629 lines
28 KiB
Python
629 lines
28 KiB
Python
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#!/usr/bin/env python3
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"""
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差分进化算法优化TTA-E集成参数
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使用SciPy的差分进化算法优化gru_weight和tta_weights参数，目标是最小化PER
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"""
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import os
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import sys
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import torch
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import numpy as np
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import pandas as pd
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from omegaconf import OmegaConf
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import time
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from tqdm import tqdm
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import editdistance
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import pickle
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import multiprocessing as mp
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from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
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from scipy.optimize import differential_evolution
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import warnings
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warnings.filterwarnings("ignore")
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# Add parent directories to path to import models
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sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'model_training'))
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sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'model_training_lstm'))
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from model_training.rnn_model import GRUDecoder
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from model_training_lstm.rnn_model import LSTMDecoder
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from model_training.evaluate_model_helpers import *
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class TTAEnsembleCache:
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"""高效缓存系统，存储GRU和LSTM在5种增强方式下的预测结果"""
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def __init__(self, cache_dir='./tta_cache'):
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self.cache_dir = cache_dir
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os.makedirs(cache_dir, exist_ok=True)
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self.gru_cache = {}
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self.lstm_cache = {}
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self.augmentation_types = ['original', 'noise', 'scale', 'shift', 'smooth']
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def _get_cache_key(self, session, trial, aug_type):
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"""生成缓存键"""
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return f"{session}_{trial}_{aug_type}"
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def _get_cache_file(self, model_type):
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"""获取缓存文件路径"""
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return os.path.join(self.cache_dir, f'{model_type}_predictions.pkl')
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def save_cache(self):
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"""保存缓存到磁盘"""
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with open(self._get_cache_file('gru'), 'wb') as f:
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pickle.dump(self.gru_cache, f)
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with open(self._get_cache_file('lstm'), 'wb') as f:
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pickle.dump(self.lstm_cache, f)
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def load_cache(self):
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"""从磁盘加载缓存"""
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try:
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with open(self._get_cache_file('gru'), 'rb') as f:
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self.gru_cache = pickle.load(f)
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with open(self._get_cache_file('lstm'), 'rb') as f:
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self.lstm_cache = pickle.load(f)
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return True
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except FileNotFoundError:
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return False
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def add_prediction(self, model_type, session, trial, aug_type, prediction):
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"""添加预测结果到缓存"""
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cache = self.gru_cache if model_type == 'gru' else self.lstm_cache
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key = self._get_cache_key(session, trial, aug_type)
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cache[key] = prediction
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def get_prediction(self, model_type, session, trial, aug_type):
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"""从缓存获取预测结果"""
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cache = self.gru_cache if model_type == 'gru' else self.lstm_cache
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key = self._get_cache_key(session, trial, aug_type)
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return cache.get(key, None)
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def is_complete(self, sessions, trials_per_session):
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"""检查缓存是否完整"""
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total_expected = 0
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total_cached_gru = 0
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total_cached_lstm = 0
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for session in sessions:
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for trial in range(trials_per_session[session]):
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for aug_type in self.augmentation_types:
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total_expected += 1
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if self.get_prediction('gru', session, trial, aug_type) is not None:
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total_cached_gru += 1
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if self.get_prediction('lstm', session, trial, aug_type) is not None:
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total_cached_lstm += 1
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return (total_cached_gru == total_expected and
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total_cached_lstm == total_expected)
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class TTAEDifferentialEvolutionOptimizer:
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"""使用差分进化算法优化TTA-E参数的主类"""
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def __init__(self,
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gru_model_path='/root/autodl-tmp/nejm-brain-to-text/data/t15_pretrained_rnn_baseline',
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lstm_model_path='/root/autodl-tmp/nejm-brain-to-text/model_training_lstm/trained_models/baseline_rnn',
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data_dir='../data/hdf5_data_final',
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csv_path='../data/t15_copyTaskData_description.csv',
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gpu_number=0):
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self.gru_model_path = gru_model_path
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self.lstm_model_path = lstm_model_path
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self.data_dir = data_dir
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self.csv_path = csv_path
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self.gpu_number = gpu_number
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# 初始化设备
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if torch.cuda.is_available() and gpu_number >= 0:
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self.device = torch.device(f'cuda:{gpu_number}')
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print(f'Using {self.device} for model inference.')
