Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
*
* * Copyright 2015 Skymind,Inc.
* *
* * 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 org.nd4j.linalg.dataset;
import com.google.common.base.Function;
import com.google.common.collect.Lists;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.executioner.GridExecutioner;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.BooleanIndexing;
import org.nd4j.linalg.indexing.INDArrayIndex;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.indexing.conditions.Condition;
import org.nd4j.linalg.util.ArrayUtil;
import org.nd4j.linalg.util.FeatureUtil;
import org.nd4j.linalg.util.MathUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.*;
import java.util.*;
/**
* A data transform (example/outcome pairs)
* The outcomes are specifically for neural network encoding such that
* any labels that are considered true are 1s. The rest are zeros.
*
* @author Adam Gibson
*/
public class DataSet implements org.nd4j.linalg.dataset.api.DataSet {
private static final long serialVersionUID = 1935520764586513365L;
private static final Logger log = LoggerFactory.getLogger(DataSet.class);
private static final byte BITMASK_FEATURES_PRESENT = 1;
private static final byte BITMASK_LABELS_PRESENT = 1 << 1;
private static final byte BITMASK_LABELS_SAME_AS_FEATURES = 1 << 2;
private static final byte BITMASK_FEATURE_MASK_PRESENT = 1 << 3;
private static final byte BITMASK_LABELS_MASK_PRESENT = 1 << 4;
private List columnNames = new ArrayList<>();
private List labelNames = new ArrayList<>();
private INDArray features, labels;
private INDArray featuresMask;
private INDArray labelsMask;
private transient boolean preProcessed = false;
public DataSet() {
this(null,null);
}
/**
* Creates a dataset with the specified input matrix and labels
*
* @param first the feature matrix
* @param second the labels (these should be binarized label matrices such that the specified label
* has a value of 1 in the desired column with the label)
*/
public DataSet(INDArray first, INDArray second) {
this(first,second,null,null);
}
/**Create a dataset with the specified input INDArray and labels (output) INDArray, plus (optionally) mask arrays
* for the features and labels
* @param features Features (input)
* @param labels Labels (output)
* @param featuresMask Mask array for features, may be null
* @param labelsMask Mask array for labels, may be null
*/
public DataSet(INDArray features, INDArray labels, INDArray featuresMask, INDArray labelsMask) {
this.features = features;
this.labels = labels;
this.featuresMask = featuresMask;
this.labelsMask = labelsMask;
// we want this dataset to be fully committed to device
if (Nd4j.getExecutioner() instanceof GridExecutioner)
((GridExecutioner) Nd4j.getExecutioner()).flushQueueBlocking();
}
public boolean isPreProcessed() {
return preProcessed;
}
public void markAsPreProcessed() {
this.preProcessed = true;
}
/**
* Returns a single dataset (all fields are null)
*
* @return an empty dataset (all fields are null)
*/
public static DataSet empty() {
return new DataSet(null,null);
}
/**
* Merge the list of datasets in to one list.
* All the rows are merged in to one dataset
*
* @param data the data to merge
* @param clone whether to clone the data
* or use a reference
* @return a single dataset
*/
public static DataSet merge(List data,boolean clone) {
if (data.isEmpty())
throw new IllegalArgumentException("Unable to merge empty dataset");
DataSet first = data.get(0);
int rankFeatures = first.getFeatures().rank();
int rankLabels = first.getLabels().rank();
INDArray[] featuresToMerge = new INDArray[data.size()];
INDArray[] labelsToMerge = new INDArray[data.size()];
int count = 0;
boolean hasFeaturesMaskArray = false;
boolean hasLabelsMaskArray = false;
for(DataSet ds : data) {
featuresToMerge[count] = ds.getFeatureMatrix();
labelsToMerge[count++] = ds.getLabels();
if(rankFeatures == 3 || rankLabels == 3) {
hasFeaturesMaskArray = hasFeaturesMaskArray | (ds.getFeaturesMaskArray() != null);
hasLabelsMaskArray = hasLabelsMaskArray | (ds.getLabelsMaskArray() != null);
}
}
INDArray featuresOut;
INDArray labelsOut;
INDArray featuresMaskOut;
INDArray labelsMaskOut;
switch (rankFeatures){
case 2:
featuresOut = merge2d(featuresToMerge);
featuresMaskOut = null;
break;
case 3:
//Time series data: may also have mask arrays...
