如何将不同的输入放入 sklearn Pipeline 中？

我正在使用 sklearn 的 Pipeline 对文本进行分类。

在此示例管道中，我有一个 TfIDF 矢量器和一些用 FeatureUnion 包装的自定义功能和分类器作为管道步骤，然后我拟合训练数据并进行预测：

from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC

X = ['I am a sentence', 'an example']
Y = [1, 2]
X_dev = ['another sentence']

# load custom features and FeatureUnion with Vectorizer
features = []
measure_features = MeasureFeatures() # this class includes my custom features
features.append(('measure_features', measure_features))

countVecWord = TfidfVectorizer(ngram_range=(1, 3), max_features= 4000)
features.append(('ngram', countVecWord))

all_features = FeatureUnion(features)

# classifier
LinearSVC1 = LinearSVC(tol=1e-4,  C = 0.10000000000000001)

pipeline = Pipeline(
    [('all', all_features ),
    ('clf', LinearSVC1),
    ])

pipeline.fit(X, Y)
y_pred = pipeline.predict(X_dev)

# etc.

上面的代码工作得很好，但有一个问题。我想对文本进行词性标记，并在标记文本上使用不同的矢量化器。

X = ['I am a sentence', 'an example']
X_tagged = do_tagging(X) 
# X_tagged = ['PP AUX DET NN', 'DET NN']
Y = [1, 2]
X_dev = ['another sentence']
X_dev_tagged = do_tagging(X_dev)

# load custom featues and FeatureUnion with Vectorizer
features = []
measure_features = MeasureFeatures() # this class includes my custom features
features.append(('measure_features', measure_features))

countVecWord = TfidfVectorizer(ngram_range=(1, 3), max_features= 4000)
# new POS Vectorizer
countVecPOS = TfidfVectorizer(ngram_range=(1, 4), max_features= 2000)

features.append(('ngram', countVecWord))
features.append(('pos_ngram', countVecWord))

all_features = FeatureUnion(features)

# classifier
LinearSVC1 = LinearSVC(tol=1e-4,  C = 0.10000000000000001)

pipeline = Pipeline(
    [('all', all_features ),
    ('clf', LinearSVC1),
    ])

# how do I fit both X and X_tagged here
# how can the different vectorizers get either X or X_tagged?
pipeline.fit(X, Y)
y_pred = pipeline.predict(X_dev)

# etc.

如何正确拟合此类数据？两个向量化器如何区分原始文本和 pos 文本？我有什么选择？

我也有自定义功能，其中一些会采用原始​​文本，另一些则采用 POS 文本。

编辑：添加了 MeasureFeatures()

from sklearn.base import BaseEstimator
import numpy as np

class MeasureFeatures(BaseEstimator):

    def __init__(self):
        pass

    def get_feature_names(self):
        return np.array(['type_token', 'count_nouns'])

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

    def transform(self, x_dataset):


        X_type_token = list()
        X_count_nouns = list()

        for sentence in x_dataset:

            # takes raw text and calculates type token ratio
            X_type_token.append(type_token_ratio(sentence))

            # takes pos tag text and counts number of noun pos tags (NN, NNS etc.)
            X_count_nouns.append(count_nouns(sentence))

        X = np.array([X_type_token, X_count_nouns]).T

        print X
        print X.shape

        if not hasattr(self, 'scalar'):
            self.scalar = StandardScaler().fit(X)
        return self.scalar.transform(X)

然后，该特征转换器需要为 count_nouns() 函数获取标记文本或为 type_token_ratio() 获取原始文本

我认为你必须做一个特征联盟2 个 Transformer（TfidfTransformer 和POS变压器）。当然，您需要定义 POSTransformer。
也许这个article http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html会帮助你。

也许你的管道会是这样的。

pipeline = Pipeline([
  ('features', FeatureUnion([
    ('ngram_tf_idf', Pipeline([
      ('counts_ngram', CountVectorizer()),
      ('tf_idf_ngram', TfidfTransformer())
    ])),
    ('pos_tf_idf', Pipeline([
      ('pos', POSTransformer()),          
      ('counts_pos', CountVectorizer()),
      ('tf_idf_pos', TfidfTransformer())
    ])),
    ('measure_features', MeasureFeatures())
  ])),
  ('classifier', LinearSVC())
])

这假设测量特征 and POS变压器Transformer 是否符合 sklearn API。