From 11ec939510eed219fc10444106a660977562deb1 Mon Sep 17 00:00:00 2001 From: posij118 Date: Mon, 5 Feb 2024 16:35:26 +0800 Subject: [PATCH] Add loss minimization rating algorithm with data and results --- data/games.json | 25134 ++++++++++++++++++++++++++++ results/min_loss_rating_list.json | 468 + src/constants.py | 2 +- src/minimize_loss.py | 382 + 4 files changed, 25985 insertions(+), 1 deletion(-) create mode 100644 data/games.json create mode 100644 results/min_loss_rating_list.json create mode 100644 src/minimize_loss.py diff --git a/data/games.json b/data/games.json new file mode 100644 index 0000000..7c44dcd --- /dev/null +++ b/data/games.json @@ -0,0 +1,25134 @@ +[ + { + "game_id": 1103393, + "num_players": 4, + "users": ["aara", "benzloeb", "Vivarus", "wtfitsnotbutter"], + "user_ids": [59, 55, 67, 64], + "user_rating_changes": [-8.706185, -4.3530927, -8.706185, -8.706185], + "user_ratings_after": [1611.212, 1796.6962, 1648.4856, 1740.7194], + "variant_id": 51, + "variant_name": "Clue Starved (6 Suits)", + "num_suits": 6, + "rating_type": 1, + "seed": "p4v51s10", + "score": 29, + "num_turns": 64, + "datetime_finished": "2024-01-31T06:23:13.968054+00:00", + "league_id": 2903, + "num_bdrs": 3, + "num_crits_lost": 0, + "game_outcomes": ["Loss"], + "variant_rating_change": 1.45, + "variant_rating_after": 1864.78 + }, + { + "game_id": 1103376, + "num_players": 4, + "users": ["aara", "benzloeb", "Vivarus", "wtfitsnotbutter"], + "user_ids": [59, 55, 67, 64], + "user_rating_changes": [-9.022096, -4.511048, -9.022096, -9.022096], + "user_ratings_after": [1619.9182, 1801.0493, 1657.1918, 1749.4255], + "variant_id": 51, + "variant_name": "Clue Starved (6 Suits)", + "num_suits": 6, + "rating_type": 1, + "seed": "p4v51s9", + "score": 0, + "num_turns": 36, + "datetime_finished": "2024-01-31T05:55:38.917338+00:00", + "league_id": 2901, + "num_bdrs": 0, + "num_crits_lost": 1, + "game_outcomes": ["Discard Critical", "Vote to Kill"], + "variant_rating_change": 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["Win"], + "variant_rating_change": -3.62, + "variant_rating_after": 1397.9 + }, + { + "game_id": 1083651, + "num_players": 3, + "users": ["posij118", "spring", "str8tsknacker"], + "user_ids": [28, 38, 40], + "user_rating_changes": [38.029655, 38.029655, 38.029655], + "user_ratings_after": [1238.0297, 1238.0297, 1238.0297], + "variant_id": 0, + "variant_name": "No Variant", + "num_suits": 5, + "rating_type": 0, + "seed": "p3v0s200", + "score": 25, + "num_turns": 52, + "datetime_finished": "2023-12-22T19:42:57.442872+00:00", + "league_id": 4, + "num_bdrs": 0, + "num_crits_lost": 0, + "game_outcomes": ["Win"], + "variant_rating_change": -3.8, + "variant_rating_after": 1392.37 + }, + { + "game_id": 1083649, + "num_players": 3, + "users": ["Eliclax", "Jay", "vEnhance"], + "user_ids": [7, 15, 43], + "user_rating_changes": [48.51027, 48.51027, 48.51027], + "user_ratings_after": [1247.0082, 1247.0082, 1247.0082], + "variant_id": 52, + "variant_name": "Clue Starved (5 Suits)", + "num_suits": 5, + "rating_type": 1, + "seed": "p3v52s38", + "score": 25, + "num_turns": 54, + "datetime_finished": "2023-12-22T19:40:16.764870+00:00", + "league_id": 3, + "num_bdrs": 1, + "num_crits_lost": 0, + "game_outcomes": ["Win"], + "variant_rating_change": -4.85, + "variant_rating_after": 1795.12 + }, + { + "game_id": 1083634, + "num_players": 4, + "users": ["Eliclax", "Jay", "pianoblook", "vEnhance"], + "user_ids": [7, 15, 27, 43], + "user_rating_changes": [-1.5020181, -1.5020181, -1.5020181, -1.5020181], + "user_ratings_after": [1198.4979, 1198.4979, 1198.4979, 1198.4979], + "variant_id": 52, + "variant_name": "Clue Starved (5 Suits)", + "num_suits": 5, + "rating_type": 1, + "seed": "p4v52s25", + "score": 24, + "num_turns": 49, + "datetime_finished": "2023-12-22T18:44:22.203869+00:00", + "league_id": 2, + "num_bdrs": 0, + "num_crits_lost": 0, + "game_outcomes": ["Loss"], + "variant_rating_change": 0.15, + "variant_rating_after": 1800.15 + }, + { + "game_id": 1083612, + "num_players": 3, + "users": ["Eliclax", "pianoblook", "vEnhance"], + "user_ids": [7, 27, 43], + "user_rating_changes": [38.2276, 38.2276, 38.2276], + "user_ratings_after": [1238.2275, 1238.2275, 1238.2275], + "variant_id": 0, + "variant_name": "No Variant", + "num_suits": 5, + "rating_type": 0, + "seed": "p3v0s195", + "score": 25, + "num_turns": 50, + "datetime_finished": "2023-12-22T17:40:23.567344+00:00", + "league_id": 1, + "num_bdrs": 0, + "num_crits_lost": 0, + "game_outcomes": ["Win"], + "variant_rating_change": -3.82, + "variant_rating_after": 1396.18 + } +] diff --git a/results/min_loss_rating_list.json b/results/min_loss_rating_list.json new file mode 100644 index 0000000..aa225ee --- /dev/null +++ b/results/min_loss_rating_list.json @@ -0,0 +1,468 @@ +{ + "players": [ + { + "id": 1, + "rating": 1685.2675415133688, + "username": "asaelr", + "\u03c3": 66.31240370576553 + }, + { + "id": 6, + "rating": 1431.613182299928, + "username": "ElenaDhynho", + "\u03c3": 29.98866321273064 + }, + { + "id": 7, 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"username": "TheDaniMan", + "\u03c3": 134.82661666333226 + }, + { + "id": 79, + "rating": 1482.0644773366396, + "username": "Kaznad", + "\u03c3": 405.28035311445757 + }, + { + "id": 80, + "rating": 1086.1355090178276, + "username": "Kernel", + "\u03c3": 211.39242310002254 + }, + { + "id": 81, + "rating": 1290.2977493468493, + "username": "Le Codex", + "\u03c3": 128.6528429208025 + }, + { + "id": 82, + "rating": 1554.5865377995306, + "username": "Nipalup", + "\u03c3": 186.7194364889647 + } + ], + "variants": [ + { + "id": 0, + "rating": 1344.571218042761, + "num_players": 3, + "name": "No Variant", + "num_suits": 5, + "\u03c3": 31.335534554470627 + }, + { + "id": 1, + "rating": 1380.3108266495492, + "num_players": 3, + "name": "6 Suits", + "num_suits": 6, + "\u03c3": 33.55653633693724 + }, + { + "id": 51, + "rating": 1751.8509769680584, + "num_players": 3, + "name": "Clue Starved (6 Suits)", + "num_suits": 6, + "\u03c3": 11.424872579830076 + }, + { + "id": 52, + "rating": 1677.1056853935763, + "num_players": 3, + "name": "Clue Starved (5 Suits)", + "num_suits": 5, + "\u03c3": 40.42763640571963 + }, + { + "id": 0, + "rating": 1436.6029781659563, + "num_players": 4, + "name": "No Variant", + "num_suits": 5, + "\u03c3": 45.77891871651097 + }, + { + "id": 1, + "rating": 1408.254084838167, + "num_players": 4, + "name": "6 Suits", + "num_suits": 6, + "\u03c3": 14.953524984939428 + }, + { + "id": 51, + "rating": 1820.7964465875705, + "num_players": 4, + "name": "Clue Starved (6 Suits)", + "num_suits": 6, + "\u03c3": 81.2114821701358 + }, + { + "id": 52, + "rating": 1719.526642788024, + "num_players": 4, + "name": "Clue Starved (5 Suits)", + "num_suits": 5, + "\u03c3": 38.5663698641606 + }, + { + "id": 0, + "rating": 1373.7429515570884, + "num_players": 5, + "name": "No Variant", + "num_suits": 5, + "\u03c3": 72.00085092075848 + }, + { + "id": 1, + "rating": 1522.642598263524, + "num_players": 5, + "name": "6 Suits", + "num_suits": 6, + "\u03c3": 36.55838248550461 + }, + { + "id": 51, + "rating": 1895.801292104732, + "num_players": 5, + "name": null, + "num_suits": 6, + "\u03c3": 14.915817466505452 + }, + { + "id": 52, + "rating": 1968.4174574449692, + "num_players": 5, + "name": "Clue Starved (5 