repository stringclasses 11
values | repo_id stringlengths 1 3 | target_module_path stringlengths 16 72 | prompt stringlengths 298 21.7k | relavent_test_path stringlengths 50 99 | full_function stringlengths 336 33.8k | function_name stringlengths 2 51 | content_class stringclasses 3
values | external_dependencies stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|
scikit-learn | 222 | sklearn/mixture/_gaussian_mixture.py | def _compute_log_det_cholesky(matrix_chol, covariance_type, n_features):
"""Compute the log-det of the cholesky decomposition of matrices.
Parameters
----------
matrix_chol : array-like
Cholesky decompositions of the matrices.
'full' : shape of (n_components, n_features, n_features)
... | /usr/src/app/target_test_cases/failed_tests__compute_log_det_cholesky.txt | def _compute_log_det_cholesky(matrix_chol, covariance_type, n_features):
"""Compute the log-det of the cholesky decomposition of matrices.
Parameters
----------
matrix_chol : array-like
Cholesky decompositions of the matrices.
'full' : shape of (n_components, n_features, n_features)
... | _compute_log_det_cholesky | self-contained | external |
scikit-learn | 223 | sklearn/mixture/_gaussian_mixture.py | def _compute_precision_cholesky(covariances, covariance_type):
"""Compute the Cholesky decomposition of the precisions.
Parameters
----------
covariances : array-like
The covariance matrix of the current components.
The shape depends of the covariance_type.
covariance_type : {'full... | /usr/src/app/target_test_cases/failed_tests__compute_precision_cholesky.txt | def _compute_precision_cholesky(covariances, covariance_type):
"""Compute the Cholesky decomposition of the precisions.
Parameters
----------
covariances : array-like
The covariance matrix of the current components.
The shape depends of the covariance_type.
covariance_type : {'full... | _compute_precision_cholesky | self-contained | external |
scikit-learn | 224 | sklearn/utils/_testing.py | def _convert_container(
container,
constructor_name,
columns_name=None,
dtype=None,
minversion=None,
categorical_feature_names=None,
):
"""Convert a given container to a specific array-like with a dtype.
Parameters
----------
container : array-like
The container to conve... | /usr/src/app/target_test_cases/failed_tests__convert_container.txt | def _convert_container(
container,
constructor_name,
columns_name=None,
dtype=None,
minversion=None,
categorical_feature_names=None,
):
"""Convert a given container to a specific array-like with a dtype.
Parameters
----------
container : array-like
The container to conve... | _convert_container | repository-level | external |
scikit-learn | 225 | sklearn/discriminant_analysis.py | def _cov(X, shrinkage=None, covariance_estimator=None):
"""Estimate covariance matrix (using optional covariance_estimator).
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
shrinkage : {'empirical', 'auto'} or float, default=None
Shrinkage parameter... | /usr/src/app/target_test_cases/failed_tests__cov.txt | def _cov(X, shrinkage=None, covariance_estimator=None):
"""Estimate covariance matrix (using optional covariance_estimator).
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.
shrinkage : {'empirical', 'auto'} or float, default=None
Shrinkage parameter... | _cov | repository-level | external |
scikit-learn | 226 | sklearn/metrics/_ranking.py | def _dcg_sample_scores(y_true, y_score, k=None, log_base=2, ignore_ties=False):
"""Compute Discounted Cumulative Gain.
Sum the true scores ranked in the order induced by the predicted scores,
after applying a logarithmic discount.
This ranking metric yields a high value if true labels are ranked high ... | /usr/src/app/target_test_cases/failed_tests__dcg_sample_scores.txt | def _dcg_sample_scores(y_true, y_score, k=None, log_base=2, ignore_ties=False):
"""Compute Discounted Cumulative Gain.
Sum the true scores ranked in the order induced by the predicted scores,
after applying a logarithmic discount.
This ranking metric yields a high value if true labels are ranked high ... | _dcg_sample_scores | file-level | external |
scikit-learn | 227 | sklearn/linear_model/_ransac.py | def _dynamic_max_trials(n_inliers, n_samples, min_samples, probability):
"""Determine number trials such that at least one outlier-free subset is
sampled for the given inlier/outlier ratio.
Parameters
----------
n_inliers : int
Number of inliers in the data.
n_samples : int
Tot... | /usr/src/app/target_test_cases/failed_tests__dynamic_max_trials.txt | def _dynamic_max_trials(n_inliers, n_samples, min_samples, probability):
"""Determine number trials such that at least one outlier-free subset is
sampled for the given inlier/outlier ratio.
Parameters
----------
n_inliers : int
Number of inliers in the data.
n_samples : int
Tot... | _dynamic_max_trials | file-level | external |
scikit-learn | 228 | sklearn/utils/_encode.py | def _encode(values, *, uniques, check_unknown=True):
"""Helper function to encode values into [0, n_uniques - 1].
Uses pure python method for object dtype, and numpy method for
all other dtypes.
The numpy method has the limitation that the `uniques` need to
be sorted. Importantly, this is not check... | /usr/src/app/target_test_cases/failed_tests__encode.txt | def _encode(values, *, uniques, check_unknown=True):
"""Helper function to encode values into [0, n_uniques - 1].
Uses pure python method for object dtype, and numpy method for
all other dtypes.
The numpy method has the limitation that the `uniques` need to
be sorted. Importantly, this is not check... | _encode | repository-level | non_external |
scikit-learn | 229 | sklearn/mixture/_gaussian_mixture.py | def _estimate_gaussian_covariances_full(resp, X, nk, means, reg_covar):
"""Estimate the full covariance matrices.
Parameters
----------
resp : array-like of shape (n_samples, n_components)
X : array-like of shape (n_samples, n_features)
nk : array-like of shape (n_components,)
means : ar... | /usr/src/app/target_test_cases/failed_tests__estimate_gaussian_covariances_full.txt | def _estimate_gaussian_covariances_full(resp, X, nk, means, reg_covar):
"""Estimate the full covariance matrices.
Parameters
----------
resp : array-like of shape (n_samples, n_components)
X : array-like of shape (n_samples, n_features)
nk : array-like of shape (n_components,)
means : ar... | _estimate_gaussian_covariances_full | file-level | external |
scikit-learn | 230 | sklearn/mixture/_gaussian_mixture.py | def _estimate_gaussian_parameters(X, resp, reg_covar, covariance_type):
"""Estimate the Gaussian distribution parameters.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data array.
resp : array-like of shape (n_samples, n_components)
The responsibil... | /usr/src/app/target_test_cases/failed_tests__estimate_gaussian_parameters.txt | def _estimate_gaussian_parameters(X, resp, reg_covar, covariance_type):
"""Estimate the Gaussian distribution parameters.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data array.
resp : array-like of shape (n_samples, n_components)
The responsibil... | _estimate_gaussian_parameters | file-level | external |
scikit-learn | 231 | sklearn/feature_extraction/image.py | def _extract_patches(arr, patch_shape=8, extraction_step=1):
"""Extracts patches of any n-dimensional array in place using strides.
Given an n-dimensional array it will return a 2n-dimensional array with
the first n dimensions indexing patch position and the last n indexing
the patch content. This oper... | /usr/src/app/target_test_cases/failed_tests__extract_patches.txt | def _extract_patches(arr, patch_shape=8, extraction_step=1):
"""Extracts patches of any n-dimensional array in place using strides.
Given an n-dimensional array it will return a 2n-dimensional array with
the first n dimensions indexing patch position and the last n indexing
the patch content. This oper... | _extract_patches | file-level | external |
scikit-learn | 232 | sklearn/datasets/_base.py | def _fetch_remote(remote, dirname=None, n_retries=3, delay=1):
"""Helper function to download a remote dataset.
Fetch a dataset pointed by remote's url, save into path using remote's
filename and ensure its integrity based on the SHA256 checksum of the
downloaded file.
.. versionchanged:: 1.6
... | /usr/src/app/target_test_cases/failed_tests__fetch_remote.txt | def _fetch_remote(remote, dirname=None, n_retries=3, delay=1):
"""Helper function to download a remote dataset.
Fetch a dataset pointed by remote's url, save into path using remote's
filename and ensure its integrity based on the SHA256 checksum of the
downloaded file.
.. versionchanged:: 1.6
... | _fetch_remote | file-level | external |
scikit-learn | 233 | sklearn/ensemble/_hist_gradient_boosting/binning.py | def _find_binning_thresholds(col_data, max_bins):
"""Extract quantiles from a continuous feature.
Missing values are ignored for finding the thresholds.
Parameters
----------
col_data : array-like, shape (n_samples,)
The continuous feature to bin.
max_bins: int
The maximum numb... | /usr/src/app/target_test_cases/failed_tests__find_binning_thresholds.txt | def _find_binning_thresholds(col_data, max_bins):
"""Extract quantiles from a continuous feature.
Missing values are ignored for finding the thresholds.
