贝叶斯优化lstmpython

使用贝叶斯优化改进LSTM模型
安装依赖库
为了在Python中使用贝叶斯优化来调整LSTM模型的超参数，首先需要安装必要的软件包。这通常包括scikit-optimize作为贝叶斯优化工具以及常用的机器学习框架如TensorFlow或PyTorch。
pip install scikit-optimize tensorflow numpy pandas matplotlib bayesian-optimization
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导入所需模块
接下来，在脚本顶部导入所需的Python库：
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
from skopt import gp_minimize
from skopt.space import Real, Integer
from skopt.utils import use_named_args
from skopt.plots import plot_convergence
import matplotlib.pyplot as plt
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准备数据集
假设有一个时间序列数据集存储在一个CSV文件中，可以按照如下方式加载并预处理这些数据[^2]:
data = pd.read_csv('time_series_data.csv')
values = data['value'].values.reshape(-1, 1)

# 创建滞后特征
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
X.append(a)
Y.append(dataset[i + look_back, 0])
return np.array(X), np.array(Y)

look_back = 3
X, y = create_dataset(values, look_back)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)

# 将输入重塑成 [样本数, 时间步长, 特征数量] 的形式
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
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构建LSTM模型函数
定义一个创建LSTM模型的功能，并允许通过传递不同配置来自定义其架构:
def build_lstm_model(input_shape, units, dropout_rate, learning_rate):
model = Sequential()
model.add(LSTM(units=units,
input_shape=input_shape,
activation='relu',
recurrent_dropout=dropout_rate))
model.add(Dense(1))

from tensorflow.keras.optimizers import Adam
optimizer = Adam(lr=learning_rate)

model.compile(loss="mean_squared_error", metrics=['mae'], optimizer=optimizer)
return model
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设置搜索空间
指定想要优化的超参数范围及其对应的分布类型:
dim_units = Integer(low=8, high=64, name='units')
dim_learning_rate = Real(low=1e-4, high=1e-2, prior='log-uniform', name='learning_rate')
dim_dropout_rate = Real(low=0.0, high=0.5, name='dropout_rate')

dimensions = [dim_units, dim_learning_rate, dim_dropout_rate]

default_parameters = [32, 1e-3, 0.2]
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定义评估指标
编写一个计算给定一组超参数下模型性能得分的方法:
@use_named_args(dimensions=dimensions)
def fitness(units, learning_rate, dropout_rate):

# 建立新的LSTM模型实例
model = build_lstm_model(
(look_back, 1),
int(units),
float(dropout_rate),
float(learning_rate)
)

history = model.fit(X_train, y_train,
epochs=10,
batch_size=int(np.sqrt(len(y_train))),
validation_split=0.2,
verbose=0).history

val_loss = min(history["val_loss"])
print(f'Validation Loss: {val_loss:.4f}')
return val_loss
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执行贝叶斯优化过程
利用高斯进程最小化器执行实际的贝叶斯优化迭代:
search_result = gp_minimize(func=fitness,
dimensions=dimensions,
acq_func='EI', # Acquisition function EI or PI etc.
n_calls=20,
x0=default_parameters)

print("Best parameters found:")
best_params = dict(zip(['units', 'learning_rate', 'dropout_rate'], search_result.x))
for k, v in best_params.items():
print(k, '=', v)
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可视化收敛情况
最后绘制图表以直观显示寻优过程中验证误差的变化趋势:
plot_convergence(search_result)
plt.show()
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