valsize_mult = len(trainX_mult) - math.floor(0.2 * len(trainX_mult))
valX_mult = array(trainX_mult[valsize_mult:len(trainX_mult)])
valY_mult = array(trainY_mult[valsize_mult:len(trainX_mult)])
trainX = array(trainX[:valsize])
trainY = array(trainY[:valsize])
trainX_mult = array(trainX_mult[:valsize_mult])
trainY_mult = array(trainY_mult[:valsize_mult])
testX = array(testX)
testY = array(testY)
print("Creating and Training GRU Bi-Classification Model ...")
bi_history, gru_bi_prediction = nn_models.GatedRecurrentUnit(
trainX, trainY, valX, valY, testX, testY, e)
print("Creating and Training LSTM Bi-Classification Model ...")
bi_history, lstm_bi_prediction = awekar_models.lstm(
trainX, trainY, valX, valY, testX, testY, e)
print("Creating and Training Bi-Classification Model ...")
bi_history, bl_bi_prediction = awekar_models.blstm(trainX, trainY, valX,
valY, testX, testY, e)
### INSERT CONSENSUS ###
cons_dict = []
for g, l, b in zip(gru_bi_prediction, lstm_bi_prediction,
bl_bi_prediction):
testY.append(labels[ts])
# VALIDATION DATA
valsize = len(trainX) - math.floor(0.2 * len(trainX))
valX = array(trainX[valsize:len(trainX)])
valY = array(trainY[valsize:len(trainX)])
trainX = array(trainX[:valsize])
trainY = array(trainY[:valsize])
testX = array(testX)
testY = array(testY)
##GRU MODEL
print("Creating and Training GRU Model ...")
history, pred = nn_models.GatedRecurrentUnit(trainX,trainY,valX,valY,testX,testY,e)
f_mes = f1_score(testY,pred,average='weighted')
a_mes = accuracy_score(testY,pred)
p_mes = precision_score(testY,pred,average='weighted')
r_mes = recall_score(testY,pred,average='weighted')
bcount=0
bully_count = 0
nbcount=0
non_bully_count = 0
for x in range(len(testX)):
if testY[x] == 1:
bully_count = bully_count + 1
if pred[x] == 1:
bcount = bcount + 1