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https://github.com/eve-ning/opal
Rhythm Game Score Estimation through Neural Collaborative Filtering
https://github.com/eve-ning/opal
osu osumania pytorch pytorch-lightning streamlit
Last synced: 9 days ago
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Rhythm Game Score Estimation through Neural Collaborative Filtering
- Host: GitHub
- URL: https://github.com/eve-ning/opal
- Owner: Eve-ning
- Created: 2022-07-02T09:00:48.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-10-01T01:43:33.000Z (about 1 year ago)
- Last Synced: 2024-10-15T18:18:44.461Z (21 days ago)
- Topics: osu, osumania, pytorch, pytorch-lightning, streamlit
- Language: Python
- Homepage: https://opal-ai.streamlit.app/
- Size: 98.3 MB
- Stars: 8
- Watchers: 2
- Forks: 0
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
Awesome Lists containing this project
README
# :arrow_forward: [**Try Out Opal on Streamlit**](https://opal-ai.streamlit.app/)
[](https://pypi.org/project/opal-net/)
[](https://github.com/Eve-ning/opal/actions/workflows/pipeline-test.yml)
# :comet: opal-net
opal is an accuracy-prediction model.
It uses a Matrix Factorization branch & Multi-layered Perceptron branch to learn associations between user and maps,
then use those associations to predict new scores never before seen.
## :hourglass_flowing_sand: Project Status
Currently, it's in its early access, that means, it'll have many problems!
However, we're working on it to minimize these issues o wo)b
## :arrow_double_down: Dataset Used
I used the top 10K mania users data from https://data.ppy.sh.
After preprocessing, we use
- ~10m scores for training
- ~1m scores for validation and testing each
After preprocessing, we found ~30K valid users, ~10K valid maps
This models can thus help predict ~300m unplayed scores!
### Users
We deem a player on separate years as a different user. This is to reflect
the improvement of the player after time.
## :high_brightness: Usage
To use this, install `opal-net`
```bash
pip install opal-net
```
Then in a python script
> Tip: GPU doesn't speed this up significantly, you can use a CPU.
```py
from opal import OpalNet
# Load in the model
# You can explicitly specify map_location='cpu' or 'cuda' in map_location=...
opal = OpalNet.load()
# You can predict a single instance.
#
# The 1st arg: "/",
# The 2nd arg: "/"
# is the year of the user to test.
# can be {-1, 0, or 1} for {HT, NT, DT}
#
# For example:
# Predict Evening on Year 2020, on the map Triumph & Regret [Regret] at Double Time
pred = opal.predict("2193881/2020", "767046/1")
# You can predict multiple entries at the same time. This is much faster that looping the above.
# Note that both lists must be of the same length!
# Note: If you're predicting millions, partition the predictions to reduce GPU memory usage!
preds = opal.predict(["2193881/2020", "2193881/2017"], ["767046/0", "767046/1"])
# Note that if the prediction doesn't exist, then it'll raise a ValueError
try:
opal.predict("2193881/2018", "767046/0")
except ValueError:
print("Prediction Failed!")
```
## :brain: AlphaOsu!
Currently, opal doesn't provide recommendations, however, you can try out [AlphaOsu!](https://alphaosu.keytoix.vip/).
- [AlphaOsu! GitHub](https://github.com/AlphaOSU)
- [Support AlphaOsu!](https://alphaosu.keytoix.vip/support)
## Annex
### Why not Score Metric?
Score is not straightforward to calculate, and may be difficult to debug. Furthermore, score isn't of interest when
calculating performance points anymore.
[osu!mania ScoreV1 Reference](https://osu.ppy.sh/wiki/en/Gameplay/Score/ScoreV1/osu%21mania)