def __init__(self):
###############################################################
#
# Sets up all default requirements
#
# - Helpers: Useful global functions
# - Data: Data functions
#
###############################################################
self.Helpers = Helpers()
self._confs = self.Helpers.loadConfigs()
self.Data = Data()
def __init__(self, jumpWay):
self.Helpers = Helpers()
self._confs = self.Helpers.loadConfigs()
self.LogFile = self.Helpers.setLogFile(self._confs["aiCore"]["Logs"] +
"Train/")
self.jumpwayCl = jumpWay
self.intentMap = {}
self.words = []
self.classes = []
self.dataCorpus = []
self.Model = Model()
self.Data = Data()
def __init__(self, jumpWay):
self.Helpers = Helpers()
self.Logging = Logging()
self.jumpwayCl = jumpWay
self._confs = self.Helpers.loadConfigs()
self.LogFile = self.Logging.setLogFile(self._confs["AI"]["Logs"] +
"Train/")
self.Logging.logMessage(self.LogFile, "LogFile", "INFO",
"NLU Trainer LogFile Set")
self.Model = Model()
self.Data = Data(self.Logging, self.LogFile)
self.intentMap = {}
self.words = []
self.classes = []
self.dataCorpus = []
self.setupData()
self.setupEntities()
def initNLU(self):
###############################################################
#
# Initiates the NLU setting up the data, NLU / entities models
# and required modules such as context and extensions.
#
###############################################################
self.Data = Data()
self.trainingData = self.Data.loadTrainingData()
self.trainedData = self.Data.loadTrainedData()
self.Model = Model()
self.Context = Context()
self.Extensions = Extensions()
self.restoreData()
self.restoreNER()
self.restoreNLU()
self.initiateSession()
self.setThresholds()
def setup(self):
self.Logging.logMessage(self.LogFile, "NLU", "INFO",
"NLU Classifier Initiating")
self.Data = Data(self.Logging, self.LogFile)
self.Model = Model()
self.Context = Context()
self.user = {}
self.ner = None
self.trainingData = self.Data.loadTrainingData()
self.trainedData = self.Data.loadTrainedData()
self.trainedWords = self.trainedData["words"]
self.trainedClasses = self.trainedData["classes"]
self.x = self.trainedData["x"]
self.y = self.trainedData["y"]
self.intentMap = self.trainedData["iMap"][0]
self.restoreEntitiesModel()
self.restoreModel()
self.Logging.logMessage(self.LogFile, "NLU", "INFO", "NLU Ready")
elif sys.argv[1] == 'test':
t = Test()
t.test_scrape_today_buysell()
# elif sys.argv[1] == 'bm':
# b = Benchmark(start_path)
# df, exists = b.get()
# recent_date = list(df.ix[len(df)-1])[0].replace('-', '')
# if exists:
# print('Recent update: ' + recent_date)
# else:
# print('Downloaded data to: ' + recent_date)
#
elif sys.argv[1] == 'data':
d = Data(start_path)
if sys.argv[2] == 'send':
if sys.argv[3] == 'ticker':
d.send_ticker()
elif sys.argv[3] == 'bm':
d.send_bm()
elif sys.argv[3] == 'ohlcv':
d.send_ohlcv()
elif sys.argv[2] == 'update':
if sys.argv[3] == 'ohlcv':
d.update_ohlcv()
elif sys.argv[3] == 'ohlcv_with_date_1':
d.upd_ohlcv_1()
elif sys.argv[3] == 'ohlcv_with_date_2':
d.upd_ohlcv_2()
elif sys.argv[3] == 'ohlcv_with_date_3':
def loadTest(self, testFilename):
self.test = Data(testFilename)
self.test.parse()
def loadTrain(self, trainFilename):
self.train = Data(trainFilename)
self.train.parse()