initial commit to RL stats

.gitignore

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+__pycache__/

app.css

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+
+.infoPoint h1 {
+    font-size: 30px;
+    text-decoration: bold;
+    
+    }
+    
+a {
+    text-decoration: underline; 
+    color: #1f3b54 ;
+}
+
+table {
+  
+    margin: 25px 0;
+    font-size: 0.9em;
+    font-family: sans-serif;
+    min-width: 400px;
+    box-shadow: 0 0 20px rgba(0, 0, 0, 0.15);
+}
+
+table th,
+table td {
+    padding: 12px 15px;
+}
+
+tr {
+text-align: left;
+}
+thead tr {
+text-align: left;
+}

app.py

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+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 = {}
+
+def get_data(rl_env):
+    global LOADED_MODEL_IDS 
+    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)
+        if meta is None:
+            continue
+        row={}
+        row["metadata"] = meta
+       
+        data.append(row)
+    return pd.DataFrame.from_records(data)
+
+def get_data_per_env(rl_env):
+    dataframe = get_data(rl_env)
+    return dataframe,dataframe.empty   
+
+
+
+rl_leaderboard = DeepRL_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('LunarLander-v2',"The Lunar Lander 🌕 Leaderboard 🚀")
+rl_leaderboard.add_leaderboard('BipedalWalker-v3',"The BipedalWalker Leaderboard 🚀")
+rl_leaderboard.add_leaderboard('Taxi-v3','The Taxi-v3🚖 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('SpaceInvadersNoFrameskip-v4','The SpaceInvadersNoFrameskip-v4 Leaderboard 🚀')
+
+RL_ENVS = rl_leaderboard.get_ids()
+RL_DETAILS = rl_leaderboard.get_data()
+
+
+def update_data(rl_env):
+    global LOADED_MODEL_IDS
+    data = []
+    model_ids = [x for x in get_model_ids(rl_env) if x not in LOADED_MODEL_IDS[rl_env]]
+    LOADED_MODEL_IDS[rl_env]+=model_ids
+
+    for model_id in tqdm(model_ids):
+        meta = get_metadata(model_id)
+        if meta is None:
+            continue
+        row = {}
+        row["metadata"] = meta
+        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("")
+
+    dataframe = pd.concat([old_dataframe,new_dataframe])
+
+    return 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 lower bound result to sort the models: mean_reward - std_reward. </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 your model? Read this <a href="https://github.com/huggingface/deep-rl-class/blob/Unit1/unit1/README.md" target="_blank">Unit 1</a> of Deep Reinforcement Learning Class.
+        </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! Click `Show Statistics` to see the current statistics.</p>
+                </div>
+               """    
+    return html            
+
+
+def reload_leaderboard(rl_env):
+    global RL_DETAILS
+ 
+    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     
+            
+def get_units_stat():
+    # gets the number of models per unit
+    units={'Unit 1':[],'Unit 2':[],'Unit 3':[]}
+    for rl_env in RL_ENVS:
+        rl_env_metadata,is_empty = RL_DETAILS[rl_env]['data']
+        if is_empty is False:
+            # All good! Carry on
+            metadata_list = rl_env_metadata['metadata'].values
+            units['Unit 1'].extend([m for m in metadata_list if 'stable-baselines3' in m['tags']])
+            units['Unit 2'].extend([m for m in metadata_list if 'custom-implementation' in m['tags']])
+            units['Unit 3'].extend([m for m in metadata_list if 'stable-baselines3' in m['tags'] and 'SpaceInvadersNoFrameskip-v4'.lower() in [tag.lower for tag in m['tags']]])
+
+    # get count
+    for k in units.keys():
+        units[k] = len(units[k])
+
+    return plot_bar(value = list(units.values),name = list(units.keys()),x_name = "Units",y_name = "Number of model submissions",title="Number of model submissions per unit")
+
+    
+      
+def get_models_stat():
+    # gets the number of models per unit
+    units={}
+    for rl_env in RL_ENVS:
+        rl_env_metadata,is_empty = RL_DETAILS[rl_env]['data']
+        if is_empty is False:
+            # All good! Carry on
+            metadata_list = rl_env_metadata['metadata'].values
+            units[rl_env] = [m for m in metadata_list]
+
+    # get count
+    for k in units.keys():
+        units[k] = len(units[k])
+
+    return plot_bar(value = list(units.values),name = list(units.keys()),x_name = "RL Environment",y_name = "Number of model submissions",title="Number of model submissions per RL environment")
+
+def get_user_stat():
+    # gets the number of models per unit
+    users={}
+    for rl_env in RL_ENVS:
+        rl_env_metadata,is_empty = RL_DETAILS[rl_env]['data']
+        if is_empty is False:
+            # All good! Carry on
+            metadata_list = rl_env_metadata['metadata'].values
+            users[rl_env] = [m['model_id'].split('/')[0] for m in metadata_list]
+
+    # get count
+    for k in users.keys():
+        users[k] = len(set(users[k]))
+
+    return plot_bar(value = list(users.values),name = list(users.keys()),x_name = "RL Environment",y_name = "Number of user submissions",title="Number of user submissions per RL environment")
+
+def get_stat():
+    # gets the number of models per unit
+    units={'Unit 1':[],'Unit 2':[],'Unit 3':[]}
+    users={}
+    models={}
+    for rl_env in RL_ENVS:
+        rl_env_metadata,is_empty = RL_DETAILS[rl_env]['data']
+        if is_empty is False:
+            # All good! Carry on
+            metadata_list = rl_env_metadata['metadata'].values
+            units['Unit 1'].extend([m for m in metadata_list if 'stable-baselines3' in m['tags']])
+            units['Unit 2'].extend([m for m in metadata_list if 'custom-implementation' in m['tags']])
+            units['Unit 3'].extend([m for m in metadata_list if 'stable-baselines3' in m['tags'] and 'spaceinvadersNoFrameskip-v4'.lower() in [tag.lower() for tag in m['tags']]])
+
+            users[rl_env] = [m['model_id'].split('/')[0] for m in metadata_list]
+            models[rl_env] = [m for m in metadata_list]
+
+    # get count
+    for k in units.keys():
+        units[k] = len(units[k])
+    for k in users.keys():
+        users[k] = len(set(users[k]))  
+    for k in models.keys():
+        models[k] = len(models[k])      
+
+    units_plot = plot_bar(value = list(units.values()),name = list(units.keys()),x_name = "Units",y_name = "Number of model submissions",title="Number of model submissions per unit")
+    user_plot  =  plot_barh(value = list(users.values()),name = list(users.keys()),x_name = "RL Environment",y_name = "Number of unique user submissions",title="Number of unique user submissions per RL environment")
+    model_plot =  plot_barh(value = list(models.values()),name = list(models.keys()),x_name = "RL Environment",y_name = "Number of model submissions",title="Number of model submissions per RL environment")
+    return units_plot,user_plot,model_plot
+
+                 
+
+
+
+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():
+        with gr.TabItem("Dashboard") as rl_tab:
+            # Stats of user submission per units
+            # 2. # model submissions per environment
+            # 3. # unique users per environment
+            # get_units_stat() 
+            #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(default_value=rl_env)  
+            #output_markdown = gr.HTML(markdown)
+            reload = gr.Button('Show Statistics')
+
+            units_plot = gr.Plot(type="matplotlib")
+            model_plot = gr.Plot(type="matplotlib")
+            user_plot = gr.Plot(type="matplotlib")
+            #plot_gender = gr.Plot(type="matplotlib")
+
+            #output_html = gr.HTML(data_html)
+
+            reload.click(get_stat,[],[units_plot,user_plot,model_plot])
+            #rl_tab.select(reload_leaderboard,inputs=[env_state],outputs=[output_markdown,output_html])
+
+block.launch()    

