src/populate.py

import json
import os
import pandas as pd
from typing import List, Dict, Tuple

from src.display.formatting import has_no_nan_values, make_clickable_model
from src.display.utils import AutoEvalColumn, AutoEvalColumnMultimodal, EvalQueueColumn
from src.leaderboard.read_evals import get_raw_eval_results, get_raw_eval_results_mib_subgraph, get_raw_eval_results_mib_causalgraph
from src.about import TasksMib_Causalgraph

def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
    """Creates a dataframe from all the individual experiment results"""
    # print(f"results_path is {results_path}, requests_path is {requests_path}")
    raw_data = get_raw_eval_results(results_path, requests_path)
    # print(f"raw_data is {raw_data}")
    all_data_json = [v.to_dict() for v in raw_data]
    # print(f"all_data_json is {pd.DataFrame.from_records(all_data_json)}")
    all_data_json_filtered = []
    for item in all_data_json:
        item["Track"] = item["eval_name"].split("_")[-1]
        item["ioi"] = 0
        item["mcqa"] = 0
        if "VQA" in benchmark_cols and "VQA" in item:
            all_data_json_filtered.append(item)
        if "VQA" not in benchmark_cols and "VQA" not in item:
            all_data_json_filtered.append(item)
            
    all_data_json = all_data_json_filtered

    df = pd.DataFrame.from_records(all_data_json)
    df = df.sort_values(by=[AutoEvalColumn.text_average.name], ascending=False)
    # df = df.sort_values(by=[Tasks.task0.value.col_name], ascending=False)
    # df = df.sort_values(by=[AutoEvalColumn.track.name], ascending=False)
    
    # print(f"df is {df}")
    
    # df = df[cols].round(decimals=1)

    # filter out if any of the benchmarks have not been produced
    df = df[has_no_nan_values(df, benchmark_cols)]
    return df



def get_leaderboard_df_mib_subgraph(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
    """Creates a dataframe from all the MIB experiment results"""
    # print(f"results_path is {results_path}, requests_path is {requests_path}")
    raw_data = get_raw_eval_results_mib_subgraph(results_path, requests_path)
    # print(f"raw_data is {raw_data}")
    
    # Convert each result to dict format
    all_data_json = [v.to_dict() for v in raw_data]
    # print(f"all_data_json is {pd.DataFrame.from_records(all_data_json)}")

    # Convert to dataframe
    df = pd.DataFrame.from_records(all_data_json)
    
    # Sort by Average score descending
    if 'Average' in df.columns:
        # Convert '-' to NaN for sorting purposes
        df['Average'] = pd.to_numeric(df['Average'], errors='coerce')
        df = df.sort_values(by=['Average'], ascending=False, na_position='last')
        # Convert NaN back to '-'
        df['Average'] = df['Average'].fillna('-')
    
    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"""
#     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
#     numeric_df = df_copy.select_dtypes(include=['float64', 'int64'])
    
#     # Group by base method name and take the max
#     aggregated_df = numeric_df.groupby(level=0).max().round(3)
    
#     return aggregated_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"""
    df_copy = df.copy()
    
    # Set Method as index if it isn't already
    if 'Method' in df_copy.columns:
        df_copy.set_index('Method', inplace=True)
    
    # Extract base method names (remove _2, _3, etc. suffixes)
    base_methods = [name.split('_')[0] if '_' in str(name) and str(name).split('_')[-1].isdigit() 
                   else name for name in df_copy.index]
    df_copy.index = base_methods
    
    # Convert scores to numeric values
    numeric_df = df_copy.select_dtypes(include=['float64', 'int64'])
    
    # Group by base method name and take the max
    aggregated_df = numeric_df.groupby(level=0).max().round(3)
    
    # Reset index to get Method as a column
    aggregated_df.reset_index(inplace=True)
    aggregated_df.rename(columns={'index': 'Method'}, inplace=True)
    
    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"""
#     df_copy = df.copy()
    
#     # Remove the Method column and eval_name if present
#     columns_to_drop = ['Method', 'eval_name']
#     df_copy = df_copy.drop(columns=[col for col in columns_to_drop if col in df_copy.columns])
    
#     # Group columns by model_task
#     model_task_groups = {}
#     for col in df_copy.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: df_copy[cols].mean(axis=1).round(3)
#         for model_task, cols in model_task_groups.items()
#     })
    
#     return averaged_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"""
#     df_copy = df.copy()
    
#     # Store Method column if it exists
#     method_col = None
#     if 'Method' in df_copy.columns:
#         method_col = df_copy['Method']
#         df_copy = df_copy.drop('Method', axis=1)
    
#     # Remove eval_name if present
#     if 'eval_name' in df_copy.columns:
#         df_copy = df_copy.drop('eval_name', axis=1)
    
#     # Group columns by model_task
#     model_task_groups = {}
#     for col in df_copy.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: df_copy[cols].mean(axis=1).round(3)
#         for model_task, cols in model_task_groups.items()
#     })
    
#     # Add Method column back
#     if method_col is not None:
#         averaged_df.insert(0, 'Method', method_col)
    
#     return averaged_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"""
    df_copy = df.copy()
    
    # Store Method column
    method_col = None
    if 'Method' in df_copy.columns:
        method_col = df_copy['Method']
        df_copy = df_copy.drop('Method', axis=1)
    
    if 'eval_name' in df_copy.columns:
        df_copy = df_copy.drop('eval_name', axis=1)
    
