code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def _num_combinations(
n_features, min_degree, max_degree, interaction_only, include_bias
):
"""Calculate number of terms in polynomial expansion
This should be equivalent to counting the number of terms returned by
_combinations(...) but much faster.
"""
if interac... | Calculate number of terms in polynomial expansion
This should be equivalent to counting the number of terms returned by
_combinations(...) but much faster.
| _num_combinations | python | scikit-learn/scikit-learn | sklearn/preprocessing/_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_polynomial.py | BSD-3-Clause |
def powers_(self):
"""Exponent for each of the inputs in the output."""
check_is_fitted(self)
combinations = self._combinations(
n_features=self.n_features_in_,
min_degree=self._min_degree,
max_degree=self._max_degree,
interaction_only=self.intera... | Exponent for each of the inputs in the output. | powers_ | python | scikit-learn/scikit-learn | sklearn/preprocessing/_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_polynomial.py | BSD-3-Clause |
def get_feature_names_out(self, input_features=None):
"""Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Input features.
- If `input_features is None`, then `feature_names_in_` is
... | Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Input features.
- If `input_features is None`, then `feature_names_in_` is
used as feature names in. If `feature_names_in_` is not d... | get_feature_names_out | python | scikit-learn/scikit-learn | sklearn/preprocessing/_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_polynomial.py | BSD-3-Clause |
def fit(self, X, y=None):
"""
Compute number of output features.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The data.
y : Ignored
Not used, present here for API consistency by convention.
Retur... |
Compute number of output features.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The data.
y : Ignored
Not used, present here for API consistency by convention.
Returns
-------
self : obj... | fit | python | scikit-learn/scikit-learn | sklearn/preprocessing/_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_polynomial.py | BSD-3-Clause |
def transform(self, X):
"""Transform data to polynomial features.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The data to transform, row by row.
Prefer CSR over CSC for sparse input (for speed), but CSC is
r... | Transform data to polynomial features.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The data to transform, row by row.
Prefer CSR over CSC for sparse input (for speed), but CSC is
required if the degree is 4 or highe... | transform | python | scikit-learn/scikit-learn | sklearn/preprocessing/_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_polynomial.py | BSD-3-Clause |
def _get_base_knot_positions(X, n_knots=10, knots="uniform", sample_weight=None):
"""Calculate base knot positions.
Base knots such that first knot <= feature <= last knot. For the
B-spline construction with scipy.interpolate.BSpline, 2*degree knots
beyond the base interval are added.
... | Calculate base knot positions.
Base knots such that first knot <= feature <= last knot. For the
B-spline construction with scipy.interpolate.BSpline, 2*degree knots
beyond the base interval are added.
Returns
-------
knots : ndarray of shape (n_knots, n_features), dtype... | _get_base_knot_positions | python | scikit-learn/scikit-learn | sklearn/preprocessing/_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_polynomial.py | BSD-3-Clause |
def fit(self, X, y=None, sample_weight=None):
"""Compute knot positions of splines.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data.
y : None
Ignored.
sample_weight : array-like of shape (n_samples,), default = Non... | Compute knot positions of splines.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data.
y : None
Ignored.
sample_weight : array-like of shape (n_samples,), default = None
Individual weights for each sample. Used to... | fit | python | scikit-learn/scikit-learn | sklearn/preprocessing/_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_polynomial.py | BSD-3-Clause |
def transform(self, X):
"""Transform each feature data to B-splines.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data to transform.
Returns
-------
XBS : {ndarray, sparse matrix} of shape (n_samples, n_features * n_splin... | Transform each feature data to B-splines.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data to transform.
Returns
-------
XBS : {ndarray, sparse matrix} of shape (n_samples, n_features * n_splines)
The matrix of featu... | transform | python | scikit-learn/scikit-learn | sklearn/preprocessing/_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_polynomial.py | BSD-3-Clause |
def fit_transform(self, X, y):
"""Fit :class:`TargetEncoder` and transform X with the target encoding.
.. note::
`fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a
:term:`cross fitting` scheme is used in `fit_transform` for encoding.
See the :ref... | Fit :class:`TargetEncoder` and transform X with the target encoding.
.. note::
`fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a
:term:`cross fitting` scheme is used in `fit_transform` for encoding.
See the :ref:`User Guide <target_encoder>`. for detail... | fit_transform | python | scikit-learn/scikit-learn | sklearn/preprocessing/_target_encoder.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_target_encoder.py | BSD-3-Clause |
def transform(self, X):
"""Transform X with the target encoding.
.. note::
`fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a
:term:`cross fitting` scheme is used in `fit_transform` for encoding.
See the :ref:`User Guide <target_encoder>`. for de... | Transform X with the target encoding.
.. note::
`fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a
:term:`cross fitting` scheme is used in `fit_transform` for encoding.
See the :ref:`User Guide <target_encoder>`. for details.
Parameters
... | transform | python | scikit-learn/scikit-learn | sklearn/preprocessing/_target_encoder.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_target_encoder.py | BSD-3-Clause |
def _fit_encodings_all(self, X, y):
"""Fit a target encoding with all the data."""
# avoid circular import
from ..preprocessing import (
LabelBinarizer,
LabelEncoder,
)
check_consistent_length(X, y)
self._fit(X, handle_unknown="ignore", ensure_all... | Fit a target encoding with all the data. | _fit_encodings_all | python | scikit-learn/scikit-learn | sklearn/preprocessing/_target_encoder.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_target_encoder.py | BSD-3-Clause |
def _fit_encoding_multiclass(self, X_ordinal, y, n_categories, target_mean):
"""Learn multiclass encodings.
