feat: Suggestions for the unified platform

benches/bimodal_ke.rs

@@ -1,6 +1,6 @@
 use anyhow::Result;
 use criterion::{criterion_group, criterion_main, Criterion};
-use pmcore::prelude::*;
+use pmcore::{algorithms::Algorithm, prelude::*};
 
 use std::hint::black_box;
 
@@ -22,74 +22,87 @@ fn create_equation() -> equation::ODE {
     }
 }
 
-fn create_error_model() -> AssayErrorModel {
-    AssayErrorModel::additive(ErrorPoly::new(0.0, 0.5, 0.0, 0.0), 0.0)
-}
-
 fn load_data() -> Result<data::Data> {
     Ok(data::read_pmetrics("examples/bimodal_ke/bimodal_ke.csv")?)
 }
 
-fn setup_npag() -> Result<EstimationProblem<equation::ODE>> {
+fn setup_npag() -> Result<EstimationProblem<equation::ODE, NonParametric>> {
     let data = load_data()?;
-    EstimationProblem::builder(create_equation(), data)
-        .parameter(Parameter::bounded("ke", 0.001, 3.0))?
-        .parameter(Parameter::bounded("v", 25.0, 250.0))?
-        .method(Npag::new())
-        .error("outeq_1", create_error_model())?
-        .progress(false)
-        .prior(Prior::sobol(2048, 22))
-        .build()
+    let parameters = ParameterSpace::bounded()
+        .add("ke", 0.001, 3.0)
+        .add("v", 25.0, 250.0);
+    let prior = Theta::sobol_default(&parameters)?;
+    let error_models = AssayErrorModels::new().add(
+        "outeq_1",
+        AssayErrorModel::additive(ErrorPoly::new(0.0, 0.5, 0.0, 0.0), 0.0),
+    )?;
+    EstimationProblem::nonparametric(create_equation(), data, prior, error_models)
 }
 
-fn setup_npod() -> Result<EstimationProblem<equation::ODE>> {
+fn setup_npod() -> Result<EstimationProblem<equation::ODE, NonParametric>> {
     let data = load_data()?;
-    EstimationProblem::builder(create_equation(), data)
-        .parameter(Parameter::bounded("ke", 0.001, 3.0))?
-        .parameter(Parameter::bounded("v", 25.0, 250.0))?
-        .method(Npod::new())
-        .error("outeq_1", create_error_model())?
-        .progress(false)
-        .prior(Prior::sobol(2048, 22))
-        .build()
+    let parameters = ParameterSpace::bounded()
+        .add("ke", 0.001, 3.0)
+        .add("v", 25.0, 250.0);
+    let prior = Theta::sobol_default(&parameters)?;
+    let error_models = AssayErrorModels::new().add(
+        "outeq_1",
+        AssayErrorModel::additive(ErrorPoly::new(0.0, 0.5, 0.0, 0.0), 0.0),
+    )?;
+    EstimationProblem::nonparametric(create_equation(), data, prior, error_models)
 }
 
-fn setup_postprob() -> Result<EstimationProblem<equation::ODE>> {
+fn setup_postprob() -> Result<EstimationProblem<equation::ODE, NonParametric>> {
     let data = load_data()?;
-    EstimationProblem::builder(create_equation(), data)
-        .parameter(Parameter::bounded("ke", 0.001, 3.0))?
-        .parameter(Parameter::bounded("v", 25.0, 250.0))?
-        .method(PostProb::new())
-        .error("outeq_1", create_error_model())?
-        .progress(false)
-        .prior(Prior::sobol(2048, 22))
-        .build()
+    let parameters = ParameterSpace::bounded()
+        .add("ke", 0.001, 3.0)
+        .add("v", 25.0, 250.0);
+    let prior = Theta::sobol_default(&parameters)?;
+    let error_models = AssayErrorModels::new().add(
+        "outeq_1",
+        AssayErrorModel::additive(ErrorPoly::new(0.0, 0.5, 0.0, 0.0), 0.0),
+    )?;
+    EstimationProblem::nonparametric(create_equation(), data, prior, error_models)
 }
 