utils.py

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+import pandas as pd
+import requests
+from tqdm.auto import tqdm
+from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub.repocard import metadata_load
+import matplotlib.pyplot as plt
+
+
+def plot_bar(value,name,x_name,y_name,title):
+    fig, ax = plt.subplots(figsize=(10,4),tight_layout=True)
+
+    ax.set(xlabel=x_name, ylabel=y_name,title=title)
+
+    ax.bar(name, value)
+   
+  
+    return ax.figure 
+def plot_barh(value,name,x_name,y_name,title):
+    fig, ax = plt.subplots(figsize=(10,4),tight_layout=True)
+
+    ax.set(xlabel=x_name, ylabel=y_name,title=title)
+
+    ax.barh(name, value)
+   
+  
+    return ax.figure 
+# Based on Omar Sanseviero work
+# Make model clickable link
+def make_clickable_model(model_name):
+    # remove user from model name
+    model_name_show = ' '.join(model_name.split('/')[1:])
+
+    link = "https://huggingface.co/" + model_name
+    return f'<a target="_blank" href="{link}">{model_name_show}</a>'
+
+# Make user clickable link
+def make_clickable_user(user_id):
+    link = "https://huggingface.co/" + user_id
+    return f'<a  target="_blank" href="{link}">{user_id}</a>'
+    
+
+
+def get_model_ids(rl_env):
+    api = HfApi()
+    models = api.list_models(filter=rl_env)
+    model_ids = [x.modelId for x in models]
+    return model_ids
+    
+def get_metadata(model_id):
+    try:
+        readme_path = hf_hub_download(model_id, filename="README.md")
+        metadata = metadata_load(readme_path)
+        metadata['model_id'] = model_id 
+        return metadata
+    except requests.exceptions.HTTPError:
+        # 404 README.md not found
+        return None
+        
+def parse_metrics_accuracy(meta):
+    if "model-index" not in meta:
+        return None
+    result = meta["model-index"][0]["results"]
+    metrics = result[0]["metrics"]
+    accuracy = metrics[0]["value"]
+    return accuracy
+
+# We keep the worst case episode
+def parse_rewards(accuracy):
+    default_std = -1000
+    default_reward=-1000
+    if accuracy !=  None:
+        parsed =  accuracy.split(' +/- ')
+        if len(parsed)>1:
+            mean_reward = float(parsed[0])
+            std_reward =  float(parsed[1])
+        else: 
+            mean_reward = float(default_std)
+            std_reward = float(default_reward)
+
+    else:
+        mean_reward = float(default_std)
+        std_reward = float(default_reward)
+    return mean_reward, std_reward
+