code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def predict(self, X, **predict_params):
"""Predict using the base regressor, applying inverse.
The regressor is used to predict and the `inverse_func` or
`inverse_transform` is applied before returning the prediction.
Parameters
----------
X : {array-like, sparse matrix... | Predict using the base regressor, applying inverse.
The regressor is used to predict and the `inverse_func` or
`inverse_transform` is applied before returning the prediction.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Samp... | predict | python | scikit-learn/scikit-learn | sklearn/compose/_target.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/_target.py | BSD-3-Clause |
def n_features_in_(self):
"""Number of features seen during :term:`fit`."""
# For consistency with other estimators we raise a AttributeError so
# that hasattr() returns False the estimator isn't fitted.
try:
check_is_fitted(self)
except NotFittedError as nfe:
... | Number of features seen during :term:`fit`. | n_features_in_ | python | scikit-learn/scikit-learn | sklearn/compose/_target.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/_target.py | BSD-3-Clause |
def get_metadata_routing(self):
"""Get metadata routing of this object.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
.. versionadded:: 1.6
Returns
-------
routing : MetadataRouter
A :class:`~sklearn.utils.met... | Get metadata routing of this object.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
.. versionadded:: 1.6
Returns
-------
routing : MetadataRouter
A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating... | get_metadata_routing | python | scikit-learn/scikit-learn | sklearn/compose/_target.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/_target.py | BSD-3-Clause |
def test_column_transformer_remainder_dtypes(cols1, cols2, expected_remainder_cols):
"""Check that the remainder columns format matches the format of the other
columns when they're all strings or masks.
"""
X = np.ones((1, 3))
if isinstance(cols1, list) and isinstance(cols1[0], str):
pd = p... | Check that the remainder columns format matches the format of the other
columns when they're all strings or masks.
| test_column_transformer_remainder_dtypes | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_force_int_remainder_cols_deprecation(force_int_remainder_cols):
"""Check that ColumnTransformer raises a FutureWarning when
force_int_remainder_cols is set.
"""
X = np.ones((1, 3))
ct = ColumnTransformer(
[("T1", Trans(), [0]), ("T2", Trans(), [1])],
remainder="passthrough",... | Check that ColumnTransformer raises a FutureWarning when
force_int_remainder_cols is set.
| test_force_int_remainder_cols_deprecation | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_feature_names_out_pandas(selector):
"""Checks name when selecting only the second column"""
pd = pytest.importorskip("pandas")
df = pd.DataFrame({"col1": ["a", "a", "b"], "col2": ["z", "z", "z"]})
ct = ColumnTransformer([("ohe", OneHotEncoder(), selector)])
ct.fit(df)
assert_array_equa... | Checks name when selecting only the second column | test_feature_names_out_pandas | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_feature_names_out_non_pandas(selector):
"""Checks name when selecting the second column with numpy array"""
X = [["a", "z"], ["a", "z"], ["b", "z"]]
ct = ColumnTransformer([("ohe", OneHotEncoder(), selector)])
ct.fit(X)
assert_array_equal(ct.get_feature_names_out(), ["ohe__x1_z"]) | Checks name when selecting the second column with numpy array | test_feature_names_out_non_pandas | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_column_transformer_reordered_column_names_remainder(
explicit_colname, remainder
):
"""Test the interaction between remainder and column transformer"""
pd = pytest.importorskip("pandas")
X_fit_array = np.array([[0, 1, 2], [2, 4, 6]]).T
X_fit_df = pd.DataFrame(X_fit_array, columns=["first",... | Test the interaction between remainder and column transformer | test_column_transformer_reordered_column_names_remainder | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_feature_name_validation_missing_columns_drop_passthough():
"""Test the interaction between {'drop', 'passthrough'} and
missing column names."""
pd = pytest.importorskip("pandas")
X = np.ones(shape=(3, 4))
df = pd.DataFrame(X, columns=["a", "b", "c", "d"])
df_dropped = df.drop("c", axi... | Test the interaction between {'drop', 'passthrough'} and
missing column names. | test_feature_name_validation_missing_columns_drop_passthough | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_feature_names_in_():
"""Feature names are stored in column transformer.
Column transformer deliberately does not check for column name consistency.
It only checks that the non-dropped names seen in `fit` are seen
in `transform`. This behavior is already tested in
`test_feature_name_validat... | Feature names are stored in column transformer.
Column transformer deliberately does not check for column name consistency.
It only checks that the non-dropped names seen in `fit` are seen
in `transform`. This behavior is already tested in
`test_feature_name_validation_missing_columns_drop_passthough` | test_feature_names_in_ | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_column_transform_set_output_mixed(remainder, fit_transform):
"""Check ColumnTransformer outputs mixed types correctly."""
pd = pytest.importorskip("pandas")
df = pd.DataFrame(
{
"pet": pd.Series(["dog", "cat", "snake"], dtype="category"),
"color": pd.Series(["green",... | Check ColumnTransformer outputs mixed types correctly. | test_column_transform_set_output_mixed | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_transformers_with_pandas_out_but_not_feature_names_out(
trans_1, expected_verbose_names, expected_non_verbose_names
):
"""Check that set_config(transform="pandas") is compatible with more transformers.
Specifically, if transformers returns a DataFrame, but does not define
`get_feature_names_ou... | Check that set_config(transform="pandas") is compatible with more transformers.
Specifically, if transformers returns a DataFrame, but does not define
`get_feature_names_out`.
| test_transformers_with_pandas_out_but_not_feature_names_out | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_empty_selection_pandas_output(empty_selection):
"""Check that pandas output works when there is an empty selection.
Non-regression test for gh-25487
"""
pd = pytest.importorskip("pandas")
X = pd.DataFrame([[1.0, 2.2], [3.0, 1.0]], columns=["a", "b"])
ct = ColumnTransformer(
[
... | Check that pandas output works when there is an empty selection.
Non-regression test for gh-25487
| test_empty_selection_pandas_output | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_raise_error_if_index_not_aligned():
"""Check column transformer raises error if indices are not aligned.
Non-regression test for gh-26210.
"""
pd = pytest.importorskip("pandas")
X = pd.DataFrame([[1.0, 2.2], [3.0, 1.0]], columns=["a", "b"], index=[8, 3])
reset_index_transformer = Func... | Check column transformer raises error if indices are not aligned.
