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def get_n_splits(self, X, y=None, groups=None):
"""Returns the number of splitting iterations in the cross-validator.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` ... | Returns the number of splitting iterations in the cross-validator.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
y : object
... | get_n_splits | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def get_n_splits(self, X, y=None, groups=None):
"""Returns the number of splitting iterations in the cross-validator.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` ... | Returns the number of splitting iterations in the cross-validator.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
y : object
... | get_n_splits | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def split(self, X, y=None, groups=None):
"""Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number o... | Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
y : array-like of shape (n_samp... | split | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def split(self, X, y, groups=None):
"""Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of fea... | Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Note that providing ``y`` i... | split | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def split(self, X, y=None, groups=None):
"""Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number o... | Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
y : array-like of shape (n_samp... | split | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def _split(self, X):
"""Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
... | Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Yields
------
t... | _split | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def split(self, X, y=None, groups=None):
"""Generates indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number ... | Generates indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
y : array-like of shape (n_sam... | split | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def get_n_splits(self, X=None, y=None, groups=None):
"""Returns the number of splitting iterations in the cross-validator.
Parameters
----------
X : object
Always ignored, exists for compatibility.
``np.zeros(n_samples)`` may be used as a placeholder.
y ... | Returns the number of splitting iterations in the cross-validator.
Parameters
----------
X : object
Always ignored, exists for compatibility.
``np.zeros(n_samples)`` may be used as a placeholder.
y : object
Always ignored, exists for compatibility.
... | get_n_splits | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def split(self, X, y=None, groups=None):
"""Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number o... | Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
y : array-like of shape (n_samp... | split | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def split(self, X, y, groups=None):
"""Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of fea... | Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Note that providing ``y`` i... | split | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def _validate_shuffle_split(n_samples, test_size, train_size, default_test_size=None):
"""
Validation helper to check if the train/test sizes are meaningful w.r.t. the
size of the data (n_samples).
"""
if test_size is None and train_size is None:
test_size = default_test_size
test_size_... |
Validation helper to check if the train/test sizes are meaningful w.r.t. the
size of the data (n_samples).
| _validate_shuffle_split | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def split(self, X=None, y=None, groups=None):
"""Generate indices to split data into training and test set.
Parameters
----------
X : object
Always ignored, exists for compatibility.
y : object
Always ignored, exists for compatibility.
groups : ... | Generate indices to split data into training and test set.
Parameters
----------
X : object
Always ignored, exists for compatibility.
y : object
Always ignored, exists for compatibility.
groups : object
Always ignored, exists for compatibili... | split | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def _split(self):
"""Generate indices to split data into training and test set.
Yields
------
train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.
"""
ind = np.arange(len(self.tes... | Generate indices to split data into training and test set.
Yields
------
train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.
| _split | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def _iter_test_masks(self):
"""Generates boolean masks corresponding to test sets."""
for f in self.unique_folds:
test_index = np.where(self.test_fold == f)[0]
test_mask = np.zeros(len(self.test_fold), dtype=bool)
test_mask[test_index] = True
yield test_ma... | Generates boolean masks corresponding to test sets. | _iter_test_masks | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def check_cv(cv=5, y=None, *, classifier=False):
"""Input checker utility for building a cross-validator.
Parameters
----------
cv : int, cross-validation generator, iterable or None, default=5
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- Non... | Input checker utility for building a cross-validator.
Parameters
----------
cv : int, cross-validation generator, iterable or None, default=5
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 5-fold cross validation,
-... | check_cv | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def train_test_split(
*arrays,
test_size=None,
train_size=None,
random_state=None,
shuffle=True,
stratify=None,
):
"""Split arrays or matrices into random train and test subsets.
Quick utility that wraps input validation,
``next(ShuffleSplit().split(X, y))``, and application to inpu... | Split arrays or matrices into random train and test subsets.
Quick utility that wraps input validation,
``next(ShuffleSplit().split(X, y))``, and application to input data
into a single call for splitting (and optionally subsampling) data into a
one-liner.
Read more in the :ref:`User Guide <cross_... | train_test_split | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def _pprint(params, offset=0, printer=repr):
"""Pretty print the dictionary 'params'
Parameters
----------
params : dict
The dictionary to pretty print
offset : int, default=0
The offset in characters to add at the begin of each line.
printer : callable, default=repr
T... | Pretty print the dictionary 'params'
Parameters
----------
params : dict
The dictionary to pretty print
offset : int, default=0
The offset in characters to add at the begin of each line.
printer : callable, default=repr
The function to convert entries to strings, typically... | _pprint | python | scikit-learn/scikit-learn | sklearn/model_selection/_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py | BSD-3-Clause |
def _check_params_groups_deprecation(fit_params, params, groups, version):
"""A helper function to check deprecations on `groups` and `fit_params`.
# TODO(SLEP6): To be removed when set_config(enable_metadata_routing=False) is not
# possible.
"""
if params is not None and fit_params is not None:
... | A helper function to check deprecations on `groups` and `fit_params`.
