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| 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 | |
| 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 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]: | |
| """Creates three dataframes 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) | |
| # Create aggregated df | |
| aggregated_df = aggregate_methods(detailed_df) | |
| # Create intervention-averaged df | |
| intervention_averaged_df = create_intervention_averaged_df(aggregated_df) | |
| 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] |