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["extension-module", "abi3-py312"] } + +[lints.clippy] +too_many_arguments = "allow" diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py deleted file mode 100644 index 0ec41709e955ed543a20e80900440804438634d8..0000000000000000000000000000000000000000 --- a/experiments/elo/elo/bga_spider.py +++ /dev/null @@ -1,126 +0,0 @@ -from collections.abc import Generator -from datetime import datetime, timezone -import json -import math -import re -from typing import Any - -from scrapy import FormRequest, Request, Spider -from scrapy.http import Response, TextResponse - - -class BaseFilter: - type_name: str - - def __init__(self, feed_options: dict[str, Any] | None) -> None: - self.feed_options = feed_options - - def accepts(self, item: Any) -> bool: - if "type" in item and item["type"] == self.type_name: - return True - return False - - -class GameFilter(BaseFilter): - type_name = "game" - - -class RankingFilter(BaseFilter): - type_name = "ranking" - - -class BgaSpider(Spider): - name = "bga" - start_urls = ("https://en.boardgamearena.com/gamelist?allGames=",) - - custom_settings = { - "DOWNLOAD_DELAY": 1, - "CONCURRENT_REQUESTS_PER_DOMAIN": 4, - "FEED_EXPORT_BATCH_ITEM_COUNT": 10_000, - "FEEDS": { - "results/games-%(time)s-%(batch_id)05d.jl": { - "format": "jsonlines", - "item_filter": GameFilter, - "overwrite": False, - "store_empty": False, - }, - "results/rankings-%(time)s-%(batch_id)05d.jl": { - "format": "jsonlines", - "item_filter": RankingFilter, - "overwrite": False, - "store_empty": False, - }, - }, - "JOBDIR": ".jobs", - } - - max_rank_scraped = None - regex = re.compile("globalUserInfos=(.+);$", flags=re.MULTILINE) - - def parse(self, response: Response) -> Generator[dict | Request]: - now = datetime.now(timezone.utc).isoformat(timespec="seconds") - - for text in response.xpath( - "//script[@type = 'text/javascript']/text()" - ).getall(): - match = self.regex.search(text) - if not match: - continue - - try: - payload = json.loads(match.group(1)) - except json.JSONDecodeError: - continue - - if not isinstance(payload, dict) or "game_list" not in payload: - continue - - for game in payload["game_list"]: - game["type"] = "game" - game["scraped_at"] = now - yield game - - yield FormRequest( - url=response.urljoin("/gamepanel/gamepanel/getRanking.html"), - method="POST", - formdata={ - "game": str(game["id"]), - "start": "0", - "mode": "elo", - }, - callback=self.parse_ranking, - meta={"game_id": game["id"]}, - priority=0, - ) - - def parse_ranking(self, response: Response) -> Generator[dict | Request]: - if not isinstance(response, TextResponse): - self.logger.warning("Response <%s> is not a TextResponse", response.url) - return - - now = datetime.now(timezone.utc).isoformat(timespec="seconds") - - game_id = response.meta["game_id"] - payload = response.json() - - rank_nos = [] - - for entry in payload["data"]["ranks"]: - entry["game_id"] = game_id - entry["type"] = "ranking" - entry["scraped_at"] = now - yield entry - rank_nos.append(int(entry["rank_no"])) - - max_rank_no = max(rank_nos, default=math.inf) - - if not self.max_rank_scraped or max_rank_no < self.max_rank_scraped: - yield response.request.replace( - formdata={ - "game": str(game_id), - "start": str(max_rank_no), - "mode": "elo", - }, - meta={"game_id": game_id}, - priority=-max_rank_no, - ) diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py deleted file mode 100644 index ccc3c01a46d91a15ebd776c3fd9edcc5030aac6d..0000000000000000000000000000000000000000 --- a/experiments/elo/elo/elo_ratings.py +++ /dev/null @@ -1,64 +0,0 @@ -from __future__ import annotations -from collections import defaultdict -from collections.abc import Iterable, Mapping -from typing import TypeVar - -ID_TYPE = TypeVar("ID_TYPE") - - -def elo_probability(diff: float, scale: float = 400) -> float: - return 1 / (1 + 10 ** (-diff / scale)) - - -def calculate_elo_ratings( - *, - player_1_ids: Iterable[ID_TYPE], - player_2_ids: Iterable[ID_TYPE], - player_1_outcomes: Iterable[float], - init_elo_ratings: Mapping[ID_TYPE, float] | None = None, - elo_initial: float = 0, - elo_k: float = 32, - elo_scale: float = 400, - full_results: bool = False, - progress_bar: bool = False, -) -> ( - defaultdict[ID_TYPE, float] - | tuple[defaultdict[ID_TYPE, float], list[float], list[float]] -): - elo_ratings = defaultdict( - lambda: elo_initial, - init_elo_ratings if init_elo_ratings is not None else {}, - ) - - player_1_win_probs: list[float] = [] - elo_updates: list[float] = [] - - if progress_bar: - from tqdm import tqdm - - player_1_ids = tqdm(player_1_ids, desc="Processing games") - - for player_1_id, player_2_id, player_1_outcome in zip( - player_1_ids, - player_2_ids, - player_1_outcomes, - strict=True, - ): - elo_1 = elo_ratings[player_1_id] - elo_2 = elo_ratings[player_2_id] - - diff = elo_1 - elo_2 - player_1_win_prob = elo_probability(diff, elo_scale) - player_1_update = elo_k * (player_1_outcome - player_1_win_prob) - - # Update ratings - elo_ratings[player_1_id] += player_1_update - elo_ratings[player_2_id] -= player_1_update - - if full_results: - player_1_win_probs.append(player_1_win_prob) - elo_updates.append(player_1_update) - - if full_results: - return elo_ratings, player_1_win_probs, elo_updates - return elo_ratings diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py deleted file mode 100644 index c4b1cb34290666b05906c4fdee98418164b20839..0000000000000000000000000000000000000000 --- a/experiments/elo/elo/optimal_k.py +++ /dev/null @@ -1,71 +0,0 @@ -from __future__ import annotations - -from collections.abc import Iterable -from typing import TypeVar - -import numpy as np -from scipy.optimize import minimize_scalar - -from elo.elo_ratings import calculate_elo_ratings - - -ID_TYPE = TypeVar("ID_TYPE") - - -def calculate_loss( - *, - player_1_ids: Iterable[ID_TYPE], - player_2_ids: Iterable[ID_TYPE], - player_1_outcomes: Iterable[float], - elo_k: float, - elo_scale: float, - progress_bar: bool = False, -) -> np.float64: - if not isinstance(player_1_outcomes, np.ndarray): - player_1_outcomes = np.array(list(player_1_outcomes)) - - _, player_1_win_probs_list, _ = calculate_elo_ratings( - player_1_ids=player_1_ids, - player_2_ids=player_2_ids, - player_1_outcomes=player_1_outcomes, - elo_k=elo_k, - elo_scale=elo_scale, - full_results=True, - progress_bar=progress_bar, - ) - - player_1_win_probs = np.array(player_1_win_probs_list) - errors = player_1_outcomes - player_1_win_probs - - return np.mean(errors**2) - - -def approximate_optimal_k( - *, - player_1_ids: Iterable[ID_TYPE], - player_2_ids: Iterable[ID_TYPE], - player_1_outcomes: Iterable[float], - min_elo_k: float = 0, - max_elo_k: float = 160, - elo_scale: float = 400, -) -> np.float64: - player_1_ids = list(player_1_ids) - player_2_ids = list(player_2_ids) - player_1_outcomes = np.array(list(player_1_outcomes)) - - def loss(elo_k: float) -> np.float64: - return calculate_loss( - player_1_ids=player_1_ids, - player_2_ids=player_2_ids, - player_1_outcomes=player_1_outcomes, - elo_k=elo_k, - elo_scale=elo_scale, - ) - - result = minimize_scalar( - fun=loss, - method="bounded", - bounds=(min_elo_k, max_elo_k), - ) - - return result.x diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py deleted file mode 100644 index a62942c9b7bfd19e11945e2bb08f5df8c0fc933f..0000000000000000000000000000000000000000 --- a/experiments/elo/elo/p_deterministic.py +++ /dev/null @@ -1,83 +0,0 @@ -from __future__ import annotations -from collections import defaultdict -import numpy as np - -from elo.elo_ratings import calculate_elo_ratings - - -def simulate_p_deterministic_games( - rng: np.random.Generator, - num_players: int, - num_games: int, - p_deterministic: float, -) -> tuple[np.ndarray, np.ndarray, np.ndarray]: - assert 0 <= p_deterministic <= 1, "p_deterministic must be between 0 and 1" - assert num_players > 1, "num_players must be greater than 1" - assert num_games > 0, "num_games must be greater than 0" - - players_a = rng.integers(low=0, high=num_players, size=num_games) - # Sample offset for second player, ensuring it's not equal to the first - offset = rng.integers(low=1, high=num_players, size=num_games) - players_b = (players_a + offset) % num_players - assert np.all(players_a != players_b), "Players A and B must be different" - - if p_deterministic == 0: - # If p_deterministic is 0, all games are probabilistic - return players_a, players_b, rng.random(size=num_games) < 0.5 - - if p_deterministic == 1: - # If p_deterministic is 1, all games are deterministic - return players_a, players_b, players_a < players_b - - # Otherwise, mix deterministic and probabilistic outcomes - prob_player_a_wins = rng.random(size=num_games) < 0.5 - det_player_a_wins = players_a < players_b - - deterministic = rng.random(size=num_games) < p_deterministic - player_a_wins = np.where(deterministic, det_player_a_wins, prob_player_a_wins) - - return players_a, players_b, player_a_wins - - -def update_elo_ratings_p_deterministic( - rng: np.random.Generator, - elo_ratings: np.ndarray, - num_games: int, - p_deterministic: float, - elo_initial: float = 0, - elo_k: float = 32, - elo_scale: float = 400, - inplace: bool = True, - progress_bar: bool = False, -) -> np.ndarray: - assert elo_ratings.ndim == 1 - num_players = len(elo_ratings) - - if not inplace: - elo_ratings = np.copy(elo_ratings) - - players_a, players_b, player_a_wins = simulate_p_deterministic_games( - rng=rng, - num_players=num_players, - num_games=num_games, - p_deterministic=p_deterministic, - ) - - results = calculate_elo_ratings( - player_1_ids=players_a, - player_2_ids=players_b, - player_1_outcomes=player_a_wins, - init_elo_ratings=dict(enumerate(elo_ratings)), - elo_initial=elo_initial, - elo_k=elo_k, - elo_scale=elo_scale, - full_results=False, - progress_bar=progress_bar, - ) - - assert isinstance(results, defaultdict) - - for player_id, elo_rating in results.items(): - elo_ratings[player_id] = elo_rating - - return elo_ratings diff --git a/experiments/elo/notebooks/BGA Elo.py b/experiments/elo/notebooks/BGA Elo.py new file mode 100644 index 0000000000000000000000000000000000000000..0659e7bdf1bfec9b712eb727383ecd4ca13e5a20 --- /dev/null +++ b/experiments/elo/notebooks/BGA Elo.py @@ -0,0 +1,116 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.17.1 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% +import itertools +import jupyter_black +import networkx as nx +import polars as pl +from elo.elo_ratings import RankOrderedLogitElo +from elo.optimal_k import approximate_optimal_k +from tqdm import tqdm + +jupyter_black.load() +pl.Config.set_tbl_rows(100) + +num_matches_regulars = 25 +game_id = 1741 # Ark Nova + +# %% +data = pl.read_ipc(f"results/arrow/matches/{game_id}.arrow", memory_map=False) +num_all_matches = len(data) +data.shape + +# %% +graph = nx.Graph() +for player_ids in tqdm(data["player_ids"]): + graph.add_edges_from(itertools.combinations(player_ids, 2)) +num_all_players = graph.number_of_nodes() +graph.number_of_nodes(), graph.number_of_edges() + +# %% +largest_community = max(nx.connected_components(graph), key=len) +num_players = len(largest_community) +num_players + +# %% +data = data.filter( + pl.col("player_ids").list.eval(pl.element().is_in(largest_community)).list.any() +) +num_matches = len(data) +data.shape + +# %% +data.sample(10) + +# %% +data.describe() + +# %% +players = ( + data.lazy() + .select(pl.col("player_ids").explode().value_counts()) + .unnest("player_ids") +) +player_stats = players.describe( + percentiles=(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99) +) +num_matches_max = int(player_stats["count"][-1]) +player_stats + +# %% +num_regular_players = ( + players.filter(pl.col("count") >= num_matches_regulars) + .select(pl.len()) + .collect() + .item() +) +num_regular_players + +# %% +print(f"Number of matches: {num_all_matches} (all) / {num_matches} (connected only)") +print( + f"Number of players: {num_all_players} (all) / {num_players} (connected only) / {num_regular_players} (regulars only)" +) +print(f"Maximum number of plays by a single player: {num_matches_max}") + +# %% +matches = ( + dict(zip(player_ids, payoffs)) + for player_ids, payoffs in zip(data["player_ids"], data["payoffs"]) +) + +# %% +elo = RankOrderedLogitElo() +elo.update_elo_ratings_batch( + matches, + full_results=True, + progress_bar=True, + tqdm_kwargs={"total": len(data)}, +) +len(elo.elo_ratings) + +# %% +sorted(elo.elo_ratings.items(), key=lambda x: -x[1])[:100] + +# %% +# This will take very very long if there's a large number of matches +optimal_k = approximate_optimal_k( + matches=matches, + min_elo_k=0, + max_elo_k=200, + elo_scale=400, +) +optimal_k diff --git a/experiments/elo/EDA.py b/experiments/elo/notebooks/EDA.py similarity index 100% rename from experiments/elo/EDA.py rename to experiments/elo/notebooks/EDA.py diff --git a/experiments/elo/notebooks/K investigations.py b/experiments/elo/notebooks/K investigations.py new file mode 100644 index 0000000000000000000000000000000000000000..c865feccfb0b84f464a9daad6dc9f26bd1e8b94c --- /dev/null +++ b/experiments/elo/notebooks/K investigations.py @@ -0,0 +1,77 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.17.1 