# TODO(SLEP6): To be removed when set_config(enable_metadata_routing=False) is not
# possible.
| _check_params_groups_deprecation | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def cross_validate(
estimator,
X,
y=None,
*,
groups=None,
scoring=None,
cv=None,
n_jobs=None,
verbose=0,
params=None,
pre_dispatch="2*n_jobs",
return_train_score=False,
return_estimator=False,
return_indices=False,
error_score=np.nan,
):
"""Evaluate metric... | Evaluate metric(s) by cross-validation and also record fit/score times.
Read more in the :ref:`User Guide <multimetric_cross_validation>`.
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
X : {array-like, sparse matrix} of shape (n_s... | cross_validate | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def _insert_error_scores(results, error_score):
"""Insert error in `results` by replacing them inplace with `error_score`.
This only applies to multimetric scores because `_fit_and_score` will
handle the single metric case.
"""
successful_score = None
failed_indices = []
for i, result in en... | Insert error in `results` by replacing them inplace with `error_score`.
This only applies to multimetric scores because `_fit_and_score` will
handle the single metric case.
| _insert_error_scores | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def _normalize_score_results(scores, scaler_score_key="score"):
"""Creates a scoring dictionary based on the type of `scores`"""
if isinstance(scores[0], dict):
# multimetric scoring
return _aggregate_score_dicts(scores)
# scaler
return {scaler_score_key: scores} | Creates a scoring dictionary based on the type of `scores` | _normalize_score_results | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
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 estimator and compute scores for a given dataset split.
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
X : array-like of shape (n_samples, n_features)
The data to fit.
y : array-like of shape (n_samples,) or (n_samples,... | _fit_and_score | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def _score(estimator, X_test, y_test, scorer, score_params, error_score="raise"):
"""Compute the score(s) of an estimator on a given test set.
Will return a dict of floats if `scorer` is a _MultiMetricScorer, otherwise a single
float is returned.
"""
score_params = {} if score_params is None else s... | Compute the score(s) of an estimator on a given test set.
Will return a dict of floats if `scorer` is a _MultiMetricScorer, otherwise a single
float is returned.
| _score | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def cross_val_predict(
estimator,
X,
y=None,
*,
groups=None,
cv=None,
n_jobs=None,
verbose=0,
params=None,
pre_dispatch="2*n_jobs",
method="predict",
):
"""Generate cross-validated estimates for each input data point.
The data is split according to the cv parameter. ... | Generate cross-validated estimates for each input data point.
The data is split according to the cv parameter. Each sample belongs
to exactly one test set, and its prediction is computed with an
estimator fitted on the corresponding training set.
Passing these predictions into an evaluation metric may... | cross_val_predict | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def _fit_and_predict(estimator, X, y, train, test, fit_params, method):
"""Fit estimator and predict values for a given dataset split.
Read more in the :ref:`User Guide <cross_validation>`.
Parameters
----------
estimator : estimator object implementing 'fit' and 'predict'
The object to us... | Fit estimator and predict values for a given dataset split.
Read more in the :ref:`User Guide <cross_validation>`.
Parameters
----------
estimator : estimator object implementing 'fit' and 'predict'
The object to use to fit the data.
X : array-like of shape (n_samples, n_features)
... | _fit_and_predict | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def _enforce_prediction_order(classes, predictions, n_classes, method):
"""Ensure that prediction arrays have correct column order
When doing cross-validation, if one or more classes are
not present in the subset of data used for training,
then the output prediction array might not have the same
co... | Ensure that prediction arrays have correct column order
When doing cross-validation, if one or more classes are
not present in the subset of data used for training,
then the output prediction array might not have the same
columns as other folds. Use the list of class names
(assumed to be ints) to e... | _enforce_prediction_order | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def _check_is_permutation(indices, n_samples):
"""Check whether indices is a reordering of the array np.arange(n_samples)
Parameters
----------
indices : ndarray
int array to test
n_samples : int
number of expected elements
Returns
-------
is_partition : bool
Tr... | Check whether indices is a reordering of the array np.arange(n_samples)
Parameters
----------
indices : ndarray
int array to test
n_samples : int
number of expected elements
Returns
-------
is_partition : bool
True iff sorted(indices) is np.arange(n)
| _check_is_permutation | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def permutation_test_score(
estimator,
X,
y,
*,
groups=None,
cv=None,
n_permutations=100,
n_jobs=None,
random_state=0,
verbose=0,
scoring=None,
fit_params=None,
params=None,
):
"""Evaluate the significance of a cross-validated score with permutations.
Permute... | Evaluate the significance of a cross-validated score with permutations.
Permutes targets to generate 'randomized data' and compute the empirical
p-value against the null hypothesis that features and targets are
independent.
The p-value represents the fraction of randomized data sets where the
esti... | permutation_test_score | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def _shuffle(y, groups, random_state):
"""Return a shuffled copy of y eventually shuffle among same groups."""
if groups is None:
indices = random_state.permutation(len(y))
else:
indices = np.arange(len(groups))
for group in np.unique(groups):
this_mask = groups == group
... | Return a shuffled copy of y eventually shuffle among same groups. | _shuffle | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def learning_curve(
estimator,
X,
y,
*,
groups=None,
train_sizes=np.linspace(0.1, 1.0, 5),
cv=None,
scoring=None,
exploit_incremental_learning=False,
n_jobs=None,
pre_dispatch="all",
verbose=0,
shuffle=False,
random_state=None,
error_score=np.nan,
return_t... | Learning curve.
