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Declarative Machine Learning
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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.apache.sysml.runtime.transform;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
import org.apache.wink.json4j.JSONException;
import org.apache.sysml.runtime.matrix.CSVReblockMR.OffsetCount;
import org.apache.sysml.runtime.matrix.mapred.MRConfigurationNames;
public class GTFMTDMapper implements Mapper{
private OutputCollector _collector = null;
private int _mapTaskID = -1;
TfUtils _agents = null;
private boolean _partFileWithHeader = false;
private boolean _firstRecordInSplit = true;
private String _partFileName = null;
private long _offsetInPartFile = -1;
// ----------------------------------------------------------------------------------------------
/**
* Configure the information used in the mapper, and setup transformation agents.
*/
@Override
public void configure(JobConf job) {
String[] parts = job.get(MRConfigurationNames.MR_TASK_ATTEMPT_ID).split("_");
if ( parts[0].equalsIgnoreCase("task")) {
_mapTaskID = Integer.parseInt(parts[parts.length-1]);
}
else if ( parts[0].equalsIgnoreCase("attempt")) {
_mapTaskID = Integer.parseInt(parts[parts.length-2]);
}
else {
throw new RuntimeException("Unrecognized format for taskID: " + job.get(MRConfigurationNames.MR_TASK_ATTEMPT_ID));
}
try {
_partFileName = TfUtils.getPartFileName(job);
_partFileWithHeader = TfUtils.isPartFileWithHeader(job);
_agents = new TfUtils(job);
} catch(IOException e) { throw new RuntimeException(e); }
catch(JSONException e) { throw new RuntimeException(e); }
}
public void map(LongWritable rawKey, Text rawValue, OutputCollector out, Reporter reporter) throws IOException {
if(_firstRecordInSplit)
{
_firstRecordInSplit = false;
_collector = out;
_offsetInPartFile = rawKey.get();
}
// ignore header
if (_agents.hasHeader() && rawKey.get() == 0 && _partFileWithHeader)
return;
_agents.prepareTfMtd(rawValue.toString());
}
@Override
public void close() throws IOException {
_agents.getMVImputeAgent().mapOutputTransformationMetadata(_collector, _mapTaskID, _agents);
_agents.getRecodeAgent().mapOutputTransformationMetadata(_collector, _mapTaskID, _agents);
_agents.getBinAgent().mapOutputTransformationMetadata(_collector, _mapTaskID, _agents);
// Output part-file offsets to create OFFSETS_FILE, which is to be used in csv reblocking.
// OffsetCount is denoted as a DistinctValue by concatenating parfile name and offset within partfile.
if(_collector != null) {
IntWritable key = new IntWritable((int)_agents.getNumCols()+1);
DistinctValue val = new DistinctValue(new OffsetCount(_partFileName, _offsetInPartFile, _agents.getValid()));
_collector.collect(key, val);
}
// reset global variables, required when the jvm is reused.
_firstRecordInSplit = true;
_offsetInPartFile = -1;
_partFileWithHeader = false;
}
}