Non-regression test for gh-26210.
| test_raise_error_if_index_not_aligned | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_remainder_set_output():
"""Check that the output is set for the remainder.
Non-regression test for #26306.
"""
pd = pytest.importorskip("pandas")
df = pd.DataFrame({"a": [True, False, True], "b": [1, 2, 3]})
ct = make_column_transformer(
(VarianceThreshold(), make_column_sele... | Check that the output is set for the remainder.
Non-regression test for #26306.
| test_remainder_set_output | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_transform_pd_na():
"""Check behavior when a tranformer's output contains pandas.NA
It should raise an error unless the output config is set to 'pandas'.
"""
pd = pytest.importorskip("pandas")
if not hasattr(pd, "Float64Dtype"):
pytest.skip(
"The issue with pd.NA tested ... | Check behavior when a tranformer's output contains pandas.NA
It should raise an error unless the output config is set to 'pandas'.
| test_transform_pd_na | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_dataframe_different_dataframe_libraries():
"""Check fitting and transforming on pandas and polars dataframes."""
pd = pytest.importorskip("pandas")
pl = pytest.importorskip("polars")
X_train_np = np.array([[0, 1], [2, 4], [4, 5]])
X_test_np = np.array([[1, 2], [1, 3], [2, 3]])
# Fit on... | Check fitting and transforming on pandas and polars dataframes. | test_dataframe_different_dataframe_libraries | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_column_transformer_remainder_passthrough_naming_consistency(transform_output):
"""Check that when `remainder="passthrough"`, inconsistent naming is handled
correctly by the underlying `FunctionTransformer`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/28232
... | Check that when `remainder="passthrough"`, inconsistent naming is handled
correctly by the underlying `FunctionTransformer`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/28232
| test_column_transformer_remainder_passthrough_naming_consistency | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_column_transformer_column_renaming(dataframe_lib):
"""Check that we properly rename columns when using `ColumnTransformer` and
selected columns are redundant between transformers.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/28260
"""
lib = pytest.import... | Check that we properly rename columns when using `ColumnTransformer` and
selected columns are redundant between transformers.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/28260
| test_column_transformer_column_renaming | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_column_transformer_error_with_duplicated_columns(dataframe_lib):
"""Check that we raise an error when using `ColumnTransformer` and
the columns names are duplicated between transformers."""
lib = pytest.importorskip(dataframe_lib)
df = lib.DataFrame({"x1": [1, 2, 3], "x2": [10, 20, 30], "x3": ... | Check that we raise an error when using `ColumnTransformer` and
the columns names are duplicated between transformers. | test_column_transformer_error_with_duplicated_columns | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_column_transformer_auto_memmap():
"""Check that ColumnTransformer works in parallel with joblib's auto-memmapping.
non-regression test for issue #28781
"""
X = np.random.RandomState(0).uniform(size=(3, 4))
scaler = StandardScaler(copy=False)
transformer = ColumnTransformer(
t... | Check that ColumnTransformer works in parallel with joblib's auto-memmapping.
non-regression test for issue #28781
| test_column_transformer_auto_memmap | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_routing_passed_metadata_not_supported(method):
"""Test that the right error message is raised when metadata is passed while
not supported when `enable_metadata_routing=False`."""
X = np.array([[0, 1, 2], [2, 4, 6]]).T
y = [1, 2, 3]
trs = ColumnTransformer([("trans", Trans(), [0])]).fit(X, ... | Test that the right error message is raised when metadata is passed while
not supported when `enable_metadata_routing=False`. | test_routing_passed_metadata_not_supported | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_metadata_routing_for_column_transformer(method):
"""Test that metadata is routed correctly for column transformer."""
X = np.array([[0, 1, 2], [2, 4, 6]]).T
y = [1, 2, 3]
registry = _Registry()
sample_weight, metadata = [1], "a"
trs = ColumnTransformer(
[
(
... | Test that metadata is routed correctly for column transformer. | test_metadata_routing_for_column_transformer | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_metadata_routing_no_fit_transform():
"""Test metadata routing when the sub-estimator doesn't implement
``fit_transform``."""
class NoFitTransform(BaseEstimator):
def fit(self, X, y=None, sample_weight=None, metadata=None):
assert sample_weight
assert metadata
... | Test metadata routing when the sub-estimator doesn't implement
``fit_transform``. | test_metadata_routing_no_fit_transform | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_metadata_routing_error_for_column_transformer(method):
"""Test that the right error is raised when metadata is not requested."""
X = np.array([[0, 1, 2], [2, 4, 6]]).T
y = [1, 2, 3]
sample_weight, metadata = [1], "a"
trs = ColumnTransformer([("trans", ConsumingTransformer(), [0])])
err... | Test that the right error is raised when metadata is not requested. | test_metadata_routing_error_for_column_transformer | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_column_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_column_transformer.py | BSD-3-Clause |
def test_transform_target_regressor_not_warns_with_global_output_set(output_format):
"""Test that TransformedTargetRegressor will not raise warnings if
set_config(transform_output="pandas"/"polars") is set globally; regression test for
issue #29361."""
X, y = datasets.make_regression()
y = np.abs(y)... | Test that TransformedTargetRegressor will not raise warnings if
set_config(transform_output="pandas"/"polars") is set globally; regression test for
issue #29361. | test_transform_target_regressor_not_warns_with_global_output_set | python | scikit-learn/scikit-learn | sklearn/compose/tests/test_target.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/compose/tests/test_target.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Fit the EllipticEnvelope model.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : Ignored
Not used, present for API consistency by convention.
Returns
-------
se... | Fit the EllipticEnvelope model.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : Ignored
Not used, present for API consistency by convention.
Returns
-------
self : object
Returns the i... | fit | python | scikit-learn/scikit-learn | sklearn/covariance/_elliptic_envelope.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_elliptic_envelope.py | BSD-3-Clause |
def decision_function(self, X):
"""Compute the decision function of the given observations.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
Returns
-------
decision : ndarray of shape (n_samples,)
De... | Compute the decision function of the given observations.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
Returns
-------
decision : ndarray of shape (n_samples,)
Decision function of the samples.
