)
with h5py.File(os.path.join(directory,filename),'r') as dat:
keys_avail = dat[fullname(varnames[0])].keys()
keys_avail.sort(lambda a,b: int.__cmp__(int(a),int(b)))
if keys == 'all':
keys = keys_avail
elif type(keys) is str:
keys = [keys,]
elif type(keys) is int:
keys = [keys_avail[keys],]
elif type(keys) is slice:
keys = keys_avail[keys]
elif type(keys) is list:
if all(type(k) is int for k in keys):
keys = [keys_avail[k] for k in keys]
elif all(type(k) is str for k in keys):
pass
else:
raise RuntimeError("Keys requested cannot be processed -- should be 'all', int, or str key, or list of ints or strs.")
# get data
vals = np.zeros((len(varnames)+2, len(keys), len(xyz)), 'd')
for i,key in enumerate(keys):
if deformable:
xyz = mesh.meshElemCentroids(mesh_filename, directory)
vals[0,i,:] = xyz[xyz_sorting,num_order[0]]
vals[1,i,:] = xyz[xyz_sorting,num_order[1]]
for j,varname in enumerate(varnames):
vals[j+2,i,:] = dat[fullname(varname)][key][:,0][xyz_sorting]
# reshape the data
# determine nx
nx = len(set(vals[0,0,:]))
nz = vals.shape[2] / nx
if (nx * nz != vals.shape[2]):
raise RuntimeError("Assumption about first coordinate being cleanly binnable is falling apart -- ask Ethan to rethink this algorithm!")
shp = vals.shape
if not return_map:
return vals.reshape(shp[0], shp[1], nx, nz)
else:
return vals.reshape(shp[0], shp[1], nx, nz), xyz_sorting.reshape(nx, nz)
def plot(dataset, ax, cax=None, vmin=None, vmax=None, cmap="jet",
label=None, mesh_filename="visdump_mesh.h5", directory=".", y_coord=0.0,
linewidths=1):
"""Draws a dataset on an ax."""
import matplotlib.collections
from matplotlib import pyplot as plt
if vmin is None:
vmin = dataset.min()
if vmax is None:
vmax = dataset.max()
# get the mesh and collapse to 2D
etype, coords, conn = mesh.meshElemXYZ(filename=mesh_filename, directory=directory)
if etype is not 'HEX':
raise RuntimeError("Only works for Hexs")
coords2 = np.array([[coords[i][0::2] for i in c[1:] if abs(coords[i][1] - y_coord) < 1.e-8] for c in conn])
try:
assert coords2.shape[2] == 2
assert coords2.shape[1] == 4
except AssertionError:
print(coords2.shape)
for c in conn:
if len(c) != 9:
print c
raise RuntimeError("what is a conn?")
coords3 = np.array([coords[i][:] for i in c[1:] if abs(coords[i][1] - y_coord) < 1.e-8])
if coords3.shape[0] != 4:
print coords
raise RuntimeError("Unable to squash to 2D")
# reorder anti-clockwise
for i,c in enumerate(coords2):
centroid = c.mean(axis=0)
def angle(p1,p2):
a1 = np.arctan2((p1[1]-centroid[1]),(p1[0]-centroid[0]))
a2 = np.arctan2((p2[1]-centroid[1]),(p2[0]-centroid[0]))
if a1 < a2:
return -1
elif a2 < a1:
return 1
else:
return 0
c2 = np.array(sorted(c,angle))
coords2[i] = c2
polygons = matplotlib.collections.PolyCollection(coords2, edgecolor='k', cmap=cmap, linewidths=linewidths)
polygons.set_array(dataset)
polygons.set_clim(vmin,vmax)
ax.add_collection(polygons)
xmin = min(c[0] for c in coords.itervalues())
xmax = max(c[0] for c in coords.itervalues())
zmin = min(c[2] for c in coords.itervalues())
zmax = max(c[2] for c in coords.itervalues())
ax.set_xlim(xmin,xmax)
ax.set_ylim(zmin,zmax)
if cax is not None:
cb = plt.colorbar(polygons, cax=cax)
if label is not None:
cb.set_label(label)
return ((xmin,xmax),(zmin,zmax))
| bsd-3-clause |
lbishal/scikit-learn | examples/gaussian_process/plot_gpc_isoprobability.py | 45 | 3025 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=================================================================
Iso-probability lines for Gaussian Processes classification (GPC)
=================================================================
A two-dimensional classification example showing iso-probability lines for
the predicted probabilities.
"""
print(__doc__)
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# Adapted to GaussianProcessClassifier:
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Licence: BSD 3 clause
import numpy as np
from matplotlib import pyplot as pl
from matplotlib import cm
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import DotProduct, ConstantKernel as C
# A few constants
lim = 8
def g(x):
"""The function to predict (classification will then consist in predicting
whether g(x) <= 0 or not)"""
return 5. - x[:, 1] - .5 * x[:, 0] ** 2.
# Design of experiments
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]])
# Observations
y = np.array(g(X) > 0, dtype=int)
# Instanciate and fit Gaussian Process Model
kernel = C(0.1, (1e-5, np.inf)) * DotProduct(sigma_0=0.1) ** 2
gp = GaussianProcessClassifier(kernel=kernel)
gp.fit(X, y)
print("Learned kernel: %s " % gp.kernel_)
# Evaluate real function and the predicted probability
res = 50
x1, x2 = np.meshgrid(np.linspace(- lim, lim, res),
np.linspace(- lim, lim, res))
xx = np.vstack([x1.reshape(x1.size), x2.reshape(x2.size)]).T
y_true = g(xx)
y_prob = gp.predict_proba(xx)[:, 1]
y_true = y_true.reshape((res, res))
y_prob = y_prob.reshape((res, res))
# Plot the probabilistic classification iso-values
fig = pl.figure(1)
ax = fig.gca()
ax.axes.set_aspect('equal')
pl.xticks([])
pl.yticks([])
ax.set_xticklabels([])
ax.set_yticklabels([])
pl.xlabel('$x_1$')
pl.ylabel('$x_2$')
cax = pl.imshow(y_prob, cmap=cm.gray_r, alpha=0.8,
extent=(-lim, lim, -lim, lim))
norm = pl.matplotlib.colors.Normalize(vmin=0., vmax=0.9)
cb = pl.colorbar(cax, ticks=[0., 0.2, 0.4, 0.6, 0.8, 1.], norm=norm)
cb.set_label('${\\rm \mathbb{P}}\left[\widehat{G}(\mathbf{x}) \leq 0\\right]$')
pl.clim(0, 1)
pl.plot(X[y <= 0, 0], X[y <= 0, 1], 'r.', markersize=12)
pl.plot(X[y > 0, 0], X[y > 0, 1], 'b.', markersize=12)
cs = pl.contour(x1, x2, y_true, [0.], colors='k', linestyles='dashdot')
cs = pl.contour(x1, x2, y_prob, [0.666], colors='b',
linestyles='solid')
pl.clabel(cs, fontsize=11)
cs = pl.contour(x1, x2, y_prob, [0.5], colors='k',
linestyles='dashed')
pl.clabel(cs, fontsize=11)
cs = pl.contour(x1, x2, y_prob, [0.334], colors='r',
linestyles='solid')
pl.clabel(cs, fontsize=11)
pl.show()
| bsd-3-clause |
montagnero/political-affiliation-prediction | newsreader.py | 2 | 11936 | # -*- coding: utf-8 -*-
from sklearn.decomposition import KernelPCA
from sklearn.metrics.pairwise import pairwise_distances
from scipy.stats.mstats import zscore
import glob
import json
import re
import datetime
import os
import cPickle
import codecs
import itertools
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy import double,triu,ones,hstack,arange,reshape,zeros,setdiff1d,array,zeros,eye,argmax,percentile
def get_news(sources=['spiegel','faz','welt','zeit'], folder='model'):
'''
Collects all news articles from political ressort of major German newspapers
Articles are transformed to BoW vectors and assigned to a political party
For better visualization, articles' BoW vectors are also clustered into topics
INPUT
folder the model folder containing classifier and BoW transformer
sources a list of strings for each newspaper for which a crawl is implemented
default ['zeit','sz']
'''
import classifier
from bs4 import BeautifulSoup
from api import fetch_url
import urllib2
news = dict([(source,[]) for source in sources])
# the classifier for prediction of political affiliation
clf = classifier.Classifier(folder=folder)
for source in sources:
if source is 'spiegel':
# fetching articles from sueddeutsche.de/politik
url = 'http://www.spiegel.de/politik'
site = BeautifulSoup(urllib2.urlopen(url).read())
titles = site.findAll("div", { "class" : "teaser" })
urls = ['http://www.spiegel.de'+a.findNext('a')['href'] for a in titles]
if source is 'faz':
# fetching articles from sueddeutsche.de/politik
url = 'http://www.faz.net/aktuell/politik'
site = BeautifulSoup(urllib2.urlopen(url).read())
titles = site.findAll("a", { "class" : "TeaserHeadLink" })
urls = ['http://www.faz.net'+a['href'] for a in titles]
if source is 'welt':
# fetching articles from sueddeutsche.de/politik
url = 'http://www.welt.de/politik'
site = BeautifulSoup(urllib2.urlopen(url).read())
titles = site.findAll("a", { "class" : "as_teaser-kicker" })
urls = [a['href'] for a in titles]
if source is 'sz-without-readability':
# fetching articles from sueddeutsche.de/politik
url = 'http://www.sueddeutsche.de/politik'
site = BeautifulSoup(urllib2.urlopen(url).read())
titles = site.findAll("div", { "class" : "teaser" })
urls = [a.findNext('a')['href'] for a in titles]
if source is 'zeit':
# fetching articles from zeit.de/politik
url = 'http://www.zeit.de/politik'
site = BeautifulSoup(urllib2.urlopen(url).read())
titles = site.findAll("span", { "class" : "supertitle" })
urls = [a.parent['href'] for a in titles if a.parent['href'].find('/2015-')>0]
print "Found %d articles on %s"%(len(urls),url)
# predict party from url for this source
print "Predicting %s"%source
articles = []
for url in urls:
try:
title,text = fetch_url(url)
prediction = clf.predict(text)
prediction['url'] = url
articles.append((title,prediction))
except:
print('Could not get text from %s'%url)
pass
news[source] = dict(articles)
# save results
datestr = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
open(folder+'/news-%s'%(datestr) + '.json', 'wb').write(json.dumps(news,ensure_ascii=False).encode('utf8'))
def all_saved_news(folder='model'):
import glob
from string import digits
# get just the most recent news articles file (assuming date label ordering)
news = json.load(open(glob.glob(folder+'/news*.json')[-1],"r"))
# collect text data from all articles
articles, data = [], []
for source in news.keys():
for title, article in news[source].items():
# remove numbers
for d in digits: article['text'] = article['text'].replace(d,'')
data.append(article['text'])
predictions = [prediction['probability'] for prediction in article['prediction']]
articles.append({
'source':source,
'title':title,
'url':article['url'],
'prediction':article['prediction'],
'predictedLabel':article['prediction'][argmax(predictions)]['party']
})
return articles, data
def pairwise_dists(data, nneighbors=10, folder='model', dist='l2'):
'''
Computes pairwise distances between bag-of-words vectors of articles
INPUT
folder model folder
nneighbors number of closest neighbors to include in distance list
'''
stopwords = codecs.open("stopwords.txt", "r", encoding="utf-8", errors='ignore').readlines()[5:]
stops = map(lambda x:x.lower().strip(),stopwords)
# using now stopwords and filtering out digits
bow = TfidfVectorizer(min_df=2,stop_words=stops)
X = bow.fit_transform(data)
print 'Computing %s pairwise distances'%dist
# KPCA transform bow vectors
if dist is 'l2_kpca_zscore':
K = pairwise_distances(X,metric='l2',n_jobs=1)
perc = 50.0
width = percentile(K.flatten(),perc)
Xc = zscore(KernelPCA(n_components=50,kernel='rbf',gamma=width).fit_transform(X))
K = pairwise_distances(Xc,metric='l2',n_jobs=1)
elif dist is 'l2_kpca':
K = pairwise_distances(X,metric='l2',n_jobs=1)
perc = 100./len(data)
width = percentile(K.flatten(),perc)
Xc = KernelPCA(n_components=50,kernel='rbf',gamma=width).fit_transform(X)
K = pairwise_distances(Xc,metric='l2',n_jobs=1)
elif dist is 'l2':
K = pairwise_distances(X,metric='l2',n_jobs=1)
elif dist is 'l1':
K = pairwise_distances(X,metric='l1',n_jobs=1)
# collect closest neighbors
distances = []
for urlidx in range(len(data)):
idx = (K[urlidx,:]).argsort()[1:nneighbors+1]
for sidx in idx:
distances.append([urlidx,sidx,(idx==sidx).nonzero()[0][0]])
return distances
def load_sentiment(negative='SentiWS_v1.8c/SentiWS_v1.8c_Negative.txt',\
positive='SentiWS_v1.8c/SentiWS_v1.8c_Positive.txt'):
words = dict()
for line in open(negative).readlines():
parts = line.strip('\n').split('\t')
words[parts[0].split('|')[0]] = double(parts[1])
if len(parts)>2:
for inflection in parts[2].strip('\n').split(','):
words[inflection] = double(parts[1])
for line in open(positive).readlines():
parts = line.strip('\n').split('\t')
words[parts[0].split('|')[0]] = double(parts[1])
if len(parts)>2:
for inflection in parts[2].strip('\n').split(','):
words[inflection] = double(parts[1])
return words
def get_sentiments(data):
# filtering out some noise words
stops = map(lambda x:x.lower().strip(),open('stopwords.txt').readlines()[6:])
# vectorize non-stopwords
bow = TfidfVectorizer(min_df=2,stop_words=stops)
X = bow.fit_transform(data)
# map sentiment vector to bow space
words = load_sentiment()
sentiment_vec = zeros(X.shape[1])
for key in words.keys():
if bow.vocabulary_.has_key(key):
sentiment_vec[bow.vocabulary_[key]] = words[key]
# compute sentiments
return X.dot(sentiment_vec)
def kpca_cluster(data,nclusters=100,ncomponents=40,topwhat=10,zscored=False):
'''
Computes clustering of bag-of-words vectors of articles
INPUT
folder model folder
nclusters number of clusters
'''
from sklearn.cluster import KMeans
# filtering out some noise words
stops = map(lambda x:x.lower().strip(),open('stopwords.txt').readlines()[6:])
# vectorize non-stopwords
bow = TfidfVectorizer(min_df=2,stop_words=stops)
X = bow.fit_transform(data)
# creating bow-index-to-word map
idx2word = dict(zip(bow.vocabulary_.values(),bow.vocabulary_.keys()))
# using now stopwords and filtering out digits
print 'Computing pairwise distances'
K = pairwise_distances(X,metric='l2',n_jobs=1)
perc = 50.0
width = percentile(K.flatten(),perc)
# KPCA transform bow vectors
Xc = KernelPCA(n_components=ncomponents,kernel='rbf',gamma=width).fit_transform(X)
if zscored:
Xc = zscore(Xc)
# compute clusters
km = KMeans(n_clusters=nclusters).fit(Xc)
Xc = km.predict(Xc)
clusters = []
for icluster in range(nclusters):
nmembers = (Xc==icluster).sum()
if True:#nmembers < len(data) / 5.0 and nmembers > 1: # only group clusters big enough but not too big
members = (Xc==icluster).nonzero()[0]
topwordidx = array(X[members,:].sum(axis=0))[0].argsort()[-topwhat:][::-1]
topwords = ' '.join([idx2word[wi] for wi in topwordidx])
meanDist = triu(pairwise_distances(X[members,:],metric='l2',n_jobs=1)).sum()
meanDist = meanDist / (len(members) + (len(members)**2 - len(members))/2.0)
# print u'Cluster %d'%icluster + u' %d members'%nmembers + u' mean Distance %f'%meanDist + u'\n\t'+topwords
clusters.append({
'name':'Cluster-%d'%icluster,
'description': topwords,
'members': list(members),
'meanL2Distances': meanDist
})
return clusters
def party_cluster(articles):
clusters = []
keyf = lambda a: a[1]['predictedLabel']
for k, group in itertools.groupby(sorted(enumerate(articles), key=keyf), keyf):
clusters.append({
'name': k,
'description': k,
'members': [index_article_tuple[0] for index_article_tuple in group]
})
return clusters
def write_distances_json(folder='model'):
articles, data = all_saved_news(folder)
dists = ['l2_kpca']
distances_json = {
'articles': articles,
'sentiments': json.dumps(get_sentiments(data).tolist()),
'distances': [
{ 'name': dist, 'distances': pairwise_dists(data,dist = dist) } for dist in dists
],
'clusterings': [
{ 'name': 'Parteivorhersage', 'clusters': party_cluster(articles) },
{ 'name': 'Ähnlichkeit', 'clusters': kpca_cluster(data,nclusters=len(articles)/2,ncomponents=40,zscored=False) },
]
}
# save article with party prediction and distances to closest articles
datestr = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
open(folder+'/distances-%s'%(datestr)+'.json', 'wb').write(json.dumps(distances_json))
# also save that latest version for the visualization
open(folder+'/distances.json', 'wb').write(json.dumps(distances_json))
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(\
description='Downloads, transforms and clusters news articles')
parser.add_argument('-f','--folder',help='Folder to store text files [./model]',\
default='model')
parser.add_argument('-d','--download',help='If files should be downloaded',\
action='store_true', default=False)
parser.add_argument('-p','--distances',help='If pairwise distances of text should be computed',\
action='store_true', default=False)
args = vars(parser.parse_args())
if not os.path.isdir(args['folder']):
os.mkdir(args['folder'])
if args['download']:
get_news(folder=args['folder'])
if args['distances']:
write_distances_json(folder=args['folder'])
| mit |
bikong2/scikit-learn | benchmarks/bench_plot_approximate_neighbors.py | 244 | 6011 | """
Benchmark for approximate nearest neighbor search using
locality sensitive hashing forest.
There are two types of benchmarks.
First, accuracy of LSHForest queries are measured for various
hyper-parameters and index sizes.
Second, speed up of LSHForest queries compared to brute force
method in exact nearest neighbors is measures for the
aforementioned settings. In general, speed up is increasing as
the index size grows.
"""
from __future__ import division
import numpy as np
from tempfile import gettempdir
from time import time
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors.approximate import LSHForest
from sklearn.datasets import make_blobs
from sklearn.externals.joblib import Memory
m = Memory(cachedir=gettempdir())
@m.cache()
def make_data(n_samples, n_features, n_queries, random_state=0):
"""Create index and query data."""
print('Generating random blob-ish data')
X, _ = make_blobs(n_samples=n_samples + n_queries,
n_features=n_features, centers=100,
shuffle=True, random_state=random_state)
# Keep the last samples as held out query vectors: note since we used
# shuffle=True we have ensured that index and query vectors are
# samples from the same distribution (a mixture of 100 gaussians in this
# case)
return X[:n_samples], X[n_samples:]
def calc_exact_neighbors(X, queries, n_queries, n_neighbors):
"""Measures average times for exact neighbor queries."""
print ('Building NearestNeighbors for %d samples in %d dimensions' %
(X.shape[0], X.shape[1]))
nbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X)
average_time = 0
t0 = time()
neighbors = nbrs.kneighbors(queries, n_neighbors=n_neighbors,
return_distance=False)
average_time = (time() - t0) / n_queries
return neighbors, average_time
def calc_accuracy(X, queries, n_queries, n_neighbors, exact_neighbors,
average_time_exact, **lshf_params):
"""Calculates accuracy and the speed up of LSHForest."""
print('Building LSHForest for %d samples in %d dimensions' %
(X.shape[0], X.shape[1]))
lshf = LSHForest(**lshf_params)
t0 = time()
lshf.fit(X)
lshf_build_time = time() - t0
print('Done in %0.3fs' % lshf_build_time)
accuracy = 0
t0 = time()
approx_neighbors = lshf.kneighbors(queries, n_neighbors=n_neighbors,
return_distance=False)
average_time_approx = (time() - t0) / n_queries
for i in range(len(queries)):
accuracy += np.in1d(approx_neighbors[i], exact_neighbors[i]).mean()
accuracy /= n_queries
speed_up = average_time_exact / average_time_approx
print('Average time for lshf neighbor queries: %0.3fs' %
average_time_approx)
print ('Average time for exact neighbor queries: %0.3fs' %
average_time_exact)
print ('Average Accuracy : %0.2f' % accuracy)
print ('Speed up: %0.1fx' % speed_up)
return speed_up, accuracy
if __name__ == '__main__':
import matplotlib.pyplot as plt
# Initialize index sizes
n_samples = [int(1e3), int(1e4), int(1e5), int(1e6)]
n_features = int(1e2)
n_queries = 100
n_neighbors = 10
X_index, X_query = make_data(np.max(n_samples), n_features, n_queries,
random_state=0)
params_list = [{'n_estimators': 3, 'n_candidates': 50},
{'n_estimators': 5, 'n_candidates': 70},
{'n_estimators': 10, 'n_candidates': 100}]
accuracies = np.zeros((len(n_samples), len(params_list)), dtype=float)
speed_ups = np.zeros((len(n_samples), len(params_list)), dtype=float)
for i, sample_size in enumerate(n_samples):
print ('==========================================================')
print ('Sample size: %i' % sample_size)
print ('------------------------')
exact_neighbors, average_time_exact = calc_exact_neighbors(
X_index[:sample_size], X_query, n_queries, n_neighbors)
for j, params in enumerate(params_list):
print ('LSHF parameters: n_estimators = %i, n_candidates = %i' %
(params['n_estimators'], params['n_candidates']))
speed_ups[i, j], accuracies[i, j] = calc_accuracy(
X_index[:sample_size], X_query, n_queries, n_neighbors,
exact_neighbors, average_time_exact, random_state=0, **params)
print ('')
print ('==========================================================')
# Set labels for LSHForest parameters
colors = ['c', 'm', 'y']
legend_rects = [plt.Rectangle((0, 0), 0.1, 0.1, fc=color)
for color in colors]
legend_labels = ['n_estimators={n_estimators}, '
'n_candidates={n_candidates}'.format(**p)
for p in params_list]
# Plot precision
plt.figure()
plt.legend(legend_rects, legend_labels,
loc='upper left')
for i in range(len(params_list)):
plt.scatter(n_samples, accuracies[:, i], c=colors[i])
plt.plot(n_samples, accuracies[:, i], c=colors[i])
plt.ylim([0, 1.3])
plt.xlim(np.min(n_samples), np.max(n_samples))
plt.semilogx()
plt.ylabel("Precision@10")
plt.xlabel("Index size")
plt.grid(which='both')
plt.title("Precision of first 10 neighbors with index size")
# Plot speed up
plt.figure()
plt.legend(legend_rects, legend_labels,
loc='upper left')
for i in range(len(params_list)):
plt.scatter(n_samples, speed_ups[:, i], c=colors[i])
plt.plot(n_samples, speed_ups[:, i], c=colors[i])
plt.ylim(0, np.max(speed_ups))
plt.xlim(np.min(n_samples), np.max(n_samples))
plt.semilogx()
plt.ylabel("Speed up")
plt.xlabel("Index size")
plt.grid(which='both')
plt.title("Relationship between Speed up and index size")
plt.show()
| bsd-3-clause |
hsuantien/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_01_language_train_model.py | 254 | 2253 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivier.grisel@ensta.org>
# License: Simplified BSD
import sys
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import Perceptron
from sklearn.pipeline import Pipeline
from sklearn.datasets import load_files
from sklearn.cross_validation import train_test_split
from sklearn import metrics
# The training data folder must be passed as first argument
languages_data_folder = sys.argv[1]
dataset = load_files(languages_data_folder)
# 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=0.5)
# TASK: Build a an vectorizer that splits strings into sequence of 1 to 3
# characters instead of word tokens
vectorizer = TfidfVectorizer(ngram_range=(1, 3), analyzer='char',
use_idf=False)
# TASK: Build a vectorizer / classifier pipeline using the previous analyzer
# the pipeline instance should stored in a variable named clf
clf = Pipeline([
('vec', vectorizer),
('clf', Perceptron()),
])
# TASK: Fit the pipeline on the training set
clf.fit(docs_train, y_train)
# TASK: Predict the outcome on the testing set in a variable named y_predicted
y_predicted = clf.predict(docs_test)
# Print the classification report
print(metrics.classification_report(y_test, y_predicted,
target_names=dataset.target_names))
# Plot the confusion matrix
cm = metrics.confusion_matrix(y_test, y_predicted)
print(cm)
#import pylab as pl
#pl.matshow(cm, cmap=pl.cm.jet)
#pl.show()
# Predict the result on some short new sentences:
sentences = [
u'This is a language detection test.',
u'Ceci est un test de d\xe9tection de la langue.',
u'Dies ist ein Test, um die Sprache zu erkennen.',
]
predicted = clf.predict(sentences)
for s, p in zip(sentences, predicted):
print(u'The language of "%s" is "%s"' % (s, dataset.target_names[p]))
| bsd-3-clause |
MostafaGazar/tensorflow | tensorflow/contrib/learn/python/learn/dataframe/dataframe.py | 85 | 4704 | # 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 DataFrame is a container for ingesting and preprocessing data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from .series import Series
from .transform import Transform
class DataFrame(object):
"""A DataFrame is a container for ingesting and preprocessing data."""
def __init__(self):
self._columns = {}
def columns(self):
"""Set of the column names."""
return frozenset(self._columns.keys())
def __len__(self):
"""The number of columns in the DataFrame."""
return len(self._columns)
def assign(self, **kwargs):
"""Adds columns to DataFrame.
Args:
**kwargs: assignments of the form key=value where key is a string
and value is an `inflow.Series`, a `pandas.Series` or a numpy array.
Raises:
TypeError: keys are not strings.
TypeError: values are not `inflow.Series`, `pandas.Series` or
`numpy.ndarray`.
TODO(jamieas): pandas assign method returns a new DataFrame. Consider
switching to this behavior, changing the name or adding in_place as an
argument.
"""
for k, v in kwargs.items():
if not isinstance(k, str):
raise TypeError("The only supported type for keys is string; got %s" %
type(k))
if v is None:
del self._columns[k]
elif isinstance(v, Series):
self._columns[k] = v
elif isinstance(v, Transform) and v.input_valency() == 0:
self._columns[k] = v()
else:
raise TypeError(
"Column in assignment must be an inflow.Series, inflow.Transform,"
" or None; got type '%s'." % type(v).__name__)
def select_columns(self, keys):
"""Returns a new DataFrame with a subset of columns.
Args:
keys: A list of strings. Each should be the name of a column in the
DataFrame.
Returns:
A new DataFrame containing only the specified columns.
"""
result = type(self)()
for key in keys:
result[key] = self._columns[key]
return result
def exclude_columns(self, exclude_keys):
"""Returns a new DataFrame with all columns not excluded via exclude_keys.
Args:
exclude_keys: A list of strings. Each should be the name of a column in
the DataFrame. These columns will be excluded from the result.
Returns:
A new DataFrame containing all columns except those specified.
"""
result = type(self)()
for key, value in self._columns.items():
if key not in exclude_keys:
result[key] = value
return result
def __getitem__(self, key):
"""Indexing functionality for DataFrames.
Args:
key: a string or an iterable of strings.
Returns:
A Series or list of Series corresponding to the given keys.
"""
if isinstance(key, str):
return self._columns[key]
elif isinstance(key, collections.Iterable):
for i in key:
if not isinstance(i, str):
raise TypeError("Expected a String; entry %s has type %s." %
(i, type(i).__name__))
return [self.__getitem__(i) for i in key]
raise TypeError(
"Invalid index: %s of type %s. Only strings or lists of strings are "
"supported." % (key, type(key)))
def __setitem__(self, key, value):
if isinstance(key, str):
key = [key]
if isinstance(value, Series):
value = [value]
self.assign(**dict(zip(key, value)))
def __delitem__(self, key):
if isinstance(key, str):
key = [key]
value = [None for _ in key]
self.assign(**dict(zip(key, value)))
def build(self, **kwargs):
# We do not allow passing a cache here, because that would encourage
# working around the rule that DataFrames cannot be expected to be
# synced with each other (e.g., they shuffle independently).
cache = {}
tensors = {name: c.build(cache, **kwargs)
for name, c in self._columns.items()}
return tensors
| apache-2.0 |
mtconley/turntable | test/lib/python2.7/site-packages/scipy/stats/tests/test_morestats.py | 7 | 38719 | # Author: Travis Oliphant, 2002
#
# Further enhancements and tests added by numerous SciPy developers.
#
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from numpy.random import RandomState
from numpy.testing import (TestCase, run_module_suite, assert_array_equal,
assert_almost_equal, assert_array_less, assert_array_almost_equal,
assert_raises, assert_, assert_allclose, assert_equal, dec, assert_warns)
from scipy import stats
# Matplotlib is not a scipy dependency but is optionally used in probplot, so
# check if it's available
try:
import matplotlib.pyplot as plt
have_matplotlib = True
except:
have_matplotlib = False
g1 = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000]
g2 = [0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988]
g3 = [0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996]
g4 = [1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996]
g5 = [0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996]
g6 = [1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996]
g7 = [0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002]
g8 = [0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006]
g9 = [1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991]
g10 = [0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997]
class TestShapiro(TestCase):
def test_basic(self):
x1 = [0.11,7.87,4.61,10.14,7.95,3.14,0.46,
4.43,0.21,4.75,0.71,1.52,3.24,
0.93,0.42,4.97,9.53,4.55,0.47,6.66]
w,pw = stats.shapiro(x1)
assert_almost_equal(w,0.90047299861907959,6)
assert_almost_equal(pw,0.042089745402336121,6)
x2 = [1.36,1.14,2.92,2.55,1.46,1.06,5.27,-1.11,
3.48,1.10,0.88,-0.51,1.46,0.52,6.20,1.69,
0.08,3.67,2.81,3.49]
w,pw = stats.shapiro(x2)
assert_almost_equal(w,0.9590270,6)
assert_almost_equal(pw,0.52460,3)
def test_bad_arg(self):
# Length of x is less than 3.
x = [1]
assert_raises(ValueError, stats.shapiro, x)
class TestAnderson(TestCase):
def test_normal(self):
rs = RandomState(1234567890)
x1 = rs.standard_exponential(size=50)
x2 = rs.standard_normal(size=50)
A,crit,sig = stats.anderson(x1)
assert_array_less(crit[:-1], A)
A,crit,sig = stats.anderson(x2)
assert_array_less(A, crit[-2:])
def test_expon(self):
rs = RandomState(1234567890)
x1 = rs.standard_exponential(size=50)
x2 = rs.standard_normal(size=50)
A,crit,sig = stats.anderson(x1,'expon')
assert_array_less(A, crit[-2:])
olderr = np.seterr(all='ignore')
try:
A,crit,sig = stats.anderson(x2,'expon')
finally:
np.seterr(**olderr)
assert_(A > crit[-1])
def test_bad_arg(self):
assert_raises(ValueError, stats.anderson, [1], dist='plate_of_shrimp')
class TestAndersonKSamp(TestCase):
def test_example1a(self):
# Example data from Scholz & Stephens (1987), originally
# published in Lehmann (1995, Nonparametrics, Statistical
# Methods Based on Ranks, p. 309)
# Pass a mixture of lists and arrays
t1 = [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0]
t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0])
t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8])
assert_warns(UserWarning, stats.anderson_ksamp, (t1, t2, t3, t4),
midrank=False)
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='approximate p-value')
Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=False)
assert_almost_equal(Tk, 4.449, 3)
assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459],
tm, 4)
assert_almost_equal(p, 0.0021, 4)
def test_example1b(self):
# Example data from Scholz & Stephens (1987), originally
# published in Lehmann (1995, Nonparametrics, Statistical
# Methods Based on Ranks, p. 309)
# Pass arrays
t1 = np.array([38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0])
t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0])
t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8])
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='approximate p-value')
Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=True)
assert_almost_equal(Tk, 4.480, 3)
assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459],
tm, 4)
assert_almost_equal(p, 0.0020, 4)
def test_example2a(self):
# Example data taken from an earlier technical report of
# Scholz and Stephens
# Pass lists instead of arrays
t1 = [194, 15, 41, 29, 33, 181]
t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118]
t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34]
t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29,
118, 25, 156, 310, 76, 26, 44, 23, 62]
t5 = [130, 208, 70, 101, 208]
t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27]
t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33]
t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5,
12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95]
t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82,
54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24]
t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36,
22, 139, 210, 97, 30, 23, 13, 14]
t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438]
t12 = [50, 254, 5, 283, 35, 12]
t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130]
t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66,
61, 34]
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='approximate p-value')
Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4, t5, t6, t7, t8,
t9, t10, t11, t12, t13, t14),
midrank=False)
assert_almost_equal(Tk, 3.288, 3)
assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009],
tm, 4)
assert_almost_equal(p, 0.0041, 4)
def test_example2b(self):
# Example data taken from an earlier technical report of
# Scholz and Stephens
t1 = [194, 15, 41, 29, 33, 181]
t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118]
t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34]
t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29,
118, 25, 156, 310, 76, 26, 44, 23, 62]
t5 = [130, 208, 70, 101, 208]
t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27]
t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33]
t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5,
12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95]
t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82,
54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24]
t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36,
22, 139, 210, 97, 30, 23, 13, 14]
t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438]
t12 = [50, 254, 5, 283, 35, 12]
t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130]
t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66,
61, 34]
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='approximate p-value')
Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4, t5, t6, t7, t8,
t9, t10, t11, t12, t13, t14),
midrank=True)
assert_almost_equal(Tk, 3.294, 3)
assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009],
tm, 4)
assert_almost_equal(p, 0.0041, 4)
def test_not_enough_samples(self):
assert_raises(ValueError, stats.anderson_ksamp, np.ones(5))
def test_no_distinct_observations(self):
assert_raises(ValueError, stats.anderson_ksamp,
(np.ones(5), np.ones(5)))
def test_empty_sample(self):
assert_raises(ValueError, stats.anderson_ksamp, (np.ones(5), []))
class TestAnsari(TestCase):
def test_small(self):
x = [1,2,3,3,4]
y = [3,2,6,1,6,1,4,1]
W, pval = stats.ansari(x,y)
assert_almost_equal(W,23.5,11)
assert_almost_equal(pval,0.13499256881897437,11)
def test_approx(self):
ramsay = np.array((111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
101, 96, 97, 102, 107, 113, 116, 113, 110, 98))
parekh = np.array((107, 108, 106, 98, 105, 103, 110, 105, 104,
100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99))
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
message="Ties preclude use of exact statistic.")
W, pval = stats.ansari(ramsay, parekh)
assert_almost_equal(W,185.5,11)
assert_almost_equal(pval,0.18145819972867083,11)
def test_exact(self):
W,pval = stats.ansari([1,2,3,4],[15,5,20,8,10,12])
assert_almost_equal(W,10.0,11)
assert_almost_equal(pval,0.533333333333333333,7)
def test_bad_arg(self):
assert_raises(ValueError, stats.ansari, [], [1])
assert_raises(ValueError, stats.ansari, [1], [])
class TestBartlett(TestCase):
def test_data(self):
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
T, pval = stats.bartlett(*args)
assert_almost_equal(T,20.78587342806484,7)
assert_almost_equal(pval,0.0136358632781,7)
def test_bad_arg(self):
# Too few args raises ValueError.
assert_raises(ValueError, stats.bartlett, [1])
class TestLevene(TestCase):
def test_data(self):
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
W, pval = stats.levene(*args)
assert_almost_equal(W,1.7059176930008939,7)
assert_almost_equal(pval,0.0990829755522,7)
def test_trimmed1(self):
# Test that center='trimmed' gives the same result as center='mean'
# when proportiontocut=0.
W1, pval1 = stats.levene(g1, g2, g3, center='mean')
W2, pval2 = stats.levene(g1, g2, g3, center='trimmed', proportiontocut=0.0)
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_trimmed2(self):
x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0]
y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0]
np.random.seed(1234)
x2 = np.random.permutation(x)
# Use center='trimmed'
W0, pval0 = stats.levene(x, y, center='trimmed', proportiontocut=0.125)
W1, pval1 = stats.levene(x2, y, center='trimmed', proportiontocut=0.125)
# Trim the data here, and use center='mean'
W2, pval2 = stats.levene(x[1:-1], y[1:-1], center='mean')
# Result should be the same.
assert_almost_equal(W0, W2)
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_equal_mean_median(self):
x = np.linspace(-1,1,21)
np.random.seed(1234)
x2 = np.random.permutation(x)
y = x**3
W1, pval1 = stats.levene(x, y, center='mean')
W2, pval2 = stats.levene(x2, y, center='median')
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_bad_keyword(self):
x = np.linspace(-1,1,21)
assert_raises(TypeError, stats.levene, x, x, portiontocut=0.1)
def test_bad_center_value(self):
x = np.linspace(-1,1,21)
assert_raises(ValueError, stats.levene, x, x, center='trim')
def test_too_few_args(self):
assert_raises(ValueError, stats.levene, [1])
class TestBinomP(TestCase):
def test_data(self):
pval = stats.binom_test(100,250)
assert_almost_equal(pval,0.0018833009350757682,11)
pval = stats.binom_test(201,405)
assert_almost_equal(pval,0.92085205962670713,11)
pval = stats.binom_test([682,243],p=3.0/4)
assert_almost_equal(pval,0.38249155957481695,11)
def test_bad_len_x(self):
# Length of x must be 1 or 2.
assert_raises(ValueError, stats.binom_test, [1,2,3])
def test_bad_n(self):
# len(x) is 1, but n is invalid.
# Missing n
assert_raises(ValueError, stats.binom_test, [100])
# n less than x[0]
assert_raises(ValueError, stats.binom_test, [100], n=50)
def test_bad_p(self):
assert_raises(ValueError, stats.binom_test, [50, 50], p=2.0)
class TestFindRepeats(TestCase):
def test_basic(self):
a = [1,2,3,4,1,2,3,4,1,2,5]
res,nums = stats.find_repeats(a)
assert_array_equal(res,[1,2,3,4])
assert_array_equal(nums,[3,3,2,2])
def test_empty_result(self):
# Check that empty arrays are returned when there are no repeats.
a = [10, 20, 50, 30, 40]
repeated, counts = stats.find_repeats(a)
assert_array_equal(repeated, [])
assert_array_equal(counts, [])
class TestFligner(TestCase):
def test_data(self):
# numbers from R: fligner.test in package stats
x1 = np.arange(5)
assert_array_almost_equal(stats.fligner(x1,x1**2),
(3.2282229927203536, 0.072379187848207877), 11)
def test_trimmed1(self):
# Test that center='trimmed' gives the same result as center='mean'
# when proportiontocut=0.
Xsq1, pval1 = stats.fligner(g1, g2, g3, center='mean')
Xsq2, pval2 = stats.fligner(g1, g2, g3, center='trimmed', proportiontocut=0.0)
assert_almost_equal(Xsq1, Xsq2)
assert_almost_equal(pval1, pval2)
def test_trimmed2(self):
x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0]
y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0]
# Use center='trimmed'
Xsq1, pval1 = stats.fligner(x, y, center='trimmed', proportiontocut=0.125)
# Trim the data here, and use center='mean'
Xsq2, pval2 = stats.fligner(x[1:-1], y[1:-1], center='mean')
# Result should be the same.
assert_almost_equal(Xsq1, Xsq2)
assert_almost_equal(pval1, pval2)
# The following test looks reasonable at first, but fligner() uses the
# function stats.rankdata(), and in one of the cases in this test,
# there are ties, while in the other (because of normal rounding
# errors) there are not. This difference leads to differences in the
# third significant digit of W.
#
#def test_equal_mean_median(self):
# x = np.linspace(-1,1,21)
# y = x**3
# W1, pval1 = stats.fligner(x, y, center='mean')
# W2, pval2 = stats.fligner(x, y, center='median')
# assert_almost_equal(W1, W2)
# assert_almost_equal(pval1, pval2)
def test_bad_keyword(self):
x = np.linspace(-1,1,21)
assert_raises(TypeError, stats.fligner, x, x, portiontocut=0.1)
def test_bad_center_value(self):
x = np.linspace(-1,1,21)
assert_raises(ValueError, stats.fligner, x, x, center='trim')
def test_bad_num_args(self):
# Too few args raises ValueError.
assert_raises(ValueError, stats.fligner, [1])
class TestMood(TestCase):
def test_mood(self):
# numbers from R: mood.test in package stats
x1 = np.arange(5)
assert_array_almost_equal(stats.mood(x1, x1**2),
(-1.3830857299399906, 0.16663858066771478), 11)
def test_mood_order_of_args(self):
# z should change sign when the order of arguments changes, pvalue
# should not change
np.random.seed(1234)
x1 = np.random.randn(10, 1)
x2 = np.random.randn(15, 1)
z1, p1 = stats.mood(x1, x2)
z2, p2 = stats.mood(x2, x1)
assert_array_almost_equal([z1, p1], [-z2, p2])
def test_mood_with_axis_none(self):
#Test with axis = None, compare with results from R
x1 = [-0.626453810742332, 0.183643324222082, -0.835628612410047,
1.59528080213779, 0.329507771815361, -0.820468384118015,
0.487429052428485, 0.738324705129217, 0.575781351653492,
-0.305388387156356, 1.51178116845085, 0.389843236411431,
-0.621240580541804, -2.2146998871775, 1.12493091814311,
-0.0449336090152309, -0.0161902630989461, 0.943836210685299,
0.821221195098089, 0.593901321217509]
x2 = [-0.896914546624981, 0.184849184646742, 1.58784533120882,
-1.13037567424629, -0.0802517565509893, 0.132420284381094,
0.707954729271733, -0.23969802417184, 1.98447393665293,
-0.138787012119665, 0.417650750792556, 0.981752777463662,
-0.392695355503813, -1.03966897694891, 1.78222896030858,
-2.31106908460517, 0.878604580921265, 0.035806718015226,
1.01282869212708, 0.432265154539617, 2.09081920524915,
-1.19992581964387, 1.58963820029007, 1.95465164222325,
0.00493777682814261, -2.45170638784613, 0.477237302613617,
-0.596558168631403, 0.792203270299649, 0.289636710177348]
x1 = np.array(x1)
x2 = np.array(x2)
x1.shape = (10, 2)
x2.shape = (15, 2)
assert_array_almost_equal(stats.mood(x1, x2, axis=None),
[-1.31716607555, 0.18778296257])
def test_mood_2d(self):
# Test if the results of mood test in 2-D case are consistent with the
# R result for the same inputs. Numbers from R mood.test().
ny = 5
np.random.seed(1234)
x1 = np.random.randn(10, ny)
x2 = np.random.randn(15, ny)
z_vectest, pval_vectest = stats.mood(x1, x2)
for j in range(ny):
assert_array_almost_equal([z_vectest[j], pval_vectest[j]],
stats.mood(x1[:, j], x2[:, j]))
# inverse order of dimensions
x1 = x1.transpose()
x2 = x2.transpose()
z_vectest, pval_vectest = stats.mood(x1, x2, axis=1)
for i in range(ny):
# check axis handling is self consistent
assert_array_almost_equal([z_vectest[i], pval_vectest[i]],
stats.mood(x1[i, :], x2[i, :]))
def test_mood_3d(self):
shape = (10, 5, 6)
np.random.seed(1234)
x1 = np.random.randn(*shape)
x2 = np.random.randn(*shape)
for axis in range(3):
z_vectest, pval_vectest = stats.mood(x1, x2, axis=axis)
# Tests that result for 3-D arrays is equal to that for the
# same calculation on a set of 1-D arrays taken from the
# 3-D array
axes_idx = ([1, 2], [0, 2], [0, 1]) # the two axes != axis
for i in range(shape[axes_idx[axis][0]]):
for j in range(shape[axes_idx[axis][1]]):
if axis == 0:
slice1 = x1[:, i, j]
slice2 = x2[:, i, j]
elif axis == 1:
slice1 = x1[i, :, j]
slice2 = x2[i, :, j]
else:
slice1 = x1[i, j, :]
slice2 = x2[i, j, :]
assert_array_almost_equal([z_vectest[i, j],
pval_vectest[i, j]],
stats.mood(slice1, slice2))
def test_mood_bad_arg(self):
# Raise ValueError when the sum of the lengths of the args is less than 3
assert_raises(ValueError, stats.mood, [1], [])
class TestProbplot(TestCase):
def test_basic(self):
np.random.seed(12345)
x = stats.norm.rvs(size=20)
osm, osr = stats.probplot(x, fit=False)
osm_expected = [-1.8241636, -1.38768012, -1.11829229, -0.91222575,
-0.73908135, -0.5857176, -0.44506467, -0.31273668,
-0.18568928, -0.06158146, 0.06158146, 0.18568928,
0.31273668, 0.44506467, 0.5857176, 0.73908135,
0.91222575, 1.11829229, 1.38768012, 1.8241636]
assert_allclose(osr, np.sort(x))
assert_allclose(osm, osm_expected)
res, res_fit = stats.probplot(x, fit=True)
res_fit_expected = [1.05361841, 0.31297795, 0.98741609]
assert_allclose(res_fit, res_fit_expected)
def test_sparams_keyword(self):
np.random.seed(123456)
x = stats.norm.rvs(size=100)
# Check that None, () and 0 (loc=0, for normal distribution) all work
# and give the same results
osm1, osr1 = stats.probplot(x, sparams=None, fit=False)
osm2, osr2 = stats.probplot(x, sparams=0, fit=False)
osm3, osr3 = stats.probplot(x, sparams=(), fit=False)
assert_allclose(osm1, osm2)
assert_allclose(osm1, osm3)
assert_allclose(osr1, osr2)
assert_allclose(osr1, osr3)
# Check giving (loc, scale) params for normal distribution
osm, osr = stats.probplot(x, sparams=(), fit=False)
def test_dist_keyword(self):
np.random.seed(12345)
x = stats.norm.rvs(size=20)
osm1, osr1 = stats.probplot(x, fit=False, dist='t', sparams=(3,))
osm2, osr2 = stats.probplot(x, fit=False, dist=stats.t, sparams=(3,))
assert_allclose(osm1, osm2)
assert_allclose(osr1, osr2)
assert_raises(ValueError, stats.probplot, x, dist='wrong-dist-name')
assert_raises(AttributeError, stats.probplot, x, dist=[])
class custom_dist(object):
"""Some class that looks just enough like a distribution."""
def ppf(self, q):
return stats.norm.ppf(q, loc=2)
osm1, osr1 = stats.probplot(x, sparams=(2,), fit=False)
osm2, osr2 = stats.probplot(x, dist=custom_dist(), fit=False)
assert_allclose(osm1, osm2)
assert_allclose(osr1, osr2)
@dec.skipif(not have_matplotlib)
def test_plot_kwarg(self):
np.random.seed(7654321)
fig = plt.figure()
fig.add_subplot(111)
x = stats.t.rvs(3, size=100)
res1, fitres1 = stats.probplot(x, plot=plt)
plt.close()
res2, fitres2 = stats.probplot(x, plot=None)
res3 = stats.probplot(x, fit=False, plot=plt)
plt.close()
res4 = stats.probplot(x, fit=False, plot=None)
# Check that results are consistent between combinations of `fit` and
# `plot` keywords.
assert_(len(res1) == len(res2) == len(res3) == len(res4) == 2)
assert_allclose(res1, res2)
assert_allclose(res1, res3)
assert_allclose(res1, res4)
assert_allclose(fitres1, fitres2)
# Check that a Matplotlib Axes object is accepted
fig = plt.figure()
ax = fig.add_subplot(111)
stats.probplot(x, fit=False, plot=ax)
plt.close()
def test_probplot_bad_args(self):
# Raise ValueError when given an invalid distribution.
assert_raises(ValueError, stats.probplot, [1], dist="plate_of_shrimp")
def test_wilcoxon_bad_arg():
# Raise ValueError when two args of different lengths are given or
# zero_method is unknown.
assert_raises(ValueError, stats.wilcoxon, [1], [1,2])
assert_raises(ValueError, stats.wilcoxon, [1,2], [1,2], "dummy")
def test_mvsdist_bad_arg():
# Raise ValueError if fewer than two data points are given.
data = [1]
assert_raises(ValueError, stats.mvsdist, data)
def test_kstat_bad_arg():
# Raise ValueError if n > 4 or n > 1.
data = [1]
n = 10
assert_raises(ValueError, stats.kstat, data, n=n)
def test_kstatvar_bad_arg():
# Raise ValueError is n is not 1 or 2.
data = [1]
n = 10
assert_raises(ValueError, stats.kstatvar, data, n=n)
def test_ppcc_max_bad_arg():
# Raise ValueError when given an invalid distribution.
data = [1]
assert_raises(ValueError, stats.ppcc_max, data, dist="plate_of_shrimp")
class TestBoxcox_llf(TestCase):
def test_basic(self):
np.random.seed(54321)
x = stats.norm.rvs(size=10000, loc=10)
lmbda = 1
llf = stats.boxcox_llf(lmbda, x)
llf_expected = -x.size / 2. * np.log(np.sum(x.std()**2))
assert_allclose(llf, llf_expected)
def test_array_like(self):
np.random.seed(54321)
x = stats.norm.rvs(size=100, loc=10)
lmbda = 1
llf = stats.boxcox_llf(lmbda, x)
llf2 = stats.boxcox_llf(lmbda, list(x))
assert_allclose(llf, llf2, rtol=1e-12)
def test_2d_input(self):
# Note: boxcox_llf() was already working with 2-D input (sort of), so
# keep it like that. boxcox() doesn't work with 2-D input though, due
# to brent() returning a scalar.
np.random.seed(54321)
x = stats.norm.rvs(size=100, loc=10)
lmbda = 1
llf = stats.boxcox_llf(lmbda, x)
llf2 = stats.boxcox_llf(lmbda, np.vstack([x, x]).T)
assert_allclose([llf, llf], llf2, rtol=1e-12)
def test_empty(self):
assert_(np.isnan(stats.boxcox_llf(1, [])))
class TestBoxcox(TestCase):
def test_fixed_lmbda(self):
np.random.seed(12345)
x = stats.loggamma.rvs(5, size=50) + 5
xt = stats.boxcox(x, lmbda=1)
assert_allclose(xt, x - 1)
xt = stats.boxcox(x, lmbda=-1)
assert_allclose(xt, 1 - 1/x)
xt = stats.boxcox(x, lmbda=0)
assert_allclose(xt, np.log(x))
# Also test that array_like input works
xt = stats.boxcox(list(x), lmbda=0)
assert_allclose(xt, np.log(x))
def test_lmbda_None(self):
np.random.seed(1234567)
# Start from normal rv's, do inverse transform to check that
# optimization function gets close to the right answer.
np.random.seed(1245)
lmbda = 2.5
x = stats.norm.rvs(loc=10, size=50000)
x_inv = (x * lmbda + 1)**(-lmbda)
xt, maxlog = stats.boxcox(x_inv)
assert_almost_equal(maxlog, -1 / lmbda, decimal=2)
def test_alpha(self):
np.random.seed(1234)
x = stats.loggamma.rvs(5, size=50) + 5
# Some regular values for alpha, on a small sample size
_, _, interval = stats.boxcox(x, alpha=0.75)
assert_allclose(interval, [4.004485780226041, 5.138756355035744])
_, _, interval = stats.boxcox(x, alpha=0.05)
assert_allclose(interval, [1.2138178554857557, 8.209033272375663])
# Try some extreme values, see we don't hit the N=500 limit
x = stats.loggamma.rvs(7, size=500) + 15
_, _, interval = stats.boxcox(x, alpha=0.001)
assert_allclose(interval, [0.3988867, 11.40553131])
_, _, interval = stats.boxcox(x, alpha=0.999)
assert_allclose(interval, [5.83316246, 5.83735292])
def test_boxcox_bad_arg(self):
# Raise ValueError if any data value is negative.
x = np.array([-1])
assert_raises(ValueError, stats.boxcox, x)
def test_empty(self):
assert_(stats.boxcox([]).shape == (0,))
class TestBoxcoxNormmax(TestCase):
def setUp(self):
np.random.seed(12345)
self.x = stats.loggamma.rvs(5, size=50) + 5
def test_pearsonr(self):
maxlog = stats.boxcox_normmax(self.x)
assert_allclose(maxlog, 1.804465, rtol=1e-6)
def test_mle(self):
maxlog = stats.boxcox_normmax(self.x, method='mle')
assert_allclose(maxlog, 1.758101, rtol=1e-6)
# Check that boxcox() uses 'mle'
_, maxlog_boxcox = stats.boxcox(self.x)
assert_allclose(maxlog_boxcox, maxlog)
def test_all(self):
maxlog_all = stats.boxcox_normmax(self.x, method='all')
assert_allclose(maxlog_all, [1.804465, 1.758101], rtol=1e-6)
class TestBoxcoxNormplot(TestCase):
def setUp(self):
np.random.seed(7654321)
self.x = stats.loggamma.rvs(5, size=500) + 5
def test_basic(self):
N = 5
lmbdas, ppcc = stats.boxcox_normplot(self.x, -10, 10, N=N)
ppcc_expected = [0.57783375, 0.83610988, 0.97524311, 0.99756057,
0.95843297]
assert_allclose(lmbdas, np.linspace(-10, 10, num=N))
assert_allclose(ppcc, ppcc_expected)
@dec.skipif(not have_matplotlib)
def test_plot_kwarg(self):
# Check with the matplotlib.pyplot module
fig = plt.figure()
fig.add_subplot(111)
stats.boxcox_normplot(self.x, -20, 20, plot=plt)
plt.close()
# Check that a Matplotlib Axes object is accepted
fig.add_subplot(111)
ax = fig.add_subplot(111)
stats.boxcox_normplot(self.x, -20, 20, plot=ax)
plt.close()
def test_invalid_inputs(self):
# `lb` has to be larger than `la`
assert_raises(ValueError, stats.boxcox_normplot, self.x, 1, 0)
# `x` can not contain negative values
assert_raises(ValueError, stats.boxcox_normplot, [-1, 1], 0, 1)
def test_empty(self):
assert_(stats.boxcox_normplot([], 0, 1).size == 0)
class TestCircFuncs(TestCase):
def test_circfuncs(self):
x = np.array([355,5,2,359,10,350])
M = stats.circmean(x, high=360)
Mval = 0.167690146
assert_allclose(M, Mval, rtol=1e-7)
V = stats.circvar(x, high=360)
Vval = 42.51955609
assert_allclose(V, Vval, rtol=1e-7)
S = stats.circstd(x, high=360)
Sval = 6.520702116
assert_allclose(S, Sval, rtol=1e-7)
def test_circfuncs_small(self):
x = np.array([20,21,22,18,19,20.5,19.2])
M1 = x.mean()
M2 = stats.circmean(x, high=360)
assert_allclose(M2, M1, rtol=1e-5)
V1 = x.var()
V2 = stats.circvar(x, high=360)
assert_allclose(V2, V1, rtol=1e-4)
S1 = x.std()
S2 = stats.circstd(x, high=360)
assert_allclose(S2, S1, rtol=1e-4)
def test_circmean_axis(self):
x = np.array([[355,5,2,359,10,350],
[351,7,4,352,9,349],
[357,9,8,358,4,356]])
M1 = stats.circmean(x, high=360)
M2 = stats.circmean(x.ravel(), high=360)
assert_allclose(M1, M2, rtol=1e-14)
M1 = stats.circmean(x, high=360, axis=1)
M2 = [stats.circmean(x[i], high=360) for i in range(x.shape[0])]
assert_allclose(M1, M2, rtol=1e-14)
M1 = stats.circmean(x, high=360, axis=0)
M2 = [stats.circmean(x[:,i], high=360) for i in range(x.shape[1])]
assert_allclose(M1, M2, rtol=1e-14)
def test_circvar_axis(self):
x = np.array([[355,5,2,359,10,350],
[351,7,4,352,9,349],
[357,9,8,358,4,356]])
V1 = stats.circvar(x, high=360)
V2 = stats.circvar(x.ravel(), high=360)
assert_allclose(V1, V2, rtol=1e-11)
V1 = stats.circvar(x, high=360, axis=1)
V2 = [stats.circvar(x[i], high=360) for i in range(x.shape[0])]
assert_allclose(V1, V2, rtol=1e-11)
V1 = stats.circvar(x, high=360, axis=0)
V2 = [stats.circvar(x[:,i], high=360) for i in range(x.shape[1])]
assert_allclose(V1, V2, rtol=1e-11)
def test_circstd_axis(self):
x = np.array([[355,5,2,359,10,350],
[351,7,4,352,9,349],
[357,9,8,358,4,356]])
S1 = stats.circstd(x, high=360)
S2 = stats.circstd(x.ravel(), high=360)
assert_allclose(S1, S2, rtol=1e-11)
S1 = stats.circstd(x, high=360, axis=1)
S2 = [stats.circstd(x[i], high=360) for i in range(x.shape[0])]
assert_allclose(S1, S2, rtol=1e-11)
S1 = stats.circstd(x, high=360, axis=0)
S2 = [stats.circstd(x[:,i], high=360) for i in range(x.shape[1])]
assert_allclose(S1, S2, rtol=1e-11)
def test_circfuncs_array_like(self):
x = [355,5,2,359,10,350]
assert_allclose(stats.circmean(x, high=360), 0.167690146, rtol=1e-7)
assert_allclose(stats.circvar(x, high=360), 42.51955609, rtol=1e-7)
assert_allclose(stats.circstd(x, high=360), 6.520702116, rtol=1e-7)
def test_empty(self):
assert_(np.isnan(stats.circmean([])))
assert_(np.isnan(stats.circstd([])))
assert_(np.isnan(stats.circvar([])))
def test_accuracy_wilcoxon():
freq = [1, 4, 16, 15, 8, 4, 5, 1, 2]
nums = range(-4, 5)
x = np.concatenate([[u] * v for u, v in zip(nums, freq)])
y = np.zeros(x.size)
T, p = stats.wilcoxon(x, y, "pratt")
assert_allclose(T, 423)
assert_allclose(p, 0.00197547303533107)
T, p = stats.wilcoxon(x, y, "zsplit")
assert_allclose(T, 441)
assert_allclose(p, 0.0032145343172473055)
T, p = stats.wilcoxon(x, y, "wilcox")
assert_allclose(T, 327)
assert_allclose(p, 0.00641346115861)
# Test the 'correction' option, using values computed in R with:
# > wilcox.test(x, y, paired=TRUE, exact=FALSE, correct={FALSE,TRUE})
x = np.array([120, 114, 181, 188, 180, 146, 121, 191, 132, 113, 127, 112])
y = np.array([133, 143, 119, 189, 112, 199, 198, 113, 115, 121, 142, 187])
T, p = stats.wilcoxon(x, y, correction=False)
assert_equal(T, 34)
assert_allclose(p, 0.6948866, rtol=1e-6)
T, p = stats.wilcoxon(x, y, correction=True)
assert_equal(T, 34)
assert_allclose(p, 0.7240817, rtol=1e-6)
def test_wilcoxon_tie():
# Regression test for gh-2391.
# Corresponding R code is:
# > result = wilcox.test(rep(0.1, 10), exact=FALSE, correct=FALSE)
# > result$p.value
# [1] 0.001565402
# > result = wilcox.test(rep(0.1, 10), exact=FALSE, correct=TRUE)
# > result$p.value
# [1] 0.001904195
stat, p = stats.wilcoxon([0.1] * 10)
expected_p = 0.001565402
assert_equal(stat, 0)
assert_allclose(p, expected_p, rtol=1e-6)
stat, p = stats.wilcoxon([0.1] * 10, correction=True)
expected_p = 0.001904195
assert_equal(stat, 0)
assert_allclose(p, expected_p, rtol=1e-6)
class TestMedianTest(TestCase):
def test_bad_n_samples(self):
# median_test requires at least two samples.
assert_raises(ValueError, stats.median_test, [1, 2, 3])
def test_empty_sample(self):
# Each sample must contain at least one value.
assert_raises(ValueError, stats.median_test, [], [1, 2, 3])
def test_empty_when_ties_ignored(self):
# The grand median is 1, and all values in the first argument are
# equal to the grand median. With ties="ignore", those values are
# ignored, which results in the first sample being (in effect) empty.
# This should raise a ValueError.
assert_raises(ValueError, stats.median_test,
[1, 1, 1, 1], [2, 0, 1], [2, 0], ties="ignore")
def test_empty_contingency_row(self):
# The grand median is 1, and with the default ties="below", all the
# values in the samples are counted as being below the grand median.
# This would result a row of zeros in the contingency table, which is
# an error.
assert_raises(ValueError, stats.median_test, [1, 1, 1], [1, 1, 1])
# With ties="above", all the values are counted as above the
# grand median.
assert_raises(ValueError, stats.median_test, [1, 1, 1], [1, 1, 1],
ties="above")
def test_bad_ties(self):
assert_raises(ValueError, stats.median_test, [1, 2, 3], [4, 5], ties="foo")
def test_bad_keyword(self):
assert_raises(TypeError, stats.median_test, [1, 2, 3], [4, 5], foo="foo")
def test_simple(self):
x = [1, 2, 3]
y = [1, 2, 3]
stat, p, med, tbl = stats.median_test(x, y)
# The median is floating point, but this equality test should be safe.
assert_equal(med, 2.0)
assert_array_equal(tbl, [[1, 1], [2, 2]])
# The expected values of the contingency table equal the contingency table,
# so the statistic should be 0 and the p-value should be 1.
assert_equal(stat, 0)
assert_equal(p, 1)
def test_ties_options(self):
# Test the contingency table calculation.
x = [1, 2, 3, 4]
y = [5, 6]
z = [7, 8, 9]
# grand median is 5.
# Default 'ties' option is "below".
stat, p, m, tbl = stats.median_test(x, y, z)
assert_equal(m, 5)
assert_equal(tbl, [[0, 1, 3], [4, 1, 0]])
stat, p, m, tbl = stats.median_test(x, y, z, ties="ignore")
assert_equal(m, 5)
assert_equal(tbl, [[0, 1, 3], [4, 0, 0]])
stat, p, m, tbl = stats.median_test(x, y, z, ties="above")
assert_equal(m, 5)
assert_equal(tbl, [[0, 2, 3], [4, 0, 0]])
def test_basic(self):
# median_test calls chi2_contingency to compute the test statistic
# and p-value. Make sure it hasn't screwed up the call...
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8]
stat, p, m, tbl = stats.median_test(x, y)
assert_equal(m, 4)
assert_equal(tbl, [[1, 2], [4, 2]])
exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl)
assert_allclose(stat, exp_stat)
assert_allclose(p, exp_p)
stat, p, m, tbl = stats.median_test(x, y, lambda_=0)
assert_equal(m, 4)
assert_equal(tbl, [[1, 2], [4, 2]])
exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl, lambda_=0)
assert_allclose(stat, exp_stat)
assert_allclose(p, exp_p)
stat, p, m, tbl = stats.median_test(x, y, correction=False)
assert_equal(m, 4)
assert_equal(tbl, [[1, 2], [4, 2]])
exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl, correction=False)
assert_allclose(stat, exp_stat)
assert_allclose(p, exp_p)
if __name__ == "__main__":
run_module_suite()
| mit |
mjgrav2001/scikit-learn | sklearn/decomposition/base.py | 313 | 5647 | """Principal Component Analysis Base Classes"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis A. Engemann <d.engemann@fz-juelich.de>
# Kyle Kastner <kastnerkyle@gmail.com>
#
# License: BSD 3 clause
import numpy as np
from scipy import linalg
from ..base import BaseEstimator, TransformerMixin
from ..utils import check_array
from ..utils.extmath import fast_dot
from ..utils.validation import check_is_fitted
from ..externals import six
from abc import ABCMeta, abstractmethod
class _BasePCA(six.with_metaclass(ABCMeta, BaseEstimator, TransformerMixin)):
"""Base class for PCA methods.
Warning: This class should not be used directly.
Use derived classes instead.
"""
def get_covariance(self):
"""Compute data covariance with the generative model.
``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)``
where S**2 contains the explained variances, and sigma2 contains the
noise variances.
Returns
-------
cov : array, shape=(n_features, n_features)
Estimated covariance of data.
"""
components_ = self.components_
exp_var = self.explained_variance_
if self.whiten:
components_ = components_ * np.sqrt(exp_var[:, np.newaxis])
exp_var_diff = np.maximum(exp_var - self.noise_variance_, 0.)
cov = np.dot(components_.T * exp_var_diff, components_)
cov.flat[::len(cov) + 1] += self.noise_variance_ # modify diag inplace
return cov
def get_precision(self):
"""Compute data precision matrix with the generative model.
Equals the inverse of the covariance but computed with
the matrix inversion lemma for efficiency.
Returns
-------
precision : array, shape=(n_features, n_features)
Estimated precision of data.
"""
n_features = self.components_.shape[1]
# handle corner cases first
if self.n_components_ == 0:
return np.eye(n_features) / self.noise_variance_
if self.n_components_ == n_features:
return linalg.inv(self.get_covariance())
# Get precision using matrix inversion lemma
components_ = self.components_
exp_var = self.explained_variance_
if self.whiten:
components_ = components_ * np.sqrt(exp_var[:, np.newaxis])
exp_var_diff = np.maximum(exp_var - self.noise_variance_, 0.)
precision = np.dot(components_, components_.T) / self.noise_variance_
precision.flat[::len(precision) + 1] += 1. / exp_var_diff
precision = np.dot(components_.T,
np.dot(linalg.inv(precision), components_))
precision /= -(self.noise_variance_ ** 2)
precision.flat[::len(precision) + 1] += 1. / self.noise_variance_
return precision
@abstractmethod
def fit(X, y=None):
"""Placeholder for fit. Subclasses should implement this method!
Fit the model with X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and
n_features is the number of features.
Returns
-------
self : object
Returns the instance itself.
"""
def transform(self, X, y=None):
"""Apply dimensionality reduction to X.
X is projected on the first principal components previously extracted
from a training set.
Parameters
----------
X : array-like, shape (n_samples, n_features)
New data, where n_samples is the number of samples
and n_features is the number of features.
Returns
-------
X_new : array-like, shape (n_samples, n_components)
Examples
--------
>>> import numpy as np
>>> from sklearn.decomposition import IncrementalPCA
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> ipca = IncrementalPCA(n_components=2, batch_size=3)
>>> ipca.fit(X)
IncrementalPCA(batch_size=3, copy=True, n_components=2, whiten=False)
>>> ipca.transform(X) # doctest: +SKIP
"""
check_is_fitted(self, ['mean_', 'components_'], all_or_any=all)
X = check_array(X)
if self.mean_ is not None:
X = X - self.mean_
X_transformed = fast_dot(X, self.components_.T)
if self.whiten:
X_transformed /= np.sqrt(self.explained_variance_)
return X_transformed
def inverse_transform(self, X, y=None):
"""Transform data back to its original space.
In other words, return an input X_original whose transform would be X.
Parameters
----------
X : array-like, shape (n_samples, n_components)
New data, where n_samples is the number of samples
and n_components is the number of components.
Returns
-------
X_original array-like, shape (n_samples, n_features)
Notes
-----
If whitening is enabled, inverse_transform will compute the
exact inverse operation, which includes reversing whitening.
"""
if self.whiten:
return fast_dot(X, np.sqrt(self.explained_variance_[:, np.newaxis]) *
self.components_) + self.mean_
else:
return fast_dot(X, self.components_) + self.mean_
| bsd-3-clause |
markhamstra/spark | examples/src/main/python/sql/arrow.py | 13 | 3997 | #
# 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.
#
"""
A simple example demonstrating Arrow in Spark.
Run with:
./bin/spark-submit examples/src/main/python/sql/arrow.py
"""
from __future__ import print_function
from pyspark.sql import SparkSession
from pyspark.sql.utils import require_minimum_pandas_version, require_minimum_pyarrow_version
require_minimum_pandas_version()
require_minimum_pyarrow_version()
def dataframe_with_arrow_example(spark):
# $example on:dataframe_with_arrow$
import numpy as np
import pandas as pd
# Enable Arrow-based columnar data transfers
spark.conf.set("spark.sql.execution.arrow.enabled", "true")
# Generate a Pandas DataFrame
pdf = pd.DataFrame(np.random.rand(100, 3))
# Create a Spark DataFrame from a Pandas DataFrame using Arrow
df = spark.createDataFrame(pdf)
# Convert the Spark DataFrame back to a Pandas DataFrame using Arrow
result_pdf = df.select("*").toPandas()
# $example off:dataframe_with_arrow$
print("Pandas DataFrame result statistics:\n%s\n" % str(result_pdf.describe()))
def scalar_pandas_udf_example(spark):
# $example on:scalar_pandas_udf$
import pandas as pd
from pyspark.sql.functions import col, pandas_udf
from pyspark.sql.types import LongType
# Declare the function and create the UDF
def multiply_func(a, b):
return a * b
multiply = pandas_udf(multiply_func, returnType=LongType())
# The function for a pandas_udf should be able to execute with local Pandas data
x = pd.Series([1, 2, 3])
print(multiply_func(x, x))
# 0 1
# 1 4
# 2 9
# dtype: int64
# Create a Spark DataFrame, 'spark' is an existing SparkSession
df = spark.createDataFrame(pd.DataFrame(x, columns=["x"]))
# Execute function as a Spark vectorized UDF
df.select(multiply(col("x"), col("x"))).show()
# +-------------------+
# |multiply_func(x, x)|
# +-------------------+
# | 1|
# | 4|
# | 9|
# +-------------------+
# $example off:scalar_pandas_udf$
def grouped_map_pandas_udf_example(spark):
# $example on:grouped_map_pandas_udf$
from pyspark.sql.functions import pandas_udf, PandasUDFType
df = spark.createDataFrame(
[(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
("id", "v"))
@pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP)
def substract_mean(pdf):
# pdf is a pandas.DataFrame
v = pdf.v
return pdf.assign(v=v - v.mean())
df.groupby("id").apply(substract_mean).show()
# +---+----+
# | id| v|
# +---+----+
# | 1|-0.5|
# | 1| 0.5|
# | 2|-3.0|
# | 2|-1.0|
# | 2| 4.0|
# +---+----+
# $example off:grouped_map_pandas_udf$
if __name__ == "__main__":
spark = SparkSession \
.builder \
.appName("Python Arrow-in-Spark example") \
.getOrCreate()
print("Running Pandas to/from conversion example")
dataframe_with_arrow_example(spark)
print("Running pandas_udf scalar example")
scalar_pandas_udf_example(spark)
print("Running pandas_udf grouped map example")
grouped_map_pandas_udf_example(spark)
spark.stop()
| apache-2.0 |
elkingtonmcb/scikit-learn | sklearn/setup.py | 225 | 2856 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.append('m')
config = Configuration('sklearn', parent_package, top_path)
config.add_subpackage('__check_build')
config.add_subpackage('svm')
config.add_subpackage('datasets')
config.add_subpackage('datasets/tests')
config.add_subpackage('feature_extraction')
config.add_subpackage('feature_extraction/tests')
config.add_subpackage('cluster')
config.add_subpackage('cluster/tests')
config.add_subpackage('covariance')
config.add_subpackage('covariance/tests')
config.add_subpackage('cross_decomposition')
config.add_subpackage('decomposition')
config.add_subpackage('decomposition/tests')
config.add_subpackage("ensemble")
config.add_subpackage("ensemble/tests")
config.add_subpackage('feature_selection')
config.add_subpackage('feature_selection/tests')
config.add_subpackage('utils')
config.add_subpackage('utils/tests')
config.add_subpackage('externals')
config.add_subpackage('mixture')
config.add_subpackage('mixture/tests')
config.add_subpackage('gaussian_process')
config.add_subpackage('gaussian_process/tests')
config.add_subpackage('neighbors')
config.add_subpackage('neural_network')
config.add_subpackage('preprocessing')
config.add_subpackage('manifold')
config.add_subpackage('metrics')
config.add_subpackage('semi_supervised')
config.add_subpackage("tree")
config.add_subpackage("tree/tests")
config.add_subpackage('metrics/tests')
config.add_subpackage('metrics/cluster')
config.add_subpackage('metrics/cluster/tests')
# add cython extension module for isotonic regression
config.add_extension(
'_isotonic',
sources=['_isotonic.c'],
include_dirs=[numpy.get_include()],
libraries=libraries,
)
# some libs needs cblas, fortran-compiled BLAS will not be sufficient
blas_info = get_info('blas_opt', 0)
if (not blas_info) or (
('NO_ATLAS_INFO', 1) in blas_info.get('define_macros', [])):
config.add_library('cblas',
sources=[join('src', 'cblas', '*.c')])
warnings.warn(BlasNotFoundError.__doc__)
# the following packages depend on cblas, so they have to be build
# after the above.
config.add_subpackage('linear_model')
config.add_subpackage('utils')
# add the test directory
config.add_subpackage('tests')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(**configuration(top_path='').todict())
| bsd-3-clause |
Vimos/scikit-learn | sklearn/utils/testing.py | 29 | 25405 | """Testing utilities."""
# Copyright (c) 2011, 2012
# Authors: Pietro Berkes,
# Andreas Muller
# Mathieu Blondel
# Olivier Grisel
# Arnaud Joly
# Denis Engemann
# Giorgio Patrini
# Thierry Guillemot
# License: BSD 3 clause
import os
import inspect
import pkgutil
import warnings
import sys
import struct
import scipy as sp
import scipy.io
from functools import wraps
from operator import itemgetter
try:
# Python 2
from urllib2 import urlopen
from urllib2 import HTTPError
except ImportError:
# Python 3+
from urllib.request import urlopen
from urllib.error import HTTPError
import tempfile
import shutil
import os.path as op
import atexit
import unittest
# WindowsError only exist on Windows
try:
WindowsError
except NameError:
WindowsError = None
import sklearn
from sklearn.base import BaseEstimator
from sklearn.externals import joblib
from nose.tools import raises
from nose import with_setup
from numpy.testing import assert_almost_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_less
from numpy.testing import assert_approx_equal
import numpy as np
from sklearn.base import (ClassifierMixin, RegressorMixin, TransformerMixin,
ClusterMixin)
from sklearn.cluster import DBSCAN
__all__ = ["assert_equal", "assert_not_equal", "assert_raises",
"assert_raises_regexp", "raises", "with_setup", "assert_true",
"assert_false", "assert_almost_equal", "assert_array_equal",
"assert_array_almost_equal", "assert_array_less",
"assert_less", "assert_less_equal",
"assert_greater", "assert_greater_equal",
"assert_approx_equal", "SkipTest"]
_dummy = unittest.TestCase('__init__')
assert_equal = _dummy.assertEqual
assert_not_equal = _dummy.assertNotEqual
assert_true = _dummy.assertTrue
assert_false = _dummy.assertFalse
assert_raises = _dummy.assertRaises
SkipTest = unittest.case.SkipTest
assert_dict_equal = _dummy.assertDictEqual
assert_in = _dummy.assertIn
assert_not_in = _dummy.assertNotIn
assert_less = _dummy.assertLess
assert_greater = _dummy.assertGreater
assert_less_equal = _dummy.assertLessEqual
assert_greater_equal = _dummy.assertGreaterEqual
try:
assert_raises_regex = _dummy.assertRaisesRegex
except AttributeError:
# Python 2.7
assert_raises_regex = _dummy.assertRaisesRegexp
# assert_raises_regexp is deprecated in Python 3.4 in favor of
# assert_raises_regex but lets keep the backward compat in scikit-learn with
# the old name for now
assert_raises_regexp = assert_raises_regex
def assert_warns(warning_class, func, *args, **kw):
"""Test that a certain warning occurs.
Parameters
----------
warning_class : the warning class
The class to test for, e.g. UserWarning.
func : callable
Calable object to trigger warnings.
*args : the positional arguments to `func`.
**kw : the keyword arguments to `func`
Returns
-------
result : the return value of `func`
"""
# very important to avoid uncontrolled state propagation
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# Trigger a warning.
result = func(*args, **kw)
if hasattr(np, 'VisibleDeprecationWarning'):
# Filter out numpy-specific warnings in numpy >= 1.9
w = [e for e in w
if e.category is not np.VisibleDeprecationWarning]
# Verify some things
if not len(w) > 0:
raise AssertionError("No warning raised when calling %s"
% func.__name__)
found = any(warning.category is warning_class for warning in w)
if not found:
raise AssertionError("%s did not give warning: %s( is %s)"
% (func.__name__, warning_class, w))
return result
def assert_warns_message(warning_class, message, func, *args, **kw):
# very important to avoid uncontrolled state propagation
"""Test that a certain warning occurs and with a certain message.
Parameters
----------
warning_class : the warning class
The class to test for, e.g. UserWarning.
message : str | callable
The entire message or a substring to test for. If callable,
it takes a string as argument and will trigger an assertion error
if it returns `False`.
func : callable
Calable object to trigger warnings.
*args : the positional arguments to `func`.
**kw : the keyword arguments to `func`.
Returns
-------
result : the return value of `func`
"""
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
if hasattr(np, 'VisibleDeprecationWarning'):
# Let's not catch the numpy internal DeprecationWarnings
warnings.simplefilter('ignore', np.VisibleDeprecationWarning)
# Trigger a warning.
result = func(*args, **kw)
# Verify some things
if not len(w) > 0:
raise AssertionError("No warning raised when calling %s"
% func.__name__)
found = [issubclass(warning.category, warning_class) for warning in w]
if not any(found):
raise AssertionError("No warning raised for %s with class "
"%s"
% (func.__name__, warning_class))
message_found = False
# Checks the message of all warnings belong to warning_class
for index in [i for i, x in enumerate(found) if x]:
# substring will match, the entire message with typo won't
msg = w[index].message # For Python 3 compatibility
msg = str(msg.args[0] if hasattr(msg, 'args') else msg)
if callable(message): # add support for certain tests
check_in_message = message
else:
check_in_message = lambda msg: message in msg
if check_in_message(msg):
message_found = True
break
if not message_found:
raise AssertionError("Did not receive the message you expected "
"('%s') for <%s>, got: '%s'"
% (message, func.__name__, msg))
return result
# To remove when we support numpy 1.7
def assert_no_warnings(func, *args, **kw):
# very important to avoid uncontrolled state propagation
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
result = func(*args, **kw)
if hasattr(np, 'VisibleDeprecationWarning'):
# Filter out numpy-specific warnings in numpy >= 1.9
w = [e for e in w
if e.category is not np.VisibleDeprecationWarning]
if len(w) > 0:
raise AssertionError("Got warnings when calling %s: [%s]"
% (func.__name__,
', '.join(str(warning) for warning in w)))
return result
def ignore_warnings(obj=None, category=Warning):
"""Context manager and decorator to ignore warnings.
Note. Using this (in both variants) will clear all warnings
from all python modules loaded. In case you need to test
cross-module-warning-logging this is not your tool of choice.
Parameters
----------
category : warning class, defaults to Warning.
The category to filter. If Warning, all categories will be muted.
Examples
--------
>>> with ignore_warnings():
... warnings.warn('buhuhuhu')
>>> def nasty_warn():
... warnings.warn('buhuhuhu')
... print(42)
>>> ignore_warnings(nasty_warn)()
42
"""
if callable(obj):
return _IgnoreWarnings(category=category)(obj)
else:
return _IgnoreWarnings(category=category)
class _IgnoreWarnings(object):
"""Improved and simplified Python warnings context manager and decorator.
This class allows to ignore the warnings raise by a function.
Copied from Python 2.7.5 and modified as required.
Parameters
----------
category : tuple of warning class, defaut to Warning
The category to filter. By default, all the categories will be muted.
"""
def __init__(self, category):
self._record = True
self._module = sys.modules['warnings']
self._entered = False
self.log = []
self.category = category
def __call__(self, fn):
"""Decorator to catch and hide warnings without visual nesting."""
@wraps(fn)
def wrapper(*args, **kwargs):
# very important to avoid uncontrolled state propagation
clean_warning_registry()
with warnings.catch_warnings():
warnings.simplefilter("ignore", self.category)
return fn(*args, **kwargs)
return wrapper
def __repr__(self):
args = []
if self._record:
args.append("record=True")
if self._module is not sys.modules['warnings']:
args.append("module=%r" % self._module)
name = type(self).__name__
return "%s(%s)" % (name, ", ".join(args))
def __enter__(self):
clean_warning_registry() # be safe and not propagate state + chaos
warnings.simplefilter("ignore", self.category)
if self._entered:
raise RuntimeError("Cannot enter %r twice" % self)
self._entered = True
self._filters = self._module.filters
self._module.filters = self._filters[:]
self._showwarning = self._module.showwarning
def __exit__(self, *exc_info):
if not self._entered:
raise RuntimeError("Cannot exit %r without entering first" % self)
self._module.filters = self._filters
self._module.showwarning = self._showwarning
self.log[:] = []
clean_warning_registry() # be safe and not propagate state + chaos
assert_less = _dummy.assertLess
assert_greater = _dummy.assertGreater
def _assert_allclose(actual, desired, rtol=1e-7, atol=0,
err_msg='', verbose=True):
actual, desired = np.asanyarray(actual), np.asanyarray(desired)
if np.allclose(actual, desired, rtol=rtol, atol=atol):
return
msg = ('Array not equal to tolerance rtol=%g, atol=%g: '
'actual %s, desired %s') % (rtol, atol, actual, desired)
raise AssertionError(msg)
if hasattr(np.testing, 'assert_allclose'):
assert_allclose = np.testing.assert_allclose
else:
assert_allclose = _assert_allclose
def assert_raise_message(exceptions, message, function, *args, **kwargs):
"""Helper function to test error messages in exceptions.
