private static void TestLinearASGD()
{
// http://leon.bottou.org/projects/sgd
string codebookPath = "codebook.bin";
string x_train_fn = "x_train.txt.gz";
string x_test_fn = "x_test.txt.gz";
Sparse <double>[] xTrain = null, xTest = null;
bool[] yTrain = null, yTest = null;
// Check if we have the precomputed dataset on disk
if (!File.Exists(x_train_fn) || !File.Exists(x_train_fn))
{
Console.WriteLine("Downloading dataset");
RCV1v2 rcv1v2 = new RCV1v2(@"C:\Temp\");
// Note: Leon Bottou's SGD inverts training and
// testing when benchmarking in this dataset
var trainWords = rcv1v2.Testing.Item1;
var testWords = rcv1v2.Training.Item1;
string positiveClass = "CCAT";
yTrain = rcv1v2.Testing.Item2.Apply(x => x.Contains(positiveClass));
yTest = rcv1v2.Training.Item2.Apply(x => x.Contains(positiveClass));
TFIDF tfidf;
if (!File.Exists(codebookPath))
{
Console.WriteLine("Learning TD-IDF");
// Create a TF-IDF considering only words that
// exist in both the training and testing sets
tfidf = new TFIDF(testWords)
{
Tf = TermFrequency.Log,
Idf = InverseDocumentFrequency.Default,
};
// Learn the training set
tfidf.Learn(trainWords);
Console.WriteLine("Saving codebook");
tfidf.Save(codebookPath);
}
else
{
Console.WriteLine("Loading codebook");
Serializer.Load(codebookPath, out tfidf);
}
if (!File.Exists(x_train_fn))
{
// Transform and normalize training set
Console.WriteLine("Pre-processing training set");
xTrain = tfidf.Transform(trainWords, out xTrain);
Console.WriteLine("Post-processing training set");
xTrain = xTrain.Divide(Norm.Euclidean(xTrain, dimension: 1), result: xTrain);
Console.WriteLine("Saving training set to disk");
SparseFormat.Save(xTrain, yTrain, x_train_fn, compression: SerializerCompression.GZip);
}
if (!File.Exists(x_test_fn))
{
// Transform and normalize testing set
Console.WriteLine("Pre-processing testing set");
xTest = tfidf.Transform(testWords, out xTest);
Console.WriteLine("Post-processing testing set");
xTest = xTest.Divide(Norm.Euclidean(xTest, dimension: 1), result: xTest);
Console.WriteLine("Saving testing set to disk");
SparseFormat.Save(xTest, yTest, x_test_fn, compression: SerializerCompression.GZip);
}
}
else
{
Console.WriteLine("Loading dataset from disk");
if (xTrain == null || yTrain == null)
{
SparseFormat.Load(x_train_fn, out xTrain, out yTrain, compression: SerializerCompression.GZip);
}
if (xTest == null || yTest == null)
{
SparseFormat.Load(x_test_fn, out xTest, out yTest, compression: SerializerCompression.GZip);
}
}
int positiveTrain = yTrain.Count(x => x);
int positiveTest = yTest.Count(x => x);
int negativeTrain = yTrain.Length - positiveTrain;
int negativeTest = yTest.Length - positiveTest;
Console.WriteLine("Training samples: {0} [{1}+, {2}-]", positiveTrain + negativeTrain, positiveTrain, negativeTrain);
Console.WriteLine("Negative samples: {0} [{1}+, {2}-]", positiveTest + negativeTest, positiveTest, negativeTest);
// Create and learn a linear sparse binary support vector machine
var learn = new AveragedStochasticGradientDescent <Linear, Sparse <double> >()
{
MaxIterations = 5,
Tolerance = 0,
};
Console.WriteLine("Learning training set");
Stopwatch sw = Stopwatch.StartNew();
var svm = learn.Learn(xTrain, yTrain);
Console.WriteLine(sw.Elapsed);
Console.WriteLine("Predicting training set");
sw = Stopwatch.StartNew();
bool[] trainPred = svm.Decide(xTrain);
Console.WriteLine(sw.Elapsed);
var train = new ConfusionMatrix(trainPred, yTrain);
Console.WriteLine("Train acc: " + train.Accuracy);
Console.WriteLine("Predicting testing set");
sw = Stopwatch.StartNew();
bool[] testPred = svm.Decide(xTest);
Console.WriteLine(sw.Elapsed);
var test = new ConfusionMatrix(testPred, yTest);
Console.WriteLine("Test acc: " + test.Accuracy);
}