LogressUDTF.java

/*
 * Hivemall: Hive scalable Machine Learning Library
 *
 * Copyright (C) 2015 Makoto YUI
 * Copyright (C) 2013-2015 National Institute of Advanced Industrial Science and Technology (AIST)
 *
 * 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 hivemall.regression;

import hivemall.common.EtaEstimator;
import hivemall.common.LossFunctions;

import hivemall.io.IWeightValue;
import hivemall.io.WeightValue;
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.StructObjectInspector;

public final class LogressUDTF extends RegressionBaseUDTF {

    private EtaEstimator etaEstimator;

    @Override
    public StructObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException {
        final int numArgs = argOIs.length;
        if(numArgs != 2 && numArgs != 3) {
            throw new UDFArgumentException("LogressUDTF takes 2 or 3 arguments: List<Text|Int|BitInt> features, float target [, constant string options]");
        }

        return super.initialize(argOIs);
    }

    @Override
    protected Options getOptions() {
        Options opts = super.getOptions();
        opts.addOption("t", "total_steps", true, "a total of n_samples * epochs time steps");
        opts.addOption("power_t", true, "The exponent for inverse scaling learning rate [default 0.1]");
        opts.addOption("eta0", true, "The initial learning rate [default 0.1]");
        return opts;
    }

    @Override
    protected CommandLine processOptions(ObjectInspector[] argOIs) throws UDFArgumentException {
        CommandLine cl = super.processOptions(argOIs);
        
        this.etaEstimator = EtaEstimator.get(cl);
        return cl;
    }

    @Override
    protected void checkTargetValue(final float target) throws UDFArgumentException {
        if(target < 0.f || target > 1.f) {
            throw new UDFArgumentException("target must be in range 0 to 1: " + target);
        }
    }

    @Override
    protected float computeUpdate(final float target, final float predicted) {
        float eta = etaEstimator.eta(count);
        float gradient = LossFunctions.logisticLoss(target, predicted);
        return eta * gradient;
    }

    @Override
    protected IWeightValue getNewWeight(IWeightValue old_w, float delta) {
        float oldWeight = 0.f;
        if (old_w != null) {
            oldWeight = old_w.get();
        }
        return new WeightValue(oldWeight + (delta / sampled));
    }

}