def removeX(filename, resultFileTag="a2"):
documents = STTools.loadSet(filename)
newFilename = os.path.join(tempfile.tempdir,
filename.rsplit(".", 2)[0] + "-no-X.tar.gz")
STTools.writeSet(documents,
newFilename,
resultFileTag=resultFileTag,
writeExtra=False,
files=["a2", "rel"])
return newFilename
def convert(datasets, outdir, corpusName):
# Depends on CO-conversion
bigfileName = corpusName + "-" + "-and-".join([x[0] for x in datasets])
documents = []
for pair in datasets:
print >> sys.stderr, "Reading", pair[0], "set,",
docs = ST.loadSet(pair[1], pair[0])
print >> sys.stderr, len(docs), "documents"
documents.extend(docs)
print >> sys.stderr, "Converting to", bigfileName+"-documents.xml"
xml = STConvert.toInteractionXML(documents, corpusName, bigfileName+"-documents.xml")
print >> sys.stderr, "Making sentences"
xml = Tools.GeniaSentenceSplitter.makeSentences(xml, bigfileName+"-sentences.xml")
#print >> sys.stderr, "Copying parses"
#parsePath = "/home/jari/biotext/BioNLP2011/data/CO/co-devel-and-train-and-test.xml"
#InteractionXML.CopyParse.copyParse(bigfileName+"-sentences.xml", parsePath, bigfileName+"-copied-parses.xml", "split-McClosky", "split-McClosky")
print >> sys.stderr, "Parsing"
Tools.CharniakJohnsonParser.parse(bigfileName+"-sentences.xml", bigfileName+"-parsed.xml", tokenizationName=None, parseName="McClosky", requireEntities=False)
print >> sys.stderr, "Stanford Conversion"
Tools.StanfordParser.convertXML("McClosky", bigfileName+"-parsed.xml", bigfileName+"-stanford.xml")
print >> sys.stderr, "Protein Name Splitting"
splitterCommand = "python /home/jari/cvs_checkout/PPI_Learning/Analysers/ProteinNameSplitter.py -f " + bigfileName+"-stanford.xml" + " -o " + bigfileName+"-split.xml" + " -p " + "McClosky" + " -t " + "McClosky" + " -s split-McClosky" + " -n split-McClosky"
subprocess.call(splitterCommand, shell=True)
print >> sys.stderr, "Head Detection"
xml = FindHeads.findHeads(bigfileName+"-split.xml", "split-McClosky", tokenization=None, output=bigfileName+".xml", removeExisting=True)
print >> sys.stderr, "Dividing into sets"
InteractionXML.DivideSets.processCorpus(bigfileName+".xml", outDir, corpusName + "-", ".xml", [("devel", "train")])
if "devel" in [x[0] for x in datasets]:
print >> sys.stderr, "Creating empty devel set"
deletionRules = {"interaction":{},"entity":{"isName":"False"}}
InteractionXML.DeleteElements.processCorpus(corpusName + "-devel.xml", corpusName + "-devel-empty.xml", deletionRules)
return xml
def convert(datasets, outdir, corpusName):
bigfileName = corpusName + "-" + "-and-".join([x[0] for x in datasets])
documents = []
for pair in datasets:
print >> sys.stderr, "Reading", pair[0], "set,",
docs = ST.loadSet(pair[1], pair[0])
print >> sys.stderr, len(docs), "documents"
documents.extend(docs)
# print >> sys.stderr, "Converting to", bigfileName+"-documents.xml"
# xml = STConvert.toInteractionXML(documents, corpusName, bigfileName+"-documents.xml")
# print >> sys.stderr, "Making sentences"
# xml = Tools.GeniaSentenceSplitter.makeSentences(xml, bigfileName+"-sentences.xml")
# print >> sys.stderr, "Parsing"
# Tools.CharniakJohnsonParser.parse(xml, bigfileName+"-parsed.xml", tokenizationName=None, parseName="McClosky", requireEntities=False)
# print >> sys.stderr, "Stanford Conversion"
# Tools.StanfordParser.convertXML("McClosky", bigfileName+"-parsed.xml", bigfileName+"-stanford.xml")
# print >> sys.stderr, "Protein Name Splitting"
# splitterCommand = "python /home/jari/cvs_checkout/PPI_Learning/Analysers/ProteinNameSplitter.py -f " + bigfileName+"-stanford.xml" + " -o " + bigfileName+"-split.xml" + " -p " + "McClosky" + " -t " + "McClosky" + " -s split-McClosky" + " -n split-McClosky"
# subprocess.call(splitterCommand, shell=True)
# print >> sys.stderr, "Fix AltOffsets"
# import InteractionXML.FixAltOffsets
# xml = InteractionXML.FixAltOffsets.fixAltOffsets(bigfileName+"-split.xml")
# print >> sys.stderr, "Head Detection"
# xml = FindHeads.findHeads(xml, "split-McClosky", tokenization=None, output=bigfileName+".xml", removeExisting=True)
print >> sys.stderr, "Dividing into sets"
