bvslusa

Install

pip install bvslusa

How to use

import pandas as pd
from bvslusa.validate import remove_restritivos, target_mapping
from bvslusa.evaluate import evaluate_bvs_scores
from bvslusa.ratings import get_ratings

df = pd.read_csv('../data/AVZA_FB727003.csv', sep=';')
df
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DOC_NUMBER SAFRA QTD_SCPC VL_SCPC QTD_CCF QTD_PROTESTO VL_PROTESTO FLAG_RESTRITIVO SCRCRDMERPJ3 SCRCRDMERPJ4 SCRCRDMERPJ5 SCRCRDATACAD SCRCRDMERMEI PERF_MERC_60D6M_EVER
0 891026 202203 0 0 1 0 0 1 267 256 58 185 5 MAU
1 982383 202203 0 0 0 0 0 0 283 256 460 619 30 BOM
2 176129 202203 0 0 0 0 0 0 283 256 460 712 34 BOM
3 566081 202203 0 0 0 0 0 0 283 256 460 781 30 BOM
4 760613 202203 0 0 0 0 0 0 283 256 460 712 34 BOM
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
35734 776859 202203 0 0 0 4 911 1 484 767 513 969 902 MAU
35735 94325 202203 0 0 0 4 911 1 484 767 513 969 902 MAU
35736 315930 202203 0 0 0 4 911 1 484 767 513 969 902 MAU
35737 668323 202203 0 0 0 4 911 1 484 767 513 969 902 MAU
35738 140483 202203 0 0 0 2 5285 1 487 767 531 956 131 BOM

35739 rows × 14 columns

df = remove_restritivos(df)
df = target_mapping(df, target='PERF_MERC_60D6M_EVER', map_dict={'BOM': 0, 'MAU': 1})
df.pipe(evaluate_bvs_scores)
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score auc ks
0 SCRCRDMERPJ5 0.694770 30.86
1 SCRCRDATACAD 0.666147 28.28
2 SCRCRDMERMEI 0.610453 19.22
3 SCRCRDMERPJ3 0.598126 16.93
4 SCRCRDMERPJ4 0.574876 13.56

As the SCRCRDMERPJ5 is the best bvs score, let’s proceed to build ratings with it:

df_ratings = get_ratings(df, target='PERF_MERC_60D6M_EVER', score='SCRCRDMERPJ5')
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Bin Count Count (%) Non-event Event Event rate WoE IV JS
0 (-inf, 294.50) 3341 0.142900 2709 632 0.189165 -0.892188 0.163145 1.974255e-02
1 [294.50, 357.50) 1336 0.057143 1107 229 0.171407 -0.771947 0.046611 5.685828e-03
2 [357.50, 405.50) 1380 0.059025 1184 196 0.142029 -0.549094 0.022291 2.751878e-03
3 [405.50, 457.50) 1469 0.062831 1299 170 0.115725 -0.314082 0.007055 8.782459e-04
4 [457.50, 491.50) 1406 0.060137 1275 131 0.093172 -0.072129 0.000322 4.028209e-05
5 [491.50, 535.50) 1547 0.066168 1413 134 0.086619 0.007997 0.000004 5.272191e-07
6 [535.50, 570.50) 1189 0.050855 1095 94 0.079058 0.107581 0.000563 7.034663e-05
7 [570.50, 597.50) 1276 0.054577 1200 76 0.059561 0.41171 0.007813 9.698149e-04
8 [597.50, 629.50) 1345 0.057528 1269 76 0.056506 0.467618 0.010386 1.286575e-03
9 [629.50, 682.50) 2340 0.100086 2223 117 0.050000 0.596806 0.027941 3.441700e-03
10 [682.50, 748.50) 3183 0.136142 3068 115 0.036129 0.936216 0.081821 9.869692e-03
11 [748.50, inf) 3568 0.152609 3498 70 0.019619 1.563818 0.202677 2.303251e-02
12 Special 0 0.000000 0 0 0.000000 0.0 0.000000 0.000000e+00
13 Missing 0 0.000000 0 0 0.000000 0.0 0.000000 0.000000e+00
Totals 23380 1.000000 21340 2040 0.087254 0.570628 6.776995e-02

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Credits

Author: João Nogueira

License

Apache Software License (Apache Software License 2.0)

Requires

Python >=3.7

Tags

nbdev jupyter notebook python

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