python.learner.TrainingStopPolicy.TrainingStopPolicy Class Reference

The class implements heuristics for testing if the learn process should be finished before learner is overfitted. More...

Inheritance diagram for python.learner.TrainingStopPolicy.TrainingStopPolicy:

Public Member Functions
def	__init__ (self)
	Contructor. More...
def	fromJson (cls, jsonObject)
	Restores the TrainingStopPolicy object from the json object with attributes. More...
def	getAccuracyLimit (self)
	Returns the accuracy on validation data, that have to be achieved. More...
def	getAdaptiveIterationLimit (self)
	Returns the maximal number of refinement iterations. More...
def	getEpochsLimit (self)
	Returns the maximal number of iterations, during which accuracy can decreases. More...
def	getGridSize (self, learner)
def	getGridSizeLimit (self)
	Returns the maximal grid size. More...
def	getMSELimit (self)
	Returns MSE on validation data, that have to be achieved. More...
def	hasGridSizeChanged (self, learner)
def	hasLimitReached (self, learner)
def	isTrainingComplete (self, learner)
	Checks if learning process have to be stopped. More...
def	setAccuracyLimit (self, limit)
	Setter for accuracy limit. More...
def	setAdaptiveIterationLimit (self, limit)
def	setEpochsLimit (self, limit)
	Setter for epochs limit. More...
def	setGridSizeLimit (self, limit)
	Setter for maximal grid size. More...
def	setMSELimit (self, limit)
	Setter for MSE limit. More...
def	toString (self)
	Returns a string that represents the object. More...

Detailed Description

The class implements heuristics for testing if the learn process should be finished before learner is overfitted.

The test is made by calling method isTrainingComplete(learner) of the class, which returns True if training process should be finished.

Constructor & Destructor Documentation

__init__()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.__init__

(

self

)

Contructor.

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__accuracyLimit, python.learner.TrainingStopPolicy.TrainingStopPolicy.__adaptiveIterationLimit, python.learner.TrainingStopPolicy.TrainingStopPolicy.__epochsLimit, python.learner.TrainingStopPolicy.TrainingStopPolicy.__gridSizeLimit, python.learner.TrainingStopPolicy.TrainingStopPolicy.__MSELimit, and python.learner.TrainingStopPolicy.TrainingStopPolicy.__oldGridSize.

Member Function Documentation

fromJson()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.fromJson	(		cls,
			jsonObject
	)

Restores the TrainingStopPolicy object from the json object with attributes.

Parameters

cls	python keyword (do not specify)
jsonObject	A json object.

Returns

The restored TrainingStopPolicy object.

Referenced by python.uq.sampler.asgc.ASGCSampler.ASGCSampler.setMemento(), python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.setMemento(), python.uq.learner.Learner.Learner.setMemento(), and python.uq.uq_setting.UQSetting.UQSetting.setMemento().

getAccuracyLimit()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.getAccuracyLimit

(

self

)

Returns the accuracy on validation data, that have to be achieved.

Returns

: accuracy on validation data, that have to be achieved

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__accuracyLimit.

getAdaptiveIterationLimit()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.getAdaptiveIterationLimit

(

self

)

Returns the maximal number of refinement iterations.

Returns

: the maximal number of refinement iterations

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__adaptiveIterationLimit.

getEpochsLimit()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.getEpochsLimit

(

self

)

Returns the maximal number of iterations, during which accuracy can decreases.

Returns

: the maximal number of iterations, during which accuracy can decreases

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__epochsLimit.

getGridSize()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.getGridSize	(		self,
			learner
	)

Referenced by python.learner.TrainingStopPolicy.TrainingStopPolicy.hasGridSizeChanged(), and python.learner.TrainingStopPolicy.TrainingStopPolicy.isTrainingComplete().

getGridSizeLimit()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.getGridSizeLimit

(

self

)

Returns the maximal grid size.

Returns

: maximal grid size

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__gridSizeLimit.

Referenced by python.uq.sampler.asgc.ASGCSamplerStopPolicy.ASGCSamplerStopPolicy.hasLimitReached(), and python.learner.TrainingStopPolicy.TrainingStopPolicy.hasLimitReached().

getMSELimit()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.getMSELimit

(

self

)

Returns MSE on validation data, that have to be achieved.

Returns

: MSE on validation data, that have to be achieved

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__MSELimit.

Referenced by python.learner.TrainingStopPolicy.TrainingStopPolicy.hasLimitReached().

hasGridSizeChanged()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.hasGridSizeChanged	(		self,
			learner
	)

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__oldGridSize, and python.learner.TrainingStopPolicy.TrainingStopPolicy.getGridSize().

Referenced by python.uq.sampler.asgc.ASGCSamplerStopPolicy.ASGCSamplerStopPolicy.isTrainingComplete(), and python.learner.TrainingStopPolicy.TrainingStopPolicy.isTrainingComplete().

hasLimitReached()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.hasLimitReached	(		self,
			learner
	)

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__adaptiveIterationLimit, python.learner.TrainingStopPolicy.TrainingStopPolicy.getGridSizeLimit(), and python.learner.TrainingStopPolicy.TrainingStopPolicy.getMSELimit().

Referenced by python.uq.sampler.asgc.ASGCSamplerStopPolicy.ASGCSamplerStopPolicy.isTrainingComplete(), and python.learner.TrainingStopPolicy.TrainingStopPolicy.isTrainingComplete().

isTrainingComplete()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.isTrainingComplete	(		self,
			learner
	)

Checks if learning process have to be stopped.

Parameters

learner

Learner object

Returns

: boolean value, true if learning has to stop, false otherwise

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__oldGridSize, python.learner.TrainingStopPolicy.TrainingStopPolicy.getGridSize(), python.learner.TrainingStopPolicy.TrainingStopPolicy.hasGridSizeChanged(), and python.learner.TrainingStopPolicy.TrainingStopPolicy.hasLimitReached().

setAccuracyLimit()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.setAccuracyLimit	(		self,
			limit
	)

Setter for accuracy limit.

Parameters

limit

double accuracy on validation data, that have to be achieved

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__accuracyLimit.

setAdaptiveIterationLimit()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.setAdaptiveIterationLimit	(		self,
			limit
	)

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__adaptiveIterationLimit.

setEpochsLimit()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.setEpochsLimit	(		self,
			limit
	)

Setter for epochs limit.

Parameters

limit

integer Maximal number of iterations, during which accuracy can decreases

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__epochsLimit.

setGridSizeLimit()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.setGridSizeLimit	(		self,
			limit
	)

Setter for maximal grid size.

Parameters

limit

integer maximal grid size

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__gridSizeLimit.

setMSELimit()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.setMSELimit	(		self,
			limit
	)

Setter for MSE limit.

Parameters

limit

double minimal MSE on validation data, that have to be achieved

References python.learner.TrainingStopPolicy.TrainingStopPolicy.__MSELimit.

toString()

def python.learner.TrainingStopPolicy.TrainingStopPolicy.toString

(

self

)

Returns a string that represents the object.

Returns

A string that represents the object. )

References create_dataset.type.

Referenced by python.uq.learner.Learner.Learner.createMemento().

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The documentation for this class was generated from the following file:

• datadriven/python/learner/TrainingStopPolicy.py