def evaluate(cls, path, use_backend=True, pre_stocks=None):
validation_accuracy = {}
global stocks
if pre_stocks:
stocks = pre_stocks
for stockName in stocks:
model = cls.load(path, stockName)
if not model:
continue
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)
if use_backend:
testSet = datasetBuilder.GetDatasetFromBackend(stockName)
else:
testSet = datasetBuilder.GetDatasetFromXLSXFile(
xlsxTrainFileName, stockName)
if len(testSet) < NMIN_SET:
continue
testSet = testSet[:NVALID]
print('Using model for %s' % stockName)
configFileFeaturesExtractor = os.path.join(
'FeaturesExtractor', 'Configurations',
'Configurations-Tasi.xml')
testFeaturesExtractor = FeaturesExtractor(
configFileFeaturesExtractor, None, None,
model['languageModel_lexicon'], testSet)
#testFeaturesExtractor.ExtractLexiconFeatures()
testFeaturesExtractor.ExtractNumTfFeatures(sparse=True)
model[
'classifier_Lexicon'].testFeatures = testFeaturesExtractor.sparse_features
model[
'classifier_Lexicon'].testTargets = testFeaturesExtractor.labels
label, acc, val = model['classifier_Lexicon'].Test()
print(acc, val)
validation_accuracy[stockName] = {
'accuracy': acc,
'training_samples': model['training_samples']
}
return validation_accuracy
def init(cls, save_path, use_backend=True, pre_stocks=None):
'''
Constructor
:type self:
'''
global stocks
if pre_stocks:
stocks = pre_stocks
for stock in stocks:
print('Buildind model for %s' % stock)
stock_model = {}
# 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)
if use_backend:
datasetBuilder.trainSet = datasetBuilder.GetDatasetFromBackend(
stock)
else:
datasetBuilder.trainSet = datasetBuilder.GetDatasetFromXLSXFile(
xlsxTrainFileName, stock)
if len(datasetBuilder.trainSet) < NMIN_SET:
print("Not enough data: ", len(datasetBuilder.trainSet))
continue
datasetBuilder.trainSet = datasetBuilder.trainSet[NVALID:]
# Configurations file xml of the language model
configFileLanguageModel_lexicon = 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
stock_model['languageModel_lexicon'] = LanguageModel(
configFileLanguageModel_lexicon, stopWordsFileName,
languageModelSerializationFile, linksDBFile,
datasetBuilder.trainSet)
stock_model['languageModel_lexicon'].BuildLanguageModel()
# Configurations file xml of the features extractor
configFileFeaturesExtractor_Lexicon = 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,
stock_model['languageModel_lexicon'], datasetBuilder.trainSet)
trainFeaturesExtractor_Lexicon.ExtractNumTfFeatures(sparse=True)
#print(trainFeaturesExtractor_Lexicon.features[0])
# The serialization file to save the features
configFileClassifier_Lexicon = os.path.join(
'Classifier', 'Configurations', 'Configurations-Tasi.xml')
modelSerializationFile = os.path.join('Classifier', 'Output',
'classifier_model.bin')
# Start the Classifier:
#---------------------
stock_model['classifier_Lexicon'] = Classifier(
configFileClassifier_Lexicon, modelSerializationFile,
trainFeaturesExtractor_Lexicon.sparse_features,
trainFeaturesExtractor_Lexicon.labels, [], [])
#stock_model['classifier_Lexicon'] = Classifier(configFileClassifier_Lexicon, modelSerializationFile, trainFeaturesExtractor_Lexicon.features, trainFeaturesExtractor_Lexicon.labels, [], [])
#print(trainFeaturesExtractor_Lexicon.labels[:4])
#print([i['label'] for i in trainFeaturesExtractor_Lexicon.dataSet[:4]])
# Train
stock_model['classifier_Lexicon'].Train()
stock_model['training_samples'] = len(datasetBuilder.trainSet)
cls.save(save_path, stock, stock_model)
print("----------------------------------------------------")