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bayesHMMTestClasses.py
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# bayesHMMTestClasses.py
# ------------------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
import testClasses
import bayesNet
import random
import layout
import hunters
from copy import deepcopy
from tempfile import mkstemp
import time
from shutil import move
from os import remove, close
import util
from util import manhattanDistance
import busters
import bustersAgents
from game import Agent
from game import Actions
from game import Directions
import re
from inference import ParticleFilter
class GraphEqualityTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(GraphEqualityTest, self).__init__(question, testDict)
layoutText = testDict['layout']
self.layoutName = testDict['layoutName']
lay = layout.Layout([row.strip() for row in layoutText.split('\n')])
self.startState = hunters.GameState()
self.startState.initialize(lay, 0)
def getEmptyStudentBayesNet(self, moduleDict):
inferenceModule = moduleDict['inference']
studentComputation = inferenceModule.constructBayesNet
net = studentComputation(self.startState)
return net
def execute(self, grades, moduleDict, solutionDict):
# load student code and staff code solutions
studentNet = self.getEmptyStudentBayesNet(moduleDict)
goldNet = bayesNet.constructEmptyBayesNetFromString(solutionDict['solutionString'])
correct = studentNet.sameGraph(goldNet)
sameValues = studentNet.sameValuesDict(goldNet)
if correct and sameValues:
return self.testPass(grades)
self.addMessage('Bayes net graphs are not equal.')
missingVars = goldNet.variablesSet() - studentNet.variablesSet()
extraVars = studentNet.variablesSet() - goldNet.variablesSet()
if missingVars:
self.addMessage('Student solution is missing variables: ' + str(missingVars) + '\n')
if extraVars:
self.addMessage('Student solution has extra variables: ' + str(extraVars) + '\n')
studentEdges = set([str(fromVar) + " -> " + str(toVar) for toVar in studentNet.variablesSet() for fromVar in studentNet.inEdges()[toVar]])
goldEdges = set([str(fromVar) + " -> " + str(toVar) for toVar in goldNet.variablesSet() for fromVar in goldNet.inEdges()[toVar]])
missingEdges = goldEdges - studentEdges
extraEdges = studentEdges - goldEdges
if missingEdges:
self.addMessage('Student solution is missing edges:')
for edge in sorted(missingEdges):
self.addMessage(' ' + str(edge))
self.addMessage('\n')
if extraEdges:
self.addMessage('Student solution has extra edges:')
for edge in sorted(extraEdges):
self.addMessage(' ' + str(edge))
self.addMessage('\n')
if not sameValues:
self.addMessage('Student solution has incorrect values dictionary.')
studentDict = studentNet.variableDomainsDict()
goldDict = goldNet.variableDomainsDict()
missingDictVars = set(goldDict) - set(studentDict)
extraDictVars = set(studentDict) - set(goldDict)
if missingDictVars:
self.addMessage('Student dictionary is missing variables: ' + str(missingDictVars))
if extraDictVars:
self.addMessage('Student dictionary has extra variables: ' + str(extraDictVars))
for variable, assignments in goldDict.items():
if variable not in studentDict:
continue
studentAssignments = studentDict[variable]
missing = set(assignments) - set(studentAssignments)
extra = set(studentAssignments) - set(assignments)
if missing:
self.addMessage('Student dictionary for ' + variable + ' is missing assignments: ' + str(missing))
if extra:
self.addMessage('Student dictionary for ' + variable + ' has extra assignments: ' + str(extra))
return self.testFail(grades)
def writeSolution(self, moduleDict, filePath):
inferenceModule = moduleDict['inference']
with open(filePath, 'w') as handle:
handle.write('# This is the solution file for %s.\n\nsolutionString: """\n' % self.path)
net = inferenceModule.constructBayesNet(self.startState)
handle.write(str(net))
handle.write('\n"""\n')
return True
def createPublicVersion(self):
pass
class BayesNetEqualityTest(GraphEqualityTest):
def execute(self, grades, moduleDict, solutionDict):
# load student code and staff code solutions
studentNet = self.getEmptyStudentBayesNet(moduleDict)
goldNet = parseSolutionBayesNet(solutionDict)
if not studentNet.sameGraph(goldNet):
self.addMessage('Bayes net graphs are not equivalent. Please check that your Q1 implementation is correct.')
