Cargo.toml

@@ -6,7 +6,6 @@ edition = "2021"
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 
 [dependencies]
-
 ndarray = {version="*", features=["approx"]}
 thiserror = "*"
 rand = "*"

src/parameter_learning.rs

@@ -153,3 +153,20 @@ impl ParameterLearning for BayesianApproach {
         return (CIM, M, T);
     }
 }
+
+
+pub struct Cache<P: ParameterLearning> {
+    parameter_learning: P,
+    dataset: tools::Dataset,
+}
+
+impl<P: ParameterLearning> Cache<P> {
+    pub fn fit<T:network::Network>(
+        &mut self,
+        net: &T,
+        node: usize,
+        parent_set: Option<BTreeSet<usize>>,
+    ) -> (Array3<f64>, Array3<usize>, Array2<f64>) {
+        self.parameter_learning.fit(net, &self.dataset, node, parent_set)
+    }
+}

src/structure_learning.rs

@@ -1,5 +1,7 @@
 pub mod score_function;
 pub mod score_based_algorithm;
+pub mod constraint_based_algorithm;
+pub mod hypothesis_test;
 use crate::network;
 use crate::tools;
 

src/structure_learning/constraint_based_algorithm.rs

@@ -0,0 +1,5 @@
+
+//pub struct CTPC {
+//
+//}
+

src/structure_learning/hypothesis_test.rs

@@ -0,0 +1,135 @@
+use ndarray::Array2;
+use ndarray::Array3;
+use ndarray::Axis;
+use statrs::distribution::{ChiSquared, ContinuousCDF};
+
+use crate::network;
+use crate::parameter_learning;
+use crate::params::ParamsTrait;
+use std::collections::BTreeSet;
+
+pub trait HypothesisTest {
+
+    fn call<T, P>(
+        &self,
+        net: &T,
+        child_node: usize,
+        parent_node: usize,
+        separation_set: &BTreeSet<usize>,
+        cache: &mut parameter_learning::Cache<P>
+    ) -> bool
+    where
+        T: network::Network,
+        P: parameter_learning::ParameterLearning;
+
+}
+
+
+pub struct ChiSquare {
+    alpha: f64,
+}
+
+pub struct F {
+
+}
+
+impl ChiSquare {
+    pub fn new( alpha: f64) -> ChiSquare {
+        ChiSquare {
+            alpha
+        }
+    }
+    pub fn compare_matrices(
+        &self,
+        i: usize,
+        M1: &Array3<usize>,
+        j: usize,
+        M2: &Array3<usize>
+    ) -> bool {
+        // Bregoli, A., Scutari, M. and Stella, F., 2021.
+        // A constraint-based algorithm for the structural learning of
+        // continuous-time Bayesian networks.
+        // International Journal of Approximate Reasoning, 138, pp.105-122.
+        // Also: https://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/chi2samp.htm
+        //
+        // M  = M            M  = M
+        //  1    xx'|s        2    xx'|y,s
+        let M1 = M1.index_axis(Axis(0), i).mapv(|x| x as f64);
+        let M2 = M2.index_axis(Axis(0), j).mapv(|x| x as f64);
+        //                   __________________
+        //                  /    ===
+        //                 /     \       M
+        //                /      /        xx'|s
+        //               /       ===
+        //              /    x'ϵVal /X \
+        //             /            \ i/                  1
+        //K =         /      ------------------       L = -
+        //           /           ===                      K
+        //          /            \       M
+        //         /             /        xx'|y,s
+        //        /              ===
+        //       /           x'ϵVal /X \
+        //   \  /                   \ i/
+        //    \/
+        let K = M1.sum_axis(Axis(1)) / M2.sum_axis(Axis(1));
+        let K = K.mapv(f64::sqrt);
+        // Reshape to column vector.
+        let K = {
+            let n = K.len();
+            K.into_shape((n, 1)).unwrap()
+        };
+        let L = 1.0 / &K;
+        //        =====                   2
+        //         \      (K . M  - L . M)
+        //          \           2        1
+        //          /      ---------------
+        //         /           M  + M
+        //        =====         2    1
+        //     x'ϵVal /X \
+        //            \ i/
+        let mut X_2 = ( &K * &M2 - &L * &M1 ).mapv(|a| a.powi(2)) / (&M2 + &M1);
+        println!("M1: {:?}", M1);
+        println!("M2: {:?}", M2);
+        println!("L*M1: {:?}", (L * &M1));
+        println!("K*M2: {:?}", (K * &M2));
+        println!("X_2: {:?}", X_2);
+        X_2.diag_mut().fill(0.0);
+        let X_2 = X_2.sum_axis(Axis(1));
+        let n = ChiSquared::new((X_2.dim() - 1) as f64).unwrap();
+        println!("CHI^2: {:?}", n);
+        println!("CHI^2 CDF: {:?}", X_2.mapv(|x| n.cdf(x)));
+        X_2.into_iter().all(|x| n.cdf(x) < (1.0 - self.alpha))
+    }
+}
+
+impl HypothesisTest for ChiSquare {
+    fn call<T, P>(
+        &self,
+        net: &T,
+        child_node: usize,
+        parent_node: usize,
+        separation_set: &BTreeSet<usize>,
+        cache: &mut parameter_learning::Cache<P>
+    ) -> bool
+    where
+        T: network::Network,
+        P: parameter_learning::ParameterLearning {
+        // Prendo dalla cache l'apprendimento dei parametri, che sarebbe una CIM
+        // di dimensione nxn
+        //  (CIM, M, T)
+        let ( _, M_small, _) = cache.fit(net, child_node, Some(separation_set.clone()));
+        // 
+        let mut extended_separation_set = separation_set.clone();
+        extended_separation_set.insert(parent_node);
+        let ( _, M_big, _) = cache.fit(net, child_node, Some(extended_separation_set.clone()));
+        // Commentare qui
+        let partial_cardinality_product:usize = extended_separation_set.iter().take_while(|x| **x != parent_node).map(|x| net.get_node(*x).get_reserved_space_as_parent()).product();
+        for idx_M_big in 0..M_big.shape()[0] {
+            let idx_M_small: usize = idx_M_big%partial_cardinality_product + (idx_M_big/(partial_cardinality_product*net.get_node(parent_node).get_reserved_space_as_parent()))*partial_cardinality_product;
+            if ! self.compare_matrices(idx_M_small, &M_small, idx_M_big, &M_big) {
+                return false;
+            }
+        }
+        return true;
+    }
+}

