Conditional independence tests with mixed data

[kenneth-lee-ch]

Can someone show an example how to use this library to conduct conditional independence tests with mixed data? Suppose I have the following data. I am confused on how to use MutualInformation as it says it can take continuous and categorical data, but I got an error once I pass the df into it.

Set seed for reproducibility

import pandas as pd
import numpy as np
from pybnesian import MutualInformation

np.random.seed(42)

# Generate continuous data
data_size = 100
continuous_data_1 = np.random.normal(loc=50, scale=10, size=data_size)
continuous_data_2 = np.random.normal(loc=30, scale=5, size=data_size)

# Generate categorical data
categories = np.random.choice(['Category A', 'Category B', 'Category C'], size=data_size)

# Create DataFrame
df = pd.DataFrame({
    'Continuous_1': continuous_data_1,
    'Continuous_2': continuous_data_2,
    'Category': categories
})

kmi = MutualInformation(df=data)