... | decision_function | python | scikit-learn/scikit-learn | sklearn/covariance/_elliptic_envelope.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_elliptic_envelope.py | BSD-3-Clause |
def predict(self, X):
"""
Predict labels (1 inlier, -1 outlier) of X according to fitted model.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
Returns
-------
is_inlier : ndarray of shape (n_samples,)
... |
Predict labels (1 inlier, -1 outlier) of X according to fitted model.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix.
Returns
-------
is_inlier : ndarray of shape (n_samples,)
Returns -1 for anomali... | predict | python | scikit-learn/scikit-learn | sklearn/covariance/_elliptic_envelope.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_elliptic_envelope.py | BSD-3-Clause |
def log_likelihood(emp_cov, precision):
"""Compute the sample mean of the log_likelihood under a covariance model.
Computes the empirical expected log-likelihood, allowing for universal
comparison (beyond this software package), and accounts for normalization
terms and scaling.
Parameters
----... | Compute the sample mean of the log_likelihood under a covariance model.
Computes the empirical expected log-likelihood, allowing for universal
comparison (beyond this software package), and accounts for normalization
terms and scaling.
Parameters
----------
emp_cov : ndarray of shape (n_featur... | log_likelihood | python | scikit-learn/scikit-learn | sklearn/covariance/_empirical_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_empirical_covariance.py | BSD-3-Clause |
def empirical_covariance(X, *, assume_centered=False):
"""Compute the Maximum likelihood covariance estimator.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Data from which to compute the covariance estimate.
assume_centered : bool, default=False
If `True`, dat... | Compute the Maximum likelihood covariance estimator.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Data from which to compute the covariance estimate.
assume_centered : bool, default=False
If `True`, data will not be centered before computation.
Useful when... | empirical_covariance | python | scikit-learn/scikit-learn | sklearn/covariance/_empirical_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_empirical_covariance.py | BSD-3-Clause |
def _set_covariance(self, covariance):
"""Saves the covariance and precision estimates
Storage is done accordingly to `self.store_precision`.
Precision stored only if invertible.
Parameters
----------
covariance : array-like of shape (n_features, n_features)
... | Saves the covariance and precision estimates
Storage is done accordingly to `self.store_precision`.
Precision stored only if invertible.
Parameters
----------
covariance : array-like of shape (n_features, n_features)
Estimated covariance matrix to be stored, and fro... | _set_covariance | python | scikit-learn/scikit-learn | sklearn/covariance/_empirical_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_empirical_covariance.py | BSD-3-Clause |
def get_precision(self):
"""Getter for the precision matrix.
Returns
-------
precision_ : array-like of shape (n_features, n_features)
The precision matrix associated to the current covariance object.
"""
if self.store_precision:
precision = self.... | Getter for the precision matrix.
Returns
-------
precision_ : array-like of shape (n_features, n_features)
The precision matrix associated to the current covariance object.
| get_precision | python | scikit-learn/scikit-learn | sklearn/covariance/_empirical_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_empirical_covariance.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Fit the maximum likelihood covariance estimator to X.
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 :... | Fit the maximum likelihood covariance estimator to X.
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 : Ignored
Not used, presen... | fit | python | scikit-learn/scikit-learn | sklearn/covariance/_empirical_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_empirical_covariance.py | BSD-3-Clause |
def score(self, X_test, y=None):
"""Compute the log-likelihood of `X_test` under the estimated Gaussian model.
The Gaussian model is defined by its mean and covariance matrix which are
represented respectively by `self.location_` and `self.covariance_`.
Parameters
----------
... | Compute the log-likelihood of `X_test` under the estimated Gaussian model.
The Gaussian model is defined by its mean and covariance matrix which are
represented respectively by `self.location_` and `self.covariance_`.
Parameters
----------
X_test : array-like of shape (n_sample... | score | python | scikit-learn/scikit-learn | sklearn/covariance/_empirical_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_empirical_covariance.py | BSD-3-Clause |
def error_norm(self, comp_cov, norm="frobenius", scaling=True, squared=True):
"""Compute the Mean Squared Error between two covariance estimators.
Parameters
----------
comp_cov : array-like of shape (n_features, n_features)
The covariance to compare with.
norm : {"... | Compute the Mean Squared Error between two covariance estimators.
Parameters
----------
comp_cov : array-like of shape (n_features, n_features)
The covariance to compare with.
norm : {"frobenius", "spectral"}, default="frobenius"
The type of norm used to compute... | error_norm | python | scikit-learn/scikit-learn | sklearn/covariance/_empirical_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_empirical_covariance.py | BSD-3-Clause |
def mahalanobis(self, X):
"""Compute the squared Mahalanobis distances of given observations.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The observations, the Mahalanobis distances of the which we
compute. Observations are assumed to be... | Compute the squared Mahalanobis distances of given observations.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The observations, the Mahalanobis distances of the which we
compute. Observations are assumed to be drawn from the same
dist... | mahalanobis | python | scikit-learn/scikit-learn | sklearn/covariance/_empirical_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_empirical_covariance.py | BSD-3-Clause |
def _objective(mle, precision_, alpha):
"""Evaluation of the graphical-lasso objective function
the objective function is made of a shifted scaled version of the
normalized log-likelihood (i.e. its empirical mean over the samples) and a
penalisation term to promote sparsity
"""
p = precision_.s... | Evaluation of the graphical-lasso objective function
the objective function is made of a shifted scaled version of the
normalized log-likelihood (i.e. its empirical mean over the samples) and a
penalisation term to promote sparsity
| _objective | python | scikit-learn/scikit-learn | sklearn/covariance/_graph_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_graph_lasso.py | BSD-3-Clause |
def _dual_gap(emp_cov, precision_, alpha):
"""Expression of the dual gap convergence criterion
The specific definition is given in Duchi "Projected Subgradient Methods
for Learning Sparse Gaussians".
"""
gap = np.sum(emp_cov * precision_)
gap -= precision_.shape[0]
gap += alpha * (np.abs(pr... | Expression of the dual gap convergence criterion
The specific definition is given in Duchi "Projected Subgradient Methods
for Learning Sparse Gaussians".
| _dual_gap | python | scikit-learn/scikit-learn | sklearn/covariance/_graph_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_graph_lasso.py | BSD-3-Clause |
def alpha_max(emp_cov):
"""Find the maximum alpha for which there are some non-zeros off-diagonal.
