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# -*- coding: utf-8 -*- # 导入库pip install openpyxl -i https://pypi.tuna.tsinghua.edu.cn/simple import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.metrics import mean_squared_error # 评价指标 import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM, GRU from keras import optimizers import keras import tensorflow as tf # mse rmse mae rmape # adam sgd plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False import warnings warnings.filterwarnings("ignore") # 忽略一些警告 不影响运行 from date_process import data_read_csv train_x,train_y=data_read_csv() # 序列长度 int_sequence_len = train_x.shape[1] # 每个序列的长度 int_a = train_x.shape[2] # 输出几个元素 几步： out_len = train_y.shape[1] # 划分验证集和测试集 x_train, x_test, y_train, y_test = train_test_split(np.array(train_x), np.array(train_y), test_size=0.2, random_state=1) print(x_train.shape) print(len(x_train), len(x_test)) # 1243 311 x_train = x_train.reshape(len(x_train),int_sequence_len, int_a) # 三维度数据 全部数据长度 序列长度 每个序列维度 y_train = y_train.reshape(len(x_train),out_len) print(x_train.shape) print(y_train.shape) x_test = x_test.reshape(len(x_test),int_sequence_len, int_a) y_test = y_test.reshape(len(x_test),out_len) print(x_test.shape) print(y_test.shape) def create_model_1(): model = keras.models.Sequential([ keras.layers.LSTM(10, activation='relu', input_shape=(int_sequence_len, int_a)), # (1,9) 要与三维度的(1243,1,9) 一一对应 # 序列长度 每个序列维度 # (1,9) 要与三维度的(1243,1,9) 一一对应 # 序列长度 每个序列维度 keras.layers.Dense(32, activation='relu'), # 全连接 keras.layers.Dense(32), # 全连接 keras.layers.Dense(out_len) # 1个全链接 ]) op=optimizers.adam_v2.Adam(lr=0.001) model.compile(loss='mean_absolute_error', optimizer=op) # 回归损失函数和优化器 Adam SGD return model model1 = create_model_1() model1.summary() history=model1.fit(x_train, y_train, validation_data=(x_train, y_train), epochs=20, batch_size=2, shuffle=True) # 训练世代 batch model1.save_weights('lstmmoxing') # 模型保存 import matplotlib.pyplot as plt training_loss = history.history['loss'] test_loss = history.history['val_loss'] # 创建迭代数量 epoch_count = range(1, len(training_loss) + 1) # 可视化损失历史 plt.plot(epoch_count, training_loss, 'r--') plt.plot(epoch_count, test_loss, 'b-') plt.legend(['Training Loss', 'Test Loss']) plt.title("train loss and test loss") plt.xlabel('Epoch') plt.ylabel('Loss') plt.show() from sklearn.metrics import mean_squared_error # 均方误差 from sklearn.metrics import mean_absolute_error # 平方绝对误差 from sklearn.metrics import r2_score # R square # 调用 # 引用上边的模型实例 model_jiazai_1 = create_model_1() # 加载保存好的模型 model_jiazai_1.load_weights('lstmmoxing') y1_pred_lstm = model_jiazai_1.predict(x_test) y_true=[] y_pred=[] for i in range(len(y1_pred_lstm)): y_true.extend(y_test[i]) y_pred.extend(y1_pred_lstm[i]) from metra import metric mae, mse, rmse, mape, mspe,r2=metric(np.array(y_pred), np.array(y_true)) print('mae, mse, rmse, mape, mspe,r2') print(mae, mse, rmse, mape, mspe,r2) # 设置Seaborn样式 import seaborn as sns import matplotlib.pyplot as plt sns.set(style="darkgrid") x = range(len(y_test)) data = pd.DataFrame({'x': x, 'y_pred': y_pred, 'y_true': y_true}) # 绘制y_pred的折线图 sns.lineplot(x='x', y='y_pred', data=data, linewidth=1, label='y_pred') # 绘制y_true的折线图 sns.lineplot(x='x', y='y_true', data=data, linewidth=1, label='y_true') # 添加标题和标签 plt.title('Prediction vs True') plt.xlabel('Date') plt.ylabel('Values') # 显示图形 plt.show()