文本分类实例和Word2vec实例

1. 文本分类

本节的代码做了一下简单的文本分类，文本选用的是sklearn中自带的文本，加载后使用td-idf将文本向量化，之后采取了多种分类器进行了分类，并比较了各个分类器之间的误差。

import numpy as np
from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import RidgeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
from time import time
from pprint import pprint
import matplotlib.pyplot as plt
import matplotlib as mpl


# def test_clf(clf):
#     print (u'分类器：', clf)
#     alpha_can = np.logspace(-3, 2, 10)
#     model = GridSearchCV(clf, param_grid={'alpha': alpha_can}, cv=5)
#     m = alpha_can.size
#     if hasattr(clf, 'alpha'):
#         model.set_params(param_grid={'alpha': alpha_can})
#         m = alpha_can.size
#     if hasattr(clf, 'n_neighbors'):
#         neighbors_can = np.arange(1, 15)
#         model.set_params(param_grid={'n_neighbors': neighbors_can})
#         m = neighbors_can.size
#     if hasattr(clf, 'C'):
#         C_can = np.logspace(1, 3, 3)
#         gamma_can = np.logspace(-3, 0, 3)
#         model.set_params(param_grid={'C':C_can, 'gamma':gamma_can})
#         m = C_can.size * gamma_can.size
#     if hasattr(clf, 'max_depth'):
#         max_depth_can = np.arange(4, 10)
#         model.set_params(param_grid={'max_depth': max_depth_can})
#         m = max_depth_can.size
#     t_start = time()
#     model.fit(x_train, y_train)
#     t_end = time()
#     t_train = (t_end - t_start) / (5*m)
#     print (u'5折交叉验证的训练时间为：%.3f秒/(5*%d)=%.3f秒' % ((t_end - t_start), m, t_train))
#     print (u'最优超参数为：', model.best_params_)
#     t_start = time()
#     y_hat = model.predict(x_test)
#     t_end = time()
#     t_test = t_end - t_start
#     print (u'测试时间：%.3f秒' % t_test)
#     acc = metrics.accuracy_score(y_test, y_hat)
#     print (u'测试集准确率：%.2f%%' % (100 * acc))
#     name = str(clf).split('(')[0]
#     index = name.find('Classifier')
#     if index != -1:
#         name = name[:index]     # 去掉末尾的Classifier
#     if name == 'SVC':
#         name = 'SVM'
#     return t_train, t_test, 1-acc, name

def test_clf(clf):
    print (u'分类器：', clf)


    if hasattr(clf, 'alpha'):
        alpha_can = np.logspace(-3, 2, 10)
        model = GridSearchCV(clf, param_grid={
     'alpha': alpha_can}, cv=5)

        m = alpha_can.size
    if hasattr(clf, 'n_neighbors'):
        neighbors_can = np.arange(1, 3)
        model = GridSearchCV(clf,param_grid={
     'n_neighbors': neighbors_can},cv=5)

        m = neighbors_can.size
    if hasattr(clf, 'C'):
        C_can = np.logspace(1, 3, 3)
        gamma_can = np.logspace(-3, 0, 3)

        model = GridSearchCV(clf, param_grid={
     'C':C_can, 'gamma':gamma_can}, cv=5)
        m = C_can.size * gamma_can.size
    if hasattr(clf, 'max_depth'):
        max_depth_can = np.arange(4, 6)

        model = GridSearchCV(clf, param_grid={
     'max_depth': max_depth_can}, cv=5)
        m = max_depth_can.size
    t_start = time()
    model.fit(x_train, y_train)
    t_end = time()
    t_train = (t_end - t_start) / (5*m)
    print (u'5折交叉验证的训练时间为：%.3f秒/(5*%d)=%.3f秒' % ((t_end - t_start), m, t_train))
    print (u'最优超参数为：', model.best_params_)
    t_start = time()
    y_hat = model.predict(x_test)
    t_end = time()
    t_test = t_end - t_start
    print (u'测试时间：%.3f秒' % t_test)
    acc = metrics.accuracy_score(y_test, y_hat)
    print (u'测试集准确率：%.2f%%' % (100 * acc))
    name = str(clf).split('(')[0]
    index = name.find('Classifier')
    if index != -1:
        name = name[:index]     # 去掉末尾的Classifier
    if name == 'SVC':
        name = 'SVM'
    return t_train, t_test, 1-acc, name

if __name__ == "__main__":
    print (u'开始下载/加载数据...')
    t_start = time()
    # remove = ('headers', 'footers', 'quotes')
    remove = ()
    categories = 'alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space'
    # categories = None     # 若分类所有类别，请注意内存是否够用
    data_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=0, remove=remove)
    data_test = fetch_20newsgroups(subset='test', categories=categories, shuffle=True, random_state=0, remove=remove)
    t_end = time()
    print (u'下载/加载数据完成，耗时%.3f秒' % (t_end - t_start))
    print (u'数据类型：', type(data_train))
    print (u'训练集包含的文本数目：', len(data_train.data))
    print (u'测试集包含的文本数目：', len(data_test.data))
    print (u'训练集和测试集使用的%d个类别的名称：' % len(categories))
    categories = data_train.target_names
    pprint(categories)
    y_train = data_train.target
    y_test = data_test.target
    print (u' -- 前10个文本 -- ')
    for i in np.arange(10):
        print (u'文本%d(属于类别 - %s)：' % (i+1, categories[y_train[i]]))
        print (data_train.data[i])
        print ('\n\n')
    vectorizer = TfidfVectorizer(input='content', stop_words='english', max_df=0.5, sublinear_tf=True)
    x_train = vectorizer.fit_transform(data_train.data)  # x_train是稀疏的，scipy.sparse.csr.csr_matrix
    x_test = vectorizer.transform(data_test.data)
    print (u'训练集样本个数：%d，特征个数：%d' % x_train.shape)
    print (u'停止词:\n',)
    pprint(vectorizer.get_stop_words())
    feature_names = np.asarray(vectorizer.get_feature_names())