Determines cross-validated training and test scores for different training
set sizes.
A cross-validation generator splits the whole dataset k times in training
and test data. Subsets of the training set with varying sizes will be used
to train the estimator and a score for each tra... | learning_curve | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def _translate_train_sizes(train_sizes, n_max_training_samples):
"""Determine absolute sizes of training subsets and validate 'train_sizes'.
Examples:
_translate_train_sizes([0.5, 1.0], 10) -> [5, 10]
_translate_train_sizes([5, 10], 10) -> [5, 10]
Parameters
----------
train_sizes ... | Determine absolute sizes of training subsets and validate 'train_sizes'.
Examples:
_translate_train_sizes([0.5, 1.0], 10) -> [5, 10]
_translate_train_sizes([5, 10], 10) -> [5, 10]
Parameters
----------
train_sizes : array-like of shape (n_ticks,)
Numbers of training examples th... | _translate_train_sizes | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def _incremental_fit_estimator(
estimator,
X,
y,
classes,
train,
test,
train_sizes,
scorer,
return_times,
error_score,
fit_params,
score_params,
):
"""Train estimator on training subsets incrementally and compute scores."""
train_scores, test_scores, fit_times, sc... | Train estimator on training subsets incrementally and compute scores. | _incremental_fit_estimator | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def validation_curve(
estimator,
X,
y,
*,
param_name,
param_range,
groups=None,
cv=None,
scoring=None,
n_jobs=None,
pre_dispatch="all",
verbose=0,
error_score=np.nan,
fit_params=None,
params=None,
):
"""Validation curve.
Determine training and test sc... | Validation curve.
Determine training and test scores for varying parameter values.
Compute scores for an estimator with different values of a specified
parameter. This is similar to grid search with one parameter. However, this
will also compute training scores and is merely a utility for plotting the... | validation_curve | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def _aggregate_score_dicts(scores):
"""Aggregate the list of dict to dict of np ndarray
The aggregated output of _aggregate_score_dicts will be a list of dict
of form [{'prec': 0.1, 'acc':1.0}, {'prec': 0.1, 'acc':1.0}, ...]
Convert it to a dict of array {'prec': np.array([0.1 ...]), ...}
Paramete... | Aggregate the list of dict to dict of np ndarray
The aggregated output of _aggregate_score_dicts will be a list of dict
of form [{'prec': 0.1, 'acc':1.0}, {'prec': 0.1, 'acc':1.0}, ...]
Convert it to a dict of array {'prec': np.array([0.1 ...]), ...}
Parameters
----------
scores : list of dic... | _aggregate_score_dicts | python | scikit-learn/scikit-learn | sklearn/model_selection/_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py | BSD-3-Clause |
def test_fit_and_score_over_thresholds_curve_scorers():
"""Check that `_fit_and_score_over_thresholds` returns thresholds in ascending order
for the different accepted curve scorers."""
X, y = make_classification(n_samples=100, random_state=0)
train_idx, val_idx = np.arange(50), np.arange(50, 100)
c... | Check that `_fit_and_score_over_thresholds` returns thresholds in ascending order
for the different accepted curve scorers. | test_fit_and_score_over_thresholds_curve_scorers | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_fit_and_score_over_thresholds_prefit():
"""Check the behaviour with a prefit classifier."""
X, y = make_classification(n_samples=100, random_state=0)
# `train_idx is None` to indicate that the classifier is prefit
train_idx, val_idx = None, np.arange(50, 100)
classifier = DecisionTreeClass... | Check the behaviour with a prefit classifier. | test_fit_and_score_over_thresholds_prefit | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_fit_and_score_over_thresholds_sample_weight():
"""Check that we dispatch the sample-weight to fit and score the classifier."""
X, y = load_iris(return_X_y=True)
X, y = X[:100], y[:100] # only 2 classes
# create a dataset and repeat twice the sample of class #0
X_repeated, y_repeated = np.... | Check that we dispatch the sample-weight to fit and score the classifier. | test_fit_and_score_over_thresholds_sample_weight | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_fit_and_score_over_thresholds_fit_params(fit_params_type):
"""Check that we pass `fit_params` to the classifier when calling `fit`."""
X, y = make_classification(n_samples=100, random_state=0)
fit_params = {
"a": _convert_container(y, fit_params_type),
"b": _convert_container(y, fit... | Check that we pass `fit_params` to the classifier when calling `fit`. | test_fit_and_score_over_thresholds_fit_params | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_tuned_threshold_classifier_no_binary(data):
"""Check that we raise an informative error message for non-binary problem."""
err_msg = "Only binary classification is supported."
with pytest.raises(ValueError, match=err_msg):
TunedThresholdClassifierCV(LogisticRegression()).fit(*data) | Check that we raise an informative error message for non-binary problem. | test_tuned_threshold_classifier_no_binary | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_tuned_threshold_classifier_conflict_cv_refit(params, err_type, err_msg):
"""Check that we raise an informative error message when `cv` and `refit`
cannot be used together.
