324 lines
10 KiB
Python
324 lines
10 KiB
Python
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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.cluster import KMeans
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from sklearn.datasets import make_blobs, make_classification, make_regression
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.feature_selection import SequentialFeatureSelector
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from sklearn.linear_model import LinearRegression
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from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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from sklearn.utils.fixes import CSR_CONTAINERS
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def test_bad_n_features_to_select():
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n_features = 5
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X, y = make_regression(n_features=n_features)
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sfs = SequentialFeatureSelector(LinearRegression(), n_features_to_select=n_features)
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with pytest.raises(ValueError, match="n_features_to_select must be < n_features"):
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sfs.fit(X, y)
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@pytest.mark.parametrize("direction", ("forward", "backward"))
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@pytest.mark.parametrize("n_features_to_select", (1, 5, 9, "auto"))
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def test_n_features_to_select(direction, n_features_to_select):
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# Make sure n_features_to_select is respected
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n_features = 10
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X, y = make_regression(n_features=n_features, random_state=0)
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sfs = SequentialFeatureSelector(
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LinearRegression(),
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n_features_to_select=n_features_to_select,
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direction=direction,
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cv=2,
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)
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sfs.fit(X, y)
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if n_features_to_select == "auto":
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n_features_to_select = n_features // 2
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assert sfs.get_support(indices=True).shape[0] == n_features_to_select
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assert sfs.n_features_to_select_ == n_features_to_select
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assert sfs.transform(X).shape[1] == n_features_to_select
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@pytest.mark.parametrize("direction", ("forward", "backward"))
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def test_n_features_to_select_auto(direction):
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"""Check the behaviour of `n_features_to_select="auto"` with different
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values for the parameter `tol`.
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"""
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n_features = 10
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tol = 1e-3
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X, y = make_regression(n_features=n_features, random_state=0)
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sfs = SequentialFeatureSelector(
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LinearRegression(),
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n_features_to_select="auto",
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tol=tol,
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direction=direction,
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cv=2,
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)
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sfs.fit(X, y)
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max_features_to_select = n_features - 1
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assert sfs.get_support(indices=True).shape[0] <= max_features_to_select
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assert sfs.n_features_to_select_ <= max_features_to_select
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assert sfs.transform(X).shape[1] <= max_features_to_select
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assert sfs.get_support(indices=True).shape[0] == sfs.n_features_to_select_
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@pytest.mark.parametrize("direction", ("forward", "backward"))
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def test_n_features_to_select_stopping_criterion(direction):
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"""Check the behaviour stopping criterion for feature selection
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depending on the values of `n_features_to_select` and `tol`.
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When `direction` is `'forward'`, select a new features at random
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among those not currently selected in selector.support_,
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build a new version of the data that includes all the features
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in selector.support_ + this newly selected feature.
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And check that the cross-validation score of the model trained on
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this new dataset variant is lower than the model with
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the selected forward selected features or at least does not improve
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by more than the tol margin.
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When `direction` is `'backward'`, instead of adding a new feature
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to selector.support_, try to remove one of those selected features at random
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And check that the cross-validation score is either decreasing or
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not improving by more than the tol margin.
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"""
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X, y = make_regression(n_features=50, n_informative=10, random_state=0)
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tol = 1e-3
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sfs = SequentialFeatureSelector(
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LinearRegression(),
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n_features_to_select="auto",
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tol=tol,
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direction=direction,
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cv=2,
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)
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sfs.fit(X, y)
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selected_X = sfs.transform(X)
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rng = np.random.RandomState(0)
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added_candidates = list(set(range(X.shape[1])) - set(sfs.get_support(indices=True)))
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added_X = np.hstack(
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[
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selected_X,
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(X[:, rng.choice(added_candidates)])[:, np.newaxis],
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]
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)
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removed_candidate = rng.choice(list(range(sfs.n_features_to_select_)))
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removed_X = np.delete(selected_X, removed_candidate, axis=1)
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plain_cv_score = cross_val_score(LinearRegression(), X, y, cv=2).mean()
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sfs_cv_score = cross_val_score(LinearRegression(), selected_X, y, cv=2).mean()
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added_cv_score = cross_val_score(LinearRegression(), added_X, y, cv=2).mean()
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removed_cv_score = cross_val_score(LinearRegression(), removed_X, y, cv=2).mean()
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assert sfs_cv_score >= plain_cv_score
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if direction == "forward":
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assert (sfs_cv_score - added_cv_score) <= tol
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assert (sfs_cv_score - removed_cv_score) >= tol
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else:
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assert (added_cv_score - sfs_cv_score) <= tol
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assert (removed_cv_score - sfs_cv_score) <= tol
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@pytest.mark.parametrize("direction", ("forward", "backward"))
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@pytest.mark.parametrize(
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"n_features_to_select, expected",
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(
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(0.1, 1),
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(1.0, 10),
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(0.5, 5),
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),
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)
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def test_n_features_to_select_float(direction, n_features_to_select, expected):
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# Test passing a float as n_features_to_select
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X, y = make_regression(n_features=10)
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sfs = SequentialFeatureSelector(
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LinearRegression(),
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n_features_to_select=n_features_to_select,
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direction=direction,
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cv=2,
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)
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sfs.fit(X, y)
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assert sfs.n_features_to_select_ == expected
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@pytest.mark.parametrize("seed", range(10))
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@pytest.mark.parametrize("direction", ("forward", "backward"))
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@pytest.mark.parametrize(
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"n_features_to_select, expected_selected_features",
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[
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(2, [0, 2]), # f1 is dropped since it has no predictive power
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(1, [2]), # f2 is more predictive than f0 so it's kept
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],
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)
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def test_sanity(seed, direction, n_features_to_select, expected_selected_features):
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# Basic sanity check: 3 features, only f0 and f2 are correlated with the
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# target, f2 having a stronger correlation than f0. We expect f1 to be
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# dropped, and f2 to always be selected.
