com.pervasive.datarush.analytics.regression

Class LogisticRegressionLearner

java.lang.Object

com.pervasive.datarush.operators.AbstractLogicalOperator

com.pervasive.datarush.operators.CompositeOperator

com.pervasive.datarush.analytics.regression.LogisticRegressionLearner

All Implemented Interfaces:

LogicalOperator

public class LogisticRegressionLearner
extends CompositeOperator

Fits a multinomial logistic regression model to the training data. The output is a PMML model describing the resulting classification model.

Constructor Summary

Constructors

Constructor and Description
LogisticRegressionLearner()

Method Summary

Methods

Modifier and Type	Method and Description
protected void	compose(CompositionContext ctx) Compose the body of this operator.
RecordPort	getInput()
List<String>	getLearningColumns()
double	getLearningRate() Gets the learning rate, alpha.
int	getMaxDistinctNominalValues() Returns the maximum number of distinct nominal values to allow.
int	getMaxIterations()
PMMLPort	getModelOutput()
double	getRidge()
long	getSeed()
String	getTargetColumn()
double	getTolerance()
void	setLearningColumns(List<String> learningColumns) Controls which fields are treated as independent variables.
void	setLearningRate(double learningRate) Set the learning rate that should be used at the start of the computation.
void	setMaxDistinctNominalValues(int maxDistinctNominalValues) Sets the maximum number of distinct nominal values to allow.
void	setMaxIterations(int maxIterations) Control the maximum number of iterations attempted before generating a model.
void	setRidge(double ridge) Sets the regularization constant, lambda.
void	setSeed(long seed) Sets the seed for the random numbers used by the algorithm.
void	setTargetColumn(String targetColumn)
void	setTolerance(double threshold) Sets the strictness of the convergence criterion, as a fraction of the total length of the coefficient vector.

Methods inherited from class com.pervasive.datarush.operators.AbstractLogicalOperator

disableParallelism, getInputPorts, getOutputPorts, newInput, newInput, newOutput, newRecordInput, newRecordInput, newRecordOutput, notifyError

Methods inherited from class java.lang.Object

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

Constructor Detail

LogisticRegressionLearner

public LogisticRegressionLearner()

Method Detail

getLearningColumns

public List<String> getLearningColumns()

Returns:

the field names of the columns containing the independent variables

setLearningColumns

public void setLearningColumns(List<String> learningColumns)

Controls which fields are treated as independent variables. Fields other than the learning and target columns are ignored

Parameters:

learningColumns - the names of the fields that are treated as independent variables.

getTargetColumn

public String getTargetColumn()

Returns:

the field name of the dependent (predicted) variable.

setTargetColumn

public void setTargetColumn(String targetColumn)

Parameters:

targetColumn - the field name of the desired dependent (predicted) variable.

getInput

public RecordPort getInput()

Returns:

the input for training data

getModelOutput

public PMMLPort getModelOutput()

Returns:

the main output, a ModelPort for a Logistic Regression model

getMaxDistinctNominalValues

public int getMaxDistinctNominalValues()

Returns the maximum number of distinct nominal values to allow. Attributes with more than this number of distinct values will be filtered from the model. The default is 1000.

Returns:

the maximum number of distinct nominal values to allow.

setMaxDistinctNominalValues

public void setMaxDistinctNominalValues(int maxDistinctNominalValues)

Sets the maximum number of distinct nominal values to allow. Attributes with more than this number of distinct values will be filtered from the model. The default is 1000.

compose

protected void compose(CompositionContext ctx)

Description copied from class: CompositeOperator

Compose the body of this operator. Implementations should do the following:

1. Perform any validation of configuration, input types, etc
2. Instantiate and configure sub-operators, adding them to the provided context via the method OperatorComposable.add(O)
3. Create necessary connections via the method OperatorComposable.connect(P, P). This includes connections from the composite's input ports to sub-operators, connections between sub-operators, and connections from sub-operators output ports to the composite's output ports

Specified by:

compose in class CompositeOperator

Parameters:

ctx - the context

getRidge

public double getRidge()

Returns:

the regularization constant (a.k.a lambda)

setRidge

public void setRidge(double ridge)

Sets the regularization constant, lambda. The regularization constant penalizes very large coefficients and is sometimes necessary for convergence. The regularization constant MUST be at least 0 and less than 1 and SHOULD generally be small.

Parameters:

ridge - the desired regularization parameter.

Throws:

com.pervasive.datarush.graphs.physical.InvalidPropertyValueException - if ridge is not between 0 and 1.

getLearningRate

public double getLearningRate()

Gets the learning rate, alpha. Note that learning rates that are high enough to cause unstable behavior may be automatically reduced.

Returns:

the learning rate, alpha at the start of the computation.

setLearningRate

public void setLearningRate(double learningRate)

Set the learning rate that should be used at the start of the computation. This is a maximum value; the algorithm may reduce the learning rate if it is likely to result in divergence. The learning rate MUST be positive (or the algorithm won't work) and SHOULD be less than one (or it will probably diverge or waste time before being adjusted to something reasonable.)

Parameters:

learningRate - the learning rate alpha that should be used at the start of the computation.

Throws:

com.pervasive.datarush.graphs.physical.InvalidPropertyValueException - if the learning rate is set to 0 or less.

getMaxIterations

public int getMaxIterations()

Returns:

the maximum number of iterations attempted before generating a model

setMaxIterations

public void setMaxIterations(int maxIterations)

Control the maximum number of iterations attempted before generating a model. In general, the number of iterations should be directly proportional to the available time and inversely proportional to the size of the data. (Note also that automatically adjusting the learning rate may require a full iteration)

Parameters:

maxIterations - the desired maximum number of passes over the dataset

Throws:

com.pervasive.datarush.graphs.physical.InvalidPropertyValueException - if maxIterations is set to a negative value

getTolerance

public double getTolerance()

Returns:

the strictness of the convergence criterion, as a fraction of the total length of the coefficient vector.

setTolerance

public void setTolerance(double threshold)

Sets the strictness of the convergence criterion, as a fraction of the total length of the coefficient vector. Note that a threshold much higher than the learning rate (> alpha*sqrt(# training examples)), or larger than 1, is very likely to result in premature convergence detection. Set to 0 for a single-full-pass algorithm.

Parameters:

threshold - the desired convergence threshold

Throws:

com.pervasive.datarush.graphs.physical.InvalidPropertyValueException - if threshold is negative

getSeed

public long getSeed()

Returns:

the randomness seed used by the algorithm.

setSeed

public void setSeed(long seed)

Sets the seed for the random numbers used by the algorithm. The main use of the seed is to randomly reorder the input. Note that even with the same seed, results may vary based on engine settings such as the number of partitions.

Parameters:

seed - the desired base seed for the random number generator