com.pervasive.datarush.analytics.text

Class DictionaryFilter

java.lang.Object

com.pervasive.datarush.operators.AbstractLogicalOperator

com.pervasive.datarush.operators.StreamingOperator

com.pervasive.datarush.operators.ExecutableOperator

com.pervasive.datarush.analytics.text.DictionaryFilter

All Implemented Interfaces:

LogicalOperator, PipelineOperator<RecordPort>, RecordPipelineOperator

public class DictionaryFilter
extends ExecutableOperator
implements RecordPipelineOperator

Filters a tokenized text field using a dictionary. This operator can be used to filter a tokenized text field based on a dictionary. The DictionaryFilter operator has four properties: input field, output field, dictionary input field, and whether the filter should be inverted. The input field must be a tokenized text object. The tokenized text object will be filtered of all words that are specified in the dictionary input. This will produce a new tokenized text object that will be encoded into the output field. This object can then be used for further text processing tasks.

Constructor Summary

Constructors

Constructor and Description
DictionaryFilter() Default constructor.
DictionaryFilter(String textField) Constructor specifying the tokenized text field to filter.
DictionaryFilter(String textField, String dictionaryField) Constructor specifying the tokenized text field to filter and the dictionary field in the dictionary input.

Method Summary

Methods

Modifier and Type	Method and Description
protected void	computeMetadata(StreamingMetadataContext ctx) Implementations must adhere to the following contracts
protected void	execute(ExecutionContext ctx) Executes the operator.
String	getDictionaryField() Get the dictionary field in the dictionary input.
RecordPort	getDictionaryInput() Get the optional dictionary input port of this operator.
RecordPort	getInput() Get the input port of this operator.
String	getInputField() Get the tokenized text field to filter.
boolean	getInverted() Get whether the filter should be inverted
RecordPort	getOutput() Get the output port of this operator.
String	getOutputField() Get the name of the filtered output field.
void	setDictionaryField(String dictionaryField) Set the dictionary field in the dictionary input.
void	setInputField(String textField) Set the field to filter.
void	setInverted(boolean inverted) Set whether the filter should be inverted.
void	setOutputField(String tokenField) Set the name of the filtered output field.

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

cloneForExecution, getNumInputCopies, getPortSettings, handleInactiveOutput

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

Methods inherited from interface com.pervasive.datarush.operators.LogicalOperator

disableParallelism, getInputPorts, getOutputPorts

Constructor Detail

DictionaryFilter

public DictionaryFilter()

Default constructor. Use setInputField(String) and setOutputField(String) to set the name of the text field to filter and its output field.

DictionaryFilter

public DictionaryFilter(String textField)

Constructor specifying the tokenized text field to filter.

Parameters:

textField - name of the field to filter

DictionaryFilter

public DictionaryFilter(String textField,
                String dictionaryField)

Constructor specifying the tokenized text field to filter and the dictionary field in the dictionary input.

Parameters:

textField - name of the field to filter

dictionaryField - name of the dicitonary field

Method Detail

setInputField

public void setInputField(String textField)

Set the field to filter.

If this field does not exist in the input, or is not of type TokenizedText, an exception will be thrown at composition time.

Parameters:

textField - the name of the field to filter

getInputField

public String getInputField()

Get the tokenized text field to filter.

Returns:

The name of the field to filter

setOutputField

public void setOutputField(String tokenField)

Set the name of the filtered output field.

Parameters:

tokenField - name of the token output field

getOutputField

public String getOutputField()

Get the name of the filtered output field.

Returns:

The name of the filtered output field

setDictionaryField

public void setDictionaryField(String dictionaryField)

Set the dictionary field in the dictionary input.

Parameters:

dictionaryField - name of the dictionary field

getDictionaryField

public String getDictionaryField()

Get the dictionary field in the dictionary input.

Returns:

name of the dictionary field

setInverted

public void setInverted(boolean inverted)

Set whether the filter should be inverted.

