test_glm_classifier.py 文件源码

def _conversion_and_evaluation_helper_for_linear_svc(self, class_labels):
        ARGS = [ {},
                 {'C' : .75, 'loss': 'hinge'},
                 {'penalty': 'l1', 'dual': False},
                 {'tol': 0.001, 'fit_intercept': False},
                 {'intercept_scaling': 1.5}
        ]

        x, y = GlmCassifierTest._generate_random_data(class_labels)
        column_names = ['x1', 'x2']
        df = pd.DataFrame(x, columns=column_names)

        for cur_args in ARGS:
            print(class_labels, cur_args)
            cur_model = LinearSVC(**cur_args)
            cur_model.fit(x, y)

            spec = convert(cur_model, input_features=column_names,
                           output_feature_names='target')

            df['prediction'] = cur_model.predict(x)

            cur_eval_metics = evaluate_classifier(spec, df, verbose=False)
            self.assertEquals(cur_eval_metics['num_errors'], 0)