105 lines
3.3 KiB
Python
105 lines
3.3 KiB
Python
|
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
|
||
|
|
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
|
||
|
|
#
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from sklearn.utils.testing import assert_array_equal
|
||
|
|
from sklearn.utils.testing import assert_array_almost_equal
|
||
|
|
from sklearn.utils.testing import assert_almost_equal
|
||
|
|
from sklearn.utils.testing import SkipTest
|
||
|
|
from sklearn.linear_model.bayes import BayesianRidge, ARDRegression
|
||
|
|
from sklearn.linear_model import Ridge
|
||
|
|
from sklearn import datasets
|
||
|
|
|
||
|
|
|
||
|
|
def test_bayesian_on_diabetes():
|
||
|
|
# Test BayesianRidge on diabetes
|
||
|
|
raise SkipTest("XFailed Test")
|
||
|
|
diabetes = datasets.load_diabetes()
|
||
|
|
X, y = diabetes.data, diabetes.target
|
||
|
|
|
||
|
|
clf = BayesianRidge(compute_score=True)
|
||
|
|
|
||
|
|
# Test with more samples than features
|
||
|
|
clf.fit(X, y)
|
||
|
|
# Test that scores are increasing at each iteration
|
||
|
|
assert_array_equal(np.diff(clf.scores_) > 0, True)
|
||
|
|
|
||
|
|
# Test with more features than samples
|
||
|
|
X = X[:5, :]
|
||
|
|
y = y[:5]
|
||
|
|
clf.fit(X, y)
|
||
|
|
# Test that scores are increasing at each iteration
|
||
|
|
assert_array_equal(np.diff(clf.scores_) > 0, True)
|
||
|
|
|
||
|
|
|
||
|
|
def test_bayesian_ridge_parameter():
|
||
|
|
# Test correctness of lambda_ and alpha_ parameters (GitHub issue #8224)
|
||
|
|
X = np.array([[1, 1], [3, 4], [5, 7], [4, 1], [2, 6], [3, 10], [3, 2]])
|
||
|
|
y = np.array([1, 2, 3, 2, 0, 4, 5]).T
|
||
|
|
|
||
|
|
# A Ridge regression model using an alpha value equal to the ratio of
|
||
|
|
# lambda_ and alpha_ from the Bayesian Ridge model must be identical
|
||
|
|
br_model = BayesianRidge(compute_score=True).fit(X, y)
|
||
|
|
rr_model = Ridge(alpha=br_model.lambda_ / br_model.alpha_).fit(X, y)
|
||
|
|
assert_array_almost_equal(rr_model.coef_, br_model.coef_)
|
||
|
|
assert_almost_equal(rr_model.intercept_, br_model.intercept_)
|
||
|
|
|
||
|
|
|
||
|
|
def test_toy_bayesian_ridge_object():
|
||
|
|
# Test BayesianRidge on toy
|
||
|
|
X = np.array([[1], [2], [6], [8], [10]])
|
||
|
|
Y = np.array([1, 2, 6, 8, 10])
|
||
|
|
clf = BayesianRidge(compute_score=True)
|
||
|
|
clf.fit(X, Y)
|
||
|
|
|
||
|
|
# Check that the model could approximately learn the identity function
|
||
|
|
test = [[1], [3], [4]]
|
||
|
|
assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_toy_ard_object():
|
||
|
|
# Test BayesianRegression ARD classifier
|
||
|
|
X = np.array([[1], [2], [3]])
|
||
|
|
Y = np.array([1, 2, 3])
|
||
|
|
clf = ARDRegression(compute_score=True)
|
||
|
|
clf.fit(X, Y)
|
||
|
|
|
||
|
|
# Check that the model could approximately learn the identity function
|
||
|
|
test = [[1], [3], [4]]
|
||
|
|
assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_return_std():
|
||
|
|
# Test return_std option for both Bayesian regressors
|
||
|
|
def f(X):
|
||
|
|
return np.dot(X, w) + b
|
||
|
|
|
||
|
|
def f_noise(X, noise_mult):
|
||
|
|
return f(X) + np.random.randn(X.shape[0]) * noise_mult
|
||
|
|
|
||
|
|
d = 5
|
||
|
|
n_train = 50
|
||
|
|
n_test = 10
|
||
|
|
|
||
|
|
w = np.array([1.0, 0.0, 1.0, -1.0, 0.0])
|
||
|
|
b = 1.0
|
||
|
|
|
||
|
|
X = np.random.random((n_train, d))
|
||
|
|
X_test = np.random.random((n_test, d))
|
||
|
|
|
||
|
|
for decimal, noise_mult in enumerate([1, 0.1, 0.01]):
|
||
|
|
y = f_noise(X, noise_mult)
|
||
|
|
|
||
|
|
m1 = BayesianRidge()
|
||
|
|
m1.fit(X, y)
|
||
|
|
y_mean1, y_std1 = m1.predict(X_test, return_std=True)
|
||
|
|
assert_array_almost_equal(y_std1, noise_mult, decimal=decimal)
|
||
|
|
|
||
|
|
m2 = ARDRegression()
|
||
|
|
m2.fit(X, y)
|
||
|
|
y_mean2, y_std2 = m2.predict(X_test, return_std=True)
|
||
|
|
assert_array_almost_equal(y_std2, noise_mult, decimal=decimal)
|