From 9999a631bf1082fc9897c7507a264c50c91ce30f Mon Sep 17 00:00:00 2001 From: sven Date: Mon, 27 Jul 2020 22:02:33 -0700 Subject: [PATCH 01/10] all in there, but some bugs --- goko/src/covertree/builders.rs | 259 ++++++++++++++-------- goko/src/covertree/data_caches.rs | 239 ++++++++++++++++++-- goko/src/covertree/layer.rs | 4 +- goko/src/covertree/mod.rs | 6 +- goko/src/covertree/node.rs | 56 +++-- goko/src/covertree/tree.rs | 161 ++++++++------ goko/src/lib.rs | 1 - goko/src/monomap/inner.rs | 4 +- goko/src/monomap/mod.rs | 4 +- goko/src/plugins/distributions/mod.rs | 12 +- goko/src/plugins/labels.rs | 60 +++-- goko/src/query_interface/mod.rs | 136 +++++++----- pointcloud/src/base_traits.rs | 33 ++- pointcloud/src/data_sources/memmap_ram.rs | 9 +- pointcloud/src/glued_data_cloud.rs | 27 ++- pointcloud/src/label_sources/mod.rs | 39 ++-- pointcloud/src/lib.rs | 1 - pointcloud/src/loaders/yaml_loaders.rs | 34 +-- pointcloud/src/summaries/mod.rs | 18 +- pygoko/src/layer.rs | 24 +- pygoko/src/node.rs | 8 +- pygoko/src/plugins.rs | 5 +- pygoko/src/tree.rs | 35 ++- pygoko/tests/ember2018.py | 2 +- 24 files changed, 778 insertions(+), 399 deletions(-) diff --git a/goko/src/covertree/builders.rs b/goko/src/covertree/builders.rs index 5ccd0f5..b5347ff 100644 --- a/goko/src/covertree/builders.rs +++ b/goko/src/covertree/builders.rs @@ -17,15 +17,15 @@ * under the License. */ -use crate::plugins::TreePluginSet; -use crate::*; -use super::*; use super::data_caches::*; use super::layer::*; use super::node::*; +use super::*; +use crate::plugins::TreePluginSet; +use crate::*; use pbr::ProgressBar; -use std::fs::read_to_string; use std::cmp::{max, min}; +use std::fs::read_to_string; use std::path::Path; use std::sync::{atomic, Arc, RwLock}; use yaml_rust::YamlLoader; @@ -45,8 +45,15 @@ struct BuilderNode { type NodeSplitResult = GokoResult<(i32, PointIndex, CoverNode)>; impl BuilderNode { - fn new(parameters: &CoverTreeParameters) -> GokoResult { - let covered = CoveredData::new::(¶meters.point_cloud)?; + fn new( + parameters: &CoverTreeParameters, + partition: &str, + ) -> GokoResult { + let covered = if partition == "first" { + CoveredData::FirstCoveredData(FirstCoveredData::new::(¶meters.point_cloud)?) + } else { + CoveredData::NearestCoveredData(NearestCoveredData::new::(¶meters.point_cloud)?) + }; let scale_index = (covered.max_distance()).log(parameters.scale_base).ceil() as i32; Ok(BuilderNode { parent_address: None, @@ -57,7 +64,7 @@ impl BuilderNode { #[inline] fn address(&self) -> NodeAddress { - (self.scale_index, self.covered.center_index) + (self.scale_index, self.covered.center_index()) } fn split_parallel( @@ -85,93 +92,44 @@ impl BuilderNode { self, parameters: &Arc>, ) -> GokoResult<(CoverNode, Vec)> { - //println!("====================="); - //println!("Splitting node with address {:?} and covered: {:?}", self.address(),self.covered); - let scale_index = self.scale_index; - let covered = self.covered; - let current_address = (scale_index, covered.center_index); - let mut node = CoverNode::new(self.parent_address,current_address); - let radius = covered.max_distance(); - let mut new_nodes = Vec::new(); + let current_address = (scale_index, self.covered.center_index()); + let mut node = CoverNode::new(self.parent_address, current_address); + let radius = self.covered.max_distance(); node.set_radius(radius); /* Occasionally there's a small cluster split off of at a low min_res_index. This brings the scale-index down/min_res_index up quickly, locally. */ - if covered.len() <= parameters.leaf_cutoff || scale_index < parameters.min_res_index { - //println!("== This is getting cut down by parameters =="); - node.insert_singletons(covered.into_indexes()); - } else { - let next_scale_index = min( - scale_index - 1, - max( - radius.log(parameters.scale_base).ceil() as i32, - parameters.min_res_index, - ), - ); - let next_scale = parameters.scale_base.powi(next_scale_index); - - /* - We get a bunch of points (close) that are within `next_scale` of the center. - we also get a bunch of points further out (new_fars). For these we need to find centers. - */ - - let (close, mut fars) = covered.split(next_scale).unwrap(); - //println!("== Split loop setup with scale {}, and scale index {} ==", next_scale, next_scale_index); - //println!("\tCovered: {:?}", close); - //println!("\tNot Covered: {:?}", fars); - - node.insert_nested_child(next_scale_index, close.len())?; - let new_node = BuilderNode { - parent_address: Some(current_address), - scale_index: next_scale_index, - covered: close, - }; - new_nodes.push(new_node); - parameters - .total_nodes - .fetch_add(1, atomic::Ordering::SeqCst); - /* - First we make the covered child. This child has the same center as it's parent and it - covers the points that are in the "close" set. - */ - - /* - We have the core loop that makes new points. We check that the new_fars' exist (split - returns None if there arn't any points more than next_scale from the center), then if - it does we split it again. - - The DistCache is responsible for picking new centers each time there's a split (to - ensure it always returns a valid DistCache). - */ - - while fars.len() > 0 { - let new_close = fars.pick_center(next_scale, ¶meters.point_cloud)?; - //println!("\t\t [{}] New Covered: {:?}",split_count, new_close); - if new_close.len() == 1 && parameters.use_singletons { - /* - We have a vast quantity of internal ourliers. These are singleton points that are - at least next_scale away from each other. These could be fully fledged leaf nodes, - or we can short circut them and just store a reference. - - On malware data 80% of the data are outliers of this type. References are a significant - ram savings. - */ - node.insert_singleton(new_close.center_index); - } else { - node.insert_child((next_scale_index, new_close.center_index), new_close.len())?; - let new_node = BuilderNode { - parent_address: Some(current_address), - scale_index: next_scale_index, - covered: new_close, - }; - new_nodes.push(new_node); - parameters - .total_nodes - .fetch_add(1, atomic::Ordering::SeqCst); + let mut new_nodes = + if self.covered.len() <= parameters.leaf_cutoff || scale_index < parameters.min_res_index { + //println!("== This is getting cut down by parameters =="); + node.insert_singletons(self.covered.into_indexes()); + vec![] + } else { + let next_scale_index = min( + scale_index - 1, + max( + radius.log(parameters.scale_base).ceil() as i32, + parameters.min_res_index, + ), + ); + match self.covered { + CoveredData::FirstCoveredData(covered) => BuilderNode::split_first( + &mut node, + current_address, + covered, + next_scale_index, + parameters, + )?, + CoveredData::NearestCoveredData(covered) => BuilderNode::split_nearest( + &mut node, + current_address, + covered, + next_scale_index, + parameters, + )?, } - } - } + }; if new_nodes.len() == 1 && new_nodes[0].covered.len() == 1 { node.remove_children(); @@ -185,6 +143,115 @@ impl BuilderNode { //println!("====================="); Ok((node, new_nodes)) } + + fn split_nearest( + parent_node: &mut CoverNode, + parent_address: NodeAddress, + covered: NearestCoveredData, + split_scale_index: i32, + parameters: &Arc>, + ) -> GokoResult> { + let next_scale = parameters.scale_base.powi(split_scale_index); + let mut splits = covered.split(next_scale,¶meters.point_cloud)?; + let mut new_nodes = Vec::new(); + println!("Splits : {:#?}", splits); + + for potential in splits.drain(0..) { + if potential.len() > 1 && parameters.use_singletons { + parent_node.insert_singleton(potential.center_index); + } else { + parent_node + .insert_child((split_scale_index, potential.center_index), potential.len())?; + let new_node = BuilderNode { + parent_address: Some(parent_address), + scale_index: split_scale_index, + covered: CoveredData::NearestCoveredData(potential), + }; + new_nodes.push(new_node); + parameters + .total_nodes + .fetch_add(1, atomic::Ordering::SeqCst); + } + } + + Ok(new_nodes) + } + + fn split_first( + parent_node: &mut CoverNode, + parent_address: NodeAddress, + covered: FirstCoveredData, + split_scale_index: i32, + parameters: &Arc>, + ) -> GokoResult> { + let mut new_nodes = Vec::new(); + + let next_scale = parameters.scale_base.powi(split_scale_index); + + /* + We get a bunch of points (close) that are within `next_scale` of the center. + we also get a bunch of points further out (new_fars). For these we need to find centers. + */ + + let (close, mut fars) = covered.split(next_scale).unwrap(); + //println!("== Split loop setup with scale {}, and scale index {} ==", next_scale, next_scale_index); + //println!("\tCovered: {:?}", close); + //println!("\tNot Covered: {:?}", fars); + + parent_node.insert_nested_child(split_scale_index, close.len())?; + let new_node = BuilderNode { + parent_address: Some(parent_address), + scale_index: split_scale_index, + covered: CoveredData::FirstCoveredData(close), + }; + new_nodes.push(new_node); + parameters + .total_nodes + .fetch_add(1, atomic::Ordering::SeqCst); + /* + First we make the covered child. This child has the same center as it's parent and it + covers the points that are in the "close" set. + */ + + /* + We have the core loop that makes new points. We check that the new_fars' exist (split + returns None if there arn't any points more than next_scale from the center), then if + it does we split it again. + + The DistCache is responsible for picking new centers each time there's a split (to + ensure it always returns a valid DistCache). + */ + + while fars.len() > 0 { + let new_close = fars.pick_center(next_scale, ¶meters.point_cloud)?; + //println!("\t\t [{}] New Covered: {:?}",split_count, new_close); + if new_close.len() == 1 && parameters.use_singletons { + /* + We have a vast quantity of internal ourliers. These are singleton points that are + at least next_scale away from each other. These could be fully fledged leaf nodes, + or we can short circut them and just store a reference. + + On malware data 80% of the data are outliers of this type. References are a significant + ram savings. + */ + parent_node.insert_singleton(new_close.center_index); + } else { + parent_node + .insert_child((split_scale_index, new_close.center_index), new_close.len())?; + let new_node = BuilderNode { + parent_address: Some(parent_address), + scale_index: split_scale_index, + covered: CoveredData::FirstCoveredData(new_close), + }; + new_nodes.push(new_node); + parameters + .total_nodes + .fetch_add(1, atomic::Ordering::SeqCst); + } + } + + Ok(new_nodes) + } } /// A construction object for a covertree. @@ -268,7 +335,7 @@ impl CoverTreeBuilder { plugins: RwLock::new(TreePluginSet::new()), }; - let root = BuilderNode::new(¶meters)?; + let root = BuilderNode::new(¶meters, "first")?; let root_address = root.address(); let scale_range = root_address.0 - parameters.min_res_index; let mut layers = Vec::with_capacity(scale_range as usize); @@ -305,10 +372,14 @@ impl CoverTreeBuilder { if let Ok(res) = node_receiver.recv() { let (scale_index, point_index, new_node) = res.unwrap(); for singleton in new_node.singletons() { - cover_tree.final_addresses.insert(*singleton,(scale_index, point_index)); + cover_tree + .final_addresses + .insert(*singleton, (scale_index, point_index)); } if new_node.is_leaf() { - cover_tree.final_addresses.insert(point_index,(scale_index, point_index)); + cover_tree + .final_addresses + .insert(point_index, (scale_index, point_index)); } unsafe { cover_tree.insert_raw(scale_index, point_index, new_node); @@ -372,13 +443,13 @@ mod tests { data.push(0.0); let test_parameters = create_test_parameters(data, 1); - let build_node = BuilderNode::new(&test_parameters).unwrap(); + let build_node = BuilderNode::new(&test_parameters,"nearest").unwrap(); let (scale_index, center_index) = build_node.address(); println!("{:?}", build_node); println!( "The center_index for the covered data should be 19 but is {}", - build_node.covered.center_index + build_node.covered.center_index() ); assert!(center_index == 19); println!("The scale_index should be 0, but is {}", scale_index); @@ -405,7 +476,7 @@ mod tests { let data = vec![0.49, 0.491, -0.49, 0.0]; let test_parameters = create_test_parameters(data, 1); - let build_node = BuilderNode::new(&test_parameters).unwrap(); + let build_node = BuilderNode::new(&test_parameters, "nearest").unwrap(); let (node_sender, node_receiver): ( Sender>)>>, diff --git a/goko/src/covertree/data_caches.rs b/goko/src/covertree/data_caches.rs index 897b310..6893bc9 100644 --- a/goko/src/covertree/data_caches.rs +++ b/goko/src/covertree/data_caches.rs @@ -19,12 +19,48 @@ use crate::errors::GokoResult; use pointcloud::*; +use rand::seq::SliceRandom; use rand::{thread_rng, Rng}; -use std::fmt; +use std::cmp::Ordering; use std::sync::Arc; -#[derive(Clone)] -pub(crate) struct CoveredData { +#[derive(Clone,Debug)] +pub(crate) enum CoveredData { + FirstCoveredData(FirstCoveredData), + NearestCoveredData(NearestCoveredData), +} + +impl CoveredData { + pub(crate) fn max_distance(&self) -> f32 { + match &self { + Self::FirstCoveredData(a) => a.max_distance(), + Self::NearestCoveredData(a) => a.max_distance(), + } + } + pub(crate) fn into_indexes(self) -> Vec { + match self { + Self::FirstCoveredData(a) => a.into_indexes(), + Self::NearestCoveredData(a) => a.into_indexes(), + } + } + + pub(crate) fn len(&self) -> usize { + match self { + Self::FirstCoveredData(a) => a.len(), + Self::NearestCoveredData(a) => a.len(), + } + } + + pub(crate) fn center_index(&self) -> PointIndex { + match &self { + Self::FirstCoveredData(a) => a.center_index, + Self::NearestCoveredData(a) => a.center_index, + } + } +} + +#[derive(Clone, Debug)] +pub(crate) struct FirstCoveredData { dists: Vec, coverage: Vec, pub(crate) center_index: PointIndex, @@ -40,7 +76,7 @@ impl UncoveredData { &mut self, radius: f32, point_cloud: &Arc, - ) -> GokoResult { + ) -> GokoResult { let mut rng = thread_rng(); let new_center: usize = rng.gen_range(0, self.coverage.len()); let center_index = self.coverage.remove(new_center); @@ -57,7 +93,7 @@ impl UncoveredData { far.push(*i); } } - let close = CoveredData { + let close = FirstCoveredData { coverage: close_index, dists: close_dist, center_index, @@ -71,16 +107,6 @@ impl UncoveredData { } } -impl fmt::Debug for CoveredData { - fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { - write!( - f, - "CoveredData {{ center_index: {:?},coverage: {:?} }}", - self.center_index, self.coverage - ) - } -} - fn find_split(dist_indexes: &[(f32, usize)], thresh: f32) -> usize { let mut smaller = 0; let mut larger = dist_indexes.len() - 1; @@ -101,19 +127,19 @@ fn find_split(dist_indexes: &[(f32, usize)], thresh: f32) -> usize { smaller } -impl CoveredData { - pub(crate) fn new(point_cloud: &Arc) -> GokoResult { +impl FirstCoveredData { + pub(crate) fn new(point_cloud: &Arc) -> GokoResult { let mut coverage = point_cloud.reference_indexes(); let center_index = coverage.pop().unwrap(); let dists = point_cloud.distances_to_point_index(center_index, &coverage)?; - Ok(CoveredData { + Ok(FirstCoveredData { dists, coverage, center_index, }) } - pub(crate) fn split(self, thresh: f32) -> GokoResult<(CoveredData, UncoveredData)> { + pub(crate) fn split(self, thresh: f32) -> GokoResult<(FirstCoveredData, UncoveredData)> { let mut close_index = Vec::with_capacity(self.coverage.len()); let mut close_dist = Vec::with_capacity(self.coverage.len()); let mut far = Vec::new(); @@ -125,7 +151,7 @@ impl CoveredData { far.push(*i); } } - let close = CoveredData { + let close = FirstCoveredData { coverage: close_index, dists: close_dist, center_index: self.center_index, @@ -150,6 +176,118 @@ impl CoveredData { } } +#[derive(Clone, Debug)] +pub(crate) struct NearestCoveredData { + centers: Vec, + dists: Vec>, + point_indexes: Vec, + pub(crate) center_index: PointIndex, +} + +impl NearestCoveredData { + pub(crate) fn new(point_cloud: &Arc) -> GokoResult { + let mut point_indexes = point_cloud.reference_indexes(); + let center_index = point_indexes.pop().unwrap(); + let new_dists = point_cloud.distances_to_point_index(center_index, &point_indexes)?; + let dists = vec![new_dists]; + let centers = vec![center_index]; + Ok(NearestCoveredData { + centers, + dists, + point_indexes, + center_index, + }) + } + + fn cover_thyself( + &mut self, + radius: f32, + point_cloud: &Arc, + ) -> GokoResult<()> { + let mut coverage: Vec = self.dists[0].iter().map(|d| d < &radius).collect(); + let mut rng = thread_rng(); + let mut i = 5; + while coverage.iter().any(|b| !b) && i > 0 { + let uncovered_indexes: Vec = self + .point_indexes + .iter() + .zip(&coverage) + .filter(|(_, b)| !**b) + .map(|(pi, _)| *pi) + .collect(); + let center_index = *uncovered_indexes.choose(&mut rng).unwrap(); + let new_dists = + point_cloud.distances_to_point_index(center_index, &self.point_indexes)?; + coverage + .iter_mut() + .zip(&new_dists) + .for_each(|(a, d)| *a = *a || (d < &radius)); + self.dists.push(new_dists); + self.centers.push(center_index); + i -= 1; + } + + Ok(()) + } + + fn add_point(&mut self, point_index: PointIndex, distance: f32) { + if point_index != self.center_index { + self.dists[0].push(distance); + self.point_indexes.push(point_index); + } + } + + fn assign_to_nearest(&self) -> Vec { + let mut new_coverage: Vec = self + .centers + .iter() + .map(|center_index| NearestCoveredData { + centers: Vec::new(), + dists: vec![vec![]], + point_indexes: Vec::new(), + center_index: *center_index, + }) + .collect(); + + for (i, pi) in self.point_indexes.iter().enumerate() { + let (index, d) = self + .dists + .iter() + .enumerate() + .map(|(dist_index, dists)| (dist_index, dists[i])) + .min_by(|(_di, d), (_ci, c)| d.partial_cmp(c).unwrap_or(Ordering::Equal)) + .unwrap(); + new_coverage[index].add_point(*pi, d); + } + + new_coverage + } + + pub(crate) fn split( + mut self, + radius: f32, + point_cloud: &Arc, + ) -> GokoResult> { + self.cover_thyself(radius,point_cloud)?; + Ok(self.assign_to_nearest()) + } + + pub(crate) fn into_indexes(self) -> Vec { + self.point_indexes + } + + pub(crate) fn max_distance(&self) -> f32 { + self.dists[0] + .iter() + .cloned() + .fold(-1. / 0. /* -inf */, f32::max) + } + + pub(crate) fn len(&self) -> usize { + self.point_indexes.len() + 1 + } +} + #[cfg(test)] mod tests { use super::*; @@ -167,7 +305,7 @@ mod tests { let point_cloud = DefaultLabeledCloud::::new_simple(data, 1, labels); - let cache = CoveredData::new(&Arc::new(point_cloud)).unwrap(); + let cache = FirstCoveredData::new(&Arc::new(point_cloud)).unwrap(); let (close, far) = cache.split(1.0).unwrap(); assert_eq!(1, close.len()); @@ -214,7 +352,7 @@ mod tests { //data.sort_unstable_by(|a, b| (a).partial_cmp(&b).unwrap_or(Ordering::Equal)); let point_cloud = DefaultLabeledCloud::::new_simple(data.clone(), 1, labels); - let cache = CoveredData::new(&Arc::new(point_cloud)).unwrap(); + let cache = FirstCoveredData::new(&Arc::new(point_cloud)).unwrap(); let thresh = 0.5; let mut true_close = Vec::new(); @@ -233,6 +371,63 @@ mod tests { assert_eq!(*tc, c); } } + + #[test] + fn nearest_splits_correctly_1() { + let mut data = Vec::with_capacity(5); + for _i in 0..4 { + data.push(rand::random::() + 3.0); + } + data.push(0.0); + + let labels: Vec = data.iter().map(|x| if *x > 0.5 { 1 } else { 0 }).collect(); + + let point_cloud = Arc::new(DefaultLabeledCloud::::new_simple(data, 1, labels)); + + let mut cache = NearestCoveredData::new(&point_cloud).unwrap(); + cache.cover_thyself(1.0,&point_cloud).unwrap(); + + assert_eq!(2, cache.dists.len()); + assert_eq!(4, cache.dists[0].len()); + assert_eq!(4, cache.dists[1].len()); + + println!("{:#?}", cache); + let splits = cache.assign_to_nearest(); + println!("{:#?}", splits); + + assert_eq!(splits.len(), 2); + assert_eq!(splits[0].len(), 1); + assert_eq!(splits[1].len(), 4); + } + + #[test] + fn nearest_splits_nearest_1() { + let cache = NearestCoveredData { + center_index: 1, + dists: vec![ + vec![2.0, 1.0, 2.0, 0.0, 1.0], + vec![0.0, 2.0, 0.0, 1.0, 2.0], + vec![1.0, 0.0, 1.0, 2.0, 0.0], + ], + point_indexes: vec![0, 2, 3, 4, 5], + centers: vec![1, 0, 2], + }; + + let splits = cache.assign_to_nearest(); + assert_eq!(splits.len(), 3); + assert_eq!(splits[0].center_index, 1); + assert_eq!(splits[1].center_index, 0); + assert_eq!(splits[2].center_index, 2); + + assert_eq!(splits[0].point_indexes[0], 4); + assert_eq!(splits[1].point_indexes[0], 3); + assert_eq!(splits[2].point_indexes[0], 5); + + assert_eq!(splits[0].dists[0], vec![0.0]); + assert_eq!(splits[1].dists[0], vec![0.0]); + assert_eq!(splits[2].dists[0], vec![0.0]); + } + /* #[test] fn correct_split_1() { diff --git a/goko/src/covertree/layer.rs b/goko/src/covertree/layer.rs index c8d277a..816c1d0 100644 --- a/goko/src/covertree/layer.rs +++ b/goko/src/covertree/layer.rs @@ -35,10 +35,10 @@ use crate::monomap::{MonoReadHandle, MonoWriteHandle}; use pointcloud::*; //use rayon; -use crate::*; use super::node::*; -use std::iter::FromIterator; use crate::tree_file_format::*; +use crate::*; +use std::iter::FromIterator; /// Actual reader, primarily contains a read head to the hash-map. /// This also contains a reference to the scale_index so that it is easy to save and load. It is largely redundant, diff --git a/goko/src/covertree/mod.rs b/goko/src/covertree/mod.rs index 0c781c6..3a9bbbe 100644 --- a/goko/src/covertree/mod.rs +++ b/goko/src/covertree/mod.rs @@ -1,7 +1,5 @@ - - -pub(crate) mod data_caches; pub(crate) mod builders; +pub(crate) mod data_caches; pub mod layer; pub mod node; pub mod query_tools; @@ -9,4 +7,4 @@ pub mod query_tools; mod tree; pub use builders::CoverTreeBuilder; -pub use tree::*; \ No newline at end of file +pub use tree::*; diff --git a/goko/src/covertree/node.rs b/goko/src/covertree/node.rs index 5283fff..767b3c7 100644 --- a/goko/src/covertree/node.rs +++ b/goko/src/covertree/node.rs @@ -20,9 +20,12 @@ //! # The Node //! This is the workhorse of the library. Each node //! -use crate::errors::{GokoError, GokoResult}; -use crate::plugins::{NodePlugin, NodePluginSet, labels::{NodeLabelSummary,NodeMetaSummary}}; use super::query_tools::{RoutingQueryHeap, SingletonQueryHeap}; +use crate::errors::{GokoError, GokoResult}; +use crate::plugins::{ + labels::{NodeLabelSummary, NodeMetaSummary}, + NodePlugin, NodePluginSet, +}; use crate::tree_file_format::*; use crate::NodeAddress; @@ -77,20 +80,24 @@ impl Clone for CoverNode { impl CoverNode { /// If the node has a summary attached, this returns the summary. pub fn label_summary(&self) -> Option>> { - self.plugins.get::>().map(|c| Arc::clone(&c.summary)) + self.plugins + .get::>() + .map(|c| Arc::clone(&c.summary)) } } impl CoverNode { /// If the node has a summary attached, this returns the summary. pub fn metasummary(&self) -> Option>> { - self.plugins.get::>().map(|c| Arc::clone(&c.summary)) + self.plugins + .get::>() + .map(|c| Arc::clone(&c.summary)) } } impl CoverNode { /// Creates a new blank node - pub fn new(parent_address: Option,address: NodeAddress) -> CoverNode { + pub fn new(parent_address: Option, address: NodeAddress) -> CoverNode { CoverNode { parent_address, address, @@ -113,7 +120,7 @@ impl CoverNode { self.radius } - /// Number of decendents of this node + /// Number of decendents of this node pub fn cover_count(&self) -> usize { self.cover_count } @@ -322,11 +329,7 @@ impl CoverNode { } /// Inserts a routing child into the node. Make sure the child node is also in the tree or you get a dangling reference - pub(crate) fn insert_child( - &mut self, - address: NodeAddress, - coverage: usize, - ) -> GokoResult<()> { + pub(crate) fn insert_child(&mut self, address: NodeAddress, coverage: usize) -> GokoResult<()> { self.cover_count += coverage; if let Some(children) = &mut self.children { children.addresses.push(address); @@ -364,14 +367,18 @@ impl CoverNode { .map(|i| *i as PointIndex) .collect(); let radius = node_proto.get_radius(); - let address = (node_proto.get_scale_index(), node_proto.get_center_index() as usize); + let address = ( + node_proto.get_scale_index(), + node_proto.get_center_index() as usize, + ); let parent_scale_index = node_proto.get_parent_scale_index(); let parent_center_index = node_proto.get_parent_center_index(); - let parent_address = if parent_scale_index == std::i32::MIN && parent_center_index == std::u64::MAX { - None - } else { - Some((parent_scale_index, parent_center_index as usize)) - }; + let parent_address = + if parent_scale_index == std::i32::MIN && parent_center_index == std::u64::MAX { + None + } else { + Some((parent_scale_index, parent_center_index as usize)) + }; let cover_count = node_proto.get_cover_count() as usize; let children = if node_proto.get_is_leaf() { None @@ -410,11 +417,11 @@ impl CoverNode { Some(parent_address) => { proto.set_parent_scale_index(parent_address.0); proto.set_parent_center_index(parent_address.1 as u64); - }, + } None => { proto.set_parent_scale_index(std::i32::MIN); proto.set_parent_center_index(std::u64::MAX); - }, + } } proto.set_radius(self.radius); @@ -458,10 +465,10 @@ mod tests { use super::*; use std::env; + use crate::covertree::tests::build_mnist_tree; use crate::query_tools::knn_query_heap::tests::clone_unvisited_nodes; use crate::query_tools::query_items::QueryAddress; use crate::query_tools::KnnQueryHeap; - use crate::covertree::tests::build_mnist_tree; fn create_test_node() -> CoverNode { let children = Some(NodeChildren { @@ -714,8 +721,11 @@ mod tests { assert_eq!(&reconstructed_node.singles_indexes[..], &[4, 5, 6]); let reconstructed_children = reconstructed_node.children.unwrap(); - assert_eq!(reconstructed_children.nested_scale,0); - assert_eq!(&reconstructed_children.addresses[..], &[(-4, 1), (-4, 2), (-4, 3)]); + assert_eq!(reconstructed_children.nested_scale, 0); + assert_eq!( + &reconstructed_children.addresses[..], + &[(-4, 1), (-4, 2), (-4, 3)] + ); } #[test] @@ -724,7 +734,7 @@ mod tests { let proto = node.save(); let reconstructed_node = CoverNode::>::load(&proto); - assert_eq!(reconstructed_node.parent_address, Some((1,0))); + assert_eq!(reconstructed_node.parent_address, Some((1, 0))); assert_eq!(reconstructed_node.address, (0, 0)); assert_eq!(reconstructed_node.radius, 1.0); assert_eq!(reconstructed_node.cover_count, 8); diff --git a/goko/src/covertree/tree.rs b/goko/src/covertree/tree.rs index 30cfce8..ec80f76 100644 --- a/goko/src/covertree/tree.rs +++ b/goko/src/covertree/tree.rs @@ -33,18 +33,18 @@ //! //! The hashmap pair idea is in `layer` and originally comes from Jon Gjengset. -use crate::*; use super::layer::*; use super::node::*; +use crate::*; //use pointcloud::*; -use std::sync::{atomic, Arc, RwLock}; -use crate::tree_file_format::*; use crate::monomap::{MonoReadHandle, MonoWriteHandle}; +use crate::tree_file_format::*; +use std::sync::{atomic, Arc, RwLock}; -use crate::plugins::{GokoPlugin, TreePluginSet}; use super::query_tools::{KnnQueryHeap, MultiscaleQueryHeap, RoutingQueryHeap}; -use errors::{GokoResult,GokoError}; +use crate::plugins::{GokoPlugin, TreePluginSet}; +use errors::{GokoError, GokoResult}; use std::collections::HashMap; use std::iter::Iterator; use std::iter::Rev; @@ -103,10 +103,9 @@ pub struct CoverTreeReader { parameters: Arc>, layers: Vec>, root_address: NodeAddress, - final_addresses: MonoReadHandle, + final_addresses: MonoReadHandle, } - impl Clone for CoverTreeReader { fn clone(&self) -> CoverTreeReader { CoverTreeReader { @@ -124,8 +123,7 @@ impl CoverTreeReader { pub fn get_node_label_summary( &self, node_address: (i32, PointIndex), - ) -> Option>> - { + ) -> Option>> { self.layers[self.parameters.internal_index(node_address.0)] .get_node_and(node_address.1, |n| n.label_summary()) .flatten() @@ -138,8 +136,7 @@ impl CoverTreeReader { pub fn get_node_metasummary( &self, node_address: (i32, PointIndex), - ) -> Option>> - { + ) -> Option>> { self.layers[self.parameters.internal_index(node_address.0)] .get_node_and(node_address.1, |n| n.metasummary()) .flatten() @@ -380,10 +377,7 @@ impl CoverTreeReader { } /// # Dry Insert Query - pub fn path<'a, T: Into>>( - &self, - point: T, - ) -> GokoResult> { + pub fn path<'a, T: Into>>(&self, point: T) -> GokoResult> { let point: PointRef<'a> = point.into(); let root_center = self.parameters.point_cloud.point(self.root_address.1)?; let mut current_distance = D::Metric::dist(&root_center, point)?; @@ -408,20 +402,26 @@ impl CoverTreeReader { Ok(trace) } - /// - pub fn known_path(&self, point_index: PointIndex) -> GokoResult> { - self.final_addresses.get_and(&point_index,|addr| { - let mut path = Vec::with_capacity((self.root_address().0 - addr.0) as usize); - let mut parent = Some(*addr); - while let Some(addr) = parent { - path.push(addr); - parent = self.get_node_and(addr,|n| n.parent_address()).flatten(); - } - (&mut path[..]).reverse(); - let point_indexes: Vec = path.iter().map(|na| na.1).collect(); - let dists = self.parameters.point_cloud.distances_to_point_index(point_index,&point_indexes[..]).unwrap(); - dists.iter().zip(path).map(|(d,a)|(*d,a)).collect() - }).ok_or(GokoError::IndexNotInTree(point_index)) + /// + pub fn known_path(&self, point_index: PointIndex) -> GokoResult> { + self.final_addresses + .get_and(&point_index, |addr| { + let mut path = Vec::with_capacity((self.root_address().0 - addr.0) as usize); + let mut parent = Some(*addr); + while let Some(addr) = parent { + path.push(addr); + parent = self.get_node_and(addr, |n| n.parent_address()).flatten(); + } + (&mut path[..]).reverse(); + let point_indexes: Vec = path.iter().map(|na| na.1).collect(); + let dists = self + .parameters + .point_cloud + .distances_to_point_index(point_index, &point_indexes[..]) + .unwrap(); + dists.iter().zip(path).map(|(d, a)| (*d, a)).collect() + }) + .ok_or(GokoError::IndexNotInTree(point_index)) } ///Computes the fractal dimension of a node @@ -546,19 +546,19 @@ pub struct CoverTreeWriter { pub(crate) parameters: Arc>, pub(crate) layers: Vec>, pub(crate) root_address: NodeAddress, - pub(crate) final_addresses: MonoWriteHandle, + pub(crate) final_addresses: MonoWriteHandle, } impl CoverTreeWriter { - /// - pub fn generate_summaries(&mut self){ + /// + pub fn generate_summaries(&mut self) { self.add_plugin::(TreeLabelSummary::default()) } } impl CoverTreeWriter { - /// - pub fn generate_meta_summaries(&mut self){ + /// + pub fn generate_meta_summaries(&mut self) { self.add_plugin::(TreeMetaSummary::default()) } } @@ -656,17 +656,19 @@ impl CoverTreeWriter { let mut unvisited_nodes: Vec = vec![self.root_address]; while !unvisited_nodes.is_empty() { let cur_add = unvisited_nodes.pop().unwrap(); - reader.get_node_and(cur_add, |n| { - for singleton in n.singletons() { - self.final_addresses.insert(*singleton,cur_add); - } - if let Some((nested_si,child_addresses)) = n.children() { - unvisited_nodes.extend(child_addresses); - unvisited_nodes.push((nested_si,cur_add.1)); - } else { - self.final_addresses.insert(cur_add.1,cur_add); - } - }).unwrap(); + reader + .get_node_and(cur_add, |n| { + for singleton in n.singletons() { + self.final_addresses.insert(*singleton, cur_add); + } + if let Some((nested_si, child_addresses)) = n.children() { + unvisited_nodes.extend(child_addresses); + unvisited_nodes.push((nested_si, cur_add.1)); + } else { + self.final_addresses.insert(cur_add.1, cur_add); + } + }) + .unwrap(); } self.final_addresses.refresh(); @@ -817,21 +819,31 @@ pub(crate) mod tests { for i in 0..5 { let trace = reader.known_path(i).unwrap(); println!("i {}, trace {:?}", i, trace); - println!("final address: {:?}", reader.final_addresses.get_and(&i,|i| *i)); + println!( + "final address: {:?}", + reader.final_addresses.get_and(&i, |i| *i) + ); let ad = trace.last().unwrap().1; - reader.get_node_and(ad,|n| { - if !n.is_leaf() { - assert!(n.singletons().contains(&i)); - } else { - assert!((ad.1 != i && n.singletons().contains(&i)) || (ad.1 == i && !n.singletons().contains(&i))); - - } - }).unwrap(); + reader + .get_node_and(ad, |n| { + if !n.is_leaf() { + assert!(n.singletons().contains(&i)); + } else { + assert!( + (ad.1 != i && n.singletons().contains(&i)) + || (ad.1 == i && !n.singletons().contains(&i)) + ); + } + }) + .unwrap(); } let known_trace = reader.known_path(4).unwrap(); let trace = reader.path(&[0.0f32][..]).unwrap(); - println!("Testing known: {:?} matches unknown {:?}", known_trace, trace); - for (p,kp) in trace.iter().zip(known_trace) { + println!( + "Testing known: {:?} matches unknown {:?}", + known_trace, trace + ); + for (p, kp) in trace.iter().zip(known_trace) { assert_eq!(*p, kp); } } @@ -876,14 +888,16 @@ pub(crate) mod tests { tree.generate_summaries(); let reader = tree.reader(); - for (_,layer) in reader.layers() { - layer.for_each_node(|_,n| println!("{:?}", n.label_summary())); + for (_, layer) in reader.layers() { + layer.for_each_node(|_, n| println!("{:?}", n.label_summary())); } - let l = reader.get_node_label_summary(reader.root_address()).unwrap(); - assert_eq!(l.summary.items.len(),2); - assert_eq!(l.nones,0); - assert_eq!(l.errors,0); + let l = reader + .get_node_label_summary(reader.root_address()) + .unwrap(); + assert_eq!(l.summary.items.len(), 2); + assert_eq!(l.nones, 0); + assert_eq!(l.errors, 0); } #[test] @@ -928,26 +942,27 @@ pub(crate) mod tests { assert_eq!(reader.layers.len(), proto.get_layers().len()); - for (layer,proto_layer) in reader.layers.iter().zip(proto.get_layers()) { + for (layer, proto_layer) in reader.layers.iter().zip(proto.get_layers()) { assert_eq!(layer.len(), proto_layer.get_nodes().len()); } - let reconstructed_tree_writer = CoverTreeWriter::load(&proto,Arc::clone(&point_cloud)).unwrap(); + let reconstructed_tree_writer = + CoverTreeWriter::load(&proto, Arc::clone(&point_cloud)).unwrap(); let reconstructed_tree = reconstructed_tree_writer.reader(); - assert_eq!(reader.layers.len(), reconstructed_tree.layers.len()); - for (layer,reconstructed_layer) in reader.layers.iter().zip(reconstructed_tree.layers) { + for (layer, reconstructed_layer) in reader.layers.iter().zip(reconstructed_tree.layers) { assert_eq!(layer.len(), reconstructed_layer.len()); - layer.for_each_node(|pi,n| { - reconstructed_layer.get_node_and(*pi,|rn| { - assert_eq!(n.address(), rn.address()); - assert_eq!(n.parent_address(), rn.parent_address()); - assert_eq!(n.singletons(), rn.singletons()); - }).unwrap(); + layer.for_each_node(|pi, n| { + reconstructed_layer + .get_node_and(*pi, |rn| { + assert_eq!(n.address(), rn.address()); + assert_eq!(n.parent_address(), rn.parent_address()); + assert_eq!