def getDDI13Result(output, numFolds=10, catenate=False):
global mainTEESDir
foldPaths = []
scores = []
matrix = defaultdict(lambda:defaultdict(int))
for fold in range(numFolds):
foldPath = os.path.join(output, "DDI13-fold" + str(fold), "classification-test", "test-pred.xml.gz")
foldPaths.append(foldPath)
logPath = os.path.join(output, "DDI13-fold" + str(fold), "log.txt")
getDDI13ResultLine(logPath, "DDI13-fold" + str(fold), scores)
foldPath = os.path.join(output, "DDI13-fold" + str(fold), "classification-test")
classPath = os.path.join(output, "DDI13-fold" + str(fold), "model-test", "trigger-ids.classes")
if not os.path.exists(classPath):
classPath = os.path.join(output, "DDI13-fold" + str(fold), "model-test", "edge-ids.classes")
addExamples(os.path.join(foldPath, "test-edge-examples.gz"), os.path.join(foldPath, "test-edge-classifications"), classPath, matrix)
else:
addExamples(os.path.join(foldPath, "test-trigger-examples.gz"), os.path.join(foldPath, "test-trigger-classifications"), classPath, matrix)
#parameterPaths = [[":TRAIN:END-MODEL", "Selected parameters"]]
#print "DDI13-fold" + str(fold) + ": " + getResultLine(logPath, parameterPaths)
print "-----"
for testSet in ["DDI13-test9.1", "DDI13-test9.2"]:
logPath = os.path.join(output, testSet, "log.txt")
getDDI13ResultLine(logPath, testSet)
#parameterPaths = [[":TRAIN:END-MODEL", "Selected parameters"]]
#print testSet + ": " + getResultLine(logPath, parameterPaths)
predPath = os.path.join(output, testSet, "classification-test", "test-pred.xml.gz")
DDITools.makeDDI13SubmissionFile(predPath, os.path.join(output, testSet + "-interactions.txt"), "interactions")
DDITools.makeDDI13SubmissionFile(predPath, os.path.join(output, testSet + "-entities.txt"), "entities")
print "-----"
print "Avg-score: ", stats.mean(scores), "stdev", stats.stdev(scores)
print "-----"
print matrixToString(matrix)
print matrixToString(matrix, True)
if catenate and len(foldPaths) > 1:
catPath = os.path.join(output, "DDI13-train-analyses.xml.gz")
Catenate.catenate(foldPaths, catPath, fast=True)
DDITools.makeDDI13SubmissionFile(catPath, os.path.join(output, "DDI13-train-interactions.txt"), "interactions")
DDITools.makeDDI13SubmissionFile(catPath, os.path.join(output, "DDI13-train-entities.txt"), "entities")
def getDDI13Result(output, numFolds=10, catenate=False):
global mainTEESDir
foldPaths = []
scores = []
matrix = defaultdict(lambda:defaultdict(int))
for fold in range(numFolds):
foldPath = os.path.join(output, "DDI13-fold" + str(fold), "classification-test", "test-pred.xml.gz")
foldPaths.append(foldPath)
logPath = os.path.join(output, "DDI13-fold" + str(fold), "log.txt")
getDDI13ResultLine(logPath, "DDI13-fold" + str(fold), scores)
foldPath = os.path.join(output, "DDI13-fold" + str(fold), "classification-test")
classPath = os.path.join(output, "DDI13-fold" + str(fold), "model-test", "trigger-ids.classes")
if not os.path.exists(classPath):
classPath = os.path.join(output, "DDI13-fold" + str(fold), "model-test", "edge-ids.classes")
addExamples(os.path.join(foldPath, "test-edge-examples.gz"), os.path.join(foldPath, "test-edge-classifications"), classPath, matrix)
else:
addExamples(os.path.join(foldPath, "test-trigger-examples.gz"), os.path.join(foldPath, "test-trigger-classifications"), classPath, matrix)
#parameterPaths = [[":TRAIN:END-MODEL", "Selected parameters"]]
#print "DDI13-fold" + str(fold) + ": " + getResultLine(logPath, parameterPaths)
print "-----"
for testSet in ["DDI13-test9.1", "DDI13-test9.2"]:
logPath = os.path.join(output, testSet, "log.txt")
