def runAssignment(assignment, students, args, helpers):
clusters = {}
allowPartners = assignment.args["allowPartners"]
# for each file
files = assignment.args["files"]
for filename in files:
# find collisions
for student in students:
safeFilename = common.makeFilenameSafe(filename) + args["sourceSuffix"]
studentText = helpers.readFromPreprocessed(student, assignment.name, safeFilename)
if studentText != None:
hashedText = hashText(studentText)
if hashedText not in clusters:
clusters[hashedText] = common.Cluster(allowPartners, filename, 100)
member = common.Member(student, assignment.name, helpers)
clusters[hashedText].add(member)
# process the clusters
clusterArray = []
for key in clusters:
clusterArray.append(clusters[key])
# write the results for this assignment
# clustersToStandardJSON will make sure that there are at least two people in the cluster
common.clustersToStandardJSON(clusterArray, assignment.name, args["resultsSuffix"], helpers)
# say we're done with this assignment
helpers.printf("Finished assignment '{}'...\n".format(assignment.name))
def runEntry(filename, students, helpers, assignment, args, allowPartners):
# get the data
assignName = assignment.name
sourceSuffix = args["sourceSuffix"]
resultsSuffix = args["resultsSuffix"]
threshold = assignment.args["threshold"]
above = args["above"]
minThreshold = None
if assignment.args.has_key("minThreshold"):
minThreshold = assignment.args["minThreshold"]
safeFilename = common.makeFilenameSafe(filename) + sourceSuffix
filepath = helpers.getProcessedPath(assignName, safeFilename)
if filepath != None:
rawData = common.PairResults(assignName, safeFilename, helpers)
data = []
# convert into python objects
for pair in rawData.iterate():
data.append(pair)
# get the mean
mean = getMean(data)
# get the deviation
deviation = getDeviation(data, mean)
helpers.printf("{}/{}: mean {}, deviation {}\n".format(
assignName, filename, mean, deviation))
# filter out data
filtered = filterData(data, mean, deviation, threshold, above,
minThreshold)
# create the clusters
clusters = createClusters(filtered, filename, assignName,
allowPartners, helpers)
# flush to disk
common.clustersToStandardJSON(
clusters, assignName,
common.makeFilenameSafe(filename) + resultsSuffix, helpers)
# all done!
helpers.printf("Finished '{}', with {} results!\n".format(
assignName, len(clusters)))
def runEntry(filename, students, helpers, assignment, args, allowPartners):
# get the data
assignName = assignment.name
sourceSuffix = args["sourceSuffix"]
resultsSuffix = args["resultsSuffix"]
threshold = assignment.args["threshold"]
above = args["above"]
minThreshold = None
if assignment.args.has_key("minThreshold"):
minThreshold = assignment.args["minThreshold"]
safeFilename = common.makeFilenameSafe(filename) + sourceSuffix
filepath = helpers.getProcessedPath(assignName, safeFilename)
if filepath != None:
rawData = common.PairResults(assignName, safeFilename, helpers)
data = []
# convert into python objects
for pair in rawData.iterate():
data.append(pair)
# get the mean
mean = getMean(data)
# get the deviation
deviation = getDeviation(data, mean)
helpers.printf("{}/{}: mean {}, deviation {}\n".format(assignName, filename, mean, deviation))
# filter out data
filtered = filterData(data, mean, deviation, threshold, above, minThreshold)
# create the clusters
clusters = createClusters(filtered, filename, assignName, allowPartners, helpers)
# flush to disk
common.clustersToStandardJSON(clusters, assignName, common.makeFilenameSafe(filename) + resultsSuffix, helpers)