"""
X, y = make_classification(n_samples=100, random_state=0)
with pytest.raises(err_type, match=err_msg):
Tune... | Check that we raise an informative error message when `cv` and `refit`
cannot be used together.
| test_tuned_threshold_classifier_conflict_cv_refit | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_threshold_classifier_estimator_response_methods(
ThresholdClassifier, estimator, response_method
):
"""Check that `TunedThresholdClassifierCV` exposes the same response methods as the
underlying estimator.
"""
X, y = make_classification(n_samples=100, random_state=0)
model = ThresholdC... | Check that `TunedThresholdClassifierCV` exposes the same response methods as the
underlying estimator.
| test_threshold_classifier_estimator_response_methods | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_tuned_threshold_classifier_without_constraint_value(response_method):
"""Check that `TunedThresholdClassifierCV` is optimizing a given objective
metric."""
X, y = load_breast_cancer(return_X_y=True)
# remove feature to degrade performances
X = X[:, :5]
# make the problem completely imb... | Check that `TunedThresholdClassifierCV` is optimizing a given objective
metric. | test_tuned_threshold_classifier_without_constraint_value | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_tuned_threshold_classifier_metric_with_parameter():
"""Check that we can pass a metric with a parameter in addition check that
`f_beta` with `beta=1` is equivalent to `f1` and different from `f_beta` with
`beta=2`.
"""
X, y = load_breast_cancer(return_X_y=True)
lr = make_pipeline(Standa... | Check that we can pass a metric with a parameter in addition check that
`f_beta` with `beta=1` is equivalent to `f1` and different from `f_beta` with
`beta=2`.
| test_tuned_threshold_classifier_metric_with_parameter | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_tuned_threshold_classifier_with_string_targets(response_method, metric):
"""Check that targets represented by str are properly managed.
Also, check with several metrics to be sure that `pos_label` is properly
dispatched.
"""
X, y = load_breast_cancer(return_X_y=True)
# Encode numeric ta... | Check that targets represented by str are properly managed.
Also, check with several metrics to be sure that `pos_label` is properly
dispatched.
| test_tuned_threshold_classifier_with_string_targets | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_tuned_threshold_classifier_refit(with_sample_weight, global_random_seed):
"""Check the behaviour of the `refit` parameter."""
rng = np.random.RandomState(global_random_seed)
X, y = make_classification(n_samples=100, random_state=0)
if with_sample_weight:
sample_weight = rng.randn(X.shap... | Check the behaviour of the `refit` parameter. | test_tuned_threshold_classifier_refit | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_tuned_threshold_classifier_cv_zeros_sample_weights_equivalence():
"""Check that passing removing some sample from the dataset `X` is
equivalent to passing a `sample_weight` with a factor 0."""
X, y = load_iris(return_X_y=True)
# Scale the data to avoid any convergence issue
X = StandardScal... | Check that passing removing some sample from the dataset `X` is
equivalent to passing a `sample_weight` with a factor 0. | test_tuned_threshold_classifier_cv_zeros_sample_weights_equivalence | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_tuned_threshold_classifier_thresholds_array():
"""Check that we can pass an array to `thresholds` and it is used as candidate
threshold internally."""
X, y = make_classification(random_state=0)
estimator = LogisticRegression()
thresholds = np.linspace(0, 1, 11)
tuned_model = TunedThresh... | Check that we can pass an array to `thresholds` and it is used as candidate
threshold internally. | test_tuned_threshold_classifier_thresholds_array | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_tuned_threshold_classifier_store_cv_results(store_cv_results):
"""Check that if `cv_results_` exists depending on `store_cv_results`."""
X, y = make_classification(random_state=0)
estimator = LogisticRegression()
tuned_model = TunedThresholdClassifierCV(
estimator, store_cv_results=stor... | Check that if `cv_results_` exists depending on `store_cv_results`. | test_tuned_threshold_classifier_store_cv_results | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_tuned_threshold_classifier_cv_float():
"""Check the behaviour when `cv` is set to a float."""
X, y = make_classification(random_state=0)
# case where `refit=False` and cv is a float: the underlying estimator will be fit
# on the training set given by a ShuffleSplit. We check that we get the sa... | Check the behaviour when `cv` is set to a float. | test_tuned_threshold_classifier_cv_float | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_tuned_threshold_classifier_error_constant_predictor():
"""Check that we raise a ValueError if the underlying classifier returns constant
probabilities such that we cannot find any threshold.
"""
X, y = make_classification(random_state=0)
estimator = DummyClassifier(strategy="constant", cons... | Check that we raise a ValueError if the underlying classifier returns constant
probabilities such that we cannot find any threshold.
| test_tuned_threshold_classifier_error_constant_predictor | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_fixed_threshold_classifier_equivalence_default(response_method):
"""Check that `FixedThresholdClassifier` has the same behaviour as the vanilla
classifier.
"""
X, y = make_classification(random_state=0)
classifier = LogisticRegression().fit(X, y)
classifier_default_threshold = FixedThre... | Check that `FixedThresholdClassifier` has the same behaviour as the vanilla
classifier.
| test_fixed_threshold_classifier_equivalence_default | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_fixed_threshold_classifier(response_method, threshold, pos_label):
"""Check that applying `predict` lead to the same prediction as applying the
threshold to the output of the response method.
