def train_linear_classificator(challenge, new=False):
if new:
unigram_tagger, st = spamFilter.prepare_tagger()
idealist = list(
importDataHelper.readcsvdata(variables.ideadbpath + challenge + '.csv'))
featurelist = {}
for idea in idealist:
idea['TRIGGERED'] = []
idea['PREDICTION'] = "Ham"
idea, ideafeatures = spamFilter.classify_and_get_idea(idea, unigram_tagger, st)
if "unusable" in idea["STATUS"] or 'spam' in idea.get("SPAM", ""):
ideafeatures["Spam"] = 1
else:
ideafeatures["Spam"] = 0
for key in ideafeatures.keys():
featurelist[key] = featurelist.get(key, [])
featurelist[key].append(ideafeatures[key])
else:
if challenge == "all":
idealist = []
for file in listdir(variables.linclasstrainingsdatapath):
if isfile(join(variables.linclasstrainingsdatapath, file)):
idealist += list(importDataHelper.readcsvdata(join(variables.linclasstrainingsdatapath, file)))
else:
idealist = list(importDataHelper.readcsvdata(variables.linclasstrainingsdatapath + challenge + ".csv"))
featurelist = {}
for key in idealist[0].keys():
featurelist[key] = [int(x) for x in idealist[0][key].replace('[', '').replace(']', '').split(',')]
testdata = pd.DataFrame(featurelist)
X = testdata.drop('Spam', axis=1)
y = testdata['Spam']
importDataHelper.writecsvfiledict(variables.linclasstrainingsdatapath + challenge + ".csv", featurelist.keys(), featurelist)
clf = RidgeClassifier().fit(X, y)
print(clf.score(X, y))
return clf, clf.coef_
def eval_all():
challengedict = {
"TCO":
list(importDataHelper.readcsvdata("Data/DBs/ideaDB/TCO.csv")),
"bionicRadar":
list(importDataHelper.readcsvdata("Data/DBs/ideaDB/bionicRadar.csv")),
"fabricDisplay":
list(importDataHelper.readcsvdata("Data/DBs/ideaDB/fabricDisplay.csv"))
}
dupdict = {}
for key in challengedict.keys():
idealist = []
for key2 in challengedict.keys():
if key2 is not key:
idealist += challengedict[key2].copy()
X_train, X_test = train_test_split(challengedict[key], test_size=0.33)
idealist += X_train.copy()
X_ndtrain = duplicateDetection.filterduplikates(
X_train, variables.resultpath + "eval2" + key + ".csv")
dupdict[key] = len(X_train) - len(X_ndtrain)
X_ndtest = X_test.copy()
idealist_test = X_test.copy()
idealist_nodups_test = X_test.copy()
results = evaluate_system(X_train, X_test, key)
importDataHelper.writecsvfiledict(
"Data/ResultsAllNew/evaluation" + key + ".csv", results.keys(),
results)
print("Done first set")
results2 = evaluate_system(X_ndtrain, X_ndtest, key, dups=True)
importDataHelper.writecsvfiledict(
"Data/ResultsAllNew/evaluationResultsNoDups" + key + ".csv",
results2.keys(), results2)
print("Challenge training done", key)
idealist_nodups = duplicateDetection.filterduplikates(
idealist, variables.resultpath + "eval" + key + ".csv")
dupdict[key + " All"] = len(idealist) - len(idealist_nodups)
results = evaluate_system(idealist, idealist_test)
importDataHelper.writecsvfiledict(
"Data/ResultsAllNew/evaluationAll" + key + ".csv", results.keys(),
results)
print("Done first set")
results2 = evaluate_system(idealist_nodups,
idealist_nodups_test,
dups=True)
importDataHelper.writecsvfiledict(
"Data/ResultsAllNew/evaluationResultsNoDupsAll" + key + ".csv",
results2.keys(), results2)
print("All training done", key)
print(dupdict)
importDataHelper.writecsvfiledict("Data/ResultsAllNew/dupNums.csv",
dupdict.keys(), dupdict)
def extend_noun_corpus():
idealist = list(
importDataHelper.readcsvdata(variables.importpathclassified +
'cscw19-unapproved-ideas_import.csv'))
nouncorpus = read_noun_corpus()
unigram_tagger = prepare_tagger()
for idea in idealist:
nouns = get_Nouns(idea['DESCRIPTION'], unigram_tagger)
for noun in nouns:
if noun not in nouncorpus:
nouncorpus[noun] = "unclassified"
importDataHelper.writecsvfiledict(variables.dbpath + 'NLPdata/NounDB.csv',
