mirror of
https://github.com/c-sooyoung/bo-ptycho.git
synced 2026-09-17 22:29:07 +09:00
78 lines
2.0 KiB
Python
78 lines
2.0 KiB
Python
import os
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import copy
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import numpy as np
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def initialize(config):
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bo_params = [
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key
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for key, value in config['bo']['params'].items()
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if value is not None
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]
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bo_state = {
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'algorithm': 'random',
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'params': bo_params,
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'train_x': np.empty((0, len(bo_params))),
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'train_y': np.empty((0,)),
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}
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train_x_path = config['bo'].get('train_x')
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train_y_path = config['bo'].get('train_y')
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if train_x_path is not None and train_y_path is not None:
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if os.path.exists(train_x_path) and os.path.exists(train_y_path):
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train_x = np.load(train_x_path)
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train_y = np.load(train_y_path)
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if (
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train_x.ndim == 2
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and train_x.shape[1] == len(bo_params)
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and train_y.ndim == 1
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and train_y.shape[0] == train_x.shape[0]
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):
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bo_state['train_x'] = train_x
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bo_state['train_y'] = train_y
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return bo_state
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def ask(config, bo_state):
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next_config = copy.deepcopy(config)
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for param in bo_state['params']:
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max_modulation = config['bo']['params'][param]
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center_value = config['ptycho']['params'][param]
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modulation = max_modulation * (np.random.rand() - 0.5) * 2
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next_config['ptycho']['params'][param] = center_value + modulation
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return next_config
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def tell(config, job_config, bo_state, y_value):
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x_value = []
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for param in bo_state['params']:
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x_value.append(job_config['ptycho']['params'][param])
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x_value = np.array(x_value).reshape(1, -1)
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y_value = np.array([y_value])
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bo_state['train_x'] = np.vstack([
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bo_state['train_x'],
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x_value,
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])
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bo_state['train_y'] = np.concatenate([
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bo_state['train_y'],
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y_value,
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])
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result_dir = config['io']['result_dir']
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np.save(os.path.join(result_dir, 'train_x.npy'), bo_state['train_x'])
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np.save(os.path.join(result_dir, 'train_y.npy'), bo_state['train_y'])
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return bo_state |