How to get the Weight of Evidence (WOE) and Information Value (IV) in Python/pandas?

Written by- Sharif2983 times views

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To calculate the Weight of Evidence (WOE) and Information Value (IV) in Python/pandas, you can use the woe() and iv() functions provided by the WeightOfEvidence class in the pywoe library.
Here is an example of how you can use these functions:
import pandas as pd
from pywoe.pywoe import WeightOfEvidence

# Load the data into a pandas DataFrame
df = pd.read_csv('data.csv')

# Select the target column and the feature columns
target_col = 'target'
feature_cols = ['feature1', 'feature2', 'feature3']

# Create a WeightOfEvidence object
woe = WeightOfEvidence()

# Calculate the WOE for each feature
woe_dict = woe.woe(df, target_col, feature_cols)

# Calculate the IV for each feature
iv_dict = woe.iv(df, target_col, feature_cols)

# Print the WOE and IV for each feature
for feature, woe_val in woe_dict.items():
    iv_val = iv_dict[feature]
    print(f'Feature: {feature}, WOE: {woe_val}, IV: {iv_val}')
Alternatively, you can also use the category_encoders library to calculate the WOE and IV. Here is an example of how you can do that:
import pandas as pd
import category_encoders as ce

# Load the data into a pandas DataFrame
df = pd.read_csv('data.csv')

# Select the target column and the feature columns
target_col = 'target'
feature_cols = ['feature1', 'feature2', 'feature3']

# Create a WOE encoder
encoder = ce.WOEEncoder(cols=feature_cols)

# Fit the encoder on the data
encoder.fit(df[feature_cols], df[target_col])

# Transform the data using the encoder
df_woe = encoder.transform(df[feature_cols])

# Print the WOE and IV for each feature
for col, woe_val, iv_val in zip(df_woe.columns, encoder.woe_, encoder.iv_):
    print(f'Feature: {col}, WOE: {woe_val}, IV: {iv_val}')
I hope this helps! Let us know if you have any questions.