295 lines
9.2 KiB
Python
295 lines
9.2 KiB
Python
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import pytest
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import numpy as np
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from scipy.sparse import csr_matrix
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from numpy.testing import assert_array_equal
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from sklearn._config import config_context, get_config
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from sklearn.utils._set_output import _wrap_in_pandas_container
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from sklearn.utils._set_output import _safe_set_output
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from sklearn.utils._set_output import _SetOutputMixin
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from sklearn.utils._set_output import _get_output_config
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def test__wrap_in_pandas_container_dense():
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"""Check _wrap_in_pandas_container for dense data."""
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pd = pytest.importorskip("pandas")
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X = np.asarray([[1, 0, 3], [0, 0, 1]])
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columns = np.asarray(["f0", "f1", "f2"], dtype=object)
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index = np.asarray([0, 1])
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dense_named = _wrap_in_pandas_container(X, columns=lambda: columns, index=index)
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assert isinstance(dense_named, pd.DataFrame)
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assert_array_equal(dense_named.columns, columns)
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assert_array_equal(dense_named.index, index)
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def test__wrap_in_pandas_container_dense_update_columns_and_index():
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"""Check that _wrap_in_pandas_container overrides columns and index."""
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pd = pytest.importorskip("pandas")
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X_df = pd.DataFrame([[1, 0, 3], [0, 0, 1]], columns=["a", "b", "c"])
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new_columns = np.asarray(["f0", "f1", "f2"], dtype=object)
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new_index = [10, 12]
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new_df = _wrap_in_pandas_container(X_df, columns=new_columns, index=new_index)
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assert_array_equal(new_df.columns, new_columns)
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# Index does not change when the input is a DataFrame
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assert_array_equal(new_df.index, X_df.index)
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def test__wrap_in_pandas_container_error_validation():
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"""Check errors in _wrap_in_pandas_container."""
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X = np.asarray([[1, 0, 3], [0, 0, 1]])
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X_csr = csr_matrix(X)
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match = "Pandas output does not support sparse data"
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with pytest.raises(ValueError, match=match):
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_wrap_in_pandas_container(X_csr, columns=["a", "b", "c"])
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class EstimatorWithoutSetOutputAndWithoutTransform:
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pass
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class EstimatorNoSetOutputWithTransform:
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def transform(self, X, y=None):
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return X # pragma: no cover
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class EstimatorWithSetOutput(_SetOutputMixin):
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def fit(self, X, y=None):
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self.n_features_in_ = X.shape[1]
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return self
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def transform(self, X, y=None):
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return X
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def get_feature_names_out(self, input_features=None):
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return np.asarray([f"X{i}" for i in range(self.n_features_in_)], dtype=object)
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def test__safe_set_output():
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"""Check _safe_set_output works as expected."""
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# Estimator without transform will not raise when setting set_output for transform.
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est = EstimatorWithoutSetOutputAndWithoutTransform()
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_safe_set_output(est, transform="pandas")
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# Estimator with transform but without set_output will raise
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est = EstimatorNoSetOutputWithTransform()
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with pytest.raises(ValueError, match="Unable to configure output"):
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_safe_set_output(est, transform="pandas")
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est = EstimatorWithSetOutput().fit(np.asarray([[1, 2, 3]]))
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_safe_set_output(est, transform="pandas")
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config = _get_output_config("transform", est)
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assert config["dense"] == "pandas"
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_safe_set_output(est, transform="default")
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config = _get_output_config("transform", est)
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assert config["dense"] == "default"
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# transform is None is a no-op, so the config remains "default"
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_safe_set_output(est, transform=None)
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config = _get_output_config("transform", est)
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assert config["dense"] == "default"
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class EstimatorNoSetOutputWithTransformNoFeatureNamesOut(_SetOutputMixin):
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def transform(self, X, y=None):
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return X # pragma: no cover
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def test_set_output_mixin():
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"""Estimator without get_feature_names_out does not define `set_output`."""
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est = EstimatorNoSetOutputWithTransformNoFeatureNamesOut()
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assert not hasattr(est, "set_output")
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def test__safe_set_output_error():
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"""Check transform with invalid config."""
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X = np.asarray([[1, 0, 3], [0, 0, 1]])
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est = EstimatorWithSetOutput()
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_safe_set_output(est, transform="bad")
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msg = "output config must be 'default'"
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with pytest.raises(ValueError, match=msg):
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est.transform(X)
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def test_set_output_method():
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"""Check that the output is pandas."""
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pd = pytest.importorskip("pandas")
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X = np.asarray([[1, 0, 3], [0, 0, 1]])
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est = EstimatorWithSetOutput().fit(X)
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# transform=None is a no-op
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est2 = est.set_output(transform=None)
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assert est2 is est
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X_trans_np = est2.transform(X)
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assert isinstance(X_trans_np, np.ndarray)
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est.set_output(transform="pandas")
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X_trans_pd = est.transform(X)
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assert isinstance(X_trans_pd, pd.DataFrame)
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def test_set_output_method_error():
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"""Check transform fails with invalid transform."""
