Java tutorial
/* * 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 <http://www.gnu.org/licenses/>. */ /* * ReplaceMissingValues.java * Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand * */ package weka.filters.unsupervised.attribute; import weka.core.*; import weka.core.Capabilities.Capability; import weka.filters.Sourcable; import weka.filters.UnsupervisedFilter; /** * <!-- globalinfo-start --> Replaces all missing values for nominal and numeric * attributes in a dataset with the modes and means from the training data. The * class attribute is skipped by default. * <p/> * <!-- globalinfo-end --> * * <!-- options-start --> Valid options are: * <p/> * * <pre> * -unset-class-temporarily * Unsets the class index temporarily before the filter is * applied to the data. * (default: no) * </pre> * * <!-- options-end --> * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @version $Revision$ */ public class ReplaceMissingValues extends PotentialClassIgnorer implements UnsupervisedFilter, Sourcable, WeightedInstancesHandler, WeightedAttributesHandler { /** for serialization */ static final long serialVersionUID = 8349568310991609867L; /** The modes and means */ private double[] m_ModesAndMeans = null; /** * Returns a string describing this filter * * @return a description of the filter suitable for displaying in the * explorer/experimenter gui */ public String globalInfo() { return "Replaces all missing values for nominal and numeric attributes in a " + "dataset with the modes and means from the training data. The class attribute is skipped by default."; } /** * Returns the Capabilities of this filter. * * @return the capabilities of this object * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); result.disableAll(); // attributes result.enableAllAttributes(); result.enable(Capability.MISSING_VALUES); // class result.enableAllClasses(); result.enable(Capability.MISSING_CLASS_VALUES); result.enable(Capability.NO_CLASS); return result; } /** * Sets the format of the input instances. * * @param instanceInfo an Instances object containing the input instance * structure (any instances contained in the object are ignored - * only the structure is required). * @return true if the outputFormat may be collected immediately * @throws Exception if the input format can't be set successfully */ public boolean setInputFormat(Instances instanceInfo) throws Exception { super.setInputFormat(instanceInfo); setOutputFormat(instanceInfo); m_ModesAndMeans = null; return true; } /** * Input an instance for filtering. Filter requires all training instances be * read before producing output. * * @param instance the input instance * @return true if the filtered instance may now be collected with output(). * @throws IllegalStateException if no input format has been set. */ public boolean input(Instance instance) { if (getInputFormat() == null) { throw new IllegalStateException("No input instance format defined"); } if (m_NewBatch) { resetQueue(); m_NewBatch = false; } if (m_ModesAndMeans == null) { bufferInput(instance); return false; } else { convertInstance(instance); return true; } } /** * Signify that this batch of input to the filter is finished. If the filter * requires all instances prior to filtering, output() may now be called to * retrieve the filtered instances. * * @return true if there are instances pending output * @throws IllegalStateException if no input structure has been defined */ public boolean batchFinished() { if (getInputFormat() == null) { throw new IllegalStateException("No input instance format defined"); } if (m_ModesAndMeans == null) { // Compute modes and means double sumOfWeights = getInputFormat().sumOfWeights(); double[][] counts = new double[getInputFormat().numAttributes()][]; for (int i = 0; i < getInputFormat().numAttributes(); i++) { if (getInputFormat().attribute(i).isNominal()) { counts[i] = new double[getInputFormat().attribute(i).numValues()]; if (counts[i].length > 0) counts[i][0] = sumOfWeights; } } double[] sums = new double[getInputFormat().numAttributes()]; for (int i = 0; i < sums.length; i++) { sums[i] = sumOfWeights; } double[] results = new double[getInputFormat().numAttributes()]; for (int j = 0; j < getInputFormat().numInstances(); j++) { Instance inst = getInputFormat().instance(j); for (int i = 0; i < inst.numValues(); i++) { if (!inst.isMissingSparse(i)) { double value = inst.valueSparse(i); if (inst.attributeSparse(i).isNominal()) { if (counts[inst.index(i)].length > 0) { counts[inst.index(i)][(int) value] += inst.weight(); counts[inst.index(i)][0] -= inst.weight(); } } else if (inst.attributeSparse(i).isNumeric()) { results[inst.index(i)] += inst.weight() * inst.valueSparse(i); } } else { if (inst.attributeSparse(i).isNominal()) { if (counts[inst.index(i)].length > 0) { counts[inst.index(i)][0] -= inst.weight(); } } else if (inst.attributeSparse(i).isNumeric()) { sums[inst.index(i)] -= inst.weight(); } } } } m_ModesAndMeans = new double[getInputFormat().numAttributes()]; for (int i = 0; i < getInputFormat().numAttributes(); i++) { if (getInputFormat().attribute(i).isNominal()) { if (counts[i].length == 0) m_ModesAndMeans[i] = Utils.missingValue(); else m_ModesAndMeans[i] = (double) Utils.maxIndex(counts[i]); } else if (getInputFormat().attribute(i).isNumeric()) { if (Utils.gr(sums[i], 0)) { m_ModesAndMeans[i] = results[i] / sums[i]; } } } // Convert pending input instances for (int i = 0; i < getInputFormat().numInstances(); i++) { convertInstance(getInputFormat().instance(i)); } } // Free memory flushInput(); m_NewBatch = true; return (numPendingOutput() != 0); } /** * Convert a single instance over. The converted instance is added to the end * of the output queue. * * @param instance the instance to convert */ private void convertInstance(Instance instance) { Instance inst = instance; boolean hasMissing = instance.hasMissingValue(); if (hasMissing) { if (instance instanceof SparseInstance) { double[] vals = new