def test_different_shape():
n, p, k = 100, 200, 5
X, y, important_betas = _generate_dummy_regression_data(n=n, k=k)
base_estimator = Pipeline([('scaler', StandardScaler()),
('model', Lasso())])
lambdas_grid = np.logspace(-1, 1, num=10)
selector = StabilitySelection(base_estimator=base_estimator,
lambda_name='model__alpha',
lambda_grid=lambdas_grid)
selector.fit(X, y)
selector.transform(X[:, :-2])
def test_stability_selection_classification():
n, p, k = 1000, 1000, 5
X, y, important_betas = _generate_dummy_classification_data(n=n, k=k)
selector = StabilitySelection(lambda_grid=np.logspace(-5, -1, 25),
verbose=1)
selector.fit(X, y)
chosen_betas = selector.get_support(indices=True)
X_r = selector.transform(X)
assert_almost_equal(important_betas, chosen_betas)
assert (X_r.shape == (n, k))
assert (selector.stability_scores_.shape == (
p, selector.lambda_grid.shape[0]))
def test_no_features():
n, p, k = 100, 200, 0
X, y, important_betas = _generate_dummy_regression_data(n=n, k=k)
base_estimator = Pipeline([('scaler', StandardScaler()),
('model', Lasso())])
lambdas_grid = np.logspace(-1, 1, num=10)
selector = StabilitySelection(base_estimator=base_estimator,
lambda_name='model__alpha',
lambda_grid=lambdas_grid)
selector.fit(X, y)
assert_almost_equal(selector.transform(X),
np.empty(0).reshape((X.shape[0], 0)))