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else:
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self.device = torch.device('cpu')
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print('Using CPU for model inference.')
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# 初始化缓存
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self.cache = TTAEnsembleCache()
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# TTA参数
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self.tta_noise_std = 0.01
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self.tta_smooth_range = 0.5
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self.tta_scale_range = 0.05
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self.tta_cut_max = 3
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# 差分进化算法参数
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self.population_size = 15 # 种群大小倍数 (实际种群 = 15 * 6维 = 90)
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self.max_iterations = 50 # 最大迭代次数
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self.tolerance = 1e-6 # 收敛容忍度
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self.mutation_factor = 0.7 # 变异因子 [0.5, 2.0]
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self.crossover_prob = 0.9 # 交叉概率 [0, 1]
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# 评估计数器
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self.evaluation_count = 0
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self.best_per_history = []
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# 加载模型和数据
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self._load_models()
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self._load_data()
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def _load_models(self):
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"""加载GRU和LSTM模型"""
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print("Loading models...")
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# 加载模型参数
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self.gru_model_args = OmegaConf.load(os.path.join(self.gru_model_path, 'checkpoint/args.yaml'))
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self.lstm_model_args = OmegaConf.load(os.path.join(self.lstm_model_path, 'checkpoint/args.yaml'))
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# 定义GRU模型
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self.gru_model = GRUDecoder(
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neural_dim=self.gru_model_args['model']['n_input_features'],
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n_units=self.gru_model_args['model']['n_units'],
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n_days=len(self.gru_model_args['dataset']['sessions']),
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n_classes=self.gru_model_args['dataset']['n_classes'],
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rnn_dropout=self.gru_model_args['model']['rnn_dropout'],
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input_dropout=self.gru_model_args['model']['input_network']['input_layer_dropout'],
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n_layers=self.gru_model_args['model']['n_layers'],
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patch_size=self.gru_model_args['model']['patch_size'],
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patch_stride=self.gru_model_args['model']['patch_stride'],
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)
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# 加载GRU模型权重
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gru_checkpoint = torch.load(os.path.join(self.gru_model_path, 'checkpoint/best_checkpoint'),
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weights_only=False, map_location=self.device)
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# 处理模型键名
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for key in list(gru_checkpoint['model_state_dict'].keys()):
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gru_checkpoint['model_state_dict'][key.replace("module.", "")] = gru_checkpoint['model_state_dict'].pop(key)
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gru_checkpoint['model_state_dict'][key.replace("_orig_mod.", "")] = gru_checkpoint['model_state_dict'].pop(key)
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self.gru_model.load_state_dict(gru_checkpoint['model_state_dict'])
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# 定义LSTM模型
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self.lstm_model = LSTMDecoder(
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neural_dim=self.lstm_model_args['model']['n_input_features'],
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n_units=self.lstm_model_args['model']['n_units'],
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n_days=len(self.lstm_model_args['dataset']['sessions']),
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n_classes=self.lstm_model_args['dataset']['n_classes'],
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rnn_dropout=self.lstm_model_args['model']['rnn_dropout'],
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input_dropout=self.lstm_model_args['model']['input_network']['input_layer_dropout'],
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n_layers=self.lstm_model_args['model']['n_layers'],
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patch_size=self.lstm_model_args['model']['patch_size'],
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patch_stride=self.lstm_model_args['model']['patch_stride'],
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)
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# 加载LSTM模型权重
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lstm_checkpoint = torch.load(os.path.join(self.lstm_model_path, 'checkpoint/best_checkpoint'),
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weights_only=False, map_location=self.device)
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# 处理模型键名
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for key in list(lstm_checkpoint['model_state_dict'].keys()):
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lstm_checkpoint['model_state_dict'][key.replace("module.", "")] = lstm_checkpoint['model_state_dict'].pop(key)
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lstm_checkpoint['model_state_dict'][key.replace("_orig_mod.", "")] = lstm_checkpoint['model_state_dict'].pop(key)
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self.lstm_model.load_state_dict(lstm_checkpoint['model_state_dict'])
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# 移动模型到设备并设置为评估模式
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self.gru_model.to(self.device)
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self.lstm_model.to(self.device)
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self.gru_model.eval()
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self.lstm_model.eval()
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print("Models loaded successfully!")
