static IReadOnlyList <(string Classification, IndexList Data)> _BuildIndexedClassifications(IReadOnlyList <Tuple <string[], string> > data, StringTableBuilder stringTable)
{
return(data
.Select(d => (d.Item2, IndexList.Create(d.Item1.Select(str => stringTable.GetIndex(str)).ToArray())))
.ToList()
);
}
/// <summary>
/// Classifies text into either positive or negative sentiment
/// The data files can be downloaded from https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences
/// </summary>
/// <param name="dataFilesPath">Path to extracted data files</param>
public static void SentimentClassification(string dataFilesPath)
{
var files = new[] { "amazon_cells_labelled.txt", "imdb_labelled.txt", "yelp_labelled.txt" };
var LINE_SEPARATOR = "\n".ToCharArray();
var SEPARATOR = "\t".ToCharArray();
var stringTable = new StringTableBuilder();
var sentimentData = files.SelectMany(f =>
File.ReadAllText(dataFilesPath + f).Split(LINE_SEPARATOR).
Where(l => !string.IsNullOrWhiteSpace(l)).Select(l => l.Split(SEPARATOR)).
Select(s => Tuple.Create(Tokenise(s[0]), s[1][0] == '1' ? "positive" : "negative")).
Where(d => d.Item1.Any())).Shuffle(0).ToList();
var splitSentimentData = sentimentData.Split();
// build training and test classification bag
var trainingClassificationBag =
BuildIndexedClassifications(splitSentimentData.Training, stringTable);
var testClassificationBag =
BuildIndexedClassifications(splitSentimentData.Test, stringTable);
// train a bernoulli naive bayes classifier
var bernoulli = trainingClassificationBag.TrainBernoulliNaiveBayes();
Console.WriteLine("Bernoulli accuracy: {0:P}",
testClassificationBag.Classify(bernoulli.CreateClassifier()).Average(r => r.Score));
// train a multinomial naive bayes classifier
var multinomial = trainingClassificationBag.TrainMultinomialNaiveBayes();
Console.WriteLine("Multinomial accuracy: {0:P}",
testClassificationBag.Classify(multinomial.CreateClassifier()).Average(r => r.Score));
// convert the index lists to vectors and normalise along the way
var sentimentDataTable = BuildIndexedClassifications(sentimentData, stringTable).
ConvertToTable().Normalise(NormalisationType.Standard);
var vectoriser = sentimentDataTable.GetVectoriser();
var sentimentDataSet = sentimentDataTable.Split(0);
var dataTableAnalysis = sentimentDataTable.GetAnalysis();
using (var lap = BrightWireProvider.CreateLinearAlgebra())
{
var graph = new GraphFactory(lap);
var trainingData = graph.CreateDataSource(sentimentDataSet.Training, vectoriser);
var testData = graph.CreateDataSource(sentimentDataSet.Test, vectoriser);
var indexListEncoder = (IIndexListEncoder)trainingData;
// use a one hot encoding error metric, rmsprop gradient descent and xavier weight initialisation
var errorMetric = graph.ErrorMetric.OneHotEncoding;
var propertySet = graph.CurrentPropertySet.Use(graph.GradientDescent.RmsProp).
Use(graph.WeightInitialisation.Xavier);
var engine = graph.CreateTrainingEngine(trainingData, 0.3f);
engine.LearningContext.ScheduleLearningRate(5, 0.1f);
engine.LearningContext.ScheduleLearningRate(11, 1f);
engine.LearningContext.ScheduleLearningRate(15, 0.3f);
// train a neural network classifier
var neuralNetworkWire = graph.Connect(engine).AddFeedForward(512, "layer1")
//.AddBatchNormalisation()
.Add(graph.ReluActivation()).AddDropOut(0.5f).
AddFeedForward(trainingData.OutputSize, "layer2").Add(graph.ReluActivation()).
