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Merge pull request #42 from myui/feature/arow_regression
Feature/arow regression
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,144 @@ | ||
| /* | ||
| * Hivemall: Hive scalable Machine Learning Library | ||
| * | ||
| * Copyright (C) 2013 | ||
| * National Institute of Advanced Industrial Science and Technology (AIST) | ||
| * Registration Number: H25PRO-1520 | ||
| * | ||
| * This library is free software; you can redistribute it and/or | ||
| * modify it under the terms of the GNU Lesser General Public | ||
| * License as published by the Free Software Foundation. | ||
| * | ||
| * This library 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 | ||
| * Lesser General Public License for more details. | ||
| * | ||
| * You should have received a copy of the GNU Lesser General Public | ||
| * License along with this library; if not, write to the Free Software | ||
| * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA | ||
| */ | ||
| package hivemall.regression; | ||
|
|
||
| import hivemall.common.FeatureValue; | ||
| import hivemall.common.PredictionResult; | ||
| import hivemall.common.WeightValue; | ||
|
|
||
| import java.util.Collection; | ||
|
|
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| import org.apache.commons.cli.CommandLine; | ||
| import org.apache.commons.cli.Options; | ||
| import org.apache.hadoop.hive.ql.exec.UDFArgumentException; | ||
| import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector; | ||
| import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils; | ||
| import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector; | ||
|
|
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| public class AROWRegressionUDTF extends OnlineRegressionUDTF { | ||
|
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| /** Regularization parameter r */ | ||
| protected float r; | ||
|
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| @Override | ||
| public StructObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException { | ||
| final int numArgs = argOIs.length; | ||
| if(numArgs != 2 && numArgs != 3) { | ||
| throw new UDFArgumentException(getClass().getSimpleName() | ||
| + " takes arguments: List<Int|BigInt|Text> features, float target [, constant string options]"); | ||
| } | ||
|
|
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| return super.initialize(argOIs); | ||
| } | ||
|
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| @Override | ||
| protected Options getOptions() { | ||
| Options opts = super.getOptions(); | ||
| opts.addOption("r", "regularization", true, "Regularization parameter for some r > 0 [default 0.1]"); | ||
| return opts; | ||
| } | ||
|
|
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| @Override | ||
| protected CommandLine processOptions(ObjectInspector[] argOIs) throws UDFArgumentException { | ||
| final CommandLine cl = super.processOptions(argOIs); | ||
|
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| float r = 0.1f; | ||
| if(cl != null) { | ||
| String r_str = cl.getOptionValue("r"); | ||
| if(r_str != null) { | ||
| r = Float.parseFloat(r_str); | ||
| if(!(r > 0)) { | ||
| throw new UDFArgumentException("Regularization parameter must be greater than 0: " | ||
| + r_str); | ||
| } | ||
| } | ||
| } | ||
|
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| this.r = r; | ||
| return cl; | ||
| } | ||
|
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| @Override | ||
| protected void train(Collection<?> features, float target) { | ||
| PredictionResult margin = calcScoreAndVariance(features); | ||
| float predicted = margin.getScore(); | ||
|
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| float loss = loss(target, predicted); | ||
|
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| float var = margin.getVariance(); | ||
| float beta = 1.f / (var + r); | ||
|
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| update(features, loss, beta); | ||
| } | ||
|
|
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| /** | ||
| * @return target - predicted | ||
| */ | ||
| protected float loss(float target, float predicted) { | ||
| return target - predicted; // y - m^Tx | ||
| } | ||
|
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| @Override | ||
| protected void update(final Collection<?> features, final float loss, final float beta) { | ||
| final ObjectInspector featureInspector = featureListOI.getListElementObjectInspector(); | ||
|
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| for(Object f : features) { | ||
| final Object k; | ||
| final float v; | ||
| if(parseX) { | ||
| FeatureValue fv = FeatureValue.parse(f, feature_hashing); | ||
| k = fv.getFeature(); | ||
| v = fv.getValue(); | ||
| } else { | ||
| k = ObjectInspectorUtils.copyToStandardObject(f, featureInspector); | ||
| v = 1.f; | ||
| } | ||
| WeightValue old_w = weights.get(k); | ||
| WeightValue new_w = getNewWeight(old_w, v, loss, beta); | ||
| weights.put(k, new_w); | ||
| } | ||
|
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| if(biasKey != null) { | ||
| WeightValue old_bias = weights.get(biasKey); | ||
| WeightValue new_bias = getNewWeight(old_bias, bias, loss, beta); | ||
| weights.put(biasKey, new_bias); | ||
| } | ||
| } | ||
|
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| private static WeightValue getNewWeight(final WeightValue old, final float x, final float loss, final float beta) { | ||
| final float old_w; | ||
| final float old_cov; | ||
| if(old == null) { | ||
| old_w = 0.f; | ||
| old_cov = 1.f; | ||
| } else { | ||
| old_w = old.getValue(); | ||
| old_cov = old.getCovariance(); | ||
| } | ||
|
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| float cov_x = old_cov * x; | ||
| float new_w = old_w + loss * cov_x * beta; | ||
| float new_cov = old_cov - (beta * cov_x * cov_x); | ||
|
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| return new WeightValue(new_w, new_cov); | ||
| } | ||
|
|
||
| } |
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