def runTesting(self,
dataPath='../data/icsmData',
resultsPath='../data/icsmResults',
nrIterations=10,
nrProcesses=1):
#For each iteration, load the file with the results for each parameter combination. (../data/icsmResults/resultsTraining_0.csv)
#Grab the parameter combination with the highest f-measure.
#Run the LSA algorithm on all testing sets for this iteration with the selected parameter combination. (for 0 <= i <= 9 :: ../data/icsmData/test_0_i.csv)
#Write the results of the testing to file. (../data/icsmResults/resultsTesting_0.csv)
#Make sure the resultsPath exists. If not, create it.
if not os.path.exists(resultsPath):
os.makedirs(resultsPath)
#Similar for the resultsPath/testing directory, as training and testing is separated.
if not os.path.exists(resultsPath + '/testing'):
os.makedirs(resultsPath + '/testing')
#Load the oracle for the data
aliasToIdName = MyDict(os.path.join(dataPath,
'aliasToIdNameUTF8.dict'))
#Loop all iterations
for itIdx in range(nrIterations):
resultList = []
#Load the data
f = open(
os.path.join(resultsPath, 'training',
'resultsTraining_%d.csv' % itIdx), 'rb')
reader = csv.reader(f, delimiter=';')
for row in reader:
header = row #skip header
break
for row in reader:
#levThr;minLen;k;cosThr;tp;fp;fn;precision;recall;f
levThr = row[0]
minLen = row[1]
k = row[2]
cosThr = row[3]
tp = row[4]
fp = row[5]
fn = row[6]
tn = row[7]
precision = row[8]
recall = row[9]
fMeasure = row[10]
resultList.append((levThr, minLen, k, cosThr, tp, fp, fn, tn,
precision, recall, fMeasure))
f.close()
#Order results by f-measure
resultList = sorted(resultList,
key=lambda tuple: -float(tuple[10]))
#Grab the first record, containing the best parameters.
bestLevThr = float(resultList[0][0])
bestMinLen = int(resultList[0][1])
bestK = float(resultList[0][2])
bestCosThr = float(resultList[0][3])
g = open(
os.path.join(resultsPath, 'testing',
'resultsTesting_%d.csv' % itIdx), 'wb')
writer = csv.writer(g, delimiter=';')
writer.writerow([
'levThr', 'minLen', 'k', 'cosThr', 'tp', 'fp', 'fn', 'tn',
'precision', 'recall', 'f'
])
parameters = {}
parameters["levenshteinSimRatio"] = bestLevThr
parameters["minLength"] = bestMinLen
parameters["rankReductionRatio"] = bestK
parameters["cosineSimRatio"] = bestCosThr
#Run the LSA algorithm on all testing subsets for this iteration
for i in range(10):
#Read the data from the testing subset
nameEmailData = MyDict()
f = open(os.path.join(dataPath, 'test_%d_%d.csv' % (itIdx, i)),
'rb')
reader = UnicodeReader(f)
idx = 0
for row in reader:
try:
alias = row[0]
email = unspam(row[1])
nameEmailData[idx] = (alias, email)
except:
print row
idx += 1
f.close()
lsaAlgo = LSAAlgo(
nameEmailData,
parameters,
dataDir=resultsPath,
dataSaveDir='testing',
resultsFileName='results_%d_%d_preoracle.csv' % (itIdx, i),
resultsHumanReadable=False,
numberOfProcesses=nrProcesses,
runProfiler=False,
profilerOutputDir='profilerOutput',
gensimLogging=False,
progressLogging=False)
lsaAlgo.run()
#Compute the oracle to verify results
oracle = computeOracle(nameEmailData, aliasToIdName)
#Now check the results using the oracle
[tp, fp, fn, tn, precision, recall,
fmeasure] = self.computeResults(
os.path.join(resultsPath, 'testing'),
'results_%d_%d_preoracle.csv' % (itIdx, i),
'results_%d_%d.csv' % (itIdx, i), oracle, nameEmailData)
writer.writerow([
bestLevThr, bestMinLen, bestK, bestCosThr, tp, fp, fn, tn,
precision, recall, fmeasure
])
print 'Done computing results on iteration %d, subset %d' % (
itIdx, i)
g.close()
def runTesting(self, dataPath='../data/icsmData', resultsPath='../data/icsmResults', nrIterations=10, nrProcesses=1):
#For each iteration, load the file with the results for each parameter combination. (../data/icsmResults/resultsTraining_0.csv)
#Grab the parameter combination with the highest f-measure.
