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Upgrade gpredomics to v0.7.7 #6

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2 changes: 1 addition & 1 deletion man/Population.Rd
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6 changes: 2 additions & 4 deletions man/caret-integration-Gpredomics-Summary-Function-for-Caret.Rd
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4 changes: 2 additions & 2 deletions sample/param.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,7 @@ cv:

importance:
compute_importance: false # Should importance be computed?
n_permutations_oob: 100 # Number of permutations per feature for OOB importance.
n_permutations_mda: 100 # Number of permutations per feature for MDA importance.
scaled_importance: true # Scale importance by feature prevalence inside folds.
importance_aggregation: mean # Aggregation method for importances: "mean" or "median".

Expand Down Expand Up @@ -93,7 +93,7 @@ beam:
method: LimitedExhaustive # LimitedExhaustive: generate all combinations (k out of features_to_keep). ParallelForward: extend each extendable model by one feature chosen from features_to_keep.
kmin: 2 # Number of variables used in the initial population.
kmax: 100 # Maximum number of variables to consider in a single model (variable count limit for beam algorithm).
best_models_ci_alpha: 1e-5 # Alpha for the family of best models confidence interval based on the best fit. Smaller alpha -> larger best_model range.
best_models_criterion: 10 # If <=1: alpha for FBM confidence interval. If >1: keep that many best models per k. Smaller alpha -> larger best_model range.
max_nb_of_models: 20000 # Limits the number of features_to_keep at each epoch according to the number of models made possible by them (truncated according to significance).

mcmc:
Expand Down
4 changes: 2 additions & 2 deletions src/rust/Cargo.lock
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2 changes: 1 addition & 1 deletion src/rust/Cargo.toml
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Expand Up @@ -9,7 +9,7 @@ name = 'rgpredomicsR'

[dependencies]
extendr-api = '*'
gpredomics = { git = "https://github.com/predomics/gpredomics.git", tag="v0.7.5" }
gpredomics = { git = "https://github.com/predomics/gpredomics.git", tag="v0.7.7" }
flexi_logger = "0.27"
chrono = "0.4"
log = "0.4"
Expand Down
28 changes: 14 additions & 14 deletions src/rust/src/lib.rs
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Expand Up @@ -275,8 +275,8 @@ impl Param {
("max_epochs", Robj::from(self.intern.ga.max_epochs)),
("min_epochs", Robj::from(self.intern.ga.min_epochs)),
("max_age_best_model", Robj::from(self.intern.ga.max_age_best_model)),
("kmin", Robj::from(self.intern.ga.kmin)),
("kmax", Robj::from(self.intern.ga.kmax)),
("kmin", Robj::from(self.intern.ga.k_min)),
("kmax", Robj::from(self.intern.ga.k_max)),
("select_elite_pct", Robj::from(self.intern.ga.select_elite_pct)),
("select_niche_pct", Robj::from(self.intern.ga.select_niche_pct)),
("select_random_pct", Robj::from(self.intern.ga.select_random_pct)),
Expand All @@ -292,8 +292,8 @@ impl Param {
// Convert GA fields
let beam = List::from_pairs(vec![
("method", Robj::from(format!("{:?}", self.intern.beam.method))),
("kmin", Robj::from(self.intern.beam.kmin)),
("kmax", Robj::from(self.intern.beam.kmax)),
("kmin", Robj::from(self.intern.beam.k_start)),
("kmax", Robj::from(self.intern.beam.k_stop)),
("best_models_criterion", Robj::from(self.intern.beam.best_models_criterion)),
("max_nb_of_models", Robj::from(self.intern.beam.max_nb_of_models)),
]);
Expand All @@ -311,7 +311,7 @@ impl Param {
// Convert Importance fields
let importance = List::from_pairs(vec![
("compute_importance", Robj::from(self.intern.importance.compute_importance)),
("n_permutations_oob",Robj::from(self.intern.importance.n_permutations_oob)),
("n_permutations_mda",Robj::from(self.intern.importance.n_permutations_mda)),
("scaled_importance", Robj::from(self.intern.importance.scaled_importance)),
("importance_aggregation", Robj::from(format!("{:?}",self.intern.importance.importance_aggregation)))
]);
Expand Down Expand Up @@ -389,8 +389,8 @@ impl Param {
"max_epochs" => self.intern.ga.max_epochs = value as usize,
"min_epochs" => self.intern.ga.min_epochs = value as usize,
"max_age_best_model" => self.intern.ga.max_age_best_model = value as usize,
"k_min" => self.intern.ga.kmin = value as usize,
"k_max" => self.intern.ga.kmax = value as usize,
"k_min" => self.intern.ga.k_min = value as usize,
"k_max" => self.intern.ga.k_max = value as usize,
"select_elite_pct" => self.intern.ga.select_elite_pct = value,
"select_niche_pct" => self.intern.ga.select_niche_pct = value,
"select_random_pct" => self.intern.ga.select_random_pct = value,
Expand Down Expand Up @@ -428,7 +428,7 @@ impl Param {
"threshold_windows_pct" => self.intern.voting.threshold_windows_pct = value,

