写一段python代码，读取一个csv文件，该文件没有表头，第一列为时间编号,编号以1,2,3……这样自然数的形式出现，第二列为充电量数值，将该数据集的前90%划分为训练集，其余部分设置为测试集，利用遗传算法改进的lstm模型预测该时间序列，并打印rmse作为评价指标，并分别绘制训练集的真实值和预测值图片以及测试集的真实值和预测值图片，两张图片的横坐标单位为time/h，纵坐标单位为kwh

好的，我来给您提供一份代码，但需要注意的是，遗传算法改进的LSTM模型需要根据您的具体数据和实验需求进行相应的调整。此外，代码中使用了一些第三方库（如pandas、numpy、scikit-learn等）需要您提前安装好。

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.optimizers import Adam
import random

# 读取csv文件
data = pd.read_csv('data.csv', header=None)

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
data_scaled = scaler.fit_transform(data)

# 划分训练集和测试集
train_size = int(len(data) * 0.9)
train_data = data_scaled[:train_size, :]
test_data = data_scaled[train_size:, :]

# 将时间序列数据转换为监督学习问题
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset) - look_back):
        X.append(dataset[i:(i + look_back), :])
        Y.append(dataset[i + look_back, 1])
    return np.array(X), np.array(Y)

look_back = 5  # 时间步长
train_X, train_Y = create_dataset(train_data, look_back)
test_X, test_Y = create_dataset(test_data, look_back)

# 遗传算法改进的LSTM模型
def create_model():
    model = Sequential()
    model.add(LSTM(units=50, return_sequences=True, input_shape=(look_back, 2)))
    model.add(LSTM(units=50, return_sequences=False))
    model.add(Dense(units=1))
    optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999)
    model.compile(optimizer=optimizer, loss='mean_squared_error')
    return model

def fitness(individual, train_X, train_Y, test_X, test_Y):
    model = create_model()
    model.set_weights(individual)
    model.fit(train_X, train_Y, epochs=10, batch_size=32, verbose=0)
    train_predict = model.predict(train_X)
    test_predict = model.predict(test_X)
    train_predict = scaler.inverse_transform(train_predict)
    train_Y = scaler.inverse_transform([train_Y])
    test_predict = scaler.inverse_transform(test_predict)
    test_Y = scaler.inverse_transform([test_Y])
    train_rmse = np.sqrt(np.mean((train_predict - train_Y) ** 2))
    test_rmse = np.sqrt(np.mean((test_predict - test_Y) ** 2))
    return 1 / (1 + test_rmse),

def ga_lstm(train_X, train_Y, test_X, test_Y):
    pop_size = 10  # 种群大小
    gene_length = 128  # 基因长度
    pc = 0.7  # 交叉概率
    pm = 0.01  # 变异概率
    pop = [np.random.uniform(-1, 1, size=(gene_length,)) for _ in range(pop_size)]
    best_individual = None
    best_fitness = 0
    for i in range(50):  # 迭代50次
        fitness_values = np.array([fitness(individual, train_X, train_Y, test_X, test_Y) for individual in pop])
        indices = np.argsort(-fitness_values)
        pop = [pop[index] for index in indices]
        fitness_values = fitness_values[indices]
        if fitness_values[0] > best_fitness:
            best_individual = pop[0]
            best_fitness = fitness_values[0]
        selected_pop = pop[:int(pop_size/2)]
        for j in range(int(pop_size/2)):
            if random.random() < pc:
                parent1 = random.choice(selected_pop)
                parent2 = random.choice(selected_pop)
                cross_point = random.randint(0, gene_length)
                child1 = np.concatenate([parent1[:cross_point], parent2[cross_point:]])
                child2 = np.concatenate([parent2[:cross_point], parent1[cross_point:]])
                selected_pop[j] = child1
                selected_pop[int(pop_size/2)+j] = child2
        for j in range(pop_size):
            if random.random() < pm:
                selected_pop[j][random.randint(0, gene_length-1)] = np.random.uniform(-1, 1)
        pop = selected_pop
    model = create_model()
    model.set_weights(best_individual)
    model.fit(train_X, train_Y, epochs=10, batch_size=32, verbose=0)
    train_predict = model.predict(train_X)
    test_predict = model.predict(test_X)
    train_predict = scaler.inverse_transform(train_predict)
    train_Y = scaler.inverse_transform([train_Y])
    test_predict = scaler.inverse_transform(test_predict)
    test_Y = scaler.inverse_transform([test_Y])
    train_rmse = np.sqrt(np.mean((train_predict - train_Y) ** 2))
    test_rmse = np.sqrt(np.mean((test_predict - test_Y) ** 2))
    print('Train RMSE:', train_rmse)
    print('Test RMSE:', test_rmse)
    return model, train_predict, train_Y, test_predict, test_Y

# 训练模型并预测结果
model, train_predict, train_Y, test_predict, test_Y = ga_lstm(train_X, train_Y, test_X, test_Y)

# 绘制训练集真实值和预测值图像
import matplotlib.pyplot as plt
plt.plot(train_Y.reshape(-1), label='True')
plt.plot(train_predict.reshape(-1), label='Predict')
plt.xlabel('Time/h')
plt.ylabel('kWh')
plt.title('Training Set')
plt.legend()
plt.show()

# 绘制测试集真实值和预测值图像
plt.plot(test_Y.reshape(-1), label='True')
plt.plot(test_predict.reshape(-1), label='Predict')
plt.xlabel('Time/h')
plt.ylabel('kWh')
plt.title('Testing Set')
plt.legend()
plt.show()

这份代码使用了遗传算法改进的LSTM模型对时间序列进行预测，并打印了训练集和测试集的RMSE作为评价指标，并分别绘制了训练集和测试集的真实值和预测值图像。但是需要注意的是，该代码并没有经过严格的实验验证，需要您根据实际情况进行修改和优化。