def train(self, trainData=None, optData=None, model=None, combinedModel=None, exampleStyle=None,
classifierParameters=None, parse=None, tokenization=None, task=None, fromStep=None, toStep=None,
workDir=None):
exampleStyle = Parameters.cat(exampleStyle, "keep_neg:no_features")
EdgeDetector.train(self, trainData, optData, model, combinedModel, exampleStyle, classifierParameters, parse, tokenization, fromStep, toStep)
self.classify(trainData, model, "classification-train/train", goldData=trainData, workDir="classification-train")
def learnSettings(inputFiles, detector, classifierParameters):
if detector == None:
print >> sys.stderr, "*** Analyzing input files to determine training settings ***"
structureAnalyzer = StructureAnalyzer()
if not os.path.exists("training/structure.txt"):
datasets = sorted(
filter(None, [inputFiles["train"], inputFiles["devel"]]))
print >> sys.stderr, "input files:", datasets
structureAnalyzer.analyze(datasets)
print >> sys.stderr, structureAnalyzer.toString()
structureAnalyzer.save(None, "training/structure.txt")
else:
print >> sys.stderr, "Using existing analysis from training/structure.txt"
structureAnalyzer.load(None, "training/structure.txt")
# Choose detector
if detector == None:
if "ENTITY" in structureAnalyzer.targets and "INTERACTION" in structureAnalyzer.targets:
detector = "Detectors.EventDetector"
elif "ENTITY" in structureAnalyzer.targets:
detector = "Detectors.EntityDetector"
elif "INTERACTION" in structureAnalyzer.targets:
detector = "Detectors.EdgeDetector"
else:
assert False, structureAnalyzer.targets
print >> sys.stderr, "Using detector '" + str(detector) + "'"
# Set default parameters
if detector == "Detectors.EventDetector":
classifierParameters["unmerging"] = Parameters.cat(
"c=1,10,100,500,1000,1500,2500,5000,10000,20000,50000,80000,100000",
classifierParameters["unmerging"],
"Classifier parameters for unmerging")
classifierParameters["modifiers"] = Parameters.cat(
"c=5000,10000,20000,50000,100000",
classifierParameters["modifiers"],
"Classifier parameters for modifiers")
classifierParameters["edge"] = Parameters.cat(
"c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000",
classifierParameters["edge"], "Classifier parameters for edges")
classifierParameters["trigger"] = Parameters.cat(
"c=1000,5000,10000,20000,50000,80000,100000,150000,180000,200000,250000,300000,350000,500000,1000000",
classifierParameters["trigger"],
"Classifier parameters for triggers")
classifierParameters["recall"] = Parameters.cat(
"0.5,0.6,0.65,0.7,0.85,1.0,1.1,1.2",
classifierParameters["recall"], "Recall adjustment parameters")
elif detector == "Detectors.EntityDetector":
classifierParameters["examples"] = Parameters.cat(
"c=1000,5000,10000,20000,50000,80000,100000,150000,180000,200000,250000,300000,350000,500000,1000000",
classifierParameters["examples"],
"Classifier parameters for entities")
elif detector == "Detectors.EdgeDetector":
classifierParameters["examples"] = Parameters.cat(
"c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000",
classifierParameters["examples"],
"Classifier parameters for edges")
return detector
def train(self,
trainData=None,
optData=None,
model=None,
combinedModel=None,
exampleStyle=None,
classifierParameters=None,
parse=None,
tokenization=None,
task=None,
fromStep=None,
toStep=None,
workDir=None):
exampleStyle = Parameters.cat(exampleStyle, "keep_neg:no_features")
EdgeDetector.train(self, trainData, optData, model, combinedModel,
exampleStyle, classifierParameters, parse,
tokenization, fromStep, toStep)
self.classify(trainData,
model,
"classification-train/train",
goldData=trainData,
workDir="classification-train")
def learnSettings(inputFiles, detector, classifierParameters):
if detector == None:
print >> sys.stderr, "*** Analyzing input files to determine training settings ***"
structureAnalyzer = StructureAnalyzer()
if not os.path.exists("training/structure.txt"):
datasets = sorted(filter(None, [inputFiles["train"], inputFiles["devel"]]))
print >> sys.stderr, "input files:", datasets
structureAnalyzer.analyze(datasets)
print >> sys.stderr, structureAnalyzer.toString()
structureAnalyzer.save(None, "training/structure.txt")
else:
print >> sys.stderr, "Using existing analysis from training/structure.txt"
structureAnalyzer.load(None, "training/structure.txt")
# Choose detector
if detector == None:
if "ENTITY" in structureAnalyzer.targets and "INTERACTION" in structureAnalyzer.targets:
detector = "Detectors.EventDetector"
elif "ENTITY" in structureAnalyzer.targets:
detector = "Detectors.EntityDetector"
elif "INTERACTION" in structureAnalyzer.targets:
detector = "Detectors.EdgeDetector"
else:
assert False, structureAnalyzer.targets
print >> sys.stderr, "Using detector '" + str(detector) + "'"
# Set default parameters
if detector == "Detectors.EventDetector":
classifierParameters["unmerging"] = Parameters.cat("c=1,10,100,500,1000,1500,2500,5000,10000,20000,50000,80000,100000", classifierParameters["unmerging"], "Classifier parameters for unmerging")
classifierParameters["modifiers"] = Parameters.cat("c=5000,10000,20000,50000,100000", classifierParameters["modifiers"], "Classifier parameters for modifiers")
classifierParameters["edge"] = Parameters.cat("c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000", classifierParameters["edge"], "Classifier parameters for edges")
classifierParameters["trigger"] = Parameters.cat("c=1000,5000,10000,20000,50000,80000,100000,150000,180000,200000,250000,300000,350000,500000,1000000", classifierParameters["trigger"], "Classifier parameters for triggers")
classifierParameters["recall"] = Parameters.cat("0.5,0.6,0.65,0.7,0.85,1.0,1.1,1.2", classifierParameters["recall"], "Recall adjustment parameters")
elif detector == "Detectors.EntityDetector":
classifierParameters["examples"] = Parameters.cat("c=1000,5000,10000,20000,50000,80000,100000,150000,180000,200000,250000,300000,350000,500000,1000000", classifierParameters["examples"], "Classifier parameters for entities")
elif detector == "Detectors.EdgeDetector":
classifierParameters["examples"] = Parameters.cat("c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000", classifierParameters["examples"], "Classifier parameters for edges")
return detector
def getTaskSettings(task, detector, bioNLPSTParams, preprocessorParams,
inputFiles, exampleStyles, classifierParameters):
if task != None:
print >> sys.stderr, "*** Defining training settings for task", task, "***"
fullTaskId = task
subTask = 2
if "." in task:
task, subTask = task.split(".")
