Uploaded Test files

venv/Lib/site-packages/sklearn/feature_selection/tests/test_base.py

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+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))