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checksum = "8e5180c00cd44c9b1c88adb3693291f1cd93605ded80c250a75d472756b4d071"
|
||||
dependencies = [
|
||||
"windows_aarch64_gnullvm 0.42.2",
|
||||
"windows_aarch64_msvc 0.42.2",
|
||||
"windows_i686_gnu 0.42.2",
|
||||
"windows_i686_msvc 0.42.2",
|
||||
"windows_x86_64_gnu 0.42.2",
|
||||
"windows_x86_64_gnullvm 0.42.2",
|
||||
"windows_x86_64_msvc 0.42.2",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows-targets"
|
||||
version = "0.48.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "7b1eb6f0cd7c80c79759c929114ef071b87354ce476d9d94271031c0497adfd5"
|
||||
dependencies = [
|
||||
"windows_aarch64_gnullvm 0.48.0",
|
||||
"windows_aarch64_msvc 0.48.0",
|
||||
"windows_i686_gnu 0.48.0",
|
||||
"windows_i686_msvc 0.48.0",
|
||||
"windows_x86_64_gnu 0.48.0",
|
||||
"windows_x86_64_gnullvm 0.48.0",
|
||||
"windows_x86_64_msvc 0.48.0",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows_aarch64_gnullvm"
|
||||
version = "0.42.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "597a5118570b68bc08d8d59125332c54f1ba9d9adeedeef5b99b02ba2b0698f8"
|
||||
|
||||
[[package]]
|
||||
name = "windows_aarch64_gnullvm"
|
||||
version = "0.48.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "91ae572e1b79dba883e0d315474df7305d12f569b400fcf90581b06062f7e1bc"
|
||||
|
||||
[[package]]
|
||||
name = "windows_aarch64_msvc"
|
||||
version = "0.42.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "e08e8864a60f06ef0d0ff4ba04124db8b0fb3be5776a5cd47641e942e58c4d43"
|
||||
|
||||
[[package]]
|
||||
name = "windows_aarch64_msvc"
|
||||
version = "0.48.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "b2ef27e0d7bdfcfc7b868b317c1d32c641a6fe4629c171b8928c7b08d98d7cf3"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_gnu"
|
||||
version = "0.42.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "c61d927d8da41da96a81f029489353e68739737d3beca43145c8afec9a31a84f"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_gnu"
|
||||
version = "0.48.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "622a1962a7db830d6fd0a69683c80a18fda201879f0f447f065a3b7467daa241"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_msvc"
|
||||
version = "0.42.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "44d840b6ec649f480a41c8d80f9c65108b92d89345dd94027bfe06ac444d1060"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_msvc"
|
||||
version = "0.48.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "4542c6e364ce21bf45d69fdd2a8e455fa38d316158cfd43b3ac1c5b1b19f8e00"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_gnu"
|
||||
version = "0.42.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "8de912b8b8feb55c064867cf047dda097f92d51efad5b491dfb98f6bbb70cb36"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_gnu"
|
||||
version = "0.48.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "ca2b8a661f7628cbd23440e50b05d705db3686f894fc9580820623656af974b1"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_gnullvm"
|
||||
version = "0.42.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "26d41b46a36d453748aedef1486d5c7a85db22e56aff34643984ea85514e94a3"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_gnullvm"
|
||||
version = "0.48.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "7896dbc1f41e08872e9d5e8f8baa8fdd2677f29468c4e156210174edc7f7b953"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_msvc"
|
||||
version = "0.42.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "9aec5da331524158c6d1a4ac0ab1541149c0b9505fde06423b02f5ef0106b9f0"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_msvc"
|
||||
version = "0.48.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1a515f5799fe4961cb532f983ce2b23082366b898e52ffbce459c86f67c8378a"
|
||||
17
Cargo.toml
Normal file
17
Cargo.toml
Normal file
@ -0,0 +1,17 @@
|
||||
[package]
|
||||
name = "hpstat"
|
||||
version = "0.1.0"
|
||||
edition = "2021"
|
||||
|
||||
[dependencies]
|
||||
clap = { version = "4.2.1", features = ["derive"] }
|
||||
indicatif = {version = "0.17.3", features = ["rayon"]}
|
||||
nalgebra = "0.32.2"
|
||||
prettytable-rs = "0.10.0"
|
||||
rayon = "1.7.0"
|
||||
serde = { version = "1.0.160", features = ["derive"] }
|
||||
serde_json = "1.0.96"
|
||||
|
||||
[profile.perf]
|
||||
inherits = "release"
|
||||
debug = true
|
||||
596
src/intcox.rs
Normal file
596
src/intcox.rs
Normal file
@ -0,0 +1,596 @@
|
||||
// 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/>.
|
||||
|
||||
const Z_97_5: f64 = 1.959964; // This is the limit of resolution for an f64
|
||||
|
||||
use std::fs;
|
||||
use std::io;
|
||||
|
||||
use clap::{Args, ValueEnum};
|
||||
use indicatif::{ParallelProgressIterator, ProgressBar, ProgressStyle};
|
||||
use nalgebra::{DMatrix, DVector, Matrix1xX};
|
||||
use prettytable::{Table, format, row};
|
||||
use rayon::prelude::*;
|
||||
use serde::{Serialize, Deserialize};
|
||||
|
||||
#[derive(Args)]
|
||||
pub struct IntCoxArgs {
|
||||
/// Path to CSV input file containing the observations
|
||||
#[arg()]
|
||||
input: String,
|
||||
|
||||
/// Output format
|
||||
#[arg(long, value_enum, default_value="text")]
|
||||
output: OutputFormat,
|
||||
|
||||
/// Maximum number of E-M iterations to attempt
|
||||
#[arg(long, default_value="100")]
|
||||
max_iterations: u32,
|
||||
|
||||
/// 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,
|
||||
|
||||
/// Estimate baseline hazard function using Turnbull innermost intervals
|
||||
#[arg(long)]
|
||||
reduced: bool,
|
||||
}
|
||||
|
||||
#[derive(ValueEnum, Clone)]
|
||||
enum OutputFormat {
|
||||
Text,
|
||||
Json
|
||||
}
|
||||
|
||||
pub fn main(args: IntCoxArgs) {
|
||||
let lines: Vec<String>;
|
||||
if args.input == "-" {
|
||||
lines = io::stdin().lines().map(|l| l.unwrap()).collect();
|
||||
} else {
|
||||
let contents = fs::read_to_string(args.input).unwrap();
|
||||
lines = contents.trim_end().split("\n").map(|s| s.to_string()).collect();
|
||||
}
|
||||
|
||||
// Read data into matrices
|
||||
|
||||
let mut data_times: DMatrix<f64> = DMatrix::zeros(
|
||||
2, // Left time, right time
|
||||
lines.len() - 1 // Minus 1 row for header row
|
||||
);
|
||||
|
||||
// Called "Z" in the paper and "X" in the C++ code
|
||||
let mut data_indep: DMatrix<f64> = DMatrix::zeros(
|
||||
lines[0].split(",").count() - 2,
|
||||
lines.len() - 1 // Minus 1 row for header row
|
||||
);
|
||||
|
||||
// Read header row
|
||||
let indep_names: Vec<&str> = lines[0].split(",").skip(2).collect();
|
||||
|
||||
// Read data
|
||||
// FIXME: Parse CSV more robustly
|
||||
for (i, row) in lines.iter().skip(1).enumerate() {
|
||||
for (j, item) in row.split(",").enumerate() {
|
||||
let value = match item {
|
||||
"inf" => f64::INFINITY,
|
||||
_ => item.parse().expect("Malformed float")
|
||||
};
|
||||
|
||||
if j < 2 {
|
||||
data_times[(j, i)] = value;
|
||||
} else {
|
||||
data_indep[(j - 2, i)] = value;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Fit regression
|
||||
let progress_bar = match args.output {
|
||||
OutputFormat::Text => ProgressBar::new(0),
|
||||
OutputFormat::Json => ProgressBar::hidden(),
|
||||
};
|
||||
let result = fit_interval_censored_cox(data_times, data_indep, args.max_iterations, args.tolerance, args.reduced, progress_bar);
|
||||
|
||||
// Display output
|
||||
match args.output {
|
||||
OutputFormat::Text => {
|
||||
println!();
|
||||
println!();
|
||||
println!("LL-Model = {:.5}", result.ll_model);
|
||||
println!("LL-Null = {:.5}", result.ll_null);
|
||||
|
||||
let mut summary = Table::new();
|
||||
let format = format::FormatBuilder::new()
|
||||
.separators(&[format::LinePosition::Top, format::LinePosition::Title, format::LinePosition::Bottom], format::LineSeparator::new('-', '+', '+', '+'))
|
||||
.padding(2, 2)
|
||||
.build();
