The main entry point to the project is aib_analysis/main.py
To set up the project please run: Installing dependencies Make sure you have python and poetry installed (poetry is a python package manager).
If you don't have poetry installed run the below:
sudo apt update -y
sudo apt install -y pipx
pipx install poetry
# Optional
poetry config virtualenvs.in-project true
Inside the terminal, go to the directory you cloned the repository into and run the following command:
poetry install
to install all required dependencies.
To run the analysis please execute:
poetry run streamlit run aib_analysis/main.py
This will bring up the visuals for the analysis. Make sure you have chosen the right input data in this script.
Check the logs for warnings to see edge cases that have come up.
Make sure to restart the site or click "clear cache" in the triple dot menu on the site if you change the underlying data or simulated_tournament loading/intialization code.
The project is focused around the SimulatedTournament object. This is init
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ialized with a number of Forecast objects (see data_models.py) and used to create other parts of a tournament (Users, Scores, etc). Every data analysis item we care about is just a tournament of some sort. Often this is a filter, aggregation, intersection, etc of forecasts from another tournament, but even a comparison of 2 people is a tournament.
Refactor advantages
- Pydantic model validation (type safety, and consistency checks at initialization time)
- Easier to unit test
- More reusable (e.g. We can use this structure for regular peer score comparisons in the future)
- Git diffs are easier to read
- We can eventually publish our results as an interactive app if we want to (and if not, we can more easily check and interact with the data ourselves)