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JPMML Apache Spark ML to PMML converter
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/*
* Copyright (c) 2016 Villu Ruusmann
*
* This file is part of JPMML-SparkML
*
* JPMML-SparkML is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* JPMML-SparkML is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with JPMML-SparkML. If not, see .
*/
package org.jpmml.sparkml.feature;
import java.util.ArrayList;
import java.util.List;
import org.apache.spark.ml.feature.MinMaxScalerModel;
import org.apache.spark.ml.linalg.Vector;
import org.dmg.pmml.DataType;
import org.dmg.pmml.DerivedField;
import org.dmg.pmml.Expression;
import org.dmg.pmml.OpType;
import org.dmg.pmml.PMMLFunctions;
import org.jpmml.converter.ContinuousFeature;
import org.jpmml.converter.ExpressionUtil;
import org.jpmml.converter.Feature;
import org.jpmml.converter.SchemaUtil;
import org.jpmml.converter.ValueUtil;
import org.jpmml.sparkml.FeatureConverter;
import org.jpmml.sparkml.SparkMLEncoder;
public class MinMaxScalerModelConverter extends FeatureConverter {
public MinMaxScalerModelConverter(MinMaxScalerModel transformer){
super(transformer);
}
@Override
public List encodeFeatures(SparkMLEncoder encoder){
MinMaxScalerModel transformer = getTransformer();
double rescaleFactor = (transformer.getMax() - transformer.getMin());
double rescaleConstant = transformer.getMin();
Vector originalMin = transformer.originalMin();
Vector originalMax = transformer.originalMax();
List features = encoder.getFeatures(transformer.getInputCol());
SchemaUtil.checkSize(Math.max(originalMin.size(), originalMax.size()), features);
List result = new ArrayList<>();
for(int i = 0, length = features.size(); i < length; i++){
Feature feature = features.get(i);
ContinuousFeature continuousFeature = feature.toContinuousFeature();
double min = originalMin.apply(i);
double max = originalMax.apply(i);
Expression expression = ExpressionUtil.createApply(PMMLFunctions.DIVIDE, ExpressionUtil.createApply(PMMLFunctions.SUBTRACT, continuousFeature.ref(), ExpressionUtil.createConstant(min)), ExpressionUtil.createConstant(max - min));
if(!ValueUtil.isOne(rescaleFactor)){
expression = ExpressionUtil.createApply(PMMLFunctions.MULTIPLY, expression, ExpressionUtil.createConstant(rescaleFactor));
} // End if
if(!ValueUtil.isZero(rescaleConstant)){
expression = ExpressionUtil.createApply(PMMLFunctions.ADD, expression, ExpressionUtil.createConstant(rescaleConstant));
}
DerivedField derivedField = encoder.createDerivedField(formatName(transformer, i, length), OpType.CONTINUOUS, DataType.DOUBLE, expression);
result.add(new ContinuousFeature(encoder, derivedField));
}
return result;
}
}
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