turnbull: Refactor to aid profiling

src/turnbull.rs

@@ -172,18 +172,7 @@ pub fn fit_turnbull(data_times: MatrixXx2<f64>, progress_bar: ProgressBar, max_i
 	// Prepare for regression
 	
 	// Get Turnbull intervals
-	let mut all_time_points: Vec<(f64, bool)> = Vec::new();  // Vec of (time, is_left)
+	let intervals = get_turnbull_intervals(&data_times);
-	all_time_points.extend(data_times.column(1).iter().map(|t| (*t, false)));  // So we have right bounds before left bounds when sorted - ensures correct behaviour since intervals are left-open
-	all_time_points.extend(data_times.column(0).iter().map(|t| (*t, true)));
-	all_time_points.dedup();
-	all_time_points.sort_by(|(t1, _), (t2, _)| t1.partial_cmp(t2).unwrap());
-	
-	let mut intervals: Vec<(f64, f64)> = Vec::new();
-	for i in 1..all_time_points.len() {
-		if all_time_points[i - 1].1 == true && all_time_points[i].1 == false {
-			intervals.push((all_time_points[i - 1].0, all_time_points[i].0));
-		}
-	}
 	
 	// Recode times as indexes
 	let data_time_interval_indexes: Vec<(usize, usize)> = data_times.row_iter().map(|t| {
@@ -202,7 +191,7 @@ pub fn fit_turnbull(data_times: MatrixXx2<f64>, progress_bar: ProgressBar, max_i
 	// Initialise s
 	let mut s = DVector::repeat(intervals.len(), 1.0 / intervals.len() as f64);
 	
-	let data = TurnbullData {
+	let mut data = TurnbullData {
 		data_time_interval_indexes: data_time_interval_indexes,
 		intervals: intervals,
 	};
@@ -215,48 +204,7 @@ pub fn fit_turnbull(data_times: MatrixXx2<f64>, progress_bar: ProgressBar, max_i
 	progress_bar.reset();
 	progress_bar.println("Running iterative algorithm to fit Turnbull estimator");
 	
-	let mut iteration = 1;
+	s = fit_turnbull_estimator(&mut data, progress_bar.clone(), max_iterations, fail_prob_tolerance, s);
-	loop {
-		// Get total failure probability for each observation (denominator of μ_ij)
-		let sum_fail_prob = DVector::from_iterator(
-			data.num_obs(),
-			data.data_time_interval_indexes
-				.iter()
-				.map(|(idx_left, idx_right)| s.view((*idx_left, 0), (*idx_right - *idx_left + 1, 1)).sum())
-		);
-		
-		// Compute π_j
-		let mut pi: DVector<f64> = DVector::zeros(data.num_intervals());
-		for (i, (idx_left, idx_right)) in data.data_time_interval_indexes.iter().enumerate() {
-			for j in *idx_left..(*idx_right + 1) {
-				pi[j] += s[j] / sum_fail_prob[i] / data.num_obs() as f64;
-			}
-		}
-		
-		let largest_delta_s = s.iter().zip(pi.iter()).map(|(x, y)| (y - x).abs()).max_by(|a, b| a.partial_cmp(b).unwrap()).unwrap();
-		
-		let converged = largest_delta_s <= fail_prob_tolerance;
-		
-		s = pi;
-		
-		// Estimate progress bar according to either the order of magnitude of the largest_delta_s relative to tolerance, or iteration/max_iterations
-		let progress2 = (iteration as f64 / max_iterations as f64 * u64::MAX as f64) as u64;
-		let progress3 = ((-largest_delta_s.log10()).max(0.0) / -fail_prob_tolerance.log10() * u64::MAX as f64) as u64;
-		
-		// Update progress bar
-		progress_bar.set_position(progress_bar.position().max(progress3.max(progress2)));
-		progress_bar.set_message(format!("Iteration {} (max Δs = {:.4})", iteration + 1, largest_delta_s));
-		
-		if converged {
-			progress_bar.println(format!("Converged in {} iterations", iteration));
-			break;
-		}
-		
-		iteration += 1;
-		if iteration > max_iterations {
-			panic!("Exceeded --max-iterations");
-		}
-	}
 	
 	// Get survival probabilities (1 - cumulative failure probability), excluding at t=0 (prob=1) and t=inf (prob=0)
 	let mut survival_prob: Vec<f64> = Vec::with_capacity(data.num_intervals() - 1);
@@ -274,32 +222,7 @@ pub fn fit_turnbull(data_times: MatrixXx2<f64>, progress_bar: ProgressBar, max_i
 	progress_bar.reset();
 	progress_bar.println("Computing standard errors for survival probabilities");
 	
-	let mut hessian: DMatrix<f64> = DMatrix::zeros(data.num_intervals() - 1, data.num_intervals() - 1);
+	let hessian = compute_hessian(&data, progress_bar.clone(), &s);
-	
-	for (idx_left, idx_right) in data.data_time_interval_indexes.iter().progress_with(progress_bar.clone()) {
-		let mut hessian_denominator = s.view((*idx_left, 0), (*idx_right - *idx_left + 1, 1)).sum();
-		hessian_denominator = hessian_denominator.powi(2);
-		
-		let idx_start = if *idx_left > 0 { *idx_left - 1 } else { 0 };  // To cover the h+1 case
-		let idx_end = (*idx_right + 1).min(data.num_intervals() - 1);   // Go up to and including idx_right but don't go beyond hessian
-		
-		for h in idx_start..idx_end {
-			let i_h = if h >= *idx_left && h <= *idx_right { 1.0 } else { 0.0 };
-			let i_h1 = if h + 1 >= *idx_left && h + 1 <= *idx_right { 1.0 } else { 0.0 };
-			
-			hessian[(h, h)] -= (i_h - i_h1) * (i_h - i_h1) / hessian_denominator;
-			
-			for k in idx_start..h {
-				let i_k = if k >= *idx_left && k <= *idx_right { 1.0 } else { 0.0 };
-				let i_k1 = if k + 1 >= *idx_left && k + 1 <= *idx_right { 1.0 } else { 0.0 };
-				
-				let value = (i_h - i_h1) * (i_k - i_k1) / hessian_denominator;
-				hessian[(h, k)] -= value;
-				hessian[(k, h)] -= value;
-			}
-		}
-	}
-	progress_bar.finish();
 	
 	let mut survival_prob_se: DVector<f64>;
 	
