120 lines
3.6 KiB
Python
120 lines
3.6 KiB
Python
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import numpy as np
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import pytest
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from scipy import sparse as sp
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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 import check_array
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class StepSelector(SelectorMixin, BaseEstimator):
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"""Retain every `step` features (beginning with 0)"""
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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 = check_array(X, accept_sparse='csc')
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self.n_input_feats = X.shape[1]
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return self
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def _get_support_mask(self):
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mask = np.zeros(self.n_input_feats, dtype=bool)
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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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def test_transform_sparse():
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sparse = sp.csc_matrix
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sel = StepSelector()
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Xt_actual = sel.fit(sparse(X)).transform(sparse(X))
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Xt_actual2 = sel.fit_transform(sparse(X))
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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(sparse(X).astype(np.int32)).dtype
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assert np.float32 == sel.transform(sparse(X).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 ==
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sel.inverse_transform(Xt.astype(np.int32)).dtype)
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assert (np.float32 ==
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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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def test_inverse_transform_sparse():
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sparse = sp.csc_matrix
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sel = StepSelector()
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Xinv_actual = sel.fit(sparse(X)).inverse_transform(sparse(Xt))
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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 ==
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sel.inverse_transform(sparse(Xt).astype(np.int32)).dtype)
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assert (np.float32 ==
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sel.inverse_transform(sparse(Xt).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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