return self.testFail(grades)
moduleDict['bayesAgents'].fillCPTs(studentNet, self.startState)
for variable in goldNet.variablesSet():
try:
studentFactor = studentNet.getCPT(variable)
except KeyError:
self.addMessage('Student Bayes net missing CPT for variable ' + str(variable))
return self.testFail(grades)
goldFactor = goldNet.getCPT(variable)
if not studentFactor == goldFactor:
self.addMessage('First factor in which student answer differs from solution: P({} | {})'.format(studentFactor.unconditionedVariables(), studentFactor.conditionedVariables()))
self.addMessage('Student Factor:\n' + str(studentFactor))
self.addMessage('Correct Factor:\n' + str(goldFactor))
return self.testFail(grades)
return self.testPass(grades)
def writeSolution(self, moduleDict, filePath):
bayesAgentsModule = moduleDict['bayesAgents']
with open(filePath, 'w') as handle:
handle.write('# This is the solution file for %s.\n\n' % self.path)
net, _ = bayesAgentsModule.constructBayesNet(self.startState)
bayesAgentsModule.fillCPTs(net, self.startState)
handle.write(net.easierToParseString(printVariableDomainsDict=True))
return True
class FactorEqualityTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(FactorEqualityTest, self).__init__(question, testDict)
self.seed = self.testDict['seed']
random.seed(self.seed)
self.alg = self.testDict['alg']
self.max_points = int(self.testDict['max_points'])
self.testPath = testDict['path']
self.constructRandomly = testDict['constructRandomly']
def execute(self, grades, moduleDict, solutionDict):
# load student code and staff code solutions
studentFactor = self.solveProblem(moduleDict)
goldenFactor = parseFactorFromFileDict(solutionDict)
# compare computed factor to stored factor
self.addMessage('Executed FactorEqualityTest')
if studentFactor == goldenFactor:
# extra condition for test passing for this test type:
if self.alg == 'inferenceByVariableElimination':
goldenCallTrackingList = eval(solutionDict['callTrackingList'])
if self.callTrackingList != goldenCallTrackingList:
self.addMessage('Order of joining by variables and elimination by variables is incorrect for variable elimination')
self.addMessage('Student performed the following operations in order: ' + str(self.callTrackingList) + '\n')
self.addMessage('Correct order of operations: ' + str(goldenCallTrackingList) + '\n')
return self.testFail(grades)
return self.testPass(grades)
else:
self.addMessage('Factors are not equal.\n')
self.addMessage('Student generated factor:\n\n' + str(studentFactor) + '\n\n')
self.addMessage('Correct factor:\n\n' + str(goldenFactor) + '\n')
studentProbabilityTotal = sum([studentFactor.getProbability(assignmentDict) for assignmentDict in studentFactor.getAllPossibleAssignmentDicts()])
correctProbabilityTotal = sum([goldenFactor.getProbability(assignmentDict) for assignmentDict in goldenFactor.getAllPossibleAssignmentDicts()])
if abs(studentProbabilityTotal - correctProbabilityTotal) > 10e-12:
self.addMessage('Sum of probability in student generated factor is not the same as in correct factor')
self.addMessage('Student sum of probability: ' + str(studentProbabilityTotal))
self.addMessage('Correct sum of probability: ' + str(correctProbabilityTotal))
return self.testFail(grades)
def writeSolution(self, moduleDict, filePath):
if self.constructRandomly:
if self.alg == 'joinFactors' or self.alg == 'eliminate' or \
self.alg == 'normalize':
replaceTestFile(self.testPath, "Factors", self.factorsDict)
elif self.alg == 'inferenceByVariableElimination' or \
self.alg == 'inferenceByLikelihoodWeightingSampling':
replaceTestFile(self.testPath, "BayesNet", self.problemBayesNet)
factor = self.solveProblem(moduleDict)
with open(filePath, 'w') as handle:
handle.write('# This is the solution file for %s.\n' % self.path)
printString = factor.easierToParseString()
handle.write('%s\n' % (printString))
if self.alg == 'inferenceByVariableElimination':
handle.write('callTrackingList: "' + repr(self.callTrackingList) + '"\n')
return True
class FactorInputFactorEqualityTest(FactorEqualityTest):
