import numpy as np import pytest from scipy import sparse as sp from numpy.testing import assert_array_equal from sklearn.base import BaseEstimator from sklearn.feature_selection._base import SelectorMixin from sklearn.utils import check_array class StepSelector(SelectorMixin, BaseEstimator): """Retain every `step` features (beginning with 0)""" def __init__(self, step=2): self.step = step def fit(self, X, y=None): X = check_array(X, accept_sparse='csc') self.n_input_feats = X.shape[1] return self def _get_support_mask(self): mask = np.zeros(self.n_input_feats, dtype=bool) mask[::self.step] = True return mask support = [True, False] * 5 support_inds = [0, 2, 4, 6, 8] X = np.arange(20).reshape(2, 10) Xt = np.arange(0, 20, 2).reshape(2, 5) Xinv = X.copy() Xinv[:, 1::2] = 0 y = [0, 1] feature_names = list('ABCDEFGHIJ') feature_names_t = feature_names[::2] feature_names_inv = np.array(feature_names) feature_names_inv[1::2] = '' def test_transform_dense(): sel = StepSelector() Xt_actual = sel.fit(X, y).transform(X) Xt_actual2 = StepSelector().fit_transform(X, y) assert_array_equal(Xt, Xt_actual) assert_array_equal(Xt, Xt_actual2) # Check dtype matches assert np.int32 == sel.transform(X.astype(np.int32)).dtype assert np.float32 == sel.transform(X.astype(np.float32)).dtype # Check 1d list and other dtype: names_t_actual = sel.transform([feature_names]) assert_array_equal(feature_names_t, names_t_actual.ravel()) # Check wrong shape raises error with pytest.raises(ValueError): sel.transform(np.array([[1], [2]])) def test_transform_sparse(): sparse = sp.csc_matrix sel = StepSelector() Xt_actual = sel.fit(sparse(X)).transform(sparse(X)) Xt_actual2 = sel.fit_transform(sparse(X)) assert_array_equal(Xt, Xt_actual.toarray()) assert_array_equal(Xt, Xt_actual2.toarray()) # Check dtype matches assert np.int32 == sel.transform(sparse(X).astype(np.int32)).dtype assert np.float32 == sel.transform(sparse(X).astype(np.float32)).dtype # Check wrong shape raises error with pytest.raises(ValueError): sel.transform(np.array([[1], [2]])) def test_inverse_transform_dense(): sel = StepSelector() Xinv_actual = sel.fit(X, y).inverse_transform(Xt) assert_array_equal(Xinv, Xinv_actual) # Check dtype matches assert (np.int32 == sel.inverse_transform(Xt.astype(np.int32)).dtype) assert (np.float32 == sel.inverse_transform(Xt.astype(np.float32)).dtype) # Check 1d list and other dtype: names_inv_actual = sel.inverse_transform([feature_names_t]) assert_array_equal(feature_names_inv, names_inv_actual.ravel()) # Check wrong shape raises error with pytest.raises(ValueError): sel.inverse_transform(np.array([[1], [2]])) def test_inverse_transform_sparse(): sparse = sp.csc_matrix sel = StepSelector() Xinv_actual = sel.fit(sparse(X)).inverse_transform(sparse(Xt)) assert_array_equal(Xinv, Xinv_actual.toarray()) # Check dtype matches assert (np.int32 == sel.inverse_transform(sparse(Xt).astype(np.int32)).dtype) assert (np.float32 == sel.inverse_transform(sparse(Xt).astype(np.float32)).dtype) # Check wrong shape raises error with pytest.raises(ValueError): sel.inverse_transform(np.array([[1], [2]])) def test_get_support(): sel = StepSelector() sel.fit(X, y) assert_array_equal(support, sel.get_support()) assert_array_equal(support_inds, sel.get_support(indices=True))