INDArray[] featuresMasks = null;
if(hasFeaturesMaskArray) {
featuresMasks = new INDArray[featuresToMerge.length];
count = 0;
for(DataSet ds : data){
featuresMasks[count++] = ds.getFeaturesMaskArray();
}
}
INDArray[] temp = mergeTimeSeries(featuresToMerge, featuresMasks);
featuresOut = temp[0];
featuresMaskOut = temp[1];
break;
case 4:
featuresOut = merge4dCnnData(featuresToMerge);
featuresMaskOut = null;
break;
default:
throw new IllegalStateException("Cannot merge examples: features rank must be in range 2 to 4 inclusive. First example features shape: " + Arrays.toString(data.get(0).getFeatureMatrix().shape()));
}
switch (rankLabels){
case 2:
labelsOut = merge2d(labelsToMerge);
labelsMaskOut = null;
break;
case 3:
//Time series data: may also have mask arrays...
INDArray[] labelsMasks = null;
if(hasLabelsMaskArray){
labelsMasks = new INDArray[labelsToMerge.length];
count = 0;
for(DataSet ds : data){
labelsMasks[count++] = ds.getLabelsMaskArray();
}
}
INDArray[] temp = mergeTimeSeries(labelsToMerge, labelsMasks);
labelsOut = temp[0];
labelsMaskOut = temp[1];
break;
case 4:
labelsOut = merge4dCnnData(featuresToMerge);
labelsMaskOut = null;
break;
default:
throw new IllegalStateException("Cannot merge examples: labels rank must be in range 2 to 4 inclusive. First example labels shape: " + Arrays.toString(data.get(0).getLabels().shape()));
}
return new DataSet(featuresOut,labelsOut,featuresMaskOut,labelsMaskOut);
}
private static INDArray merge2d(INDArray[] data){
if(data.length == 0) return data[0];
int totalRows = 0;
for(INDArray arr : data) totalRows += arr.rows();
INDArray out = Nd4j.create(totalRows, data[0].columns());
totalRows = 0;
for (INDArray i : data) {
if (i.size(0) == 1) out.putRow(totalRows++, i);
else {
out.put(new INDArrayIndex[]{NDArrayIndex.interval(totalRows, totalRows + i.size(0)), NDArrayIndex.all()}, i);
totalRows += i.size(0);
}
}
return out;
}
private static INDArray merge4dCnnData(INDArray[] data){
if(data.length == 1) return data[0];
int[] outSize = Arrays.copyOf(data[0].shape(),4); //[examples,depth,width,height]
for( int i=1; i data) {
if (data.isEmpty())
throw new IllegalArgumentException("Unable to merge empty dataset");
return merge(data,false);
}
private static int totalExamples(Collection coll) {
int count = 0;
for (DataSet d : coll)
count += d.numExamples();
return count;
}
@Override
public org.nd4j.linalg.dataset.api.DataSet getRange(int from, int to) {
if (hasMaskArrays()) {
INDArray featureMaskHere = featuresMask != null ? featuresMask.get(NDArrayIndex.interval(from, to)) : null;
INDArray labelMaskHere = labelsMask != null ? labelsMask.get(NDArrayIndex.interval(from, to)) : null;
return new DataSet(features.get(NDArrayIndex.interval(from, to)), labels.get(NDArrayIndex.interval(from, to)), featureMaskHere, labelMaskHere);
}
return new DataSet(features.get(NDArrayIndex.interval(from,to)),labels.get(NDArrayIndex.interval(from,to)));
}
@Override
public void load(InputStream from) {
try {
BufferedInputStream bis = new BufferedInputStream(from);
DataInputStream dis = new DataInputStream(bis);
byte included = dis.readByte();
boolean hasFeatures = (included & BITMASK_FEATURES_PRESENT) != 0;
boolean hasLabels = (included & BITMASK_LABELS_PRESENT) != 0;
boolean hasLabelsSameAsFeatures = (included & BITMASK_LABELS_SAME_AS_FEATURES) != 0;
boolean hasFeaturesMask = (included & BITMASK_FEATURE_MASK_PRESENT) != 0;
boolean hasLabelsMask = (included & BITMASK_LABELS_MASK_PRESENT) != 0;
features = (hasFeatures ? Nd4j.read(dis) : null);
if(hasLabels){
labels = Nd4j.read(dis);
} else if(hasLabelsSameAsFeatures){
labels = features;
} else {
labels = null;
}
featuresMask = (hasFeaturesMask ? Nd4j.read(dis) : null);
labelsMask = (hasLabelsMask ? Nd4j.read(dis) : null);
dis.close();
} catch (Exception e) {
e.printStackTrace();
}
}
@Override
public void load(File from) {
try{
load(new FileInputStream(from));
}catch(IOException e){
throw new RuntimeException(e);
}
}
@Override
public void save(OutputStream to) {
byte included = 0;
if(features != null) included |= BITMASK_FEATURES_PRESENT;
if(labels != null){
if(labels == features){
//Same object. Don't serialize the same data twice!
