本文整理汇总了Python中sklearn.linear_model.coordinate_descent.MultiTaskElasticNetCV类的典型用法代码示例。如果您正苦于以下问题：Python MultiTaskElasticNetCV类的具体用法？Python MultiTaskElasticNetCV怎么用？Python MultiTaskElasticNetCV使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了MultiTaskElasticNetCV类的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_enet_path
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_)示例2: test_1d_multioutput_enet_and_multitask_enet_cv
def test_1d_multioutput_enet_and_multitask_enet_cv():
X, y, _, _ = build_dataset(n_features=10)
y = y[:, np.newaxis]
clf = ElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf.fit(X, y[:, 0])
clf1 = MultiTaskElasticNetCV(n_alphas=5, eps=2e-3, l1_ratio=[0.5, 0.7])
clf1.fit(X, y)
assert_almost_equal(clf.l1_ratio_, clf1.l1_ratio_)
assert_almost_equal(clf.alpha_, clf1.alpha_)
assert_almost_equal(clf.coef_, clf1.coef_[0])
assert_almost_equal(clf.intercept_, clf1.intercept_[0])示例3: test_multitask_enet_and_lasso_cv
def test_multitask_enet_and_lasso_cv():
X, y, _, _ = build_dataset(n_features=50, n_targets=3)
clf = MultiTaskElasticNetCV().fit(X, y)
assert_almost_equal(clf.alpha_, 0.00556, 3)
clf = MultiTaskLassoCV().fit(X, y)
assert_almost_equal(clf.alpha_, 0.00278, 3)
X, y, _, _ = build_dataset(n_targets=3)
clf = MultiTaskElasticNetCV(n_alphas=10, eps=1e-3, max_iter=100, l1_ratio=[0.3, 0.5], tol=1e-3)
clf.fit(X, y)
assert_equal(0.5, clf.l1_ratio_)
assert_equal((3, X.shape[1]), clf.coef_.shape)
assert_equal((3,), clf.intercept_.shape)
assert_equal((2, 10, 3), clf.mse_path_.shape)
assert_equal((2, 10), clf.alphas_.shape)
X, y, _, _ = build_dataset(n_targets=3)
clf = MultiTaskLassoCV(n_alphas=10, eps=1e-3, max_iter=100, tol=1e-3)
clf.fit(X, y)
assert_equal((3, X.shape[1]), clf.coef_.shape)
assert_equal((3,), clf.intercept_.shape)
assert_equal((10, 3), clf.mse_path_.shape)
assert_equal(10, len(clf.alphas_))