#InteractionXML.DivideSets.processCorpus(bigfileName+".xml", outDir, corpusName + "-", ".xml", [("devel", "train")])
if "devel" in [x[0] for x in datasets]:
print >> sys.stderr, "Creating empty devel set"
deletionRules = {"interaction": {}, "entity": {"isName": "False"}}
InteractionXML.DeleteElements.processCorpus(
corpusName + "-devel.xml", corpusName + "-devel-empty.xml",
deletionRules)
return xml
def convert(datasets, outdir, corpusName):
bigfileName = corpusName + "-" + "-and-".join([x[0] for x in datasets])
documents = []
for pair in datasets:
print >> sys.stderr, "Reading", pair[0], "set,",
docs = ST.loadSet(pair[1], pair[0], "a1")
print >> sys.stderr, len(docs), "documents"
documents.extend(docs)
print >> sys.stderr, "Converting to", bigfileName+"-documents.xml"
xml = STConvert.toInteractionXML(documents, corpusName, bigfileName+"-documents.xml")
print >> sys.stderr, "Making sentences"
xml = Tools.GeniaSentenceSplitter.makeSentences(xml, bigfileName+"-sentences.xml", postProcess=False)
print >> sys.stderr, "Parsing"
Tools.CharniakJohnsonParser.parse(bigfileName+"-sentences.xml", bigfileName+"-parsed.xml", tokenizationName=None, parseName="McClosky", requireEntities=False)
print >> sys.stderr, "Stanford Conversion"
Tools.StanfordParser.convertXML("McClosky", bigfileName+"-parsed.xml", bigfileName+"-stanford.xml")
print >> sys.stderr, "Protein Name Splitting"
splitterCommand = "python /home/jari/cvs_checkout/PPI_Learning/Analysers/ProteinNameSplitter.py -f " + bigfileName+"-stanford.xml" + " -o " + bigfileName+"-split.xml" + " -p " + "McClosky" + " -t " + "McClosky" + " -s split-McClosky" + " -n split-McClosky"
subprocess.call(splitterCommand, shell=True)
print >> sys.stderr, "Head Detection"
xml = FindHeads.findHeads(bigfileName+"-split.xml", "split-McClosky", tokenization=None, output=bigfileName+".xml", removeExisting=True)
print >> sys.stderr, "Dividing into sets"
InteractionXML.DivideSets.processCorpus(bigfileName+".xml", outdir, corpusName + "-", ".xml", [("devel", "train")])
def convert(datasets, analysisTags, analysisPath, corpusName):
global moveBI
bigfileName = corpusName + "-" + "-and-".join([x[0] for x in datasets])
documents = []
for pair in datasets:
print >> sys.stderr, "Reading", pair[0], "set,",
sitesAreArguments = False
if corpusName == "EPI":
sitesAreArguments = True
docs = ST.loadSet(pair[1],
pair[0],
"a2",
sitesAreArguments=sitesAreArguments)
print >> sys.stderr, len(docs), "documents"
documents.extend(docs)
print >> sys.stderr, "Resolving equivalences"
STFormat.Equiv.process(documents)
print >> sys.stderr, "Checking data validity"
for doc in documents:
STFormat.Validate.validate(doc.events,
simulation=True,
verbose=True,
docId=doc.id)
print >> sys.stderr, "Writing all documents to geniaformat"
ST.writeSet(documents,
"all-geniaformat",
resultFileTag="a2",
debug=False,
task=2,
validate=False)
print >> sys.stderr, "Converting to", bigfileName + "-documents.xml"
xml = STConvert.toInteractionXML(documents, corpusName,
bigfileName + "-documents.xml")
if corpusName == "BI":
InteractionXML.MixSets.mixSets(xml, None, set(moveBI), "train",
"devel")
for pair in datasets:
if True: #corpusName != "BI":
print >> sys.stderr, "Adding analyses for set", pair[0]
addAnalyses(xml, analysisTags[pair[0]], analysisPath, bigfileName)
ETUtils.write(xml, bigfileName + "-sentences.xml")
processParses(corpusName, xml)
# Write out converted data
ETUtils.write(xml, bigfileName + ".xml")
InteractionXML.MergeDuplicateEntities.mergeAll(xml,
bigfileName + "-nodup.xml")
for sourceTag in ["", "-nodup"]:
print >> sys.stderr, "Dividing into sets"
InteractionXML.DivideSets.processCorpus(
bigfileName + sourceTag + ".xml", "./", corpusName + "-",
sourceTag + ".xml", [("devel", "train")])
if "devel" in [x[0] for x in datasets]:
print >> sys.stderr, "Converting back"
STConvert.toSTFormat(corpusName + "-devel" + sourceTag + ".xml",