included |= BITMASK_LABELS_SAME_AS_FEATURES;
} else {
included |= BITMASK_LABELS_PRESENT;
}
}
if(featuresMask != null) included |= BITMASK_FEATURE_MASK_PRESENT;
if(labelsMask != null) included |= BITMASK_LABELS_MASK_PRESENT;
try {
BufferedOutputStream bos = new BufferedOutputStream(to);
DataOutputStream dos = new DataOutputStream(bos);
dos.writeByte(included);
if(features != null) Nd4j.write(features, dos);
if(labels != null && labels != features) Nd4j.write(labels, dos);
if(featuresMask != null) Nd4j.write(featuresMask, dos);
if(labelsMask != null) Nd4j.write(labelsMask, dos);
dos.flush();
dos.close();
} catch (Exception e) {
e.printStackTrace();
}
}
@Override
public void save(File to) {
try{
save(new FileOutputStream(to,false));
}catch(IOException e){
throw new RuntimeException(e);
}
}
@Override
public DataSetIterator iterateWithMiniBatches() {
return null;
}
@Override
public String id() {
return "";
}
@Override
public INDArray getFeatures() {
return features;
}
@Override
public void setFeatures(INDArray features) {
this.features = features;
}
@Override
public Map labelCounts() {
Map ret = new HashMap<>();
if (labels == null)
return ret;
int nTensors = labels.tensorssAlongDimension(1);
for( int i=0; i function) {
BooleanIndexing.applyWhere(getFeatureMatrix(), condition, function);
}
/**
* Clone the dataset
*
* @return a clone of the dataset
*/
@Override
public DataSet copy() {
DataSet ret = new DataSet(getFeatures().dup(), getLabels().dup());
ret.setColumnNames(getColumnNames());
ret.setLabelNames(getLabelNames());
return ret;
}
/**
* Reshapes the input in to the given rows and columns
*
* @param rows the row size
* @param cols the column size
* @return a copy of this data op with the input resized
*/
@Override
public DataSet reshape(int rows, int cols) {
DataSet ret = new DataSet(getFeatures().reshape(new int[]{rows, cols}), getLabels());
return ret;
}
@Override
public void multiplyBy(double num) {
getFeatures().muli(Nd4j.scalar(num));
}
@Override
public void divideBy(int num) {
getFeatures().divi(Nd4j.scalar(num));
}
@Override
public void shuffle() {
long seed = System.currentTimeMillis();
shuffle(seed);
}
/**
* Shuffles the dataset in place, given a seed for a random number generator. For reproducibility
* This will modify the dataset in place!!
