class Trainer():
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 setupData(self):
self.trainingData = self.Data.loadTrainingData()
self.Logging.logMessage(self.LogFile, "Trainer", "INFO",
"Loaded NLU Training Data")
self.words, self.classes, self.dataCorpus, self.intentMap = self.Data.prepareData(
self.trainingData)
self.x, self.y = self.Data.finaliseData(self.classes, self.dataCorpus,
self.words)
self.Logging.logMessage(self.LogFile, "TRAIN", "INFO",
"NLU Trainer Data Ready")
def setupEntities(self):
if self._confs["ClassifierSettings"]["Entities"] == "Mitie":
self.entityExtractor = Entities()
self.Logging.logMessage(self.LogFile, "TRAIN", "OK",
"NLU Trainer Entity Extractor Ready")
self.entityExtractor.trainEntities(
self._confs["ClassifierSettings"]["Mitie"]["ModelLocation"],
self.trainingData)
def trainModel(self):
while True:
self.Logging.logMessage(self.LogFile, "TRAIN", "ACTION",
"Ready To Begin Training ? (Yes/No)")
userInput = input(">")
if userInput == 'Yes': break
if userInput == 'No': exit()
humanStart, trainingStart = self.Helpers.timerStart()
self.Logging.logMessage(self.LogFile, "TRAIN", "INFO",
"NLU Model Training At " + humanStart)
self.jumpwayCl.publishToDeviceChannel(
"Training", {
"NeuralNet": "NLU",
"Start": trainingStart,
"End": "In Progress",
"Total": "In Progress",
"Message": "NLU Model Training At " + humanStart
})
self.Model.trainDNN(self.x, self.y, self.words, self.classes,
self.intentMap)
trainingEnd, trainingTime, humanEnd = self.Helpers.timerEnd(
trainingStart)
self.Logging.logMessage(
self.LogFile, "TRAIN", "OK", "NLU Model Trained At " + humanEnd +
" In " + str(trainingEnd) + " Seconds")
self.jumpwayCl.publishToDeviceChannel(
"Training", {
"NeuralNet":
"NLU",
"Start":
trainingStart,
"End":
trainingEnd,
"Total":
trainingTime,
"Message":
"NLU Model Trained At " + humanEnd + " In " +
str(trainingEnd) + " Seconds"
})
class Trainer():
###############################################################
#
# Sets up all default requirements and placeholders
# needed for the NLU engine to run.
#
# - Helpers: Useful global functions
# - JumpWay/jumpWayClient: iotJumpWay class and connection
# - Logging: Logging class
#
###############################################################
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 setupData(self):
self.trainingData = self.Data.loadTrainingData()
self.words, self.classes, self.dataCorpus, self.intentMap = self.Data.prepareData(
self.trainingData)
self.x, self.y = self.Data.finaliseData(self.classes, self.dataCorpus,
self.words)
self.Helpers.logMessage(self.LogFile, "TRAIN", "INFO",
"NLU Training Data Ready")
def setupEntities(self):
if self._confs["NLU"]["Entities"] == "Mitie":
self.entityController = Entities()
self.entityController.trainEntities(
self._confs["NLU"]["Mitie"]["ModelLocation"],
self.trainingData)
self.Helpers.logMessage(self.LogFile, "TRAIN", "OK",
"NLU Trainer Entities Ready")
def trainModel(self):
while True:
self.Helpers.logMessage(self.LogFile, "TRAIN", "ACTION",
"Ready To Begin Training ? (Yes/No)")
userInput = input(">")
if userInput == 'Yes': break
if userInput == 'No': exit()
self.setupData()
self.setupEntities()
humanStart, trainingStart = self.Helpers.timerStart()
self.jumpwayCl.publishToDeviceChannel(
"Training", {
"NeuralNet": "NLU",
"Start": trainingStart,
"End": "In Progress",
"Total": "In Progress",
"Message": "NLU Model Training At " + humanStart
})
self.Model.trainDNN(self.x, self.y, self.words, self.classes,
self.intentMap)
trainingEnd, trainingTime, humanEnd = self.Helpers.timerEnd(
trainingStart)
self.Helpers.logMessage(
self.LogFile, "TRAIN", "OK", "NLU Model Trained At " + humanEnd +
" In " + str(trainingEnd) + " Seconds")
self.jumpwayCl.publishToDeviceChannel(
"Training", {
"NeuralNet":
"NLU",
"Start":
trainingStart,
"End":
trainingEnd,
"Total":
trainingTime,
"Message":
"NLU Model Trained At " + humanEnd + " In " +
str(trainingEnd) + " Seconds"
})
