Uploaded Test files

venv/Lib/site-packages/sklearn/tests/test_kernel_ridge.py

@@ -0,0 +1,85 @@
+import numpy as np
+import scipy.sparse as sp
+
+from sklearn.datasets import make_regression
+from sklearn.linear_model import Ridge
+from sklearn.kernel_ridge import KernelRidge
+from sklearn.metrics.pairwise import pairwise_kernels
+from sklearn.utils._testing import ignore_warnings
+
+from sklearn.utils._testing import assert_array_almost_equal
+
+
+X, y = make_regression(n_features=10, random_state=0)
+Xcsr = sp.csr_matrix(X)
+Xcsc = sp.csc_matrix(X)
+Y = np.array([y, y]).T
+
+
+def test_kernel_ridge():
+    pred = Ridge(alpha=1, fit_intercept=False).fit(X, y).predict(X)
+    pred2 = KernelRidge(kernel="linear", alpha=1).fit(X, y).predict(X)
+    assert_array_almost_equal(pred, pred2)
+
+
+def test_kernel_ridge_csr():
+    pred = Ridge(alpha=1, fit_intercept=False,
+                 solver="cholesky").fit(Xcsr, y).predict(Xcsr)
+    pred2 = KernelRidge(kernel="linear", alpha=1).fit(Xcsr, y).predict(Xcsr)
+    assert_array_almost_equal(pred, pred2)
+
+
+def test_kernel_ridge_csc():
+    pred = Ridge(alpha=1, fit_intercept=False,
+                 solver="cholesky").fit(Xcsc, y).predict(Xcsc)
+    pred2 = KernelRidge(kernel="linear", alpha=1).fit(Xcsc, y).predict(Xcsc)
+    assert_array_almost_equal(pred, pred2)
+
+
+def test_kernel_ridge_singular_kernel():
+    # alpha=0 causes a LinAlgError in computing the dual coefficients,
+    # which causes a fallback to a lstsq solver. This is tested here.
+    pred = Ridge(alpha=0, fit_intercept=False).fit(X, y).predict(X)
+    kr = KernelRidge(kernel="linear", alpha=0)
+    ignore_warnings(kr.fit)(X, y)
+    pred2 = kr.predict(X)
+    assert_array_almost_equal(pred, pred2)
+
+
+def test_kernel_ridge_precomputed():
+    for kernel in ["linear", "rbf", "poly", "cosine"]:
+        K = pairwise_kernels(X, X, metric=kernel)
+        pred = KernelRidge(kernel=kernel).fit(X, y).predict(X)
+        pred2 = KernelRidge(kernel="precomputed").fit(K, y).predict(K)
+        assert_array_almost_equal(pred, pred2)
+
+
+def test_kernel_ridge_precomputed_kernel_unchanged():
+    K = np.dot(X, X.T)
+    K2 = K.copy()
+    KernelRidge(kernel="precomputed").fit(K, y)
+    assert_array_almost_equal(K, K2)
+
+
+def test_kernel_ridge_sample_weights():
+    K = np.dot(X, X.T)  # precomputed kernel
+    sw = np.random.RandomState(0).rand(X.shape[0])
+
+    pred = Ridge(alpha=1,
+                 fit_intercept=False).fit(X, y, sample_weight=sw).predict(X)
+    pred2 = KernelRidge(kernel="linear",
+                        alpha=1).fit(X, y, sample_weight=sw).predict(X)
+    pred3 = KernelRidge(kernel="precomputed",
+                        alpha=1).fit(K, y, sample_weight=sw).predict(K)
+    assert_array_almost_equal(pred, pred2)
+    assert_array_almost_equal(pred, pred3)
+
+
+def test_kernel_ridge_multi_output():
+    pred = Ridge(alpha=1, fit_intercept=False).fit(X, Y).predict(X)
+    pred2 = KernelRidge(kernel="linear", alpha=1).fit(X, Y).predict(X)
+    assert_array_almost_equal(pred, pred2)
+
+    pred3 = KernelRidge(kernel="linear", alpha=1).fit(X, y).predict(X)
+    pred3 = np.array([pred3, pred3]).T
+    assert_array_almost_equal(pred2, pred3)