Update process to run on a single space per env

app.py

@@ -8,17 +8,23 @@ from huggingface_hub import HfApi, hf_hub_download
 from huggingface_hub.repocard import metadata_load
 import pandas as pd
 from matchmaking import *
+from background_task import init_matchmaking
+from apscheduler.schedulers.background import BackgroundScheduler
 
 
 block = gr.Blocks()
-envs = [
+env = [
     {
-        "name": "Snowball-Fight",
+        "name": "Soccer",
         "global": None,
     },
 ]
 matchmaking = Matchmaking()
 
+scheduler = BackgroundScheduler()
+scheduler.add_job(func=init_matchmaking, trigger="interval", seconds=60)
+scheduler.start()
+
 
 def update_elos():
     matchmaking.read_history()
@@ -26,9 +32,9 @@ def update_elos():
     matchmaking.save_elo_data()
 
 
-def get_env_data(env_name) -> pd.DataFrame:
-    # data = pd.read_csv(f"env_elos/{env_name}.csv")
-    data = pd.DataFrame(columns=["user", "model", "elo", "games_played"])
+def get_env_data() -> pd.DataFrame:
+    data = pd.read_csv(f"env_elos/elo.csv")
+    # data = pd.DataFrame(columns=["user", "model", "elo", "games_played"])
     return data
 
 
@@ -38,7 +44,7 @@ with block:
 
     This is the leaderboard of trained agents during the Deep Reinforcement Learning Course. A free course from beginner to expert.
 
-    Just choose which environment you trained your agent on and with Ctrl+F find your rank 🏆
+    This is the Soccer environment leaderboard, use Ctrl+F to find your rank 🏆
 
     We use an ELO rating to sort the models.
     You **can click on the model's name** to be redirected to its model card which includes documentation.
@@ -50,17 +56,15 @@ with block:
     🔧 There is an **environment missing?** Please open an issue.
     """)
 
-    for i, env in enumerate(envs):
-        with gr.TabItem(env["name"]) as tab:
-            with gr.Row():
-                refresh_data = gr.Button("Refresh")
-                val = gr.Variable(value=[env["name"]])
-                refresh_data.click(get_env_data, inputs=[val], outputs=env["global"])
-            with gr.Row():
-                env["global"] = gr.components.DataFrame(
-                    get_env_data(env["name"]),
-                    headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "ELO 🏆", "Games played 🎮"],
-                    datatype=["number", "markdown", "markdown", "number", "number"]
-                )
+    with gr.Row():
+        refresh_data = gr.Button("Refresh")
+        val = gr.Variable(value=[env["name"]])
+        refresh_data.click(get_env_data, inputs=[val], outputs=env["global"])
+    with gr.Row():
+        env["global"] = gr.components.DataFrame(
+            get_env_data(),
+            headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "ELO 🚀", "Games played 🎮"],
+            datatype=["number", "markdown", "markdown", "number", "number"]
+        )
 
 block.launch()

