caulsal_metric.py

import json
import os
import pandas as pd
import numpy as np
from typing import List, Dict, Any, Tuple
from collections import defaultdict

def average_counterfactuals(json_files: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Averages scores across counterfactuals for each layer.
    """
    processed_jsons = []
    
    for json_file in json_files:
        new_json = {
            'method_name': json_file['method_name'],
            'results': []
        }
        
        for result in json_file['results']:
            new_result = {
                'model_id': result['model_id'],
                'task_scores': {}
            }
            
            for task, scores in result['task_scores'].items():
                new_scores = []
                
                for layer_data in scores:
                    new_layer_data = {
                        'layer': layer_data['layer'],
                        'layer_scores': []
                    }
                    
                    for intervention_data in layer_data['layer_scores']:
                        avg_score = np.mean([cf['score'] for cf in intervention_data['counterfactual_scores']])
                        if np.isnan(avg_score):
                            avg_score = 0.0
                        new_layer_data['layer_scores'].append({
                            'intervention': intervention_data['intervention'],
                            'average_score': avg_score
                        })
                    
                    new_scores.append(new_layer_data)
                
                new_result['task_scores'][task] = new_scores
            
            new_json['results'].append(new_result)
        
        processed_jsons.append(new_json)
    
    return processed_jsons

def find_layer_averages(json_files: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Averages scores across layers for each intervention.
    """
    processed_jsons = []
    
    for json_file in json_files:
        new_json = {
            'method_name': json_file['method_name'],
            'results': []
        }
        
        for result in json_file['results']:
            new_result = {
                'model_id': result['model_id'],
                'task_scores': {}
            }
            
            for task, scores in result['task_scores'].items():
                # Group by intervention first
                intervention_scores = defaultdict(list)
                for layer_data in scores:
                    for intervention_data in layer_data['layer_scores']:
                        intervention_key = '_'.join(intervention_data['intervention'])
                        intervention_scores[intervention_key].append(intervention_data['average_score'])
                
                # Average across layers for each intervention
                new_result['task_scores'][task] = [
                    {
                        'intervention': intervention.split('_'),
                        'average_score': np.mean(layer_scores) if layer_scores else 0.0
                    }
                    for intervention, layer_scores in intervention_scores.items()
                ]
            
            new_json['results'].append(new_result)
        
        processed_jsons.append(new_json)
    
    return processed_jsons

def create_summary_dataframe(json_files: List[Dict[str, Any]]) -> pd.DataFrame:
    """
    Creates a summary DataFrame with methods as rows and MODEL_TASK_INTERVENTION as columns.
    Handles duplicate method names by adding a counter suffix.
    """
    data = {}
    method_counters = defaultdict(int)
    
    for json_file in json_files:
        method_name = json_file['method_name']
        # Increment counter for this method name
        method_counters[method_name] += 1
        
        # If this is a duplicate method name, append a counter
        unique_method_name = f"{method_name}_{method_counters[method_name]}"
            
        method_scores = []
        column_names = []
        
        for result in json_file['results']:
            model = result['model_id']
            for task, scores in result['task_scores'].items():
                for score_data in scores:
                    intervention = '_'.join(score_data['intervention'])
                    column = f"{model}_{task}_{intervention}"
                    score = f"{score_data['average_score']:.3f}"
                    method_scores.append((column, score))
        
        # Sort by column names for consistency
        method_scores.sort(key=lambda x: x[0])
        scores_only = [float(score) for _, score in method_scores]
        avg_score = np.mean(scores_only)
        
        # Add average as first column
        data[unique_method_name] = {
            **{col: score for col, score in method_scores}
        }
    
    df = pd.DataFrame.from_dict(data, orient='index')
    
    
    return df

def aggregate_methods(df: pd.DataFrame) -> pd.DataFrame:
    """
    Aggregates rows with the same base method name by taking the max value for each column.
    """
    # Create a copy of the DataFrame
    df_copy = df.copy()
    
    # Extract base method names (remove _2, _3, etc. suffixes)
    base_methods = [name.split('_')[0] if '_' in name and name.split('_')[-1].isdigit() 
                   else name for name in df_copy.index]
    df_copy.index = base_methods
    
    # Convert scores to numeric values
    def extract_score(score_str):
        if isinstance(score_str, str):
            return float(score_str)
        return 0.0
    
    numeric_df = df_copy.applymap(extract_score)
    
    # Group by base method name and take the mean
    aggregated_df = numeric_df.groupby(level=0).max().round(3)
    
    # Convert back to string format
    aggregated_df = aggregated_df.applymap(lambda x: f"{x:.3f}")
    
    return aggregated_df

def create_intervention_averaged_df(df: pd.DataFrame) -> pd.DataFrame:
    """
    Creates a DataFrame where columns are model_task and cells are averaged over interventions.
    """
    # Create a copy of the DataFrame
    df_copy = df.copy()
    
    # Remove the Average column if it exists
    if 'Average' in df_copy.columns:
        df_copy = df_copy.drop('Average', axis=1)
    
    # Function to extract score value from string
    def extract_score(score_str):
        if isinstance(score_str, str):
            return float(score_str.split()[0])
        return 0.0
    
    # Convert all scores to numeric values
    numeric_df = df_copy.applymap(extract_score)
    
    # Group columns by model_task
    model_task_groups = {}
    for col in numeric_df.columns:
        model_task = '_'.join(col.split('_')[:2])  # Get model_task part
        if model_task not in model_task_groups:
            model_task_groups[model_task] = []
        model_task_groups[model_task].append(col)
    
    # Create new DataFrame with averaged intervention scores
    averaged_df = pd.DataFrame({
        model_task: numeric_df[cols].mean(axis=1).round(3)
        for model_task, cols in model_task_groups.items()
    })
    
    # Add overall average column
    averaged_df['Average'] = averaged_df.mean(axis=1).round(3)
    
    # Sort by Average column
    averaged_df = averaged_df.sort_values('Average', ascending=False)
    
    return averaged_df

def process_json_folder(folder_path: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
    """
    Processes all JSON files in a folder and returns three DataFrames:
    1. Detailed DataFrame showing all results including duplicates (with layer-averaged scores)
    2. Aggregated DataFrame showing average scores for each base method
    3. Intervention-averaged DataFrame showing means across interventions
    """
    json_files = []
    
    # Read all JSON files
    for filename in os.listdir(folder_path):
        if filename.endswith('.json'):
            with open(os.path.join(folder_path, filename), 'r') as f:
                json_files.append(json.load(f))
    
    # Process the files through each step
    averaged_cf = average_counterfactuals(json_files)
    layer_averaged = find_layer_averages(averaged_cf)
    detailed_df = create_summary_dataframe(layer_averaged)
    aggregated_df = aggregate_methods(detailed_df)
    intervention_averaged_df = create_intervention_averaged_df(aggregated_df)
    
    return detailed_df, aggregated_df, intervention_averaged_df

# Example usage:
if __name__ == "__main__":
    # Replace with your folder path
    folder_path = "./json_files"
    detailed_df, aggregated_df, intervention_averaged_df = process_json_folder(folder_path)
    
    print("Detailed Results (including duplicates):")
    print(detailed_df)
    print("\nAggregated Results (max scores per method):")
    print(aggregated_df)
    print("\nIntervention-Averaged Results:")
    print(intervention_averaged_df)