def __init__(self, question, testDict):
super(FactorInputFactorEqualityTest, self).__init__(question, testDict)
self.factorArgs = self.testDict['factorArgs']
eliminateToPerform = (self.alg == 'eliminate')
evidenceAssignmentToPerform = (self.alg == 'normalize')
parseDict = parseFactorInputProblem(testDict, goingToEliminate=eliminateToPerform,
goingToEvidenceAssign=evidenceAssignmentToPerform)
self.variableDomainsDict = parseDict['variableDomainsDict']
self.factorsDict = parseDict['factorsDict']
if eliminateToPerform:
self.eliminateVariable = parseDict['eliminateVariable']
if evidenceAssignmentToPerform:
self.evidenceDict = parseDict['evidenceDict']
self.max_points = int(self.testDict['max_points'])
def solveProblem(self, moduleDict):
factorOperationsModule = moduleDict['factorOperations']
studentComputation = getattr(factorOperationsModule, self.alg)
if self.alg == 'joinFactors':
solvedFactor = studentComputation(self.factorsDict.values())
elif self.alg == 'eliminate':
solvedFactor = studentComputation(list(self.factorsDict.values())[0],
self.eliminateVariable)
elif self.alg == 'normalize':
newVariableDomainsDict = deepcopy(self.variableDomainsDict)
for variable, value in self.evidenceDict.items():
newVariableDomainsDict[variable] = [value]
origFactor = list(self.factorsDict.values())[0]
specializedFactor = origFactor.specializeVariableDomains(newVariableDomainsDict)
solvedFactor = studentComputation(specializedFactor)
return solvedFactor
class BayesNetInputFactorEqualityTest(FactorEqualityTest):
def __init__(self, question, testDict):
super(BayesNetInputFactorEqualityTest, self).__init__(question, testDict)
parseDict = parseBayesNetProblem(testDict)
self.queryVariables = parseDict['queryVariables']
self.evidenceDict = parseDict['evidenceDict']
if self.alg == 'inferenceByVariableElimination':
self.callTrackingList = []
self.variableEliminationOrder = parseDict['variableEliminationOrder']
elif self.alg == 'inferenceByLikelihoodWeightingSampling':
self.numSamples = parseDict['numSamples']
self.problemBayesNet = parseDict['problemBayesNet']
self.max_points = int(self.testDict['max_points'])
def solveProblem(self, moduleDict):
inferenceModule = moduleDict['inference']
if self.alg == 'inferenceByVariableElimination':
studentComputationWithCallTracking = getattr(inferenceModule, self.alg + 'WithCallTracking')
studentComputation = studentComputationWithCallTracking(self.callTrackingList)
solvedFactor = studentComputation(self.problemBayesNet, self.queryVariables, self.evidenceDict, self.variableEliminationOrder)
elif self.alg == 'inferenceByLikelihoodWeightingSampling':
randomSource = util.FixedRandom().random
studentComputationRandomSource = getattr(inferenceModule, self.alg + 'RandomSource')
studentComputation = studentComputationRandomSource(randomSource)
#random.seed(self.seed) # reset seed so that if we had to compute the bayes net we still have the initial seed
solvedFactor = studentComputation(self.problemBayesNet, self.queryVariables, self.evidenceDict, self.numSamples)
return solvedFactor
class MostLikelyFoodHousePositionTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(MostLikelyFoodHousePositionTest, self).__init__(question, testDict)
layoutText = testDict['layout']
self.layoutName = testDict['layoutName']
lay = layout.Layout([row.strip() for row in layoutText.split('\n')])
self.startState = hunters.GameState()
self.startState.initialize(lay, 0)
self.evidence = eval(testDict['evidence'])
self.eliminationOrder = eval(testDict['eliminationOrder'])
def execute(self, grades, moduleDict, solutionDict):
# load student code and staff code solutions
bayesAgentsModule = moduleDict['bayesAgents']
FOOD_HOUSE_VAR = bayesAgentsModule.FOOD_HOUSE_VAR
studentBayesNet, _ = bayesAgentsModule.constructBayesNet(self.startState)
bayesAgentsModule.fillCPTs(studentBayesNet, self.startState)
studentFunction = bayesAgentsModule.getMostLikelyFoodHousePosition
studentPosition = studentFunction(self.evidence, studentBayesNet, self.eliminationOrder)[FOOD_HOUSE_VAR]
goldPosition = solutionDict['answer']
