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turnbull: Clarify likelihood-ratio CI multithreading code

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Remove unnecessary use of RwLock and use map-reduce instead
This commit is contained in:
RunasSudo 2023-12-26 23:41:12 +11:00
parent 204571d6cb
commit 162e415e07
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
1 changed files with 17 additions and 25 deletions
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42
src/turnbull.rs
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@ -19,7 +19,7 @@ const CHI2_1DF_95: f64 = 3.8414588;
use std::fs::File; use std::fs::File;
use std::io::{self, BufReader}; use std::io::{self, BufReader};
use std::sync::{Arc, RwLock}; use std::sync::Arc;
use clap::{Args, ValueEnum}; use clap::{Args, ValueEnum};
use indicatif::{ProgressBar, ProgressDrawTarget, ProgressStyle}; use indicatif::{ProgressBar, ProgressDrawTarget, ProgressStyle};
@ -250,32 +250,27 @@ pub fn fit_turnbull(data_times: Matrix2xX<f64>, progress_bar: ProgressBar, max_i
progress_bar.reset(); progress_bar.reset();
progress_bar.println("Computing confidence intervals by likelihood ratio test"); progress_bar.println("Computing confidence intervals by likelihood ratio test");
// (CI left, (CI left lower, CI left upper), CI right, (CI right lower, CI right upper))
// TODO: Refactor this (unsafe code?) - each thread reads/writes only one value so there is no need for locking
let ci_with_bounds = Arc::new(
Vec::from_iter((1..data.num_intervals()).map(|_| RwLock::new((f64::NAN, (f64::NAN, f64::NAN), f64::NAN, (f64::NAN, f64::NAN)))))
);
// First do intervals with nonzero failure probability // First do intervals with nonzero failure probability
(1..data.num_intervals()).into_par_iter() let ci_with_bounds: Vec<(f64, (f64, f64), f64, (f64, f64))> = (1..data.num_intervals()).into_par_iter()
.for_each(|j| { .map(|j| {
if p[j - 1] <= 0.0001 { // To see if the survival probability at the j-th time index is the same as (j-1)-th, check the (j-1)-th failure probability if p[j - 1] <= 0.0001 { // To see if the survival probability at the j-th time index is the same as (j-1)-th, check the (j-1)-th failure probability
return; return (f64::NAN, (f64::NAN, f64::NAN), f64::NAN, (f64::NAN, f64::NAN));
} }
let ci = survival_prob_likelihood_ratio_ci(&data, ProgressBar::hidden(), max_iterations, ll_tolerance, ci_precision, &p, ll, &s, &oim_se, j, None); let ci = survival_prob_likelihood_ratio_ci(&data, ProgressBar::hidden(), max_iterations, ll_tolerance, ci_precision, &p, ll, &s, &oim_se, j, None);
let mut r = ci_with_bounds[j - 1].write().unwrap();
*r = ci;
progress_bar.inc(1); progress_bar.inc(1);
}); return ci; // (CI left, (CI left lower, CI left upper), CI right, (CI right lower, CI right upper))
})
.collect();
let ci_with_bounds = Arc::new(ci_with_bounds);
// Fill initial guesses for intervals with zero failure probability // Fill initial guesses for intervals with zero failure probability
let mut initial_guesses = Vec::with_capacity(data.num_intervals() - 1); let mut initial_guesses = Vec::with_capacity(data.num_intervals() - 1);
for j in 1..data.num_intervals() { for j in 1..data.num_intervals() {
if p[j - 1] > 0.0001 { if p[j - 1] > 0.0001 {
let r = ci_with_bounds[j - 1].read().unwrap(); initial_guesses.push(Some((ci_with_bounds[j - 1].1, ci_with_bounds[j - 1].3)));
initial_guesses.push(Some((r.1, r.3)));
} else if j >= 2 { } else if j >= 2 {
initial_guesses.push(initial_guesses[j - 2]); // Carry forward final bounds from last time point initial_guesses.push(initial_guesses[j - 2]); // Carry forward final bounds from last time point
} else { } else {
@ -284,24 +279,21 @@ pub fn fit_turnbull(data_times: Matrix2xX<f64>, progress_bar: ProgressBar, max_i
} }
// Now do intervals with zero failure probability // Now do intervals with zero failure probability
(1..data.num_intervals()).into_par_iter() let ci_with_bounds: Vec<(f64, (f64, f64), f64, (f64, f64))> = (1..data.num_intervals()).into_par_iter()
.for_each(|j| { .map(|j| {
if p[j - 1] > 0.0001 { if p[j - 1] > 0.0001 {
return; return ci_with_bounds[j - 1];
} }
let ci = survival_prob_likelihood_ratio_ci(&data, ProgressBar::hidden(), max_iterations, ll_tolerance, ci_precision, &p, ll, &s, &oim_se, j, initial_guesses[j - 1]); let ci = survival_prob_likelihood_ratio_ci(&data, ProgressBar::hidden(), max_iterations, ll_tolerance, ci_precision, &p, ll, &s, &oim_se, j, initial_guesses[j - 1]);
let mut r = ci_with_bounds[j - 1].write().unwrap();
*r = ci;
progress_bar.inc(1); progress_bar.inc(1);
}); return ci;
})
.collect();
let confidence_intervals = ci_with_bounds.iter() let confidence_intervals = ci_with_bounds.iter()
.map(|x| { .map(|x| (x.0, x.2))
let r = x.read().unwrap();
(r.0, r.2)
})
.collect(); .collect();
survival_prob_ci = Some(confidence_intervals); survival_prob_ci = Some(confidence_intervals);