*
* @param seed Seed to use for the random Number Generator
*/
public void shuffle(long seed) {
//note here we use the same seed with different random objects guaranteeing same order
/*
if (getFeatures().rank() == 2 && getLabels().rank() == 2) {
Nd4j.shuffle(Arrays.asList(getFeatures(), getLabels()), 1);
} else {
Nd4j.shuffle(Arrays.asList(getFeatures(), getLabels()), ArrayUtil.range(1,getFeatures().rank()));
}
*/
List arrays = new ArrayList<>();
List dimensions = new ArrayList<>();
arrays.add(getFeatures());
dimensions.add(ArrayUtil.range(1,getFeatures().rank()));
arrays.add(getLabels());
dimensions.add(ArrayUtil.range(1,getLabels().rank()));
if (featuresMask != null) {
arrays.add(getFeaturesMaskArray());
dimensions.add(ArrayUtil.range(1,getFeaturesMaskArray().rank()));
}
if (featuresMask != null) {
arrays.add(getLabelsMaskArray());
dimensions.add(ArrayUtil.range(1,getLabelsMaskArray().rank()));
}
Nd4j.shuffle(arrays, new Random(seed), dimensions);
/*
int[] nonzeroDimsFeat = ArrayUtil.range(1,getFeatures().rank());
int[] nonzeroDimsLab = ArrayUtil.range(1,getLabels().rank());
Nd4j.shuffle(getFeatureMatrix(),new Random(seed),nonzeroDimsFeat);
Nd4j.shuffle(getLabels(),new Random(seed),nonzeroDimsLab);
if(getFeaturesMaskArray() != null) {
Nd4j.shuffle(getFeaturesMaskArray(),new Random(seed),nonzeroDimsFeat);
}
if(getLabelsMaskArray() != null) {
Nd4j.shuffle(getLabelsMaskArray(),new Random(seed),nonzeroDimsLab);
}
*/
}
/**
* Squeezes input data to a max and a min
*
* @param min the min value to occur in the dataset
* @param max the max value to ccur in the dataset
*/
@Override
public void squishToRange(double min, double max) {
for (int i = 0; i < getFeatures().length(); i++) {
double curr = (double) getFeatures().getScalar(i).element();
if (curr < min)
getFeatures().put(i, Nd4j.scalar(min));
else if (curr > max)
getFeatures().put(i, Nd4j.scalar(max));
}
}
@Override
public void scaleMinAndMax(double min, double max) {
FeatureUtil.scaleMinMax(min, max, getFeatureMatrix());
}
/**
* Divides the input data transform
* by the max number in each row
*/
@Override
public void scale() {
FeatureUtil.scaleByMax(getFeatures());
}
/**
* Adds a feature for each example on to the current feature vector
*
* @param toAdd the feature vector to add
*/
@Override
public void addFeatureVector(INDArray toAdd) {
setFeatures(Nd4j.hstack(getFeatureMatrix(), toAdd));
}
/**
* The feature to add, and the example/row number
*
* @param feature the feature vector to add
* @param example the number of the example to append to
*/
@Override
public void addFeatureVector(INDArray feature, int example) {
getFeatures().putRow(example, feature);
}
@Override
public void normalize() {
//FeatureUtil.normalizeMatrix(getFeatures());
NormalizerStandardize inClassPreProcessor = new NormalizerStandardize();
inClassPreProcessor.fit(this);
inClassPreProcessor.transform(this);
}
/**
* Same as calling binarize(0)
*/
@Override
public void binarize() {
binarize(0);
}
/**
* Binarizes the dataset such that any number greater than cutoff is 1 otherwise zero
*
* @param cutoff the cutoff point
*/
@Override
public void binarize(double cutoff) {
INDArray linear = getFeatureMatrix().linearView();
for (int i = 0; i < getFeatures().length(); i++) {
double curr = linear.getDouble(i);
if (curr > cutoff)
getFeatures().putScalar(i, 1);
else
getFeatures().putScalar(i, 0);
}
}
/**
* @Deprecated
* Subtract by the column means and divide by the standard deviation
*/
@Deprecated
@Override
public void normalizeZeroMeanZeroUnitVariance() {
INDArray columnMeans = getFeatures().mean(0);
INDArray columnStds = getFeatureMatrix().std(0);
setFeatures(getFeatures().subiRowVector(columnMeans));
columnStds.addi(Nd4j.scalar(Nd4j.EPS_THRESHOLD));
setFeatures(getFeatures().diviRowVector(columnStds));
}
/**
* The number of inputs in the feature matrix
*
* @return
*/
@Override
public int numInputs() {
return getFeatures().size(1);
}
@Override
public void validate() {
if (getFeatures().size(0) != getLabels().size(0))
throw new IllegalStateException("Invalid dataset");
}
@Override
public int outcome() {
return Nd4j.getBlasWrapper().iamax(getLabels());