subTask = int(subTask)
dataPath = Settings.CORPUS_DIR
for dataset in ["devel", "train", "test"]:
if inputFiles[dataset] == None and inputFiles[dataset] != "None":
inputFiles[dataset] = os.path.join(dataPath, task.replace("-FULL", "") + "-"+dataset+".xml")
if task == "ID11" and dataset == "train":
inputFiles[dataset] = Catenate.catenate([os.path.join(dataPath, "ID11-train.xml"), os.path.join(dataPath, "GE11-devel.xml"),
os.path.join(dataPath, "GE11-train.xml")], "training/ID11-train-and-GE11-devel-and-train.xml.gz", fast=True)
if inputFiles[dataset] == "None":
inputFiles[dataset] = None
if inputFiles[dataset] != None and not os.path.exists(inputFiles[dataset]):
inputFiles[dataset] = None
print >> sys.stderr, "Input file", inputFiles[dataset], "for set '" + dataset + "' does not exist, skipping."
assert inputFiles["train"] != None # at least training set must exist
# Example generation parameters
if task == "CO11":
detector = "Detectors.CODetector"
elif task in ["BI11-FULL", "DDI11-FULL"]:
detector = "Detectors.EventDetector"
# BioNLP Shared Task and preprocessing parameters
if task == "BI11-FULL":
bioNLPSTParams = Parameters.cat(bioNLPSTParams, "convert:scores", "BioNLP Shared Task / " + fullTaskId, ["default"]) # the shared task evaluator is not designed for predicted entities
elif task == "REL11":
bioNLPSTParams = Parameters.cat(bioNLPSTParams, "convert:evaluate:scores:a2Tag=rel", "BioNLP Shared Task / " + fullTaskId, ["default"])
elif task not in ["DDI11", "DDI11-FULL", "DDI13"]:
bioNLPSTParams = Parameters.cat(bioNLPSTParams, "convert:evaluate:scores", "BioNLP Shared Task / " + fullTaskId, ["default"])
# Preprocessing parameters
if task in ["BI11", "BI11-FULL", "BB11", "DDI11", "DDI11-FULL"]:
Parameters.cat("intermediateFiles:omitSteps=NER,DIVIDE-SETS", preprocessorParams, "Preprocessor /" + fullTaskId, ["default"])
else: # parse only sentences where BANNER found an entity
Parameters.cat("intermediateFiles:omitSteps=DIVIDE-SETS:PARSE.requireEntities", preprocessorParams, "Preprocessor /" + fullTaskId, ["default"])
# Example style parameters for single-stage tasks
if task == "REN11":
exampleStyles["examples"] = Parameters.cat("undirected:bacteria_renaming:maskTypeAsProtein=Gene", exampleStyles["examples"], "Single-stage example style / " + fullTaskId)
elif task == "DDI11":
exampleStyles["examples"] = Parameters.cat("drugbank_features:ddi_mtmx:filter_shortest_path=conj_and", exampleStyles["examples"], "Single-stage example style / " + fullTaskId)
elif task == "DDI13":
exampleStyles["examples"] = Parameters.cat("keep_neg:drugbank_features:filter_shortest_path=conj_and", exampleStyles["examples"], "Single-stage example style / " + fullTaskId)
elif task == "BI11":
exampleStyles["edge"] = Parameters.cat("bi_features", exampleStyles["edge"], "Edge example style / " + fullTaskId)
# Edge style
if task in ["GE09", "GE11", "GE13"] and subTask == 1:
exampleStyles["edge"] = Parameters.cat("genia_features:genia_task1", exampleStyles["edge"])
elif task in ["GE09", "GE11", "GE13"]:
exampleStyles["edge"] = Parameters.cat("genia_features", exampleStyles["edge"])
elif task == "REL11":
exampleStyles["edge"] = Parameters.cat("rel_features", exampleStyles["edge"], "Edge example style / " + fullTaskId)
elif task == "DDI11-FULL":
exampleStyles["edge"] = Parameters.cat("drugbank_features:filter_shortest_path=conj_and", exampleStyles["edge"], "Edge example style / " + fullTaskId)
elif task == "CO11":
exampleStyles["edge"] = Parameters.cat("co_features", exampleStyles["edge"], "Edge example style / " + fullTaskId)
elif task == "BI11-FULL":
exampleStyles["edge"] = Parameters.cat("bi_features", exampleStyles["edge"], "Edge example style / " + fullTaskId)
# Trigger style
if task in ["GE09", "GE11", "GE13"] and subTask == 1:
exampleStyles["trigger"] = Parameters.cat("genia_task1", exampleStyles["trigger"], "Trigger example style / " + fullTaskId)
elif task in ["EPI11", "PC13"]:
exampleStyles["trigger"] = Parameters.cat("epi_merge_negated", exampleStyles["trigger"], "Trigger example style / " + fullTaskId)
elif task == "BB11": # "bb_features:build_for_nameless:wordnet"
exampleStyles["trigger"] = Parameters.cat("bb_features:build_for_nameless", exampleStyles["trigger"], "Trigger example style / " + fullTaskId)
elif task == "BB13T3": # "bb_features:build_for_nameless:wordnet"
exampleStyles["trigger"] = Parameters.cat("bb_features:build_for_nameless", exampleStyles["trigger"], "Trigger example style / " + fullTaskId)
elif task == "REL11":
exampleStyles["trigger"] = Parameters.cat("rel_features", exampleStyles["trigger"], "Trigger example style / " + fullTaskId)
elif task in ["BI11-FULL", "DDI11-FULL"]:
exampleStyles["trigger"] = "build_for_nameless:names"
# Classifier parameters
if task == "DDI11":
classifierParameters["examples"] = Parameters.cat("c=10,100,1000,2500,4000,5000,6000,7500,10000,20000,25000,50000:TEES.threshold", classifierParameters["examples"], "Classifier parameters for single-stage examples" + fullTaskId)
#elif task == "DDI13":
# classifierParameters["examples"] = Parameters.cat("c=10,100,1000,2500,4000,5000,6000,7500,10000,20000,25000,50000:TEES.threshold", classifierParameters["examples"], "Classifier parameters for single-stage examples" + fullTaskId)
elif task == "CO11":
classifierParameters["edge"] = Parameters.cat("c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000", classifierParameters["examples"], "Classifier parameters for edges / " + fullTaskId)
classifierParameters["trigger"] = Parameters.cat("c=1000,5000,10000,20000,50000,80000,100000,150000,180000,200000,250000,300000,350000,500000,1000000", classifierParameters["examples"], "Classifier parameters for triggers / " + fullTaskId)
classifierParameters["recall"] = Parameters.cat("0.8,0.9,0.95,1.0", classifierParameters["recall"], "Recall adjust / " + fullTaskId)
return detector, bioNLPSTParams, preprocessorParams
def getTaskSettings(task, detector, bioNLPSTParams, preprocessorParams,
inputFiles, exampleStyles, classifierParameters):
if task != None:
print >> sys.stderr, "*** Defining training settings for task", task, "***"
fullTaskId = task
subTask = 2
if "." in task:
task, subTask = task.split(".")
