test_NuSVR.py 文件源码

def _test_evaluation(self, allow_slow):
        """
        Test that the same predictions are made
        """

        # Generate some smallish (some kernels take too long on anything else) random data
        x, y = [], []
        for _ in range(50):
            cur_x1, cur_x2 = random.gauss(2,3), random.gauss(-1,2)
            x.append([cur_x1, cur_x2])
            y.append( 1 + 2*cur_x1 + 3*cur_x2 )

        input_names = ['x1', 'x2']
        df = pd.DataFrame(x, columns=input_names)

        # Parameters to test
        kernel_parameters = [{}, {'kernel': 'rbf', 'gamma': 1.2},
                             {'kernel': 'linear'},
                             {'kernel': 'poly'},  {'kernel': 'poly', 'degree': 2},  {'kernel': 'poly', 'gamma': 0.75},
                                 {'kernel': 'poly', 'degree': 0, 'gamma': 0.9, 'coef0':2},
                             {'kernel': 'sigmoid'}, {'kernel': 'sigmoid', 'gamma': 1.3}, {'kernel': 'sigmoid', 'coef0': 0.8},
                                 {'kernel': 'sigmoid', 'coef0': 0.8, 'gamma': 0.5}
                             ]
        non_kernel_parameters = [{}, {'C': 1}, {'C': 1.5, 'shrinking': True}, {'C': 0.5, 'shrinking': False, 'nu': 0.9}]

        # Test
        for param1 in non_kernel_parameters:
            for param2 in kernel_parameters:
                cur_params = param1.copy()
                cur_params.update(param2)

                cur_model = NuSVR(**cur_params)
                cur_model.fit(x, y)
                df['prediction'] = cur_model.predict(x)

                spec = scikit_converter.convert(cur_model, input_names, 'target')

                metrics = evaluate_regressor(spec, df)
                self.assertAlmostEquals(metrics['max_error'], 0)

                if not allow_slow:
                    break

            if not allow_slow:
                break