public void TestEvaluate()
{
DataModel model = GetDataModel();
RecommenderBuilder builder = new SlopeOneRecommenderBuilder();
RecommenderEvaluator evaluator = new RMSRecommenderEvaluator();
double eval = evaluator.Evaluate(builder, model, 0.75, 1.0);
Assert.AreEqual(0.26387685767414826, eval, EPSILON);
}
public void TestEvaluate()
{
DataModel model = GetDataModel();
RecommenderBuilder builder = new SlopeOneRecommenderBuilder();
RecommenderEvaluator evaluator = new RMSRecommenderEvaluator();
double eval = evaluator.Evaluate(builder, model, 0.75, 1.0);
Assert.AreEqual(0.26387685767414826, eval, EPSILON);
}
public void TestEvaluate()
{
DataModel model = GetDataModel();
RecommenderBuilder builder = new SlopeOneRecommenderBuilder();
RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
IRStatistics stats = evaluator.Evaluate(builder, model, 5, 0.2, 1.0);
Assert.IsNotNull(stats);
Assert.AreEqual(0.2, stats.Precision, EPSILON);
Assert.AreEqual(1.0, stats.Recall, EPSILON);
Assert.AreEqual(0.33333, stats.GetF1Measure(), EPSILON);
}
public void TestEvaluate()
{
DataModel model = GetDataModel();
RecommenderBuilder builder = new SlopeOneRecommenderBuilder();
RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
IRStatistics stats = evaluator.Evaluate(builder, model, 5, 0.2, 1.0);
Assert.IsNotNull(stats);
Assert.AreEqual(0.2, stats.Precision, EPSILON);
Assert.AreEqual(1.0, stats.Recall, EPSILON);
Assert.AreEqual(0.33333, stats.GetF1Measure(), EPSILON);
}