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/*
* * Copyright 2016 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.deeplearning4j.spark.datavec;
import org.apache.spark.api.java.function.Function;
import org.datavec.api.io.WritableConverter;
import org.datavec.api.writable.Writable;
import org.datavec.common.data.NDArrayWritable;
import org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.DataSetPreProcessor;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.util.FeatureUtil;
import java.io.Serializable;
import java.util.Iterator;
import java.util.List;
/**Map {@code Collection>} objects (out of a datavec-spark sequence record reader function) to
* DataSet objects for Spark training.
* Analogous to {@link SequenceRecordReaderDataSetIterator}, but in the context of Spark.
* Supports loading data from a single source only (hence no masknig arrays, many-to-one etc here)
* see {@link DataVecSequencePairDataSetFunction} for the separate collections for input and labels version
* @author Alex Black
*/
public class DataVecSequenceDataSetFunction implements Function>,DataSet>, Serializable {
private final boolean regression;
private final int labelIndex;
private final int numPossibleLabels;
private final DataSetPreProcessor preProcessor;
private final WritableConverter converter;
/**
* @param labelIndex Index of the label column
* @param numPossibleLabels Number of classes for classification (not used if regression = true)
* @param regression False for classification, true for regression
*/
public DataVecSequenceDataSetFunction(int labelIndex, int numPossibleLabels, boolean regression){
this(labelIndex, numPossibleLabels, regression, null, null);
}
/**
* @param labelIndex Index of the label column
* @param numPossibleLabels Number of classes for classification (not used if regression = true)
* @param regression False for classification, true for regression
* @param preProcessor DataSetPreprocessor (may be null)
* @param converter WritableConverter (may be null)
*/
public DataVecSequenceDataSetFunction(int labelIndex, int numPossibleLabels, boolean regression,
DataSetPreProcessor preProcessor, WritableConverter converter){
this.labelIndex = labelIndex;
this.numPossibleLabels = numPossibleLabels;
this.regression = regression;
this.preProcessor = preProcessor;
this.converter = converter;
}
@Override
public DataSet call(List> input) throws Exception {
Iterator> iter = input.iterator();
INDArray features = null;
INDArray labels = Nd4j.zeros(1, (regression ? 1 : numPossibleLabels), input.size());
int[] fIdx = new int[3];
int[] lIdx = new int[3];
int i=0;
while(iter.hasNext()){
List step = iter.next();
if (i == 0) {
features = Nd4j.zeros(1, step.size()-1, input.size());
}
Iterator timeStepIter = step.iterator();
int countIn = 0;
int countFeatures = 0;
while (timeStepIter.hasNext()) {
Writable current = timeStepIter.next();
if(converter != null) current = converter.convert(current);
if(countIn++ == labelIndex){
//label
if(regression){
lIdx[2] = i;
labels.putScalar(lIdx,current.toDouble());
} else {
INDArray line = FeatureUtil.toOutcomeVector(current.toInt(), numPossibleLabels);
labels.tensorAlongDimension(i,1).assign(line); //1d from [1,nOut,timeSeriesLength] -> tensor i along dimension 1 is at time i
}
} else {
//feature
fIdx[1] = countFeatures++;
fIdx[2] = i;
try {
features.putScalar(fIdx, current.toDouble());
} catch (UnsupportedOperationException e) {
// This isn't a scalar, so check if we got an array already
if (current instanceof NDArrayWritable) {
features.get(NDArrayIndex.point(fIdx[0]), NDArrayIndex.all(), NDArrayIndex.point(fIdx[2]))
.putRow(0, ((NDArrayWritable)current).get());
} else {
throw e;
}
}
}
}
i++;
}
DataSet ds = new DataSet(features,labels);
if(preProcessor != null) preProcessor.preProcess(ds);
return ds;
}
}
