classifier.SaveModel()
################## INDEPENDENT PROCESS TEST #################################
# Load language model from the the serialization file languageModelSerializationFile which was saved in training
testLanguageModel = LanguageModel(configFileLanguageModel, stopWordsFileName,
languageModelSerializationFile, linksDBFile,
datasetBuilder.trainSet)
testLanguageModel.LoadModel()
# Extract features
testFeaturesExtractor = FeaturesExtractor(configFileFeaturesExtractor,
testFeaturesSerializationFile,
testLabelsSerializationFile,
testLanguageModel,
datasetBuilder.testSet)
testFeaturesExtractor.ExtractNumTfFeatures()
# Load classifier
testClassifier = Classifier(configFileClassifier, modelSerializationFile,
trainFeaturesExtractor.features,
trainFeaturesExtractor.labels,
testFeaturesExtractor.features,
testFeaturesExtractor.labels)
testClassifier.LoadModel()
labels, acc, val = testClassifier.Test()
# Build the confusion matrix
mConfusionMatrix, mNormalConfusionMatrix, vNumTrainExamplesPerClass, vAccuracyPerClass, nOverallAccuracy = classifier.BuildConfusionMatrix(
testFeaturesExtractor.labels, labels)
print(mConfusionMatrix)
if DUMP_FEATURES:
trainFeaturesExtractor.DumpFeaturesToTxt(trainExportFileName)
testFeaturesExtractor.DumpFeaturesToTxt(testExportFileName)
if (CLASSIFIER):
# The serialization file to save the features
modelSerializationFile = ".\\Classifier\\Output\classifier_model.bin"
# Start the Classifier:
#---------------------
if (SVM_CLASSIFIER):
classifier = Classifier(modelSerializationFile, 'SVM',
trainFeaturesExtractor.features,
trainFeaturesExtractor.labels,
testFeaturesExtractor.features,
testFeaturesExtractor.labels)
if not LOAD_MODEL:
# Train
classifier.Train()
classifier.SaveModel()
else:
classifier.LoadModel()
# Test
labels, acc, val = classifier.Test()
# Build the confusion matrix
mConfusionMatrix, mNormalConfusionMatrix, vNumTrainExamplesPerClass, vAccuracyPerClass, nOverallAccuracy = classifier.BuildConfusionMatrix(
testFeaturesExtractor.labels, labels)
print(mConfusionMatrix)
class Filter(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
:type self:
'''
# Start the DatasetBuilder
#-------------------------
# Configurations file xml of the dataset builder
configFileDatasetBuilder = os.path.join('DatasetBuilder','Configurations','Configurations.xml')
# The serialization file to save the dataset
datasetSerializationFile = os.path.join('DatasetBuilder','Output', 'dataset.bin')
# The XLSX file name for train set
xlsxTrainFileName = os.path.join('DatasetBuilder','Input','train')
# Initialize the DatasetBuilder from serialization file
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [], datasetSerializationFile)
datasetBuilder.trainSet = datasetBuilder.GetDatasetFromXLSXFile(xlsxTrainFileName)
# Configurations file xml of the language model
configFileLanguageModel_lexicon = os.path.join('LanguageModel', 'Configurations', 'Configurations-lexicon.xml')
configFileLanguageModel_Tasi = os.path.join('LanguageModel', 'Configurations', 'Configurations-Tasi.xml')
stopWordsFileName = os.path.join('LanguageModel', 'Input', 'stop_words.txt')
linksDBFile = os.path.join('LanguageModel', 'Output', 'links_database.txt')
# The serialization file to save the model
languageModelSerializationFile = os.path.join('LanguageModel', 'Output', 'language_model.bin')
# Start the LanguageModel:
# Initialize the LanguageModel_Lexicon
self.languageModel_lexicon = LanguageModel(configFileLanguageModel_lexicon, stopWordsFileName, languageModelSerializationFile, linksDBFile, datasetBuilder.trainSet)
self.languageModel_lexicon.BuildLanguageModel()
