test_imputer.py 文件源码

def test_conversion_boston(self):

        from sklearn.datasets import load_boston

        scikit_data = load_boston()

        sh = scikit_data.data.shape 

        rn.seed(0)
        missing_value_indices = [(rn.randint(sh[0]), rn.randint(sh[1])) 
                                    for k in range(sh[0])]

        for strategy in ["mean", "median", "most_frequent"]: 
            for missing_value in [0, 'NaN', -999]:

                X = np.array(scikit_data.data).copy()

                for i, j in missing_value_indices:
                    X[i,j] = missing_value

                model = Imputer(missing_values = missing_value, strategy = strategy)
                model = model.fit(X)

                tr_X = model.transform(X.copy())

                spec = converter.convert(model, scikit_data.feature_names, 'out')

                input_data = [dict(zip(scikit_data.feature_names, row)) 
                                for row in X]

                output_data = [{"out" : row} for row in tr_X]

                result = evaluate_transformer(spec, input_data, output_data)

                assert result["num_errors"] == 0