Ability to execute a battery of benchmarks from a json and print results to a csv

.gitignore

@@ -1,2 +1,8 @@
 /target
 Cargo.lock
+
+*.json
+*.csv
+
+# Heaptrack
+*.zst

Cargo.toml

@@ -6,3 +6,5 @@ edition = "2021"
 [dependencies]
 reCTBN = { path = "deps/reCTBN/reCTBN", package = "reCTBN", version="0.1.0" }
 json = "0.12.*"
+csv = "1.2.*"
+duration-string = "0.2.*"

src/main.rs

@@ -1,6 +1,11 @@
 #![allow(non_snake_case)]
 
+use csv;
+use duration_string::DurationString;
+use json;
 use std::collections::BTreeSet;
+use std::fs;
+use std::time::Instant;
 
 use reCTBN::parameter_learning::MLE;
 use reCTBN::params::DiscreteStatesContinousTimeParams;
@@ -17,10 +22,19 @@ use reCTBN::tools::RandomParametersGenerator;
 use reCTBN::tools::UniformGraphGenerator;
 use reCTBN::tools::UniformParametersGenerator;
 
-fn uniform_parameters_generator_right_densities_ctmp() -> (CtbnNetwork, Dataset) {
+fn uniform_parameters_generator_right_densities_ctmp(
+    nodes_cardinality: usize,
+    domain_cardinality: usize,
+    density: f64,
+    interval_s: f64,
+    interval_f: f64,
+    n_trajectories: u64,
+    t_end: f64,
+    sg_seed: Option<u64>,
+    cg_seed: Option<u64>,
+    tg_seed: Option<u64>,
+) -> (CtbnNetwork, Dataset) {
     let mut net = CtbnNetwork::new();
-    let nodes_cardinality = 20;
-    let domain_cardinality = 3;
     for node in 0..nodes_cardinality {
         // Create the domain for a discrete node
         let mut domain = BTreeSet::new();
@@ -37,22 +51,41 @@ fn uniform_parameters_generator_right_densities_ctmp() -> (CtbnNetwork, Dataset)
 
     // Initialize the Graph Generator using the one with an
     // uniform distribution
-    let mut structure_generator = UniformGraphGenerator::new(1.0 / 3.0, Some(7641630759785120));
+    let mut structure_generator = UniformGraphGenerator::new(density, sg_seed);
 
     // 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));
+    let mut cim_generator = UniformParametersGenerator::new(interval_s..interval_f, cg_seed);
 
     // Generate CIMs with uniformly distributed parameters.
     cim_generator.generate_parameters(&mut net);
 
-    let dataset = trajectory_generator(&net, 300, 200.0, Some(30230423));
+    let dataset = trajectory_generator(&net, n_trajectories, t_end, tg_seed);
 
     return (net, dataset);
 }
 
+fn write_csv_record<I, T>(file_name: &str, record: I) -> std::io::Result<()>
+where
+    I: IntoIterator<Item = T>,
+    T: AsRef<[u8]>,
+{
+    let file = fs::OpenOptions::new()
+        .write(true)
+        .create(true)
+        .append(true)
+        .open(file_name)
+        .unwrap();
+    let mut wtr = csv::Writer::from_writer(file);
+
+    //let mut wtr = csv::Writer::from_path(file_name)?;
+    wtr.write_record(record)?;
+    wtr.flush()?;
+    Ok(())
+}
+
 fn structure_learning_CTPC(net: CtbnNetwork, dataset: &Dataset) {
     // Initialize the hypothesis tests to pass to the CTPC with their
     // respective significance level `alpha`
@@ -67,6 +100,72 @@ fn structure_learning_CTPC(net: CtbnNetwork, dataset: &Dataset) {
 }
 
 fn main() {
-    let (net, dataset) = uniform_parameters_generator_right_densities_ctmp();
+    let file_path = "./networks-settings.json";
+    let csv_path = "./results.csv";
+    eprintln!("Opening file {}...", file_path);
+    let file_content =
+        fs::read_to_string(file_path).expect("File not found! Check the README for instructions!");
+    let parsed_json = json::parse(&file_content).unwrap();
+
+    write_csv_record(
+        csv_path,
+        &[
+            "nodes_cardinality",
+            "domain_cardinality",
+            "density",
+            "duration",
+        ],
+    )
+    .unwrap();
+
+    let benchmarks_cardinality = parsed_json.len();
+    for idx in 0..benchmarks_cardinality {
+        eprintln!(
+            "[{}/{}] Generating network...",
+            idx + 1,
+            benchmarks_cardinality
+        );
+        let (net, dataset) = uniform_parameters_generator_right_densities_ctmp(
+            parsed_json[idx]["nodes_cardinality"].as_usize().unwrap(),
+            parsed_json[idx]["domain_cardinality"].as_usize().unwrap(),
+            parsed_json[idx]["density"].as_f64().unwrap(),
+            parsed_json[idx]["interval_s"].as_f64().unwrap(),
+            parsed_json[idx]["interval_f"].as_f64().unwrap(),
+            parsed_json[idx]["n_trajectories"].as_u64().unwrap(),
+            parsed_json[idx]["t_end"].as_f64().unwrap(),
+            parsed_json[idx]["sg_seed"].as_u64(),
+            parsed_json[idx]["cg_seed"].as_u64(),
+            parsed_json[idx]["tg_seed"].as_u64(),
+        );
+        eprintln!(
+            "[{}/{}] Structure learning CTPC...",
+            idx + 1,
+            benchmarks_cardinality
+        );
+        let start = Instant::now();
         structure_learning_CTPC(net, &dataset);
+        let duration = start.elapsed();
+        eprintln!(
+            "[{}/{}] Strucure learned with CTPC in {:?}.",
+            idx + 1,
+            benchmarks_cardinality,
+            duration
+        );
+        write_csv_record(
+            csv_path,
+            &[
+                parsed_json[idx]["nodes_cardinality"]
+                    .as_usize()
+                    .unwrap()
+                    .to_string(),
+                parsed_json[idx]["domain_cardinality"]
+                    .as_usize()
+                    .unwrap()
+                    .to_string(),
+                parsed_json[idx]["density"].as_f64().unwrap().to_string(),
+                DurationString::from(duration).to_string(),
+            ],
+        )
+        .unwrap();
+    }
 }