-fn benchmark_algorithm<F>(c: &mut Criterion, bench_name: &str, setup_fn: F)
+fn benchmark_algorithm<F, A>(c: &mut Criterion, bench_name: &str, setup_fn: F, config: A)
 where
-    F: Fn() -> Result<EstimationProblem<equation::ODE>>,
+    F: Fn() -> Result<EstimationProblem<equation::ODE, NonParametric>>,
+    A: Algorithm<equation::ODE, NonParametric> + Clone,
 {
-    let problem = setup_fn().unwrap();
-
     c.bench_function(bench_name, |b| {
         b.iter_with_setup(
-            || problem.clone(),
-            |problem| black_box(problem.fit().unwrap()),
+            || setup_fn().unwrap(),
+            |problem| black_box(problem.fit_with(config.clone()).unwrap()),
         )
     });
 }
 
 fn benchmark_bimodal_ke_npag(c: &mut Criterion) {
-    benchmark_algorithm(c, "bimodal_ke_npag", setup_npag);
+    benchmark_algorithm(
+        c,
+        "bimodal_ke_npag",
+        setup_npag,
+        NonParametricAlgorithm::npag(),
+    );
 }
 
 fn benchmark_bimodal_ke_npod(c: &mut Criterion) {
-    benchmark_algorithm(c, "bimodal_ke_npod", setup_npod);
+    benchmark_algorithm(
+        c,
+        "bimodal_ke_npod",
+        setup_npod,
+        NonParametricAlgorithm::npod(),
+    );
 }
 
 fn benchmark_bimodal_ke_postprob(c: &mut Criterion) {
-    benchmark_algorithm(c, "bimodal_ke_postprob", setup_postprob);
+    benchmark_algorithm(
+        c,
+        "bimodal_ke_postprob",
+        setup_postprob,
+        NonParametricAlgorithm::npmap(),
+    );
 }
 
 criterion_group! {
@@ -98,3 +111,4 @@ criterion_group! {
     targets = benchmark_bimodal_ke_npag, benchmark_bimodal_ke_npod, benchmark_bimodal_ke_postprob
 }
 criterion_main!(benches);
+

examples/bestdose.rs

@@ -20,9 +20,9 @@ fn main() -> Result<()> {
         },
     };
 
-    let parameter_space = ParameterSpace::new()
-        .add(Parameter::bounded("ke", 0.001, 3.0))
-        .add(Parameter::bounded("v", 25.0, 250.0));
+    let parameter_space = ParameterSpace::<BoundedParameter>::new()
+        .add("ke", 0.001, 3.0)
+        .add("v", 25.0, 250.0);
 
     let ems = AssayErrorModels::new().add(
         0,
@@ -65,7 +65,8 @@ fn main() -> Result<()> {
         .observation(18.0, conc(6.0, 75.0) + conc(18.0, 150.0), 0)
         .build();
 
-    let (theta, prior) = read_prior("examples/bimodal_ke/output/theta.csv", &parameter_space)?;
+    let (theta, prior) =
+        Theta::from_file("examples/bimodal_ke/output/theta.csv", &parameter_space)?;
 
     let posterior = BestDosePosterior::compute(
         &theta,

examples/bestdose_auc.rs

@@ -28,9 +28,9 @@ fn main() -> Result<()> {
     };
 
     // Minimal parameter ranges
-    let parameter_space = ParameterSpace::new()
-        .add(Parameter::bounded("ke", 0.001, 3.0))
-        .add(Parameter::bounded("v", 25.0, 250.0));
+    let parameter_space = ParameterSpace::<BoundedParameter>::new()
+        .add("ke", 0.001, 3.0)
+        .add("v", 25.0, 250.0);
 
     let ems = AssayErrorModels::new().add(
         0,
@@ -43,7 +43,10 @@ fn main() -> Result<()> {
 
     // Load realistic prior from previous NPAG run (47 support points)
     println!("Loading prior from bimodal_ke example...");
-    let (theta, prior) = read_prior("examples/bimodal_ke/output/theta.csv", &parameter_space)?;
+    let (theta, prior) = Theta::from_file(
+        "examples/bimodal_ke/output/theta.csv",
+        &parameter_space,
+    )?;
     let weights = prior.as_ref().unwrap();
 
     println!("Prior: {} support points\n", theta.matrix().nrows());
@@ -118,9 +121,8 @@ fn main() -> Result<()> {
         }
     }
 
