Package index • celecx

Package

celecx celecx-package: celecx: Computer Experiment LEarning Curve eXtrapolation

Active Learning

High-level entry points for running active learning.

optimize_active(): Run Active Learning
optimizer_active_learning(): Active Learning Optimizer Factory
optimizer_pool_al(): Convenience Constructor for Pool-Based Active Learning Optimizers

Search Instance

Instance, context, and error for managing a search run.

SearchInstance: Search Instance
search_instance(): Create Search Instance
ContextSearch: Context for Search Instance
search_terminated_error(): Search Terminated Error

Optimizers

Optimizer base classes and concrete optimizers over search spaces or candidate pools.

OptimizerSearchAbstract: Abstract Base Class for Search-Compatible Optimizers
OptimizerPoolAbstract: Abstract Base Class for Pool-Aware Optimizers
OptimizerAL: Proposer-Based Active Learning Optimizer
mlr_optimizers_pool_random OptimizerPoolRandom: Random Search on Pool-Restricted or Discrete Objectives
mlr_optimizers_pool_sample OptimizerPoolSample: Sampler-Based Search on Pool-Restricted or Discrete Objectives

Active Learning Proposers

Proposers that build active-learning batches inside OptimizerAL.

ALContext: Active Learning Proposal Context
ALProposer: Active Learning Proposer
ALProposerScoreAbstract: Abstract Base Class for Score-Based Active Learning Proposers
ALProposerScore: Score-Based Active Learning Proposer
ALProposerSequentialScore: Sequential Score-Based Active Learning Proposer
ALProposerSequentialReference: Sequential Reference Active Learning Proposer
ALProposerPseudoLabel: Pseudo-Label Active Learning Proposer
ALProposerPortfolio: Portfolio Active Learning Proposer

Score Modifiers

Modifiers that adjust acquisition scores during sequential batch construction.

ALScoreModifier: Active Learning Score Modifier
ALScoreModifierDiversity: Diversity Active Learning Score Modifier
ALScoreModifierLocalPenalization: Local-Penalization Active Learning Score Modifier
ALScoreModifierNone: No-Op Active Learning Score Modifier

Acquisition Functions

Distance-aware acquisition functions for active learning.

AcqFunctionDist: Distance-Aware Acquisition Function Base Class
AcqFunctionDistGSx: Distance-Aware GSx Acquisition Function
AcqFunctionDistIDEAL: Distance-Aware IDEAL Acquisition Function
AcqFunctionDistIGS: Distance-Aware iGS Acquisition Function
AcqFunctionGSy: GSy Acquisition Function

Active Learning Distances

Distance objects, their dictionary, and sugar constructors.

ALDistance: Active Learning Distance Base Class
ALDistanceGeometry: Geometry-Based Active Learning Distance
mlr_al_distances: Dictionary of Active Learning Distances
mlr_al_distances_affine ALDistanceAffine: Affine Active Learning Distance
mlr_al_distances_gower ALDistanceGower: Gower Active Learning Distance
mlr_al_distances_standardize ALDistanceStandardize: Standardized Active Learning Distance
clx_ald(): Syntactic Sugar Active Learning Distance Construction
clx_alds(): Syntactic Sugar Active Learning Distances Construction

Space Samplers

Space-filling and pool-based samplers, their dictionary, and sugar constructors.

SpaceSampler: Space Sampler Base Class
SpaceSamplerDistance: Distance-Based Space Sampler
mlr_space_samplers: Dictionary of Space Samplers
mlr_space_samplers_chain SpaceSamplerChain: Chained Space Sampler
mlr_space_samplers_conditional SpaceSamplerConditional: Conditional Space Sampler
mlr_space_samplers_gsx SpaceSamplerGSx: GSx Space Sampler
mlr_space_samplers_kmeans SpaceSamplerKMeans: K-Means Space Sampler
mlr_space_samplers_kmedoids SpaceSamplerKMedoids: K-Medoids Space Sampler
mlr_space_samplers_lhs SpaceSamplerLhs: LHS Space Sampler
mlr_space_samplers_relational_kmeans SpaceSamplerRelationalKMeans: Relational K-Means Space Sampler
mlr_space_samplers_sobol SpaceSamplerSobol: Sobol Space Sampler
mlr_space_samplers_uniform SpaceSamplerUniform: Uniform Space Sampler
clx_sps(): Syntactic Sugar Space Sampler Construction
clx_spss(): Syntactic Sugar Space Samplers Construction

Objectives

Objective functions and pool-restricted objective wrappers.