#Run the LSA algorithm on all testing sets for this iteration with the selected parameter combination. (for 0 <= i <= 9 :: ../data/icsmData/test_0_i.csv)
#Write the results of the testing to file. (../data/icsmResults/resultsTesting_0.csv)
#Make sure the resultsPath exists. If not, create it.
if not os.path.exists(resultsPath):
os.makedirs(resultsPath)
#Similar for the resultsPath/testing directory, as training and testing is separated.
if not os.path.exists(resultsPath + '/testingWorse'):
os.makedirs(resultsPath + '/testingWorse')
#Load the oracle for the data
aliasToIdName = MyDict(os.path.join(dataPath, 'aliasToIdNameUTF8.dict'))
#Loop all iterations
for itIdx in range(nrIterations):
resultList = []
#Load the data
f = open(os.path.join(resultsPath, 'trainingWorse', 'resultsTrainingWorse_%d.csv' % itIdx), 'rb')
reader = csv.reader(f, delimiter=';')
for row in reader:
header = row #skip header
break
for row in reader:
#levThr;minLen;k;cosThr;tp;fp;fn;precision;recall;f
levThr = row[0]
minLen = row[1]
k = row[2]
cosThr = row[3]
tp = row[4]
fp = row[5]
fn = row[6]
tn = row[7]
precision = row[8]
recall = row[9]
fMeasure = row[10]
resultList.append((levThr, minLen, k, cosThr, tp, fp, fn, tn, precision, recall, fMeasure))
f.close()
#Order results by f-measure
resultList = sorted(resultList, key=lambda tuple: -float(tuple[10]))
#Grab the first record, containing the best parameters.
bestLevThr = float(resultList[0][0])
bestMinLen = int(resultList[0][1])
bestK = float(resultList[0][2])
bestCosThr = float(resultList[0][3])
g = open(os.path.join(resultsPath, 'testingWorse', 'resultsTestingWorse_%d.csv' % itIdx), 'wb')
writer = csv.writer(g, delimiter=';')
writer.writerow(['levThr','minLen','k','cosThr','tp','fp','fn','tn','precision','recall','f'])
parameters = {}
parameters["levenshteinSimRatio"] = bestLevThr
parameters["minLength"] = bestMinLen
parameters["rankReductionRatio"] = bestK
parameters["cosineSimRatio"] = bestCosThr
#Read the data from the testing subset
nameEmailData = MyDict()
f = open(os.path.join(dataPath, 'testWorse_%d.csv' % (itIdx)), 'rb')
reader = UnicodeReader(f)
idx = 0
for row in reader:
try:
alias = row[0]
email = unspam(row[1])
nameEmailData[idx] = (alias, email)
except:
print row
idx += 1
f.close()
lsaAlgo = LSAAlgo(nameEmailData, parameters, dataDir=resultsPath, dataSaveDir='testingWorse', resultsFileName='resultsWorse_%d_preoracle.csv' % (itIdx), resultsHumanReadable=False, numberOfProcesses=nrProcesses, runProfiler=False, profilerOutputDir='profilerOutput', gensimLogging=False, progressLogging=False)
lsaAlgo.run()
#Compute the oracle to verify results
oracle = computeOracle(nameEmailData, aliasToIdName)
#Now check the results using the oracle
[tp, fp, fn, tn, precision, recall, fmeasure] = self.computeResults(os.path.join(resultsPath, 'testingWorse'), 'resultsWorse_%d_preoracle.csv' % (itIdx), 'resultsWorse_%d.csv' % (itIdx), oracle, nameEmailData)
writer.writerow([bestLevThr,bestMinLen,bestK,bestCosThr,tp,fp,fn,tn,precision,recall,fmeasure])
print 'Done computing results on iteration %d' % (itIdx)
g.close()
def runTraining(self,
dataPath='../data/icsmData',
resultsPath='../data/icsmResults',
levThrRange=[0.7, 0.8, 0.9],
minLenRange=[2, 3, 4],
kRange=[0.9, 0.95, 1.0],
cosThrRange=[0.7, 0.75, 0.8, 0.85],
nrIterations=10,
nrProcesses=1):
#Load iteration of the training set. (../data/icsmData/training_0.csv)
#Run this iteration on all parameter combinations.
