from ae_simple_siamese import trainNet as trainNet
from ae_simple_siamese import top10Siamese as top10
from ae_simple_siamese import optimal_optimisation as oo
from ae_simple_siamese import evalSet
from getandsavestuff import getAndSaveEncodings
#uncomment to train
"""
epochs = 1000
data_path="../data/trainingset_final_v2.csv"
learning_rate = 0.0001
batch_size = 4
layer_amount = 5
layer_size = 32
factor = 6
trainNet(epochs,learning_rate,batch_size,data_path,layer_amount,layer_size,factor,save=True,trainFrom=False)
"""
#uncomment to create encodings
#getAndSaveEncodings(filepath="../data/trainingset_final_v2.csv",filename="training_siamese",ntype="ae",network_path="AE_earlystopsave_simple_siamese_v3.pth")
#getAndSaveEncodings(filepath="../data/testset_final_v2.csv",filename="test_siamese",ntype="ae",network_path="AE_earlystopsave_simple_siamese_v3.pth")

#uncomment to evaluate
#evalSet("../data/testset_final_v2.csv","AE_earlystopsave_simple_siamese_v3.pth")
#top10("AE_earlystopsave_simple_siamese_v3.pth", "ae_training_siamese_encodings_simple_v3.npy", "ae_training_siamese_ids_simple_v3.npy", "ae_test_siamese_encodings_simple_v3.npy", "ae_test_siamese_ids_simple_v3.npy")