Replacing the Weight-of-Evidence (WoE) with its corresponding va

ghz 10days ago ⋅ 5 views

Replacing the Weight-of-Evidence (WoE) with its corresponding value

I have variable called x whose minimum value is zero and maximum is 2 million. So I cut the value into bins like this code:

bins = [0,1,10000,20000,50000,60000,70000,100000,2000000]
df_input['X_bins'] = pd.cut(df_input['X'], bins,right=False)

enter image description here

Currently I am using for-loop to replace each bin with their Weight-of-Evidence value:

def flag_dfstd(df_input):
    if (df_input['X'] >=0) & (df_input['X'] <100) :
        return '-0.157688'
    elif (df_input['X'] >=100) & (df_input['X'] < 10000) :
        return '-0.083307'
    elif (df_input['X'] >=10000) & (df_input['X'] < 20000) :
        return '0.381819'
    elif (df_input['X'] >=20000) & (df_input['X'] < 50000):
        return '0.364365'
    else:
        return '0'
df_input['X_WOE'] = df_input.apply(flag_dfstd, axis = 1).astype(str)

Is there way that I can replace the Weight of Evidence without using for loop?

Answer

Yes, you can replace the Weight of Evidence (WOE) values without using a for loop by utilizing pandas' pd.cut() to create the bins and pd.Series.replace() or map() to assign the corresponding WOE values.

Here’s how you can do it:

  1. Use pd.cut() to create the bins.
  2. Create a mapping of the bin ranges to their WOE values.
  3. Use map() to replace the bin labels with the corresponding WOE values.

Here’s an optimized version of your code:

import pandas as pd

# Define the bins and the corresponding WOE values
bins = [0, 1, 10000, 20000, 50000, 60000, 70000, 100000, 2000000]
woe_values = {
    pd.Interval(0, 1, closed='left'): '-0.157688',
    pd.Interval(1, 10000, closed='left'): '-0.083307',
    pd.Interval(10000, 20000, closed='left'): '0.381819',
    pd.Interval(20000, 50000, closed='left'): '0.364365',
    pd.Interval(50000, 60000, closed='left'): '0',
    pd.Interval(60000, 70000, closed='left'): '0',
    pd.Interval(70000, 100000, closed='left'): '0',
    pd.Interval(100000, 2000000, closed='left'): '0'
}

# Use pd.cut to assign bins
df_input['X_bins'] = pd.cut(df_input['X'], bins, right=False)

# Map WOE values based on the bin labels
df_input['X_WOE'] = df_input['X_bins'].map(woe_values)

# Display the result
print(df_input[['X', 'X_bins', 'X_WOE']])

This eliminates the need for a for loop and should be more efficient when working with large datasets.

#py