def test_regularization_path(self):
# Check results using logistic path
num_samples = 10
num_feat = 5
X, y = make_classification(n_samples=num_samples, n_features=num_feat, n_informative=3,
n_classes=2, random_state=0, weights=[0.5, 0.5])
matrix = np.zeros((num_samples, num_feat + 2))
matrix[:,:-2] = X
matrix[:, -2] = np.ones(num_samples)
matrix[:, -1] = y
# Betas to test
logitfitL1 = LogisticRegressionL1()
lambda_grid = np.exp(-1 * np.linspace(1, 17, 200))
path = logitfitL1.fit(matrix, lambda_grid)
# Sklearn
cs = l1_min_c(X, y, loss='log') * np.logspace(0, 3)
# Computing regularization path using sklearn
clf = LogisticRegression(C=1.0, penalty='l1', tol=1e-6)
coefs_ = []
for c in cs:
clf.set_params(C=c)
clf.fit(X, y)
coefs_.append(clf.coef_.ravel().copy())
skbetas = np.append(clf.intercept_[0], clf.coef_)
np.testing.assert_almost_equal(skbetas, logitfitL1.coef_, 1)