-    // =========================================================================
     // EXAMPLE 2: Interval AUC (AUCFromLastDose)
-    // =========================================================================
+
     println!("\n\n");
     println!("════════════════════════════════════════════════════════");
     println!("  EXAMPLE 2: Interval AUC (AUCFromLastDose)");

examples/bestdose_bounds.rs

@@ -27,9 +27,9 @@ fn main() -> Result<()> {
         },
     };
 
-    let parameter_space = ParameterSpace::new()
-        .add(Parameter::bounded("ke", 0.001, 3.0))
-        .add(Parameter::bounded("v", 25.0, 250.0));
+    let parameter_space = ParameterSpace::<BoundedParameter>::new()
+        .add("ke", 0.001, 3.0)
+        .add("v", 25.0, 250.0);
 
     let ems = AssayErrorModels::new().add(
         0,
@@ -40,7 +40,10 @@ fn main() -> Result<()> {
 
     // Load realistic prior from previous NPAG run
     println!("Loading prior from bimodal_ke example...");
-    let (theta, prior) = read_prior("examples/bimodal_ke/output/theta.csv", &parameter_space)?;
+    let (theta, prior) = Theta::from_file(
+        "examples/bimodal_ke/output/theta.csv",
+        &parameter_space,
+    )?;
     let weights = prior.as_ref().unwrap();
 
     println!("Prior: {} support points\n", theta.matrix().nrows());

examples/bestdose_cov.rs

@@ -1,7 +1,5 @@
 use anyhow::Result;
-use pmcore::bestdose; // bestdose new
-                      // use pmcore::bestdose::bestdose_old as bestdose; // bestdose old
-
+use pmcore::bestdose::{BestDoseConfig, BestDosePosterior, DoseRange, Target};
 use pmcore::prelude::*;
 
 fn main() -> Result<()> {
@@ -23,17 +21,16 @@ fn main() -> Result<()> {
         },
     };
 
-    let parameter_space = ParameterSpace::new()
-        .add(Parameter::bounded("ke", 0.001, 3.0))
-        .add(Parameter::bounded("v", 25.0 / 70.0, 250.0 / 70.0));
+    let parameter_space = ParameterSpace::<BoundedParameter>::new()
+        .add("ke", 0.001, 3.0)
+        .add("v", 25.0 / 70.0, 250.0 / 70.0);
 
     let ems = AssayErrorModels::new().add(
         0,
         AssayErrorModel::additive(ErrorPoly::new(0.0, 0.20, 0.0, 0.0), 0.0),
     )?;
 
-    let config =
-        bestdose::BestDoseConfig::new(parameter_space.clone(), ems.clone()).with_progress(false);
+    let config = BestDoseConfig::new(parameter_space.clone(), ems.clone()).with_progress(false);
 
     // Generate a patient with known parameters
     // Ke = 0.5, V = 50
@@ -93,29 +90,22 @@ fn main() -> Result<()> {
         .observation(18.0, conc(6.0, 75.0) + conc(18.0, 150.0), 0)
         .build();
 
-    let (mut theta, prior) = read_prior("examples/bimodal_ke/output/theta.csv", &parameter_space)?;
+    let (mut theta, prior) = Theta::from_file(
+        "examples/bimodal_ke/output/theta.csv",
+        &parameter_space,
+    )?;
 
     let m_t = theta.matrix_mut();
     for i in 0..m_t.nrows() {
         m_t[(i, 1)] /= 70.0;
     }
 
-    // Example usage - using new() constructor which calculates NPAGFULL11 posterior
-    // max_cycles controls NPAGFULL refinement:
-    //   0 = NPAGFULL11 only (fast but less accurate)
-    //   100 = moderate refinement
-    //   500 = full refinement (Fortran default, slow but most accurate)
-    let problem = bestdose::BestDoseProblem::new(
+    let posterior = BestDosePosterior::compute(
         &theta,
         &prior.unwrap(),
         Some(past_data.clone()), // Optional: past data for Bayesian updating
-        target_data.clone(),
-        None,
         eq.clone(),
-        bestdose::DoseRange::new(0.0, 300.0),
-        0.0,
         config.clone(),
-        bestdose::Target::Concentration, // Target concentrations (not AUCs)
     )?;
 
