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 from src.submission.check_validity import parse_huggingface_url def get_leaderboard_df_mib_subgraph(results_path: str, cols: list, benchmark_cols: list, metric_type = "CPR") -> 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) all_data_json = [v.to_dict(metric_type=metric_type) 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) ascending = False if metric_type == "CPR" else True # 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=ascending, 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() # 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(2) # 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() # 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(2) return averaged_df def get_leaderboard_df_mib_causalgraph(results_path: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: aggregated_df, intervention_averaged_df = get_raw_eval_results_mib_causalgraph(results_path) print(f"Columns in aggregated_df: {aggregated_df.columns.tolist()}") print(f"Columns in intervention_averaged_df: {intervention_averaged_df.columns.tolist()}") return aggregated_df, intervention_averaged_df def get_evaluation_queue_df(save_path: str, cols: list, track: str) -> 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 = [] print(track) 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", "PREVALIDATION"]] finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL" or e["status"] == "FAILED"] for list in (pending_list, finished_list): for item in list: item["track"] = track item["hf_repo"] = parse_huggingface_url(item["hf_repo"])[0] df_pending = pd.DataFrame.from_records(pending_list, columns=cols) df_finished = pd.DataFrame.from_records(finished_list, columns=cols) return df_finished[cols], df_pending[cols]