Parameters
----------
exceptions : exception or tuple of exception
Name of the estimator
function : callable
Calable object to raise error
*args : the positional arguments to `function`.
**kw : the keyword arguments to `function`
"""
try:
function(*args, **kwargs)
except exceptions as e:
error_message = str(e)
if message not in error_message:
raise AssertionError("Error message does not include the expected"
" string: %r. Observed error message: %r" %
(message, error_message))
else:
# concatenate exception names
if isinstance(exceptions, tuple):
names = " or ".join(e.__name__ for e in exceptions)
else:
names = exceptions.__name__
raise AssertionError("%s not raised by %s" %
(names, function.__name__))
def fake_mldata(columns_dict, dataname, matfile, ordering=None):
"""Create a fake mldata data set.
Parameters
----------
columns_dict : dict, keys=str, values=ndarray
Contains data as columns_dict[column_name] = array of data.
dataname : string
Name of data set.
matfile : string or file object
The file name string or the file-like object of the output file.
ordering : list, default None
List of column_names, determines the ordering in the data set.
Notes
-----
This function transposes all arrays, while fetch_mldata only transposes
'data', keep that into account in the tests.
"""
datasets = dict(columns_dict)
# transpose all variables
for name in datasets:
datasets[name] = datasets[name].T
if ordering is None:
ordering = sorted(list(datasets.keys()))
# NOTE: setting up this array is tricky, because of the way Matlab
# re-packages 1D arrays
datasets['mldata_descr_ordering'] = sp.empty((1, len(ordering)),
dtype='object')
for i, name in enumerate(ordering):
datasets['mldata_descr_ordering'][0, i] = name
scipy.io.savemat(matfile, datasets, oned_as='column')
class mock_mldata_urlopen(object):
def __init__(self, mock_datasets):
"""Object that mocks the urlopen function to fake requests to mldata.
`mock_datasets` is a dictionary of {dataset_name: data_dict}, or
{dataset_name: (data_dict, ordering).
`data_dict` itself is a dictionary of {column_name: data_array},
and `ordering` is a list of column_names to determine the ordering
in the data set (see `fake_mldata` for details).
When requesting a dataset with a name that is in mock_datasets,
this object creates a fake dataset in a StringIO object and
returns it. Otherwise, it raises an HTTPError.
"""
self.mock_datasets = mock_datasets
def __call__(self, urlname):
dataset_name = urlname.split('/')[-1]
if dataset_name in self.mock_datasets:
resource_name = '_' + dataset_name
from io import BytesIO
matfile = BytesIO()
dataset = self.mock_datasets[dataset_name]
ordering = None
if isinstance(dataset, tuple):
dataset, ordering = dataset
fake_mldata(dataset, resource_name, matfile, ordering)
matfile.seek(0)
return matfile
else:
raise HTTPError(urlname, 404, dataset_name + " is not available",
[], None)
def install_mldata_mock(mock_datasets):
# Lazy import to avoid mutually recursive imports
from sklearn import datasets
datasets.mldata.urlopen = mock_mldata_urlopen(mock_datasets)
def uninstall_mldata_mock():
# Lazy import to avoid mutually recursive imports
from sklearn import datasets
datasets.mldata.urlopen = urlopen
# Meta estimators need another estimator to be instantiated.
META_ESTIMATORS = ["OneVsOneClassifier", "MultiOutputEstimator",
"MultiOutputRegressor", "MultiOutputClassifier",
"OutputCodeClassifier", "OneVsRestClassifier",
"RFE", "RFECV", "BaseEnsemble"]
# estimators that there is no way to default-construct sensibly
OTHER = ["Pipeline", "FeatureUnion", "GridSearchCV", "RandomizedSearchCV",
"SelectFromModel"]
# some trange ones
DONT_TEST = ['SparseCoder', 'EllipticEnvelope', 'DictVectorizer',
'LabelBinarizer', 'LabelEncoder',
'MultiLabelBinarizer', 'TfidfTransformer',
'TfidfVectorizer', 'IsotonicRegression',
'OneHotEncoder', 'RandomTreesEmbedding',
'FeatureHasher', 'DummyClassifier', 'DummyRegressor',
'TruncatedSVD', 'PolynomialFeatures',
'GaussianRandomProjectionHash', 'HashingVectorizer',
'CheckingClassifier', 'PatchExtractor', 'CountVectorizer',
# GradientBoosting base estimators, maybe should
# exclude them in another way
'ZeroEstimator', 'ScaledLogOddsEstimator',
'QuantileEstimator', 'MeanEstimator',
'LogOddsEstimator', 'PriorProbabilityEstimator',
'_SigmoidCalibration', 'VotingClassifier']
def all_estimators(include_meta_estimators=False,
include_other=False, type_filter=None,
include_dont_test=False):
"""Get a list of all estimators from sklearn.
This function crawls the module and gets all classes that inherit
from BaseEstimator. Classes that are defined in test-modules are not
included.
By default meta_estimators such as GridSearchCV are also not included.
Parameters
----------
include_meta_estimators : boolean, default=False
Whether to include meta-estimators that can be constructed using
an estimator as their first argument. These are currently
BaseEnsemble, OneVsOneClassifier, OutputCodeClassifier,
OneVsRestClassifier, RFE, RFECV.
include_other : boolean, default=False
Wether to include meta-estimators that are somehow special and can
not be default-constructed sensibly. These are currently
Pipeline, FeatureUnion and GridSearchCV
include_dont_test : boolean, default=False
Whether to include "special" label estimator or test processors.
type_filter : string, list of string, or None, default=None
Which kind of estimators should be returned. If None, no filter is
applied and all estimators are returned. Possible values are
'classifier', 'regressor', 'cluster' and 'transformer' to get
estimators only of these specific types, or a list of these to
get the estimators that fit at least one of the types.
Returns
-------
estimators : list of tuples
List of (name, class), where ``name`` is the class name as string
and ``class`` is the actuall type of the class.
"""
def is_abstract(c):
if not(hasattr(c, '__abstractmethods__')):
return False
if not len(c.__abstractmethods__):
return False
return True
all_classes = []
# get parent folder
path = sklearn.__path__
for importer, modname, ispkg in pkgutil.walk_packages(
path=path, prefix='sklearn.', onerror=lambda x: None):
if (".tests." in modname):
continue
module = __import__(modname, fromlist="dummy")
classes = inspect.getmembers(module, inspect.isclass)
all_classes.extend(classes)
all_classes = set(all_classes)
estimators = [c for c in all_classes
if (issubclass(c[1], BaseEstimator) and
c[0] != 'BaseEstimator')]
# get rid of abstract base classes
estimators = [c for c in estimators if not is_abstract(c[1])]
if not include_dont_test:
estimators = [c for c in estimators if not c[0] in DONT_TEST]
if not include_other:
estimators = [c for c in estimators if not c[0] in OTHER]
# possibly get rid of meta estimators
if not include_meta_estimators:
estimators = [c for c in estimators if not c[0] in META_ESTIMATORS]
if type_filter is not None:
if not isinstance(type_filter, list):
type_filter = [type_filter]
else:
type_filter = list(type_filter) # copy
filtered_estimators = []
filters = {'classifier': ClassifierMixin,
'regressor': RegressorMixin,
'transformer': TransformerMixin,
'cluster': ClusterMixin}
for name, mixin in filters.items():
if name in type_filter:
type_filter.remove(name)
filtered_estimators.extend([est for est in estimators
if issubclass(est[1], mixin)])
estimators = filtered_estimators
if type_filter:
raise ValueError("Parameter type_filter must be 'classifier', "
"'regressor', 'transformer', 'cluster' or "
"None, got"
" %s." % repr(type_filter))
# drop duplicates, sort for reproducibility
# itemgetter is used to ensure the sort does not extend to the 2nd item of
# the tuple
return sorted(set(estimators), key=itemgetter(0))
def set_random_state(estimator, random_state=0):
"""Set random state of an estimator if it has the `random_state` param.
Classes for whom random_state is deprecated are ignored. Currently DBSCAN
is one such class.
"""
if isinstance(estimator, DBSCAN):
return
if "random_state" in estimator.get_params():
estimator.set_params(random_state=random_state)
def if_matplotlib(func):
"""Test decorator that skips test if matplotlib not installed."""
@wraps(func)
def run_test(*args, **kwargs):
try:
import matplotlib
matplotlib.use('Agg', warn=False)
# this fails if no $DISPLAY specified
import matplotlib.pyplot as plt
plt.figure()
except ImportError:
raise SkipTest('Matplotlib not available.')
else:
return func(*args, **kwargs)
return run_test
def skip_if_32bit(func):
"""Test decorator that skips tests on 32bit platforms."""
@wraps(func)
def run_test(*args, **kwargs):
bits = 8 * struct.calcsize("P")
if bits == 32:
raise SkipTest('Test skipped on 32bit platforms.')
else:
return func(*args, **kwargs)
return run_test
def if_safe_multiprocessing_with_blas(func):
"""Decorator for tests involving both BLAS calls and multiprocessing.
Under POSIX (e.g. Linux or OSX), using multiprocessing in conjunction with
some implementation of BLAS (or other libraries that manage an internal
posix thread pool) can cause a crash or a freeze of the Python process.
In practice all known packaged distributions (from Linux distros or
Anaconda) of BLAS under Linux seems to be safe. So we this problem seems to
only impact OSX users.
This wrapper makes it possible to skip tests that can possibly cause
this crash under OS X with.
Under Python 3.4+ it is possible to use the `forkserver` start method
for multiprocessing to avoid this issue. However it can cause pickling
errors on interactively defined functions. It therefore not enabled by
default.
"""
@wraps(func)
def run_test(*args, **kwargs):
if sys.platform == 'darwin':
raise SkipTest(
"Possible multi-process bug with some BLAS")
return func(*args, **kwargs)
return run_test
def clean_warning_registry():
"""Safe way to reset warnings."""
warnings.resetwarnings()
reg = "__warningregistry__"
for mod_name, mod in list(sys.modules.items()):
if 'six.moves' in mod_name:
continue
if hasattr(mod, reg):
getattr(mod, reg).clear()
def check_skip_network():
if int(os.environ.get('SKLEARN_SKIP_NETWORK_TESTS', 0)):
raise SkipTest("Text tutorial requires large dataset download")
def check_skip_travis():
"""Skip test if being run on Travis."""
if os.environ.get('TRAVIS') == "true":
raise SkipTest("This test needs to be skipped on Travis")
def _delete_folder(folder_path, warn=False):
"""Utility function to cleanup a temporary folder if still existing.
Copy from joblib.pool (for independence).
"""
try:
if os.path.exists(folder_path):
# This can fail under windows,
# but will succeed when called by atexit
shutil.rmtree(folder_path)
except WindowsError:
if warn:
warnings.warn("Could not delete temporary folder %s" % folder_path)
class TempMemmap(object):
def __init__(self, data, mmap_mode='r'):
self.temp_folder = tempfile.mkdtemp(prefix='sklearn_testing_')
self.mmap_mode = mmap_mode
self.data = data
def __enter__(self):
fpath = op.join(self.temp_folder, 'data.pkl')
joblib.dump(self.data, fpath)
data_read_only = joblib.load(fpath, mmap_mode=self.mmap_mode)
atexit.register(lambda: _delete_folder(self.temp_folder, warn=True))
return data_read_only
def __exit__(self, exc_type, exc_val, exc_tb):
_delete_folder(self.temp_folder)
with_network = with_setup(check_skip_network)
with_travis = with_setup(check_skip_travis)
class _named_check(object):
"""Wraps a check to show a useful description
Parameters
----------
check : function
Must have ``__name__`` and ``__call__``
arg_text : str
A summary of arguments to the check
"""
# Setting the description on the function itself can give incorrect results
# in failing tests
def __init__(self, check, arg_text):
self.check = check
self.description = ("{0[1]}.{0[3]}:{1.__name__}({2})".format(
inspect.stack()[1], check, arg_text))
def __call__(self, *args, **kwargs):
return self.check(*args, **kwargs)
| bsd-3-clause |
neale/CS-program | 434-MachineLearning/final_project/linearClassifier/sklearn/datasets/mlcomp.py | 289 | 3855 | # Copyright (c) 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
"""Glue code to load http://mlcomp.org data as a scikit.learn dataset"""
import os
import numbers
from sklearn.datasets.base import load_files
def _load_document_classification(dataset_path, metadata, set_=None, **kwargs):
if set_ is not None:
dataset_path = os.path.join(dataset_path, set_)
return load_files(dataset_path, metadata.get('description'), **kwargs)
LOADERS = {
'DocumentClassification': _load_document_classification,
# TODO: implement the remaining domain formats
}
def load_mlcomp(name_or_id, set_="raw", mlcomp_root=None, **kwargs):
"""Load a datasets as downloaded from http://mlcomp.org
Parameters
----------
name_or_id : the integer id or the string name metadata of the MLComp
dataset to load
set_ : select the portion to load: 'train', 'test' or 'raw'
mlcomp_root : the filesystem path to the root folder where MLComp datasets
are stored, if mlcomp_root is None, the MLCOMP_DATASETS_HOME
environment variable is looked up instead.
**kwargs : domain specific kwargs to be passed to the dataset loader.
Read more in the :ref:`User Guide <datasets>`.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'filenames', the files holding the raw to learn, 'target', the
classification labels (integer index), 'target_names',
the meaning of the labels, and 'DESCR', the full description of the
dataset.
Note on the lookup process: depending on the type of name_or_id,
will choose between integer id lookup or metadata name lookup by
looking at the unzipped archives and metadata file.
TODO: implement zip dataset loading too
"""
if mlcomp_root is None:
try:
mlcomp_root = os.environ['MLCOMP_DATASETS_HOME']
except KeyError:
raise ValueError("MLCOMP_DATASETS_HOME env variable is undefined")
mlcomp_root = os.path.expanduser(mlcomp_root)
mlcomp_root = os.path.abspath(mlcomp_root)
mlcomp_root = os.path.normpath(mlcomp_root)
if not os.path.exists(mlcomp_root):
raise ValueError("Could not find folder: " + mlcomp_root)
# dataset lookup
if isinstance(name_or_id, numbers.Integral):
# id lookup
dataset_path = os.path.join(mlcomp_root, str(name_or_id))
else:
# assume name based lookup
dataset_path = None
expected_name_line = "name: " + name_or_id
for dataset in os.listdir(mlcomp_root):
metadata_file = os.path.join(mlcomp_root, dataset, 'metadata')
if not os.path.exists(metadata_file):
continue
with open(metadata_file) as f:
for line in f:
if line.strip() == expected_name_line:
dataset_path = os.path.join(mlcomp_root, dataset)
break
if dataset_path is None:
raise ValueError("Could not find dataset with metadata line: " +
expected_name_line)
# loading the dataset metadata
metadata = dict()
metadata_file = os.path.join(dataset_path, 'metadata')
if not os.path.exists(metadata_file):
raise ValueError(dataset_path + ' is not a valid MLComp dataset')
with open(metadata_file) as f:
for line in f:
if ":" in line:
key, value = line.split(":", 1)
metadata[key.strip()] = value.strip()
format = metadata.get('format', 'unknow')
loader = LOADERS.get(format)
if loader is None:
raise ValueError("No loader implemented for format: " + format)
return loader(dataset_path, metadata, set_=set_, **kwargs)
| unlicense |
antoinecarme/pyaf | tests/perf/test_ozone_debug_perf.py | 1 | 1566 | import pandas as pd
import numpy as np
# from memory_profiler import profile
# from memprof import *
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
#get_ipython().magic('matplotlib inline')
# @memprof
def test_ozone_debug_perf():
b1 = tsds.load_ozone()
df = b1.mPastData
# df.tail(10)
# df[:-10].tail()
# df[:-10:-1]
# df.describe()
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon;
lEngine.mOptions.mDebugPerformance = True;
lEngine.mOptions.mEnableCycles = False;
lEngine.mOptions.mEnableTimeBasedTrends = False;
lEngine.mOptions.mEnableARModels = False;
lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H);
lEngine.getModelInfo();
print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine.standardPlots("outputs/my_ozone");
dfapp_in = df.copy();
dfapp_in.tail()
dfapp_out = lEngine.forecast(dfapp_in, H);
#dfapp_out.to_csv("outputs/ozone_apply_out.csv")
dfapp_out.tail(2 * H)
print("Forecast Columns " , dfapp_out.columns);
Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']]
print(Forecast_DF.info())
print("Forecasts\n" , Forecast_DF.tail(H).values);
print("\n\n<ModelInfo>")
print(lEngine.to_json());
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2*H).to_json(date_format='iso'))
print("</Forecast>\n\n")
test_ozone_debug_perf();
| bsd-3-clause |
nkhuyu/seizure-prediction | ensemble.py | 2 | 9703 | #!/usr/bin/env python2.7
from multiprocessing import Pool
import sys
import numpy as np
from sklearn.metrics import roc_auc_score
from seizure_prediction.classifiers import make_svm, make_simple_lr, make_lr
from seizure_prediction.feature_selection import generate_feature_masks
from seizure_prediction.fft_bins import *
from seizure_prediction.pipeline import Pipeline, FeatureConcatPipeline, InputSource
from seizure_prediction.scores import get_score_summary, print_results
from seizure_prediction.tasks import make_csv_for_target_predictions, write_submission_file, \
cross_validation_score, check_training_data_loaded, check_test_data_loaded, make_submission_predictions
from seizure_prediction.transforms import Windower, Correlation, FreqCorrelation, FFT, \
Magnitude, PIBSpectralEntropy, Log10, FreqBinning, FlattenChannels, Preprocess, HFD, PFD, Hurst
from seizure_prediction.settings import load_settings
from main import run_prepare_data_for_cross_validation
def run_make_submission(settings, targets_and_pipelines, split_ratio):
pool = Pool(settings.N_jobs)
for i, (target, pipeline, feature_masks, classifier, classifier_name) in enumerate(targets_and_pipelines):
for j, feature_mask in enumerate(feature_masks):
progress_str = 'T=%d/%d M=%d/%d' % (i+1, len(targets_and_pipelines), j+1, len(feature_masks))
pool.apply_async(make_submission_predictions, [settings, target, pipeline, classifier, classifier_name],
{'feature_mask': feature_mask, 'progress_str': progress_str, 'quiet': True})
pool.close()
pool.join()
guesses = ['clip,preictal']
num_masks = None
classifier_names = []
for target, pipeline, feature_masks, classifier, classifier_name in targets_and_pipelines:
classifier_names.append(classifier_name)
if num_masks is None:
num_masks = len(feature_masks)
else:
assert num_masks == len(feature_masks)
test_predictions = []
for feature_mask in feature_masks:
data = make_submission_predictions(settings, target, pipeline, classifier, classifier_name, feature_mask=feature_mask)
test_predictions.append(data.mean_predictions)
predictions = np.mean(test_predictions, axis=0)
guesses += make_csv_for_target_predictions(target, predictions)
output = '\n'.join(guesses)
write_submission_file(settings, output, 'ensemble n=%d split_ratio=%s' % (num_masks, split_ratio), None, str(classifier_names), targets_and_pipelines)
def run_prepare_data(settings, targets, pipelines, train=True, test=False, quiet=False):
for pipeline in pipelines:
for target in targets:
print 'Preparing data for', target
if train:
check_training_data_loaded(settings, target, pipeline, quiet=quiet)
if test:
check_test_data_loaded(settings, target, pipeline, quiet=quiet)
def run_cross_validation(settings, targets, pipelines, mask_range, split_ratios, classifiers):
pool = Pool(settings.N_jobs)
for i, pipeline in enumerate(pipelines):
for j, (classifier, classifier_name) in enumerate(classifiers):
for k, target in enumerate(targets):
pool.apply_async(cross_validation_score, [settings, target, pipeline, classifier, classifier_name], {'quiet': True})
for split_num, split_ratio in enumerate(split_ratios):
masks = generate_feature_masks(settings, target, pipeline, np.max(mask_range), split_ratio, random_state=0, quiet=True)
for mask_num, mask in enumerate(masks):
progress_str = 'P=%d/%d C=%d/%d T=%d/%d S=%d/%d M=%d/%d' % (i+1, len(pipelines), j+1, len(classifiers), k+1, len(targets), split_num+1, len(split_ratios), mask_num+1, len(masks))
cross_validation_score(settings, target, pipeline, classifier, classifier_name, feature_mask=mask, quiet=True, return_data=False, pool=pool, progress_str=progress_str)
pool.close()
pool.join()
print 'Finished cross validation mp'
summaries = []
for p_num, pipeline in enumerate(pipelines):
for classifier, classifier_name in classifiers:
scores_full = []
scores_masked = [[[] for y in mask_range] for x in split_ratios]
for i, target in enumerate(targets):
run_prepare_data_for_cross_validation(settings, [target], [pipeline], quiet=True)
data = cross_validation_score(settings, target, pipeline, classifier, classifier_name, pool=None, quiet=True)
scores_full.append(data.mean_score)
for split_index, split_ratio in enumerate(split_ratios):
masks = generate_feature_masks(settings, target, pipeline, np.max(mask_range), split_ratio, random_state=0, quiet=True)
for mask_index, num_masks in enumerate(mask_range):
predictions = []
y_cvs = None
for mask in masks[0:num_masks]:
data = cross_validation_score(settings, target, pipeline, classifier, classifier_name, feature_mask=mask, pool=None, quiet=True)
predictions.append(data.mean_predictions)
if y_cvs is None:
y_cvs = data.y_cvs
else:
for y_cv_1, y_cv_2 in zip(y_cvs, data.y_cvs):
assert np.alltrue(y_cv_1 == y_cv_2)
predictions = np.mean(predictions, axis=0)
scores = [roc_auc_score(y_cv, p) for p, y_cv in zip(predictions, y_cvs)]
score = np.mean(scores)
scores_masked[split_index][mask_index].append(score)
summary = get_score_summary('%s p=%d full' % (classifier_name, p_num), scores_full)
summaries.append((summary, np.mean(scores_full)))
for split_index, split_ratio in enumerate(split_ratios):
for mask_index, num_masks in enumerate(mask_range):
scores = scores_masked[split_index][mask_index]
summary = get_score_summary('%s p=%d split_ratio=%s masks=%d' % (classifier_name, p_num, split_ratio, num_masks), scores)
summaries.append((summary, np.mean(scores)))
print summary
print_results(summaries)
def main():
settings = load_settings()
pipelines = [
FeatureConcatPipeline(
Pipeline(InputSource(), Preprocess(), Windower(75), Correlation('none')),
Pipeline(InputSource(), Preprocess(), Windower(75), FreqCorrelation(1, None, 'none')),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), FreqBinning(winning_bins, 'mean'), Log10(), FlattenChannels()),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 1, 1.75, 2.5, 3.25, 4, 5, 8.5, 12, 15.5, 19.5, 24])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 2, 3.5, 6, 15, 24])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 2, 3.5, 6, 15])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 2, 3.5])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([6, 15, 24])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([2, 3.5, 6])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([3.5, 6, 15])),
Pipeline(InputSource(), Preprocess(), Windower(75), HFD(2)),
Pipeline(InputSource(), Preprocess(), Windower(75), PFD()),
Pipeline(InputSource(), Preprocess(), Windower(75), Hurst()),
),
]
targets = [
'Dog_1',
'Dog_2',
'Dog_3',
'Dog_4',
'Dog_5',
'Patient_1',
'Patient_2'
]
classifiers = [
make_svm(gamma=0.0079, C=2.7),
make_svm(gamma=0.0068, C=2.0),
make_svm(gamma=0.003, C=150.0),
make_lr(C=0.04),
make_simple_lr(),
]
make_submission = len(sys.argv) >= 2 and sys.argv[1] == 'submission'
do_cv = not make_submission
if do_cv:
mask_range = [3]
split_ratios = [0.4, 0.525, 0.6]
run_prepare_data_for_cross_validation(settings, targets, pipelines)
run_cross_validation(settings, targets, pipelines, mask_range, split_ratios, classifiers)
if make_submission:
num_masks = 10
split_ratio = 0.525
classifiers = [
# make_svm(gamma=0.0079, C=2.7),
make_svm(gamma=0.0068, C=2.0),
# make_svm(gamma=0.003, C=150.0),
# make_lr(C=0.04),
# make_simple_lr(),
]
targets_and_pipelines = []
pipeline = pipelines[0]
for classifier, classifier_name in classifiers:
for i, target in enumerate(targets):
run_prepare_data(settings, [target], [pipeline], test=True)
feature_masks = generate_feature_masks(settings, target, pipeline, num_masks, split_ratio, random_state=0, quiet=True)
targets_and_pipelines.append((target, pipeline, feature_masks, classifier, classifier_name))
run_make_submission(settings, targets_and_pipelines, split_ratio)
if __name__ == "__main__":
main()
| mit |
keir-rex/zipline | zipline/utils/tradingcalendar_tse.py | 24 | 10413 | #
# Copyright 2014 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.
import pandas as pd
import pytz
from datetime import datetime
from dateutil import rrule
from zipline.utils.tradingcalendar import end, canonicalize_datetime
start = pd.Timestamp('1994-01-01', tz='UTC')
def get_non_trading_days(start, end):
non_trading_rules = []
start = canonicalize_datetime(start)
end = canonicalize_datetime(end)
weekends = rrule.rrule(
rrule.YEARLY,
byweekday=(rrule.SA, rrule.SU),
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(weekends)
new_years = rrule.rrule(
rrule.MONTHLY,
byyearday=1,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(new_years)
new_years_sunday = rrule.rrule(
rrule.MONTHLY,
byyearday=2,
byweekday=rrule.MO,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(new_years_sunday)
new_years_saturday = rrule.rrule(
rrule.MONTHLY,
byyearday=3,
byweekday=rrule.MO,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(new_years_saturday)
# Family day in Ontario, starting in 2008, third monday of February
family_day = rrule.rrule(
rrule.MONTHLY,
bymonth=2,
byweekday=(rrule.MO(3)),
cache=True,
dtstart=datetime(2008, 1, 1, tzinfo=pytz.utc),
until=end
)
non_trading_rules.append(family_day)
good_friday = rrule.rrule(
rrule.DAILY,
byeaster=-2,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(good_friday)
# Monday prior to May 25th.
victoria_day = rrule.rrule(
rrule.MONTHLY,
bymonth=5,
byweekday=rrule.MO,
bymonthday=[24, 23, 22, 21, 20, 19, 18],
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(victoria_day)
july_1st = rrule.rrule(
rrule.MONTHLY,
bymonth=7,
bymonthday=1,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(july_1st)
july_1st_sunday = rrule.rrule(
rrule.MONTHLY,
bymonth=7,
bymonthday=2,
byweekday=rrule.MO,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(july_1st_sunday)
july_1st_saturday = rrule.rrule(
rrule.MONTHLY,
bymonth=7,
bymonthday=3,
byweekday=rrule.MO,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(july_1st_saturday)
civic_holiday = rrule.rrule(
rrule.MONTHLY,
bymonth=8,
byweekday=rrule.MO(1),
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(civic_holiday)
labor_day = rrule.rrule(
rrule.MONTHLY,
bymonth=9,
byweekday=(rrule.MO(1)),
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(labor_day)
thanksgiving = rrule.rrule(
rrule.MONTHLY,
bymonth=10,
byweekday=(rrule.MO(2)),
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(thanksgiving)
christmas = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=25,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(christmas)
# If Christmas is a Sunday then the 26th, a Monday is observed.
# (but that would be boxing day), so the 27th is also observed.
christmas_sunday = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=27,
byweekday=rrule.TU,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(christmas_sunday)
# If Christmas is a Saturday then the 27th, a monday is observed.
christmas_saturday = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=27,
byweekday=rrule.MO,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(christmas_saturday)
boxing_day = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=26,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(boxing_day)
# if boxing day is a sunday, the Christmas was saturday.
# Christmas is observed on the 27th, a month and boxing day is observed
# on the 28th, a tuesday.
boxing_day_sunday = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=28,
byweekday=rrule.TU,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(boxing_day_sunday)
# If boxing day is a Saturday then the 28th, a monday is observed.
boxing_day_saturday = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=28,
byweekday=rrule.MO,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(boxing_day_saturday)
non_trading_ruleset = rrule.rruleset()
for rule in non_trading_rules:
non_trading_ruleset.rrule(rule)
non_trading_days = non_trading_ruleset.between(start, end, inc=True)
# Add September 11th closings
# The TSX was open for 71 minutes on September 11, 2011.
# It was closed on the 12th and reopened on the 13th.
# http://www.cbc.ca/news2/interactives/map-tsx/
#
# September 2001
# Su Mo Tu We Th Fr Sa
# 1
# 2 3 4 5 6 7 8
# 9 10 11 12 13 14 15
# 16 17 18 19 20 21 22
# 23 24 25 26 27 28 29
# 30
non_trading_days.append(
datetime(2001, 9, 12, tzinfo=pytz.utc))
non_trading_days.sort()
return pd.DatetimeIndex(non_trading_days)
non_trading_days = get_non_trading_days(start, end)
trading_day = pd.tseries.offsets.CDay(holidays=non_trading_days)
def get_trading_days(start, end, trading_day=trading_day):
return pd.date_range(start=start.date(),
end=end.date(),
freq=trading_day).tz_localize('UTC')
trading_days = get_trading_days(start, end)
# Days in Environment but not in Calendar (using ^GSPTSE as bm_symbol):
# --------------------------------------------------------------------
# Used http://web.tmxmoney.com/pricehistory.php?qm_page=61468&qm_symbol=^TSX
# to check whether exchange was open on these days.
# 1994-07-01 - July 1st, Yahoo Finance has Volume = 0
# 1996-07-01 - July 1st, Yahoo Finance has Volume = 0
# 1996-08-05 - Civic Holiday, Yahoo Finance has Volume = 0
# 1997-07-01 - July 1st, Yahoo Finance has Volume = 0
# 1997-08-04 - Civic Holiday, Yahoo Finance has Volume = 0
# 2001-05-21 - Victoria day, Yahoo Finance has Volume = 0
# 2004-10-11 - Closed, Thanksgiving - Confirmed closed
# 2004-12-28 - Closed, Boxing Day - Confirmed closed
# 2012-10-08 - Closed, Thanksgiving - Confirmed closed
# Days in Calendar but not in Environment using ^GSPTSE as bm_symbol:
# --------------------------------------------------------------------
# Used http://web.tmxmoney.com/pricehistory.php?qm_page=61468&qm_symbol=^TSX
# to check whether exchange was open on these days.
# 2000-06-28 - No data this far back, can't confirm
# 2000-08-28 - No data this far back, can't confirm
# 2000-08-29 - No data this far back, can't confirm
# 2001-09-11 - TSE Open for 71 min.
# 2002-02-01 - Confirm TSE Open
# 2002-06-14 - Confirm TSE Open
# 2002-07-02 - Confirm TSE Open
# 2002-11-11 - TSX website has no data for 2 weeks in 2002
# 2003-07-07 - Confirm TSE Open
# 2003-12-16 - Confirm TSE Open
def get_early_closes(start, end):
# TSX closed at 1:00 PM on december 24th.
start = canonicalize_datetime(start)
end = canonicalize_datetime(end)
start = max(start, datetime(1993, 1, 1, tzinfo=pytz.utc))
end = max(end, datetime(1993, 1, 1, tzinfo=pytz.utc))
# Not included here are early closes prior to 1993
# or unplanned early closes
early_close_rules = []
christmas_eve = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=24,
byweekday=(rrule.MO, rrule.TU, rrule.WE, rrule.TH, rrule.FR),
cache=True,
dtstart=start,
until=end
)
early_close_rules.append(christmas_eve)
early_close_ruleset = rrule.rruleset()
for rule in early_close_rules:
early_close_ruleset.rrule(rule)
early_closes = early_close_ruleset.between(start, end, inc=True)
early_closes.sort()
return pd.DatetimeIndex(early_closes)
early_closes = get_early_closes(start, end)
def get_open_and_closes(trading_days, early_closes, tz='US/Eastern'):
open_and_closes = pd.DataFrame(index=trading_days,
columns=('market_open', 'market_close'))
for day in trading_days:
market_open = pd.Timestamp(
datetime(
year=day.year,
month=day.month,
day=day.day,
hour=9,
minute=31),
tz='US/Eastern').tz_convert('UTC')
# 1 PM if early close, 4 PM otherwise
close_hour = 13 if day in early_closes else 16
market_close = pd.Timestamp(
datetime(
year=day.year,
month=day.month,
day=day.day,
hour=close_hour),
tz='US/Eastern').tz_convert('UTC')
open_and_closes.loc[day, 'market_open'] = market_open
open_and_closes.loc[day, 'market_close'] = market_close
return open_and_closes
open_and_closes = get_open_and_closes(trading_days, early_closes)
| apache-2.0 |
Sentient07/scikit-learn | sklearn/tests/test_grid_search.py | 27 | 29492 | """
Testing for grid search module (sklearn.grid_search)
"""
from collections import Iterable, Sized
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.externals.six.moves import xrange
from itertools import chain, product
import pickle
import warnings
import sys
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_false, assert_true
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_no_warnings
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.mocking import CheckingClassifier, MockDataFrame
from scipy.stats import bernoulli, expon, uniform
from sklearn.externals.six.moves import zip
from sklearn.base import BaseEstimator
from sklearn.datasets import make_classification
from sklearn.datasets import make_blobs
from sklearn.datasets import make_multilabel_classification
from sklearn.svm import LinearSVC, SVC
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeClassifier
from sklearn.cluster import KMeans
from sklearn.neighbors import KernelDensity
from sklearn.metrics import f1_score
from sklearn.metrics import make_scorer
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import Ridge
from sklearn.exceptions import ChangedBehaviorWarning
from sklearn.exceptions import FitFailedWarning
with warnings.catch_warnings():
warnings.simplefilter('ignore')
from sklearn.grid_search import (GridSearchCV, RandomizedSearchCV,
ParameterGrid, ParameterSampler)
from sklearn.cross_validation import KFold, StratifiedKFold
from sklearn.preprocessing import Imputer
from sklearn.pipeline import Pipeline
# Neither of the following two estimators inherit from BaseEstimator,
# to test hyperparameter search on user-defined classifiers.
class MockClassifier(object):
"""Dummy classifier to test the cross-validation"""
def __init__(self, foo_param=0):
self.foo_param = foo_param
def fit(self, X, Y):
assert_true(len(X) == len(Y))
return self
def predict(self, T):
return T.shape[0]
predict_proba = predict
decision_function = predict
transform = predict
def score(self, X=None, Y=None):
if self.foo_param > 1:
score = 1.
else:
score = 0.
return score
def get_params(self, deep=False):
return {'foo_param': self.foo_param}
def set_params(self, **params):
self.foo_param = params['foo_param']
return self
class LinearSVCNoScore(LinearSVC):
"""An LinearSVC classifier that has no score method."""
@property
def score(self):
raise AttributeError
X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
y = np.array([1, 1, 2, 2])
def assert_grid_iter_equals_getitem(grid):
assert_equal(list(grid), [grid[i] for i in range(len(grid))])
def test_parameter_grid():
# Test basic properties of ParameterGrid.
params1 = {"foo": [1, 2, 3]}
grid1 = ParameterGrid(params1)
assert_true(isinstance(grid1, Iterable))
assert_true(isinstance(grid1, Sized))
assert_equal(len(grid1), 3)
assert_grid_iter_equals_getitem(grid1)
params2 = {"foo": [4, 2],
"bar": ["ham", "spam", "eggs"]}
grid2 = ParameterGrid(params2)
assert_equal(len(grid2), 6)
# loop to assert we can iterate over the grid multiple times
for i in xrange(2):
# tuple + chain transforms {"a": 1, "b": 2} to ("a", 1, "b", 2)
points = set(tuple(chain(*(sorted(p.items())))) for p in grid2)
assert_equal(points,
set(("bar", x, "foo", y)
for x, y in product(params2["bar"], params2["foo"])))
assert_grid_iter_equals_getitem(grid2)
# Special case: empty grid (useful to get default estimator settings)
empty = ParameterGrid({})
assert_equal(len(empty), 1)
assert_equal(list(empty), [{}])
assert_grid_iter_equals_getitem(empty)
assert_raises(IndexError, lambda: empty[1])
has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}])
assert_equal(len(has_empty), 4)
assert_equal(list(has_empty), [{'C': 1}, {'C': 10}, {}, {'C': .5}])
assert_grid_iter_equals_getitem(has_empty)
def test_grid_search():
# Test that the best estimator contains the right value for foo_param
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, verbose=3)
# make sure it selects the smallest parameter in case of ties
old_stdout = sys.stdout
sys.stdout = StringIO()
grid_search.fit(X, y)
sys.stdout = old_stdout
assert_equal(grid_search.best_estimator_.foo_param, 2)
for i, foo_i in enumerate([1, 2, 3]):
assert_true(grid_search.grid_scores_[i][0]
== {'foo_param': foo_i})
# Smoke test the score etc:
grid_search.score(X, y)
grid_search.predict_proba(X)
grid_search.decision_function(X)
grid_search.transform(X)
# Test exception handling on scoring
grid_search.scoring = 'sklearn'
assert_raises(ValueError, grid_search.fit, X, y)
@ignore_warnings
def test_grid_search_no_score():
# Test grid-search on classifier that has no score function.
clf = LinearSVC(random_state=0)
X, y = make_blobs(random_state=0, centers=2)
Cs = [.1, 1, 10]
clf_no_score = LinearSVCNoScore(random_state=0)
grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy')
grid_search.fit(X, y)
grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs},
scoring='accuracy')
# smoketest grid search
grid_search_no_score.fit(X, y)
# check that best params are equal
assert_equal(grid_search_no_score.best_params_, grid_search.best_params_)
# check that we can call score and that it gives the correct result
assert_equal(grid_search.score(X, y), grid_search_no_score.score(X, y))
# giving no scoring function raises an error
grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs})
assert_raise_message(TypeError, "no scoring", grid_search_no_score.fit,
[[1]])
def test_grid_search_score_method():
X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2,
random_state=0)
clf = LinearSVC(random_state=0)
grid = {'C': [.1]}
search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y)
search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y)
search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid,
scoring='roc_auc').fit(X, y)
search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y)
# Check warning only occurs in situation where behavior changed:
# estimator requires score method to compete with scoring parameter
score_no_scoring = assert_no_warnings(search_no_scoring.score, X, y)
score_accuracy = assert_warns(ChangedBehaviorWarning,
search_accuracy.score, X, y)
score_no_score_auc = assert_no_warnings(search_no_score_method_auc.score,
X, y)
score_auc = assert_warns(ChangedBehaviorWarning,
search_auc.score, X, y)
# ensure the test is sane
assert_true(score_auc < 1.0)
assert_true(score_accuracy < 1.0)
assert_not_equal(score_auc, score_accuracy)
assert_almost_equal(score_accuracy, score_no_scoring)
assert_almost_equal(score_auc, score_no_score_auc)
def test_trivial_grid_scores():
# Test search over a "grid" with only one point.
# Non-regression test: grid_scores_ wouldn't be set by GridSearchCV.
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1]})
grid_search.fit(X, y)
assert_true(hasattr(grid_search, "grid_scores_"))
random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1)
random_search.fit(X, y)
assert_true(hasattr(random_search, "grid_scores_"))
def test_no_refit():
# Test that grid search can be used for model selection only
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=False)
grid_search.fit(X, y)
assert_true(hasattr(grid_search, "best_params_"))
def test_grid_search_error():
# Test that grid search will capture errors on data with different
# length
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
assert_raises(ValueError, cv.fit, X_[:180], y_)
def test_grid_search_iid():
# test the iid parameter
# noise-free simple 2d-data
X, y = make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]], random_state=0,
cluster_std=0.1, shuffle=False, n_samples=80)
# split dataset into two folds that are not iid
# first one contains data of all 4 blobs, second only from two.
mask = np.ones(X.shape[0], dtype=np.bool)
mask[np.where(y == 1)[0][::2]] = 0
mask[np.where(y == 2)[0][::2]] = 0
# this leads to perfect classification on one fold and a score of 1/3 on
# the other
svm = SVC(kernel='linear')
# create "cv" for splits
cv = [[mask, ~mask], [~mask, mask]]
# once with iid=True (default)
grid_search = GridSearchCV(svm, param_grid={'C': [1, 10]}, cv=cv)
grid_search.fit(X, y)
first = grid_search.grid_scores_[0]
assert_equal(first.parameters['C'], 1)
assert_array_almost_equal(first.cv_validation_scores, [1, 1. / 3.])
# for first split, 1/4 of dataset is in test, for second 3/4.
# take weighted average
assert_almost_equal(first.mean_validation_score,
1 * 1. / 4. + 1. / 3. * 3. / 4.)
# once with iid=False
grid_search = GridSearchCV(svm, param_grid={'C': [1, 10]}, cv=cv,
iid=False)
grid_search.fit(X, y)
first = grid_search.grid_scores_[0]
assert_equal(first.parameters['C'], 1)
# scores are the same as above
assert_array_almost_equal(first.cv_validation_scores, [1, 1. / 3.])
# averaged score is just mean of scores
assert_almost_equal(first.mean_validation_score,
np.mean(first.cv_validation_scores))
def test_grid_search_one_grid_point():
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
param_dict = {"C": [1.0], "kernel": ["rbf"], "gamma": [0.1]}
clf = SVC()
cv = GridSearchCV(clf, param_dict)
cv.fit(X_, y_)
clf = SVC(C=1.0, kernel="rbf", gamma=0.1)
clf.fit(X_, y_)
assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_)
def test_grid_search_bad_param_grid():
param_dict = {"C": 1.0}
clf = SVC()
assert_raises(ValueError, GridSearchCV, clf, param_dict)
param_dict = {"C": []}
clf = SVC()
assert_raises(ValueError, GridSearchCV, clf, param_dict)
param_dict = {"C": np.ones(6).reshape(3, 2)}
clf = SVC()
assert_raises(ValueError, GridSearchCV, clf, param_dict)
def test_grid_search_sparse():
# Test that grid search works with both dense and sparse matrices
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
cv.fit(X_[:180], y_[:180])
y_pred = cv.predict(X_[180:])
C = cv.best_estimator_.C
X_ = sp.csr_matrix(X_)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
cv.fit(X_[:180].tocoo(), y_[:180])
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator_.C
assert_true(np.mean(y_pred == y_pred2) >= .9)
assert_equal(C, C2)
def test_grid_search_sparse_scoring():
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring="f1")
cv.fit(X_[:180], y_[:180])
y_pred = cv.predict(X_[180:])
C = cv.best_estimator_.C
X_ = sp.csr_matrix(X_)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring="f1")
cv.fit(X_[:180], y_[:180])
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator_.C
assert_array_equal(y_pred, y_pred2)
assert_equal(C, C2)
# Smoke test the score
# np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]),
# cv.score(X_[:180], y[:180]))
# test loss where greater is worse
def f1_loss(y_true_, y_pred_):
return -f1_score(y_true_, y_pred_)
F1Loss = make_scorer(f1_loss, greater_is_better=False)
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss)
cv.fit(X_[:180], y_[:180])
y_pred3 = cv.predict(X_[180:])
C3 = cv.best_estimator_.C
assert_equal(C, C3)
assert_array_equal(y_pred, y_pred3)
def test_grid_search_precomputed_kernel():
# Test that grid search works when the input features are given in the
# form of a precomputed kernel matrix
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
# compute the training kernel matrix corresponding to the linear kernel
K_train = np.dot(X_[:180], X_[:180].T)
y_train = y_[:180]
clf = SVC(kernel='precomputed')
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
cv.fit(K_train, y_train)
assert_true(cv.best_score_ >= 0)
# compute the test kernel matrix
K_test = np.dot(X_[180:], X_[:180].T)
y_test = y_[180:]
y_pred = cv.predict(K_test)
assert_true(np.mean(y_pred == y_test) >= 0)
# test error is raised when the precomputed kernel is not array-like
# or sparse
assert_raises(ValueError, cv.fit, K_train.tolist(), y_train)
def test_grid_search_precomputed_kernel_error_nonsquare():
# Test that grid search returns an error with a non-square precomputed
# training kernel matrix
K_train = np.zeros((10, 20))
y_train = np.ones((10, ))
clf = SVC(kernel='precomputed')
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
assert_raises(ValueError, cv.fit, K_train, y_train)
def test_grid_search_precomputed_kernel_error_kernel_function():
# Test that grid search returns an error when using a kernel_function
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
kernel_function = lambda x1, x2: np.dot(x1, x2.T)
clf = SVC(kernel=kernel_function)
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
assert_raises(ValueError, cv.fit, X_, y_)
class BrokenClassifier(BaseEstimator):
"""Broken classifier that cannot be fit twice"""
def __init__(self, parameter=None):
self.parameter = parameter
def fit(self, X, y):
assert_true(not hasattr(self, 'has_been_fit_'))
self.has_been_fit_ = True
def predict(self, X):
return np.zeros(X.shape[0])
@ignore_warnings
def test_refit():
# Regression test for bug in refitting
# Simulates re-fitting a broken estimator; this used to break with
# sparse SVMs.
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}],
scoring="precision", refit=True)
clf.fit(X, y)
def test_gridsearch_nd():
# Pass X as list in GridSearchCV
X_4d = np.arange(10 * 5 * 3 * 2).reshape(10, 5, 3, 2)
y_3d = np.arange(10 * 7 * 11).reshape(10, 7, 11)
check_X = lambda x: x.shape[1:] == (5, 3, 2)
check_y = lambda x: x.shape[1:] == (7, 11)
clf = CheckingClassifier(check_X=check_X, check_y=check_y)
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]})
grid_search.fit(X_4d, y_3d).score(X, y)
assert_true(hasattr(grid_search, "grid_scores_"))
def test_X_as_list():
# Pass X as list in GridSearchCV
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = CheckingClassifier(check_X=lambda x: isinstance(x, list))
cv = KFold(n=len(X), n_folds=3)
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv)
grid_search.fit(X.tolist(), y).score(X, y)
assert_true(hasattr(grid_search, "grid_scores_"))
def test_y_as_list():
# Pass y as list in GridSearchCV
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = CheckingClassifier(check_y=lambda x: isinstance(x, list))
cv = KFold(n=len(X), n_folds=3)
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv)
grid_search.fit(X, y.tolist()).score(X, y)
assert_true(hasattr(grid_search, "grid_scores_"))
def test_pandas_input():
# check cross_val_score doesn't destroy pandas dataframe
types = [(MockDataFrame, MockDataFrame)]
try:
from pandas import Series, DataFrame
types.append((DataFrame, Series))
except ImportError:
pass
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
for InputFeatureType, TargetType 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)
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]})
grid_search.fit(X_df, y_ser).score(X_df, y_ser)
grid_search.predict(X_df)
assert_true(hasattr(grid_search, "grid_scores_"))
def test_unsupervised_grid_search():
# test grid-search with unsupervised estimator
X, y = make_blobs(random_state=0)
km = KMeans(random_state=0)
grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]),
scoring='adjusted_rand_score')
grid_search.fit(X, y)
# ARI can find the right number :)
assert_equal(grid_search.best_params_["n_clusters"], 3)
# Now without a score, and without y
grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]))
grid_search.fit(X)
assert_equal(grid_search.best_params_["n_clusters"], 4)
def test_gridsearch_no_predict():
# test grid-search with an estimator without predict.
# slight duplication of a test from KDE
def custom_scoring(estimator, X):
return 42 if estimator.bandwidth == .1 else 0
X, _ = make_blobs(cluster_std=.1, random_state=1,
centers=[[0, 1], [1, 0], [0, 0]])
search = GridSearchCV(KernelDensity(),
param_grid=dict(bandwidth=[.01, .1, 1]),
scoring=custom_scoring)
search.fit(X)
assert_equal(search.best_params_['bandwidth'], .1)
assert_equal(search.best_score_, 42)
def test_param_sampler():
# test basic properties of param sampler
param_distributions = {"kernel": ["rbf", "linear"],
"C": uniform(0, 1)}
sampler = ParameterSampler(param_distributions=param_distributions,
n_iter=10, random_state=0)
samples = [x for x in sampler]
assert_equal(len(samples), 10)
for sample in samples:
assert_true(sample["kernel"] in ["rbf", "linear"])
assert_true(0 <= sample["C"] <= 1)
def test_randomized_search_grid_scores():
# Make a dataset with a lot of noise to get various kind of prediction
# errors across CV folds and parameter settings
X, y = make_classification(n_samples=200, n_features=100, n_informative=3,
random_state=0)
# XXX: as of today (scipy 0.12) it's not possible to set the random seed
# of scipy.stats distributions: the assertions in this test should thus
# not depend on the randomization
params = dict(C=expon(scale=10),
gamma=expon(scale=0.1))
n_cv_iter = 3
n_search_iter = 30
search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_cv_iter,
param_distributions=params, iid=False)
search.fit(X, y)
assert_equal(len(search.grid_scores_), n_search_iter)
# Check consistency of the structure of each cv_score item
for cv_score in search.grid_scores_:
assert_equal(len(cv_score.cv_validation_scores), n_cv_iter)
# Because we set iid to False, the mean_validation score is the
# mean of the fold mean scores instead of the aggregate sample-wise
# mean score
assert_almost_equal(np.mean(cv_score.cv_validation_scores),
cv_score.mean_validation_score)
assert_equal(list(sorted(cv_score.parameters.keys())),
list(sorted(params.keys())))
# Check the consistency with the best_score_ and best_params_ attributes
sorted_grid_scores = list(sorted(search.grid_scores_,
key=lambda x: x.mean_validation_score))
best_score = sorted_grid_scores[-1].mean_validation_score
assert_equal(search.best_score_, best_score)
tied_best_params = [s.parameters for s in sorted_grid_scores
if s.mean_validation_score == best_score]
assert_true(search.best_params_ in tied_best_params,
"best_params_={0} is not part of the"
" tied best models: {1}".format(
search.best_params_, tied_best_params))
def test_grid_search_score_consistency():
# test that correct scores are used
clf = LinearSVC(random_state=0)
X, y = make_blobs(random_state=0, centers=2)
Cs = [.1, 1, 10]
for score in ['f1', 'roc_auc']:
grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score)
grid_search.fit(X, y)
cv = StratifiedKFold(n_folds=3, y=y)
for C, scores in zip(Cs, grid_search.grid_scores_):
clf.set_params(C=C)
scores = scores[2] # get the separate runs from grid scores
i = 0
for train, test in cv:
clf.fit(X[train], y[train])
if score == "f1":
correct_score = f1_score(y[test], clf.predict(X[test]))
elif score == "roc_auc":
dec = clf.decision_function(X[test])
correct_score = roc_auc_score(y[test], dec)
assert_almost_equal(correct_score, scores[i])
i += 1
def test_pickle():
# Test that a fit search can be pickled
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True)
grid_search.fit(X, y)
pickle.dumps(grid_search) # smoke test
random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]},
refit=True, n_iter=3)
random_search.fit(X, y)
pickle.dumps(random_search) # smoke test
def test_grid_search_with_multioutput_data():
# Test search with multi-output estimator
X, y = make_multilabel_classification(random_state=0)
est_parameters = {"max_depth": [1, 2, 3, 4]}
cv = KFold(y.shape[0], random_state=0)
estimators = [DecisionTreeRegressor(random_state=0),
DecisionTreeClassifier(random_state=0)]
# Test with grid search cv
for est in estimators:
grid_search = GridSearchCV(est, est_parameters, cv=cv)
grid_search.fit(X, y)
for parameters, _, cv_validation_scores in grid_search.grid_scores_:
est.set_params(**parameters)
for i, (train, test) in enumerate(cv):
est.fit(X[train], y[train])
correct_score = est.score(X[test], y[test])
assert_almost_equal(correct_score,
cv_validation_scores[i])
# Test with a randomized search
for est in estimators:
random_search = RandomizedSearchCV(est, est_parameters,
cv=cv, n_iter=3)
random_search.fit(X, y)
for parameters, _, cv_validation_scores in random_search.grid_scores_:
est.set_params(**parameters)
for i, (train, test) in enumerate(cv):
est.fit(X[train], y[train])
correct_score = est.score(X[test], y[test])
assert_almost_equal(correct_score,
cv_validation_scores[i])
def test_predict_proba_disabled():
# Test predict_proba when disabled on estimator.
X = np.arange(20).reshape(5, -1)
y = [0, 0, 1, 1, 1]
clf = SVC(probability=False)
gs = GridSearchCV(clf, {}, cv=2).fit(X, y)
assert_false(hasattr(gs, "predict_proba"))
def test_grid_search_allows_nans():
# Test GridSearchCV with Imputer
X = np.arange(20, dtype=np.float64).reshape(5, -1)
X[2, :] = np.nan
y = [0, 0, 1, 1, 1]
p = Pipeline([
('imputer', Imputer(strategy='mean', missing_values='NaN')),
('classifier', MockClassifier()),
])
GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y)
class FailingClassifier(BaseEstimator):
"""Classifier that raises a ValueError on fit()"""
FAILING_PARAMETER = 2
def __init__(self, parameter=None):
self.parameter = parameter
def fit(self, X, y=None):
if self.parameter == FailingClassifier.FAILING_PARAMETER:
raise ValueError("Failing classifier failed as required")
def predict(self, X):
return np.zeros(X.shape[0])
def test_grid_search_failing_classifier():
# GridSearchCV with on_error != 'raise'
# Ensures that a warning is raised and score reset where appropriate.
X, y = make_classification(n_samples=20, n_features=10, random_state=0)
clf = FailingClassifier()
# refit=False because we only want to check that errors caused by fits
# to individual folds will be caught and warnings raised instead. If
# refit was done, then an exception would be raised on refit and not
# caught by grid_search (expected behavior), and this would cause an
# error in this test.
gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',
refit=False, error_score=0.0)
assert_warns(FitFailedWarning, gs.fit, X, y)
# Ensure that grid scores were set to zero as required for those fits
# that are expected to fail.
assert all(np.all(this_point.cv_validation_scores == 0.0)
for this_point in gs.grid_scores_
if this_point.parameters['parameter'] ==
FailingClassifier.FAILING_PARAMETER)
gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',
refit=False, error_score=float('nan'))
assert_warns(FitFailedWarning, gs.fit, X, y)
assert all(np.all(np.isnan(this_point.cv_validation_scores))
for this_point in gs.grid_scores_
if this_point.parameters['parameter'] ==
FailingClassifier.FAILING_PARAMETER)
def test_grid_search_failing_classifier_raise():
# GridSearchCV with on_error == 'raise' raises the error
X, y = make_classification(n_samples=20, n_features=10, random_state=0)
clf = FailingClassifier()
# refit=False because we want to test the behaviour of the grid search part
gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',
refit=False, error_score='raise')
# FailingClassifier issues a ValueError so this is what we look for.
assert_raises(ValueError, gs.fit, X, y)
def test_parameters_sampler_replacement():
# raise error if n_iter too large
params = {'first': [0, 1], 'second': ['a', 'b', 'c']}
sampler = ParameterSampler(params, n_iter=7)
assert_raises(ValueError, list, sampler)
# degenerates to GridSearchCV if n_iter the same as grid_size
sampler = ParameterSampler(params, n_iter=6)
samples = list(sampler)
assert_equal(len(samples), 6)
for values in ParameterGrid(params):
assert_true(values in samples)
# test sampling without replacement in a large grid
params = {'a': range(10), 'b': range(10), 'c': range(10)}
sampler = ParameterSampler(params, n_iter=99, random_state=42)
samples = list(sampler)
assert_equal(len(samples), 99)
hashable_samples = ["a%db%dc%d" % (p['a'], p['b'], p['c'])
for p in samples]
assert_equal(len(set(hashable_samples)), 99)
# doesn't go into infinite loops
params_distribution = {'first': bernoulli(.5), 'second': ['a', 'b', 'c']}
sampler = ParameterSampler(params_distribution, n_iter=7)
samples = list(sampler)
assert_equal(len(samples), 7)
def test_classes__property():
# Test that classes_ property matches best_esimator_.classes_
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
Cs = [.1, 1, 10]
grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs})
grid_search.fit(X, y)
assert_array_equal(grid_search.best_estimator_.classes_,
grid_search.classes_)
# Test that regressors do not have a classes_ attribute
grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]})
grid_search.fit(X, y)
assert_false(hasattr(grid_search, 'classes_'))
| bsd-3-clause |
fdudatamining/framework | tests/draw/test_simple.py | 1 | 1233 | import numpy as np
import pandas as pd
from unittest import TestCase
from framework import draw
X = np.array([1, 2, 3, 4, 5])
class TestSimplePlots(TestCase):
def test_kinds(self):
self.assertIsNotNone(draw.draw_kinds)
def test_line(self):
draw.draw(clear=True, kind='line', x=X, y=X)
draw.draw(clear=True, kind='line', y=X)
def test_scatter(self):
draw.draw(clear=True, kind='scatter', x=X, y=X)
draw.draw(clear=True, kind='scatter', y=X)
def test_stem(self):
draw.draw(clear=True, kind='stem', x=X, y=X)
draw.draw(clear=True, kind='stem', y=X)
def test_errorbar(self):
draw.draw(clear=True, kind='errorbar', x=X, y=X, xerr=X, yerr=X)
draw.draw(clear=True, kind='errorbar', y=X, yerr=X)
def test_boxplot(self):
draw.draw(clear=True, kind='boxplot', x=X)
def test_barplot(self):
draw.draw(clear=True, kind='barplot', x=X, y=X, width=1)
draw.draw(clear=True, kind='barplot', x=X, y=X)
draw.draw(clear=True, kind='barplot', y=X)
def test_contour(self):
draw.draw(clear=True, kind='contour', z=[[1, 2, 3], [4, 5, 6], [7, 8, 9]])
def test_hist(self):
draw.draw(clear=True, kind='hist', x=X, bins=2)
draw.draw(clear=True, kind='hist', x=X)
| gpl-2.0 |
stevertaylor/NX01 | newcmaps.py | 28 | 50518 | # New matplotlib colormaps by Nathaniel J. Smith, Stefan van der Walt,
# and (in the case of viridis) Eric Firing.
#
# This file and the colormaps in it are released under the CC0 license /
# public domain dedication. We would appreciate credit if you use or
# redistribute these colormaps, but do not impose any legal restrictions.
#
# To the extent possible under law, the persons who associated CC0 with
# mpl-colormaps have waived all copyright and related or neighboring rights
# to mpl-colormaps.
#
# You should have received a copy of the CC0 legalcode along with this
# work. If not, see <http://creativecommons.org/publicdomain/zero/1.0/>.
__all__ = ['magma', 'inferno', 'plasma', 'viridis']
_magma_data = [[0.001462, 0.000466, 0.013866],
[0.002258, 0.001295, 0.018331],
[0.003279, 0.002305, 0.023708],
[0.004512, 0.003490, 0.029965],
[0.005950, 0.004843, 0.037130],
[0.007588, 0.006356, 0.044973],
[0.009426, 0.008022, 0.052844],
[0.011465, 0.009828, 0.060750],
[0.013708, 0.011771, 0.068667],
[0.016156, 0.013840, 0.076603],
[0.018815, 0.016026, 0.084584],
[0.021692, 0.018320, 0.092610],
[0.024792, 0.020715, 0.100676],
[0.028123, 0.023201, 0.108787],
[0.031696, 0.025765, 0.116965],
[0.035520, 0.028397, 0.125209],
[0.039608, 0.031090, 0.133515],
[0.043830, 0.033830, 0.141886],
[0.048062, 0.036607, 0.150327],
[0.052320, 0.039407, 0.158841],
[0.056615, 0.042160, 0.167446],
[0.060949, 0.044794, 0.176129],
[0.065330, 0.047318, 0.184892],
[0.069764, 0.049726, 0.193735],
[0.074257, 0.052017, 0.202660],
[0.078815, 0.054184, 0.211667],
[0.083446, 0.056225, 0.220755],
[0.088155, 0.058133, 0.229922],
[0.092949, 0.059904, 0.239164],
[0.097833, 0.061531, 0.248477],
[0.102815, 0.063010, 0.257854],
[0.107899, 0.064335, 0.267289],
[0.113094, 0.065492, 0.276784],
[0.118405, 0.066479, 0.286321],
[0.123833, 0.067295, 0.295879],
[0.129380, 0.067935, 0.305443],
[0.135053, 0.068391, 0.315000],
[0.140858, 0.068654, 0.324538],
[0.146785, 0.068738, 0.334011],
[0.152839, 0.068637, 0.343404],
[0.159018, 0.068354, 0.352688],
[0.165308, 0.067911, 0.361816],
[0.171713, 0.067305, 0.370771],
[0.178212, 0.066576, 0.379497],
[0.184801, 0.065732, 0.387973],
[0.191460, 0.064818, 0.396152],
[0.198177, 0.063862, 0.404009],
[0.204935, 0.062907, 0.411514],
[0.211718, 0.061992, 0.418647],
[0.218512, 0.061158, 0.425392],
[0.225302, 0.060445, 0.431742],
[0.232077, 0.059889, 0.437695],
[0.238826, 0.059517, 0.443256],
[0.245543, 0.059352, 0.448436],
[0.252220, 0.059415, 0.453248],
[0.258857, 0.059706, 0.457710],
[0.265447, 0.060237, 0.461840],
[0.271994, 0.060994, 0.465660],
[0.278493, 0.061978, 0.469190],
[0.284951, 0.063168, 0.472451],
[0.291366, 0.064553, 0.475462],
[0.297740, 0.066117, 0.478243],
[0.304081, 0.067835, 0.480812],
[0.310382, 0.069702, 0.483186],
[0.316654, 0.071690, 0.485380],
[0.322899, 0.073782, 0.487408],
[0.329114, 0.075972, 0.489287],
[0.335308, 0.078236, 0.491024],
[0.341482, 0.080564, 0.492631],
[0.347636, 0.082946, 0.494121],
[0.353773, 0.085373, 0.495501],
[0.359898, 0.087831, 0.496778],
[0.366012, 0.090314, 0.497960],
[0.372116, 0.092816, 0.499053],
[0.378211, 0.095332, 0.500067],
[0.384299, 0.097855, 0.501002],
[0.390384, 0.100379, 0.501864],
[0.396467, 0.102902, 0.502658],
[0.402548, 0.105420, 0.503386],
[0.408629, 0.107930, 0.504052],
[0.414709, 0.110431, 0.504662],
[0.420791, 0.112920, 0.505215],
[0.426877, 0.115395, 0.505714],
[0.432967, 0.117855, 0.506160],
[0.439062, 0.120298, 0.506555],
[0.445163, 0.122724, 0.506901],
[0.451271, 0.125132, 0.507198],
[0.457386, 0.127522, 0.507448],
[0.463508, 0.129893, 0.507652],
[0.469640, 0.132245, 0.507809],
[0.475780, 0.134577, 0.507921],
[0.481929, 0.136891, 0.507989],
[0.488088, 0.139186, 0.508011],
[0.494258, 0.141462, 0.507988],
[0.500438, 0.143719, 0.507920],
[0.506629, 0.145958, 0.507806],
[0.512831, 0.148179, 0.507648],
[0.519045, 0.150383, 0.507443],
[0.525270, 0.152569, 0.507192],
[0.531507, 0.154739, 0.506895],
[0.537755, 0.156894, 0.506551],
[0.544015, 0.159033, 0.506159],
[0.550287, 0.161158, 0.505719],
[0.556571, 0.163269, 0.505230],
[0.562866, 0.165368, 0.504692],
[0.569172, 0.167454, 0.504105],
[0.575490, 0.169530, 0.503466],
[0.581819, 0.171596, 0.502777],
[0.588158, 0.173652, 0.502035],
[0.594508, 0.175701, 0.501241],
[0.600868, 0.177743, 0.500394],
[0.607238, 0.179779, 0.499492],
[0.613617, 0.181811, 0.498536],
[0.620005, 0.183840, 0.497524],
[0.626401, 0.185867, 0.496456],
[0.632805, 0.187893, 0.495332],
[0.639216, 0.189921, 0.494150],
[0.645633, 0.191952, 0.492910],
[0.652056, 0.193986, 0.491611],
[0.658483, 0.196027, 0.490253],
[0.664915, 0.198075, 0.488836],
[0.671349, 0.200133, 0.487358],
[0.677786, 0.202203, 0.485819],
[0.684224, 0.204286, 0.484219],
[0.690661, 0.206384, 0.482558],
[0.697098, 0.208501, 0.480835],
[0.703532, 0.210638, 0.479049],
[0.709962, 0.212797, 0.477201],
[0.716387, 0.214982, 0.475290],
[0.722805, 0.217194, 0.473316],
[0.729216, 0.219437, 0.471279],
[0.735616, 0.221713, 0.469180],
[0.742004, 0.224025, 0.467018],
[0.748378, 0.226377, 0.464794],
[0.754737, 0.228772, 0.462509],
[0.761077, 0.231214, 0.460162],
[0.767398, 0.233705, 0.457755],
[0.773695, 0.236249, 0.455289],
[0.779968, 0.238851, 0.452765],
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[0.352360, 0.783011, 0.392636],
[0.360741, 0.785964, 0.387814],
[0.369214, 0.788888, 0.382914],
[0.377779, 0.791781, 0.377939],
[0.386433, 0.794644, 0.372886],
[0.395174, 0.797475, 0.367757],
[0.404001, 0.800275, 0.362552],
[0.412913, 0.803041, 0.357269],
[0.421908, 0.805774, 0.351910],
[0.430983, 0.808473, 0.346476],
[0.440137, 0.811138, 0.340967],
[0.449368, 0.813768, 0.335384],
[0.458674, 0.816363, 0.329727],
[0.468053, 0.818921, 0.323998],
[0.477504, 0.821444, 0.318195],
[0.487026, 0.823929, 0.312321],
[0.496615, 0.826376, 0.306377],
[0.506271, 0.828786, 0.300362],
[0.515992, 0.831158, 0.294279],
[0.525776, 0.833491, 0.288127],
[0.535621, 0.835785, 0.281908],
[0.545524, 0.838039, 0.275626],
[0.555484, 0.840254, 0.269281],
[0.565498, 0.842430, 0.262877],
[0.575563, 0.844566, 0.256415],
[0.585678, 0.846661, 0.249897],
[0.595839, 0.848717, 0.243329],
[0.606045, 0.850733, 0.236712],
[0.616293, 0.852709, 0.230052],
[0.626579, 0.854645, 0.223353],
[0.636902, 0.856542, 0.216620],
[0.647257, 0.858400, 0.209861],
[0.657642, 0.860219, 0.203082],
[0.668054, 0.861999, 0.196293],
[0.678489, 0.863742, 0.189503],
[0.688944, 0.865448, 0.182725],
[0.699415, 0.867117, 0.175971],
[0.709898, 0.868751, 0.169257],
[0.720391, 0.870350, 0.162603],
[0.730889, 0.871916, 0.156029],
[0.741388, 0.873449, 0.149561],
[0.751884, 0.874951, 0.143228],
[0.762373, 0.876424, 0.137064],
[0.772852, 0.877868, 0.131109],
[0.783315, 0.879285, 0.125405],
[0.793760, 0.880678, 0.120005],
[0.804182, 0.882046, 0.114965],
[0.814576, 0.883393, 0.110347],
[0.824940, 0.884720, 0.106217],
[0.835270, 0.886029, 0.102646],
[0.845561, 0.887322, 0.099702],
[0.855810, 0.888601, 0.097452],
[0.866013, 0.889868, 0.095953],
[0.876168, 0.891125, 0.095250],
[0.886271, 0.892374, 0.095374],
[0.896320, 0.893616, 0.096335],
[0.906311, 0.894855, 0.098125],
[0.916242, 0.896091, 0.100717],
[0.926106, 0.897330, 0.104071],
[0.935904, 0.898570, 0.108131],
[0.945636, 0.899815, 0.112838],
[0.955300, 0.901065, 0.118128],
[0.964894, 0.902323, 0.123941],
[0.974417, 0.903590, 0.130215],
[0.983868, 0.904867, 0.136897],
[0.993248, 0.906157, 0.143936]]
from matplotlib.colors import ListedColormap
cmaps = {}
for (name, data) in (('magma', _magma_data),
('inferno', _inferno_data),
('plasma', _plasma_data),
('viridis', _viridis_data)):
cmaps[name] = ListedColormap(data, name=name)
magma = cmaps['magma']
inferno = cmaps['inferno']
plasma = cmaps['plasma']
viridis = cmaps['viridis']
| mit |
anntzer/scikit-learn | sklearn/ensemble/__init__.py | 12 | 1655 | """
The :mod:`sklearn.ensemble` module includes ensemble-based methods for
classification, regression and anomaly detection.
"""
import typing
from ._base import BaseEnsemble
from ._forest import RandomForestClassifier
from ._forest import RandomForestRegressor
from ._forest import RandomTreesEmbedding
from ._forest import ExtraTreesClassifier
from ._forest import ExtraTreesRegressor
from ._bagging import BaggingClassifier
from ._bagging import BaggingRegressor
from ._iforest import IsolationForest
from ._weight_boosting import AdaBoostClassifier
from ._weight_boosting import AdaBoostRegressor
from ._gb import GradientBoostingClassifier
from ._gb import GradientBoostingRegressor
from ._voting import VotingClassifier
from ._voting import VotingRegressor
from ._stacking import StackingClassifier
from ._stacking import StackingRegressor
if typing.TYPE_CHECKING:
# Avoid errors in type checkers (e.g. mypy) for experimental estimators.
# TODO: remove this check once the estimator is no longer experimental.
from ._hist_gradient_boosting.gradient_boosting import ( # noqa
HistGradientBoostingRegressor, HistGradientBoostingClassifier
)
__all__ = ["BaseEnsemble",
"RandomForestClassifier", "RandomForestRegressor",
"RandomTreesEmbedding", "ExtraTreesClassifier",
"ExtraTreesRegressor", "BaggingClassifier",
"BaggingRegressor", "IsolationForest", "GradientBoostingClassifier",
"GradientBoostingRegressor", "AdaBoostClassifier",
"AdaBoostRegressor", "VotingClassifier", "VotingRegressor",
"StackingClassifier", "StackingRegressor",
]
| bsd-3-clause |
natj/bender | paper/figs/fig9.py | 1 | 4141 | import numpy as np
import math
from pylab import *
from palettable.wesanderson import Zissou_5 as wsZ
import matplotlib.ticker as mtick
from scipy.interpolate import interp1d
from scipy.interpolate import griddata
from scipy.signal import savgol_filter
def smooth(xx, yy):
yy = savgol_filter(yy, 7, 2)
np.clip(yy, 0.0, 1000.0, out=yy)
yy[0] = 0.0
yy[-1] = 0.0
return xx, yy
#Read JN files
def read_lineprof(fname):
da = np.genfromtxt(fname, delimiter=",")
des = np.diff(da[:,0])[2]
norm = np.sum(des*da[:,1])
return da[:,0],da[:,1]/norm
#Read JN files
def read_csv(fname):
da = np.genfromtxt(fname, delimiter=",")
des = np.diff(da[:,0])[2]
norm = np.sum(des*da[:,1])
return da[:,0],da[:,1] #/norm
## Plot
fig = figure(figsize=(5,3), dpi=80)
rc('font', family='serif')
rc('xtick', labelsize='xx-small')
rc('ytick', labelsize='xx-small')
gs = GridSpec(1, 1)
#gs.update(wspace = 0.34)
#gs.update(hspace = 0.4)
lsize = 10.0
xmin = 0.69
xmax = 0.82
#error window limits
eymin = -0.5
eymax = 0.5
#path to files
#path_JN = "../../out3/lines/"
path_JN = "../../out/lines2/"
#labels size
tsize = 10.0
nu = '700'
#fig.text(0.5, 0.92, '$\\theta_s = 18^{\\circ}$', ha='center', va='center', size=tsize)
#fig.text(0.5, 0.72, '$\\theta_s = 45^{\\circ}$', ha='center', va='center', size=tsize)
#fig.text(0.5, 0.52, '$\\theta_s = 90^{\\circ}$', ha='center', va='center', size=tsize)
#fig.text(0.5, 0.32, 'Hopf $\\theta_s = 45^{\circ}$', ha='center', va='center', size=tsize)
#fig.text(0.5, 0.12, 'Phase',ha='center', va='center', size=lsize)
ax1 = subplot(gs[0,0])
ax1.minorticks_on()
ax1.set_xlim(xmin, xmax)
ax1.set_ylim(0.0, 30)
ax1.set_ylabel('Normalized flux',size=lsize)
ax1.set_xlabel('Energy $E/E\'$',size=lsize)
#xx1, yy1 = read_lineprof(path_JN+'lineprof_f700pbbr10m1.4i20.csv')
#ax1.plot(xx1, yy1, "k--")
#xx2, yy2 = read_lineprof(path_JN+'lineprof_obl_HTq0_f700pbbr10m1.4i20.csv')
#ax1.plot(xx2, yy2, "k-")
#lineprof_obl_HTq3_f700pbbr10m1.4i20.csv
#lineprof_obl_HTq5_f700pbbr10m1.4i20.csv
#lineprof_obl_HTq2_f700pbbr10m1.4i20.csv
files_JN = [
"lineprof_f700pbbr10m1.4i20.csv",
"lineprof_obl_f700pbbr10m1.4i20.csv",
#"lineprof_sph2_HTqfix_f700pbbr10m1.4i20.csv"]
#"lineprof_obl_HTq0_f700pbbr10m1.4i20.csv",
"lineprof_obl_HTq1_f700pbbr10m1.4i20.csv"]
#"lineprof_obl_HTq4_f700pbbr10m1.4i20.csv"]
files_JN = ['sch/lineprofile_f700_bb_r10_m1.4_i20.csv',
'obl/lineprofile_f700_bb_r10_m1.4_i20.csv',
'q/lineprofile_f700_bb_r10_m1.4_i20.csv']
cols = ["black",
"blue",
"red",
"magenta"]
i = 0
for file_name in files_JN:
xx, yy = read_lineprof(path_JN+file_name)
xx, yy = smooth(xx, yy)
ax1.plot(xx, yy, color=cols[i], linestyle="solid")
i += 1
#path_JN = "../../out3/lines/"
xx, yy = read_lineprof("../../out3/lines/lineprof_obl_HTq4_f700pbbr10m1.4i20.csv")
ax1.plot(xx, yy, color="red", linestyle="dashed")
#files_Bau = [
#"sch+dopp.csv",
#"sch+dopp+obl.csv",
#"HT.csv",
#"HT_obl.csv"]
files_Bau = ['sch.csv', 'obl.csv', 'ht.csv']
i = 0
for file_name in files_Bau:
xx, yy = read_csv(path_JN+file_name)
#rescale xx for correct scaling
#xx = (xx-0.72)/(0.89-0.72)*(0.8-0.72) + 0.72
#ax1.plot(xx, yy, color=cols[i], linestyle="dashed")
i += 1
############ q's
#xx3, yy3 = read_lineprof(path_JN+'lineprof_obl_HTq1_f700pbbr10m1.4i20.csv')
#ax1.plot(xx3, yy3, "k-", label="$q = -0.268$")
#
#xx4, yy4 = read_lineprof(path_JN+'lineprof_obl_HTq2_f700pbbr10m1.4i20.csv')
#ax1.plot(xx4, yy4, "r-", label="$q \\times 2$")
#
#xx5, yy5 = read_lineprof(path_JN+'lineprof_obl_HTq3_f700pbbr10m1.4i20.csv')
#ax1.plot(xx5, yy5, "g-", label="$q \\times 3$")
#
#xx6, yy6 = read_lineprof(path_JN+'lineprof_obl_HTq4_f700pbbr10m1.4i20.csv')
#ax1.plot(xx6, yy6, "b-", label="$q \\times 4$")
#
#xx7, yy7 = read_lineprof(path_JN+'lineprof_obl_HTq5_f700pbbr10m1.4i20.csv')
#ax1.plot(xx7, yy7, "m-", label="$q \\times 5$")
#
#legend = ax1.legend(loc='upper left', shadow=False, labelspacing=0.1)
#for label in legend.get_texts():
# label.set_fontsize('x-small')
savefig('fig9_testi.pdf', bbox_inches='tight')
| mit |
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 |
mhue/scikit-learn | examples/text/document_classification_20newsgroups.py | 222 | 10500 | """
======================================================
Classification of text documents using sparse features
======================================================
This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.sparse
matrix to store the features and demonstrates various classifiers that can
efficiently handle sparse matrices.
The dataset used in this example is the 20 newsgroups dataset. It will be
automatically downloaded, then cached.
The bar plot indicates the accuracy, training time (normalized) and test time
(normalized) of each classifier.
"""
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause
from __future__ import print_function
import logging
import numpy as np
from optparse import OptionParser
import sys
from time import time
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.linear_model import RidgeClassifier
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC
from sklearn.linear_model import SGDClassifier
from sklearn.linear_model import Perceptron
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import NearestCentroid
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils.extmath import density
from sklearn import metrics
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
# parse commandline arguments
op = OptionParser()
op.add_option("--report",
action="store_true", dest="print_report",
help="Print a detailed classification report.")
op.add_option("--chi2_select",
action="store", type="int", dest="select_chi2",
help="Select some number of features using a chi-squared test")
op.add_option("--confusion_matrix",
action="store_true", dest="print_cm",
help="Print the confusion matrix.")
op.add_option("--top10",
action="store_true", dest="print_top10",
help="Print ten most discriminative terms per class"
" for every classifier.")
op.add_option("--all_categories",
action="store_true", dest="all_categories",
help="Whether to use all categories or not.")
op.add_option("--use_hashing",
action="store_true",
help="Use a hashing vectorizer.")
op.add_option("--n_features",
action="store", type=int, default=2 ** 16,
help="n_features when using the hashing vectorizer.")
op.add_option("--filtered",
action="store_true",
help="Remove newsgroup information that is easily overfit: "
"headers, signatures, and quoting.")
(opts, args) = op.parse_args()
if len(args) > 0:
op.error("this script takes no arguments.")
sys.exit(1)
print(__doc__)
op.print_help()
print()
###############################################################################
# Load some categories from the training set
if opts.all_categories:
categories = None
else:
categories = [
'alt.atheism',
'talk.religion.misc',
'comp.graphics',
'sci.space',
]
if opts.filtered:
remove = ('headers', 'footers', 'quotes')
else:
remove = ()
print("Loading 20 newsgroups dataset for categories:")
print(categories if categories else "all")
data_train = fetch_20newsgroups(subset='train', categories=categories,
shuffle=True, random_state=42,
remove=remove)
data_test = fetch_20newsgroups(subset='test', categories=categories,
shuffle=True, random_state=42,
remove=remove)
print('data loaded')
categories = data_train.target_names # for case categories == None
def size_mb(docs):
return sum(len(s.encode('utf-8')) for s in docs) / 1e6
data_train_size_mb = size_mb(data_train.data)
data_test_size_mb = size_mb(data_test.data)
print("%d documents - %0.3fMB (training set)" % (
len(data_train.data), data_train_size_mb))
print("%d documents - %0.3fMB (test set)" % (
len(data_test.data), data_test_size_mb))
print("%d categories" % len(categories))
print()
# split a training set and a test set
y_train, y_test = data_train.target, data_test.target
print("Extracting features from the training data using a sparse vectorizer")
t0 = time()
if opts.use_hashing:
vectorizer = HashingVectorizer(stop_words='english', non_negative=True,
n_features=opts.n_features)
X_train = vectorizer.transform(data_train.data)
else:
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
stop_words='english')
X_train = vectorizer.fit_transform(data_train.data)
duration = time() - t0
print("done in %fs at %0.3fMB/s" % (duration, data_train_size_mb / duration))
print("n_samples: %d, n_features: %d" % X_train.shape)
print()
print("Extracting features from the test data using the same vectorizer")
t0 = time()
X_test = vectorizer.transform(data_test.data)
duration = time() - t0
print("done in %fs at %0.3fMB/s" % (duration, data_test_size_mb / duration))
print("n_samples: %d, n_features: %d" % X_test.shape)
print()
# mapping from integer feature name to original token string
if opts.use_hashing:
feature_names = None
else:
feature_names = vectorizer.get_feature_names()
if opts.select_chi2:
print("Extracting %d best features by a chi-squared test" %
opts.select_chi2)
t0 = time()
ch2 = SelectKBest(chi2, k=opts.select_chi2)
X_train = ch2.fit_transform(X_train, y_train)
X_test = ch2.transform(X_test)
if feature_names:
# keep selected feature names
feature_names = [feature_names[i] for i
in ch2.get_support(indices=True)]
print("done in %fs" % (time() - t0))
print()
if feature_names:
feature_names = np.asarray(feature_names)
def trim(s):
"""Trim string to fit on terminal (assuming 80-column display)"""
return s if len(s) <= 80 else s[:77] + "..."
