def describe(self):
accuracy = [
Measure("Accuracy: " + t,
"A " + t,
minimal=0,
maximal=1,
direction=Sorting.DESCENDING) for t in self.tags
]
robustness = [
Measure("Robutsness" + t,
"R " + t,
minimal=0,
maximal=1,
direction=Sorting.DESCENDING) for t in self.tags
]
length = [None] * len(self.tags)
return tuple(
functools.reduce(
operator.add,
[[a, r, n] for a, r, n in zip(accuracy, robustness, length)]))
def describe(self):
return Measure("Expected average overlap", "EAO", 0, 1,
Sorting.DESCENDING),
def describe(self):
return Measure("Accuracy", "A", minimal=0, maximal=1, direction=Sorting.DESCENDING), \
Measure("Robustness", "R", minimal=0, direction=Sorting.ASCENDING), \
Point("AR plot", dimensions=2, abbreviation="AR", minimal=(0, 0), \
maximal=(1, 1), labels=("Robustness", "Accuracy"), trait="ar"), \
None
def describe(self):
return Measure("Accuracy", "AUC", 0, 1, Sorting.DESCENDING),
def describe(self):
return Measure("Failures", "F", 0, None, Sorting.ASCENDING),
def describe(self):
return Measure("Precision", "Pr", minimal=0, maximal=1, direction=Sorting.DESCENDING), \
Measure("Recall", "Re", minimal=0, maximal=1, direction=Sorting.DESCENDING), \
Measure("F Score", "F", minimal=0, maximal=1, direction=Sorting.DESCENDING)
def describe(self):
return tuple([
Measure(t, t, minimal=0, maximal=1, direction=Sorting.DESCENDING)
for t in self.tags
] + [None] * len(self.tags))
def describe(self):
return Measure("Expected average overlap", "EAO", minimal=0, maximal=1, direction=Sorting.DESCENDING),
