Merge 4ac3b3c into ed7a94c

.dockerignore

@@ -1,8 +1,6 @@
 data
 ui
 logs
-experimental/logs
-experimental/models
 assets
 venv
 .git

.github/workflows/python-app.yml

@@ -5,9 +5,9 @@ name: Lint and Tests
 
 on:
   push:
-    branches: [master]
+    branches: [master, staging]
   pull_request:
-    branches: [master]
+    branches: [master, staging]
 
 jobs:
   build:
@@ -23,9 +23,16 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install -r dev-requirements.txt
+          pip install -e catanatron_core
+          pip install -e catanatron_gym
+          pip install -e catanatron_server
+          pip install -e catanatron_experimental
       - name: Lint with black
         run: |
-          black catanatron/ --check
+          black catanatron_core --check
+          black catanatron_gym --check
+          black catanatron_server --check
+          black catanatron_experimental --check
       - name: Test with pytest
         run: |
           coverage run --source=catanatron -m pytest tests/

Dockerfile.paperspace

@@ -1,10 +1,10 @@
 # This is a Container we can use to run Experiments in Paperspace.
 #   Edit, build, and publish to DockerHub.
 #   `docker build . -f Dockerfile.paperspace -t bcollazo/paperspace-rl` to build.
-#   `docker run --rm -it bcollazo/paperspace-rl python3.8 experimental/play.py` to ensure it works.
+#   `docker run --rm -it bcollazo/paperspace-rl catanatron-play` to ensure it works.
 #   `docker push bcollazo/paperspace-rl` to publish.
 # FROM paperspace/tensorflow:2.0.0-gpu-py3-jupyter-lab
-FROM tensorflow/tensorflow:2.4.1-gpu-jupyter-py38
+FROM tensorflow/tensorflow:2.4.1-gpu-jupyter
 
 # Install Python3.8
 RUN apt update && \
@@ -20,13 +20,15 @@ WORKDIR /app
 COPY requirements.txt /app
 
 RUN python3.8 -m pip install --upgrade pip
-RUN python3.8 -m pip install -r requirements.txt
+RUN python3.8 -m pip install -r dev-requirements.txt
 
 # For some reason we can't include the -e . dependency of the 
 #   catanatron package in the requirements.txt. So we install
 #   it separately, here:
 COPY . .
-
-RUN python3.8 -m pip install -e .
+RUN python3.8 -m pip install -e catanatron_core
+RUN python3.8 -m pip install -e catanatron_gym
+RUN python3.8 -m pip install -e catanatron_server
+RUN python3.8 -m pip install -e catanatron_experimental
 
 EXPOSE 8888

Dockerfile.web

@@ -6,11 +6,11 @@ ENV FLASK_ENV=development
 ENV FLASK_APP=catanatron_server
 ENV FLASK_RUN_HOST=0.0.0.0
 
-# We can't leverage Docker caching too much since we
-#   depend on the catanatron package via "-e ." in requirements.txt
-COPY . .
 RUN pip install --upgrade pip
-RUN pip install -r requirements.txt
+
+COPY . .
+RUN pip install -e catanatron_core
+RUN pip install -e catanatron_server
 
 EXPOSE 5000
 

Makefile

@@ -0,0 +1,36 @@
+clean: clean-build clean-pyc clean-test
+
+clean-build:
+	rm -fr build/
+	rm -fr dist/
+	rm -fr .eggs/
+	find . -name '*.egg-info' -exec rm -fr {} +
+	find . -name '*.egg' -exec rm -f {} +
+
+clean-pyc:
+	find . -name '*.pyc' -exec rm -f {} +
+	find . -name '*.pyo' -exec rm -f {} +
+	find . -name '__pycache__' -exec rm -fr {} +
+	# find . -name '*~' -exec rm -f {} +
+
+clean-test:
+	rm -fr .tox/
+	rm -f .coverage
+	rm -fr htmlcov/
+
+build-catanatron: clean
+	python catanatron_core/setup.py sdist bdist_wheel
+	ls -l dist
+	twine check dist/*
+
+
+build-catanatron-gym: clean
+	python catanatron_gym/setup.py sdist bdist_wheel
+	ls -l dist
+	twine check dist/*
+
+upload:
+	twine upload --repository-url https://test.pypi.org/legacy/ dist/*
+
+upload-production:
+	twine upload dist/*

README.md

@@ -19,7 +19,7 @@ Install with pip:
 pip install catanatron
 ```
 
-Make your own bot by implementing the following API (see examples in [catanatron/players](catanatron/players) and [experimental/machine_learning/players](experimental/machine_learning/players)):
+Make your own bot by implementing the following API (see examples in [catanatron_core/catanatron/players](catanatron_core/catanatron/players) and [catanatron_experimental/catanatron_experimental/machine_learning/players](catanatron_experimental/catanatron_experimental/machine_learning/players)):
 
 ```python
 from catanatron.game import Game
@@ -62,7 +62,7 @@ For watching these games in a UI see [watching games](#watching-games).
 
 ## Advanced Usage
 
-Cloning the repo and using directly will allow you to access additional tools not included in the core package. In particular, a web UI for watching games and a `experimental/play.py` script that provides a blueprint to run many games, collect summary statistics (avg vps, avg game length, etc...),
+Cloning the repo and using directly will allow you to access additional tools not included in the core package. In particular, a web UI for watching games and a `catanatron-play` CLI script that provides a blueprint to run many games, collect summary statistics (avg vps, avg game length, etc...),
 save game for viewing in browser, and/or generate machine learning datasets.
 
 Create a virtualenv with Python 3.8 and install requirements:
@@ -71,12 +71,16 @@ Create a virtualenv with Python 3.8 and install requirements:
 python3.8 -m venv venv
 source ./venv/bin/activate
 pip install -r dev-requirements.txt
+pip install -e catanatron_core
+pip install -e catanatron_server
+pip install -e catanatron_gym
+pip install -e catanatron_experimental
 ```
 
 Run games with the `play.py` script. It provides extra options you can explore with `--help`:
 
 ```
-python experimental/play.py --num=100
+catanatron-play --num=100
 ```
 
 Currently, we can execute one game in ~76 milliseconds.
@@ -172,13 +176,13 @@ ptw --ignore=tests/integration_tests/ --nobeep
 Generate data (GZIP CSVs of features and PGSQL rows) by running:
 
 ```
-python experimental/play.py --num=100 --outpath=my-data-path/
+catanatron-play --num=100 --outpath=my-data-path/
 ```
 
 You can then use this data to build a machine learning model, and then
 implement a `Player` subclass that implements the corresponding "predict"
 step of your model. There are some examples of these type of
-players in `experimental/machine_learning/players/reinforcement.py`.
+players in [reinforcement.py](catanatron_experimental/catanatron_experimental/machine_learning/players/reinforcement.py).
 
 # Appendix
 
@@ -252,7 +256,7 @@ docker run -it --rm -v $(realpath ./notebooks):/tf/notebooks -p 8888:8888 tensor
 ### Testing Performance
 
 ```
-python -m cProfile -o profile.pstats experimental/play.py --num=5
+python -m cProfile -o profile.pstats catanatron_experimental/catanatron_experimental/play.py --num=5
 snakeviz profile.pstats
 ```
 
@@ -273,13 +277,9 @@ In [3]: x.get_chunk(10)
 catanatron Package
 
