def mutate(self, model: Model):
# this mutate does not have any effect, but it is recorded in the mutation history
for node in model.get_nodes_by_label(self.label):
n_chosen = self.number_of_chosen(node)
if n_chosen is None:
candidates = [i for i in self.candidates(node) if self.choice([False, True])]
# FIXME This is a hack to make choice align with the previous format
# For example, it will convert [False, True, True] into [1, 2].
self._cur_samples = candidates
else:
for _ in range(n_chosen):
self.choice(self.candidates(node))
break
def mutate(self, model: Model):
max_num_edges = cast(int, None)
for node in model.get_nodes_by_label(self.label):
max_num_edges = node.operation.parameters['max_num_edges']
break
assert max_num_edges is not None
mutation_dict = {
mut.mutator.label: mut.samples
for mut in model.history
}
num_nodes = mutation_dict[f'{self.label}/num_nodes'][0]
adjacency_list = [
mutation_dict[f'{self.label}/input{i}']
for i in range(1, num_nodes)
]
if sum([len(e) for e in adjacency_list]) > max_num_edges:
raise InvalidMutation(
f'Expected {max_num_edges} edges, found: {adjacency_list}')
matrix = _NasBench101CellFixed.build_connection_matrix(
adjacency_list, num_nodes)
operations = ['IN'] + [
mutation_dict[f'{self.label}/op{i}'][0]
for i in range(1, num_nodes - 1)
] + ['OUT']
assert len(operations) == len(matrix)
matrix, operations = prune(
matrix, operations) # possible to raise InvalidMutation inside
# NOTE: a hack to maintain a clean copy of what nasbench101 cell looks like
self._cur_samples = {}
for i in range(1, len(matrix)):
if i + 1 < len(matrix):
self._cur_samples[f'op{i}'] = operations[i]
self._cur_samples[f'input{i}'] = [
k for k in range(i) if matrix[k, i]
]
self._cur_samples = [self._cur_samples
] # by design, _cur_samples is a list of samples