Python计算KS值并绘制KS曲线

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python实现KS曲线，相关使用方法请参考上篇博客-R语言实现KS曲线

代码如下：

####################### PlotKS ##########################def PlotKS(preds, labels, n, asc):    # preds is score: asc=1    # preds is prob: asc=0    pred = preds  # 预测值    bad = labels  # 取1为bad, 0为good    ksds = DataFrame({'bad': bad, 'pred': pred})    ksds['good'] = 1 - ksds.bad    if asc == 1:        ksds1 = ksds.sort_values(by=['pred', 'bad'], ascending=[True, True])    elif asc == 0:        ksds1 = ksds.sort_values(by=['pred', 'bad'], ascending=[False, True])    ksds1.index = range(len(ksds1.pred))    ksds1['cumsum_good1'] = 1.0*ksds1.good.cumsum()/sum(ksds1.good)    ksds1['cumsum_bad1'] = 1.0*ksds1.bad.cumsum()/sum(ksds1.bad)    if asc == 1:        ksds2 = ksds.sort_values(by=['pred', 'bad'], ascending=[True, False])    elif asc == 0:        ksds2 = ksds.sort_values(by=['pred', 'bad'], ascending=[False, False])    ksds2.index = range(len(ksds2.pred))    ksds2['cumsum_good2'] = 1.0*ksds2.good.cumsum()/sum(ksds2.good)    ksds2['cumsum_bad2'] = 1.0*ksds2.bad.cumsum()/sum(ksds2.bad)    # ksds1 ksds2 -> average    ksds = ksds1[['cumsum_good1', 'cumsum_bad1']]    ksds['cumsum_good2'] = ksds2['cumsum_good2']    ksds['cumsum_bad2'] = ksds2['cumsum_bad2']    ksds['cumsum_good'] = (ksds['cumsum_good1'] + ksds['cumsum_good2'])/2    ksds['cumsum_bad'] = (ksds['cumsum_bad1'] + ksds['cumsum_bad2'])/2    # ks    ksds['ks'] = ksds['cumsum_bad'] - ksds['cumsum_good']    ksds['tile0'] = range(1, len(ksds.ks) + 1)    ksds['tile'] = 1.0*ksds['tile0']/len(ksds['tile0'])    qe = list(np.arange(0, 1, 1.0/n))    qe.append(1)    qe = qe[1:]    ks_index = Series(ksds.index)    ks_index = ks_index.quantile(q = qe)    ks_index = np.ceil(ks_index).astype(int)    ks_index = list(ks_index)    ksds = ksds.loc[ks_index]    ksds = ksds[['tile', 'cumsum_good', 'cumsum_bad', 'ks']]    ksds0 = np.array([[0, 0, 0, 0]])    ksds = np.concatenate([ksds0, ksds], axis=0)    ksds = DataFrame(ksds, columns=['tile', 'cumsum_good', 'cumsum_bad', 'ks'])    ks_value = ksds.ks.max()    ks_pop = ksds.tile[ksds.ks.idxmax()]    print ('ks_value is ' + str(np.round(ks_value, 4)) + ' at pop = ' + str(np.round(ks_pop, 4)))    # chart    plt.plot(ksds.tile, ksds.cumsum_good, label='cum_good',                         color='blue', linestyle='-', linewidth=2)    plt.plot(ksds.tile, ksds.cumsum_bad, label='cum_bad',                        color='red', linestyle='-', linewidth=2)    plt.plot(ksds.tile, ksds.ks, label='ks',                   color='green', linestyle='-', linewidth=2)    plt.axvline(ks_pop, color='gray', linestyle='--')    plt.axhline(ks_value, color='green', linestyle='--')    plt.axhline(ksds.loc[ksds.ks.idxmax(), 'cumsum_good'], color='blue', linestyle='--')    plt.axhline(ksds.loc[ksds.ks.idxmax(),'cumsum_bad'], color='red', linestyle='--')    plt.title('KS=%s ' %np.round(ks_value, 4) +                  'at Pop=%s' %np.round(ks_pop, 4), fontsize=15)    return ksds####################### over ##########################

作图效果如下：