hivemall.classifier.multiclass.MulticlassAROWClassifierUDTF Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of hivemall-all
The newest version!
/*
 * 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 hivemall.classifier.multiclass;

import hivemall.model.FeatureValue;
import hivemall.model.IWeightValue;
import hivemall.model.Margin;
import hivemall.model.PredictionModel;
import hivemall.model.WeightValue.WeightValueWithCovar;

import javax.annotation.Nonnull;

import org.apache.commons.cli.CommandLine;
import org.apache.commons.cli.Options;
import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;

/**
 * Multi-class Adaptive Regularization of Weight Vectors (AROW) classifier.
 * 
 *  * [1] K. Crammer, A. Kulesza, and M. Dredze, "Adaptive Regularization of Weight Vectors",
 *     In Proc. NIPS, 2009.
 * 
 */
@Description(name = "train_multiclass_arow",
        value = "_FUNC_(list features, {int|string} label [, const string options])"
                + " - Returns a relation consists of <{int|string} label, {string|int|bigint} feature, float weight, float covar>",
        extended = "Build a prediction model by Adaptive Regularization of Weight Vectors (AROW) multiclass classifier")
public class MulticlassAROWClassifierUDTF extends MulticlassOnlineClassifierUDTF {

    /** Regularization parameter r */
    protected float r;

    @Override
    public StructObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException {
        final int numArgs = argOIs.length;
        if (numArgs != 2 && numArgs != 3) {
            throw new UDFArgumentException(
                "_FUNC_ takes 2 or 3 arguments: List features, {Int|String} label [, constant String options]");
        }

        return super.initialize(argOIs);
    }

    @Override
    protected boolean useCovariance() {
        return true;
    }

    @Override
    protected Options getOptions() {
        Options opts = super.getOptions();
        opts.addOption("r", "regularization", true,
            "Regularization parameter for some r > 0 [default 0.1]");
        return opts;
    }

    @Override
    protected CommandLine processOptions(ObjectInspector[] argOIs) throws UDFArgumentException {
        final CommandLine cl = super.processOptions(argOIs);

        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);
                }
            }
        }

        this.r = r;
        return cl;
    }

    @Override
    protected void train(@Nonnull final FeatureValue[] features, @Nonnull Object actual_label) {
        Margin margin = getMarginAndVariance(features, actual_label);
        float m = margin.get();

        if (m >= 1.f) {
            return;
        }

        float var = margin.getVariance();
        float beta = 1.f / (var + r);
        float alpha = (1.f - m) * beta;

        Object missed_label = margin.getMaxIncorrectLabel();
        update(features, actual_label, missed_label, alpha, beta);
    }

    protected void update(@Nonnull final FeatureValue[] features, final Object actual_label,
            final Object missed_label, final float alpha, final float beta) {
        assert (actual_label != null);
        if (actual_label.equals(missed_label)) {
            throw new IllegalArgumentException(
                "Actual label equals to missed label: " + actual_label);
        }

        PredictionModel model2add = label2model.get(actual_label);
        if (model2add == null) {
            model2add = createModel();
            label2model.put(actual_label, model2add);
        }
        PredictionModel model2sub = null;
        if (missed_label != null) {
            model2sub = label2model.get(missed_label);
            if (model2sub == null) {
                model2sub = createModel();
                label2model.put(missed_label, model2sub);
            }
        }

        for (FeatureValue f : features) {// w[f] += y * x[f]
            if (f == null) {
                continue;
            }
            final Object k = f.getFeature();
            final float v = f.getValueAsFloat();

            IWeightValue old_correctclass_w = model2add.get(k);
            IWeightValue new_correctclass_w =
                    getNewWeight(old_correctclass_w, v, alpha, beta, true);
            model2add.set(k, new_correctclass_w);

            if (model2sub != null) {
                IWeightValue old_wrongclass_w = model2sub.get(k);
                IWeightValue new_wrongclass_w =
                        getNewWeight(old_wrongclass_w, v, alpha, beta, false);
                model2sub.set(k, new_wrongclass_w);
            }
        }
    }

    private static IWeightValue getNewWeight(final IWeightValue old, final float v,
            final float alpha, final float beta, final boolean positive) {
        final float old_v;
        final float old_cov;
        if (old == null) {
            old_v = 0.f;
            old_cov = 1.f;
        } else {
            old_v = old.get();
            old_cov = old.getCovariance();
        }

        float cv = old_cov * v;
        float new_w = positive ? old_v + (alpha * cv) : old_v - (alpha * cv);
        float new_cov = old_cov - (beta * cv * cv);

        return new WeightValueWithCovar(new_w, new_cov);
    }

    @Description(name = "train_multiclass_arowh",
            value = "_FUNC_(list features, int|string label [, const string options])"
                    + " - Returns a relation consists of ",
            extended = "Build a prediction model by Adaptive Regularization of Weight Vectors (AROW) multiclass classifier using hinge loss")
    public static final class AROWh extends MulticlassAROWClassifierUDTF {

        /** Aggressiveness parameter */
        protected float c;

        @Override
        protected Options getOptions() {
            Options opts = super.getOptions();
            opts.addOption("c", "aggressiveness", true, "Aggressiveness parameter C [default 1.0]");
            return opts;
        }

        @Override
        protected CommandLine processOptions(ObjectInspector[] argOIs) throws UDFArgumentException {
            final CommandLine cl = super.processOptions(argOIs);

            float c = 1.f;
            if (cl != null) {
                String c_str = cl.getOptionValue("c");
                if (c_str != null) {
                    c = Float.parseFloat(c_str);
                    if (!(c > 0.f)) {
                        throw new UDFArgumentException(
                            "Aggressiveness parameter C must be C > 0: " + c);
                    }
                }
            }

            this.c = c;
            return cl;
        }

        @Override
        protected void train(@Nonnull final FeatureValue[] features, @Nonnull Object actual_label) {
            Margin margin = getMarginAndVariance(features, actual_label);

            float loss = loss(margin);
            if (loss > 0.f) {
                float var = margin.getVariance();
                float beta = 1.f / (var + r);
                float alpha = loss * beta;

                Object missed_label = margin.getMaxIncorrectLabel();
                update(features, actual_label, missed_label, alpha, beta);
            }
        }

        /**
         * @return C - m
         */
        protected float loss(Margin margin) {
            return c - margin.get();
        }
    }
}