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rng = np.random.RandomState(seed)
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n_samples = 100
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X = rng.randn(n_samples, 3)
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y = 3 * X[:, 0] - 10 * X[:, 2]
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sfs = SequentialFeatureSelector(
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LinearRegression(),
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n_features_to_select=n_features_to_select,
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direction=direction,
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cv=2,
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)
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sfs.fit(X, y)
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assert_array_equal(sfs.get_support(indices=True), expected_selected_features)
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_sparse_support(csr_container):
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# Make sure sparse data is supported
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X, y = make_regression(n_features=10)
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X = csr_container(X)
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sfs = SequentialFeatureSelector(
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LinearRegression(), n_features_to_select="auto", cv=2
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)
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sfs.fit(X, y)
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sfs.transform(X)
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def test_nan_support():
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# Make sure nans are OK if the underlying estimator supports nans
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rng = np.random.RandomState(0)
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n_samples, n_features = 40, 4
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X, y = make_regression(n_samples, n_features, random_state=0)
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nan_mask = rng.randint(0, 2, size=(n_samples, n_features), dtype=bool)
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X[nan_mask] = np.nan
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sfs = SequentialFeatureSelector(
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HistGradientBoostingRegressor(), n_features_to_select="auto", cv=2
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)
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sfs.fit(X, y)
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sfs.transform(X)
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with pytest.raises(ValueError, match="Input X contains NaN"):
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# LinearRegression does not support nans
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SequentialFeatureSelector(
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LinearRegression(), n_features_to_select="auto", cv=2
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).fit(X, y)
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def test_pipeline_support():
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# Make sure that pipelines can be passed into SFS and that SFS can be
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# passed into a pipeline
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n_samples, n_features = 50, 3
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X, y = make_regression(n_samples, n_features, random_state=0)
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# pipeline in SFS
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pipe = make_pipeline(StandardScaler(), LinearRegression())
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sfs = SequentialFeatureSelector(pipe, n_features_to_select="auto", cv=2)
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sfs.fit(X, y)
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sfs.transform(X)
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# SFS in pipeline
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sfs = SequentialFeatureSelector(
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LinearRegression(), n_features_to_select="auto", cv=2
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)
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pipe = make_pipeline(StandardScaler(), sfs)
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pipe.fit(X, y)
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pipe.transform(X)
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@pytest.mark.parametrize("n_features_to_select", (2, 3))
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def test_unsupervised_model_fit(n_features_to_select):
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# Make sure that models without classification labels are not being
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# validated
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X, y = make_blobs(n_features=4)
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sfs = SequentialFeatureSelector(
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KMeans(n_init=1),
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n_features_to_select=n_features_to_select,
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)
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sfs.fit(X)
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assert sfs.transform(X).shape[1] == n_features_to_select
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@pytest.mark.parametrize("y", ("no_validation", 1j, 99.9, np.nan, 3))
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def test_no_y_validation_model_fit(y):
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# Make sure that other non-conventional y labels are not accepted
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X, clusters = make_blobs(n_features=6)
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sfs = SequentialFeatureSelector(
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KMeans(),
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n_features_to_select=3,
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)
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with pytest.raises((TypeError, ValueError)):
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sfs.fit(X, y)
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def test_forward_neg_tol_error():
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"""Check that we raise an error when tol<0 and direction='forward'"""
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X, y = make_regression(n_features=10, random_state=0)
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sfs = SequentialFeatureSelector(
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LinearRegression(),
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n_features_to_select="auto",
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direction="forward",
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tol=-1e-3,
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)
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with pytest.raises(ValueError, match="tol must be positive"):
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sfs.fit(X, y)
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def test_backward_neg_tol():
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"""Check that SequentialFeatureSelector works negative tol
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non-regression test for #25525
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"""
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X, y = make_regression(n_features=10, random_state=0)
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lr = LinearRegression()
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initial_score = lr.fit(X, y).score(X, y)
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sfs = SequentialFeatureSelector(
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lr,
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n_features_to_select="auto",
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direction="backward",
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tol=-1e-3,
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)
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Xr = sfs.fit_transform(X, y)
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new_score = lr.fit(Xr, y).score(Xr, y)
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assert 0 < sfs.get_support().sum() < X.shape[1]
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assert new_score < initial_score
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def test_cv_generator_support():
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"""Check that no exception raised when cv is generator
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non-regression test for #25957
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"""
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X, y = make_classification(random_state=0)
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groups = np.zeros_like(y, dtype=int)
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groups[y.size // 2 :] = 1
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cv = LeaveOneGroupOut()
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splits = cv.split(X, y, groups=groups)
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knc = KNeighborsClassifier(n_neighbors=5)
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sfs = SequentialFeatureSelector(knc, n_features_to_select=5, cv=splits)
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sfs.fit(X, y)
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