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import numpy as np
import pytest
from numpy.testing import assert_array_equal
from sklearn.base import BaseEstimator
from sklearn.feature_selection._base import SelectorMixin
from sklearn.utils.fixes import CSC_CONTAINERS
class StepSelector(SelectorMixin, BaseEstimator):
"""Retain every `step` features (beginning with 0).
If `step < 1`, then no features are selected.
"""
def __init__(self, step=2):
self.step = step
def fit(self, X, y=None):
X = self._validate_data(X, accept_sparse="csc")
return self
def _get_support_mask(self):
mask = np.zeros(self.n_features_in_, dtype=bool)
if self.step >= 1:
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]]))
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_transform_sparse(csc_container):
X_sp = csc_container(X)
sel = StepSelector()
Xt_actual = sel.fit(X_sp).transform(X_sp)
Xt_actual2 = sel.fit_transform(X_sp)
assert_array_equal(Xt, Xt_actual.toarray())
assert_array_equal(Xt, Xt_actual2.toarray())
# Check dtype matches
assert np.int32 == sel.transform(X_sp.astype(np.int32)).dtype
assert np.float32 == sel.transform(X_sp.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]]))
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_inverse_transform_sparse(csc_container):
X_sp = csc_container(X)
Xt_sp = csc_container(Xt)
sel = StepSelector()
Xinv_actual = sel.fit(X_sp).inverse_transform(Xt_sp)
assert_array_equal(Xinv, Xinv_actual.toarray())
# Check dtype matches
assert np.int32 == sel.inverse_transform(Xt_sp.astype(np.int32)).dtype
assert np.float32 == sel.inverse_transform(Xt_sp.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))
def test_output_dataframe():
"""Check output dtypes for dataframes is consistent with the input dtypes."""
pd = pytest.importorskip("pandas")
X = pd.DataFrame(
{
"a": pd.Series([1.0, 2.4, 4.5], dtype=np.float32),
"b": pd.Series(["a", "b", "a"], dtype="category"),
"c": pd.Series(["j", "b", "b"], dtype="category"),
"d": pd.Series([3.0, 2.4, 1.2], dtype=np.float64),
}
)
for step in [2, 3]:
sel = StepSelector(step=step).set_output(transform="pandas")
sel.fit(X)
output = sel.transform(X)
for name, dtype in output.dtypes.items():
assert dtype == X.dtypes[name]
# step=0 will select nothing
sel0 = StepSelector(step=0).set_output(transform="pandas")
sel0.fit(X, y)
msg = "No features were selected"
with pytest.warns(UserWarning, match=msg):
output0 = sel0.transform(X)
assert_array_equal(output0.index, X.index)
assert output0.shape == (X.shape[0], 0)
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