# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import pickle
import re
import warnings
import numpy as np
import pytest
import scipy.sparse as sp
from numpy.testing import assert_allclose
import sklearn
from sklearn import config_context, datasets
from sklearn.base import (
BaseEstimator,
OutlierMixin,
TransformerMixin,
clone,
is_classifier,
is_clusterer,
is_outlier_detector,
is_regressor,
)
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.ensemble import IsolationForest
from sklearn.exceptions import InconsistentVersionWarning
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC, SVR
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.utils._mocking import MockDataFrame
from sklearn.utils._set_output import _get_output_config
from sklearn.utils._testing import (
_convert_container,
assert_array_equal,
)
from sklearn.utils.validation import _check_n_features, validate_data
#############################################################################
# A few test classes
class MyEstimator(BaseEstimator):
def __init__(self, l1=0, empty=None):
self.l1 = l1
self.empty = empty
class K(BaseEstimator):
def __init__(self, c=None, d=None):
self.c = c
self.d = d
class T(BaseEstimator):
def __init__(self, a=None, b=None):
self.a = a
self.b = b
class NaNTag(BaseEstimator):
def __sklearn_tags__(self):
tags = super().__sklearn_tags__()
tags.input_tags.allow_nan = True
return tags
class NoNaNTag(BaseEstimator):
def __sklearn_tags__(self):
tags = super().__sklearn_tags__()
tags.input_tags.allow_nan = False
return tags
class OverrideTag(NaNTag):
def __sklearn_tags__(self):
tags = super().__sklearn_tags__()
tags.input_tags.allow_nan = False
return tags
class DiamondOverwriteTag(NaNTag, NoNaNTag):
pass
class InheritDiamondOverwriteTag(DiamondOverwriteTag):
pass
class ModifyInitParams(BaseEstimator):
"""Deprecated behavior.
Equal parameters but with a type cast.
Doesn't fulfill a is a
"""
def __init__(self, a=np.array([0])):
self.a = a.copy()
class Buggy(BaseEstimator):
"A buggy estimator that does not set its parameters right."
def __init__(self, a=None):
self.a = 1
class NoEstimator:
def __init__(self):
pass
def fit(self, X=None, y=None):
return self
def predict(self, X=None):
return None
class VargEstimator(BaseEstimator):
"""scikit-learn estimators shouldn't have vargs."""
def __init__(self, *vargs):
pass
#############################################################################
# The tests
def test_clone():
# Tests that clone creates a correct deep copy.
# We create an estimator, make a copy of its original state
# (which, in this case, is the current state of the estimator),
# and check that the obtained copy is a correct deep copy.
from sklearn.feature_selection import SelectFpr, f_classif
selector = SelectFpr(f_classif, alpha=0.1)
new_selector = clone(selector)
assert selector is not new_selector
assert selector.get_params() == new_selector.get_params()
selector = SelectFpr(f_classif, alpha=np.zeros((10, 2)))
new_selector = clone(selector)
assert selector is not new_selector
def test_clone_2():
# Tests that clone doesn't copy everything.
# We first create an estimator, give it an own attribute, and
# make a copy of its original state. Then we check that the copy doesn't
# have the specific attribute we manually added to the initial estimator.
from sklearn.feature_selection import SelectFpr, f_classif
selector = SelectFpr(f_classif, alpha=0.1)
selector.own_attribute = "test"
new_selector = clone(selector)
assert not hasattr(new_selector, "own_attribute")
def test_clone_buggy():
