gov.sandia.cognition.learning.data.DatasetUtil Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of cognitive-foundry Show documentation
Show all versions of cognitive-foundry Show documentation
A single jar with all the Cognitive Foundry components.
/*
* File: DatasetUtil.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright September 11, 2007, Sandia Corporation. Under the terms of Contract
* DE-AC04-94AL85000, there is a non-exclusive license for use of this work by
* or on behalf of the U.S. Government. Export of this program may require a
* license from the United States Government. See CopyrightHistory.txt for
* complete details.
*
*/
package gov.sandia.cognition.learning.data;
import gov.sandia.cognition.collection.DefaultMultiCollection;
import gov.sandia.cognition.collection.MultiCollection;
import gov.sandia.cognition.math.matrix.DimensionalityMismatchException;
import gov.sandia.cognition.math.matrix.VectorFactory;
import gov.sandia.cognition.math.matrix.Matrix;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.math.matrix.VectorUtil;
import gov.sandia.cognition.math.matrix.Vectorizable;
import gov.sandia.cognition.math.matrix.mtj.SparseMatrixFactoryMTJ;
import gov.sandia.cognition.statistics.DataDistribution;
import gov.sandia.cognition.statistics.distribution.DefaultDataDistribution;
import gov.sandia.cognition.util.DefaultPair;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.LinkedHashMap;
import java.util.LinkedHashSet;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Set;
/**
* Static class containing utility methods for handling Collections of data
* in the learning package.
*
* @author Kevin R. Dixon
* @since 2.0
*
*/
public class DatasetUtil
{
/**
* Appends a bias (constant 1.0) to the end of each Vector in the dataset,
* the original dataset is unmodified. The resulting Vectors will have one
* greater dimension and look like: [ x1 x2 ] -> [ x1 x2 1.0 ]
* @param dataset
* Dataset to append a bias term to, Vectors can be of different
* dimensionality
* @return
* Dataset with 1.0 appended to each Vector in the dataset
*/
public static ArrayList appendBias(
Collection dataset)
{
return DatasetUtil.appendBias(dataset, 1.0);
}
/**
* Appends "biasValue" to the end of each Vector in the dataset,
* the original dataset is unmodified. The resulting Vectors will have one
* greater dimension and look like: [ x1 x2 ] -> [ x1 x2 1.0 ]
* @param dataset
* Dataset to append a bias term to, Vectors can be of different
* dimensionality
* @param biasValue
* Bias value to append to the samples
* @return
* Dataset with "biasValue" appended to each Vector in the dataset
*/
public static ArrayList appendBias(
Collection dataset,
double biasValue)
{
ArrayList biasDataset = new ArrayList(dataset.size());
Vector bias = VectorFactory.getDefault().copyValues(biasValue);
for (Vector data : dataset)
{
biasDataset.add(data.stack(bias));
}
return biasDataset;
}
/**
* Takes a set of equal-dimension Vector-Vector InputOutputPairs and
* turns them into a collection of Double-Double InputOutputPairs. This
* is useful when one can treat each element of the Vector-Vector pairs
* as independent of the other elements
* @param dataset
* Collection of Vector-Vector InputOutputPairs. All Vectors (both inputs
* and outputs) must have equal dimension!!
* @return
* ArrayList of ArrayList of Double-Double InputOutputPairs. The outer
* ArrayList contains a dataset for each element in the vector (and thus
* there are as many ArrayList> as there
* are elements in the Vectors). Each ArrayList>
* has as many elements as the original dataset
*/
public static ArrayList>> decoupleVectorPairDataset(
Collection> dataset)
{
int numSamples = dataset.size();
int M = dataset.iterator().next().getInput().getDimensionality();
ArrayList>> retval =
new ArrayList>>(M);
for (int i = 0; i < M; i++)
{
retval.add(new ArrayList>(numSamples));
}
for (InputOutputPair pair : dataset)
{
if ((pair.getInput().getDimensionality() != M) ||
(pair.getOutput().getDimensionality() != M))
{
throw new IllegalArgumentException(
"All input-output Vectors must have same dimension!");
}
for (int i = 0; i < M; i++)
{
double x = pair.getInput().getElement(i);
double y = pair.getOutput().getElement(i);
InputOutputPair rowPair;
if (pair instanceof WeightedInputOutputPair)
{
double weight = ((WeightedInputOutputPair) pair).getWeight();
rowPair = new DefaultWeightedInputOutputPair(
x, y, weight);
}
else
{
rowPair = new DefaultInputOutputPair(x, y);
}
retval.get(i).add(rowPair);
}
}
return retval;
}
/**
* Takes a dataset of M-dimensional Vectors and turns it into M
* datasets of Doubles
* @param dataset
* M-dimensional Vectors, throws IllegalArgumentException if all Vectors
* aren't the same dimensionality
* @return
* M datasets of dataset.size() Doubles
*/
static public ArrayList> decoupleVectorDataset(
Collection dataset)
{
int M = dataset.iterator().next().getDimensionality();
int num = dataset.size();
ArrayList> decoupledDatasets =
new ArrayList>(M);
for (int i = 0; i < M; i++)
{
decoupledDatasets.add(new ArrayList(num));
}
for (Vector data : dataset)
{
if (M != data.getDimensionality())
{
throw new IllegalArgumentException(
"All vectors in the dataset must be the same size");
}
for (int i = 0; i < M; i++)
{
decoupledDatasets.get(i).add(data.getElement(i));
}
}
return decoupledDatasets;
}
/**
* Splits a dataset of input-output pair into two datasets, one for the
* inputs that have a "true" output and another for the inputs that have
* a "false" output
*
* @param The type of the data.
