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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.

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/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see .
 */

/*
 * InMemory.java
 * Copyright (C) 2018 University of Waikato, Hamilton, New Zealand
 */

package weka.classifiers.evaluation.output.prediction;

import weka.classifiers.Classifier;
import weka.classifiers.evaluation.NominalPrediction;
import weka.classifiers.evaluation.NumericPrediction;
import weka.classifiers.evaluation.Prediction;
import weka.core.Attribute;
import weka.core.Instance;

import java.io.File;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

/**
 * 
 * * Stores the predictions in memory for programmatic retrieval.
* * Stores the instance, a prediction object and a map of attribute names with their associated values if an attribute was defined in a container per prediction.
* * The list of predictions can get retrieved using the getPredictions() method.
* * File output is disabled and buffer doesn't need to be supplied. * *

* * * * * Valid options are:

* * * *

 -p <range>
 * *  The range of attributes to print in addition to the classification.
 * *  (default: none)
* * * *
 -distribution
 * *  Whether to turn on the output of the class distribution.
 * *  Only for nominal class attributes.
 * *  (default: off)
* * * *
 -decimals <num>
 * *  The number of digits after the decimal point.
 * *  (default: 3)
* * * *
 -file <path>
 * *  The file to store the output in, instead of outputting it on stdout.
 * *  Gets ignored if the supplied path is a directory.
 * *  (default: .)
* * * *
 -suppress
 * *  In case the data gets stored in a file, then this flag can be used
 * *  to suppress the regular output.
 * *  (default: not suppressed)
* * * * * @author fracpete (fracpete at waikato dot ac dot nz) * @version $Revision: 14789 $ */ public class InMemory extends AbstractOutput { /** for serialization. */ private static final long serialVersionUID = 3401604538169573720L; /** * Container for storing the predictions alongside the additional attributes. */ public static class PredictionContainer { /** the instance. */ public Instance instance = null; /** the prediction. */ public Prediction prediction = null; /** the associated attribute values (attribute-name - value). */ public Map attributeValues = new HashMap<>(); /** * Returns a string representation of the container. * * @return the string representation */ @Override public String toString() { return instance + " - " + prediction + " - " + attributeValues; } } /** for storing the predictions. */ protected List m_Predictions; /** * Returns a string describing the output generator. * * @return a description suitable for displaying in the GUI */ @Override public String globalInfo() { return "Stores the predictions in memory for programmatic retrieval.\n" + "Stores the instance, a prediction object and a map of attribute names " + "with their associated values if an attribute was defined in a container " + "per prediction.\n" + "The list of predictions can get retrieved using the getPredictions() method.\n" + "File output is disabled and buffer doesn't need to be supplied."; } /** * Returns a short display text, to be used in comboboxes. * * @return a short display text */ @Override public String getDisplay() { return "InMemory"; } /** * Ignored, as it does not generate any output. * * @param value ignored */ @Override public void setOutputFile(File value) { super.setOutputFile(new File(".")); } /** * Performs checks whether everything is correctly setup for the header. * * @return null if everything is in order, otherwise the error message */ protected String checkHeader() { if (m_Buffer == null) m_Buffer = new StringBuffer(); return super.checkHeader(); } /** * Performs the actual printing of the header. */ @Override protected void doPrintHeader() { m_Predictions = new ArrayList<>(); } /** * Returns the additional attribute values as map. * * @param instance the current instance * @return the generated map (attribute-name - value) */ protected Map attributeValuesToMap(Instance instance) { Map result; result = new HashMap<>(); m_Attributes.setUpper(instance.numAttributes() - 1); for (int i = 0; i < instance.numAttributes(); i++) { if (m_Attributes.isInRange(i) && i != instance.classIndex()) { switch (instance.attribute(i).type()) { case Attribute.NOMINAL: case Attribute.STRING: case Attribute.RELATIONAL: case Attribute.DATE: result.put(instance.attribute(i).name(), instance.stringValue(i)); break; case Attribute.NUMERIC: result.put(instance.attribute(i).name(), instance.value(i)); break; default: throw new IllegalStateException( "Unhandled attribute type for attribute '" + instance.attribute(i).name() + ": " + Attribute.typeToString(instance.attribute(i).type())); } } } return result; } /** * Store the prediction made by the classifier as a string. * * @param dist the distribution to use * @param inst the instance to generate text from * @param index the index in the dataset * @throws Exception if something goes wrong */ @Override protected void doPrintClassification(double[] dist, Instance inst, int index) throws Exception { PredictionContainer cont; cont = new PredictionContainer(); cont.instance = inst; if (inst.classAttribute().isNominal()) cont.prediction = new NominalPrediction(inst.classValue(), dist, inst.weight()); else cont.prediction = new NumericPrediction(inst.classValue(), dist[0], inst.weight()); cont.attributeValues.putAll(attributeValuesToMap(inst)); m_Predictions.add(cont); } /** * Store the prediction made by the classifier as a string. * * @param classifier the classifier to use * @param inst the instance to generate text from * @param index the index in the dataset * @throws Exception if something goes wrong */ @Override protected void doPrintClassification(Classifier classifier, Instance inst, int index) throws Exception { double[] d = classifier.distributionForInstance(inst); doPrintClassification(d, inst, index); } /** * Does nothing. */ @Override protected void doPrintFooter() { } /** * Returns the collected predictions. * * @return the predictions */ public List getPredictions() { return m_Predictions; } }




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