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% +import jupyter_black +import numpy as np +import polars as pl +import seaborn as sns +from matplotlib import pyplot as plt +from scipy.optimize import curve_fit + +jupyter_black.load() + +# %% +data = ( + pl.scan_csv("results/p_deterministic*.csv") + # .filter(pl.col("num_matches") > 1000) + .with_columns(matches_per_player=pl.col("num_matches") / pl.col("num_players")) + .sort("p_deterministic") + .collect() +) +data.shape + + +# %% +def power_law(x: np.ndarray, a: float, b: float, c: float) -> np.ndarray: + return a / (x**b) + c + + +def fit_and_plot(data): + data = data.filter(pl.col("matches_per_player") > 10) + + if len(data) < 10: + print(f"Only {len(data)} row(s) provided, skipping…") + return + + x = data["matches_per_player"].to_numpy() + y = data["elo_k"].to_numpy() + popt, pcov = curve_fit(power_law, x, y, bounds=(0, np.inf)) + a, b, c = popt + print(f"Fitted parameters: a={a:.4f}, b={b:.4f}, c={c:.4f}") + x_fit = np.linspace(x.min(), x.max(), 100) + y_fit = power_law(x_fit, *popt) + + _, ax = plt.subplots() + sns.scatterplot( + data=data, + x="matches_per_player", + y="elo_k", + hue="players_per_match", + size="num_players", + ax=ax, + ) + sns.lineplot(x=x_fit, y=y_fit, ax=ax) + + return ax + + +# %% +for (p_deterministic,), group in data.group_by("p_deterministic", maintain_order=True): + print(f"{p_deterministic=}") + fit_and_plot(group) + plt.show() + +# %% diff --git a/experiments/elo/notebooks/Matches.py b/experiments/elo/notebooks/Matches.py new file mode 100644 index 0000000000000000000000000000000000000000..528af25776bbfe1465b2f2230dd4d0765cf532f8 --- /dev/null +++ b/experiments/elo/notebooks/Matches.py @@ -0,0 +1,68 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.17.0 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% +import jupyter_black +import polars as pl + +jupyter_black.load() +pl.Config.set_tbl_rows(100) +seed = 13 + +# %% +matches = ( + pl.scan_ndjson("matches.jl") + .drop("id", "scraped_at") + .with_columns(pl.col("timestamp").cast(pl.Int64)) + .with_columns( + pl.from_epoch("timestamp").dt.convert_time_zone(time_zone="UTC"), + num_players=pl.col("players").list.len(), + ) + .filter(pl.col("num_players") > 1) + .filter( + pl.col("players") + .list.eval(pl.element().struct.field("place").is_not_null()) + .list.all() + ) +) + +# %% +matches.head(10).collect() + +# %% +games_info = pl.scan_ndjson("games.jl").select( + pl.col("id").alias("game_id"), + pl.col("name").alias("game_slug"), + "bgg_id", + "display_name_en", + "games_played", +) +games_matches = matches.group_by("game_id").agg( + num_matches=pl.len(), + min_players=pl.col("num_players").min(), + max_players=pl.col("num_players").max(), + first=pl.col("timestamp").min(), + last=pl.col("timestamp").max(), +) +all_games = games_info.join(games_matches, on="game_id", how="full").collect() +all_games.shape + +# %% +all_games.describe() + +# %% +all_games.filter(pl.col("min_players") == 2).filter( + pl.col("max_players") == 2 +).describe() diff --git a/experiments/elo/Odds parsing.py b/experiments/elo/notebooks/Odds parsing.py similarity index 100% rename from experiments/elo/Odds parsing.py rename to experiments/elo/notebooks/Odds parsing.py diff --git a/experiments/elo/Plot sigmoid.py b/experiments/elo/notebooks/Plot sigmoid.py similarity index 100% rename from experiments/elo/Plot sigmoid.py rename to experiments/elo/notebooks/Plot sigmoid.py diff --git a/experiments/elo/notebooks/Process matches.py b/experiments/elo/notebooks/Process matches.py new file mode 100644 index 0000000000000000000000000000000000000000..5a1e6bb47d93baff84bc64327a26e911cb7eb8ff --- /dev/null +++ b/experiments/elo/notebooks/Process matches.py @@ -0,0 +1,101 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.17.1 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% +import jupyter_black +import polars as pl +from datetime import datetime +from itertools import batched +from pathlib import Path +from tqdm import tqdm + +jupyter_black.load() +pl.Config.set_tbl_rows(100) + +# %% +in_dir = Path("results").resolve() +out_dir = in_dir / "arrow" +match_dir = out_dir / "matches" +out_dir.mkdir(parents=True, exist_ok=True) +match_dir.mkdir(parents=True, exist_ok=True) +in_dir, out_dir, match_dir + +# %% +file_batch_size = 10 +ts = datetime.now().replace(microsecond=0).isoformat().replace(":", "-") +suffix = f"{ts}-euler-" +file_batch_size, suffix + +# %% +schema = { + "id": pl.String, + "timestamp": pl.String, + "game_id": pl.Int64, + "players": pl.List( + pl.Struct( + { + "player_id": pl.Int64, + "place": pl.Int64, + "score": pl.Float64, + } + ) + ), + "scraped_at": pl.String, +} + +# %% +for i, batch in enumerate(batched(tqdm(in_dir.glob("matches-*.jl")), file_batch_size)): + df = ( + pl.scan_ndjson(list(batch), schema=schema) + .with_columns( + pl.col("id").cast(pl.Int64), + pl.col("timestamp").cast(pl.Int64), + ) + .with_columns( + pl.from_epoch("timestamp").dt.convert_time_zone(time_zone="UTC"), + pl.col("scraped_at").str.to_datetime(time_zone="UTC"), + ) + ) + df.sink_ipc(out_dir / f"matches-{suffix}{i:05d}.arrow") + +# %% +matches = pl.scan_ipc(out_dir / "matches-*.arrow") +game_ids = matches.select(pl.col("game_id").unique()).collect()["game_id"] +game_ids.shape + +# %% +for game_id in tqdm(game_ids): + match_data = ( + matches.filter(pl.col("game_id") == game_id) + .sort("scraped_at", "timestamp") + .unique("id", keep="first") + .select( + num_players=pl.col("players").list.len(), + player_ids=pl.col("players").list.eval( + pl.element().struct.field("player_id") + ), + places=pl.col("players").list.eval(pl.element().struct.field("place")), + ) + .filter(pl.col("num_players") >= 2) + .filter(pl.col("player_ids").list.eval(pl.element().is_not_null()).list.all()) + .filter( + pl.col("places") + .list.eval(pl.element().is_not_null() & (pl.element() >= 1)) + .list.all() + ) + .with_columns(payoffs=pl.col("places").list.eval(pl.len() - pl.element())) + .filter(pl.col("payoffs").list.eval(pl.element() >= 0).list.all()) + ) + match_data.sink_ipc(match_dir / f"{game_id}.arrow") diff --git a/experiments/elo/Snooker API.py b/experiments/elo/notebooks/Snooker API.py similarity index 100% rename from experiments/elo/Snooker API.py rename to experiments/elo/notebooks/Snooker API.py diff --git a/experiments/elo/Snooker data.py b/experiments/elo/notebooks/Snooker data.py similarity index 100% rename from experiments/elo/Snooker data.py rename to experiments/elo/notebooks/Snooker data.py diff --git a/experiments/elo/Snooker tournament.py b/experiments/elo/notebooks/Snooker tournament.py similarity index 100% rename from experiments/elo/Snooker tournament.py rename to experiments/elo/notebooks/Snooker tournament.py diff --git a/experiments/elo/notebooks/p-deterministic distribution.py b/experiments/elo/notebooks/p-deterministic distribution.py new file mode 100644 index 0000000000000000000000000000000000000000..2c3741e14326caf10c06043a2392c57bfbe497bf --- /dev/null +++ b/experiments/elo/notebooks/p-deterministic distribution.py @@ -0,0 +1,107 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.18.1 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% +import dataclasses +import jupyter_black +import numpy as np +import polars as pl +import seaborn as sns +from elo.p_deterministic import p_deterministic_experiments +from tqdm import tqdm + +jupyter_black.load() + +seed = 13 + +# %% +num_players = (100,) +num_matches = (10_000,) +players_per_match = (2, 3, 6, 9) +p_deterministic = np.linspace(start=0.0, stop=0.95, num=96) +elo_scale = (400,) +num_experiments = ( + len(num_players) + * len(num_matches) + * len(players_per_match) + * len(p_deterministic) + * len(elo_scale) +) +num_experiments + +# %% +experiments = p_deterministic_experiments( + seed=seed, + num_players=num_players, + num_matches=num_matches, + players_per_match=players_per_match, + p_deterministic=p_deterministic, + elo_scale=elo_scale, +) +experiments = tqdm(experiments, desc="Running experiments", total=num_experiments) +results = ( + pl.LazyFrame(dataclasses.asdict(experiment) for experiment in experiments) + .sort("num_players", "num_matches", "players_per_match", "p_deterministic") + .collect() +) +results.shape + +# %% +results.sample(10, seed=seed) + +# %% +results.write_csv("results/p_deterministic.csv", float_precision=5) + +# %% +sns.scatterplot( + data=results, + x="p_deterministic", + y="elo_k", + hue="players_per_match", +) + +# %% +results_log = results.filter(pl.col("elo_k") > 0).select( + "p_deterministic", + pl.col("elo_k").log().alias("log(elo_k)"), + "players_per_match", +) +sns.scatterplot( + data=results_log, + x="p_deterministic", + y="log(elo_k)", + hue="players_per_match", +) + +# %% +sns.scatterplot( + data=results, + x="p_deterministic", + y="std_dev", + hue="players_per_match", +) + +# %% +results_log = results.filter(pl.col("std_dev") > 0).select( + "p_deterministic", + pl.col("std_dev").log().alias("log(std_dev)"), + "players_per_match", +) +sns.scatterplot( + data=results_log, + x="p_deterministic", + y="log(std_dev)", + hue="players_per_match", +) diff --git a/experiments/elo/p-deterministic games.py b/experiments/elo/notebooks/p-deterministic games.py similarity index 60% rename from experiments/elo/p-deterministic games.py rename to experiments/elo/notebooks/p-deterministic games.py index 5d500d9a36c569cfda957dd987254a7adc280b92..73fb147500a7153995c1fca6967d893167d1234f 100644 --- a/experiments/elo/p-deterministic games.py +++ b/experiments/elo/notebooks/p-deterministic games.py @@ -6,7 +6,7 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.17.0 +# jupytext_version: 1.18.1 # kernelspec: # display_name: Python 3 (ipykernel) # language: python @@ -19,8 +19,11 @@ import numpy as np import polars as pl import seaborn as sns from elo.optimal_k import approximate_optimal_k -from elo.p_deterministic import simulate_p_deterministic_games, update_elo_ratings_p_deterministic -from elo.elo_ratings import elo_probability +from elo.p_deterministic import ( + simulate_p_deterministic_matches, + update_elo_ratings_p_deterministic, +) +from elo.elo_ratings import RankOrderedLogitElo, TwoPlayerElo, elo_probability jupyter_black.load() @@ -28,21 +31,25 @@ jupyter_black.load() seed = 13 rng = np.random.default_rng(seed) num_players = 1000 -num_games = 1_000_000 +num_matches = 1_000_000 +players_per_match = 2 p_deterministic = 0.5 elo_scale = 400 # %% -player_1_ids, player_2_ids, player_1_outcomes = simulate_p_deterministic_games( +matches = simulate_p_deterministic_matches( rng=rng, num_players=num_players, - num_games=num_games, + num_matches=num_matches, + players_per_match=players_per_match, p_deterministic=p_deterministic, ) +matches.shape + +# %% elo_k = approximate_optimal_k( - player_1_ids=player_1_ids, - player_2_ids=player_2_ids, - player_1_outcomes=player_1_outcomes, + matches=matches, + two_player_only=players_per_match == 2, min_elo_k=0, max_elo_k=elo_scale / 2, elo_scale=elo_scale, @@ -50,20 +57,22 @@ elo_k = approximate_optimal_k( elo_k # %% -elo_ratings = np.zeros(num_players) -elo_ratings.shape, elo_ratings.dtype - -# %% -elo_scores = update_elo_ratings_p_deterministic( +elo_kwargs = {"elo_k": elo_k, "elo_scale": elo_scale} +elo = ( + TwoPlayerElo(**elo_kwargs) + if players_per_match == 2 + else RankOrderedLogitElo(**elo_kwargs) +) +elo = update_elo_ratings_p_deterministic( rng=rng, - elo_ratings=elo_ratings, - num_games=num_games, + elo=elo, + num_players=num_players, + num_matches=num_matches, + players_per_match=players_per_match, p_deterministic=p_deterministic, - elo_k=elo_k, - elo_scale=elo_scale, - inplace=True, progress_bar=True, ) +elo_ratings = np.array(list(elo.elo_ratings.values())) elo_ratings.shape, elo_ratings.dtype # %% jp-MarkdownHeadingCollapsed=true diff --git a/experiments/elo/notebooks/p-deterministic multi.py b/experiments/elo/notebooks/p-deterministic multi.py new file mode 100644 index 0000000000000000000000000000000000000000..af8cf9eff5fb9e7487180d2a0cf228d8e8620a33 --- /dev/null +++ b/experiments/elo/notebooks/p-deterministic multi.py @@ -0,0 +1,63 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.18.1 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% +import jupyter_black +import numpy as np +from elo.elo_ratings import RankOrderedLogitElo +from elo.optimal_k import approximate_optimal_k + +jupyter_black.load() +rng = np.random.default_rng() + +# %% +p_deterministic = 0.5 +elo_scale = 400 +num_players = 1000 +num_matches = 100_000 +player_per_match = 4 +players = np.arange(num_players) + +# %% +random_outcomes = np.array( + [ + rng.choice( + players, + size=player_per_match, + replace=False, + ) + for _ in range(num_matches) + ] +) +deterministic_outcomes = np.sort(random_outcomes, axis=1) +mask = rng.random(size=num_matches) < p_deterministic +outcomes = np.where(mask[:, np.newaxis], deterministic_outcomes, random_outcomes) +random_outcomes.shape, deterministic_outcomes.shape, outcomes.shape + +# %% +optimal_k = approximate_optimal_k( + matches=outcomes, + elo_scale=elo_scale, + min_elo_k=0, + max_elo_k=elo_scale / 2, +) +optimal_k + +# %% +elo = RankOrderedLogitElo(elo_k=optimal_k, elo_scale=elo_scale) +elo.update_elo_ratings_batch(matches=outcomes, progress_bar=True) + +# %% +np.std(tuple(elo.elo_ratings.values())) diff --git a/experiments/elo/p-deterministic distribution.py b/experiments/elo/p-deterministic distribution.py deleted file mode 100644 index b0ee9d650df5a4c21e471880397e6c077a05408a..0000000000000000000000000000000000000000 --- a/experiments/elo/p-deterministic distribution.py +++ /dev/null @@ -1,120 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.17.0 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# %% -import jupyter_black -import numpy as np -import polars as pl -import seaborn as sns -from elo.optimal_k import approximate_optimal_k -from elo.p_deterministic import simulate_p_deterministic_games, update_elo_ratings_p_deterministic - -jupyter_black.load() - - -# %% -def generate_distributions( - num_players=1000, - num_games=1_000_000, - p_deterministic_min=0.01, - p_deterministic_max=0.99, - num_steps=99, - elo_scale=400, - seed=None, - progress_bar: bool = False, -): - rng = np.random.default_rng(seed) - - p_deterministics = np.linspace( - start=p_deterministic_min, - stop=p_deterministic_max, - num=num_steps, - ) - - if progress_bar: - from tqdm import tqdm - - p_deterministics = tqdm(p_deterministics) - - for p_deterministic in p_deterministics: - player_1_ids, player_2_ids, player_1_outcomes = simulate_p_deterministic_games( - rng=rng, - num_players=num_players, - num_games=num_games, - p_deterministic=p_deterministic, - ) - - elo_k = approximate_optimal_k( - player_1_ids=player_1_ids, - player_2_ids=player_2_ids, - player_1_outcomes=player_1_outcomes, - min_elo_k=0, - max_elo_k=elo_scale / 2, - elo_scale=elo_scale, - ) - - elo_ratings = update_elo_ratings_p_deterministic( - rng=rng, - elo_ratings=np.zeros(num_players), - num_games=num_games, - p_deterministic=p_deterministic, - elo_k=elo_k, - elo_scale=elo_scale, - inplace=True, - progress_bar=False, - ) - - yield p_deterministic, elo_k, elo_ratings - - -# %% -p_deterministics, elo_ks, elo_ratingss = zip( - *generate_distributions(seed=13, progress_bar=True) -) - -# %% -elo_ratingss_array = np.array(elo_ratingss) -elo_ratingss_array.shape, elo_ratingss_array.dtype - -# %% -elo_ratings_stds = np.std(elo_ratingss_array, axis=1) -elo_ratings_stds.shape, elo_ratings_stds.dtype - -# %% -results = pl.DataFrame( - data=elo_ratingss_array, - schema=[f"elo_{i:03d}" for i in range(elo_ratingss_array.shape[1])], -).select( - pl.Series("p_deterministic", p_deterministics), - pl.Series("elo_k", elo_ks), - pl.Series("elo_ratings_std", elo_ratings_stds), - pl.all(), -) -results.shape - -# %% -results.write_csv("results/p_deterministic.csv", float_precision=5) - -# %% -sns.scatterplot(data=results, x="p_deterministic", y="elo_k") - -# %% -sns.scatterplot(x=results["p_deterministic"], y=np.log(results["elo_k"])) - -# %% -sns.scatterplot(data=results, x="p_deterministic", y="elo_ratings_std") - -# %% -sns.scatterplot(x=results["p_deterministic"], y=np.log(results["elo_ratings_std"])) diff --git 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-jupytext = "*" -mypy = "*" -ruff = "*" -types-tqdm = "*" +description = "Elo implementation and experiments" +authors = [ + { name = "Markus Shepherd", email = "markus@recommend.games" }, +] -[tool.ruff] -target-version = "py312" +requires-python = "==3.13.*" +dependencies = [ + "board-game-merger", + "funcy", + "jmespath", + "matplotlib", + "networkx", + "nevergrad", + "numpy", + "polars[numpy,pandas,pyarrow]", + "requests", + "scikit-learn", + "scipy!=1.14.1", + "scrapy", + "scrapy-extensions>=1.0.1", + "seaborn", + "statsmodels", + "tabulate", + "tqdm", +] -[tool.mypy] -python_version = "3.12" +packages = [ + { include = "elo", from = "python" } +] + +[project.scripts] +elo = "elo.__main__:main" +p_deterministic = "elo.p_deterministic:main" + +[dependency-groups] +dev = [ + "codetiming", + "funcy-typing", + "jupyter-black", + "jupyterlab", + "jupytext", + "maturin", + "mypy", + "ruff", + "scipy-stubs", + "types-jmespath", + "types-networkx", + "types-tqdm", +] [build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +requires = ["maturin>=1.0,<2.0"] +build-backend = "maturin" + +[tool.maturin] +# "extension-module" tells pyo3 we want to build an extension module (skips linking against libpython.so) +features = ["pyo3/extension-module"] +module-name = "elo._rust" +python-packages = ["elo"] +python-source = "python" + +[tool.uv] +cache-keys = [ + { file = "pyproject.toml" }, + { file = "src/**/*.rs" }, + { file = "Cargo.toml" }, + { file = "Cargo.lock" }, +] + +[tool.ruff] +target-version = "py313" + +[tool.mypy] +python_version = "3.13" diff --git a/experiments/elo/elo/__init__.py b/experiments/elo/python/elo/__init__.py similarity index 100% rename from experiments/elo/elo/__init__.py rename to experiments/elo/python/elo/__init__.py diff --git a/experiments/elo/python/elo/__main__.py b/experiments/elo/python/elo/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..c0decf33df147b76d5b4f7df6346d2f3c451ecda --- /dev/null +++ b/experiments/elo/python/elo/__main__.py @@ -0,0 +1,156 @@ +import sys + +import polars as pl +import numpy as np +from numpy import typing as npt + +from elo._rust import ( + calculate_elo_ratings_multi_players_rust, + calculate_elo_ratings_two_players_rust, +) +from elo.elo_ratings import ( + calculate_elo_ratings_multi_players_python, + calculate_elo_ratings_two_players_python, +) +from elo.optimal_k import approximate_optimal_k + + +def matches_from_arrow_file( + file_path: str, + *, + player_count: int = 2, +) -> npt.NDArray[np.int32]: + assert file_path.endswith(".arrow"), "Input file must be an Arrow file (.arrow)" + assert player_count >= 2, "Player count must be at least 2" + + data = ( + pl.scan_ipc(file_path) + .select(pl.col("player_ids").list.to_struct(upper_bound=player_count)) + .unnest("player_ids") + .drop_nulls() + .collect() + ) + print(f"Loaded {len(data)} matches with {player_count} players from {file_path}") + + return data.to_numpy().astype(np.int32) + + +def main(): + from codetiming import Timer + + if len(sys.argv) < 2: + print("Usage: python -m elo __main__.py ") + sys.exit(1) + + file_path = sys.argv[1] + player_count = int(sys.argv[2]) if len(sys.argv) >= 3 else 2 + + matches = matches_from_arrow_file( + file_path=file_path, + player_count=player_count, + ) + + if len(matches) == 0: + print("No matches found in the input file.") + return + + elo_initial = 0.0 + elo_k = 32.0 + elo_scale = 400.0 + + if player_count == 2: + print("Elo ratings (two player Python implementation):") + with Timer( + text="Calculated Elo ratings (Python) in {:.3f} seconds", logger=print + ): + elo_ratings = calculate_elo_ratings_two_players_python( + matches=matches, + elo_initial=elo_initial, + elo_k=elo_k, + elo_scale=elo_scale, + progress_bar=True, + ) + sorted_ratings = sorted(elo_ratings.items(), key=lambda x: x[1], reverse=True) + if len(sorted_ratings) > 20: + sorted_ratings = sorted_ratings[:10] + [(0, 0)] + sorted_ratings[-10:] + for player, rating in sorted_ratings: + print(f"Player {player:10d}:\tElo {rating:10.3f}") + + print("Elo ratings (multi player Python implementation):") + with Timer(text="Calculated Elo ratings (Python) in {:.3f} seconds", logger=print): + elo_ratings = calculate_elo_ratings_multi_players_python( + matches=matches, + elo_initial=elo_initial, + elo_k=elo_k, + elo_scale=elo_scale, + progress_bar=True, + ) + sorted_ratings = sorted(elo_ratings.items(), key=lambda x: x[1], reverse=True) + if len(sorted_ratings) > 20: + sorted_ratings = sorted_ratings[:10] + [(0, 0)] + sorted_ratings[-10:] + for player, rating in sorted_ratings: + print(f"Player {player:10d}:\tElo {rating:10.3f}") + + if player_count == 2: + print("Elo ratings (two player Rust implementation):") + with Timer( + text="Calculated Elo ratings (Rust) in {:.3f} seconds", + logger=print, + ): + elo_ratings = calculate_elo_ratings_two_players_rust( + matches=matches, + elo_initial=elo_initial, + elo_k=elo_k, + elo_scale=elo_scale, + ) + sorted_ratings = sorted(elo_ratings.items(), key=lambda x: x[1], reverse=True) + if len(sorted_ratings) > 20: + sorted_ratings = sorted_ratings[:10] + [(0, 0)] + sorted_ratings[-10:] + for player, rating in sorted_ratings: + print(f"Player {player:10d}:\tElo {rating:10.3f}") + + print("Elo ratings (multi player Rust implementation):") + with Timer(text="Calculated Elo ratings (Rust) in {:.3f} seconds", logger=print): + elo_ratings = calculate_elo_ratings_multi_players_rust( + matches=matches, + elo_initial=elo_initial, + elo_k=elo_k, + elo_scale=elo_scale, + ) + sorted_ratings = sorted(elo_ratings.items(), key=lambda x: x[1], reverse=True) + if len(sorted_ratings) > 20: + sorted_ratings = sorted_ratings[:10] + [(0, 0)] + sorted_ratings[-10:] + for player, rating in sorted_ratings: + print(f"Player {player:10d}:\tElo {rating:10.3f}") + + print("Approximate optimal K (Python implementation):") + with Timer(text="Calculated optimal K (Python) in {:.3f} seconds", logger=print): + optimal_k = approximate_optimal_k( + matches=matches, + two_player_only=(player_count == 2), + min_elo_k=0, + max_elo_k=200, + elo_scale=elo_scale, + max_iterations=None, + x_absolute_tol=None, + python=True, + ) + print(f"Approximate optimal K (Python): {optimal_k:.3f}") + + print("Approximate optimal K (Rust implementation):") + with Timer(text="Calculated optimal K (Rust) in {:.3f} seconds", logger=print): + optimal_k = approximate_optimal_k( + matches=matches, + two_player_only=(player_count == 2), + min_elo_k=0, + max_elo_k=200, + elo_scale=elo_scale, + max_iterations=None, + x_absolute_tol=None, + python=False, + ) + print(f"Approximate optimal K (Rust): {optimal_k:.3f}") + + +if __name__ == "__main__": + main() diff --git a/experiments/elo/python/elo/_rust.pyi b/experiments/elo/python/elo/_rust.pyi new file mode 100644 index 0000000000000000000000000000000000000000..00b1c4092be08b4fd9c60f39419605befee8673c --- /dev/null +++ b/experiments/elo/python/elo/_rust.pyi @@ -0,0 +1,29 @@ +import numpy as np +import numpy.typing as npt + +def approx_optimal_k_rust( + *, + players: npt.NDArray[np.int64], + payoffs: npt.NDArray[np.float64] | None, + row_splits: npt.NDArray[np.int64], + two_player_only: bool, + min_elo_k: np.float64 | float, + max_elo_k: np.float64 | float, + elo_scale: np.float64 | float, + max_iterations: int | None = None, + x_absolute_tol: np.float64 | float | None = None, +) -> np.float64: ... +def calculate_elo_ratings_two_players_rust( + *, + matches: npt.NDArray[np.int32], + elo_initial: np.float64 | float, + elo_k: np.float64 | float, + elo_scale: np.float64 | float, +) -> dict[int, np.float64 | float]: ... +def calculate_elo_ratings_multi_players_rust( + *, + matches: npt.NDArray[np.int32], + elo_initial: np.float64 | float, + elo_k: np.float64 | float, + elo_scale: np.float64 | float, +) -> dict[int, np.float64 | float]: ... diff --git a/experiments/elo/python/elo/bga_spider.py b/experiments/elo/python/elo/bga_spider.py new file mode 100644 index 0000000000000000000000000000000000000000..6ff9563c3ee08c55547a20933663d57a5a5005d5 --- /dev/null +++ b/experiments/elo/python/elo/bga_spider.py @@ -0,0 +1,327 @@ +from __future__ import annotations + +import json +import math +import re +import time +from datetime import datetime, timezone +from typing import Any, TYPE_CHECKING + +import funcy +import jmespath +from scrapy import FormRequest, Request, Selector, Spider +from scrapy.http import Response, TextResponse + +if TYPE_CHECKING: + from collections.abc import Generator, Iterable + + +class BaseFilter: + type_name: str + + def __init__(self, feed_options: dict[str, Any] | None) -> None: + self.feed_options = feed_options + + def accepts(self, item: Any) -> bool: + if "type" in item and item["type"] == self.type_name: + return True + return False + + +class GameFilter(BaseFilter): + type_name = "game" + + +class RankingFilter(BaseFilter): + type_name = "ranking" + + +class MatchFilter(BaseFilter): + type_name = "match" + + +class BgaSpider(Spider): + name = "bga" + base_url = "https://en.boardgamearena.com/" + start_urls = (base_url,) + + custom_settings = { + "COOKIES_ENABLED": True, + "COOKIES_DEBUG": False, + "DOWNLOAD_DELAY": 10, + "CONCURRENT_REQUESTS_PER_DOMAIN": 16, + "LOG_FORMATTER": "scrapy_extensions.QuietLogFormatter", + "FEED_EXPORT_BATCH_ITEM_COUNT": 100_000, + "FEEDS": { + "results/games-%(time)s-%(batch_id)05d.jl": { + "format": "jsonlines", + "item_filter": GameFilter, + "overwrite": False, + "store_empty": False, + }, + "results/rankings-%(time)s-%(batch_id)05d.jl": { + "format": "jsonlines", + "item_filter": RankingFilter, + "overwrite": False, + "store_empty": False, + }, + "results/matches-%(time)s-%(batch_id)05d.jl": { + "format": "jsonlines", + "item_filter": MatchFilter, + "overwrite": False, + "store_empty": False, + }, + }, + "JOBDIR": ".jobs", + } + + request_token_url = "/account/auth/getRequestToken.html" + request_token_path = jmespath.compile("data.request_token") + request_token: str | None = None + + game_list_url = "/gamelist?allGames=" + game_list_regex = re.compile("globalUserInfos=(.