gov.sandia.cognition.learning.algorithm.factor.machine

Class FactorizationMachineAlternatingLeastSquares

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm<ResultType>

gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,ResultType>

gov.sandia.cognition.learning.algorithm.AbstractAnytimeSupervisedBatchLearner<Vector,java.lang.Double,FactorizationMachine>

gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner

gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineAlternatingLeastSquares

All Implemented Interfaces:

AnytimeAlgorithm<FactorizationMachine>, IterativeAlgorithm, MeasurablePerformanceAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vector,java.lang.Double>>,FactorizationMachine>, BatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vector,java.lang.Double>>,FactorizationMachine>, SupervisedBatchLearner<Vector,java.lang.Double,FactorizationMachine>, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

@PublicationReference(title="Fast Context-aware Recommendations with Factorization Machines",author={"Steffen Rendle","Zeno Gantner","Christoph Freudenthaler","Lars Schmidt-Thieme"},year=2011,type=Conference,publication="Proceeding of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR)",url="http://www.inf.uni-konstanz.de/~rendle/pdf/Rendle2011-CARS.pdf") @PublicationReference(title="Factorization Machines with libFM",author="Steffen Rendle",year=2012,type=Journal,publication="ACM Transactions on Intelligent Systems Technology",url="http://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf",notes="Algorithm 2: Alternating Least Squares (ALS)")
public class FactorizationMachineAlternatingLeastSquares
extends AbstractFactorizationMachineLearner

Implements an Alternating Least Squares (ALS) algorithm for learning a Factorization Machine.

Since:

3.4.1

Author:

Justin Basilico

See Also:

FactorizationMachine, Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected java.util.ArrayList<? extends InputOutputPair<? extends Vector,java.lang.Double>>	dataList The data in the form that it can be accessed in O(1) as a list.
protected int	dataSize The size of the data.
static double	DEFAULT_MIN_CHANGE The default minimum change is 1.0E-5.
protected java.util.ArrayList<Vector>	inputsTransposed A list representing a transposed form of the matrix of inputs.
protected double	minChange The minimum change allowed in an iteration.
protected double	totalChange The total change from the current iteration.
protected double	totalError The total error from the current iteration.

Fields inherited from class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner

biasEnabled, biasRegularization, DEFAULT_BIAS_ENABLED, DEFAULT_BIAS_REGULARIZATION, DEFAULT_FACTOR_COUNT, DEFAULT_FACTOR_REGULARIZATION, DEFAULT_MAX_ITERATIONS, DEFAULT_SEED_SCALE, DEFAULT_WEIGHT_REGULARIZATION, DEFAULT_WEIGHTS_ENABLED, dimensionality, factorCount, factorRegularization, random, result, seedScale, weightRegularization, weightsEnabled

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

data, keepGoing

Fields inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

maxIterations

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

DEFAULT_ITERATION, iteration

Constructor Summary

Constructors

Constructor and Description
FactorizationMachineAlternatingLeastSquares() Creates a new FactorizationMachineAlternatingLeastSquares with default parameter values.
FactorizationMachineAlternatingLeastSquares(int factorCount, double biasRegularization, double weightRegularization, double factorRegularization, double seedScale, int maxIterations, double minChange, java.util.Random random) Creates a new FactorizationMachineAlternatingLeastSquares.

Method Summary

Modifier and Type	Method and Description
protected void	cleanupAlgorithm() Called to clean up the learning algorithm's state after learning has finished.
double	computeObjective() Gets the total objective, which is the squared error plus the regularization terms.
double	getMinChange() Gets the minimum change allowed in an iteration.
double	getObjective() Gets the total objective, which is the squared error plus the regularization terms.
NamedValue<? extends java.lang.Number>	getPerformance() Gets the name-value pair that describes the current performance of the algorithm.
double	getRegularizationPenalty() Gets the regularization penalty term in the error for the objective.
double	getTotalChange() Gets the total change in the model parameters from the current iteration.
double	getTotalError() Gets the total squared error from the current iteration.
protected boolean	initializeAlgorithm() Called to initialize the learning algorithm's state based on the data that is stored in the data field.
void	setMinChange(double minChange) Sets the minimum change allowed in an iteration.
protected boolean	step() Called to take a single step of the learning algorithm.

Methods inherited from class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner

getBiasRegularization, getFactorCount, getFactorRegularization, getRandom, getResult, getSeedScale, getWeightRegularization, isBiasEnabled, isFactorsEnabled, isWeightsEnabled, setBiasEnabled, setBiasRegularization, setFactorCount, setFactorRegularization, setRandom, setSeedScale, setWeightRegularization, setWeightsEnabled

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

clone, getData, getKeepGoing, learn, setData, setKeepGoing, stop

Methods inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

getMaxIterations, isResultValid, setMaxIterations

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListeners

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

learn

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm

getMaxIterations, setMaxIterations

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm

addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm

isResultValid

Field Detail

DEFAULT_MIN_CHANGE

public static final double DEFAULT_MIN_CHANGE

The default minimum change is 1.0E-5.