background_task.py

@@ -0,0 +1,160 @@
+import random
+import pandas as pd
+from datetime import datetime
+from huggingface_hub import HfApi
+
+
+env = "Soccer"
+
+
+class Model:
+    """
+    Class containing the info of a model.
+
+    :param name: Name of the model
+    :param elo: Elo rating of the model
+    :param games_played: Number of games played by the model (useful if we implement sigma uncertainty)
+    """
+    def __init__(self, author, name, elo=1200, games_played=0):
+        self.author = author
+        self.name = name
+        self.elo = elo
+        self.games_played = games_played
+
+
+class Matchmaking:
+    """
+    Class managing the matchmaking between the models.
+
+    :param models: List of models
+    :param queue: Temporary list of models used for the matching process
+    :param k: Dev coefficient
+    :param max_diff: Maximum difference considered between two models' elo
+    :param matches: Dictionary containing the match history (to later upload as CSV)
+    """
+    def __init__(self, models):
+        self.models = models
+        self.queue = self.models.copy()
+        self.k = 20
+        self.max_diff = 500
+        self.matches = {
+            "model1": [],
+            "model2": [],
+            "result": [],
+            "datetime": [],
+            "env": []
+        }
+
+    def run(self):
+        """
+        Run the matchmaking process.
+        Add models to the queue, shuffle it, and match the models one by one to models with close ratings.
+        Compute the new elo for each model after each match and add the match to the match history.
+        """
+        self.queue = self.models.copy()
+        random.shuffle(self.queue)
+        while len(self.queue) > 1:
+            model1 = self.queue.pop(0)
+            model2 = self.queue.pop(self.find_n_closest_indexes(model1, 10))
+            result = match(model1, model2)
+            self.compute_elo(model1, model2, result)
+            self.matches["model1"].append(model1.name)
+            self.matches["model2"].append(model2.name)
+            self.matches["result"].append(result)
+            self.matches["datetime"].append(datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"))
+            self.matches["env"].append(env)
+
+    def compute_elo(self, model1, model2, result):
+        """ Compute the new elo for each model based on a match result. """
+        delta = model1.elo - model2.elo
+        win_probability = 1 / (1 + 10 ** (-delta / 500))
+        model1.elo += self.k * (result - win_probability)
+        model2.elo -= self.k * (result - win_probability)
+
+    def find_n_closest_indexes(self, model, n) -> int:
+        """
+        Get a model index with a fairly close rating. If no model is found, return the last model in the queue.
+        We don't always pick the closest rating to add variety to the matchups.
+
+        :param model: Model to compare
+        :param n: Number of close models from which to pick a candidate
+        :return: id of the chosen candidate
+        """
+        indexes = []
+        closest_diffs = [9999999] * n
+        for i, m in enumerate(self.queue):
+            if m.name == model.name:
+                continue
+            diff = abs(m.elo - model.elo)
+            if diff < max(closest_diffs):
+                closest_diffs.append(diff)
+                closest_diffs.sort()
+                closest_diffs.pop()
+                indexes.append(i)
+        random.shuffle(indexes)
+        return indexes[0]
+
+    def to_csv(self):
+        """ Save the match history as a CSV file to the hub. """
+        data_dict = {"author": [], "name": [], "elo": [], "games_played": []}
+        for model in self.models:
+            data_dict["author"].append(model.author)
+            data_dict["name"].append(model.name)
+            data_dict["elo"].append(model.elo)
+            data_dict["games_played"].append(model.games_played)
+
+        df = pd.DataFrame(data_dict)
+        df.to_csv('env_elos/elo.csv', index=False)
+        df_matches = pd.DataFrame(self.matches)
+        date = datetime.now()
+        df_matches.to_csv(f"matches/{date.strftime('%Y-%m-%d_%H-%M-%S_%f')}.csv", index=False)
+
+
+def match(model1, model2) -> float:
+    """
+    !!! Current code is placeholder !!!
+    TODO: Launch a Unity process with the 2 models and get the result of the match
+
+    :param model1: First Model object
+    :param model2: Second Model object
+    :return: match result (0: model1 lost, 0.5: draw, 1: model1 won)
+    """
+    result = random.randint(0, 2) / 2
+
+    model1.games_played += 1
+    model2.games_played += 1
+    return result
+
+
+def get_models_list() -> list:
+    """
+    !!! Current code is placeholder !!!
+    TODO: Create a list of Model objects from the models found on the hub
+
+    :return: list of Model objects
+    """
+    models = []
+    models_names = []
+    data = pd.read_csv("env_elos/elo.csv")
+    # models_on_hub = api.list_models(filter=["reinforcement-learning", env, "stable-baselines3"])
+    models_on_hub = []
+    for i, row in data.iterrows():
+        models.append(Model(row["author"], row["name"], row["elo"], row["games_played"]))
+        models_names.append(row["name"])
+    for model in models_on_hub:
+        if model.modelId not in models_names:
+            models.append(Model(model.author, model.modelId))
+    return models
+
+
+def init_matchmaking():
+    models = get_models_list()
+    matchmaking = Matchmaking(models)
+    matchmaking.run()
+    matchmaking.to_csv()
+
+
+if __name__ == "__main__":
+    print("It's running!")
+    api = HfApi()
+    init_matchmaking()

env_elos/elo.csv

@@ -1,3 +1,5 @@
-model,elo,env
-model1,1200,snowballfight
-,,
+author,model,elo,games_played
+CarlCochet,model1,1234,8
+CarlCochet,model2,1000,5
+CarlCochet,model3,1431,4
+CarlCochet,model4,1200,0

match_history/2022-12-21_11-07-43_445125.csv

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match_history/2022-12-21_11-09-12_484328.csv

@@ -1,131 +0,0 @@
-model1,model2,result,datetime
-model-n,model-b,0.0,2022-12-21 11:09:12.462323
-model-r,model-t,0.5,2022-12-21 11:09:12.463322
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-model-z,model-m,0.0,2022-12-21 11:09:12.463322
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matchmaking.py

@@ -11,11 +11,10 @@ class Model:
     :param elo: Elo rating of the model
     :param games_played: Number of games played by the model (useful if we implement sigma uncertainty)
     """
-    def __init__(self, name, elo, env):
+    def __init__(self, name, elo):
         self.name = name
         self.elo = elo
         self.games_played = 0
-        self.env = env
 
 
 class Matchmaking:
@@ -47,7 +46,7 @@ class Matchmaking:
         self.matches = self.matches.sort_values("datetime")
         self.matches.reset_index(drop=True, inplace=True)
         model_names = self.matches["model1"].unique()
-        self.models = [Model(name, self.start_elo, self.matches[self.matches["name"] == name].iloc[0]["env"]) for name in model_names]
+        self.models = [Model(name, self.start_elo) for name in model_names]
 
     def compute_elo(self):
         """ Compute the elo for each model after each match. """