def applyLearnedDictPlusHumanDict(
testOrigPath,
normOutPath=None,
learnedDictPath=u'./005learnedDict/ororaAbbreviationDict.json',
humanDictPath=u'./005learnedDict/humanMadeDict/humanMadeOroraAbbreviationDict.json'
):
''' apply the normalization dict'''
#open the dicts
learnedDict = myUtils.openJsonFileAsDict(learnedDictPath)
humanDict = myUtils.openJsonFileAsDict(humanDictPath)
#open the test dataframe from the path
testOrigDf = myUtils.getDataFrameFromArgs(testOrigPath, header=False)[0]
for index, testComment in testOrigDf.iteritems():
#use the human dict FIRST (priority to the human-made dicts)
normOutput = ororaZeAbbreviations(testComment,
learnedDict,
listTheVariations=False)
#use the learned dict
normOutput = ororaZeAbbreviations(normOutput,
humanDict,
listTheVariations=False)
#save into pandas series
testOrigDf[index] = normOutput
#dump normalized output
if normOutPath != None:
testOrigDf.to_csv(normOutPath, sep=u'\t', index=False)
return testOrigDf
def dumpDictIntersectAutoAndHuman(autoDict, humanDict=None, outputPath=None):
''' given 2 dicts makes the intersection of the 2 (keys in common in both of them), then returns and dumps it '''
newDict = {}
nonIntersectAutoDict = {}
#get the dicts if they are not given directly as dicts
if humanDict == None:
humanDict = myUtils.openJsonFileAsDict(
u'./005learnedDict/humanMadeDict/humanMadeOroraAbbreviationDict.json'
)
elif type(humanDict) is str:
humanDict = myUtils.openJsonFileAsDict(humanDict)
if type(autoDict) is str:
autoDict = myUtils.openJsonFileAsDict(autoDict)
# get the intersection of keys in both dicts
intersection = set(autoDict.keys()) & set(humanDict.keys())
for key in intersection:
newDict[key] = autoDict[key]
# get the auto keys not appearing in the intersection
for key, val in autoDict.items():
if key not in intersection:
nonIntersectAutoDict[key] = val
#dump it
if outputPath == None:
outputPath = u'./005learnedDict/intersectionHumanAutoDict/humanAutoDict.json'
myUtils.dumpDictToJsonFile(newDict,
pathOutputFile=outputPath,
overwrite=True)
myUtils.dumpDictToJsonFile(nonIntersectAutoDict,
pathOutputFile=outputPath.replace(
u'HumanAutoDict/',
u'HumanAutoDict/nonIntersect/').replace(
u'.json', u'NonIntersect.json'),
overwrite=True)
return newDict, nonIntersectAutoDict
def applyNormalisation(testOrigPath,
normOutPath=None,
normalization=None,
*args):
''' apply the normalization dict'''
#if we are given a path to the place where the dict is
if type(normalization) is str:
normalization = myUtils.openJsonFileAsDict(normalization)
#start an empty dejavuDict
dejavuDict = {}
c = 0
#open the test dataframe from the path
testOrigDf = myUtils.getDataFrameFromArgs(testOrigPath, header=False)[0]
for index, testComment in testOrigDf.iteritems():
if normalization == None:
normOutput = testComment
#use the dict as a normalization
elif type(normalization) is dict:
normOutput, c = ororaZeAbbreviations(testComment,
normalization,
listTheVariations=True,
c=c)
else:
#detect french feminin accord and fossilize the word by modifying its structure to something unchanged by the normalization function
normOutput = frenchFemininAccordsCodification(originalComment,
isInput=True)
#apply the spell corrector or other normalization function
normOutput, dejavuDict = normalizationFunction(
normOutput.lower(), dejavuDict, *args)
#reverse back the code for the feminin accord into its original form
normOutput = frenchFemininAccordsCodification(normOutput,
isInput=False)
#save into pandas series
testOrigDf[index] = normOutput
#dump normalized output
if normOutPath != None:
testOrigDf.to_csv(normOutPath, sep=u'\t', index=False)
print(22222, c, len(testOrigDf), c / len(testOrigDf))
return testOrigDf
def ororaZeAbbreviations(string,
abbrDict=None,
listTheVariations=False,
c=0,
e=0):
'''
ABBR --> ABBREVIATION
'''
def makeReplacements(
token
): #replace diacritical characters with non diacritical characters
replacements = [(u'A', u'ÀÂ'), (u'E', u'ÉÈÊ'), (u'I', u'ÎÏ'),
(u'O', u'Ô'), (u'U', u'ÙÛÜ'), (u'C', u'Ç')]
for replaceTuple in replacements:
for char in replaceTuple[1]:
token = token.replace(char, replaceTuple[0])
token = token.replace(char.lower(), replaceTuple[0].lower())
return token
#open the abbreviation dict
if abbrDict == None:
abbrDict = myUtils.openJsonFileAsDict(