Parameters
----------
col_data : array-like, shape (n_samples,)
The continuous feature to bin.
max_bins: int
The maximum numb... | _find_binning_thresholds | repository-level | external |
scikit-learn | 234 | sklearn/model_selection/_validation.py | def _fit_and_score(
estimator,
X,
y,
*,
scorer,
train,
test,
verbose,
parameters,
fit_params,
score_params,
return_train_score=False,
return_parameters=False,
return_n_test_samples=False,
return_times=False,
return_estimator=False,
split_progress=None,... | /usr/src/app/target_test_cases/failed_tests__fit_and_score.txt | def _fit_and_score(
estimator,
X,
y,
*,
scorer,
train,
test,
verbose,
parameters,
fit_params,
score_params,
return_train_score=False,
return_parameters=False,
return_n_test_samples=False,
return_times=False,
return_estimator=False,
split_progress=None,... | _fit_and_score | repository-level | external |
scikit-learn | 235 | sklearn/model_selection/_classification_threshold.py | def _fit_and_score_over_thresholds(
classifier,
X,
y,
*,
fit_params,
train_idx,
val_idx,
curve_scorer,
score_params,
):
"""Fit a classifier and compute the scores for different decision thresholds.
Parameters
----------
classifier : estimator instance
The cla... | /usr/src/app/target_test_cases/failed_tests__fit_and_score_over_thresholds.txt | def _fit_and_score_over_thresholds(
classifier,
X,
y,
*,
fit_params,
train_idx,
val_idx,
curve_scorer,
score_params,
):
"""Fit a classifier and compute the scores for different decision thresholds.
Parameters
----------
classifier : estimator instance
The cla... | _fit_and_score_over_thresholds | repository-level | non_external |
scikit-learn | 236 | sklearn/utils/graph.py | def _fix_connected_components(
X,
graph,
n_connected_components,
component_labels,
mode="distance",
metric="euclidean",
**kwargs,
):
"""Add connections to sparse graph to connect unconnected components.
For each pair of unconnected components, compute all pairwise distances
from... | /usr/src/app/target_test_cases/failed_tests__fix_connected_components.txt | def _fix_connected_components(
X,
graph,
n_connected_components,
component_labels,
mode="distance",
metric="euclidean",
**kwargs,
):
"""Add connections to sparse graph to connect unconnected components.
For each pair of unconnected components, compute all pairwise distances
from... | _fix_connected_components | repository-level | external |
scikit-learn | 237 | sklearn/cluster/_agglomerative.py | def _fix_connectivity(X, connectivity, affinity):
"""
Fixes the connectivity matrix.
The different steps are:
- copies it
- makes it symmetric
- converts it to LIL if necessary
- completes it if necessary.
Parameters
----------
X : array-like of shape (n_samples, n_features)
... | /usr/src/app/target_test_cases/failed_tests__fix_connectivity.txt | def _fix_connectivity(X, connectivity, affinity):
"""
Fixes the connectivity matrix.
The different steps are:
- copies it
- makes it symmetric
- converts it to LIL if necessary
- completes it if necessary.
Parameters
----------
X : array-like of shape (n_samples, n_features)
... | _fix_connectivity | repository-level | external |
scikit-learn | 238 | sklearn/inspection/_pd_utils.py | def _get_feature_index(fx, feature_names=None):
"""Get feature index.
Parameters
----------
fx : int or str
Feature index or name.
feature_names : list of str, default=None
All feature names from which to search the indices.
Returns
-------
idx : int
Feature in... | /usr/src/app/target_test_cases/failed_tests__get_feature_index.txt | def _get_feature_index(fx, feature_names=None):
"""Get feature index.
Parameters
----------
fx : int or str
Feature index or name.
feature_names : list of str, default=None
All feature names from which to search the indices.
Returns
-------
idx : int
Feature in... | _get_feature_index | self-contained | non_external |
scikit-learn | 239 | sklearn/ensemble/_forest.py | def _get_n_samples_bootstrap(n_samples, max_samples):
"""
Get the number of samples in a bootstrap sample.
Parameters
----------
n_samples : int
Number of samples in the dataset.
max_samples : int or float
The maximum number of samples to draw from the total available:
... | /usr/src/app/target_test_cases/failed_tests__get_n_samples_bootstrap.txt | def _get_n_samples_bootstrap(n_samples, max_samples):
"""
Get the number of samples in a bootstrap sample.
Parameters
----------
n_samples : int
Number of samples in the dataset.
max_samples : int or float
The maximum number of samples to draw from the total available:
... | _get_n_samples_bootstrap | self-contained | external |
scikit-learn | 240 | sklearn/utils/_response.py | def _get_response_values(
estimator,
X,
response_method,
pos_label=None,
return_response_method_used=False,
):
"""Compute the response values of a classifier, an outlier detector, or a regressor.
The response values are predictions such that it follows the following shape:
- for binary... | /usr/src/app/target_test_cases/failed_tests__get_response_values.txt | def _get_response_values(
estimator,
X,
response_method,
pos_label=None,
return_response_method_used=False,
):
"""Compute the response values of a classifier, an outlier detector, or a regressor.
The response values are predictions such that it follows the following shape:
- for binary... | _get_response_values | repository-level | non_external |
scikit-learn | 241 | sklearn/utils/_response.py | def _get_response_values_binary(
estimator, X, response_method, pos_label=None, return_response_method_used=False
):
"""Compute the response values of a binary classifier.
Parameters
----------
estimator : estimator instance
Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
... | /usr/src/app/target_test_cases/failed_tests__get_response_values_binary.txt | def _get_response_values_binary(
estimator, X, response_method, pos_label=None, return_response_method_used=False
):
"""Compute the response values of a binary classifier.
Parameters
----------
estimator : estimator instance
Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
... | _get_response_values_binary | repository-level | non_external |
scikit-learn | 242 | sklearn/manifold/_t_sne.py | def _gradient_descent(
objective,
p0,
it,
max_iter,
n_iter_check=1,
n_iter_without_progress=300,
momentum=0.8,
learning_rate=200.0,
min_gain=0.01,
min_grad_norm=1e-7,
verbose=0,
args=None,
kwargs=None,
):
"""Batch gradient descent with momentum and individual gain... | /usr/src/app/target_test_cases/failed_tests__gradient_descent.txt | def _gradient_descent(
objective,
p0,
it,
max_iter,
n_iter_check=1,
n_iter_without_progress=300,
momentum=0.8,
learning_rate=200.0,
min_gain=0.01,
min_grad_norm=1e-7,
verbose=0,
args=None,
kwargs=None,
):
"""Batch gradient descent with momentum and individual gain... | _gradient_descent | file-level | external |
scikit-learn | 243 | sklearn/manifold/_spectral_embedding.py | def _graph_connected_component(graph, node_id):
"""Find the largest graph connected components that contains one
given node.
Parameters
----------
graph : array-like of shape (n_samples, n_samples)
Adjacency matrix of the graph, non-zero weight means an edge
between the nodes.
... | /usr/src/app/target_test_cases/failed_tests__graph_connected_component.txt | def _graph_connected_component(graph, node_id):
"""Find the largest graph connected components that contains one
given node.
Parameters
----------
graph : array-like of shape (n_samples, n_samples)
Adjacency matrix of the graph, non-zero weight means an edge
between the nodes.
... | _graph_connected_component | file-level | external |
scikit-learn | 244 | sklearn/manifold/_spectral_embedding.py | def _graph_is_connected(graph):
"""Return whether the graph is connected (True) or Not (False).
Parameters
----------
graph : {array-like, sparse matrix} of shape (n_samples, n_samples)
Adjacency matrix of the graph, non-zero weight means an edge
between the nodes.
Returns
----... | /usr/src/app/target_test_cases/failed_tests__graph_is_connected.txt | def _graph_is_connected(graph):
"""Return whether the graph is connected (True) or Not (False).
Parameters
----------
graph : {array-like, sparse matrix} of shape (n_samples, n_samples)
Adjacency matrix of the graph, non-zero weight means an edge
between the nodes.
Returns
----... | _graph_is_connected | repository-level | external |
scikit-learn | 245 | sklearn/inspection/_partial_dependence.py | def _grid_from_X(X, percentiles, is_categorical, grid_resolution):
"""Generate a grid of points based on the percentiles of X.
The grid is a cartesian product between the columns of ``values``. The
ith column of ``values`` consists in ``grid_resolution`` equally-spaced
points between the percentiles of... | /usr/src/app/target_test_cases/failed_tests__grid_from_X.txt | def _grid_from_X(X, percentiles, is_categorical, grid_resolution):
"""Generate a grid of points based on the percentiles of X.
The grid is a cartesian product between the columns of ``values``. The
ith column of ``values`` consists in ``grid_resolution`` equally-spaced
points between the percentiles of... | _grid_from_X | repository-level | external |
scikit-learn | 246 | sklearn/cluster/_agglomerative.py | def _hc_cut(n_clusters, children, n_leaves):
"""Function cutting the ward tree for a given number of clusters.
Parameters
----------
n_clusters : int or ndarray
The number of clusters to form.
children : ndarray of shape (n_nodes-1, 2)
The children of each non-leaf node. Values les... | /usr/src/app/target_test_cases/failed_tests__hc_cut.txt | def _hc_cut(n_clusters, children, n_leaves):
"""Function cutting the ward tree for a given number of clusters.
Parameters
----------
n_clusters : int or ndarray
The number of clusters to form.
children : ndarray of shape (n_nodes-1, 2)
The children of each non-leaf node. Values les... | _hc_cut | self-contained | external |
scikit-learn | 247 | sklearn/linear_model/_huber.py | def _huber_loss_and_gradient(w, X, y, epsilon, alpha, sample_weight=None):
"""Returns the Huber loss and the gradient.
