/*
* Carrot2 project.
*
* Copyright (C) 2002-2014, Dawid Weiss, Stanisław Osiński.
* All rights reserved.
*
* Refer to the full license file "carrot2.LICENSE"
* in the root folder of the repository checkout or at:
* http://www.carrot2.org/carrot2.LICENSE
*/
package org.carrot2.examples.clustering;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import org.carrot2.clustering.lingo.LingoClusteringAlgorithm;
import org.carrot2.clustering.lingo.LingoClusteringAlgorithmDescriptor;
import org.carrot2.core.Cluster;
import org.carrot2.core.Controller;
import org.carrot2.core.ControllerFactory;
import org.carrot2.core.Document;
import org.carrot2.core.ProcessingResult;
import org.carrot2.core.attribute.CommonAttributesDescriptor;
import org.carrot2.examples.ConsoleFormatter;
import org.carrot2.examples.SampleDocumentData;
import org.carrot2.matrix.factorization.IterationNumberGuesser.FactorizationQuality;
import org.carrot2.source.microsoft.Bing3WebDocumentSource;
import org.carrot2.source.microsoft.Bing3WebDocumentSourceDescriptor;
/**
* This example shows how to customize the behaviour of clustering algorithms and
* document sources by setting attributes. For a complete summary of the available
* attributes, please see Carrot2 manual.
*/
public class UsingAttributes
{
@SuppressWarnings("unused")
public static void main(String [] args)
{
/* [[[start:using-attributes-raw-map-intro]]]
* <div>
* <p>
* You can change the default behaviour of clustering algorithms and document sources
* by changing their <em>attributes</em>. For a complete list of available attributes,
* their identifiers, types and allowed values, please see Carrot2 manual.
* </p>
* <p>
* To pass attributes to Carrot2, put them into a {@link java.util.Map},
* along with query or documents being clustered. The code shown below searches the
* web using {@link org.carrot2.source.microsoft.Bing3WebDocumentSource}
* and clusters the results using {@link org.carrot2.clustering.lingo.LingoClusteringAlgorithm}
* customized to create fewer clusters than by default.
* </p>
* </div>
* [[[end:using-attributes-raw-map-intro]]]
*/
{
// [[[start:using-attributes-raw-map]]]
/* A controller to manage the processing pipeline. */
final Controller controller = ControllerFactory.createSimple();
/* Prepare attribute map */
final Map<String, Object> attributes = new HashMap<String, Object>();
/* Put attribute values using direct keys. */
attributes.put(CommonAttributesDescriptor.Keys.QUERY, "data mining");
attributes.put(CommonAttributesDescriptor.Keys.RESULTS, 100);
attributes.put("LingoClusteringAlgorithm.desiredClusterCountBase", 15);
/* Put your own API key here! */
attributes.put(Bing3WebDocumentSourceDescriptor.Keys.APPID, BingKeyAccess.getKey());
/* Perform processing */
final ProcessingResult result = controller.process(attributes,
Bing3WebDocumentSource.class, LingoClusteringAlgorithm.class);
/* Documents fetched from the document source, clusters created by Carrot2. */
final List<Document> documents = result.getDocuments();
final List<Cluster> clusters = result.getClusters();
// [[[end:using-attributes-raw-map]]]
ConsoleFormatter.displayResults(result);
}
/* [[[start:using-attributes-builders-intro]]]
*
* <div>
* <p>
* As an alternative to the raw attribute map used in the previous example, you
* can use attribute map builders. Attribute map builders have a number of advantages:
* </p>
*
* <ul>
* <li>Type-safety: the correct type of the value will be enforced at compile time</li>
* <li>Error prevention: unexpected results caused by typos in attribute name strings are avoided</li>
* <li>Early error detection: in case an attribute's key changes, your compiler will detect that</li>
* <li>IDE support: your IDE will suggest the right method names and parameters</li>
* </ul>
*
* <p>
* A possible disadvantage of attribute builders is that one algorithm's attributes can
* be divided into a number of builders and hence not readily available in your IDE's auto
* complete window. Please consult attribute documentation in Carrot2 manual for pointers to
* the appropriate builder classes and methods.
* </p>
*
* <p>
* The code shown below fetches 100 results for query <em>data mining</em> from
* {@link org.carrot2.source.microsoft.Bing3WebDocumentSource} and clusters them using
* the {@link org.carrot2.clustering.lingo.LingoClusteringAlgorithm} tuned to create slightly
* fewer clusters than by default. Please note how the API key is passed and use your own
* key in production deployments.
* </p>
* </div>
*
* [[[end:using-attributes-builders-intro]]]
*/
{
/// [[[start:using-attributes-builders]]]
/* A controller to manage the processing pipeline. */
final Controller controller = ControllerFactory.createSimple();
/* Prepare attribute map */
final Map<String, Object> attributes = new HashMap<String, Object>();
/* Put values using attribute builders */
CommonAttributesDescriptor
.attributeBuilder(attributes)
.query("data mining")
.results(100);
LingoClusteringAlgorithmDescriptor
.attributeBuilder(attributes)
.desiredClusterCountBase(15)
.matrixReducer()
.factorizationQuality(FactorizationQuality.HIGH);
Bing3WebDocumentSourceDescriptor
.attributeBuilder(attributes)
.appid(BingKeyAccess.getKey()); // use your own key here
/* Perform processing */
final ProcessingResult result = controller.process(attributes,
Bing3WebDocumentSource.class, LingoClusteringAlgorithm.class);
/* Documents fetched from the document source, clusters created by Carrot2. */
final List<Document> documents = result.getDocuments();
final List<Cluster> clusters = result.getClusters();
/// [[[end:using-attributes-builders]]]
ConsoleFormatter.displayResults(result);
}
/* [[[start:using-attributes-output-intro]]]
* <div>
* <p>
* Some algorithms apart from clusters can produce additional, usually
* diagnostic, output. The output is present in the attributes map contained
* in the {@link org.carrot2.core.ProcessingResult}. You can read the contents
* of that map directly or through the attribute map builders. Carrot2 manual
* lists and describes in detail the output attributes of each component.
* </p>
* <p>
* The code shown below clusters clusters an example collection of
* {@link org.carrot2.core.Document}s using the Lingo algorithm. Lingo can
* optionally use native platform-specific matrix computation libraries. The
* example code reads an attribute to find out whether such libraries were
* successfully loaded and used.
* </p>
* </div>
* [[[end:using-attributes-output-intro]]]
*/
{
/// [[[start:using-attributes-output]]]
/* A controller to manage the processing pipeline. */
final Controller controller = ControllerFactory.createSimple();
/* Prepare attribute map */
final Map<String, Object> attributes = new HashMap<String, Object>();
CommonAttributesDescriptor
.attributeBuilder(attributes)
.documents(SampleDocumentData.DOCUMENTS_DATA_MINING);
LingoClusteringAlgorithmDescriptor
.attributeBuilder(attributes)
.desiredClusterCountBase(15)
.matrixReducer()
.factorizationQuality(FactorizationQuality.HIGH);
/* Perform processing */
final ProcessingResult result = controller.process(attributes,
LingoClusteringAlgorithm.class);
/* Clusters created by Carrot2, read processing time */
final List<Cluster> clusters = result.getClusters();
final Long clusteringTime = CommonAttributesDescriptor.attributeBuilder(
result.getAttributes()).processingTimeAlgorithm();
/// [[[end:using-attributes-output]]]
ConsoleFormatter.displayResults(result);
}
}
}