(n.singletons(), rn.singletons()); + }) + .unwrap(); }) } - } } diff --git a/goko/src/lib.rs b/goko/src/lib.rs index fefe03c..7238f1d 100644 --- a/goko/src/lib.rs +++ b/goko/src/lib.rs @@ -73,7 +73,6 @@ pub mod utils; pub mod plugins; - /// The data structure explicitly seperates the covertree by layer, and the addressing schema for nodes /// is a pair for the layer index and the center point index of that node. pub type NodeAddress = (i32, usize); diff --git a/goko/src/monomap/inner.rs b/goko/src/monomap/inner.rs index bcc2e4e..632dd3b 100644 --- a/goko/src/monomap/inner.rs +++ b/goko/src/monomap/inner.rs @@ -1,9 +1,9 @@ use std::hash::{BuildHasher, Hash}; -#[cfg(feature = "indexed")] -use indexmap::IndexMap as MapImpl; #[cfg(not(feature = "indexed"))] use hashbrown::HashMap as MapImpl; +#[cfg(feature = "indexed")] +use indexmap::IndexMap as MapImpl; pub(crate) struct Inner where diff --git a/goko/src/monomap/mod.rs b/goko/src/monomap/mod.rs index 5630a23..23acc51 100644 --- a/goko/src/monomap/mod.rs +++ b/goko/src/monomap/mod.rs @@ -32,10 +32,10 @@ mod inner; pub(crate) type Epochs = Arc>>>; //use std::collections::hash_map::RandomState; -use std::fmt; -use std::hash::{BuildHasher, Hash}; use fxhash::FxBuildHasher; use inner::Inner; +use std::fmt; +use std::hash::{BuildHasher, Hash}; /// Merge object for reducing the entries of a entry to a single one. pub struct Updater(pub(crate) Box); diff --git a/goko/src/plugins/distributions/mod.rs b/goko/src/plugins/distributions/mod.rs index 809a7b5..31cf408 100644 --- a/goko/src/plugins/distributions/mod.rs +++ b/goko/src/plugins/distributions/mod.rs @@ -52,7 +52,6 @@ pub trait ContinousBayesianDistribution: ContinousDistribution + Clone + 'static fn add_observation(&mut self, point: &PointRef); } - /// Tracks the KL divergence for a given distribution. pub trait DiscreteBayesianSequenceTracker: Debug { /// The. underlying distribution that this is tracking. @@ -106,10 +105,13 @@ pub trait DiscreteBayesianSequenceTracker: Debug { nz_count += 1; } }); - let weighted_layer_totals: Vec = layer_node_counts.iter().map(|counts| { - let max: f32 = *counts.iter().max().unwrap_or(&1) as f32; - counts.iter().fold(0.0, |a,c| a + (*c as f32)/max) - }).collect(); + let weighted_layer_totals: Vec = layer_node_counts + .iter() + .map(|counts| { + let max: f32 = *counts.iter().max().unwrap_or(&1) as f32; + counts.iter().fold(0.0, |a, c| a + (*c as f32) / max) + }) + .collect(); KLDivergenceStats { max, min, diff --git a/goko/src/plugins/labels.rs b/goko/src/plugins/labels.rs index 568017d..b4e6f4f 100644 --- a/goko/src/plugins/labels.rs +++ b/goko/src/plugins/labels.rs @@ -1,16 +1,16 @@ //! Plugin for labels and metadata +use super::*; use crate::covertree::node::CoverNode; use crate::covertree::CoverTreeReader; -use pointcloud::*; -use super::*; +//use pointcloud::*; use std::sync::Arc; /// Wrapper around the summary found in the point cloud #[derive(Debug, Default)] pub struct NodeLabelSummary { /// The summary object, refenced counted to eliminate duplicates - pub summary: Arc> + pub summary: Arc>, } impl Clone for NodeLabelSummary { @@ -25,7 +25,7 @@ impl NodePlugin for NodeLabelSummary, _my_tree: &CoverTreeReader) {} } -/// +/// #[derive(Debug, Clone, Default)] pub struct TreeLabelSummary {} @@ -33,7 +33,7 @@ impl TreePlugin for TreeLabelSummary { fn update(&mut self, _my_tree: &CoverTreeReader) {} } -/// Plug in that allows for summaries of labels to be attached to +/// Plug in that allows for summaries of labels to be attached to #[derive(Debug, Clone, Default)] pub struct LabelSummaryPlugin {} @@ -45,7 +45,11 @@ impl GokoPlugin for LabelSummaryPlugin { my_node: &CoverNode, my_tree: &CoverTreeReader, ) -> Self::NodeComponent { - let mut bucket = my_tree.parameters().point_cloud.label_summary(my_node.singletons()).unwrap(); + let mut bucket = my_tree + .parameters() + .point_cloud + .label_summary(my_node.singletons()) + .unwrap(); // If we're a routing node then grab the childen's values if let Some((nested_scale, child_addresses)) = my_node.children() { my_tree.get_node_plugin_and::( @@ -54,14 +58,17 @@ impl GokoPlugin for LabelSummaryPlugin { ); for ca in child_addresses { - my_tree.get_node_plugin_and::( - *ca, - |p| bucket.combine(p.summary.as_ref()), - ); + my_tree.get_node_plugin_and::(*ca, |p| { + bucket.combine(p.summary.as_ref()) + }); } - } else { - bucket.add(my_tree.parameters().point_cloud.label(*my_node.center_index())); + bucket.add( + my_tree + .parameters() + .point_cloud + .label(*my_node.center_index()), + ); } NodeLabelSummary { summary: Arc::new(bucket), @@ -73,7 +80,7 @@ impl GokoPlugin for LabelSummaryPlugin { #[derive(Debug, Default)] pub struct NodeMetaSummary { /// The summary object, refenced counted to eliminate duplicates - pub summary: Arc> + pub summary: Arc>, } impl Clone for NodeMetaSummary { @@ -88,7 +95,7 @@ impl NodePlugin for NodeMetaSummary, _my_tree: &CoverTreeReader) {} } -/// +/// #[derive(Debug, Clone, Default)] pub struct TreeMetaSummary {} @@ -96,7 +103,7 @@ impl TreePlugin for TreeMetaSummary { fn update(&mut self, _my_tree: &CoverTreeReader) {} } -/// Plug in that allows for summaries of Metas to be attached to +/// Plug in that allows for summaries of Metas to be attached to #[derive(Debug, Clone, Default)] pub struct MetaSummaryPlugin {} @@ -108,7 +115,11 @@ impl GokoPlugin for MetaSummaryPlugin { my_node: &CoverNode, my_tree: &CoverTreeReader, ) -> Self::NodeComponent { - let mut bucket = my_tree.parameters().point_cloud.metasummary(my_node.singletons()).unwrap(); + let mut bucket = my_tree + .parameters() + .point_cloud + .metasummary(my_node.singletons()) + .unwrap(); // If we're a routing node then grab the childen's values if let Some((nested_scale, child_addresses)) = my_node.children() { my_tree.get_node_plugin_and::( @@ -117,17 +128,20 @@ impl GokoPlugin for MetaSummaryPlugin { ); for ca in child_addresses { - my_tree.get_node_plugin_and::( - *ca, - |p| bucket.combine(p.summary.as_ref()), - ); + my_tree.get_node_plugin_and::(*ca, |p| { + bucket.combine(p.summary.as_ref()) + }); } - } else { - bucket.add(my_tree.parameters().point_cloud.metadata(*my_node.center_index())); + bucket.add( + my_tree + .parameters() + .point_cloud + .metadata(*my_node.center_index()), + ); } NodeMetaSummary { summary: Arc::new(bucket), } } -} \ No newline at end of file +} diff --git a/goko/src/query_interface/mod.rs b/goko/src/query_interface/mod.rs index d335970..acd57fc 100644 --- a/goko/src/query_interface/mod.rs +++ b/goko/src/query_interface/mod.rs @@ -4,77 +4,107 @@ use crate::*; use rayon::iter::repeatn; - /// Inteface for bulk queries. Handles cloning the readers for you pub struct BulkInterface { reader: CoverTreeReader, } impl BulkInterface { - /// Creates a new one. + /// Creates a new one. pub fn new(reader: CoverTreeReader) -> Self { - BulkInterface { - reader, - } + BulkInterface { reader } } /// Bulk known path - pub fn known_path(&self, point_indexes: &[PointIndex]) -> Vec>> { + pub fn known_path( + &self, + point_indexes: &[PointIndex], + ) -> Vec>> { let indexes_iter = point_indexes.par_chunks(100); let reader_copies = indexes_iter.len(); - let mut chunked_results: Vec>>> = indexes_iter - .zip(repeatn(self.reader.clone(),reader_copies)) - .map(|(chunk_indexes,reader)| { - chunk_indexes.iter().map(|i| { - reader.known_path(*i) - }).collect() - }).collect(); - chunked_results.drain(..).fold_first(|mut a, mut x| {a.extend(x.drain(..)); a}).unwrap() + let mut chunked_results: Vec>>> = indexes_iter + .zip(repeatn(self.reader.clone(), reader_copies)) + .map(|(chunk_indexes, reader)| { + chunk_indexes + .iter() + .map(|i| reader.known_path(*i)) + .collect() + }) + .collect(); + chunked_results + .drain(..) + .fold_first(|mut a, mut x| { + a.extend(x.drain(..)); + a + }) + .unwrap() } /// Bulk path - pub fn path<'a>(&self, points: &[PointRef<'a>]) -> Vec>> { + pub fn path<'a>(&self, points: &[PointRef<'a>]) -> Vec>> { let point_iter = points.par_chunks(100); let reader_copies = point_iter.len(); - let mut chunked_results: Vec>>> = point_iter - .zip(repeatn(self.reader.clone(),reader_copies)) - .map(|(chunk_points,reader)| { - chunk_points.iter().map(|p| { - reader.path(p) - }).collect() - }).collect(); - - chunked_results.drain(..).fold_first(|mut a, mut x| {a.extend(x.drain(..)); a}).unwrap() + let mut chunked_results: Vec>>> = point_iter + .zip(repeatn(self.reader.clone(), reader_copies)) + .map(|(chunk_points, reader)| chunk_points.iter().map(|p| reader.path(p)).collect()) + .collect(); + + chunked_results + .drain(..) + .fold_first(|mut a, mut x| { + a.extend(x.drain(..)); + a + }) + .unwrap() } /// Bulk knn - pub fn knn<'a>(&self, points: &[PointRef<'a>], k: usize) -> Vec>> { + pub fn knn<'a>( + &self, + points: &[PointRef<'a>], + k: usize, + ) -> Vec>> { let point_iter = points.par_chunks(1); let reader_copies = point_iter.len(); - let mut chunked_results: Vec>>> = point_iter - .zip(repeatn(self.reader.clone(),reader_copies)) - .map(|(chunk_points,reader)| { - chunk_points.iter().map(|p| { - reader.knn(p,k) - }).collect() - }).collect(); - - chunked_results.drain(..).fold_first(|mut a, mut x| {a.extend(x.drain(..)); a}).unwrap() + let mut chunked_results: Vec>>> = point_iter + .zip(repeatn(self.reader.clone(), reader_copies)) + .map(|(chunk_points, reader)| chunk_points.iter().map(|p| reader.knn(p, k)).collect()) + .collect(); + + chunked_results + .drain(..) + .fold_first(|mut a, mut x| { + a.extend(x.drain(..)); + a + }) + .unwrap() } /// Bulk routing knn - pub fn routing_knn<'a>(&self, points: &[PointRef<'a>], k: usize) -> Vec>> { + pub fn routing_knn<'a>( + &self, + points: &[PointRef<'a>], + k: usize, + ) -> Vec>> { let point_iter = points.par_chunks(1); let reader_copies = point_iter.len(); - let mut chunked_results: Vec>>> = point_iter - .zip(repeatn(self.reader.clone(),reader_copies)) - .map(|(chunk_points,reader)| { - chunk_points.iter().map(|p| { - reader.routing_knn(p,k) - }).collect() - }).collect(); - - chunked_results.drain(..).fold_first(|mut a, mut x| {a.extend(x.drain(..)); a}).unwrap() + let mut chunked_results: Vec>>> = point_iter + .zip(repeatn(self.reader.clone(), reader_copies)) + .map(|(chunk_points, reader)| { + chunk_points + .iter() + .map(|p| reader.routing_knn(p, k)) + .collect() + }) + .collect(); + + chunked_results + .drain(..) + .fold_first(|mut a, mut x| { + a.extend(x.drain(..)); + a + }) + .unwrap() } } @@ -96,11 +126,11 @@ pub(crate) mod tests { let points: Vec = (0..100).map(|i| cloud.point(i).unwrap()).collect(); let path_results = interface.path(&points); - for (i,path) in path_results.iter().enumerate() { + for (i, path) in path_results.iter().enumerate() { let old_path = reader.path(cloud.point(i).unwrap()).unwrap(); - for ((d1,a1),(d2,a2)) in (path.as_ref().unwrap()).iter().zip(old_path) { - assert_approx_eq!(*d1,d2); - assert_eq!(*a1,a2); + for ((d1, a1), (d2, a2)) in (path.as_ref().unwrap()).iter().zip(old_path) { + assert_approx_eq!(*d1, d2); + assert_eq!(*a1, a2); } } } @@ -117,13 +147,13 @@ pub(crate) mod tests { let points: Vec = (0..10).map(|i| cloud.point(i).unwrap()).collect(); let knn_results = interface.knn(&points, 5); - for (i,knn) in knn_results.iter().enumerate() { + for (i, knn) in knn_results.iter().enumerate() { let old_knn = reader.knn(cloud.point(i).unwrap(), 5).unwrap(); - for ((d1,a1),(d2,a2)) in (knn.as_ref().unwrap()).iter().zip(old_knn) { - assert_approx_eq!(*d1,d2); - assert_eq!(*a1,a2); + for ((d1, a1), (d2, a2)) in (knn.as_ref().unwrap()).iter().zip(old_knn) { + assert_approx_eq!(*d1, d2); + assert_eq!(*a1, a2); } } } } -} \ No newline at end of file +} diff --git a/pointcloud/src/base_traits.rs b/pointcloud/src/base_traits.rs index 5acf95f..f751a90 100644 --- a/pointcloud/src/base_traits.rs +++ b/pointcloud/src/base_traits.rs @@ -4,10 +4,10 @@ use rayon::prelude::*; use std::cmp::min; use std::fmt::Debug; -use serde::{Deserialize, Serialize}; use crate::distances::*; use crate::pc_errors::*; use crate::*; +use serde::{Deserialize, Serialize}; #[inline] fn chunk(data_dim: usize) -> usize { @@ -274,7 +274,10 @@ pub trait LabelSet: Debug + Send + Sync + 'static { /// and partially labeled datasets with the option. fn label(&self, pn: PointIndex) -> PointCloudResult>; /// Grabs a label summary of a set of indexes. - fn label_summary(&self, pns: &[PointIndex]) -> PointCloudResult>; + fn label_summary( + &self, + pns: &[PointIndex], + ) -> PointCloudResult>; } /// A point cloud that is labeled @@ -287,11 +290,14 @@ pub trait LabeledCloud: PointCloud { /// and partially labeled datasets with the option. fn label(&self, pn: PointIndex) -> PointCloudResult>; /// Grabs a label summary of a set of indexes. - fn label_summary(&self, pns: &[PointIndex]) -> PointCloudResult>; + fn label_summary( + &self, + pns: &[PointIndex], + ) -> PointCloudResult>; } /// Simply shoves together a point cloud and a label set, for a modular label system -#[derive(Default,Debug, Serialize, Deserialize)] +#[derive(Default, Debug, Serialize, Deserialize)] pub struct SummaryCounter { /// The categorical summary pub summary: S, @@ -301,7 +307,7 @@ pub struct SummaryCounter { pub errors: usize, } -impl SummaryCounter { +impl SummaryCounter { /// adds an element to the summary, handling errors pub fn add(&mut self, v: PointCloudResult>) { if let Ok(vv) = v { @@ -328,7 +334,7 @@ impl SummaryCounter { } /// the number of samples this summarieses - pub fn count(&self) -> usize { + pub fn count(&self) -> usize { self.summary.count() + self.nones + self.errors } @@ -389,14 +395,17 @@ impl LabeledCloud for SimpleLabeledCloud { fn label(&self, pn: PointIndex) -> PointCloudResult> { self.labels.label(pn) } - fn label_summary(&self, pns: &[PointIndex]) -> PointCloudResult> { + fn label_summary( + &self, + pns: &[PointIndex], + ) -> PointCloudResult> { self.labels.label_summary(pns) } } -/// Enables the points in the underlying cloud to be named with strings. +/// Enables the points in the underlying cloud to be named with strings. pub trait NamedCloud: PointCloud { - /// Grabs the name of the point. + /// Grabs the name of the point. /// Returns an error if the access errors out, and a None if the name is unknown fn name(&self, pi: PointIndex) -> PointCloudResult>; /// Converts a name to an index you can use @@ -415,6 +424,8 @@ pub trait MetaCloud: PointCloud { /// Expensive metadata object for the sample fn metadata(&self, pn: PointIndex) -> PointCloudResult>; /// Expensive metadata summary over the samples - fn metasummary(&self, pns: &[PointIndex]) -> PointCloudResult>; + fn metasummary( + &self, + pns: &[PointIndex], + ) -> PointCloudResult>; } - diff --git a/pointcloud/src/data_sources/memmap_ram.rs b/pointcloud/src/data_sources/memmap_ram.rs index e45f6b3..5410dbc 100644 --- a/pointcloud/src/data_sources/memmap_ram.rs +++ b/pointcloud/src/data_sources/memmap_ram.rs @@ -157,8 +157,8 @@ make_point_cloud!(DataMemmap); pub mod tests { use super::*; use crate::distances::*; - use crate::{Point, PointRef}; use crate::label_sources::SmallIntLabels; + use crate::{Point, PointRef}; use rand; use std::iter; @@ -207,12 +207,7 @@ pub mod tests { data_dim, ) .unwrap(); - let labels = SmallIntLabels::new( - (0..count) - .map(|i| i as i64) - .collect(), - None, - ); + let labels = SmallIntLabels::new((0..count).map(|i| i as i64).collect(), None); SimpleLabeledCloud::new(data, labels) } diff --git a/pointcloud/src/glued_data_cloud.rs b/pointcloud/src/glued_data_cloud.rs index 09cada3..88b370b 100644 --- a/pointcloud/src/glued_data_cloud.rs +++ b/pointcloud/src/glued_data_cloud.rs @@ -92,7 +92,10 @@ impl LabeledCloud for HashGluedCloud { let (i, j) = self.get_address(pn)?; self.data_sources[i].label(j) } - fn label_summary(&self, pns: &[PointIndex]) -> PointCloudResult> { + fn label_summary( + &self, + pns: &[PointIndex], + ) -> PointCloudResult> { let mut summary = SummaryCounter::::default(); for pn in pns { let (i, j) = self.get_address(*pn)?; @@ -117,11 +120,19 @@ impl NamedCloud for HashGluedCloud { Err(PointCloudError::NameNotInCloud(pn.clone())) } fn names(&self) -> Vec { - self.addresses.values().filter_map(|(i,j)| self.data_sources[*i].name(*j).ok().flatten().map(|s| s.clone())).collect() + self.addresses + .values() + .filter_map(|(i, j)| { + self.data_sources[*i] + .name(*j) + .ok() + .flatten() + .map(|s| s.clone()) + }) + .collect() } } - impl MetaCloud for HashGluedCloud { type Metadata = D::Metadata; type MetaSummary = D::MetaSummary; @@ -130,7 +141,10 @@ impl MetaCloud for HashGluedCloud { let (i, j) = self.get_address(pn)?; self.data_sources[i].metadata(j) } - fn metasummary(&self, pns: &[PointIndex]) -> PointCloudResult> { + fn metasummary( + &self, + pns: &[PointIndex], + ) -> PointCloudResult> { let mut summary = SummaryCounter::::default(); for pn in pns { let (i, j) = self.get_address(*pn)?; @@ -140,7 +154,6 @@ impl MetaCloud for HashGluedCloud { } } - #[cfg(test)] mod tests { use super::*; @@ -248,7 +261,7 @@ mod tests { println!("{:?}", label_summary); assert_eq!(label_summary.nones, 0); assert_eq!(label_summary.errors, 0); - assert_eq!(label_summary.summary.items[0], (1,5)); + assert_eq!(label_summary.summary.items[0], (1, 5)); } #[test] @@ -268,6 +281,4 @@ mod tests { assert_approx_eq!(3.0f32.sqrt(), d); } } - - } diff --git a/pointcloud/src/label_sources/mod.rs b/pointcloud/src/label_sources/mod.rs index c7bcac6..ac01e92 100644 --- a/pointcloud/src/label_sources/mod.rs +++ b/pointcloud/src/label_sources/mod.rs @@ -33,7 +33,10 @@ impl LabelSet for SmallIntLabels { Ok(self.labels.get(pn)) } } - fn label_summary(&self, pns: &[PointIndex]) -> PointCloudResult> { + fn label_summary( + &self, + pns: &[PointIndex], + ) -> PointCloudResult> { let mut summary = CategorySummary::default(); let mut nones = 0; if let Some(mask) = &self.mask { @@ -124,7 +127,7 @@ impl VecLabels { .iter() .enumerate() .filter(|(_i, x)| *x > &0.5) - .map(|(i,_x)| i as i64) + .map(|(i, _x)| i as i64) .next() .unwrap_or(self.label_dim as i64); if label == self.label_dim as i64 { @@ -134,9 +137,9 @@ impl VecLabels { }) .collect(); SmallIntLabels { - labels, - mask: Some(mask), - } + labels, + mask: Some(mask), + } } /// coverts a binary encoding to a integer label set @@ -156,9 +159,9 @@ impl VecLabels { }) .collect(); SmallIntLabels { - labels, - mask: Some(mask), - } + labels, + mask: Some(mask), + } } } @@ -187,25 +190,31 @@ impl LabelSet for VecLabels { .get(self.label_dim * (pn as usize)..self.label_dim * (pn as usize + 1))) } } - fn label_summary(&self, pns: &[PointIndex]) -> PointCloudResult> { + fn label_summary( + &self, + pns: &[PointIndex], + ) -> PointCloudResult> { let mut summary = Self::LabelSummary::default(); let mut nones = 0; if let Some(mask) = &self.mask { for i in pns { if mask[*i] { summary.add( - self - .labels - .get(self.label_dim * (*i as usize)..self.label_dim * (*i as usize + 1)).unwrap()); + self.labels + .get(self.label_dim * (*i as usize)..self.label_dim * (*i as usize + 1)) + .unwrap(), + ); } else { nones += 1; } } } else { for i in pns { - summary.add(self - .labels - .get(self.label_dim * (*i as usize)..self.label_dim * (*i as usize + 1)).unwrap()); + summary.add( + self.labels + .get(self.label_dim * (*i as usize)..self.label_dim * (*i as usize + 1)) + .unwrap(), + ); } } Ok(SummaryCounter { diff --git a/pointcloud/src/lib.rs b/pointcloud/src/lib.rs index 48d1e38..9239053 100644 --- a/pointcloud/src/lib.rs +++ b/pointcloud/src/lib.rs @@ -191,4 +191,3 @@ impl<'a> From<&'a Point> for PointRef<'a> { } } } - diff --git a/pointcloud/src/loaders/yaml_loaders.rs b/pointcloud/src/loaders/yaml_loaders.rs index 970ba9c..33f5c82 100644 --- a/pointcloud/src/loaders/yaml_loaders.rs +++ b/pointcloud/src/loaders/yaml_loaders.rs @@ -1,11 +1,11 @@ use glob::{glob_with, MatchOptions}; +use std::cmp::Ordering; use std::fs; use yaml_rust::YamlLoader; -use std::cmp::Ordering; -use crate::{DefaultCloud, DefaultLabeledCloud}; -use crate::distances::L2; use super::*; +use crate::distances::L2; +use crate::{DefaultCloud, DefaultLabeledCloud}; /// Given a yaml file on disk, it builds a point cloud. Minimal example below. /// ```yaml @@ -22,10 +22,7 @@ pub fn labeled_ram_from_yaml, M: Metric>( let label_set = labels_from_yaml(&path)?; let data_set = ram_from_yaml(&path)?; - Ok(SimpleLabeledCloud::new( - data_set, - label_set, - )) + Ok(SimpleLabeledCloud::new(data_set, label_set)) } /// Given a yaml file on disk, it builds a point cloud. Minimal example below. @@ -128,26 +125,33 @@ pub fn labels_from_yaml>(path: P) -> PointCloudResult = labels_path .iter() - .map( - |path| match (path.extension().unwrap().to_str().unwrap(), labels_index, labels_dim) { + .map(|path| { + match ( + path.extension().unwrap().to_str().unwrap(), + labels_index, + labels_dim, + ) { ("csv", Some(index), _) | ("gz", Some(index), _) => open_int_csv(&path, index), ("dat", _, Some(dim)) => { let labels: VecLabels = DataMemmap::::new(dim, &path)?.convert_to_labels(); match dim.cmp(&1) { Ordering::Greater => Ok(labels.one_hot_to_int()), - Ordering::Less => { + Ordering::Less => { panic!( "Could not determine if labels are one hot or binary. {:?}, {:?}", path, dim ); - }, - Ordering::Equal => Ok(labels.binary_to_int()) + } + Ordering::Equal => Ok(labels.binary_to_int()), } } - _ => panic!("Unable to detemine label source. {:?}, index: {:?}, dim: {:?}", path, labels_index, labels_dim), - }, - ) + _ => panic!( + "Unable to detemine label source. {:?}, index: {:?}, dim: {:?}", + path, labels_index, labels_dim + ), + } + }) .collect::>>()?; Ok(label_set diff --git a/pointcloud/src/summaries/mod.rs b/pointcloud/src/summaries/mod.rs index 4766a43..cdec9c6 100644 --- a/pointcloud/src/summaries/mod.rs +++ b/pointcloud/src/summaries/mod.rs @@ -86,13 +86,16 @@ impl Summary for VecSummary { .for_each(|(m, x)| *m += x * x); self.count += 1; } else { - panic!("Combining a vec of len {:?} and of len {:?}", self.moment1.len(), val.len()); + panic!( + "Combining a vec of len {:?} and of len {:?}", + self.moment1.len(), + val.len() + ); } } else { self.moment1.extend(val); self.moment2.extend(val.iter().map(|x| x * x)) } - } fn combine(&mut self, other: &VecSummary) { self.moment1 @@ -112,7 +115,7 @@ impl Summary for VecSummary { } /// Summary of a bunch of underlying floats -#[derive(Clone,Debug, Serialize, Deserialize, Default)] +#[derive(Clone, Debug, Serialize, Deserialize, Default)] pub struct FloatSummary { /// First moment, see https://en.wikipedia.org/wiki/Moment_(mathematics) pub moment1: f64, @@ -127,7 +130,7 @@ impl Summary for FloatSummary { fn add(&mut self, val: &f64) { self.moment1 += val; - self.moment2 += val*val; + self.moment2 += val * val; self.count += 1; } fn combine(&mut self, other: &FloatSummary) { @@ -142,7 +145,7 @@ impl Summary for FloatSummary { } /// Summary of a bunch of underlying integers, more accurate for int than the float summary -#[derive(Clone,Debug, Serialize, Deserialize, Default)] +#[derive(Clone, Debug, Serialize, Deserialize, Default)] pub struct IntSummary { /// First moment, see https://en.wikipedia.org/wiki/Moment_(mathematics) pub moment1: i64, @@ -157,7 +160,7 @@ impl Summary for IntSummary { fn add(&mut self, val: &i64) { self.moment1 += val; - self.moment2 += val*val; + self.moment2 += val * val; self.count += 1; } fn combine(&mut self, other: &IntSummary) { @@ -171,9 +174,8 @@ impl Summary for IntSummary { } } - /// A summary for a small number of categories. -#[derive(Clone,Debug, Serialize, Deserialize)] +#[derive(Clone, Debug, Serialize, Deserialize)] pub struct StringSummary { /// Hashmap that counts how many of each instance of string there is pub items: HashMap, diff --git a/pygoko/src/layer.rs b/pygoko/src/layer.rs index 2bab67b..16f7753 100644 --- a/pygoko/src/layer.rs +++ b/pygoko/src/layer.rs @@ -104,7 +104,13 @@ impl PyLayer { let mut centers_indexes = Vec::with_capacity(self.layer().len()); self.layer().for_each_node(|pi, _n| { centers_indexes.push(*pi); - centers.extend(self.parameters.point_cloud.point(*pi).unwrap().dense_iter(dim)); + centers.extend( + self.parameters + .point_cloud + .point(*pi) + .unwrap() + .dense_iter(dim), + ); }); let py_center_indexes = Array::from(centers_indexes); let py_centers = Array2::from_shape_vec( @@ -135,7 +141,13 @@ impl PyLayer { .dense_iter(dim), ); for na in child_addresses { - centers.extend(self.parameters.point_cloud.point(na.1).unwrap().dense_iter(dim)); + centers.extend( + self.parameters + .point_cloud + .point(na.1) + .unwrap() + .dense_iter(dim), + ); } let py_centers = Array2::from_shape_vec((count, dim), centers).unwrap(); let gil = GILGuard::acquire(); @@ -149,7 +161,13 @@ impl PyLayer { let singletons = node.singletons(); let mut centers: Vec = Vec::with_capacity(singletons.len() * dim); for pi in singletons { - centers.extend(self.parameters.point_cloud.point(*pi).unwrap().dense_iter(dim)); + centers.extend( + self.parameters + .point_cloud + .point(*pi) + .unwrap() + .dense_iter(dim), + ); } let py_centers = Array2::from_shape_vec((singletons.len(), dim), centers).unwrap(); let gil = GILGuard::acquire(); diff --git a/pygoko/src/node.rs b/pygoko/src/node.rs index 42545d3..4ccaa3e 100644 --- a/pygoko/src/node.rs +++ b/pygoko/src/node.rs @@ -103,17 +103,13 @@ impl PyNode { let mut ret_matrix = Vec::with_capacity(len * dim); self.tree.get_node_and(self.address, |n| { n.singletons().iter().for_each(|pi| { - if let Ok(p) = self.parameters - .point_cloud - .point(*pi) { + if let Ok(p) = self.parameters.point_cloud.point(*pi) { ret_matrix.extend(p.dense_iter(dim)); } }); if n.is_leaf() { - if let Ok(p) = self.parameters - .point_cloud - .point(*n.center_index()) { + if let Ok(p) = self.parameters.point_cloud.point(*n.center_index()) { ret_matrix.extend(p.dense_iter(dim)); } } diff --git a/pygoko/src/plugins.rs b/pygoko/src/plugins.rs index 810120a..aa1de0f 100644 --- a/pygoko/src/plugins.rs +++ b/pygoko/src/plugins.rs @@ -32,7 +32,10 @@ pub struct PyBayesCategoricalTracker { #[pymethods] impl PyBayesCategoricalTracker { pub fn push(&mut self, point: &PyArray1) { - let results = self.tree.path(point.readonly().as_slice().unwrap()).unwrap(); + let results = self + .tree + .path(point.readonly().as_slice().unwrap()) + .unwrap(); self.hkl.add_path(results); } diff --git a/pygoko/src/tree.rs b/pygoko/src/tree.rs index 6e0573b..772c788 100644 --- a/pygoko/src/tree.rs +++ b/pygoko/src/tree.rs @@ -26,8 +26,8 @@ use std::sync::Arc; use goko::plugins::distributions::*; use goko::*; -use pointcloud::*; use pointcloud::loaders::labeled_ram_from_yaml; +use pointcloud::*; use crate::layer::*; use crate::node::*; @@ -78,7 +78,7 @@ impl CoverTree { } pub fn load_yaml_config(&mut self, file_name: String) -> PyResult<()> { let path = Path::new(&file_name); - let point_cloud = Arc::new(labeled_ram_from_yaml::<_,L2>(&path).unwrap()); + let point_cloud = Arc::new(labeled_ram_from_yaml::<_, L2>(&path).unwrap()); let builder = CoverTreeBuilder::from_yaml(&path); self.builder = Some(builder); self.temp_point_cloud = Some(point_cloud); @@ -89,17 +89,17 @@ impl CoverTree { self.metric = metric_name; } - pub fn fit(&mut self, data: Option<&PyArray2>, labels: Option<&PyArray1>) -> PyResult<()> { + pub fn fit( + &mut self, + data: Option<&PyArray2>, + labels: Option<&PyArray1>, + ) -> PyResult<()> { let point_cloud = if let Some(data) = data { let len = data.shape()[0]; let data_dim = data.shape()[1]; let my_labels: Vec = match labels { - Some(labels) => { - Vec::from(labels.readonly().as_slice().unwrap()) - } - None => { - vec![0; len] - } + Some(labels) => Vec::from(labels.readonly().as_slice().unwrap()), + None => vec![0; len], }; Arc::new(DefaultLabeledCloud::::new_simple( Vec::from(data.readonly().as_slice().unwrap()), @@ -113,7 +113,7 @@ impl CoverTree { panic!("No known point_cloud"); } }; - + let builder = self.builder.take(); self.writer = Some(builder.unwrap().build(point_cloud).unwrap()); let writer = self.writer.as_mut().unwrap(); @@ -140,7 +140,9 @@ impl CoverTree { //pub fn layers(&self) -> pub fn top_scale(&self) -> Option { - self.writer.as_ref().map(|w| w.reader().scale_range().end - 1) + self.writer + .as_ref() + .map(|w| w.reader().scale_range().end - 1) } pub fn bottom_scale(&self) -> Option { @@ -185,22 +187,17 @@ impl CoverTree { pub fn knn(&self, point: &PyArray1, k: usize) -> Vec<(f32, usize)> { let reader = self.writer.as_ref().unwrap().reader(); - reader - .knn(point.readonly().as_slice().unwrap(), k) - .unwrap() + reader.knn(point.readonly().as_slice().unwrap(), k).unwrap() } pub fn known_path(&self, point_index: usize) -> Vec<(f32, (i32, usize))> { let reader = self.writer.as_ref().unwrap().reader(); - reader - .known_path(point_index).unwrap() + reader.known_path(point_index).unwrap() } pub fn path(&self, point: &PyArray1) -> Vec<(f32, (i32, usize))> { let reader = self.writer.as_ref().unwrap().reader(); - reader - .path(point.readonly().as_slice().unwrap()) - .unwrap() + reader.path(point.readonly().as_slice().unwrap()).unwrap() } pub fn kl_div_dirichlet( diff --git a/pygoko/tests/ember2018.py b/pygoko/tests/ember2018.py index d9b7ee7..cebbbd4 100644 --- a/pygoko/tests/ember2018.py +++ b/pygoko/tests/ember2018.py @@ -2,7 +2,7 @@ import pygoko tree = pygoko.CoverTree() -tree.load_yaml_config("../../data/ember_complex.yml") +tree.load_yaml_config("../data/ember_complex.yml") tree.fit() From f62c49358dfd6af3da288787ffad03ad42e590b2 Mon Sep 17 00:00:00 2001 From: sven Date: Tue, 28 Jul 2020 16:13:13 -0700 Subject: [PATCH 02/10] Finished second partition type --- goko/examples/ember_sequence_track.rs | 60 ++--- goko/protos/tree_file_format.proto | 1 + goko/src/covertree/builders.rs | 217 ++++++++++++++---- goko/src/covertree/data_caches.rs | 97 ++++---- goko/src/covertree/node.rs | 2 +- goko/src/covertree/tree.rs | 53 ++++- goko/src/lib.rs | 3 +- goko/src/plugins/distributions/categorical.rs | 20 +- goko/src/plugins/distributions/dirichlet.rs | 109 +++++++-- goko/src/plugins/distributions/mod.rs | 26 ++- goko/src/tree_file_format.rs | 55 ++++- 11 files changed, 463 insertions(+), 180 deletions(-) diff --git a/goko/examples/ember_sequence_track.rs b/goko/examples/ember_sequence_track.rs index aefd270..acb618a 100644 --- a/goko/examples/ember_sequence_track.rs +++ b/goko/examples/ember_sequence_track.rs @@ -32,42 +32,21 @@ use std::sync::Arc; use std::time; use goko::plugins::distributions::*; -use rand::prelude::*; - -use rayon::prelude::*; +use goko::query_interface::BulkInterface; fn build_tree() -> CoverTreeWriter, VecLabels>> { - let file_name = "../data/ember_complex.yml"; + let file_name = "data/ember_complex.yml"; let path = Path::new(file_name); if !path.exists() { panic!(file_name.to_owned() + &" does not exist".to_string()); } let builder = CoverTreeBuilder::from_yaml(&path); - let point_cloud = vec_labeled_ram_from_yaml("../data/ember_complex_test.yml").unwrap(); + let point_cloud = vec_labeled_ram_from_yaml("data/ember_complex.yml").unwrap(); builder.build(Arc::new(point_cloud)).unwrap() } fn build_test_set() -> SimpleLabeledCloud, VecLabels> { - vec_labeled_ram_from_yaml("../data/ember_complex_test.yml").unwrap() -} - -fn test_run( - mut tracker: BayesCategoricalTracker, VecLabels>>, - test_set: &SimpleLabeledCloud, VecLabels>, - count: usize, -) -> Vec { - let mut rng = thread_rng(); - let mut stats = Vec::new(); - for i in 0..count { - let point_index: usize = rng.gen_range(0, test_set.len()); - let point = test_set.point(point_index).unwrap(); - let trace = tracker.tree_reader().path(point).unwrap(); - tracker.add_path(trace); - if i % 5 == 0 { - stats.push(tracker.current_stats()); - } - } - stats + vec_labeled_ram_from_yaml("data/ember_complex_test.yml").unwrap() } fn main() { @@ -80,25 +59,26 @@ fn main() { println!("Tree has {} nodes", ct_reader.node_count()); let prior_weight = 1.0; let observation_weight = 1.3; - let window_size = 40; - let sequence_len = 1000; - - let trackers: Vec, VecLabels>>> = (0 - ..1000) - .map(|_| { - BayesCategoricalTracker::new(prior_weight, observation_weight, window_size, ct.reader()) - }) - .collect(); + let window_size = 0; + let mut tracker = + BayesCategoricalTracker::new(prior_weight, observation_weight, window_size, ct.reader()); let start = time::Instant::now(); - let _stats: Vec> = trackers - .into_par_iter() - .map(|tracker| test_run(tracker, &test_set, sequence_len)) + + let points: Vec> = (0..test_set.len()) + .map(|i| test_set.point(i).unwrap()) .collect(); - let elapse = start.elapsed().as_secs(); + let bulk = BulkInterface::new(tracker.tree_reader().clone()); + let mut paths = bulk.path(&points); + for path in paths.drain(0..) { + tracker.add_path(path.unwrap()); + } + + let elapse = start.elapsed().as_millis(); println!( - "Time elapsed {:?}, time per sequence {}", + "Time elapsed {:?} milliseconds, time per sequence {} milliseconds", elapse, - (elapse as f64) / 1000.0 + (elapse as f64) / (test_set.len() as f64) ); + println!("stats: {:?}", tracker.current_stats()); } diff --git a/goko/protos/tree_file_format.proto b/goko/protos/tree_file_format.proto index 5064477..723f597 100644 --- a/goko/protos/tree_file_format.proto +++ b/goko/protos/tree_file_format.proto @@ -30,6 +30,7 @@ message CoreProto { float scale_base = 2; uint64 cutoff = 3; sint32 resolution = 4; + string partition_type = 5; uint64 dim = 7; uint64 count = 8; diff --git a/goko/src/covertree/builders.rs b/goko/src/covertree/builders.rs index b5347ff..956b62d 100644 --- a/goko/src/covertree/builders.rs +++ b/goko/src/covertree/builders.rs @@ -47,12 +47,15 @@ type NodeSplitResult = GokoResult<(i32, PointIndex, CoverNode)>; impl BuilderNode { fn new( parameters: &CoverTreeParameters, - partition: &str, + partition_type: PartitionType, ) -> GokoResult { - let covered = if partition == "first" { - CoveredData::FirstCoveredData(FirstCoveredData::new::(¶meters.point_cloud)?) - } else { - CoveredData::NearestCoveredData(NearestCoveredData::new::(¶meters.point_cloud)?) + let covered = match partition_type { + PartitionType::Nearest => CoveredData::NearestCoveredData( + NearestCoveredData::new::(¶meters.point_cloud)?, + ), + PartitionType::First => { + CoveredData::FirstCoveredData(FirstCoveredData::new::(¶meters.point_cloud)?) + } }; let scale_index = (covered.max_distance()).log(parameters.scale_base).ceil() as i32; Ok(BuilderNode { @@ -100,36 +103,37 @@ impl BuilderNode { /* Occasionally there's a small cluster split off of at a low min_res_index. This brings the scale-index down/min_res_index up quickly, locally. */ - let mut new_nodes = - if self.covered.len() <= parameters.leaf_cutoff || scale_index < parameters.min_res_index { - //println!