精品--基于深度学习的溶解氧时间序列预测模型.zip

import numpy as np import pandas as pd from pandas import read_csv from pandas import DataFrame from datetime import datetime from matplotlib import pyplot from pylab import mpl from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import LabelEncoder from sklearn.metrics import mean_squared_error from sklearn.metrics import mean_absolute_error from sklearn.metrics import r2_score from sklearn.ensemble import IsolationForest from pandas import concat from PyEMD import EEMD from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from keras.callbacks import ModelCheckpoint from keras.layers import Dropout from keras.layers import Activation from scipy import interpolate, math import matplotlib.pyplot as plt from keras import Input, Model from keras.layers import Dense from keras.models import load_model def data_split(data, train_len, lookback_window): train = data[:train_len] # 标志训练集 test = data[train_len:] # 标志测试集 # X1[]代表移动窗口中的10个数 # Y1[]代表相应的移动窗口需要预测的数 # X2, Y2 同理 X1, Y1 = [], [] for i in range(lookback_window, len(train)): X1.append(train[i - lookback_window:i]) Y1.append(train[i]) Y_train = np.array(Y1) X_train = np.array(X1) X2, Y2 = [], [] for i in range(lookback_window, len(test)): X2.append(test[i - lookback_window:i]) Y2.append(test[i]) y_test = np.array(Y2) X_test = np.array(X2) return (X_train, Y_train, X_test, y_test) def data_split_LSTM(X_train, Y_train, X_test, y_test): # data split f X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1) X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1) Y_train = Y_train.reshape(Y_train.shape[0], 1) y_test = y_test.reshape(y_test.shape[0], 1) return (X_train, Y_train, X_test, y_test) def imf_data(data, lookback_window): X1 = [] for i in range(lookback_window, len(data)): X1.append(data[i - lookback_window:i]) X1.append(data[len(data) - 1:len(data)]) X_train = np.array(X1) return X_train def visualize(history): plt.rcParams['figure.figsize'] = (10.0, 6.0) # Plot training & validation loss values plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') plt.show() def LSTM_Model(X_train, Y_train,i): filepath = 'res/' + str(i) + '-{epoch:02d}-{val_acc:.2f}.h5' checkpoint = ModelCheckpoint(filepath, monitor='loss',verbose=1,save_best_only=False,mode='auto',period=10) callbacks_list = [checkpoint] model = Sequential() model.add(LSTM(50,activation='relu', input_shape=(X_train.shape[1], X_train.shape[2]))) # 已经确定10步长 ''' ,return_sequences = True 如果设置return_sequences = True，该LSTM层会返回每一个time step的h， 那么该层返回的就是1个由多个h组成的2维数组了，如果下一层不是可以接收2维数组 的层，就会报错。所以一般LSTM层后面接LSTM层的话，设置return_sequences = True， 如果接全连接层的话，设置return_sequences = False。 ''' model.add(Dense(50, activation='relu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') model.fit(X_train, Y_train, epochs=20, batch_size=16, validation_split=0.1, verbose=2, shuffle=True) return (model) def isolutionforest(DO): rng = np.random.RandomState(42) clf = IsolationForest(random_state=rng, contamination=0.025) # contamination为异常样本比例 clf.fit(DO) DO_copy = DO m = 0 pre = clf.predict(DO) for i in range(len(pre)): if pre[i] == -1: DO_copy = np.delete(DO_copy, i - m, 0) print(i) m += 1 return DO_copy def plot_curve(true_data, predicted): rmse=format(RMSE(test,prediction),'.4f') mape=format(MAPE(test,prediction),'.4f') plt.plot(true_data, label='True