    # Group columns by model and intervention
    result_cols = {}
    for task in TasksMib_Causalgraph:
        for model in task.value.models:  # Will iterate over all three models
            for intervention in task.value.interventions:
                for counterfactual in task.value.counterfactuals:
                    col_pattern = f"{model}_layer.*_{intervention}_{counterfactual}"
                    matching_cols = [c for c in df_copy.columns if pd.Series(c).str.match(col_pattern).any()]
                    if matching_cols:
                        col_name = f"{model}_{intervention}_{counterfactual}"
                        result_cols[col_name] = matching_cols
    
    averaged_df = pd.DataFrame()
    if method_col is not None:
        averaged_df['Method'] = method_col
    
    for col_name, cols in result_cols.items():
        averaged_df[col_name] = df_copy[cols].mean(axis=1).round(3)
    
    return averaged_df

# def get_leaderboard_df_mib_causalgraph(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
#     """Creates a dataframe from all the MIB causal graph experiment results"""
#     print(f"results_path is {results_path}, requests_path is {requests_path}")
#     raw_data = get_raw_eval_results_mib_causalgraph(results_path, requests_path)
#     print(f"raw_data is {raw_data}")
    
#     # Convert each result to dict format for detailed df
#     all_data_json = [v.to_dict() for v in raw_data]
#     detailed_df = pd.DataFrame.from_records(all_data_json)
#     print(f"detailed_df is: {detailed_df}")

#     # Create and print other views for debugging/reference
#     aggregated_df = aggregate_methods(detailed_df)
#     print(f"aggregated_df is: {aggregated_df}")
    
#     intervention_averaged_df = create_intervention_averaged_df(aggregated_df)
#     print(f"intervention_averaged_df is: {intervention_averaged_df}")
    
#     # Only return detailed_df for display
#     return detailed_df

# def get_leaderboard_df_mib_causalgraph(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
#     print(f"results_path is {results_path}, requests_path is {requests_path}")
#     raw_data = get_raw_eval_results_mib_causalgraph(results_path, requests_path)
    
#     # Convert each result to dict format for detailed df
#     all_data_json = [v.to_dict() for v in raw_data]
#     detailed_df = pd.DataFrame.from_records(all_data_json)
#     print("Columns in detailed_df:", detailed_df.columns.tolist())  # Print actual columns
    
#     # Create aggregated df
#     aggregated_df = aggregate_methods(detailed_df)
#     print("Columns in aggregated_df:", aggregated_df.columns.tolist())
    
#     # Create intervention-averaged df
#     intervention_averaged_df = create_intervention_averaged_df(aggregated_df)
#     print("Columns in intervention_averaged_df:", intervention_averaged_df.columns.tolist())
    
#     return detailed_df, aggregated_df, intervention_averaged_df

def get_leaderboard_df_mib_causalgraph(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
    # print(f"results_path is {results_path}, requests_path is {requests_path}")
    raw_data = get_raw_eval_results_mib_causalgraph(results_path, requests_path)
    
    # Convert each result to dict format for detailed df
    all_data_json = [v.to_dict() for v in raw_data]
    detailed_df = pd.DataFrame.from_records(all_data_json)

    # Print the actual columns for debugging
    # print("Original columns:", detailed_df.columns.tolist())
    
    # Rename columns to match schema
    column_mapping = {}
    for col in detailed_df.columns:
        if col in ['eval_name', 'Method']:
            continue
        # Ensure consistent casing for the column names
        new_col = col.replace('Qwen2ForCausalLM', 'qwen2forcausallm') \
                    .replace('Gemma2ForCausalLM', 'gemma2forcausallm') \
                    .replace('LlamaForCausalLM', 'llamaforcausallm')
        column_mapping[col] = new_col
    
    detailed_df = detailed_df.rename(columns=column_mapping)
    
    # Create aggregated df
    aggregated_df = aggregate_methods(detailed_df)
    
    # Create intervention-averaged df
    intervention_averaged_df = create_intervention_averaged_df(aggregated_df)
    
    # print("Transformed columns:", detailed_df.columns.tolist())
    
    return detailed_df, aggregated_df, intervention_averaged_df


def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
    """Creates the different dataframes for the evaluation queues requests"""
    entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
    all_evals = []

    for entry in entries:
        if ".json" in entry:
            file_path = os.path.join(save_path, entry)
            with open(file_path) as fp:
                data = json.load(fp)

            if "still_on_hub" in data and data["still_on_hub"]:
                data[EvalQueueColumn.model.name] = make_clickable_model(data["hf_repo"], data["model"])
                data[EvalQueueColumn.revision.name] = data.get("revision", "main")
            else:
                data[EvalQueueColumn.model.name] = data["model"]
                data[EvalQueueColumn.revision.name] = "N/A"

            all_evals.append(data)
        elif ".md" not in entry:
            # this is a folder
            sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if os.path.isfile(e) and not e.startswith(".")]
            for sub_entry in sub_entries:
                file_path = os.path.join(save_path, entry, sub_entry)
                with open(file_path) as fp:
                    data = json.load(fp)

                data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
                data[EvalQueueColumn.revision.name] = data.get("revision", "main")
                all_evals.append(data)

    pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
    running_list = [e for e in all_evals if e["status"] == "RUNNING"]
    finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
    df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
    df_running = pd.DataFrame.from_records(running_list, columns=cols)
    df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
    return df_finished[cols], df_running[cols], df_pending[cols]