Merge pull request #25 from alan-turing-institute/visualisation

Visualisation using sprites

src/p2lab/poke_stats/POKE_STATS.md

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+# Poke Stats
+
+This directory includes the statistical analysis tools necessary for analysing
+the outcomes of the fights.
+
+So far this includes:
+
+- Benchmarking the method via clustering the results of 1v1 fights against base
+  stats, looking to recreate the tier list.
+  - This can be achieved by running 1v1_fights.py, the base stats are found in
+    poke_base_stats.pkl

src/p2lab/poke_stats/delete_pngs.py

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+import os
+import pickle
+import re
+
+# Directory path where the files are located
+directory = "/Users/lfrance/OneDrive - The Alan Turing Institute/002_Projects/Pokemon/poke-images/pokesprite/pokemon-gen8/regular"
+
+pickle_file = "poke_base_stats.pkl"
+
+with open(pickle_file, "rb") as file:
+    pokemon_dict = pickle.load(file)
+# Get a list of all filenames in the directory
+filenames = os.listdir(directory)
+
+print(pokemon_dict.keys())
+
+pokemon_names = list(pokemon_dict.keys())
+for filename in filenames:
+    file_path = os.path.join(directory, filename)
+
+    # Extract the Pokémon name from the filename
+    pokemon_name = filename[:-4]  # Remove the ".png" extension
+
+    # Check if the Pokémon name is not present in the dictionary
+    if pokemon_name not in pokemon_names:
+        # Delete the file
+        os.remove(file_path)
+        print(f"Deleted file: {filename}")

