Added constraint based structure learning algorithms #71
Merged
meliurwen
merged 21 commits from 8-feature-constraint-based-structure-learning-algorithm-for-ctbn into dev 2 years ago
@ -1,5 +1,164 @@ |
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//! Module containing constraint based algorithms like CTPC and Hiton.
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//pub struct CTPC {
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//
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//}
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use crate::params::Params; |
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use itertools::Itertools; |
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use rayon::iter::{IntoParallelIterator, ParallelIterator}; |
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use rayon::prelude::ParallelExtend; |
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use std::collections::{BTreeSet, HashMap}; |
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use std::mem; |
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use std::usize; |
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use super::hypothesis_test::*; |
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use crate::parameter_learning::ParameterLearning; |
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use crate::process; |
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use crate::structure_learning::StructureLearningAlgorithm; |
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use crate::tools::Dataset; |
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pub struct Cache<'a, P: ParameterLearning> { |
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parameter_learning: &'a P, |
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cache_persistent_small: HashMap<Option<BTreeSet<usize>>, Params>, |
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cache_persistent_big: HashMap<Option<BTreeSet<usize>>, Params>, |
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parent_set_size_small: usize, |
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} |
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impl<'a, P: ParameterLearning> Cache<'a, P> { |
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pub fn new(parameter_learning: &'a P) -> Cache<'a, P> { |
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Cache { |
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parameter_learning, |
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cache_persistent_small: HashMap::new(), |
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cache_persistent_big: HashMap::new(), |
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parent_set_size_small: 0, |
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} |
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} |
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pub fn fit<T: process::NetworkProcess>( |
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&mut self, |
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net: &T, |
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dataset: &Dataset, |
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node: usize, |
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parent_set: Option<BTreeSet<usize>>, |
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) -> Params { |
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let parent_set_len = parent_set.as_ref().unwrap().len(); |
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if parent_set_len > self.parent_set_size_small + 1 { |
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//self.cache_persistent_small = self.cache_persistent_big;
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mem::swap( |
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&mut self.cache_persistent_small, |
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&mut self.cache_persistent_big, |
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); |
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self.cache_persistent_big = HashMap::new(); |
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self.parent_set_size_small += 1; |
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} |
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if parent_set_len > self.parent_set_size_small { |
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match self.cache_persistent_big.get(&parent_set) { |
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// TODO: Better not clone `params`, useless clock cycles, RAM use and I/O
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// not cloning requires a minor and reasoned refactoring across the library
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Some(params) => params.clone(), |
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None => { |
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let params = |
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self.parameter_learning |
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.fit(net, dataset, node, parent_set.clone()); |
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self.cache_persistent_big.insert(parent_set, params.clone()); |
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params |
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} |
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} |
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} else { |
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match self.cache_persistent_small.get(&parent_set) { |
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// TODO: Better not clone `params`, useless clock cycles, RAM use and I/O
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// not cloning requires a minor and reasoned refactoring across the library
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Some(params) => params.clone(), |
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None => { |
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let params = |
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self.parameter_learning |
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.fit(net, dataset, node, parent_set.clone()); |
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self.cache_persistent_small |
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.insert(parent_set, params.clone()); |
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params |
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} |
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} |
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} |
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} |
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} |
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pub struct CTPC<P: ParameterLearning> { |
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parameter_learning: P, |
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Ftest: F, |
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Chi2test: ChiSquare, |
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} |
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impl<P: ParameterLearning> CTPC<P> { |
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pub fn new(parameter_learning: P, Ftest: F, Chi2test: ChiSquare) -> CTPC<P> { |
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CTPC { |
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parameter_learning, |
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Ftest, |
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Chi2test, |
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} |
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} |
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} |
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impl<P: ParameterLearning> StructureLearningAlgorithm for CTPC<P> { |
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fn fit_transform<T>(&self, net: T, dataset: &Dataset) -> T |
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where |
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T: process::NetworkProcess, |
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{ |
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//Check the coherence between dataset and network
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if net.get_number_of_nodes() != dataset.get_trajectories()[0].get_events().shape()[1] { |
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panic!("Dataset and Network must have the same number of variables.") |
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} |
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//Make the network mutable.
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let mut net = net; |
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net.initialize_adj_matrix(); |
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let mut learned_parent_sets: Vec<(usize, BTreeSet<usize>)> = vec![]; |
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learned_parent_sets.par_extend(net.get_node_indices().into_par_iter().map(|child_node| { |
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let mut cache = Cache::new(&self.parameter_learning); |
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let mut candidate_parent_set: BTreeSet<usize> = net |
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.get_node_indices() |
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.into_iter() |
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.filter(|x| x != &child_node) |
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.collect(); |
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let mut separation_set_size = 0; |
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while separation_set_size < candidate_parent_set.len() { |
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let mut candidate_parent_set_TMP = candidate_parent_set.clone(); |
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for parent_node in candidate_parent_set.iter() { |
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for separation_set in candidate_parent_set |
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.iter() |
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.filter(|x| x != &parent_node) |
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.map(|x| *x) |
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.combinations(separation_set_size) |
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{ |
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let separation_set = separation_set.into_iter().collect(); |
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if self.Ftest.call( |
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&net, |
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child_node, |
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*parent_node, |
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&separation_set, |
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dataset, |
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&mut cache, |
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) && self.Chi2test.call( |
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&net, |
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child_node, |
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*parent_node, |
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&separation_set, |
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dataset, |
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&mut cache, |
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) { |
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candidate_parent_set_TMP.remove(parent_node); |
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break; |
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} |
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} |
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} |
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candidate_parent_set = candidate_parent_set_TMP; |
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separation_set_size += 1; |
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} |
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(child_node, candidate_parent_set) |
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})); |
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for (child_node, candidate_parent_set) in learned_parent_sets { |
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for parent_node in candidate_parent_set.iter() { |
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net.add_edge(*parent_node, child_node); |
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} |
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} |
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net |
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} |
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} |
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