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def _load_data(self):
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"""加载验证数据集"""
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print("Loading validation data...")
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# 加载CSV文件
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b2txt_csv_df = pd.read_csv(self.csv_path)
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# 加载验证数据
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self.test_data = {}
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self.trials_per_session = {}
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total_test_trials = 0
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for session in self.gru_model_args['dataset']['sessions']:
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files = [f for f in os.listdir(os.path.join(self.data_dir, session)) if f.endswith('.hdf5')]
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if 'data_val.hdf5' in files:
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eval_file = os.path.join(self.data_dir, session, 'data_val.hdf5')
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data = load_h5py_file(eval_file, b2txt_csv_df)
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self.test_data[session] = data
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self.trials_per_session[session] = len(data["neural_features"])
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total_test_trials += len(data["neural_features"])
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print(f'Loaded {len(data["neural_features"])} validation trials for session {session}.')
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print(f'Total number of validation trials: {total_test_trials}')
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def _apply_augmentation(self, x, aug_type):
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"""应用数据增强"""
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x_augmented = x.clone()
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if aug_type == 'original':
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pass
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elif aug_type == 'noise':
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noise_scale = self.tta_noise_std * (0.5 + 0.5 * np.random.rand())
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noise = torch.randn_like(x_augmented) * noise_scale
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x_augmented = x_augmented + noise
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elif aug_type == 'scale':
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scale_factor = 1.0 + (torch.rand(1).item() - 0.5) * 2 * self.tta_scale_range
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x_augmented = x_augmented * scale_factor
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elif aug_type == 'shift' and self.tta_cut_max > 0:
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shift_amount = np.random.randint(1, min(self.tta_cut_max + 1, x_augmented.shape[1] // 8))
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x_augmented = torch.cat([x_augmented[:, shift_amount:, :],
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x_augmented[:, :shift_amount, :]], dim=1)
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elif aug_type == 'smooth':
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smooth_variation = (torch.rand(1).item() - 0.5) * 2 * self.tta_smooth_range
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varied_smooth_std = max(0.3, self.gru_model_args['dataset']['data_transforms']['smooth_kernel_std'] + smooth_variation)
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return x_augmented
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def _get_model_prediction(self, model, model_args, x_smoothed, input_layer):
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"""获取单个模型的预测结果"""
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with torch.no_grad():
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logits, _ = model(
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x=x_smoothed,
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day_idx=torch.tensor([input_layer], device=self.device),
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states=None,
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return_state=True,
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)
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probs = torch.softmax(logits, dim=-1)
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return probs
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def generate_all_predictions(self):
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"""生成所有模型在所有增强方式下的预测结果并缓存"""
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print("Generating all TTA predictions for caching...")
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# 尝试加载现有缓存
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if self.cache.load_cache():
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if self.cache.is_complete(self.test_data.keys(), self.trials_per_session):
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print("Complete cache found, skipping prediction generation.")
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return
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else:
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print("Incomplete cache found, generating missing predictions...")