See Also:

Constant Field Values

minChange

protected double minChange

The minimum change allowed in an iteration. The algorithm stops if the change is less than this value. Cannot be negative.

dataSize

protected transient int dataSize

The size of the data.

dataList

protected transient java.util.ArrayList<? extends InputOutputPair<? extends Vector,java.lang.Double>> dataList

The data in the form that it can be accessed in O(1) as a list.

inputsTransposed

protected transient java.util.ArrayList<Vector> inputsTransposed

A list representing a transposed form of the matrix of inputs. It is a d by n sparse matrix stored as an array list of sparse vectors. This is used to speed up the computation of the per-coordinate updates.

totalChange

protected double totalChange

The total change from the current iteration.

totalError

protected double totalError

The total error from the current iteration.

Constructor Detail

FactorizationMachineAlternatingLeastSquares

public FactorizationMachineAlternatingLeastSquares()

Creates a new FactorizationMachineAlternatingLeastSquares with default parameter values.

FactorizationMachineAlternatingLeastSquares

public FactorizationMachineAlternatingLeastSquares(int factorCount,
                                                   double biasRegularization,
                                                   double weightRegularization,
                                                   double factorRegularization,
                                                   double seedScale,
                                                   int maxIterations,
                                                   double minChange,
                                                   java.util.Random random)

Creates a new FactorizationMachineAlternatingLeastSquares.

Parameters:

factorCount - The number of factors to use. Zero means no factors. Cannot be negative.

biasRegularization - The regularization term for the bias. Cannot be negative.

weightRegularization - The regularization term for the linear weights. Cannot be negative.

factorRegularization - The regularization term for the factor matrix. Cannot be negative.

seedScale - The random initialization scale for the factors. Multiplied by a random Gaussian to initialize each factor value. Cannot be negative.

maxIterations - The maximum number of iterations for the algorithm to run. Cannot be negative.

minChange - The minimum change allowed in an iteration. The algorithm stops if the change is less than this value. Cannot be negative.

random - The random number generator.

Method Detail

initializeAlgorithm

protected boolean initializeAlgorithm()

Description copied from class: AbstractAnytimeBatchLearner

Called to initialize the learning algorithm's state based on the data that is stored in the data field. The return value indicates if the algorithm can be run or not based on the initialization.

Overrides:

initializeAlgorithm in class AbstractFactorizationMachineLearner

Returns:

True if the learning algorithm can be run and false if it cannot.

step

protected boolean step()

Description copied from class: AbstractAnytimeBatchLearner

Called to take a single step of the learning algorithm.

Specified by:

step in class AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vector,java.lang.Double>>,FactorizationMachine>

Returns:

True if another step can be taken and false it the algorithm should halt.

cleanupAlgorithm

protected void cleanupAlgorithm()

Description copied from class: AbstractAnytimeBatchLearner

Called to clean up the learning algorithm's state after learning has finished.

Specified by:

cleanupAlgorithm in class AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vector,java.lang.Double>>,FactorizationMachine>

getTotalChange

public double getTotalChange()

Gets the total change in the model parameters from the current iteration.

Returns:

The total change in model parameters.

getTotalError

public double getTotalError()

Gets the total squared error from the current iteration.

Returns:

The total squared error.

getRegularizationPenalty

public double getRegularizationPenalty()

Gets the regularization penalty term in the error for the objective.

Returns:

The regularization penalty term.

getObjective

public double getObjective()

Gets the total objective, which is the squared error plus the regularization terms.

Returns:

The value of the optimization objective.

computeObjective

public double computeObjective()

Gets the total objective, which is the squared error plus the regularization terms.

Returns:

The value of the optimization objective.

getPerformance

public NamedValue<? extends java.lang.Number> getPerformance()

Description copied from interface: MeasurablePerformanceAlgorithm

Gets the name-value pair that describes the current performance of the algorithm. For most algorithms, this is the value that they are attempting to optimize.

Returns:

The name-value pair that describes the current performance of the algorithm.

getMinChange

public double getMinChange()

Gets the minimum change allowed in an iteration. The algorithm stops if the change is less than this value.

Returns:

The minimum change. Cannot be negative.

setMinChange

public void setMinChange(double minChange)

Sets the minimum change allowed in an iteration. The algorithm stops if the change is less than this value.

Parameters:

minChange - The minimum change. Cannot be negative.