From 77f656a785d4c616a2c1141a016c0753824ddc4e Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 09:52:00 +0300 Subject: [PATCH 001/147] Added scrape_rankings flag to BgaSpider --- experiments/elo/elo/bga_spider.py | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index 0ec41709..cfdb4079 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -54,6 +54,7 @@ class BgaSpider(Spider): "JOBDIR": ".jobs", } + scrape_rankings = False max_rank_scraped = None regex = re.compile("globalUserInfos=(.+);$", flags=re.MULTILINE) @@ -80,18 +81,19 @@ class BgaSpider(Spider): game["scraped_at"] = now yield game - yield FormRequest( - url=response.urljoin("/gamepanel/gamepanel/getRanking.html"), - method="POST", - formdata={ - "game": str(game["id"]), - "start": "0", - "mode": "elo", - }, - callback=self.parse_ranking, - meta={"game_id": game["id"]}, - priority=0, - ) + if self.scrape_rankings: + yield FormRequest( + url=response.urljoin("/gamepanel/gamepanel/getRanking.html"), + method="POST", + formdata={ + "game": str(game["id"]), + "start": "0", + "mode": "elo", + }, + callback=self.parse_ranking, + meta={"game_id": game["id"]}, + priority=0, + ) def parse_ranking(self, response: Response) -> Generator[dict | Request]: if not isinstance(response, TextResponse): -- GitLab From b947e04f427408b80c56eb487aa05c4ce8f2f60a Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 10:24:50 +0300 Subject: [PATCH 002/147] First version of BgaSpider.build_match_url() and parse_matches() --- experiments/elo/elo/bga_spider.py | 57 +++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index cfdb4079..16b702a3 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -29,6 +29,10 @@ class RankingFilter(BaseFilter): type_name = "ranking" +class MatchFilter(BaseFilter): + type_name = "match" + + class BgaSpider(Spider): name = "bga" start_urls = ("https://en.boardgamearena.com/gamelist?allGames=",) @@ -50,6 +54,12 @@ class BgaSpider(Spider): "overwrite": False, "store_empty": False, }, + "results/matches-%(time)s-%(batch_id)05d.jl": { + "format": "jsonlines", + "item_filter": MatchFilter, + "overwrite": False, + "store_empty": False, + }, }, "JOBDIR": ".jobs", } @@ -58,6 +68,10 @@ class BgaSpider(Spider): max_rank_scraped = None regex = re.compile("globalUserInfos=(.+);$", flags=re.MULTILINE) + scrape_matches = False + base_match_url = "https://en.boardgamearena.com/message/board" + max_matches_per_page = 9500 + def parse(self, response: Response) -> Generator[dict | Request]: now = datetime.now(timezone.utc).isoformat(timespec="seconds") @@ -95,6 +109,13 @@ class BgaSpider(Spider): priority=0, ) + if self.scrape_matches: + yield Request( + url=self.build_match_url(game["id"]), + callback=self.parse_matches, + meta={"game_id": game["id"]}, + ) + 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) @@ -126,3 +147,39 @@ class BgaSpider(Spider): meta={"game_id": game_id}, priority=-max_rank_no, ) + + def build_match_url(self, game_id: int, from_time: int = None, from_id: int = None): + params = { + "type": "lastresult", + "id": str(game_id), + "social": "false", + "per_page": str(self.max_matches_per_page), + "dojo.preventCache": "1", # TODO: timestamp + } + + 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()) + return f"{self.base_match_url}?{query}" + + 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() + + for match in payload["data"]["news"]: + match["game_id"] = game_id + match["type"] = "match" + match["scraped_at"] = now + # TODO: parse match["html"] for match results + yield match + + # TODO: request next page -- GitLab From 1507ad5d745db38f07b80317a206701fd586369c Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 14:15:12 +0300 Subject: [PATCH 003/147] Added request token fetching and parsing --- experiments/elo/elo/bga_spider.py | 44 ++++++++++++++++++++++++++++--- 1 file changed, 41 insertions(+), 3 deletions(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index 16b702a3..937a3018 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -35,9 +35,11 @@ class MatchFilter(BaseFilter): class BgaSpider(Spider): name = "bga" - start_urls = ("https://en.boardgamearena.com/gamelist?allGames=",) + start_urls = ("https://en.boardgamearena.com/",) custom_settings = { + "COOKIES_ENABLED": True, + "COOKIES_DEBUG": True, "DOWNLOAD_DELAY": 1, "CONCURRENT_REQUESTS_PER_DOMAIN": 4, "FEED_EXPORT_BATCH_ITEM_COUNT": 10_000, @@ -64,6 +66,13 @@ class BgaSpider(Spider): "JOBDIR": ".jobs", } + request_token_url = ( + "https://en.boardgamearena.com/account/auth/getRequestToken.html" + ) + request_token: str | None = None + + game_list_url = "https://en.boardgamearena.com/gamelist?allGames=" + scrape_rankings = False max_rank_scraped = None regex = re.compile("globalUserInfos=(.+);$", flags=re.MULTILINE) @@ -72,7 +81,29 @@ class BgaSpider(Spider): base_match_url = "https://en.boardgamearena.com/message/board" max_matches_per_page = 9500 - def parse(self, response: Response) -> Generator[dict | Request]: + def parse(self, response: Response) -> Request: + return FormRequest( + url=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() + self.request_token = payload["data"]["request_token"] + + return Request( + url=self.game_list_url, + callback=self.parse_games, + headers={"X-Request-Token": self.request_token}, + ) + + def parse_games(self, response: Response) -> Generator[dict | Request]: now = datetime.now(timezone.utc).isoformat(timespec="seconds") for text in response.xpath( @@ -107,6 +138,7 @@ class BgaSpider(Spider): callback=self.parse_ranking, meta={"game_id": game["id"]}, priority=0, + headers={"X-Request-Token": self.request_token}, ) if self.scrape_matches: @@ -114,6 +146,7 @@ class BgaSpider(Spider): url=self.build_match_url(game["id"]), callback=self.parse_matches, meta={"game_id": game["id"]}, + headers={"X-Request-Token": self.request_token}, ) def parse_ranking(self, response: Response) -> Generator[dict | Request]: @@ -148,7 +181,12 @@ class BgaSpider(Spider): priority=-max_rank_no, ) - def build_match_url(self, game_id: int, from_time: int = None, from_id: int = None): + def build_match_url( + self, + game_id: int, + from_time: int = None, + from_id: int = None, + ) -> str: params = { "type": "lastresult", "id": str(game_id), -- GitLab From b7d4bd1211c4a27eb2fbed94e9e9644c91efffec Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 14:17:58 +0300 Subject: [PATCH 004/147] Added jmespath --- experiments/elo/poetry.lock | 483 ++++++++++++++++----------------- experiments/elo/pyproject.toml | 1 + 2 files changed, 238 insertions(+), 246 deletions(-) diff --git a/experiments/elo/poetry.lock b/experiments/elo/poetry.lock index 0538c890..4ed49941 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a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index 899cd6a0..a169e9c6 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -8,6 +8,7 @@ authors = ["Markus Shepherd "] python = "3.12.*" board-game-merger = "*" +jmespath = "*" matplotlib = "*" numpy = "*" polars = {version = "*", extras = ["numpy", "pandas", "pyarrow"]} -- GitLab From 46f2467ec5948621a23e5be484a4f439af5047f4 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 14:26:04 +0300 Subject: [PATCH 005/147] Using jmsepath --- experiments/elo/elo/bga_spider.py | 34 +++++++++++++++++++++++++------ 1 file changed, 28 insertions(+), 6 deletions(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index 937a3018..7f0f499c 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -1,5 +1,6 @@ from collections.abc import Generator from datetime import datetime, timezone +import jmespath import json import math import re @@ -69,16 +70,19 @@ class BgaSpider(Spider): request_token_url = ( "https://en.boardgamearena.com/account/auth/getRequestToken.html" ) + request_token_path = jmespath.compile("data.request_token") request_token: str | None = None game_list_url = "https://en.boardgamearena.com/gamelist?allGames=" + game_list_regex = re.compile("globalUserInfos=(.+);$", flags=re.MULTILINE) scrape_rankings = False + ranking_path = jmespath.compile("data.ranks") max_rank_scraped = None - regex = re.compile("globalUserInfos=(.+);$", flags=re.MULTILINE) scrape_matches = False base_match_url = "https://en.boardgamearena.com/message/board" + match_path = jmespath.compile("data.news") max_matches_per_page = 9500 def parse(self, response: Response) -> Request: @@ -95,12 +99,18 @@ class BgaSpider(Spider): return payload = response.json() - self.request_token = payload["data"]["request_token"] + 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=self.game_list_url, callback=self.parse_games, - headers={"X-Request-Token": self.request_token}, + headers={"X-Request-Token": request_token}, ) def parse_games(self, response: Response) -> Generator[dict | Request]: @@ -109,7 +119,7 @@ class BgaSpider(Spider): for text in response.xpath( "//script[@type = 'text/javascript']/text()" ).getall(): - match = self.regex.search(text) + match = self.game_list_regex.search(text) if not match: continue @@ -159,9 +169,15 @@ class BgaSpider(Spider): 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 payload["data"]["ranks"]: + for entry in entries: entry["game_id"] = game_id entry["type"] = "ranking" entry["scraped_at"] = now @@ -213,7 +229,13 @@ class BgaSpider(Spider): game_id = response.meta["game_id"] payload = response.json() - for match in payload["data"]["news"]: + matches = self.match_path.search(payload) + + if not matches: + self.logger.error("Matches not found in response <%s>", response.url) + return + + for match in matches: match["game_id"] = game_id match["type"] = "match" match["scraped_at"] = now -- GitLab From 23a05395c1d27d30b29b755c4f81e4b24129c8bd Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 14:54:18 +0300 Subject: [PATCH 006/147] More compact URLs --- experiments/elo/elo/bga_spider.py | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index 7f0f499c..be364a41 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -36,14 +36,15 @@ class MatchFilter(BaseFilter): class BgaSpider(Spider): name = "bga" - start_urls = ("https://en.boardgamearena.com/",) + base_url = "https://en.boardgamearena.com/" + start_urls = (base_url,) custom_settings = { "COOKIES_ENABLED": True, "COOKIES_DEBUG": True, "DOWNLOAD_DELAY": 1, "CONCURRENT_REQUESTS_PER_DOMAIN": 4, - "FEED_EXPORT_BATCH_ITEM_COUNT": 10_000, + "FEED_EXPORT_BATCH_ITEM_COUNT": 100_000, "FEEDS": { "results/games-%(time)s-%(batch_id)05d.jl": { "format": "jsonlines", @@ -67,13 +68,11 @@ class BgaSpider(Spider): "JOBDIR": ".jobs", } - request_token_url = ( - "https://en.boardgamearena.com/account/auth/getRequestToken.html" - ) + request_token_url = "/account/auth/getRequestToken.html" request_token_path = jmespath.compile("data.request_token") request_token: str | None = None - game_list_url = "https://en.boardgamearena.com/gamelist?allGames=" + game_list_url = "/gamelist?allGames=" game_list_regex = re.compile("globalUserInfos=(.+);$", flags=re.MULTILINE) scrape_rankings = False @@ -81,13 +80,13 @@ class BgaSpider(Spider): max_rank_scraped = None scrape_matches = False - base_match_url = "https://en.boardgamearena.com/message/board" + base_match_url = "/message/board" match_path = jmespath.compile("data.news") max_matches_per_page = 9500 def parse(self, response: Response) -> Request: return FormRequest( - url=self.request_token_url, + url=response.urljoin(self.request_token_url), formdata={"bgapp": "bga"}, callback=self.parse_request_token, method="POST", @@ -108,7 +107,7 @@ class BgaSpider(Spider): self.request_token = request_token return Request( - url=self.game_list_url, + url=response.urljoin(self.game_list_url), callback=self.parse_games, headers={"X-Request-Token": request_token}, ) @@ -153,7 +152,7 @@ class BgaSpider(Spider): if self.scrape_matches: yield Request( - url=self.build_match_url(game["id"]), + url=self.build_match_url(response, game["id"]), callback=self.parse_matches, meta={"game_id": game["id"]}, headers={"X-Request-Token": self.request_token}, @@ -199,6 +198,7 @@ class BgaSpider(Spider): def build_match_url( self, + response: Response, game_id: int, from_time: int = None, from_id: int = None, @@ -217,7 +217,8 @@ class BgaSpider(Spider): params["from_id"] = str(from_id) query = "&".join(f"{k}={v}" for k, v in params.items()) - return f"{self.base_match_url}?{query}" + 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): -- GitLab From 0732b68996f8827a63e30443f4729e18bc11c1e1 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 14:57:24 +0300 Subject: [PATCH 007/147] Added scrapy-extensions, using QuietLogFormatter --- experiments/elo/elo/bga_spider.py | 1 + experiments/elo/poetry.lock | 23 ++++++++++++++++++++++- experiments/elo/pyproject.toml | 1 + 3 files changed, 24 insertions(+), 1 deletion(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index be364a41..478c9684 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -44,6 +44,7 @@ class BgaSpider(Spider): "COOKIES_DEBUG": True, "DOWNLOAD_DELAY": 1, "CONCURRENT_REQUESTS_PER_DOMAIN": 4, + "LOG_FORMATTER": "scrapy_extensions.QuietLogFormatter", "FEED_EXPORT_BATCH_ITEM_COUNT": 100_000, "FEEDS": { "results/games-%(time)s-%(batch_id)05d.jl": { diff --git a/experiments/elo/poetry.lock b/experiments/elo/poetry.lock index 4ed49941..b1612c92 100644 --- a/experiments/elo/poetry.lock +++ b/experiments/elo/poetry.lock @@ -3380,6 +3380,27 @@ Twisted = ">=21.7.0" w3lib = ">=1.17.0" "zope.interface" = ">=5.1.0" +[[package]] +name = "scrapy-extensions" +version = "1.0.2" +description = "A collection of Scrapy extensions and other utilities" +optional = false +python-versions = "<4.0,>=3.9.0" +files = [ + {file = "scrapy_extensions-1.0.2-py3-none-any.whl", hash = "sha256:bdd2d931e67af8ceb9bf71c5d1230ad93b7587ec0d0ebd3794f392c4cd5e72a1"}, + {file = "scrapy_extensions-1.0.2.tar.gz", hash = "sha256:1a29d9d250dc5a6a89f1cb64e7b795766e69c9400da22c6aef9c6ba2da282e64"}, +] + +[package.dependencies] +itemadapter = "*" +jmespath = "*" +scrapy = ">=2.0.0,<3.0.0" +twisted = "*" +w3lib = ">=2.0.0,<3.0" + +[package.extras] +blurhash = ["blurhash-numba[pillow]", "numpy", "pillow (>=4.0.0)"] + [[package]] name = "seaborn" version = "0.13.2" @@ -3960,4 +3981,4 @@ testing = ["coverage[toml]", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" python-versions = "3.12.*" -content-hash = "6a7eccea95cc27430baaa17f60b919e2431f2b7f08147bea112e2a28d9a49bbf" +content-hash = "ac855e9d941ba4dc041d553e018cd808886e32250ef32e288ab637652c1c4a60" diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index a169e9c6..ef3ca1c7 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -16,6 +16,7 @@ requests = "*" scikit-learn = "*" scipy = "!= 1.14.1" scrapy = "*" +scrapy-extensions = ">=1.0.1" seaborn = "*" statsmodels = "*" tabulate = "*" -- GitLab From 4e467dd622614f1eb1c3c56bd09e35874237f59c Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 15:20:21 +0300 Subject: [PATCH 008/147] First version of parse_match_html() --- experiments/elo/elo/bga_spider.py | 27 +++++++++++++++++++++++++-- 1 file changed, 25 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index 478c9684..9e9a54ad 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -6,7 +6,7 @@ import math import re from typing import Any -from scrapy import FormRequest, Request, Spider +from scrapy import FormRequest, Request, Selector, Spider from scrapy.http import Response, TextResponse @@ -154,8 +154,10 @@ class BgaSpider(Spider): if self.scrape_matches: yield Request( url=self.build_match_url(response, game["id"]), + method="GET", callback=self.parse_matches, meta={"game_id": game["id"]}, + priority=0, headers={"X-Request-Token": self.request_token}, ) @@ -241,7 +243,28 @@ class BgaSpider(Spider): match["game_id"] = game_id match["type"] = "match" match["scraped_at"] = now - # TODO: parse match["html"] for match results + match["players"] = list(self.parse_match_html(match["html"])) yield match # TODO: request next page + + def parse_match_html(self, html: str) -> Generator[dict[str, Any]]: + selector = Selector(text=html) + for player_div in selector.css("div.board-score-entry"): + rank_text = player_div.xpath("text()[1]").get() + match = re.search(r"(\d+)(?:st|nd|rd|th)", rank_text or "") + place = int(match.group(1)) if match else None + + player_link = player_div.css("a.playername") + player_id = int(player_link.xpath("@href").get().split("=")[-1]) + player_name = player_link.xpath("text()").get() + score_text = player_div.xpath("following-sibling::text()[1]").get() + score_str = re.search(r"(\d+)", score_text or "").group(1) + score = int(score_str) if score_str else None + + yield { + "player_id": player_id, + "player_name": player_name, + "place": place, + "score": score, + } -- GitLab From 4ec96a2bd430ac9f01e963d60ca950e9cf0ba896 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 16:18:48 +0300 Subject: [PATCH 009/147] More robust parse_match_html() --- experiments/elo/elo/bga_spider.py | 25 +++++++++++++++++-------- 1 file changed, 17 insertions(+), 8 deletions(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index 9e9a54ad..8c59957b 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -85,6 +85,9 @@ class BgaSpider(Spider): match_path = jmespath.compile("data.news") max_matches_per_page = 9500 + ordinal_regex = re.compile(r"(\d+)(st|nd|rd|th)") + integer_regex = re.compile(r"(\d+)") + def parse(self, response: Response) -> Request: return FormRequest( url=response.urljoin(self.request_token_url), @@ -243,24 +246,30 @@ class BgaSpider(Spider): match["game_id"] = game_id match["type"] = "match" match["scraped_at"] = now - match["players"] = list(self.parse_match_html(match["html"])) + try: + match["players"] = list(self.parse_match_html(match["html"])) + except Exception: + match["players"] = None yield match # TODO: request next page def parse_match_html(self, html: str) -> Generator[dict[str, Any]]: - selector = Selector(text=html) + selector = Selector(text=html, type="html") + for player_div in selector.css("div.board-score-entry"): - rank_text = player_div.xpath("text()[1]").get() - match = re.search(r"(\d+)(?:st|nd|rd|th)", rank_text or "") + rank_text = player_div.xpath("text()[1]").get() or "" + match = self.ordinal_regex.search(rank_text) place = int(match.group(1)) if match else None player_link = player_div.css("a.playername") - player_id = int(player_link.xpath("@href").get().split("=")[-1]) + 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_div.xpath("following-sibling::text()[1]").get() - score_str = re.search(r"(\d+)", score_text or "").group(1) - score = int(score_str) if score_str else None + score_text = player_div.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, -- GitLab From 57d36c02a11ebf1c8fefb1d4be80bd536a868b8f Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 16:30:09 +0300 Subject: [PATCH 010/147] Fix a bug in parse_match_html() --- experiments/elo/elo/bga_spider.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index 8c59957b..08981b29 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -267,7 +267,7 @@ class BgaSpider(Spider): player_id = int(player_href.split("=")[-1]) if "=" in player_href else None player_name = player_link.xpath("text()").get() - score_text = player_div.xpath("following-sibling::text()[1]").get() or "" + 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 -- GitLab From 2fd7fc28ac0ba551a55dd177f771f8572af4a4e9 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 16:44:13 +0300 Subject: [PATCH 011/147] Request more matches --- experiments/elo/elo/bga_spider.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index 08981b29..fb434f11 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -242,6 +242,9 @@ class BgaSpider(Spider): 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" @@ -250,9 +253,19 @@ class BgaSpider(Spider): 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 match - # TODO: request next page + 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") -- GitLab From 25f92cd29398185c7249d50f9377e117ba3feec3 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 16:46:58 +0300 Subject: [PATCH 012/147] dojo.preventCache --- experiments/elo/elo/bga_spider.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index fb434f11..d6202cfc 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -4,6 +4,7 @@ import jmespath import json import math import re +import time from typing import Any from scrapy import FormRequest, Request, Selector, Spider @@ -214,7 +215,7 @@ class BgaSpider(Spider): "id": str(game_id), "social": "false", "per_page": str(self.max_matches_per_page), - "dojo.preventCache": "1", # TODO: timestamp + "dojo.preventCache": str(int(time.time() * 1000)), } if from_time is not None and from_id is not None: -- GitLab From 19839e42c685aaa717d61e4b00e796fab6d3a8b3 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 16:50:40 +0300 Subject: [PATCH 013/147] Go! --- experiments/elo/elo/bga_spider.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index d6202cfc..cd36726f 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -42,7 +42,7 @@ class BgaSpider(Spider): custom_settings = { "COOKIES_ENABLED": True, - "COOKIES_DEBUG": True, + "COOKIES_DEBUG": False, "DOWNLOAD_DELAY": 1, "CONCURRENT_REQUESTS_PER_DOMAIN": 4, "LOG_FORMATTER": "scrapy_extensions.QuietLogFormatter", @@ -81,7 +81,7 @@ class BgaSpider(Spider): ranking_path = jmespath.compile("data.ranks") max_rank_scraped = None - scrape_matches = False + scrape_matches = True base_match_url = "/message/board" match_path = jmespath.compile("data.news") max_matches_per_page = 9500 -- GitLab From 08f95edac92708e4eefad975e5b6582369fbf1ae Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 25 Apr 2025 18:49:08 +0300 Subject: [PATCH 014/147] Slow down downloads --- experiments/elo/elo/bga_spider.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index cd36726f..3241828b 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -43,8 +43,8 @@ class BgaSpider(Spider): custom_settings = { "COOKIES_ENABLED": True, "COOKIES_DEBUG": False, - "DOWNLOAD_DELAY": 1, - "CONCURRENT_REQUESTS_PER_DOMAIN": 4, + "DOWNLOAD_DELAY": 10, + "CONCURRENT_REQUESTS_PER_DOMAIN": 16, "LOG_FORMATTER": "scrapy_extensions.QuietLogFormatter", "FEED_EXPORT_BATCH_ITEM_COUNT": 100_000, "FEEDS": { -- GitLab From d9802666cd25cff0ceb93db73714fb60f971db17 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 26 Apr 2025 14:34:29 +0300 Subject: [PATCH 015/147] Added merge_matches() --- experiments/elo/elo/data.py | 45 +++++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/experiments/elo/elo/data.py b/experiments/elo/elo/data.py index d3743361..f07f4308 100644 --- a/experiments/elo/elo/data.py +++ b/experiments/elo/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.Int64, + "timestamp": pl.Int64, + "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, -- GitLab From 56ab9dbe9970f840f628158d2de57b4bb3f0f792 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 26 Apr 2025 22:29:17 +0300 Subject: [PATCH 016/147] Added merge_matches() to data.__main__ --- experiments/elo/elo/data.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/data.py b/experiments/elo/elo/data.py index f07f4308..0ccfb0d9 100644 --- a/experiments/elo/elo/data.py +++ b/experiments/elo/elo/data.py @@ -93,8 +93,8 @@ def merge_matches( progress_bar: bool = False, ) -> None: schema = { - "id": pl.Int64, - "timestamp": pl.Int64, + "id": pl.String, + "timestamp": pl.String, "game_id": pl.Int64, "players": pl.List( pl.Struct( @@ -239,3 +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, + ) -- GitLab From 5b159f84a986f9037824f0680a3fbef0b19eb4bd Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 26 Apr 2025 22:29:37 +0300 Subject: [PATCH 017/147] First version of Matches.py --- experiments/elo/Matches.py | 69 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 experiments/elo/Matches.py diff --git a/experiments/elo/Matches.py b/experiments/elo/Matches.py new file mode 100644 index 00000000..1743ce09 --- /dev/null +++ b/experiments/elo/Matches.py @@ -0,0 +1,69 @@ +# --- +# 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() -- GitLab From 4af16260bf140da079eecd1b9b0a8b897d8f9a3c Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 26 Apr 2025 22:40:25 +0300 Subject: [PATCH 018/147] Better priority handling --- experiments/elo/elo/bga_spider.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index 3241828b..fa6d6ff3 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -156,12 +156,14 @@ class BgaSpider(Spider): ) 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=0, + priority=priority, headers={"X-Request-Token": self.request_token}, ) @@ -200,7 +202,7 @@ class BgaSpider(Spider): "mode": "elo", }, meta={"game_id": game_id}, - priority=-max_rank_no, + priority=response.request.priority - 1, ) def build_match_url( -- GitLab From 9de56cc00e7ecbd8fae8ab40e12da7732ca23da2 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 26 Apr 2025 22:55:07 +0300 Subject: [PATCH 019/147] Blackened Matches.py --- experiments/elo/Matches.py | 33 ++++++++++++++++----------------- 1 file changed, 16 insertions(+), 17 deletions(-) diff --git a/experiments/elo/Matches.py b/experiments/elo/Matches.py index 1743ce09..528af257 100644 --- a/experiments/elo/Matches.py +++ b/experiments/elo/Matches.py @@ -43,27 +43,26 @@ 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(), - ) + 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 = 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() +all_games.filter(pl.col("min_players") == 2).filter( + pl.col("max_players") == 2 +).describe() -- GitLab From 997a630a9f0b759e193f1998fc160d464fb4d72f Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 26 Apr 2025 22:58:20 +0300 Subject: [PATCH 020/147] Factor out ranking_url --- experiments/elo/elo/bga_spider.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index fa6d6ff3..7cfb35e1 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -78,6 +78,7 @@ class BgaSpider(Spider): game_list_regex = re.compile("globalUserInfos=(.+);$", flags=re.MULTILINE) scrape_rankings = False + ranking_url = "/gamepanel/gamepanel/getRanking.html" ranking_path = jmespath.compile("data.ranks") max_rank_scraped = None @@ -142,7 +143,7 @@ class BgaSpider(Spider): if self.scrape_rankings: yield FormRequest( - url=response.urljoin("/gamepanel/gamepanel/getRanking.html"), + url=response.urljoin(self.ranking_url), method="POST", formdata={ "game": str(game["id"]), -- GitLab From eaf33f2682ebde8af1d0fd7df9745ad5625785ac Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 29 Apr 2025 13:52:59 +0300 Subject: [PATCH 021/147] poetry update --- experiments/elo/poetry.lock | 139 ++++++++++++++++++++---------------- 1 file changed, 76 insertions(+), 63 deletions(-) diff --git a/experiments/elo/poetry.lock b/experiments/elo/poetry.lock index b1612c92..a6780355 100644 --- 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description = "Easily download, build, install, upgrade, and uninstall Python packages" optional = false python-versions = ">=3.9" files = [ - {file = "setuptools-79.0.1-py3-none-any.whl", hash = "sha256:e147c0549f27767ba362f9da434eab9c5dc0045d5304feb602a0af001089fc51"}, - {file = "setuptools-79.0.1.tar.gz", hash = "sha256:128ce7b8f33c3079fd1b067ecbb4051a66e8526e7b65f6cec075dfc650ddfa88"}, + {file = "setuptools-80.0.0-py3-none-any.whl", hash = "sha256:a38f898dcd6e5380f4da4381a87ec90bd0a7eec23d204a5552e80ee3cab6bd27"}, + {file = "setuptools-80.0.0.tar.gz", hash = "sha256:c40a5b3729d58dd749c0f08f1a07d134fb8a0a3d7f87dc33e7c5e1f762138650"}, ] [package.extras] -- GitLab From 65c043ad1f76f6281fb082610e3585b3a8dd1b0e Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 29 Apr 2025 14:40:25 +0300 Subject: [PATCH 022/147] Added IPC.py --- experiments/elo/IPC.py | 63 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 63 insertions(+) create mode 100644 experiments/elo/IPC.py diff --git a/experiments/elo/IPC.py b/experiments/elo/IPC.py new file mode 100644 index 00000000..f230e8bf --- /dev/null +++ b/experiments/elo/IPC.py @@ -0,0 +1,63 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.17.1 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% +import jupyter_black +import polars as pl +from itertools import batched +from pathlib import Path +from tqdm import tqdm + +jupyter_black.load() +pl.Config.set_tbl_rows(100) + +# %% +file_batch_size = 10 +in_dir = Path("results").resolve() +out_dir = in_dir / "arrow" +out_dir.mkdir(parents=True, exist_ok=True) +file_batch_size, in_dir, out_dir + +# %% +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_{i:05d}.arrow") -- GitLab From c59224ae56f527cd6a7a84c037c82dbc51037369 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 29 Apr 2025 14:46:04 +0300 Subject: [PATCH 023/147] Added date and machine suffix --- experiments/elo/IPC.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/experiments/elo/IPC.py b/experiments/elo/IPC.py index f230e8bf..79649886 100644 --- a/experiments/elo/IPC.py +++ b/experiments/elo/IPC.py @@ -16,6 +16,7 @@ # %% import jupyter_black import polars as pl +from datetime import datetime from itertools import batched from pathlib import Path from tqdm import tqdm @@ -24,11 +25,16 @@ jupyter_black.load() pl.Config.set_tbl_rows(100) # %% -file_batch_size = 10 in_dir = Path("results").resolve() out_dir = in_dir / "arrow" out_dir.mkdir(parents=True, exist_ok=True) -file_batch_size, in_dir, out_dir +in_dir, out_dir + +# %% +file_batch_size = 10 +ts = datetime.now().replace(microsecond=0).isoformat().replace(":", "-") +suffix = f"{ts}-euler-" +file_batch_size, suffix # %% schema = { @@ -60,4 +66,4 @@ for i, batch in enumerate(batched(tqdm(in_dir.glob("matches-*.jl")), file_batch_ pl.col("scraped_at").str.to_datetime(time_zone="UTC"), ) ) - df.sink_ipc(out_dir / f"matches_{i:05d}.arrow") + df.sink_ipc(out_dir / f"matches-{suffix}{i:05d}.arrow") -- GitLab From fbefcc00f7d725cc2646fd22cd902404fd46a66e Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 29 Apr 2025 21:13:15 +0300 Subject: [PATCH 024/147] ChatGPT version of RankOrderedLogitElo --- experiments/elo/elo/elo_ratings_multi.py | 140 +++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 experiments/elo/elo/elo_ratings_multi.py diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py new file mode 100644 index 00000000..1320e0a3 --- /dev/null +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -0,0 +1,140 @@ +from __future__ import annotations + +from collections import defaultdict +from itertools import permutations +import math +import random + + +class RankOrderedLogitElo: + """ + Multiplayer Elo using the Plackett–Luce (rank‑ordered logit) model. + + Parameters + ---------- + k : float, default 32 + Learning rate (just as in standard Elo). Tune this on historical + data by minimising a Brier‑type loss, exactly like in the paper. + """ + + def __init__(self, k: float = 32.0): + self.k = k + self.R = defaultdict(float) # default rating = 0.0 + + # ------------------------------------------------------------------ # + # Internal helpers + # ------------------------------------------------------------------ # + + @staticmethod + def _pl_prob(order, expR): + """ + Probability of a specific ranking *order* under Plackett‑Luce. + + order : tuple[int] + One permutation of player indices, winner first. + expR : list[float] + pre‑computed exponentials of the players' ratings. + """ + p = 1.0 + denom = sum(expR) + for idx in order: + p *= expR[idx] / denom + denom -= expR[idx] + return p + + def _expected_normalised_scores(self, players, prizes, max_enum=6, mc_samples=5000): + """ + Expected normalised prize for each player. + + players : list[hashable] + prizes : list[float] (len == len(players)) + """ + n = len(players) + assert n == len(prizes), "players and prizes length mismatch" + max_prize = max(prizes) + norm_prize = [p / max_prize for p in prizes] + ratings = [self.R[p] for p in players] + expR = [math.exp(r) for r in ratings] + + expected = [0.0] * n + + # ---- exact enumeration (factorial) for small fields ---- # + if n <= max_enum: + for order in permutations(range(n)): + prob = self._pl_prob(order, expR) + for pos, idx in enumerate(order): + expected[idx] += norm_prize[pos] * prob + else: + # ---- Monte‑Carlo approximation for larger n ---- # + for _ in range(mc_samples): + remaining = list(range(n)) + ranking = [] + while remaining: + # draw winner according to current weights + weights = [expR[i] for i in remaining] + chosen_idx = random.choices(remaining, weights)[0] + ranking.append(chosen_idx) + remaining.remove(chosen_idx) + + # compute prob of this ranking (needed for importance‑weighting) + prob = self._pl_prob(tuple(ranking), expR) + for pos, idx in enumerate(ranking): + expected[idx] += norm_prize[pos] * prob # prob already weights + + # normalise because probabilities sum to 1 in expectation + total_prob = ( + sum(self._pl_prob(order, expR) for order in permutations(range(n))) + if n <= max_enum + else 1.0 + ) + expected = [e / total_prob for e in expected] + + return expected + + # ------------------------------------------------------------------ # + # Public API + # ------------------------------------------------------------------ # + + def rate_match(self, players, prizes, actual_prizes): + """ + Update ratings after one multiplayer match. + + players : list[hashable] ordered arbitrarily + prizes : list[float] prize money (or points) by finishing position + actual_prizes : list[float] prize actually earned by each *player* + (same order as `players`) + """ + max_prize = max(prizes) + norm_actual = [s / max_prize for s in actual_prizes] + + expected = self._expected_normalised_scores(players, prizes) + + for idx, p in enumerate(players): + self.R[p] += self.k * (norm_actual[idx] - expected[idx]) + + # ------------------------------------------------------------------ # + # Convenience + # ------------------------------------------------------------------ # + + def rating(self, player): + return self.R[player] + + +# -------------------------------------------------------------------------- # +# Demo: 4‑player free‑for‑all (prizes 10‑6‑4‑2) +# -------------------------------------------------------------------------- # +elo = RankOrderedLogitElo(k=24) + +players = ["A", "B", "C", "D"] +prizes = [10, 6, 4, 2] # winner‑takes‑most + +# Suppose the first match ends A‑B‑C‑D +actual_prizes = [10, 6, 4, 2] +print(elo.rate_match(players, prizes, actual_prizes)) + +# Second match D‑B‑A‑C +actual_prizes = [10, 6, 4, 2] # but reorder to players' list order +print(elo.rate_match(players, prizes, [4, 6, 2, 10])) + +# Show current ratings +print({p: round(elo.rating(p), 2) for p in players}) -- GitLab From f0ccd1958f5e63157ef5f523aa033cac1cac4934 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 29 Apr 2025 21:33:04 +0300 Subject: [PATCH 025/147] Using numpy --- experiments/elo/elo/elo_ratings_multi.py | 217 ++++++++++------------- 1 file changed, 91 insertions(+), 126 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 1320e0a3..ee60f7b5 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -1,140 +1,105 @@ from __future__ import annotations -from collections import defaultdict -from itertools import permutations -import math -import random +import numpy as np +import itertools +from typing import List, Any, Optional class RankOrderedLogitElo: """ - Multiplayer Elo using the Plackett–Luce (rank‑ordered logit) model. - - Parameters - ---------- - k : float, default 32 - Learning rate (just as in standard Elo). Tune this on historical - data by minimising a Brier‑type loss, exactly like in the paper. + A vectorised, numpy-based implementation of the rank-ordered-logit (Plackett–Luce) Elo. + Defaults to linear pseudo-scores if no prize structure is given. """ def __init__(self, k: float = 32.0): self.k = k - self.R = defaultdict(float) # default rating = 0.0 - - # ------------------------------------------------------------------ # - # Internal helpers - # ------------------------------------------------------------------ # - - @staticmethod - def _pl_prob(order, expR): + self.ratings = {} # player_id -> rating (float), default 0.0 + + def _get_rating_array(self, players: List[Any]) -> np.ndarray: + # Fetch current ratings (default 0.0) + return np.array([self.ratings.get(p, 0.0) for p in players], dtype=float) + + def _set_rating_array(self, players: List[Any], arr: np.ndarray): + # Write back updated ratings + for p, r in zip(players, arr): + self.ratings[p] = float(r) + + def rate( + self, + players: List[Any], + ranks: List[int], + prizes: Optional[List[float]] = None, + ): """ - Probability of a specific ranking *order* under Plackett‑Luce. - - order : tuple[int] - One permutation of player indices, winner first. - expR : list[float] - pre‑computed exponentials of the players' ratings. - """ - p = 1.0 - denom = sum(expR) - for idx in order: - p *= expR[idx] / denom - denom -= expR[idx] - return p - - def _expected_normalised_scores(self, players, prizes, max_enum=6, mc_samples=5000): - """ - Expected normalised prize for each player. - - players : list[hashable] - prizes : list[float] (len == len(players)) + Update ratings for one match. + + players : list of player IDs + ranks : list of finishing positions (1 = first, 2 = second, ...). + Equal ranks are allowed (ties). + prizes : optional list of payoffs π_k for each position in [1..n]; + length must equal len(players). If None, uses linear + pseudo-scores (n - rank)/(n - 1). """ n = len(players) - assert n == len(prizes), "players and prizes length mismatch" - max_prize = max(prizes) - norm_prize = [p / max_prize for p in prizes] - ratings = [self.R[p] for p in players] - expR = [math.exp(r) for r in ratings] - - expected = [0.0] * n - - # ---- exact enumeration (factorial) for small fields ---- # - if n <= max_enum: - for order in permutations(range(n)): - prob = self._pl_prob(order, expR) - for pos, idx in enumerate(order): - expected[idx] += norm_prize[pos] * prob - else: - # ---- Monte‑Carlo approximation for larger n ---- # - for _ in range(mc_samples): - remaining = list(range(n)) - ranking = [] - while remaining: - # draw winner according to current weights - weights = [expR[i] for i in remaining] - chosen_idx = random.choices(remaining, weights)[0] - ranking.append(chosen_idx) - remaining.remove(chosen_idx) - - # compute prob of this ranking (needed for importance‑weighting) - prob = self._pl_prob(tuple(ranking), expR) - for pos, idx in enumerate(ranking): - expected[idx] += norm_prize[pos] * prob # prob already weights - - # normalise because probabilities sum to 1 in expectation - total_prob = ( - sum(self._pl_prob(order, expR) for order in permutations(range(n))) - if n <= max_enum - else 1.0 - ) - expected = [e / total_prob for e in expected] - - return expected - - # ------------------------------------------------------------------ # - # Public API - # ------------------------------------------------------------------ # - - def rate_match(self, players, prizes, actual_prizes): - """ - Update ratings after one multiplayer match. - - players : list[hashable] ordered arbitrarily - prizes : list[float] prize money (or points) by finishing position - actual_prizes : list[float] prize actually earned by each *player* - (same order as `players`) - """ - max_prize = max(prizes) - norm_actual = [s / max_prize for s in actual_prizes] + ratings_arr = self._get_rating_array(players) + exp_r = np.exp(ratings_arr) - expected = self._expected_normalised_scores(players, prizes) - - for idx, p in enumerate(players): - self.R[p] += self.k * (norm_actual[idx] - expected[idx]) - - # ------------------------------------------------------------------ # - # Convenience - # ------------------------------------------------------------------ # - - def rating(self, player): - return self.R[player] - - -# -------------------------------------------------------------------------- # -# Demo: 4‑player free‑for‑all (prizes 10‑6‑4‑2) -# -------------------------------------------------------------------------- # -elo = RankOrderedLogitElo(k=24) - -players = ["A", "B", "C", "D"] -prizes = [10, 6, 4, 2] # winner‑takes‑most - -# Suppose the first match ends A‑B‑C‑D -actual_prizes = [10, 6, 4, 2] -print(elo.rate_match(players, prizes, actual_prizes)) - -# Second match D‑B‑A‑C -actual_prizes = [10, 6, 4, 2] # but reorder to players' list order -print(elo.rate_match(players, prizes, [4, 6, 2, 10])) - -# Show current ratings -print({p: round(elo.rating(p), 2) for p in players}) + # Build the pay-off vector π + if prizes is not None: + pi = np.array(prizes, dtype=float) + else: + ranks_arr = np.array(ranks, dtype=float) + pi = (n - ranks_arr) / (n - 1) + + pi_max = pi.max() + S_hat = pi / pi_max + + # Enumerate all permutations of 0..n-1 + perms = np.array( + list(itertools.permutations(range(n))), + dtype=int, + ) # shape (F, n) + F = perms.shape[0] + + # Compute Plackett–Luce probabilities for each perm + # stage_probs[k] = exp_r[perms[:,k]] / sum(exp_r[perms[:,k:]], axis=1) + stage_probs = [] + for k in range(n): + num = exp_r[perms[:, k]] + denom = np.sum(exp_r[perms[:, k:]], axis=1) + stage_probs.append(num / denom) + perm_probs = np.prod(np.stack(stage_probs, axis=1), axis=1) # shape (F,) + + # Build payoff matrix: payoff_mat[j,i] = π at the position where player i sits in perm j + payoff_mat = np.zeros((F, n), dtype=float) + rows = np.arange(F)[:, None] + payoff_mat[rows, perms] = pi[None, :] + + # Expected raw payoff + E_raw = (perm_probs[:, None] * payoff_mat).sum(axis=0) # shape (n,) + E_hat = E_raw / pi_max + + # Rating update + ratings_arr += self.k * (S_hat - E_hat) + self._set_rating_array(players, ratings_arr) + + def get_rating(self, player: Any) -> float: + return self.ratings.get(player, 0.0) + + def batch_ratings(self, players: List[Any]) -> np.ndarray: + return self._get_rating_array(players) + + +# Example usage: +if __name__ == "__main__": + elo = RankOrderedLogitElo(k=24.0) + players = ["Alice", "Bob", "Carol", "Dave"] + ranks = [1, 2, 2, 4] # Carol tied second with Bob + # No prizes → linear pseudo-scores used + elo.rate(players, ranks) + print({p: elo.get_rating(p) for p in players}) + + # With explicit prize structure: + prizes = [10, 6, 4, 2] + elo.rate(players, ranks, prizes=prizes) + print({p: elo.get_rating(p) for p in players}) -- GitLab From 6652cd2134eab95c17d567d9321eee19b07b8af4 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 29 Apr 2025 21:36:42 +0300 Subject: [PATCH 026/147] Bring back MC --- experiments/elo/elo/elo_ratings_multi.py | 115 +++++++++++------------ 1 file changed, 55 insertions(+), 60 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index ee60f7b5..fd51dab5 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -1,26 +1,24 @@ -from __future__ import annotations - import numpy as np import itertools +import numpy.random as npr from typing import List, Any, Optional class RankOrderedLogitElo: """ - A vectorised, numpy-based implementation of the rank-ordered-logit (Plackett–Luce) Elo. - Defaults to linear pseudo-scores if no prize structure is given. + Numpy-based rank-ordered-logit Elo with Monte-Carlo fallback for large fields. """ - def __init__(self, k: float = 32.0): + def __init__(self, k: float = 32.0, max_exact: int = 6, mc_samples: int = 5000): self.k = k - self.ratings = {} # player_id -> rating (float), default 0.0 + self.max_exact = max_exact + self.mc_samples = mc_samples + self.ratings = {} # player_id -> rating - def _get_rating_array(self, players: List[Any]) -> np.ndarray: - # Fetch current ratings (default 0.0) + def _get_ratings(self, players: List[Any]) -> np.ndarray: return np.array([self.ratings.get(p, 0.0) for p in players], dtype=float) - def _set_rating_array(self, players: List[Any], arr: np.ndarray): - # Write back updated ratings + def _set_ratings(self, players: List[Any], arr: np.ndarray): for p, r in zip(players, arr): self.ratings[p] = float(r) @@ -30,21 +28,11 @@ class RankOrderedLogitElo: ranks: List[int], prizes: Optional[List[float]] = None, ): - """ - Update ratings for one match. - - players : list of player IDs - ranks : list of finishing positions (1 = first, 2 = second, ...). - Equal ranks are allowed (ties). - prizes : optional list of payoffs π_k for each position in [1..n]; - length must equal len(players). If None, uses linear - pseudo-scores (n - rank)/(n - 1). - """ n = len(players) - ratings_arr = self._get_rating_array(players) - exp_r = np.exp(ratings_arr) + R = self._get_ratings(players) + expR = np.exp(R) - # Build the pay-off vector π + # define payoff vector π if prizes is not None: pi = np.array(prizes, dtype=float) else: @@ -54,52 +42,59 @@ class RankOrderedLogitElo: pi_max = pi.max() S_hat = pi / pi_max - # Enumerate all permutations of 0..n-1 - perms = np.array( - list(itertools.permutations(range(n))), - dtype=int, - ) # shape (F, n) - F = perms.shape[0] - - # Compute Plackett–Luce probabilities for each perm - # stage_probs[k] = exp_r[perms[:,k]] / sum(exp_r[perms[:,k:]], axis=1) - stage_probs = [] - for k in range(n): - num = exp_r[perms[:, k]] - denom = np.sum(exp_r[perms[:, k:]], axis=1) - stage_probs.append(num / denom) - perm_probs = np.prod(np.stack(stage_probs, axis=1), axis=1) # shape (F,) - - # Build payoff matrix: payoff_mat[j,i] = π at the position where player i sits in perm j - payoff_mat = np.zeros((F, n), dtype=float) - rows = np.arange(F)[:, None] - payoff_mat[rows, perms] = pi[None, :] + # compute expected payoffs + if n <= self.max_exact: + perms = np.array(list(itertools.permutations(range(n))), dtype=int) + # Plackett-Luce probability for each perm + stage_probs = [] + for k in range(n): + num = expR[perms[:, k]] + denom = np.sum(expR[perms[:, k:]], axis=1) + stage_probs.append(num / denom) + perm_probs = np.prod(np.stack(stage_probs, axis=1), axis=1) + # payoff per perm + rows = np.arange(perms.shape[0])[:, None] + payoff_mat = np.zeros_like( + perm_probs[:, None] * np.zeros((perms.shape[0], n)) + ) + payoff_mat[rows, perms] = pi[None, :] + E_raw = (perm_probs[:, None] * payoff_mat).sum(axis=0) + else: + # Monte Carlo fallback + E_raw = np.zeros(n, dtype=float) + for _ in range(self.mc_samples): + remaining = list(range(n)) + denom = expR.copy() + ranking_pos = [None] * n + for k in range(n): + weights = denom[remaining] + weights_sum = weights.sum() + probs = weights / weights_sum + chosen = npr.choice(remaining, p=probs) + ranking_pos[chosen] = k + remaining.remove(chosen) + # accumulate payoff + for i in range(n): + E_raw[i] += pi[ranking_pos[i]] + E_raw /= self.mc_samples - # Expected raw payoff - E_raw = (perm_probs[:, None] * payoff_mat).sum(axis=0) # shape (n,) E_hat = E_raw / pi_max - # Rating update - ratings_arr += self.k * (S_hat - E_hat) - self._set_rating_array(players, ratings_arr) + # update ratings + R += self.k * (S_hat - E_hat) + self._set_ratings(players, R) def get_rating(self, player: Any) -> float: return self.ratings.get(player, 0.0) def batch_ratings(self, players: List[Any]) -> np.ndarray: - return self._get_rating_array(players) + return self._get_ratings(players) -# Example usage: +# Example usage if __name__ == "__main__": - elo = RankOrderedLogitElo(k=24.0) - players = ["Alice", "Bob", "Carol", "Dave"] - ranks = [1, 2, 2, 4] # Carol tied second with Bob - # No prizes → linear pseudo-scores used - elo.rate(players, ranks) - print({p: elo.get_rating(p) for p in players}) - - # With explicit prize structure: - prizes = [10, 6, 4, 2] - elo.rate(players, ranks, prizes=prizes) + elo = RankOrderedLogitElo(k=24.0, max_exact=6, mc_samples=10000) + players = ["A", "B", "C", "D", "E", "F", "G"] + ranks = [1, 2, 3, 4, 5, 6, 7] + elo.rate(players, ranks) # uses Monte Carlo for n>6 print({p: elo.get_rating(p) for p in players}) -- GitLab From a235a6b6860aa8f0ef0a4da96e91056599d58184 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 08:43:45 +0300 Subject: [PATCH 027/147] Minor cleaning up --- experiments/elo/elo/elo_ratings_multi.py | 36 ++++++++++++++++-------- 1 file changed, 25 insertions(+), 11 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index fd51dab5..ea8e8815 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -1,26 +1,40 @@ import numpy as np import itertools import numpy.random as npr -from typing import List, Any, Optional +from typing import List, Any, Optional, Generic, TypeVar +from collections import defaultdict +ID_TYPE = TypeVar("ID_TYPE") -class RankOrderedLogitElo: + +class RankOrderedLogitElo(Generic[ID_TYPE]): """ - Numpy-based rank-ordered-logit Elo with Monte-Carlo fallback for large fields. + Rank-ordered-logit Elo with Monte-Carlo fallback for large fields. """ - def __init__(self, k: float = 32.0, max_exact: int = 6, mc_samples: int = 5000): - self.k = k + def __init__( + self, + elo_initial: float = 0, + elo_k: float = 32, + elo_scale: float = 400, + max_exact: int = 6, + mc_samples: int = 5_000, + ): + self.elo_initial = elo_initial + self.elo_k = elo_k + self.elo_scale = elo_scale self.max_exact = max_exact self.mc_samples = mc_samples - self.ratings = {} # player_id -> rating + self.elo_ratings: defaultdict[ID_TYPE, float] = defaultdict( + lambda: self.elo_initial + ) def _get_ratings(self, players: List[Any]) -> np.ndarray: - return np.array([self.ratings.get(p, 0.0) for p in players], dtype=float) + return np.array([self.elo_ratings.get(p, 0.0) for p in players], dtype=float) def _set_ratings(self, players: List[Any], arr: np.ndarray): for p, r in zip(players, arr): - self.ratings[p] = float(r) + self.elo_ratings[p] = float(r) def rate( self, @@ -81,11 +95,11 @@ class RankOrderedLogitElo: E_hat = E_raw / pi_max # update ratings - R += self.k * (S_hat - E_hat) + R += self.elo_k * (S_hat - E_hat) self._set_ratings(players, R) def get_rating(self, player: Any) -> float: - return self.ratings.get(player, 0.0) + return self.elo_ratings.get(player, 0.0) def batch_ratings(self, players: List[Any]) -> np.ndarray: return self._get_ratings(players) @@ -93,7 +107,7 @@ class RankOrderedLogitElo: # Example usage if __name__ == "__main__": - elo = RankOrderedLogitElo(k=24.0, max_exact=6, mc_samples=10000) + elo = RankOrderedLogitElo(elo_k=24.0, max_exact=6, mc_samples=10000) players = ["A", "B", "C", "D", "E", "F", "G"] ranks = [1, 2, 3, 4, 5, 6, 7] elo.rate(players, ranks) # uses Monte Carlo for n>6 -- GitLab From e7726f7a56fd0f091156c87c09eb98e500720de2 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 08:46:53 +0300 Subject: [PATCH 028/147] Updated type hints --- experiments/elo/elo/elo_ratings_multi.py | 28 +++++++++++++++--------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index ea8e8815..2f6081b1 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -1,11 +1,16 @@ +from __future__ import annotations + import numpy as np import itertools import numpy.random as npr -from typing import List, Any, Optional, Generic, TypeVar +from typing import Generic, TypeVar, TYPE_CHECKING from collections import defaultdict ID_TYPE = TypeVar("ID_TYPE") +if TYPE_CHECKING: + from collections.abc import Mapping + class RankOrderedLogitElo(Generic[ID_TYPE]): """ @@ -14,33 +19,36 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): def __init__( self, + *, + init_elo_ratings: Mapping[ID_TYPE, float] | None = None, elo_initial: float = 0, elo_k: float = 32, elo_scale: float = 400, max_exact: int = 6, mc_samples: int = 5_000, - ): + ) -> None: self.elo_initial = elo_initial self.elo_k = elo_k self.elo_scale = elo_scale self.max_exact = max_exact self.mc_samples = mc_samples self.elo_ratings: defaultdict[ID_TYPE, float] = defaultdict( - lambda: self.elo_initial + lambda: self.elo_initial, + init_elo_ratings if init_elo_ratings is not None else {}, ) - def _get_ratings(self, players: List[Any]) -> np.ndarray: + def _get_ratings(self, players: list[ID_TYPE]) -> np.ndarray: return np.array([self.elo_ratings.get(p, 0.0) for p in players], dtype=float) - def _set_ratings(self, players: List[Any], arr: np.ndarray): + def _set_ratings(self, players: list[ID_TYPE], arr: np.ndarray): for p, r in zip(players, arr): self.elo_ratings[p] = float(r) def rate( self, - players: List[Any], - ranks: List[int], - prizes: Optional[List[float]] = None, + players: list[ID_TYPE], + ranks: list[int], + prizes: list[float] | None = None, ): n = len(players) R = self._get_ratings(players) @@ -98,10 +106,10 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): R += self.elo_k * (S_hat - E_hat) self._set_ratings(players, R) - def get_rating(self, player: Any) -> float: + def get_rating(self, player: ID_TYPE) -> float: return self.elo_ratings.get(player, 0.0) - def batch_ratings(self, players: List[Any]) -> np.ndarray: + def batch_ratings(self, players: list[ID_TYPE]) -> np.ndarray: return self._get_ratings(players) -- GitLab From d4ec1685f4695cb5811dc37a3b51ea346b2b53c3 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 09:25:33 +0300 Subject: [PATCH 029/147] First version of calculate_probability_matrix() --- experiments/elo/elo/elo_ratings_multi.py | 47 ++++++++++++++++++++++-- 1 file changed, 43 insertions(+), 4 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 2f6081b1..0fc5350e 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -9,7 +9,7 @@ from collections import defaultdict ID_TYPE = TypeVar("ID_TYPE") if TYPE_CHECKING: - from collections.abc import Mapping + from collections.abc import Iterable, Mapping class RankOrderedLogitElo(Generic[ID_TYPE]): @@ -37,10 +37,49 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): init_elo_ratings if init_elo_ratings is not None else {}, ) - def _get_ratings(self, players: list[ID_TYPE]) -> np.ndarray: - return np.array([self.elo_ratings.get(p, 0.0) for p in players], dtype=float) + def calculate_probability_matrix(self, players: Iterable[ID_TYPE]) -> np.ndarray: + players = tuple(players) + num_players = len(players) + if num_players > self.max_exact: + raise NotImplementedError( + f"Monte Carlo fallback not implemented for n>{self.max_exact}" + ) + + probs = np.zeros((num_players, num_players)) + + ratings = self._get_ratings(players) + exp_ratings = 10 ** (ratings / self.elo_scale) - def _set_ratings(self, players: list[ID_TYPE], arr: np.ndarray): + for perm in itertools.permutations(range(num_players)): + prob = 1 + denom = np.sum(exp_ratings) + for i in range(num_players - 1): + prob *= exp_ratings[perm[i]] / denom + denom -= exp_ratings[perm[i]] + for player, position in enumerate(perm): + probs[player, position] += prob + + def _calculate_player_scores(self, players: Mapping[ID_TYPE, float]) -> np.ndarray: + scores = np.array(list(players.values()), dtype=float) + assert np.all(scores >= 0) + assert np.any(scores > 0) + scores /= scores.max() + return scores + + def calculate_elo_ratings( + self, + players: Mapping[ID_TYPE, float], + ) -> defaultdict[ID_TYPE, float]: + # player_ids = list(players.keys()) + # player_scores = self._calculate_player_scores(players) + # probs = self.calculate_probability_matrix(player_ids) + # TODO: finish calculations + return self.elo_ratings + + def _get_ratings(self, players: Iterable[ID_TYPE]) -> np.ndarray: + return np.array([self.elo_ratings[p] for p in players], dtype=float) + + def _set_ratings(self, players: Iterable[ID_TYPE], arr: np.ndarray): for p, r in zip(players, arr): self.elo_ratings[p] = float(r) -- GitLab From 7ac37a4cc58a3d33dbb6400c4cd0b2d269e0f39e Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 10:12:31 +0300 Subject: [PATCH 030/147] Correct bug in calculate_probability_matrix() --- experiments/elo/elo/elo_ratings_multi.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 0fc5350e..304fc6b6 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -40,15 +40,19 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): def calculate_probability_matrix(self, players: Iterable[ID_TYPE]) -> np.ndarray: players = tuple(players) num_players = len(players) + + if num_players < 2: + raise ValueError("At least 2 players are required") + if num_players > self.max_exact: + # TODO: implement Monte Carlo fallback raise NotImplementedError( f"Monte Carlo fallback not implemented for n>{self.max_exact}" ) - probs = np.zeros((num_players, num_players)) - ratings = self._get_ratings(players) exp_ratings = 10 ** (ratings / self.elo_scale) + probs = np.zeros((num_players, num_players)) for perm in itertools.permutations(range(num_players)): prob = 1 @@ -56,9 +60,14 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): for i in range(num_players - 1): prob *= exp_ratings[perm[i]] / denom denom -= exp_ratings[perm[i]] - for player, position in enumerate(perm): + for position, player in enumerate(perm): probs[player, position] += prob + assert np.allclose(probs.sum(axis=0), 1), "Probabilities must sum to 1" + assert np.allclose(probs.sum(axis=1), 1), "Probabilities must sum to 1" + + return probs + def _calculate_player_scores(self, players: Mapping[ID_TYPE, float]) -> np.ndarray: scores = np.array(list(players.values()), dtype=float) assert np.all(scores >= 0) -- GitLab From 368d538ea8f4123a894a2c214c2663cb5f312f1c Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 10:37:28 +0300 Subject: [PATCH 031/147] Added calculate_expected_payoff() --- experiments/elo/elo/elo_ratings_multi.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 304fc6b6..02f2e52b 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -68,6 +68,18 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): return probs + def calculate_expected_payoff( + self, + players: Iterable[ID_TYPE], + rank_payoffs: np.ndarray, + ) -> np.ndarray: + assert rank_payoffs.ndim == 1, "Rank payoffs must be a 1D array" + probs = self.calculate_probability_matrix(players) + assert len(rank_payoffs) == len(probs), ( + "Rank payoffs must be the same length as the number of players" + ) + return probs @ rank_payoffs + def _calculate_player_scores(self, players: Mapping[ID_TYPE, float]) -> np.ndarray: scores = np.array(list(players.values()), dtype=float) assert np.all(scores >= 0) @@ -79,9 +91,6 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): self, players: Mapping[ID_TYPE, float], ) -> defaultdict[ID_TYPE, float]: - # player_ids = list(players.keys()) - # player_scores = self._calculate_player_scores(players) - # probs = self.calculate_probability_matrix(player_ids) # TODO: finish calculations return self.elo_ratings -- GitLab From 1f24f6183c6138acc4ecbc4c1cda9f6610d13dd8 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 10:57:27 +0300 Subject: [PATCH 032/147] Added update_elo_ratings() --- experiments/elo/elo/elo_ratings_multi.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 02f2e52b..26caf111 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -87,20 +87,23 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): scores /= scores.max() return scores - def calculate_elo_ratings( + def update_elo_ratings( self, players: Mapping[ID_TYPE, float], - ) -> defaultdict[ID_TYPE, float]: - # TODO: finish calculations - return self.elo_ratings + ) -> None: + player_ids = tuple(players.keys()) + payoffs = np.array(list(players.values()), dtype=float) + 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.calculate_expected_payoff(player_ids, 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 def _get_ratings(self, players: Iterable[ID_TYPE]) -> np.ndarray: return np.array([self.elo_ratings[p] for p in players], dtype=float) - def _set_ratings(self, players: Iterable[ID_TYPE], arr: np.ndarray): - for p, r in zip(players, arr): - self.elo_ratings[p] = float(r) - def rate( self, players: list[ID_TYPE], -- GitLab From a6d7de76fdad215c02d4e134e989fce8855ffd48 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 11:01:37 +0300 Subject: [PATCH 033/147] Removed unused code --- experiments/elo/elo/elo_ratings_multi.py | 83 +----------------------- 1 file changed, 1 insertion(+), 82 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 26caf111..fbabb776 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -2,7 +2,6 @@ from __future__ import annotations import numpy as np import itertools -import numpy.random as npr from typing import Generic, TypeVar, TYPE_CHECKING from collections import defaultdict @@ -50,7 +49,7 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): f"Monte Carlo fallback not implemented for n>{self.max_exact}" ) - ratings = self._get_ratings(players) + ratings = np.array([self.elo_ratings[p] for p in players], dtype=float) exp_ratings = 10 ** (ratings / self.elo_scale) probs = np.zeros((num_players, num_players)) @@ -100,83 +99,3 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): diffs = (payoffs - expected_payoffs) / max_payoff for player_id, diff in zip(player_ids, diffs): self.elo_ratings[player_id] += self.elo_k * diff - - def _get_ratings(self, players: Iterable[ID_TYPE]) -> np.ndarray: - return np.array([self.elo_ratings[p] for p in players], dtype=float) - - def rate( - self, - players: list[ID_TYPE], - ranks: list[int], - prizes: list[float] | None = None, - ): - n = len(players) - R = self._get_ratings(players) - expR = np.exp(R) - - # define payoff vector π - if prizes is not None: - pi = np.array(prizes, dtype=float) - else: - ranks_arr = np.array(ranks, dtype=float) - pi = (n - ranks_arr) / (n - 1) - - pi_max = pi.max() - S_hat = pi / pi_max - - # compute expected payoffs - if n <= self.max_exact: - perms = np.array(list(itertools.permutations(range(n))), dtype=int) - # Plackett-Luce probability for each perm - stage_probs = [] - for k in range(n): - num = expR[perms[:, k]] - denom = np.sum(expR[perms[:, k:]], axis=1) - stage_probs.append(num / denom) - perm_probs = np.prod(np.stack(stage_probs, axis=1), axis=1) - # payoff per perm - rows = np.arange(perms.shape[0])[:, None] - payoff_mat = np.zeros_like( - perm_probs[:, None] * np.zeros((perms.shape[0], n)) - ) - payoff_mat[rows, perms] = pi[None, :] - E_raw = (perm_probs[:, None] * payoff_mat).sum(axis=0) - else: - # Monte Carlo fallback - E_raw = np.zeros(n, dtype=float) - for _ in range(self.mc_samples): - remaining = list(range(n)) - denom = expR.copy() - ranking_pos = [None] * n - for k in range(n): - weights = denom[remaining] - weights_sum = weights.sum() - probs = weights / weights_sum - chosen = npr.choice(remaining, p=probs) - ranking_pos[chosen] = k - remaining.remove(chosen) - # accumulate payoff - for i in range(n): - E_raw[i] += pi[ranking_pos[i]] - E_raw /= self.mc_samples - - E_hat = E_raw / pi_max - - # update ratings - R += self.elo_k * (S_hat - E_hat) - self._set_ratings(players, R) - - def get_rating(self, player: ID_TYPE) -> float: - return self.elo_ratings.get(player, 0.0) - - def batch_ratings(self, players: list[ID_TYPE]) -> np.ndarray: - return self._get_ratings(players) - - -# Example usage -if __name__ == "__main__": - elo = RankOrderedLogitElo(elo_k=24.0, max_exact=6, mc_samples=10000) - players = ["A", "B", "C", "D", "E", "F", "G"] - ranks = [1, 2, 3, 4, 5, 6, 7] - elo.rate(players, ranks) # uses Monte Carlo for n>6 - print({p: elo.get_rating(p) for p in players}) -- GitLab From 69e0075847ad2bd00091cc99ffa2b99f7c51feaf Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 11:05:50 +0300 Subject: [PATCH 034/147] Small cleanup --- experiments/elo/elo/elo_ratings_multi.py | 17 +++++------------ 1 file changed, 5 insertions(+), 12 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index fbabb776..847d1ff9 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -26,15 +26,15 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): max_exact: int = 6, mc_samples: int = 5_000, ) -> None: - self.elo_initial = elo_initial - self.elo_k = elo_k - self.elo_scale = elo_scale - self.max_exact = max_exact - self.mc_samples = mc_samples self.elo_ratings: defaultdict[ID_TYPE, float] = defaultdict( lambda: self.elo_initial, init_elo_ratings if init_elo_ratings is not None else {}, ) + self.elo_k = elo_k + self.elo_scale = elo_scale + + self.max_exact = max_exact + self.mc_samples = mc_samples def calculate_probability_matrix(self, players: Iterable[ID_TYPE]) -> np.ndarray: players = tuple(players) @@ -79,13 +79,6 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): ) return probs @ rank_payoffs - def _calculate_player_scores(self, players: Mapping[ID_TYPE, float]) -> np.ndarray: - scores = np.array(list(players.values()), dtype=float) - assert np.all(scores >= 0) - assert np.any(scores > 0) - scores /= scores.max() - return scores - def update_elo_ratings( self, players: Mapping[ID_TYPE, float], -- GitLab From 5879fbe98e3a6f67a8d1957c6d962a43faee73b6 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 11:12:56 +0300 Subject: [PATCH 035/147] Automatically assign payoffs if none are explicitly given --- experiments/elo/elo/elo_ratings_multi.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 847d1ff9..fd9770e4 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -81,12 +81,18 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): def update_elo_ratings( self, - players: Mapping[ID_TYPE, float], + players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], ) -> None: - player_ids = tuple(players.keys()) - payoffs = np.array(list(players.values()), dtype=float) + if isinstance(players, Mapping): + player_ids = tuple(players.keys()) + payoffs = np.array(list(players.values()), dtype=float) + else: + player_ids = tuple(players) + payoffs = np.arange(len(player_ids) - 1, -1, -1, dtype=float) + 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.calculate_expected_payoff(player_ids, payoffs) diffs = (payoffs - expected_payoffs) / max_payoff -- GitLab From 6b8c6ccdbee172b31fd70b31a8cd13ee5b729c7b Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 11:21:34 +0300 Subject: [PATCH 036/147] Small changes --- experiments/elo/elo/elo_ratings_multi.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index fd9770e4..49d9c55c 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -43,6 +43,8 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): if num_players < 2: raise ValueError("At least 2 players are required") + # TODO: For two players, default back to regular Elo + if num_players > self.max_exact: # TODO: implement Monte Carlo fallback raise NotImplementedError( @@ -73,10 +75,11 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): rank_payoffs: np.ndarray, ) -> np.ndarray: assert rank_payoffs.ndim == 1, "Rank payoffs must be a 1D array" - probs = self.calculate_probability_matrix(players) - assert len(rank_payoffs) == len(probs), ( + players = tuple(players) + assert len(rank_payoffs) == len(players), ( "Rank payoffs must be the same length as the number of players" ) + probs = self.calculate_probability_matrix(players) return probs @ rank_payoffs def update_elo_ratings( -- GitLab From ceaea4aaae0feae84c86088629b30b84d4816259 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 11:47:16 +0300 Subject: [PATCH 037/147] Simplify prob calculation for two players --- experiments/elo/elo/elo_ratings_multi.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 49d9c55c..06db4496 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -4,6 +4,7 @@ import numpy as np import itertools from typing import Generic, TypeVar, TYPE_CHECKING from collections import defaultdict +from elo.elo_ratings import elo_probability ID_TYPE = TypeVar("ID_TYPE") @@ -36,6 +37,17 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): self.max_exact = max_exact self.mc_samples = mc_samples + def _calculate_probability_matrix_two_players( + self, + players: tuple[ID_TYPE, ID_TYPE], + ) -> np.ndarray: + 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.array([[prob_a, prob_b], [prob_b, prob_a]]) + def calculate_probability_matrix(self, players: Iterable[ID_TYPE]) -> np.ndarray: players = tuple(players) num_players = len(players) @@ -43,7 +55,8 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): if num_players < 2: raise ValueError("At least 2 players are required") - # TODO: For two players, default back to regular Elo + if num_players == 2: + return self._calculate_probability_matrix_two_players(players) if num_players > self.max_exact: # TODO: implement Monte Carlo fallback -- GitLab From aa7704cae8f91996c4260483c632b5b10829c8ae Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 11:49:15 +0300 Subject: [PATCH 038/147] _calculate_probability_matrix_from_simulations() stub --- experiments/elo/elo/elo_ratings_multi.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 06db4496..211703fc 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -48,6 +48,13 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): prob_b = 1 - prob_a return np.array([[prob_a, prob_b], [prob_b, prob_a]]) + def _calculate_probability_matrix_from_simulations( + self, + players: tuple[ID_TYPE, ...], + ) -> np.ndarray: + # TODO: implement Monte Carlo fallback + raise NotImplementedError + def calculate_probability_matrix(self, players: Iterable[ID_TYPE]) -> np.ndarray: players = tuple(players) num_players = len(players) @@ -59,10 +66,7 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): return self._calculate_probability_matrix_two_players(players) if num_players > self.max_exact: - # TODO: implement Monte Carlo fallback - raise NotImplementedError( - f"Monte Carlo fallback not implemented for n>{self.max_exact}" - ) + return self._calculate_probability_matrix_from_simulations(players) ratings = np.array([self.elo_ratings[p] for p in players], dtype=float) exp_ratings = 10 ** (ratings / self.elo_scale) -- GitLab From 44ed95727d21c7399181272f819200d5ca35ac33 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 11:51:58 +0300 Subject: [PATCH 039/147] Factor out _calculate_probability_matrix_from_permutations() --- experiments/elo/elo/elo_ratings_multi.py | 31 +++++++++++++++--------- 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 211703fc..a408aeea 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -55,19 +55,11 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): # TODO: implement Monte Carlo fallback raise NotImplementedError - def calculate_probability_matrix(self, players: Iterable[ID_TYPE]) -> np.ndarray: - players = tuple(players) + def _calculate_probability_matrix_from_permutations( + self, + players: tuple[ID_TYPE, ...], + ) -> np.ndarray: num_players = len(players) - - if num_players < 2: - raise ValueError("At least 2 players are required") - - if num_players == 2: - return self._calculate_probability_matrix_two_players(players) - - if num_players > self.max_exact: - return self._calculate_probability_matrix_from_simulations(players) - ratings = np.array([self.elo_ratings[p] for p in players], dtype=float) exp_ratings = 10 ** (ratings / self.elo_scale) probs = np.zeros((num_players, num_players)) @@ -86,6 +78,21 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): return probs + def calculate_probability_matrix(self, players: Iterable[ID_TYPE]) -> np.ndarray: + players = tuple(players) + num_players = len(players) + + if num_players < 2: + raise ValueError("At least 2 players are required") + + if num_players == 2: + return self._calculate_probability_matrix_two_players(players) + + if num_players > self.max_exact: + return self._calculate_probability_matrix_from_simulations(players) + + return self._calculate_probability_matrix_from_permutations(players) + def calculate_expected_payoff( self, players: Iterable[ID_TYPE], -- GitLab From 846d6a45b784bf9ea1d4a57a760be51ec1118fbb Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 12:15:25 +0300 Subject: [PATCH 040/147] Capture differences for loss calculations --- experiments/elo/elo/elo_ratings_multi.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index a408aeea..534433cd 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -109,7 +109,7 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): def update_elo_ratings( self, players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], - ) -> None: + ) -> np.ndarray: if isinstance(players, Mapping): player_ids = tuple(players.keys()) payoffs = np.array(list(players.values()), dtype=float) @@ -125,3 +125,5 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): 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 -- GitLab From c16c0982e2ceefc515ce5cc9eae1885487759351 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 12:21:23 +0300 Subject: [PATCH 041/147] Added update_elo_ratings_batch() --- experiments/elo/elo/elo_ratings_multi.py | 26 +++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 534433cd..89116b4e 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -9,7 +9,7 @@ from elo.elo_ratings import elo_probability ID_TYPE = TypeVar("ID_TYPE") if TYPE_CHECKING: - from collections.abc import Iterable, Mapping + from collections.abc import Generator, Iterable, Mapping class RankOrderedLogitElo(Generic[ID_TYPE]): @@ -127,3 +127,27 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): self.elo_ratings[player_id] += self.elo_k * diff return diffs + + def _update_elo_ratings_batch( + self, + matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], + ) -> Generator[np.ndarray]: + for match in matches: + yield self.update_elo_ratings(match) + + def update_elo_ratings_batch( + self, + matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], + full_results: bool = False, + progress_bar: bool = False, + ) -> np.ndarray | None: + if progress_bar: + from tqdm import tqdm + + matches = tqdm(matches) + + if full_results: + return np.array(list(self._update_elo_ratings_batch(matches))) + + for _ in self._update_elo_ratings_batch(matches): + pass -- GitLab From d8fd42655fbadfd2d03ac5b5fd0bb82c32b10de3 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 12:24:58 +0300 Subject: [PATCH 042/147] Added calculate_loss_multi() --- experiments/elo/elo/optimal_k.py | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py index c4b1cb34..ab17c244 100644 --- a/experiments/elo/elo/optimal_k.py +++ b/experiments/elo/elo/optimal_k.py @@ -1,13 +1,15 @@ from __future__ import annotations -from collections.abc import Iterable -from typing import TypeVar +from typing import TypeVar, TYPE_CHECKING import numpy as np from scipy.optimize import minimize_scalar from elo.elo_ratings import calculate_elo_ratings +from elo.elo_ratings_multi import RankOrderedLogitElo +if TYPE_CHECKING: + from collections.abc import Iterable, Mapping ID_TYPE = TypeVar("ID_TYPE") @@ -40,6 +42,22 @@ def calculate_loss( return np.mean(errors**2) +def calculate_loss_multi( + *, + matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], + elo_k: float, + elo_scale: float, + progress_bar: bool = False, +) -> np.float64: + elo = RankOrderedLogitElo(elo_k=elo_k, elo_scale=elo_scale) + errors = elo.update_elo_ratings_batch( + matches, + full_results=True, + progress_bar=progress_bar, + ) + return np.mean(errors**2) + + def approximate_optimal_k( *, player_1_ids: Iterable[ID_TYPE], -- GitLab From db70a74b9ae075c58b85d47bdf7ff5d0033f9ae5 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 12:27:26 +0300 Subject: [PATCH 043/147] Added approximate_optimal_k_multi() --- experiments/elo/elo/optimal_k.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py index ab17c244..70d0c7dc 100644 --- a/experiments/elo/elo/optimal_k.py +++ b/experiments/elo/elo/optimal_k.py @@ -87,3 +87,28 @@ def approximate_optimal_k( ) return result.x + + +def approximate_optimal_k_multi( + *, + matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], + min_elo_k: float = 0, + max_elo_k: float = 160, + elo_scale: float = 400, +) -> np.float64: + matches = list(matches) + + def loss(elo_k: float) -> np.float64: + return calculate_loss_multi( + matches=matches, + 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 -- GitLab From 427a5de7e600a33f0c7708904e84dc4193dda58c Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 13:20:27 +0300 Subject: [PATCH 044/147] Fix type checking --- experiments/elo/elo/elo_ratings.py | 7 +++++-- experiments/elo/elo/elo_ratings_multi.py | 5 +++-- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py index ccc3c01a..4e84a4c2 100644 --- a/experiments/elo/elo/elo_ratings.py +++ b/experiments/elo/elo/elo_ratings.py @@ -1,7 +1,10 @@ from __future__ import annotations + from collections import defaultdict -from collections.abc import Iterable, Mapping -from typing import TypeVar +from typing import TypeVar, TYPE_CHECKING + +if TYPE_CHECKING: + from collections.abc import Iterable, Mapping ID_TYPE = TypeVar("ID_TYPE") diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 89116b4e..0f98b4ff 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -2,14 +2,15 @@ from __future__ import annotations import numpy as np import itertools -from typing import Generic, TypeVar, TYPE_CHECKING from collections import defaultdict +from collections.abc import Mapping +from typing import Generic, TypeVar, TYPE_CHECKING from elo.elo_ratings import elo_probability ID_TYPE = TypeVar("ID_TYPE") if TYPE_CHECKING: - from collections.abc import Generator, Iterable, Mapping + from collections.abc import Generator, Iterable class RankOrderedLogitElo(Generic[ID_TYPE]): -- GitLab From f5d84bb3e0f0563a865d377ea2025e8ea4f2dc28 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 14:51:54 +0300 Subject: [PATCH 045/147] bugfix --- experiments/elo/elo/elo_ratings_multi.