# all done!
helpers.printf("Finished '{}', with {} results!\n".format(assignName, len(clusters)))
def runAssignment(assignment, students, args, helpers):
clusters = {}
allowPartners = assignment.args["allowPartners"]
# for each file
files = assignment.args["files"]
for filename in files:
# find collisions
for student in students:
safeFilename = common.makeFilenameSafe(
filename) + args["sourceSuffix"]
studentText = helpers.readFromPreprocessed(student,
assignment.name,
safeFilename)
if studentText != None:
hashedText = hashText(studentText)
if hashedText not in clusters:
clusters[hashedText] = common.Cluster(
allowPartners, filename, 100)
member = common.Member(student, assignment.name, helpers)
clusters[hashedText].add(member)
# process the clusters
clusterArray = []
for key in clusters:
clusterArray.append(clusters[key])
# write the results for this assignment
# clustersToStandardJSON will make sure that there are at least two people in the cluster
common.clustersToStandardJSON(clusterArray, assignment.name,
args["resultsSuffix"], helpers)
# say we're done with this assignment
helpers.printf("Finished assignment '{}'...\n".format(assignment.name))
def runEntry(filename, students, helpers, assignment, args, allowPartners):
# get the data
assignName = assignment.name
sourceSuffix = args["sourceSuffix"]
resultsSuffix = args["resultsSuffix"]
percent = float(args["percent"]) / 100.0
top = args["top"]
safeFilename = common.makeFilenameSafe(filename) + sourceSuffix
filepath = helpers.getProcessedPath(assignName, safeFilename)
if filepath != None:
rawData = common.PairResults(assignName, safeFilename, helpers)
data = []
# convert into python objects
i = 0
for pair in rawData.iterate():
data.append(pair)
i += 1
if i % 100000 == 0:
gc.collect()
# sort them
data.sort(sortFun)
# calculate and print stats
mean = getMean(data)
dev = getDeviation(data, mean)
helpers.printf("{}/{} mean: {}, std. devation: {}\n".format(assignName, filename, mean, dev))
# take to the top bottom percent
takeNum = math.floor(float(len(data)) * percent)
if "maxResults" in args:
takeNum = min(args["maxResults"], takeNum)
if top:
data = data[::-1] # conveniently reverse
results = []
taken = 0
index = 0
while taken < takeNum:
current = data[index]
member1 = common.Member(current.pair[0], assignName, helpers)
member2 = common.Member(current.pair[1], assignName, helpers)
cluster = common.Cluster(allowPartners, filename, current.score)
cluster.add(member1)
cluster.add(member2)
if cluster.hasCheating() == False:
# student are partners, ignore
index += 1
continue
# take this entry
taken += 1
index += 1
results.append(current)
if index % 50000 == 0:
gc.collect()
# create the clusters
clusters = createClusters(results, filename, assignName, allowPartners, helpers)
# group pairs if necessary
if args.has_key("groupPairs") and args["groupPairs"] == True:
clusters = common.groupPairClusters(clusters, top)
# free up RAM
gc.collect()
# flush to disk
common.clustersToStandardJSON(clusters, assignName, common.makeFilenameSafe(filename) + resultsSuffix, helpers)
# all done!
helpers.printf("Finished '{}/{}', with {} results!\n".format(assignName, filename, len(clusters)))
def runEntry(filename, students, helpers, assignment, args, allowPartners):
# get the data
assignName = assignment.name
sourceSuffix = args["sourceSuffix"]
resultsSuffix = args["resultsSuffix"]
percent = float(args["percent"]) / 100.0
top = args["top"]
safeFilename = common.makeFilenameSafe(filename) + sourceSuffix
filepath = helpers.getProcessedPath(assignName, safeFilename)
if filepath != None:
rawData = common.PairResults(assignName, safeFilename, helpers)
data = []
# convert into python objects
i = 0
for pair in rawData.iterate():
data.append(pair)
i += 1
if i % 100000 == 0:
gc.collect()
# sort them
data.sort(sortFun)
# calculate and print stats
mean = getMean(data)
dev = getDeviation(data, mean)
helpers.printf("{}/{} mean: {}, std. devation: {}\n".format(
assignName, filename, mean, dev))
# take to the top bottom percent
takeNum = math.floor(float(len(data)) * percent)
if "maxResults" in args:
takeNum = min(args["maxResults"], takeNum)
if top:
data = data[::-1] # conveniently reverse
results = []
taken = 0
index = 0
while taken < takeNum:
current = data[index]
member1 = common.Member(current.pair[0], assignName, helpers)
member2 = common.Member(current.pair[1], assignName, helpers)
cluster = common.Cluster(allowPartners, filename, current.score)
cluster.add(member1)
cluster.add(member2)
if cluster.hasCheating() == False:
# student are partners, ignore
index += 1
continue
# take this entry
taken += 1
index += 1
results.append(current)
if index % 50000 == 0:
gc.collect()
# create the clusters
clusters = createClusters(results, filename, assignName, allowPartners,
helpers)
# group pairs if necessary
if args.has_key("groupPairs") and args["groupPairs"] == True:
clusters = common.groupPairClusters(clusters, top)
# free up RAM
gc.collect()
# flush to disk
common.clustersToStandardJSON(
clusters, assignName,
common.makeFilenameSafe(filename) + resultsSuffix, helpers)
# all done!
helpers.printf("Finished '{}/{}', with {} results!\n".format(
assignName, filename, len(clusters)))