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total_trials = sum(self.trials_per_session.values())
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total_predictions = total_trials * len(self.cache.augmentation_types) * 2 # 2 models
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with tqdm(total=total_predictions, desc='Generating cached predictions', unit='pred') as pbar:
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for session, data in self.test_data.items():
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input_layer = self.gru_model_args['dataset']['sessions'].index(session)
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for trial in range(len(data['neural_features'])):
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# 获取神经输入
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neural_input = data['neural_features'][trial]
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neural_input = np.expand_dims(neural_input, axis=0)
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neural_input = torch.tensor(neural_input, device=self.device, dtype=torch.bfloat16)
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# 为每种增强方式生成预测
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for aug_type in self.cache.augmentation_types:
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# 检查是否已缓存
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if (self.cache.get_prediction('gru', session, trial, aug_type) is not None and
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self.cache.get_prediction('lstm', session, trial, aug_type) is not None):
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pbar.update(2)
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continue
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# 应用增强
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x_augmented = self._apply_augmentation(neural_input, aug_type)
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# 应用高斯平滑
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default_smooth_std = self.gru_model_args['dataset']['data_transforms']['smooth_kernel_std']
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|||
|
|
default_smooth_size = self.gru_model_args['dataset']['data_transforms']['smooth_kernel_size']
|
|||
|
|
|
|||
|
|
if aug_type == 'smooth':
|
|||
|
|
smooth_variation = (torch.rand(1).item() - 0.5) * 2 * self.tta_smooth_range
|
|||
|
|
varied_smooth_std = max(0.3, default_smooth_std + smooth_variation)
|
|||
|
|
else:
|
|||
|
|
varied_smooth_std = default_smooth_std
|
|||
|
|
|
|||
|
|
with torch.autocast(device_type="cuda", enabled=self.gru_model_args['use_amp'], dtype=torch.bfloat16):
|
|||
|
|
x_smoothed = gauss_smooth(
|
|||
|
|
inputs=x_augmented,
|
|||
|
|
device=self.device,
|
|||
|
|
smooth_kernel_std=varied_smooth_std,
|
|||
|
|
smooth_kernel_size=default_smooth_size,
|
|||
|
|
padding='valid',
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# GRU预测
|
|||
|
|
if self.cache.get_prediction('gru', session, trial, aug_type) is None:
|
|||
|
|
gru_probs = self._get_model_prediction(
|
|||
|
|
self.gru_model, self.gru_model_args, x_smoothed, input_layer
|
|||
|
|
)
|
|||
|
|
self.cache.add_prediction('gru', session, trial, aug_type,
|
|||
|
|
gru_probs.cpu().numpy())
|
|||
|
|
pbar.update(1)
|
|||
|
|
|
|||
|
|
# LSTM预测
|
|||
|
|
if self.cache.get_prediction('lstm', session, trial, aug_type) is None:
|
|||
|
|
lstm_probs = self._get_model_prediction(
|
|||
|
|
self.lstm_model, self.lstm_model_args, x_smoothed, input_layer
|
|||
|
|
)
|
|||
|
|
self.cache.add_prediction('lstm', session, trial, aug_type,
|
|||
|
|
lstm_probs.cpu().numpy())
|
|||
|
|
pbar.update(1)
|
|||
|
|
|
|||
|
|
# 保存缓存
|
|||
|
|
print("Saving cache to disk...")
|
|||
|
|
self.cache.save_cache()
|
|||
|
|
print("Cache generation completed!")
