Add --ll_tolerance parameter for intcox

src/intcox.rs

@@ -44,7 +44,11 @@ pub struct IntCoxArgs {
 	
 	/// Terminate E-M algorithm when the maximum absolute change in all parameters is less than this tolerance
 	#[arg(long, default_value="0.001")]
-	tolerance: f64,
+	param_tolerance: f64,
+	
+	/// Terminate E-M algorithm when the absolute change in log-likelihood is less than this tolerance
+	#[arg(long)]
+	ll_tolerance: Option<f64>,
 	
 	/// Estimate baseline hazard function using Turnbull innermost intervals
 	#[arg(long)]
@@ -63,7 +67,7 @@ pub fn main(args: IntCoxArgs) {
 	
 	// Fit regression
 	let progress_bar = ProgressBar::with_draw_target(Some(0), ProgressDrawTarget::term_like(Box::new(UnconditionalTermLike::stderr())));
-	let result = fit_interval_censored_cox(data_times, data_indep, args.max_iterations, args.tolerance, args.reduced, progress_bar);
+	let result = fit_interval_censored_cox(data_times, data_indep, args.max_iterations, args.param_tolerance, args.ll_tolerance, args.reduced, progress_bar);
 	
 	// Display output
 	match args.output {
@@ -172,7 +176,7 @@ impl IntervalCensoredCoxData {
 	}
 }
 
-pub fn fit_interval_censored_cox(data_times: DMatrix<f64>, mut data_indep: DMatrix<f64>, max_iterations: u32, tolerance: f64, reduced: bool, progress_bar: ProgressBar) -> IntervalCensoredCoxResult {
+pub fn fit_interval_censored_cox(data_times: DMatrix<f64>, mut data_indep: DMatrix<f64>, max_iterations: u32, param_tolerance: f64, ll_tolerance: Option<f64>, reduced: bool, progress_bar: ProgressBar) -> IntervalCensoredCoxResult {
 	// ----------------------
 	// Prepare for regression
 	
@@ -256,6 +260,7 @@ pub fn fit_interval_censored_cox(data_times: DMatrix<f64>, mut data_indep: DMatr
 	progress_bar.println("Running E-M algorithm to fit interval-censored Cox model");
 	
 	let mut iteration: u32 = 0;
+	let mut ll_model: f64 = 0.0;
 	loop {
 		// Pre-compute exp(β^T * Z_ik)
 		let exp_beta_z: Matrix1xX<f64> = (beta.transpose() * &data.data_indep).apply_into(|x| { *x = x.exp(); });
@@ -266,17 +271,31 @@ pub fn fit_interval_censored_cox(data_times: DMatrix<f64>, mut data_indep: DMatr
 		// Do M-step
 		let (new_beta, new_lambda) = do_m_step(&data, &exp_beta_z, &beta, posterior_weight);
 		
-		// Check for convergence
+		// Check for convergence (param_tolerance)
-		let (coef_change, converged) = em_check_convergence(&beta, &lambda, &new_beta, &new_lambda, tolerance);
+		let (coef_change, mut converged) = em_check_convergence(&beta, &lambda, &new_beta, &new_lambda, param_tolerance);
 		beta = new_beta;
 		lambda = new_lambda;
 		
-		// Update progress bar
+		// Estimate progress bar according to either the order of magnitude of the coef_change relative to tolerance, or iteration/max_iterations
-		// Estimate progress according to either the order of magnitude of the coef_change relative to tolerance, or iteration/max_iterations
+		let progress1 = ((-coef_change.log10()).max(0.0) / -param_tolerance.log10() * u64::MAX as f64) as u64;
-		let progress1 = ((-coef_change.log10()).max(0.0) / -tolerance.log10() * u64::MAX as f64) as u64;
 		let progress2 = (iteration as f64 / max_iterations as f64 * u64::MAX as f64) as u64;
-		progress_bar.set_position(progress_bar.position().max(progress1).max(progress2));
+		
-		progress_bar.set_message(format!("Iter {} (delta = {:.6})", iteration + 1, coef_change));
+		if let Some(ll_tolerance_amount) = ll_tolerance {
+			// Check for convergence (ll_tolerance)
+			let new_ll = log_likelihood_obs(&data, &beta, &lambda).sum();
+			let ll_change = new_ll - ll_model;
+			converged = converged && (ll_change < ll_tolerance_amount);
+			ll_model = new_ll;
+			
+			// Update progress bar
+			let progress3 = ((-ll_change.log10()).max(0.0) / -ll_tolerance_amount.log10() * u64::MAX as f64) as u64;
+			progress_bar.set_position(progress_bar.position().max(progress1.min(progress3)).max(progress2));
+			progress_bar.set_message(format!("Iteration {} (Δparams = {:.6}, ΔLL = {:.4})", iteration + 1, coef_change, ll_change));
+		} else {
+			// Update progress bar
+			progress_bar.set_position(progress_bar.position().max(progress1).max(progress2));
+			progress_bar.set_message(format!("Iteration {} (Δparams = {:.6})", iteration + 1, coef_change));
+		}
 		
 		if converged {
 			progress_bar.finish();
@@ -290,7 +309,11 @@ pub fn fit_interval_censored_cox(data_times: DMatrix<f64>, mut data_indep: DMatr
 	}
 	
 	// Compute log-likelihood
-	let ll_model = log_likelihood_obs(&data, &beta, &lambda).sum();
+	if let Some(_) = ll_tolerance {
+		// Already computed above
+	} else {
+		ll_model = log_likelihood_obs(&data, &beta, &lambda).sum();
+	}
 	
 	// Unstandardise betas
 	let mut beta_unstandardised: DVector<f64> = DVector::zeros(data.num_covs());
@@ -313,15 +336,15 @@ pub fn fit_interval_censored_cox(data_times: DMatrix<f64>, mut data_indep: DMatr
 	// pll_toggle_zero = log-likelihoods for each observation at final beta
 	// pll_toggle_one = log-likelihoods for each observation at toggled beta
 	
-	let ll_null = profile_log_likelihood_obs(&data, DVector::zeros(data.num_covs()), lambda.clone(), max_iterations, tolerance).sum();
+	let ll_null = profile_log_likelihood_obs(&data, DVector::zeros(data.num_covs()), lambda.clone(), max_iterations, param_tolerance).sum();
 	
-	let pll_toggle_zero: DVector<f64> = profile_log_likelihood_obs(&data, beta.clone(), lambda.clone(), max_iterations, tolerance);
+	let pll_toggle_zero: DVector<f64> = profile_log_likelihood_obs(&data, beta.clone(), lambda.clone(), max_iterations, param_tolerance);
 	progress_bar.inc(1);
 	
 	let pll_toggle_one: Vec<DVector<f64>> = (0..data.num_covs()).into_par_iter().map(|j| {
 		let mut pll_beta = beta.clone();
 		pll_beta[j] += h;
-		profile_log_likelihood_obs(&data, pll_beta, lambda.clone(), max_iterations, tolerance)
+		profile_log_likelihood_obs(&data, pll_beta, lambda.clone(), max_iterations, param_tolerance)
 	})
 	.progress_with(progress_bar.clone())
 	.collect();