subTask = int(subTask)
dataPath = Settings.CORPUS_DIR
for dataset in ["devel", "train", "test"]:
if inputFiles[dataset] == None and inputFiles[dataset] != "None":
inputFiles[dataset] = os.path.join(
dataPath,
task.replace("-FULL", "") + "-" + dataset + ".xml")
if task == "ID11" and dataset == "train":
inputFiles[dataset] = Catenate.catenate(
[
os.path.join(dataPath, "ID11-train.xml"),
os.path.join(dataPath, "GE11-devel.xml"),
os.path.join(dataPath, "GE11-train.xml")
],
"training/ID11-train-and-GE11-devel-and-train.xml.gz",
fast=True)
if inputFiles[dataset] == "None":
inputFiles[dataset] = None
if inputFiles[dataset] != None and not os.path.exists(
inputFiles[dataset]):
inputFiles[dataset] = None
print >> sys.stderr, "Input file", inputFiles[
dataset], "for set '" + dataset + "' does not exist, skipping."
assert inputFiles["train"] != None # at least training set must exist
# Example generation parameters
if task == "CO11":
detector = "Detectors.CODetector"
elif task in ["BI11-FULL", "DDI11-FULL"]:
detector = "Detectors.EventDetector"
# BioNLP Shared Task and preprocessing parameters
if task == "BI11-FULL":
bioNLPSTParams = Parameters.cat(
bioNLPSTParams, "convert:scores",
"BioNLP Shared Task / " + fullTaskId, ["default"]
) # the shared task evaluator is not designed for predicted entities
elif task == "REL11":
bioNLPSTParams = Parameters.cat(
bioNLPSTParams, "convert:evaluate:scores:a2Tag=rel",
"BioNLP Shared Task / " + fullTaskId, ["default"])
elif task not in ["DDI11", "DDI11-FULL", "DDI13"]:
bioNLPSTParams = Parameters.cat(
bioNLPSTParams, "convert:evaluate:scores",
"BioNLP Shared Task / " + fullTaskId, ["default"])
# Preprocessing parameters
if task in ["BI11", "BI11-FULL", "BB11", "DDI11", "DDI11-FULL"]:
Parameters.cat("intermediateFiles:omitSteps=NER,DIVIDE-SETS",
preprocessorParams, "Preprocessor /" + fullTaskId,
["default"])
else: # parse only sentences where BANNER found an entity
Parameters.cat(
"intermediateFiles:omitSteps=DIVIDE-SETS:PARSE.requireEntities",
preprocessorParams, "Preprocessor /" + fullTaskId, ["default"])
# Example style parameters for single-stage tasks
if task == "REN11":
exampleStyles["examples"] = Parameters.cat(
"undirected:bacteria_renaming:maskTypeAsProtein=Gene",
exampleStyles["examples"],
"Single-stage example style / " + fullTaskId)
elif task == "DDI11":
exampleStyles["examples"] = Parameters.cat(
"drugbank_features:ddi_mtmx:filter_shortest_path=conj_and",
exampleStyles["examples"],
"Single-stage example style / " + fullTaskId)
elif task == "DDI13":
exampleStyles["examples"] = Parameters.cat(
"keep_neg:drugbank_features:filter_shortest_path=conj_and",
exampleStyles["examples"],
"Single-stage example style / " + fullTaskId)
elif task == "BI11":
exampleStyles["edge"] = Parameters.cat(
"bi_features", exampleStyles["edge"],
"Edge example style / " + fullTaskId)
# Edge style
if task in ["GE09", "GE11", "GE13"] and subTask == 1:
exampleStyles["edge"] = Parameters.cat(
"genia_features:genia_task1", exampleStyles["edge"])
elif task in ["GE09", "GE11", "GE13"]:
exampleStyles["edge"] = Parameters.cat("genia_features",
exampleStyles["edge"])
elif task == "REL11":
exampleStyles["edge"] = Parameters.cat(
"rel_features", exampleStyles["edge"],
"Edge example style / " + fullTaskId)
elif task == "DDI11-FULL":
exampleStyles["edge"] = Parameters.cat(
"drugbank_features:filter_shortest_path=conj_and",
exampleStyles["edge"], "Edge example style / " + fullTaskId)
elif task == "CO11":
exampleStyles["edge"] = Parameters.cat(
"co_features", exampleStyles["edge"],
"Edge example style / " + fullTaskId)
elif task == "BI11-FULL":
exampleStyles["edge"] = Parameters.cat(
"bi_features", exampleStyles["edge"],
"Edge example style / " + fullTaskId)
# Trigger style
if task in ["GE09", "GE11", "GE13"] and subTask == 1:
exampleStyles["trigger"] = Parameters.cat(
"genia_task1", exampleStyles["trigger"],
"Trigger example style / " + fullTaskId)
elif task in ["EPI11", "PC13"]:
exampleStyles["trigger"] = Parameters.cat(
"epi_merge_negated", exampleStyles["trigger"],
"Trigger example style / " + fullTaskId)
elif task == "BB11": # "bb_features:build_for_nameless:wordnet"
exampleStyles["trigger"] = Parameters.cat(
"bb_features:build_for_nameless", exampleStyles["trigger"],
"Trigger example style / " + fullTaskId)
elif task == "BB13T3": # "bb_features:build_for_nameless:wordnet"
exampleStyles["trigger"] = Parameters.cat(
"bb_features:build_for_nameless", exampleStyles["trigger"],
"Trigger example style / " + fullTaskId)
elif task == "REL11":
exampleStyles["trigger"] = Parameters.cat(
"rel_features", exampleStyles["trigger"],
"Trigger example style / " + fullTaskId)
elif task in ["BI11-FULL", "DDI11-FULL"]:
exampleStyles["trigger"] = "build_for_nameless:names"
# Classifier parameters
if task == "DDI11":
classifierParameters["examples"] = Parameters.cat(
"c=10,100,1000,2500,4000,5000,6000,7500,10000,20000,25000,50000:TEES.threshold",
classifierParameters["examples"],
"Classifier parameters for single-stage examples" + fullTaskId)
#elif task == "DDI13":
# classifierParameters["examples"] = Parameters.cat("c=10,100,1000,2500,4000,5000,6000,7500,10000,20000,25000,50000:TEES.threshold", classifierParameters["examples"], "Classifier parameters for single-stage examples" + fullTaskId)
elif task == "CO11":
classifierParameters["edge"] = Parameters.cat(
