def createDotplotFromResult(result, showIds=False):
"""Creates a dotplot from the result (PlagResult) of a similarity check.
It uses the tiles of the PlagResult. Therefore it does not need w/k
parameters.
"""
#check preconditions
if type(result) != type(PlagResult()):
raise NoValidArgumentError, 'Input must be of type PlagResult.'
if result == None:
raise NoValidArgumentError, 'Input must be of type PlagResult not None.'
maxX = result.getIdStringLength()[0]
maxY = result.getIdStringLength()[1]
#create image (B/W)
img = Image.new("L", (maxX, maxY), 255)
tiles = result.getTiles()
for tile in tiles:
for i in range(0, tile[2]):
img.putpixel((tile[0] + i, tile[1] + i), 0)
if showIds:
img = addIds(img, result.getIdentifier(), result.getIdStringLength())
return img
def run(s1, s2, mML=3, treshold=0.5):
"""This method runs a comparison on the two given strings s1 and s2 returning
a PlagResult object containing the similarity value, the similarities as
list of tiles and a boolean value indicating suspected plagiarism.
Input: s1 and s2 : normalized Strings
mML : minimumMatchingLength
treshold : a single value between 0 and 1
Output: PlagResult
"""
#check if the preconditions are fullfilled
if mML<1:
raise OutOfRangeError, 'minimum Matching Length mML needs to be greater than 0'
if not (0 <= treshold <= 1):
raise OutOfRangeError, 'treshold t needs to be 0<=t<=1'
if s1 == None or s2 == None:
raise NoValidArgumentError, 'input must be of type string not None'
if type(s1) != type('') or type(s2) != type(''):
raise NoValidArgumentError, 'input must be of type string'
if s1 == '' or s2 == '':
return PlagResult(hash(s1), hash(s2)) #TODO: Identifierbildung ueberdenken..
#compute tiles
global tiles, matchList
tiles = [] #TODO: anders regeln ? tiles
matchList = [] #TODO: anders regeln`? matchList
tiles = RKR_GST(s1, s2, mML)
#compute similarity
simResult = calcSimilarity(s1.split(), s2.split(), tiles, treshold)
similarity = simResult[0]
if similarity>1: similarity = 1
#create PlagResult and set attributes
result = PlagResult()
result.setIdentifier(hash(s1), hash(s2))
result.setTiles(tiles)
result.setSimilarity(similarity)
result.setSuspectedPlagiarism(simResult[1])
#return result of similarity check as PlagResult object
return result
def getPositiveResults(resultList):
"""Returns a list with all positive PlagResult objects from the result list.
@param resultList list containing PlagResult objects
@return A list containing only positive, i.e. suspected, PlagResult objects.
"""
return [r for r in resultList if r.isSuspectPlagiarism()]
#===============================================================================
# Test
#===============================================================================
if __name__ == '__main__':
print "Start Tests - PlagResultList helper methods"
#create Test PlagResult1
plagResult = PlagResult("Test1", "Test2")
plagResult.setAlgorithmName("NGRAM")
plagResult.setNormalizerName("NORMAL")
plagResult.setSimilarity(0.65)
plagResult.setSuspectedPlagiarism(True)
plagResult.setIdStringLength(52, 45)
plagResult.setTiles([(3, 5, 4), (12, 23, 5), (34, 2, 3)])
#create Test PlagResult2
plagResult2 = PlagResult("Test3", "Test4")
plagResult2.setAlgorithmName("NGRAM")
plagResult2.setNormalizerName("NORMAL")
plagResult2.setSimilarity(0.45)
plagResult2.setSuspectedPlagiarism(False)
plagResult2.setIdStringLength(152, 145)
plagResult2.setTiles([(3, 5, 4), (12, 23, 5), (34, 2, 3)])
#create Test PlagResult3
def run(s1, s2, mML=3, treshold=0.5):
"""This method runs a comparison on the two given strings s1 and s2 returning
a PlagResult object containing the similarity value, the similarities as
list of tiles and a boolean value indicating suspected plagiarism.
Input: s1 and s2 : normalized Strings
mML : minimumMatchingLength
treshold : a single value between 0 and 1
Output: PlagResult
"""
#check if the preconditions are fullfilled
if mML < 1:
raise OutOfRangeError, 'minimum Matching Length mML needs to be greater than 0'
if not (0 <= treshold <= 1):
raise OutOfRangeError, 'treshold t needs to be 0<=t<=1'
if s1 == None or s2 == None:
raise NoValidArgumentError, 'input must be of type string not None'
if type(s1) != type('') or type(s2) != type(''):
raise NoValidArgumentError, 'input must be of type string'
if s1 == '' or s2 == '':
return PlagResult(hash(s1),
hash(s2)) #TODO: Identifierbildung ueberdenken..