nouncorpus.keys(), nouncorpus)
def evaluate_fun():
idealist = list(
importDataHelper.readcsvdata("Data/DBs/ideaDB/bionicRadar.csv"))
X_train, X_test = train_test_split(idealist, test_size=0.33)
X_ndtrain = duplicateDetection.filterduplikates(
X_train, variables.resultpath + "evalbionicRadar.csv")
X_ndtest = X_test.copy()
results = evaluate_system(X_train, X_test, "bionicRadar")
importDataHelper.writecsvfiledict(
"Data/ResultsNew/evaluationResultsbionicRadar.csv", results.keys(),
results)
print("Done first set")
results2 = evaluate_system(X_ndtrain, X_ndtest, "bionicRadar", dups=True)
importDataHelper.writecsvfiledict(
"Data/ResultsNew/evaluationResultsNoDupsbionicRadar.csv",
results2.keys(), results2)
print("Done")
def classify_noun_corpus():
nouncorpus = read_noun_corpus()
print(
"Enter \'y\' if noun is concrete, \'n\' if noun is abstract, \'skip\' to skip the word or \'stop\' to safe results and stop classifying"
)
for noun in nouncorpus.keys():
if "unclassified" in nouncorpus[noun]:
answer = input(noun + ": ")
if 'y' in answer:
nouncorpus[noun] = "C"
elif 'n' in answer:
nouncorpus[noun] = "A"
elif 'skip' in answer:
pass
else:
importDataHelper.writecsvfiledict(
variables.dbpath + 'NLPdata/NounDB.csv', nouncorpus.keys(),
nouncorpus)
break
def trainbayes(idealist, challenge=None, delete=False, duplicates=False):
if delete:
bayesspamwords = {}
bayeshamwords = {}
else:
bayesspamwords = getspamtokens(challenge, duplicates)
bayeshamwords = gethamtokens(challenge, duplicates)
nspam = int(bayesspamwords.pop("<IdeaCount>", 0))
nham = int(bayeshamwords.pop("<IdeaCount>", 0))
for idea in idealist:
if idea.get("STATUS", "") == "unusable":
bayesspamwords = updatedb(idea['DESCRIPTION'], bayesspamwords)
nspam += 1
elif idea.get("STATUS", "") == "usable":
nham += 1
bayeshamwords = updatedb(idea['DESCRIPTION'], bayeshamwords)
elif idea.get("STATUS", "") == "unreviewed" and "spam" in idea.get(
'SPAM', ""):
bayesspamwords = updatedb(idea['DESCRIPTION'], bayesspamwords)
nspam += 1
elif idea.get("STATUS", "") == "unreviewed" and "ham" in idea.get(
'SPAM', ""):
nham += 1
bayeshamwords = updatedb(idea['DESCRIPTION'], bayeshamwords)
# if "spam" in idea.get('SPAM', "") or "unusable" in idea.get("STATUS"):
# bayesspamwords = updatedb(idea['DESCRIPTION'], bayesspamwords)
# nspam += 1
# else:
# nham += 1
# bayeshamwords = updatedb(idea['DESCRIPTION'], bayeshamwords)
bayesspamwords["<IdeaCount>"] = nspam
bayeshamwords["<IdeaCount>"] = nham
if challenge is None:
if duplicates:
importDataHelper.writecsvfiledict(
variables.complexbayesmixedpath +
'duplicateBayesSpamToken.csv', bayesspamwords.keys(),
bayesspamwords)
importDataHelper.writecsvfiledict(
variables.complexbayesmixedpath + 'duplicateBayesHamToken.csv',
bayeshamwords.keys(), bayeshamwords)
probslist = calculateprobs(bayesspamwords, bayeshamwords, nspam,
nham)
importDataHelper.writecsvfiledict(
variables.complexbayesmixedpath +
'duplicateBayesTokenProbs.csv', probslist.keys(), probslist)
else:
importDataHelper.writecsvfiledict(
variables.complexbayesmixedpath + 'bayesSpamToken.csv',
bayesspamwords.keys(), bayesspamwords)
importDataHelper.writecsvfiledict(
variables.complexbayesmixedpath + 'bayesHamToken.csv',
bayeshamwords.keys(), bayeshamwords)
probslist = calculateprobs(bayesspamwords, bayeshamwords, nspam,
nham)
importDataHelper.writecsvfiledict(
variables.complexbayesmixedpath + 'bayesTokenProbs.csv',
probslist.keys(), probslist)
else:
if not os.path.exists(variables.complexbayeschallengebasedpath +
challenge):
try:
os.mkdir(variables.complexbayeschallengebasedpath + challenge)
except OSError:
print(
"Path for Challenge does not exist and could not be created"
)
if duplicates:
importDataHelper.writecsvfiledict(
