コード例 #1
0
def test_feature_importance(X_y_binary):
    X, y = X_y_binary

    sk_clf = SKElasticNetClassifier(loss="log",
                                    penalty="elasticnet",
                                    alpha=0.5,
                                    l1_ratio=0.5,
                                    n_jobs=1,
                                    random_state=0)
    sk_clf.fit(X, y)

    clf = ElasticNetClassifier(n_jobs=1)
    clf.fit(X, y)

    np.testing.assert_almost_equal(sk_clf.coef_.flatten(), clf.feature_importance, decimal=5)
コード例 #2
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def test_feature_importance_multi(X_y_multi):
    X, y = X_y_multi

    sk_clf = SKElasticNetClassifier(loss="log",
                                    penalty="elasticnet",
                                    alpha=0.5,
                                    l1_ratio=0.5,
                                    n_jobs=1,
                                    random_state=0)
    sk_clf.fit(X, y)

    clf = ElasticNetClassifier(n_jobs=1)
    clf.fit(X, y)

    sk_features = np.linalg.norm(sk_clf.coef_, axis=0, ord=2)

    np.testing.assert_almost_equal(sk_features, clf.feature_importance, decimal=5)
コード例 #3
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def test_overwrite_loss_parameter_in_kwargs():

    with pytest.warns(expected_warning=UserWarning) as warnings:
        en = ElasticNetClassifier(loss="hinge")

    assert len(warnings) == 1
    # check that the message matches
    assert warnings[0].message.args[0] == ("Parameter loss is being set to 'log' so that ElasticNetClassifier can predict probabilities"
                                           ". Originally received 'hinge'.")

    assert en.parameters['loss'] == 'log'
コード例 #4
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def test_fit_predict_multi(X_y_multi):
    X, y = X_y_multi

    sk_clf = SKElasticNetClassifier(loss="log",
                                    penalty="elasticnet",
                                    alpha=0.5,
                                    l1_ratio=0.5,
                                    n_jobs=-1,
                                    random_state=0)
    sk_clf.fit(X, y)
    y_pred_sk = sk_clf.predict(X)
    y_pred_proba_sk = sk_clf.predict_proba(X)

    clf = ElasticNetClassifier()
    fitted = clf.fit(X, y)
    assert isinstance(fitted, ElasticNetClassifier)

    y_pred = clf.predict(X)
    y_pred_proba = clf.predict_proba(X)

    np.testing.assert_almost_equal(y_pred_sk, y_pred.to_series().values, decimal=5)
    np.testing.assert_almost_equal(y_pred_proba_sk, y_pred_proba.to_dataframe().values, decimal=5)
コード例 #5
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def test_fit_predict_binary(X_y_binary):
    X, y = X_y_binary

    sk_clf = SKElasticNetClassifier(loss="log",
                                    penalty="elasticnet",
                                    alpha=0.5,
                                    l1_ratio=0.5,
                                    n_jobs=-1,
                                    random_state=0)
    sk_clf.fit(X, y)
    y_pred_sk = sk_clf.predict(X)
    y_pred_proba_sk = sk_clf.predict_proba(X)

    clf = ElasticNetClassifier()
    clf.fit(X, y)
    y_pred = clf.predict(X)
    y_pred_proba = clf.predict_proba(X)

    np.testing.assert_almost_equal(y_pred, y_pred_sk, decimal=5)
    np.testing.assert_almost_equal(y_pred_proba, y_pred_proba_sk, decimal=5)