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| import pandas as pd
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| import numpy as np
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| import streamlit as st
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| import gc
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|
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| @st.cache_data(show_spinner=False)
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| def get_baseline_default_rate(df,target_col='bad_loan'):
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| baseline_rate = df[target_col].mean()
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| return baseline_rate
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|
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| @st.cache_data(show_spinner=False)
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| def calculate_woe_iv(df, feature, target='bad_loan'):
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| """
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| Transforms a categorical variable into Weight of Evidence (WoE) scores
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| and computes the Total Information Value (IV) using vectorized operations.
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| """
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|
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| subset = df[[feature, target]].copy()
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| stats = subset.groupby(feature, observed=True)[target].agg([("Total", "count"), ("Bad", "sum")])
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|
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| stats['Good'] = stats['Total'] - stats['Bad']
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|
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| stats['Dist_Bad'] = stats['Bad'] / stats['Bad'].sum()
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| stats['Dist_Good'] = stats['Good'] / stats['Good'].sum()
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| stats['WoE'] = np.log((stats['Dist_Good'] + 0.001) / (stats['Dist_Bad'] + 0.001))
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| stats['IV_Contribution'] = (stats['Dist_Good'] - stats['Dist_Bad']) * stats['WoE']
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|
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| woe_df = stats.sort_values(by='WoE', ascending=False).reset_index()
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| total_iv = woe_df['IV_Contribution'].sum()
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|
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| del subset, stats
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| gc.collect()
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| return woe_df, total_iv
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|
