v1ad98 / woebinningpandas

woeBinningPandas

This code generates a supervised fine and coarse classing of numeric variables and factors with respect to a dichotomous target variable. Its parameters provide flexibility in finding a binning that fits specific data characteristics and practical needs.

The basis of this code was taken woeBinning code (https://github.com/cran/woeBinning/blob/master/R/woe.binning.R) and changed from R to Python.

Used programs versions

Spyder (Python 3.7)

Pandas 0.23.4

Used Python libraries

import pandas as pd

import numpy as np

import math

import warnings

import copy

Using

Cloning a repository from GitHub

Use Git CMD

cd YOUR LINK FOLDER

git clone https://github.com/V1ad98/woeBinningPandas.git

File woeBinningPandas is ready to go!

Set your variable CSV file

yourvariable = pd.read_csv('Yourfile.csv')

Set the df variable and specify the column names from your CSV file, which you want to use.

df = yourvariable[['columnnames1', 'columnnames2','columnnames3']]

At THE END of the code in the function call woe_binning set the values of the arguments

binning = woe_binning(df, target_var, pred_var, min_perc_total, min_perc_class, stop_limit, abbrev_fact_levels, event_class)

df - Name of data frame with input data.

target_var - Name of dichotomous target variable in quotes. Only target variables with two distinct values (0 or 1).

pred_var - Name of predictor variables to be binned in quotes. Values can be either numeric or factors.

min_perc_total - For numeric variables this parameter defines the number of initial classes before any merging is applied. WOE starts. Increasing the min_perc_total} parameter will avoid sparse bins. Accepted range: 0.0001-0.2; default: 0.05.

min_perc_ class - If a column percentage of one of the target classes within a bin is below this limit (e.g. below 0.01=1%) then the respective bin will be joined with others. In case of numeric variables adjacent predictor classes are merged. Setting min_perc_class > 0 may provide more reliable WOE values. Accepted range: 0-0.2; default: 0, i.e. no merging with respect to sparse target classes is applied.

stop_limit - Stops WOE based merging of the predictor's classes/levels in case the resulting information value (IV) decreases more than (e.g. 0.05 = 5%) compared to the preceding binning step. stop_limit=0 will skip any WOE based merging. Increasing the stop_limit will simplify the binning solution and may avoid overfitting. Accepted range: 0-0.5; default: 0.1.

abbrev_fact_levels - Abbreviates the names of new (merged) factor levels via the base abbreviate function in case the specified number of characters is exceeded.

event_class - Optional parameter for specifying the class of the target event. This class typically indicates a negative event like a loan default or a disease. Use characters in quotes (e.g. bad). This class will be represented by negative WOE values then.

Using with PIP package

Download PIP package woeBinningPandas

pip install woeBinningPandas

Add use package

import woeBinningPandas

Set variables and call a function

yourvariable = woeBinningPandas.pd.read_csv('Yourfile.csv')

df = yourvariable[['columnnames1', 'columnnames2','columnnames3']]

Pass your values to functions

binning = woeBinningPandas.woe_binning (df, target_var, pred_var, min_perc_total, min_perc_class, stop_limit, abbrev_fact_levels, event_class)

Examples

import woeBinningPandas

germancredit = woeBinningPandas.pd.read_csv('GermanCredit.csv')

df = germancredit[['credit_risk', 'amount','duration']]

binning = woeBinningPandas.woe_binning(df, 'credit_risk', 'duration', 0.05, 0, 0.1, 50, 'bad')