// Evaluate Non-negative Matrix Factorization
public void EvaluateNMF(ITransformer model)
{
Console.WriteLine("\nEvaluating NMF...");
MLContext mlContext = new MLContext();
// get test data
DataManager dm = new DataManager();
// test data
IDataView testData = dm.GetDataView(ModelChoice.NMF, mlContext, DataPurpose.TEST);
Data[] test_data = dm.GetRecipes(ModelChoice.NMF, DataPurpose.TEST);
// train data
IDataView trainData = dm.GetDataView(ModelChoice.NMF, mlContext, DataPurpose.TRAIN);
Data[] train_data = dm.GetRecipes(ModelChoice.NMF, DataPurpose.TRAIN);
// features
string[] features = dm.GetFeatures();
int[] recipeArray = testData.GetColumn <int>(testData.Schema["recipeId"]).ToArray();
Results results = new Results(0);
Recommender recommender = new Recommender();
// distinct test recipes
int[] distinct_recipes = recipeArray.Distinct().ToArray();
// for each test recipe
foreach (int r in distinct_recipes)
{
Recommendation[] recommendations = new Recommendation[features.Length];
Data[] recipe = test_data.Where(d => d.recipeId == r && d.score == 1).ToArray();
Data[] trecipe = train_data.Where(d => d.recipeId == r && d.score == 1).ToArray();
// get recipe r
Data[] combined = recipe.Concat(trecipe).ToArray();
int[] current_recipe = dm.GetRecipe(combined.ToArray());
// iterate through all features
for (int i = 0; i < dm.GetFeatures().Length; i++)
{
// make prediction (get score)
double prediction = recommender.SinglePrediction(mlContext, model, i, r);
// save score of ingredient
recommendations[i] = new Recommendation(new Ingredient(i, features[i]), prediction);
}
// sort
recommendations = recommendations.OrderByDescending(d => d.score).ToArray();
results = GetResults(results, recommendations, current_recipe);
}
// Display accuracy results
results.ShowResults();
Console.WriteLine();
}
// Evaluate different models
static void Evaluate(ModelChoice choice)
{
Evaluation eval = new Evaluation();
// Evaluate Non-negative Matrix Factorization (12 minutes)
if (choice.Equals(ModelChoice.NMF))
{
Recommender nmf = new Recommender();
eval.EvaluateNMF(nmf.GetModel());
}
// Evaluate Naive Bayes (5 seconds each)
else if (choice.Equals(ModelChoice.NB))
{
NaiveBayes nb = new NaiveBayes();
double[][] model = nb.GetModel();
Console.WriteLine("1: Evaluate ALL versions of Naive Bayes");
Console.WriteLine("2: Evaluate only the BEST version of Naive Bayes (normalization, laplace, uniform prior)");
string option = Console.ReadLine();
// ALL
if (option == "1")
{
eval.EvaluateNB(model, false, false, true);
eval.EvaluateNB(model, true, false, true); // with laplace smoothing
eval.EvaluateNB(model, false, true, true); // with normalization (best results)
eval.EvaluateNB(model, true, true, true); // with laplace smoothing and normalization
// uniform prior
eval.EvaluateNB(model, false, false, false);
eval.EvaluateNB(model, true, false, false); // with laplace smoothing
eval.EvaluateNB(model, false, true, false); // with normalization (best results)
}
// BEST
eval.EvaluateNB(model, true, true, false); // with laplace smoothing, normalization, and uniform prior
}
// Evaluate KNN (35 seconds)
else if (choice.Equals(ModelChoice.KNN))
{
eval.EvaluateKNN(6, DistanceChoice.Jaccard, Voting.Unweighted);
}
// Evaluate Modified KNN (35 seconds)
else
{
eval.EvaluateModifiedKNN(DistanceChoice.Jaccard_Similarity, Voting.Unweighted);
}
}
