src/Lucene.Net.Classification/KNearestNeighborClassifier.cs

/*
 * 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.
 */

namespace Lucene.Net.Classification
{
    using Lucene.Net.Analysis;
    using Lucene.Net.Index;
    using Lucene.Net.Queries.Mlt;
    using Lucene.Net.Search;
    using Lucene.Net.Util;
    using System;
    using System.Collections.Generic;
    using System.IO;

    /// <summary>
    /// A k-Nearest Neighbor classifier (see <code>http://en.wikipedia.org/wiki/K-nearest_neighbors</code>) based
    /// on {@link MoreLikeThis}
    ///
    /// @lucene.experimental
    /// </summary>
    public class KNearestNeighborClassifier : IClassifier<BytesRef>
    {

        private MoreLikeThis _mlt;
        private String[] _textFieldNames;
        private String _classFieldName;
        private IndexSearcher _indexSearcher;
        private readonly int _k;
        private Query _query;

        private int _minDocsFreq;
        private int _minTermFreq;

        /// <summary>Create a {@link Classifier} using kNN algorithm</summary>
        /// <param name="k">the number of neighbors to analyze as an <code>int</code></param>
        public KNearestNeighborClassifier(int k)
        {
            _k = k;
        }

        /// <summary>Create a {@link Classifier} using kNN algorithm</summary>
        /// <param name="k">the number of neighbors to analyze as an <code>int</code></param>
        /// <param name="minDocsFreq">the minimum number of docs frequency for MLT to be set with {@link MoreLikeThis#setMinDocFreq(int)}</param>
        /// <param name="minTermFreq">the minimum number of term frequency for MLT to be set with {@link MoreLikeThis#setMinTermFreq(int)}</param>
        public KNearestNeighborClassifier(int k, int minDocsFreq, int minTermFreq)
        {
            _k = k;
            _minDocsFreq = minDocsFreq;
            _minTermFreq = minTermFreq;
        }

        public ClassificationResult<BytesRef> AssignClass(String text)
        {
            if (_mlt == null)
            {
                throw new IOException("You must first call Classifier#train");
            }

            BooleanQuery mltQuery = new BooleanQuery();
            foreach (String textFieldName in _textFieldNames)
            {
                mltQuery.Add(new BooleanClause(_mlt.Like(new StringReader(text), textFieldName), BooleanClause.Occur.SHOULD));
            }
            Query classFieldQuery = new WildcardQuery(new Term(_classFieldName, "*"));
            mltQuery.Add(new BooleanClause(classFieldQuery, BooleanClause.Occur.MUST));
            if (_query != null)
            {
                mltQuery.Add(_query, BooleanClause.Occur.MUST);
            }
            TopDocs topDocs = _indexSearcher.Search(mltQuery, _k);
            return SelectClassFromNeighbors(topDocs);
        }

        private ClassificationResult<BytesRef> SelectClassFromNeighbors(TopDocs topDocs)
        {
            // TODO : improve the nearest neighbor selection
            Dictionary<BytesRef, int> classCounts = new Dictionary<BytesRef, int>();

            foreach (ScoreDoc scoreDoc in topDocs.ScoreDocs)
            {
                BytesRef cl = new BytesRef(_indexSearcher.Doc(scoreDoc.Doc).GetField(_classFieldName).StringValue);
                if (classCounts.ContainsKey(cl))
                {
                    classCounts[cl] = classCounts[cl] + 1;
                }
                else
                {
                    classCounts.Add(cl, 1);
                }
            }
            double max = 0;
            BytesRef assignedClass = new BytesRef();
            foreach (KeyValuePair<BytesRef, int> entry in classCounts)
            {
                int count = entry.Value;
                if (count > max)
                {
                    max = count;
                    assignedClass = (BytesRef)entry.Key.Clone();
                }
            }
            double score = max / (double) _k;
            return new ClassificationResult<BytesRef>(assignedClass, score);
        }

        public void Train(AtomicReader atomicReader, String textFieldName, String classFieldName, Analyzer analyzer)
        {
            Train(atomicReader, textFieldName, classFieldName, analyzer, null);
        }


        public void Train(AtomicReader atomicReader, String textFieldName, String classFieldName, Analyzer analyzer, Query query)
        {
            Train(atomicReader, new String[]{textFieldName}, classFieldName, analyzer, query);
        }

        public void Train(AtomicReader atomicReader, String[] textFieldNames, String classFieldName, Analyzer analyzer, Query query)
        {
            _textFieldNames = textFieldNames;
            _classFieldName = classFieldName;
            _mlt = new MoreLikeThis(atomicReader);
            _mlt.Analyzer = analyzer;
            _mlt.FieldNames = _textFieldNames;
            _indexSearcher = new IndexSearcher(atomicReader);
            if (_minDocsFreq > 0)
            {
                _mlt.MinDocFreq = _minDocsFreq;
            }
            if (_minTermFreq > 0)
            {
                _mlt.MinTermFreq = _minTermFreq;
            }
            _query = query;
        }
    }
}