mirror of
https://github.com/c-sooyoung/bo-ptycho.git
synced 2026-09-17 18:29:07 +09:00
113 lines
3.9 KiB
Python
113 lines
3.9 KiB
Python
import os
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import copy
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import numpy as np
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from bo.bo_base import BOEngine
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class RandomBOEngine(BOEngine):
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def __init__(self, config):
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super().__init__(config)
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bo_params = [
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key for key, spec in config["bo"]["params"].items() if spec is not None
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]
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bo_param_types = {
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key: config["bo"]["params"][key].get("type", "float") for key in bo_params
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}
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integer_params = [
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key for key in bo_params if bo_param_types[key] == "int"
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]
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integer_indices = [
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bo_params.index(key) for key in integer_params
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]
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bounds = np.empty((2, len(bo_params)))
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for i, param in enumerate(bo_params):
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center = config["ptycho"]["params"][param]
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radius = config["bo"]["params"][param]["radius"]
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bounds[0, i] = center - radius
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bounds[1, i] = center + radius
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state = {
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"method": "random",
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"acquisition": "",
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"params": bo_params,
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"param_types": bo_param_types,
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"integer_params": integer_params,
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"integer_indices": integer_indices,
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"bounds": bounds, # shape: (2, BOparam)
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"train_x": np.empty((0, len(bo_params))), # shape: (BOiter, BOparam)
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"train_y": np.empty((0,)), # shape: (BOiter,)
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"train_info": [] # shape: (BOiter,)
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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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assert train_x.ndim == 2, "loaded train_x must be 2D"
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assert train_x.shape[1] == len(bo_params), "loaded train_x shape(1) does not match number of variable parameters"
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assert train_y.ndim == 1, "loaded train_y must be 1D"
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assert train_y.shape[0] == train_x.shape[0], "loaded train_x and train_y shape(0) have unequal iterations"
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state["train_x"] = train_x
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state["train_y"] = train_y
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self.state = state
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def ask(self):
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config = self.config
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state = self.state
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next_config = copy.deepcopy(config)
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for param in state['params']:
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radius = config['bo']['params'][param]['radius']
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center = config['ptycho']['params'][param]
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modulation = radius * (np.random.rand() - 0.5) * 2
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next_value = center + modulation
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if state['param_types'][param] == 'int':
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next_value = round(next_value)
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next_config['ptycho']['params'][param] = next_value
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return next_config
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def tell(self, job_config, y_value):
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config = self.config
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state = self.state
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x_value = []
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for param in state['params']:
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x_value.append(job_config['ptycho']['params'][param])
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state['train_x'] = np.vstack([
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state['train_x'],
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np.array(x_value).reshape(1, -1)
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])
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state['train_y'] = np.concatenate([
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state['train_y'],
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np.array([y_value])
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])
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state['train_info'].append(state['method'])
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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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np.save(train_x_path, state['train_x'])
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np.save(train_y_path, state['train_y'])
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else:
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result_dir = config['io']['result_dir']
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np.save(os.path.join(result_dir, 'train_x.npy'), state['train_x'])
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np.save(os.path.join(result_dir, 'train_y.npy'), state['train_y'])
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