###############################################################################
# Benchmark classifiers
def benchmark(clf):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.accuracy_score(y_test, pred)
print("accuracy: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
if opts.print_top10 and feature_names is not None:
print("top 10 keywords per class:")
for i, category in enumerate(categories):
top10 = np.argsort(clf.coef_[i])[-10:]
print(trim("%s: %s"
% (category, " ".join(feature_names[top10]))))
print()
if opts.print_report:
print("classification report:")
print(metrics.classification_report(y_test, pred,
target_names=categories))
if opts.print_cm:
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
results = []
for clf, name in (
(RidgeClassifier(tol=1e-2, solver="lsqr"), "Ridge Classifier"),
(Perceptron(n_iter=50), "Perceptron"),
(PassiveAggressiveClassifier(n_iter=50), "Passive-Aggressive"),
(KNeighborsClassifier(n_neighbors=10), "kNN"),
(RandomForestClassifier(n_estimators=100), "Random forest")):
print('=' * 80)
print(name)
results.append(benchmark(clf))
for penalty in ["l2", "l1"]:
print('=' * 80)
print("%s penalty" % penalty.upper())
# Train Liblinear model
results.append(benchmark(LinearSVC(loss='l2', penalty=penalty,
dual=False, tol=1e-3)))
# Train SGD model
results.append(benchmark(SGDClassifier(alpha=.0001, n_iter=50,
penalty=penalty)))
# Train SGD with Elastic Net penalty
print('=' * 80)
print("Elastic-Net penalty")
results.append(benchmark(SGDClassifier(alpha=.0001, n_iter=50,
penalty="elasticnet")))
# Train NearestCentroid without threshold
print('=' * 80)
print("NearestCentroid (aka Rocchio classifier)")
results.append(benchmark(NearestCentroid()))
# Train sparse Naive Bayes classifiers
print('=' * 80)
print("Naive Bayes")
results.append(benchmark(MultinomialNB(alpha=.01)))
results.append(benchmark(BernoulliNB(alpha=.01)))
print('=' * 80)
print("LinearSVC with L1-based feature selection")
# The smaller C, the stronger the regularization.
# The more regularization, the more sparsity.
results.append(benchmark(Pipeline([
('feature_selection', LinearSVC(penalty="l1", dual=False, tol=1e-3)),
('classification', LinearSVC())
])))
# make some plots
indices = np.arange(len(results))
results = [[x[i] for x in results] for i in range(4)]
clf_names, score, training_time, test_time = results
training_time = np.array(training_time) / np.max(training_time)
test_time = np.array(test_time) / np.max(test_time)
plt.figure(figsize=(12, 8))
plt.title("Score")
plt.barh(indices, score, .2, label="score", color='r')
plt.barh(indices + .3, training_time, .2, label="training time", color='g')
plt.barh(indices + .6, test_time, .2, label="test time", color='b')
plt.yticks(())
plt.legend(loc='best')
plt.subplots_adjust(left=.25)
plt.subplots_adjust(top=.95)
plt.subplots_adjust(bottom=.05)
for i, c in zip(indices, clf_names):
plt.text(-.3, i, c)
plt.show()
| bsd-3-clause |
vermouthmjl/scikit-learn | sklearn/gaussian_process/tests/test_gpr.py | 23 | 11915 | """Testing for Gaussian process regression """
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Licence: BSD 3 clause
import numpy as np
from scipy.optimize import approx_fprime
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels \
import RBF, ConstantKernel as C, WhiteKernel
from sklearn.utils.testing \
import (assert_true, assert_greater, assert_array_less,
assert_almost_equal, assert_equal)
def f(x):
return 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()
fixed_kernel = RBF(length_scale=1.0, length_scale_bounds="fixed")
kernels = [RBF(length_scale=1.0), fixed_kernel,
RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)),
C(1.0, (1e-2, 1e2)) *
RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)),
C(1.0, (1e-2, 1e2)) *
RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)) +
C(1e-5, (1e-5, 1e2)),
C(0.1, (1e-2, 1e2)) *
RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)) +
C(1e-5, (1e-5, 1e2))]
def test_gpr_interpolation():
"""Test the interpolating property for different kernels."""
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_pred, y_cov = gpr.predict(X, return_cov=True)
assert_true(np.allclose(y_pred, y))
assert_true(np.allclose(np.diag(y_cov), 0.))
def test_lml_improving():
""" Test that hyperparameter-tuning improves log-marginal likelihood. """
for kernel in kernels:
if kernel == fixed_kernel:
continue
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
assert_greater(gpr.log_marginal_likelihood(gpr.kernel_.theta),
gpr.log_marginal_likelihood(kernel.theta))
def test_lml_precomputed():
""" Test that lml of optimized kernel is stored correctly. """
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
assert_equal(gpr.log_marginal_likelihood(gpr.kernel_.theta),
gpr.log_marginal_likelihood())
def test_converged_to_local_maximum():
""" Test that we are in local maximum after hyperparameter-optimization."""
for kernel in kernels:
if kernel == fixed_kernel:
continue
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
lml, lml_gradient = \
gpr.log_marginal_likelihood(gpr.kernel_.theta, True)
assert_true(np.all((np.abs(lml_gradient) < 1e-4) |
(gpr.kernel_.theta == gpr.kernel_.bounds[:, 0]) |
(gpr.kernel_.theta == gpr.kernel_.bounds[:, 1])))
def test_solution_inside_bounds():
""" Test that hyperparameter-optimization remains in bounds"""
for kernel in kernels:
if kernel == fixed_kernel:
continue
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
bounds = gpr.kernel_.bounds
max_ = np.finfo(gpr.kernel_.theta.dtype).max
tiny = 1e-10
bounds[~np.isfinite(bounds[:, 1]), 1] = max_
assert_array_less(bounds[:, 0], gpr.kernel_.theta + tiny)
assert_array_less(gpr.kernel_.theta, bounds[:, 1] + tiny)
def test_lml_gradient():
""" Compare analytic and numeric gradient of log marginal likelihood. """
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
lml, lml_gradient = gpr.log_marginal_likelihood(kernel.theta, True)
lml_gradient_approx = \
approx_fprime(kernel.theta,
lambda theta: gpr.log_marginal_likelihood(theta,
False),
1e-10)
assert_almost_equal(lml_gradient, lml_gradient_approx, 3)
def test_prior():
""" Test that GP prior has mean 0 and identical variances."""
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel)
y_mean, y_cov = gpr.predict(X, return_cov=True)
assert_almost_equal(y_mean, 0, 5)
if len(gpr.kernel.theta) > 1:
# XXX: quite hacky, works only for current kernels
assert_almost_equal(np.diag(y_cov), np.exp(kernel.theta[0]), 5)
else:
assert_almost_equal(np.diag(y_cov), 1, 5)
def test_sample_statistics():
""" Test that statistics of samples drawn from GP are correct."""
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_mean, y_cov = gpr.predict(X2, return_cov=True)
samples = gpr.sample_y(X2, 300000)
# More digits accuracy would require many more samples
assert_almost_equal(y_mean, np.mean(samples, 1), 1)
assert_almost_equal(np.diag(y_cov) / np.diag(y_cov).max(),
np.var(samples, 1) / np.diag(y_cov).max(), 1)
def test_no_optimizer():
""" Test that kernel parameters are unmodified when optimizer is None."""
kernel = RBF(1.0)
gpr = GaussianProcessRegressor(kernel=kernel, optimizer=None).fit(X, y)
assert_equal(np.exp(gpr.kernel_.theta), 1.0)
def test_predict_cov_vs_std():
""" Test that predicted std.-dev. is consistent with cov's diagonal."""
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_mean, y_cov = gpr.predict(X2, return_cov=True)
y_mean, y_std = gpr.predict(X2, return_std=True)
assert_almost_equal(np.sqrt(np.diag(y_cov)), y_std)
def test_anisotropic_kernel():
""" Test that GPR can identify meaningful anisotropic length-scales. """
# We learn a function which varies in one dimension ten-times slower
# than in the other. The corresponding length-scales should differ by at
# least a factor 5
rng = np.random.RandomState(0)
X = rng.uniform(-1, 1, (50, 2))
y = X[:, 0] + 0.1 * X[:, 1]
kernel = RBF([1.0, 1.0])
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
assert_greater(np.exp(gpr.kernel_.theta[1]),
np.exp(gpr.kernel_.theta[0]) * 5)
def test_random_starts():
"""
Test that an increasing number of random-starts of GP fitting only
increases the log marginal likelihood of the chosen theta.
"""
n_samples, n_features = 25, 2
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) \
+ rng.normal(scale=0.1, size=n_samples)
kernel = C(1.0, (1e-2, 1e2)) \
* RBF(length_scale=[1.0] * n_features,
length_scale_bounds=[(1e-4, 1e+2)] * n_features) \
+ WhiteKernel(noise_level=1e-5, noise_level_bounds=(1e-5, 1e1))
last_lml = -np.inf
for n_restarts_optimizer in range(5):
gp = GaussianProcessRegressor(
kernel=kernel, n_restarts_optimizer=n_restarts_optimizer,
random_state=0,).fit(X, y)
lml = gp.log_marginal_likelihood(gp.kernel_.theta)
assert_greater(lml, last_lml - np.finfo(np.float32).eps)
last_lml = lml
def test_y_normalization():
""" Test normalization of the target values in GP
Fitting non-normalizing GP on normalized y and fitting normalizing GP
on unnormalized y should yield identical results
"""
y_mean = y.mean(0)
y_norm = y - y_mean
for kernel in kernels:
# Fit non-normalizing GP on normalized y
gpr = GaussianProcessRegressor(kernel=kernel)
gpr.fit(X, y_norm)
# Fit normalizing GP on unnormalized y
gpr_norm = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
gpr_norm.fit(X, y)
# Compare predicted mean, std-devs and covariances
y_pred, y_pred_std = gpr.predict(X2, return_std=True)
y_pred = y_mean + y_pred
y_pred_norm, y_pred_std_norm = gpr_norm.predict(X2, return_std=True)
assert_almost_equal(y_pred, y_pred_norm)
assert_almost_equal(y_pred_std, y_pred_std_norm)
_, y_cov = gpr.predict(X2, return_cov=True)
_, y_cov_norm = gpr_norm.predict(X2, return_cov=True)
assert_almost_equal(y_cov, y_cov_norm)
def test_y_multioutput():
""" Test that GPR can deal with multi-dimensional target values"""
y_2d = np.vstack((y, y * 2)).T
# Test for fixed kernel that first dimension of 2d GP equals the output
# of 1d GP and that second dimension is twice as large
kernel = RBF(length_scale=1.0)
gpr = GaussianProcessRegressor(kernel=kernel, optimizer=None,
normalize_y=False)
gpr.fit(X, y)
gpr_2d = GaussianProcessRegressor(kernel=kernel, optimizer=None,
normalize_y=False)
gpr_2d.fit(X, y_2d)
y_pred_1d, y_std_1d = gpr.predict(X2, return_std=True)
y_pred_2d, y_std_2d = gpr_2d.predict(X2, return_std=True)
_, y_cov_1d = gpr.predict(X2, return_cov=True)
_, y_cov_2d = gpr_2d.predict(X2, return_cov=True)
assert_almost_equal(y_pred_1d, y_pred_2d[:, 0])
assert_almost_equal(y_pred_1d, y_pred_2d[:, 1] / 2)
# Standard deviation and covariance do not depend on output
assert_almost_equal(y_std_1d, y_std_2d)
assert_almost_equal(y_cov_1d, y_cov_2d)
y_sample_1d = gpr.sample_y(X2, n_samples=10)
y_sample_2d = gpr_2d.sample_y(X2, n_samples=10)
assert_almost_equal(y_sample_1d, y_sample_2d[:, 0])
# Test hyperparameter optimization
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
gpr.fit(X, y)
gpr_2d = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
gpr_2d.fit(X, np.vstack((y, y)).T)
assert_almost_equal(gpr.kernel_.theta, gpr_2d.kernel_.theta, 4)
def test_custom_optimizer():
""" Test that GPR can use externally defined optimizers. """
# Define a dummy optimizer that simply tests 50 random hyperparameters
def optimizer(obj_func, initial_theta, bounds):
rng = np.random.RandomState(0)
theta_opt, func_min = \
initial_theta, obj_func(initial_theta, eval_gradient=False)
for _ in range(50):
theta = np.atleast_1d(rng.uniform(np.maximum(-2, bounds[:, 0]),
np.minimum(1, bounds[:, 1])))
f = obj_func(theta, eval_gradient=False)
if f < func_min:
theta_opt, func_min = theta, f
return theta_opt, func_min
for kernel in kernels:
if kernel == fixed_kernel:
continue
gpr = GaussianProcessRegressor(kernel=kernel, optimizer=optimizer)
gpr.fit(X, y)
# Checks that optimizer improved marginal likelihood
assert_greater(gpr.log_marginal_likelihood(gpr.kernel_.theta),
gpr.log_marginal_likelihood(gpr.kernel.theta))
def test_duplicate_input():
""" Test GPR can handle two different output-values for the same input. """
for kernel in kernels:
gpr_equal_inputs = \
GaussianProcessRegressor(kernel=kernel, alpha=1e-2)
gpr_similar_inputs = \
GaussianProcessRegressor(kernel=kernel, alpha=1e-2)
X_ = np.vstack((X, X[0]))
y_ = np.hstack((y, y[0] + 1))
gpr_equal_inputs.fit(X_, y_)
X_ = np.vstack((X, X[0] + 1e-15))
y_ = np.hstack((y, y[0] + 1))
gpr_similar_inputs.fit(X_, y_)
X_test = np.linspace(0, 10, 100)[:, None]
y_pred_equal, y_std_equal = \
gpr_equal_inputs.predict(X_test, return_std=True)
y_pred_similar, y_std_similar = \
gpr_similar_inputs.predict(X_test, return_std=True)
assert_almost_equal(y_pred_equal, y_pred_similar)
assert_almost_equal(y_std_equal, y_std_similar)
| bsd-3-clause |
kushalbhola/MyStuff | Practice/PythonApplication/env/Lib/site-packages/pandas/tseries/frequencies.py | 2 | 15559 | from datetime import timedelta
import re
from typing import Dict
import numpy as np
from pytz import AmbiguousTimeError
from pandas._libs.algos import unique_deltas
from pandas._libs.tslibs import Timedelta, Timestamp
from pandas._libs.tslibs.ccalendar import MONTH_ALIASES, int_to_weekday
from pandas._libs.tslibs.fields import build_field_sarray
import pandas._libs.tslibs.frequencies as libfreqs
from pandas._libs.tslibs.offsets import _offset_to_period_map
import pandas._libs.tslibs.resolution as libresolution
from pandas._libs.tslibs.resolution import Resolution
from pandas._libs.tslibs.timezones import UTC
from pandas._libs.tslibs.tzconversion import tz_convert
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.common import (
is_datetime64_dtype,
is_period_arraylike,
is_timedelta64_dtype,
)
from pandas.core.dtypes.generic import ABCSeries
from pandas.core.algorithms import unique
from pandas.tseries.offsets import (
DateOffset,
Day,
Hour,
Micro,
Milli,
Minute,
Nano,
Second,
prefix_mapping,
)
_ONE_MICRO = 1000
_ONE_MILLI = _ONE_MICRO * 1000
_ONE_SECOND = _ONE_MILLI * 1000
_ONE_MINUTE = 60 * _ONE_SECOND
_ONE_HOUR = 60 * _ONE_MINUTE
_ONE_DAY = 24 * _ONE_HOUR
# ---------------------------------------------------------------------
# Offset names ("time rules") and related functions
#: cache of previously seen offsets
_offset_map = {} # type: Dict[str, DateOffset]
def get_period_alias(offset_str):
""" alias to closest period strings BQ->Q etc"""
return _offset_to_period_map.get(offset_str, None)
_name_to_offset_map = {
"days": Day(1),
"hours": Hour(1),
"minutes": Minute(1),
"seconds": Second(1),
"milliseconds": Milli(1),
"microseconds": Micro(1),
"nanoseconds": Nano(1),
}
def to_offset(freq):
"""
Return DateOffset object from string or tuple representation
or datetime.timedelta object
Parameters
----------
freq : str, tuple, datetime.timedelta, DateOffset or None
Returns
-------
DateOffset
None if freq is None.
Raises
------
ValueError
If freq is an invalid frequency
See Also
--------
DateOffset
Examples
--------
>>> to_offset('5min')
<5 * Minutes>
>>> to_offset('1D1H')
<25 * Hours>
>>> to_offset(('W', 2))
<2 * Weeks: weekday=6>
>>> to_offset((2, 'B'))
<2 * BusinessDays>
>>> to_offset(datetime.timedelta(days=1))
<Day>
>>> to_offset(Hour())
<Hour>
"""
if freq is None:
return None
if isinstance(freq, DateOffset):
return freq
if isinstance(freq, tuple):
name = freq[0]
stride = freq[1]
if isinstance(stride, str):
name, stride = stride, name
name, _ = libfreqs._base_and_stride(name)
delta = get_offset(name) * stride
elif isinstance(freq, timedelta):
delta = None
freq = Timedelta(freq)
try:
for name in freq.components._fields:
offset = _name_to_offset_map[name]
stride = getattr(freq.components, name)
if stride != 0:
offset = stride * offset
if delta is None:
delta = offset
else:
delta = delta + offset
except Exception:
raise ValueError(libfreqs.INVALID_FREQ_ERR_MSG.format(freq))
else:
delta = None
stride_sign = None
try:
splitted = re.split(libfreqs.opattern, freq)
if splitted[-1] != "" and not splitted[-1].isspace():
# the last element must be blank
raise ValueError("last element must be blank")
for sep, stride, name in zip(
splitted[0::4], splitted[1::4], splitted[2::4]
):
if sep != "" and not sep.isspace():
raise ValueError("separator must be spaces")
prefix = libfreqs._lite_rule_alias.get(name) or name
if stride_sign is None:
stride_sign = -1 if stride.startswith("-") else 1
if not stride:
stride = 1
if prefix in Resolution._reso_str_bump_map.keys():
stride, name = Resolution.get_stride_from_decimal(
float(stride), prefix
)
stride = int(stride)
offset = get_offset(name)
offset = offset * int(np.fabs(stride) * stride_sign)
if delta is None:
delta = offset
else:
delta = delta + offset
except Exception:
raise ValueError(libfreqs.INVALID_FREQ_ERR_MSG.format(freq))
if delta is None:
raise ValueError(libfreqs.INVALID_FREQ_ERR_MSG.format(freq))
return delta
def get_offset(name):
"""
Return DateOffset object associated with rule name
Examples
--------
get_offset('EOM') --> BMonthEnd(1)
"""
if name not in libfreqs._dont_uppercase:
name = name.upper()
name = libfreqs._lite_rule_alias.get(name, name)
name = libfreqs._lite_rule_alias.get(name.lower(), name)
else:
name = libfreqs._lite_rule_alias.get(name, name)
if name not in _offset_map:
try:
split = name.split("-")
klass = prefix_mapping[split[0]]
# handles case where there's no suffix (and will TypeError if too
# many '-')
offset = klass._from_name(*split[1:])
except (ValueError, TypeError, KeyError):
# bad prefix or suffix
raise ValueError(libfreqs.INVALID_FREQ_ERR_MSG.format(name))
# cache
_offset_map[name] = offset
return _offset_map[name]
# ---------------------------------------------------------------------
# Period codes
def infer_freq(index, warn=True):
"""
Infer the most likely frequency given the input index. If the frequency is
uncertain, a warning will be printed.
Parameters
----------
index : DatetimeIndex or TimedeltaIndex
if passed a Series will use the values of the series (NOT THE INDEX)
warn : boolean, default True
Returns
-------
str or None
None if no discernible frequency
TypeError if the index is not datetime-like
ValueError if there are less than three values.
"""
import pandas as pd
if isinstance(index, ABCSeries):
values = index._values
if not (
is_datetime64_dtype(values)
or is_timedelta64_dtype(values)
or values.dtype == object
):
raise TypeError(
"cannot infer freq from a non-convertible dtype "
"on a Series of {dtype}".format(dtype=index.dtype)
)
index = values
if is_period_arraylike(index):
raise TypeError(
"PeriodIndex given. Check the `freq` attribute "
"instead of using infer_freq."
)
elif is_timedelta64_dtype(index):
# Allow TimedeltaIndex and TimedeltaArray
inferer = _TimedeltaFrequencyInferer(index, warn=warn)
return inferer.get_freq()
if isinstance(index, pd.Index) and not isinstance(index, pd.DatetimeIndex):
if isinstance(index, (pd.Int64Index, pd.Float64Index)):
raise TypeError(
"cannot infer freq from a non-convertible index "
"type {type}".format(type=type(index))
)
index = index.values
if not isinstance(index, pd.DatetimeIndex):
try:
index = pd.DatetimeIndex(index)
except AmbiguousTimeError:
index = pd.DatetimeIndex(index.asi8)
inferer = _FrequencyInferer(index, warn=warn)
return inferer.get_freq()
class _FrequencyInferer:
"""
Not sure if I can avoid the state machine here
"""
def __init__(self, index, warn=True):
self.index = index
self.values = index.asi8
# This moves the values, which are implicitly in UTC, to the
# the timezone so they are in local time
if hasattr(index, "tz"):
if index.tz is not None:
self.values = tz_convert(self.values, UTC, index.tz)
self.warn = warn
if len(index) < 3:
raise ValueError("Need at least 3 dates to infer frequency")
self.is_monotonic = (
self.index._is_monotonic_increasing or self.index._is_monotonic_decreasing
)
@cache_readonly
def deltas(self):
return unique_deltas(self.values)
@cache_readonly
def deltas_asi8(self):
return unique_deltas(self.index.asi8)
@cache_readonly
def is_unique(self):
return len(self.deltas) == 1
@cache_readonly
def is_unique_asi8(self):
return len(self.deltas_asi8) == 1
def get_freq(self):
"""
Find the appropriate frequency string to describe the inferred
frequency of self.values
Returns
-------
str or None
"""
if not self.is_monotonic or not self.index._is_unique:
return None
delta = self.deltas[0]
if _is_multiple(delta, _ONE_DAY):
return self._infer_daily_rule()
# Business hourly, maybe. 17: one day / 65: one weekend
if self.hour_deltas in ([1, 17], [1, 65], [1, 17, 65]):
return "BH"
# Possibly intraday frequency. Here we use the
# original .asi8 values as the modified values
# will not work around DST transitions. See #8772
elif not self.is_unique_asi8:
return None
delta = self.deltas_asi8[0]
if _is_multiple(delta, _ONE_HOUR):
# Hours
return _maybe_add_count("H", delta / _ONE_HOUR)
elif _is_multiple(delta, _ONE_MINUTE):
# Minutes
return _maybe_add_count("T", delta / _ONE_MINUTE)
elif _is_multiple(delta, _ONE_SECOND):
# Seconds
return _maybe_add_count("S", delta / _ONE_SECOND)
elif _is_multiple(delta, _ONE_MILLI):
# Milliseconds
return _maybe_add_count("L", delta / _ONE_MILLI)
elif _is_multiple(delta, _ONE_MICRO):
# Microseconds
return _maybe_add_count("U", delta / _ONE_MICRO)
else:
# Nanoseconds
return _maybe_add_count("N", delta)
@cache_readonly
def day_deltas(self):
return [x / _ONE_DAY for x in self.deltas]
@cache_readonly
def hour_deltas(self):
return [x / _ONE_HOUR for x in self.deltas]
@cache_readonly
def fields(self):
return build_field_sarray(self.values)
@cache_readonly
def rep_stamp(self):
return Timestamp(self.values[0])
def month_position_check(self):
return libresolution.month_position_check(self.fields, self.index.dayofweek)
@cache_readonly
def mdiffs(self):
nmonths = self.fields["Y"] * 12 + self.fields["M"]
return unique_deltas(nmonths.astype("i8"))
@cache_readonly
def ydiffs(self):
return unique_deltas(self.fields["Y"].astype("i8"))
def _infer_daily_rule(self):
annual_rule = self._get_annual_rule()
if annual_rule:
nyears = self.ydiffs[0]
month = MONTH_ALIASES[self.rep_stamp.month]
alias = "{prefix}-{month}".format(prefix=annual_rule, month=month)
return _maybe_add_count(alias, nyears)
quarterly_rule = self._get_quarterly_rule()
if quarterly_rule:
nquarters = self.mdiffs[0] / 3
mod_dict = {0: 12, 2: 11, 1: 10}
month = MONTH_ALIASES[mod_dict[self.rep_stamp.month % 3]]
alias = "{prefix}-{month}".format(prefix=quarterly_rule, month=month)
return _maybe_add_count(alias, nquarters)
monthly_rule = self._get_monthly_rule()
if monthly_rule:
return _maybe_add_count(monthly_rule, self.mdiffs[0])
if self.is_unique:
days = self.deltas[0] / _ONE_DAY
if days % 7 == 0:
# Weekly
day = int_to_weekday[self.rep_stamp.weekday()]
return _maybe_add_count("W-{day}".format(day=day), days / 7)
else:
return _maybe_add_count("D", days)
if self._is_business_daily():
return "B"
wom_rule = self._get_wom_rule()
if wom_rule:
return wom_rule
def _get_annual_rule(self):
if len(self.ydiffs) > 1:
return None
if len(unique(self.fields["M"])) > 1:
return None
pos_check = self.month_position_check()
return {"cs": "AS", "bs": "BAS", "ce": "A", "be": "BA"}.get(pos_check)
def _get_quarterly_rule(self):
if len(self.mdiffs) > 1:
return None
if not self.mdiffs[0] % 3 == 0:
return None
pos_check = self.month_position_check()
return {"cs": "QS", "bs": "BQS", "ce": "Q", "be": "BQ"}.get(pos_check)
def _get_monthly_rule(self):
if len(self.mdiffs) > 1:
return None
pos_check = self.month_position_check()
return {"cs": "MS", "bs": "BMS", "ce": "M", "be": "BM"}.get(pos_check)
def _is_business_daily(self):
# quick check: cannot be business daily
if self.day_deltas != [1, 3]:
return False
# probably business daily, but need to confirm
first_weekday = self.index[0].weekday()
shifts = np.diff(self.index.asi8)
shifts = np.floor_divide(shifts, _ONE_DAY)
weekdays = np.mod(first_weekday + np.cumsum(shifts), 7)
return np.all(
((weekdays == 0) & (shifts == 3))
| ((weekdays > 0) & (weekdays <= 4) & (shifts == 1))
)
def _get_wom_rule(self):
# wdiffs = unique(np.diff(self.index.week))
# We also need -47, -49, -48 to catch index spanning year boundary
# if not lib.ismember(wdiffs, set([4, 5, -47, -49, -48])).all():
# return None
weekdays = unique(self.index.weekday)
if len(weekdays) > 1:
return None
week_of_months = unique((self.index.day - 1) // 7)
# Only attempt to infer up to WOM-4. See #9425
week_of_months = week_of_months[week_of_months < 4]
if len(week_of_months) == 0 or len(week_of_months) > 1:
return None
# get which week
week = week_of_months[0] + 1
wd = int_to_weekday[weekdays[0]]
return "WOM-{week}{weekday}".format(week=week, weekday=wd)
class _TimedeltaFrequencyInferer(_FrequencyInferer):
def _infer_daily_rule(self):
if self.is_unique:
days = self.deltas[0] / _ONE_DAY
if days % 7 == 0:
# Weekly
wd = int_to_weekday[self.rep_stamp.weekday()]
alias = "W-{weekday}".format(weekday=wd)
return _maybe_add_count(alias, days / 7)
else:
return _maybe_add_count("D", days)
def _is_multiple(us, mult):
return us % mult == 0
def _maybe_add_count(base, count):
if count != 1:
assert count == int(count)
count = int(count)
return "{count}{base}".format(count=count, base=base)
else:
return base
| apache-2.0 |
jmargeta/scikit-learn | sklearn/tests/test_pls.py | 6 | 9383 | import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.datasets import load_linnerud
from sklearn import pls
def test_pls():
d = load_linnerud()
X = d.data
Y = d.target
# 1) Canonical (symetric) PLS (PLS 2 blocks canonical mode A)
# ===========================================================
# Compare 2 algo.: nipals vs. svd
# ------------------------------
pls_bynipals = pls.PLSCanonical(n_components=X.shape[1])
pls_bynipals.fit(X, Y)
pls_bysvd = pls.PLSCanonical(algorithm="svd", n_components=X.shape[1])
pls_bysvd.fit(X, Y)
# check equalities of loading (up to the sign of the second column)
assert_array_almost_equal(
pls_bynipals.x_loadings_,
np.multiply(pls_bysvd.x_loadings_, np.array([1, -1, 1])), decimal=5,
err_msg="nipals and svd implementation lead to different x loadings")
assert_array_almost_equal(
pls_bynipals.y_loadings_,
np.multiply(pls_bysvd.y_loadings_, np.array([1, -1, 1])), decimal=5,
err_msg="nipals and svd implementation lead to different y loadings")
# Check PLS properties (with n_components=X.shape[1])
# ---------------------------------------------------
plsca = pls.PLSCanonical(n_components=X.shape[1])
plsca.fit(X, Y)
T = plsca.x_scores_
P = plsca.x_loadings_
Wx = plsca.x_weights_
U = plsca.y_scores_
Q = plsca.y_loadings_
Wy = plsca.y_weights_
def check_ortho(M, err_msg):
K = np.dot(M.T, M)
assert_array_almost_equal(K, np.diag(np.diag(K)), err_msg=err_msg)
# Orthogonality of weights
# ~~~~~~~~~~~~~~~~~~~~~~~~
check_ortho(Wx, "x weights are not orthogonal")
check_ortho(Wy, "y weights are not orthogonal")
# Orthogonality of latent scores
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
check_ortho(T, "x scores are not orthogonal")
check_ortho(U, "y scores are not orthogonal")
# Check X = TP' and Y = UQ' (with (p == q) components)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# center scale X, Y
Xc, Yc, x_mean, y_mean, x_std, y_std =\
pls._center_scale_xy(X.copy(), Y.copy(), scale=True)
assert_array_almost_equal(Xc, np.dot(T, P.T), err_msg="X != TP'")
assert_array_almost_equal(Yc, np.dot(U, Q.T), err_msg="Y != UQ'")
# Check that rotations on training data lead to scores
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Xr = plsca.transform(X)
assert_array_almost_equal(Xr, plsca.x_scores_,
err_msg="rotation on X failed")
Xr, Yr = plsca.transform(X, Y)
assert_array_almost_equal(Xr, plsca.x_scores_,
err_msg="rotation on X failed")
assert_array_almost_equal(Yr, plsca.y_scores_,
err_msg="rotation on Y failed")
# "Non regression test" on canonical PLS
# --------------------------------------
# The results were checked against the R-package plspm
pls_ca = pls.PLSCanonical(n_components=X.shape[1])
pls_ca.fit(X, Y)
x_weights = np.array(
[[-0.61330704, 0.25616119, -0.74715187],
[-0.74697144, 0.11930791, 0.65406368],
[-0.25668686, -0.95924297, -0.11817271]])
assert_array_almost_equal(pls_ca.x_weights_, x_weights)
x_rotations = np.array(
[[-0.61330704, 0.41591889, -0.62297525],
[-0.74697144, 0.31388326, 0.77368233],
[-0.25668686, -0.89237972, -0.24121788]])
assert_array_almost_equal(pls_ca.x_rotations_, x_rotations)
y_weights = np.array(
[[+0.58989127, 0.7890047, 0.1717553],
[+0.77134053, -0.61351791, 0.16920272],
[-0.23887670, -0.03267062, 0.97050016]])
assert_array_almost_equal(pls_ca.y_weights_, y_weights)
y_rotations = np.array(
[[+0.58989127, 0.7168115, 0.30665872],
[+0.77134053, -0.70791757, 0.19786539],
[-0.23887670, -0.00343595, 0.94162826]])
assert_array_almost_equal(pls_ca.y_rotations_, y_rotations)
# 2) Regression PLS (PLS2): "Non regression test"
# ===============================================
# The results were checked against the R-packages plspm, misOmics and pls
pls_2 = pls.PLSRegression(n_components=X.shape[1])
pls_2.fit(X, Y)
x_weights = np.array(
[[-0.61330704, -0.00443647, 0.78983213],
[-0.74697144, -0.32172099, -0.58183269],
[-0.25668686, 0.94682413, -0.19399983]])
assert_array_almost_equal(pls_2.x_weights_, x_weights)
x_loadings = np.array(
[[-0.61470416, -0.24574278, 0.78983213],
[-0.65625755, -0.14396183, -0.58183269],
[-0.51733059, 1.00609417, -0.19399983]])
assert_array_almost_equal(pls_2.x_loadings_, x_loadings)
y_weights = np.array(
[[+0.32456184, 0.29892183, 0.20316322],
[+0.42439636, 0.61970543, 0.19320542],
[-0.13143144, -0.26348971, -0.17092916]])
assert_array_almost_equal(pls_2.y_weights_, y_weights)
y_loadings = np.array(
[[+0.32456184, 0.29892183, 0.20316322],
[+0.42439636, 0.61970543, 0.19320542],
[-0.13143144, -0.26348971, -0.17092916]])
assert_array_almost_equal(pls_2.y_loadings_, y_loadings)
# 3) Another non-regression test of Canonical PLS on random dataset
# =================================================================
# The results were checked against the R-package plspm
n = 500
p_noise = 10
q_noise = 5
# 2 latents vars:
np.random.seed(11)
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 = np.concatenate(
(X, np.random.normal(size=p_noise * n).reshape(n, p_noise)), axis=1)
Y = np.concatenate(
(Y, np.random.normal(size=q_noise * n).reshape(n, q_noise)), axis=1)
np.random.seed(None)
pls_ca = pls.PLSCanonical(n_components=3)
pls_ca.fit(X, Y)
x_weights = np.array(
[[0.65803719, 0.19197924, 0.21769083],
[0.7009113, 0.13303969, -0.15376699],
[0.13528197, -0.68636408, 0.13856546],
[0.16854574, -0.66788088, -0.12485304],
[-0.03232333, -0.04189855, 0.40690153],
[0.1148816, -0.09643158, 0.1613305],
[0.04792138, -0.02384992, 0.17175319],
[-0.06781, -0.01666137, -0.18556747],
[-0.00266945, -0.00160224, 0.11893098],
[-0.00849528, -0.07706095, 0.1570547],
[-0.00949471, -0.02964127, 0.34657036],
[-0.03572177, 0.0945091, 0.3414855],
[0.05584937, -0.02028961, -0.57682568],
[0.05744254, -0.01482333, -0.17431274]])
assert_array_almost_equal(pls_ca.x_weights_, x_weights)
x_loadings = np.array(
[[0.65649254, 0.1847647, 0.15270699],
[0.67554234, 0.15237508, -0.09182247],
[0.19219925, -0.67750975, 0.08673128],
[0.2133631, -0.67034809, -0.08835483],
[-0.03178912, -0.06668336, 0.43395268],
[0.15684588, -0.13350241, 0.20578984],
[0.03337736, -0.03807306, 0.09871553],
[-0.06199844, 0.01559854, -0.1881785],
[0.00406146, -0.00587025, 0.16413253],
[-0.00374239, -0.05848466, 0.19140336],
[0.00139214, -0.01033161, 0.32239136],
[-0.05292828, 0.0953533, 0.31916881],
[0.04031924, -0.01961045, -0.65174036],
[0.06172484, -0.06597366, -0.1244497]])
assert_array_almost_equal(pls_ca.x_loadings_, x_loadings)
y_weights = np.array(
[[0.66101097, 0.18672553, 0.22826092],
[0.69347861, 0.18463471, -0.23995597],
[0.14462724, -0.66504085, 0.17082434],
[0.22247955, -0.6932605, -0.09832993],
[0.07035859, 0.00714283, 0.67810124],
[0.07765351, -0.0105204, -0.44108074],
[-0.00917056, 0.04322147, 0.10062478],
[-0.01909512, 0.06182718, 0.28830475],
[0.01756709, 0.04797666, 0.32225745]])
assert_array_almost_equal(pls_ca.y_weights_, y_weights)
y_loadings = np.array(
[[0.68568625, 0.1674376, 0.0969508],
[0.68782064, 0.20375837, -0.1164448],
[0.11712173, -0.68046903, 0.12001505],
[0.17860457, -0.6798319, -0.05089681],
[0.06265739, -0.0277703, 0.74729584],
[0.0914178, 0.00403751, -0.5135078],
[-0.02196918, -0.01377169, 0.09564505],
[-0.03288952, 0.09039729, 0.31858973],
[0.04287624, 0.05254676, 0.27836841]])
assert_array_almost_equal(pls_ca.y_loadings_, y_loadings)
# Orthogonality of weights
# ~~~~~~~~~~~~~~~~~~~~~~~~
check_ortho(pls_ca.x_weights_, "x weights are not orthogonal")
check_ortho(pls_ca.y_weights_, "y weights are not orthogonal")
# Orthogonality of latent scores
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
check_ortho(pls_ca.x_scores_, "x scores are not orthogonal")
check_ortho(pls_ca.y_scores_, "y scores are not orthogonal")
def test_scale():
d = load_linnerud()
X = d.data
Y = d.target
# causes X[:, -1].std() to be zero
X[:, -1] = 1.0
for clf in [pls.PLSCanonical(), pls.PLSRegression(), pls.CCA(),
pls.PLSSVD()]:
clf.set_params(scale=True)
clf.fit(X, Y)
| bsd-3-clause |
Shatki/PyIMU | test/magnetosphere.py | 1 | 1580 | from mpl_toolkits.mplot3d import axes3d
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from socket import *
import time
# Объявляем все глобальные переменные
HOST = '192.168.0.76'
PORT = 21566
BUFSIZ = 512
ADDR = (HOST, PORT)
bad_packet = 0
good_packet = 0
# fig, ax = plt.subplots()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# Socket
# tcpCliSock = socket(AF_INET, SOCK_STREAM)
# tcpCliSock.connect(ADDR)
# Запрет на ожидание
plt.ion()
tstart = time.time()
# real-time plotting loop
X, Y, Z = [], [], []
while True:
try:
# читаем данные из сети
tcpCliSock.c
data = tcpCliSock.recv(BUFSIZ)
if data:
print(len(X), data)
data = data.decode().split(',')
if len(data) == 9:
# print('Data received', data)
# tcpCliSock.send(b'Ok')
good_packet += 1
else:
bad_packet += 1
# читаем данные из сети
data = tcpCliSock.recv(BUFSIZ)
X.append(data[0])
Y.append(data[1])
Z.append(data[2])
frame = ax.scatter(X, Y, Z, c='b', marker='o')
# Remove old line collection before drawing
#if oldcol is not None:
# ax.collections.remove(oldcol)
plt.pause(0.001 / len(X))
except KeyboardInterrupt:
tcpCliSock.close()
print('FPS: %f' % (len(X) / (time.time() - tstart)))
break
| gpl-3.0 |
DamCB/tyssue | tyssue/draw/ipv_draw.py | 2 | 8114 | """3D visualisation inside the notebook.
"""
import warnings
import numpy as np
import pandas as pd
from matplotlib import cm
from ipywidgets import interact
from ..config.draw import sheet_spec
from ..utils.utils import spec_updater, get_sub_eptm
try:
import ipyvolume as ipv
except ImportError:
print(
"""
This module needs ipyvolume to work.
You can install it with:
$ conda install -c conda-forge ipyvolume
"""
)
def browse_history(history, coords=["x", "y", "z"], **draw_specs_kw):
times = history.time_stamps
num_frames = times.size
draw_specs = sheet_spec()
spec_updater(draw_specs, draw_specs_kw)
sheet = history.retrieve(0)
ipv.clear()
fig, meshes = sheet_view(sheet, coords, **draw_specs_kw)
lim_inf = sheet.vert_df[sheet.coords].min().min()
lim_sup = sheet.vert_df[sheet.coords].max().max()
ipv.xyzlim(lim_inf, lim_sup)
def set_frame(i=0):
fig.animation = 0
t = times[i]
meshes = _get_meshes(history.retrieve(t), coords, draw_specs)
update_view(fig, meshes)
ipv.show()
interact(set_frame, i=(0, num_frames - 1))
def update_view(fig, meshes):
for old, new in zip(fig.meshes, meshes):
old.x = new.x
old.y = new.y
old.z = new.z
old.color = new.color
old.triangles = new.triangles
old.lines = new.lines
def sheet_view(sheet, coords=["x", "y", "z"], **draw_specs_kw):
"""
Creates a javascript renderer of the edge lines to be displayed
in Jupyter Notebooks
Returns
-------
fig: a :class:`ipyvolume.widgets.Figure` widget
mesh: a :class:`ipyvolume.widgets.Mesh` mesh widget
"""
# ipv.style.use(["dark", "minimal"])
draw_specs = sheet_spec()
spec_updater(draw_specs, draw_specs_kw)
fig = ipv.gcf()
fig.meshes = fig.meshes + _get_meshes(sheet, coords, draw_specs)
box_size = max(*(np.ptp(sheet.vert_df[u]) for u in sheet.coords))
border = 0.05 * box_size
lim_inf = sheet.vert_df[sheet.coords].min().min() - border
lim_sup = sheet.vert_df[sheet.coords].max().max() + border
ipv.xyzlim(lim_inf, lim_sup)
return fig, fig.meshes
def view_ipv(sheet, coords=["x", "y", "z"], **edge_specs):
"""
Creates a javascript renderer of the edge lines to be displayed
in Jupyter Notebooks
Returns
-------
fig: a :class:`ipyvolume.widgets.Figure` widget
mesh: a :class:`ipyvolume.widgets.Mesh` mesh widget
"""
warnings.warn("`view_ipv` is deprecated, use the more generic `sheet_view`")
mesh = edge_mesh(sheet, coords, **edge_specs)
fig = ipv.gcf()
fig.meshes = fig.meshes + [mesh]
box_size = max(*(np.ptp(sheet.vert_df[u]) for u in sheet.coords))
border = 0.05 * box_size
lim_inf = sheet.vert_df[sheet.coords].min().min() - border
lim_sup = sheet.vert_df[sheet.coords].max().max() + border
ipv.xyzlim(lim_inf, lim_sup)
return fig, mesh
def edge_mesh(sheet, coords, **edge_specs):
"""
Creates a ipyvolume Mesh of the edge lines to be displayed
in Jupyter Notebooks
Returns
-------
mesh: a :class:`ipyvolume.widgets.Mesh` mesh widget
"""
spec = sheet_spec()["edge"]
spec.update(**edge_specs)
if callable(spec["color"]):
spec["color"] = spec["color"](sheet)
if isinstance(spec["color"], str):
color = spec["color"]
elif hasattr(spec["color"], "__len__"):
color = _wire_color_from_sequence(spec, sheet)[:, :3]
u, v, w = coords
mesh = ipv.Mesh(
x=sheet.vert_df[u],
y=sheet.vert_df[v],
z=sheet.vert_df[w],
lines=sheet.edge_df[["srce", "trgt"]].astype(dtype=np.uint32),
color=color,
)
return mesh
def face_mesh(sheet, coords, **face_draw_specs):
"""
Creates a ipyvolume Mesh of the face polygons
"""
Ne, Nf = sheet.Ne, sheet.Nf
if callable(face_draw_specs["color"]):
face_draw_specs["color"] = face_draw_specs["color"](sheet)
if isinstance(face_draw_specs["color"], str):
color = face_draw_specs["color"]
elif hasattr(face_draw_specs["color"], "__len__"):
color = _face_color_from_sequence(face_draw_specs, sheet)[:, :3]
if "visible" in sheet.face_df.columns:
edges = sheet.edge_df[sheet.upcast_face(sheet.face_df["visible"])].index
_sheet = get_sub_eptm(sheet, edges)
if _sheet is not None:
sheet = _sheet
if isinstance(color, np.ndarray):
faces = sheet.face_df["face_o"].values.astype(np.uint32)
edges = edges.values.astype(np.uint32)
indexer = np.concatenate([faces, edges + Nf, edges + Ne + Nf])
color = color.take(indexer, axis=0)
epsilon = face_draw_specs.get("epsilon", 0)
up_srce = sheet.edge_df[["s" + c for c in coords]]
up_trgt = sheet.edge_df[["t" + c for c in coords]]
Ne, Nf = sheet.Ne, sheet.Nf
if epsilon > 0:
up_face = sheet.edge_df[["f" + c for c in coords]].values
up_srce = (up_srce - up_face) * (1 - epsilon) + up_face
up_trgt = (up_trgt - up_face) * (1 - epsilon) + up_face
mesh_ = np.concatenate(
[sheet.face_df[coords].values, up_srce.values, up_trgt.values]
)
triangles = np.vstack(
[sheet.edge_df["face"], np.arange(Ne) + Nf, np.arange(Ne) + Ne + Nf]
).T.astype(dtype=np.uint32)
mesh = ipv.Mesh(
x=mesh_[:, 0], y=mesh_[:, 1], z=mesh_[:, 2], triangles=triangles, color=color
)
return mesh
def _wire_color_from_sequence(edge_spec, sheet):
"""
"""
color_ = edge_spec["color"]
cmap = cm.get_cmap(edge_spec.get("colormap", "viridis"))
if color_.shape in [(sheet.Nv, 3), (sheet.Nv, 4)]:
return np.asarray(color_)
if color_.shape == (sheet.Nv,):
if np.ptp(color_) < 1e-10:
return np.ones((sheet.Nv, 3)) * 0.7
return cmap((color_ - color_.min()) / np.ptp(color_))
if color_.shape in [(sheet.Ne, 3), (sheet.Ne, 4)]:
color_ = pd.DataFrame(color_, index=sheet.edge_df.index)
color_["srce"] = sheet.edge_df["srce"]
color_ = color_.groupby("srce").mean().values
return color_
if color_.shape == (sheet.Ne,):
color_ = pd.DataFrame(color_, index=sheet.edge_df.index)
color_["srce"] = sheet.edge_df["srce"]
color_ = color_.groupby("srce").mean().values.ravel()
if np.ptp(color_) < 1e-10:
warnings.warn("Attempting to draw a colormap " "with a uniform value")
return np.ones((sheet.Nv, 3)) * 0.7
return cmap((color_ - color_.min()) / np.ptp(color_))
else:
raise ValueError("The 'color' value of the spec doesn't have a correct shape.")
def _face_color_from_sequence(face_spec, sheet):
color_ = face_spec["color"]
cmap = cm.get_cmap(face_spec.get("colormap", "viridis"))
Nf, Ne = sheet.Nf, sheet.Ne
color_min, color_max = face_spec.get("color_range", (color_.min(), color_.max()))
face_mesh_shape = Nf + 2 * Ne
if color_.shape in [(sheet.Nf, 3), (sheet.Nf, 4)]:
return np.concatenate([color_, color_, color_])
elif color_.shape == (sheet.Nf,):
if np.ptp(color_) < 1e-10:
# warnings.warn("Attempting to draw a colormap with a uniform value")
return np.ones((face_mesh_shape, 3)) * 0.5
normed = (color_ - color_min) / (color_max - color_min)
up_color = sheet.upcast_face(normed).values
return cmap(np.concatenate([normed, up_color, up_color]))
else:
raise ValueError(
"shape of `face_spec['color']` must be either (Nf, 3), (Nf, 4) or (Nf,)"
)
def _get_meshes(sheet, coords, draw_specs):
meshes = []
edge_spec = draw_specs["edge"]
if edge_spec["visible"]:
edges = edge_mesh(sheet, coords, **edge_spec)
meshes.append(edges)
else:
edges = None
face_spec = draw_specs["face"]
if face_spec["visible"]:
faces = face_mesh(sheet, coords, **face_spec)
meshes.append(faces)
else:
faces = None
return meshes
| gpl-3.0 |
LiaoPan/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 |
ephes/scikit-learn | examples/decomposition/plot_faces_decomposition.py | 204 | 4452 | """
============================
Faces dataset decompositions
============================
This example applies to :ref:`olivetti_faces` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
:ref:`decompositions`) .
"""
print(__doc__)
# Authors: Vlad Niculae, Alexandre Gramfort
# License: BSD 3 clause
import logging
from time import time
from numpy.random import RandomState
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.cluster import MiniBatchKMeans
from sklearn import decomposition
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
n_row, n_col = 2, 3
n_components = n_row * n_col
image_shape = (64, 64)
rng = RandomState(0)
###############################################################################
# Load faces data
dataset = fetch_olivetti_faces(shuffle=True, random_state=rng)
faces = dataset.data
n_samples, n_features = faces.shape
# global centering
faces_centered = faces - faces.mean(axis=0)
# local centering
faces_centered -= faces_centered.mean(axis=1).reshape(n_samples, -1)
print("Dataset consists of %d faces" % n_samples)
###############################################################################
def plot_gallery(title, images, n_col=n_col, n_row=n_row):
plt.figure(figsize=(2. * n_col, 2.26 * n_row))
plt.suptitle(title, size=16)
for i, comp in enumerate(images):
plt.subplot(n_row, n_col, i + 1)
vmax = max(comp.max(), -comp.min())
plt.imshow(comp.reshape(image_shape), cmap=plt.cm.gray,
interpolation='nearest',
vmin=-vmax, vmax=vmax)
plt.xticks(())
plt.yticks(())
plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)
###############################################################################
# List of the different estimators, whether to center and transpose the
# problem, and whether the transformer uses the clustering API.
estimators = [
('Eigenfaces - RandomizedPCA',
decomposition.RandomizedPCA(n_components=n_components, whiten=True),
True),
('Non-negative components - NMF',
decomposition.NMF(n_components=n_components, init='nndsvda', beta=5.0,
tol=5e-3, sparseness='components'),
False),
('Independent components - FastICA',
decomposition.FastICA(n_components=n_components, whiten=True),
True),
('Sparse comp. - MiniBatchSparsePCA',
decomposition.MiniBatchSparsePCA(n_components=n_components, alpha=0.8,
n_iter=100, batch_size=3,
random_state=rng),
True),
('MiniBatchDictionaryLearning',
decomposition.MiniBatchDictionaryLearning(n_components=15, alpha=0.1,
n_iter=50, batch_size=3,
random_state=rng),
True),
('Cluster centers - MiniBatchKMeans',
MiniBatchKMeans(n_clusters=n_components, tol=1e-3, batch_size=20,
max_iter=50, random_state=rng),
True),
('Factor Analysis components - FA',
decomposition.FactorAnalysis(n_components=n_components, max_iter=2),
True),
]
###############################################################################
# Plot a sample of the input data
plot_gallery("First centered Olivetti faces", faces_centered[:n_components])
###############################################################################
# Do the estimation and plot it
for name, estimator, center in estimators:
print("Extracting the top %d %s..." % (n_components, name))
t0 = time()
data = faces
if center:
data = faces_centered
estimator.fit(data)
train_time = (time() - t0)
print("done in %0.3fs" % train_time)
if hasattr(estimator, 'cluster_centers_'):
components_ = estimator.cluster_centers_
else:
components_ = estimator.components_
if hasattr(estimator, 'noise_variance_'):
plot_gallery("Pixelwise variance",
estimator.noise_variance_.reshape(1, -1), n_col=1,
n_row=1)
plot_gallery('%s - Train time %.1fs' % (name, train_time),
components_[:n_components])
plt.show()
| bsd-3-clause |
devanshdalal/scikit-learn | examples/ensemble/plot_isolation_forest.py | 39 | 2361 | """
==========================================
IsolationForest example
==========================================
An example using IsolationForest for anomaly detection.
The IsolationForest 'isolates' observations by randomly selecting a feature
and then randomly selecting a split value between the maximum and minimum
values of the selected feature.
Since recursive partitioning can be represented by a tree structure, the
number of splittings required to isolate a sample is equivalent to the path
length from the root node to the terminating node.
This path length, averaged over a forest of such random trees, is a measure
of normality and our decision function.
Random partitioning produces noticeable shorter paths for anomalies.
Hence, when a forest of random trees collectively produce shorter path lengths
for particular samples, they are highly likely to be anomalies.
.. [1] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest."
Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest
rng = np.random.RandomState(42)
# Generate train data
X = 0.3 * rng.randn(100, 2)
X_train = np.r_[X + 2, X - 2]
# Generate some regular novel observations
X = 0.3 * rng.randn(20, 2)
X_test = np.r_[X + 2, X - 2]
# Generate some abnormal novel observations
X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
# fit the model
clf = IsolationForest(max_samples=100, random_state=rng)
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)
# plot the line, the samples, and the nearest vectors to the plane
xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.title("IsolationForest")
plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)
b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white')
b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green')
c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red')
plt.axis('tight')
plt.xlim((-5, 5))
plt.ylim((-5, 5))
plt.legend([b1, b2, c],
["training observations",
"new regular observations", "new abnormal observations"],
loc="upper left")
plt.show()
| bsd-3-clause |
thp44/delphin_6_automation | data_process/2d_1d/archieve/moisture_content_comparison.py | 1 | 18274 | __author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import pandas as pd
import matplotlib.pyplot as plt
# RiBuild Modules
# -------------------------------------------------------------------------------------------------------------------- #
# RIBuild
out_folder = r'C:\Users\ocni\PycharmProjects\delphin_6_automation\data_process\2d_1d\processed_data'
graphic_folder = r'U:\RIBuild\2D_1D\Processed Results\4A'
hdf_file = out_folder + '/relative_moisture_content.h5'
# Open HDF
# Uninsulated
dresdenzp_highratio_uninsulated_4a = pd.read_hdf(hdf_file, 'dresden_zp_high_ratio_uninsulated_4a')
dresdenzd_highratio_uninsulated_4a = pd.read_hdf(hdf_file, 'dresden_zd_high_ratio_uninsulated_4a')
postdam_highratio_uninsulated_4a = pd.read_hdf(hdf_file, 'potsdam_high_ratio_uninsulated_4a')
dresdenzp_lowratio_uninsulated_4a = pd.read_hdf(hdf_file, 'dresden_zp_low_ratio_uninsulated_4a')
dresdenzd_lowratio_uninsulated_4a = pd.read_hdf(hdf_file, 'dresden_zd_low_ratio_uninsulated_4a')
postdam_lowratio_uninsulated_4a = pd.read_hdf(hdf_file, 'potsdam_low_ratio_uninsulated_4a')
total_uninsulated_4a = pd.concat([dresdenzp_highratio_uninsulated_4a, dresdenzd_highratio_uninsulated_4a,
postdam_highratio_uninsulated_4a, dresdenzp_lowratio_uninsulated_4a,
dresdenzd_lowratio_uninsulated_4a, postdam_lowratio_uninsulated_4a])
# Insulated
dresdenzp_highratio_insulated_4a = pd.read_hdf(hdf_file, 'dresden_zp_high_ratio_insulated_4a')
dresdenzd_highratio_insulated_4a = pd.read_hdf(hdf_file, 'dresden_zd_high_ratio_insulated_4a')
postdam_highratio_insulated_4a = pd.read_hdf(hdf_file, 'potsdam_high_ratio_insulated_4a')
dresdenzp_lowratio_insulated_4a = pd.read_hdf(hdf_file, 'dresden_zp_low_ratio_insulated_4a')
dresdenzd_lowratio_insulated_4a = pd.read_hdf(hdf_file, 'dresden_zd_low_ratio_insulated_4a')
postdam_lowratio_insulated_4a = pd.read_hdf(hdf_file, 'potsdam_low_ratio_insulated_4a')
total_insulated_4a = pd.concat([dresdenzp_highratio_insulated_4a, dresdenzd_highratio_insulated_4a,
postdam_highratio_insulated_4a, dresdenzp_lowratio_insulated_4a,
dresdenzd_lowratio_insulated_4a, postdam_lowratio_insulated_4a])
def plots(plot, save=False):
"""
Creates box plots from all the wall scenarios
"""
if plot == 'uninsulated' or plot == 'all':
plt.figure('dresdenzp_highratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzp_highratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZP - Mortar: High Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/dresdenzp_highratio_uninsulated_4a_moisture")
plt.figure('dresdenzd_highratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzd_highratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZD - Mortar: High Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/dresdenzd_highratio_uninsulated_4a_moisture")
plt.figure('postdam_highratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
postdam_highratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Potsdam - Mortar: High Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/postdam_highratio_uninsulated_4a_moisture")
plt.figure('dresdenzp_lowratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzp_lowratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZP - Mortar: Low Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/dresdenzp_lowratio_uninsulated_4a_moisture")
plt.figure('dresdenzd_lowratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzd_lowratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZD - Mortar: Low Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/dresdenzd_lowratio_uninsulated_4a_moisture")
plt.figure('postdam_lowratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
postdam_lowratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Potsdam - Mortar: Low Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/postdam_lowratio_uninsulated_4a_moisture")
plt.figure('total_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
total_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: All - Mortar: All - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/total_uninsulated_4a_moisture")
if plot == 'insulated' or plot == 'all':
plt.figure('dresdenzp_highratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzp_highratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZP - Mortar: High Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/dresdenzp_highratio_insulated_4a_moisture")
plt.figure('dresdenzd_highratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzd_highratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZD - Mortar: High Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/dresdenzd_highratio_insulated_4a_moisture")
plt.figure('postdam_highratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
postdam_highratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Potsdam - Mortar: High Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/postdam_highratio_insulated_4a_moisture")
plt.figure('dresdenzp_lowratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzp_lowratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZP - Mortar: Low Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/dresdenzp_lowratio_insulated_4a_moisture")
plt.figure('dresdenzd_lowratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzd_lowratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZD - Mortar: Low Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/dresdenzd_lowratio_insulated_4a_moisture")
plt.figure('postdam_lowratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
postdam_lowratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Potsdam - Mortar: Low Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/postdam_lowratio_insulated_4a_moisture")
plt.figure('total_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
total_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: All - Mortar: All - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/total_insulated_4a_moisture")
plt.show()
plots('all', False)
def std3_ratio(print_=False, excel=False):
"""Computes ratio of outliers in the data sets. Outliers is here defined as data points deviating with more
the 3 standard deviations from the mean."""
std3_uninsulated_ratio_ = uninsulated()
std3_insulated_ratio_ = insulated()
if print_:
print('Uninsulated')
print(std3_uninsulated_ratio_)
print('')
print('Insulated')
print(std3_insulated_ratio_)
if excel:
writer = pd.ExcelWriter(f'{out_folder}/moisture_std_ratios.xlsx')
std3_uninsulated_ratio_.to_excel(writer, 'Uninsulated')
std3_insulated_ratio_.to_excel(writer, 'Insulated')
writer.save()
def uninsulated():
"""Computes the outliers for the uninsulated cases"""
outliers_total_uninsulated = (total_uninsulated_4a.shape[0] -
total_uninsulated_4a.sub(total_uninsulated_4a.mean())
.div(total_uninsulated_4a.std()).abs().lt(3).sum()) / total_uninsulated_4a.shape[0]
outliers_zd_high_uninsulated = (dresdenzd_highratio_uninsulated_4a.shape[0] -
dresdenzd_highratio_uninsulated_4a.sub(dresdenzd_highratio_uninsulated_4a.mean())
.div(dresdenzd_highratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzd_highratio_uninsulated_4a.shape[0]
outliers_zp_high_uninsulated = (dresdenzp_highratio_uninsulated_4a.shape[0] -
dresdenzp_highratio_uninsulated_4a.sub(dresdenzp_highratio_uninsulated_4a.mean())
.div(dresdenzp_highratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzp_highratio_uninsulated_4a.shape[0]
outliers_pd_high_uninsulated = (postdam_highratio_uninsulated_4a.shape[0] -
postdam_highratio_uninsulated_4a.sub(postdam_highratio_uninsulated_4a.mean())
.div(postdam_highratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ postdam_highratio_uninsulated_4a.shape[0]
outliers_zd_low_uninsulated = (dresdenzd_lowratio_uninsulated_4a.shape[0] -
dresdenzd_lowratio_uninsulated_4a.sub(dresdenzd_lowratio_uninsulated_4a.mean())
.div(dresdenzd_lowratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzd_lowratio_uninsulated_4a.shape[0]
outliers_zp_low_uninsulated = (dresdenzp_lowratio_uninsulated_4a.shape[0] -
dresdenzp_lowratio_uninsulated_4a.sub(dresdenzp_lowratio_uninsulated_4a.mean())
.div(dresdenzp_lowratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzp_lowratio_uninsulated_4a.shape[0]
outliers_pd_low_uninsulated = (postdam_lowratio_uninsulated_4a.shape[0] -
postdam_lowratio_uninsulated_4a.sub(postdam_lowratio_uninsulated_4a.mean())
.div(postdam_lowratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ postdam_lowratio_uninsulated_4a.shape[0]
outliers_uninsulated_ratio_ = pd.concat([outliers_total_uninsulated, outliers_zd_high_uninsulated,
outliers_zp_high_uninsulated, outliers_pd_high_uninsulated,
outliers_zd_low_uninsulated, outliers_zp_low_uninsulated,
outliers_pd_low_uninsulated], axis=1)
outliers_uninsulated_ratio_.columns = ["Brick: All - Mortar: All - Insulation: None",
"Brick: Dresden ZD - Mortar: High Cement Ratio - Insulation: None",
"Brick: Dresden ZP - Mortar: High Cement Ratio - Insulation: None",
"Brick: Potsdam - Mortar: High Cement Ratio - Insulation: None",
"Brick: Dresden ZD - Mortar: Low Cement Ratio - Insulation: None",
"Brick: Dresden ZP - Mortar: Low Cement Ratio - Insulation: None",
"Brick: Potsdam - Mortar: Low Cement Ratio - Insulation: None"]
return outliers_uninsulated_ratio_
def insulated():
"""Computes the outliers for the insulated cases"""
outliers_total_insulated = (total_insulated_4a.shape[0] - total_insulated_4a.sub(total_insulated_4a.mean())
.div(total_insulated_4a.std()).abs().lt(3).sum()) / total_insulated_4a.shape[0]
outliers_zd_high_insulated = (dresdenzd_highratio_insulated_4a.shape[0] -
dresdenzd_highratio_insulated_4a.sub(dresdenzd_highratio_insulated_4a.mean())
.div(dresdenzd_highratio_insulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzd_highratio_insulated_4a.shape[0]
outliers_zp_high_insulated = (dresdenzp_highratio_insulated_4a.shape[0] -
dresdenzp_highratio_insulated_4a.sub(dresdenzp_highratio_insulated_4a.mean())
.div(dresdenzp_highratio_insulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzp_highratio_insulated_4a.shape[0]
outliers_pd_high_insulated = (postdam_highratio_insulated_4a.shape[0] -
postdam_highratio_insulated_4a.sub(postdam_highratio_insulated_4a.mean())
.div(postdam_highratio_insulated_4a.std()).abs().lt(3).sum()) \
/ postdam_highratio_insulated_4a.shape[0]
outliers_zd_low_insulated = (dresdenzd_lowratio_insulated_4a.shape[0] -
dresdenzd_lowratio_insulated_4a.sub(dresdenzd_lowratio_insulated_4a.mean())
.div(dresdenzd_lowratio_insulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzd_lowratio_insulated_4a.shape[0]
outliers_zp_low_insulated = (dresdenzp_lowratio_insulated_4a.shape[0] -
dresdenzp_lowratio_insulated_4a.sub(dresdenzp_lowratio_insulated_4a.mean())
.div(dresdenzp_lowratio_insulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzp_lowratio_insulated_4a.shape[0]
outliers_pd_low_insulated = (postdam_lowratio_insulated_4a.shape[0] -
postdam_lowratio_insulated_4a.sub(postdam_lowratio_insulated_4a.mean())
.div(postdam_lowratio_insulated_4a.std()).abs().lt(3).sum()) \
/ postdam_lowratio_insulated_4a.shape[0]
std2_insulated_ratio_ = pd.concat([outliers_total_insulated, outliers_zd_high_insulated,
outliers_zp_high_insulated, outliers_pd_high_insulated,
outliers_zd_low_insulated, outliers_zp_low_insulated,
outliers_pd_low_insulated], axis=1)
std2_insulated_ratio_.columns = ["Brick: All - Mortar: All - Insulation: None",
"Brick: Dresden ZD - Mortar: High Cement Ratio - Insulation: Calcium Silicate",
"Brick: Dresden ZP - Mortar: High Cement Ratio - Insulation: Calcium Silicate",
"Brick: Potsdam - Mortar: High Cement Ratio - Insulation: Calcium Silicate",
"Brick: Dresden ZD - Mortar: Low Cement Ratio - Insulation: Calcium Silicate",
"Brick: Dresden ZP - Mortar: Low Cement Ratio - Insulation: Calcium Silicate",
"Brick: Potsdam - Mortar: Low Cement Ratio - Insulation: Calcium Silicate"]
return std2_insulated_ratio_
#std3_ratio(False, True)
| mit |
miaecle/deepchem | devtools/archive/jenkins/generate_graph.py | 2 | 5220 | import csv
import os
import numpy as np
import matplotlib.pyplot as plt
import time
plt.switch_backend('agg')
TODO = {
('tox21', 'random'): [
'weave', 'graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg',
'textcnn'
],
('clintox', 'random'): [
'weave', 'graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg',
'textcnn'
],
('sider', 'random'): [
'weave', 'graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg',
'textcnn'
],
('bbbp', 'scaffold'):
['weave', 'graphconv', 'tf', 'irv', 'xgb', 'logreg', 'textcnn'],
('bace_c', 'scaffold'):
['weave', 'graphconv', 'tf', 'irv', 'xgb', 'logreg', 'textcnn'],
('hiv', 'scaffold'):
['weave', 'graphconv', 'tf', 'irv', 'xgb', 'logreg', 'textcnn'],
('muv', 'random'): ['graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg'],
('delaney', 'random'): [
'weave_regression', 'graphconvreg', 'tf_regression', 'xgb_regression',
'krr', 'textcnn_regression', 'dag_regression', 'mpnn'
],
('sampl', 'random'): [
'weave_regression', 'graphconvreg', 'tf_regression', 'xgb_regression',
'krr', 'textcnn_regression', 'dag_regression', 'mpnn'
],
('lipo', 'random'): [
'weave_regression', 'graphconvreg', 'tf_regression', 'xgb_regression',
'krr', 'textcnn_regression', 'dag_regression', 'mpnn'
],
('qm7', 'stratified'): [
'dtnn', 'graphconvreg', 'tf_regression_ft', 'krr_ft'
],
('qm8', 'random'): [
'dtnn', 'graphconvreg', 'weave_regression', 'textcnn_regression',
'mpnn', 'tf_regression', 'tf_regression_ft'
],
}
ORDER = [
'logreg', 'rf', 'rf_regression', 'xgb', 'xgb_regression', 'kernelsvm',
'krr', 'krr_ft', 'tf', 'tf_regression', 'tf_regression_ft', 'tf_robust',
'irv', 'textcnn', 'textcnn_regression', 'graphconv', 'graphconvreg', 'dag',
'dag_regression', 'ani', 'weave', 'weave_regression', 'dtnn', 'mpnn'
]
COLOR = {
'logreg': '#3F3F3F',
'rf': '#67AD4F',
'rf_regression': '#67AD4F',
'xgb': '#0E766C',
'xgb_regression': '#0E766C',
'kernelsvm': '#FC926B',
'krr': '#FC926B',
'krr_ft': '#5A372A',
'tf': '#2B6596',
'tf_regression': '#2B6596',
'tf_regression_ft': '#162939',
'tf_robust': '#775183',
'irv': '#D9D9D9',
'graphconv': '#A4D192',
'graphconvreg': '#A4D192',
'dag': '#D06329',
'dag_regression': '#D06329',
'ani': '#D9D9D9',
'weave': '#8196AE',
'weave_regression': '#8196AE',
'textcnn': '#811B18',
'textcnn_regression': '#811B18',
'dtnn': '#D06329',
'mpnn': '#7B0A48'
}
TODO_list = set()
for key in TODO.keys():
for val in TODO[key]:
TODO_list.add((key[0], key[1], val))
def read_results(path):
Results = set()
with open(path, 'r') as f:
reader = csv.reader(f)
for line in reader:
Results.add((line[0], line[1], line[3]))
return Results
def run_benchmark(path, deepchem_dir):
finished = read_results(path)
os.chdir(deepchem_dir)
os.chdir('./examples')
while len(TODO_list - finished) > 0:
todo = TODO_list - finished
for p in todo:
os.system('python benchmark.py --seed 123 -d ' + p[0] + ' -s ' + p[1] +
' -m ' + p[2])
def plot(dataset, split, path, out_path):
if dataset in [
'bace_c', 'bbbp', 'clintox', 'hiv', 'muv', 'pcba', 'pcba_146',
'pcba_2475', 'sider', 'tox21', 'toxcast'
]:
mode = 'classification'
else:
mode = 'regression'
data = {}
with open(path, 'r') as f:
reader = csv.reader(f)
for line in reader:
if line[0] == dataset and line[1] == split:
data[line[3]] = line[8]
labels = []
values = []
colors = []
for model in ORDER:
if model in data.keys():
labels.append(model)
colors.append(COLOR[model])
values.append(float(data[model]))
y_pos = np.arange(len(labels))
plt.rcdefaults()
fig, ax = plt.subplots()
ax.barh(y_pos, values, align='center', color='green')
ax.set_yticks(y_pos)
ax.set_yticklabels(labels)
ax.invert_yaxis()
if mode == 'regression':
ax.set_xlabel('R square')
ax.set_xlim(left=0., right=1.)
else:
ax.set_xlabel('ROC-AUC')
ax.set_xlim(left=0.4, right=1.)
t = time.localtime(time.time())
ax.set_title("Performance on %s (%s split), %i-%i-%i" %
(dataset, split, t.tm_year, t.tm_mon, t.tm_mday))
plt.tight_layout()
for i in range(len(colors)):
ax.get_children()[i].set_color(colors[i])
ax.text(
values[i] - 0.1, y_pos[i] + 0.1, str("%.3f" % values[i]), color='white')
fig.savefig(os.path.join(out_path, dataset + '_' + split + '.png'))
#plt.show()
if __name__ == '__main__':
current_dir = os.path.dirname(os.path.realpath(__file__))
DEEPCHEM_DIR = os.path.split(os.path.split(current_dir)[0])[0]
FILE = os.path.join(os.path.join(DEEPCHEM_DIR, 'examples'), 'results.csv')
#run_benchmark(FILE, DEEPCHEM_DIR)
save_dir = os.path.join(DEEPCHEM_DIR, 'datasets/MolNet_pic')
if not os.path.exists(save_dir):
os.mkdir(save_dir)
for pair in TODO.keys():
plot(pair[0], pair[1], FILE, save_dir)
os.system('aws s3 sync ' + save_dir +
' s3://deepchem.io/trained_models/MolNet_pic')
| mit |
xiongzhenggang/xiongzhenggang.github.io | AI/data/deeplearning24054/planar_utils.py | 2 | 2271 | import matplotlib.pyplot as plt
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.linear_model
def plot_decision_boundary(model, X, Y):
# Set min and max values and give it some padding
x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
h = 0.01
# Generate a grid of points with distance h between them
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Predict the function value for the whole grid
Z = model(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# Plot the contour and training examples
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
plt.ylabel('x2')
plt.xlabel('x1')
plt.scatter(X[0, :], X[1, :], c=Y.reshape(X[0,:].shape), cmap=plt.cm.Spectral)
def sigmoid(x):
"""
Compute the sigmoid of x
Arguments:
x -- A scalar or numpy array of any size.
Return:
s -- sigmoid(x)
"""
s = 1/(1+np.exp(-x))
return s
def load_planar_dataset():
np.random.seed(1)
m = 400 # number of examples
N = int(m/2) # number of points per class
D = 2 # dimensionality
X = np.zeros((m,D)) # data matrix where each row is a single example
Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue)
a = 4 # maximum ray of the flower
for j in range(2):
ix = range(N*j,N*(j+1))
t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta
r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius
X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
Y[ix] = j
X = X.T
Y = Y.T
return X, Y
def load_extra_datasets():
N = 200
noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)
noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)
blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)
gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None)
no_structure = np.random.rand(N, 2), np.random.rand(N, 2)
return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure | gpl-3.0 |
airanmehr/bio | Scripts/TimeSeriesPaper/Plot/topSNPs.py | 1 | 1589 | '''
Copyleft Oct 14, 2016 Arya Iranmehr, PhD Student, Bafna Lab, UC San Diego, Email: airanmehr@gmail.com
'''
import numpy as np;
np.set_printoptions(linewidth=200, precision=5, suppress=True)
import pandas as pd;
pd.options.display.max_rows = 20;
pd.options.display.expand_frame_repr = False
import seaborn as sns
import matplotlib as mpl
import os;
home = os.path.expanduser('~') + '/'
import Utils.Util as utl
import Scripts.TimeSeriesPaper.RealData.Utils as rutl
a = rutl.loadAllScores().groupby(level='h', axis=1).apply(rutl.HstatisticAll)
df = pd.read_pickle(utl.outpath + 'real/scores.df')
i = df.lrd.sort_values().index[-1]
df.loc[i]
cd = pd.read_pickle(utl.outpath + 'real/CD.F59.df')
import Utils.Plots as pplt
import pylab as plt
names = rutl.loadSNPIDs()
sns.set_style("white", {"grid.color": "0.9", 'axes.linewidth': .5, "grid.linewidth": "9.99"})
mpl.rc('font', **{'family': 'serif', 'serif': ['Computer Modern']});
mpl.rc('text', usetex=True)
reload(pplt)
f, ax = plt.subplots(1, 2, sharey=True, dpi=300, figsize=(4, 2))
i = a[0.5].sort_values().index[-1]
sns.set_context("notebook", font_scale=1, rc={"lines.linewidth": 1.2})
pplt.plotSiteReal(cd.loc[i], ax=ax[0], legend=True)
ax[0].set_title('{}:{:.0f} ({})'.format(i[0], i[1], names.loc[i]), fontsize=8)
i = df.lrdiff.sort_values().index[-1]
pplt.plotSiteReal(cd.loc[i], ax=ax[1])
sns.set_context("notebook", font_scale=1, rc={"lines.linewidth": 1.2})
ax[1].set_title('{}:{:.0f} ({})'.format(i[0], i[1], names.loc[i]), fontsize=8)
plt.gcf().subplots_adjust(bottom=0.2)
pplt.savefig('topSNPs', 300)
plt.show()
| mit |
sighingnow/sighingnow.github.io | resource/k_nearest_neighbors/dating.py | 1 | 3622 | #! /usr/bin/env python
# -*- coding: utf-8
'''
Name: dating.py(KNN algorithm)
Training and test dataset: dating.txt
Created on Feb 8, 2015
@author: Tao He
'''
__author__ = 'Tao He'
from numpy import array as nmarray
from matplotlib import pyplot as plt
LABEL_MAP = {
'didntLike': 1,
'smallDoses': 2,
'largeDoses': 3,
}
ATTR_MAP = {
1: 'Number of frequent flyer miles earned per year',
2: 'Percentage of time spent playing video games',
3: 'Liters of ice cream consumed per week',
}
def create_dataset(filename=None):
''' Return data group and labels.
Get the data from file.
If the filename is not specialed, return None.
dataformat: flyerMiles, gameTime, icecream, label.
'''
def normalize_data(data=None):
''' Normalized dataset.
Normalize all data to range 0-1.
'''
if data is None:
return None
for column in range(data[0].__len__()):
max_val, min_val = max(data[:, column]), min(data[:, column])
for row in range(data.__len__()):
data[row][column] = (data[row][column]-min_val)/(max_val-min_val)
return data
if filename == None:
return (None, None)
group = []
labels = []
with open(filename, mode='r') as fp_data:
for line in fp_data:
group.append([float(num) for num in line[:-1].split('\t')[0:3]])
labels.append(LABEL_MAP[line[:-1].split('\t')[3]])
return normalize_data(nmarray(group)), labels
def draw_pic(group=None, labels=None, x=0, y=0):
''' Draw a subplot from data group.
'''
if group is None or labels is None:
return None
name = 'knn-dating'
figure = plt.figure(num=name, dpi=100)
ax_main = figure.add_subplot(1, 1, 1, xlabel=ATTR_MAP[x+1], ylabel=ATTR_MAP[y+1], title=name)
ax_main.scatter(group[:, x], group[:, y],
s=15*nmarray(labels),
c=[[i/LABEL_MAP.__len__()] for i in labels])
plt.show()
## plt.savefig('%s.png'%name, format='png', dpi=100)
def knn_classify(group, labels, attrs, ratio=0.5, item=0, k=3):
''' Return the type of item.
knn classify function.
'''
def get_dist(i, j):
''' Return the distence of group[i] and group[j].
'''
dist = 0.0
for attr in attrs:
dist += (group[i][attr]-group[j][attr])*(group[i][attr]-group[j][attr])
return dist
length = group.__len__()
distence = []
for i in range(int(length*ratio), length):
distence.append((i, get_dist(item, i)))
cnt = {}
distence.sort(key=lambda item: item[1])
for i in range(k):
label = labels[distence[i][0]]
if label in cnt:
cnt[label] += 1
else:
cnt[label] = 1
return sorted(cnt.items(), key=lambda item: item[1], reverse=True)[0][0]
def knn():
''' KNN classify algorithm.
'''
data, labels = create_dataset('dating.txt')
ratio, attr = 0.5, [0, 1, 2]
cnt, cnt_correct = 0, 0
length = data.__len__()
for i in range(0, int(length*ratio)):
cnt += 1
knn_type = knn_classify(data, labels, attr, ratio, i, 3)
# print('case[%d]: real: %d, knn: %d'%(i, labels[i], knn_type))
if knn_type == labels[i]:
cnt_correct += 1
print('total: %d, correct: %d, correct ratio: %f'%(cnt, cnt_correct, cnt_correct/cnt))
if __name__ == '__main__':
knn()
# vim: set sw=4, ts=4, fileencoding=utf-8
| mit |
ZenDevelopmentSystems/scikit-learn | sklearn/cluster/mean_shift_.py | 96 | 15434 | """Mean shift clustering algorithm.
Mean shift clustering aims to discover *blobs* in a smooth density of
samples. It is a centroid based algorithm, which works by updating candidates
for centroids to be the mean of the points within a given region. These
candidates are then filtered in a post-processing stage to eliminate
near-duplicates to form the final set of centroids.
Seeding is performed using a binning technique for scalability.
"""
# Authors: Conrad Lee <conradlee@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Martino Sorbaro <martino.sorbaro@ed.ac.uk>
import numpy as np
import warnings
from collections import defaultdict
from ..externals import six
from ..utils.validation import check_is_fitted
from ..utils import extmath, check_random_state, gen_batches, check_array
from ..base import BaseEstimator, ClusterMixin
from ..neighbors import NearestNeighbors
from ..metrics.pairwise import pairwise_distances_argmin
from ..externals.joblib import Parallel
from ..externals.joblib import delayed
def estimate_bandwidth(X, quantile=0.3, n_samples=None, random_state=0):
"""Estimate the bandwidth to use with the mean-shift algorithm.
That this function takes time at least quadratic in n_samples. For large
datasets, it's wise to set that parameter to a small value.
Parameters
----------
X : array-like, shape=[n_samples, n_features]
Input points.
quantile : float, default 0.3
should be between [0, 1]
0.5 means that the median of all pairwise distances is used.
n_samples : int, optional
The number of samples to use. If not given, all samples are used.
random_state : int or RandomState
Pseudo-random number generator state used for random sampling.
Returns
-------
bandwidth : float
The bandwidth parameter.
"""
random_state = check_random_state(random_state)
if n_samples is not None:
idx = random_state.permutation(X.shape[0])[:n_samples]
X = X[idx]
nbrs = NearestNeighbors(n_neighbors=int(X.shape[0] * quantile))
nbrs.fit(X)
bandwidth = 0.
for batch in gen_batches(len(X), 500):
d, _ = nbrs.kneighbors(X[batch, :], return_distance=True)
bandwidth += np.max(d, axis=1).sum()
return bandwidth / X.shape[0]
# separate function for each seed's iterative loop
def _mean_shift_single_seed(my_mean, X, nbrs, max_iter):
# For each seed, climb gradient until convergence or max_iter
bandwidth = nbrs.get_params()['radius']
stop_thresh = 1e-3 * bandwidth # when mean has converged
completed_iterations = 0
while True:
# Find mean of points within bandwidth
i_nbrs = nbrs.radius_neighbors([my_mean], bandwidth,
return_distance=False)[0]
points_within = X[i_nbrs]
if len(points_within) == 0:
break # Depending on seeding strategy this condition may occur
my_old_mean = my_mean # save the old mean
my_mean = np.mean(points_within, axis=0)
# If converged or at max_iter, adds the cluster
if (extmath.norm(my_mean - my_old_mean) < stop_thresh or
completed_iterations == max_iter):
return tuple(my_mean), len(points_within)
completed_iterations += 1
def mean_shift(X, bandwidth=None, seeds=None, bin_seeding=False,
min_bin_freq=1, cluster_all=True, max_iter=300,
max_iterations=None, n_jobs=1):
"""Perform mean shift clustering of data using a flat kernel.
Read more in the :ref:`User Guide <mean_shift>`.
Parameters
----------
X : array-like, shape=[n_samples, n_features]
Input data.
bandwidth : float, optional
Kernel bandwidth.
If bandwidth is not given, it is determined using a heuristic based on
the median of all pairwise distances. This will take quadratic time in
the number of samples. The sklearn.cluster.estimate_bandwidth function
can be used to do this more efficiently.
seeds : array-like, shape=[n_seeds, n_features] or None
Point used as initial kernel locations. If None and bin_seeding=False,
each data point is used as a seed. If None and bin_seeding=True,
see bin_seeding.
bin_seeding : boolean, default=False
If true, initial kernel locations are not locations of all
points, but rather the location of the discretized version of
points, where points are binned onto a grid whose coarseness
corresponds to the bandwidth. Setting this option to True will speed
up the algorithm because fewer seeds will be initialized.
Ignored if seeds argument is not None.
min_bin_freq : int, default=1
To speed up the algorithm, accept only those bins with at least
min_bin_freq points as seeds.
cluster_all : boolean, default True
If true, then all points are clustered, even those orphans that are
not within any kernel. Orphans are assigned to the nearest kernel.