data') plt.plot(predicted, label='Predicted data') plt.legend() plt.text(1, 1, 'RMSE:' + str(rmse)+' \n '+'MAPE:'+str(mape), color = "r",style='italic', wrap=True) #plt.text(2, 2, "RMSE:" + str(format(RMSE(true_data,predicted),'.4f'))+" \n "+"MAPE:"+str(format(MAPE(true_data,predicted),'.4f')), style='italic', ha='center', wrap=True) #plt.savefig('result_EEMD_LSTM_E5B16.png') #plt.show() def RMSE(test, predicted): rmse = math.sqrt(mean_squared_error(test, predicted)) return rmse def MAPE(Y_true, Y_pred): Y_true, Y_pred = np.array(Y_true), np.array(Y_pred) return np.mean(np.fabs((Y_true - Y_pred) / Y_true)) * 100 if __name__ == '__main__': plt.rcParams['figure.figsize'] = (10.0, 5.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' dataset = pd.read_csv('../csv/Water Quality Record.csv', header=0, index_col=0, parse_dates=True) data = dataset.values.reshape(-1) values = dataset.values groups = [0, 1, 2, 3] # fig, axs = plt.subplots(1) df = pd.DataFrame(dataset) # 整体数据的全部字典类型 do = df['Dissolved Oxygen'] # 返回溶解氧那一列，用字典的方式 DO = [] for i in range(0, len(do)): DO.append([do[i]]) scaler_DO = MinMaxScaler(feature_range=(0, 1)) DO = scaler_DO.fit_transform(DO) DO=DO[:100] # DO = isolutionforest(DO) eemd = EEMD() eemd.noise_seed(12345) imfs = eemd.eemd(DO.reshape(-1),None,8) print(imfs.shape) c = int(len(DO) * .85) lookback_window = 10 imfs_prediction = [] i = 1 for imf in imfs: plt.subplot(len(imfs), 1, i) plt.plot(imf) i += 1 # plt.savefig('result_imf.png') plt.show() test = np.zeros([len(DO) - c - lookback_window, 1]) i = 1 for imf in imfs: print('-' * 45) print('This is ' + str(i) + ' time(s)') print('*' * 45) X1_train, Y1_train, X1_test, Y1_test = data_split(imf_data(imf, 1), c, lookback_window) X2_train, Y2_train, X2_test, Y2_test = data_split_LSTM(X1_train, Y1_train, X1_test, Y1_test) test += Y2_test model = load_model('../B16LSTM50tanh/EEMD-LSTM-imf' + str(i) + '-90.h5') prediction_Y = model.predict(X2_test) imfs_prediction.append(prediction_Y) i += 1; # i = 1 # for imf in imfs: # print('-'*45) # print('This is ' + str(i) + ' time(s)') # print('*'*45) # X1_train, Y1_train, X1_test, Y1_test = data_split(imf_data(imf,1), c, lookback_window) # X2_train, Y2_train, X2_test, Y2_test = data_split_LSTM(X1_train, Y1_train, X1_test, Y1_test) # test += Y2_test # model = LSTM_Model(X2_train,Y2_train,i) # model.save('EEMD_LSTM_5050/EEMD-LSTM-imf' + str(i) + '-50.h5') # prediction_Y = model.predict(X2_test) # imfs_prediction.append(prediction_Y) # i+=1; imfs_prediction = np.array(imfs_prediction) prediction = [0.0 for i in range(len(test))] prediction = np.array(prediction) for i in range(len(test)): t = 0.0 for imf_prediction in imfs_prediction: t += imf_prediction[i][0] prediction[i] = t prediction = prediction.reshape(prediction.shape[0], 1) # test = scaler_DO.inverse_transform(test) # prediction = scaler_DO.inverse_transform(prediction) # plot_curve(test, prediction) # print(RMSE(test, prediction)) # print(MAPE(test, prediction)) rmse=format(RMSE(test,prediction),'.4f') mape=format(MAPE(test,prediction),'.4f') r2 = format(r2_score(test, prediction), '.4f') mae = format(mean_absolute_error(test, prediction), '.4f') print('RMSE:' + str(rmse) + '\n' + 'MAE:' + str(mae) + '\n' + 'MAPE:' + str(mape) + '\n' +