wfolsom/cs6601p2
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Work in progress.

Browse Source
This commit is contained in:
Woody Folsom
2012-03-12 14:33:41 -04:00
parent c1f68f6f5c
commit 16a97ba39a
15 changed files with 1424 additions and 753 deletions
Download Patch File Download Diff File Expand all files Collapse all files

97
src/dkohl/bayes/builders/FoodNetBuilder.java
View File

@@ -23,14 +23,19 @@ import dkohl.onthology.Ontology;
public class FoodNetBuilder {
public static final String TASTE = "Taste";
public static final String SOMEONE_VEGETARIAN = "Vegetarian";
public static final String SOMEONE_VEGETARIAN = "vegetarian";
public static final String SOMEONE_ALLERGIC_NUTS = "allergic-nuts";
public static final String CONTAINS_MEAT = "Meat";
public static final String CONTAINS_VEGETABLE = "Vegetable";
public static final String CONTAINS_BEEF = TYPE.BEEF.toString();
public static final String CONTAINS_PORK = TYPE.PORK.toString();
public static final String CONTAINS_TOMATOS = TYPE.TOMATO.toString();
public static final String CONTAINS_POTATOS = TYPE.POTATO.toString();
public static final String CONTAINS_NUTS = "Nuts";
public static final String CONTAINS_GENERIC_NUTS = TYPE.GENERIC_NUTS.toString();
public static final String TRUE_VALUE = "true";
public static final String FALSE_VALUE = "false";
@@ -39,27 +44,30 @@ public class FoodNetBuilder {
public static final String RATING_DOMAIN[] = { "1", "2", "3", "4", "5",
"6", "7", "8", "9", "10" };
private static final String[] VARIABLES = { SOMEONE_VEGETARIAN,
private static final String[] VARIABLES = { SOMEONE_VEGETARIAN, SOMEONE_ALLERGIC_NUTS,
CONTAINS_BEEF, CONTAINS_MEAT, CONTAINS_PORK, CONTAINS_POTATOS,
CONTAINS_TOMATOS, CONTAINS_VEGETABLE, TASTE };
CONTAINS_TOMATOS, CONTAINS_VEGETABLE, CONTAINS_NUTS, CONTAINS_GENERIC_NUTS, TASTE };
private static final String[] OBSERVED = { CONTAINS_BEEF, CONTAINS_PORK,
CONTAINS_POTATOS, CONTAINS_TOMATOS, };
CONTAINS_POTATOS, CONTAINS_TOMATOS, CONTAINS_GENERIC_NUTS};
public static Ontology createOntology() {
HashSet<String> classes = new HashSet<String>();
classes.add(CONTAINS_MEAT);
classes.add(CONTAINS_NUTS);
classes.add(CONTAINS_VEGETABLE);
Ontology onthology = new Ontology(classes);
Ontology ontology = new Ontology(classes);
onthology.define(CONTAINS_PORK, CONTAINS_MEAT);
onthology.define(CONTAINS_BEEF, CONTAINS_MEAT);
ontology.define(CONTAINS_PORK, CONTAINS_MEAT);
ontology.define(CONTAINS_BEEF, CONTAINS_MEAT);
onthology.define(CONTAINS_TOMATOS, CONTAINS_VEGETABLE);
onthology.define(CONTAINS_POTATOS, CONTAINS_VEGETABLE);
return onthology;
ontology.define(CONTAINS_GENERIC_NUTS, CONTAINS_NUTS);
ontology.define(CONTAINS_TOMATOS, CONTAINS_VEGETABLE);
ontology.define(CONTAINS_POTATOS, CONTAINS_VEGETABLE);
return ontology;
}
private static Assignment build(String varible, String value) {
@@ -86,14 +94,13 @@ public class FoodNetBuilder {
return normPoint;
}
public static DataSet getSurveyDataSet(Survey survey, RecipeBook recipeBook) {
public static DataSet getSurveyDataSet(Survey survey, RecipeBook recipeBook, int startIndex, int endIndex) {
DataSet data = FoodExampleBuilder.examples();
Ontology onto = createOntology();
int nDishes = survey.getDishCount();
for (int dinerIndex = 0; dinerIndex < survey.getDinerCount(); dinerIndex++) {
Diner diner = survey.getDiner(dinerIndex);
for (int dishIndex = 0; dishIndex < nDishes; dishIndex++) {
for (int dishIndex = startIndex; dishIndex < endIndex; dishIndex++) {
data.add(normalize(createDataPoint(recipeBook, survey.getDish(dishIndex), diner.getRating(dishIndex)),onto));
}
}
@@ -118,20 +125,41 @@ public class FoodNetBuilder {
if (ingredients.contains(TYPE.TOMATO)) {
point.add(build(CONTAINS_TOMATOS, TRUE_VALUE));
}
if (ingredients.contains(TYPE.GENERIC_NUTS)) {
point.add(build(CONTAINS_GENERIC_NUTS, TRUE_VALUE));
}
point.add(build(TASTE, "" + weight));
return point;
}
public static ProbabilityDistribution vegi() {
public static ProbabilityDistribution vegi(Survey survey) {
String names[] = { SOMEONE_VEGETARIAN };
ProbabilityTable table = new ProbabilityTable(names);
table.setProbabilityForAssignment("true;", new Probability(0));
table.setProbabilityForAssignment("false;", new Probability(1));
if (survey.isDiner("vegetarian")) {
table.setProbabilityForAssignment("true;", new Probability(1));
table.setProbabilityForAssignment("false;", new Probability(0));
} else {
table.setProbabilityForAssignment("true;", new Probability(0));
table.setProbabilityForAssignment("false;", new Probability(1));
}
return table;
}
public static ProbabilityDistribution allergicNuts(Survey survey) {
String names[] = { SOMEONE_ALLERGIC_NUTS };
ProbabilityTable table = new ProbabilityTable(names);
if (survey.isDiner("allergic-nuts")) {
table.setProbabilityForAssignment("true;", new Probability(1));
table.setProbabilityForAssignment("false;", new Probability(0));
} else {
table.setProbabilityForAssignment("true;", new Probability(0));
table.setProbabilityForAssignment("false;", new Probability(1));
}
return table;
}
public static ProbabilityDistribution beef() {
String names[] = { CONTAINS_MEAT, CONTAINS_BEEF };
ProbabilityTable table = new ProbabilityTable(names);
@@ -143,13 +171,25 @@ public class FoodNetBuilder {
ProbabilityTable table = new ProbabilityTable(names);
return table;
}
public static ProbabilityDistribution meet() {
public static ProbabilityDistribution meat() {
String names[] = { SOMEONE_VEGETARIAN, CONTAINS_MEAT };
ProbabilityTable table = new ProbabilityTable(names);
return table;
}
public static ProbabilityDistribution genericNuts() {
String names[] = { CONTAINS_NUTS, CONTAINS_GENERIC_NUTS };
ProbabilityTable table = new ProbabilityTable(names);
return table;
}
public static ProbabilityDistribution nuts() {
String names[] = { SOMEONE_ALLERGIC_NUTS, CONTAINS_NUTS };
ProbabilityTable table = new ProbabilityTable(names);
return table;
}
public static ProbabilityDistribution tomatos() {
String names[] = { CONTAINS_VEGETABLE, CONTAINS_TOMATOS };
ProbabilityTable table = new ProbabilityTable(names);
@@ -170,27 +210,34 @@ public class FoodNetBuilder {
public static ProbabilityDistribution taste() {
String names[] = { TASTE, CONTAINS_BEEF, CONTAINS_PORK,
CONTAINS_POTATOS, CONTAINS_TOMATOS };
CONTAINS_POTATOS, CONTAINS_TOMATOS, CONTAINS_GENERIC_NUTS };
ContinousDistribution distribution = new ContinousDistribution(names, 0);
return distribution;
}
public static BayesNet createDishNet(Survey survey, RecipeBook recipeBook) {
public static BayesNet createDishNet(Survey survey, RecipeBook recipeBook, int startIndex, int endIndex) {
BayesNet net = new BayesNet(VARIABLES);
net.setDistribution(new Variable(SOMEONE_VEGETARIAN, DOMAIN), vegi());
net.setDistribution(new Variable(CONTAINS_MEAT, DOMAIN), meet());
net.setDistribution(new Variable(SOMEONE_VEGETARIAN, DOMAIN), vegi(survey));
net.setDistribution(new Variable(SOMEONE_ALLERGIC_NUTS, DOMAIN), allergicNuts(survey));
net.setDistribution(new Variable(CONTAINS_MEAT, DOMAIN), meat());
net.setDistribution(new Variable(CONTAINS_NUTS, DOMAIN), nuts());
net.setDistribution(new Variable(CONTAINS_VEGETABLE, DOMAIN),
vegetables());
net.setDistribution(new Variable(CONTAINS_BEEF, DOMAIN), beef());
net.setDistribution(new Variable(CONTAINS_PORK, DOMAIN), pork());
net.setDistribution(new Variable(CONTAINS_POTATOS, DOMAIN), potatos());
net.setDistribution(new Variable(CONTAINS_TOMATOS, DOMAIN), tomatos());
net.setDistribution(new Variable(CONTAINS_GENERIC_NUTS, DOMAIN), genericNuts());
net.setDistribution(new Variable(TASTE, RATING_DOMAIN), taste());
Ontology ontology = createOntology();
for (String category : ontology.getClasses()) {
net.connect(category, SOMEONE_VEGETARIAN);
net.connect(category, SOMEONE_ALLERGIC_NUTS);
}
for (String thing : OBSERVED) {
@@ -198,7 +245,7 @@ public class FoodNetBuilder {
net.connect(TASTE, thing);
}
DataSet dataSet = getSurveyDataSet(survey, recipeBook);
DataSet dataSet = getSurveyDataSet(survey, recipeBook, startIndex, endIndex);
for (String category : ontology.getClasses()) {
MaximumLikelihoodEstimation.estimate(dataSet, net, category);

47
src/dkohl/util/RootMeanSquareError.java
View File

@@ -4,32 +4,27 @@ import java.util.LinkedList;
public class RootMeanSquareError {
private LinkedList<Double> expected;
private LinkedList<Double> groundTruth;
public RootMeanSquareError() {
expected = new LinkedList<Double>();
groundTruth = new LinkedList<Double>();
}
public void push(double predicted, double actual) {
expected.add(predicted);
groundTruth.add(actual);
}
public double error() {
double mean = 0.0;
for(Double val : groundTruth) {
mean += val;
private LinkedList<Double> expected;
private LinkedList<Double> groundTruth;
public RootMeanSquareError() {
expected = new LinkedList<Double>();
groundTruth = new LinkedList<Double>();
}
mean /= groundTruth.size();
double err = 0.0;
for(Double val : expected) {
err += Math.pow(mean - val, 2);
public void push(double predicted, double actual) {
expected.add(predicted);
groundTruth.add(actual);
}
return Math.sqrt(err);
}
public double error() {
double mean = 0.0;
for (int i = 0; i < expected.size(); i++){
mean += Math.pow(expected.get(i) - groundTruth.get(i), 2);
}
mean /= groundTruth.size();
return Math.sqrt(mean);
}
}

55
src/net/woodyfolsom/cs6601/p2/BayesChef.java
View File

@@ -6,24 +6,22 @@ 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.inference.EnumerateAll;
import dkohl.bayes.probability.Assignment;
import dkohl.bayes.probability.ProbabilityAssignment;
import dkohl.bayes.probability.Variable;
import dkohl.util.RootMeanSquareError;
public class BayesChef {
public static void main(String... args) {
System.out.println("Reading recipe book.");
RecipeBook recipeBook = RecipeBookReader.readRecipeBook(new File("data/short_recipebook.xml"));
RecipeBook recipeBook = RecipeBookReader.readRecipeBook(new File("data/long_recipebook.xml"));
System.out.println("Reading survey data.");
Survey survey = SurveyReader.readSurvey(new File("data/short_survey.xml"));
Survey survey = SurveyReader.readSurvey(new File("data/long_survey.xml"));
new BayesChef().reportBestMeal(recipeBook,survey);
}
@@ -36,29 +34,39 @@ public class BayesChef {
System.out.println("Read data for " + numDiners + " diner(s).");
System.out.println("Creating Bayes net for survey dataset...");
BayesNet net = FoodNetBuilder.createDishNet(survey, recipeBook);
System.out.println("Querying Bayes net for individual flavor preferences: ");
double[][] flavorPrefs = new double[4][];
int numSurveyDishes = survey.getDishCount();
int bayesNetIndexStart = 0;
int bayesNetIndexEnd = numSurveyDishes / 2;
flavorPrefs[0] = new double[] {0.0,printPreference(net, TYPE.PORK)};
flavorPrefs[1] = new double[] {1.0,printPreference(net, TYPE.BEEF)};
flavorPrefs[2] = new double[] {2.0,printPreference(net, TYPE.POTATO)};
flavorPrefs[3] = new double[] {3.0,printPreference(net, TYPE.TOMATO)};
int comparisonIndexStart = bayesNetIndexEnd;
int comparisonIndexEnd = numSurveyDishes;
SimpleRegression simpleRegression = new SimpleRegression();
simpleRegression.addData(flavorPrefs);
//TODO change this to include min/max recipe indices for building the net
BayesNet net = FoodNetBuilder.createDishNet(survey, recipeBook, bayesNetIndexStart, bayesNetIndexEnd);
System.out.println("Individual flavor pref MSE: " + simpleRegression.getMeanSquareError());
simpleRegression.clear();
System.out.println("Querying Bayes net for predicted flavor preferences: ");
//TODO change this to query actual flavor combos from latter half of survey
double[] predictedRating = new double[numSurveyDishes - bayesNetIndexEnd];
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)));
for (int i = comparisonIndexStart; i < comparisonIndexEnd; i++) {
predictedRating[i - comparisonIndexStart] = printPreference(net, recipeBook.getRecipe(survey.getDish(i)));
}
System.out.println("Recipe flavor pref MSE: " + simpleRegression.getMeanSquareError());
System.out.println("Querying survey dataset for actual recipe flavor preferences: ");
double[] actualRating = new double[predictedRating.length];
for (int i = comparisonIndexStart; i < comparisonIndexEnd; i++) {
actualRating[i - comparisonIndexStart] = survey.getAverageRating(i);
System.out.println("Actual average rating for " + survey.getDish(i) + ": " + actualRating[i - comparisonIndexStart]);
}
RootMeanSquareError rmse = new RootMeanSquareError();
for (int i = 0; i < actualRating.length; i++) {
rmse.push(predictedRating[i], actualRating[i]);
}
System.out.println("Root Mean Squared Error (predicted vs. actual): " + rmse.error());
}
int printPreference(BayesNet net, Recipe recipe) {
@@ -69,7 +77,7 @@ public class BayesChef {
net, createQuery(ingredientTypes.toArray(new TYPE[ingredientTypes.size()])));
double max_val = 0.0;
String max_arg = "N/A";
String max_arg = "0";
for (ProbabilityAssignment p : probs) {
if (p.getProbability() > max_val) {
max_val = p.getProbability();
@@ -106,6 +114,7 @@ public class BayesChef {
case BEEF:
case POTATO:
case TOMATO:
case GENERIC_NUTS:
assignment.add(new Assignment(new Variable(type.toString(), FoodNetBuilder.DOMAIN),
FoodNetBuilder.TRUE_VALUE));
break;

