def test_ridge():
"""Ridge regression convergence test using score
TODO: for this test to be robust, we should use a dataset instead
of np.random.
"""
alpha = 1.0
# With more samples than features
n_samples, n_features = 6, 5
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
ridge = Ridge(alpha=alpha)
ridge.fit(X, y)
assert_equal(ridge.coef_.shape, (X.shape[1], ))
assert ridge.score(X, y) > 0.5
ridge.fit(X, y, sample_weight=np.ones(n_samples))
assert ridge.score(X, y) > 0.5
# With more features than samples
n_samples, n_features = 5, 10
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
ridge = Ridge(alpha=alpha)
ridge.fit(X, y)
assert ridge.score(X, y) > .9
ridge.fit(X, y, sample_weight=np.ones(n_samples))
assert ridge.score(X, y) > 0.9
def test_ridge():
"""Ridge regression convergence test using score
TODO: for this test to be robust, we should use a dataset instead
of np.random.
"""
alpha = 1.0
# With more samples than features
n_samples, n_features = 6, 5
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
ridge = Ridge(alpha=alpha)
ridge.fit(X, y)
assert ridge.score(X, y) > 0.5
ridge.fit(X, y, sample_weight=np.ones(n_samples))
assert ridge.score(X, y) > 0.5
# With more features than samples
n_samples, n_features = 5, 10
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
ridge = Ridge(alpha=alpha)
ridge.fit(X, y)
assert ridge.score(X, y) > .9
ridge.fit(X, y, sample_weight=np.ones(n_samples))
assert ridge.score(X, y) > 0.9
def _test_tolerance(filter_):
ridge = Ridge(tol=1e-5)
ridge.fit(filter_(X_diabetes), y_diabetes)
score = ridge.score(filter_(X_diabetes), y_diabetes)
ridge2 = Ridge(tol=1e-3)
ridge2.fit(filter_(X_diabetes), y_diabetes)
score2 = ridge2.score(filter_(X_diabetes), y_diabetes)
assert score >= score2
def _test_tolerance(filter_):
ridge = Ridge(tol=1e-5)
ridge.fit(filter_(X_diabetes), y_diabetes)
score = ridge.score(filter_(X_diabetes), y_diabetes)
ridge2 = Ridge(tol=1e-3)
ridge2.fit(filter_(X_diabetes), y_diabetes)
score2 = ridge2.score(filter_(X_diabetes), y_diabetes)
assert score >= score2
def _test_ridge_diabetes(filter_):
ridge = Ridge(fit_intercept=False)
ridge.fit(filter_(X_diabetes), y_diabetes)
return np.round(ridge.score(filter_(X_diabetes), y_diabetes), 5)
def _test_ridge_diabetes(filter_):
ridge = Ridge(fit_intercept=False)
ridge.fit(filter_(X_diabetes), y_diabetes)
return np.round(ridge.score(filter_(X_diabetes), y_diabetes), 5)
