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/*
*
* * 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.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.BooleanIndexing;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.indexing.conditions.Condition;
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 Logger log = LoggerFactory.getLogger(DataSet.class);
private List columnNames = new ArrayList<>();
private List labelNames = new ArrayList<>();
private INDArray features, labels;
private String id = UUID.randomUUID().toString();
public DataSet() {
this(Nd4j.zeros(new int[]{1,1}), Nd4j.zeros(new int[]{1,1}));
}
/**
* 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) {
if (first.size(0) != second.size(0))
throw new IllegalStateException("Invalid data transform; first and second do not have equal rows. First was " + first.size(0) + " second was " + second.size(0));
this.features = first;
this.labels = second;
}
/**
* Returns a single dataset
*
* @return an empty dataset with 2 1x1 zero matrices
*/
public static DataSet empty() {
return new DataSet(Nd4j.zeros(new int[]{1,1}), Nd4j.zeros(new int[]{1,1}));
}
/**
* 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 numExamples = totalExamples(data);
INDArray in = Nd4j.create(numExamples, first.getFeatures().columns());
INDArray out = Nd4j.create(numExamples, first.getLabels().columns());
int count = 0;
for (int i = 0; i < data.size(); i++) {
DataSet d1 = data.get(i);
for (int j = 0; j < d1.numExamples(); j++) {
DataSet example = d1.get(j);
in.putRow(count, clone ? example.getFeatures().dup() : example.getFeatures());
out.putRow(count, clone ? example.getLabels().dup() : example.getLabels());
count++;
}
}
return new DataSet(in, out);
}
/**
* 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");
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) {
return new DataSet(features.get(NDArrayIndex.interval(from,to)),labels.get(NDArrayIndex.interval(from,to)));
}
@Override
public void load(File from) {
try {
BufferedInputStream bis = new BufferedInputStream(new FileInputStream(from));
DataInputStream dis = new DataInputStream(bis);
features = Nd4j.read(dis);
labels = Nd4j.read(dis);
dis.close();
} catch (Exception e) {
e.printStackTrace();
}
}
@Override
public void save(File to) {
try {
BufferedOutputStream bos = new BufferedOutputStream(new FileOutputStream(to));
DataOutputStream dis = new DataOutputStream(bos);
Nd4j.write(getFeatureMatrix(),dis);
Nd4j.write(getLabels(),dis);
dis.flush();
dis.close();
} catch (Exception e) {
e.printStackTrace();
}
}
@Override
public DataSetIterator iterateWithMiniBatches() {
return null;
}
@Override
public String id() {
return id;
}
@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;
for (int i = 0; i < labels.rows(); i++) {
INDArray label = labels.getRow(i);
int maxIdx = Nd4j.getBlasWrapper().iamax(label);
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;
}
@Override
public void apply(Condition condition, Function 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() {
//note here we use the same seed with different random objects guaranteeing same order
long seed = System.currentTimeMillis();
Nd4j.shuffle(getFeatureMatrix(),new Random(seed),0);
Nd4j.shuffle(getLabels(),new Random(seed),0);
}
/**
* 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());
}
/**
* 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);
}
}
/**
* Subtract by the column means and divide by the standard deviation
*/
@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().columns();
}
@Override
public void validate() {
if (getFeatures().size(0) != getLabels().size(0))
throw new IllegalStateException("Invalid dataset");
}
@Override
public int outcome() {
if (this.numExamples() > 1)
throw new IllegalStateException("Unable to derive outcome for dataset greater than one row");
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;
return new DataSet(getFeatures().getRow(i), getLabels().getRow(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) {
return Lists.partition(asList(), num);
}
/**
* 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 Lists.partition(asList(), numOutcomes());
}
@Override
public List> batchByNumLabels() {
return Lists.partition(asList(), numOutcomes());
}
@Override
public List asList() {
List list = new ArrayList<>(numExamples());
for (int i = 0; i < numExamples(); i++) {
list.add(new DataSet(getFeatures().getRow(i), getLabels().getRow(i)));
}
return list;
}
/**
* 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, Random rng) {
if (numHoldout >= numExamples())
throw new IllegalArgumentException("Unable to split on size larger than the number of rows");
DataSet first = new DataSet(getFeatureMatrix().get(NDArrayIndex.interval(0,numHoldout)),getLabels().get(NDArrayIndex.interval(0,numHoldout)));
DataSet second = new DataSet(getFeatureMatrix().get(NDArrayIndex.interval(numHoldout,numExamples())),getLabels().get(NDArrayIndex.interval(numHoldout,numExamples())));
return new SplitTestAndTrain(first, second);
}
@Override
public SplitTestAndTrain splitTestAndTrain(int numHoldout) {
return splitTestAndTrain(numHoldout, new Random());
}
/**
* Returns the labels for the dataset
*
* @return the labels for the dataset
*/
@Override
public INDArray getLabels() {
return labels;
}
@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().columns();
}
@Override
public int numExamples() {
return getFeatures().size(0);
}
@Override
public String toString() {
StringBuilder builder = new StringBuilder();
builder.append("===========INPUT===================\n")
.append(getFeatures().toString().replaceAll(";", "\n"))
.append("\n=================OUTPUT==================\n")
.append(getLabels().toString().replaceAll(";", "\n"));
return builder.toString();
}
/**
* Gets the optional label names
*
* @return
*/
@Override
public List getLabelNames() {
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
*/
@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
*/
@Override
public void setColumnNames(List columnNames) {
this.columnNames = columnNames;
}
@Override
public SplitTestAndTrain splitTestAndTrain(double percentTrain) {
int numPercent = (int) (percentTrain * numExamples());
return splitTestAndTrain(numPercent);
}
@Override
public Iterator iterator() {
return asList().iterator();
}
@Override
public boolean equals(Object o) {
if (this == o) return true;
if (!(o instanceof DataSet)) return false;
DataSet dataSet = (DataSet) o;
if (getFeatures() != null ? !getFeatures().equals(dataSet.getFeatures()) : dataSet.getFeatures() != null)
return false;
return !(getLabels() != null ? !getLabels().equals(dataSet.getLabels()) : dataSet.getLabels() != null);
}
@Override
public int hashCode() {
int result = getFeatures() != null ? getFeatures().hashCode() : 0;
result = 31 * result + (getLabels() != null ? getLabels().hashCode() : 0);
return result;
}
}