tests/structure_learning.rs

@@ -7,6 +7,7 @@ use reCTBN::network::Network;
 use reCTBN::params;
 use reCTBN::structure_learning::score_function::*;
 use reCTBN::structure_learning::{score_based_algorithm::*, StructureLearningAlgorithm};
+use reCTBN::structure_learning::hypothesis_test::*;
 use reCTBN::tools::*;
 use std::collections::BTreeSet;
 
@@ -315,3 +316,75 @@ pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic_1_parent_constraint()
     let hl = HillClimbing::new(bic, Some(1));
     learn_mixed_discrete_net_3_nodes_1_parent_constraint(hl);
 }
+
+#[test]
+pub fn chi_square_compare_matrices () {
+    let i: usize = 1;
+    let M1 = arr3(&[
+       [[ 0,  2,  3],
+        [ 4,  0,  6],
+        [ 7,  8,  0]],
+       [[0, 12,  90],
+        [ 3, 0,  40],
+        [ 6, 40,  0]],
+       [[ 0,  2,  3],
+        [ 4,  0,  6],
+        [ 44, 66, 0]]
+    ]);
+    let j: usize = 0;
+    let M2 = arr3(&[
+       [[ 0,  200,  300],
+        [ 400,  0,  600],
+        [  700, 800,  0]]
+    ]);
+    let chi_sq = ChiSquare::new(0.1);
+    assert!(!chi_sq.compare_matrices( i, &M1, j, &M2));
+}
+
+#[test]
+pub fn chi_square_compare_matrices_2 () {
+    let i: usize = 1;
+    let M1 = arr3(&[
+       [[ 0,  2,  3],
+        [ 4,  0,  6],
+        [ 7,  8,  0]],
+       [[0, 20,  30],
+        [ 40, 0,  60],
+        [ 70, 80,  0]],
+       [[ 0,  2,  3],
+        [ 4,  0,  6],
+        [ 44, 66, 0]]
+    ]);
+    let j: usize = 0;
+    let M2 = arr3(&[
+       [[ 0,  200,  300],
+        [ 400,  0,  600],
+        [  700, 800,  0]]
+    ]);
+    let chi_sq = ChiSquare::new(0.1);
+    assert!(chi_sq.compare_matrices( i, &M1, j, &M2));
+}
+
+#[test]
+pub fn chi_square_compare_matrices_3 () {
+    let i: usize = 1;
+    let M1 = arr3(&[
+       [[ 0,  2,  3],
+        [ 4,  0,  6],
+        [ 7,  8,  0]],
+       [[0, 21,  31],
+        [ 41, 0,  59],
+        [ 71, 79,  0]],
+       [[ 0,  2,  3],
+        [ 4,  0,  6],
+        [ 44, 66, 0]]
+    ]);
+    let j: usize = 0;
+    let M2 = arr3(&[
+       [[ 0,  200,  300],
+        [ 400,  0,  600],
+        [  700, 800,  0]]
+    ]);
+    let chi_sq = ChiSquare::new(0.1);
+    assert!(chi_sq.compare_matrices( i, &M1, j, &M2));
+}