#compute tiles
global tiles, matchList
tiles = [] #TODO: anders regeln ? tiles
matchList = [] #TODO: anders regeln`? matchList
tiles = RKR_GST(s1, s2, mML)
#compute similarity
simResult = calcSimilarity(s1.split(), s2.split(), tiles, treshold)
similarity = simResult[0]
if similarity > 1: similarity = 1
#create PlagResult and set attributes
result = PlagResult()
result.setIdentifier(hash(s1), hash(s2))
result.setTiles(tiles)
result.setSimilarity(similarity)
result.setSuspectedPlagiarism(simResult[1])
#return result of similarity check as PlagResult object
return result
def run(s1, s2, mML=3, treshold=[0.03, 0.31]):
"""This method runs a comparison on the two given strings s1 and s2 returning
a PlagResult object containing the similarity value, the similarities as
list of tiles and a boolean value indicating suspected plagiarism.
Input: s1 and s2 : normalized Strings
Options:
========
mML : minimumMatchingLength
treshold : could be a single number(Resemblance)
or a list of two numbers(Resemblance
and Containment)
Output: PlagResult
"""
#check if the preconditions are fullfilled
if mML<1:
raise OutOfRangeError, 'minimum Matching Length mML needs to be greater than 0'
if type(treshold) == type([]):
if len(treshold) != 2:
raise AssertionError, 'Treshold must be a single Value (Resemblance) or a list of two Values [Resemblance, Containment]'
elif not (0 <= treshold[0] <= 1) or not (0 <= treshold[1] <= 1):
raise OutOfRangeError, 'tresholds values need to be 0<=t<=1'
elif not (0 <= treshold <= 1):
raise OutOfRangeError, 'treshold t needs to be 0<=t<=1'
if s1 == None or s2 == None:
raise NoValidArgumentError, 'input must be of type string not None'
if type(s1) != type('') or type(s2) != type(''):
raise NoValidArgumentError, 'input must be of type string'
if s1 == '' or s2 == '':
return PlagResult(hash(s1), hash(s2)) #TODO: Identifierbildung ueberdenken..
#create NGrams for strings
ngramDict1 = createNGrams(s1, mML)
ngramDict2 = createNGrams(s2, mML)
#no nGrams found -> return empty initialized PlagResult
if ngramDict1 == {} or ngramDict2 == {}:
return PlagResult(hash(s1), hash(s2))
#compute similarity
simResult = calcSimilarity(set(ngramDict1.keys()), set(ngramDict2.keys()), treshold)
similarity = simResult[0]
#compute tiles
tiles = calcTiles(ngramDict1, ngramDict2, simResult[1], mML)
#create PlagResult and set attributes
result = PlagResult()
result.setIdentifier(hash(s1), hash(s2))
result.setTiles(tiles)
result.setSimilarity(similarity)
result.setSuspectedPlagiarism(simResult[2])
result.setIdStringLength(len(s1.split()), len(s2.split()))
#return result of similarity check as PlagResult object
return result
def resultFromXML(xmlString):
"""Returns a PlagResult object from the given XML representation.
@param xmlString XML representation (whole file with a single PlagResult)
@return A PlagResult object of the given XML representation.
