python写贝叶斯优化LSTM预测PM2.5

贝叶斯优化可以用来寻找LSTM模型的最佳超参数组合，从而提高PM2.5预测的准确性。下面是一个基于Python的贝叶斯优化LSTM预测PM2.5的示例代码：

首先，我们需要导入一些必要的库：

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense
from bayes_opt import BayesianOptimization

然后，我们需要读取PM2.5数据集，进行数据预处理：

# Load data
data = pd.read_csv('PM2.5.csv', header=0, index_col=0)
data.index = pd.to_datetime(data.index)
data = data.dropna()

# Normalize data
scaler = MinMaxScaler(feature_range=(0, 1))
data_scaled = scaler.fit_transform(data)

接下来，我们定义LSTM模型和目标函数：

def create_model(neurons, dropout, batch_size, epochs):
    # Define model
    model = Sequential()
    model.add(LSTM(units=int(neurons), input_shape=(1, 1)))
    model.add(Dropout(dropout))
    model.add(Dense(units=1))

    # Compile and fit model
    model.compile(loss='mean_squared_error', optimizer='adam')
    model.fit(X_train, y_train, batch_size=int(batch_size), epochs=int(epochs), verbose=0)

    # Predict and evaluate
    y_pred = model.predict(X_test)
    rmse = np.sqrt(np.mean(np.square(y_test - y_pred)))

    return -rmse

# Define search space
pbounds = {'neurons': (5, 50), 'dropout': (0, 0.5), 'batch_size': (1, 10), 'epochs': (10, 100)}

# Define optimizer
optimizer = BayesianOptimization(f=create_model, pbounds=pbounds, verbose=2)

最后，我们运行优化器来寻找最佳超参数：

# Split data into train and test sets
train_size = int(len(data_scaled) * 0.8)
test_size = len(data_scaled) - train_size
train, test = data_scaled[0:train_size,:], data_scaled[train_size:len(data_scaled),:]

X_train, y_train = train[:,0], train[:,0]
X_test, y_test = test[:,0], test[:,0]

X_train = np.reshape(X_train, (X_train.shape[0], 1, 1))
X_test = np.reshape(X_test, (X_test.shape[0], 1, 1))

# Run optimization
optimizer.maximize(init_points=10, n_iter=30)

运行完成后，我们可以得到最佳的超参数组合，然后使用这些超参数来训练LSTM模型进行PM2.5预测。