Question

我一直在尝试使用DataFrameMapper将我的数据框上的多个预处理转换添加到我的scikit-learn Pipeline中。

url = "https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data"
names = ['Sex', 'Length', 'Diameter', 'Height', 'Whole weight', 'Schuked weight', 'Viscera weight', 'Shell weight', 'Rings']

df = pd.read_csv(url, names=names)

mapper = DataFrameMapper(
    [('Height', Normalizer()), ('Sex', LabelBinarizer())]
)

stages = []

stages += [("mapper", mapper)]

estimator = DecisionTreeClassifier()

stages += [("dtree", estimator)]

pipeline = Pipeline(stages)

labelCol = 'Rings'
target = df[labelCol]
data = df.drop(labelCol, axis=1)

train_data, test_data, train_target, expected = train_test_split(data, target, test_size=0.25, random_state=33)

model = pipeline.fit(train_data, train_target)

但是，我收到以下错误：

Traceback (most recent call last):
  File "app/experimenter/sklearn/transformations.py", line 65, in <module>
    model = pipeline.fit(train_data, train_target)
  File "/Library/Python/2.7/site-packages/sklearn/pipeline.py", line 268, in fit
    Xt, fit_params = self._fit(X, y, **fit_params)
  File "/Library/Python/2.7/site-packages/sklearn/pipeline.py", line 234, in _fit
    Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
  File "/Library/Python/2.7/site-packages/sklearn/base.py", line 497, in fit_transform
    return self.fit(X, y, **fit_params).transform(X)
  File "/Library/Python/2.7/site-packages/sklearn_pandas/dataframe_mapper.py", line 225, in transform
    stacked = np.hstack(extracted)
  File "/Library/Python/2.7/site-packages/numpy/core/shape_base.py", line 288, in hstack
    return _nx.concatenate(arrs, 1)
ValueError: all the input array dimensions except for the concatenation axis must match exactly

我错过了什么？

谢谢：）

Answer 1

您必须改变DataFrameMapper：

的构造

mapper = DataFrameMapper(
    [(['Height'], Normalizer()), ('Sex', LabelBinarizer())]
)

这是一个细微的细节，可以在sklearn_pandas的文档中找到：

将列映射到转换

将列选择器指定为'column'（作为简单字符串）和['column']（作为具有一个元素的列表）之间的区别在于传递给转换器的数组的形状。在第一种情况下，将传递一维数组，而在第二种情况下，它将是具有一列的二维数组，即列向量。

[...]

请注意，某些变换器需要一维输入（面向标签的输入），而其他一些变换器（如OneHotEncoder或Imputer）需要二维输入，形状为{{1 }}

DataFrameMapper scikit-learn ValueError：除了连接轴之外的所有输入数组维度必须完全匹配

1 个答案: