加入自注意力机制进行特征筛选

在LSTM模型中加入自注意力机制的代码示例如下：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Model, Sequential
from keras.layers import Dense, LSTM, Input, Dropout, Lambda, Concatenate, Layer, Activation

# 读取数据
data = pd.read_csv('commodity_price.csv', index_col='date', parse_dates=True)

# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data)

# 准备训练数据和测试数据
train_data = scaled_data[:int(len(scaled_data)*0.8)]
test_data = scaled_data[int(len(scaled_data)*0.8):]

# 定义函数，将数据转换为LSTM的输入格式
def create_dataset(dataset, look_back):
    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)

# 定义自注意力层
class Attention(Layer):
    def __init__(self, step_dim, W_regularizer=None, b_regularizer=None, **kwargs):
        self.supports_masking = True
        self.init = initializers.get('glorot_uniform')
        self.W_regularizer = regularizers.get(W_regularizer)
        self.b_regularizer = regularizers.get(b_regularizer)
        self.step_dim = step_dim
        self.features_dim = 0
        super(Attention, self).__init__(**kwargs)

    def build(self, input_shape):
        assert len(input_shape) == 3
        self.W = self.add_weight(name='{}_W'.format(self.name), shape=(input_shape[-1],),
                                 initializer=self.init, regularizer=self.W_regularizer, trainable=True)
        self.features_dim = input_shape[-1]
        super(Attention, self).build(input_shape)

    def call(self, x, mask=None):
        eij = K.reshape(K.dot(K.reshape(x, (-1, self.features_dim)), K.reshape(self.W, (self.features_dim, 1))), (-1, self.step_dim))
        ai = K.exp(eij - K.max(eij, axis=1, keepdims=True))
        weights = ai / K.sum(ai, axis=1, keepdims=True)
        weighted_input = x * K.expand_dims(weights)
        return K.sum(weighted_input, axis=1)

    def compute_output_shape(self, input_shape):
        return input_shape[0], self.features_dim

# 定义LSTM模型
look_back = 30
inputs = Input(shape=(look_back, 1))
lstm1 = LSTM(64, return_sequences=True)(inputs)
attention = Attention(look_back)(lstm1)
dropout = Dropout(0.2)(attention)
output = Dense(1)(dropout)
model = Model(inputs=inputs, outputs=output)
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
train_X, train_Y = create_dataset(train_data, look_back)
train_X = np.reshape(train_X, (train_X.shape[0], train_X.shape[1], 1))
model.fit(train_X, train_Y, epochs=100, batch_size=32)

# 预测未来价格
test_X, test_Y = create_dataset(test_data, look_back)
test_X = np.reshape(test_X, (test_X.shape[0], test_X.shape[1], 1))
future_price = model.predict(test_X)

# 反归一化
future_price = scaler.inverse_transform(future_price)

# 可视化预测结果
plt.plot(data[int(len(data)*0.8):])
plt.plot(pd.date_range(start=data.index[-1], periods=len(future_price), freq='D'), future_price, label='Prediction')
plt.title('Commodity Price Prediction using LSTM with Self-Attention')
plt.legend()
plt.show()

这个代码示例在LSTM模型中加入了自注意力机制。首先，我们定义了一个 `Attention` 类，用于实现自注意力层。然后，我们定义了一个包含自注意力层的LSTM模型，并训练模型。最后，我们使用模型预测未来价格，并将预测结果反归一化并可视化。通过加入自注意力机制，模型可以自动地筛选出对预测结果最有帮助的特征，从而提高模型的预测精度。