Merge pull request #29 from ALLAN-DIP/add-baseline-lr-advisor

Add baseline LR advisor

Dockerfile

@@ -4,6 +4,8 @@
 # Use the command `hadolint Dockerfile` to test
 # Adding Hadolint to `pre-commit` is non-trivial, so the command must be run manually
 
+FROM ghcr.io/allan-dip/chiron-utils:baseline-lr-model-2025-01-14 AS baseline-lr-model
+
 FROM python:3.11.11-slim-bookworm AS base
 
 WORKDIR /bot
@@ -34,3 +36,7 @@ RUN pip install --no-cache-dir --no-deps -e .[$TARGET]
 ENTRYPOINT ["python", "-m", "chiron_utils.scripts.run_bot"]
 
 LABEL org.opencontainers.image.source=https://github.com/ALLAN-DIP/chiron-utils
+
+FROM base AS baseline-lr
+
+COPY --from=baseline-lr-model lr_model/ lr_model/

Makefile

@@ -118,3 +118,8 @@ build:
 		--tag ghcr.io/allan-dip/chiron-utils:$(TAG) \
 		--target $(TARGET) \
 		.
+
+.PHONY: build-baseline-lr
+build-baseline-lr:
+	TARGET=baseline-lr \
+	make build

README.md

@@ -49,6 +49,12 @@ Both the bot and game running commands support a `--help` argument to list avail
   - Messages are proposals to carry out a set of valid moves, which is also randomly selected. One such proposal is sent to each opponent.
   - Due to the random nature of play, a game consisting entirely of `RandomProposerPlayer`s can last for a very long time. I (Alex) have observed multiple games lasting past 1950 without a clear winner.
   - `RandomProposerPlayer` uses very few resources, so it is useful as stand-ins for other players.
+- [`LrBot`](src/chiron_utils/bots/lr_bot.py) (`LrAdvisor` and `LrPlayer`):
+  - A logistic regression model is used to predict orders for each available unit, given current game state.
+  - To build the bot, run `make build-baseline-lr` to generate the OCI image to run with Docker
+    - When running the bot outside of a container, download the latest model file from [lr_models - Google Drive](https://drive.google.com/drive/folders/1FuG3qY51wRkR8RgEBVY49-loln06W-Ro). The filename includes the model release date in `YYYYMMDD` format).
+    - Edit the `MODEL_PATH` constant in `lr_bot.py` to point to the unzipped model folder.
+  - Code for model training can be found at <https://github.com/ALLAN-DIP/baseline-models>
 
 ## Contributing
 

pyproject.toml

@@ -27,17 +27,22 @@ dependencies = [
   "daidepp @ git+https://git@github.com/SHADE-AI/daidepp.git@f84f7279fb4a31083ad8e4b5fcf6d1ed290bae0e",
   # Pinned `diplomacy` to `main`
   "diplomacy @ git+https://git@github.com/ALLAN-DIP/diplomacy.git@5733af848f3e5b35cd9ff8a1eba77bb40e09c946",
+  'importlib-metadata ; python_version < "3.10"',
   "tornado",
 ]
 
 [project.optional-dependencies]
 # `all` extra allows an easy install of all optional dependencies
 all = [
   "chiron_utils[base]",
+  "chiron_utils[baseline-lr]",
   "chiron_utils[dev]",
 ]
 # Default extra to make OCI image builds easier
 base = []
+baseline-lr = [
+  "baseline-models @ git+https://git@github.com/ALLAN-DIP/baseline-models.git@2fd62f89963eb804f5f98ba0a1321656e28e594a",
+]
 dev = [
   # Use older `mypy` version to keep code compatible with Python 3.7
   "mypy<1.9",

requirements-lock.txt

@@ -1,4 +1,5 @@
 astroid==3.3.8
+baseline_models @ git+https://git@github.com/ALLAN-DIP/baseline-models.git@2fd62f89963eb804f5f98ba0a1321656e28e594a
 bcrypt==4.2.1
 certifi==2024.12.14
 cfgv==3.4.0
@@ -14,10 +15,12 @@ identify==2.6.6
 idna==3.10
 iniconfig==2.0.0
 isort==6.0.0
+joblib==1.4.2
 mccabe==0.7.0
 mypy==1.8.0
 mypy-extensions==1.0.0
 nodeenv==1.9.1
+numpy==1.26.4
 packaging==24.2
 parsimonious==0.9.0
 platformdirs==4.3.6
@@ -30,8 +33,11 @@ pytz==2024.2
 PyYAML==6.0.2
 regex==2024.11.6
 requests==2.32.3
+scikit-learn==1.4.1.post1
+scipy==1.15.1
 setuptools==75.8.0
 six==1.17.0
+threadpoolctl==3.5.0
 tomlkit==0.13.2
 tornado==6.4.2
 tqdm==4.67.1