* @param data
* Collection of InputOutputPairs to split according to the output flag
* @return
* DefaultPair of LinkedLists where the first dataset corresponds to the inputs
* where the output was "true" and the second dataset corresponds to the
* inputs where the output was "false"
*/
public static DefaultPair, LinkedList> splitDatasets(
Collection> data)
{
LinkedList dtrue = new LinkedList();
LinkedList dfalse = new LinkedList();
for (InputOutputPair pair : data)
{
if (pair.getOutput())
{
dtrue.add(pair.getInput());
}
else
{
dfalse.add(pair.getInput());
}
}
return DefaultPair.create( dtrue, dfalse );
}
/**
* Splits a dataset according to its output value (usually a category) so
* that all the inputs for that category are given in a list. It maps the
* category value to its list.
*
* @param
* The the of the input values.
* @param
* The type of the output values.
* @param data
* The input-output pairs to split.
* @return
* A mapping of category to a list of all of the inputs for that
* category.
*/
public static Map> splitOnOutput(
final Iterable> data)
{
final LinkedHashMap> result =
new LinkedHashMap>();
for (InputOutputPair example
: data)
{
final CategoryType category = example.getOutput();
List examplesForCategory = result.get(category);
if (examplesForCategory == null)
{
examplesForCategory = new ArrayList();
result.put(category, examplesForCategory);
}
examplesForCategory.add(example.getInput());
}
return result;
}
/**
* Computes the outer-product Matrix of the given set of data:
* XXt = [ x1 x2 ... xn ] * [ x1 x2 ... xn ]^T.
* The outer-product data Matrix is useful in things like computing the
* Principal Components Analysis of the dataset. For exapmle, finding the
* eigenvectors of the outer-product data Matrix is equivalent to finding
* the left singular Vectors ("U" from the SingularValueDecomposition) of
* the dataset.
* Note that if the input dataset has a size of "N" and each Vector in the
* dataset has "M" dimensions, then the return Matrix (XXt) is an (MxM)
* Matrix. This method computes the return Matrix without explicitly
* forming the data matrix, potentially saving quite a lot of memory.
* @param data
* Input dataset where each of "N" Vectors has dimension of "M"
* @return
* Outer product Matrix of the input dataset, having dimensions
* (MxM).
*/
public static Matrix computeOuterProductDataMatrix(
ArrayList data)
{
// This is X*transpose(X), if X is a (MxN) resulting in an (MxM) matrix
// [ x1 x2 ... xn ] * [ x1 x2 ... xn ]^T
// We're avoiding storing the entire matrices
int M = data.iterator().next().getDimensionality();
Matrix XXt = SparseMatrixFactoryMTJ.INSTANCE.createMatrix(M, M);
for (int j = 0; j < M; j++)
{
for (int i = 0; i < M; i++)
{
double sum = 0.0;
for (int k = 0; k < data.size(); k++)
{
Vector dk = data.get(k);
sum += dk.getElement(i) * dk.getElement(j);
}
if( sum != 0.0 )
{
XXt.setElement(i, j, sum);
}
}
}
return XXt;
}
/**
* Computes the mean of the output data.
*
* @param data The data to compute the mean of the output.
* @return The mean of the output values of the given data.
*/
public static double computeOutputMean(
final Collection> data)
{
if (data == null)
{
// No data, so zero mean.
return 0.0;
}
// Compute the sum in order to find the mean.
double sum = 0.0;
int count = 0;
for (InputOutputPair example : data)
{
sum += example.getOutput().doubleValue();
count++;
}
if (count <= 0)
{
// No elements, so zero mean.
return 0.0;
}
else
{
return sum / count;
}
}
/**
* Computes the mean of the output data.
*
* @param data The data to compute the mean of the output.
