def train_SaveClassifierRandom(posURLs,negURLs,classifierFileName):
posDocs = getWebpageText(posURLs)
posDocs = [d['title'] + " " + d['text'] for d in posDocs if d]
negDocs = getWebpageText(negURLs)
negDocs = [d['title'] + " " + d['text'] for d in negDocs if d]
posLen = len(posDocs)
print posLen
negLen = len(negDocs)
print negLen
posLabels = [1]* posLen
negLabels = [0]*negLen
dataSetDocs = posDocs + negDocs
dataSetLabels = posLabels + negLabels
dataDocLabels = zip(dataSetDocs,dataSetLabels)
random.shuffle(dataDocLabels)
sep = int(0.7*len(dataDocLabels))
trainingDocLabels = dataDocLabels[:sep]
testDocLabels = dataDocLabels[sep:]
trainingLabels = [v for _,v in trainingDocLabels]
trainingDocs = [k for k,_ in trainingDocLabels]
testDocs = [d for d,_ in testDocLabels]
test_labels=[l for _,l in testDocLabels]
classifier = NaiveBayesClassifier()
trainingLabelsArr = np.array(trainingLabels)
classifier.trainClassifier(trainingDocs,trainingLabelsArr)
print classifier.score(trainingDocs, trainingLabelsArr)
print metrics.classification_report(trainingLabelsArr, classifier.predicted)
test_labelsArr = np.array(test_labels)
print classifier.score(testDocs, test_labelsArr)
print metrics.classification_report(test_labelsArr, classifier.predicted)
classifierFile = open(classifierFileName,"wb")
pickle.dump(classifier,classifierFile)
classifierFile.close()
return classifier
def train_SaveClassifier(posURLs,negURLs,classifierFileName):
#posDocs = getWebpageText(posURLs)
posDocs = getWebpageText_NoURLs(posURLs)
posDocs = [d['text'] for d in posDocs if d]
#negDocs = getWebpageText(negURLs)
negDocs = getWebpageText_NoURLs(negURLs)
negDocs = [d['text'] for d in negDocs if d]
#negTraining = [d['title'] + " " + d['text'] for d in negTraining if d]
#negTesting = [d['title'] + " " + d['text'] for d in negTesting if d]
posLen = len(posDocs)
posSep = int(0.7*posLen)
posTraining = posDocs[:posSep]
posTest = posDocs[posSep:]
negLen = len(negDocs)
negSep = int(0.7*negLen)
negTraining = negDocs[:negSep]
negTest = negDocs[negSep:]
trainingDocs = posTraining + negTraining
trainingLabels = [1]* len(posTraining) + [0]*len(negTraining)
testingDocs = posTest + negTest
testingLabels = [1]*len(posTest) + [0]*len(negTest)
classifier = NaiveBayesClassifier()
#classifier = SVMClassifier()
trainingLabelsArr = np.array(trainingLabels)
classifier.trainClassifier(trainingDocs,trainingLabelsArr)
print classifier.score(trainingDocs, trainingLabelsArr)
print metrics.classification_report(trainingLabelsArr, classifier.predicted)
test_labelsArr = np.array(testingLabels)
print classifier.score(testingDocs, test_labelsArr)
print metrics.classification_report(test_labelsArr, classifier.predicted)
#print classifier.classifier.feature_log_prob_
#print classifier.classifier.coef_
classifierFile = open(classifierFileName,"wb")
pickle.dump(classifier,classifierFile)
classifierFile.close()
return classifier
def train_SaveClassifierFolder(posURLs,negURLs,classifierFileName):
posDocs = getWebpageText(posURLs)
posDocs = [d['title'] + " " + d['text'] for d in posDocs if d]
negDocsList = []
for n in negURLs:
negDocsList.append(getWebpageText(n))
negTraining = []
negTesting =[]
for nu in negDocsList:
ns = int(len(nu)*0.7)
negTraining.extend(nu[:ns])
negTesting.extend(nu[ns:])
negTraining = [d['title'] + " " + d['text'] for d in negTraining if d]
negTesting = [d['title'] + " " + d['text'] for d in negTesting if d]
posLen = len(posDocs)
posSep = int(0.7*posLen)
posTraining = posDocs[:posSep]
posTest = posDocs[posSep:]
trainingDocs = posTraining + negTraining
trainingLabels = [1]* len(posTraining) + [0]*len(negTraining)
testingDocs = posTest + negTesting
testingLabels = [1]*len(posTest) + [0]*len(negTesting)
classifier = NaiveBayesClassifier()
#classifier = SVMClassifier()
trainingLabelsArr = np.array(trainingLabels)
classifier.trainClassifier(trainingDocs,trainingLabelsArr)
