scikit-learn 中带有 FeatureUnion 的自定义转换器 mixin

Question

我正在 scikit-learn 中编写自定义转换器，以便对数组执行特定操作。为此，我使用 TransformerMixin 类的继承。 当我只处理一个变压器时它工作正常。 但是，当我尝试使用 FeatureUnion（或 make_union）链接它们时，数组被复制了 n 次。 我能做些什么来避免这种情况？ 我是否按照应有的方式使用 scikit-learn？

import numpy as np
from sklearn.base import TransformerMixin
from sklearn.pipeline import FeatureUnion

# creation of array
s1 = np.array(['foo', 'bar', 'baz'])
s2 = np.array(['a', 'b', 'c'])
X = np.column_stack([s1, s2])
print('base array: \n', X, '\n')

# A fake example that appends a column (Could be a score, ...) calculated on specific columns from X
class DummyTransformer(TransformerMixin):
    def __init__(self, value=None):
        TransformerMixin.__init__(self)
        self.value = value

    def fit(self, *_):
        return self

    def transform(self, X):
        # appends a column (in this case, a constant) to X
        s = np.full(X.shape[0], self.value)
        X = np.column_stack([X, s])
        return X

# as such, the transformer gives what I need first
transfo = DummyTransformer(value=1)
print('single transformer: \n', transfo.fit_transform(X), '\n')

# but when I try to chain them and create a pipeline I run into the replication of existing columns
stages = []
for i in range(2):
    transfo = DummyTransformer(value=i+1)
    stages.append(('step'+str(i+1),transfo))
pipeunion = FeatureUnion(stages)
print('Given result of the Feature union pipeline: \n', pipeunion.fit_transform(X), '\n')
# columns 1&2 from X are replicated

# I would expect:
expected = np.column_stack([X, np.full(X.shape[0], 1), np.full(X.shape[0], 2) ])
print('Expected result of the Feature Union pipeline: \n', expected, '\n')

输出：

base array: 
 [['foo' 'a']
 ['bar' 'b']
 ['baz' 'c']] 

single transformer: 
 [['foo' 'a' '1']
 ['bar' 'b' '1']
 ['baz' 'c' '1']] 

Given result of the Feature union pipeline: 
 [['foo' 'a' '1' 'foo' 'a' '2']
 ['bar' 'b' '1' 'bar' 'b' '2']
 ['baz' 'c' '1' 'baz' 'c' '2']] 

Expected result of the Feature Union pipeline: 
   [['foo' 'a' '1' '2']
   ['bar' 'b' '1' '2']
   ['baz' 'c' '1' '2']] 

非常感谢

Answer 1

FeatureUnion 只会连接它从内部变压器中得到的东西。现在在您的内部转换器中，您从每个转换器发送相同的列。由转换器正确发送正确的数据。

我建议您只从内部转换器返回新数据，然后从FeatureUnion 外部或内部连接剩余的列。

如果你还没有看这个例子：

• http://scikit-learn.org/stable/auto_examples/hetero_feature_union.html

例如，您可以这样做：

# This dont do anything, just pass the data as it is
class DataPasser(TransformerMixin):

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return X

# Your transformer
class DummyTransformer(TransformerMixin):
    def __init__(self, value=None):
        TransformerMixin.__init__(self)
        self.value = value

    def fit(self, *_):
        return self

    # Changed this to only return new column after some operation on X
    def transform(self, X):
        s = np.full(X.shape[0], self.value)
        return s.reshape(-1,1)

之后，在您的代码中进一步更改：

stages = []    

# Append our DataPasser here, so original data is at the beginning
stages.append(('no_change', DataPasser()))


for i in range(2):
    transfo = DummyTransformer(value=i+1)
    stages.append(('step'+str(i+1),transfo))

pipeunion = FeatureUnion(stages)

运行这个新代码有结果：

('Given result of the Feature union pipeline: \n', 
array([['foo', 'a', '1', '2'],
       ['bar', 'b', '1', '2'],
       ['baz', 'c', '1', '2']], dtype='|S21'), '\n')
('Expected result of the Feature Union pipeline: \n', 
array([['foo', 'a', '1', '2'],
       ['bar', 'b', '1', '2'],
       ['baz', 'c', '1', '2']], dtype='|S21'), '\n')