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models.py
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import keras
import warnings
import numpy as np
from preprocess import load_data
from keras.models import Sequential
from myLayers.attention import MultiHeadAttention
from keras.layers import Bidirectional,LSTM,Dense,Activation,Conv1D,GlobalMaxPool1D,Input,Flatten,Dropout
warnings.filterwarnings('ignore')
class LstmEncoderDecoder():
def __init__(self,window_size,file_train,file_test,split,batch_size,learning_rate,epoch,dataSave=False):
self.model = 'LSTM-Encoder-Decoder'
self.window_size = window_size
self.metric = 'mse'
self.file_train = file_train
self.file_test = file_test
self.split =split
self.batch_size = batch_size
self.lr = learning_rate
self.epoch = epoch
self.config = {'LSTM_unit1':50,
'LSTM_unit2':100,
'Dense_unit':1}
print('>>> DataPrecessing')
self.train_x, self.train_y = load_data(self.file_train,
self.window_size,
self.split,
train=True)
self.test_x, self.test_y = load_data(self.file_test,
self.window_size,
self.split,
train=False)
if dataSave:
self.ndarrayData()
def ndarrayData(self):
np.save('./npData/train_x.npy',self.train_x)
np.save('./npData/train_y.npy',self.train_y)
np.save('./npData/test_x.npy',self.test_x)
np.save('./npData/test_y.npy',self.test_y)
def _create_model(self):
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=self.config['LSTM_unit1'], return_sequences=True))
model.add(LSTM(self.config['LSTM_unit2'], return_sequences=False))
model.add(Dense(output_dim=self.config['Dense_unit']))
model.add(Dropout(rate=0.2))
model.add(Activation('linear'))
model.compile(loss=self.metric,
optimizer=keras.optimizers.Adam(lr=0.001, beta_1=0.9,
beta_2=0.999, epsilon=None,
decay=0.0, amsgrad=False))
print(model.summary())
return model
def fit(self):
model = self._create_model()
model.fit(x=self.train_x,
y=self.train_y,
epochs = self.epoch,
batch_size = self.batch_size,
validation_data = (self.test_x,self.test_y))
model.save('./modelCheckPoint/EncDec.h5')
def predict(self):
model = keras.models.load_model('./modelCheckPoint/EncDec.h5')
y_hat = model.predict(self.test_x,batch_size = self.batch_size)
np.save('./result/lstmEncDnc_yHat.npy',y_hat)
return y_hat
class Conv1DAutoEncoder():
def __init__(self,window_size,file_train,file_test,split,batch_size,learning_rate,epoch,dataSave=False):
self.model = 'Conv1DAutoEncoder'
self.window_size = window_size
self.metric = 'mse'
self.file_train = file_train
self.file_test = file_test
self.split =split
self.batch_size = batch_size
self.lr = learning_rate
self.epoch = epoch
self.config = {'filters':256,
'kernel_size':7,
'conv_activation':'relu',
'padding':'same',
'Dense_unit':1}
print('>>> DataPrecessing')
self.train_x, self.train_y = load_data(self.file_train,
self.window_size,
self.split,
train=True)
self.test_x, self.test_y = load_data(self.file_test,
self.window_size,
self.split,
train=False)
if dataSave:
self.ndarrayData()
def ndarrayData(self):
np.save('./npData/train_x.npy',self.train_x)
np.save('./npData/train_y.npy',self.train_y)
np.save('./npData/test_x.npy',self.test_x)
np.save('./npData/test_y.npy',self.test_y)
def _create_model(self):
model = Sequential()
model.add(Conv1D(filters = self.config['filters'],
kernel_size = self.config['kernel_size'],
activation = self.config['conv_activation'],
input_dim = 1,
padding = self.config['padding'])
)
model.add(GlobalMaxPool1D())
model.add(Dense(output_dim=self.config['Dense_unit']))
model.add(Dropout(rate = 0.2))
model.add(Activation('linear'))
model.compile(loss=self.metric,
optimizer=keras.optimizers.Adam(lr=self.lr, beta_1=0.9,
beta_2=0.999, epsilon=None,
decay=0.0, amsgrad=False))
return model
def fit(self):
model = self._create_model()
model.fit(x=self.train_x,
y=self.train_y,
epochs = self.epoch,
batch_size = self.batch_size,
validation_data = (self.test_x,self.test_y))
model.save('./modelCheckPoint/Conv1DAutoEncoder.h5')
def predict(self):
model = keras.models.load_model('./modelCheckPoint/Conv1DAutoEncoder.h5')
y_hat = model.predict(self.test_x,batch_size = self.batch_size)
np.save('./result/Conv1DAutoEncoder_yHat.npy',y_hat)
return y_hat
class Conv1DLSTM():
def __init__(self,window_size,file_train,file_test,split,batch_size,learning_rate,epoch,dataSave=False):
self.model = 'Conv1DLSTM'
self.window_size = window_size
self.metric = 'mse'
self.file_train = file_train
self.file_test = file_test
self.split =split
self.batch_size = batch_size
