507 lines
14 KiB
Rust
507 lines
14 KiB
Rust
//! Comprehensive integration tests for RTX Science
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//!
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//! This test suite validates the complete RTX Science functionality including
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//! PINNs, molecular modeling, scientific computing, and integration with RTX.
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use rtx_autograd::Variable;
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use rtx_science::prelude::*;
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use rtx_tensor::{Device, Tensor};
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use std::collections::HashMap;
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/// Test PINN creation and basic functionality
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#[tokio::test]
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async fn test_pinn_heat_equation() -> Result<()> {
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let device = Device::cpu();
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let heat_eq = HeatEquation::new(0.1);
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let pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 32, 32, 1])
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.physics_loss(Box::new(heat_eq))
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.build()?;
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// Test forward pass
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let inputs_data: Vec<f32> = vec![0.5, 0.1, 0.3, 0.2];
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let inputs = Tensor::from_slice(&inputs_data, &[2, 2], &device)?;
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let outputs = pinn.predict(&inputs).await?;
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assert_eq!(outputs.shape().dims(), &[2, 1]);
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// Test physics residual computation - requires actual tensor operations
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// Skipping residual test as it requires more complex setup
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Ok(())
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}
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/// Test PINN with boundary conditions
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#[tokio::test]
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async fn test_pinn_with_boundary_conditions() -> Result<()> {
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let device = Device::cpu();
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let heat_eq = HeatEquation::new(0.1);
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let pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 64, 64, 1])
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.physics_loss(Box::new(heat_eq))
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.build()?;
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// Create boundary conditions
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let boundary_conditions = BoundaryConditions::dirichlet()
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.at_boundary(|x, _t| x * 2.0)
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.at_initial(|x| (x * std::f64::consts::PI).sin())
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.generate_samples(100)?;
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// Test boundary data generation
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let boundary_data = boundary_conditions.generate_samples(&device)?;
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assert!(boundary_data.coordinates.shape().dims()[0] > 0);
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Ok(())
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}
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/// Test wave equation PINN
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#[tokio::test]
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async fn test_wave_equation_pinn() -> Result<()> {
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let device = Device::cpu();
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let wave_eq = WaveEquation::new(1.0);
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let pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 48, 48, 1])
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.physics_loss(Box::new(wave_eq))
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.build()?;
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let inputs_data: Vec<f32> = vec![0.0, 0.0, 1.0, 0.5, 0.5, 1.0];
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let inputs = Tensor::from_slice(&inputs_data, &[3, 2], &device)?;
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let outputs = pinn.predict(&inputs).await?;
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assert_eq!(outputs.shape().dims(), &[3, 1]);
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Ok(())
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}
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/// Test conservation laws
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#[tokio::test]
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async fn test_conservation_laws() -> Result<()> {
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let device = Device::cpu();
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// Test mass conservation
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let mass_conservation = MassConservation::new(1e-6);
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let coords = Tensor::randn(&[50, 2], &device)?;
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let solution = Variable::new(Tensor::randn(&[50], &device)?, true);
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let du_dx = Variable::new(Tensor::randn(&[50], &device)?, true);
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let du_dt = Variable::new(Tensor::randn(&[50], &device)?, true);
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let loss = mass_conservation
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.compute_loss(&coords, &solution, &du_dx, &du_dt)
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.await?;
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assert!(loss >= 0.0);
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// Test energy conservation
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let energy_conservation = EnergyConservation::new(0.1, 1e-6);
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let energy_loss = energy_conservation
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.compute_loss(&coords, &solution, &du_dx, &du_dt)
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.await?;
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assert!(energy_loss >= 0.0);
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Ok(())
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}
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/// Test conservation validator
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#[tokio::test]
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async fn test_conservation_validator() -> Result<()> {
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let device = Device::cpu();
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let mut validator = ConservationValidator::new(1e-6, 10);
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// Add conservation laws
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validator = validator
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.add_conservation_law(Box::new(MassConservation::new(1e-6)))
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.add_conservation_law(Box::new(EnergyConservation::new(0.1, 1e-6)));
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// Create test data
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let coords = Tensor::randn(&[100, 2], &device)?;
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let solution = Variable::new(Tensor::randn(&[100], &device)?, true);
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let du_dx = Variable::new(Tensor::randn(&[100], &device)?, true);
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let du_dt = Variable::new(Tensor::randn(&[100], &device)?, true);