}
/**
* Clears the outcome matrix setting a new number of labels
*
* @param labels the number of labels/columns in the outcome matrix
* Note that this clears the labels for each example
*/
@Override
public void setNewNumberOfLabels(int labels) {
int examples = numExamples();
INDArray newOutcomes = Nd4j.create(examples, labels);
setLabels(newOutcomes);
}
/**
* Sets the outcome of a particular example
*
* @param example the example to transform
* @param label the label of the outcome
*/
@Override
public void setOutcome(int example, int label) {
if (example > numExamples())
throw new IllegalArgumentException("No example at " + example);
if (label > numOutcomes() || label < 0)
throw new IllegalArgumentException("Illegal label");
INDArray outcome = FeatureUtil.toOutcomeVector(label, numOutcomes());
getLabels().putRow(example, outcome);
}
/**
* Gets a copy of example i
*
* @param i the example to getFromOrigin
* @return the example at i (one example)
*/
@Override
public DataSet get(int i) {
if (i > numExamples() || i < 0)
throw new IllegalArgumentException("invalid example number");
if(i == 0 && numExamples() == 1)
return this;
if(getFeatureMatrix().rank() == 4) {
//ensure rank is preserved
INDArray slice = getFeatureMatrix().slice(i);
return new DataSet(slice.reshape(ArrayUtil.combine(new int[]{1},slice.shape())),getLabels().slice(i));
}
return new DataSet(getFeatures().slice(i), getLabels().slice(i));
}
/**
* Gets a copy of example i
*
* @param i the example to getFromOrigin
* @return the example at i (one example)
*/
@Override
public DataSet get(int[] i) {
return new DataSet(getFeatures().getRows(i), getLabels().getRows(i));
}
/**
* Partitions a dataset in to mini batches where
* each dataset in each list is of the specified number of examples
*
* @param num the number to split by
* @return the partitioned datasets
*/
@Override
public List batchBy(int num) {
List batched = Lists.newArrayList();
for(List splitBatch : Lists.partition(asList(), num)) {
batched.add(DataSet.merge(splitBatch));
}
return batched;
}
/**
* Strips the data transform of all but the passed in labels
*
* @param labels strips the data transform of all but the passed in labels
* @return the dataset with only the specified labels
*/
@Override
public DataSet filterBy(int[] labels) {
List list = asList();
List newList = new ArrayList<>();
List labelList = new ArrayList<>();
for (int i : labels)
labelList.add(i);
for (DataSet d : list) {
int outcome = d.outcome();
if (labelList.contains(outcome)) {
newList.add(d);
}
}
return DataSet.merge(newList);
}
/**
* Strips the dataset down to the specified labels
* and remaps them
*
* @param labels the labels to strip down to
*/
@Override
public void filterAndStrip(int[] labels) {
DataSet filtered = filterBy(labels);
List newLabels = new ArrayList<>();
//map new labels to index according to passed in labels
Map labelMap = new HashMap<>();
for (int i = 0; i < labels.length; i++)
labelMap.put(labels[i], i);
//map examples
for (int i = 0; i < filtered.numExamples(); i++) {
DataSet example = filtered.get(i);
int o2 = example.outcome();
Integer outcome = labelMap.get(o2);
newLabels.add(outcome);
}
INDArray newLabelMatrix = Nd4j.create(filtered.numExamples(), labels.length);
if (newLabelMatrix.rows() != newLabels.size())
throw new IllegalStateException("Inconsistent label sizes");
for (int i = 0; i < newLabelMatrix.rows(); i++) {
Integer i2 = newLabels.get(i);
if (i2 == null)
throw new IllegalStateException("Label not found on row " + i);
INDArray newRow = FeatureUtil.toOutcomeVector(i2, labels.length);
newLabelMatrix.putRow(i, newRow);
}
setFeatures(filtered.getFeatures());
setLabels(newLabelMatrix);
}
/**
* Partitions the data transform by the specified number.
*
* @param num the number to split by
* @return the partitioned data transform
*/
@Override
public List dataSetBatches(int num) {
List> list = Lists.partition(asList(), num);
List ret = new ArrayList<>();
for (List l : list)
ret.add(DataSet.merge(l));
return ret;
}
/**
* Sorts the dataset by label:
* Splits the data transform such that examples are sorted by their labels.
