def getModelScore(model, trainIterations, trainMaxTime, testsMaxTime,
**modelParams):
"""
@param model
@param trainIterations: Number of iterations the model is
going to be trained.
@param trainMaxTime: Training stops if maxTime (in minutes) is
exceeded. Note that this may interrupt an ongoing train
ireration. -1 is no time restrictions.
@param testsMaxTime: If maxTime (in minutes) is exceeded, the tests
will end. Note that this won't interrupt an ongoing test.
The TestSuite will wait until the sequence is passed to
the model and the corresponding result is processed.
@param modelParams
"""
paramsByModule = organizeParamsByModule(modelParams)
tempModel = copy.deepcopy(model)
tempModel.iterationsTrained = 0
for moduleName in paramsByModule:
tempModel.modules[moduleName] = tempModel.modules[
moduleName].__class__(**paramsByModule[moduleName])
print('\n-----------------------------\n')
print(tempModel.spParametersStr())
print(tempModel.tmParametersStr())
print('Training...')
tempModel.train(trainIterations, maxTime=trainMaxTime, verbosity=0)
results = TestSuite.testModel(tempModel,
testSet.trainingData,
maxTime=testsMaxTime,
saveResults=False)
print("Success: {}%".format(results['successPercent']))
return results['successPercent']
def getModelScore(model, trainIterations, trainMaxTime, testsMaxTime,
**modelParams):
"""
@param model
@param trainIterations: Number of iterations the model is
going to be trained.
@param trainMaxTime: Training stops if maxTime (in minutes) is
exceeded. Note that this may interrupt an ongoing train
ireration. -1 is no time restrictions.
@param testsMaxTime: If maxTime (in minutes) is exceeded, the tests
will end. Note that this won't interrupt an ongoing test.
The TestSuite will wait until the sequence is passed to
the model and the corresponding result is processed.
@param modelParams
"""
paramsByModule = organizeParamsByModule(modelParams)
tempModel = copy.deepcopy(model)
tempModel.iterationsTrained = 0
for moduleName in paramsByModule:
tempModel.modules[moduleName] = tempModel.modules[moduleName].__class__(
**paramsByModule[moduleName])
print('\n-----------------------------\n')
print(tempModel.spParametersStr())
print(tempModel.tmParametersStr())
print('Training...')
tempModel.train(trainIterations, maxTime=trainMaxTime, verbosity=0)
results = TestSuite.testModel(tempModel, testSet.trainingData,
maxTime=testsMaxTime, saveResults=False)
print("Success: {}%".format(results['successPercent']))
return results['successPercent']
encoderName = wordEncoder.__class__.__name__
model = CurrentModel(wordEncoder, actionEncoder, trainingSet,
BestResults.bestFindings[0])
modelName = model.__class__.__name__
print(modelName)
print(encoderName)
model.train(30, maxTime=-1, verbosity=1)
fileName = 'Results/'
# Strips the 'Model' fron the name
fileName += modelName[:-5] + setsName + '-'
# Appends only the Capital letters
fileName += ''.join(cap for cap in encoderName if cap.isupper())
#fileName += 'OneRegionExp32'
TestSuite.testModel(model, testSet.trainingData,
fileName=(fileName + '_Results'))
#print("Saving the model...")
#with open((fileName + '.pck'), 'wb') as modelFile:
# cPickle.dump(model, modelFile, -1)
#print("Done!")
#app = QApplication([])
#window = MainWindow(model)
#app.exec_()
#sys.exit(app.exec_())
model = CurrentModel(wordEncoder, actionEncoder, trainingSet,
BestParameters.bestFindings[0])
modelName = model.__class__.__name__
print(modelName)
print(encoderName)
model.train(iterations, maxTime=-1, verbosity=1)
fileName = 'Results/'
# Strips the 'Model' fron the name
fileName += modelName[:-5] + setsName + '-'
# Appends only the Capital letters
fileName += ''.join(cap for cap in encoderName if cap.isupper())
#fileName += 'OneRegionExp32'
if whether_tests:
TestSuite.testModel(model,
testSet.trainingData,
fileName=(fileName + '_Results'))
if whether_save:
print("Saving the model...")
with open((fileName + '.pck'), 'wb') as modelFile:
cPickle.dump(model, modelFile, -1)
print("Done!")
if whether_gui:
app = QApplication([])
window = MainWindow(model)
sys.exit(app.exec_())
