Sklearn pipeline throws ValueError: too many values to unpack (expected 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

自定义转换器

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))

我的目标就是实现这个目标。 X 是带有文本值的 pandas 系列。这行得通。

    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())])

但是 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

我认为您需要将 fit_transform 方法添加到您的 class AverageWordLengthExtractor。

Answer 2

你的括号在错误的位置/你在创建管道时缺少括号，应该是一个元组列表：

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

Sklearn pipeline throws ValueError: too many values to unpack (expected 2)

Sklearn pipeline throws ValueError: too many values to unpack (expected 2)

python

pipeline

python-3.x

pandas

scikit-learn