"""
from xml.dom import minidom
dom_object = minidom.parseString(xmlString)
#get all PlagResult tags in the XML representation
list = dom_object.getElementsByTagName("PlagResult")
#get the first PlagResult tag
plagResultTag = list[0]
#create a PlagResult object
plagResult = PlagResult()
#read attributes from XML and fill PlagResult
plagResult.setIdentifier(plagResultTag.getElementsByTagName("Identifier")[0].getAttribute("Id1"),
plagResultTag.getElementsByTagName("Identifier")[0].getAttribute("Id2"))
plagResult.setAlgorithmName(plagResultTag.getElementsByTagName("Algorithm")[0].firstChild.data)
plagResult.setNormalizerName(plagResultTag.getElementsByTagName("Normalizer")[0].firstChild.data)
plagResult.setSimilarity(float(plagResultTag.getElementsByTagName("Similarity")[0].firstChild.data))
plagResult.setSuspectedPlagiarism(bool(plagResultTag.getElementsByTagName("SuspectedPlagiarism")[0].firstChild.data))
tileTags = plagResultTag.getElementsByTagName("Tiles")[0].getElementsByTagName("Tile")
tiles = []
for tileTag in tileTags:
tile = (int(tileTag.getAttribute("posId1")),
int(tileTag.getAttribute("posId2")),
int(tileTag.getAttribute("length")))
tiles.append(tile)
plagResult.setTiles(tiles)
plagResult.setIdStringLength(int(plagResultTag.getElementsByTagName("IdStringLength")[0].getAttribute("lengthId1")),
int(plagResultTag.getElementsByTagName("IdStringLength")[0].getAttribute("lengthId2")))
return plagResult
tile = (int(tileTag.getAttribute("posId1")),
int(tileTag.getAttribute("posId2")),
int(tileTag.getAttribute("length")))
tiles.append(tile)
plagResult.setTiles(tiles)
plagResult.setIdStringLength(int(plagResultTag.getElementsByTagName("IdStringLength")[0].getAttribute("lengthId1")),
int(plagResultTag.getElementsByTagName("IdStringLength")[0].getAttribute("lengthId2")))
return plagResult
#===============================================================================
# Test
#===============================================================================
if __name__ == '__main__':
print "Start Test - Transfrom PlagResult to XML and Back"
#create Test PlagResult
plagResult = PlagResult("Test1", "Test2")
plagResult.setAlgorithmName("NGRAM")
plagResult.setNormalizerName("NORMAL")
plagResult.setSimilarity(0.65)
plagResult.setSuspectedPlagiarism(True)
plagResult.setIdStringLength(52, 45)
plagResult.setTiles([(3,5,4),(12,23,5),(34,2,3)])
#test transfrom to xml and back should result in the same PlagResult object
result = resultFromXML(resultToXML(plagResult))
assert plagResult.__eq__(plagResult), "plagResult changed through transformation to xml and back"
print "End Test - Transfrom PlagResult to XML and Back"
def getPositiveResults(resultList):
"""Returns a list with all positive PlagResult objects from the result list.
@param resultList list containing PlagResult objects
@return A list containing only positive, i.e. suspected, PlagResult objects.
"""
return [r for r in resultList if r.isSuspectPlagiarism()]
#===============================================================================
# Test
#===============================================================================
if __name__ == '__main__':
print "Start Tests - PlagResultList helper methods"
#create Test PlagResult1
plagResult = PlagResult("Test1", "Test2")
plagResult.setAlgorithmName("NGRAM")
plagResult.setNormalizerName("NORMAL")
plagResult.setSimilarity(0.65)
plagResult.setSuspectedPlagiarism(True)
plagResult.setIdStringLength(52, 45)
plagResult.setTiles([(3,5,4),(12,23,5),(34,2,3)])
#create Test PlagResult2
plagResult2 = PlagResult("Test3", "Test4")
plagResult2.setAlgorithmName("NGRAM")
plagResult2.setNormalizerName("NORMAL")
plagResult2.setSimilarity(0.45)
plagResult2.setSuspectedPlagiarism(False)
plagResult2.setIdStringLength(152, 145)
plagResult2.setTiles([(3,5,4),(12,23,5),(34,2,3)])
#create Test PlagResult3
def resultsToTorc(resultList, colored=False):
"""Takes the result and returns an image showing a Torc indicating the
similarity relations of the compared texts in the results.
A Torc is a kind of overview which allows the user to recognize the similarity
relations between different texts. Therefore all texts are arranged on a circle.
For each relation of similarity between two texts a connecting line is drawn.
"""
#check preconditions
if type(resultList) != type([]):
raise NoValidArgumentError, 'Input must be of type list'
elif len(resultList) == 0:
return None
else:
for result in resultList:
if type(result) != type(PlagResult()):
raise NoValidArgumentError, 'Input list should only contain values of type PlagResult.'
#1. get all identifiers of the results
idSet = set()
for result in resultList:
for id in result.getIdentifier():
idSet.add(id)
idSet = list(idSet)
idSet.sort()
#2. create a circle with a size depending on the number of identifier
font = ImageFont.load_default()
freespace = computeMaxIdLength(idSet, font)
margin = 10
radius = computeRadius(
len(idSet)) # computes radius depending on number of ids
xM = freespace + radius + margin #middle x pos of circle
yM = freespace + radius + margin #middle y pos of circle
img = Image.new('RGB', (2 * xM, 2 * yM), (255, 255, 255))
draw = ImageDraw.Draw(img)
draw.arc((freespace + margin, freespace + margin, freespace + margin +
(2 * radius), freespace + margin + (2 * radius)),
0,
360,
fill=(150, 150, 150))
#3. arrange the ids along the circle and save the coordinates for each id
distToNextId = 360 / len(idSet)
angles = range(0, 360, distToNextId)
idPosDict = {}
for idNr in xrange(0, len(idSet)):
# x = xM + r * cos phi und y = yM + r * sin phi
pos = (xM + (radius * cos(radians(angles[idNr]))),
yM + (radius * sin(radians(angles[idNr]))))
idPosDict.setdefault(idSet[idNr], pos)
# use a truetype font and draw the id names
for id in idPosDict:
draw.text(computeFontPos(font, draw, str(id), idPosDict.get(id), xM,
yM),
str(id),
font=font,
fill=(0, 0, 0))
#4. walk through the results and plot the similarity relations as lines between the Ids
if colored:
#TODO: Params von aussen eingeben?