|
||||
summary.set_format(format);
|
||||
|
||||
summary.set_titles(row!["Parameter", c->"β", c->"Std Err.", c->"exp(β)", H2c->"(95% CI)"]);
|
||||
for (i, indep_name) in indep_names.iter().enumerate() {
|
||||
summary.add_row(row![
|
||||
indep_name,
|
||||
r->format!("{:.5}", result.params[i]),
|
||||
r->format!("{:.5}", result.params_se[i]),
|
||||
r->format!("{:.5}", result.params[i].exp()),
|
||||
r->format!("({:.5},", (result.params[i] - Z_97_5 * result.params_se[i]).exp()),
|
||||
format!("{:.5})", (result.params[i] + Z_97_5 * result.params_se[i]).exp()),
|
||||
]);
|
||||
}
|
||||
summary.printstd();
|
||||
}
|
||||
OutputFormat::Json => {
|
||||
println!("{}", serde_json::to_string(&result).unwrap());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
struct IntervalCensoredCoxData {
|
||||
data_times: DMatrix<f64>,
|
||||
data_indep: DMatrix<f64>,
|
||||
|
||||
// Cached intermediate values
|
||||
time_points: Vec<f64>,
|
||||
r_star_indicator: DMatrix<f64>,
|
||||
z_z_transpose: Vec<DMatrix<f64>>,
|
||||
}
|
||||
|
||||
impl IntervalCensoredCoxData {
|
||||
fn num_obs(&self) -> usize {
|
||||
return self.data_indep.ncols();
|
||||
}
|
||||
|
||||
fn num_covs(&self) -> usize {
|
||||
return self.data_indep.nrows();
|
||||
}
|
||||
|
||||
fn num_times(&self) -> usize {
|
||||
return self.time_points.len();
|
||||
}
|
||||
}
|
||||
|
||||
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 {
|
||||
// ----------------------
|
||||
// Prepare for regression
|
||||
|
||||
// Standardise values
|
||||
let indep_means = data_indep.column_mean();
|
||||
let indep_stdev = data_indep.column_variance().apply_into(|x| { *x = (*x * data_indep.ncols() as f64 / (data_indep.ncols() - 1) as f64).sqrt(); });
|
||||
for j in 0..data_indep.nrows() {
|
||||
data_indep.row_mut(j).apply(|x| *x = (*x - indep_means[j]) / indep_stdev[j]);
|
||||
}
|
||||
|
||||
// Get time points (t_0 = 0, t_1, ..., t_m)
|
||||
let mut time_points: Vec<f64>;
|
||||
if reduced {
|
||||
// Turnbull intervals
|
||||
let mut all_time_points: Vec<(f64, bool)> = Vec::new(); // Vec of (time, is_left)
|
||||
all_time_points.extend(data_times.row(0).iter().map(|t| (*t, true)));
|
||||
all_time_points.extend(data_times.row(1).iter().map(|t| (*t, false)));
|
||||
all_time_points.sort_by(|(t1, _), (t2, _)| t1.partial_cmp(t2).unwrap());
|
||||
|
||||
time_points = Vec::new();
|
||||
for i in 1..all_time_points.len() {
|
||||
if all_time_points[i - 1].1 == true && all_time_points[i].1 == false {
|
||||
time_points.push(all_time_points[i - 1].0);
|
||||
time_points.push(all_time_points[i].0);
|
||||
}
|
||||
}
|
||||
time_points.push(0.0); // Ensure 0 is in the list
|
||||
time_points.retain(|t| t.is_finite()); // Remove infinity
|
||||
time_points.sort_by(|a, b| a.partial_cmp(b).unwrap()); // Cannot use .sort() as f64 does not implement Ord
|
||||
time_points.dedup();
|
||||
} else {
|
||||
// All observed intervals
|
||||
time_points = data_times.iter().copied().collect();
|
||||
time_points.push(0.0); // Ensure 0 is in the list
|
||||
time_points.retain(|t| t.is_finite()); // Remove infinity
|
||||
time_points.sort_by(|a, b| a.partial_cmp(b).unwrap()); // Cannot use .sort() as f64 does not implement Ord
|
||||
time_points.dedup();
|
||||
}
|
||||
|
||||
// Initialise β, λ
|
||||
let mut beta = DVector::zeros(data_indep.nrows());