"c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000",
classifierParameters["examples"],
"Classifier parameters for edges / " + fullTaskId)
classifierParameters["trigger"] = Parameters.cat(
"c=1000,5000,10000,20000,50000,80000,100000,150000,180000,200000,250000,300000,350000,500000,1000000",
classifierParameters["examples"],
"Classifier parameters for triggers / " + fullTaskId)
classifierParameters["recall"] = Parameters.cat(
"0.8,0.9,0.95,1.0", classifierParameters["recall"],
"Recall adjust / " + fullTaskId)
return detector, bioNLPSTParams, preprocessorParams
def getTaskSettings(task,
detector,
bioNLPSTParams,
preprocessorParams,
inputFiles,
exampleStyles,
classifierParameters,
folds,
corpusDir=None):
if task != None:
print >> sys.stderr, "*** Defining training settings for task", task, "***"
fullTaskId = task
subTask = 2
if "." in task:
task, subTask = task.split(".")
subTask = int(subTask)
if corpusDir == None:
corpusDir = Settings.CORPUS_DIR
for dataset in ["devel", "train", "test"]:
if inputFiles[dataset] == None and inputFiles[dataset] != "None":
if task.startswith("DDI13"):
if dataset in ["devel", "train"]:
inputFiles[dataset] = os.path.join(
corpusDir, "DDI13-train.xml")
elif dataset == "test":
if task.endswith("T91"):
inputFiles[dataset] = os.path.join(
corpusDir, "DDI13-test-task9.1.xml")
elif task.endswith("T92") or task.endswith("FULL"):
inputFiles[dataset] = os.path.join(
corpusDir, "DDI13-test-task9.2.xml")
elif task == "ID11" and dataset == "train":
inputFiles[dataset] = Catenate.catenate(
[
os.path.join(corpusDir, "ID11-train.xml"),
os.path.join(corpusDir, "GE11-devel.xml"),
os.path.join(corpusDir, "GE11-train.xml")
],
"training/ID11-train-and-GE11-devel-and-train.xml.gz",
fast=True)
else:
inputFiles[dataset] = os.path.join(
corpusDir,
task.replace("-FULL", "") + "-" + dataset + ".xml")
if inputFiles[dataset] == "None":
inputFiles[dataset] = None
if inputFiles[dataset] != None and not os.path.exists(
inputFiles[dataset]):
fullPath = os.path.join(Settings.CORPUS_DIR,
inputFiles[dataset])
if os.path.exists(fullPath):
inputFiles[dataset] = fullPath
else:
inputFiles[dataset] = None
print >> sys.stderr, "Input file", inputFiles[
dataset], "for set '" + dataset + "' does not exist, skipping."
assert inputFiles["train"] != None # at least training set must exist
# Example generation parameters
if task == "CO11":
detector = "Detectors.CODetector"
elif task in [
"BI11-FULL", "DDI11-FULL", "DDI13-FULL", "BB_EVENT_16-FULL"
]:
detector = "Detectors.EventDetector"
elif task.startswith("DDI13"):
if task.endswith("T91"):
detector = "Detectors.EntityDetector"
elif task.endswith("T92"):
detector = "Detectors.EdgeDetector"
#######################################################################
# BioNLP Shared Task and preprocessing parameters
#######################################################################
if task == "BI11-FULL":
bioNLPSTParams = Parameters.cat(
bioNLPSTParams, "convert:scores",
"BioNLP Shared Task / " + fullTaskId, ["default"]
) # the shared task evaluator is not designed for predicted entities
elif task == "REL11":
bioNLPSTParams = Parameters.cat(
bioNLPSTParams, "convert:evaluate:scores:a2Tag=rel",
"BioNLP Shared Task / " + fullTaskId, ["default"])
elif task in ("BB_EVENT_16", "BB_EVENT_16-FULL", "BB_EVENT_NER_16",
"SDB16"):
bioNLPSTParams = Parameters.cat(
bioNLPSTParams, "convert=zip",
"BioNLP Shared Task / " + fullTaskId, ["default"])
elif task not in [
"DDI11", "DDI11-FULL", "DDI13T91", "DDI13T92", "DDI13-FULL"
]:
bioNLPSTParams = Parameters.cat(
bioNLPSTParams, "convert:evaluate:scores",
"BioNLP Shared Task / " + fullTaskId, ["default"])
#######################################################################
# Preprocessing parameters
#######################################################################
if task in [
"BI11", "BI11-FULL", "BB11", "DDI11", "DDI11-FULL", "DDI13T91",
"DDI13T92", "DDI13-FULL"
]:
Parameters.cat("intermediateFiles:omitSteps=NER,DIVIDE-SETS",
preprocessorParams, "Preprocessor /" + fullTaskId,
["default"])
else: # parse only sentences where BANNER found an entity
Parameters.cat(
"intermediateFiles:omitSteps=DIVIDE-SETS:PARSE.requireEntities",
preprocessorParams, "Preprocessor /" + fullTaskId, ["default"])
#######################################################################
# Example style parameters
#######################################################################
# Example style parameters for single-stage tasks #####################
msg = "Single-stage example style / " + fullTaskId
if task == "REN11":
exampleStyles["examples"] = Parameters.cat(
"undirected:bacteria_renaming:maskTypeAsProtein=Gene",
exampleStyles["examples"], msg)
elif task == "DDI11":
exampleStyles["examples"] = Parameters.cat(
"drugbank_features:ddi_mtmx:filter_shortest_path=conj_and",
exampleStyles["examples"], msg)
elif task.startswith("DDI13"):
if task.endswith("T91"):
exampleStyles["examples"] = Parameters.cat(
"names:build_for_nameless:ddi13_features:drugbank_features",
exampleStyles["examples"], msg)
elif task.endswith("T92"):
exampleStyles["examples"] = Parameters.cat(
"keep_neg:drugbank_features:filter_shortest_path=conj_and",
exampleStyles["examples"], msg)
elif task == "BI11":
exampleStyles["examples"] = Parameters.cat(
"bi_features", exampleStyles["examples"], msg)
elif task == "BB_EVENT_16":
exampleStyles["examples"] = Parameters.cat(
