org.nd4j.linalg.dataset.DataSet Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.dataset;
import org.nd4j.shade.guava.collect.Lists;
import lombok.extern.slf4j.Slf4j;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.DataSetUtil;
import org.nd4j.linalg.dataset.api.MultiDataSet;
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.common.primitives.Pair;
import org.nd4j.common.util.ArrayUtil;
import org.nd4j.linalg.util.FeatureUtil;
import org.nd4j.common.util.MathUtils;
import java.io.*;
import java.util.*;
import static org.nd4j.linalg.indexing.NDArrayIndex.all;
import static org.nd4j.linalg.indexing.NDArrayIndex.interval;
/**
* 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
*/
@Slf4j
public class DataSet implements org.nd4j.linalg.dataset.api.DataSet {
private static final long serialVersionUID = 1935520764586513365L;
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 static final byte BITMASK_METADATA_PRESET = 1 << 5;
private List columnNames = new ArrayList<>();
private List labelNames = new ArrayList<>();
private INDArray features, labels;
private INDArray featuresMask;
private INDArray labelsMask;
private List exampleMetaData;
private transient boolean preProcessed = false;
public DataSet() {
this(null, null);
}
@Override
public List getExampleMetaData() {
return exampleMetaData;
}
@Override
public List getExampleMetaData(Class metaDataType) {
return (List) exampleMetaData;
}
@Override
public void setExampleMetaData(List exampleMetaData) {
this.exampleMetaData = (List) exampleMetaData;
}
/**
* 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
Nd4j.getExecutioner().commit();
}
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
* @return a single dataset
*/
public static DataSet merge(List data) {
if (data.isEmpty())
throw new IllegalArgumentException("Unable to merge empty dataset");
int nonEmpty = 0;
boolean anyFeaturesPreset = false;
boolean anyLabelsPreset = false;
boolean first = true;
for(org.nd4j.linalg.dataset.api.DataSet ds : data){
if(ds.isEmpty()){
continue;
}
nonEmpty++;
if(anyFeaturesPreset && ds.getFeatures() == null || (!first && !anyFeaturesPreset && ds.getFeatures() != null)){
throw new IllegalStateException("Cannot merge features: encountered null features in one or more DataSets");
}
if(anyLabelsPreset && ds.getLabels() == null || (!first && !anyLabelsPreset && ds.getLabels() != null)){
throw new IllegalStateException("Cannot merge labels: enountered null labels in one or more DataSets");
}
anyFeaturesPreset |= ds.getFeatures() != null;
anyLabelsPreset |= ds.getLabels() != null;
first = false;
}
INDArray[] featuresToMerge = new INDArray[nonEmpty];
INDArray[] labelsToMerge = new INDArray[nonEmpty];
INDArray[] featuresMasksToMerge = null;
INDArray[] labelsMasksToMerge = null;
int count = 0;
for (org.nd4j.linalg.dataset.api.DataSet ds : data) {
if(ds.isEmpty())
continue;
featuresToMerge[count] = ds.getFeatures();
labelsToMerge[count] = ds.getLabels();
if (ds.getFeaturesMaskArray() != null) {
if (featuresMasksToMerge == null) {
featuresMasksToMerge = new INDArray[nonEmpty];
}
featuresMasksToMerge[count] = ds.getFeaturesMaskArray();
}
if (ds.getLabelsMaskArray() != null) {
if (labelsMasksToMerge == null) {
labelsMasksToMerge = new INDArray[nonEmpty];
}
labelsMasksToMerge[count] = ds.getLabelsMaskArray();
}
count++;
}
INDArray featuresOut;
INDArray labelsOut;
INDArray featuresMaskOut;
INDArray labelsMaskOut;
Pair fp = DataSetUtil.mergeFeatures(featuresToMerge, featuresMasksToMerge);
featuresOut = fp.getFirst();
featuresMaskOut = fp.getSecond();
Pair lp = DataSetUtil.mergeLabels(labelsToMerge, labelsMasksToMerge);
labelsOut = lp.getFirst();
labelsMaskOut = lp.getSecond();
DataSet dataset = new DataSet(featuresOut, labelsOut, featuresMaskOut, labelsMaskOut);
List meta = null;
