Python机器学习(6)——逻辑回归分类

#-*- coding: utf-8 -*-
from matplotlib importpyplot
importscipy as sp
importnumpy as np
from matplotlib importpylab
from sklearn.datasets importload_files
from sklearn.cross_validation importtrain_test_split
from sklearn.feature_extraction.text importCountVectorizer
from sklearn.feature_extraction.text importTfidfVectorizer
from sklearn.naive_bayes importMultinomialNB
from sklearn.metrics importprecision_recall_curve, roc_curve, auc
from sklearn.metrics importclassification_report
from sklearn.linear_model importLogisticRegression
importtime
 
start_time =time.time()
 
#绘制R/P曲线
def plot_pr(auc_score, precision, recall, label=None):
    pylab.figure(num=None, figsize=(6, 5))
    pylab.xlim([0.0, 1.0])
    pylab.ylim([0.0, 1.0])
    pylab.xlabel('Recall')
    pylab.ylabel('Precision')
    pylab.title('P/R (AUC=%0.2f) / %s' %(auc_score, label))
    pylab.fill_between(recall, precision, alpha=0.5)
    pylab.grid(True, linestyle='-', color='0.75')
    pylab.plot(recall, precision, lw=1)    
    pylab.show()
 
#读取
movie_data   = sp.load('movie_data.npy')
movie_target = sp.load('movie_target.npy')
x =movie_data
y =movie_target
 
#BOOL型特征下的向量空间模型，注意，测试样本调用的是transform接口
count_vec = TfidfVectorizer(binary = False, decode_error = 'ignore',
                            stop_words = 'english')
average =0
testNum = 10
for i inrange(0, testNum):
    #加载数据集，切分数据集80%训练，20%测试
x_train, x_test, y_train, y_test
        = train_test_split(movie_data, movie_target, test_size = 0.2)
    x_train =count_vec.fit_transform(x_train)
    x_test  =count_vec.transform(x_test)
 
    #训练LR分类器
    clf =LogisticRegression()
    clf.fit(x_train, y_train)
    y_pred =clf.predict(x_test)
    p = np.mean(y_pred ==y_test)
    print(p)
    average +=p
 
    
#准确率与召回率
answer = clf.predict_proba(x_test)[:,1]
precision, recall, thresholds =precision_recall_curve(y_test, answer)    
report = answer > 0.5
print(classification_report(y_test, report, target_names = ['neg', 'pos']))
print("average precision:", average/testNum)
print("time spent:", time.time() -start_time)
 
plot_pr(0.5, precision, recall, "pos")