If false, then orphans are given cluster label -1.
max_iter : int, default 300
Maximum number of iterations, per seed point before the clustering
operation terminates (for that seed point), if has not converged yet.
n_jobs : int
The number of jobs to use for the computation. This works by computing
each of the n_init runs in parallel.
If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
Returns
-------
cluster_centers : array, shape=[n_clusters, n_features]
Coordinates of cluster centers.
labels : array, shape=[n_samples]
Cluster labels for each point.
Notes
-----
See examples/cluster/plot_meanshift.py for an example.
"""
# FIXME To be removed in 0.18
if max_iterations is not None:
warnings.warn("The `max_iterations` parameter has been renamed to "
"`max_iter` from version 0.16. The `max_iterations` "
"parameter will be removed in 0.18", DeprecationWarning)
max_iter = max_iterations
if bandwidth is None:
bandwidth = estimate_bandwidth(X)
elif bandwidth <= 0:
raise ValueError("bandwidth needs to be greater than zero or None,\
got %f" % bandwidth)
if seeds is None:
if bin_seeding:
seeds = get_bin_seeds(X, bandwidth, min_bin_freq)
else:
seeds = X
n_samples, n_features = X.shape
center_intensity_dict = {}
nbrs = NearestNeighbors(radius=bandwidth).fit(X)
# execute iterations on all seeds in parallel
all_res = Parallel(n_jobs=n_jobs)(
delayed(_mean_shift_single_seed)
(seed, X, nbrs, max_iter) for seed in seeds)
# copy results in a dictionary
for i in range(len(seeds)):
if all_res[i] is not None:
center_intensity_dict[all_res[i][0]] = all_res[i][1]
if not center_intensity_dict:
# nothing near seeds
raise ValueError("No point was within bandwidth=%f of any seed."
" Try a different seeding strategy \
or increase the bandwidth."
% bandwidth)
# POST PROCESSING: remove near duplicate points
# If the distance between two kernels is less than the bandwidth,
# then we have to remove one because it is a duplicate. Remove the
# one with fewer points.
sorted_by_intensity = sorted(center_intensity_dict.items(),
key=lambda tup: tup[1], reverse=True)
sorted_centers = np.array([tup[0] for tup in sorted_by_intensity])
unique = np.ones(len(sorted_centers), dtype=np.bool)
nbrs = NearestNeighbors(radius=bandwidth).fit(sorted_centers)
for i, center in enumerate(sorted_centers):
if unique[i]:
neighbor_idxs = nbrs.radius_neighbors([center],
return_distance=False)[0]
unique[neighbor_idxs] = 0
unique[i] = 1 # leave the current point as unique
cluster_centers = sorted_centers[unique]
# ASSIGN LABELS: a point belongs to the cluster that it is closest to
nbrs = NearestNeighbors(n_neighbors=1).fit(cluster_centers)
labels = np.zeros(n_samples, dtype=np.int)
distances, idxs = nbrs.kneighbors(X)
if cluster_all:
labels = idxs.flatten()
else:
labels.fill(-1)
bool_selector = distances.flatten() <= bandwidth
labels[bool_selector] = idxs.flatten()[bool_selector]
return cluster_centers, labels
def get_bin_seeds(X, bin_size, min_bin_freq=1):
"""Finds seeds for mean_shift.
Finds seeds by first binning data onto a grid whose lines are
spaced bin_size apart, and then choosing those bins with at least
min_bin_freq points.
Parameters
----------
X : array-like, shape=[n_samples, n_features]
Input points, the same points that will be used in mean_shift.
bin_size : float
Controls the coarseness of the binning. Smaller values lead
to more seeding (which is computationally more expensive). If you're
not sure how to set this, set it to the value of the bandwidth used
in clustering.mean_shift.
min_bin_freq : integer, optional
Only bins with at least min_bin_freq will be selected as seeds.
Raising this value decreases the number of seeds found, which
makes mean_shift computationally cheaper.
Returns
-------
bin_seeds : array-like, shape=[n_samples, n_features]
Points used as initial kernel positions in clustering.mean_shift.
"""
# Bin points
bin_sizes = defaultdict(int)
for point in X:
binned_point = np.round(point / bin_size)
bin_sizes[tuple(binned_point)] += 1
# Select only those bins as seeds which have enough members
bin_seeds = np.array([point for point, freq in six.iteritems(bin_sizes) if
freq >= min_bin_freq], dtype=np.float32)
if len(bin_seeds) == len(X):
warnings.warn("Binning data failed with provided bin_size=%f,"
" using data points as seeds." % bin_size)
return X
bin_seeds = bin_seeds * bin_size
return bin_seeds
class MeanShift(BaseEstimator, ClusterMixin):
"""Mean shift clustering using a flat kernel.
Mean shift clustering aims to discover "blobs" in a smooth density of
samples. It is a centroid-based algorithm, which works by updating
candidates for centroids to be the mean of the points within a given
region. These candidates are then filtered in a post-processing stage to
eliminate near-duplicates to form the final set of centroids.
Seeding is performed using a binning technique for scalability.
Read more in the :ref:`User Guide <mean_shift>`.
Parameters
----------
bandwidth : float, optional
Bandwidth used in the RBF kernel.
If not given, the bandwidth is estimated using
sklearn.cluster.estimate_bandwidth; see the documentation for that
function for hints on scalability (see also the Notes, below).
seeds : array, shape=[n_samples, n_features], optional
Seeds used to initialize kernels. If not set,
the seeds are calculated by clustering.get_bin_seeds
with bandwidth as the grid size and default values for
other parameters.
bin_seeding : boolean, optional
If true, initial kernel locations are not locations of all
points, but rather the location of the discretized version of
points, where points are binned onto a grid whose coarseness
corresponds to the bandwidth. Setting this option to True will speed
up the algorithm because fewer seeds will be initialized.
default value: False
Ignored if seeds argument is not None.
min_bin_freq : int, optional
To speed up the algorithm, accept only those bins with at least
min_bin_freq points as seeds. If not defined, set to 1.
cluster_all : boolean, default True
If true, then all points are clustered, even those orphans that are
not within any kernel. Orphans are assigned to the nearest kernel.
If false, then orphans are given cluster label -1.
n_jobs : int
The number of jobs to use for the computation. This works by computing
each of the n_init runs in parallel.
If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
Attributes
----------
cluster_centers_ : array, [n_clusters, n_features]
Coordinates of cluster centers.
labels_ :
Labels of each point.
Notes
-----
Scalability:
Because this implementation uses a flat kernel and
a Ball Tree to look up members of each kernel, the complexity will is
to O(T*n*log(n)) in lower dimensions, with n the number of samples
and T the number of points. In higher dimensions the complexity will
tend towards O(T*n^2).
Scalability can be boosted by using fewer seeds, for example by using
a higher value of min_bin_freq in the get_bin_seeds function.
Note that the estimate_bandwidth function is much less scalable than the
mean shift algorithm and will be the bottleneck if it is used.
References
----------
Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward
feature space analysis". IEEE Transactions on Pattern Analysis and
Machine Intelligence. 2002. pp. 603-619.
"""
def __init__(self, bandwidth=None, seeds=None, bin_seeding=False,
min_bin_freq=1, cluster_all=True, n_jobs=1):
self.bandwidth = bandwidth
self.seeds = seeds
self.bin_seeding = bin_seeding
self.cluster_all = cluster_all
self.min_bin_freq = min_bin_freq
self.n_jobs = n_jobs
def fit(self, X, y=None):
"""Perform clustering.
Parameters
-----------
X : array-like, shape=[n_samples, n_features]
Samples to cluster.
"""
X = check_array(X)
self.cluster_centers_, self.labels_ = \
mean_shift(X, bandwidth=self.bandwidth, seeds=self.seeds,
min_bin_freq=self.min_bin_freq,
bin_seeding=self.bin_seeding,
cluster_all=self.cluster_all, n_jobs=self.n_jobs)
return self
def predict(self, X):
"""Predict the closest cluster each sample in X belongs to.
Parameters
----------
X : {array-like, sparse matrix}, shape=[n_samples, n_features]
New data to predict.
Returns
-------
labels : array, shape [n_samples,]
Index of the cluster each sample belongs to.
"""
check_is_fitted(self, "cluster_centers_")
return pairwise_distances_argmin(X, self.cluster_centers_)
| bsd-3-clause |
Obus/scikit-learn | benchmarks/bench_plot_approximate_neighbors.py | 85 | 6377 | """
Benchmark for approximate nearest neighbor search using
locality sensitive hashing forest.
There are two types of benchmarks.
First, accuracy of LSHForest queries are measured for various
hyper-parameters and index sizes.
Second, speed up of LSHForest queries compared to brute force
method in exact nearest neighbors is measures for the
aforementioned settings. In general, speed up is increasing as
the index size grows.
"""
from __future__ import division
import numpy as np
from tempfile import gettempdir
from time import time
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors.approximate import LSHForest
from sklearn.datasets import make_blobs
from sklearn.externals.joblib import Memory
m = Memory(cachedir=gettempdir())
@m.cache()
def make_data(n_samples, n_features, n_queries, random_state=0):
"""Create index and query data."""
print('Generating random blob-ish data')
X, _ = make_blobs(n_samples=n_samples + n_queries,
n_features=n_features, centers=100,
shuffle=True, random_state=random_state)
# Keep the last samples as held out query vectors: note since we used
# shuffle=True we have ensured that index and query vectors are
# samples from the same distribution (a mixture of 100 gaussians in this
# case)
return X[:n_samples], X[n_samples:]
def calc_exact_neighbors(X, queries, n_queries, n_neighbors):
"""Measures average times for exact neighbor queries."""
print ('Building NearestNeighbors for %d samples in %d dimensions' %
(X.shape[0], X.shape[1]))
nbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X)
average_time = 0
t0 = time()
neighbors = nbrs.kneighbors(queries, n_neighbors=n_neighbors,
return_distance=False)
average_time = (time() - t0) / n_queries
return neighbors, average_time
def calc_accuracy(X, queries, n_queries, n_neighbors, exact_neighbors,
average_time_exact, **lshf_params):
"""Calculates accuracy and the speed up of LSHForest."""
print('Building LSHForest for %d samples in %d dimensions' %
(X.shape[0], X.shape[1]))
lshf = LSHForest(**lshf_params)
t0 = time()
lshf.fit(X)
lshf_build_time = time() - t0
print('Done in %0.3fs' % lshf_build_time)
accuracy = 0
t0 = time()
approx_neighbors = lshf.kneighbors(queries, n_neighbors=n_neighbors,
return_distance=False)
average_time_approx = (time() - t0) / n_queries
for i in range(len(queries)):
accuracy += np.in1d(approx_neighbors[i], exact_neighbors[i]).mean()
accuracy /= n_queries
speed_up = average_time_exact / average_time_approx
print('Average time for lshf neighbor queries: %0.3fs' %
average_time_approx)
print ('Average time for exact neighbor queries: %0.3fs' %
average_time_exact)
print ('Average Accuracy : %0.2f' % accuracy)
print ('Speed up: %0.1fx' % speed_up)
return speed_up, accuracy
if __name__ == '__main__':
import matplotlib.pyplot as plt
# Initialize index sizes
n_samples = [int(1e3), int(1e4), int(1e5), int(1e6)]
n_features = int(1e2)
n_queries = 100
n_neighbors = 10
X_index, X_query = make_data(np.max(n_samples), n_features, n_queries,
random_state=0)
params_list = [{'n_estimators': 3, 'n_candidates': 50},
{'n_estimators': 5, 'n_candidates': 70},
{'n_estimators': 10, 'n_candidates': 100}]
accuracies = np.zeros((len(n_samples), len(params_list)), dtype=float)
speed_ups = np.zeros((len(n_samples), len(params_list)), dtype=float)
for i, sample_size in enumerate(n_samples):
print ('==========================================================')
print ('Sample size: %i' % sample_size)
print ('------------------------')
exact_neighbors, average_time_exact = calc_exact_neighbors(
X_index[:sample_size], X_query, n_queries, n_neighbors)
for j, params in enumerate(params_list):
print ('LSHF parameters: n_estimators = %i, n_candidates = %i' %
(params['n_estimators'], params['n_candidates']))
speed_ups[i, j], accuracies[i, j] = calc_accuracy(
X_index[:sample_size], X_query, n_queries, n_neighbors,
exact_neighbors, average_time_exact, random_state=0, **params)
print ('')
print ('==========================================================')
# Set labels for LSHForest parameters
colors = ['c', 'm', 'y']
p1 = plt.Rectangle((0, 0), 0.1, 0.1, fc=colors[0])
p2 = plt.Rectangle((0, 0), 0.1, 0.1, fc=colors[1])
p3 = plt.Rectangle((0, 0), 0.1, 0.1, fc=colors[2])
labels = ['n_estimators=' + str(params_list[0]['n_estimators']) +
', n_candidates=' + str(params_list[0]['n_candidates']),
'n_estimators=' + str(params_list[1]['n_estimators']) +
', n_candidates=' + str(params_list[1]['n_candidates']),
'n_estimators=' + str(params_list[2]['n_estimators']) +
', n_candidates=' + str(params_list[2]['n_candidates'])]
# Plot precision
plt.figure()
plt.legend((p1, p2, p3), (labels[0], labels[1], labels[2]),
loc='upper left')
for i in range(len(params_list)):
plt.scatter(n_samples, accuracies[:, i], c=colors[i])
plt.plot(n_samples, accuracies[:, i], c=colors[i])
plt.ylim([0, 1.3])
plt.xlim(np.min(n_samples), np.max(n_samples))
plt.semilogx()
plt.ylabel("Precision@10")
plt.xlabel("Index size")
plt.grid(which='both')
plt.title("Precision of first 10 neighbors with index size")
# Plot speed up
plt.figure()
plt.legend((p1, p2, p3), (labels[0], labels[1], labels[2]),
loc='upper left')
for i in range(len(params_list)):
plt.scatter(n_samples, speed_ups[:, i], c=colors[i])
plt.plot(n_samples, speed_ups[:, i], c=colors[i])
plt.ylim(0, np.max(speed_ups))
plt.xlim(np.min(n_samples), np.max(n_samples))
plt.semilogx()
plt.ylabel("Speed up")
plt.xlabel("Index size")
plt.grid(which='both')
plt.title("Relationship between Speed up and index size")
plt.show()
| bsd-3-clause |
nightjean/Deep-Learning | tensorflow/examples/learn/text_classification_character_rnn.py | 61 | 3350 | # 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.
"""This is an example of using recurrent neural networks over characters for DBpedia dataset to predict class from description of an entity.
This model is similar to one described in this paper:
"Character-level Convolutional Networks for Text Classification"
http://arxiv.org/abs/1509.01626
and is somewhat alternative to the Lua code from here:
https://github.com/zhangxiangxiao/Crepe
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import numpy as np
import pandas
from sklearn import metrics
import tensorflow as tf
learn = tf.contrib.learn
FLAGS = None
MAX_DOCUMENT_LENGTH = 100
HIDDEN_SIZE = 20
def char_rnn_model(features, target):
"""Character level recurrent neural network model to predict classes."""
target = tf.one_hot(target, 15, 1, 0)
byte_list = tf.one_hot(features, 256, 1, 0)
byte_list = tf.unstack(byte_list, axis=1)
cell = tf.contrib.rnn.GRUCell(HIDDEN_SIZE)
_, encoding = tf.contrib.rnn.static_rnn(cell, byte_list, dtype=tf.float32)
logits = tf.contrib.layers.fully_connected(encoding, 15, activation_fn=None)
loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
train_op = tf.contrib.layers.optimize_loss(
loss,
tf.contrib.framework.get_global_step(),
optimizer='Adam',
learning_rate=0.01)
return ({
'class': tf.argmax(logits, 1),
'prob': tf.nn.softmax(logits)
}, loss, train_op)
def main(unused_argv):
# Prepare training and testing data
dbpedia = learn.datasets.load_dataset(
'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
x_train = pandas.DataFrame(dbpedia.train.data)[1]
y_train = pandas.Series(dbpedia.train.target)
x_test = pandas.DataFrame(dbpedia.test.data)[1]
y_test = pandas.Series(dbpedia.test.target)
# Process vocabulary
char_processor = learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)
x_train = np.array(list(char_processor.fit_transform(x_train)))
x_test = np.array(list(char_processor.transform(x_test)))
# Build model
classifier = learn.Estimator(model_fn=char_rnn_model)
# Train and predict
classifier.fit(x_train, y_train, steps=100)
y_predicted = [
p['class'] for p in classifier.predict(
x_test, as_iterable=True)
]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--test_with_fake_data',
default=False,
help='Test the example code with fake data.',
action='store_true')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
| apache-2.0 |
cswiercz/sympy | sympy/physics/quantum/state.py | 58 | 29186 | """Dirac notation for states."""
from __future__ import print_function, division
from sympy import (cacheit, conjugate, Expr, Function, integrate, oo, sqrt,
Tuple)
from sympy.core.compatibility import u, range
from sympy.printing.pretty.stringpict import stringPict
from sympy.physics.quantum.qexpr import QExpr, dispatch_method
__all__ = [
'KetBase',
'BraBase',
'StateBase',
'State',
'Ket',
'Bra',
'TimeDepState',
'TimeDepBra',
'TimeDepKet',
'Wavefunction'
]
#-----------------------------------------------------------------------------
# States, bras and kets.
#-----------------------------------------------------------------------------
# ASCII brackets
_lbracket = "<"
_rbracket = ">"
_straight_bracket = "|"
# Unicode brackets
# MATHEMATICAL ANGLE BRACKETS
_lbracket_ucode = u("\N{MATHEMATICAL LEFT ANGLE BRACKET}")
_rbracket_ucode = u("\N{MATHEMATICAL RIGHT ANGLE BRACKET}")
# LIGHT VERTICAL BAR
_straight_bracket_ucode = u("\N{LIGHT VERTICAL BAR}")
# Other options for unicode printing of <, > and | for Dirac notation.
# LEFT-POINTING ANGLE BRACKET
# _lbracket = u"\u2329"
# _rbracket = u"\u232A"
# LEFT ANGLE BRACKET
# _lbracket = u"\u3008"
# _rbracket = u"\u3009"
# VERTICAL LINE
# _straight_bracket = u"\u007C"
class StateBase(QExpr):
"""Abstract base class for general abstract states in quantum mechanics.
All other state classes defined will need to inherit from this class. It
carries the basic structure for all other states such as dual, _eval_adjoint
and label.
This is an abstract base class and you should not instantiate it directly,
instead use State.
"""
@classmethod
def _operators_to_state(self, ops, **options):
""" Returns the eigenstate instance for the passed operators.
This method should be overridden in subclasses. It will handle being
passed either an Operator instance or set of Operator instances. It
should return the corresponding state INSTANCE or simply raise a
NotImplementedError. See cartesian.py for an example.
"""
raise NotImplementedError("Cannot map operators to states in this class. Method not implemented!")
def _state_to_operators(self, op_classes, **options):
""" Returns the operators which this state instance is an eigenstate
of.
This method should be overridden in subclasses. It will be called on
state instances and be passed the operator classes that we wish to make
into instances. The state instance will then transform the classes
appropriately, or raise a NotImplementedError if it cannot return
operator instances. See cartesian.py for examples,
"""
raise NotImplementedError(
"Cannot map this state to operators. Method not implemented!")
@property
def operators(self):
"""Return the operator(s) that this state is an eigenstate of"""
from .operatorset import state_to_operators # import internally to avoid circular import errors
return state_to_operators(self)
def _enumerate_state(self, num_states, **options):
raise NotImplementedError("Cannot enumerate this state!")
def _represent_default_basis(self, **options):
return self._represent(basis=self.operators)
#-------------------------------------------------------------------------
# Dagger/dual
#-------------------------------------------------------------------------
@property
def dual(self):
"""Return the dual state of this one."""
return self.dual_class()._new_rawargs(self.hilbert_space, *self.args)
@classmethod
def dual_class(self):
"""Return the class used to construt the dual."""
raise NotImplementedError(
'dual_class must be implemented in a subclass'
)
def _eval_adjoint(self):
"""Compute the dagger of this state using the dual."""
return self.dual
#-------------------------------------------------------------------------
# Printing
#-------------------------------------------------------------------------
def _pretty_brackets(self, height, use_unicode=True):
# Return pretty printed brackets for the state
# Ideally, this could be done by pform.parens but it does not support the angled < and >
# Setup for unicode vs ascii
if use_unicode:
lbracket, rbracket = self.lbracket_ucode, self.rbracket_ucode
slash, bslash, vert = u('\N{BOX DRAWINGS LIGHT DIAGONAL UPPER RIGHT TO LOWER LEFT}'), \
u('\N{BOX DRAWINGS LIGHT DIAGONAL UPPER LEFT TO LOWER RIGHT}'), \
u('\N{BOX DRAWINGS LIGHT VERTICAL}')
else:
lbracket, rbracket = self.lbracket, self.rbracket
slash, bslash, vert = '/', '\\', '|'
# If height is 1, just return brackets
if height == 1:
return stringPict(lbracket), stringPict(rbracket)
# Make height even
height += (height % 2)
brackets = []
for bracket in lbracket, rbracket:
# Create left bracket
if bracket in set([_lbracket, _lbracket_ucode]):
bracket_args = [ ' ' * (height//2 - i - 1) +
slash for i in range(height // 2)]
bracket_args.extend(
[ ' ' * i + bslash for i in range(height // 2)])
# Create right bracket
elif bracket in set([_rbracket, _rbracket_ucode]):
bracket_args = [ ' ' * i + bslash for i in range(height // 2)]
bracket_args.extend([ ' ' * (
height//2 - i - 1) + slash for i in range(height // 2)])
# Create straight bracket
elif bracket in set([_straight_bracket, _straight_bracket_ucode]):
bracket_args = [vert for i in range(height)]
else:
raise ValueError(bracket)
brackets.append(
stringPict('\n'.join(bracket_args), baseline=height//2))
return brackets
def _sympystr(self, printer, *args):
contents = self._print_contents(printer, *args)
return '%s%s%s' % (self.lbracket, contents, self.rbracket)
def _pretty(self, printer, *args):
from sympy.printing.pretty.stringpict import prettyForm
# Get brackets
pform = self._print_contents_pretty(printer, *args)
lbracket, rbracket = self._pretty_brackets(
pform.height(), printer._use_unicode)
# Put together state
pform = prettyForm(*pform.left(lbracket))
pform = prettyForm(*pform.right(rbracket))
return pform
def _latex(self, printer, *args):
contents = self._print_contents_latex(printer, *args)
# The extra {} brackets are needed to get matplotlib's latex
# rendered to render this properly.
return '{%s%s%s}' % (self.lbracket_latex, contents, self.rbracket_latex)
class KetBase(StateBase):
"""Base class for Kets.
This class defines the dual property and the brackets for printing. This is
an abstract base class and you should not instantiate it directly, instead
use Ket.
"""
lbracket = _straight_bracket
rbracket = _rbracket
lbracket_ucode = _straight_bracket_ucode
rbracket_ucode = _rbracket_ucode
lbracket_latex = r'\left|'
rbracket_latex = r'\right\rangle '
@classmethod
def default_args(self):
return ("psi",)
@classmethod
def dual_class(self):
return BraBase
def __mul__(self, other):
"""KetBase*other"""
from sympy.physics.quantum.operator import OuterProduct
if isinstance(other, BraBase):
return OuterProduct(self, other)
else:
return Expr.__mul__(self, other)
def __rmul__(self, other):
"""other*KetBase"""
from sympy.physics.quantum.innerproduct import InnerProduct
if isinstance(other, BraBase):
return InnerProduct(other, self)
else:
return Expr.__rmul__(self, other)
#-------------------------------------------------------------------------
# _eval_* methods
#-------------------------------------------------------------------------
def _eval_innerproduct(self, bra, **hints):
"""Evaluate the inner product betweeen this ket and a bra.
This is called to compute <bra|ket>, where the ket is ``self``.
This method will dispatch to sub-methods having the format::
``def _eval_innerproduct_BraClass(self, **hints):``
Subclasses should define these methods (one for each BraClass) to
teach the ket how to take inner products with bras.
"""
return dispatch_method(self, '_eval_innerproduct', bra, **hints)
def _apply_operator(self, op, **options):
"""Apply an Operator to this Ket.
This method will dispatch to methods having the format::
``def _apply_operator_OperatorName(op, **options):``
Subclasses should define these methods (one for each OperatorName) to
teach the Ket how operators act on it.
Parameters
==========
op : Operator
The Operator that is acting on the Ket.
options : dict
A dict of key/value pairs that control how the operator is applied
to the Ket.
"""
return dispatch_method(self, '_apply_operator', op, **options)
class BraBase(StateBase):
"""Base class for Bras.
This class defines the dual property and the brackets for printing. This
is an abstract base class and you should not instantiate it directly,
instead use Bra.
"""
lbracket = _lbracket
rbracket = _straight_bracket
lbracket_ucode = _lbracket_ucode
rbracket_ucode = _straight_bracket_ucode
lbracket_latex = r'\left\langle '
rbracket_latex = r'\right|'
@classmethod
def _operators_to_state(self, ops, **options):
state = self.dual_class().operators_to_state(ops, **options)
return state.dual
def _state_to_operators(self, op_classes, **options):
return self.dual._state_to_operators(op_classes, **options)
def _enumerate_state(self, num_states, **options):
dual_states = self.dual._enumerate_state(num_states, **options)
return [x.dual for x in dual_states]
@classmethod
def default_args(self):
return self.dual_class().default_args()
@classmethod
def dual_class(self):
return KetBase
def __mul__(self, other):
"""BraBase*other"""
from sympy.physics.quantum.innerproduct import InnerProduct
if isinstance(other, KetBase):
return InnerProduct(self, other)
else:
return Expr.__mul__(self, other)
def __rmul__(self, other):
"""other*BraBase"""
from sympy.physics.quantum.operator import OuterProduct
if isinstance(other, KetBase):
return OuterProduct(other, self)
else:
return Expr.__rmul__(self, other)
def _represent(self, **options):
"""A default represent that uses the Ket's version."""
from sympy.physics.quantum.dagger import Dagger
return Dagger(self.dual._represent(**options))
class State(StateBase):
"""General abstract quantum state used as a base class for Ket and Bra."""
pass
class Ket(State, KetBase):
"""A general time-independent Ket in quantum mechanics.
Inherits from State and KetBase. This class should be used as the base
class for all physical, time-independent Kets in a system. This class
and its subclasses will be the main classes that users will use for
expressing Kets in Dirac notation [1]_.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the
ket. This will usually be its symbol or its quantum numbers. For
time-dependent state, this will include the time.
Examples
========
Create a simple Ket and looking at its properties::
>>> from sympy.physics.quantum import Ket, Bra
>>> from sympy import symbols, I
>>> k = Ket('psi')
>>> k
|psi>
>>> k.hilbert_space
H
>>> k.is_commutative
False
>>> k.label
(psi,)
Ket's know about their associated bra::
>>> k.dual
<psi|
>>> k.dual_class()
<class 'sympy.physics.quantum.state.Bra'>
Take a linear combination of two kets::
>>> k0 = Ket(0)
>>> k1 = Ket(1)
>>> 2*I*k0 - 4*k1
2*I*|0> - 4*|1>
Compound labels are passed as tuples::
>>> n, m = symbols('n,m')
>>> k = Ket(n,m)
>>> k
|nm>
References
==========
.. [1] http://en.wikipedia.org/wiki/Bra-ket_notation
"""
@classmethod
def dual_class(self):
return Bra
class Bra(State, BraBase):
"""A general time-independent Bra in quantum mechanics.
Inherits from State and BraBase. A Bra is the dual of a Ket [1]_. This
class and its subclasses will be the main classes that users will use for
expressing Bras in Dirac notation.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the
ket. This will usually be its symbol or its quantum numbers. For
time-dependent state, this will include the time.
Examples
========
Create a simple Bra and look at its properties::
>>> from sympy.physics.quantum import Ket, Bra
>>> from sympy import symbols, I
>>> b = Bra('psi')
>>> b
<psi|
>>> b.hilbert_space
H
>>> b.is_commutative
False
Bra's know about their dual Ket's::
>>> b.dual
|psi>
>>> b.dual_class()
<class 'sympy.physics.quantum.state.Ket'>
Like Kets, Bras can have compound labels and be manipulated in a similar
manner::
>>> n, m = symbols('n,m')
>>> b = Bra(n,m) - I*Bra(m,n)
>>> b
-I*<mn| + <nm|
Symbols in a Bra can be substituted using ``.subs``::
>>> b.subs(n,m)
<mm| - I*<mm|
References
==========
.. [1] http://en.wikipedia.org/wiki/Bra-ket_notation
"""
@classmethod
def dual_class(self):
return Ket
#-----------------------------------------------------------------------------
# Time dependent states, bras and kets.
#-----------------------------------------------------------------------------
class TimeDepState(StateBase):
"""Base class for a general time-dependent quantum state.
This class is used as a base class for any time-dependent state. The main
difference between this class and the time-independent state is that this
class takes a second argument that is the time in addition to the usual
label argument.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the ket. This
will usually be its symbol or its quantum numbers. For time-dependent
state, this will include the time as the final argument.
"""
#-------------------------------------------------------------------------
# Initialization
#-------------------------------------------------------------------------
@classmethod
def default_args(self):
return ("psi", "t")
#-------------------------------------------------------------------------
# Properties
#-------------------------------------------------------------------------
@property
def label(self):
"""The label of the state."""
return self.args[:-1]
@property
def time(self):
"""The time of the state."""
return self.args[-1]
#-------------------------------------------------------------------------
# Printing
#-------------------------------------------------------------------------
def _print_time(self, printer, *args):
return printer._print(self.time, *args)
_print_time_repr = _print_time
_print_time_latex = _print_time
def _print_time_pretty(self, printer, *args):
pform = printer._print(self.time, *args)
return pform
def _print_contents(self, printer, *args):
label = self._print_label(printer, *args)
time = self._print_time(printer, *args)
return '%s;%s' % (label, time)
def _print_label_repr(self, printer, *args):
label = self._print_sequence(self.label, ',', printer, *args)
time = self._print_time_repr(printer, *args)
return '%s,%s' % (label, time)
def _print_contents_pretty(self, printer, *args):
label = self._print_label_pretty(printer, *args)
time = self._print_time_pretty(printer, *args)
return printer._print_seq((label, time), delimiter=';')
def _print_contents_latex(self, printer, *args):
label = self._print_sequence(
self.label, self._label_separator, printer, *args)
time = self._print_time_latex(printer, *args)
return '%s;%s' % (label, time)
class TimeDepKet(TimeDepState, KetBase):
"""General time-dependent Ket in quantum mechanics.
This inherits from ``TimeDepState`` and ``KetBase`` and is the main class
that should be used for Kets that vary with time. Its dual is a
``TimeDepBra``.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the ket. This
will usually be its symbol or its quantum numbers. For time-dependent
state, this will include the time as the final argument.
Examples
========
Create a TimeDepKet and look at its attributes::
>>> from sympy.physics.quantum import TimeDepKet
>>> k = TimeDepKet('psi', 't')
>>> k
|psi;t>
>>> k.time
t
>>> k.label
(psi,)
>>> k.hilbert_space
H
TimeDepKets know about their dual bra::
>>> k.dual
<psi;t|
>>> k.dual_class()
<class 'sympy.physics.quantum.state.TimeDepBra'>
"""
@classmethod
def dual_class(self):
return TimeDepBra
class TimeDepBra(TimeDepState, BraBase):
"""General time-dependent Bra in quantum mechanics.
This inherits from TimeDepState and BraBase and is the main class that
should be used for Bras that vary with time. Its dual is a TimeDepBra.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the ket. This
will usually be its symbol or its quantum numbers. For time-dependent
state, this will include the time as the final argument.
Examples
========
>>> from sympy.physics.quantum import TimeDepBra
>>> from sympy import symbols, I
>>> b = TimeDepBra('psi', 't')
>>> b
<psi;t|
>>> b.time
t
>>> b.label
(psi,)
>>> b.hilbert_space
H
>>> b.dual
|psi;t>
"""
@classmethod
def dual_class(self):
return TimeDepKet
class Wavefunction(Function):
"""Class for representations in continuous bases
This class takes an expression and coordinates in its constructor. It can
be used to easily calculate normalizations and probabilities.
Parameters
==========
expr : Expr
The expression representing the functional form of the w.f.
coords : Symbol or tuple
The coordinates to be integrated over, and their bounds
Examples
========
Particle in a box, specifying bounds in the more primitive way of using
Piecewise:
>>> from sympy import Symbol, Piecewise, pi, N
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x = Symbol('x', real=True)
>>> n = 1
>>> L = 1
>>> g = Piecewise((0, x < 0), (0, x > L), (sqrt(2//L)*sin(n*pi*x/L), True))
>>> f = Wavefunction(g, x)
>>> f.norm
1
>>> f.is_normalized
True
>>> p = f.prob()
>>> p(0)
0
>>> p(L)
0
>>> p(0.5)
2
>>> p(0.85*L)
2*sin(0.85*pi)**2
>>> N(p(0.85*L))
0.412214747707527
Additionally, you can specify the bounds of the function and the indices in
a more compact way:
>>> from sympy import symbols, pi, diff
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x, L = symbols('x,L', positive=True)
>>> n = symbols('n', integer=True, positive=True)
>>> g = sqrt(2/L)*sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.norm
1
>>> f(L+1)
0
>>> f(L-1)
sqrt(2)*sin(pi*n*(L - 1)/L)/sqrt(L)
>>> f(-1)
0
>>> f(0.85)
sqrt(2)*sin(0.85*pi*n/L)/sqrt(L)
>>> f(0.85, n=1, L=1)
sqrt(2)*sin(0.85*pi)
>>> f.is_commutative
False
All arguments are automatically sympified, so you can define the variables
as strings rather than symbols:
>>> expr = x**2
>>> f = Wavefunction(expr, 'x')
>>> type(f.variables[0])
<class 'sympy.core.symbol.Symbol'>
Derivatives of Wavefunctions will return Wavefunctions:
>>> diff(f, x)
Wavefunction(2*x, x)
"""
#Any passed tuples for coordinates and their bounds need to be
#converted to Tuples before Function's constructor is called, to
#avoid errors from calling is_Float in the constructor
def __new__(cls, *args, **options):
new_args = [None for i in args]
ct = 0
for arg in args:
if isinstance(arg, tuple):
new_args[ct] = Tuple(*arg)
else:
new_args[ct] = arg
ct += 1
return super(Function, cls).__new__(cls, *new_args, **options)
def __call__(self, *args, **options):
var = self.variables
if len(args) != len(var):
raise NotImplementedError(
"Incorrect number of arguments to function!")
ct = 0
#If the passed value is outside the specified bounds, return 0
for v in var:
lower, upper = self.limits[v]
#Do the comparison to limits only if the passed symbol is actually
#a symbol present in the limits;
#Had problems with a comparison of x > L
if isinstance(args[ct], Expr) and \
not (lower in args[ct].free_symbols
or upper in args[ct].free_symbols):
continue
if (args[ct] < lower) == True or (args[ct] > upper) == True:
return 0
ct += 1
expr = self.expr
#Allows user to make a call like f(2, 4, m=1, n=1)
for symbol in list(expr.free_symbols):
if str(symbol) in options.keys():
val = options[str(symbol)]
expr = expr.subs(symbol, val)
return expr.subs(zip(var, args))
def _eval_derivative(self, symbol):
expr = self.expr
deriv = expr._eval_derivative(symbol)
return Wavefunction(deriv, *self.args[1:])
def _eval_conjugate(self):
return Wavefunction(conjugate(self.expr), *self.args[1:])
def _eval_transpose(self):
return self
@property
def free_symbols(self):
return self.expr.free_symbols
@property
def is_commutative(self):
"""
Override Function's is_commutative so that order is preserved in
represented expressions
"""
return False
@classmethod
def eval(self, *args):
return None
@property
def variables(self):
"""
Return the coordinates which the wavefunction depends on
Examples
========
>>> from sympy.physics.quantum.state import Wavefunction
>>> from sympy import symbols
>>> x,y = symbols('x,y')
>>> f = Wavefunction(x*y, x, y)
>>> f.variables
(x, y)
>>> g = Wavefunction(x*y, x)
>>> g.variables
(x,)
"""
var = [g[0] if isinstance(g, Tuple) else g for g in self._args[1:]]
return tuple(var)
@property
def limits(self):
"""
Return the limits of the coordinates which the w.f. depends on If no
limits are specified, defaults to ``(-oo, oo)``.
Examples
========
>>> from sympy.physics.quantum.state import Wavefunction
>>> from sympy import symbols
>>> x, y = symbols('x, y')
>>> f = Wavefunction(x**2, (x, 0, 1))
>>> f.limits
{x: (0, 1)}
>>> f = Wavefunction(x**2, x)
>>> f.limits
{x: (-oo, oo)}
>>> f = Wavefunction(x**2 + y**2, x, (y, -1, 2))
>>> f.limits
{x: (-oo, oo), y: (-1, 2)}
"""
limits = [(g[1], g[2]) if isinstance(g, Tuple) else (-oo, oo)
for g in self._args[1:]]
return dict(zip(self.variables, tuple(limits)))
@property
def expr(self):
"""
Return the expression which is the functional form of the Wavefunction
Examples
========
>>> from sympy.physics.quantum.state import Wavefunction
>>> from sympy import symbols
>>> x, y = symbols('x, y')
>>> f = Wavefunction(x**2, x)
>>> f.expr
x**2
"""
return self._args[0]
@property
def is_normalized(self):
"""
Returns true if the Wavefunction is properly normalized
Examples
========
>>> from sympy import symbols, pi
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x, L = symbols('x,L', positive=True)
>>> n = symbols('n', integer=True, positive=True)
>>> g = sqrt(2/L)*sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.is_normalized
True
"""
return (self.norm == 1.0)
@property
@cacheit
def norm(self):
"""
Return the normalization of the specified functional form.
This function integrates over the coordinates of the Wavefunction, with
the bounds specified.
Examples
========
>>> from sympy import symbols, pi
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x, L = symbols('x,L', positive=True)
>>> n = symbols('n', integer=True, positive=True)
>>> g = sqrt(2/L)*sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.norm
1
>>> g = sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.norm
sqrt(2)*sqrt(L)/2
"""
exp = self.expr*conjugate(self.expr)
var = self.variables
limits = self.limits
for v in var:
curr_limits = limits[v]
exp = integrate(exp, (v, curr_limits[0], curr_limits[1]))
return sqrt(exp)
def normalize(self):
"""
Return a normalized version of the Wavefunction
Examples
========
>>> from sympy import symbols, pi
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x = symbols('x', real=True)
>>> L = symbols('L', positive=True)
>>> n = symbols('n', integer=True, positive=True)
>>> g = sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.normalize()
Wavefunction(sqrt(2)*sin(pi*n*x/L)/sqrt(L), (x, 0, L))
"""
const = self.norm
if const == oo:
raise NotImplementedError("The function is not normalizable!")
else:
return Wavefunction((const)**(-1)*self.expr, *self.args[1:])
def prob(self):
"""
Return the absolute magnitude of the w.f., `|\psi(x)|^2`
Examples
========
>>> from sympy import symbols, pi
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x, L = symbols('x,L', real=True)
>>> n = symbols('n', integer=True)
>>> g = sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.prob()
Wavefunction(sin(pi*n*x/L)**2, x)
"""
return Wavefunction(self.expr*conjugate(self.expr), *self.variables)
| bsd-3-clause |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/doc/mpl_examples/pylab_examples/demo_bboximage.py | 12 | 1805 | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage
from matplotlib.transforms import Bbox, TransformedBbox
if __name__ == "__main__":
fig = plt.figure(1)
ax = plt.subplot(121)
txt = ax.text(0.5, 0.5, "test", size=30, ha="center", color="w")
kwargs = dict()
bbox_image = BboxImage(txt.get_window_extent,
norm = None,
origin=None,
clip_on=False,
**kwargs
)
a = np.arange(256).reshape(1,256)/256.
bbox_image.set_data(a)
ax.add_artist(bbox_image)
ax = plt.subplot(122)
a = np.linspace(0, 1, 256).reshape(1,-1)
a = np.vstack((a,a))
maps = sorted(m for m in plt.cm.datad if not m.endswith("_r"))
#nmaps = len(maps) + 1
#fig.subplots_adjust(top=0.99, bottom=0.01, left=0.2, right=0.99)
ncol = 2
nrow = len(maps)//ncol + 1
xpad_fraction = 0.3
dx = 1./(ncol + xpad_fraction*(ncol-1))
ypad_fraction = 0.3
dy = 1./(nrow + ypad_fraction*(nrow-1))
for i,m in enumerate(maps):
ix, iy = divmod(i, nrow)
#plt.figimage(a, 10, i*10, cmap=plt.get_cmap(m), origin='lower')
bbox0 = Bbox.from_bounds(ix*dx*(1+xpad_fraction),
1.-iy*dy*(1+ypad_fraction)-dy,
dx, dy)
bbox = TransformedBbox(bbox0, ax.transAxes)
bbox_image = BboxImage(bbox,
cmap = plt.get_cmap(m),
norm = None,
origin=None,
**kwargs
)
bbox_image.set_data(a)
ax.add_artist(bbox_image)
plt.draw()
plt.show()
| gpl-2.0 |
bikong2/scikit-learn | sklearn/tests/test_discriminant_analysis.py | 19 | 11711 | try:
# Python 2 compat
reload
except NameError:
# Regular Python 3+ import
from importlib import reload
import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import ignore_warnings
from sklearn.datasets import make_blobs
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
# Data is just 6 separable points in the plane
X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]], dtype='f')
y = np.array([1, 1, 1, 2, 2, 2])
y3 = np.array([1, 1, 2, 2, 3, 3])
# Degenerate data with only one feature (still should be separable)
X1 = np.array([[-2, ], [-1, ], [-1, ], [1, ], [1, ], [2, ]], dtype='f')
# Data is just 9 separable points in the plane
X6 = np.array([[0, 0], [-2, -2], [-2, -1], [-1, -1], [-1, -2],
[1, 3], [1, 2], [2, 1], [2, 2]])
y6 = np.array([1, 1, 1, 1, 1, 2, 2, 2, 2])
y7 = np.array([1, 2, 3, 2, 3, 1, 2, 3, 1])
# Degenerate data with 1 feature (still should be separable)
X7 = np.array([[-3, ], [-2, ], [-1, ], [-1, ], [0, ], [1, ], [1, ],
[2, ], [3, ]])
# Data that has zero variance in one dimension and needs regularization
X2 = np.array([[-3, 0], [-2, 0], [-1, 0], [-1, 0], [0, 0], [1, 0], [1, 0],
[2, 0], [3, 0]])
# One element class
y4 = np.array([1, 1, 1, 1, 1, 1, 1, 1, 2])
# Data with less samples in a class than n_features
X5 = np.c_[np.arange(8), np.zeros((8, 3))]
y5 = np.array([0, 0, 0, 0, 0, 1, 1, 1])
solver_shrinkage = [('svd', None), ('lsqr', None), ('eigen', None),
('lsqr', 'auto'), ('lsqr', 0), ('lsqr', 0.43),
('eigen', 'auto'), ('eigen', 0), ('eigen', 0.43)]
def test_lda_predict():
# Test LDA classification.
# This checks that LDA implements fit and predict and returns correct
# values for simple toy data.
for test_case in solver_shrinkage:
solver, shrinkage = test_case
clf = LinearDiscriminantAnalysis(solver=solver, shrinkage=shrinkage)
y_pred = clf.fit(X, y).predict(X)
assert_array_equal(y_pred, y, 'solver %s' % solver)
# Assert that it works with 1D data
y_pred1 = clf.fit(X1, y).predict(X1)
assert_array_equal(y_pred1, y, 'solver %s' % solver)
# Test probability estimates
y_proba_pred1 = clf.predict_proba(X1)
assert_array_equal((y_proba_pred1[:, 1] > 0.5) + 1, y,
'solver %s' % solver)
y_log_proba_pred1 = clf.predict_log_proba(X1)
assert_array_almost_equal(np.exp(y_log_proba_pred1), y_proba_pred1,
8, 'solver %s' % solver)
# Primarily test for commit 2f34950 -- "reuse" of priors
y_pred3 = clf.fit(X, y3).predict(X)
# LDA shouldn't be able to separate those
assert_true(np.any(y_pred3 != y3), 'solver %s' % solver)
# Test invalid shrinkages
clf = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=-0.2231)
assert_raises(ValueError, clf.fit, X, y)
clf = LinearDiscriminantAnalysis(solver="eigen", shrinkage="dummy")
assert_raises(ValueError, clf.fit, X, y)
clf = LinearDiscriminantAnalysis(solver="svd", shrinkage="auto")
assert_raises(NotImplementedError, clf.fit, X, y)
# Test unknown solver
clf = LinearDiscriminantAnalysis(solver="dummy")
assert_raises(ValueError, clf.fit, X, y)
def test_lda_priors():
# Test priors (negative priors)
priors = np.array([0.5, -0.5])
clf = LinearDiscriminantAnalysis(priors=priors)
msg = "priors must be non-negative"
assert_raise_message(ValueError, msg, clf.fit, X, y)
# Test that priors passed as a list are correctly handled (run to see if
# failure)
clf = LinearDiscriminantAnalysis(priors=[0.5, 0.5])
clf.fit(X, y)
# Test that priors always sum to 1
priors = np.array([0.5, 0.6])
prior_norm = np.array([0.45, 0.55])
clf = LinearDiscriminantAnalysis(priors=priors)
clf.fit(X, y)
assert_array_almost_equal(clf.priors_, prior_norm, 2)
def test_lda_coefs():
# Test if the coefficients of the solvers are approximately the same.
n_features = 2
n_classes = 2
n_samples = 1000
X, y = make_blobs(n_samples=n_samples, n_features=n_features,
centers=n_classes, random_state=11)
clf_lda_svd = LinearDiscriminantAnalysis(solver="svd")
clf_lda_lsqr = LinearDiscriminantAnalysis(solver="lsqr")
clf_lda_eigen = LinearDiscriminantAnalysis(solver="eigen")
clf_lda_svd.fit(X, y)
clf_lda_lsqr.fit(X, y)
clf_lda_eigen.fit(X, y)
assert_array_almost_equal(clf_lda_svd.coef_, clf_lda_lsqr.coef_, 1)
assert_array_almost_equal(clf_lda_svd.coef_, clf_lda_eigen.coef_, 1)
assert_array_almost_equal(clf_lda_eigen.coef_, clf_lda_lsqr.coef_, 1)
def test_lda_transform():
# Test LDA transform.
clf = LinearDiscriminantAnalysis(solver="svd", n_components=1)
X_transformed = clf.fit(X, y).transform(X)
assert_equal(X_transformed.shape[1], 1)
clf = LinearDiscriminantAnalysis(solver="eigen", n_components=1)
X_transformed = clf.fit(X, y).transform(X)
assert_equal(X_transformed.shape[1], 1)
clf = LinearDiscriminantAnalysis(solver="lsqr", n_components=1)
clf.fit(X, y)
msg = "transform not implemented for 'lsqr'"
assert_raise_message(NotImplementedError, msg, clf.transform, X)
def test_lda_explained_variance_ratio():
# Test if the sum of the normalized eigen vectors values equals 1
n_features = 2
n_classes = 2
n_samples = 1000
X, y = make_blobs(n_samples=n_samples, n_features=n_features,
centers=n_classes, random_state=11)
clf_lda_eigen = LinearDiscriminantAnalysis(solver="eigen")
clf_lda_eigen.fit(X, y)
assert_almost_equal(clf_lda_eigen.explained_variance_ratio_.sum(), 1.0, 3)
def test_lda_orthogonality():
# arrange four classes with their means in a kite-shaped pattern
# the longer distance should be transformed to the first component, and
# the shorter distance to the second component.
means = np.array([[0, 0, -1], [0, 2, 0], [0, -2, 0], [0, 0, 5]])
# We construct perfectly symmetric distributions, so the LDA can estimate
# precise means.
scatter = np.array([[0.1, 0, 0], [-0.1, 0, 0], [0, 0.1, 0], [0, -0.1, 0],
[0, 0, 0.1], [0, 0, -0.1]])
X = (means[:, np.newaxis, :] + scatter[np.newaxis, :, :]).reshape((-1, 3))
y = np.repeat(np.arange(means.shape[0]), scatter.shape[0])
# Fit LDA and transform the means
clf = LinearDiscriminantAnalysis(solver="svd").fit(X, y)
means_transformed = clf.transform(means)
d1 = means_transformed[3] - means_transformed[0]
d2 = means_transformed[2] - means_transformed[1]
d1 /= np.sqrt(np.sum(d1 ** 2))
d2 /= np.sqrt(np.sum(d2 ** 2))
# the transformed within-class covariance should be the identity matrix
assert_almost_equal(np.cov(clf.transform(scatter).T), np.eye(2))
# the means of classes 0 and 3 should lie on the first component
assert_almost_equal(np.abs(np.dot(d1[:2], [1, 0])), 1.0)
# the means of classes 1 and 2 should lie on the second component
assert_almost_equal(np.abs(np.dot(d2[:2], [0, 1])), 1.0)
def test_lda_scaling():
# Test if classification works correctly with differently scaled features.
n = 100
rng = np.random.RandomState(1234)
# use uniform distribution of features to make sure there is absolutely no
# overlap between classes.
x1 = rng.uniform(-1, 1, (n, 3)) + [-10, 0, 0]
x2 = rng.uniform(-1, 1, (n, 3)) + [10, 0, 0]
x = np.vstack((x1, x2)) * [1, 100, 10000]
y = [-1] * n + [1] * n
for solver in ('svd', 'lsqr', 'eigen'):
clf = LinearDiscriminantAnalysis(solver=solver)
# should be able to separate the data perfectly
assert_equal(clf.fit(x, y).score(x, y), 1.0,
'using covariance: %s' % solver)
def test_qda():
# QDA classification.
# This checks that QDA implements fit and predict and returns
# correct values for a simple toy dataset.
clf = QuadraticDiscriminantAnalysis()
y_pred = clf.fit(X6, y6).predict(X6)
assert_array_equal(y_pred, y6)
# Assure that it works with 1D data
y_pred1 = clf.fit(X7, y6).predict(X7)
assert_array_equal(y_pred1, y6)
# Test probas estimates
y_proba_pred1 = clf.predict_proba(X7)
assert_array_equal((y_proba_pred1[:, 1] > 0.5) + 1, y6)
y_log_proba_pred1 = clf.predict_log_proba(X7)
assert_array_almost_equal(np.exp(y_log_proba_pred1), y_proba_pred1, 8)
y_pred3 = clf.fit(X6, y7).predict(X6)
# QDA shouldn't be able to separate those
assert_true(np.any(y_pred3 != y7))
# Classes should have at least 2 elements
assert_raises(ValueError, clf.fit, X6, y4)
def test_qda_priors():
clf = QuadraticDiscriminantAnalysis()
y_pred = clf.fit(X6, y6).predict(X6)
n_pos = np.sum(y_pred == 2)
neg = 1e-10
clf = QuadraticDiscriminantAnalysis(priors=np.array([neg, 1 - neg]))
y_pred = clf.fit(X6, y6).predict(X6)
n_pos2 = np.sum(y_pred == 2)
assert_greater(n_pos2, n_pos)
def test_qda_store_covariances():
# The default is to not set the covariances_ attribute
clf = QuadraticDiscriminantAnalysis().fit(X6, y6)
assert_true(not hasattr(clf, 'covariances_'))
# Test the actual attribute:
clf = QuadraticDiscriminantAnalysis().fit(X6, y6, store_covariances=True)
assert_true(hasattr(clf, 'covariances_'))
assert_array_almost_equal(
clf.covariances_[0],
np.array([[0.7, 0.45], [0.45, 0.7]])
)
assert_array_almost_equal(
clf.covariances_[1],
np.array([[0.33333333, -0.33333333], [-0.33333333, 0.66666667]])
)
def test_qda_regularization():
# the default is reg_param=0. and will cause issues
# when there is a constant variable
clf = QuadraticDiscriminantAnalysis()
with ignore_warnings():
y_pred = clf.fit(X2, y6).predict(X2)
assert_true(np.any(y_pred != y6))
# adding a little regularization fixes the problem
clf = QuadraticDiscriminantAnalysis(reg_param=0.01)
with ignore_warnings():
clf.fit(X2, y6)
y_pred = clf.predict(X2)
assert_array_equal(y_pred, y6)
# Case n_samples_in_a_class < n_features
clf = QuadraticDiscriminantAnalysis(reg_param=0.1)
with ignore_warnings():
clf.fit(X5, y5)
y_pred5 = clf.predict(X5)
assert_array_equal(y_pred5, y5)
def test_deprecated_lda_qda_deprecation():
def import_lda_module():
import sklearn.lda
# ensure that we trigger DeprecationWarning even if the sklearn.lda
# was loaded previously by another test.
reload(sklearn.lda)
return sklearn.lda
lda = assert_warns(DeprecationWarning, import_lda_module)
assert lda.LDA is LinearDiscriminantAnalysis
def import_qda_module():
import sklearn.qda
# ensure that we trigger DeprecationWarning even if the sklearn.qda
# was loaded previously by another test.
reload(sklearn.qda)
return sklearn.qda
qda = assert_warns(DeprecationWarning, import_qda_module)
assert qda.QDA is QuadraticDiscriminantAnalysis
| bsd-3-clause |
kgullikson88/General | Analyze_CCF.py | 1 | 9048 | """
This is a module to read in an HDF5 file with CCFs.
Use this to determine the best parameters, and plot the best CCF for each star/date
"""
from collections import defaultdict
import logging
import h5py
import numpy as np
import pandas as pd
from scipy.interpolate import InterpolatedUnivariateSpline as spline
class CCF_Interface(object):
def __init__(self, filename, vel=np.arange(-900, 900, 1)):
self.hdf5 = h5py.File(filename, 'r')
self.velocities = vel
self._df = None
def __getitem__(self, path):
return self.hdf5[path]
def list_stars(self, print2screen=False):
"""
List the stars available in the HDF5 file, and the dates available for each
:return: A list of the stars
"""
if print2screen:
for star in sorted(self.hdf5.keys()):
print(star)
for date in sorted(self.hdf5[star].keys()):
print('\t{}'.format(date))
return sorted(self.hdf5.keys())
def list_dates(self, star, print2screen=False):
"""
List the dates available for the given star
:param star: The name of the star
:return: A list of dates the star was observed
"""
if print2screen:
for date in sorted(self.hdf5[star].keys()):
print(date)
return sorted(self.hdf5[star].keys())
def load_cache(self, addmode='simple'):
"""
Read in the whole HDF5 file. This will take a while and take a few Gb of memory, but will speed things up considerably
:keyword addmode: The way the individual CCFs were added. Options are:
- 'simple'
- 'ml'
- 'all' (saves all addmodes)
"""
self._df = self._compile_data(addmode=addmode)
def _compile_data(self, starname=None, date=None, addmode='simple', read_ccf=True):
"""
Private function. This reads in all the datasets for the given star and date
:param starname: the name of the star. Must be in self.hdf5
:param date: The date to search. Must be in self.hdf5[star]
:keyword addmode: The way the individual CCFs were added. Options are:
- 'simple'
- 'ml'
- 'all' (saves all addmodes)
:return: a pandas DataFrame with the columns:
- star
- date
- temperature
- log(g)
- [Fe/H]
- vsini
- addmode
- rv (at maximum CCF value)
- CCF height (maximum)
"""
if starname is None:
df_list = []
star_list = self.list_stars()
for star in star_list:
date_list = self.list_dates(star)
for date in date_list:
logging.debug('Reading in metadata for star {}, date {}'.format(star, date))
df_list.append(self._compile_data(star, date, addmode=addmode, read_ccf=read_ccf))
return pd.concat(df_list, ignore_index=True)
elif starname is not None and date is None:
df_list = []
date_list = self.list_dates(starname)
for date in date_list:
logging.debug('Reading in metadata for date {}'.format(date))
df_list.append(self._compile_data(starname, date, addmode=addmode, read_ccf=read_ccf))
return pd.concat(df_list, ignore_index=True)
else:
if self._df is not None:
return self._df.loc[(self._df['Star'] == starname) & (self._df['Date'] == date)].copy()
#print('Stars: ', self.list_stars())
datasets = self.hdf5[starname][date].keys()
data = defaultdict(list)
for ds_name, ds in self.hdf5[starname][date].iteritems(): # in datasets:
#ds = self.hdf5[starname][date][ds_name]
try:
am = ds.attrs['addmode']
if addmode == 'all' or addmode == am:
data['T'].append(ds.attrs['T'])
data['logg'].append(ds.attrs['logg'])
data['[Fe/H]'].append(ds.attrs['[Fe/H]'])
data['vsini'].append(ds.attrs['vsini'])
data['addmode'].append(am)
data['name'].append(ds.name)
try:
data['ccf_max'].append(ds.attrs['ccf_max'])
data['vel_max'].append(ds.attrs['vel_max'])
except KeyError:
vel, corr = ds.value
idx = np.argmax(corr)
data['ccf_max'].append(corr[idx])
data['vel_max'].append(vel[idx])
if read_ccf:
v = ds.value
vel, corr = v[0], v[1]
sorter = np.argsort(vel)
fcn = spline(vel[sorter], corr[sorter])
data['ccf'].append(fcn(self.velocities))
except:
raise IOError('Something weird happened with dataset {}!'.format(ds.name))
data['Star'] = [starname] * len(data['T'])
data['Date'] = [date] * len(data['T'])
df = pd.DataFrame(data=data)
return df
def get_temperature_run(self, starname=None, date=None, df=None):
"""
Return the maximum ccf height for each temperature. Either starname AND date, or df must be given
:param starname: The name of the star
:param date: The date of the observation
:param df: Input dataframe, such as from _compile_data. Overrides starname and date, if given
:return: a pandas DataFrame with all the best parameters for each temperature
"""
# Get the dataframe if it isn't given
if df is None:
if starname is None or date is None:
raise ValueError('Must give either starname or date to get_temperature_run!')
df = self._compile_data(starname, date)
# Find the maximum CCF for each set of parameters
fcn = lambda row: (np.max(row), self.velocities[np.argmax(row)])
vals = df['ccf'].map(fcn)
df['ccf_max'] = vals.map(lambda l: l[0])
df['rv'] = vals.map(lambda l: l[1])
# Find the best parameters for each temperature
d = defaultdict(list)
temperatures = pd.unique(df['T'])
for T in temperatures:
good = df.loc[df['T'] == T]
best = good.loc[good.ccf_max == good.ccf_max.max()]
d['vsini'].append(best['vsini'].item())
d['logg'].append(best['logg'].item())
d['[Fe/H]'].append(best['[Fe/H]'].item())
d['rv'].append(best['rv'].item())
d['ccf_value'].append(best.ccf_max.item())
d['T'].append(T)
d['metal'].append(best['[Fe/H]'].item())
return pd.DataFrame(data=d)
def get_ccf(self, params, df=None):
"""
Get the ccf with the given parameters. A dataframe can be given to speed things up
:param params: All the parameters necessary to define a single ccf. This should be
a python dictionary with the keys:
- 'starname': The name of the star. Try self.list_stars() for the options.
- 'date': The UT date of the observations. Try self.list_dates() for the options.
- 'T': temperature of the model
- 'logg': the log(g) of the model
- 'vsini': the vsini by which the model was broadened before correlation
- '[Fe/H]': the metallicity of the model
- 'addmode': The way the order CCFs were added to make a total one. Can be:
- 'simple'
- 'ml'
- 'weighted'
- 'dc'
:param df: a pandas DataFrame such as outputted by _compile_data
:return: a pandas DataFrame with columns of velocity and CCF power
"""
if df is None:
try:
df = self._compile_data(params['starname'], params['date'])
except KeyError:
raise KeyError('Must give get_ccf params with starname and date keywords, if df is not given!')
Tvals = df['T'].unique()
T = Tvals[np.argmin(abs(Tvals - params['T']))]
good = df.loc[(df['T'] == T) & (df.logg == params['logg']) & (df.vsini == params['vsini']) \
& (df['[Fe/H]'] == params['[Fe/H]']) & (df.addmode == params['addmode'])]
return pd.DataFrame(data={'velocity': self.velocities, 'CCF': good['ccf'].item()})
| gpl-3.0 |
Obus/scikit-learn | sklearn/setup.py | 225 | 2856 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.append('m')
config = Configuration('sklearn', parent_package, top_path)
config.add_subpackage('__check_build')
config.add_subpackage('svm')
config.add_subpackage('datasets')
config.add_subpackage('datasets/tests')
config.add_subpackage('feature_extraction')
config.add_subpackage('feature_extraction/tests')
config.add_subpackage('cluster')
config.add_subpackage('cluster/tests')
config.add_subpackage('covariance')
config.add_subpackage('covariance/tests')
config.add_subpackage('cross_decomposition')
config.add_subpackage('decomposition')
config.add_subpackage('decomposition/tests')
config.add_subpackage("ensemble")
config.add_subpackage("ensemble/tests")
config.add_subpackage('feature_selection')
config.add_subpackage('feature_selection/tests')
config.add_subpackage('utils')
config.add_subpackage('utils/tests')
config.add_subpackage('externals')
config.add_subpackage('mixture')
config.add_subpackage('mixture/tests')
config.add_subpackage('gaussian_process')
config.add_subpackage('gaussian_process/tests')
config.add_subpackage('neighbors')
config.add_subpackage('neural_network')
config.add_subpackage('preprocessing')
config.add_subpackage('manifold')
config.add_subpackage('metrics')
config.add_subpackage('semi_supervised')
config.add_subpackage("tree")
config.add_subpackage("tree/tests")
config.add_subpackage('metrics/tests')
config.add_subpackage('metrics/cluster')
config.add_subpackage('metrics/cluster/tests')
# add cython extension module for isotonic regression
config.add_extension(
'_isotonic',
sources=['_isotonic.c'],
include_dirs=[numpy.get_include()],
libraries=libraries,
)
# some libs needs cblas, fortran-compiled BLAS will not be sufficient
blas_info = get_info('blas_opt', 0)
if (not blas_info) or (
('NO_ATLAS_INFO', 1) in blas_info.get('define_macros', [])):
config.add_library('cblas',
sources=[join('src', 'cblas', '*.c')])
warnings.warn(BlasNotFoundError.__doc__)
# the following packages depend on cblas, so they have to be build
# after the above.
config.add_subpackage('linear_model')
config.add_subpackage('utils')
# add the test directory
config.add_subpackage('tests')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(**configuration(top_path='').todict())
| bsd-3-clause |
cle1109/scot | doc/sphinxext/inheritance_diagram.py | 4 | 13650 | """
Defines a docutils directive for inserting inheritance diagrams.
Provide the directive with one or more classes or modules (separated
by whitespace). For modules, all of the classes in that module will
be used.
Example::
Given the following classes:
class A: pass
class B(A): pass
class C(A): pass
class D(B, C): pass
class E(B): pass
.. inheritance-diagram: D E
Produces a graph like the following:
A
/ \
B C
/ \ /
E D
The graph is inserted as a PNG+image map into HTML and a PDF in
LaTeX.
"""
import inspect
import os
import re
import subprocess
try:
from hashlib import md5
except ImportError:
from md5 import md5
from docutils.nodes import Body, Element
from docutils.parsers.rst import directives
from sphinx.roles import xfileref_role
def my_import(name):
"""Module importer - taken from the python documentation.
This function allows importing names with dots in them."""
mod = __import__(name)
components = name.split('.')
for comp in components[1:]:
mod = getattr(mod, comp)
return mod
class DotException(Exception):
pass
class InheritanceGraph(object):
"""
Given a list of classes, determines the set of classes that
they inherit from all the way to the root "object", and then
is able to generate a graphviz dot graph from them.
"""
def __init__(self, class_names, show_builtins=False):
"""
*class_names* is a list of child classes to show bases from.
If *show_builtins* is True, then Python builtins will be shown
in the graph.
"""
self.class_names = class_names
self.classes = self._import_classes(class_names)
self.all_classes = self._all_classes(self.classes)
if len(self.all_classes) == 0:
raise ValueError("No classes found for inheritance diagram")
self.show_builtins = show_builtins
py_sig_re = re.compile(r'''^([\w.]*\.)? # class names
(\w+) \s* $ # optionally arguments
''', re.VERBOSE)
def _import_class_or_module(self, name):
"""
Import a class using its fully-qualified *name*.
"""
try:
path, base = self.py_sig_re.match(name).groups()
except:
raise ValueError(
"Invalid class or module '%s' specified for inheritance diagram" % name)
fullname = (path or '') + base
path = (path and path.rstrip('.'))
if not path:
path = base
try:
module = __import__(path, None, None, [])
# We must do an import of the fully qualified name. Otherwise if a
# subpackage 'a.b' is requested where 'import a' does NOT provide
# 'a.b' automatically, then 'a.b' will not be found below. This
# second call will force the equivalent of 'import a.b' to happen
# after the top-level import above.
my_import(fullname)
except ImportError:
raise ValueError(
"Could not import class or module '%s' specified for inheritance diagram" % name)
try:
todoc = module
for comp in fullname.split('.')[1:]:
todoc = getattr(todoc, comp)
except AttributeError:
raise ValueError(
"Could not find class or module '%s' specified for inheritance diagram" % name)
# If a class, just return it
if inspect.isclass(todoc):
return [todoc]
elif inspect.ismodule(todoc):
classes = []
for cls in todoc.__dict__.values():
if inspect.isclass(cls) and cls.__module__ == todoc.__name__:
classes.append(cls)
return classes
raise ValueError(
"'%s' does not resolve to a class or module" % name)
def _import_classes(self, class_names):
"""
Import a list of classes.
"""
classes = []
for name in class_names:
classes.extend(self._import_class_or_module(name))
return classes
def _all_classes(self, classes):
"""
Return a list of all classes that are ancestors of *classes*.
"""
all_classes = {}
def recurse(cls):
all_classes[cls] = None
for c in cls.__bases__:
if c not in all_classes:
recurse(c)
for cls in classes:
recurse(cls)
return all_classes.keys()
def class_name(self, cls, parts=0):
"""
Given a class object, return a fully-qualified name. This
works for things I've tested in matplotlib so far, but may not
be completely general.
"""
module = cls.__module__
if module == '__builtin__':
fullname = cls.__name__
else:
fullname = "%s.%s" % (module, cls.__name__)
if parts == 0:
return fullname
name_parts = fullname.split('.')
return '.'.join(name_parts[-parts:])
def get_all_class_names(self):
"""
Get all of the class names involved in the graph.
"""
return [self.class_name(x) for x in self.all_classes]
# These are the default options for graphviz
default_graph_options = {
"rankdir": "LR",
"size": '"8.0, 12.0"'
}
default_node_options = {
"shape": "box",
"fontsize": 10,
"height": 0.25,
"fontname": "Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",
"style": '"setlinewidth(0.5)"'
}
default_edge_options = {
"arrowsize": 0.5,
"style": '"setlinewidth(0.5)"'
}
def _format_node_options(self, options):
return ','.join(["%s=%s" % x for x in options.items()])
def _format_graph_options(self, options):
return ''.join(["%s=%s;\n" % x for x in options.items()])
def generate_dot(self, fd, name, parts=0, urls={},
graph_options={}, node_options={},
edge_options={}):
"""
Generate a graphviz dot graph from the classes that
were passed in to __init__.
*fd* is a Python file-like object to write to.
*name* is the name of the graph
*urls* is a dictionary mapping class names to http urls
*graph_options*, *node_options*, *edge_options* are
dictionaries containing key/value pairs to pass on as graphviz
properties.
"""
g_options = self.default_graph_options.copy()
g_options.update(graph_options)
n_options = self.default_node_options.copy()
n_options.update(node_options)
e_options = self.default_edge_options.copy()
e_options.update(edge_options)
fd.write('digraph %s {\n' % name)
fd.write(self._format_graph_options(g_options))
for cls in self.all_classes:
if not self.show_builtins and cls in __builtins__.values():
continue
name = self.class_name(cls, parts)
# Write the node
this_node_options = n_options.copy()
url = urls.get(self.class_name(cls))
if url is not None:
this_node_options['URL'] = '"%s"' % url
fd.write(' "%s" [%s];\n' %
(name, self._format_node_options(this_node_options)))
# Write the edges
for base in cls.__bases__:
if not self.show_builtins and base in __builtins__.values():
continue
base_name = self.class_name(base, parts)
fd.write(' "%s" -> "%s" [%s];\n' %
(base_name, name,
self._format_node_options(e_options)))
fd.write('}\n')
def run_dot(self, args, name, parts=0, urls={},
graph_options={}, node_options={}, edge_options={}):
"""
Run graphviz 'dot' over this graph, returning whatever 'dot'
writes to stdout.
*args* will be passed along as commandline arguments.
*name* is the name of the graph
*urls* is a dictionary mapping class names to http urls
Raises DotException for any of the many os and
installation-related errors that may occur.
"""
try:
dot = subprocess.Popen(['dot'] + list(args),
stdin=subprocess.PIPE, stdout=subprocess.PIPE,
close_fds=True)
except OSError:
raise DotException("Could not execute 'dot'. Are you sure you have 'graphviz' installed?")
except ValueError:
raise DotException("'dot' called with invalid arguments")
except:
raise DotException("Unexpected error calling 'dot'")
self.generate_dot(dot.stdin, name, parts, urls, graph_options,
node_options, edge_options)
dot.stdin.close()
result = dot.stdout.read()
returncode = dot.wait()
if returncode != 0:
raise DotException("'dot' returned the errorcode %d" % returncode)
return result
class inheritance_diagram(Body, Element):
"""
A docutils node to use as a placeholder for the inheritance
diagram.
"""
pass
def inheritance_diagram_directive(name, arguments, options, content, lineno,
content_offset, block_text, state,
state_machine):
"""
Run when the inheritance_diagram directive is first encountered.
"""
node = inheritance_diagram()
class_names = arguments
# Create a graph starting with the list of classes
graph = InheritanceGraph(class_names)
# Create xref nodes for each target of the graph's image map and
# add them to the doc tree so that Sphinx can resolve the
# references to real URLs later. These nodes will eventually be
# removed from the doctree after we're done with them.
for name in graph.get_all_class_names():
refnodes, x = xfileref_role(
'class', ':class:`%s`' % name, name, 0, state)
node.extend(refnodes)
# Store the graph object so we can use it to generate the
# dot file later
node['graph'] = graph
# Store the original content for use as a hash
node['parts'] = options.get('parts', 0)
node['content'] = " ".join(class_names)
return [node]
def get_graph_hash(node):
return md5(node['content'] + str(node['parts'])).hexdigest()[-10:]
def html_output_graph(self, node):
"""
Output the graph for HTML. This will insert a PNG with clickable
image map.
"""
graph = node['graph']
parts = node['parts']
graph_hash = get_graph_hash(node)
name = "inheritance%s" % graph_hash
path = '_images'
dest_path = os.path.join(setup.app.builder.outdir, path)
if not os.path.exists(dest_path):
os.makedirs(dest_path)
png_path = os.path.join(dest_path, name + ".png")
path = setup.app.builder.imgpath
# Create a mapping from fully-qualified class names to URLs.
urls = {}
for child in node:
if child.get('refuri') is not None:
urls[child['reftitle']] = child.get('refuri')
elif child.get('refid') is not None:
urls[child['reftitle']] = '#' + child.get('refid')
# These arguments to dot will save a PNG file to disk and write
# an HTML image map to stdout.
image_map = graph.run_dot(['-Tpng', '-o%s' % png_path, '-Tcmapx'],
name, parts, urls)
return ('<img src="%s/%s.png" usemap="#%s" class="inheritance"/>%s' %
(path, name, name, image_map))
def latex_output_graph(self, node):
"""
Output the graph for LaTeX. This will insert a PDF.
"""
graph = node['graph']
parts = node['parts']
graph_hash = get_graph_hash(node)
name = "inheritance%s" % graph_hash
dest_path = os.path.abspath(os.path.join(setup.app.builder.outdir, '_images'))
if not os.path.exists(dest_path):
os.makedirs(dest_path)
pdf_path = os.path.abspath(os.path.join(dest_path, name + ".pdf"))
graph.run_dot(['-Tpdf', '-o%s' % pdf_path],
name, parts, graph_options={'size': '"6.0,6.0"'})
return '\n\\includegraphics{%s}\n\n' % pdf_path
def visit_inheritance_diagram(inner_func):
"""
This is just a wrapper around html/latex_output_graph to make it
easier to handle errors and insert warnings.
"""
def visitor(self, node):
try:
content = inner_func(self, node)
except DotException as e:
# Insert the exception as a warning in the document
warning = self.document.reporter.warning(str(e), line=node.line)
warning.parent = node
node.children = [warning]
else:
source = self.document.attributes['source']
self.body.append(content)
node.children = []
return visitor
def do_nothing(self, node):
pass
def setup(app):
setup.app = app
setup.confdir = app.confdir
app.add_node(
inheritance_diagram,
latex=(visit_inheritance_diagram(latex_output_graph), do_nothing),
html=(visit_inheritance_diagram(html_output_graph), do_nothing))
app.add_directive(
'inheritance-diagram', inheritance_diagram_directive,
False, (1, 100, 0), parts = directives.nonnegative_int)
| mit |
great-expectations/great_expectations | tests/dataset/test_sparkdfdataset.py | 1 | 14191 | import importlib.util
import json
from unittest import mock
import pandas as pd
import pytest
from great_expectations.dataset.sparkdf_dataset import SparkDFDataset
from great_expectations.util import is_library_loadable
def test_sparkdfdataset_persist(spark_session):
df = pd.DataFrame({"a": [1, 2, 3]})
sdf = spark_session.createDataFrame(df)
sdf.persist = mock.MagicMock()
_ = SparkDFDataset(sdf, persist=True)
sdf.persist.assert_called_once()
sdf = spark_session.createDataFrame(df)
sdf.persist = mock.MagicMock()
_ = SparkDFDataset(sdf, persist=False)
sdf.persist.assert_not_called()
sdf = spark_session.createDataFrame(df)
sdf.persist = mock.MagicMock()
_ = SparkDFDataset(sdf)
sdf.persist.assert_called_once()
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
@pytest.fixture
def test_dataframe(spark_session):
from pyspark.sql.types import IntegerType, StringType, StructField, StructType
schema = StructType(
[
StructField("name", StringType(), True),
StructField("age", IntegerType(), True),
StructField(
"address",
StructType(
[
StructField("street", StringType(), True),
StructField("city", StringType(), True),
StructField("house_number", IntegerType(), True),
]
),
False,
),
StructField("name_duplicate", StringType(), True),
StructField("non.nested", StringType(), True),
StructField("name_with_duplicates", StringType(), True),
StructField("age_with_duplicates", IntegerType(), True),
StructField(
"address_with_duplicates",
StructType(
[
StructField("street", StringType(), True),
StructField("city", StringType(), True),
StructField("house_number", IntegerType(), True),
]
),
False,
),
]
)
rows = [
(
"Alice",
1,
("Street 1", "Alabama", 10),
"Alice",
"a",
"Alice",
1,
("Street 1", "Alabama", 12),
),
(
"Bob",
2,
("Street 2", "Brooklyn", 11),
"Bob",
"b",
"Bob",
2,
("Street 1", "Brooklyn", 12),
),
(
"Charlie",
3,
("Street 3", "Alabama", 12),
"Charlie",
"c",
"Charlie",
3,
("Street 1", "Alabama", 12),
),
(
"Dan",
4,
("Street 4", "Boston", 12),
"Dan",
"d",
"Charlie",
3,
("Street 1", "Boston", 12),
),
]
rdd = spark_session.sparkContext.parallelize(rows)
df = spark_session.createDataFrame(rdd, schema)
return SparkDFDataset(df, persist=True)
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
def test_expect_column_values_to_be_of_type(spark_session, test_dataframe):
"""
data asset expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_column_values_to_be_of_type(
"address.street", "StringType"
).success
assert test_dataframe.expect_column_values_to_be_of_type(
"`non.nested`", "StringType"
).success
assert test_dataframe.expect_column_values_to_be_of_type(
"name", "StringType"
).success
with pytest.raises(AnalysisException):
test_dataframe.expect_column_values_to_be_of_type("non.nested", "StringType")
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
def test_expect_column_values_to_be_of_type(spark_session, test_dataframe):
"""
data asset expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_column_values_to_be_of_type(
"address.street", "StringType"
).success
assert test_dataframe.expect_column_values_to_be_of_type(
"`non.nested`", "StringType"
).success
assert test_dataframe.expect_column_values_to_be_of_type(
"name", "StringType"
).success
with pytest.raises(AnalysisException):
test_dataframe.expect_column_values_to_be_of_type("non.nested", "StringType")
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
def test_expect_column_values_to_be_in_type_list(spark_session, test_dataframe):
"""
data asset expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_column_values_to_be_in_type_list(
"address.street", ["StringType", "IntegerType"]
).success
assert test_dataframe.expect_column_values_to_be_in_type_list(
"`non.nested`", ["StringType", "IntegerType"]
).success
assert test_dataframe.expect_column_values_to_be_in_type_list(
"name", ["StringType", "IntegerType"]
).success
with pytest.raises(AnalysisException):
test_dataframe.expect_column_values_to_be_of_type("non.nested", "StringType")
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
def test_expect_column_pair_values_to_be_equal(spark_session, test_dataframe):
"""
column_pair_map_expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_column_pair_values_to_be_equal(
"name", "name_duplicate"
).success
assert not test_dataframe.expect_column_pair_values_to_be_equal(
"name", "address.street"
).success
assert not test_dataframe.expect_column_pair_values_to_be_equal(
"name", "`non.nested`"
).success
# Expectation should fail when no `` surround a non-nested column with dot notation
with pytest.raises(AnalysisException):
test_dataframe.expect_column_pair_values_to_be_equal("name", "non.nested")
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
def test_expect_column_pair_values_A_to_be_greater_than_B(
spark_session, test_dataframe
):
"""
column_pair_map_expectation
"""
assert test_dataframe.expect_column_pair_values_A_to_be_greater_than_B(
"address.house_number", "age"
).success
assert test_dataframe.expect_column_pair_values_A_to_be_greater_than_B(
"age", "age", or_equal=True
).success
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
def test_expect_select_column_values_to_be_unique_within_record(
spark_session, test_dataframe
):
"""
multicolumn_map_expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_select_column_values_to_be_unique_within_record(
["name", "age"]
).success
assert test_dataframe.expect_select_column_values_to_be_unique_within_record(
["address.street", "name"]
).success
assert test_dataframe.expect_select_column_values_to_be_unique_within_record(
["address.street", "`non.nested`"]
).success
# Expectation should fail when no `` surround a non-nested column with dot notation
with pytest.raises(AnalysisException):
test_dataframe.expect_select_column_values_to_be_unique_within_record(
["address.street", "non.nested"]
)
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
def test_expect_compound_columns_to_be_unique(spark_session, test_dataframe):
"""
multicolumn_map_expectation
"""
from pyspark.sql.utils import AnalysisException
# Positive tests
assert test_dataframe.expect_compound_columns_to_be_unique(["name", "age"]).success
assert test_dataframe.expect_compound_columns_to_be_unique(
["address.street", "name"]
).success
assert test_dataframe.expect_compound_columns_to_be_unique(
["address.street", "address.city"]
).success
assert test_dataframe.expect_compound_columns_to_be_unique(
["name_with_duplicates", "age_with_duplicates", "name"]
).success
assert test_dataframe.expect_compound_columns_to_be_unique(
["address.street", "`non.nested`"]
).success
assert test_dataframe.expect_compound_columns_to_be_unique(
["name", "name_with_duplicates"]
).success
assert test_dataframe.expect_compound_columns_to_be_unique(
[
"name",
"name_with_duplicates",
"address_with_duplicates.street",
"address_with_duplicates.city",
"address_with_duplicates.house_number",
]
).success
# Negative tests
assert not test_dataframe.expect_compound_columns_to_be_unique(
["address_with_duplicates.city", "address_with_duplicates.house_number"]
).success
assert not test_dataframe.expect_compound_columns_to_be_unique(
["name_with_duplicates"]
).success
assert not test_dataframe.expect_compound_columns_to_be_unique(
["name_with_duplicates", "address_with_duplicates.street"]
).success
assert not test_dataframe.expect_compound_columns_to_be_unique(
[
"name_with_duplicates",
"address_with_duplicates.street",
"address_with_duplicates.house_number",
]
).success
# Expectation should fail when no `` surround a non-nested column with dot notation
with pytest.raises(AnalysisException):
test_dataframe.expect_compound_columns_to_be_unique(
["address.street", "non.nested"]
)
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
def test_expect_column_values_to_be_unique(spark_session, test_dataframe):
"""
column_map_expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_column_values_to_be_unique("name").success
assert not test_dataframe.expect_column_values_to_be_unique("address.city").success
assert test_dataframe.expect_column_values_to_be_unique("`non.nested`").success
# Expectation should fail when no `` surround a non-nested column with dot notation
with pytest.raises(AnalysisException):
test_dataframe.expect_column_values_to_be_unique("non.nested")
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
def test_expect_column_value_lengths_to_be_between(spark_session, test_dataframe):
"""
column_map_expectation
"""
assert test_dataframe.expect_column_value_lengths_to_be_between(
"name", 3, 7
).success
assert test_dataframe.expect_column_value_lengths_to_be_between(
"address.street", 1, 10
).success
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
def test_expect_column_value_lengths_to_equal(spark_session, test_dataframe):
"""
column_map_expectation
"""
assert test_dataframe.expect_column_value_lengths_to_equal("age", 1).success
assert test_dataframe.expect_column_value_lengths_to_equal(
"address.street", 8
).success
@pytest.mark.skipif(
not is_library_loadable(library_name="pyspark"),
reason="pyspark must be installed",
)
def test_expect_column_values_to_be_json_parseable(spark_session):
d1 = json.dumps({"i": [1, 2, 3], "j": 35, "k": {"x": "five", "y": 5, "z": "101"}})
d2 = json.dumps({"i": 1, "j": 2, "k": [3, 4, 5]})
d3 = json.dumps({"i": "a", "j": "b", "k": "c"})
d4 = json.dumps(
{"i": [4, 5], "j": [6, 7], "k": [8, 9], "l": {4: "x", 5: "y", 6: "z"}}
)
inner = {
"json_col": [d1, d2, d3, d4],
"not_json": [4, 5, 6, 7],
"py_dict": [
{"a": 1, "out": 1},
{"b": 2, "out": 4},
{"c": 3, "out": 9},
{"d": 4, "out": 16},
],
"most": [d1, d2, d3, "d4"],
}
data_reshaped = list(zip(*[v for _, v in inner.items()]))
df = spark_session.createDataFrame(
data_reshaped, ["json_col", "not_json", "py_dict", "most"]
)
D = SparkDFDataset(df)
D.set_default_expectation_argument("result_format", "COMPLETE")
T = [
{
"in": {"column": "json_col"},
"out": {
"success": True,
"unexpected_list": [],
},
},
{
"in": {"column": "not_json"},
"out": {
"success": False,
"unexpected_list": [4, 5, 6, 7],
},
},
{
"in": {"column": "py_dict"},
"out": {
"success": False,
"unexpected_list": [
{"a": 1, "out": 1},
{"b": 2, "out": 4},
{"c": 3, "out": 9},
{"d": 4, "out": 16},
],
},
},
{
"in": {"column": "most"},
"out": {
"success": False,
"unexpected_list": ["d4"],
},
},
{
"in": {"column": "most", "mostly": 0.75},
"out": {
"success": True,
"unexpected_index_list": [3],
"unexpected_list": ["d4"],
},
},
]
for t in T:
out = D.expect_column_values_to_be_json_parseable(**t["in"])
assert t["out"]["success"] == out.success
assert t["out"]["unexpected_list"] == out.result["unexpected_list"]
| apache-2.0 |
hdmetor/scikit-learn | examples/text/hashing_vs_dict_vectorizer.py | 284 | 3265 | """
===========================================
FeatureHasher and DictVectorizer Comparison
===========================================
Compares FeatureHasher and DictVectorizer by using both to vectorize
text documents.
The example demonstrates syntax and speed only; it doesn't actually do
anything useful with the extracted vectors. See the example scripts
{document_classification_20newsgroups,clustering}.py for actual learning
on text documents.
A discrepancy between the number of terms reported for DictVectorizer and
for FeatureHasher is to be expected due to hash collisions.
"""
# Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause
from __future__ import print_function
from collections import defaultdict
import re
import sys
from time import time
import numpy as np
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction import DictVectorizer, FeatureHasher
def n_nonzero_columns(X):
"""Returns the number of non-zero columns in a CSR matrix X."""
return len(np.unique(X.nonzero()[1]))
def tokens(doc):
"""Extract tokens from doc.
This uses a simple regex to break strings into tokens. For a more
principled approach, see CountVectorizer or TfidfVectorizer.
"""
return (tok.lower() for tok in re.findall(r"\w+", doc))
def token_freqs(doc):
"""Extract a dict mapping tokens from doc to their frequencies."""
freq = defaultdict(int)
for tok in tokens(doc):
freq[tok] += 1
return freq
categories = [
'alt.atheism',
'comp.graphics',
'comp.sys.ibm.pc.hardware',
'misc.forsale',
'rec.autos',
'sci.space',
'talk.religion.misc',
]
# Uncomment the following line to use a larger set (11k+ documents)
#categories = None
print(__doc__)
print("Usage: %s [n_features_for_hashing]" % sys.argv[0])
print(" The default number of features is 2**18.")
print()
try:
n_features = int(sys.argv[1])
except IndexError:
n_features = 2 ** 18
except ValueError:
print("not a valid number of features: %r" % sys.argv[1])
sys.exit(1)
print("Loading 20 newsgroups training data")
raw_data = fetch_20newsgroups(subset='train', categories=categories).data
data_size_mb = sum(len(s.encode('utf-8')) for s in raw_data) / 1e6
print("%d documents - %0.3fMB" % (len(raw_data), data_size_mb))
print()
print("DictVectorizer")
t0 = time()
vectorizer = DictVectorizer()
vectorizer.fit_transform(token_freqs(d) for d in raw_data)
duration = time() - t0
print("done in %fs at %0.3fMB/s" % (duration, data_size_mb / duration))
print("Found %d unique terms" % len(vectorizer.get_feature_names()))
print()
print("FeatureHasher on frequency dicts")
t0 = time()
hasher = FeatureHasher(n_features=n_features)
X = hasher.transform(token_freqs(d) for d in raw_data)
duration = time() - t0
print("done in %fs at %0.3fMB/s" % (duration, data_size_mb / duration))
print("Found %d unique terms" % n_nonzero_columns(X))
print()
print("FeatureHasher on raw tokens")
t0 = time()
hasher = FeatureHasher(n_features=n_features, input_type="string")
X = hasher.transform(tokens(d) for d in raw_data)
duration = time() - t0
print("done in %fs at %0.3fMB/s" % (duration, data_size_mb / duration))
print("Found %d unique terms" % n_nonzero_columns(X))
| bsd-3-clause |
davidtrem/ThunderStorm | thunderstorm/lightning/utils.py | 1 | 5027 | # -*- coding: utf-8 -*-
# Copyright (C) 2010-2013 Trémouilles David
#This file is part of Thunderstorm.
#
#ThunderStrom 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.
#
#ThunderStorm 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 ThunderStorm. If not, see <http://www.gnu.org/licenses/>.
"""
Various utility functions
"""
import matplotlib
from weakref import WeakValueDictionary
from weakref import WeakKeyDictionary
import warnings
class UniversalCursors(object):
def __init__(self):
self.all_cursor_orient = WeakKeyDictionary()
self.all_canvas = WeakValueDictionary()
self.all_axes = WeakValueDictionary()
self.backgrounds = {}
self.visible = True
self.needclear = False
def _onmove(self, event):
for canvas in self.all_canvas.values():
if not canvas.widgetlock.available(self):
return
if event.inaxes is None or not self.visible:
if self.needclear:
self._update(event)
for canvas in self.all_canvas.values():
canvas.draw()
self.needclear = False
return
self._update(event)
def _update(self, event):
# 1/ Reset background
for canvas in self.all_canvas.values():
canvas.restore_region(self.backgrounds[id(canvas)])
# 2/ update cursors
for cursors in self.all_cursor_orient.keys():
orient = self.all_cursor_orient[cursors]
if (event.inaxes in [line.get_axes() for line in cursors]
and self.visible):
visible = True
self.needclear = True
else:
visible = False
for line in cursors:
if orient == 'vertical':
line.set_xdata((event.xdata, event.xdata))
if orient == 'horizontal':
line.set_ydata((event.ydata, event.ydata))
line.set_visible(visible)
ax = line.get_axes()
ax.draw_artist(line)
# 3/ update canvas
for canvas in self.all_canvas.values():
canvas.blit(canvas.figure.bbox)
def _clear(self, event):
"""clear the cursor"""
self.backgrounds = {}
for canvas in self.all_canvas.values():
self.backgrounds[id(canvas)] = (
canvas.copy_from_bbox(canvas.figure.bbox))
for cursor in self.all_cursor_orient.keys():
for line in cursor:
line.set_visible(False)
def add_cursor(self, axes=(), orient='vertical', **lineprops):
class CursorList(list):
def __hash__(self):
return hash(tuple(self))
cursors = CursorList() # Required to keep weakref
for ax in axes:
self.all_axes[id(ax)] = ax
ax_canvas = ax.get_figure().canvas
if ax_canvas not in self.all_canvas.values():
#if not ax_canvas.supports_blit:
# warnings.warn("Must use canvas that support blit")
# return
self.all_canvas[id(ax_canvas)] = ax_canvas
ax_canvas.mpl_connect('motion_notify_event', self._onmove)
ax_canvas.mpl_connect('draw_event', self._clear)
if orient == 'vertical':
line = ax.axvline(ax.get_xbound()[0], visible=False,
animated=True, **lineprops)
if orient == 'horizontal':
line = ax.axhline(ax.get_ybound()[0], visible=False,
animated=True, **lineprops)
cursors.append(line)
self.all_cursor_orient[cursors] = orient
return cursors
def autoscale_visible_lines(axs):
"""
Function to autoscale only on visible lines.
"""
mplt_ver = [int(elem) for elem in matplotlib.__version__.split('.')[0:2]]
ignore = True
for line in (axs.lines):
if not line.get_visible():
continue # jump to next line if this one is not visible
if mplt_ver[0] == 0 and mplt_ver[1] < 98:
axs.dataLim.update_numerix(line.get_xdata(),
line.get_ydata(),
ignore)
else:
axs.dataLim.update_from_data_xy(line.get_xydata(),
ignore)
ignore = False
axs.autoscale_view()
return None
def neg_bool_list(a_list):
return [not elem for elem in a_list]
| gpl-3.0 |
klahnakoski/ActiveData | vendor/mo_testing/fuzzytestcase.py | 1 | 9712 | # encoding: utf-8
#
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this file,
# You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Contact: Kyle Lahnakoski (kyle@lahnakoski.com)
#
from __future__ import unicode_literals
import datetime
import types
import unittest
from mo_collections.unique_index import UniqueIndex
import mo_dots
from mo_dots import coalesce, is_container, is_list, literal_field, unwrap, to_data, is_data, is_many
from mo_future import is_text, zip_longest, first
from mo_logs import Except, Log, suppress_exception
from mo_logs.strings import expand_template, quote
import mo_math
from mo_math import is_number, log10
from mo_times import dates
class FuzzyTestCase(unittest.TestCase):
"""
COMPARE STRUCTURE AND NUMBERS!
ONLY THE ATTRIBUTES IN THE expected STRUCTURE ARE TESTED TO EXIST
EXTRA ATTRIBUTES ARE IGNORED.
NUMBERS ARE MATCHED BY ...
* places (UP TO GIVEN SIGNIFICANT DIGITS)
* digits (UP TO GIVEN DECIMAL PLACES, WITH NEGATIVE MEANING LEFT-OF-UNITS)
* delta (MAXIMUM ABSOLUTE DIFFERENCE FROM expected)
"""
def __init__(self, *args, **kwargs):
unittest.TestCase.__init__(self, *args, **kwargs)
self.default_places=15
def set_default_places(self, places):
"""
WHEN COMPARING float, HOW MANY DIGITS ARE SIGNIFICANT BY DEFAULT
"""
self.default_places=places
def assertAlmostEqual(self, test_value, expected, msg=None, digits=None, places=None, delta=None):
if delta or digits:
assertAlmostEqual(test_value, expected, msg=msg, digits=digits, places=places, delta=delta)
else:
assertAlmostEqual(test_value, expected, msg=msg, digits=digits, places=coalesce(places, self.default_places), delta=delta)
def assertEqual(self, test_value, expected, msg=None, digits=None, places=None, delta=None):
self.assertAlmostEqual(test_value, expected, msg=msg, digits=digits, places=places, delta=delta)
def assertRaises(self, problem=None, function=None, *args, **kwargs):
if function is None:
return RaiseContext(self, problem=problem or Exception)
with RaiseContext(self, problem=problem):
function(*args, **kwargs)
class RaiseContext(object):
def __init__(self, this, problem=Exception):
self.this = this
self.problem = problem
def __enter__(self):
pass
def __exit__(self, exc_type, exc_val, exc_tb):
if not exc_val:
Log.error("Expecting an error")
f = Except.wrap(exc_val)
if isinstance(self.problem, (list, tuple)):
problems = self.problem
else:
problems = [self.problem]
causes = []
for problem in problems:
if isinstance(problem, object.__class__) and issubclass(problem, BaseException) and isinstance(exc_val, problem):
return True
try:
self.this.assertIn(problem, f)
return True
except Exception as cause:
causes.append(cause)
Log.error("problem is not raised", cause=first(causes))
def assertAlmostEqual(test, expected, digits=None, places=None, msg=None, delta=None):
show_detail = True
test = unwrap(test)
expected = unwrap(expected)
try:
if test is None and (is_null_op(expected) or expected is None):
return
elif test is expected:
return
elif is_text(expected):
assertAlmostEqualValue(test, expected, msg=msg, digits=digits, places=places, delta=delta)
elif isinstance(test, UniqueIndex):
if test ^ expected:
Log.error("Sets do not match")
elif is_data(expected) and is_data(test):
for k, e in unwrap(expected).items():
t = test.get(k)
assertAlmostEqual(t, e, msg=coalesce(msg, "")+"key "+quote(k)+": ", digits=digits, places=places, delta=delta)
elif is_data(expected):
if is_many(test):
test = list(test)
if len(test) != 1:
Log.error("Expecting data, not a list")
test = test[0]
for k, e in expected.items():
try:
t = test[k]
assertAlmostEqual(t, e, msg=msg, digits=digits, places=places, delta=delta)
continue
except:
pass
t = mo_dots.get_attr(test, literal_field(k))
assertAlmostEqual(t, e, msg=msg, digits=digits, places=places, delta=delta)
elif is_container(test) and isinstance(expected, set):
test = set(to_data(t) for t in test)
if len(test) != len(expected):
Log.error(
"Sets do not match, element count different:\n{{test|json|indent}}\nexpecting{{expectedtest|json|indent}}",
test=test,
expected=expected
)
try:
return len(set(test)|expected) == len(expected)
except:
for e in expected:
for t in test:
try:
assertAlmostEqual(t, e, msg=msg, digits=digits, places=places, delta=delta)
break
except Exception as _:
pass
else:
Log.error("Sets do not match. {{value|json}} not found in {{test|json}}", value=e, test=test)
elif isinstance(expected, types.FunctionType):
return expected(test)
elif hasattr(test, "__iter__") and hasattr(expected, "__iter__"):
if test.__class__.__name__ == "ndarray": # numpy
test = test.tolist()
elif test.__class__.__name__ == "DataFrame": # pandas
test = test[test.columns[0]].values.tolist()
elif test.__class__.__name__ == "Series": # pandas
test = test.values.tolist()
if not expected and test == None:
return
if expected == None:
expected = [] # REPRESENT NOTHING
for t, e in zip_longest(test, expected):
assertAlmostEqual(t, e, msg=msg, digits=digits, places=places, delta=delta)
else:
assertAlmostEqualValue(test, expected, msg=msg, digits=digits, places=places, delta=delta)
except Exception as cause:
Log.error(
"{{test|json|limit(10000)}} does not match expected {{expected|json|limit(10000)}}",
test=test if show_detail else "[can not show]",
expected=expected if show_detail else "[can not show]",
cause=cause
)
def assertAlmostEqualValue(test, expected, digits=None, places=None, msg=None, delta=None):
"""
Snagged from unittest/case.py, then modified (Aug2014)
"""
if is_null_op(expected):
if test == None: # pandas dataframes reject any comparision with an exception!
return
else:
raise AssertionError(expand_template("{{test|json}} != NULL", locals()))
if expected == None: # None has no expectations
return
if test == expected:
# shortcut
return
if isinstance(expected, (dates.Date, datetime.datetime, datetime.date)):
return assertAlmostEqualValue(
dates.Date(test).unix,
dates.Date(expected).unix,
msg=msg,
digits=digits,
places=places,
delta=delta
)
if not is_number(expected):
# SOME SPECIAL CASES, EXPECTING EMPTY CONTAINERS IS THE SAME AS EXPECTING NULL
if is_list(expected) and len(expected) == 0 and test == None:
return
if is_data(expected) and not expected.keys() and test == None:
return
if test != expected:
raise AssertionError(expand_template("{{test|json}} != {{expected|json}}", locals()))
return
elif not is_number(test):
try:
# ASSUME IT IS A UTC DATE
test = dates.parse(test).unix
except Exception as e:
raise AssertionError(expand_template("{{test|json}} != {{expected}}", locals()))
num_param = 0
if digits != None:
num_param += 1
if places != None:
num_param += 1
if delta != None:
num_param += 1
if num_param > 1:
raise TypeError("specify only one of digits, places or delta")
if digits is not None:
with suppress_exception:
diff = log10(abs(test-expected))
if diff < digits:
return
standardMsg = expand_template("{{test|json}} != {{expected|json}} within {{digits}} decimal places", locals())
elif delta is not None:
if abs(test - expected) <= delta:
return
standardMsg = expand_template("{{test|json}} != {{expected|json}} within {{delta}} delta", locals())
else:
if places is None:
places = 15
with suppress_exception:
diff = mo_math.log10(abs(test-expected))
if diff == None:
return # Exactly the same
if diff < mo_math.ceiling(mo_math.log10(abs(test)))-places:
return
standardMsg = expand_template("{{test|json}} != {{expected|json}} within {{places}} places", locals())
raise AssertionError(coalesce(msg, "") + ": (" + standardMsg + ")")
def is_null_op(v):
return v.__class__.__name__ == "NullOp"
| mpl-2.0 |
tbullmann/heuhaufen | publication/generators_and_depth/aggregate.py | 1 | 5320 | import os
import pandas
import numpy as np
from bokeh.palettes import Viridis4 as palette
from bokeh.layouts import layout, column, row
from bokeh.plotting import figure, output_file, show, ColumnDataSource
from bokeh.models import HoverTool, Div, DataTable, TableColumn, NumberFormatter, LinearAxis, Select, CustomJS, Slider, Button
import json # must be imported after bokeh
def main(test_path='temp/publication/how_deep/test'):
labels = ['membranes', 'synapses', 'mitochondria']
# concatenate the evaluation and parameters for all runs
dfs = []
for label in labels:
for run in range(1,21):
df = read_run_from_json_and_csv(test_path, run, label)
dfs.append(df)
data = pandas.concat(dfs)
# save aggregated data (in long format)
data.to_csv(os.path.join(test_path, 'summary_long.csv'))
# convert long to wide: label x metric --> label_metric
metrics = data.columns.to_series().groupby(data.dtypes).groups[np.dtype('float64')]
data2 = data.pivot_table(index=['generator', 'layers', 'sample'], columns='label', values=metrics)
data2.columns = ['{}_{}'.format(x, y) for x, y in
zip(data2.columns.get_level_values(1), data2.columns.get_level_values(0))]
data2 = data2.reset_index()
# save aggregated data (in wide format)
data2.to_csv(os.path.join(test_path, 'summary_wide.csv'))
# TODO: interactive plot with bokeh
# bokeh_plot(data2, test_path) # not fully functional, e.g. cannot change label and metric
def read_run_from_json_and_csv(test_path, run, label):
# path to the test result for a particular model
base_path = os.path.join(test_path, '%d' % run)
# getting parameters from the options json file
with open(os.path.join(base_path, "options.json")) as f:
options = dict(json.loads(f.read()).items())
generator = options['generator']
# calculate the number of layers depending on generator network and its specific parameters
if generator == 'unet':
layers = options['u_depth'] * 2 # 1 for down sampling and 1 for up sampling at each level
elif generator == 'densenet':
layers = options['n_dense_blocks'] * options['n_dense_layers'] + 6 # 3 for each encoder and decoder
elif generator == 'resnet':
layers = options['n_res_blocks'] * 2 + 6 # 2 for transformation, 3 for each encoder and decoder
elif generator == 'highwaynet':
layers = options['n_highway_units'] * 2 + 6 # 2 for transformation, 3 for each encoder and decoder
# read evaluation results
df = pandas.read_csv(os.path.join(base_path, 'evaluation/%s.csv' % label)) # no index_col
# add parameters
df['generator'] = generator
df['layers'] = layers
df['label'] = label
df['run'] = run
return df
def bokeh_plot(data, test_path):
networks = ['unet', 'resnet', 'highwaynet', 'densenet']
# assuming all float values are metrics
metrics = data.columns.to_series().groupby(data.dtypes).groups[np.dtype('float64')]
# calculate mean for each
data_mean = data.groupby(['generator', 'layers'])[metrics].mean().reset_index()
source = dict()
source_mean = dict()
for network in networks:
source[network] = ColumnDataSource(data[data.generator == network])
source_mean[network] = ColumnDataSource(data_mean[data_mean.generator == network])
output_file(os.path.join(test_path, "select.html"))
description = Div(text="""
<h1>Evaluation of network type and depth for generator</h1>
<p>
Interact with the widgets to select metric and evaluated label.
</p>
""", width=1000)
fig = figure(plot_width=1000, plot_height=1000, tools=['box_select', 'reset'])
fig.xaxis.axis_label = "layers"
fig.yaxis.axis_label = "value of metric"
plots = []
for network, column_color in zip(networks, palette):
plot = fig.line('layers', metrics[0], legend=dict(value=network), color=column_color,
source=source_mean[network])
plot = fig.scatter('layers', metrics[0], legend=dict(value=network), color=column_color, source=source[network])
# legend which can hide/select a specific metric
fig.legend.location = "bottom_right"
fig.legend.click_policy = "hide"
choices = metrics
axis = 'y'
axis_callback_code = """
plot.glyph.{axis}.field = cb_obj.value
axis.attributes.axis_label = cb_obj.value;
axis.trigger('change');
source.change.emit();
"""
if axis == 'x':
fig.xaxis.visible = None
position = 'below'
initial_choice = 0
else:
fig.yaxis.visible = None
position = 'left'
initial_choice = 1
linear_axis = LinearAxis(axis_label=choices[initial_choice])
fig.add_layout(linear_axis, position)
callback1 = CustomJS(args=dict(source=source[network], axis=linear_axis, plot=plot),
code=axis_callback_code.format(axis=axis))
ticker = Select(value=choices[initial_choice], options=choices, title=axis + '-axis')
ticker.js_on_change('value', callback1)
l = layout([
[description],
[ticker],
[fig]
], sizing_mode='fixed')
show(l)
if __name__ == "__main__":
main()
else:
main()
| mit |
attia42/twitter_word2vec | kmeans/experimentm.py | 1 | 3559 | import csv
import nltk
from nltk.tokenize import word_tokenize
import string
from nltk import pos_tag
from gensim.models.word2vec import Word2Vec
from gensim import matutils
from numpy import array, float32 as REAL
from sklearn.cluster import MiniBatchKMeans, KMeans
from multiprocessing import Pool
from collections import Counter
#string.punctuation
#string.digits
file = 'training.1600000.processed.noemoticon2.csv'
#file = 'testdata.manual.2009.06.14.csv'
tags = ["NNP", "NN", "NNS"]
ncls = 1000
niters = 1000
nreplay_kmeans = 1
lower = False
redundant = ["aw", "aww", "awww", "awwww", "haha", "lol", "wow", "wtf", "xd", "yay", "http", "www", "com", "ah", "ahh", "ahhh", "amp"]
def preprocess(tweet):
ret_tweet = ""
i = -1
nn = []
raw_tweet = tweet
for ch in string.punctuation.replace("'","") + string.digits:
tweet = tweet.replace(ch, " ")
tweet_pos = {}
if lower:
tweet = tweet.lower()
try:
toks = word_tokenize(tweet)
pos = pos_tag(toks)
nn = [p for p in pos if p[1] in tags]
#nn = [p for p in pos if p == 'NNP']
except:
pass
if(len(nn)):
tweet_pos["NN"] = nn
ret_tweet = tweet_pos
return ret_tweet
raw = []
with open(file, 'rb') as csvfile:
content = csv.reader(csvfile, delimiter=',', quotechar='"')
for row in content:
tweet = row[5]
raw.append(tweet)
p = Pool(6)
tweets = p.map(preprocess, raw)
t1 = []
t2 = []
for i in range(len(tweets)):
if len(tweets[i]):
t1.append(raw[i])
t2.append(tweets[i])
raw = t1
tweets = t2
print "Loading model..."
wv = Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)
vectors = []
for i in range(len(tweets)):
tweet = tweets[i]
nns = tweet['NN']
vector = []
#print nns
mean = []
no_wv_tweet = True
for w in nns:
if len(w[0]) > 1 and w[0] in wv and w[0].lower() not in redundant:
no_wv_tweet = False
#print w[0]
weight = 1
if w[1] == 'NNP':
weight = 100
mean.append(weight * wv[w[0]])
if(len(mean)):
vectors.append(matutils.unitvec(array(mean).mean(axis=0)).astype(REAL))
else:
vectors.append([])
t1 = []
t2 = []
t3 = []
for i in range(len(vectors)):
if vectors[i] != None and len(vectors[i]):
t1.append(raw[i])
t2.append(tweets[i])
t3.append(vectors[i])
raw = t1
tweets = t2
vectors = t3
#kmeans = KMeans(init='k-means++', n_clusters=ncls, n_init=1)
kmeans = MiniBatchKMeans(init='k-means++', n_clusters=ncls, n_init=nreplay_kmeans, max_iter=niters)
kmeans.fit(vectors)
clss = kmeans.predict(vectors)
clusters = [[] for i in range(ncls)]
for i in range(len(vectors)):
cls = clss[i]
clusters[cls].append(i)
clusterstags = [[] for i in range(ncls)]
countarr = []
for c in clusters:
counts = Counter()
for i in c:
t = [x[0] for x in tweets[i]["NN"] ]#if x[1] == "NNP"]
#tn = [x[1] for x in tweets[i]["NN"]]
sentence = " ".join(t) #+ tn)
counts.update(word.strip('.,?!"\'').lower() for word in sentence.split())
countarr.append(counts)
output = ""
for i in range(ncls):
output = "Most common words for this cluster:\n"
output += str(countarr[i].most_common(12))
output += "\n\n\n\n\n\n"
output += "Word2vec space of related words:\n"
wv_rel = wv.most_similar([kmeans.cluster_centers_[i]], topn=10)
output += str(wv_rel)
output += "\n\n\n\n\n\n"
for t in clusters[i]:
output += str(raw[t]) + "\n"
#output += "\n\n\n"
nm = [x[0] for x in countarr[i].most_common(5)]
nm = str(" ".join(nm))
for ch in string.punctuation:
nm = nm.replace(ch, " ")
f = open('clusters/' + nm +'.txt', 'wb')
f.write(output)
f.close()
| mit |
mtconley/turntable | test/lib/python2.7/site-packages/scipy/interpolate/fitpack2.py | 7 | 57978 | """
fitpack --- curve and surface fitting with splines
fitpack is based on a collection of Fortran routines DIERCKX
by P. Dierckx (see http://www.netlib.org/dierckx/) transformed
to double routines by Pearu Peterson.
"""
# Created by Pearu Peterson, June,August 2003
from __future__ import division, print_function, absolute_import
__all__ = [
'UnivariateSpline',
'InterpolatedUnivariateSpline',
'LSQUnivariateSpline',
'BivariateSpline',
'LSQBivariateSpline',
'SmoothBivariateSpline',
'LSQSphereBivariateSpline',
'SmoothSphereBivariateSpline',
'RectBivariateSpline',
'RectSphereBivariateSpline']
import warnings
from numpy import zeros, concatenate, alltrue, ravel, all, diff, array, ones
import numpy as np
from . import fitpack
from . import dfitpack
################ Univariate spline ####################
_curfit_messages = {1:"""
The required storage space exceeds the available storage space, as
specified by the parameter nest: nest too small. If nest is already
large (say nest > m/2), it may also indicate that s is too small.
The approximation returned is the weighted least-squares spline
according to the knots t[0],t[1],...,t[n-1]. (n=nest) the parameter fp
gives the corresponding weighted sum of squared residuals (fp>s).
""",
2:"""
A theoretically impossible result was found during the iteration
proces for finding a smoothing spline with fp = s: s too small.
There is an approximation returned but the corresponding weighted sum
of squared residuals does not satisfy the condition abs(fp-s)/s < tol.""",
3:"""
The maximal number of iterations maxit (set to 20 by the program)
allowed for finding a smoothing spline with fp=s has been reached: s
too small.
There is an approximation returned but the corresponding weighted sum
of squared residuals does not satisfy the condition abs(fp-s)/s < tol.""",
10:"""
Error on entry, no approximation returned. The following conditions
must hold:
xb<=x[0]<x[1]<...<x[m-1]<=xe, w[i]>0, i=0..m-1
if iopt=-1:
xb<t[k+1]<t[k+2]<...<t[n-k-2]<xe"""
}
# UnivariateSpline, ext parameter can be an int or a string
_extrap_modes = {0: 0, 'extrapolate': 0,
1: 1, 'zeros': 1,
2: 2, 'raise': 2,
3: 3, 'const': 3}
class UnivariateSpline(object):
"""
One-dimensional smoothing spline fit to a given set of data points.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data. `s`
specifies the number of knots by specifying a smoothing condition.
Parameters
----------
x : (N,) array_like
1-D array of independent input data. Must be increasing.
y : (N,) array_like
1-D array of dependent input data, of the same length as `x`.
w : (N,) array_like, optional
Weights for spline fitting. Must be positive. If None (default),
weights are all equal.
bbox : (2,) array_like, optional
2-sequence specifying the boundary of the approximation interval. If
None (default), ``bbox=[x[0], x[-1]]``.
k : int, optional
Degree of the smoothing spline. Must be <= 5.
Default is k=3, a cubic spline.
s : float or None, optional
Positive smoothing factor used to choose the number of knots. Number
of knots will be increased until the smoothing condition is satisfied::
sum((w[i] * (y[i]-spl(x[i])))**2, axis=0) <= s
If None (default), ``s = len(w)`` which should be a good value if
``1/w[i]`` is an estimate of the standard deviation of ``y[i]``.
If 0, spline will interpolate through all data points.
ext : int or str, optional
Controls the extrapolation mode for elements
not in the interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 of 'const', return the boundary value.
The default value is 0.
See Also
--------
InterpolatedUnivariateSpline : Subclass with smoothing forced to 0
LSQUnivariateSpline : Subclass in which knots are user-selected instead of
being set by smoothing condition
splrep : An older, non object-oriented wrapping of FITPACK
splev, sproot, splint, spalde
BivariateSpline : A similar class for two-dimensional spline interpolation
Notes
-----
The number of data points must be larger than the spline degree `k`.
**NaN handling**: If the input arrays contain ``nan`` values, the result
is not useful, since the underlying spline fitting routines cannot deal
with ``nan`` . A workaround is to use zero weights for not-a-number
data points:
>>> w = np.isnan(y)
>>> y[w] = 0.
>>> spl = UnivariateSpline(x, y, w=~w)
Notice the need to replace a ``nan`` by a numerical value (precise value
does not matter as long as the corresponding weight is zero.)
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from scipy.interpolate import UnivariateSpline
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
>>> plt.plot(x, y, 'ro', ms=5)
Use the default value for the smoothing parameter:
>>> spl = UnivariateSpline(x, y)
>>> xs = np.linspace(-3, 3, 1000)
>>> plt.plot(xs, spl(xs), 'g', lw=3)
Manually change the amount of smoothing:
>>> spl.set_smoothing_factor(0.5)
>>> plt.plot(xs, spl(xs), 'b', lw=3)
>>> plt.show()
"""
def __init__(self, x, y, w=None, bbox=[None]*2, k=3, s=None, ext=0):
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
try:
self.ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
data = dfitpack.fpcurf0(x,y,k,w=w,
xb=bbox[0],xe=bbox[1],s=s)
if data[-1] == 1:
# nest too small, setting to maximum bound
data = self._reset_nest(data)
self._data = data
self._reset_class()
@classmethod
def _from_tck(cls, tck, ext=0):
"""Construct a spline object from given tck"""
self = cls.__new__(cls)
t, c, k = tck
self._eval_args = tck
#_data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
self._data = (None,None,None,None,None,k,None,len(t),t,
c,None,None,None,None)
self.ext = ext
return self
def _reset_class(self):
data = self._data
n,t,c,k,ier = data[7],data[8],data[9],data[5],data[-1]
self._eval_args = t[:n],c[:n],k
if ier == 0:
# the spline returned has a residual sum of squares fp
# such that abs(fp-s)/s <= tol with tol a relative
# tolerance set to 0.001 by the program
pass
elif ier == -1:
# the spline returned is an interpolating spline
self._set_class(InterpolatedUnivariateSpline)
elif ier == -2:
# the spline returned is the weighted least-squares
# polynomial of degree k. In this extreme case fp gives
# the upper bound fp0 for the smoothing factor s.
self._set_class(LSQUnivariateSpline)
else:
# error
if ier == 1:
self._set_class(LSQUnivariateSpline)
message = _curfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
def _set_class(self, cls):
self._spline_class = cls
if self.__class__ in (UnivariateSpline, InterpolatedUnivariateSpline,
LSQUnivariateSpline):
self.__class__ = cls
else:
# It's an unknown subclass -- don't change class. cf. #731
pass
def _reset_nest(self, data, nest=None):
n = data[10]
if nest is None:
k,m = data[5],len(data[0])
nest = m+k+1 # this is the maximum bound for nest
else:
if not n <= nest:
raise ValueError("`nest` can only be increased")
t, c, fpint, nrdata = [np.resize(data[j], nest) for j in [8,9,11,12]]
args = data[:8] + (t,c,n,fpint,nrdata,data[13])
data = dfitpack.fpcurf1(*args)
return data
def set_smoothing_factor(self, s):
""" Continue spline computation with the given smoothing
factor s and with the knots found at the last call.
This routine modifies the spline in place.
"""
data = self._data
if data[6] == -1:
warnings.warn('smoothing factor unchanged for'
'LSQ spline with fixed knots')
return
args = data[:6] + (s,) + data[7:]
data = dfitpack.fpcurf1(*args)
if data[-1] == 1:
# nest too small, setting to maximum bound
data = self._reset_nest(data)
self._data = data
self._reset_class()
def __call__(self, x, nu=0, ext=None):
"""
Evaluate spline (or its nu-th derivative) at positions x.
Parameters
----------
x : array_like
A 1-D array of points at which to return the value of the smoothed
spline or its derivatives. Note: x can be unordered but the
evaluation is more efficient if x is (partially) ordered.
nu : int
The order of derivative of the spline to compute.
ext : int
Controls the value returned for elements of ``x`` not in the
interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 or 'const', return the boundary value.
The default value is 0, passed from the initialization of
UnivariateSpline.
"""
x = np.asarray(x)
# empty input yields empty output
if x.size == 0:
return array([])
# if nu is None:
# return dfitpack.splev(*(self._eval_args+(x,)))
# return dfitpack.splder(nu=nu,*(self._eval_args+(x,)))
if ext is None:
ext = self.ext
else:
try:
ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
return fitpack.splev(x, self._eval_args, der=nu, ext=ext)
def get_knots(self):
""" Return positions of (boundary and interior) knots of the spline.
"""
data = self._data
k,n = data[5],data[7]
return data[8][k:n-k]
def get_coeffs(self):
"""Return spline coefficients."""
data = self._data
k,n = data[5],data[7]
return data[9][:n-k-1]
def get_residual(self):
"""Return weighted sum of squared residuals of the spline
approximation: ``sum((w[i] * (y[i]-spl(x[i])))**2, axis=0)``.
"""
return self._data[10]
def integral(self, a, b):
""" Return definite integral of the spline between two given points.
"""
return dfitpack.splint(*(self._eval_args+(a,b)))
def derivatives(self, x):
""" Return all derivatives of the spline at the point x."""
d,ier = dfitpack.spalde(*(self._eval_args+(x,)))
if not ier == 0:
raise ValueError("Error code returned by spalde: %s" % ier)
return d
def roots(self):
""" Return the zeros of the spline.
Restriction: only cubic splines are supported by fitpack.
"""
k = self._data[5]
if k == 3:
z,m,ier = dfitpack.sproot(*self._eval_args[:2])
if not ier == 0:
raise ValueError("Error code returned by spalde: %s" % ier)
return z[:m]
raise NotImplementedError('finding roots unsupported for '
'non-cubic splines')
def derivative(self, n=1):
"""
Construct a new spline representing the derivative of this spline.
Parameters
----------
n : int, optional
Order of derivative to evaluate. Default: 1
Returns
-------
spline : UnivariateSpline
Spline of order k2=k-n representing the derivative of this
spline.
See Also
--------
splder, antiderivative
Notes
-----
.. versionadded:: 0.13.0
Examples
--------
This can be used for finding maxima of a curve:
>>> from scipy.interpolate import UnivariateSpline
>>> x = np.linspace(0, 10, 70)
>>> y = np.sin(x)
>>> spl = UnivariateSpline(x, y, k=4, s=0)
Now, differentiate the spline and find the zeros of the
derivative. (NB: `sproot` only works for order 3 splines, so we
fit an order 4 spline):
>>> spl.derivative().roots() / np.pi
array([ 0.50000001, 1.5 , 2.49999998])
This agrees well with roots :math:`\pi/2 + n\pi` of `cos(x) = sin'(x)`.
"""
tck = fitpack.splder(self._eval_args, n)
return UnivariateSpline._from_tck(tck, self.ext)
def antiderivative(self, n=1):
"""
Construct a new spline representing the antiderivative of this spline.
Parameters
----------
n : int, optional
Order of antiderivative to evaluate. Default: 1
Returns
-------
spline : UnivariateSpline
Spline of order k2=k+n representing the antiderivative of this
spline.
Notes
-----
.. versionadded:: 0.13.0
See Also
--------
splantider, derivative
Examples
--------
>>> from scipy.interpolate import UnivariateSpline
>>> x = np.linspace(0, np.pi/2, 70)
>>> y = 1 / np.sqrt(1 - 0.8*np.sin(x)**2)
>>> spl = UnivariateSpline(x, y, s=0)
The derivative is the inverse operation of the antiderivative,
although some floating point error accumulates:
>>> spl(1.7), spl.antiderivative().derivative()(1.7)
(array(2.1565429877197317), array(2.1565429877201865))
Antiderivative can be used to evaluate definite integrals:
>>> ispl = spl.antiderivative()
>>> ispl(np.pi/2) - ispl(0)
2.2572053588768486
This is indeed an approximation to the complete elliptic integral
:math:`K(m) = \\int_0^{\\pi/2} [1 - m\\sin^2 x]^{-1/2} dx`:
>>> from scipy.special import ellipk
>>> ellipk(0.8)
2.2572053268208538
"""
tck = fitpack.splantider(self._eval_args, n)
return UnivariateSpline._from_tck(tck, self.ext)
class InterpolatedUnivariateSpline(UnivariateSpline):
"""
One-dimensional interpolating spline for a given set of data points.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data. Spline
function passes through all provided points. Equivalent to
`UnivariateSpline` with s=0.
Parameters
----------
x : (N,) array_like
Input dimension of data points -- must be increasing
y : (N,) array_like
input dimension of data points
w : (N,) array_like, optional
Weights for spline fitting. Must be positive. If None (default),
weights are all equal.
bbox : (2,) array_like, optional
2-sequence specifying the boundary of the approximation interval. If
None (default), ``bbox=[x[0], x[-1]]``.
k : int, optional
Degree of the smoothing spline. Must be 1 <= `k` <= 5.
ext : int or str, optional
Controls the extrapolation mode for elements
not in the interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 of 'const', return the boundary value.
The default value is 0.
See Also
--------
UnivariateSpline : Superclass -- allows knots to be selected by a
smoothing condition
LSQUnivariateSpline : spline for which knots are user-selected
splrep : An older, non object-oriented wrapping of FITPACK
splev, sproot, splint, spalde
BivariateSpline : A similar class for two-dimensional spline interpolation
Notes
-----
The number of data points must be larger than the spline degree `k`.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from scipy.interpolate import InterpolatedUnivariateSpline
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
>>> spl = InterpolatedUnivariateSpline(x, y)
>>> plt.plot(x, y, 'ro', ms=5)
>>> xs = np.linspace(-3, 3, 1000)
>>> plt.plot(xs, spl(xs), 'g', lw=3, alpha=0.7)
>>> plt.show()
Notice that the ``spl(x)`` interpolates `y`:
>>> spl.get_residual()
0.0
"""
def __init__(self, x, y, w=None, bbox=[None]*2, k=3, ext=0):
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
self._data = dfitpack.fpcurf0(x,y,k,w=w,
xb=bbox[0],xe=bbox[1],s=0)
self._reset_class()
try:
self.ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
_fpchec_error_string = """The input parameters have been rejected by fpchec. \
This means that at least one of the following conditions is violated:
1) k+1 <= n-k-1 <= m
2) t(1) <= t(2) <= ... <= t(k+1)
t(n-k) <= t(n-k+1) <= ... <= t(n)
3) t(k+1) < t(k+2) < ... < t(n-k)
4) t(k+1) <= x(i) <= t(n-k)
5) The conditions specified by Schoenberg and Whitney must hold
for at least one subset of data points, i.e., there must be a
subset of data points y(j) such that
t(j) < y(j) < t(j+k+1), j=1,2,...,n-k-1
"""
class LSQUnivariateSpline(UnivariateSpline):
"""
One-dimensional spline with explicit internal knots.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data. `t`
specifies the internal knots of the spline
Parameters
----------
x : (N,) array_like
Input dimension of data points -- must be increasing
y : (N,) array_like
Input dimension of data points
t : (M,) array_like
interior knots of the spline. Must be in ascending order and::
bbox[0] < t[0] < ... < t[-1] < bbox[-1]
w : (N,) array_like, optional
weights for spline fitting. Must be positive. If None (default),
weights are all equal.
bbox : (2,) array_like, optional
2-sequence specifying the boundary of the approximation interval. If
None (default), ``bbox = [x[0], x[-1]]``.
k : int, optional
Degree of the smoothing spline. Must be 1 <= `k` <= 5.
Default is k=3, a cubic spline.
ext : int or str, optional
Controls the extrapolation mode for elements
not in the interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 of 'const', return the boundary value.
The default value is 0.
Raises
------
ValueError
If the interior knots do not satisfy the Schoenberg-Whitney conditions
See Also
--------
UnivariateSpline : Superclass -- knots are specified by setting a
smoothing condition
InterpolatedUnivariateSpline : spline passing through all points
splrep : An older, non object-oriented wrapping of FITPACK
splev, sproot, splint, spalde
BivariateSpline : A similar class for two-dimensional spline interpolation
Notes
-----
The number of data points must be larger than the spline degree `k`.
Knots `t` must satisfy the Schoenberg-Whitney conditions,
i.e., there must be a subset of data points ``x[j]`` such that
``t[j] < x[j] < t[j+k+1]``, for ``j=0, 1,...,n-k-2``.
Examples
--------
>>> from scipy.interpolate import LSQUnivariateSpline
>>> import matplotlib.pyplot as plt
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
Fit a smoothing spline with a pre-defined internal knots:
>>> t = [-1, 0, 1]
>>> spl = LSQUnivariateSpline(x, y, t)
>>> xs = np.linspace(-3, 3, 1000)
>>> plt.plot(x, y, 'ro', ms=5)
>>> plt.plot(xs, spl(xs), 'g-', lw=3)
>>> plt.show()
Check the knot vector:
>>> spl.get_knots()
array([-3., -1., 0., 1., 3.])
"""
def __init__(self, x, y, t, w=None, bbox=[None]*2, k=3, ext=0):
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
xb = bbox[0]
xe = bbox[1]
if xb is None:
xb = x[0]
if xe is None:
xe = x[-1]
t = concatenate(([xb]*(k+1), t, [xe]*(k+1)))
n = len(t)
if not alltrue(t[k+1:n-k]-t[k:n-k-1] > 0, axis=0):
raise ValueError('Interior knots t must satisfy '
'Schoenberg-Whitney conditions')
if not dfitpack.fpchec(x, t, k) == 0:
raise ValueError(_fpchec_error_string)
data = dfitpack.fpcurfm1(x, y, k, t, w=w, xb=xb, xe=xe)
self._data = data[:-3] + (None, None, data[-1])
self._reset_class()
try:
self.ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
################ Bivariate spline ####################
class _BivariateSplineBase(object):
""" Base class for Bivariate spline s(x,y) interpolation on the rectangle
[xb,xe] x [yb, ye] calculated from a given set of data points
(x,y,z).
See Also
--------
bisplrep, bisplev : an older wrapping of FITPACK
BivariateSpline :
implementation of bivariate spline interpolation on a plane grid
SphereBivariateSpline :
implementation of bivariate spline interpolation on a spherical grid
"""
def get_residual(self):
""" Return weighted sum of squared residuals of the spline
approximation: sum ((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0)
"""
return self.fp
def get_knots(self):
""" Return a tuple (tx,ty) where tx,ty contain knots positions
of the spline with respect to x-, y-variable, respectively.
The position of interior and additional knots are given as
t[k+1:-k-1] and t[:k+1]=b, t[-k-1:]=e, respectively.
"""
return self.tck[:2]
def get_coeffs(self):
""" Return spline coefficients."""
return self.tck[2]
def __call__(self, x, y, mth=None, dx=0, dy=0, grid=True):
"""
Evaluate the spline or its derivatives at given positions.
Parameters
----------
x, y : array-like
Input coordinates.
If `grid` is False, evaluate the spline at points ``(x[i],
y[i]), i=0, ..., len(x)-1``. Standard Numpy broadcasting
is obeyed.
If `grid` is True: evaluate spline at the grid points
defined by the coordinate arrays x, y. The arrays must be
sorted to increasing order.
dx : int
Order of x-derivative
.. versionadded:: 0.14.0
dy : int
Order of y-derivative
.. versionadded:: 0.14.0
grid : bool
Whether to evaluate the results on a grid spanned by the
input arrays, or at points specified by the input arrays.
.. versionadded:: 0.14.0
mth : str
Deprecated argument. Has no effect.
"""
x = np.asarray(x)
y = np.asarray(y)
if mth is not None:
warnings.warn("The `mth` argument is deprecated and will be removed",
FutureWarning)
tx, ty, c = self.tck[:3]
kx, ky = self.degrees
if grid:
if x.size == 0 or y.size == 0:
return np.zeros((x.size, y.size), dtype=self.tck[2].dtype)
if dx or dy:
z,ier = dfitpack.parder(tx,ty,c,kx,ky,dx,dy,x,y)
if not ier == 0:
raise ValueError("Error code returned by parder: %s" % ier)
else:
z,ier = dfitpack.bispev(tx,ty,c,kx,ky,x,y)
if not ier == 0:
raise ValueError("Error code returned by bispev: %s" % ier)
else:
# standard Numpy broadcasting
if x.shape != y.shape:
x, y = np.broadcast_arrays(x, y)
shape = x.shape
x = x.ravel()
y = y.ravel()
if x.size == 0 or y.size == 0:
return np.zeros(shape, dtype=self.tck[2].dtype)
if dx or dy:
z,ier = dfitpack.pardeu(tx,ty,c,kx,ky,dx,dy,x,y)
if not ier == 0:
raise ValueError("Error code returned by pardeu: %s" % ier)
else:
z,ier = dfitpack.bispeu(tx,ty,c,kx,ky,x,y)
if not ier == 0:
raise ValueError("Error code returned by bispeu: %s" % ier)
z = z.reshape(shape)
return z
_surfit_messages = {1:"""
The required storage space exceeds the available storage space: nxest
or nyest too small, or s too small.
The weighted least-squares spline corresponds to the current set of
knots.""",
2:"""
A theoretically impossible result was found during the iteration
process for finding a smoothing spline with fp = s: s too small or
badly chosen eps.
Weighted sum of squared residuals does not satisfy abs(fp-s)/s < tol.""",
3:"""
the maximal number of iterations maxit (set to 20 by the program)
allowed for finding a smoothing spline with fp=s has been reached:
s too small.
Weighted sum of squared residuals does not satisfy abs(fp-s)/s < tol.""",
4:"""
No more knots can be added because the number of b-spline coefficients
(nx-kx-1)*(ny-ky-1) already exceeds the number of data points m:
either s or m too small.
The weighted least-squares spline corresponds to the current set of
knots.""",
5:"""
No more knots can be added because the additional knot would (quasi)
coincide with an old one: s too small or too large a weight to an
inaccurate data point.
The weighted least-squares spline corresponds to the current set of
knots.""",
10:"""
Error on entry, no approximation returned. The following conditions
must hold:
xb<=x[i]<=xe, yb<=y[i]<=ye, w[i]>0, i=0..m-1
If iopt==-1, then
xb<tx[kx+1]<tx[kx+2]<...<tx[nx-kx-2]<xe
yb<ty[ky+1]<ty[ky+2]<...<ty[ny-ky-2]<ye""",
-3:"""
The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank deficient
system (deficiency=%i). If deficiency is large, the results may be
inaccurate. Deficiency may strongly depend on the value of eps."""
}
class BivariateSpline(_BivariateSplineBase):
"""
Base class for bivariate splines.
This describes a spline ``s(x, y)`` of degrees ``kx`` and ``ky`` on
the rectangle ``[xb, xe] * [yb, ye]`` calculated from a given set
of data points ``(x, y, z)``.
This class is meant to be subclassed, not instantiated directly.
To construct these splines, call either `SmoothBivariateSpline` or
`LSQBivariateSpline`.
See Also
--------
UnivariateSpline : a similar class for univariate spline interpolation
SmoothBivariateSpline :
to create a BivariateSpline through the given points
LSQBivariateSpline :
to create a BivariateSpline using weighted least-squares fitting
SphereBivariateSpline :
bivariate spline interpolation in spherical cooridinates
bisplrep : older wrapping of FITPACK
bisplev : older wrapping of FITPACK
"""
@classmethod
def _from_tck(cls, tck):
"""Construct a spline object from given tck and degree"""
self = cls.__new__(cls)
if len(tck) != 5:
raise ValueError("tck should be a 5 element tuple of tx, ty, c, kx, ky")
self.tck = tck[:3]
self.degrees = tck[3:]
return self
def ev(self, xi, yi, dx=0, dy=0):
"""
Evaluate the spline at points
Returns the interpolated value at ``(xi[i], yi[i]),
i=0,...,len(xi)-1``.
Parameters
----------
xi, yi : array-like
Input coordinates. Standard Numpy broadcasting is obeyed.
dx : int
Order of x-derivative
.. versionadded:: 0.14.0
dy : int
Order of y-derivative
.. versionadded:: 0.14.0
"""
return self.__call__(xi, yi, dx=dx, dy=dy, grid=False)
def integral(self, xa, xb, ya, yb):
"""
Evaluate the integral of the spline over area [xa,xb] x [ya,yb].
Parameters
----------
xa, xb : float
The end-points of the x integration interval.
ya, yb : float
The end-points of the y integration interval.
Returns
-------
integ : float
The value of the resulting integral.
"""
tx,ty,c = self.tck[:3]
kx,ky = self.degrees
return dfitpack.dblint(tx,ty,c,kx,ky,xa,xb,ya,yb)
class SmoothBivariateSpline(BivariateSpline):
"""
Smooth bivariate spline approximation.
Parameters
----------
x, y, z : array_like
1-D sequences of data points (order is not important).
w : array_like, optional
Positive 1-D sequence of weights, of same length as `x`, `y` and `z`.
bbox : array_like, optional
Sequence of length 4 specifying the boundary of the rectangular
approximation domain. By default,
``bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)]``.
kx, ky : ints, optional
Degrees of the bivariate spline. Default is 3.
s : float, optional
Positive smoothing factor defined for estimation condition:
``sum((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s``
Default ``s=len(w)`` which should be a good value if ``1/w[i]`` is an
estimate of the standard deviation of ``z[i]``.
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
See Also
--------
bisplrep : an older wrapping of FITPACK
bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
LSQUnivariateSpline : to create a BivariateSpline using weighted
Notes
-----
The length of `x`, `y` and `z` should be at least ``(kx+1) * (ky+1)``.
"""
def __init__(self, x, y, z, w=None, bbox=[None] * 4, kx=3, ky=3, s=None,
eps=None):
xb,xe,yb,ye = bbox
nx,tx,ny,ty,c,fp,wrk1,ier = dfitpack.surfit_smth(x,y,z,w,
xb,xe,yb,ye,
kx,ky,s=s,
eps=eps,lwrk2=1)
if ier > 10: # lwrk2 was to small, re-run
nx,tx,ny,ty,c,fp,wrk1,ier = dfitpack.surfit_smth(x,y,z,w,
xb,xe,yb,ye,
kx,ky,s=s,
eps=eps,lwrk2=ier)
if ier in [0,-1,-2]: # normal return
pass
else:
message = _surfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx[:nx],ty[:ny],c[:(nx-kx-1)*(ny-ky-1)]
self.degrees = kx,ky
class LSQBivariateSpline(BivariateSpline):
"""
Weighted least-squares bivariate spline approximation.
Parameters
----------
x, y, z : array_like
1-D sequences of data points (order is not important).
tx, ty : array_like
Strictly ordered 1-D sequences of knots coordinates.
w : array_like, optional
Positive 1-D array of weights, of the same length as `x`, `y` and `z`.
bbox : (4,) array_like, optional
Sequence of length 4 specifying the boundary of the rectangular
approximation domain. By default,
``bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)]``.
kx, ky : ints, optional
Degrees of the bivariate spline. Default is 3.
s : float, optional
Positive smoothing factor defined for estimation condition:
``sum((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s``
Default ``s=len(w)`` which should be a good value if ``1/w[i]`` is an
estimate of the standard deviation of ``z[i]``.
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
See Also
--------
bisplrep : an older wrapping of FITPACK
bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
SmoothBivariateSpline : create a smoothing BivariateSpline
Notes
-----
The length of `x`, `y` and `z` should be at least ``(kx+1) * (ky+1)``.
"""
def __init__(self, x, y, z, tx, ty, w=None, bbox=[None]*4, kx=3, ky=3,
eps=None):
nx = 2*kx+2+len(tx)
ny = 2*ky+2+len(ty)
tx1 = zeros((nx,),float)
ty1 = zeros((ny,),float)
tx1[kx+1:nx-kx-1] = tx
ty1[ky+1:ny-ky-1] = ty
xb,xe,yb,ye = bbox
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,
xb,xe,yb,ye,
kx,ky,eps,lwrk2=1)
if ier > 10:
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,
xb,xe,yb,ye,
kx,ky,eps,lwrk2=ier)
if ier in [0,-1,-2]: # normal return
pass
else:
if ier < -2:
deficiency = (nx-kx-1)*(ny-ky-1)+ier
message = _surfit_messages.get(-3) % (deficiency)
else:
message = _surfit_messages.get(ier, 'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx1, ty1, c
self.degrees = kx, ky
class RectBivariateSpline(BivariateSpline):
"""
Bivariate spline approximation over a rectangular mesh.
Can be used for both smoothing and interpolating data.
Parameters
----------
x,y : array_like
1-D arrays of coordinates in strictly ascending order.
z : array_like
2-D array of data with shape (x.size,y.size).
bbox : array_like, optional
Sequence of length 4 specifying the boundary of the rectangular
approximation domain. By default,
``bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)]``.
kx, ky : ints, optional
Degrees of the bivariate spline. Default is 3.
s : float, optional
Positive smoothing factor defined for estimation condition:
``sum((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s``
Default is ``s=0``, which is for interpolation.
See Also
--------
SmoothBivariateSpline : a smoothing bivariate spline for scattered data
bisplrep : an older wrapping of FITPACK
bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
"""
def __init__(self, x, y, z, bbox=[None] * 4, kx=3, ky=3, s=0):
x, y = ravel(x), ravel(y)
if not all(diff(x) > 0.0):
raise TypeError('x must be strictly increasing')
if not all(diff(y) > 0.0):
raise TypeError('y must be strictly increasing')
if not ((x.min() == x[0]) and (x.max() == x[-1])):
raise TypeError('x must be strictly ascending')
if not ((y.min() == y[0]) and (y.max() == y[-1])):
raise TypeError('y must be strictly ascending')
if not x.size == z.shape[0]:
raise TypeError('x dimension of z must have same number of '
'elements as x')
if not y.size == z.shape[1]:
raise TypeError('y dimension of z must have same number of '
'elements as y')
z = ravel(z)
xb, xe, yb, ye = bbox
nx, tx, ny, ty, c, fp, ier = dfitpack.regrid_smth(x, y, z, xb, xe, yb,
ye, kx, ky, s)
if ier not in [0, -1, -2]:
msg = _surfit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(msg)
self.fp = fp
self.tck = tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)]
self.degrees = kx, ky
_spherefit_messages = _surfit_messages.copy()
_spherefit_messages[10] = """
ERROR. On entry, the input data are controlled on validity. The following
restrictions must be satisfied:
-1<=iopt<=1, m>=2, ntest>=8 ,npest >=8, 0<eps<1,
0<=teta(i)<=pi, 0<=phi(i)<=2*pi, w(i)>0, i=1,...,m
lwrk1 >= 185+52*v+10*u+14*u*v+8*(u-1)*v**2+8*m
kwrk >= m+(ntest-7)*(npest-7)
if iopt=-1: 8<=nt<=ntest , 9<=np<=npest
0<tt(5)<tt(6)<...<tt(nt-4)<pi
0<tp(5)<tp(6)<...<tp(np-4)<2*pi
if iopt>=0: s>=0
if one of these conditions is found to be violated,control
is immediately repassed to the calling program. in that
case there is no approximation returned."""
_spherefit_messages[-3] = """
WARNING. The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank
deficient system (deficiency=%i, rank=%i). Especially if the rank
deficiency, which is computed by 6+(nt-8)*(np-7)+ier, is large,
the results may be inaccurate. They could also seriously depend on
the value of eps."""
class SphereBivariateSpline(_BivariateSplineBase):
"""
Bivariate spline s(x,y) of degrees 3 on a sphere, calculated from a
given set of data points (theta,phi,r).
.. versionadded:: 0.11.0
See Also
--------
bisplrep, bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
SmoothUnivariateSpline :
to create a BivariateSpline through the given points
LSQUnivariateSpline :
to create a BivariateSpline using weighted least-squares fitting
"""
def __call__(self, theta, phi, dtheta=0, dphi=0, grid=True):
"""
Evaluate the spline or its derivatives at given positions.
Parameters
----------
theta, phi : array-like
Input coordinates.
If `grid` is False, evaluate the spline at points
``(theta[i], phi[i]), i=0, ..., len(x)-1``. Standard
Numpy broadcasting is obeyed.
If `grid` is True: evaluate spline at the grid points
defined by the coordinate arrays theta, phi. The arrays
must be sorted to increasing order.
dtheta : int
Order of theta-derivative
.. versionadded:: 0.14.0
dphi : int
Order of phi-derivative
.. versionadded:: 0.14.0
grid : bool
Whether to evaluate the results on a grid spanned by the
input arrays, or at points specified by the input arrays.
.. versionadded:: 0.14.0
"""
theta = np.asarray(theta)
phi = np.asarray(phi)
if theta.size > 0 and (theta.min() < 0. or theta.max() > np.pi):
raise ValueError("requested theta out of bounds.")
if phi.size > 0 and (phi.min() < 0. or phi.max() > 2. * np.pi):
raise ValueError("requested phi out of bounds.")
return _BivariateSplineBase.__call__(self, theta, phi,
dx=dtheta, dy=dphi, grid=grid)
def ev(self, theta, phi, dtheta=0, dphi=0):
"""
Evaluate the spline at points
Returns the interpolated value at ``(theta[i], phi[i]),
i=0,...,len(theta)-1``.
Parameters
----------
theta, phi : array-like
Input coordinates. Standard Numpy broadcasting is obeyed.
dtheta : int
Order of theta-derivative
.. versionadded:: 0.14.0
dphi : int
Order of phi-derivative
.. versionadded:: 0.14.0
"""
return self.__call__(theta, phi, dtheta=dtheta, dphi=dphi, grid=False)
class SmoothSphereBivariateSpline(SphereBivariateSpline):
"""
Smooth bivariate spline approximation in spherical coordinates.
.. versionadded:: 0.11.0
Parameters
----------
theta, phi, r : array_like
1-D sequences of data points (order is not important). Coordinates
must be given in radians. Theta must lie within the interval (0, pi),
and phi must lie within the interval (0, 2pi).
w : array_like, optional
Positive 1-D sequence of weights.
s : float, optional
Positive smoothing factor defined for estimation condition:
``sum((w(i)*(r(i) - s(theta(i), phi(i))))**2, axis=0) <= s``
Default ``s=len(w)`` which should be a good value if 1/w[i] is an
estimate of the standard deviation of r[i].
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
Notes
-----
For more information, see the FITPACK_ site about this function.
.. _FITPACK: http://www.netlib.org/dierckx/sphere.f
Examples
--------
Suppose we have global data on a coarse grid (the input data does not
have to be on a grid):
>>> theta = np.linspace(0., np.pi, 7)
>>> phi = np.linspace(0., 2*np.pi, 9)
>>> data = np.empty((theta.shape[0], phi.shape[0]))
>>> data[:,0], data[0,:], data[-1,:] = 0., 0., 0.
>>> data[1:-1,1], data[1:-1,-1] = 1., 1.
>>> data[1,1:-1], data[-2,1:-1] = 1., 1.
>>> data[2:-2,2], data[2:-2,-2] = 2., 2.
>>> data[2,2:-2], data[-3,2:-2] = 2., 2.
>>> data[3,3:-2] = 3.
>>> data = np.roll(data, 4, 1)
We need to set up the interpolator object
>>> lats, lons = np.meshgrid(theta, phi)
>>> from scipy.interpolate import SmoothSphereBivariateSpline
>>> lut = SmoothSphereBivariateSpline(lats.ravel(), lons.ravel(),
data.T.ravel(),s=3.5)
As a first test, we'll see what the algorithm returns when run on the
input coordinates
>>> data_orig = lut(theta, phi)
Finally we interpolate the data to a finer grid
>>> fine_lats = np.linspace(0., np.pi, 70)
>>> fine_lons = np.linspace(0., 2 * np.pi, 90)
>>> data_smth = lut(fine_lats, fine_lons)
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(131)
>>> ax1.imshow(data, interpolation='nearest')
>>> ax2 = fig.add_subplot(132)
>>> ax2.imshow(data_orig, interpolation='nearest')
>>> ax3 = fig.add_subplot(133)
>>> ax3.imshow(data_smth, interpolation='nearest')
>>> plt.show()
"""
def __init__(self, theta, phi, r, w=None, s=0., eps=1E-16):
if np.issubclass_(w, float):
w = ones(len(theta)) * w
nt_, tt_, np_, tp_, c, fp, ier = dfitpack.spherfit_smth(theta, phi,
r, w=w, s=s,
eps=eps)
if ier not in [0, -1, -2]:
message = _spherefit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(message)
self.fp = fp
self.tck = tt_[:nt_], tp_[:np_], c[:(nt_ - 4) * (np_ - 4)]
self.degrees = (3, 3)
class LSQSphereBivariateSpline(SphereBivariateSpline):
"""
Weighted least-squares bivariate spline approximation in spherical
coordinates.
.. versionadded:: 0.11.0
Parameters
----------
theta, phi, r : array_like
1-D sequences of data points (order is not important). Coordinates
must be given in radians. Theta must lie within the interval (0, pi),
and phi must lie within the interval (0, 2pi).
tt, tp : array_like
Strictly ordered 1-D sequences of knots coordinates.
Coordinates must satisfy ``0 < tt[i] < pi``, ``0 < tp[i] < 2*pi``.
w : array_like, optional
Positive 1-D sequence of weights, of the same length as `theta`, `phi`
and `r`.
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
Notes
-----
For more information, see the FITPACK_ site about this function.
.. _FITPACK: http://www.netlib.org/dierckx/sphere.f
Examples
--------
Suppose we have global data on a coarse grid (the input data does not
have to be on a grid):
>>> theta = np.linspace(0., np.pi, 7)
>>> phi = np.linspace(0., 2*np.pi, 9)
>>> data = np.empty((theta.shape[0], phi.shape[0]))
>>> data[:,0], data[0,:], data[-1,:] = 0., 0., 0.
>>> data[1:-1,1], data[1:-1,-1] = 1., 1.
>>> data[1,1:-1], data[-2,1:-1] = 1., 1.
>>> data[2:-2,2], data[2:-2,-2] = 2., 2.
>>> data[2,2:-2], data[-3,2:-2] = 2., 2.
>>> data[3,3:-2] = 3.
>>> data = np.roll(data, 4, 1)
We need to set up the interpolator object. Here, we must also specify the
coordinates of the knots to use.
>>> lats, lons = np.meshgrid(theta, phi)
>>> knotst, knotsp = theta.copy(), phi.copy()
>>> knotst[0] += .0001
>>> knotst[-1] -= .0001
>>> knotsp[0] += .0001
>>> knotsp[-1] -= .0001
>>> from scipy.interpolate import LSQSphereBivariateSpline
>>> lut = LSQSphereBivariateSpline(lats.ravel(), lons.ravel(),
data.T.ravel(),knotst,knotsp)
As a first test, we'll see what the algorithm returns when run on the
input coordinates
>>> data_orig = lut(theta, phi)
Finally we interpolate the data to a finer grid
>>> fine_lats = np.linspace(0., np.pi, 70)
>>> fine_lons = np.linspace(0., 2*np.pi, 90)
>>> data_lsq = lut(fine_lats, fine_lons)
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(131)
>>> ax1.imshow(data, interpolation='nearest')
>>> ax2 = fig.add_subplot(132)
>>> ax2.imshow(data_orig, interpolation='nearest')
>>> ax3 = fig.add_subplot(133)
>>> ax3.imshow(data_lsq, interpolation='nearest')
>>> plt.show()
"""
def __init__(self, theta, phi, r, tt, tp, w=None, eps=1E-16):
if np.issubclass_(w, float):
w = ones(len(theta)) * w
nt_, np_ = 8 + len(tt), 8 + len(tp)
tt_, tp_ = zeros((nt_,), float), zeros((np_,), float)
tt_[4:-4], tp_[4:-4] = tt, tp
tt_[-4:], tp_[-4:] = np.pi, 2. * np.pi
tt_, tp_, c, fp, ier = dfitpack.spherfit_lsq(theta, phi, r, tt_, tp_,
w=w, eps=eps)
if ier < -2:
deficiency = 6 + (nt_ - 8) * (np_ - 7) + ier
message = _spherefit_messages.get(-3) % (deficiency, -ier)
warnings.warn(message)
elif ier not in [0, -1, -2]:
message = _spherefit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(message)
self.fp = fp
self.tck = tt_, tp_, c
self.degrees = (3, 3)
_spfit_messages = _surfit_messages.copy()
_spfit_messages[10] = """
ERROR: on entry, the input data are controlled on validity
the following restrictions must be satisfied.
-1<=iopt(1)<=1, 0<=iopt(2)<=1, 0<=iopt(3)<=1,
-1<=ider(1)<=1, 0<=ider(2)<=1, ider(2)=0 if iopt(2)=0.
-1<=ider(3)<=1, 0<=ider(4)<=1, ider(4)=0 if iopt(3)=0.
mu >= mumin (see above), mv >= 4, nuest >=8, nvest >= 8,
kwrk>=5+mu+mv+nuest+nvest,
lwrk >= 12+nuest*(mv+nvest+3)+nvest*24+4*mu+8*mv+max(nuest,mv+nvest)
0< u(i-1)<u(i)< pi,i=2,..,mu,
-pi<=v(1)< pi, v(1)<v(i-1)<v(i)<v(1)+2*pi, i=3,...,mv
if iopt(1)=-1: 8<=nu<=min(nuest,mu+6+iopt(2)+iopt(3))
0<tu(5)<tu(6)<...<tu(nu-4)< pi
8<=nv<=min(nvest,mv+7)
v(1)<tv(5)<tv(6)<...<tv(nv-4)<v(1)+2*pi
the schoenberg-whitney conditions, i.e. there must be
subset of grid co-ordinates uu(p) and vv(q) such that
tu(p) < uu(p) < tu(p+4) ,p=1,...,nu-4
(iopt(2)=1 and iopt(3)=1 also count for a uu-value
tv(q) < vv(q) < tv(q+4) ,q=1,...,nv-4
(vv(q) is either a value v(j) or v(j)+2*pi)
if iopt(1)>=0: s>=0
if s=0: nuest>=mu+6+iopt(2)+iopt(3), nvest>=mv+7
if one of these conditions is found to be violated,control is
immediately repassed to the calling program. in that case there is no
approximation returned."""
class RectSphereBivariateSpline(SphereBivariateSpline):
"""
Bivariate spline approximation over a rectangular mesh on a sphere.
Can be used for smoothing data.
.. versionadded:: 0.11.0
Parameters
----------
u : array_like
1-D array of latitude coordinates in strictly ascending order.
Coordinates must be given in radians and lie within the interval
(0, pi).
v : array_like
1-D array of longitude coordinates in strictly ascending order.
Coordinates must be given in radians, and must lie within (0, 2pi).
r : array_like
2-D array of data with shape ``(u.size, v.size)``.
s : float, optional
Positive smoothing factor defined for estimation condition
(``s=0`` is for interpolation).
pole_continuity : bool or (bool, bool), optional
Order of continuity at the poles ``u=0`` (``pole_continuity[0]``) and
``u=pi`` (``pole_continuity[1]``). The order of continuity at the pole
will be 1 or 0 when this is True or False, respectively.
Defaults to False.
pole_values : float or (float, float), optional
Data values at the poles ``u=0`` and ``u=pi``. Either the whole
parameter or each individual element can be None. Defaults to None.
pole_exact : bool or (bool, bool), optional
Data value exactness at the poles ``u=0`` and ``u=pi``. If True, the
value is considered to be the right function value, and it will be
fitted exactly. If False, the value will be considered to be a data
value just like the other data values. Defaults to False.
pole_flat : bool or (bool, bool), optional
For the poles at ``u=0`` and ``u=pi``, specify whether or not the
approximation has vanishing derivatives. Defaults to False.
See Also
--------
RectBivariateSpline : bivariate spline approximation over a rectangular
mesh
Notes
-----
Currently, only the smoothing spline approximation (``iopt[0] = 0`` and
``iopt[0] = 1`` in the FITPACK routine) is supported. The exact
least-squares spline approximation is not implemented yet.
When actually performing the interpolation, the requested `v` values must
lie within the same length 2pi interval that the original `v` values were
chosen from.
For more information, see the FITPACK_ site about this function.
.. _FITPACK: http://www.netlib.org/dierckx/spgrid.f
Examples
--------
Suppose we have global data on a coarse grid
>>> lats = np.linspace(10, 170, 9) * np.pi / 180.
>>> lons = np.linspace(0, 350, 18) * np.pi / 180.
>>> data = np.dot(np.atleast_2d(90. - np.linspace(-80., 80., 18)).T,
np.atleast_2d(180. - np.abs(np.linspace(0., 350., 9)))).T
We want to interpolate it to a global one-degree grid
>>> new_lats = np.linspace(1, 180, 180) * np.pi / 180
>>> new_lons = np.linspace(1, 360, 360) * np.pi / 180
>>> new_lats, new_lons = np.meshgrid(new_lats, new_lons)
We need to set up the interpolator object
>>> from scipy.interpolate import RectSphereBivariateSpline
>>> lut = RectSphereBivariateSpline(lats, lons, data)
Finally we interpolate the data. The `RectSphereBivariateSpline` object
only takes 1-D arrays as input, therefore we need to do some reshaping.
>>> data_interp = lut.ev(new_lats.ravel(),
... new_lons.ravel()).reshape((360, 180)).T
Looking at the original and the interpolated data, one can see that the
interpolant reproduces the original data very well:
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(211)
>>> ax1.imshow(data, interpolation='nearest')
>>> ax2 = fig.add_subplot(212)
>>> ax2.imshow(data_interp, interpolation='nearest')
>>> plt.show()
Chosing the optimal value of ``s`` can be a delicate task. Recommended
values for ``s`` depend on the accuracy of the data values. If the user
has an idea of the statistical errors on the data, she can also find a
proper estimate for ``s``. By assuming that, if she specifies the
right ``s``, the interpolator will use a spline ``f(u,v)`` which exactly
reproduces the function underlying the data, she can evaluate
``sum((r(i,j)-s(u(i),v(j)))**2)`` to find a good estimate for this ``s``.
For example, if she knows that the statistical errors on her
``r(i,j)``-values are not greater than 0.1, she may expect that a good
``s`` should have a value not larger than ``u.size * v.size * (0.1)**2``.
If nothing is known about the statistical error in ``r(i,j)``, ``s`` must
be determined by trial and error. The best is then to start with a very
large value of ``s`` (to determine the least-squares polynomial and the
corresponding upper bound ``fp0`` for ``s``) and then to progressively
decrease the value of ``s`` (say by a factor 10 in the beginning, i.e.
``s = fp0 / 10, fp0 / 100, ...`` and more carefully as the approximation
shows more detail) to obtain closer fits.
The interpolation results for different values of ``s`` give some insight
into this process:
>>> fig2 = plt.figure()
>>> s = [3e9, 2e9, 1e9, 1e8]
>>> for ii in xrange(len(s)):
>>> lut = RectSphereBivariateSpline(lats, lons, data, s=s[ii])
>>> data_interp = lut.ev(new_lats.ravel(),
... new_lons.ravel()).reshape((360, 180)).T
>>> ax = fig2.add_subplot(2, 2, ii+1)
>>> ax.imshow(data_interp, interpolation='nearest')
>>> ax.set_title("s = %g" % s[ii])
>>> plt.show()
"""
def __init__(self, u, v, r, s=0., pole_continuity=False, pole_values=None,
pole_exact=False, pole_flat=False):
iopt = np.array([0, 0, 0], dtype=int)
ider = np.array([-1, 0, -1, 0], dtype=int)
if pole_values is None:
pole_values = (None, None)
elif isinstance(pole_values, (float, np.float32, np.float64)):
pole_values = (pole_values, pole_values)
if isinstance(pole_continuity, bool):
pole_continuity = (pole_continuity, pole_continuity)
if isinstance(pole_exact, bool):
pole_exact = (pole_exact, pole_exact)
if isinstance(pole_flat, bool):
pole_flat = (pole_flat, pole_flat)
r0, r1 = pole_values
iopt[1:] = pole_continuity
if r0 is None:
ider[0] = -1
else:
ider[0] = pole_exact[0]
if r1 is None:
ider[2] = -1
else:
ider[2] = pole_exact[1]
ider[1], ider[3] = pole_flat
u, v = np.ravel(u), np.ravel(v)
if not np.all(np.diff(u) > 0.0):
raise TypeError('u must be strictly increasing')
if not np.all(np.diff(v) > 0.0):
raise TypeError('v must be strictly increasing')
if not u.size == r.shape[0]:
raise TypeError('u dimension of r must have same number of '
'elements as u')
if not v.size == r.shape[1]:
raise TypeError('v dimension of r must have same number of '
'elements as v')
if pole_continuity[1] is False and pole_flat[1] is True:
raise TypeError('if pole_continuity is False, so must be '
'pole_flat')
if pole_continuity[0] is False and pole_flat[0] is True:
raise TypeError('if pole_continuity is False, so must be '
'pole_flat')
r = np.ravel(r)
nu, tu, nv, tv, c, fp, ier = dfitpack.regrid_smth_spher(iopt, ider,
u.copy(), v.copy(), r.copy(), r0, r1, s)
if ier not in [0, -1, -2]:
msg = _spfit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(msg)
self.fp = fp
self.tck = tu[:nu], tv[:nv], c[:(nu - 4) * (nv-4)]
self.degrees = (3, 3)
| mit |
nilbody/h2o-3 | h2o-py/tests/testdir_golden/pyunit_svd_1_golden.py | 1 | 2402 | from __future__ import print_function
from builtins import zip
import sys
sys.path.insert(1,"../../")
import h2o
from tests import pyunit_utils
def svd_1_golden():
print("Importing USArrests.csv data...")
arrestsH2O = h2o.upload_file(pyunit_utils.locate("smalldata/pca_test/USArrests.csv"))
print("Compare with SVD")
fitH2O = h2o.svd(x=arrestsH2O[0:4], nv=4, transform="NONE", max_iterations=2000)
print("Compare singular values (D)")
h2o_d = fitH2O._model_json['output']['d']
r_d = [1419.06139509772, 194.825846110138, 45.6613376308754, 18.0695566224677]
print("R Singular Values: {0}".format(r_d))
print("H2O Singular Values: {0}".format(h2o_d))
for r, h in zip(r_d, h2o_d): assert abs(r - h) < 1e-6, "H2O got {0}, but R got {1}".format(h, r)
print("Compare right singular vectors (V)")
h2o_v = h2o.as_list(h2o.get_frame(fitH2O._model_json['output']['v_key']['name']), use_pandas=False)
h2o_v.pop(0)
r_v = [[-0.04239181, 0.01616262, -0.06588426, 0.99679535],
[-0.94395706, 0.32068580, 0.06655170, -0.04094568],
[-0.30842767, -0.93845891, 0.15496743, 0.01234261],
[-0.10963744, -0.12725666, -0.98347101, -0.06760284]]
print("R Right Singular Vectors: {0}".format(r_v))
print("H2O Right Singular Vectors: {0}".format(h2o_v))
for rl, hl in zip(r_v, h2o_v):
for r, h in zip(rl, hl): assert abs(abs(r) - abs(float(h))) < 1e-5, "H2O got {0}, but R got {1}".format(h, r)
print("Compare left singular vectors (U)")
h2o_u = h2o.as_list(h2o.get_frame(fitH2O._model_json['output']['u_key']['name']), use_pandas=False)
h2o_u.pop(0)
r_u = [[-0.1716251, 0.096325710, 0.06515480, 0.15369551],
[-0.1891166, 0.173452566, -0.42665785, -0.17801438],
[-0.2155930, 0.078998111, 0.02063740, -0.28070784],
[-0.1390244, 0.059889811, 0.01392269, 0.01610418],
[-0.2067788, -0.009812026, -0.17633244, -0.21867425],
[-0.1558794, -0.064555293, -0.28288280, -0.11797419]]
print("R Left Singular Vectors: {0}".format(r_u))
print("H2O Left Singular Vectors: {0}".format(h2o_u))
for rl, hl in zip(r_u, h2o_u):
for r, h in zip(rl, hl): assert abs(abs(r) - abs(float(h))) < 1e-5, "H2O got {0}, but R got {1}".format(h, r)
if __name__ == "__main__":
pyunit_utils.standalone_test(svd_1_golden)
else:
svd_1_golden()
| apache-2.0 |
UKPLab/emnlp2017-claim-identification | src/main/python/process_data_se_WithDevel.py | 1 | 4976 | import cPickle
import numpy as np
import pandas as pd
import re
import sys
from collections import defaultdict
def build_data_cv(data_folder, cv=10, clean_string=True):
"""
Loads data.
"""
revs = []
pos_file = data_folder[0] # train file
neg_file = data_folder[1] # test file
devel_file = data_folder[2]
vocab = defaultdict(float)
for (mysplit,myfile) in [(0,pos_file),(1,neg_file),(2,devel_file)]:
with open(myfile, "rb") as f:
for line in f:
rev = []
strippedLine = line.strip()
try:
lline,label = strippedLine.split("\t")
except ValueError:
lline = ""
label = strippedLine
rev.append(lline.strip())
if clean_string:
orig_rev = clean_str(" ".join(rev))
else:
orig_rev = " ".join(rev).lower()
words = set(orig_rev.split())
for word in words:
vocab[word] += 1
datum = {"y":int(label),
"text": orig_rev,
"num_words": len(orig_rev.split()),
"split": mysplit}
revs.append(datum)
#print revs
return revs, vocab
def get_W(word_vecs, k=300):
"""
Get word matrix. W[i] is the vector for word indexed by i
"""
vocab_size = len(word_vecs)
word_idx_map = dict()
W = np.zeros(shape=(vocab_size+1, k), dtype='float32')
W[0] = np.zeros(k, dtype='float32')
i = 1
for word in word_vecs:
W[i] = word_vecs[word]
word_idx_map[word] = i
i += 1
return W, word_idx_map
def load_bin_vec(fname, vocab):
"""
Loads 300x1 word vecs from Google (Mikolov) word2vec
"""
word_vecs = {}
with open(fname, "rb") as f:
header = f.readline()
vocab_size, layer1_size = map(int, header.split())
binary_len = np.dtype('float32').itemsize * layer1_size
for line in xrange(vocab_size):
word = []
while True:
ch = f.read(1)
if ch == ' ':
word = ''.join(word)
break
if ch != '\n':
word.append(ch)
if word in vocab:
word_vecs[word] = np.fromstring(f.read(binary_len), dtype='float32')
else:
f.read(binary_len)
return word_vecs
def add_unknown_words(word_vecs, vocab, min_df=1, k=300):
"""
For words that occur in at least min_df documents, create a separate word vector.
0.25 is chosen so the unknown vectors have (approximately) same variance as pre-trained ones
"""
for word in vocab:
if word not in word_vecs and vocab[word] >= min_df:
word_vecs[word] = np.random.uniform(-0.25,0.25,k)
def clean_str(string, TREC=False):
"""
Tokenization/string cleaning for all datasets except for SST.
