88 lines
3.5 KiB
Rust
88 lines
3.5 KiB
Rust
// hpstat: High-performance statistics implementations
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// Copyright © 2023 Lee Yingtong Li (RunasSudo)
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//
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// This program is free software: you can redistribute it and/or modify
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// it under the terms of the GNU Affero General Public License as published by
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// the Free Software Foundation, either version 3 of the License, or
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// (at your option) any later version.
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//
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// This program is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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// GNU Affero General Public License for more details.
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//
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// You should have received a copy of the GNU Affero General Public License
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// along with this program. If not, see <https://www.gnu.org/licenses/>.
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use std::fs;
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use indicatif::ProgressBar;
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use nalgebra::DMatrix;
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use hpstat::intcox::fit_interval_censored_cox;
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#[test]
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fn test_intcox_zeng_mao_lin() {
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// Compare "Bangkok Metropolitan Administration HIV" data from Zeng, Mao & Lin (2016) with IntCens 0.2 output
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let contents = fs::read_to_string("tests/zeng_mao_lin.csv").unwrap();
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let lines: Vec<String> = contents.trim_end().split("\n").map(|s| s.to_string()).collect();
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// Read data into matrices
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let mut data_times: DMatrix<f64> = DMatrix::zeros(
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2, // Left time, right time
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lines.len() - 1 // Minus 1 row for header row
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);
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// Called "Z" in the paper and "X" in the C++ code
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let mut data_indep: DMatrix<f64> = DMatrix::zeros(
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lines[0].split(",").count() - 2,
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lines.len() - 1 // Minus 1 row for header row
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);
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// Read data
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// FIXME: Parse CSV more robustly
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for (i, row) in lines.iter().skip(1).enumerate() {
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for (j, item) in row.split(",").enumerate() {
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let value = match item {
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"inf" => f64::INFINITY,
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_ => item.parse().expect("Malformed float")
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};
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if j < 2 {
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data_times[(j, i)] = value;
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} else {
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data_indep[(j - 2, i)] = value;
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}
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}
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}
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// Fit regression
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let progress_bar = ProgressBar::hidden();
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//let result = fit_interval_censored_cox(data_times, data_indep, 200, 0.00005, false, progress_bar);
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let result = fit_interval_censored_cox(data_times, data_indep, 100, 0.0001, false, progress_bar);
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// ./unireg --in zeng_mao_lin.csv --out out.txt --r 0.0 --model "(Left_Time, Right_Time) = Needle + Needle2 + LogAge + GenderM + RaceO + RaceW + GenderM_RaceO + GenderM_RaceW" --sep , --inf_char inf --convergence_threshold 0.002
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assert!((result.ll_model - -603.205).abs() < 1.0);
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assert!((result.params[0] - -0.18636961816695094).abs() < 0.01);
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assert!((result.params[1] - 0.080478699024478656).abs() < 0.01);
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assert!((result.params[2] - -0.71260450817296639).abs() < 0.01);
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assert!((result.params[3] - -0.22937443803422858).abs() < 0.01);
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assert!((result.params[4] - -0.14101449484871434).abs() < 0.01);
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assert!((result.params[5] - -0.43894526362102332).abs() < 0.01);
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assert!((result.params[6] - 0.064533885082884768).abs() < 0.01);
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assert!((result.params[7] - 0.20970425315378016).abs() < 0.01);
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assert!((result.params_se[0] - 0.41496954829036448).abs() < 0.01);
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assert!((result.params_se[1] - 0.15086156546712554).abs() < 0.01);
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assert!((result.params_se[2] - 0.36522062865858951).abs() < 0.01);
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assert!((result.params_se[3] - 0.32195496906604004).abs() < 0.01);
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assert!((result.params_se[4] - 0.3912241733944129).abs() < 0.01);
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assert!((result.params_se[5] - 0.41907763222198746).abs() < 0.01);
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assert!((result.params_se[6] - 0.45849947730170948).abs() < 0.01);
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assert!((result.params_se[7] - 0.48803508171247434).abs() < 0.01);
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}
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