double[instance.numValues()]; int[] indices = new int[instance.numValues()]; int num = 0; for (int j = 0; j < instance.numValues(); j++) { if (instance.isMissingSparse(j) && (getInputFormat().classIndex() != instance.index(j)) && (instance.attributeSparse(j).isNominal() || instance.attributeSparse(j).isNumeric())) { if (m_ModesAndMeans[instance.index(j)] != 0.0) { vals[num] = m_ModesAndMeans[instance.index(j)]; indices[num] = instance.index(j); num++; } } else { vals[num] = instance.valueSparse(j); indices[num] = instance.index(j); num++; } } if (num == instance.numValues()) { inst = new SparseInstance(instance.weight(), vals, indices, instance.numAttributes()); } else { double[] tempVals = new double[num]; int[] tempInd = new int[num]; System.arraycopy(vals, 0, tempVals, 0, num); System.arraycopy(indices, 0, tempInd, 0, num); inst = new SparseInstance(instance.weight(), tempVals, tempInd, instance.numAttributes()); } } else { double[] vals = new double[getInputFormat().numAttributes()]; for (int j = 0; j < instance.numAttributes(); j++) { if (instance.isMissing(j) && (getInputFormat().classIndex() != j) && (getInputFormat().attribute(j).isNominal() || getInputFormat().attribute(j).isNumeric())) { vals[j] = m_ModesAndMeans[j]; } else { vals[j] = instance.value(j); } } inst = new DenseInstance(instance.weight(), vals); } } inst.setDataset(instance.dataset()); push(inst, !hasMissing); // No need to shallow copy if we've deep copied already } /** * Returns a string that describes the filter as source. The filter will be * contained in a class with the given name (there may be auxiliary classes), * and will contain two methods with these signatures: * * <pre> * <code> * // converts one row * public static Object[] filter(Object[] i); * // converts a full dataset (first dimension is row index) * public static Object[][] filter(Object[][] i); * </code> * </pre> * * where the array <code>i</code> contains elements that are either Double, * String, with missing values represented as null. The generated code is * public domain and comes with no warranty. * * @param className the name that should be given to the source class. * @param data the dataset used for initializing the filter * @return the object source described by a string * @throws Exception if the source can't be computed */ public String toSource(String className, Instances data) throws Exception { StringBuffer result; boolean[] numeric; boolean[] nominal; String[] modes; double[] means; int i; result = new StringBuffer(); // determine what attributes were processed numeric = new boolean[data.numAttributes()]; nominal = new boolean[data.numAttributes()]; modes = new String[data.numAttributes()]; means = new double[data.numAttributes()]; for (i = 0; i < data.numAttributes(); i++) { numeric[i] = (data.attribute(i).isNumeric() && (i != data.classIndex())); nominal[i] = (data.attribute(i).isNominal() && (i != data.classIndex())); if (numeric[i]) means[i] = m_ModesAndMeans[i]; else means[i] = Double.NaN; if (nominal[i]) modes[i] = data.attribute(i).value((int) m_ModesAndMeans[i]); else modes[i] = null; } result.append("class " + className + " {\n"); result.append("\n"); result.append(" /** lists which numeric attributes will be processed */\n"); result.append(" protected final static boolean[] NUMERIC = new boolean[]{" + Utils.arrayToString(numeric) + "};\n"); result.append("\n"); result.append(" /** lists which nominal attributes will be processed */\n"); result.append(" protected final static boolean[] NOMINAL = new boolean[]{" + Utils.arrayToString(nominal) + "};\n"); result.append("\n"); result.append(" /** the means */\n"); result.append(" protected final static double[] MEANS = new double[]{" + Utils.arrayToString(means).replaceAll("NaN", "Double.NaN") + "};\n"); result.append("\n"); result.append(" /** the modes */\n"); result.append(" protected final static String[] MODES = new String[]{"); for (i = 0; i < modes.length; i++) { if (i > 0) result.append(","); if (nominal[i]) result.append("\"" + Utils.quote(modes[i]) + "\""); else result.append(modes[i]); } result.append("};\n"); result.append("\n"); result.append(" /**\n"); result.append(" * filters a single row\n"); result.append(" * \n"); result.append(" * @param i the row to process\n"); result.append(" * @return the processed row\n"); result.append(" */\n"); result.append(" public static Object[] filter(Object[] i) {\n"); result.append(" Object[] result;\n"); result.append("\n"); result.append(" result = new Object[i.length];\n"); result.append(" for (int n = 0; n < i.length; n++) {\n"); result.append(" if (i[n] == null) {\n"); result.append(" if (NUMERIC[n])\n"); result.append(" result[n] = MEANS[n];\n"); result.append(" else if (NOMINAL[n])\n"); result.append(" result[n] = MODES[n];\n"); result.append(" else\n"); result.append(" result[n] = i[n];\n"); result.append(" }\n"); result.append(" else {\n"); result.append(" result[n] = i[n];\n"); result.append(" }\n"); result.append(" }\n"); result.append("\n"); result.append(" return result;\n"); result.append(" }\n"); result.append("\n"); result.append(" /**\n"); result.append(" * filters multiple rows\n"); result.append(" * \n"); result.append(" * @param i the rows to process\n"); result.append(" * @return the processed rows\n"); result.append(" */\n"); result.append(" public static Object[][] filter(Object[][] i) {\n"); result.append(" Object[][] result;\n"); result.append("\n"); result.append(" result = new Object[i.length][];\n"); result.append(" for (int n = 0; n < i.length; n++) {\n"); result.append(" result[n] = filter(i[n]);\n"); result.append(" }\n"); result.append("\n"); result.append(" return result;\n"); result.append(" }\n"); result.append("}\n"); return result.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision$"); } /** * Main method for testing this class. * * @param argv should contain arguments to the filter: use -h for help */ public static void main(String[] argv) { runFilter(new ReplaceMissingValues(), argv); } }