* @return The mean of the output values of the given data.
*/
public static double computeWeightedOutputMean(
final Collection> data)
{
if (data == null || data.size() <= 0.0)
{
// No data, so zero mean.
return 0.0;
}
// Compute the sum in order to find the mean.
double sum = 0.0;
double weightSum = 0.0;
for (InputOutputPair example : data)
{
final double weight = DatasetUtil.getWeight(example);
sum += weight * example.getOutput().doubleValue();
weightSum += weight;
}
if (weightSum == 0.0)
{
// No elements, so zero mean.
return 0.0;
}
else
{
return sum / weightSum;
}
}
/**
* Computes the variance of the output of a given set of input-output pairs.
*
* @param data The data.
* @return The variance of the output of the data.
*/
public static double computeOutputVariance(
final Collection> data)
{
if (data == null)
{
// No data, so zero variance.
return 0.0;
}
final int count = data.size();
if (count <= 0)
{
// No elements, so zero variance.
return 0.0;
}
// Compute the mean.
final double mean = computeOutputMean(data);
// Now compute the variance.
double sum = 0.0;
for (InputOutputPair example : data)
{
final double difference = example.getOutput().doubleValue() - mean;
sum += difference * difference;
}
// Compute the variance and return it.
final double variance = sum / count;
return variance;
}
/**
* Creates a set containing the unique output values from the given data.
*
* @param
* The type of the output values.
* @param data
* The data to collect the unique output values from.
* @return
* The set of unique output values. Implemented as a linked hash set.
*/
public static Set findUniqueOutputs(
final Iterable> data)
{
// Create the result set.
final Set outputs = new LinkedHashSet();
if (data != null)
{
// Go through and add each output.
for (InputOutputPair example : data)
{
outputs.add(example.getOutput());
}
}
return outputs;
}
/**
* Creates a data histogram over the output values from the given data.
*
* @param
* The type of the output values.
* @param data
* The data to collect the output values from.
* @return
* The histogram of output values.
*/
public static DataDistribution countOutputValues(
final Iterable> data)
{
// Create the result set.
final DataDistribution outputs =
new DefaultDataDistribution();
if (data != null)
{
// Go through and add each output.
for (InputOutputPair example : data)
{
outputs.increment(example.getOutput());
}
}
return outputs;
}
/**
* Creates a list containing all of the input values from the given data.
*
* @param
* The type of the input values.
* @param data
* The data to collect the input values from.
* @return
* A list containing the output values.
*/
public static List inputsList(
final Iterable> data)
{
// TODO: Make this a proxy object rather than copying the list.
// Create the result list.
final ArrayList inputs = new ArrayList();
if (data != null)
{
// Go through and add each input.
for (InputOutputPair example : data)
{
inputs.add(example.getInput());
}
}
return inputs;
}
/**
* Creates a list containing all of the output values from the given data.
*
* @param
* The type of the output values.
* @param data
* The data to collect the output values from.
* @return
* A list containing the output values.
*/
public static List outputsList(
final Iterable> data)
{
// TODO: Make this a proxy object rather than copying the list.
// Create the result list.
final ArrayList outputs = new ArrayList();
if (data != null)
{
// Go through and add each input.
for (InputOutputPair example : data)
{
outputs.add(example.getOutput());
}
}
return outputs;
}
/**
* Takes a collection and returns a multi-collection version of that
* collection. If the given collection is a multi-collection, it casts it
* to that value and returns it. If it is not a multi-collection, it creates
* a new, singleton multi-collection with the given collection and returns
* it.
*
* @param
* The entry type of the collection.
* @param collection
* A collection.
* @return
* A multi-collection version of the given collection.
*/
public static MultiCollection asMultiCollection(
final Collection collection)
{
if (collection instanceof MultiCollection)
{
return (MultiCollection) collection;
}
else
{
return new DefaultMultiCollection(
Collections.singletonList(collection));
}
}
/**
* Takes a collection of {@code Vectorizable} objects and returns a
* collection of {@code Vector} objects of the same size.
*
* @param collection
* The collection of {@code Vectorizable} objects to convert.
* @return
* The corresponding collection of {@code Vector} objects.
*/
public static Collection asVectorCollection(
final Collection collection)
{
// TODO: Have this class make a wrapper collection that doesn't actually
// recreate the whole collection of vectors, but instead calls convertToVector
// when needed.
// Convert the values to vector form.
final Collection result = new ArrayList(
collection.size());
for (Vectorizable value : collection)
{
result.add(value.convertToVector());
}
return result;
}
/**
* Gets the dimensionality of the input vectors in given set of input-output
* pairs. It finds the first non-null vector and returns its dimensionality.
* If there are non-null vectors, then -1 is returned.