AddBackpropagation(errorMetric, "first-network");
// train the network
Console.WriteLine("Training neural network classifier...");
const int TRAINING_ITERATIONS = 10;
GraphModel bestNetwork = null;
engine.Train(TRAINING_ITERATIONS, testData, errorMetric, network => bestNetwork = network);
if (bestNetwork != null)
{
engine.LoadParametersFrom(bestNetwork.Graph);
}
var firstClassifier = graph.CreateEngine(engine.Graph);
// stop the backpropagation to the first neural network
engine.LearningContext.EnableNodeUpdates(neuralNetworkWire.Find("layer1"), false);
engine.LearningContext.EnableNodeUpdates(neuralNetworkWire.Find("layer2"), false);
// create the bernoulli classifier wire
var bernoulliClassifier = bernoulli.CreateClassifier();
var bernoulliWire = graph.Connect(engine).AddClassifier(bernoulliClassifier,
sentimentDataSet.Training, dataTableAnalysis);
// create the multinomial classifier wire
var multinomialClassifier = multinomial.CreateClassifier();
var multinomialWire = graph.Connect(engine).AddClassifier(multinomialClassifier,
sentimentDataSet.Training, dataTableAnalysis);
// join the bernoulli, multinomial and neural network classification outputs
var firstNetwork = neuralNetworkWire.Find("first-network");
var joined = graph.Join(multinomialWire,
graph.Join(bernoulliWire, graph.Connect(trainingData.OutputSize, firstNetwork)));
// train an additional classifier on the output of the previous three classifiers
joined.AddFeedForward(outputSize: 64).Add(graph.ReluActivation()).
AddDropOut(dropOutPercentage: 0.5f).AddFeedForward(trainingData.OutputSize).
Add(graph.ReluActivation()).AddBackpropagation(errorMetric);
// train the network again
Console.WriteLine("Training stacked neural network classifier...");
GraphModel bestStackedNetwork = null;
engine.Train(10, testData, errorMetric, network => bestStackedNetwork = network);
if (bestStackedNetwork != null)
{
engine.LoadParametersFrom(bestStackedNetwork.Graph);
}
Console.WriteLine("Enter some text to test the classifiers...");
while (true)
{
Console.Write(">");
var line = Console.ReadLine();
if (string.IsNullOrWhiteSpace(line))
{
break;
}
var tokens = Tokenise(line);
var indexList = new List <uint>();
foreach (var token in tokens)
{
if (stringTable.TryGetIndex(token, out uint stringIndex))
{
indexList.Add(stringIndex);
}
}
if (indexList.Any())
{
var queryTokens = indexList.GroupBy(d => d).
Select(g => Tuple.Create(g.Key, (float)g.Count())).ToList();
var vector = new float[trainingData.InputSize];
foreach (var token in queryTokens)
{
vector[token.Item1] = token.Item2;
}
var indexList2 = IndexList.Create(indexList.ToArray());
var encodedInput = indexListEncoder.Encode(indexList2);
Console.WriteLine("Bernoulli classification: " +
bernoulliClassifier.Classify(indexList2).First().Label);
Console.WriteLine("Multinomial classification: " +
multinomialClassifier.Classify(indexList2).First().Label);
var result = firstClassifier.Execute(encodedInput);
var classification = vectoriser.GetOutputLabel(1,
(result.Output[0].Data[0] > result.Output[0].Data[1]) ? 0 : 1);
Console.WriteLine("Neural network classification: " + classification);
var stackedResult = engine.Execute(encodedInput);
var stackedClassification = vectoriser.GetOutputLabel(1,
(stackedResult.Output[0].Data[0] > stackedResult.Output[0].Data[1]) ? 0 : 1);
Console.WriteLine("Stack classification: " + stackedClassification);
}
else
{
Console.WriteLine("Sorry, none of those words have been seen before.");
}
Console.WriteLine();
}
}
Console.WriteLine();
}