 ```
-pip install twine
-rm -rf build
-rm -rf dist
-python setup.py sdist bdist_wheel
-twine check dist/*
-twine upload --repository-url https://test.pypi.org/legacy/ dist/*
-twine upload dist/*
+make build-catanatron
+make upload
+make upload-production
 ```
 
 ### Building Docs
@@ -292,7 +292,7 @@ sphinx-build -b html docs/source/ docs/build/html
 
 # Contributing
 
-I am new to Open Source Development, so open to suggestions on this section. The best contributions would be to make the core bot stronger by tinkering with the weights of each of the hand-crafted features in `experimental/machine_learning/players/minimax.py`, or coming up with new hand-crafted features! In particular, you can edit the `CONTENDER_WEIGHTS` and/or `contender_fn` function and run a command like: `python experimental/play.py --players=AB:2:False:C,AB:2 --num=100` to see if your changes improve the main bot.
+I am new to Open Source Development, so open to suggestions on this section. The best contributions would be to make the core bot stronger by tinkering with the weights of each of the hand-crafted features in [minimax.py](catanatron_experimental/catanatron_experimental/machine_learning/players/minimax.py), or coming up with new hand-crafted features! In particular, you can edit the `CONTENDER_WEIGHTS` and/or `contender_fn` function and run a command like: `catanatron-play --players=AB:2:False:C,AB:2 --num=100` to see if your changes improve the main bot.
 
 Here is also a list of ideas:
 

setup.py → catanatron_core/setup.py

@@ -1,27 +1,26 @@
 import setuptools
+import os
 
-with open("README.md", "r") as fh:
+readme_path = os.path.abspath(os.path.join(__file__, "..", "..", "README.md"))
+
+with open(readme_path, "r") as fh:
     long_description = fh.read()
 
 setuptools.setup(
     name="catanatron",
-    version="2.0.0",
+    version="3.0.0",
     author="Bryan Collazo",
     author_email="bcollazo2010@gmail.com",
     description="Fast Settlers of Catan Python Implementation",
     long_description=long_description,
     long_description_content_type="text/markdown",
     url="https://github.com/bcollazo/catanatron",
-    packages=setuptools.find_packages(
-        exclude=[
-            "catanatron_server",
-        ]
-    ),
+    packages=setuptools.find_packages(exclude=["catanatron_server"]),
     classifiers=[
         "Programming Language :: Python :: 3",
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
     ],
     python_requires=">=3.8",
-    install_requires=["networkx", "gym"],
+    install_requires=["networkx"],
 )

experimental/alphatan/benchmark.py → catanatron_experimental/catanatron_experimental/alphatan/benchmark.py

@@ -1,9 +1,9 @@
 from catanatron.game import Game
 
 from catanatron.models.player import Color, Player, RandomPlayer
-from experimental.alphatan.mcts import AlphaMCTS, game_end_value
+from catanatron_experimental.alphatan.mcts import AlphaMCTS, game_end_value
 
-from experimental.alphatan.simple_alpha_zero import (
+from catanatron_experimental.alphatan.simple_alpha_zero import (
     AlphaTan,
     create_model,
     load_replay_memory,

experimental/alphatan/mcts.py → catanatron_experimental/catanatron_experimental/alphatan/mcts.py

@@ -4,8 +4,10 @@
 import tensorflow as tf
 import numpy as np
 
-from experimental.machine_learning.players.reinforcement import ACTION_SPACE_SIZE
-from experimental.dqn_player import to_action_space, from_action_space
+from catanatron_experimental.machine_learning.players.reinforcement import (
+    ACTION_SPACE_SIZE,
+)
+from catanatron_experimental.dqn_player import to_action_space, from_action_space
 from catanatron_gym.features import create_sample_vector
 
 EPS = 1e-8

experimental/alphatan/offline_alpha_test.py → catanatron_experimental/catanatron_experimental/alphatan/offline_alpha_test.py

@@ -1,4 +1,6 @@
-from experimental.machine_learning.players.reinforcement import ACTION_SPACE_SIZE
+from catanatron_experimental.machine_learning.players.reinforcement import (
+    ACTION_SPACE_SIZE,
+)
 import time
 from pathlib import Path
 
@@ -9,7 +11,7 @@
 
 # Read datasets. Try to fit model
 from simple_alpha_zero import create_model, load_replay_memory, pit
