lstm-forecast-最经典案例

逃离我推掉我的手 2021-11-16 07:16 272阅读 0赞

# -*- coding: utf-8 -*-
    """
    Created on 2019/5/21 23:32
    @Author: Johnson
    @Email:593956670@qq.com
    @File: LSTM_forecast.py
    """
    # -*- coding: utf-8 -*-
    
    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd
    from keras.layers.core import Dense, Activation, Dropout
    from keras.models import Sequential
    import time
    import math
    from keras.layers import LSTM
    from sklearn.preprocessing import MinMaxScaler
    from sklearn.metrics import mean_squared_error
    from keras import optimizers
    import os
    
    def create_dataset(dataset, look_back):
        dataX, dataY = [], []
        for i in range(len(dataset)-look_back):
            a = dataset[i:(i+look_back), 0]
            dataX.append(a)
            dataY.append(dataset[i + look_back, 0])
        return np.array(dataX), np.array(dataY)
    
    
    #####当激活函数为 sigmoid 或者 tanh 时，要把数据正则话，此时 LSTM 比较敏感 设定 99.2% 是训练数据，余下的是测试数据
    def create_train_test_data(dataset,look_back ):
    
        ############正则化
        scaler = MinMaxScaler(feature_range=(0, 1))
        dataset = scaler.fit_transform(dataset)
        print(dataset.shape)
        datasetX, datasetY = create_dataset(dataset, look_back)
    
        ##########分割数据集
        train_size = int(len(datasetX) * 0.992)
    
    
        ####test_size = len(dataset) - train_size
        ################X=t and Y=t+1 时的数据，并且此时的维度为 [samples, features]
        trainX, trainY, testX, testY = datasetX[0:train_size,:],datasetY[0:train_size],datasetX[train_size:len(datasetX),:],datasetY[train_size:len(datasetX)]
    
        ###################投入到 LSTM 的 X 需要有这样的结构： [samples, timesteps, features]，所以做一下变换
        trainX = trainX.reshape (trainX.shape[0],look_back ,1)
        testX = testX.reshape(testX.shape[0], look_back , 1)
    
        return scaler,trainX,trainY,testX,testY
    
    def build_model(layers,seq_len):  #layers [1,50,100,1]
        model = Sequential()
    
        model.add(LSTM(layers[1], input_shape=(seq_len, layers[0]),return_sequences=True))
        #model.add(Dropout(0.2))
    
        model.add(LSTM(layers[2],return_sequences=False))
        #model.add(Dropout(0.2))
    
        model.add(Dense(units=layers[3], activation='tanh'))
    
        start = time.time()
        #rmsprop = optimizers.RMSprop(lr=0.0001)
        #model.compile(loss="mse", optimizer=rmsprop)
        #model.compile(loss="mse", optimizer="adagrad")
        model.compile(loss="mse", optimizer="rmsprop")
        print("Compilation Time : ", time.time() - start)
        return model
    
    #直接全部预测
    def predict_point_by_point(model, data):
        predicted = model.predict(data)
        print('predicted shape:',np.array(predicted).shape)  #(412L,1L)
        predicted = np.reshape(predicted, (len(predicted),))
        return predicted
    #结果展示
    def plot_results(predicted_data, true_data,predicted_next):
        PredictionNextX=[i for i in range(len(predicted_data),(len(predicted_data)+len(predicted_next)))]
        fig = plt.figure(facecolor='white',figsize=(10,5))
        #ax = fig.add_subplot(111)
        #ax.plot(true_data, label='True Data')
        plt.plot(true_data,color="black", label='True Data')
        plt.plot(predicted_data,color="blue", label='Prediction')
        plt.plot(PredictionNextX,predicted_next,color="red", label='PredictionNext')
        plt.xlabel("Time(s)") #X轴标签
        plt.ylabel("precipitation")  #Y轴标签
        plt.title("amount of precipitation") #图标题
        plt.legend()
        plt.show()
        #plt.savefig(filename+'.png')
    
    def predict_next(model,scaler,dataset_pre,look_back,next_num):
        dataset_pre = scaler.transform(dataset[len(dataset)-look_back:len(dataset), 0])
        next_predicted_list=[]
        for i in range(next_num):
            dataXNext = dataset_pre[len(dataset_pre)-look_back:len(dataset_pre)]
            dataPreNext = dataXNext.reshape(1,look_back,1)
            next_predicted = model.predict(dataPreNext)
            next_predicted_array = np.array(next_predicted[0])
            next_predicted_list.append(next_predicted[0][0])
            dataset_pre = np.concatenate((dataset_pre,next_predicted_array))
            print("未来",i,"天的降水量预测值为：",next_predicted[0][0])
        return scaler.inverse_transform(next_predicted_list)
    
    if __name__=='__main__':
        global_start_time = time.time()
        nb_epoch  = 100#迭代次数
        seq_len = 30#步长
        batchSize=365
        lstmFirstLayer=10#第一层lstm每步神经元个数
        lstmSecondLayer=20#第二层lstm每步神经元个数
        future_predict_num=30#连续预测未来30个数据
    
        print('> Loading data... ')
        dataframe = pd.read_csv('dy_rain_20_20_56691_.csv', usecols=[0])
        dataset = dataframe.values
        dataset = dataset.astype('float32')
        scaler,trainX,trainY,testX,testY = create_train_test_data(dataset,seq_len)
    
        print('X_train shape:',trainX.shape)  #(3709L, 50L, 1L)
        print('y_train shape:',trainY.shape)  #(3709L,)
        print('X_test shape:',testX.shape)    #(412L, 50L, 1L)
        print('y_test shape:',testY.shape)    #(412L,)
        #预测未来数据
        #predict_next(model,dataset,seq_len,10)
    
        print('> Data Loaded. Compiling...')
        #建模
        model = build_model([1, lstmFirstLayer, lstmSecondLayer, 1],seq_len)
        #模型训练
        history = model.fit(trainX,trainY,batch_size=batchSize,epochs=nb_epoch,validation_split=0.05,verbose=1,shuffle=True)
    
        #测试数据预测
        testPredict = predict_point_by_point(model, testX)
        #测试数据反归一化
        testPredict = scaler.inverse_transform(testPredict)
        testY = scaler.inverse_transform([testY])
        #计算测试数据集的rmse
        testScore = math.sqrt(mean_squared_error(testY[0], testPredict))
        print ('Test Score: %.2f RMSE' % (testScore))
        #预测未来数据
        next_predicted_list = predict_next(model,scaler,dataset,seq_len,future_predict_num)
        #结果展示
        plot_results(testPredict,testY[0],next_predicted_list)
        #保存模型
        hyperparams_name=str(seq_len)+"-"+str(lstmFirstLayer)+"-"+str(lstmSecondLayer)+"-"+str(testScore)
        model.save(os.path.join(
            'MODEL', '{}_cont.h5'.format(hyperparams_name)), overwrite=True)