Learn encodings for each class (c) then reorder encodings such that
the same features (f) are grouped together. `reorder_index` enables
converting from:
f0_c0, f1_c0, f0_c... | Learn multiclass encodings.
Learn encodings for each class (c) then reorder encodings such that
the same features (f) are grouped together. `reorder_index` enables
converting from:
f0_c0, f1_c0, f0_c1, f1_c1, f0_c2, f1_c2
to:
f0_c0, f0_c1, f0_c2, f1_c0, f1_c1, f1_c2
... | _fit_encoding_multiclass | python | scikit-learn/scikit-learn | sklearn/preprocessing/_target_encoder.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_target_encoder.py | BSD-3-Clause |
def _transform_X_ordinal(
self,
X_out,
X_ordinal,
X_unknown_mask,
row_indices,
encodings,
target_mean,
):
"""Transform X_ordinal using encodings.
In the multiclass case, `X_ordinal` and `X_unknown_mask` have column
(axis=1) size `n_fea... | Transform X_ordinal using encodings.
In the multiclass case, `X_ordinal` and `X_unknown_mask` have column
(axis=1) size `n_features`, while `encodings` has length of size
`n_features * n_classes`. `feat_idx` deals with this by repeating
feature indices by `n_classes` E.g., for 3 feature... | _transform_X_ordinal | python | scikit-learn/scikit-learn | sklearn/preprocessing/_target_encoder.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_target_encoder.py | BSD-3-Clause |
def get_feature_names_out(self, input_features=None):
"""Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Not used, present here for API consistency by convention.
Returns
-------
... | Get output feature names for transformation.
Parameters
----------
input_features : array-like of str or None, default=None
Not used, present here for API consistency by convention.
Returns
-------
feature_names_out : ndarray of str objects
Trans... | get_feature_names_out | python | scikit-learn/scikit-learn | sklearn/preprocessing/_target_encoder.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_target_encoder.py | BSD-3-Clause |
def test_quantile_transform_subsampling_disabled():
"""Check the behaviour of `QuantileTransformer` when `subsample=None`."""
X = np.random.RandomState(0).normal(size=(200, 1))
n_quantiles = 5
transformer = QuantileTransformer(n_quantiles=n_quantiles, subsample=None).fit(X)
expected_references = n... | Check the behaviour of `QuantileTransformer` when `subsample=None`. | test_quantile_transform_subsampling_disabled | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_data.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_data.py | BSD-3-Clause |
def test_power_transformer_box_cox_raise_all_nans_col():
"""Check that box-cox raises informative when a column contains all nans.
Non-regression test for gh-26303
"""
X = rng.random_sample((4, 5))
X[:, 0] = np.nan
err_msg = "Column must not be all nan."
pt = PowerTransformer(method="box-... | Check that box-cox raises informative when a column contains all nans.
Non-regression test for gh-26303
| test_power_transformer_box_cox_raise_all_nans_col | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_data.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_data.py | BSD-3-Clause |
def test_standard_scaler_raise_error_for_1d_input():
"""Check that `inverse_transform` from `StandardScaler` raises an error
with 1D array.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/19518
"""
scaler = StandardScaler().fit(X_2d)
err_msg = "Expected 2D array,... | Check that `inverse_transform` from `StandardScaler` raises an error
with 1D array.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/19518
| test_standard_scaler_raise_error_for_1d_input | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_data.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_data.py | BSD-3-Clause |
def test_power_transformer_significantly_non_gaussian():
"""Check that significantly non-Gaussian data before transforms correctly.
For some explored lambdas, the transformed data may be constant and will
be rejected. Non-regression test for
https://github.com/scikit-learn/scikit-learn/issues/14959
... | Check that significantly non-Gaussian data before transforms correctly.
For some explored lambdas, the transformed data may be constant and will
be rejected. Non-regression test for
https://github.com/scikit-learn/scikit-learn/issues/14959
| test_power_transformer_significantly_non_gaussian | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_data.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_data.py | BSD-3-Clause |
def test_one_to_one_features(Transformer):
"""Check one-to-one transformers give correct feature names."""
tr = Transformer().fit(iris.data)
names_out = tr.get_feature_names_out(iris.feature_names)
assert_array_equal(names_out, iris.feature_names) | Check one-to-one transformers give correct feature names. | test_one_to_one_features | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_data.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_data.py | BSD-3-Clause |
def test_power_transformer_constant_feature(standardize):
"""Check that PowerTransfomer leaves constant features unchanged."""
X = [[-2, 0, 2], [-2, 0, 2], [-2, 0, 2]]
pt = PowerTransformer(method="yeo-johnson", standardize=standardize).fit(X)
assert_allclose(pt.lambdas_, [1, 1, 1])
Xft = pt.fit_... | Check that PowerTransfomer leaves constant features unchanged. | test_power_transformer_constant_feature | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_data.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_data.py | BSD-3-Clause |
def test_power_transformer_no_warnings():
"""Verify that PowerTransformer operates without raising any warnings on valid data.
This test addresses numerical issues with floating point numbers (mostly
overflows) with the Yeo-Johnson transform, see
https://github.com/scikit-learn/scikit-learn/issues/2331... | Verify that PowerTransformer operates without raising any warnings on valid data.
