def model_sort_criterion(
chunk: Tuple[ModelRepr, Tuple[Penalty,
List[InstanceRepr]]]) -> Any:
model, (penalty, instances) = chunk
return (
model_get_ordering_key(model),
len(instances),
len(model_get_name(model)),
)
def export_compact(
self
) -> Iterator[Optional[Tuple[Optional[str], Union[int, str], str, int]]]:
for subject in map(subject_from_response_bytes, self.subjects):
subject_name: Optional[str] = subject.name
for model, model_penalty, instances in subject.best_models:
yield (subject_name, model_penalty.to_csv(),
model_get_name(model), len(instances))
subject_name = None # don't repeat these
yield None # bump progress
def export_detailed(
self
) -> Iterator[Optional[Tuple[str, Optional[int], int, str, str]]]:
for subject in map(subject_from_response_bytes, self.subjects):
for model, penalty, instances in subject.best_models:
for instance in sorted(instances):
yield (subject.name,
penalty.lower_bound if penalty.lower_bound
== penalty.upper_bound else None,
penalty.upper_bound, model_get_name(model),
base64.b64encode(instance).decode('ascii'))
yield None # bump progress
def __init__(self, parent_node: 'EstimationResult.Subject', row: int,
model: model.Model, penalty: Penalty,
instances: List[InstanceRepr]) -> None:
subject = parent_node.subject
Node.__init__(
self,
parent_node,
row,
fields=(model_get_name(model), penalty,
'%d instances' % len(instances)),
child_count=len(instances),
)
self.instances = instances
self.subject = subject