Parameters
----------
w : ndarray, shape (n_features + 1,) or (n_features + 2,)
Feature vector.
w[:n_features] gives the coefficients
w[-1] gives the scale fact... | /usr/src/app/target_test_cases/failed_tests__huber_loss_and_gradient.txt | def _huber_loss_and_gradient(w, X, y, epsilon, alpha, sample_weight=None):
"""Returns the Huber loss and the gradient.
Parameters
----------
w : ndarray, shape (n_features + 1,) or (n_features + 2,)
Feature vector.
w[:n_features] gives the coefficients
w[-1] gives the scale fact... | _huber_loss_and_gradient | repository-level | external |
scikit-learn | 248 | sklearn/manifold/_t_sne.py | def _joint_probabilities(distances, desired_perplexity, verbose):
"""Compute joint probabilities p_ij from distances.
Parameters
----------
distances : ndarray of shape (n_samples * (n_samples-1) / 2,)
Distances of samples are stored as condensed matrices, i.e.
we omit the diagonal and ... | /usr/src/app/target_test_cases/failed_tests__joint_probabilities.txt | def _joint_probabilities(distances, desired_perplexity, verbose):
"""Compute joint probabilities p_ij from distances.
Parameters
----------
distances : ndarray of shape (n_samples * (n_samples-1) / 2,)
Distances of samples are stored as condensed matrices, i.e.
we omit the diagonal and ... | _joint_probabilities | file-level | external |
scikit-learn | 249 | sklearn/manifold/_t_sne.py | def _joint_probabilities_nn(distances, desired_perplexity, verbose):
"""Compute joint probabilities p_ij from distances using just nearest
neighbors.
This method is approximately equal to _joint_probabilities. The latter
is O(N), but limiting the joint probability to nearest neighbors improves
this... | /usr/src/app/target_test_cases/failed_tests__joint_probabilities_nn.txt | def _joint_probabilities_nn(distances, desired_perplexity, verbose):
"""Compute joint probabilities p_ij from distances using just nearest
neighbors.
This method is approximately equal to _joint_probabilities. The latter
is O(N), but limiting the joint probability to nearest neighbors improves
this... | _joint_probabilities_nn | file-level | external |
scikit-learn | 250 | sklearn/manifold/_t_sne.py | def _kl_divergence(
params,
P,
degrees_of_freedom,
n_samples,
n_components,
skip_num_points=0,
compute_error=True,
):
"""t-SNE objective function: gradient of the KL divergence
of p_ijs and q_ijs and the absolute error.
Parameters
----------
params : ndarray of shape (n_... | /usr/src/app/target_test_cases/failed_tests__kl_divergence.txt | def _kl_divergence(
params,
P,
degrees_of_freedom,
n_samples,
n_components,
skip_num_points=0,
compute_error=True,
):
"""t-SNE objective function: gradient of the KL divergence
of p_ijs and q_ijs and the absolute error.
Parameters
----------
params : ndarray of shape (n_... | _kl_divergence | file-level | external |
scikit-learn | 251 | sklearn/manifold/_t_sne.py | def _kl_divergence_bh(
params,
P,
degrees_of_freedom,
n_samples,
n_components,
angle=0.5,
skip_num_points=0,
verbose=False,
compute_error=True,
num_threads=1,
):
"""t-SNE objective function: KL divergence of p_ijs and q_ijs.
Uses Barnes-Hut tree methods to calculate the ... | /usr/src/app/target_test_cases/failed_tests__kl_divergence_bh.txt | def _kl_divergence_bh(
params,
P,
degrees_of_freedom,
n_samples,
n_components,
angle=0.5,
skip_num_points=0,
verbose=False,
compute_error=True,
num_threads=1,
):
"""t-SNE objective function: KL divergence of p_ijs and q_ijs.
Uses Barnes-Hut tree methods to calculate the ... | _kl_divergence_bh | self-contained | external |
scikit-learn | 252 | sklearn/cluster/_kmeans.py | def _labels_inertia(X, sample_weight, centers, n_threads=1, return_inertia=True):
"""E step of the K-means EM algorithm.
Compute the labels and the inertia of the given samples and centers.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The input sample... | /usr/src/app/target_test_cases/failed_tests__labels_inertia.txt | def _labels_inertia(X, sample_weight, centers, n_threads=1, return_inertia=True):
"""E step of the K-means EM algorithm.
Compute the labels and the inertia of the given samples and centers.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The input sample... | _labels_inertia | self-contained | external |
scikit-learn | 253 | sklearn/linear_model/_least_angle.py | def _lars_path_residues(
X_train,
y_train,
X_test,
y_test,
Gram=None,
copy=True,
method="lar",
verbose=False,
fit_intercept=True,
max_iter=500,
eps=np.finfo(float).eps,
positive=False,
):
"""Compute the residues on left-out data for a full LARS path
Parameters
... | /usr/src/app/target_test_cases/failed_tests__lars_path_residues.txt | def _lars_path_residues(
X_train,
y_train,
X_test,
y_test,
Gram=None,
copy=True,
method="lar",
verbose=False,
fit_intercept=True,
max_iter=500,
eps=np.finfo(float).eps,
positive=False,
):
"""Compute the residues on left-out data for a full LARS path
Parameters
... | _lars_path_residues | repository-level | external |
scikit-learn | 254 | sklearn/linear_model/_logistic.py | def _log_reg_scoring_path(
X,
y,
train,
test,
*,
pos_class,
Cs,
scoring,
fit_intercept,
max_iter,
tol,
class_weight,
verbose,
solver,
penalty,
dual,
intercept_scaling,
multi_class,
random_state,
max_squared_sum,
sample_weight,
l1_ra... | /usr/src/app/target_test_cases/failed_tests__log_reg_scoring_path.txt | def _log_reg_scoring_path(
X,
y,
train,
test,
*,
pos_class,
Cs,
scoring,
fit_intercept,
max_iter,
tol,
class_weight,
verbose,
solver,
penalty,
dual,
intercept_scaling,
multi_class,
random_state,
max_squared_sum,
sample_weight,
l1_ra... | _log_reg_scoring_path | repository-level | external |
scikit-learn | 255 | sklearn/mixture/_bayesian_mixture.py | def _log_wishart_norm(degrees_of_freedom, log_det_precisions_chol, n_features):
"""Compute the log of the Wishart distribution normalization term.
Parameters
----------
degrees_of_freedom : array-like of shape (n_components,)
The number of degrees of freedom on the covariance Wishart
di... | /usr/src/app/target_test_cases/failed_tests__log_wishart_norm.txt | def _log_wishart_norm(degrees_of_freedom, log_det_precisions_chol, n_features):
"""Compute the log of the Wishart distribution normalization term.
Parameters
----------
degrees_of_freedom : array-like of shape (n_components,)
The number of degrees of freedom on the covariance Wishart
di... | _log_wishart_norm | self-contained | external |
scikit-learn | 256 | sklearn/linear_model/_logistic.py | def _logistic_regression_path(
X,
y,
pos_class=None,
Cs=10,
fit_intercept=True,
max_iter=100,
tol=1e-4,
verbose=0,
solver="lbfgs",
coef=None,
class_weight=None,
dual=False,
penalty="l2",
intercept_scaling=1.0,
multi_class="auto",
random_state=None,
che... | /usr/src/app/target_test_cases/failed_tests__logistic_regression_path.txt | def _logistic_regression_path(
X,
y,
pos_class=None,
Cs=10,
fit_intercept=True,
max_iter=100,
tol=1e-4,
verbose=0,
solver="lbfgs",
coef=None,
class_weight=None,
dual=False,
penalty="l2",
intercept_scaling=1.0,
multi_class="auto",
random_state=None,
che... | _logistic_regression_path | repository-level | external |
scikit-learn | 257 | sklearn/cluster/_kmeans.py | def _mini_batch_step(
X,
sample_weight,
centers,
centers_new,
weight_sums,
random_state,
random_reassign=False,
reassignment_ratio=0.01,
verbose=False,
n_threads=1,
):
"""Incremental update of the centers for the Minibatch K-Means algorithm.
Parameters
----------
... | /usr/src/app/target_test_cases/failed_tests__mini_batch_step.txt | def _mini_batch_step(
X,
sample_weight,
centers,
centers_new,
weight_sums,
random_state,
random_reassign=False,
reassignment_ratio=0.01,
verbose=False,
n_threads=1,
):
"""Incremental update of the centers for the Minibatch K-Means algorithm.
Parameters
----------
... | _mini_batch_step | file-level | external |
scikit-learn | 258 | sklearn/linear_model/_theil_sen.py | def _modified_weiszfeld_step(X, x_old):
"""Modified Weiszfeld step.
This function defines one iteration step in order to approximate the
spatial median (L1 median). It is a form of an iteratively re-weighted
least squares method.
Parameters
----------
X : array-like of shape (n_samples, n_... | /usr/src/app/target_test_cases/failed_tests__modified_weiszfeld_step.txt | def _modified_weiszfeld_step(X, x_old):
"""Modified Weiszfeld step.
This function defines one iteration step in order to approximate the
spatial median (L1 median). It is a form of an iteratively re-weighted
least squares method.
Parameters
----------
X : array-like of shape (n_samples, n_... | _modified_weiszfeld_step | file-level | external |
scikit-learn | 259 | sklearn/utils/extmath.py | def _nanaverage(a, weights=None):
"""Compute the weighted average, ignoring NaNs.
