# from makeit.utilities.historian.chemicals import ChemHistorian
# chemhistorian = ChemHistorian()
# chemhistorian.load_from_file()
chemhistorian = None
from makeit.prioritization.precursors.scscore import SCScorePrecursorPrioritizer
scscorer = SCScorePrecursorPrioritizer()
scscorer.load_model(model_tag='1024bool')
print('Loaded SCScorer on website')
print(scscorer.get_score_from_smiles('CCCC', noprice=True))
# Solvent choices - the save file is created by the template-based forward predictor
solvent_choices = []
from makeit.utilities.io.files import get_abraham_solvents_path
file_path = get_abraham_solvents_path()
if os.path.isfile(file_path):
with open(file_path, 'rb') as fid:
solvent_name_to_smiles = pickle.load(fid)
solvent_choices = [{
'smiles': v,
'name': k
} for (k, v) in solvent_name_to_smiles.items()]
else:
db_client = MongoClient(gc.MONGO['path'],
gc.MONGO['id'],
connect=gc.MONGO['connect'])
db = db_client[gc.SOLVENTS['database']]
SOLVENT_DB = db[gc.SOLVENTS['collection']]
for doc in SOLVENT_DB.find({'_id': {'$ne': 'default'}}):
solvent_choices.append({
def load(self, folder="", worker_no = 0):
'''Load a neural network scoring model'''
if worker_no==0:
MyLogger.print_and_log('Starting to load scorer...', template_nn_scorer_loc)
# First load neural network
if not folder:
MyLogger.print_and_log(
'Cannot load neural network without the directory in which the parameters are saved. Exiting...', template_nn_scorer_loc, level=3)
# Get model args
ARGS_FPATH = os.path.join(folder, 'args.json')
with open(ARGS_FPATH, 'r') as fid:
args = json.load(fid)
N_h2 = int(args['Nh2'])
N_h1 = int(args['Nh1'])
N_h3 = int(args['Nh3'])
N_hf = int(args['Nhf'])
l2v = float(args['l2'])
lr = float(args['lr'])
context_weight = float(args['context_weight'])
enhancement_weight = float(args['enhancement_weight'])
optimizer = args['optimizer']
inner_act = args['inner_act']
TARGET_YIELD = False
self.model = build(F_atom=self.F_atom, F_bond=self.F_bond, N_h1=N_h1,
N_h2=N_h2, N_h3=N_h3, N_hf=N_hf, l2v=l2v, inner_act=inner_act,
context_weight=context_weight, enhancement_weight=enhancement_weight, TARGET_YIELD=TARGET_YIELD,
absolute_score=True)
WEIGHTS_FPATH = os.path.join(folder, 'weights.h5')
self.model.load_weights(WEIGHTS_FPATH, by_name=True)
# Now load solvent information
# Try to load from file first
from makeit.utilities.io.files import get_abraham_solvents_path
file_path = get_abraham_solvents_path()
if os.path.isfile(file_path):
with open(file_path, 'rb') as fid:
self.solvent_name_to_smiles = pickle.load(fid)
self.solvent_smiles_to_params = pickle.load(fid)
else:
db_client = MongoClient(gc.MONGO['path'], gc.MONGO[
'id'], connect=gc.MONGO['connect'])
db = db_client[gc.SOLVENTS['database']]
SOLVENT_DB = db[gc.SOLVENTS['collection']]
for doc in SOLVENT_DB.find():
try:
if doc['_id'] == 'default':
self.solvent_name_to_smiles['default'] = doc['_id']
else:
self.solvent_name_to_smiles[doc['name']] = doc['_id']
self.solvent_smiles_to_params[doc['_id']] = doc
except KeyError:
MyLogger.print_and_log('Solvent doc {} missing a name'.format(
doc), template_nn_scorer_loc, level=1)
with open(file_path, 'wb') as fid:
pickle.dump(self.solvent_name_to_smiles, fid)
pickle.dump(self.solvent_smiles_to_params, fid)
if worker_no == 0:
MyLogger.print_and_log('Scorer has been loaded.', template_nn_scorer_loc)