def was_described(self,by=None,as_being=None,at_time=None,while_ignoring=0,haste=False,at_x=[-1],at_y=[-1],online=True,record=True,realize_confusion=True,laplace_smoothing=0):
# TODO: CPD: make likelihood into an attribute? if so, I need to
# probably wipe it here to avoid silent bugs (likelihood not updating,
# etc)
# Rename some variables:
a_few_at_the_start = while_ignoring
if online:
# Update agent:
by.N += 1
if by==None or as_being==None or by.kind == 'banned':
pass
# Optional: skip straight past inactive subjects.
# It would be nice to warn the user that inactive subjects
# should not be being classified: this can happen if the
# subject has not been cleanly retired in the Zooniverse
# database. However, this leads to a huge stream of warnings,
# so best not do that... Note also that training subjects
# cannot go inactive - but we need to ignore classifications of
# them after they cross threshold, for the training set to
# be useful in giving us the selection function. What you see in
# the trajectory plot is the *status* of the training subjects,
# not their *state*.
elif haste and ( self.state == 'inactive' \
or self.status == 'detected' \
or self.status == 'rejected' ):
# print "SWAP: WARNING: subject "+self.ID+" is inactive, but appears to have been just classified"
pass
else:
# update the annotation history
if record:
as_being_dict = {'LENS': 1, 'NOT': 0}
self.annotationhistory['Name'] = np.append(self.annotationhistory['Name'], by.name)
self.annotationhistory['ItWas'] = np.append(self.annotationhistory['ItWas'], as_being_dict[as_being])
self.annotationhistory['At_X'].append(at_x)
self.annotationhistory['At_Y'].append(at_y)
self.annotationhistory['PL'] = np.append(self.annotationhistory['PL'], by.PL)
self.annotationhistory['PD'] = np.append(self.annotationhistory['PD'], by.PD)
self.annotationhistory['At_Time'].append(at_time)
# Deal with active subjects. Ignore the classifier until they
# have seen NT > a_few_at_the_start (ie they've had a
# certain amount of training - at least one training image, for example):
if by.NT > a_few_at_the_start:
# Calculate likelihood for all Ntrajectory trajectories, generating as many binomial deviates
if realize_confusion:
PL_realization=by.get_PL_realization(Ntrajectory);
PD_realization=by.get_PD_realization(Ntrajectory);
else:
PL_realization=np.ones(Ntrajectory) * by.PL
PD_realization=np.ones(Ntrajectory) * by.PD
prior_probability=self.probability*1.0; # TODO: not used?!
if as_being == 'LENS':
likelihood = PL_realization + laplace_smoothing
likelihood /= (PL_realization*self.probability + (1-PD_realization)*(1-self.probability) + 2 * laplace_smoothing)
as_being_number = 1
elif as_being == 'NOT':
likelihood = (1-PL_realization) + laplace_smoothing
likelihood /= ((1-PL_realization)*self.probability + PD_realization*(1-self.probability) + 2 * laplace_smoothing)
as_being_number = 0
else:
raise Exception("Unrecognised classification result: "+as_being)
if online:
# Update subject:
self.probability = likelihood*self.probability
idx=np.where(self.probability < swap.pmin)
self.probability[idx]=swap.pmin
#if self.probability < swap.pmin: self.probability = swap.pmin
posterior_probability=self.probability*1.0;
self.trajectory = np.append(self.trajectory,self.probability)
self.exposure += 1
self.update_state(at_time)
# Update agent - training history is taken care of in agent.heard(),
# which also keeps agent.skill up to date.
if self.kind == 'test' and record:
by.testhistory['ID'] = np.append(by.testhistory['ID'], self.ID)
by.testhistory['I'] = np.append(by.testhistory['I'], swap.informationGain(self.mean_probability, by.PL, by.PD, as_being))
by.testhistory['Skill'] = np.append(by.testhistory['Skill'], by.skill)
by.testhistory['ItWas'] = np.append(by.testhistory['ItWas'], as_being_number)
by.testhistory['At_Time'] = np.append(by.testhistory['At_Time'], at_time)
by.contribution += by.skill
else:
# offline
return likelihood
else:
# Still advance exposure, even if by.NT <= ignore:
# it would be incorrect to calculate mean classns/retirement
# different from strict and alt-strict:
self.exposure += 1
return
def was_described(self,by=None,as_being=None,at_time=None,while_ignoring=0,haste=False,at_x=[-1],at_y=[-1]):
# Rename some variables:
a_few_at_the_start = while_ignoring
# Update agent:
by.N += 1
if by==None or as_being==None:
pass
# Optional: skip straight past inactive subjects.
# It would be nice to warn the user that inactive subjects
# should not be being classified: this can happen if the
# subject has not been cleanly retired in the Zooniverse
# database. However, this leads to a huge stream of warnings,
# so best not do that... Note also that training subjects
# cannot go inactive - but we need to ignore classifications of
# them after they cross threshold, for the training set to
# be useful in giving us the selection function. What you see in
# the trajectory plot is the *status* of the training subjects,
# not their *state*.
elif haste and ( self.state == 'inactive' \
or self.status == 'detected' \
or self.status == 'rejected' ):
# print "SWAP: WARNING: subject "+self.ID+" is inactive, but appears to have been just classified"
pass
else:
# Deal with active subjects. Ignore the classifier until they
# have seen NT > a_few_at_the_start (ie they've had a
# certain amount of training - at least one training image, for example):
if by.NT > a_few_at_the_start:
# Calculate likelihood for all Ntrajectory trajectories, generating as many binomial deviates
PL_realization=by.get_PL_realization(Ntrajectory);
PD_realization=by.get_PD_realization(Ntrajectory);
prior_probability=self.probability*1.0;
if as_being == 'LENS':
likelihood = PL_realization
likelihood /= (PL_realization*self.probability + (1-PD_realization)*(1-self.probability))
as_being_number = 1
elif as_being == 'NOT':
likelihood = (1-PL_realization)
likelihood /= ((1-PL_realization)*self.probability + PD_realization*(1-self.probability))
as_being_number = 0
else:
raise Exception("Unrecognised classification result: "+as_being)
# Update subject:
self.probability = likelihood*self.probability
idx=np.where(self.probability < swap.pmin)
self.probability[idx]=swap.pmin
#if self.probability < swap.pmin: self.probability = swap.pmin
posterior_probability=self.probability*1.0;
self.trajectory = np.append(self.trajectory,self.probability)
self.exposure += 1
# Update median probability
self.mean_probability=10.0**(sum(np.log10(self.probability))/Ntrajectory)
self.median_probability=np.sort(self.probability)[Ntrajectory/2]
# Should we count it as a detection, or a rejection?
# Only test subjects get de-activated:
if self.mean_probability < self.rejection_threshold:
self.status = 'rejected'
if self.kind == 'test':
self.state = 'inactive'
self.retirement_time = at_time
self.retirement_age = self.exposure
elif self.mean_probability > self.detection_threshold:
self.status = 'detected'
if self.kind == 'test':
# Let's keep the detections live!
# self.state = 'inactive'
# self.retirement_time = at_time
# self.retirement_age = self.exposure
pass
else:
# Keep the subject alive! This code is only reached if
# we are not being hasty.
self.status = 'undecided'
if self.kind == 'test':
self.state = 'active'
self.retirement_time = 'not yet'
self.retirement_age = 0.0
# Update agent - training history is taken care of in agent.heard(),
# which also keeps agent.skill up to date.
if self.kind == 'test':
by.testhistory['ID'] = np.append(by.testhistory['ID'], self.ID)
by.testhistory['I'] = np.append(by.testhistory['I'], swap.informationGain(self.mean_probability, by.PL, by.PD, as_being))
by.testhistory['Skill'] = np.append(by.testhistory['Skill'], by.skill)
by.testhistory['ItWas'] = np.append(by.testhistory['ItWas'], as_being_number)
by.contribution += by.skill
# update the annotation history
self.annotationhistory['Name'] = np.append(self.annotationhistory['Name'], by.name)
self.annotationhistory['ItWas'] = np.append(self.annotationhistory['ItWas'], as_being_number)
self.annotationhistory['At_X'].append(at_x)
self.annotationhistory['At_Y'].append(at_y)
self.annotationhistory['PL'] = np.append(self.annotationhistory['PL'], by.PL)
self.annotationhistory['PD'] = np.append(self.annotationhistory['PD'], by.PD)
else:
# Still advance exposure, even if by.NT <= ignore:
# it would be incorrect to calculate mean classns/retirement
# different from strict and alt-strict:
self.exposure += 1
return
def was_described(self,
by=None,
as_being=None,
at_time=None,
while_ignoring=0,
haste=False,
at_x=[-1],
at_y=[-1],
online=True,
record=True,
realize_confusion=True):
# TODO: CPD: make likelihood into an attribute? if so, I need to
# probably wipe it here to avoid silent bugs (likelihood not updating,
# etc)
# Rename some variables:
a_few_at_the_start = while_ignoring
if online:
# Update agent:
by.N += 1
if by == None or as_being == None or by.kind == 'banned':
