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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.
/*
* 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
* -unset-class-temporarily
* Unsets the class index temporarily before the filter is
* applied to the data.
* (default: no)
*
*
*
*
* @author Eibe Frank ([email protected])
* @version $Revision: 14796 $
*/
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:
*
*
*
* // converts one row
* public static Object[] filter(Object[] i);
* // converts a full dataset (first dimension is row index)
* public static Object[][] filter(Object[][] i);
*
*
*
* where the array i 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: 14796 $");
}
/**
* 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);
}
}