turnbull: Introduce analytical solution to computing Hessian

Makes runtime of computing Hessian negligible!

src/turnbull.rs

@@ -21,7 +21,7 @@ use std::io;
 
 use clap::{Args, ValueEnum};
 use csv::{Reader, StringRecord};
-use indicatif::{ProgressBar, ProgressDrawTarget, ProgressIterator, ProgressStyle};
+use indicatif::{ProgressBar, ProgressDrawTarget, ProgressStyle};
 use nalgebra::{Const, DMatrix, DVector, Dyn, MatrixXx2};
 use prettytable::{Table, format, row};
 use serde::{Serialize, Deserialize};
@@ -217,12 +217,7 @@ pub fn fit_turnbull(data_times: MatrixXx2<f64>, progress_bar: ProgressBar, max_i
 	// --------------------------------------------------
 	// Compute standard errors for survival probabilities
 	
-	progress_bar.set_style(ProgressStyle::with_template("[{elapsed_precise}] {bar:40} Compute Hessian {pos}/{len}").unwrap());
+	let hessian = compute_hessian(&data, &s);
-	progress_bar.set_length(data.num_obs() as u64);
-	progress_bar.reset();
-	progress_bar.println("Computing standard errors for survival probabilities");
-	
-	let hessian = compute_hessian(&data, progress_bar.clone(), &s);
 	
 	let mut survival_prob_se: DVector<f64>;
 	
@@ -333,33 +328,50 @@ fn fit_turnbull_estimator(data: &mut TurnbullData, progress_bar: ProgressBar, ma
 	return s;
 }
 
-fn compute_hessian(data: &TurnbullData, progress_bar: ProgressBar, s: &DVector<f64>) -> DMatrix<f64> {
+fn compute_hessian(data: &TurnbullData, s: &DVector<f64>) -> DMatrix<f64> {
 	let mut hessian: DMatrix<f64> = DMatrix::zeros(data.num_intervals() - 1, data.num_intervals() - 1);
 	
-	for (idx_left, idx_right) in data.data_time_interval_indexes.iter().progress_with(progress_bar.clone()) {
+	for (idx_left, idx_right) in data.data_time_interval_indexes.iter() {
-		let mut hessian_denominator = s.view((*idx_left, 0), (*idx_right - *idx_left + 1, 1)).sum();
+		// Compute 1 / (Σ_j α_{i,j} s_j)
-		hessian_denominator = hessian_denominator.powi(2);
+		let mut one_over_hessian_denominator = s.view((*idx_left, 0), (*idx_right - *idx_left + 1, 1)).sum();
+		one_over_hessian_denominator = one_over_hessian_denominator.powi(-2);
 		
-		let idx_start = if *idx_left > 0 { *idx_left - 1 } else { 0 };  // To cover the h+1 case
+		// The numerator of the log-likelihood is -(α_{i,h} - α_{i,h+1})(α_{i,k} - α_{i,k+1})
-		let idx_end = (*idx_right + 1).min(data.num_intervals() - 1);   // Go up to and including idx_right but don't go beyond hessian
+		// This is nonzero only when α_{i,h} ≠ α_{i,h+1} AND α_{i,k} ≠ α_{i,k+1}
+		// Since each observation spans a continuous sequence of intervals, this is true only at two each of h and k at the boundaries of the observation
+		// h = last interval not involving the observation, h + 1 = first interval involving the observation, etc.
 		
-		for h in idx_start..idx_end {
+		// if *idx_left > 0 { h1 = idx_left - 1; }
-			let i_h = if h >= *idx_left && h <= *idx_right { 1.0 } else { 0.0 };
+		// if *idx_right < data.num_intervals() - 1 { h2 = *idx_right; }
-			let i_h1 = if h + 1 >= *idx_left && h + 1 <= *idx_right { 1.0 } else { 0.0 };
+		
+		if *idx_left > 0 {
+			let h1 = idx_left - 1;
 			
-			hessian[(h, h)] -= (i_h - i_h1) * (i_h - i_h1) / hessian_denominator;
+			// (h, k) = (h1, h1)
+			// numerator is -(0 - 1)(0 - 1) = -1
+			hessian[(h1, h1)] -= one_over_hessian_denominator;
+		}
+		
+		if *idx_right < data.num_intervals() - 1 {
+			let h2 = *idx_right;
 			
-			for k in idx_start..h {
+			// (h, k) = (h2, h2)
-				let i_k = if k >= *idx_left && k <= *idx_right { 1.0 } else { 0.0 };
+			// numerator is -(1 - 0)(1 - 0) = -1
-				let i_k1 = if k + 1 >= *idx_left && k + 1 <= *idx_right { 1.0 } else { 0.0 };
+			hessian[(h2, h2)] -= one_over_hessian_denominator;
+			
+			if *idx_left > 0 {
+				let h1 = idx_left - 1;
 				
-				let value = (i_h - i_h1) * (i_k - i_k1) / hessian_denominator;
+				// (h, k) = (h1, h2)
-				hessian[(h, k)] -= value;
+				// numerator is -(0 - 1)(1 - 0) = 1
-				hessian[(k, h)] -= value;
+				hessian[(h1, h2)] += one_over_hessian_denominator;
+				
+				// (h, k) = (h2, h1)
+				// numerator is -(1 - 0)(0 - 1) = 1
+				hessian[(h2, h1)] += one_over_hessian_denominator;
 			}
 		}
 	}
-	progress_bar.finish();
 	
 	return hessian;
 }