This test addresses numerical issues with floating point numbers (mostly
overflows) with the Yeo-Johnson transform, see
https://github.com/scikit-learn/scikit-learn/issues/23319#issuecomment-1464933635
| test_power_transformer_no_warnings | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_data.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_data.py | BSD-3-Clause |
def _test_no_warnings(data):
"""Internal helper to test for unexpected warnings."""
with warnings.catch_warnings(record=True) as caught_warnings:
warnings.simplefilter("always") # Ensure all warnings are captured
PowerTransformer(method="yeo-johnson", standardize=True).fit_trans... | Internal helper to test for unexpected warnings. | _test_no_warnings | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_data.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_data.py | BSD-3-Clause |
def test_yeojohnson_for_different_scipy_version():
"""Check that the results are consistent across different SciPy versions."""
pt = PowerTransformer(method="yeo-johnson").fit(X_1col)
pt.lambdas_[0] == pytest.approx(0.99546157, rel=1e-7) | Check that the results are consistent across different SciPy versions. | test_yeojohnson_for_different_scipy_version | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_data.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_data.py | BSD-3-Clause |
def test_kbinsdiscretizer_effect_sample_weight():
"""Check the impact of `sample_weight` one computed quantiles."""
X = np.array([[-2], [-1], [1], [3], [500], [1000]])
# add a large number of bins such that each sample with a non-null weight
# will be used as bin edge
est = KBinsDiscretizer(
... | Check the impact of `sample_weight` one computed quantiles. | test_kbinsdiscretizer_effect_sample_weight | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_discretization.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_discretization.py | BSD-3-Clause |
def test_kbinsdiscretizer_no_mutating_sample_weight(strategy):
"""Make sure that `sample_weight` is not changed in place."""
if strategy == "quantile":
est = KBinsDiscretizer(
n_bins=3,
encode="ordinal",
strategy=strategy,
quantile_method="averaged_invert... | Make sure that `sample_weight` is not changed in place. | test_kbinsdiscretizer_no_mutating_sample_weight | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_discretization.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_discretization.py | BSD-3-Clause |
def test_kbinsdiscrtizer_get_feature_names_out(encode, expected_names):
"""Check get_feature_names_out for different settings.
Non-regression test for #22731
"""
X = [[-2, 1, -4], [-1, 2, -3], [0, 3, -2], [1, 4, -1]]
kbd = KBinsDiscretizer(
n_bins=4, encode=encode, quantile_method="averaged... | Check get_feature_names_out for different settings.
Non-regression test for #22731
| test_kbinsdiscrtizer_get_feature_names_out | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_discretization.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_discretization.py | BSD-3-Clause |
def test_one_hot_encoder_custom_feature_name_combiner():
"""Check the behaviour of `feature_name_combiner` as a callable."""
def name_combiner(feature, category):
return feature + "_" + repr(category)
enc = OneHotEncoder(feature_name_combiner=name_combiner)
X = np.array([["None", None]], dtype... | Check the behaviour of `feature_name_combiner` as a callable. | test_one_hot_encoder_custom_feature_name_combiner | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_one_hot_encoder_inverse_transform_raise_error_with_unknown(
X, X_trans, sparse_
):
"""Check that `inverse_transform` raise an error with unknown samples, no
dropped feature, and `handle_unknow="error`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/14934
""... | Check that `inverse_transform` raise an error with unknown samples, no
dropped feature, and `handle_unknow="error`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/14934
| test_one_hot_encoder_inverse_transform_raise_error_with_unknown | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_encoder_nan_ending_specified_categories(Encoder):
"""Test encoder for specified categories that nan is at the end.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27088
"""
cats = [np.array([0, np.nan, 1])]
enc = Encoder(categories=cats)
X = np.array([[... | Test encoder for specified categories that nan is at the end.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27088
| test_encoder_nan_ending_specified_categories | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_two_levels(kwargs, categories):
"""Test that different parameters for combine 'a', 'c', and 'd' into
the infrequent category works as expected."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
categories=categories,
... | Test that different parameters for combine 'a', 'c', and 'd' into
the infrequent category works as expected. | test_ohe_infrequent_two_levels | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_two_levels_drop_frequent(drop):
"""Test two levels and dropping the frequent category."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist",
sparse_output=False,
max_categories=2,... | Test two levels and dropping the frequent category. | test_ohe_infrequent_two_levels_drop_frequent | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_two_levels_drop_infrequent_errors(drop):
"""Test two levels and dropping any infrequent category removes the
whole infrequent category."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist",
... | Test two levels and dropping any infrequent category removes the
whole infrequent category. | test_ohe_infrequent_two_levels_drop_infrequent_errors | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_three_levels(kwargs):
"""Test that different parameters for combing 'a', and 'd' into
the infrequent category works as expected."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist", sparse_o... | Test that different parameters for combing 'a', and 'd' into
the infrequent category works as expected. | test_ohe_infrequent_three_levels | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_three_levels_drop_frequent(drop):
"""Test three levels and dropping the frequent category."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist",
sparse_output=False,
max_categorie... | Test three levels and dropping the frequent category. | test_ohe_infrequent_three_levels_drop_frequent | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_three_levels_drop_infrequent_errors(drop):
"""Test three levels and dropping the infrequent category."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist",
sparse_output=False,
max... | Test three levels and dropping the infrequent category. | test_ohe_infrequent_three_levels_drop_infrequent_errors | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_handle_unknown_error():
"""Test that different parameters for combining 'a', and 'd' into
the infrequent category works as expected."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ohe = OneHotEncoder(
handle_unknown="error", sparse_output=Fals... | Test that different parameters for combining 'a', and 'd' into
the infrequent category works as expected. | test_ohe_infrequent_handle_unknown_error | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_two_levels_user_cats_one_frequent(kwargs):
"""'a' is the only frequent category, all other categories are infrequent."""