#Compare to oracle and augment the output file from the LSA algorithm.
#Write the results from each parameter combination, including precision, recall and f-measure. (../data/icsmResults/resultsTraining_0.csv)
#Make sure the resultsPath exists. If not, create it.
if not os.path.exists(resultsPath):
os.makedirs(resultsPath)
#Similar for the resultsPath/training directory, as training and testing is separated.
if not os.path.exists(resultsPath + '/training'):
os.makedirs(resultsPath + '/training')
#Loop all iterations
for itIdx in range(nrIterations):
#Load the data
nameEmailData = MyDict()
f = open(os.path.join(dataPath, 'training_%d.csv' % itIdx), 'rb')
reader = UnicodeReader(f)
idx = 0
for row in reader:
try:
alias = row[0]
email = unspam(row[1])
nameEmailData[idx] = (alias, email)
except:
print row
idx += 1
f.close()
#Load the oracle for the data
aliasToIdName = MyDict(
os.path.join(dataPath, 'aliasToIdNameUTF8.dict'))
oracle = computeOracle(nameEmailData, aliasToIdName)
nrRuns = len(minLenRange) * len(kRange) * len(levThrRange) * len(
cosThrRange)
g = open(
os.path.join(resultsPath, 'training',
'resultsTraining_%d.csv' % itIdx), 'wb')
writer = csv.writer(g, delimiter=';')
writer.writerow([
'levThr', 'minLen', 'k', 'cosThr', 'tp', 'fp', 'fn', 'tn',
'precision', 'recall', 'f'
])
run = 0
for levThr in levThrRange:
for minLen in minLenRange:
for k in kRange:
for cosThr in cosThrRange:
#Load the parameters
parameters = {}
parameters["levenshteinSimRatio"] = levThr
parameters["minLength"] = minLen
parameters["cosineSimRatio"] = cosThr
parameters["rankReductionRatio"] = k
#Run the LSA algorithm on these parameters
lsaAlgo = LSAAlgo(
nameEmailData,
parameters,
dataDir=resultsPath,
dataSaveDir='training',
resultsFileName=
'results_%d_%.2f_%d_%.2f_%.2f_preoracle.csv' %
(itIdx, levThr, minLen, k, cosThr),
resultsHumanReadable=False,
numberOfProcesses=nrProcesses,
runProfiler=False,
profilerOutputDir='profilerOutput',
gensimLogging=False,
progressLogging=False)
lsaAlgo.run()
#Now check the results using the oracle
[tp, fp, fn, tn, precision, recall,
fmeasure] = self.computeResults(
os.path.join(resultsPath, 'training'),
'results_%d_%.2f_%d_%.2f_%.2f_preoracle.csv' %
(itIdx, levThr, minLen, k, cosThr),
'results_%d_%.2f_%d_%.2f_%.2f.csv' %
(itIdx, levThr, minLen, k, cosThr), oracle,
nameEmailData)
writer.writerow([
levThr, minLen, k, cosThr, tp, fp, fn, tn,
precision, recall, fmeasure
])
run += 1
print 'Run %d out of %d in iteration %d...' % (
run, nrRuns, itIdx)
g.close()
def runTraining(self, dataPath='../data/icsmData', resultsPath='../data/icsmResults', levThrRange=[0.7, 0.8, 0.9], minLenRange=[2, 3, 4], kRange=[0.9, 0.95, 1.0], cosThrRange=[0.7, 0.75, 0.8, 0.85], nrIterations=10, nrProcesses=1):
#Load iteration of the training set. (../data/icsmData/training_0.csv)
#Run this iteration on all parameter combinations.
