def test_dtype_match_cholesky():
# Test different alphas in cholesky solver to ensure full coverage.
# This test is separated from test_dtype_match for clarity.
rng = np.random.RandomState(0)
alpha = (1.0, 0.5)
n_samples, n_features, n_target = 6, 7, 2
X_64 = rng.randn(n_samples, n_features)
y_64 = rng.randn(n_samples, n_target)
X_32 = X_64.astype(np.float32)
y_32 = y_64.astype(np.float32)
# Check type consistency 32bits
ridge_32 = Ridge(alpha=alpha, solver='cholesky')
ridge_32.fit(X_32, y_32)
coef_32 = ridge_32.coef_
# Check type consistency 64 bits
ridge_64 = Ridge(alpha=alpha, solver='cholesky')
ridge_64.fit(X_64, y_64)
coef_64 = ridge_64.coef_
# Do all the checks at once, like this is easier to debug
assert coef_32.dtype == X_32.dtype
assert coef_64.dtype == X_64.dtype
assert ridge_32.predict(X_32).dtype == X_32.dtype
assert ridge_64.predict(X_64).dtype == X_64.dtype
assert_almost_equal(ridge_32.coef_, ridge_64.coef_, decimal=5)
def test_dtype_match(solver):
rng = np.random.RandomState(0)
alpha = 1.0
n_samples, n_features = 6, 5
X_64 = rng.randn(n_samples, n_features)
y_64 = rng.randn(n_samples)
X_32 = X_64.astype(np.float32)
y_32 = y_64.astype(np.float32)
tol = 2 * np.finfo(np.float32).resolution
# Check type consistency 32bits
ridge_32 = Ridge(alpha=alpha, solver=solver, max_iter=500, tol=tol)
ridge_32.fit(X_32, y_32)
coef_32 = ridge_32.coef_
# Check type consistency 64 bits
ridge_64 = Ridge(alpha=alpha, solver=solver, max_iter=500, tol=tol)
ridge_64.fit(X_64, y_64)
coef_64 = ridge_64.coef_
# Do the actual checks at once for easier debug
assert coef_32.dtype == X_32.dtype
assert coef_64.dtype == X_64.dtype
assert ridge_32.predict(X_32).dtype == X_32.dtype
assert ridge_64.predict(X_64).dtype == X_64.dtype
assert_allclose(ridge_32.coef_, ridge_64.coef_, rtol=1e-4, atol=5e-4)
def _test_multi_ridge_diabetes(filter_):
# simulate several responses
Y = np.vstack((y_diabetes, y_diabetes)).T
n_features = X_diabetes.shape[1]
ridge = Ridge(fit_intercept=False)
ridge.fit(filter_(X_diabetes), Y)
assert ridge.coef_.shape == (2, n_features)
Y_pred = ridge.predict(filter_(X_diabetes))
ridge.fit(filter_(X_diabetes), y_diabetes)
y_pred = ridge.predict(filter_(X_diabetes))
assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3)
def test_regression_scorers():
# Test regression scorers.
diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = Ridge()
clf.fit(X_train, y_train)
score1 = get_scorer('r2')(clf, X_test, y_test)
score2 = r2_score(y_test, clf.predict(X_test))
assert_almost_equal(score1, score2)
def test_toy_ridge_object():
# Test BayesianRegression ridge classifier
# TODO: test also n_samples > n_features
X = np.array([[1], [2]])
Y = np.array([1, 2])
reg = Ridge(alpha=0.0)
reg.fit(X, Y)
X_test = [[1], [2], [3], [4]]
assert_almost_equal(reg.predict(X_test), [1., 2, 3, 4])
assert len(reg.coef_.shape) == 1
assert type(reg.intercept_) == np.float64
Y = np.vstack((Y, Y)).T
reg.fit(X, Y)
X_test = [[1], [2], [3], [4]]
assert len(reg.coef_.shape) == 2
assert type(reg.intercept_) == np.ndarray