private Problem LoadData(bool withScaling = false)
{
var file = Path.Combine(TestContext.CurrentContext.TestDirectory, @".\Data\data.arff");
var arff = ArffDataSet.Load <PositivityType>(file);
IProblemFactory factory = new ProblemFactory(arff);
if (withScaling)
{
factory = factory.WithRangeScaling();
}
return(factory.Construct(arff).GetProblem());
}
public static IMachineSentiment Load(string path)
{
if (string.IsNullOrEmpty(path))
{
throw new ArgumentException("Value cannot be null or empty.", nameof(path));
}
log.LogInformation("Loading {0}...", path);
IArffDataSet reviews = ArffDataSet.Load <PositivityType>(Path.Combine(path, "data.arff"));
var classifier = new Classifier();
classifier.Load(Path.Combine(path, "training.model"));
return(new MachineSentiment(reviews, classifier));
}
public void Classify(string arff, string modelName, object result)
{
var file = Path.Combine(TestContext.CurrentContext.TestDirectory, "data", arff);
var dataSet = result.GetType() == typeof(PositivityType)
? ArffDataSet.Load <PositivityType>(file)
: ArffDataSet.LoadSimple(file);
file = Path.Combine(TestContext.CurrentContext.TestDirectory, "data", modelName);
var model = Model.Read(file);
IProblemFactory factory = new ProblemFactory(dataSet);
var client = new SvmTesting(model, factory);
var dataHolder = dataSet.CreateDataSet("Test");
var review = dataHolder.AddDocument();
review.AddRecord("Good").Value = 2;
review.AddRecord("Bad").Value = 1;
client.Classify(dataHolder);
Assert.AreEqual(result, review.Class.Value);
}
