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  • 宵论文最终结果

    SVM

    mean_precision:0.94,mean_recall:0.82,mean_f:0.88,mean_accuracy:0.85,mean_auc:0.88

    Gaussian Naive Bayes

    mean_precision:0.95,mean_recall:0.78,mean_f:0.86,mean_accuracy:0.83,mean_auc:0.91

     logisClassifier

    mean_precision:0.90,mean_recall:0.84,mean_f:0.87,mean_accuracy:0.83,mean_auc:0.90

    SGD

    mean_precision:0.94,mean_recall:0.84,mean_f:0.89,mean_accuracy:0.86,mean_auc:0.93

    去除一类属性之后:

    mean_precision:0.89,mean_recall:0.64,mean_f:0.75,mean_accuracy:0.71,mean_auc:0.76

    mean_precision:0.90,mean_recall:0.76,mean_f:0.82,mean_accuracy:0.79,mean_auc:0.84

    mean_precision:0.90,mean_recall:0.82,mean_f:0.86,mean_accuracy:0.82,mean_auc:0.88

    mean_precision:0.88,mean_recall:0.72,mean_f:0.79,mean_accuracy:0.75,mean_auc:0.79

    mean_precision:0.88,mean_recall:0.70,mean_f:0.78,mean_accuracy:0.74,mean_auc:0.76

    mean_precision:0.89,mean_recall:0.81,mean_f:0.85,mean_accuracy:0.81,mean_auc:0.86

    mean_precision:0.88,mean_recall:0.77,mean_f:0.82,mean_accuracy:0.78,mean_auc:0.80

    最后一次实验的脚本是:

    #!/usr/python
    #!-*-coding=utf8-*-
    #本周主要工作是对比几种分类算法的差别
    import numpy as np
    import random
    import myUtil
    
    from sklearn import metrics
    from sklearn import cross_validation
    from sklearn import svm
    from sklearn import naive_bayes
    from sklearn import metrics
    from sklearn import linear_model
    from sklearn import ensemble
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.feature_extraction.text import TfidfVectorizer
    import pylab as pl
    
    root_dir="/media/新加卷/小论文实验/data/liweibo"
    
    def loadAllFileWithSuffix(suffix):
        file_list=list()
    #myUtil.traverseFile(root_dir,suffix,file_list)
    #    file_list.append(root_dir+"/团圆饭/团圆饭.result")
    #    file_list.append(root_dir+"/时间都去哪儿/时间都去哪儿.result")
        file_list.append(root_dir+"/bobo.pincou")
        return file_list
    
    def loadData():
        print "start to preDataByMyOwn..."
        all_file_list=loadAllFileWithSuffix(['result'])
        for file_name in all_file_list:
            print file_name
            label_list=list()
            data_list=list()
            with open(file_name) as in_file:
                for line in in_file:
                    line_list=list()
                    label_list.append(int(line.strip().split('	')[0]))
                    all_features=line.strip().split('	')[1:]
                    for  feature in all_features:
                        line_list.append(float(feature))
                    data_list.append(line_list)
            data_list=np.array(data_list)
            label_list=np.array(label_list)
            return (data_list,label_list)
        
        
    def trainModel(data,classifier,n_folds=5):
        print "start to trainModel..."
        x=data[0]
        y=data[1]
    
        #shupple samples
        n_samples,n_features=x.shape
        print "n_samples:"+str(n_samples)+"n_features:"+str(n_features)
        p=range(n_samples)
        random.seed(0)
        random.shuffle(p)
        x,y=x[p],y[p]
    
        #cross_validation
        cv=cross_validation.KFold(len(y),n_folds=5)
        mean_tpr=0.0
        mean_fpr=np.linspace(0,1,100)
    
        mean_recall=0.0
        mean_accuracy=0.0
        mean_f=0.0
        mean_precision=0.0
    
        for i,(train,test) in enumerate(cv):
            print "the "+str(i)+"times validation..."
            classifier.fit(x[train],y[train])
            y_true,y_pred=y[test],classifier.predict(x[test])
            mean_precision+=metrics.precision_score(y_true,y_pred)
            mean_recall+=metrics.recall_score(y_true,y_pred)
    #        mean_accuracy+=metrics.accuracy_score(y_true,y_pred)
            mean_accuracy+=classifier.score(x[test],y_true)
            mean_f+=metrics.fbeta_score(y_true,y_pred,beta=1)
            
            probas_=classifier.predict_proba(x[test])
            fpr,tpr,thresholds=metrics.roc_curve(y[test],probas_[:,1])
            mean_tpr+=np.interp(mean_fpr,fpr,tpr)
            mean_tpr[0]=0.0
            roc_auc=metrics.auc(fpr,tpr)
            pl.plot(fpr,tpr,lw=1,label='ROC fold %d (area=%0.2f)'%(i,roc_auc))
        pl.plot([0,1],[0,1],'--',color=(0.6,0.6,0.6),label='luck')
    
        mean_precision/=len(cv)
        mean_recall/=len(cv)
        mean_f/=len(cv)
        mean_accuracy/=len(cv)
    
        mean_tpr/=len(cv)
        mean_tpr[-1]=1.0
        mean_auc=metrics.auc(mean_fpr,mean_tpr)
        print("mean_precision:%0.2f,mean_recall:%0.2f,mean_f:%0.2f,mean_accuracy:%0.2f,mean_auc:%0.2f " % (mean_precision,mean_recall,mean_f,mean_accuracy,mean_auc))
        pl.plot(mean_fpr,mean_tpr,'k--',label='Mean ROC (area=%0.2f)'% mean_auc,lw=2)
    
        pl.xlim([-0.05,1.05])
        pl.ylim([-0.05,1.05])
        pl.xlabel('False Positive Rate')
        pl.ylabel('True Positive Rate')
        pl.title('ROC')
        pl.legend(loc="lower right")
        #pl.show()
    
    
    def chooseSomeFeaturesThenTrain(data,clf,choose_index):
        x=data[0]
        y=data[1]
        (n_samples,n_features)=x.shape
        result_data=np.zeros(n_samples).reshape(n_samples,1)
        for i in choose_index:
                if i<1 or i > n_features:
                print "error feture comination..."
                    return
            choose_column=x[:,(i-1)].reshape(n_samples,1)
                result_data=np.column_stack((result_data,choose_column))
        result_data=(result_data[:,1:],y)        
        trainModel(result_data,clf)
    
    def main():
    #尝试四种分类方法
        data=loadData()
        #print "classify by svm:"
    #    clf_svm=svm.SVC(kernel='linear',C=1,probability=True,random_state=0)
    #    trainModel(data,clf_svm)
    #    #采用朴素贝页斯作为分类器
    #    print "classify by naive_bayes_multinomialNB:"
    #    clf_mnb=naive_bayes.GaussianNB()
    #    trainModel(data,clf_mnb)
    #    #采用逻辑回归作为分类器
    #    print "classify by logisClassifier"
    #    clf_sgd=linear_model.SGDClassifier(loss='log',penalty='l1')
    #    trainModel(data,clf_sgd)
    #    #利用梯度增强树作为分类器
        print "classify by gradientBoostingClassifier"
        clf_gbc=ensemble.GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)
        #trainModel(data,clf_gbc)
        #尝试不同的属性组合
        chooseSomeFeaturesThenTrain(data,clf_gbc,[5,6])
    
        
    if __name__=='__main__':
        main()
    最后一次实验的脚本文件
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  • 原文地址:https://www.cnblogs.com/bobodeboke/p/3662384.html
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