Copyright (c) 2019 The Python Packaging Authority
The package is used for risk control modeling in Python. It mainly provides some basic and commonly data analysis methods for partners who want to learn Python data analysis or machine learning in the Internet financial industry. It also contains a large number of solutions to the problems encountered by the authors in their daily work. I hope you can actively use it. If you have any questions, please contact me at hsliu_em@126.com.
riskcontrol
Riskcontrol is used for risk control modeling in Python.
It provides intuitive tools for
- feature information-value (iv)
- plot bad rate of bins based on decision tree
- ks plot
- feature info describe for external data validation(contain missing analysis)
- two feature heat map for cross analysis
- logistic credit card
- gps near geohash coding
install
pip install riskcontrol
Usage
import riskcontrol as rc
import pandas as pd
data = pd.read_csv('test.csv')
rc.feature_miss_ana(data) # data analysis report
| col | col_type | iv | ... | mode_v | mode_pect | mode_bad_rate | risk_monotonicity |
|---|---|---|---|---|---|---|---|
| ios | float64 | 0.0273 | ... | 0 | 0.528996 | 0.054911 | 强 |
| sex_m | float64 | 0.0486 | ... | 1 | 0.723311 | 0.053726 | 强 |
| age | float64 | 0.0408 | ... | 22 | 0.095423 | 0.053391 | 弱 |
| income | float64 | 0.0302 | ... | 100000 | 0.147488 | 0.041681 | 弱 |
| edu | float64 | 0.0367 | ... | 0 | 0.828286 | 0.051514 | 强 |
| freeram | float64 | 0.0087 | ... | 482864 | 0.000104 | 0.125 | 无 |
| joblevel | float64 | 0.0411 | ... | 4 | 0.383198 | 0.056743 | 强 |
- miss_bad_rate:缺失值对应的bad_rate
- notnull_bad_rate：非缺失值对应的bad_rate
- zero_pect：0值占比
- zero_bad_rate：0值bad_rate
- mode_v：众数
- mode_pect：众数占比
- mode_bad_rate：众数对应的bad_rate
- risk_monotonicity：分箱后的逾期单调性
rc.split_box_plot_new(data, col_name) # bins bad rate plot
rc.create_geohash(latitude, longitude, radius, precision)
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