scikit-learn
diff --git a/‎doc/whats_new/v0.24.rst
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diff --git a/‎sklearn/gaussian_process/_gpr.py
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+9-15Lines changed: 9 additions & 15 deletions b/‎sklearn/gaussian_process/_gpr.py
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+9-15Lines changed: 9 additions & 15 deletions
diff --git a/‎sklearn/gaussian_process/tests/test_gpr.py
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+33-25Lines changed: 33 additions & 25 deletions b/‎sklearn/gaussian_process/tests/test_gpr.py
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+33-25Lines changed: 33 additions & 25 deletions
@@ -45,6 +45,13 @@ Changelog
 :mod:`sklearn.gaussian_process`
 ...............................
 
+- |Fix| Avoid explicitly forming inverse covariance matrix in
+  :class:`gaussian_process.GaussianProcessRegressor` when set to output
+  standard deviation. With certain covariance matrices this inverse is unstable
+  to compute explicitly. Calling Cholesky solver mitigates this issue in
+  computation.
+  :pr:`19939` by :user:`Ian Halvic <iwhalvic>`.
+
 - |Fix| Avoid division by zero when scaling constant target in
   :class:`gaussian_process.GaussianProcessRegressor`. It was due to a std. dev.
   equal to 0. Now, such case is detected and the std. dev. is affected to 1
@@ -59,7 +66,7 @@ Changelog
 - |Fix|: Fixed a bug in :class:`linear_model.LogisticRegression`: the
   sample_weight object is not modified anymore. :pr:`19182` by
   :user:`Yosuke KOBAYASHI <m7142yosuke>`.
-  
+
 :mod:`sklearn.metrics`
 ......................
 
 
@@ -8,7 +8,7 @@
 from operator import itemgetter
 
 import numpy as np
-from scipy.linalg import cholesky, cho_solve, solve_triangular
+from scipy.linalg import cholesky, cho_solve
 import scipy.optimize
 
 from ..base import BaseEstimator, RegressorMixin, clone
@@ -271,8 +271,6 @@ def obj_func(theta, eval_gradient=True):
         K[np.diag_indices_from(K)] += self.alpha
         try:
             self.L_ = cholesky(K, lower=True)  # Line 2
-            # self.L_ changed, self._K_inv needs to be recomputed
-            self._K_inv = None
         except np.linalg.LinAlgError as exc:
             exc.args = ("The kernel, %s, is not returning a "
                         "positive definite matrix. Try gradually "
@@ -345,31 +343,27 @@ def predict(self, X, return_std=False, return_cov=False):
         else:  # Predict based on GP posterior
             K_trans = self.kernel_(X, self.X_train_)
             y_mean = K_trans.dot(self.alpha_)  # Line 4 (y_mean = f_star)
-
             # undo normalisation
             y_mean = self._y_train_std * y_mean + self._y_train_mean
 
             if return_cov:
-                v = cho_solve((self.L_, True), K_trans.T)  # Line 5
-                y_cov = self.kernel_(X) - K_trans.dot(v)  # Line 6
+                # Solve K @ V = K_trans.T
+                V = cho_solve((self.L_, True), K_trans.T)  # Line 5
+                y_cov = self.kernel_(X) - K_trans.dot(V)  # Line 6
 
                 # undo normalisation
                 y_cov = y_cov * self._y_train_std**2
 
                 return y_mean, y_cov
             elif return_std:
-                # cache result of K_inv computation
-                if self._K_inv is None:
-                    # compute inverse K_inv of K based on its Cholesky
-                    # decomposition L and its inverse L_inv
-                    L_inv = solve_triangular(self.L_.T,
-                                             np.eye(self.L_.shape[0]))
-                    self._K_inv = L_inv.dot(L_inv.T)
+                # Solve K @ V = K_trans.T
+                V = cho_solve((self.L_, True), K_trans.T)  # Line 5
 
                 # Compute variance of predictive distribution
+                # Use einsum to avoid explicitly forming the large matrix
+                # K_trans @ V just to extract its diagonal afterward.
                 y_var = self.kernel_.diag(X)
-                y_var -= np.einsum("ij,ij->i",
-                                   np.dot(K_trans, self._K_inv), K_trans)
+                y_var -= np.einsum("ij,ji->i", K_trans, V)
 
                 # Check if any of the variances is negative because of
                 # numerical issues. If yes: set the variance to 0.
 