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 0f98b4ff..1db5e857 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -29,7 +29,7 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): mc_samples: int = 5_000, ) -> None: self.elo_ratings: defaultdict[ID_TYPE, float] = defaultdict( - lambda: self.elo_initial, + lambda: elo_initial, init_elo_ratings if init_elo_ratings is not None else {}, ) self.elo_k = elo_k -- GitLab From 60d586702d749015ffefdd1c5d662545f08ad69d Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 14:54:59 +0300 Subject: [PATCH 046/147] First version of p-deterministic multi.py --- experiments/elo/p-deterministic multi.py | 63 ++++++++++++++++++++++++ 1 file changed, 63 insertions(+) create mode 100644 experiments/elo/p-deterministic multi.py diff --git a/experiments/elo/p-deterministic multi.py b/experiments/elo/p-deterministic multi.py new file mode 100644 index 00000000..2ff9fce2 --- /dev/null +++ b/experiments/elo/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: 1.17.1 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% +import jupyter_black +import numpy as np +from elo.elo_ratings_multi import RankOrderedLogitElo +from elo.optimal_k import approximate_optimal_k_multi + +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_multi( + 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())) -- GitLab From b14c36cb618afdb78fd4321942c505501a271a25 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 19:01:11 +0300 Subject: [PATCH 047/147] Account for varying player counts in update_elo_ratings_batch() and calculate_loss_multi() --- experiments/elo/elo/elo_ratings_multi.py | 16 +++++++++++++++- experiments/elo/elo/optimal_k.py | 2 +- 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 1db5e857..8dd96887 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -148,7 +148,21 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): matches = tqdm(matches) if full_results: - return np.array(list(self._update_elo_ratings_batch(matches))) + return _padded_numpy_array(self._update_elo_ratings_batch(matches)) for _ in self._update_elo_ratings_batch(matches): pass + + +def _padded_numpy_array( + arrays: Iterable[np.ndarray], + *, + pad_value: float = np.nan, + dtype: np.dtype = np.float64, +) -> np.ndarray: + arrays = tuple(arrays) + max_len = max(a.shape[0] for a in arrays) + result = np.full((len(arrays), max_len), pad_value, dtype=dtype) + for i, a in enumerate(arrays): + result[i, : a.shape[0]] = a + return result diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py index 70d0c7dc..27a0be59 100644 --- a/experiments/elo/elo/optimal_k.py +++ b/experiments/elo/elo/optimal_k.py @@ -55,7 +55,7 @@ def calculate_loss_multi( full_results=True, progress_bar=progress_bar, ) - return np.mean(errors**2) + return np.nanmean(errors**2) def approximate_optimal_k( -- GitLab From 4e730c7b85e3bf6c23533b5845affcf27795d9c9 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 19:02:15 +0300 Subject: [PATCH 048/147] First version of BGA Elo.py --- experiments/elo/BGA Elo.py | 78 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 experiments/elo/BGA Elo.py diff --git a/experiments/elo/BGA Elo.py b/experiments/elo/BGA Elo.py new file mode 100644 index 00000000..ef4ac39b --- /dev/null +++ b/experiments/elo/BGA Elo.py @@ -0,0 +1,78 @@ +# --- +# 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 elo.elo_ratings_multi import RankOrderedLogitElo +from elo.optimal_k import approximate_optimal_k_multi + +jupyter_black.load() + +game_id = 1741 # Ark Nova + +# %% +data = ( + pl.scan_ipc("results/arrow/matches-*.arrow") + .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()) + .collect() +) +data.shape + +# %% +data.sample(10) + +# %% +data.describe() + +# %% +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) +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_multi( + matches=matches, + min_elo_k=0, + max_elo_k=200, + elo_scale=400, +) +optimal_k -- GitLab From acba0474a15addf17809199aaf481bd2dc3e35c3 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 21:49:03 +0300 Subject: [PATCH 049/147] Added funcy --- experiments/elo/poetry.lock | 13 ++++++++++++- experiments/elo/pyproject.toml | 1 + 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/experiments/elo/poetry.lock b/experiments/elo/poetry.lock index a6780355..9cb4220f 100644 --- a/experiments/elo/poetry.lock +++ b/experiments/elo/poetry.lock @@ -870,6 +870,17 @@ files = [ {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"}, ] +[[package]] +name = "funcy" +version = "2.0" +description = "A fancy and practical functional tools" +optional = false +python-versions = "*" +files = [ + {file = "funcy-2.0-py2.py3-none-any.whl", hash = "sha256:53df23c8bb1651b12f095df764bfb057935d49537a56de211b098f4c79614bb0"}, + {file = "funcy-2.0.tar.gz", hash = "sha256:3963315d59d41c6f30c04bc910e10ab50a3ac4a225868bfa96feed133df075cb"}, +] + [[package]] name = "h11" version = "0.16.0" @@ -3994,4 +4005,4 @@ testing = ["coverage[toml]", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" python-versions = "3.12.*" -content-hash = "ac855e9d941ba4dc041d553e018cd808886e32250ef32e288ab637652c1c4a60" +content-hash = "ba42c03221b3499c2f1697c5376dde71778eefb58cd4c9f3c50eb463aef8e6b1" diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index ef3ca1c7..c213f230 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -8,6 +8,7 @@ authors = ["Markus Shepherd "] python = "3.12.*" board-game-merger = "*" +funcy = "*" jmespath = "*" matplotlib = "*" numpy = "*" -- GitLab From caa931d4a7b07a058fb0ba7d99e1134d55a6b344 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 21:58:16 +0300 Subject: [PATCH 050/147] Only store a subset of keys --- experiments/elo/elo/bga_spider.py | 33 ++++++++++++++++++++++++------- 1 file changed, 26 insertions(+), 7 deletions(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index 7cfb35e1..356032c4 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -1,15 +1,20 @@ -from collections.abc import Generator -from datetime import datetime, timezone -import jmespath +from __future__ import annotations + import json import math import re import time -from typing import Any +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 @@ -76,16 +81,28 @@ class BgaSpider(Spider): 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)") integer_regex = re.compile(r"(\d+)") @@ -139,7 +156,7 @@ class BgaSpider(Spider): for game in payload["game_list"]: game["type"] = "game" game["scraped_at"] = now - yield game + yield funcy.project(game, self.game_keys) if self.game_keys else game if self.scrape_rankings: yield FormRequest( @@ -190,7 +207,9 @@ class BgaSpider(Spider): entry["game_id"] = game_id entry["type"] = "ranking" entry["scraped_at"] = now - yield entry + 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) @@ -259,7 +278,7 @@ class BgaSpider(Spider): match["players"] = None last_id = match.get("id") last_timestamp = match.get("timestamp") - yield match + yield funcy.project(match, self.match_keys) if self.match_keys else match if last_id and last_timestamp: yield Request( -- GitLab From 3a38f95690360d9d90b993950326a79408114a6a Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 30 Apr 2025 22:16:27 +0300 Subject: [PATCH 051/147] Updated .gitignore --- experiments/elo/.gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/elo/.gitignore b/experiments/elo/.gitignore index 37467a5f..d0d2a5f7 100644 --- a/experiments/elo/.gitignore +++ b/experiments/elo/.gitignore @@ -1,4 +1,4 @@ .env .jobs/ -results/ +results*/ *.jl -- GitLab From d782006e7d6914282486bf7aa780a07563da2624 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 2 May 2025 22:55:10 +0300 Subject: [PATCH 052/147] Better game processing --- experiments/elo/BGA Elo.py | 37 ++++++++----------- .../elo/{IPC.py => Process matches.py} | 34 ++++++++++++++++- 2 files changed, 49 insertions(+), 22 deletions(-) rename experiments/elo/{IPC.py => Process matches.py} (56%) diff --git a/experiments/elo/BGA Elo.py b/experiments/elo/BGA Elo.py index ef4ac39b..b1d6eadc 100644 --- a/experiments/elo/BGA Elo.py +++ b/experiments/elo/BGA Elo.py @@ -20,31 +20,13 @@ from elo.elo_ratings_multi import RankOrderedLogitElo from elo.optimal_k import approximate_optimal_k_multi jupyter_black.load() +pl.Config.set_tbl_rows(100) +num_matches_regulars = 25 game_id = 1741 # Ark Nova # %% -data = ( - pl.scan_ipc("results/arrow/matches-*.arrow") - .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()) - .collect() -) +data = pl.read_ipc(f"results/arrow/matches/{game_id}.arrow", memory_map=False) data.shape # %% @@ -53,6 +35,19 @@ data.sample(10) # %% data.describe() +# %% +players = ( + data.lazy() + .select(pl.col("player_ids").explode().value_counts()) + .unnest("player_ids") +) +players.describe(percentiles=(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99)) + +# %% +players.select( + frac_regulars=(pl.col("count") >= num_matches_regulars).sum() / pl.len() +).collect() + # %% matches = ( dict(zip(player_ids, payoffs)) diff --git a/experiments/elo/IPC.py b/experiments/elo/Process matches.py similarity index 56% rename from experiments/elo/IPC.py rename to experiments/elo/Process matches.py index 79649886..5a1e6bb4 100644 --- a/experiments/elo/IPC.py +++ b/experiments/elo/Process matches.py @@ -27,8 +27,10 @@ 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) -in_dir, out_dir +match_dir.mkdir(parents=True, exist_ok=True) +in_dir, out_dir, match_dir # %% file_batch_size = 10 @@ -67,3 +69,33 @@ for i, batch in enumerate(batched(tqdm(in_dir.glob("matches-*.jl")), file_batch_ ) ) 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") -- GitLab From 7feb76227d996d884a05aab10a93912b99def7f1 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 3 May 2025 21:11:57 +0300 Subject: [PATCH 053/147] Added networkx --- experiments/elo/poetry.lock | 343 +++++++++++++++++---------------- experiments/elo/pyproject.toml | 1 + 2 files changed, 183 insertions(+), 161 deletions(-) diff --git a/experiments/elo/poetry.lock b/experiments/elo/poetry.lock index 9cb4220f..abe35265 100644 --- a/experiments/elo/poetry.lock +++ b/experiments/elo/poetry.lock @@ -374,103 +374,103 @@ pycparser = "*" [[package]] name = "charset-normalizer" -version = "3.4.1" +version = "3.4.2" description = "The Real First Universal Charset Detector. 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b/experiments/elo/pyproject.toml @@ -11,6 +11,7 @@ board-game-merger = "*" funcy = "*" jmespath = "*" matplotlib = "*" +networkx = "*" numpy = "*" polars = {version = "*", extras = ["numpy", "pandas", "pyarrow"]} requests = "*" -- GitLab From 32186399699562791073df04fbfe23985f2d25a4 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 3 May 2025 21:47:25 +0300 Subject: [PATCH 054/147] Added tqdm_kwargs to update_elo_ratings_batch() --- experiments/elo/elo/elo_ratings_multi.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py index 8dd96887..e6e94908 100644 --- a/experiments/elo/elo/elo_ratings_multi.py +++ b/experiments/elo/elo/elo_ratings_multi.py @@ -10,7 +10,7 @@ from elo.elo_ratings import elo_probability ID_TYPE = TypeVar("ID_TYPE") if TYPE_CHECKING: - from collections.abc import Generator, Iterable + from collections.abc import Any, Generator, Iterable class RankOrderedLogitElo(Generic[ID_TYPE]): @@ -141,11 +141,13 @@ class RankOrderedLogitElo(Generic[ID_TYPE]): matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], full_results: bool = False, progress_bar: bool = False, + tqdm_kwargs: dict[str, Any] | None = None, ) -> np.ndarray | None: if progress_bar: from tqdm import tqdm - matches = tqdm(matches) + tqdm_kwargs = tqdm_kwargs or {} + matches = tqdm(matches, **tqdm_kwargs) if full_results: return _padded_numpy_array(self._update_elo_ratings_batch(matches)) -- GitLab From 8ce2a83bf5f67d99fdd81aeeeaacb2a1efa7226b Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 3 May 2025 22:54:05 +0300 Subject: [PATCH 055/147] Only consider the largest connected component --- experiments/elo/BGA Elo.py | 53 ++++++++++++++++++++++++++++++++++---- 1 file changed, 48 insertions(+), 5 deletions(-) diff --git a/experiments/elo/BGA Elo.py b/experiments/elo/BGA Elo.py index b1d6eadc..69eb8295 100644 --- a/experiments/elo/BGA Elo.py +++ b/experiments/elo/BGA Elo.py @@ -14,10 +14,13 @@ # --- # %% +import itertools import jupyter_black +import networkx as nx import polars as pl from elo.elo_ratings_multi import RankOrderedLogitElo from elo.optimal_k import approximate_optimal_k_multi +from tqdm import tqdm jupyter_black.load() pl.Config.set_tbl_rows(100) @@ -27,6 +30,26 @@ 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 # %% @@ -41,12 +64,27 @@ players = ( .select(pl.col("player_ids").explode().value_counts()) .unnest("player_ids") ) -players.describe(percentiles=(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99)) +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 # %% -players.select( - frac_regulars=(pl.col("count") >= num_matches_regulars).sum() / pl.len() -).collect() +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 players by a single player: {num_matches_max}") # %% matches = ( @@ -56,7 +94,12 @@ matches = ( # %% elo = RankOrderedLogitElo() -elo.update_elo_ratings_batch(matches, full_results=True, progress_bar=True) +elo.update_elo_ratings_batch( + matches, + full_results=True, + progress_bar=True, + tqdm_kwargs={"total": len(data)}, +) len(elo.elo_ratings) # %% -- GitLab From 738390e47e7105b12e9fb1b9d4a86f707d67f7ed Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 3 May 2025 23:22:59 +0300 Subject: [PATCH 056/147] First version of game_stats() --- experiments/elo/elo/game_stats.py | 57 +++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 experiments/elo/elo/game_stats.py diff --git a/experiments/elo/elo/game_stats.py b/experiments/elo/elo/game_stats.py new file mode 100644 index 00000000..50acbaa1 --- /dev/null +++ b/experiments/elo/elo/game_stats.py @@ -0,0 +1,57 @@ +from __future__ import annotations + +import itertools +import logging +from pathlib import Path +from typing import TYPE_CHECKING + +import networkx as nx +import polars as pl +from tqdm import tqdm + +if TYPE_CHECKING: + pass + +LOGGER = logging.getLogger(__name__) + + +def game_stats( + matches_path: Path | str, + threshold_matches_regulars: int = 25, +) -> 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() + for player_ids in tqdm(data["player_ids"]): + 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 -- GitLab From 28dab55dcd4459aafe8463cb6e5bd6f35f099c86 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 3 May 2025 23:33:12 +0300 Subject: [PATCH 057/147] games_stats() --- experiments/elo/elo/game_stats.py | 78 +++++++++++++++++++++++++++++-- 1 file changed, 75 insertions(+), 3 deletions(-) diff --git a/experiments/elo/elo/game_stats.py b/experiments/elo/elo/game_stats.py index 50acbaa1..e39bf18a 100644 --- a/experiments/elo/elo/game_stats.py +++ b/experiments/elo/elo/game_stats.py @@ -1,23 +1,26 @@ from __future__ import annotations import itertools +import json import logging from pathlib import Path from typing import TYPE_CHECKING import networkx as nx import polars as pl -from tqdm import tqdm + if TYPE_CHECKING: - pass + 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) @@ -26,8 +29,16 @@ def game_stats( num_all_matches = len(data) graph = nx.Graph() - for player_ids in tqdm(data["player_ids"]): + + 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) @@ -55,3 +66,64 @@ def game_stats( 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() + + if progress_bar: + from tqdm import tqdm + + games_dicts = tqdm(games_dicts) + + for game in games_dicts: + game_id = game["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) + continue + + stats = game_stats( + matches_path, + threshold_matches_regulars=threshold_matches_regulars, + progress_bar=False, + ) + 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") -- GitLab From 4b857c297f049941ac10d5013ca94f5baa11ca9d Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 3 May 2025 23:41:43 +0300 Subject: [PATCH 058/147] Added _main() method --- experiments/elo/elo/game_stats.py | 27 ++++++++++++++++++++------- 1 file changed, 20 insertions(+), 7 deletions(-) diff --git a/experiments/elo/elo/game_stats.py b/experiments/elo/elo/game_stats.py index e39bf18a..490904ec 100644 --- a/experiments/elo/elo/game_stats.py +++ b/experiments/elo/elo/game_stats.py @@ -3,6 +3,7 @@ from __future__ import annotations import itertools import json import logging +import sys from pathlib import Path from typing import TYPE_CHECKING @@ -84,22 +85,19 @@ def _games_stats( games = pl.read_ndjson(games_path) games_dicts = games.to_dicts() - if progress_bar: - from tqdm import tqdm - - games_dicts = tqdm(games_dicts) - for game in games_dicts: - game_id = game["game_id"] + 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 stats = game_stats( matches_path, threshold_matches_regulars=threshold_matches_regulars, - progress_bar=False, + progress_bar=progress_bar, ) yield game | stats @@ -127,3 +125,18 @@ def games_stats( 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() -- GitLab From 8c1fd5a94137506a00f850a874358e9213af0ad2 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 01:05:28 +0300 Subject: [PATCH 059/147] Error handling --- experiments/elo/elo/game_stats.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/experiments/elo/elo/game_stats.py b/experiments/elo/elo/game_stats.py index 490904ec..52186812 100644 --- a/experiments/elo/elo/game_stats.py +++ b/experiments/elo/elo/game_stats.py @@ -94,12 +94,17 @@ def _games_stats( yield game continue - stats = game_stats( - matches_path, - threshold_matches_regulars=threshold_matches_regulars, - progress_bar=progress_bar, - ) - yield game | stats + 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.") -- GitLab From 32225b3e1dff45641321ada6cd4dafa2494509b5 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 16:20:21 +0300 Subject: [PATCH 060/147] simulate_p_deterministic_games -> simulate_p_deterministic_matches --- experiments/elo/elo/p_deterministic.py | 23 +++++++++++-------- .../elo/p-deterministic distribution.py | 6 ++--- experiments/elo/p-deterministic games.py | 4 ++-- 3 files changed, 18 insertions(+), 15 deletions(-) diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py index a62942c9..b0788d9e 100644 --- a/experiments/elo/elo/p_deterministic.py +++ b/experiments/elo/elo/p_deterministic.py @@ -5,35 +5,38 @@ import numpy as np from elo.elo_ratings import calculate_elo_ratings -def simulate_p_deterministic_games( +def simulate_p_deterministic_matches( + *, rng: np.random.Generator, num_players: int, - num_games: int, + num_matches: int, + players_per_match: int = 2, 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" + 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_a = rng.integers(low=0, high=num_players, size=num_games) + players_a = rng.integers(low=0, high=num_players, size=num_matches) # Sample offset for second player, ensuring it's not equal to the first - offset = rng.integers(low=1, high=num_players, size=num_games) + offset = rng.integers(low=1, high=num_players, size=num_matches) 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 + return players_a, players_b, rng.random(size=num_matches) < 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 + prob_player_a_wins = rng.random(size=num_matches) < 0.5 det_player_a_wins = players_a < players_b - deterministic = rng.random(size=num_games) < p_deterministic + deterministic = rng.random(size=num_matches) < p_deterministic player_a_wins = np.where(deterministic, det_player_a_wins, prob_player_a_wins) return players_a, players_b, player_a_wins @@ -56,10 +59,10 @@ def update_elo_ratings_p_deterministic( if not inplace: elo_ratings = np.copy(elo_ratings) - players_a, players_b, player_a_wins = simulate_p_deterministic_games( + players_a, players_b, player_a_wins = simulate_p_deterministic_matches( rng=rng, num_players=num_players, - num_games=num_games, + num_matches=num_games, p_deterministic=p_deterministic, ) diff --git a/experiments/elo/p-deterministic distribution.py b/experiments/elo/p-deterministic distribution.py index b0ee9d65..06f70071 100644 --- a/experiments/elo/p-deterministic distribution.py +++ b/experiments/elo/p-deterministic distribution.py @@ -6,7 +6,7 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.17.0 +# jupytext_version: 1.17.1 # kernelspec: # display_name: Python 3 (ipykernel) # language: python @@ -19,7 +19,7 @@ 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.p_deterministic import simulate_p_deterministic_matches, update_elo_ratings_p_deterministic jupyter_black.load() @@ -49,7 +49,7 @@ def generate_distributions( p_deterministics = tqdm(p_deterministics) for p_deterministic in p_deterministics: - player_1_ids, player_2_ids, player_1_outcomes = simulate_p_deterministic_games( + player_1_ids, player_2_ids, player_1_outcomes = simulate_p_deterministic_matches( rng=rng, num_players=num_players, num_games=num_games, diff --git a/experiments/elo/p-deterministic games.py b/experiments/elo/p-deterministic games.py index 5d500d9a..f0c13f23 100644 --- a/experiments/elo/p-deterministic games.py +++ b/experiments/elo/p-deterministic games.py @@ -19,7 +19,7 @@ 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.p_deterministic import simulate_p_deterministic_matches, update_elo_ratings_p_deterministic from elo.elo_ratings import elo_probability jupyter_black.load() @@ -33,7 +33,7 @@ p_deterministic = 0.5 elo_scale = 400 # %% -player_1_ids, player_2_ids, player_1_outcomes = simulate_p_deterministic_games( +player_1_ids, player_2_ids, player_1_outcomes = simulate_p_deterministic_matches( rng=rng, num_players=num_players, num_games=num_games, -- GitLab From 0dbad6301d8ead92484bb87939b850230fac055d Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 16:31:45 +0300 Subject: [PATCH 061/147] Simplify simulate_p_deterministic_matches() --- experiments/elo/elo/p_deterministic.py | 40 ++++++++++++++------------ 1 file changed, 21 insertions(+), 19 deletions(-) diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py index b0788d9e..3aaa5275 100644 --- a/experiments/elo/elo/p_deterministic.py +++ b/experiments/elo/elo/p_deterministic.py @@ -12,34 +12,36 @@ def simulate_p_deterministic_matches( num_matches: int, players_per_match: int = 2, p_deterministic: float, -) -> tuple[np.ndarray, 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_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" + assert 2 <= players_per_match <= num_players, ( + "players_per_match must be between 2 and num_players" + ) - players_a = rng.integers(low=0, high=num_players, size=num_matches) - # Sample offset for second player, ensuring it's not equal to the first - offset = rng.integers(low=1, high=num_players, size=num_matches) - players_b = (players_a + offset) % num_players - assert np.all(players_a != players_b), "Players A and B must be different" + 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: - # If p_deterministic is 0, all games are probabilistic - return players_a, players_b, rng.random(size=num_matches) < 0.5 + return random_outcomes - 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_matches) < 0.5 - det_player_a_wins = players_a < players_b + deterministic_outcomes = np.sort(random_outcomes, axis=1) - deterministic = rng.random(size=num_matches) < p_deterministic - player_a_wins = np.where(deterministic, det_player_a_wins, prob_player_a_wins) + if p_deterministic == 1: + return deterministic_outcomes - return players_a, players_b, player_a_wins + mask = rng.random(size=num_matches) < p_deterministic + return np.where(mask[:, np.newaxis], deterministic_outcomes, random_outcomes) def update_elo_ratings_p_deterministic( -- GitLab From 7f730a01f95420f2d04cf42192dbf23e7bc9478b Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 17:11:17 +0300 Subject: [PATCH 062/147] Added base class EloRatingSystem --- experiments/elo/elo/elo_ratings.py | 64 ++++++++++++++++++++++++++++-- 1 file changed, 60 insertions(+), 4 deletions(-) diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py index 4e84a4c2..18e0555c 100644 --- a/experiments/elo/elo/elo_ratings.py +++ b/experiments/elo/elo/elo_ratings.py @@ -1,19 +1,20 @@ from __future__ import annotations from collections import defaultdict -from typing import TypeVar, TYPE_CHECKING +from typing import TYPE_CHECKING + +import numpy as np if TYPE_CHECKING: from collections.abc import Iterable, Mapping - -ID_TYPE = TypeVar("ID_TYPE") + from typing import Any def elo_probability(diff: float, scale: float = 400) -> float: return 1 / (1 + 10 ** (-diff / scale)) -def calculate_elo_ratings( +def calculate_elo_ratings[ID_TYPE]( *, player_1_ids: Iterable[ID_TYPE], player_2_ids: Iterable[ID_TYPE], @@ -65,3 +66,58 @@ def calculate_elo_ratings( if full_results: return elo_ratings, player_1_win_probs, elo_updates return elo_ratings + + +class EloRatingSystem[ID_TYPE]: + elo_initial: float = 0 + elo_k: float = 32 + elo_scale: float = 400 + + elo_ratings: defaultdict[ID_TYPE, float] + + def __init__( + self, + *, + init_elo_ratings: Mapping[ID_TYPE, float] | None = None, + elo_initial: float = 0, + elo_k: float = 32, + elo_scale: float = 400, + ) -> None: + self.elo_initial = elo_initial + self.elo_k = elo_k + self.elo_scale = elo_scale + + self.elo_ratings = defaultdict( + lambda: elo_initial, + init_elo_ratings if init_elo_ratings is not None else {}, + ) + + def update_elo_ratings( + self, + players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], + ) -> np.ndarray: + raise NotImplementedError + + def update_elo_ratings_batch( + self, + matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], + *, + full_results: bool = False, + progress_bar: bool = False, + tqdm_kwargs: dict[str, Any] | None = None, + ) -> np.ndarray | None: + raise NotImplementedError + + def probability_matrix( + self, + players: Iterable[ID_TYPE], + ) -> np.ndarray: + raise NotImplementedError + + def expected_outcome( + self, + players: Iterable[ID_TYPE], + *, + rank_payoffs: np.ndarray | None = None, + ) -> np.ndarray: + raise NotImplementedError -- GitLab From 924566ea2d919704cd4056b0d5915e077f383521 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 17:32:30 +0300 Subject: [PATCH 063/147] Implemented TwoPlayerElo --- experiments/elo/elo/elo_ratings.py | 73 +++++++++++++++++++++++++++++- 1 file changed, 72 insertions(+), 1 deletion(-) diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py index 18e0555c..389f6ddf 100644 --- a/experiments/elo/elo/elo_ratings.py +++ b/experiments/elo/elo/elo_ratings.py @@ -1,12 +1,13 @@ from __future__ import annotations +import warnings from collections import defaultdict from typing import TYPE_CHECKING import numpy as np if TYPE_CHECKING: - from collections.abc import Iterable, Mapping + from collections.abc import Iterable, Generator, Mapping from typing import Any @@ -121,3 +122,73 @@ class EloRatingSystem[ID_TYPE]: rank_payoffs: np.ndarray | None = None, ) -> np.ndarray: raise NotImplementedError + + +class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): + def expected_outcome( + self, + players: Iterable[ID_TYPE], + *, + rank_payoffs: np.ndarray | None = None, + ) -> np.ndarray: + 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.array([prob, 1 - prob]) + + def probability_matrix( + self, + players: Iterable[ID_TYPE], + ) -> np.ndarray: + expected_outcome = self.expected_outcome(players) + return np.array([expected_outcome, 1 - expected_outcome]) + + def update_elo_ratings( + self, + players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], + ) -> np.ndarray: + if isinstance(players, Mapping): + player_ids = tuple(players.keys()) + outcomes = np.array(list(players.values()), dtype=float) + else: + player_ids = tuple(players) + outcomes = np.array([1, 0], dtype=float) + + 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, float] | Iterable[ID_TYPE]], + ) -> Generator[np.ndarray]: + for match in matches: + yield self.update_elo_ratings(match) + + def update_elo_ratings_batch( + self, + matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], + *, + full_results: bool = False, + progress_bar: bool = False, + tqdm_kwargs: dict[str, Any] | None = None, + ) -> np.ndarray | 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.array(list(self._update_elo_ratings_batch(matches))) + + for _ in self._update_elo_ratings_batch(matches): + pass + return None -- GitLab From 87ffa5329e6d6decf755252a5751bfe0db4d1325 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 20:38:40 +0300 Subject: [PATCH 064/147] Moved RankOrderedLogitElo to elo_ratings --- experiments/elo/elo/elo_ratings.py | 166 ++++++++++++++++++++++ experiments/elo/elo/elo_ratings_multi.py | 170 ----------------------- 2 files changed, 166 insertions(+), 170 deletions(-) delete mode 100644 experiments/elo/elo/elo_ratings_multi.py diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py index 389f6ddf..a106f616 100644 --- a/experiments/elo/elo/elo_ratings.py +++ b/experiments/elo/elo/elo_ratings.py @@ -1,5 +1,6 @@ from __future__ import annotations +import itertools import warnings from collections import defaultdict from typing import TYPE_CHECKING @@ -192,3 +193,168 @@ class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): for _ in self._update_elo_ratings_batch(matches): pass return None + + +class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): + """ + Rank-ordered-logit Elo with Monte-Carlo fallback for large fields. + """ + + def __init__( + self, + *, + init_elo_ratings: Mapping[ID_TYPE, float] | None = None, + elo_initial: float = 0, + elo_k: float = 32, + elo_scale: float = 400, + max_exact: int = 6, + mc_samples: int = 5_000, + ) -> None: + super().__init__( + init_elo_ratings=init_elo_ratings, + elo_initial=elo_initial, + elo_k=elo_k, + elo_scale=elo_scale, + ) + + self.max_exact = max_exact + self.mc_samples = mc_samples + + def _calculate_probability_matrix_two_players( + self, + players: tuple[ID_TYPE, ID_TYPE], + ) -> np.ndarray: + 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.array([[prob_a, prob_b], [prob_b, prob_a]]) + + def _calculate_probability_matrix_from_simulations( + self, + players: tuple[ID_TYPE, ...], + ) -> np.ndarray: + # TODO: implement Monte Carlo fallback + raise NotImplementedError + + def _calculate_probability_matrix_from_permutations( + self, + players: tuple[ID_TYPE, ...], + ) -> np.ndarray: + num_players = len(players) + ratings = np.array([self.elo_ratings[p] for p in players], dtype=float) + exp_ratings = 10 ** (ratings / self.elo_scale) + probs = np.zeros((num_players, num_players)) + + for perm in itertools.permutations(range(num_players)): + prob = 1 + denom = np.sum(exp_ratings) + for i in range(num_players - 1): + prob *= exp_ratings[perm[i]] / denom + denom -= exp_ratings[perm[i]] + for position, player in enumerate(perm): + probs[player, position] += prob + + assert np.allclose(probs.sum(axis=0), 1), "Probabilities must sum to 1" + assert np.allclose(probs.sum(axis=1), 1), "Probabilities must sum to 1" + + return probs + + def probability_matrix( + self, + players: Iterable[ID_TYPE], + ) -> np.ndarray: + players = tuple(players) + + if len(players) < 2: + raise ValueError("At least 2 players are required") + + if len(players) == 2: + return self._calculate_probability_matrix_two_players(players) + + if len(players) > self.max_exact: + return self._calculate_probability_matrix_from_simulations(players) + + return self._calculate_probability_matrix_from_permutations(players) + + def expected_outcome( + self, + players: Iterable[ID_TYPE], + *, + rank_payoffs: np.ndarray | None = None, + ) -> np.ndarray: + players = tuple(players) + if rank_payoffs is None: + rank_payoffs = np.arange(len(players) - 1, -1, -1, dtype=float) + + 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 + + def update_elo_ratings( + self, + players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], + ) -> np.ndarray: + if isinstance(players, Mapping): + player_ids = tuple(players.keys()) + payoffs = np.array(list(players.values()), dtype=float) + else: + player_ids = tuple(players) + payoffs = np.arange(len(player_ids) - 1, -1, -1, dtype=float) + + 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, float] | Iterable[ID_TYPE]], + ) -> Generator[np.ndarray]: + for match in matches: + yield self.update_elo_ratings(match) + + def update_elo_ratings_batch( + self, + matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], + *, + full_results: bool = False, + progress_bar: bool = False, + tqdm_kwargs: dict[str, Any] | None = None, + ) -> np.ndarray | 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[np.ndarray], + *, + pad_value: float = np.nan, + dtype: np.dtype | None = None, +) -> np.ndarray: + arrays = tuple(arrays) + max_len = max(a.shape[0] for a in arrays) + result = np.full((len(arrays), max_len), pad_value, dtype=dtype or np.float64) + for i, a in enumerate(arrays): + result[i, : a.shape[0]] = a + return result diff --git a/experiments/elo/elo/elo_ratings_multi.py b/experiments/elo/elo/elo_ratings_multi.py deleted file mode 100644 index e6e94908..00000000 --- a/experiments/elo/elo/elo_ratings_multi.py +++ /dev/null @@ -1,170 +0,0 @@ -from __future__ import annotations - -import numpy as np -import itertools -from collections import defaultdict -from collections.abc import Mapping -from typing import Generic, TypeVar, TYPE_CHECKING -from elo.elo_ratings import elo_probability - -ID_TYPE = TypeVar("ID_TYPE") - -if TYPE_CHECKING: - from collections.abc import Any, Generator, Iterable - - -class RankOrderedLogitElo(Generic[ID_TYPE]): - """ - Rank-ordered-logit Elo with Monte-Carlo fallback for large fields. - """ - - def __init__( - self, - *, - init_elo_ratings: Mapping[ID_TYPE, float] | None = None, - elo_initial: float = 0, - elo_k: float = 32, - elo_scale: float = 400, - max_exact: int = 6, - mc_samples: int = 5_000, - ) -> None: - self.elo_ratings: defaultdict[ID_TYPE, float] = defaultdict( - lambda: elo_initial, - init_elo_ratings if init_elo_ratings is not None else {}, - ) - self.elo_k = elo_k - self.elo_scale = elo_scale - - self.max_exact = max_exact - self.mc_samples = mc_samples - - def _calculate_probability_matrix_two_players( - self, - players: tuple[ID_TYPE, ID_TYPE], - ) -> np.ndarray: - 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.array([[prob_a, prob_b], [prob_b, prob_a]]) - - def _calculate_probability_matrix_from_simulations( - self, - players: tuple[ID_TYPE, ...], - ) -> np.ndarray: - # TODO: implement Monte Carlo fallback - raise NotImplementedError - - def _calculate_probability_matrix_from_permutations( - self, - players: tuple[ID_TYPE, ...], - ) -> np.ndarray: - num_players = len(players) - ratings = np.array([self.elo_ratings[p] for p in players], dtype=float) - exp_ratings = 10 ** (ratings / self.elo_scale) - probs = np.zeros((num_players, num_players)) - - for perm in itertools.permutations(range(num_players)): - prob = 1 - denom = np.sum(exp_ratings) - for i in range(num_players - 1): - prob *= exp_ratings[perm[i]] / denom - denom -= exp_ratings[perm[i]] - for position, player in enumerate(perm): - probs[player, position] += prob - - assert np.allclose(probs.sum(axis=0), 1), "Probabilities must sum to 1" - assert np.allclose(probs.sum(axis=1), 1), "Probabilities must sum to 1" - - return probs - - def calculate_probability_matrix(self, players: Iterable[ID_TYPE]) -> np.ndarray: - players = tuple(players) - num_players = len(players) - - if num_players < 2: - raise ValueError("At least 2 players are required") - - if num_players == 2: - return self._calculate_probability_matrix_two_players(players) - - if num_players > self.max_exact: - return self._calculate_probability_matrix_from_simulations(players) - - return self._calculate_probability_matrix_from_permutations(players) - - def calculate_expected_payoff( - self, - players: Iterable[ID_TYPE], - rank_payoffs: np.ndarray, - ) -> np.ndarray: - assert rank_payoffs.ndim == 1, "Rank payoffs must be a 1D array" - players = tuple(players) - assert len(rank_payoffs) == len(players), ( - "Rank payoffs must be the same length as the number of players" - ) - probs = self.calculate_probability_matrix(players) - return probs @ rank_payoffs - - def update_elo_ratings( - self, - players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], - ) -> np.ndarray: - if isinstance(players, Mapping): - player_ids = tuple(players.keys()) - payoffs = np.array(list(players.values()), dtype=float) - else: - player_ids = tuple(players) - payoffs = np.arange(len(player_ids) - 1, -1, -1, dtype=float) - - 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.calculate_expected_payoff(player_ids, 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, float] | Iterable[ID_TYPE]], - ) -> Generator[np.ndarray]: - for match in matches: - yield self.update_elo_ratings(match) - - def update_elo_ratings_batch( - self, - matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], - full_results: bool = False, - progress_bar: bool = False, - tqdm_kwargs: dict[str, Any] | None = None, - ) -> np.ndarray | 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 - - -def _padded_numpy_array( - arrays: Iterable[np.ndarray], - *, - pad_value: float = np.nan, - dtype: np.dtype = np.float64, -) -> np.ndarray: - arrays = tuple(arrays) - max_len = max(a.shape[0] for a in arrays) - result = np.full((len(arrays), max_len), pad_value, dtype=dtype) - for i, a in enumerate(arrays): - result[i, : a.shape[0]] = a - return result -- GitLab From ae26b1107dc603e05777af8075359d83064187e0 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 20:49:43 +0300 Subject: [PATCH 065/147] Updated update_elo_ratings_p_deterministic() --- experiments/elo/elo/p_deterministic.py | 48 ++++++++++---------------- 1 file changed, 18 insertions(+), 30 deletions(-) diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py index 3aaa5275..af511a10 100644 --- a/experiments/elo/elo/p_deterministic.py +++ b/experiments/elo/elo/p_deterministic.py @@ -1,8 +1,7 @@ from __future__ import annotations -from collections import defaultdict import numpy as np -from elo.elo_ratings import calculate_elo_ratings +from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo def simulate_p_deterministic_matches( @@ -45,44 +44,33 @@ def simulate_p_deterministic_matches( def update_elo_ratings_p_deterministic( + *, rng: np.random.Generator, - elo_ratings: np.ndarray, - num_games: int, + elo: EloRatingSystem | None = None, + num_players: int, + num_matches: int, + players_per_match: int = 2, 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) +) -> EloRatingSystem: + if elo is None: + elo = TwoPlayerElo() if players_per_match == 2 else RankOrderedLogitElo() - if not inplace: - elo_ratings = np.copy(elo_ratings) - - players_a, players_b, player_a_wins = simulate_p_deterministic_matches( + matches = simulate_p_deterministic_matches( rng=rng, num_players=num_players, - num_matches=num_games, + num_matches=num_matches, + players_per_match=players_per_match, 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, + 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}, ) - assert isinstance(results, defaultdict) - - for player_id, elo_rating in results.items(): - elo_ratings[player_id] = elo_rating - - return elo_ratings + return elo -- GitLab From d29257ba5bb47b829e86ffb6d2d6690da7888f88 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 20:51:37 +0300 Subject: [PATCH 066/147] Removed old calculate_elo_ratings() --- experiments/elo/elo/elo_ratings.py | 54 ------------------------------ 1 file changed, 54 deletions(-) diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py index a106f616..24caf05d 100644 --- a/experiments/elo/elo/elo_ratings.py +++ b/experiments/elo/elo/elo_ratings.py @@ -16,60 +16,6 @@ def elo_probability(diff: float, scale: float = 400) -> float: return 1 / (1 + 10 ** (-diff / scale)) -def calculate_elo_ratings[ID_TYPE]( - *, - 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 - - class EloRatingSystem[ID_TYPE]: elo_initial: float = 0 elo_k: float = 32 -- GitLab From 8ad66a3b538b2b3ef7065c33dee8e8ab3fd2da6f Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 21:00:18 +0300 Subject: [PATCH 067/147] Simplify optimal_k.py --- experiments/elo/elo/optimal_k.py | 86 +++++--------------------------- 1 file changed, 13 insertions(+), 73 deletions(-) diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py index 27a0be59..01404be1 100644 --- a/experiments/elo/elo/optimal_k.py +++ b/experiments/elo/elo/optimal_k.py @@ -1,108 +1,48 @@ from __future__ import annotations -from typing import TypeVar, TYPE_CHECKING +from typing import TYPE_CHECKING import numpy as np from scipy.optimize import minimize_scalar -from elo.elo_ratings import calculate_elo_ratings -from elo.elo_ratings_multi import RankOrderedLogitElo +from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo if TYPE_CHECKING: from collections.abc import Iterable, Mapping -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 calculate_loss_multi( +def calculate_loss[ID_TYPE]( *, + elo: EloRatingSystem[ID_TYPE], matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], - elo_k: float, - elo_scale: float, progress_bar: bool = False, ) -> np.float64: - elo = RankOrderedLogitElo(elo_k=elo_k, elo_scale=elo_scale) errors = elo.update_elo_ratings_batch( matches, full_results=True, progress_bar=progress_bar, ) + assert errors is not None return np.nanmean(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 - - -def approximate_optimal_k_multi( +def approximate_optimal_k[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 = 160, + max_elo_k: float = 200, elo_scale: float = 400, ) -> np.float64: matches = list(matches) def loss(elo_k: float) -> np.float64: - return calculate_loss_multi( + return calculate_loss( + elo=TwoPlayerElo(elo_k=elo_k, elo_scale=elo_scale) + if two_player_only + else RankOrderedLogitElo(elo_k=elo_k, elo_scale=elo_scale), matches=matches, - elo_k=elo_k, - elo_scale=elo_scale, + progress_bar=False, ) result = minimize_scalar( -- GitLab From bfda6e4cba9878948d4eb007e62bac989ace411e Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 21:12:33 +0300 Subject: [PATCH 068/147] Better processing of matches in approximate_optimal_k() --- experiments/elo/elo/optimal_k.