CTreeMissingHandler.java

/*
 * Apache License
 * Version 2.0, January 2004
 * http://www.apache.org/licenses/
 *
 *    Copyright 2013 Aurelian Tutuianu
 *    Copyright 2014 Aurelian Tutuianu
 *    Copyright 2015 Aurelian Tutuianu
 *    Copyright 2016 Aurelian Tutuianu
 *
 *    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 rapaio.ml.classifier.tree;

import rapaio.core.RandomSource;
import rapaio.data.Frame;
import rapaio.data.Mapping;
import rapaio.data.Var;
import rapaio.data.stream.FSpot;
import rapaio.util.Pair;
import rapaio.util.Tagged;
import rapaio.util.func.SPredicate;

import java.io.Serializable;
import java.util.*;
import java.util.stream.IntStream;

import static java.util.stream.Collectors.toList;

/**
 * Created by <a href="mailto:padreati@yahoo.com>Aurelian Tutuianu</a>.
 */
public interface CTreeMissingHandler extends Tagged, Serializable {

    CTreeMissingHandler Ignored = new CTreeMissingHandler() {
        private static final long serialVersionUID = -9017265383541294518L;

        @Override
        public String name() {
            return "Ignored";
        }

        @Override
        public Pair<List<Frame>, List<Var>> performSplit(Frame df, Var weights, CTreeCandidate candidate) {
            List<SPredicate<FSpot>> p = candidate.getGroupPredicates();
            List<Mapping> mappings = IntStream.range(0, p.size()).boxed().map(i -> Mapping.empty()).collect(toList());

            df.stream().forEach(s -> {
                for (int i = 0; i < p.size(); i++) {
                    if (p.get(i).test(s)) {
                        mappings.get(i).add(s.row());
                        break;
                    }
                }
            });
            return Pair.from(
                    mappings.stream().map(df::mapRows).collect(toList()),
                    mappings.stream().map(weights::mapRows).collect(toList())
            );
        }

    };
    CTreeMissingHandler ToMajority = new CTreeMissingHandler() {
        private static final long serialVersionUID = -5858151664805703831L;

        @Override
        public String name() {
            return "ToMajority";
        }

        @Override
        public Pair<List<Frame>, List<Var>> performSplit(Frame df, Var weights, CTreeCandidate candidate) {
            List<SPredicate<FSpot>> p = candidate.getGroupPredicates();
            List<Mapping> mappings = IntStream.range(0, p.size()).boxed().map(i -> Mapping.empty()).collect(toList());

            List<Integer> missingSpots = new LinkedList<>();
            df.stream().forEach(s -> {
                for (int i = 0; i < p.size(); i++) {
                    if (p.get(i).test(s)) {
                        mappings.get(i).add(s.row());
                        return;
                    }
                }
                missingSpots.add(s.row());
            });
            List<Integer> lens = mappings.stream().map(Mapping::size).collect(toList());
            Collections.shuffle(lens);
            int majorityGroup = 0;
            int majoritySize = 0;
            for (int i = 0; i < mappings.size(); i++) {
                if (mappings.get(i).size() > majoritySize) {
                    majorityGroup = i;
                    majoritySize = mappings.get(i).size();
                }
            }
            final int index = majorityGroup;

            mappings.get(index).addAll(missingSpots);

            return Pair.from(
                    mappings.stream().map(df::mapRows).collect(toList()),
                    mappings.stream().map(weights::mapRows).collect(toList())
            );
        }
    };
    CTreeMissingHandler ToAllWeighted = new CTreeMissingHandler() {
        private static final long serialVersionUID = 5936044048099571710L;

        @Override
        public Pair<List<Frame>, List<Var>> performSplit(Frame df, Var weights, CTreeCandidate candidate) {
            List<SPredicate<FSpot>> pred = candidate.getGroupPredicates();
            List<Mapping> mappings = IntStream.range(0, pred.size()).boxed().map(i -> Mapping.empty()).collect(toList());

            List<Integer> missingSpots = new ArrayList<>();
            df.stream().forEach(s -> {
                for (int i = 0; i < pred.size(); i++) {
                    if (pred.get(i).test(s)) {
                        mappings.get(i).add(s.row());
                        return;
                    }
                }
                missingSpots.add(s.row());
            });

            final double[] p = new double[mappings.size()];
            double n = 0;
            for (int i = 0; i < mappings.size(); i++) {
                p[i] = mappings.get(i).size();
                n += p[i];
            }
            for (int i = 0; i < p.length; i++) {
                p[i] /= n;
            }
            List<Var> weightsList = mappings.stream().map(weights::mapRows).map(Var::solidCopy).collect(toList());
            for (int i = 0; i < mappings.size(); i++) {
                final int ii = i;
                missingSpots.forEach(row -> {
                    mappings.get(ii).add(row);
                    weightsList.get(ii).addValue(weights.missing(row) ? p[ii] : weights.value(row) * p[ii]);
                });
            }
            List<Frame> frames = mappings.stream().map(df::mapRows).collect(toList());
            return Pair.from(frames, weightsList);
        }

        @Override
        public String name() {
            return "ToAllWeighted";
        }
    };
    CTreeMissingHandler ToRandom = new CTreeMissingHandler() {
        private static final long serialVersionUID = -4762758695801141929L;

        @Override
        public Pair<List<Frame>, List<Var>> performSplit(Frame df, Var weights, CTreeCandidate candidate) {
            List<SPredicate<FSpot>> pred = candidate.getGroupPredicates();
            List<Mapping> mappings = IntStream.range(0, pred.size()).boxed().map(i -> Mapping.empty()).collect(toList());

            final Set<Integer> missingSpots = new HashSet<>();
            df.stream().forEach(s -> {
                for (int i = 0; i < pred.size(); i++) {
                    if (pred.get(i).test(s)) {
                        mappings.get(i).add(s.row());
                        return;
                    }
                }
                missingSpots.add(s.row());
            });
            missingSpots.forEach(rowId -> mappings.get(RandomSource.nextInt(mappings.size())).add(rowId));
            List<Frame> frameList = mappings.stream().map(df::mapRows).collect(toList());
            List<Var> weightList = mappings.stream().map(weights::mapRows).collect(toList());
            return Pair.from(frameList, weightList);
        }

        @Override
        public String name() {
            return "ToRandom";
        }
    };

    Pair<List<Frame>, List<Var>> performSplit(Frame df, Var weights, CTreeCandidate candidate);
}