001/*
002 * Copyright (c) 2015-2020, Oracle and/or its affiliates. All rights reserved.
003 *
004 * Licensed under the Apache License, Version 2.0 (the "License");
005 * you may not use this file except in compliance with the License.
006 * You may obtain a copy of the License at
007 *
008 *     http://www.apache.org/licenses/LICENSE-2.0
009 *
010 * Unless required by applicable law or agreed to in writing, software
011 * distributed under the License is distributed on an "AS IS" BASIS,
012 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express implied.
013 * See the License for the specific language governing permissions and
014 * limitations under the License.
015 */
016
017package org.tribuo.classification.sgd;
018
019import com.oracle.labs.mlrg.olcut.util.Pair;
020import org.tribuo.common.sgd.SGDObjective;
021import org.tribuo.math.la.SGDVector;
022import org.tribuo.math.util.VectorNormalizer;
023
024/**
025 * An interface for single label prediction objectives.
026 * <p>
027 * An objective knows if it generates a probabilistic model or not,
028 * and what kind of normalization needs to be applied to produce probability values.
029 */
030public interface LabelObjective extends SGDObjective<Integer> {
031
032    /**
033     * Scores a prediction, returning the loss and a vector of per label gradients.
034     *
035     * @deprecated In 4.1, to migrate to the new name {@link #lossAndGradient}.
036     * @param truth      The true label id.
037     * @param prediction The prediction for each label id.
038     * @return The score and per label gradient.
039     */
040    @Deprecated
041    Pair<Double, SGDVector> valueAndGradient(int truth, SGDVector prediction);
042
043    @Override
044    default Pair<Double, SGDVector> lossAndGradient(Integer truth, SGDVector prediction) {
045        return valueAndGradient(truth, prediction);
046    }
047
048    /**
049     * Generates a new {@link VectorNormalizer} which normalizes the predictions into [0,1].
050     * @return The vector normalizer for this objective.
051     */
052    public VectorNormalizer getNormalizer();
053
054    /**
055     * Does the objective function score probabilities or not?
056     * @return boolean.
057     */
058    public boolean isProbabilistic();
059
060}