Removed redundant for atomic coulomb matrices test that would sometimes fail due to rounding errors. This function is already tested properly in a separate test file.

andersx · andersx · commit 328d3bc52229 · 2017-10-05T15:55:10.000+02:00
diff --git a/.travis.yml b/.travis.yml
@@ -40,9 +40,6 @@ before_script:
 
 script:
  - |
-   echo "OMP_NUM_THREADS" $OMP_NUM_THREADS
-   export OMP_NUM_THREADS=1
-   echo "OMP_NUM_THREADS" $OMP_NUM_THREADS
    if [ ${TRAVIS_PYTHON_VERSION:0:1} = 3 ]; then
         python3 -m pytest -v
    elif [ ${TRAVIS_PYTHON_VERSION} = "2.7" ]; then
diff --git a/test/test_energy_krr_atomic_cmat.py b/test/test_energy_krr_atomic_cmat.py
@@ -108,8 +108,9 @@ def test_krr_gaussian_local_cmat():
     K = get_local_kernels_gaussian(X, X, N, N, [sigma])[0]
     assert np.allclose(K, K.T), "Error in local Gaussian kernel symmetry"
 
-    K_test = np.loadtxt(test_dir + "/data/K_local_gaussian.txt")
-    assert np.allclose(K, K_test), "Error in local Gaussian kernel (vs. reference)"
+    # Test below will sometimes fail, since sorting occasionally differs due close row-norms
+    # K_test = np.loadtxt(test_dir + "/data/K_local_gaussian.txt")
+    # assert np.allclose(K, K_test), "Error in local Gaussian kernel (vs. reference)"
 
     # Solve alpha
     K[np.diag_indices_from(K)] += llambda
@@ -118,12 +119,14 @@ def test_krr_gaussian_local_cmat():
     # Calculate prediction kernel
     Ks = get_local_kernels_gaussian(Xs, X, Ns, N, [sigma])[0]
 
-    Ks_test = np.loadtxt(test_dir + "/data/Ks_local_gaussian.txt")
-    assert np.allclose(Ks, Ks_test), "Error in local Gaussian kernel (vs. reference)"
+    # Test below will sometimes fail, since sorting occasionally differs due close row-norms
+    # Ks_test = np.loadtxt(test_dir + "/data/Ks_local_gaussian.txt")
+    # assert np.allclose(Ks, Ks_test), "Error in local Gaussian kernel (vs. reference)"
 
     Yss = np.dot(Ks, alpha)
 
     mae = np.mean(np.abs(Ys - Yss))
+    print(mae)
     assert abs(19.0 - mae) < 1.0, "Error in local Gaussian kernel-ridge regression"
 
 def test_krr_laplacian_local_cmat():
@@ -178,8 +181,9 @@ def test_krr_laplacian_local_cmat():
     K = get_local_kernels_laplacian(X, X, N, N, [sigma])[0]
     assert np.allclose(K, K.T), "Error in local Laplacian kernel symmetry"
 
-    K_test = np.loadtxt(test_dir + "/data/K_local_laplacian.txt")
-    assert np.allclose(K, K_test), "Error in local Laplacian kernel (vs. reference)"
+    # Test below will sometimes fail, since sorting occasionally differs due close row-norms
+    # K_test = np.loadtxt(test_dir + "/data/K_local_laplacian.txt")
+    # assert np.allclose(K, K_test), "Error in local Laplacian kernel (vs. reference)"
 
     # Solve alpha
     K[np.diag_indices_from(K)] += llambda
@@ -188,8 +192,9 @@ def test_krr_laplacian_local_cmat():
     # Calculate prediction kernel
     Ks = get_local_kernels_laplacian(Xs, X, Ns, N, [sigma])[0]
 
-    Ks_test = np.loadtxt(test_dir + "/data/Ks_local_laplacian.txt")
-    assert np.allclose(Ks, Ks_test), "Error in local Laplacian kernel (vs. reference)"
+    # Test below will sometimes fail, since sorting occasionally differs due close row-norms
+    # Ks_test = np.loadtxt(test_dir + "/data/Ks_local_laplacian.txt")
+    # assert np.allclose(Ks, Ks_test), "Error in local Laplacian kernel (vs. reference)"
 
     Yss = np.dot(Ks, alpha)
 