("== This is getting cut down by parameters =="); - node.insert_singletons(self.covered.into_indexes()); - vec![] - } else { - let next_scale_index = min( - scale_index - 1, - max( - radius.log(parameters.scale_base).ceil() as i32, - parameters.min_res_index, - ), - ); - match self.covered { - CoveredData::FirstCoveredData(covered) => BuilderNode::split_first( - &mut node, - current_address, - covered, - next_scale_index, - parameters, - )?, - CoveredData::NearestCoveredData(covered) => BuilderNode::split_nearest( - &mut node, - current_address, - covered, - next_scale_index, - parameters, - )?, - } - }; + let mut new_nodes = if self.covered.len() <= parameters.leaf_cutoff + || scale_index < parameters.min_res_index + { + //println!("== This is getting cut down by parameters =="); + node.insert_singletons(self.covered.into_indexes()); + vec![] + } else { + let next_scale_index = min( + scale_index - 1, + max( + radius.log(parameters.scale_base).ceil() as i32, + parameters.min_res_index, + ), + ); + match self.covered { + CoveredData::FirstCoveredData(covered) => BuilderNode::split_first( + &mut node, + current_address, + covered, + next_scale_index, + parameters, + )?, + CoveredData::NearestCoveredData(covered) => BuilderNode::split_nearest( + &mut node, + current_address, + covered, + next_scale_index, + parameters, + )?, + } + }; if new_nodes.len() == 1 && new_nodes[0].covered.len() == 1 { node.remove_children(); @@ -152,16 +156,17 @@ impl BuilderNode { parameters: &Arc>, ) -> GokoResult> { let next_scale = parameters.scale_base.powi(split_scale_index); - let mut splits = covered.split(next_scale,¶meters.point_cloud)?; + let (nested_potential, mut splits) = covered.split(next_scale, ¶meters.point_cloud)?; let mut new_nodes = Vec::new(); - println!("Splits : {:#?}", splits); + + let mut inserts = Vec::new(); for potential in splits.drain(0..) { - if potential.len() > 1 && parameters.use_singletons { + if potential.len() == 1 && parameters.use_singletons { parent_node.insert_singleton(potential.center_index); } else { - parent_node - .insert_child((split_scale_index, potential.center_index), potential.len())?; + inserts.push(((split_scale_index, potential.center_index), potential.len())); + let new_node = BuilderNode { parent_address: Some(parent_address), scale_index: split_scale_index, @@ -173,6 +178,24 @@ impl BuilderNode { .fetch_add(1, atomic::Ordering::SeqCst); } } + if inserts.len() > 0 || !(nested_potential.len() == 1 && parameters.use_singletons) { + parent_node.insert_nested_child(split_scale_index, nested_potential.len())?; + + let new_node = BuilderNode { + parent_address: Some(parent_address), + scale_index: split_scale_index, + covered: CoveredData::NearestCoveredData(nested_potential), + }; + new_nodes.push(new_node); + parameters + .total_nodes + .fetch_add(1, atomic::Ordering::SeqCst); + + for ((split_scale_index, potential_center_index), potential_len) in inserts { + parent_node + .insert_child((split_scale_index, potential_center_index), potential_len)?; + } + } Ok(new_nodes) } @@ -255,7 +278,7 @@ impl BuilderNode { } /// A construction object for a covertree. -#[derive(Debug, Default)] +#[derive(Debug)] pub struct CoverTreeBuilder { /// See paper or main description, governs the number of children of each node. Higher is more. pub scale_base: f32, @@ -265,11 +288,26 @@ pub struct CoverTreeBuilder { pub min_res_index: i32, /// If you don't want singletons messing with your tree and want everything to be a node or a element of leaf node, make this true. pub use_singletons: bool, + /// Partition type of the tree + pub partition_type: PartitionType, /// Printing verbosity. 2 is the default and gives a progress bar. Still not fully pulled thru the codebase. /// This should be replaced by a logging solution pub verbosity: u32, } +impl Default for CoverTreeBuilder { + fn default() -> CoverTreeBuilder { + CoverTreeBuilder { + scale_base: 2.0, + leaf_cutoff: 1, + min_res_index: -10, + use_singletons: true, + partition_type: PartitionType::Nearest, + verbosity: 2, + } + } +} + impl CoverTreeBuilder { /// Creates a new builder with sensible defaults. pub fn new() -> CoverTreeBuilder { @@ -278,6 +316,7 @@ impl CoverTreeBuilder { leaf_cutoff: 1, min_res_index: -10, use_singletons: true, + partition_type: PartitionType::Nearest, verbosity: 2, } } @@ -287,11 +326,17 @@ impl CoverTreeBuilder { let config = read_to_string(&path).expect("Unable to read config file"); let params_files = YamlLoader::load_from_str(&config).unwrap(); let params = ¶ms_files[0]; + let partition_type = if "first" == params["partition_type"].as_str().unwrap_or("nearest") { + PartitionType::First + } else { + PartitionType::Nearest + }; CoverTreeBuilder { scale_base: params["scale_base"].as_f64().unwrap_or(2.0) as f32, leaf_cutoff: params["leaf_cutoff"].as_i64().unwrap_or(1) as usize, min_res_index: params["min_res_index"].as_i64().unwrap_or(-10) as i32, use_singletons: params["use_singletons"].as_bool().unwrap_or(true), + partition_type, verbosity: params["verbosity"].as_i64().unwrap_or(2) as u32, } } @@ -330,12 +375,13 @@ impl CoverTreeBuilder { leaf_cutoff: self.leaf_cutoff, min_res_index: self.min_res_index, use_singletons: self.use_singletons, + partition_type: self.partition_type, point_cloud, verbosity: self.verbosity, plugins: RwLock::new(TreePluginSet::new()), }; - let root = BuilderNode::new(¶meters, "first")?; + let root = BuilderNode::new(¶meters, self.partition_type)?; let root_address = root.address(); let scale_range = root_address.0 - parameters.min_res_index; let mut layers = Vec::with_capacity(scale_range as usize); @@ -428,6 +474,7 @@ mod tests { leaf_cutoff: 0, min_res_index: -9, use_singletons: true, + partition_type: PartitionType::Nearest, point_cloud, verbosity: 0, plugins: RwLock::new(TreePluginSet::new()), @@ -435,7 +482,45 @@ mod tests { } #[test] - fn splits_conditions() { + fn nearest_splits_conditions() { + let mut data = Vec::with_capacity(20); + for _i in 0..19 { + data.push(rand::random::()); + } + data.push(0.0); + + let test_parameters = create_test_parameters(data, 1); + let build_node = BuilderNode::new(&test_parameters, PartitionType::Nearest).unwrap(); + let (scale_index, center_index) = build_node.address(); + + println!("{:?}", build_node); + println!( + "The center_index for the covered data should be 19 but is {}", + build_node.covered.center_index() + ); + assert!(center_index == 19); + println!("The scale_index should be 0, but is {}", scale_index); + assert!(scale_index == 0); + + let (new_node, unfinished_nodes) = build_node.split(&test_parameters).unwrap(); + println!("New Node: {:#?}", new_node); + let split_count = test_parameters.total_nodes.load(atomic::Ordering::SeqCst) - 1; + println!( + "We should have split count be equal to the work count: split {} , work {}", + split_count, + unfinished_nodes.len() + ); + println!("We shouldn't be a leaf: {}", new_node.is_leaf()); + assert!(!new_node.is_leaf()); + println!( + "We should have children count be equal to the split count: {}", + new_node.children_len() + ); + assert!(new_node.children_len() == split_count); + } + + #[test] + fn first_splits_conditions() { let mut data = Vec::with_capacity(20); for _i in 0..19 { data.push(rand::random::()); @@ -443,7 +528,7 @@ mod tests { data.push(0.0); let test_parameters = create_test_parameters(data, 1); - let build_node = BuilderNode::new(&test_parameters,"nearest").unwrap(); + let build_node = BuilderNode::new(&test_parameters, PartitionType::First).unwrap(); let (scale_index, center_index) = build_node.address(); println!("{:?}", build_node); @@ -472,11 +557,48 @@ mod tests { } #[test] - fn tree_structure_condition() { + fn tree_first_structure_condition() { + let data = vec![0.49, 0.491, -0.49, 0.0]; + let test_parameters = create_test_parameters(data, 1); + + let build_node = BuilderNode::new(&test_parameters, PartitionType::First).unwrap(); + + let (node_sender, node_receiver): ( + Sender>)>>, + Receiver>)>>, + ) = unbounded(); + let node_sender = Arc::new(node_sender); + + build_node.split_parallel(&test_parameters, &node_sender); + thread::sleep(time::Duration::from_millis(100)); + let split_count = test_parameters.total_nodes.load(atomic::Ordering::SeqCst) - 1; + println!( + "Split count {}, node_receiver {}", + split_count, + node_receiver.len() + ); + assert!(split_count + 1 == node_receiver.len()); + assert!(split_count == 3); + while let Ok(pat) = node_receiver.try_recv() { + let (scale_index, center_index, node) = pat.unwrap(); + println!("{:?}", node); + match (scale_index, center_index) { + (-1, 3) => assert!(!node.is_leaf()), + (-2, 3) => assert!(node.is_leaf()), + (-2, 2) => assert!(node.is_leaf()), + (-2, 0) => assert!(!node.is_leaf()), + (-2, 1) => assert!(!node.is_leaf()), + _ => {} + }; + } + } + + #[test] + fn tree_nearest_structure_condition() { let data = vec![0.49, 0.491, -0.49, 0.0]; let test_parameters = create_test_parameters(data, 1); - let build_node = BuilderNode::new(&test_parameters, "nearest").unwrap(); + let build_node = BuilderNode::new(&test_parameters, PartitionType::Nearest).unwrap(); let (node_sender, node_receiver): ( Sender>)>>, @@ -497,6 +619,7 @@ mod tests { while let Ok(pat) = node_receiver.try_recv() { let (scale_index, center_index, node) = pat.unwrap(); println!("{:?}", node); + match (scale_index, center_index) { (-1, 3) => assert!(!node.is_leaf()), (-2, 3) => assert!(node.is_leaf()), @@ -519,6 +642,7 @@ mod tests { min_res_index: -9, use_singletons: true, verbosity: 0, + partition_type: PartitionType::First, }; let tree = builder.build(point_cloud).unwrap(); let reader = tree.reader(); @@ -561,6 +685,7 @@ mod tests { min_res_index: -9, use_singletons: false, verbosity: 0, + partition_type: PartitionType::First, }; let tree = builder.build(point_cloud).unwrap(); let reader = tree.reader(); diff --git a/goko/src/covertree/data_caches.rs b/goko/src/covertree/data_caches.rs index 6893bc9..6026b7a 100644 --- a/goko/src/covertree/data_caches.rs +++ b/goko/src/covertree/data_caches.rs @@ -24,7 +24,7 @@ use rand::{thread_rng, Rng}; use std::cmp::Ordering; use std::sync::Arc; -#[derive(Clone,Debug)] +#[derive(Clone, Debug)] pub(crate) enum CoveredData { FirstCoveredData(FirstCoveredData), NearestCoveredData(NearestCoveredData), @@ -181,6 +181,7 @@ pub(crate) struct NearestCoveredData { centers: Vec, dists: Vec>, point_indexes: Vec, + center_dists: Vec, pub(crate) center_index: PointIndex, } @@ -188,14 +189,15 @@ impl NearestCoveredData { pub(crate) fn new(point_cloud: &Arc) -> GokoResult { let mut point_indexes = point_cloud.reference_indexes(); let center_index = point_indexes.pop().unwrap(); - let new_dists = point_cloud.distances_to_point_index(center_index, &point_indexes)?; - let dists = vec![new_dists]; - let centers = vec![center_index]; + let center_dists = point_cloud.distances_to_point_index(center_index, &point_indexes)?; + let dists = vec![]; + let centers = vec![]; Ok(NearestCoveredData { centers, dists, point_indexes, center_index, + center_dists, }) } @@ -204,10 +206,10 @@ impl NearestCoveredData { radius: f32, point_cloud: &Arc, ) -> GokoResult<()> { - let mut coverage: Vec = self.dists[0].iter().map(|d| d < &radius).collect(); + let mut coverage: Vec = self.center_dists.iter().map(|d| d < &radius).collect(); let mut rng = thread_rng(); - let mut i = 5; - while coverage.iter().any(|b| !b) && i > 0 { + + while coverage.iter().any(|b| !b) { let uncovered_indexes: Vec = self .point_indexes .iter() @@ -224,7 +226,6 @@ impl NearestCoveredData { .for_each(|(a, d)| *a = *a || (d < &radius)); self.dists.push(new_dists); self.centers.push(center_index); - i -= 1; } Ok(()) @@ -232,20 +233,28 @@ impl NearestCoveredData { fn add_point(&mut self, point_index: PointIndex, distance: f32) { if point_index != self.center_index { - self.dists[0].push(distance); + self.center_dists.push(distance); self.point_indexes.push(point_index); } } - fn assign_to_nearest(&self) -> Vec { + fn assign_to_nearest(&self) -> (NearestCoveredData, Vec) { + let mut new_center_coverage = NearestCoveredData { + centers: vec![], + dists: vec![], + point_indexes: Vec::new(), + center_index: self.center_index, + center_dists: Vec::new(), + }; let mut new_coverage: Vec = self .centers .iter() .map(|center_index| NearestCoveredData { - centers: Vec::new(), - dists: vec![vec![]], + centers: vec![], + dists: vec![], point_indexes: Vec::new(), center_index: *center_index, + center_dists: Vec::new(), }) .collect(); @@ -256,19 +265,23 @@ impl NearestCoveredData { .enumerate() .map(|(dist_index, dists)| (dist_index, dists[i])) .min_by(|(_di, d), (_ci, c)| d.partial_cmp(c).unwrap_or(Ordering::Equal)) - .unwrap(); - new_coverage[index].add_point(*pi, d); + .unwrap_or((0, f32::MAX)); + if self.center_dists[i] < d { + new_center_coverage.add_point(*pi, self.center_dists[i]); + } else { + new_coverage[index].add_point(*pi, d); + } } - new_coverage + (new_center_coverage, new_coverage) } pub(crate) fn split( mut self, radius: f32, point_cloud: &Arc, - ) -> GokoResult> { - self.cover_thyself(radius,point_cloud)?; + ) -> GokoResult<(NearestCoveredData, Vec)> { + self.cover_thyself(radius, point_cloud)?; Ok(self.assign_to_nearest()) } @@ -277,7 +290,7 @@ impl NearestCoveredData { } pub(crate) fn max_distance(&self) -> f32 { - self.dists[0] + self.center_dists .iter() .cloned() .fold(-1. / 0. /* -inf */, f32::max) @@ -385,47 +398,47 @@ mod tests { let point_cloud = Arc::new(DefaultLabeledCloud::::new_simple(data, 1, labels)); let mut cache = NearestCoveredData::new(&point_cloud).unwrap(); - cache.cover_thyself(1.0,&point_cloud).unwrap(); + cache.cover_thyself(1.0, &point_cloud).unwrap(); - assert_eq!(2, cache.dists.len()); + assert_eq!(1, cache.dists.len()); + assert_eq!(4, cache.center_dists.len()); assert_eq!(4, cache.dists[0].len()); - assert_eq!(4, cache.dists[1].len()); println!("{:#?}", cache); - let splits = cache.assign_to_nearest(); + let (nested_split, splits) = cache.assign_to_nearest(); println!("{:#?}", splits); - assert_eq!(splits.len(), 2); - assert_eq!(splits[0].len(), 1); - assert_eq!(splits[1].len(), 4); + assert_eq!(splits.len(), 1); + assert_eq!(nested_split.len(), 1); + assert_eq!(splits[0].len(), 4); } #[test] fn nearest_splits_nearest_1() { let cache = NearestCoveredData { center_index: 1, - dists: vec![ - vec![2.0, 1.0, 2.0, 0.0, 1.0], - vec![0.0, 2.0, 0.0, 1.0, 2.0], - vec![1.0, 0.0, 1.0, 2.0, 0.0], - ], + dists: vec![vec![0.0, 2.0, 0.0, 1.0, 2.0], vec![1.0, 0.0, 1.0, 2.0, 0.0]], point_indexes: vec![0, 2, 3, 4, 5], - centers: vec![1, 0, 2], + centers: vec![0, 2], + center_dists: vec![2.0, 1.0, 2.0, 0.0, 1.0], }; - let splits = cache.assign_to_nearest(); - assert_eq!(splits.len(), 3); - assert_eq!(splits[0].center_index, 1); - assert_eq!(splits[1].center_index, 0); - assert_eq!(splits[2].center_index, 2); + let (nested_split, splits) = cache.assign_to_nearest(); + + println!("Nested Split: {:?}", nested_split); + println!("Splits: {:?}", splits); + assert_eq!(splits.len(), 2); + assert_eq!(nested_split.center_index, 1); + assert_eq!(splits[0].center_index, 0); + assert_eq!(splits[1].center_index, 2); - assert_eq!(splits[0].point_indexes[0], 4); - assert_eq!(splits[1].point_indexes[0], 3); - assert_eq!(splits[2].point_indexes[0], 5); + assert_eq!(nested_split.point_indexes[0], 4); + assert_eq!(splits[0].point_indexes[0], 3); + assert_eq!(splits[1].point_indexes[0], 5); - assert_eq!(splits[0].dists[0], vec![0.0]); - assert_eq!(splits[1].dists[0], vec![0.0]); - assert_eq!(splits[2].dists[0], vec![0.0]); + assert_eq!(nested_split.center_dists, vec![0.0]); + assert_eq!(splits[0].center_dists, vec![0.0]); + assert_eq!(splits[1].center_dists, vec![0.0]); } /* diff --git a/goko/src/covertree/node.rs b/goko/src/covertree/node.rs index 767b3c7..7e17e91 100644 --- a/goko/src/covertree/node.rs +++ b/goko/src/covertree/node.rs @@ -301,7 +301,7 @@ impl CoverNode { /// Gives the child that the point would be inserted into if the /// point just happened to never be picked as a center. This is the first child node that covers /// the point. - pub fn covering_child<'a, P: Into>>( + pub fn first_covering_child<'a, P: Into>>( &self, scale_base: f32, dist_to_center: f32, diff --git a/goko/src/covertree/tree.rs b/goko/src/covertree/tree.rs index ec80f76..eaf0961 100644 --- a/goko/src/covertree/tree.rs +++ b/goko/src/covertree/tree.rs @@ -53,6 +53,17 @@ use std::slice::Iter; use plugins::labels::*; +/// When 2 spheres overlap under a node, and there is a point in the overlap we have to decide +/// to which sphere it belongs. As we create the nodes in a particular sequence, we can assign them +/// to the first to be created or we can assign it to the nearest. +#[derive(Debug, Copy, Clone)] +pub enum PartitionType { + /// Conflicts assigning a point to several eligible nodes are assigned to the nearest node. + Nearest, + /// Conflicts assigning a point to several eligible nodes are assigned to the first node to be created. + First, +} + /// Container for the parameters governing the construction of the covertree #[derive(Debug)] pub struct CoverTreeParameters { @@ -67,6 +78,8 @@ pub struct CoverTreeParameters { pub min_res_index: i32, /// If you don't want singletons messing with your tree and want everything to be a node or a element of leaf node, make this true. pub use_singletons: bool, + /// The partition type of the tree + pub partition_type: PartitionType, /// The point cloud this tree references pub point_cloud: Arc, /// This should be replaced by a logging solution @@ -383,14 +396,22 @@ impl CoverTreeReader { let mut current_distance = D::Metric::dist(&root_center, point)?; let mut current_address = self.root_address; let mut trace = vec![(current_distance, current_address)]; - while let Some(nearest) = self.get_node_and(current_address, |n| { - n.covering_child( - self.parameters.scale_base, - current_distance, - point, - &self.parameters.point_cloud, - ) - }) { + while let Some(nearest) = + self.get_node_and(current_address, |n| match self.parameters.partition_type { + PartitionType::Nearest => n.nearest_covering_child( + self.parameters.scale_base, + current_distance, + point, + &self.parameters.point_cloud, + ), + PartitionType::First => n.first_covering_child( + self.parameters.scale_base, + current_distance, + point, + &self.parameters.point_cloud, + ), + }) + { if let Some(nearest) = nearest? { trace.push(nearest); current_distance = nearest.0; @@ -616,6 +637,12 @@ impl CoverTreeWriter { /// Loads a tree from a protobuf. There's a `load_tree` in `utils` that handles loading from a path to a protobuf file. pub fn load(cover_proto: &CoreProto, point_cloud: Arc) -> GokoResult> { + let partition_type = if cover_proto.partition_type == "first" { + PartitionType::First + } else { + PartitionType::Nearest + }; + let parameters = Arc::new(CoverTreeParameters { total_nodes: atomic::AtomicUsize::new(0), use_singletons: cover_proto.use_singletons, @@ -624,6 +651,7 @@ impl CoverTreeWriter { min_res_index: cover_proto.resolution as i32, point_cloud, verbosity: 2, + partition_type, plugins: RwLock::new(TreePluginSet::new()), }); let root_address = ( @@ -678,6 +706,10 @@ impl CoverTreeWriter { /// Encodes the tree into a protobuf. See `utils::save_tree` for saving to a file on disk. pub fn save(&self) -> CoreProto { let mut cover_proto = CoreProto::new(); + match self.parameters.partition_type { + PartitionType::First => cover_proto.set_partition_type("first".to_string()), + PartitionType::Nearest => cover_proto.set_partition_type("nearest".to_string()), + } cover_proto.set_scale_base(self.parameters.scale_base); cover_proto.set_cutoff(self.parameters.leaf_cutoff as u64); cover_proto.set_resolution(self.parameters.min_res_index); @@ -724,6 +756,7 @@ pub(crate) mod tests { leaf_cutoff: 1, min_res_index: -9, use_singletons: true, + partition_type: PartitionType::Nearest, verbosity: 0, }; builder.build(Arc::new(point_cloud)).unwrap() @@ -769,6 +802,7 @@ pub(crate) mod tests { leaf_cutoff: 1, min_res_index: -9, use_singletons: false, + partition_type: PartitionType::Nearest, verbosity: 0, }; let tree = builder.build(Arc::new(point_cloud)).unwrap(); @@ -882,6 +916,7 @@ pub(crate) mod tests { leaf_cutoff: 1, min_res_index: -9, use_singletons: false, + partition_type: PartitionType::Nearest, verbosity: 0, }; let mut tree = builder.build(Arc::new(point_cloud)).unwrap(); @@ -911,6 +946,7 @@ pub(crate) mod tests { leaf_cutoff: 1, min_res_index: -9, use_singletons: false, + partition_type: PartitionType::Nearest, verbosity: 0, }; let tree = builder.build(Arc::new(point_cloud)).unwrap(); @@ -934,6 +970,7 @@ pub(crate) mod tests { leaf_cutoff: 1, min_res_index: -9, use_singletons: false, + partition_type: PartitionType::Nearest, verbosity: 0, }; let tree = builder.build(Arc::clone(&point_cloud)).unwrap(); diff --git a/goko/src/lib.rs b/goko/src/lib.rs index 7238f1d..022287f 100644 --- a/goko/src/lib.rs +++ b/goko/src/lib.rs @@ -18,11 +18,12 @@ */ #![allow(dead_code)] -#![deny(warnings)] +//#![deny(warnings)] #![warn(missing_docs)] #![doc(test(attr(allow(unused_variables), deny(warnings))))] #![feature(binary_heap_into_iter_sorted)] #![feature(iterator_fold_self)] +#![feature(associated_type_defaults)] //! # Goko //! This is an lock-free efficient implementation of a covertree for data science. The traditional diff --git a/goko/src/plugins/distributions/categorical.rs b/goko/src/plugins/distributions/categorical.rs index 7af09bc..24c1f40 100644 --- a/goko/src/plugins/distributions/categorical.rs +++ b/goko/src/plugins/distributions/categorical.rs @@ -12,14 +12,15 @@ use crate::plugins::*; /// Stored as a flat vector in the order of the node addresses. #[derive(Debug, Clone, Default)] pub struct Categorical { - child_counts: Vec<(NodeAddress, f64)>, - singleton_count: f64, + pub(crate) child_counts: Vec<(NodeAddress, f64)>, + pub(crate) singleton_count: f64, } impl DiscreteDistribution for Categorical { /// Pass none if you want to test for a singleton, returns 0 if fn ln_prob(&self, loc: Option<&NodeAddress>) -> Option { - if self.total() > 0.0 { + let total = self.total(); + if total > 0.0 { let ax = match loc { Some(ca) => self .child_counts @@ -28,7 +29,7 @@ impl DiscreteDistribution for Categorical { .unwrap_or(0.0), None => self.singleton_count, }; - Some(ax.ln() - self.total().ln()) + Some(ax.ln() - total.ln()) } else { None } @@ -79,7 +80,14 @@ impl Categorical { .fold(0.0, |x, a| x + a) } - fn add_child_pop(&mut self, loc: Option, count: f64) { + pub(crate) fn merge(&mut self, other: &Categorical) { + for (na, c) in &other.child_counts { + self.add_child_pop(Some(*na), *c); + } + self.add_child_pop(None, other.singleton_count); + } + + pub(crate) fn add_child_pop(&mut self, loc: Option, count: f64) { match loc { Some(ca) => match self.child_counts.binary_search_by_key(&ca, |&(a, _)| a) { Ok(index) => self.child_counts[index].1 += count, @@ -89,7 +97,7 @@ impl Categorical { } } - fn remove_child_pop(&mut self, loc: Option, count: f64) { + pub(crate) fn remove_child_pop(&mut self, loc: Option, count: f64) { match loc { Some(ca) => { if let Ok(index) = self.child_counts.binary_search_by_key(&ca, |&(a, _)| a) { diff --git a/goko/src/plugins/distributions/dirichlet.rs b/goko/src/plugins/distributions/dirichlet.rs index b4d6a02..e02eb59 100644 --- a/goko/src/plugins/distributions/dirichlet.rs +++ b/goko/src/plugins/distributions/dirichlet.rs @@ -83,9 +83,49 @@ impl Dirichlet { } } impl DiscreteBayesianDistribution for Dirichlet { + type Evidence = Categorical; + fn add_observation(&mut self, loc: Option) { self.add_child_pop(loc, 1.0); } + + fn add_evidence(&mut self, other: &Categorical) { + for (na, c) in &other.child_counts { + self.add_child_pop(Some(*na), *c); + } + self.add_child_pop(None, other.singleton_count); + } + + fn posterior_kl_divergence(&self, other: &Categorical) -> Option { + let my_total = self.total(); + let other_total = other.total() + my_total; + let mut my_total_lng = 0.0; + let mut other_total_lng = 0.0; + let mut digamma_portion = 0.0; + if self.singleton_count > 0.0 { + other_total_lng += ln_gamma(other.singleton_count + self.singleton_count); + my_total_lng += ln_gamma(self.singleton_count); + digamma_portion -= other.singleton_count + * (digamma(self.singleton_count) - digamma(my_total)); + } + for (other_ca, other_ca_count) in other.child_counts.iter() + { + let ca_count = self.child_counts[self.child_counts.binary_search_by_key(other_ca, |&(a, _)| a).unwrap()].1; + my_total_lng += ln_gamma(ca_count); + other_total_lng += ln_gamma(*other_ca_count + ca_count); + digamma_portion -= *other_ca_count * (digamma(ca_count) - digamma(my_total)); + } + + let kld = ln_gamma(my_total) - my_total_lng - ln_gamma(other_total) + + other_total_lng + + digamma_portion; + // for floating point errors, sometimes this is -0.000000001 + if kld < 0.0 { + Some(0.0) + } else { + Some(kld) + } + } } impl DiscreteDistribution for Dirichlet { fn ln_prob(&self, loc: Option<&NodeAddress>) -> Option { @@ -186,7 +226,7 @@ impl GokoPlugin for GokoDirichlet { /// Computes a frequentist KL divergence calculation on each node the sequence touches. pub struct BayesCategoricalTracker { - running_distributions: HashMap, + running_evidence: HashMap, sequence: VecDeque>, window_size: usize, prior_weight: f64, @@ -198,8 +238,8 @@ impl fmt::Debug for BayesCategoricalTracker { fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { write!( f, - "PointCloud {{ sequence: {:?}, window_size: {} prior_weight: {}, observation_weight: {}, running_distributions: {:#?}}}", - self.sequence, self.window_size, self.prior_weight, self.observation_weight, self.running_distributions, + "PointCloud {{ sequence: {:?}, window_size: {} prior_weight: {}, observation_weight: {}, running_evidence: {:#?}}}", + self.sequence, self.window_size, self.prior_weight, self.observation_weight, self.running_evidence, ) } } @@ -213,7 +253,7 @@ impl BayesCategoricalTracker { reader: CoverTreeReader, ) -> BayesCategoricalTracker { BayesCategoricalTracker { - running_distributions: HashMap::new(), + running_evidence: HashMap::new(), sequence: VecDeque::new(), window_size, prior_weight, @@ -222,9 +262,16 @@ impl BayesCategoricalTracker { } } - /// Accessor for the reader associated to this tracker - pub fn reader(&self) -> &CoverTreeReader { - &self.reader + /// Appends a tracker to this one, + pub fn append(mut self, other: &Self) -> Self { + for (k, v) in other.running_evidence.iter() { + self.running_evidence + .entry(*k) + .and_modify(|e| e.merge(v)) + .or_insert(v.clone()); + } + self.sequence.extend(other.sequence.iter().cloned()); + self } fn get_distro(&self, address: NodeAddress) -> Dirichlet { @@ -245,23 +292,15 @@ impl BayesCategoricalTracker { let mut child_address_iter = trace.iter().map(|(_, ca)| ca); child_address_iter.next(); for (parent, child) in parent_address_iter.zip(child_address_iter) { - if !self.running_distributions.contains_key(parent) { - self.running_distributions - .insert(*parent, self.get_distro(*parent)); - } - self.running_distributions - .get_mut(parent) - .unwrap() + self.running_evidence + .entry(*parent) + .or_default() .add_child_pop(Some(*child), self.observation_weight); } let last = trace.last().unwrap().1; - if !self.running_distributions.contains_key(&last) { - self.running_distributions - .insert(last, self.get_distro(last)); - } - self.running_distributions - .get_mut(&last) - .unwrap() + self.running_evidence + .entry(last) + .or_default() .add_child_pop(None, self.observation_weight); } @@ -270,13 +309,13 @@ impl BayesCategoricalTracker { let mut child_address_iter = trace.iter().map(|(_, ca)| ca); child_address_iter.next(); for (parent, child) in parent_address_iter.zip(child_address_iter) { - self.running_distributions + self.running_evidence .get_mut(parent) .unwrap() .remove_child_pop(Some(*child), self.observation_weight); } let last = trace.last().unwrap().1; - self.running_distributions + self.running_evidence .get_mut(&last) .unwrap() .remove_child_pop(None, self.observation_weight); @@ -293,8 +332,8 @@ impl DiscreteBayesianSequenceTracker for BayesCategoricalTrack self.remove_trace_from_pdfs(&oldest); } } - fn running_distributions(&self) -> &HashMap { - &self.running_distributions + fn running_evidence(&self) -> &HashMap { + &self.running_evidence } fn tree_reader(&self) -> &CoverTreeReader { &self.reader @@ -405,6 +444,26 @@ pub(crate) mod tests { assert_approx_eq!(buckets.kl_divergence(&buckets).unwrap(), 0.0); } + #[test] + fn dirichlet_posterior_sanity_test() { + let mut buckets = Dirichlet::new(); + buckets.add_child_pop(None, 3.0); + buckets.add_child_pop(Some((0, 0)), 5.0); + + let mut categorical = Categorical::new(); + categorical.add_child_pop(None, 2.0); + + let mut buckets_posterior = buckets.clone(); + buckets_posterior.add_evidence(&categorical); + + println!("Buckets: {:?}", buckets); + println!("Buckets Posterior: {:?}", buckets_posterior); + println!("Evidence: {:?}", categorical); + assert_approx_eq!(buckets_posterior.ln_prob(None).unwrap(), 0.5f64.ln()); + assert_approx_eq!(buckets_posterior.ln_prob(Some(&(0, 0))).unwrap(), 0.5f64.ln()); + assert_approx_eq!(buckets.kl_divergence(&buckets_posterior).unwrap(), buckets.posterior_kl_divergence(&categorical).unwrap()); + } + #[test] fn dirichlet_kl_sanity_test() { let mut bucket1 = Dirichlet::new(); diff --git a/goko/src/plugins/distributions/mod.rs b/goko/src/plugins/distributions/mod.rs index 31cf408..253a771 100644 --- a/goko/src/plugins/distributions/mod.rs +++ b/goko/src/plugins/distributions/mod.rs @@ -40,9 +40,15 @@ pub trait ContinousDistribution: Clone + 'static { /// pub trait DiscreteBayesianDistribution: DiscreteDistribution + Clone + 'static { + /// The distribution to which this is a conjugate prior + type Evidence: 'static; /// Adds an observation to the distribution. /// This currently shifts the underlying parameters of the distribution rather than be tracked. fn add_observation(&mut self, loc: Option); + /// Adds several observations + fn add_evidence(&mut self, evidence: &Self::Evidence); + /// Computes the KL divergence between the prior and the posterior, with the given evidence + fn posterior_kl_divergence(&self, other: &Self::Evidence) -> Option; } /// @@ -57,10 +63,11 @@ pub trait DiscreteBayesianSequenceTracker: Debug { /// The. underlying distribution that this is tracking. type Distribution: DiscreteBayesianDistribution + NodePlugin + 'static; + /// Adds a dry insert. fn add_path(&mut self, trace: Vec<(f32, NodeAddress)>); /// The current distributions that a dry insert touched. - fn running_distributions(&self) -> &HashMap; + fn running_evidence(&self) -> &HashMap>::Distribution as DiscreteBayesianDistribution>::Evidence>; /// Helper function, each sequence tracker should carry it's own reader. fn tree_reader(&self) -> &CoverTreeReader; /// The length of the sequence @@ -76,15 +83,24 @@ pub trait DiscreteBayesianSequenceTracker: Debug { let mut layer_totals: Vec = vec![0; self.tree_reader().len()]; let mut layer_node_counts = vec![Vec::::new(); self.tree_reader().len()]; let parameters = self.tree_reader().parameters(); - self.running_distributions() + self.running_evidence() .iter() .for_each(|(address, sequence_pdf)| { let kl = self .tree_reader() .get_node_plugin_and::(*address, |p| { - p.kl_divergence(sequence_pdf).unwrap() + let kl = p.posterior_kl_divergence(sequence_pdf).unwrap(); + /* + if kl == f64::INFINITY { + println!("Found an infinity at {:?}", address); + println!("sequence pdf: {:#?}", sequence_pdf); + println!("tree pdf: {:#?