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

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/*
 * 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 hivemall.utils.math.StatsUtils;

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;

/**
 * A multi-class confidence-weighted linear classification.
 * 
 *  * [1] Mark Dredze, Koby Crammer and Fernando Pereira. "Confidence-weighted linear classification",
 *     In Proc. ICML, pp.264-271, 2008.
 * [2] Koby Crammer, Mark Dredze and Alex Kulesza. "Multi-class confidence weighted algorithms",
 *     In Proc. EMNLP, Vol. 2, pp.496-504, 2008.
 * 
 * 
 * @link http://dl.acm.org/citation.cfm?id=1390190
 * @link http://dl.acm.org/citation.cfm?id=1699577
 */
@Description(name = "train_multiclass_cw",
        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 Confidence-Weighted (CW) multiclass classifier")
public class MulticlassConfidenceWeightedUDTF extends MulticlassOnlineClassifierUDTF {

    /** confidence parameter phi */
    protected float phi;

    @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("phi", "confidence", true, "Confidence parameter [default 1.0]");
        opts.addOption("eta", "hyper_c", true,
            "Confidence hyperparameter eta in range (0.5, 1] [default 0.85]");
        return opts;
    }

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

        float phi = 1.f;
        if (cl != null) {
            String phi_str = cl.getOptionValue("phi");
            if (phi_str == null) {
                String eta_str = cl.getOptionValue("eta");
                if (eta_str != null) {
                    double eta = Double.parseDouble(eta_str);
                    if (eta <= 0.5 || eta > 1) {
                        throw new UDFArgumentException(
                            "Confidence hyperparameter eta must be in range (0.5, 1]: " + eta_str);
                    }
                    phi = (float) StatsUtils.probit(eta, 5d);
                }
            } else {
                phi = Float.parseFloat(phi_str);
            }
        }

        this.phi = phi;
        return cl;
    }

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

        if (gamma > 0.f) {// alpha = max(0, gamma)                   
            Object missed_label = margin.getMaxIncorrectLabel();
            update(features, gamma, actual_label, missed_label);
        }
    }

    protected final float getGamma(Margin margin) {
        float m = margin.get();
        float var = margin.getVariance();
        assert (var != 0);

        float b = 1.f + 2.f * phi * m;
        float gamma_numer = -b + (float) Math.sqrt(b * b - 8.f * phi * (m - phi * var));
        float gamma_denom = 4.f * phi * var;
        if (gamma_denom == 0.f) {// avoid divide-by-zero
            return 0.f;
        }
        return gamma_numer / gamma_denom;
    }

    protected void update(@Nonnull final FeatureValue[] features, float alpha, Object actual_label,
            Object missed_label) {
        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, phi, 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, phi, false);
                model2sub.set(k, new_wrongclass_w);
            }
        }
    }

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

        float delta_w = alpha * old_cov * x;
        float new_w = positive ? old_w + delta_w : old_w - delta_w;
        float new_cov = 1.f / (1.f / old_cov + (2.f * alpha * phi * x * x));
        return new WeightValueWithCovar(new_w, new_cov);
    }

}