#Compare to oracle and augment the output file from the LSA algorithm.
#Write the results from each parameter combination, including precision, recall and f-measure. (../data/icsmResults/resultsTraining_0.csv)
#Make sure the resultsPath exists. If not, create it.
if not os.path.exists(resultsPath):
os.makedirs(resultsPath)
#Similar for the resultsPath/training directory, as training and testing is separated.
if not os.path.exists(resultsPath + '/trainingWorse'):
os.makedirs(resultsPath + '/trainingWorse')
#Loop all iterations
for itIdx in range(nrIterations):
#Load the data
nameEmailData = MyDict()
f = open(os.path.join(dataPath, 'trainingWorse_%d.csv' % itIdx), 'rb')
reader = UnicodeReader(f)
idx = 0
for row in reader:
try:
alias = row[0]
email = unspam(row[1])
nameEmailData[idx] = (alias, email)
except:
print row
idx += 1
f.close()
#Load the oracle for the data
aliasToIdName = MyDict(os.path.join(dataPath, 'aliasToIdNameUTF8.dict'))
oracle = computeOracle(nameEmailData, aliasToIdName)
nrRuns = len(minLenRange) * len(kRange) * len(levThrRange) * len(cosThrRange)
g = open(os.path.join(resultsPath, 'trainingWorse', 'resultsTrainingWorse_%d.csv' % itIdx), 'wb')
writer = csv.writer(g, delimiter=';')
writer.writerow(['levThr','minLen','k','cosThr','tp','fp','fn','tn','precision','recall','f'])
run = 0
for levThr in levThrRange:
for minLen in minLenRange:
for k in kRange:
for cosThr in cosThrRange:
#Load the parameters
parameters = {}
parameters["levenshteinSimRatio"] = levThr
parameters["minLength"] = minLen
parameters["cosineSimRatio"] = cosThr
parameters["rankReductionRatio"] = k
#Run the LSA algorithm on these parameters
lsaAlgo = LSAAlgo(nameEmailData, parameters, dataDir=resultsPath, dataSaveDir='trainingWorse', resultsFileName='resultsWorse_%d_%.2f_%d_%.2f_%.2f_preoracle.csv' % (itIdx, levThr, minLen, k, cosThr), resultsHumanReadable=False, numberOfProcesses=nrProcesses, runProfiler=False, profilerOutputDir='profilerOutput', gensimLogging=False, progressLogging=False)
lsaAlgo.run()
#Now check the results using the oracle
[tp, fp, fn, tn, precision, recall, fmeasure] = self.computeResults(os.path.join(resultsPath, 'trainingWorse'), 'resultsWorse_%d_%.2f_%d_%.2f_%.2f_preoracle.csv' % (itIdx, levThr, minLen, k, cosThr), 'resultsWorse_%d_%.2f_%d_%.2f_%.2f.csv' % (itIdx, levThr, minLen, k, cosThr), oracle, nameEmailData)
writer.writerow([levThr,minLen,k,cosThr,tp,fp,fn,tn,precision,recall,fmeasure])
run += 1
print 'Run %d out of %d in iteration %d...' % (run, nrRuns, itIdx)
g.close()