     println!("Optimizing dose...");
@@ -125,7 +115,13 @@ fn main() -> Result<()> {
 
     for bias_weight in &bias_weights {
         println!("Running optimization with bias weight: {}", bias_weight);
-        let optimal = problem.clone().with_bias_weight(*bias_weight).optimize()?;
+        let optimal = posterior.optimize(
+            target_data.clone(),
+            None,
+            DoseRange::new(0.0, 300.0),
+            *bias_weight,
+            Target::Concentration,
+        )?;
         results.push((bias_weight, optimal));
     }
 

examples/bimodal_ke/main.rs

@@ -1,8 +1,8 @@
 use anyhow::Result;
-use pmcore::{output::logging::Logger, prelude::*};
+use pmcore::prelude::*;
 
 fn main() -> Result<()> {
-    Logger::new().init()?;
+    Logger::new().stdout(true).init()?;
 
     let eq = ode! {
         name: "bimodal_ke",
@@ -23,15 +23,20 @@ fn main() -> Result<()> {
 
     let data = data::read_pmetrics("examples/bimodal_ke/bimodal_ke.csv")?;
 
-    let _result = EstimationProblem::builder(eq, data)
-        .method(Npag::default())
-        .parameter(Parameter::bounded("ke", 0.001, 3.0))?
-        .parameter(Parameter::bounded("v", 25.0, 250.0))?
-        .error(
-            "outeq_1",
-            AssayErrorModel::additive(ErrorPoly::new(0.0, 0.5, 0.0, 0.0), 0.0),
-        )?
-        .fit()?;
+    let parameters = ParameterSpace::bounded()
+        .add("ke", 0.001, 3.0)
+        .add("v", 25.0, 250.0);
+
+    let prior = Theta::sobol_default(&parameters)?;
+
+    let error_models = AssayErrorModels::new().add(
+        "outeq_1",
+        AssayErrorModel::additive(ErrorPoly::new(0.0, 0.5, 0.0, 0.0), 0.0),
+    )?;
+
+    let problem = EstimationProblem::nonparametric(eq, data, prior, error_models)?;
+
+    let _result = problem.fit_with(NonParametricAlgorithm::npag())?;
 
     Ok(())
 }

examples/bimodal_ke_backend_compare.rs

@@ -202,17 +202,16 @@ fn run_case<E: pharmsol::Equation + Clone + Send + 'static + EquationMetadataSou
 ) -> Result<ComparisonResult> {
     let data = data::read_pmetrics(DATA_PATH)?;
     let fit_started = Instant::now();
-    let result = EstimationProblem::builder(equation, data)
-        .parameter(Parameter::bounded("ke", 0.001, 3.0))?
-        .parameter(Parameter::bounded("v", 25.0, 250.0))?
-        .method(Npag::new())
-        .error(
-            "outeq_1",
-            AssayErrorModel::additive(ErrorPoly::new(0.0, 0.5, 0.0, 0.0), 0.0),
-        )?
-        .cache(true)
-        .progress(false)
-        .fit()?;
+    let parameters = ParameterSpace::bounded()
+        .add("ke", 0.001, 3.0)
+        .add("v", 25.0, 250.0);
+    let prior = Theta::sobol_default(&parameters)?;
+    let error_models = AssayErrorModels::new().add(
+        "outeq_1",
+        AssayErrorModel::additive(ErrorPoly::new(0.0, 0.5, 0.0, 0.0), 0.0),
+    )?;
+    let result = EstimationProblem::nonparametric(equation, data, prior, error_models)?
+        .fit_with(NonParametricAlgorithm::npag())?;
     let fit_time = fit_started.elapsed();
 
     summarize_result(label, compile_time, fit_time, &result)
@@ -222,12 +221,8 @@ fn summarize_result<E: pharmsol::Equation>(
     label: &'static str,
     compile_time: Duration,
     fit_time: Duration,
-    result: &FitResult<E>,
+    result: &NonParametricResult<E>,
 ) -> Result<ComparisonResult> {
-    result
-        .as_nonparametric()
-        .ok_or_else(|| anyhow!("expected nonparametric result for {label}"))?;
-
     Ok(ComparisonResult {
         label,
         compile_time,
@@ -318,3 +313,4 @@ fn print_summary(results: &[ComparisonResult]) -> Result<()> {
 
     Ok(())
 }
+