* A ten label dataset would produce lists with batches like the following:
* x1 y = 1
* x2 y = 2
* ...
* x10 y = 10
*
* @return a list of data sets partitioned by outcomes
*/
@Override
public List sortAndBatchByNumLabels() {
sortByLabel();
return batchByNumLabels();
}
@Override
public List batchByNumLabels() {
return batchBy(numOutcomes());
}
@Override
public List asList() {
List list = new ArrayList<>(numExamples());
INDArray featuresHere, labelsHere, featureMaskHere, labelMaskHere;
int rank = getFeatures().rank();
int labelsRank = getLabels().rank();
// Preserving the dimension of the dataset - essentially a minibatch size of 1
for (int i = 0; i < numExamples(); i++) {
switch (rank){
case 2:
featuresHere = getFeatures().get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all());
featureMaskHere = featuresMask != null ? featuresMask.get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all()) : null;
break;
case 3:
featuresHere = getFeatures().get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all(),NDArrayIndex.all());
featureMaskHere = featuresMask != null ? featuresMask.get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all()) : null;
break;
case 4:
featuresHere = getFeatures().get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all(),NDArrayIndex.all(),NDArrayIndex.all());
featureMaskHere = featuresMask != null ? featuresMask.get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all()) : null;
break;
default:
throw new IllegalStateException("Cannot convert to list: feature set rank must be in range 2 to 4 inclusive. First example labels shape: " + Arrays.toString(getFeatures().shape()));
}
switch (labelsRank) {
case 2:
labelsHere = getLabels().get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all());
labelMaskHere = labelsMask != null ? labelsMask.get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all()) : null;
break;
case 3:
labelsHere = getLabels().get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all(),NDArrayIndex.all());
labelMaskHere = labelsMask != null ? labelsMask.get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all()) : null;
break;
case 4:
labelsHere = getLabels().get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all(),NDArrayIndex.all(),NDArrayIndex.all());
labelMaskHere = labelsMask != null ? labelsMask.get(NDArrayIndex.interval(i,i,true), NDArrayIndex.all()) : null;
break;
default:
throw new IllegalStateException("Cannot convert to list: feature set rank must be in range 2 to 4 inclusive. First example labels shape: " + Arrays.toString(getFeatures().shape()));
}
list.add(new DataSet(featuresHere,labelsHere,featureMaskHere,labelMaskHere));
}
return list;
}
/**
* Splits a dataset in to test and train randomly.
* This will modify the dataset in place to shuffle it before splitting into test/train!
*
* @param numHoldout the number to hold out for training
* @param rng Random Number Generator to use to shuffle the dataset
* @return the pair of datasets for the train test split
*/
@Override
public SplitTestAndTrain splitTestAndTrain(int numHoldout, Random rng) {
long seed = rng.nextLong();
this.shuffle(seed);
return splitTestAndTrain(numHoldout);
}
/**
* Splits a dataset in to test and train
*
* @param numHoldout the number to hold out for training
* @return the pair of datasets for the train test split
*/
@Override
public SplitTestAndTrain splitTestAndTrain(int numHoldout) {
int numExamples = numExamples();
if(numExamples <= 1) throw new IllegalStateException("Cannot split DataSet with <= 1 rows (data set has " + numExamples + " example)");
if (numHoldout >= numExamples)
throw new IllegalArgumentException("Unable to split on size equal or larger than the number of rows (# numExamples=" + numExamples + ", numHoldout=" + numHoldout + ")");
DataSet first = new DataSet(getFeatureMatrix().get(NDArrayIndex.interval(0,numHoldout), NDArrayIndex.all()),getLabels().get(NDArrayIndex.interval(0,numHoldout),NDArrayIndex.all()));
DataSet second = new DataSet(getFeatureMatrix().get(NDArrayIndex.interval(numHoldout,numExamples()), NDArrayIndex.all()),getLabels().get(NDArrayIndex.interval(numHoldout,numExamples), NDArrayIndex.all()));
return new SplitTestAndTrain(first, second);
}
/**
* Returns the labels for the dataset
*
* @return the labels for the dataset
*/
@Override
public INDArray getLabels() {
return labels;
}
/**
* @param idx the index to pullRows the string label value out of the list if it exists
* @return the label name
*/
@Override
public String getLabelName(int idx) {
return labelNames.get(idx);
}
/**
* @param idxs list of index to pullRows the string label value out of the list if it exists
* @return the label name
*/
@Override
public List getLabelNames(INDArray idxs) {
List ret = new ArrayList<>();
for(int i = 0; i < idxs.length(); i++) {
ret.add(i, getLabelName(i));
}
return ret;
}
@Override
public void setLabels(INDArray labels) {
this.labels = labels;
}
/**
* Get the feature matrix (inputs for the data)
*
* @return the feature matrix for the dataset
*/
@Override
public INDArray getFeatureMatrix() {
return getFeatures();
}
/**
* Organizes the dataset to minimize sampling error
* while still allowing efficient batching.
