diff --git a/src/config.rs b/src/config.rs
index b5496e9c8bf6cf9369edd946053695418c1a1e83..1f265e451b8d39da9975326abceff74d9fb1419c 100644
--- a/src/config.rs
+++ b/src/config.rs
@@ -19,7 +19,7 @@ use crate::{
pub mod check;
pub(crate) mod defaults;
-mod deserialize;
+pub(crate) mod deserialize;
// The final value handed out to user.
pub struct Config {
diff --git a/src/config/check.rs b/src/config/check.rs
index 4602eb5cb4c938772e0b43252d27fa2eea18b9f3..d824e461b95da42eccb966dbe5f91fa821d30013 100644
--- a/src/config/check.rs
+++ b/src/config/check.rs
@@ -8,6 +8,7 @@ use std::{
};
use arrayvec::ArrayVec;
+use colored::Colorize;
use regex::Regex;
use snafu::{ensure, Snafu};
@@ -143,7 +144,7 @@ impl TryFrom<&'_ raw::Config> for Search {
init_parms: Parameters::try_from(&raw.init, &raw.gf_times)?,
init_data_reduction_factor,
init_descent: (&raw.search.init_descent).try_into()?,
- bfgs: (&raw.search.bfgs).try_into()?,
+ bfgs: BfgsConfig::try_from(&raw.search.bfgs, raw.gf_times.len())?,
final_descent: raw
.search
.final_descent
@@ -169,16 +170,15 @@ impl TryFrom<&'_ raw::GdConfig> for GdConfig {
}
}
-impl TryFrom<&'_ raw::BfgsConfig> for BfgsConfig {
- type Error = Error;
- fn try_from(raw: &raw::BfgsConfig) -> Result<Self, Self::Error> {
+impl BfgsConfig {
+ fn try_from(raw: &raw::BfgsConfig, n_gf_times: usize) -> Result<Self, Error> {
let &raw::BfgsConfig {
max_iter,
ref wolfe_search,
step_size_threshold,
ref slope_tracking,
- ref main_trace_path,
- ref linsearch_trace_path,
+ ref main_trace,
+ ref linsearch_trace,
} = raw;
ensure!(
0. <= step_size_threshold,
@@ -187,19 +187,45 @@ impl TryFrom<&'_ raw::BfgsConfig> for BfgsConfig {
must be null or positive. Received: {step_size_threshold}.")
)
);
- let check_path = |path: &Option<PathBuf>| -> Result<_, Self::Error> {
+ let check_path = |path: &Option<PathBuf>| -> Result<_, Error> {
let Some(path) = path else {
return Ok(None);
};
- let path = io::canonicalize(path)?;
if path.is_file() {
+ let path = io::canonicalize(path)?;
eprintln!("Will override {:?}.", path.to_string_lossy());
- };
- if let Some(parent) = path.parent() {
- ensure!(parent.exists(), NoSuchFolderErr { path });
+ } else {
+ ensure!(
+ !path.is_dir(),
+ err!(("Path designates a folder: {:?}", path.to_string_lossy()))
+ );
+ if let Some(parent) = path.parent() {
+ ensure!(parent.exists(), NoSuchFolderErr { path: parent });
+ } else {
+ return err!((
+ "Path does not exist and has no parent: {:?}",
+ path.to_string_lossy()
+ ))
+ .fail();
+ }
}
- Ok(Some(path))
+ Ok(Some(path.clone()))
};
+ let main_trace_path = check_path(main_trace)?;
+ let linsearch_trace_path = check_path(linsearch_trace)?;
+ if let (None, Some(path)) = (&main_trace_path, &linsearch_trace) {
+ return err!((
+ "A path is specified with {linopt} \
+ to log the detailed trace of BFGS linear search during each step, \
+ but none is specified to log the global trace of each step. \
+ Consider setting {mainopt} option.\n\
+ The path given was: {path}",
+ linopt = "`search.bfgs.linsearch_trace`".blue(),
+ mainopt = "`search.bfgs.main_trace`".blue(),
+ path = format!("{:?}", path.to_string_lossy()).black(),
+ ))
+ .fail();
+ }
Ok(Self {
max_iter,
slope_tracking: slope_tracking
@@ -208,9 +234,20 @@ impl TryFrom<&'_ raw::BfgsConfig> for BfgsConfig {
.transpose()?,
wolfe: wolfe_search.try_into()?,
small_step: step_size_threshold,
- // TODO: expose.