Parameters
----------
a : ndarray
Array containing data to be averaged.
weights : array-like, default=None
An array of weights associated with the values in a. Each value in a
contributes to th... | /usr/src/app/target_test_cases/failed_tests__nanaverage.txt | def _nanaverage(a, weights=None):
"""Compute the weighted average, ignoring NaNs.
Parameters
----------
a : ndarray
Array containing data to be averaged.
weights : array-like, default=None
An array of weights associated with the values in a. Each value in a
contributes to th... | _nanaverage | self-contained | external |
scikit-learn | 260 | sklearn/utils/optimize.py | def _newton_cg(
grad_hess,
func,
grad,
x0,
args=(),
tol=1e-4,
maxiter=100,
maxinner=200,
line_search=True,
warn=True,
verbose=0,
):
"""
Minimization of scalar function of one or more variables using the
Newton-CG algorithm.
Parameters
----------
grad_... | /usr/src/app/target_test_cases/failed_tests__newton_cg.txt | def _newton_cg(
grad_hess,
func,
grad,
x0,
args=(),
tol=1e-4,
maxiter=100,
maxinner=200,
line_search=True,
warn=True,
verbose=0,
):
"""
Minimization of scalar function of one or more variables using the
Newton-CG algorithm.
Parameters
----------
grad_... | _newton_cg | repository-level | external |
scikit-learn | 261 | sklearn/datasets/_openml.py | def _open_openml_url(
openml_path: str, data_home: Optional[str], n_retries: int = 3, delay: float = 1.0
):
"""
Returns a resource from OpenML.org. Caches it to data_home if required.
Parameters
----------
openml_path : str
OpenML URL that will be accessed. This will be prefixes with
... | /usr/src/app/target_test_cases/failed_tests__open_openml_url.txt | def _open_openml_url(
openml_path: str, data_home: Optional[str], n_retries: int = 3, delay: float = 1.0
):
"""
Returns a resource from OpenML.org. Caches it to data_home if required.
Parameters
----------
openml_path : str
OpenML URL that will be accessed. This will be prefixes with
... | _open_openml_url | file-level | external |
scikit-learn | 262 | sklearn/utils/multiclass.py | def _ovr_decision_function(predictions, confidences, n_classes):
"""Compute a continuous, tie-breaking OvR decision function from OvO.
It is important to include a continuous value, not only votes,
to make computing AUC or calibration meaningful.
Parameters
----------
predictions : array-like ... | /usr/src/app/target_test_cases/failed_tests__ovr_decision_function.txt | def _ovr_decision_function(predictions, confidences, n_classes):
"""Compute a continuous, tie-breaking OvR decision function from OvO.
It is important to include a continuous value, not only votes,
to make computing AUC or calibration meaningful.
Parameters
----------
predictions : array-like ... | _ovr_decision_function | self-contained | external |
scikit-learn | 263 | sklearn/inspection/_partial_dependence.py | def _partial_dependence_brute(
est, grid, features, X, response_method, sample_weight=None
):
"""Calculate partial dependence via the brute force method.
The brute method explicitly averages the predictions of an estimator over a
grid of feature values.
For each `grid` value, all the samples from ... | /usr/src/app/target_test_cases/failed_tests__partial_dependence_brute.txt | def _partial_dependence_brute(
est, grid, features, X, response_method, sample_weight=None
):
"""Calculate partial dependence via the brute force method.
The brute method explicitly averages the predictions of an estimator over a
grid of feature values.
For each `grid` value, all the samples from ... | _partial_dependence_brute | repository-level | external |
scikit-learn | 264 | sklearn/datasets/_arff_parser.py | def _post_process_frame(frame, feature_names, target_names):
"""Post process a dataframe to select the desired columns in `X` and `y`.
Parameters
----------
frame : dataframe
The dataframe to split into `X` and `y`.
feature_names : list of str
The list of feature names to populate ... | /usr/src/app/target_test_cases/failed_tests__post_process_frame.txt | def _post_process_frame(frame, feature_names, target_names):
"""Post process a dataframe to select the desired columns in `X` and `y`.
Parameters
----------
frame : dataframe
The dataframe to split into `X` and `y`.
feature_names : list of str
The list of feature names to populate ... | _post_process_frame | self-contained | non_external |
scikit-learn | 265 | sklearn/utils/random.py | def _random_choice_csc(n_samples, classes, class_probability=None, random_state=None):
"""Generate a sparse random matrix given column class distributions
Parameters
----------
n_samples : int,
Number of samples to draw in each column.
classes : list of size n_outputs of arrays of size (n_... | /usr/src/app/target_test_cases/failed_tests__random_choice_csc.txt | def _random_choice_csc(n_samples, classes, class_probability=None, random_state=None):
"""Generate a sparse random matrix given column class distributions
Parameters
----------
n_samples : int,
Number of samples to draw in each column.
classes : list of size n_outputs of arrays of size (n_... | _random_choice_csc | repository-level | external |
scikit-learn | 266 | sklearn/utils/extmath.py | def _randomized_eigsh(
M,
n_components,
*,
n_oversamples=10,
n_iter="auto",
power_iteration_normalizer="auto",
selection="module",
random_state=None,
):
"""Computes a truncated eigendecomposition using randomized methods
This method solves the fixed-rank approximation problem de... | /usr/src/app/target_test_cases/failed_tests__randomized_eigsh.txt | def _randomized_eigsh(
M,
n_components,
*,
n_oversamples=10,
n_iter="auto",
power_iteration_normalizer="auto",
selection="module",
random_state=None,
):
"""Computes a truncated eigendecomposition using randomized methods
This method solves the fixed-rank approximation problem de... | _randomized_eigsh | file-level | external |
scikit-learn | 267 | sklearn/linear_model/_base.py | def _rescale_data(X, y, sample_weight, inplace=False):
"""Rescale data sample-wise by square root of sample_weight.
For many linear models, this enables easy support for sample_weight because
(y - X w)' S (y - X w)
with S = diag(sample_weight) becomes
||y_rescaled - X_rescaled w||_2^2
... | /usr/src/app/target_test_cases/failed_tests__rescale_data.txt | def _rescale_data(X, y, sample_weight, inplace=False):
"""Rescale data sample-wise by square root of sample_weight.
For many linear models, this enables easy support for sample_weight because
(y - X w)' S (y - X w)
with S = diag(sample_weight) becomes
||y_rescaled - X_rescaled w||_2^2
... | _rescale_data | repository-level | external |
scikit-learn | 268 | sklearn/utils/_indexing.py | def _safe_assign(X, values, *, row_indexer=None, column_indexer=None):
"""Safe assignment to a numpy array, sparse matrix, or pandas dataframe.
Parameters
----------
X : {ndarray, sparse-matrix, dataframe}
Array to be modified. It is expected to be 2-dimensional.
values : ndarray
T... | /usr/src/app/target_test_cases/failed_tests__safe_assign.txt | def _safe_assign(X, values, *, row_indexer=None, column_indexer=None):
"""Safe assignment to a numpy array, sparse matrix, or pandas dataframe.
Parameters
----------
X : {ndarray, sparse-matrix, dataframe}
Array to be modified. It is expected to be 2-dimensional.
values : ndarray
T... | _safe_assign | self-contained | external |
scikit-learn | 269 | sklearn/utils/_set_output.py | def _safe_set_output(estimator, *, transform=None):
"""Safely call estimator.set_output and error if it not available.
This is used by meta-estimators to set the output for child estimators.
Parameters
----------
estimator : estimator instance
Estimator instance.
transform : {"default... | /usr/src/app/target_test_cases/failed_tests__safe_set_output.txt | def _safe_set_output(estimator, *, transform=None):
"""Safely call estimator.set_output and error if it not available.
This is used by meta-estimators to set the output for child estimators.
Parameters
----------
estimator : estimator instance
Estimator instance.
transform : {"default... | _safe_set_output | self-contained | non_external |
scikit-learn | 270 | sklearn/utils/metaestimators.py | def _safe_split(estimator, X, y, indices, train_indices=None):
"""Create subset of dataset and properly handle kernels.
Slice X, y according to indices for cross-validation, but take care of
precomputed kernel-matrices or pairwise affinities / distances.
If ``estimator._pairwise is True``, X needs to ... | /usr/src/app/target_test_cases/failed_tests__safe_split.txt | def _safe_split(estimator, X, y, indices, train_indices=None):
"""Create subset of dataset and properly handle kernels.
Slice X, y according to indices for cross-validation, but take care of
precomputed kernel-matrices or pairwise affinities / distances.
If ``estimator._pairwise is True``, X needs to ... | _safe_split | repository-level | external |
scikit-learn | 271 | sklearn/linear_model/_coordinate_descent.py | def _set_order(X, y, order="C"):
"""Change the order of X and y if necessary.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
y : ndarray of shape (n_samples,)
Target values.
order : {None, 'C', 'F'}
If 'C', dense a... | /usr/src/app/target_test_cases/failed_tests__set_order.txt | def _set_order(X, y, order="C"):
"""Change the order of X and y if necessary.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
y : ndarray of shape (n_samples,)
Target values.
order : {None, 'C', 'F'}
If 'C', dense a... | _set_order | self-contained | external |
scikit-learn | 272 | sklearn/ensemble/_base.py | def _set_random_states(estimator, random_state=None):
"""Set fixed random_state parameters for an estimator.