// Importance parameters
"n_permutations_oob" => self.intern.importance.n_permutations_oob = value as usize,
"n_permutations_mda" => self.intern.importance.n_permutations_mda = value as usize,

// GPU parameters
"max_total_memory_mb" => self.intern.gpu.max_total_memory_mb = value as u64,
Expand Down Expand Up @@ -1900,7 +1900,7 @@ impl Individual {
feature_seeds.insert(f, seeds);
}

let ic: ImportanceCollection = self.intern.compute_oob_feature_importance(&data.intern, permutations, &features_to_process, &feature_seeds);
let ic: ImportanceCollection = self.intern.compute_mda_feature_importance(&data.intern, permutations, &features_to_process, &feature_seeds);

let mut feature = Vec::with_capacity(ic.importances.len());
let mut importance = Vec::with_capacity(ic.importances.len());
Expand Down Expand Up @@ -2598,7 +2598,7 @@ impl Experiment {
/// @name Experiment$compute_cv_importance
/// @description
/// Compute cross-validated feature importance (MDA-like) aggregated across CV folds.
/// This uses the native CV::compute_cv_oob_feature_importance logic on the original
/// This uses the native CV::compute_cv_mda_feature_importance logic on the original
/// training data and CV folds stored in the Experiment.
///
/// @param n_perm Number of permutations (default: 1000)
Expand Down Expand Up @@ -2643,7 +2643,7 @@ impl Experiment {

// 3) Native CV importance (agrégé across folds)
let mut rng = ChaCha8Rng::seed_from_u64(seed);
let mut ic: ImportanceCollection = cv.compute_cv_oob_feature_importance(
let mut ic: ImportanceCollection = cv.compute_cv_mda_feature_importance(
&self.param_arc,
permutations,
&mut rng,
Expand Down Expand Up @@ -2845,7 +2845,7 @@ impl Experiment {

let mut rng = ChaCha8Rng::seed_from_u64(base_seed);
let mut ic: ImportanceCollection = last_pop
.compute_pop_oob_feature_importance(
.compute_pop_mda_feature_importance(
&fold_data,
permutations,
&mut rng,
Expand Down Expand Up @@ -4094,7 +4094,7 @@ impl Population {
let pool = ThreadPoolBuilder::new().num_threads(threads).build().unwrap();

let mut ic: ImportanceCollection = pool.install(|| {
self.intern.compute_pop_oob_feature_importance(
self.intern.compute_pop_mda_feature_importance(
&data.intern,
permutations,
&mut rng,
Expand Down Expand Up @@ -4212,7 +4212,7 @@ impl Population {

let mut col = vec![0.0_f64; n_feat];

let ic = ind.compute_oob_feature_importance(
let ic = ind.compute_mda_feature_importance(
&data.intern,
permutations,
&feats_for_ind,
Expand Down