pmcore/algorithms/
npod.rs

1use crate::{
2    algorithms::Status,
3    prelude::{
4        algorithms::Algorithms,
5        routines::{
6            evaluation::{ipm::burke, qr},
7            output::{CycleLog, NPCycle, NPResult},
8            settings::Settings,
9        },
10    },
11    structs::{
12        psi::{calculate_psi, Psi},
13        theta::Theta,
14    },
15};
16use anyhow::bail;
17use anyhow::Result;
18use faer::Col;
19use faer_ext::IntoNdarray;
20use pharmsol::{prelude::ErrorModel, ErrorModels};
21use pharmsol::{
22    prelude::{data::Data, simulator::Equation},
23    Subject,
24};
25
26use ndarray::{
27    parallel::prelude::{IntoParallelRefMutIterator, ParallelIterator},
28    Array, Array1, ArrayBase, Dim, OwnedRepr,
29};
30
31use crate::routines::{initialization, optimization::SppOptimizer};
32
33const THETA_F: f64 = 1e-2;
34const THETA_D: f64 = 1e-4;
35
36pub struct NPOD<E: Equation> {
37    equation: E,
38    psi: Psi,
39    theta: Theta,
40    lambda: Col<f64>,
41    w: Col<f64>,
42    last_objf: f64,
43    objf: f64,
44    cycle: usize,
45    gamma_delta: Vec<f64>,
46    error_models: ErrorModels,
47    converged: bool,
48    status: Status,
49    cycle_log: CycleLog,
50    data: Data,
51    settings: Settings,
52}
53
54impl<E: Equation> Algorithms<E> for NPOD<E> {
55    fn new(settings: Settings, equation: E, data: Data) -> Result<Box<Self>, anyhow::Error> {
56        Ok(Box::new(Self {
57            equation,
58            psi: Psi::new(),
59            theta: Theta::new(),
60            lambda: Col::zeros(0),
61            w: Col::zeros(0),
62            last_objf: -1e30,
63            objf: f64::NEG_INFINITY,
64            cycle: 0,
65            gamma_delta: vec![0.1; settings.errormodels().len()],
66            error_models: settings.errormodels().clone().into(),
67            converged: false,
68            status: Status::Starting,
69            cycle_log: CycleLog::new(),
70            settings,
71            data,
72        }))
73    }
74    fn into_npresult(&self) -> NPResult<E> {
75        NPResult::new(
76            self.equation.clone(),
77            self.data.clone(),
78            self.theta.clone(),
79            self.psi.clone(),
80            self.w.clone(),
81            -2. * self.objf,
82            self.cycle,
83            self.status.clone(),
84            self.settings.clone(),
85            self.cycle_log.clone(),
86        )
87    }
88
89    fn equation(&self) -> &E {
90        &self.equation
91    }
92
93    fn get_settings(&self) -> &Settings {
94        &self.settings
95    }
96
97    fn get_data(&self) -> &Data {
98        &self.data
99    }
100
101    fn get_prior(&self) -> Theta {
102        initialization::sample_space(&self.settings).unwrap()
103    }
104
105    fn inc_cycle(&mut self) -> usize {
106        self.cycle += 1;
107        self.cycle
108    }
109
110    fn get_cycle(&self) -> usize {
111        self.cycle
112    }
113
114    fn set_theta(&mut self, theta: Theta) {
115        self.theta = theta;
116    }
117
118    fn theta(&self) -> &Theta {
119        &self.theta
120    }
121
122    fn psi(&self) -> &Psi {
123        &self.psi
124    }
125
126    fn likelihood(&self) -> f64 {
127        self.objf
128    }
129
130    fn set_status(&mut self, status: Status) {
131        self.status = status;
132    }
133
134    fn status(&self) -> &Status {
135        &self.status
136    }
137
138    fn convergence_evaluation(&mut self) {
139        if (self.last_objf - self.objf).abs() <= THETA_F {
140            tracing::info!("Objective function convergence reached");
141            self.converged = true;
142            self.status = Status::Converged;
143        }
144
145        // Stop if we have reached maximum number of cycles
146        if self.cycle >= self.settings.config().cycles {
147            tracing::warn!("Maximum number of cycles reached");
148            self.converged = true;
149            self.status = Status::MaxCycles;
150        }
151
152        // Stop if stopfile exists
153        if std::path::Path::new("stop").exists() {
154            tracing::warn!("Stopfile detected - breaking");
155            self.converged = true;
156            self.status = Status::ManualStop;
157        }
158
159        // Create state object
160        let state = NPCycle {
161            cycle: self.cycle,
162            objf: -2. * self.objf,
163            delta_objf: (self.last_objf - self.objf).abs(),
164            nspp: self.theta.nspp(),
165            theta: self.theta.clone(),
166            error_models: self.error_models.clone(),
167            status: self.status.clone(),
168        };
169
170        // Write cycle log
171        self.cycle_log.push(state);
172        self.last_objf = self.objf;
173    }
174
175    fn converged(&self) -> bool {
176        self.converged
177    }
178
179    fn evaluation(&mut self) -> Result<()> {
180        let error_model: ErrorModels = self.error_models.clone();
181
182        self.psi = calculate_psi(
183            &self.equation,
184            &self.data,
185            &self.theta,
186            &error_model,
187            self.cycle == 1 && self.settings.config().progress,
188            self.cycle != 1,
189        )?;
190
191        if let Err(err) = self.validate_psi() {
192            bail!(err);
193        }
194
195        (self.lambda, _) = match burke(&self.psi) {
196            Ok((lambda, objf)) => (lambda, objf),
197            Err(err) => {
198                bail!(err);
199            }
200        };
201        Ok(())
202    }
203
204    fn condensation(&mut self) -> Result<()> {
205        let max_lambda = self
206            .lambda
207            .iter()
208            .fold(f64::NEG_INFINITY, |acc, &x| x.max(acc));
209