- main_trace_path: check_path(main_trace_path)?,
- linsearch_trace_path: check_path(linsearch_trace_path)?,
+ main_trace_path,
+ linsearch_trace_path,
+ // Unexposed: the meaning of variables is bound to the program internals.
+ // /!\ Order here must match order within the optimised tensors.
+ variable_names: Some({
+ let mut names = ["theta", "tau_1", "delta_tau", "gf", "ac", "bc", "ancient"]
+ .iter()
+ .map(ToString::to_string)
+ .collect::<Vec<_>>();
+ for i in 0..n_gf_times {
+ names.push(format!("gf_time_{i}"));
+ }
+ names
+ }),
})
}
}
@@ -572,7 +609,7 @@ pub enum Error {
Io { source: io::Error },
#[snafu(transparent)]
Parse { source: raw::Error },
- #[snafu(display("Configuration error: {mess}"))]
+ #[snafu(display("Configuration: {mess}"))]
Config { mess: String },
#[snafu(transparent)]
Optim { source: optim::Error },
diff --git a/src/config/defaults.rs b/src/config/defaults.rs
index dbe968142e8dc1dca4fc2f066dad8b09bd072bdd..37dcd8d951ca539138b878ccc8937ccdbc5a5810 100644
--- a/src/config/defaults.rs
+++ b/src/config/defaults.rs
@@ -34,7 +34,7 @@ pub(crate) fn search() -> Search {
Search {
init_data_reduction_factor: init_data_reduction_factor(),
init_descent: init_descent(),
- bfgs: bfgs(),
+ bfgs: BfgsConfig::default(),
final_descent: None,
}
}
@@ -51,37 +51,50 @@ pub(crate) fn init_descent() -> GdConfig {
}
}
-pub(crate) fn bfgs() -> BfgsConfig {
- BfgsConfig {
- max_iter: 1_000,
- wolfe_search: wolfe_search(),
- step_size_threshold: 1e-9,
- slope_tracking: Some(SlopeTrackingConfig { sample_size: 20, threshold: 1e-3, grain: 5 }),
- main_trace_path: None,
- linsearch_trace_path: None,
+impl Default for BfgsConfig {
+ fn default() -> Self {
+ BfgsConfig {
+ max_iter: 1_000,
+ wolfe_search: WolfeSearchConfig::default(),
+ step_size_threshold: 1e-9,
+ slope_tracking: Some(SlopeTrackingConfig {
+ sample_size: 20,
+ threshold: 1e-3,
+ grain: 5,
+ }),
+ main_trace: None,
+ linsearch_trace: None,
+ }
}
}
-pub(crate) fn wolfe_search() -> WolfeSearchConfig {
- // Taken from Nocedal and Wright 2006.
- let c1 = 1e-4;
- let c2 = 0.1;
- WolfeSearchConfig {
- c1,
- c2,
- init_step_size: 1.0,
- step_decrease_factor: c2,
- step_increase_factor: 10., // (custom addition)
- flat_gradient: 1e-20,
- bisection_threshold: 1e-1,
+impl Default for WolfeSearchConfig {
+ fn default() -> Self {
+ // Taken from Nocedal and Wright 2006.
+ let c1 = 1e-4;
+ let c2 = 0.1;
+ WolfeSearchConfig {
+ c1,
+ c2,
+ init_step_size: 1.0,
+ step_decrease_factor: c2,
+ step_increase_factor: 10., // (custom addition)
+ flat_gradient: 1e-20,
+ bisection_threshold: 1e-1,
+ }
}
}
-#[allow(dead_code)] // Not the default anymore.
