def main():
decoded_file = GRAMMAR_WEIGHTS.split(".")[0] + "_decRes.txt"
priors_file = GRAMMAR_WEIGHTS.split(".")[0] + "_priorsRes.txt"
generation_file = GRAMMAR_WEIGHTS.split(".")[0] + "_generationRes.txt"
grammar_model = molecule_vae.ZincCharacterModel(GRAMMAR_WEIGHTS)
XTE = read_zinc()
XTE = XTE[0:5000]
# rember to comment and uncomment the line in the #moelcule_vae file
decoded_result = reconstruction(grammar_model, XTE)
save_decoded_results(XTE, decoded_result, decoded_file)
# decoded_priors = prior(grammar_model)
# save_decoded_priors(decoded_priors, priors_file)
decoded_generation = generation(grammar_model)
save_decoded_priors(decoded_generation, generation_file)
def main():
char_weights = '../../Dropbox/model/zinc_vae_str_L56_E100_val.hdf5'
accuracy_save_file = char_weights + '.reconstruct_accuracy.txt'
decode_result_save_file = char_weights + '.reconstruct_decode_result.txt'
model = molecule_vae.ZincCharacterModel(char_weights)
decode_result = reconstruct(model, smiles)
accuracy = cal_accuracy(decode_result)
print('accuracy:', accuracy)
save_result = True
if save_result:
with open(accuracy_save_file, 'w') as fout:
print('accuracy:', accuracy, file=fout)
save_decode_result(decode_result, decode_result_save_file)
print 'Train RMSE: ', error
print 'Train ll: ', trainll
# We load the decoder to obtain the molecules
from rdkit.Chem import MolFromSmiles, MolToSmiles
from rdkit.Chem import Draw
import image
import copy
import time
import sys
sys.path.insert(0, '../../../')
import molecule_vae
grammar_weights = '../../../pretrained/zinc_vae_str_L56_E100_val.hdf5'
grammar_model = molecule_vae.ZincCharacterModel(grammar_weights)
# We pick the next 50 inputs
next_inputs = sgp.batched_greedy_ei(50, np.min(X_train, 0),
np.max(X_train, 0))
valid_smiles_final = decode_from_latent_space(next_inputs, grammar_model)
from rdkit.Chem import Descriptors
from rdkit.Chem import MolFromSmiles, MolToSmiles
new_features = next_inputs
save_object(valid_smiles_final,
"results/valid_smiles{}.dat".format(iteration))