1、摘要

本文主要讲解：PSO粒子群优化-LSTM-优化神经网络神经元个数dropout和batch_size，目标为对沪深300价格进行预测
主要思路：

1. PSO Parameters ：粒子数量、搜索维度、所有粒子的位置和速度、个体经历的最佳位置和全局最佳位置、每个个体的历史最佳适应值
2. LSTM Parameters 神经网络第一层神经元个数、神经网络第二层神经元个数、dropout比率、batch_size
3. 开始搜索：初始粒子适应度计算、计算初始全局最优、计算适应值、初始全局最优参数、适应度函数、更新个体最优、更新全局最优、全局最优参数
4. 训练模型，使用PSO找到的最好的全局最优参数
5. plt.show()

2、数据介绍

[‘SP’, ‘High’, ‘Low’, ‘KP’, ‘QSP’, ‘ZDE’, ‘ZDF’, ‘CJL’]

需要数据的话去我其他文章找到我的联系方式，有偿
数据链接

3、相关技术

PSO好的地方就是论文多，好写引用文献
不过说实话，算法优化我并不推荐用PSO，虽然说PSO的论文多，但是都被用烂了，AutoML-NNI，hyperopt，optuna，ray都是很好很先进的优化框架，里面集成了很多效果非常好的优化算法，推荐大家学习。

4、完整代码和步骤

代码输出如下：

主运行程序入口

import random
import time

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from keras.models import Sequential
from sklearn.metrics import r2_score  # R2
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.keras.models import Sequential

batch_size = 128
epochs = 2
steps = 10

def process_data():
    dataset = pd.read_csv("D:\项目\量化交易\沪深300/hs300.csv", engine='python', parse_dates=['date'], index_col=['date'])
    columns = ['SP', 'High', 'Low', 'KP', 'QSP', 'ZDE', 'ZDF', 'CJL']

    for col in columns:
        scaler = MinMaxScaler()
        dataset[col] = scaler.fit_transform(dataset[col].values.reshape(-1, 1))
    X = dataset.drop(columns=['SP'], axis=1)
    y = dataset['SP']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.24, shuffle=False, random_state=666)

    return X_train, y_train, X_test, y_test


def create_dataset(X, y, seq_len=10):
    features = []
    targets = []  # 标签

    for i in range(0, len(X) - seq_len, 1):  # 此处的1表示步长，每隔一步滑一下
        data = X.iloc[i:i + seq_len]  # 序列数据；前闭后开
        label = y.iloc[i + seq_len]  # 标签数据
        # 保存到features和labels
        features.append(data)
        targets.append(label)

    return np.array(features), np.array(targets)


X_train, y_train, X_test, y_test = process_data()
train_dataset, train_labels = create_dataset(X_train, y_train, seq_len=10)
X_test, y_test = create_dataset(X_test, y_test, seq_len=10)

from tensorflow.keras import Sequential, layers

def build_model(neurons, dropout):
    model = Sequential([
        layers.LSTM(units=neurons, input_shape=train_dataset.shape[-2:], return_sequences=True),
        # units=256表示有256个神经元；return_sequences=True表示将结果传到下一步
        layers.Dropout(dropout),  # 表示删除一些神经元
        layers.LSTM(units=256, return_sequences=True),
        layers.Dropout(dropout),
        layers.LSTM(units=128, return_sequences=True),
        layers.LSTM(units=32),
        layers.Dense(1)  # 因为只有一个特征值的输出
    ])
    return model


def training(X):
    neurons = int(X[0])
    dropout = round(X[1], 6)
    batch_size = int(X[2])
    model = build_model(neurons, dropout)
    model.compile(optimizer='adam',
                  loss='mse')
    model.fit(
        train_dataset,
        train_labels,
        batch_size=batch_size,
        epochs=1,
        validation_data=(X_test, y_test),
        verbose=1)
    model.save(
        'neurons' + str(int(X[0])) + '_dropout' + str(dropout) + '_batch_size' + str(batch_size) + '.h5')
    # 训练完成后可直接加载模型
    # model_lstm = load_model('LSTM_bus_' + str(X[0]) + '_' + str(X[1]) + '_' + str(X[2]) + '_' + '.h5')
    pred = model.predict(X_test)
    le = len(pred)
    y_t = y_test.reshape(-1, 1)
    return pred, le, y_t


def function(ps, test, le):
    ss = sum(((abs(test - ps)) / test) / le)
    return ss