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py index 01404be1..e719cdfa 100644 --- a/experiments/elo/elo/optimal_k.py +++ b/experiments/elo/elo/optimal_k.py @@ -34,14 +34,19 @@ def approximate_optimal_k[ID_TYPE]( max_elo_k: float = 200, elo_scale: float = 400, ) -> np.float64: - matches = list(matches) + match_list: list[Mapping[ID_TYPE, float]] = [ + dict(zip((m := tuple(match)), range(len(m) - 1, -1, -1))) + if isinstance(match, Iterable) + else match + for match in matches + ] def loss(elo_k: float) -> np.float64: return calculate_loss( elo=TwoPlayerElo(elo_k=elo_k, elo_scale=elo_scale) if two_player_only else RankOrderedLogitElo(elo_k=elo_k, elo_scale=elo_scale), - matches=matches, + matches=match_list, progress_bar=False, ) -- GitLab From e6ab25222efb49156872d938a186f345289525dd Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 21:24:25 +0300 Subject: [PATCH 069/147] Correcting imports --- experiments/elo/elo/elo_ratings.py | 3 ++- experiments/elo/elo/optimal_k.py | 5 +++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py index 24caf05d..b5ca081a 100644 --- a/experiments/elo/elo/elo_ratings.py +++ b/experiments/elo/elo/elo_ratings.py @@ -3,12 +3,13 @@ from __future__ import annotations import itertools import warnings from collections import defaultdict +from collections.abc import Mapping from typing import TYPE_CHECKING import numpy as np if TYPE_CHECKING: - from collections.abc import Iterable, Generator, Mapping + from collections.abc import Iterable, Generator from typing import Any diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py index e719cdfa..4cbf6c66 100644 --- a/experiments/elo/elo/optimal_k.py +++ b/experiments/elo/elo/optimal_k.py @@ -1,5 +1,6 @@ from __future__ import annotations +from collections.abc import Mapping from typing import TYPE_CHECKING import numpy as np @@ -8,7 +9,7 @@ from scipy.optimize import minimize_scalar from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo if TYPE_CHECKING: - from collections.abc import Iterable, Mapping + from collections.abc import Iterable def calculate_loss[ID_TYPE]( @@ -36,7 +37,7 @@ def approximate_optimal_k[ID_TYPE]( ) -> np.float64: match_list: list[Mapping[ID_TYPE, float]] = [ dict(zip((m := tuple(match)), range(len(m) - 1, -1, -1))) - if isinstance(match, Iterable) + if not isinstance(match, Mapping) else match for match in matches ] -- GitLab From 02c7fe366924c879c71010e259ceba528604feb4 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 21:24:41 +0300 Subject: [PATCH 070/147] Updating p-deterministic games.py to new format --- experiments/elo/p-deterministic games.py | 47 ++++++++++++++---------- 1 file changed, 28 insertions(+), 19 deletions(-) diff --git a/experiments/elo/p-deterministic games.py b/experiments/elo/p-deterministic games.py index f0c13f23..f4edde1d 100644 --- a/experiments/elo/p-deterministic games.py +++ b/experiments/elo/p-deterministic games.py @@ -6,7 +6,7 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.17.0 +# jupytext_version: 1.17.1 # 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_matches, 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_matches( +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 -- GitLab From a532b4e27977246cb08dcdf590b0ea4b5df7961f Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 21:49:26 +0300 Subject: [PATCH 071/147] memoize --- experiments/elo/elo/optimal_k.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py index 4cbf6c66..af5590a4 100644 --- a/experiments/elo/elo/optimal_k.py +++ b/experiments/elo/elo/optimal_k.py @@ -1,5 +1,6 @@ from __future__ import annotations +import functools from collections.abc import Mapping from typing import TYPE_CHECKING @@ -42,6 +43,7 @@ def approximate_optimal_k[ID_TYPE]( for match in matches ] + @functools.cache def loss(elo_k: float) -> np.float64: return calculate_loss( elo=TwoPlayerElo(elo_k=elo_k, elo_scale=elo_scale) -- GitLab From 7d77684fced931788dade108f07a4dfb4012f395 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 21:50:22 +0300 Subject: [PATCH 072/147] nevergrad --- experiments/elo/poetry.lock | 88 ++++++++++++++++++++++++++++++---- experiments/elo/pyproject.toml | 1 + 2 files changed, 81 insertions(+), 8 deletions(-) diff --git a/experiments/elo/poetry.lock b/experiments/elo/poetry.lock index abe35265..f27c076e 100644 --- a/experiments/elo/poetry.lock +++ b/experiments/elo/poetry.lock @@ -181,6 +181,22 @@ files = [ [package.extras] dev = ["backports.zoneinfo", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata"] +[[package]] +name = "bayesian-optimization" +version = "1.4.0" +description = "Bayesian Optimization package" +optional = false +python-versions = "*" +files = [ + {file = "bayesian-optimization-1.4.0.tar.gz", hash = "sha256:5e7b5ada7826a8a342932d67f8ae5e638ade588a12b7f7df058fb7a2defe5866"}, + {file = "bayesian_optimization-1.4.0-py3-none-any.whl", hash = "sha256:0984cfcbc9472284778962416f0d4892286da3f54e1df2767ef89263b6e22263"}, +] + +[package.dependencies] +numpy = ">=1.9.0" +scikit-learn = ">=0.18.0" +scipy = ">=1.0.0" + [[package]] name = "beautifulsoup4" version = "4.13.4" @@ -487,6 +503,24 @@ files = [ [package.dependencies] colorama = {version = "*", markers = "platform_system == \"Windows\""} +[[package]] +name = "cma" +version = "4.0.0" +description = "CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python" +optional = false +python-versions = "*" +files = [ + {file = "cma-4.0.0-py3-none-any.whl", hash = "sha256:97b86ba1ac9f1cbb189a06c4d4a78f591f0878e5dd3e55c95e88e622e78c1a10"}, + {file = "cma-4.0.0.tar.gz", hash = "sha256:fd28ce56983bf2fca0e614189d60134ebb80bf604f070d1ea095ea4e856f13a5"}, +] + +[package.dependencies] +numpy = "*" + +[package.extras] +constrained-solution-tracking = ["moarchiving"] +plotting = ["matplotlib"] + [[package]] name = "colorama" version = "0.4.6" @@ -744,6 +778,20 @@ files = [ {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"}, ] +[[package]] +name = "directsearch" +version = "1.0" +description = "A derivative-free solver for unconstrained minimization" +optional = false +python-versions = "*" +files = [ + {file = "directsearch-1.0.tar.gz", hash = "sha256:8093ecc401a3d5eff28f053d4ef1b726f5a9c577bd33d6a8b2413a5ba753c734"}, +] + +[package.dependencies] +numpy = ">=1.11" +scipy = ">=1.0" + [[package]] name = "executing" version = "2.2.0" @@ -1157,13 +1205,13 @@ files = [ [[package]] name = "joblib" -version = "1.4.2" +version = "1.5.0" description = "Lightweight pipelining with Python functions" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" files = [ - {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"}, - {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"}, + {file = "joblib-1.5.0-py3-none-any.whl", hash = "sha256:206144b320246485b712fc8cc51f017de58225fa8b414a1fe1764a7231aca491"}, + {file = "joblib-1.5.0.tar.gz", hash = "sha256:d8757f955389a3dd7a23152e43bc297c2e0c2d3060056dad0feefc88a06939b5"}, ] [[package]] @@ -2098,6 +2146,30 @@ example = ["cairocffi (>=1.7)", "contextily (>=1.6)", "igraph (>=0.11)", "momepy extra = ["lxml (>=4.6)", "pydot (>=3.0.1)", "pygraphviz (>=1.14)", "sympy (>=1.10)"] test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"] +[[package]] +name = "nevergrad" +version = "1.0.12" +description = "A Python toolbox for performing gradient-free optimization" +optional = false +python-versions = ">=3.6" +files = [ + {file = "nevergrad-1.0.12-py3-none-any.whl", hash = "sha256:56ff65d6a2f497ecd79af5a796968ee946c05705a6a69ca616eae5988cc5d999"}, + {file = "nevergrad-1.0.12.tar.gz", hash = "sha256:ade90dacbb676f6dfc9fee3d4f9c628ec29eec9e3d995a31026ae8a61988b803"}, +] + +[package.dependencies] +bayesian-optimization = "1.4.0" +cma = ">=2.6.0" +directsearch = "*" +numpy = ">=1.24.0" +pandas = "*" +typing-extensions = ">=3.6.6" + +[package.extras] +all = ["IOHexperimenter (>=0.2.9.2)", "Keras-Preprocessing", "Pillow (>=10.3.0)", "Py-BOBYQA (>=1.2)", "autodocsumm (>=0.1.11)", "autograd (>=1.2)", "ax-platform", "bayes-optim", "black (==24.3.0)", "directsearch (>=1.0)", "docutils (==0.17.1)", "fcmaes", "glfw", "gym (==0.24.1)", "hiplot", "hyperopt (>=0.2.5)", "image-quality (>=1.2.7)", "keras (>=2.4.3)", "loguru", "matplotlib (==3.7.0)", "mixsimulator (>=0.3.3)", "mujoco", "mypy (>=0.800)", "nlopt", "numpy (>=1.16.2)", "olymp (>=0.0.1b0)", "opencv-python (>=4.1.2.30)", "openpyxl (>=3.0.0)", "pandas (>=0.23.4)", "poap", "pySOT", "pybullet (>=3.2.2)", "pygame (>=2.1.2)", "pylint (>=2.4.4)", "pymoo (==0.5.0)", "pyomo (==5.7.1)", "pyparsing (>=2.2.1)", "pyproj", "pytest (>=4.3.0)", "pytest-circleci-parallelized", "pytest-cov (>=2.6.1)", "pytest-timeout", "pytest-xdist", "recommonmark (>=0.5.0)", "requests (>=2.21.0)", "scikit-image (>=0.15.0)", "scikit-image (>=0.18.3)", "scikit-learn (>=1.0.1)", "scikit-optimize", "scipy", "scipy (>=1.2.1)", "setuptools (==70.0.0)", "silence-tensorflow", "smac (>=2.0.1)", "sphinx (>=3.3.0)", "sphinx-rtd-theme (>=0.4.3)", "tensorflow (>=2.7.0)", "tensorflow-estimator (>=2.7.0)", "tensorflow-probability", "torch (>=1.7.0)", "torchvision (>=0.11.1)", "tqdm", "twine (>=3.1.1)", "wheel (>=0.33.6)", "xlrd (>=1.2.0)", "xlwt (>=1.3.0)"] +benchmark = ["IOHexperimenter (>=0.2.9.2)", "Keras-Preprocessing", "Pillow (>=10.3.0)", "Py-BOBYQA (>=1.2)", "autograd (>=1.2)", "ax-platform", "bayes-optim", "directsearch (>=1.0)", "fcmaes", "glfw", "gym (==0.24.1)", "hiplot", "hyperopt (>=0.2.5)", "image-quality (>=1.2.7)", "keras (>=2.4.3)", "loguru", "matplotlib (==3.7.0)", "mixsimulator (>=0.3.3)", "mujoco", "nlopt", "numpy (>=1.16.2)", "olymp (>=0.0.1b0)", "opencv-python (>=4.1.2.30)", "openpyxl (>=3.0.0)", "pandas (>=0.23.4)", "poap", "pySOT", "pybullet (>=3.2.2)", "pygame (>=2.1.2)", "pymoo (==0.5.0)", "pyomo (==5.7.1)", "pyproj", "pytest-circleci-parallelized", "pytest-timeout", "pytest-xdist", "requests (>=2.21.0)", "scikit-image (>=0.15.0)", "scikit-image (>=0.18.3)", "scikit-learn (>=1.0.1)", "scikit-optimize", "scipy", "scipy (>=1.2.1)", "silence-tensorflow", "smac (>=2.0.1)", "tensorflow (>=2.7.0)", "tensorflow-estimator (>=2.7.0)", "tensorflow-probability", "torch (>=1.7.0)", "torchvision (>=0.11.1)", "tqdm", "xlrd (>=1.2.0)", "xlwt (>=1.3.0)"] +dev = ["autodocsumm (>=0.1.11)", "black (==24.3.0)", "docutils (==0.17.1)", "mypy (>=0.800)", "pandas (>=0.23.4)", "pylint (>=2.4.4)", "pyparsing (>=2.2.1)", "pytest (>=4.3.0)", "pytest-cov (>=2.6.1)", "recommonmark (>=0.5.0)", "setuptools (==70.0.0)", "sphinx (>=3.3.0)", "sphinx-rtd-theme (>=0.4.3)", "twine (>=3.1.1)", "wheel (>=0.33.6)"] + [[package]] name = "notebook-shim" version = "0.2.4" @@ -3509,13 +3581,13 @@ tests = ["coverage[toml] (>=5.0.2)", "pytest"] [[package]] name = "setuptools" -version = "80.3.0" +version = "80.3.1" description = "Easily download, build, install, upgrade, and uninstall Python packages" optional = false python-versions = ">=3.9" files = [ - {file = "setuptools-80.3.0-py3-none-any.whl", hash = "sha256:a65cffc4fb86167e3020b3ef58e08226baad8b29a3b34ce2c9d07e901bac481d"}, - {file = "setuptools-80.3.0.tar.gz", hash = "sha256:ec8308eb180b2312062b1c5523204acf872cd8b0a9e6c2ae76431b22bc4065d7"}, + {file = "setuptools-80.3.1-py3-none-any.whl", hash = "sha256:ea8e00d7992054c4c592aeb892f6ad51fe1b4d90cc6947cc45c45717c40ec537"}, + {file = "setuptools-80.3.1.tar.gz", hash = "sha256:31e2c58dbb67c99c289f51c16d899afedae292b978f8051efaf6262d8212f927"}, ] [package.extras] @@ -4026,4 +4098,4 @@ testing = ["coverage[toml]", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" python-versions = "3.12.*" -content-hash = "42d2618362442f359c6b9b7596c3ab3f5033b000f3db6a27d9e9de5f8fffbbb9" +content-hash = "5cc0393b2dbe033b45cce40403e71191318873911010be2c40630a113b2c5c48" diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index 47c4b0fd..31c3e9c3 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -12,6 +12,7 @@ funcy = "*" jmespath = "*" matplotlib = "*" networkx = "*" +nevergrad = "*" numpy = "*" polars = {version = "*", extras = ["numpy", "pandas", "pyarrow"]} requests = "*" -- GitLab From 793430a983757a2fbaee9612adbfd9e13a62cc61 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 22:19:22 +0300 Subject: [PATCH 073/147] Updated p-deterministic distribution.py --- .../elo/p-deterministic distribution.py | 37 ++++++++++++------- 1 file changed, 24 insertions(+), 13 deletions(-) diff --git a/experiments/elo/p-deterministic distribution.py b/experiments/elo/p-deterministic distribution.py index 06f70071..d4266f13 100644 --- a/experiments/elo/p-deterministic distribution.py +++ b/experiments/elo/p-deterministic distribution.py @@ -18,8 +18,12 @@ import jupyter_black import numpy as np import polars as pl import seaborn as sns +from elo.elo_ratings import RankOrderedLogitElo, TwoPlayerElo from elo.optimal_k import approximate_optimal_k -from elo.p_deterministic import simulate_p_deterministic_matches, update_elo_ratings_p_deterministic +from elo.p_deterministic import ( + simulate_p_deterministic_matches, + update_elo_ratings_p_deterministic, +) jupyter_black.load() @@ -27,7 +31,8 @@ jupyter_black.load() # %% def generate_distributions( num_players=1000, - num_games=1_000_000, + num_matches=1_000_000, + players_per_match=2, p_deterministic_min=0.01, p_deterministic_max=0.99, num_steps=99, @@ -49,32 +54,38 @@ def generate_distributions( p_deterministics = tqdm(p_deterministics) for p_deterministic in p_deterministics: - player_1_ids, player_2_ids, player_1_outcomes = simulate_p_deterministic_matches( + 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, ) 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, ) - elo_ratings = 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=np.zeros(num_players), - 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=False, ) + elo_ratings = np.array(list(elo.elo_ratings.values())) yield p_deterministic, elo_k, elo_ratings -- GitLab From 6bc954811ec5d37fda2c40cd42d697b6f19fe826 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 22:36:53 +0300 Subject: [PATCH 074/147] Added p_deterministic_experiment() --- experiments/elo/elo/p_deterministic.py | 44 ++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py index af511a10..1bb48f66 100644 --- a/experiments/elo/elo/p_deterministic.py +++ b/experiments/elo/elo/p_deterministic.py @@ -1,7 +1,9 @@ from __future__ import annotations + import numpy as np from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo +from elo.optimal_k import approximate_optimal_k def simulate_p_deterministic_matches( @@ -74,3 +76,45 @@ def update_elo_ratings_p_deterministic( ) return elo + + +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, + progress_bar: bool = False, +) -> tuple[float, float, np.ndarray]: + 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, + ) + + 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, + ) + + 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=False, + ) + elo_ratings = np.array(list(elo.elo_ratings.values())) + + return p_deterministic, elo_k, elo_ratings -- GitLab From b6003dcac33a7edc0161c435e45b6cb3b33a674b Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 22:42:57 +0300 Subject: [PATCH 075/147] Added ExperimentResult --- experiments/elo/elo/p_deterministic.py | 43 ++++++++++++++++++++++++-- 1 file changed, 41 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py index 1bb48f66..dfc2a57a 100644 --- a/experiments/elo/elo/p_deterministic.py +++ b/experiments/elo/elo/p_deterministic.py @@ -1,5 +1,7 @@ from __future__ import annotations +import dataclasses + import numpy as np from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo @@ -78,6 +80,27 @@ def update_elo_ratings_p_deterministic( 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, @@ -87,7 +110,7 @@ def p_deterministic_experiment( p_deterministic: float, elo_scale: float = 400, progress_bar: bool = False, -) -> tuple[float, float, np.ndarray]: +) -> ExperimentResult: matches = simulate_p_deterministic_matches( rng=rng, num_players=num_players, @@ -117,4 +140,20 @@ def p_deterministic_experiment( ) elo_ratings = np.array(list(elo.elo_ratings.values())) - return p_deterministic, elo_k, elo_ratings + 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(), + ) -- GitLab From 09c72779894e1cab4fade590e7b11b40bf08e39a Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 23:02:28 +0300 Subject: [PATCH 076/147] Added p_deterministic_experiments() incl parallel execution --- experiments/elo/elo/p_deterministic.py | 71 +++++++++++++++++++++++++- 1 file changed, 70 insertions(+), 1 deletion(-) diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py index dfc2a57a..2a9e1097 100644 --- a/experiments/elo/elo/p_deterministic.py +++ b/experiments/elo/elo/p_deterministic.py @@ -1,12 +1,20 @@ from __future__ import annotations import dataclasses +import itertools +import time +from concurrent.futures import ProcessPoolExecutor, as_completed +from typing import TYPE_CHECKING import numpy as np from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo from elo.optimal_k import approximate_optimal_k +if TYPE_CHECKING: + from collections.abc import Iterable, Generator + from concurrent.futures import Future + def simulate_p_deterministic_matches( *, @@ -136,7 +144,7 @@ def p_deterministic_experiment( num_matches=num_matches, players_per_match=players_per_match, p_deterministic=p_deterministic, - progress_bar=False, + progress_bar=progress_bar, ) elo_ratings = np.array(list(elo.elo_ratings.values())) @@ -157,3 +165,64 @@ def p_deterministic_experiment( p99=np.percentile(elo_ratings, 99), p100=elo_ratings.max(), ) + + +def _p_deterministic_experiment_futures( + *, + executor: ProcessPoolExecutor, + seed: int, + num_playerss: Iterable[int], + num_matchess: Iterable[int], + players_per_matchs: Iterable[int] = (2,), + p_deterministics: Iterable[float], + elo_scales: Iterable[float] = (400,), +) -> Generator[Future[ExperimentResult]]: + for i, ( + num_players, + num_matches, + players_per_match, + p_deterministic, + elo_scale, + ) in enumerate( + itertools.product( + num_playerss, + num_matchess, + players_per_matchs, + p_deterministics, + 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, + progress_bar=False, + ) + + +def p_deterministic_experiments( + *, + seed: int | None = None, + num_players: Iterable[int], + num_matches: Iterable[int], + players_per_match: Iterable[int] = (2,), + p_deterministic: Iterable[float], + elo_scale: Iterable[float] = (400,), +) -> 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, + ) + for future in as_completed(futures): + yield future.result() -- GitLab From 7956be9db6a4c5940baf8ccc3ee2e2642a113cbe Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 23:05:37 +0300 Subject: [PATCH 077/147] Simplify signature --- experiments/elo/elo/p_deterministic.py | 34 ++++++++++++++------------ 1 file changed, 19 insertions(+), 15 deletions(-) diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py index 2a9e1097..bffd1cce 100644 --- a/experiments/elo/elo/p_deterministic.py +++ b/experiments/elo/elo/p_deterministic.py @@ -171,11 +171,11 @@ def _p_deterministic_experiment_futures( *, executor: ProcessPoolExecutor, seed: int, - num_playerss: Iterable[int], - num_matchess: Iterable[int], - players_per_matchs: Iterable[int] = (2,), - p_deterministics: Iterable[float], - elo_scales: Iterable[float] = (400,), + 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, ) -> Generator[Future[ExperimentResult]]: for i, ( num_players, @@ -185,11 +185,15 @@ def _p_deterministic_experiment_futures( elo_scale, ) in enumerate( itertools.product( - num_playerss, - num_matchess, - players_per_matchs, - p_deterministics, - elo_scales, + (num_playerss,) if isinstance(num_playerss, int) else num_playerss, + (num_matchess,) if isinstance(num_matchess, int) else num_matchess, + (players_per_matchs,) + if isinstance(players_per_matchs, int) + else players_per_matchs, + (p_deterministics,) + if isinstance(p_deterministics, float) + else p_deterministics, + (elo_scales,) if isinstance(elo_scales, float) else elo_scales, ) ): rng = np.random.default_rng(seed + i) @@ -208,11 +212,11 @@ def _p_deterministic_experiment_futures( def p_deterministic_experiments( *, seed: int | None = None, - num_players: Iterable[int], - num_matches: Iterable[int], - players_per_match: Iterable[int] = (2,), - p_deterministic: Iterable[float], - elo_scale: Iterable[float] = (400,), + 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, ) -> Generator[ExperimentResult]: with ProcessPoolExecutor() as executor: futures = _p_deterministic_experiment_futures( -- GitLab From ab0da3400344d65e4f362a83dfa112192d773587 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 23:13:32 +0300 Subject: [PATCH 078/147] Fix iterables --- experiments/elo/elo/p_deterministic.py | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py index bffd1cce..3da89c0e 100644 --- a/experiments/elo/elo/p_deterministic.py +++ b/experiments/elo/elo/p_deterministic.py @@ -3,6 +3,7 @@ from __future__ import annotations import dataclasses import itertools import time +from collections.abc import Iterable from concurrent.futures import ProcessPoolExecutor, as_completed from typing import TYPE_CHECKING @@ -12,7 +13,7 @@ from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo from elo.optimal_k import approximate_optimal_k if TYPE_CHECKING: - from collections.abc import Iterable, Generator + from collections.abc import Generator from concurrent.futures import Future @@ -167,6 +168,10 @@ def p_deterministic_experiment( ) +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, @@ -185,15 +190,11 @@ def _p_deterministic_experiment_futures( elo_scale, ) in enumerate( itertools.product( - (num_playerss,) if isinstance(num_playerss, int) else num_playerss, - (num_matchess,) if isinstance(num_matchess, int) else num_matchess, - (players_per_matchs,) - if isinstance(players_per_matchs, int) - else players_per_matchs, - (p_deterministics,) - if isinstance(p_deterministics, float) - else p_deterministics, - (elo_scales,) if isinstance(elo_scales, float) else elo_scales, + _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) -- GitLab From d135c0cb91165135b7fa6c7e73204c5c2ee81952 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 23:24:00 +0300 Subject: [PATCH 079/147] Simplify p-deterministic distribution.py --- .../elo/p-deterministic distribution.py | 101 +++--------------- 1 file changed, 15 insertions(+), 86 deletions(-) diff --git a/experiments/elo/p-deterministic distribution.py b/experiments/elo/p-deterministic distribution.py index d4266f13..dbe96c20 100644 --- a/experiments/elo/p-deterministic distribution.py +++ b/experiments/elo/p-deterministic distribution.py @@ -14,107 +14,36 @@ # --- # %% +import dataclasses import jupyter_black import numpy as np import polars as pl import seaborn as sns -from elo.elo_ratings import RankOrderedLogitElo, TwoPlayerElo -from elo.optimal_k import approximate_optimal_k -from elo.p_deterministic import ( - simulate_p_deterministic_matches, - update_elo_ratings_p_deterministic, -) +from elo.p_deterministic import p_deterministic_experiments jupyter_black.load() +seed = 13 # %% -def generate_distributions( +experiments = p_deterministic_experiments( + seed=seed, num_players=1000, num_matches=1_000_000, players_per_match=2, - p_deterministic_min=0.01, - p_deterministic_max=0.99, - num_steps=99, + p_deterministic=np.linspace(start=0.01, stop=0.99, num=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: - 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, - ) - - 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, - ) - - 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=elo, - num_players=num_players, - num_matches=num_matches, - players_per_match=players_per_match, - p_deterministic=p_deterministic, - progress_bar=False, - ) - elo_ratings = np.array(list(elo.elo_ratings.values())) - - 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 = ( + 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) @@ -125,7 +54,7 @@ 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(data=results, x="p_deterministic", y="std_dev") # %% -sns.scatterplot(x=results["p_deterministic"], y=np.log(results["elo_ratings_std"])) +sns.scatterplot(x=results["p_deterministic"], y=np.log(results["std_dev"])) -- GitLab From 38b12fdf8f76210f873842642bcb7f9e7258f49a Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 4 May 2025 23:28:02 +0300 Subject: [PATCH 080/147] Added tqdm --- experiments/elo/p-deterministic distribution.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/experiments/elo/p-deterministic distribution.py b/experiments/elo/p-deterministic distribution.py index dbe96c20..c6992b04 100644 --- a/experiments/elo/p-deterministic distribution.py +++ b/experiments/elo/p-deterministic distribution.py @@ -20,6 +20,7 @@ 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() @@ -35,7 +36,7 @@ experiments = p_deterministic_experiments( elo_scale=400, ) results = ( - pl.LazyFrame(dataclasses.asdict(experiment) for experiment in experiments) + pl.LazyFrame(dataclasses.asdict(experiment) for experiment in tqdm(experiments)) .sort("num_players", "num_matches", "players_per_match", "p_deterministic") .collect() ) -- GitLab From 82077ce57c6db1474330662a2cc727376d7ad2fa Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 5 May 2025 11:22:31 +0300 Subject: [PATCH 081/147] abc --- experiments/elo/elo/elo_ratings.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py index b5ca081a..1cfc7b94 100644 --- a/experiments/elo/elo/elo_ratings.py +++ b/experiments/elo/elo/elo_ratings.py @@ -2,6 +2,7 @@ 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 @@ -17,7 +18,7 @@ def elo_probability(diff: float, scale: float = 400) -> float: return 1 / (1 + 10 ** (-diff / scale)) -class EloRatingSystem[ID_TYPE]: +class EloRatingSystem[ID_TYPE](ABC): elo_initial: float = 0 elo_k: float = 32 elo_scale: float = 400 @@ -41,12 +42,13 @@ class EloRatingSystem[ID_TYPE]: init_elo_ratings if init_elo_ratings is not None else {}, ) + @abstractmethod def update_elo_ratings( self, players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], - ) -> np.ndarray: - raise NotImplementedError + ) -> np.ndarray: ... + @abstractmethod def update_elo_ratings_batch( self, matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], @@ -54,22 +56,21 @@ class EloRatingSystem[ID_TYPE]: full_results: bool = False, progress_bar: bool = False, tqdm_kwargs: dict[str, Any] | None = None, - ) -> np.ndarray | None: - raise NotImplementedError + ) -> np.ndarray | None: ... + @abstractmethod def probability_matrix( self, players: Iterable[ID_TYPE], - ) -> np.ndarray: - raise NotImplementedError + ) -> np.ndarray: ... + @abstractmethod def expected_outcome( self, players: Iterable[ID_TYPE], *, rank_payoffs: np.ndarray | None = None, - ) -> np.ndarray: - raise NotImplementedError + ) -> np.ndarray: ... class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): -- GitLab From fab4951931069f552599426cb910419d8237b31d Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 5 May 2025 11:26:55 +0300 Subject: [PATCH 082/147] @override --- experiments/elo/elo/elo_ratings.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py index 1cfc7b94..c7cc6852 100644 --- a/experiments/elo/elo/elo_ratings.py +++ b/experiments/elo/elo/elo_ratings.py @@ -5,7 +5,7 @@ import warnings from abc import ABC, abstractmethod from collections import defaultdict from collections.abc import Mapping -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, override import numpy as np @@ -74,6 +74,7 @@ class EloRatingSystem[ID_TYPE](ABC): class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): + @override def expected_outcome( self, players: Iterable[ID_TYPE], @@ -90,6 +91,7 @@ class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): prob = elo_probability(diff, self.elo_scale) return np.array([prob, 1 - prob]) + @override def probability_matrix( self, players: Iterable[ID_TYPE], @@ -97,6 +99,7 @@ class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): expected_outcome = self.expected_outcome(players) return np.array([expected_outcome, 1 - expected_outcome]) + @override def update_elo_ratings( self, players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], @@ -121,6 +124,7 @@ class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): for match in matches: yield self.update_elo_ratings(match) + @override def update_elo_ratings_batch( self, matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], @@ -209,6 +213,7 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): return probs + @override def probability_matrix( self, players: Iterable[ID_TYPE], @@ -226,6 +231,7 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): return self._calculate_probability_matrix_from_permutations(players) + @override def expected_outcome( self, players: Iterable[ID_TYPE], @@ -243,6 +249,7 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): probs = self.probability_matrix(players) return probs @ rank_payoffs + @override def update_elo_ratings( self, players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], @@ -272,6 +279,7 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): for match in matches: yield self.update_elo_ratings(match) + @override def update_elo_ratings_batch( self, matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], -- GitLab From e9f977f1e3f6758993b04486ba4d1aead8d50f58 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 6 May 2025 08:48:59 +0300 Subject: [PATCH 083/147] Added K investigations.py --- experiments/elo/K investigations.py | 74 +++++++++++++++++++++++++++++ 1 file changed, 74 insertions(+) create mode 100644 experiments/elo/K investigations.py diff --git a/experiments/elo/K investigations.py b/experiments/elo/K investigations.py new file mode 100644 index 00000000..0ca3f9ec --- /dev/null +++ b/experiments/elo/K investigations.py @@ -0,0 +1,74 @@ +# --- +# 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")) + .collect() +) +data.shape + + +# %% +def power_law(x: np.ndarray, a: float) -> np.ndarray: + return a / (x**0.5) + + +def fit_and_plot(data): + 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,) = popt + print(f"Fitted parameters: a={a:.4f}, b={.5:.4f}, c={.0:.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 + + +# %% +fit_and_plot(data.filter(pl.col("p_deterministic") == 0.25)) +plt.show() + +# %% +fit_and_plot(data.filter(pl.col("p_deterministic") == 0.5)) +plt.show() + +# %% +fit_and_plot(data.filter(pl.col("p_deterministic") == 0.75)) +plt.show() -- GitLab From 81e2e1cfcc23ae15bf6b20e81a5e04b9bb1691c6 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 7 May 2025 08:17:38 +0300 Subject: [PATCH 084/147] Updated K investigations.py --- experiments/elo/K investigations.py | 31 ++++++++++++++++------------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/experiments/elo/K investigations.py b/experiments/elo/K investigations.py index 0ca3f9ec..c865fecc 100644 --- a/experiments/elo/K investigations.py +++ b/experiments/elo/K investigations.py @@ -25,25 +25,32 @@ jupyter_black.load() # %% data = ( - pl.scan_csv("results/p_deterministic.csv") - .filter(pl.col("num_matches") > 1000) + 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) -> np.ndarray: - return a / (x**0.5) +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,) = popt - print(f"Fitted parameters: a={a:.4f}, b={.5:.4f}, c={.0:.4f}") + 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) @@ -62,13 +69,9 @@ def fit_and_plot(data): # %% -fit_and_plot(data.filter(pl.col("p_deterministic") == 0.25)) -plt.show() - -# %% -fit_and_plot(data.filter(pl.col("p_deterministic") == 0.5)) -plt.show() +for (p_deterministic,), group in data.group_by("p_deterministic", maintain_order=True): + print(f"{p_deterministic=}") + fit_and_plot(group) + plt.show() # %% -fit_and_plot(data.filter(pl.col("p_deterministic") == 0.75)) -plt.show() -- GitLab From c3a0c58a7566c9dfc0dab0ba7140cf302f18dacf Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 11 May 2025 23:07:23 +0300 Subject: [PATCH 085/147] Winners and losers --- experiments/elo/elo/bga_spider.py | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py index 356032c4..6ff9563c 100644 --- a/experiments/elo/elo/bga_spider.py +++ b/experiments/elo/elo/bga_spider.py @@ -104,7 +104,7 @@ class BgaSpider(Spider): ) ) - ordinal_regex = re.compile(r"(\d+)(st|nd|rd|th)") + 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: @@ -293,10 +293,22 @@ class BgaSpider(Spider): def parse_match_html(self, html: str) -> Generator[dict[str, Any]]: selector = Selector(text=html, type="html") - for player_div in selector.css("div.board-score-entry"): + 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) - place = int(match.group(1)) if match else None + 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 "" -- GitLab From d1f259e46f380a891ed2156ab9b64e359b75e660 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 27 May 2025 08:16:02 +0300 Subject: [PATCH 086/147] Small fixes --- experiments/elo/BGA Elo.py | 6 +++--- experiments/elo/elo/elo_ratings.