"""
X, y = make_classification(n_samples=50, random_state=0)
logistic_regression = LogisticRegressi... | Check that applying `predict` lead to the same prediction as applying the
threshold to the output of the response method.
| test_fixed_threshold_classifier | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_fixed_threshold_classifier_metadata_routing():
"""Check that everything works with metadata routing."""
X, y = make_classification(random_state=0)
sample_weight = np.ones_like(y)
sample_weight[::2] = 2
classifier = LogisticRegression().set_fit_request(sample_weight=True)
classifier.fit(... | Check that everything works with metadata routing. | test_fixed_threshold_classifier_metadata_routing | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_fixed_threshold_classifier_fitted_estimator(method):
"""Check that if the underlying estimator is already fitted, no fit is required."""
X, y = make_classification(random_state=0)
classifier = LogisticRegression().fit(X, y)
fixed_threshold_classifier = FixedThresholdClassifier(estimator=classif... | Check that if the underlying estimator is already fitted, no fit is required. | test_fixed_threshold_classifier_fitted_estimator | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_fixed_threshold_classifier_classes_():
"""Check that the classes_ attribute is properly set."""
X, y = make_classification(random_state=0)
with pytest.raises(
AttributeError, match="The underlying estimator is not fitted yet."
):
FixedThresholdClassifier(estimator=LogisticRegres... | Check that the classes_ attribute is properly set. | test_fixed_threshold_classifier_classes_ | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_classification_threshold.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_classification_threshold.py | BSD-3-Clause |
def test_curve_display_parameters_validation(
pyplot, data, params, err_type, err_msg, CurveDisplay, specific_params
):
"""Check that we raise a proper error when passing invalid parameters."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
with pytest.raises(err_type, match=err_msg... | Check that we raise a proper error when passing invalid parameters. | test_curve_display_parameters_validation | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_plot.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_plot.py | BSD-3-Clause |
def test_learning_curve_display_default_usage(pyplot, data):
"""Check the default usage of the LearningCurveDisplay class."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
train_sizes = [0.3, 0.6, 0.9]
display = LearningCurveDisplay.from_estimator(
estimator, X, y, train_si... | Check the default usage of the LearningCurveDisplay class. | test_learning_curve_display_default_usage | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_plot.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_plot.py | BSD-3-Clause |
def test_validation_curve_display_default_usage(pyplot, data):
"""Check the default usage of the ValidationCurveDisplay class."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
param_name, param_range = "max_depth", [1, 3, 5]
display = ValidationCurveDisplay.from_estimator(
... | Check the default usage of the ValidationCurveDisplay class. | test_validation_curve_display_default_usage | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_plot.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_plot.py | BSD-3-Clause |
def test_curve_display_negate_score(pyplot, data, CurveDisplay, specific_params):
"""Check the behaviour of the `negate_score` parameter calling `from_estimator` and
`plot`.
"""
X, y = data
estimator = DecisionTreeClassifier(max_depth=1, random_state=0)
negate_score = False
display = CurveD... | Check the behaviour of the `negate_score` parameter calling `from_estimator` and
`plot`.
| test_curve_display_negate_score | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_plot.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_plot.py | BSD-3-Clause |
def test_curve_display_score_name(
pyplot, data, score_name, ylabel, CurveDisplay, specific_params
):
"""Check that we can overwrite the default score name shown on the y-axis."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
display = CurveDisplay.from_estimator(
estimator... | Check that we can overwrite the default score name shown on the y-axis. | test_curve_display_score_name | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_plot.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_plot.py | BSD-3-Clause |
def test_learning_curve_display_score_type(pyplot, data, std_display_style):
"""Check the behaviour of setting the `score_type` parameter."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
train_sizes = [0.3, 0.6, 0.9]
train_sizes_abs, train_scores, test_scores = learning_curve(
... | Check the behaviour of setting the `score_type` parameter. | test_learning_curve_display_score_type | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_plot.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_plot.py | BSD-3-Clause |
def test_curve_display_xscale_auto(
pyplot, data, CurveDisplay, specific_params, expected_xscale
):
"""Check the behaviour of the x-axis scaling depending on the data provided."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
display = CurveDisplay.from_estimator(estimator, X, y, *... | Check the behaviour of the x-axis scaling depending on the data provided. | test_curve_display_xscale_auto | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_plot.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_plot.py | BSD-3-Clause |
def test_curve_display_std_display_style(pyplot, data, CurveDisplay, specific_params):
"""Check the behaviour of the parameter `std_display_style`."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
import matplotlib as mpl
std_display_style = None
display = CurveDisplay.from_es... | Check the behaviour of the parameter `std_display_style`. | test_curve_display_std_display_style | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_plot.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_plot.py | BSD-3-Clause |
def test_curve_display_plot_kwargs(pyplot, data, CurveDisplay, specific_params):
"""Check the behaviour of the different plotting keyword arguments: `line_kw`,
`fill_between_kw`, and `errorbar_kw`."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
std_display_style = "fill_between"
... | Check the behaviour of the different plotting keyword arguments: `line_kw`,
`fill_between_kw`, and `errorbar_kw`. | test_curve_display_plot_kwargs | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_plot.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_plot.py | BSD-3-Clause |
def test_validation_curve_xscale_from_param_range_provided_as_a_list(
pyplot, data, param_range, xscale
):
"""Check the induced xscale from the provided param_range values."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
param_name = "max_depth"
display = ValidationCurveDispla... | Check the induced xscale from the provided param_range values. | test_validation_curve_xscale_from_param_range_provided_as_a_list | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_plot.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_plot.py | BSD-3-Clause |
def test_refit_callable():
"""
Test refit=callable, which adds flexibility in identifying the
"best" estimator.