    print (u'\n\n===================\n分类器的比较：\n')
    clfs = (MultinomialNB(),                # 0.87(0.017), 0.002, 90.39%
            BernoulliNB(),                  # 1.592(0.032), 0.010, 88.54%
            KNeighborsClassifier(),         # 19.737(0.282), 0.208, 86.03%
            RidgeClassifier(),              # 25.6(0.512), 0.003, 89.73%
            RandomForestClassifier(n_estimators=200),   # 59.319(1.977), 0.248, 77.01%
            SVC()                           # 236.59(5.258), 1.574, 90.10%
            )
    result = []
    for clf in clfs:
        a = test_clf(clf)
        result.append(a)
        print ('\n')
    result = np.array(result)
    time_train, time_test, err, names = result.T
    time_train = time_train.astype(np.float)
    time_test = time_test.astype(np.float)
    err = err.astype(np.float)
    x = np.arange(len(time_train))
    mpl.rcParams['font.sans-serif'] = [u'simHei']
    mpl.rcParams['axes.unicode_minus'] = False
    plt.figure(figsize=(10, 7), facecolor='w')
    ax = plt.axes()
    b1 = ax.bar(x, err, width=0.25, color='#77E0A0')
    ax_t = ax.twinx()
    b2 = ax_t.bar(x+0.25, time_train, width=0.25, color='#FFA0A0')
    b3 = ax_t.bar(x+0.5, time_test, width=0.25, color='#FF8080')
    plt.xticks(x+0.5, names)
    plt.legend([b1[0], b2[0], b3[0]], (u'错误率', u'训练时间', u'测试时间'), loc='upper left', shadow=True)
    plt.title(u'新闻组文本数据不同分类器间的比较', fontsize=18)
    plt.xlabel(u'分类器名称')
    plt.grid(True)
    plt.tight_layout(2)
    plt.show()

2. Word2vec实例

Word2vec是nlp中常用的文本向量化工具。在这里，我们使用gensim包中的Word2vec函数对文字之间的相似性等性质进行了探索。

from time import time
from gensim.models import Word2Vec
import sys
import os


reload(sys)
sys.setdefaultencoding('utf-8')


class LoadCorpora(object):
    def __init__(self, s):
        self.path = s

    def __iter__(self):
        f = open(self.path,'r')
        for line in f:
            yield line.split(' ')


def print_list(a):
    for i, s in enumerate(a):
        if i != 0:
            print ('+',)
        print (s,)


if __name__ == '__main__':
    if not os.path.exists('news.model'):
        sentences = LoadCorpora('news.dat')
        t_start = time()
        model = Word2Vec(sentences, size=200, min_count=5, workers=8)  # 词向量维度为200，丢弃出现次数少于5次的词
        model.save('news.model')
        print ('OK:', time() - t_start)

    model = Word2Vec.load('news.model')
    print ('词典中词的个数：', len(model.vocab))
    for i, word in enumerate(model.vocab):
        print (word,)
        if i % 25 == 24:
            print()
    print()

    intrested_words = ('中国', '手机', '学习', '人民', '名义')
    print ('特征向量：')
    for word in intrested_words:
        print (word, len(model[word]), model[word])
    for word in intrested_words:
        result = model.most_similar(word)
        print ('与', word, '最相近的词：')
        for w, s in result:
            print ('\t', w, s)

    words = ('中国', '祖国', '人民')
    for i in range(len(words)):
        w1 = words[i]
        for j in range(i+1, len(words)):
            w2 = words[j]
            print ('%s 和 %s 的相似度为：%.6f' % (w1, w2, model.similarity(w1, w2)))

    print ('========================')
    opposites = ((['中国', '城市'], ['学生']),
                 (['男', '工作'], ['女']),
                 (['俄罗斯', '美国', '英国'], ['日本']))
    for positive, negative in opposites:
        result = model.most_similar(positive=positive, negative=negative)
        print_list(positive)
        print ('-',)
        print_list(negative)
        print ('：')
        for word, similar in result:
            print ('\t', word, similar)

    print ('========================')
    words_list = ('苹果 三星 美的 海尔', '中国 日本 韩国 美国 北京',
                  '医院 手术 护士 医生 感染 福利', '爸爸 妈妈 舅舅 爷爷 叔叔 阿姨 老婆')
    for words in words_list:
        print (words, '离群词：', model.doesnt_match(words.split(' ')))