Finds all parameters ending ``random_state`` and sets them to integers
derived from ``random_state``.
Parameters
----------
estimator : estimator supporting get/set_params
Estim... | /usr/src/app/target_test_cases/failed_tests__set_random_states.txt | def _set_random_states(estimator, random_state=None):
"""Set fixed random_state parameters for an estimator.
Finds all parameters ending ``random_state`` and sets them to integers
derived from ``random_state``.
Parameters
----------
estimator : estimator supporting get/set_params
Estim... | _set_random_states | repository-level | external |
scikit-learn | 273 | sklearn/calibration.py | def _sigmoid_calibration(
predictions, y, sample_weight=None, max_abs_prediction_threshold=30
):
"""Probability Calibration with sigmoid method (Platt 2000)
Parameters
----------
predictions : ndarray of shape (n_samples,)
The decision function or predict proba for the samples.
y : nda... | /usr/src/app/target_test_cases/failed_tests__sigmoid_calibration.txt | def _sigmoid_calibration(
predictions, y, sample_weight=None, max_abs_prediction_threshold=30
):
"""Probability Calibration with sigmoid method (Platt 2000)
Parameters
----------
predictions : ndarray of shape (n_samples,)
The decision function or predict proba for the samples.
y : nda... | _sigmoid_calibration | repository-level | external |
scikit-learn | 274 | sklearn/metrics/cluster/_unsupervised.py | def _silhouette_reduce(D_chunk, start, labels, label_freqs):
"""Accumulate silhouette statistics for vertical chunk of X.
Parameters
----------
D_chunk : {array-like, sparse matrix} of shape (n_chunk_samples, n_samples)
Precomputed distances for a chunk. If a sparse matrix is provided,
... | /usr/src/app/target_test_cases/failed_tests__silhouette_reduce.txt | def _silhouette_reduce(D_chunk, start, labels, label_freqs):
"""Accumulate silhouette statistics for vertical chunk of X.
Parameters
----------
D_chunk : {array-like, sparse matrix} of shape (n_chunk_samples, n_samples)
Precomputed distances for a chunk. If a sparse matrix is provided,
... | _silhouette_reduce | self-contained | external |
scikit-learn | 275 | sklearn/utils/fixes.py | def _smallest_admissible_index_dtype(arrays=(), maxval=None, check_contents=False):
"""Based on input (integer) arrays `a`, determine a suitable index data
type that can hold the data in the arrays.
This function returns `np.int64` if it either required by `maxval` or based on the
largest precision of ... | /usr/src/app/target_test_cases/failed_tests__smallest_admissible_index_dtype.txt | def _smallest_admissible_index_dtype(arrays=(), maxval=None, check_contents=False):
"""Based on input (integer) arrays `a`, determine a suitable index data
type that can hold the data in the arrays.
This function returns `np.int64` if it either required by `maxval` or based on the
largest precision of ... | _smallest_admissible_index_dtype | self-contained | external |
scikit-learn | 276 | sklearn/decomposition/_dict_learning.py | def _update_dict(
dictionary,
Y,
code,
A=None,
B=None,
verbose=False,
random_state=None,
positive=False,
):
"""Update the dense dictionary factor in place.
Parameters
----------
dictionary : ndarray of shape (n_components, n_features)
Value of the dictionary at t... | /usr/src/app/target_test_cases/failed_tests__update_dict.txt | def _update_dict(
dictionary,
Y,
code,
A=None,
B=None,
verbose=False,
random_state=None,
positive=False,
):
"""Update the dense dictionary factor in place.
Parameters
----------
dictionary : ndarray of shape (n_components, n_features)
Value of the dictionary at t... | _update_dict | repository-level | external |
scikit-learn | 277 | sklearn/utils/stats.py | def _weighted_percentile(array, sample_weight, percentile=50):
"""Compute weighted percentile
Computes lower weighted percentile. If `array` is a 2D array, the
`percentile` is computed along the axis 0.
.. versionchanged:: 0.24
Accepts 2D `array`.
Parameters
----------
arr... | /usr/src/app/target_test_cases/failed_tests__weighted_percentile.txt | def _weighted_percentile(array, sample_weight, percentile=50):
"""Compute weighted percentile
Computes lower weighted percentile. If `array` is a 2D array, the
`percentile` is computed along the axis 0.
.. versionchanged:: 0.24
Accepts 2D `array`.
Parameters
----------
arr... | _weighted_percentile | repository-level | external |
scikit-learn | 278 | sklearn/utils/_estimator_html_repr.py | def _write_label_html(
out,
name,
name_details,
name_caption=None,
doc_link_label=None,
outer_class="sk-label-container",
inner_class="sk-label",
checked=False,
doc_link="",
is_fitted_css_class="",
is_fitted_icon="",
):
"""Write labeled html with or without a dropdown wit... | /usr/src/app/target_test_cases/failed_tests__write_label_html.txt | def _write_label_html(
out,
name,
name_details,
name_caption=None,
doc_link_label=None,
outer_class="sk-label-container",
inner_class="sk-label",
checked=False,
doc_link="",
is_fitted_css_class="",
is_fitted_icon="",
):
"""Write labeled html with or without a dropdown wit... | _write_label_html | file-level | external |
scikit-learn | 279 | sklearn/model_selection/_search.py | def _yield_masked_array_for_each_param(candidate_params):
"""
Yield a masked array for each candidate param.
`candidate_params` is a sequence of params which were used in
a `GridSearchCV`. We use masked arrays for the results, as not
all params are necessarily present in each element of
`candid... | /usr/src/app/target_test_cases/failed_tests__yield_masked_array_for_each_param.txt | def _yield_masked_array_for_each_param(candidate_params):
"""
Yield a masked array for each candidate param.
`candidate_params` is a sequence of params which were used in
a `GridSearchCV`. We use masked arrays for the results, as not
all params are necessarily present in each element of
`candid... | _yield_masked_array_for_each_param | self-contained | external |
scikit-learn | 280 | sklearn/utils/_testing.py | def assert_allclose_dense_sparse(x, y, rtol=1e-07, atol=1e-9, err_msg=""):
"""Assert allclose for sparse and dense data.
Both x and y need to be either sparse or dense, they
can't be mixed.
Parameters
----------
x : {array-like, sparse matrix}
First array to compare.
y : {array-li... | /usr/src/app/target_test_cases/failed_tests_assert_allclose_dense_sparse.txt | def assert_allclose_dense_sparse(x, y, rtol=1e-07, atol=1e-9, err_msg=""):
"""Assert allclose for sparse and dense data.
Both x and y need to be either sparse or dense, they
can't be mixed.
Parameters
----------
x : {array-like, sparse matrix}
First array to compare.
y : {array-li... | assert_allclose_dense_sparse | file-level | external |
scikit-learn | 281 | sklearn/metrics/tests/test_pairwise_distances_reduction.py | def assert_compatible_argkmin_results(
neighbors_dists_a,
neighbors_dists_b,
neighbors_indices_a,
neighbors_indices_b,
rtol=1e-5,
atol=1e-6,
):
"""Assert that argkmin results are valid up to rounding errors.
This function asserts that the results of argkmin queries are valid up to:
... | /usr/src/app/target_test_cases/failed_tests_assert_compatible_argkmin_results.txt | def assert_compatible_argkmin_results(
neighbors_dists_a,
neighbors_dists_b,
neighbors_indices_a,
neighbors_indices_b,
rtol=1e-5,
atol=1e-6,
):
"""Assert that argkmin results are valid up to rounding errors.
This function asserts that the results of argkmin queries are valid up to:
... | assert_compatible_argkmin_results | file-level | external |
scikit-learn | 282 | sklearn/metrics/tests/test_pairwise_distances_reduction.py | def assert_compatible_radius_results(
neighbors_dists_a,
neighbors_dists_b,
neighbors_indices_a,
neighbors_indices_b,
radius,
check_sorted=True,
rtol=1e-5,
atol=1e-6,
):
"""Assert that radius neighborhood results are valid up to:
- relative and absolute tolerance on computed d... | /usr/src/app/target_test_cases/failed_tests_assert_compatible_radius_results.txt | def assert_compatible_radius_results(
neighbors_dists_a,
neighbors_dists_b,
neighbors_indices_a,
neighbors_indices_b,
radius,
check_sorted=True,
rtol=1e-5,
atol=1e-6,
):
"""Assert that radius neighborhood results are valid up to:
- relative and absolute tolerance on computed d... | assert_compatible_radius_results | file-level | external |
scikit-learn | 283 | sklearn/utils/_testing.py | def assert_run_python_script_without_output(source_code, pattern=".+", timeout=60):
"""Utility to check assertions in an independent Python subprocess.
The script provided in the source code should return 0 and the stdtout +
stderr should not match the pattern `pattern`.
This is a port from cloudpickl... | /usr/src/app/target_test_cases/failed_tests_assert_run_python_script_without_output.txt | def assert_run_python_script_without_output(source_code, pattern=".+", timeout=60):
"""Utility to check assertions in an independent Python subprocess.
The script provided in the source code should return 0 and the stdtout +
stderr should not match the pattern `pattern`.
This is a port from cloudpickl... | assert_run_python_script_without_output | repository-level | external |
scikit-learn | 284 | sklearn/manifold/_locally_linear.py | def barycenter_kneighbors_graph(X, n_neighbors, reg=1e-3, n_jobs=None):
"""Computes the barycenter weighted graph of k-Neighbors for points in X
Parameters
----------
X : {array-like, NearestNeighbors}
Sample data, shape = (n_samples, n_features), in the form of a
numpy array or a Neare... | /usr/src/app/target_test_cases/failed_tests_barycenter_kneighbors_graph.txt | def barycenter_kneighbors_graph(X, n_neighbors, reg=1e-3, n_jobs=None):
"""Computes the barycenter weighted graph of k-Neighbors for points in X
Parameters
----------
X : {array-like, NearestNeighbors}
Sample data, shape = (n_samples, n_features), in the form of a
numpy array or a Neare... | barycenter_kneighbors_graph | repository-level | external |
scikit-learn | 285 | sklearn/utils/validation.py | def check_X_y(
X,
y,
accept_sparse=False,
*,
accept_large_sparse=True,
dtype="numeric",
order=None,
copy=False,
force_writeable=False,
force_all_finite="deprecated",
ensure_all_finite=None,
ensure_2d=True,
allow_nd=False,
multi_output=False,
ensure_min_samples... | /usr/src/app/target_test_cases/failed_tests_check_X_y.txt | def check_X_y(
X,
y,
accept_sparse=False,
*,
accept_large_sparse=True,
dtype="numeric",
order=None,
copy=False,
force_writeable=False,
force_all_finite="deprecated",
ensure_all_finite=None,
ensure_2d=True,
allow_nd=False,
multi_output=False,
ensure_min_samples... | check_X_y | repository-level | non_external |
scikit-learn | 286 | sklearn/utils/validation.py | def check_array(
array,
accept_sparse=False,
*,
accept_large_sparse=True,
dtype="numeric",
order=None,
copy=False,
force_writeable=False,
force_all_finite="deprecated",
ensure_all_finite=None,
ensure_non_negative=False,
ensure_2d=True,
allow_nd=False,
ensure_min_s... | /usr/src/app/target_test_cases/failed_tests_check_array.txt | def check_array(
array,
accept_sparse=False,
*,
accept_large_sparse=True,
dtype="numeric",
order=None,
copy=False,
force_writeable=False,
force_all_finite="deprecated",
ensure_all_finite=None,
ensure_non_negative=False,
ensure_2d=True,
allow_nd=False,
ensure_min_s... | check_array | repository-level | external |
scikit-learn | 287 | sklearn/metrics/pairwise.py | def check_paired_arrays(X, Y):
"""Set X and Y appropriately and checks inputs for paired distances.
All paired distance metrics should use this function first to assert that
the given parameters are correct and safe to use.
Specifically, this function first ensures that both X and Y are arrays,
th... | /usr/src/app/target_test_cases/failed_tests_check_paired_arrays.txt | def check_paired_arrays(X, Y):
"""Set X and Y appropriately and checks inputs for paired distances.
All paired distance metrics should use this function first to assert that
the given parameters are correct and safe to use.
Specifically, this function first ensures that both X and Y are arrays,
th... | check_paired_arrays | file-level | non_external |
scikit-learn | 288 | sklearn/metrics/pairwise.py | def check_pairwise_arrays(
X,
Y,
*,
precomputed=False,
dtype="infer_float",
accept_sparse="csr",
force_all_finite="deprecated",
ensure_all_finite=None,
ensure_2d=True,
copy=False,
):
"""Set X and Y appropriately and checks inputs.
If Y is None, it is set as a pointer to ... | /usr/src/app/target_test_cases/failed_tests_check_pairwise_arrays.txt | def check_pairwise_arrays(
X,
Y,
*,
precomputed=False,
dtype="infer_float",
accept_sparse="csr",
force_all_finite="deprecated",
ensure_all_finite=None,
ensure_2d=True,
copy=False,
):
"""Set X and Y appropriately and checks inputs.
If Y is None, it is set as a pointer to ... | check_pairwise_arrays | repository-level | external |
scikit-learn | 289 | sklearn/tests/metadata_routing_common.py | def check_recorded_metadata(obj, method, parent, split_params=tuple(), **kwargs):
"""Check whether the expected metadata is passed to the object's method.
Parameters
----------
obj : estimator object
sub-estimator to check routed params for
method : str
sub-estimator's method where ... | /usr/src/app/target_test_cases/failed_tests_check_recorded_metadata.txt | def check_recorded_metadata(obj, method, parent, split_params=tuple(), **kwargs):
"""Check whether the expected metadata is passed to the object's method.
Parameters
----------
obj : estimator object
sub-estimator to check routed params for
method : str
sub-estimator's method where ... | check_recorded_metadata | self-contained | external |
scikit-learn | 290 | sklearn/utils/multiclass.py | def class_distribution(y, sample_weight=None):
"""Compute class priors from multioutput-multiclass target data.
Parameters
----------
y : {array-like, sparse matrix} of size (n_samples, n_outputs)
The labels for each example.
sample_weight : array-like of shape (n_samples,), default=None
... | /usr/src/app/target_test_cases/failed_tests_class_distribution.txt | def class_distribution(y, sample_weight=None):
"""Compute class priors from multioutput-multiclass target data.
Parameters
----------
y : {array-like, sparse matrix} of size (n_samples, n_outputs)
The labels for each example.
sample_weight : array-like of shape (n_samples,), default=None
... | class_distribution | self-contained | external |
scikit-learn | 291 | sklearn/utils/sparsefuncs.py | def count_nonzero(X, axis=None, sample_weight=None):
"""A variant of X.getnnz() with extension to weighting on axis 0.
Useful in efficiently calculating multilabel metrics.
Parameters
----------
X : sparse matrix of shape (n_samples, n_labels)
Input data. It should be of CSR format.
a... | /usr/src/app/target_test_cases/failed_tests_count_nonzero.txt | def count_nonzero(X, axis=None, sample_weight=None):
"""A variant of X.getnnz() with extension to weighting on axis 0.
Useful in efficiently calculating multilabel metrics.
Parameters
----------
X : sparse matrix of shape (n_samples, n_labels)
Input data. It should be of CSR format.
a... | count_nonzero | self-contained | external |
scikit-learn | 292 | sklearn/utils/sparsefuncs.py | def csc_median_axis_0(X):
"""Find the median across axis 0 of a CSC matrix.
It is equivalent to doing np.median(X, axis=0).
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Input data. It should be of CSC format.
Returns
-------
median : ndarray of shap... | /usr/src/app/target_test_cases/failed_tests_csc_median_axis_0.txt | def csc_median_axis_0(X):
"""Find the median across axis 0 of a CSC matrix.
It is equivalent to doing np.median(X, axis=0).
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Input data. It should be of CSC format.
Returns
-------
median : ndarray of shap... | csc_median_axis_0 | file-level | external |
scikit-learn | 293 | sklearn/cluster/_spectral.py | def discretize(
vectors, *, copy=True, max_svd_restarts=30, n_iter_max=20, random_state=None
):
"""Search for a partition matrix which is closest to the eigenvector embedding.
This implementation was proposed in [1]_.
Parameters
----------
vectors : array-like of shape (n_samples, n_clusters)
... | /usr/src/app/target_test_cases/failed_tests_discretize.txt | def discretize(
vectors, *, copy=True, max_svd_restarts=30, n_iter_max=20, random_state=None
):
"""Search for a partition matrix which is closest to the eigenvector embedding.
This implementation was proposed in [1]_.
Parameters
----------
vectors : array-like of shape (n_samples, n_clusters)
... | discretize | repository-level | external |
scikit-learn | 294 | sklearn/feature_selection/_univariate_selection.py | def f_oneway(*args):
"""Perform a 1-way ANOVA.
The one-way ANOVA tests the null hypothesis that 2 or more groups have
the same population mean. The test is applied to samples from two or
more groups, possibly with differing sizes.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
... | /usr/src/app/target_test_cases/failed_tests_f_oneway.txt | def f_oneway(*args):
"""Perform a 1-way ANOVA.
The one-way ANOVA tests the null hypothesis that 2 or more groups have
the same population mean. The test is applied to samples from two or
more groups, possibly with differing sizes.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
... | f_oneway | repository-level | external |
scikit-learn | 295 | sklearn/covariance/_robust_covariance.py | def fast_mcd(
X,
support_fraction=None,
cov_computation_method=empirical_covariance,
random_state=None,
):
"""Estimate the Minimum Covariance Determinant matrix.
Read more in the :ref:`User Guide <robust_covariance>`.
Parameters
----------
X : array-like of shape (n_samples, n_feat... | /usr/src/app/target_test_cases/failed_tests_fast_mcd.txt | def fast_mcd(
X,
support_fraction=None,
cov_computation_method=empirical_covariance,
random_state=None,
):
"""Estimate the Minimum Covariance Determinant matrix.
Read more in the :ref:`User Guide <robust_covariance>`.
Parameters
----------
X : array-like of shape (n_samples, n_feat... | fast_mcd | repository-level | external |
scikit-learn | 296 | sklearn/linear_model/_sag.py | def get_auto_step_size(
max_squared_sum, alpha_scaled, loss, fit_intercept, n_samples=None, is_saga=False
):
"""Compute automatic step size for SAG solver.