for (org.nd4j.linalg.dataset.api.DataSet ds : data) {
if (ds.getExampleMetaData() == null || ds.getExampleMetaData().size() != ds.numExamples()) {
meta = null;
break;
}
if (meta == null)
meta = new ArrayList<>();
meta.addAll(ds.getExampleMetaData());
}
if (meta != null) {
dataset.setExampleMetaData(meta);
}
return dataset;
}
@Override
public org.nd4j.linalg.dataset.api.DataSet getRange(int from, int to) {
if (hasMaskArrays()) {
INDArray featureMaskHere = featuresMask != null ? featuresMask.get(interval(from, to)) : null;
INDArray labelMaskHere = labelsMask != null ? labelsMask.get(interval(from, to)) : null;
return new DataSet(features.get(interval(from, to)), labels.get(interval(from, to)), featureMaskHere,
labelMaskHere);
}
return new DataSet(features.get(interval(from, to)), labels.get(interval(from, to)));
}
@Override
public void load(InputStream from) {
try {
DataInputStream dis = from instanceof BufferedInputStream ? new DataInputStream(from)
: new DataInputStream(new BufferedInputStream(from));
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;
boolean hasMetaData = (included & BITMASK_METADATA_PRESET) != 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);
if(hasMetaData){
ObjectInputStream ois = new ObjectInputStream(dis);
exampleMetaData = (List)ois.readObject();
}
dis.close();
} catch (Exception e) {
throw new RuntimeException("Error loading DataSet",e);
}
}
@Override
public void load(File from) {
try (FileInputStream fis = new FileInputStream(from);
BufferedInputStream bis = new BufferedInputStream(fis, 1024 * 1024)) {
load(bis);
} 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;
if (exampleMetaData != null && exampleMetaData.size() > 0)
included |= BITMASK_METADATA_PRESET;
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);
if(exampleMetaData != null && exampleMetaData.size() > 0){
ObjectOutputStream oos = new ObjectOutputStream(bos);
oos.writeObject(exampleMetaData);
oos.flush();
}
dos.flush();
dos.close();
} catch (Exception e) {
log.error("",e);
}
}
@Override
public void save(File to) {
try (FileOutputStream fos = new FileOutputStream(to, false);
BufferedOutputStream bos = new BufferedOutputStream(fos)) {
save(bos);
} 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;
long nTensors = labels.tensorsAlongDimension(1);
for (int i = 0; i < nTensors; i++) {
INDArray row = labels.tensorAlongDimension(i, 1);
INDArray javaRow = labels.tensorAlongDimension(i, 1);
int maxIdx = Nd4j.getBlasWrapper().iamax(row);
int maxIdxJava = Nd4j.getBlasWrapper().iamax(javaRow);
if (maxIdx < 0)
throw new IllegalStateException("Please check the iamax implementation for "
+ Nd4j.getBlasWrapper().getClass().getName());
if (ret.get(maxIdx) == null)
ret.put(maxIdx, 1.0);
else
ret.put(maxIdx, ret.get(maxIdx) + 1.0);
}
return ret;
}
/**
* Clone the dataset
*
* @return a clone of the dataset
*/
@Override
public DataSet copy() {
DataSet ret = new DataSet(getFeatures().dup(), getLabels().dup());
if (getLabelsMaskArray() != null)
ret.setLabelsMaskArray(getLabelsMaskArray().dup());
if (getFeaturesMaskArray() != null)
ret.setFeaturesMaskArray(getFeaturesMaskArray().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 long[] {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) {
// just skip shuffle if there's only 1 example
if (numExamples() < 2)
return;
//note here we use the same seed with different random objects guaranteeing same order
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 (labelsMask != null) {
arrays.add(getLabelsMaskArray());
dimensions.add(ArrayUtil.range(1, getLabelsMaskArray().rank()));
}
Nd4j.shuffle(arrays, new Random(seed), dimensions);
//As per CpuNDArrayFactory.shuffle(List arrays, Random rnd, List dimensions) and libnd4j transforms.h shuffleKernelGeneric
if (exampleMetaData != null) {