|
||||
let mut lambda = DVector::repeat(time_points.len(), 1.0 / (time_points.len() - 1) as f64);
|
||||
|
||||
// Compute I(t_k <= R*_i)
|
||||
// Where R*_i is R_i if R_i ≠ ∞, otherwise it is L_i
|
||||
let mut r_star_indicator = DMatrix::zeros(data_indep.ncols(), time_points.len());
|
||||
for (i, observation) in data_times.column_iter().enumerate() {
|
||||
let time_right_star = if observation[1].is_finite() { observation[1] } else { observation[0] };
|
||||
for (k, time) in time_points.iter().enumerate() {
|
||||
if *time <= time_right_star {
|
||||
// t_k <= R*_i
|
||||
r_star_indicator[(i, k)] = 1.0;
|
||||
} else {
|
||||
r_star_indicator[(i, k)] = 0.0;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Pre-compute Z * Z^T
|
||||
// Indexed by observation -> Matrix (num-covariates, num-covariates)
|
||||
let mut z_z_transpose: Vec<DMatrix<f64>> = Vec::new();
|
||||
for i in 0..data_indep.ncols() {
|
||||
let covariates = data_indep.column(i);
|
||||
z_z_transpose.push(covariates * covariates.transpose());
|
||||
}
|
||||
|
||||
let data = IntervalCensoredCoxData {
|
||||
data_times: data_times,
|
||||
data_indep: data_indep,
|
||||
time_points: time_points,
|
||||
r_star_indicator: r_star_indicator,
|
||||
z_z_transpose: z_z_transpose,
|
||||
};
|
||||
|
||||
// -------------------
|
||||
// Apply E-M algorithm
|
||||
|
||||
progress_bar.set_length(u64::MAX);
|
||||
progress_bar.reset();
|
||||
progress_bar.set_style(ProgressStyle::with_template("[{elapsed_precise}] {bar:40} {msg}").unwrap());
|
||||
progress_bar.println("Running E-M algorithm to fit interval-censored Cox model");
|
||||
|
||||
let mut iteration: u32 = 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(); });
|
||||
|
||||
// Do E-step
|
||||
let posterior_weight = do_e_step(&data, &exp_beta_z, &lambda);
|
||||
|
||||
// Do M-step
|
||||
let (new_beta, new_lambda) = do_m_step(&data, &exp_beta_z, &beta, posterior_weight);
|
||||
|
||||
// Check for convergence
|
||||
let (coef_change, converged) = em_check_convergence(&beta, &lambda, &new_beta, &new_lambda, tolerance);
|
||||
beta = new_beta;
|
||||
lambda = new_lambda;
|
||||
|
||||
// Update progress bar
|
||||
// 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) / -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 converged {
|
||||
progress_bar.finish();
|
||||
break;
|
||||
}
|
||||
|
||||
iteration += 1;
|
||||
if iteration >= max_iterations {
|
||||
panic!("Exceeded --max-iterations");
|
||||
}
|
||||
}
|
||||
|
||||
// Compute log-likelihood
|
||||
let ll_model = log_likelihood_obs(&data, &beta, &lambda).sum();
|
||||
|
||||
// Unstandardise betas
|
||||
let mut beta_unstandardised: DVector<f64> = DVector::zeros(data.num_covs());
|
||||
for (j, beta_value) in beta.iter().enumerate() {
|
||||
beta_unstandardised[j] = beta_value / indep_stdev[j];
|
||||
}
|
||||
|
||||
// -------------------------
|
||||
// Compute covariance matrix
|
||||
|
||||
// Compute profile log-likelihoods
|
||||
let h = 5.0 / (data.num_obs() as f64).sqrt(); // "a constant of order n^(-1/2)"
|
||||
|
||||
progress_bar.set_length(data.num_covs() as u64 + 2);
|
||||
progress_bar.reset();
|
||||
progress_bar.set_style(ProgressStyle::with_template("[{elapsed_precise}] {bar:40} Profile LL {pos}/{len}").unwrap());
|
||||
