def test_enet_l1_ratio(): # Test that an error message is raised if an estimator that # uses _alpha_grid is called with l1_ratio=0 msg = ("Automatic alpha grid generation is not supported for l1_ratio=0. " "Please supply a grid by providing your estimator with the " "appropriate `alphas=` argument.") X = np.array([[1, 2, 4, 5, 8], [3, 5, 7, 7, 8]]).T y = np.array([12, 10, 11, 21, 5]) assert_raise_message(ValueError, msg, ElasticNetCV(l1_ratio=0, random_state=42).fit, X, y) assert_raise_message( ValueError, msg, MultiTaskElasticNetCV(l1_ratio=0, random_state=42).fit, X, y[:, None]) # Test that l1_ratio=0 is allowed if we supply a grid manually alphas = [0.1, 10] estkwds = {'alphas': alphas, 'random_state': 42} est_desired = ElasticNetCV(l1_ratio=0.00001, **estkwds) est = ElasticNetCV(l1_ratio=0, **estkwds) with ignore_warnings(): est_desired.fit(X, y) est.fit(X, y) assert_array_almost_equal(est.coef_, est_desired.coef_, decimal=5) est_desired = MultiTaskElasticNetCV(l1_ratio=0.00001, **estkwds) est = MultiTaskElasticNetCV(l1_ratio=0, **estkwds) with ignore_warnings(): est.fit(X, y[:, None]) est_desired.fit(X, y[:, None]) assert_array_almost_equal(est.coef_, est_desired.coef_, decimal=5)
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_))
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_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])
def test_uniform_targets(): enet = ElasticNetCV(fit_intercept=True, n_alphas=3) m_enet = MultiTaskElasticNetCV(fit_intercept=True, n_alphas=3) lasso = LassoCV(fit_intercept=True, n_alphas=3) m_lasso = MultiTaskLassoCV(fit_intercept=True, n_alphas=3) models_single_task = (enet, lasso) models_multi_task = (m_enet, m_lasso) rng = np.random.RandomState(0) X_train = rng.random_sample(size=(10, 3)) X_test = rng.random_sample(size=(10, 3)) y1 = np.empty(10) y2 = np.empty((10, 2)) for model in models_single_task: for y_values in (0, 5): y1.fill(y_values) assert_array_equal(model.fit(X_train, y1).predict(X_test), y1) assert_array_equal(model.alphas_, [np.finfo(float).resolution]*3) for model in models_multi_task: for y_values in (0, 5): y2[:, 0].fill(y_values) y2[:, 1].fill(2 * y_values) assert_array_equal(model.fit(X_train, y2).predict(X_test), y2) assert_array_equal(model.alphas_, [np.finfo(float).resolution]*3)
def __init__(self, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, max_iter=1000, tol=0.0001, cv=3, copy_X=True, verbose=0, n_jobs=None, random_state=None, selection='cyclic'): self._hyperparams = { 'l1_ratio': l1_ratio, 'eps': eps, 'n_alphas': n_alphas, 'alphas': alphas, 'fit_intercept': fit_intercept, 'normalize': normalize, 'max_iter': max_iter, 'tol': tol, 'cv': cv, 'copy_X': copy_X, 'verbose': verbose, 'n_jobs': n_jobs, 'random_state': random_state, 'selection': selection } self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None): self._sklearn_model = SKLModel(**self._hyperparams) if (y is not None): self._sklearn_model.fit(X, y) else: self._sklearn_model.fit(X) return self
class MultiTaskElasticNetCVImpl(): def __init__(self, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, max_iter=1000, tol=0.0001, cv=3, copy_X=True, verbose=0, n_jobs=None, random_state=None, selection='cyclic'): self._hyperparams = { 'l1_ratio': l1_ratio, 'eps': eps, 'n_alphas': n_alphas, 'alphas': alphas, 'fit_intercept': fit_intercept, 'normalize': normalize, 'max_iter': max_iter, 'tol': tol, 'cv': cv, 'copy_X': copy_X, 'verbose': verbose, 'n_jobs': n_jobs, 'random_state': random_state, 'selection': selection } self._wrapped_model = Op(**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_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_))
def fit(self, x_train, y_train): self.kwargs['cv'] = 3 elastic_net = MultiTaskElasticNetCV(**self.kwargs) self._update_pipeline_and_fit(x_train, y_train, [elastic_net])
'LinearSVR':LinearSVR(), 'LocallyLinearEmbedding':LocallyLinearEmbedding(), 'LogisticRegression':LogisticRegression(), 'LogisticRegressionCV':LogisticRegressionCV(), 'MDS':MDS(), 'MLPClassifier':MLPClassifier(), 'MLPRegressor':MLPRegressor(), 'MaxAbsScaler':MaxAbsScaler(), 'MeanShift':MeanShift(), 'MinCovDet':MinCovDet(), 'MinMaxScaler':MinMaxScaler(), 'MiniBatchDictionaryLearning':MiniBatchDictionaryLearning(), 'MiniBatchKMeans':MiniBatchKMeans(), 'MiniBatchSparsePCA':MiniBatchSparsePCA(), 'MultiTaskElasticNet':MultiTaskElasticNet(), 'MultiTaskElasticNetCV':MultiTaskElasticNetCV(), 'MultiTaskLasso':MultiTaskLasso(), 'MultiTaskLassoCV':MultiTaskLassoCV(), 'MultinomialNB':MultinomialNB(), 'NMF':NMF(), 'NearestCentroid':NearestCentroid(), 'NearestNeighbors':NearestNeighbors(), 'Normalizer':Normalizer(), 'NuSVC':NuSVC(), 'NuSVR':NuSVR(), 'Nystroem':Nystroem(), 'OAS':OAS(), 'OneClassSVM':OneClassSVM(), 'OrthogonalMatchingPursuit':OrthogonalMatchingPursuit(), 'OrthogonalMatchingPursuitCV':OrthogonalMatchingPursuitCV(), 'PCA':PCA(),