X_train = np.array([["a"] * 5 + ["e"] * 30], dtype=object).T
ohe = OneHotEncoder(
categories=[["c", "d", "a", "b"]],
sparse_output=False,
... | 'a' is the only frequent category, all other categories are infrequent. | test_ohe_infrequent_two_levels_user_cats_one_frequent | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_two_levels_user_cats():
"""Test that the order of the categories provided by a user is respected."""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object
).T
ohe = OneHotEncoder(
categories=[["c", "d", "a", "b"]],
sparse_outp... | Test that the order of the categories provided by a user is respected. | test_ohe_infrequent_two_levels_user_cats | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_three_levels_user_cats():
"""Test that the order of the categories provided by a user is respected.
In this case 'c' is encoded as the first category and 'b' is encoded
as the second one."""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=obj... | Test that the order of the categories provided by a user is respected.
In this case 'c' is encoded as the first category and 'b' is encoded
as the second one. | test_ohe_infrequent_three_levels_user_cats | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_mixed():
"""Test infrequent categories where feature 0 has infrequent categories,
and feature 1 does not."""
# X[:, 0] 1 and 2 are infrequent
# X[:, 1] nothing is infrequent
X = np.c_[[0, 1, 3, 3, 3, 3, 2, 0, 3], [0, 0, 0, 0, 1, 1, 1, 1, 1]]
ohe = OneHotEncoder(max_cate... | Test infrequent categories where feature 0 has infrequent categories,
and feature 1 does not. | test_ohe_infrequent_mixed | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_multiple_categories():
"""Test infrequent categories with feature matrix with 3 features."""
X = np.c_[
[0, 1, 3, 3, 3, 3, 2, 0, 3],
[0, 0, 5, 1, 1, 10, 5, 5, 0],
[1, 0, 1, 0, 1, 0, 1, 0, 1],
]
ohe = OneHotEncoder(
categories="auto", max_categori... | Test infrequent categories with feature matrix with 3 features. | test_ohe_infrequent_multiple_categories | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_multiple_categories_dtypes():
"""Test infrequent categories with a pandas dataframe with multiple dtypes."""
pd = pytest.importorskip("pandas")
X = pd.DataFrame(
{
"str": ["a", "f", "c", "f", "f", "a", "c", "b", "b"],
"int": [5, 3, 0, 10, 10, 12, 0, 3... | Test infrequent categories with a pandas dataframe with multiple dtypes. | test_ohe_infrequent_multiple_categories_dtypes | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_infrequent_one_level_errors(kwargs):
"""All user provided categories are infrequent."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 2]).T
ohe = OneHotEncoder(
handle_unknown="infrequent_if_exist", sparse_output=False, **kwargs
)
ohe.fit(X_train)
X_tra... | All user provided categories are infrequent. | test_ohe_infrequent_one_level_errors | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_encoders_string_categories(input_dtype, category_dtype, array_type):
"""Check that encoding work with object, unicode, and byte string dtypes.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/15616
https://github.com/scikit-learn/scikit-learn/issues/15726
https:/... | Check that encoding work with object, unicode, and byte string dtypes.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/15616
https://github.com/scikit-learn/scikit-learn/issues/15726
https://github.com/scikit-learn/scikit-learn/issues/19677
| test_encoders_string_categories | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_mixed_string_bytes_categoricals():
"""Check that this mixture of predefined categories and X raises an error.
Categories defined as bytes can not easily be compared to data that is
a string.
"""
# data as unicode
X = np.array([["b"], ["a"]], dtype="U")
# predefined categories as by... | Check that this mixture of predefined categories and X raises an error.
Categories defined as bytes can not easily be compared to data that is
a string.
| test_mixed_string_bytes_categoricals | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_drop_first_handle_unknown_ignore_warns(handle_unknown):
"""Check drop='first' and handle_unknown='ignore'/'infrequent_if_exist'
during transform."""
X = [["a", 0], ["b", 2], ["b", 1]]
ohe = OneHotEncoder(
drop="first", sparse_output=False, handle_unknown=handle_unknown
)
X_... | Check drop='first' and handle_unknown='ignore'/'infrequent_if_exist'
during transform. | test_ohe_drop_first_handle_unknown_ignore_warns | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_drop_if_binary_handle_unknown_ignore_warns(handle_unknown):
"""Check drop='if_binary' and handle_unknown='ignore' during transform."""
X = [["a", 0], ["b", 2], ["b", 1]]
ohe = OneHotEncoder(
drop="if_binary", sparse_output=False, handle_unknown=handle_unknown
)
X_trans = ohe.fi... | Check drop='if_binary' and handle_unknown='ignore' during transform. | test_ohe_drop_if_binary_handle_unknown_ignore_warns | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_drop_first_explicit_categories(handle_unknown):
"""Check drop='first' and handle_unknown='ignore'/'infrequent_if_exist'
during fit with categories passed in."""
X = [["a", 0], ["b", 2], ["b", 1]]
ohe = OneHotEncoder(
drop="first",
sparse_output=False,
handle_unknow... | Check drop='first' and handle_unknown='ignore'/'infrequent_if_exist'
during fit with categories passed in. | test_ohe_drop_first_explicit_categories | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ohe_more_informative_error_message():
"""Raise informative error message when pandas output and sparse_output=True."""
pd = pytest.importorskip("pandas")
df = pd.DataFrame({"a": [1, 2, 3], "b": ["z", "b", "b"]}, columns=["a", "b"])
ohe = OneHotEncoder(sparse_output=True)
ohe.set_output(tra... | Raise informative error message when pandas output and sparse_output=True. | test_ohe_more_informative_error_message | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_passthrough_missing_values_float_errors_dtype():
"""Test ordinal encoder with nan passthrough fails when dtype=np.int32."""
