如何添加另一個功能（文本的長度）到當前包的單詞分類？ Scikit-learn

Question

我正在使用字袋來分類文本。它運行良好，但我想知道如何添加一個不是單詞的功能。

這是我的示例代碼。

import numpy as np 
from sklearn.pipeline import Pipeline 
from sklearn.feature_extraction.text import CountVectorizer 
from sklearn.svm import LinearSVC 
from sklearn.feature_extraction.text import TfidfTransformer 
from sklearn.multiclass import OneVsRestClassifier 

X_train = np.array(["new york is a hell of a town", 
        "new york was originally dutch", 
        "new york is also called the big apple", 
        "nyc is nice", 
        "the capital of great britain is london. london is a huge metropolis which has a great many number of people living in it. london is also a very old town with a rich and vibrant cultural history.", 
        "london is in the uk. they speak english there. london is a sprawling big city where it's super easy to get lost and i've got lost many times.", 
        "london is in england, which is a part of great britain. some cool things to check out in london are the museum and buckingham palace.", 
        "london is in great britain. it rains a lot in britain and london's fogs are a constant theme in books based in london, such as sherlock holmes. the weather is really bad there.",]) 
y_train = [[0],[0],[0],[0],[1],[1],[1],[1]] 

X_test = np.array(["it's a nice day in nyc", 
        'i loved the time i spent in london, the weather was great, though there was a nip in the air and i had to wear a jacket.' 
        ]) 
target_names = ['Class 1', 'Class 2'] 

classifier = Pipeline([ 
    ('vectorizer', CountVectorizer(min_df=1,max_df=2)), 
    ('tfidf', TfidfTransformer()), 
    ('clf', OneVsRestClassifier(LinearSVC()))]) 
classifier.fit(X_train, y_train) 
predicted = classifier.predict(X_test) 
for item, labels in zip(X_test, predicted): 
    print '%s => %s' % (item, ', '.join(target_names[x] for x in labels)) 

現在很清楚，關於倫敦的文字往往比關於紐約的文字長得多。我將如何添加文本的長度作爲一個功能？ 我是否必須使用另一種分類方式，然後結合這兩種預測？有沒有什麼辦法可以把這些文字一起做呢？一些示例代碼會很好 - 我對機器學習和scikit學習非常陌生。

Answer 1

如評論所示，這是FunctionTransformer，FeaturePipeline和FeatureUnion的組合。

import numpy as np 
from sklearn.pipeline import Pipeline, FeatureUnion 
from sklearn.feature_extraction.text import CountVectorizer 
from sklearn.svm import LinearSVC 
from sklearn.feature_extraction.text import TfidfTransformer 
from sklearn.multiclass import OneVsRestClassifier 
from sklearn.preprocessing import FunctionTransformer 

X_train = np.array(["new york is a hell of a town", 
        "new york was originally dutch", 
        "new york is also called the big apple", 
        "nyc is nice", 
        "the capital of great britain is london. london is a huge metropolis which has a great many number of people living in it. london is also a very old town with a rich and vibrant cultural history.", 
        "london is in the uk. they speak english there. london is a sprawling big city where it's super easy to get lost and i've got lost many times.", 
        "london is in england, which is a part of great britain. some cool things to check out in london are the museum and buckingham palace.", 
        "london is in great britain. it rains a lot in britain and london's fogs are a constant theme in books based in london, such as sherlock holmes. the weather is really bad there.",]) 
y_train = np.array([[0],[0],[0],[0],[1],[1],[1],[1]]) 

X_test = np.array(["it's a nice day in nyc", 
        'i loved the time i spent in london, the weather was great, though there was a nip in the air and i had to wear a jacket.' 
        ]) 
target_names = ['Class 1', 'Class 2'] 


def get_text_length(x): 
    return np.array([len(t) for t in x]).reshape(-1, 1) 

classifier = Pipeline([ 
    ('features', FeatureUnion([ 
     ('text', Pipeline([ 
      ('vectorizer', CountVectorizer(min_df=1,max_df=2)), 
      ('tfidf', TfidfTransformer()), 
     ])), 
     ('length', Pipeline([ 
      ('count', FunctionTransformer(get_text_length, validate=False)), 
     ])) 
    ])), 
    ('clf', OneVsRestClassifier(LinearSVC()))]) 

classifier.fit(X_train, y_train) 
predicted = classifier.predict(X_test) 
predicted 

這會將文本的長度添加到分類器使用的特徵中。