Add custom sorting for playlists
This commit is contained in:
parent
82d229346b
commit
e9e63f961c
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@ -1,5 +1,8 @@
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# Changelog
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# Changelog
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## bliss 0.3.5
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* Add custom sorting methods for playlist-making.
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## bliss 0.3.4
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## bliss 0.3.4
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* Bump ffmpeg's version to avoid building ffmpeg when building bliss.
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* Bump ffmpeg's version to avoid building ffmpeg when building bliss.
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@ -1,6 +1,6 @@
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[package]
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[package]
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name = "bliss-audio"
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name = "bliss-audio"
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version = "0.3.4"
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version = "0.3.5"
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authors = ["Polochon-street <polochonstreet@gmx.fr>"]
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authors = ["Polochon-street <polochonstreet@gmx.fr>"]
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edition = "2018"
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edition = "2018"
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license = "GPL-3.0-only"
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license = "GPL-3.0-only"
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154
src/distance.rs
154
src/distance.rs
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@ -7,10 +7,12 @@
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//! They will yield different styles of playlists, so don't hesitate to
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//! They will yield different styles of playlists, so don't hesitate to
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//! experiment with them if the default (euclidean distance for now) doesn't
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//! experiment with them if the default (euclidean distance for now) doesn't
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//! suit you.
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//! suit you.
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use crate::NUMBER_FEATURES;
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#[cfg(doc)]
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#[cfg(doc)]
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use crate::{Library, Song};
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use crate::Library;
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use crate::Song;
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use crate::NUMBER_FEATURES;
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use ndarray::{Array, Array1};
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use ndarray::{Array, Array1};
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use noisy_float::prelude::*;
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/// Convenience trait for user-defined distance metrics.
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/// Convenience trait for user-defined distance metrics.
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pub trait DistanceMetric: Fn(&Array1<f32>, &Array1<f32>) -> f32 {}
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pub trait DistanceMetric: Fn(&Array1<f32>, &Array1<f32>) -> f32 {}
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@ -36,10 +38,158 @@ pub fn cosine_distance(a: &Array1<f32>, b: &Array1<f32>) -> f32 {
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1. - similarity
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1. - similarity
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}
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}
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/// Sort `songs` in place by putting songs close to `first_song` first
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/// using the `distance` metric. Deduplicate identical songs.
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pub fn closest_to_first_song(
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first_song: &Song,
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songs: &mut Vec<Song>,
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distance: impl DistanceMetric,
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) {
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songs.sort_by_cached_key(|song| n32(first_song.custom_distance(song, &distance)));
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songs.dedup_by_key(|song| n32(first_song.custom_distance(song, &distance)));
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}
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/// Sort `songs` in place using the `distance` metric and ordering by
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/// the smallest distance between each song. Deduplicate identical songs.
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///
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/// If the generated playlist is `[song1, song2, song3, song4]`, it means
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/// song2 is closest to song1, song3 is closest to song2, and song4 is closest
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/// to song3.
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pub fn song_to_song(first_song: &Song, songs: &mut Vec<Song>, distance: impl DistanceMetric) {
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let mut new_songs = vec![first_song.to_owned()];
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let mut song = first_song.to_owned();
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loop {
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if songs.is_empty() {
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break;
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}
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songs
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.retain(|s| n32(song.custom_distance(s, &distance)) != 0.);
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songs.sort_by_key(|s| n32(song.custom_distance(s, &distance)));
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song = songs.remove(0);
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new_songs.push(song.to_owned());
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}
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*songs = new_songs;
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}
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#[cfg(test)]
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#[cfg(test)]
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mod test {
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mod test {
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use super::*;
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use super::*;
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use crate::Analysis;
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use ndarray::arr1;
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use ndarray::arr1;
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use std::path::Path;
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#[test]
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fn test_song_to_song() {
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let first_song = Song {
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path: Path::new("path-to-first").to_path_buf(),
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analysis: Analysis::new([
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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]),
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..Default::default()
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};
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let first_song_dupe = Song {
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path: Path::new("path-to-dupe").to_path_buf(),
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analysis: Analysis::new([
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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]),
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..Default::default()
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};
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let second_song = Song {
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path: Path::new("path-to-second").to_path_buf(),
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analysis: Analysis::new([
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2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 1.9, 1., 1., 1.,
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]),
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..Default::default()
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};
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let third_song = Song {
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path: Path::new("path-to-third").to_path_buf(),
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analysis: Analysis::new([
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2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.5, 1., 1., 1.,
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]),
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..Default::default()
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};
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let fourth_song = Song {
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path: Path::new("path-to-fourth").to_path_buf(),
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analysis: Analysis::new([
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2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 1., 1., 1.,
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]),
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..Default::default()
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};
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let mut songs = vec![
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first_song.to_owned(),
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first_song_dupe.to_owned(),
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second_song.to_owned(),
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third_song.to_owned(),
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fourth_song.to_owned(),
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];
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song_to_song(&first_song, &mut songs, euclidean_distance);
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assert_eq!(
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songs,
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vec![first_song, second_song, third_song, fourth_song],