Parameters
----------
emp_cov : ndarray of shape (n_features, n_features)
The sample covariance matrix.
Notes
-----
This results from the bound for the all the Lasso that are solved
... | Find the maximum alpha for which there are some non-zeros off-diagonal.
Parameters
----------
emp_cov : ndarray of shape (n_features, n_features)
The sample covariance matrix.
Notes
-----
This results from the bound for the all the Lasso that are solved
in GraphicalLasso: each time... | alpha_max | python | scikit-learn/scikit-learn | sklearn/covariance/_graph_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_graph_lasso.py | BSD-3-Clause |
def graphical_lasso(
emp_cov,
alpha,
*,
mode="cd",
tol=1e-4,
enet_tol=1e-4,
max_iter=100,
verbose=False,
return_costs=False,
eps=np.finfo(np.float64).eps,
return_n_iter=False,
):
"""L1-penalized covariance estimator.
Read more in the :ref:`User Guide <sparse_inverse_... | L1-penalized covariance estimator.
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
.. versionchanged:: v0.20
graph_lasso has been renamed to graphical_lasso
Parameters
----------
emp_cov : array-like of shape (n_features, n_features)
Empirical covariance from which... | graphical_lasso | python | scikit-learn/scikit-learn | sklearn/covariance/_graph_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_graph_lasso.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Fit the GraphicalLasso model to X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data from which to compute the covariance estimate.
y : Ignored
Not used, present for API consistency by convention.
... | Fit the GraphicalLasso model to X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data from which to compute the covariance estimate.
y : Ignored
Not used, present for API consistency by convention.
Returns
-------
... | fit | python | scikit-learn/scikit-learn | sklearn/covariance/_graph_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_graph_lasso.py | BSD-3-Clause |
def graphical_lasso_path(
X,
alphas,
cov_init=None,
X_test=None,
mode="cd",
tol=1e-4,
enet_tol=1e-4,
max_iter=100,
verbose=False,
eps=np.finfo(np.float64).eps,
):
"""l1-penalized covariance estimator along a path of decreasing alphas
Read more in the :ref:`User Guide <sp... | l1-penalized covariance estimator along a path of decreasing alphas
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Data from which to compute the covariance estimate.
alphas : array-like of shape (n_alphas... | graphical_lasso_path | python | scikit-learn/scikit-learn | sklearn/covariance/_graph_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_graph_lasso.py | BSD-3-Clause |
def fit(self, X, y=None, **params):
"""Fit the GraphicalLasso covariance model to X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data from which to compute the covariance estimate.
y : Ignored
Not used, present for API consisten... | Fit the GraphicalLasso covariance model to X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data from which to compute the covariance estimate.
y : Ignored
Not used, present for API consistency by convention.
**params : dict, def... | fit | python | scikit-learn/scikit-learn | sklearn/covariance/_graph_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_graph_lasso.py | BSD-3-Clause |
def c_step(
X,
n_support,
remaining_iterations=30,
initial_estimates=None,
verbose=False,
cov_computation_method=empirical_covariance,
random_state=None,
):
"""C_step procedure described in [Rouseeuw1984]_ aiming at computing MCD.
Parameters
----------
X : array-like of shap... | C_step procedure described in [Rouseeuw1984]_ aiming at computing MCD.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data set in which we look for the n_support observations whose
scatter matrix has minimum determinant.
n_support : int
Number of observat... | c_step | python | scikit-learn/scikit-learn | sklearn/covariance/_robust_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_robust_covariance.py | BSD-3-Clause |
def select_candidates(
X,
n_support,
n_trials,
select=1,
n_iter=30,
verbose=False,
cov_computation_method=empirical_covariance,
random_state=None,
):
"""Finds the best pure subset of observations to compute MCD from it.
The purpose of this function is to find the best sets of n_... | Finds the best pure subset of observations to compute MCD from it.
The purpose of this function is to find the best sets of n_support
observations with respect to a minimization of their covariance
matrix determinant. Equivalently, it removes n_samples-n_support
observations to construct what we call a... | select_candidates | python | scikit-learn/scikit-learn | sklearn/covariance/_robust_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_robust_covariance.py | BSD-3-Clause |
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... | Estimate the Minimum Covariance Determinant matrix.
Read more in the :ref:`User Guide <robust_covariance>`.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data matrix, with p features and n samples.
support_fraction : float, default=None
The proportion o... | fast_mcd | python | scikit-learn/scikit-learn | sklearn/covariance/_robust_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_robust_covariance.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Fit a Minimum Covariance Determinant with the FastMCD algorithm.
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 feature... | Fit a Minimum Covariance Determinant with the FastMCD algorithm.
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 : Ignored
N... | fit | python | scikit-learn/scikit-learn | sklearn/covariance/_robust_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_robust_covariance.py | BSD-3-Clause |
def correct_covariance(self, data):
"""Apply a correction to raw Minimum Covariance Determinant estimates.
Correction using the empirical correction factor suggested
by Rousseeuw and Van Driessen in [RVD]_.
Parameters
----------
data : array-like of shape (n_samples, n_... | Apply a correction to raw Minimum Covariance Determinant estimates.
Correction using the empirical correction factor suggested
by Rousseeuw and Van Driessen in [RVD]_.
Parameters
----------
data : array-like of shape (n_samples, n_features)
The data matrix, with p f... | correct_covariance | python | scikit-learn/scikit-learn | sklearn/covariance/_robust_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_robust_covariance.py | BSD-3-Clause |
def reweight_covariance(self, data):
"""Re-weight raw Minimum Covariance Determinant estimates.
Re-weight observations using Rousseeuw's method (equivalent to
deleting outlying observations from the data set before
computing location and covariance estimates) described
in [RVDri... | Re-weight raw Minimum Covariance Determinant estimates.
Re-weight observations using Rousseeuw's method (equivalent to
deleting outlying observations from the data set before
computing location and covariance estimates) described
in [RVDriessen]_.