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/experiments/elo/BGA Elo.py b/experiments/elo/BGA Elo.py index 69eb8295..cf28d8c2 100644 --- a/experiments/elo/BGA Elo.py +++ b/experiments/elo/BGA Elo.py @@ -18,8 +18,8 @@ import itertools import jupyter_black import networkx as nx import polars as pl -from elo.elo_ratings_multi import RankOrderedLogitElo -from elo.optimal_k import approximate_optimal_k_multi +from elo.elo_ratings import RankOrderedLogitElo +from elo.optimal_k import approximate_optimal_k from tqdm import tqdm jupyter_black.load() @@ -107,7 +107,7 @@ 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_multi( +optimal_k = approximate_optimal_k( matches=matches, min_elo_k=0, max_elo_k=200, diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py index c7cc6852..a662123f 100644 --- a/experiments/elo/elo/elo_ratings.py +++ b/experiments/elo/elo/elo_ratings.py @@ -309,7 +309,7 @@ def _padded_numpy_array( dtype: np.dtype | None = None, ) -> np.ndarray: arrays = tuple(arrays) - max_len = max(a.shape[0] for a in arrays) + max_len = max((a.shape[0] for a in arrays), default=0) result = np.full((len(arrays), max_len), pad_value, dtype=dtype or np.float64) for i, a in enumerate(arrays): result[i, : a.shape[0]] = a -- GitLab From a3076ea5740605bf761e26cbbf3798ec8fc87810 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 24 Jun 2025 23:12:05 +0300 Subject: [PATCH 087/147] Fix a typo --- experiments/elo/BGA Elo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/elo/BGA Elo.py b/experiments/elo/BGA Elo.py index cf28d8c2..0659e7bd 100644 --- a/experiments/elo/BGA Elo.py +++ b/experiments/elo/BGA Elo.py @@ -84,7 +84,7 @@ print(f"Number of matches: {num_all_matches} (all) / {num_matches} (connected on print( f"Number of players: {num_all_players} (all) / {num_players} (connected only) / {num_regular_players} (regulars only)" ) -print(f"Maximum number of players by a single player: {num_matches_max}") +print(f"Maximum number of plays by a single player: {num_matches_max}") # %% matches = ( -- GitLab From 97d55a572750e91bd8547acfb4d66d71baf9c6bb Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 5 Aug 2025 21:03:12 +0300 Subject: [PATCH 088/147] poetry update --- experiments/elo/poetry.lock | 2490 ++++++++++++++++++----------------- 1 file changed, 1293 insertions(+), 1197 deletions(-) diff --git a/experiments/elo/poetry.lock b/experiments/elo/poetry.lock index f27c076e..ffb7a339 100644 --- a/experiments/elo/poetry.lock +++ b/experiments/elo/poetry.lock @@ -2,13 +2,13 @@ [[package]] name = "anyio" -version = "4.9.0" -description = "High level compatibility layer for multiple asynchronous event loop implementations" +version = "4.10.0" +description = "High-level concurrency and networking framework on top of asyncio or Trio" optional = false python-versions = ">=3.9" files = [ - {file = "anyio-4.9.0-py3-none-any.whl", hash = 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100644 --- a/experiments/elo/poetry.lock +++ b/experiments/elo/poetry.lock @@ -1,23 +1,23 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. [[package]] name = "anyio" -version = "4.10.0" +version = "4.11.0" description = "High-level concurrency and networking framework on top of asyncio or Trio" optional = false python-versions = ">=3.9" +groups = ["dev"] files = [ - {file = "anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1"}, - {file = "anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6"}, + {file = "anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc"}, + {file = "anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4"}, ] [package.dependencies] idna = ">=2.8" sniffio = ">=1.1" -typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""} [package.extras] -trio = ["trio (>=0.26.1)"] +trio = ["trio (>=0.31.0)"] [[package]] name = "appnope" @@ -25,6 +25,8 @@ version = "0.1.4" description = "Disable App Nap on macOS >= 10.9" optional = false python-versions = ">=3.6" +groups = ["dev"] +markers = "platform_system == \"Darwin\"" files = [ {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"}, {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"}, @@ -36,6 +38,7 @@ version = "25.1.0" description = "Argon2 for Python" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741"}, {file = "argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1"}, @@ -50,6 +53,7 @@ version = "25.1.0" description = "Low-level CFFI bindings for Argon2" optional = false python-versions = ">=3.9" +groups = ["dev"] files = [ {file = 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[PATCH 090/147] Updated p-deterministic notebooks --- experiments/elo/p-deterministic distribution.py | 2 +- experiments/elo/p-deterministic games.py | 2 +- experiments/elo/p-deterministic multi.py | 8 ++++---- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/experiments/elo/p-deterministic distribution.py b/experiments/elo/p-deterministic distribution.py index c6992b04..921ebf17 100644 --- a/experiments/elo/p-deterministic distribution.py +++ b/experiments/elo/p-deterministic distribution.py @@ -6,7 +6,7 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.17.1 +# jupytext_version: 1.18.1 # kernelspec: # display_name: Python 3 (ipykernel) # language: python diff --git a/experiments/elo/p-deterministic games.py b/experiments/elo/p-deterministic games.py index f4edde1d..73fb1475 100644 --- a/experiments/elo/p-deterministic games.py +++ b/experiments/elo/p-deterministic games.py @@ -6,7 +6,7 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.17.1 +# jupytext_version: 1.18.1 # kernelspec: # display_name: Python 3 (ipykernel) # language: python diff --git a/experiments/elo/p-deterministic multi.py b/experiments/elo/p-deterministic multi.py index 2ff9fce2..af8cf9ef 100644 --- a/experiments/elo/p-deterministic multi.py +++ b/experiments/elo/p-deterministic multi.py @@ -6,7 +6,7 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.17.1 +# jupytext_version: 1.18.1 # kernelspec: # display_name: Python 3 (ipykernel) # language: python @@ -16,8 +16,8 @@ # %% import jupyter_black import numpy as np -from elo.elo_ratings_multi import RankOrderedLogitElo -from elo.optimal_k import approximate_optimal_k_multi +from elo.elo_ratings import RankOrderedLogitElo +from elo.optimal_k import approximate_optimal_k jupyter_black.load() rng = np.random.default_rng() @@ -47,7 +47,7 @@ outcomes = np.where(mask[:, np.newaxis], deterministic_outcomes, random_outcomes random_outcomes.shape, deterministic_outcomes.shape, outcomes.shape # %% -optimal_k = approximate_optimal_k_multi( +optimal_k = approximate_optimal_k( matches=outcomes, elo_scale=elo_scale, min_elo_k=0, -- GitLab From f773d22f575c0555d76de2dd8b31f8c60c482aa3 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 20 Oct 2025 13:49:16 +0300 Subject: [PATCH 091/147] Updated pyproject.toml --- experiments/elo/poetry.lock | 56 ++++++++++++++++++++++++++++++++-- experiments/elo/pyproject.toml | 1 + 2 files changed, 55 insertions(+), 2 deletions(-) diff --git a/experiments/elo/poetry.lock b/experiments/elo/poetry.lock index d358e984..aa07c739 100644 --- a/experiments/elo/poetry.lock +++ b/experiments/elo/poetry.lock @@ -2444,7 +2444,7 @@ version = "2.3.4" description = "Fundamental package for array computing in Python" optional = false python-versions = ">=3.11" -groups = ["main"] +groups = ["main", "dev"] files = [ {file = "numpy-2.3.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = 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Blocks for Precise & Flexible Type Hints" +optional = false +python-versions = ">=3.11" +groups = ["dev"] +files = [ + {file = "optype-0.14.0-py3-none-any.whl", hash = "sha256:50d02edafd04edf2e5e27d6249760a51b2198adb9f6ffd778030b3d2806b026b"}, + {file = "optype-0.14.0.tar.gz", hash = "sha256:925cf060b7d1337647f880401f6094321e7d8e837533b8e159b9a92afa3157c6"}, +] + +[package.dependencies] +numpy = {version = ">=1.25", optional = true, markers = "extra == \"numpy\""} +numpy-typing-compat = {version = ">=20250818.1.25,<20250819", optional = true, markers = "extra == \"numpy\""} + +[package.extras] +numpy = ["numpy (>=1.25)", "numpy-typing-compat (>=20250818.1.25,<20250819)"] + [[package]] name = "packaging" version = "25.0" @@ -3883,6 +3917,24 @@ dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodest doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.19.1)", "jupytext", "linkify-it-py", "matplotlib (>=3.5)", "myst-nb (>=1.2.0)", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.2.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"] test = ["Cython", "array-api-strict (>=2.3.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest (>=8.0.0)", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] +[[package]] +name = "scipy-stubs" +version = "1.16.2.4" +description = "Type annotations for SciPy" +optional = false +python-versions = ">=3.11" +groups = ["dev"] +files = [ + {file = "scipy_stubs-1.16.2.4-py3-none-any.whl", hash = "sha256:8e47684fe5f8b823e06ec6513e4dbb5ae43a5a064d10d8228b7e3c3d243ec673"}, + {file = "scipy_stubs-1.16.2.4.tar.gz", hash = "sha256:dc303e0ba2272aa3832660f0e55f7b461ab32e98f452090f3e28a338f3920e67"}, +] + +[package.dependencies] +optype = {version = ">=0.14.0,<0.15", extras = ["numpy"]} + +[package.extras] +scipy = ["scipy (>=1.16.2,<1.17)"] + [[package]] name = "scrapy" version = "2.13.3" @@ -4530,4 +4582,4 @@ testing = ["coverage[toml]", "zope.event", "zope.testing"] [metadata] lock-version = "2.1" python-versions = "3.13.*" -content-hash = "b34088e560df8d0cfafb65ad27e24e724b546d262222bb55b962e42022cdb224" +content-hash = "40f899dfd41111803cf14d44cc52596924ecf016f4f1355302b44454fbd79d43" diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index 8e63ecad..8d7ada61 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -31,6 +31,7 @@ jupyterlab = "*" jupytext = "*" mypy = "*" ruff = "*" +scipy-stubs = "*" types-tqdm = "*" [tool.ruff] -- GitLab From c76561b35c1475ca566b6fb5302cce25ecfc9a9c Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 20 Oct 2025 14:13:34 +0300 Subject: [PATCH 092/147] GPT suggested improvements for approximate_optimal_k() --- experiments/elo/elo/optimal_k.py | 100 ++++++++++++++++++++++++++----- 1 file changed, 85 insertions(+), 15 deletions(-) diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py index af5590a4..21570b31 100644 --- a/experiments/elo/elo/optimal_k.py +++ b/experiments/elo/elo/optimal_k.py @@ -1,6 +1,5 @@ from __future__ import annotations -import functools from collections.abc import Mapping from typing import TYPE_CHECKING @@ -11,6 +10,9 @@ from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo if TYPE_CHECKING: from collections.abc import Iterable + from typing import TypeVar + + ID_TYPE = TypeVar("ID_TYPE") def calculate_loss[ID_TYPE]( @@ -32,10 +34,45 @@ def approximate_optimal_k[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, + min_elo_k: float | np.float64 = 1e-6, + max_elo_k: float | np.float64 = 200.0, + elo_scale: float | np.float64 = 400.0, + coarse_points: int | np.int64 = 12, # number of thetas in coarse scan + final_bracket_halfwidth: float | np.float64 = np.log(2.0), # half-width in log-k + xatol: float | np.float64 = 1e-3, # tolerance in log-k for Brent refine ) -> np.float64: + """ + Compute k* for the *given* population of players and matches ONLY. + + This routine explicitly treats k* as *data-dependent*. It performs: + 1) A coarse scan of L(k) on the full set of matches to find a good bracket + in theta = log(k)-space; and + 2) A Brent refinement on the full match list within that bracket. + + Parameters + ---------- + matches : iterable of Mapping or iterable of player IDs + Chronological stream of matches (Elo is order-sensitive). + two_player_only : bool + Use TwoPlayerElo if True, else RankOrderedLogitElo. + min_elo_k, max_elo_k : float + Search bounds for k. min_elo_k must be > 0 to define log(k). + elo_scale : float + Logistic scale parameter (e.g., 400). + coarse_points : int + Grid size for coarse scan in log-space. + final_bracket_halfwidth : float + Half-width of the final bracket in log-space around the coarse best. + xatol : float + Absolute tolerance in log(k) for Brent; corresponds to relative tol in k. + + Returns + ------- + float + The k* that minimises mean squared prediction error on the PROVIDED matches. + """ + + # 0) Normalise matches ONCE; k* is specific to this realised sample. match_list: list[Mapping[ID_TYPE, float]] = [ dict(zip((m := tuple(match)), range(len(m) - 1, -1, -1))) if not isinstance(match, Mapping) @@ -43,20 +80,53 @@ def approximate_optimal_k[ID_TYPE]( for match in matches ] - @functools.cache - def loss(elo_k: float) -> np.float64: - return calculate_loss( - elo=TwoPlayerElo(elo_k=elo_k, elo_scale=elo_scale) + n = len(match_list) + if not n: + raise ValueError("approximate_optimal_k: empty match stream") + + def _loss(elo_k: np.float64) -> 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), - matches=match_list, - progress_bar=False, + else RankOrderedLogitElo(elo_k=elo_k, elo_scale=elo_scale) ) + return calculate_loss(elo=elo, matches=match_list, progress_bar=False) + + # 1) Coarse bracket on the FULL sample in theta = log(k) + th_min = np.log(min_elo_k) + th_max = np.log(max_elo_k) + thetas = np.linspace(th_min, th_max, max(5, coarse_points)) + # Evaluate coarse grid on FULL data + coarse_vals = [_loss(np.exp(th)) for th in thetas] + # Pick best theta and form a local bracket [a, b, c] in log-space + best_i = int(np.argmin(coarse_vals)) + # Keep b away from edges for a proper (a < b < c) triple + b = thetas[np.clip(best_i, 1, len(thetas) - 2)] + # Symmetric bracket around b in log-space, clipped to bounds. + # Ensure non-degenerate width by using at least half a grid step. + grid_step = thetas[1] - thetas[0] + half = max(final_bracket_halfwidth, 0.5 * grid_step) + # Form bracket + a = max(th_min, b - half) + c = min(th_max, b + half) + + if not (a < b < c): + # Degenerate cases: zero half-width, collapsed bounds, or numerical ties. + eps = 1e-9 + mid = 0.5 * (th_min + th_max) + a, b, c = th_min, mid, th_max + if not (a < b < c): # th_min == th_max edge case + c = a + eps + + # 2) Final Brent refine on the FULL sample + def _loss_theta(theta: np.float64) -> np.float64: + return _loss(np.exp(theta)) result = minimize_scalar( - fun=loss, - method="bounded", - bounds=(min_elo_k, max_elo_k), + fun=_loss_theta, + method="brent", + bracket=(a, b, c), + options={"xtol": xatol, "maxiter": 80}, ) - return result.x + return np.exp(result.x) -- GitLab From 85f14e1e0c925252fb50a326edec7afc21f15482 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 20 Oct 2025 15:56:18 +0300 Subject: [PATCH 093/147] Better search for Brent brackets; fall back to bounded minimisation if necessary --- experiments/elo/elo/optimal_k.py | 87 ++++++++++++++++++++------------ 1 file changed, 56 insertions(+), 31 deletions(-) diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py index 21570b31..8fedb370 100644 --- a/experiments/elo/elo/optimal_k.py +++ b/experiments/elo/elo/optimal_k.py @@ -1,5 +1,7 @@ from __future__ import annotations +import logging + from collections.abc import Mapping from typing import TYPE_CHECKING @@ -14,6 +16,8 @@ if TYPE_CHECKING: ID_TYPE = TypeVar("ID_TYPE") +LOGGER = logging.getLogger(__name__) + def calculate_loss[ID_TYPE]( *, @@ -38,8 +42,8 @@ def approximate_optimal_k[ID_TYPE]( max_elo_k: float | np.float64 = 200.0, elo_scale: float | np.float64 = 400.0, coarse_points: int | np.int64 = 12, # number of thetas in coarse scan - final_bracket_halfwidth: float | np.float64 = np.log(2.0), # half-width in log-k xatol: float | np.float64 = 1e-3, # tolerance in log-k for Brent refine + maxiter: int | np.int64 = 80, ) -> np.float64: """ Compute k* for the *given* population of players and matches ONLY. @@ -84,7 +88,21 @@ def approximate_optimal_k[ID_TYPE]( if not n: raise ValueError("approximate_optimal_k: empty match stream") - def _loss(elo_k: np.float64) -> np.float64: + # Validate and sanitise bounds + min_k = min_elo_k + max_k = max_elo_k + if not (np.isfinite(min_k) and np.isfinite(max_k)): + raise ValueError("min_elo_k and max_elo_k must be finite") + if min_k <= 0.0: + # Avoid log(0); use the smallest positive *normal* float, not subnormal + min_k = np.finfo(float).tiny + if max_k <= min_k: + raise ValueError( + f"max_elo_k ({max_k}) must be greater than min_elo_k ({min_k})." + ) + + def _loss_theta(theta: np.float64) -> np.float64: + elo_k = np.exp(theta) elo: EloRatingSystem[ID_TYPE] = ( TwoPlayerElo(elo_k=elo_k, elo_scale=elo_scale) if two_player_only @@ -93,40 +111,47 @@ def approximate_optimal_k[ID_TYPE]( return calculate_loss(elo=elo, matches=match_list, progress_bar=False) # 1) Coarse bracket on the FULL sample in theta = log(k) - th_min = np.log(min_elo_k) - th_max = np.log(max_elo_k) + th_min: np.float64 = np.log(min_k) + th_max: np.float64 = np.log(max_k) thetas = np.linspace(th_min, th_max, max(5, coarse_points)) # Evaluate coarse grid on FULL data - coarse_vals = [_loss(np.exp(th)) for th in thetas] - # Pick best theta and form a local bracket [a, b, c] in log-space - best_i = int(np.argmin(coarse_vals)) - # Keep b away from edges for a proper (a < b < c) triple - b = thetas[np.clip(best_i, 1, len(thetas) - 2)] - # Symmetric bracket around b in log-space, clipped to bounds. - # Ensure non-degenerate width by using at least half a grid step. - grid_step = thetas[1] - thetas[0] - half = max(final_bracket_halfwidth, 0.5 * grid_step) - # Form bracket - a = max(th_min, b - half) - c = min(th_max, b + half) - - if not (a < b < c): - # Degenerate cases: zero half-width, collapsed bounds, or numerical ties. - eps = 1e-9 - mid = 0.5 * (th_min + th_max) - a, b, c = th_min, mid, th_max - if not (a < b < c): # th_min == th_max edge case - c = a + eps - - # 2) Final Brent refine on the FULL sample - def _loss_theta(theta: np.float64) -> np.float64: - return _loss(np.exp(theta)) + coarse_vals = np.array([_loss_theta(th) for th in thetas]) + + # Pick a bracket that satisfies Brent's requirement: f(b) < f(a) and f(b) < f(c) + if not np.all(np.isfinite(coarse_vals)): + raise ValueError("Non-finite loss encountered during coarse scan.") + + # Find local minima on the coarse grid + candidates = [ + i + for i in range(1, len(coarse_vals) - 1) + if (coarse_vals[i] < coarse_vals[i - 1]) + and (coarse_vals[i] < coarse_vals[i + 1]) + ] + + if not candidates: + LOGGER.debug( + "approximate_optimal_k: No strict local minimum found on coarse grid; " + "falling back to bounded search.", + ) + LOGGER.debug("thetas: %s; values: %s", thetas, coarse_vals) + # No strict local minimum on the coarse grid (flat/noisy surface): fall back to bounded search + result = minimize_scalar( # type: ignore[call-overload] + fun=_loss_theta, + method="bounded", + bounds=(th_min, th_max), + options={"xatol": xatol, "maxiter": maxiter * 2}, + ) + return np.exp(result.x) + + # Choose the lowest local minimum and its immediate neighbours as a strict bracket + i = min(candidates, key=lambda j: coarse_vals[j]) + a, b, c = thetas[i - 1], thetas[i], thetas[i + 1] - result = minimize_scalar( + result = minimize_scalar( # type: ignore[call-overload] fun=_loss_theta, method="brent", bracket=(a, b, c), - options={"xtol": xatol, "maxiter": 80}, + options={"xtol": xatol, "maxiter": maxiter}, ) - return np.exp(result.x) -- GitLab From 041fc554e4099037d9a172e77850cbcf554ca98d Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 20 Oct 2025 19:56:50 +0300 Subject: [PATCH 094/147] Type stubs --- experiments/elo/poetry.lock | 53 ++++++++++++++++++++++++++++++---- experiments/elo/pyproject.toml | 3 ++ 2 files changed, 51 insertions(+), 5 deletions(-) diff --git a/experiments/elo/poetry.lock b/experiments/elo/poetry.lock index aa07c739..766078df 100644 --- a/experiments/elo/poetry.lock +++ b/experiments/elo/poetry.lock @@ -1023,12 +1023,28 @@ version = "2.0" description = "A fancy and practical functional tools" optional = false python-versions = "*" -groups = ["main"] +groups = ["main", "dev"] files = [ {file = "funcy-2.0-py2.py3-none-any.whl", hash = "sha256:53df23c8bb1651b12f095df764bfb057935d49537a56de211b098f4c79614bb0"}, {file = "funcy-2.0.tar.gz", hash = "sha256:3963315d59d41c6f30c04bc910e10ab50a3ac4a225868bfa96feed133df075cb"}, ] +[[package]] +name = "funcy-typing" +version = "2.0.8" +description = "Stubs for funcy package" +optional = false +python-versions = ">=3.8" +groups = ["dev"] +files = [ + {file = "funcy_typing-2.0.8-py3-none-any.whl", hash = "sha256:8a561a5a4cb6c6292694fd2e02c688107bcb3aa17ab08571569a79c8ace2b605"}, + {file = "funcy_typing-2.0.8.tar.gz", hash = "sha256:7f1279cd42adac47740b19edaa566e066808ce59383faf9d1e0717ada59162c7"}, +] + +[package.dependencies] +funcy = ">=2.0,<3.0" +typing-extensions = ">=4.11" + [[package]] name = "h11" version = "0.16.0" @@ -2725,14 +2741,14 @@ files = [ [[package]] name = "patsy" -version = "1.0.1" +version = "1.0.2" description = "A Python package for describing statistical models and for building design matrices." optional = false python-versions = ">=3.6" groups = ["main"] files = [ - {file = "patsy-1.0.1-py2.py3-none-any.whl", hash = "sha256:751fb38f9e97e62312e921a1954b81e1bb2bcda4f5eeabaf94db251ee791509c"}, - {file = "patsy-1.0.1.tar.gz", hash = "sha256:e786a9391eec818c054e359b737bbce692f051aee4c661f4141cc88fb459c0c4"}, + {file = "patsy-1.0.2-py2.py3-none-any.whl", hash = "sha256:37bfddbc58fcf0362febb5f54f10743f8b21dd2aa73dec7e7ef59d1b02ae668a"}, + {file = "patsy-1.0.2.tar.gz", hash = "sha256:cdc995455f6233e90e22de72c37fcadb344e7586fb83f06696f54d92f8ce74c0"}, ] [package.dependencies] @@ -4381,6 +4397,33 @@ tls = ["idna (>=2.4)", "pyopenssl (>=21.0.0)", "service-identity (>=18.1.0)"] websocket = ["wsproto"] windows-platform = ["appdirs (>=1.4.0)", "appdirs (>=1.4.0)", "bcrypt (>=3.1.3)", "bcrypt (>=3.1.3)", "cryptography (>=3.3)", "cryptography (>=3.3)", "cython-test-exception-raiser (>=1.0.2,<2)", "cython-test-exception-raiser (>=1.0.2,<2)", "h2 (>=3.2,<5.0)", "h2 (>=3.2,<5.0)", "httpx[http2] (>=0.27)", "httpx[http2] (>=0.27)", "hypothesis (>=6.56)", "hypothesis (>=6.56)", "idna (>=2.4)", "idna (>=2.4)", "priority (>=1.1.0,<2.0)", "priority (>=1.1.0,<2.0)", "pyhamcrest (>=2)", "pyhamcrest (>=2)", "pyopenssl (>=21.0.0)", "pyopenssl (>=21.0.0)", "pyserial (>=3.0)", "pyserial (>=3.0)", "pywin32 (!=226)", "pywin32 (!=226)", "pywin32 (!=226) ; platform_system == \"Windows\"", "pywin32 (!=226) ; platform_system == \"Windows\"", "service-identity (>=18.1.0)", "service-identity (>=18.1.0)", "twisted-iocpsupport (>=1.0.2)", "twisted-iocpsupport (>=1.0.2)", "wsproto", "wsproto"] +[[package]] +name = "types-jmespath" +version = "1.0.2.20250809" +description = "Typing stubs for jmespath" +optional = false +python-versions = ">=3.9" +groups = ["dev"] +files = [ + {file = "types_jmespath-1.0.2.20250809-py3-none-any.whl", hash = "sha256:4147d17cc33454f0dac7e78b4e18e532a1330c518d85f7f6d19e5818ab83da21"}, + {file = "types_jmespath-1.0.2.20250809.tar.gz", hash = "sha256:e194efec21c0aeae789f701ae25f17c57c25908e789b1123a5c6f8d915b4adff"}, +] + +[[package]] +name = "types-networkx" +version = "3.5.0.20251001" +description = "Typing stubs for networkx" +optional = false +python-versions = ">=3.10" +groups = ["dev"] +files = [ + {file = "types_networkx-3.5.0.20251001-py3-none-any.whl", hash = "sha256:4bb9dd0378a52ca57d68f8215f8662e736d800a8ff892b457646eccd828a0230"}, + {file = "types_networkx-3.5.0.20251001.tar.gz", hash = "sha256:8e3c5c491ba5870d75e175751d70ddeac81df43caf2a64bae161e181f5e8ea7a"}, +] + +[package.dependencies] +numpy = ">=1.20" + [[package]] name = "types-requests" version = "2.32.4.20250913" @@ -4582,4 +4625,4 @@ testing = ["coverage[toml]", "zope.event", "zope.testing"] [metadata] lock-version = "2.1" python-versions = "3.13.*" -content-hash = "40f899dfd41111803cf14d44cc52596924ecf016f4f1355302b44454fbd79d43" +content-hash = "7f3ea2fecd60ac736d90a0412a947727a47d2641e9af3e380bdbfcfa3a544c06" diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index 8d7ada61..86b94897 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -26,12 +26,15 @@ tabulate = "*" tqdm = "*" [tool.poetry.group.dev.dependencies] +funcy-typing = "*" jupyter-black = "*" jupyterlab = "*" jupytext = "*" mypy = "*" ruff = "*" scipy-stubs = "*" +types-jmespath = "*" +types-networkx = "*" types-tqdm = "*" [tool.ruff] -- GitLab From e164c550c0b48595bb5621e12083e4882bb82b7c Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 20 Oct 2025 20:22:08 +0300 Subject: [PATCH 095/147] Revert approximate_optimal_k() --- experiments/elo/elo/optimal_k.py | 128 ++++++------------------------- 1 file changed, 23 insertions(+), 105 deletions(-) diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py index 8fedb370..6507bdef 100644 --- a/experiments/elo/elo/optimal_k.py +++ b/experiments/elo/elo/optimal_k.py @@ -1,8 +1,7 @@ from __future__ import annotations -import logging - from collections.abc import Mapping +import functools from typing import TYPE_CHECKING import numpy as np @@ -12,11 +11,6 @@ from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo if TYPE_CHECKING: from collections.abc import Iterable - from typing import TypeVar - - ID_TYPE = TypeVar("ID_TYPE") - -LOGGER = logging.getLogger(__name__) def calculate_loss[ID_TYPE]( @@ -38,45 +32,12 @@ def approximate_optimal_k[ID_TYPE]( *, matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], two_player_only: bool = False, - min_elo_k: float | np.float64 = 1e-6, - max_elo_k: float | np.float64 = 200.0, - elo_scale: float | np.float64 = 400.0, - coarse_points: int | np.int64 = 12, # number of thetas in coarse scan - xatol: float | np.float64 = 1e-3, # tolerance in log-k for Brent refine - maxiter: int | np.int64 = 80, + 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: - """ - Compute k* for the *given* population of players and matches ONLY. - - This routine explicitly treats k* as *data-dependent*. It performs: - 1) A coarse scan of L(k) on the full set of matches to find a good bracket - in theta = log(k)-space; and - 2) A Brent refinement on the full match list within that bracket. - - Parameters - ---------- - matches : iterable of Mapping or iterable of player IDs - Chronological stream of matches (Elo is order-sensitive). - two_player_only : bool - Use TwoPlayerElo if True, else RankOrderedLogitElo. - min_elo_k, max_elo_k : float - Search bounds for k. min_elo_k must be > 0 to define log(k). - elo_scale : float - Logistic scale parameter (e.g., 400). - coarse_points : int - Grid size for coarse scan in log-space. - final_bracket_halfwidth : float - Half-width of the final bracket in log-space around the coarse best. - xatol : float - Absolute tolerance in log(k) for Brent; corresponds to relative tol in k. - - Returns - ------- - float - The k* that minimises mean squared prediction error on the PROVIDED matches. - """ - - # 0) Normalise matches ONCE; k* is specific to this realised sample. match_list: list[Mapping[ID_TYPE, float]] = [ dict(zip((m := tuple(match)), range(len(m) - 1, -1, -1))) if not isinstance(match, Mapping) @@ -84,74 +45,31 @@ def approximate_optimal_k[ID_TYPE]( for match in matches ] - n = len(match_list) - if not n: - raise ValueError("approximate_optimal_k: empty match stream") - - # Validate and sanitise bounds - min_k = min_elo_k - max_k = max_elo_k - if not (np.isfinite(min_k) and np.isfinite(max_k)): - raise ValueError("min_elo_k and max_elo_k must be finite") - if min_k <= 0.0: - # Avoid log(0); use the smallest positive *normal* float, not subnormal - min_k = np.finfo(float).tiny - if max_k <= min_k: - raise ValueError( - f"max_elo_k ({max_k}) must be greater than min_elo_k ({min_k})." - ) - - def _loss_theta(theta: np.float64) -> np.float64: - elo_k = np.exp(theta) + @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) - - # 1) Coarse bracket on the FULL sample in theta = log(k) - th_min: np.float64 = np.log(min_k) - th_max: np.float64 = np.log(max_k) - thetas = np.linspace(th_min, th_max, max(5, coarse_points)) - # Evaluate coarse grid on FULL data - coarse_vals = np.array([_loss_theta(th) for th in thetas]) - - # Pick a bracket that satisfies Brent's requirement: f(b) < f(a) and f(b) < f(c) - if not np.all(np.isfinite(coarse_vals)): - raise ValueError("Non-finite loss encountered during coarse scan.") - # Find local minima on the coarse grid - candidates = [ - i - for i in range(1, len(coarse_vals) - 1) - if (coarse_vals[i] < coarse_vals[i - 1]) - and (coarse_vals[i] < coarse_vals[i + 1]) - ] - - if not candidates: - LOGGER.debug( - "approximate_optimal_k: No strict local minimum found on coarse grid; " - "falling back to bounded search.", + return calculate_loss( + elo=elo, + matches=match_list, + progress_bar=False, ) - LOGGER.debug("thetas: %s; values: %s", thetas, coarse_vals) - # No strict local minimum on the coarse grid (flat/noisy surface): fall back to bounded search - result = minimize_scalar( # type: ignore[call-overload] - fun=_loss_theta, - method="bounded", - bounds=(th_min, th_max), - options={"xatol": xatol, "maxiter": maxiter * 2}, - ) - return np.exp(result.x) - # Choose the lowest local minimum and its immediate neighbours as a strict bracket - i = min(candidates, key=lambda j: coarse_vals[j]) - a, b, c = thetas[i - 1], thetas[i], thetas[i + 1] + 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_theta, - method="brent", - bracket=(a, b, c), - options={"xtol": xatol, "maxiter": maxiter}, + fun=_loss, + method="bounded", + bounds=(min_elo_k, max_elo_k), + options=options, ) - return np.exp(result.x) + + return result.x -- GitLab From 87da05d1f64d245ae91616537080cae27b821e89 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 20 Oct 2025 20:54:18 +0300 Subject: [PATCH 096/147] Added p=0 and 1 to p_deterministic_experiment --- experiments/elo/elo/p_deterministic.py | 41 ++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py index 3da89c0e..2dc4f1aa 100644 --- a/experiments/elo/elo/p_deterministic.py +++ b/experiments/elo/elo/p_deterministic.py @@ -120,6 +120,47 @@ def p_deterministic_experiment( elo_scale: float = 400, 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, -- GitLab From 6243199994a74e1b109c763785f40b1e4abf3173 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 20 Oct 2025 20:55:45 +0300 Subject: [PATCH 097/147] More general handling of the args to p_deterministic_experiments in p-deterministic distribution.py --- .../elo/p-deterministic distribution.py | 54 +++++++++++++++---- 1 file changed, 44 insertions(+), 10 deletions(-) diff --git a/experiments/elo/p-deterministic distribution.py b/experiments/elo/p-deterministic distribution.py index 921ebf17..0fc0c3c9 100644 --- a/experiments/elo/p-deterministic distribution.py +++ b/experiments/elo/p-deterministic distribution.py @@ -26,17 +26,33 @@ jupyter_black.load() seed = 13 +# %% +num_players = (1000,) +num_matches = (1_000_000,) +players_per_match = (2,) +p_deterministic = np.linspace(start=0.00, stop=0.99, num=100) +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=1000, - num_matches=1_000_000, - players_per_match=2, - p_deterministic=np.linspace(start=0.01, stop=0.99, num=99), - elo_scale=400, + 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 tqdm(experiments)) + pl.LazyFrame(dataclasses.asdict(experiment) for experiment in experiments) .sort("num_players", "num_matches", "players_per_match", "p_deterministic") .collect() ) @@ -49,13 +65,31 @@ 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") +sns.scatterplot( + data=results, + x="p_deterministic", + y="elo_k", + hue="players_per_match", +) # %% -sns.scatterplot(x=results["p_deterministic"], y=np.log(results["elo_k"])) +sns.scatterplot( + x=results["p_deterministic"], + y=np.log(results["elo_k"].alias("log(elo_k)")), + hue=results["players_per_match"], +) # %% -sns.scatterplot(data=results, x="p_deterministic", y="std_dev") +sns.scatterplot( + data=results, + x="p_deterministic", + y="std_dev", + hue="players_per_match", +) # %% -sns.scatterplot(x=results["p_deterministic"], y=np.log(results["std_dev"])) +sns.scatterplot( + x=results["p_deterministic"], + y=np.log(results["std_dev"].alias("log(std_dev)")), + hue=results["players_per_match"], +) -- GitLab From 67f1a678418dc5a04296c88ad5faf85334d39b38 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 20 Oct 2025 21:16:58 +0300 Subject: [PATCH 098/147] Added max_iterations and x_absolute_tol to p-deterministic experiments --- experiments/elo/elo/p_deterministic.py | 12 ++++++++++++ experiments/elo/p-deterministic distribution.py | 4 ++-- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py index 2dc4f1aa..bf68b845 100644 --- a/experiments/elo/elo/p_deterministic.py +++ b/experiments/elo/elo/p_deterministic.py @@ -118,6 +118,8 @@ def p_deterministic_experiment( 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: @@ -175,6 +177,8 @@ def p_deterministic_experiment( min_elo_k=0, max_elo_k=elo_scale / 2, elo_scale=elo_scale, + max_iterations=max_iterations, + x_absolute_tol=x_absolute_tol, ) elo = update_elo_ratings_p_deterministic( @@ -222,6 +226,8 @@ def _p_deterministic_experiment_futures( 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, @@ -247,6 +253,8 @@ def _p_deterministic_experiment_futures( 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, ) @@ -259,6 +267,8 @@ def p_deterministic_experiments( 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( @@ -269,6 +279,8 @@ def p_deterministic_experiments( 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() diff --git a/experiments/elo/p-deterministic distribution.py b/experiments/elo/p-deterministic distribution.py index 0fc0c3c9..00e5c259 100644 --- a/experiments/elo/p-deterministic distribution.py +++ b/experiments/elo/p-deterministic distribution.py @@ -29,8 +29,8 @@ seed = 13 # %% num_players = (1000,) num_matches = (1_000_000,) -players_per_match = (2,) -p_deterministic = np.linspace(start=0.00, stop=0.99, num=100) +players_per_match = (2, 3) +p_deterministic = np.linspace(start=0.00, stop=0.99, num=10) elo_scale = (400,) num_experiments = ( len(num_players) -- GitLab From 44124d1fe143a583ea46aeb8df9b4f2042f66137 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 20 Oct 2025 23:12:35 +0300 Subject: [PATCH 099/147] Recalibrate probablities if necessary --- experiments/elo/elo/elo_ratings.py | 25 +++++++++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py index a662123f..defb159e 100644 --- a/experiments/elo/elo/elo_ratings.py +++ b/experiments/elo/elo/elo_ratings.py @@ -193,6 +193,8 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): def _calculate_probability_matrix_from_permutations( self, players: tuple[ID_TYPE, ...], + rtol: float = 1e-3, + atol: float = 1e-4, ) -> np.ndarray: num_players = len(players) ratings = np.array([self.elo_ratings[p] for p in players], dtype=float) @@ -208,8 +210,27 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): for position, player in enumerate(perm): probs[player, position] += prob - assert np.allclose(probs.sum(axis=0), 1), "Probabilities must sum to 1" - assert np.allclose(probs.sum(axis=1), 1), "Probabilities must sum to 1" + col_sum = probs.sum(axis=0, keepdims=True) + row_sum = probs.sum(axis=1, keepdims=True) + while not ( + np.allclose(col_sum, 1.0, rtol=rtol, atol=atol) + and np.allclose(row_sum, 1.0, rtol=rtol, atol=atol) + ): + col_sum[col_sum == 0.0] = 1.0 + probs /= col_sum + row_sum[row_sum == 0.0] = 1.0 + probs /= row_sum + col_sum = probs.sum(axis=0, keepdims=True) + row_sum = probs.sum(axis=1, keepdims=True) + + # Robust checks for probability matrix + assert np.all(np.isfinite(probs)), "Probability matrix has non-finite values" + assert np.allclose(col_sum, 1.0, rtol=rtol, atol=atol), ( + f"Probabilities must sum to 1 over players per position (got {col_sum})" + ) + assert np.allclose(row_sum, 1.0, rtol=rtol, atol=atol), ( + f"Probabilities must sum to 1 over positions per player (got {row_sum})" + ) return probs -- GitLab From 47eee01ab7c238c23bb36b3db853ab89bd70d4ce Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 21 Oct 2025 08:06:12 +0300 Subject: [PATCH 100/147] Fix the probability calibration in _calculate_probability_matrix_from_permutations() --- experiments/elo/elo/elo_ratings.py | 1 + 1 file changed, 1 insertion(+) diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py index defb159e..548c0a5c 100644 --- a/experiments/elo/elo/elo_ratings.py +++ b/experiments/elo/elo/elo_ratings.py @@ -218,6 +218,7 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): ): col_sum[col_sum == 0.0] = 1.0 probs /= col_sum + row_sum = probs.sum(axis=1, keepdims=True) row_sum[row_sum == 0.0] = 1.0 probs /= row_sum col_sum = probs.sum(axis=0, keepdims=True) -- GitLab From 5073a8dc421755e375803d4c6d251662ef9f57cf Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 21 Oct 2025 08:06:30 +0300 Subject: [PATCH 101/147] Better plotting in p-deterministic distribution.py --- .../elo/p-deterministic distribution.py | 24 ++++++++++++++----- 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/experiments/elo/p-deterministic distribution.py b/experiments/elo/p-deterministic distribution.py index 00e5c259..c6fc4c65 100644 --- a/experiments/elo/p-deterministic distribution.py +++ b/experiments/elo/p-deterministic distribution.py @@ -73,10 +73,16 @@ sns.scatterplot( ) # %% +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( - x=results["p_deterministic"], - y=np.log(results["elo_k"].alias("log(elo_k)")), - hue=results["players_per_match"], + data=results_log, + x="p_deterministic", + y="log(elo_k)", + hue="players_per_match", ) # %% @@ -88,8 +94,14 @@ sns.scatterplot( ) # %% +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( - x=results["p_deterministic"], - y=np.log(results["std_dev"].alias("log(std_dev)")), - hue=results["players_per_match"], + data=results_log, + x="p_deterministic", + y="log(std_dev)", + hue="players_per_match", ) -- GitLab From b78c251216969758f28ec7951392e0704cbee15b Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 21 Oct 2025 10:52:26 +0300 Subject: [PATCH 102/147] poetry -> uv --- experiments/elo/poetry.lock | 4628 ----------------- experiments/elo/poetry.toml | 2 - experiments/elo/pyproject.toml | 78 +- experiments/elo/{ => src}/elo/__init__.py | 0 experiments/elo/{ => src}/elo/bga_spider.py | 0 experiments/elo/{ => src}/elo/data.py | 0 experiments/elo/{ => src}/elo/elo_ratings.py | 0 experiments/elo/{ => src}/elo/game_stats.py | 0 experiments/elo/{ => src}/elo/optimal_k.py | 0 .../elo/{ => src}/elo/p_deterministic.py | 0 experiments/elo/src/elo/py.typed | 0 .../elo/{ => src}/elo/snooker_spider.py | 0 experiments/elo/uv.lock | 2481 +++++++++ 13 files changed, 2522 insertions(+), 4667 deletions(-) delete mode 100644 experiments/elo/poetry.lock delete mode 100644 experiments/elo/poetry.toml rename experiments/elo/{ => src}/elo/__init__.py (100%) rename experiments/elo/{ => src}/elo/bga_spider.py (100%) rename experiments/elo/{ => src}/elo/data.py (100%) rename experiments/elo/{ => src}/elo/elo_ratings.py (100%) rename experiments/elo/{ => src}/elo/game_stats.py (100%) rename experiments/elo/{ => src}/elo/optimal_k.py (100%) rename experiments/elo/{ => src}/elo/p_deterministic.py (100%) create mode 100644 experiments/elo/src/elo/py.typed rename experiments/elo/{ => src}/elo/snooker_spider.py (100%) create mode 100644 experiments/elo/uv.lock diff --git a/experiments/elo/poetry.lock 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"0.22.4", features = ["extension-module", "abi3-py312"] } diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index 08609fd1..fdde6a2d 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -27,6 +27,9 @@ dependencies = [ "tqdm", ] +[project.scripts] +elo = "elo:main" + [dependency-groups] dev = [ "funcy-typing", @@ -43,8 +46,23 @@ dev = [ ] [build-system] -requires = ["uv_build>=0.9.4,<0.10.0"] -build-backend = "uv_build" +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._core" +python-packages = ["elo"] +python-source = "src" + +[tool.uv] +cache-keys = [ + { file = "pyproject.toml" }, + { file = "src/**/*.rs" }, + { file = "Cargo.toml" }, + { file = "Cargo.lock" }, +] [tool.ruff] target-version = "py313" diff --git a/experiments/elo/src/elo/__init__.py b/experiments/elo/src/elo/__init__.py index e69de29b..8b588872 100644 --- a/experiments/elo/src/elo/__init__.py +++ b/experiments/elo/src/elo/__init__.py @@ -0,0 +1,5 @@ +from elo._core import hello_from_bin + + +def main() -> None: + print(hello_from_bin()) diff --git a/experiments/elo/src/elo/_core.pyi b/experiments/elo/src/elo/_core.pyi new file mode 100644 index 00000000..d52129eb --- /dev/null +++ b/experiments/elo/src/elo/_core.pyi @@ -0,0 +1 @@ +def hello_from_bin() -> str: ... diff --git a/experiments/elo/src/lib.rs b/experiments/elo/src/lib.rs new file mode 100644 index 00000000..53781f9b --- /dev/null +++ b/experiments/elo/src/lib.rs @@ -0,0 +1,15 @@ +use pyo3::prelude::*; + +#[pyfunction] +fn hello_from_bin() -> String { + "Hello from example-ext!".to_string() +} + +/// 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 _core(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_function(wrap_pyfunction!(hello_from_bin, m)?)