Scikit-learn管道TypeError：zip参数＃2必须支持迭代

Question

我正在尝试为sklearn管道创建一个自定义转换器，它将提取特定文本的平均字长，然后对其应用标准缩放器以标准化数据集。我正在将一系列文本传递给管道。

class AverageWordLengthExtractor(BaseEstimator, TransformerMixin):

    def __init__(self):
        pass
    def average_word_length(self, text):
        return np.mean([len(word) for word in text.split( )])
    def fit(self, x, y=None):
        return self
    def transform(self, x , y=None):
        return pd.DataFrame(pd.Series(x).apply(self.average_word_length))

然后我创建了这样的管道。

pipeline = Pipeline(['text_length', AverageWordLengthExtractor(), 
                         'scale', StandardScaler()])

当我在这个管道上执行fit_transform时，我收到错误，

 File "custom_transformer.py", line 48, in <module>
    main()
  File "custom_transformer.py", line 43, in main
    'scale', StandardScaler()])
  File "/opt/conda/lib/python3.6/site-packages/sklearn/pipeline.py", line 114, in __init__
    self._validate_steps()
  File "/opt/conda/lib/python3.6/site-packages/sklearn/pipeline.py", line 146, in _validate_steps
    names, estimators = zip(*self.steps)
TypeError: zip argument #2 must support iteration

Answer 1

Pipeline构造函数需要一个参数steps，它是一个元组列表。

更正版本：

pipeline = Pipeline([('text_length', AverageWordLengthExtractor()), 
                     ('scale', StandardScaler())])

更多信息在官方docs。