progress_bar.println("Profiling log-likelihood to compute covariance matrix");
|
||||
|
||||
// ll_null = log-likelihood for null model
|
||||
// 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 pll_toggle_zero: DVector<f64> = profile_log_likelihood_obs(&data, beta.clone(), lambda.clone(), max_iterations, 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)
|
||||
})
|
||||
.progress_with(progress_bar.clone())
|
||||
.collect();
|
||||
progress_bar.finish();
|
||||
|
||||
let mut pll_matrix: DMatrix<f64> = DMatrix::zeros(data.num_covs(), data.num_covs());
|
||||
for i in 0..data.num_obs() {
|
||||
let toggle_none_i = pll_toggle_zero[i];
|
||||
let mut ps_i: DVector<f64> = DVector::zeros(data.num_covs());
|
||||
for p in 0..data.num_covs() {
|
||||
ps_i[p] = (pll_toggle_one[p][i] - toggle_none_i) / h;
|
||||
}
|
||||
pll_matrix += ps_i.clone() * ps_i.transpose();
|
||||
}
|
||||
|
||||
let vcov = pll_matrix.try_inverse().expect("Matrix not invertible");
|
||||
|
||||
// Unstandardise SEs
|
||||
let beta_se = vcov.diagonal().apply_into(|x| { *x = x.sqrt(); });
|
||||
let mut beta_se_unstandardised: DVector<f64> = DVector::zeros(data.num_covs());
|
||||
for (j, se) in beta_se.iter().enumerate() {
|
||||
beta_se_unstandardised[j] = se / indep_stdev[j];
|
||||
}
|
||||
|
||||
return IntervalCensoredCoxResult {
|
||||
params: beta_unstandardised.data.as_vec().clone(),
|
||||
params_se: beta_se_unstandardised.data.as_vec().clone(),
|
||||
ll_model: ll_model,
|
||||
ll_null: ll_null,
|
||||
};
|
||||
}
|
||||
|
||||
fn do_e_step(data: &IntervalCensoredCoxData, exp_beta_z: &Matrix1xX<f64>, lambda: &DVector<f64>) -> DMatrix<f64> {
|
||||
// Compute S_L and S_R (S_i1 and S_i2 in the paper)
|
||||
let s_left = e_step_compute_s(data, &exp_beta_z, lambda, 0);
|
||||
let s_right = e_step_compute_s(data, &exp_beta_z, lambda, 1);
|
||||
|
||||
// In the paper, consideration is given to G(x)
|
||||
// But in a proportional hazards model, G(x) = x
|
||||
// So we omit the details
|
||||
// As a consequence, the posterior ξ_i are always 1
|
||||
|
||||
// Compute posterior weights (W_ik, "posterior mean" in C++)
|
||||
let mut posterior_weight: DMatrix<f64> = DMatrix::zeros(data.num_obs(), data.num_times());
|
||||
for (i, observation) in data.data_times.column_iter().enumerate() {
|
||||
let time_left = observation[0];
|
||||
let time_right = observation[1];
|
||||
|
||||
for (k, time) in data.time_points.iter().enumerate() {
|
||||
if *time <= time_left {
|
||||
// t_k <= L_i
|
||||
posterior_weight[(i, k)] = 0.0;
|
||||
} else if *time <= time_right && time_right.is_finite() {
|
||||
// L_i < t_k <= R_i, with R_i < ∞
|
||||
// Assumes r = 0
|
||||
posterior_weight[(i, k)] = lambda[k] * exp_beta_z[i] / (1.0 - (s_left[i] - s_right[i]).exp());
|
||||
} else {
|
||||
// None of the above circumstances
|
||||
// C++ says the weight is unused in this case
|
||||
// Set this to a non-NaN value so we can still do elementwise vector multiplication for masking
|
||||
posterior_weight[(i, k)] = 0.0;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return posterior_weight;
|
||||
}
|
||||
|
||||
fn e_step_compute_s(data: &IntervalCensoredCoxData, exp_beta_z: &Matrix1xX<f64>, lambda: &DVector<f64>, time_index: usize) -> DVector<f64> {
|
||||
let mut s: DVector<f64> = DVector::zeros(data.num_obs());
|
||||
