154 lines
4.7 KiB
Python
154 lines
4.7 KiB
Python
import numpy as np
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import pytest
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from numpy.testing import assert_array_equal
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from sklearn.base import BaseEstimator
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from sklearn.feature_selection._base import SelectorMixin
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from sklearn.utils.fixes import CSC_CONTAINERS
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class StepSelector(SelectorMixin, BaseEstimator):
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"""Retain every `step` features (beginning with 0).
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If `step < 1`, then no features are selected.
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"""
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def __init__(self, step=2):
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self.step = step
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def fit(self, X, y=None):
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X = self._validate_data(X, accept_sparse="csc")
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return self
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def _get_support_mask(self):
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mask = np.zeros(self.n_features_in_, dtype=bool)
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if self.step >= 1:
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mask[:: self.step] = True
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return mask
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support = [True, False] * 5
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support_inds = [0, 2, 4, 6, 8]
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X = np.arange(20).reshape(2, 10)
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Xt = np.arange(0, 20, 2).reshape(2, 5)
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Xinv = X.copy()
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Xinv[:, 1::2] = 0
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y = [0, 1]
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feature_names = list("ABCDEFGHIJ")
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feature_names_t = feature_names[::2]
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feature_names_inv = np.array(feature_names)
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feature_names_inv[1::2] = ""
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def test_transform_dense():
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sel = StepSelector()
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Xt_actual = sel.fit(X, y).transform(X)
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Xt_actual2 = StepSelector().fit_transform(X, y)
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assert_array_equal(Xt, Xt_actual)
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assert_array_equal(Xt, Xt_actual2)
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# Check dtype matches
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assert np.int32 == sel.transform(X.astype(np.int32)).dtype
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assert np.float32 == sel.transform(X.astype(np.float32)).dtype
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# Check 1d list and other dtype:
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names_t_actual = sel.transform([feature_names])
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assert_array_equal(feature_names_t, names_t_actual.ravel())
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# Check wrong shape raises error
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with pytest.raises(ValueError):
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sel.transform(np.array([[1], [2]]))
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@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
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def test_transform_sparse(csc_container):
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X_sp = csc_container(X)
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sel = StepSelector()
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Xt_actual = sel.fit(X_sp).transform(X_sp)
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Xt_actual2 = sel.fit_transform(X_sp)
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assert_array_equal(Xt, Xt_actual.toarray())
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assert_array_equal(Xt, Xt_actual2.toarray())
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# Check dtype matches
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assert np.int32 == sel.transform(X_sp.astype(np.int32)).dtype
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assert np.float32 == sel.transform(X_sp.astype(np.float32)).dtype
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# Check wrong shape raises error
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with pytest.raises(ValueError):
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sel.transform(np.array([[1], [2]]))
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def test_inverse_transform_dense():
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sel = StepSelector()
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Xinv_actual = sel.fit(X, y).inverse_transform(Xt)
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assert_array_equal(Xinv, Xinv_actual)
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# Check dtype matches
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assert np.int32 == sel.inverse_transform(Xt.astype(np.int32)).dtype
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assert np.float32 == sel.inverse_transform(Xt.astype(np.float32)).dtype
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# Check 1d list and other dtype:
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names_inv_actual = sel.inverse_transform([feature_names_t])
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assert_array_equal(feature_names_inv, names_inv_actual.ravel())
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# Check wrong shape raises error
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with pytest.raises(ValueError):
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sel.inverse_transform(np.array([[1], [2]]))
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@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
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def test_inverse_transform_sparse(csc_container):
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X_sp = csc_container(X)
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Xt_sp = csc_container(Xt)
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sel = StepSelector()
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Xinv_actual = sel.fit(X_sp).inverse_transform(Xt_sp)
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assert_array_equal(Xinv, Xinv_actual.toarray())
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# Check dtype matches
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assert np.int32 == sel.inverse_transform(Xt_sp.astype(np.int32)).dtype
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assert np.float32 == sel.inverse_transform(Xt_sp.astype(np.float32)).dtype
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# Check wrong shape raises error
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with pytest.raises(ValueError):
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sel.inverse_transform(np.array([[1], [2]]))
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def test_get_support():
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sel = StepSelector()
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sel.fit(X, y)
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assert_array_equal(support, sel.get_support())
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assert_array_equal(support_inds, sel.get_support(indices=True))
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def test_output_dataframe():
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"""Check output dtypes for dataframes is consistent with the input dtypes."""
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pd = pytest.importorskip("pandas")
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X = pd.DataFrame(
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{
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"a": pd.Series([1.0, 2.4, 4.5], dtype=np.float32),
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"b": pd.Series(["a", "b", "a"], dtype="category"),
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"c": pd.Series(["j", "b", "b"], dtype="category"),
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"d": pd.Series([3.0, 2.4, 1.2], dtype=np.float64),
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}
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)
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for step in [2, 3]:
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sel = StepSelector(step=step).set_output(transform="pandas")
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sel.fit(X)
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output = sel.transform(X)
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for name, dtype in output.dtypes.items():
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assert dtype == X.dtypes[name]
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# step=0 will select nothing
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sel0 = StepSelector(step=0).set_output(transform="pandas")
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sel0.fit(X, y)
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msg = "No features were selected"
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with pytest.warns(UserWarning, match=msg):
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output0 = sel0.transform(X)
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assert_array_equal(output0.index, X.index)
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assert output0.shape == (X.shape[0], 0)
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