correct = studentPosition == goldPosition
if not correct:
self.addMessage('Student answer: ' + str(studentPosition))
self.addMessage('Correct answer: ' + str(goldPosition))
return self.testPass(grades) if correct else self.testFail(grades)
def writeSolution(self, moduleDict, filePath):
bayesAgentsModule = moduleDict['bayesAgents']
staffBayesNet, _ = bayesAgentsModule.constructBayesNet(self.startState)
FOOD_HOUSE_VAR = bayesAgentsModule.FOOD_HOUSE_VAR
bayesAgentsModule.fillCPTs(staffBayesNet, self.startState)
staffFunction = bayesAgentsModule.getMostLikelyFoodHousePosition
answer = staffFunction(self.evidence, staffBayesNet, self.eliminationOrder)[FOOD_HOUSE_VAR]
with open(filePath, 'w') as handle:
handle.write('# This is the solution file for %s.\n\nanswer: """\n' % self.path)
handle.write(str(answer))
handle.write('\n"""\n')
return True
def createPublicVersion(self):
pass
class VPITest(testClasses.TestCase):
def __init__(self, question, testDict):
super(VPITest, self).__init__(question, testDict)
self.targetFunction = testDict['function']
layoutText = testDict['layout']
self.layoutName = testDict['layoutName']
lay = layout.Layout([row.strip() for row in layoutText.split('\n')])
self.startState = hunters.GameState()
self.startState.initialize(lay, 0)
self.evidence = eval(testDict['evidence'])
self.eliminationOrder = eval(testDict['eliminationOrder'])
def execute(self, grades, moduleDict, solutionDict):
# load student code and staff code solutions
bayesAgentsModule = moduleDict['bayesAgents']
studentAgent = bayesAgentsModule.VPIAgent()
studentAgent.registerInitialState(self.startState)
studentAnswer = eval('studentAgent.{}(self.evidence, self.eliminationOrder)'.format(self.targetFunction))
goldAnswer = eval(solutionDict['answer'])
if type(studentAnswer) == float:
correct = closeNums(studentAnswer, goldAnswer)
else:
correct = closeNums(studentAnswer[0], goldAnswer[0]) & closeNums(studentAnswer[1], goldAnswer[1])
if not correct:
self.addMessage('Student answer differed from solution by at least .0001')
self.addMessage('Student answer: ' + str(studentAnswer))
self.addMessage('Correct answer: ' + str(goldAnswer))
return self.testPass(grades) if correct else self.testFail(grades)
def writeSolution(self, moduleDict, filePath):
bayesAgentsModule = moduleDict['bayesAgents']
agent = bayesAgentsModule.VPIAgent()
agent.registerInitialState(self.startState)
answer = eval('agent.{}(self.evidence, self.eliminationOrder)'.format(self.targetFunction))
with open(filePath, 'w') as handle:
handle.write('# This is the solution file for %s.\n\nanswer: """\n' % self.path)
handle.write(str(answer))
handle.write('\n"""\n')
return True
def createPublicVersion(self):
pass
def closeNums(x, y):
return abs(x - y) < 1e-4
def parseFactorInputProblem(testDict, goingToEliminate=False, goingToEvidenceAssign=False):
parseDict = {}
variableDomainsDict = {}
for line in testDict['variableDomainsDict'].split('\n'):
variable, domain = line.split(' : ')
variableDomainsDict[variable] = domain.split(' ')
parseDict['variableDomainsDict'] = variableDomainsDict
factorsDict = {} # assume args is a list of factor names and maybe a variable name at the end
if goingToEliminate:
eliminateVariable = testDict["eliminateVariable"]
parseDict['eliminateVariable'] = eliminateVariable
# for normalize need evidence so that normalize is nontrivial
if goingToEvidenceAssign:
evidenceAssignmentString = testDict["evidenceDict"]
evidenceDict = {}
for line in evidenceAssignmentString.split('\n'):
if(line.count(' : ')): #so we can pass empty dicts for unnormalized variables
evidenceVariable, evidenceAssignment = line.split(' : ')
evidenceDict[evidenceVariable] = evidenceAssignment
parseDict['evidenceDict'] = evidenceDict
for factorName in testDict["factorArgs"].split(' '):
# construct a dict from names to factors and
# load a factor from the test file for each
currentFactor = parseFactorFromFileDict(testDict, variableDomainsDict=variableDomainsDict,
prefix=factorName)
factorsDict[factorName] = currentFactor