"""
def refit_callable(cv_results):
"""
A dummy function tests `refit=callable` interface.
Return the index of a model that has the least
`mean_test_score`... |
Test refit=callable, which adds flexibility in identifying the
"best" estimator.
| test_refit_callable | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def refit_callable(cv_results):
"""
A dummy function tests `refit=callable` interface.
Return the index of a model that has the least
`mean_test_score`.
"""
# Fit a dummy clf with `refit=True` to get a list of keys in
# clf.cv_results_.
X, y = make_classif... |
A dummy function tests `refit=callable` interface.
Return the index of a model that has the least
`mean_test_score`.
| refit_callable | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_refit_callable_invalid_type():
"""
Test implementation catches the errors when 'best_index_' returns an
invalid result.
"""
def refit_callable_invalid_type(cv_results):
"""
A dummy function tests when returned 'best_index_' is not integer.
"""
return None
... |
Test implementation catches the errors when 'best_index_' returns an
invalid result.
| test_refit_callable_invalid_type | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_refit_callable_out_bound(out_bound_value, search_cv):
"""
Test implementation catches the errors when 'best_index_' returns an
out of bound result.
"""
def refit_callable_out_bound(cv_results):
"""
A dummy function tests when returned 'best_index_' is out of bounds.
... |
Test implementation catches the errors when 'best_index_' returns an
out of bound result.
| test_refit_callable_out_bound | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_refit_callable_multi_metric():
"""
Test refit=callable in multiple metric evaluation setting
"""
def refit_callable(cv_results):
"""
A dummy function tests `refit=callable` interface.
Return the index of a model that has the least
`mean_test_prec`.
"""
... |
Test refit=callable in multiple metric evaluation setting
| test_refit_callable_multi_metric | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def compare_cv_results_multimetric_with_single(search_multi, search_acc, search_rec):
"""Compare multi-metric cv_results with the ensemble of multiple
single metric cv_results from single metric grid/random search"""
assert search_multi.multimetric_
assert_array_equal(sorted(search_multi.scorer_), ("ac... | Compare multi-metric cv_results with the ensemble of multiple
single metric cv_results from single metric grid/random search | compare_cv_results_multimetric_with_single | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit):
"""Compare refit multi-metric search methods with single metric methods"""
assert search_acc.refit == refit
if refit:
assert search_multi.refit == "accuracy"
else:
assert not search_multi.refit
return... | Compare refit multi-metric search methods with single metric methods | compare_refit_methods_when_refit_with_acc | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_unsupported_sample_weight_scorer():
"""Checks that fitting with sample_weight raises a warning if the scorer does not
support sample_weight"""
def fake_score_func(y_true, y_pred):
"Fake scoring function that does not support sample_weight"
return 0.5
fake_scorer = make_scorer(... | Checks that fitting with sample_weight raises a warning if the scorer does not
support sample_weight | test_unsupported_sample_weight_scorer | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_search_cv_pairwise_property_delegated_to_base_estimator(pairwise):
"""
Test implementation of BaseSearchCV has the pairwise tag
which matches the pairwise tag of its estimator.
This test make sure pairwise tag is delegated to the base estimator.
Non-regression test for issue #13920.
""... |
Test implementation of BaseSearchCV has the pairwise tag
which matches the pairwise tag of its estimator.
This test make sure pairwise tag is delegated to the base estimator.
Non-regression test for issue #13920.
| test_search_cv_pairwise_property_delegated_to_base_estimator | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_search_cv__pairwise_property_delegated_to_base_estimator():
"""
Test implementation of BaseSearchCV has the pairwise property
which matches the pairwise tag of its estimator.
This test make sure pairwise tag is delegated to the base estimator.
Non-regression test for issue #13920.
"""
... |
Test implementation of BaseSearchCV has the pairwise property
which matches the pairwise tag of its estimator.
This test make sure pairwise tag is delegated to the base estimator.
Non-regression test for issue #13920.
| test_search_cv__pairwise_property_delegated_to_base_estimator | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_search_cv_pairwise_property_equivalence_of_precomputed():
"""
Test implementation of BaseSearchCV has the pairwise tag
which matches the pairwise tag of its estimator.
This test ensures the equivalence of 'precomputed'.
Non-regression test for issue #13920.
"""
n_samples = 50
n... |
Test implementation of BaseSearchCV has the pairwise tag
which matches the pairwise tag of its estimator.
This test ensures the equivalence of 'precomputed'.
Non-regression test for issue #13920.
| test_search_cv_pairwise_property_equivalence_of_precomputed | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_search_cv_verbose_3(capsys, return_train_score):
"""Check that search cv with verbose>2 shows the score for single
metrics. non-regression test for #19658."""