+);$", flags=re.MULTILINE) + game_keys: Iterable[str] | None = None + + scrape_rankings = False + ranking_url = "/gamepanel/gamepanel/getRanking.html" + ranking_path = jmespath.compile("data.ranks") + max_rank_scraped = None + ranking_keys: Iterable[str] | None = None + + scrape_matches = True + base_match_url = "/message/board" + match_path = jmespath.compile("data.news") + max_matches_per_page = 9500 + match_keys: Iterable[str] | None = frozenset( + ( + "id", + "timestamp", + "game_id", + "players", + "scraped_at", + "type", + ) + ) + + ordinal_regex = re.compile(r"(\d+)(st|nd|rd|th)|winner|loser", re.IGNORECASE) + integer_regex = re.compile(r"(\d+)") + + def parse(self, response: Response) -> Request: + return FormRequest( + url=response.urljoin(self.request_token_url), + formdata={"bgapp": "bga"}, + callback=self.parse_request_token, + method="POST", + ) + + def parse_request_token(self, response: Response) -> Request | None: + if not isinstance(response, TextResponse): + self.logger.warning("Response <%s> is not a TextResponse", response.url) + return + + payload = response.json() + request_token = self.request_token_path.search(payload) + + if not request_token: + self.logger.error("Request token not found in response <%s>", response.url) + return + + self.request_token = request_token + + return Request( + url=response.urljoin(self.game_list_url), + callback=self.parse_games, + headers={"X-Request-Token": request_token}, + ) + + def parse_games(self, response: Response) -> Generator[dict | Request]: + now = datetime.now(timezone.utc).isoformat(timespec="seconds") + + for text in response.xpath( + "//script[@type = 'text/javascript']/text()" + ).getall(): + match = self.game_list_regex.search(text) + if not match: + continue + + try: + payload = json.loads(match.group(1)) + except json.JSONDecodeError: + continue + + if not isinstance(payload, dict) or "game_list" not in payload: + continue + + for game in payload["game_list"]: + game["type"] = "game" + game["scraped_at"] = now + yield funcy.project(game, self.game_keys) if self.game_keys else game + + if self.scrape_rankings: + yield FormRequest( + url=response.urljoin(self.ranking_url), + method="POST", + formdata={ + "game": str(game["id"]), + "start": "0", + "mode": "elo", + }, + callback=self.parse_ranking, + meta={"game_id": game["id"]}, + priority=0, + headers={"X-Request-Token": self.request_token}, + ) + + if self.scrape_matches: + games_played = int(game.get("games_played", 0)) + priority = games_played // (2 * self.max_matches_per_page) + yield Request( + url=self.build_match_url(response, game["id"]), + method="GET", + callback=self.parse_matches, + meta={"game_id": game["id"]}, + priority=priority, + headers={"X-Request-Token": self.request_token}, + ) + + def parse_ranking(self, response: Response) -> Generator[dict | Request]: + if not isinstance(response, TextResponse): + self.logger.warning("Response <%s> is not a TextResponse", response.url) + return + + now = datetime.now(timezone.utc).isoformat(timespec="seconds") + + game_id = response.meta["game_id"] + payload = response.json() + + entries = self.ranking_path.search(payload) + + if not entries: + self.logger.error("Ranking not found in response <%s>", response.url) + return + + rank_nos = [] + + for entry in entries: + entry["game_id"] = game_id + entry["type"] = "ranking" + entry["scraped_at"] = now + yield ( + funcy.project(entry, self.ranking_keys) if self.ranking_keys else entry + ) + rank_nos.append(int(entry["rank_no"])) + + max_rank_no = max(rank_nos, default=math.inf) + + if not self.max_rank_scraped or max_rank_no < self.max_rank_scraped: + yield response.request.replace( + formdata={ + "game": str(game_id), + "start": str(max_rank_no), + "mode": "elo", + }, + meta={"game_id": game_id}, + priority=response.request.priority - 1, + ) + + def build_match_url( + self, + response: Response, + game_id: int, + from_time: int = None, + from_id: int = None, + ) -> str: + params = { + "type": "lastresult", + "id": str(game_id), + "social": "false", + "per_page": str(self.max_matches_per_page), + "dojo.preventCache": str(int(time.time() * 1000)), + } + + if from_time is not None and from_id is not None: + params["page"] = "0" + params["from_time"] = str(from_time) + params["from_id"] = str(from_id) + + query = "&".join(f"{k}={v}" for k, v in params.items()) + url = f"{self.base_match_url}?{query}" + return response.urljoin(url) + + def parse_matches(self, response: Response) -> Generator[dict[str, Any] | Request]: + if not isinstance(response, TextResponse): + self.logger.warning("Response <%s> is not a TextResponse", response.url) + return + + now = datetime.now(timezone.utc).isoformat(timespec="seconds") + + game_id = response.meta["game_id"] + payload = response.json() + + matches = self.match_path.search(payload) + + if not matches: + self.logger.error("Matches not found in response <%s>", response.url) + return + + last_id = None + last_timestamp = None + + for match in matches: + match["game_id"] = game_id + match["type"] = "match" + match["scraped_at"] = now + try: + match["players"] = list(self.parse_match_html(match["html"])) + except Exception: + match["players"] = None + last_id = match.get("id") + last_timestamp = match.get("timestamp") + yield funcy.project(match, self.match_keys) if self.match_keys else match + + if last_id and last_timestamp: + yield Request( + url=self.build_match_url(response, game_id, last_timestamp, last_id), + method="GET", + callback=self.parse_matches, + meta={"game_id": game_id}, + priority=response.request.priority - 1, + headers={"X-Request-Token": self.request_token}, + ) + + def parse_match_html(self, html: str) -> Generator[dict[str, Any]]: + selector = Selector(text=html, type="html") + + player_divs = selector.css("div.board-score-entry") + num_players = len(player_divs) + + for player_div in player_divs: + rank_text = player_div.xpath("text()[1]").get() or "" + match = self.ordinal_regex.search(rank_text) + if not match: + place = None + elif match.group(1): + place = int(match.group(1)) + elif match.group(0).lower() == "winner": + place = 1 + elif match.group(0).lower() == "loser": + place = num_players + else: + raise ValueError(f"Invalid place: {place}") + + player_link = player_div.css("a.playername") + player_href = player_link.xpath("@href").get() or "" + player_id = int(player_href.split("=")[-1]) if "=" in player_href else None + + player_name = player_link.xpath("text()").get() + score_text = player_link.xpath("following-sibling::text()[1]").get() or "" + match = self.integer_regex.search(score_text) + score = int(match.group(1)) if match else None + + yield { + "player_id": player_id, + "player_name": player_name, + "place": place, + "score": score, + } diff --git a/experiments/elo/elo/data.py b/experiments/elo/python/elo/data.py similarity index 81% rename from experiments/elo/elo/data.py rename to experiments/elo/python/elo/data.py index d374336103b54d3243333a55a1412b193d5b7c2b..0ccfb0d9fe06be7622b71af48ff126a00fcfe761 100644 --- a/experiments/elo/elo/data.py +++ b/experiments/elo/python/elo/data.py @@ -86,6 +86,51 @@ def merge_rankings( ) +def merge_matches( + matches_path: str | Path | list[str] | list[Path], + output_path: str | Path, + overwrite: bool = False, + progress_bar: bool = False, +) -> None: + schema = { + "id": pl.String, + "timestamp": pl.String, + "game_id": pl.Int64, + "players": pl.List( + pl.Struct( + { + "player_id": pl.Int64, + "player_name": pl.String, + "place": pl.Int64, + "score": pl.Float64, + } + ) + ), + "scraped_at": pl.String, + } + + config = MergeConfig( + schema=pl.Schema(schema), + in_paths=matches_path, + out_path=output_path, + key_col="id", + latest_col=pl.col("scraped_at").str.to_datetime(time_zone="UTC"), + sort_fields=[ + "game_id", + "timestamp", + "id", + ], + ) + + merge_files( + merge_config=config, + overwrite=overwrite, + drop_empty=True, + sort_keys=True, + progress_bar=progress_bar, + ) + + def load_data( *, bgg_games_path: str | Path, @@ -194,3 +239,9 @@ if __name__ == "__main__": overwrite=True, progress_bar=True, ) + merge_matches( + matches_path="results/matches-*.jl", + output_path="matches.jl", + overwrite=True, + progress_bar=True, + ) diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py new file mode 100644 index 0000000000000000000000000000000000000000..dd41f174937e2bd8af9aca93b71e8aa0149983e6 --- /dev/null +++ b/experiments/elo/python/elo/elo_ratings.py @@ -0,0 +1,492 @@ +from __future__ import annotations + +import itertools +import warnings +from abc import ABC, abstractmethod +from collections import defaultdict +from collections.abc import Mapping +from typing import TYPE_CHECKING, override + +import numpy as np +from numpy import typing as npt +from scipy.special import comb + +if TYPE_CHECKING: + from collections.abc import Iterable, Generator + from typing import Any + +NP_ZERO = np.float64(0.0) +NP_ONE = np.float64(1.0) +NP_TEN = np.float64(10.0) +NP_THIRTYTWO = np.float64(32.0) +NP_FOURHUNDRED = np.float64(400.0) + + +def elo_probability( + diff: np.float64 | float, + scale: np.float64 | float = NP_FOURHUNDRED, +) -> np.float64: + return NP_ONE / (NP_ONE + np.power(NP_TEN, -np.float64(diff) / np.float64(scale))) + + +class EloRatingSystem[ID_TYPE](ABC): + elo_initial: np.float64 = NP_ZERO + elo_k: np.float64 = NP_THIRTYTWO + elo_scale: np.float64 = NP_FOURHUNDRED + + elo_ratings: defaultdict[ID_TYPE, np.float64] + + def __init__( + self, + *, + init_elo_ratings: Mapping[ID_TYPE, np.float64 | float] | None = None, + elo_initial: np.float64 | float = NP_ZERO, + elo_k: np.float64 | float = NP_THIRTYTWO, + elo_scale: np.float64 | float = NP_FOURHUNDRED, + ) -> None: + self.elo_initial = np.float64(elo_initial) + self.elo_k = np.float64(elo_k) + self.elo_scale = np.float64(elo_scale) + + init_elo_ratings_converted: dict[ID_TYPE, np.float64] = ( + {k: np.float64(v) for k, v in init_elo_ratings.items()} + if init_elo_ratings + else {} + ) + self.elo_ratings = defaultdict( + lambda: self.elo_initial, + init_elo_ratings_converted, + ) + + @abstractmethod + def update_elo_ratings( + self, + players: Mapping[ID_TYPE, np.float64 | float] | Iterable[ID_TYPE], + ) -> npt.NDArray[np.float64]: ... + + @abstractmethod + def update_elo_ratings_batch( + self, + matches: Iterable[Mapping[ID_TYPE, np.float64 | float] | Iterable[ID_TYPE]], + *, + full_results: bool = False, + progress_bar: bool = False, + tqdm_kwargs: dict[str, Any] | None = None, + ) -> npt.NDArray[np.float64] | None: ... + + @abstractmethod + def probability_matrix( + self, + players: Iterable[ID_TYPE], + ) -> npt.NDArray[np.float64]: ... + + @abstractmethod + def expected_outcome( + self, + players: Iterable[ID_TYPE], + *, + rank_payoffs: npt.NDArray[np.float64] | None = None, + ) -> npt.NDArray[np.float64]: ... + + +class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): + @override + def expected_outcome( + self, + players: Iterable[ID_TYPE], + *, + rank_payoffs: npt.NDArray[np.float64] | None = None, + ) -> npt.NDArray[np.float64]: + if rank_payoffs is not None: + warnings.warn("rank_payoffs are ignored for two-player elo") + + player_1, player_2 = players + elo_1 = self.elo_ratings[player_1] + elo_2 = self.elo_ratings[player_2] + diff = elo_1 - elo_2 + prob = elo_probability(diff, self.elo_scale) + return np.asarray([prob, 1 - prob], dtype=np.float64) + + @override + def probability_matrix( + self, + players: Iterable[ID_TYPE], + ) -> npt.NDArray[np.float64]: + expected_outcome = self.expected_outcome(players) + return np.asarray([expected_outcome, 1 - expected_outcome], dtype=np.float64) + + @override + def update_elo_ratings( + self, + players: Mapping[ID_TYPE, np.float64 | float] | Iterable[ID_TYPE], + ) -> npt.NDArray[np.float64]: + if isinstance(players, Mapping): + player_ids = tuple(players.keys()) + outcomes = np.asarray(list(players.values()), dtype=np.float64) + else: + player_ids = tuple(players) + outcomes = np.asarray([1.0, 0.0], dtype=np.float64) + + diffs = outcomes - self.expected_outcome(player_ids) + for player_id, diff in zip(player_ids, diffs): + self.elo_ratings[player_id] += self.elo_k * diff + + return diffs + + def _update_elo_ratings_batch( + self, + matches: Iterable[Mapping[ID_TYPE, np.float64 | float] | Iterable[ID_TYPE]], + ) -> Generator[npt.NDArray[np.float64]]: + for match in matches: + yield self.update_elo_ratings(match) + + @override + def update_elo_ratings_batch( + self, + matches: Iterable[Mapping[ID_TYPE, np.float64 | float] | Iterable[ID_TYPE]], + *, + full_results: bool = False, + progress_bar: bool = False, + tqdm_kwargs: dict[str, Any] | None = None, + ) -> npt.NDArray[np.float64] | None: + if progress_bar: + from tqdm import tqdm + + tqdm_kwargs = tqdm_kwargs or {} + matches = tqdm(matches, desc="Updating elo ratings", **tqdm_kwargs) + + if full_results: + return np.asarray( + list(self._update_elo_ratings_batch(matches)), + dtype=np.float64, + ) + + for _ in self._update_elo_ratings_batch(matches): + pass + return None + + +class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): + """ + Rank-ordered-logit Elo with MC/DP for larger fields. + """ + + def __init__( + self, + *, + init_elo_ratings: Mapping[ID_TYPE, np.float64 | float] | None = None, + elo_initial: np.float64 | float = NP_ZERO, + elo_k: np.float64 | float = NP_THIRTYTWO, + elo_scale: np.float64 | float = NP_FOURHUNDRED, + max_permutations: int = 6, + max_dynamic_programming: int = 12, + monte_carlo_samples: int = 5_000, + rng: np.random.Generator | int | None = None, + ) -> None: + super().