def test_enet_path():
# We use a large number of samples and of informative features so that
# the l1_ratio selected is more toward ridge than lasso
X, y, X_test, y_test = build_dataset(n_samples=200, n_features=100,
n_informative_features=100)
max_iter = 150
# Here we have a small number of iterations, and thus the
# ElasticNet might not converge. This is to speed up tests
clf = ElasticNetCV(alphas=[0.01, 0.05, 0.1], eps=2e-3,
l1_ratio=[0.5, 0.7], cv=3,
max_iter=max_iter)
ignore_warnings(clf.fit)(X, y)
# Well-conditioned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have selected an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
clf = ElasticNetCV(alphas=[0.01, 0.05, 0.1], eps=2e-3,
l1_ratio=[0.5, 0.7], cv=3,
max_iter=max_iter, precompute=True)
ignore_warnings(clf.fit)(X, y)
# Well-conditioned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have selected an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
# We are in well-conditioned settings with low noise: we should
# have a good test-set performance
assert_greater(clf.score(X_test, y_test), 0.99)
# Multi-output/target case
X, y, X_test, y_test = build_dataset(n_features=10, n_targets=3)
clf = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7],
cv=3, max_iter=max_iter)
ignore_warnings(clf.fit)(X, y)
# We are in well-conditioned settings with low noise: we should
# have a good test-set performance
assert_greater(clf.score(X_test, y_test), 0.99)
assert_equal(clf.coef_.shape, (3, 10))
# Mono-output should have same cross-validated alpha_ and l1_ratio_
# in both cases.
X, y, _, _ = build_dataset(n_features=10)
clf1 = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf1.fit(X, y)
clf2 = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf2.fit(X, y[:, np.newaxis])
assert_almost_equal(clf1.l1_ratio_, clf2.l1_ratio_)
assert_almost_equal(clf1.alpha_, clf2.alpha_)
def test_enet_path():
# We use a large number of samples and of informative features so that
# the l1_ratio selected is more toward ridge than lasso
X, y, X_test, y_test = build_dataset(n_samples=200, n_features=100,
n_informative_features=100)
max_iter = 150
# Here we have a small number of iterations, and thus the
# ElasticNet might not converge. This is to speed up tests
clf = ElasticNetCV(alphas=[0.01, 0.05, 0.1], eps=2e-3,
l1_ratio=[0.5, 0.7], cv=3,
max_iter=max_iter)
ignore_warnings(clf.fit)(X, y)
# Well-conditioned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have selected an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
clf = ElasticNetCV(alphas=[0.01, 0.05, 0.1], eps=2e-3,
l1_ratio=[0.5, 0.7], cv=3,
max_iter=max_iter, precompute=True)
ignore_warnings(clf.fit)(X, y)
# Well-conditioned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have selected an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
# We are in well-conditioned settings with low noise: we should
# have a good test-set performance
assert_greater(clf.score(X_test, y_test), 0.99)
# Multi-output/target case
X, y, X_test, y_test = build_dataset(n_features=10, n_targets=3)
clf = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7],
cv=3, max_iter=max_iter)
ignore_warnings(clf.fit)(X, y)
# We are in well-conditioned settings with low noise: we should
# have a good test-set performance
assert_greater(clf.score(X_test, y_test), 0.99)
assert_equal(clf.coef_.shape, (3, 10))
# Mono-output should have same cross-validated alpha_ and l1_ratio_
# in both cases.
X, y, _, _ = build_dataset(n_features=10)
clf1 = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf1.fit(X, y)
clf2 = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf2.fit(X, y[:, np.newaxis])
assert_almost_equal(clf1.l1_ratio_, clf2.l1_ratio_)
assert_almost_equal(clf1.alpha_, clf2.alpha_)
def test_enet_path():
X, y, X_test, y_test = build_dataset()
max_iter = 150
with warnings.catch_warnings():
# Here we have a small number of iterations, and thus the
# ElasticNet might not converge. This is to speed up tests
warnings.simplefilter("ignore", UserWarning)
clf = ElasticNetCV(n_alphas=5,
eps=2e-3,
l1_ratio=[0.9, 0.95],
cv=3,
max_iter=max_iter)
clf.fit(X, y)
assert_almost_equal(clf.alpha_, 0.002, 2)
assert_equal(clf.l1_ratio_, 0.95)
clf = ElasticNetCV(n_alphas=5,
eps=2e-3,
l1_ratio=[0.9, 0.95],
cv=3,
max_iter=max_iter,
precompute=True)
clf.fit(X, y)
assert_almost_equal(clf.alpha_, 0.002, 2)
assert_equal(clf.l1_ratio_, 0.95)
# test set
assert_greater(clf.score(X_test, y_test), 0.99)
def test_enet_path():
# We use a large number of samples and of informative features so that
# the l1_ratio selected is more toward ridge than lasso
X, y, X_test, y_test = build_dataset(n_samples=200, n_features=100, n_informative_features=100)
max_iter = 150
with warnings.catch_warnings():