*/
@Override
public void sortByLabel() {
Map> map = new HashMap<>();
List data = asList();
int numLabels = numOutcomes();
int examples = numExamples();
for (DataSet d : data) {
int label = d.outcome();
Queue q = map.get(label);
if (q == null) {
q = new ArrayDeque<>();
map.put(label, q);
}
q.add(d);
}
for (Map.Entry> label : map.entrySet()) {
log.info("Label " + label + " has " + label.getValue().size() + " elements");
}
//ideal input splits: 1 of each label in each batch
//after we run out of ideal batches: fall back to a new strategy
boolean optimal = true;
for (int i = 0; i < examples; i++) {
if (optimal) {
for (int j = 0; j < numLabels; j++) {
Queue q = map.get(j);
if (q == null) {
optimal = false;
break;
}
DataSet next = q.poll();
//add a row; go to next
if (next != null) {
addRow(next, i);
i++;
} else {
optimal = false;
break;
}
}
} else {
DataSet add = null;
for (Queue q : map.values()) {
if (!q.isEmpty()) {
add = q.poll();
break;
}
}
addRow(add, i);
}
}
}
@Override
public void addRow(DataSet d, int i) {
if (i > numExamples() || d == null)
throw new IllegalArgumentException("Invalid index for adding a row");
getFeatures().putRow(i, d.getFeatures());
getLabels().putRow(i, d.getLabels());
}
private int getLabel(DataSet data) {
Float f = data.getLabels().maxNumber().floatValue();
return f.intValue();
}
@Override
public INDArray exampleSums() {
return getFeatures().sum(1);
}
@Override
public INDArray exampleMaxs() {
return getFeatures().max(1);
}
@Override
public INDArray exampleMeans() {
return getFeatures().mean(1);
}
/**
* Sample without replacement and a random rng
*
* @param numSamples the number of samples to getFromOrigin
* @return a sample data transform without replacement
*/
@Override
public DataSet sample(int numSamples) {
return sample(numSamples,Nd4j.getRandom());
}
/**
* Sample without replacement
*
* @param numSamples the number of samples to getFromOrigin
* @param rng the rng to use
* @return the sampled dataset without replacement
*/
@Override
public DataSet sample(int numSamples, org.nd4j.linalg.api.rng.Random rng) {
return sample(numSamples, rng, false);
}
/**
* Sample a dataset numSamples times
*
* @param numSamples the number of samples to getFromOrigin
* @param withReplacement the rng to use
* @return the sampled dataset without replacement
*/
@Override
public DataSet sample(int numSamples, boolean withReplacement) {
return sample(numSamples, Nd4j.getRandom(), withReplacement);
}
/**
* Sample a dataset
*
* @param numSamples the number of samples to getFromOrigin
* @param rng the rng to use
* @param withReplacement whether to allow duplicates (only tracked by example row number)
* @return the sample dataset
*/
@Override
public DataSet sample(int numSamples, org.nd4j.linalg.api.rng.Random rng, boolean withReplacement) {
INDArray examples = Nd4j.create(numSamples, getFeatures().columns());
INDArray outcomes = Nd4j.create(numSamples, numOutcomes());
Set added = new HashSet<>();
for (int i = 0; i < numSamples; i++) {
int picked = rng.nextInt(numExamples());
if (!withReplacement)
while (added.contains(picked))
picked = rng.nextInt(numExamples());
examples.putRow(i, get(picked).getFeatures());
outcomes.putRow(i, get(picked).getLabels());
}
return new DataSet(examples, outcomes);
}
@Override
public void roundToTheNearest(int roundTo) {
for (int i = 0; i < getFeatures().length(); i++) {
double curr = (double) getFeatures().getScalar(i).element();
getFeatures().put(i, Nd4j.scalar(MathUtils.roundDouble(curr, roundTo)));
}
}
@Override
public int numOutcomes() {