Parameters:

inverted - if true invert filter

getInverted

public boolean getInverted()

Get whether the filter should be inverted

Returns:

true if filter inverted

getInput

public RecordPort getInput()

Get the input port of this operator.

Specified by:

getInput in interface PipelineOperator<RecordPort>

Returns:

input port

getOutput

public RecordPort getOutput()

Get the output port of this operator.

Specified by:

getOutput in interface PipelineOperator<RecordPort>

Returns:

output port

getDictionaryInput

public RecordPort getDictionaryInput()

Get the optional dictionary input port of this operator.

Returns:

dictionary port

computeMetadata

protected void computeMetadata(StreamingMetadataContext ctx)

Description copied from class: StreamingOperator

Implementations must adhere to the following contracts

General

Regardless of input ports/output port types, all implementations must do the following:

1. Validation. Validation of configuration should always be performed first.
2. Declare parallelizability.. Implementations must declare parallelizability by calling StreamingMetadataContext.parallelize(ParallelismStrategy).

Input record ports

Implementations with input record ports must declare the following:

1. Required data ordering:
Implementations that have data ordering requirements must declare them by calling RecordPort.setRequiredDataOrdering(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.ports.record.DataOrdering), otherwise data may arrive in any order.
2. Required data distribution (only applies to parallelizable operators):
Implementations that have data distribution requirements must declare them by calling RecordPort.setRequiredDataDistribution(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.ports.record.DataDistribution), otherwise data will arrive in an unspecified partial distribution.

Note that if the upstream operator's output distribution/ordering is compatible with those required, we avoid a re-sort/re-distribution which is generally a very large savings from a performance standpoint. In addition, some operators may chose to query the upstream output distribution/ordering by calling RecordPort.getSourceDataDistribution(com.pervasive.datarush.operators.MetadataContext) and RecordPort.getSourceDataOrdering(com.pervasive.datarush.operators.MetadataContext). These should be viewed as a hints to help chose a more efficient algorithm. In such cases, though, operators must still declare data ordering and data distribution requirements; otherwise there is no guarantee that data will arrive sorted/distributed as required.

Output record ports

Implementations with output record ports must declare the following:

1. Type: Implementations must declare their output type by calling RecordPort.setType(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.types.RecordTokenType).

Implementations with output record ports may declare the following:

1. Output data ordering: Implementations that can make guarantees as to their output ordering may do so by calling RecordPort.setOutputDataOrdering(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.ports.record.DataOrdering)
2. Output data distribution (only applies to parallelizable operators): Implementations that can make guarantees as to their output distribution may do so by calling RecordPort.setOutputDataDistribution(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.ports.record.DataDistribution)

Note that both of these properties are optional; if unspecified, performance may suffer since the framework may unnecessarily re-sort/re-distributed the data.

Input model ports

In general, there is nothing special to declare for input model ports. Models are implicitly duplicated to all partitions when going from non-parallel to parallel operators. The case of a model going from a parallel to a non-parallel node is a special case of a "model reducer" operator. In the case of a model reducer, the downstream operator, must declare the following:

1. Merge handler: Model reducers must declare a merge handler by calling AbstractModelPort.setMergeHandler(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.ports.model.ModelMergeHandler<T>).

Note that MergeModel is a convenient, re-usable model reducer, parameterized with a merge-handler.

Output model ports

SimpleModelPort's have no associated metadata and therefore there is never any output metadata to declare. PMMLPort's, on the other hand, do have associated metadata. For all PMMLPorts, implementations must declare the following:

1. pmmlModelSpec: Implementations must declare the PMML model spec by calling PMMLPort.setPMMLModelSpec.

Specified by:

computeMetadata in class StreamingOperator

Parameters:

ctx - the context

execute

protected void execute(ExecutionContext ctx)

Description copied from class: ExecutableOperator

Executes the operator. Implementations should adhere to the following contracts:

1. Following execution, all input ports must be at end-of-data.
2. Following execution, all output ports must be at end-of-data.

Specified by:

execute in class ExecutableOperator

Parameters:

ctx - context in which to lookup physical ports bound to logical ports