Python绘制KS曲线的实现方法


            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：



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