easy_predict.py 文件源码

def predict_eval_model(dtest,model_path):
    labels = dtest.get_label()
    bst = xgb.Booster(model_file=model_path)
    preds_prob = bst.predict(data=dtest)
    preds_original = bst.predict(data=dtest,output_margin=True)
    preds_label = []
    for pred_prob in preds_prob:
        if pred_prob>=0.5:
            preds_label.append(1)
        else:
            preds_label.append(0)
    print 'true label:\n',labels
    from sklearn.metrics import accuracy_score
    accuracy_score = accuracy_score(labels,preds_label)
    # accuracy_score_num = accuracy_score(labels,preds_label,normalize=True)
    # print 'accuracy_score : %f\tnum of the predicted truly : %d/%d'%accuracy_score,accuracy_score_num,len(labels)
    print 'accuracy_score : %f'%accuracy_score
    print 'average_precision_score : %f'%average_precision_score(labels,preds_prob)
    print classification_report(labels,preds_label,target_names=['class 0','class 1'])
    print confusion_matrix(labels,preds_label,labels=[0,1])
    f1_score_s = f1_score(labels,preds_label,pos_label=1)
    print 'f1 score : %f'%f1_score_s
    print 'precision_score : %f'%precision_score(labels,preds_label,pos_label=1)
    print 'recall_score : %f'%recall_score(labels,preds_label,pos_label=1)
    print 'roc_auc_score  : %f'%roc_auc_score(labels,preds_prob)

    fpr, tpr, thresholds = roc_curve(labels, preds_prob)
    print fpr,tpr,thresholds
    roc_auc = auc(fpr, tpr)
    precision,recall,thresholds_pr = precision_recall_curve(labels,preds_prob,pos_label=1)
    plt.plot(precision,recall,label='P-R f1 score %f'%(f1_score_s))
    # plt.plot(fpr, tpr, lw=1, label='ROC (area = %0.2f)' % (roc_auc))

    # average_precision_score = average_precision_score(labels,preds_prob)
    # print 'average_precision_score : %f'%average_precision_score