Every dataset is lower cased except for TREC
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'ve", " \'ve", string)
string = re.sub(r"n\'t", " n\'t", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\(", " \( ", string)
string = re.sub(r"\)", " \) ", string)
string = re.sub(r"\?", " \? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip() if TREC else string.strip().lower()
def clean_str_sst(string):
"""
Tokenization/string cleaning for the SST dataset
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip().lower()
if __name__=="__main__":
w2v_file = sys.argv[1]
trainFile = sys.argv[2]
testFile = sys.argv[3]
develFile = sys.argv[4]
saveFile = sys.argv[5]
data_folder = [trainFile,testFile,develFile]
print "loading data...",
revs, vocab = build_data_cv(data_folder, cv=10, clean_string=True)
max_l = np.max(pd.DataFrame(revs)["num_words"])
print "data loaded!"
print "number of sentences: " + str(len(revs))
print "vocab size: " + str(len(vocab))
print "max sentence length: " + str(max_l)
print "loading word2vec vectors...",
sys.stdout.flush()
w2v = load_bin_vec(w2v_file, vocab)
print "word2vec loaded!"
print "num words already in word2vec: " + str(len(w2v))
add_unknown_words(w2v, vocab)
W, word_idx_map = get_W(w2v)
rand_vecs = {}
add_unknown_words(rand_vecs, vocab)
W2, _ = get_W(rand_vecs)
cPickle.dump([revs, W, W2, word_idx_map, vocab], open(saveFile, "wb"))
print "dataset created!"
#sys.exit(1) # SE
| apache-2.0 |
imatge-upc/saliency | shallow/train.py | 2 | 3064 | # add to kfkd.py
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet,BatchIterator
import os
import numpy as np
from sklearn.utils import shuffle
import cPickle as pickle
import matplotlib.pyplot as plt
import Image
import ImageOps
from scipy import misc
import scipy.io
import theano
def load():
f = file('data_Salicon_T.cPickle', 'rb')
loaded_obj = pickle.load(f)
f.close()
X, y = loaded_obj
return X, y
def float32(k):
return np.cast['float32'](k)
class AdjustVariable(object):
def __init__(self, name, start=0.03, stop=0.001):
self.name = name
self.start, self.stop = start, stop
self.ls = None
def __call__(self, nn, train_history):
if self.ls is None:
self.ls = np.linspace(self.start, self.stop, nn.max_epochs)
epoch = train_history[-1]['epoch']
new_value = float32(self.ls[epoch - 1])
getattr(nn, self.name).set_value(new_value)
class FlipBatchIterator(BatchIterator):
def transform(self, Xb, yb):
Xb, yb = super(FlipBatchIterator, self).transform(Xb, yb)
# Flip half of the images in this batch at random:
bs = Xb.shape[0]
indices = np.random.choice(bs, bs / 2, replace=False)
Xb[indices] = Xb[indices, :, :, ::-1]
tmp = yb[indices].reshape(bs/2,1,48,48)
mirror = tmp[ :,:,:, ::-1]
yb[indices] = mirror.reshape(bs/2,48*48)
return Xb, yb
net2 = NeuralNet(
layers=[
('input', layers.InputLayer),
('conv1', layers.Conv2DLayer),
('pool1', layers.MaxPool2DLayer),
('conv2', layers.Conv2DLayer),
('pool2', layers.MaxPool2DLayer),
('conv3', layers.Conv2DLayer),
('pool3', layers.MaxPool2DLayer),
('hidden4', layers.DenseLayer),
('maxout6',layers.FeaturePoolLayer),
('output', layers.DenseLayer),
],
input_shape=(None, 3, 96, 96),
conv1_num_filters=32, conv1_filter_size=(5, 5), pool1_pool_size=(2, 2),
conv2_num_filters=64, conv2_filter_size=(3, 3), pool2_pool_size=(2, 2),
conv3_num_filters=64, conv3_filter_size=(3, 3), pool3_pool_size=(2, 2),
hidden4_num_units=48*48*2,
maxout6_pool_size=2,output_num_units=48*48,output_nonlinearity=None,
update_learning_rate=theano.shared(float32(0.05)),
update_momentum=theano.shared(float32(0.9)),
regression=True,
on_epoch_finished=[
AdjustVariable('update_learning_rate', start=0.05, stop=0.0001),
AdjustVariable('update_momentum', start=0.9, stop=0.999),
],
batch_iterator_train=FlipBatchIterator(batch_size=128),
max_epochs=1200,
verbose=1,
)
X, y = load()
print("X.shape == {}; X.min == {:.3f}; X.max == {:.3f}".format(
X.shape, X.min(), X.max()))
print("y.shape == {}; y.min == {:.3f}; y.max == {:.3f}".format(
y.shape, y.min(), y.max()))
X = X.astype(np.float32)
y = y.astype(np.float32)
net.fit(X, y)
with open('JuntingNet_SALICON.pickle', 'wb') as f:
pickle.dump(net2, f, -1) | mit |
bromjiri/Presto | predictor/predictor_new.py | 1 | 8137 | import settings
import pandas as pd
import numpy as np
import os
from datetime import datetime
from datetime import timedelta
import predictor.predictor_classifier as cls
import predictor.predictor_statistic as stat
import random
import nltk
class Stock:
def __init__(self, subject):
input_file = settings.PREDICTOR_STOCK + "/" + subject + ".csv"
self.stock_df = pd.read_csv(input_file, sep=',', index_col='Date')
def create_dict(self, from_date, to_date):
self.stock_ser = self.stock_df['Diff'].loc[from_date:to_date]
# binning
self.stock_ser = self.stock_ser.apply(binning_none)
self.stock_dict = self.stock_ser.dropna().astype(int).to_dict()
def get_dict(self):
return self.stock_dict
def get_stock_dates(self):
return self.stock_ser.index.values
class Sent:
def __init__(self, subject, source):
input_file = settings.PREDICTOR_SENTIMENT + "/" + source + "/" + source + "-sent-" + subject + ".csv"
self.sent_df = pd.read_csv(input_file, sep=',', index_col='Date')
def get_weekend(self, col_name, stock_dates):
weekend_df = np.round(self.sent_df, 2)
aggreg = 0
days = 1
for idx, row in weekend_df.iterrows():
value = row[col_name]
date = pd.to_datetime(idx)
date_plus = date + timedelta(days=1)
if str(date_plus.date()) not in stock_dates:
# print("weekend")
value += aggreg
aggreg = value
days += 1
else:
total = value + aggreg
mean = total / days
aggreg = 0
days = 1
weekend_df.set_value(idx, col_name, mean)
# print(date.date(), row[col_name], value)
return np.round(weekend_df[col_name].diff().loc[stock_dates], 2)
def create_dict(self, precision, method, from_date, to_date, stock_dates, binning):
sentiment_col = "Sent" + precision
sent_ser = self.sent_df[sentiment_col]
if method == "Natural":
sent_ser = sent_ser.diff().loc[from_date:to_date]
elif method == "Friday":
sent_ser = sent_ser.loc[stock_dates].diff()
elif method == "Sunday":
sent_ser = sent_ser.diff().loc[stock_dates]
elif method == "Weekend":
sent_ser = self.get_weekend(sentiment_col, stock_dates)
# binning
std_dev1 = sent_ser.std() / 4
std_dev2 = sent_ser.std()
if binning == 'none':
sent_ser_new = sent_ser.apply(binning_none)
elif binning == 'low':
sent_ser_new = sent_ser.apply(binning_low, args=(std_dev1,))
else:
sent_ser_new = sent_ser.apply(binning_high, args=(std_dev1, std_dev2,))
# print(pd.concat([sent_ser, sent_ser_new], axis=1))
self.sent_dict = sent_ser_new.dropna().astype(int).to_dict()
self.key_list = sorted(self.sent_dict.keys())
def get_dict(self):
return self.sent_dict
def get_features(self, key):
index = self.key_list.index(key)
features = dict()
features['d1'] = self.sent_dict[self.key_list[index-3]]
features['d2'] = self.sent_dict[self.key_list[index-2]]
features['d3'] = self.sent_dict[self.key_list[index-1]]
return features
def binning_none(row):
if row > 0:
return 4
elif row < 0:
return 0
else:
return row
def binning_low(row, std_dev1):
if row > std_dev1:
return 4
elif row < std_dev1 and row > -std_dev1:
return 2
elif row < -std_dev1:
return 0
else:
return row
def binning_high(row, std_dev1, std_dev2):
if row > std_dev2:
return 4
elif row < std_dev2 and row > std_dev1:
return 3
elif row < std_dev1 and row > -std_dev1:
return 2
elif row < -std_dev1 and row > -std_dev2:
return 1
elif row < -std_dev2:
return 0
else:
return row
def run_one(source, subject, precision, method, from_date, to_date, binning, filename_nltk, filename_skl):
# stock dataframe
stock = Stock(subject)
stock.create_dict(from_date, to_date)
stock_dict = stock.get_dict()
# print(sorted(stock_dict.items()))
indexes = ["djia", "snp", "nasdaq"]
# if subject in indexes:
# subject = "the"
# sentiment dataframe
sent = Sent(subject, source)
sent.create_dict(precision, method, from_date, to_date, stock.get_stock_dates(), binning)
# print(sorted(sent.get_dict().items()))
# features
features_list = list()
for key in sorted(stock_dict)[3:]:
features = sent.get_features(key)
features_list.append([features, stock_dict[key]])
# print([key, sorted(features.items()), stock_dict[key]])
features_list_pos = list()
features_list_neg = list()
for feature in features_list:
if feature[1] == 0:
features_list_neg.append(feature)
else:
features_list_pos.append(feature)
statistic = stat.Statistic(source, subject, precision, method, binning)
# print(len(features_list), len(features_list_pos), len(features_list_neg))
max_half = min(len(features_list_pos), len(features_list_neg))
train_border = int(max_half * 4 / 5)
# print(train_border, max_half)
# exit()
cycles = 50
for x in range(0, cycles):
random.shuffle(features_list_pos)
random.shuffle(features_list_neg)
# random.shuffle(features_list)
trainfeats = features_list_pos[:train_border] + features_list_neg[:train_border]
testfeats = features_list_pos[train_border:max_half] + features_list_neg[train_border:max_half]
# print(len(trainfeats), len(testfeats))
# trainfeats = features_list[:170]
# testfeats = features_list[170:]
nlt_output, skl_output = cls.train(trainfeats, testfeats, nlt=nltk_run, skl=sklearn_run)
# print(nlt_output['most1'])
# exit()
if nltk_run:
statistic.add_nltk(nlt_output)
if sklearn_run:
statistic.add_skl(skl_output)
if nltk_run:
statistic.mean_nltk(cycles)
statistic.print_nltk()
# statistic.write_nltk(filename_nltk)
if sklearn_run:
statistic.mean_skl(cycles)
statistic.print_skl()
statistic.print_stddev()
# statistic.write_skl(filename_skl)
nltk_run = True
sklearn_run = True
from_date = '2016-11-01'
to_date = '2017-08-31'
source = "stwits-comb"
binnings = ['none', 'low', 'high']
# subjects = ["snp", "djia", "nasdaq"]
subjects = ["djia", "snp", "nasdaq"]
# subjects = ["microsoft"]
precisions = ["0.6", "0.8", "1.0"]
# precisions = ["0.6"]
methods = ["Friday", "Natural", "Weekend", "Sunday"]
# methods = ["Friday"]
for subject in subjects:
folder = settings.PREDICTOR_PREDICTION + '/' + source + '/' + subject + '/'
os.makedirs(folder, exist_ok=True)
filename_nltk = folder + source + '-prediction-' + subject + "-nltk.csv"
filename_skl = folder + source + '-prediction-' + subject + "-skl.csv"
# if nltk_run:
# open(filename_nltk, 'w').close()
#
# if sklearn_run:
# open(filename_skl, 'w').close()
for method in methods:
# if nltk_run:
# f = open(filename_nltk, 'a')
# f.write(source + ", " + subject + ", " + method + ", NLTK\n")
# f.write("precision, binning, accuracy, pos_prec, neg_prec, pos_rec, neg_rec, d1, d2, d3\n")
# f.close()
#
# if sklearn_run:
# f = open(filename_skl, 'a')
# f.write(source + ", " + subject + ", " + method + ", SKL\n")
# f.write("precision, binning, mnb, bnb, lr, lsvc, nsvc, voted\n")
# f.close()
for precision in precisions:
for binning in binnings:
# print(source, subject, precision, method)
run_one(source, subject, precision, method, from_date, to_date, binning, filename_nltk, filename_skl)
| mit |
dingmingliu/quanttrade | bt/core.py | 1 | 37660 | """
Contains the core building blocks of the framework.
"""
import math
from copy import deepcopy
import pandas as pd
import numpy as np
import cython as cy
class Node(object):
"""
The Node is the main building block in bt's tree structure design.
Both StrategyBase and SecurityBase inherit Node. It contains the
core functionality of a tree node.
Args:
* name (str): The Node name
* parent (Node): The parent Node
* children (dict, list): A collection of children. If dict,
the format is {name: child}, if list then list of children.
Attributes:
* name (str): Node name
* parent (Node): Node parent
* root (Node): Root node of the tree (topmost node)
* children (dict): Node's children
* now (datetime): Used when backtesting to store current date
* stale (bool): Flag used to determine if Node is stale and need
updating
* prices (TimeSeries): Prices of the Node. Prices for a security will
be the security's price, for a strategy it will be an index that
reflects the value of the strategy over time.
* price (float): last price
* value (float): last value
* weight (float): weight in parent
* full_name (str): Name including parents' names
* members (list): Current Node + node's children
"""
_price = cy.declare(cy.double)
_value = cy.declare(cy.double)
_weight = cy.declare(cy.double)
_issec = cy.declare(cy.bint)
_has_strat_children = cy.declare(cy.bint)
def __init__(self, name, parent=None, children=None):
self.name = name
# strategy children helpers
self._has_strat_children = False
self._strat_children = []
# if children is not None, we assume that we want to limit the
# available children space to the provided list.
if children is not None:
if isinstance(children, list):
# if all strings - just save as universe_filter
if all(isinstance(x, str) for x in children):
self._universe_tickers = children
# empty dict - don't want to uselessly create
# tons of children when they might not be needed
children = {}
else:
# this will be case if we pass in children
# (say a bunch of sub-strategies)
tmp = {}
ut = []
for c in children:
if type(c) == str:
tmp[c] = SecurityBase(c)
ut.append(c)
else:
# deepcopy object for possible later reuse
tmp[c.name] = deepcopy(c)
# if strategy, turn on flag and add name to list
# strategy children have special treatment
if isinstance(c, StrategyBase):
self._has_strat_children = True
self._strat_children.append(c.name)
# if not strategy, then we will want to add this to
# universe_tickers to filter on setup
else:
ut.append(c.name)
children = tmp
# we want to keep whole universe in this case
# so set to None
self._universe_tickers = ut
if parent is None:
self.parent = self
self.root = self
else:
self.parent = parent
self.root = parent.root
parent._add_child(self)
# default children
if children is None:
children = {}
self._universe_tickers = None
self.children = children
self._childrenv = children.values()
for c in self._childrenv:
c.parent = self
c.root = self.root
# set default value for now
self.now = 0
# make sure root has stale flag
# used to avoid unncessary update
# sometimes we change values in the tree and we know that we will need
# to update if another node tries to access a given value (say weight).
# This avoid calling the update until it is actually needed.
self.root.stale = False
# helper vars
self._price = 0
self._value = 0
self._weight = 0
# is security flag - used to avoid updating 0 pos securities
self._issec = False
def __getitem__(self, key):
return self.children[key]
@property
def prices(self):
"""
A TimeSeries of the Node's price.
"""
# can optimize depending on type -
# securities don't need to check stale to
# return latest prices, whereas strategies do...
raise NotImplementedError()
@property
def price(self):
"""
Current price of the Node
"""
# can optimize depending on type -
# securities don't need to check stale to
# return latest prices, whereas strategies do...
raise NotImplementedError()
@property
def value(self):
"""
Current value of the Node
"""
if self.root.stale:
self.root.update(self.root.now, None)
return self._value
@property
def weight(self):
"""
Current weight of the Node (with respect to the parent).
"""
if self.root.stale:
self.root.update(self.root.now, None)
return self._weight
def setup(self, dates):
"""
Setup method used to initialize a Node with a set of dates.
"""
raise NotImplementedError()
def _add_child(self, child):
child.parent = self
child.root = self.root
if self.children is None:
self.children = {child.name: child}
else:
self.children[child.name] = child
self._childrenv = self.children.values()
def update(self, date, data=None, inow=None):
"""
Update Node with latest date, and optionally some data.
"""
raise NotImplementedError()
def adjust(self, amount, update=True, isflow=True):
"""
Adjust Node value by amount.
"""
raise NotImplementedError()
def allocate(self, amount, update=True):
"""
Allocate capital to Node.
"""
raise NotImplementedError()
@property
def members(self):
"""
Node members. Members include current node as well as Node's
children.
"""
res = [self]
for c in self.children.values():
res.extend(c.members)
return res
@property
def full_name(self):
if self.parent == self:
return self.name
else:
return '%s>%s' % (self.parent.full_name, self.name)
class StrategyBase(Node):
"""
Strategy Node. Used to define strategy logic within a tree.
A Strategy's role is to allocate capital to it's children
based on a function.
Args:
* name (str): Strategy name
* children (dict, list): A collection of children. If dict,
the format is {name: child}, if list then list of children.
Children can be any type of Node.
* parent (Node): The parent Node
Attributes:
* name (str): Strategy name
* parent (Strategy): Strategy parent
* root (Strategy): Root node of the tree (topmost node)
* children (dict): Strategy's children
* now (datetime): Used when backtesting to store current date
* stale (bool): Flag used to determine if Strategy is stale and need
updating
* prices (TimeSeries): Prices of the Strategy - basically an index that
reflects the value of the strategy over time.
* price (float): last price
* value (float): last value
* weight (float): weight in parent
* full_name (str): Name including parents' names
* members (list): Current Strategy + strategy's children
* commission_fn (fn(quantity, price)): A function used to determine the
commission (transaction fee) amount. Could be used to model slippage
(implementation shortfall). Note that often fees are symmetric for
buy and sell and absolute value of quantity should be used for
calculation.
* capital (float): Capital amount in Strategy - cash
* universe (DataFrame): Data universe available at the current time.
Universe contains the data passed in when creating a Backtest. Use
this data to determine strategy logic.
"""
_capital = cy.declare(cy.double)
_net_flows = cy.declare(cy.double)
_last_value = cy.declare(cy.double)
_last_price = cy.declare(cy.double)
_last_fee = cy.declare(cy.double)
_paper_trade = cy.declare(cy.bint)
bankrupt = cy.declare(cy.bint)
def __init__(self, name, children=None, parent=None):
Node.__init__(self, name, children=children, parent=parent)
self._capital = 0
self._weight = 1
self._value = 0
self._price = 100
# helper vars
self._net_flows = 0
self._last_value = 0
self._last_price = 100
self._last_fee = 0
# default commission function
self.commission_fn = self._dflt_comm_fn
self._paper_trade = False
self._positions = None
self.bankrupt = False
@property
def price(self):
"""
Current price.
"""
if self.root.stale:
self.root.update(self.now, None)
return self._price
@property
def prices(self):
"""
TimeSeries of prices.
"""
if self.root.stale:
self.root.update(self.now, None)
return self._prices.ix[:self.now]
@property
def values(self):
"""
TimeSeries of values.
"""
if self.root.stale:
self.root.update(self.now, None)
return self._values.ix[:self.now]
@property
def capital(self):
"""
Current capital - amount of unallocated capital left in strategy.
"""
# no stale check needed
return self._capital
@property
def cash(self):
"""
TimeSeries of unallocated capital.
"""
# no stale check needed
return self._cash
@property
def fees(self):
"""
TimeSeries of fees.
"""
# no stale check needed
return self._fees
@property
def universe(self):
"""
Data universe available at the current time.
Universe contains the data passed in when creating a Backtest.
Use this data to determine strategy logic.
"""
# avoid windowing every time
# if calling and on same date return
# cached value
if self.now == self._last_chk:
return self._funiverse
else:
self._last_chk = self.now
self._funiverse = self._universe.ix[:self.now]
return self._funiverse
@property
def positions(self):
"""
TimeSeries of positions.
"""
# if accessing and stale - update first
if self.root.stale:
self.root.update(self.root.now, None)
if self._positions is not None:
return self._positions
else:
vals = pd.DataFrame({x.name: x.positions for x in self.members
if isinstance(x, SecurityBase)})
self._positions = vals
return vals
def setup(self, universe):
"""
Setup strategy with universe. This will speed up future calculations
and updates.
"""
# save full universe in case we need it
self._original_data = universe
# determine if needs paper trading
# and setup if so
if self is not self.parent:
self._paper_trade = True
self._paper_amount = 1000000
paper = deepcopy(self)
paper.parent = paper
paper.root = paper
paper._paper_trade = False
paper.setup(self._original_data)
paper.adjust(self._paper_amount)
self._paper = paper
# setup universe
funiverse = universe
if self._universe_tickers is not None:
# if we have universe_tickers defined, limit universe to
# those tickers
valid_filter = list(set(universe.columns)
.intersection(self._universe_tickers))
funiverse = universe[valid_filter].copy()
# if we have strat children, we will need to create their columns
# in the new universe
if self._has_strat_children:
for c in self._strat_children:
funiverse[c] = np.nan
# must create to avoid pandas warning
funiverse = pd.DataFrame(funiverse)
self._universe = funiverse
# holds filtered universe
self._funiverse = funiverse
self._last_chk = None
# We're not bankrupt yet
self.bankrupt = False
# setup internal data
self.data = pd.DataFrame(index=funiverse.index,
columns=['price', 'value', 'cash', 'fees'],
data=0.0)
self._prices = self.data['price']
self._values = self.data['value']
self._cash = self.data['cash']
self._fees = self.data['fees']
# setup children as well - use original universe here - don't want to
# pollute with potential strategy children in funiverse
if self.children is not None:
[c.setup(universe) for c in self._childrenv]
@cy.locals(newpt=cy.bint, val=cy.double, ret=cy.double)
def update(self, date, data=None, inow=None):
"""
Update strategy. Updates prices, values, weight, etc.
"""
# resolve stale state
self.root.stale = False
# update helpers on date change
# also set newpt flag
newpt = False
if self.now == 0:
newpt = True
elif date != self.now:
self._net_flows = 0
self._last_price = self._price
self._last_value = self._value
self._last_fee = 0.0
newpt = True
# update now
self.now = date
if inow is None:
if self.now == 0:
inow = 0
else:
inow = self.data.index.get_loc(date)
# update children if any and calculate value
val = self._capital # default if no children
if self.children is not None:
for c in self._childrenv:
# avoid useless update call
if c._issec and not c._needupdate:
continue
c.update(date, data, inow)
val += c.value
if self.root == self:
if (val < 0) and not self.bankrupt:
# Declare a bankruptcy
self.bankrupt = True
self.flatten()
# update data if this value is different or
# if now has changed - avoid all this if not since it
# won't change
if newpt or self._value != val:
self._value = val
self._values.values[inow] = val
try:
ret = self._value / (self._last_value
+ self._net_flows) - 1
except ZeroDivisionError:
if self._value == 0:
ret = 0
else:
raise ZeroDivisionError(
'Could not update %s. Last value '
'was %s and net flows were %s. Current'
'value is %s. Therefore, '
'we are dividing by zero to obtain the return '
'for the period.' % (self.name,
self._last_value,
self._net_flows,
self._value))
self._price = self._last_price * (1 + ret)
self._prices.values[inow] = self._price
# update children weights
if self.children is not None:
for c in self._childrenv:
# avoid useless update call
if c._issec and not c._needupdate:
continue
try:
c._weight = c.value / val
except ZeroDivisionError:
c._weight = 0.0
# if we have strategy children, we will need to update them in universe
if self._has_strat_children:
for c in self._strat_children:
# TODO: optimize ".loc" here as well
self._universe.loc[date, c] = self.children[c].price
# Cash should track the unallocated capital at the end of the day, so
# we should update it every time we call "update".
# Same for fees
self._cash.values[inow] = self._capital
self._fees.values[inow] = self._last_fee
# update paper trade if necessary
if newpt and self._paper_trade:
self._paper.update(date)
self._paper.run()
self._paper.update(date)
# update price
self._price = self._paper.price
self._prices.values[inow] = self._price
@cy.locals(amount=cy.double, update=cy.bint, flow=cy.bint, fees=cy.double)
def adjust(self, amount, update=True, flow=True, fee=0.0):
"""
Adjust capital - used to inject capital to a Strategy. This injection
of capital will have no effect on the children.
Args:
* amount (float): Amount to adjust by.
* update (bool): Force update?
* flow (bool): Is this adjustment a flow? Basically a flow will
have an impact on the price index. Examples of flows are
commissions.
"""
# adjust capital
self._capital += amount
self._last_fee += fee
# if flow - increment net_flows - this will not affect
# performance. Commissions and other fees are not flows since
# they have a performance impact
if flow:
self._net_flows += amount
if update:
# indicates that data is now stale and must
# be updated before access
self.root.stale = True
@cy.locals(amount=cy.double, update=cy.bint)
def allocate(self, amount, child=None, update=True):
"""
Allocate capital to Strategy. By default, capital is allocated
recursively down the children, proportionally to the children's
weights. If a child is specified, capital will be allocated
to that specific child.
Allocation also have a side-effect. They will deduct the same amount
from the parent's "account" to offset the allocation. If there is
remaining capital after allocation, it will remain in Strategy.
Args:
* amount (float): Amount to allocate.
* child (str): If specified, allocation will be directed to child
only. Specified by name.
* update (bool): Force update.
"""
# allocate to child
if child is not None:
if child not in self.children:
c = SecurityBase(child)
c.setup(self._universe)
# update to bring up to speed
c.update(self.now)
# add child to tree
self._add_child(c)
# allocate to child
self.children[child].allocate(amount)
# allocate to self
else:
# adjust parent's capital
# no need to update now - avoids repetition
if self.parent == self:
self.parent.adjust(-amount, update=False, flow=True)
else:
# do NOT set as flow - parent will be another strategy
# and therefore should not incur flow
self.parent.adjust(-amount, update=False, flow=False)
# adjust self's capital
self.adjust(amount, update=False, flow=True)
# push allocation down to children if any
# use _weight to avoid triggering an update
if self.children is not None:
[c.allocate(amount * c._weight, update=False)
for c in self._childrenv]
# mark as stale if update requested
if update:
self.root.stale = True
@cy.locals(delta=cy.double, weight=cy.double, base=cy.double)
def rebalance(self, weight, child, base=np.nan, update=True):
"""
Rebalance a child to a given weight.
This is a helper method to simplify code logic. This method is used
when we want to se the weight of a particular child to a set amount.
It is similar to allocate, but it calculates the appropriate allocation
based on the current weight.
Args:
* weight (float): The target weight. Usually between -1.0 and 1.0.
* child (str): child to allocate to - specified by name.
* base (float): If specified, this is the base amount all weight
delta calculations will be based off of. This is useful when we
determine a set of weights and want to rebalance each child
given these new weights. However, as we iterate through each
child and call this method, the base (which is by default the
current value) will change. Therefore, we can set this base to
the original value before the iteration to ensure the proper
allocations are made.
* update (bool): Force update?
"""
# if weight is 0 - we want to close child
if weight == 0:
if child in self.children:
return self.close(child)
else:
return
# if no base specified use self's value
if np.isnan(base):
base = self.value
# else make sure we have child
if child not in self.children:
c = SecurityBase(child)
c.setup(self._universe)
# update child to bring up to speed
c.update(self.now)
self._add_child(c)
# allocate to child
# figure out weight delta
c = self.children[child]
delta = weight - c.weight
c.allocate(delta * base)
def close(self, child):
"""
Close a child position - alias for rebalance(0, child). This will also
flatten (close out all) the child's children.
Args:
* child (str): Child, specified by name.
"""
c = self.children[child]
# flatten if children not None
if c.children is not None and len(c.children) != 0:
c.flatten()
c.allocate(-c.value)
def flatten(self):
"""
Close all child positions.
"""
# go right to base alloc
[c.allocate(-c.value) for c in self._childrenv if c.value != 0]
def run(self):
"""
This is the main logic method. Override this method to provide some
algorithm to execute on each date change. This method is called by
backtester.
"""
pass
def set_commissions(self, fn):
"""
Set commission (transaction fee) function.
Args:
fn (fn(quantity, price)): Function used to determine commission
amount.
"""
self.commission_fn = fn
for c in self._childrenv:
if isinstance(c, StrategyBase):
c.set_commissions(fn)
@cy.locals(q=cy.double, p=cy.double)
def _dflt_comm_fn(self, q, p):
return max(1, abs(q) * 0.01)
class SecurityBase(Node):
"""
Security Node. Used to define a security within a tree.
A Security's has no children. It simply models an asset that can be bought
or sold.
Args:
* name (str): Security name
* multiplier (float): security multiplier - typically used for
derivatives.
Attributes:
* name (str): Security name
* parent (Security): Security parent
* root (Security): Root node of the tree (topmost node)
* now (datetime): Used when backtesting to store current date
* stale (bool): Flag used to determine if Security is stale and need
updating
* prices (TimeSeries): Security prices.
* price (float): last price
* value (float): last value - basically position * price * multiplier
* weight (float): weight in parent
* full_name (str): Name including parents' names
* members (list): Current Security + strategy's children
* position (float): Current position (quantity).
"""
_last_pos = cy.declare(cy.double)
_position = cy.declare(cy.double)
multiplier = cy.declare(cy.double)
_prices_set = cy.declare(cy.bint)
_needupdate = cy.declare(cy.bint)
@cy.locals(multiplier=cy.double)
def __init__(self, name, multiplier=1):
Node.__init__(self, name, parent=None, children=None)
self._value = 0
self._price = 0
self._weight = 0
self._position = 0
self.multiplier = multiplier
# opt
self._last_pos = 0
self._issec = True
self._needupdate = True
@property
def price(self):
"""
Current price.
"""
# if accessing and stale - update first
if self._needupdate or self.now != self.parent.now:
self.update(self.root.now)
return self._price
@property
def prices(self):
"""
TimeSeries of prices.
"""
# if accessing and stale - update first
if self._needupdate or self.now != self.parent.now:
self.update(self.root.now)
return self._prices.ix[:self.now]
@property
def values(self):
"""
TimeSeries of values.
"""
# if accessing and stale - update first
if self._needupdate or self.now != self.parent.now:
self.update(self.root.now)
if self.root.stale:
self.root.update(self.root.now, None)
return self._values.ix[:self.now]
@property
def position(self):
"""
Current position
"""
# no stale check needed
return self._position
@property
def positions(self):
"""
TimeSeries of positions.
"""
# if accessing and stale - update first
if self._needupdate:
self.update(self.root.now)
if self.root.stale:
self.root.update(self.root.now, None)
return self._positions.ix[:self.now]
def setup(self, universe):
"""
Setup Security with universe. Speeds up future runs.
Args:
* universe (DataFrame): DataFrame of prices with security's name as
one of the columns.
"""
# if we already have all the prices, we will store them to speed up
# future udpates
try:
prices = universe[self.name]
except KeyError:
prices = None
# setup internal data
if prices is not None:
self._prices = prices
self.data = pd.DataFrame(index=universe.index,
columns=['value', 'position'],
data=0.0)
self._prices_set = True
else:
self.data = pd.DataFrame(index=universe.index,
columns=['price', 'value', 'position'])
self._prices = self.data['price']
self._prices_set = False
self._values = self.data['value']
self._positions = self.data['position']
@cy.locals(prc=cy.double)
def update(self, date, data=None, inow=None):
"""
Update security with a given date and optionally, some data.
This will update price, value, weight, etc.
"""
# filter for internal calls when position has not changed - nothing to
# do. Internal calls (stale root calls) have None data. Also want to
# make sure date has not changed, because then we do indeed want to
# update.
if date == self.now and self._last_pos == self._position:
return
if inow is None:
if date == 0:
inow = 0
else:
inow = self.data.index.get_loc(date)
# date change - update price
if date != self.now:
# update now
self.now = date
if self._prices_set:
self._price = self._prices.values[inow]
# traditional data update
elif data is not None:
prc = data[self.name]
self._price = prc
self._prices.values[inow] = prc
self._positions.values[inow] = self._position
self._last_pos = self._position
self._value = self._position * self._price * self.multiplier
self._values.values[inow] = self._value
if self._weight == 0 and self._position == 0:
self._needupdate = False
@cy.locals(amount=cy.double, update=cy.bint, q=cy.double, outlay=cy.double)
def allocate(self, amount, update=True):
"""
This allocates capital to the Security. This is the method used to
buy/sell the security.
A given amount of shares will be determined on the current price, a
commisison will be calculated based on the parent's commission fn, and
any remaining capital will be passed back up to parent as an
adjustment.
Args:
* amount (float): Amount of adjustment.
* update (bool): Force update?
"""
# will need to update if this has been idle for a while...
# update if needupdate or if now is stale
# fetch parent's now since our now is stale
if self._needupdate or self.now != self.parent.now:
self.update(self.parent.now)
# ignore 0 alloc
# Note that if the price of security has dropped to zero, then it should
# never be selected by SelectAll, SelectN etc. I.e. we should not open
# the position at zero price. At the same time, we are able to close
# it at zero price, because at that point amount=0.
# Note also that we don't erase the position in an asset which price has
# dropped to zero (though the weight will indeed be = 0)
if amount == 0:
return
if self.parent is self or self.parent is None:
raise Exception(
'Cannot allocate capital to a parentless security')
if self._price == 0 or np.isnan(self._price):
raise Exception(
'Cannot allocate capital to '
'%s because price is 0 or nan as of %s'
% (self.name, self.parent.now))
# buy/sell
# determine quantity - must also factor in commission
# closing out?
if amount == -self._value:
q = -self._position
else:
if (self._position > 0) or ((self._position == 0) and (amount > 0)):
# if we're going long or changing long position
q = math.floor(amount / (self._price * self.multiplier))
else:
# if we're going short or changing short position
q = math.ceil(amount / (self._price * self.multiplier))
# if q is 0 nothing to do
if q == 0 or np.isnan(q):
return
# this security will need an update, even if pos is 0 (for example if
# we close the positions, value and pos is 0, but still need to do that
# last update)
self._needupdate = True
# adjust position & value
self._position += q
# calculate proper adjustment for parent
# parent passed down amount so we want to pass
# -outlay back up to parent to adjust for capital
# used
outlay, fee = self.outlay(q)
# call parent
self.parent.adjust(-outlay, update=update, flow=False, fee=fee)
@cy.locals(q=cy.double, p=cy.double)
def commission(self, q, p):
"""
Calculates the commission (transaction fee) based on quantity and price.
Uses the parent's commission_fn.
Args:
* q (float): quantity
* p (float): price
"""
return self.parent.commission_fn(q, p)
@cy.locals(q=cy.double)
def outlay(self, q):
"""
Determines the complete cash outlay (including commission) necessary
given a quantity q.
Second returning parameter is a commission itself.
Args:
* q (float): quantity
"""
fee = self.commission(q, self._price * self.multiplier)
full_outlay = q * self._price * self.multiplier + fee
return full_outlay, fee
def run(self):
"""
Does nothing - securities have nothing to do on run.
"""
pass
class Algo(object):
"""
Algos are used to modularize strategy logic so that strategy logic becomes
modular, composable, more testable and less error prone. Basically, the
Algo should follow the unix philosophy - do one thing well.
In practice, algos are simply a function that receives one argument, the
Strategy (refered to as target) and are expected to return a bool.
When some state preservation is necessary between calls, the Algo
object can be used (this object). The __call___ method should be
implemented and logic defined therein to mimic a function call. A
simple function may also be used if no state preservation is neceesary.
Args:
* name (str): Algo name
"""
def __init__(self, name=None):
self._name = name
@property
def name(self):
"""
Algo name.
"""
if self._name is None:
self._name = self.__class__.__name__
return self._name
def __call__(self, target):
raise NotImplementedError("%s not implemented!" % self.name)
class AlgoStack(Algo):
"""
An AlgoStack derives from Algo runs multiple Algos until a
failure is encountered.
The purpose of an AlgoStack is to group a logic set of Algos together. Each
Algo in the stack is run. Execution stops if one Algo returns False.
Args:
* algos (list): List of algos.
"""
def __init__(self, *algos):
super(AlgoStack, self).__init__()
self.algos = algos
self.check_run_always = any(hasattr(x, 'run_always')
for x in self.algos)
def __call__(self, target):
# normal runing mode
if not self.check_run_always:
for algo in self.algos:
if not algo(target):
return False
return True
# run mode when at least one algo has a run_always attribute
else:
# store result in res
# allows continuation to check for and run
# algos that have run_always set to True
res = True
for algo in self.algos:
if res:
res = algo(target)
elif hasattr(algo, 'run_always'):
if algo.run_always:
algo(target)
return res
class Strategy(StrategyBase):
"""
Strategy expands on the StrategyBase and incorporates Algos.
Basically, a Strategy is built by passing in a set of algos. These algos
will be placed in an Algo stack and the run function will call the stack.
Furthermore, two class attributes are created to pass data between algos.
perm for permanent data, temp for temporary data.
Args:
* name (str): Strategy name
* algos (list): List of Algos to be passed into an AlgoStack
* children (dict, list): Children - useful when you want to create
strategies of strategies
Attributes:
* stack (AlgoStack): The stack
* temp (dict): A dict containing temporary data - cleared on each call
to run. This can be used to pass info to other algos.
* perm (dict): Permanent data used to pass info from one algo to
another. Not cleared on each pass.
"""
def __init__(self, name, algos=[], children=None):
super(Strategy, self).__init__(name, children=children)
self.stack = AlgoStack(*algos)
self.temp = {}
self.perm = {}
def run(self):
# clear out temp data
self.temp = {}
# run algo stack
self.stack(self)
# run children
for c in self.children.values():
c.run()
| apache-2.0 |
MikeLing/shogun | examples/undocumented/python/graphical/interactive_svm_demo.py | 6 | 12586 | """
Shogun demo, based on PyQT Demo by Eli Bendersky
Christian Widmer
Soeren Sonnenburg
License: GPLv3
"""
import numpy
import sys, os, csv
from PyQt4.QtCore import *
from PyQt4.QtGui import *
import matplotlib
from matplotlib.colorbar import make_axes, Colorbar
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
from shogun import *
import util
class Form(QMainWindow):
def __init__(self, parent=None):
super(Form, self).__init__(parent)
self.setWindowTitle('SHOGUN interactive demo')
self.data = DataHolder()
self.series_list_model = QStandardItemModel()
self.create_menu()
self.create_main_frame()
self.create_status_bar()
self.on_show()
def load_file(self, filename=None):
filename = QFileDialog.getOpenFileName(self,
'Open a data file', '.', 'CSV files (*.csv);;All Files (*.*)')
if filename:
self.data.load_from_file(filename)
self.fill_series_list(self.data.series_names())
self.status_text.setText("Loaded " + filename)
def on_show(self):
self.axes.clear()
self.axes.grid(True)
self.axes.plot(self.data.x1_pos, self.data.x2_pos, 'ro')
self.axes.plot(self.data.x1_neg, self.data.x2_neg, 'bo')
self.axes.set_xlim((-5,5))
self.axes.set_ylim((-5,5))
self.canvas.draw()
self.fill_series_list(self.data.get_stats())
def on_about(self):
msg = __doc__
QMessageBox.about(self, "About the demo", msg.strip())
def fill_series_list(self, names):
self.series_list_model.clear()
for name in names:
item = QStandardItem(name)
item.setCheckState(Qt.Unchecked)
item.setCheckable(False)
self.series_list_model.appendRow(item)
def onclick(self, event):
print 'button=%d, x=%d, y=%d, xdata=%f, ydata=%f'%(event.button, event.x, event.y, event.xdata, event.ydata)
if event.button==1:
label = 1.0
else:
label = -1.0
self.data.add_example(event.xdata, event.ydata, label)
self.on_show()
def clear(self):
self.data.clear()
self.on_show()
def enable_widgets(self):
kernel_name = self.kernel_combo.currentText()
if kernel_name == "LinearKernel":
self.sigma.setDisabled(True)
self.degree.setDisabled(True)
elif kernel_name == "PolynomialKernel":
self.sigma.setDisabled(True)
self.degree.setEnabled(True)
elif kernel_name == "GaussianKernel":
self.sigma.setEnabled(True)
self.degree.setDisabled(True)
def train_svm(self):
width = float(self.sigma.text())
degree = int(self.degree.text())
self.axes.clear()
self.axes.grid(True)
self.axes.plot(self.data.x1_pos, self.data.x2_pos, 'ro')
self.axes.plot(self.data.x1_neg, self.data.x2_neg, 'bo')
# train svm
labels = self.data.get_labels()
print type(labels)
lab = BinaryLabels(labels)
features = self.data.get_examples()
train = RealFeatures(features)
kernel_name = self.kernel_combo.currentText()
print "current kernel is %s" % (kernel_name)
if kernel_name == "LinearKernel":
gk = LinearKernel(train, train)
gk.set_normalizer(IdentityKernelNormalizer())
elif kernel_name == "PolynomialKernel":
gk = PolyKernel(train, train, degree, True)
gk.set_normalizer(IdentityKernelNormalizer())
elif kernel_name == "GaussianKernel":
gk = GaussianKernel(train, train, width)
cost = float(self.cost.text())
print "cost", cost
svm = LibSVM(cost, gk, lab)
svm.train()
svm.set_epsilon(1e-2)
x, y, z = util.compute_output_plot_isolines(svm, gk, train)
plt=self.axes.pcolor(x, y, z)
CS=self.axes.contour(x, y, z, [-1,0,1], linewidths=1, colors='black', hold=True)
#CS=self.axes.contour(x, y, z, linewidths=1, colors='black', hold=True)
#CS=self.axes.contour(x, y, z, 5, linewidths=1, colors='black', hold=True)
matplotlib.pyplot.clabel(CS, inline=1, fontsize=10)
self.axes.set_xlim((-5,5))
self.axes.set_ylim((-5,5))
cmap = matplotlib.cm.jet
norm = matplotlib.colors.Normalize(numpy.min(z), numpy.max(z))
print CS.get_clim()
if not self.cax:
self.cax, kw = make_axes(self.axes)
# ColorbarBase derives from ScalarMappable and puts a colorbar
# in a specified axes, so it has everything needed for a
# standalone colorbar. There are many more kwargs, but the
# following gives a basic continuous colorbar with ticks
# and labels.
cb1 = matplotlib.colorbar.ColorbarBase(self.cax, cmap=cmap,
norm=norm)
self.canvas.draw()
def create_main_frame(self):
self.main_frame = QWidget()
plot_frame = QWidget()
self.dpi = 100
self.fig = Figure((6.0, 6.0), dpi=self.dpi)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self.main_frame)
cid = self.canvas.mpl_connect('button_press_event', self.onclick)
self.axes = self.fig.add_subplot(111)
self.cax = None
#self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame)
log_label = QLabel("Number of examples:")
self.series_list_view = QListView()
self.series_list_view.setModel(self.series_list_model)
cost_label = QLabel('C')
#self.cost = QSpinBox()#QLineEdit()
self.cost = QLineEdit()
self.cost.setText("1.0")
#self.cost.setMinimum(1)
spin_label2 = QLabel('sigma')
self.sigma = QLineEdit()
self.sigma.setText("1.2")
#self.sigma.setMinimum(1)
self.degree = QLineEdit()
self.degree.setText("2")
#self.sigma.setMinimum(1)
spins_hbox = QHBoxLayout()
spins_hbox.addWidget(cost_label)
spins_hbox.addWidget(self.cost)
spins_hbox.addWidget(spin_label2)
spins_hbox.addWidget(self.sigma)
spins_hbox.addWidget(self.degree)
spins_hbox.addStretch(1)
self.legend_cb = QCheckBox("Show Support Vectors")
self.legend_cb.setChecked(False)
self.show_button = QPushButton("&Train SVM")
self.connect(self.show_button, SIGNAL('clicked()'), self.train_svm)
self.clear_button = QPushButton("&Clear")
self.connect(self.clear_button, SIGNAL('clicked()'), self.clear)
self.kernel_combo = QComboBox()
self.kernel_combo.insertItem(-1, "GaussianKernel")
self.kernel_combo.insertItem(-1, "PolynomialKernel")
self.kernel_combo.insertItem(-1, "LinearKernel")
self.kernel_combo.maximumSize = QSize(300, 50)
self.connect(self.kernel_combo, SIGNAL("currentIndexChanged(QString)"), self.enable_widgets)
left_vbox = QVBoxLayout()
left_vbox.addWidget(self.canvas)
#left_vbox.addWidget(self.mpl_toolbar)
right0_vbox = QVBoxLayout()
right0_vbox.addWidget(log_label)
right0_vbox.addWidget(self.series_list_view)
#right0_vbox.addWidget(self.legend_cb)
right0_vbox.addStretch(1)
right2_vbox = QVBoxLayout()
right2_label = QLabel("Settings")
right2_vbox.addWidget(right2_label)
right2_vbox.addWidget(self.show_button)
right2_vbox.addWidget(self.kernel_combo)
right2_vbox.addLayout(spins_hbox)
right2_clearlabel = QLabel("Remove Data")
right2_vbox.addWidget(right2_clearlabel)
right2_vbox.addWidget(self.clear_button)
right2_vbox.addStretch(1)
right_vbox = QHBoxLayout()
right_vbox.addLayout(right0_vbox)
right_vbox.addLayout(right2_vbox)
hbox = QVBoxLayout()
hbox.addLayout(left_vbox)
hbox.addLayout(right_vbox)
self.main_frame.setLayout(hbox)
self.setCentralWidget(self.main_frame)
self.enable_widgets()
def create_status_bar(self):
self.status_text = QLabel("")
self.statusBar().addWidget(self.status_text, 1)
def create_menu(self):
self.file_menu = self.menuBar().addMenu("&File")
load_action = self.create_action("&Load file",
shortcut="Ctrl+L", slot=self.load_file, tip="Load a file")
quit_action = self.create_action("&Quit", slot=self.close,
shortcut="Ctrl+Q", tip="Close the application")
self.add_actions(self.file_menu,
(load_action, None, quit_action))
self.help_menu = self.menuBar().addMenu("&Help")
about_action = self.create_action("&About",
shortcut='F1', slot=self.on_about,
tip='About the demo')
self.add_actions(self.help_menu, (about_action,))
def add_actions(self, target, actions):
for action in actions:
if action is None:
target.addSeparator()
else:
target.addAction(action)
def create_action( self, text, slot=None, shortcut=None,
icon=None, tip=None, checkable=False,
signal="triggered()"):
action = QAction(text, self)
if icon is not None:
action.setIcon(QIcon(":/%s.png" % icon))
if shortcut is not None:
action.setShortcut(shortcut)
if tip is not None:
action.setToolTip(tip)
action.setStatusTip(tip)
if slot is not None:
self.connect(action, SIGNAL(signal), slot)
if checkable:
action.setCheckable(True)
return action
class DataHolder(object):
""" Just a thin wrapper over a dictionary that holds integer
data series. Each series has a name and a list of numbers
as its data. The length of all series is assumed to be
the same.
The series can be read from a CSV file, where each line
is a separate series. In each series, the first item in
the line is the name, and the rest are data numbers.
"""
def __init__(self, filename=None):
self.clear()
self.load_from_file(filename)
def clear(self):
self.x1_pos = []
self.x2_pos = []
self.x1_neg = []
self.x2_neg = []
def get_stats(self):
num_neg = len(self.x1_neg)
num_pos = len(self.x1_pos)
str_neg = "num negative examples: %i" % num_neg
str_pos = "num positive examples: %i" % num_pos
return (str_neg, str_pos)
def get_labels(self):
return numpy.array([1]*len(self.x1_pos) + [-1]*len(self.x1_neg), dtype=numpy.float64)
def get_examples(self):
num_pos = len(self.x1_pos)
num_neg = len(self.x1_neg)
examples = numpy.zeros((2,num_pos+num_neg))
for i in xrange(num_pos):
examples[0,i] = self.x1_pos[i]
examples[1,i] = self.x2_pos[i]
for i in xrange(num_neg):
examples[0,i+num_pos] = self.x1_neg[i]
examples[1,i+num_pos] = self.x2_neg[i]
return examples
def add_example(self, x1, x2, label):
if label==1:
self.x1_pos.append(x1)
self.x2_pos.append(x2)
else:
self.x1_neg.append(x1)
self.x2_neg.append(x2)
def load_from_file(self, filename=None):
self.data = {}
self.names = []
if filename:
for line in csv.reader(open(filename, 'rb')):
self.names.append(line[0])
self.data[line[0]] = map(int, line[1:])
self.datalen = len(line[1:])
def series_names(self):
""" Names of the data series
"""
return self.names
def series_len(self):
""" Length of a data series
"""
return self.datalen
def series_count(self):
return len(self.data)
def get_series_data(self, name):
return self.data[name]
def main():
app = QApplication(sys.argv)
form = Form()
form.show()
app.exec_()
if __name__ == "__main__":
main()
#~ dh = DataHolder('qt_mpl_data.csv')
#~ print dh.data
#~ print dh.get_series_data('1991 Sales')
#~ print dh.series_names()
#~ print dh.series_count()
| gpl-3.0 |
jlcarmic/producthunt_simulator | venv/lib/python2.7/site-packages/scipy/integrate/odepack.py | 62 | 9420 | # Author: Travis Oliphant
from __future__ import division, print_function, absolute_import
__all__ = ['odeint']
from . import _odepack
from copy import copy
import warnings
class ODEintWarning(Warning):
pass
_msgs = {2: "Integration successful.",
1: "Nothing was done; the integration time was 0.",
-1: "Excess work done on this call (perhaps wrong Dfun type).",
-2: "Excess accuracy requested (tolerances too small).",
-3: "Illegal input detected (internal error).",
-4: "Repeated error test failures (internal error).",
-5: "Repeated convergence failures (perhaps bad Jacobian or tolerances).",
-6: "Error weight became zero during problem.",
-7: "Internal workspace insufficient to finish (internal error)."
}
def odeint(func, y0, t, args=(), Dfun=None, col_deriv=0, full_output=0,
ml=None, mu=None, rtol=None, atol=None, tcrit=None, h0=0.0,
hmax=0.0, hmin=0.0, ixpr=0, mxstep=0, mxhnil=0, mxordn=12,
mxords=5, printmessg=0):
"""
Integrate a system of ordinary differential equations.
Solve a system of ordinary differential equations using lsoda from the
FORTRAN library odepack.
Solves the initial value problem for stiff or non-stiff systems
of first order ode-s::
dy/dt = func(y, t0, ...)
where y can be a vector.
*Note*: The first two arguments of ``func(y, t0, ...)`` are in the
opposite order of the arguments in the system definition function used
by the `scipy.integrate.ode` class.
Parameters
----------
func : callable(y, t0, ...)
Computes the derivative of y at t0.
y0 : array
Initial condition on y (can be a vector).
t : array
A sequence of time points for which to solve for y. The initial
value point should be the first element of this sequence.
args : tuple, optional
Extra arguments to pass to function.
Dfun : callable(y, t0, ...)
Gradient (Jacobian) of `func`.
col_deriv : bool, optional
True if `Dfun` defines derivatives down columns (faster),
otherwise `Dfun` should define derivatives across rows.
full_output : bool, optional
True if to return a dictionary of optional outputs as the second output
printmessg : bool, optional
Whether to print the convergence message
Returns
-------
y : array, shape (len(t), len(y0))
Array containing the value of y for each desired time in t,
with the initial value `y0` in the first row.
infodict : dict, only returned if full_output == True
Dictionary containing additional output information
======= ============================================================
key meaning
======= ============================================================
'hu' vector of step sizes successfully used for each time step.
'tcur' vector with the value of t reached for each time step.
(will always be at least as large as the input times).
'tolsf' vector of tolerance scale factors, greater than 1.0,
computed when a request for too much accuracy was detected.
'tsw' value of t at the time of the last method switch
(given for each time step)
'nst' cumulative number of time steps
'nfe' cumulative number of function evaluations for each time step
'nje' cumulative number of jacobian evaluations for each time step
'nqu' a vector of method orders for each successful step.
'imxer' index of the component of largest magnitude in the
weighted local error vector (e / ewt) on an error return, -1
otherwise.
'lenrw' the length of the double work array required.
'leniw' the length of integer work array required.
'mused' a vector of method indicators for each successful time step:
1: adams (nonstiff), 2: bdf (stiff)
======= ============================================================
Other Parameters
----------------
ml, mu : int, optional
If either of these are not None or non-negative, then the
Jacobian is assumed to be banded. These give the number of
lower and upper non-zero diagonals in this banded matrix.
For the banded case, `Dfun` should return a matrix whose
rows contain the non-zero bands (starting with the lowest diagonal).
Thus, the return matrix `jac` from `Dfun` should have shape
``(ml + mu + 1, len(y0))`` when ``ml >=0`` or ``mu >=0``.
The data in `jac` must be stored such that ``jac[i - j + mu, j]``
holds the derivative of the `i`th equation with respect to the `j`th
state variable. If `col_deriv` is True, the transpose of this
`jac` must be returned.
rtol, atol : float, optional
The input parameters `rtol` and `atol` determine the error
control performed by the solver. The solver will control the
vector, e, of estimated local errors in y, according to an
inequality of the form ``max-norm of (e / ewt) <= 1``,
where ewt is a vector of positive error weights computed as
``ewt = rtol * abs(y) + atol``.
rtol and atol can be either vectors the same length as y or scalars.
Defaults to 1.49012e-8.
tcrit : ndarray, optional
Vector of critical points (e.g. singularities) where integration
care should be taken.
h0 : float, (0: solver-determined), optional
The step size to be attempted on the first step.
hmax : float, (0: solver-determined), optional
The maximum absolute step size allowed.
hmin : float, (0: solver-determined), optional
The minimum absolute step size allowed.
ixpr : bool, optional
Whether to generate extra printing at method switches.
mxstep : int, (0: solver-determined), optional
Maximum number of (internally defined) steps allowed for each
integration point in t.
mxhnil : int, (0: solver-determined), optional
Maximum number of messages printed.
mxordn : int, (0: solver-determined), optional
Maximum order to be allowed for the non-stiff (Adams) method.
mxords : int, (0: solver-determined), optional
Maximum order to be allowed for the stiff (BDF) method.
See Also
--------
ode : a more object-oriented integrator based on VODE.
quad : for finding the area under a curve.
Examples
--------
The second order differential equation for the angle `theta` of a
pendulum acted on by gravity with friction can be written::
theta''(t) + b*theta'(t) + c*sin(theta(t)) = 0
where `b` and `c` are positive constants, and a prime (') denotes a
derivative. To solve this equation with `odeint`, we must first convert
it to a system of first order equations. By defining the angular
velocity ``omega(t) = theta'(t)``, we obtain the system::
theta'(t) = omega(t)
omega'(t) = -b*omega(t) - c*sin(theta(t))
Let `y` be the vector [`theta`, `omega`]. We implement this system
in python as:
>>> def pend(y, t, b, c):
... theta, omega = y
... dydt = [omega, -b*omega - c*np.sin(theta)]
... return dydt
...
We assume the constants are `b` = 0.25 and `c` = 5.0:
>>> b = 0.25
>>> c = 5.0
For initial conditions, we assume the pendulum is nearly vertical
with `theta(0)` = `pi` - 0.1, and it initially at rest, so
`omega(0)` = 0. Then the vector of initial conditions is
>>> y0 = [np.pi - 0.1, 0.0]
We generate a solution 101 evenly spaced samples in the interval
0 <= `t` <= 10. So our array of times is:
>>> t = np.linspace(0, 10, 101)
Call `odeint` to generate the solution. To pass the parameters
`b` and `c` to `pend`, we give them to `odeint` using the `args`
argument.
>>> from scipy.integrate import odeint
>>> sol = odeint(pend, y0, t, args=(b, c))
The solution is an array with shape (101, 2). The first column
is `theta(t)`, and the second is `omega(t)`. The following code
plots both components.
>>> import matplotlib.pyplot as plt
>>> plt.plot(t, sol[:, 0], 'b', label='theta(t)')
>>> plt.plot(t, sol[:, 1], 'g', label='omega(t)')
>>> plt.legend(loc='best')
>>> plt.xlabel('t')
>>> plt.grid()
>>> plt.show()
"""
if ml is None:
ml = -1 # changed to zero inside function call
if mu is None:
mu = -1 # changed to zero inside function call
t = copy(t)
y0 = copy(y0)
output = _odepack.odeint(func, y0, t, args, Dfun, col_deriv, ml, mu,
full_output, rtol, atol, tcrit, h0, hmax, hmin,
ixpr, mxstep, mxhnil, mxordn, mxords)
if output[-1] < 0:
warning_msg = _msgs[output[-1]] + " Run with full_output = 1 to get quantitative information."
warnings.warn(warning_msg, ODEintWarning)
elif printmessg:
warning_msg = _msgs[output[-1]]
warnings.warn(warning_msg, ODEintWarning)
if full_output:
output[1]['message'] = _msgs[output[-1]]
output = output[:-1]
if len(output) == 1:
return output[0]
else:
return output
| mit |
blab/antibody-response-pulse | bcell-array/code/Virus_Bcell_IgM_IgG_Infection_OAS_new.py | 1 | 13195 |
# coding: utf-8
# # Antibody Response Pulse
# https://github.com/blab/antibody-response-pulse
#
# ### B-cells evolution --- cross-reactive antibody response after influenza virus infection or vaccination
# ### Adaptive immune response for repeated infection
# In[3]:
'''
author: Alvason Zhenhua Li
date: 04/09/2015
'''
get_ipython().magic(u'matplotlib inline')
import numpy as np
import matplotlib.pyplot as plt
import os
from matplotlib.ticker import FuncFormatter
import alva_machinery_event_OAS_new as alva
AlvaFontSize = 23
AlvaFigSize = (15, 5)
numberingFig = 0
# plotting
dir_path = '/Users/al/Desktop/GitHub/antibody-response-pulse/bcell-array/figure'
file_name = 'Virus-Bcell-IgM-IgG'
figure_name = '-equation'
file_suffix = '.png'
save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)
numberingFig = numberingFig + 1
plt.figure(numberingFig, figsize=(12, 5))
plt.axis('off')
plt.title(r'$ Virus-Bcell-IgM-IgG \ equations \ (antibody-response \ for \ repeated-infection) $'
, fontsize = AlvaFontSize)
plt.text(0, 7.0/9, r'$ \frac{\partial V_n(t)}{\partial t} = +\mu_{v}V_{n}(t)(1 - \frac{V_n(t)}{V_{max}}) - \phi_{m} M_{n}(t) V_{n}(t) - \phi_{g} G_{n}(t) V_{n}(t) $'
, fontsize = 1.2*AlvaFontSize)
plt.text(0, 5.0/9, r'$ \frac{\partial B_n(t)}{\partial t} = +\mu_{b}V_{n}(t)(1 - \frac{V_n(t)}{V_{max}}) + (\beta_{m} + \beta_{g}) V_{n}(t) B_{n}(t) - \mu_{b} B_{n}(t) + m_b V_{n}(t)\frac{B_{i-1}(t) - 2B_i(t) + B_{i+1}(t)}{(\Delta i)^2} $'
, fontsize = 1.2*AlvaFontSize)
plt.text(0, 3.0/9,r'$ \frac{\partial M_n(t)}{\partial t} = +\xi_{m} B_{n}(t) - \phi_{m} M_{n}(t) V_{n}(t) - \mu_{m} M_{n}(t) $'
, fontsize = 1.2*AlvaFontSize)
plt.text(0, 1.0/9,r'$ \frac{\partial G_n(t)}{\partial t} = +\xi_{g} B_{n}(t) - \phi_{g} G_{n}(t) V_{n}(t) - \mu_{g} G_{n}(t) + m_a V_{n}(t)\frac{G_{i-1}(t) - 2G_i(t) + G_{i+1}(t)}{(\Delta i)^2} $'
, fontsize = 1.2*AlvaFontSize)
plt.savefig(save_figure, dpi = 100)
plt.show()
# define the V-M-G partial differential equations
def dVdt_array(VBMGxt = [], *args):
# naming
V = VBMGxt[0]
B = VBMGxt[1]
M = VBMGxt[2]
G = VBMGxt[3]
x_totalPoint = VBMGxt.shape[1]
# there are n dSdt
dV_dt_array = np.zeros(x_totalPoint)
# each dSdt with the same equation form
dV_dt_array[:] = +inRateV*V[:]*(1 - V[:]/maxV) - killRateVm*M[:]*V[:] - killRateVg*G[:]*V[:]
return(dV_dt_array)
def dBdt_array(VBMGxt = [], *args):
# naming
V = VBMGxt[0]
B = VBMGxt[1]
M = VBMGxt[2]
G = VBMGxt[3]
x_totalPoint = VBMGxt.shape[1]
# there are n dSdt
dB_dt_array = np.zeros(x_totalPoint)
# each dSdt with the same equation form
Bcopy = np.copy(B)
centerX = Bcopy[:]
leftX = np.roll(Bcopy[:], 1)
rightX = np.roll(Bcopy[:], -1)
leftX[0] = centerX[0]
rightX[-1] = centerX[-1]
dB_dt_array[:] = +inRateB*V[:]*(1 - V[:]/maxV) + (actRateBm + alva.event_active + alva.event_OAS_B)*V[:]*B[:] - outRateB*B[:] + mutatRateB*V[:]*(leftX[:] - 2*centerX[:] + rightX[:])/(dx**2)
return(dB_dt_array)
def dMdt_array(VBMGxt = [], *args):
# naming
V = VBMGxt[0]
B = VBMGxt[1]
M = VBMGxt[2]
G = VBMGxt[3]
x_totalPoint = VBMGxt.shape[1]
# there are n dSdt
dM_dt_array = np.zeros(x_totalPoint)
# each dSdt with the same equation form
dM_dt_array[:] = +inRateM*B[:] - consumeRateM*M[:]*V[:] - outRateM*M[:]
return(dM_dt_array)
def dGdt_array(VBMGxt = [], *args):
# naming
V = VBMGxt[0]
B = VBMGxt[1]
M = VBMGxt[2]
G = VBMGxt[3]
x_totalPoint = VBMGxt.shape[1]
# there are n dSdt
dG_dt_array = np.zeros(x_totalPoint)
# each dSdt with the same equation form
Gcopy = np.copy(G)
centerX = Gcopy[:]
leftX = np.roll(Gcopy[:], 1)
rightX = np.roll(Gcopy[:], -1)
leftX[0] = centerX[0]
rightX[-1] = centerX[-1]
dG_dt_array[:] = +(inRateG + alva.event_OAS)*B[:] - consumeRateG*G[:]*V[:] - outRateG*G[:] + mutatRateA*(leftX[:] - 2*centerX[:] + rightX[:])/(dx**2)
return(dG_dt_array)
# In[7]:
# setting parameter
timeUnit = 'day'
if timeUnit == 'hour':
hour = float(1)
day = float(24)
elif timeUnit == 'day':
day = float(1)
hour = float(1)/24
elif timeUnit == 'year':
year = float(1)
day = float(1)/365
hour = float(1)/24/365
maxV = float(50) # max virus/micro-liter
inRateV = 0.2/hour # in-rate of virus
killRateVm = 0.0003/hour # kill-rate of virus by antibody-IgM
killRateVg = killRateVm # kill-rate of virus by antibody-IgG
inRateB = 0.06/hour # in-rate of B-cell
outRateB = inRateB/8 # out-rate of B-cell
actRateBm = killRateVm # activation rate of naive B-cell
inRateM = 0.16/hour # in-rate of antibody-IgM from naive B-cell
outRateM = inRateM/1 # out-rate of antibody-IgM from naive B-cell
consumeRateM = killRateVm # consume-rate of antibody-IgM by cleaning virus
inRateG = inRateM/10 # in-rate of antibody-IgG from memory B-cell
outRateG = outRateM/250 # out-rate of antibody-IgG from memory B-cell
consumeRateG = killRateVg # consume-rate of antibody-IgG by cleaning virus
mutatRateB = 0.00003/hour # B-cell mutation rate
mutatRateA = 0.0001/hour # antibody mutation rate
mutatRateB = 0.0000/hour # B-cell mutation rate
mutatRateA = 0.000/hour # antibody mutation rate
# time boundary and griding condition
minT = float(0)
maxT = float(6*28*day)
totalPoint_T = int(1*10**3 + 1)
gT = np.linspace(minT, maxT, totalPoint_T)
spacingT = np.linspace(minT, maxT, num = totalPoint_T, retstep = True)
gT = spacingT[0]
dt = spacingT[1]
# space boundary and griding condition
minX = float(0)
maxX = float(3)
totalPoint_X = int(maxX - minX + 1)
gX = np.linspace(minX, maxX, totalPoint_X)
gridingX = np.linspace(minX, maxX, num = totalPoint_X, retstep = True)
gX = gridingX[0]
dx = gridingX[1]
gV_array = np.zeros([totalPoint_X, totalPoint_T])
gB_array = np.zeros([totalPoint_X, totalPoint_T])
gM_array = np.zeros([totalPoint_X, totalPoint_T])
gG_array = np.zeros([totalPoint_X, totalPoint_T])
# initial output condition
#gV_array[1, 0] = float(2)
#[pre-parameter, post-parameter, recovered-day, OAS+, OSA-]
actRateBg_1st = 0.0002/hour # activation rate of memory B-cell at 1st time (pre-)
actRateBg_2nd = actRateBg_1st*10 # activation rate of memory B-cell at 2nd time (post-)
origin_virus = int(1)
current_virus = int(2)
event_parameter = np.array([[actRateBg_1st,
actRateBg_2nd,
14*day,
+5/hour,
-actRateBm - actRateBg_1st + (actRateBm + actRateBg_1st)/3,
origin_virus,
current_virus]])
# [viral population, starting time, first]
# [viral population, starting time] ---first
infection_period = 1*28*day
viral_population = np.zeros(int(maxX + 1))
viral_population[origin_virus:current_virus + 1] = 3
infection_starting_time = np.arange(int(maxX + 1))*infection_period
event_1st = np.zeros([int(maxX + 1), 2])
event_1st[:, 0] = viral_population
event_1st[:, 1] = infection_starting_time
print ('event_1st = {:}'.format(event_1st))
# [viral population, starting time] ---2nd]
viral_population = np.zeros(int(maxX + 1))
viral_population[origin_virus:current_virus + 1] = 0
infection_starting_time = np.arange(int(maxX + 1))*0
event_2nd = np.zeros([int(maxX + 1), 2])
event_2nd[:, 0] = viral_population
event_2nd[:, 1] = infection_starting_time
print ('event_2nd = {:}'.format(event_2nd))
event_table = np.array([event_parameter, event_1st, event_2nd])
# Runge Kutta numerical solution
pde_array = np.array([dVdt_array, dBdt_array, dMdt_array, dGdt_array])
initial_Out = np.array([gV_array, gB_array, gM_array, gG_array])
gOut_array = alva.AlvaRungeKutta4XT(pde_array, initial_Out, minX, maxX, totalPoint_X, minT, maxT, totalPoint_T, event_table)
# plotting
gV = gOut_array[0]
gB = gOut_array[1]
gM = gOut_array[2]
gG = gOut_array[3]
numberingFig = numberingFig + 1
for i in range(totalPoint_X):
figure_name = '-response-%i'%(i)
figure_suffix = '.png'
save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)
plt.figure(numberingFig, figsize = AlvaFigSize)
plt.plot(gT, gV[i], color = 'red', label = r'$ V_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)
plt.plot(gT, gM[i], color = 'blue', label = r'$ IgM_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)
plt.plot(gT, gG[i], color = 'green', label = r'$ IgG_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)
plt.plot(gT, gM[i] + gG[i], color = 'gray', linewidth = 5.0, alpha = 0.5, linestyle = 'dashed'
, label = r'$ IgM_{%i}(t) + IgG_{%i}(t) $'%(i, i))
plt.grid(True, which = 'both')
plt.title(r'$ Antibody \ from \ Virus-{%i} $'%(i), fontsize = AlvaFontSize)
plt.xlabel(r'$time \ (%s)$'%(timeUnit), fontsize = AlvaFontSize)
plt.ylabel(r'$ Neutralization \ \ titer $', fontsize = AlvaFontSize)
plt.xlim([minT, maxT])
plt.xticks(fontsize = AlvaFontSize*0.6)
plt.yticks(fontsize = AlvaFontSize*0.6)
plt.ylim([2**0, 2**14])
plt.yscale('log', basey = 2)
plt.legend(loc = (1,0), fontsize = AlvaFontSize)
plt.savefig(save_figure, dpi = 100)
plt.show()
# In[5]:
# Experimental lab data from OAS paper
gT_lab = np.array([28, 28 + 7, 28 + 14, 28 + 28]) + 28
gPR8_lab = np.array([2**(9 + 1.0/10), 2**(13 - 1.0/5), 2**(13 + 1.0/3), 2**(13 - 1.0/4)])
standard_PR8 = gPR8_lab**(3.0/4)
gFM1_lab = np.array([0, 2**(6 - 1.0/5), 2**(7 - 1.0/4), 2**(8 + 1.0/4)])
standard_FM1 = gFM1_lab**(3.0/4)
bar_width = 2.0
# Sequential immunization graph
numberingFig = numberingFig + 1
plt.figure(numberingFig, figsize = (12, 6))
plt.subplot(111)
plt.plot(gT, (gM[origin_virus] + gG[origin_virus]), linewidth = 5.0, alpha = 0.5, color = 'gray'
, label = r'$ Origin-virus $')
plt.plot(gT, (gM[origin_virus + 1] + gG[origin_virus + 1]), linewidth = 5.0, alpha = 0.5, color = 'red'
, label = r'$ Subsequence-virus $')
plt.bar(gT_lab - bar_width/2, gPR8_lab, bar_width, alpha = 0.6, color = 'gray', yerr = standard_PR8
, error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ PR8-virus $')
plt.bar(gT_lab + bar_width/2, gFM1_lab, bar_width, alpha = 0.6, color = 'red', yerr = standard_FM1
, error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ FM1-virus $')
plt.grid(True, which = 'both')
plt.title(r'$ Original \ Antigenic \ Sin \ (sequential-infection)$', fontsize = AlvaFontSize)
plt.xlabel(r'$time \ (%s)$'%(timeUnit), fontsize = AlvaFontSize)
plt.ylabel(r'$ Neutralization \ \ titer $', fontsize = AlvaFontSize)
plt.xticks(fontsize = AlvaFontSize*0.6)
plt.yticks(fontsize = AlvaFontSize*0.6)
plt.xlim([minT, 6*30*day])
plt.ylim([2**5, 2**14])
plt.yscale('log', basey = 2)
# gca()---GetCurrentAxis and Format the ticklabel to be 2**x
plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, pos: int(2**(np.log(x)/np.log(2)))))
#plt.gca().xaxis.set_major_locator(plt.MultipleLocator(7))
plt.legend(loc = (1, 0), fontsize = AlvaFontSize)
plt.show()
# In[6]:
# Experimental lab data from OAS paper
gT_lab = np.array([28, 28 + 7, 28 + 14, 28 + 28]) + 28
gPR8_lab = np.array([2**(9 + 1.0/10), 2**(13 - 1.0/5), 2**(13 + 1.0/3), 2**(13 - 1.0/4)])
standard_PR8 = gPR8_lab**(3.0/4)
gFM1_lab = np.array([0, 2**(6 - 1.0/5), 2**(7 - 1.0/4), 2**(8 + 1.0/4)])
standard_FM1 = gFM1_lab**(3.0/4)
bar_width = 1.0
# Sequential immunization graph
figure_name = '-Original-Antigenic-Sin-infection'
figure_suffix = '.png'
save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)
numberingFig = numberingFig + 1
plt.figure(numberingFig, figsize = (12, 6))
plt.subplot(111)
plt.plot(gT, (gM[origin_virus] + gG[origin_virus]), linewidth = 5.0, alpha = 0.5, color = 'gray'
, label = r'$ Origin-virus $')
plt.plot(gT, (gM[origin_virus + 1] + gG[origin_virus + 1]), linewidth = 5.0, alpha = 0.5, color = 'red'
, label = r'$ Subsequence-virus $')
plt.bar(gT_lab - bar_width/2, gPR8_lab, bar_width, alpha = 0.6, color = 'gray', yerr = standard_PR8
, error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ PR8-virus $')
plt.bar(gT_lab + bar_width/2, gFM1_lab, bar_width, alpha = 0.6, color = 'red', yerr = standard_FM1
, error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ FM1-virus $')
plt.grid(True, which = 'both')
plt.title(r'$ Original \ Antigenic \ Sin \ (sequential-infection)$', fontsize = AlvaFontSize)
plt.xlabel(r'$time \ (%s)$'%(timeUnit), fontsize = AlvaFontSize)
plt.ylabel(r'$ Neutralization \ \ titer $', fontsize = AlvaFontSize)
plt.xticks(fontsize = AlvaFontSize*0.6)
plt.yticks(fontsize = AlvaFontSize*0.6)
plt.xlim([minT, 3*30*day])
plt.ylim([2**5, 2**14])
plt.yscale('log', basey = 2)
# gca()---GetCurrentAxis and Format the ticklabel to be 2**x
plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, pos: int(2**(np.log(x)/np.log(2)))))
plt.gca().xaxis.set_major_locator(plt.MultipleLocator(7))
plt.legend(loc = (1, 0), fontsize = AlvaFontSize)
plt.savefig(save_figure, dpi = 100, bbox_inches='tight')
plt.show()
# In[ ]:
| gpl-2.0 |
adhix11/pmtk3 | python/demos/linregDemo1.py | 26 | 1104 | #!/usr/bin/python2.4
import numpy
import scipy.stats
import matplotlib.pyplot as plt
def main():
# true parameters
w = 2
w0 = 3
sigma = 2
# make data
numpy.random.seed(1)
Ntrain = 20
xtrain = numpy.linspace(0,10,Ntrain)
ytrain = w*xtrain + w0 + numpy.random.random(Ntrain)*sigma
Ntest = 100
xtest = numpy.linspace(0,10,Ntest)
ytest = w*xtest + w0 + numpy.random.random(Ntest)*sigma
# from http://www2.warwick.ac.uk/fac/sci/moac/students/peter_cock/python/lin_reg/
# fit
west, w0est, r_value, p_value, std_err = scipy.stats.linregress(xtrain, ytrain)
# display
print "Param \t True \t Est"
print "w0 \t %5.3f \t %5.3f" % (w0, w0est)
print "w \t %5.3f \t %5.3f" % (w, west)
# plot
plt.close()
plt.plot(xtrain, ytrain, 'ro')
plt.hold(True)
#plt.plot(xtest, ytest, 'ka-')
ytestPred = west*xtest + w0est
#ndx = range(0, Ntest, 10)
#h = plt.plot(xtest[ndx], ytestPred[ndx], 'b*')
h = plt.plot(xtest, ytestPred, 'b-')
plt.setp(h, 'markersize', 12)
if __name__ == '__main__':
main()
| mit |
marcusmueller/gnuradio | gr-filter/examples/fft_filter_ccc.py | 7 | 4367 | #!/usr/bin/env python
#
# Copyright 2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# GNU Radio is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GNU Radio; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from gnuradio import gr, filter
from gnuradio import analog
from gnuradio import blocks
from gnuradio import eng_notation
from gnuradio.eng_arg import eng_float, intx
from argparse import ArgumentParser
import sys
import numpy
try:
from matplotlib import pyplot
except ImportError:
print("Error: could not from matplotlib import pyplot (http://matplotlib.sourceforge.net/)")
sys.exit(1)
class example_fft_filter_ccc(gr.top_block):
def __init__(self, N, fs, bw0, bw1, tw, atten, D):
gr.top_block.__init__(self)
self._nsamps = N
self._fs = fs
self._bw0 = bw0
self._bw1 = bw1
self._tw = tw
self._at = atten
self._decim = D
taps = filter.firdes.complex_band_pass_2(1, self._fs,
self._bw0, self._bw1,
self._tw, self._at)
print("Num. Taps: ", len(taps))
self.src = analog.noise_source_c(analog.GR_GAUSSIAN, 1)
self.head = blocks.head(gr.sizeof_gr_complex, self._nsamps)
self.filt0 = filter.fft_filter_ccc(self._decim, taps)
self.vsnk_src = blocks.vector_sink_c()
self.vsnk_out = blocks.vector_sink_c()
self.connect(self.src, self.head, self.vsnk_src)
self.connect(self.head, self.filt0, self.vsnk_out)
def main():
parser = ArgumentParser(conflict_handler="resolve")
parser.add_argument("-N", "--nsamples", type=int, default=10000,
help="Number of samples to process [default=%(default)r]")
parser.add_argument("-s", "--samplerate", type=eng_float, default=8000,
help="System sample rate [default=%(default)r]")
parser.add_argument("-S", "--start-pass", type=eng_float, default=1000,
help="Start of Passband [default=%(default)r]")
parser.add_argument("-E", "--end-pass", type=eng_float, default=2000,
help="End of Passband [default=%(default)r]")
parser.add_argument("-T", "--transition", type=eng_float, default=100,
help="Transition band [default=%(default)r]")
parser.add_argument("-A", "--attenuation", type=eng_float, default=80,
help="Stopband attenuation [default=%(default)r]")
parser.add_argument("-D", "--decimation", type=int, default=1,
help="Decmation factor [default=%(default)r]")
args = parser.parse_args()
put = example_fft_filter_ccc(args.nsamples,
args.samplerate,
args.start_pass,
args.end_pass,
args.transition,
args.attenuation,
args.decimation)
put.run()
data_src = numpy.array(put.vsnk_src.data())
data_snk = numpy.array(put.vsnk_out.data())
# Plot the signals PSDs
nfft = 1024
f1 = pyplot.figure(1, figsize=(12,10))
s1 = f1.add_subplot(1,1,1)
s1.psd(data_src, NFFT=nfft, noverlap=nfft / 4,
Fs=args.samplerate)
s1.psd(data_snk, NFFT=nfft, noverlap=nfft / 4,
Fs=args.samplerate)
f2 = pyplot.figure(2, figsize=(12,10))
s2 = f2.add_subplot(1,1,1)
s2.plot(data_src)
s2.plot(data_snk.real, 'g')
pyplot.show()
if __name__ == "__main__":
try:
main()
except KeyboardInterrupt:
pass
| gpl-3.0 |
devs1991/test_edx_docmode | venv/lib/python2.7/site-packages/sklearn/lda.py | 3 | 9301 | """
The :mod:`sklearn.lda` module implements Linear Discriminant Analysis (LDA).
"""
# Authors: Matthieu Perrot
# Mathieu Blondel
import warnings
import numpy as np
from scipy import linalg
from .base import BaseEstimator, ClassifierMixin, TransformerMixin
from .utils.extmath import logsumexp
from .utils.fixes import unique
from .utils import check_arrays
__all__ = ['LDA']
class LDA(BaseEstimator, ClassifierMixin, TransformerMixin):
"""
Linear Discriminant Analysis (LDA)
A classifier with a linear decision boundary, generated
by fitting class conditional densities to the data
and using Bayes' rule.
The model fits a Gaussian density to each class, assuming that
all classes share the same covariance matrix.
The fitted model can also be used to reduce the dimensionality
of the input, by projecting it to the most discriminative
directions.
Parameters
----------
n_components: int
Number of components (< n_classes - 1) for dimensionality reduction
priors : array, optional, shape = [n_classes]
Priors on classes
Attributes
----------
`means_` : array-like, shape = [n_classes, n_features]
Class means
`xbar_` : float, shape = [n_features]
Over all mean
`priors_` : array-like, shape = [n_classes]
Class priors (sum to 1)
`covariance_` : array-like, shape = [n_features, n_features]
Covariance matrix (shared by all classes)
Examples
--------
>>> import numpy as np
>>> from sklearn.lda import LDA
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> clf = LDA()
>>> clf.fit(X, y)
LDA(n_components=None, priors=None)
>>> print(clf.predict([[-0.8, -1]]))
[1]
See also
--------
sklearn.qda.QDA: Quadratic discriminant analysis
"""
def __init__(self, n_components=None, priors=None):
self.n_components = n_components
self.priors = np.asarray(priors) if priors is not None else None
if self.priors is not None:
if (self.priors < 0).any():
raise ValueError('priors must be non-negative')
if self.priors.sum() != 1:
print 'warning: the priors do not sum to 1. Renormalizing'
self.priors = self.priors / self.priors.sum()
def fit(self, X, y, store_covariance=False, tol=1.0e-4):
"""
Fit the LDA model according to the given training data and parameters.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and
n_features is the number of features.
y : array, shape = [n_samples]
Target values (integers)
store_covariance : boolean
If True the covariance matrix (shared by all classes) is computed
and stored in `self.covariance_` attribute.
