def filter(self, run_counts, criteria):
wrong_confidence = criteria['wrong_confidence']
below_t = wrong_confidence <= self.t
filtered_counts = deep_copy(run_counts)
for key in filtered_counts:
filtered_counts[key] = filtered_counts[key][below_t]
return filtered_counts
def filter(self, run_counts, criteria):
correctness = criteria['correctness']
assert correctness.dtype == np.bool
filtered_counts = deep_copy(run_counts)
for key in filtered_counts:
filtered_counts[key] = filtered_counts[key][correctness]
return filtered_counts
def start(self, run_counts):
_logger.info("Started working on a MaxConfidence goal")
_logger.info("Threshold: " + str(self.t))
if self.new_work_goal is None:
if self.t >= 1.:
_logger.info("This goal will run forever")
else:
_logger.info("This goal will run until all examples have confidence"
+ " greater than " + str(self.t) + ", which may never"
+ " happen.")
self.work_before = deep_copy(run_counts)
def start(self, run_counts):
for key in run_counts:
value = run_counts[key]
assert value.ndim == 1
_logger.info("Started working on a Misclassify goal")
self.work_before = deep_copy(run_counts)