X, y = make_classification(n_samples=100, n_classes=2, flip_y=0.2, random_state=0)
clf = LinearSVC(random_state=0)
grid = {"C": [0.1... | Check that search cv with verbose>2 shows the score for single
metrics. non-regression test for #19658. | test_search_cv_verbose_3 | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_search_html_repr():
"""Test different HTML representations for GridSearchCV."""
X, y = make_classification(random_state=42)
pipeline = Pipeline([("scale", StandardScaler()), ("clf", DummyClassifier())])
param_grid = {"clf": [DummyClassifier(), LogisticRegression()]}
# Unfitted shows the o... | Test different HTML representations for GridSearchCV. | test_search_html_repr | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_multi_metric_search_forwards_metadata(SearchCV, param_search):
"""Test that *SearchCV forwards metadata correctly when passed multiple metrics."""
X, y = make_classification(random_state=42)
n_samples = _num_samples(X)
rng = np.random.RandomState(0)
score_weights = rng.rand(n_samples)
s... | Test that *SearchCV forwards metadata correctly when passed multiple metrics. | test_multi_metric_search_forwards_metadata | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_score_rejects_params_with_no_routing_enabled(SearchCV, param_search):
"""*SearchCV should reject **params when metadata routing is not enabled
since this is added only when routing is enabled."""
X, y = make_classification(random_state=42)
est = LinearSVC()
param_grid_search = {param_search... | *SearchCV should reject **params when metadata routing is not enabled
since this is added only when routing is enabled. | test_score_rejects_params_with_no_routing_enabled | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_cv_results_dtype_issue_29074():
"""Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/29074"""
class MetaEstimator(BaseEstimator, ClassifierMixin):
def __init__(
self,
base_clf,
parameter1=None,
parameter2=None,
... | Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/29074 | test_cv_results_dtype_issue_29074 | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_search_with_estimators_issue_29157():
"""Check cv_results_ for estimators with a `dtype` parameter, e.g. OneHotEncoder."""
pd = pytest.importorskip("pandas")
df = pd.DataFrame(
{
"numeric_1": [1, 2, 3, 4, 5],
"object_1": ["a", "a", "a", "a", "a"],
"target... | Check cv_results_ for estimators with a `dtype` parameter, e.g. OneHotEncoder. | test_search_with_estimators_issue_29157 | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_cv_results_multi_size_array():
"""Check that GridSearchCV works with params that are arrays of different sizes.
Non-regression test for #29277.
"""
n_features = 10
X, y = make_classification(n_features=10)
spline_reg_pipe = make_pipeline(
SplineTransformer(extrapolation="perio... | Check that GridSearchCV works with params that are arrays of different sizes.
Non-regression test for #29277.
| test_cv_results_multi_size_array | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_search.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_search.py | BSD-3-Clause |
def test_train_test_split_32bit_overflow():
"""Check for integer overflow on 32-bit platforms.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/20774
"""
# A number 'n' big enough for expression 'n * n * train_size' to cause
# an overflow for signed 32-bit integer
... | Check for integer overflow on 32-bit platforms.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/20774
| test_train_test_split_32bit_overflow | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_split.py | BSD-3-Clause |
def test_splitter_set_split_request(cv):
"""Check set_split_request is defined for group splitters and not for others."""
if cv in GROUP_SPLITTERS:
assert hasattr(cv, "set_split_request")
elif cv in NO_GROUP_SPLITTERS:
assert not hasattr(cv, "set_split_request") | Check set_split_request is defined for group splitters and not for others. | test_splitter_set_split_request | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_split.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_split.py | BSD-3-Clause |
def test_nan_handling(HalvingSearch, fail_at):
"""Check the selection of the best scores in presence of failure represented by
NaN values."""
n_samples = 1_000
X, y = make_classification(n_samples=n_samples, random_state=0)
search = HalvingSearch(
SometimesFailClassifier(),
{f"fail_... | Check the selection of the best scores in presence of failure represented by
NaN values. | test_nan_handling | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_successive_halving.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_successive_halving.py | BSD-3-Clause |
def test_min_resources_null(SearchCV):
"""Check that we raise an error if the minimum resources is set to 0."""
base_estimator = FastClassifier()
param_grid = {"a": [1]}
X = np.empty(0).reshape(0, 3)
search = SearchCV(base_estimator, param_grid, min_resources="smallest")
err_msg = "min_resourc... | Check that we raise an error if the minimum resources is set to 0. | test_min_resources_null | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_successive_halving.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_successive_halving.py | BSD-3-Clause |
def test_select_best_index(SearchCV):
"""Check the selection strategy of the halving search."""
results = { # this isn't a 'real world' result dict
"iter": np.array([0, 0, 0, 0, 1, 1, 2, 2, 2]),
"mean_test_score": np.array([4, 3, 5, 1, 11, 10, 5, 6, 9]),
"params": np.array(["a", "b", "c... | Check the selection strategy of the halving search. | test_select_best_index | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_successive_halving.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_successive_halving.py | BSD-3-Clause |
def test_halving_random_search_list_of_dicts():
"""Check the behaviour of the `HalvingRandomSearchCV` with `param_distribution`
being a list of dictionary.
