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Adding support for ElasticNet regularization.
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datumbox-framework-common/src/test/resources/datasets/housing.csv
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...main/java/com/datumbox/framework/core/utilities/regularization/ElasticNetRegularizer.java
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| /** | ||
| * Copyright (C) 2013-2016 Vasilis Vryniotis <bbriniotis@datumbox.com> | ||
| * | ||
| * Licensed 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 com.datumbox.framework.core.utilities.regularization; | ||
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| import java.util.Map; | ||
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| /** | ||
| * Utility class for ElasticNet regularization. | ||
| * | ||
| * https://web.stanford.edu/~hastie/Papers/B67.2%20(2005)%20301-320%20Zou%20&%20Hastie.pdf | ||
| * http://web.stanford.edu/~hastie/TALKS/enet_talk.pdf | ||
| * | ||
| * @author Vasilis Vryniotis <bbriniotis@datumbox.com> | ||
| */ | ||
| public class ElasticNetRegularizer { | ||
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| /** | ||
| * Updates the weights by applying the ElasticNet regularization. | ||
| * | ||
| * @param l1 | ||
| * @param l2 | ||
| * @param learningRate | ||
| * @param weights | ||
| * @param newWeights | ||
| * @param <K> | ||
| */ | ||
| public static <K> void updateWeights(double l1, double l2, double learningRate, Map<K, Double> weights, Map<K, Double> newWeights) { | ||
| //Equivalent to Naive elastic net | ||
| //L2Regularizer.updateWeights(l2, learningRate, weights, newWeights); | ||
| //L1Regularizer.updateWeights(l1, learningRate, weights, newWeights); | ||
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| for(Map.Entry<K, Double> e : newWeights.entrySet()) { | ||
| double w = e.getValue(); | ||
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| //formula 6 on Zou and Hastie paper | ||
| double net_w = Math.abs(w) - l1/2.0; //naive elastic net beta | ||
| if(net_w>0) { | ||
| net_w /= (1.0 + l2); | ||
| net_w *= Math.signum(w); | ||
| } | ||
| else { | ||
| net_w = 0.0; | ||
| } | ||
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| newWeights.put(e.getKey(), net_w); | ||
| } | ||
| } | ||
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| /** | ||
| * Estimates the penalty by adding the ElasticNet regularization. | ||
| * | ||
| * @param l1 | ||
| * @param l2 | ||
| * @param weights | ||
| * @param <K> | ||
| * @return | ||
| */ | ||
| public static <K> double estimatePenalty(double l1, double l2, Map<K, Double> weights) { | ||
| //Equivalent to Naive elastic net | ||
| //double penalty = 0.0; | ||
| //penalty += L2Regularizer.estimatePenalty(l2, weights); | ||
| //penalty += L1Regularizer.estimatePenalty(l1, weights); | ||
| //return penalty; | ||
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| double sumAbsWeights = 0.0; | ||
| double sumWeightsSquared = 0.0; | ||
| for(double w : weights.values()) { | ||
| sumAbsWeights += Math.abs(w); | ||
| sumWeightsSquared += w*w; | ||
| } | ||
| return l1*sumAbsWeights + | ||
| l2*sumWeightsSquared;//not divided by 2 as in the implementation of L2Regularizer | ||
| } | ||
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| /** | ||
| * Applies a correction on the weights, turning the naive elastic net weights to elastic net weights. | ||
| * | ||
| * @param l2 | ||
| * @param finalWeights | ||
| * @param <K> | ||
| */ | ||
| public static <K> void weightCorrection(double l2, Map<K, Double> finalWeights) { | ||
| for(Map.Entry<K, Double> e : finalWeights.entrySet()) { | ||
| double w = e.getValue(); | ||
| if(w != 0.0) { | ||
| finalWeights.put(e.getKey(), (1.0+l2)*w); //equation 12 on Zou and Hastie paper | ||
| } | ||
| } | ||
| } | ||
| } |
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