def test_micro(
tp: np.array,
fp: np.array,
fn: np.array,
support: np.array,
zero_division: int,
true_answer: Tuple[float],
):
"""
Test micro metrics averaging
Args:
tp: true positive statistic
fp: false positive statistic
fn: false negative statistic
support: support statistic
zero_division: 0 or 1
true_answer: true metric value
"""
_, micro, _, _ = get_aggregated_metrics(tp=tp,
fp=fp,
fn=fn,
support=support,
zero_division=zero_division)
assert micro[-1] is None
for pred, real in zip(micro[:-1], true_answer):
assert abs(pred - real) < EPS
def compute(self) -> Any:
"""
Compute precision, recall, f1 score and support.
Compute micro, macro and weighted average for the metrics.
Returns:
list of aggregated metrics: per-class, micro, macro and weighted averaging of
precision, recall, f1 score and support metrics
"""
# ddp hotfix, could be done better
# but metric must handle DDP on it's own
if self._ddp_backend == "xla":
device = get_device()
for key in self.statistics:
key_statistics = torch.tensor([self.statistics[key]],
device=device)
key_statistics = xm.all_gather(key_statistics).sum(
dim=0).cpu().numpy()
self.statistics[key] = key_statistics
elif self._ddp_backend == "ddp":
for key in self.statistics:
value: List[np.ndarray] = all_gather(self.statistics[key])
value: np.ndarray = np.sum(np.vstack(value), axis=0)
self.statistics[key] = value
per_class, micro, macro, weighted = get_aggregated_metrics(
tp=self.statistics["tp"],
fp=self.statistics["fp"],
fn=self.statistics["fn"],
support=self.statistics["support"],
zero_division=self.zero_division,
)
return per_class, micro, macro, weighted
def update(
self, outputs: torch.Tensor, targets: torch.Tensor
) -> Tuple[Any, Any, Any, Any]:
"""
Update statistics and return intermediate metrics results
Args:
outputs: prediction values
targets: true answers
Returns:
tuple of metrics intermediate results with per-class, micro, macro and
weighted averaging
"""
tn, fp, fn, tp, support, num_classes = super().update(
outputs=outputs, targets=targets
)
per_class, micro, macro, weighted = get_aggregated_metrics(
tp=tp, fp=fp, fn=fn, support=support, zero_division=self.zero_division
)
if self.num_classes is None:
self.num_classes = num_classes
return per_class, micro, macro, weighted
def compute(self) -> Any:
"""
Compute precision, recall, f1 score and support.
Compute micro, macro and weighted average for the metrics.
Returns:
list of aggregated metrics: per-class, micro, macro and weighted averaging of
precision, recall, f1 score and support metrics
"""
per_class, micro, macro, weighted = get_aggregated_metrics(
tp=self.statistics["tp"],
fp=self.statistics["fp"],
fn=self.statistics["fn"],
support=self.statistics["support"],
zero_division=self.zero_division,
)
return per_class, micro, macro, weighted
