BayesChef now reports MSE of evidence and hidden recipe flavor combos.

Also, improved formatting of output to show exactly which flavors were included in the calculations.

src/net/woodyfolsom/cs6601/p2/BayesChef.java

@@ -3,11 +3,14 @@ package net.woodyfolsom.cs6601.p2;
 import java.io.File;
 import java.util.LinkedList;
 import java.util.List;
+import java.util.Set;
 
 import net.woodyfolsom.cs6601.p2.Ingredient.TYPE;
+
+import org.apache.commons.math3.stat.regression.SimpleRegression;
+
 import dkohl.bayes.bayesnet.BayesNet;
 import dkohl.bayes.builders.FoodNetBuilder;
-import dkohl.bayes.example.builders.FoodExampleBuilder;
 import dkohl.bayes.inference.EnumerateAll;
 import dkohl.bayes.probability.Assignment;
 import dkohl.bayes.probability.ProbabilityAssignment;
@@ -32,54 +35,53 @@ public class BayesChef {
 		int numDiners = survey.getDinerCount();
 		System.out.println("Read data for " + numDiners + " diner(s).");
 		
-		/*
+		System.out.println("Creating Bayes net for survey dataset...");
-		System.out.println("Setting evidence for first 11 recipes.");
+		BayesNet net = FoodNetBuilder.createDishNet(survey, recipeBook);
 		
-		System.out.println("Evaluating preference for remaining recipes.");
+		System.out.println("Querying Bayes net for individual flavor preferences: ");
+		double[][] flavorPrefs = new double[4][];
 		
-		//read survey prefs from survey.xml
+		flavorPrefs[0] = new double[] {0.0,printPreference(net, TYPE.PORK)};
-		double[][] surveyPrefs = new double[numDiners][];
+		flavorPrefs[1] = new double[] {1.0,printPreference(net, TYPE.BEEF)};
-		for (int i = 0; i < numDiners; i++) {
+		flavorPrefs[2] = new double[] {2.0,printPreference(net, TYPE.POTATO)};
-			surveyPrefs[i] = new double[numRecipes/2];
+		flavorPrefs[3] = new double[] {3.0,printPreference(net, TYPE.TOMATO)};
-			for (int j = 0; j < numRecipes/2; j++) {
+		
-				surveyPrefs[i][j] = survey.getDiner(i).getRating(j);
+		SimpleRegression simpleRegression = new SimpleRegression();	
+		simpleRegression.addData(flavorPrefs);
+		
+		System.out.println("Individual flavor pref MSE: " + simpleRegression.getMeanSquareError());
+		simpleRegression.clear();
+		
+		System.out.println("Querying Bayes net for recipe flavor preferences: ");
+		flavorPrefs = new double[recipeBook.getSize()][];
+		
+		for (int i = 0; i < recipeBook.getSize(); i++) {
+			simpleRegression.addData(i,printPreference(net, recipeBook.getRecipe(i)));
+		}
+		System.out.println("Recipe flavor pref MSE: " + simpleRegression.getMeanSquareError());
+	}
+	
+	int printPreference(BayesNet net, Recipe recipe) {
+		Set<TYPE> ingredientTypes = recipe.getIngredients().getTypes();
+		
+		List<ProbabilityAssignment> probs = EnumerateAll.enumerateAsk(new Variable(
+				FoodNetBuilder.TASTE, FoodNetBuilder.RATING_DOMAIN),
+				net, createQuery(ingredientTypes.toArray(new TYPE[ingredientTypes.size()])));
+		
+		double max_val = 0.0;
+		String max_arg = "N/A";
+		for (ProbabilityAssignment p : probs) {
+			if (p.getProbability() > max_val) {
+				max_val = p.getProbability();
+				max_arg = p.getValue();
 			}
 		}
 