|
|||
|
|
|
|||
|
|
def evaluate_parameters(self, gru_weight, tta_weights):
|
|||
|
|
"""评估给定参数组合的PER性能"""
|
|||
|
|
lstm_weight = 1.0 - gru_weight
|
|||
|
|
|
|||
|
|
# 将tta_weights转换为字典
|
|||
|
|
tta_weights_dict = {
|
|||
|
|
'original': tta_weights[0],
|
|||
|
|
'noise': tta_weights[1],
|
|||
|
|
'scale': tta_weights[2],
|
|||
|
|
'shift': tta_weights[3],
|
|||
|
|
'smooth': tta_weights[4]
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
total_true_length = 0
|
|||
|
|
total_edit_distance = 0
|
|||
|
|
|
|||
|
|
for session, data in self.test_data.items():
|
|||
|
|
for trial in range(len(data['neural_features'])):
|
|||
|
|
# 收集所有增强方式的预测结果
|
|||
|
|
all_gru_probs = []
|
|||
|
|
all_lstm_probs = []
|
|||
|
|
sample_weights = []
|
|||
|
|
|
|||
|
|
for aug_type in self.cache.augmentation_types:
|
|||
|
|
if tta_weights_dict[aug_type] <= 0:
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# 从缓存获取预测结果
|
|||
|
|
gru_probs = self.cache.get_prediction('gru', session, trial, aug_type)
|
|||
|
|
lstm_probs = self.cache.get_prediction('lstm', session, trial, aug_type)
|
|||
|
|
|
|||
|
|
if gru_probs is not None and lstm_probs is not None:
|
|||
|
|
all_gru_probs.append(torch.tensor(gru_probs))
|
|||
|
|
all_lstm_probs.append(torch.tensor(lstm_probs))
|
|||
|
|
sample_weights.append(tta_weights_dict[aug_type])
|
|||
|
|
|
|||
|
|
if len(all_gru_probs) == 0:
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# TTA融合
|
|||
|
|
if len(all_gru_probs) > 1:
|
|||
|
|
min_length = min([probs.shape[1] for probs in all_gru_probs + all_lstm_probs])
|
|||
|
|
|
|||
|
|
# 截断到最小长度
|
|||
|
|
truncated_gru_probs = []
|
|||
|
|
truncated_lstm_probs = []
|
|||
|
|
for gru_probs, lstm_probs in zip(all_gru_probs, all_lstm_probs):
|
|||
|
|
if gru_probs.shape[1] > min_length:
|
|||
|
|
truncated_gru_probs.append(gru_probs[:, :min_length, :])
|
|||
|
|
else:
|
|||
|
|
truncated_gru_probs.append(gru_probs)
|
|||
|
|
|
|||
|
|
if lstm_probs.shape[1] > min_length:
|
|||
|
|
truncated_lstm_probs.append(lstm_probs[:, :min_length, :])
|
|||
|
|
else:
|
|||
|
|
truncated_lstm_probs.append(lstm_probs)
|
|||
|
|
|
|||
|
|
# 加权平均
|
|||
|
|
sample_weights_tensor = torch.tensor(sample_weights, dtype=torch.float32)
|
|||
|
|
sample_weights_tensor = sample_weights_tensor / sample_weights_tensor.sum()
|
|||
|
|
|
|||
|
|
weighted_gru_probs = torch.zeros_like(truncated_gru_probs[0])
|
|||
|
|
weighted_lstm_probs = torch.zeros_like(truncated_lstm_probs[0])
|
|||
|
|
|
|||
|
|
for i, (gru_probs, lstm_probs, weight) in enumerate(zip(truncated_gru_probs, truncated_lstm_probs, sample_weights_tensor)):
|
|||
|
|
weighted_gru_probs += weight * gru_probs
|
|||
|
|
weighted_lstm_probs += weight * lstm_probs
|
|||
|
|
|
|||
|
|
avg_gru_probs = weighted_gru_probs
|
|||
|
|
avg_lstm_probs = weighted_lstm_probs
|
|||
|
|
else:
|
|||