X = np.array([[np.nan, 3.0, 1.0, 3.0]]).T
oe = OrdinalEncoder(dtype=np.int32)
msg = (
r"There are missing values in features \[0\]. For OrdinalEn... | Test ordinal encoder with nan passthrough fails when dtype=np.int32. | test_ordinal_encoder_passthrough_missing_values_float_errors_dtype | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_passthrough_missing_values_float(encoded_missing_value):
"""Test ordinal encoder with nan on float dtypes."""
X = np.array([[np.nan, 3.0, 1.0, 3.0]], dtype=np.float64).T
oe = OrdinalEncoder(encoded_missing_value=encoded_missing_value).fit(X)
assert len(oe.categories_) == 1
... | Test ordinal encoder with nan on float dtypes. | test_ordinal_encoder_passthrough_missing_values_float | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_missing_value_support_pandas_categorical(
pd_nan_type, encoded_missing_value
):
"""Check ordinal encoder is compatible with pandas."""
# checks pandas dataframe with categorical features
pd = pytest.importorskip("pandas")
pd_missing_value = pd.NA if pd_nan_type == "pd.NA" e... | Check ordinal encoder is compatible with pandas. | test_ordinal_encoder_missing_value_support_pandas_categorical | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_specified_categories_missing_passthrough(
X, X2, cats, cat_dtype
):
"""Test ordinal encoder for specified categories."""
oe = OrdinalEncoder(categories=cats)
exp = np.array([[0.0], [np.nan]])
assert_array_equal(oe.fit_transform(X), exp)
# manually specified categories sh... | Test ordinal encoder for specified categories. | test_ordinal_encoder_specified_categories_missing_passthrough | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_encoder_duplicate_specified_categories(Encoder):
"""Test encoder for specified categories have duplicate values.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27088
"""
cats = [np.array(["a", "b", "a"], dtype=object)]
enc = Encoder(categories=cats)
X ... | Test encoder for specified categories have duplicate values.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27088
| test_encoder_duplicate_specified_categories | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_handle_missing_and_unknown(X, expected_X_trans, X_test):
"""Test the interaction between missing values and handle_unknown"""
oe = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1)
X_trans = oe.fit_transform(X)
assert_allclose(X_trans, expected_X_trans)
... | Test the interaction between missing values and handle_unknown | test_ordinal_encoder_handle_missing_and_unknown | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_sparse(csr_container):
"""Check that we raise proper error with sparse input in OrdinalEncoder.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/19878
"""
X = np.array([[3, 2, 1], [0, 1, 1]])
X_sparse = csr_container(X)
encoder = OrdinalE... | Check that we raise proper error with sparse input in OrdinalEncoder.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/19878
| test_ordinal_encoder_sparse | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_fit_with_unseen_category():
"""Check OrdinalEncoder.fit works with unseen category when
`handle_unknown="use_encoded_value"`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/19872
"""
X = np.array([0, 0, 1, 0, 2, 5])[:, np.newaxis]
oe = O... | Check OrdinalEncoder.fit works with unseen category when
`handle_unknown="use_encoded_value"`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/19872
| test_ordinal_encoder_fit_with_unseen_category | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_handle_unknown_string_dtypes(X_train, X_test):
"""Checks that `OrdinalEncoder` transforms string dtypes.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/19872
"""
enc = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-9)
enc.... | Checks that `OrdinalEncoder` transforms string dtypes.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/19872
| test_ordinal_encoder_handle_unknown_string_dtypes | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_python_integer():
"""Check that `OrdinalEncoder` accepts Python integers that are potentially
larger than 64 bits.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/20721
"""
X = np.array(
[
44253463435747313673,
... | Check that `OrdinalEncoder` accepts Python integers that are potentially
larger than 64 bits.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/20721
| test_ordinal_encoder_python_integer | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_features_names_out_pandas():
"""Check feature names out is same as the input."""
pd = pytest.importorskip("pandas")
names = ["b", "c", "a"]
X = pd.DataFrame([[1, 2, 3]], columns=names)
enc = OrdinalEncoder().fit(X)
feature_names_out = enc.get_feature_names_out()
as... | Check feature names out is same as the input. | test_ordinal_encoder_features_names_out_pandas | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_unknown_missing_interaction():
"""Check interactions between encode_unknown and missing value encoding."""
X = np.array([["a"], ["b"], [np.nan]], dtype=object)
oe = OrdinalEncoder(
handle_unknown="use_encoded_value",
unknown_value=np.nan,
encoded_missing_va... | Check interactions between encode_unknown and missing value encoding. | test_ordinal_encoder_unknown_missing_interaction | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_encoded_missing_value_error(with_pandas):
"""Check OrdinalEncoder errors when encoded_missing_value is used by
an known category."""
X = np.array([["a", "dog"], ["b", "cat"], ["c", np.nan]], dtype=object)
# The 0-th feature has no missing values so it is not included in the lis... | Check OrdinalEncoder errors when encoded_missing_value is used by
an known category. | test_ordinal_encoder_encoded_missing_value_error | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_unknown_missing_interaction_both_nan(
X_train, X_test_trans_expected, X_roundtrip_expected
):
"""Check transform when unknown_value and encoded_missing_value is nan.
Non-regression test for #24082.
"""
oe = OrdinalEncoder(
handle_unknown="use_encoded_value",
... | Check transform when unknown_value and encoded_missing_value is nan.
Non-regression test for #24082.
| test_ordinal_encoder_unknown_missing_interaction_both_nan | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_predefined_categories_dtype():
"""Check that the categories_ dtype is `object` for string categories
Regression test for gh-25171.
