han.py

from keras.models import Model
from keras.layers import Dense, Input, Activation, multiply, Lambda
from keras.layers import TimeDistributed, GRU, Bidirectional
from keras import backend as K
import numpy as np
from sklearn.preprocessing import LabelEncoder
from keras.utils.np_utils import to_categorical


def han():
# refer to 4.2 in the paper whil reading the following code

    # Input for one day : max article per day =40, dim_vec=200
    input1 = Input(shape=(40, 200), dtype='float32')

    # Attention Layer
    dense_layer = Dense(200, activation='tanh')(input1)
    softmax_layer = Activation('softmax')(dense_layer)
    attention_mul = multiply([softmax_layer,input1])
	#end attention layer
	
	
    vec_sum = Lambda(lambda x: K.sum(x, axis=1))(attention_mul)
    pre_model1 = Model(input1, vec_sum)
	pre_model2 = Model(input1, vec_sum)

    # Input of the HAN shape (None,11,0,200)
	# 11 = Window size = N in the paper 40 = max articles per day, dim_vec = 200
    input2 = Input(shape=(11, 40, 200), dtype='float32')

    # TimeDistributed is used to apply a layer to every temporal slice of an input 
	# So we use it here to apply our attention layer ( pre_model ) to every article in one day
	# to focus on the most critical article
    pre_gru = TimeDistributed(pre_model1)(input2)

	# bidirectional gru
    l_gru = Bidirectional(GRU(100, return_sequences=True))(pre_gru)
	
	# We apply attention layer to every day to focus on the most critical day	
    post_gru = TimeDistributed(pre_model2)(l_gru)

    # MLP to perform classification
    dense1 = Dense(100, activation='tanh')(post_gru)
    dense2 = Dense(3, activation='tanh')(dense1)
    final = Activation('softmax')(dense2)
    final_model = Model(input2, final)
    final_model.summary()

    return final_model

'''
def load_data(model, x_train_file, x_test_file, y_train_file, y_test_file):
    x_train = np.load(x_train_file)
    y_train = np.load(y_train_file)

    x_test = np.load(x_test_file)
    y_test = np.load(y_test_file)


    encoder = LabelEncoder()
    encoder.fit(y_train)
    encoded_Y = encoder.transform(y_train)
    y_train_end = to_categorical(encoded_Y)


    encoder2 = LabelEncoder()
    encoder.fit(y_test)
    encoded_Y2 = encoder.transform(y_test)
    y_test_end = to_categorical(encoded_Y2)
    print(y_test_end.shape)



    print("model compiling - Hierachical attention network")

    model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])


    print("model fitting - Hierachical attention network")

    print(x_train.shape, y_test_end.shape)

    model.fit(x_train, y_train_end, epochs=200)


    print("validation_test")

    final_x_test_predict = model.predict(x_train)



    print("Prediction de Y ", final_x_test_predict)
    print("vrai valeur Y ", y_train)

    return

''''



def twin_creation( x_train_folder, y_train_folder) :
''' Here we create a list of twins ( duo_list) 
	Twin = [CompanyA_x_train_filepath, CompanyA_y_train_filepath]'''
	x_train_list= os.listdir(x_train_folder)
	x_train_list=sorted(x_train_list)

	y_train_list=os.listdir(y_train_folder)
	y_train_list=sorted(y_train_list)

	duo_list=[]
	for i in range ( len(y_train_list) ):
		duo=[x_train_list[i],y_train_list[i]]
		duo_list.append(duo)
	duo_list=[duo for duo in duo_list if duo[0][-4:]=='.npy']
	
	return duo_list

def training(x_name,y_name,model):
    x_train = np.load('./x_train/'+x_name)
    y_train = np.load('./y_train/'+y_name)

    y_oh_list=[] # y one hot for one hot encoding
	
	# Transforming Y so that it has 3 dim for a 3 class classification
    for trend in y_train:
        new_value=trend+1
        code = [0 for _ in range(3)]
        code[new_value]=1
        y_oh_list.append(code)
        
    y_train_end=np.asarray(y_oh_list)
    print(y_train_end.shape)

	# Encoding y
    encoder = LabelEncoder()
    encoder.fit(y_train)
    encoded_Y = encoder.transform(y_train)
  
    model.train_on_batch(x_train, y_train_end)
    print("model fitting on "+x_name)

if __name__ == "__main__":
    model = han()
	model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])
    # Put your training data folder path
	x_train_folder=''
	y_train_folder=''
	epochs=60
	
	duo = twin_creation( x_train_folder, y_train_folder)
	for epoch in range(epochs):
		for k,duo in enumerate(duo_list):
		    print('fitting on firm nb {} out of 494 epoch {}'.format(k,epoch))
		    training(duo[0],duo[1],model)
		epoch += 1
	
	model.save('your_model_{}epochs.hdf5'.format(epochs))
    
#load_data(model, '', '', '', '')