-pub(crate) fn final_descent() -> GdConfig {
- GdConfig {
- max_iter: 1_000,
- learning_rate: 1e-5,
- slope_tracking: Some(SlopeTrackingConfig { sample_size: 100, threshold: 1e-3, grain: 50 }),
+impl Default for GdConfig {
+ fn default() -> Self {
+ GdConfig {
+ max_iter: 1_000,
+ learning_rate: 1e-5,
+ slope_tracking: Some(SlopeTrackingConfig {
+ sample_size: 100,
+ threshold: 1e-3,
+ grain: 50,
+ }),
+ }
}
}
diff --git a/src/config/deserialize.rs b/src/config/deserialize.rs
index d5d51d17e3957f9f941c9c75f8a081055c710c73..70f56e6c580a334380d1c80c1bf8d82259d38584 100644
--- a/src/config/deserialize.rs
+++ b/src/config/deserialize.rs
@@ -129,25 +129,24 @@ pub(crate) struct Search {
pub(crate) init_data_reduction_factor: f64,
#[serde(default = "defaults::init_descent")]
pub(crate) init_descent: GdConfig,
- #[serde(default = "defaults::bfgs")]
+ #[serde(default)]
pub(crate) bfgs: BfgsConfig,
pub(crate) final_descent: Option<GdConfig>,
}
#[derive(Deserialize)]
-#[serde(deny_unknown_fields)]
+#[serde(deny_unknown_fields, default)]
pub(crate) struct BfgsConfig {
pub(crate) max_iter: u64,
- #[serde(default = "defaults::wolfe_search")]
pub(crate) wolfe_search: WolfeSearchConfig,
pub(crate) step_size_threshold: f64,
pub(crate) slope_tracking: Option<SlopeTrackingConfig>,
- pub(crate) main_trace_path: Option<PathBuf>,
- pub(crate) linsearch_trace_path: Option<PathBuf>,
+ pub(crate) main_trace: Option<PathBuf>,
+ pub(crate) linsearch_trace: Option<PathBuf>,
}
#[derive(Deserialize)]
-#[serde(deny_unknown_fields)]
+#[serde(deny_unknown_fields, default)]
pub(crate) struct WolfeSearchConfig {
pub(crate) c1: f64,
pub(crate) c2: f64,
@@ -159,7 +158,7 @@ pub(crate) struct WolfeSearchConfig {
}
#[derive(Deserialize)]
-#[serde(deny_unknown_fields)]
+#[serde(deny_unknown_fields, default)]
pub(crate) struct GdConfig {
pub(crate) max_iter: u64,
pub(crate) learning_rate: f64,
diff --git a/src/learn.rs b/src/learn.rs
index 58c6dbfb283e50f164632ad00694a698301e1a4e..e332d32a55c3799b7438838b7ed1e004492863a4 100644
--- a/src/learn.rs
+++ b/src/learn.rs
@@ -16,7 +16,7 @@ use crate::{
scenarios::Scenario,
scores::Scores,
},
- optim::{Error as OptimError, Optim, OptimResult, OptimTensor},
+ optim::{Error as OptimError, OptimResult, OptimTensor},
GeneTriplet, Parameters,
};
@@ -146,7 +146,8 @@ pub fn optimize_likelihood(
println!("-- BFGS learning.");
let mut opt: Box<dyn OptimResult>;
- opt = Box::new(search.bfgs.minimize(f, &p, 0)?);
+ // Log every step if a file was provided.
+ opt = Box::new(search.bfgs.minimize(f, &p)?);
let p = opt.best_vars();
n_eval += opt.n_eval();
n_diff += opt.n_diff();
diff --git a/src/lib.rs b/src/lib.rs
index 386a2b31fc50e835e5dcb6c1489abee92c249aaa..7630d75620dfe88485116dc74fd27468208d4c0a 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -4,7 +4,7 @@ pub mod genes_forest;
pub mod interner;
mod io;
pub mod it_mean;
-mod learn;
+pub mod learn;
mod lexer;
mod model;
mod optim;
diff --git a/src/optim.rs b/src/optim.rs
index fa7eb1f4a9f33814c86d954c6cac7bd545afc30d..8b31ca136a2b478d2a3a8ad322fd041a4d21c3a6 100644
--- a/src/optim.rs
+++ b/src/optim.rs
@@ -1,6 +1,12 @@
// Disatisfied with tch-rs optimisers,
// here is a collection of homebrew optimisers procedures
// built on tch-rs Tensors and autograd.
