本文整理匯總了Python中Helper.Helper.parseGenderBlogDatasetWithLabels方法的典型用法代碼示例。如果您正苦於以下問題：Python Helper.parseGenderBlogDatasetWithLabels方法的具體用法？Python Helper.parseGenderBlogDatasetWithLabels怎麽用？Python Helper.parseGenderBlogDatasetWithLabels使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類Helper.Helper的用法示例。
在下文中一共展示了Helper.parseGenderBlogDatasetWithLabels方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: tryOnlineLearn
# 需要導入模塊: from Helper import Helper [as 別名]
# 或者: from Helper.Helper import parseGenderBlogDatasetWithLabels [as 別名]
def tryOnlineLearn():
testShare = 0.25
valShare = 0.25
trainingFeatures, trainingLabels = Helper.parseGenderBlogDatasetWithLabels('blog-gender-dataset.csv')
#trainingFeatures = random.shuffle(trainingFeatures)
if(onlineLearning_HW2.FEATURE_TYPE == 'word2vec'):
trainingFeatures = onlineLearning_HW2.readDataVecs('genderBlogDatasetVectors.txt')
else:
vocab = onlineLearning_HW2.buildVocab(trainingFeatures)
trainingFeatures = onlineLearning_HW2.data2bow(trainingFeatures,vocab)
tln = len(trainingFeatures)
valData = trainingFeatures[0:int(tln*valShare)]
valLabel = trainingLabels[0:int(tln*valShare)]
vln = len(valData)
testData = trainingFeatures[vln:int(vln+tln*testShare)]
testLabel = trainingLabels[vln:int(vln+tln*testShare)]
ln = len(valData)+len(testData)
trainingFeatures = trainingFeatures[ln:]
trainingLabels = trainingLabels[ln:]
model = onlineLearning_HW2.avgPerceptron(trainingFeatures, trainingLabels)
predictionsTraining = onlineLearning_HW2.classify(trainingFeatures, trainingLabels, model, trainingFeatures, trainingLabels)
predictionsVal = onlineLearning_HW2.classify(valData, valLabel, model, trainingFeatures, trainingLabels)
predictionsTest = onlineLearning_HW2.classify(testData, testLabel, model, trainingFeatures, trainingLabels)
print('Training set:')
res = onlineLearning_HW2.checkPerformance(trainingLabels, predictionsTraining)
print(res)
print('Validation set:')
res = onlineLearning_HW2.checkPerformance(valLabel, predictionsVal)
print(res)
print('Test set:')
res = onlineLearning_HW2.checkPerformance(testLabel, predictionsTest)
print(res)示例2: paramSelectOnlineLearning
# 需要導入模塊: from Helper import Helper [as 別名]
# 或者: from Helper.Helper import parseGenderBlogDatasetWithLabels [as 別名]
def paramSelectOnlineLearning():
writer = open('results_log.txt','w')
# reading the training data, validation data, test data
print ('>> Parsing all data sets ...')
testShare = 0.25
valShare = 0.25
trainingData, trainingLabels = Helper.parseGenderBlogDatasetWithLabels('blog-gender-dataset.csv')
print ('>> Building vocabulary ...')
#vocab = buildVocab(trainingData, valData)
bestVal = 0
bestValParam = ''
bestTest = 0
bestTestParam = ''
bestTrain = 0
bestTrainParam = ''
for feat in onlineLearning_HW2.FEATURE_TYPE_LIST:
global FEATURE_TYPE
FEATURE_TYPE = feat
for boolean in onlineLearning_HW2.BOOLEAN_TYPE_LIST:
global BOOLEAN_TYPE
BOOLEAN_TYPE = boolean
if(feat != 'word2vec'):
vocab = onlineLearning_HW2.buildVocab(trainingData)
trainingFeatures = onlineLearning_HW2.data2bow(trainingData,vocab)
if(feat == 'word2vec'):
trainingFeatures = onlineLearning_HW2.readDataVecs('genderBlogDatasetVectors.txt')
tln = len(trainingFeatures)
valFeatures = trainingFeatures[0:int(tln*valShare)]
valLabel = trainingLabels[0:int(tln*valShare)]
vln = len(valFeatures)
testFeatures = trainingFeatures[vln:int(vln+tln*testShare)]
testLabel = trainingLabels[vln:int(vln+tln*testShare)]
ln = len(valFeatures)+len(testFeatures)
trainingFeatures = trainingFeatures[ln:]
trainingOnlyLabels = trainingLabels[ln:]
#print ('>> Starting Training ...')
for typee in onlineLearning_HW2.LEARNING_TYPE_LIST:
for margin in onlineLearning_HW2.MARGIN_LIST:
for maxIter in onlineLearning_HW2.MAX_ITERATION_LIST:
for lrate in onlineLearning_HW2.LEARNING_RATE_LIST:
#try:
#global onlineLearning_HW2.MARGIN
onlineLearning_HW2.MARGIN = margin
#global onlineLearning_HW2.MAX_ITERATION
onlineLearning_HW2.MAX_ITERATION = maxIter
#global onlineLearning_HW2.LEARNING_RATE
onlineLearning_HW2.LEARNING_RATE = lrate
#global onlineLearning_HW2.LEARNING_TYPE
onlineLearning_HW2.LEARNING_TYPE = typee
myStr = 'FEATURE_TYPE='+str(feat) +' BOOLEAN_FEATURES='+str(boolean) +' LEARNING_TYPE='+str(typee)+ ' MARGIN='+str(margin) + ' MAX_ITERATION='+str(maxIter)+' LEARNING_RATE='+str(lrate)
writer.write('\n'+myStr)
writer.flush()
print (myStr)
#print ('>> Starting training ...')
if(onlineLearning_HW2.LEARNING_TYPE == 'p'):
model = onlineLearning_HW2.perceptron(trainingFeatures, trainingOnlyLabels)
elif(onlineLearning_HW2.LEARNING_TYPE == 'avgP'):
model = onlineLearning_HW2.avgPerceptron(trainingFeatures, trainingOnlyLabels)
elif(onlineLearning_HW2.LEARNING_TYPE == 'w'):
model = onlineLearning_HW2.winnow(trainingFeatures, trainingOnlyLabels)
else:
model = onlineLearning_HW2.kernelPerceptron(trainingFeatures, trainingOnlyLabels)
#print ('>> Making predictions ...')
predictionsTraining = onlineLearning_HW2.classify(trainingFeatures, trainingOnlyLabels, model, trainingFeatures, trainingOnlyLabels)
predictionsVal = onlineLearning_HW2.classify(valFeatures, valLabel, model, trainingFeatures, trainingOnlyLabels)
predictionsTest = onlineLearning_HW2.classify(testFeatures, testLabel, model, trainingFeatures, trainingOnlyLabels)
#print ('>> Calculating performance ...')
#print('Training set:')
res, trainAcc = onlineLearning_HW2.checkPerformance(trainingOnlyLabels, predictionsTraining)
writer.write('\nTRAIN: '+res)
#print('Validation set:')
res, valAcc = onlineLearning_HW2.checkPerformance(valLabel, predictionsVal)
writer.write('\nVAL : '+res)
#print('Test set:')
res, testAcc = onlineLearning_HW2.checkPerformance(testLabel, predictionsTest)
writer.write('\nTEST : '+res)
writer.flush()
# except:
# writer.write('>> Expection !')
#.........這裏部分代碼省略.........