| /* |
| * 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.solr.client.solrj.io.eval; |
| |
| import java.io.IOException; |
| import java.util.ArrayList; |
| import java.util.List; |
| import java.util.Map; |
| import java.util.HashMap; |
| |
| import org.apache.commons.math3.stat.regression.MultipleLinearRegression; |
| import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression; |
| import org.apache.solr.client.solrj.io.Tuple; |
| import org.apache.solr.client.solrj.io.stream.expr.StreamExpression; |
| import org.apache.solr.client.solrj.io.stream.expr.StreamFactory; |
| |
| public class OLSRegressionEvaluator extends RecursiveObjectEvaluator implements ManyValueWorker { |
| protected static final long serialVersionUID = 1L; |
| |
| public OLSRegressionEvaluator(StreamExpression expression, StreamFactory factory) throws IOException{ |
| super(expression, factory); |
| } |
| |
| @Override |
| @SuppressWarnings({"unchecked"}) |
| public Object doWork(Object ... values) throws IOException { |
| |
| Matrix observations = null; |
| List<Number> outcomes = null; |
| |
| if(values[0] instanceof Matrix) { |
| observations = (Matrix)values[0]; |
| } else { |
| throw new IOException("The first parameter for olsRegress should be the observation matrix."); |
| } |
| |
| if(values[1] instanceof List) { |
| outcomes = (List) values[1]; |
| } else { |
| throw new IOException("The second parameter for olsRegress should be outcome array. "); |
| } |
| |
| double[][] observationData = observations.getData(); |
| double[] outcomeData = new double[outcomes.size()]; |
| for(int i=0; i<outcomeData.length; i++) { |
| outcomeData[i] = outcomes.get(i).doubleValue(); |
| } |
| |
| OLSMultipleLinearRegression multipleLinearRegression = (OLSMultipleLinearRegression)regress(observationData, outcomeData); |
| |
| @SuppressWarnings({"rawtypes"}) |
| Map map = new HashMap(); |
| |
| map.put("regressandVariance", multipleLinearRegression.estimateRegressandVariance()); |
| map.put("regressionParameters", list(multipleLinearRegression.estimateRegressionParameters())); |
| map.put("RSquared", multipleLinearRegression.calculateRSquared()); |
| map.put("adjustedRSquared", multipleLinearRegression.calculateAdjustedRSquared()); |
| map.put("residualSumSquares", multipleLinearRegression.calculateResidualSumOfSquares()); |
| |
| try { |
| map.put("regressionParametersStandardErrors", list(multipleLinearRegression.estimateRegressionParametersStandardErrors())); |
| map.put("regressionParametersVariance", new Matrix(multipleLinearRegression.estimateRegressionParametersVariance())); |
| } catch (Exception e) { |
| //Exception is thrown if the matrix is singular |
| } |
| |
| return new MultipleRegressionTuple(multipleLinearRegression, map); |
| } |
| |
| @SuppressWarnings({"unchecked"}) |
| private List<Number> list(double[] values) { |
| @SuppressWarnings({"rawtypes"}) |
| List list = new ArrayList(); |
| for(double d : values) { |
| list.add(d); |
| } |
| return list; |
| } |
| |
| protected MultipleLinearRegression regress(double[][] observations, double[] outcomes) { |
| OLSMultipleLinearRegression olsMultipleLinearRegression = new OLSMultipleLinearRegression(); |
| olsMultipleLinearRegression.newSampleData(outcomes, observations); |
| return olsMultipleLinearRegression; |
| } |
| |
| public static class MultipleRegressionTuple extends Tuple { |
| |
| private MultipleLinearRegression multipleLinearRegression; |
| |
| |
| public MultipleRegressionTuple(MultipleLinearRegression multipleLinearRegression, Map<?,?> map) { |
| super(map); |
| this.multipleLinearRegression = multipleLinearRegression; |
| } |
| |
| public double predict(double[] values) { |
| @SuppressWarnings({"unchecked"}) |
| List<Number> weights = (List<Number>)get("regressionParameters"); |
| double prediction = 0.0; |
| List<Number> predictors = new ArrayList<>(); |
| predictors.add(1.0D); |
| for(double d : values) { |
| predictors.add(d); |
| } |
| for(int i=0; i< predictors.size(); i++) { |
| prediction += weights.get(i).doubleValue()*predictors.get(i).doubleValue(); |
| } |
| |
| return prediction; |
| } |
| } |
| } |
| |