u'./005learnedDict/ororaAbbreviationDict.json')
#open the abbr dict file if it's a path
elif type(abbrDict) is str:
abbrDict = myUtils.openJsonFileAsDict(abbrDict)
#abbreviation replacement
stringList = string.split(u' ')
if type(abbrDict[list(abbrDict.keys())[0]]) is list:
for index, token in enumerate(stringList):
#if the token is in the dict
if makeReplacements(token).upper() in abbrDict:
minScore = 0.55
#if we search only for the first and most common option
if listTheVariations == False:
#if the token has a reliable score
if abbrDict[makeReplacements(
token).upper()][0][1] >= minScore:
stringList[index] = abbrDict[makeReplacements(
token).upper()][0][0]
#if we want to return a list of all the possibilities in decreasing order
else:
for var in abbrDict[makeReplacements(token).upper()]:
if var[1] >= minScore:
e = 1
variations = [
var[0]
for var in abbrDict[makeReplacements(token).upper()]
if var[1] >= minScore
]
stringList[index] = u'¤'.join(variations) if len(
variations) != 0 else makeReplacements(token).upper()
if e == 1:
c += 1
#stringList = [ token if makeReplacements(token).upper() not in abbrDict else abbrDict[makeReplacements(token).upper()][0][0] for token in stringList ]
else:
stringList = [
token if makeReplacements(token).upper() not in abbrDict else
abbrDict[makeReplacements(token).upper()] for token in stringList
]
#elimination of the empty elements u'' or u'∅' if they got in (somehow)
#stringList = [ token for token in stringList if token not in [u'', u'∅'] ]
string = u' '.join(stringList)
return string, c
def makeDictFromTsvTrain(pathNonMatch, trainedDict={}, outputDictFilePath=False):
''' '''
#open the dict
if type(trainedDict) == str:
trainedDict = myUtils.openJsonFileAsDict(trainedDict)
#open as a list the non identical elements between the original and the gold
if type(pathNonMatch) is str:
with open(pathNonMatch) as nonMatchFile:
nonMatchList = []
line = nonMatchFile.readline()
while line:
nonMatchList.append(ast.literal_eval(line.replace(u', \n', u'').replace(u',\n', u'').replace(u'\n', u'')))
line = nonMatchFile.readline()
#get the gold standard data to which compare the training data
for nonMatchingAlignment in nonMatchList:
#if the list is not empty
if nonMatchingAlignment:
for nonMatchTupl in nonMatchingAlignment:
#use the original token as a key
trainedDict[nonMatchTupl[0]] = trainedDict.get( nonMatchTupl[0], list() )+[nonMatchTupl[1]]
#clean the dict
for origKey, goldValList in dict(trainedDict).items():
#eliminate all the elements in the dict that have an empty symbol as a value
if set(goldValList) == {u'∅'}:
del trainedDict[origKey]
#########elif len(goldValList) != 1:
######### del trainedDict[origKey]
#eliminate the elements containing a number character
elif myUtils.detectNbChar(origKey) == True:
del trainedDict[origKey]
else:
#change the goldValList into a sorted list with a count of the recurrences
goldValSortedList = []
#eliminate the empty symbol from the list
goldValList = [ elem for elem in goldValList if elem not in [u'∅', u''] ]
for goldVal in set(goldValList):
#count their instances
counter = 0
for gv in goldValList:
if goldVal == gv:
counter += 1
#add the token and the normalized score
goldValSortedList.append( (goldVal, float(counter)/float(len(goldValList))) )
#sort the list
goldValSortedList.sort(reverse=True, key=lambda x: x[1])
trainedDict[origKey] = goldValSortedList
'''
#eliminate all the elements in the dict that have multiple possible outputs or if the value is an empty symbol
if len(goldValList) != 1 or set(goldValList) == {u'∅'}:
del trainedDict[origKey]
#eleiminate the elements containing a number character
elif myUtils.detectNbChar(origKey) == True:
del trainedDict[origKey]
else:
trainedDict[origKey] = goldValList[0]
'''
'''
#eliminate all the elements in the dict that have multiple possible outputs or if the value is an empty symbol
if len(goldValList) != 1 or set(goldValList) == {u'∅'}:
del trainedDict[origKey]
#eleiminate the elements containing a number character
elif myUtils.detectNbChar(origKey) == True:
del trainedDict[origKey]
else:
trainedDict[origKey] = goldValList[0]
'''
print(len(trainedDict))
#dump the dict
if outputDictFilePath != False:
myUtils.dumpDictToJsonFile(trainedDict, outputDictFilePath, overwrite=True)
return trainedDict
def makeDictFromTsvTrain(pathNonMatchList,