return getLabels().size(1);
}
@Override
public int numExamples() {
return getLabels().size(0);
}
@Override
public String toString() {
StringBuilder builder = new StringBuilder();
if (features != null && labels != null) {
builder.append("===========INPUT===================\n")
.append(getFeatures().toString().replaceAll(";", "\n"))
.append("\n=================OUTPUT==================\n")
.append(getLabels().toString().replaceAll(";", "\n"));
if (featuresMask != null) {
builder.append("\n===========INPUT MASK===================\n")
.append(getFeaturesMaskArray().toString().replaceAll(";","\n"));
}
if (labelsMask != null) {
builder.append("\n===========OUTPUT MASK===================\n")
.append(getLabelsMaskArray().toString().replaceAll(";","\n"));
}
return builder.toString();
}
else {
log.info("Features or labels are null values");
return "";
}
}
/**
* Gets the optional label names
*
* @return
*/
@Deprecated
@Override
public List getLabelNames() {
return labelNames;
}
/**
* Gets the optional label names
*
* @return
*/
@Override
public List getLabelNamesList() {
return labelNames;
}
/**
* Sets the label names, will throw an exception if the passed
* in label names doesn't equal the number of outcomes
*
* @param labelNames the label names to use
*/
@Override
public void setLabelNames(List labelNames) {
this.labelNames = labelNames;
}
/**
* Optional column names of the data transform, this is mainly used
* for interpeting what columns are in the dataset
*
* @return
*/
@Deprecated
@Override
public List getColumnNames() {
return columnNames;
}
/**
* Sets the column names, will throw an exception if the column names
* don't match the number of columns
*
* @param columnNames
*/
@Deprecated
@Override
public void setColumnNames(List columnNames) {
this.columnNames = columnNames;
}
@Override
public SplitTestAndTrain splitTestAndTrain(double percentTrain) {
int numPercent = (int) (percentTrain * numExamples());
if(numPercent <= 0) numPercent = 1;
return splitTestAndTrain(numPercent);
}
@Override
public Iterator iterator() {
return asList().iterator();
}
@Override
public INDArray getFeaturesMaskArray() {
return featuresMask;
}
@Override
public void setFeaturesMaskArray(INDArray featuresMask) {
this.featuresMask = featuresMask;
}
@Override
public INDArray getLabelsMaskArray() {
return labelsMask;
}
@Override
public void setLabelsMaskArray(INDArray labelsMask) {
this.labelsMask = labelsMask;
}
@Override
public boolean hasMaskArrays() {
return labelsMask != null || featuresMask != null;
}
@Override
public boolean equals(Object o) {
if (this == o) return true;
if (!(o instanceof DataSet)) return false;
DataSet d = (DataSet) o;
if(!equalOrBothNull(features,d.features)) return false;
if(!equalOrBothNull(labels, d.labels)) return false;
if(!equalOrBothNull(featuresMask, d.featuresMask)) return false;
return equalOrBothNull(labelsMask, d.labelsMask);
}
private static boolean equalOrBothNull(INDArray first, INDArray second){
if(first == null && second == null) return true; //Both are null: ok
if(first == null || second == null) return false; //Only one is null, not both
return first.equals(second);
}
@Override
public int hashCode() {
int result = getFeatures() != null ? getFeatures().hashCode() : 0;
result = 31 * result + (getLabels() != null ? getLabels().hashCode() : 0);
result = 31 * result + (getFeaturesMaskArray() != null ? getFeaturesMaskArray().hashCode() : 0);
result = 31 * result + (getLabelsMaskArray() != null ? getLabelsMaskArray().hashCode() : 0);
return result;
}
}