?; + Ok(()) +} -- GitLab From 093a7f75e2f2979dc446f865113bc51a4f119734 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 22 Oct 2025 21:03:58 +0300 Subject: [PATCH 105/147] Some Rust shenanigans --- experiments/elo/.cargo/config.toml | 2 ++ experiments/elo/Cargo.toml | 4 ++-- experiments/elo/pyproject.toml | 2 +- experiments/elo/src/elo/__init__.py | 2 +- experiments/elo/src/elo/{_core.pyi => _rust.pyi} | 0 experiments/elo/src/lib.rs | 12 ++++++++++-- experiments/elo/src/main.rs | 5 +++++ 7 files changed, 21 insertions(+), 6 deletions(-) create mode 100644 experiments/elo/.cargo/config.toml rename experiments/elo/src/elo/{_core.pyi => _rust.pyi} (100%) create mode 100644 experiments/elo/src/main.rs diff --git a/experiments/elo/.cargo/config.toml b/experiments/elo/.cargo/config.toml new file mode 100644 index 00000000..67ba795e --- /dev/null +++ b/experiments/elo/.cargo/config.toml @@ -0,0 +1,2 @@ +[target.'cfg(target_os = "macos")'] +rustflags = ["-C", "link-args=-Wl,-undefined,dynamic_lookup"] diff --git a/experiments/elo/Cargo.toml b/experiments/elo/Cargo.toml index 4c635785..0c1c19e4 100644 --- a/experiments/elo/Cargo.toml +++ b/experiments/elo/Cargo.toml @@ -4,9 +4,9 @@ version = "0.1.0" edition = "2021" [lib] -name = "_core" +name = "elo" # "cdylib" is necessary to produce a shared library for Python to import from. -crate-type = ["cdylib"] +crate-type = ["cdylib", "rlib"] [dependencies] rand = "0.9.0" diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index fdde6a2d..da400144 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -52,7 +52,7 @@ 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._core" +module-name = "elo._rust" python-packages = ["elo"] python-source = "src" diff --git a/experiments/elo/src/elo/__init__.py b/experiments/elo/src/elo/__init__.py index 8b588872..87679b81 100644 --- a/experiments/elo/src/elo/__init__.py +++ b/experiments/elo/src/elo/__init__.py @@ -1,4 +1,4 @@ -from elo._core import hello_from_bin +from elo._rust import hello_from_bin def main() -> None: diff --git a/experiments/elo/src/elo/_core.pyi b/experiments/elo/src/elo/_rust.pyi similarity index 100% rename from experiments/elo/src/elo/_core.pyi rename to experiments/elo/src/elo/_rust.pyi diff --git a/experiments/elo/src/lib.rs b/experiments/elo/src/lib.rs index 53781f9b..dfebfa1c 100644 --- a/experiments/elo/src/lib.rs +++ b/experiments/elo/src/lib.rs @@ -1,15 +1,23 @@ use pyo3::prelude::*; +fn hello_core() -> String { + "Hello from example-ext!".to_string() +} + #[pyfunction] fn hello_from_bin() -> String { - "Hello from example-ext!".to_string() + hello_core() +} + +pub fn hello_for_rust() -> String { + hello_core() } /// 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 _core(m: &Bound<'_, PyModule>) -> PyResult<()> { +fn _rust(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_function(wrap_pyfunction!(hello_from_bin, m)?)?; Ok(()) } diff --git a/experiments/elo/src/main.rs b/experiments/elo/src/main.rs new file mode 100644 index 00000000..b1d66c8a --- /dev/null +++ b/experiments/elo/src/main.rs @@ -0,0 +1,5 @@ +use elo::hello_for_rust; + +fn main() { + println!("{}", hello_for_rust()); +} -- GitLab From 245b584d76d6d4567d3749bc7fb25203022b0e91 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Wed, 22 Oct 2025 23:28:24 +0300 Subject: [PATCH 106/147] Added permutations --- experiments/elo/Cargo.lock | 137 ++++++++++++++++++++++++++++++++++-- experiments/elo/Cargo.toml | 3 + experiments/elo/src/lib.rs | 29 ++++++++ experiments/elo/src/main.rs | 8 ++- 4 files changed, 171 insertions(+), 6 deletions(-) diff --git a/experiments/elo/Cargo.lock b/experiments/elo/Cargo.lock index d04a684e..3c0761df 100644 --- a/experiments/elo/Cargo.lock +++ b/experiments/elo/Cargo.lock @@ -14,12 +14,57 @@ version = "1.0.4" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9330f8b2ff13f34540b44e946ef35111825727b38d33286ef986142615121801" +[[package]] +name = "crossbeam-deque" +version = "0.8.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "9dd111b7b7f7d55b72c0a6ae361660ee5853c9af73f70c3c2ef6858b950e2e51" +dependencies = [ + "crossbeam-epoch", + "crossbeam-utils", +] + +[[package]] +name = "crossbeam-epoch" +version = "0.9.18" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5b82ac4a3c2ca9c3460964f020e1402edd5753411d7737aa39c3714ad1b5420e" +dependencies = [ + "crossbeam-utils", +] + +[[package]] +name = "crossbeam-utils" +version = "0.8.21" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d0a5c400df2834b80a4c3327b3aad3a4c4cd4de0629063962b03235697506a28" + +[[package]] +name = "either" +version = "1.15.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "48c757948c5ede0e46177b7add2e67155f70e33c07fea8284df6576da70b3719" + [[package]] name = "elo" version = "0.1.0" dependencies = [ + "once_cell", "pyo3", - "rand", + "rand 0.9.2", + "rand_distr", + "rayon", +] + +[[package]] +name = "getrandom" +version = "0.2.16" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "335ff9f135e4384c8150d6f27c6daed433577f86b4750418338c01a1a2528592" +dependencies = [ + "cfg-if", + "libc", + "wasi", ] [[package]] @@ -55,6 +100,12 @@ version = "0.2.177" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "2874a2af47a2325c2001a6e6fad9b16a53b802102b528163885171cf92b15976" +[[package]] +name = "libm" +version = "0.2.15" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f9fbbcab51052fe104eb5e5d351cf728d30a5be1fe14d9be8a3b097481fb97de" + [[package]] name = "memoffset" version = "0.9.1" @@ -64,6 +115,16 @@ dependencies = [ "autocfg", ] +[[package]] +name = "num-traits" +version = "0.2.19" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "071dfc062690e90b734c0b2273ce72ad0ffa95f0c74596bc250dcfd960262841" +dependencies = [ + "autocfg", + "libm", +] + [[package]] name = "once_cell" version = "1.21.3" @@ -172,14 +233,35 @@ version = "5.3.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "69cdb34c158ceb288df11e18b4bd39de994f6657d83847bdffdbd7f346754b0f" +[[package]] +name = "rand" +version = "0.8.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "34af8d1a0e25924bc5b7c43c079c942339d8f0a8b57c39049bef581b46327404" +dependencies = [ + "libc", + "rand_chacha 0.3.1", + "rand_core 0.6.4", +] + [[package]] name = "rand" version = "0.9.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "6db2770f06117d490610c7488547d543617b21bfa07796d7a12f6f1bd53850d1" dependencies = [ - "rand_chacha", - "rand_core", + "rand_chacha 0.9.0", + "rand_core 0.9.3", +] + +[[package]] +name = "rand_chacha" +version = "0.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e6c10a63a0fa32252be49d21e7709d4d4baf8d231c2dbce1eaa8141b9b127d88" +dependencies = [ + "ppv-lite86", + "rand_core 0.6.4", ] [[package]] @@ -189,7 +271,16 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d3022b5f1df60f26e1ffddd6c66e8aa15de382ae63b3a0c1bfc0e4d3e3f325cb" dependencies = [ "ppv-lite86", - "rand_core", + "rand_core 0.9.3", +] + +[[package]] +name = "rand_core" +version = "0.6.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ec0be4795e2f6a28069bec0b5ff3e2ac9bafc99e6a9a7dc3547996c5c816922c" +dependencies = [ + "getrandom 0.2.16", ] [[package]] @@ -198,7 +289,37 @@ version = "0.9.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "99d9a13982dcf210057a8a78572b2217b667c3beacbf3a0d8b454f6f82837d38" dependencies = [ - "getrandom", + "getrandom 0.3.4", +] + +[[package]] +name = "rand_distr" +version = "0.4.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "32cb0b9bc82b0a0876c2dd994a7e7a2683d3e7390ca40e6886785ef0c7e3ee31" +dependencies = [ + "num-traits", + "rand 0.8.5", +] + +[[package]] +name = "rayon" +version = "1.11.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "368f01d005bf8fd9b1206fb6fa653e6c4a81ceb1466406b81792d87c5677a58f" +dependencies = [ + "either", + "rayon-core", +] + +[[package]] +name = "rayon-core" +version = "1.13.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "22e18b0f0062d30d4230b2e85ff77fdfe4326feb054b9783a3460d8435c8ab91" +dependencies = [ + "crossbeam-deque", + "crossbeam-utils", ] [[package]] @@ -236,6 +357,12 @@ version = "0.2.4" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7264e107f553ccae879d21fbea1d6724ac785e8c3bfc762137959b5802826ef3" +[[package]] +name = "wasi" +version = "0.11.1+wasi-snapshot-preview1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ccf3ec651a847eb01de73ccad15eb7d99f80485de043efb2f370cd654f4ea44b" + [[package]] name = "wasip2" version = "1.0.1+wasi-0.2.4" diff --git a/experiments/elo/Cargo.toml b/experiments/elo/Cargo.toml index 0c1c19e4..12f680d5 100644 --- a/experiments/elo/Cargo.toml +++ b/experiments/elo/Cargo.toml @@ -11,3 +11,6 @@ crate-type = ["cdylib", "rlib"] [dependencies] rand = "0.9.0" pyo3 = { version = "0.22.4", features = ["extension-module", "abi3-py312"] } +rand_distr = "0.4" +rayon = "1.10" # optional for MC sampling +once_cell = "1.19" # for cached permutations diff --git a/experiments/elo/src/lib.rs b/experiments/elo/src/lib.rs index dfebfa1c..1ee29ca9 100644 --- a/experiments/elo/src/lib.rs +++ b/experiments/elo/src/lib.rs @@ -1,5 +1,34 @@ +use once_cell::sync::Lazy; use pyo3::prelude::*; +pub static PERMS: Lazy>>> = Lazy::new(|| { + // PERMS[n] = all permutations of 0..n-1, for n up to 6 + let mut out: Vec>> = vec![vec![]; 7]; + for n in 0..=6 { + let mut perms = Vec::new(); + let mut a: Vec = (0..n).collect(); + heap_permute(n, &mut a, &mut perms); + out[n] = perms; + } + out +}); + +fn heap_permute(k: usize, a: &mut [usize], out: &mut Vec>) { + if k <= 1 { + out.push(a.to_vec()); + return; + } + heap_permute(k - 1, a, out); + for i in 0..(k - 1) { + if k % 2 == 0 { + a.swap(i, k - 1); + } else { + a.swap(0, k - 1); + } + heap_permute(k - 1, a, out); + } +} + fn hello_core() -> String { "Hello from example-ext!".to_string() } diff --git a/experiments/elo/src/main.rs b/experiments/elo/src/main.rs index b1d66c8a..03df4c6f 100644 --- a/experiments/elo/src/main.rs +++ b/experiments/elo/src/main.rs @@ -1,5 +1,11 @@ -use elo::hello_for_rust; +use elo::{hello_for_rust, PERMS}; fn main() { println!("{}", hello_for_rust()); + for n in 0..=6 { + println!("n={} perms={}", n, PERMS[n].len()); + for p in &PERMS[n] { + println!("{:?}", p); + } + } } -- GitLab From 2d9c18b5d37a59ba01c62d85363407519ec6858e Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Thu, 23 Oct 2025 21:40:19 +0300 Subject: [PATCH 107/147] Separate Python and Rust code --- experiments/elo/Cargo.toml | 5 +++++ experiments/elo/pyproject.toml | 6 +++++- experiments/elo/{src => python}/elo/__init__.py | 0 experiments/elo/{src => python}/elo/_rust.pyi | 0 experiments/elo/{src => python}/elo/bga_spider.py | 0 experiments/elo/{src => python}/elo/data.py | 0 experiments/elo/{src => python}/elo/elo_ratings.py | 0 experiments/elo/{src => python}/elo/game_stats.py | 0 experiments/elo/{src => python}/elo/optimal_k.py | 0 experiments/elo/{src => python}/elo/p_deterministic.py | 0 experiments/elo/{src => python}/elo/py.typed | 0 experiments/elo/{src => python}/elo/snooker_spider.py | 0 experiments/elo/{src => rust}/lib.rs | 0 experiments/elo/{src => rust}/main.rs | 0 14 files changed, 10 insertions(+), 1 deletion(-) rename experiments/elo/{src => python}/elo/__init__.py (100%) rename experiments/elo/{src => python}/elo/_rust.pyi (100%) rename experiments/elo/{src => python}/elo/bga_spider.py (100%) rename experiments/elo/{src => python}/elo/data.py (100%) rename experiments/elo/{src => python}/elo/elo_ratings.py (100%) rename experiments/elo/{src => python}/elo/game_stats.py (100%) rename experiments/elo/{src => python}/elo/optimal_k.py (100%) rename experiments/elo/{src => python}/elo/p_deterministic.py (100%) rename experiments/elo/{src => python}/elo/py.typed (100%) rename experiments/elo/{src => python}/elo/snooker_spider.py (100%) rename experiments/elo/{src => rust}/lib.rs (100%) rename experiments/elo/{src => rust}/main.rs (100%) diff --git a/experiments/elo/Cargo.toml b/experiments/elo/Cargo.toml index 12f680d5..683b22c4 100644 --- a/experiments/elo/Cargo.toml +++ b/experiments/elo/Cargo.toml @@ -5,9 +5,14 @@ edition = "2021" [lib] name = "elo" +path = "rust/lib.rs" # "cdylib" is necessary to produce a shared library for Python to import from. crate-type = ["cdylib", "rlib"] +[[bin]] +name = "elo" +path = "rust/main.rs" + [dependencies] rand = "0.9.0" pyo3 = { version = "0.22.4", features = ["extension-module", "abi3-py312"] } diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index da400144..c08154c7 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -27,6 +27,10 @@ dependencies = [ "tqdm", ] +packages = [ + { include = "elo", from = "python" } +] + [project.scripts] elo = "elo:main" @@ -54,7 +58,7 @@ build-backend = "maturin" features = ["pyo3/extension-module"] module-name = "elo._rust" python-packages = ["elo"] -python-source = "src" +python-source = "python" [tool.uv] cache-keys = [ diff --git a/experiments/elo/src/elo/__init__.py b/experiments/elo/python/elo/__init__.py similarity index 100% rename from experiments/elo/src/elo/__init__.py rename to experiments/elo/python/elo/__init__.py diff --git a/experiments/elo/src/elo/_rust.pyi b/experiments/elo/python/elo/_rust.pyi similarity index 100% rename from experiments/elo/src/elo/_rust.pyi rename to experiments/elo/python/elo/_rust.pyi diff --git a/experiments/elo/src/elo/bga_spider.py b/experiments/elo/python/elo/bga_spider.py similarity index 100% rename from experiments/elo/src/elo/bga_spider.py rename to experiments/elo/python/elo/bga_spider.py diff --git a/experiments/elo/src/elo/data.py b/experiments/elo/python/elo/data.py similarity index 100% rename from experiments/elo/src/elo/data.py rename to experiments/elo/python/elo/data.py diff --git a/experiments/elo/src/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py similarity index 100% rename from experiments/elo/src/elo/elo_ratings.py rename to experiments/elo/python/elo/elo_ratings.py diff --git a/experiments/elo/src/elo/game_stats.py b/experiments/elo/python/elo/game_stats.py similarity index 100% rename from experiments/elo/src/elo/game_stats.py rename to experiments/elo/python/elo/game_stats.py diff --git a/experiments/elo/src/elo/optimal_k.py b/experiments/elo/python/elo/optimal_k.py similarity index 100% rename from experiments/elo/src/elo/optimal_k.py rename to experiments/elo/python/elo/optimal_k.py diff --git a/experiments/elo/src/elo/p_deterministic.py b/experiments/elo/python/elo/p_deterministic.py similarity index 100% rename from experiments/elo/src/elo/p_deterministic.py rename to experiments/elo/python/elo/p_deterministic.py diff --git a/experiments/elo/src/elo/py.typed b/experiments/elo/python/elo/py.typed similarity index 100% rename from experiments/elo/src/elo/py.typed rename to experiments/elo/python/elo/py.typed diff --git a/experiments/elo/src/elo/snooker_spider.py b/experiments/elo/python/elo/snooker_spider.py similarity index 100% rename from experiments/elo/src/elo/snooker_spider.py rename to experiments/elo/python/elo/snooker_spider.py diff --git a/experiments/elo/src/lib.rs b/experiments/elo/rust/lib.rs similarity index 100% rename from experiments/elo/src/lib.rs rename to experiments/elo/rust/lib.rs diff --git a/experiments/elo/src/main.rs b/experiments/elo/rust/main.rs similarity index 100% rename from experiments/elo/src/main.rs rename to experiments/elo/rust/main.rs -- GitLab From 6a49c2dc38a0e84734307f69580020431b6da8f4 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Thu, 23 Oct 2025 21:54:38 +0300 Subject: [PATCH 108/147] Added rating_system.rs --- experiments/elo/rust/core.rs | 9 ++ experiments/elo/rust/core/rating_system.rs | 135 +++++++++++++++++++++ experiments/elo/rust/lib.rs | 2 + 3 files changed, 146 insertions(+) create mode 100644 experiments/elo/rust/core.rs create mode 100644 experiments/elo/rust/core/rating_system.rs diff --git a/experiments/elo/rust/core.rs b/experiments/elo/rust/core.rs new file mode 100644 index 00000000..70a47692 --- /dev/null +++ b/experiments/elo/rust/core.rs @@ -0,0 +1,9 @@ +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/rating_system.rs b/experiments/elo/rust/core/rating_system.rs new file mode 100644 index 00000000..d78dbc02 --- /dev/null +++ b/experiments/elo/rust/core/rating_system.rs @@ -0,0 +1,135 @@ +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 an iterator of matches. If `collect_diffs` is true, + /// returns a ragged list of per-match diffs; otherwise returns `None`. + fn update_elo_ratings_batch( + &mut self, + matches: I, + collect_diffs: bool, + ) -> Option>> + where + I: IntoIterator>, + { + if collect_diffs { + let mut all = Vec::new(); + for m in matches { + all.push(self.update_elo_ratings(m)); + } + Some(all) + } else { + for m in matches { + let _ = self.update_elo_ratings(m); + } + 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.is_empty(), "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/lib.rs b/experiments/elo/rust/lib.rs index 1ee29ca9..b0a57514 100644 --- a/experiments/elo/rust/lib.rs +++ b/experiments/elo/rust/lib.rs @@ -1,6 +1,8 @@ use once_cell::sync::Lazy; use pyo3::prelude::*; +pub mod core; + pub static PERMS: Lazy>>> = Lazy::new(|| { // PERMS[n] = all permutations of 0..n-1, for n up to 6 let mut out: Vec>> = vec![vec![]; 7]; -- GitLab From 0055e0b53d132a22155475272425769f0b87d3c7 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Thu, 23 Oct 2025 21:56:37 +0300 Subject: [PATCH 109/147] two_player.rs --- experiments/elo/rust/core.rs | 2 +- experiments/elo/rust/core/two_player.rs | 69 +++++++++++++++++++++++++ 2 files changed, 70 insertions(+), 1 deletion(-) create mode 100644 experiments/elo/rust/core/two_player.rs diff --git a/experiments/elo/rust/core.rs b/experiments/elo/rust/core.rs index 70a47692..5779f290 100644 --- a/experiments/elo/rust/core.rs +++ b/experiments/elo/rust/core.rs @@ -1,5 +1,5 @@ pub mod rating_system; -// pub mod two_player; +pub mod two_player; /// Elo win probability for player A vs B given rating difference `diff = r_a - r_b`. #[inline] diff --git a/experiments/elo/rust/core/two_player.rs b/experiments/elo/rust/core/two_player.rs new file mode 100644 index 00000000..b9d55170 --- /dev/null +++ b/experiments/elo/rust/core/two_player.rs @@ -0,0 +1,69 @@ +use std::collections::HashMap; +use std::hash::Hash; + +use super::elo_probability; +use super::rating_system::{EloConfig, EloRatingSystem, Match}; + +/// 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(), + } + } + + /// Convenience: update with explicit outcomes (id, outcome in [0,1]) or ordered (winner first). + pub fn update(&mut self, m: Match) -> Vec { + self.update_elo_ratings(m) + } +} + +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]]] + } +} -- GitLab From f2836ad62012a1f3855393263afbe097f4da7c19 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Thu, 23 Oct 2025 22:04:44 +0300 Subject: [PATCH 110/147] Updated main.rs --- experiments/elo/rust/main.rs | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/experiments/elo/rust/main.rs b/experiments/elo/rust/main.rs index 03df4c6f..f03dad4e 100644 --- a/experiments/elo/rust/main.rs +++ b/experiments/elo/rust/main.rs @@ -1,11 +1,19 @@ -use elo::{hello_for_rust, PERMS}; +use elo::core::rating_system::{EloConfig, EloRatingSystem, Match}; +use elo::core::two_player::TwoPlayerElo; fn main() { - println!("{}", hello_for_rust()); - for n in 0..=6 { - println!("n={} perms={}", n, PERMS[n].len()); - for p in &PERMS[n] { - println!("{:?}", p); - } - } + 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 players: winner first + elo.update(Match::Ordered(vec!["A", "B"])); + // Or explicit outcomes + elo.update(Match::Outcomes(vec![("A", 1.0), ("B", 0.0)])); + + let expected = elo.expected_outcome(&["A", "B"], None); + println!("P(A wins) = {}", expected[0]); } -- GitLab From d89ff0cd85e4c37e1b7151e6f634f1710aa8765c Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Thu, 23 Oct 2025 22:28:28 +0300 Subject: [PATCH 111/147] Added elo.__main__ --- experiments/elo/pyproject.toml | 2 +- experiments/elo/python/elo/__init__.py | 5 ----- experiments/elo/python/elo/__main__.py | 9 +++++++++ 3 files changed, 10 insertions(+), 6 deletions(-) create mode 100644 experiments/elo/python/elo/__main__.py diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index c08154c7..3442679a 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -32,7 +32,7 @@ packages = [ ] [project.scripts] -elo = "elo:main" +elo = "elo.__main__:main" [dependency-groups] dev = [ diff --git a/experiments/elo/python/elo/__init__.py b/experiments/elo/python/elo/__init__.py index 87679b81..e69de29b 100644 --- a/experiments/elo/python/elo/__init__.py +++ b/experiments/elo/python/elo/__init__.py @@ -1,5 +0,0 @@ -from elo._rust import hello_from_bin - - -def main() -> None: - print(hello_from_bin()) diff --git a/experiments/elo/python/elo/__main__.py b/experiments/elo/python/elo/__main__.py new file mode 100644 index 00000000..d05dccaa --- /dev/null +++ b/experiments/elo/python/elo/__main__.py @@ -0,0 +1,9 @@ +from elo._rust import hello_from_bin + + +def main() -> None: + print(hello_from_bin()) + + +if __name__ == "__main__": + main() -- GitLab From 3ded10a454ef7cdacdbef3f44c8f7279d95795fd Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 13:11:00 +0300 Subject: [PATCH 112/147] Added simple calculate_elo_ratings() --- experiments/elo/python/elo/elo_ratings.py | 32 +++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py index 548c0a5c..d78fbcd8 100644 --- a/experiments/elo/python/elo/elo_ratings.py +++ b/experiments/elo/python/elo/elo_ratings.py @@ -8,6 +8,7 @@ from collections.abc import Mapping from typing import TYPE_CHECKING, override import numpy as np +from numpy import typing as npt if TYPE_CHECKING: from collections.abc import Iterable, Generator @@ -336,3 +337,34 @@ def _padded_numpy_array( for i, a in enumerate(arrays): result[i, : a.shape[0]] = a return result + + +def calculate_elo_ratings_python( + *, + matches: npt.NDArray[np.int32], + elo_initial: float = 0, + elo_k: float = 32, + elo_scale: float = 400, + progress_bar: bool = False, +) -> dict[int, float]: + 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 _main(): + matches = np.array([[0, 1], [0, 1]], dtype=np.int32) + elo_ratings = calculate_elo_ratings_python(matches=matches, progress_bar=True) + for player, rating in sorted(elo_ratings.items(), key=lambda x: x[1], reverse=True): + print(f"Player {player:4d}:\tElo {rating:10.3f}") + + +if __name__ == "__main__": + _main() -- GitLab From 166d3704332b92a1df72ea76d2ecfe0744cf9e1e Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 13:19:56 +0300 Subject: [PATCH 113/147] Updated Rust dependencies --- experiments/elo/Cargo.lock | 108 ++++++++++++++++++++++++++++++++----- experiments/elo/Cargo.toml | 6 ++- 2 files changed, 98 insertions(+), 16 deletions(-) diff --git a/experiments/elo/Cargo.lock b/experiments/elo/Cargo.lock index 3c0761df..8a4d4ae5 100644 --- a/experiments/elo/Cargo.lock +++ b/experiments/elo/Cargo.lock @@ -49,6 +49,8 @@ checksum = "48c757948c5ede0e46177b7add2e67155f70e33c07fea8284df6576da70b3719" name = "elo" version = "0.1.0" dependencies = [ + "ndarray", + "numpy", "once_cell", "pyo3", "rand 0.9.2", @@ -106,6 +108,16 @@ version = "0.2.15" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f9fbbcab51052fe104eb5e5d351cf728d30a5be1fe14d9be8a3b097481fb97de" +[[package]] +name = "matrixmultiply" +version = "0.3.10" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a06de3016e9fae57a36fd14dba131fccf49f74b40b7fbdb472f96e361ec71a08" +dependencies = [ + "autocfg", + "rawpointer", +] + [[package]] name = "memoffset" version = "0.9.1" @@ -115,6 +127,39 @@ dependencies = [ "autocfg", ] +[[package]] +name = "ndarray" +version = "0.16.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "882ed72dce9365842bf196bdeedf5055305f11fc8c03dee7bb0194a6cad34841" +dependencies = [ + "matrixmultiply", + "num-complex", + "num-integer", + "num-traits", + "portable-atomic", + "portable-atomic-util", + "rawpointer", +] + +[[package]] +name = "num-complex" +version = "0.4.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495" +dependencies = [ + "num-traits", +] + +[[package]] +name = "num-integer" +version = "0.1.46" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7969661fd2958a5cb096e56c8e1ad0444ac2bbcd0061bd28660485a44879858f" +dependencies = [ + "num-traits", +] + [[package]] name = "num-traits" version = "0.2.19" @@ -125,6 +170,22 @@ dependencies = [ "libm", ] +[[package]] +name = "numpy" +version = "0.27.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0fa24ffc88cf9d43f7269d6b6a0d0a00010924a8cc90604a21ef9c433b66998d" +dependencies = [ + "libc", + "ndarray", + "num-complex", + "num-integer", + "num-traits", + "pyo3", + "pyo3-build-config", + "rustc-hash", +] + [[package]] name = "once_cell" version = "1.21.3" @@ -137,6 +198,15 @@ version = "1.11.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f84267b20a16ea918e43c6a88433c2d54fa145c92a811b5b047ccbe153674483" +[[package]] +name = "portable-atomic-util" +version = "0.2.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d8a2f0d8d040d7848a709caf78912debcc3f33ee4b3cac47d73d1e1069e83507" +dependencies = [ + "portable-atomic", +] + [[package]] name = "ppv-lite86" version = "0.2.21" @@ -157,11 +227,10 @@ dependencies = [ [[package]] name = "pyo3" -version = "0.22.6" +version = "0.27.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f402062616ab18202ae8319da13fa4279883a2b8a9d9f83f20dbade813ce1884" +checksum = "37a6df7eab65fc7bee654a421404947e10a0f7085b6951bf2ea395f4659fb0cf" dependencies = [ - "cfg-if", "indoc", "libc", "memoffset", @@ -175,19 +244,18 @@ dependencies = [ [[package]] name = "pyo3-build-config" -version = "0.22.6" +version = "0.27.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b14b5775b5ff446dd1056212d778012cbe8a0fbffd368029fd9e25b514479c38" +checksum = "f77d387774f6f6eec64a004eac0ed525aab7fa1966d94b42f743797b3e395afb" dependencies = [ - "once_cell", "target-lexicon", ] [[package]] name = "pyo3-ffi" -version = "0.22.6" +version = "0.27.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9ab5bcf04a2cdcbb50c7d6105de943f543f9ed92af55818fd17b660390fc8636" +checksum = "2dd13844a4242793e02df3e2ec093f540d948299a6a77ea9ce7afd8623f542be" dependencies = [ "libc", "pyo3-build-config", @@ -195,9 +263,9 @@ dependencies = [ [[package]] name = "pyo3-macros" -version = "0.22.6" +version = "0.27.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0fd24d897903a9e6d80b968368a34e1525aeb719d568dba8b3d4bfa5dc67d453" +checksum = "eaf8f9f1108270b90d3676b8679586385430e5c0bb78bb5f043f95499c821a71" dependencies = [ "proc-macro2", "pyo3-macros-backend", @@ -207,9 +275,9 @@ dependencies = [ [[package]] name = "pyo3-macros-backend" -version = "0.22.6" +version = "0.27.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "36c011a03ba1e50152b4b394b479826cad97e7a21eb52df179cd91ac411cbfbe" +checksum = "70a3b2274450ba5288bc9b8c1b69ff569d1d61189d4bff38f8d22e03d17f932b" dependencies = [ "heck", "proc-macro2", @@ -302,6 +370,12 @@ dependencies = [ "rand 0.8.5", ] +[[package]] +name = "rawpointer" +version = "0.2.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "60a357793950651c4ed0f3f52338f53b2f809f32d83a07f72909fa13e4c6c1e3" + [[package]] name = "rayon" version = "1.11.0" @@ -322,6 +396,12 @@ dependencies = [ "crossbeam-utils", ] +[[package]] +name = "rustc-hash" +version = "2.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "357703d41365b4b27c590e3ed91eabb1b663f07c4c084095e60cbed4362dff0d" + [[package]] name = "rustversion" version = "1.0.22" @@ -341,9 +421,9 @@ dependencies = [ [[package]] name = "target-lexicon" -version = "0.12.16" +version = "0.13.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1" +checksum = "df7f62577c25e07834649fc3b39fafdc597c0a3527dc1c60129201ccfcbaa50c" [[package]] name = "unicode-ident" diff --git a/experiments/elo/Cargo.toml b/experiments/elo/Cargo.toml index 683b22c4..a976d18f 100644 --- a/experiments/elo/Cargo.toml +++ b/experiments/elo/Cargo.toml @@ -14,8 +14,10 @@ name = "elo" path = "rust/main.rs" [dependencies] +ndarray = "0.16.1" +numpy = "0.27.0" +once_cell = "1.19" # for cached permutations +pyo3 = { version = "0.27.0", features = ["extension-module", "abi3-py312"] } rand = "0.9.0" -pyo3 = { version = "0.22.4", features = ["extension-module", "abi3-py312"] } rand_distr = "0.4" rayon = "1.10" # optional for MC sampling -once_cell = "1.19" # for cached permutations -- GitLab From 4f6a3ac1948b3851e8343ee36aa5ec40a6ff52ff Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 15:40:38 +0300 Subject: [PATCH 114/147] Python and Rust implementation and binding of calculate_elo_ratings_*() --- experiments/elo/python/elo/_rust.pyi | 10 ++++++ experiments/elo/python/elo/elo_ratings.py | 35 ++++++++++++++++++-- experiments/elo/rust/lib.rs | 39 +++++++++++++++++++++-- 3 files changed, 80 insertions(+), 4 deletions(-) diff --git a/experiments/elo/python/elo/_rust.pyi b/experiments/elo/python/elo/_rust.pyi index d52129eb..5034b1b7 100644 --- a/experiments/elo/python/elo/_rust.pyi +++ b/experiments/elo/python/elo/_rust.pyi @@ -1 +1,11 @@ +import numpy as np +import numpy.typing as npt + def hello_from_bin() -> str: ... +def calculate_elo_ratings_rust( + *, + matches: npt.NDArray[np.int32], + elo_initial: float, + elo_k: float, + elo_scale: float, +) -> dict[int, float]: ... diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py index d78fbcd8..6991ccd9 100644 --- a/experiments/elo/python/elo/elo_ratings.py +++ b/experiments/elo/python/elo/elo_ratings.py @@ -360,9 +360,40 @@ def calculate_elo_ratings_python( def _main(): + from elo._rust import calculate_elo_ratings_rust + matches = np.array([[0, 1], [0, 1]], dtype=np.int32) - elo_ratings = calculate_elo_ratings_python(matches=matches, progress_bar=True) - for player, rating in sorted(elo_ratings.items(), key=lambda x: x[1], reverse=True): + elo_initial = 0.0 + elo_k = 32.0 + elo_scale = 400.0 + + print("Elo ratings (Python implementation):") + elo_ratings = calculate_elo_ratings_python( + matches=matches, + elo_initial=elo_initial, + elo_k=elo_k, + elo_scale=elo_scale, + progress_bar=True, + ) + for player, rating in sorted( + elo_ratings.items(), + key=lambda x: x[1], + reverse=True, + ): + print(f"Player {player:4d}:\tElo {rating:10.3f}") + + print("Elo ratings (Rust implementation):") + elo_ratings_rust = calculate_elo_ratings_rust( + matches=matches, + elo_initial=elo_initial, + elo_k=elo_k, + elo_scale=elo_scale, + ) + for player, rating in sorted( + elo_ratings_rust.items(), + key=lambda x: x[1], + reverse=True, + ): print(f"Player {player:4d}:\tElo {rating:10.3f}") diff --git a/experiments/elo/rust/lib.rs b/experiments/elo/rust/lib.rs index b0a57514..a61da103 100644 --- a/experiments/elo/rust/lib.rs +++ b/experiments/elo/rust/lib.rs @@ -1,7 +1,13 @@ +pub mod core; + +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 once_cell::sync::Lazy; use pyo3::prelude::*; - -pub mod core; +use std::collections::HashMap; pub static PERMS: Lazy>>> = Lazy::new(|| { // PERMS[n] = all permutations of 0..n-1, for n up to 6 @@ -44,11 +50,40 @@ pub fn hello_for_rust() -> String { hello_core() } +#[pyfunction] +fn calculate_elo_ratings_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(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!(hello_from_bin, m)?)?; + m.add_function(wrap_pyfunction!(calculate_elo_ratings_rust, m)?)?; Ok(()) } -- GitLab From 9f37fbcbfc0456c3c3fa2fd2d3a725275d9eeeb7 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 16:06:32 +0300 Subject: [PATCH 115/147] Load matches from arrow files --- experiments/elo/python/elo/elo_ratings.py | 38 ++++++++++++++++------- 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py index 6991ccd9..aaeb9e51 100644 --- a/experiments/elo/python/elo/elo_ratings.py +++ b/experiments/elo/python/elo/elo_ratings.py @@ -359,10 +359,26 @@ def calculate_elo_ratings_python( return dict(elo.elo_ratings) +def matches_from_arrow_file(file_path: str) -> npt.NDArray[np.int32]: + import polars as pl + + data = ( + pl.scan_ipc(file_path) + .select(pl.col("player_ids").list.to_struct(upper_bound=2)) + .unnest("player_ids") + .drop_nulls() + .collect() + ) + print(f"Loaded {len(data)} matches from {file_path}") + return data.to_numpy().astype(np.int32) + + def _main(): + import sys from elo._rust import calculate_elo_ratings_rust - matches = np.array([[0, 1], [0, 1]], dtype=np.int32) + matches = matches_from_arrow_file(sys.argv[1]) + elo_initial = 0.0 elo_k = 32.0 elo_scale = 400.0 @@ -375,25 +391,23 @@ def _main(): elo_scale=elo_scale, progress_bar=True, ) - for player, rating in sorted( - elo_ratings.items(), - key=lambda x: x[1], - reverse=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:4d}:\tElo {rating:10.3f}") print("Elo ratings (Rust implementation):") - elo_ratings_rust = calculate_elo_ratings_rust( + elo_ratings = calculate_elo_ratings_rust( matches=matches, elo_initial=elo_initial, elo_k=elo_k, elo_scale=elo_scale, ) - for player, rating in sorted( - elo_ratings_rust.items(), - key=lambda x: x[1], - reverse=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:4d}:\tElo {rating:10.3f}") -- GitLab From e35405d0195766ec4c83a2f872273e3f2554b36a Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 16:08:58 +0300 Subject: [PATCH 116/147] Change __main__ --- experiments/elo/python/elo/__main__.py | 54 ++++++++++++++++++++-- experiments/elo/python/elo/elo_ratings.py | 56 ----------------------- 2 files changed, 51 insertions(+), 59 deletions(-) diff --git a/experiments/elo/python/elo/__main__.py b/experiments/elo/python/elo/__main__.py index d05dccaa..5dd9f083 100644 --- a/experiments/elo/python/elo/__main__.py +++ b/experiments/elo/python/elo/__main__.py @@ -1,8 +1,56 @@ -from elo._rust import hello_from_bin +import polars as pl +import numpy as np +from numpy import typing as npt +import sys +from elo._rust import calculate_elo_ratings_rust +from elo.elo_ratings import calculate_elo_ratings_python -def main() -> None: - print(hello_from_bin()) +def matches_from_arrow_file(file_path: str) -> npt.NDArray[np.int32]: + data = ( + pl.scan_ipc(file_path) + .select(pl.col("player_ids").list.to_struct(upper_bound=2)) + .unnest("player_ids") + .drop_nulls() + .collect() + ) + print(f"Loaded {len(data)} matches from {file_path}") + return data.to_numpy().astype(np.int32) + + +def main(): + matches = matches_from_arrow_file(sys.argv[1]) + + elo_initial = 0.0 + elo_k = 32.0 + elo_scale = 400.0 + + print("Elo ratings (Python implementation):") + elo_ratings = calculate_elo_ratings_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:4d}:\tElo {rating:10.3f}") + + print("Elo ratings (Rust implementation):") + elo_ratings = calculate_elo_ratings_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:4d}:\tElo {rating:10.3f}") if __name__ == "__main__": diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py index aaeb9e51..e0a8b80c 100644 --- a/experiments/elo/python/elo/elo_ratings.py +++ b/experiments/elo/python/elo/elo_ratings.py @@ -357,59 +357,3 @@ def calculate_elo_ratings_python( ) elo.update_elo_ratings_batch(matches, progress_bar=progress_bar) return dict(elo.elo_ratings) - - -def matches_from_arrow_file(file_path: str) -> npt.NDArray[np.int32]: - import polars as pl - - data = ( - pl.scan_ipc(file_path) - .select(pl.col("player_ids").list.to_struct(upper_bound=2)) - .unnest("player_ids") - .drop_nulls() - .collect() - ) - print(f"Loaded {len(data)} matches from {file_path}") - return data.to_numpy().astype(np.int32) - - -def _main(): - import sys - from elo._rust import calculate_elo_ratings_rust - - matches = matches_from_arrow_file(sys.argv[1]) - - elo_initial = 0.0 - elo_k = 32.0 - elo_scale = 400.0 - - print("Elo ratings (Python implementation):") - elo_ratings = calculate_elo_ratings_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:4d}:\tElo {rating:10.3f}") - - print("Elo ratings (Rust implementation):") - elo_ratings = calculate_elo_ratings_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:4d}:\tElo {rating:10.3f}") - - -if __name__ == "__main__": - _main() -- GitLab From d99b05c918926bb88530a774223f383ddfa80f54 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 16:11:21 +0300 Subject: [PATCH 117/147] Remove old example --- experiments/elo/python/elo/_rust.pyi | 1 - experiments/elo/rust/lib.rs | 43 ---------------------------- 2 files changed, 44 deletions(-) diff --git a/experiments/elo/python/elo/_rust.pyi b/experiments/elo/python/elo/_rust.pyi index 5034b1b7..4fb1fbfc 100644 --- a/experiments/elo/python/elo/_rust.pyi +++ b/experiments/elo/python/elo/_rust.pyi @@ -1,7 +1,6 @@ import numpy as np import numpy.typing as npt -def hello_from_bin() -> str: ... def calculate_elo_ratings_rust( *, matches: npt.NDArray[np.int32], diff --git a/experiments/elo/rust/lib.rs b/experiments/elo/rust/lib.rs index a61da103..df9bd795 100644 --- a/experiments/elo/rust/lib.rs +++ b/experiments/elo/rust/lib.rs @@ -5,51 +5,9 @@ use crate::core::rating_system::{EloConfig, Match}; use crate::core::two_player::TwoPlayerElo; use ndarray::ArrayView2; use numpy::PyReadonlyArray2; -use once_cell::sync::Lazy; use pyo3::prelude::*; use std::collections::HashMap; -pub static PERMS: Lazy>>> = Lazy::new(|| { - // PERMS[n] = all permutations of 0..n-1, for n up to 6 - let mut out: Vec>> = vec![vec![]; 7]; - for n in 0..=6 { - let mut perms = Vec::new(); - let mut a: Vec = (0..n).collect(); - heap_permute(n, &mut a, &mut perms); - out[n] = perms; - } - out -}); - -fn heap_permute(k: usize, a: &mut [usize], out: &mut Vec>) { - if k <= 1 { - out.push(a.to_vec()); - return; - } - heap_permute(k - 1, a, out); - for i in 0..(k - 1) { - if k % 2 == 0 { - a.swap(i, k - 1); - } else { - a.swap(0, k - 1); - } - heap_permute(k - 1, a, out); - } -} - -fn hello_core() -> String { - "Hello from example-ext!".to_string() -} - -#[pyfunction] -fn hello_from_bin() -> String { - hello_core() -} - -pub fn hello_for_rust() -> String { - hello_core() -} - #[pyfunction] fn calculate_elo_ratings_rust<'py>( matches: PyReadonlyArray2<'py, i32>, // shape: (n_matches, 2) @@ -83,7 +41,6 @@ fn calculate_elo_ratings_rust<'py>( /// import the module. #[pymodule] fn _rust(m: &Bound<'_, PyModule>) -> PyResult<()> { - m.add_function(wrap_pyfunction!(hello_from_bin, m)?)?; m.add_function(wrap_pyfunction!(calculate_elo_ratings_rust, m)?)?; Ok(()) } -- GitLab From 5aa2399c96adbde0ffca2f6300a80a2c2d160836 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 20:43:49 +0300 Subject: [PATCH 118/147] Added codetiming --- experiments/elo/pyproject.toml | 1 + experiments/elo/uv.lock | 11 +++++++++++ 2 files changed, 12 insertions(+) diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index 3442679a..e4ae1aa9 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -36,6 +36,7 @@ elo = "elo.__main__:main" [dependency-groups] dev = [ + "codetiming", "funcy-typing", "jupyter-black", "jupyterlab", diff --git a/experiments/elo/uv.lock b/experiments/elo/uv.lock index a19603c2..866c6c15 100644 --- a/experiments/elo/uv.lock +++ b/experiments/elo/uv.lock @@ -280,6 +280,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/1e/b4/b3c1258c014cb18fdc1920f08bada2eccb7a49e39b9f68c3552ec8acfadf/cma-4.4.0-py3-none-any.whl", hash = "sha256:46da7f95056b02496f4117269026dce3953ef0ae89717267540566effb85b052", size = 303789, upload-time = "2025-09-20T20:40:26.583Z" }, ] +[[package]] +name = "codetiming" +version = "1.4.