getDDI13ResultLine(logPath, testSet)
#parameterPaths = [[":TRAIN:END-MODEL", "Selected parameters"]]
#print testSet + ": " + getResultLine(logPath, parameterPaths)
predPath = os.path.join(output, testSet, "classification-test", "test-pred.xml.gz")
DDITools.makeDDI13SubmissionFile(predPath, os.path.join(output, testSet + "-interactions.txt"), "interactions")
DDITools.makeDDI13SubmissionFile(predPath, os.path.join(output, testSet + "-entities.txt"), "entities")
print "-----"
print "Avg-score: ", stats.mean(scores), "stdev", stats.stdev(scores)
print "-----"
print matrixToString(matrix)
print matrixToString(matrix, True)
if catenate and len(foldPaths) > 1:
catPath = os.path.join(output, "DDI13-train-analyses.xml.gz")
Catenate.catenate(foldPaths, catPath, fast=True)
DDITools.makeDDI13SubmissionFile(catPath, os.path.join(output, "DDI13-train-interactions.txt"), "interactions")
DDITools.makeDDI13SubmissionFile(catPath, os.path.join(output, "DDI13-train-entities.txt"), "entities")
def getDDI13Result(output, numFolds=10):
global mainTEESDir
from batch import batch
foldPaths = []
for fold in range(numFolds):
foldPath = os.path.join(output, "DDI13-fold" + str(fold),
"classification-test", "test-pred.xml.gz")
logPath = os.path.join(output, "DDI13-fold" + str(fold), "log.txt")
tagPaths = [[
"------------ Test set classification ------------",
"##### EvaluateInteractionXML #####", "Interactions", "micro p/n:"
]]
print "DDI13-fold" + str(fold) + ": " + getResultLine(
logPath, tagPaths)
foldPaths.append(foldPath)
if len(foldPaths) > 1:
Catenate.catenate(foldPaths,
os.path.join(output, "DDI13-train-analyses.xml.gz"),
fast=True)
def getTaskSettings(task, detector, processUnmerging, processModifiers, isSingleStage,
bioNLPSTParams, preprocessorParams,
inputFiles, exampleStyles, classifierParameters):
if task != None:
print >> sys.stderr, "Determining training settings for task", task
assert task.replace("-MINI", "") in ["GE09", "GE09.1", "GE09.2", "GE", "GE.1", "GE.2", "EPI", "ID", "BB", "BI", "BI-FULL", "CO", "REL", "REN", "DDI", "DDI-FULL"], task
fullTaskId = task
subTask = 2
if "." in task:
task, subTask = task.split(".")
subTask = int(subTask)
#dataPath = os.path.expanduser("~/biotext/BioNLP2011/data/main-tasks/")
dataPath = Settings.CORPUS_DIR
# Optional overrides for input files
#if inputFiles["devel"] == None: inputFiles["devel"] = dataPath + task + "/" + task + "-devel.xml"
#if inputFiles["train"] == None: inputFiles["train"] = dataPath + task + "/" + task + "-train.xml"
#if inputFiles["test"] == None: inputFiles["test"] = dataPath + task + "/" + task + "-test.xml"
if inputFiles["devel"] == None and inputFiles["devel"] != "None":
inputFiles["devel"] = os.path.join(dataPath, task.replace("-FULL", "") + "-devel.xml")
if inputFiles["train"] == None and inputFiles["train"] != "None":
if task == "ID": # add GE-task data to the ID training set
inputFiles["train"] = Catenate.catenate([os.path.join(dataPath, "ID-train.xml"),
os.path.join(dataPath, "GE-devel.xml"),
os.path.join(dataPath, "GE-train.xml")],
"training/ID-train-and-GE-devel-and-train.xml.gz", fast=True)
else:
inputFiles["train"] = os.path.join(dataPath, task.replace("-FULL", "") + "-train.xml")
if inputFiles["test"] == None and inputFiles["test"] != "None":
inputFiles["test"] = os.path.join(dataPath, task.replace("-FULL", "") + "-test.xml")
task = task.replace("-MINI", "")
# Example generation parameters
if detector == None:
detector = "Detectors.EventDetector"
if task == "CO":
detector = "Detectors.CODetector"
elif task in ["REN", "BI", "DDI"]:
detector = "Detectors.EdgeDetector"
