scikit learn中不同数据类型的自定义管道

Question

我目前正试图根据一堆整数和一些文本特征来预测一个 kickstarter 项目是否会成功。我正在考虑构建一个看起来像这样的管道

参考：http://scikit-learn.org/stable/auto_examples/hetero_feature_union.html#sphx-glr-auto-examples-hetero-feature-union-py

这是我的 ItemSelector 和管道代码

class ItemSelector(BaseEstimator, TransformerMixin):    
    def __init__(self, keys):
        self.keys = keys

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

    def transform(self, data_dict):
        return data_dict[self.keys]

我验证了 ItemSelector 正在按预期工作

t = ItemSelector(['cleaned_text'])
t.transform(df)

And it extract the necessary columns

管道

pipeline = Pipeline([
    # Use FeatureUnion to combine the features from subject and body
    ('union', FeatureUnion(
        transformer_list=[
            # Pipeline for pulling features from the post's subject line
            ('text', Pipeline([
                ('selector', ItemSelector(['cleaned_text'])),
                ('counts', CountVectorizer()),
                ('tf_idf', TfidfTransformer())
            ])),

            # Pipeline for pulling ad hoc features from post's body
            ('integer_features', ItemSelector(int_features)),
        ]
    )),

    # Use a SVC classifier on the combined features
    ('svc', SVC(kernel='linear')),
])

但是当我运行 pipeline.fit(X_train, y_train) 时，我收到了这个错误。知道如何解决这个问题吗？

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-27-317e1c402966> in <module>()
----> 1 pipeline.fit(X_train, y_train)

~/Anaconda/anaconda/envs/ds/lib/python3.5/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
    266             This estimator
    267         """
--> 268         Xt, fit_params = self._fit(X, y, **fit_params)
    269         if self._final_estimator is not None:
    270             self._final_estimator.fit(Xt, y, **fit_params)

~/Anaconda/anaconda/envs/ds/lib/python3.5/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params)
    232                 pass
    233             elif hasattr(transform, "fit_transform"):
--> 234                 Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
    235             else:
    236                 Xt = transform.fit(Xt, y, **fit_params_steps[name]) \

~/Anaconda/anaconda/envs/ds/lib/python3.5/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
    740         self._update_transformer_list(transformers)
    741         if any(sparse.issparse(f) for f in Xs):
--> 742             Xs = sparse.hstack(Xs).tocsr()
    743         else:
    744             Xs = np.hstack(Xs)

~/Anaconda/anaconda/envs/ds/lib/python3.5/site-packages/scipy/sparse/construct.py in hstack(blocks, format, dtype)
    456 
    457     """
--> 458     return bmat([blocks], format=format, dtype=dtype)
    459 
    460 

~/Anaconda/anaconda/envs/ds/lib/python3.5/site-packages/scipy/sparse/construct.py in bmat(blocks, format, dtype)
    577                                                     exp=brow_lengths[i],
    578                                                     got=A.shape[0]))
--> 579                     raise ValueError(msg)
    580 
    581                 if bcol_lengths[j] == 0:

ValueError: blocks[0,:] has incompatible row dimensions. Got blocks[0,1].shape[0] == 81096, expected 1.

Answer 1

ItemSelector 返回的是一个数据框，而不是一个数组。这就是scipy.hstack 抛出错误的原因。更改 ItemSelector 如下：

class ItemSelector(BaseEstimator, TransformerMixin):    
    ....
    ....
    ....

    def transform(self, data_dict):
        return data_dict[self.keys].as_matrix()

错误发生在管道的integer_features 部分。对于第一部分text，ItemSelector 下方的转换器支持 Dataframe，因此可以正确地将其转换为数组。但第二部分只有 ItemSelector 并返回 Dataframe。

更新：

在评论中，您提到要对从 ItemSelector 返回的结果 Dataframe 执行一些操作。因此，您可以创建一个新的 Transformer 并将其附加到管道的第二部分，而不是修改 ItemSelector 的转换方法。

class DataFrameToArrayTransformer(BaseEstimator, TransformerMixin):    
    def __init__(self):

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

    def transform(self, X):
        return X.as_matrix()

那么你的管道应该是这样的：

pipeline = Pipeline([
    # Use FeatureUnion to combine the features from subject and body
    ('union', FeatureUnion(
        transformer_list=[
            # Pipeline for pulling features from the post's subject line
            ('text', Pipeline([
                ('selector', ItemSelector(['cleaned_text'])),
                ('counts', CountVectorizer()),
                ('tf_idf', TfidfTransformer())
            ])),

            # Pipeline for pulling ad hoc features from post's body
            ('integer', Pipeline([
                ('integer_features', ItemSelector(int_features)),
                ('array', DataFrameToArrayTransformer()),
            ])),
        ]
    )),

    # Use a SVC classifier on the combined features
    ('svc', SVC(kernel='linear')),
])

这里要理解的主要内容是，FeatureUnion 在组合它们时只会处理二维数组，因此像 DataFrame 这样的任何其他类型都可能会出现问题。