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)
class ARDRegressionImpl():
def __init__(self, n_iter=300, tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, compute_score=False, threshold_lambda=10000.0, fit_intercept=True, normalize=False, copy_X=True, verbose=False):
self._hyperparams = {
'n_iter': n_iter,
'tol': tol,
'alpha_1': alpha_1,
'alpha_2': alpha_2,
'lambda_1': lambda_1,
'lambda_2': lambda_2,
'compute_score': compute_score,
'threshold_lambda': threshold_lambda,
'fit_intercept': fit_intercept,
'normalize': normalize,
'copy_X': copy_X,
'verbose': verbose}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
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)
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_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)
test = [[1], [3], [4]]
assert(np.abs(clf.predict(test) - [1, 3, 4]).sum() < 1.e-3) # identity
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)
test = [[1], [3], [4]]
assert (np.abs(clf.predict(test) - [1, 3, 4]).sum() < 1.e-3) # identity
def test_update_of_sigma_in_ard():
# Checks that `sigma_` is updated correctly after the last iteration
# of the ARDRegression algorithm. See issue #10128.
X = np.array([[1, 0], [0, 0]])
y = np.array([0, 0])
clf = ARDRegression(n_iter=1)
clf.fit(X, y)
# With the inputs above, ARDRegression prunes one of the two coefficients
# in the first iteration. Hence, the expected shape of `sigma_` is (1, 1).
assert_equal(clf.sigma_.shape, (1, 1))
# Ensure that no error is thrown at prediction stage
clf.predict(X, return_std=True)
def test_update_of_sigma_in_ard():
# Checks that `sigma_` is updated correctly after the last iteration
# of the ARDRegression algorithm. See issue #10128.
X = np.array([[1, 0],
[0, 0]])
y = np.array([0, 0])
clf = ARDRegression(n_iter=1)
clf.fit(X, y)
# With the inputs above, ARDRegression prunes one of the two coefficients
# in the first iteration. Hence, the expected shape of `sigma_` is (1, 1).
assert_equal(clf.sigma_.shape, (1, 1))
# Ensure that no error is thrown at prediction stage
clf.predict(X, return_std=True)
def test_ard_accuracy_on_easy_problem():
# Check that ARD converges with reasonable accuracy on an easy problem
# (Github issue #14055)
# This particular seed seems to converge poorly in the failure-case
# (scipy==1.3.0, sklearn==0.21.2)
seed = 45
X = np.random.RandomState(seed=seed).normal(size=(250, 3))
y = X[:, 1]
regressor = ARDRegression()
regressor.fit(X, y)
abs_coef_error = np.abs(1 - regressor.coef_[1])
# Expect an accuracy of better than 1E-4 in most cases -
# Failure-case produces 0.16!
assert abs_coef_error < 0.01