isSingleStage = True
print >> sys.stderr, "Detector undefined, using default '" + detector + "' for task", fullTaskId
if bioNLPSTParams == None and task not in ["DDI", "DDI-FULL"]:
bioNLPSTParams = "convert:evaluate:scores"
if task == "BI-FULL":
bioNLPSTParams = "convert:scores" # the shared task evaluator is not designed for predicted entities
print >> sys.stderr, "BioNLP Shared Task parameters undefined, using default '" + bioNLPSTParams + "' for task", fullTaskId
if preprocessorParams == None:
preprocessorParams = ["intermediateFiles"]
if task in ["BI", "BI-FULL", "BB", "DDI", "DDI-FULL"]:
preprocessorParams += ["omitSteps=NER,DIVIDE-SETS"]
else:
preprocessorParams += ["omitSteps=DIVIDE-SETS"]
preprocessorParams += ["PARSE.requireEntities"] # parse only sentences where BANNER found an entity
preprocessorParams = ":".join(preprocessorParams)
print >> sys.stderr, "Preprocessor parameters undefined, using default '" + preprocessorParams + "' for task", fullTaskId
if processUnmerging == None and not isSingleStage:
processUnmerging = True
if task in ["CO", "REL", "BB", "BI-FULL", "DDI-FULL"]:
processUnmerging = False
print >> sys.stderr, "Unmerging undefined, using default", processUnmerging, "for task", fullTaskId
if processModifiers == None:
processModifiers = False
if task in ["GE", "EPI", "ID"]:
processModifiers = True
print >> sys.stderr, "Modifier prediction undefined, using default", processModifiers, " for task", fullTaskId
if exampleStyles["examples"] == None and isSingleStage:
if task == "REN":
exampleStyles["examples"] = "trigger_features:typed:no_linear:entities:noMasking:maxFeatures:bacteria_renaming:maskTypeAsProtein=Gene"
elif task == "BI":
exampleStyles["examples"] = "trigger_features:typed:directed:no_linear:entities:noMasking:maxFeatures:bi_limits"
elif task == "DDI":
exampleStyles["examples"] = "trigger_features:typed:no_linear:entities:noMasking:maxFeatures:ddi_features:ddi_mtmx:filter_shortest_path=conj_and"
print >> sys.stderr, "Single-stage examples style undefined, using default '" + exampleStyles["examples"] + "' for task", fullTaskId
if exampleStyles["edge"] == None and not isSingleStage:
print >> sys.stderr, "Edge example style undefined, using default for task", fullTaskId
if task in ["GE09", "GE"]:
exampleStyles["edge"]="trigger_features:typed:directed:no_linear:entities:genia_limits:noMasking:maxFeatures" #,multipath"
if subTask == 1:
exampleStyles["edge"] += ":genia_task1"
elif task in ["BB"]:
exampleStyles["edge"]="trigger_features:typed:directed:no_linear:entities:bb_limits:noMasking:maxFeatures"
elif task == "EPI":
exampleStyles["edge"]="trigger_features:typed:directed:no_linear:entities:epi_limits:noMasking:maxFeatures"
elif task == "ID":
exampleStyles["edge"]="trigger_features:typed:directed:no_linear:entities:id_limits:noMasking:maxFeatures"
elif task == "REL":
exampleStyles["edge"]="trigger_features:typed:directed:no_linear:entities:noMasking:maxFeatures:rel_limits:rel_features"
elif task == "CO":
exampleStyles["edge"]="trigger_features:typed:directed:no_linear:entities:noMasking:maxFeatures:co_limits"
elif task == "BI-FULL":
exampleStyles["edge"] = "trigger_features:typed:directed:no_linear:entities:noMasking:maxFeatures:bi_limits"
elif task == "DDI-FULL":
exampleStyles["edge"] = "trigger_features:typed:no_linear:entities:noMasking:maxFeatures:ddi_features:filter_shortest_path=conj_and"
else:
exampleStyles["edge"]="trigger_features:typed:directed:no_linear:entities:noMasking:maxFeatures"
if exampleStyles["trigger"] == None and not isSingleStage:
print >> sys.stderr, "Trigger example style undefined, using default for task", fullTaskId
if task in ["GE09", "GE"] and subTask == 1:
exampleStyles["trigger"] = "genia_task1"
elif task == "EPI":
exampleStyles["trigger"] = "epi_merge_negated"
elif task == "BB":
exampleStyles["trigger"] = "bb_features:build_for_nameless:wordnet"
elif task == "REL":
exampleStyles["trigger"] = "rel_features"
elif task == "CO":
options.triggerExampleBuilder = "PhraseTriggerExampleBuilder"
elif task in ["BI-FULL", "DDI-FULL"]:
exampleStyles["trigger"] = "build_for_nameless:names"
if exampleStyles["unmerging"] == None and not isSingleStage:
exampleStyles["unmerging"] = "trigger_features:typed:directed:no_linear:entities:genia_limits:noMasking:maxFeatures"
#if task == "ID": # Do not use catenated GE for unmerging examples
# exampleStyles["unmerging"] += ":sentenceLimit=id.ID"
# Classifier parameters
if classifierParameters["examples"] == None and isSingleStage:
print >> sys.stderr, "Classifier parameters for single-stage examples undefined, using default for task", fullTaskId
if task == "REN":
classifierParameters["examples"] = "10,100,1000,2000,3000,4000,4500,5000,5500,6000,7500,10000,20000,25000,28000,50000,60000"
elif task == "BI":
classifierParameters["examples"] = "10,100,1000,2500,5000,7500,10000,20000,25000,28000,50000,60000,65000,80000,100000,150000"
elif task == "DDI":
classifierParameters["examples"] = "c=10,100,1000,2500,4000,5000,6000,7500,10000,20000,25000,50000:TEES.threshold"
if classifierParameters["trigger"] == None and not isSingleStage:
print >> sys.stderr, "Classifier parameters for trigger examples undefined, using default for task", fullTaskId
classifierParameters["trigger"] = "1000,5000,10000,20000,50000,80000,100000,150000,180000,200000,250000,300000,350000,500000,1000000"
if classifierParameters["recall"] == None and not isSingleStage:
print >> sys.stderr, "Recall adjust parameter undefined, using default for task", fullTaskId
classifierParameters["recall"] = "0.5,0.6,0.65,0.7,0.85,1.0,1.1,1.2"
if task == "CO":
classifierParameters["recall"] = "0.8,0.9,0.95,1.0"
if classifierParameters["edge"] == None and not isSingleStage:
print >> sys.stderr, "Classifier parameters for edge examples undefined, using default for task", fullTaskId
classifierParameters["edge"] = "5000,7500,10000,20000,25000,27500,28000,29000,30000,35000,40000,50000,60000,65000"
if task in ["REL", "CO"]:
classifierParameters["edge"] = "10,100,1000,5000,7500,10000,20000,25000,28000,50000,60000,65000,100000,500000,1000000"
if classifierParameters["unmerging"] == None and not isSingleStage:
print >> sys.stderr, "Classifier parameters for unmerging examples undefined, using default for task", fullTaskId
classifierParameters["unmerging"] = "1,10,100,500,1000,1500,2500,5000,10000,20000,50000,80000,100000"
if classifierParameters["modifiers"] == None and not isSingleStage:
print >> sys.stderr, "Classifier parameters for modifier examples undefined, using default for task", fullTaskId
classifierParameters["modifiers"] = "5000,10000,20000,50000,100000"
if isSingleStage and exampleStyles["examples"] != None and "names" in exampleStyles["examples"]:
removeNamesFromEmpty = True
elif (not isSingleStage) and exampleStyles["trigger"] != None and "names" in exampleStyles["trigger"]:
removeNamesFromEmpty = True
else:
removeNamesFromEmpty = False
return detector, processUnmerging, processModifiers, isSingleStage, bioNLPSTParams, preprocessorParams, exampleStyles, classifierParameters, removeNamesFromEmpty
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