ai-content-maker/.venv/Lib/site-packages/sklearn/gaussian_process/tests/test_gpc.py

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2024-05-03 04:18:51 +03:00
"""Testing for Gaussian process classification """
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause
import warnings
import numpy as np
import pytest
from scipy.optimize import approx_fprime
from sklearn.exceptions import ConvergenceWarning
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import (
RBF,
CompoundKernel,
WhiteKernel,
)
from sklearn.gaussian_process.kernels import (
ConstantKernel as C,
)
from sklearn.gaussian_process.tests._mini_sequence_kernel import MiniSeqKernel
from sklearn.utils._testing import assert_almost_equal, assert_array_equal
def f(x):
return np.sin(x)
X = np.atleast_2d(np.linspace(0, 10, 30)).T
X2 = np.atleast_2d([2.0, 4.0, 5.5, 6.5, 7.5]).T
y = np.array(f(X).ravel() > 0, dtype=int)
fX = f(X).ravel()
y_mc = np.empty(y.shape, dtype=int) # multi-class
y_mc[fX < -0.35] = 0
y_mc[(fX >= -0.35) & (fX < 0.35)] = 1
y_mc[fX > 0.35] = 2
fixed_kernel = RBF(length_scale=1.0, length_scale_bounds="fixed")
kernels = [
RBF(length_scale=0.1),
fixed_kernel,
RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)),
C(1.0, (1e-2, 1e2)) * RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)),
]
non_fixed_kernels = [kernel for kernel in kernels if kernel != fixed_kernel]
@pytest.mark.parametrize("kernel", kernels)
def test_predict_consistent(kernel):
# Check binary predict decision has also predicted probability above 0.5.
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
assert_array_equal(gpc.predict(X), gpc.predict_proba(X)[:, 1] >= 0.5)
def test_predict_consistent_structured():
# Check binary predict decision has also predicted probability above 0.5.
X = ["A", "AB", "B"]
y = np.array([True, False, True])
kernel = MiniSeqKernel(baseline_similarity_bounds="fixed")
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
assert_array_equal(gpc.predict(X), gpc.predict_proba(X)[:, 1] >= 0.5)
@pytest.mark.parametrize("kernel", non_fixed_kernels)
def test_lml_improving(kernel):
# Test that hyperparameter-tuning improves log-marginal likelihood.
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
assert gpc.log_marginal_likelihood(gpc.kernel_.theta) > gpc.log_marginal_likelihood(
kernel.theta
)
@pytest.mark.parametrize("kernel", kernels)
def test_lml_precomputed(kernel):
# Test that lml of optimized kernel is stored correctly.
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
assert_almost_equal(
gpc.log_marginal_likelihood(gpc.kernel_.theta), gpc.log_marginal_likelihood(), 7
)
@pytest.mark.parametrize("kernel", kernels)
def test_lml_without_cloning_kernel(kernel):
# Test that clone_kernel=False has side-effects of kernel.theta.
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
input_theta = np.ones(gpc.kernel_.theta.shape, dtype=np.float64)
gpc.log_marginal_likelihood(input_theta, clone_kernel=False)
assert_almost_equal(gpc.kernel_.theta, input_theta, 7)
@pytest.mark.parametrize("kernel", non_fixed_kernels)
def test_converged_to_local_maximum(kernel):
# Test that we are in local maximum after hyperparameter-optimization.
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
lml, lml_gradient = gpc.log_marginal_likelihood(gpc.kernel_.theta, True)
assert np.all(
(np.abs(lml_gradient) < 1e-4)
| (gpc.kernel_.theta == gpc.kernel_.bounds[:, 0])
| (gpc.kernel_.theta == gpc.kernel_.bounds[:, 1])
)
@pytest.mark.parametrize("kernel", kernels)
def test_lml_gradient(kernel):
# Compare analytic and numeric gradient of log marginal likelihood.
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
lml, lml_gradient = gpc.log_marginal_likelihood(kernel.theta, True)
lml_gradient_approx = approx_fprime(
kernel.theta, lambda theta: gpc.log_marginal_likelihood(theta, False), 1e-10
)
assert_almost_equal(lml_gradient, lml_gradient_approx, 3)
def test_random_starts(global_random_seed):
# Test that an increasing number of random-starts of GP fitting only
# increases the log marginal likelihood of the chosen theta.
n_samples, n_features = 25, 2
rng = np.random.RandomState(global_random_seed)
X = rng.randn(n_samples, n_features) * 2 - 1
y = (np.sin(X).sum(axis=1) + np.sin(3 * X).sum(axis=1)) > 0
kernel = C(1.0, (1e-2, 1e2)) * RBF(
length_scale=[1e-3] * n_features, length_scale_bounds=[(1e-4, 1e2)] * n_features
)
last_lml = -np.inf
for n_restarts_optimizer in range(5):
gp = GaussianProcessClassifier(
kernel=kernel,
n_restarts_optimizer=n_restarts_optimizer,
random_state=global_random_seed,
).fit(X, y)
lml = gp.log_marginal_likelihood(gp.kernel_.theta)
assert lml > last_lml - np.finfo(np.float32).eps
last_lml = lml
@pytest.mark.parametrize("kernel", non_fixed_kernels)
def test_custom_optimizer(kernel, global_random_seed):
# Test that GPC can use externally defined optimizers.
# Define a dummy optimizer that simply tests 10 random hyperparameters
def optimizer(obj_func, initial_theta, bounds):
rng = np.random.RandomState(global_random_seed)
theta_opt, func_min = initial_theta, obj_func(
initial_theta, eval_gradient=False
)
for _ in range(10):
theta = np.atleast_1d(
rng.uniform(np.maximum(-2, bounds[:, 0]), np.minimum(1, bounds[:, 1]))
)
f = obj_func(theta, eval_gradient=False)
if f < func_min:
theta_opt, func_min = theta, f
return theta_opt, func_min
gpc = GaussianProcessClassifier(kernel=kernel, optimizer=optimizer)
gpc.fit(X, y_mc)
# Checks that optimizer improved marginal likelihood
assert gpc.log_marginal_likelihood(
gpc.kernel_.theta
) >= gpc.log_marginal_likelihood(kernel.theta)
@pytest.mark.parametrize("kernel", kernels)
def test_multi_class(kernel):
# Test GPC for multi-class classification problems.
gpc = GaussianProcessClassifier(kernel=kernel)
gpc.fit(X, y_mc)
y_prob = gpc.predict_proba(X2)
assert_almost_equal(y_prob.sum(1), 1)
y_pred = gpc.predict(X2)
assert_array_equal(np.argmax(y_prob, 1), y_pred)
@pytest.mark.parametrize("kernel", kernels)
def test_multi_class_n_jobs(kernel):
# Test that multi-class GPC produces identical results with n_jobs>1.
gpc = GaussianProcessClassifier(kernel=kernel)
gpc.fit(X, y_mc)
gpc_2 = GaussianProcessClassifier(kernel=kernel, n_jobs=2)
gpc_2.fit(X, y_mc)
y_prob = gpc.predict_proba(X2)
y_prob_2 = gpc_2.predict_proba(X2)
assert_almost_equal(y_prob, y_prob_2)
def test_warning_bounds():
kernel = RBF(length_scale_bounds=[1e-5, 1e-3])
gpc = GaussianProcessClassifier(kernel=kernel)
warning_message = (
"The optimal value found for dimension 0 of parameter "
"length_scale is close to the specified upper bound "
"0.001. Increasing the bound and calling fit again may "
"find a better value."
)
with pytest.warns(ConvergenceWarning, match=warning_message):
gpc.fit(X, y)
kernel_sum = WhiteKernel(noise_level_bounds=[1e-5, 1e-3]) + RBF(
length_scale_bounds=[1e3, 1e5]
)
gpc_sum = GaussianProcessClassifier(kernel=kernel_sum)
with warnings.catch_warnings(record=True) as record:
warnings.simplefilter("always")
gpc_sum.fit(X, y)
assert len(record) == 2
assert issubclass(record[0].category, ConvergenceWarning)
assert (
record[0].message.args[0]
== "The optimal value found for "
"dimension 0 of parameter "
"k1__noise_level is close to the "
"specified upper bound 0.001. "
"Increasing the bound and calling "
"fit again may find a better value."
)
assert issubclass(record[1].category, ConvergenceWarning)
assert (
record[1].message.args[0]
== "The optimal value found for "
"dimension 0 of parameter "
"k2__length_scale is close to the "
"specified lower bound 1000.0. "
"Decreasing the bound and calling "
"fit again may find a better value."
)
X_tile = np.tile(X, 2)
kernel_dims = RBF(length_scale=[1.0, 2.0], length_scale_bounds=[1e1, 1e2])
gpc_dims = GaussianProcessClassifier(kernel=kernel_dims)
with warnings.catch_warnings(record=True) as record:
warnings.simplefilter("always")
gpc_dims.fit(X_tile, y)
assert len(record) == 2
assert issubclass(record[0].category, ConvergenceWarning)
assert (
record[0].message.args[0]
== "The optimal value found for "
"dimension 0 of parameter "
"length_scale is close to the "
"specified upper bound 100.0. "
"Increasing the bound and calling "
"fit again may find a better value."
)
assert issubclass(record[1].category, ConvergenceWarning)
assert (
record[1].message.args[0]
== "The optimal value found for "
"dimension 1 of parameter "
"length_scale is close to the "
"specified upper bound 100.0. "
"Increasing the bound and calling "
"fit again may find a better value."
)
@pytest.mark.parametrize(
"params, error_type, err_msg",
[
(
{"kernel": CompoundKernel(0)},
ValueError,
"kernel cannot be a CompoundKernel",
)
],
)
def test_gpc_fit_error(params, error_type, err_msg):
"""Check that expected error are raised during fit."""
gpc = GaussianProcessClassifier(**params)
with pytest.raises(error_type, match=err_msg):
gpc.fit(X, y)