// Get ingredients sorted by what is most recommended
public Recommendation[] GetRecommendations(ModelChoice model_choice, int[] recipe)
{
Recommendation[] recommendations = null;
if (model_choice.Equals(ModelChoice.NB))
{
NaiveBayes nb = new NaiveBayes();
recommendations = nb.RecipeRecommendations(nb.GetModel(), recipe, true, true, false);
}
else if (model_choice.Equals(ModelChoice.KNN))
{
KNN knn = new KNN();
recommendations = knn.GetRecommendations(6, DistanceChoice.Jaccard, recipe, Voting.Unweighted);
}
else if (model_choice.Equals(ModelChoice.MKNN))
{
KNN knn = new KNN();
int new_k = 0;
recommendations = knn.GetRecommendations_ModifiedKNN(recipe, DistanceChoice.Jaccard_Similarity, Voting.Unweighted, ref new_k);
}
return(recommendations);
}
// Ingredients to ADD to or REMOVE from a recipe
public void TopRecommendations(int top, string[] recipe_str, ModelChoice model_choice, bool add, bool include_recipe_ingrs)
{
DataManager dm = new DataManager();
// get training data
Data[] data = dm.GetRecipes(model_choice, DataPurpose.TRAIN);
Console.WriteLine("You model choice: " + model_choice.ToString());
// input recipe
int[] recipe = new int[recipe_str.Length];
for (int i = 0; i < recipe_str.Length; i++)
{
try
{
// trim and make lowercase
recipe_str[i] = recipe_str[i].Trim().ToLower();
// find ingredient
recipe[i] = data.Where(d => d.ingredient.name.Equals(recipe_str[i])).ToArray()[0].ingredient.id;
}
catch
{
// get features (ingredients)
string[] features = GetAllIngredients(false);
bool found = false;
// try finding a similar ingredient
foreach (string ingr in features)
{
if (ingr.StartsWith(recipe_str[i]) || ingr.Contains(recipe_str[i]))
{
recipe_str[i] = ingr;
recipe[i] = data.Where(d => d.ingredient.name.Equals(ingr)).ToArray()[0].ingredient.id;
found = true;
break;
}
}
// ingredient not found
if (found == false)
{
Console.WriteLine("Ingredient [" + recipe_str[i] + "] was not found");
return;
}
}
}
Console.Write("Your recipe: ");
PrintRecipe(recipe_str);
// keep track of ingredient recommendations
Recommendation[] recommendations = null;
// Naive Bayes
if (model_choice.Equals(ModelChoice.NB))
{
NaiveBayes nb = new NaiveBayes();
recommendations = nb.RecipeRecommendations(nb.GetModel(), recipe, true, true, false);
}
// k Nearest Neighbors
else if (model_choice.Equals(ModelChoice.KNN))
{
KNN knn = new KNN();
recommendations = knn.GetRecommendations(6, DistanceChoice.Jaccard, recipe, Voting.Unweighted);
}
// Modified k Nearest Neighbors (dynamic k)
else if (model_choice.Equals(ModelChoice.MKNN))
{
KNN knn = new KNN();
int new_k = 0;
recommendations = knn.GetRecommendations_ModifiedKNN(recipe, DistanceChoice.Jaccard_Similarity, Voting.Unweighted, ref new_k);
}
else
{
return;
}
// Ingredients to Add
if (add == true)
{
Console.WriteLine("Your recommendations:");
for (int i = 0; i < top; i++)
{
// skip ingredients in recipe
if (include_recipe_ingrs == false && recipe_str.Contains(recommendations[i].ingredient.name))
{
top++;
continue;
}
Console.WriteLine(recommendations[i].ingredient.name);
}
}
// Ingredients to Remove
else
{
Console.WriteLine("Ingredients ordered by what to remove first:");
// only keep scores of ingredients in input recipe
recommendations = recommendations.Where(d => recipe.Contains(d.ingredient.id)).ToArray();
// sort by score
recommendations = recommendations.OrderBy(d => d.score).ToArray();
for (int i = 0; i < recipe.Length; i++)
{
Console.WriteLine(recommendations[i].ingredient.name);
}
}
Console.WriteLine();
}
// Evaluate Naive Bayes
public void EvaluateNB(double[][] model, bool laplace, bool normalize, bool prior)
{
MLContext ml = new MLContext();
DataManager dm = new DataManager();
NaiveBayes nb = new NaiveBayes();
// get test data
IDataView testData = dm.GetDataView(ModelChoice.NB, ml, DataPurpose.TEST);
// get features
IDataView features = dm.GetDataView(ModelChoice.NB, ml, DataPurpose.FEATURES);
Results results = new Results(0);
// get distinct recipes
int[] recipes = testData.GetColumn <int>(testData.Schema["recipeId"]).ToArray().Distinct().ToArray();
Data[] data = dm.GetData(testData, features);
Console.Write("\nEvaluating Naive Bayes");
if (laplace == true)
{
Console.Write(" with laplace smoothing");
if (normalize == true)
{
Console.Write(" and normalization");
}
if (prior == false)
{
Console.Write(" and uniform prior");
}
}
else if (normalize == true)
{
Console.Write(" with normalization");
if (prior == false)
{
Console.Write(" and uniform prior");
}
}
else if (prior == false)
{
Console.Write(" with uniform prior");
}
Console.WriteLine("...");
// iterate through all recipes
for (int r = 0; r < recipes.Length; r++)
{
// current recipe
Data[] current_recipe = data.Where(d => d.recipeId == recipes[r] && d.score == 1).ToArray();
int[] recipe = dm.GetRecipe(current_recipe);
// get likelihoods of all possible ingredients
Recommendation[] recommendations = nb.RecipeRecommendations(model, recipe, laplace, normalize, prior);
// get results
results = GetResults(results, recommendations, recipe);
}
// Display results
results.ShowResults();
Console.WriteLine();
}