-from experimental.machine_learning.board_tensor_features import (
+from catanatron_experimental.machine_learning.board_tensor_features import (
     HEIGHT,
     WIDTH,
     create_board_tensor,

experimental/alphatan/pit.py → catanatron_experimental/catanatron_experimental/alphatan/pit.py

@@ -1,13 +1,13 @@
 import uuid
 
-from experimental.alphatan.simple_alpha_zero import (
+from catanatron_experimental.alphatan.simple_alpha_zero import (
     AlphaTan,
     create_model,
     load_replay_memory,
     pit,
 )
 from catanatron.models.player import Color, Player, RandomPlayer
-from experimental.play import play_batch
+from catanatron_experimental.play import play_batch
 
 save_in_db = False
 model = create_model()

experimental/alphatan/simple_alpha_zero.py → catanatron_experimental/catanatron_experimental/alphatan/simple_alpha_zero.py

@@ -13,18 +13,18 @@
 import tensorflow as tf
 
 from catanatron.game import Game
-from experimental.alphatan.mcts import AlphaMCTS, game_end_value
-from experimental.machine_learning.utils import ensure_dir
+from catanatron_experimental.alphatan.mcts import AlphaMCTS, game_end_value
+from catanatron_experimental.machine_learning.utils import ensure_dir
 from catanatron.models.player import Color, Player, RandomPlayer
-from experimental.play import play_batch
+from catanatron_experimental.play import play_batch
 from catanatron_gym.envs.catanatron_env import (
     ACTIONS_ARRAY,
     ACTION_SPACE_SIZE,
     from_action_space,
     to_action_space,
 )
-from experimental.dqn_player import epsilon_greedy_policy
-from experimental.machine_learning.board_tensor_features import (
+from catanatron_experimental.dqn_player import epsilon_greedy_policy
+from catanatron_experimental.machine_learning.board_tensor_features import (
     CHANNELS,
     HEIGHT,
     WIDTH,
@@ -108,7 +108,7 @@ def main():
 
         # TODO: Save checkpoint
         model.save_weights(f"data/checkpoints/{name}")
-        model.save(f"experimental/models/{name}")
+        model.save(f"data/models/{name}")
 
 
 def save_replay_memory(data_directory, examples):

experimental/bayesian.py → catanatron_experimental/catanatron_experimental/bayesian.py

@@ -1,13 +1,13 @@
 from bayes_opt import BayesianOptimization
 from bayes_opt.logger import JSONLogger
 
-from experimental.machine_learning.players.minimax import (
+from catanatron_experimental.machine_learning.players.minimax import (
     AlphaBetaPlayer,
     DEFAULT_WEIGHTS,
     ValueFunctionPlayer,
 )
 from catanatron.models.player import Color
-from experimental.play import play_batch
+from catanatron_experimental.play import play_batch
 
 
 def black_box_function(a, b, c, d, e, f, g, h, i, j, k, l):

experimental/benchmarks/benchmark_bot_strength.py → catanatron_experimental/catanatron_experimental/benchmarks/benchmark_bot_strength.py

@@ -1,4 +1,4 @@
-from experimental.play import simulate
+from catanatron_experimental.play import simulate
 
 simulate(50, "AB:2,F", None, False, False)
 

experimental/cross-entropy-method.py → catanatron_experimental/catanatron_experimental/cross-entropy-method.py

@@ -6,8 +6,10 @@
 import tensorflow as tf
 import kerastuner as kt
 
-from experimental.machine_learning.players.reinforcement import ACTION_SPACE_SIZE
-from experimental.train import NUM_FEATURES
+from catanatron_experimental.machine_learning.players.reinforcement import (
+    ACTION_SPACE_SIZE,
+)
+from catanatron_experimental.train import NUM_FEATURES
 
 
 # ===== Configuration

experimental/data_logger.py → catanatron_experimental/catanatron_experimental/data_logger.py

@@ -7,7 +7,7 @@
     create_sample_vector,
     get_feature_ordering,
 )
-from experimental.machine_learning.board_tensor_features import (
+from catanatron_experimental.machine_learning.board_tensor_features import (
     CHANNELS,
     HEIGHT,
     WIDTH,

experimental/datasets.py → catanatron_experimental/catanatron_experimental/datasets.py

@@ -3,7 +3,11 @@
 import tensorflow as tf
 import pandas as pd
 
-from experimental.machine_learning.board_tensor_features import CHANNELS, HEIGHT, WIDTH
+from catanatron_experimental.machine_learning.board_tensor_features import (
+    CHANNELS,
+    HEIGHT,
+    WIDTH,
+)
 
 
 def read_data(