parseDict['factorsDict'] = factorsDict
return parseDict
def replaceTestFile(file_path, typeOfTest, inputToTest):
#Create temp file
fh, abs_path = mkstemp()
with open(abs_path,'w') as new_file:
with open(file_path) as old_file:
# Assumes that variableDomainsDict is the last
# entry in the test file before the factors start to
# get enumerated
for line in old_file:
new_file.write(line)
if 'endOfNonFactors' in line:
break
if typeOfTest == 'BayesNet':
new_file.write("\n" + inputToTest.easierToParseString())
elif typeOfTest == 'Factors':
new_file.write("\n" + "\n".join([factor.easierToParseString(prefix=name,
printVariableDomainsDict=False) for
name, factor in inputToTest.items()]))
close(fh)
#Remove original file
remove(file_path)
#Move new file
move(abs_path, file_path)
def parseFactorFromFileDict(fileDict, variableDomainsDict=None, prefix=None):
if prefix is None:
prefix = ''
if variableDomainsDict is None:
variableDomainsDict = {}
for line in fileDict['variableDomainsDict'].split('\n'):
variable, domain = line.split(' : ')
variableDomainsDict[variable] = domain.split(' ')
# construct a dict from names to factors and
# load a factor from the test file for each
unconditionedVariables = []
for variable in fileDict[prefix + "unconditionedVariables"].split(' '):
unconditionedVariable = variable.strip()
unconditionedVariables.append(unconditionedVariable)
conditionedVariables = []
for variable in fileDict[prefix + "conditionedVariables"].split(' '):
conditionedVariable = variable.strip()
if variable != '':
conditionedVariables.append(conditionedVariable)
if 'constructRandomly' not in fileDict or fileDict['constructRandomly'] == 'False':
currentFactor = bayesNet.Factor(unconditionedVariables, conditionedVariables,
variableDomainsDict)
for line in fileDict[prefix + 'FactorTable'].split('\n'):
assignments, probability = line.split(" = ")
assignmentList = [assignment for assignment in assignments.split(', ')]
assignmentsDict = {}
for assignment in assignmentList:
var, value = assignment.split(' : ')
assignmentsDict[var] = value
currentFactor.setProbability(assignmentsDict, float(probability))
elif fileDict['constructRandomly'] == 'True':
currentFactor = bayesNet.constructAndFillFactorRandomly(unconditionedVariables, conditionedVariables, variableDomainsDict)
return currentFactor
def parseSolutionBayesNet(solutionDict):
# needs to be able to parse in a bayes net
variableDomainsDict = {}
for line in solutionDict['variableDomainsDict'].split('\n'):
variable, domain = line.split(' : ')
variableDomainsDict[variable] = domain.split(' ')
variables = list(variableDomainsDict.keys())
edgeList = []
for variable in variables:
parents = solutionDict[variable + 'conditionedVariables'].split(' ')
for parent in parents:
if parent != '':
edgeList.append((parent, variable))
net = bayesNet.constructEmptyBayesNet(variables, edgeList, variableDomainsDict)
factors = {}
for variable in variables:
net.setCPT(variable, parseFactorFromFileDict(solutionDict, variableDomainsDict, variable))
return net
def parseBayesNetProblem(testDict):
# needs to be able to parse in a bayes net,
# and figure out what type of operation to perform and on what
parseDict = {}
variableDomainsDict = {}
for line in testDict['variableDomainsDict'].split('\n'):
variable, domain = line.split(' : ')
variableDomainsDict[variable] = domain.split(' ')
parseDict['variableDomainsDict'] = variableDomainsDict
variables = []
for line in testDict["variables"].split('\n'):
variable = line.strip()
variables.append(variable)
edges = []
for line in testDict["edges"].split('\n'):
tokens = line.strip().split()
if len(tokens) == 2:
edges.append((tokens[0], tokens[1]))
else:
raise Exception("[parseBayesNetProblem] Bad evaluation line: |%s|" % (line,))
# inference query args
queryVariables = testDict['queryVariables'].split(' ')
parseDict['queryVariables'] = queryVariables
evidenceDict = {}
for line in testDict['evidenceDict'].split('\n'):
if(line.count(' : ')): #so we can pass empty dicts for unnormalized variables
(evidenceVariable, evidenceValue) = line.split(' : ')
