如何将不同的输入适配到一个sklearn管道中？

Question

如何将不同的输入适配到一个sklearn管道中？

pythonmachine-learningscikit-learnclassificationpipeline

9

我正在使用sklearn的Pipeline来对文本进行分类。

在这个例子的Pipeline中，我使用了一个TfIDF向量化器和一些自定义特征，将它们与FeatureUnion包装起来，并将分类器作为Pipeline的步骤。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.

以上代码运行正常，但有一个细节。我想对文本进行词性标注，并在标记文本上使用不同的向量化器。

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.

如何正确地适配这种类型的数据？这两个向量化器如何区分原始文本和POS文本？我的选择有哪些？

我还有自定义特征，其中一些需要使用原始文本，而另一些需要使用POS文本。

编辑：添加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)

这个特征转换器需要接收标记文本以供count_nouns()函数使用，或者接收原始文本以供type_token_ratio()函数使用。

- Ivan Bilan

1个回答

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- dooms · Accepted Answer

我认为您需要在两个转换器（TfidfTransformer和POSTransformer）上进行FeatureUnion。当然，您需要定义POSTransformer。
或许这篇文章可以帮到您：article。
您的管道可能会像这样。

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())
])

假设MeasureFeatures和POSTransformer都是符合sklearn API的Transformer。