# Check that clone raises an error on buggy estimators.
buggy = Buggy()
buggy.a = 2
with pytest.raises(RuntimeError):
clone(buggy)
no_estimator = NoEstimator()
with pytest.raises(TypeError):
clone(no_estimator)
varg_est = VargEstimator()
with pytest.raises(RuntimeError):
clone(varg_est)
est = ModifyInitParams()
with pytest.raises(RuntimeError):
clone(est)
def test_clone_empty_array():
# Regression test for cloning estimators with empty arrays
clf = MyEstimator(empty=np.array([]))
clf2 = clone(clf)
assert_array_equal(clf.empty, clf2.empty)
clf = MyEstimator(empty=sp.csr_matrix(np.array([[0]])))
clf2 = clone(clf)
assert_array_equal(clf.empty.data, clf2.empty.data)
def test_clone_nan():
# Regression test for cloning estimators with default parameter as np.nan
clf = MyEstimator(empty=np.nan)
clf2 = clone(clf)
assert clf.empty is clf2.empty
def test_clone_dict():
# test that clone creates a clone of a dict
orig = {"a": MyEstimator()}
cloned = clone(orig)
assert orig["a"] is not cloned["a"]
def test_clone_sparse_matrices():
sparse_matrix_classes = [
cls
for name in dir(sp)
if name.endswith("_matrix") and type(cls := getattr(sp, name)) is type
]
for cls in sparse_matrix_classes:
sparse_matrix = cls(np.eye(5))
clf = MyEstimator(empty=sparse_matrix)
clf_cloned = clone(clf)
assert clf.empty.__class__ is clf_cloned.empty.__class__
assert_array_equal(clf.empty.toarray(), clf_cloned.empty.toarray())
def test_clone_estimator_types():
# Check that clone works for parameters that are types rather than
# instances
clf = MyEstimator(empty=MyEstimator)
clf2 = clone(clf)
assert clf.empty is clf2.empty
def test_clone_class_rather_than_instance():
# Check that clone raises expected error message when
# cloning class rather than instance
msg = "You should provide an instance of scikit-learn estimator"
with pytest.raises(TypeError, match=msg):
clone(MyEstimator)
def test_repr():
# Smoke test the repr of the base estimator.
my_estimator = MyEstimator()
repr(my_estimator)
test = T(K(), K())
assert repr(test) == "T(a=K(), b=K())"
some_est = T(a=["long_params"] * 1000)
assert len(repr(some_est)) == 485
def test_str():
# Smoke test the str of the base estimator
my_estimator = MyEstimator()
str(my_estimator)
def test_get_params():
test = T(K(), K)
assert "a__d" in test.get_params(deep=True)
assert "a__d" not in test.get_params(deep=False)
test.set_params(a__d=2)
assert test.a.d == 2
with pytest.raises(ValueError):
test.set_params(a__a=2)
# TODO(1.8): Remove this test when the deprecation is removed
def test_is_estimator_type_class():
with pytest.warns(FutureWarning, match="passing a class to.*is deprecated"):
assert is_classifier(SVC)
with pytest.warns(FutureWarning, match="passing a class to.*is deprecated"):
assert is_regressor(SVR)
with pytest.warns(FutureWarning, match="passing a class to.*is deprecated"):
assert is_clusterer(KMeans)
with pytest.warns(FutureWarning, match="passing a class to.*is deprecated"):
assert is_outlier_detector(IsolationForest)
@pytest.mark.parametrize(
"estimator, expected_result",
[
(SVC(), True),
(GridSearchCV(SVC(), {"C": [0.1, 1]}), True),
(Pipeline([("svc", SVC())]), True),
(Pipeline([("svc_cv", GridSearchCV(SVC(), {"C": [0.1, 1]}))]), True),
(SVR(), False),
(GridSearchCV(SVR(), {"C": [0.1, 1]}), False),
(Pipeline([("svr", SVR())]), False),
(Pipeline([("svr_cv", GridSearchCV(SVR(), {"C": [0.1, 1]}))]), False),
],
)
def test_is_classifier(estimator, expected_result):
assert is_classifier(estimator) == expected_result
@pytest.mark.parametrize(
"estimator, expected_result",
[
(SVR(), True),
(GridSearchCV(SVR(), {"C": [0.1, 1]}), True),
(Pipeline([("svr", SVR())]), True),
(Pipeline([("svr_cv", GridSearchCV(SVR(), {"C": [0.1, 1]}))]), True),
