FIX heisenfailure on 32 bit python + speedup

Author: ogrisel

sklearn/gaussian_process/tests/test_gaussian_process.py

@@ -13,6 +13,7 @@
 from sklearn.gaussian_process import GaussianProcess
 from sklearn.gaussian_process import regression_models as regression
 from sklearn.gaussian_process import correlation_models as correlation
+from sklearn.utils.testing import assert_greater
 
 
 f = lambda x: x * np.sin(x)
@@ -148,20 +149,19 @@ def test_random_starts():
     Test that an increasing number of random-starts of GP fitting only
     increases the reduced likelihood function of the optimal theta.
     """
-    n_input_dims = 3
-    n_samples = 100
+    n_samples, n_features = 50, 3
     np.random.seed(0)
-    X = np.random.random(n_input_dims*n_samples).reshape(n_samples,
-                                                         n_input_dims) * 2 - 1
-    y = np.sin(X).sum(axis=1) + np.sin(3*X).sum(axis=1)
+    rng = np.random.RandomState(0)
+    X = rng.randn(n_samples, n_features) * 2 - 1
+    y = np.sin(X).sum(axis=1) + np.sin(3 * X).sum(axis=1)
     best_likelihood = -np.inf
-    for random_start in range(1, 10):
+    for random_start in range(1, 5):
         gp = GaussianProcess(regr="constant", corr="squared_exponential",
-                             theta0=[1e-0]*n_input_dims,
-                             thetaL=[1e-4]*n_input_dims,
-                             thetaU=[1e+1]*n_input_dims,
+                             theta0=[1e-0] * n_features,
+                             thetaL=[1e-4] * n_features,
+                             thetaU=[1e+1] * n_features,
                              random_start=random_start, random_state=0,
                              verbose=False).fit(X, y)
         rlf = gp.reduced_likelihood_function()[0]
-        assert_true(rlf >= best_likelihood)
+        assert_greater(rlf, best_likelihood - np.finfo(np.float32).eps)
         best_likelihood = rlf