def NER():
trainFiles = util.findTrainingFiles()
testFiles = util.findTestFiles()
trainCorpus, usedTrainFiles = util.findCorpus(trainFiles)
testCorpus, usedTestFiles = util.findCorpus(testFiles)
for text in trainCorpus:
nerText = "Chelsea began their Premier League title defence with a thrilling 2-2 draw against Swansea, as Thibaut Courtois was sent off and will now miss the crunch clash with Manchester City"
for sent in nltk.sent_tokenize(nerText):
for chunk in nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sent))):
if hasattr(chunk, 'node'):
print chunk.node, ' '.join(c[0] for c in chunk.leaves())
def ConnectionClarity():
todayDate = util.getYesterdayDateFolder()
lastClarityDate = util.loadSettings(Constants.LAST_CLARITY_DIR)
lastSuggClarityDate = util.loadSettings(Constants.LAST_SUGG_CLARITY_DIR)
if lastClarityDate:
util.logger.info("Google Clarity done last for =" + lastClarityDate)
else:
util.logger.info("Google Clarity done last for none")
if lastSuggClarityDate:
util.logger.info("Sugg Clarity done last for =" + lastClarityDate)
else:
util.logger.info("Sugg Clarity done last for none")
if todayDate == lastClarityDate and todayDate == lastSuggClarityDate:
util.logger.info("Clarity signal done for today =" + todayDate)
return True
trainFiles = util.findTrainingFiles()
trainFiles = util.random_select(trainFiles)
trainCorpus, usedTrainFiles = util.findCorpus(trainFiles)
normalClarity = True
if todayDate != lastClarityDate:
testFiles = util.findTestFiles()
testCorpus, usedTestFiles = util.findCorpus(testFiles)
clarityobj = Clarity(trainCorpus, testCorpus)
clarityScore = clarityobj.ClarityScore()
normalClarity = printNormalRankedDocs(clarityScore, usedTestFiles)
if normalClarity == True:
util.saveSettings(Constants.LAST_CLARITY_DIR, todayDate)
util.logger.info("Google Clarity info just completed for =" +
todayDate)
suggClarity = True
if todayDate != lastClarityDate:
testFiles = util.findSuggTestFiles()
testCorpus, usedTestFiles = util.findCorpus(testFiles)
clarityobj = Clarity(trainCorpus, testCorpus)
clarityScore = clarityobj.ClarityScore()
suggClarity = printSuggRankedDocs(clarityScore, usedTestFiles)
if suggClarity == True:
util.saveSettings(Constants.LAST_SUGG_CLARITY_DIR, todayDate)
util.logger.info("SuggGoogle Clarity info just completed for =" +
todayDate)
return normalClarity or suggClarity
def ConnectionClarity():
todayDate = util.getTodayDateFolder()
lastClarityDate = util.loadSettings(Constants.LAST_CLARITY_DIR)
if todayDate == lastClarityDate :
util.logger.info("Clarity signal done for today =" + todayDate)
return True
trainFiles = util.findTrainingFiles()
testFiles = util.findTestFiles()
trainCorpus, usedTrainFiles = util.findCorpus(trainFiles)
testCorpus, usedTestFiles = util.findCorpus(testFiles)
clarityobj = Clarity(trainCorpus,testCorpus)
clarityScore = clarityobj.ClarityScore()
ret = printRankedDocs(clarityScore, usedTestFiles)
if ret == True:
util.saveSettings(Constants.LAST_CLARITY_DIR, todayDate)
util.logger.info("Clarity info just completed for ="+todayDate)
return ret
def Relevance():
todayDate = util.getTodayDateFolder()
lastRelevanceDate = util.loadSettings(Constants.LAST_RELEVANCE_DIR)
if todayDate == lastRelevanceDate :
util.logger.info("Relevance signal already done for today :" + todayDate)
return True
trainFiles = util.findTrainingFiles()
testFiles = util.findTestFiles()
trainCorpus, usedTrainFiles = util.findCorpus(trainFiles)
testCorpus, usedTestFiles = util.findCorpus(testFiles)
all_tokens = sum(trainCorpus, [])
tokens_once = set(word for word in set(all_tokens) if all_tokens.count(word) == 1)
texts = [[word for word in text if word not in tokens_once]
for text in trainCorpus]
pass
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
tfidf = models.TfidfModel(corpus=corpus, id2word=dictionary,normalize=True)
index = similarities.SparseMatrixSimilarity(tfidf[corpus],num_features=len(dictionary))
count = 0
testJson = {}
for text in testCorpus:
vec=dictionary.doc2bow(text)
sims = index[tfidf[vec]]
score = sum(sims)
#print(list(enumerate(sims)))
testJson[usedTestFiles[count]] = score
count = count + 1
ret = printRankedDocs(testJson)
if ret == True:
util.saveSettings(Constants.LAST_RELEVANCE_DIR, todayDate)
util.logger.info("Relevance info just completed for ="+todayDate)
return ret
def Relevance():
todayDate = util.getYesterdayDateFolder()
lastRelevanceDate = util.loadSettings(Constants.LAST_RELEVANCE_DIR)
lastSuggRelevanceDate = util.loadSettings(
Constants.LAST_SUGG_RELEVANCE_DIR)
if lastRelevanceDate:
util.logger.info("Google Relevance done last for =" +
lastRelevanceDate)
else:
util.logger.info("Google Relevance done last for None")
if lastSuggRelevanceDate:
util.logger.info("Sugg Relevance done last for =" + lastRelevanceDate)
else:
util.logger.info("Sugg Relevance done last for None")
if todayDate == lastRelevanceDate and todayDate == lastSuggRelevanceDate:
util.logger.info("Relevance signal already done for today :" +
todayDate)
return True
trainFiles = util.findTrainingFiles()
trainCorpus, usedTrainFiles = util.findCorpus(trainFiles)
all_tokens = sum(trainCorpus, [])
tokens_once = set(word for word in set(all_tokens)
if all_tokens.count(word) == 1)
texts = [[word for word in text if word not in tokens_once]
for text in trainCorpus]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
tfidf = models.TfidfModel(corpus=corpus,
id2word=dictionary,
normalize=True)
index = similarities.SparseMatrixSimilarity(tfidf[corpus],
num_features=len(dictionary))
normalRelevance = True
if todayDate != lastRelevanceDate:
testFiles = util.findTestFiles()
testCorpus, usedTestFiles = util.findCorpus(testFiles)
count = 0
testJson = {}
for text in testCorpus:
vec = dictionary.doc2bow(text)
sims = index[tfidf[vec]]
score = sum(sims)
#print(list(enumerate(sims)))
testJson[usedTestFiles[count]] = score
count = count + 1
normalRelevance = printNormalRankedDocs(testJson)
if normalRelevance == True:
util.saveSettings(Constants.LAST_RELEVANCE_DIR, todayDate)
util.logger.info("Google Relevance info just completed for =" +
todayDate)
suggRelevance = True
if todayDate != lastSuggRelevanceDate:
testFiles = util.findSuggTestFiles()
testCorpus, usedTestFiles = util.findCorpus(testFiles)
count = 0
testJson = {}
for text in testCorpus:
vec = dictionary.doc2bow(text)
sims = index[tfidf[vec]]
score = sum(sims)
#print(list(enumerate(sims)))
testJson[usedTestFiles[count]] = score
count = count + 1
suggRelevance = printSuggRankedDocs(testJson)
if suggRelevance == True:
util.saveSettings(Constants.LAST_SUGG_RELEVANCE_DIR, todayDate)
util.logger.info("Google Relevance info just completed for =" +
todayDate)
return normalRelevance or suggRelevance
def Smoothness():
todayDate = util.getYesterdayDateFolder()
