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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 java.util.Map.Entry;
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 grouped aggregates
*
*/
public class GroupedAggregate extends Lop
{
private HashMap _inputParams;
private static final String opcode = "groupedagg";
public static final String COMBINEDINPUT = "combinedinput";
private boolean _weights = false;
//spark-specific parameters
private boolean _broadcastGroups = false;
//cp-specific parameters
private int _numThreads = 1;
/**
* Constructor to perform grouped aggregate.
* inputParameterLops <- parameters required to compute different aggregates (hashmap)
* "combinedinput" -- actual data
* "function" -- aggregate function
*/
public GroupedAggregate(
HashMap inputParameterLops, boolean weights,
DataType dt, ValueType vt) {
this(inputParameterLops, dt, vt, ExecType.MR);
_weights = weights;
}
public GroupedAggregate(
HashMap inputParameterLops,
DataType dt, ValueType vt, ExecType et) {
super(Lop.Type.GroupedAgg, dt, vt);
init(inputParameterLops, dt, vt, et);
}
public GroupedAggregate(
HashMap inputParameterLops,
DataType dt, ValueType vt, ExecType et, boolean broadcastGroups) {
super(Lop.Type.GroupedAgg, dt, vt);
init(inputParameterLops, dt, vt, et);
_broadcastGroups = broadcastGroups;
}
public GroupedAggregate(
HashMap inputParameterLops,
DataType dt, ValueType vt, ExecType et, int k) {
super(Lop.Type.GroupedAgg, dt, vt);
init(inputParameterLops, dt, vt, et);
_numThreads = k;
}
/**
*
* @param inputParameterLops
* @param dt
* @param vt
* @param et
*/
private void init(HashMap inputParameterLops,
DataType dt, ValueType vt, ExecType et) {
if ( et == ExecType.MR ) {
/*
* Inputs to ParameterizedBuiltinOp can be in an arbitrary order. However,
* piggybacking (Dag.java:getAggAndOtherInstructions()) expects the first
* input to be the data (named as "combinedinput") on which the aggregate
* needs to be computed. Make sure that "combinedinput" is the first input
* to GroupedAggregate lop.
*/
this.addInput(inputParameterLops.get(COMBINEDINPUT));
inputParameterLops.get(COMBINEDINPUT).addOutput(this);
// process remaining parameters
for ( Entry e : inputParameterLops.entrySet() ) {
String k = e.getKey();
Lop lop = e.getValue();
if ( !k.equalsIgnoreCase(COMBINEDINPUT) ) {
this.addInput(lop);
lop.addOutput(this);
}
}
_inputParams = inputParameterLops;
boolean breaksAlignment = false;
boolean aligner = false;
boolean definesMRJob = true;
lps.addCompatibility(JobType.GROUPED_AGG);
this.lps.setProperties(inputs, et, ExecLocation.MapAndReduce, breaksAlignment, aligner, definesMRJob);
}
else {
boolean breaksAlignment = false;
boolean aligner = false;
boolean definesMRJob = false;
// First, add inputs corresponding to "target" and "groups"
this.addInput(inputParameterLops.get(Statement.GAGG_TARGET));
inputParameterLops.get(Statement.GAGG_TARGET).addOutput(this);
this.addInput(inputParameterLops.get(Statement.GAGG_GROUPS));
inputParameterLops.get(Statement.GAGG_GROUPS).addOutput(this);
// process remaining parameters
for ( Entry e : inputParameterLops.entrySet() ) {
String k = e.getKey();
Lop lop = e.getValue();
if ( !k.equalsIgnoreCase(Statement.GAGG_TARGET) && !k.equalsIgnoreCase(Statement.GAGG_GROUPS) ) {
this.addInput(lop);
lop.addOutput(this);
}
}
_inputParams = inputParameterLops;
lps.addCompatibility(JobType.INVALID);
this.lps.setProperties(inputs, et, ExecLocation.ControlProgram, breaksAlignment, aligner, definesMRJob);
}
}
@Override
public String toString() {
return "Operation = GroupedAggregate";
}
/**
* Function to generate CP Grouped Aggregate Instructions.