variables.complexbayeschallengebasedpath +
challenge + '/duplicateBayesSpamToken.csv',
bayesspamwords.keys(), bayesspamwords)
importDataHelper.writecsvfiledict(
variables.complexbayeschallengebasedpath + challenge +
'/duplicateBayesHamToken.csv', bayeshamwords.keys(),
bayeshamwords)
probslist = calculateprobs(bayesspamwords, bayeshamwords, nspam,
nham)
importDataHelper.writecsvfiledict(
variables.complexbayeschallengebasedpath + challenge +
'/duplicateBayesTokenProbs.csv', probslist.keys(), probslist)
else:
importDataHelper.writecsvfiledict(
variables.complexbayeschallengebasedpath + challenge +
'/bayesSpamToken.csv', bayesspamwords.keys(), bayesspamwords)
importDataHelper.writecsvfiledict(
variables.complexbayeschallengebasedpath + challenge +
'/bayesHamToken.csv', bayeshamwords.keys(), bayeshamwords)
probslist = calculateprobs(bayesspamwords, bayeshamwords, nspam,
nham)
importDataHelper.writecsvfiledict(
variables.complexbayeschallengebasedpath + challenge +
'/bayesTokenProbs.csv', probslist.keys(), probslist)
def evaluate_system(X_train, X_test, challenge=None, dups=False):
unigram_tagger, st = spamFilter.prepare_tagger()
features = {}
data = {"DESCRIPTION": [], "Spam": []}
for idea in X_train:
idea, feature = spamFilter.classify_and_get_idea(
idea, unigram_tagger, st)
data["DESCRIPTION"].append(idea["DESCRIPTION"])
for key in feature.keys():
features[key] = features.get(key, [])
features[key].append(feature[key])
if idea["STATUS"] == "unusable":
features["Spam"] = features.get("Spam", [])
features["Spam"].append(1)
data["Spam"].append(1)
elif idea["STATUS"] == "usable":
features["Spam"] = features.get("Spam", [])
features["Spam"].append(0)
data["Spam"].append(0)
elif "spam" in idea["SPAM"]:
features["Spam"] = features.get("Spam", [])
features["Spam"].append(1)
data["Spam"].append(1)
else:
features["Spam"] = features.get("Spam", [])
features["Spam"].append(0)
data["Spam"].append(0)
print("prepared")
bayes.trainbayes(X_train,
challenge=challenge,
delete=True,
duplicates=dups)
complexBayes.trainbayes(X_train,
challenge=challenge,
delete=True,
duplicates=dups)
wordprobs = bayes.gettokenprobs(challenge=challenge, duplicates=dups)
comwordprobs = complexBayes.gettokenprobs(challenge=challenge,
duplicates=dups)
linClass, coeff = linearClassifier.train_linear_classifier(features)
useest = USEClassifier.train_classifier_idealist(pd.DataFrame(data))
print(coeff)
actual = []
predbay = []
predcombay = []
predUSE = []
predLin = []
features = {}
for idea in X_test:
if not idea["STATUS"] == "unreviewed":
actual.append(idea["STATUS"] == "unusable")
else:
actual.append("spam" in idea["SPAM"])
predbay.append(bayes.classify(idea["DESCRIPTION"], wordprobs))
predcombay.append(
complexBayes.classify(idea["DESCRIPTION"], comwordprobs))
if idea["DESCRIPTION"] == "" or idea["DESCRIPTION"] == []:
predUSE.append((0, 0.0))
else:
predUSE.append(
(USEClassifier.classify(useest,
{"DESCRIPTION": idea["DESCRIPTION"]})))
idea["TRIGGERED"] = idea.get("TRIGGERED", [])
idea, ideadata = spamFilter.classify_and_get_idea(
idea, unigram_tagger, st)
test = False
for ideakey in ideadata.keys():
if ideadata[ideakey] == 1:
features[ideakey] = features.get(ideakey, [])
features[ideakey].append(1)
test = True
else:
features[ideakey] = features.get(ideakey, [])
features[ideakey].append(0)
ideadata[ideakey] = [ideadata[ideakey]]
if test:
predLin.append(
linearClassifier.classify(pd.DataFrame(ideadata), linClass))
else:
predLin.append((0, 0.0))
results = {
"actual": actual,
"bayes": predbay,
"complexbayes": predcombay,
"USE": predUSE,
"linCLassifier": predLin,
"linClassCo": coeff,
"Filter": features
}
importDataHelper.writecsvfiledict(
variables.resultpath + "evaluationResults.csv", results.keys(),
results)
return results
