182 lines
6.6 KiB
Python
182 lines
6.6 KiB
Python
"""Testing for Gaussian process classification """
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# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
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# License: BSD 3 clause
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import numpy as np
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from scipy.optimize import approx_fprime
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import pytest
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from sklearn.gaussian_process import GaussianProcessClassifier
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from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
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from sklearn.gaussian_process.tests._mini_sequence_kernel import MiniSeqKernel
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from sklearn.utils._testing import assert_almost_equal, assert_array_equal
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def f(x):
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return np.sin(x)
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X = np.atleast_2d(np.linspace(0, 10, 30)).T
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X2 = np.atleast_2d([2., 4., 5.5, 6.5, 7.5]).T
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y = np.array(f(X).ravel() > 0, dtype=int)
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fX = f(X).ravel()
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y_mc = np.empty(y.shape, dtype=int) # multi-class
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y_mc[fX < -0.35] = 0
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y_mc[(fX >= -0.35) & (fX < 0.35)] = 1
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y_mc[fX > 0.35] = 2
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fixed_kernel = RBF(length_scale=1.0, length_scale_bounds="fixed")
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kernels = [RBF(length_scale=0.1), fixed_kernel,
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RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)),
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C(1.0, (1e-2, 1e2)) *
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RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3))]
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non_fixed_kernels = [kernel for kernel in kernels
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if kernel != fixed_kernel]
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@pytest.mark.parametrize('kernel', kernels)
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def test_predict_consistent(kernel):
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# Check binary predict decision has also predicted probability above 0.5.
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gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
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assert_array_equal(gpc.predict(X),
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gpc.predict_proba(X)[:, 1] >= 0.5)
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def test_predict_consistent_structured():
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# Check binary predict decision has also predicted probability above 0.5.
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X = ['A', 'AB', 'B']
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y = np.array([True, False, True])
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kernel = MiniSeqKernel(baseline_similarity_bounds='fixed')
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gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
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assert_array_equal(gpc.predict(X),
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gpc.predict_proba(X)[:, 1] >= 0.5)
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@pytest.mark.parametrize('kernel', non_fixed_kernels)
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def test_lml_improving(kernel):
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# Test that hyperparameter-tuning improves log-marginal likelihood.
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gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
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assert (gpc.log_marginal_likelihood(gpc.kernel_.theta) >
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gpc.log_marginal_likelihood(kernel.theta))
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@pytest.mark.parametrize('kernel', kernels)
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def test_lml_precomputed(kernel):
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# Test that lml of optimized kernel is stored correctly.
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gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
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assert_almost_equal(gpc.log_marginal_likelihood(gpc.kernel_.theta),
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gpc.log_marginal_likelihood(), 7)
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@pytest.mark.parametrize('kernel', kernels)
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def test_lml_without_cloning_kernel(kernel):
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# Test that clone_kernel=False has side-effects of kernel.theta.
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gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
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input_theta = np.ones(gpc.kernel_.theta.shape, dtype=np.float64)
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gpc.log_marginal_likelihood(input_theta, clone_kernel=False)
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assert_almost_equal(gpc.kernel_.theta, input_theta, 7)
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@pytest.mark.parametrize('kernel', non_fixed_kernels)
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def test_converged_to_local_maximum(kernel):
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# Test that we are in local maximum after hyperparameter-optimization.
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gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
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lml, lml_gradient = \
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gpc.log_marginal_likelihood(gpc.kernel_.theta, True)
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assert np.all((np.abs(lml_gradient) < 1e-4) |
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(gpc.kernel_.theta == gpc.kernel_.bounds[:, 0]) |
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(gpc.kernel_.theta == gpc.kernel_.bounds[:, 1]))
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@pytest.mark.parametrize('kernel', kernels)
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def test_lml_gradient(kernel):
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# Compare analytic and numeric gradient of log marginal likelihood.
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gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
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lml, lml_gradient = gpc.log_marginal_likelihood(kernel.theta, True)
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lml_gradient_approx = \
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approx_fprime(kernel.theta,
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lambda theta: gpc.log_marginal_likelihood(theta,
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False),
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1e-10)
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assert_almost_equal(lml_gradient, lml_gradient_approx, 3)
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def test_random_starts():
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# Test that an increasing number of random-starts of GP fitting only
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# increases the log marginal likelihood of the chosen theta.
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n_samples, n_features = 25, 2
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rng = np.random.RandomState(0)
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X = rng.randn(n_samples, n_features) * 2 - 1
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y = (np.sin(X).sum(axis=1) + np.sin(3 * X).sum(axis=1)) > 0
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kernel = C(1.0, (1e-2, 1e2)) \
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* RBF(length_scale=[1e-3] * n_features,
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length_scale_bounds=[(1e-4, 1e+2)] * n_features)
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last_lml = -np.inf
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for n_restarts_optimizer in range(5):
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gp = GaussianProcessClassifier(
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kernel=kernel, n_restarts_optimizer=n_restarts_optimizer,
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random_state=0).fit(X, y)
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lml = gp.log_marginal_likelihood(gp.kernel_.theta)
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assert lml > last_lml - np.finfo(np.float32).eps
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last_lml = lml
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@pytest.mark.parametrize('kernel', non_fixed_kernels)
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def test_custom_optimizer(kernel):
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# Test that GPC can use externally defined optimizers.
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# Define a dummy optimizer that simply tests 10 random hyperparameters
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def optimizer(obj_func, initial_theta, bounds):
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rng = np.random.RandomState(0)
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theta_opt, func_min = \
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initial_theta, obj_func(initial_theta, eval_gradient=False)
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for _ in range(10):
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theta = np.atleast_1d(rng.uniform(np.maximum(-2, bounds[:, 0]),
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np.minimum(1, bounds[:, 1])))
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f = obj_func(theta, eval_gradient=False)
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if f < func_min:
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theta_opt, func_min = theta, f
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return theta_opt, func_min
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gpc = GaussianProcessClassifier(kernel=kernel, optimizer=optimizer)
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gpc.fit(X, y_mc)
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# Checks that optimizer improved marginal likelihood
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assert (gpc.log_marginal_likelihood(gpc.kernel_.theta) >
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gpc.log_marginal_likelihood(kernel.theta))
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@pytest.mark.parametrize('kernel', kernels)
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def test_multi_class(kernel):
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# Test GPC for multi-class classification problems.
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gpc = GaussianProcessClassifier(kernel=kernel)
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gpc.fit(X, y_mc)
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y_prob = gpc.predict_proba(X2)
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assert_almost_equal(y_prob.sum(1), 1)
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y_pred = gpc.predict(X2)
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assert_array_equal(np.argmax(y_prob, 1), y_pred)
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@pytest.mark.parametrize('kernel', kernels)
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def test_multi_class_n_jobs(kernel):
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# Test that multi-class GPC produces identical results with n_jobs>1.
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gpc = GaussianProcessClassifier(kernel=kernel)
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gpc.fit(X, y_mc)
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gpc_2 = GaussianProcessClassifier(kernel=kernel, n_jobs=2)
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gpc_2.fit(X, y_mc)
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y_prob = gpc.predict_proba(X2)
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y_prob_2 = gpc_2.predict_proba(X2)
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assert_almost_equal(y_prob, y_prob_2)
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