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：
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####################### 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：
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://www.cnblogs.com/bigdatafengkong/p/9079093.html