for (i, observation) in data.data_times.column_iter().enumerate() {
|
||||
let time_cutoff = observation[time_index];
|
||||
if time_cutoff.is_infinite() {
|
||||
s[i] = f64::INFINITY;
|
||||
} else {
|
||||
for (k, time) in data.time_points.iter().enumerate() {
|
||||
if *time <= time_cutoff {
|
||||
// time is t_k <= L_i, or t_k <= R_i, as applicable
|
||||
s[i] += lambda[k] * exp_beta_z[i]; // Row 0, because all covariates are time-independent
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
return s;
|
||||
}
|
||||
|
||||
fn do_m_step(data: &IntervalCensoredCoxData, exp_beta_z: &Matrix1xX<f64>, beta: &DVector<f64>, posterior_weight: DMatrix<f64>) -> (DVector<f64>, DVector<f64>) {
|
||||
// ComputeSummandTerm
|
||||
// Covariates are time-independent in this model
|
||||
// And ξ_i is always 1, as discussed above
|
||||
// So we can skip this step and let xi_exp_beta_z = exp_beta_z
|
||||
let xi_exp_beta_z = &exp_beta_z;
|
||||
|
||||
// Split these steps into functions to make profiling easier
|
||||
let (mut s0, s1, s2) = m_step_compute_s_values(data, xi_exp_beta_z);
|
||||
let sigma = m_step_compute_sigma(data, &posterior_weight, &s0, &s1, &s2);
|
||||
let new_beta = m_step_compute_new_beta(data, &posterior_weight, &s0, &s1, sigma, beta);
|
||||
s0 = m_step_compute_s0(data, beta);
|
||||
let new_lambda = m_step_compute_new_lambda(data, &posterior_weight, &s0);
|
||||
|
||||
return (new_beta, new_lambda);
|
||||
}
|
||||
|
||||
fn m_step_compute_s_values(data: &IntervalCensoredCoxData, xi_exp_beta_z: &Matrix1xX<f64>) -> (DVector<f64>, Vec<DVector<f64>>, Vec<DMatrix<f64>>) {
|
||||
// ComputeSValues
|
||||
|
||||
// Compute s0
|
||||
let mut s0: DVector<f64> = DVector::zeros(data.num_times()); // Elements are f64
|
||||
for i in 0..data.num_obs() {
|
||||
let s0_contrib = xi_exp_beta_z[i];
|
||||
s0 += data.r_star_indicator.row(i).transpose() * s0_contrib;
|
||||
}
|
||||
|
||||
// Precompute s1, s2 contributions for each observation
|
||||
let mut s1_contrib: Vec<DVector<f64>> = vec![DVector::zeros(data.num_covs()); data.num_obs()]; // Elements are DVector of len num-covariates
|
||||
let mut s2_contrib: Vec<DMatrix<f64>> = vec![DMatrix::zeros(data.num_covs(), data.num_covs()); data.num_obs()]; // Elements are (num-covariates, num-covariates)
|
||||
for i in 0..data.num_obs() {
|
||||
s1_contrib[i] = xi_exp_beta_z[i] * data.data_indep.column(i);
|
||||
s2_contrib[i] = xi_exp_beta_z[i] * &data.z_z_transpose[i]; // Observations are time-independent
|
||||
}
|
||||
|
||||
let s1 = (0..data.num_times()).into_par_iter().map(|k| {
|
||||
let mut s1_k = DVector::zeros(data.num_covs());
|
||||
for i in 0..data.num_obs() {
|
||||
if data.r_star_indicator[(i, k)] == 1.0 {
|
||||
s1_k += &s1_contrib[i];
|
||||
}
|
||||
}
|
||||
s1_k
|
||||
}).collect();
|
||||
|
||||
let s2 = (0..data.num_times()).into_par_iter().map(|k| {
|
||||
let mut s2_k = DMatrix::zeros(data.num_covs(), data.num_covs());
|
||||
for i in 0..data.num_obs() {
|
||||
if data.r_star_indicator[(i, k)] == 1.0 {
|
||||
s2_k += &s2_contrib[i];
|
||||
}
|
||||
}
|
||||
s2_k
|
||||
}).collect();
|
||||
|
||||
return (s0, s1, s2);
|
||||
}
|
||||
|
||||
fn m_step_compute_sigma(data: &IntervalCensoredCoxData, posterior_weight: &DMatrix<f64>, s0: &DVector<f64>, s1: &Vec<DVector<f64>>, s2: &Vec<DMatrix<f64>>) -> DMatrix<f64> {