evidenceDict[evidenceVariable] = evidenceValue
parseDict['evidenceDict'] = evidenceDict
if testDict['constructRandomly'] == 'False':
# load from test file
problemBayesNet = bayesNet.constructEmptyBayesNet(variables, edges, variableDomainsDict)
for variable in variables:
currentFactor = bayesNet.Factor([variable], problemBayesNet.inEdges()[variable], variableDomainsDict)
for line in testDict[variable + 'FactorTable'].split('\n'):
assignments, probability = line.split(" = ")
assignmentList = [assignment for assignment in assignments.split(', ')]
assignmentsDict = {}
for assignment in assignmentList:
var, value = assignment.split(' : ')
assignmentsDict[var] = value
currentFactor.setProbability(assignmentsDict, float(probability))
problemBayesNet.setCPT(variable, currentFactor)
elif testDict['constructRandomly'] == 'True':
problemBayesNet = bayesNet.constructRandomlyFilledBayesNet(variables, edges, variableDomainsDict)
parseDict['problemBayesNet'] = problemBayesNet
if testDict['alg'] == 'inferenceByVariableElimination':
variableEliminationOrder = testDict['variableEliminationOrder'].split(' ')
parseDict['variableEliminationOrder'] = variableEliminationOrder
elif testDict['alg'] == 'inferenceByLikelihoodWeightingSampling':
numSamples = int(testDict['numSamples'])
parseDict['numSamples'] = numSamples
return parseDict
###################################
####### From fa21 Tracking Project
fixed_order = ['West', 'East', 'Stop', 'South', 'North']
class GameScoreTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(GameScoreTest, self).__init__(question, testDict)
self.maxMoves = int(self.testDict['maxMoves'])
self.inference = self.testDict['inference']
self.layout_str = self.testDict['layout_str'].split('\n')
self.numRuns = int(self.testDict['numRuns'])
self.numWinsForCredit = int(self.testDict['numWinsForCredit'])
self.numGhosts = int(self.testDict['numGhosts'])
self.layout_name = self.testDict['layout_name']
self.min_score = int(self.testDict['min_score'])
self.observe_enable = self.testDict['observe'] == 'True'
self.elapse_enable = self.testDict['elapse'] == 'True'
def execute(self, grades, moduleDict, solutionDict):
ghosts = [SeededRandomGhostAgent(i) for i in range(1,self.numGhosts+1)]
print(self.inference)
pac = bustersAgents.GreedyBustersAgent(0, inference = self.inference, ghostAgents = ghosts, observeEnable = self.observe_enable, elapseTimeEnable = self.elapse_enable)
#if self.inference == "ExactInference":
# pac.inferenceModules = [moduleDict['inference'].ExactInference(a) for a in ghosts]
#else:
# print "Error inference type %s -- not implemented" % self.inference
# return
stats = run(self.layout_str, pac, ghosts, self.question.getDisplay(), nGames=self.numRuns, maxMoves=self.maxMoves, quiet = False)
aboveCount = [s >= self.min_score for s in stats['scores']].count(True)
msg = "%s) Games won on %s with score above %d: %d/%d" % (self.layout_name, grades.currentQuestion, self.min_score, aboveCount, self.numRuns)
grades.addMessage(msg)
if aboveCount >= self.numWinsForCredit:
grades.assignFullCredit()
return self.testPass(grades)
else:
return self.testFail(grades)
def writeSolution(self, moduleDict, filePath):
handle = open(filePath, 'w')
handle.write('# You must win at least %d/10 games with at least %d points' % (self.numWinsForCredit, self.min_score))
handle.close()
def createPublicVersion(self):
pass
class ZeroWeightTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(ZeroWeightTest, self).__init__(question, testDict)
self.maxMoves = int(self.testDict['maxMoves'])
self.inference = self.testDict['inference']
self.layout_str = self.testDict['layout'].split('\n')
self.numGhosts = int(self.testDict['numGhosts'])
self.observe_enable = self.testDict['observe'] == 'True'
self.elapse_enable = self.testDict['elapse'] == 'True'
self.ghost = self.testDict['ghost']
self.seed = int(self.testDict['seed'])
def execute(self, grades, moduleDict, solutionDict):
random.seed(self.seed)
inferenceFunction = getattr(moduleDict['inference'], self.inference)
