TPE

c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization

Abstract This package aims to reproduce the TPE algorithm used in the paper published at IJCAI'23: c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization The default parameter set of this sampler is the recommended setup from the paper and the experiments in the paper can also be reproduced by this sampler. Note that this sampler is officially implemented by the first author of the original paper. The performance was verified, c.

NSGAIISampler Using TPESampler for the Initialization

Abstract This sampler uses TPESampler instead of RandomSampler for the initialization of NSGAIISampler. APIs NSGAIIWithTPEWarmupSampler This class takes the identical interface as the Optuna NSGAIISampler. Example from __future__ import annotations import optuna import optunahub def objective(trial: optuna.Trial) -> tuple[float, float]: x = trial.suggest_float("x", -5, 5) y = trial.suggest_float("y", -5, 5) return x**2 + y**2, (x - 2) ** 2 + (y - 2) ** 2 package_name = "samplers/nsgaii_with_tpe_warmup" sampler = optunahub.

TPE Acquisition Visualizer

Abstract This package is the TPE’s Acquisition Visualizer. It provides callback and plot functions. APIs Class: TPEAcquisitionVisualizer TPEAcquisitionVisualizer() __call__(self, study: optuna.study.Study, trial: optuna.trial.FrozenTrial) -> None Callback function to collect tpe sampler’s acquisition information. Args: study (optuna.study.Study): The study object. trial (optuna.trial.FrozenTrial): The trial object for which the callback is called. Returns: None plot(self, study: optuna.study.Study, trial_number: int, param_name: str,) -> plt.Figure Plots the TPE acquisition for a given trial and parameter.

Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance

Abstract This package aims to reproduce the TPE algorithm used in the paper: Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance The default parameter set of this sampler is the recommended setup from the paper and the experiments in the paper can also be reproduced by this sampler. Class or Function Names CustomizableTPESampler Installation The version constraint of this package is Optuna v4.0.0 or later.