# Python calculates KS values and plots KS curves

Source: Internet
Author: User

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Python implementation of the KS curve, the relevant use of the method please refer to the previous blog-R language Implementation 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 # predictive Value bad = labels # take 1 for bad, 0 for good Ksds = DataFrame ({' Bad ': Bad, ' pred ': P Red}) 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 = Ran GE (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.C  Umsum ()/sum (Ksds2.bad)  # ksds1 ksds2, average Ksds = ksds1[[' cumsum_good1 ', ' cumsum_bad1 '] [ksds[' cumsum_good2 '] = ksds2[' Cumsum_goo D2 '] 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.quan  Tile (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)) # Char T Plt.plot (Ksds.tile, Ksds.cumsum_good, label= ' Cum_good ', color= ' Blue ', linestyle= '-', Linewidt h=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.axvlin E (Ks_pop, color= ' Gray ', linestyle= '--') Plt.axhline (ks_value, color= ' green ', linestyle= '--') Plt.axhline (ksds.loc[ks Ds.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 drawing effect is as follows:

Python calculates KS values and plots KS curves

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