# Here we have a small number of iterations, and thus the
# ElasticNet might not converge. This is to speed up tests
warnings.simplefilter("ignore", UserWarning)
clf = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7], cv=3, max_iter=max_iter)
clf.fit(X, y)
# Well-conditionned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have seleted an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
clf = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7], cv=3, max_iter=max_iter, precompute=True)
clf.fit(X, y)
# Well-conditionned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have seleted an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
# We are in well-conditionned settings with low noise: we should
# have a good test-set performance
assert_greater(clf.score(X_test, y_test), 0.99)
def test_enet_path():
# build an ill-posed linear regression problem with many noisy features and
# comparatively few samples
n_samples, n_features, max_iter = 50, 200, 50
random_state = np.random.RandomState(0)
w = random_state.randn(n_features)
w[10:] = 0.0 # only the top 10 features are impacting the model
X = random_state.randn(n_samples, n_features)
y = np.dot(X, w)
clf = ElasticNetCV(n_alphas=10, eps=1e-3, rho=0.95, cv=5,
max_iter=max_iter)
clf.fit(X, y)
assert_almost_equal(clf.alpha, 0.002, 2)
clf = ElasticNetCV(n_alphas=10, eps=1e-3, rho=0.95, cv=5,
max_iter=max_iter, precompute=True)
clf.fit(X, y)
assert_almost_equal(clf.alpha, 0.002, 2)
# test set
X_test = random_state.randn(n_samples, n_features)
y_test = np.dot(X_test, w)
assert clf.score(X_test, y_test) > 0.99
def test_enet_path():
# We use a large number of samples and of informative features so that
# the l1_ratio selected is more toward ridge than lasso
X, y, X_test, y_test = build_dataset(n_samples=200, n_features=100,
n_informative_features=100)
max_iter = 150
with warnings.catch_warnings():
# Here we have a small number of iterations, and thus the
# ElasticNet might not converge. This is to speed up tests
warnings.simplefilter("ignore", UserWarning)
clf = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7], cv=3,
max_iter=max_iter)
clf.fit(X, y)
# Well-conditioned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have seleted an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
clf = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7], cv=3,
max_iter=max_iter, precompute=True)
clf.fit(X, y)
# Well-conditioned settings, we should have selected our
# smallest penalty
assert_almost_equal(clf.alpha_, min(clf.alphas_))
# Non-sparse ground truth: we should have seleted an elastic-net
# that is closer to ridge than to lasso
assert_equal(clf.l1_ratio_, min(clf.l1_ratio))
# We are in well-conditioned settings with low noise: we should
# have a good test-set performance
assert_greater(clf.score(X_test, y_test), 0.99)
def test_enet_path():
# build an ill-posed linear regression problem with many noisy features and
# comparatively few samples
n_samples, n_features, max_iter = 50, 200, 50
random_state = np.random.RandomState(0)
w = random_state.randn(n_features)
w[10:] = 0.0 # only the top 10 features are impacting the model
X = random_state.randn(n_samples, n_features)
y = np.dot(X, w)
clf = ElasticNetCV(n_alphas=10,
eps=1e-3,
rho=0.95,
cv=5,
max_iter=max_iter)
clf.fit(X, y)
assert_almost_equal(clf.alpha, 0.002, 2)
clf = ElasticNetCV(n_alphas=10,
eps=1e-3,
rho=0.95,
cv=5,
max_iter=max_iter,
precompute=True)
clf.fit(X, y)
assert_almost_equal(clf.alpha, 0.002, 2)
# test set
X_test = random_state.randn(n_samples, n_features)
y_test = np.dot(X_test, w)
assert clf.score(X_test, y_test) > 0.99
def test_enet_path():
X, y, X_test, y_test = build_dataset()
max_iter = 50
clf = ElasticNetCV(n_alphas=10, eps=1e-3, rho=0.95, cv=5,
max_iter=max_iter)
clf.fit(X, y)
assert_almost_equal(clf.alpha, 0.002, 2)
clf = ElasticNetCV(n_alphas=10, eps=1e-3, rho=0.95, cv=5,
max_iter=max_iter, precompute=True)
clf.fit(X, y)
assert_almost_equal(clf.alpha, 0.002, 2)
# test set
assert clf.score(X_test, y_test) > 0.99
def test_enet_path():
X, y, X_test, y_test = build_dataset()
max_iter = 150
with warnings.catch_warnings():
# Here we have a small number of iterations, and thus the
# ElasticNet might not converge. This is to speed up tests
warnings.simplefilter("ignore", UserWarning)
clf = ElasticNetCV(n_alphas=5, eps=2e-3, rho=[0.9, 0.95], cv=3, max_iter=max_iter)
clf.fit(X, y)
assert_almost_equal(clf.alpha, 0.002, 2)
assert_equal(clf.rho_, 0.95)
clf = ElasticNetCV(n_alphas=5, eps=2e-3, rho=[0.9, 0.95], cv=3, max_iter=max_iter, precompute=True)
clf.fit(X, y)
assert_almost_equal(clf.alpha, 0.002, 2)
assert_equal(clf.rho_, 0.95)
# test set
assert_greater(clf.score(X_test, y_test), 0.99)
