test_normalizer.py 文件源码

def test_random(self):
        # Generate some random data_imputeValue.multiArrayValue[i]
        X = _np.random.random(size = (50, 3))

        for param in ('l1', 'l2', 'max'):

            cur_model= Normalizer(norm=param)

            output = cur_model.fit_transform(X)

            spec = converter.convert(cur_model, ["a", 'b', 'c'], 'out')

            metrics = evaluate_transformer(spec, 
                    [dict(zip(["a", "b", "c"], row)) for row in X], 
                    [{"out" : row} for row in output])