Sklearn管道抛出ValueError：解压缩的值太多（预期2）

Question

我正在尝试创建一个sklearn管道，它将首先提取文本中的平均字长，然后使用StandardScaler对其进行标准化。

定制变压器

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))

我的目标是实现这一目标。 X是带有文本值的熊猫系列。这有效。

    extractor=AverageWordLengthExtractor()
    print(extractor.transform(X[:10]))
    sc=StandardScaler()
    print(sc.fit_transform(extractor.transform(X[:10])))

我为此创建的管道是。

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

但pipeline.fit_transform()产生以下错误。

Traceback (most recent call last):
  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)
ValueError: too many values to unpack (expected 2)

Answer 1

您的括号位于错误的位置/创建管道时缺少括号，应该是元组列表：

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

Answer 2

我想你需要在你的班级fit_transform上添加AverageWordLengthExtractor方法。