Merge pull request #577 from alan-turing-institute/develop

REG Hack Week Changes

.github/workflows/docker-ci.yml

@@ -0,0 +1,36 @@
+name: CI
+
+# Enable Buildkit and let compose use it to speed up image building
+env:
+  DOCKER_BUILDKIT: 1
+  COMPOSE_DOCKER_CLI_BUILD: 1
+
+on:
+  pull_request:
+    branches: [ "main", "develop" ]
+
+  push:
+    branches: [ "main", "develop" ]
+
+concurrency:
+  group: ${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+
+      - name: Checkout Code Repository
+        uses: actions/checkout@main
+
+      - name: Build AIrsenal
+        run:  docker build -t airsenal .
+
+      - name: Code quality checks
+        run: |
+          docker run airsenal poetry run pre-commit run --all-files
+      - name: Tests
+        run: |
+          docker run -e "FPL_TEAM_ID=2779516" airsenal poetry run pytest

.pre-commit-config.yaml

@@ -2,15 +2,15 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
 -   repo: https://github.com/pycqa/isort
-    rev: 5.10.1
+    rev: 5.12.0
     hooks:
     - id: isort
 -   repo: https://github.com/ambv/black
     rev: 22.3.0
     hooks:
     - id: black
       language_version: python3.9
--   repo: https://gitlab.com/pycqa/flake8
+-   repo: https://github.com/pycqa/flake8
     rev: 3.9.2
     hooks:
     - id: flake8

Dockerfile

@@ -1,10 +1,13 @@
-FROM python:3.8-slim-buster
+FROM python:3.9-slim-buster
+
+RUN apt-get update && \
+    apt-get install build-essential git sqlite3 curl -y && \
+    pip install -U setuptools pygmo poetry
 
 WORKDIR /airsenal
 
 COPY . /airsenal
 
-RUN apt-get update && apt-get install build-essential git sqlite3 -y && \
-    pip install pygmo && pip install .
+RUN poetry install --extras "api"
 
-CMD ["airsenal_run_pipeline"]
+CMD ["poetry", "run", "airsenal_run_pipeline"]

NOTES.md

@@ -40,7 +40,6 @@ The database is filled with data from the previous three FPL seasons (stored in
 
 - **TransferSuggestion:** Stores recommended transfers from AIrsenal optimisation runs.
 
-
 The database schema is defined using `sqlalchemy` in `airsenal.framework.schema.py`.
 
 ### Interacting with the FPL API
@@ -97,12 +96,11 @@ Player points predictions are generated from three main components:
 
 ### Team Model
 
-BPL package (written by Angus, one of the original AIrsenal developers): https://github.com/anguswilliams91/bpl
-
+BPL package (written by Angus, one of the original AIrsenal developers): https://github.com/anguswilliams91/bpl-next
 
 ### Player Model
 
-Stan model definition: `airsenal/stan/player_forecasts.stan`
+NumPyro model definition: `airsenal/framework/player_model.py`
 
 ### How Predicted Points are Calculated
 

README.md

@@ -12,9 +12,9 @@ We welcome contributions and comments - if you'd like to join the AIrsenal commu
 
 We have made a mini-league **"Prem-AI League"** for players using this software.  To join, login to the FPL website, and navigate to the page to join a league: https://fantasy.premierleague.com/leagues/create-join then click "join a league or cup".
 The code to join is: **8wec9c**.
-Hope to see your AI team there! :)
+Hope to see your AI team there!! :)
 
-Our own AIrsenal team's id for the 2022/23 season is **[2779516](https://fantasy.premierleague.com/entry/2779516/history)**.
+Our own AIrsenal team's ID for the 2022/23 season is **[2779516](https://fantasy.premierleague.com/entry/2779516/history)**.
 
 ## Installation
 
@@ -63,23 +63,51 @@ AIrsenal has an optional optimisation algorithm using the PyGMO package, which i
 
 Build the docker-image:
 
-```shell
-docker build -t airsenal .
+```console
+$ docker build -t airsenal .
 ```
 
+If `docker build` fails due to a `RuntimeError` like 
+
+```console
+Unable to find installation candidates for jaxlib (0.4.11)
+```
+
+this may be a lack of maintained versions of a package for `m1` on Linux.
+
+A slow solution for this error is to force a `linux/amd64` build like 
+
+```console
+$ docker build --platform linux/amd64 -t airsenal .
+```
+
+If that fails try 
+
+```console
+$ docker build --platform linux/amd64 --no-cache -t airsenal .
+```
+
+See ticket [#547](https://github.com/alan-turing-institute/AIrsenal/issues/574) for latest on this issue.
+
 Create a volume for data persistance:
 
-```shell
-docker volume create airsenal_data
+```console
+$ docker volume create airsenal_data
 ```
 
 Run commands with your configuration as environment variables, eg:
 
-```shell
-docker run -it --rm -v airsenal_data:/tmp/ -e "FPL_TEAM_ID=<your_id>" -e "AIRSENAL_HOME=/tmp" airsenal [airsenal_run_pipeline]
+```console
+$ docker run -it --rm -v airsenal_data:/tmp/ -e "FPL_TEAM_ID=<your_id>" -e "AIRSENAL_HOME=/tmp" airsenal bash
+```
+
+or 
+
+```console
+$ docker run -it --rm -v airsenal_data:/tmp/ -e "FPL_TEAM_ID=<your_id>" -e "AIRSENAL_HOME=/tmp" airsenal airsenal_run_pipeline
 ```
 
-```airsenal_run_pipeline``` is the default command.
+`airsenal_run_pipeline` is the default command.
 
 ## Optional dependencies
 
@@ -159,7 +187,7 @@ with more recent data, using the command
 airsenal_update_db
 ```
 
-The next step is to use the team- and player-level Stan models to predict the expected points for all players for the next fixtures.  This is done using the command
+The next step is to use the team- and player-level NumPyro models to predict the expected points for all players for the next fixtures.  This is done using the command
 
 ```shell
 airsenal_run_prediction --weeks_ahead 3

airsenal/data/fifa_team_ratings_1718.csv

@@ -18,4 +18,9 @@ Bournemouth,76,74,75,75
 Burnley,74,75,75,75
 Newcastle United,74,75,75,75
 Brighton & Hove Albion,74,74,73,74
-Huddersfield Town,73,73,72,73
+Huddersfield Town,73,73,72,73
+Norwich City,73,73,74,73
+Aston Villa,74,73,73,73
+Sunderland,81,76,73,76
+Hull City,76,74,75,75
+Middlesbrough,76,74,74,75