self.lr = learning_rate
self.epoch = epoch
self.config = {'filters':256,
'kernel_size':7,
'conv_activation':'relu',
'padding':'same',
'LSTM_unit':50,
'Dense_unit':1}
print('>>> DataPrecessing')
self.train_x, self.train_y = load_data(self.file_train,
self.window_size,
self.split,
train=True)
self.test_x, self.test_y = load_data(self.file_test,
self.window_size,
self.split,
train=False)
if dataSave:
self.ndarrayData()
def ndarrayData(self):
np.save('./npData/train_x.npy',self.train_x)
np.save('./npData/train_y.npy',self.train_y)
np.save('./npData/test_x.npy',self.test_x)
np.save('./npData/test_y.npy',self.test_y)
def _create_model(self):
model = Sequential()
model.add(Conv1D(filters = self.config['filters'],
kernel_size = self.config['kernel_size'],
activation = self.config['conv_activation'],
input_dim = 1,
padding = self.config['padding'])
)
model.add(Dropout(rate = 0.2))
model.add(LSTM(self.config['LSTM_unit'], return_sequences=False))
model.add(Dropout(rate = 0.2))
model.add(Dense(output_dim=self.config['Dense_unit']))
model.add(Dropout(rate = 0.2))
model.add(Activation('linear'))
model.compile(loss=self.metric,
optimizer=keras.optimizers.Adam(lr=self.lr, beta_1=0.9,
beta_2=0.999, epsilon=None,
decay=0.0, amsgrad=False))
return model
def fit(self):
model = self._create_model()
model.fit(x=self.train_x,
y=self.train_y,
epochs = self.epoch,
batch_size = self.batch_size,
validation_data = (self.test_x,self.test_y))
model.save('./modelCheckPoint/Conv1DLSTM.h5')
def predict(self):
model = keras.models.load_model('./modelCheckPoint/Conv1DLSTM.h5')
y_hat = model.predict(self.test_x,batch_size = self.batch_size)
np.save('./result/conv1DLSTM_yHat.npy', y_hat)
return y_hat
class AttLSTM():
def __init__(self, window_size, file_train, file_test, split, batch_size, learning_rate, epoch, dataSave=False):
self.model = 'AttentionLSTM'
self.window_size = window_size
self.metric = 'mse'
self.file_train = file_train
self.file_test = file_test
self.split = split
self.batch_size = batch_size
self.lr = learning_rate
self.epoch = epoch
self.config = {'LSTM_unit1': 50,
'LSTM_unit2': 1}
print('>>> DataPrecessing')
self.train_x, self.train_y = load_data(self.file_train,
self.window_size,
self.split,
train=True)
self.test_x, self.test_y = load_data(self.file_test,
self.window_size,
self.split,
train=False)
if dataSave:
self.ndarrayData()
def ndarrayData(self):
np.save('./npData/train_x.npy', self.train_x)
np.save('./npData/train_y.npy', self.train_y)
np.save('./npData/test_x.npy', self.test_x)
np.save('./npData/test_y.npy', self.test_y)
def _create_model(self):
i = Input(shape=(self.window_size, 1))
enc = LSTM(self.config['LSTM_unit1'], return_sequences=True)(i)
dec,attn = MultiHeadAttention(2,100,0.2,1)(enc,enc,enc)
result = LSTM(units=self.config['LSTM_unit2'], activation='linear')(dec)
model = keras.Model(inputs=i, outputs=result)
model.compile(loss=self.metric,
optimizer=keras.optimizers.Adam(lr=self.lr, beta_1=0.9,
beta_2=0.999, epsilon=None,
decay=0.0, amsgrad=False))
return model
def fit(self):
model = self._create_model()
model.fit(x=self.train_x,
y=self.train_y,
epochs=self.epoch,
batch_size=self.batch_size,
validation_data=(self.test_x, self.test_y))
return model
def predict(self):
model = self.fit()
y_hat = model.predict(self.test_x, batch_size=self.batch_size)
np.save('./result/AttnBi-LSTM.npy', y_hat)
return y_hat
if __name__ == '__main__':
MODEL = 'Conv1DLSTM'
if MODEL == 'LSTM_enc_dec':
LSTM_enc_dec = LstmEncoderDecoder(window_size=50,
file_train = './data/nyc_taxi_train.csv',
file_test='./data/nyc_taxi_test.csv',
split = 1.,
batch_size = 32,
learning_rate=.001,
epoch = 20,
dataSave = True)
LSTM_enc_dec.fit()
LSTM_enc_dec.predict()
elif MODEL == 'Conv1D':
conv1D = Conv1DAutoEncoder(window_size=50,
file_train='./data/nyc_taxi_train.csv',
file_test='./data/nyc_taxi_test.csv',
split = 1.,
batch_size = 32,
learning_rate=.001,
epoch = 20,
dataSave = False)
conv1D.fit()
conv1D.predict()
elif MODEL == 'Conv1DLSTM':
conv1dLSTM = Conv1DLSTM(window_size=50,
file_train='./data/nyc_taxi_train.csv',
file_test='./data/nyc_taxi_test.csv',
split = 1.,
batch_size = 32,
learning_rate=.001,
epoch = 20,
dataSave = False)
conv1dLSTM.fit()
conv1dLSTM.predict()
elif MODEL == 'AttentionLSTM':
attLSTM = AttLSTM(window_size=50,
file_train='./data/nyc_taxi_train.csv',
file_test='./data/nyc_taxi_test.csv',
split = 1,
batch_size = 32,
learning_rate=.015,
epoch = 20,
dataSave = False)
attLSTM.predict()