|
||||
// ComputeSigma
|
||||
let mut sigma: DMatrix<f64> = DMatrix::zeros(data.num_covs(), data.num_covs());
|
||||
for k in 0..data.num_times() {
|
||||
let factor_k = (s1[k].clone() / s0[k]) * (s1[k].transpose() / s0[k]) - (s2[k].clone() / s0[k]);
|
||||
let sum_posterior_weight = data.r_star_indicator.column(k).component_mul(&posterior_weight.column(k)).sum();
|
||||
sigma += sum_posterior_weight * factor_k.clone();
|
||||
}
|
||||
return sigma;
|
||||
}
|
||||
|
||||
fn m_step_compute_new_beta(data: &IntervalCensoredCoxData, posterior_weight: &DMatrix<f64>, s0: &DVector<f64>, s1: &Vec<DVector<f64>>, sigma: DMatrix<f64>, beta: &DVector<f64>) -> DVector<f64> {
|
||||
// ComputeNewBeta
|
||||
assert!(sigma.clone().full_piv_lu().is_invertible(), "Sigma is not invertible");
|
||||
|
||||
let mut sum: DVector<f64> = DVector::zeros(data.num_covs());
|
||||
for k in 0..data.num_times() {
|
||||
let quotient_k = s1[k].clone() / s0[k];
|
||||
for i in 0..data.num_obs() {
|
||||
if data.r_star_indicator[(i, k)] == 1.0 {
|
||||
sum += posterior_weight[(i, k)] * (data.data_indep.column(i) - "ient_k);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let new_beta = beta.clone() - sigma.try_inverse().unwrap() * sum;
|
||||
return new_beta;
|
||||
}
|
||||
|
||||
fn m_step_compute_s0(data: &IntervalCensoredCoxData, beta: &DVector<f64>) -> DVector<f64> {
|
||||
// ComputeS0
|
||||
let mut s0: DVector<f64> = DVector::zeros(data.num_times());
|
||||
for i in 0..data.num_obs() {
|
||||
// let s0_contrib = posterior_xi[i] * self.beta.dot(&data_indep.column(i)).exp();
|
||||
let s0_contrib = beta.dot(&data.data_indep.column(i)).exp();
|
||||
s0 += data.r_star_indicator.row(i).transpose() * s0_contrib;
|
||||
}
|
||||
return s0;
|
||||
}
|
||||
|
||||
fn m_step_compute_new_lambda(data: &IntervalCensoredCoxData, posterior_weight: &DMatrix<f64>, s0: &DVector<f64>) -> DVector<f64> {
|
||||
// ComputeNewLambda
|
||||
let mut new_lambda: DVector<f64> = DVector::zeros(data.num_times());
|
||||
for k in 0..data.num_times() {
|
||||
let mut numerator_k = 0.0;
|
||||
for i in 0..data.num_obs() {
|
||||
if data.r_star_indicator[(i, k)] == 1.0 {
|
||||
numerator_k += posterior_weight[(i, k)];
|
||||
}
|
||||
}
|
||||
new_lambda[k] = numerator_k / s0[k];
|
||||
}
|
||||
return new_lambda;
|
||||
}
|
||||
|
||||
fn em_check_convergence(beta: &DVector<f64>, lambda: &DVector<f64>, new_beta: &DVector<f64>, new_lambda: &DVector<f64>, tolerance: f64) -> (f64, bool) {
|
||||
let beta_diff = max_abs_difference(beta, new_beta);
|
||||
|
||||
let old_cumulative_hazard = cumulative_hazard(lambda);
|
||||
let new_cumulative_hazard = cumulative_hazard(new_lambda);
|
||||
let lambda_diff = max_abs_difference(&old_cumulative_hazard, &new_cumulative_hazard);
|
||||
|
||||
let max_diff = beta_diff.max(lambda_diff);
|
||||
|
||||
return (max_diff, max_diff < tolerance);
|
||||
}
|
||||
|
||||
fn log_likelihood_obs(data: &IntervalCensoredCoxData, beta: &DVector<f64>, lambda: &DVector<f64>) -> DVector<f64> {
|
||||
// Pre-compute exp(β^T * Z_ik)
|
||||
let exp_beta_z: Matrix1xX<f64> = (beta.transpose() * &data.data_indep).apply_into(|x| { *x = x.exp(); });
|
||||
|
||||
// Compute S_L and S_R (S_i1 and S_i2 in the paper)
|
||||
let s_left = e_step_compute_s(data, &exp_beta_z, lambda, 0);
|
||||
