如何将另一个特征(文本长度)添加到当前的词袋分类中?Scikit-学习 [英] How to add another feature (length of text) to current bag of words classification? Scikit-learn

问题描述

我正在使用词袋对文本进行分类.它运行良好，但我想知道如何添加一个不是单词的功能.

I am using bag of words to classify text. It's working well but I am wondering how to add a feature which is not a word.

这是我的示例代码.

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))

现在很明显，关于伦敦的文本往往比关于纽约的文本长得多.我将如何添加文本长度作为特征?我是否必须使用另一种分类方式，然后将两种预测结合起来?有没有办法和词袋一起做?一些示例代码会很棒——我对机器学习和 scikit 学习非常陌生.

Now it is clear that the text about London tends to be much longer than the text about New York. How would I add length of the text as a feature? Do I have to use another way of classification and then combine the two predictions? Is there any way of doing it along with the bag of words? Some sample code would be great -- I'm very new to machine learning and scikit learn.

推荐答案

如评论所示，这是一个 FunctionTransformer、一个 FeaturePipeline 和一个 <代码>FeatureUnion.

As shown in the comments, this is a combination of a FunctionTransformer, a FeaturePipeline and a FeatureUnion.

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

这会将文本的长度添加到分类器使用的特征中.

This will add the length of the text to the features used by the classifier.

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