# Initialize the LanguageModel_Tasi
self.languageModel_Tasi = LanguageModel(configFileLanguageModel_Tasi, stopWordsFileName, languageModelSerializationFile, linksDBFile, datasetBuilder.trainSet)
self.languageModel_Tasi.BuildLanguageModel()
# Configurations file xml of the features extractor
configFileFeaturesExtractor_Lexicon = os.path.join('FeaturesExtractor', 'Configurations', 'Configurations-lexicon.xml')
configFileFeaturesExtractor_Tasi = os.path.join('FeaturesExtractor', 'Configurations', 'Configurations-Tasi.xml')
# The serialization file to save the features
trainFeaturesSerializationFile = os.path.join('FeaturesExtractor', 'Output', 'train_features.bin')
trainLabelsSerializationFile = os.path.join('FeaturesExtractor', 'Output', 'train_labels.bin')
# Start the FeaturesExtractor:
#-----------------------------
# Initialize the FeaturesExtractor _ Lexicon
trainFeaturesExtractor_Lexicon = FeaturesExtractor(configFileFeaturesExtractor_Lexicon, trainFeaturesSerializationFile, trainLabelsSerializationFile, self.languageModel_lexicon, datasetBuilder.trainSet)
trainFeaturesExtractor_Lexicon.ExtractNumTfFeatures()
# Initialize the FeaturesExtractor _ Tasi
trainFeaturesExtractor_Tasi = FeaturesExtractor(configFileFeaturesExtractor_Tasi, trainFeaturesSerializationFile, trainLabelsSerializationFile, self.languageModel_Tasi, datasetBuilder.trainSet)
trainFeaturesExtractor_Tasi.ExtractNumTfFeatures()
# The serialization file to save the features
configFileClassifier_Lexicon = os.path.join('Classifier', 'Configurations', 'Configurations-lexicon.xml')
configFileClassifier_Tasi = os.path.join('Classifier', 'Configurations', 'Configurations-Tasi.xml')
modelSerializationFile = os.path.join('Classifier', 'Output', 'classifier_model.bin')
# Start the Classifier:
#---------------------
print(trainFeaturesExtractor_Tasi.labels[:4])
print([i['label'] for i in trainFeaturesExtractor_Lexicon.dataSet[:4]])
self.classifier_Lexicon = Classifier(configFileClassifier_Lexicon, modelSerializationFile, trainFeaturesExtractor_Lexicon.features, trainFeaturesExtractor_Lexicon.labels, [], [])
self.classifier_Tasi = Classifier(configFileClassifier_Tasi, modelSerializationFile, trainFeaturesExtractor_Tasi.features,
trainFeaturesExtractor_Tasi.labels, [],[])
# Train
self.classifier_Lexicon.Train()
self.classifier_Tasi.Train()
def Classify(self, text, stockName):
testSet = []
for t in text:
testSet.append({'label' : '', 'text' : t})
if stockName == 'Tasi':
# Configurations file xml of the features extractor
configFileFeaturesExtractor = os.path.join('FeaturesExtractor', 'Configurations', 'Configurations-Tasi.xml')
testFeaturesExtractor = FeaturesExtractor(configFileFeaturesExtractor, None, None, self.languageModel_Tasi, testSet)
testFeaturesExtractor.ExtractNumTfFeatures()
self.classifier_Tasi.testFeatures = testFeaturesExtractor.features
self.classifier_Tasi.testTargets = []
for i in range(len(self.classifier_Tasi.testFeatures)):
#self.classifier_Tasi.testTargets[i] = 1
self.classifier_Tasi.testTargets.append(1)
label, acc, val = self.classifier_Tasi.Test()
else:
configFileFeaturesExtractor = os.path.join('FeaturesExtractor', 'Configurations', 'Configurations-lexicon.xml')
testFeaturesExtractor = FeaturesExtractor(configFileFeaturesExtractor, None, None, self.languageModel_lexicon, testSet)
self.classifier_Lexicon.testFeatures = testFeaturesExtractor.features
self.classifier_Lexicon.testTargets = []
for i in range(len(self.classifier_Lexicon.testFeatures)):
self.classifier_Lexicon.testTargets[i] = 1
label, acc, val = self.classifier_Lexicon.Test()
return label
