def crossValidation(tweetList, k,maxNgram):
m=80
n=50
for i in xrange(k):
trainSet,testSet = divideDataset(tweetList,k,i)
trainDist = utils.obtainNgrams(trainSet,maxNgram)
confidenceDict=utils.learnNgramConfidencefromData(trainDist,trainSet)
predicted, true=utils.evaluateNgramRakingSet(testSet,trainDist, confidenceDict,m,n)
# utils.printJeroni(true,predicted,i)
utils.printResults(testSet, predicted, i)
def crossValidationLinearInterpolation(tweetList, k, maxNgram):
for i in xrange(k):
trainSet, testSet = divideDataset(tweetList, k, i)
trainDist, arrayLanguages, languagesAll = utils.obtainNgrams(trainSet, maxNgram)
linearCoefficients = linear.getlinearcoefficientsForLanguageArray(arrayLanguages, maxNgram, trainDist)
print linearCoefficients
count = 0
tot = 0
for tweet in testSet:
predictedLanguage, probability = linear.getPredictedLanguageForTweet(linearCoefficients, tweet.text, maxNgram, trainDist)
utils.printResultTXT(predictedLanguage, tweet)
if(predictedLanguage == tweet.language):
count = count + 1;
tot = tot +1
# print str(count)+'/'+str(tot)
print 'correct tweets fold '+str(i)+' = '+str(count)+'/'+str(tot)
tweetListPreProcessed_train = preprocess.main(tweetList_train)
tweetListPreProcessed_test = preprocess.main(tweetList_test)
# shuffle(tweetListPreProcessed)
# 3-. Algorithms
# 3.1-. Algorithms: Bayesian Networks
# 3.2.1-. Linear interpolation
# Generate linear coefficients: input (n-grams and language)
# Smooth data
# cv.crossValidationLinearInterpolation(tweetListPreProcessed_train, 3, maxNgram)
linearCoefficientsAll = list()
trainDist, arrayLanguages, languagesAll = utils.obtainNgrams(tweetListPreProcessed_train, maxNgram)
for gram in xrange(1, maxNgram+1):
linearCoefficientsAll.append(linear.getlinearcoefficientsForLanguageArray(arrayLanguages, gram, trainDist))
print linearCoefficientsAll
# linearCoefficientsALL = read.readLinearCoefficients(LI_Coefficients)
count = 4 # Desde que gram empezar
for i in xrange(count, maxNgram):
count = count + 1
t0 = time.time()
for tweet in tweetListPreProcessed_test:
tweetListtest = read.read_tweets_dataset(test)
# 2-. Pre-process state
tweetListPreProcessed = preprocess.main(tweetList)
tweetListPreProcessedtest= preprocess.main(tweetListtest)
shuffle(tweetListPreProcessed)
# Raw data -> tweetList
# Clean data -> tweetListPreProcessed
#utils.printTweets(tweetListPreProcessed)
# 3-. Algorithms
#
# 3.1-. OBTAIN N-GRAMS
corpusNgrams, arrayLanguages, arrayLanguagesFull = utils.obtainNgrams(tweetListPreProcessed, maxNgram+1)
arrayLanguagesFull = utils.orderVector(arrayLanguagesFull)
# Example: print(corpusNgrams.get(str(3)).get('pt'))
# 3.2-. Algorithms: Bayesian Networks
# 3.2.1-. Linear interpolation
# Generate linear coefficients: input (n-grams and language)
# Smooth data
tweetEN = "Tomorrow is going to be a good day to go to the beach."
tweetPT = "Amanhã será um dia muito bom, como ir para a praia."
tweetCA = "Demà farà un dia molt bo, com per anar a la platja."
tweetEU = "Bihar egun oso ona egingo du, hondartzara joateko modukoa."
# _____________________________________________________________________________
# 1-. Read dataset and create tweetList fullfilled of Tweet object*
dataset = sys.argv[1]
maxNgram = int(sys.argv[2])
filename = os.path.basename(dataset).split('.')
tweetList = read.read_tweets_dataset(dataset)
# 2-. Pre-process state
# Raw data -> tweetList
# Clean data -> tweetListPreProcessed
tweetListPreProcessed = preprocess.main(tweetList)
# 3-. OBTAIN N-GRAMS and Linear Coefficients
for i in xrange(5, maxNgram+1):
corpusNgrams, arrayLanguages,arrayLanguagesFull = utils.obtainNgrams(tweetListPreProcessed, i+1)
linearCoefficients = linear.getlinearcoefficientsForLanguageArray(arrayLanguages, i, corpusNgrams)
# print linearCoefficients
file = open('../Dataset/LICoefficients_'+str(maxNgram)+'gram_for-'+str(filename[0])+'.txt', 'a+')
for li in linearCoefficients:
file.write(str(i)+"\t"+str(li[0]))
for co in xrange(1, i+1):
file.write("\t"+str(li[co]))
file.write("\n")
file.close()
