com.pervasive.datarush.analytics.naivebayes.learner

Class NaiveBayesLearner

java.lang.Object

com.pervasive.datarush.operators.AbstractLogicalOperator

com.pervasive.datarush.operators.CompositeOperator

com.pervasive.datarush.analytics.naivebayes.learner.NaiveBayesLearner

All Implemented Interfaces:

LogicalOperator

public final class NaiveBayesLearner
extends CompositeOperator

Operator responsible for building a Naive Bayes PMML model from input data. The base algorithm used is specified here here, with the following differences:

1. Provides the ability to predict based on numerical data. For numerical data, we compute probability based on the assumption of a Gaussian distribution.
2. We use Laplace smoothing in place of the "threshold" parameter.
3. We provide an option to count missing values. If selected, missing values are treated like any other single distinct value. Probability is calculated in terms of the ration of missing to non-missing.
4. Calculation is performed in terms of log-likelihood rather than likelihood.

Constructor Summary

Constructors

Constructor and Description
NaiveBayesLearner() The default constructor.
NaiveBayesLearner(String targetColumn) Creates a new instance of NaiveBayesLearner, specifying the minimal set of required parameters.

Method Summary

Methods

Modifier and Type	Method and Description
protected void	compose(CompositionContext ctx) Compose the body of this operator.
RecordPort	getInput() The input data.
List<String>	getLearningColumns() Returns the list of columns to be used to predict the output value.
PMMLPort	getModel() Returns the output PMML model port.
String	getTargetColumn() Gets the column to be predicted.
void	setLearningColumns(List<String> learningColumns) Sets the list of columns to be used to predict the output value.
void	setTargetColumn(String targetColumn) Sets the column to be predicted.

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

NaiveBayesLearner

public NaiveBayesLearner()

The default constructor. Prior to graph compilation the following required properties must be specified or an exception will be raised:

• targetColumn

NaiveBayesLearner

public NaiveBayesLearner(String targetColumn)

Creates a new instance of NaiveBayesLearner, specifying the minimal set of required parameters.

Parameters:

targetColumn - the target column to predict. Must be of type StringValued.

Method Detail

getInput

public RecordPort getInput()

The input data. String fields are assumed to be categorical. Double fields are assumed to be numerical. All other fields are ignored.

Returns:

the input data

getModel

public PMMLPort getModel()

Returns the output PMML model port.

Returns:

the output PMML model port.

getLearningColumns

public final List<String> getLearningColumns()

Returns the list of columns to be used to predict the output value. Default of empty list means "everything but targetColumn".

Returns:

The list of columns to be used to predict the output value.

setLearningColumns

public final void setLearningColumns(List<String> learningColumns)

Sets the list of columns to be used to predict the output value. Default of empty list means "everything but targetColumn".

Parameters:

learningColumns - The list of columns to be used to predict the output value.

setTargetColumn

public void setTargetColumn(String targetColumn)

Sets the column to be predicted. Must be of type string

Parameters:

targetColumn - the column to be predicted

getTargetColumn

public String getTargetColumn()

Gets the column to be predicted. Must be of type string.

Returns:

the column to be predicted

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