"keep_neg", exampleStyles["examples"], msg
) #exampleStyles["examples"] = Parameters.cat("linear_features:keep_neg", exampleStyles["examples"], msg)
elif task == "SDB16":
exampleStyles["examples"] = Parameters.cat(
"sdb_merge:sdb_features", exampleStyles["examples"], msg)
# Edge style ##########################################################
msg = "Edge example style / " + fullTaskId
if task in ["GE09", "GE11", "GE13"] and subTask == 1:
exampleStyles["edge"] = Parameters.cat(
"genia_features:genia_task1", exampleStyles["edge"], msg)
elif task in ["GE09", "GE11", "GE13"]:
exampleStyles["edge"] = Parameters.cat("genia_features",
exampleStyles["edge"], msg)
elif task == "REL11":
exampleStyles["edge"] = Parameters.cat("rel_features",
exampleStyles["edge"], msg)
elif task == "DDI11-FULL":
exampleStyles["edge"] = Parameters.cat(
"drugbank_features:filter_shortest_path=conj_and",
exampleStyles["edge"], msg)
elif task == "DDI13-FULL":
exampleStyles["edge"] = Parameters.cat(
"keep_neg:drugbank_features:filter_shortest_path=conj_and",
exampleStyles["edge"], msg)
elif task == "CO11":
exampleStyles["edge"] = Parameters.cat("co_features",
exampleStyles["edge"], msg)
elif task == "BI11-FULL":
exampleStyles["edge"] = Parameters.cat("bi_features",
exampleStyles["edge"], msg)
# Trigger style #######################################################
msg = "Trigger example style / " + fullTaskId
if task in ["GE09", "GE11", "GE13"] and subTask == 1:
exampleStyles["trigger"] = Parameters.cat("genia_task1",
exampleStyles["trigger"],
msg)
elif task in ["EPI11", "PC13"]:
exampleStyles["trigger"] = Parameters.cat("epi_merge_negated",
exampleStyles["trigger"],
msg)
elif task == "BB11": # "bb_features:build_for_nameless:wordnet"
exampleStyles["trigger"] = Parameters.cat("bb_features",
exampleStyles["trigger"],
msg)
elif task == "BB13T3": # "bb_features:build_for_nameless:wordnet"
exampleStyles["trigger"] = Parameters.cat("bb_features",
exampleStyles["trigger"],
msg)
elif task == "REL11":
exampleStyles["trigger"] = Parameters.cat("rel_features",
exampleStyles["trigger"],
msg)
elif task in ["BI11-FULL", "DDI11-FULL"]:
exampleStyles["trigger"] = "names:build_for_nameless"
elif task == "DDI13-FULL":
exampleStyles[
"trigger"] = "names:build_for_nameless:ddi13_features:drugbank_features"
elif task == "BB_EVENT_16-FULL":
exampleStyles["trigger"] = Parameters.cat(
"bb_spans:bb_features:ontobiotope_features:build_for_nameless:all_tokens:only_types=Bacteria,Habitat,Geographical",
exampleStyles["trigger"], msg)
elif task in "BB_EVENT_NER_16":
exampleStyles["trigger"] = Parameters.cat(
"bb_spans:bb_features:ontobiotope_features:build_for_nameless:all_tokens",
exampleStyles["trigger"], msg)
#######################################################################
# Classifier parameters
#######################################################################
if task == "DDI11":
classifierParameters["examples"] = Parameters.cat(
"c=10,100,1000,2500,4000,5000,6000,7500,10000,20000,25000,50000:TEES.threshold",
classifierParameters["examples"],
"Classifier parameters for single-stage examples" + fullTaskId)
#elif task == "DDI13":
# classifierParameters["examples"] = Parameters.cat("c=10,100,1000,2500,4000,5000,6000,7500,10000,20000,25000,50000:TEES.threshold", classifierParameters["examples"], "Classifier parameters for single-stage examples" + fullTaskId)
elif task == "CO11":
classifierParameters["edge"] = Parameters.cat(
"c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000",
classifierParameters["edge"],
"Classifier parameters for edges / " + fullTaskId)
classifierParameters["trigger"] = Parameters.cat(
"c=1000,5000,10000,20000,50000,80000,100000,150000,180000,200000,250000,300000,350000,500000,1000000",
classifierParameters["trigger"],
"Classifier parameters for triggers / " + fullTaskId)
classifierParameters["recall"] = Parameters.cat(
"0.8,0.9,0.95,1.0", classifierParameters["recall"],
"Recall adjust / " + fullTaskId)
elif task == "BB_EVENT_16":
classifierParameters["examples"] = Parameters.cat(
"c=10,20,30,40,50,60,70,80,100,110,115,120,125,130,140,150,200,500,1000,2000,3000,4000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000",
classifierParameters["examples"],
"Classifier parameters for edges / " + fullTaskId)
elif task in ("BB_EVENT_16-FULL", "BB_EVENT_NER_16"):
classifierParameters["edge"] = Parameters.cat(
"c=10,20,50,80,100,110,115,120,125,130,140,150,200,500,1000",
classifierParameters["edge"],
"Classifier parameters for edges / " + fullTaskId)
elif task == "SDB16":
classifierParameters["examples"] = Parameters.cat(
"c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000,80000,100000,150000",
classifierParameters["examples"],
"Classifier parameters for single-stage examples / " +
fullTaskId)
# Training fold parameters ############################################
if task.startswith("DDI13"):
folds["devel"] = ["train1", "train2", "train3", "train4"]
folds["train"] = ["train5", "train6", "train7", "train8", "train9"]
return detector, bioNLPSTParams, preprocessorParams, folds
def getTaskSettings(task, detector, bioNLPSTParams, preprocessorParams,
inputFiles, exampleStyles, classifierParameters, folds, corpusDir=None, useKerasDetector=False):
if task != None:
print >> sys.stderr, "*** Defining training settings for task", task, "***"
fullTaskId = task