(SVC(), False),
(GridSearchCV(SVC(), {"C": [0.1, 1]}), False),
(Pipeline([("svc", SVC())]), False),
(Pipeline([("svc_cv", GridSearchCV(SVC(), {"C": [0.1, 1]}))]), False),
],
)
def test_is_regressor(estimator, expected_result):
assert is_regressor(estimator) == expected_result
@pytest.mark.parametrize(
"estimator, expected_result",
[
(KMeans(), True),
(GridSearchCV(KMeans(), {"n_clusters": [3, 8]}), True),
(Pipeline([("km", KMeans())]), True),
(Pipeline([("km_cv", GridSearchCV(KMeans(), {"n_clusters": [3, 8]}))]), True),
(SVC(), False),
(GridSearchCV(SVC(), {"C": [0.1, 1]}), False),
(Pipeline([("svc", SVC())]), False),
(Pipeline([("svc_cv", GridSearchCV(SVC(), {"C": [0.1, 1]}))]), False),
],
)
def test_is_clusterer(estimator, expected_result):
assert is_clusterer(estimator) == expected_result
def test_set_params():
# test nested estimator parameter setting
clf = Pipeline([("svc", SVC())])
# non-existing parameter in svc
with pytest.raises(ValueError):
clf.set_params(svc__stupid_param=True)
# non-existing parameter of pipeline
with pytest.raises(ValueError):
clf.set_params(svm__stupid_param=True)
# we don't currently catch if the things in pipeline are estimators
# bad_pipeline = Pipeline([("bad", NoEstimator())])
# with pytest.raises(AttributeError):
# bad_pipeline.set_params(bad__stupid_param=True)
def test_set_params_passes_all_parameters():
# Make sure all parameters are passed together to set_params
# of nested estimator. Regression test for #9944
class TestDecisionTree(DecisionTreeClassifier):
def set_params(self, **kwargs):
super().set_params(**kwargs)
# expected_kwargs is in test scope
assert kwargs == expected_kwargs
return self
expected_kwargs = {"max_depth": 5, "min_samples_leaf": 2}
for est in [
Pipeline([("estimator", TestDecisionTree())]),
GridSearchCV(TestDecisionTree(), {}),
]:
est.set_params(estimator__max_depth=5, estimator__min_samples_leaf=2)
def test_set_params_updates_valid_params():
# Check that set_params tries to set SVC().C, not
# DecisionTreeClassifier().C
gscv = GridSearchCV(DecisionTreeClassifier(), {})
gscv.set_params(estimator=SVC(), estimator__C=42.0)
assert gscv.estimator.C == 42.0
@pytest.mark.parametrize(
"tree,dataset",
[
(
DecisionTreeClassifier(max_depth=2, random_state=0),
datasets.make_classification(random_state=0),
),
(
DecisionTreeRegressor(max_depth=2, random_state=0),
datasets.make_regression(random_state=0),
),
],
)
def test_score_sample_weight(tree, dataset):
rng = np.random.RandomState(0)
# check that the score with and without sample weights are different
X, y = dataset
tree.fit(X, y)
# generate random sample weights
sample_weight = rng.randint(1, 10, size=len(y))
score_unweighted = tree.score(X, y)
score_weighted = tree.score(X, y, sample_weight=sample_weight)
msg = "Unweighted and weighted scores are unexpectedly equal"
assert score_unweighted != score_weighted, msg
def test_clone_pandas_dataframe():
class DummyEstimator(TransformerMixin, BaseEstimator):
"""This is a dummy class for generating numerical features
This feature extractor extracts numerical features from pandas data
frame.
Parameters
----------
df: pandas data frame
The pandas data frame parameter.
Notes
-----
"""
def __init__(self, df=None, scalar_param=1):
self.df = df
self.scalar_param = scalar_param
def fit(self, X, y=None):
pass
def transform(self, X):
pass
# build and clone estimator
d = np.arange(10)
df = MockDataFrame(d)
e = DummyEstimator(df, scalar_param=1)
cloned_e = clone(e)
# the test
assert (e.df == cloned_e.df).values.all()
assert e.scalar_param == cloned_e.scalar_param
def test_clone_protocol():
"""Checks that clone works with `__sklearn_clone__` protocol."""
