def createTeam(firstIndex,
secondIndex,
isRed,
first='OffensiveAgent',
second='DefensiveDummyAgent'):
"""
This function should return a list of two agents that will form the
team, initialized using firstIndex and secondIndex as their agent
index numbers. isRed is True if the red team is being created, and
will be False if the blue team is being created.
As a potentially helpful development aid, this function can take
additional string-valued keyword arguments ("first" and "second" are
such arguments in the case of this function), which will come from
the --redOpts and --blueOpts command-line arguments to capture.py.
For the nightly contest, however, your team will be created without
any extra arguments, so you should make sure that the default
behavior is what you want for the nightly contest.
"""
locationFinder = Finder()
locationFinder.__init__()
# The following line is an example only; feel free to change it.
return [
eval(first)(firstIndex, locationFinder),
eval(second)(secondIndex, locationFinder)
]
def __init__(self, ensembleType, finder, nClassifiers, modelPath, percentOfTraining=0,\
duplicatesAllowed=False, randomSeed=42, rerankType='vote', countOther=True):
""" Create an ensemble classifier.
type = 'feature', 'featureType', 'abstract'
finder = classifier to create copies of
nClassifiers = number of classifiers in the ensemble
percentOfTraining = is percentage of the training set used for each classifier's training set
(if 0, then the training sets are disjoint and there is no overlap)
duplicates = are duplicate training examples allowed
entityTypes = list of mention types (e.g. group, outcome) to find
"""
Finder.__init__(self, finder.entityTypes)
self.finderType = 'ensemble'
self.type = ensembleType
self.finder = finder
self.nClassifiers = nClassifiers
self.duplicatesAllowed = duplicatesAllowed
self.percentOfTraining = percentOfTraining
self.randomSeed = randomSeed
self.baggedFeatures = []
self.modelPath = modelPath
if self.modelPath[-1] != '/':
self.modelPath = self.modelPath + '/'
self.countOther = countOther
self.rerankType = rerankType
self.modelFilenames = []
for i in range(self.nClassifiers):
self.modelFilenames.append('%s%s.%d.train.model' %(self.modelPath,self.entityTypesString,i))
self.ensembleTypes = set([])
for i in range(self.nClassifiers):
for eType in self.entityTypes:
self.ensembleTypes.add(self.toEnsembleLabel(eType, i))
def __init__(self, entityType, sentenceFilter, useDetected=True):
""" create a component that can be trained cluster similar mentions
of a given type.
useDetected = True if detected mentions should be clustered.
Otherwise cluster annotated mentions
"""
Finder.__init__(self, [entityType])
self.finderType = 'clusterer'
self.sentenceFilter = sentenceFilter
self.useDetected = useDetected
def __init__(self, entityType, sentenceFilter, useDetected=True):
""" create a component that can be trained cluster similar mentions
of a given type.
useDetected = True if detected mentions should be clustered.
Otherwise cluster annotated mentions
"""
Finder.__init__(self, [entityType])
self.finderType = 'clusterer'
self.sentenceFilter = sentenceFilter
self.useDetected = useDetected
def __init__(self, entityTypes, tokenClassifier):
""" Create a new mention finder to find a given list of mention types.
entityTypes = list of mention types (e.g. group, outcome) to find
"""
Finder.__init__(self, entityTypes)
self.tokenClassifier = tokenClassifier
if self.tokenClassifier != None:
self.finderType = 'mention.'+self.tokenClassifier.classifierType
else:
self.finderType = 'mention'
def __init__(self, groupFinder, outcomeFinder, eventrateFinder, numberFinder, modelPath, jointAssignment=True, \
useRules=False, maxTopK=2, theta=0.8):
""" Create a new re-ranker.
"""
Finder.__init__(self, ['group', 'outcome', 'eventrate', 'on', 'gs'])
self.groupFinder = groupFinder
self.outcomeFinder = outcomeFinder
self.eventrateFinder = eventrateFinder
self.numberFinder = numberFinder
self.modelPath = modelPath
self.featureIds = {}
self.trainFolds = 5
self.maxTopK = maxTopK
self.jointAssignment = jointAssignment
self.useRules = useRules
self.theta = theta
self.labelingWeights = []
# self.labelingWeights = self.poissonWeights()
self.labelingWeights = self.linearWeights()
# self.labelingWeights = self.exponentialWeights()
for i, w in enumerate(self.labelingWeights):
print '%2d %.8f' % (i, w)
def __init__(self, entityType1, entityType2, useLabels=True):
""" create a new mention-quantity associator given a specific mention type
and quantity type. """
Finder.__init__(self, [entityType1, entityType2])
self.finderType = 'associator'
self.useLabels = useLabels
def __init__(self, entityType1, entityType2, useLabels=True):
""" create a new mention-quantity associator given a specific mention type
and quantity type. """
Finder.__init__(self, [entityType1, entityType2])
self.finderType = 'associator'
self.useLabels = useLabels
def __init__(self): Finder.__init__(self)
def __init__(self, entityTypes=[]):
""" Does nothing """
Finder.__init__(self, entityTypes)