}", p); + } + */ + kl }) .unwrap(); + if kl > 1.0e-10 { layer_totals[parameters.internal_index(address.0)] += 1; layer_node_counts[parameters.internal_index(address.0)].push( @@ -126,13 +142,13 @@ pub trait DiscreteBayesianSequenceTracker: Debug { /// Gives the per-node KL divergence, with the node address fn all_node_kl(&self) -> Vec<(f64, NodeAddress)> { - self.running_distributions() + self.running_evidence() .iter() .map(|(address, sequence_pdf)| { let kl = self .tree_reader() .get_node_plugin_and::(*address, |p| { - p.kl_divergence(sequence_pdf).unwrap() + p.posterior_kl_divergence(sequence_pdf).unwrap() }) .unwrap(); (kl, *address) diff --git a/goko/src/tree_file_format.rs b/goko/src/tree_file_format.rs index 0851918..7ae9588 100644 --- a/goko/src/tree_file_format.rs +++ b/goko/src/tree_file_format.rs @@ -793,6 +793,7 @@ pub struct CoreProto { pub scale_base: f32, pub cutoff: u64, pub resolution: i32, + pub partition_type: ::std::string::String, pub dim: u64, pub count: u64, pub root_scale: i32, @@ -874,6 +875,32 @@ impl CoreProto { self.resolution = v; } + // string partition_type = 5; + + + pub fn get_partition_type(&self) -> &str { + &self.partition_type + } + pub fn clear_partition_type(&mut self) { + self.partition_type.clear(); + } + + // Param is passed by value, moved + pub fn set_partition_type(&mut self, v: ::std::string::String) { + self.partition_type = v; + } + + // Mutable pointer to the field. + // If field is not initialized, it is initialized with default value first. + pub fn mut_partition_type(&mut self) -> &mut ::std::string::String { + &mut self.partition_type + } + + // Take field + pub fn take_partition_type(&mut self) -> ::std::string::String { + ::std::mem::replace(&mut self.partition_type, ::std::string::String::new()) + } + // uint64 dim = 7; @@ -1002,6 +1029,9 @@ impl ::protobuf::Message for CoreProto { let tmp = is.read_sint32()?; self.resolution = tmp; }, + 5 => { + ::protobuf::rt::read_singular_proto3_string_into(wire_type, is, &mut self.partition_type)?; + }, 7 => { if wire_type != ::protobuf::wire_format::WireTypeVarint { return ::std::result::Result::Err(::protobuf::rt::unexpected_wire_type(wire_type)); @@ -1057,6 +1087,9 @@ impl ::protobuf::Message for CoreProto { if self.resolution != 0 { my_size += ::protobuf::rt::value_varint_zigzag_size(4, self.resolution); } + if !self.partition_type.is_empty() { + my_size += ::protobuf::rt::string_size(5, &self.partition_type); + } if self.dim != 0 { my_size += ::protobuf::rt::value_size(7, self.dim, ::protobuf::wire_format::WireTypeVarint); } @@ -1091,6 +1124,9 @@ impl ::protobuf::Message for CoreProto { if self.resolution != 0 { os.write_sint32(4, self.resolution)?; } + if !self.partition_type.is_empty() { + os.write_string(5, &self.partition_type)?; + } if self.dim != 0 { os.write_uint64(7, self.dim)?; } @@ -1166,6 +1202,11 @@ impl ::protobuf::Message for CoreProto { |m: &CoreProto| { &m.resolution }, |m: &mut CoreProto| { &mut m.resolution }, )); + fields.push(::protobuf::reflect::accessor::make_simple_field_accessor::<_, ::protobuf::types::ProtobufTypeString>( + "partition_type", + |m: &CoreProto| { &m.partition_type }, + |m: &mut CoreProto| { &mut m.partition_type }, + )); fields.push(::protobuf::reflect::accessor::make_simple_field_accessor::<_, ::protobuf::types::ProtobufTypeUint64>( "dim", |m: &CoreProto| { &m.dim }, @@ -1211,6 +1252,7 @@ impl ::protobuf::Clear for CoreProto { self.scale_base = 0.; self.cutoff = 0; self.resolution = 0; + self.partition_type.clear(); self.dim = 0; self.count = 0; self.root_scale = 0; @@ -1247,14 +1289,15 @@ static file_descriptor_proto_data: &'static [u8] = b"\ \x01(\tR\x12outlierSummaryJson\x12\x16\n\x06radius\x18\x0c\x20\x01(\x02R\ \x06radius\"Y\n\nLayerProto\x12\x1f\n\x0bscale_index\x18\x01\x20\x01(\ \x05R\nscaleIndex\x12*\n\x05nodes\x18\x02\x20\x03(\x0b2\x14.CoverTree.No\ - deProtoR\x05nodes\"\x9e\x02\n\tCoreProto\x12%\n\x0euse_singletons\x18\ + deProtoR\x05nodes\"\xc5\x02\n\tCoreProto\x12%\n\x0euse_singletons\x18\ \x01\x20\x01(\x08R\ruseSingletons\x12\x1d\n\nscale_base\x18\x02\x20\x01(\ \x02R\tscaleBase\x12\x16\n\x06cutoff\x18\x03\x20\x01(\x04R\x06cutoff\x12\ - \x1e\n\nresolution\x18\x04\x20\x01(\x11R\nresolution\x12\x10\n\x03dim\ - \x18\x07\x20\x01(\x04R\x03dim\x12\x14\n\x05count\x18\x08\x20\x01(\x04R\ - \x05count\x12\x1d\n\nroot_scale\x18\t\x20\x01(\x05R\trootScale\x12\x1d\n\ - \nroot_index\x18\n\x20\x01(\x04R\trootIndex\x12-\n\x06layers\x18\x0b\x20\ - \x03(\x0b2\x15.CoverTree.LayerProtoR\x06layersb\x06proto3\ + \x1e\n\nresolution\x18\x04\x20\x01(\x11R\nresolution\x12%\n\x0epartition\ + _type\x18\x05\x20\x01(\tR\rpartitionType\x12\x10\n\x03dim\x18\x07\x20\ + \x01(\x04R\x03dim\x12\x14\n\x05count\x18\x08\x20\x01(\x04R\x05count\x12\ + \x1d\n\nroot_scale\x18\t\x20\x01(\x05R\trootScale\x12\x1d\n\nroot_index\ + \x18\n\x20\x01(\x04R\trootIndex\x12-\n\x06layers\x18\x0b\x20\x03(\x0b2\ + \x15.CoverTree.LayerProtoR\x06layersb\x06proto3\ "; static file_descriptor_proto_lazy: ::protobuf::rt::LazyV2<::protobuf::descriptor::FileDescriptorProto> = ::protobuf::rt::LazyV2::INIT; From a0a796133c1ab67a4d4f68acf0cf5198ffc19b58 Mon Sep 17 00:00:00 2001 From: sven Date: Thu, 13 Aug 2020 19:43:48 -0700 Subject: [PATCH 03/10] fixed the partition --- .../test_set_attacks/attack_graphs.ipynb | 115893 --------------- goko/examples/ember_sequence_track.rs | 20 +- goko/src/plugins/distributions/categorical.rs | 12 + goko/src/plugins/distributions/dirichlet.rs | 173 +- goko/src/plugins/distributions/mod.rs | 175 +- pygoko/src/node.rs | 12 + pygoko/src/plugins.rs | 101 +- pygoko/src/tree.rs | 37 +- pygoko/tests/ember2018.py | 25 +- pygoko/tests/one_d_viz.py | 157 +- pygoko/tests/test_one_d_viz.py | 20 + pygoko/tests/two_d_viz.py | 70 +- 12 files changed, 591 insertions(+), 116104 deletions(-) delete mode 100644 experiments/test_set_attacks/attack_graphs.ipynb diff --git a/experiments/test_set_attacks/attack_graphs.ipynb b/experiments/test_set_attacks/attack_graphs.ipynb deleted file mode 100644 index d95bc50..0000000 --- a/experiments/test_set_attacks/attack_graphs.ipynb +++ /dev/null @@ -1,115893 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import numpy as np\n", - "import pandas as pd\n", - "import plotly.graph_objects as go\n", - "\n", - "skip = 5\n", - "def open_experiment(folder_name):\n", - " with open(f\"{folder_name}expected_ember_stats.json\",\"r\") as file:\n", - " training_runs = [json.loads(line) for line in file]\n", - " training_keys = list(training_runs[0].keys())\n", - " training_arrays = {k:np.array([training_run[k][skip:] for training_run in training_runs]) for k in training_keys}\n", - "\n", - " expected_runs = {}\n", - " for k,v in training_arrays.items():\n", - " expected_runs[f\"mean_{k}\"] = np.mean(v,axis=0)\n", - " expected_runs[f\"stddev_{k}\"] = np.sqrt(np.var(v,axis=0))\n", - "\n", - " expected_runs[\"moment1_nz\"] = np.sum(training_arrays[\"moment1_nz\"],axis=0)\n", - " expected_runs[\"moment2_nz\"] = np.sum(training_arrays[\"moment2_nz\"],axis=0)\n", - " expected_runs[\"nz_count\"] = np.sum(training_arrays[\"nz_count\"],axis=0)\n", - " expected_runs[\"mean_nz_mean\"] = expected_runs[\"moment1_nz\"]/expected_runs[\"nz_count\"]\n", - " expected_runs[\"stddev_nz_mean\"] = np.sqrt(\n", - " expected_runs[\"moment2_nz\"]/expected_runs[\"nz_count\"] - \n", - " np.square(expected_runs[\"mean_nz_mean\"]))\n", - "\n", - " expected_df = pd.DataFrame(expected_runs)\n", - " with open(f\"{folder_name}normal_runs_ember_stats.json\",\"r\") as file:\n", - " normal_runs = [{k: np.array(v[skip:]) for k,v in json.loads(line).items()} for line in file]\n", - " for run in normal_runs:\n", - " run[\"nz_mean\"] = run[\"moment1_nz\"]/run[\"nz_count\"]\n", - " run[\"nz_stddev\"] = np.sqrt(run[\"moment2_nz\"]/run[\"nz_count\"] - np.square(run[\"nz_mean\"]))\n", - " normal_runs = [pd.DataFrame(run) for run in normal_runs]\n", - "\n", - " with open(f\"{folder_name}attack_runs_ember_stats.json\",\"r\") as file:\n", - " attack_runs = [{k: np.array(v[skip:]) for k,v in json.loads(line).items()} for line in file]\n", - " for run in attack_runs:\n", - " run[\"nz_mean\"] = run[\"moment1_nz\"]/run[\"nz_count\"]\n", - " run[\"nz_stddev\"] = np.sqrt(run[\"moment2_nz\"]/run[\"nz_count\"] - np.square(run[\"nz_mean\"]))\n", - " attack_runs = [pd.DataFrame(run) for run in attack_runs]\n", - " return expected_runs, normal_runs, attack_runs\n", - "\n", - "experiment = open_experiment(\"normal_0-8_1-3_50_400_50/\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_traces(expected_runs,normal_runs,attack_runs,col_name):\n", - " expected_runs[f'mean_p_stddev_{col_name}'] = expected_runs[f\"mean_{col_name}\"]+expected_runs[f'stddev_{col_name}']\n", - " expected_runs[f'mean_n_stddev_{col_name}'] = expected_runs[f\"mean_{col_name}\"]-expected_runs[f'stddev_{col_name}']\n", - " x = np.arange(skip,len(expected_runs[f\"mean_{col_name}\"]))\n", - " upper_bound = go.Scatter(x=x,y=expected_runs[f'mean_p_stddev_{col_name}'],\n", - " mode='lines',\n", - " line_color='rgba(0,100,80,0)',\n", - " showlegend=False,\n", - " fillcolor='rgba(0,100,80, 0.3)',\n", - " legendgroup=\"baseline\",\n", - " fill='tonexty')\n", - " trace = go.Scatter(x=x,y=expected_runs[f\"mean_{col_name}\"],\n", - " mode='lines',\n", - " line_color='rgb(0,100,80)',\n", - " name='Mean & Stddev of
training sequences',\n", - " fillcolor='rgba(0,100,80, 0.3)',\n", - " legendgroup=\"baseline\",\n", - " fill='tonexty')\n", - " lower_bound = go.Scatter(x=x,y=expected_runs[f'mean_n_stddev_{col_name}'],\n", - " mode='lines',\n", - " line_color='rgba(0,100,80,0)',\n", - " legendgroup=\"baseline\",\n", - " showlegend=False)\n", - "\n", - " data = [lower_bound, trace, upper_bound]\n", - " attack_trace = go.Scatter(x=x,y=attack_runs[0][col_name],\n", - " mode='lines',\n", - " line_color='rgba(200,10,10,0.8)',\n", - " name='Attack Sequence',\n", - " legendgroup=\"attack\")\n", - " data.append(attack_trace)\n", - " for run in attack_runs[1:10]:\n", - " attack_trace = go.Scatter(x=x,y=run[col_name],\n", - " mode='lines',\n", - " line_color='rgba(200,10,10,0.8)',\n", - " name='Attack Sequence',\n", - " legendgroup=\"attack\",\n", - " showlegend=False)\n", - " data.append(attack_trace)\n", - " normal_trace = go.Scatter(x=x,y=normal_runs[0][col_name],\n", - " mode='lines',\n", - " line_color='rgba(10,10,200,0.8)',\n", - " name='Normal Sequence',\n", - " legendgroup=\"normal\")\n", - " data.append(normal_trace)\n", - " for run in normal_runs[1:10]:\n", - " normal_trace = go.Scatter(x=x,y=run[col_name],\n", - " mode='lines',\n", - " line_color='rgba(10,10,200,0.8)',\n", - " name='Normal Sequence',\n", - " legendgroup=\"normal\",\n", - " showlegend=False)\n", - " data.append(normal_trace)\n", - " return data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "legendgroup": "baseline", - "line": { - "color": "rgba(0,100,80,0)" - }, - "mode": "lines", - "showlegend": false, - "type": "scatter", - "x": [ - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data = generate_traces(*experiment,\"max\")\n", - "fig = go.Figure()\n", - "layout = go.Layout(\n", - " yaxis=dict(title='Max KL Divergence'),\n", - " title='Maximum of the KL Divergence of Query Sequences',\n", - " yaxis_type=\"log\")\n", - "\n", - "fig = go.Figure(data=data, layout=layout)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "legendgroup": "baseline", - "line": { - "color": "rgba(0,100,80,0)" - }, - "mode": "lines", - "showlegend": false, - "type": "scatter", - "x": [ - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data = generate_traces(*experiment,\"min\")\n", - "fig = go.Figure()\n", - "layout = go.Layout(\n", - " yaxis=dict(title='Min KL Divergence'),\n", - " title='Minimum of the KL Divergence of Query Sequences',\n", - " yaxis_type=\"log\")\n", - "\n", - "fig = go.Figure(data=data, layout=layout)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "legendgroup": "baseline", - "line": { - "color": "rgba(0,100,80,0)" - }, - "mode": "lines", - "showlegend": false, - "type": "scatter", - "x": [ - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 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"linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Mean of the non-zero KL divergences" - }, - "yaxis": { - "title": { - "text": "Mean of non-zero KL Divergences" - }, - "type": "log" - } - } - }, - "text/html": [ - "
\n", - " \n", - " \n", - "
\n", - " \n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data = generate_traces(*experiment,\"nz_mean\")\n", - "fig = go.Figure()\n", - "layout = go.Layout(\n", - " yaxis=dict(title='Mean of non-zero KL Divergences'),\n", - " title='Mean of the non-zero KL divergences',\n", - " yaxis_type=\"log\")\n", - "\n", - "fig = go.Figure(data=data, layout=layout)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "def sequence_analysis(expected_runs,sequence_run,col_name):\n", - " stat_column = sequence_run[col_name]\n", - " index = sequence_run[\"index\"]\n", - " std_dev_up = expected_runs[f\"mean_{col_name}\"]+expected_runs[f'stddev_{col_name}']\n", - " std_dev_down = expected_runs[f\"mean_{col_name}\"]-expected_runs[f'stddev_{col_name}']\n", - " \n", - " true_explotation_time = None\n", - " i = 0\n", - " while true_explotation_time is None and i + 2 < len(index):\n", - " if index[i] == index[i+1] and index[i+1] == index[i+2]:\n", - " true_explotation_time = i\n", - " print(true_explotation_time)\n", - " i += 1\n", - " \n", - " detection_time = None\n", - " i = 0\n", - " while detection_time is None and i < len(index):\n", - " if stat_column[i] > std_dev_up[i] or stat_column[i] < std_dev_down[i]:\n", - " detection_time = i\n", - " print(detection_time)\n", - " i += 1\n", - " return true_explotation_time, detection_time\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "175\n", - "66\n", - "37\n", - "55\n", - "12\n", - "38\n", - "1\n", - "7\n", - "72\n", - "94\n", - "189\n", - "390\n", - "detection_mean_lag 27.333333333333332\n", - "true_positive 1.0\n", - "false_positive 0.0\n", - "true_negative 1.0\n", - "false_negative 0.0\n" - ] - } - ], - "source": [ - "expected_runs,normal_runs,attack_runs = experiment\n", - "\n", - "attack_results = []\n", - "for run in attack_runs[:5]:\n", - " attack_results.append(sequence_analysis(expected_runs,run,\"nz_mean\"))\n", - "normal_results = []\n", - "for run in normal_runs[:5]:\n", - " normal_results.append(sequence_analysis(expected_runs,run,\"nz_mean\"))\n", - "positives = 0\n", - "negatives = 0\n", - "results = attack_results + normal_results\n", - "true_positive = 0.0\n", - "false_positive = 0.0\n", - "true_negative = 0.0\n", - "false_negative = 0.0\n", - "detection_mean_lag = 0.0\n", - "for attack_start,detection in results:\n", - " if attack_start is None:\n", - " negatives += 1.0\n", - " if detection is None:\n", - " true_negative += 1.0\n", - " else:\n", - " false_negative += 1.0\n", - " else:\n", - " positives += 1.0\n", - " if detection is None:\n", - " false_positive += 1.0\n", - " else:\n", - " true_positive += 1.0\n", - " detection_mean_lag += detection - attack_start\n", - "detection_mean_lag /= true_positive\n", - "true_positive /= positives\n", - "false_positive /= positives\n", - "true_negative /= negatives\n", - "false_negative /= negatives\n", - "print(\"detection_mean_lag\",detection_mean_lag)\n", - "print(\"true_positive\",true_positive)\n", - "print(\"false_positive\",false_positive)\n", - "print(\"true_negative\",true_negative)\n", - "print(\"false_negative\",false_negative)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/goko/examples/ember_sequence_track.rs b/goko/examples/ember_sequence_track.rs index acb618a..4873d3a 100644 --- a/goko/examples/ember_sequence_track.rs +++ b/goko/examples/ember_sequence_track.rs @@ -60,6 +60,24 @@ fn main() { let prior_weight = 1.0; let observation_weight = 1.3; let window_size = 0; + let num_sequence = 8; + + let mut baseline = DirichletBaseline::new(); + baseline.set_window_size(window_size); + baseline.set_observation_weight(observation_weight); + baseline.set_prior_weight(prior_weight); + baseline.set_sequence_len(test_set.len()); + baseline.set_num_sequences(num_sequence); + println!("Gathering baseline with prior_weight: {}, observation_weight: {}, and window_size: {}", prior_weight, observation_weight, window_size); + let baseline_start = time::Instant::now(); + let baseline_data = baseline.train(ct.reader()); + let baseline_elapse = baseline_start.elapsed().as_millis(); + + println!( + "BASELINE: Time elapsed {:?} milliseconds, time per sequence {} milliseconds", + baseline_elapse, + (baseline_elapse as f64) / ((test_set.len()*num_sequence) as f64) + ); let mut tracker = BayesCategoricalTracker::new(prior_weight, observation_weight, window_size, ct.reader()); @@ -80,5 +98,5 @@ fn main() { elapse, (elapse as f64) / (test_set.len() as f64) ); - println!("stats: {:?}", tracker.current_stats()); + println!("stats: {:?}", tracker.kl_div_stats()); } diff --git a/goko/src/plugins/distributions/categorical.rs b/goko/src/plugins/distributions/categorical.rs index 24c1f40..730ce0b 100644 --- a/goko/src/plugins/distributions/categorical.rs +++ b/goko/src/plugins/distributions/categorical.rs @@ -80,6 +80,18 @@ impl Categorical { .fold(0.0, |x, a| x + a) } + /// Gives the probability vector for this + pub fn prob_vector(&self) -> Option<(Vec<(NodeAddress,f64)>,f64)> { + + let total = self.total(); + if total > 0.0 { + let v: Vec<(NodeAddress,f64)> = self.child_counts.iter().map(|(na,f)| (*na,f/total)).collect(); + Some((v,self.singleton_count/total)) + } else { + None + } + } + pub(crate) fn merge(&mut self, other: &Categorical) { for (na, c) in &other.child_counts { self.add_child_pop(Some(*na), *c); diff --git a/goko/src/plugins/distributions/dirichlet.rs b/goko/src/plugins/distributions/dirichlet.rs index e02eb59..bddd292 100644 --- a/goko/src/plugins/distributions/dirichlet.rs +++ b/goko/src/plugins/distributions/dirichlet.rs @@ -17,7 +17,8 @@ use crate::plugins::*; use super::*; use std::fmt; -use rand::{thread_rng, Rng}; +use rand::prelude::*; +use rayon::iter::repeatn; use statrs::function::gamma::{digamma, ln_gamma}; use std::collections::{HashMap, VecDeque}; @@ -51,6 +52,19 @@ impl Dirichlet { .map(|(_, c)| c) .fold(0.0, |x, a| x + a) } + + /// Gives the probability vector for this + pub fn prob_vector(&self) -> Option<(Vec<(NodeAddress,f64)>,f64)> { + + let total = self.total(); + if total > 0.0 { + let v: Vec<(NodeAddress,f64)> = self.child_counts.iter().map(|(na,f)| (*na,f/total)).collect(); + Some((v,self.singleton_count/total)) + } else { + None + } + } + fn add_child_pop(&mut self, loc: Option, count: f64) { match loc { Some(ca) => match self.child_counts.binary_search_by_key(&ca, |&(a, _)| a) { @@ -110,7 +124,11 @@ impl DiscreteBayesianDistribution for Dirichlet { } for (other_ca, other_ca_count) in other.child_counts.iter() { - let ca_count = self.child_counts[self.child_counts.binary_search_by_key(other_ca, |&(a, _)| a).unwrap()].1; + let ca_count = match self.child_counts.binary_search_by_key(other_ca, |&(a, _)| a) { + Ok(ind) => self.child_counts[ind].1, + Err(_) => return None, + }; + my_total_lng += ln_gamma(ca_count); other_total_lng += ln_gamma(*other_ca_count + ca_count); digamma_portion -= *other_ca_count * (digamma(ca_count) - digamma(my_total)); @@ -227,7 +245,8 @@ impl GokoPlugin for GokoDirichlet { /// Computes a frequentist KL divergence calculation on each node the sequence touches. pub struct BayesCategoricalTracker { running_evidence: HashMap, - sequence: VecDeque>, + sequence_queue: VecDeque>, + sequence_count: usize, window_size: usize, prior_weight: f64, observation_weight: f64, @@ -238,8 +257,8 @@ impl fmt::Debug for BayesCategoricalTracker { fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { write!( f, - "PointCloud {{ sequence: {:?}, window_size: {} prior_weight: {}, observation_weight: {}, running_evidence: {:#?}}}", - self.sequence, self.window_size, self.prior_weight, self.observation_weight, self.running_evidence, + "PointCloud {{ sequence_queue: {:?}, window_size: {} prior_weight: {}, observation_weight: {}, running_evidence: {:?}}}", + self.sequence_queue, self.window_size, self.prior_weight, self.observation_weight, self.running_evidence, ) } } @@ -254,7 +273,8 @@ impl BayesCategoricalTracker { ) -> BayesCategoricalTracker { BayesCategoricalTracker { running_evidence: HashMap::new(), - sequence: VecDeque::new(), + sequence_queue: VecDeque::new(), + sequence_count: 0, window_size, prior_weight, observation_weight, @@ -270,7 +290,7 @@ impl BayesCategoricalTracker { .and_modify(|e| e.merge(v)) .or_insert(v.clone()); } - self.sequence.extend(other.sequence.iter().cloned()); + self.sequence_queue.extend(other.sequence_queue.iter().cloned()); self } @@ -309,10 +329,10 @@ impl BayesCategoricalTracker { let mut child_address_iter = trace.iter().map(|(_, ca)| ca); child_address_iter.next(); for (parent, child) in parent_address_iter.zip(child_address_iter) { - self.running_evidence + let mut parent_evidence = self.running_evidence .get_mut(parent) - .unwrap() - .remove_child_pop(Some(*child), self.observation_weight); + .unwrap(); + parent_evidence.remove_child_pop(Some(*child), self.observation_weight); } let last = trace.last().unwrap().1; self.running_evidence @@ -320,16 +340,37 @@ impl BayesCategoricalTracker { .unwrap() .remove_child_pop(None, self.observation_weight); } + + /// Gives the probability vector for this + pub fn prob_vector(&self,na:NodeAddress) -> Option<(Vec<(NodeAddress,f64)>,f64)> { + self.reader + .get_node_plugin_and::(na, |p| { + let mut dir = p.clone(); + if let Some(e) = self.running_evidence.get(&na) { + dir.add_evidence(e) + } + dir.prob_vector() + }).flatten() + } + + /// Gives the probability vector for this + pub fn evidence_prob_vector(&self,na:NodeAddress) -> Option<(Vec<(NodeAddress,f64)>,f64)> { + self.running_evidence.get(&na).map(|e| e.prob_vector()).flatten() + } } impl DiscreteBayesianSequenceTracker for BayesCategoricalTracker { type Distribution = Dirichlet; /// Adds an element to the trace fn add_path(&mut self, trace: Vec<(f32, NodeAddress)>) { self.add_trace_to_pdfs(&trace); - self.sequence.push_back(trace); - if self.sequence.len() > self.window_size && self.window_size != 0 { - let oldest = self.sequence.pop_front().unwrap(); - self.remove_trace_from_pdfs(&oldest); + self.sequence_count += 1; + if self.window_size != 0 { + self.sequence_queue.push_back(trace); + + if self.sequence_queue.len() > self.window_size { + let oldest = self.sequence_queue.pop_front().unwrap(); + self.remove_trace_from_pdfs(&oldest); + } } } fn running_evidence(&self) -> &HashMap { @@ -339,31 +380,31 @@ impl DiscreteBayesianSequenceTracker for BayesCategoricalTrack &self.reader } fn sequence_len(&self) -> usize { - self.sequence.len() + self.sequence_count } } /// Trains a baseline by sampling randomly from the training set (used to create the tree) /// This baseline is _not_ realistic. -pub struct DirichletBaseline { +pub struct DirichletBaseline { + sample_rate: usize, sequence_len: usize, num_sequences: usize, window_size: usize, prior_weight: f64, observation_weight: f64, - reader: CoverTreeReader, } -impl DirichletBaseline { +impl DirichletBaseline { /// New with sensible defaults - pub fn new(reader: CoverTreeReader) -> DirichletBaseline { + pub fn new() -> DirichletBaseline { DirichletBaseline { - sequence_len: 200, - num_sequences: 100, + sample_rate: 100, + sequence_len: 0, + num_sequences: 8, window_size: 50, prior_weight: 1.0, observation_weight: 1.0, - reader, } } /// Sets a new training sequence window_size, default 200. Stats for each total lenght of sequence are returned @@ -386,9 +427,13 @@ impl DirichletBaseline { pub fn set_observation_weight(&mut self, observation_weight: f64) { self.observation_weight = observation_weight; } + /// Samples at the following rate, then interpolates for sequence lengths between the following. + pub fn set_sample_rate(&mut self, sample_rate: usize) { + self.sample_rate = sample_rate; + } /// Trains the sequences up. - pub fn train(&self) -> GokoResult>> { + pub fn train(&self,reader: CoverTreeReader) -> GokoResult { /* let chunk_size = 10; let (results_sender, results_receiver): ( @@ -396,26 +441,44 @@ impl DirichletBaseline { Receiver, ) = unbounded(); */ - let mut results: Vec> = (0..self.num_sequences) - .map(|_| Vec::with_capacity(self.sequence_len)) - .collect(); - let point_cloud = self.reader.point_cloud(); - for seq_results in results.iter_mut() { + let point_indexes = reader.point_cloud().reference_indexes(); + let sequence_len = if self.sequence_len == 0 { + point_indexes.len() + } else { + self.sequence_len + }; + + let results: Vec> = repeatn(reader.clone(), self.num_sequences).map(|reader| { let mut tracker = BayesCategoricalTracker::new( self.prior_weight, self.observation_weight, self.window_size, - self.reader.clone(), + reader, ); - for _ in 0..self.sequence_len { - let mut rng = thread_rng(); - let query_point = point_cloud.point(rng.gen_range(0, point_cloud.len()))?; - tracker.add_path(self.reader.path(&query_point)?); - seq_results.push(tracker.current_stats()); + (&point_indexes[..]).choose_multiple(&mut thread_rng(),sequence_len).enumerate().filter_map(|(i,pi)| { + tracker.add_path(tracker.reader.known_path(*pi).unwrap()); + if i % self.sample_rate == 0 { + Some(tracker.kl_div_stats()) + } else { + None + } + }).collect() + }).collect(); + let len = results[0].len(); + let mut sequence_len = Vec::with_capacity(len); + let mut stats: Vec = std::iter::repeat_with(|| KLDivergenceBaselineStats::default()).take(len).collect(); + + for i in 0..len { + for result_vec in &results { + stats[i].add(&result_vec[i]); } + sequence_len.push(results[0][i].sequence_len); } - - Ok(results) + Ok(KLDivergenceBaseline { + num_sequences: results.len(), + stats, + sequence_len, + }) } } @@ -464,6 +527,44 @@ pub(crate) mod tests { assert_approx_eq!(buckets.kl_divergence(&buckets_posterior).unwrap(), buckets.posterior_kl_divergence(&categorical).unwrap()); } + #[test] + fn dirichlet_posterior_sanity_test_2() { + let mut buckets = Dirichlet::new(); + buckets.add_child_pop(None, 3.0); + buckets.add_child_pop(Some((0, 0)), 2.0); + + let mut categorical = Categorical::new(); + categorical.add_child_pop(None,3.0); + categorical.add_child_pop(Some((0, 0)), 2.0); + + let mut buckets_posterior = buckets.clone(); + buckets_posterior.add_evidence(&categorical); + + println!("Buckets: {:?}", buckets); + println!("Buckets Posterior: {:?}", buckets_posterior); + println!("Evidence: {:?}", categorical); + assert_approx_eq!(buckets.kl_divergence(&buckets_posterior).unwrap(), buckets.posterior_kl_divergence(&categorical).unwrap()); + assert_approx_eq!(buckets.kl_divergence(&buckets_posterior).unwrap(), 0.0); + } + + #[test] + fn dirichlet_posterior_sanity_test_3() { + let mut buckets = Dirichlet::new(); + buckets.add_child_pop(None, 3.0); + + let mut categorical = Categorical::new(); + categorical.add_child_pop(None,3.0); + + let mut buckets_posterior = buckets.clone(); + buckets_posterior.add_evidence(&categorical); + + println!("Buckets: {:?}", buckets); + println!("Buckets Posterior: {:?}", buckets_posterior); + println!("Evidence: {:?}", categorical); + assert_approx_eq!(buckets.kl_divergence(&buckets_posterior).unwrap(), buckets.posterior_kl_divergence(&categorical).unwrap()); + assert_approx_eq!(buckets.kl_divergence(&buckets_posterior).unwrap(), 0.0); + } + #[test] fn dirichlet_kl_sanity_test() { let mut bucket1 = Dirichlet::new(); diff --git a/goko/src/plugins/distributions/mod.rs b/goko/src/plugins/distributions/mod.rs index 253a771..09c4f86 100644 --- a/goko/src/plugins/distributions/mod.rs +++ b/goko/src/plugins/distributions/mod.rs @@ -41,7 +41,7 @@ pub trait ContinousDistribution: Clone + 'static { /// pub trait DiscreteBayesianDistribution: DiscreteDistribution + Clone + 'static { /// The distribution to which this is a conjugate prior - type Evidence: 'static; + type Evidence: 'static + Debug; /// Adds an observation to the distribution. /// This currently shifts the underlying parameters of the distribution rather than be tracked. fn add_observation(&mut self, loc: Option); @@ -63,7 +63,6 @@ pub trait DiscreteBayesianSequenceTracker: Debug { /// The. underlying distribution that this is tracking. type Distribution: DiscreteBayesianDistribution + NodePlugin + 'static; - /// Adds a dry insert. fn add_path(&mut self, trace: Vec<(f32, NodeAddress)>); /// The current distributions that a dry insert touched. @@ -72,43 +71,25 @@ pub trait DiscreteBayesianSequenceTracker: Debug { fn tree_reader(&self) -> &CoverTreeReader; /// The length of the sequence fn sequence_len(&self) -> usize; + /// A set of stats for the sequence that are helpful. - fn current_stats(&self) -> KLDivergenceStats { + fn kl_div_stats(&self) -> KLDivergenceStats { let mut max = f64::MIN; let mut min = f64::MAX; let mut nz_count = 0; let mut moment1_nz = 0.0; let mut moment2_nz = 0.0; - // For computing the fracta dimensions - let mut layer_totals: Vec = vec![0; self.tree_reader().len()]; - let mut layer_node_counts = vec![Vec::::new(); self.tree_reader().len()]; - let parameters = self.tree_reader().parameters(); self.running_evidence() .iter() .for_each(|(address, sequence_pdf)| { let kl = self .tree_reader() .get_node_plugin_and::(*address, |p| { - let kl = p.posterior_kl_divergence(sequence_pdf).unwrap(); - /* - if kl == f64::INFINITY { - println!("Found an infinity at {:?}", address); - println!("sequence pdf: {:#?}", sequence_pdf); - println!("tree pdf: {:#?}", p); - } - */ - kl + p.posterior_kl_divergence(sequence_pdf).unwrap() }) .unwrap(); if kl > 1.0e-10 { - layer_totals[parameters.internal_index(address.0)] += 1; - layer_node_counts[parameters.internal_index(address.0)].push( - self.tree_reader() - .get_node_and(*address, |n| n.cover_count()) - .unwrap(), - ); - moment1_nz += kl; moment2_nz += kl * kl; if max < kl { @@ -121,6 +102,31 @@ pub trait DiscreteBayesianSequenceTracker: Debug { nz_count += 1; } }); + KLDivergenceStats { + max, + min, + nz_count, + moment1_nz, + moment2_nz, + sequence_len: self.sequence_len(), + } + } + + /// A set of stats for the sequence that are helpful. + fn fractal_dim_stats(&self) -> FractalDimStats { + let mut layer_totals: Vec = vec![0; self.tree_reader().len()]; + let mut layer_node_counts = vec![Vec::::new(); self.tree_reader().len()]; + let parameters = self.tree_reader().parameters(); + self.running_evidence() + .iter() + .for_each(|(address, _sequence_pdf)| { + layer_totals[parameters.internal_index(address.0)] += 1; + layer_node_counts[parameters.internal_index(address.0)].push( + self.tree_reader() + .get_node_and(*address, |n| n.cover_count()) + .unwrap(), + ); + }); let weighted_layer_totals: Vec = layer_node_counts .iter() .map(|counts| { @@ -128,18 +134,14 @@ pub trait DiscreteBayesianSequenceTracker: Debug { counts.iter().fold(0.0, |a, c| a + (*c as f32) / max) }) .collect(); - KLDivergenceStats { - max, - min, - nz_count, - moment1_nz, - moment2_nz, - sequence_len: self.sequence_len() as u64, + FractalDimStats { + sequence_len: self.sequence_len(), layer_totals, weighted_layer_totals, } } + /// Gives the per-node KL divergence, with the node address fn all_node_kl(&self) -> Vec<(f64, NodeAddress)> { self.running_evidence() @@ -157,7 +159,7 @@ pub trait DiscreteBayesianSequenceTracker: Debug { } } -/// Tracks the non-zero (all KL divergences above 1e-10) +/// Tracks the non-zero KL div (all KL divergences above 1e-10) #[derive(Debug)] pub struct KLDivergenceStats { /// The maximum non-zero KL divergence @@ -172,9 +174,118 @@ pub struct KLDivergenceStats { pub moment2_nz: f64, /// The number of sequence elements that went into calculating this stat. This is not the total lenght /// We can drop old sequence elements - pub sequence_len: u64, + pub sequence_len: usize, +} + +/// Stats that let you compute the fractal dim of the query dataset wrt the base covertree +pub struct FractalDimStats { + /// The number of sequence elements that went into calculating this stat. This is not the total lenght + /// We can drop old sequence elements + pub sequence_len: usize, /// The number of nodes per layer this sequence touches pub layer_totals: Vec, /// The number of nodes per layer this sequence touches pub weighted_layer_totals: Vec, } + + +/// Tracks the non-zero (all KL divergences above 1e-10) +#[derive(Debug, Default)] +pub struct KLDivergenceBaselineStats { + /// The maximum non-zero KL divergence + pub max: (f64,f64), + /// The minimum non-zero KL divergence + pub min: (f64,f64), + /// The number of nodes that have a non-zero divergence + pub nz_count: (f64,f64), + /// The first moment, use this with the `nz_count` to get the mean + pub moment1_nz: (f64,f64), + /// The second moment, use this with the `nz_count` and first moment to get the variance + pub moment2_nz: (f64,f64), +} + +impl KLDivergenceBaselineStats { + pub(crate) fn add(&mut self, stats:&KLDivergenceStats) { + self.max.0 += stats.max; + self.max.1 += stats.max*stats.max; + self.min.0 += stats.min; + self.min.1 += stats.min*stats.min; + self.nz_count.0 += stats.nz_count as f64; + self.nz_count.1 += (stats.nz_count*stats.nz_count) as f64; + self.moment1_nz.0 += stats.moment1_nz; + self.moment1_nz.1 += stats.moment1_nz*stats.moment1_nz; + self.moment2_nz.0 += stats.moment2_nz; + self.moment2_nz.1 += stats.moment2_nz*stats.moment2_nz; + } + + fn to_mean_var(&self, count: f64) -> KLDivergenceBaselineStats { + let max_mean = self.max.0/count; + let min_mean = self.min.0/count; + let nz_count_mean = self.nz_count.0/count; + let moment1_nz_mean = self.moment1_nz.0/count; + let moment2_nz_mean = self.moment2_nz.0/count; + + let max_var = self.max.1/count - max_mean*max_mean; + let min_var = self.min.1/count - min_mean*min_mean; + let nz_count_var = self.nz_count.1/count - nz_count_mean*nz_count_mean; + let moment1_nz_var = self.moment1_nz.1/count - moment1_nz_mean*moment1_nz_mean; + let moment2_nz_var = self.moment2_nz.1/count - moment2_nz_mean*moment2_nz_mean; + + KLDivergenceBaselineStats { + max: (max_mean, max_var), + min: (min_mean, min_var), + nz_count: (nz_count_mean, nz_count_var), + moment1_nz: (moment1_nz_mean, moment1_nz_var), + moment2_nz: (moment2_nz_mean, moment2_nz_var), + } + } + + fn interpolate(mut self, other:&Self, w: f64) -> Self { + self.max.0 += w*(other.max.0 - self.max.0); + self.max.1 += w*(other.max.1 - self.max.1); + + self.min.0 += w*(other.min.0 - self.min.0); + self.min.1 += w*(other.min.1 - self.min.1); + + self.nz_count.0 += w*(other.nz_count.0 - self.nz_count.0); + self.nz_count.1 += w*(other.nz_count.1 - self.nz_count.1); + + self.moment1_nz.0 += w*(other.moment1_nz.0 - self.moment1_nz.0); + self.moment1_nz.1 += w*(other.moment1_nz.1 - self.moment1_nz.1); + + self.moment2_nz.0 += w*(other.moment2_nz.0 - self.moment2_nz.0); + self.moment2_nz.1 += w*(other.moment2_nz.1 - self.moment2_nz.1); + self + } +} + +pub struct KLDivergenceBaseline { + pub num_sequences: usize, + pub sequence_len: Vec, + pub stats: Vec, +} + +impl KLDivergenceBaseline { + pub fn stats(&self,i:usize) -> KLDivergenceBaselineStats { + match self.sequence_len.binary_search(&i) { + Ok(index) => { + self.stats[index].to_mean_var(self.num_sequences as f64) + } + Err(index) => { + if index == 0 { + KLDivergenceBaselineStats::default() + } else if index == self.sequence_len.len() { + let stats1 = self.stats[index-2].to_mean_var(self.num_sequences as f64); + let stats2 = self.stats[index-1].to_mean_var(self.num_sequences as f64); + let weight = ((i- self.sequence_len[index-2]) as f64)/((self.sequence_len[index-1] - self.sequence_len[index-2]) as f64); + stats1.interpolate(&stats2,weight) + } else { + let stats1 = self.stats[index-1].to_mean_var(self.num_sequences as f64); + let stats2 = self.stats[index].to_mean_var(self.num_sequences as f64); + let weight = ((i- self.sequence_len[index-1]) as f64)/((self.sequence_len[index] - self.sequence_len[index-1]) as f64); + stats1.interpolate(&stats2,weight) + } + }, + } + } +} diff --git a/pygoko/src/node.rs b/pygoko/src/node.rs index 4ccaa3e..252296c 100644 --- a/pygoko/src/node.rs +++ b/pygoko/src/node.rs @@ -59,6 +59,12 @@ impl PyNode { self.address } + pub fn is_leaf(&self) -> bool { + self.tree + .get_node_and(self.address, |n| n.is_leaf()) + .unwrap() + } + pub fn coverage_count(&self) -> usize { self.tree .get_node_and(self.address, |n| n.cover_count()) @@ -76,6 +82,12 @@ impl PyNode { .collect() } + pub fn children_probs(&self) -> Option<(Vec<((i32, usize),f64)>,f64)> { + self.tree.get_node_plugin_and(self.address, |p: &Dirichlet| { + p.prob_vector() + }).flatten() + } + pub fn children_addresses(&self) -> Vec<(i32, usize)> { self.tree .get_node_and(self.address, |n| { diff --git a/pygoko/src/plugins.rs b/pygoko/src/plugins.rs index aa1de0f..6ae6435 100644 --- a/pygoko/src/plugins.rs +++ b/pygoko/src/plugins.rs @@ -5,6 +5,7 @@ use pyo3::PyObjectProtocol; use goko::plugins::distributions::*; use goko::*; use pointcloud::*; +use pyo3::types::PyDict; /* pub #[derive(Debug)] @@ -43,63 +44,69 @@ impl PyBayesCategoricalTracker { println!("{:#?