|
|
avg_gru_probs = all_gru_probs[0]
|
|||
|
|
avg_lstm_probs = all_lstm_probs[0]
|
|||
|
|
|
|||
|
|
# 集成融合 (几何平均)
|
|||
|
|
epsilon = 1e-8
|
|||
|
|
avg_gru_probs = avg_gru_probs + epsilon
|
|||
|
|
avg_lstm_probs = avg_lstm_probs + epsilon
|
|||
|
|
|
|||
|
|
log_ensemble_probs = (gru_weight * torch.log(avg_gru_probs) +
|
|||
|
|
lstm_weight * torch.log(avg_lstm_probs))
|
|||
|
|
ensemble_probs = torch.exp(log_ensemble_probs)
|
|||
|
|
ensemble_probs = ensemble_probs / ensemble_probs.sum(dim=-1, keepdim=True)
|
|||
|
|
|
|||
|
|
# 转换为预测序列
|
|||
|
|
logits = torch.log(ensemble_probs + epsilon)
|
|||
|
|
pred_seq = np.argmax(logits[0].numpy(), axis=-1)
|
|||
|
|
|
|||
|
|
# 移除空白和连续重复
|
|||
|
|
pred_seq = [int(p) for p in pred_seq if p != 0]
|
|||
|
|
pred_seq = [pred_seq[i] for i in range(len(pred_seq)) if i == 0 or pred_seq[i] != pred_seq[i-1]]
|
|||
|
|
pred_seq = [LOGIT_TO_PHONEME[p] for p in pred_seq]
|
|||
|
|
|
|||
|
|
# 获取真实序列
|
|||
|
|
true_seq = data['seq_class_ids'][trial][0:data['seq_len'][trial]]
|
|||
|
|
true_seq = [LOGIT_TO_PHONEME[p] for p in true_seq]
|
|||
|
|
|
|||
|
|
# 计算编辑距离
|
|||
|
|
ed = editdistance.eval(true_seq, pred_seq)
|
|||
|
|
total_true_length += len(true_seq)
|
|||
|
|
total_edit_distance += ed
|
|||
|
|
|
|||
|
|
# 计算PER
|
|||
|
|
if total_true_length == 0:
|
|||
|
|
return 100.0 # 返回最大PER作为惩罚
|
|||
|
|
|
|||
|
|
per = 100 * total_edit_distance / total_true_length
|
|||
|
|
return per
|
|||
|
|
|
|||
|
|
def objective_function(self, params):
|
|||
|
|
"""差分进化的目标函数"""
|
|||
|
|
self.evaluation_count += 1
|
|||
|
|
|
|||
|
|
# 解码参数
|
|||
|
|
gru_weight = params[0] # 范围 [0, 1]
|
|||
|
|
tta_weights = params[1:6] # 5个TTA权重
|
|||
|
|
|
|||
|
|
# 确保权重非负
|
|||
|
|
tta_weights = np.maximum(tta_weights, 0)
|
|||
|
|
|
|||
|
|
# 如果所有TTA权重都为0，返回最大PER作为惩罚
|
|||
|
|
if np.sum(tta_weights) == 0:
|
|||
|
|
return 100.0
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
per = self.evaluate_parameters(gru_weight, tta_weights)
|
|||
|
|
|
|||
|
|
# 记录历史最佳
|
|||
|
|
if len(self.best_per_history) == 0 or per < min(self.best_per_history):
|
|||
|
|
print(f"🎯 Eval {self.evaluation_count}: New best PER = {per:.4f}%")
|
|||
|
|
print(f" GRU weight = {gru_weight:.4f}, TTA weights = {tta_weights}")
|
|||
|
|
|
|||
|
|
self.best_per_history.append(per)
|
|||
|
|
|
|||
|
|
# 每10次评估输出进度
|
|||
|
|
if self.evaluation_count % 10 == 0:
|
|||
|
|
current_best = min(self.best_per_history)
|
|||
|
|
print(f"📊 Progress: {self.evaluation_count} evaluations, Best PER = {current_best:.4f}%")
|
|||
|
|
|
|||
|
|
return per # 差分进化默认最小化目标函数
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error in objective function evaluation: {e}")
|
|||
|
|
return 100.0
|
|||
|
|
|
|||
|
|
def optimize(self):
|
|||
|
|
"""运行差分进化算法优化"""
|
|||
|
|
print("Starting Differential Evolution optimization...")