6
src/net/woodyfolsom/cs6601/p2/Diner.java
View File

@@ -9,7 +9,7 @@ import com.thoughtworks.xstream.annotations.XStreamAlias;
public class Diner {
private int id;
private Map<Integer,Integer> ratings = new HashMap<Integer,Integer>();
private Map<Integer,Boolean> allergies = new HashMap<Integer,Boolean>();
private Map<Integer,Boolean> categories = new HashMap<Integer,Boolean>();
public int getId() {
return id;
@@ -19,7 +19,7 @@ public class Diner {
return ratings.get(dishId);
}
public boolean isAllergic(int categoryId) {
return allergies.get(categoryId);
public boolean isCategory(int categoryId) {
return categories.get(categoryId);
}
}

12
src/net/woodyfolsom/cs6601/p2/Ingredient.java
View File

@@ -1,15 +1,13 @@
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")
public class Ingredient {
public enum TYPE { ALCOHOL, BEEF, DAIRY, EGGS, FISH, GLUTEN, GRAIN, NUTS, PORK, POULTRY, POTATO, SHELLFISH, SPICE, SUGAR, TOMATO}
public enum TYPE { ALCOHOL, BEEF, DAIRY, EGGS, FISH, GENERIC_NUTS, GLUTEN, GRAIN, PORK, POULTRY, POTATO, SHELLFISH, SPICE, SUGAR, TOMATO}
private String item;
@@ -30,11 +28,15 @@ public class Ingredient {
public boolean isType(TYPE type) {
switch (type) {
case BEEF :
return item.contains("beef");
//For our purposes, veal is just expensive beef
return item.contains("beef") || item.contains("veal");
case DAIRY :
return item.contains("margarine");
return item.contains("margarine") || item.contains("milk");
case EGGS :
return item.equals("egg") || item.equals("eggs");
case GENERIC_NUTS :
//cashews, peanuts or generic nuts
return item.contains("cashew") || item.contains("peanut") || item.contains("nuts");
case GLUTEN :
return item.contains("flour");
case PORK :

27
src/net/woodyfolsom/cs6601/p2/Survey.java
View File

@@ -4,6 +4,7 @@ import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import com.thoughtworks.xstream.annotations.XStreamAlias;
import com.thoughtworks.xstream.annotations.XStreamImplicit;
@@ -16,6 +17,32 @@ public class Survey {
@XStreamImplicit(itemFieldName="diner")
private List<Diner> diners = new ArrayList<Diner>();
public double getAverageRating(int recipeIndex) {
double total = 0.0;
for (Diner diner : diners) {
total += diner.getRating(recipeIndex);
}
return total/diners.size();
}
public boolean isDiner(String category) {
for (int i = 0; i < diners.size(); i++) {
if (isCategory(i,category)) {
return true;
}
}
return false;
}
public boolean isCategory(int dinerIndex, String category) {
for (Entry<Integer,String> entry : categories.entrySet()) {
if (entry.getValue().equals(category)) {
return diners.get(dinerIndex).isCategory(entry.getKey());
}
}
return false;
}
public Diner getDiner(int dinerIndex) {
return diners.get(dinerIndex);
}