int[] map = ArrayUtil.buildInterleavedVector(new Random(seed), numExamples());
ArrayUtil.shuffleWithMap(exampleMetaData, map);
}
}
/**
* 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, getFeatures());
}
/**
* 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(getFeatures(), 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 = getFeatures().reshape(-1);
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 = getFeatures().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 (int) 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: must be 0 to " + (numExamples()-1) + ", got " + i);
if (i == 0 && numExamples() == 1)
return this;
return new DataSet(getHelper(features,i), getHelper(labels, i), getHelper(featuresMask,i), getHelper(labelsMask,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) {
List list = new ArrayList<>();
for(int ex : i){
list.add(get(ex));
}
return DataSet.merge(list);
}
/**
* 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++) {
featuresHere = getHelper(getFeatures(), i);
featureMaskHere = getHelper(featuresMask, i);
labelsHere = getHelper(labels, i);
labelMaskHere = getHelper(labelsMask, i);
DataSet ds = new DataSet(featuresHere, labelsHere, featureMaskHere, labelMaskHere);
if (exampleMetaData != null && exampleMetaData.size() > i) {
ds.setExampleMetaData(Collections.singletonList(exampleMetaData.get(i)));
}
list.add(ds);
}
return list;
}
private INDArray getHelper(INDArray from, int i){
if(from == null){
return null;
}
switch (from.rank()) {
case 2:
return from.get(interval(i, i, true), all());
case 3:
return from.get(interval(i, i, true), all(), all());
case 4:
return from.get(interval(i, i, true), all(), all(), all());
case 5:
return from.get(interval(i, i, true), all(), all(), all(), all());
default:
throw new IllegalStateException(
"Cannot convert to list: feature set rank must be in range 2 to 5 inclusive. Got shape: "
+ Arrays.toString(from.shape()));
}
}
/**
* 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();
DataSet second = new DataSet();
switch (features.rank()) {
case 2:
first.setFeatures(features.get(interval(0, numHoldout), all()));
second.setFeatures(features.get(interval(numHoldout, numExamples), all()));
break;
case 3:
first.setFeatures(features.get(interval(0, numHoldout), all(), all()));
second.setFeatures(features.get(interval(numHoldout, numExamples), all(), all()));
break;
case 4:
first.setFeatures(features.get(interval(0, numHoldout), all(), all(), all()));
second.setFeatures(features.get(interval(numHoldout, numExamples), all(), all(), all()));
break;
default:
throw new UnsupportedOperationException("Features rank: " + features.rank());
}
switch (labels.rank()) {
case 2:
first.setLabels(labels.get(interval(0, numHoldout), all()));
second.setLabels(labels.get(interval(numHoldout, numExamples), all()));
break;
case 3:
first.setLabels(labels.get(interval(0, numHoldout), all(), all()));
second.setLabels(labels.get(interval(numHoldout, numExamples), all(), all()));
break;
case 4:
first.setLabels(labels.get(interval(0, numHoldout), all(), all(), all()));
second.setLabels(labels.get(interval(numHoldout, numExamples), all(), all(), all()));
break;
default:
throw new UnsupportedOperationException("Labels rank: " + features.rank());
}
if (featuresMask != null) {
first.setFeaturesMaskArray(featuresMask.get(interval(0, numHoldout), all()));
second.setFeaturesMaskArray(featuresMask.get(interval(numHoldout, numExamples), all()));
}
if (labelsMask != null) {
first.setLabelsMaskArray(labelsMask.get(interval(0, numHoldout), all()));
second.setLabelsMaskArray(labelsMask.get(interval(numHoldout, numExamples), all()));
}
if (exampleMetaData != null) {
List meta1 = new ArrayList<>();
List meta2 = new ArrayList<>();
for (int i = 0; i < numHoldout && i < exampleMetaData.size(); i++) {