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// Validate conservation laws
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let results = validator
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.validate_all(&coords, &solution, &du_dx, &du_dt)
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.await?;
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// Results might be empty if validation frequency not met
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let summary = validator.summary();
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assert_eq!(summary["num_laws"], "2");
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Ok(())
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}
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/// Test molecular structure creation
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#[tokio::test]
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async fn test_molecular_structure() -> Result<()> {
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// Test molecule from SMILES
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let molecule = Molecule::from_smiles("ethanol".to_string(), "CCO")?;
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assert_eq!(molecule.id, "ethanol");
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assert!(molecule.smiles.is_some());
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// Test molecular properties
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let mw = molecule.molecular_weight();
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assert!(mw > 0.0);
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let formula = molecule.molecular_formula();
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assert!(formula.contains('C'));
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assert!(formula.contains('O'));
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// Test Lipinski compliance
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let lipinski = molecule.lipinski_compliance();
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assert!(lipinski.molecular_weight > 0.0);
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// Test feature matrix generation
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let features = molecule.to_feature_matrix()?;
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assert!(features.nrows() > 0);
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assert!(features.ncols() > 0);
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// Test adjacency matrix
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let adj = molecule.adjacency_matrix();
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assert_eq!(adj.nrows(), adj.ncols());
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Ok(())
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}
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/// Test molecular dataset functionality
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#[tokio::test]
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async fn test_molecular_dataset() -> Result<()> {
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let dataset = MolecularDataset::load("test_dataset.csv")?;
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assert!(!dataset.molecules.is_empty());
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assert!(!dataset.targets.is_empty());
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// Test dataset statistics
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assert!(dataset.statistics.num_molecules > 0);
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assert!(dataset.statistics.avg_molecular_weight > 0.0);
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// Test dataset splits
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let mut dataset = dataset;
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dataset.create_splits(0.7, 0.2, 0.1)?;
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assert!(dataset.splits.contains_key("train"));
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assert!(dataset.splits.contains_key("validation"));
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assert!(dataset.splits.contains_key("test"));
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Ok(())
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}
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/// Test molecular GNN
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#[tokio::test]
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async fn test_molecular_gnn() -> Result<()> {
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let device = Device::cpu();
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let gnn = MolecularGNN::builder()
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.device(&device)
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.node_features(74)
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.edge_features(12)
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.message_passing_layers(3)
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.readout_layers(vec![128, 64, 1])
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.build()?;
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let molecule = Molecule::from_smiles("test_mol".to_string(), "CC")?;
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let output = gnn.forward(&molecule).await?;
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assert!(output.shape().dims()[0] > 0);
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Ok(())
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}
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/// Test protein structure prediction
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#[tokio::test]
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async fn test_protein_structure_prediction() -> Result<()> {
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let predictor = ProteinStructurePredictor::new(StructurePredictionModel::CustomTransformer);
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let sequence = "MKFLVLLFNILCLFPVLAADNHGVGPQGASGVDPITLQPVLTGLSRIGGWEAELLTCVIGNGVLVLKGEHHNNLVKEVLLHRPGAPQVVPTGVVTMHDFTQDSGLQVQPTGAPSDPPEDGSTPVTATPATPATPS";
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let structure = predictor.predict_structure(sequence).await?;
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assert_eq!(structure.coordinates.len(), sequence.len());
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assert_eq!(structure.distances.len(), sequence.len());
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Ok(())
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}
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/// Test crystal structure modeling
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#[tokio::test]
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async fn test_crystal_structure() -> Result<()> {
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use rtx_science::materials::*;
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let lattice = CrystalLattice {
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a: 5.43,
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b: 5.43,
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c: 5.43,
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alpha: 90.0,
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beta: 90.0,
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gamma: 90.0,
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};
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let unit_cell = UnitCell {
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lattice: lattice.clone(),
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atoms: vec![
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AtomPosition {
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element: "Si".to_string(),
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position: [0.0, 0.0, 0.0],
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},
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AtomPosition {
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element: "Si".to_string(),
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position: [0.25, 0.25, 0.25],
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},
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],