__init__( + init_elo_ratings=init_elo_ratings, + elo_initial=elo_initial, + elo_k=elo_k, + elo_scale=elo_scale, + ) + self.max_permutations = max_permutations + self.max_dynamic_programming = max_dynamic_programming + self.monte_carlo_samples = monte_carlo_samples + self._rng = ( + rng if isinstance(rng, np.random.Generator) else np.random.default_rng(rng) + ) + + def _calculate_probability_matrix_two_players( + self, + players: tuple[ID_TYPE, ID_TYPE], + ) -> npt.NDArray[np.float64]: + rating_a = self.elo_ratings[players[0]] + rating_b = self.elo_ratings[players[1]] + diff = rating_a - rating_b + prob_a = elo_probability(diff, self.elo_scale) + prob_b = 1 - prob_a + return np.asarray([[prob_a, prob_b], [prob_b, prob_a]], dtype=np.float64) + + def _calculate_probability_matrix_permutations( + self, + players: tuple[ID_TYPE, ...], + *, + rtol: np.float64 | float = np.float64(1e-3), + atol: np.float64 | float = np.float64(1e-4), + ) -> npt.NDArray[np.float64]: + ratings = np.asarray([self.elo_ratings[p] for p in players], dtype=np.float64) + weights = np.power(NP_TEN, ratings / self.elo_scale) + n = len(players) + probs = np.zeros((n, n), dtype=np.float64) + + for perm in itertools.permutations(range(n)): + denom = weights.sum(dtype=np.float64) + p_perm = NP_ONE + for player in range(n - 1): + w = weights[perm[player]] + p_perm *= w / denom + denom -= w + + for position, player_idx in enumerate(perm): + probs[player_idx, position] += p_perm + + col_sum = probs.sum(axis=0, dtype=np.float64) + row_sum = probs.sum(axis=1, dtype=np.float64) + assert np.allclose(col_sum, NP_ONE, rtol=rtol, atol=atol), ( + f"Probabilities must sum to 1 over players per position (got {col_sum})" + ) + assert np.allclose(row_sum, NP_ONE, rtol=rtol, atol=atol), ( + f"Probabilities must sum to 1 over positions per player (got {row_sum})" + ) + return probs + + def _calculate_probability_matrix_dynamic_programming( + self, + players: tuple[ID_TYPE, ...], + *, + rtol: np.float64 | float = np.float64(1e-3), + atol: np.float64 | float = np.float64(1e-4), + ) -> npt.NDArray[np.float64]: + n = len(players) + ratings = np.asarray([self.elo_ratings[p] for p in players], dtype=np.float64) + w = np.power(NP_TEN, ratings / self.elo_scale) + w_total = w.sum(dtype=np.float64) + + probs = np.zeros((n, n), dtype=np.float64) + for i in range(n): + other_idx = [j for j in range(n) if j != i] + m = n - 1 + other_w = w[other_idx] + + num_masks = 1 << m + subset_sum = np.zeros(num_masks, dtype=np.float64) + for mask in range(1, num_masks): + lsb = mask & -mask + bit = lsb.bit_length() - 1 + subset_sum[mask] = subset_sum[mask ^ lsb] + other_w[bit] + + p = np.zeros(m + 1, dtype=np.float64) + wi = w[i] # NumPy scalar float64 + + for mask in range(num_masks): + tsize = int(mask.bit_count()) + denom = w_total - subset_sum[mask] + base = wi / denom # NumPy scalar + if tsize <= m: + ks = np.arange(tsize, m + 1, dtype=np.int64) + coeffs = comb(m - tsize, ks - tsize, exact=False) + signs = np.where(((ks + tsize) & 1) == 1, -1.0, 1.0).astype( + np.float64 + ) + p[tsize : m + 1] += (base * signs * coeffs).astype(np.float64) + + p = np.clip(p, 0.0, 1.0) + s = p.sum(dtype=np.float64) + if not np.isclose(s, 1.0, rtol=rtol, atol=atol): + if s > 0: + p /= s + else: + p.fill(1.0 / (m + 1)) + + probs[i, : m + 1] = p + + col_sum = probs.sum(axis=0, dtype=np.float64) + row_sum = probs.sum(axis=1, dtype=np.float64) + assert np.allclose(col_sum, NP_ONE, rtol=rtol, atol=atol) + assert np.allclose(row_sum, NP_ONE, rtol=rtol, atol=atol) + return probs + + def _calculate_probability_matrix_monte_carlo( + self, + players: tuple[ID_TYPE, ...], + ) -> npt.NDArray[np.float64]: + n = len(players) + ratings = np.asarray([self.elo_ratings[p] for p in players], dtype=np.float64) + weights = np.power(NP_TEN, ratings / self.elo_scale) + counts = np.zeros((n, n), dtype=np.float64) + + idx_all = np.arange(n, dtype=np.int64) + for _ in range(self.monte_carlo_samples): + remaining = idx_all.tolist() + order: list[int] = [] + w = weights.copy() + while remaining: + rem_arr = np.array(remaining, dtype=np.int64) + probs = w[rem_arr] + tot = probs.sum(dtype=np.float64) + probs = np.divide( + probs, + tot, + out=np.full_like(probs, NP_ONE / probs.size), + where=tot > NP_ZERO, + ) + chosen = int(self._rng.choice(rem_arr, p=probs)) + order.append(chosen) + remaining.remove(chosen) + w[chosen] = NP_ZERO + for pos, player_idx in enumerate(order): + counts[player_idx, pos] += NP_ONE + + probs = np.clip(counts / np.float64(self.monte_carlo_samples), NP_ZERO, NP_ONE) + col_sums = probs.sum(axis=0, keepdims=True) + col_sums[col_sums == NP_ZERO] = NP_ONE + return probs / col_sums + + @override + def probability_matrix( + self, + players: Iterable[ID_TYPE], + ) -> npt.NDArray[np.float64]: + players = tuple(players) + n = len(players) + + if n < 2: + raise ValueError("At least 2 players are required") + + if n == 2: + assert len(players) == 2 + return self._calculate_probability_matrix_two_players(players) + + if n <= self.max_permutations: + return self._calculate_probability_matrix_permutations(players) + + if n <= self.max_dynamic_programming: + return self._calculate_probability_matrix_dynamic_programming(players) + + return self._calculate_probability_matrix_monte_carlo(players) + + @override + def expected_outcome( + self, + players: Iterable[ID_TYPE], + *, + rank_payoffs: npt.NDArray[np.float64] | None = None, + ) -> npt.NDArray[np.float64]: + players = tuple(players) + if rank_payoffs is None: + rank_payoffs = np.arange(len(players) - 1, -1, -1, dtype=np.float64) + + assert rank_payoffs.ndim == 1, "Rank payoffs must be a 1D array" + assert len(rank_payoffs) == len(players), ( + "Rank payoffs must be the same length as the number of players" + ) + probs = self.probability_matrix(players) + return probs @ rank_payoffs + + @override + def update_elo_ratings( + self, + players: Mapping[ID_TYPE, np.float64 | float] | Iterable[ID_TYPE], + ) -> npt.NDArray[np.float64]: + if isinstance(players, Mapping): + player_ids = tuple(players.keys()) + payoffs = np.asarray(list(players.values()), dtype=np.float64) + else: + player_ids = tuple(players) + payoffs = np.arange(len(player_ids) - 1, -1, -1, dtype=np.float64) + + assert np.all(payoffs >= 0), "Payoffs must be non-negative" + assert np.any(payoffs > 0), "At least one payoff must be positive" + + max_payoff = payoffs.max() + expected_payoffs = self.expected_outcome(player_ids, rank_payoffs=payoffs) + diffs = (payoffs - expected_payoffs) / max_payoff + for player_id, diff in zip(player_ids, diffs): + self.elo_ratings[player_id] += self.elo_k * diff + + return diffs + + def _update_elo_ratings_batch( + self, + matches: Iterable[Mapping[ID_TYPE, np.float64 | float] | Iterable[ID_TYPE]], + ) -> Generator[npt.NDArray[np.float64]]: + for match in matches: + yield self.update_elo_ratings(match) + + @override + def update_elo_ratings_batch( + self, + matches: Iterable[Mapping[ID_TYPE, np.float64 | float] | Iterable[ID_TYPE]], + *, + full_results: bool = False, + progress_bar: bool = False, + tqdm_kwargs: dict[str, Any] | None = None, + ) -> npt.NDArray[np.float64] | None: + if progress_bar: + from tqdm import tqdm + + tqdm_kwargs = tqdm_kwargs or {} + matches = tqdm(matches, **tqdm_kwargs) + + if full_results: + return _padded_numpy_array(self._update_elo_ratings_batch(matches)) + + for _ in self._update_elo_ratings_batch(matches): + pass + return None + + +def _padded_numpy_array( + arrays: Iterable[npt.NDArray[np.float64]], + *, + pad_value: np.float64 | float = np.nan, + dtype: np.dtype | None = None, +) -> npt.NDArray[np.float64]: + arrays = tuple(arrays) + max_len = max((a.shape[0] for a in arrays), default=0) + result = np.full( + shape=(len(arrays), max_len), + fill_value=pad_value, + dtype=dtype or np.float64, + ) + + for i, a in enumerate(arrays): + result[i, : a.shape[0]] = a + + return result + + +def calculate_elo_ratings_two_players_python( + *, + matches: npt.NDArray[np.int32], + elo_initial: np.float64 | float = NP_ZERO, + elo_k: np.float64 | float = NP_THIRTYTWO, + elo_scale: np.float64 | float = NP_FOURHUNDRED, + progress_bar: bool = False, +) -> dict[int, np.float64]: + assert matches.ndim == 2 and matches.shape[1] == 2, ( + "Matches must be a 2D array with shape (num_matches, 2)" + ) + elo: TwoPlayerElo = TwoPlayerElo( + elo_initial=elo_initial, + elo_k=elo_k, + elo_scale=elo_scale, + ) + elo.update_elo_ratings_batch(matches, progress_bar=progress_bar) + return dict(elo.elo_ratings) + + +def calculate_elo_ratings_multi_players_python( + *, + matches: npt.NDArray[np.int32], + elo_initial: np.float64 | float = NP_ZERO, + elo_k: np.float64 | float = NP_THIRTYTWO, + elo_scale: np.float64 | float = NP_FOURHUNDRED, + progress_bar: bool = False, +) -> dict[int, np.float64]: + assert matches.ndim == 2 and matches.shape[1] >= 2, ( + "Matches must be a 2D array with shape (num_matches, num_players>=2)" + ) + elo: RankOrderedLogitElo = RankOrderedLogitElo( + elo_initial=elo_initial, + elo_k=elo_k, + elo_scale=elo_scale, + ) + num_matches = len(matches) + matches_iter = (match[~np.isnan(match)].astype(np.int32) for match in matches) + elo.update_elo_ratings_batch( + matches_iter, + progress_bar=progress_bar, + tqdm_kwargs={"total": num_matches}, + ) + return dict(elo.elo_ratings) diff --git a/experiments/elo/python/elo/game_stats.py b/experiments/elo/python/elo/game_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..521868123bef1828048759117b395796cef6ab2c --- /dev/null +++ b/experiments/elo/python/elo/game_stats.py @@ -0,0 +1,147 @@ +from __future__ import annotations + +import itertools +import json +import logging +import sys +from pathlib import Path +from typing import TYPE_CHECKING + +import networkx as nx +import polars as pl + + +if TYPE_CHECKING: + from typing import Generator, Any + +LOGGER = logging.getLogger(__name__) + + +def game_stats( + matches_path: Path | str, + *, + threshold_matches_regulars: int = 25, + progress_bar: bool = False, +) -> dict[str, int]: + matches_path = Path(matches_path).resolve() + LOGGER.info("Reading matches from %s", matches_path) + + data = pl.read_ipc(matches_path, memory_map=False) + num_all_matches = len(data) + + graph = nx.Graph() + + player_ids_col = data["player_ids"] + if progress_bar: + from tqdm import tqdm + + player_ids_col = tqdm(player_ids_col) + + for player_ids in player_ids_col: + graph.add_edges_from(itertools.combinations(player_ids, 2)) + + num_all_players = graph.number_of_nodes() + largest_community = max(nx.connected_components(graph), key=len) + num_players = len(largest_community) + + data = data.filter( + pl.col("player_ids").list.eval(pl.element().is_in(largest_community)).list.any() + ) + num_matches = len(data) + + result = ( + data.lazy() + .select(pl.col("player_ids").explode().value_counts()) + .unnest("player_ids") + .select( + num_matches_max=pl.col("count").max(), + num_regular_players=(pl.col("count") >= threshold_matches_regulars).sum(), + ) + .collect() + .to_dicts()[0] + ) + + result["num_all_matches"] = num_all_matches + result["num_matches"] = num_matches + result["num_all_players"] = num_all_players + result["num_players"] = num_players + + return result + + +def _games_stats( + *, + games_path: Path | str, + matches_dir: Path | str, + threshold_matches_regulars: int = 25, + progress_bar: bool = False, +) -> Generator[dict[str, Any]]: + games_path = Path(games_path).resolve() + matches_dir = Path(matches_dir).resolve() + + LOGGER.info("Reading games from %s", games_path) + LOGGER.info("Reading matches from %s", matches_dir) + + games = pl.read_ndjson(games_path) + games_dicts = games.to_dicts() + + for game in games_dicts: + LOGGER.info("Processing game %s", game["display_name_en"]) + game_id = game["id"] + matches_path = matches_dir / f"{game_id}.arrow" + if not matches_path.exists(): + LOGGER.warning("Matches file %s does not exist", matches_path) + yield game + continue + + try: + stats = game_stats( + matches_path, + threshold_matches_regulars=threshold_matches_regulars, + progress_bar=progress_bar, + ) + except Exception: + LOGGER.exception("Error processing game %s", game["display_name_en"]) + yield game + else: + yield game | stats + + LOGGER.info("Done.") + + +def games_stats( + *, + games_path: Path | str, + matches_dir: Path | str, + output_path: Path | str, + threshold_matches_regulars: int = 25, + progress_bar: bool = False, +): + output_path = Path(output_path).resolve() + output_path.parent.mkdir(parents=True, exist_ok=True) + + LOGGER.info("Writing games stats to %s", output_path) + + with output_path.open("w") as file: + for game in _games_stats( + games_path=games_path, + matches_dir=matches_dir, + threshold_matches_regulars=threshold_matches_regulars, + progress_bar=progress_bar, + ): + file.write(json.dumps(game) + "\n") + + +def _main(): + logging.basicConfig(level=logging.INFO, stream=sys.stdout) + games_stats( + games_path="games.jl", + matches_dir="results/arrow/matches", + output_path="games_stats.jl", + threshold_matches_regulars=25, + progress_bar=True, + ) + + +if __name__ == "__main__": + _main() diff --git a/experiments/elo/python/elo/optimal_k.py b/experiments/elo/python/elo/optimal_k.py new file mode 100644 index 0000000000000000000000000000000000000000..611c4b4c68b4ae9dae30d811774997d97968e516 --- /dev/null +++ b/experiments/elo/python/elo/optimal_k.py @@ -0,0 +1,197 @@ +from __future__ import annotations + +from collections.abc import Mapping +import functools +import itertools +from typing import TYPE_CHECKING + +import numpy as np +from numpy import typing as npt +from scipy.optimize import minimize_scalar + +from elo._rust import approx_optimal_k_rust +from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo + +if TYPE_CHECKING: + from collections.abc import Iterable + + +def _calculate_loss[ID_TYPE]( + *, + elo: EloRatingSystem[ID_TYPE], + matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], + progress_bar: bool = False, +) -> np.float64: + errors = elo.update_elo_ratings_batch( + matches, + full_results=True, + progress_bar=progress_bar, + ) + assert errors is not None + return np.nanmean(errors**2, dtype=np.float64) + + +def approximate_optimal_k_python[ID_TYPE]( + *, + matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], + two_player_only: bool = False, + min_elo_k: float = 0, + max_elo_k: float = 200, + elo_scale: float = 400, + max_iterations: int | None = None, + x_absolute_tol: float | None = None, +) -> np.float64: + match_list: list[Mapping[ID_TYPE, float]] = [ + dict(zip((m := tuple(match)), range(len(m) - 1, -1, -1))) + if not isinstance(match, Mapping) + else match + for match in matches + ] + + @functools.cache + def _loss(elo_k: float) -> np.float64: + elo: EloRatingSystem[ID_TYPE] = ( + TwoPlayerElo(elo_k=elo_k, elo_scale=elo_scale) + if two_player_only + else RankOrderedLogitElo(elo_k=elo_k, elo_scale=elo_scale) + ) + + return _calculate_loss( + elo=elo, + matches=match_list, + progress_bar=False, + ) + + options: dict[str, float | int] = {} + if max_iterations is not None: + options["maxiter"] = max_iterations + if x_absolute_tol is not None: + options["xatol"] = x_absolute_tol + + result = minimize_scalar( # type: ignore[call-overload] + fun=_loss, + method="bounded", + bounds=(min_elo_k, max_elo_k), + options=options, + ) + + return result.x + + +def _match_mapping_to_lists[ID_TYPE]( + matches: Iterable[Mapping[ID_TYPE, float]], +) -> tuple[list[ID_TYPE], list[float], list[int]]: + players: list[ID_TYPE] = [] + payoffs: list[float] = [] + row_splits: list[int] = [0] + + for match in matches: + for player, payoff in match.items(): + players.append(player) + payoffs.append(payoff) + row_splits.append(len(players)) + + return players, payoffs, row_splits + + +def _match_iterable_to_lists[ID_TYPE]( + matches: Iterable[Iterable[ID_TYPE]], +) -> tuple[list[ID_TYPE], list[int]]: + players: list[ID_TYPE] = [] + row_splits: list[int] = [0] + + for match in matches: + for player in match: + players.append(player) + row_splits.append(len(players)) + + return players, row_splits + + +def _player_ids_to_ints[ID_TYPE]( + players: list[ID_TYPE], +) -> npt.NDArray[np.int64]: + unique_players = tuple(frozenset(players)) + mapping = dict(zip(unique_players, range(len(unique_players)))) + return np.asarray([mapping[p] for p in players], dtype=np.int64) + + +def _matches_to_arrays[ID_TYPE]( + matches: Iterable[Mapping[ID_TYPE, float]] + | Iterable[Iterable[ID_TYPE]] + | npt.NDArray[np.int64], +) -> tuple[ + npt.NDArray[np.int64], + npt.NDArray[np.float64] | None, + npt.NDArray[np.int64], +]: + if ( + isinstance(matches, np.ndarray) + and matches.dtype in (np.int8, np.int16, np.int32, np.int64) + and matches.ndim == 2 + ): + return ( + matches.flatten().astype(np.int64), + None, + np.arange(0, matches.size + 1, matches.shape[1], dtype=np.int64), + ) + + matches_iter = iter(matches) + first_match = next(matches_iter) + if isinstance(first_match, Mapping): + match_mappings: Iterable[Mapping[ID_TYPE, float]] = itertools.chain( + (first_match,), + matches_iter, # type: ignore[arg-type] + ) + players, payoffs, row_splits = _match_mapping_to_lists(match_mappings) + payoffs_array = np.asarray(payoffs, dtype=np.float64) + else: + match_iterables: Iterable[Iterable[ID_TYPE]] = itertools.chain( + (first_match,), + matches_iter, + ) + players, row_splits = _match_iterable_to_lists(match_iterables) + payoffs_array = None + + players_array = _player_ids_to_ints(players) + row_splits_array = np.asarray(row_splits, dtype=np.int64) + + return players_array, payoffs_array, row_splits_array + + +def approximate_optimal_k[ID_TYPE]( + *, + matches: Iterable[Mapping[ID_TYPE, float]] + | Iterable[Iterable[ID_TYPE]] + | npt.NDArray[np.int64], + two_player_only: bool = False, + min_elo_k: float = 0, + max_elo_k: float = 200, + elo_scale: float = 400, + max_iterations: int | None = None, + x_absolute_tol: float | None = None, + python: bool = False, +) -> np.float64: + if python: + return approximate_optimal_k_python( + matches=matches, + two_player_only=two_player_only, + min_elo_k=min_elo_k, + max_elo_k=max_elo_k, + elo_scale=elo_scale, + max_iterations=max_iterations, + x_absolute_tol=x_absolute_tol, + ) + + players_array, payoffs_array, row_splits_array = _matches_to_arrays(matches) + return approx_optimal_k_rust( + players=players_array, + payoffs=payoffs_array, + row_splits=row_splits_array, + two_player_only=two_player_only, + min_elo_k=min_elo_k, + max_elo_k=max_elo_k, + elo_scale=elo_scale, + max_iterations=max_iterations, + x_absolute_tol=x_absolute_tol, + ) diff --git a/experiments/elo/python/elo/p_deterministic.py b/experiments/elo/python/elo/p_deterministic.py new file mode 100644 index 0000000000000000000000000000000000000000..f38a0a08638b668d443124a3ff12f05776149b36 --- /dev/null +++ b/experiments/elo/python/elo/p_deterministic.py @@ -0,0 +1,475 @@ +from __future__ import annotations + +import argparse +import dataclasses +import itertools +import logging +import sys +import time +from collections.abc import Iterable +from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path +from typing import TYPE_CHECKING + +import numpy as np +import polars as pl + +from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo +from elo.optimal_k import approximate_optimal_k + +if TYPE_CHECKING: + from collections.abc import Generator + from concurrent.futures import Future + +LOGGER = logging.getLogger(__name__) + + +def simulate_p_deterministic_matches( + *, + rng: np.random.Generator, + num_players: int, + num_matches: int, + players_per_match: int = 2, + p_deterministic: float, +) -> np.ndarray: + assert 0 <= p_deterministic <= 1, "p_deterministic must be between 0 and 1" + assert num_players > 1, "num_players must be greater than 1" + assert num_matches > 0, "num_games must be greater than 0" + assert 2 <= players_per_match <= num_players, ( + "players_per_match must be between 2 and num_players" + ) + + players = np.arange(num_players) + random_outcomes = np.array( + [ + rng.choice( + players, + size=players_per_match, + replace=False, + ) + for _ in range(num_matches) + ] + ) + + if p_deterministic == 0: + return random_outcomes + + deterministic_outcomes = np.sort(random_outcomes, axis=1) + + if p_deterministic == 1: + return deterministic_outcomes + + mask = rng.random(size=num_matches) < p_deterministic + return np.where(mask[:, np.newaxis], deterministic_outcomes, random_outcomes) + + +def update_elo_ratings_p_deterministic( + *, + rng: np.random.Generator, + elo: EloRatingSystem | None = None, + num_players: int, + num_matches: int, + players_per_match: int = 2, + p_deterministic: float, + progress_bar: bool = False, +) -> EloRatingSystem: + if elo is None: + elo = TwoPlayerElo() if players_per_match == 2 else RankOrderedLogitElo() + + matches = simulate_p_deterministic_matches( + rng=rng, + num_players=num_players, + num_matches=num_matches, + players_per_match=players_per_match, + p_deterministic=p_deterministic, + ) + + payoffs = np.arange(players_per_match - 1, -1, -1, dtype=float) + + elo.update_elo_ratings_batch( + matches=(dict(zip(match, payoffs)) for match in matches), + full_results=False, + progress_bar=progress_bar, + tqdm_kwargs={"total": num_matches}, + ) + + return elo + + +@dataclasses.dataclass(frozen=True, kw_only=True) +class ExperimentResult: + num_players: int + num_matches: int + players_per_match: int + + p_deterministic: float + elo_k: float + elo_scale: float + + mean: float + std_dev: float + p00: float + p01: float + p25: float + p50: float + p75: float + p99: float + p100: float + + +def p_deterministic_experiment( + *, + rng: np.random.Generator, + num_players: int, + num_matches: int, + players_per_match: int = 2, + p_deterministic: float, + elo_scale: float = 400, + max_iterations: int | None = None, + x_absolute_tol: float | None = None, + progress_bar: bool = False, +) -> ExperimentResult: + if p_deterministic < 0 or p_deterministic > 1: + raise ValueError("p_deterministic must be between 0 and 1") + + if np.isclose(p_deterministic, 0.0): + return ExperimentResult( + num_players=num_players, + num_matches=num_matches, + players_per_match=players_per_match, + p_deterministic=0.0, + elo_k=0.0, + elo_scale=elo_scale, + mean=0.0, + std_dev=0.0, + p00=0.0, + p01=0.0, + p25=0.0, + p50=0.0, + p75=0.0, + p99=0.0, + p100=0.0, + ) + + if np.isclose(p_deterministic, 1.0): + return ExperimentResult( + num_players=num_players, + num_matches=num_matches, + players_per_match=players_per_match, + p_deterministic=1.0, + elo_k=float("inf"), + elo_scale=elo_scale, + mean=0.0, + std_dev=float("inf"), + p00=-float("inf"), + p01=-float("inf"), + p25=-float("inf"), + p50=0.0, + p75=float("inf"), + p99=float("inf"), + p100=float("inf"), + ) + + matches = simulate_p_deterministic_matches( + rng=rng, + num_players=num_players, + num_matches=num_matches, + players_per_match=players_per_match, + p_deterministic=p_deterministic, + ) + + try: + elo_k = approximate_optimal_k( + matches=matches, + two_player_only=players_per_match == 2, + min_elo_k=0, + max_elo_k=elo_scale / 2, + elo_scale=elo_scale, + max_iterations=max_iterations, + x_absolute_tol=x_absolute_tol, + ) + except Exception: + return ExperimentResult( + num_players=num_players, + num_matches=num_matches, + players_per_match=players_per_match, + p_deterministic=p_deterministic, + elo_k=float("nan"), + elo_scale=elo_scale, + mean=float("nan"), + std_dev=float("nan"), + p00=float("nan"), + p01=float("nan"), + p25=float("nan"), + p50=float("nan"), + p75=float("nan"), + p99=float("nan"), + p100=float("nan"), + ) + + elo = update_elo_ratings_p_deterministic( + rng=rng, + elo=TwoPlayerElo(elo_k=elo_k, elo_scale=elo_scale) + if players_per_match == 2 + else RankOrderedLogitElo(elo_k=elo_k, elo_scale=elo_scale), + num_players=num_players, + num_matches=num_matches, + players_per_match=players_per_match, + p_deterministic=p_deterministic, + progress_bar=progress_bar, + ) + elo_ratings = np.array(list(elo.elo_ratings.values())) + + return ExperimentResult( + num_players=num_players, + num_matches=num_matches, + players_per_match=players_per_match, + p_deterministic=p_deterministic, + elo_k=elo_k, + elo_scale=elo_scale, + mean=elo_ratings.mean(), + std_dev=elo_ratings.std(), + p00=elo_ratings.min(), + p01=np.percentile(elo_ratings, 1), + p25=np.percentile(elo_ratings, 25), + p50=np.percentile(elo_ratings, 50), + p75=np.percentile(elo_ratings, 75), + p99=np.percentile(elo_ratings, 99), + p100=elo_ratings.max(), + ) + + +def _make_iterable[T](value: T | Iterable[T]) -> Iterable[T]: + return value if isinstance(value, Iterable) else (value,) + + +def _p_deterministic_experiment_futures( + *, + executor: ProcessPoolExecutor, + seed: int, + num_playerss: Iterable[int] | int, + num_matchess: Iterable[int] | int, + players_per_matchs: Iterable[int] | int, + p_deterministics: Iterable[float] | float, + elo_scales: Iterable[float] | float, + max_iterations: int | None = None, + x_absolute_tol: float | None = None, +) -> Generator[Future[ExperimentResult]]: + for i, ( + num_players, + num_matches, + players_per_match, + p_deterministic, + elo_scale, + ) in enumerate( + itertools.product( + _make_iterable(num_playerss), + _make_iterable(num_matchess), + _make_iterable(players_per_matchs), + _make_iterable(p_deterministics), + _make_iterable(elo_scales), + ) + ): + rng = np.random.default_rng(seed + i) + yield executor.submit( + p_deterministic_experiment, + rng=rng, + num_players=num_players, + num_matches=num_matches, + players_per_match=players_per_match, + p_deterministic=p_deterministic, + elo_scale=elo_scale, + max_iterations=max_iterations, + x_absolute_tol=x_absolute_tol, + progress_bar=False, + ) + + +def p_deterministic_experiments( + *, + seed: int | None = None, + num_players: Iterable[int] | int, + num_matches: Iterable[int] | int, + players_per_match: Iterable[int] | int = 2, + p_deterministic: Iterable[float] | float, + elo_scale: Iterable[float] | float = 400, + max_iterations: int | None = None, + x_absolute_tol: float | None = None, +) -> Generator[ExperimentResult]: + with ProcessPoolExecutor() as executor: + futures = _p_deterministic_experiment_futures( + executor=executor, + seed=seed or