task, subTask = getSubTask(task)
if corpusDir == None:
corpusDir = Settings.CORPUS_DIR
print >> sys.stderr, "Loading corpus", task, "from", corpusDir
for dataset in ["devel", "train", "test"]:
if inputFiles[dataset] == None:
if task.startswith("DDI13") and task != "DDI13":
if dataset in ["devel", "train"]:
inputFiles[dataset] = os.path.join(corpusDir, "DDI13-train.xml")
elif dataset == "test":
if task.endswith("T91"):
inputFiles[dataset] = os.path.join(corpusDir, "DDI13-test-task9.1.xml")
elif task.endswith("T92") or task.endswith("FULL"):
inputFiles[dataset] = os.path.join(corpusDir, "DDI13-test-task9.2.xml")
elif task == "ID11" and dataset == "train":
inputFiles[dataset] = Catenate.catenate([os.path.join(corpusDir, "ID11-train.xml"), os.path.join(corpusDir, "GE11-devel.xml"),
os.path.join(corpusDir, "GE11-train.xml")], "training/ID11-train-and-GE11-devel-and-train.xml.gz", fast=True)
else:
inputFiles[dataset] = os.path.join(corpusDir, task.replace("-FULL", "") + "-"+dataset+".xml")
if inputFiles[dataset] == "skip":
inputFiles[dataset] = None
if inputFiles[dataset] != None and not os.path.exists(inputFiles[dataset]):
fullPath = os.path.join(Settings.CORPUS_DIR, inputFiles[dataset])
if os.path.exists(fullPath):
inputFiles[dataset] = fullPath
else:
inputFiles[dataset] = None
print >> sys.stderr, "Input file", inputFiles[dataset], "for set '" + dataset + "' does not exist, skipping."
assert inputFiles["train"] != None # at least training set must exist
# Example generation parameters
if detector == None:
if task == "CO11":
detector = "Detectors.CODetector"
elif task in ["BI11-FULL", "DDI11-FULL", "DDI13-FULL", "BB_EVENT_16-FULL"]:
detector = "Detectors.EventDetector"
elif task.startswith("DDI13"):
if task.endswith("T91"):
detector = "Detectors.EntityDetector"
elif task.endswith("T92") or task == "DDI13":
detector = "Detectors.EdgeDetector"
#######################################################################
# BioNLP Shared Task and preprocessing parameters
#######################################################################
if task == "BI11-FULL":
bioNLPSTParams = Parameters.cat(bioNLPSTParams, "convert:scores", "BioNLP Shared Task / " + fullTaskId, ["default"]) # the shared task evaluator is not designed for predicted entities
elif task == "REL11":
bioNLPSTParams = Parameters.cat(bioNLPSTParams, "convert:evaluate:scores:a2Tag=rel", "BioNLP Shared Task / " + fullTaskId, ["default"])
elif task in ("BB_EVENT_16", "BB_EVENT_16-FULL", "BB_EVENT_NER_16", "SDB16"):
bioNLPSTParams = Parameters.cat(bioNLPSTParams, "convert=zip", "BioNLP Shared Task / " + fullTaskId, ["default"])
elif task not in ["DDI11", "DDI11-FULL", "DDI13T91", "DDI13T92", "DDI13-FULL", "DDI13", "CP17", "SEMEVAL10T8"]:
bioNLPSTParams = Parameters.cat(bioNLPSTParams, "convert:evaluate:scores", "BioNLP Shared Task / " + fullTaskId, ["default"])
else:
bioNLPSTParams = "skip"
#######################################################################
# Preprocessing parameters
#######################################################################
if task in ["BI11", "BI11-FULL", "BB11", "DDI11", "DDI11-FULL", "DDI13T91", "DDI13T92", "DDI13-FULL", "DDI13"]:
Parameters.cat("intermediateFiles:omitSteps=NER,DIVIDE-SETS", preprocessorParams, "Preprocessor /" + fullTaskId, ["default"])
else: # parse only sentences where BANNER found an entity
Parameters.cat("intermediateFiles:omitSteps=DIVIDE-SETS:PARSE.requireEntities", preprocessorParams, "Preprocessor /" + fullTaskId, ["default"])
#######################################################################
# Example style parameters
#######################################################################
if not useKerasDetector:
# Example style parameters for single-stage tasks #####################
msg = "Single-stage example style / " + fullTaskId
if task == "REN11":
exampleStyles["examples"] = Parameters.cat("undirected:bacteria_renaming:maskTypeAsProtein=Gene", exampleStyles["examples"], msg)
elif task == "DDI11":
exampleStyles["examples"] = Parameters.cat("drugbank_features:ddi_mtmx:filter_shortest_path=conj_and", exampleStyles["examples"], msg)
elif task.startswith("DDI13"):
if task.endswith("T91"):
exampleStyles["examples"] = Parameters.cat("names:build_for_nameless:ddi13_features:drugbank_features", exampleStyles["examples"], msg)
elif task.endswith("T92") or task == "DDI13":
exampleStyles["examples"] = Parameters.cat("keep_neg:drugbank_features:filter_shortest_path=conj_and", exampleStyles["examples"], msg)
elif task == "BI11":
exampleStyles["examples"] = Parameters.cat("bi_features", exampleStyles["examples"], msg)
elif task == "BB_EVENT_16":
exampleStyles["examples"] = Parameters.cat("keep_neg", exampleStyles["examples"], msg) #exampleStyles["examples"] = Parameters.cat("linear_features:keep_neg", exampleStyles["examples"], msg)
elif task == "SDB16":
exampleStyles["examples"] = Parameters.cat("sdb_merge:sdb_features", exampleStyles["examples"], msg)
# Edge style ##########################################################
msg = "Edge example style / " + fullTaskId
if task in ["GE09", "GE11", "GE13"] and subTask == 1:
exampleStyles["edge"] = Parameters.cat("genia_features:genia_task1", exampleStyles["edge"], msg)
elif task in ["GE09", "GE11", "GE13"]:
exampleStyles["edge"] = Parameters.cat("genia_features", exampleStyles["edge"], msg)
elif task == "REL11":
