turnbull: Refactor root-finding code

src/lib.rs

@@ -3,4 +3,5 @@ pub mod turnbull;
 
 mod csv;
 mod pava;
+mod root_finding;
 mod term;

src/root_finding.rs

@@ -0,0 +1,61 @@
+// hpstat: High-performance statistics implementations
+// Copyright © 2023  Lee Yingtong Li (RunasSudo)
+// 
+// This program is free software: you can redistribute it and/or modify
+// it under the terms of the GNU Affero General Public License as published by
+// the Free Software Foundation, either version 3 of the License, or
+// (at your option) any later version.
+// 
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU Affero General Public License for more details.
+// 
+// You should have received a copy of the GNU Affero General Public License
+// along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+pub struct BisectionRootFinder {
+	bound_lower: f64,
+	bound_upper: f64,
+	value_lower: f64,
+	value_upper: f64
+}
+
+impl BisectionRootFinder {
+	pub fn new(bound_lower: f64, bound_upper: f64, value_lower: f64, value_upper: f64,) -> BisectionRootFinder {
+		return BisectionRootFinder {
+			bound_lower: bound_lower,
+			bound_upper: bound_upper,
+			value_lower: value_lower,
+			value_upper: value_upper
+		}
+	}
+	
+	pub fn update(&mut self, guess: f64, value: f64) {
+		if value > 0.0 {
+			if self.value_lower > 0.0 || self.value_upper < 0.0 {
+				self.bound_lower = guess;
+				self.value_lower = value;
+			} else {
+				self.bound_upper = guess;
+				self.value_upper = value;
+			}
+		} else {
+			if self.value_lower < 0.0 || self.value_upper > 0.0 {
+				self.bound_lower = guess;
+				self.value_lower = value;
+			} else {
+				self.bound_upper = guess;
+				self.value_upper = value;
+			}
+		}
+	}
+	
+	pub fn next_guess(&self) -> f64 {
+		return (self.bound_lower + self.bound_upper) / 2.0;
+	}
+	
+	pub fn precision(&self) -> f64 {
+		return self.bound_upper - self.bound_lower;
+	}
+}

src/turnbull.rs

@@ -29,6 +29,7 @@ use serde::{Serialize, Deserialize};
 
 use crate::csv::read_csv;
 use crate::pava::monotonic_regression_pava;
+use crate::root_finding::BisectionRootFinder;
 use crate::term::UnconditionalTermLike;
 
 #[derive(Args)]
@@ -621,11 +622,14 @@ fn compute_hessian(data: &TurnbullData, p: &Vec<f64>) -> DMatrix<f64> {
 
 fn survival_prob_likelihood_ratio_ci(data: &TurnbullData, progress_bar: ProgressBar, max_iterations: u32, ll_tolerance: f64, ci_precision: f64, p: &Vec<f64>, ll_model: f64, s: &Vec<f64>, oim_se: &Vec<f64>, time_index: usize) -> (f64, f64) {
 	// Compute lower confidence limit
-	let mut ci_bound_lower = 0.0;
+	let mut root_finder = BisectionRootFinder::new(
-	let mut ci_bound_upper = s[time_index];
+		0.0, s[time_index],
+		f64::NAN, -CHI2_1DF_95  // Value of (lr_statistic - CHI2_1DF_95), which we are seeking the roots of
+	);
+	
 	let mut ci_estimate = s[time_index] - Z_97_5 * oim_se[time_index - 1];
 	if ci_estimate < 0.0 {
-		ci_estimate = (ci_bound_lower + ci_bound_upper) / 2.0;
+		ci_estimate = root_finder.next_guess();  // Returns interval midpoint in this case
 	}
 	
 	let mut iteration = 1;
@@ -639,17 +643,10 @@ fn survival_prob_likelihood_ratio_ci(data: &TurnbullData, progress_bar: Progress
 		let (_p, ll_test) = fit_turnbull_estimator(data, progress_bar.clone(), max_iterations, ll_tolerance, p_test, Some(Constraint { time_index: time_index, survival_prob: ci_estimate }));
 		let lr_statistic = 2.0 * (ll_model - ll_test);
 		
-		if lr_statistic > CHI2_1DF_95 {
+		root_finder.update(ci_estimate, lr_statistic - CHI2_1DF_95);
-			// CI is too wide
+		ci_estimate = root_finder.next_guess();
-			ci_bound_lower = ci_estimate;
-		} else {
-			// CI is too narrow
-			ci_bound_upper = ci_estimate;
-		}
 		
-		ci_estimate = (ci_bound_lower + ci_bound_upper) / 2.0;
+		if root_finder.precision() <= ci_precision {
-		
-		if ci_bound_upper - ci_bound_lower <= ci_precision {
 			// Desired precision has been reached
 			break;
 		}
@@ -663,11 +660,14 @@ fn survival_prob_likelihood_ratio_ci(data: &TurnbullData, progress_bar: Progress
 	let ci_lower = ci_estimate;
 	
 	// Compute upper confidence limit
-	ci_bound_lower = s[time_index];
+	root_finder = BisectionRootFinder::new(
-	ci_bound_upper = 1.0;
+		s[time_index], 1.0,
+		-CHI2_1DF_95, f64::NAN
+	);
+	
 	ci_estimate = s[time_index] + Z_97_5 * oim_se[time_index - 1];
 	if ci_estimate > 1.0 {
-		ci_estimate = (ci_bound_lower + ci_bound_upper) / 2.0;
+		ci_estimate = root_finder.next_guess();
 	}
 	
 	let mut iteration = 1;
@@ -681,17 +681,10 @@ fn survival_prob_likelihood_ratio_ci(data: &TurnbullData, progress_bar: Progress
 		let (_p, ll_test) = fit_turnbull_estimator(data, progress_bar.clone(), max_iterations, ll_tolerance, p_test, Some(Constraint { time_index: time_index, survival_prob: ci_estimate }));
 		let lr_statistic = 2.0 * (ll_model - ll_test);
 		
-		if lr_statistic > CHI2_1DF_95 {
+		root_finder.update(ci_estimate, lr_statistic - CHI2_1DF_95);
-			// CI is too wide
+		ci_estimate = root_finder.next_guess();
-			ci_bound_upper = ci_estimate;
-		} else {
-			// CI is too narrow
-			ci_bound_lower = ci_estimate;
-		}
 		
-		ci_estimate = (ci_bound_lower + ci_bound_upper) / 2.0;
+		if root_finder.precision() <= ci_precision {
-		
-		if ci_bound_upper - ci_bound_lower <= ci_precision {
 			// Desired precision has been reached
 			break;
 		}