class FrozenEstimator(BaseEstimator):
def __init__(self, fitted_estimator):
self.fitted_estimator = fitted_estimator
def __getattr__(self, name):
return getattr(self.fitted_estimator, name)
def __sklearn_clone__(self):
return self
def fit(self, *args, **kwargs):
return self
def fit_transform(self, *args, **kwargs):
return self.fitted_estimator.transform(*args, **kwargs)
X = np.array([[-1, -1], [-2, -1], [-3, -2]])
pca = PCA().fit(X)
components = pca.components_
frozen_pca = FrozenEstimator(pca)
assert_allclose(frozen_pca.components_, components)
# Calling PCA methods such as `get_feature_names_out` still works
assert_array_equal(frozen_pca.get_feature_names_out(), pca.get_feature_names_out())
# Fitting on a new data does not alter `components_`
X_new = np.asarray([[-1, 2], [3, 4], [1, 2]])
frozen_pca.fit(X_new)
assert_allclose(frozen_pca.components_, components)
# `fit_transform` does not alter state
frozen_pca.fit_transform(X_new)
assert_allclose(frozen_pca.components_, components)
# Cloning estimator is a no-op
clone_frozen_pca = clone(frozen_pca)
assert clone_frozen_pca is frozen_pca
assert_allclose(clone_frozen_pca.components_, components)
def test_pickle_version_warning_is_not_raised_with_matching_version():
iris = datasets.load_iris()
tree = DecisionTreeClassifier().fit(iris.data, iris.target)
tree_pickle = pickle.dumps(tree)
assert b"_sklearn_version" in tree_pickle
with warnings.catch_warnings():
warnings.simplefilter("error")
tree_restored = pickle.loads(tree_pickle)
# test that we can predict with the restored decision tree classifier
score_of_original = tree.score(iris.data, iris.target)
score_of_restored = tree_restored.score(iris.data, iris.target)
assert score_of_original == score_of_restored
class TreeBadVersion(DecisionTreeClassifier):
def __getstate__(self):
return dict(self.__dict__.items(), _sklearn_version="something")
pickle_error_message = (
"Trying to unpickle estimator {estimator} from "
"version {old_version} when using version "
"{current_version}. This might "
"lead to breaking code or invalid results. "
"Use at your own risk."
)
def test_pickle_version_warning_is_issued_upon_different_version():
iris = datasets.load_iris()
tree = TreeBadVersion().fit(iris.data, iris.target)
tree_pickle_other = pickle.dumps(tree)
message = pickle_error_message.format(
estimator="TreeBadVersion",
old_version="something",
current_version=sklearn.__version__,
)
with pytest.warns(UserWarning, match=message) as warning_record:
pickle.loads(tree_pickle_other)
message = warning_record.list[0].message
assert isinstance(message, InconsistentVersionWarning)
assert message.estimator_name == "TreeBadVersion"
assert message.original_sklearn_version == "something"
assert message.current_sklearn_version == sklearn.__version__
class TreeNoVersion(DecisionTreeClassifier):
def __getstate__(self):
return self.__dict__
def test_pickle_version_warning_is_issued_when_no_version_info_in_pickle():
iris = datasets.load_iris()
# TreeNoVersion has no getstate, like pre-0.18
tree = TreeNoVersion().fit(iris.data, iris.target)
tree_pickle_noversion = pickle.dumps(tree)
assert b"_sklearn_version" not in tree_pickle_noversion
message = pickle_error_message.format(
estimator="TreeNoVersion",
old_version="pre-0.18",
current_version=sklearn.__version__,
)
# check we got the warning about using pre-0.18 pickle
with pytest.warns(UserWarning, match=message):
pickle.loads(tree_pickle_noversion)
def test_pickle_version_no_warning_is_issued_with_non_sklearn_estimator():
iris = datasets.load_iris()
tree = TreeNoVersion().fit(iris.data, iris.target)
tree_pickle_noversion = pickle.dumps(tree)
try:
module_backup = TreeNoVersion.__module__
TreeNoVersion.__module__ = "notsklearn"
with warnings.catch_warnings():
warnings.simplefilter("error")
pickle.loads(tree_pickle_noversion)
finally:
TreeNoVersion.__module__ = module_backup
class DontPickleAttributeMixin:
def __getstate__(self):
data = self.__dict__.copy()
data["_attribute_not_pickled"] = None
return data
def __setstate__(self, state):