Parameters
----------
... | reweight_covariance | python | scikit-learn/scikit-learn | sklearn/covariance/_robust_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_robust_covariance.py | BSD-3-Clause |
def _ledoit_wolf(X, *, assume_centered, block_size):
"""Estimate the shrunk Ledoit-Wolf covariance matrix."""
# for only one feature, the result is the same whatever the shrinkage
if len(X.shape) == 2 and X.shape[1] == 1:
if not assume_centered:
X = X - X.mean()
return np.atleast... | Estimate the shrunk Ledoit-Wolf covariance matrix. | _ledoit_wolf | python | scikit-learn/scikit-learn | sklearn/covariance/_shrunk_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_shrunk_covariance.py | BSD-3-Clause |
def _oas(X, *, assume_centered=False):
"""Estimate covariance with the Oracle Approximating Shrinkage algorithm.
The formulation is based on [1]_.
[1] "Shrinkage algorithms for MMSE covariance estimation.",
Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O.
IEEE Transactions on Signal Proces... | Estimate covariance with the Oracle Approximating Shrinkage algorithm.
The formulation is based on [1]_.
[1] "Shrinkage algorithms for MMSE covariance estimation.",
Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O.
IEEE Transactions on Signal Processing, 58(10), 5016-5029, 2010.
https:/... | _oas | python | scikit-learn/scikit-learn | sklearn/covariance/_shrunk_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_shrunk_covariance.py | BSD-3-Clause |
def shrunk_covariance(emp_cov, shrinkage=0.1):
"""Calculate covariance matrices shrunk on the diagonal.
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters
----------
emp_cov : array-like of shape (..., n_features, n_features)
Covariance matrices to be shrunk, at least 2D nd... | Calculate covariance matrices shrunk on the diagonal.
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters
----------
emp_cov : array-like of shape (..., n_features, n_features)
Covariance matrices to be shrunk, at least 2D ndarray.
shrinkage : float, default=0.1
Coe... | shrunk_covariance | python | scikit-learn/scikit-learn | sklearn/covariance/_shrunk_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_shrunk_covariance.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Fit the shrunk covariance model to X.
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 : Ignored
... | Fit the shrunk covariance model to X.
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 : Ignored
Not used, present for API co... | fit | python | scikit-learn/scikit-learn | sklearn/covariance/_shrunk_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_shrunk_covariance.py | BSD-3-Clause |
def ledoit_wolf_shrinkage(X, assume_centered=False, block_size=1000):
"""Estimate the shrunk Ledoit-Wolf covariance matrix.
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data from which to compute the Ledoit-Wo... | Estimate the shrunk Ledoit-Wolf covariance matrix.
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data from which to compute the Ledoit-Wolf shrunk covariance shrinkage.
assume_centered : bool, default=False
... | ledoit_wolf_shrinkage | python | scikit-learn/scikit-learn | sklearn/covariance/_shrunk_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_shrunk_covariance.py | BSD-3-Clause |
def ledoit_wolf(X, *, assume_centered=False, block_size=1000):
"""Estimate the shrunk Ledoit-Wolf covariance matrix.
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data from which to compute the covariance estim... | Estimate the shrunk Ledoit-Wolf covariance matrix.
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data from which to compute the covariance estimate.
assume_centered : bool, default=False
If True, data ... | ledoit_wolf | python | scikit-learn/scikit-learn | sklearn/covariance/_shrunk_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_shrunk_covariance.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Fit the Ledoit-Wolf shrunk covariance model to X.
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 : ... | Fit the Ledoit-Wolf shrunk covariance model to X.
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 : Ignored
Not used, present... | fit | python | scikit-learn/scikit-learn | sklearn/covariance/_shrunk_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_shrunk_covariance.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Fit the Oracle Approximating Shrinkage covariance model to X.
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.
... | Fit the Oracle Approximating Shrinkage covariance model to X.
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 : Ignored
Not u... | fit | python | scikit-learn/scikit-learn | sklearn/covariance/_shrunk_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_shrunk_covariance.py | BSD-3-Clause |
def test_ledoit_wolf_empty_array(ledoit_wolf_fitting_function):
"""Check that we validate X and raise proper error with 0-sample array."""
X_empty = np.zeros((0, 2))
with pytest.raises(ValueError, match="Found array with 0 sample"):
ledoit_wolf_fitting_function(X_empty) | Check that we validate X and raise proper error with 0-sample array. | test_ledoit_wolf_empty_array | python | scikit-learn/scikit-learn | sklearn/covariance/tests/test_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/tests/test_covariance.py | BSD-3-Clause |
def test_EmpiricalCovariance_validates_mahalanobis():
"""Checks that EmpiricalCovariance validates data with mahalanobis."""
cov = EmpiricalCovariance().fit(X)
msg = f"X has 2 features, but \\w+ is expecting {X.shape[1]} features as input"
with pytest.raises(ValueError, match=msg):
cov.mahalano... | Checks that EmpiricalCovariance validates data with mahalanobis. | test_EmpiricalCovariance_validates_mahalanobis | python | scikit-learn/scikit-learn | sklearn/covariance/tests/test_covariance.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/tests/test_covariance.py | BSD-3-Clause |
def test_graphical_lassos(random_state=1):
"""Test the graphical lasso solvers.
This checks is unstable for some random seeds where the covariance found with "cd"
and "lars" solvers are different (4 cases / 100 tries).
"""
# Sample data from a sparse multivariate normal
dim = 20
n_samples =... | Test the graphical lasso solvers.
This checks is unstable for some random seeds where the covariance found with "cd"
and "lars" solvers are different (4 cases / 100 tries).
| test_graphical_lassos | python | scikit-learn/scikit-learn | sklearn/covariance/tests/test_graphical_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/tests/test_graphical_lasso.py | BSD-3-Clause |
def test_graphical_lasso_when_alpha_equals_0():
"""Test graphical_lasso's early return condition when alpha=0."""
