run2-pareto-optimization

name	run2_pareto-optimization
description	Finding the Pareto frontier for multi-objective optimization using dominance conditions.

Pareto Optimization (Dominance Condition)

When you need to optimize multiple conflicting objectives (e.g., maximizing F1, minimizing error), you can identify the Pareto frontier by explicitly checking if a point is "dominated" by another point.

Installation

Ensure you have numpy and pandas installed: pip install numpy pandas

Usage

import numpy as np
import pandas as pd

def get_pareto_frontier(df, obj_max_cols, obj_min_cols):
    """
    Finds non-dominated rows in a DataFrame.
    """
    pareto_mask = np.ones(len(df), dtype=bool)
    
    for i in range(len(df)):
        # Assume self is not dominated unless proven otherwise
        row_i = df.iloc[i]
        
        # Check against all other rows
        better_or_equal = np.ones(len(df), dtype=bool)
        strictly_better = np.zeros(len(df), dtype=bool)
        
        for col in obj_max_cols:
            better_or_equal &= (df[col] >= row_i[col])
            strictly_better |= (df[col] > row_i[col])
            
        for col in obj_min_cols:
            better_or_equal &= (df[col] <= row_i[col])
            strictly_better |= (df[col] < row_i[col])
            
        # If any other point is better or equal in all, AND strictly better in at least one
        if np.any(better_or_equal & strictly_better):
            pareto_mask[i] = False
            
    return df[pareto_mask]

# pareto_df = get_pareto_frontier(results_df, ['F1'], ['delta'])