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https://github.com/meilisearch/MeiliSearch
synced 2024-11-04 20:18:55 +01:00
Reworked the best proximity algo a little bit
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302866ad73
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@ -3,8 +3,6 @@ use std::time::Instant;
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use pathfinding::directed::astar::astar_bag;
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use crate::SmallVec16;
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const ONE_ATTRIBUTE: u32 = 1000;
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const MAX_DISTANCE: u32 = 8;
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@ -28,47 +26,57 @@ fn extract_position(position: u32) -> (u32, u32) {
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(position / ONE_ATTRIBUTE, position % ONE_ATTRIBUTE)
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}
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#[derive(Debug, Default, Clone, PartialOrd, Ord, PartialEq, Eq, Hash)]
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struct Path(SmallVec16<u32>);
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#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord, Hash)]
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enum Node {
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// Is this node is the first node.
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Uninit,
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Init {
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// The layer where this node located.
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layer: usize,
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// The position where this node is located.
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position: u32,
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},
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}
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impl Path {
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// TODO we must skip the successors that have already been sent
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impl Node {
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// TODO we must skip the successors that have already been seen
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// TODO we must skip the successors that doesn't return any documents
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// this way we are able to skip entire paths
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fn successors(&self, positions: &[Vec<u32>], best_proximity: u32) -> Vec<(Path, u32)> {
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let next_positions = match positions.get(self.0.len()) {
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Some(positions) => positions,
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None => return vec![],
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};
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next_positions.iter()
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.filter_map(|p| {
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let mut path = self.clone();
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path.0.push(*p);
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let proximity = path.proximity();
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if path.is_complete(positions) && proximity < best_proximity {
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None
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} else {
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Some((path, proximity))
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}
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})
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.inspect(|p| eprintln!("{:?}", p))
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.collect()
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}
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fn proximity(&self) -> u32 {
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self.0.windows(2).map(|ps| positions_proximity(ps[0], ps[1])).sum::<u32>()
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}
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fn heuristic(&self, positions: &[Vec<u32>]) -> u32 {
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let remaining = (positions.len() - self.0.len()) as u32;
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self.proximity() + remaining * MAX_DISTANCE
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fn successors(&self, positions: &[Vec<u32>], best_proximity: u32) -> Vec<(Node, u32)> {
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match self {
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Node::Uninit => {
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positions[0].iter().map(|p| (Node::Init { layer: 0, position: *p }, 0)).collect()
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},
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// We reached the highest layer
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n @ Node::Init { .. } if n.is_complete(positions) => vec![],
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Node::Init { layer, position } => {
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let layer = layer + 1;
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positions[layer].iter().filter_map(|p| {
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let proximity = positions_proximity(*position, *p);
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let node = Node::Init { layer, position: *p };
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// We do not produce the nodes we have already seen in previous iterations loops.
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if node.is_complete(positions) && proximity < best_proximity {
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None
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} else {
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Some((node, proximity))
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}
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}).collect()
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}
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}
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}
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fn is_complete(&self, positions: &[Vec<u32>]) -> bool {
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let res = positions.len() == self.0.len();
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eprintln!("is_complete: {:?} {}", self, res);
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res
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match self {
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Node::Uninit => false,
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Node::Init { layer, .. } => *layer == positions.len() - 1,
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}
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}
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fn position(&self) -> Option<u32> {
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match self {
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Node::Uninit => None,
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Node::Init { position, .. } => Some(*position),
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}
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}
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}
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@ -93,13 +101,11 @@ impl Iterator for BestProximity {
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return None;
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}
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// We start with nothing
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let start = Path::default();
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let result = astar_bag(
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&start,
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|p| p.successors(&self.positions, self.best_proximity),
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|p| p.heuristic(&self.positions),
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|p| p.is_complete(&self.positions), // success
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&Node::Uninit, // start
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|n| n.successors(&self.positions, self.best_proximity),
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|_| 0, // heuristic
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|n| n.is_complete(&self.positions), // success
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);
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eprintln!("BestProximity::next() took {:.02?}", before.elapsed());
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@ -108,9 +114,7 @@ impl Iterator for BestProximity {
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Some((paths, proximity)) => {
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self.best_proximity = proximity + 1;
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// We retrieve the last path that we convert into a Vec
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let paths: Vec<_> = paths.map(|p| {
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p.last().unwrap().0.to_vec()
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}).collect();
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let paths: Vec<_> = paths.map(|p| p.iter().filter_map(Node::position).collect()).collect();
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eprintln!("result: {} {:?}", proximity, paths);
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Some((proximity, paths))
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},
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