mpdstats is a plugin for beets that collects statistics about your listening
habits from MPD. It collects the following information about tracks:
- play_count: The number of times you fully listened to this track.
- skip_count: The number of times you skipped this track.
- last_played: UNIX timestamp when you last played this track.
- rating: A rating based on play_count and skip_count.
This plugin requires the python-mpd2 library in order to talk to the MPD server.
Install the library from pip, like so:
$ pip install python-mpd2
mpdstats plugin to your configuration (see Using Plugins).
mpdstats command to fire it up:
$ beet mpdstats
To configure the plugin, make an
mpd: section in your
configuration file. The available options are:
- host: The MPD server hostname.
- port: The MPD server port. Default: 6600.
- password: The MPD server password. Default: None.
- music_directory: If your MPD library is at a different location from the beets library (e.g., because one is mounted on a NFS share), specify the path here. Default: The beets library directory.
- rating: Enable rating updates.
- rating_mix: Tune the way rating is calculated (see below). Default: 0.75.
A Word on Ratings¶
Ratings are calculated based on the play_count, skip_count and the last action (play or skip). It consists in one part of a stable_rating and in another part on a rolling_rating. The stable_rating is calculated like this:
stable_rating = (play_count + 1.0) / (play_count + skip_count + 2.0)
So if the play_count equals the skip_count, the stable_rating is always 0.5. More play_counts adjust the rating up to 1.0. More skip_counts adjust it down to 0.0. One of the disadvantages of this rating system, is that it doesn’t really cover recent developments. e.g. a song that you loved last year and played over 50 times will keep a high rating even if you skipped it the last 10 times. That’s were the rolling_rating comes in.
If a song has been fully played, the rolling_rating is calculated like this:
rolling_rating = old_rating + (1.0 - old_rating) / 2.0
If a song has been skipped, like this:
rolling_rating = old_rating - old_rating / 2.0
So rolling_rating adapts pretty fast to recent developments. But it’s too fast. Taking the example from above, your old favorite with 50 plays will get a negative rating (<0.5) the first time you skip it. Also not good.
To take the best of both worlds, we mix the ratings together with the
rating_mix factor. A
rating_mix of 0.0 means all
rolling and 1.0 means all stable. We found 0.75 to be a good compromise,
but fell free to play with that.