【Python】神经网络回归预测模型,Python代码实现
场景描述:基于物联网检测设备,大量采集流量数据和液位高度数据存储在时序数据库(influxdb)中。并采用下面代码对数据进行预测
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# Author:李智敏
# Wechat:anark919
# Date:2024-01-05 10:17
# Title:
import os.path
import uuid
import pandas as pd
class Neural_networks():
def __init__(self,para_fields:list,outcome_fields:list,model_dir:str,model_name:str=uuid.uuid4().hex):
'''
:param para_fields: 自变量
:param outcome_fields: 因变量
:param model_dir: 模型存储地址
:param model_name: 模型名称,不传,以uuid生成唯一识别名称
'''
self.para_fields=para_fields
self.outcome_fields=outcome_fields
self.model_name = model_name
self.model_dir = os.path.join(model_dir,model_name)
if not os.path.exists(self.model_dir ):
os.makedirs(self.model_dir )
def training(self,data:pd.DataFrame,epochs=50000):
'''模型训练'''
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from keras import regularizers
# 加载数据
df = data
rain_dir = os.path.join(self.model_dir ,f"train_{self.model_name}.csv")
df.to_csv(rain_dir, index=False)
x = df[self.para_fields].values
y = df[self.outcome_fields].values
# 数据归一化
x_scaler = MinMaxScaler(feature_range=(-1, 1))
y_scaler = MinMaxScaler(feature_range=(-1, 1))
x = x_scaler.fit_transform(x)
y = y_scaler.fit_transform(y)
# 定义神经网络模型
model = Sequential()
# model.add(Dense(10, activation='relu', input_shape=(3,), kernel_regularizer=regularizers.l2(0.01)))
model.add(Dense(10, activation='relu', input_shape=(len(self.para_fields),), kernel_regularizer=regularizers.l2(0.01)))
model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
model.add(Dense(1, activation='linear'))
# 误差记录
optimizer = Adam(lr=0.0001)
model.compile(optimizer=optimizer, loss='mse')
# 训练模型
history = model.fit(x, y, epochs=epochs, batch_size=67)
# 评估模型
mse = model.evaluate(x, y)
print('Validation MSE:', mse)
# 保存模型的权重和偏差
model_dir = os.path.join(self.model_dir ,f"{self.model_name}.h5")
model.save(model_dir)
# 误差曲线
# 设置中文显示和解决负号显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.plot(history.history['loss'])
plt.title("模型误差")
plt.ylabel("误差")
plt.xlabel("循环次数")
img_dir = os.path.join(self.model_dir ,f"误差曲线_{self.model_name}.png")
plt.savefig(img_dir)
plt.show()
# 预测值输出
y_pred = model.predict(x)
# 预测值反归一化
y = y_scaler.inverse_transform(y)
y_pred = y_scaler.inverse_transform(y_pred)
print("the prediction is:", y_pred)
# 将预测值存储到Excel表中
df_out = pd.DataFrame(y_pred, columns=self.outcome_fields)
Prediction_dir = os.path.join(self.model_dir ,f"prediction_train_{self.model_name}.xlsx")
df_out.to_excel(Prediction_dir, index=False)
# 实际值与预测值的对比图
# 设置中文显示和解决负号显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.scatter(y, y_pred)
plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)
plt.xlabel("实际值")
plt.ylabel("预测值")
img_dir = os.path.join(self.model_dir ,f"对比图_{self.model_name}.png")
plt.savefig(img_dir)
plt.show()
return True
def forecast(self,data:pd.DataFrame):
'''模型预测'''
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import load_model
# 数据预处理(归一化)
rain_dir = os.path.join(self.model_dir ,f"train_{self.model_name}.csv")
df = pd.read_csv(rain_dir)
x = df[self.para_fields].values
y = df[self.outcome_fields].values
x_scaler = MinMaxScaler(feature_range=(-1, 1))
y_scaler = MinMaxScaler(feature_range=(-1, 1))
x = x_scaler.fit_transform(x)
y = y_scaler.fit_transform(y)
# 加载预测数据
df_test = data
x_test = df_test[self.para_fields].values
# 预测数据归一化
x_test = x_scaler.transform(x_test)
# 加载训练好的神经网络模型
# 中文路径会报编码错误
model_dir = os.path.join(self.model_dir ,f"{self.model_name}.h5")
model = load_model(model_dir)
# 对预测数据进行预测
y_pred = model.predict(x_test)
y_pred = y_scaler.inverse_transform(y_pred)
# print(y_pred)
# 将预测值存储到Excel表中
df_out = pd.DataFrame(y_pred, columns=self.outcome_fields)
df_out = pd.merge(df_test,df_out,how='outer',left_index=True,right_index=True)
Prediction_dir = os.path.join(self.model_dir ,f"prediction_{self.model_name}.xlsx")
df_out.to_excel(Prediction_dir, index=False)
return df_out.T.to_dict().values()
if __name__ == '__main__':
# df = pd.read_csv('train.csv')
from 数据库操作 import influxdb_query
data1 = influxdb_query('SELECT para505 FROM "device_YL_315103022220A6D3_1" limit 300')
data = influxdb_query('SELECT para168 FROM "device_LD_716001012220A5E8_1" limit 300')
# print(data[0]['time'])
# print(data1)
df = pd.DataFrame([{'time':k['time'],'para505':k['para505'],'para168':v['para168']} for k,v in zip(data1[:100],data[:100])])
df1 = pd.DataFrame([{'time':k['time'],'para505':k['para505']} for k,v in zip(data1[100:],data[100:])])
# print(df,df1)
# # df1 = pd.read_csv('test.csv')
os.chdir('D://')
# n = Neural_networks(['x1', 'x2', 'x3'],['y'],model_dir='.',model_name='f39e6103681244a5a092ef9e2759b61c')
n = Neural_networks(['para505'],['para168'],model_dir='.',model_name='f39e6103681244a5a092ef9e2759b61c')
# print(n.training(df,epochs=5000))
print(n.forecast(df1))
原文地址:https://blog.csdn.net/qq_42874996/article/details/139871161
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