"""
categories = [["as", "mmas", "eas", "ras", "acs"], ["1", "2"]]
enc = OneHotEncoder(categories=categories)
enc.fit([["as", "1"]])
assert len(cate... | Check that the categories_ dtype is `object` for string categories
Regression test for gh-25171.
| test_predefined_categories_dtype | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_missing_unknown_encoding_max():
"""Check missing value or unknown encoding can equal the cardinality."""
X = np.array([["dog"], ["cat"], [np.nan]], dtype=object)
X_trans = OrdinalEncoder(encoded_missing_value=2).fit_transform(X)
assert_allclose(X_trans, [[1], [0], [2]])
enc... | Check missing value or unknown encoding can equal the cardinality. | test_ordinal_encoder_missing_unknown_encoding_max | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_drop_idx_infrequent_categories():
"""Check drop_idx is defined correctly with infrequent categories.
Non-regression test for gh-25550.
"""
X = np.array(
[["a"] * 2 + ["b"] * 4 + ["c"] * 4 + ["d"] * 4 + ["e"] * 4], dtype=object
).T
ohe = OneHotEncoder(min_frequency=4, sparse_out... | Check drop_idx is defined correctly with infrequent categories.
Non-regression test for gh-25550.
| test_drop_idx_infrequent_categories | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_infrequent_three_levels(kwargs):
"""Test parameters for grouping 'a', and 'd' into the infrequent category."""
X_train = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3]).T
ordinal = OrdinalEncoder(
handle_unknown="use_encoded_value", unknown_value=-1, **kwargs
... | Test parameters for grouping 'a', and 'd' into the infrequent category. | test_ordinal_encoder_infrequent_three_levels | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_infrequent_three_levels_user_cats():
"""Test that the order of the categories provided by a user is respected.
In this case 'c' is encoded as the first category and 'b' is encoded
as the second one.
"""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d... | Test that the order of the categories provided by a user is respected.
In this case 'c' is encoded as the first category and 'b' is encoded
as the second one.
| test_ordinal_encoder_infrequent_three_levels_user_cats | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_infrequent_mixed():
"""Test when feature 0 has infrequent categories and feature 1 does not."""
X = np.column_stack(([0, 1, 3, 3, 3, 3, 2, 0, 3], [0, 0, 0, 0, 1, 1, 1, 1, 1]))
ordinal = OrdinalEncoder(max_categories=3).fit(X)
assert_array_equal(ordinal.infrequent_categories_[... | Test when feature 0 has infrequent categories and feature 1 does not. | test_ordinal_encoder_infrequent_mixed | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_infrequent_multiple_categories_dtypes():
"""Test infrequent categories with a pandas DataFrame with multiple dtypes."""
pd = pytest.importorskip("pandas")
categorical_dtype = pd.CategoricalDtype(["bird", "cat", "dog", "snake"])
X = pd.DataFrame(
{
"str": ["a... | Test infrequent categories with a pandas DataFrame with multiple dtypes. | test_ordinal_encoder_infrequent_multiple_categories_dtypes | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_infrequent_custom_mapping():
"""Check behavior of unknown_value and encoded_missing_value with infrequent."""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3 + [np.nan]], dtype=object
).T
ordinal = OrdinalEncoder(
handle_unknown="use_encoded... | Check behavior of unknown_value and encoded_missing_value with infrequent. | test_ordinal_encoder_infrequent_custom_mapping | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_all_frequent(kwargs):
"""All categories are considered frequent have same encoding as default encoder."""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object
).T
adjusted_encoder = OrdinalEncoder(
**kwargs, handle_unknown="use_enc... | All categories are considered frequent have same encoding as default encoder. | test_ordinal_encoder_all_frequent | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_all_infrequent(kwargs):
"""When all categories are infrequent, they are all encoded as zero."""
X_train = np.array(
[["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object
).T
encoder = OrdinalEncoder(
**kwargs, handle_unknown="use_encoded_value", unknown... | When all categories are infrequent, they are all encoded as zero. | test_ordinal_encoder_all_infrequent | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_missing_appears_frequent():
"""Check behavior when missing value appears frequently."""
X = np.array(
[[np.nan] * 20 + ["dog"] * 10 + ["cat"] * 5 + ["snake"] + ["deer"]],
dtype=object,
).T
ordinal = OrdinalEncoder(max_categories=3).fit(X)
X_test = np.array([... | Check behavior when missing value appears frequently. | test_ordinal_encoder_missing_appears_frequent | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_ordinal_encoder_missing_appears_infrequent():
"""Check behavior when missing value appears infrequently."""
# feature 0 has infrequent categories
# feature 1 has no infrequent categories
X = np.array(
[
[np.nan] + ["dog"] * 10 + ["cat"] * 5 + ["snake"] + ["deer"],
... | Check behavior when missing value appears infrequently. | test_ordinal_encoder_missing_appears_infrequent | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_encoder_not_fitted(Encoder):
"""Check that we raise a `NotFittedError` by calling transform before fit with
the encoders.
One could expect that the passing the `categories` argument to the encoder
would make it stateless. However, `fit` is making a couple of check, such as the
position of ... | Check that we raise a `NotFittedError` by calling transform before fit with
the encoders.
