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