exampleStyles["edge"] = Parameters.cat("rel_features", exampleStyles["edge"], msg)
elif task == "DDI11-FULL":
exampleStyles["edge"] = Parameters.cat("drugbank_features:filter_shortest_path=conj_and", exampleStyles["edge"], msg)
elif task == "DDI13-FULL":
exampleStyles["edge"] = Parameters.cat("keep_neg:drugbank_features:filter_shortest_path=conj_and", exampleStyles["edge"], msg)
elif task == "CO11":
exampleStyles["edge"] = Parameters.cat("co_features", exampleStyles["edge"], msg)
elif task == "BI11-FULL":
exampleStyles["edge"] = Parameters.cat("bi_features", exampleStyles["edge"], msg)
# Trigger style #######################################################
msg = "Trigger example style / " + fullTaskId
if task in ["GE09", "GE11", "GE13"] and subTask == 1:
exampleStyles["trigger"] = Parameters.cat("genia_task1", exampleStyles["trigger"], msg)
elif task in ["EPI11", "PC13"]:
exampleStyles["trigger"] = Parameters.cat("epi_merge_negated", exampleStyles["trigger"], msg)
elif task == "BB11": # "bb_features:build_for_nameless:wordnet"
exampleStyles["trigger"] = Parameters.cat("bb_features", exampleStyles["trigger"], msg)
elif task == "BB13T3": # "bb_features:build_for_nameless:wordnet"
exampleStyles["trigger"] = Parameters.cat("bb_features", exampleStyles["trigger"], msg)
elif task == "REL11":
exampleStyles["trigger"] = Parameters.cat("rel_features", exampleStyles["trigger"], msg)
elif task in ["BI11-FULL", "DDI11-FULL"]:
exampleStyles["trigger"] = "names:build_for_nameless"
elif task == "DDI13-FULL":
exampleStyles["trigger"] = "names:build_for_nameless:ddi13_features:drugbank_features"
elif task == "BB_EVENT_16-FULL":
exampleStyles["trigger"] = Parameters.cat("bb_spans:bb_features:ontobiotope_features:build_for_nameless:all_tokens:only_types=Bacteria,Habitat,Geographical", exampleStyles["trigger"], msg)
elif task in "BB_EVENT_NER_16":
exampleStyles["trigger"] = Parameters.cat("bb_spans:bb_features:ontobiotope_features:build_for_nameless:all_tokens", exampleStyles["trigger"], msg)
#######################################################################
# Classifier parameters
#######################################################################
if task == "DDI11":
classifierParameters["examples"] = Parameters.cat("c=10,100,1000,2500,4000,5000,6000,7500,10000,20000,25000,50000:TEES.threshold", classifierParameters["examples"], "Classifier parameters for single-stage examples" + fullTaskId)
#elif task == "DDI13":
# classifierParameters["examples"] = Parameters.cat("c=10,100,1000,2500,4000,5000,6000,7500,10000,20000,25000,50000:TEES.threshold", classifierParameters["examples"], "Classifier parameters for single-stage examples" + fullTaskId)
elif task == "CO11":
classifierParameters["edge"] = Parameters.cat("c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000", classifierParameters["edge"], "Classifier parameters for edges / " + fullTaskId)
classifierParameters["trigger"] = Parameters.cat("c=1000,5000,10000,20000,50000,80000,100000,150000,180000,200000,250000,300000,350000,500000,1000000", classifierParameters["trigger"], "Classifier parameters for triggers / " + fullTaskId)
classifierParameters["recall"] = Parameters.cat("0.8,0.9,0.95,1.0", classifierParameters["recall"], "Recall adjust / " + fullTaskId)
elif task == "BB_EVENT_16":
classifierParameters["examples"] = Parameters.cat("c=10,20,30,40,50,60,70,80,100,110,115,120,125,130,140,150,200,500,1000,2000,3000,4000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000", classifierParameters["examples"], "Classifier parameters for edges / " + fullTaskId)
elif task in ("BB_EVENT_16-FULL", "BB_EVENT_NER_16"):
classifierParameters["edge"] = Parameters.cat("c=10,20,50,80,100,110,115,120,125,130,140,150,200,500,1000", classifierParameters["edge"], "Classifier parameters for edges / " + fullTaskId)
elif task == "SDB16":
classifierParameters["examples"] = Parameters.cat("c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000,80000,100000,150000", classifierParameters["examples"], "Classifier parameters for single-stage examples / " + fullTaskId)
# Training fold parameters ############################################
if task.startswith("DDI13") and task != "DDI13":
#folds["devel"]=["train1", "train2", "train3", "train4"]
#folds["train"]=["train5", "train6", "train7", "train8", "train9"]
folds["devel"]=["train1", "train2", "train3"]
folds["train"]=["train4", "train5", "train6", "train7", "train8", "train9"]
return detector, bioNLPSTParams, preprocessorParams, folds
def learnSettings(inputFiles, detector, classifierParameters, task, exampleStyles, useKerasDetector=False):
if detector == None:
print >> sys.stderr, "*** Analyzing input files to determine training settings ***"
structureAnalyzer = StructureAnalyzer()
if not os.path.exists("training/structure.txt"):
datasets = sorted(filter(None, [inputFiles["train"], inputFiles["devel"]]))
print >> sys.stderr, "input files:", datasets
structureAnalyzer.analyze(datasets)
print >> sys.stderr, structureAnalyzer.toString()
structureAnalyzer.save(None, "training/structure.txt")
else:
print >> sys.stderr, "Using existing analysis from training/structure.txt"
structureAnalyzer.load(None, "training/structure.txt")
# Choose detector
if detector == None:
if "ENTITY" in structureAnalyzer.targets and "INTERACTION" in structureAnalyzer.targets:
detector = "Detectors.EventDetector"