210        let mut keep = Vec::<usize>::new();
211        for (index, lam) in self.lambda.iter().enumerate() {
212            if *lam > max_lambda / 1000_f64 {
213                keep.push(index);
214            }
215        }
216        if self.psi.matrix().ncols() != keep.len() {
217            tracing::debug!(
218                "Lambda (max/1000) dropped {} support point(s)",
219                self.psi.matrix().ncols() - keep.len(),
220            );
221        }
222
223        self.theta.filter_indices(keep.as_slice());
224        self.psi.filter_column_indices(keep.as_slice());
225
226        //Rank-Revealing Factorization
227        let (r, perm) = qr::qrd(&self.psi)?;
228
229        let mut keep = Vec::<usize>::new();
230
231        // The minimum between the number of subjects and the actual number of support points
232        let keep_n = self.psi.matrix().ncols().min(self.psi.matrix().nrows());
233        for i in 0..keep_n {
234            let test = r.col(i).norm_l2();
235            let r_diag_val = r.get(i, i);
236            let ratio = r_diag_val / test;
237            if ratio.abs() >= 1e-8 {
238                keep.push(*perm.get(i).unwrap());
239            }
240        }
241
242        // If a support point is dropped, log it as a debug message
243        if self.psi.matrix().ncols() != keep.len() {
244            tracing::debug!(
245                "QR decomposition dropped {} support point(s)",
246                self.psi.matrix().ncols() - keep.len(),
247            );
248        }
249
250        self.theta.filter_indices(keep.as_slice());
251        self.psi.filter_column_indices(keep.as_slice());
252
253        (self.lambda, self.objf) = match burke(&self.psi) {
254            Ok((lambda, objf)) => (lambda, objf),
255            Err(err) => {
256                return Err(anyhow::anyhow!("Error in IPM: {:?}", err));
257            }
258        };
259        self.w = self.lambda.clone();
260        Ok(())
261    }
262
263    fn optimizations(&mut self) -> Result<()> {
264        self.error_models
265            .clone()
266            .iter_mut()
267            .filter_map(|(outeq, em)| match em {
268                ErrorModel::None => None,
269                _ => Some((outeq, em)),
270            })
271            .try_for_each(|(outeq, em)| -> Result<()> {
272                // OPTIMIZATION
273
274                let gamma_up = em.scalar()? * (1.0 + self.gamma_delta[outeq]);
275                let gamma_down = em.scalar()? / (1.0 + self.gamma_delta[outeq]);
276
277                let mut error_model_up = self.error_models.clone();
278                error_model_up.set_scalar(outeq, gamma_up)?;
279
280                let mut error_model_down = self.error_models.clone();
281                error_model_down.set_scalar(outeq, gamma_down)?;
282
283                let psi_up = calculate_psi(
284                    &self.equation,
285                    &self.data,
286                    &self.theta,
287                    &error_model_up,
288                    false,
289                    true,
290                )?;
291                let psi_down = calculate_psi(
292                    &self.equation,
293                    &self.data,
294                    &self.theta,
295                    &error_model_down,
296                    false,
297                    true,
298                )?;
299
300                let (lambda_up, objf_up) = match burke(&psi_up) {
301                    Ok((lambda, objf)) => (lambda, objf),
302                    Err(err) => {
303                        //todo: write out report
304                        return Err(anyhow::anyhow!("Error in IPM during optim: {:?}", err));
305                    }
306                };
307                let (lambda_down, objf_down) = match burke(&psi_down) {
308                    Ok((lambda, objf)) => (lambda, objf),
309                    Err(err) => {
310                        //todo: write out report
311                        //panic!("Error in IPM: {:?}", err);
312                        return Err(anyhow::anyhow!("Error in IPM during optim: {:?}", err));
313                        //(Array1::zeros(1), f64::NEG_INFINITY)
314                    }
315                };
316                if objf_up > self.objf {
317                    self.error_models.set_scalar(outeq, gamma_up)?;
318                    self.objf = objf_up;
319                    self.gamma_delta[outeq] *= 4.;
320                    self.lambda = lambda_up;
321                    self.psi = psi_up;
322                }
323                if objf_down > self.objf {
324                    self.error_models.set_scalar(outeq, gamma_down)?;
325                    self.objf = objf_down;
326                    self.gamma_delta[outeq] *= 4.;
327                    self.lambda = lambda_down;
328                    self.psi = psi_down;
329                }
330                self.gamma_delta[outeq] *= 0.5;
331                if self.gamma_delta[outeq] <= 0.01 {
332                    self.gamma_delta[outeq] = 0.1;
333                }
334                Ok(())
335            })?;
336
337        Ok(())
338    }
339
340    fn logs(&self) {
341        tracing::info!("Objective function = {:.4}", -2.0 * self.objf);
342        tracing::debug!("Support points: {}", self.theta.nspp());
343        self.error_models.iter().for_each(|(outeq, em)| {
344            if ErrorModel::None == *em {
345                return;
346            }
347            tracing::debug!(
348                "Error model for outeq {}: {:.16}",
349                outeq,
350                em.scalar().unwrap_or_default()
351            );
352        });
353        // Increasing objf signals instability or model misspecification.
