python实现ks算法_Python计算KS值并绘制KS曲线

####################### 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 ##########################

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