The step size is set to 1 / (alpha_scaled + L + fit_intercept) where L is
the max sum of squares for over all samples.
Parameters
--------... | /usr/src/app/target_test_cases/failed_tests_get_auto_step_size.txt | def get_auto_step_size(
max_squared_sum, alpha_scaled, loss, fit_intercept, n_samples=None, is_saga=False
):
"""Compute automatic step size for SAG solver.
The step size is set to 1 / (alpha_scaled + L + fit_intercept) where L is
the max sum of squares for over all samples.
Parameters
--------... | get_auto_step_size | self-contained | non_external |
scikit-learn | 297 | sklearn/cluster/_mean_shift.py | def get_bin_seeds(X, bin_size, min_bin_freq=1):
"""Find 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 of shape (n_samples, n_fe... | /usr/src/app/target_test_cases/failed_tests_get_bin_seeds.txt | def get_bin_seeds(X, bin_size, min_bin_freq=1):
"""Find 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 of shape (n_samples, n_fe... | get_bin_seeds | self-contained | external |
scikit-learn | 298 | sklearn/utils/_chunking.py | def get_chunk_n_rows(row_bytes, *, max_n_rows=None, working_memory=None):
"""Calculate how many rows can be processed within `working_memory`.
Parameters
----------
row_bytes : int
The expected number of bytes of memory that will be consumed
during the processing of each row.
max_n_... | /usr/src/app/target_test_cases/failed_tests_get_chunk_n_rows.txt | def get_chunk_n_rows(row_bytes, *, max_n_rows=None, working_memory=None):
"""Calculate how many rows can be processed within `working_memory`.
Parameters
----------
row_bytes : int
The expected number of bytes of memory that will be consumed
during the processing of each row.
max_n_... | get_chunk_n_rows | repository-level | external |
scikit-learn | 299 | sklearn/utils/sparsefuncs.py | def incr_mean_variance_axis(X, *, axis, last_mean, last_var, last_n, weights=None):
"""Compute incremental mean and variance along an axis on a CSR or CSC matrix.
last_mean, last_var are the statistics computed at the last step by this
function. Both must be initialized to 0-arrays of the proper size, i.e.... | /usr/src/app/target_test_cases/failed_tests_incr_mean_variance_axis.txt | def incr_mean_variance_axis(X, *, axis, last_mean, last_var, last_n, weights=None):
"""Compute incremental mean and variance along an axis on a CSR or CSC matrix.
last_mean, last_var are the statistics computed at the last step by this
function. Both must be initialized to 0-arrays of the proper size, i.e.... | incr_mean_variance_axis | repository-level | external |
scikit-learn | 300 | sklearn/neighbors/_graph.py | def kneighbors_graph(
X,
n_neighbors,
*,
mode="connectivity",
metric="minkowski",
p=2,
metric_params=None,
include_self=False,
n_jobs=None,
):
"""Compute the (weighted) graph of k-Neighbors for points in X.
Read more in the :ref:`User Guide <unsupervised_neighbors>`.
Pa... | /usr/src/app/target_test_cases/failed_tests_kneighbors_graph.txt | def kneighbors_graph(
X,
n_neighbors,
*,
mode="connectivity",
metric="minkowski",
p=2,
metric_params=None,
include_self=False,
n_jobs=None,
):
"""Compute the (weighted) graph of k-Neighbors for points in X.
Read more in the :ref:`User Guide <unsupervised_neighbors>`.
Pa... | kneighbors_graph | repository-level | external |
scikit-learn | 301 | sklearn/cluster/_agglomerative.py | def linkage_tree(
X,
connectivity=None,
n_clusters=None,
linkage="complete",
affinity="euclidean",
return_distance=False,
):
"""Linkage agglomerative clustering based on a Feature matrix.
The inertia matrix uses a Heapq-based representation.
This is the structured version, that tak... | /usr/src/app/target_test_cases/failed_tests_linkage_tree.txt | def linkage_tree(
X,
connectivity=None,
n_clusters=None,
linkage="complete",
affinity="euclidean",
return_distance=False,
):
"""Linkage agglomerative clustering based on a Feature matrix.
The inertia matrix uses a Heapq-based representation.
This is the structured version, that tak... | linkage_tree | repository-level | external |
scikit-learn | 302 | sklearn/datasets/_arff_parser.py | def load_arff_from_gzip_file(
gzip_file,
parser,
output_type,
openml_columns_info,
feature_names_to_select,
target_names_to_select,
shape=None,
read_csv_kwargs=None,
):
"""Load a compressed ARFF file using a given parser.
Parameters
----------
gzip_file : GzipFile instan... | /usr/src/app/target_test_cases/failed_tests_load_arff_from_gzip_file.txt | def load_arff_from_gzip_file(
gzip_file,
parser,
output_type,
openml_columns_info,
feature_names_to_select,
target_names_to_select,
shape=None,
read_csv_kwargs=None,
):
"""Load a compressed ARFF file using a given parser.
Parameters
----------
gzip_file : GzipFile instan... | load_arff_from_gzip_file | file-level | non_external |
scikit-learn | 303 | sklearn/datasets/_base.py | def load_diabetes(*, return_X_y=False, as_frame=False, scaled=True):
"""Load and return the diabetes dataset (regression).
============== ==================
Samples total 442
Dimensionality 10
Features real, -.2 < x < .2
Targets integer 25 - 346
============== ====... | /usr/src/app/target_test_cases/failed_tests_load_diabetes.txt | def load_diabetes(*, return_X_y=False, as_frame=False, scaled=True):
"""Load and return the diabetes dataset (regression).
============== ==================
Samples total 442
Dimensionality 10
Features real, -.2 < x < .2
Targets integer 25 - 346
============== ====... | load_diabetes | repository-level | non_external |
scikit-learn | 304 | sklearn/neural_network/_base.py | def log_loss(y_true, y_prob):
"""Compute Logistic loss for classification.
Parameters
----------
y_true : array-like or label indicator matrix
Ground truth (correct) labels.
y_prob : array-like of float, shape = (n_samples, n_classes)
Predicted probabilities, as returned by a class... | /usr/src/app/target_test_cases/failed_tests_log_loss.txt | def log_loss(y_true, y_prob):
"""Compute Logistic loss for classification.
Parameters
----------
y_true : array-like or label indicator matrix
Ground truth (correct) labels.
y_prob : array-like of float, shape = (n_samples, n_classes)
Predicted probabilities, as returned by a class... | log_loss | self-contained | external |
scikit-learn | 305 | sklearn/datasets/_samples_generator.py | def make_blobs(
n_samples=100,
n_features=2,
*,
centers=None,
cluster_std=1.0,
center_box=(-10.0, 10.0),
shuffle=True,
random_state=None,
return_centers=False,
):
"""Generate isotropic Gaussian blobs for clustering.
For an example of usage, see
:ref:`sphx_glr_auto_exampl... | /usr/src/app/target_test_cases/failed_tests_make_blobs.txt | def make_blobs(
n_samples=100,
n_features=2,
*,
centers=None,
cluster_std=1.0,
center_box=(-10.0, 10.0),
shuffle=True,
random_state=None,
return_centers=False,
):
"""Generate isotropic Gaussian blobs for clustering.
For an example of usage, see
:ref:`sphx_glr_auto_exampl... | make_blobs | repository-level | external |
scikit-learn | 306 | sklearn/compose/_column_transformer.py | def make_column_transformer(
*transformers,
remainder="drop",
sparse_threshold=0.3,
n_jobs=None,
verbose=False,
verbose_feature_names_out=True,
force_int_remainder_cols=True,
):
"""Construct a ColumnTransformer from the given transformers.
This is a shorthand for the ColumnTransform... | /usr/src/app/target_test_cases/failed_tests_make_column_transformer.txt | def make_column_transformer(
*transformers,
remainder="drop",
sparse_threshold=0.3,
n_jobs=None,
verbose=False,
verbose_feature_names_out=True,
force_int_remainder_cols=True,
):
"""Construct a ColumnTransformer from the given transformers.
This is a shorthand for the ColumnTransform... | make_column_transformer | file-level | non_external |
scikit-learn | 307 | sklearn/linear_model/_base.py | def make_dataset(X, y, sample_weight, random_state=None):
"""Create ``Dataset`` abstraction for sparse and dense inputs.
This also returns the ``intercept_decay`` which is different
for sparse datasets.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data
... | /usr/src/app/target_test_cases/failed_tests_make_dataset.txt | def make_dataset(X, y, sample_weight, random_state=None):
"""Create ``Dataset`` abstraction for sparse and dense inputs.
This also returns the ``intercept_decay`` which is different
for sparse datasets.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data
... | make_dataset | repository-level | external |
scikit-learn | 308 | sklearn/utils/sparsefuncs.py | def min_max_axis(X, axis, ignore_nan=False):
"""Compute minimum and maximum along an axis on a CSR or CSC matrix.
Optionally ignore NaN values.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Input data. It should be of CSR or CSC format.
axis : {0, 1}
... | /usr/src/app/target_test_cases/failed_tests_min_max_axis.txt | def min_max_axis(X, axis, ignore_nan=False):
"""Compute minimum and maximum along an axis on a CSR or CSC matrix.