*
* @param data
* The data to find the input dimensionality of.
* @return
* The dimensionality of the first non-null in put in the given data.
* -1 if there are no non-null inputs.
*/
public static int getInputDimensionality(
final Iterable> data)
{
if (data != null)
{
for (InputOutputPair example : data)
{
if (example == null)
{
// Keep looking since this was null.
continue;
}
final Vectorizable input = example.getInput();
if (input == null)
{
// Keep looking since this was null.
continue;
}
final Vector vector = input.convertToVector();
if (vector != null)
{
// Found a non-null vector. Get its dimensionality.
return vector.getDimensionality();
}
// else - The vector was null. Keep looking.
}
}
// No valid vectors.
return -1;
}
/**
* Asserts that all of the dimensionalities of the input vectors in the
* given set of input-output pairs are the same.
*
* @param data
* A collection of input-output pairs.
* @throws DimensionalityMismatchException
* If the dimensionalities are not all equal.
*/
public static void assertInputDimensionalitiesAllEqual(
final Iterable> data)
{
assertInputDimensionalitiesAllEqual(data, getInputDimensionality(data));
}
/**
* Asserts that all of the dimensionalities of the input vectors in the
* given set of input-output pairs equal the given dimensionality.
*
* @param data
* A collection of input-output pairs.
* @param dimensionality
* The dimensionality that all the inputs must have.
* @throws DimensionalityMismatchException
* If the dimensionalities are not all equal.
*/
public static void assertInputDimensionalitiesAllEqual(
final Iterable> data,
final int dimensionality)
{
if (data != null)
{
for (InputOutputPair example : data)
{
if (example == null)
{
// Ignore null examples.
continue;
}
final Vectorizable input = example.getInput();
if (input == null)
{
// Ignre null inputs.
continue;
}
final Vector vector = input.convertToVector();
if (vector == null)
{
// Ignore null vectors.
continue;
}
// Make sure this dimensionality is correct.
vector.assertDimensionalityEquals(dimensionality);
}
}
}
/**
* Gets the dimensionality of the vectors in given set of data. It finds
* the first non-null vector and returns its dimensionality. If there are
* non-null vectors, then -1 is returned.
*
* @param data
* The data to find the dimensionality of.
* @return
* The dimensionality of the first non-null vector in the given data.
* -1 if there are no non-null vector.
*/
public static int getDimensionality(
final Iterable data)
{
if (data != null)
{
for (Vectorizable example : data)
{
if (example == null)
{
// Keep looking since this was null.
continue;
}
final Vector vector = example.convertToVector();
if (vector != null)
{
// Found a non-null vector. Get its dimensionality.
return vector.getDimensionality();
}
// else - The vector was null. Keep looking.
}
}
// No valid vectors.
return -1;
}
/**
* Asserts that all of the dimensionalities of the vectors in the
* given set of data are the same.
*
* @param data
* A collection of data.
* @throws DimensionalityMismatchException
* If the dimensionalities are not all equal.
*/
public static void assertDimensionalitiesAllEqual(
final Iterable data)
{
VectorUtil.assertDimensionalitiesAllEqual(data, getDimensionality(data));
}
/**
* Gets the weight of a given input-output pair. If it is a weighted
* input-output pair (implements the {@code WeightedInputOutputPair}
* interface, then it casts it to retrieve its weight. Otherwise, it
* returns 1.0.
*
* @param pair
* The pair to get the weight of.
* @return
* The weight of the given pair, if it exists, otherwise 1.0.
*/
public static double getWeight(
final InputOutputPair pair)
{
if (pair instanceof WeightedInputOutputPair)
{
return ((WeightedInputOutputPair) pair).getWeight();
}
else
{
return 1.0;
}
}
/**
* Gets the weight of a given target-estimate pair. If it is a weighted
* target-estimate pair (implements the {@code WeightedTargetEstimatePair}
* interface, then it casts it to retrieve its weight. Otherwise, it
* returns 1.0.
*
* @param pair
* The pair to get the weight of.
* @return
* The weight of the given pair, if it exists, otherwise 1.0.
*/
public static double getWeight(
final TargetEstimatePair pair)
{
if (pair instanceof WeightedTargetEstimatePair)
{
return ((WeightedTargetEstimatePair) pair).getWeight();
}
else
{
return 1.0;
}
}
/**
* Gets the sum of the weights of the weights of the elements of the
* dataset. It loops over the items and calls getWeight on each one.
*
* @param data
* The dataset to compute the sum of the weights
* @return
* The sum of the weights of the elements in the dataset.
*/
public static double sumWeights(
final Collection> data)
{
double result = 0.0;
for (InputOutputPair example : data)
{
result += getWeight(example);
}
return result;
}
}