+//
+// These don't share a common 'Optim' trait (anymore),
+// but they implement some `minimize(f, init, ..)` method
+// to minimize the given scalar function y = f(x),
+// assuming that the tensor 'x'
+// contains all and only the gradient-tracking leaves of it.
pub(crate) mod bfgs;
pub(crate) mod gd;
@@ -16,24 +22,7 @@ use tch::Tensor;
use tensor::Loggable;
pub(crate) use tensor::OptimTensor;
-// Abstract trait shared by optimisers.
-pub(crate) trait Optim {
- type Error: Into<Error>;
- type Result: OptimResult;
- // Minimize the given scalar function y = f(x).
- // assuming that the tensor 'x'
- // contains all and only the gradient-tracking leaves of it.
- // The 'log' parameters sets the number of steps to wait before displaying one.
- // Set to 0 to not log anything.
- fn minimize(
- &self,
- f: impl Fn(&Tensor) -> Tensor,
- init: &Tensor,
- log: u64,
- ) -> Result<Self::Result, Error>;
-}
-
-// Abstact over the possible return values of the minimisation function.
+// Abstact over the possible optimisations results.
pub(crate) trait OptimResult {
fn best_vars(&self) -> &Tensor;
fn best_loss(&self) -> f64;
@@ -211,7 +200,7 @@ impl<'c> SlopeTracker<'c> {
#[derive(Debug, Snafu)]
#[snafu(context(suffix(Err)))]
pub enum Error {
- #[snafu(display("Configuration error:\n{mess}"))]
+ #[snafu(display("Configuration:\n{mess}"))]
Config { mess: String },
#[snafu(display("Gradient descent failure:\n{source}"))]
Gd { source: gd::Error },
@@ -247,7 +236,7 @@ error_convert! {
#[cfg(test)]
mod tests {
- use std::{fs::File, io::Write, path::PathBuf};
+ use std::{fs::File, io::Write, num::NonZero, path::PathBuf};
use float_eq::float_eq;
use rand::{
@@ -258,12 +247,10 @@ mod tests {
use rand_distr::StandardNormal;
use tch::{Device, Kind, Tensor};
- use crate::{
- config::defaults,
- optim::{
- bfgs::Config as BfgsConfig, gd::Config as GdConfig, History, Optim, OptimResult,
- OptimTensor, SlopeTrackingConfig,
- },
+ use super::wolfe_search::Config as WolfeSearchConfig;
+ use crate::optim::{
+ bfgs::Config as BfgsConfig, gd::Config as GdConfig, History, OptimResult, OptimTensor,
+ SlopeTrackingConfig,
};
// Generate files to debug simple optimisation tests.
@@ -294,10 +281,27 @@ mod tests {
}
fn bfgs_config() -> BfgsConfig {
- let mut bfgs: BfgsConfig = (&defaults::bfgs()).try_into().unwrap();
- bfgs.main_trace_path = Some(PathBuf::from("./target/bfgs.csv"));
- bfgs.linsearch_trace_path = Some(PathBuf::from("./target/bfgs_linsearch.csv"));
- bfgs
+ BfgsConfig {
+ max_iter: 1_000,
+ wolfe: WolfeSearchConfig {
+ c1: 1e-4,
+ c2: 0.1,
+ init_step: 1.0,
+ step_decrease: 0.1,
+ step_increase: 10., // (custom addition)
+ flat_gradient: 1e-20,
+ bisection_threshold: 1e-1,
+ },
+ small_step: 1e-9,
+ slope_tracking: Some(SlopeTrackingConfig {
+ history_size: NonZero::new(20).unwrap(),
+ threshold: 1e-3,
+ grain: 5,
+ }),
+ main_trace_path: Some(PathBuf::from("./target/bfgs.csv")),
+ linsearch_trace_path: Some(PathBuf::from("./target/bfgs_linsearch.csv")),
+ variable_names: None,
+ }
}
#[test]
@@ -333,7 +337,7 @@ mod tests {
// Fit to data.