"""
X, y = check_arrays(X, y, sparse_format='dense')
self.classes_, y = unique(y, return_inverse=True)
n_samples, n_features = X.shape
n_classes = len(self.classes_)
if n_classes < 2:
raise ValueError('y has less than 2 classes')
if self.priors is None:
self.priors_ = np.bincount(y) / float(n_samples)
else:
self.priors_ = self.priors
# Group means n_classes*n_features matrix
means = []
Xc = []
cov = None
if store_covariance:
cov = np.zeros((n_features, n_features))
for ind in xrange(n_classes):
Xg = X[y == ind, :]
meang = Xg.mean(0)
means.append(meang)
# centered group data
Xgc = Xg - meang
Xc.append(Xgc)
if store_covariance:
cov += np.dot(Xgc.T, Xgc)
if store_covariance:
cov /= (n_samples - n_classes)
self.covariance_ = cov
self.means_ = np.asarray(means)
Xc = np.concatenate(Xc, 0)
# ----------------------------
# 1) within (univariate) scaling by with classes std-dev
std = Xc.std(axis=0)
# avoid division by zero in normalization
std[std == 0] = 1.
fac = float(1) / (n_samples - n_classes)
# ----------------------------
# 2) Within variance scaling
X = np.sqrt(fac) * (Xc / std)
# SVD of centered (within)scaled data
U, S, V = linalg.svd(X, full_matrices=0)
rank = np.sum(S > tol)
if rank < n_features:
warnings.warn("Variables are collinear")
# Scaling of within covariance is: V' 1/S
scaling = (V[:rank] / std).T / S[:rank]
## ----------------------------
## 3) Between variance scaling
# Overall mean
xbar = np.dot(self.priors_, self.means_)
# Scale weighted centers
X = np.dot(((np.sqrt((n_samples * self.priors_) * fac)) *
(means - xbar).T).T, scaling)
# Centers are living in a space with n_classes-1 dim (maximum)
# Use svd to find projection in the space spanned by the
# (n_classes) centers
_, S, V = linalg.svd(X, full_matrices=0)
rank = np.sum(S > tol * S[0])
# compose the scalings
self.scaling = np.dot(scaling, V.T[:, :rank])
self.xbar_ = xbar
# weight vectors / centroids
self.coef_ = np.dot(self.means_ - self.xbar_, self.scaling)
self.intercept_ = -0.5 * np.sum(self.coef_ ** 2, axis=1) + \
np.log(self.priors_)
return self
@property
def classes(self):
warnings.warn("LDA.classes is deprecated and will be removed in 0.14. "
"Use LDA.classes_ instead.", DeprecationWarning,
stacklevel=2)
return self.classes_
def _decision_function(self, X):
X = np.asarray(X)
# center and scale data
X = np.dot(X - self.xbar_, self.scaling)
return np.dot(X, self.coef_.T) + self.intercept_
def decision_function(self, X):
"""
This function return the decision function values related to each
class on an array of test vectors X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array, shape = [n_samples, n_classes] or [n_samples,]
Decision function values related to each class, per sample.
In the two-class case, the shape is [n_samples,], giving the
log likelihood ratio of the positive class.
"""
dec_func = self._decision_function(X)
if len(self.classes_) == 2:
return dec_func[:, 1] - dec_func[:, 0]
return dec_func
def transform(self, X):
"""
Project the data so as to maximize class separation (large separation
between projected class means and small variance within each class).
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
X_new : array, shape = [n_samples, n_components]
"""
X = np.asarray(X)
# center and scale data
X = np.dot(X - self.xbar_, self.scaling)
n_comp = X.shape[1] if self.n_components is None else self.n_components
return np.dot(X, self.coef_[:n_comp].T)
def predict(self, X):
"""
This function does classification on an array of test vectors X.
The predicted class C for each sample in X is returned.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array, shape = [n_samples]
"""
d = self._decision_function(X)
y_pred = self.classes_.take(d.argmax(1))
return y_pred
def predict_proba(self, X):
"""
This function return posterior probabilities of classification
according to each class on an array of test vectors X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array, shape = [n_samples, n_classes]
"""
values = self._decision_function(X)
# compute the likelihood of the underlying gaussian models
# up to a multiplicative constant.
likelihood = np.exp(values - values.max(axis=1)[:, np.newaxis])
# compute posterior probabilities
return likelihood / likelihood.sum(axis=1)[:, np.newaxis]
def predict_log_proba(self, X):
"""
This function return posterior log-probabilities of classification
according to each class on an array of test vectors X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array, shape = [n_samples, n_classes]
"""
values = self._decision_function(X)
loglikelihood = (values - values.max(axis=1)[:, np.newaxis])
normalization = logsumexp(loglikelihood, axis=1)
return loglikelihood - normalization[:, np.newaxis]
| agpl-3.0 |
grimfang/panda3d | samples/carousel/main.py | 25 | 9571 | #!/usr/bin/env python
# Author: Shao Zhang, Phil Saltzman, and Eddie Canaan
# Last Updated: 2015-03-13
#
# This tutorial will demonstrate some uses for intervals in Panda
# to move objects in your panda world.
# Intervals are tools that change a value of something, like position,
# rotation or anything else, linearly, over a set period of time. They can be
# also be combined to work in sequence or in Parallel
#
# In this lesson, we will simulate a carousel in motion using intervals.
# The carousel will spin using an hprInterval while 4 pandas will represent
# the horses on a traditional carousel. The 4 pandas will rotate with the
# carousel and also move up and down on their poles using a LerpFunc interval.
# Finally there will also be lights on the outer edge of the carousel that
# will turn on and off by switching their texture with intervals in Sequence
# and Parallel
from direct.showbase.ShowBase import ShowBase
from panda3d.core import AmbientLight, DirectionalLight, LightAttrib
from panda3d.core import NodePath
from panda3d.core import LVector3
from direct.interval.IntervalGlobal import * # Needed to use Intervals
from direct.gui.DirectGui import *
# Importing math constants and functions
from math import pi, sin
class CarouselDemo(ShowBase):
def __init__(self):
# Initialize the ShowBase class from which we inherit, which will
# create a window and set up everything we need for rendering into it.
ShowBase.__init__(self)
# This creates the on screen title that is in every tutorial
self.title = OnscreenText(text="Panda3D: Tutorial - Carousel",
parent=base.a2dBottomCenter,
fg=(1, 1, 1, 1), shadow=(0, 0, 0, .5),
pos=(0, .1), scale=.1)
base.disableMouse() # Allow manual positioning of the camera
camera.setPosHpr(0, -8, 2.5, 0, -9, 0) # Set the cameras' position
# and orientation
self.loadModels() # Load and position our models
self.setupLights() # Add some basic lighting
self.startCarousel() # Create the needed intervals and put the
# carousel into motion
def loadModels(self):
# Load the carousel base
self.carousel = loader.loadModel("models/carousel_base")
self.carousel.reparentTo(render) # Attach it to render
# Load the modeled lights that are on the outer rim of the carousel
# (not Panda lights)
# There are 2 groups of lights. At any given time, one group will have
# the "on" texture and the other will have the "off" texture.
self.lights1 = loader.loadModel("models/carousel_lights")
self.lights1.reparentTo(self.carousel)
# Load the 2nd set of lights
self.lights2 = loader.loadModel("models/carousel_lights")
# We need to rotate the 2nd so it doesn't overlap with the 1st set.
self.lights2.setH(36)
self.lights2.reparentTo(self.carousel)
# Load the textures for the lights. One texture is for the "on" state,
# the other is for the "off" state.
self.lightOffTex = loader.loadTexture("models/carousel_lights_off.jpg")
self.lightOnTex = loader.loadTexture("models/carousel_lights_on.jpg")
# Create an list (self.pandas) with filled with 4 dummy nodes attached
# to the carousel.
# This uses a python concept called "Array Comprehensions." Check the
# Python manual for more information on how they work
self.pandas = [self.carousel.attachNewNode("panda" + str(i))
for i in range(4)]
self.models = [loader.loadModel("models/carousel_panda")
for i in range(4)]
self.moves = [0] * 4
for i in range(4):
# set the position and orientation of the ith panda node we just created
# The Z value of the position will be the base height of the pandas.
# The headings are multiplied by i to put each panda in its own position
# around the carousel
self.pandas[i].setPosHpr(0, 0, 1.3, i * 90, 0, 0)
# Load the actual panda model, and parent it to its dummy node
self.models[i].reparentTo(self.pandas[i])
# Set the distance from the center. This distance is based on the way the
# carousel was modeled in Maya
self.models[i].setY(.85)
# Load the environment (Sky sphere and ground plane)
self.env = loader.loadModel("models/env")
self.env.reparentTo(render)
self.env.setScale(7)
# Panda Lighting
def setupLights(self):
# Create some lights and add them to the scene. By setting the lights on
# render they affect the entire scene
# Check out the lighting tutorial for more information on lights
ambientLight = AmbientLight("ambientLight")
ambientLight.setColor((.4, .4, .35, 1))
directionalLight = DirectionalLight("directionalLight")
directionalLight.setDirection(LVector3(0, 8, -2.5))
directionalLight.setColor((0.9, 0.8, 0.9, 1))
render.setLight(render.attachNewNode(directionalLight))
render.setLight(render.attachNewNode(ambientLight))
# Explicitly set the environment to not be lit
self.env.setLightOff()
def startCarousel(self):
# Here's where we actually create the intervals to move the carousel
# The first type of interval we use is one created directly from a NodePath
# This interval tells the NodePath to vary its orientation (hpr) from its
# current value (0,0,0) to (360,0,0) over 20 seconds. Intervals created from
# NodePaths also exist for position, scale, color, and shear
self.carouselSpin = self.carousel.hprInterval(20, LVector3(360, 0, 0))
# Once an interval is created, we need to tell it to actually move.
# start() will cause an interval to play once. loop() will tell an interval
# to repeat once it finished. To keep the carousel turning, we use
# loop()
self.carouselSpin.loop()
# The next type of interval we use is called a LerpFunc interval. It is
# called that becuase it linearly interpolates (aka Lerp) values passed to
# a function over a given amount of time.
# In this specific case, horses on a carousel don't move contantly up,
# suddenly stop, and then contantly move down again. Instead, they start
# slowly, get fast in the middle, and slow down at the top. This motion is
# close to a sine wave. This LerpFunc calls the function oscillatePanda
# (which we will create below), which changes the height of the panda based
# on the sin of the value passed in. In this way we achieve non-linear
# motion by linearly changing the input to a function
for i in range(4):
self.moves[i] = LerpFunc(
self.oscillatePanda, # function to call
duration=3, # 3 second duration
fromData=0, # starting value (in radians)
toData=2 * pi, # ending value (2pi radians = 360 degrees)
# Additional information to pass to
# self.oscialtePanda
extraArgs=[self.models[i], pi * (i % 2)]
)
# again, we want these to play continuously so we start them with
# loop()
self.moves[i].loop()
# Finally, we combine Sequence, Parallel, Func, and Wait intervals,
# to schedule texture swapping on the lights to simulate the lights turning
# on and off.
# Sequence intervals play other intervals in a sequence. In other words,
# it waits for the current interval to finish before playing the next
# one.
# Parallel intervals play a group of intervals at the same time
# Wait intervals simply do nothing for a given amount of time
# Func intervals simply make a single function call. This is helpful because
# it allows us to schedule functions to be called in a larger sequence. They
# take virtually no time so they don't cause a Sequence to wait.
self.lightBlink = Sequence(
# For the first step in our sequence we will set the on texture on one
# light and set the off texture on the other light at the same time
Parallel(
Func(self.lights1.setTexture, self.lightOnTex, 1),
Func(self.lights2.setTexture, self.lightOffTex, 1)),
Wait(1), # Then we will wait 1 second
# Then we will switch the textures at the same time
Parallel(
Func(self.lights1.setTexture, self.lightOffTex, 1),
Func(self.lights2.setTexture, self.lightOnTex, 1)),
Wait(1) # Then we will wait another second
)
self.lightBlink.loop() # Loop this sequence continuously
def oscillatePanda(self, rad, panda, offset):
# This is the oscillation function mentioned earlier. It takes in a
# degree value, a NodePath to set the height on, and an offset. The
# offset is there so that the different pandas can move opposite to
# each other. The .2 is the amplitude, so the height of the panda will
# vary from -.2 to .2
panda.setZ(sin(rad + offset) * .2)
demo = CarouselDemo()
demo.run()
| 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 |
ttthy1/2017sejongAI | week14/Mnist.py | 1 | 2273 | # Lab 7 Learning rate and Evaluation
import tensorflow as tf
import random
import matplotlib.pyplot as plt
tf.set_random_seed(777) # for reproducibility
from tensorflow.examples.tutorials.mnist import input_data
# Check out https://www.tensorflow.org/get_started/mnist/beginners for
# more information about the mnist dataset
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
nb_classes = 10
# MNIST data image of shape 28 * 28 = 784
X = tf.placeholder(tf.float32, [None, 784])
# 0 - 9 digits recognition = 10 classes
Y = tf.placeholder(tf.float32, [None, nb_classes])
W = tf.Variable(tf.random_normal([784, nb_classes]))
b = tf.Variable(tf.random_normal([nb_classes]))
# Hypothesis (using softmax)
hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
# Test model
is_correct = tf.equal(tf.arg_max(hypothesis, 1), tf.arg_max(Y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
# parameters
training_epochs = 15
batch_size = 100
with tf.Session() as sess:
# Initialize TensorFlow variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0
total_batch = int(mnist.train.num_examples / batch_size)
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
c, _ = sess.run([cost, optimizer], feed_dict={
X: batch_xs, Y: batch_ys})
avg_cost += c / total_batch
print('Epoch:', '%04d' % (epoch + 1),
'cost =', '{:.9f}'.format(avg_cost))
print("Learning finished")
# Test the model using test sets
print("Accuracy: ", accuracy.eval(session=sess, feed_dict={
X: mnist.test.images, Y: mnist.test.labels}))
# Get one and predict
r = random.randint(0, mnist.test.num_examples - 1)
print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
print("Prediction: ", sess.run(
tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]}))
plt.imshow(
mnist.test.images[r:r + 1].reshape(28, 28),
cmap='Greys',
interpolation='nearest')
plt.show()
| gpl-3.0 |
jaidevd/scikit-learn | sklearn/externals/joblib/testing.py | 23 | 3042 | """
Helper for testing.
"""
import sys
import warnings
import os.path
import re
import subprocess
import threading
from sklearn.externals.joblib._compat import PY3_OR_LATER
def warnings_to_stdout():
""" Redirect all warnings to stdout.
"""
showwarning_orig = warnings.showwarning
def showwarning(msg, cat, fname, lno, file=None, line=0):
showwarning_orig(msg, cat, os.path.basename(fname), line, sys.stdout)
warnings.showwarning = showwarning
#warnings.simplefilter('always')
try:
from nose.tools import assert_raises_regex
except ImportError:
# For Python 2.7
try:
from nose.tools import assert_raises_regexp as assert_raises_regex
except ImportError:
# for Python 2.6
def assert_raises_regex(expected_exception, expected_regexp,
callable_obj=None, *args, **kwargs):
"""Helper function to check for message patterns in exceptions"""
not_raised = False
try:
callable_obj(*args, **kwargs)
not_raised = True
except Exception as e:
error_message = str(e)
if not re.compile(expected_regexp).search(error_message):
raise AssertionError("Error message should match pattern "
"%r. %r does not." %
(expected_regexp, error_message))
if not_raised:
raise AssertionError("Should have raised %r" %
expected_exception(expected_regexp))
def check_subprocess_call(cmd, timeout=1, stdout_regex=None,
stderr_regex=None):
"""Runs a command in a subprocess with timeout in seconds.
Also checks returncode is zero, stdout if stdout_regex is set, and
stderr if stderr_regex is set.
"""
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
def kill_process():
proc.kill()
timer = threading.Timer(timeout, kill_process)
try:
timer.start()
stdout, stderr = proc.communicate()
if PY3_OR_LATER:
stdout, stderr = stdout.decode(), stderr.decode()
if proc.returncode != 0:
message = (
'Non-zero return code: {0}.\nStdout:\n{1}\n'
'Stderr:\n{2}').format(
proc.returncode, stdout, stderr)
raise ValueError(message)
if (stdout_regex is not None and
not re.search(stdout_regex, stdout)):
raise ValueError(
"Unexpected stdout: {0!r} does not match:\n{1!r}".format(
stdout_regex, stdout))
if (stderr_regex is not None and
not re.search(stderr_regex, stderr)):
raise ValueError(
"Unexpected stderr: {0!r} does not match:\n{1!r}".format(
stderr_regex, stderr))
finally:
timer.cancel()
| bsd-3-clause |
trungnt13/scikit-learn | sklearn/feature_selection/__init__.py | 244 | 1088 | """
The :mod:`sklearn.feature_selection` module implements feature selection
algorithms. It currently includes univariate filter selection methods and the
recursive feature elimination algorithm.
"""
from .univariate_selection import chi2
from .univariate_selection import f_classif
from .univariate_selection import f_oneway
from .univariate_selection import f_regression
from .univariate_selection import SelectPercentile
from .univariate_selection import SelectKBest
from .univariate_selection import SelectFpr
from .univariate_selection import SelectFdr
from .univariate_selection import SelectFwe
from .univariate_selection import GenericUnivariateSelect
from .variance_threshold import VarianceThreshold
from .rfe import RFE
from .rfe import RFECV
__all__ = ['GenericUnivariateSelect',
'RFE',
'RFECV',
'SelectFdr',
'SelectFpr',
'SelectFwe',
'SelectKBest',
'SelectPercentile',
'VarianceThreshold',
'chi2',
'f_classif',
'f_oneway',
'f_regression']
| bsd-3-clause |
Micket/CCBuilder | make_cc.py | 1 | 8680 | #!/usr/bin/env python3
from __future__ import print_function
from __future__ import division
import argparse
import pickle
import time
import CCBuilder as ccb
import CCBuilder_c as ccb_c
import numpy as np
import scipy.special
def uniform_dist(x):
""" Returns uniform distributions of given range """
return lambda: np.random.uniform(x[0], x[1])
def weibull_dist(a, mu):
""" Returns Weibull distributions for given shape parameter and average """
return lambda: np.random.weibull(a) * mu / scipy.special.gamma(1/a + 1)
def parse_dist(arg):
# Parses input string for given distribution.
# Returns a distribution, and the average
d, params = arg.split(':')
params = [float(x) for x in params.split(',')]
if d == 'U':
return uniform_dist(params), np.mean(params)
elif d == 'W':
a, mu = params
return weibull_dist(a, mu), mu
parser = argparse.ArgumentParser(description='''Generate a WC microstructure.
Grain shape/size supports 2 types of distributions:
Uniform: U:low,high
Weibull: U:a,mu (a=k in some notation, mu=mean)
''',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# parser.add_argument('-V', dest='verbose', action='store_true', help='Verbose mode.')
parser.add_argument('-f', dest='fname', metavar='basename', required=True, help='Output base filename.')
parser.add_argument('-L', dest='L', metavar='length', required=True, type=float, help='Cell length (volume is L^3)')
parser.add_argument('-m', dest='m', metavar='m', required=True, type=int,
help='Grid resolution. Total number of voxels are (m*L)^3')
parser.add_argument('--vol_frac_goal', dest="vol_frac_goal", metavar='v', required=True, type=float,
help='Goal for volume fraction WC (excluding overlap)')
parser.add_argument('-s', dest='seed', metavar='s', default=None, type=int,
help='Seed for RNG. Given identical parameters, ' +
'CCBuilder will generate identical output given a controlled seed.')
parser.add_argument('--stray_cleanup', action='store_true', help='Clean up stray voxels')
group = parser.add_argument_group('WC grain shape')
group.add_argument('-k', dest='k_dist', metavar='type,[params]', default='U:0.4,1.4',
help='k distribution')
group.add_argument('-r', dest='r_dist', metavar='type,[params]', default='U:0.1,0.4',
help='r distribution')
group.add_argument('-d', dest='d_dist', metavar='type,[params]', default='U:0.5,1.5',
help='d distribution')
group = parser.add_argument_group('Packing')
group.add_argument('--use_potential', action='store_true', help='Use repulsive potential.')
group.add_argument('--nr_tries', dest='nr_tries', metavar='n', default=2500, type=int,
help='Number of random translations.')
group.add_argument('--delta', dest='delta', metavar='d', type=float,
help='Maximum distance for randomized translations.')
group.add_argument('--m_coarse', dest="m_coarse", metavar='mc', default=10,
help='Grid resolution during packing.')
group = parser.add_argument_group('Potts simulation')
group.add_argument('--mc_steps', dest="mc_steps", metavar='steps', default=0.05, type=float,
help='Monte-Carlo steps (scales with (m*L)^4. Set to zero to turn off.')
group.add_argument('--tau', dest='tau', metavar='t', default=0.5, type=float,
help='Ficticious temperature in Potts model.')
options = parser.parse_args()
if options.seed is not None:
np.random.seed(options.seed)
# Heuristic mapping from actual to goal volume fraction
# vol_frac_goal = (alpha - 2)/(2 * alpha) + 1/alpha * np.sqrt(1 - alpha * np.log(-2*(vol_frac - 1)))
d_eq, d_0 = parse_dist(options.d_dist)
r, r_0 = parse_dist(options.r_dist)
k, k_0 = parse_dist(options.k_dist)
fname = options.fname
# to avoid confusion with types:
m = np.int(options.m)
m_coarse = np.int(options.m_coarse)
L = np.float(options.L)
mc_steps = np.float(options.mc_steps)
vol_frac_goal = np.double(options.vol_frac_goal)
tau = np.double(options.tau)
nr_tries = np.int(options.nr_tries)
delta_x = d_0/float(m)
M = np.int(m * L / d_0)
M_coarse = np.int(m_coarse * L / d_0)
idelta = M
idelta_coarse = M_coarse
if options.delta:
idelta = np.int(M * options.delta / L)
idelta_coarse = np.int(M_coarse * options.delta / L)
trunc_triangles = ccb.prepare_triangles(vol_frac_goal, L, r, k, d_eq)
# trunc_triangles = trunc_triangles[:1]
# trunc_triangles[0].rot_matrix = np.eye(3)
# trunc_triangles[0].rot_matrix_tr = np.eye(3)
# trunc_triangles[0].midpoint = np.array([2., 2., 2.])
# Sort triangles w.r.t. volume, so that large triangles are added to the box first (better packing)
trunc_triangles.sort(key=lambda x: x.volume, reverse=True)
print('Prepared', len(trunc_triangles), 'triangles')
if options.use_potential:
ccb.optimize_midpoints(L, trunc_triangles)
if m_coarse == m:
grain_ids, overlaps, voxel_indices = ccb_c.populate_voxels(M, L, trunc_triangles, nr_tries, idelta, 1.0)
else:
if nr_tries > 0:
# Optimization: Use coarser grid for packing, then insert packed grains into fine grid
# No need to get the return values, trunc_triangles
ccb_c.populate_voxels(M_coarse, L, trunc_triangles, nr_tries, idelta_coarse, 1.0)
grain_ids, overlaps, voxel_indices = ccb_c.populate_voxels(M, L, trunc_triangles, 1, 0, 1.0)
if mc_steps > 0:
start_time = time.time()
# Do Potts on coarse grid first for an improved initial guess.
M_coarseMC = M//2
grain_ids_coarse, overlaps_coarse, voxel_indices_coarse = ccb_c.populate_voxels(M_coarseMC, L, trunc_triangles, 0, 0, 1.0)
_, gb_voxels_coarse, _ = ccb_c.calc_surface_prop(M_coarseMC, grain_ids_coarse)
ccb_c.make_mcp_bound(M_coarseMC, grain_ids_coarse, gb_voxels_coarse, overlaps_coarse, voxel_indices_coarse,
np.int(mc_steps * M_coarseMC**4), tau)
# Copy over that solution to the overlap regions of the fine grid as a starting point
M2 = M**2
i = np.nonzero(overlaps)[0]
iz = i // M2
iy = (i - iz*M2) // M
ix = i - iz*M2 - iy*M
cix = ix * M_coarseMC // M
ciy = iy * M_coarseMC // M
ciz = iz * M_coarseMC // M
ci = cix + ciy*M_coarseMC + ciz*M_coarseMC**2
gid = grain_ids_coarse[ci]
# Could use a Cython implementation for efficiency.
for ii, g in zip(i, gid):
if g != grain_ids[ii] and np.searchsorted(voxel_indices[g-2], ii) < len(voxel_indices[g-2]):
grain_ids[ii] = g
# This might change a few voxels to a value that they shouldn't obtain, but it's barely noticeable
# grain_ids_1[i] = grain_ids_coarse[ci]
_, gb_voxels, _ = ccb_c.calc_surface_prop(M, grain_ids)
# and run the full resolution MCP:
ccb_c.make_mcp_bound(M, grain_ids, gb_voxels, overlaps, voxel_indices, np.int(mc_steps * M ** 4), tau)
print('Potts model took {} seconds'.format(np.str(time.time() - start_time)))
if options.stray_cleanup:
start_time = time.time()
ccb_c.stray_cleanup(M, grain_ids)
print('Stray voxel cleanup took {} seconds'.format(np.str(time.time() - start_time)))
surface_voxels, gb_voxels, interface_voxels = ccb_c.calc_surface_prop(M, grain_ids)
phases, good_voxels, euler_angles = ccb_c.calc_grain_prop(M, grain_ids, trunc_triangles)
phase_volumes = np.bincount(phases)
vol_frac_WC = phase_volumes[2] / np.float(M ** 3)
vol_frac_Co = 1 - vol_frac_WC
mass_frac_WC = ccb.mass_fraction(vol_frac_WC)
sum_gb_voxels = np.sum(gb_voxels)
contiguity = sum_gb_voxels / np.float(sum_gb_voxels + np.sum(interface_voxels))
print('Contiguity {:5f}, Co volume frac {:.5f}, mass frac {:.5f}'.format(
contiguity, 1 - vol_frac_WC, ccb.mass_fraction(vol_frac_WC)))
ccb.write_dream3d(fname, 3 * [M], 3 * [delta_x], trunc_triangles, grain_ids, phases, good_voxels,
euler_angles, surface_voxels, gb_voxels, interface_voxels, overlaps)
with open(fname + '_trunc_triangles.data', 'wb') as f:
pickle.dump([t.rot_matrix for t in trunc_triangles], f)
# Saving grain volume data
if False:
grain_volumes = np.bincount(grain_ids)
d_eq = ccb.volume_to_eq_d(grain_volumes[2:] * delta_x ** 3)
# np.savetxt(fname + '_d_orig.txt', [t.d_eq for t in trunc_triangles])
np.savetxt(fname + '_d.txt', d_eq)
# Plot initial and final distributions
import matplotlib.pyplot as plt
plt.hist(np.array([t.d_eq for t in trunc_triangles]), alpha=0.5, bins=15, normed=True, label='Initial')
plt.hist(d_eq, alpha=0.5, bins=15, normed=True, label='Final')
plt.legend(loc='upper right')
plt.show()
| gpl-3.0 |
andersgs/dingo | dingo/random_forest.py | 1 | 2551 | '''
Some functions to fit a random forest
'''
import sklearn.ensemble
import pandas
import progressbar
bar = progressbar.ProgressBar()
def test_max_features(max_features):
if (max_features not in ['sqrt', 'auto', 'log2', None]):
try:
max_features = int(max_features)
except ValueError:
print("max_features has to be an integer or one of 'sqrt', 'auto', 'log2' or None.")
raise
return max_features
def learn(X,y, n_trees = 10, criterion = 'entropy', max_features = "sqrt", max_depth = None, min_samples_split = 2, min_samples_leaf = 1, min_weight_fraction_leaf = 0, max_leaf_nodes = None, min_impurity_split = 1e-7, bootstrap = False, oob_score = False, n_jobs = 10, random_state = None, warm_start = False, class_weight = 'balanced_subsample'):
rf = sklearn.ensemble.RandomForestClassifier(n_estimators = n_trees, \
criterion = criterion, \
max_features = max_features, \
max_depth = max_depth, \
min_samples_split = min_samples_split, \
min_samples_leaf = min_samples_leaf, \
min_weight_fraction_leaf = min_weight_fraction_leaf, \
max_leaf_nodes = max_leaf_nodes, \
min_impurity_split = min_impurity_split, \
bootstrap = bootstrap, \
oob_score = oob_score, \
n_jobs = n_jobs, \
random_state = random_state, \
warm_start = warm_start, \
class_weight = class_weight, \
verbose = 1
)
rf.fit(X, y)
return rf
def importance(rf, kmers):
importance = rf.estimators_[0].feature_importances_
for est in bar(rf.estimators_[1:]):
importance += est.feature_importances_
importance = importance/rf.n_estimators
d = {"kmer": kmers,
"importance": importance}
d = pandas.DataFrame(d)
d = d.sort_values(by = "importance", ascending = 0)
d = d.loc[d.importance > 0]
return d
| bsd-3-clause |
BlueBrain/NEST | testsuite/manualtests/cross_check_test_mip_corrdet.py | 13 | 2594 | # -*- coding: utf-8 -*-
#
# cross_check_test_mip_corrdet.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
# Script to check correlation_detector.
# Calculates spike cross correlation function of both spike trains in
# spike_detector-0-0-3.gdf. The file is generated after running the
# testscript testsuite/unittests/test_mip_corrdet.sli
#
# Author: Helias
# Date: 08-04-07
#
from scipy import *
from matplotlib.pylab import * # for plot
# Auto- and crosscorrelation functions for spike trains.
#
# A time bin of size tbin is centered around the time difference it
# represents If the correlation function is calculated for tau in
# [-tau_max, tau_max], the pair events contributing to the left-most
# bin are those for which tau in [-tau_max-tbin/2, tau_max+tbin/2) and
# so on.
# correlate two spike trains with each other
# assumes spike times to be ordered in time
# tau > 0 means spike2 is later than spike1
#
# tau_max: maximum time lag in ms correlation function
# tbin: bin size
# spike1: first spike train [tspike...]
# spike2: second spike train [tspike...]
#
def corr_spikes_sorted(spike1, spike2, tbin, tau_max, h):
tau_max_i = int(tau_max/h)
tbin_i = int(tbin/h)
cross = zeros(int(2*tau_max_i/tbin_i+1), 'd')
j0 = 0
for spki in spike1:
j = j0
while j < len(spike2) and spike2[j] - spki < -tau_max_i - tbin_i/2.0:
j += 1
j0 = j
while j < len(spike2) and spike2[j] - spki < tau_max_i + tbin_i/2.0:
cross[int((spike2[j] - spki + tau_max_i + 0.5*tbin_i)/tbin_i)] += 1.0
j += 1
return cross
def main():
# resolution
h = 0.1
tau_max = 100.0 # ms correlation window
t_bin = 10.0 # ms bin size
# read input from spike detector
spikes = load('spike_detector-0-0-3.gdf')
sp1 = spikes[find(spikes[:,0] == 4), 1]
sp2 = spikes[find(spikes[:,0] == 5), 1]
cross = corr_spikes_sorted(sp1, sp2, t_bin, tau_max, h)
print cross
print sum(cross)
main()
| gpl-2.0 |
NicovincX2/Python-3.5 | Algèbre/Opération/scalar_product.py | 1 | 1933 | # -*- coding: utf-8 -*-
import os
import seaborn
seaborn.set()
colors = seaborn.color_palette()
import utils
# For 3D plotting we need to import some extra stuff
from mpl_toolkits.mplot3d import Axes3D
# First create two random vectors in 3 dimensional space
v1 = rand(3, 1)
v2 = rand(3, 1)
# And scale them to unit length
v1 = v1 / norm(v1)
v2 = v2 / norm(v2)
# Plot the vectors
o = zeros(3) # origin
# We'll use the object oriented plotting interface
f = figure(figsize=(8, 8))
ax = f.add_subplot(111, projection="3d", axisbg="white")
ax.plot(*[[o[i], v1[i]] for i in range(3)], linewidth=3, label="vector1")
ax.plot(*[[o[i], v2[i]] for i in range(3)], linewidth=3, label="vector2")
for axisl in ["x", "y", "z"]:
getattr(ax, "set_%slabel" % axisl)(axisl) # Here's a fun trick
legend()
f = figure(figsize=(8, 8))
ax = f.add_subplot(111, projection="3d", axisbg="white")
ax.plot(*[[o[i], v1[i]] for i in range(3)], linewidth=3, label="vector1")
ax.plot(*[[o[i], v2[i]] for i in range(3)], linewidth=3, label="vector2")
for axisl in ["x", "y", "z"]:
getattr(ax, "set_%slabel" % axisl)(axisl) # Here's a fun trick
legend()
for i in range(100):
# generate a point that is a weighted sum of the 2 vectors
w1 = randn(1)
w2 = randn(1)
point = w1 * v1 + w2 * v2
ax.plot(*point, marker=".", color="k")
# We can find a vector that is orthogonal to the plane defined by v1 and v2
# by taking the vector cross product. See the wikipedia page for a
# definition of cross product
# Must be right shape for cross()
v3 = cross(v1.reshape(1, 3), v2.reshape(1, 3)).squeeze()
ax.plot(*[[o[i], v3[i]] for i in range(3)],
linewidth=3, label="orthogonal vector")
legend()
print(v3[0] * v1[0] + v3[1] * v1[1] + v3[2] * v1[2])
print(dot(v3, v1))
theta = arccos(dot(v2.T, v1)).squeeze()
# and radians can be converted to degrees
theta_deg = theta * (180 / pi)
print(theta, theta_deg)
os.system("pause")
| gpl-3.0 |
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 |
giorgiop/scikit-learn | sklearn/linear_model/__init__.py | 83 | 3139 | """
The :mod:`sklearn.linear_model` module implements generalized linear models. It
includes Ridge regression, Bayesian Regression, Lasso and Elastic Net
estimators computed with Least Angle Regression and coordinate descent. It also
implements Stochastic Gradient Descent related algorithms.
"""
# See http://scikit-learn.sourceforge.net/modules/sgd.html and
# http://scikit-learn.sourceforge.net/modules/linear_model.html for
# complete documentation.
from .base import LinearRegression
from .bayes import BayesianRidge, ARDRegression
from .least_angle import (Lars, LassoLars, lars_path, LarsCV, LassoLarsCV,
LassoLarsIC)
from .coordinate_descent import (Lasso, ElasticNet, LassoCV, ElasticNetCV,
lasso_path, enet_path, MultiTaskLasso,
MultiTaskElasticNet, MultiTaskElasticNetCV,
MultiTaskLassoCV)
from .huber import HuberRegressor
from .sgd_fast import Hinge, Log, ModifiedHuber, SquaredLoss, Huber
from .stochastic_gradient import SGDClassifier, SGDRegressor
from .ridge import (Ridge, RidgeCV, RidgeClassifier, RidgeClassifierCV,
ridge_regression)
from .logistic import (LogisticRegression, LogisticRegressionCV,
logistic_regression_path)
from .omp import (orthogonal_mp, orthogonal_mp_gram, OrthogonalMatchingPursuit,
OrthogonalMatchingPursuitCV)
from .passive_aggressive import PassiveAggressiveClassifier
from .passive_aggressive import PassiveAggressiveRegressor
from .perceptron import Perceptron
from .randomized_l1 import (RandomizedLasso, RandomizedLogisticRegression,
lasso_stability_path)
from .ransac import RANSACRegressor
from .theil_sen import TheilSenRegressor
__all__ = ['ARDRegression',
'BayesianRidge',
'ElasticNet',
'ElasticNetCV',
'Hinge',
'HuberRegressor',
'Lars',
'LarsCV',
'Lasso',
'LassoCV',
'LassoLars',
'LassoLarsCV',
'LassoLarsIC',
'LinearRegression',
'Log',
'LogisticRegression',
'LogisticRegressionCV',
'ModifiedHuber',
'MultiTaskElasticNet',
'MultiTaskElasticNetCV',
'MultiTaskLasso',
'MultiTaskLassoCV',
'OrthogonalMatchingPursuit',
'OrthogonalMatchingPursuitCV',
'PassiveAggressiveClassifier',
'PassiveAggressiveRegressor',
'Perceptron',
'RandomizedLasso',
'RandomizedLogisticRegression',
'Ridge',
'RidgeCV',
'RidgeClassifier',
'RidgeClassifierCV',
'SGDClassifier',
'SGDRegressor',
'SquaredLoss',
'TheilSenRegressor',
'enet_path',
'lars_path',
'lasso_path',
'lasso_stability_path',
'logistic_regression_path',
'orthogonal_mp',
'orthogonal_mp_gram',
'ridge_regression',
'RANSACRegressor']
| bsd-3-clause |
nik-hil/fastai | deeplearning2/rossman_exp.py | 10 | 5451 | train_ratio=0.9
use_dict=True
use_scaler=False
init_emb=False
split_contins=True
samp_size = 100000
#samp_size = 0
import math, keras, datetime, pandas as pd, numpy as np, keras.backend as K
import matplotlib.pyplot as plt, xgboost, operator, random, pickle, os
from sklearn_pandas import DataFrameMapper
from sklearn.preprocessing import LabelEncoder, Imputer, StandardScaler
from keras.models import Model
from keras.layers import merge, Input
from keras.layers.core import Dense, Activation, Reshape, Flatten, Dropout
from keras.layers.embeddings import Embedding
from keras.optimizers import Adam
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
from keras import initializations
np.set_printoptions(4)
cfg = K.tf.ConfigProto()
cfg.gpu_options.allow_growth = True
K.set_session(K.tf.Session(config=cfg))
os.chdir('data/rossman')
cat_var_dict = {'Store': 50, 'DayOfWeek': 6, 'Year': 2, 'Month': 6,
'Day': 10, 'StateHoliday': 3, 'CompetitionMonthsOpen': 2,
'Promo2Weeks': 1, 'StoreType': 2, 'Assortment': 3, 'PromoInterval': 3,
'CompetitionOpenSinceYear': 4, 'Promo2SinceYear': 4, 'State': 6,
'Week': 2, 'Events': 4, 'Promo_fw': 1,
'Promo_bw': 1, 'StateHoliday_fw': 1,
'StateHoliday_bw': 1, 'SchoolHoliday_fw': 1,
'SchoolHoliday_bw': 1}
cats, contins= [o for n,o in np.load('vars.npz').items()]
y = np.load('deps.npz').items()[0][1]
if samp_size != 0:
np.random.seed(42)
idxs = sorted(np.random.choice(len(y), samp_size, replace=False))
cats= cats[idxs]
contins= contins[idxs]
y= y[idxs]
n=len(y)
train_size = int(n*train_ratio)
contins_trn_orig, contins_val_orig = contins[:train_size], contins[train_size:]
cats_trn, cats_val = cats[:train_size], cats[train_size:]
y_trn, y_val = y[:train_size], y[train_size:]
contin_map_fit = pickle.load(open('contin_maps.pickle', 'rb'))
cat_map_fit = pickle.load(open('cat_maps.pickle', 'rb'))
def cat_map_info(feat): return feat[0], len(feat[1].classes_)
co_enc = StandardScaler().fit(contins_trn_orig)
tf_contins_trn = co_enc.transform(contins_trn_orig)
tf_contins_val = co_enc.transform(contins_val_orig)
"""
def rmspe(y_pred, targ = y_valid_orig):
return math.sqrt(np.square((targ - y_pred)/targ).mean())
def log_max_inv(preds, mx = max_log_y): return np.exp(preds * mx)
def normalize_inv(preds): return preds * ystd + ymean
"""
def split_cols(arr): return np.hsplit(arr,arr.shape[1])
def emb_init(shape, name=None):
return initializations.uniform(shape, scale=0.6/shape[1], name=name)
def get_emb(feat):
name, c = cat_map_info(feat)
if use_dict:
c2 = cat_var_dict[name]
else:
c2 = (c+2)//3
if c2>50: c2=50
inp = Input((1,), dtype='int64', name=name+'_in')
if init_emb:
u = Flatten(name=name+'_flt')(Embedding(c, c2, input_length=1)(inp))
else:
u = Flatten(name=name+'_flt')(Embedding(c, c2, input_length=1, init=emb_init)(inp))
return inp,u
def get_contin(feat):
name = feat[0][0]
inp = Input((1,), name=name+'_in')
return inp, Dense(1, name=name+'_d')(inp)
def split_data():
if split_contins:
map_train = split_cols(cats_trn) + split_cols(contins_trn)
map_valid = split_cols(cats_val) + split_cols(contins_val)
else:
map_train = split_cols(cats_trn) + [contins_trn]
map_valid = split_cols(cats_val) + [contins_val]
return (map_train, map_valid)
def get_contin_one():
n_contin = contins_trn.shape[1]
contin_inp = Input((n_contin,), name='contin')
contin_out = BatchNormalization()(contin_inp)
return contin_inp, contin_out
def train(model, map_train, map_valid, bs=128, ne=10):
return model.fit(map_train, y_trn, batch_size=bs, nb_epoch=ne,
verbose=0, validation_data=(map_valid, y_val))
def get_model():
if split_contins:
conts = [get_contin(feat) for feat in contin_map_fit.features]
cont_out = [d for inp,d in conts]
cont_inp = [inp for inp,d in conts]
else:
contin_inp, contin_out = get_contin_one()
cont_out = [contin_out]
cont_inp = [contin_inp]
embs = [get_emb(feat) for feat in cat_map_fit.features]
x = merge([emb for inp,emb in embs] + cont_out, mode='concat')
x = Dropout(0.02)(x)
x = Dense(1000, activation='relu', init='uniform')(x)
x = Dense(500, activation='relu', init='uniform')(x)
x = Dense(1, activation='sigmoid')(x)
model = Model([inp for inp,emb in embs] + cont_inp, x)
model.compile('adam', 'mean_absolute_error')
#model.compile(Adam(), 'mse')
return model
for split_contins in [True, False]:
for use_dict in [True, False]:
for use_scaler in [True, False]:
for init_emb in [True, False]:
print ({'split_contins':split_contins, 'use_dict':use_dict,
'use_scaler':use_scaler, 'init_emb':init_emb})
if use_scaler:
contins_trn = tf_contins_trn
contins_val = tf_contins_val
else:
contins_trn = contins_trn_orig
contins_val = contins_val_orig
map_train, map_valid = split_data()
model = get_model()
hist = np.array(train(model, map_train, map_valid, 128, 10)
.history['val_loss'])
print(hist)
print(hist.min())
| apache-2.0 |
adiIspas/Machine-Learning_A-Z | Machine Learning A-Z/Part 7 - Natural Language Processing/Section 36 - Natural Language Processing/natural_language_processing.py | 3 | 1452 | # Natural Language Processing
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\t', quoting = 3)
# Cleaning the texts
import re
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
corpus = []
for i in range(0, 1000):
review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
review = review.lower()
review = review.split()
ps = PorterStemmer()
review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
review = ' '.join(review)
corpus.append(review)
# Creating the Bag of Words model
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features = 1500)
X = cv.fit_transform(corpus).toarray()
y = dataset.iloc[:, 1].values
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
# Fitting Naive Bayes to the Training set
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred) | mit |
hsuantien/scikit-learn | sklearn/metrics/regression.py | 23 | 16771 | """Metrics to assess performance on regression task
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Arnaud Joly <a.joly@ulg.ac.be>
# Jochen Wersdorfer <jochen@wersdoerfer.de>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Joel Nothman <joel.nothman@gmail.com>
# Noel Dawe <noel@dawe.me>
# Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Michael Eickenberg <michael.eickenberg@gmail.com>
# Konstantin Shmelkov <konstantin.shmelkov@polytechnique.edu>
# License: BSD 3 clause
from __future__ import division
import numpy as np
from ..utils.validation import check_array, check_consistent_length
from ..utils.validation import column_or_1d
import warnings
__ALL__ = [
"mean_absolute_error",
"mean_squared_error",
"median_absolute_error",
"r2_score",
"explained_variance_score"
]
def _check_reg_targets(y_true, y_pred, multioutput):
"""Check that y_true and y_pred belong to the same regression task
Parameters
----------
y_true : array-like,
y_pred : array-like,
multioutput : array-like or string in ['raw_values', uniform_average',
'variance_weighted'] or None
None is accepted due to backward compatibility of r2_score().
Returns
-------
type_true : one of {'continuous', continuous-multioutput'}
The type of the true target data, as output by
'utils.multiclass.type_of_target'
y_true : array-like of shape = (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples, n_outputs)
Estimated target values.
multioutput : array-like of shape = (n_outputs) or string in ['raw_values',
uniform_average', 'variance_weighted'] or None
Custom output weights if ``multioutput`` is array-like or
just the corresponding argument if ``multioutput`` is a
correct keyword.
"""
check_consistent_length(y_true, y_pred)
y_true = check_array(y_true, ensure_2d=False)
y_pred = check_array(y_pred, ensure_2d=False)
if y_true.ndim == 1:
y_true = y_true.reshape((-1, 1))
if y_pred.ndim == 1:
y_pred = y_pred.reshape((-1, 1))
if y_true.shape[1] != y_pred.shape[1]:
raise ValueError("y_true and y_pred have different number of output "
"({0}!={1})".format(y_true.shape[1], y_pred.shape[1]))
n_outputs = y_true.shape[1]
multioutput_options = (None, 'raw_values', 'uniform_average',
'variance_weighted')
if multioutput not in multioutput_options:
multioutput = check_array(multioutput, ensure_2d=False)
if n_outputs == 1:
raise ValueError("Custom weights are useful only in "
"multi-output cases.")
elif n_outputs != len(multioutput):
raise ValueError(("There must be equally many custom weights "
"(%d) as outputs (%d).") %
(len(multioutput), n_outputs))
y_type = 'continuous' if n_outputs == 1 else 'continuous-multioutput'
return y_type, y_true, y_pred, multioutput
def mean_absolute_error(y_true, y_pred,
sample_weight=None,
multioutput='uniform_average'):
"""Mean absolute error regression loss
Read more in the :ref:`User Guide <mean_absolute_error>`.
Parameters
----------
y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape = (n_samples), optional
Sample weights.
multioutput : string in ['raw_values', 'uniform_average']
or array-like of shape (n_outputs)
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats
If multioutput is 'raw_values', then mean absolute error is returned
for each output separately.
If multioutput is 'uniform_average' or an ndarray of weights, then the
weighted average of all output errors is returned.
MAE output is non-negative floating point. The best value is 0.0.
Examples
--------
>>> from sklearn.metrics import mean_absolute_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_absolute_error(y_true, y_pred)
0.5
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> mean_absolute_error(y_true, y_pred)
0.75
>>> mean_absolute_error(y_true, y_pred, multioutput='raw_values')
array([ 0.5, 1. ])
>>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.849...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput)
output_errors = np.average(np.abs(y_pred - y_true),
weights=sample_weight, axis=0)
if multioutput == 'raw_values':
return output_errors
elif multioutput == 'uniform_average':
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput)
def mean_squared_error(y_true, y_pred,
sample_weight=None,
multioutput='uniform_average'):
"""Mean squared error regression loss
Read more in the :ref:`User Guide <mean_squared_error>`.
Parameters
----------
y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape = (n_samples), optional
Sample weights.
multioutput : string in ['raw_values', 'uniform_average']
or array-like of shape (n_outputs)
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats
A non-negative floating point value (the best value is 0.0), or an
array of floating point values, one for each individual target.
Examples
--------
>>> from sklearn.metrics import mean_squared_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_squared_error(y_true, y_pred)
0.375
>>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
>>> y_pred = [[0, 2],[-1, 2],[8, -5]]
>>> mean_squared_error(y_true, y_pred) # doctest: +ELLIPSIS
0.708...
>>> mean_squared_error(y_true, y_pred, multioutput='raw_values')
... # doctest: +ELLIPSIS
array([ 0.416..., 1. ])
>>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.824...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput)
output_errors = np.average((y_true - y_pred) ** 2, axis=0,
weights=sample_weight)
if multioutput == 'raw_values':
return output_errors
elif multioutput == 'uniform_average':
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput)
def median_absolute_error(y_true, y_pred):
"""Median absolute error regression loss
Read more in the :ref:`User Guide <median_absolute_error>`.
Parameters
----------
y_true : array-like of shape = (n_samples)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples)
Estimated target values.
Returns
-------
loss : float
A positive floating point value (the best value is 0.0).
Examples
--------
>>> from sklearn.metrics import median_absolute_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> median_absolute_error(y_true, y_pred)
0.5
"""
y_type, y_true, y_pred, _ = _check_reg_targets(y_true, y_pred,
'uniform_average')
if y_type == 'continuous-multioutput':
raise ValueError("Multioutput not supported in median_absolute_error")
return np.median(np.abs(y_pred - y_true))
def explained_variance_score(y_true, y_pred,
sample_weight=None,
multioutput='uniform_average'):
"""Explained variance regression score function
Best possible score is 1.0, lower values are worse.
Read more in the :ref:`User Guide <explained_variance_score>`.
Parameters
----------
y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape = (n_samples), optional
Sample weights.
multioutput : string in ['raw_values', 'uniform_average', \
'variance_weighted'] or array-like of shape (n_outputs)
Defines aggregating of multiple output scores.
Array-like value defines weights used to average scores.
'raw_values' :
Returns a full set of scores in case of multioutput input.
'uniform_average' :
Scores of all outputs are averaged with uniform weight.
'variance_weighted' :
Scores of all outputs are averaged, weighted by the variances
of each individual output.
Returns
-------
score : float or ndarray of floats
The explained variance or ndarray if 'multioutput' is 'raw_values'.
Notes
-----
This is not a symmetric function.
Examples
--------
>>> from sklearn.metrics import explained_variance_score
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> explained_variance_score(y_true, y_pred) # doctest: +ELLIPSIS
0.957...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> explained_variance_score(y_true, y_pred, multioutput='uniform_average')
... # doctest: +ELLIPSIS
0.983...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput)
y_diff_avg = np.average(y_true - y_pred, weights=sample_weight, axis=0)
numerator = np.average((y_true - y_pred - y_diff_avg) ** 2,
weights=sample_weight, axis=0)
y_true_avg = np.average(y_true, weights=sample_weight, axis=0)
denominator = np.average((y_true - y_true_avg) ** 2,
weights=sample_weight, axis=0)
nonzero_numerator = numerator != 0
nonzero_denominator = denominator != 0
valid_score = nonzero_numerator & nonzero_denominator
output_scores = np.ones(y_true.shape[1])
output_scores[valid_score] = 1 - (numerator[valid_score] /
denominator[valid_score])
output_scores[nonzero_numerator & ~nonzero_denominator] = 0.
if multioutput == 'raw_values':
# return scores individually
return output_scores
elif multioutput == 'uniform_average':
# passing to np.average() None as weights results is uniform mean
avg_weights = None
elif multioutput == 'variance_weighted':
avg_weights = denominator
else:
avg_weights = multioutput
return np.average(output_scores, weights=avg_weights)
def r2_score(y_true, y_pred,
sample_weight=None,
multioutput=None):
"""R^2 (coefficient of determination) regression score function.
Best possible score is 1.0, lower values are worse.
Read more in the :ref:`User Guide <r2_score>`.
Parameters
----------
y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape = (n_samples), optional
Sample weights.
multioutput : string in ['raw_values', 'uniform_average',
'variance_weighted'] or None or array-like of shape (n_outputs)
Defines aggregating of multiple output scores.
Array-like value defines weights used to average scores.
Default value correponds to 'variance_weighted', but
will be changed to 'uniform_average' in next versions.
'raw_values' :
Returns a full set of scores in case of multioutput input.
'uniform_average' :
Scores of all outputs are averaged with uniform weight.
'variance_weighted' :
Scores of all outputs are averaged, weighted by the variances
of each individual output.
Returns
-------
z : float or ndarray of floats
The R^2 score or ndarray of scores if 'multioutput' is
'raw_values'.
Notes
-----
This is not a symmetric function.
Unlike most other scores, R^2 score may be negative (it need not actually
be the square of a quantity R).
References
----------
.. [1] `Wikipedia entry on the Coefficient of determination
<http://en.wikipedia.org/wiki/Coefficient_of_determination>`_
Examples
--------
>>> from sklearn.metrics import r2_score
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> r2_score(y_true, y_pred) # doctest: +ELLIPSIS
0.948...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> r2_score(y_true, y_pred, multioutput='variance_weighted') # doctest: +ELLIPSIS
0.938...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput)
if sample_weight is not None:
sample_weight = column_or_1d(sample_weight)
weight = sample_weight[:, np.newaxis]
else:
weight = 1.
numerator = (weight * (y_true - y_pred) ** 2).sum(axis=0,
dtype=np.float64)
denominator = (weight * (y_true - np.average(
y_true, axis=0, weights=sample_weight)) ** 2).sum(axis=0,
dtype=np.float64)
nonzero_denominator = denominator != 0
nonzero_numerator = numerator != 0
valid_score = nonzero_denominator & nonzero_numerator
output_scores = np.ones([y_true.shape[1]])
output_scores[valid_score] = 1 - (numerator[valid_score] /
denominator[valid_score])
# arbitrary set to zero to avoid -inf scores, having a constant
# y_true is not interesting for scoring a regression anyway
output_scores[nonzero_numerator & ~nonzero_denominator] = 0.
if multioutput is None and y_true.shape[1] != 1:
# @FIXME change in 0.18
warnings.warn("Default 'multioutput' behavior now corresponds to "
"'variance_weighted' value, it will be changed "
"to 'uniform_average' in 0.18.",
DeprecationWarning)
multioutput = 'variance_weighted'
if multioutput == 'raw_values':
# return scores individually
return output_scores
elif multioutput == 'uniform_average':
# passing None as weights results is uniform mean
avg_weights = None
elif multioutput == 'variance_weighted':
avg_weights = denominator
# avoid fail on constant y or one-element arrays
if not np.any(nonzero_denominator):
if not np.any(nonzero_numerator):
return 1.0
else:
return 0.0
else:
avg_weights = multioutput
return np.average(output_scores, weights=avg_weights)
| bsd-3-clause |
yunfeilu/scikit-learn | sklearn/decomposition/tests/test_sparse_pca.py | 160 | 6028 | # Author: Vlad Niculae
# License: BSD 3 clause
import sys
import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import if_safe_multiprocessing_with_blas
from sklearn.decomposition import SparsePCA, MiniBatchSparsePCA
from sklearn.utils import check_random_state
def generate_toy_data(n_components, n_samples, image_size, random_state=None):
n_features = image_size[0] * image_size[1]
rng = check_random_state(random_state)
U = rng.randn(n_samples, n_components)
V = rng.randn(n_components, n_features)
centers = [(3, 3), (6, 7), (8, 1)]
sz = [1, 2, 1]
for k in range(n_components):
img = np.zeros(image_size)
xmin, xmax = centers[k][0] - sz[k], centers[k][0] + sz[k]
ymin, ymax = centers[k][1] - sz[k], centers[k][1] + sz[k]
img[xmin:xmax][:, ymin:ymax] = 1.0
V[k, :] = img.ravel()
# Y is defined by : Y = UV + noise
Y = np.dot(U, V)
Y += 0.1 * rng.randn(Y.shape[0], Y.shape[1]) # Add noise
return Y, U, V
# SparsePCA can be a bit slow. To avoid having test times go up, we
# test different aspects of the code in the same test
def test_correct_shapes():
rng = np.random.RandomState(0)
X = rng.randn(12, 10)
spca = SparsePCA(n_components=8, random_state=rng)
U = spca.fit_transform(X)
assert_equal(spca.components_.shape, (8, 10))
assert_equal(U.shape, (12, 8))
# test overcomplete decomposition
spca = SparsePCA(n_components=13, random_state=rng)
U = spca.fit_transform(X)
assert_equal(spca.components_.shape, (13, 10))
assert_equal(U.shape, (12, 13))
def test_fit_transform():
alpha = 1
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
random_state=0)
spca_lars.fit(Y)
# Test that CD gives similar results
spca_lasso = SparsePCA(n_components=3, method='cd', random_state=0,
alpha=alpha)
spca_lasso.fit(Y)
assert_array_almost_equal(spca_lasso.components_, spca_lars.components_)
@if_safe_multiprocessing_with_blas
def test_fit_transform_parallel():
alpha = 1
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
random_state=0)
spca_lars.fit(Y)
U1 = spca_lars.transform(Y)
# Test multiple CPUs
spca = SparsePCA(n_components=3, n_jobs=2, method='lars', alpha=alpha,
random_state=0).fit(Y)
U2 = spca.transform(Y)
assert_true(not np.all(spca_lars.components_ == 0))
assert_array_almost_equal(U1, U2)
def test_transform_nan():
# Test that SparsePCA won't return NaN when there is 0 feature in all
# samples.
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
Y[:, 0] = 0
estimator = SparsePCA(n_components=8)
assert_false(np.any(np.isnan(estimator.fit_transform(Y))))
def test_fit_transform_tall():
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 65, (8, 8), random_state=rng) # tall array
spca_lars = SparsePCA(n_components=3, method='lars',
random_state=rng)
U1 = spca_lars.fit_transform(Y)
spca_lasso = SparsePCA(n_components=3, method='cd', random_state=rng)
U2 = spca_lasso.fit(Y).transform(Y)
assert_array_almost_equal(U1, U2)
def test_initialization():
rng = np.random.RandomState(0)
U_init = rng.randn(5, 3)
V_init = rng.randn(3, 4)
model = SparsePCA(n_components=3, U_init=U_init, V_init=V_init, max_iter=0,
random_state=rng)
model.fit(rng.randn(5, 4))
assert_array_equal(model.components_, V_init)
def test_mini_batch_correct_shapes():
rng = np.random.RandomState(0)
X = rng.randn(12, 10)
pca = MiniBatchSparsePCA(n_components=8, random_state=rng)
U = pca.fit_transform(X)
assert_equal(pca.components_.shape, (8, 10))
assert_equal(U.shape, (12, 8))
# test overcomplete decomposition
pca = MiniBatchSparsePCA(n_components=13, random_state=rng)
U = pca.fit_transform(X)
assert_equal(pca.components_.shape, (13, 10))
assert_equal(U.shape, (12, 13))
def test_mini_batch_fit_transform():
raise SkipTest("skipping mini_batch_fit_transform.")
alpha = 1
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
spca_lars = MiniBatchSparsePCA(n_components=3, random_state=0,
alpha=alpha).fit(Y)
U1 = spca_lars.transform(Y)
# Test multiple CPUs
if sys.platform == 'win32': # fake parallelism for win32
import sklearn.externals.joblib.parallel as joblib_par
_mp = joblib_par.multiprocessing
joblib_par.multiprocessing = None
try:
U2 = MiniBatchSparsePCA(n_components=3, n_jobs=2, alpha=alpha,
random_state=0).fit(Y).transform(Y)
finally:
joblib_par.multiprocessing = _mp
else: # we can efficiently use parallelism
U2 = MiniBatchSparsePCA(n_components=3, n_jobs=2, alpha=alpha,
random_state=0).fit(Y).transform(Y)
assert_true(not np.all(spca_lars.components_ == 0))
assert_array_almost_equal(U1, U2)
# Test that CD gives similar results
spca_lasso = MiniBatchSparsePCA(n_components=3, method='cd', alpha=alpha,
random_state=0).fit(Y)
assert_array_almost_equal(spca_lasso.components_, spca_lars.components_)
| bsd-3-clause |
winklerand/pandas | pandas/tests/reshape/test_merge_ordered.py | 2 | 2966 | import pandas as pd
from pandas import DataFrame, merge_ordered
from pandas.util import testing as tm
from pandas.util.testing import assert_frame_equal
from numpy import nan
class TestMergeOrdered(object):
def setup_method(self, method):
self.left = DataFrame({'key': ['a', 'c', 'e'],
'lvalue': [1, 2., 3]})
self.right = DataFrame({'key': ['b', 'c', 'd', 'f'],
'rvalue': [1, 2, 3., 4]})
def test_basic(self):
result = merge_ordered(self.left, self.right, on='key')
expected = DataFrame({'key': ['a', 'b', 'c', 'd', 'e', 'f'],
'lvalue': [1, nan, 2, nan, 3, nan],
'rvalue': [nan, 1, 2, 3, nan, 4]})
assert_frame_equal(result, expected)
def test_ffill(self):
result = merge_ordered(
self.left, self.right, on='key', fill_method='ffill')
expected = DataFrame({'key': ['a', 'b', 'c', 'd', 'e', 'f'],
'lvalue': [1., 1, 2, 2, 3, 3.],
'rvalue': [nan, 1, 2, 3, 3, 4]})
assert_frame_equal(result, expected)
def test_multigroup(self):
left = pd.concat([self.left, self.left], ignore_index=True)
left['group'] = ['a'] * 3 + ['b'] * 3
result = merge_ordered(left, self.right, on='key', left_by='group',
fill_method='ffill')
expected = DataFrame({'key': ['a', 'b', 'c', 'd', 'e', 'f'] * 2,
'lvalue': [1., 1, 2, 2, 3, 3.] * 2,
'rvalue': [nan, 1, 2, 3, 3, 4] * 2})
expected['group'] = ['a'] * 6 + ['b'] * 6
assert_frame_equal(result, expected.loc[:, result.columns])
result2 = merge_ordered(self.right, left, on='key', right_by='group',
fill_method='ffill')
assert_frame_equal(result, result2.loc[:, result.columns])
result = merge_ordered(left, self.right, on='key', left_by='group')
assert result['group'].notna().all()
def test_merge_type(self):
class NotADataFrame(DataFrame):
@property
def _constructor(self):
return NotADataFrame
nad = NotADataFrame(self.left)
result = nad.merge(self.right, on='key')
assert isinstance(result, NotADataFrame)
def test_empty_sequence_concat(self):
# GH 9157
empty_pat = "[Nn]o objects"
none_pat = "objects.*None"
test_cases = [
((), empty_pat),
([], empty_pat),
({}, empty_pat),
([None], none_pat),
([None, None], none_pat)
]
for df_seq, pattern in test_cases:
tm.assert_raises_regex(ValueError, pattern, pd.concat, df_seq)
pd.concat([pd.DataFrame()])
pd.concat([None, pd.DataFrame()])
pd.concat([pd.DataFrame(), None])
| bsd-3-clause |
nomadcube/scikit-learn | examples/bicluster/plot_spectral_coclustering.py | 276 | 1736 | """
==============================================
A demo of the Spectral Co-Clustering algorithm
==============================================
This example demonstrates how to generate a dataset and bicluster it
using the the Spectral Co-Clustering algorithm.
The dataset is generated using the ``make_biclusters`` function, which
creates a matrix of small values and implants bicluster with large
values. The rows and columns are then shuffled and passed to the
Spectral Co-Clustering algorithm. Rearranging the shuffled matrix to
make biclusters contiguous shows how accurately the algorithm found
the biclusters.
"""
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_biclusters
from sklearn.datasets import samples_generator as sg
from sklearn.cluster.bicluster import SpectralCoclustering
from sklearn.metrics import consensus_score
data, rows, columns = make_biclusters(
shape=(300, 300), n_clusters=5, noise=5,
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 = SpectralCoclustering(n_clusters=5, random_state=0)
model.fit(data)
score = consensus_score(model.biclusters_,
(rows[:, row_idx], columns[:, col_idx]))
print("consensus score: {:.3f}".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.show()
| bsd-3-clause |
tayebzaidi/snova_analysis | Miscellaneous/typ1a_features.py | 1 | 2252 | import matplotlib.pyplot as plt
import scipy.interpolate as scinterp
import numpy as np
import peakfinding
import peak_original
import smoothing
import plotter
import random
import readin
import sys
import os
if __name__== '__main__':
Mbdata = []
delM15data = []
path = "/Users/zaidi/Documents/REU/restframe/"
filenames = os.listdir(path)
random.shuffle(filenames)
for filename in filenames:
current_file = os.path.join(path, filename)
data= readin.readin_SNrest(filename)
indB = np.where((data.band == 'B'))
Bdata = data[indB]
Bdata = np.sort(Bdata)
if len(Bdata.phase) > 3:
spl = scinterp.UnivariateSpline(Bdata.phase, Bdata.mag)
spl.set_smoothing_factor(2./len(Bdata.phase))
phase_new = np.arange(Bdata.phase[0], Bdata.phase[-1], 1)
mag_new = spl(phase_new)
maxp, minp = peak_original.peakdet(mag_new, 0.5, phase_new)
if len(minp) > 0 and minp[0][0] < 5 and minp[0][0] > -5:
Mb = minp[0][1]
delM15 = minp[0][1] - spl(minp[0][0]+15)
Mbdata.append(Mb)
delM15data.append(delM15)
if delM15 > 0 or delM15 < -5:
print minp
print filename
print spl(minp[0][0] + 15)
fig = plt.figure(1)
ax = fig.add_subplot(1,1,1)
ax.plot(phase_new, mag_new)
ax.plot(Bdata.phase, Bdata.mag)
if len(minp) > 0:
ax.scatter(minp[:,0],minp[:,1])
plt.show(fig)
'''
maxp, minp = peakfinding.peakdetect(mag_new, phase_new, 200, 1.5)
if len(minp) > 0:
print minp
print filename
fig = plt.figure(1)
ax = fig.add_subplot(1,1,1)
#ax.scatter(minp[:,0], minp[:,1],'bo')
#ax.plot(Bdata.phase, Bdata.mag)
#plt.show(fig)
'''
#interp = smoothing.Interpolate1D(data.phase
print Mbdata
print delM15data
fig = plt.figure(2)
ax = fig.add_subplot(1,1,1)
ax.scatter(Mbdata, delM15data)
plt.show(fig)
| gpl-3.0 |
billbrod/spatial-frequency-preferences | sfp/image_computable.py | 1 | 6815 | #!/usr/bin/python
"""code to help run the image-computable version of the model
we're using this primarily to check the effect of vignetting, but this does make our project
image-computable (though it's a linear model and so will fail in some trivial cases)
"""
import itertools
import argparse
import numpy as np
import pandas as pd
import pyrtools as pt
from scipy import interpolate
def upsample(signal, target_shape):
"""upsample a signal to target_shape
this uses scipy's interpolate.interp2d (and so will end up with a smoothed signal)
"""
x = np.linspace(-(signal.shape[0]-1)/2, (signal.shape[0]-1)/2, num=signal.shape[0])
y = np.linspace(-(signal.shape[1]-1)/2, (signal.shape[1]-1)/2, num=signal.shape[1])
f = interpolate.interp2d(x, y, signal)
x = np.linspace(-(signal.shape[0]-1)/2, (signal.shape[0]-1)/2, num=target_shape[0])
y = np.linspace(-(signal.shape[1]-1)/2, (signal.shape[1]-1)/2, num=target_shape[1])
return f(x,y)
def calc_energy_and_filters(stim, stim_df, n_orientations=6, save_path_template=None):
"""this creates the energy and filter arrays
We assume the stimuli have natural groups, here indexed by the "class_idx" column in stim_df,
and all stimuli within these groups should be considered the same stimuli, that is, we sum the
energy across all of them. for the spatial frequency project, these are the different phases of
the gratings (because of how we structure our experiment, we estimate a response amplitude to
all phases together).
Note that this will take a while to run (~10 or 20 minutes). Since it only needs to run once
per experiment, didn't bother to make it efficient at all. The outputs will also be very large,
totalling about 11GB
Parameters
----------
stim : np.ndarray
The stimuli to produce energy for. Should have shape (n, *img_size), where n is the number
of total stimuli.
stim_df : pd.DataFrame
The DataFrame describing the stimuli. Must contain the column "class_idx", which indexes
the different stimulus classes (see above)
n_orientations : int
the number of orientations in the steerable pyramid. 6 is the number used to model fMRI
voxels in Roth, Z. N., Heeger, D., & Merriam, E. (2018). Stimulus vignetting and
orientation selectivity in human visual cortex. bioRxiv.
save_path_template : str or None
the template string for the save path we'll use for energy and filters. should end in .npy
and contain one %s, which we'll replace with "energy" and "filters".
Returns
-------
energy : np.ndarray
energy has shape (stim_df.class_idx.nunique(), max_ht, n_orientations, *img_size) and
contains the energy (square and absolute value the complex valued output of
SteerablePyramidFreq; equivalently, square and sum the output of the quadrature pair of
filters that make up the pyramid) for each image, at each scale and orientation. the energy
has all been upsampled to the size of the initial image.
filters : np.ndarray
filters has shape (max_ht, n_orientations, *img_size) and is the fourier transform of the
filters at each scale and orientation, zero-padded so they all have the same size. we only
have one set of filters (instead of one per stimulus class) because the same pyramid was
used for each of them; we ensure this by getting the filters for each stimulus class and
checking that they're individually equal to the average across classes.
"""
img_size = stim.shape[1:]
# this computation comes from the SteerablePyramidFreq code
max_ht = int(np.floor(np.log2(min(img_size))) - 2)
energy = np.zeros((stim_df.class_idx.nunique(), max_ht, n_orientations, *img_size),
dtype=np.float32)
filters = np.zeros_like(energy)
for i, g in stim_df.groupby('class_idx'):
idx = g.index
filled_filters = False
for j in idx:
pyr = pt.pyramids.SteerablePyramidFreq(stim[j], order=n_orientations-1, is_complex=True)
for k, l in itertools.product(range(max_ht), range(n_orientations)):
energy[int(i), k, l, :, :] += upsample(np.abs(pyr.pyr_coeffs[(k, l)])**2, img_size)
# we only want to run this once per stimulus class
if not filled_filters:
if k > 0:
lomask = pyr._lomasks[k-1]
else:
lomask = pyr._lo0mask
filt = pyr._anglemasks[k][l] * pyr._himasks[k] * lomask
pad_num = []
for m in range(2):
pad_num.append([(img_size[m] - filt.shape[m])//2, (img_size[m] - filt.shape[m])//2])
if filt.shape[m] + 2*pad_num[m][0] != img_size[m]:
pad_num[m][0] += img_size[m] - (filt.shape[m] + 2*pad_num[m][0])
filters[int(i), k, l, :, :] = np.pad(filt, pad_num, 'constant', constant_values=0)
filled_filters = True
filter_mean = np.mean(filters, 0)
for i in range(filters.shape[0]):
if not(np.allclose(filter_mean, filters[i,:,:,:,:])):
raise Exception("Something has gone terribly wrong, the filters for stim class %d are different than the rest!" % i)
filters = filter_mean
if save_path_template is not None:
np.save(save_path_template % "energy", energy)
np.save(save_path_template % "filters", filters)
return energy, filters
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description=("Calculate and save the energy for each stimulus class, as well as the Fourier"
" transform of the filters of the steerable pyramid we use to get this. For "
"use with image-computable version of this model"),
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("stimuli",
help=("Path to the stimulus .npy file."))
parser.add_argument("stimuli_description_df",
help=("Path to the stimulus description dataframe .csv file."))
parser.add_argument("save_path_template",
help=("Path template (with .npy extension) where we'll save the results. "
"Should contain one %s."))
parser.add_argument('--n_orientations', '-n', default=6, type=int,
help=("The number of orientations in the steerable pyramid used here."))
args = vars(parser.parse_args())
stim = np.load(args.pop('stimuli'))
stim_df = pd.read_csv(args.pop('stimuli_description_df'))
calc_energy_and_filters(stim, stim_df, **args)
| mit |
IshankGulati/scikit-learn | sklearn/feature_selection/variance_threshold.py | 123 | 2572 | # Author: Lars Buitinck
# License: 3-clause BSD
import numpy as np
from ..base import BaseEstimator
from .base import SelectorMixin
from ..utils import check_array
from ..utils.sparsefuncs import mean_variance_axis
from ..utils.validation import check_is_fitted
class VarianceThreshold(BaseEstimator, SelectorMixin):
"""Feature selector that removes all low-variance features.
This feature selection algorithm looks only at the features (X), not the
desired outputs (y), and can thus be used for unsupervised learning.
Read more in the :ref:`User Guide <variance_threshold>`.
Parameters
----------
threshold : float, optional
Features with a training-set variance lower than this threshold will
be removed. The default is to keep all features with non-zero variance,
i.e. remove the features that have the same value in all samples.
Attributes
----------
variances_ : array, shape (n_features,)
Variances of individual features.
Examples
--------
The following dataset has integer features, two of which are the same
in every sample. These are removed with the default setting for threshold::
>>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
>>> selector = VarianceThreshold()
>>> selector.fit_transform(X)
array([[2, 0],
[1, 4],
[1, 1]])
"""
def __init__(self, threshold=0.):
self.threshold = threshold
def fit(self, X, y=None):
"""Learn empirical variances from X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Sample vectors from which to compute variances.
y : any
Ignored. This parameter exists only for compatibility with
sklearn.pipeline.Pipeline.
Returns
-------
self
"""
X = check_array(X, ('csr', 'csc'), dtype=np.float64)
if hasattr(X, "toarray"): # sparse matrix
_, self.variances_ = mean_variance_axis(X, axis=0)
else:
self.variances_ = np.var(X, axis=0)
if np.all(self.variances_ <= self.threshold):
msg = "No feature in X meets the variance threshold {0:.5f}"
if X.shape[0] == 1:
msg += " (X contains only one sample)"
raise ValueError(msg.format(self.threshold))
return self
def _get_support_mask(self):
check_is_fitted(self, 'variances_')
return self.variances_ > self.threshold
| bsd-3-clause |
Winand/pandas | pandas/tests/io/formats/test_eng_formatting.py | 22 | 8085 | import numpy as np
import pandas as pd
from pandas import DataFrame
from pandas.compat import u
import pandas.io.formats.format as fmt
from pandas.util import testing as tm
class TestEngFormatter(object):
def test_eng_float_formatter(self):
df = DataFrame({'A': [1.41, 141., 14100, 1410000.]})
fmt.set_eng_float_format()
result = df.to_string()
expected = (' A\n'
'0 1.410E+00\n'
'1 141.000E+00\n'
'2 14.100E+03\n'
'3 1.410E+06')
assert result == expected
fmt.set_eng_float_format(use_eng_prefix=True)
result = df.to_string()
expected = (' A\n'
'0 1.410\n'
'1 141.000\n'
'2 14.100k\n'
'3 1.410M')
assert result == expected
fmt.set_eng_float_format(accuracy=0)
result = df.to_string()
expected = (' A\n'
'0 1E+00\n'
'1 141E+00\n'
'2 14E+03\n'
'3 1E+06')
assert result == expected
tm.reset_display_options()
def compare(self, formatter, input, output):
formatted_input = formatter(input)
assert formatted_input == output
def compare_all(self, formatter, in_out):
"""
Parameters:
-----------
formatter: EngFormatter under test
in_out: list of tuples. Each tuple = (number, expected_formatting)
It is tested if 'formatter(number) == expected_formatting'.
*number* should be >= 0 because formatter(-number) == fmt is also
tested. *fmt* is derived from *expected_formatting*
"""
for input, output in in_out:
self.compare(formatter, input, output)
self.compare(formatter, -input, "-" + output[1:])
def test_exponents_with_eng_prefix(self):
formatter = fmt.EngFormatter(accuracy=3, use_eng_prefix=True)
f = np.sqrt(2)
in_out = [
(f * 10 ** -24, " 1.414y"), (f * 10 ** -23, " 14.142y"),
(f * 10 ** -22, " 141.421y"), (f * 10 ** -21, " 1.414z"),
(f * 10 ** -20, " 14.142z"), (f * 10 ** -19, " 141.421z"),
(f * 10 ** -18, " 1.414a"), (f * 10 ** -17, " 14.142a"),
(f * 10 ** -16, " 141.421a"), (f * 10 ** -15, " 1.414f"),
(f * 10 ** -14, " 14.142f"), (f * 10 ** -13, " 141.421f"),
(f * 10 ** -12, " 1.414p"), (f * 10 ** -11, " 14.142p"),
(f * 10 ** -10, " 141.421p"), (f * 10 ** -9, " 1.414n"),
(f * 10 ** -8, " 14.142n"), (f * 10 ** -7, " 141.421n"),
(f * 10 ** -6, " 1.414u"), (f * 10 ** -5, " 14.142u"),
(f * 10 ** -4, " 141.421u"), (f * 10 ** -3, " 1.414m"),
(f * 10 ** -2, " 14.142m"), (f * 10 ** -1, " 141.421m"),
(f * 10 ** 0, " 1.414"), (f * 10 ** 1, " 14.142"),
(f * 10 ** 2, " 141.421"), (f * 10 ** 3, " 1.414k"),
(f * 10 ** 4, " 14.142k"), (f * 10 ** 5, " 141.421k"),
(f * 10 ** 6, " 1.414M"), (f * 10 ** 7, " 14.142M"),
(f * 10 ** 8, " 141.421M"), (f * 10 ** 9, " 1.414G"),
(f * 10 ** 10, " 14.142G"), (f * 10 ** 11, " 141.421G"),
(f * 10 ** 12, " 1.414T"), (f * 10 ** 13, " 14.142T"),
(f * 10 ** 14, " 141.421T"), (f * 10 ** 15, " 1.414P"),
(f * 10 ** 16, " 14.142P"), (f * 10 ** 17, " 141.421P"),
(f * 10 ** 18, " 1.414E"), (f * 10 ** 19, " 14.142E"),
(f * 10 ** 20, " 141.421E"), (f * 10 ** 21, " 1.414Z"),
(f * 10 ** 22, " 14.142Z"), (f * 10 ** 23, " 141.421Z"),
(f * 10 ** 24, " 1.414Y"), (f * 10 ** 25, " 14.142Y"),
(f * 10 ** 26, " 141.421Y")]
self.compare_all(formatter, in_out)
def test_exponents_without_eng_prefix(self):
formatter = fmt.EngFormatter(accuracy=4, use_eng_prefix=False)
f = np.pi
in_out = [
(f * 10 ** -24, " 3.1416E-24"),
(f * 10 ** -23, " 31.4159E-24"),
(f * 10 ** -22, " 314.1593E-24"),
(f * 10 ** -21, " 3.1416E-21"),
(f * 10 ** -20, " 31.4159E-21"),
(f * 10 ** -19, " 314.1593E-21"),
(f * 10 ** -18, " 3.1416E-18"),
(f * 10 ** -17, " 31.4159E-18"),
(f * 10 ** -16, " 314.1593E-18"),
(f * 10 ** -15, " 3.1416E-15"),
(f * 10 ** -14, " 31.4159E-15"),
(f * 10 ** -13, " 314.1593E-15"),
(f * 10 ** -12, " 3.1416E-12"),
(f * 10 ** -11, " 31.4159E-12"),
(f * 10 ** -10, " 314.1593E-12"),
(f * 10 ** -9, " 3.1416E-09"),
(f * 10 ** -8, " 31.4159E-09"),
(f * 10 ** -7, " 314.1593E-09"),
(f * 10 ** -6, " 3.1416E-06"),
(f * 10 ** -5, " 31.4159E-06"),
(f * 10 ** -4, " 314.1593E-06"),
(f * 10 ** -3, " 3.1416E-03"),
(f * 10 ** -2, " 31.4159E-03"),
(f * 10 ** -1, " 314.1593E-03"),
(f * 10 ** 0, " 3.1416E+00"),
(f * 10 ** 1, " 31.4159E+00"),
(f * 10 ** 2, " 314.1593E+00"),
(f * 10 ** 3, " 3.1416E+03"),
(f * 10 ** 4, " 31.4159E+03"),
(f * 10 ** 5, " 314.1593E+03"),
(f * 10 ** 6, " 3.1416E+06"),
(f * 10 ** 7, " 31.4159E+06"),
(f * 10 ** 8, " 314.1593E+06"),
(f * 10 ** 9, " 3.1416E+09"),
(f * 10 ** 10, " 31.4159E+09"),
(f * 10 ** 11, " 314.1593E+09"),
(f * 10 ** 12, " 3.1416E+12"),
(f * 10 ** 13, " 31.4159E+12"),
(f * 10 ** 14, " 314.1593E+12"),
(f * 10 ** 15, " 3.1416E+15"),
(f * 10 ** 16, " 31.4159E+15"),
(f * 10 ** 17, " 314.1593E+15"),
(f * 10 ** 18, " 3.1416E+18"),
(f * 10 ** 19, " 31.4159E+18"),
(f * 10 ** 20, " 314.1593E+18"),
(f * 10 ** 21, " 3.1416E+21"),
(f * 10 ** 22, " 31.4159E+21"),
(f * 10 ** 23, " 314.1593E+21"),
(f * 10 ** 24, " 3.1416E+24"),
(f * 10 ** 25, " 31.4159E+24"),
(f * 10 ** 26, " 314.1593E+24")]
self.compare_all(formatter, in_out)
def test_rounding(self):
formatter = fmt.EngFormatter(accuracy=3, use_eng_prefix=True)
in_out = [(5.55555, ' 5.556'), (55.5555, ' 55.556'),
(555.555, ' 555.555'), (5555.55, ' 5.556k'),
(55555.5, ' 55.556k'), (555555, ' 555.555k')]
self.compare_all(formatter, in_out)
formatter = fmt.EngFormatter(accuracy=1, use_eng_prefix=True)
in_out = [(5.55555, ' 5.6'), (55.5555, ' 55.6'), (555.555, ' 555.6'),
(5555.55, ' 5.6k'), (55555.5, ' 55.6k'), (555555, ' 555.6k')]
self.compare_all(formatter, in_out)
formatter = fmt.EngFormatter(accuracy=0, use_eng_prefix=True)
in_out = [(5.55555, ' 6'), (55.5555, ' 56'), (555.555, ' 556'),
(5555.55, ' 6k'), (55555.5, ' 56k'), (555555, ' 556k')]
self.compare_all(formatter, in_out)
formatter = fmt.EngFormatter(accuracy=3, use_eng_prefix=True)
result = formatter(0)
assert result == u(' 0.000')
def test_nan(self):
# Issue #11981
formatter = fmt.EngFormatter(accuracy=1, use_eng_prefix=True)
result = formatter(np.nan)
assert result == u('NaN')
df = pd.DataFrame({'a': [1.5, 10.3, 20.5],
'b': [50.3, 60.67, 70.12],
'c': [100.2, 101.33, 120.33]})
pt = df.pivot_table(values='a', index='b', columns='c')
fmt.set_eng_float_format(accuracy=1)
result = pt.to_string()
assert 'NaN' in result
tm.reset_display_options()
def test_inf(self):
# Issue #11981
formatter = fmt.EngFormatter(accuracy=1, use_eng_prefix=True)
result = formatter(np.inf)
assert result == u('inf')
| bsd-3-clause |
gmatteo/pymatgen | pymatgen/io/gaussian.py | 2 | 59623 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module implements input and output processing from Gaussian.