*
*/
@Override
public String getInstructions(String output)
throws LopsException
{
StringBuilder sb = new StringBuilder();
sb.append( getExecType() );
sb.append( Lop.OPERAND_DELIMITOR );
sb.append( opcode );
sb.append( Lop.OPERAND_DELIMITOR );
if ( _inputParams.get(Statement.GAGG_TARGET) == null || _inputParams.get(Statement.GAGG_GROUPS) == null || _inputParams.get("fn") == null )
throw new LopsException(this.printErrorLocation() + "Invalid parameters to groupedAggregate -- \"target\", \"groups\", \"fn\" must be provided");
String targetVar = _inputParams.get(Statement.GAGG_TARGET).getOutputParameters().getLabel();
String groupsVar = _inputParams.get(Statement.GAGG_GROUPS).getOutputParameters().getLabel();
sb.append( Statement.GAGG_TARGET );
sb.append( Lop.NAME_VALUE_SEPARATOR );
sb.append( targetVar );
sb.append( Lop.OPERAND_DELIMITOR );
sb.append( Statement.GAGG_GROUPS );
sb.append( Lop.NAME_VALUE_SEPARATOR );
sb.append( groupsVar );
if ( _inputParams.get(Statement.GAGG_WEIGHTS) != null )
{
sb.append( Lop.OPERAND_DELIMITOR );
sb.append( Statement.GAGG_WEIGHTS );
sb.append( Lop.NAME_VALUE_SEPARATOR );
sb.append( _inputParams.get(Statement.GAGG_WEIGHTS).getOutputParameters().getLabel() );
}
// Process all other name=value parameters, which are scalars
String name, valueString;
Lop value;
for(Entry e : _inputParams.entrySet()) {
name = e.getKey();
if ( !name.equalsIgnoreCase(Statement.GAGG_TARGET) && !name.equalsIgnoreCase(Statement.GAGG_GROUPS) && !name.equalsIgnoreCase(Statement.GAGG_WEIGHTS) ) {
value = e.getValue();
valueString = value.prepScalarLabel();
sb.append( OPERAND_DELIMITOR );
sb.append( name );
sb.append( Lop.NAME_VALUE_SEPARATOR );
sb.append( valueString );
}
}
if( getExecType()==ExecType.CP ) {
sb.append( OPERAND_DELIMITOR );
sb.append( "k" );
sb.append( Lop.NAME_VALUE_SEPARATOR );
sb.append( _numThreads );
}
else if( getExecType()==ExecType.SPARK ) {
sb.append( OPERAND_DELIMITOR );
sb.append( "broadcast" );
sb.append( Lop.NAME_VALUE_SEPARATOR );
sb.append( _broadcastGroups );
}
sb.append( OPERAND_DELIMITOR );
sb.append( prepOutputOperand(output));
return sb.toString();
}
@Override
public String getInstructions(int input_index, int output_index)
{
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(input_index));
// get the aggregate function
sb.append( OPERAND_DELIMITOR );
Lop funcLop = _inputParams.get(Statement.GAGG_FN);
sb.append( funcLop.prepScalarInputOperand(getExecType()));
// get the "optional" parameters
if ( _inputParams.get(Statement.GAGG_FN_CM_ORDER) != null ) {
sb.append( OPERAND_DELIMITOR );
Lop orderLop = _inputParams.get(Statement.GAGG_FN_CM_ORDER);
sb.append( orderLop.prepScalarInputOperand(getExecType()));
}
// add output_index to instruction
sb.append( OPERAND_DELIMITOR );
sb.append( prepOutputOperand(output_index) );
sb.append( OPERAND_DELIMITOR );
sb.append( _weights );
sb.append( OPERAND_DELIMITOR );
Lop ngroups = _inputParams.get(Statement.GAGG_NUM_GROUPS);
sb.append( (ngroups!=null)? ngroups.prepScalarInputOperand(getExecType()) : "-1" );
return sb.toString();
}
}