clusters = getClusters(resultList,
onlyPositives=False,
onlyNonZeroSimilarities=False)
for result in resultList:
if result.isSuspectPlagiarism():
ids = result.getIdentifier()
if colored:
color = getColorForScope(
getClusterNr(ids[0], ids[1], clusters),
range(len(clusters)))
else:
color = (0, 0, 0)
draw.line([idPosDict.get(ids[0]),
idPosDict.get(ids[1])],
fill=color)
del draw #free draw instance
#5. return the image
return img
def run(s1, s2, mML=3, treshold=0.5):
"""Tuned Version of GST
Input: s1 and s2 : normalized Strings
mML : minimumMatchingLength
treshold : a single value between 0 and 1
Output: PlagResult
"""
#check if the preconditions are fullfilled
if mML<1:
raise OutOfRangeError, 'minimum Matching Length mML needs to be greater than 0'
if not (0 <= treshold <= 1):
raise OutOfRangeError, 'treshold t needs to be 0<=t<=1'
if s1 == None or s2 == None:
raise NoValidArgumentError, 'input must be of type string not None'
if type(s1) != type('') or type(s2) != type(''):
raise NoValidArgumentError, 'input must be of type string'
if s1 == '' or s2 == '':
return PlagResult(hash(s1), hash(s2)) #TODO: Identifierbildung ueberdenken..
#compute tiles
tiles = []
tiles = GSTPrechelt(s1, s2, mML)
#compute similarity
simResult = calcSimilarity(s1.split(), s2.split(), tiles, treshold)
similarity = simResult[0]
if similarity>1: similarity = 1
#create PlagResult and set attributes
result = PlagResult()
result.setIdentifier(hash(s1), hash(s2))
result.setTiles(tiles)
result.setSimilarity(similarity)
result.setSuspectedPlagiarism(simResult[1])
#return result of similarity check as PlagResult object
return result
def run(s1, s2, mML=3, treshold=[0.03, 0.31]):
"""This method runs a comparison on the two given strings s1 and s2 returning
a PlagResult object containing the similarity value, the similarities as
list of tiles and a boolean value indicating suspected plagiarism.
Input: s1 and s2 : normalized Strings
Options:
========
mML : minimumMatchingLength
treshold : could be a single number(Resemblance)
or a list of two numbers(Resemblance
and Containment)
Output: PlagResult
"""
#check if the preconditions are fullfilled
if mML < 1:
raise OutOfRangeError, 'minimum Matching Length mML needs to be greater than 0'
if type(treshold) == type([]):
if len(treshold) != 2:
raise AssertionError, 'Treshold must be a single Value (Resemblance) or a list of two Values [Resemblance, Containment]'
elif not (0 <= treshold[0] <= 1) or not (0 <= treshold[1] <= 1):
raise OutOfRangeError, 'tresholds values need to be 0<=t<=1'
elif not (0 <= treshold <= 1):
raise OutOfRangeError, 'treshold t needs to be 0<=t<=1'
if s1 == None or s2 == None:
raise NoValidArgumentError, 'input must be of type string not None'
if type(s1) != type('') or type(s2) != type(''):
raise NoValidArgumentError, 'input must be of type string'
if s1 == '' or s2 == '':
return PlagResult(hash(s1),
hash(s2)) #TODO: Identifierbildung ueberdenken..
#create NGrams for strings
ngramDict1 = createNGrams(s1, mML)
ngramDict2 = createNGrams(s2, mML)
#no nGrams found -> return empty initialized PlagResult
if ngramDict1 == {} or ngramDict2 == {}:
return PlagResult(hash(s1), hash(s2))
#compute similarity
simResult = calcSimilarity(set(ngramDict1.keys()), set(ngramDict2.keys()),
treshold)
similarity = simResult[0]
#compute tiles
tiles = calcTiles(ngramDict1, ngramDict2, simResult[1], mML)
#create PlagResult and set attributes
result = PlagResult()
result.setIdentifier(hash(s1), hash(s2))
result.setTiles(tiles)
result.setSimilarity(similarity)
result.setSuspectedPlagiarism(simResult[2])
result.setIdStringLength(len(s1.split()), len(s2.split()))
#return result of similarity check as PlagResult object
return result