"""
import re
import warnings
import numpy as np
import scipy.constants as cst
from monty.io import zopen
from pymatgen.core.composition import Composition
from pymatgen.core.periodic_table import Element
from pymatgen.core.structure import Molecule
from pymatgen.core.operations import SymmOp
from pymatgen.core.units import Ha_to_eV
from pymatgen.electronic_structure.core import Spin
from pymatgen.util.coord import get_angle
__author__ = "Shyue Ping Ong, Germain Salvato-Vallverdu, Xin Chen"
__copyright__ = "Copyright 2013, The Materials Virtual Lab"
__version__ = "0.1"
__maintainer__ = "Shyue Ping Ong"
__email__ = "ongsp@ucsd.edu"
__date__ = "8/1/15"
float_patt = re.compile(r"\s*([+-]?\d+\.\d+)")
def read_route_line(route):
"""
read route line in gaussian input/output and return functional basis_set
and a dictionary of other route parameters
Args:
route (str) : the route line
return
functional (str) : the method (HF, PBE ...)
basis_set (str) : the basis set
route (dict) : dictionary of parameters
"""
scrf_patt = re.compile(r"^([sS][cC][rR][fF])\s*=\s*(.+)")
multi_params_patt = re.compile(r"^([A-z]+[0-9]*)[\s=]+\((.*)\)$")
functional = None
basis_set = None
route_params = {}
dieze_tag = None
if route:
if "/" in route:
tok = route.split("/")
functional = tok[0].split()[-1]
basis_set = tok[1].split()[0]
for tok in [functional, basis_set, "/"]:
route = route.replace(tok, "")
for tok in route.split():
if scrf_patt.match(tok):
m = scrf_patt.match(tok)
route_params[m.group(1)] = m.group(2)
elif tok.upper() in ["#", "#N", "#P", "#T"]:
# does not store # in route to avoid error in input
if tok == "#":
dieze_tag = "#N"
else:
dieze_tag = tok
continue
else:
m = re.match(multi_params_patt, tok.strip("#"))
if m:
pars = {}
for par in m.group(2).split(","):
p = par.split("=")
pars[p[0]] = None if len(p) == 1 else p[1]
route_params[m.group(1)] = pars
else:
d = tok.strip("#").split("=")
route_params[d[0]] = None if len(d) == 1 else d[1]
return functional, basis_set, route_params, dieze_tag
class GaussianInput:
"""
An object representing a Gaussian input file.
"""
# Commonly used regex patterns
_zmat_patt = re.compile(r"^(\w+)*([\s,]+(\w+)[\s,]+(\w+))*[\-\.\s,\w]*$")
_xyz_patt = re.compile(r"^(\w+)[\s,]+([\d\.eE\-]+)[\s,]+([\d\.eE\-]+)[\s,]+" r"([\d\.eE\-]+)[\-\.\s,\w.]*$")
def __init__(
self,
mol,
charge=None,
spin_multiplicity=None,
title=None,
functional="HF",
basis_set="6-31G(d)",
route_parameters=None,
input_parameters=None,
link0_parameters=None,
dieze_tag="#P",
gen_basis=None,
):
"""
Args:
mol: Input molecule. It can either be a Molecule object,
a string giving the geometry in a format supported by Guassian,
or ``None``. If the molecule is ``None``, you will need to use
read it in from a checkpoint. Consider adding ``CHK`` to the
``link0_parameters``.
charge: Charge of the molecule. If None, charge on molecule is used.
Defaults to None. This allows the input file to be set a
charge independently from the molecule itself.
If ``mol`` is not a Molecule object, then you must specify a charge.
spin_multiplicity: Spin multiplicity of molecule. Defaults to None,
which means that the spin multiplicity is set to 1 if the
molecule has no unpaired electrons and to 2 if there are
unpaired electrons. If ``mol`` is not a Molecule object, then you
must specify the multiplicity
title: Title for run. Defaults to formula of molecule if None.
functional: Functional for run.
basis_set: Basis set for run.
route_parameters: Additional route parameters as a dict. For example,
{'SP':"", "SCF":"Tight"}
input_parameters: Additional input parameters for run as a dict. Used
for example, in PCM calculations. E.g., {"EPS":12}
link0_parameters: Link0 parameters as a dict. E.g., {"%mem": "1000MW"}
dieze_tag: # preceding the route line. E.g. "#p"
gen_basis: allows a user-specified basis set to be used in a Gaussian
calculation. If this is not None, the attribute ``basis_set`` will
be set to "Gen".
"""
self._mol = mol
# Determine multiplicity and charge settings
if isinstance(mol, Molecule):
self.charge = charge if charge is not None else mol.charge
nelectrons = mol.charge + mol.nelectrons - self.charge
if spin_multiplicity is not None:
self.spin_multiplicity = spin_multiplicity
if (nelectrons + spin_multiplicity) % 2 != 1:
raise ValueError(
"Charge of {} and spin multiplicity of {} is"
" not possible for this molecule".format(self.charge, spin_multiplicity)
)
else:
self.spin_multiplicity = 1 if nelectrons % 2 == 0 else 2
# Get a title from the molecule name
self.title = title if title else self._mol.composition.formula
else:
self.charge = charge
self.spin_multiplicity = spin_multiplicity
# Set a title
self.title = title if title else "Restart"
# Store the remaining settings
self.functional = functional
self.basis_set = basis_set
self.link0_parameters = link0_parameters if link0_parameters else {}
self.route_parameters = route_parameters if route_parameters else {}
self.input_parameters = input_parameters if input_parameters else {}
self.dieze_tag = dieze_tag if dieze_tag[0] == "#" else "#" + dieze_tag
self.gen_basis = gen_basis
if gen_basis is not None:
self.basis_set = "Gen"
@property
def molecule(self):
"""
Returns molecule associated with this GaussianInput.
"""
return self._mol
@staticmethod
def _parse_coords(coord_lines):
"""
Helper method to parse coordinates.
"""
paras = {}
var_pattern = re.compile(r"^([A-Za-z]+\S*)[\s=,]+([\d\-\.]+)$")
for l in coord_lines:
m = var_pattern.match(l.strip())
if m:
paras[m.group(1).strip("=")] = float(m.group(2))
species = []
coords = []
# Stores whether a Zmatrix format is detected. Once a zmatrix format
# is detected, it is assumed for the remaining of the parsing.
zmode = False
for l in coord_lines:
l = l.strip()
if not l:
break
if (not zmode) and GaussianInput._xyz_patt.match(l):
m = GaussianInput._xyz_patt.match(l)
species.append(m.group(1))
toks = re.split(r"[,\s]+", l.strip())
if len(toks) > 4:
coords.append([float(i) for i in toks[2:5]])
else:
coords.append([float(i) for i in toks[1:4]])
elif GaussianInput._zmat_patt.match(l):
zmode = True
toks = re.split(r"[,\s]+", l.strip())
species.append(toks[0])
toks.pop(0)
if len(toks) == 0:
coords.append(np.array([0, 0, 0]))
else:
nn = []
parameters = []
while len(toks) > 1:
ind = toks.pop(0)
data = toks.pop(0)
try:
nn.append(int(ind))
except ValueError:
nn.append(species.index(ind) + 1)
try:
val = float(data)
parameters.append(val)
except ValueError:
if data.startswith("-"):
parameters.append(-paras[data[1:]])
else:
parameters.append(paras[data])
if len(nn) == 1:
coords.append(np.array([0, 0, parameters[0]]))
elif len(nn) == 2:
coords1 = coords[nn[0] - 1]
coords2 = coords[nn[1] - 1]
bl = parameters[0]
angle = parameters[1]
axis = [0, 1, 0]
op = SymmOp.from_origin_axis_angle(coords1, axis, angle, False)
coord = op.operate(coords2)
vec = coord - coords1
coord = vec * bl / np.linalg.norm(vec) + coords1
coords.append(coord)
elif len(nn) == 3:
coords1 = coords[nn[0] - 1]
coords2 = coords[nn[1] - 1]
coords3 = coords[nn[2] - 1]
bl = parameters[0]
angle = parameters[1]
dih = parameters[2]
v1 = coords3 - coords2
v2 = coords1 - coords2
axis = np.cross(v1, v2)
op = SymmOp.from_origin_axis_angle(coords1, axis, angle, False)
coord = op.operate(coords2)
v1 = coord - coords1
v2 = coords1 - coords2
v3 = np.cross(v1, v2)
adj = get_angle(v3, axis)
axis = coords1 - coords2
op = SymmOp.from_origin_axis_angle(coords1, axis, dih - adj, False)
coord = op.operate(coord)
vec = coord - coords1
coord = vec * bl / np.linalg.norm(vec) + coords1
coords.append(coord)
def _parse_species(sp_str):
"""
The species specification can take many forms. E.g.,
simple integers representing atomic numbers ("8"),
actual species string ("C") or a labelled species ("C1").
Sometimes, the species string is also not properly capitalized,
e.g, ("c1"). This method should take care of these known formats.
"""
try:
return int(sp_str)
except ValueError:
sp = re.sub(r"\d", "", sp_str)
return sp.capitalize()
species = [_parse_species(sp) for sp in species]
return Molecule(species, coords)
@staticmethod
def from_string(contents):
"""
Creates GaussianInput from a string.
Args:
contents: String representing an Gaussian input file.
Returns:
GaussianInput object
"""
lines = [l.strip() for l in contents.split("\n")]
link0_patt = re.compile(r"^(%.+)\s*=\s*(.+)")
link0_dict = {}
for i, l in enumerate(lines):
if link0_patt.match(l):
m = link0_patt.match(l)
link0_dict[m.group(1).strip("=")] = m.group(2)
route_patt = re.compile(r"^#[sSpPnN]*.*")
route = ""
route_index = None
for i, l in enumerate(lines):
if route_patt.match(l):
route += " " + l
route_index = i
# This condition allows for route cards spanning multiple lines
elif (l == "" or l.isspace()) and route_index:
break
functional, basis_set, route_paras, dieze_tag = read_route_line(route)
ind = 2
title = []
while lines[route_index + ind].strip():
title.append(lines[route_index + ind].strip())
ind += 1
title = " ".join(title)
ind += 1
toks = re.split(r"[,\s]+", lines[route_index + ind])
charge = int(float(toks[0]))
spin_mult = int(toks[1])
coord_lines = []
spaces = 0
input_paras = {}
ind += 1
for i in range(route_index + ind, len(lines)):
if lines[i].strip() == "":
spaces += 1
if spaces >= 2:
d = lines[i].split("=")
if len(d) == 2:
input_paras[d[0]] = d[1]
else:
coord_lines.append(lines[i].strip())
mol = GaussianInput._parse_coords(coord_lines)
mol.set_charge_and_spin(charge, spin_mult)
return GaussianInput(
mol,
charge=charge,
spin_multiplicity=spin_mult,
title=title,
functional=functional,
basis_set=basis_set,
route_parameters=route_paras,
input_parameters=input_paras,
link0_parameters=link0_dict,
dieze_tag=dieze_tag,
)
@staticmethod
def from_file(filename):
"""
Creates GaussianInput from a file.
Args:
filename: Gaussian input filename
Returns:
GaussianInput object
"""
with zopen(filename, "r") as f:
return GaussianInput.from_string(f.read())
def _find_nn_pos_before_site(self, siteindex):
"""
Returns index of nearest neighbor atoms.
"""
alldist = [(self._mol.get_distance(siteindex, i), i) for i in range(siteindex)]
alldist = sorted(alldist, key=lambda x: x[0])
return [d[1] for d in alldist]
def get_zmatrix(self):
"""
Returns a z-matrix representation of the molecule.
"""
output = []
outputvar = []
for i, site in enumerate(self._mol):
if i == 0:
output.append("{}".format(site.specie))
elif i == 1:
nn = self._find_nn_pos_before_site(i)
bondlength = self._mol.get_distance(i, nn[0])
output.append("{} {} B{}".format(self._mol[i].specie, nn[0] + 1, i))
outputvar.append("B{}={:.6f}".format(i, bondlength))
elif i == 2:
nn = self._find_nn_pos_before_site(i)
bondlength = self._mol.get_distance(i, nn[0])
angle = self._mol.get_angle(i, nn[0], nn[1])
output.append("{} {} B{} {} A{}".format(self._mol[i].specie, nn[0] + 1, i, nn[1] + 1, i))
outputvar.append("B{}={:.6f}".format(i, bondlength))
outputvar.append("A{}={:.6f}".format(i, angle))
else:
nn = self._find_nn_pos_before_site(i)
bondlength = self._mol.get_distance(i, nn[0])
angle = self._mol.get_angle(i, nn[0], nn[1])
dih = self._mol.get_dihedral(i, nn[0], nn[1], nn[2])
output.append(
"{} {} B{} {} A{} {} D{}".format(self._mol[i].specie, nn[0] + 1, i, nn[1] + 1, i, nn[2] + 1, i)
)
outputvar.append("B{}={:.6f}".format(i, bondlength))
outputvar.append("A{}={:.6f}".format(i, angle))
outputvar.append("D{}={:.6f}".format(i, dih))
return "\n".join(output) + "\n\n" + "\n".join(outputvar)
def get_cart_coords(self):
"""
Return the cartesian coordinates of the molecule
"""
def to_s(x):
return "%0.6f" % x
outs = []
for i, site in enumerate(self._mol):
outs.append(" ".join([site.species_string, " ".join([to_s(j) for j in site.coords])]))
return "\n".join(outs)
def __str__(self):
return self.to_string()
def to_string(self, cart_coords=False):
"""
Return GaussianInput string
Option: whe cart_coords sets to True return the cartesian coordinates
instead of the z-matrix
"""
def para_dict_to_string(para, joiner=" "):
para_str = []
# sorted is only done to make unittests work reliably
for par, val in sorted(para.items()):
if val is None or val == "":
para_str.append(par)
elif isinstance(val, dict):
val_str = para_dict_to_string(val, joiner=",")
para_str.append("{}=({})".format(par, val_str))
else:
para_str.append("{}={}".format(par, val))
return joiner.join(para_str)
output = []
if self.link0_parameters:
output.append(para_dict_to_string(self.link0_parameters, "\n"))
# Handle functional or basis set set to None, empty string or whitespace
func_str = "" if self.functional is None else self.functional.strip()
bset_str = "" if self.basis_set is None else self.basis_set.strip()
if func_str != "" and bset_str != "":
func_bset_str = " {}/{}".format(func_str, bset_str)
else:
# don't use the slash if either or both are set as empty
func_bset_str = " {}{}".format(func_str, bset_str).rstrip()
output.append(
"{diez}{func_bset} {route}".format(
diez=self.dieze_tag,
func_bset=func_bset_str,
route=para_dict_to_string(self.route_parameters),
)
)
output.append("")
output.append(self.title)
output.append("")
charge_str = "" if self.charge is None else "%d" % self.charge
multip_str = "" if self.spin_multiplicity is None else " %d" % self.spin_multiplicity
output.append("{}{}".format(charge_str, multip_str))
if isinstance(self._mol, Molecule):
if cart_coords is True:
output.append(self.get_cart_coords())
else:
output.append(self.get_zmatrix())
elif self._mol is not None:
output.append(str(self._mol))
output.append("")
if self.gen_basis is not None:
output.append("{:s}\n".format(self.gen_basis))
output.append(para_dict_to_string(self.input_parameters, "\n"))
output.append("\n")
return "\n".join(output)
def write_file(self, filename, cart_coords=False):
"""
Write the input string into a file
Option: see __str__ method
"""
with zopen(filename, "w") as f:
f.write(self.to_string(cart_coords))
def as_dict(self):
"""
:return: MSONable dict
"""
return {
"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"molecule": self.molecule.as_dict(),
"functional": self.functional,
"basis_set": self.basis_set,
"route_parameters": self.route_parameters,
"title": self.title,
"charge": self.charge,
"spin_multiplicity": self.spin_multiplicity,
"input_parameters": self.input_parameters,
"link0_parameters": self.link0_parameters,
"dieze_tag": self.dieze_tag,
}
@classmethod
def from_dict(cls, d):
"""
:param d: dict
:return: GaussianInput
"""
return GaussianInput(
mol=Molecule.from_dict(d["molecule"]),
functional=d["functional"],
basis_set=d["basis_set"],
route_parameters=d["route_parameters"],
title=d["title"],
charge=d["charge"],
spin_multiplicity=d["spin_multiplicity"],
input_parameters=d["input_parameters"],
link0_parameters=d["link0_parameters"],
)
class GaussianOutput:
"""
Parser for Gaussian output files.
.. note::
Still in early beta.
Attributes:
.. attribute:: structures
All structures from the calculation in the standard orientation. If the
symmetry is not considered, the standard orientation is not printed out
and the input orientation is used instead. Check the `standard_orientation`
attribute.
.. attribute:: structures_input_orientation
All structures from the calculation in the input orientation or the
Z-matrix orientation (if an opt=z-matrix was requested).
.. attribute:: opt_structures
All optimized structures from the calculation in the standard orientation,
if the attribute 'standard_orientation' is True, otherwise in the input
or the Z-matrix orientation.
.. attribute:: energies
All energies from the calculation.
.. attribute:: eigenvalues
List of eigenvalues for the last geometry
.. attribute:: MO_coefficients
Matrix of MO coefficients for the last geometry
.. attribute:: cart_forces
All cartesian forces from the calculation.
.. attribute:: frequencies
A list for each freq calculation and for each mode of a dict with
{
"frequency": freq in cm-1,
"symmetry": symmetry tag
"r_mass": Reduce mass,
"f_constant": force constant,
"IR_intensity": IR Intensity,
"mode": normal mode
}
The normal mode is a 1D vector of dx, dy dz of each atom.
.. attribute:: hessian
Matrix of second derivatives of the energy with respect to cartesian
coordinates in the **input orientation** frame. Need #P in the
route section in order to be in the output.
.. attribute:: properly_terminated
True if run has properly terminated
.. attribute:: is_pcm
True if run is a PCM run.
.. attribute:: is_spin
True if it is an unrestricted run
.. attribute:: stationary_type
If it is a relaxation run, indicates whether it is a minimum (Minimum)
or a saddle point ("Saddle").
.. attribute:: corrections
Thermochemical corrections if this run is a Freq run as a dict. Keys
are "Zero-point", "Thermal", "Enthalpy" and "Gibbs Free Energy"
.. attribute:: functional
Functional used in the run.
.. attribute:: basis_set
Basis set used in the run
.. attribute:: route
Additional route parameters as a dict. For example,
{'SP':"", "SCF":"Tight"}
.. attribute:: dieze_tag
# preceding the route line, e.g. "#P"
.. attribute:: link0
Link0 parameters as a dict. E.g., {"%mem": "1000MW"}
.. attribute:: charge
Charge for structure
.. attribute:: spin_multiplicity
Spin multiplicity for structure
.. attribute:: num_basis_func
Number of basis functions in the run.
.. attribute:: electrons
number of alpha and beta electrons as (N alpha, N beta)
.. attribute:: pcm
PCM parameters and output if available.
.. attribute:: errors
error if not properly terminated (list to be completed in error_defs)
.. attribute:: Mulliken_charges
Mulliken atomic charges
.. attribute:: eigenvectors
Matrix of shape (num_basis_func, num_basis_func). Each column is an
eigenvectors and contains AO coefficients of an MO.
eigenvectors[Spin] = mat(num_basis_func, num_basis_func)
.. attribute:: molecular_orbital
MO development coefficients on AO in a more convenient array dict
for each atom and basis set label.
mo[Spin][OM j][atom i] = {AO_k: coeff, AO_k: coeff ... }
.. attribute:: atom_basis_labels
Labels of AO for each atoms. These labels are those used in the output
of molecular orbital coefficients (POP=Full) and in the
molecular_orbital array dict.
atom_basis_labels[iatom] = [AO_k, AO_k, ...]
.. attribute:: resumes
List of gaussian data resume given at the end of the output file before
the quotation. The resumes are given as string.
.. attribute:: title
Title of the gaussian run.
.. attribute:: standard_orientation
If True, the geometries stored in the structures are in the standard
orientation. Else, the geometries are in the input orientation.
.. attribute:: bond_orders
Dict of bond order values read in the output file such as:
{(0, 1): 0.8709, (1, 6): 1.234, ...}
The keys are the atom indexes and the values are the Wiberg bond indexes
that are printed using `pop=NBOREAD` and `$nbo bndidx $end`.
Methods:
.. method:: to_input()
Return a GaussianInput object using the last geometry and the same
calculation parameters.
.. method:: read_scan()
Read a potential energy surface from a gaussian scan calculation.
.. method:: get_scan_plot()
Get a matplotlib plot of the potential energy surface
.. method:: save_scan_plot()
Save a matplotlib plot of the potential energy surface to a file
"""
def __init__(self, filename):
"""
Args:
filename: Filename of Gaussian output file.
"""
self.filename = filename
self._parse(filename)
@property
def final_energy(self):
"""
:return: Final energy in Gaussian output.
"""
return self.energies[-1]
@property
def final_structure(self):
"""
:return: Final structure in Gaussian output.
"""
return self.structures[-1]
def _parse(self, filename):
start_patt = re.compile(r" \(Enter \S+l101\.exe\)")
route_patt = re.compile(r" #[pPnNtT]*.*")
link0_patt = re.compile(r"^\s(%.+)\s*=\s*(.+)")
charge_mul_patt = re.compile(r"Charge\s+=\s*([-\d]+)\s+" r"Multiplicity\s+=\s*(\d+)")
num_basis_func_patt = re.compile(r"([0-9]+)\s+basis functions")
num_elec_patt = re.compile(r"(\d+)\s+alpha electrons\s+(\d+)\s+beta electrons")
pcm_patt = re.compile(r"Polarizable Continuum Model")
stat_type_patt = re.compile(r"imaginary frequencies")
scf_patt = re.compile(r"E\(.*\)\s*=\s*([-\.\d]+)\s+")
mp2_patt = re.compile(r"EUMP2\s*=\s*(.*)")
oniom_patt = re.compile(r"ONIOM:\s+extrapolated energy\s*=\s*(.*)")
termination_patt = re.compile(r"(Normal|Error) termination")
error_patt = re.compile(r"(! Non-Optimized Parameters !|Convergence failure)")
mulliken_patt = re.compile(r"^\s*(Mulliken charges|Mulliken atomic charges)")
mulliken_charge_patt = re.compile(r"^\s+(\d+)\s+([A-Z][a-z]?)\s*(\S*)")
end_mulliken_patt = re.compile(r"(Sum of Mulliken )(.*)(charges)\s*=\s*(\D)")
std_orientation_patt = re.compile(r"Standard orientation")
input_orientation_patt = re.compile(r"Input orientation|Z-Matrix orientation")
orbital_patt = re.compile(r"(Alpha|Beta)\s*\S+\s*eigenvalues --(.*)")
thermo_patt = re.compile(r"(Zero-point|Thermal) correction(.*)=" r"\s+([\d\.-]+)")
forces_on_patt = re.compile(r"Center\s+Atomic\s+Forces\s+\(Hartrees/Bohr\)")
forces_off_patt = re.compile(r"Cartesian\s+Forces:\s+Max.*RMS.*")
forces_patt = re.compile(r"\s+(\d+)\s+(\d+)\s+([0-9\.-]+)\s+([0-9\.-]+)\s+([0-9\.-]+)")
freq_on_patt = re.compile(r"Harmonic\sfrequencies\s+\(cm\*\*-1\),\sIR\sintensities.*Raman.*")
normal_mode_patt = re.compile(r"\s+(\d+)\s+(\d+)\s+([0-9\.-]{4,5})\s+([0-9\.-]{4,5}).*")
mo_coeff_patt = re.compile(r"Molecular Orbital Coefficients:")
mo_coeff_name_patt = re.compile(r"\d+\s((\d+|\s+)\s+([a-zA-Z]{1,2}|\s+))\s+(\d+\S+)")
hessian_patt = re.compile(r"Force constants in Cartesian coordinates:")
resume_patt = re.compile(r"^\s1\\1\\GINC-\S*")
resume_end_patt = re.compile(r"^\s.*\\\\@")
bond_order_patt = re.compile(r"Wiberg bond index matrix in the NAO basis:")
self.properly_terminated = False
self.is_pcm = False
self.stationary_type = "Minimum"
self.corrections = {}
self.energies = []
self.pcm = None
self.errors = []
self.Mulliken_charges = {}
self.link0 = {}
self.cart_forces = []
self.frequencies = []
self.eigenvalues = []
self.is_spin = False
self.hessian = None
self.resumes = []
self.title = None
self.bond_orders = {}
read_coord = 0
read_mulliken = False
read_eigen = False
eigen_txt = []
parse_stage = 0
num_basis_found = False
terminated = False
parse_forces = False
forces = []
parse_freq = False
frequencies = []
read_mo = False
parse_hessian = False
routeline = ""
standard_orientation = False
parse_bond_order = False
input_structures = list()
std_structures = list()
geom_orientation = None
opt_structures = list()
with zopen(filename) as f:
for line in f:
if parse_stage == 0:
if start_patt.search(line):
parse_stage = 1
elif link0_patt.match(line):
m = link0_patt.match(line)
self.link0[m.group(1)] = m.group(2)
elif route_patt.search(line) or routeline != "":
if set(line.strip()) == {"-"}:
params = read_route_line(routeline)
self.functional = params[0]
self.basis_set = params[1]
self.route_parameters = params[2]
route_lower = {k.lower(): v for k, v in self.route_parameters.items()}
self.dieze_tag = params[3]
parse_stage = 1
else:
routeline += line.strip()
elif parse_stage == 1:
if set(line.strip()) == {"-"} and self.title is None:
self.title = ""
elif self.title == "":
self.title = line.strip()
elif charge_mul_patt.search(line):
m = charge_mul_patt.search(line)
self.charge = int(m.group(1))
self.spin_multiplicity = int(m.group(2))
parse_stage = 2
elif parse_stage == 2:
if self.is_pcm:
self._check_pcm(line)
if "freq" in route_lower and thermo_patt.search(line):
m = thermo_patt.search(line)
if m.group(1) == "Zero-point":
self.corrections["Zero-point"] = float(m.group(3))
else:
key = m.group(2).strip(" to ")
self.corrections[key] = float(m.group(3))
if read_coord:
[f.readline() for i in range(3)]
line = f.readline()
sp = []
coords = []
while set(line.strip()) != {"-"}:
toks = line.split()
sp.append(Element.from_Z(int(toks[1])))
coords.append([float(x) for x in toks[3:6]])
line = f.readline()
read_coord = False
if geom_orientation == "input":
input_structures.append(Molecule(sp, coords))
elif geom_orientation == "standard":
std_structures.append(Molecule(sp, coords))
if parse_forces:
m = forces_patt.search(line)
if m:
forces.extend([float(_v) for _v in m.groups()[2:5]])
elif forces_off_patt.search(line):
self.cart_forces.append(forces)
forces = []
parse_forces = False
# read molecular orbital eigenvalues
if read_eigen:
m = orbital_patt.search(line)
if m:
eigen_txt.append(line)
else:
read_eigen = False
self.eigenvalues = {Spin.up: []}
for eigenline in eigen_txt:
if "Alpha" in eigenline:
self.eigenvalues[Spin.up] += [float(e) for e in float_patt.findall(eigenline)]
elif "Beta" in eigenline:
if Spin.down not in self.eigenvalues:
self.eigenvalues[Spin.down] = []
self.eigenvalues[Spin.down] += [float(e) for e in float_patt.findall(eigenline)]
eigen_txt = []
# read molecular orbital coefficients
if (not num_basis_found) and num_basis_func_patt.search(line):
m = num_basis_func_patt.search(line)
self.num_basis_func = int(m.group(1))
num_basis_found = True
elif read_mo:
# build a matrix with all coefficients
all_spin = [Spin.up]
if self.is_spin:
all_spin.append(Spin.down)
mat_mo = {}
for spin in all_spin:
mat_mo[spin] = np.zeros((self.num_basis_func, self.num_basis_func))
nMO = 0
end_mo = False
while nMO < self.num_basis_func and not end_mo:
f.readline()
f.readline()
self.atom_basis_labels = []
for i in range(self.num_basis_func):
line = f.readline()
# identify atom and OA labels
m = mo_coeff_name_patt.search(line)
if m.group(1).strip() != "":
iat = int(m.group(2)) - 1
# atname = m.group(3)
self.atom_basis_labels.append([m.group(4)])
else:
self.atom_basis_labels[iat].append(m.group(4))
# MO coefficients
coeffs = [float(c) for c in float_patt.findall(line)]
for j, c in enumerate(coeffs):
mat_mo[spin][i, nMO + j] = c
nMO += len(coeffs)
line = f.readline()
# manage pop=regular case (not all MO)
if nMO < self.num_basis_func and (
"Density Matrix:" in line or mo_coeff_patt.search(line)
):
end_mo = True
warnings.warn("POP=regular case, matrix " "coefficients not complete")
f.readline()
self.eigenvectors = mat_mo
read_mo = False
# build a more convenient array dict with MO
# coefficient of each atom in each MO.
# mo[Spin][OM j][atom i] =
# {AO_k: coeff, AO_k: coeff ... }
mo = {}
for spin in all_spin:
mo[spin] = [
[{} for iat in range(len(self.atom_basis_labels))] for j in range(self.num_basis_func)
]
for j in range(self.num_basis_func):
i = 0
for iat in range(len(self.atom_basis_labels)):
for label in self.atom_basis_labels[iat]:
mo[spin][j][iat][label] = self.eigenvectors[spin][i, j]
i += 1
self.molecular_orbital = mo
elif parse_freq:
while line.strip() != "": # blank line
ifreqs = [int(val) - 1 for val in line.split()]
for ifreq in ifreqs:
frequencies.append(
{
"frequency": None,
"r_mass": None,
"f_constant": None,
"IR_intensity": None,
"symmetry": None,
"mode": [],
}
)
# read freq, intensity, masses, symmetry ...
while "Atom AN" not in line:
if "Frequencies --" in line:
freqs = map(float, float_patt.findall(line))
for ifreq, freq in zip(ifreqs, freqs):
frequencies[ifreq]["frequency"] = freq
elif "Red. masses --" in line:
r_masses = map(float, float_patt.findall(line))
for ifreq, r_mass in zip(ifreqs, r_masses):
frequencies[ifreq]["r_mass"] = r_mass
elif "Frc consts --" in line:
f_consts = map(float, float_patt.findall(line))
for ifreq, f_const in zip(ifreqs, f_consts):
frequencies[ifreq]["f_constant"] = f_const
elif "IR Inten --" in line:
IR_intens = map(float, float_patt.findall(line))
for ifreq, intens in zip(ifreqs, IR_intens):
frequencies[ifreq]["IR_intensity"] = intens
else:
syms = line.split()[:3]
for ifreq, sym in zip(ifreqs, syms):
frequencies[ifreq]["symmetry"] = sym
line = f.readline()
# read normal modes
line = f.readline()
while normal_mode_patt.search(line):
values = list(map(float, float_patt.findall(line)))
for i, ifreq in zip(range(0, len(values), 3), ifreqs):
frequencies[ifreq]["mode"].extend(values[i : i + 3])
line = f.readline()
parse_freq = False
self.frequencies.append(frequencies)
frequencies = []
elif parse_hessian:
# read Hessian matrix under "Force constants in Cartesian coordinates"
# Hessian matrix is in the input orientation framework
# WARNING : need #P in the route line
parse_hessian = False
ndf = 3 * len(input_structures[0])
self.hessian = np.zeros((ndf, ndf))
j_indices = range(5)
jndf = 0
while jndf < ndf:
for i in range(jndf, ndf):
line = f.readline()
vals = re.findall(r"\s*([+-]?\d+\.\d+[eEdD]?[+-]\d+)", line)
vals = [float(val.replace("D", "E")) for val in vals]
for jval, val in enumerate(vals):
j = j_indices[jval]
self.hessian[i, j] = val
self.hessian[j, i] = val
jndf += len(vals)
line = f.readline()
j_indices = [j + 5 for j in j_indices]
elif parse_bond_order:
# parse Wiberg bond order
line = f.readline()
line = f.readline()
nat = len(input_structures[0])
matrix = list()
for iat in range(nat):
line = f.readline()
matrix.append([float(v) for v in line.split()[2:]])
self.bond_orders = dict()
for iat in range(nat):
for jat in range(iat + 1, nat):
self.bond_orders[(iat, jat)] = matrix[iat][jat]
parse_bond_order = False
elif termination_patt.search(line):
m = termination_patt.search(line)
if m.group(1) == "Normal":
self.properly_terminated = True
terminated = True
elif error_patt.search(line):
error_defs = {
"! Non-Optimized Parameters !": "Optimization " "error",
"Convergence failure": "SCF convergence error",
}
m = error_patt.search(line)
self.errors.append(error_defs[m.group(1)])
elif num_elec_patt.search(line):
m = num_elec_patt.search(line)
self.electrons = (int(m.group(1)), int(m.group(2)))
elif (not self.is_pcm) and pcm_patt.search(line):
self.is_pcm = True
self.pcm = {}
elif "freq" in route_lower and "opt" in route_lower and stat_type_patt.search(line):
self.stationary_type = "Saddle"
elif mp2_patt.search(line):
m = mp2_patt.search(line)
self.energies.append(float(m.group(1).replace("D", "E")))
elif oniom_patt.search(line):
m = oniom_patt.matcher(line)
self.energies.append(float(m.group(1)))
elif scf_patt.search(line):
m = scf_patt.search(line)
self.energies.append(float(m.group(1)))
elif std_orientation_patt.search(line):
standard_orientation = True
geom_orientation = "standard"
read_coord = True
elif input_orientation_patt.search(line):
geom_orientation = "input"
read_coord = True
elif "Optimization completed." in line:
line = f.readline()
if " -- Stationary point found." not in line:
warnings.warn(
"\n" + self.filename + ": Optimization complete but this is not a stationary point"
)
if standard_orientation:
opt_structures.append(std_structures[-1])
else:
opt_structures.append(input_structures[-1])
elif not read_eigen and orbital_patt.search(line):
eigen_txt.append(line)
read_eigen = True
elif mulliken_patt.search(line):
mulliken_txt = []
read_mulliken = True
elif not parse_forces and forces_on_patt.search(line):
parse_forces = True
elif freq_on_patt.search(line):
parse_freq = True
[f.readline() for i in range(3)]
elif mo_coeff_patt.search(line):
if "Alpha" in line:
self.is_spin = True
read_mo = True
elif hessian_patt.search(line):
parse_hessian = True
elif resume_patt.search(line):
resume = []
while not resume_end_patt.search(line):
resume.append(line)
line = f.readline()
# security if \\@ not in one line !
if line == "\n":
break
resume.append(line)
resume = "".join([r.strip() for r in resume])
self.resumes.append(resume)
elif bond_order_patt.search(line):
parse_bond_order = True
if read_mulliken:
if not end_mulliken_patt.search(line):
mulliken_txt.append(line)
else:
m = end_mulliken_patt.search(line)
mulliken_charges = {}
for line in mulliken_txt:
if mulliken_charge_patt.search(line):
m = mulliken_charge_patt.search(line)
dic = {int(m.group(1)): [m.group(2), float(m.group(3))]}
mulliken_charges.update(dic)
read_mulliken = False
self.Mulliken_charges = mulliken_charges
# store the structures. If symmetry is considered, the standard orientation
# is used. Else the input orientation is used.
if standard_orientation:
self.structures = std_structures
self.structures_input_orientation = input_structures
else:
self.structures = input_structures
self.structures_input_orientation = input_structures
# store optimized structure in input orientation
self.opt_structures = opt_structures
if not terminated:
warnings.warn("\n" + self.filename + ": Termination error or bad Gaussian output file !")
def _check_pcm(self, line):
energy_patt = re.compile(r"(Dispersion|Cavitation|Repulsion) energy" r"\s+\S+\s+=\s+(\S*)")
total_patt = re.compile(r"with all non electrostatic terms\s+\S+\s+" r"=\s+(\S*)")
parameter_patt = re.compile(r"(Eps|Numeral density|RSolv|Eps" r"\(inf[inity]*\))\s+=\s*(\S*)")
if energy_patt.search(line):
m = energy_patt.search(line)
self.pcm["{} energy".format(m.group(1))] = float(m.group(2))
elif total_patt.search(line):
m = total_patt.search(line)
self.pcm["Total energy"] = float(m.group(1))
elif parameter_patt.search(line):
m = parameter_patt.search(line)
self.pcm[m.group(1)] = float(m.group(2))
def as_dict(self):
"""
Json-serializable dict representation.
"""
structure = self.final_structure
d = {
"has_gaussian_completed": self.properly_terminated,
"nsites": len(structure),
}
comp = structure.composition
d["unit_cell_formula"] = comp.as_dict()
d["reduced_cell_formula"] = Composition(comp.reduced_formula).as_dict()
d["pretty_formula"] = comp.reduced_formula
d["is_pcm"] = self.is_pcm
d["errors"] = self.errors
d["Mulliken_charges"] = self.Mulliken_charges
unique_symbols = sorted(list(d["unit_cell_formula"].keys()))
d["elements"] = unique_symbols
d["nelements"] = len(unique_symbols)
d["charge"] = self.charge
d["spin_multiplicity"] = self.spin_multiplicity
vin = {
"route": self.route_parameters,
"functional": self.functional,
"basis_set": self.basis_set,
"nbasisfunctions": self.num_basis_func,
"pcm_parameters": self.pcm,
}
d["input"] = vin
nsites = len(self.final_structure)
vout = {
"energies": self.energies,
"final_energy": self.final_energy,
"final_energy_per_atom": self.final_energy / nsites,
"molecule": structure.as_dict(),
"stationary_type": self.stationary_type,
"corrections": self.corrections,
}
d["output"] = vout
d["@module"] = self.__class__.__module__
d["@class"] = self.__class__.__name__
return d
def read_scan(self):
"""
Read a potential energy surface from a gaussian scan calculation.
Returns:
A dict: {"energies": [ values ],
"coords": {"d1": [ values ], "A2", [ values ], ... }}
"energies" are the energies of all points of the potential energy
surface. "coords" are the internal coordinates used to compute the
potential energy surface and the internal coordinates optimized,
labelled by their name as defined in the calculation.
"""
def floatList(l):
"""return a list of float from a list of string"""
return [float(v) for v in l]
scan_patt = re.compile(r"^\sSummary of the potential surface scan:")
optscan_patt = re.compile(r"^\sSummary of Optimized Potential Surface Scan")
coord_patt = re.compile(r"^\s*(\w+)((\s*[+-]?\d+\.\d+)+)")
# data dict return
data = {"energies": list(), "coords": dict()}
# read in file
with zopen(self.filename, "r") as f:
line = f.readline()
while line != "":
if optscan_patt.match(line):
f.readline()
line = f.readline()
endScan = False
while not endScan:
data["energies"] += floatList(float_patt.findall(line))
line = f.readline()
while coord_patt.match(line):
icname = line.split()[0].strip()
if icname in data["coords"]:
data["coords"][icname] += floatList(float_patt.findall(line))
else:
data["coords"][icname] = floatList(float_patt.findall(line))
line = f.readline()
if not re.search(r"^\s+((\s*\d+)+)", line):
endScan = True
else:
line = f.readline()
elif scan_patt.match(line):
line = f.readline()
data["coords"] = {icname: list() for icname in line.split()[1:-1]}
f.readline()
line = f.readline()
while not re.search(r"^\s-+", line):
values = floatList(line.split())
data["energies"].append(values[-1])
for i, icname in enumerate(data["coords"]):
data["coords"][icname].append(values[i + 1])
line = f.readline()
else:
line = f.readline()
return data
def get_scan_plot(self, coords=None):
"""
Get a matplotlib plot of the potential energy surface.
Args:
coords: internal coordinate name to use as abcissa.
"""
from pymatgen.util.plotting import pretty_plot
plt = pretty_plot(12, 8)
d = self.read_scan()
if coords and coords in d["coords"]:
x = d["coords"][coords]
plt.xlabel(coords)
else:
x = range(len(d["energies"]))
plt.xlabel("points")
plt.ylabel("Energy (eV)")
e_min = min(d["energies"])
y = [(e - e_min) * Ha_to_eV for e in d["energies"]]
plt.plot(x, y, "ro--")
return plt
def save_scan_plot(self, filename="scan.pdf", img_format="pdf", coords=None):
"""
Save matplotlib plot of the potential energy surface to a file.
Args:
filename: Filename to write to.
img_format: Image format to use. Defaults to EPS.
coords: internal coordinate name to use as abcissa.
"""
plt = self.get_scan_plot(coords)
plt.savefig(filename, format=img_format)
def read_excitation_energies(self):
"""
Read a excitation energies after a TD-DFT calculation.
Returns:
A list: A list of tuple for each transition such as
[(energie (eV), lambda (nm), oscillatory strength), ... ]
"""
transitions = list()
# read in file
with zopen(self.filename, "r") as f:
line = f.readline()
td = False
while line != "":
if re.search(r"^\sExcitation energies and oscillator strengths:", line):
td = True
if td:
if re.search(r"^\sExcited State\s*\d", line):
val = [float(v) for v in float_patt.findall(line)]
transitions.append(tuple(val[0:3]))
line = f.readline()
return transitions
def get_spectre_plot(self, sigma=0.05, step=0.01):
"""
Get a matplotlib plot of the UV-visible xas. Transition are plotted
as vertical lines and as a sum of normal functions with sigma with. The
broadening is applied in energy and the xas is plotted as a function
of the wavelength.
Args:
sigma: Full width at half maximum in eV for normal functions.
step: bin interval in eV
Returns:
A dict: {"energies": values, "lambda": values, "xas": values}
where values are lists of abscissa (energies, lamba) and
the sum of gaussian functions (xas).
A matplotlib plot.
"""
from scipy.stats import norm
from pymatgen.util.plotting import pretty_plot
plt = pretty_plot(12, 8)
transitions = self.read_excitation_energies()
minval = min([val[0] for val in transitions]) - 5.0 * sigma
maxval = max([val[0] for val in transitions]) + 5.0 * sigma
npts = int((maxval - minval) / step) + 1
eneval = np.linspace(minval, maxval, npts) # in eV
lambdaval = [cst.h * cst.c / (val * cst.e) * 1.0e9 for val in eneval] # in nm
# sum of gaussian functions
spectre = np.zeros(npts)
for trans in transitions:
spectre += trans[2] * norm(eneval, trans[0], sigma)
spectre /= spectre.max()
plt.plot(lambdaval, spectre, "r-", label="spectre")
data = {"energies": eneval, "lambda": lambdaval, "xas": spectre}
# plot transitions as vlines
plt.vlines(
[val[1] for val in transitions],
0.0,
[val[2] for val in transitions],
color="blue",
label="transitions",
linewidth=2,
)
plt.xlabel("$\\lambda$ (nm)")
plt.ylabel("Arbitrary unit")
plt.legend()
return data, plt
def save_spectre_plot(self, filename="spectre.pdf", img_format="pdf", sigma=0.05, step=0.01):
"""
Save matplotlib plot of the spectre to a file.
Args:
filename: Filename to write to.
img_format: Image format to use. Defaults to EPS.
sigma: Full width at half maximum in eV for normal functions.
step: bin interval in eV
"""
d, plt = self.get_spectre_plot(sigma, step)
plt.savefig(filename, format=img_format)
def to_input(
self,
mol=None,
charge=None,
spin_multiplicity=None,
title=None,
functional=None,
basis_set=None,
route_parameters=None,
input_parameters=None,
link0_parameters=None,
dieze_tag=None,
cart_coords=False,
):
"""
Create a new input object using by default the last geometry read in
the output file and with the same calculation parameters. Arguments
are the same as GaussianInput class.
Returns
gaunip (GaussianInput) : the gaussian input object
"""
if not mol:
mol = self.final_structure
if charge is None:
charge = self.charge
if spin_multiplicity is None:
spin_multiplicity = self.spin_multiplicity
if not title:
title = self.title
if not functional:
functional = self.functional
if not basis_set:
basis_set = self.basis_set
if not route_parameters:
route_parameters = self.route_parameters
if not link0_parameters:
link0_parameters = self.link0
if not dieze_tag:
dieze_tag = self.dieze_tag
return GaussianInput(
mol=mol,
charge=charge,
spin_multiplicity=spin_multiplicity,
title=title,
functional=functional,
basis_set=basis_set,
route_parameters=route_parameters,
input_parameters=input_parameters,
link0_parameters=link0_parameters,
dieze_tag=dieze_tag,
)
| mit |
CalvinNeo/EasyMLPlatform | py/graphic/tree.py | 1 | 4067 | #coding:utf8
import numpy as np
import math
import pylab as pl
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import json
class GraphTree:
def __init__(self):
self.jsonobj = {}
self.leafNode = dict(boxstyle = 'round4',fc = '0.8')
self.branchNode = dict(boxstyle = 'sawtooth',fc = '0.8')
self.arrow = dict(arrowstyle = '<-')
self.depth = 0
self.leafcount = 0
def get_depth_leafcount(self,root):
current_node = root.keys()[0] #name of choice node(string)
branch_dict = root[current_node]
maxdepth, thisdepth, thisleafcount = 0,0,0
for current_node in branch_dict.keys():
# print current_node,type(branch_dict[current_node]).__name__
if type(branch_dict[current_node]).__name__ == 'dict':
temp = self.get_depth_leafcount(branch_dict[current_node])
thisdepth = 1 + temp[0]
thisleafcount += temp[1]
else:
thisdepth = 1
thisleafcount += 1
if thisdepth > maxdepth:
maxdepth = thisdepth
return maxdepth,thisleafcount
def load(self,strjson):
self.jsonobj = dict(strjson)
self.depth,self.leafcount = self.get_depth_leafcount(self.jsonobj)
def plotMidText(self, cntrPt, parentPt, txtString):
xMid = (parentPt[0] - cntrPt[0]) / 2.0 + cntrPt[0]
yMid = (parentPt[1] - cntrPt[1]) / 2.0 + cntrPt[1]
self.ax1.text(xMid, yMid, txtString)
def plotNode(self, nodeTxt, cntrPt, parentPt, nodeType):
self.ax1.annotate(nodeTxt, xy = parentPt, xycoords = 'axes fraction', xytext = cntrPt, \
textcoords = 'axes fraction', va = 'center', ha = 'center', bbox = nodeType, arrowprops = self.arrow)
def plotTree(self, myTree, parentPt, nodeTxt):
depth, leaves = self.get_depth_leafcount(myTree)
current_node = myTree.keys()[0]
cntrPt = (self.xOff + (1.0 + leaves) / 2.0 / self.leafcount, self.yOff)
self.plotMidText(cntrPt, parentPt, nodeTxt)
self.plotNode(current_node, cntrPt, parentPt, self.branchNode)
branch_dict = myTree[current_node]
self.yOff -= 1.0 / self.depth
for current_node in branch_dict.keys():
if type(branch_dict[current_node]).__name__ == 'dict':
self.plotTree(branch_dict[current_node], cntrPt, str(current_node))
else:
self.xOff += 1.0 / self.leafcount
self.plotNode(branch_dict[current_node], (self.xOff, self.yOff), cntrPt, self.leafNode)
self.plotMidText((self.xOff, self.yOff), cntrPt, str(current_node))
self.yOff += 1.0 / self.depth
def createPlot(self, show = True, save = ''):
fig = plt.figure(1, facecolor = 'white')
fig.clf()
axprops = dict(xticks = [], yticks = [])
self.ax1 = plt.subplot(111,frameon = False, **axprops)
self.xOff, self.yOff = -0.5 / self.leafcount, 1.0
self.plotTree(self.jsonobj, (0.5,1.0), '')
import StringIO, urllib, base64
if show:
plt.show()
else:
imgdata = StringIO.StringIO()
fig.savefig(imgdata, format='png')
imgdata.seek(0) # rewind the data
uri = 'data:image/png;base64,' + urllib.quote(base64.b64encode(imgdata.buf))
imgdata.close()
return uri
def showPlot(self):
plt.show()
if __name__ == '__main__':
tr = GraphTree()
# aa = '{"no surfacing":{"0":"no","1":{"flippers":{"0":"no","1":"yes"}}}}'
# tr.load(json.loads(aa))
#JSON can't have non-string key
aa = {"aged":{"0":"no","1":{"male":{"0":"no","1":"yes"}}}}
# aa = {'water': {0: 1, 1: {'foot': {0: "'no'", 1: "'yes'"}}}}
print dict(aa)
# aa = {"no surfacing":{0:"no",1:{"flippers":{0:"no",1:"yes"}}}}
# print dict(aa)
tr.load(aa)
print tr.leafcount,tr.depth
tr.createPlot(show=True)
| apache-2.0 |
khiner/aubio | python/demos/demo_waveform_plot.py | 10 | 2099 | #! /usr/bin/env python
import sys
from aubio import pvoc, source
from numpy import zeros, hstack
def get_waveform_plot(filename, samplerate = 0, block_size = 4096, ax = None, downsample = 2**4):
import matplotlib.pyplot as plt
if not ax:
fig = plt.figure()
ax = fig.add_subplot(111)
hop_s = block_size
allsamples_max = zeros(0,)
downsample = downsample # to plot n samples / hop_s
a = source(filename, samplerate, hop_s) # source file
if samplerate == 0: samplerate = a.samplerate
total_frames = 0
while True:
samples, read = a()
# keep some data to plot it later
new_maxes = (abs(samples.reshape(hop_s/downsample, downsample))).max(axis=0)
allsamples_max = hstack([allsamples_max, new_maxes])
total_frames += read
if read < hop_s: break
allsamples_max = (allsamples_max > 0) * allsamples_max
allsamples_max_times = [ ( float (t) / downsample ) * hop_s for t in range(len(allsamples_max)) ]
ax.plot(allsamples_max_times, allsamples_max, '-b')
ax.plot(allsamples_max_times, -allsamples_max, '-b')
ax.axis(xmin = allsamples_max_times[0], xmax = allsamples_max_times[-1])
set_xlabels_sample2time(ax, allsamples_max_times[-1], samplerate)
return ax
def set_xlabels_sample2time(ax, latest_sample, samplerate):
ax.axis(xmin = 0, xmax = latest_sample)
if latest_sample / float(samplerate) > 60:
ax.set_xlabel('time (mm:ss)')
ax.set_xticklabels([ "%02d:%02d" % (t/float(samplerate)/60, (t/float(samplerate))%60) for t in ax.get_xticks()[:-1]], rotation = 50)
else:
ax.set_xlabel('time (ss.mm)')
ax.set_xticklabels([ "%02d.%02d" % (t/float(samplerate), 100*((t/float(samplerate))%1) ) for t in ax.get_xticks()[:-1]], rotation = 50)
if __name__ == '__main__':
import matplotlib.pyplot as plt
if len(sys.argv) < 2:
print "Usage: %s <filename>" % sys.argv[0]
else:
for soundfile in sys.argv[1:]:
get_waveform_plot(soundfile)
# display graph
plt.show()
| gpl-3.0 |
Ziqi-Li/bknqgis | pandas/pandas/tests/io/test_packers.py | 7 | 31902 | import pytest
from warnings import catch_warnings
import os
import datetime
import numpy as np
import sys
from distutils.version import LooseVersion
from pandas import compat
from pandas.compat import u, PY3
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
date_range, period_range, Index, Categorical)
from pandas.errors import PerformanceWarning
from pandas.io.packers import to_msgpack, read_msgpack
import pandas.util.testing as tm
from pandas.util.testing import (ensure_clean,
assert_categorical_equal,
assert_frame_equal,
assert_index_equal,
assert_series_equal,
patch)
from pandas.tests.test_panel import assert_panel_equal
import pandas
from pandas import Timestamp, NaT
from pandas._libs.tslib import iNaT
nan = np.nan
try:
import blosc # NOQA
except ImportError:
_BLOSC_INSTALLED = False
else:
_BLOSC_INSTALLED = True
try:
import zlib # NOQA
except ImportError:
_ZLIB_INSTALLED = False
else:
_ZLIB_INSTALLED = True
@pytest.fixture(scope='module')
def current_packers_data():
# our current version packers data
from pandas.tests.io.generate_legacy_storage_files import (
create_msgpack_data)
return create_msgpack_data()
@pytest.fixture(scope='module')
def all_packers_data():
# our all of our current version packers data
from pandas.tests.io.generate_legacy_storage_files import (
create_data)
return create_data()
def check_arbitrary(a, b):
if isinstance(a, (list, tuple)) and isinstance(b, (list, tuple)):
assert(len(a) == len(b))
for a_, b_ in zip(a, b):
check_arbitrary(a_, b_)
elif isinstance(a, Panel):
assert_panel_equal(a, b)
elif isinstance(a, DataFrame):
assert_frame_equal(a, b)
elif isinstance(a, Series):
assert_series_equal(a, b)
elif isinstance(a, Index):
assert_index_equal(a, b)
elif isinstance(a, Categorical):
# Temp,
# Categorical.categories is changed from str to bytes in PY3
# maybe the same as GH 13591
if PY3 and b.categories.inferred_type == 'string':
pass
else:
tm.assert_categorical_equal(a, b)
elif a is NaT:
assert b is NaT
elif isinstance(a, Timestamp):
assert a == b
assert a.freq == b.freq
else:
assert(a == b)
class TestPackers(object):
def setup_method(self, method):
self.path = '__%s__.msg' % tm.rands(10)
def teardown_method(self, method):
pass
def encode_decode(self, x, compress=None, **kwargs):
with ensure_clean(self.path) as p:
to_msgpack(p, x, compress=compress, **kwargs)
return read_msgpack(p, **kwargs)
class TestAPI(TestPackers):
def test_string_io(self):
df = DataFrame(np.random.randn(10, 2))
s = df.to_msgpack(None)
result = read_msgpack(s)
tm.assert_frame_equal(result, df)
s = df.to_msgpack()
result = read_msgpack(s)
tm.assert_frame_equal(result, df)
s = df.to_msgpack()
result = read_msgpack(compat.BytesIO(s))
tm.assert_frame_equal(result, df)
s = to_msgpack(None, df)
result = read_msgpack(s)
tm.assert_frame_equal(result, df)
with ensure_clean(self.path) as p:
s = df.to_msgpack()
fh = open(p, 'wb')
fh.write(s)
fh.close()
result = read_msgpack(p)
tm.assert_frame_equal(result, df)
def test_path_pathlib(self):
df = tm.makeDataFrame()
result = tm.round_trip_pathlib(df.to_msgpack, read_msgpack)
tm.assert_frame_equal(df, result)
def test_path_localpath(self):
df = tm.makeDataFrame()
result = tm.round_trip_localpath(df.to_msgpack, read_msgpack)
tm.assert_frame_equal(df, result)
def test_iterator_with_string_io(self):
dfs = [DataFrame(np.random.randn(10, 2)) for i in range(5)]
s = to_msgpack(None, *dfs)
for i, result in enumerate(read_msgpack(s, iterator=True)):
tm.assert_frame_equal(result, dfs[i])
def test_invalid_arg(self):
# GH10369
class A(object):
def __init__(self):
self.read = 0
pytest.raises(ValueError, read_msgpack, path_or_buf=None)
pytest.raises(ValueError, read_msgpack, path_or_buf={})
pytest.raises(ValueError, read_msgpack, path_or_buf=A())
class TestNumpy(TestPackers):
def test_numpy_scalar_float(self):
x = np.float32(np.random.rand())
x_rec = self.encode_decode(x)
tm.assert_almost_equal(x, x_rec)
def test_numpy_scalar_complex(self):
x = np.complex64(np.random.rand() + 1j * np.random.rand())
x_rec = self.encode_decode(x)
assert np.allclose(x, x_rec)
def test_scalar_float(self):
x = np.random.rand()
x_rec = self.encode_decode(x)
tm.assert_almost_equal(x, x_rec)
def test_scalar_complex(self):
x = np.random.rand() + 1j * np.random.rand()
x_rec = self.encode_decode(x)
assert np.allclose(x, x_rec)
def test_list_numpy_float(self):
x = [np.float32(np.random.rand()) for i in range(5)]
x_rec = self.encode_decode(x)
# current msgpack cannot distinguish list/tuple
tm.assert_almost_equal(tuple(x), x_rec)
x_rec = self.encode_decode(tuple(x))
tm.assert_almost_equal(tuple(x), x_rec)
def test_list_numpy_float_complex(self):
if not hasattr(np, 'complex128'):
pytest.skip('numpy cant handle complex128')
x = [np.float32(np.random.rand()) for i in range(5)] + \
[np.complex128(np.random.rand() + 1j * np.random.rand())
for i in range(5)]
x_rec = self.encode_decode(x)
assert np.allclose(x, x_rec)
def test_list_float(self):
x = [np.random.rand() for i in range(5)]
x_rec = self.encode_decode(x)
# current msgpack cannot distinguish list/tuple
tm.assert_almost_equal(tuple(x), x_rec)
x_rec = self.encode_decode(tuple(x))
tm.assert_almost_equal(tuple(x), x_rec)
def test_list_float_complex(self):
x = [np.random.rand() for i in range(5)] + \
[(np.random.rand() + 1j * np.random.rand()) for i in range(5)]
x_rec = self.encode_decode(x)
assert np.allclose(x, x_rec)
def test_dict_float(self):
x = {'foo': 1.0, 'bar': 2.0}
x_rec = self.encode_decode(x)
tm.assert_almost_equal(x, x_rec)
def test_dict_complex(self):
x = {'foo': 1.0 + 1.0j, 'bar': 2.0 + 2.0j}
x_rec = self.encode_decode(x)
tm.assert_dict_equal(x, x_rec)
for key in x:
tm.assert_class_equal(x[key], x_rec[key], obj="complex value")
def test_dict_numpy_float(self):
x = {'foo': np.float32(1.0), 'bar': np.float32(2.0)}
x_rec = self.encode_decode(x)
tm.assert_almost_equal(x, x_rec)
def test_dict_numpy_complex(self):
x = {'foo': np.complex128(1.0 + 1.0j),
'bar': np.complex128(2.0 + 2.0j)}
x_rec = self.encode_decode(x)
tm.assert_dict_equal(x, x_rec)
for key in x:
tm.assert_class_equal(x[key], x_rec[key], obj="numpy complex128")
def test_numpy_array_float(self):
# run multiple times
for n in range(10):
x = np.random.rand(10)
for dtype in ['float32', 'float64']:
x = x.astype(dtype)
x_rec = self.encode_decode(x)
tm.assert_almost_equal(x, x_rec)
def test_numpy_array_complex(self):
x = (np.random.rand(5) + 1j * np.random.rand(5)).astype(np.complex128)
x_rec = self.encode_decode(x)
assert (all(map(lambda x, y: x == y, x, x_rec)) and
x.dtype == x_rec.dtype)
def test_list_mixed(self):
x = [1.0, np.float32(3.5), np.complex128(4.25), u('foo')]
x_rec = self.encode_decode(x)
# current msgpack cannot distinguish list/tuple
tm.assert_almost_equal(tuple(x), x_rec)
x_rec = self.encode_decode(tuple(x))
tm.assert_almost_equal(tuple(x), x_rec)
class TestBasic(TestPackers):
def test_timestamp(self):
for i in [Timestamp(
'20130101'), Timestamp('20130101', tz='US/Eastern'),
Timestamp('201301010501')]:
i_rec = self.encode_decode(i)
assert i == i_rec
def test_nat(self):
nat_rec = self.encode_decode(NaT)
assert NaT is nat_rec
def test_datetimes(self):
# fails under 2.6/win32 (np.datetime64 seems broken)
if LooseVersion(sys.version) < '2.7':
pytest.skip('2.6 with np.datetime64 is broken')
for i in [datetime.datetime(2013, 1, 1),
datetime.datetime(2013, 1, 1, 5, 1),
datetime.date(2013, 1, 1),
np.datetime64(datetime.datetime(2013, 1, 5, 2, 15))]:
i_rec = self.encode_decode(i)
assert i == i_rec
def test_timedeltas(self):
for i in [datetime.timedelta(days=1),
datetime.timedelta(days=1, seconds=10),
np.timedelta64(1000000)]:
i_rec = self.encode_decode(i)
assert i == i_rec
class TestIndex(TestPackers):
def setup_method(self, method):
super(TestIndex, self).setup_method(method)
self.d = {
'string': tm.makeStringIndex(100),
'date': tm.makeDateIndex(100),
'int': tm.makeIntIndex(100),
'rng': tm.makeRangeIndex(100),
'float': tm.makeFloatIndex(100),
'empty': Index([]),
'tuple': Index(zip(['foo', 'bar', 'baz'], [1, 2, 3])),
'period': Index(period_range('2012-1-1', freq='M', periods=3)),
'date2': Index(date_range('2013-01-1', periods=10)),
'bdate': Index(bdate_range('2013-01-02', periods=10)),
'cat': tm.makeCategoricalIndex(100)
}
self.mi = {
'reg': MultiIndex.from_tuples([('bar', 'one'), ('baz', 'two'),
('foo', 'two'),
('qux', 'one'), ('qux', 'two')],
names=['first', 'second']),
}
def test_basic_index(self):
for s, i in self.d.items():
i_rec = self.encode_decode(i)
tm.assert_index_equal(i, i_rec)
# datetime with no freq (GH5506)
i = Index([Timestamp('20130101'), Timestamp('20130103')])
i_rec = self.encode_decode(i)
tm.assert_index_equal(i, i_rec)
# datetime with timezone
i = Index([Timestamp('20130101 9:00:00'), Timestamp(
'20130103 11:00:00')]).tz_localize('US/Eastern')
i_rec = self.encode_decode(i)
tm.assert_index_equal(i, i_rec)
def test_multi_index(self):
for s, i in self.mi.items():
i_rec = self.encode_decode(i)
tm.assert_index_equal(i, i_rec)
def test_unicode(self):
i = tm.makeUnicodeIndex(100)
i_rec = self.encode_decode(i)
tm.assert_index_equal(i, i_rec)
def categorical_index(self):
# GH15487
df = DataFrame(np.random.randn(10, 2))
df = df.astype({0: 'category'}).set_index(0)
result = self.encode_decode(df)
tm.assert_frame_equal(result, df)
class TestSeries(TestPackers):
def setup_method(self, method):
super(TestSeries, self).setup_method(method)
self.d = {}
s = tm.makeStringSeries()
s.name = 'string'
self.d['string'] = s
s = tm.makeObjectSeries()
s.name = 'object'
self.d['object'] = s
s = Series(iNaT, dtype='M8[ns]', index=range(5))
self.d['date'] = s
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E': [0., 1, Timestamp('20100101'), 'foo', 2.],
'F': [Timestamp('20130102', tz='US/Eastern')] * 2 +
[Timestamp('20130603', tz='CET')] * 3,
'G': [Timestamp('20130102', tz='US/Eastern')] * 5,
'H': Categorical([1, 2, 3, 4, 5]),
'I': Categorical([1, 2, 3, 4, 5], ordered=True),
}
self.d['float'] = Series(data['A'])
self.d['int'] = Series(data['B'])
self.d['mixed'] = Series(data['E'])
self.d['dt_tz_mixed'] = Series(data['F'])
self.d['dt_tz'] = Series(data['G'])
self.d['cat_ordered'] = Series(data['H'])
self.d['cat_unordered'] = Series(data['I'])
def test_basic(self):
# run multiple times here
for n in range(10):
for s, i in self.d.items():
i_rec = self.encode_decode(i)
assert_series_equal(i, i_rec)
class TestCategorical(TestPackers):
def setup_method(self, method):
super(TestCategorical, self).setup_method(method)
self.d = {}
self.d['plain_str'] = Categorical(['a', 'b', 'c', 'd', 'e'])
self.d['plain_str_ordered'] = Categorical(['a', 'b', 'c', 'd', 'e'],
ordered=True)
self.d['plain_int'] = Categorical([5, 6, 7, 8])
self.d['plain_int_ordered'] = Categorical([5, 6, 7, 8], ordered=True)
def test_basic(self):
# run multiple times here
for n in range(10):
for s, i in self.d.items():
i_rec = self.encode_decode(i)
assert_categorical_equal(i, i_rec)
class TestNDFrame(TestPackers):
def setup_method(self, method):
super(TestNDFrame, self).setup_method(method)
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E': [0., 1, Timestamp('20100101'), 'foo', 2.],
'F': [Timestamp('20130102', tz='US/Eastern')] * 5,
'G': [Timestamp('20130603', tz='CET')] * 5,
'H': Categorical(['a', 'b', 'c', 'd', 'e']),
'I': Categorical(['a', 'b', 'c', 'd', 'e'], ordered=True),
}
self.frame = {
'float': DataFrame(dict(A=data['A'], B=Series(data['A']) + 1)),
'int': DataFrame(dict(A=data['B'], B=Series(data['B']) + 1)),
'mixed': DataFrame(data)}
with catch_warnings(record=True):
self.panel = {
'float': Panel(dict(ItemA=self.frame['float'],
ItemB=self.frame['float'] + 1))}
def test_basic_frame(self):
for s, i in self.frame.items():
i_rec = self.encode_decode(i)
assert_frame_equal(i, i_rec)
def test_basic_panel(self):
with catch_warnings(record=True):
for s, i in self.panel.items():
i_rec = self.encode_decode(i)
assert_panel_equal(i, i_rec)
def test_multi(self):
i_rec = self.encode_decode(self.frame)
for k in self.frame.keys():
assert_frame_equal(self.frame[k], i_rec[k])
l = tuple([self.frame['float'], self.frame['float'].A,
self.frame['float'].B, None])
l_rec = self.encode_decode(l)
check_arbitrary(l, l_rec)
# this is an oddity in that packed lists will be returned as tuples
l = [self.frame['float'], self.frame['float']
.A, self.frame['float'].B, None]
l_rec = self.encode_decode(l)
assert isinstance(l_rec, tuple)
check_arbitrary(l, l_rec)
def test_iterator(self):
l = [self.frame['float'], self.frame['float']
.A, self.frame['float'].B, None]
with ensure_clean(self.path) as path:
to_msgpack(path, *l)
for i, packed in enumerate(read_msgpack(path, iterator=True)):
check_arbitrary(packed, l[i])
def tests_datetimeindex_freq_issue(self):
# GH 5947
# inferring freq on the datetimeindex
df = DataFrame([1, 2, 3], index=date_range('1/1/2013', '1/3/2013'))
result = self.encode_decode(df)
assert_frame_equal(result, df)
df = DataFrame([1, 2], index=date_range('1/1/2013', '1/2/2013'))
result = self.encode_decode(df)
assert_frame_equal(result, df)
def test_dataframe_duplicate_column_names(self):
# GH 9618
expected_1 = DataFrame(columns=['a', 'a'])
expected_2 = DataFrame(columns=[1] * 100)
expected_2.loc[0] = np.random.randn(100)
expected_3 = DataFrame(columns=[1, 1])
expected_3.loc[0] = ['abc', np.nan]
result_1 = self.encode_decode(expected_1)
result_2 = self.encode_decode(expected_2)
result_3 = self.encode_decode(expected_3)
assert_frame_equal(result_1, expected_1)
assert_frame_equal(result_2, expected_2)
assert_frame_equal(result_3, expected_3)
class TestSparse(TestPackers):
def _check_roundtrip(self, obj, comparator, **kwargs):
# currently these are not implemetned
# i_rec = self.encode_decode(obj)
# comparator(obj, i_rec, **kwargs)
pytest.raises(NotImplementedError, self.encode_decode, obj)
def test_sparse_series(self):
s = tm.makeStringSeries()
s[3:5] = np.nan
ss = s.to_sparse()
self._check_roundtrip(ss, tm.assert_series_equal,
check_series_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_roundtrip(ss2, tm.assert_series_equal,
check_series_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_roundtrip(ss3, tm.assert_series_equal,
check_series_type=True)
def test_sparse_frame(self):
s = tm.makeDataFrame()
s.loc[3:5, 1:3] = np.nan
s.loc[8:10, -2] = np.nan
ss = s.to_sparse()
self._check_roundtrip(ss, tm.assert_frame_equal,
check_frame_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_roundtrip(ss2, tm.assert_frame_equal,
check_frame_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_roundtrip(ss3, tm.assert_frame_equal,
check_frame_type=True)
class TestCompression(TestPackers):
"""See https://github.com/pandas-dev/pandas/pull/9783
"""
def setup_method(self, method):
try:
from sqlalchemy import create_engine
self._create_sql_engine = create_engine
except ImportError:
self._SQLALCHEMY_INSTALLED = False
else:
self._SQLALCHEMY_INSTALLED = True
super(TestCompression, self).setup_method(method)
data = {
'A': np.arange(1000, dtype=np.float64),
'B': np.arange(1000, dtype=np.int32),
'C': list(100 * 'abcdefghij'),
'D': date_range(datetime.datetime(2015, 4, 1), periods=1000),
'E': [datetime.timedelta(days=x) for x in range(1000)],
}
self.frame = {
'float': DataFrame(dict((k, data[k]) for k in ['A', 'A'])),
'int': DataFrame(dict((k, data[k]) for k in ['B', 'B'])),
'mixed': DataFrame(data),
}
def test_plain(self):
i_rec = self.encode_decode(self.frame)
for k in self.frame.keys():
assert_frame_equal(self.frame[k], i_rec[k])
def _test_compression(self, compress):
i_rec = self.encode_decode(self.frame, compress=compress)
for k in self.frame.keys():
value = i_rec[k]
expected = self.frame[k]
assert_frame_equal(value, expected)
# make sure that we can write to the new frames
for block in value._data.blocks:
assert block.values.flags.writeable
def test_compression_zlib(self):
if not _ZLIB_INSTALLED:
pytest.skip('no zlib')
self._test_compression('zlib')
def test_compression_blosc(self):
if not _BLOSC_INSTALLED:
pytest.skip('no blosc')
self._test_compression('blosc')
def _test_compression_warns_when_decompress_caches(self, compress):
not_garbage = []
control = [] # copied data
compress_module = globals()[compress]
real_decompress = compress_module.decompress
def decompress(ob):
"""mock decompress function that delegates to the real
decompress but caches the result and a copy of the result.
"""
res = real_decompress(ob)
not_garbage.append(res) # hold a reference to this bytes object
control.append(bytearray(res)) # copy the data here to check later
return res
# types mapped to values to add in place.
rhs = {
np.dtype('float64'): 1.0,
np.dtype('int32'): 1,
np.dtype('object'): 'a',
np.dtype('datetime64[ns]'): np.timedelta64(1, 'ns'),
np.dtype('timedelta64[ns]'): np.timedelta64(1, 'ns'),
}
with patch(compress_module, 'decompress', decompress), \
tm.assert_produces_warning(PerformanceWarning) as ws:
i_rec = self.encode_decode(self.frame, compress=compress)
for k in self.frame.keys():
value = i_rec[k]
expected = self.frame[k]
assert_frame_equal(value, expected)
# make sure that we can write to the new frames even though
# we needed to copy the data
for block in value._data.blocks:
assert block.values.flags.writeable
# mutate the data in some way
block.values[0] += rhs[block.dtype]
for w in ws:
# check the messages from our warnings
assert str(w.message) == ('copying data after decompressing; '
'this may mean that decompress is '
'caching its result')
for buf, control_buf in zip(not_garbage, control):
# make sure none of our mutations above affected the
# original buffers
assert buf == control_buf
def test_compression_warns_when_decompress_caches_zlib(self):
if not _ZLIB_INSTALLED:
pytest.skip('no zlib')
self._test_compression_warns_when_decompress_caches('zlib')
def test_compression_warns_when_decompress_caches_blosc(self):
if not _BLOSC_INSTALLED:
pytest.skip('no blosc')
self._test_compression_warns_when_decompress_caches('blosc')
def _test_small_strings_no_warn(self, compress):
empty = np.array([], dtype='uint8')
with tm.assert_produces_warning(None):
empty_unpacked = self.encode_decode(empty, compress=compress)
tm.assert_numpy_array_equal(empty_unpacked, empty)
assert empty_unpacked.flags.writeable
char = np.array([ord(b'a')], dtype='uint8')
with tm.assert_produces_warning(None):
char_unpacked = self.encode_decode(char, compress=compress)
tm.assert_numpy_array_equal(char_unpacked, char)
assert char_unpacked.flags.writeable
# if this test fails I am sorry because the interpreter is now in a
# bad state where b'a' points to 98 == ord(b'b').
char_unpacked[0] = ord(b'b')
# we compare the ord of bytes b'a' with unicode u'a' because the should
# always be the same (unless we were able to mutate the shared
# character singleton in which case ord(b'a') == ord(b'b').
assert ord(b'a') == ord(u'a')
tm.assert_numpy_array_equal(
char_unpacked,
np.array([ord(b'b')], dtype='uint8'),
)
def test_small_strings_no_warn_zlib(self):
if not _ZLIB_INSTALLED:
pytest.skip('no zlib')
self._test_small_strings_no_warn('zlib')
def test_small_strings_no_warn_blosc(self):
if not _BLOSC_INSTALLED:
pytest.skip('no blosc')
self._test_small_strings_no_warn('blosc')
def test_readonly_axis_blosc(self):
# GH11880
if not _BLOSC_INSTALLED:
pytest.skip('no blosc')
df1 = DataFrame({'A': list('abcd')})
df2 = DataFrame(df1, index=[1., 2., 3., 4.])
assert 1 in self.encode_decode(df1['A'], compress='blosc')
assert 1. in self.encode_decode(df2['A'], compress='blosc')
def test_readonly_axis_zlib(self):
# GH11880
df1 = DataFrame({'A': list('abcd')})
df2 = DataFrame(df1, index=[1., 2., 3., 4.])
assert 1 in self.encode_decode(df1['A'], compress='zlib')
assert 1. in self.encode_decode(df2['A'], compress='zlib')
def test_readonly_axis_blosc_to_sql(self):
# GH11880
if not _BLOSC_INSTALLED:
pytest.skip('no blosc')
if not self._SQLALCHEMY_INSTALLED:
pytest.skip('no sqlalchemy')
expected = DataFrame({'A': list('abcd')})
df = self.encode_decode(expected, compress='blosc')
eng = self._create_sql_engine("sqlite:///:memory:")
df.to_sql('test', eng, if_exists='append')
result = pandas.read_sql_table('test', eng, index_col='index')
result.index.names = [None]
assert_frame_equal(expected, result)
def test_readonly_axis_zlib_to_sql(self):
# GH11880
if not _ZLIB_INSTALLED:
pytest.skip('no zlib')
if not self._SQLALCHEMY_INSTALLED:
pytest.skip('no sqlalchemy')
expected = DataFrame({'A': list('abcd')})
df = self.encode_decode(expected, compress='zlib')
eng = self._create_sql_engine("sqlite:///:memory:")
df.to_sql('test', eng, if_exists='append')
result = pandas.read_sql_table('test', eng, index_col='index')
result.index.names = [None]
assert_frame_equal(expected, result)
class TestEncoding(TestPackers):
def setup_method(self, method):
super(TestEncoding, self).setup_method(method)
data = {
'A': [compat.u('\u2019')] * 1000,
'B': np.arange(1000, dtype=np.int32),
'C': list(100 * 'abcdefghij'),
'D': date_range(datetime.datetime(2015, 4, 1), periods=1000),
'E': [datetime.timedelta(days=x) for x in range(1000)],
'G': [400] * 1000
}
self.frame = {
'float': DataFrame(dict((k, data[k]) for k in ['A', 'A'])),
'int': DataFrame(dict((k, data[k]) for k in ['B', 'B'])),
'mixed': DataFrame(data),
}
self.utf_encodings = ['utf8', 'utf16', 'utf32']
def test_utf(self):
# GH10581
for encoding in self.utf_encodings:
for frame in compat.itervalues(self.frame):
result = self.encode_decode(frame, encoding=encoding)
assert_frame_equal(result, frame)
def test_default_encoding(self):
for frame in compat.itervalues(self.frame):
result = frame.to_msgpack()
expected = frame.to_msgpack(encoding='utf8')
assert result == expected
result = self.encode_decode(frame)
assert_frame_equal(result, frame)
def legacy_packers_versions():
# yield the packers versions
path = tm.get_data_path('legacy_msgpack')
for v in os.listdir(path):
p = os.path.join(path, v)
if os.path.isdir(p):
yield v
class TestMsgpack(object):
"""
How to add msgpack tests:
1. Install pandas version intended to output the msgpack.
TestPackers
2. Execute "generate_legacy_storage_files.py" to create the msgpack.
$ python generate_legacy_storage_files.py <output_dir> msgpack
3. Move the created pickle to "data/legacy_msgpack/<version>" directory.
"""
minimum_structure = {'series': ['float', 'int', 'mixed',
'ts', 'mi', 'dup'],
'frame': ['float', 'int', 'mixed', 'mi'],
'panel': ['float'],
'index': ['int', 'date', 'period'],
'mi': ['reg2']}
def check_min_structure(self, data, version):
for typ, v in self.minimum_structure.items():
assert typ in data, '"{0}" not found in unpacked data'.format(typ)
for kind in v:
msg = '"{0}" not found in data["{1}"]'.format(kind, typ)
assert kind in data[typ], msg
def compare(self, current_data, all_data, vf, version):
# GH12277 encoding default used to be latin-1, now utf-8
if LooseVersion(version) < '0.18.0':
data = read_msgpack(vf, encoding='latin-1')
else:
data = read_msgpack(vf)
self.check_min_structure(data, version)
for typ, dv in data.items():
assert typ in all_data, ('unpacked data contains '
'extra key "{0}"'
.format(typ))
for dt, result in dv.items():
assert dt in current_data[typ], ('data["{0}"] contains extra '
'key "{1}"'.format(typ, dt))
try:
expected = current_data[typ][dt]
except KeyError:
continue
# use a specific comparator
# if available
comp_method = "compare_{typ}_{dt}".format(typ=typ, dt=dt)
comparator = getattr(self, comp_method, None)
if comparator is not None:
comparator(result, expected, typ, version)
else:
check_arbitrary(result, expected)
return data
def compare_series_dt_tz(self, result, expected, typ, version):
# 8260
# dtype is object < 0.17.0
if LooseVersion(version) < '0.17.0':
expected = expected.astype(object)
tm.assert_series_equal(result, expected)
else:
tm.assert_series_equal(result, expected)
def compare_frame_dt_mixed_tzs(self, result, expected, typ, version):
# 8260
# dtype is object < 0.17.0
if LooseVersion(version) < '0.17.0':
expected = expected.astype(object)
tm.assert_frame_equal(result, expected)
else:
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize('version', legacy_packers_versions())
def test_msgpacks_legacy(self, current_packers_data, all_packers_data,
version):
pth = tm.get_data_path('legacy_msgpack/{0}'.format(version))
n = 0
for f in os.listdir(pth):
# GH12142 0.17 files packed in P2 can't be read in P3
if (compat.PY3 and version.startswith('0.17.') and
f.split('.')[-4][-1] == '2'):
continue
vf = os.path.join(pth, f)
try:
with catch_warnings(record=True):
self.compare(current_packers_data, all_packers_data,
vf, version)
except ImportError:
# blosc not installed
continue
n += 1
assert n > 0, 'Msgpack files are not tested'
| gpl-2.0 |
jeffzheng1/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py | 14 | 3569 | # 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.
# ==============================================================================
"""Methods to allow pandas.DataFrame."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
try:
# pylint: disable=g-import-not-at-top
import pandas as pd
HAS_PANDAS = True
except ImportError:
HAS_PANDAS = False
PANDAS_DTYPES = {
'int8': 'int',
'int16': 'int',
'int32': 'int',
'int64': 'int',
'uint8': 'int',
'uint16': 'int',
'uint32': 'int',
'uint64': 'int',
'float16': 'float',
'float32': 'float',
'float64': 'float',
'bool': 'i'
}
def extract_pandas_data(data):
"""Extract data from pandas.DataFrame for predictors.
Given a DataFrame, will extract the values and cast them to float. The
DataFrame is expected to contain values of type int, float or bool.
Args:
data: `pandas.DataFrame` containing the data to be extracted.
Returns:
A numpy `ndarray` of the DataFrame's values as floats.
Raises:
ValueError: if data contains types other than int, float or bool.
"""
if not isinstance(data, pd.DataFrame):
return data
bad_data = [column for column in data
if data[column].dtype.name not in PANDAS_DTYPES]
if not bad_data:
return data.values.astype('float')
else:
error_report = [("'" + str(column) + "' type='" +
data[column].dtype.name + "'") for column in bad_data]
raise ValueError('Data types for extracting pandas data must be int, '
'float, or bool. Found: ' + ', '.join(error_report))
def extract_pandas_matrix(data):
"""Extracts numpy matrix from pandas DataFrame.
Args:
data: `pandas.DataFrame` containing the data to be extracted.
Returns:
A numpy `ndarray` of the DataFrame's values.
"""
if not isinstance(data, pd.DataFrame):
return data
return data.as_matrix()
def extract_pandas_labels(labels):
"""Extract data from pandas.DataFrame for labels.
Args:
labels: `pandas.DataFrame` or `pandas.Series` containing one column of
labels to be extracted.
Returns:
A numpy `ndarray` of labels from the DataFrame.
Raises:
ValueError: if more than one column is found or type is not int, float or
bool.
"""
if isinstance(labels,
pd.DataFrame): # pandas.Series also belongs to DataFrame
if len(labels.columns) > 1:
raise ValueError('Only one column for labels is allowed.')
bad_data = [column for column in labels
if labels[column].dtype.name not in PANDAS_DTYPES]
if not bad_data:
return labels.values
else:
error_report = ["'" + str(column) + "' type="
+ str(labels[column].dtype.name) for column in bad_data]
raise ValueError('Data types for extracting labels must be int, '
'float, or bool. Found: ' + ', '.join(error_report))
else:
return labels
| apache-2.0 |