state["_restored"] = True
self.__dict__.update(state)
class MultiInheritanceEstimator(DontPickleAttributeMixin, BaseEstimator):
def __init__(self, attribute_pickled=5):
self.attribute_pickled = attribute_pickled
self._attribute_not_pickled = None
def test_pickling_when_getstate_is_overwritten_by_mixin():
estimator = MultiInheritanceEstimator()
estimator._attribute_not_pickled = "this attribute should not be pickled"
serialized = pickle.dumps(estimator)
estimator_restored = pickle.loads(serialized)
assert estimator_restored.attribute_pickled == 5
assert estimator_restored._attribute_not_pickled is None
assert estimator_restored._restored
def test_pickling_when_getstate_is_overwritten_by_mixin_outside_of_sklearn():
try:
estimator = MultiInheritanceEstimator()
text = "this attribute should not be pickled"
estimator._attribute_not_pickled = text
old_mod = type(estimator).__module__
type(estimator).__module__ = "notsklearn"
serialized = estimator.__getstate__()
assert serialized == {"_attribute_not_pickled": None, "attribute_pickled": 5}
serialized["attribute_pickled"] = 4
estimator.__setstate__(serialized)
assert estimator.attribute_pickled == 4
assert estimator._restored
finally:
type(estimator).__module__ = old_mod
class SingleInheritanceEstimator(BaseEstimator):
def __init__(self, attribute_pickled=5):
self.attribute_pickled = attribute_pickled
self._attribute_not_pickled = None
def __getstate__(self):
state = super().__getstate__()
state["_attribute_not_pickled"] = None
return state
def test_pickling_works_when_getstate_is_overwritten_in_the_child_class():
estimator = SingleInheritanceEstimator()
estimator._attribute_not_pickled = "this attribute should not be pickled"
serialized = pickle.dumps(estimator)
estimator_restored = pickle.loads(serialized)
assert estimator_restored.attribute_pickled == 5
assert estimator_restored._attribute_not_pickled is None
def test_tag_inheritance():
# test that changing tags by inheritance is not allowed
nan_tag_est = NaNTag()
no_nan_tag_est = NoNaNTag()
assert nan_tag_est.__sklearn_tags__().input_tags.allow_nan
assert not no_nan_tag_est.__sklearn_tags__().input_tags.allow_nan
redefine_tags_est = OverrideTag()
assert not redefine_tags_est.__sklearn_tags__().input_tags.allow_nan
diamond_tag_est = DiamondOverwriteTag()
assert diamond_tag_est.__sklearn_tags__().input_tags.allow_nan
inherit_diamond_tag_est = InheritDiamondOverwriteTag()
assert inherit_diamond_tag_est.__sklearn_tags__().input_tags.allow_nan
def test_raises_on_get_params_non_attribute():
class MyEstimator(BaseEstimator):
def __init__(self, param=5):
pass
def fit(self, X, y=None):
return self
est = MyEstimator()
msg = "'MyEstimator' object has no attribute 'param'"
with pytest.raises(AttributeError, match=msg):
est.get_params()
def test_repr_mimebundle_():
# Checks the display configuration flag controls the json output
tree = DecisionTreeClassifier()
output = tree._repr_mimebundle_()
assert "text/plain" in output
assert "text/html" in output
with config_context(display="text"):
output = tree._repr_mimebundle_()
assert "text/plain" in output
assert "text/html" not in output
def test_repr_html_wraps():
# Checks the display configuration flag controls the html output
tree = DecisionTreeClassifier()
output = tree._repr_html_()
assert "<style>" in output
with config_context(display="text"):
msg = "_repr_html_ is only defined when"
with pytest.raises(AttributeError, match=msg):
output = tree._repr_html_()
def test_n_features_in_validation():
"""Check that `_check_n_features` validates data when reset=False"""
est = MyEstimator()
X_train = [[1, 2, 3], [4, 5, 6]]
_check_n_features(est, X_train, reset=True)
assert est.n_features_in_ == 3
msg = "X does not contain any features, but MyEstimator is expecting 3 features"
with pytest.raises(ValueError, match=msg):
_check_n_features(est, "invalid X", reset=False)
def test_n_features_in_no_validation():
"""Check that `_check_n_features` does not validate data when
n_features_in_ is not defined."""
