TwitterResults.py 文件源码

def print_metrics(clf):

    #scores = cross_validation.cross_val_score(clf,features,labels,cv=5,scoring='accuracy')
    #print 'Accuracy:',scores.mean()

    cv = cross_validation.StratifiedKFold(labels,n_folds=5)

    mean_tpr = 0.0
    mean_fpr = np.linspace(0,1,100)
    all_tpr = []

    for i, (train,test) in enumerate(cv):
        probas_ = clf.fit(features[train],labels[train]).predict_proba(features[test])

        fpr,tpr,thresholds = metrics.roc_curve(labels[test],probas_[:,1])
        mean_tpr += interp(mean_fpr,fpr,tpr)
        mean_tpr[0] = 0.0
        roc_auc = metrics.auc(fpr,tpr)

        plt.plot(fpr,tpr,lw=1,label='ROC fold %d (area = %0.2f)' % (i,roc_auc))

    plt.plot([0,1],[0,1],'--',color=(0.6,0.6,0.6),label='Luck')

    mean_tpr /= len(cv)
    mean_tpr[-1] = 1.0
    mean_auc = metrics.auc(mean_fpr, mean_tpr)
    plt.plot(mean_fpr, mean_tpr, 'k--',
             label='Mean ROC (area = %0.2f)' % mean_auc, lw=2)

    plt.xlim([-0.05, 1.05])
    plt.ylim([-0.05, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic')
    plt.legend(loc="lower right")
    plt.savefig('auc_sent.png')