def classify_run(path_run, single_classification=False, constraints=CLASSIFICATIONS):
import common_metric
import wfutil
classifications = []
def __nonzero(field):
value = wfutil.get_parameter(path_run, field)
return float(value) != 0
def __not(field, notvalue):
value = wfutil.get_parameter(path_run, field)
return value != notvalue
def __true(field):
value = wfutil.get_parameter(path_run, field)
return value == "True"
#
# Fitness
#
if __nonzero("ComplexityFitnessWeight") or __nonzero("HeuristicFitnessWeight"):
classifications.append("Fitness")
#
# Driven/Passive
#
if __true("PassiveLockstep"):
classifications.append("Passive")
else:
classifications.append("Driven")
#
# Random
#
classifications += map(lambda x: "Random_" + x, common_metric.get_random_classifications(path_run))
#
# Apply Constraints
#
if constraints != None:
classifications_constrained = list_intersection(classifications, constraints)
else:
classifications_constrained = classifications
#
# Apply Preemptions
#
for c in classifications_constrained:
try:
preempted = CLASSIFICATION_PREEMPTION[c]
classifications_constrained = list_difference(classifications_constrained, preempted)
except KeyError:
pass
#
# Validate Result
#
if len(classifications_constrained) == 0:
if len(classifications) == 0:
raise ClassificationError("notfound", path_run + " cannot be classified")
else:
raise ClassificationError("notfound", path_run + " classification not in {" + ",".join(constraints) + "}")
if single_classification and len(classifications_constrained) > 1:
raise ClassificationError(
"ambiguous", path_run + " classification ambiguous (" + ",".join(classifications_constrained) + ")"
)
if single_classification:
return classifications_constrained[0]
else:
return classifications_constrained
def classify_run(path_run,
single_classification = False,
constraints = CLASSIFICATIONS):
import common_metric
import wfutil
classifications = []
def __nonzero(field):
value = wfutil.get_parameter(path_run, field)
return float(value) != 0
def __not(field, notvalue):
value = wfutil.get_parameter(path_run, field)
return value != notvalue
def __true(field):
value = wfutil.get_parameter(path_run, field)
return value == 'True'
#
# Fitness
#
if __nonzero("ComplexityFitnessWeight") or __nonzero("HeuristicFitnessWeight"):
classifications.append('Fitness')
#
# Driven/Passive
#
if __true("PassiveLockstep"):
classifications.append('Passive')
else:
classifications.append('Driven')
#
# Random
#
classifications += map( lambda x: 'Random_' + x,
common_metric.get_random_classifications(path_run) )
#
# Apply Constraints
#
if constraints != None:
classifications_constrained = list_intersection( classifications,
constraints )
else:
classifications_constrained = classifications
#
# Apply Preemptions
#
for c in classifications_constrained:
try:
preempted = CLASSIFICATION_PREEMPTION[c]
classifications_constrained = list_difference(classifications_constrained,
preempted)
except KeyError:
pass
#
# Validate Result
#
if len(classifications_constrained) == 0:
if len(classifications) == 0:
raise ClassificationError( 'notfound', path_run + ' cannot be classified' )
else:
raise ClassificationError( 'notfound', path_run + ' classification not in {' + ','.join(constraints) + '}' )
if single_classification and len(classifications_constrained) > 1:
raise ClassificationError( 'ambiguous', path_run + ' classification ambiguous (' + ','.join(classifications_constrained) + ')' )
if single_classification:
return classifications_constrained[0]
else:
return classifications_constrained
def classify_run(path_run,
single_classification = False,
constraints = CLASSIFICATIONS,
dependents = None):
classifications = []
def __nonzero(field):
value = read_worldfile_parameter(path_run,
field)
return value != None and float(value) != 0
def __not(field, notvalue, default = None):
value = read_worldfile_parameter(path_run,
field)
if value == None:
if default == None:
value = notvalue
else:
value = default
return value != notvalue
#
# Fitness
#
if __nonzero("complexityFitnessWeight") or __nonzero("heuristicFitnessWeight") or __not("useComplexityAsFitness", "O"):
classifications.append('Fitness')
#
# Driven/Passive
#
if __nonzero("LockStepWithBirthsDeathsLog"):
classifications.append('Passive')
else:
classifications.append('Driven')
#
# Random
#
classifications += map( lambda x: 'Random_' + x,
common_metric.get_random_classifications(path_run) )
#
# Apply Constraints
#
if constraints != None:
classifications_constrained = list_intersection( classifications,
constraints )
else:
classifications_constrained = classifications
#
# Apply Preemptions
#
for c in classifications_constrained:
try:
preempted = CLASSIFICATION_PREEMPTION[c]