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ad/4e/c40bf151af20ba2748bd6ea24e484d7b6196b1056ba3a1a4ee33b6939c37/codetiming-1.4.0.tar.gz", hash = "sha256:4937bf913a2814258b87eaaa43d9a1bb24711ffd3557a9ab6934fa1fe3ba0dbc", size = 14899, upload-time = "2022-11-08T07:12:29.361Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bc/91/e4a2b7c64e738beefddfa24b409d6eecb16c378bde01578918b6ea722a09/codetiming-1.4.0-py3-none-any.whl", hash = "sha256:3b80f409bef00941a9755c5524071ce2f72eaa4520f4bc35b33869cde024ccbd", size = 7165, upload-time = "2022-11-08T07:12:23.553Z" }, +] + [[package]] name = "colorama" version = "0.4.6" @@ -466,6 +475,7 @@ dependencies = [ [package.dev-dependencies] dev = [ + { name = "codetiming" }, { name = "funcy-typing" }, { name = "jupyter-black" }, { name = "jupyterlab" }, @@ -502,6 +512,7 @@ requires-dist = [ [package.metadata.requires-dev] dev = [ + { name = "codetiming" }, { name = "funcy-typing" }, { name = "jupyter-black" }, { name = "jupyterlab" }, -- GitLab From 32f32a31774d4532b1c58765af1bd08f92ec97c9 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 20:49:10 +0300 Subject: [PATCH 119/147] Added timer --- experiments/elo/python/elo/__main__.py | 34 ++++++++++++++------------ 1 file changed, 19 insertions(+), 15 deletions(-) diff --git a/experiments/elo/python/elo/__main__.py b/experiments/elo/python/elo/__main__.py index 5dd9f083..09a35209 100644 --- a/experiments/elo/python/elo/__main__.py +++ b/experiments/elo/python/elo/__main__.py @@ -19,6 +19,8 @@ def matches_from_arrow_file(file_path: str) -> npt.NDArray[np.int32]: def main(): + from codetiming import Timer + matches = matches_from_arrow_file(sys.argv[1]) elo_initial = 0.0 @@ -26,31 +28,33 @@ def main(): elo_scale = 400.0 print("Elo ratings (Python implementation):") - elo_ratings = calculate_elo_ratings_python( - matches=matches, - elo_initial=elo_initial, - elo_k=elo_k, - elo_scale=elo_scale, - progress_bar=True, - ) + with Timer(text="Calculated Elo ratings (Python) in {:.3f} seconds", logger=print): + elo_ratings = calculate_elo_ratings_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:4d}:\tElo {rating:10.3f}") + print(f"Player {player:10d}:\tElo {rating:10.3f}") print("Elo ratings (Rust implementation):") - elo_ratings = calculate_elo_ratings_rust( - matches=matches, - elo_initial=elo_initial, - elo_k=elo_k, - elo_scale=elo_scale, - ) + with Timer(text="Calculated Elo ratings (Rust) in {:.3f} seconds", logger=print): + elo_ratings = calculate_elo_ratings_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:4d}:\tElo {rating:10.3f}") + print(f"Player {player:10d}:\tElo {rating:10.3f}") if __name__ == "__main__": -- GitLab From 91b8ecf03471f6885f0d7de6e472de930b774bad Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 21:26:15 +0300 Subject: [PATCH 120/147] Move notebooks into their own folder --- experiments/elo/{ => notebooks}/BGA Elo.py | 0 experiments/elo/{ => notebooks}/EDA.py | 0 experiments/elo/{ => notebooks}/K investigations.py | 0 experiments/elo/{ => notebooks}/Matches.py | 0 experiments/elo/{ => notebooks}/Odds parsing.py | 0 experiments/elo/{ => notebooks}/Plot sigmoid.py | 0 experiments/elo/{ => notebooks}/Process matches.py | 0 experiments/elo/{ => notebooks}/Snooker API.py | 0 experiments/elo/{ => notebooks}/Snooker data.py | 0 experiments/elo/{ => notebooks}/Snooker tournament.py | 0 experiments/elo/{ => notebooks}/p-deterministic distribution.py | 0 experiments/elo/{ => notebooks}/p-deterministic games.py | 0 experiments/elo/{ => notebooks}/p-deterministic multi.py | 0 13 files changed, 0 insertions(+), 0 deletions(-) rename experiments/elo/{ => notebooks}/BGA Elo.py (100%) rename experiments/elo/{ => notebooks}/EDA.py (100%) rename experiments/elo/{ => notebooks}/K investigations.py (100%) rename experiments/elo/{ => notebooks}/Matches.py (100%) rename experiments/elo/{ => notebooks}/Odds parsing.py (100%) rename experiments/elo/{ => notebooks}/Plot sigmoid.py (100%) rename experiments/elo/{ => notebooks}/Process matches.py (100%) rename experiments/elo/{ => notebooks}/Snooker API.py (100%) rename experiments/elo/{ => notebooks}/Snooker data.py (100%) rename experiments/elo/{ => notebooks}/Snooker tournament.py (100%) rename experiments/elo/{ => notebooks}/p-deterministic distribution.py (100%) rename experiments/elo/{ => notebooks}/p-deterministic games.py (100%) rename experiments/elo/{ => notebooks}/p-deterministic multi.py (100%) diff --git a/experiments/elo/BGA Elo.py b/experiments/elo/notebooks/BGA Elo.py similarity index 100% rename from experiments/elo/BGA Elo.py rename to experiments/elo/notebooks/BGA Elo.py 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/K investigations.py b/experiments/elo/notebooks/K investigations.py similarity index 100% rename from experiments/elo/K investigations.py rename to experiments/elo/notebooks/K investigations.py diff --git a/experiments/elo/Matches.py b/experiments/elo/notebooks/Matches.py similarity index 100% rename from experiments/elo/Matches.py rename to experiments/elo/notebooks/Matches.py 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/Process matches.py b/experiments/elo/notebooks/Process matches.py similarity index 100% rename from experiments/elo/Process matches.py rename to experiments/elo/notebooks/Process matches.py 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/p-deterministic distribution.py b/experiments/elo/notebooks/p-deterministic distribution.py similarity index 100% rename from experiments/elo/p-deterministic distribution.py rename to experiments/elo/notebooks/p-deterministic distribution.py diff --git a/experiments/elo/p-deterministic games.py b/experiments/elo/notebooks/p-deterministic games.py similarity index 100% rename from experiments/elo/p-deterministic games.py rename to experiments/elo/notebooks/p-deterministic games.py diff --git a/experiments/elo/p-deterministic multi.py b/experiments/elo/notebooks/p-deterministic multi.py similarity index 100% rename from experiments/elo/p-deterministic multi.py rename to experiments/elo/notebooks/p-deterministic multi.py -- GitLab From 1041e0e413b52a99570e8e2e692dd31d5d7183c0 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 21:31:35 +0300 Subject: [PATCH 121/147] Remove unused deps --- experiments/elo/Cargo.lock | 216 ------------------------------------- experiments/elo/Cargo.toml | 4 - 2 files changed, 220 deletions(-) diff --git a/experiments/elo/Cargo.lock b/experiments/elo/Cargo.lock index 8a4d4ae5..ac8a6e6b 100644 --- a/experiments/elo/Cargo.lock +++ b/experiments/elo/Cargo.lock @@ -8,77 +8,13 @@ version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "c08606f8c3cbf4ce6ec8e28fb0014a2c086708fe954eaa885384a6165172e7e8" -[[package]] -name = "cfg-if" 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"quote", - "syn", -] diff --git a/experiments/elo/Cargo.toml b/experiments/elo/Cargo.toml index a976d18f..3eb9c335 100644 --- a/experiments/elo/Cargo.toml +++ b/experiments/elo/Cargo.toml @@ -16,8 +16,4 @@ path = "rust/main.rs" [dependencies] ndarray = "0.16.1" numpy = "0.27.0" -once_cell = "1.19" # for cached permutations pyo3 = { version = "0.27.0", features = ["extension-module", "abi3-py312"] } -rand = "0.9.0" -rand_distr = "0.4" -rayon = "1.10" # optional for MC sampling -- GitLab From 6a570c1c1c0dfd999e27a53f6e298fec67df03ef Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 21:48:14 +0300 Subject: [PATCH 122/147] .vscode/settings.json --- .vscode/settings.json | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..a52790ab --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,7 @@ +{ + "rust-analyzer.linkedProjects": [ + "${workspaceFolder}/experiments/elo/Cargo.toml" + ], + "rust-analyzer.cargo.autoreload": true, + "rust-analyzer.procMacro.enable": true +} -- GitLab From 522b475d19be14f7100e04f393359e002b9aa83d Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 22:06:35 +0300 Subject: [PATCH 123/147] Added optimal_k.rs --- experiments/elo/rust/core.rs | 1 + experiments/elo/rust/core/optimal_k.rs | 111 +++++++++++++++++++++++++ 2 files changed, 112 insertions(+) create mode 100644 experiments/elo/rust/core/optimal_k.rs diff --git a/experiments/elo/rust/core.rs b/experiments/elo/rust/core.rs index 5779f290..3face455 100644 --- a/experiments/elo/rust/core.rs +++ b/experiments/elo/rust/core.rs @@ -1,3 +1,4 @@ +pub mod optimal_k; pub mod rating_system; pub mod two_player; diff --git a/experiments/elo/rust/core/optimal_k.rs b/experiments/elo/rust/core/optimal_k.rs new file mode 100644 index 00000000..be62d6b6 --- /dev/null +++ b/experiments/elo/rust/core/optimal_k.rs @@ -0,0 +1,111 @@ +// src/optimal_k.rs (or wherever you keep helpers) +use std::collections::HashMap; +use std::hash::Hash; + +use crate::core::rating_system::{EloConfig, EloRatingSystem, Match}; +use crate::core::two_player::TwoPlayerElo; + +/// 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 S, matches: &[Match]) -> f64 +where + Id: Eq + Hash + Clone, + S: EloRatingSystem, +{ + let Some(all_diffs) = elo.update_elo_ratings_batch(matches.iter().cloned(), 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) + } +} + +/// Simple golden-section search for bounded scalar minimisation. +/// Returns the x that (approximately) minimises f on [a, b]. +fn golden_section_search(mut a: f64, mut b: f64, tol: f64, max_iters: usize, f: F) -> f64 +where + F: Fn(f64) -> f64, +{ + // φ = (1 + sqrt(5)) / 2; 1 - 1/φ = 1/φ^2 ≈ 0.381966... + let gr = 0.5 * (5.0_f64.sqrt() + 1.0); + let inv_gr2 = 1.0 / (gr * gr); + + let mut c = b - (b - a) * inv_gr2; + let mut d = a + (b - a) * inv_gr2; + let mut fc = f(c); + let mut fd = f(d); + + for _ in 0..max_iters { + if (b - a).abs() <= tol { + break; + } + if fc < fd { + b = d; + d = c; + fd = fc; + c = b - (b - a) * inv_gr2; + fc = f(c); + } else { + a = c; + c = d; + fc = fd; + d = a + (b - a) * inv_gr2; + fd = f(d); + } + } + // Return the best of c/d region + if fc < fd { + c + } else { + d + } +} + +/// 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_two_player( + matches: &[Match], + 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); + let tol = x_absolute_tol.unwrap_or(1e-3); + + // Loss as a function of k: create a fresh system each time (like Python). + let loss = |k: f64| -> f64 { + // Guard against degenerate inputs + if !k.is_finite() || k < 0.0 { + return f64::INFINITY; + } + let mut sys: TwoPlayerElo = TwoPlayerElo::new( + EloConfig { + elo_k: k, + elo_scale, + ..EloConfig::default() + }, + Some(HashMap::new()), + ); + calculate_loss(&mut sys, matches) + }; + + golden_section_search(min_k, max_k, tol, max_iters, loss) +} -- GitLab From f7e835ce51709efbaf9299b41c1421f11bdb8300 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 22:18:40 +0300 Subject: [PATCH 124/147] approx_optimal_k_two_player_rust() --- experiments/elo/rust/lib.rs | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/experiments/elo/rust/lib.rs b/experiments/elo/rust/lib.rs index df9bd795..0aa7d926 100644 --- a/experiments/elo/rust/lib.rs +++ b/experiments/elo/rust/lib.rs @@ -1,5 +1,6 @@ pub mod core; +use crate::core::optimal_k::approx_optimal_k_two_player; use crate::core::rating_system::EloRatingSystem; use crate::core::rating_system::{EloConfig, Match}; use crate::core::two_player::TwoPlayerElo; @@ -8,6 +9,39 @@ use numpy::PyReadonlyArray2; use pyo3::prelude::*; use std::collections::HashMap; +#[pyfunction] +fn approx_optimal_k_two_player_rust<'py>( + matches: PyReadonlyArray2<'py, i32>, // shape: (n_matches, 2) + min_k: f64, + max_k: f64, + elo_scale: f64, + max_iterations: Option, // Python's `maxiter` + x_absolute_tol: Option, // Python's `xatol` +) -> PyResult { + 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 matches = arr + .rows() + .into_iter() + .map(|r| Match::Ordered(r.to_vec())) + .collect::>>(); + let optimal_k = approx_optimal_k_two_player( + &matches, + min_k, + max_k, + elo_scale, + max_iterations, + x_absolute_tol, + ); + + Ok(optimal_k) +} + #[pyfunction] fn calculate_elo_ratings_rust<'py>( matches: PyReadonlyArray2<'py, i32>, // shape: (n_matches, 2) @@ -41,6 +75,7 @@ fn calculate_elo_ratings_rust<'py>( /// import the module. #[pymodule] fn _rust(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_function(wrap_pyfunction!(approx_optimal_k_two_player_rust, m)?)?; m.add_function(wrap_pyfunction!(calculate_elo_ratings_rust, m)?)?; Ok(()) } -- GitLab From 2b5e28f41eea9d105c641327f4f332126ea90335 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 22:28:08 +0300 Subject: [PATCH 125/147] Compare Python and Rust approximate_optimal_k*() --- experiments/elo/python/elo/__main__.py | 28 +++++++++++++++++++++++++- experiments/elo/python/elo/_rust.pyi | 9 +++++++++ 2 files changed, 36 insertions(+), 1 deletion(-) diff --git a/experiments/elo/python/elo/__main__.py b/experiments/elo/python/elo/__main__.py index 09a35209..41da255d 100644 --- a/experiments/elo/python/elo/__main__.py +++ b/experiments/elo/python/elo/__main__.py @@ -2,8 +2,9 @@ import polars as pl import numpy as np from numpy import typing as npt import sys -from elo._rust import calculate_elo_ratings_rust +from elo._rust import approx_optimal_k_two_player_rust, calculate_elo_ratings_rust from elo.elo_ratings import calculate_elo_ratings_python +from elo.optimal_k import approximate_optimal_k def matches_from_arrow_file(file_path: str) -> npt.NDArray[np.int32]: @@ -56,6 +57,31 @@ def main(): 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=True, + min_elo_k=0, + max_elo_k=200, + elo_scale=elo_scale, + max_iterations=None, + x_absolute_tol=None, + ) + 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 = approx_optimal_k_two_player_rust( + matches=matches, + min_k=0, + max_k=200, + elo_scale=elo_scale, + max_iterations=None, + x_absolute_tol=None, + ) + 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 index 4fb1fbfc..30061910 100644 --- a/experiments/elo/python/elo/_rust.pyi +++ b/experiments/elo/python/elo/_rust.pyi @@ -1,6 +1,15 @@ import numpy as np import numpy.typing as npt +def approx_optimal_k_two_player_rust( + *, + matches: npt.NDArray[np.int32], + min_k: float, + max_k: float, + elo_scale: float, + max_iterations: int | None = None, + x_absolute_tol: float | None = None, +) -> float: ... def calculate_elo_ratings_rust( *, matches: npt.NDArray[np.int32], -- GitLab From 7f8cbb75a3992bacc1dffcece8830f03a68b2f26 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Fri, 24 Oct 2025 23:08:56 +0300 Subject: [PATCH 126/147] Brent optimisation from argmin --- experiments/elo/Cargo.lock | 261 +++++++++++++++++++++++++ experiments/elo/Cargo.toml | 1 + experiments/elo/rust/core/optimal_k.rs | 111 ++++++----- 3 files changed, 320 insertions(+), 53 deletions(-) diff --git a/experiments/elo/Cargo.lock b/experiments/elo/Cargo.lock index ac8a6e6b..24a9bcfc 100644 --- a/experiments/elo/Cargo.lock +++ b/experiments/elo/Cargo.lock @@ -2,21 +2,83 @@ # It is not intended for manual editing. version = 4 +[[package]] +name = "anyhow" +version = "1.0.100" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a23eb6b1614318a8071c9b2521f36b424b2c83db5eb3a0fead4a6c0809af6e61" + +[[package]] +name = "argmin" +version = "0.11.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e7ab7ca97779074715a402e5e8045fae27e7191acaec9b4c5653276316e9e404" +dependencies = [ + "anyhow", + "argmin-math", + "num-traits", + "paste", + "rand", + "rand_xoshiro", + "thiserror", + "web-time", +] + +[[package]] +name = "argmin-math" +version = "0.5.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ba6958a87117ff5e1d0d7716856a4303752518012ec4a67a68446b6631a1a54d" +dependencies = [ + "anyhow", + "cfg-if", + "num-complex", + "num-integer", + "num-traits", + "rand", + "thiserror", +] + [[package]] name = "autocfg" version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "c08606f8c3cbf4ce6ec8e28fb0014a2c086708fe954eaa885384a6165172e7e8" +[[package]] +name = "bumpalo" +version = "3.19.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "46c5e41b57b8bba42a04676d81cb89e9ee8e859a1a66f80a5a72e1cb76b34d43" + +[[package]] +name = "cfg-if" +version = "1.0.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "9330f8b2ff13f34540b44e946ef35111825727b38d33286ef986142615121801" + [[package]] name = "elo" version = "0.1.0" dependencies = [ + "argmin", "ndarray", "numpy", "pyo3", ] +[[package]] +name = "getrandom" +version = "0.3.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "899def5c37c4fd7b2664648c28120ecec138e4d395b459e5ca34f9cce2dd77fd" +dependencies = [ + "cfg-if", + "libc", + "r-efi", + "wasip2", +] + [[package]] name = "heck" version = "0.5.0" @@ -32,12 +94,28 @@ dependencies = [ "rustversion", ] +[[package]] +name = "js-sys" +version = "0.3.81" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ec48937a97411dcb524a265206ccd4c90bb711fca92b2792c407f268825b9305" +dependencies = [ + "once_cell", + "wasm-bindgen", +] + [[package]] name = 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"registry+https://github.com/rust-lang/crates.io-index" +checksum = "88d2b8d9c68ad2b9e4340d7832716a4d21a22a1154777ad56ea55c51a9cf3831" +dependencies = [ + "proc-macro2", + "quote", + "syn", +] diff --git a/experiments/elo/Cargo.toml b/experiments/elo/Cargo.toml index 3eb9c335..4fee8d66 100644 --- a/experiments/elo/Cargo.toml +++ b/experiments/elo/Cargo.toml @@ -14,6 +14,7 @@ name = "elo" path = "rust/main.rs" [dependencies] +argmin = "0.11" ndarray = "0.16.1" numpy = "0.27.0" pyo3 = { version = "0.27.0", features = ["extension-module", "abi3-py312"] } diff --git a/experiments/elo/rust/core/optimal_k.rs b/experiments/elo/rust/core/optimal_k.rs index be62d6b6..c3c3c580 100644 --- a/experiments/elo/rust/core/optimal_k.rs +++ b/experiments/elo/rust/core/optimal_k.rs @@ -4,6 +4,8 @@ use std::hash::Hash; 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. @@ -33,44 +35,33 @@ where } } -/// Simple golden-section search for bounded scalar minimisation. -/// Returns the x that (approximately) minimises f on [a, b]. -fn golden_section_search(mut a: f64, mut b: f64, tol: f64, max_iters: usize, f: F) -> 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, +} + +impl<'a, Id> CostFunction for BrentKProblem<'a, Id> where - F: Fn(f64) -> f64, + Id: Eq + Hash + Clone, { - // φ = (1 + sqrt(5)) / 2; 1 - 1/φ = 1/φ^2 ≈ 0.381966... - let gr = 0.5 * (5.0_f64.sqrt() + 1.0); - let inv_gr2 = 1.0 / (gr * gr); - - let mut c = b - (b - a) * inv_gr2; - let mut d = a + (b - a) * inv_gr2; - let mut fc = f(c); - let mut fd = f(d); + type Param = f64; + type Output = f64; - for _ in 0..max_iters { - if (b - a).abs() <= tol { - break; - } - if fc < fd { - b = d; - d = c; - fd = fc; - c = b - (b - a) * inv_gr2; - fc = f(c); - } else { - a = c; - c = d; - fc = fd; - d = a + (b - a) * inv_gr2; - fd = f(d); + fn cost(&self, k: &Self::Param) -> Result { + // Guard against degenerate inputs + if !k.is_finite() || *k < 0.0 { + return Ok(f64::INFINITY); } - } - // Return the best of c/d region - if fc < fd { - c - } else { - d + let mut sys: TwoPlayerElo = TwoPlayerElo::new( + EloConfig { + elo_k: *k, + elo_scale: self.elo_scale, + ..EloConfig::default() + }, + Some(HashMap::new()), + ); + Ok(calculate_loss(&mut sys, self.matches)) } } @@ -87,25 +78,39 @@ pub fn approx_optimal_k_two_player( where Id: Eq + Hash + Clone, { - let max_iters = max_iterations.unwrap_or(200); - let tol = x_absolute_tol.unwrap_or(1e-3); + let max_iters = max_iterations.unwrap_or(200) as u64; - // Loss as a function of k: create a fresh system each time (like Python). - let loss = |k: f64| -> f64 { - // Guard against degenerate inputs - if !k.is_finite() || k < 0.0 { - return f64::INFINITY; - } - let mut sys: TwoPlayerElo = TwoPlayerElo::new( - EloConfig { - elo_k: k, - elo_scale, - ..EloConfig::default() - }, - Some(HashMap::new()), - ); - calculate_loss(&mut sys, matches) - }; + // 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 }; - golden_section_search(min_k, max_k, tol, max_iters, loss) + 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 + } + } } -- GitLab From 6c39463708c7f76ec81a037b0d3a3ae33b5dc79d Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 14:35:41 +0300 Subject: [PATCH 127/147] .gitignore --- experiments/elo/.gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/experiments/elo/.gitignore b/experiments/elo/.gitignore index 910a829c..f55b3765 100644 --- a/experiments/elo/.gitignore +++ b/experiments/elo/.gitignore @@ -2,4 +2,5 @@ .jobs/ results*/ /target +/supplemental/ *.jl -- GitLab From dd3f8a5f24223a0733fc270ca80decbc59b35078 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 14:57:28 +0300 Subject: [PATCH 128/147] Tweaked the implementation of RankOrderedLogitElo._calculate_probability_matrix_from_permutations() --- experiments/elo/python/elo/elo_ratings.py | 50 ++++++++++------------- 1 file changed, 22 insertions(+), 28 deletions(-) diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py index e0a8b80c..419fbb42 100644 --- a/experiments/elo/python/elo/elo_ratings.py +++ b/experiments/elo/python/elo/elo_ratings.py @@ -169,7 +169,6 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): elo_k=elo_k, elo_scale=elo_scale, ) - self.max_exact = max_exact self.mc_samples = mc_samples @@ -198,35 +197,30 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): atol: float = 1e-4, ) -> np.ndarray: num_players = len(players) - ratings = np.array([self.elo_ratings[p] for p in players], dtype=float) - exp_ratings = 10 ** (ratings / self.elo_scale) - probs = np.zeros((num_players, num_players)) + ratings = np.array([self.elo_ratings[p] for p in players], dtype=np.float64) + # Plackett–Luce weights (paper §2.2): w_i = 10^(R_i/scale) + weights = np.power(10.0, ratings / self.elo_scale) + + # probs[i, k] = P(player i finishes at rank k), k=0 is best rank + probs = np.zeros((num_players, num_players), dtype=np.float64) for perm in itertools.permutations(range(num_players)): - prob = 1 - denom = np.sum(exp_ratings) - for i in range(num_players - 1): - prob *= exp_ratings[perm[i]] / denom - denom -= exp_ratings[perm[i]] - for position, player in enumerate(perm): - probs[player, position] += prob - - col_sum = probs.sum(axis=0, keepdims=True) - row_sum = probs.sum(axis=1, keepdims=True) - while not ( - np.allclose(col_sum, 1.0, rtol=rtol, atol=atol) - and np.allclose(row_sum, 1.0, rtol=rtol, atol=atol) - ): - col_sum[col_sum == 0.0] = 1.0 - probs /= col_sum - row_sum = probs.sum(axis=1, keepdims=True) - row_sum[row_sum == 0.0] = 1.0 - probs /= row_sum - col_sum = probs.sum(axis=0, keepdims=True) - row_sum = probs.sum(axis=1, keepdims=True) - - # Robust checks for probability matrix - assert np.all(np.isfinite(probs)), "Probability matrix has non-finite values" + # Probability of this exact ranking under PL: + # \prod_{l=0}^{n-2} w_{perm[l]} / \sum_{j=l}^{n-1} w_{perm[j]} + denom = weights.sum() + p_perm = np.float64(1.0) + for player in range(num_players - 1): + w = weights[perm[player]] + p_perm *= w / denom + denom -= w # remove the chosen player's weight + + # Accumulate into player/position probabilities + for position, player_idx in enumerate(perm): + probs[player_idx, position] += p_perm + + # Numerical sanity checks (no balancing; PL is already doubly-stochastic up to FP error) + col_sum = probs.sum(axis=0) + row_sum = probs.sum(axis=1) assert np.allclose(col_sum, 1.0, rtol=rtol, atol=atol), ( f"Probabilities must sum to 1 over players per position (got {col_sum})" ) -- GitLab From 67beeae2845a20d26ecbb027a1e4a0f39e0206ea Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 15:55:33 +0300 Subject: [PATCH 129/147] Small renaming --- experiments/elo/python/elo/__main__.py | 11 +++++++---- experiments/elo/python/elo/_rust.pyi | 2 +- experiments/elo/python/elo/elo_ratings.py | 2 +- experiments/elo/rust/lib.rs | 4 ++-- 4 files changed, 11 insertions(+), 8 deletions(-) diff --git a/experiments/elo/python/elo/__main__.py b/experiments/elo/python/elo/__main__.py index 41da255d..f47097dc 100644 --- a/experiments/elo/python/elo/__main__.py +++ b/experiments/elo/python/elo/__main__.py @@ -2,8 +2,11 @@ import polars as pl import numpy as np from numpy import typing as npt import sys -from elo._rust import approx_optimal_k_two_player_rust, calculate_elo_ratings_rust -from elo.elo_ratings import calculate_elo_ratings_python +from elo._rust import ( + approx_optimal_k_two_player_rust, + calculate_elo_ratings_two_players_rust, +) +from elo.elo_ratings import calculate_elo_ratings_two_players_python from elo.optimal_k import approximate_optimal_k @@ -30,7 +33,7 @@ def main(): print("Elo ratings (Python implementation):") with Timer(text="Calculated Elo ratings (Python) in {:.3f} seconds", logger=print): - elo_ratings = calculate_elo_ratings_python( + elo_ratings = calculate_elo_ratings_two_players_python( matches=matches, elo_initial=elo_initial, elo_k=elo_k, @@ -45,7 +48,7 @@ def main(): print("Elo ratings (Rust implementation):") with Timer(text="Calculated Elo ratings (Rust) in {:.3f} seconds", logger=print): - elo_ratings = calculate_elo_ratings_rust( + elo_ratings = calculate_elo_ratings_two_players_rust( matches=matches, elo_initial=elo_initial, elo_k=elo_k, diff --git a/experiments/elo/python/elo/_rust.pyi b/experiments/elo/python/elo/_rust.pyi index 30061910..9a0c6e68 100644 --- a/experiments/elo/python/elo/_rust.pyi +++ b/experiments/elo/python/elo/_rust.pyi @@ -10,7 +10,7 @@ def approx_optimal_k_two_player_rust( max_iterations: int | None = None, x_absolute_tol: float | None = None, ) -> float: ... -def calculate_elo_ratings_rust( +def calculate_elo_ratings_two_players_rust( *, matches: npt.NDArray[np.int32], elo_initial: float, diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py index 419fbb42..d775bf48 100644 --- a/experiments/elo/python/elo/elo_ratings.py +++ b/experiments/elo/python/elo/elo_ratings.py @@ -333,7 +333,7 @@ def _padded_numpy_array( return result -def calculate_elo_ratings_python( +def calculate_elo_ratings_two_players_python( *, matches: npt.NDArray[np.int32], elo_initial: float = 0, diff --git a/experiments/elo/rust/lib.rs b/experiments/elo/rust/lib.rs index 0aa7d926..36ea71c3 100644 --- a/experiments/elo/rust/lib.rs +++ b/experiments/elo/rust/lib.rs @@ -43,7 +43,7 @@ fn approx_optimal_k_two_player_rust<'py>( } #[pyfunction] -fn calculate_elo_ratings_rust<'py>( +fn calculate_elo_ratings_two_players_rust<'py>( matches: PyReadonlyArray2<'py, i32>, // shape: (n_matches, 2) elo_initial: f64, elo_k: f64, @@ -76,6 +76,6 @@ fn calculate_elo_ratings_rust<'py>( #[pymodule] fn _rust(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_function(wrap_pyfunction!(approx_optimal_k_two_player_rust, m)?)?; - m.add_function(wrap_pyfunction!(calculate_elo_ratings_rust, m)?)?; + m.add_function(wrap_pyfunction!(calculate_elo_ratings_two_players_rust, m)?)?; Ok(()) } -- GitLab From b3ee5aa3730658cc76ef83887d474eb348aafa3f Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 16:02:17 +0300 Subject: [PATCH 130/147] Introduce calculate_elo_ratings_multi_players_python() --- experiments/elo/python/elo/elo_ratings.py | 27 +++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py index d775bf48..e1729422 100644 --- a/experiments/elo/python/elo/elo_ratings.py +++ b/experiments/elo/python/elo/elo_ratings.py @@ -351,3 +351,30 @@ def calculate_elo_ratings_two_players_python( ) 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: float = 0, + elo_k: float = 32, + elo_scale: float = 400, + progress_bar: bool = False, +) -> dict[int, float]: + assert matches.ndim == 2 and matches.shape[1] == 2, ( + "Matches must be a 2D array with shape (num_matches, 2)" + ) + elo: RankOrderedLogitElo = RankOrderedLogitElo( + elo_initial=elo_initial, + elo_k=elo_k, + elo_scale=elo_scale, + ) + num_matches = len(matches) + # Iterate over matches and drop nans from each match + 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) -- GitLab From 3515624f655beacd3f40a7905e7f5a338e5245f2 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 16:12:23 +0300 Subject: [PATCH 131/147] Added comparison with multi player implementation --- experiments/elo/python/elo/__main__.py | 24 ++++++++++++++++++++--- experiments/elo/python/elo/elo_ratings.py | 4 ++-- 2 files changed, 23 insertions(+), 5 deletions(-) diff --git a/experiments/elo/python/elo/__main__.py b/experiments/elo/python/elo/__main__.py index f47097dc..9e05f201 100644 --- a/experiments/elo/python/elo/__main__.py +++ b/experiments/elo/python/elo/__main__.py @@ -6,7 +6,10 @@ from elo._rust import ( approx_optimal_k_two_player_rust, calculate_elo_ratings_two_players_rust, ) -from elo.elo_ratings import calculate_elo_ratings_two_players_python +from elo.elo_ratings import ( + calculate_elo_ratings_two_players_python, + calculate_elo_ratings_multi_players_python, +) from elo.optimal_k import approximate_optimal_k @@ -31,7 +34,7 @@ def main(): elo_k = 32.0 elo_scale = 400.0 - print("Elo ratings (Python implementation):") + 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, @@ -46,7 +49,22 @@ def main(): for player, rating in sorted_ratings: print(f"Player {player:10d}:\tElo {rating:10.3f}") - print("Elo ratings (Rust implementation):") + 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}") + + 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, diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py index e1729422..3190fc21 100644 --- a/experiments/elo/python/elo/elo_ratings.py +++ b/experiments/elo/python/elo/elo_ratings.py @@ -361,8 +361,8 @@ def calculate_elo_ratings_multi_players_python( elo_scale: float = 400, progress_bar: bool = False, ) -> dict[int, float]: - assert matches.ndim == 2 and matches.shape[1] == 2, ( - "Matches must be a 2D array with shape (num_matches, 2)" + 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, -- GitLab From f84c54f8b9646d708a732b96232c93c04d8ddb6c Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 16:40:18 +0300 Subject: [PATCH 132/147] Make player count in __main__ more flexible --- experiments/elo/python/elo/__main__.py | 121 ++++++++++++++++--------- 1 file changed, 76 insertions(+), 45 deletions(-) diff --git a/experiments/elo/python/elo/__main__.py b/experiments/elo/python/elo/__main__.py index 9e05f201..db049934 100644 --- a/experiments/elo/python/elo/__main__.py +++ b/experiments/elo/python/elo/__main__.py @@ -1,53 +1,80 @@ +import sys + import polars as pl import numpy as np from numpy import typing as npt -import sys + from elo._rust import ( approx_optimal_k_two_player_rust, calculate_elo_ratings_two_players_rust, ) from elo.elo_ratings import ( - calculate_elo_ratings_two_players_python, 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) -> npt.NDArray[np.int32]: +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=2)) + .select(pl.col("player_ids").list.to_struct(upper_bound=player_count)) .unnest("player_ids") .drop_nulls() .collect() ) - print(f"Loaded {len(data)} matches from {file_path}") + 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 - matches = matches_from_arrow_file(sys.argv[1]) + 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 - 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}") + 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): @@ -64,25 +91,28 @@ def main(): for player, rating in sorted_ratings: print(f"Player {player:10d}:\tElo {rating:10.3f}") - 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}") + 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("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=True, + two_player_only=(player_count == 2), min_elo_k=0, max_elo_k=200, elo_scale=elo_scale, @@ -91,17 +121,18 @@ def main(): ) 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 = approx_optimal_k_two_player_rust( - matches=matches, - min_k=0, - max_k=200, - elo_scale=elo_scale, - max_iterations=None, - x_absolute_tol=None, - ) - print(f"Approximate optimal K (Rust): {optimal_k:.3f}") + if player_count == 2: + print("Approximate optimal K (Rust implementation):") + with Timer(text="Calculated optimal K (Rust) in {:.3f} seconds", logger=print): + optimal_k = approx_optimal_k_two_player_rust( + matches=matches, + min_k=0, + max_k=200, + elo_scale=elo_scale, + max_iterations=None, + x_absolute_tol=None, + ) + print(f"Approximate optimal K (Rust): {optimal_k:.3f}") if __name__ == "__main__": -- GitLab From 315b58eade872ea300993ec53d19665f7f5fa7ed Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 17:52:02 +0300 Subject: [PATCH 133/147] Added DP and MC implementations for RankOrderedLogitElo.probability_matrix() --- experiments/elo/python/elo/elo_ratings.py | 107 ++++++++++++++++++++-- 1 file changed, 97 insertions(+), 10 deletions(-) diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py index 3190fc21..114e6ae1 100644 --- a/experiments/elo/python/elo/elo_ratings.py +++ b/experiments/elo/python/elo/elo_ratings.py @@ -1,6 +1,7 @@ from __future__ import annotations import itertools +import math import warnings from abc import ABC, abstractmethod from collections import defaultdict @@ -161,7 +162,9 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): elo_k: float = 32, elo_scale: float = 400, max_exact: int = 6, + max_dp: int = 12, mc_samples: int = 5_000, + rng: np.random.Generator | int | None = None, ) -> None: super().__init__( init_elo_ratings=init_elo_ratings, @@ -170,7 +173,11 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): elo_scale=elo_scale, ) self.max_exact = max_exact + self.max_dp = max_dp self.mc_samples = mc_samples + self._rng = ( + rng if isinstance(rng, np.random.Generator) else np.random.default_rng(rng) + ) def _calculate_probability_matrix_two_players( self, @@ -183,13 +190,6 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): prob_b = 1 - prob_a return np.array([[prob_a, prob_b], [prob_b, prob_a]]) - def _calculate_probability_matrix_from_simulations( - self, - players: tuple[ID_TYPE, ...], - ) -> np.ndarray: - # TODO: implement Monte Carlo fallback - raise NotImplementedError - def _calculate_probability_matrix_from_permutations( self, players: tuple[ID_TYPE, ...], @@ -230,6 +230,90 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): return probs + def _calculate_probability_matrix_from_dp( + self, + players: tuple[ID_TYPE, ...], + *, + rtol: float = 1e-9, + atol: float = 1e-12, + ) -> np.ndarray: + n = len(players) + ratings = np.array([self.elo_ratings[p] for p in players], dtype=np.float64) + w = np.power(10.0, ratings / self.elo_scale) + W_total = float(w.sum()) + + 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 = float(w[i]) + for mask in range(num_masks): + tsize = mask.bit_count() + denom = W_total - subset_sum[mask] + base = wi / denom + sign_mask = -1.0 if (tsize & 1) else 1.0 + for k in range(tsize, m + 1): + sign_k = -1.0 if (k & 1) else 1.0 + coeff = math.comb(m - tsize, k - tsize) + P[k] += base * sign_mask * sign_k * coeff + + P = np.clip(P, 0.0, 1.0) + s = P.sum() + if not np.isclose(s, 1.0, rtol=rtol, atol=atol): + P = (P / s) if s > 0 else np.full_like(P, 1.0 / (m + 1)) + probs[i, : m + 1] = P + + col_sum = probs.sum(axis=0) + row_sum = probs.sum(axis=1) + assert np.allclose(col_sum, 1.0, rtol=1e-6, atol=1e-8) + assert np.allclose(row_sum, 1.0, rtol=1e-6, atol=1e-8) + return probs + + def _calculate_probability_matrix_from_simulations( + self, + players: tuple[ID_TYPE, ...], + ) -> np.ndarray: + n = len(players) + ratings = np.array([self.elo_ratings[p] for p in players], dtype=np.float64) + weights = np.power(10.0, ratings / self.elo_scale) + counts = np.zeros((n, n), dtype=np.float64) + + rng = self._rng + idx_all = np.arange(n, dtype=np.int64) + for _ in range(self.mc_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() + if not np.isfinite(tot) or tot <= 0: + probs = np.ones_like(probs, dtype=np.float64) + tot = probs.sum() + probs = probs / tot + chosen = int(rng.choice(rem_arr, p=probs)) + order.append(chosen) + remaining.remove(chosen) + w[chosen] = 0.0 + for pos, player_idx in enumerate(order): + counts[player_idx, pos] += 1.0 + + probs = np.clip(counts / float(self.mc_samples), 0.0, 1.0) + col_sums = probs.sum(axis=0, keepdims=True) + col_sums[col_sums == 0.0] = 1.0 + return probs / col_sums + @override def probability_matrix( self, @@ -243,10 +327,13 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): if len(players) == 2: return self._calculate_probability_matrix_two_players(players) - if len(players) > self.max_exact: - return self._calculate_probability_matrix_from_simulations(players) + if len(players) <= self.max_exact: + return self._calculate_probability_matrix_from_permutations(players) + + if len(players) <= self.max_dp: + return self._calculate_probability_matrix_from_dp(players) - return self._calculate_probability_matrix_from_permutations(players) + return self._calculate_probability_matrix_from_simulations(players) @override def expected_outcome( -- GitLab From f93ed10a3088202128b26858b151a4b042e27ad6 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 22:53:32 +0300 Subject: [PATCH 134/147] Numpy everywhere --- experiments/elo/python/elo/elo_ratings.py | 301 ++++++++++++---------- experiments/elo/python/elo/optimal_k.py | 2 +- 2 files changed, 164 insertions(+), 139 deletions(-) diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py index 114e6ae1..6b75e19e 100644 --- a/experiments/elo/python/elo/elo_ratings.py +++ b/experiments/elo/python/elo/elo_ratings.py @@ -1,7 +1,6 @@ from __future__ import annotations import itertools -import math import warnings from abc import ABC, abstractmethod from collections import defaultdict @@ -10,69 +9,84 @@ 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: float, scale: float = 400) -> float: - return 1 / (1 + 10 ** (-diff / scale)) + +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: float = 0 - elo_k: float = 32 - elo_scale: float = 400 + elo_initial: np.float64 = NP_ZERO + elo_k: np.float64 = NP_THIRTYTWO + elo_scale: np.float64 = NP_FOURHUNDRED - elo_ratings: defaultdict[ID_TYPE, float] + elo_ratings: defaultdict[ID_TYPE, np.float64] def __init__( self, *, - init_elo_ratings: Mapping[ID_TYPE, float] | None = None, - elo_initial: float = 0, - elo_k: float = 32, - elo_scale: float = 400, + 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 = elo_initial - self.elo_k = elo_k - self.elo_scale = elo_scale - + 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: elo_initial, - init_elo_ratings if init_elo_ratings is not None else {}, + lambda: self.elo_initial, + init_elo_ratings_converted, ) @abstractmethod def update_elo_ratings( self, - players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], - ) -> np.ndarray: ... + 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, float] | Iterable[ID_TYPE]], + 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, - ) -> np.ndarray | None: ... + ) -> npt.NDArray[np.float64] | None: ... @abstractmethod def probability_matrix( self, players: Iterable[ID_TYPE], - ) -> np.ndarray: ... + ) -> npt.NDArray[np.float64]: ... @abstractmethod def expected_outcome( self, players: Iterable[ID_TYPE], *, - rank_payoffs: np.ndarray | None = None, - ) -> np.ndarray: ... + rank_payoffs: npt.NDArray[np.float64] | None = None, + ) -> npt.NDArray[np.float64]: ... class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): @@ -81,8 +95,8 @@ class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): self, players: Iterable[ID_TYPE], *, - rank_payoffs: np.ndarray | None = None, - ) -> np.ndarray: + 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") @@ -91,27 +105,27 @@ class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): elo_2 = self.elo_ratings[player_2] diff = elo_1 - elo_2 prob = elo_probability(diff, self.elo_scale) - return np.array([prob, 1 - prob]) + return np.asarray([prob, 1 - prob], dtype=np.float64) @override def probability_matrix( self, players: Iterable[ID_TYPE], - ) -> np.ndarray: + ) -> npt.NDArray[np.float64]: expected_outcome = self.expected_outcome(players) - return np.array([expected_outcome, 1 - expected_outcome]) + return np.asarray([expected_outcome, 1 - expected_outcome], dtype=np.float64) @override def update_elo_ratings( self, - players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], - ) -> np.ndarray: + 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.array(list(players.values()), dtype=float) + outcomes = np.asarray(list(players.values()), dtype=np.float64) else: player_ids = tuple(players) - outcomes = np.array([1, 0], dtype=float) + 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): @@ -121,20 +135,20 @@ class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): def _update_elo_ratings_batch( self, - matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], - ) -> Generator[np.ndarray]: + 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, float] | Iterable[ID_TYPE]], + 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, - ) -> np.ndarray | None: + ) -> npt.NDArray[np.float64] | None: if progress_bar: from tqdm import tqdm @@ -142,7 +156,10 @@ class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): matches = tqdm(matches, desc="Updating elo ratings", **tqdm_kwargs) if full_results: - return np.array(list(self._update_elo_ratings_batch(matches))) + return np.asarray( + list(self._update_elo_ratings_batch(matches)), + dtype=np.float64, + ) for _ in self._update_elo_ratings_batch(matches): pass @@ -151,16 +168,16 @@ class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]): class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): """ - Rank-ordered-logit Elo with Monte-Carlo fallback for large fields. + Rank-ordered-logit Elo with MC/DP for larger fields. """ def __init__( self, *, - init_elo_ratings: Mapping[ID_TYPE, float] | None = None, - elo_initial: float = 0, - elo_k: float = 32, - elo_scale: float = 400, + 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_exact: int = 6, max_dp: int = 12, mc_samples: int = 5_000, @@ -182,65 +199,57 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): def _calculate_probability_matrix_two_players( self, players: tuple[ID_TYPE, ID_TYPE], - ) -> np.ndarray: + ) -> 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.array([[prob_a, prob_b], [prob_b, prob_a]]) + return np.asarray([[prob_a, prob_b], [prob_b, prob_a]], dtype=np.float64) def _calculate_probability_matrix_from_permutations( self, players: tuple[ID_TYPE, ...], - rtol: float = 1e-3, - atol: float = 1e-4, - ) -> np.ndarray: - num_players = len(players) - ratings = np.array([self.elo_ratings[p] for p in players], dtype=np.float64) - # Plackett–Luce weights (paper §2.2): w_i = 10^(R_i/scale) - weights = np.power(10.0, ratings / self.elo_scale) - - # probs[i, k] = P(player i finishes at rank k), k=0 is best rank - probs = np.zeros((num_players, num_players), dtype=np.float64) - - for perm in itertools.permutations(range(num_players)): - # Probability of this exact ranking under PL: - # \prod_{l=0}^{n-2} w_{perm[l]} / \sum_{j=l}^{n-1} w_{perm[j]} - denom = weights.sum() - p_perm = np.float64(1.0) - for player in range(num_players - 1): + 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 # remove the chosen player's weight + denom -= w - # Accumulate into player/position probabilities for position, player_idx in enumerate(perm): probs[player_idx, position] += p_perm - # Numerical sanity checks (no balancing; PL is already doubly-stochastic up to FP error) - col_sum = probs.sum(axis=0) - row_sum = probs.sum(axis=1) - assert np.allclose(col_sum, 1.0, rtol=rtol, atol=atol), ( + 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, 1.0, rtol=rtol, atol=atol), ( + 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_from_dp( self, players: tuple[ID_TYPE, ...], *, - rtol: float = 1e-9, - atol: float = 1e-12, - ) -> np.ndarray: + 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.array([self.elo_ratings[p] for p in players], dtype=np.float64) - w = np.power(10.0, ratings / self.elo_scale) - W_total = float(w.sum()) + 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): @@ -255,40 +264,46 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): bit = lsb.bit_length() - 1 subset_sum[mask] = subset_sum[mask ^ lsb] + other_w[bit] - P = np.zeros(m + 1, dtype=np.float64) - wi = float(w[i]) + p = np.zeros(m + 1, dtype=np.float64) + wi = w[i] # NumPy scalar float64 + for mask in range(num_masks): - tsize = mask.bit_count() - denom = W_total - subset_sum[mask] - base = wi / denom - sign_mask = -1.0 if (tsize & 1) else 1.0 - for k in range(tsize, m + 1): - sign_k = -1.0 if (k & 1) else 1.0 - coeff = math.comb(m - tsize, k - tsize) - P[k] += base * sign_mask * sign_k * coeff - - P = np.clip(P, 0.0, 1.0) - s = P.sum() + 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): - P = (P / s) if s > 0 else np.full_like(P, 1.0 / (m + 1)) - probs[i, : m + 1] = P + if s > 0: + p /= s + else: + p.fill(1.0 / (m + 1)) + + probs[i, : m + 1] = p - col_sum = probs.sum(axis=0) - row_sum = probs.sum(axis=1) - assert np.allclose(col_sum, 1.0, rtol=1e-6, atol=1e-8) - assert np.allclose(row_sum, 1.0, rtol=1e-6, atol=1e-8) + 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_from_simulations( self, players: tuple[ID_TYPE, ...], - ) -> np.ndarray: + ) -> npt.NDArray[np.float64]: n = len(players) - ratings = np.array([self.elo_ratings[p] for p in players], dtype=np.float64) - weights = np.power(10.0, ratings / self.elo_scale) + 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) - rng = self._rng idx_all = np.arange(n, dtype=np.int64) for _ in range(self.mc_samples): remaining = idx_all.tolist() @@ -297,40 +312,45 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): while remaining: rem_arr = np.array(remaining, dtype=np.int64) probs = w[rem_arr] - tot = probs.sum() - if not np.isfinite(tot) or tot <= 0: - probs = np.ones_like(probs, dtype=np.float64) - tot = probs.sum() - probs = probs / tot - chosen = int(rng.choice(rem_arr, p=probs)) + tot = probs.sum(dtype=np.float64) + # Robustness for degenerate/underflow cases + 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] = 0.0 + w[chosen] = NP_ZERO for pos, player_idx in enumerate(order): - counts[player_idx, pos] += 1.0 + counts[player_idx, pos] += NP_ONE - probs = np.clip(counts / float(self.mc_samples), 0.0, 1.0) + probs = np.clip(counts / np.float64(self.mc_samples), NP_ZERO, NP_ONE) col_sums = probs.sum(axis=0, keepdims=True) - col_sums[col_sums == 0.0] = 1.0 + col_sums[col_sums == NP_ZERO] = NP_ONE return probs / col_sums @override def probability_matrix( self, players: Iterable[ID_TYPE], - ) -> np.ndarray: + ) -> npt.NDArray[np.float64]: players = tuple(players) + n = len(players) - if len(players) < 2: + if n < 2: raise ValueError("At least 2 players are required") - if len(players) == 2: + if n == 2: + assert len(players) == 2 return self._calculate_probability_matrix_two_players(players) - if len(players) <= self.max_exact: + if n <= self.max_exact: return self._calculate_probability_matrix_from_permutations(players) - if len(players) <= self.max_dp: + if n <= self.max_dp: return self._calculate_probability_matrix_from_dp(players) return self._calculate_probability_matrix_from_simulations(players) @@ -340,11 +360,11 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): self, players: Iterable[ID_TYPE], *, - rank_payoffs: np.ndarray | None = None, - ) -> np.ndarray: + 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=float) + 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), ( @@ -356,14 +376,14 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): @override def update_elo_ratings( self, - players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE], - ) -> np.ndarray: + 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.array(list(players.values()), dtype=float) + payoffs = np.asarray(list(players.values()), dtype=np.float64) else: player_ids = tuple(players) - payoffs = np.arange(len(player_ids) - 1, -1, -1, dtype=float) + 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" @@ -378,20 +398,20 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): def _update_elo_ratings_batch( self, - matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], - ) -> Generator[np.ndarray]: + 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, float] | Iterable[ID_TYPE]], + 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, - ) -> np.ndarray | None: + ) -> npt.NDArray[np.float64] | None: if progress_bar: from tqdm import tqdm @@ -407,27 +427,33 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): def _padded_numpy_array( - arrays: Iterable[np.ndarray], + arrays: Iterable[npt.NDArray[np.float64]], *, - pad_value: float = np.nan, + pad_value: np.float64 | float = np.nan, dtype: np.dtype | None = None, -) -> np.ndarray: +) -> npt.NDArray[np.float64]: arrays = tuple(arrays) max_len = max((a.shape[0] for a in arrays), default=0) - result = np.full((len(arrays), max_len), pad_value, dtype=dtype or np.float64) + 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: float = 0, - elo_k: float = 32, - elo_scale: float = 400, + 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, float]: +) -> dict[int, np.float64]: assert matches.ndim == 2 and matches.shape[1] == 2, ( "Matches must be a 2D array with shape (num_matches, 2)" ) @@ -443,11 +469,11 @@ def calculate_elo_ratings_two_players_python( def calculate_elo_ratings_multi_players_python( *, matches: npt.NDArray[np.int32], - elo_initial: float = 0, - elo_k: float = 32, - elo_scale: float = 400, + 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, float]: +) -> 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)" ) @@ -457,7 +483,6 @@ def calculate_elo_ratings_multi_players_python( elo_scale=elo_scale, ) num_matches = len(matches) - # Iterate over matches and drop nans from each match matches_iter = (match[~np.isnan(match)].astype(np.int32) for match in matches) elo.update_elo_ratings_batch( matches_iter, diff --git a/experiments/elo/python/elo/optimal_k.py b/experiments/elo/python/elo/optimal_k.py index 6507bdef..07caf916 100644 --- a/experiments/elo/python/elo/optimal_k.py +++ b/experiments/elo/python/elo/optimal_k.py @@ -25,7 +25,7 @@ def calculate_loss[ID_TYPE]( progress_bar=progress_bar, ) assert errors is not None - return np.nanmean(errors**2) + return np.nanmean(errors**2, dtype=np.float64) def approximate_optimal_k[ID_TYPE]( -- GitLab From 440faf8160d7b2cbdb14c45b9a79eb26a2f4aace Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 23:00:49 +0300 Subject: [PATCH 135/147] Rename some methods --- experiments/elo/python/elo/elo_ratings.py | 34 +++++++++++------------ 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/experiments/elo/python/elo/elo_ratings.py b/experiments/elo/python/elo/elo_ratings.py index 6b75e19e..dd41f174 100644 --- a/experiments/elo/python/elo/elo_ratings.py +++ b/experiments/elo/python/elo/elo_ratings.py @@ -178,9 +178,9 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): elo_initial: np.float64 | float = NP_ZERO, elo_k: np.float64 | float = NP_THIRTYTWO, elo_scale: np.float64 | float = NP_FOURHUNDRED, - max_exact: int = 6, - max_dp: int = 12, - mc_samples: int = 5_000, + 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__( @@ -189,9 +189,9 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): elo_k=elo_k, elo_scale=elo_scale, ) - self.max_exact = max_exact - self.max_dp = max_dp - self.mc_samples = mc_samples + 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) ) @@ -207,9 +207,10 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): prob_b = 1 - prob_a return np.asarray([[prob_a, prob_b], [prob_b, prob_a]], dtype=np.float64) - def _calculate_probability_matrix_from_permutations( + 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]: @@ -239,7 +240,7 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): ) return probs - def _calculate_probability_matrix_from_dp( + def _calculate_probability_matrix_dynamic_programming( self, players: tuple[ID_TYPE, ...], *, @@ -295,7 +296,7 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): assert np.allclose(row_sum, NP_ONE, rtol=rtol, atol=atol) return probs - def _calculate_probability_matrix_from_simulations( + def _calculate_probability_matrix_monte_carlo( self, players: tuple[ID_TYPE, ...], ) -> npt.NDArray[np.float64]: @@ -305,7 +306,7 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): counts = np.zeros((n, n), dtype=np.float64) idx_all = np.arange(n, dtype=np.int64) - for _ in range(self.mc_samples): + for _ in range(self.monte_carlo_samples): remaining = idx_all.tolist() order: list[int] = [] w = weights.copy() @@ -313,7 +314,6 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): rem_arr = np.array(remaining, dtype=np.int64) probs = w[rem_arr] tot = probs.sum(dtype=np.float64) - # Robustness for degenerate/underflow cases probs = np.divide( probs, tot, @@ -327,7 +327,7 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): for pos, player_idx in enumerate(order): counts[player_idx, pos] += NP_ONE - probs = np.clip(counts / np.float64(self.mc_samples), NP_ZERO, 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 @@ -347,13 +347,13 @@ class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]): assert len(players) == 2 return self._calculate_probability_matrix_two_players(players) - if n <= self.max_exact: - return self._calculate_probability_matrix_from_permutations(players) + if n <= self.max_permutations: + return self._calculate_probability_matrix_permutations(players) - if n <= self.max_dp: - return self._calculate_probability_matrix_from_dp(players) + if n <= self.max_dynamic_programming: + return self._calculate_probability_matrix_dynamic_programming(players) - return self._calculate_probability_matrix_from_simulations(players) + return self._calculate_probability_matrix_monte_carlo(players) @override def expected_outcome( -- GitLab From 7615908f7b8449dda654208cb90abb29d31e3ec8 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 23:19:51 +0300 Subject: [PATCH 136/147] Added multi_player.rs --- experiments/elo/rust/core.rs | 1 + experiments/elo/rust/core/multi_player.rs | 267 ++++++++++++++++++++++ 2 files changed, 268 insertions(+) create mode 100644 experiments/elo/rust/core/multi_player.rs diff --git a/experiments/elo/rust/core.rs b/experiments/elo/rust/core.rs index 3face455..afb768e8 100644 --- a/experiments/elo/rust/core.rs +++ b/experiments/elo/rust/core.rs @@ -1,3 +1,4 @@ +pub mod multi_player; pub mod optimal_k; pub mod rating_system; pub mod two_player; diff --git a/experiments/elo/rust/core/multi_player.rs b/experiments/elo/rust/core/multi_player.rs new file mode 100644 index 00000000..02c9e742 --- /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-9, "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-9, "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-6, "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-6, "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) + } +} -- GitLab From 360e07930e1eedd58284b99565cf47f9f68c1ddb Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 23:39:44 +0300 Subject: [PATCH 137/147] Added calculate_elo_ratings_multi_players_rust() Python binding --- experiments/elo/python/elo/_rust.pyi | 25 +++++++++++------- experiments/elo/rust/core/two_player.rs | 7 +---- experiments/elo/rust/lib.rs | 35 ++++++++++++++++++++++++- experiments/elo/rust/main.rs | 4 +-- 4 files changed, 53 insertions(+), 18 deletions(-) diff --git a/experiments/elo/python/elo/_rust.pyi b/experiments/elo/python/elo/_rust.pyi index 9a0c6e68..99beb866 100644 --- a/experiments/elo/python/elo/_rust.pyi +++ b/experiments/elo/python/elo/_rust.pyi @@ -4,16 +4,23 @@ import numpy.typing as npt def approx_optimal_k_two_player_rust( *, matches: npt.NDArray[np.int32], - min_k: float, - max_k: float, - elo_scale: float, + min_k: np.float64 | float, + max_k: np.float64 | float, + elo_scale: np.float64 | float, max_iterations: int | None = None, - x_absolute_tol: float | None = None, -) -> float: ... + x_absolute_tol: np.float64 | float | None = None, +) -> np.float64: ... def calculate_elo_ratings_two_players_rust( *, matches: npt.NDArray[np.int32], - elo_initial: float, - elo_k: float, - elo_scale: float, -) -> dict[int, float]: ... + 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/rust/core/two_player.rs b/experiments/elo/rust/core/two_player.rs index b9d55170..6b4461be 100644 --- a/experiments/elo/rust/core/two_player.rs +++ b/experiments/elo/rust/core/two_player.rs @@ -2,7 +2,7 @@ use std::collections::HashMap; use std::hash::Hash; use super::elo_probability; -use super::rating_system::{EloConfig, EloRatingSystem, Match}; +use super::rating_system::{EloConfig, EloRatingSystem}; /// Two-player Elo. Mirrors your Python `TwoPlayerElo`. #[derive(Debug, Clone)] @@ -24,11 +24,6 @@ where ratings: initial_ratings.unwrap_or_default(), } } - - /// Convenience: update with explicit outcomes (id, outcome in [0,1]) or ordered (winner first). - pub fn update(&mut self, m: Match) -> Vec { - self.update_elo_ratings(m) - } } impl EloRatingSystem for TwoPlayerElo diff --git a/experiments/elo/rust/lib.rs b/experiments/elo/rust/lib.rs index 36ea71c3..d05e2550 100644 --- a/experiments/elo/rust/lib.rs +++ b/experiments/elo/rust/lib.rs @@ -1,5 +1,6 @@ pub mod core; +use crate::core::multi_player::RankOrderedLogitElo; use crate::core::optimal_k::approx_optimal_k_two_player; use crate::core::rating_system::EloRatingSystem; use crate::core::rating_system::{EloConfig, Match}; @@ -64,7 +65,35 @@ fn calculate_elo_ratings_two_players_rust<'py>( let mut elo = TwoPlayerElo::::new(cfg, None); for row in arr.rows() { - elo.update(Match::Ordered(row.to_vec())); + 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 { + return Err(pyo3::exceptions::PyValueError::new_err( + "matches must be shape (n_matches, n_players) 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()) @@ -77,5 +106,9 @@ fn calculate_elo_ratings_two_players_rust<'py>( fn _rust(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_function(wrap_pyfunction!(approx_optimal_k_two_player_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 index f03dad4e..2fdfd9ae 100644 --- a/experiments/elo/rust/main.rs +++ b/experiments/elo/rust/main.rs @@ -10,9 +10,9 @@ fn main() { let mut elo = TwoPlayerElo::<&'static str>::new(cfg, None); // Match as ordered players: winner first - elo.update(Match::Ordered(vec!["A", "B"])); + elo.update_elo_ratings(Match::Ordered(vec!["A", "B"])); // Or explicit outcomes - elo.update(Match::Outcomes(vec![("A", 1.0), ("B", 0.0)])); + elo.update_elo_ratings(Match::Outcomes(vec![("A", 1.0), ("B", 0.0)])); let expected = elo.expected_outcome(&["A", "B"], None); println!("P(A wins) = {}", expected[0]); -- GitLab From b73fbd731406148cfe0ad24a20c4e1aeba3b1dd0 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sat, 25 Oct 2025 23:48:53 +0300 Subject: [PATCH 138/147] Added calculate_elo_ratings_multi_players_rust() to __main__.py --- experiments/elo/python/elo/__main__.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/experiments/elo/python/elo/__main__.py b/experiments/elo/python/elo/__main__.py index db049934..62aa3641 100644 --- a/experiments/elo/python/elo/__main__.py +++ b/experiments/elo/python/elo/__main__.py @@ -6,6 +6,7 @@ from numpy import typing as npt from elo._rust import ( approx_optimal_k_two_player_rust, + calculate_elo_ratings_multi_players_rust, calculate_elo_ratings_two_players_rust, ) from elo.elo_ratings import ( @@ -94,7 +95,8 @@ def main(): if player_count == 2: print("Elo ratings (two player Rust implementation):") with Timer( - text="Calculated Elo ratings (Rust) in {:.3f} seconds", logger=print + text="Calculated Elo ratings (Rust) in {:.3f} seconds", + logger=print, ): elo_ratings = calculate_elo_ratings_two_players_rust( matches=matches, @@ -108,6 +110,20 @@ def main(): 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( -- GitLab From 8e7ff27a130432ba14a318d5c4e4d1cdb3bb1f8b Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 26 Oct 2025 20:00:32 +0200 Subject: [PATCH 139/147] approx_optimal_k_rust() implementation and binding --- experiments/elo/python/elo/__main__.py | 26 ++++++------ experiments/elo/python/elo/_rust.pyi | 7 ++-- experiments/elo/rust/core/optimal_k.rs | 47 +++++++++++++++------- experiments/elo/rust/core/rating_system.rs | 19 ++++----- experiments/elo/rust/lib.rs | 33 ++++++++------- 5 files changed, 76 insertions(+), 56 deletions(-) diff --git a/experiments/elo/python/elo/__main__.py b/experiments/elo/python/elo/__main__.py index 62aa3641..641b4414 100644 --- a/experiments/elo/python/elo/__main__.py +++ b/experiments/elo/python/elo/__main__.py @@ -5,7 +5,7 @@ import numpy as np from numpy import typing as npt from elo._rust import ( - approx_optimal_k_two_player_rust, + approx_optimal_k_rust, calculate_elo_ratings_multi_players_rust, calculate_elo_ratings_two_players_rust, ) @@ -137,18 +137,18 @@ def main(): ) print(f"Approximate optimal K (Python): {optimal_k:.3f}") - if player_count == 2: - print("Approximate optimal K (Rust implementation):") - with Timer(text="Calculated optimal K (Rust) in {:.3f} seconds", logger=print): - optimal_k = approx_optimal_k_two_player_rust( - matches=matches, - min_k=0, - max_k=200, - elo_scale=elo_scale, - max_iterations=None, - x_absolute_tol=None, - ) - print(f"Approximate optimal K (Rust): {optimal_k:.3f}") + print("Approximate optimal K (Rust implementation):") + with Timer(text="Calculated optimal K (Rust) in {:.3f} seconds", logger=print): + optimal_k = approx_optimal_k_rust( + 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, + ) + print(f"Approximate optimal K (Rust): {optimal_k:.3f}") if __name__ == "__main__": diff --git a/experiments/elo/python/elo/_rust.pyi b/experiments/elo/python/elo/_rust.pyi index 99beb866..1dce1e2e 100644 --- a/experiments/elo/python/elo/_rust.pyi +++ b/experiments/elo/python/elo/_rust.pyi @@ -1,11 +1,12 @@ import numpy as np import numpy.typing as npt -def approx_optimal_k_two_player_rust( +def approx_optimal_k_rust( *, matches: npt.NDArray[np.int32], - min_k: np.float64 | float, - max_k: np.float64 | float, + 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, diff --git a/experiments/elo/rust/core/optimal_k.rs b/experiments/elo/rust/core/optimal_k.rs index c3c3c580..bd104e57 100644 --- a/experiments/elo/rust/core/optimal_k.rs +++ b/experiments/elo/rust/core/optimal_k.rs @@ -2,6 +2,7 @@ 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}; @@ -9,12 +10,11 @@ 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 S, matches: &[Match]) -> f64 +fn calculate_loss(elo: &mut dyn EloRatingSystem, matches: &[Match]) -> f64 where Id: Eq + Hash + Clone, - S: EloRatingSystem, { - let Some(all_diffs) = elo.update_elo_ratings_batch(matches.iter().cloned(), true) else { + let Some(all_diffs) = elo.update_elo_ratings_batch_slice(matches, true) else { return f64::INFINITY; }; @@ -39,6 +39,7 @@ where struct BrentKProblem<'a, Id> { matches: &'a [Match], elo_scale: f64, + two_player_only: bool, } impl<'a, Id> CostFunction for BrentKProblem<'a, Id> @@ -53,22 +54,36 @@ where if !k.is_finite() || *k < 0.0 { return Ok(f64::INFINITY); } - let mut sys: TwoPlayerElo = TwoPlayerElo::new( - EloConfig { - elo_k: *k, - elo_scale: self.elo_scale, - ..EloConfig::default() - }, - Some(HashMap::new()), - ); - Ok(calculate_loss(&mut sys, self.matches)) + 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_two_player( +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() @@ -85,7 +100,11 @@ where let abs_tol = x_absolute_tol.unwrap_or(1e-3); let eps = f64::EPSILON.sqrt(); - let problem = BrentKProblem { matches, elo_scale }; + let problem = BrentKProblem { + matches, + elo_scale, + two_player_only, + }; let solver = BrentOpt::new(min_k, max_k).set_tolerance(eps, abs_tol); diff --git a/experiments/elo/rust/core/rating_system.rs b/experiments/elo/rust/core/rating_system.rs index d78dbc02..09955149 100644 --- a/experiments/elo/rust/core/rating_system.rs +++ b/experiments/elo/rust/core/rating_system.rs @@ -64,25 +64,22 @@ where } } - /// Batch update over an iterator of matches. If `collect_diffs` is true, - /// returns a ragged list of per-match diffs; otherwise returns `None`. - fn update_elo_ratings_batch( + /// 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: I, + matches: &[Match], collect_diffs: bool, - ) -> Option>> - where - I: IntoIterator>, - { + ) -> Option>> { if collect_diffs { - let mut all = Vec::new(); + let mut all = Vec::with_capacity(matches.len()); for m in matches { - all.push(self.update_elo_ratings(m)); + all.push(self.update_elo_ratings(m.clone())); } Some(all) } else { for m in matches { - let _ = self.update_elo_ratings(m); + let _ = self.update_elo_ratings(m.clone()); } None } diff --git a/experiments/elo/rust/lib.rs b/experiments/elo/rust/lib.rs index d05e2550..78d8a614 100644 --- a/experiments/elo/rust/lib.rs +++ b/experiments/elo/rust/lib.rs @@ -1,7 +1,7 @@ pub mod core; use crate::core::multi_player::RankOrderedLogitElo; -use crate::core::optimal_k::approx_optimal_k_two_player; +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; @@ -11,18 +11,19 @@ use pyo3::prelude::*; use std::collections::HashMap; #[pyfunction] -fn approx_optimal_k_two_player_rust<'py>( - matches: PyReadonlyArray2<'py, i32>, // shape: (n_matches, 2) - min_k: f64, - max_k: f64, +fn approx_optimal_k_rust<'py>( + matches: PyReadonlyArray2<'py, i32>, // shape: (n_matches, n_players) + two_player_only: bool, + min_elo_k: f64, + max_elo_k: f64, elo_scale: f64, - max_iterations: Option, // Python's `maxiter` - x_absolute_tol: Option, // Python's `xatol` + max_iterations: Option, + x_absolute_tol: Option, ) -> PyResult { let arr: ArrayView2<'_, i32> = matches.as_array(); - if arr.ndim() != 2 || arr.shape()[1] != 2 { + if arr.ndim() != 2 || arr.shape()[1] < 2 { return Err(pyo3::exceptions::PyValueError::new_err( - "matches must be shape (n_matches, 2) of int32", + "matches must be shape (n_matches, n_players>=2) of int32", )); } @@ -31,10 +32,12 @@ fn approx_optimal_k_two_player_rust<'py>( .into_iter() .map(|r| Match::Ordered(r.to_vec())) .collect::>>(); - let optimal_k = approx_optimal_k_two_player( + + let optimal_k = approx_optimal_k( &matches, - min_k, - max_k, + two_player_only, + min_elo_k, + max_elo_k, elo_scale, max_iterations, x_absolute_tol, @@ -80,9 +83,9 @@ fn calculate_elo_ratings_multi_players_rust<'py>( ) -> PyResult> { // Simple, straightforward implementation (ordered players). let arr: ArrayView2<'_, i32> = matches.as_array(); - if arr.ndim() != 2 { + if arr.ndim() != 2 || arr.shape()[1] < 2 { return Err(pyo3::exceptions::PyValueError::new_err( - "matches must be shape (n_matches, n_players) of int32", + "matches must be shape (n_matches, n_players>=2) of int32", )); } let cfg = EloConfig { @@ -104,7 +107,7 @@ fn calculate_elo_ratings_multi_players_rust<'py>( /// import the module. #[pymodule] fn _rust(m: &Bound<'_, PyModule>) -> PyResult<()> { - m.add_function(wrap_pyfunction!(approx_optimal_k_two_player_rust, m)?)?; + 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, -- GitLab From 4ace8c4b35a10706ceead77e329eb650ef456720 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 26 Oct 2025 21:14:03 +0200 Subject: [PATCH 140/147] Small correction --- experiments/elo/rust/core/rating_system.rs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/elo/rust/core/rating_system.rs b/experiments/elo/rust/core/rating_system.rs index 09955149..ea19be5a 100644 --- a/experiments/elo/rust/core/rating_system.rs +++ b/experiments/elo/rust/core/rating_system.rs @@ -101,7 +101,7 @@ where /// Shared path: compute diffs = (outcomes - expected) / max_outcome, then apply. fn apply_diffs_from_outcomes(&mut self, ids: &[Id], outcomes: &[f64]) -> Vec { - assert!(!ids.is_empty(), "At least 2 players are required"); + assert!(ids.len() >= 2, "At least 2 players are required"); assert!( outcomes.iter().all(|&x| x >= 0.0), "Payoffs must be non-negative" -- GitLab From 017bbacfc284444c249359798fa5a9fe2db76eff Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 26 Oct 2025 22:33:35 +0200 Subject: [PATCH 141/147] Unify interface for approximate_optimal_k(); expect matches in CSR format for approx_optimal_k_rust() --- experiments/elo/Cargo.toml | 3 + experiments/elo/python/elo/__main__.py | 5 +- experiments/elo/python/elo/_rust.pyi | 4 +- experiments/elo/python/elo/optimal_k.py | 111 +++++++++++++++++++++++- experiments/elo/rust/lib.rs | 40 +++++++-- 5 files changed, 148 insertions(+), 15 deletions(-) diff --git a/experiments/elo/Cargo.toml b/experiments/elo/Cargo.toml index 4fee8d66..3535ad50 100644 --- a/experiments/elo/Cargo.toml +++ b/experiments/elo/Cargo.toml @@ -18,3 +18,6 @@ argmin = "0.11" ndarray = "0.16.1" numpy = "0.27.0" pyo3 = { version = "0.27.0", features = ["extension-module", "abi3-py312"] } + +[lints.clippy] +too_many_arguments = "allow" diff --git a/experiments/elo/python/elo/__main__.py b/experiments/elo/python/elo/__main__.py index 641b4414..c0decf33 100644 --- a/experiments/elo/python/elo/__main__.py +++ b/experiments/elo/python/elo/__main__.py @@ -5,7 +5,6 @@ import numpy as np from numpy import typing as npt from elo._rust import ( - approx_optimal_k_rust, calculate_elo_ratings_multi_players_rust, calculate_elo_ratings_two_players_rust, ) @@ -134,12 +133,13 @@ def main(): 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 = approx_optimal_k_rust( + optimal_k = approximate_optimal_k( matches=matches, two_player_only=(player_count == 2), min_elo_k=0, @@ -147,6 +147,7 @@ def main(): elo_scale=elo_scale, max_iterations=None, x_absolute_tol=None, + python=False, ) print(f"Approximate optimal K (Rust): {optimal_k:.3f}") diff --git a/experiments/elo/python/elo/_rust.pyi b/experiments/elo/python/elo/_rust.pyi index 1dce1e2e..00b1c409 100644 --- a/experiments/elo/python/elo/_rust.pyi +++ b/experiments/elo/python/elo/_rust.pyi @@ -3,7 +3,9 @@ import numpy.typing as npt def approx_optimal_k_rust( *, - matches: npt.NDArray[np.int32], + 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, diff --git a/experiments/elo/python/elo/optimal_k.py b/experiments/elo/python/elo/optimal_k.py index 07caf916..a86899c0 100644 --- a/experiments/elo/python/elo/optimal_k.py +++ b/experiments/elo/python/elo/optimal_k.py @@ -2,18 +2,21 @@ 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]( +def _calculate_loss[ID_TYPE]( *, elo: EloRatingSystem[ID_TYPE], matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], @@ -28,7 +31,7 @@ def calculate_loss[ID_TYPE]( return np.nanmean(errors**2, dtype=np.float64) -def approximate_optimal_k[ID_TYPE]( +def approximate_optimal_k_python[ID_TYPE]( *, matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]], two_player_only: bool = False, @@ -53,7 +56,7 @@ def approximate_optimal_k[ID_TYPE]( else RankOrderedLogitElo(elo_k=elo_k, elo_scale=elo_scale) ) - return calculate_loss( + return _calculate_loss( elo=elo, matches=match_list, progress_bar=False, @@ -73,3 +76,105 @@ def approximate_optimal_k[ID_TYPE]( ) 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]], +) -> tuple[ + npt.NDArray[np.int64], npt.NDArray[np.float64] | None, npt.NDArray[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]], + 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/rust/lib.rs b/experiments/elo/rust/lib.rs index 78d8a614..31e58109 100644 --- a/experiments/elo/rust/lib.rs +++ b/experiments/elo/rust/lib.rs @@ -12,7 +12,9 @@ use std::collections::HashMap; #[pyfunction] fn approx_optimal_k_rust<'py>( - matches: PyReadonlyArray2<'py, i32>, // shape: (n_matches, n_players) + 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, @@ -20,18 +22,38 @@ fn approx_optimal_k_rust<'py>( max_iterations: Option, x_absolute_tol: Option, ) -> PyResult { - let arr: ArrayView2<'_, i32> = matches.as_array(); - if arr.ndim() != 2 || arr.shape()[1] < 2 { + 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( - "matches must be shape (n_matches, n_players>=2) of int32", + "row_splits must start at 0 and end at players.len()", )); } - let matches = arr - .rows() - .into_iter() - .map(|r| Match::Ordered(r.to_vec())) - .collect::>>(); + 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, -- GitLab From 23b6b217e1da91116ed04d5d8dc67bd7e298aac4 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 26 Oct 2025 22:54:41 +0200 Subject: [PATCH 142/147] Be smarter if we already receive numpy arrays as matches --- experiments/elo/python/elo/optimal_k.py | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/experiments/elo/python/elo/optimal_k.py b/experiments/elo/python/elo/optimal_k.py index a86899c0..611c4b4c 100644 --- a/experiments/elo/python/elo/optimal_k.py +++ b/experiments/elo/python/elo/optimal_k.py @@ -117,10 +117,25 @@ def _player_ids_to_ints[ID_TYPE]( def _matches_to_arrays[ID_TYPE]( - matches: Iterable[Mapping[ID_TYPE, float]] | Iterable[Iterable[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] + 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): @@ -146,7 +161,9 @@ def _matches_to_arrays[ID_TYPE]( def approximate_optimal_k[ID_TYPE]( *, - matches: Iterable[Mapping[ID_TYPE, float]] | Iterable[Iterable[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, -- GitLab From 5832f6054285624663eac357454fec8c7619b592 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 26 Oct 2025 22:55:07 +0200 Subject: [PATCH 143/147] Much less strict accuracy requirements --- experiments/elo/rust/core/multi_player.rs | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/experiments/elo/rust/core/multi_player.rs b/experiments/elo/rust/core/multi_player.rs index 02c9e742..9ed4ef7c 100644 --- a/experiments/elo/rust/core/multi_player.rs +++ b/experiments/elo/rust/core/multi_player.rs @@ -98,11 +98,11 @@ where // 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-9, "column {} sum {}", rank, s); + assert!((s - 1.0).abs() <= 1e-3, "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-9, "row {} sum {}", i, s); + assert!((s - 1.0).abs() <= 1e-3, "row {} sum {}", i, s); } probs } @@ -191,11 +191,11 @@ where // sanity for rank in 0..n { let s: f64 = probs.iter().map(|row| row[rank]).sum(); - assert!((s - 1.0).abs() <= 1e-6, "column {} sum {}", rank, s); + assert!((s - 1.0).abs() <= 1e-3, "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-6, "row {} sum {}", i, s); + assert!((s - 1.0).abs() <= 1e-3, "row {} sum {}", i, s); } probs } -- GitLab From 9dcfc3c0c83ec1cd26f8fa8300308c26e7fea18c Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Sun, 26 Oct 2025 23:25:26 +0200 Subject: [PATCH 144/147] More error catching --- experiments/elo/python/elo/p_deterministic.py | 37 ++++++++++++++----- experiments/elo/rust/core/multi_player.rs | 8 ++-- 2 files changed, 32 insertions(+), 13 deletions(-) diff --git a/experiments/elo/python/elo/p_deterministic.py b/experiments/elo/python/elo/p_deterministic.py index bf68b845..975e0d83 100644 --- a/experiments/elo/python/elo/p_deterministic.py +++ b/experiments/elo/python/elo/p_deterministic.py @@ -171,15 +171,34 @@ def p_deterministic_experiment( p_deterministic=p_deterministic, ) - 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, - ) + 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, diff --git a/experiments/elo/rust/core/multi_player.rs b/experiments/elo/rust/core/multi_player.rs index 9ed4ef7c..a419b179 100644 --- a/experiments/elo/rust/core/multi_player.rs +++ b/experiments/elo/rust/core/multi_player.rs @@ -98,11 +98,11 @@ where // 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-3, "column {} sum {}", rank, s); + 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-3, "row {} sum {}", i, s); + assert!((s - 1.0).abs() <= 1e-2, "row {} sum {}", i, s); } probs } @@ -191,11 +191,11 @@ where // sanity for rank in 0..n { let s: f64 = probs.iter().map(|row| row[rank]).sum(); - assert!((s - 1.0).abs() <= 1e-3, "column {} sum {}", rank, s); + 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-3, "row {} sum {}", i, s); + assert!((s - 1.0).abs() <= 1e-2, "row {} sum {}", i, s); } probs } -- GitLab From 0c76eb0a954c45e1aeb37b33cdf03cd83551bd97 Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Mon, 27 Oct 2025 09:50:13 +0200 Subject: [PATCH 145/147] Different p-deterministic experiment config --- experiments/elo/notebooks/p-deterministic distribution.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/experiments/elo/notebooks/p-deterministic distribution.py b/experiments/elo/notebooks/p-deterministic distribution.py index c6fc4c65..2c3741e1 100644 --- a/experiments/elo/notebooks/p-deterministic distribution.py +++ b/experiments/elo/notebooks/p-deterministic distribution.py @@ -27,10 +27,10 @@ jupyter_black.load() seed = 13 # %% -num_players = (1000,) -num_matches = (1_000_000,) -players_per_match = (2, 3) -p_deterministic = np.linspace(start=0.00, stop=0.99, num=10) +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) -- GitLab From 9a97f535a94a0c4c019c5ae5cafafedf332bd9af Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 28 Oct 2025 10:58:53 +0200 Subject: [PATCH 146/147] Added main method to python/elo/p_deterministic.py --- experiments/elo/python/elo/p_deterministic.py | 170 ++++++++++++++++++ 1 file changed, 170 insertions(+) diff --git a/experiments/elo/python/elo/p_deterministic.py b/experiments/elo/python/elo/p_deterministic.py index 975e0d83..f38a0a08 100644 --- a/experiments/elo/python/elo/p_deterministic.py +++ b/experiments/elo/python/elo/p_deterministic.py @@ -1,13 +1,18 @@ 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 @@ -16,6 +21,8 @@ if TYPE_CHECKING: from collections.abc import Generator from concurrent.futures import Future +LOGGER = logging.getLogger(__name__) + def simulate_p_deterministic_matches( *, @@ -303,3 +310,166 @@ def p_deterministic_experiments( ) 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() -- GitLab From 40cb9bd0ce4e26c7750671709ab082100b70131d Mon Sep 17 00:00:00 2001 From: Markus Shepherd Date: Tue, 28 Oct 2025 13:31:23 +0200 Subject: [PATCH 147/147] Added p_deterministic script to pyproject.toml --- experiments/elo/pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml index e4ae1aa9..950fa6fd 100644 --- a/experiments/elo/pyproject.toml +++ b/experiments/elo/pyproject.toml @@ -33,6 +33,7 @@ packages = [ [project.scripts] elo = "elo.__main__:main" +p_deterministic = "elo.p_deterministic:main" [dependency-groups] dev = [ -- GitLab