mainPEP.py 文件源码

def analyzeResult_temp(data,model,DataVecs):
    predict = model.predict(DataVecs)
    data['predict'] = predict
    print ("Accuracy: %f %%" % (100. * sum(data["label"] == data["predict"]) / len(data["label"])))
    answer1 = data[data["label"] == 1]
    answer2 = data[data["label"] == 0]
    print ("Positive Accuracy: %f %%" % (100. * sum(answer1["label"] == answer1["predict"]) / len(answer1["label"])))
    print ("Negative Accuracy: %f %%" % (100. * sum(answer2["label"] == answer2["predict"]) / len(answer2["label"])))
    try:
        result_auc = model.predict_proba(DataVecs)
        print ("Roc:%f\nAUPR:%f\n" % (roc_auc_score(data["label"],result_auc[:,1]),
            average_precision_score(data["label"],result_auc[:,1])))
        print("Precision:%f\nRecall:%f\nF1score:%f\nMCC:%f\n" %(precision_score(data["label"],data["predict"]),
            recall_score(data["label"],data["predict"]),
            f1_score(data["label"],data["predict"]),
            matthews_corrcoef(data["label"],data["predict"])))
    except:
        print "ROC unavailable"

# Performance evaluation and result analysis uing adjusted thresholds