108 lines
3.3 KiB
Python
108 lines
3.3 KiB
Python
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import numpy as np
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import pytest
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from sklearn.impute._base import _BaseImputer
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from sklearn.impute._iterative import _assign_where
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from sklearn.utils._mask import _get_mask
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from sklearn.utils._testing import _convert_container, assert_allclose
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@pytest.fixture
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def data():
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X = np.random.randn(10, 2)
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X[::2] = np.nan
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return X
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class NoFitIndicatorImputer(_BaseImputer):
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def fit(self, X, y=None):
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return self
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def transform(self, X, y=None):
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return self._concatenate_indicator(X, self._transform_indicator(X))
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class NoTransformIndicatorImputer(_BaseImputer):
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def fit(self, X, y=None):
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mask = _get_mask(X, value_to_mask=np.nan)
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super()._fit_indicator(mask)
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return self
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def transform(self, X, y=None):
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return self._concatenate_indicator(X, None)
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class NoPrecomputedMaskFit(_BaseImputer):
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def fit(self, X, y=None):
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self._fit_indicator(X)
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return self
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def transform(self, X):
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return self._concatenate_indicator(X, self._transform_indicator(X))
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class NoPrecomputedMaskTransform(_BaseImputer):
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def fit(self, X, y=None):
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mask = _get_mask(X, value_to_mask=np.nan)
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self._fit_indicator(mask)
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return self
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def transform(self, X):
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return self._concatenate_indicator(X, self._transform_indicator(X))
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def test_base_imputer_not_fit(data):
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imputer = NoFitIndicatorImputer(add_indicator=True)
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err_msg = "Make sure to call _fit_indicator before _transform_indicator"
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with pytest.raises(ValueError, match=err_msg):
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imputer.fit(data).transform(data)
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with pytest.raises(ValueError, match=err_msg):
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imputer.fit_transform(data)
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def test_base_imputer_not_transform(data):
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imputer = NoTransformIndicatorImputer(add_indicator=True)
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err_msg = (
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"Call _fit_indicator and _transform_indicator in the imputer implementation"
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)
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with pytest.raises(ValueError, match=err_msg):
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imputer.fit(data).transform(data)
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with pytest.raises(ValueError, match=err_msg):
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imputer.fit_transform(data)
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def test_base_no_precomputed_mask_fit(data):
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imputer = NoPrecomputedMaskFit(add_indicator=True)
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err_msg = "precomputed is True but the input data is not a mask"
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with pytest.raises(ValueError, match=err_msg):
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imputer.fit(data)
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with pytest.raises(ValueError, match=err_msg):
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imputer.fit_transform(data)
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def test_base_no_precomputed_mask_transform(data):
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imputer = NoPrecomputedMaskTransform(add_indicator=True)
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err_msg = "precomputed is True but the input data is not a mask"
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imputer.fit(data)
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with pytest.raises(ValueError, match=err_msg):
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imputer.transform(data)
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with pytest.raises(ValueError, match=err_msg):
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imputer.fit_transform(data)
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@pytest.mark.parametrize("X1_type", ["array", "dataframe"])
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def test_assign_where(X1_type):
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"""Check the behaviour of the private helpers `_assign_where`."""
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rng = np.random.RandomState(0)
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n_samples, n_features = 10, 5
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X1 = _convert_container(rng.randn(n_samples, n_features), constructor_name=X1_type)
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X2 = rng.randn(n_samples, n_features)
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mask = rng.randint(0, 2, size=(n_samples, n_features)).astype(bool)
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_assign_where(X1, X2, mask)
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if X1_type == "dataframe":
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X1 = X1.to_numpy()
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assert_allclose(X1[mask], X2[mask])
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