X = np.random.randn(100, 10)
emp_cov = empirical_covariance(X, assume_centered=True)
model = GraphicalLasso(alpha=0, covariance="precomputed").fit(emp_cov)
assert_allclose(model.precisi... | Test graphical_lasso's early return condition when alpha=0. | test_graphical_lasso_when_alpha_equals_0 | python | scikit-learn/scikit-learn | sklearn/covariance/tests/test_graphical_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/tests/test_graphical_lasso.py | BSD-3-Clause |
def test_graphical_lasso_cv_alphas_iterable(alphas_container_type):
"""Check that we can pass an array-like to `alphas`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/22489
"""
true_cov = np.array(
[
[0.8, 0.0, 0.2, 0.0],
[0.0, 0.4, 0.0... | Check that we can pass an array-like to `alphas`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/22489
| test_graphical_lasso_cv_alphas_iterable | python | scikit-learn/scikit-learn | sklearn/covariance/tests/test_graphical_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/tests/test_graphical_lasso.py | BSD-3-Clause |
def test_graphical_lasso_cv_alphas_invalid_array(alphas, err_type, err_msg):
"""Check that if an array-like containing a value
outside of (0, inf] is passed to `alphas`, a ValueError is raised.
Check if a string is passed, a TypeError is raised.
"""
true_cov = np.array(
[
[0.8, 0... | Check that if an array-like containing a value
outside of (0, inf] is passed to `alphas`, a ValueError is raised.
Check if a string is passed, a TypeError is raised.
| test_graphical_lasso_cv_alphas_invalid_array | python | scikit-learn/scikit-learn | sklearn/covariance/tests/test_graphical_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/tests/test_graphical_lasso.py | BSD-3-Clause |
def test_graphical_lasso_cv_scores_with_routing(global_random_seed):
"""Check that `GraphicalLassoCV` internally dispatches metadata to
the splitter.
"""
splits = 5
n_alphas = 5
n_refinements = 3
true_cov = np.array(
[
[0.8, 0.0, 0.2, 0.0],
[0.0, 0.4, 0.0, 0.0... | Check that `GraphicalLassoCV` internally dispatches metadata to
the splitter.
| test_graphical_lasso_cv_scores_with_routing | python | scikit-learn/scikit-learn | sklearn/covariance/tests/test_graphical_lasso.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/tests/test_graphical_lasso.py | BSD-3-Clause |
def _get_first_singular_vectors_power_method(
X, y, mode="A", max_iter=500, tol=1e-06, norm_y_weights=False
):
"""Return the first left and right singular vectors of X'y.
Provides an alternative to the svd(X'y) and uses the power method instead.
With norm_y_weights to True and in mode A, this correspon... | Return the first left and right singular vectors of X'y.
Provides an alternative to the svd(X'y) and uses the power method instead.
With norm_y_weights to True and in mode A, this corresponds to the
algorithm section 11.3 of the Wegelin's review, except this starts at the
"update saliences" part.
| _get_first_singular_vectors_power_method | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/_pls.py | BSD-3-Clause |
def _get_first_singular_vectors_svd(X, y):
"""Return the first left and right singular vectors of X'y.
Here the whole SVD is computed.
"""
C = np.dot(X.T, y)
U, _, Vt = svd(C, full_matrices=False)
return U[:, 0], Vt[0, :] | Return the first left and right singular vectors of X'y.
Here the whole SVD is computed.
| _get_first_singular_vectors_svd | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/_pls.py | BSD-3-Clause |
def _center_scale_xy(X, y, scale=True):
"""Center X, y and scale if the scale parameter==True
Returns
-------
X, y, x_mean, y_mean, x_std, y_std
"""
# center
x_mean = X.mean(axis=0)
X -= x_mean
y_mean = y.mean(axis=0)
y -= y_mean
# scale
if scale:
x_std = X.s... | Center X, y and scale if the scale parameter==True
Returns
-------
X, y, x_mean, y_mean, x_std, y_std
| _center_scale_xy | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/_pls.py | BSD-3-Clause |
def _svd_flip_1d(u, v):
"""Same as svd_flip but works on 1d arrays, and is inplace"""
# svd_flip would force us to convert to 2d array and would also return 2d
# arrays. We don't want that.
biggest_abs_val_idx = np.argmax(np.abs(u))
sign = np.sign(u[biggest_abs_val_idx])
u *= sign
v *= sign | Same as svd_flip but works on 1d arrays, and is inplace | _svd_flip_1d | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/_pls.py | BSD-3-Clause |
def fit(self, X, y):
"""Fit model to data.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of predictors.
y : array-like of shape (n_samples... | Fit model to data.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of predictors.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
... | fit | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/_pls.py | BSD-3-Clause |
def transform(self, X, y=None, copy=True):
"""Apply the dimension reduction.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Samples to transform.
y : array-like of shape (n_samples, n_targets), default=None
Target vectors.
... | Apply the dimension reduction.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Samples to transform.
y : array-like of shape (n_samples, n_targets), default=None
Target vectors.
copy : bool, default=True
Whether to copy... | transform | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/_pls.py | BSD-3-Clause |
def inverse_transform(self, X, y=None):
"""Transform data back to its original space.
Parameters
----------
X : array-like of shape (n_samples, n_components)
New data, where `n_samples` is the number of samples
and `n_components` is the number of pls components.
... | Transform data back to its original space.
Parameters
----------
X : array-like of shape (n_samples, n_components)
New data, where `n_samples` is the number of samples
and `n_components` is the number of pls components.
y : array-like of shape (n_samples,) or (n... | inverse_transform | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/_pls.py | BSD-3-Clause |
def predict(self, X, copy=True):
"""Predict targets of given samples.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Samples.
copy : bool, default=True
Whether to copy `X` or perform in-place normalization.
Returns
... | Predict targets of given samples.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Samples.
copy : bool, default=True
Whether to copy `X` or perform in-place normalization.
Returns
-------
y_pred : ndarray of shape (... | predict | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/_pls.py | BSD-3-Clause |
def fit(self, X, y):
"""Fit model to data.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training samples.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Targets.
Returns
-------
self : obj... | Fit model to data.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training samples.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Targets.
Returns
-------
self : object
Fitted estimator... | fit | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/_pls.py | BSD-3-Clause |
def transform(self, X, y=None):
"""
Apply the dimensionality reduction.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Samples to be transformed.
y : array-like of shape (n_samples,) or (n_samples, n_targets), \
default... |
Apply the dimensionality reduction.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Samples to be transformed.
y : array-like of shape (n_samples,) or (n_samples, n_targets), default=None
Targets.