# (1) PSO Parameters
MAX_EPISODES = 2
MAX_EP_STEPS = 2
c1 = 1
c2 = 1
w = 0.5
pN = 1  # 粒子数量

# (2) LSTM Parameters
dim = 3  # 搜索维度
X = np.zeros((pN, dim))  # 所有粒子的位置和速度
V = np.zeros((pN, dim))
pbest = np.zeros((pN, dim))  # 个体经历的最佳位置和全局最佳位置
gbest = np.zeros(dim)
p_fit = np.zeros(pN)  # 每个个体的历史最佳适应值
print(p_fit.shape)
print(p_fit.shape)
t1 = time.time()

'''
神经网络第一层神经元个数： 256-259
dropout比率： 0.03-0.19
batch_size： 64-128
'''
UP = [259, 0.19, 128]
DOWN = [256, 0.03, 64]

# (4) 开始搜索
for i_episode in range(MAX_EPISODES):
    """初始化s"""
    random.seed(8)
    fit = -1e5  # 全局最佳适应值
    # 初始粒子适应度计算
    print("计算初始全局最优")
    for i in range(pN):
        for j in range(dim):
            V[i][j] = random.uniform(0, 1)
            if j == 1:
                X[i][j] = random.uniform(DOWN[j], UP[j])
            else:
                X[i][j] = round(random.randint(DOWN[j], UP[j]), 0)
        pbest[i] = X[i]
        le, pred, y_t = training(X[i])
        NN = 1
        tmp = function(pred, y_t, le)
        p_fit[i] = tmp
        if tmp > fit:
            fit = tmp
            gbest = X[i]
    print("初始全局最优参数：{:}".format(gbest))

    fitness = []  # 适应度函数
    for j in range(MAX_EP_STEPS):
        fit2 = []
        plt.title("第{}次迭代".format(i_episode))
        for i in range(pN):
            le, pred, y_t = training(X[i])
            temp = function(pred, y_t, le)
            fit2.append(temp / 1000)
            if temp > p_fit[i]:  # 更新个体最优
                p_fit[i] = temp
                pbest[i] = X[i]
                if p_fit[i] > fit:  # 更新全局最优
                    gbest = X[i]
                    fit = p_fit[i]
        print("搜索步数：{:}".format(j))
        print("个体最优参数：{:}".format(pbest))
        print("全局最优参数：{:}".format(gbest))
        # [30.          0.14277071 95.        ]
        for i in range(pN):
            V[i] = w * V[i] + c1 * random.uniform(0, 1) * (pbest[i] - X[i]) + c2 * random.uniform(0, 1) * (gbest - X[i])
            ww = 1
            for k in range(dim):
                if DOWN[k] < X[i][k] + V[i][k] < UP[k]:
                    continue
                else:
                    ww = 0
            X[i] = X[i] + V[i] * ww
        fitness.append(fit)

print('Running time: ', time.time() - t1)

# 训练模型  使用PSO找到的最好的神经元个数
neurons = int(gbest[0])
dropout = gbest[1]
batch_size = int(gbest[2])
model = build_model(neurons, dropout)
model.compile(optimizer='adam',
              loss='mse')
model.summary()
history = model.fit(train_dataset, train_labels, epochs=epochs, batch_size=batch_size, verbose=2)

# 模型预测数据
test_preds = model.predict(X_test)
test_preds = test_preds[:, 0] # 获取数组中的第1列值


# 计算r2值
score = r2_score(y_test, test_preds)
print("r^2 值为： ", score)

# 绘制 预测与真值结果
plt.figure(figsize=(16,8))
plt.plot(y_test[:1149], label="True value")
plt.plot(test_preds[:1149], label="Pred value")#预测值
plt.legend(loc='best')
plt.show()
# 显示训练结果

plt.figure(figsize=(16,8))
plt.plot(history.history['loss'], label='train loss')
plt.legend(loc='best')
plt.show()

from sklearn import metrics
#MSE
print(metrics.mean_squared_error(y_test,test_preds))
#RMSE
print(np.sqrt(metrics.mean_squared_error(y_test,test_preds)))
#MAE
print(metrics.mean_absolute_error(y_test,test_preds))

代码比较复杂，如需帮忙请私聊
完整代码和数据链接

5、学习链接

PSO粒子群优化-LSTM-pyswarms框架-实现期货价格预测

https://pypi.org/project/pyswarms/
ljvmiranda921/pyswarms
PySwarms(Python粒子群优化工具包)的使用:GlobalBestPSO例子解析