est = MyEstimator()
_check_n_features(est, "invalid X", reset=True)
assert not hasattr(est, "n_features_in_")
# does not raise
_check_n_features(est, "invalid X", reset=False)
def test_feature_names_in():
"""Check that feature_name_in are recorded by `_validate_data`"""
pd = pytest.importorskip("pandas")
iris = datasets.load_iris()
X_np = iris.data
df = pd.DataFrame(X_np, columns=iris.feature_names)
class NoOpTransformer(TransformerMixin, BaseEstimator):
def fit(self, X, y=None):
validate_data(self, X)
return self
def transform(self, X):
validate_data(self, X, reset=False)
return X
# fit on dataframe saves the feature names
trans = NoOpTransformer().fit(df)
assert_array_equal(trans.feature_names_in_, df.columns)
# fit again but on ndarray does not keep the previous feature names (see #21383)
trans.fit(X_np)
assert not hasattr(trans, "feature_names_in_")
trans.fit(df)
msg = "The feature names should match those that were passed"
df_bad = pd.DataFrame(X_np, columns=iris.feature_names[::-1])
with pytest.raises(ValueError, match=msg):
trans.transform(df_bad)
# warns when fitted on dataframe and transforming a ndarray
msg = (
"X does not have valid feature names, but NoOpTransformer was "
"fitted with feature names"
)
with pytest.warns(UserWarning, match=msg):
trans.transform(X_np)
# warns when fitted on a ndarray and transforming dataframe
msg = "X has feature names, but NoOpTransformer was fitted without feature names"
trans = NoOpTransformer().fit(X_np)
with pytest.warns(UserWarning, match=msg):
trans.transform(df)
# fit on dataframe with all integer feature names works without warning
df_int_names = pd.DataFrame(X_np)
trans = NoOpTransformer()
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
trans.fit(df_int_names)
# fit on dataframe with no feature names or all integer feature names
# -> do not warn on transform
Xs = [X_np, df_int_names]
for X in Xs:
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
trans.transform(X)
# fit on dataframe with feature names that are mixed raises an error:
df_mixed = pd.DataFrame(X_np, columns=["a", "b", 1, 2])
trans = NoOpTransformer()
msg = re.escape(
"Feature names are only supported if all input features have string names, "
"but your input has ['int', 'str'] as feature name / column name types. "
"If you want feature names to be stored and validated, you must convert "
"them all to strings, by using X.columns = X.columns.astype(str) for "
"example. Otherwise you can remove feature / column names from your input "
"data, or convert them all to a non-string data type."
)
with pytest.raises(TypeError, match=msg):
trans.fit(df_mixed)
# transform on feature names that are mixed also raises:
with pytest.raises(TypeError, match=msg):
trans.transform(df_mixed)
def test_validate_data_skip_check_array():
"""Check skip_check_array option of _validate_data."""
pd = pytest.importorskip("pandas")
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
y = pd.Series(iris.target)
class NoOpTransformer(TransformerMixin, BaseEstimator):
pass
no_op = NoOpTransformer()
X_np_out = validate_data(no_op, df, skip_check_array=False)
assert isinstance(X_np_out, np.ndarray)
assert_allclose(X_np_out, df.to_numpy())
X_df_out = validate_data(no_op, df, skip_check_array=True)
assert X_df_out is df
y_np_out = validate_data(no_op, y=y, skip_check_array=False)
assert isinstance(y_np_out, np.ndarray)
assert_allclose(y_np_out, y.to_numpy())
y_series_out = validate_data(no_op, y=y, skip_check_array=True)
assert y_series_out is y
X_np_out, y_np_out = validate_data(no_op, df, y, skip_check_array=False)
assert isinstance(X_np_out, np.ndarray)
assert_allclose(X_np_out, df.to_numpy())
assert isinstance(y_np_out, np.ndarray)
assert_allclose(y_np_out, y.to_numpy())
X_df_out, y_series_out = validate_data(no_op, df, y, skip_check_array=True)
assert X_df_out is df
assert y_series_out is y
msg = "Validation should be done on X, y or both."
with pytest.raises(ValueError, match=msg):
validate_data(no_op)
def test_clone_keeps_output_config():
"""Check that clone keeps the set_output config."""
ss = StandardScaler().set_output(transform="pandas")
config = _get_output_config("transform", ss)
ss_clone = clone(ss)
config_clone = _get_output_config("transform", ss_clone)
assert config == config_clone
class _Empty:
pass
class EmptyEstimator(_Empty, BaseEstimator):
pass
@pytest.mark.parametrize("estimator", [BaseEstimator(), EmptyEstimator()])
def test_estimator_empty_instance_dict(estimator):
"""Check that ``__getstate__`` returns an empty ``dict`` with an empty
instance.