classifications_constrained = list_difference(classifications_constrained,
preempted)
except KeyError:
pass
#
# Apply Dependents
#
if dependents != None:
nondependents = list_difference(classifications_constrained,
dependents)
if len(nondependents) == 0:
classifications_constrained = []
#
# Validate Result
#
if len(classifications_constrained) == 0:
if len(classifications) == 0:
raise ClassificationError( 'notfound', path_run + ' cannot be classified' )
else:
raise ClassificationError( 'notfound', path_run + ' classification not in {' + ','.join(constraints) + '}' )
if single_classification and len(classifications_constrained) > 1:
raise ClassificationError( 'ambiguous', path_run + ' classification ambiguous (' + ','.join(classifications_constrained) + ')' )
return classifications_constrained
def classify_run(path_run,
single_classification=False,
constraints=CLASSIFICATIONS,
dependents=None):
classifications = []
def __nonzero(field):
value = read_worldfile_parameter(path_run, field)
return value != None and float(value) != 0
def __not(field, notvalue, default=None):
value = read_worldfile_parameter(path_run, field)
if value == None:
if default == None:
value = notvalue
else:
value = default
return value != notvalue
#
# Fitness
#
if __nonzero("complexityFitnessWeight") or __nonzero(
"heuristicFitnessWeight") or __not("useComplexityAsFitness", "O"):
classifications.append('Fitness')
#
# Driven/Passive
#
if __nonzero("LockStepWithBirthsDeathsLog"):
classifications.append('Passive')
else:
classifications.append('Driven')
#
# Random
#
classifications += map(lambda x: 'Random_' + x,
common_metric.get_random_classifications(path_run))
#
# Apply Constraints
#
if constraints != None:
classifications_constrained = list_intersection(
classifications, constraints)
else:
classifications_constrained = classifications
#
# Apply Preemptions
#
for c in classifications_constrained:
try:
preempted = CLASSIFICATION_PREEMPTION[c]
classifications_constrained = list_difference(
classifications_constrained, preempted)
except KeyError:
pass
#
# Apply Dependents
#
if dependents != None:
nondependents = list_difference(classifications_constrained,
dependents)
if len(nondependents) == 0:
classifications_constrained = []
#
# Validate Result
#
if len(classifications_constrained) == 0:
if len(classifications) == 0:
raise ClassificationError('notfound',
path_run + ' cannot be classified')
else:
raise ClassificationError(
'notfound', path_run + ' classification not in {' +
','.join(constraints) + '}')
if single_classification and len(classifications_constrained) > 1:
raise ClassificationError(
'ambiguous', path_run + ' classification ambiguous (' +
','.join(classifications_constrained) + ')')
return classifications_constrained
def classify_run(path_run,
single_classification = False,
constraints = CLASSIFICATIONS,
dependents = None):
import common_metric
import wfutil
classifications = []
def __nonzero(field):
value = wfutil.get_parameter(path_run, field)
return float(value) != 0
def __not(field, notvalue):
value = wfutil.get_parameter(path_run, field)
return value != notvalue
def __true(field):
value = wfutil.get_parameter(path_run, field)
return value == 'True'
#
# Fitness
#
if __nonzero("ComplexityFitnessWeight") or __nonzero("HeuristicFitnessWeight"):
classifications.append('Fitness')
#
# Driven/Passive
#
if __true("PassiveLockstep"):
classifications.append('Passive')
else:
classifications.append('Driven')
#
# Random
#
classifications += map( lambda x: 'Random_' + x,
common_metric.get_random_classifications(path_run) )
#
# Apply Constraints
#
if constraints != None:
classifications_constrained = list_intersection( classifications,
constraints )
else:
classifications_constrained = classifications
#
# Apply Preemptions
#
for c in classifications_constrained:
try:
preempted = CLASSIFICATION_PREEMPTION[c]
classifications_constrained = list_difference(classifications_constrained,
preempted)
except KeyError:
pass
#
# Apply Dependents
#
if dependents != None:
nondependents = list_difference(classifications_constrained,
dependents)
if len(nondependents) == 0:
classifications_constrained = []
#
# Validate Result
#
if len(classifications_constrained) == 0:
if len(classifications) == 0:
raise ClassificationError( 'notfound', path_run + ' cannot be classified' )
else:
raise ClassificationError( 'notfound', path_run + ' classification not in {' + ','.join(constraints) + '}' )
if single_classification and len(classifications_constrained) > 1:
raise ClassificationError( 'ambiguous', path_run + ' classification ambiguous (' + ','.join(classifications_constrained) + ')' )
return classifications_constrained
