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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.lops;
import java.util.HashMap;
import org.apache.sysml.lops.LopProperties.ExecLocation;
import org.apache.sysml.lops.LopProperties.ExecType;
import org.apache.sysml.lops.compile.JobType;
import org.apache.sysml.parser.Statement;
import org.apache.sysml.parser.Expression.*;
/**
* Lop to perform mr map-side grouped aggregates
* (restriction: sum, w/o weights, ngroups), groups broadcasted
*
*/
public class GroupedAggregateM extends Lop
{
public static final String OPCODE = "mapgroupedagg";
public enum CacheType {
RIGHT,
RIGHT_PART,
}
private HashMap _inputParams;
private CacheType _cacheType = null;
public GroupedAggregateM(HashMap inputParameterLops,
DataType dt, ValueType vt, boolean partitioned, ExecType et) {
super(Lop.Type.GroupedAggM, dt, vt);
init(inputParameterLops, dt, vt, et);
_inputParams = inputParameterLops;
_cacheType = partitioned ? CacheType.RIGHT_PART : CacheType.RIGHT;
}
private void init(HashMap inputParameterLops,
DataType dt, ValueType vt, ExecType et)
{
addInput(inputParameterLops.get(Statement.GAGG_TARGET));
inputParameterLops.get(Statement.GAGG_TARGET).addOutput(this);
addInput(inputParameterLops.get(Statement.GAGG_GROUPS));
inputParameterLops.get(Statement.GAGG_GROUPS).addOutput(this);
if( et == ExecType.MR )
{
//setup MR parameters
boolean breaksAlignment = true;
boolean aligner = false;
boolean definesMRJob = false;
lps.addCompatibility(JobType.GMR);
lps.addCompatibility(JobType.DATAGEN);
lps.setProperties( inputs, ExecType.MR, ExecLocation.Map, breaksAlignment, aligner, definesMRJob );
}
else //SPARK
{
//setup Spark parameters
boolean breaksAlignment = false;
boolean aligner = false;
boolean definesMRJob = false;
lps.addCompatibility(JobType.INVALID);
lps.setProperties( inputs, et, ExecLocation.ControlProgram, breaksAlignment, aligner, definesMRJob );
}
}
@Override
public String toString() {
return "Operation = MapGroupedAggregate";
}
@Override
public String getInstructions(int input1, int input2, int output) {
return getInstructions(
String.valueOf(input1),
String.valueOf(input2),
String.valueOf(output) );
}
@Override
public String getInstructions(String input1, String input2, String output)
{
StringBuilder sb = new StringBuilder();
sb.append( getExecType() );
sb.append( Lop.OPERAND_DELIMITOR );
sb.append( OPCODE );
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(0).prepInputOperand(input1));
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(1).prepInputOperand(input2));
sb.append( OPERAND_DELIMITOR );
sb.append( prepOutputOperand(output) );
sb.append( OPERAND_DELIMITOR );
sb.append( _inputParams.get(Statement.GAGG_NUM_GROUPS)
.prepScalarInputOperand(getExecType()) );
sb.append( OPERAND_DELIMITOR );
sb.append( _cacheType.toString() );
return sb.toString();
}
@Override
public boolean usesDistributedCache() {
return (getExecType()==ExecType.MR);
}
@Override
public int[] distributedCacheInputIndex() {
return (getExecType()==ExecType.MR) ?
new int[]{2} : new int[]{-1};
}
}