lastSmoothnessDate = util.loadSettings(Constants.LAST_SMOOTHNESS_DIR)
lastSuggSmoothnessDate = util.loadSettings(
Constants.LAST_SUGG_SMOOTHNESS_DIR)
if lastSmoothnessDate:
util.logger.info("Google Smoothness done last for =" +
lastSmoothnessDate)
else:
util.logger.info("Google Smoothness done last for none")
if lastSuggSmoothnessDate:
util.logger.info("Sugg Google Smoothness done last for =" +
lastSuggSmoothnessDate)
else:
util.logger.info("Sugg Google Smoothness done last for none")
if todayDate == lastSmoothnessDate and todayDate == lastSuggSmoothnessDate:
util.logger.info("Smoothness signal done for today" + todayDate)
return True
trainFiles = util.findTrainingFiles()
trainFiles = util.random_select(trainFiles)
trainCorpus, usedTrainFiles = util.findCorpus(trainFiles)
bm25obj = Bm25(trainCorpus)
trainUniqueWords = []
for trainText in trainCorpus:
trainUniqueWords.append(set(trainText))
normalSmoothness = True
if todayDate != lastSmoothnessDate:
testFiles = util.findTestFiles()
testCorpus, usedTestFiles = util.findCorpus(testFiles)
testJson = {}
testUniqueWords = []
smoothness = zeros((len(testCorpus), len(trainCorpus)))
for testText in testCorpus:
testUniqueWords.append(set(testText))
for testDoc in range(len(testCorpus)):
uniqueTest = testUniqueWords[testDoc]
SminusDcontext = zeros(bm25obj.N)
DminusScontext = zeros(bm25obj.N)
for trainDoc in range(len(trainCorpus)):
uniqueTrain = trainUniqueWords[trainDoc]
# t0 = time()
SminusD = [
word for word in trainCorpus[trainDoc]
if word not in uniqueTest
]
# t1 = time()
#print "time 1 = "+str(t1-t0)
DminusS = [
word for word in testCorpus[testDoc]
if word not in uniqueTrain
]
# t2 = time()
# print "time 2 = "+str(t2-t1)
SminusDcontext = bm25obj.BM25Score(SminusD)
# t3 = time()
# print "time 3 = "+str(t3-t2)
DminusScontext = bm25obj.BM25Score(DminusS)
# t4 = time()
# print "time 4 = "+str(t4-t3)
smoothness[testDoc][trainDoc] = np.dot(SminusDcontext,
DminusScontext)
# t5 = time()
#print "time 5 = "+str(t5-t4)
normalSmoothness = printNormalRankedDocs(smoothness, usedTestFiles)
if normalSmoothness == True:
util.saveSettings(Constants.LAST_SMOOTHNESS_DIR, todayDate)
util.logger.info("Google Smoothness info just completed for =" +
todayDate)
suggSmoothness = True
if todayDate != lastSuggSmoothnessDate:
testFiles = util.findSuggTestFiles()
testCorpus, usedTestFiles = util.findCorpus(testFiles)
testJson = {}
testUniqueWords = []
smoothness = zeros((len(testCorpus), len(trainCorpus)))
for testText in testCorpus:
testUniqueWords.append(set(testText))
for testDoc in range(len(testCorpus)):
uniqueTest = testUniqueWords[testDoc]
SminusDcontext = zeros(bm25obj.N)
DminusScontext = zeros(bm25obj.N)
for trainDoc in range(len(trainCorpus)):
uniqueTrain = trainUniqueWords[trainDoc]
SminusD = [
word for word in trainCorpus[trainDoc]
if word not in uniqueTest
]
DminusS = [
word for word in testCorpus[testDoc]
if word not in uniqueTrain
]
SminusDcontext = bm25obj.BM25Score(SminusD)
DminusScontext = bm25obj.BM25Score(DminusS)
smoothness[testDoc][trainDoc] = np.dot(SminusDcontext,
DminusScontext)
suggSmoothness = printSuggRankedDocs(smoothness, usedTestFiles)
if suggSmoothness == True:
util.saveSettings(Constants.LAST_SUGG_SMOOTHNESS_DIR, todayDate)
util.logger.info("Sugg Smoothness info just completed for =" +
todayDate)
return normalSmoothness or suggSmoothness