Suits)", + "num_suits": 5, + "\u03c3": 50.09621953978423 + } + ] +} diff --git a/src/constants.py b/src/constants.py index 24ca63f..1aeffc2 100644 --- a/src/constants.py +++ b/src/constants.py @@ -28,7 +28,7 @@ DB_TABLE_NAMES = [ DATABASE_SCHEMA_PATH = 'install/database_schema.sql' DEFAULT_DB_CONFIG_PATH = 'install/default_db_config.yaml' -DEFAULT_CONFIG_PATH = 'install/default_config.yaml' +DEFAULT_CONFIG_PATH = '../install/default_config.yaml' VARIANTS_JSON_URL = 'https://raw.githubusercontent.com/Hanabi-Live/hanabi-live/main/packages/game/src/json/variants.json' diff --git a/src/minimize_loss.py b/src/minimize_loss.py new file mode 100644 index 0000000..d155641 --- /dev/null +++ b/src/minimize_loss.py @@ -0,0 +1,382 @@ +from typing import Iterable, List + +from config import config_manager +from constants import UNWINNABLE_SEED_FRACTION + +from dataclasses import dataclass + +import json +import datetime + +import numpy as np +from numpy.typing import NDArray +from scipy.optimize import minimize, Bounds + +MAX_RATING_DIFF = 1200 +RATING_PRIOR = 1600 +# This allows us to do integer optimization on more fine-grained grid. +LCM_FACTOR = 60 +NUM_RANDOM_INITS = 50 +NUM_CV_ITERS = 50 + +VARIANT_DEFAULT_RATINGS = np.array([1400, 1400, 1800, 1800]).reshape((1, 4)) +VARIANT_NUM_PLAYERS_MODIFIERS = np.array([0, 0, 100]).reshape((3, 1)) + +# As of season 1, this is [1400 1400 1800 1800 1400 1400 1800 1800 1500 1500 1900 1900]. +VARIANT_RATING_PRIORS = np.ravel( + VARIANT_DEFAULT_RATINGS + VARIANT_NUM_PLAYERS_MODIFIERS +) + + +@dataclass +class GlobalInfo: + rated_ids: Iterable[int] + variant_ids: Iterable[int] + player_counts: Iterable[int] + user_names: Iterable[str] + game_counts: Iterable[int] + + @property + def rating_list_length(self): + return len(self.rated_ids) + len(self.variant_ids) * len(self.player_counts) + + @property + def rated_id_indices_in_rating_list(self): + return {id: index for index, id in dict(enumerate(self.rated_ids)).items()} + + +@dataclass +class GameRow: + game_id: int + num_players: int + users: List[str] + user_ids: List[int] + user_rating_changes: List[float] + user_ratings_after: List[float] + variant_id: int + variant_name: str + num_suits: id + rating_type: int + seed: str + score: int + num_turns: int + datetime_finished: datetime.datetime + league_id: int + num_bdrs: int + num_crits_lost: int + game_outcomes: List[str] + variant_rating_change: float + variant_rating_after: float + + +def calculate_loss( + rating_list: Iterable[int], + game_list: Iterable[GameRow], + global_info: GlobalInfo, + p_win_lookup_table: NDArray[np.float32], + player_rating_priors: Iterable[int], + player_prior_weight: float, + variant_rating_priors: Iterable[int], + variant_prior_weight: float, +): + loss = np.float32(0) + + for game in game_list: + team_ratings = [ + rating_list[global_info.rated_id_indices_in_rating_list[user_id]] + for user_id in game["user_ids"] + ] + team_rating = sum(team_ratings) / len(team_ratings) + + variant_rating = rating_list[ + calculate_variant_index_in_rating_list( + len(game["users"]), game["variant_id"], global_info + ) + ] + + rating_diff = int(np.round(LCM_FACTOR * (team_rating - variant_rating))) + p_win = calculate_p_win(rating_diff, p_win_lookup_table) + + game_won = game["game_outcomes"].count("Win") > 0 + loss += calculate_loss_for_one_game(p_win, game_won) + + # Add two dummy single-player games for each player with prior_weight * square-root of number of games weight - one won and lost + for player_rating_prior, rated_id, game_count in zip( + player_rating_priors, global_info.rated_ids, global_info.game_counts + ): + team_rating = rating_list[global_info.rated_id_indices_in_rating_list[rated_id]] + variant_rating = player_rating_prior + + rating_diff = int(np.round(LCM_FACTOR * (team_rating - variant_rating))) + p_win = calculate_p_win(rating_diff, p_win_lookup_table) + + loss += ( + np.sqrt(game_count) + * player_prior_weight + * calculate_loss_for_one_game(p_win, True) + ) + loss += ( + np.sqrt(game_count) + * player_prior_weight + * calculate_loss_for_one_game(p_win, False) + ) + + # Add two dummy single-player games for each player with prior_weight * square-root of number of games weight - one won and lost + for variant_rating, variant_rating_prior in zip( + rating_list[len(global_info.rated_ids) :], + variant_rating_priors, + ): + team_rating = variant_rating_prior + + rating_diff = int(np.round(LCM_FACTOR * (team_rating - variant_rating))) + p_win = calculate_p_win(rating_diff, p_win_lookup_table) + + loss += ( + np.sqrt(game_count) + * variant_prior_weight + * calculate_loss_for_one_game(p_win, True) + ) + loss += ( + np.sqrt(game_count) + * variant_prior_weight + * calculate_loss_for_one_game(p_win, False) + ) + + return loss + + +def calculate_loss_gradient(rating_list, *args): + y = calculate_loss(rating_list, *args) + eye = np.eye(len(rating_list), dtype=int) + + gradient = [ + calculate_loss(rating_list + eye[i], *args) - y for i in range(len(rating_list)) + ] + + return gradient + + +def calculate_minloss_ratings( + game_list: List[GameRow], + global_info: GlobalInfo, + p_win_lookup_table: NDArray[np.float32], + player_rating_priors: int | Iterable[int] = RATING_PRIOR, + player_random_init_stdev: int = 0, + player_prior_weight=0.5, + variant_rating_priors: int | Iterable[int] = RATING_PRIOR, + variant_prior_weight=0.5, + variant_random_init_stdev: int = 0, +): + player_rating_priors = np.broadcast_to(player_rating_priors, len(rated_ids)) + variant_rating_priors = np.broadcast_to( + variant_rating_priors, len(variant_ids) * len(player_counts) + ) + + prior_rating_list = np.concatenate( + ( + player_rating_priors + + np.random.normal(0, player_random_init_stdev, len(player_rating_priors)), + variant_rating_priors + + np.random.normal( + 0, variant_random_init_stdev, len(variant_rating_priors) + ), + ), + dtype=np.float32, + ) + + rating_list = minimize( + calculate_loss, + prior_rating_list, + ( + game_list, + global_info, + p_win_lookup_table, + player_rating_priors, + player_prior_weight, + variant_rating_priors, + variant_prior_weight, + ), + bounds=Bounds(RATING_PRIOR - MAX_RATING_DIFF, RATING_PRIOR + MAX_RATING_DIFF), + jac=calculate_loss_gradient, + tol=0.0001, + ) + + return rating_list + + +def calculate_p_win_lookup_table(rating_diff_indices: Iterable[int]): + lookup_table = np.full(2 * MAX_RATING_DIFF * LCM_FACTOR + 1, 0.5, dtype=np.float32) + + for rating_diff_index in rating_diff_indices: + p_win = calculate_p_win(rating_diff_index, None) + lookup_table[rating_diff_index] = p_win + + return lookup_table + + +def calculate_p_win( + rating_diff_index: int, lookup_table: NDArray[np.float32] | None = None +): + if lookup_table is None: + return (1 - UNWINNABLE_SEED_FRACTION) * ( + 1 / (1 + 10 ** (-(rating_diff_index / LCM_FACTOR) / 400)) + ) + else: + return lookup_table[rating_diff_index] + + +# Cross-entropy loss +def calculate_loss_for_one_game(p_win: np.float32, game_won: bool): + if game_won: + loss = -np.log(p_win) + else: + loss = -np.log(1 - p_win) + + return loss + + +def calculate_variant_index_in_rating_list( + player_count: int, variant_id: int, global_info: GlobalInfo +): + return ( + len(global_info.rated_ids) + + global_info.player_counts.index(player_count) * len(global_info.variant_ids) + + global_info.variant_ids.index(variant_id) + ) + + +def