Question

我正在尝试使用OneVsRestClassifier对一组注释进行多标记分类。我的目标是将每个评论标记为可能的主题列表。我的自定义分类器在csv中使用手动策划的单词列表及其对应的标记来标记每个注释。我试图使用VotingClassifier将Bag of Words技术和我的自定义分类器获得的结果结合起来。这是我现有代码的一部分：

import numpy as np

from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.ensemble import VotingClassifier
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.grid_search import GridSearchCV
from sklearn.linear_model import SGDClassifier
from sklearn.multiclass import OneVsRestClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MultiLabelBinarizer

class CustomClassifier(BaseEstimator, ClassifierMixin):
    def __init__(self, word_to_tag):
        self.word_to_tag = word_to_tag

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

    def predict_proba(self, X):
        prob = np.zeros(shape=(len(self.word_to_tag), 2))

        for index, comment in np.ndenumerate(X):
            prob[index] = [0.5, 0.5]
            for word, label in self.word_to_tag.iteritems():
                if (label == self.class_label) and (comment.find(word) >= 0):
                    prob[index] = [0, 1]
                    break

        return prob

    def _get_label(self, ...):
        # Need to have a way of knowing which label being classified
        # by OneVsRestClassifier (self.class_label)

bow_clf = Pipeline([('vect', CountVectorizer(stop_words='english', min_df=1, max_df=0.9)), 
                    ('tfidf', TfidfTransformer(use_idf=False)),
                    ('clf', SGDClassifier(loss='log', penalty='l2', alpha=1e-3, n_iter=5)),
                   ])
custom_clf = CustomClassifier(word_to_tag_dict)

ovr_clf = OneVsRestClassifier(VotingClassifier(estimators=[('bow', bow_clf), ('custom', custom_clf)],
                                               voting='soft'))

params = { 'estimator_weights': ([1, 1], [1, 2], [2, 1]) }
gs_clf = GridSearchCV(ovr_clf, params, n_jobs=-1, verbose=1, scoring='precision_samples')

binarizer = MultiLabelBinarizer()

gs_clf.fit(X, binarizer.fit_transform(y))

我的目的是使用这个手动策划的单词列表，通过几种启发式方法获得，通过单独应用单词来改善所获得的结果。目前，我正在努力寻找一种方法来了解在预测时对哪个标签进行分类，因为使用OneVsRestClassifier为每个标签创建了CustomClassifier的副本。

Answer 1

我认为您正在寻找classes_属性：http://scikit-learn.org/dev/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier

使用自定义分类器与GridSearchCV和OneVsRestClassifier进行多标签分类

1 个答案: