ThomasSimonini
HF staff
Duplicate from huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
7dfd834
| import requests | |
| import pandas as pd | |
| from tqdm.auto import tqdm | |
| from utils import * | |
| import gradio as gr | |
| from huggingface_hub import HfApi, hf_hub_download | |
| from huggingface_hub.repocard import metadata_load | |
| class DeepRL_Leaderboard: | |
| def __init__(self) -> None: | |
| self.leaderboard= {} | |
| def add_leaderboard(self,id=None, title=None): | |
| if id is not None and title is not None: | |
| id = id.strip() | |
| title = title.strip() | |
| self.leaderboard.update({id:{'title':title,'data':get_data_per_env(id)}}) | |
| def get_data(self): | |
| return self.leaderboard | |
| def get_ids(self): | |
| return list(self.leaderboard.keys()) | |
| # CSS file for the | |
| with open('app.css','r') as f: | |
| BLOCK_CSS = f.read() | |
| LOADED_MODEL_IDS = {} | |
| LOADED_MODEL_METADATA = {} | |
| def get_data(rl_env): | |
| global LOADED_MODEL_IDS ,LOADED_MODEL_METADATA | |
| data = [] | |
| model_ids = get_model_ids(rl_env) | |
| LOADED_MODEL_IDS[rl_env]=model_ids | |
| for model_id in tqdm(model_ids): | |
| meta = get_metadata(model_id) | |
| LOADED_MODEL_METADATA[model_id] = meta if meta is not None else '' | |
| if meta is None: | |
| continue | |
| user_id = model_id.split('/')[0] | |
| row = {} | |
| row["User"] = user_id | |
| row["Model"] = model_id | |
| accuracy = parse_metrics_accuracy(meta) | |
| mean_reward, std_reward = parse_rewards(accuracy) | |
| mean_reward = mean_reward if not pd.isna(mean_reward) else 0 | |
| std_reward = std_reward if not pd.isna(std_reward) else 0 | |
| row["Results"] = mean_reward - std_reward | |
| row["Mean Reward"] = mean_reward | |
| row["Std Reward"] = std_reward | |
| data.append(row) | |
| return pd.DataFrame.from_records(data) | |
| def get_data_per_env(rl_env): | |
| dataframe = get_data(rl_env) | |
| dataframe = dataframe.fillna("") | |
| if not dataframe.empty: | |
| # turn the model ids into clickable links | |
| dataframe["User"] = dataframe["User"].apply(make_clickable_user) | |
| dataframe["Model"] = dataframe["Model"].apply(make_clickable_model) | |
| dataframe = dataframe.sort_values(by=['Results'], ascending=False) | |
| if not 'Ranking' in dataframe.columns: | |
| dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)]) | |
| else: | |
| dataframe['Ranking'] = [i for i in range(1,len(dataframe)+1)] | |
| table_html = dataframe.to_html(escape=False, index=False,justify = 'left') | |
| return table_html,dataframe,dataframe.empty | |
| else: | |
| html = """<div style="color: green"> | |
| <p> ⌛ Please wait. Results will be out soon... </p> | |
| </div> | |
| """ | |
| return html,dataframe,dataframe.empty | |
| rl_leaderboard = DeepRL_Leaderboard() | |
| rl_leaderboard.add_leaderboard('CartPole-v1','The Cartpole-v1 Leaderboard') | |
| rl_leaderboard.add_leaderboard('LunarLander-v2',"The Lunar Lander 🌕 Leaderboard") | |
| rl_leaderboard.add_leaderboard('FrozenLake-v1-4x4-no_slippery','The FrozenLake-v1-4x4-no_slippery Leaderboard') | |
| rl_leaderboard.add_leaderboard('FrozenLake-v1-8x8-no_slippery','The FrozenLake-v1-8x8-no_slippery Leaderboard') | |
| rl_leaderboard.add_leaderboard('FrozenLake-v1-4x4','The FrozenLake-v1-4x4 Leaderboard') | |
| rl_leaderboard.add_leaderboard('FrozenLake-v1-8x8','The FrozenLake-v1-8x8 Leaderboard') | |
| rl_leaderboard.add_leaderboard('Taxi-v3','The Taxi-v3🚖 Leaderboard') | |
| rl_leaderboard.add_leaderboard('CarRacing-v0'," The Car Racing 🏎️ Leaderboard") | |
| rl_leaderboard.add_leaderboard('MountainCar-v0',"The Mountain Car ⛰️ 🚗 Leaderboard") | |
| rl_leaderboard.add_leaderboard('BipedalWalker-v3',"The BipedalWalker Leaderboard") | |
| rl_leaderboard.add_leaderboard('SpaceInvadersNoFrameskip-v4','The SpaceInvadersNoFrameskip-v4 Leaderboard') | |
| rl_leaderboard.add_leaderboard('Pixelcopter-PLE-v0','The Pixelcopter-PLE-v0 🚁 Leaderboard') | |
| rl_leaderboard.add_leaderboard('Pong-PLE-v0','The Pong-PLE-v0 🎾 Leaderboard') | |
| rl_leaderboard.add_leaderboard('Walker2DBulletEnv-v0','The Walker2DBulletEnv-v0 🤖 Leaderboard') | |
| rl_leaderboard.add_leaderboard('AntBulletEnv-v0','The AntBulletEnv-v0 🕸️ Leaderboard') | |
| rl_leaderboard.add_leaderboard('HalfCheetahBulletEnv-v0','The HalfCheetahBulletEnv-v0 🤖 Leaderboard') | |
| RL_ENVS = rl_leaderboard.get_ids() | |
| RL_DETAILS = rl_leaderboard.get_data() | |
| def update_data(rl_env): | |
| global LOADED_MODEL_IDS,LOADED_MODEL_METADATA | |
| data = [] | |
| model_ids = [x for x in get_model_ids(rl_env)] #if x not in LOADED_MODEL_IDS[rl_env]] # For now let's update all | |
| LOADED_MODEL_IDS[rl_env]+=model_ids | |
| for model_id in tqdm(model_ids): | |
| meta = get_metadata(model_id) | |
| LOADED_MODEL_METADATA[model_id] = meta if meta is not None else '' | |
| if meta is None: | |
| continue | |
| user_id = model_id.split('/')[0] | |
| row = {} | |
| row["User"] = user_id | |
| row["Model"] = model_id | |
| accuracy = parse_metrics_accuracy(meta) | |
| mean_reward, std_reward = parse_rewards(accuracy) | |
| mean_reward = mean_reward if not pd.isna(mean_reward) else 0 | |
| std_reward = std_reward if not pd.isna(std_reward) else 0 | |
| row["Results"] = mean_reward - std_reward | |
| row["Mean Reward"] = mean_reward | |
| row["Std Reward"] = std_reward | |
| data.append(row) | |
| return pd.DataFrame.from_records(data) | |
| def update_data_per_env(rl_env): | |
| global RL_DETAILS | |
| _,old_dataframe,_ = RL_DETAILS[rl_env]['data'] | |
| new_dataframe = update_data(rl_env) | |
| new_dataframe = new_dataframe.fillna("") | |
| if not new_dataframe.empty: | |
| new_dataframe["User"] = new_dataframe["User"].apply(make_clickable_user) | |
| new_dataframe["Model"] = new_dataframe["Model"].apply(make_clickable_model) | |
| dataframe = pd.concat([old_dataframe,new_dataframe]) | |
| if not dataframe.empty: | |
| dataframe = dataframe.sort_values(by=['Results'], ascending=False) | |
| if not 'Ranking' in dataframe.columns: | |
| dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)]) | |
| else: | |
| dataframe['Ranking'] = [i for i in range(1,len(dataframe)+1)] | |
| table_html = dataframe.to_html(escape=False, index=False,justify = 'left') | |
| return table_html,dataframe,dataframe.empty | |
| else: | |
| html = """<div style="color: green"> | |
| <p> ⌛ Please wait. Results will be out soon... </p> | |
| </div> | |
| """ | |
| return html,dataframe,dataframe.empty | |
| def get_info_display(dataframe,env_name,name_leaderboard,is_empty): | |
| if not is_empty: | |
| markdown = """ | |
| <div class='infoPoint'> | |
| <h1> {name_leaderboard} </h1> | |
| <br> | |
| <p> This is a leaderboard of <b>{len_dataframe}</b> agents, from <b>{num_unique_users}</b> unique users, playing {env_name} 👩🚀. </p> | |
| <br> | |
| <p> We use <b>lower bound result to sort the models: mean_reward - std_reward.</b> </p> | |
| <br> | |
| <p> You can click on the model's name to be redirected to its model card which includes documentation. </p> | |
| <br> | |
| <p> You want to try to train your agents? <a href="http://eepurl.com/h1pElX" target="_blank">Sign up to the Hugging Face free Deep Reinforcement Learning Class 🤗 </a>. | |
| </p> | |
| <br> | |
| <p> You want to compare two agents? <a href="https://huggingface.co/spaces/ThomasSimonini/Compare-Reinforcement-Learning-Agents" target="_blank">It's possible using this Spaces demo 👀 </a>. | |
| </p> | |
| </div> | |
| """.format(len_dataframe = len(dataframe),env_name = env_name,name_leaderboard = name_leaderboard,num_unique_users = len(set(dataframe['User'].values))) | |
| else: | |
| markdown = """ | |
| <div class='infoPoint'> | |
| <h1> {name_leaderboard} </h1> | |
| <br> | |
| </div> | |
| """.format(name_leaderboard = name_leaderboard) | |
| return markdown | |
| def reload_all_data(): | |
| global RL_DETAILS,RL_ENVS | |
| for rl_env in RL_ENVS: | |
| RL_DETAILS[rl_env]['data'] = update_data_per_env(rl_env) | |
| html = """<div style="color: green"> | |
| <p> ✅ Leaderboard updated! </p> | |
| </div> | |
| """ | |
| return html | |
| def reload_leaderboard(rl_env): | |
| global RL_DETAILS | |
| data_html,data_dataframe,is_empty = RL_DETAILS[rl_env]['data'] | |
| markdown = get_info_display(data_dataframe,rl_env,RL_DETAILS[rl_env]['title'],is_empty) | |
| return markdown,data_html | |
| block = gr.Blocks(css=BLOCK_CSS) | |
| with block: | |
| notification = gr.HTML("""<div style="color: green"> | |
| <p> ⌛ Updating leaderboard... </p> | |
| </div> | |
| """) | |
| block.load(reload_all_data,[],[notification]) | |
| with gr.Tabs(): | |
| for rl_env in RL_ENVS: | |
| with gr.TabItem(rl_env) as rl_tab: | |
| data_html,data_dataframe,is_empty = RL_DETAILS[rl_env]['data'] | |
| markdown = get_info_display(data_dataframe,rl_env,RL_DETAILS[rl_env]['title'],is_empty) | |
| env_state =gr.Variable(value=f'\"{rl_env}\"') | |
| output_markdown = gr.HTML(markdown) | |
| output_html = gr.HTML(data_html) | |
| rl_tab.select(reload_leaderboard,inputs=[env_state],outputs=[output_markdown,output_html]) | |
| block.launch() | |