ryanbehdad

# SHAP's force plot does not label all the important features
# We usually need to get the top (20) feautures that affect a decision for a particular instance
# In addition to their name, the features' values and their shapley values are also required.
# The below snippet 
# 1. creates a dataframe containing all the features, their shapley value and their actual value
# 2. and exports the dataframe to a csv file
# 3. It also displays the force plot

import shap
shap.initjs()

ryanbehdad

def compare_df_columns(df1, df2):
    """
    Compare the columns of two dataframes (including their types)
    """
    matched = True
    
    # Compare number of rows
    if df1.shape[0] != df2.shape[0]:
        print(f'Row numbers do not match {df1.shape[0]:,} vs {df2.shape[0]:,}')
        matched=False   

ryanbehdad

Using nbstripout to Clean Outputs of Jupyter Notebooks before Commits

To improve version control for Jupyter notebooks, consider using nbstripout. This tool automatically removes output cells from notebooks before committing them to Git.

Benefits

• Cleaner Version History: Eliminates unnecessary output data, making diffs more readable.
• Reduced Repository Size: Keeps the repository lightweight by excluding bulky output files.
• Consistent Results: Ensures that notebooks run consistently across different environments.