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View Code? Open in Web Editor NEWRhythm Game Score Estimation through Neural Collaborative Filtering
Rhythm Game Score Estimation through Neural Collaborative Filtering
I just found that the newest model didn't filter out non-active maps and players, this was a mistake due to the main repo using 0 for samples.
On this topic, maybe we should have a debug option for pipeline.sh
and its stages?
Currently, we just use the whole dataset, which can be
To even the training, we should try to uniformly sample across the sample space for a more representative measure and training process
Additionally, if it's possible, fetch env vars from shell
Not sure why, but The Living Tombstone - Nippontradamus (Everest Hope) [October's 7K Insane].osu
is highly rated.
This caused heavy bias towards maps that are non-competitive.
Currently, we have train.py
setting the model path on the .env
file.
I don't think it's a great approach, as it widens the scope of train, which should just train the model, make the model, and possibly return the model path. The problem is that the model path return is not trivial, thus we resorted to I/O-ing to the .env
file.
A better solution is to specify a unique model name, which is the pipeline run id, then we can grep
the model from opal/models
.
Currently, maps such as Backbeat Maniac & Perthed are highly overrated by the rankings.
For the future purposes of beatmap difficulty estimation, we should simply remove them.
To do so, we'll estimate the svness of each map, to find an optimal threshold where a map is "sv" or not.
Currently in #29 , the preprocessing is done via a run.sh
, while others are done with docker compose up --build
.
This inconsistency is ugly, and furthermore, it can be annoying to dig into a long run.sh
Currently, the pipeline script in #29 ALWAYS runs the dataset preprocessing. This can be wasteful especially if
An idea would be to hash the SQL+Dataset string as a unique identifier for the dataset. However, it'd be good if we can also include how the dataset was generated, i.e. metadata for the .csv
.
There should be enough neurons in the network to reasonably yield a distribution, furthermore i've regularized it.
Let's see if this works
Currently, we threshold maps with a hard SR. which is fine, however,
We need to threshold maps w.r.t. map and speed.
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