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);
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}
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#[test]
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fn test_sort_closest_to_first_song() {
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let first_song = Song {
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path: Path::new("path-to-first").to_path_buf(),
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analysis: Analysis::new([
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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]),
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..Default::default()
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};
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let first_song_dupe = Song {
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path: Path::new("path-to-dupe").to_path_buf(),
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analysis: Analysis::new([
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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]),
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..Default::default()
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};
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let second_song = Song {
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path: Path::new("path-to-second").to_path_buf(),
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analysis: Analysis::new([
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2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 1.9, 1., 1., 1.,
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]),
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..Default::default()
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};
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let third_song = Song {
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path: Path::new("path-to-third").to_path_buf(),
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analysis: Analysis::new([
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2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.5, 1., 1., 1.,
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]),
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..Default::default()
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};
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let fourth_song = Song {
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path: Path::new("path-to-fourth").to_path_buf(),
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analysis: Analysis::new([
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2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 1., 1., 1.,
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]),
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..Default::default()
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};
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let fifth_song = Song {
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path: Path::new("path-to-fifth").to_path_buf(),
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analysis: Analysis::new([
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2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 1., 1., 1.,
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]),
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..Default::default()
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};
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let mut songs = vec![
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first_song.to_owned(),
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first_song_dupe.to_owned(),
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second_song.to_owned(),
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third_song.to_owned(),
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fourth_song.to_owned(),
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fifth_song.to_owned(),
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];
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closest_to_first_song(&first_song, &mut songs, euclidean_distance);
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assert_eq!(
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songs,
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vec![first_song, second_song, fourth_song, third_song],
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);
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}
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#[test]
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#[test]
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fn test_euclidean_distance() {
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fn test_euclidean_distance() {
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118
src/library.rs
118
src/library.rs
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//! MPD](https://github.com/Polochon-street/blissify-rs) could also be useful.
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//! MPD](https://github.com/Polochon-street/blissify-rs) could also be useful.
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#[cfg(doc)]
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#[cfg(doc)]
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use crate::distance;
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use crate::distance;
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use crate::distance::DistanceMetric;
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use crate::distance::{closest_to_first_song, DistanceMetric, euclidean_distance};
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use crate::{BlissError, BlissResult, Song};
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use crate::{BlissError, BlissResult, Song};
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use log::{debug, error, info};
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use log::{debug, error, info};
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use noisy_float::prelude::*;
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use std::sync::mpsc;
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use std::sync::mpsc;
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use std::sync::mpsc::{Receiver, Sender};
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use std::sync::mpsc::{Receiver, Sender};
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use std::thread;
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use std::thread;
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/// once.
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/// once.
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fn get_stored_songs(&self) -> BlissResult<Vec<Song>>;
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fn get_stored_songs(&self) -> BlissResult<Vec<Song>>;
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/// Return a list of songs that are similar to ``first_song``.
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/// Return a list of `playlist_length` songs that are similar
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/// to ``first_song``, deduplicating identical songs.
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///
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///
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/// # Arguments
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/// # Arguments
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///
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///
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///
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///
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/// # Returns
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/// # Returns
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///
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///
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/// A vector of `playlist_length` Songs, including `first_song`, that you
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/// A vector of `playlist_length` songs, including `first_song`, that you
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/// most likely want to plug in your audio player by using something like
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/// most likely want to plug in your audio player by using something like
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/// `ret.map(|song| song.path.to_owned()).collect::<Vec<String>>()`.
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/// `ret.map(|song| song.path.to_owned()).collect::<Vec<String>>()`.
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// TODO return an iterator and not a Vec
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fn playlist_from_song(
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fn playlist_from_song(
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&self,
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&self,
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first_song: Song,
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first_song: Song,
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playlist_length: usize,
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playlist_length: usize,
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) -> BlissResult<Vec<Song>> {
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) -> BlissResult<Vec<Song>> {
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let mut songs = self.get_stored_songs()?;
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let playlist = self.playlist_from_song_custom(
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songs.sort_by_cached_key(|song| n32(first_song.distance(&song)));
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first_song,
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playlist_length,
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euclidean_distance,
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closest_to_first_song,
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)?;
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let playlist = songs
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.into_iter()
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.take(playlist_length)
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.collect::<Vec<Song>>();
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debug!(
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debug!(
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"Playlist created: {}",
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"Playlist created: {}",
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playlist
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playlist
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}
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}
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/// Return a list of songs that are similar to ``first_song``, using a
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/// Return a list of songs that are similar to ``first_song``, using a
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/// custom distance metric.