src/chiron_utils/bots/__init__.py

@@ -2,6 +2,11 @@
 
 from typing import List, Type
 
+try:
+    import importlib.metadata as importlib_metadata
+except ImportError:
+    import importlib_metadata
+
 from chiron_utils.bots.baseline_bot import BaselineBot as BaselineBot
 from chiron_utils.bots.random_proposer_bot import (
     RandomProposerAdvisor as RandomProposerAdvisor,
@@ -12,6 +17,25 @@
     RandomProposerAdvisor,
     RandomProposerPlayer,
 ]
+# Import bots only if their direct third-party dependencies are satisfied
+# This unfortunately requires hardcoding the list of required modules,
+# but there currently isn't a way to check if a given extra was used during installation.
+importable_modules = set(importlib_metadata.packages_distributions())
+if {"baseline_models"} < importable_modules:
+    from chiron_utils.bots.lr_bot import (
+        LrAdvisor as LrAdvisor,
+        LrPlayer as LrPlayer,
+    )
+
+    BOTS.extend(
+        [
+            LrAdvisor,
+            LrPlayer,
+        ]
+    )
+# Alphabetize list of classes
+BOTS.sort(key=lambda t: t.__name__)
+
 NAMES_TO_BOTS = {bot.__name__: bot for bot in BOTS}
 
 DEFAULT_BOT_TYPE = RandomProposerPlayer  # pylint: disable=invalid-name

src/chiron_utils/bots/lr_bot.py

@@ -0,0 +1,92 @@
+"""Bots that carry out orders generated by a logistic regressor model."""
+
+from abc import ABC
+from dataclasses import dataclass
+from pathlib import Path
+from typing import List, Sequence
+
+from baseline_models.model_code.predict import predict
+from diplomacy.utils import strings as diplomacy_strings
+from diplomacy.utils.constants import SuggestionType
+
+from chiron_utils.bots.baseline_bot import BaselineBot, BotType
+from chiron_utils.utils import return_logger
+
+logger = return_logger(__name__)
+
+MODEL_PATH = Path() / "lr_model"
+
+
+@dataclass
+class LrBot(BaselineBot, ABC):
+    """Baseline logistic regressor model.
+
+    `MODEL_PATH` should point to folder containing model .pkl files.
+
+    Each model corresponds to a (unit, location, phase) combination.
+
+    Unit types are 'A', 'F'.
+
+    Location types include all possible locations on the board, such as 'BRE', 'LON', 'STP_SC', etc.
+
+    Phase types are 'SM', 'FM', 'WA, 'SR', 'FR', 'CD'
+    """
+
+    player_type = diplomacy_strings.NO_PRESS_BOT
+
+    def __post_init__(self) -> None:
+        """Verify that model path exists when instantiated."""
+        super().__post_init__()
+        if not MODEL_PATH.is_dir():
+            raise NotADirectoryError(
+                f"Model directory {str(MODEL_PATH)!r} does not exist or is not a directory."
+            )
+
+    def get_orders(self) -> List[str]:
+        """Get order predictions from model.
+
+        Returns:
+            List of predicted orders.
+        """
+        state = self.game.get_state()
+        orders: List[str] = predict(MODEL_PATH, state, self.power_name)
+
+        logger.info("Orders to suggest: %s", orders)
+
+        return orders
+
+    async def gen_orders(self) -> List[str]:
+        """Generate orders for a turn.
+
+        Returns:
+            List of orders to carry out.
+        """
+        orders = self.get_orders()
+        if self.bot_type == BotType.ADVISOR:
+            await self.suggest_orders(orders)
+        elif self.bot_type == BotType.PLAYER:
+            await self.send_orders(orders, wait=True)
+        return orders
+
+    async def do_messaging_round(self, orders: Sequence[str]) -> List[str]:
+        """Carry out one round of messaging.
+
+        Returns:
+            List of orders to carry out.
+        """
+        return list(orders)
+
+
+@dataclass
+class LrAdvisor(LrBot):
+    """Advisor form of `LrBot`."""
+
+    bot_type = BotType.ADVISOR
+    default_suggestion_type = SuggestionType.MOVE
+
+
+@dataclass
+class LrPlayer(LrBot):
+    """Player form of `LrBot`."""
+
+    bot_type = BotType.PLAYER