learning_kernel.py 文件源码

def MyEvaluation(y_test,predicted):
    def norm_me(x):
        if str(type(x)).find("int")>-1:
            return x
        zix = np.argmax(x)
        x1 = [0]*len(x)
        x1[zix] = 1
        return x1
    predicted = [norm_me(x) for x in predicted]
    predicted = np.array(predicted,dtype="uint8")

    target_names  = ['normal','malware']
    inv_map = {v: k for k, v in KLABEL.items()}
    target_names = [inv_map[x] for x in range(WORKING_KLABEL)]
    result = classification_report(y_test,predicted,target_names=target_names)
    print result

    averagelabel = 'binary'
    if B_MULTICLASS: averaegelabel = "macro"

    v_precision = precision_score(y_test,predicted, average=averagelabel)
    v_recall = recall_score(y_test,predicted, average=averagelabel)    

    (TP, FP, TN, FN) = perf_measure(y_test, predicted,KLABEL["malicious"])
    return v_precision,v_recall,TP, FP, TN, FN