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)