ghosts = [globals()[self.ghost](i) for i in range(1, self.numGhosts+1)]
if self.inference == 'MarginalInference':
moduleDict['inference'].jointInference = moduleDict['inference'].JointParticleFilter()
disp = self.question.getDisplay()
pac = ZeroWeightAgent(inferenceFunction, ghosts, grades, self.seed, disp, elapse=self.elapse_enable, observe=self.observe_enable)
if self.inference == "ParticleFilter":
for pfilter in pac.inferenceModules: pfilter.setNumParticles(5000)
elif self.inference == "MarginalInference":
moduleDict['inference'].jointInference.setNumParticles(5000)
run(self.layout_str, pac, ghosts, disp, maxMoves = self.maxMoves)
if pac.getReset():
grades.addMessage('%s) successfully handled all weights = 0' % grades.currentQuestion)
return self.testPass(grades)
else:
grades.addMessage('%s) error handling all weights = 0' % grades.currentQuestion)
return self.testFail(grades)
def writeSolution(self, moduleDict, filePath):
handle = open(filePath, 'w')
handle.write('# This test checks that you successfully handle the case when all particle weights are set to 0\n')
handle.close()
def createPublicVersion(self):
self.testDict['seed'] = '188'
self.seed = 188
class DoubleInferenceAgentTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(DoubleInferenceAgentTest, self).__init__(question, testDict)
self.seed = int(self.testDict['seed'])
self.layout_str = self.testDict['layout'].split('\n')
self.observe = (self.testDict['observe'] == "True")
self.elapse = (self.testDict['elapse'] == "True")
self.checkUniform = (self.testDict['checkUniform'] == 'True')
self.maxMoves = int(self.testDict['maxMoves'])
self.numParticles = int(self.testDict['numParticles'])
self.numGhosts = int(self.testDict['numGhosts'])
self.inference = self.testDict['inference']
self.errorMsg = self.testDict['errorMsg']
self.L2Tolerance = float(self.testDict['L2Tolerance'])
self.ghost = self.testDict['ghost']
def execute(self, grades, moduleDict, solutionDict):
random.seed(self.seed)
lines = solutionDict['correctActions'].split('\n')
moves = []
# Collect solutions
for l in lines:
m = re.match(r'(\d+) (\w+) (.*)', l)
moves.append((m.group(1), m.group(2), eval(m.group(3))))
inferenceFunction = getattr(moduleDict['inference'], self.inference)
ghosts = [globals()[self.ghost](i) for i in range(1, self.numGhosts+1)]
if self.inference == 'MarginalInference':
moduleDict['inference'].jointInference = moduleDict['inference'].JointParticleFilter()
disp = self.question.getDisplay()
pac = DoubleInferenceAgent(inferenceFunction, moves, ghosts, grades, self.seed, disp, self.inference, elapse=self.elapse,
observe=self.observe, L2Tolerance=self.L2Tolerance, checkUniform = self.checkUniform)
if self.inference == "ParticleFilter":
for pfilter in pac.inferenceModules: pfilter.setNumParticles(self.numParticles)
elif self.inference == "MarginalInference":
moduleDict['inference'].jointInference.setNumParticles(self.numParticles)
run(self.layout_str, pac, ghosts, disp, maxMoves=self.maxMoves)
msg = self.errorMsg % pac.errors
grades.addMessage(("%s) " % (grades.currentQuestion))+msg)
if pac.errors == 0:
grades.addPoints(2)
return self.testPass(grades)
else:
return self.testFail(grades)
def writeSolution(self, moduleDict, filePath):
random.seed(self.seed)
if self.inference == 'ParticleFilter':
self.inference = 'ExactInference' # use exact inference to generate solution
inferenceFunction = getattr(moduleDict['inference'], self.inference)
ghosts = [globals()[self.ghost](i) for i in range(1, self.numGhosts+1)]
if self.inference == 'MarginalInference':
moduleDict['inference'].jointInference = moduleDict['inference'].JointParticleFilter()
moduleDict['inference'].jointInference.setNumParticles(self.numParticles)
pac = InferenceAgent(inferenceFunction, ghosts, self.seed, elapse=self.elapse, observe=self.observe)
run(self.layout_str, pac, ghosts, self.question.getDisplay(), maxMoves=self.maxMoves)
# run our gold code here and then write it to a solution file
answerList = pac.answerList
handle = open(filePath, 'w')
handle.write('# move_number action likelihood_dictionary\n')