let s_right = e_step_compute_s(data, &exp_beta_z, lambda, 1);
|
||||
|
||||
// Compute the log-likelihood by summing log-likelihood for each observation
|
||||
// Assumes G(x) = x
|
||||
let mut result = DVector::zeros(data.num_obs());
|
||||
for i in 0..data.num_obs() {
|
||||
result[i] = ((-s_left[i]).exp() - (-s_right[i]).exp()).ln();
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
fn profile_log_likelihood_obs(data: &IntervalCensoredCoxData, beta: DVector<f64>, mut lambda: DVector<f64>, max_iterations: u32, tolerance: f64) -> DVector<f64> {
|
||||
for _iteration in 0..max_iterations {
|
||||
// Pre-compute exp(β^T * Z_ik)
|
||||
let exp_beta_z: Matrix1xX<f64> = (beta.transpose() * &data.data_indep).apply_into(|x| { *x = x.exp(); });
|
||||
|
||||
// Do E-step
|
||||
let posterior_weight = do_e_step(data, &exp_beta_z, &lambda);
|
||||
|
||||
// Do M-step (skip expensive unnecessary steps)
|
||||
let s0 = m_step_compute_s0(data, &beta);
|
||||
let new_lambda = m_step_compute_new_lambda(data, &posterior_weight, &s0);
|
||||
|
||||
// Check for convergence
|
||||
let old_cumulative_hazard = cumulative_hazard(&lambda);
|
||||
let new_cumulative_hazard = cumulative_hazard(&new_lambda);
|
||||
let lambda_diff = max_abs_difference(&old_cumulative_hazard, &new_cumulative_hazard);
|
||||
lambda = new_lambda;
|
||||
|
||||
// TODO: Incorporate into progress bar
|
||||
//println!("Profile iteration {}, estimates changed by {}", iteration + 1, lambda_diff);
|
||||
|
||||
if lambda_diff < tolerance {
|
||||
return log_likelihood_obs(data, &beta, &lambda);
|
||||
}
|
||||
}
|
||||
|
||||
panic!("Exceeded --max-iterations");
|
||||
}
|
||||
|
||||
#[derive(Serialize, Deserialize)]
|
||||
struct IntervalCensoredCoxResult {
|
||||
params: Vec<f64>,
|
||||
params_se: Vec<f64>,
|
||||
ll_model: f64,
|
||||
ll_null: f64,
|
||||
// TODO: cumulative hazard, etc.
|
||||
}
|
||||
|
||||
fn cumulative_hazard(lambda: &DVector<f64>) -> DVector<f64> {
|
||||
let mut result = DVector::zeros(lambda.nrows());
|
||||
for (i, value) in lambda.iter().enumerate() {
|
||||
if i > 0 {
|
||||
result[i] += result[i - 1];
|
||||
}
|
||||
result[i] += value;
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
fn max_abs_difference(vector_old: &DVector<f64>, vector_new: &DVector<f64>) -> f64 {
|
||||
return (vector_new - vector_old).abs().max();
|
||||
}
|
||||
40
src/main.rs
Normal file
40
src/main.rs
Normal file
@ -0,0 +1,40 @@
|
||||
// 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/>.
|
||||
|
||||
use clap::{Parser, Subcommand};
|
||||
|
||||
mod intcox;
|
||||
|
||||
#[derive(Parser)]
|
||||
#[command(about="High-performance statistics implementations")]
|
||||
struct MainArgs {
|
||||
#[clap(subcommand)]
|
||||
command: Command,
|
||||
}
|
||||
|
||||
#[derive(Subcommand)]
|
||||
enum Command {
|
||||
#[command(name="intcox", about="Interval-censored Cox regression", long_about="Fit a Cox proportional hazards model on time-independent interval-censored observations, using an E-M algorithm by Zeng, Mao & Lin (Biometrika 2016;103(2):253-71)")]
|
||||
IntCox(intcox::IntCoxArgs),
|
||||
}
|
||||
|
||||
fn main() {
|
||||
let args = MainArgs::parse();
|
||||
|
||||
match args.command {
|
||||
Command::IntCox(intcox_args) => intcox::main(intcox_args),
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user