Python 3.11+ changed behaviour by returning ``None`` instead of raising an
``AttributeError``. Non-regression test for gh-25188.
"""
state = estimator.__getstate__()
expected = {"_sklearn_version": sklearn.__version__}
assert state == expected
# this should not raise
pickle.loads(pickle.dumps(BaseEstimator()))
def test_estimator_getstate_using_slots_error_message():
"""Using a `BaseEstimator` with `__slots__` is not supported."""
class WithSlots:
__slots__ = ("x",)
class Estimator(BaseEstimator, WithSlots):
pass
msg = (
"You cannot use `__slots__` in objects inheriting from "
"`sklearn.base.BaseEstimator`"
)
with pytest.raises(TypeError, match=msg):
Estimator().__getstate__()
with pytest.raises(TypeError, match=msg):
pickle.dumps(Estimator())
@pytest.mark.parametrize(
"constructor_name, minversion",
[
("dataframe", "1.5.0"),
("pyarrow", "12.0.0"),
("polars", "0.20.23"),
],
)
def test_dataframe_protocol(constructor_name, minversion):
"""Uses the dataframe exchange protocol to get feature names."""
data = [[1, 4, 2], [3, 3, 6]]
columns = ["col_0", "col_1", "col_2"]
df = _convert_container(
data, constructor_name, columns_name=columns, minversion=minversion
)
class NoOpTransformer(TransformerMixin, BaseEstimator):
def fit(self, X, y=None):
validate_data(self, X)
return self
def transform(self, X):
return validate_data(self, X, reset=False)
no_op = NoOpTransformer()
no_op.fit(df)
assert_array_equal(no_op.feature_names_in_, columns)
X_out = no_op.transform(df)
if constructor_name != "pyarrow":
# pyarrow does not work with `np.asarray`
# https://github.com/apache/arrow/issues/34886
assert_allclose(df, X_out)
bad_names = ["a", "b", "c"]
df_bad = _convert_container(data, constructor_name, columns_name=bad_names)
with pytest.raises(ValueError, match="The feature names should match"):
no_op.transform(df_bad)
@config_context(enable_metadata_routing=True)
def test_transformer_fit_transform_with_metadata_in_transform():
"""Test that having a transformer with metadata for transform raises a
warning when calling fit_transform."""
class CustomTransformer(BaseEstimator, TransformerMixin):
def fit(self, X, y=None, prop=None):
return self
def transform(self, X, prop=None):
return X
# passing the metadata to `fit_transform` should raise a warning since it
# could potentially be consumed by `transform`
with pytest.warns(UserWarning, match="`transform` method which consumes metadata"):
CustomTransformer().set_transform_request(prop=True).fit_transform(
[[1]], [1], prop=1
)
# not passing a metadata which can potentially be consumed by `transform` should
# not raise a warning
with warnings.catch_warnings(record=True) as record:
CustomTransformer().set_transform_request(prop=True).fit_transform([[1]], [1])
assert len(record) == 0
@config_context(enable_metadata_routing=True)
def test_outlier_mixin_fit_predict_with_metadata_in_predict():
"""Test that having an OutlierMixin with metadata for predict raises a
warning when calling fit_predict."""
class CustomOutlierDetector(BaseEstimator, OutlierMixin):
def fit(self, X, y=None, prop=None):
return self
def predict(self, X, prop=None):
return X
# passing the metadata to `fit_predict` should raise a warning since it
# could potentially be consumed by `predict`
with pytest.warns(UserWarning, match="`predict` method which consumes metadata"):
CustomOutlierDetector().set_predict_request(prop=True).fit_predict(
[[1]], [1], prop=1
)
# not passing a metadata which can potentially be consumed by `predict` should
# not raise a warning
with warnings.catch_warnings(record=True) as record:
CustomOutlierDetector().set_predict_request(prop=True).fit_predict([[1]], [1])
assert len(record) == 0