One could expect that the passing the `categories` argument to the encoder
would make it stateless. However, `fit` is making a couple of check, such as the
position of `np.nan`.
| test_encoder_not_fitted | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_encoders.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_encoders.py | BSD-3-Clause |
def test_function_transformer_raise_error_with_mixed_dtype(X_type):
"""Check that `FunctionTransformer.check_inverse` raises error on mixed dtype."""
mapping = {"one": 1, "two": 2, "three": 3, 5: "five", 6: "six"}
inverse_mapping = {value: key for key, value in mapping.items()}
dtype = "object"
dat... | Check that `FunctionTransformer.check_inverse` raises error on mixed dtype. | test_function_transformer_raise_error_with_mixed_dtype | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_function_transformer.py | BSD-3-Clause |
def test_function_transformer_support_all_nummerical_dataframes_check_inverse_True():
"""Check support for dataframes with only numerical values."""
pd = pytest.importorskip("pandas")
df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
transformer = FunctionTransformer(
func=lambda x: x + 2, in... | Check support for dataframes with only numerical values. | test_function_transformer_support_all_nummerical_dataframes_check_inverse_True | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_function_transformer.py | BSD-3-Clause |
def test_function_transformer_with_dataframe_and_check_inverse_True():
"""Check error is raised when check_inverse=True.
Non-regresion test for gh-25261.
"""
pd = pytest.importorskip("pandas")
transformer = FunctionTransformer(
func=lambda x: x, inverse_func=lambda x: x, check_inverse=True
... | Check error is raised when check_inverse=True.
Non-regresion test for gh-25261.
| test_function_transformer_with_dataframe_and_check_inverse_True | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_function_transformer.py | BSD-3-Clause |
def test_function_transformer_validate_inverse():
"""Test that function transformer does not reset estimator in
`inverse_transform`."""
def add_constant_feature(X):
X_one = np.ones((X.shape[0], 1))
return np.concatenate((X, X_one), axis=1)
def inverse_add_constant(X):
return X[... | Test that function transformer does not reset estimator in
`inverse_transform`. | test_function_transformer_validate_inverse | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_function_transformer.py | BSD-3-Clause |
def test_get_feature_names_out_dataframe_with_string_data(
feature_names_out, expected, in_pipeline
):
"""Check that get_feature_names_out works with DataFrames with string data."""
pd = pytest.importorskip("pandas")
X = pd.DataFrame({"pet": ["dog", "cat"], "color": ["red", "green"]})
def func(X):
... | Check that get_feature_names_out works with DataFrames with string data. | test_get_feature_names_out_dataframe_with_string_data | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_function_transformer.py | BSD-3-Clause |
def test_set_output_func():
"""Check behavior of set_output with different settings."""
pd = pytest.importorskip("pandas")
X = pd.DataFrame({"a": [1, 2, 3], "b": [10, 20, 100]})
ft = FunctionTransformer(np.log, feature_names_out="one-to-one")
# no warning is raised when feature_names_out is defin... | Check behavior of set_output with different settings. | test_set_output_func | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_function_transformer.py | BSD-3-Clause |
def test_consistence_column_name_between_steps():
"""Check that we have a consistence between the feature names out of
`FunctionTransformer` and the feature names in of the next step in the pipeline.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27695
"""
pd = pyt... | Check that we have a consistence between the feature names out of
`FunctionTransformer` and the feature names in of the next step in the pipeline.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27695
| test_consistence_column_name_between_steps | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_function_transformer.py | BSD-3-Clause |
def test_function_transformer_overwrite_column_names(dataframe_lib, transform_output):
"""Check that we overwrite the column names when we should."""
lib = pytest.importorskip(dataframe_lib)
if transform_output != "numpy":
pytest.importorskip(transform_output)
df = lib.DataFrame({"a": [1, 2, 3]... | Check that we overwrite the column names when we should. | test_function_transformer_overwrite_column_names | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_function_transformer.py | BSD-3-Clause |
def test_function_transformer_overwrite_column_names_numerical(feature_names_out):
"""Check the same as `test_function_transformer_overwrite_column_names`
but for the specific case of pandas where column names can be numerical."""
pd = pytest.importorskip("pandas")
df = pd.DataFrame({0: [1, 2, 3], 1: [... | Check the same as `test_function_transformer_overwrite_column_names`
but for the specific case of pandas where column names can be numerical. | test_function_transformer_overwrite_column_names_numerical | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_function_transformer.py | BSD-3-Clause |
def test_function_transformer_error_column_inconsistent(
dataframe_lib, feature_names_out
):
"""Check that we raise an error when `func` returns a dataframe with new
column names that become inconsistent with `get_feature_names_out`."""
lib = pytest.importorskip(dataframe_lib)
df = lib.DataFrame({"... | Check that we raise an error when `func` returns a dataframe with new
column names that become inconsistent with `get_feature_names_out`. | test_function_transformer_error_column_inconsistent | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_function_transformer.py | BSD-3-Clause |
def test_label_binarizer_pandas_nullable(dtype, unique_first):
"""Checks that LabelBinarizer works with pandas nullable dtypes.
Non-regression test for gh-25637.
"""
pd = pytest.importorskip("pandas")
y_true = pd.Series([1, 0, 0, 1, 0, 1, 1, 0, 1], dtype=dtype)
if unique_first:
# Calli... | Checks that LabelBinarizer works with pandas nullable dtypes.
Non-regression test for gh-25637.
| test_label_binarizer_pandas_nullable | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_label.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_label.py | BSD-3-Clause |
def test_nan_label_encoder():
"""Check that label encoder encodes nans in transform.
Non-regression test for #22628.
"""
le = LabelEncoder()
le.fit(["a", "a", "b", np.nan])
y_trans = le.transform([np.nan])
assert_array_equal(y_trans, [2]) | Check that label encoder encodes nans in transform.
Non-regression test for #22628.
| test_nan_label_encoder | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_label.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_label.py | BSD-3-Clause |
def test_label_encoders_do_not_have_set_output(encoder):
"""Check that label encoders do not define set_output and work with y as a kwarg.
Non-regression test for #26854.
