test_one_hot_encoder.py 文件源码

def test_boston_OHE_pipeline(self): 
        data = load_boston()

        for categorical_features in [ [3], [8], [3, 8], [8,3] ]:

            # Put it in a pipeline so that we can test whether the output dimension
            # handling is correct. 

            model = Pipeline([("OHE", OneHotEncoder(categorical_features = categorical_features)),
                 ("Normalizer", Normalizer())])

            model.fit(data.data.copy(), data.target)

            # Convert the model
            spec = sklearn.convert(model, data.feature_names, 'out').get_spec()

            input_data = [dict(zip(data.feature_names, row)) for row in data.data]
            output_data = [{"out" : row} for row in model.transform(data.data.copy())]

            result = evaluate_transformer(spec, input_data, output_data)

            assert result["num_errors"] == 0