moa.classifiers.rules.multilabel.instancetransformers.InstanceOutputAttributesSelector Maven / Gradle / Ivy

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Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.

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/*
 *    InstanceOutputAttributesSelector.java
 *    Copyright (C) 2017 University of Porto, Portugal
 *    @author J. Duarte, J. Gama
 *
 *    Licensed under the Apache License, Version 2.0 (the "License");
 *    you may not use this file except in compliance with the License.
 *    You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 *    Unless required by applicable law or agreed to in writing, software
 *    distributed under the License is distributed on an "AS IS" BASIS,
 *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *    See the License for the specific language governing permissions and
 *    limitations under the License.
 *
 *
 */

package moa.classifiers.rules.multilabel.instancetransformers;

import java.util.ArrayList;
import java.util.List;

import moa.AbstractMOAObject;

import com.yahoo.labs.samoa.instances.Attribute;
import com.yahoo.labs.samoa.instances.Instance;
import com.yahoo.labs.samoa.instances.InstanceImpl;
import com.yahoo.labs.samoa.instances.InstancesHeader;
import com.yahoo.labs.samoa.instances.MultiLabelPrediction;
import com.yahoo.labs.samoa.instances.Prediction;
import com.yahoo.labs.samoa.instances.Range;

/**
 * Transforms instances considering only a subset of output attributes
 *
 * @author João Duarte ([email protected])
 */
public class InstanceOutputAttributesSelector extends AbstractMOAObject implements InstanceTransformer {

	private static final long serialVersionUID = 1L;

	public InstancesHeader targetInstances;
	public int [] targetOutputIndices;
	public int numSourceInstancesOutputs;

	public InstanceOutputAttributesSelector(){
		
	}
	public InstanceOutputAttributesSelector(InstancesHeader sourceInstances, int [] targetOutputIndices){
		this.targetOutputIndices=targetOutputIndices;
		this.numSourceInstancesOutputs=sourceInstances.numOutputAttributes();

		int totAttributes=sourceInstances.numInputAttributes()+this.targetOutputIndices.length;
		targetInstances= new InstancesHeader();

		List v = new ArrayList(totAttributes);
		List indexValues = new ArrayList(totAttributes);
		int numInputs=sourceInstances.numInputAttributes();
		for (int i=0; i