}", self.hkl); } + pub fn probs(&self,node_address:(i32, usize)) -> Option<(Vec<((i32, usize),f64)>,f64)> { + self.hkl.prob_vector(node_address) + } + + pub fn evidence(&self,node_address:(i32, usize)) -> Option<(Vec<((i32, usize),f64)>,f64)> { + self.hkl.evidence_prob_vector(node_address) + } + pub fn all_kl(&self) -> Vec<(f64, (i32, usize))> { self.hkl.all_node_kl() } - pub fn stats(&self) -> PyKLDivergenceStats { - PyKLDivergenceStats { - stats: self.hkl.current_stats(), - } + pub fn stats(&self) -> PyResult { + let stats = self.hkl.kl_div_stats(); + let gil = GILGuard::acquire(); + let py = gil.python(); + let dict = PyDict::new(py); + dict.set_item("max", stats.max)?; + dict.set_item("min", stats.min)?; + dict.set_item("nz_count", stats.nz_count)?; + dict.set_item("moment1_nz", stats.moment1_nz)?; + dict.set_item("moment2_nz", stats.moment2_nz)?; + dict.set_item("sequence_len", stats.sequence_len)?; + Ok(dict.into()) } } + + #[pyclass(unsendable)] -pub struct PyKLDivergenceStats { - pub stats: KLDivergenceStats, +pub struct PyKLDivergenceBaseline { + pub baseline: KLDivergenceBaseline, } #[pymethods] -impl PyKLDivergenceStats { - #[getter] - pub fn max(&self) -> f64 { - self.stats.max - } - #[getter] - pub fn min(&self) -> f64 { - self.stats.min - } - #[getter] - pub fn nz_count(&self) -> u64 { - self.stats.nz_count - } - #[getter] - pub fn moment1_nz(&self) -> f64 { - self.stats.moment1_nz - } - #[getter] - pub fn moment2_nz(&self) -> f64 { - self.stats.moment2_nz - } - #[getter] - pub fn sequence_len(&self) -> u64 { - self.stats.sequence_len - } - #[getter] - pub fn layer_totals(&self) -> Vec { - self.stats.layer_totals.clone() - } - #[getter] - pub fn weighed_layer_totals(&self) -> Vec { - self.stats.weighted_layer_totals.clone() - } -} +impl PyKLDivergenceBaseline { + pub fn stats(&self,i:usize) -> PyResult { + let stats = self.baseline.stats(i); + let gil = GILGuard::acquire(); + let dict = PyDict::new(gil.python()); + let max_dict = PyDict::new(gil.python()); + max_dict.set_item("mean", stats.max.0)?; + max_dict.set_item("var", stats.max.1)?; + dict.set_item("max", max_dict)?; + + let min_dict = PyDict::new(gil.python()); + min_dict.set_item("mean", stats.min.0)?; + min_dict.set_item("var", stats.min.1)?; + dict.set_item("min", min_dict)?; -#[pyproto] -impl PyObjectProtocol for PyKLDivergenceStats { - fn __repr__(&self) -> PyResult { - Ok(format!("{:?}", self.stats)) - } - fn __str__(&self) -> PyResult { - Ok(format!("{:?}", self.stats)) + let nz_count_dict = PyDict::new(gil.python()); + nz_count_dict.set_item("mean", stats.nz_count.0)?; + nz_count_dict.set_item("var", stats.nz_count.1)?; + dict.set_item("nz_count", nz_count_dict)?; + + let moment1_nz_dict = PyDict::new(gil.python()); + moment1_nz_dict.set_item("mean", stats.moment1_nz.0)?; + moment1_nz_dict.set_item("var", stats.moment1_nz.1)?; + dict.set_item("moment1_nz", moment1_nz_dict)?; + + let moment2_nz_dict = PyDict::new(gil.python()); + moment2_nz_dict.set_item("mean", stats.moment2_nz.0)?; + moment2_nz_dict.set_item("var", stats.moment2_nz.1)?; + dict.set_item("moment2_nz", moment2_nz_dict)?; + Ok(dict.into()) } } diff --git a/pygoko/src/tree.rs b/pygoko/src/tree.rs index 772c788..b1c1761 100644 --- a/pygoko/src/tree.rs +++ b/pygoko/src/tree.rs @@ -149,6 +149,10 @@ impl CoverTree { self.writer.as_ref().map(|w| w.reader().scale_range().start) } + pub fn scale_base(&self) -> Option { + self.writer.as_ref().map(|w| w.reader().parameters().scale_base) + } + pub fn layers(&self) -> PyResult { let reader = self.writer.as_ref().unwrap().reader(); let scale_indexes = reader.layers().map(|(si, _)| si).collect(); @@ -218,31 +222,24 @@ impl CoverTree { } } - pub fn kl_div_dirichlet_basestats( + pub fn kl_div_dirichlet_baseline( &self, prior_weight: f64, observation_weight: f64, - sequence_len: u64, - num_sequences: u64, - window_size: u64, - ) -> Vec> { + sequence_len: usize, + num_sequences: usize, + window_size: usize, + sample_rate: usize, + ) -> PyKLDivergenceBaseline { let reader = self.writer.as_ref().unwrap().reader(); - let mut trainer = DirichletBaseline::new(reader); + let mut trainer = DirichletBaseline::new(); trainer.set_prior_weight(prior_weight); trainer.set_observation_weight(observation_weight); - trainer.set_sequence_len(sequence_len as usize); - trainer.set_num_sequences(num_sequences as usize); - trainer.set_window_size(window_size as usize); - trainer - .train() - .unwrap() - .drain(0..) - .map(|mut vstats| { - vstats - .drain(0..) - .map(|stats| PyKLDivergenceStats { stats }) - .collect() - }) - .collect() + trainer.set_sequence_len(sequence_len); + trainer.set_num_sequences(num_sequences); + trainer.set_window_size(window_size); + trainer.set_sample_rate(sample_rate); + let baseline = trainer.train(reader).unwrap(); + PyKLDivergenceBaseline { baseline } } } diff --git a/pygoko/tests/ember2018.py b/pygoko/tests/ember2018.py index cebbbd4..0b7ac19 100644 --- a/pygoko/tests/ember2018.py +++ b/pygoko/tests/ember2018.py @@ -21,29 +21,34 @@ print("============= KL Divergence =============") prior_weight = 1.0 observation_weight = 1.3 -sequence_cap = 10 -sequence_len = 20 +window_size = 5000 +sequence_len = 800000 +sample_rate = 5000 sequence_count = 10 -normal_stats = tree.kl_div_dirichlet_basestats(prior_weight, +baseline = tree.kl_div_dirichlet_baseline(prior_weight, observation_weight, sequence_len, sequence_count, - sequence_cap) -for i,stats in enumerate(normal_stats[0]): - print(stats) + window_size, + sample_rate) +for i in range(0,800000,1000): + print(baseline.stats(i)) +""" print("============= KL Divergence Normal Use =============") -kl_tracker = tree.kl_div_dirichlet(prior_weight,observation_weight,sequence_cap) +kl_tracker = tree.kl_div_dirichlet(prior_weight,observation_weight,window_size) for i in range(200): kl_tracker.push(tree.data_point(i)) - print(kl_tracker.stats()) + mean, var = baseline.stats(i) + print((kl_tracker.stats() - mean)/var.sqrt()) print("============= KL Divergence Attack =============") -kl_attack_tracker = tree.kl_div_dirichlet(prior_weight,observation_weight,sequence_cap) +kl_attack_tracker = tree.kl_div_dirichlet(prior_weight,observation_weight,window_size) for i in range(10): kl_attack_tracker.push(tree.data_point(i)) print(kl_attack_tracker.stats()) for i in range(10): kl_attack_tracker.push(tree.data_point(0)) - print(kl_attack_tracker.stats()) \ No newline at end of file + print(kl_attack_tracker.stats()) +""" \ No newline at end of file diff --git a/pygoko/tests/one_d_viz.py b/pygoko/tests/one_d_viz.py index 0f202f8..dff1a61 100644 --- a/pygoko/tests/one_d_viz.py +++ b/pygoko/tests/one_d_viz.py @@ -12,63 +12,148 @@ cmap= plt.get_cmap("jet") norm = mpl.colors.Normalize(vmin=0.0, vmax=1.0) -def show1D(tree,data): +def show1D(tree,data, final_layer = -1000, color = "conditional", tracker = None, test_set = None): patches = [] lines = [] x_center = [] y_center = [] - top = tree.top_scale() - bottom = tree.bottom_scale() - for i in range(top,bottom,-1): - layer = tree.layer(i) - width = layer.radius() - scale_index = layer.scale_index() - - y = 1.5 + 0.1*scale_index - point_indexes, center_points = layer.centers() - for my_pi, c in zip(point_indexes,center_points): - x = c[0] - x_center.append(x) - y_center.append(y) - child_addresses = layer.child_addresses(my_pi) - if layer.is_leaf(my_pi): - patches.append(mpatches.Rectangle([x - width, y-0.05],2*width, 0.08, ec="none",color="orange")) - - lines.append(mlines.Line2D([x,x], [y,0.0],color="blue")) - - else: - patches.append(mpatches.Rectangle([x - width, y-0.05],2*width, 0.08, ec="none",color="blue")) - for si,pi in child_addresses: - y_next = 1.5+0.1*si - x_next = data[pi][0] - lines.append(mlines.Line2D([x,x_next], [y,y-0.05],color="blue")) - lines.append(mlines.Line2D([x_next,x_next], [y-0.05,y_next],color="blue")) - - for c in layer.singleton_points(my_pi): - lines.append(mlines.Line2D([c[0],c[0]], [y-0.05,0.0],color="orange")) - lines.append(mlines.Line2D([x,c[0]], [y,y-0.05],color="orange")) + + ungraphed_nodes = [tree.root()] + + while len(ungraphed_nodes) > 0: + node = ungraphed_nodes.pop() + (si,pi) = node.address() + x = data[pi,0] + y = 1+0.1*si + width = 2**si + x_center.append(x) + y_center.append(y) + + singleton_len = float(len(node.singletons_indexes())) + + if si < final_layer: + break + child_probs,singleton_probs = node.children_probs() + for i,(child_address,child_prob) in enumerate(child_probs): + (csi,cpi) = child_address + y_next = 1+0.1*csi + x_next = data[cpi][0] + child = tree.node(child_address) + if color == "conditional": + lines.append(mlines.Line2D([x,x_next], [y,y-0.05],color=cmap(child_prob),linewidth=4)) + lines.append(mlines.Line2D([x_next,x_next], [y-0.05,y_next],color=cmap(child_prob),linewidth=4)) + elif color == "total": + coverage_count = float(child.coverage_count()) + lines.append(mlines.Line2D([x,x_next], [y,y-0.05],color=cmap(coverage_count/len(data)),linewidth=4)) + lines.append(mlines.Line2D([x_next,x_next], [y-0.05,y_next],color=cmap(coverage_count/len(data)),linewidth=4)) + elif "post_prob" in color: + tracker_probs,_ = tracker.probs((si,pi)) + my_color = tracker_probs[i][1] + lines.append(mlines.Line2D([x,x_next], [y,y-0.05],color=cmap(my_color),linewidth=4)) + lines.append(mlines.Line2D([x_next,x_next], [y-0.05,y_next],color=cmap(my_color),linewidth=4)) + elif color == "evidence": + evidence = tracker.evidence((si,pi)) + if evidence is not None: + tracker_probs,_ = evidence + my_color = tracker_probs[i][1] + else: + my_color = 0 + lines.append(mlines.Line2D([x,x_next], [y,y-0.05],color=cmap(my_color),linewidth=4)) + lines.append(mlines.Line2D([x_next,x_next], [y-0.05,y_next],color=cmap(my_color),linewidth=4)) + + ungraphed_nodes.append(child) + for cpi in node.singletons_indexes(): + x_next = data[cpi][0] + if color == "conditional": + lines.append(mlines.Line2D([x_next,x_next], [y-0.05,0.0],color=cmap(singleton_probs),linewidth=2, alpha = 0.5)) + lines.append(mlines.Line2D([x,x_next], [y,y-0.05],color=cmap(singleton_probs),linewidth=2, alpha = 0.5)) + elif color == "total": + lines.append(mlines.Line2D([x_next,x_next], [y-0.05,0.0],color=cmap(singleton_len/len(data)),linewidth=2, alpha = 0.5)) + lines.append(mlines.Line2D([x,x_next], [y,y-0.05],color=cmap(singleton_len/len(data)),linewidth=2, alpha = 0.5)) + elif "post_prob" in color: + _,my_color = tracker.probs((si,pi)) + lines.append(mlines.Line2D([x_next,x_next], [y-0.05,0.0],color=cmap(my_color),linewidth=2, alpha = 0.5)) + lines.append(mlines.Line2D([x,x_next], [y,y-0.05],color=cmap(my_color),linewidth=2, alpha = 0.5)) + elif color == "evidence": + evidence = tracker.evidence((si,pi)) + if evidence is not None: + _,my_color = evidence + else: + my_color = 0 + lines.append(mlines.Line2D([x_next,x_next], [y-0.05,0.0],color=cmap(my_color),linewidth=4)) + lines.append(mlines.Line2D([x,x_next], [y,y-0.05],color=cmap(my_color),linewidth=4)) + if node.is_leaf(): + patches.append(mpatches.Rectangle([x - width, y-0.05],2*width, 0.08, ec="none",color="orange")) + if color == "conditional": + lines.append(mlines.Line2D([x,x], [y,0.0],color=cmap(singleton_probs),linewidth=2, alpha = 0.5)) + elif color == "total": + lines.append(mlines.Line2D([x,x], [y,0.0],color=cmap(1.0/len(data)),linewidth=2, alpha = 0.5)) + elif "post_prob" in color: + _,my_color = tracker.probs((si,pi)) + lines.append(mlines.Line2D([x,x], [y,0.0],color=cmap(my_color),linewidth=2, alpha = 0.5)) + elif color == "evidence": + evidence = tracker.evidence((si,pi)) + if evidence is not None: + _,my_color = evidence + else: + my_color = 0 + lines.append(mlines.Line2D([x,x], [y,0.0],color=cmap(my_color),linewidth=4)) + + else: + patches.append(mpatches.Rectangle([x - width, y-0.05],2*width, 0.08, ec="none",color="blue")) fig, ax = plt.subplots() + + if test_set is not None: + for c in test_set: + + path = tree.path(c) + csi,cpi = path[-1][1] + x = data[cpi][0] + y = 1+0.1*csi + x_next = c[0] + lines.append(mlines.Line2D([x,x_next], [y,0.0],color=cmap(my_color),linewidth=2)) + ax.scatter(test_set[:,0].reshape((test_set.shape[0],)),np.zeros([test_set.shape[0]]),color="red") + + ax.set_xlim((-1.01,1.01)) - ax.set_ylim((-0.1,2.1)) - ax.scatter(data[:,0].reshape((data.shape[0],)),np.zeros([data.shape[0]]),color="orange") + ax.set_ylim((-0.05,1.2)) + ax.scatter(data[:,0].reshape((data.shape[0],)),np.zeros([data.shape[0]]),color="orange", alpha= 0.5) collection = PatchCollection(patches,match_original=True, alpha=0.3) for l in lines: ax.add_line(l) ax.scatter(x_center,y_center) ax.add_collection(collection) - plt.show() + cax = fig.add_axes([0.9,0.05, 0.01, 0.9]) + + fig.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), cax=cax, orientation='vertical') + + ax.axis('off') + fig.set_size_inches(14.00, 7.00) + fig.savefig(f"1d_vis_{color}.png", bbox_inches='tight') if __name__ == '__main__': - numPoints = 42 + numPoints = 21 + numTest = 10 data1 = np.random.normal(loc=0.5,scale=0.2,size=(numPoints//3,)).reshape(numPoints//3,1) data2 = np.random.normal(loc=-0.5,scale=0.1,size=(2*numPoints//3,)).reshape(2*numPoints//3,1) data = np.concatenate([data1,data2],axis=0) data = np.concatenate([data,np.zeros([numPoints,1])],axis=1).astype(np.float32) + test_set = np.random.normal(loc=0.5,scale=0.2,size=(10,)).reshape(10,1) + test_set = np.concatenate([test_set,np.zeros([10,1])],axis=1).astype(np.float32) + tree = pygoko.CoverTree() tree.set_scale_base(2) tree.set_leaf_cutoff(0) tree.fit(data) - show1D(tree,data) \ No newline at end of file + path = tree.path(np.array([0.5],dtype=np.float32)) + + run_tracker = tree.kl_div_dirichlet(1.0,2.0,0) + for i,t in enumerate(test_set): + run_tracker.push(t) + show1D(tree,data,color=f"post_prob_{i}",tracker=run_tracker, test_set =test_set[:i]) + show1D(tree,data,color="total") + show1D(tree,data) + diff --git a/pygoko/tests/test_one_d_viz.py b/pygoko/tests/test_one_d_viz.py index a7fdded..652013d 100644 --- a/pygoko/tests/test_one_d_viz.py +++ b/pygoko/tests/test_one_d_viz.py @@ -8,8 +8,28 @@ data = np.array([[0.499], [0.48], [-0.49], [0.0]],dtype=np.float32) + tree = pygoko.CoverTree() tree.set_scale_base(2) tree.set_leaf_cutoff(0) tree.fit(data) + +print(tree.knn(tree.data_point(0),5)) + +print("============= KL Divergence =============") +prior_weight = 1.0 +observation_weight = 1.0 +window_size = 3 +sequence_len = 10 +sample_rate = 2 +sequence_count = 1 +baseline = tree.kl_div_dirichlet_baseline(prior_weight, + observation_weight, + sequence_len, + sequence_count, + window_size, + sample_rate) +for i in range(0,sequence_len,sample_rate): + print(baseline.stats(i)) + show1D(tree,data) \ No newline at end of file diff --git a/pygoko/tests/two_d_viz.py b/pygoko/tests/two_d_viz.py index 23bcd2d..2301dfc 100644 --- a/pygoko/tests/two_d_viz.py +++ b/pygoko/tests/two_d_viz.py @@ -13,38 +13,49 @@ norm = mpl.colors.Normalize(vmin=0.0, vmax=1.0) def show2D(tree,data): - patches = [] - lines = [] - centers = [] + top = tree.top_scale() bottom = tree.bottom_scale() print(top,bottom) - for i in range(top,bottom,-1): - layer = tree.layer(i) - width = layer.radius() - point_indexes, center_points = layer.centers() - for pi, c in zip(point_indexes,center_points): + for j in range(top,bottom,-1): + patches = [] + lines = [] + centers = [] + layer = tree.layer(j) + width = layer.radius()/2 + _, centers = layer.centers() + for c in centers: patches.append(mpatches.Circle(c,2*width,color="Blue")) - centers.append(c) - if not layer.is_leaf(pi): - for child in layer.child_points(pi): - lines.append(mlines.Line2D([c[0],child[0]], [c[1],child[1]],color="blue")) - - for singleton in layer.singleton_points(pi): - lines.append(mlines.Line2D([c[0],singleton[0]], [c[1],singleton[1]],color="orange")) - - centers = np.array(centers) - fig, ax = plt.subplots() - ax.set_xlim((-1.1,1.1)) - ax.set_ylim((-1.1,1.1)) - - cmap= plt.get_cmap("jet") - collection = PatchCollection(patches,match_original=True,alpha= 0.05) - for l in lines: - ax.add_line(l) - ax.scatter(centers[:,0],centers[:,1]) - ax.add_collection(collection) - plt.show() + for i in range(j,top): + parent_layer = tree.layer(i+1) + point_indexes, center_points = parent_layer.centers() + for pi, c in zip(point_indexes,center_points): + if not parent_layer.is_leaf(pi): + for child in parent_layer.child_points(pi): + lines.append(mlines.Line2D([c[0],child[0]], [c[1],child[1]],color="blue")) + + for singleton in parent_layer.singleton_points(pi): + lines.append(mlines.Line2D([c[0],singleton[0]], [c[1],singleton[1]],color="orange")) + + fig, ax = plt.subplots() + centers = np.array(centers) + + + ax.set_xlim((-1.6,1.6)) + ax.set_ylim((-1.6,1.6)) + + cmap= plt.get_cmap("jet") + collection = PatchCollection(patches,match_original=True,alpha= 0.05) + for l in lines: + ax.add_line(l) + ax.add_collection(collection) + ax.scatter(data[:,0],data[:,1],color="orange") + ax.scatter(centers[:,0],centers[:,1], color="red") + + ax.axis('off') + fig.set_size_inches(10.00, 10.00) + fig.savefig(f"2d_vis_{j:2d}.png", bbox_inches='tight') + plt.close() if __name__ == '__main__': numPoints = 120 @@ -52,8 +63,9 @@ def show2D(tree,data): data = [np.cos(data).reshape(-1,1),np.cos(data)*np.sin(data).reshape(-1,1)] data = np.concatenate(data,axis=1).astype(np.float32) - tree = pygoko.Covertree() + tree = pygoko.CoverTree() tree.set_leaf_cutoff(0) + tree.set_scale_base(2.0) tree.fit(data) show2D(tree,data) From db42e7ccbb0d450f76457e439ae2a06c7288455f Mon Sep 17 00:00:00 2001 From: sven Date: Tue, 18 Aug 2020 10:39:55 -0700 Subject: [PATCH 04/10] got some examples in there --- examples/ember_chronological_drift.py | 126 +++ experiments/.gitignore | 2 - experiments/__init__.py | 0 experiments/buildTestingNets.py | 56 - experiments/mnist_test_set_attack_basic.py | 81 -- .../poisoning/mnist_basic_poisoning.py | 0 experiments/targets/.gitignore | 1 - experiments/targets/__init__.py | 1 - experiments/targets/__init__.pyc | Bin 149 -> 0 bytes experiments/targets/cifar10Deep.py | 113 -- experiments/targets/cifar10ResNet.py | 104 -- experiments/targets/cifar10ResNet.pyc | Bin 4475 -> 0 bytes experiments/targets/cifar10VGG.py | 180 --- experiments/targets/datasets.py | 116 -- experiments/targets/datasets.pyc | Bin 5210 -> 0 bytes experiments/targets/datasets_locations.pyc | Bin 478 -> 0 bytes experiments/targets/imageNetClasses.py | 1003 ----------------- experiments/targets/imageNetClasses.pyc | Bin 39949 -> 0 bytes experiments/targets/mnistConvNet.py | 60 - experiments/targets/mnistConvNet.pyc | Bin 2912 -> 0 bytes experiments/targets/mnistMLP.py | 50 - experiments/targets/mnistMLP.pyc | Bin 2495 -> 0 bytes experiments/targets/utils.py | 137 --- .../ember2018_test_set_attack.py | 384 ------- pygoko/tests/gmm2d.py | 83 ++ pygoko/tests/gmm_test.py | 141 +++ 26 files changed, 350 insertions(+), 2288 deletions(-) create mode 100644 examples/ember_chronological_drift.py delete mode 100644 experiments/.gitignore delete mode 100644 experiments/__init__.py delete mode 100644 experiments/buildTestingNets.py delete mode 100644 experiments/mnist_test_set_attack_basic.py delete mode 100644 experiments/poisoning/mnist_basic_poisoning.py delete mode 100644 experiments/targets/.gitignore delete mode 100644 experiments/targets/__init__.py delete mode 100644 experiments/targets/__init__.pyc delete mode 100644 experiments/targets/cifar10Deep.py delete mode 100644 experiments/targets/cifar10ResNet.py delete mode 100644 experiments/targets/cifar10ResNet.pyc delete mode 100644 experiments/targets/cifar10VGG.py delete mode 100644 experiments/targets/datasets.py delete mode 100644 experiments/targets/datasets.pyc delete mode 100644 experiments/targets/datasets_locations.pyc delete mode 100644 experiments/targets/imageNetClasses.py delete mode 100644 experiments/targets/imageNetClasses.pyc delete mode 100644 experiments/targets/mnistConvNet.py delete mode 100644 experiments/targets/mnistConvNet.pyc delete mode 100644 experiments/targets/mnistMLP.py delete mode 100644 experiments/targets/mnistMLP.pyc delete mode 100644 experiments/targets/utils.py delete mode 100644 experiments/test_set_attacks/ember2018_test_set_attack.py create mode 100644 pygoko/tests/gmm2d.py create mode 100644 pygoko/tests/gmm_test.py diff --git a/examples/ember_chronological_drift.py b/examples/ember_chronological_drift.py new file mode 100644 index 0000000..04bc68d --- /dev/null +++ b/examples/ember_chronological_drift.py @@ -0,0 +1,126 @@ + #!/usr/bin/env python + +import os +import pandas as pd +import ember +import argparse +import numpy as np + +import matplotlib.pyplot as plt + +from pygoko import CoverTree + +def split(metadata,X,Y): + unique_dates = list(set(metadata['appeared'])) + + X_sorted = {k:X[metadata['appeared'] == k] for k in unique_dates} + y_sorted = {k:Y[metadata['appeared'] == k] for k in unique_dates} + + return X_sorted, y_sorted + +def normalize(baseline,stats): + basesline_stats = baseline.stats(stats["sequence_len"]) + normalized = {} + for k in basesline_stats.keys(): + n = (stats[k]-basesline_stats[k]["mean"]) + if basesline_stats[k]["var"] > 0: + n = n/np.sqrt(basesline_stats[k]["var"]) + normalized[k] = n + return normalized + +def main(): + prog = "ember_drift_calc" + descr = "Train an ember model from a directory with raw feature files" + parser = argparse.ArgumentParser(prog=prog, description=descr) + parser.add_argument("datadir", metavar="DATADIR", type=str, help="Directory with raw features") + args = parser.parse_args() + + split_date = "2018-07" + + + # Gather the data and sort it into order + train_X, train_y = ember.read_vectorized_features(args.datadir,"train") + test_X, test_y = ember.read_vectorized_features(args.datadir,"test") + + train_metadata = pd.read_csv(os.path.join(args.datadir, "train_metadata.csv"), index_col=0) + test_metadata = pd.read_csv(os.path.join(args.datadir, "test_metadata.csv"), index_col=0) + + train_X_month, train_y_month = split(train_metadata,train_X,train_y) + test_X_month, test_y_month = split(test_metadata,test_X,test_y) + + print(train_X_month.keys()) + print(test_X_month.keys()) + + dates = list(train_X_month.keys()) + dates.sort() + + training_dates = [d for d in dates if d < split_date] + all_dates = [d for d in dates] + + training_data = np.concatenate([train_X_month[k] for k in training_dates]) + training_data = np.ascontiguousarray(training_data) + + all_data = np.concatenate([train_X_month[k] for k in all_dates]) + all_data = np.ascontiguousarray(all_data) + + # Build the tree + tree = CoverTree() + tree.set_leaf_cutoff(50) + tree.set_scale_base(1.5) + tree.set_min_res_index(0) + tree.fit(training_data) + + # Gather a baseline + + prior_weight = 1.0 + observation_weight = 1.3 + # 0 sets the window to be infinite, otherwise the "dataset" you're computing against is only the last `window_size` elements + window_size = 5000 + # + sequence_len = 800000 + # Actually computes the KL div this often. All other values are linearly interpolated between these sample points + sample_rate = 5000 + # Averages over this number of sasmples + sequence_count = 50 + + baseline = tree.kl_div_dirichlet_baseline(prior_weight, + observation_weight, + sequence_len, + sequence_count, + window_size, + sample_rate) + + goko_divs = {} + + + run_tracker = tree.kl_div_dirichlet( + prior_weight, + observation_weight, + window_size) + + total_kl_div = [] + for i,datum in enumerate(all_data): + run_tracker.push(datum) + if i % 500 == 0: + goko_divs[i] = normalize(baseline,run_tracker.stats()) + total_kl_div.append(goko_divs[i]['moment1_nz']) + + + fig, ax = plt.subplots() + ax.plot(list(range(0,len(all_data),500)),total_kl_div) + ax.set_ylabel('KL Divergence') + ax.set_xlabel('Sample Timestamp') + tick_len = 0 + tick_locations = [] + for date in all_dates: + tick_len += len(train_X_month[date]) + tick_locations.append(tick_len) + ax.set_xticks(tick_locations) + ax.set_xticklabels(all_dates) + fig.tight_layout() + fig.savefig("drift.png", bbox_inches='tight') + plt.show() + plt.close() + +if __name__ == '__main__': + main() diff --git a/experiments/.gitignore b/experiments/.gitignore deleted file mode 100644 index 96c8b5f..0000000 --- a/experiments/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -*.json -.ipynb_checkpoints \ No newline at end of file diff --git a/experiments/__init__.py b/experiments/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/experiments/buildTestingNets.py b/experiments/buildTestingNets.py deleted file mode 100644 index adfc9db..0000000 --- a/experiments/buildTestingNets.py +++ /dev/null @@ -1,56 +0,0 @@ -from .targets.mnistMLP import trainMNISTMLP -from .targets.cifar10ResNet import trainCIFAR10ResNet -from .targets.cifar10Deep import trainCIFAR10Deep7,trainCIFAR10Deep10 -from .targets.mnistConvNet import trainMNISTConvNet -import torch -import os - -import argparse - -def main(): - prog = "build_target_models" - descr = "Build target models for attack testing purposes" - parser = argparse.ArgumentParser(prog=prog, description=descr) - parser.add_argument("-m", "--model", type=str, default=None, required=False, help="Target Model") - parser.add_argument("-d", "--directory", type=str, default='./', required=False, help="Base location of models") - args = parser.parse_args() - - targetModels = { - "mnistMLP":trainMNISTMLP, - "mnistConvNet":trainMNISTConvNet, - "cifar10ResNet":trainCIFAR10ResNet, - "cifar10Deep7":trainCIFAR10Deep7, - "cifar10Deep10":trainCIFAR10Deep10} - - if not (args.model in targetModels.keys() or args.model is None): - model_parse_error = "{} is not a supported model, try:\n".format(args.model) - for m in targetModels.keys(): - model_parse_error += "\t" + m + "\n" - parser.error(model_parse_error) - - if torch.cuda.is_available(): - print("Using GPU 0") - device = torch.device("cuda:0") - else: - print("No GPU, using CPU") - device = torch.device("cpu") - cpu = torch.device("cpu") - - if torch.cuda.is_available(): - if args.model is None: - print("Training all of them") - for model in targetModels.values(): - model(num_components=3,device=device,directory=args.directory) - else: - targetModels[args.model](num_components=3,device=device,directory=args.directory) - - else: - if args.model is None: - print("Training MNIST MLP due to lack of GPU") - trainMNISTMLP(num_components=3,device=device,directory=args.directory) - else: - targetModels[args.model](num_components=3,device=device,directory=args.directory) - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/experiments/mnist_test_set_attack_basic.py b/experiments/mnist_test_set_attack_basic.py deleted file mode 100644 index 9589a84..0000000 --- a/experiments/mnist_test_set_attack_basic.py +++ /dev/null @@ -1,81 +0,0 @@ -from targets.mnistMLP import trainMNISTMLP -from targets.mnistConvNet import trainMNISTConvNet -import torch -from targets.datasets import MNIST -from targets.utils import * -import pygoko -import numpy as np - -def testAttackAccuracy(model,test_set,device = None, kl_tracker=None): - if device is None: - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - cpu = torch.device("cpu") - model.to(device) - - incorrect = None - - correct = 0.0 - total = 0.0 - with torch.no_grad(): - for data in test_set: - images, labels = data - if incorrect is not None: - images,labels = incorrect - if kl_tracker is not None: - for image in images.numpy(): - kl_tracker.push(image.flatten()) - images = images.to(device) - outputs = model(images) - _, predicted = torch.max(outputs.data, 1) - _, predicted = _.to(cpu), predicted.to(cpu) - if incorrect is None: - incorrect_indexes = np.arange(predicted.shape[0])[predicted != labels] - if len(incorrect_indexes) > 0: - images = images.to(cpu) - incorrect_image = images[incorrect_indexes[0]].repeat(images.shape[0],1,1,1) - incorrect_label = labels[incorrect_indexes[0]].repeat(images.shape[0],1) - incorrect = incorrect_image,incorrect_label - - - total += labels.size(0) - correct += (predicted == labels).sum().item() - model.to(cpu) - return correct/total - -def main(): - model1 = torch.load("models/mnistMLP.pkl") - model2 = torch.load("models/mnistConvNet.pkl") - mnist = MNIST() - trainloader = mnist.training(50000) - for arr,label in trainloader: - train_set_array = arr.numpy().reshape([-1,784]).astype(np.float32) - tree = pygoko.CoverTree() - tree.set_cutoff(50) - tree.set_scale_base(1.3) - tree.set_resolution(-30) - tree.fit(train_set_array) - - print("============= KL Divergence Normal =============") - kl_tracker = tree.kl_div_sgd(0.005,0.9) - testloader = mnist.testing() - accuracy = testAccuracy(model1,testloader,kl_tracker=kl_tracker) - print(accuracy) - kl_arr = [] - for kl,address in kl_tracker.all_kl(): - kl_arr.append(kl) - print(kl,address) - kl_arr = np.array(kl_arr) - print(kl_arr.mean(),kl_arr.var(),kl_arr.max(),len(kl_arr)) - print("============= KL Divergence Attack =============") - - kl_attack_tracker = tree.kl_div_sgd(0.005,0.9) - accuracy = testAttackAccuracy(model1,testloader,kl_tracker=kl_attack_tracker) - print(accuracy) - kl_attack_arr = [] - for kl,address in kl_attack_tracker.all_kl(): - kl_attack_arr.append(kl) - print(kl,address) - kl_attack_arr = np.array(kl_attack_arr) - print(kl_attack_arr.mean(),kl_attack_arr.var(),kl_attack_arr.max(),len(kl_attack_arr)) -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/experiments/poisoning/mnist_basic_poisoning.py b/experiments/poisoning/mnist_basic_poisoning.py deleted file mode 100644 index e69de29..0000000 diff --git a/experiments/targets/.gitignore b/experiments/targets/.gitignore deleted file mode 100644 index 82d1035..0000000 --- a/experiments/targets/.gitignore +++ /dev/null @@ -1 +0,0 @@ -datasets_locations.py \ No newline at end of file diff --git a/experiments/targets/__init__.py b/experiments/targets/__init__.py deleted file mode 100644 index 8b13789..0000000 --- a/experiments/targets/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/experiments/targets/__init__.pyc b/experiments/targets/__init__.pyc deleted file mode 100644 index d5d3bc9946086d6acd95a87a9b47edcc2243c862..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 149 zcmZSn%**BAYZIN!00oRd+5w1*S%5?e14FO|NW@PANHCxg#dbh3{fzwFRQ=+z)I9y< z{FGGvg36NoqU4PD#FVnsqT@nl4AY%_{_Y_lK6N%pjr-~ T5jMH`DWy57b|8z2ftUdR53M2F diff --git a/experiments/targets/cifar10Deep.py b/experiments/targets/cifar10Deep.py deleted file mode 100644 index cb2a486..0000000 --- a/experiments/targets/cifar10Deep.py +++ /dev/null @@ -1,113 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim -import torch.nn.functional as F -import os -from .datasets import CIFAR10 -from .utils import * - -class CIFAR10Deep7(nn.Module): - def __init__(self): - super(CIFAR10Deep7, self).__init__() - self.mlp = nn.Sequential( - nn.Linear(32*32*3, 750), - nn.ReLU(True), - nn.AlphaDropout(p=0.25), - nn.Linear(750, 750), - nn.ReLU(True), - nn.Linear(750, 750), - nn.ReLU(True), - nn.Linear(750, 750), - nn.ReLU(True), - nn.Linear(750, 750), - nn.ReLU(True), - nn.Linear(750, 150), - nn.AlphaDropout(p=0.25), - nn.ReLU(True), - nn.Linear(150, 10)) - self.add_module("5_layer",self.mlp) - - - def forward(self, x): - x = x.view(-1, 32*32*3) - return self.mlp(x) - - def dataset(self): - return CIFAR10 - -def trainCIFAR10Deep7(num_components=3,device=None,directory = ''): - if device is None: - device = getDevice() - - net = CIFAR10Deep() - cifar = CIFAR10() - batch_size = 120 - trainloader = cifar.training(batch_size) - - print('Training CIFAR10 Deep MLP Model') - criterion = nn.CrossEntropyLoss() - optimizer = optim.Adam(net.parameters(), lr=0.001) - scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5) - trainModel(net,trainloader,optimizer,criterion,400,device) - - net.eval() - print('Finished Training, getting accuracy') - testloader = cifar.testing() - accuracy = testAccuracy(net,testloader) - - model_path = os.path.join(directory, "cifar10Deep7.pkl") - print('Saving as: cifar10Deep7.pkl, with accuracy %.4f'%(accuracy,)) - torch.save(net,model_path) - -class CIFAR10Deep10(nn.Module): - def __init__(self): - super(CIFAR10Deep10, self).__init__() - self.mlp = nn.Sequential( - nn.Linear(32*32*3, 750), - nn.ReLU(True), - nn.AlphaDropout(p=0.25), - nn.Linear(750, 750), - nn.ReLU(True), - nn.Linear(750, 750), - nn.ReLU(True), - nn.Linear(750, 750), - nn.ReLU(True), - nn.Linear(750, 750), - nn.ReLU(True), - nn.Linear(750, 150), - nn.AlphaDropout(p=0.25), - nn.ReLU(True), - nn.Linear(150, 10)) - self.add_module("5_layer",self.mlp) - - - def forward(self, x): - x = x.view(-1, 32*32*3) - return self.mlp(x) - - def dataset(self): - return CIFAR10 - -def trainCIFAR10Deep10(num_components=3,device=None,directory = ''): - if device is None: - device = getDevice() - - net = CIFAR10Deep() - cifar = CIFAR10() - batch_size = 120 - trainloader = cifar.training(batch_size) - - print('Training CIFAR10 Deep MLP Model') - criterion = nn.CrossEntropyLoss() - optimizer = optim.Adam(net.parameters(), lr=0.001) - scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.95) - trainModel(net,trainloader,optimizer,criterion,400,device) - - net.eval() - print('Finished Training, getting accuracy') - testloader = cifar.testing() - accuracy = testAccuracy(net,testloader) - - model_path = os.path.join(directory, "cifar10Deep10.pkl") - print('Saving as: cifar10Deep10.pkl, with accuracy %.4f'%(accuracy,)) - torch.save(net,model_path) \ No newline at end of file diff --git a/experiments/targets/cifar10ResNet.py b/experiments/targets/cifar10ResNet.py deleted file mode 100644 index 592da5a..0000000 --- a/experiments/targets/cifar10ResNet.py +++ /dev/null @@ -1,104 +0,0 @@ -import torch.nn as nn -import torch.nn.functional as F -from tqdm import tqdm -import torch -import os -import torchvision -import torch.optim as optim -import torchvision.transforms as transforms -import os - -from .datasets import CIFAR10 -from .utils import * - -class residual(nn.Module): - def __init__(self,nxn,connections,padding): - super(residual, self).__init__() - self.conv1 = nn.Conv2d(connections, connections, nxn,padding = padding) - self.bn1 = nn.BatchNorm2d(connections) - self.conv2 = nn.Conv2d(connections, connections, nxn,padding = padding) - self.bn2 = nn.BatchNorm2d(connections) - def forward(self,x): - y = F.relu(self.conv1(x)) - y = self.bn1(y) - y = F.relu(self.conv2(y)) - y = self.bn2(y) - return x + y - -class CIFAR10ResNet(nn.Module): - def __init__(self,n): - super(CIFAR10ResNet, self).