@@ -19,11 +19,12 @@
 from sklearn.gaussian_process.tests._mini_sequence_kernel import MiniSeqKernel
 from sklearn.exceptions import ConvergenceWarning
 
-from sklearn.utils._testing \
-    import (assert_array_less,
-            assert_almost_equal, assert_raise_message,
-            assert_array_almost_equal, assert_array_equal,
-            assert_allclose, assert_warns_message)
+from sklearn.utils._testing import (
+    assert_array_less,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_allclose
+)
 
 
 def f(x):
@@ -185,7 +186,8 @@ def test_no_optimizer():
 
 
 @pytest.mark.parametrize('kernel', kernels)
-def test_predict_cov_vs_std(kernel):
+@pytest.mark.parametrize("target", [y, np.ones(X.shape[0], dtype=np.float64)])
+def test_predict_cov_vs_std(kernel, target):
     if sys.maxsize <= 2 ** 32 and sys.version_info[:2] == (3, 6):
         pytest.xfail("This test may fail on 32bit Py3.6")
 
@@ -452,25 +454,6 @@ def test_no_fit_default_predict():
     assert_array_almost_equal(y_cov1, y_cov2)
 
 
-@pytest.mark.parametrize('kernel', kernels)
-def test_K_inv_reset(kernel):
-    y2 = f(X2).ravel()
-
-    # Test that self._K_inv is reset after a new fit
-    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
-    assert hasattr(gpr, '_K_inv')
-    assert gpr._K_inv is None
-    gpr.predict(X, return_std=True)
-    assert gpr._K_inv is not None
-    gpr.fit(X2, y2)
-    assert gpr._K_inv is None
-    gpr.predict(X2, return_std=True)
-    gpr2 = GaussianProcessRegressor(kernel=kernel).fit(X2, y2)
-    gpr2.predict(X2, return_std=True)
-    # the value of K_inv should be independent of the first fit
-    assert_array_equal(gpr._K_inv, gpr2._K_inv)
-
-
 def test_warning_bounds():
     kernel = RBF(length_scale_bounds=[1e-5, 1e-3])
     gpr = GaussianProcessRegressor(kernel=kernel)
@@ -566,3 +549,28 @@ def test_constant_target(kernel):
     assert_allclose(y_pred, y_constant)
     # set atol because we compare to zero
     assert_allclose(np.diag(y_cov), 0., atol=1e-9)
+
+
+def test_gpr_consistency_std_cov_non_invertible_kernel():
+    """Check the consistency between the returned std. dev. and the covariance.
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/19936
+    Inconsistencies were observed when the kernel cannot be inverted (or
+    numerically stable).
+    """
+    kernel = (C(8.98576054e+05, (1e-12, 1e12)) *
+              RBF([5.91326520e+02, 1.32584051e+03], (1e-12, 1e12)) +
+              WhiteKernel(noise_level=1e-5))
+    gpr = GaussianProcessRegressor(kernel=kernel, alpha=0, optimizer=None)
+    X_train = np.array([[0., 0.], [1.54919334, -0.77459667], [-1.54919334, 0.],
+                        [0., -1.54919334], [0.77459667, 0.77459667],
+                        [-0.77459667, 1.54919334]])
+    y_train = np.array([[-2.14882017e-10], [-4.66975823e+00], [4.01823986e+00],
+                        [-1.30303674e+00], [-1.35760156e+00],
+                        [3.31215668e+00]])
+    gpr.fit(X_train, y_train)
+    X_test = np.array([[-1.93649167, -1.93649167], [1.93649167, -1.93649167],
+                       [-1.93649167, 1.93649167], [1.93649167, 1.93649167]])
+    pred1, std = gpr.predict(X_test, return_std=True)
+    pred2, cov = gpr.predict(X_test, return_cov=True)
+    assert_allclose(std, np.sqrt(np.diagonal(cov)), rtol=1e-5)