Returns
... | transform | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/_pls.py | BSD-3-Clause |
def test_scale_and_stability(Est, X, y):
"""scale=True is equivalent to scale=False on centered/scaled data
This allows to check numerical stability over platforms as well"""
X_s, y_s, *_ = _center_scale_xy(X, y)
X_score, y_score = Est(scale=True).fit_transform(X, y)
X_s_score, y_s_score = Est(sca... | scale=True is equivalent to scale=False on centered/scaled data
This allows to check numerical stability over platforms as well | test_scale_and_stability | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/tests/test_pls.py | BSD-3-Clause |
def test_n_components_upper_bounds(Estimator):
"""Check the validation of `n_components` upper bounds for `PLS` regressors."""
rng = np.random.RandomState(0)
X = rng.randn(10, 5)
y = rng.randn(10, 3)
est = Estimator(n_components=10)
err_msg = "`n_components` upper bound is .*. Got 10 instead. Re... | Check the validation of `n_components` upper bounds for `PLS` regressors. | test_n_components_upper_bounds | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/tests/test_pls.py | BSD-3-Clause |
def test_n_components_upper_PLSRegression():
"""Check the validation of `n_components` upper bounds for PLSRegression."""
rng = np.random.RandomState(0)
X = rng.randn(20, 64)
y = rng.randn(20, 3)
est = PLSRegression(n_components=30)
err_msg = "`n_components` upper bound is 20. Got 30 instead. Re... | Check the validation of `n_components` upper bounds for PLSRegression. | test_n_components_upper_PLSRegression | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/tests/test_pls.py | BSD-3-Clause |
def test_loadings_converges(global_random_seed):
"""Test that CCA converges. Non-regression test for #19549."""
X, y = make_regression(
n_samples=200, n_features=20, n_targets=20, random_state=global_random_seed
)
cca = CCA(n_components=10, max_iter=500)
with warnings.catch_warnings():
... | Test that CCA converges. Non-regression test for #19549. | test_loadings_converges | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/tests/test_pls.py | BSD-3-Clause |
def test_pls_constant_y():
"""Checks warning when y is constant. Non-regression test for #19831"""
rng = np.random.RandomState(42)
x = rng.rand(100, 3)
y = np.zeros(100)
pls = PLSRegression()
msg = "y residual is constant at iteration"
with pytest.warns(UserWarning, match=msg):
pls... | Checks warning when y is constant. Non-regression test for #19831 | test_pls_constant_y | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/tests/test_pls.py | BSD-3-Clause |
def test_pls_coef_shape(PLSEstimator):
"""Check the shape of `coef_` attribute.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/12410
"""
d = load_linnerud()
X = d.data
y = d.target
pls = PLSEstimator(copy=True).fit(X, y)
n_targets, n_features = y.shap... | Check the shape of `coef_` attribute.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/12410
| test_pls_coef_shape | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/tests/test_pls.py | BSD-3-Clause |
def test_pls_prediction(PLSEstimator, scale):
"""Check the behaviour of the prediction function."""
d = load_linnerud()
X = d.data
y = d.target
pls = PLSEstimator(copy=True, scale=scale).fit(X, y)
y_pred = pls.predict(X, copy=True)
y_mean = y.mean(axis=0)
X_trans = X - X.mean(axis=0)
... | Check the behaviour of the prediction function. | test_pls_prediction | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/tests/test_pls.py | BSD-3-Clause |
def test_pls_regression_fit_1d_y():
"""Check that when fitting with 1d `y`, prediction should also be 1d.
Non-regression test for Issue #26549.
"""
X = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36]])
y = np.array([2, 6, 12, 20, 30, 42])
expected = y.copy()
plsr = PLSRegressio... | Check that when fitting with 1d `y`, prediction should also be 1d.
Non-regression test for Issue #26549.
| test_pls_regression_fit_1d_y | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/tests/test_pls.py | BSD-3-Clause |
def test_pls_regression_scaling_coef():
"""Check that when using `scale=True`, the coefficients are using the std. dev. from
both `X` and `y`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27964
"""
# handcrafted data where we can predict y from X with an addition... | Check that when using `scale=True`, the coefficients are using the std. dev. from
both `X` and `y`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27964
| test_pls_regression_scaling_coef | python | scikit-learn/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_decomposition/tests/test_pls.py | BSD-3-Clause |
def _split_sparse_columns(
arff_data: ArffSparseDataType, include_columns: List
) -> ArffSparseDataType:
"""Obtains several columns from sparse ARFF representation. Additionally,
the column indices are re-labelled, given the columns that are not
included. (e.g., when including [1, 2, 3], the columns wil... | Obtains several columns from sparse ARFF representation. Additionally,
the column indices are re-labelled, given the columns that are not
included. (e.g., when including [1, 2, 3], the columns will be relabelled
to [0, 1, 2]).
Parameters
----------
arff_data : tuple
A tuple of three lis... | _split_sparse_columns | python | scikit-learn/scikit-learn | sklearn/datasets/_arff_parser.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_arff_parser.py | BSD-3-Clause |
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 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 `X`.
target_names : list of str
The list of target name... | _post_process_frame | python | scikit-learn/scikit-learn | sklearn/datasets/_arff_parser.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_arff_parser.py | BSD-3-Clause |
def _liac_arff_parser(
gzip_file,
output_arrays_type,
openml_columns_info,
feature_names_to_select,
target_names_to_select,
shape=None,
):
"""ARFF parser using the LIAC-ARFF library coded purely in Python.
This parser is quite slow but consumes a generator. Currently it is needed
to... | ARFF parser using the LIAC-ARFF library coded purely in Python.
This parser is quite slow but consumes a generator. Currently it is needed
to parse sparse datasets. For dense datasets, it is recommended to instead
use the pandas-based parser, although it does not always handles the
dtypes exactly the s... | _liac_arff_parser | python | scikit-learn/scikit-learn | sklearn/datasets/_arff_parser.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_arff_parser.py | BSD-3-Clause |
def _pandas_arff_parser(
gzip_file,
output_arrays_type,
openml_columns_info,
feature_names_to_select,
target_names_to_select,
read_csv_kwargs=None,
):
"""ARFF parser using `pandas.read_csv`.