"""
assert not hasattr(encoder, "set_output")
y_encoded_with_kwarg = encoder.fit_transform(y=["a", "b", "c"])
y_encoded_posi... | Check that label encoders do not define set_output and work with y as a kwarg.
Non-regression test for #26854.
| test_label_encoders_do_not_have_set_output | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_label.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_label.py | BSD-3-Clause |
def test_polynomial_and_spline_array_order(est):
"""Test that output array has the given order."""
X = np.arange(10).reshape(5, 2)
def is_c_contiguous(a):
return np.isfortran(a.T)
assert is_c_contiguous(est().fit_transform(X))
assert is_c_contiguous(est(order="C").fit_transform(X))
ass... | Test that output array has the given order. | test_polynomial_and_spline_array_order | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_polynomial.py | BSD-3-Clause |
def test_spline_transformer_input_validation(params, err_msg):
"""Test that we raise errors for invalid input in SplineTransformer."""
X = [[1], [2]]
with pytest.raises(ValueError, match=err_msg):
SplineTransformer(**params).fit(X) | Test that we raise errors for invalid input in SplineTransformer. | test_spline_transformer_input_validation | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_polynomial.py | BSD-3-Clause |
def test_spline_transformer_integer_knots(extrapolation):
"""Test that SplineTransformer accepts integer value knot positions."""
X = np.arange(20).reshape(10, 2)
knots = [[0, 1], [1, 2], [5, 5], [11, 10], [12, 11]]
_ = SplineTransformer(
degree=3, knots=knots, extrapolation=extrapolation
).... | Test that SplineTransformer accepts integer value knot positions. | test_spline_transformer_integer_knots | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_polynomial.py | BSD-3-Clause |
def test_spline_transformer_feature_names():
"""Test that SplineTransformer generates correct features name."""
X = np.arange(20).reshape(10, 2)
splt = SplineTransformer(n_knots=3, degree=3, include_bias=True).fit(X)
feature_names = splt.get_feature_names_out()
assert_array_equal(
feature_na... | Test that SplineTransformer generates correct features name. | test_spline_transformer_feature_names | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_polynomial.py | BSD-3-Clause |
def test_split_transform_feature_names_extrapolation_degree(extrapolation, degree):
"""Test feature names are correct for different extrapolations and degree.
Non-regression test for gh-25292.
"""
X = np.arange(20).reshape(10, 2)
splt = SplineTransformer(degree=degree, extrapolation=extrapolation).... | Test feature names are correct for different extrapolations and degree.
Non-regression test for gh-25292.
| test_split_transform_feature_names_extrapolation_degree | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_polynomial.py | BSD-3-Clause |
def test_spline_transformer_unity_decomposition(degree, n_knots, knots, extrapolation):
"""Test that B-splines are indeed a decomposition of unity.
Splines basis functions must sum up to 1 per row, if we stay in between boundaries.
"""
X = np.linspace(0, 1, 100)[:, None]
# make the boundaries 0 and... | Test that B-splines are indeed a decomposition of unity.
Splines basis functions must sum up to 1 per row, if we stay in between boundaries.
| test_spline_transformer_unity_decomposition | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_polynomial.py | BSD-3-Clause |
def test_spline_transformer_linear_regression(bias, intercept):
"""Test that B-splines fit a sinusodial curve pretty well."""
X = np.linspace(0, 10, 100)[:, None]
y = np.sin(X[:, 0]) + 2 # +2 to avoid the value 0 in assert_allclose
pipe = Pipeline(
steps=[
(
"spline"... | Test that B-splines fit a sinusodial curve pretty well. | test_spline_transformer_linear_regression | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_polynomial.py | BSD-3-Clause |
def test_spline_transformer_get_base_knot_positions(
knots, n_knots, sample_weight, expected_knots
):
"""Check the behaviour to find knot positions with and without sample_weight."""
X = np.array([[0, 2], [0, 2], [2, 2], [3, 3], [4, 6], [5, 8], [6, 14]])
base_knots = SplineTransformer._get_base_knot_pos... | Check the behaviour to find knot positions with and without sample_weight. | test_spline_transformer_get_base_knot_positions | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_polynomial.py | BSD-3-Clause |
def test_spline_transformer_periodic_linear_regression(bias, intercept):
"""Test that B-splines fit a periodic curve pretty well."""
# "+ 3" to avoid the value 0 in assert_allclose
def f(x):
return np.sin(2 * np.pi * x) - np.sin(8 * np.pi * x) + 3
X = np.linspace(0, 1, 101)[:, None]
pipe =... | Test that B-splines fit a periodic curve pretty well. | test_spline_transformer_periodic_linear_regression | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_polynomial.py | BSD-3-Clause |
def test_spline_transformer_periodic_spline_backport():
"""Test that the backport of extrapolate="periodic" works correctly"""
X = np.linspace(-2, 3.5, 10)[:, None]
degree = 2
# Use periodic extrapolation backport in SplineTransformer
transformer = SplineTransformer(
degree=degree, extrapol... | Test that the backport of extrapolate="periodic" works correctly | test_spline_transformer_periodic_spline_backport | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_polynomial.py | BSD-3-Clause |
def test_spline_transformer_periodic_splines_periodicity():
"""Test if shifted knots result in the same transformation up to permutation."""
X = np.linspace(0, 10, 101)[:, None]
transformer_1 = SplineTransformer(
degree=3,
extrapolation="periodic",
knots=[[0.0], [1.0], [3.0], [4.0],... | Test if shifted knots result in the same transformation up to permutation. | test_spline_transformer_periodic_splines_periodicity | python | scikit-learn/scikit-learn | sklearn/preprocessing/tests/test_polynomial.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/tests/test_polynomial.py | BSD-3-Clause |
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