reCTBN/Cargo.toml

@@ -18,3 +18,8 @@ rayon = "~1.6"
 
 [dev-dependencies]
 approx = { package = "approx", version = "~0.5" }
+criterion = "0.4.*"
+
+[[bench]]
+name = "structure_learning"
+harness = false

reCTBN/benches/structure_learning.rs

@@ -0,0 +1,88 @@
+#![allow(non_snake_case)]
+
+use std::collections::BTreeSet;
+use std::time::Duration;
+use criterion::{criterion_group, criterion_main, Criterion};
+
+use reCTBN::params::DiscreteStatesContinousTimeParams;
+use reCTBN::params::Params::DiscreteStatesContinousTime;
+use reCTBN::process::NetworkProcess;
+use reCTBN::parameter_learning::BayesianApproach;
+use reCTBN::process::ctbn::CtbnNetwork;
+use reCTBN::structure_learning::constraint_based_algorithm::CTPC;
+use reCTBN::structure_learning::hypothesis_test::{ChiSquare, F};
+use reCTBN::structure_learning::StructureLearningAlgorithm;
+use reCTBN::tools::trajectory_generator;
+use reCTBN::tools::Dataset;
+use reCTBN::tools::RandomGraphGenerator;
+use reCTBN::tools::RandomParametersGenerator;
+use reCTBN::tools::UniformGraphGenerator;
+use reCTBN::tools::UniformParametersGenerator;
+
+
+fn uniform_parameters_generator_right_densities_ctmp() -> (CtbnNetwork, Dataset) {
+    let mut net = CtbnNetwork::new();
+    let nodes_cardinality = 5;
+    let domain_cardinality = 3;
+    for node in 0..nodes_cardinality {
+        // Create the domain for a discrete node
+        let mut domain = BTreeSet::new();
+        for dvalue in 0..domain_cardinality {
+            domain.insert(dvalue.to_string());
+        }
+        // Create the parameters for a discrete node using the domain
+        let param = DiscreteStatesContinousTimeParams::new(node.to_string(), domain);
+        //Create the node using the parameters
+        let node = DiscreteStatesContinousTime(param);
+        // Add the node to the network
+        net.add_node(node).unwrap();
+    }
+
+    // Initialize the Graph Generator using the one with an
+    // uniform distribution
+    let mut structure_generator = UniformGraphGenerator::new(1.0 / 3.0, Some(7641630759785120));
+
+    // Generate the graph directly on the network
+    structure_generator.generate_graph(&mut net);
+
+    // Initialize the parameters generator with uniform distributin
+    let mut cim_generator = UniformParametersGenerator::new(3.0..7.0, Some(7641630759785120));
+
+    // Generate CIMs with uniformly distributed parameters.
+    cim_generator.generate_parameters(&mut net);
+
+    let dataset = trajectory_generator(&net, 300, 200.0, Some(30230423));
+
+    return (net, dataset);
+}
+
+fn structure_learning_CTPC(net: CtbnNetwork, dataset: &Dataset) {
+    // Initialize the hypothesis tests to pass to the CTPC with their
+    // respective significance level `alpha`
+    let f = F::new(1e-6);
+    let chi_sq = ChiSquare::new(1e-4);
+    // Use the bayesian approach to learn the parameters
+    let parameter_learning = BayesianApproach { alpha: 1, tau: 1.0 };
+    //Initialize CTPC
+    let ctpc = CTPC::new(parameter_learning, f, chi_sq);
+    // Learn the structure of the network from the generated trajectory
+    ctpc.fit_transform(net, dataset);
+}
+
+pub fn criterion_benchmark_ctpc(c: &mut Criterion) {
+    let mut group = c.benchmark_group("structure_learning_CTPC");
+    // Configure Criterion.rs to detect smaller differences and increase sample size to improve
+    // precision and counteract the resulting noise.
+    group.sample_size(10).measurement_time(Duration::from_secs(20));
+    group.bench_function("CTPC", move |b| {
+        b.iter_batched(
+            || uniform_parameters_generator_right_densities_ctmp(),
+            |(net, dataset)| structure_learning_CTPC(net, &dataset),
+            criterion::BatchSize::PerIteration,
+        )
+    });
+    group.finish();
+}
+
+criterion_group!(benches, criterion_benchmark_ctpc);
+criterion_main!(benches);