existingDict={},
pathMatch=None,
pathNonMatch=None,
language=u'fr',
outputDictFilePath=False):
''' '''
#open the trained dict
if type(existingDict) == str:
trainedDict = myUtils.openJsonFileAsDict(trainedDict)
else:
trainedDict = dict(existingDict)
#open the matching dicts
if pathMatch != None:
matchCounterDict = myUtils.openJsonFileAsDict(pathMatch)
nonMatchCounterDict = myUtils.openJsonFileAsDict(pathNonMatch)
#open as a list the non identical elements between the original and the gold
if type(pathNonMatchList) is str:
with open(pathNonMatchList) as nonMatchFile:
nonMatchList = []
line = nonMatchFile.readline()
while line:
nonMatchList.append(
ast.literal_eval(
line.replace(u', \n',
u'').replace(u',\n',
u'').replace(u'\n', u'')))
line = nonMatchFile.readline()
#get the gold standard data to which compare the training data
for nonMatchingAlignment in nonMatchList:
#if the list is not empty
if nonMatchingAlignment:
for nonMatchTupl in nonMatchingAlignment:
#use the original token as a key
trainedDict[nonMatchTupl[0]] = trainedDict.get(
nonMatchTupl[0], list()) + [nonMatchTupl[1]]
#first cleaning: eliminate the elements that appear a lot less than the unchanged variant
if pathMatch != None:
for origKey, goldValList in dict(trainedDict).items():
try:
nbMatch = matchCounterDict[origKey]
nbNonMatch = 0
for indexGold, goldVal in enumerate(goldValList):
nbNonMatch = nonMatchCounterDict[origKey][goldVal]
###trainedDict[origKey] = goldValList #don't remove anything, no matter how uncommon
#remove the gold value if it's not a very common unmatching replacement
if float(nbMatch) / float(nbMatch + nbNonMatch) >= 0.55:
del goldValList[indexGold]
#if the list is empty delete it
if len(goldValList) == 0:
del trainedDict[origKey]
else:
trainedDict[origKey] = goldValList
elif nbNonMatch < 10:
del goldValList[indexGold]
#if the list is empty delete it
if len(goldValList) == 0:
del trainedDict[origKey]
except KeyError:
pass
#second cleaning, get rid of the empty element
if u'' in trainedDict: del trainedDict[u'']
#third cleaning, eliminate the elements whose value is way smaller than its key and
#of the entries whose key have empirically shown to induce errors and delete them
for origKey, goldValList in dict(trainedDict).items():
# if the key is undesireable
if undesireableKey(origKey) == True:
del trainedDict[origKey]
else:
indexesToDel = []
for indexVal, goldVal in enumerate(list(goldValList)):
# if the val is much smaller than the key
if float(len(goldVal)) / float(len(origKey)) <= 0.4:
indexesToDel.append(indexVal)
# if the val is undesireable
elif undesireableVal(origKey, goldVal) == True:
indexesToDel.append(indexVal)
#we delete the indexes we detected as causing problems
for indexVal in reversed(indexesToDel):
del goldValList[indexVal]
#if we have not produced an empty list, assign it to the trained dict
if len(goldValList) == 0:
del trainedDict[origKey]
else:
trainedDict[origKey] = goldValList
#fourth cleaning, ruled based cleaning
for origKey, goldValList in dict(trainedDict).items():
#eliminate all the elements in the dict that have an empty symbol as a value
if set(goldValList) == {u'∅'} or origKey == u'∅':
del trainedDict[origKey]
#eliminate all ambiguous entries
###elif len(goldValList) != 1:
### del trainedDict[origKey]
#eliminate the elements containing a number character
elif myUtils.detectNbChar(origKey) == True:
del trainedDict[origKey]
#eliminate the elements whose key is a stop-word
elif myUtils.isTokenStopWord(origKey, language) == True:
del trainedDict[origKey]
else:
#change the goldValList into a sorted list with a count of the recurrences
goldValSortedList = []
#eliminate the empty symbol from the list
goldValList = [
elem for elem in goldValList if elem not in [u'∅', u'']
]
for goldVal in set(goldValList):
#count their instances
counter = 0
for gv in goldValList:
if goldVal == gv:
counter += 1
#add the token and the normalized score
goldValSortedList.append(
(goldVal, float(counter) / float(len(goldValList))))
#sort the list and add to the dict
if len(goldValSortedList) != 0:
goldValSortedList.sort(reverse=True, key=lambda x: x[1])
trainedDict[origKey] = goldValSortedList
#dump the dict
if outputDictFilePath != False:
myUtils.dumpDictToJsonFile(trainedDict,
outputDictFilePath,
overwrite=True)
return trainedDict