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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 | |
| 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] |