calculate_player_count_and_variant_id_from_variant_index( + index: int, global_info: GlobalInfo +): + num_players = global_info.player_counts[index // len(global_info.variant_ids)] + variant_id = global_info.variant_ids[index % len(global_info.variant_ids)] + + return num_players, variant_id + + +def write_data(random_init_rating_lists, cv_rating_lists): + with open("../results/min_loss_rating_list.json", "w") as f: + rating_list = np.average(random_init_rating_lists, axis=0) + rating_stdevs = np.std(cv_rating_lists, axis=0, ddof=1) + + players = [] + for rated_id, user_name, rating, rating_stdev in zip( + global_info.rated_ids, global_info.user_names, rating_list, rating_stdevs + ): + players.append( + { + "id": rated_id, + "rating": rating, + "username": user_name, + "stdev": rating_stdev, + } + ) + + variants = [] + + for i, (rating, rating_stdev) in enumerate( + zip( + rating_list[len(global_info.rated_ids) :], + rating_stdevs[len(global_info.rated_ids) :], + ) + ): + ( + num_players, + variant_id, + ) = calculate_player_count_and_variant_id_from_variant_index(i, global_info) + variant_name = None + + for game in game_list: + if ( + len(game["users"]) == num_players + and game["variant_id"] == variant_id + ): + variant_name = game["variant_name"] + num_suits = game["num_suits"] + + variants.append( + { + "id": variant_id, + "rating": rating, + "num_players": num_players, + "name": variant_name, + "num_suits": num_suits, + "stdev": rating_stdev, + } + ) + + s = json.dumps({"players": players, "variants": variants}) + f.write(s) + + +if __name__ == "__main__": + with open("../data/games.json") as game_data: + game_list = json.loads(game_data.read()) + + p_win_lookup_table = calculate_p_win_lookup_table( + np.arange(-MAX_RATING_DIFF * LCM_FACTOR, MAX_RATING_DIFF * LCM_FACTOR + 1) + ) + + config = config_manager.get_config() + + rated_ids_dupes = [ID for game in game_list for ID in game["user_ids"]] + rated_ids = list(set(rated_ids_dupes)) + + variant_ids_dupes = [game["variant_id"] for game in game_list] + variant_ids = list(set(variant_ids_dupes)) + + nums_players = [game["num_players"] for game in game_list] + + player_counts = list(range(config.min_player_count, config.max_player_count + 1)) + game_counts = np.concatenate( + [rated_ids_dupes.count(ID) for ID in rated_ids], + np.zeros(len(variant_ids) * len(player_counts)), + ) + + user_names = list(np.full((len(rated_ids),), "")) + for game in game_list: + for user_id, user_name in zip(game["user_ids"], game["users"]): + user_names[rated_ids.index(user_id)] = user_name + + global_info = GlobalInfo( + rated_ids, variant_ids, player_counts, user_names, game_counts + ) + + for variant_id, num_players in zip(variant_ids_dupes, nums_players): + game_counts[ + len(rated_ids) + + calculate_variant_index_in_rating_list( + num_players, variant_id, global_info + ) + ] += 1 + global_info.game_counts = game_counts + + random_init_rating_lists = [] + cv_rating_lists = [] + + for _ in range(NUM_RANDOM_INITS): + random_init_rating_lists.append( + calculate_minloss_ratings( + game_list, + global_info, + p_win_lookup_table, + variant_rating_priors=VARIANT_RATING_PRIORS, + player_random_init_stdev=200, + variant_random_init_stdev=200, + ).x + ) + + for _ in range(NUM_CV_ITERS): + cv_game_list = np.random.choice(game_list, len(game_list), replace=True) + cv_rating_lists.append( + calculate_minloss_ratings( + cv_game_list, + global_info, + p_win_lookup_table, + variant_rating_priors=VARIANT_RATING_PRIORS, + player_random_init_stdev=200, + variant_random_init_stdev=200, + ).x + ) + + write_data(random_init_rating_lists, cv_rating_lists)