let bfgs = bfgs_config();
- let bfgs = bfgs.minimize(loss, &init, 1).unwrap();
+ let bfgs = bfgs.minimize(loss, &init).unwrap();
// Check that we get close enough.
let [oa, ob] = [0, 1].map(|i| bfgs.best_vars().get(i).to_double());
@@ -454,7 +458,7 @@ mod tests {
let init = Tensor::from_slice(&init);
let bfgs = bfgs_config();
- let bfgs = bfgs.minimize(&loss, &init, 1).unwrap();
+ let bfgs = bfgs.minimize(&loss, &init).unwrap();
// Extract optimum found.
for (actual, expected) in (0..=1)
@@ -560,7 +564,7 @@ mod tests {
let init = (0..20).map(|_| unif.sample(&mut rng)).collect::<Vec<_>>();
let init = Tensor::from_slice(&init);
let bfgs = bfgs_config();
- let bfgs = bfgs.minimize(&loss, &init, 1).unwrap();
+ let bfgs = bfgs.minimize(&loss, &init).unwrap();
for i in 0..20 {
let oi = bfgs.best_vars().get(i).to_double();
assert!(
diff --git a/src/optim/bfgs.rs b/src/optim/bfgs.rs
index fc006b4cf2a088c50809a441a79f95c4ea4f727d..eeb7c9ee0e8fac19b113e24d6174dd4c9e9af2b2 100644
--- a/src/optim/bfgs.rs
+++ b/src/optim/bfgs.rs
@@ -19,7 +19,7 @@ use crate::optim::{
self, Config as WolfeSearchConfig, Error as WolfeSearchError, LastStep, Location,
Summary as WolfeSearchSummary,
},
- Best, Error as OptimError, Optim, OptimTensor, SlopeTracker, SlopeTrackingConfig,
+ Best, Error as OptimError, OptimTensor, SlopeTracker, SlopeTrackingConfig,
};
// The exposed config.
@@ -29,15 +29,15 @@ pub(crate) struct Config {
pub(crate) wolfe: WolfeSearchConfig,
// If required.
pub(crate) slope_tracking: Option<SlopeTrackingConfig>,
- // Logging.
- pub(crate) main_trace_path: Option<PathBuf>,
- pub(crate) linsearch_trace_path: Option<PathBuf>,
// When gradient from one step to the next happen to be equal,
// the hessian approximation breaks with a division by zero.
// In this situation, consider that the search is over
// if the step norm was shorter than this threshold value.
pub(crate) small_step: f64,
- // Paths for recording optimisation details.
+ // Logging.
+ pub(crate) main_trace_path: Option<PathBuf>,
+ pub(crate) linsearch_trace_path: Option<PathBuf>,
+ pub(crate) variable_names: Option<Vec<String>>,
}
#[derive(Debug)]
@@ -90,15 +90,12 @@ struct BfgsSearch<'c, F: Fn(&Tensor) -> Tensor> {
lin_file: Option<File>, // Linear search on every step.
}
-impl Optim for Config {
- type Error = Error;
- type Result = BfgsResult;
- fn minimize(
+impl Config {
+ pub fn minimize(
&self,
fun: impl Fn(&Tensor) -> Tensor,
init: &Tensor,
- log: u64,
- ) -> Result<Self::Result, OptimError> {
+ ) -> Result<BfgsResult, OptimError> {
let n_parms = init.size1().unwrap_or_else(|e| {
panic!(
"Parameters must be single-dimensional. Received dimensions {:?} instead:\n{e}",
@@ -141,7 +138,6 @@ impl Optim for Config {
Ok(if let Some(path) = path {
Some(File::create(path).context(TraceErr { path })?)
} else {
- ensure!(log == 0, NoTracePathErr { log });
None
})
};
@@ -166,27 +162,31 @@ impl Optim for Config {
best,
};
- Ok(search.run_search(log)?)
+ Ok(search.run_search()?)
