mirror of
https://github.com/c-sooyoung/bo-ptycho.git
synced 2026-09-17 18:29:07 +09:00
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
# import os
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# import sys
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# import shutil
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# import numpy as np
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# import subprocess
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# import h5py
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# from PIL import Image
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# import time
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# import random
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# import torch
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# from botorch.sampling.samplers import SobolQMCNormalSampler
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# from botorch.models import SingleTaskGP
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# from botorch.fit import fit_gpytorch_model
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# from gpytorch.mlls import ExactMarginalLogLikelihood
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# from botorch.optim import optimize_acqf
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# from botorch.acquisition import UpperConfidenceBound
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# from botorch.models.transforms.outcome import Standardize
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# from botorch.acquisition.monte_carlo import qUpperConfidenceBound
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# from botorch.utils.multi_objective.box_decompositions.non_dominated import NondominatedPartitioning
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# from botorch.acquisition.multi_objective.monte_carlo import qExpectedHypervolumeImprovement
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# from botorch.utils.transforms import unnormalize
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# from bo import bo_random_config
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def initialize(config):
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pass
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# n_initial_jobs = config['bo']['parallel_jobs']
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# n_var_params = len([p for p in config['bo']['params'].values() if p is not None])
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# train_x = np.zeros([n_initial_jobs, n_var_params])
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# for i in range(n_initial_jobs):
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# config_i = bo_random_config(config)
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# train_x[i] = [p for p in config_i['bo']['params'].values() if p is not None]
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def ask(config, bo_state):
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next_config = config.copy()
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return next_config
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def tell(job_config, bo_state, y_value):
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pass |