Package index • celecx

Package

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

Active Learning

Main entry points for running active learning experiments.

optimize_active(): Run Active Learning
optimizer_active_learning(): Active Learning Optimizer Factory

Search Instance

Instance classes for managing active learning runs.

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

Objectives

Objective functions for computer experiments.

ObjectiveDataset: Objective Function Based on Pre-evaluated Dataset
ObjectiveLearner: Objective Function Based on a Fitted Learner

Surrogate Learners

Learners with uncertainty quantification for active learning.

mlr_learners_regr.bootstrap_se LearnerRegrBootstrapSE: Bootstrap Ensemble Learner with SE Prediction
mlr_learners_regr.quantile_se LearnerRegrQuantileSE: Quantile Regression Learner with SE Prediction

Batch Selection

Strategies for selecting multiple points per iteration.

BatchProposer: Batch Proposer
batch_strategies: Batch Selection Strategies
batch_strategy_diversity(): Diversity Batch Strategy
batch_strategy_greedy(): Greedy Batch Strategy
batch_strategy_local_penalization(): Local Penalization Batch Strategy

Candidate Generators

Methods for generating candidate points in pool-based optimization.

candidate_generators: Candidate Point Generators
candidate_generator_grid(): Grid Candidate Generator
candidate_generator_lhs(): LHS Candidate Generator
candidate_generator_local(): Local Candidate Generator
candidate_generator_mixed(): Mixed Candidate Generator
candidate_generator_random(): Random Candidate Generator
candidate_generator_sobol(): Sobol Candidate Generator

Optimizers

Acquisition function optimizers.

mlr_optimizers_pool OptimizerPool: Pool-Based Optimizer

Metrics Tracking

Track and record metrics during active learning.

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

Codomain Helpers

Utilities for working with objective codomains.

codomain_goal(): Determine Goal from Codomain
codomain_has_learn(): Check if Codomain Has Learning Targets
codomain_has_optimize(): Check if Codomain Has Optimization Targets
codomain_helpers: Codomain Helpers for Active Learning
codomain_learn_ids(): Get Learning Target IDs
codomain_optimize_ids(): Get Optimization Target IDs
codomain_target_ids(): Get Target IDs from Codomain

Base Classes

Abstract base classes and utilities.

ConfigurableComponent: ConfigurableComponent
ResultAssignerNull: Null Result Assigner
hash_transform(): Create Hash Digest of an Object