|
|||
|
|
print(f"Algorithm parameters:")
|
|||
|
|
print(f" - Population size multiplier: {self.population_size}")
|
|||
|
|
print(f" - Max iterations: {self.max_iterations}")
|
|||
|
|
print(f" - Mutation factor: {self.mutation_factor}")
|
|||
|
|
print(f" - Crossover probability: {self.crossover_prob}")
|
|||
|
|
print(f" - Tolerance: {self.tolerance}")
|
|||
|
|
|
|||
|
|
# 首先生成所有预测并缓存
|
|||
|
|
self.generate_all_predictions()
|
|||
|
|
|
|||
|
|
# 定义参数边界
|
|||
|
|
# params = [gru_weight, original_weight, noise_weight, scale_weight, shift_weight, smooth_weight]
|
|||
|
|
bounds = [
|
|||
|
|
(0.0, 1.0), # gru_weight: [0, 1]
|
|||
|
|
(0.0, 5.0), # original weight: [0, 5]
|
|||
|
|
(0.0, 5.0), # noise weight: [0, 5]
|
|||
|
|
(0.0, 5.0), # scale weight: [0, 5]
|
|||
|
|
(0.0, 5.0), # shift weight: [0, 5]
|
|||
|
|
(0.0, 5.0), # smooth weight: [0, 5]
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
print(f"\nParameter bounds:")
|
|||
|
|
param_names = ['GRU weight', 'Original', 'Noise', 'Scale', 'Shift', 'Smooth']
|
|||
|
|
for i, (name, (low, high)) in enumerate(zip(param_names, bounds)):
|
|||
|
|
print(f" - {name}: [{low}, {high}]")
|
|||
|
|
|
|||
|
|
# 运行差分进化优化
|
|||
|
|
print(f"\nRunning differential evolution optimization...")
|
|||
|
|
start_time = time.time()
|
|||
|
|
|
|||
|
|
result = differential_evolution(
|
|||
|
|
func=self.objective_function,
|
|||
|
|
bounds=bounds,
|
|||
|
|
popsize=self.population_size, # 种群大小倍数
|
|||
|
|
maxiter=self.max_iterations, # 最大迭代次数
|
|||
|
|
tol=self.tolerance, # 收敛容忍度
|
|||
|
|
mutation=self.mutation_factor, # 变异因子
|
|||
|
|
recombination=self.crossover_prob, # 交叉概率
|
|||
|
|
seed=42, # 随机种子确保可复现
|
|||
|
|
disp=True, # 显示优化过程
|
|||
|
|
polish=True, # 最后用局部优化算法精炼
|
|||
|
|
workers=1, # 单线程避免缓存冲突
|
|||
|
|
updating='deferred', # 延迟更新策略
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
end_time = time.time()
|
|||
|
|
|
|||
|
|
# 获取最佳解
|
|||
|
|
best_params = result.x
|
|||
|
|
best_per = result.fun
|
|||
|
|
|
|||
|
|
print("\n" + "="*60)
|
|||
|
|
print("DIFFERENTIAL EVOLUTION OPTIMIZATION COMPLETED!")
