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Python realizes KS curve. For related usage, please refer to the previous blog-R language to realize KS curve.

The code is as follows:

# PlotKS # # def PlotKS (preds, labels, n, asc): # preds is score: asc=1 # preds is prob: asc=0 pred = preds # predicted value bad = labels # take 1 as bad, 0 as 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) 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. Locks _ 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

The effect of drawing is as follows:

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