roc曲线和lift曲线是模型评价的指标，我们在建好模型后经常会用这两个指标对模型进行评估。在建模过程中发现python竟然没有自动生成roc曲线和lift曲线的包。我自己写了两个函数，能够做出roc曲线和lift曲线。希望可以给大家工作和学习带来一些方便。
函数如下，大家可以直接调用。
from scipy.stats import scoreatpercentile
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.metrics import roc_curve, auc
###result like result[['target','proba']]
def lift_curve_(result):
result.columns = ['target','proba']
result_ = result.copy()
proba_copy = result.proba.copy()
for i in range(10):
point1 = scoreatpercentile(result_.proba, i*(100/10))
point2 = scoreatpercentile(result_.proba, (i+1)*(100/10))
proba_copy[(result_.proba >= point1) & (result_.proba <= point2)] = ((i+1))
result_['grade'] = proba_copy
df_gain = result_.groupby(by=['grade'], sort=True).sum()/(len(result)/10)*100
plt.plot(df_gain['target'], color='red')
for xy in zip(df_gain['target'].reset_index().values):
plt.annotate("%s" % round(xy[0][1],2), xy=xy[0], xytext=(-20, 10), textcoords='offset points')
plt.plot(df_gain.index,[sum(result['target'])*100.0/len(result['target'])]*len(df_gain.index), color='blue')
plt.title('Lift Curve')
plt.xlabel('Decile')
plt.ylabel('Bad Rate (%)')
plt.xticks([1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0])
fig = plt.gcf()
fig.set_size_inches(10,8)
plt.savefig("train.png")
plt.show()
def roc_curve_(result):
result.columns = ['target','proba']
fpr,tpr,threshold = roc_curve(result['target'],result['proba']) ###计算真正率和假正率
roc_auc = auc(fpr,tpr) ###计算auc的值
plt.figure()
lw = 2
plt.figure(figsize=(10,8))
plt.plot(fpr, tpr, color='darkorange',
lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Roc Curve')
plt.legend(loc="lower right")
plt.show()
PS：result为两列的DataFrame，第一列为target（样本标签），第二列为probability（模型预测概率）。
lift_curve_(result)
roc_curve_(result)
