org.deeplearning4j.spark.datavec.DataVecSequenceDataSetFunction Maven / Gradle / Ivy
The newest version!
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.deeplearning4j.spark.datavec;
import org.apache.spark.api.java.function.Function;
import org.datavec.api.io.WritableConverter;
import org.datavec.api.writable.NDArrayWritable;
import org.datavec.api.writable.Writable;
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;
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;
}
}