}
}
impl<F: Fn(&Tensor) -> Tensor> BfgsSearch<'_, F> {
- fn run_search(&mut self, log: u64) -> Result<BfgsResult, Error> {
+ fn run_search(&mut self) -> Result<BfgsResult, Error> {
let cf = self.cf;
if cf.max_iter == 0 {
println!("No optimisation step asked for.");
return Ok(self.result());
}
- if log > 0 {
+ let log = cf.main_trace_path.is_some();
+ if log {
self.write_header()?;
- wolfe_search::write_header(self.lin_file.as_mut().unwrap(), self.n_vars)?;
+ if cf.linsearch_trace_path.is_some() {
+ wolfe_search::write_header(
+ self.lin_file.as_mut().unwrap(),
+ self.cf.variable_names.as_ref().ok_or(self.n_vars),
+ )?;
+ }
self.log(0.)?; // Fake initial zero step.
}
loop {
- let log_this_one = log > 0 && self.n_steps % log == 0;
-
// Pick a search direction.
self.dir = (-&self.hess).mv(&self.grad);
@@ -195,11 +195,7 @@ impl<F: Fn(&Tensor) -> Tensor> BfgsSearch<'_, F> {
Location::new(&self.fun, &self.dir, &self.vars, self.loss, &self.grad),
&self.cf.wolfe,
&mut self.best,
- (
- if log_this_one { log } else { 0 },
- self.lin_file.as_mut(),
- self.n_steps,
- ),
+ (self.lin_file.as_mut(), self.n_steps),
)?;
self.n_eval += n_eval;
self.n_diff += n_diff;
@@ -260,7 +256,7 @@ impl<F: Fn(&Tensor) -> Tensor> BfgsSearch<'_, F> {
self.loss = loss_after_step;
self.grad = grad_after_step;
- if log_this_one {
+ if log {
self.log(step_size)?;
}
@@ -288,9 +284,15 @@ impl<F: Fn(&Tensor) -> Tensor> BfgsSearch<'_, F> {
.into_iter()
.map(ToString::to_string)
.collect::<Vec<_>>();
- for vec in ["vars", "grad", "dir"] {
- for i in 0..self.n_vars {
- header.push(format!("{vec}_{i}"));
+ for vec in ["var", "grad", "dir"] {
+ if let Some(varnames) = &self.cf.variable_names {
+ for name in varnames {
+ header.push(format!("{vec}_{name}"))
+ }
+ } else {
+ for i in 0..self.n_vars {
+ header.push(format!("{vec}_{i}"));
+ }
}
}
let file = self.main_file.as_mut().unwrap();
@@ -337,9 +339,4 @@ pub enum Error {
WolfeSearch { source: WolfeSearchError },
#[snafu(display("Error writing to trace file {:?}:\n{source}", path.display()))]
Trace { path: PathBuf, source: io::Error },
- #[snafu(display(
- "A log level of {log} is required, \
- but no file path has been choosen to record traces."
- ))]
- NoTracePath { log: u64 },
}
diff --git a/src/optim/gd.rs b/src/optim/gd.rs
index 79e31aa45929182f762ec7ee19cb7f545ee3bdd9..53b110147f00bea8e9a3890146debb23017a9398 100644
--- a/src/optim/gd.rs
+++ b/src/optim/gd.rs
@@ -9,8 +9,8 @@ use snafu::Snafu;
use tch::Tensor;
use crate::optim::{
- simple_optim_result_impl, Best, Error as OptimError, Loggable, Optim, OptimTensor,
- SlopeTracker, SlopeTrackingConfig,
+ simple_optim_result_impl, Best, Error as OptimError, Loggable, OptimTensor, SlopeTracker,
+ SlopeTrackingConfig,
};
#[derive(Debug, Serialize)]
@@ -29,15 +29,13 @@ pub(crate) struct GdResult {
}
simple_optim_result_impl!(GdResult);
-impl Optim for Config {
- type Error = Error;
- type Result = GdResult;
- fn minimize(
+impl Config {
+ pub fn minimize(
&self,
fun: impl Fn(&Tensor) -> Tensor,
init: &Tensor,
log: u64,
- ) -> Result<Self::Result, OptimError> {
+ ) -> Result<GdResult, OptimError> {
let &Self { max_iter, step_size, ref slope_tracking } = self;
let kindev = (init.kind(), init.device());
let mut n_eval = 0;
diff --git a/src/optim/wolfe_search.rs b/src/optim/wolfe_search.rs
index f8657cab0563e249e851e7e891bdc8462f496e83..104a50c4c42c6189db460e1893b4ba2dd6af37c9 100644
--- a/src/optim/wolfe_search.rs
+++ b/src/optim/wolfe_search.rs
@@ -69,9 +69,6 @@ pub(crate) struct Search<'c, 's, F: Fn(&Tensor) -> Tensor> {
// Borrow situation from the calling search step.