__init__() - self.conv1 = nn.Conv2d(3, 16, 3,padding=1) - self.bn1 = nn.BatchNorm2d(16) - self.res1 = [] - for i in range(0,n): - res = residual(3,16,1) - self.add_module("residual_"+str(i),res) - self.res1.append(res) - self.conv2 = nn.Conv2d(16, 32, 3,stride=2,padding=1) - self.bn2 = nn.BatchNorm2d(32) - - self.res2 = [] - for i in range(n,2*n): - res = residual(3,32,1) - self.add_module("residual_"+str(i),res) - self.res2.append(res) - self.conv3 = nn.Conv2d(32, 64, 3,stride=2,padding=1) - self.bn3 = nn.BatchNorm2d(64) - - self.res3 = [] - for i in range(2*n,3*n): - res = residual(3,64,1) - self.add_module("residual_"+str(i),res) - self.res3.append(res) - - self.fc1 = nn.Linear(64*8*8, 10) - - self.layerCount = n*2*3 + 4 - - - def forward(self, x): - x = F.relu(self.conv1(x)) - x = self.bn1(x) - for res in self.res1: - x = res(x) - x = F.relu(self.conv2(x)) - x = self.bn2(x) - for res in self.res2: - x = res(x) - x = F.relu(self.conv3(x)) - x = self.bn3(x) - for res in self.res3: - x = res(x) - - x = x.view(-1, 64*8*8) - x = self.fc1(x) - return x - - def dataset(self): - return CIFAR10 - -def trainCIFAR10ResNet(num_components=3,device=None,directory = ''): - if device is None: - device = getDevice() - - net = CIFAR10ResNet(num_components) - cifar = CIFAR10() - batch_size = 240 - trainloader = cifar.training(batch_size) - - - print('Training CIFAR10 ResNet Model with %d layers'%(net.layerCount)) - criterion = nn.CrossEntropyLoss() - optimizer = optim.Adam(net.parameters(), lr=0.001) - scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.9) - trainModel(net,trainloader,optimizer,criterion,400) - - net.eval() - print('Finished Training, getting accuracy') - testloader = cifar.testing() - accuracy = testAccuracy(net,testloader) - - model_path = os.path.join(directory, "cifar10ResNet%d.pkl"%(net.layerCount,)) - print('Saving as: cifar10ResNet%d.pkl, with accuracy %.4f'%(net.layerCount,accuracy,)) - torch.save(net,model_path) \ No newline at end of file diff --git a/experiments/targets/cifar10ResNet.pyc b/experiments/targets/cifar10ResNet.pyc deleted file mode 100644 index ca8767bb2fc710e2c9a79fcea656532183925697..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4475 zcmcInTW=fJ5uURYDUzlxrtH|x4QiYML{UhQ{L~~!d`X-XwfV3rVw7pYu6I|`VaO%D zXGK$i^rZZNpl|Iz=&$H=U-KLC2ZEq4MS}MGW_L-&7a$Lcq&+%wE^}t)%zQJa^8HHl z_QUr-52g62;QwuW?prhw{)`Mo21a_OBt4RYiu9Ir#FJrFMpfxm#S|NA(yNuLm!-E{ z_UqECm;Hten$l~QBXjOWv;9ixeCc`xutxInm^s>Z|kT6jh&5-g5lhpDh7-CRpD%nQ)jxZzpNuK zTVH=}eS>$9OVrJ%`6P%Un;djh-zw!gn#8GJp>R~X-?xd)`u&VbBuS>?kCW+clyP;4 z?oM_Iy}Lmc9(Kp+2s7OMIN6!6*eynOJG6jaf{TayI-1N9-49Ml9Ao4I%+n$Gw~w+8ckT=wqZ(+yKbMGQEVK zEUPY-fdek>+Mc>xw?j#A@gn!t7->8_@*5z1TGDT^q33oek-v&Y7tgdkzIe;1C+v^Y zr$HKB#c73suA4Q(FTb_i8S}T`|AowET!1?D1w{c#$zZtdK6^T}vEub>Z zgoHt4BtCR4J;JV|Q7F=6VL?%(MIPweO%5@ZU6$G?*7RX-gSPY{%OJ4=bg=?nomW8m z1Wo@s8}9Q7!9Ew~;m;k40R=3$97EF}If!#i<_bV|5qgTCI9!DYCX+ac7U)s(*q!6> ztpbN{l~cEL;{%(-L8^26;f_Z80(24u9jmj*fE}Fw3&8Rl;Oe%>yMX0}c?ID7s<~p` zHf^tg-g%0^jX5tYp_`GbQTtzX7MOeOW~Ld|n@Qe-P6B8(&F!2?n7CS3Mf#9`5 zUIwN9C!bP)RXwaB*&0te!AX#s^UJ2ivsrv zA&!9bN7nx+jyH=sV>2%_HuEO|Z+4g-{P553zIpiguODh^a9KR*JNsp9!BiA?4}#Gs zIQYlk`Sb4&?bq-?cN3HM!P*_h(PnwdZ#Pl3W;`wk!=p3^j~yfRnJV^+q2rbA1yfz= z-ram|L5ESJ;o2Q1vC0L`AH`D}#(C;t6lis?FB~w^JOqou9n!ILA17Hlo*X|wS9KUq zGCNX^ccNgV#or`IQ3+VAGMBjzb2IXL$jP_uO2fj}u>Be{(3^h2i*ilK{#r~Z_ zS$$xPoi3gP*`am@W1FZ1TI}nP3#M_0M{4SdR4dgnvMJsVmbY9(nC-Y$HPA5Uj+3<#dAi? zR4`Fz?+lKkok;%Z}z zZ0#Q<`Vhm7QQrlbrw$(Ff57HFG`TU!Y-%0N$0tnAN{#5>B2DPi!FgZ)JEwfwB>mLlIeirfnO%(S}h!p=K$wl^?dL;XiwznjYq-e>0ONzGax20H+{RN5YZx-dA z_B}d1ouwoD9r?rfby=`Wa=$CprxHa`+Lm-((gpcUo%=DNmr+>y{2_d(zR*psiyT5kGb1_21m~RgJ;lh zqw2d9RWf#?vMQXXY9>C*onJ;RO?=|4cfQA$Nl|1r9_ER2jVdE_yp7^Guf-u$aXd{+ z%x<6ELkgA_$`*?2LtrzXh{R1rzhet@CEB&X%5I-u4g3N>!EXB8pzg95XU_X2ybiNb zQeC^MV+Q5edOc&299Yz294;GY;{$&hL$jvUI*&%(pnm9VKH|Pag?kINjl;6A8}5M} zZw$-SZX7-GWi>pElk|bDTvBC8zVXmj53SqqNp%1{ZA=w*1!JkeIL6vOj?bd**dWo0 z{srZSA>*X5ajcezIgh2p9AQRY)+8om+h*?1wu(&2qT5^EKNFJ*Vcl)O>+aPJ^1_|e7?F-_x-ExJG4;sPw?>p3Z5@+C?=X2no4LsF^j6Y`SxVxJNpSa z!<7|nTV-)n!YSwyCK;z?u{j!LKpKs?SMJ%dD=R(h03Ma(*`I7cS#y4R+FXt5&tYjG zXzIod=HvJRdK`URqE7T8pm%;|a{7*%{O70~H+gMTcp?lBbHb>t<3^=34jM&%r~cX) z-i$t3>2o^Ouc6}_`ih~wRc+>3)==i>n4Ixz+6r6{$1kCy_R@~3N)5ifxwpA{Yj4Os z@ZoXZMsdG^_y*+^p!0XcM~EY4UrS{0m3JDAQq^mKnB1Y<$`@BkXBwA!#=;+`M zA_7%6P`bREc<`}amD;bb(*t#%z{_`XJry=r20Zf(tVl=_09ip}#BFF?#y*|TjR zdi?^kJ3>aH9`M?!|9a7nRea}|;yY8tFR9{}riu>-Bd?f?6g;AN5u(4$+RrE&C7Mfk zRoi$t88t^Ty`ALF8hV>5nO#pyeQNFC?{8rJ|E)cSM#7dbP}uT#ATEG(`@^&HxTPAw zdrN-$QA_?v-SEhR;C@`AcGDRiJSR-5%5z{ge@r0#7$A>O533zf9mQyzTEn91r{-nW zxV#~HX8X|EJQ9ra^|VT)pwaevcfG4-zcbj`ozW=upfpO-{|1d7&m3tChE6rapHXS} z1GE9Hl0RC?8B^x)fG!j51=7aSw~Sw&F%1Y|Hoi+k!>z$Rpy2Hf&&Xpe)1&S2PHPvm zevBlW*=gV+0;MX;fNrG&_0zQccuZhx49k>wnGXfhU@G|_b6GhyJfeA>JWVyE;URKHnE(9LGcGd<821Z;atONDu*L z1Q98GV6H%#4T`H2%u>{ujv-!@g!Lj7=`y!I2 z-~?No@GlS{mCQv7T&^OCyyj1o;6G%VkU$dxcn>_)Vmq=ZT*yFPs4FAZH#3mslH6P7 zh3p<~bzO$BCs7peIP1cFt}7lhfJ#H}DUu+E;kOLJHr(y_TSAC?)cf>;y`2qxwvGnF zQOy<2u<7JQFZ+TciTEFQwWF%_V-@O1o=fO92G(!!#|rDrCg(?yaK#|aB1+)ZGI8V_QTC95$J9rPJ2Kmf``)X{>{*)Uqrp^OZuZ&xoV26-*rMas*RdKENBix+z zs-izr1RBv0$CGP>G)gx&@p^1&yvZCCtugz0_yOiEI;y(}(SxclM(e9>D1I$E+gWR` OEUk3TUO3l1xB6es^ER>o diff --git a/experiments/targets/datasets_locations.pyc b/experiments/targets/datasets_locations.pyc deleted file mode 100644 index e0fc2eaa64ac61e1bdbb7656338f0b0a36958cd2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 478 zcma)&%}N6?5XWb`?J5O958^e7hl0?w_TWWCussySgB1_!A%rBh!R|&f(?Z|Ick~6E zv=tS4Ffjilzxl{y^3Q|z$LZk3z;gSTQKPq zDfWV;J+htz%{&SU0v(A}SVUnpX}+MC2cyY{x+~gv%<*y-DkpL2YMEkWReK~n+2Jw% zRmXl0uXgyx_mk(xJ2^}hhNCA*QTtkM=w+`zP#b{f|HxgsC?7g4j-)*nrED-tQ3C7c z)^ytp9WnKlp1Ru6<`G{{>F^JANDMp7;74%G*I%DE!8-ZAq7cnYY0FKZj$Y5DCer2& lCFwZj0j{T%CaJzBiCs?H=W5qgu#367$@z>46pbQF_64v?f-wL9 diff --git a/experiments/targets/imageNetClasses.py b/experiments/targets/imageNetClasses.py deleted file mode 100644 index 9efa61f..0000000 --- a/experiments/targets/imageNetClasses.py +++ /dev/null @@ -1,1003 +0,0 @@ -imagenetDict = {0: 'tench, Tinca tinca', - 1: 'goldfish, Carassius auratus', - 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', - 3: 'tiger shark, Galeocerdo cuvieri', - 4: 'hammerhead, hammerhead shark', - 5: 'electric ray, crampfish, numbfish, torpedo', - 6: 'stingray', - 7: 'cock', - 8: 'hen', - 9: 'ostrich, Struthio camelus', - 10: 'brambling, Fringilla montifringilla', - 11: 'goldfinch, Carduelis carduelis', - 12: 'house finch, linnet, Carpodacus mexicanus', - 13: 'junco, snowbird', - 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea', - 15: 'robin, American robin, Turdus migratorius', - 16: 'bulbul', - 17: 'jay', - 18: 'magpie', - 19: 'chickadee', - 20: 'water ouzel, dipper', - 21: 'kite', - 22: 'bald eagle, American eagle, Haliaeetus leucocephalus', - 23: 'vulture', - 24: 'great grey owl, great gray owl, Strix nebulosa', - 25: 'European fire salamander, Salamandra salamandra', - 26: 'common newt, Triturus vulgaris', - 27: 'eft', - 28: 'spotted salamander, Ambystoma maculatum', - 29: 'axolotl, mud puppy, Ambystoma mexicanum', - 30: 'bullfrog, Rana catesbeiana', - 31: 'tree frog, tree-frog', - 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui', - 33: 'loggerhead, loggerhead turtle, Caretta caretta', - 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', - 35: 'mud turtle', - 36: 'terrapin', - 37: 'box turtle, box tortoise', - 38: 'banded gecko', - 39: 'common iguana, iguana, Iguana iguana', - 40: 'American chameleon, anole, Anolis carolinensis', - 41: 'whiptail, whiptail lizard', - 42: 'agama', - 43: 'frilled lizard, Chlamydosaurus kingi', - 44: 'alligator lizard', - 45: 'Gila monster, Heloderma suspectum', - 46: 'green lizard, Lacerta viridis', - 47: 'African chameleon, Chamaeleo chamaeleon', - 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis', - 49: 'African crocodile, Nile crocodile, Crocodylus niloticus', - 50: 'American alligator, Alligator mississipiensis', - 51: 'triceratops', - 52: 'thunder snake, worm snake, Carphophis amoenus', - 53: 'ringneck snake, ring-necked snake, ring snake', - 54: 'hognose snake, puff adder, sand viper', - 55: 'green snake, grass snake', - 56: 'king snake, kingsnake', - 57: 'garter snake, grass snake', - 58: 'water snake', - 59: 'vine snake', - 60: 'night snake, Hypsiglena torquata', - 61: 'boa constrictor, Constrictor constrictor', - 62: 'rock python, rock snake, Python sebae', - 63: 'Indian cobra, Naja naja', - 64: 'green mamba', - 65: 'sea snake', - 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus', - 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus', - 68: 'sidewinder, horned rattlesnake, Crotalus cerastes', - 69: 'trilobite', - 70: 'harvestman, daddy longlegs, Phalangium opilio', - 71: 'scorpion', - 72: 'black and gold garden spider, Argiope aurantia', - 73: 'barn spider, Araneus cavaticus', - 74: 'garden spider, Aranea diademata', - 75: 'black widow, Latrodectus mactans', - 76: 'tarantula', - 77: 'wolf spider, hunting spider', - 78: 'tick', - 79: 'centipede', - 80: 'black grouse', - 81: 'ptarmigan', - 82: 'ruffed grouse, partridge, Bonasa umbellus', - 83: 'prairie chicken, prairie grouse, prairie fowl', - 84: 'peacock', - 85: 'quail', - 86: 'partridge', - 87: 'African grey, African gray, Psittacus erithacus', - 88: 'macaw', - 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita', - 90: 'lorikeet', - 91: 'coucal', - 92: 'bee eater', - 93: 'hornbill', - 94: 'hummingbird', - 95: 'jacamar', - 96: 'toucan', - 97: 'drake', - 98: 'red-breasted merganser, Mergus serrator', - 99: 'goose', - 100: 'black swan, Cygnus atratus', - 101: 'tusker', - 102: 'echidna, spiny anteater, anteater', - 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus', - 104: 'wallaby, brush kangaroo', - 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', - 106: 'wombat', - 107: 'jellyfish', - 108: 'sea anemone, anemone', - 109: 'brain coral', - 110: 'flatworm, platyhelminth', - 111: 'nematode, nematode worm, roundworm', - 112: 'conch', - 113: 'snail', - 114: 'slug', - 115: 'sea slug, nudibranch', - 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore', - 117: 'chambered nautilus, pearly nautilus, nautilus', - 118: 'Dungeness crab, Cancer magister', - 119: 'rock crab, Cancer irroratus', - 120: 'fiddler crab', - 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica', - 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus', - 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish', - 124: 'crayfish, crawfish, crawdad, crawdaddy', - 125: 'hermit crab', - 126: 'isopod', - 127: 'white stork, Ciconia ciconia', - 128: 'black stork, Ciconia nigra', - 129: 'spoonbill', - 130: 'flamingo', - 131: 'little blue heron, Egretta caerulea', - 132: 'American egret, great white heron, Egretta albus', - 133: 'bittern', - 134: 'crane', - 135: 'limpkin, Aramus pictus', - 136: 'European gallinule, Porphyrio porphyrio', - 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana', - 138: 'bustard', - 139: 'ruddy turnstone, Arenaria interpres', - 140: 'red-backed sandpiper, dunlin, Erolia alpina', - 141: 'redshank, Tringa totanus', - 142: 'dowitcher', - 143: 'oystercatcher, oyster catcher', - 144: 'pelican', - 145: 'king penguin, Aptenodytes patagonica', - 146: 'albatross, mollymawk', - 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', - 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca', - 149: 'dugong, Dugong dugon', - 150: 'sea lion', - 151: 'Chihuahua', - 152: 'Japanese spaniel', - 153: 'Maltese dog, Maltese terrier, Maltese', - 154: 'Pekinese, Pekingese, Peke', - 155: 'Shih-Tzu', - 156: 'Blenheim spaniel', - 157: 'papillon', - 158: 'toy terrier', - 159: 'Rhodesian ridgeback', - 160: 'Afghan hound, Afghan', - 161: 'basset, basset hound', - 162: 'beagle', - 163: 'bloodhound, sleuthhound', - 164: 'bluetick', - 165: 'black-and-tan coonhound', - 166: 'Walker hound, Walker foxhound', - 167: 'English foxhound', - 168: 'redbone', - 169: 'borzoi, Russian wolfhound', - 170: 'Irish wolfhound', - 171: 'Italian greyhound', - 172: 'whippet', - 173: 'Ibizan hound, Ibizan Podenco', - 174: 'Norwegian elkhound, elkhound', - 175: 'otterhound, otter hound', - 176: 'Saluki, gazelle hound', - 177: 'Scottish deerhound, deerhound', - 178: 'Weimaraner', - 179: 'Staffordshire bullterrier, Staffordshire bull terrier', - 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier', - 181: 'Bedlington terrier', - 182: 'Border terrier', - 183: 'Kerry blue terrier', - 184: 'Irish terrier', - 185: 'Norfolk terrier', - 186: 'Norwich terrier', - 187: 'Yorkshire terrier', - 188: 'wire-haired fox terrier', - 189: 'Lakeland terrier', - 190: 'Sealyham terrier, Sealyham', - 191: 'Airedale, Airedale terrier', - 192: 'cairn, cairn terrier', - 193: 'Australian terrier', - 194: 'Dandie Dinmont, Dandie Dinmont terrier', - 195: 'Boston bull, Boston terrier', - 196: 'miniature schnauzer', - 197: 'giant schnauzer', - 198: 'standard schnauzer', - 199: 'Scotch terrier, Scottish terrier, Scottie', - 200: 'Tibetan terrier, chrysanthemum dog', - 201: 'silky terrier, Sydney silky', - 202: 'soft-coated wheaten terrier', - 203: 'West Highland white terrier', - 204: 'Lhasa, Lhasa apso', - 205: 'flat-coated retriever', - 206: 'curly-coated retriever', - 207: 'golden retriever', - 208: 'Labrador retriever', - 209: 'Chesapeake Bay retriever', - 210: 'German short-haired pointer', - 211: 'vizsla, Hungarian pointer', - 212: 'English setter', - 213: 'Irish setter, red setter', - 214: 'Gordon setter', - 215: 'Brittany spaniel', - 216: 'clumber, clumber spaniel', - 217: 'English springer, English springer spaniel', - 218: 'Welsh springer spaniel', - 219: 'cocker spaniel, English cocker spaniel, cocker', - 220: 'Sussex spaniel', - 221: 'Irish water spaniel', - 222: 'kuvasz', - 223: 'schipperke', - 224: 'groenendael', - 225: 'malinois', - 226: 'briard', - 227: 'kelpie', - 228: 'komondor', - 229: 'Old English sheepdog, bobtail', - 230: 'Shetland sheepdog, Shetland sheep dog, Shetland', - 231: 'collie', - 232: 'Border collie', - 233: 'Bouvier des Flandres, Bouviers des Flandres', - 234: 'Rottweiler', - 235: 'German shepherd, German shepherd dog, German police dog, alsatian', - 236: 'Doberman, Doberman pinscher', - 237: 'miniature pinscher', - 238: 'Greater Swiss Mountain dog', - 239: 'Bernese mountain dog', - 240: 'Appenzeller', - 241: 'EntleBucher', - 242: 'boxer', - 243: 'bull mastiff', - 244: 'Tibetan mastiff', - 245: 'French bulldog', - 246: 'Great Dane', - 247: 'Saint Bernard, St Bernard', - 248: 'Eskimo dog, husky', - 249: 'malamute, malemute, Alaskan malamute', - 250: 'Siberian husky', - 251: 'dalmatian, coach dog, carriage dog', - 252: 'affenpinscher, monkey pinscher, monkey dog', - 253: 'basenji', - 254: 'pug, pug-dog', - 255: 'Leonberg', - 256: 'Newfoundland, Newfoundland dog', - 257: 'Great Pyrenees', - 258: 'Samoyed, Samoyede', - 259: 'Pomeranian', - 260: 'chow, chow chow', - 261: 'keeshond', - 262: 'Brabancon griffon', - 263: 'Pembroke, Pembroke Welsh corgi', - 264: 'Cardigan, Cardigan Welsh corgi', - 265: 'toy poodle', - 266: 'miniature poodle', - 267: 'standard poodle', - 268: 'Mexican hairless', - 269: 'timber wolf, grey wolf, gray wolf, Canis lupus', - 270: 'white wolf, Arctic wolf, Canis lupus tundrarum', - 271: 'red wolf, maned wolf, Canis rufus, Canis niger', - 272: 'coyote, prairie wolf, brush wolf, Canis latrans', - 273: 'dingo, warrigal, warragal, Canis dingo', - 274: 'dhole, Cuon alpinus', - 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus', - 276: 'hyena, hyaena', - 277: 'red fox, Vulpes vulpes', - 278: 'kit fox, Vulpes macrotis', - 279: 'Arctic fox, white fox, Alopex lagopus', - 280: 'grey fox, gray fox, Urocyon cinereoargenteus', - 281: 'tabby, tabby cat', - 282: 'tiger cat', - 283: 'Persian cat', - 284: 'Siamese cat, Siamese', - 285: 'Egyptian cat', - 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor', - 287: 'lynx, catamount', - 288: 'leopard, Panthera pardus', - 289: 'snow leopard, ounce, Panthera uncia', - 290: 'jaguar, panther, Panthera onca, Felis onca', - 291: 'lion, king of beasts, Panthera leo', - 292: 'tiger, Panthera tigris', - 293: 'cheetah, chetah, Acinonyx jubatus', - 294: 'brown bear, bruin, Ursus arctos', - 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus', - 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', - 297: 'sloth bear, Melursus ursinus, Ursus ursinus', - 298: 'mongoose', - 299: 'meerkat, mierkat', - 300: 'tiger beetle', - 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle', - 302: 'ground beetle, carabid beetle', - 303: 'long-horned beetle, longicorn, longicorn beetle', - 304: 'leaf beetle, chrysomelid', - 305: 'dung beetle', - 306: 'rhinoceros beetle', - 307: 'weevil', - 308: 'fly', - 309: 'bee', - 310: 'ant, emmet, pismire', - 311: 'grasshopper, hopper', - 312: 'cricket', - 313: 'walking stick, walkingstick, stick insect', - 314: 'cockroach, roach', - 315: 'mantis, mantid', - 316: 'cicada, cicala', - 317: 'leafhopper', - 318: 'lacewing, lacewing fly', - 319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", - 320: 'damselfly', - 321: 'admiral', - 322: 'ringlet, ringlet butterfly', - 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus', - 324: 'cabbage butterfly', - 325: 'sulphur butterfly, sulfur butterfly', - 326: 'lycaenid, lycaenid butterfly', - 327: 'starfish, sea star', - 328: 'sea urchin', - 329: 'sea cucumber, holothurian', - 330: 'wood rabbit, cottontail, cottontail rabbit', - 331: 'hare', - 332: 'Angora, Angora rabbit', - 333: 'hamster', - 334: 'porcupine, hedgehog', - 335: 'fox squirrel, eastern fox squirrel, Sciurus niger', - 336: 'marmot', - 337: 'beaver', - 338: 'guinea pig, Cavia cobaya', - 339: 'sorrel', - 340: 'zebra', - 341: 'hog, pig, grunter, squealer, Sus scrofa', - 342: 'wild boar, boar, Sus scrofa', - 343: 'warthog', - 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius', - 345: 'ox', - 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis', - 347: 'bison', - 348: 'ram, tup', - 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis', - 350: 'ibex, Capra ibex', - 351: 'hartebeest', - 352: 'impala, Aepyceros melampus', - 353: 'gazelle', - 354: 'Arabian camel, dromedary, Camelus dromedarius', - 355: 'llama', - 356: 'weasel', - 357: 'mink', - 358: 'polecat, fitch, foulmart, foumart, Mustela putorius', - 359: 'black-footed ferret, ferret, Mustela nigripes', - 360: 'otter', - 361: 'skunk, polecat, wood pussy', - 362: 'badger', - 363: 'armadillo', - 364: 'three-toed sloth, ai, Bradypus tridactylus', - 365: 'orangutan, orang, orangutang, Pongo pygmaeus', - 366: 'gorilla, Gorilla gorilla', - 367: 'chimpanzee, chimp, Pan troglodytes', - 368: 'gibbon, Hylobates lar', - 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus', - 370: 'guenon, guenon monkey', - 371: 'patas, hussar monkey, Erythrocebus patas', - 372: 'baboon', - 373: 'macaque', - 374: 'langur', - 375: 'colobus, colobus monkey', - 376: 'proboscis monkey, Nasalis larvatus', - 377: 'marmoset', - 378: 'capuchin, ringtail, Cebus capucinus', - 379: 'howler monkey, howler', - 380: 'titi, titi monkey', - 381: 'spider monkey, Ateles geoffroyi', - 382: 'squirrel monkey, Saimiri sciureus', - 383: 'Madagascar cat, ring-tailed lemur, Lemur catta', - 384: 'indri, indris, Indri indri, Indri brevicaudatus', - 385: 'Indian elephant, Elephas maximus', - 386: 'African elephant, Loxodonta africana', - 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', - 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', - 389: 'barracouta, snoek', - 390: 'eel', - 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch', - 392: 'rock beauty, Holocanthus tricolor', - 393: 'anemone fish', - 394: 'sturgeon', - 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus', - 396: 'lionfish', - 397: 'puffer, pufferfish, blowfish, globefish', - 398: 'abacus', - 399: 'abaya', - 400: "academic gown, academic robe, judge's robe", - 401: 'accordion, piano accordion, squeeze box', - 402: 'acoustic guitar', - 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier', - 404: 'airliner', - 405: 'airship, dirigible', - 406: 'altar', - 407: 'ambulance', - 408: 'amphibian, amphibious vehicle', - 409: 'analog clock', - 410: 'apiary, bee house', - 411: 'apron', - 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin', - 413: 'assault rifle, assault gun', - 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack', - 415: 'bakery, bakeshop, bakehouse', - 416: 'balance beam, beam', - 417: 'balloon', - 418: 'ballpoint, ballpoint pen, ballpen, Biro', - 419: 'Band Aid', - 420: 'banjo', - 421: 'bannister, banister, balustrade, balusters, handrail', - 422: 'barbell', - 423: 'barber chair', - 424: 'barbershop', - 425: 'barn', - 426: 'barometer', - 427: 'barrel, cask', - 428: 'barrow, garden cart, lawn cart, wheelbarrow', - 429: 'baseball', - 430: 'basketball', - 431: 'bassinet', - 432: 'bassoon', - 433: 'bathing cap, swimming cap', - 434: 'bath towel', - 435: 'bathtub, bathing tub, bath, tub', - 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', - 437: 'beacon, lighthouse, beacon light, pharos', - 438: 'beaker', - 439: 'bearskin, busby, shako', - 440: 'beer bottle', - 441: 'beer glass', - 442: 'bell cote, bell cot', - 443: 'bib', - 444: 'bicycle-built-for-two, tandem bicycle, tandem', - 445: 'bikini, two-piece', - 446: 'binder, ring-binder', - 447: 'binoculars, field glasses, opera glasses', - 448: 'birdhouse', - 449: 'boathouse', - 450: 'bobsled, bobsleigh, bob', - 451: 'bolo tie, bolo, bola tie, bola', - 452: 'bonnet, poke bonnet', - 453: 'bookcase', - 454: 'bookshop, bookstore, bookstall', - 455: 'bottlecap', - 456: 'bow', - 457: 'bow tie, bow-tie, bowtie', - 458: 'brass, memorial tablet, plaque', - 459: 'brassiere, bra, bandeau', - 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty', - 461: 'breastplate, aegis, egis', - 462: 'broom', - 463: 'bucket, pail', - 464: 'buckle', - 465: 'bulletproof vest', - 466: 'bullet train, bullet', - 467: 'butcher shop, meat market', - 468: 'cab, hack, taxi, taxicab', - 469: 'caldron, cauldron', - 470: 'candle, taper, wax light', - 471: 'cannon', - 472: 'canoe', - 473: 'can opener, tin opener', - 474: 'cardigan', - 475: 'car mirror', - 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig', - 477: "carpenter's kit, tool kit", - 478: 'carton', - 479: 'car wheel', - 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM', - 481: 'cassette', - 482: 'cassette player', - 483: 'castle', - 484: 'catamaran', - 485: 'CD player', - 486: 'cello, violoncello', - 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone', - 488: 'chain', - 489: 'chainlink fence', - 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour', - 491: 'chain saw, chainsaw', - 492: 'chest', - 493: 'chiffonier, commode', - 494: 'chime, bell, gong', - 495: 'china cabinet, china closet', - 496: 'Christmas stocking', - 497: 'church, church building', - 498: 'cinema, movie theater, movie theatre, movie house, picture palace', - 499: 'cleaver, meat cleaver, chopper', - 500: 'cliff dwelling', - 501: 'cloak', - 502: 'clog, geta, patten, sabot', - 503: 'cocktail shaker', - 504: 'coffee mug', - 505: 'coffeepot', - 506: 'coil, spiral, volute, whorl, helix', - 507: 'combination lock', - 508: 'computer keyboard, keypad', - 509: 'confectionery, confectionary, candy store', - 510: 'container ship, containership, container vessel', - 511: 'convertible', - 512: 'corkscrew, bottle screw', - 513: 'cornet, horn, trumpet, trump', - 514: 'cowboy boot', - 515: 'cowboy hat, ten-gallon hat', - 516: 'cradle', - 517: 'crane', - 518: 'crash helmet', - 519: 'crate', - 520: 'crib, cot', - 521: 'Crock Pot', - 522: 'croquet ball', - 523: 'crutch', - 524: 'cuirass', - 525: 'dam, dike, dyke', - 526: 'desk', - 527: 'desktop computer', - 528: 'dial telephone, dial phone', - 529: 'diaper, nappy, napkin', - 530: 'digital clock', - 531: 'digital watch', - 532: 'dining table, board', - 533: 'dishrag, dishcloth', - 534: 'dishwasher, dish washer, dishwashing machine', - 535: 'disk brake, disc brake', - 536: 'dock, dockage, docking facility', - 537: 'dogsled, dog sled, dog sleigh', - 538: 'dome', - 539: 'doormat, welcome mat', - 540: 'drilling platform, offshore rig', - 541: 'drum, membranophone, tympan', - 542: 'drumstick', - 543: 'dumbbell', - 544: 'Dutch oven', - 545: 'electric fan, blower', - 546: 'electric guitar', - 547: 'electric locomotive', - 548: 'entertainment center', - 549: 'envelope', - 550: 'espresso maker', - 551: 'face powder', - 552: 'feather boa, boa', - 553: 'file, file cabinet, filing cabinet', - 554: 'fireboat', - 555: 'fire engine, fire truck', - 556: 'fire screen, fireguard', - 557: 'flagpole, flagstaff', - 558: 'flute, transverse flute', - 559: 'folding chair', - 560: 'football helmet', - 561: 'forklift', - 562: 'fountain', - 563: 'fountain pen', - 564: 'four-poster', - 565: 'freight car', - 566: 'French horn, horn', - 567: 'frying pan, frypan, skillet', - 568: 'fur coat', - 569: 'garbage truck, dustcart', - 570: 'gasmask, respirator, gas helmet', - 571: 'gas pump, gasoline pump, petrol pump, island dispenser', - 572: 'goblet', - 573: 'go-kart', - 574: 'golf ball', - 575: 'golfcart, golf cart', - 576: 'gondola', - 577: 'gong, tam-tam', - 578: 'gown', - 579: 'grand piano, grand', - 580: 'greenhouse, nursery, glasshouse', - 581: 'grille, radiator grille', - 582: 'grocery store, grocery, food market, market', - 583: 'guillotine', - 584: 'hair slide', - 585: 'hair spray', - 586: 'half track', - 587: 'hammer', - 588: 'hamper', - 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier', - 590: 'hand-held computer, hand-held microcomputer', - 591: 'handkerchief, hankie, hanky, hankey', - 592: 'hard disc, hard disk, fixed disk', - 593: 'harmonica, mouth organ, harp, mouth harp', - 594: 'harp', - 595: 'harvester, reaper', - 596: 'hatchet', - 597: 'holster', - 598: 'home theater, home theatre', - 599: 'honeycomb', - 600: 'hook, claw', - 601: 'hoopskirt, crinoline', - 602: 'horizontal bar, high bar', - 603: 'horse cart, horse-cart', - 604: 'hourglass', - 605: 'iPod', - 606: 'iron, smoothing iron', - 607: "jack-o'-lantern", - 608: 'jean, blue jean, denim', - 609: 'jeep, landrover', - 610: 'jersey, T-shirt, tee shirt', - 611: 'jigsaw puzzle', - 612: 'jinrikisha, ricksha, rickshaw', - 613: 'joystick', - 614: 'kimono', - 615: 'knee pad', - 616: 'knot', - 617: 'lab coat, laboratory coat', - 618: 'ladle', - 619: 'lampshade, lamp shade', - 620: 'laptop, laptop computer', - 621: 'lawn mower, mower', - 622: 'lens cap, lens cover', - 623: 'letter opener, paper knife, paperknife', - 624: 'library', - 625: 'lifeboat', - 626: 'lighter, light, igniter, ignitor', - 627: 'limousine, limo', - 628: 'liner, ocean liner', - 629: 'lipstick, lip rouge', - 630: 'Loafer', - 631: 'lotion', - 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system', - 633: "loupe, jeweler's loupe", - 634: 'lumbermill, sawmill', - 635: 'magnetic compass', - 636: 'mailbag, postbag', - 637: 'mailbox, letter box', - 638: 'maillot', - 639: 'maillot, tank suit', - 640: 'manhole cover', - 641: 'maraca', - 642: 'marimba, xylophone', - 643: 'mask', - 644: 'matchstick', - 645: 'maypole', - 646: 'maze, labyrinth', - 647: 'measuring cup', - 648: 'medicine chest, medicine cabinet', - 649: 'megalith, megalithic structure', - 650: 'microphone, mike', - 651: 'microwave, microwave oven', - 652: 'military uniform', - 653: 'milk can', - 654: 'minibus', - 655: 'miniskirt, mini', - 656: 'minivan', - 657: 'missile', - 658: 'mitten', - 659: 'mixing bowl', - 660: 'mobile home, manufactured home', - 661: 'Model T', - 662: 'modem', - 663: 'monastery', - 664: 'monitor', - 665: 'moped', - 666: 'mortar', - 667: 'mortarboard', - 668: 'mosque', - 669: 'mosquito net', - 670: 'motor scooter, scooter', - 671: 'mountain bike, all-terrain bike, off-roader', - 672: 'mountain tent', - 673: 'mouse, computer mouse', - 674: 'mousetrap', - 675: 'moving van', - 676: 'muzzle', - 677: 'nail', - 678: 'neck brace', - 679: 'necklace', - 680: 'nipple', - 681: 'notebook, notebook computer', - 682: 'obelisk', - 683: 'oboe, hautboy, hautbois', - 684: 'ocarina, sweet potato', - 685: 'odometer, hodometer, mileometer, milometer', - 686: 'oil filter', - 687: 'organ, pipe organ', - 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO', - 689: 'overskirt', - 690: 'oxcart', - 691: 'oxygen mask', - 692: 'packet', - 693: 'paddle, boat paddle', - 694: 'paddlewheel, paddle wheel', - 695: 'padlock', - 696: 'paintbrush', - 697: "pajama, pyjama, pj's, jammies", - 698: 'palace', - 699: 'panpipe, pandean pipe, syrinx', - 700: 'paper towel', - 701: 'parachute, chute', - 702: 'parallel bars, bars', - 703: 'park bench', - 704: 'parking meter', - 705: 'passenger car, coach, carriage', - 706: 'patio, terrace', - 707: 'pay-phone, pay-station', - 708: 'pedestal, plinth, footstall', - 709: 'pencil box, pencil case', - 710: 'pencil sharpener', - 711: 'perfume, essence', - 712: 'Petri dish', - 713: 'photocopier', - 714: 'pick, plectrum, plectron', - 715: 'pickelhaube', - 716: 'picket fence, paling', - 717: 'pickup, pickup truck', - 718: 'pier', - 719: 'piggy bank, penny bank', - 720: 'pill bottle', - 721: 'pillow', - 722: 'ping-pong ball', - 723: 'pinwheel', - 724: 'pirate, pirate ship', - 725: 'pitcher, ewer', - 726: "plane, carpenter's plane, woodworking plane", - 727: 'planetarium', - 728: 'plastic bag', - 729: 'plate rack', - 730: 'plow, plough', - 731: "plunger, plumber's helper", - 732: 'Polaroid camera, Polaroid Land camera', - 733: 'pole', - 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria', - 735: 'poncho', - 736: 'pool table, billiard table, snooker table', - 737: 'pop bottle, soda bottle', - 738: 'pot, flowerpot', - 739: "potter's wheel", - 740: 'power drill', - 741: 'prayer rug, prayer mat', - 742: 'printer', - 743: 'prison, prison house', - 744: 'projectile, missile', - 745: 'projector', - 746: 'puck, hockey puck', - 747: 'punching bag, punch bag, punching ball, punchball', - 748: 'purse', - 749: 'quill, quill pen', - 750: 'quilt, comforter, comfort, puff', - 751: 'racer, race car, racing car', - 752: 'racket, racquet', - 753: 'radiator', - 754: 'radio, wireless', - 755: 'radio telescope, radio reflector', - 756: 'rain barrel', - 757: 'recreational vehicle, RV, R.V.', - 758: 'reel', - 759: 'reflex camera', - 760: 'refrigerator, icebox', - 761: 'remote control, remote', - 762: 'restaurant, eating house, eating place, eatery', - 763: 'revolver, six-gun, six-shooter', - 764: 'rifle', - 765: 'rocking chair, rocker', - 766: 'rotisserie', - 767: 'rubber eraser, rubber, pencil eraser', - 768: 'rugby ball', - 769: 'rule, ruler', - 770: 'running shoe', - 771: 'safe', - 772: 'safety pin', - 773: 'saltshaker, salt shaker', - 774: 'sandal', - 775: 'sarong', - 776: 'sax, saxophone', - 777: 'scabbard', - 778: 'scale, weighing machine', - 779: 'school bus', - 780: 'schooner', - 781: 'scoreboard', - 782: 'screen, CRT screen', - 783: 'screw', - 784: 'screwdriver', - 785: 'seat belt, seatbelt', - 786: 'sewing machine', - 787: 'shield, buckler', - 788: 'shoe shop, shoe-shop, shoe store', - 789: 'shoji', - 790: 'shopping basket', - 791: 'shopping cart', - 792: 'shovel', - 793: 'shower cap', - 794: 'shower curtain', - 795: 'ski', - 796: 'ski mask', - 797: 'sleeping bag', - 798: 'slide rule, slipstick', - 799: 'sliding door', - 800: 'slot, one-armed bandit', - 801: 'snorkel', - 802: 'snowmobile', - 803: 'snowplow, snowplough', - 804: 'soap dispenser', - 805: 'soccer ball', - 806: 'sock', - 807: 'solar dish, solar collector, solar furnace', - 808: 'sombrero', - 809: 'soup bowl', - 810: 'space bar', - 811: 'space heater', - 812: 'space shuttle', - 813: 'spatula', - 814: 'speedboat', - 815: "spider web, spider's web", - 816: 'spindle', - 817: 'sports car, sport car', - 818: 'spotlight, spot', - 819: 'stage', - 820: 'steam locomotive', - 821: 'steel arch bridge', - 822: 'steel drum', - 823: 'stethoscope', - 824: 'stole', - 825: 'stone wall', - 826: 'stopwatch, stop watch', - 827: 'stove', - 828: 'strainer', - 829: 'streetcar, tram, tramcar, trolley, trolley car', - 830: 'stretcher', - 831: 'studio couch, day bed', - 832: 'stupa, tope', - 833: 'submarine, pigboat, sub, U-boat', - 834: 'suit, suit of clothes', - 835: 'sundial', - 836: 'sunglass', - 837: 'sunglasses, dark glasses, shades', - 838: 'sunscreen, sunblock, sun blocker', - 839: 'suspension bridge', - 840: 'swab, swob, mop', - 841: 'sweatshirt', - 842: 'swimming trunks, bathing trunks', - 843: 'swing', - 844: 'switch, electric switch, electrical switch', - 845: 'syringe', - 846: 'table lamp', - 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle', - 848: 'tape player', - 849: 'teapot', - 850: 'teddy, teddy bear', - 851: 'television, television system', - 852: 'tennis ball', - 853: 'thatch, thatched roof', - 854: 'theater curtain, theatre curtain', - 855: 'thimble', - 856: 'thresher, thrasher, threshing machine', - 857: 'throne', - 858: 'tile roof', - 859: 'toaster', - 860: 'tobacco shop, tobacconist shop, tobacconist', - 861: 'toilet seat', - 862: 'torch', - 863: 'totem pole', - 864: 'tow truck, tow car, wrecker', - 865: 'toyshop', - 866: 'tractor', - 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi', - 868: 'tray', - 869: 'trench coat', - 870: 'tricycle, trike, velocipede', - 871: 'trimaran', - 872: 'tripod', - 873: 'triumphal arch', - 874: 'trolleybus, trolley coach, trackless trolley', - 875: 'trombone', - 876: 'tub, vat', - 877: 'turnstile', - 878: 'typewriter keyboard', - 879: 'umbrella', - 880: 'unicycle, monocycle', - 881: 'upright, upright piano', - 882: 'vacuum, vacuum cleaner', - 883: 'vase', - 884: 'vault', - 885: 'velvet', - 886: 'vending machine', - 887: 'vestment', - 888: 'viaduct', - 889: 'violin, fiddle', - 890: 'volleyball', - 891: 'waffle iron', - 892: 'wall clock', - 893: 'wallet, billfold, notecase, pocketbook', - 894: 'wardrobe, closet, press', - 895: 'warplane, military plane', - 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin', - 897: 'washer, automatic washer, washing machine', - 898: 'water bottle', - 899: 'water jug', - 900: 'water tower', - 901: 'whiskey jug', - 902: 'whistle', - 903: 'wig', - 904: 'window screen', - 905: 'window shade', - 906: 'Windsor tie', - 907: 'wine bottle', - 908: 'wing', - 909: 'wok', - 910: 'wooden spoon', - 911: 'wool, woolen, woollen', - 912: 'worm fence, snake fence, snake-rail fence, Virginia fence', - 913: 'wreck', - 914: 'yawl', - 915: 'yurt', - 916: 'web site, website, internet site, site', - 917: 'comic book', - 918: 'crossword puzzle, crossword', - 919: 'street sign', - 920: 'traffic light, traffic signal, stoplight', - 921: 'book jacket, dust cover, dust jacket, dust wrapper', - 922: 'menu', - 923: 'plate', - 924: 'guacamole', - 925: 'consomme', - 926: 'hot pot, hotpot', - 927: 'trifle', - 928: 'ice cream, icecream', - 929: 'ice lolly, lolly, lollipop, popsicle', - 930: 'French loaf', - 931: 'bagel, beigel', - 932: 'pretzel', - 933: 'cheeseburger', - 934: 'hotdog, hot dog, red hot', - 935: 'mashed potato', - 936: 'head cabbage', - 937: 'broccoli', - 938: 'cauliflower', - 939: 'zucchini, courgette', - 940: 'spaghetti squash', - 941: 'acorn squash', - 942: 'butternut squash', - 943: 'cucumber, cuke', - 944: 'artichoke, globe artichoke', - 945: 'bell pepper', - 946: 'cardoon', - 947: 'mushroom', - 948: 'Granny Smith', - 949: 'strawberry', - 950: 'orange', - 951: 'lemon', - 952: 'fig', - 953: 'pineapple, ananas', - 954: 'banana', - 955: 'jackfruit, jak, jack', - 956: 'custard apple', - 957: 'pomegranate', - 958: 'hay', - 959: 'carbonara', - 960: 'chocolate sauce, chocolate syrup', - 961: 'dough', - 962: 'meat loaf, meatloaf', - 963: 'pizza, pizza pie', - 964: 'potpie', - 965: 'burrito', - 966: 'red wine', - 967: 'espresso', - 968: 'cup', - 969: 'eggnog', - 970: 'alp', - 971: 'bubble', - 972: 'cliff, drop, drop-off', - 973: 'coral reef', - 974: 'geyser', - 975: 'lakeside, lakeshore', - 976: 'promontory, headland, head, foreland', - 977: 'sandbar, sand bar', - 978: 'seashore, coast, seacoast, sea-coast', - 979: 'valley, vale', - 980: 'volcano', - 981: 'ballplayer, baseball player', - 982: 'groom, bridegroom', - 983: 'scuba diver', - 984: 'rapeseed', - 985: 'daisy', - 986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", - 987: 'corn', - 988: 'acorn', - 989: 'hip, rose hip, rosehip', - 990: 'buckeye, horse chestnut, conker', - 991: 'coral fungus', - 992: 'agaric', - 993: 'gyromitra', - 994: 'stinkhorn, carrion fungus', - 995: 'earthstar', - 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa', - 997: 'bolete', - 998: 'ear, spike, capitulum', -999: 'toilet tissue, toilet paper, bathroom tissue'} - -def lookup(n): - return imagenetDict[n] \ No newline at end of file diff --git a/experiments/targets/imageNetClasses.pyc b/experiments/targets/imageNetClasses.pyc deleted file mode 100644 index cdba59f38d34bf73e9f2fa0d4c20a36268943d6b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 39949 zcmeIbWt1F860Y6cW@cRnORa?~m{2ch2rw{U$_aWhF#JMn=|Tf1Go+EhlZdcO@17FAHwTzvkfLsg(OZ zYpIl_Q|ntMWmzDXvOG{oSrM2eWwQdarEGR!j+D&_%$2gafq7CkFEC%q<_8u?*@D1A zDO(s=BxQ>Ni=}LFV2PA12`rVerGZk)mI0Pc*>b@0DO&+pF=ZBr z53qX5)&SN_*;>HbDO(3vH)ZPq>!)l3V8fJc1ZDccE{oU)yPT~am$n3}R(f!$KJJFrK}_5}7y z+1|iDDccv=FJ=1!2c)bF^rox=SjqyRFJ=9}K*}62EoFm1HDyCUEoC84PuYP$BV|pX zm9jQ4J!QkdjFfeNnJGI6I5=g80EedRFyQc%9RVDfvZH{bQ+5n+Y|4%Uj!)SMz=