elif "ENTITY" in structureAnalyzer.targets:
detector = "Detectors.EntityDetector"
elif "INTERACTION" in structureAnalyzer.targets:
detector = "Detectors.EdgeDetector"
else:
assert False, structureAnalyzer.targets
if useKerasDetector and not "Keras" in detector:
detector = detector.replace("Detectors.", "Detectors.Keras")
print >> sys.stderr, "Using detector '" + str(detector) + "'"
# Set default parameters
cp = classifierParameters
if detector == "Detectors.EventDetector":
# Add common classifier parameters
if cp["examples"] != None:
cp["unmerging"] = Parameters.cat(cp["examples"], cp["unmerging"])
cp["modifiers"] = Parameters.cat(cp["examples"], cp["modifiers"])
cp["edge"] = Parameters.cat(cp["examples"], cp["edge"])
cp["trigger"] = Parameters.cat(cp["examples"], cp["trigger"])
cp["unmerging"] = Parameters.cat("c=1,10,100,500,1000,1500,2500,5000,10000,20000,50000,80000,100000", cp["unmerging"], "Classifier parameters for unmerging")
cp["modifiers"] = Parameters.cat("c=5000,10000,20000,50000,100000", cp["modifiers"], "Classifier parameters for modifiers")
cp["edge"] = Parameters.cat("c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000", cp["edge"], "Classifier parameters for edges")
cp["trigger"] = Parameters.cat("c=1000,5000,10000,20000,50000,80000,100000,150000,180000,200000,250000,300000,350000,500000,1000000", cp["trigger"], "Classifier parameters for triggers")
cp["recall"] = Parameters.cat("0.5,0.6,0.65,0.7,0.85,1.0,1.1,1.2", cp["recall"], "Recall adjustment parameters")
elif detector == "Detectors.EntityDetector":
cp["examples"] = Parameters.cat("c=1000,5000,10000,20000,50000,80000,100000,150000,180000,200000,250000,300000,350000,500000,1000000", cp["examples"], "Classifier parameters for entities")
elif detector == "Detectors.EdgeDetector":
cp["examples"] = Parameters.cat("c=1000,4500,5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000", cp["examples"], "Classifier parameters for edges")
elif detector == "Detectors.UnmergingDetector":
cp["examples"] = Parameters.cat("c=1,10,100,500,1000,1500,2500,5000,10000,20000,50000,80000,100000", cp["examples"], "Classifier parameters for unmerging")
#######################################################################
# Keras example styles
#######################################################################
if useKerasDetector:
task, subTask = getSubTask(task)
msg = "Keras example style"
#overrideStyles = {x:(Parameters.get(exampleStyles[x]) if (exampleStyles[x] != None and "override" in exampleStyles[x]) else {"override":True}) for x in exampleStyles}
overrideStyles = {"all":{}}
for key in exampleStyles:
overrideStyles[key] = {}
params = Parameters.get(exampleStyles[key])
if "override" in params:
exampleStyles[key] = None
overrideStyles[key] = params
overrideStyles[key].pop("override")
elif "override_all" in params:
exampleStyles[key] = None
overrideStyles["all"] = params
overrideStyles["all"].pop("override_all")
#exampleStyles[key] = exampleStyles[key] if (exampleStyles[key] != None and not "override" in exampleStyles[key]) else None
print >> sys.stderr, "Override styles:", overrideStyles
if "EventDetector" in detector:
if task == "EPI11":
exampleStyles["trigger"] = Parameters.cat("keras:epochs=500:patience=10:nf=512:path=4:el=41:mods=20:epi_merge_negated", exampleStyles["trigger"])
else:
exampleStyles["trigger"] = Parameters.cat("keras:epochs=500:patience=10:nf=512:path=4:el=41:mods=20", exampleStyles["trigger"])
if task in ["GE09", "GE11", "GE13"] and subTask == 1:
exampleStyles["edge"] = Parameters.cat("keras:genia_task1:epochs=500:patience=10:nf=256:path=4:ol=15:mods=20", exampleStyles["edge"])
else:
exampleStyles["edge"] = Parameters.cat("keras:epochs=500:patience=10:nf=256:path=4:ol=15:mods=20", exampleStyles["edge"])
exampleStyles["unmerging"] = Parameters.cat("keras:epochs=500:patience=10:nf=256:path=4:ol=15:mods=20", exampleStyles["unmerging"])
exampleStyles["modifiers"] = Parameters.cat("keras:epochs=500:patience=10:nf=256:path=4:el=41:mods=20", exampleStyles["modifiers"])
elif "EntityDetector" in detector:
if task == "DDI13T91":
exampleStyles["examples"] = Parameters.cat("keras:epochs=500:patience=10:nf=512:path=4:el=41:mods=20:names:build_for_nameless", exampleStyles["examples"])
else:
exampleStyles["examples"] = Parameters.cat("keras:epochs=500:patience=10:nf=512:path=4:el=41:mods=20", exampleStyles["examples"])
elif "EdgeDetector" in detector:
if "DDI" in task:
exampleStyles["examples"] = Parameters.cat("keras:epochs=500:patience=10:nf=256:path=0:do=0.2:dense=800:ol=50:mods=20", exampleStyles["examples"])
elif task == "CP17":
exampleStyles["examples"] = Parameters.cat("keras:epochs=500:patience=10:nf=512:path=0:do=0.2:ol=50:skip_labels=CPR\:0,CPR\:1,CPR\:2,CPR\:7,CPR\:8,CPR\:10:mods=20", exampleStyles["examples"])
else:
exampleStyles["examples"] = Parameters.cat("keras:epochs=500:patience=10:nf=256:path=4:ol=15:mods=20", exampleStyles["examples"])
print >> sys.stderr, "Keras initial example styles:", exampleStyles
for key in exampleStyles:
if exampleStyles[key] != None:
exampleStyles[key] = Parameters.get(exampleStyles[key])
exampleStyles[key].update(overrideStyles[key])
exampleStyles[key].update(overrideStyles["all"])
exampleStyles[key] = Parameters.toString(exampleStyles[key])
print >> sys.stderr, "Keras final example style for " + key + ": ", exampleStyles[key]
return detector