loc: Location<'s, F>,
- // Log level.
- n_log: u64,
-
// Evolvable search state.
best: &'s mut Best, // Keep track of the best loss found so far.
@@ -87,11 +84,11 @@ pub(crate) struct Search<'c, 's, F: Fn(&Tensor) -> Tensor> {
// Starting point of the linear search, supposed immutable in this context.
pub(crate) struct Location<'s, F: Fn(&Tensor) -> Tensor> {
- pub(crate) f: &'s F, // Objective function.
- pub(crate) p: &'s Tensor, // Search direction.
- pub(crate) x: &'s Tensor, // Search starting point.
- pub(crate) y: f64, // Search starting loss value = phi(0).
- pub(crate) phigrad: f64, // Derivative = phi'(0) = p . ∇f(x).
+ pub(crate) f: &'s F, // Objective function.
+ pub(crate) p: &'s Tensor, // Search direction.
+ pub(crate) x: &'s Tensor, // Search starting point.
+ pub(crate) y: f64, // Search starting loss value = phi(0).
+ pub(crate) phigrad: f64, // Derivative = phi'(0) = p . ∇f(x).
}
// Pack search variables in various types depending on the search phase.
@@ -466,10 +463,9 @@ impl<'c, 'binsearch, F: Fn(&Tensor) -> Tensor> Search<'c, 'binsearch, F> {
x: &Tensor, // short for xs = x + alpha * p
grad: Option<&Tensor>, // = ∇f(xs)
) -> Result<(), Error> {
- if self.n_log == 0 || self.id % self.n_log > 0 {
+ let Some(file) = self.trace.as_mut() else {
return Ok(());
- }
- let file = self.trace.as_mut().unwrap();
+ };
macro_rules! w {
($fmt:literal, $value:expr) => {
write!(file, $fmt, $value)?;
@@ -502,14 +498,26 @@ impl<'c, 'binsearch, F: Fn(&Tensor) -> Tensor> Search<'c, 'binsearch, F> {
}
}
-pub(crate) fn write_header(file: &mut File, n_vars: usize) -> Result<(), Error> {
+pub(crate) fn write_header(
+ file: &mut File,
+ varnames: Result<&Vec<String>, usize>, // Get at least number if there are no names.
+) -> Result<(), Error> {
let mut header = ["id", "alpha", "loss", "phigrad"]
.into_iter()
.map(ToString::to_string)
.collect::<Vec<_>>();
- for vec in ["vars", "grad"] {
- for i in 0..n_vars {
- header.push(format!("{vec}_{i}"));
+ for vec in ["var", "grad"] {
+ match varnames {
+ Ok(names) => {
+ for name in names {
+ header.push(format!("{vec}_{name}"));
+ }
+ }
+ Err(n_vars) => {
+ for i in 0..n_vars {
+ header.push(format!("{vec}_{i}"));
+ }
+ }
}
}
writeln!(file, "{}", header.join(","))?;
@@ -526,14 +534,13 @@ pub(crate) fn search<'s, F: Fn(&Tensor) -> Tensor>(
loc: Location<'s, F>,
cf: &Config,
best: &'s mut Best,
- (n_log, trace, id): (u64, Option<&'s mut File>, u64),
+ (trace, id): (Option<&'s mut File>, u64),
) -> Result<Summary, Error> {
use Error as E;
// Initialize.
let search = Search {
n_vars: loc.p.size1().unwrap().try_into().unwrap(),
loc,
- n_log,
best,
cf,
n_eval: 0,