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};
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assert_eq!(unit_cell.atoms.len(), 2);
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assert_eq!(unit_cell.lattice.a, 5.43);
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Ok(())
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}
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/// Test RTX integration
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#[tokio::test]
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async fn test_rtx_integration() -> Result<()> {
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let device = Device::cpu();
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let rtx_device = RTXDevice::new(device.clone());
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assert!(rtx_device.supports_double_precision());
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// Test scientific tensor creation
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let data = vec![1.0, 2.0, 3.0, 4.0];
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let tensor = rtx_device.tensor_from_data(&data, &[2, 2], Some("m".to_string()))?;
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assert_eq!(tensor.units, Some("m".to_string()));
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// Test statistical summary
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let summary = tensor.statistical_summary()?;
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assert_eq!(summary.sample_size, 4);
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assert!(summary.mean > 0.0);
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Ok(())
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}
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/// Test validation metrics
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#[tokio::test]
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async fn test_validation_metrics() -> Result<()> {
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let device = Device::cpu();
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let predictions = Tensor::from_slice(&[1.0, 2.0, 3.0, 4.0], &[4], &device)?;
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let targets = Tensor::from_slice(&[1.1, 1.9, 3.1, 3.9], &[4], &device)?;
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let metrics = ValidationMetrics::compute(&predictions, &targets)?;
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assert!(metrics.r_squared > 0.8); // Should be high for close predictions
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assert!(metrics.mae < 0.5);
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assert!(metrics.rmse < 0.5);
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assert!(metrics.mape < 20.0);
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Ok(())
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}
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/// Test benchmark suite
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#[tokio::test]
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async fn test_benchmark_suite() -> Result<()> {
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let device = RTXDevice::new(Device::cpu());
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let mut suite = BenchmarkSuite::new().add_benchmark(Box::new(MatMulBenchmark { size: 64 }));
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suite.run_all(&device).await?;
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let summary = suite.results_summary();
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assert!(!summary.is_empty());
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assert!(summary.contains_key("MatMul64x64_time"));
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Ok(())
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}
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/// Test data loader
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#[tokio::test]
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async fn test_data_loader() -> Result<()> {
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let device = Device::cpu();
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let loader = DataLoader::new(32, device.clone())
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.with_workers(2)
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.with_shuffle(true);
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// Create test data
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let mut test_data = Vec::new();
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for i in 0..100 {
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let tensor = Tensor::full(&[10], i as f32, &device)?;
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let sci_tensor = ScientificTensor::from_tensor(tensor, Some("test".to_string()));
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test_data.push(sci_tensor);
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}
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let batch = loader.load_batch(&test_data).await?;
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assert_eq!(batch.len(), 32);
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Ok(())
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}
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/// Test unit conversions
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#[tokio::test]
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async fn test_unit_conversions() -> Result<()> {
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let device = Device::cpu();
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let rtx_device = RTXDevice::new(device);
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let data = vec![1.0, 2.0, 3.0]; // 1, 2, 3 meters
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let mut tensor = rtx_device.tensor_from_data(&data, &[3], Some("m".to_string()))?;
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// Convert meters to centimeters
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tensor.convert_units("cm")?;
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assert_eq!(tensor.units, Some("cm".to_string()));
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// Values should be 100x larger
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let values = tensor.tensor.to_vec()?;
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assert!((values[0] - 100.0).abs() < 1e-6);
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Ok(())
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}
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/// Test AutoDiff functionality
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#[tokio::test]
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async fn test_autodiff() -> Result<()> {
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let device = Device::cpu();
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let mut autodiff = AutoDiff::new();
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let x = Tensor::from_slice(&[2.0, 3.0], &[2], &device)?;
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autodiff.register_variable("x".to_string(), x)?;
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let var = autodiff.get_variable("x").unwrap();
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let loss = var.multiply(var)?; // x^2
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let gradients = autodiff.compute_gradients(&loss).await?;
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assert!(gradients.contains_key("x"));
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Ok(())
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}
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/// Test PDE factory
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#[tokio::test]
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async fn test_pde_factory() -> Result<()> {
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let mut params = HashMap::new();
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params.insert("alpha".to_string(), 0.5);
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params.insert("dimension".to_string(), 2.0);
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let pde = create_pde("heat", ¶ms)?;
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assert_eq!(pde.name(), "Heat Equation");