pass
# Optional: skip straight past inactive subjects.
# It would be nice to warn the user that inactive subjects
# should not be being classified: this can happen if the
# subject has not been cleanly retired in the Zooniverse
# database. However, this leads to a huge stream of warnings,
# so best not do that... Note also that training subjects
# cannot go inactive - but we need to ignore classifications of
# them after they cross threshold, for the training set to
# be useful in giving us the selection function. What you see in
# the trajectory plot is the *status* of the training subjects,
# not their *state*.
elif haste and ( self.state == 'inactive' \
or self.status == 'detected' \
or self.status == 'rejected' ):
# print "SWAP: WARNING: subject "+self.ID+" is inactive, but appears to have been just classified"
pass
else:
# update the annotation history
if record:
as_being_dict = {'LENS': 1, 'NOT': 0}
self.annotationhistory['Name'] = np.append(
self.annotationhistory['Name'], by.name)
self.annotationhistory['ItWas'] = np.append(
self.annotationhistory['ItWas'], as_being_dict[as_being])
self.annotationhistory['At_X'].append(at_x)
self.annotationhistory['At_Y'].append(at_y)
self.annotationhistory['PL'] = np.append(
self.annotationhistory['PL'], by.PL)
self.annotationhistory['PD'] = np.append(
self.annotationhistory['PD'], by.PD)
self.annotationhistory['At_Time'].append(at_time)
# Deal with active subjects. Ignore the classifier until they
# have seen NT > a_few_at_the_start (ie they've had a
# certain amount of training - at least one training image, for example):
if by.NT > a_few_at_the_start:
# Calculate likelihood for all Ntrajectory trajectories, generating as many binomial deviates
if realize_confusion:
PL_realization = by.get_PL_realization(Ntrajectory)
PD_realization = by.get_PD_realization(Ntrajectory)
else:
PL_realization = np.ones(Ntrajectory) * by.PL
PD_realization = np.ones(Ntrajectory) * by.PD
prior_probability = self.probability * 1.0
# TODO: not used?!
if as_being == 'LENS':
likelihood = PL_realization
likelihood /= (PL_realization * self.probability +
(1 - PD_realization) *
(1 - self.probability))
as_being_number = 1
elif as_being == 'NOT':
likelihood = (1 - PL_realization)
likelihood /= ((1 - PL_realization) * self.probability +
PD_realization * (1 - self.probability))
as_being_number = 0
else:
raise Exception("Unrecognised classification result: " +
as_being)
if online:
# Update subject:
self.probability = likelihood * self.probability
idx = np.where(self.probability < swap.pmin)
self.probability[idx] = swap.pmin
#if self.probability < swap.pmin: self.probability = swap.pmin
posterior_probability = self.probability * 1.0
self.trajectory = np.append(self.trajectory,
self.probability)
self.exposure += 1
self.update_state(at_time)
# Update agent - training history is taken care of in agent.heard(),
# which also keeps agent.skill up to date.
if self.kind == 'test' and record:
by.testhistory['ID'] = np.append(
by.testhistory['ID'], self.ID)
by.testhistory['I'] = np.append(
by.testhistory['I'],
swap.informationGain(self.mean_probability, by.PL,
by.PD, as_being))
by.testhistory['Skill'] = np.append(
by.testhistory['Skill'], by.skill)
by.testhistory['ItWas'] = np.append(
by.testhistory['ItWas'], as_being_number)
by.testhistory['At_Time'] = np.append(
by.testhistory['At_Time'], at_time)
by.contribution += by.skill
else:
# offline
return likelihood
else:
# Still advance exposure, even if by.NT <= ignore:
# it would be incorrect to calculate mean classns/retirement
# different from strict and alt-strict:
self.exposure += 1
return
