train_encoder_decoder.py

from __future__ import print_function #為了確保代碼同時兼容Python 2和Python 3版本中的print函數# 導入標準庫和第三方庫
import os.path #導入了Python的os.path模塊，用于處理文件和目錄路徑
from os import path #從os模塊中導入了path子模塊，可以直接使用path來調用os.path中的函數import sys #導入了sys模塊，用于系統相關的參數和函數
import math #導入了math模塊，提供了數學運算函數
import numpy as np #導入了NumPy庫，并使用np作為別名，NumPy是用于科學計算的基礎庫
import pandas as pd #導入了Pandas庫，并使用pd作為別名，Pandas是用于數據分析的強大庫# 導入深度學習相關庫
import tensorflow as tf #導入了TensorFlow深度學習框架from keras import backend as K #導入了Keras的backend模塊，并使用K作為別名，用于訪問后端引擎的函數
from keras.models import Model #從Keras導入了Model類，用于定義神經網絡模型
from keras.layers import LSTM, GRU, TimeDistributed, Input, Dense, RepeatVector #從Keras導入了LSTM、Input和Dense等神經網絡層
from keras.callbacks import CSVLogger, EarlyStopping, TerminateOnNaN #從Keras導入了CSVLogger、EarlyStopping和TerminateOnNaN等回調函數，用于模型訓練時的控制和記錄
from keras import regularizers #從Keras導入了regularizers模塊，用于正則化
from keras.optimizers import Adam #從Keras導入了Adam優化器，用于編譯模型時指定優化算法# 導入其他功能模塊
from functools import partial, update_wrapper #從Python標準庫functools中導入了partial和update_wrapper函數，用于函數式編程中的功能擴展和包裝# 這個函數的作用是創建一個部分應用（partial application）的函數，并保留原始函數的文檔字符串等信息。
def wrapped_partial(func, *args, **kwargs):partial_func = partial(func, *args, **kwargs)update_wrapper(partial_func, func)return partial_func# 這是一個自定義的損失函數，計算加權的均方誤差（Mean Squared Error），其中y_true是真實值，y_pred是預測值，weights是權重。
def weighted_mse(y_true, y_pred, weights):return K.mean(K.square(y_true - y_pred) * weights, axis=-1)# 這部分代碼用于選擇使用的GPU設備。它從命令行參數中獲取一個整數值gpu，如果gpu小于3，則設置CUDA環境變量以指定使用的GPU設備
import os
gpu = int(sys.argv[-13])
if gpu < 3:os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"   # see issue #152os.environ["CUDA_VISIBLE_DEVICES"]= "{}".format(gpu)from tensorflow.python.client import device_libprint(device_lib.list_local_devices())# 這部分代碼獲取了一系列命令行參數，并將它們分別賦值給變量 
# 這些參數可能包括數據集名稱、訓練的批次數量、訓練周期數、學習率、正則化懲罰、Dropout率、耐心（用于Early Stopping）等 
imp = sys.argv[-1]
T = sys.argv[-2]
t0 = sys.argv[-3]
dataname = sys.argv[-4] 
nb_batches = sys.argv[-5]
nb_epochs = sys.argv[-6]
lr = float(sys.argv[-7])
penalty = float(sys.argv[-8])
dr = float(sys.argv[-9])
patience = sys.argv[-10]
n_hidden = int(sys.argv[-11])
hidden_activation = sys.argv[-12]# results_directory 是一個字符串，表示將要創建的結果文件夾路徑。dataname 是之前從命令行參數中獲取的數據集名稱
# 如果這個文件夾路徑不存在，就使用 os.makedirs 函數創建它。這個路徑通常用于存儲訓練模型的結果或者日志
results_directory = 'results/encoder-decoder/{}'.format(dataname)if not os.path.exists(results_directory):os.makedirs(results_directory)# 定義了一個函數 create_model，用于創建、編譯和返回一個循環神經網絡（RNN）模型
def create_model(n_pre, n_post, nb_features, output_dim, lr, penalty, dr, n_hidden, hidden_activation):""" creates, compiles and returns a RNN model @param nb_features: the number of features in the model"""# 這里定義了兩個輸入層：inputs 是一個形狀為 (n_pre, nb_features) 的輸入張量，用于模型的主輸入；weights_tensor 是一個形狀相同的張量，用于傳遞權重或其他需要的信息inputs = Input(shape=(n_pre, nb_features), name="Inputs")  weights_tensor = Input(shape=(n_pre, nb_features), name="Weights") # 這里使用了兩個 LSTM 層：lstm_1 是一個具有 n_hidden 個單元的 LSTM 層，應用了 dropout 和 recurrent_dropout，并且返回整個時間序列的輸出。lstm_2 是一個相同的 LSTM 層，但它只返回最后一個時間步的輸出。lstm_1 = LSTM(n_hidden, dropout=dr, recurrent_dropout=dr, activation=hidden_activation, return_sequences=True, name='LSTM_1')(inputs) # Encoderlstm_2 = LSTM(n_hidden, activation=hidden_activation, return_sequences=False, name='LSTM_2')(lstm_1) # Encoderrepeat = RepeatVector(n_post, name='Repeat')(lstm_2) # get the last output of the LSTM and repeats itgru_1 = GRU(n_hidden, activation=hidden_activation, return_sequences=True, name='Decoder')(repeat)  # Decoderoutput= TimeDistributed(Dense(output_dim, activation='linear', kernel_regularizer=regularizers.l2(penalty), name='Dense'), name='Outputs')(gru_1)model = Model([inputs, weights_tensor], output)# Compilecl = wrapped_partial(weighted_mse, weights=weights_tensor)model.compile(optimizer=Adam(lr=lr), loss=cl)print(model.summary()) return modeldef train_model(model, dataX, dataY, weights, nb_epoches, nb_batches):# Prepare model checkpoints and callbacksstopping = EarlyStopping(monitor='val_loss', patience=int(patience), min_delta=0, verbose=1, mode='min', restore_best_weights=True)csv_logger = CSVLogger('results/encoder-decoder/{}/training_log_{}_{}_{}_{}_{}_{}_{}_{}.csv'.format(dataname,dataname,imp,hidden_activation,n_hidden,patience,dr,penalty,nb_batches), separator=',', append=False)terminate = TerminateOnNaN()# Model fithistory = model.fit(x=[dataX,weights], y=dataY, batch_size=nb_batches, verbose=1,epochs=nb_epoches, callbacks=[stopping,csv_logger,terminate],validation_split=0.2)def test_model():n_post = int(1)n_pre =int(t0)-1seq_len = int(T)wx = np.array(pd.read_csv("data/{}-wx-{}.csv".format(dataname,imp)))print('raw wx shape', wx.shape)  wXC = []for i in range(seq_len-n_pre-n_post):wXC.append(wx[i:i+n_pre]) wXC = np.array(wXC)print('wXC shape:', wXC.shape)x = np.array(pd.read_csv("data/{}-x-{}.csv".format(dataname,imp)))print('raw x shape', x.shape) dXC, dYC = [], []for i in range(seq_len-n_pre-n_post):dXC.append(x[i:i+n_pre])dYC.append(x[i+n_pre:i+n_pre+n_post])dataXC = np.array(dXC)dataYC = np.array(dYC)print('dataXC shape:', dataXC.shape)print('dataYC shape:', dataYC.shape)nb_features = dataXC.shape[2]output_dim = dataYC.shape[2]# create and fit the encoder-decoder networkprint('creating model...')model = create_model(n_pre, n_post, nb_features, output_dim, lr, penalty, dr, n_hidden, hidden_activation)train_model(model, dataXC, dataYC, wXC, int(nb_epochs), int(nb_batches))# now testprint('Generate predictions on full training set')preds_train = model.predict([dataXC,wXC], batch_size=int(nb_batches), verbose=1)print('predictions shape =', preds_train.shape)preds_train = np.squeeze(preds_train)print('predictions shape (squeezed)=', preds_train.shape)print('Saving to results/encoder-decoder/{}/encoder-decoder-{}-train-{}-{}-{}-{}-{}-{}.csv'.format(dataname,dataname,imp,hidden_activation,n_hidden,patience,dr,penalty,nb_batches))np.savetxt("results/encoder-decoder/{}/encoder-decoder-{}-train-{}-{}-{}-{}-{}-{}.csv".format(dataname,dataname,imp,hidden_activation,n_hidden,patience,dr,penalty,nb_batches), preds_train, delimiter=",")print('Generate predictions on test set')wy = np.array(pd.read_csv("data/{}-wy-{}.csv".format(dataname,imp)))print('raw wy shape', wy.shape)  wY = []for i in range(seq_len-n_pre-n_post):wY.append(wy[i:i+n_pre]) # weights for outputswXT = np.array(wY)print('wXT shape:', wXT.shape)y = np.array(pd.read_csv("data/{}-y-{}.csv".format(dataname,imp)))print('raw y shape', y.shape)  dXT = []for i in range(seq_len-n_pre-n_post):dXT.append(y[i:i+n_pre]) # treated is inputdataXT = np.array(dXT)print('dataXT shape:', dataXT.shape)preds_test = model.predict([dataXT, wXT], batch_size=int(nb_batches), verbose=1)print('predictions shape =', preds_test.shape)preds_test = np.squeeze(preds_test)print('predictions shape (squeezed)=', preds_test.shape)print('Saving to results/encoder-decoder/{}/encoder-decoder-{}-test-{}-{}-{}-{}-{}-{}.csv'.format(dataname,dataname,imp,hidden_activation,n_hidden,patience,dr,penalty,nb_batches))np.savetxt("results/encoder-decoder/{}/encoder-decoder-{}-test-{}-{}-{}-{}-{}-{}.csv".format(dataname,dataname,imp,hidden_activation,n_hidden,patience,dr,penalty,nb_batches), preds_test, delimiter=",")def main():test_model()return 1if __name__ == "__main__":main()