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a/experiments/targets/mnistMLP.py b/experiments/targets/mnistMLP.py deleted file mode 100644 index d7149e8..0000000 --- a/experiments/targets/mnistMLP.py +++ /dev/null @@ -1,50 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim -import torch.nn.functional as F -import os -from .datasets import MNIST -from .utils import * - -class MNISTMLP(nn.Module): - def __init__(self): - super(MNISTMLP, self).__init__() - self.mlp = nn.Sequential( - nn.Linear(784, 300), - nn.ReLU(True), - nn.Linear(300, 150), - nn.ReLU(True), - nn.Linear(150, 10)) - self.add_module("3_layer",self.mlp) - - - def forward(self, x): - x = x.view(-1, 28*28) - return self.mlp(x) - - def dataset(self): - return MNIST - -def trainMNISTMLP(num_components=3,device=None,directory = ''): - if device is None: - device = getDevice() - - net = MNISTMLP() - mnist = MNIST() - batch_size = 120 - trainloader = mnist.training(batch_size) - - print('Training MNIST MLP Model') - criterion = nn.CrossEntropyLoss() - optimizer = optim.Adam(net.parameters(), lr=0.001) - scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5) - trainModel(net,trainloader,optimizer,criterion,30,device=device) - - net.eval() - print('Finished Training, getting accuracy') - testloader = mnist.testing() - accuracy = testAccuracy(net,testloader) - - model_path = os.path.join(directory, "mnistMLP.pkl") - print('Saving as: mnistMLP.pkl, with accuracy %.4f'%(accuracy,)) - torch.save(net,model_path) \ No newline at end of file diff --git a/experiments/targets/mnistMLP.pyc b/experiments/targets/mnistMLP.pyc deleted file mode 100644 index ca6d87dbaddd4770f9dae94314ffdbfa2a442593..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2495 zcmb_d&2A$_5UzF{$95c&{S%2m5DAbhv|1mKIH1)o%g^p2CDv*jgw`BJvooEfz2g}( z-S#HPfkXDpk$2#AcmiGlaYIP(ePt)v1IJjNn(D5uuD`EpR{q*(|NPzk?^9X+R`C8D zkN*WE!{10QGQ8xCjA}BhU5Hj>RF`4>V!2A`s3F6(i=Czno08%DisUUBw`JHClR;rs 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a/experiments/targets/utils.py b/experiments/targets/utils.py deleted file mode 100644 index ec8f099..0000000 --- a/experiments/targets/utils.py +++ /dev/null @@ -1,137 +0,0 @@ - -import torchvision -import numpy as np -import torch -import torch.nn.functional as F -from tqdm import tqdm -import matplotlib.pyplot as plt -from math import sqrt - -def getDevice(): - if torch.cuda.is_available(): - print("Using GPU 0") - device = torch.device("cuda:0") - else: - print("No GPU, using CPU") - device = torch.device("cpu") - return device - -def numpyImages(imgs,padding=2): - numPerRow = int(sqrt(imgs.size()[0])) - images = torchvision.utils.make_grid(imgs,numPerRow,padding=2) - images_min = images.min() - images_max = images.max() - images = images / 2 + 0.5 # unnormalize - npimages = images.cpu().detach().numpy() - images = np.transpose(npimages, (1, 2, 0)) - return np.clip(images,0,1) - -def conditionalPad(success,images,padding=2): - shape = images.size() - # Makes a background of green for success, red for failure - background = torch.zeros(( - success.size()[0], - 3, - shape[-2]+2*padding, - shape[-1]+2*padding, - )) - - for i,s in enumerate(success): - if s == 1: - background[i,1] = 1 - else: - background[i,0] = 1 - - background_mask = torch.zeros((success.size()[0],3,shape[-2],shape[-1])) - background_mask = F.pad(background_mask,(padding,padding,padding,padding), value=1) - background = torch.mul(background,background_mask) - - # Make the images RGB if not - if images.shape[1] == 1: - images = images.expand(-1,3,-1,-1) - - paddedImages = F.pad(images,(padding,padding,padding,padding)) - background = background.type_as(paddedImages) - - return paddedImages + background - -def imshow(imgs, num = 25,filename=None): - images = numpyImages(imgs[:num,]) - plt.figure() - # show images - plt.imshow(images) - if filename is None: - plt.show() - else: - plt.savefig(filename,dpi=fig.dpi*4,bbox_inches='tight') - -def trainModel(model,loader,optimizer,criterion,epochs,device=None,update_rate=20): - if device is None: - device = getDevice() - - print(f"Training device: {device}") - model.to(device) - epoch_size = len(loader) - for epoch in range(epochs): # loop over the dataset multiple times - # For tracking the loss and making the progress bar - epoch_loss = torch.zeros((1,)) - epoch_loss = epoch_loss.to(device) - update_loss = torch.zeros((1,)) - update_loss = update_loss.to(device) - dataIterator = tqdm(enumerate(loader, 0),total = epoch_size) - dataIterator.set_description("update loss: %.3f, epoch loss: %.3f" % (0,0)) - - for i, data in dataIterator: - # get the inputs - inputs, labels = data - inputs, labels = inputs.to(device), labels.to(device) - - # zero the parameter gradients - optimizer.zero_grad() - - # forward + backward + optimize - outputs = model(inputs) - loss = criterion(outputs, labels) - loss.backward() - optimizer.step() - - # Update progress bar - update_loss += loss - if i % update_rate == update_rate - 1: # print every 500 mini-batches - epoch_loss += update_loss - epoch_loss, update_loss = epoch_loss.cpu(), update_loss.cpu() - dataIterator.set_description( - "update loss: %.3f, epoch loss: %.3f" % ( - update_loss[0] / update_rate, - epoch_loss[0]/(i + 1), - )) - update_loss.zero_() - epoch_loss, update_loss = epoch_loss.to(device), update_loss.to(device) - - epoch_loss = epoch_loss.cpu() - print("Epoch %d/%d loss: %.4f" % (epoch+1,epochs,epoch_loss[0]/epoch_size)) - model.cpu() - -def testAccuracy(model,test_set,device = None, kl_tracker=None): - if device is None: - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - cpu = torch.device("cpu") - model.to(device) - - correct = 0.0 - total = 0.0 - with torch.no_grad(): - for data in test_set: - images, labels = data - if kl_tracker is not None: - for image in images.numpy(): - kl_tracker.push(image.flatten()) - images = images.to(device) - outputs = model(images) - _, predicted = torch.max(outputs.data, 1) - _, predicted = _.to(cpu), predicted.to(cpu) - - total += labels.size(0) - correct += (predicted == labels).sum().item() - model.to(cpu) - return correct/total diff --git a/experiments/test_set_attacks/ember2018_test_set_attack.py b/experiments/test_set_attacks/ember2018_test_set_attack.py deleted file mode 100644 index 35b9f20..0000000 --- a/experiments/test_set_attacks/ember2018_test_set_attack.py +++ /dev/null @@ -1,384 +0,0 @@ -import lightgbm as lgb -import pygoko -import numpy as np -from collections import defaultdict -import argparse,os -import json -from ember import read_vectorized_features -import pandas as pd -import matplotlib.pyplot as plt - -def unpack_stats(dataframe,stats): - dataframe["max"].append(stats.max) - dataframe["min"].append(stats.min) - dataframe["nz_count"].append(stats.nz_count) - dataframe["moment1_nz"].append(stats.moment1_nz) - dataframe["moment2_nz"].append(stats.moment2_nz) - dataframe["sequence_len"].append(stats.sequence_len) - -def classify(y,pred,ember_threshold): - return (ember_threshold < y and pred == 0) or (y < ember_threshold and pred == 1) - -ember_threshold = 0.8336 -skip = 0 - -def baseline(parameters,tree): - print(f"Creating the baseline for {parameters['file_folder']}") - training_runs = [] - normal_stats = tree.kl_div_dirichlet_basestats( - parameters["prior_weight"], - parameters["observation_weight"], - parameters["sequence_len"], - parameters["sequence_count"], - parameters["window_size"]) - for i,vstats in enumerate(normal_stats): - training_run = defaultdict(list) - for stats in vstats: - unpack_stats(training_run,stats) - - training_runs.append(training_run) - - with open(f"{parameters['file_folder']}/expected_ember_stats.json","w") as file: - for training_run in training_runs: - file.write(json.dumps(training_run)+"\n") - -def run_normal(parameters,tree,bst,X_test,y_test): - print(f"Creating the normal runs for {parameters['file_folder']}") - normal_runs = [] - for normal in range(parameters["sequence_count"]): - normal_run = [] - normal_test_tracker = tree.kl_div_dirichlet( - parameters["prior_weight"], - parameters["observation_weight"], - parameters["window_size"]) - normal_test_stats = defaultdict(list) - index_generator = parameters["index_factory"]() - for i in range(parameters["sequence_len"]): - i = index_generator() - x = X_test[i] - y = y_test[i] - pred = bst.predict(x.reshape(1,-1))[0] - - normal_test_tracker.push(x) - normal_test_stats["index"].append(int(i)) - normal_test_stats["y"].append(int(y)) - normal_test_stats["pred"].append(float(pred)) - unpack_stats(normal_test_stats,normal_test_tracker.stats()) - normal_runs.append(normal_test_stats) - with open(f"{parameters['file_folder']}/normal_runs_ember_stats.json","w") as file: - for normal_run in normal_runs: - file.write(json.dumps(normal_run)+"\n") - -def run_attacks(parameters,tree,bst,X_test,y_test): - print(f"Creating the attack runs for {parameters['file_folder']}") - attack_runs = [] - for attack in range(parameters["sequence_count"]): - attack_index = None - attack_run = [] - attack_test_tracker = tree.kl_div_dirichlet( - parameters["prior_weight"], - parameters["observation_weight"], - parameters["window_size"]) - attack_test_stats = defaultdict(list) - index_generator = parameters["index_factory"]() - for i in range(parameters["sequence_len"]): - if attack_index is None: - i = index_generator() - else: - i = attack_index - x = X_test[i] - y = y_test[i] - pred = bst.predict(x.reshape(1,-1))[0] - attack_test_stats["index"].append(int(i)) - attack_test_stats["y"].append(int(y)) - attack_test_stats["pred"].append(float(pred)) - if (pred < ember_threshold and y == 1) and attack_index is None: - attack_index = i - attack_test_tracker.push(x) - unpack_stats(attack_test_stats,attack_test_tracker.stats()) - attack_runs.append(attack_test_stats) - with open(f"{parameters['file_folder']}/attack_runs_ember_stats.json","w") as file: - for attack_run in attack_runs: - file.write(json.dumps(attack_run)+"\n") - -def open_experiment(folder_name): - with open(f"{folder_name}expected_ember_stats.json","r") as file: - training_runs = [json.loads(line) for line in file] - training_keys = list(training_runs[0].keys()) - training_arrays = {k:np.array([training_run[k][skip:] for training_run in training_runs]) for k in training_keys} - - expected_runs = {} - for k,v in training_arrays.items(): - expected_runs[f"mean_{k}"] = np.mean(v,axis=0) - expected_runs[f"stddev_{k}"] = np.sqrt(np.var(v,axis=0)) - - expected_runs["moment1_nz"] = np.sum(training_arrays["moment1_nz"],axis=0) - expected_runs["moment2_nz"] = np.sum(training_arrays["moment2_nz"],axis=0) - expected_runs["nz_count"] = np.sum(training_arrays["nz_count"],axis=0) - expected_runs["mean_nz_mean"] = expected_runs["moment1_nz"]/expected_runs["nz_count"] - expected_runs["stddev_nz_mean"] = np.sqrt( - expected_runs["moment2_nz"]/expected_runs["nz_count"] - - np.square(expected_runs["mean_nz_mean"])) - - - with open(f"{folder_name}normal_runs_ember_stats.json","r") as file: - normal_runs = [{k: np.array(v[skip:]) for k,v in json.loads(line).items()} for line in file] - for run in normal_runs: - run["nz_mean"] = run["moment1_nz"]/run["nz_count"] - run["nz_stddev"] = np.sqrt(run["moment2_nz"]/run["nz_count"] - np.square(run["nz_mean"])) - - with open(f"{folder_name}attack_runs_ember_stats.json","r") as file: - attack_runs = [{k: np.array(v[skip:]) for k,v in json.loads(line).items()} for line in file] - for run in attack_runs: - run["nz_mean"] = run["moment1_nz"]/run["nz_count"] - run["nz_stddev"] = np.sqrt(run["moment2_nz"]/run["nz_count"] - np.square(run["nz_mean"])) - return expected_runs, normal_runs, attack_runs - -def generate_traces(runs,col_name, yaxis_type,title, filename): - runs["normal"]["expected_runs"][f'mean_p_stddev_{col_name}'] = runs["normal"]["expected_runs"][f"mean_{col_name}"]+runs["normal"]["expected_runs"][f'stddev_{col_name}'] - runs["normal"]["expected_runs"][f'mean_n_stddev_{col_name}'] = runs["normal"]["expected_runs"][f"mean_{col_name}"]-runs["normal"]["expected_runs"][f'stddev_{col_name}'] - runs["uniform"]["expected_runs"][f'mean_p_stddev_{col_name}'] = runs["uniform"]["expected_runs"][f"mean_{col_name}"]+runs["uniform"]["expected_runs"][f'stddev_{col_name}'] - runs["uniform"]["expected_runs"][f'mean_n_stddev_{col_name}'] = runs["uniform"]["expected_runs"][f"mean_{col_name}"]-runs["uniform"]["expected_runs"][f'stddev_{col_name}'] - x = np.arange(skip,len(runs["normal"]["expected_runs"][f"mean_{col_name}"])) - - - fig,(ax2,ax1) = plt.subplots(1,2,sharey=True) - ax1.plot(x[10:],runs["normal"]["expected_runs"][f"mean_{col_name}"][10:], color="green", - label='Uniform Baseline') - ax1.fill_between( - x[10:], - runs["normal"]["expected_runs"][f'mean_p_stddev_{col_name}'][10:], - runs["normal"]["expected_runs"][f'mean_n_stddev_{col_name}'][10:], - color="green", - alpha=0.5 - ) - attack = np.stack([run[col_name] for run in runs["normal"]["attack_runs"][:10]]).T - ax1.plot(x[10:], attack[10:], 'r', color="red", label='Attack Sequence') - - normal = np.stack([run[col_name] for run in runs["normal"]["normal_runs"][:10]]).T - ax1.plot(x[10:], normal[10:], 'r', color="blue", label='Normal Sequence') - ax1.set_title("Binomial Interest") - ax1.set_xlim(10,400) - - ax2.plot(x[10:],runs["uniform"]["expected_runs"][f"mean_{col_name}"][10:], color="green", - label='Uniform Baseline') - ax2.fill_between( - x[10:], - runs["uniform"]["expected_runs"][f'mean_p_stddev_{col_name}'][10:], - runs["uniform"]["expected_runs"][f'mean_n_stddev_{col_name}'][10:], - color="green", - alpha=0.5 - ) - attack = np.stack([run[col_name] for run in runs["uniform"]["attack_runs"][:10]]).T - ax2.plot(x[10:], attack[10:], 'r', color="red", label='Attack Sequence') - - normal = np.stack([run[col_name] for run in runs["uniform"]["normal_runs"][:10]]).T - ax2.plot(x[10:], normal[10:], 'r', color="blue", label='Normal Sequence') - ax2.set_title("Uniform Interest") - ax2.set_xlim(10,400) - plt.yscale("log") - fig.set_size_inches(12.00, 5.00) - fig.set_dpi(2000) - fig.tight_layout() - handles, labels = plt.gca().get_legend_handles_labels() - labels, ids = np.unique(labels, return_index=True) - handles = [handles[i] for i in ids] - plt.legend(handles, labels, loc='best') - fig.savefig(filename, bbox_inches='tight') - plt.close() - -def sequence_analysis(expected_runs,sequence_run,col_name): - stat_column = sequence_run[col_name] - index = sequence_run["index"] - std_dev_up = expected_runs[f"mean_{col_name}"]+expected_runs[f'stddev_{col_name}'] - std_dev_down = expected_runs[f"mean_{col_name}"]-expected_runs[f'stddev_{col_name}'] - - true_explotation_time = None - i = 0 - while true_explotation_time is None and i + 2 < len(index): - if index[i] == index[i+1] and index[i+1] == index[i+2]: - true_explotation_time = i - i += 1 - - detection_time = None - i = 0 - while detection_time is None and i < len(index): - if stat_column[i] > std_dev_up[i] or stat_column[i] < std_dev_down[i]: - detection_time = i - i += 1 - return true_explotation_time, detection_time - -def generate_results(pair): - runs = {"normal":dict(),"uniform":{}} - for n in pair: - print(f"Opening {n}") - if "normal" in n: - fileprefix = n[len("normal"):] - runs["normal"]["expected_runs"], runs["normal"]["normal_runs"], runs["normal"]["attack_runs"] = open_experiment(n+"/") - else: - runs["uniform"]["expected_runs"], runs["uniform"]["normal_runs"], runs["uniform"]["attack_runs"] = open_experiment(n+"/") - - generate_traces(runs,"max", - title='Maximum of the KL Divergence of Query Sequences', - yaxis_type="log", - filename=f"{fileprefix}_max.png") - - generate_traces(runs,"min", - title='Minimum of the KL Divergence of Query Sequences', - yaxis_type="log", - filename=f"{fileprefix}_min.png") - - generate_traces(runs,"nz_count", - title="Count of non-zero KL Divergence", - yaxis_type="log", - filename=f"{fileprefix}_nz_count.png") - - generate_traces(runs,"moment1_nz", - title='KL Divergence', - yaxis_type="log", - filename=f"{fileprefix}_moment1_nz.png") - - generate_traces(runs,"moment2_nz", - title='Second Moment of KL Divergence', - yaxis_type="log", - filename=f"{fileprefix}_moment2_nz.png") - - data = generate_traces(runs,"nz_mean", - title='Mean of the non-zero KL divergences', - yaxis_type="log", - filename=f"{fileprefix}_nz_mean.png") - - """ - attack_results = [] - for run in attack_runs: - attack_results.append(sequence_analysis(expected_runs,run,"nz_mean")) - normal_results = [] - for run in normal_runs: - normal_results.append(sequence_analysis(expected_runs,run,"nz_mean")) - positives = 0 - negatives = 0 - results = attack_results + normal_results - true_positive = 0.0 - false_positive = 0.0 - true_negative = 0.0 - false_negative = 0.0 - detection_mean_lag = 0.0 - for attack_start,detection in results: - if attack_start is None: - negatives += 1.0 - if detection is None: - true_negative += 1.0 - else: - false_negative += 1.0 - else: - positives += 1.0 - if detection is None: - false_positive += 1.0 - else: - true_positive += 1.0 - detection_mean_lag += detection - attack_start - detection_mean_lag /= true_positive - true_positive /= positives - false_positive /= positives - true_negative /= negatives - false_negative /= negatives - results_summary = { - "detection_mean_lag":detection_mean_lag, - "true_positive":true_positive, - "false_positive":false_positive, - "true_negative":true_negative, - "false_negative":false_negative, - "positives": positives, - "negatives": negatives, - } - with open(f"{folder_name}/detection_results.json","w") as file: - file.write(json.dumps(results_summary)+"\n") - """ -def main(): - prog = "test set attack of ember" - parser = argparse.ArgumentParser() - parser.add_argument("-t","--treeyaml", type=str, required=True, help="gridsearch to find best parameters") - parser.add_argument("datadir", metavar="DATADIR", type=str, help="Directory with raw features") - args = parser.parse_args() - - model_dir = os.path.join(args.datadir, 'model.txt') - bst = lgb.Booster(model_file=model_dir) # init model - - X_test, y_test = read_vectorized_features(args.datadir, subset="test") - X_test = X_test.copy() - y_test = y_test.copy() - - tree = pygoko.CoverTree() - tree.load_yaml_config(args.treeyaml) - tree.fit() - def uniform_generator_factory(): - def generator(): - return np.random.randint(0,len(X_test)) - return generator - - def normal_generator_factory(): - count = np.random.randint(0,1000) - shift = np.random.randint(0,len(X_test) - count) - def generator(): - return np.random.binomial(n=count, p=0.5, size=1)[0] + shift - return generator - - parameters_list = [ - { - "prior_weight" : 1.0, - "observation_weight" : 1.3, - "window_size" : 50, - "sequence_len" : 40, - "sequence_count" : 20, - "file_folder_prefix" : "uniform", - "index_factory": uniform_generator_factory, - }, - { - "prior_weight" : 1.0, - "observation_weight" : 1.3, - "window_size" : 50, - "sequence_len" : 40, - "sequence_count" : 20, - "file_folder_prefix" : "normal", - "index_factory": normal_generator_factory, - }, - ] - file_pairs = list() - for prior_weight in [0.8,1.0]: - for observation_weight in [1.0,1.3,1.6]: - for window_size in [25,50,75]: - pair = [] - for parameters in parameters_list: - parameters["observation_weight"] = observation_weight - parameters["prior_weight"] = prior_weight - parameters["window_size"] = window_size - parameters["sequence_len"] = 400 - parameters["sequence_count"] = 100 - prior_weight_str = str(parameters["prior_weight"]).replace(".","-") - observation_weight_str = str(parameters["observation_weight"]).replace(".","-") - window_size_str = str(parameters["window_size"]) - sequence_len_str = str(parameters["sequence_len"]) - sequence_count_str = str(parameters["sequence_count"]) - name = parameters["file_folder_prefix"] +"_"+ "_".join([ - prior_weight_str, - observation_weight_str, - window_size_str, - sequence_len_str, - sequence_count_str, - ]) - parameters["file_folder"] = name - pair.append(name) - if not os.path.exists(name): - os.mkdir(name) - baseline(parameters,tree) - run_normal(parameters,tree,bst,X_test,y_test) - run_attacks(parameters,tree,bst,X_test,y_test) - - file_pairs.append(pair) - - - for pair in file_pairs: - generate_results(pair) - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/pygoko/tests/gmm2d.py b/pygoko/tests/gmm2d.py new file mode 100644 index 0000000..e664cc2 --- /dev/null +++ b/pygoko/tests/gmm2d.py @@ -0,0 +1,83 @@ +import pygoko +import numpy as np +import pandas as pd +import plotly.graph_objects as go +from collections import defaultdict +import matplotlib.pyplot as plt + + +def grab_samples(x_mean, count): + mean = np.zeros([2],dtype=np.float32) + mean[0] = x_mean + cov = np.diag(np.ones([2],dtype=np.float32)) + return np.random.multivariate_normal(mean,cov,count).astype(np.float32) + +data = grab_samples(0,1000) + + + +for i in range(6): + test_data = grab_samples(float(i)/3,50) + + + fig, ax = plt.subplots() + ax.scatter(data[:,0],data[:,1],color="orange") + ax.scatter(test_data[:,0],test_data[:,1],color="blue") + ax.set_xlim((-1.6,3.1)) + ax.set_ylim((-1.6,1.6)) + fig.set_size_inches(14.00, 7.00) + fig.savefig(f"gaussian_vis_{i}.png", bbox_inches='tight') + + + + + + + +''' +fig, ax = plt.subplots() + +ax.plot(np.linspace(0,2,100), +ax.fill_between( + x[10:], + runs["normal"]["expected_runs"][f'mean_p_stddev_{col_name}'][10:], + runs["normal"]["expected_runs"][f'mean_n_stddev_{col_name}'][10:], + color="green", + alpha=0.5 +) +attack = np.stack([run[col_name] for run in runs["normal"]["attack_runs"][:10]]).T +ax1.plot(x[10:], attack[10:], 'r', color="red", label='Attack Sequence') + +normal = np.stack([run[col_name] for run in runs["normal"]["normal_runs"][:10]]).T +ax1.plot(x[10:], normal[10:], 'r', color="blue", label='Normal Sequence') +ax1.set_title("Binomial Interest") +ax1.set_xlim(10,400) + +ax2.plot(x[10:],runs["uniform"]["expected_runs"][f"mean_{col_name}"][10:], color="green", + label='Uniform Baseline') +ax2.fill_between( + x[10:], + runs["uniform"]["expected_runs"][f'mean_p_stddev_{col_name}'][10:], + runs["uniform"]["expected_runs"][f'mean_n_stddev_{col_name}'][10:], + color="green", + alpha=0.5 +) +attack = np.stack([run[col_name] for run in runs["uniform"]["attack_runs"][:10]]).T +ax2.plot(x[10:], attack[10:], 'r', color="red", label='Attack Sequence') + +normal = np.stack([run[col_name] for run in runs["uniform"]["normal_runs"][:10]]).T +ax2.plot(x[10:], normal[10:], 'r', color="blue", label='Normal Sequence') +ax2.set_title("Uniform Interest") +ax2.set_xlim(10,400) +plt.yscale("log") +fig.set_size_inches(12.00, 5.00) +fig.set_dpi(2000) +fig.tight_layout() +handles, labels = plt.gca().get_legend_handles_labels() +labels, ids = np.unique(labels, return_index=True) +handles = [handles[i] for i in ids] +plt.legend(handles, labels, loc='best') +fig.savefig(filename, bbox_inches='tight') +plt.close() +''' + diff --git a/pygoko/tests/gmm_test.py b/pygoko/tests/gmm_test.py new file mode 100644 index 0000000..a264740 --- /dev/null +++ b/pygoko/tests/gmm_test.py @@ -0,0 +1,141 @@ +import pygoko +import numpy as np +import pandas as pd +import plotly.graph_objects as go +from collections import defaultdict +import matplotlib.pyplot as plt + + +def grab_samples(x_mean, count): + mean = np.zeros([100],dtype=np.float32) + mean[0] = x_mean + cov = np.diag(np.concatenate([np.ones([10],dtype=np.float32),0.001*np.ones([90],dtype=np.float32)])) + return np.random.multivariate_normal(mean,cov,count).astype(np.float32) + +data = grab_samples(0,100000) +tree = pygoko.CoverTree() +tree.set_leaf_cutoff(100) +tree.set_scale_base(1.5) +tree.set_min_res_index(-30) +tree.fit(data) + +print("============= KL Divergence =============") +prior_weight = 1.0 +observation_weight = 1.1 +window_size = 1000 +sequence_len = 1000 +sample_rate = 25 +sequence_count = 32 +baseline = tree.kl_div_dirichlet_baseline(prior_weight, + observation_weight, + sequence_len, + sequence_count, + window_size, + sample_rate) +for i in range(0,sequence_len,sample_rate): + print(baseline.stats(i)) + + +def unpack_stats(dataframe,stats, baseline): + for k in baseline.keys(): + normalized = (stats[k]-baseline[k]["mean"]) + if baseline[k]["var"] > 0: + normalized/np.sqrt(baseline[k]["var"]) + dataframe[k].append(normalized) + +def cumulate(dataframes): + cumulation = defaultdict(list) + for dataframe in dataframes: + for k,v in dataframe.items(): + cumulation[k].append(v) + + cumulation = {k: np.stack(v) for k,v in cumulation.items()} + cumulation = {k: np.mean(v, axis=0) for k,v in cumulation.items()} + return cumulation + + +only_cumulative = [] +for j in range(2): + run_tracker = tree.kl_div_dirichlet( + prior_weight, + observation_weight, + window_size) + run_only_tracker = tree.kl_div_dirichlet( + prior_weight, + observation_weight, + window_size) + mid_run = defaultdict(list) + only_run = defaultdict(list) + + for i in range(100): + test_data = grab_samples(0,sequence_len) + + test_data = grab_samples(float(i)/50,sequence_len) + for x in test_data: + run_only_tracker.push(x) + unpack_stats(only_run,run_only_tracker.stats(),baseline.stats(sequence_len)) + only_cumulative.append(only_run) + +print("==== Only ====") +only_cumulative = cumulate(only_cumulative) +print(only_cumulative) + +fig, ax = plt.subplots() + +ax.plot(np.linspace(0,2,100),only_cumulative["moment1_nz"]) +ax.set_ylabel('KL Divergence') +ax.set_xlabel('Distance between mean of Multinomial in 100d') +fig.tight_layout() +fig.savefig("GaussianDrift.png", bbox_inches='tight') +plt.show() +plt.close() + + + +''' +fig, ax = plt.subplots() + +ax.plot(np.linspace(0,2,100), +ax.fill_between( + x[10:], + runs["normal"]["expected_runs"][f'mean_p_stddev_{col_name}'][10:], + runs["normal"]["expected_runs"][f'mean_n_stddev_{col_name}'][10:], + color="green", + alpha=0.5 +) +attack = np.stack([run[col_name] for run in runs["normal"]["attack_runs"][:10]]).T +ax1.plot(x[10:], attack[10:], 'r', color="red", label='Attack Sequence') + +normal = np.stack([run[col_name] for run in runs["normal"]["normal_runs"][:10]]).T +ax1.plot(x[10:], normal[10:], 'r', color="blue", label='Normal Sequence') +ax1.set_title("Binomial Interest") +ax1.set_xlim(10,400) + +ax2.plot(x[10:],runs["uniform"]["expected_runs"][f"mean_{col_name}"][10:], color="green", + label='Uniform Baseline') +ax2.fill_between( + x[10:], + runs["uniform"]["expected_runs"][f'mean_p_stddev_{col_name}'][10:], + runs["uniform"]["expected_runs"][f'mean_n_stddev_{col_name}'][10:], + color="green", + alpha=0.5 +) +attack = np.stack([run[col_name] for run in runs["uniform"]["attack_runs"][:10]]).T +ax2.plot(x[10:], attack[10:], 'r', color="red", label='Attack Sequence') + +normal = np.stack([run[col_name] for run in runs["uniform"]["normal_runs"][:10]]).T +ax2.plot(x[10:], normal[10:], 'r', color="blue", label='Normal Sequence') +ax2.set_title("Uniform Interest") +ax2.set_xlim(10,400) +plt.yscale("log") +fig.set_size_inches(12.00, 5.00) +fig.set_dpi(2000) +fig.tight_layout() +handles, labels = plt.gca().get_legend_handles_labels() +labels, ids = np.unique(labels, return_index=True) +handles = [handles[i] for i in ids] +plt.legend(handles, labels, loc='best') +fig.savefig(filename, bbox_inches='tight') +plt.close() +''' + From 6398acc7fd281e7bdd15ba2ccc5ae97610ab7662 Mon Sep 17 00:00:00 2001 From: sven Date: Wed, 19 Aug 2020 16:34:28 -0700 Subject: [PATCH 05/10] better examples --- examples/ember_chronological_drift.py | 134 +++++++++++--------- goko/examples/ember_sequence_track.rs | 1 - goko/src/lib.rs | 2 +- goko/src/plugins/distributions/dirichlet.rs | 31 ++--- goko/src/plugins/distributions/mod.rs | 66 +++++----- pygoko/src/plugins.rs | 1 - pygoko/src/tree.rs | 2 - 7 files changed, 120 insertions(+), 117 deletions(-) diff --git a/examples/ember_chronological_drift.py b/examples/ember_chronological_drift.py index 04bc68d..a41285b 100644 --- a/examples/ember_chronological_drift.py +++ b/examples/ember_chronological_drift.py @@ -1,32 +1,13 @@ - #!/usr/bin/env python +#!