print classifier.score(trainingDocs, trainingLabelsArr)
print metrics.classification_report(trainingLabelsArr, classifier.predicted)
test_labelsArr = np.array(testingLabels)
print classifier.score(testingDocs, test_labelsArr)
print metrics.classification_report(test_labelsArr, classifier.predicted)
classifierFile = open(classifierFileName,"wb")
pickle.dump(classifier,classifierFile)
classifierFile.close()
return classifier
def main():
#seedUrls = ["http://www.huffingtonpost.com/news/arab-spring/","http://www.opendemocracy.net/david-hayes/arab-spring-protest-power-prospect","http://www.washingtonpost.com/wp-srv/special/world/middle-east-protests/"]
#seedUrls = ["http://www.ndtv.com/article/india/big-earthquake-in-sikkim-tremors-across-india-54-dead-over-100-injured-134537",
# "http://articles.timesofindia.indiatimes.com/2011-09-21/india/30184028_1_construction-site-teesta-urja-gangtok",
# "http://www.ndtv.com/article/india/quake-aftermath-many-villages-in-sikkim-still-cut-off-thousands-waiting-for-help-135132",
# "http://www.ndtv.com/article/india/12-dead-40-missing-at-sikkim-plant-hit-by-quake-135215"
# ]
seedUrls = [
"http://www.ndtv.com/topic/sikkim-earthquake",
"http://zeenews.india.com/tags/Sikkim_earthquake.html",
"http://earthquake-report.com/2011/09/18/very-strong-earthquake-in-sikkim-india/",
"http://articles.timesofindia.indiatimes.com/2011-09-21/india/30184028_1_construction-site-teesta-urja-gangtok"
]
'''
seedUrls = ["http://www.aljazeera.com/indepth/spotlight/anger-in-egypt/",
"http://live.reuters.com/Event/Unrest_in_Egypt?Page=0",
"http://www.guardian.co.uk/world/series/egypt-protests",
"http://www.huffingtonpost.com/2012/06/24/egypt-uprising-election-timeline_n_1622773.html",
"http://www.washingtonpost.com/wp-srv/world/special/egypt-transition-timeline/index.html",
"http://botw.org/top/Regional/Africa/Egypt/Society_and_Culture/Politics/Protests_2011/"
]
'''
#topicKeywords = ['demonstrations','protest','elections','egypt','revolution','uprising','arab','spring','tunisia','libya','military']
##topicKeywords = getTopicKeywords("manual-sikkim-earthquake-wikipedia.txt")
urls_tokens = []
title_tokens = []
docs = getrawDocs("html_files2-balanced.txt", urls_tokens, title_tokens)
#writeToFile(docs,"rawData.txt")
print("raw docs extracted")
docs_len = len(docs)
#docs_tokens = getTokenizedDocs(docs)
#print(" docs tokens extracted")
#labels = getLabels(docs_tokens, topicKeywords)
#writeToFile(labels,"labels.txt")
labels = getLabelsFromFile("labels2-balanced.txt")
print sum(labels)
##print("docs labels calcualted")
sep = int(docs_len * 0.9)
trainingDocs = docs[:sep]
trainingLabels = labels[:sep]
testDocs = docs[sep:]
test_labels = labels[sep:]
classifier = NaiveBayesClassifier()
#classifier = SVMClassifier()
trainingLabelsArr = np.array(labels)
classifier.trainClassifier(docs, trainingLabelsArr)
#print classifier.classifier.coef_
#print classifier.ch2.get_support()
trainingLabelsArr = np.array(trainingLabels)
classifier.trainClassifier(trainingDocs, trainingLabelsArr)
#print len(trainingDocs)
#print len (trainingLabelsArr)
#classifier.trainClassifier(trainingDocs,trainingLabels)
#print("classifer trained")
#print (classifier.classifier)
#print sum(test_labels)
test_labelsArr = np.array(test_labels)
print classifier.score(testDocs, test_labelsArr)
#print sum(classifier.predicted)
#print classifier.score(testDocs, test_labels)
print metrics.classification_report(test_labelsArr, classifier.predicted)
'''
def main():
conf = FCConfig("config.ini")
seedUrls = linesFromFile(conf["seedFile"])
repositoryDocNames = linesFromFile(conf["docsFile"])
if conf["labelFile"]:
print "Using labels"
labels = intLinesFromFile(conf["labelFile"])
relevantDocs = [doc for doc,lab in zip(repositoryDocNames, labels) if lab==1]
irrelevantDocs = [doc for doc,lab in zip(repositoryDocNames, labels) if lab==0]
else:
# use VSM model to label training docs
vsmModel = None
if conf["VSMFilterModel"].lower() == "tf-idf":
vsmModel = TfidfScorer(getUrlTexts(seedUrls))
elif conf["VSMFilterModel"].lower() == "lsi":
vsmModel = LSIScorer(getUrlTexts(seedUrls))
print "constructed vsm model"
relevantDocs , irrelevantDocs = vsmModel.labelDocs(
repositoryDocNames, conf["minRepositoryDocNum"],
conf["filterIrrelevantThreshold"],
conf["filterRelevantThreshold"])
print len(relevantDocs), len(irrelevantDocs)
# Train classifier
classifier = None
testSize = min(len(relevantDocs), len(irrelevantDocs))
trainSize = conf["trainDocNum"]
if (trainSize > testSize):
raise Exception("Training size is larger than test size")
trainDocs = relevantDocs[:trainSize] + irrelevantDocs[:trainSize]
trainLabels = [1]*trainSize + [0]*trainSize
if conf["classifier"].upper() == "NB":
classifier = NaiveBayesClassifier()
elif conf["classifier"].upper() == "SVM":
classifier = SVMClassifier()
classifier.trainClassifierFromNames(trainDocs, trainLabels)
print "Training complete"
# Test classifier
testSize = min(len(relevantDocs), len(irrelevantDocs))
testDocs = relevantDocs[:testSize] + irrelevantDocs[:testSize]
testLabels = [1]*testSize + [0]*testSize
predictedLabels = list(classifier.predictFromNames(testDocs))
# Statistical analysis (recall and precision)
allRelevant = testSize
allIrrelevant = testSize
predictedRelevant = predictedLabels.count(1)
predictedIrrelevant = predictedLabels.count(0)
correctlyRelevant = 0
for i in range(0, testSize):
if predictedLabels[i] == 1:
correctlyRelevant += 1
correctlyIrrelevant = 0
for i in range(testSize, 2*testSize):
if predictedLabels[i] == 0:
correctlyIrrelevant += 1
relevantRecall = float(correctlyRelevant) / allRelevant
relevantPrecision = float(correctlyRelevant) / (predictedRelevant)
irrelevantRecall = float(correctlyIrrelevant) / allIrrelevant
irrelevantPrecision = float(correctlyIrrelevant) / (predictedIrrelevant)
print relevantRecall, relevantPrecision
[(-1,p) for p in seedUrls]
priorityQueue = PriorityQueue(t)
crawler = Crawler(priorityQueue,classifier,10)
crawler.crawl()
print crawler.relevantPagesCount
print crawler.pagesCount
def main():
#seedUrls = ["http://www.huffingtonpost.com/news/arab-spring/","http://www.opendemocracy.net/david-hayes/arab-spring-protest-power-prospect","http://www.washingtonpost.com/wp-srv/special/world/middle-east-protests/"]
#seedUrls = ["http://www.ndtv.com/article/india/big-earthquake-in-sikkim-tremors-across-india-54-dead-over-100-injured-134537",
# "http://articles.timesofindia.indiatimes.com/2011-09-21/india/30184028_1_construction-site-teesta-urja-gangtok",
# "http://www.ndtv.com/article/india/quake-aftermath-many-villages-in-sikkim-still-cut-off-thousands-waiting-for-help-135132",
# "http://www.ndtv.com/article/india/12-dead-40-missing-at-sikkim-plant-hit-by-quake-135215"
# ]
seedUrls = ["http://www.ndtv.com/topic/sikkim-earthquake",
"http://zeenews.india.com/tags/Sikkim_earthquake.html",
"http://earthquake-report.com/2011/09/18/very-strong-earthquake-in-sikkim-india/",
"http://articles.timesofindia.indiatimes.com/2011-09-21/india/30184028_1_construction-site-teesta-urja-gangtok"
]
'''
seedUrls = ["http://www.aljazeera.com/indepth/spotlight/anger-in-egypt/",
"http://live.reuters.com/Event/Unrest_in_Egypt?Page=0",
"http://www.guardian.co.uk/world/series/egypt-protests",
"http://www.huffingtonpost.com/2012/06/24/egypt-uprising-election-timeline_n_1622773.html",
"http://www.washingtonpost.com/wp-srv/world/special/egypt-transition-timeline/index.html",
"http://botw.org/top/Regional/Africa/Egypt/Society_and_Culture/Politics/Protests_2011/"
]
'''
#topicKeywords = ['demonstrations','protest','elections','egypt','revolution','uprising','arab','spring','tunisia','libya','military']
##topicKeywords = getTopicKeywords("manual-sikkim-earthquake-wikipedia.txt")
urls_tokens = []
title_tokens = []
