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/*
* BotSharp.NLP Library
* Copyright (C) 2018 Haiping Chen
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
using Bigtree.Algorithm;
using Bigtree.Algorithm.Bayes;
using Bigtree.Algorithm.Estimators;
using Bigtree.Algorithm.Extensions;
using Bigtree.Algorithm.Features;
using Bigtree.Algorithm.Statistics;
using BotSharp.NLP.Featuring;
using BotSharp.NLP.Txt2Vec;
using Newtonsoft.Json;
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace BotSharp.NLP.Classify
{
/// <summary>
/// This is a simple (naive) classification method based on Bayes rule.
/// It relies on a very simple representation of the document (called the bag of words representation)
/// This technique works well for topic classification;
/// say we have a set of academic papers, and we want to classify them into different topics (computer science, biology, mathematics).
/// Naive Bayes is best for Less training data
/// </summary>
public class NaiveBayesClassifier : IClassifier
{
private List<Probability> labelDist;
private MultinomiaNaiveBayes nb = new MultinomiaNaiveBayes();
private Dictionary<string, double> condProbDictionary = new Dictionary<string, double>();
private List<string> words;
private double[] features = new double[] { 0, 1 };
public void Train(List<Sentence> sentences, ClassifyOptions options)
{
var tfidf = new TfIdfFeatureExtractor();
tfidf.Dimension = options.Dimension;
tfidf.Sentences = sentences;
tfidf.CalBasedOnCategory();
var encoder = new OneHotEncoder();
encoder.Sentences = sentences;
encoder.Words = tfidf.Keywords();
words = encoder.EncodeAll();
var featureSets = sentences.Select(x => new Tuple<string, double[]>(x.Label, x.Vector)).ToList();
labelDist = featureSets.GroupBy(x => x.Item1)
.Select(x => new Probability
{
Value = x.Key,
Freq = x.Count()
})
.OrderBy(x => x.Value)
.ToList();
nb.LabelDist = labelDist;
nb.FeatureSet = featureSets;
// calculate prior prob
labelDist.ForEach(l => l.Prob = nb.CalPriorProb(l.Value));
// calculate posterior prob
// loop features
var featureCount = nb.FeatureSet[0].Item2.Length;
labelDist.ForEach(label =>
{
for (int x = 0; x < featureCount; x++)
{
for (int v = 0; v < features.Length; v++)
{
string key = $"{label.Value} f{x} {features[v]}";
condProbDictionary[key] = nb.CalCondProb(x, label.Value, features[v]);
}
}
});
}
public List<Tuple<string, double>> Classify(Sentence sentence, ClassifyOptions options)
{
var encoder = new OneHotEncoder();
encoder.Words = words;
encoder.Encode(sentence);
var results = new List<Tuple<string, double>>();
// calculate prop
labelDist.ForEach(lf =>
{
var prob = nb.CalPosteriorProb(lf.Value, sentence.Vector, lf.Prob, condProbDictionary);
results.Add(new Tuple<string, double>(lf.Value, prob));
});
/*Parallel.ForEach(labelDist, (lf) =>
{
nb.Y = lf.Value;
lf.Prob = nb.PosteriorProb();
});*/
double total = results.Select(x => x.Item2).Sum();
return results.Select(x => new Tuple<string, double>(x.Item1, x.Item2 / total)).ToList();
}
public string SaveModel(ClassifyOptions options)
{
// save the model
var model = new MultinomiaNaiveBayesModel
{
LabelDist = labelDist,
CondProbDictionary = condProbDictionary,
Values = words
};
//save the file
using (var bw = new BinaryWriter(new FileStream(options.ModelFilePath, FileMode.Create)))
{
var bytes = Encoding.UTF8.GetBytes(JsonConvert.SerializeObject(model));
bw.Write(bytes);
}
return options.ModelFilePath;
}
public Object LoadModel(ClassifyOptions options)
{
string json = String.Empty;
//read the file
using (var br = new BinaryReader(new FileStream(options.ModelFilePath, FileMode.Open)))
{
byte[] bytes = br.ReadBytes((int)br.BaseStream.Length);
json = Encoding.UTF8.GetString(bytes);
}
var model = JsonConvert.DeserializeObject<MultinomiaNaiveBayesModel>(json);
labelDist = model.LabelDist;
condProbDictionary = model.CondProbDictionary;
words = model.Values;
return model;
}
}
public class FeaturesWithLabel
{
public List<Feature> Features { get; set; }
public string Label { get; set; }
public FeaturesWithLabel()
{
this.Features = new List<Feature>();
}
}
}