-		//generating stub evaluated preferences to test RMSE calculation
+		System.out.println("TASTE for " + recipe.getName() + " " + ingredientTypes + " : " + max_arg + " " + max_val);
-		double[][] calculatedPrefs = new double[numDiners][];
+		return Integer.valueOf(max_arg);
-		calculatedPrefs[0] = new double[numRecipes/2];
-		
-		System.out.println("RMSE for recipes #" + numRecipes/2 + "-" + numRecipes +" (calculated vs. surveyed preference):");
-		
-		for (int dinerIndex = 0; dinerIndex < survey.getDinerCount(); dinerIndex++) {
-			SimpleRegression simpleRegression = new SimpleRegression();
-			
-			for (int i = 0; i < numRecipes/2; i++) {
-				simpleRegression.addData(i,calculatedPrefs[dinerIndex][i]);
-			}
-			double calculatedMSE = simpleRegression.getMeanSquareError();
-			simpleRegression.clear();
-			
-			for (int i = 0; i < numRecipes/2; i++) {
-				simpleRegression.addData(i,surveyPrefs[dinerIndex][i]);
-			}
-			
-			double surveyMSE = simpleRegression.getMeanSquareError();
-			
-			System.out.println("Diner # " + (dinerIndex + 1) + ": " + calculatedMSE + " vs. "+ surveyMSE);
-		}*/
-		
-		BayesNet net = FoodNetBuilder.createDishNet(survey, recipeBook);
-		//BayesNet net = FoodExampleBuilder.dishNet();
-		
-		printPreference(net, TYPE.PORK);
-		printPreference(net, TYPE.BEEF);
-		printPreference(net, TYPE.POTATO);
-		printPreference(net, TYPE.TOMATO);
 	}
 	
-	void printPreference(BayesNet net, TYPE type) {
+	int printPreference(BayesNet net, TYPE type) {
 		List<ProbabilityAssignment> probs = EnumerateAll.enumerateAsk(new Variable(
 				FoodNetBuilder.TASTE, FoodNetBuilder.RATING_DOMAIN),
 				net, createQuery(type));
@@ -93,10 +95,12 @@ public class BayesChef {
 		}
 
 		System.out.println("TASTE " + type + ": " + max_arg + " " + max_val);
+		return Integer.valueOf(max_arg);
 	}
 	
-	List<Assignment> createQuery(TYPE type) {
+	List<Assignment> createQuery(TYPE... types) {
 		List<Assignment> assignment = new LinkedList<Assignment>();
+		for (TYPE type : types) {
 		switch (type) {
 		case PORK:
 		case BEEF:
@@ -107,6 +111,7 @@ public class BayesChef {
 			break;
 		default:
 		}
+		}
 		return assignment;
 	}
 }

src/net/woodyfolsom/cs6601/p2/Ingredient.java

@@ -1,5 +1,10 @@
 package net.woodyfolsom.cs6601.p2;
 
+import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Set;
+
 import com.thoughtworks.xstream.annotations.XStreamAlias;
 
 @XStreamAlias("ing")
@@ -12,6 +17,16 @@ public class Ingredient {
 		return item;
 	}
 	
+	public Set<TYPE> getTypes() {
+		Set<TYPE> types = new HashSet<TYPE>();
+		for (TYPE type : TYPE.values()) {
+			if (isType(type)) {
+				types.add(type);
+			}
+		}
+		return types;
+	}
+	
 	public boolean isType(TYPE type) {
 		switch (type) {
 			case BEEF :

src/net/woodyfolsom/cs6601/p2/Ingredients.java

@@ -1,7 +1,11 @@
 package net.woodyfolsom.cs6601.p2;
 
 import java.util.ArrayList;
+import java.util.HashSet;
 import java.util.List;
+import java.util.Set;
+
+import net.woodyfolsom.cs6601.p2.Ingredient.TYPE;
 
 import com.thoughtworks.xstream.annotations.XStreamAlias;
 import com.thoughtworks.xstream.annotations.XStreamImplicit;
@@ -31,6 +35,14 @@ public class Ingredients {
 		return ingredients;
 	}
 	
+	public Set<TYPE> getTypes() {
+		Set<TYPE> types = new HashSet<TYPE>();
+		for (Ingredient ingredient : ingredients) {
+			types.addAll(ingredient.getTypes());
+		}
+		return types;
+	}
+	
 	public void setIngredients(List<Ingredient> ingredients) {
 		this.ingredients = ingredients;
 	}

src/net/woodyfolsom/cs6601/p2/Recipe.java

@@ -14,4 +14,8 @@ public class Recipe {
 	public Ingredients getIngredients() {
 		return ingredients;
 	}
+	
+	public String getName() {
+		return head.getTitle();
+	}
 }