docs = getrawDocs("html_files2-balanced.txt",urls_tokens, title_tokens)
#writeToFile(docs,"rawData.txt")
print("raw docs extracted")
docs_len = len(docs)
#docs_tokens = getTokenizedDocs(docs)
#print(" docs tokens extracted")
#labels = getLabels(docs_tokens, topicKeywords)
#writeToFile(labels,"labels.txt")
labels = getLabelsFromFile("labels2-balanced.txt")
print sum(labels)
##print("docs labels calcualted")
sep = int(docs_len*0.9)
trainingDocs = docs[:sep]
trainingLabels = labels[:sep]
testDocs = docs[sep:]
test_labels=labels[sep:]
classifier = NaiveBayesClassifier()
#classifier = SVMClassifier()
trainingLabelsArr = np.array(labels)
classifier.trainClassifier(docs,trainingLabelsArr)
#print classifier.classifier.coef_
#print classifier.ch2.get_support()
trainingLabelsArr = np.array(trainingLabels)
classifier.trainClassifier(trainingDocs,trainingLabelsArr)
#print len(trainingDocs)
#print len (trainingLabelsArr)
#classifier.trainClassifier(trainingDocs,trainingLabels)
#print("classifer trained")
#print (classifier.classifier)
#print sum(test_labels)
test_labelsArr = np.array(test_labels)
print classifier.score(testDocs, test_labelsArr)
#print sum(classifier.predicted)
#print classifier.score(testDocs, test_labels)
print metrics.classification_report(test_labelsArr, classifier.predicted)
'''
def main():
conf = FCConfig("config.ini")
seedUrls = linesFromFile(conf["seedFile"])
repositoryDocNames = linesFromFile(conf["docsFile"])
if conf["labelFile"]:
print "Using labels"
labels = intLinesFromFile(conf["labelFile"])
relevantDocs = [
doc for doc, lab in zip(repositoryDocNames, labels) if lab == 1
]
irrelevantDocs = [
doc for doc, lab in zip(repositoryDocNames, labels) if lab == 0
]
else:
# use VSM model to label training docs
vsmModel = None
if conf["VSMFilterModel"].lower() == "tf-idf":
vsmModel = TfidfScorer(getUrlTexts(seedUrls))
elif conf["VSMFilterModel"].lower() == "lsi":
vsmModel = LSIScorer(getUrlTexts(seedUrls))
print "constructed vsm model"
relevantDocs, irrelevantDocs = vsmModel.labelDocs(
repositoryDocNames, conf["minRepositoryDocNum"],
conf["filterIrrelevantThreshold"], conf["filterRelevantThreshold"])
print len(relevantDocs), len(irrelevantDocs)
# Train classifier
classifier = None
testSize = min(len(relevantDocs), len(irrelevantDocs))
trainSize = conf["trainDocNum"]
if (trainSize > testSize):
raise Exception("Training size is larger than test size")
trainDocs = relevantDocs[:trainSize] + irrelevantDocs[:trainSize]
trainLabels = [1] * trainSize + [0] * trainSize
if conf["classifier"].upper() == "NB":
classifier = NaiveBayesClassifier()
elif conf["classifier"].upper() == "SVM":
classifier = SVMClassifier()
classifier.trainClassifierFromNames(trainDocs, trainLabels)
print "Training complete"
# Test classifier
testSize = min(len(relevantDocs), len(irrelevantDocs))
testDocs = relevantDocs[:testSize] + irrelevantDocs[:testSize]
testLabels = [1] * testSize + [0] * testSize
predictedLabels = list(classifier.predictFromNames(testDocs))
# Statistical analysis (recall and precision)
allRelevant = testSize
allIrrelevant = testSize
predictedRelevant = predictedLabels.count(1)
predictedIrrelevant = predictedLabels.count(0)
correctlyRelevant = 0
for i in range(0, testSize):
if predictedLabels[i] == 1:
correctlyRelevant += 1
correctlyIrrelevant = 0
for i in range(testSize, 2 * testSize):
if predictedLabels[i] == 0:
correctlyIrrelevant += 1
relevantRecall = float(correctlyRelevant) / allRelevant
relevantPrecision = float(correctlyRelevant) / (predictedRelevant)
irrelevantRecall = float(correctlyIrrelevant) / allIrrelevant
irrelevantPrecision = float(correctlyIrrelevant) / (predictedIrrelevant)
print relevantRecall, relevantPrecision
[(-1, p) for p in seedUrls]
priorityQueue = PriorityQueue(t)
crawler = Crawler(priorityQueue, classifier, 10)
crawler.crawl()
print crawler.relevantPagesCount
print crawler.pagesCount