def _setup_eval(self, eval_data: evdata.EvalData):
self.prepare, self.id_ = analysis.get_confidence_entry_preparation(
eval_data, 'probabilities')
self.load_params = analysis.Loader.Params(eval_data.confidence_entry)
metric = ev.ComposeEvaluation([
ev.LambdaEvaluation(lambda x: x.min(), ('probabilities', ), 'min'),
ev.LambdaEvaluation(lambda x: x.max(), ('probabilities', ), 'max')
])
hook = hooks.WriteSummaryCsvHook(
os.path.join(self.min_max_dir,
dirs.MINMAX_PLACEHOLDER.format(self.id_)),
confidence_entry=eval_data.confidence_entry)
self.eval_cases = [EvalCase(metric, hook)]
def _setup_eval(self, eval_data: evdata.EvalData):
self.prepare, self.id_ = analysis.get_probability_preparation(
eval_data,
rescale_confidence=self.rescale_confidence,
rescale_sigma=self.rescale_sigma,
min_max_dir=self.min_max_dir)
self.load_params = analysis.Loader.Params(
eval_data.confidence_entry, need_t2_mask=self.need_t2_mask)
metric = ev.ComposeEvaluation(
[*self._m, ev.DiceNumpy(),
ev.ConfusionMatrix()])
hook = hooks.ReducedComposeEvalHook([
hooks.WriteCsvHook(os.path.join(
self.out_dir, dirs.ECE_PLACEHOLDER.format(self.id_)),
entries=(*self.ece_entries, 'dice', 'tp', 'tn',
'fp', 'fn', 'n'))
])
self.eval_cases = [EvalCase(metric, hook)]
def _setup_eval(self, eval_data: evdata.EvalData):
self.prepare, self.id_ = analysis.get_probability_preparation(
eval_data,
rescale_confidence=self.rescale_confidence,
rescale_sigma=self.rescale_sigma,
min_max_dir=self.min_max_dir)
self.load_params = analysis.Loader.Params(eval_data.confidence_entry,
need_t2_mask=self.need_mask)
metric = ev.ComposeEvaluation([
ev.EceBinaryNumpy(threshold_range=None,
return_bins=True,
with_mask=self.need_mask),
ev.DiceNumpy()
])
hook = hooks.ReducedComposeEvalHook([
hooks.WriteBinsCsvHook(
os.path.join(self.out_dir,
dirs.CALIBRATION_PLACEHOLDER.format(self.id_)))
])
self.eval_cases = [EvalCase(metric, hook)]
def __init__(self) -> None:
super().__init__()
self.evaluate = ev.ComposeEvaluation([ev.DiceNumpy()])
def __init__(self) -> None:
super().__init__()
self.evaluate = eval.ComposeEvaluation([eval.SmoothDice('dice')])
def __init__(self) -> None:
super().__init__()
self.evaluate = ev.ComposeEvaluation([ev.DiceNumpy(),
# ev2.EntropyEval(entropy_threshold=0.7, with_ratios=False),
ev.LogLossSklearn()])