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Advanced methods of optimization
There is a python package for the optimizer. This package has torch as a dependency, so note it might take about half a gigabyte of space.
Install the package with the command:
python -m pip install fsrs_optimizer
You should upgrade regularly to make sure you have the most recent version of FSRS-Optimizer:
python -m pip install fsrs_optimizer --upgrade
Export your deck and cd into the folder to which you exported it.
Then you can run:
python -m fsrs_optimizer "package.(colpkg/apkg)"
You can also list multiple files, e.g.:
python -m fsrs_optimizer "file1.akpg" "file2.apkg"
Wildcards are supported:
python -m fsrs_optimizer *.apkg
There are certain options which are as follows:
options:
-h, --help show this help message and exit
-y, --yes, --no-yes If set automatically defaults on all stdin settings.
-o OUT, --out OUT File to APPEND the automatically generated profile to.
Download and install this version of the anki helper addon either by git cloning it into the anki addons folder or downloading it as a zip and extracting the zip into the anki addons folder.
Install the optimizer locally.
Please pay attention to the popup.
After that has downloaded and installed you should be able to run the optimizer from within anki.
Press the cog next to any given deck and hit the optimize option.
Anki may then hang a small while while it loads the optimizer.
Hit yes to find the optimum retention, Hit no to not or hit cancel to pick a different deck.
If all is well you should then get a toolbar popup which tells you the progress of the optimization.
You should then get the stats in a format which is easy to copy into the javascript scheduler.
These values are saved in the addons config file which can be found and edited in anki if you want to change the retention manually for example.
If there are any issues with this please mention them on this pull request here.
My representative paper at ACMKDD: A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling
My fantastic research experience on spaced repetition algorithm: How did I publish a paper in ACMKDD as an undergraduate?
The largest open-source dataset on spaced repetition with time-series features: open-spaced-repetition/FSRS-Anki-20k