time.time_ns(), + num_playerss=num_players, + num_matchess=num_matches, + players_per_matchs=players_per_match, + p_deterministics=p_deterministic, + elo_scales=elo_scale, + max_iterations=max_iterations, + x_absolute_tol=x_absolute_tol, + ) + for future in as_completed(futures): + yield future.result() + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Run p-deterministic Elo experiments and save results to CSV.", + ) + parser.add_argument( + "output", + type=str, + help="Path to save the results CSV file.", + ) + parser.add_argument( + "--num-players", + type=int, + nargs="+", + default=(100,), + help="Number of players.", + ) + parser.add_argument( + "--num-matches", + type=int, + nargs="+", + default=(10_000,), + help="Number of matches.", + ) + parser.add_argument( + "--players-per-match", + type=int, + nargs="+", + default=(2,), + help="Number of players per match.", + ) + parser.add_argument( + "--p-deterministic-start", + type=float, + default=0.0, + help="Starting value of p-deterministic.", + ) + parser.add_argument( + "--p-deterministic-stop", + type=float, + default=0.9, + help="Stopping value of p-deterministic.", + ) + parser.add_argument( + "--p-deterministic-steps", + type=int, + default=10, + help="Number of steps for p-deterministic values.", + ) + parser.add_argument( + "--elo-scale", + type=float, + nargs="+", + default=(400,), + help="Elo scale values.", + ) + parser.add_argument( + "--seed", + "-s", + type=int, + default=None, + help="Random seed for reproducibility.", + ) + parser.add_argument( + "--float-precision", + type=int, + default=5, + help="Float precision in CSV output.", + ) + parser.add_argument( + "--progress-bar", + "-p", + action="store_true", + help="Show a progress bar during experiments.", + ) + parser.add_argument( + "--verbose", + "-v", + action="count", + help="Enable verbose output (can be used multiple times).", + ) + return parser.parse_args() + + +def main(): + args = _parse_args() + verbose_level = args.verbose if args.verbose is not None else 0 + logging.basicConfig( + stream=sys.stdout, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + level=logging.DEBUG + if verbose_level > 1 + else logging.INFO + if verbose_level + else logging.WARNING, + ) + LOGGER.info(args) + LOGGER.info("Starting p-deterministic Elo experiments") + + num_players = tuple(args.num_players) + LOGGER.info("num_players: %s", num_players) + + num_matches = tuple(args.num_matches) + LOGGER.info("num_matches: %s", num_matches) + + players_per_match = tuple(args.players_per_match) + LOGGER.info("players_per_match: %s", players_per_match) + + p_deterministic = np.linspace( + start=args.p_deterministic_start, + stop=args.p_deterministic_stop, + num=args.p_deterministic_steps, + ) + LOGGER.info("p_deterministic: %s", p_deterministic) + + elo_scale = tuple(args.elo_scale) + LOGGER.info("elo_scale: %s", elo_scale) + + num_experiments = ( + len(num_players) + * len(num_matches) + * len(players_per_match) + * len(p_deterministic) + * len(elo_scale) + ) + LOGGER.info("Total number of experiments to run: %d", num_experiments) + + results_path = Path(args.output).resolve() + LOGGER.info("Results will be saved to: %s", results_path) + results_path.parent.mkdir(parents=True, exist_ok=True) + + experiments = p_deterministic_experiments( + seed=args.seed, + num_players=num_players, + num_matches=num_matches, + players_per_match=players_per_match, + p_deterministic=p_deterministic, + elo_scale=elo_scale, + ) + if args.progress_bar: + from tqdm import tqdm + + experiments = tqdm( + experiments, + desc="Running experiments", + total=num_experiments, + ) + + LOGGER.info("Running experiments, this may take a while…") + results = ( + pl.LazyFrame(dataclasses.asdict(experiment) for experiment in experiments) + .sort("num_players", "num_matches", "players_per_match", "p_deterministic") + .collect() + ) + LOGGER.info("Successfully completed %d experiments", len(results)) + + results.write_csv(results_path, float_precision=args.float_precision) + LOGGER.info("Results saved to: %s", results_path) + + +if __name__ == "__main__": + main() diff --git a/experiments/elo/python/elo/py.typed b/experiments/elo/python/elo/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/experiments/elo/elo/snooker_spider.py b/experiments/elo/python/elo/snooker_spider.py similarity index 100% rename from experiments/elo/elo/snooker_spider.py rename to experiments/elo/python/elo/snooker_spider.py diff --git a/experiments/elo/rust/core.rs b/experiments/elo/rust/core.rs new file mode 100644 index 0000000000000000000000000000000000000000..afb768e819da1881651f7196162fe564a51d54c2 --- /dev/null +++ b/experiments/elo/rust/core.rs @@ -0,0 +1,11 @@ +pub mod multi_player; +pub mod optimal_k; +pub mod rating_system; +pub mod two_player; + +/// Elo win probability for player A vs B given rating difference `diff = r_a - r_b`. +#[inline] +pub fn elo_probability(diff: f64, scale: f64) -> f64 { + // 1 / (1 + 10^(-diff/scale)) + 1.0 / (1.0 + 10f64.powf(-diff / scale)) +} diff --git a/experiments/elo/rust/core/multi_player.rs b/experiments/elo/rust/core/multi_player.rs new file mode 100644 index 0000000000000000000000000000000000000000..a419b179577fd29dd4f0cb9a2a440dccd0a54ac2 --- /dev/null +++ b/experiments/elo/rust/core/multi_player.rs @@ -0,0 +1,267 @@ +use std::collections::HashMap; +use std::hash::Hash; + +use super::rating_system::{EloConfig, EloRatingSystem}; + +// ---- Multiplayer Elo: Rank-Ordered-Logit (Plackett–Luce) -------------------- +// Exact probabilities via (1) permutation sum for small n, (2) inclusion–exclusion +// dynamic programming (subset sums) for medium n. This respects §2.2. + +#[derive(Debug, Clone)] +pub struct RankOrderedLogitElo +where + Id: Eq + Hash + Clone, +{ + cfg: EloConfig, + ratings: HashMap, + /// Use exact permutation sum up to this many players. + max_permutations: usize, + /// Use exact DP (subset inclusion–exclusion) up to this many players. + max_dynamic_programming: usize, +} + +impl RankOrderedLogitElo +where + Id: Eq + Hash + Clone, +{ + pub fn new( + cfg: EloConfig, + initial_ratings: Option>, + max_permutations: usize, + max_dynamic_programming: usize, + ) -> Self { + Self { + cfg, + ratings: initial_ratings.unwrap_or_default(), + max_permutations, + max_dynamic_programming, + } + } + + #[inline] + fn weights_for(&self, ids: &[Id]) -> Vec { + ids.iter() + .map(|id| { + let r = self.rating_of(id); + 10f64.powf(r / self.cfg.elo_scale) + }) + .collect() + } + + /// Exact probability matrix via permutation sum (Plackett–Luce exploded logit). + fn probability_matrix_permutations(&self, ids: &[Id]) -> Vec> { + let n = ids.len(); + let w = self.weights_for(ids); + let w_total: f64 = w.iter().sum(); + + let mut probs = vec![vec![0.0_f64; n]; n]; + let mut perm: Vec = (0..n).collect(); + // Heap's algorithm or std next_permutation? Simpler: use recursion by itertools-like approach. + // For n ≤ 6 this is fine to generate using next_permutation over sorted indices. + // We'll implement a simple iterative next_permutation on indices. + // Helper closure: + let mut push_perm = |permute: &[usize]| { + // Probability of this exact ranking: + let mut denom = w_total; + let mut p_perm = 1.0_f64; + for &pos in permute.iter().take(n - 1) { + let wi = w[pos]; + p_perm *= wi / denom; + denom -= wi; + } + for (rank, &pid) in permute.iter().enumerate() { + probs[pid][rank] += p_perm; + } + }; + + // Start with 0..n, iterate all permutations in lexicographic order + // using a standard next_permutation implementation. + perm.sort_unstable(); + loop { + push_perm(&perm); + // next_permutation + let mut i = n - 1; + while i > 0 && perm[i - 1] >= perm[i] { + i -= 1; + } + if i == 0 { + break; + } + let mut j = n - 1; + while perm[j] <= perm[i - 1] { + j -= 1; + } + perm.swap(i - 1, j); + perm[i..].reverse(); + } + + // sanity (allow tiny FP drift) + for rank in 0..n { + let s: f64 = probs.iter().map(|row| row[rank]).sum(); + assert!((s - 1.0).abs() <= 1e-2, "column {} sum {}", rank, s); + } + for (i, row) in probs.iter().enumerate().take(n) { + let s: f64 = row.iter().sum(); + assert!((s - 1.0).abs() <= 1e-2, "row {} sum {}", i, s); + } + probs + } + + #[inline] + fn n_choose_k(n: usize, k: usize) -> f64 { + if k > n { + return 0.0; + } + let k = k.min(n - k); + if k == 0 { + return 1.0; + } + let mut res = 1.0_f64; + for i in 1..=k { + res = res * ((n - k + i) as f64) / (i as f64); + } + res + } + + /// Exact probability matrix via subset DP / inclusion–exclusion. + /// For each focal player i, and others O (m = n-1), we compute P_i(k) for k=0..m as: + /// P_i(k) = w_i * sum_{T ⊆ O} [ (-1)^{k+|T|} * C(m-|T|, k-|T|) / (W_tot - W(T)) ]. + fn probability_matrix_dp(&self, ids: &[Id]) -> Vec> { + let n = ids.len(); + let w = self.weights_for(ids); + let w_total: f64 = w.iter().sum(); + let mut probs = vec![vec![0.0_f64; n]; n]; + + for i in 0..n { + // Build "others" list and their weights + let mut other_w = Vec::with_capacity(n - 1); + let mut other_index = Vec::with_capacity(n - 1); + for (j, &item) in w.iter().enumerate().take(n) { + if j != i { + other_w.push(item); + other_index.push(j); + } + } + let m = other_w.len(); + let num_masks = 1usize << m; + // subset_sum[mask] = sum of other_w over set bits + let mut subset_sum = vec![0.0_f64; num_masks]; + for mask in 1..num_masks { + let lsb = mask & mask.wrapping_neg(); + let bit = lsb.trailing_zeros() as usize; + subset_sum[mask] = subset_sum[mask ^ lsb] + other_w[bit]; + } + + let wi = w[i]; + let mut p = vec![0.0_f64; m + 1]; + for (mask, &item) in subset_sum.iter().enumerate().take(num_masks) { + let tsize = (mask as u32).count_ones() as usize; + let denom = w_total - item; + let base = wi / denom; + // contribute to all k >= tsize + for (k, p_k) in p.iter_mut().enumerate().take(m + 1).skip(tsize) { + let coeff = Self::n_choose_k(m - tsize, k - tsize); + let sign = if ((k + tsize) & 1) == 1 { -1.0 } else { 1.0 }; + *p_k += base * sign * coeff; + } + } + // numerical clean-up + let mut sum_p: f64 = p.iter().sum(); + if sum_p <= 0.0 { + // fallback uniform (should not happen unless extreme FP) + for v in &mut p { + *v = 1.0 / ((m + 1) as f64); + } + sum_p = 1.0; + } + for v in &mut p { + if *v < 0.0 && *v > -1e-15 { + *v = 0.0; + } + } + // renormalise + let inv = 1.0 / sum_p; + for v in &mut p { + *v *= inv; + } + // store: ranks 0..m + probs[i][..(m + 1)].copy_from_slice(&p[..(m + 1)]); + } + + // sanity + for rank in 0..n { + let s: f64 = probs.iter().map(|row| row[rank]).sum(); + assert!((s - 1.0).abs() <= 1e-2, "column {} sum {}", rank, s); + } + for (i, row) in probs.iter().enumerate().take(n) { + let s: f64 = row.iter().sum(); + assert!((s - 1.0).abs() <= 1e-2, "row {} sum {}", i, s); + } + probs + } +} + +impl EloRatingSystem for RankOrderedLogitElo +where + Id: Eq + Hash + Clone, +{ + fn ratings(&self) -> &HashMap { + &self.ratings + } + fn ratings_mut(&mut self) -> &mut HashMap { + &mut self.ratings + } + fn config(&self) -> EloConfig { + self.cfg + } + fn config_mut(&mut self) -> &mut EloConfig { + &mut self.cfg + } + + fn expected_outcome(&self, players: &[Id], rank_payoffs: Option<&[f64]>) -> Vec { + let n = players.len(); + assert!(n >= 2, "At least 2 players are required"); + + let pm = self.probability_matrix(players); + let payoffs_owned; + let pay = if let Some(p) = rank_payoffs { + assert_eq!(p.len(), n, "rank_payoffs must have length n"); + p + } else { + // default descending payoffs: n-1, ..., 0 + payoffs_owned = (0..n).rev().map(|i| i as f64).collect::>(); + &payoffs_owned + }; + + let mut expected = vec![0.0_f64; n]; + for i in 0..n { + let mut s = 0.0_f64; + for (k, &item) in pay.iter().enumerate().take(n) { + s += pm[i][k] * item; + } + expected[i] = s; + } + expected + } + + fn probability_matrix(&self, players: &[Id]) -> Vec> { + let n = players.len(); + assert!