|
|||
|
|
print("="*60)
|
|||
|
|
print(f"Optimization time: {end_time - start_time:.2f} seconds")
|
|||
|
|
print(f"Total function evaluations: {result.nfev}")
|
|||
|
|
print(f"Optimization success: {result.success}")
|
|||
|
|
print(f"Termination message: {result.message}")
|
|||
|
|
|
|||
|
|
print(f"\nBest solution:")
|
|||
|
|
print(f"Best GRU weight: {best_params[0]:.4f}")
|
|||
|
|
print(f"Best LSTM weight: {1.0 - best_params[0]:.4f}")
|
|||
|
|
print(f"Best TTA weights:")
|
|||
|
|
aug_types = ['original', 'noise', 'scale', 'shift', 'smooth']
|
|||
|
|
for i, aug_type in enumerate(aug_types):
|
|||
|
|
print(f" - {aug_type}: {best_params[i+1]:.4f}")
|
|||
|
|
print(f"Best PER: {best_per:.4f}%")
|
|||
|
|
|
|||
|
|
# 保存详细结果
|
|||
|
|
optimization_result = {
|
|||
|
|
'best_params': best_params,
|
|||
|
|
'gru_weight': best_params[0],
|
|||
|
|
'lstm_weight': 1.0 - best_params[0],
|
|||
|
|
'tta_weights': {
|
|||
|
|
'original': best_params[1],
|
|||
|
|
'noise': best_params[2],
|
|||
|
|
'scale': best_params[3],
|
|||
|
|
'shift': best_params[4],
|
|||
|
|
'smooth': best_params[5]
|
|||
|
|
},
|
|||
|
|
'best_per': best_per,
|
|||
|
|
'optimization_time': end_time - start_time,
|
|||
|
|
'function_evaluations': result.nfev,
|
|||
|
|
'success': result.success,
|
|||
|
|
'message': result.message,
|
|||
|
|
'per_history': self.best_per_history,
|
|||
|
|
'algorithm': 'differential_evolution',
|
|||
|
|
'algorithm_params': {
|
|||
|
|
'popsize': self.population_size,
|
|||
|
|
'maxiter': self.max_iterations,
|
|||
|
|
'mutation': self.mutation_factor,
|
|||
|
|
'recombination': self.crossover_prob,
|
|||
|
|
'tolerance': self.tolerance
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
# 保存到文件
|
|||
|
|
timestamp = time.strftime("%Y%m%d_%H%M%S")
|
|||
|
|
result_file = f'de_optimization_result_{timestamp}.pkl'
|
|||
|
|
with open(result_file, 'wb') as f:
|
|||
|
|
pickle.dump(optimization_result, f)
|
|||
|
|
|
|||
|
|
print(f"\nResults saved to: {result_file}")
|
|||
|
|
|
|||
|
|
# 性能分析
|
|||
|
|
print(f"\nPerformance Analysis:")
|
|||
|
|
print(f" - Average evaluation time: {(end_time - start_time) / result.nfev:.3f} seconds")
|
|||
|
|
print(f" - Evaluations per minute: {result.nfev / ((end_time - start_time) / 60):.1f}")
|
|||
|
|
if len(self.best_per_history) > 10:
|
|||
|
|
improvement = self.best_per_history[0] - min(self.best_per_history)
|
|||
|
|
print(f" - Total PER improvement: {improvement:.4f}%")
|
|||
|
|
|
|||
|
|
return optimization_result
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
"""主函数"""
|
|||
|
|
print("TTA-E Differential Evolution Optimization")
|
|||
|
|
print("="*50)
|
|||
|
|
|
|||
|
|
# 创建优化器
|
|||
|
|
optimizer = TTAEDifferentialEvolutionOptimizer(
|
|||
|
|
gru_model_path='/root/autodl-tmp/nejm-brain-to-text/data/t15_pretrained_rnn_baseline',
|
|||
|
|
lstm_model_path='/root/autodl-tmp/nejm-brain-to-text/model_training_lstm/trained_models/baseline_rnn',
|
|||
|
|
data_dir='../data/hdf5_data_final',
|
|||
|
|
csv_path='../data/t15_copyTaskData_description.csv',
|
|||
|
|
gpu_number=0
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 运行优化
|
|||
|
|
result = optimizer.optimize()
|
|||
|
|
|
|||
|
|
print("\nDifferential Evolution optimization completed successfully!")
|
|||
|
|
return result
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
# 设置环境
|
|||
|
|
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
|
|||
|
|
|
|||
|
|
# 运行主函数
|
|||
|
|
result = main()
|