ObjectiveDataset: Objective Based on Pre-evaluated Dataset
ObjectiveLearner: Objective Function Based on a Fitted Learner
ObjectivePoolAbstract: Abstract Base Class for Pool-backed Objectives
ObjectivePoolRFun: Objective Function Based on a Candidate Pool and R Function
ObjectivePoolWrapper: Objective Function Wrapping Another Objective on a Candidate Pool

Surrogate Learners

Regression learners with uncertainty quantification.

mlr_learners_regr.bootstrap_se LearnerRegrBootstrapSE: Bootstrap Ensemble Learner with SE Prediction
mlr_learners_regr.deepgp LearnerRegrDeepGP: Deep GP Regression Learner
mlr_learners_regr.gpfit LearnerRegrGPfit: GPfit Regression Learner
mlr_learners_regr.hetgp LearnerRegrHetGP: hetGP Regression Learner
mlr_learners_regr.quantile_se LearnerRegrQuantileSE: Quantile Regression Learner with SE Prediction
mlr_learners_regr.tgp LearnerRegrTGP: tgp Regression Learner

Model Marshaling

S3 methods for (un)marshaling custom learner model states.

marshal_model(<learner_regr_bootstrap_se_state>): Marshal Model for LearnerRegrBootstrapSE State
marshal_model(<learner_regr_quantile_se_state>): Marshal Model for LearnerRegrQuantileSE State
unmarshal_model(<learner_regr_bootstrap_se_state_marshaled>): Unmarshal Model for LearnerRegrBootstrapSE State
unmarshal_model(<learner_regr_quantile_se_state_marshaled>): Unmarshal Model for LearnerRegrQuantileSE State

Metrics Tracking

Tracker, callbacks, and helpers for logging metrics during a run.

MetricsTracker: Metrics Tracker
metrics_tracker(): Create Metrics Tracker
make_metric(): Make Metric Function
celecx.metrics_tracker CallbackMetricsTracker: Metrics Tracker Callback
celecx.forecast_tracker CallbackForecastTracker: Forecast Tracker Callback

Metrics

Built-in metric functions for optimization quality and model accuracy.

search_metrics: Search Metrics
metric_best_y(): Best Y Metric
metric_integrated_variance(): Integrated Variance Metric
metric_max_variance(): Maximum Variance Metric
metric_mean_variance(): Mean Variance Metric
metric_model_mae(): Model MAE Metric
metric_model_r2(): Model R-squared Metric
metric_model_rmse(): Model RMSE Metric
metric_regret(): Regret Metric
metric_simple_regret(): Simple Regret Metric
metric_worst_y(): Worst Y Metric

Codomain Helpers

Accessors for optimization / learning targets in a codomain.

codomain_goals(): Determine Goals from Codomain
codomain_target_ids(): Get Target IDs from Codomain

Design Generation

Grid and volume helpers for search spaces.

generate_design_grid_celecx(): Generate a Dependency-Aware Grid Design
generate_default_grid(): Generate Default Grid
compute_domain_volume(): Compute Domain Volume

Validation Helpers

Assertion utilities used by domain, codomain, and pool checks.

assert_data_table_param_set(): Validate data.table Against ParamSet
assert_domain_codomain(): Validate Domain and Codomain Structure
assert_learner_domain(): Validate Learner Against Domain
assert_param_type_compatible(): Assert Parameter Type Compatibility
assert_param_uty_custom_check(): Assert ParamUty Custom Checks for a data.table Column
assert_pool_objective_search_space(): Assert Pool Objective Search-Space Compatibility
objective_uses_dt_eval(): Detect Objectives with Native data.table Evaluation Semantics
param_set_topo_ids(): Topologically sort parameter IDs by dependency order
param_set_valid_mask_dt(): Get logical(nrow(dt)) mask of valid configurations in a data.table
get_col_type(): Get Column Type from data.table Column

Base Classes

Shared abstractions and small utilities.

ConfigurableComponent: ConfigurableComponent
SurrogateNull: Archive-Backed Surrogate Adapter
ResultAssignerNull: Null Result Assigner
hash_transform(): Create Hash Digest of an Object