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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.regression;

import hivemall.optimizer.EtaEstimator;
import hivemall.optimizer.LossFunctions;

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;

/**
 * Logistic regression using SGD.
 * 
 * @deprecated Use {@link hivemall.regression.GeneralRegressorUDTF} instead
 */
@Deprecated
@Description(name = "logress",
        value = "_FUNC_(array features, float target [, constant string options])"
                + " - Returns a relation consists of <{int|bigint|string} feature, float weight>")
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 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: "
                + EtaEstimator.DEFAULT_POWER_T + "]");
        opts.addOption("eta0", true,
            "The initial learning rate [default: " + EtaEstimator.DEFAULT_ETA0 + "]");
        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 computeGradient(final float target, final float predicted) {
        float eta = etaEstimator.eta(count);
        float gradient = LossFunctions.logisticLoss(target, predicted);
        return eta * gradient;
    }

}