import json import os from ast import literal_eval import pandas as pd import re from src.display.formatting import has_no_nan_values, make_clickable_model from src.display.utils import AutoEvalColumn, EvalQueueColumn from src.leaderboard.read_evals import get_raw_eval_results from src.about import ( nc_tasks, nr_tasks, lp_tasks, ) def sanitize_string(input_string): # Remove leading and trailing whitespace input_string = input_string.strip() # Remove leading whitespace on each line sanitized_string = re.sub(r'(?m)^\s+', '', input_string) return sanitized_string ''' 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""" raw_data = get_raw_eval_results(results_path, requests_path) all_data_json = [v.to_dict() for v in raw_data] df = pd.DataFrame.from_records(all_data_json) #df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) #df = df[cols].round(decimals=2) # filter out if any of the benchmarks have not been produced #df = df[has_no_nan_values(df, benchmark_cols)] return raw_data, df ''' # Function to extract the numerical part before '+' def extract_x(value): return float(value.split('+')[0]) # Function to highlight the highest (or lowest) value based on X def make_bold(df, cols, ascending): df_highlight = df.copy() def apply_highlight(s): if ascending: max_idx = s.apply(extract_x).idxmin() else: max_idx = s.apply(extract_x).idxmax() return ['font-weight: bold' if i == max_idx else '' for i in range(len(s))] styler = df_highlight.style.apply(lambda x: apply_highlight(x) if x.name in cols else ['']*len(x), axis=0) return styler def format_number(num): return f"{num:.3f}" def get_leaderboard_df(EVAL_REQUESTS_PATH, task_type) -> pd.DataFrame: if task_type in ['Node Classification', 'Entity Classification']: ascending = False tasks = nc_tasks task_type = ['Node Classification', 'Entity Classification'] elif task_type in ['Node Regression', 'Entity Regression']: ascending = True tasks = nr_tasks task_type = ['Node Regression', 'Entity Regression'] elif task_type in ['Link Prediction', 'Recommendation']: ascending = False tasks = lp_tasks task_type = ['Link Prediction', 'Recommendation'] model_result_filepaths = [] for root,_, files in os.walk(EVAL_REQUESTS_PATH): if len(files) == 0 or any([not f.endswith(".json") for f in files]): continue for file in files: model_result_filepaths.append(os.path.join(root, file)) model_res = [] for model in model_result_filepaths: import json with open(model) as f: out = json.load(f) if ('task' in out) and (out['task'] in task_type): model_res.append(out) for model in model_res: model["test"] = literal_eval(model["test"].split('}')[0]+'}') model["valid"] = literal_eval(model["valid"].split('}')[0]+'}') #model["params"] = int(model["params"]) model['submitted_time'] = model['submitted_time'].split('T')[0] #model['paper_url'] = '[Link](' + model['paper_url'] + ')' #model['github_url'] = '[Link](' + model['github_url'] + ')' name2short_name = {task.value.benchmark: task.value.benchmark for task in tasks} for model in model_res: model.update({ name2short_name[i]: (f"{format_number(model['test'][i][0])} ± {format_number(model['test'][i][1])}" if i in model['test'] else '-') for i in name2short_name }) columns_to_show = ['model', 'author', 'email', 'paper_url', 'github_url', 'submitted_time'] + list(name2short_name.values()) # Check if model_res is empty if len(model_res) > 0: df_res = pd.DataFrame([{col: model[col] for col in columns_to_show} for model in model_res]) else: # Initialize an empty DataFrame with the desired columns df_res = pd.DataFrame(columns=columns_to_show) #df_res = pd.DataFrame([{col: model[col] for col in columns_to_show} for model in model_res]) ranks = df_res[list(name2short_name.values())].rank(ascending = ascending) df_res.rename(columns={'model': 'Model', 'author': 'Author', 'email': 'Email', 'paper_url': 'Paper URL', 'github_url': 'Github URL', 'submitted_time': 'Time'}, inplace=True) df_res.Model.replace('Relbench User Study', 'Human Data Scientist', inplace=True) df_res['Average Rank⬆️'] = ranks.mean(axis=1) df_res.sort_values(by='Average Rank⬆️', ascending=True, inplace=True) #df_res = make_bold(df_res, list(name2short_name.values()), ascending = ascending) print(df_res) return df_res def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]: """Creates the different dataframes for the evaluation queues requestes""" 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) data[EvalQueueColumn.model.name] = make_clickable_model(data["model"]) data[EvalQueueColumn.revision.name] = data.get("revision", "main") 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 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]