hivemall.factorization.mf.MatrixFactorizationSGDUDTF 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.factorization.mf;

import hivemall.optimizer.EtaEstimator;

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;

@Description(name = "train_mf_sgd",
        value = "_FUNC_(INT user, INT item, FLOAT rating [, CONSTANT STRING options])"
                + " - Returns a relation consists of  Pu, array Qi [, float Bu, float Bi [, float mu]]>")
public final class MatrixFactorizationSGDUDTF extends OnlineMatrixFactorizationUDTF {

    private EtaEstimator etaEstimator;

    public MatrixFactorizationSGDUDTF() {
        super();
    }

    @Override
    protected Options getOptions() {
        Options opts = super.getOptions();
        opts.addOption("eta", true,
            "The initial learning rate [default: " + EtaEstimator.DEFAULT_ETA + "]");
        opts.addOption("eta0", true,
            "The initial learning rate [default: " + EtaEstimator.DEFAULT_ETA0 + "]");
        opts.addOption("t", "total_steps", true, "The total number of training examples");
        opts.addOption("power_t", true, "The exponent for inverse scaling learning rate [default: "
                + EtaEstimator.DEFAULT_POWER_T + "]");
        return opts;
    }

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

    @Override
    protected float eta() {
        return etaEstimator.eta(count);
    }

}