def mult_dense_train_data(): mult_dense, mult_target = make_classification(n_samples=300, n_features=100, n_informative=5, n_classes=3, random_state=0) return mult_dense, mult_target
def bin_dense_train_data(): bin_dense, bin_target = make_classification(n_samples=200, n_features=100, n_informative=5, n_classes=2, random_state=0) return bin_dense, bin_target
from scipy.linalg import svd, diagsvd from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_equal from sklearn.datasets import load_digits from lightning.impl.datasets.samples_generator import make_classification from lightning.classification import FistaClassifier from lightning.regression import FistaRegressor from lightning.impl.penalty import project_simplex, project_l1_ball, L1Penalty bin_dense, bin_target = make_classification(n_samples=200, n_features=100, n_informative=5, n_classes=2, random_state=0) bin_target = bin_target * 2 - 1 mult_dense, mult_target = make_classification(n_samples=300, n_features=100, n_informative=5, n_classes=3, random_state=0) bin_csr = sp.csr_matrix(bin_dense) mult_csr = sp.csr_matrix(mult_dense) digit = load_digits(2) def test_fista_multiclass_l1l2():
from sklearn.datasets.samples_generator import make_regression from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_array_almost_equal from lightning.impl.datasets.samples_generator import make_classification from lightning.impl.dual_cd import LinearSVC from lightning.impl.dual_cd import LinearSVR from lightning.impl.dual_cd import LinearRidge from lightning.impl.dual_cd_fast import sparse_dot from lightning.impl.dataset_fast import get_dataset bin_dense, bin_target = make_classification(n_samples=200, n_features=100, n_informative=5, n_classes=2, random_state=0) bin_csr = sp.csr_matrix(bin_dense) mult_dense, mult_target = make_classification(n_samples=300, n_features=100, n_informative=5, n_classes=3, random_state=0) mult_sparse = sp.csr_matrix(mult_dense) reg_dense, reg_target = make_regression(n_samples=200, n_features=100, n_informative=5, random_state=0) def test_sparse_dot(): for data in (bin_dense, bin_csr): K = linear_kernel(data)