meta1.add(exampleMetaData.get(i));
}
for (int i = numHoldout; i < numExamples && i < exampleMetaData.size(); i++) {
meta2.add(exampleMetaData.get(i));
}
first.setExampleMetaData(meta1);
second.setExampleMetaData(meta2);
}
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 opName
*/
@Override
public String getLabelName(int idx) {
if (!labelNames.isEmpty()) {
if (idx < labelNames.size())
return labelNames.get(idx);
else
throw new IllegalStateException(
"Index requested is longer than the number of labels used for classification.");
} else
throw new IllegalStateException(
"Label names are not defined on this dataset. Add label names in order to use getLabelName with an id.");
}
/**
* @param idxs list of index to pullRows the string label value out of the list if it exists
* @return the label opName
*/
@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;
}
/**
* 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) {
Set added = new HashSet<>();
List toMerge = new ArrayList<>();
boolean terminate = false;
for (int i = 0; i < numSamples && !terminate; i++) {
int picked = rng.nextInt(numExamples());
if (!withReplacement) {
while (added.contains(picked)) {
picked = rng.nextInt(numExamples());
if(added.size() == numExamples()){
terminate = true;
break;
}
}
}
added.add(picked);
toMerge.add(get(picked));
}
return DataSet.merge(toMerge);
}
@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 (int) getLabels().size(1);
}
@Override
public int numExamples() {
if (getFeatures() != null)
return (int) getFeatures().size(0);
else if (getLabels() != null)
return (int) getLabels().size(0);
return 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 fractionTrain) {
Preconditions.checkArgument(fractionTrain > 0.0 && fractionTrain < 1.0,
"Train fraction must be > 0.0 and < 1.0 - got %s", fractionTrain);
int numTrain = (int) (fractionTrain * numExamples());
if (numTrain <= 0)
numTrain = 1;
return splitTestAndTrain(numTrain);
}
@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;
}
/**
* This method returns memory used by this DataSet
*
* @return
*/
@Override
public long getMemoryFootprint() {
long reqMem = features.length() * Nd4j.sizeOfDataType(features.dataType());
reqMem += labels == null ? 0 : labels.length() * Nd4j.sizeOfDataType(labels.dataType());
reqMem += featuresMask == null ? 0 : featuresMask.length() * Nd4j.sizeOfDataType(featuresMask.dataType());
reqMem += labelsMask == null ? 0 : labelsMask.length() * Nd4j.sizeOfDataType(labelsMask.dataType());
return reqMem;
}
@Override
public void migrate() {
if (Nd4j.getMemoryManager().getCurrentWorkspace() != null) {
if (features != null)
features = features.migrate();
if (labels != null)
labels = labels.migrate();
if (featuresMask != null)
featuresMask = featuresMask.migrate();
if (labelsMask != null)
labelsMask = labelsMask.migrate();
}
}
@Override
public void detach() {
if (features != null)
features = features.detach();
if (labels != null)
labels = labels.detach();
if (featuresMask != null)
featuresMask = featuresMask.detach();
if (labelsMask != null)
labelsMask = labelsMask.detach();
}
@Override
public boolean isEmpty() {
return features == null && labels == null && featuresMask == null && labelsMask == null;
}
@Override
public MultiDataSet toMultiDataSet() {
INDArray f = getFeatures();
INDArray l = getLabels();
INDArray fMask = getFeaturesMaskArray();
INDArray lMask = getLabelsMaskArray();
INDArray[] fNew = f == null ? null : new INDArray[] {f};
INDArray[] lNew = l == null ? null : new INDArray[] {l};
INDArray[] fMaskNew = (fMask != null ? new INDArray[] {fMask} : null);
INDArray[] lMaskNew = (lMask != null ? new INDArray[] {lMask} : null);
return new org.nd4j.linalg.dataset.MultiDataSet(fNew, lNew, fMaskNew, lMaskNew, exampleMetaData);
}
}
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