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X = np.asarray([[1, 0, 3], [0, 0, 1]])
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est = EstimatorWithSetOutput().fit(X)
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est.set_output(transform="bad")
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msg = "output config must be 'default'"
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with pytest.raises(ValueError, match=msg):
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est.transform(X)
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def test__get_output_config():
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"""Check _get_output_config works as expected."""
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# Without a configuration set, the global config is used
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global_config = get_config()["transform_output"]
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config = _get_output_config("transform")
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assert config["dense"] == global_config
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with config_context(transform_output="pandas"):
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# with estimator=None, the global config is used
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config = _get_output_config("transform")
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assert config["dense"] == "pandas"
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est = EstimatorNoSetOutputWithTransform()
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config = _get_output_config("transform", est)
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assert config["dense"] == "pandas"
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est = EstimatorWithSetOutput()
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# If estimator has not config, use global config
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config = _get_output_config("transform", est)
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assert config["dense"] == "pandas"
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# If estimator has a config, use local config
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est.set_output(transform="default")
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config = _get_output_config("transform", est)
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assert config["dense"] == "default"
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est.set_output(transform="pandas")
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config = _get_output_config("transform", est)
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assert config["dense"] == "pandas"
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class EstimatorWithSetOutputNoAutoWrap(_SetOutputMixin, auto_wrap_output_keys=None):
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def transform(self, X, y=None):
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return X
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def test_get_output_auto_wrap_false():
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"""Check that auto_wrap_output_keys=None does not wrap."""
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est = EstimatorWithSetOutputNoAutoWrap()
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assert not hasattr(est, "set_output")
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X = np.asarray([[1, 0, 3], [0, 0, 1]])
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assert X is est.transform(X)
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def test_auto_wrap_output_keys_errors_with_incorrect_input():
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msg = "auto_wrap_output_keys must be None or a tuple of keys."
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with pytest.raises(ValueError, match=msg):
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class BadEstimator(_SetOutputMixin, auto_wrap_output_keys="bad_parameter"):
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pass
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class AnotherMixin:
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def __init_subclass__(cls, custom_parameter, **kwargs):
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super().__init_subclass__(**kwargs)
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cls.custom_parameter = custom_parameter
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def test_set_output_mixin_custom_mixin():
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"""Check that multiple init_subclasses passes parameters up."""
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class BothMixinEstimator(_SetOutputMixin, AnotherMixin, custom_parameter=123):
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def transform(self, X, y=None):
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return X
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def get_feature_names_out(self, input_features=None):
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return input_features
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est = BothMixinEstimator()
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assert est.custom_parameter == 123
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assert hasattr(est, "set_output")
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def test__wrap_in_pandas_container_column_errors():
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"""If a callable `columns` errors, it has the same semantics as columns=None."""
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pd = pytest.importorskip("pandas")
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def get_columns():
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raise ValueError("No feature names defined")
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X_df = pd.DataFrame({"feat1": [1, 2, 3], "feat2": [3, 4, 5]})
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X_wrapped = _wrap_in_pandas_container(X_df, columns=get_columns)
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assert_array_equal(X_wrapped.columns, X_df.columns)
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X_np = np.asarray([[1, 3], [2, 4], [3, 5]])
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X_wrapped = _wrap_in_pandas_container(X_np, columns=get_columns)
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assert_array_equal(X_wrapped.columns, range(X_np.shape[1]))
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def test_set_output_mro():
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"""Check that multi-inheritance resolves to the correct class method.
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Non-regression test gh-25293.
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"""
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class Base(_SetOutputMixin):
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def transform(self, X):
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return "Base" # noqa
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class A(Base):
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pass
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class B(Base):
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def transform(self, X):
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return "B"
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class C(A, B):
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pass
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assert C().transform(None) == "B"
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class EstimatorWithSetOutputIndex(_SetOutputMixin):
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def fit(self, X, y=None):
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self.n_features_in_ = X.shape[1]
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return self
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def transform(self, X, y=None):
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import pandas as pd
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# transform by giving output a new index.
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return pd.DataFrame(X.to_numpy(), index=[f"s{i}" for i in range(X.shape[0])])
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def get_feature_names_out(self, input_features=None):
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return np.asarray([f"X{i}" for i in range(self.n_features_in_)], dtype=object)
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def test_set_output_pandas_keep_index():
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"""Check that set_output does not override index.
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Non-regression test for gh-25730.
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"""
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pd = pytest.importorskip("pandas")
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X = pd.DataFrame([[1, 2, 3], [4, 5, 6]], index=[0, 1])
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est = EstimatorWithSetOutputIndex().set_output(transform="pandas")
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est.fit(X)
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X_trans = est.transform(X)
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assert_array_equal(X_trans.index, ["s0", "s1"])
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