def test_sparse_enet_not_as_toy_dataset():
    n_samples, n_features, max_iter = 100, 100, 1000
    n_informative = 10

    X, y = make_sparse_data(n_samples, n_features, n_informative)

    X_train, X_test = X[n_samples / 2:], X[:n_samples / 2]
    y_train, y_test = y[n_samples / 2:], y[:n_samples / 2]

    s_clf = SparseENet(alpha=0.1, rho=0.8, fit_intercept=False,
                       max_iter=max_iter, tol=1e-7)
    s_clf.fit(X_train, y_train)
    assert_almost_equal(s_clf.dual_gap_, 0, 4)
    assert s_clf.score(X_test, y_test) > 0.85

    # check the convergence is the same as the dense version
    d_clf = DenseENet(alpha=0.1, rho=0.8, fit_intercept=False,
                      max_iter=max_iter, tol=1e-7)
    d_clf.fit(X_train, y_train)
    assert_almost_equal(d_clf.dual_gap_, 0, 4)
    assert d_clf.score(X_test, y_test) > 0.85

    assert_almost_equal(s_clf.coef_, d_clf.coef_, 5)

    # check that the coefs are sparse
    assert np.sum(s_clf.coef_ != 0.0) < 2 * n_informative
Example #2
0
def test_sparse_enet_not_as_toy_dataset():
    n_samples, n_features, max_iter = 100, 100, 1000
    n_informative = 10

    X, y = make_sparse_data(n_samples, n_features, n_informative)

    X_train, X_test = X[n_samples / 2:], X[:n_samples / 2]
    y_train, y_test = y[n_samples / 2:], y[:n_samples / 2]

    s_clf = SparseENet(alpha=0.1, rho=0.8, fit_intercept=False)
    s_clf.fit(X_train, y_train, max_iter=max_iter, tol=1e-7)
    assert_almost_equal(s_clf.dual_gap_, 0, 4)
    assert s_clf.score(X_test, y_test) > 0.85

    # check the convergence is the same as the dense version
    d_clf = DenseENet(alpha=0.1, rho=0.8, fit_intercept=False)
    d_clf.fit(X_train, y_train, max_iter=max_iter, tol=1e-7)
    assert_almost_equal(d_clf.dual_gap_, 0, 4)
    assert d_clf.score(X_test, y_test) > 0.85

    assert_almost_equal(s_clf.coef_, d_clf.coef_, 5)

    # check that the coefs are sparse
    assert np.sum(s_clf.coef_ != 0.0) < 2 * n_informative
Example #3
0
File: core.py Project: kanzure/ffx
 def fit(self, *args, **kwargs):
     return ElasticNet.fit(self, *args, **kwargs)