src/p2lab/poke_stats/poke_1v1_fighter.py

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+# 1v1 fights for the pokemon for stats.
+# Author: Edmund Dable-Heath
+"""
+    Setting each of the 150 pokemon to fight each other pokemon until either a cutoff is
+    reached or the variation of the win ratio over a history of s fights falls below a
+    threshold.
+"""
+
+# Imports include reference to potential scripts
+# import pop_gen
+# import fight_sim
+from __future__ import annotations
+
+import pickle
+from itertools import combinations_with_replacement as cwr
+
+import numpy as np
+
+# Actual code ==========================================================================
+
+# def gen_pop() -> list(poke_objects):
+#     """Generate the population of 151 pokemon objects ready to fight.
+
+#     NB: This can be done more explicity here, i.e. implement the rejection sampling
+#         in this script, but I will simplify away from that for now.
+#     """
+#     return pop_gen.gen_pop
+
+# # def duel_to_convergence(
+#     poke_1: poke_object,
+#     poke_2: poke_object,
+#     var_threshold: float,
+#     look_back: int,
+#     cutoff: int,
+# ) -> tuple(float, float):
+#     """DUEL TO THE DEATH! (or at least a converged win ratio.....)
+
+#         For two pokemon, duel until the win ration converges or cutoff is reached.
+#         Return ratios scaled by number of fights.
+
+#         TODO: make lookback function of number of fights?
+
+#     Args:
+#         poke_1 (poke_object): pokemon_1      (GLADIATOR, READY!)
+#         poke_2 (poke_object): pokemon_2      (GLADIATOR, READY!)
+#         var_threshold (float): threshold for convergence of win ratio variance.
+#         look_back: how man previous ratio values to consider.
+#         cutoff: max iters to run this.
+
+#     Returns:
+#         (poke_wins_1, poke_wins_2) tuple(float, float):
+#             poke_wins_i = (no wins of poke_i) / (no of duels)
+#     """
+#     count = 0
+#     variance = 100
+#     poke_win_1 = 1  # Start with one win each to avoid /0 errors....
+#     poke_win_2 = 1
+#     ratios = []
+#     while (count < cutoff) or (variance > var_threshold):
+#         count += 1
+#         if duel(poke_1, poke_2):
+#             poke_win_1 += 1
+#         else:
+#             poke_win_2 += 1
+#         ratios.append(poke_win_1 / poke_win_2)
+#         if len(ratios) < look_back:
+#             variance = np.var(ratios)
+#         else:
+#             variance = np.var(ratios[-look_back:])
+#     return poke_win_1 / count, poke_win_2 / count
+
+# def duel(poke_1: poke_object, poke_2: poke_object) -> bool:
+#     """Simulate a fight between a pair of pokemon, with a result of True if poke_1
+#         wins.
+
+#     Args:
+#         poke_1 (poke_object): pokemon_1     (IN THE RED CORNER)
+#         poke_2 (poke_object): pokemon_2     (IN THE BLUE CORNER)
+
+#     Returns:
+#         bool: True if pokemon 1 is victorious.
+#     """
+#     winner = fight_sim.fight(poke_1, poke_2)
+#     return winner == poke_1
+
+
+# def main(**kwargs):
+#     # Generate a population
+#     poke_pop = gen_pop
+
+#     # Instantiate results matrix (we know n = 151)
+#     res_arr = np.zeros(151, 151)
+
+#     # Duel to convergence to get stats
+#     for dueling_partners_id in cwr(list(range(len(poke_pop))), 2):
+#         i, j = dueling_partners_id
+#         poke_ratio_1, poke_ratio_2 = duel_to_convergence(
+#             poke_pop[i],
+#             poke_pop[j],
+#             kwargs["var_threshold"],
+#             kwargs["look_back"],
+#             kwargs["cutoff"],
+#         )
+#         if i == j:
+#             res_arr[i, i] += poke_ratio_1
+#         res_arr[i, j] += poke_ratio_1
+#         res_arr[j, i] += poke_ratio_2
+
+#     # Average over to get stats
+#     avg_win_ratio = np.mean(res_arr, axis=0)
+#     return avg_win_ratio
+
+# Dummy Code ===========================================================================
+
+
+def dummy_gen_pop() -> list(str):
+    """Create a dummy list of pokemon."""
+    with open("poke_base_stats.pkl", "rb") as f:
+        p_dict = pickle.load(f)
+    return p_dict
+
+
+def dummy_duel(poke_1: str, poke_2: str) -> bool:
+    """Dummy duel method"""
+    rng = np.random.default_rng(seed=int("".join(str(ord(c)) for c in poke_1 + poke_2)))
+    prob = rng.random(1)[0]
+    # print(prob)
+    # print(1 - prob)
+    return rng.choice(a=[True, False], p=[prob, 1 - prob])
+
+
+def dummy_duel_to_convergence(
+    poke_1: str,
+    poke_2: str,
+    var_threshold: float,
+    look_back: int,
+    cutoff: int,
+) -> tuple(float, float):
+    """DUEL TO THE DEATH! (or at least a converged win ratio.....)
+
+        For two pokemon, duel until the win ration converges or cutoff is reached.
+        Return ratios scaled by number of fights.
+
+        TODO: make lookback function of number of fights?
+
+    Args:
+        poke_1 (poke_object): pokemon_1      (GLADIATOR, READY!)
+        poke_2 (poke_object): pokemon_2      (GLADIATOR, READY!)
+        var_threshold (float): threshold for convergence of win ratio variance.
+        look_back: how man previous ratio values to consider.
+        cutoff: max iters to run this.
+
+    Returns:
+        (poke_wins_1, poke_wins_2) tuple(float, float):
+            poke_wins_i = (no wins of poke_i) / (no of duels)
+    """
+    count = 0
+    variance = 100
+    poke_win_1 = 1  # Start with one win each to avoid /0 errors....
+    poke_win_2 = 1
+    ratios = []
+    # print(cutoff)
+    while count < cutoff and variance > var_threshold:  # or variance
+        # print(count)
+        count += 1
+        if dummy_duel(poke_1, poke_2):
+            poke_win_1 += 1
+        else:
+            poke_win_2 += 1
+        ratios.append(poke_win_1 / poke_win_2)
+        if count > 2:
+            if len(ratios) < look_back:
+                variance = np.var(ratios)
+            else:
+                variance = np.var(ratios[-look_back:])
+    print(variance)
+    return poke_win_1 / count, poke_win_2 / count
+
+
+def main(**kwargs):
+    # Generate a population
+    poke_dict = dummy_gen_pop()
+    poke_names = list(poke_dict.keys())
+
+    # Instantiate results matrix (we know n = 151)
+    res_arr = np.zeros((151, 151))
+
+    # Duel to convergence to get stats
+    for dueling_partners_id in cwr(list(range(len(poke_names))), 2):
+        i, j = dueling_partners_id
+        print(f"IN THE RED CORNER: {poke_names[i]}")
+        print(f"IN THE BLUE CORNER: {poke_names[j]}")
+        poke_ratio_1, poke_ratio_2 = dummy_duel_to_convergence(
+            poke_names[i],
+            poke_names[j],
+            kwargs["var_threshold"],
+            kwargs["look_back"],
+            kwargs["cutoff"],
+        )
+        if i == j:
+            res_arr[i, i] += poke_ratio_1
+        res_arr[i, j] += poke_ratio_1
+        res_arr[j, i] += poke_ratio_2
+
+    # Average over to get stats
+    avg_win_ratio = np.mean(res_arr, axis=0)
+    for i in range(len(poke_names)):
+        poke_dict[poke_names[i]]["average_win_ratio"] = avg_win_ratio[i]
+    # print(poke_dict)
+    print("for Magikarp:")
+    print(poke_dict["magikarp"])
+    return poke_dict
+
+
+if __name__ == "__main__":
+    # Pars hardcoded in for now as should be single run.
+    pars = {"var_threshold": 1e-14, "look_back": 2, "cutoff": 100}
+
+    main(**pars)