@@ -346,6 +269,101 @@ pub fn fit_turnbull(data_times: MatrixXx2<f64>, progress_bar: ProgressBar, max_i
 	};
 }
 
+fn get_turnbull_intervals(data_times: &MatrixXx2<f64>) -> Vec<(f64, f64)> {
+	let mut all_time_points: Vec<(f64, bool)> = Vec::new();  // Vec of (time, is_left)
+	all_time_points.extend(data_times.column(1).iter().map(|t| (*t, false)));  // So we have right bounds before left bounds when sorted - ensures correct behaviour since intervals are left-open
+	all_time_points.extend(data_times.column(0).iter().map(|t| (*t, true)));
+	all_time_points.dedup();
+	all_time_points.sort_by(|(t1, _), (t2, _)| t1.partial_cmp(t2).unwrap());
+	
+	let mut intervals: Vec<(f64, f64)> = Vec::new();
+	for i in 1..all_time_points.len() {
+		if all_time_points[i - 1].1 == true && all_time_points[i].1 == false {
+			intervals.push((all_time_points[i - 1].0, all_time_points[i].0));
+		}
+	}
+	
+	return intervals;
+}
+
+fn fit_turnbull_estimator(data: &mut TurnbullData, progress_bar: ProgressBar, max_iterations: u32, fail_prob_tolerance: f64, mut s: DVector<f64>) -> DVector<f64> {
+	let mut iteration = 1;
+	loop {
+		// Get total failure probability for each observation (denominator of μ_ij)
+		let sum_fail_prob = DVector::from_iterator(
+			data.num_obs(),
+			data.data_time_interval_indexes
+				.iter()
+				.map(|(idx_left, idx_right)| s.view((*idx_left, 0), (*idx_right - *idx_left + 1, 1)).sum())
+		);
+		
+		// Compute π_j
+		let mut pi: DVector<f64> = DVector::zeros(data.num_intervals());
+		for (i, (idx_left, idx_right)) in data.data_time_interval_indexes.iter().enumerate() {
+			for j in *idx_left..(*idx_right + 1) {
+				pi[j] += s[j] / sum_fail_prob[i] / data.num_obs() as f64;
+			}
+		}
+		
+		let largest_delta_s = s.iter().zip(pi.iter()).map(|(x, y)| (y - x).abs()).max_by(|a, b| a.partial_cmp(b).unwrap()).unwrap();
+		
+		let converged = largest_delta_s <= fail_prob_tolerance;
+		
+		s = pi;
+		
+		// Estimate progress bar according to either the order of magnitude of the largest_delta_s relative to tolerance, or iteration/max_iterations
+		let progress2 = (iteration as f64 / max_iterations as f64 * u64::MAX as f64) as u64;
+		let progress3 = ((-largest_delta_s.log10()).max(0.0) / -fail_prob_tolerance.log10() * u64::MAX as f64) as u64;
+		
+		// Update progress bar
+		progress_bar.set_position(progress_bar.position().max(progress3.max(progress2)));
+		progress_bar.set_message(format!("Iteration {} (max Δs = {:.4})", iteration + 1, largest_delta_s));
+		
+		if converged {
+			progress_bar.println(format!("Converged in {} iterations", iteration));
+			break;
+		}
+		
+		iteration += 1;
+		if iteration > max_iterations {
+			panic!("Exceeded --max-iterations");
+		}
+	}
+	
+	return s;
+}
+
+fn compute_hessian(data: &TurnbullData, progress_bar: ProgressBar, 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()) {
+		let mut hessian_denominator = s.view((*idx_left, 0), (*idx_right - *idx_left + 1, 1)).sum();
+		hessian_denominator = hessian_denominator.powi(2);
+		
+		let idx_start = if *idx_left > 0 { *idx_left - 1 } else { 0 };  // To cover the h+1 case
+		let idx_end = (*idx_right + 1).min(data.num_intervals() - 1);   // Go up to and including idx_right but don't go beyond hessian
+		
+		for h in idx_start..idx_end {
+			let i_h = if h >= *idx_left && h <= *idx_right { 1.0 } else { 0.0 };
+			let i_h1 = if h + 1 >= *idx_left && h + 1 <= *idx_right { 1.0 } else { 0.0 };
+			
+			hessian[(h, h)] -= (i_h - i_h1) * (i_h - i_h1) / hessian_denominator;
+			
+			for k in idx_start..h {
+				let i_k = if k >= *idx_left && k <= *idx_right { 1.0 } else { 0.0 };
+				let i_k1 = if k + 1 >= *idx_left && k + 1 <= *idx_right { 1.0 } else { 0.0 };
+				
+				let value = (i_h - i_h1) * (i_k - i_k1) / hessian_denominator;
+				hessian[(h, k)] -= value;
+				hessian[(k, h)] -= value;
+			}
+		}
+	}
+	progress_bar.finish();
+	
+	return hessian;
+}
+
 #[derive(Serialize, Deserialize)]
 pub struct TurnbullResult {
 	pub failure_intervals: Vec<(f64, f64)>,