Optionally ignore NaN values.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Input data. It should be of CSR or CSC format.
axis : {0, 1}
... | min_max_axis | repository-level | external |
scikit-learn | 309 | sklearn/utils/extmath.py | def softmax(X, copy=True):
"""
Calculate the softmax function.
The softmax function is calculated by
np.exp(X) / np.sum(np.exp(X), axis=1)
This will cause overflow when large values are exponentiated.
Hence the largest value in each row is subtracted from each data
point to prevent this.
... | /usr/src/app/target_test_cases/failed_tests_softmax.txt | def softmax(X, copy=True):
"""
Calculate the softmax function.
The softmax function is calculated by
np.exp(X) / np.sum(np.exp(X), axis=1)
This will cause overflow when large values are exponentiated.
Hence the largest value in each row is subtracted from each data
point to prevent this.
... | softmax | repository-level | external |
scikit-learn | 310 | sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py | def sqeuclidean_row_norms(X, num_threads):
"""Compute the squared euclidean norm of the rows of X in parallel.
Parameters
----------
X : ndarray or CSR matrix of shape (n_samples, n_features)
Input data. Must be c-contiguous.
num_threads : int
The number of OpenMP threads to use.
... | /usr/src/app/target_test_cases/failed_tests_sqeuclidean_row_norms.txt | def sqeuclidean_row_norms(X, num_threads):
"""Compute the squared euclidean norm of the rows of X in parallel.
Parameters
----------
X : ndarray or CSR matrix of shape (n_samples, n_features)
Input data. Must be c-contiguous.
num_threads : int
The number of OpenMP threads to use.
... | sqeuclidean_row_norms | self-contained | external |
scikit-learn | 311 | sklearn/utils/extmath.py | def stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08):
"""Use high precision for cumsum and check that final value matches sum.
Warns if the final cumulative sum does not match the sum (up to the chosen
tolerance).
Parameters
----------
arr : array-like
To be cumulatively summed as... | /usr/src/app/target_test_cases/failed_tests_stable_cumsum.txt | def stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08):
"""Use high precision for cumsum and check that final value matches sum.
Warns if the final cumulative sum does not match the sum (up to the chosen
tolerance).
Parameters
----------
arr : array-like
To be cumulatively summed as... | stable_cumsum | self-contained | external |
scikit-learn | 312 | sklearn/utils/extmath.py | def svd_flip(u, v, u_based_decision=True):
"""Sign correction to ensure deterministic output from SVD.
Adjusts the columns of u and the rows of v such that the loadings in the
columns in u that are largest in absolute value are always positive.
If u_based_decision is False, then the same sign correcti... | /usr/src/app/target_test_cases/failed_tests_svd_flip.txt | def svd_flip(u, v, u_based_decision=True):
"""Sign correction to ensure deterministic output from SVD.
Adjusts the columns of u and the rows of v such that the loadings in the
columns in u that are largest in absolute value are always positive.
If u_based_decision is False, then the same sign correcti... | svd_flip | repository-level | external |
scikit-learn | 313 | sklearn/utils/multiclass.py | def type_of_target(y, input_name=""):
"""Determine the type of data indicated by the target.
Note that this type is the most specific type that can be inferred.
For example:
* ``binary`` is more specific but compatible with ``multiclass``.
* ``multiclass`` of integers is more specific but compatib... | /usr/src/app/target_test_cases/failed_tests_type_of_target.txt | def type_of_target(y, input_name=""):
"""Determine the type of data indicated by the target.
Note that this type is the most specific type that can be inferred.
For example:
* ``binary`` is more specific but compatible with ``multiclass``.
* ``multiclass`` of integers is more specific but compatib... | type_of_target | repository-level | external |
astropy | 0 | astropy/modeling/physical_models.py | def evaluate(self, x, temperature, scale):
"""Evaluate the model.
Parameters
----------
x : float, `~numpy.ndarray`, or `~astropy.units.Quantity` ['frequency']
Frequency at which to compute the blackbody. If no units are given,
this defaults to Hz (or AA if `... | /usr/src/app/target_test_cases/failed_tests_BlackBody.evaluate.txt | def evaluate(self, x, temperature, scale):
"""Evaluate the model.
Parameters
----------
x : float, `~numpy.ndarray`, or `~astropy.units.Quantity` ['frequency']
Frequency at which to compute the blackbody. If no units are given,
this defaults to Hz (or AA if `... | BlackBody.evaluate | repository-level | external |
astropy | 1 | astropy/timeseries/periodograms/bls/core.py | def autoperiod(
self,
duration,
minimum_period=None,
maximum_period=None,
minimum_n_transit=3,
frequency_factor=1.0,
):
"""Determine a suitable grid of periods.
This method uses a set of heuristics to select a conservative period
grid that... | /usr/src/app/target_test_cases/failed_tests_BoxLeastSquares.autoperiod.txt | def autoperiod(
self,
duration,
minimum_period=None,
maximum_period=None,
minimum_n_transit=3,
frequency_factor=1.0,
):
"""Determine a suitable grid of periods.
This method uses a set of heuristics to select a conservative period
grid that... | BoxLeastSquares.autoperiod | repository-level | external |
astropy | 2 | astropy/timeseries/periodograms/bls/core.py | def compute_stats(self, period, duration, transit_time):
"""Compute descriptive statistics for a given transit model.
These statistics are commonly used for vetting of transit candidates.
Parameters
----------
period : float or `~astropy.units.Quantity` ['time']
... | /usr/src/app/target_test_cases/failed_tests_BoxLeastSquares.compute_stats.txt | def compute_stats(self, period, duration, transit_time):
"""Compute descriptive statistics for a given transit model.
These statistics are commonly used for vetting of transit candidates.
Parameters
----------
period : float or `~astropy.units.Quantity` ['time']
... | BoxLeastSquares.compute_stats | repository-level | external |
astropy | 3 | astropy/timeseries/periodograms/bls/core.py | def model(self, t_model, period, duration, transit_time):
"""Compute the transit model at the given period, duration, and phase.
Parameters
----------
t_model : array-like, `~astropy.units.Quantity`, or `~astropy.time.Time`
Times at which to compute the model.
pe... | /usr/src/app/target_test_cases/failed_tests_BoxLeastSquares.model.txt | def model(self, t_model, period, duration, transit_time):
"""Compute the transit model at the given period, duration, and phase.
Parameters
----------
t_model : array-like, `~astropy.units.Quantity`, or `~astropy.time.Time`
Times at which to compute the model.
pe... | BoxLeastSquares.model | repository-level | external |
astropy | 4 | astropy/timeseries/periodograms/bls/core.py | def power(self, period, duration, objective=None, method=None, oversample=10):
"""Compute the periodogram for a set of periods.
Parameters
----------
period : array-like or `~astropy.units.Quantity` ['time']
The periods where the power should be computed
duration... | /usr/src/app/target_test_cases/failed_tests_BoxLeastSquares.power.txt | def power(self, period, duration, objective=None, method=None, oversample=10):
"""Compute the periodogram for a set of periods.
Parameters
----------
period : array-like or `~astropy.units.Quantity` ['time']
The periods where the power should be computed
duration... | BoxLeastSquares.power | repository-level | external |
astropy | 5 | astropy/timeseries/periodograms/bls/core.py | def transit_mask(self, t, period, duration, transit_time):
"""Compute which data points are in transit for a given parameter set.
Parameters
----------
t : array-like or `~astropy.units.Quantity` ['time']
Times where the mask should be evaluated.
period : float o... | /usr/src/app/target_test_cases/failed_tests_BoxLeastSquares.transit_mask.txt | def transit_mask(self, t, period, duration, transit_time):
"""Compute which data points are in transit for a given parameter set.
Parameters
----------
t : array-like or `~astropy.units.Quantity` ['time']
Times where the mask should be evaluated.
period : float o... | BoxLeastSquares.transit_mask | repository-level | external |
astropy | 6 | astropy/nddata/ccddata.py | def to_hdu(
self,
hdu_mask="MASK",
hdu_uncertainty="UNCERT",
hdu_flags=None,
wcs_relax=True,
key_uncertainty_type="UTYPE",
as_image_hdu=False,
hdu_psf="PSFIMAGE",
):
"""Creates an HDUList object from a CCDData object.
Parameters
... | /usr/src/app/target_test_cases/failed_tests_CCDData.to_hdu.txt | def to_hdu(
self,
hdu_mask="MASK",
hdu_uncertainty="UNCERT",
hdu_flags=None,
wcs_relax=True,
key_uncertainty_type="UTYPE",
as_image_hdu=False,
hdu_psf="PSFIMAGE",
):
"""Creates an HDUList object from a CCDData object.
Parameters
... | CCDData.to_hdu | repository-level | external |
astropy | 7 | astropy/uncertainty/core.py | def pdf_histogram(self, **kwargs):
"""
Compute histogram over the samples in the distribution.
Parameters
----------
All keyword arguments are passed into `astropy.stats.histogram`. Note
That some of these options may not be valid for some multidimensional
di... | /usr/src/app/target_test_cases/failed_tests_Distribution.pdf_histogram.txt | def pdf_histogram(self, **kwargs):
"""
Compute histogram over the samples in the distribution.
Parameters
----------
All keyword arguments are passed into `astropy.stats.histogram`. Note
That some of these options may not be valid for some multidimensional
di... | Distribution.pdf_histogram | repository-level | external |
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