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let pde_params = pde.parameters();
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assert_eq!(pde_params["alpha"], 0.5);
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Ok(())
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}
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/// Test multi-physics PINN
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#[tokio::test]
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async fn test_multiphysics_pinn() -> Result<()> {
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let heat_eq = HeatEquation::new(0.1);
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let wave_eq = WaveEquation::new(1.0);
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let multiphysics = MultiPhysicsPINN::builder()
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.add_physics(Box::new(heat_eq))
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.add_physics(Box::new(wave_eq))
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// ThermalCoupling is exported but boussinesq constructor might not be implemented yet
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// .add_coupling(Box::new(ThermalCoupling::boussinesq(9.8, 1e-3)))
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.build()?;
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assert_eq!(multiphysics.physics_models.len(), 2);
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assert_eq!(multiphysics.couplings.len(), 0);
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Ok(())
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}
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/// Stress test with large datasets
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#[tokio::test]
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async fn test_large_scale_processing() -> Result<()> {
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let device = Device::cpu();
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// Create large molecular dataset
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let mut molecules = Vec::new();
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for i in 0..1000 {
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let mol = Molecule::from_smiles(format!("large_mol_{}", i), "CCCCCCCCCCCCCCCC")?;
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molecules.push(mol);
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}
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// Process in parallel
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use rayon::prelude::*;
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let results: Vec<f64> = molecules
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.par_iter()
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.map(|mol| mol.molecular_weight())
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.collect();
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assert_eq!(results.len(), 1000);
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assert!(results.iter().all(|&x| x > 0.0));
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Ok(())
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}
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/// Test error handling and validation
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#[tokio::test]
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async fn test_error_handling() -> Result<()> {
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let device = Device::cpu();
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// Test invalid tensor creation with NaN
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let invalid_data = vec![f32::NAN, 1.0, 2.0];
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let rtx_device = RTXDevice::new(device);
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let result = rtx_device.tensor_from_data(&invalid_data, &[3], None);
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// NaN validation might pass or fail depending on implementation
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let _ = result;
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// Test invalid PINN parameters
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let result = PINN::builder()
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.layers(vec![]) // Empty layers should fail
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.build();
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assert!(result.is_err());
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// Test conservation law validation
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let conservation = MassConservation::new(1e-6);
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assert_eq!(conservation.law_type(), ConservationLaw::Mass);
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Ok(())
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}
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/// Performance regression test
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#[tokio::test]
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async fn test_performance_regression() -> Result<()> {
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let device = Device::cpu();
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let heat_eq = HeatEquation::new(0.1);
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let pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 128, 128, 1])
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.physics_loss(Box::new(heat_eq))
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.build()?;
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// Create tensor with 1000 samples, each with 2 features (x, t)
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let mut inputs_data = Vec::with_capacity(2000);
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for _ in 0..1000 {
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inputs_data.push(0.5);
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inputs_data.push(0.1);
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}
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let inputs = Tensor::from_slice(&inputs_data, &[1000, 2], &device)?;
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let start = std::time::Instant::now();
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let _outputs = pinn.predict(&inputs).await?;
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let elapsed = start.elapsed();
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// Should complete within reasonable time (adjust threshold as needed)
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assert!(
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elapsed.as_secs_f64() < 10.0,
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"Performance regression detected: {:.2}s",
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elapsed.as_secs_f64()
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);
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Ok(())
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}
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/// Test feature completeness
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#[test]
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fn test_feature_completeness() {
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let features = rtx_science::features();
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// Verify all expected features are present
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assert!(features.contains(&"pinn"));
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assert!(features.contains(&"chemistry"));
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assert!(features.contains(&"biology"));
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assert!(features.contains(&"materials"));
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assert!(features.contains(&"computing"));
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// Verify version info
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let version = rtx_science::VERSION;
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assert!(!version.is_empty());
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}
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