This parser uses the metadata fetched directly from OpenML and skips the metadata
headers of... | ARFF parser using `pandas.read_csv`.
This parser uses the metadata fetched directly from OpenML and skips the metadata
headers of ARFF file itself. The data is loaded as a CSV file.
Parameters
----------
gzip_file : GzipFile instance
The GZip compressed file with the ARFF formatted payload... | _pandas_arff_parser | python | scikit-learn/scikit-learn | sklearn/datasets/_arff_parser.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_arff_parser.py | BSD-3-Clause |
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 a compressed ARFF file using a given parser.
Parameters
----------
gzip_file : GzipFile instance
The file compressed to be read.
parser : {"pandas", "liac-arff"}
The parser used to parse the ARFF file. "pandas" is recommended
but only supports loading dense datasets.
... | load_arff_from_gzip_file | python | scikit-learn/scikit-learn | sklearn/datasets/_arff_parser.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_arff_parser.py | BSD-3-Clause |
def get_data_home(data_home=None) -> str:
"""Return the path of the scikit-learn data directory.
This folder is used by some large dataset loaders to avoid downloading the
data several times.
By default the data directory is set to a folder named 'scikit_learn_data' in the
user home folder.
A... | Return the path of the scikit-learn data directory.
This folder is used by some large dataset loaders to avoid downloading the
data several times.
By default the data directory is set to a folder named 'scikit_learn_data' in the
user home folder.
Alternatively, it can be set by the 'SCIKIT_LEARN_... | get_data_home | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def load_files(
container_path,
*,
description=None,
categories=None,
load_content=True,
shuffle=True,
encoding=None,
decode_error="strict",
random_state=0,
allowed_extensions=None,
):
"""Load text files with categories as subfolder names.
Individual samples are assumed ... | Load text files with categories as subfolder names.
Individual samples are assumed to be files stored a two levels folder
structure such as the following:
.. code-block:: text
container_folder/
category_1_folder/
file_1.txt
file_2.txt
..... | load_files | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def load_csv_data(
data_file_name,
*,
data_module=DATA_MODULE,
descr_file_name=None,
descr_module=DESCR_MODULE,
encoding="utf-8",
):
"""Loads `data_file_name` from `data_module with `importlib.resources`.
Parameters
----------
data_file_name : str
Name of csv file to be ... | Loads `data_file_name` from `data_module with `importlib.resources`.
Parameters
----------
data_file_name : str
Name of csv file to be loaded from `data_module/data_file_name`.
For example `'wine_data.csv'`.
data_module : str or module, default='sklearn.datasets.data'
Module wh... | load_csv_data | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def load_wine(*, return_X_y=False, as_frame=False):
"""Load and return the wine dataset (classification).
.. versionadded:: 0.18
The wine dataset is a classic and very easy multi-class classification
dataset.
================= ==============
Classes 3
Samples pe... | Load and return the wine dataset (classification).
.. versionadded:: 0.18
The wine dataset is a classic and very easy multi-class classification
dataset.
================= ==============
Classes 3
Samples per class [59,71,48]
Samples total ... | load_wine | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def load_iris(*, return_X_y=False, as_frame=False):
"""Load and return the iris dataset (classification).
The iris dataset is a classic and very easy multi-class classification
dataset.
================= ==============
Classes 3
Samples per class 50
... | Load and return the iris dataset (classification).
The iris dataset is a classic and very easy multi-class classification
dataset.
================= ==============
Classes 3
Samples per class 50
Samples total 150
Dimensionality ... | load_iris | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def load_breast_cancer(*, return_X_y=False, as_frame=False):
"""Load and return the breast cancer Wisconsin dataset (classification).
The breast cancer dataset is a classic and very easy binary classification
dataset.
================= ==============
Classes 2
Sample... | Load and return the breast cancer Wisconsin dataset (classification).
The breast cancer dataset is a classic and very easy binary classification
dataset.
================= ==============
Classes 2
Samples per class 212(M),357(B)
Samples total 569
... | load_breast_cancer | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def load_digits(*, n_class=10, return_X_y=False, as_frame=False):
"""Load and return the digits dataset (classification).
Each datapoint is a 8x8 image of a digit.
================= ==============
Classes 10
Samples per class ~180
Samples total ... | Load and return the digits dataset (classification).
Each datapoint is a 8x8 image of a digit.
================= ==============
Classes 10
Samples per class ~180
Samples total 1797
Dimensionality 64
Features ... | load_digits | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
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 and return the diabetes dataset (regression).
============== ==================
Samples total 442
Dimensionality 10
Features real, -.2 < x < .2
Targets integer 25 - 346
============== ==================
.. note::
The meaning of each feature (i.e. `feat... | load_diabetes | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def load_linnerud(*, return_X_y=False, as_frame=False):
"""Load and return the physical exercise Linnerud dataset.
This dataset is suitable for multi-output regression tasks.
============== ============================
Samples total 20
Dimensionality 3 (for both data and target)
Feature... | Load and return the physical exercise Linnerud dataset.
This dataset is suitable for multi-output regression tasks.
============== ============================
Samples total 20
Dimensionality 3 (for both data and target)
Features integer
Targets integer
============... | load_linnerud | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def load_sample_images():
"""Load sample images for image manipulation.
Loads both, ``china`` and ``flower``.
Read more in the :ref:`User Guide <sample_images>`.
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
images... | Load sample images for image manipulation.
Loads both, ``china`` and ``flower``.
Read more in the :ref:`User Guide <sample_images>`.
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
images : list of ndarray of shape (427,... | load_sample_images | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def load_sample_image(image_name):
"""Load the numpy array of a single sample image.
Read more in the :ref:`User Guide <sample_images>`.
Parameters
----------
image_name : {`china.jpg`, `flower.jpg`}
The name of the sample image loaded.
Returns
-------
img : 3D array
T... | Load the numpy array of a single sample image.
Read more in the :ref:`User Guide <sample_images>`.
Parameters
----------
image_name : {`china.jpg`, `flower.jpg`}
The name of the sample image loaded.
Returns
-------
img : 3D array
The image as a numpy array: height x width ... | load_sample_image | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
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