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/// custom distance metric and deduplicating indentical songs.
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///
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///
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/// # Arguments
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/// # Arguments
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///
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///
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playlist_length: usize,
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playlist_length: usize,
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distance: impl DistanceMetric,
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distance: impl DistanceMetric,
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) -> BlissResult<Vec<Song>> {
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) -> BlissResult<Vec<Song>> {
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let mut songs = self.get_stored_songs()?;
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let playlist = self.playlist_from_song_custom(
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songs.sort_by_cached_key(|song| n32(first_song.custom_distance(&song, &distance)));
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first_song,
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playlist_length,
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distance,
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closest_to_first_song,
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)?;
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let playlist = songs
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.into_iter()
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.take(playlist_length)
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.collect::<Vec<Song>>();
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debug!(
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debug!(
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"Playlist created: {}",
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"Playlist created: {}",
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playlist
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playlist
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@ -116,6 +117,44 @@ pub trait Library {
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Ok(playlist)
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Ok(playlist)
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}
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}
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/// Return a playlist of songs, starting with `first_song`, sorted using
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/// the custom `sort` function, and the custom `distance` metric.
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///
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/// # Arguments
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///
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/// * `first_song` - The song the playlist will be built from.
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/// * `playlist_length` - The playlist length. If there are not enough
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/// songs in the library, it will be truncated to the size of the library.
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/// * `distance` - a user-supplied valid distance metric, either taken
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/// from the [distance](distance) module, or made from scratch.
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/// * `sort` - a user-supplied sorting function that uses the `distance`
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/// metric, either taken from the [distance](module), or made from
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/// scratch.
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///
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/// # Returns
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///
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/// A vector of `playlist_length` Songs, including `first_song`, that you
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/// most likely want to plug in your audio player by using something like
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/// `ret.map(|song| song.path.to_owned()).collect::<Vec<String>>()`.
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fn playlist_from_song_custom<F, G>(
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&self,
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first_song: Song,
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playlist_length: usize,
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distance: G,
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mut sort: F,
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) -> BlissResult<Vec<Song>>
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where
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F: FnMut(&Song, &mut Vec<Song>, G),
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G: DistanceMetric,
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{
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let mut songs = self.get_stored_songs()?;
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sort(&first_song, &mut songs, distance);
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Ok(songs
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.into_iter()
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.take(playlist_length)
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.collect::<Vec<Song>>())
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}
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/// Analyze and store songs in `paths`, using `store_song` and
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/// Analyze and store songs in `paths`, using `store_song` and
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/// `store_error_song` implementations.
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/// `store_error_song` implementations.
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///
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///
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@ -595,4 +634,49 @@ mod test {
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.unwrap()
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.unwrap()
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);
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);
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}
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}
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fn custom_sort(_: &Song, songs: &mut Vec<Song>, _: impl DistanceMetric) {
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songs.sort_by_key(|song| song.path.to_owned());
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}
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#[test]
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fn test_playlist_from_song_custom() {
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let mut test_library = TestLibrary::default();
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let first_song = Song {
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path: Path::new("path-to-first").to_path_buf(),
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analysis: Analysis::new([0.; 20]),
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..Default::default()
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};
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|
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let second_song = Song {
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|
path: Path::new("path-to-second").to_path_buf(),
|
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analysis: Analysis::new([0.1; 20]),
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..Default::default()
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};
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let third_song = Song {
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||||||
|
path: Path::new("path-to-third").to_path_buf(),
|
||||||
|
analysis: Analysis::new([10.; 20]),
|
||||||
|
..Default::default()
|
||||||
|
};
|
||||||
|
|
||||||
|
let fourth_song = Song {
|
||||||
|
path: Path::new("path-to-fourth").to_path_buf(),
|
||||||
|
analysis: Analysis::new([20.; 20]),
|
||||||
|
..Default::default()
|
||||||
|
};
|
||||||
|
|
||||||
|
test_library.internal_storage = vec![
|
||||||
|
first_song.to_owned(),
|
||||||
|
fourth_song.to_owned(),
|
||||||
|
third_song.to_owned(),
|
||||||
|
second_song.to_owned(),
|
||||||
|
];
|
||||||
|
assert_eq!(
|
||||||
|
vec![first_song.to_owned(), fourth_song, second_song],
|
||||||
|
test_library
|
||||||
|
.playlist_from_song_custom(first_song, 3, custom_distance, custom_sort)
|
||||||
|
.unwrap()
|
||||||
|
);
|
||||||
|
}
|
||||||
}
|
}
|
||||||
|
|
Loading…
Reference in New Issue
Block a user