(n >= 2, "At least 2 players are required"); + if n == 2 { + // two-player closed form + let ra = self.rating_of(&players[0]); + let rb = self.rating_of(&players[1]); + let p = 1.0 / (1.0 + 10f64.powf(-(ra - rb) / self.cfg.elo_scale)); + return vec![vec![p, 1.0 - p], vec![1.0 - p, p]]; + } + if n <= self.max_permutations { + return self.probability_matrix_permutations(players); + } + if n <= self.max_dynamic_programming { + return self.probability_matrix_dp(players); + } + // If larger than DP cutoff, fall back to DP anyway (still exact but may be slow). + // You can later replace this with a Monte Carlo sampler if a RNG dependency is added. + self.probability_matrix_dp(players) + } +} diff --git a/experiments/elo/rust/core/optimal_k.rs b/experiments/elo/rust/core/optimal_k.rs new file mode 100644 index 0000000000000000000000000000000000000000..bd104e5757d60e77eef1b24b5e3a0ef566d8e12a --- /dev/null +++ b/experiments/elo/rust/core/optimal_k.rs @@ -0,0 +1,135 @@ +// src/optimal_k.rs (or wherever you keep helpers) +use std::collections::HashMap; +use std::hash::Hash; + +use crate::core::multi_player::RankOrderedLogitElo; +use crate::core::rating_system::{EloConfig, EloRatingSystem, Match}; +use crate::core::two_player::TwoPlayerElo; +use argmin::core::{CostFunction, Error, Executor, State}; +use argmin::solver::brent::BrentOpt; + +/// Compute mean squared error of the per-player diffs produced over a batch. +/// Diffs are the normalised (observed - expected) values returned by the rating updates. +fn calculate_loss(elo: &mut dyn EloRatingSystem, matches: &[Match]) -> f64 +where + Id: Eq + Hash + Clone, +{ + let Some(all_diffs) = elo.update_elo_ratings_batch_slice(matches, true) else { + return f64::INFINITY; + }; + + let mut sum = 0.0f64; + let mut n = 0usize; + for diffs in all_diffs { + for d in diffs { + if d.is_finite() { + sum += d * d; + n += 1; + } + } + } + if n == 0 { + f64::INFINITY + } else { + sum / (n as f64) + } +} + +/// Argmin problem: minimise the Elo diff MSE over k in [min_k, max_k]. +struct BrentKProblem<'a, Id> { + matches: &'a [Match], + elo_scale: f64, + two_player_only: bool, +} + +impl<'a, Id> CostFunction for BrentKProblem<'a, Id> +where + Id: Eq + Hash + Clone, +{ + type Param = f64; + type Output = f64; + + fn cost(&self, k: &Self::Param) -> Result { + // Guard against degenerate inputs + if !k.is_finite() || *k < 0.0 { + return Ok(f64::INFINITY); + } + let mut sys: Box> = if self.two_player_only { + Box::new(TwoPlayerElo::new( + EloConfig { + elo_k: *k, + elo_scale: self.elo_scale, + ..EloConfig::default() + }, + Some(HashMap::new()), + )) + } else { + Box::new(RankOrderedLogitElo::new( + EloConfig { + elo_k: *k, + elo_scale: self.elo_scale, + ..EloConfig::default() + }, + Some(HashMap::new()), + 6, + 12, + )) + }; + Ok(calculate_loss::(sys.as_mut(), self.matches)) + } +} + +/// Port of Python's `approximate_optimal_k(..., two_player_only=True, ...)`. +/// Minimises the MSE of diffs over k in [min_k, max_k]. +pub fn approx_optimal_k( + matches: &[Match], + two_player_only: bool, + min_k: f64, + max_k: f64, + elo_scale: f64, // default 400.0 in EloConfig::default() + max_iterations: Option, // Python's `maxiter` + x_absolute_tol: Option, // Python's `xatol` +) -> f64 +where + Id: Eq + Hash + Clone, +{ + let max_iters = max_iterations.unwrap_or(200) as u64; + + // Brent uses combined tolerance: tol = eps * |x| + t. + // We map the provided absolute tolerance to `t` and keep default `eps = sqrt(machine_epsilon)`. + let abs_tol = x_absolute_tol.unwrap_or(1e-3); + let eps = f64::EPSILON.sqrt(); + + let problem = BrentKProblem { + matches, + elo_scale, + two_player_only, + }; + + let solver = BrentOpt::new(min_k, max_k).set_tolerance(eps, abs_tol); + + // Use the mid-point as an initial guess for completeness (Brent brackets anyway). + let init = 0.5 * (min_k + max_k); + + let res = Executor::new(problem, solver) + .configure(|state| state.param(init).max_iters(max_iters)) + .run(); + + match res { + Ok(res) => { + // Prefer the best parameter found; fall back to the last one if unset. + if let Some(k) = res.state().get_best_param() { + *k + } else if let Some(k) = res.state().get_param() { + *k + } else { + // Shouldn't happen; conservative fallback. + init + } + } + Err(_e) => { + // On failure, return the mid-point as a safe fallback. + init + } + } +} diff --git a/experiments/elo/rust/core/rating_system.rs b/experiments/elo/rust/core/rating_system.rs new file mode 100644 index 0000000000000000000000000000000000000000..ea19be5af0868ef6db7abdb8986745f504b5b40d --- /dev/null +++ b/experiments/elo/rust/core/rating_system.rs @@ -0,0 +1,132 @@ +use std::collections::HashMap; +use std::hash::Hash; + +/// Input for a single match: +/// - `Outcomes`: explicit per-player scores (e.g., [1.0, 0.0] for 2p), +/// - `Ordered`: player IDs ordered by rank (highest first), implicit scores n-1..0. +#[derive(Debug, Clone)] +pub enum Match { + Outcomes(Vec<(Id, f64)>), + Ordered(Vec), +} + +/// Common configuration knobs for Elo. +#[derive(Debug, Clone, Copy)] +pub struct EloConfig { + pub elo_initial: f64, + pub elo_k: f64, + pub elo_scale: f64, +} +impl Default for EloConfig { + fn default() -> Self { + Self { + elo_initial: 0.0, + elo_k: 32.0, + elo_scale: 400.0, + } + } +} + +/// Core Elo trait—mirrors your Python abstract base class. +pub trait EloRatingSystem +where + Id: Eq + Hash + Clone, +{ + /// Access to the rating map (implementors store this). + fn ratings(&self) -> &HashMap; + fn ratings_mut(&mut self) -> &mut HashMap; + fn config(&self) -> EloConfig; + fn config_mut(&mut self) -> &mut EloConfig; + + /// Expected outcome vector for the given players. + /// For n-player variants, `rank_payoffs` length must equal number of players. + fn expected_outcome(&self, players: &[Id], rank_payoffs: Option<&[f64]>) -> Vec; + + /// Probability “matrix” for the given players. + /// For 2p this is [[p, 1-p], [1-p, p]]. For n-player, implementors define semantics. + fn probability_matrix(&self, players: &[Id]) -> Vec>; + + /// Update ratings for a single match; returns the per-player diffs actually applied. + fn update_elo_ratings(&mut self, m: Match) -> Vec { + match m { + Match::Outcomes(pairs) => { + let (ids, outcomes): (Vec<_>, Vec<_>) = pairs.into_iter().unzip(); + self.apply_diffs_from_outcomes(&ids, &outcomes) + } + Match::Ordered(ids) => { + let n = ids.len(); + let mut outcomes = Vec::with_capacity(n); + for i in (0..n).rev() { + outcomes.push(i as f64); + } + self.apply_diffs_from_outcomes(&ids, &outcomes) + } + } + } + + /// Batch update over a slice (object-safe). + /// If `collect_diffs` is true, returns per-match diffs; otherwise `None`. + fn update_elo_ratings_batch_slice( + &mut self, + matches: &[Match], + collect_diffs: bool, + ) -> Option>> { + if collect_diffs { + let mut all = Vec::with_capacity(matches.len()); + for m in matches { + all.push(self.update_elo_ratings(m.clone())); + } + Some(all) + } else { + for m in matches { + let _ = self.update_elo_ratings(m.clone()); + } + None + } + } + + // ----- helpers ----- + + /// Get (or insert) a player's rating. + fn rating_of(&self, id: &Id) -> f64 { + *self.ratings().get(id).unwrap_or(&self.config().elo_initial) + } + fn rating_of_mut<'a>(&'a mut self, id: &Id) -> &'a mut f64 + where + Id: 'a, + { + let init = self.config().elo_initial; + self.ratings_mut().entry(id.clone()).or_insert(init) + } + + /// Shared path: compute diffs = (outcomes - expected) / max_outcome, then apply. + fn apply_diffs_from_outcomes(&mut self, ids: &[Id], outcomes: &[f64]) -> Vec { + assert!(ids.len() >= 2, "At least 2 players are required"); + assert!( + outcomes.iter().all(|&x| x >= 0.0), + "Payoffs must be non-negative" + ); + assert!( + outcomes.iter().any(|&x| x > 0.0), + "At least one payoff must be positive" + ); + assert_eq!(ids.len(), outcomes.len(), "Length mismatch"); + + let expected = self.expected_outcome(ids, Some(outcomes)); + let max_payoff = outcomes.iter().cloned().fold(f64::NEG_INFINITY, f64::max); + let inv = if max_payoff > 0.0 { + 1.0 / max_payoff + } else { + 1.0 + }; + let k = self.config().elo_k; + + let mut diffs = Vec::with_capacity(ids.len()); + for (id, (&obs, exp)) in ids.iter().zip(outcomes.iter().zip(expected.iter())) { + let d = (obs - exp) * inv; + *self.rating_of_mut(id) += k * d; + diffs.push(d); + } + diffs + } +} diff --git a/experiments/elo/rust/core/two_player.rs b/experiments/elo/rust/core/two_player.rs new file mode 100644 index 0000000000000000000000000000000000000000..6b4461bee9e959dc9115ceea4fd2ac5efa07bf73 --- /dev/null +++ b/experiments/elo/rust/core/two_player.rs @@ -0,0 +1,64 @@ +use std::collections::HashMap; +use std::hash::Hash; + +use super::elo_probability; +use super::rating_system::{EloConfig, EloRatingSystem}; + +/// Two-player Elo. Mirrors your Python `TwoPlayerElo`. +#[derive(Debug, Clone)] +pub struct TwoPlayerElo +where + Id: Eq + Hash + Clone, +{ + cfg: EloConfig, + ratings: HashMap, +} + +impl TwoPlayerElo +where + Id: Eq + Hash + Clone, +{ + pub fn new(cfg: EloConfig, initial_ratings: Option>) -> Self { + Self { + cfg, + ratings: initial_ratings.unwrap_or_default(), + } + } +} + +impl EloRatingSystem for TwoPlayerElo +where + Id: Eq + Hash + Clone, +{ + fn ratings(&self) -> &HashMap { + &self.ratings + } + fn ratings_mut(&mut self) -> &mut HashMap { + &mut self.ratings + } + fn config(&self) -> EloConfig { + self.cfg + } + fn config_mut(&mut self) -> &mut EloConfig { + &mut self.cfg + } + + fn expected_outcome(&self, players: &[Id], rank_payoffs: Option<&[f64]>) -> Vec { + if rank_payoffs.is_some() { + // parity with your Python: ignore rank_payoffs for 2p + // (we could assert they are length 2 and equal to [1,0], but keep it flexible) + } + assert!(players.len() == 2, "TwoPlayerElo expects exactly 2 players"); + let a = &players[0]; + let b = &players[1]; + let ra = self.rating_of(a); + let rb = self.rating_of(b); + let p = elo_probability(ra - rb, self.cfg.elo_scale); + vec![p, 1.0 - p] + } + + fn probability_matrix(&self, players: &[Id]) -> Vec> { + let e = self.expected_outcome(players, None); + vec![vec![e[0], e[1]], vec![e[1], e[0]]] + } +} diff --git a/experiments/elo/rust/lib.rs b/experiments/elo/rust/lib.rs new file mode 100644 index 0000000000000000000000000000000000000000..31e581090c2b8d335cc8f78d5a71704db7258756 --- /dev/null +++ b/experiments/elo/rust/lib.rs @@ -0,0 +1,139 @@ +pub mod core; + +use crate::core::multi_player::RankOrderedLogitElo; +use crate::core::optimal_k::approx_optimal_k; +use crate::core::rating_system::EloRatingSystem; +use crate::core::rating_system::{EloConfig, Match}; +use crate::core::two_player::TwoPlayerElo; +use ndarray::ArrayView2; +use numpy::PyReadonlyArray2; +use pyo3::prelude::*; +use std::collections::HashMap; + +#[pyfunction] +fn approx_optimal_k_rust<'py>( + players: numpy::PyReadonlyArray1<'py, i64>, + payoffs: Option>, + row_splits: numpy::PyReadonlyArray1<'py, i64>, + two_player_only: bool, + min_elo_k: f64, + max_elo_k: f64, + elo_scale: f64, + max_iterations: Option, + x_absolute_tol: Option, +) -> PyResult { + let p = players.as_slice()?; + let rs = row_splits.as_slice()?; + + if rs.len() < 2 || rs[0] != 0 || *rs.last().unwrap() as usize != p.len() { + return Err(pyo3::exceptions::PyValueError::new_err( + "row_splits must start at 0 and end at players.len()", + )); + } + + let matches: Vec> = if let Some(payoffs) = payoffs { + let payoffs = payoffs.as_slice()?; + if payoffs.len() != p.len() { + return Err(pyo3::exceptions::PyValueError::new_err( + "payoffs length must equal players length", + )); + } + rs.windows(2) + .map(|w| { + Match::Outcomes( + p[w[0] as usize..w[1] as usize] + .iter() + .cloned() + .zip(payoffs[w[0] as usize..w[1] as usize].iter().cloned()) + .collect(), + ) + }) + .collect::>() + } else { + rs.windows(2) + .map(|w| Match::Ordered(p[w[0] as usize..w[1] as usize].to_vec())) + .collect::>() + }; + + let optimal_k = approx_optimal_k( + &matches, + two_player_only, + min_elo_k, + max_elo_k, + elo_scale, + max_iterations, + x_absolute_tol, + ); + + Ok(optimal_k) +} + +#[pyfunction] +fn calculate_elo_ratings_two_players_rust<'py>( + matches: PyReadonlyArray2<'py, i32>, // shape: (n_matches, 2) + elo_initial: f64, + elo_k: f64, + elo_scale: f64, +) -> PyResult> { + // Simple, straightforward implementation (winner, loser pairs). + let arr: ArrayView2<'_, i32> = matches.as_array(); + if arr.ndim() != 2 || arr.shape()[1] != 2 { + return Err(pyo3::exceptions::PyValueError::new_err( + "matches must be shape (n_matches, 2) of int32", + )); + } + let cfg = EloConfig { + elo_initial, + elo_k, + elo_scale, + }; + let mut elo = TwoPlayerElo::::new(cfg, None); + + for row in arr.rows() { + elo.update_elo_ratings(Match::Ordered(row.to_vec())); + } + + Ok(elo.ratings().clone()) +} + +#[pyfunction] +fn calculate_elo_ratings_multi_players_rust<'py>( + matches: PyReadonlyArray2<'py, i32>, // shape: (n_matches, n_players) + elo_initial: f64, + elo_k: f64, + elo_scale: f64, +) -> PyResult> { + // Simple, straightforward implementation (ordered players). + let arr: ArrayView2<'_, i32> = matches.as_array(); + if arr.ndim() != 2 || arr.shape()[1] < 2 { + return Err(pyo3::exceptions::PyValueError::new_err( + "matches must be shape (n_matches, n_players>=2) of int32", + )); + } + let cfg = EloConfig { + elo_initial, + elo_k, + elo_scale, + }; + let mut elo = RankOrderedLogitElo::::new(cfg, None, 6, 12); + + for row in arr.rows() { + elo.update_elo_ratings(Match::Ordered(row.to_vec())); + } + + Ok(elo.ratings().clone()) +} + +/// A Python module implemented in Rust. The name of this function must match +/// the `lib.name` setting in the `Cargo.toml`, else Python will not be able to +/// import the module. +#[pymodule] +fn _rust(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_function(wrap_pyfunction!(approx_optimal_k_rust, m)?)?; + m.add_function(wrap_pyfunction!(calculate_elo_ratings_two_players_rust, m)?)?; + m.add_function(wrap_pyfunction!( + calculate_elo_ratings_multi_players_rust, + m + )?)?; + Ok(()) +} diff --git a/experiments/elo/rust/main.rs b/experiments/elo/rust/main.rs new file mode 100644 index 0000000000000000000000000000000000000000..2fdfd9aeb9f4cd548a43fbe6d68f78238c27a882 --- /dev/null +++ b/experiments/elo/rust/main.rs @@ -0,0 +1,19 @@ +use elo::core::rating_system::{EloConfig, EloRatingSystem, Match}; +use elo::core::two_player::TwoPlayerElo; + +fn main() { + let cfg = EloConfig { + elo_initial: 0.0, + elo_k: 32.0, + elo_scale: 400.0, + }; + let mut elo = TwoPlayerElo::<&'static str>::new(cfg, None); + + // Match as ordered 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