/usr/bin/env python import os import pandas as pd import ember import argparse import numpy as np - import matplotlib.pyplot as plt - from pygoko import CoverTree -def split(metadata,X,Y): - unique_dates = list(set(metadata['appeared'])) - - X_sorted = {k:X[metadata['appeared'] == k] for k in unique_dates} - y_sorted = {k:Y[metadata['appeared'] == k] for k in unique_dates} - - return X_sorted, y_sorted - -def normalize(baseline,stats): - basesline_stats = baseline.stats(stats["sequence_len"]) - normalized = {} - for k in basesline_stats.keys(): - n = (stats[k]-basesline_stats[k]["mean"]) - if basesline_stats[k]["var"] > 0: - n = n/np.sqrt(basesline_stats[k]["var"]) - normalized[k] = n - return normalized def main(): prog = "ember_drift_calc" @@ -35,34 +16,8 @@ def main(): parser.add_argument("datadir", metavar="DATADIR", type=str, help="Directory with raw features") args = parser.parse_args() - split_date = "2018-07" - - - # Gather the data and sort it into order - train_X, train_y = ember.read_vectorized_features(args.datadir,"train") - test_X, test_y = ember.read_vectorized_features(args.datadir,"test") - - train_metadata = pd.read_csv(os.path.join(args.datadir, "train_metadata.csv"), index_col=0) - test_metadata = pd.read_csv(os.path.join(args.datadir, "test_metadata.csv"), index_col=0) - - train_X_month, train_y_month = split(train_metadata,train_X,train_y) - test_X_month, test_y_month = split(test_metadata,test_X,test_y) - - print(train_X_month.keys()) - print(test_X_month.keys()) - - dates = list(train_X_month.keys()) - dates.sort() - - training_dates = [d for d in dates if d < split_date] - all_dates = [d for d in dates] - - training_data = np.concatenate([train_X_month[k] for k in training_dates]) - training_data = np.ascontiguousarray(training_data) - - all_data = np.concatenate([train_X_month[k] for k in all_dates]) - all_data = np.ascontiguousarray(all_data) - + training_data, all_data, X_month = sort_ember_dataset(datadir = args.datadir, split_date = "2018-07") + # Build the tree tree = CoverTree() tree.set_leaf_cutoff(50) @@ -71,34 +26,49 @@ def main(): tree.fit(training_data) # Gather a baseline - prior_weight = 1.0 observation_weight = 1.3 # 0 sets the window to be infinite, otherwise the "dataset" you're computing against is only the last `window_size` elements window_size = 5000 - # + # We don't use this, our sequences are windowed so we only compute the KL Div on (at most) the last window_size elements sequence_len = 800000 - # Actually computes the KL div this often. All other values are linearly interpolated between these sample points - sample_rate = 5000 - # Averages over this number of sasmples + # Actually computes the KL div this often. All other values are linearly interpolated between these sample points. + # It's too slow to calculate each value and this is accurate enough. + sample_rate = 10 + # Gets the mean and variance over this number of simulated sequence. sequence_count = 50 - baseline = tree.kl_div_dirichlet_baseline(prior_weight, + ''' + We gather a baseline object. When you feed the entire dataset the covertree was created from to itself, + you will get a non-zero KL-Div on any node that is non-trivial. This process will weight the node's posterior Dirichlet distribution, + multiplying the internal weights by (prior_weight + observation_weight). This posterior distribution has a lower variance than the prior and + the expected KL-divergence between the unknown distributions we're modeling is thus non-zero. + + This slowly builds up, but we expect a non-zero KL-div over the nodes as we feed in-distribution data in. This object estimates that, and + allows us to normalize this natural variance away. + ''' + baseline = tree.kl_div_dirichlet_baseline( + prior_weight, observation_weight, - sequence_len, + window_size, sequence_count, - window_size, sample_rate) - goko_divs = {} + """ + This is the actual object that computes the KL Divergence statistics between the samples we feed in and the new samples. + Internally, it is an evidence hashmap containing categorical distributions, and a queue of paths. + The sample's path is computed, we then push it onto the queue and update the evidence by incrementing the correct buckets + in the evidence hashmap. If the queue is full, we pop off the oldest path and decrement the correct paths in the queue. + """ run_tracker = tree.kl_div_dirichlet( prior_weight, observation_weight, window_size) total_kl_div = [] + for i,datum in enumerate(all_data): run_tracker.push(datum) if i % 500 == 0: @@ -111,16 +81,60 @@ def main(): ax.set_ylabel('KL Divergence') ax.set_xlabel('Sample Timestamp') tick_len = 0 + cutoff_len = 0 tick_locations = [] - for date in all_dates: - tick_len += len(train_X_month[date]) + dates = [d for d in X_month.keys()] + for date in dates: + if date == "2018-07": + cutoff_len = tick_len + tick_len += len(X_month[date]) tick_locations.append(tick_len) ax.set_xticks(tick_locations) - ax.set_xticklabels(all_dates) + ax.set_xticklabels(dates) + ax.axvline(x=cutoff_len, linewidth=4, color='r') fig.tight_layout() fig.savefig("drift.png", bbox_inches='tight') plt.show() plt.close() +def normalize(baseline,stats): + """ + Grabs the mean and variance from the baseline and normalizes the stats object passed in by subtracting + the norm and dividing by the standard deviation. + """ + basesline_stats = baseline.stats(stats["sequence_len"]) + normalized = {} + for k in basesline_stats.keys(): + n = (stats[k]-basesline_stats[k]["mean"]) + if basesline_stats[k]["var"] > 0: + n = n/np.sqrt(basesline_stats[k]["var"]) + normalized[k] = n + return normalized + +def sort_ember_dataset(datadir,split_date): + """ + Opens the dataset and creates a training dataset consisting of everything before the split date. + + Returns the training dataset and all data + """ + X, _ = ember.read_vectorized_features(datadir,"train") + metadata = pd.read_csv(os.path.join(datadir, "train_metadata.csv"), index_col=0) + dates = list(set(metadata['appeared'])) + dates.sort() + + X_month = {k:X[metadata['appeared'] == k] for k in dates} + + training_dates = [d for d in dates if d < split_date] + all_dates = [d for d in dates] + + training_data = np.concatenate([X_month[k] for k in training_dates]) + training_data = np.ascontiguousarray(training_data) + + all_data = np.concatenate([X_month[k] for k in all_dates]) + all_data = np.ascontiguousarray(all_data) + + return training_data, all_data, X_month + + if __name__ == '__main__': main() diff --git a/goko/examples/ember_sequence_track.rs b/goko/examples/ember_sequence_track.rs index 4873d3a..a92fae4 100644 --- a/goko/examples/ember_sequence_track.rs +++ b/goko/examples/ember_sequence_track.rs @@ -63,7 +63,6 @@ fn main() { let num_sequence = 8; let mut baseline = DirichletBaseline::new(); - baseline.set_window_size(window_size); baseline.set_observation_weight(observation_weight); baseline.set_prior_weight(prior_weight); baseline.set_sequence_len(test_set.len()); diff --git a/goko/src/lib.rs b/goko/src/lib.rs index 022287f..9b7b1fc 100644 --- a/goko/src/lib.rs +++ b/goko/src/lib.rs @@ -18,7 +18,7 @@ */ #![allow(dead_code)] -//#![deny(warnings)] +#![deny(warnings)] #![warn(missing_docs)] #![doc(test(attr(allow(unused_variables), deny(warnings))))] #![feature(binary_heap_into_iter_sorted)] diff --git a/goko/src/plugins/distributions/dirichlet.rs b/goko/src/plugins/distributions/dirichlet.rs index bddd292..ac1a582 100644 --- a/goko/src/plugins/distributions/dirichlet.rs +++ b/goko/src/plugins/distributions/dirichlet.rs @@ -329,7 +329,7 @@ impl BayesCategoricalTracker { let mut child_address_iter = trace.iter().map(|(_, ca)| ca); child_address_iter.next(); for (parent, child) in parent_address_iter.zip(child_address_iter) { - let mut parent_evidence = self.running_evidence + let parent_evidence = self.running_evidence .get_mut(parent) .unwrap(); parent_evidence.remove_child_pop(Some(*child), self.observation_weight); @@ -380,7 +380,11 @@ impl DiscreteBayesianSequenceTracker for BayesCategoricalTrack &self.reader } fn sequence_len(&self) -> usize { - self.sequence_count + if self.sequence_queue.len() > 0 { + self.sequence_queue.len() + } else { + self.sequence_count + } } } @@ -390,7 +394,6 @@ pub struct DirichletBaseline { sample_rate: usize, sequence_len: usize, num_sequences: usize, - window_size: usize, prior_weight: f64, observation_weight: f64, } @@ -402,12 +405,15 @@ impl DirichletBaseline { sample_rate: 100, sequence_len: 0, num_sequences: 8, - window_size: 50, prior_weight: 1.0, observation_weight: 1.0, } } - /// Sets a new training sequence window_size, default 200. Stats for each total lenght of sequence are returned + /// Sets a new maxium sequence length. Set this to be the window size if you're using windows, the lenght of the test set you've got, + /// or leave it alone as the default limit is the number of points in the training set. + /// + /// We sample up to this cap, linearly interpolating above this. So, the baseline produced is fairly accurate for indexes below this + /// and unreliable above this. pub fn set_sequence_len(&mut self, sequence_len: usize) { self.sequence_len = sequence_len; } @@ -415,10 +421,6 @@ impl DirichletBaseline { pub fn set_num_sequences(&mut self, num_sequences: usize) { self.num_sequences = num_sequences; } - /// Sets a new maximum lenght of sequence, before it starts forgetting data, default 50 - pub fn set_window_size(&mut self, window_size: usize) { - self.window_size = window_size; - } /// Sets a new prior weight, default 1.0. The prior is multiplied by this to increase or decrease it's importance pub fn set_prior_weight(&mut self, prior_weight: f64) { self.prior_weight = prior_weight; @@ -434,13 +436,6 @@ impl DirichletBaseline { /// Trains the sequences up. pub fn train(&self,reader: CoverTreeReader) -> GokoResult { - /* - let chunk_size = 10; - let (results_sender, results_receiver): ( - Sender, - Receiver, - ) = unbounded(); - */ let point_indexes = reader.point_cloud().reference_indexes(); let sequence_len = if self.sequence_len == 0 { point_indexes.len() @@ -452,7 +447,7 @@ impl DirichletBaseline { let mut tracker = BayesCategoricalTracker::new( self.prior_weight, self.observation_weight, - self.window_size, + 0, reader, ); (&point_indexes[..]).choose_multiple(&mut thread_rng(),sequence_len).enumerate().filter_map(|(i,pi)| { @@ -544,7 +539,7 @@ pub(crate) mod tests { println!("Buckets Posterior: {:?}", buckets_posterior); println!("Evidence: {:?}", categorical); assert_approx_eq!(buckets.kl_divergence(&buckets_posterior).unwrap(), buckets.posterior_kl_divergence(&categorical).unwrap()); - assert_approx_eq!(buckets.kl_divergence(&buckets_posterior).unwrap(), 0.0); + assert_approx_eq!(buckets.kl_divergence(&buckets_posterior).unwrap(), 0.1789970483832892); } #[test] diff --git a/goko/src/plugins/distributions/mod.rs b/goko/src/plugins/distributions/mod.rs index 09c4f86..f8507e5 100644 --- a/goko/src/plugins/distributions/mod.rs +++ b/goko/src/plugins/distributions/mod.rs @@ -67,6 +67,23 @@ pub trait DiscreteBayesianSequenceTracker: Debug { fn add_path(&mut self, trace: Vec<(f32, NodeAddress)>); /// The current distributions that a dry insert touched. fn running_evidence(&self) -> &HashMap>::Distribution as DiscreteBayesianDistribution>::Evidence>; + + /// Gives the per-node KL divergence, with the node address + fn all_node_kl(&self) -> Vec<(f64, NodeAddress)> { + self.running_evidence() + .iter() + .map(|(address, sequence_pdf)| { + let kl = self + .tree_reader() + .get_node_plugin_and::(*address, |p| { + p.posterior_kl_divergence(sequence_pdf).unwrap() + }) + .unwrap(); + (kl, *address) + }) + .collect() + } + /// Helper function, each sequence tracker should carry it's own reader. fn tree_reader(&self) -> &CoverTreeReader; /// The length of the sequence @@ -79,24 +96,17 @@ pub trait DiscreteBayesianSequenceTracker: Debug { let mut nz_count = 0; let mut moment1_nz = 0.0; let mut moment2_nz = 0.0; - self.running_evidence() + self.all_node_kl() .iter() - .for_each(|(address, sequence_pdf)| { - let kl = self - .tree_reader() - .get_node_plugin_and::(*address, |p| { - p.posterior_kl_divergence(sequence_pdf).unwrap() - }) - .unwrap(); - - if kl > 1.0e-10 { + .for_each(|(kl, _address)| { + if *kl > 1.0e-10 { moment1_nz += kl; moment2_nz += kl * kl; - if max < kl { - max = kl; + if max < *kl { + max = *kl; } - if kl < min { - min = kl; + if *kl < min { + min = *kl; } nz_count += 1; @@ -117,9 +127,9 @@ pub trait DiscreteBayesianSequenceTracker: Debug { let mut layer_totals: Vec = vec![0; self.tree_reader().len()]; let mut layer_node_counts = vec![Vec::::new(); self.tree_reader().len()]; let parameters = self.tree_reader().parameters(); - self.running_evidence() + self.all_node_kl() .iter() - .for_each(|(address, _sequence_pdf)| { + .for_each(|(_kl, address)| { layer_totals[parameters.internal_index(address.0)] += 1; layer_node_counts[parameters.internal_index(address.0)].push( self.tree_reader() @@ -139,24 +149,7 @@ pub trait DiscreteBayesianSequenceTracker: Debug { layer_totals, weighted_layer_totals, } - } - - - /// Gives the per-node KL divergence, with the node address - fn all_node_kl(&self) -> Vec<(f64, NodeAddress)> { - self.running_evidence() - .iter() - .map(|(address, sequence_pdf)| { - let kl = self - .tree_reader() - .get_node_plugin_and::(*address, |p| { - p.posterior_kl_divergence(sequence_pdf).unwrap() - }) - .unwrap(); - (kl, *address) - }) - .collect() - } + } } /// Tracks the non-zero KL div (all KL divergences above 1e-10) @@ -259,13 +252,18 @@ impl KLDivergenceBaselineStats { } } +/// Computing the KL div of each node's prior and posterior is expensive. pub struct KLDivergenceBaseline { + /// The number of sequences we're have the stats from pub num_sequences: usize, + /// The lenght of the sequences we have stats for. All other values are linearly interpolated between these. pub sequence_len: Vec, + /// The actual stats objects. These are stored by the moments, but are returned by (mean,var) pub stats: Vec, } impl KLDivergenceBaseline { + /// Gets the stats object that stores an approcimate mean and variance of the pub fn stats(&self,i:usize) -> KLDivergenceBaselineStats { match self.sequence_len.binary_search(&i) { Ok(index) => { diff --git a/pygoko/src/plugins.rs b/pygoko/src/plugins.rs index 6ae6435..ccb87d4 100644 --- a/pygoko/src/plugins.rs +++ b/pygoko/src/plugins.rs @@ -1,6 +1,5 @@ use numpy::PyArray1; use pyo3::prelude::*; -use pyo3::PyObjectProtocol; use goko::plugins::distributions::*; use goko::*; diff --git a/pygoko/src/tree.rs b/pygoko/src/tree.rs index b1c1761..58cd40c 100644 --- a/pygoko/src/tree.rs +++ b/pygoko/src/tree.rs @@ -228,7 +228,6 @@ impl CoverTree { observation_weight: f64, sequence_len: usize, num_sequences: usize, - window_size: usize, sample_rate: usize, ) -> PyKLDivergenceBaseline { let reader = self.writer.as_ref().unwrap().reader(); @@ -237,7 +236,6 @@ impl CoverTree { trainer.set_observation_weight(observation_weight); trainer.set_sequence_len(sequence_len); trainer.set_num_sequences(num_sequences); - trainer.set_window_size(window_size); trainer.set_sample_rate(sample_rate); let baseline = trainer.train(reader).unwrap(); PyKLDivergenceBaseline { baseline } From df67ccf01c51a66cc015ff70f8e7399de008d86f Mon Sep 17 00:00:00 2001 From: sven Date: Thu, 20 Aug 2020 20:05:52 -0700 Subject: [PATCH 06/10] added the routing KNN --- examples/ember_chronological_drift.py | 4 +++ examples/mnist_knn.py | 42 +++++++++++++++++++++++++++ pygoko/src/tree.rs | 5 ++++ 3 files changed, 51 insertions(+) create mode 100644 examples/mnist_knn.py diff --git a/examples/ember_chronological_drift.py b/examples/ember_chronological_drift.py index a41285b..37e957d 100644 --- a/examples/ember_chronological_drift.py +++ b/examples/ember_chronological_drift.py @@ -1,5 +1,9 @@ #!/usr/bin/env python +""" +This is an example that tracks chronological drift in the ember dataset. We train on the ember dataset on data before 2018-07, +""" + import os import pandas as pd import ember diff --git a/examples/mnist_knn.py b/examples/mnist_knn.py new file mode 100644 index 0000000..a46a3d4 --- /dev/null +++ b/examples/mnist_knn.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python + +""" +This example shows loading from a YAML file. You can specify all the parameters in the yaml file. +Thiss +""" + +import numpy as np +from pygoko import CoverTree + +tree = CoverTree() +tree.load_yaml_config("data/mnist_complex.yml") +tree.fit() + +point = np.zeros([784], dtype=np.float32) + +""" +This is a standard KNN, returning the 5 nearest nbrs. +""" + +print(tree.knn(point,5)) + +""" +This is the KNN that ignores singletons, the outliers attached to each node and the leftover indexes on the leaf +are ignored. +""" + +print(tree.routing_knn(point,5)) + +""" +This returns the indexes of the nodes along the path. We can then ask for the label summaries of +each node along the path. +""" +path = tree.path(point) +print(path) + +print("Summary of the labels of points covered by the node at address") +for dist, address in path: + node = tree.node(address) + label_summary = node.label_summary() + print(f"Address {address}: Summary: {label_summary}") + diff --git a/pygoko/src/tree.rs b/pygoko/src/tree.rs index 58cd40c..b48d049 100644 --- a/pygoko/src/tree.rs +++ b/pygoko/src/tree.rs @@ -194,6 +194,11 @@ impl CoverTree { reader.knn(point.readonly().as_slice().unwrap(), k).unwrap() } + pub fn routing_knn(&self, point: &PyArray1, k: usize) -> Vec<(f32, usize)> { + let reader = self.writer.as_ref().unwrap().reader(); + reader.routing_knn(point.readonly().as_slice().unwrap(), k).unwrap() + } + pub fn known_path(&self, point_index: usize) -> Vec<(f32, (i32, usize))> { let reader = self.writer.as_ref().unwrap().reader(); reader.known_path(point_index).unwrap() From 4c81b04d901e1bb6c4c890cde0fab05f094968e3 Mon Sep 17 00:00:00 2001 From: sven Date: Thu, 20 Aug 2020 22:06:07 -0700 Subject: [PATCH 07/10] default to no messages printed --- goko/src/covertree/builders.rs | 4 ++-- pygoko/src/tree.rs | 9 +++++++++ 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/goko/src/covertree/builders.rs b/goko/src/covertree/builders.rs index 956b62d..3c428b0 100644 --- a/goko/src/covertree/builders.rs +++ b/goko/src/covertree/builders.rs @@ -303,7 +303,7 @@ impl Default for CoverTreeBuilder { min_res_index: -10, use_singletons: true, partition_type: PartitionType::Nearest, - verbosity: 2, + verbosity: 0, } } } @@ -317,7 +317,7 @@ impl CoverTreeBuilder { min_res_index: -10, use_singletons: true, partition_type: PartitionType::Nearest, - verbosity: 2, + verbosity: 0, } } diff --git a/pygoko/src/tree.rs b/pygoko/src/tree.rs index b48d049..e6bc0eb 100644 --- a/pygoko/src/tree.rs +++ b/pygoko/src/tree.rs @@ -76,6 +76,15 @@ impl CoverTree { None => panic!("Set too late"), }; } + + pub fn set_verbosity(&mut self, x: u32) { + match &mut self.builder { + Some(builder) => builder.set_verbosity(x), + None => panic!("Set too late"), + }; + } + + set_verbosity pub fn load_yaml_config(&mut self, file_name: String) -> PyResult<()> { let path = Path::new(&file_name); let point_cloud = Arc::new(labeled_ram_from_yaml::<_, L2>(&path).unwrap()); From 7262f8854ba6071286a067412de61e9b9ea2dcac Mon Sep 17 00:00:00 2001 From: sven Date: Mon, 24 Aug 2020 13:08:10 -0700 Subject: [PATCH 08/10] examples better setup --- .gitignore | 1 + examples/ember_chronological_drift.py | 4 +- examples/gaussian_drift.py | 110 ++++++++++++++++++++ goko/src/plugins/distributions/mod.rs | 2 +- pygoko/tests/ember2018.py | 54 ---------- pygoko/tests/gmm2d.py | 83 --------------- pygoko/tests/gmm_test.py | 141 -------------------------- pygoko/tests/mnist.py | 52 ---------- pygoko/tests/save_mnist_knn.py | 25 ----- 9 files changed, 115 insertions(+), 357 deletions(-) create mode 100644 examples/gaussian_drift.py delete mode 100644 pygoko/tests/ember2018.py delete mode 100644 pygoko/tests/gmm2d.py delete mode 100644 pygoko/tests/gmm_test.py delete mode 100644 pygoko/tests/mnist.py delete mode 100644 pygoko/tests/save_mnist_knn.py diff --git a/.gitignore b/.gitignore index e329385..120d0e1 100644 --- a/.gitignore +++ b/.gitignore @@ -5,5 +5,6 @@ models build dist *.egg-info +*.png __pycache__ add_license.py \ No newline at end of file diff --git a/examples/ember_chronological_drift.py b/examples/ember_chronological_drift.py index 37e957d..90ea079 100644 --- a/examples/ember_chronological_drift.py +++ b/examples/ember_chronological_drift.py @@ -1,7 +1,9 @@ #!/usr/bin/env python """ -This is an example that tracks chronological drift in the ember dataset. We train on the ember dataset on data before 2018-07, +This is an example that tracks chronological drift in the ember dataset. We train on the ember dataset on data before 2018-07, +and then run everything through it. There's a massive increase in the total KL-div after the cutoff, so this does detect a +shift in the dataset. """ import os diff --git a/examples/gaussian_drift.py b/examples/gaussian_drift.py new file mode 100644 index 0000000..0ce2dfe --- /dev/null +++ b/examples/gaussian_drift.py @@ -0,0 +1,110 @@ +''' +This produces 2 gaussians, one that is fixed at 0, and the other that moves slowly over. +''' + +import pygoko +import numpy as np +import pandas as pd +import plotly.graph_objects as go +from collections import defaultdict +import matplotlib.pyplot as plt + +def main(): + # How many samples we grab from the fixed gaussian + fixed_sample_count = 200000 + # How many samples we grab from the moving gaussian, at each timestamp + moving_sample_count = 1000 + timestamps = np.linspace(0,2,50) + + # We treat multiply the weight vector of the Dirichlet prior by this before computing the KL-div + prior_weight = 1.0 + # We weight the evidence by this before we add it to the prior to get the posterior. + observation_weight = 1.0 + # How often we sample the KL div of the sequences + sample_rate = 25 + # How many artificial sequences do we average over + sequence_count = 32 + + tree = build_covertree(sample_from_gaussian(0,fixed_sample_count)) + + # See the ember_ chronological _drift + baseline = tree.kl_div_dirichlet_baseline( + prior_weight, + observation_weight, + moving_sample_count, # We don't need to sample sequences longer than this + sequence_count, + sample_rate) + + + + tracking_stats = [] + for t in timestamps: + run_only_tracker = tree.kl_div_dirichlet( + prior_weight, + observation_weight, + 0) + timestamps_stats = defaultdict(list) + + for i in range(10): + moving_data = sample_from_gaussian(t,moving_sample_count) + for x in moving_data: + run_only_tracker.push(x) + unpack_stats( + timestamps_stats, + run_only_tracker.stats(), + baseline.stats(moving_sample_count)) + tracking_stats.append(timestamps_stats) + + plot(timestamps,tracking_stats) + +def sample_from_gaussian(x_mean, count): + """ + Grabs count samples from a gaussian centered at [x_mean, 0, ... 0], with the identity matrix for the covariance. + """ + mean = np.zeros([100],dtype=np.float32) + mean[0] = x_mean + cov = np.diag(np.concatenate([np.ones([10],dtype=np.float32),0.001*np.ones([90],dtype=np.float32)])) + return np.random.multivariate_normal(mean,cov,count).astype(np.float32) + +def build_covertree(data): + """ + Builds a covertree on the data + """ + tree = pygoko.CoverTree() + tree.set_leaf_cutoff(100) + tree.set_scale_base(1.5) + tree.set_min_res_index(-30) + tree.fit(data) + return tree + +def unpack_stats(dataframe,stats, baseline): + """ + Normalizes the stats by the baseline + """ + for k in baseline.keys(): + normalized = (stats[k]-baseline[k]["mean"]) + if baseline[k]["var"] > 0: + normalized/np.sqrt(baseline[k]["var"]) + dataframe[k].append(normalized) + +def plot(timestamps,dataframes,statistic="moment1_nz"): + cumulation = defaultdict(list) + for dataframe in dataframes: + for k,v in dataframe.items(): + cumulation[k].append(v) + + cumulation = {k: np.stack(v) for k,v in cumulation.items()} + cumulation_mean = {k: np.mean(v, axis=1) for k,v in cumulation.items()} + fig, ax = plt.subplots() + + ax.plot(timestamps,cumulation_mean[statistic]) + ax.set_ylabel('KL Divergence') + ax.set_xlabel('Distance between mean of Multinomial in 100d') + fig.tight_layout() + fig.savefig("GaussianDrift.png", bbox_inches='tight') + plt.show() + plt.close() + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/goko/src/plugins/distributions/mod.rs b/goko/src/plugins/distributions/mod.rs index f8507e5..c78fc92 100644 --- a/goko/src/plugins/distributions/mod.rs +++ b/goko/src/plugins/distributions/mod.rs @@ -263,7 +263,7 @@ pub struct KLDivergenceBaseline { } impl KLDivergenceBaseline { - /// Gets the stats object that stores an approcimate mean and variance of the + /// Gets the stats object that stores an approximate mean and variance of the samples. pub fn stats(&self,i:usize) -> KLDivergenceBaselineStats { match self.sequence_len.binary_search(&i) { Ok(index) => { diff --git a/pygoko/tests/ember2018.py b/pygoko/tests/ember2018.py deleted file mode 100644 index 0b7ac19..0000000 --- a/pygoko/tests/ember2018.py +++ /dev/null @@ -1,54 +0,0 @@ -import numpy as np -import pygoko - -tree = pygoko.CoverTree() -tree.load_yaml_config("../data/ember_complex.yml") -tree.fit() - - -print(tree.knn(tree.data_point(0),5)) - -print("============= TRACE =============") -trace = tree.path(tree.data_point(59999)) -for distance,address in trace: - node = tree.node(address) - mean = node.cover_mean() - if mean is not None: - print(f"\tNode {node.address()}, mean: {mean.mean()}") - else: - print(f"\tNode {node.address()}, MEAN IS BROKEN") - -print("============= KL Divergence =============") -prior_weight = 1.0 -observation_weight = 1.3 -window_size = 5000 -sequence_len = 800000 -sample_rate = 5000 -sequence_count = 10 -baseline = tree.kl_div_dirichlet_baseline(prior_weight, - observation_weight, - sequence_len, - sequence_count, - window_size, - sample_rate) -for i in range(0,800000,1000): - print(baseline.stats(i)) -""" -print("============= KL Divergence Normal Use =============") -kl_tracker = tree.kl_div_dirichlet(prior_weight,observation_weight,window_size) -for i in range(200): - kl_tracker.push(tree.data_point(i)) - mean, var = baseline.stats(i) - print((kl_tracker.stats() - mean)/var.sqrt()) - - -print("============= KL Divergence Attack =============") - -kl_attack_tracker = tree.kl_div_dirichlet(prior_weight,observation_weight,window_size) -for i in range(10): - kl_attack_tracker.push(tree.data_point(i)) - print(kl_attack_tracker.stats()) -for i in range(10): - kl_attack_tracker.push(tree.data_point(0)) - print(kl_attack_tracker.stats()) -""" \ No newline at end of file diff --git a/pygoko/tests/gmm2d.py b/pygoko/tests/gmm2d.py deleted file mode 100644 index e664cc2..0000000 --- a/pygoko/tests/gmm2d.py +++ /dev/null @@ -1,83 +0,0 @@ -import pygoko -import numpy as np -import pandas as pd -import plotly.graph_objects as go -from collections import defaultdict -import matplotlib.pyplot as plt - - -def grab_samples(x_mean, count): - mean = np.zeros([2],dtype=np.float32) - mean[0] = x_mean - cov = np.diag(np.ones([2],dtype=np.float32)) - return np.random.multivariate_normal(mean,cov,count).astype(np.float32) - -data = grab_samples(0,1000) - - - -for i in range(6): - test_data = grab_samples(float(i)/3,50) - - - fig, ax = plt.subplots() - ax.scatter(data[:,0],data[:,1],color="orange") - ax.scatter(test_data[:,0],test_data[:,1],color="blue") - ax.set_xlim((-1.6,3.1)) - ax.set_ylim((-1.6,1.6)) - fig.set_size_inches(14.00, 7.00) - fig.savefig(f"gaussian_vis_{i}.png", bbox_inches='tight') - - - - - - - -''' -fig, ax = plt.subplots() - -ax.plot(np.linspace(0,2,100), -ax.fill_between( - x[10:], - runs["normal"]["expected_runs"][f'mean_p_stddev_{col_name}'][10:], - runs["normal"]["expected_runs"][f'mean_n_stddev_{col_name}'][10:], - color="green", - alpha=0.5 -) -attack = np.stack([run[col_name] for run in runs["normal"]["attack_runs"][:10]]).T -ax1.plot(x[10:], attack[10:], 'r', color="red", label='Attack Sequence') - -normal = np.stack([run[col_name] for run in runs["normal"]["normal_runs"][:10]]).T -ax1.plot(x[10:], normal[10:], 'r', color="blue", label='Normal Sequence') -ax1.set_title("Binomial Interest") -ax1.set_xlim(10,400) - -ax2.plot(x[10:],runs["uniform"]["expected_runs"][f"mean_{col_name}"][10:], color="green", - label='Uniform Baseline') -ax2.fill_between( - x[10:], - runs["uniform"]["expected_runs"][f'mean_p_stddev_{col_name}'][10:], - runs["uniform"]["expected_runs"][f'mean_n_stddev_{col_name}'][10:], - color="green", - alpha=0.5 -) -attack = np.stack([run[col_name] for run in runs["uniform"]["attack_runs"][:10]]).T -ax2.plot(x[10:], attack[10:], 'r', color="red", label='Attack Sequence') - -normal = np.stack([run[col_name] for run in runs["uniform"]["normal_runs"][:10]]).T -ax2.plot(x[10:], normal[10:], 'r', color="blue", label='Normal Sequence') -ax2.set_title("Uniform Interest") -ax2.set_xlim(10,400) -plt.yscale("log") -fig.set_size_inches(12.00, 5.00) -fig.set_dpi(2000) -fig.tight_layout() -handles, labels = plt.gca().get_legend_handles_labels() -labels, ids = np.unique(labels, return_index=True) -handles = [handles[i] for i in ids] -plt.legend(handles, labels, loc='best') -fig.savefig(filename, bbox_inches='tight') -plt.close() -''' - diff --git a/pygoko/tests/gmm_test.py b/pygoko/tests/gmm_test.py deleted file mode 100644 index a264740..0000000 --- a/pygoko/tests/gmm_test.py +++ /dev/null @@ -1,141 +0,0 @@ -import pygoko -import numpy as np -import pandas as pd -import plotly.graph_objects as go -from collections import defaultdict -import matplotlib.pyplot as plt - - -def grab_samples(x_mean, count): - mean = np.zeros([100],dtype=np.float32) - mean[0] = x_mean - cov = np.diag(np.concatenate([np.ones([10],dtype=np.float32),0.001*np.ones([90],dtype=np.float32)])) - return np.random.multivariate_normal(mean,cov,count).astype(np.float32) - -data = grab_samples(0,100000) -tree = pygoko.CoverTree() -tree.set_leaf_cutoff(100) -tree.set_scale_base(1.5) -tree.set_min_res_index(-30) -tree.fit(data) - -print("============= KL Divergence =============") -prior_weight = 1.0 -observation_weight = 1.1 -window_size = 1000 -sequence_len = 1000 -sample_rate = 25 -sequence_count = 32 -baseline = tree.kl_div_dirichlet_baseline(prior_weight, - observation_weight, - sequence_len, - sequence_count, - window_size, - sample_rate) -for i in range(0,sequence_len,sample_rate): - print(baseline.stats(i)) - - -def unpack_stats(dataframe,stats, baseline): - for k in baseline.keys(): - normalized = (stats[k]-baseline[k]["mean"]) - if baseline[k]["var"] > 0: - normalized/np.sqrt(baseline[k]["var"]) - dataframe[k].append(normalized) - -def cumulate(dataframes): - cumulation = defaultdict(list) - for dataframe in dataframes: - for k,v in dataframe.items(): - cumulation[k].append(v) - - cumulation = {k: np.stack(v) for k,v in cumulation.items()} - cumulation = {k: np.mean(v, axis=0) for k,v in cumulation.items()} - return cumulation - - -only_cumulative = [] -for j in range(2): - run_tracker = tree.kl_div_dirichlet( - prior_weight, - observation_weight, - window_size) - run_only_tracker = tree.kl_div_dirichlet( - prior_weight, - observation_weight, - window_size) - mid_run = defaultdict(list) - only_run = defaultdict(list) - - for i in range(100): - test_data = grab_samples(0,sequence_len) - - test_data = grab_samples(float(i)/50,sequence_len) - for x in test_data: - run_only_tracker.push(x) - unpack_stats(only_run,run_only_tracker.stats(),baseline.stats(sequence_len)) - only_cumulative.append(only_run) - -print("==== Only ====") -only_cumulative = cumulate(only_cumulative) -print(only_cumulative) - -fig, ax = plt.subplots() - -ax.plot(np.linspace(0,2,100),only_cumulative["moment1_nz"]) -ax.set_ylabel('KL Divergence') -ax.set_xlabel('Distance between mean of Multinomial in 100d') -fig.tight_layout() -fig.savefig("GaussianDrift.png", bbox_inches='tight') -plt.show() -plt.close() - - - -''' -fig, ax = plt.subplots() - -ax.plot(np.linspace(0,2,100), -ax.fill_between( - x[10:], - runs["normal"]["expected_runs"][f'mean_p_stddev_{col_name}'][10:], - runs["normal"]["expected_runs"][f'mean_n_stddev_{col_name}'][10:], - color="green", - alpha=0.5 -) -attack = np.stack([run[col_name] for run in runs["normal"]["attack_runs"][:10]]).T -ax1.plot(x[10:], attack[10:], 'r', color="red", label='Attack Sequence') - -normal = np.stack([run[col_name] for run in runs["normal"]["normal_runs"][:10]]).T -ax1.plot(x[10:], normal[10:], 'r', color="blue", label='Normal Sequence') -ax1.set_title("Binomial Interest") -ax1.set_xlim(10,400) - -ax2.plot(x[10:],runs["uniform"]["expected_runs"][f"mean_{col_name}"][10:], color="green", - label='Uniform Baseline') -ax2.fill_between( - x[10:], - runs["uniform"]["expected_runs"][f'mean_p_stddev_{col_name}'][10:], - runs["uniform"]["expected_runs"][f'mean_n_stddev_{col_name}'][10:], - color="green", - alpha=0.5 -) -attack = np.stack([run[col_name] for run in runs["uniform"]["attack_runs"][:10]]).T -ax2.plot(x[10:], attack[10:], 'r', color="red", label='Attack Sequence') - -normal = np.stack([run[col_name] for run in runs["uniform"]["normal_runs"][:10]]).T -ax2.plot(x[10:], normal[10:], 'r', color="blue", label='Normal Sequence') -ax2.set_title("Uniform Interest") -ax2.set_xlim(10,400) -plt.yscale("log") -fig.set_size_inches(12.00, 5.00) -fig.set_dpi(2000) -fig.tight_layout() -handles, labels = plt.gca().get_legend_handles_labels() -labels, ids = np.unique(labels, return_index=True) -handles = [handles[i] for i in ids] -plt.legend(handles, labels, loc='best') -fig.savefig(filename, bbox_inches='tight') -plt.close() -''' - diff --git a/pygoko/tests/mnist.py b/pygoko/tests/mnist.py deleted file mode 100644 index 8bf3381..0000000 --- a/pygoko/tests/mnist.py +++ /dev/null @@ -1,52 +0,0 @@ -import numpy as np -from sklearn.neighbors import KDTree -import pygoko -import pandas as pd - -data = np.memmap("../../data/mnist.dat", dtype=np.float32) -data = data.reshape([-1,784]) - -tree = pygoko.CoverTree() -tree.load_yaml_config("../../data/mnist_complex.yml") -tree.set_leaf_cutoff(0) -tree.set_scale_base(1.2) -tree.set_min_res_index(-30) -tree.fit() - -print(tree.knn(data[0],5)) - -for i,layer in enumerate(tree.layers()): - print(f"On layer {layer.scale_index()}") - if i < 2: - for node in layer.nodes(): - print(f"\tNode {node.address()} mean: {node.cover_mean().mean()}") - -print("============= TRACE =============") -trace = tree.path(data[59999]) -for address in trace: - print(address) - node = tree.node(address[1]) - mean = node.cover_mean() - if mean is not None: - print(f"\tNode {node.address()}, mean: {mean.mean()}, summary: {node.label_summary()}") - else: - print(f"\tNode {node.address()}, MEAN IS BROKEN") - -print("============= KL Divergence =============") -normal_stats = tree.kl_div_dirichlet_basestats(1.0,1.3,100,10,20) -for i,vstats in enumerate(normal_stats[:1]): - for stats in vstats: - print(stats) -print("============= KL Divergence Normal Use =============") -kl_tracker = tree.kl_div_dirichlet(1.0,1.3,20) -for x in data[:50]: - kl_tracker.push(x) - print(kl_tracker.stats()) - - -print("============= KL Divergence Attack =============") - -kl_attack_tracker = tree.kl_div_dirichlet(1.0,1.3,20) -for i in range(50): - kl_attack_tracker.push(data[0]) - print(kl_attack_tracker.stats()) diff --git a/pygoko/tests/save_mnist_knn.py b/pygoko/tests/save_mnist_knn.py deleted file mode 100644 index ad2ca30..0000000 --- a/pygoko/tests/save_mnist_knn.py +++ /dev/null @@ -1,25 +0,0 @@ -import numpy as np -from sklearn.neighbors import KDTree - -tree = KDTree(data, leaf_size=2) -dist, ind = tree.query(data[:10], k=5) -print(dist[0]) -print(ind[0]) - -dist, ind = tree.query(np.zeros([1,784],dtype=np.float32), k=5) -print(dist[0]) -print(ind[0]) - -nbrs = {"d0":dist[:,0], - "d1":dist[:,1], - "d2":dist[:,2], - "d3":dist[:,3], - "d4":dist[:,4], - "i0":ind[:,0], - "i1":ind[:,1], - "i2":ind[:,2], - "i3":ind[:,3], - "i4":ind[:,4],} - -csv = pd.DataFrame(nbrs) -csv.to_csv("../../data/mnist_nbrs.csv") \ No newline at end of file From 3bc29dfeac9c696c06ca743eca7528cecafa5351 Mon Sep 17 00:00:00 2001 From: sven Date: Mon, 24 Aug 2020 13:16:14 -0700 Subject: [PATCH 09/10] version up --- goko/Cargo.toml | 12 ++++++------ goko/src/tree_file_format.rs | 4 ++-- pointcloud/Cargo.toml | 10 +++++----- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/goko/Cargo.toml b/goko/Cargo.toml index ed4f7c4..a8acd39 100644 --- a/goko/Cargo.toml +++ b/goko/Cargo.toml @@ -1,6 +1,6 @@ [package] name = "goko" -version = "0.3.9" +version = "0.4.0" edition = "2018" description = "A lock-free, eventually consistent, concurrent covertree." @@ -30,17 +30,17 @@ path = "src/lib.rs" test = true [dependencies] -protobuf = "2.16.2" +protobuf = "2.17.0" rand = "0.7.3" yaml-rust = "0.4.4" pbr = "1.0.3" fxhash = "0.2.1" rayon = "1.3.1" -hashbrown = { version = "0.8", features = ["rayon"] } -crossbeam-channel = "0.4.2" -pointcloud = { version = "0.3.5", path = "../pointcloud" } +hashbrown = { version = "0.8.2", features = ["rayon"] } +crossbeam-channel = "0.4.3" +pointcloud = { version = "0.3.6", path = "../pointcloud" } #evmap = { git = "https://github.com/comath/rust-evmap" } -smallvec = "1.4.1" +smallvec = "1.4.2" type-map = "0.3.0" statrs = "0.13.0" diff --git a/goko/src/tree_file_format.rs b/goko/src/tree_file_format.rs index 7ae9588..813b16e 100644 --- a/goko/src/tree_file_format.rs +++ b/goko/src/tree_file_format.rs @@ -1,4 +1,4 @@ -// This file is generated by rust-protobuf 2.16.2. Do not edit +// This file is generated by rust-protobuf 2.17.0. Do not edit // @generated // https://github.com/rust-lang/rust-clippy/issues/702 @@ -21,7 +21,7 @@ /// Generated files are compatible only with the same version /// of protobuf runtime. -// const _PROTOBUF_VERSION_CHECK: () = ::protobuf::VERSION_2_16_2; +// const _PROTOBUF_VERSION_CHECK: () = ::protobuf::VERSION_2_17_0; #[derive(PartialEq,Clone,Default)] pub struct NodeProto { diff --git a/pointcloud/Cargo.toml b/pointcloud/Cargo.toml index 77f59f5..a5544a0 100644 --- a/pointcloud/Cargo.toml +++ b/pointcloud/Cargo.toml @@ -25,16 +25,16 @@ channel = "nightly" [dependencies] csv = "1" -libc = "0.2.72" +libc = "0.2.76" yaml-rust = "0.4" rayon = "1.3.1" packed_simd = "0.3.3" glob = "0.3.0" fxhash = "0.2.1" -hashbrown = { version = "0.8", features = ["rayon", "serde"] } -serde_json = "1.0.56" -serde = { version = "1.0.114", features = ["derive"] } -flate2 = "1.0.16" +hashbrown = { version = "0.8.2", features = ["rayon", "serde"] } +serde_json = "1.0.57" +serde = { version = "1.0.115", features = ["derive"] } +flate2 = "1.0.17" rand = "0.7.3" smallvec = { version = "1.3.0", features = ["serde"] } num-traits = "0.2" From c6557d2cf34c586abc7cfc971b55058dbf53877e Mon Sep 17 00:00:00 2001 From: sven Date: Mon, 24 Aug 2020 13:19:21 -0700 Subject: [PATCH 10/10] forgot the pointcloud version --- pointcloud/Cargo.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pointcloud/Cargo.toml b/pointcloud/Cargo.toml index a5544a0..5332a27 100644 --- a/pointcloud/Cargo.toml +++ b/pointcloud/Cargo.toml @@ -1,6 +1,6 @@ [package] name = "pointcloud" -version = "0.3.5" +version = "0.3.6" edition = "2018" description = "An accessor layer for goko"