"roundtrip/" + corpusName + "-devel" +
sourceTag + "-task2",
outputTag="a2",
task=2)
STConvert.toSTFormat(corpusName + "-devel" + sourceTag + ".xml",
"roundtrip/" + corpusName + "-devel" +
sourceTag + "-task1",
outputTag="a2",
task=1)
if corpusName == "GE":
print >> sys.stderr, "Evaluating task 2 back-conversion"
BioNLP11GeniaTools.evaluate("roundtrip/" + corpusName +
"-devel" + sourceTag + "-task2",
task=2,
verbose=True,
debug=False)
print >> sys.stderr, "Evaluating task 1 back-conversion"
BioNLP11GeniaTools.evaluate("roundtrip/" + corpusName +
"-devel" + sourceTag + "-task1",
task=1,
verbose=True,
debug=False)
elif corpusName in ["BI", "BB"]:
print >> sys.stderr, "Evaluating task 2 back-conversion"
BioNLP11GeniaTools.evaluateBX(
"roundtrip/" + corpusName + "-devel" + sourceTag +
"-task2", corpusName)
print >> sys.stderr, "Evaluating task 1 back-conversion"
BioNLP11GeniaTools.evaluateBX(
"roundtrip/" + corpusName + "-devel" + sourceTag +
"-task1", corpusName)
print >> sys.stderr, "Creating empty devel set"
deletionRules = {"interaction": {}, "entity": {"isName": "False"}}
InteractionXML.DeleteElements.processCorpus(
corpusName + "-devel" + sourceTag + ".xml",
corpusName + "-devel" + sourceTag + "-empty.xml",
deletionRules)
def convertDownloaded(outdir,
corpus,
files,
intermediateFiles=True,
evaluate=True):
global moveBI
workdir = outdir + "/conversion/" + corpus
if os.path.exists(workdir):
shutil.rmtree(workdir)
os.makedirs(workdir)
print >> sys.stderr, "---------------", "Converting to XML", "---------------"
# All datasets are processed as one XML, to ensure all the steps (parse modification etc.) are
# applied equally
datasets = ["devel", "train", "test"]
bigfileName = os.path.join(outdir, corpus + "-" + "-and-".join(datasets))
documents = []
for setName in datasets:
sourceFile = files[corpus + "_" + setName.upper()]
print >> sys.stderr, "Reading", setName, "set from", sourceFile, "temp at ",
sitesAreArguments = False
if corpus == "EPI":
sitesAreArguments = True
docs = ST.loadSet(sourceFile,
setName,
"a2",
sitesAreArguments=sitesAreArguments)
print >> sys.stderr, "Read", len(docs), "documents"
documents.extend(docs)
print >> sys.stderr, "Resolving equivalences"
STFormat.Equiv.process(documents)
if evaluate:
print >> sys.stderr, "Checking data validity"
for doc in documents:
STFormat.Validate.validate(doc.events,
simulation=True,
verbose=True,
docId=doc.id)
print >> sys.stderr, "Writing all documents to geniaformat"
ST.writeSet(documents,
os.path.join(workdir, "all-geniaformat"),
resultFileTag="a2",
debug=False,
task=2,
validate=False)
if intermediateFiles:
print >> sys.stderr, "Converting to XML, writing combined corpus to", bigfileName + "-documents.xml"
xml = STConvert.toInteractionXML(documents, corpus,
bigfileName + "-documents.xml")
else:
print >> sys.stderr, "Converting to XML"
xml = STConvert.toInteractionXML(documents, corpus, None)
if corpus == "BI":
InteractionXML.MixSets.mixSets(xml, None, set(moveBI), "train",
"devel")
addAnalyses(xml, corpus, datasets, files, bigfileName)
if intermediateFiles:
print >> sys.stderr, "Writing combined corpus", bigfileName + "-sentences.xml"
ETUtils.write(xml, bigfileName + "-sentences.xml")
processParses(xml)
print >> sys.stderr, "---------------", "Writing corpora", "---------------"
# Write out converted data
if intermediateFiles:
print >> sys.stderr, "Writing combined corpus", bigfileName + ".xml"
ETUtils.write(xml, bigfileName + ".xml")
print >> sys.stderr, "Dividing into sets"
InteractionXML.DivideSets.processCorpus(xml, outdir, corpus, ".xml")
if evaluate and "devel" in datasets:
print >> sys.stderr, "---------------", "Evaluating conversion", "---------------"
print >> sys.stderr, "Converting back"
STConvert.toSTFormat(os.path.join(outdir, corpus + "-devel.xml"),
workdir + "/roundtrip/" + corpus + "-devel" +
"-task1",
outputTag="a2",
task=1)
STConvert.toSTFormat(os.path.join(outdir, corpus + "-devel.xml"),
