[MRG+1] Fixes predicting std and cov without fitting in GPR by default. (scikit-learn#9177)

* If self.kernel_ is None then self.kernel_ is set to RBFKernel in predict() as fit(). Fixes scikit-learn#6573

* xxx_ attributes can not be altered outside fit. Removing self.kernel_ from predict() and using previously implemented self.kernel attribute.

* Cleanup and add non-regression-test

Author: MechCoder

doc/whats_new.rst

@@ -273,7 +273,7 @@ Bug fixes
      ``max_iter`` if finds a large inlier group early. :issue:`8251` by :user:`aivision2020`.
 
    - Fixed a bug where :class:`sklearn.naive_bayes.MultinomialNB` and :class:`sklearn.naive_bayes.BernoulliNB`
-     failed when `alpha=0`. :issue:`5814` by :user:`Yichuan Liu <yl565>` and 
+     failed when `alpha=0`. :issue:`5814` by :user:`Yichuan Liu <yl565>` and
      :user:`Herilalaina Rakotoarison <herilalaina>`.
 
    - Fixed a bug where :func:`datasets.make_moons` gives an
@@ -425,6 +425,11 @@ Bug fixes
      hence :func:`metrics.cohen_kappa_score`. :issue:`8354`, :issue:`7929`
      by `Joel Nothman`_ and :user:`Jon Crall <Erotemic>`.
 
+  -  Fixed a bug in :class:`gaussian_process.GaussianProcessRegressor`
+     when the standard deviation and covariance predicted without fit
+     would fail with a unmeaningful error by default.
+     :issue:`6573` by :user:`Quazi Marufur Rahman <qmaruf>` and
+     `Manoj Kumar`_.
 
    - Fixed the implementation of `explained_variance_`
      in :class:`decomposition.PCA`,

sklearn/gaussian_process/gpr.py

@@ -297,12 +297,17 @@ def predict(self, X, return_std=False, return_cov=False):
         X = check_array(X)
 
         if not hasattr(self, "X_train_"):  # Unfitted;predict based on GP prior
+            if self.kernel is None:
+                kernel = (C(1.0, constant_value_bounds="fixed") *
+                          RBF(1.0, length_scale_bounds="fixed"))
+            else:
+                kernel = self.kernel
             y_mean = np.zeros(X.shape[0])
             if return_cov:
-                y_cov = self.kernel(X)
+                y_cov = kernel(X)
                 return y_mean, y_cov
             elif return_std:
-                y_var = self.kernel.diag(X)
+                y_var = kernel.diag(X)
                 return y_mean, np.sqrt(y_var)
             else:
                 return y_mean

sklearn/gaussian_process/tests/test_gpr.py

@@ -14,7 +14,8 @@
 
 from sklearn.utils.testing \
     import (assert_true, assert_greater, assert_array_less,
-            assert_almost_equal, assert_equal, assert_raise_message)
+            assert_almost_equal, assert_equal, assert_raise_message,
+            assert_array_almost_equal)
 
 
 def f(x):
@@ -327,3 +328,19 @@ def test_duplicate_input():
 
         assert_almost_equal(y_pred_equal, y_pred_similar)
         assert_almost_equal(y_std_equal, y_std_similar)
+
+
+def test_no_fit_default_predict():
+    # Test that GPR predictions without fit does not break by default.
+    default_kernel = (C(1.0, constant_value_bounds="fixed") *
+                      RBF(1.0, length_scale_bounds="fixed"))
+    gpr1 = GaussianProcessRegressor()
+    _, y_std1 = gpr1.predict(X, return_std=True)
+    _, y_cov1 = gpr1.predict(X, return_cov=True)
+
+    gpr2 = GaussianProcessRegressor(kernel=default_kernel)
+    _, y_std2 = gpr2.predict(X, return_std=True)
+    _, y_cov2 = gpr2.predict(X, return_cov=True)
+
+    assert_array_almost_equal(y_std1, y_std2)
+    assert_array_almost_equal(y_cov1, y_cov2)