"""
X, y = make_classification(n_samples=150, n_features=4, random_state=42)
params = [
{"kernel": ["rbf"], "C": expon(scale=10), "gamm... | Check the behaviour of the `HalvingRandomSearchCV` with `param_distribution`
being a list of dictionary.
| test_halving_random_search_list_of_dicts | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_successive_halving.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_successive_halving.py | BSD-3-Clause |
def fit(
self,
X,
Y=None,
sample_weight=None,
class_prior=None,
sparse_sample_weight=None,
sparse_param=None,
dummy_int=None,
dummy_str=None,
dummy_obj=None,
callback=None,
):
"""The dummy arguments are to test that this... | The dummy arguments are to test that this fit function can
accept non-array arguments through cross-validation, such as:
- int
- str (this is actually array-like)
- object
- function
| fit | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_validation.py | BSD-3-Clause |
def check_cross_val_predict_binary(est, X, y, method):
"""Helper for tests of cross_val_predict with binary classification"""
cv = KFold(n_splits=3, shuffle=False)
# Generate expected outputs
if y.ndim == 1:
exp_shape = (len(X),) if method == "decision_function" else (len(X), 2)
else:
... | Helper for tests of cross_val_predict with binary classification | check_cross_val_predict_binary | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_validation.py | BSD-3-Clause |
def check_cross_val_predict_multiclass(est, X, y, method):
"""Helper for tests of cross_val_predict with multiclass classification"""
cv = KFold(n_splits=3, shuffle=False)
# Generate expected outputs
float_min = np.finfo(np.float64).min
default_values = {
"decision_function": float_min,
... | Helper for tests of cross_val_predict with multiclass classification | check_cross_val_predict_multiclass | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_validation.py | BSD-3-Clause |
def check_cross_val_predict_multilabel(est, X, y, method):
"""Check the output of cross_val_predict for 2D targets using
Estimators which provide a predictions as a list with one
element per class.
"""
cv = KFold(n_splits=3, shuffle=False)
# Create empty arrays of the correct size to hold outpu... | Check the output of cross_val_predict for 2D targets using
Estimators which provide a predictions as a list with one
element per class.
| check_cross_val_predict_multilabel | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_validation.py | BSD-3-Clause |
def test_learning_curve_partial_fit_regressors():
"""Check that regressors with partial_fit is supported.
Non-regression test for #22981.
"""
X, y = make_regression(random_state=42)
# Does not error
learning_curve(MLPRegressor(), X, y, exploit_incremental_learning=True, cv=2) | Check that regressors with partial_fit is supported.
Non-regression test for #22981.
| test_learning_curve_partial_fit_regressors | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_validation.py | BSD-3-Clause |
def test_learning_curve_some_failing_fits_warning(global_random_seed):
"""Checks for fit failures in `learning_curve` and raises the required warning"""
X, y = make_classification(
n_samples=30,
n_classes=3,
n_informative=6,
shuffle=False,
random_state=global_random_seed... | Checks for fit failures in `learning_curve` and raises the required warning | test_learning_curve_some_failing_fits_warning | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_validation.py | BSD-3-Clause |
def test_fit_param_deprecation(func, extra_args):
"""Check that we warn about deprecating `fit_params`."""
with pytest.warns(FutureWarning, match="`fit_params` is deprecated"):
func(
estimator=ConsumingClassifier(), X=X, y=y, cv=2, fit_params={}, **extra_args
)
with pytest.raise... | Check that we warn about deprecating `fit_params`. | test_fit_param_deprecation | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_validation.py | BSD-3-Clause |
def test_groups_with_routing_validation(func, extra_args):
"""Check that we raise an error if `groups` are passed to the cv method instead
of `params` when metadata routing is enabled.
"""
with pytest.raises(ValueError, match="`groups` can only be passed if"):
func(
estimator=Consumi... | Check that we raise an error if `groups` are passed to the cv method instead
of `params` when metadata routing is enabled.
| test_groups_with_routing_validation | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_validation.py | BSD-3-Clause |
def test_cross_validate_params_none(func, extra_args):
"""Test that no errors are raised when passing `params=None`, which is the
default value.
Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/30447
"""
X, y = make_classification(n_samples=100, n_classes=2, random_state=... | Test that no errors are raised when passing `params=None`, which is the
default value.
Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/30447
| test_cross_validate_params_none | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_validation.py | BSD-3-Clause |
def test_passed_unrequested_metadata(func, extra_args):
"""Check that we raise an error when passing metadata that is not
requested."""
err_msg = re.escape(
"[metadata] are passed but are not explicitly set as requested or not "
"requested for ConsumingClassifier.fit, which is used within"
... | Check that we raise an error when passing metadata that is not
requested. | test_passed_unrequested_metadata | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_validation.py | BSD-3-Clause |
def test_validation_functions_routing(func, extra_args):
"""Check that the respective cv method is properly dispatching the metadata
to the consumer."""
scorer_registry = _Registry()
scorer = ConsumingScorer(registry=scorer_registry).set_score_request(
sample_weight="score_weights", metadata="sc... | Check that the respective cv method is properly dispatching the metadata
to the consumer. | test_validation_functions_routing | python | scikit-learn/scikit-learn | sklearn/model_selection/tests/test_validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/tests/test_validation.py | BSD-3-Clause |
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