workdir + "/roundtrip/" + corpus + "-devel" +
"-task2",
outputTag="a2",
task=2)
print >> sys.stderr, "Evaluating task 1 back-conversion"
BioNLP11GeniaTools.evaluate(
workdir + "/roundtrip/" + corpus + "-devel" + "-task1",
corpus + ".1")
print >> sys.stderr, "Evaluating task 2 back-conversion"
BioNLP11GeniaTools.evaluate(
workdir + "/roundtrip/" + corpus + "-devel" + "-task2",
corpus + ".2")
print >> sys.stderr, "Note! Evaluation of Task 2 back-conversion can be less than 100% due to site-argument mapping"
def convertDownloaded(outdir, corpus, files, intermediateFiles=True, evaluate=True):
global moveBI
workdir = outdir + "/conversion/" + corpus
if os.path.exists(workdir):
shutil.rmtree(workdir)
os.makedirs(workdir)
print >> sys.stderr, "---------------", "Converting to XML", "---------------"
# All datasets are processed as one XML, to ensure all the steps (parse modification etc.) are
# applied equally
datasets = ["devel", "train", "test"]
bigfileName = os.path.join(outdir, corpus + "-" + "-and-".join(datasets))
documents = []
for setName in datasets:
sourceFile = files[corpus + "_" + setName.upper()]
print >> sys.stderr, "Reading", setName, "set from", sourceFile, "temp at ",
sitesAreArguments = False
if corpus == "EPI":
sitesAreArguments = True
docs = ST.loadSet(sourceFile, setName, "a2", sitesAreArguments=sitesAreArguments)
print >> sys.stderr, "Read", len(docs), "documents"
documents.extend(docs)
print >> sys.stderr, "Resolving equivalences"
STFormat.Equiv.process(documents)
if evaluate:
print >> sys.stderr, "Checking data validity"
for doc in documents:
STFormat.Validate.validate(doc.events, simulation=True, verbose=True, docId=doc.id)
print >> sys.stderr, "Writing all documents to geniaformat"
ST.writeSet(documents, os.path.join(workdir, "all-geniaformat"), resultFileTag="a2", debug=False, task=2, validate=False)
if intermediateFiles:
print >> sys.stderr, "Converting to XML, writing combined corpus to", bigfileName+"-documents.xml"
xml = STConvert.toInteractionXML(documents, corpus, bigfileName+"-documents.xml")
else:
print >> sys.stderr, "Converting to XML"
xml = STConvert.toInteractionXML(documents, corpus, None)
if corpus == "BI":
InteractionXML.MixSets.mixSets(xml, None, set(moveBI), "train", "devel")
addAnalyses(xml, corpus, datasets, files, bigfileName)
if intermediateFiles:
print >> sys.stderr, "Writing combined corpus", bigfileName+"-sentences.xml"
ETUtils.write(xml, bigfileName+"-sentences.xml")
processParses(xml)
print >> sys.stderr, "---------------", "Writing corpora", "---------------"
# Write out converted data
if intermediateFiles:
print >> sys.stderr, "Writing combined corpus", bigfileName+".xml"
ETUtils.write(xml, bigfileName+".xml")
print >> sys.stderr, "Dividing into sets"
InteractionXML.DivideSets.processCorpus(xml, outdir, corpus, ".xml")
if evaluate and "devel" in datasets:
print >> sys.stderr, "---------------", "Evaluating conversion", "---------------"
print >> sys.stderr, "Converting back"
STConvert.toSTFormat(os.path.join(outdir, corpus + "-devel.xml"), workdir + "/roundtrip/" + corpus + "-devel" + "-task1", outputTag="a2", task=1)
STConvert.toSTFormat(os.path.join(outdir, corpus + "-devel.xml"), workdir + "/roundtrip/" + corpus + "-devel" + "-task2", outputTag="a2", task=2)
print >> sys.stderr, "Evaluating task 1 back-conversion"
BioNLP11GeniaTools.evaluate(workdir + "/roundtrip/" + corpus + "-devel" + "-task1", corpus + ".1")
print >> sys.stderr, "Evaluating task 2 back-conversion"
BioNLP11GeniaTools.evaluate(workdir + "/roundtrip/" + corpus + "-devel" + "-task2", corpus + ".2")
print >> sys.stderr, "Note! Evaluation of Task 2 back-conversion can be less than 100% due to site-argument mapping"
def removeX(filename, resultFileTag="a2"):
documents = STTools.loadSet(filename)
newFilename = os.path.join(tempfile.tempdir, filename.rsplit(".", 2)[0] + "-no-X.tar.gz")
STTools.writeSet(documents, newFilename, resultFileTag=resultFileTag, writeExtra=False, files=["a2","rel"])
return newFilename