354        if self.last_objf > self.objf + 1e-4 {
355            tracing::warn!(
356                "Objective function decreased from {:.4} to {:.4} (delta = {})",
357                -2.0 * self.last_objf,
358                -2.0 * self.objf,
359                -2.0 * self.last_objf - -2.0 * self.objf
360            );
361        }
362    }
363
364    fn expansion(&mut self) -> Result<()> {
365        // If no stop signal, add new point to theta based on the optimization of the D function
366        let psi = self.psi().matrix().as_ref().into_ndarray().to_owned();
367        let w: Array1<f64> = self.w.clone().iter().cloned().collect();
368        let pyl = psi.dot(&w);
369
370        // Add new point to theta based on the optimization of the D function
371        let error_model: ErrorModels = self.error_models.clone();
372
373        let mut candididate_points: Vec<Array1<f64>> = Vec::default();
374        for spp in self.theta.matrix().row_iter() {
375            let candidate: Vec<f64> = spp.iter().cloned().collect();
376            let spp = Array1::from(candidate);
377            candididate_points.push(spp.to_owned());
378        }
379        candididate_points.par_iter_mut().for_each(|spp| {
380            let optimizer = SppOptimizer::new(&self.equation, &self.data, &error_model, &pyl);
381            let candidate_point = optimizer.optimize_point(spp.to_owned()).unwrap();
382            *spp = candidate_point;
383            // add spp to theta
384            // recalculate psi
385            // re-run ipm to re-calculate w
386            // re-calculate pyl
387            // re-define a new optimization
388        });
389        for cp in candididate_points {
390            self.theta.suggest_point(cp.to_vec().as_slice(), THETA_D);
391        }
392        Ok(())
393    }
394}
395
396impl<E: Equation> NPOD<E> {
397    fn validate_psi(&mut self) -> Result<()> {
398        let mut psi = self.psi().matrix().as_ref().into_ndarray().to_owned();
399        // First coerce all NaN and infinite in psi to 0.0
400        if psi.iter().any(|x| x.is_nan() || x.is_infinite()) {
401            tracing::warn!("Psi contains NaN or Inf values, coercing to 0.0");
402            for i in 0..psi.nrows() {
403                for j in 0..psi.ncols() {
404                    let val = psi.get_mut((i, j)).unwrap();
405                    if val.is_nan() || val.is_infinite() {
406                        *val = 0.0;
407                    }
408                }
409            }
410        }
411
412        // Calculate the sum of each column in psi
413        let (_, col) = psi.dim();
414        let ecol: ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>> = Array::ones(col);
415        let plam = psi.dot(&ecol);
416        let w = 1. / &plam;
417
418        // Get the index of each element in `w` that is NaN or infinite
419        let indices: Vec<usize> = w
420            .iter()
421            .enumerate()
422            .filter(|(_, x)| x.is_nan() || x.is_infinite())
423            .map(|(i, _)| i)
424            .collect::<Vec<_>>();
425
426        // If any elements in `w` are NaN or infinite, return the subject IDs for each index
427        if !indices.is_empty() {
428            let subject: Vec<&Subject> = self.data.get_subjects();
429            let zero_probability_subjects: Vec<&String> =
430                indices.iter().map(|&i| subject[i].id()).collect();
431
432            return Err(anyhow::anyhow!(
433                "The probability of one or more subjects, given the model, is zero. The following subjects have zero probability: {:?}", zero_probability_subjects
434            ));
435        }
436
437        Ok(())
438    }
439}
pmcore/algorithms/npod.rs

pmcore/algorithms/
npod.rs
