def test_get_set_params_as_vector(self):
grammar_cache = 'hyper_grammar_guac_10k_with_clique_collapse.pickle' # 'hyper_grammar.pickle'
g = HypergraphGrammar.load(grammar_cache)
m = CondtionalProbabilityModel(g)
out = m.get_params_as_vector()
out[0] = 1
m.set_params_from_vector(out)
out2 = m.get_params_as_vector()
assert out2[0] == out[0]
def test_cycle_length(self):
g = HypergraphGrammar.load('hyper_grammar.pickle')
for r in g.rules:
if r is not None:
g = r.to_nx()
cycles = nx.minimum_cycle_basis(g)
if len(cycles) > 0:
maxlen = max([len(c) for c in cycles])
if maxlen > 7:
print(maxlen)
cc = nx.number_connected_components(g)
if cc > 1:
print(cc)
def get_codec(molecules, grammar, max_seq_length):
if grammar is True:
grammar = 'classic'
# character-based models
if grammar is False:
if molecules:
charlist = [
'C', '(', ')', 'c', '1', '2', 'o', '=', 'O', 'N', '3', 'F',
'[', '@', 'H', ']', 'n', '-', '#', 'S', 'l', '+', 's', 'B',
'r', '/', '4', '\\', '5', '6', '7', 'I', 'P', '8', ' '
]
else:
charlist = [
'x', '+', '(', ')', '1', '2', '3', '*', '/', 's', 'i', 'n',
'e', 'p', ' '
]
codec = CharacterCodec(max_len=max_seq_length, charlist=charlist)
elif grammar == 'classic':
if molecules:
codec = CFGrammarCodec(max_len=max_seq_length,
grammar=grammar_zinc,
tokenizer=zinc_tokenizer)
else:
codec = CFGrammarCodec(max_len=max_seq_length,
grammar=grammar_eq,
tokenizer=eq_tokenizer)
codec.mask_gen = GrammarMaskGenerator(max_seq_length, codec.grammar)
elif grammar == 'new':
codec = CFGrammarCodec(max_len=max_seq_length,
grammar=grammar_zinc_new,
tokenizer=zinc_tokenizer_new)
codec.mask_gen = GrammarMaskGeneratorNew(max_seq_length, codec.grammar)
elif 'hypergraph' in grammar:
grammar_cache = grammar.split(':')[1]
assert grammar_cache is not None, "Invalid cached hypergraph grammar file:" + str(
grammar_cache)
codec = HypergraphGrammar.load(grammar_cache)
codec.MAX_LEN = max_seq_length
codec.normalize_conditional_frequencies()
codec.calc_terminal_distance(
) # just in case it wasn't initialized yet
codec.mask_gen = HypergraphMaskGenerator(max_seq_length, codec)
assert hasattr(codec, 'PAD_INDEX')
return codec
settings = get_settings(molecules=True, grammar='new')
thresh = 100000
# Read in the strings
f = open(settings['source_data'], 'r')
L = []
for line in f:
line = line.strip()
L.append(line)
if len(L) > thresh:
break
f.close()
fn = "rule_hypergraphs.pickle"
max_rules = 50
if os.path.isfile(fn):
rm = HypergraphGrammar.load(fn)
else:
rm = HypergraphGrammar(cache_file=fn)
bad_smiles = []
for num, smile in enumerate(L[:100]):
try:
smile = MolToSmiles(MolFromSmiles(smile))
print(smile)
mol = MolFromSmiles(smile)
actions = rm.strings_to_actions([smile])
re_smile = rm.decode_from_actions(actions)[0]
mol = MolFromSmiles(smile)
if re_smile != smile:
print("SMILES reconstruction wasn't perfect for " + smile)
print(re_smile)