Add test for scale=True

sklearn/cross_decomposition/tests/test_pls.py

@@ -1,4 +1,6 @@
 import numpy as np
+from numpy.testing import assert_approx_equal
+
 from sklearn.utils.testing import (assert_equal, assert_array_almost_equal,
                                    assert_array_equal, assert_true,
                                    assert_raise_message)
@@ -351,11 +353,38 @@ def test_scale_and_stability():
             assert_array_almost_equal(X_s_score, X_score)
             assert_array_almost_equal(Y_s_score, Y_score)
 
+
 def test_pls_errors():
     d = load_linnerud()
     X = d.data
     Y = d.target
     for clf in [pls_.PLSCanonical(), pls_.PLSRegression(),
                 pls_.PLSSVD()]:
         clf.n_components = 4
-        assert_raise_message(ValueError, "Invalid number of components", clf.fit, X, Y)
+        assert_raise_message(ValueError, "Invalid number of components",
+                             clf.fit, X, Y)
+
+
+def test_pls_scaling():
+    # sanity check for scale=True
+    n_samples = 1000
+    n_targets = 5
+    n_features = 10
+
+    rng = np.random.RandomState(0)
+
+    Q = rng.randn(n_targets, n_features)
+    Y = rng.randn(n_samples, n_targets)
+    X = np.dot(Y, Q) + 2 * rng.randn(n_samples, n_features)
+
+    scale = 1000.
+    X_scaled = scale * X
+
+    pls = pls_.PLSRegression(n_components=5, scale=True)
+    pls.fit(X, Y)
+    score = pls.score(X, Y)
+
+    pls.fit(X_scaled, Y)
+    score_scaled = pls.score(X_scaled, Y)
+
+    assert_approx_equal(score, score_scaled)