remove custom make_regression

eickenberg · eickenberg · commit 5e6c2ea72ebc · 2015-04-02T22:17:39.000+02:00
diff --git a/sklearn/linear_model/tests/test_ridge.py b/sklearn/linear_model/tests/test_ridge.py
@@ -621,34 +621,11 @@ def test_sparse_cg_max_iter():
     assert_equal(reg.coef_.shape[0], X_diabetes.shape[1])
 
 
-def make_noisy_forward_data(
-    n_samples=100,
-    n_features=200,
-    n_targets=10,
-    train_frac=.8,
-    noise_levels=None,
-    random_state=42):
-    """Creates a simple, dense, noisy forward linear model with multiple
-    output."""
-    rng = check_random_state(random_state)
-    n_train = int(train_frac * n_samples)
-    train = slice(None, n_train)
-    test = slice(n_train, None)
-    X = rng.randn(n_samples, n_features)
-    W = rng.randn(n_features, n_targets)
-    Y_clean = X.dot(W)
-    if noise_levels is None:
-        noise_levels = rng.randn(n_targets) ** 2
-    noise_levels = np.atleast_1d(noise_levels) * np.ones(n_targets)
-    noise = rng.randn(*Y_clean.shape) * noise_levels * Y_clean.std(0)
-    Y = Y_clean + noise
-    return X, Y, W, train, test
-
-
 def test_ridge_gcv_with_sample_weights():
 
     n_samples, n_features, n_targets = 20, 5, 7
-    X, Y, W, _, _ = make_noisy_forward_data(n_samples, n_features, n_targets)
+    X, Y = datasets.make_regression(n_samples, n_features,
+                                    n_targets=n_targets)
     alphas = np.logspace(-3, 3, 9)
 
     rng = np.random.RandomState(42)