handle.write('correctActions: """\n')
for (moveNum, move, dists) in answerList:
handle.write('%s %s [' % (moveNum, move))
for dist in dists:
handle.write('{')
for key in dist:
handle.write('%s: %s, ' % (key, dist[key]))
handle.write('}, ')
handle.write(']\n')
handle.write('"""\n')
handle.close()
def createPublicVersion(self):
self.testDict['seed'] = '188'
self.seed = 188
class OutputTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(OutputTest, self).__init__(question, testDict)
self.preamble = compile(testDict.get('preamble', ""), "%s.preamble" % self.getPath(), 'exec')
self.test = compile(testDict['test'], "%s.test" % self.getPath(), 'eval')
self.success = testDict['success']
self.failure = testDict['failure']
def evalCode(self, moduleDict):
bindings = dict(moduleDict)
exec(self.preamble, bindings)
return eval(self.test, bindings)
def execute(self, grades, moduleDict, solutionDict):
result = self.evalCode(moduleDict)
result = list(map(lambda x: str(x), result))
result = ' '.join(result)
if result == solutionDict['result']:
grades.addMessage('PASS: %s' % self.path)
grades.addMessage('\t%s' % self.success)
return True
else:
grades.addMessage('FAIL: %s' % self.path)
grades.addMessage('\t%s' % self.failure)
grades.addMessage('\tstudent result: "%s"' % result)
grades.addMessage('\tcorrect result: "%s"' % solutionDict['result'])
return False
def writeSolution(self, moduleDict, filePath):
handle = open(filePath, 'w')
handle.write('# This is the solution file for %s.\n' % self.path)
handle.write('# The result of evaluating the test must equal the below when cast to a string.\n')
solution = self.evalCode(moduleDict)
solution = list(map(lambda x: str(x), solution))
handle.write('result: "%s"\n' % ' '.join(solution))
handle.close()
return True
def createPublicVersion(self):
pass
def run(layout_str, pac, ghosts, disp, nGames = 1, name = 'games', maxMoves=-1, quiet = True):
"Runs a few games and outputs their statistics."
starttime = time.time()
lay = layout.Layout(layout_str)
#print '*** Running %s on' % name, layname,'%d time(s).' % nGames
games = busters.runGames(lay, pac, ghosts, disp, nGames, maxMoves)
#print '*** Finished running %s on' % name, layname, 'after %d seconds.' % (time.time() - starttime)
stats = {'time': time.time() - starttime, \
'wins': [g.state.isWin() for g in games].count(True), \
'games': games, 'scores': [g.state.getScore() for g in games]}
statTuple = (stats['wins'], len(games), sum(stats['scores']) * 1.0 / len(games))
if not quiet:
print('*** Won %d out of %d games. Average score: %f ***' % statTuple)
return stats
class InferenceAgent(bustersAgents.BustersAgent):
"Tracks ghosts and compares to reference inference modules, while moving randomly"
def __init__( self, inference, ghostAgents, seed, elapse=True, observe=True, burnIn=0):
self.inferenceModules = [inference(a) for a in ghostAgents]
self.elapse = elapse
self.observe = observe
self.burnIn = burnIn
self.numMoves = 0
#self.rand = rand
# list of tuples (move_num, move, [dist_1, dist_2, ...])
self.answerList = []
self.seed = seed
def final(self, gameState):
distributionList = []
self.numMoves += 1
for index,inf in enumerate(self.inferenceModules):
if self.observe:
inf.observe(gameState)
self.ghostBeliefs[index] = inf.getBeliefDistribution()
beliefCopy = deepcopy(self.ghostBeliefs[index])
distributionList.append(beliefCopy)
self.answerList.append((self.numMoves, None, distributionList))
random.seed(self.seed + self.numMoves)
def registerInitialState(self, gameState):
"Initializes beliefs and inference modules"
for inference in self.inferenceModules: inference.initialize(gameState)
self.ghostBeliefs = [inf.getBeliefDistribution() for inf in self.inferenceModules]
self.firstMove = True
self.answerList.append((self.numMoves,None,deepcopy(self.ghostBeliefs)))
def getAction(self, gameState):
"Updates beliefs, then chooses an action based on updated beliefs."
distributionList = []
self.numMoves += 1
for index,inf in enumerate(self.inferenceModules):
if self.elapse:
if not self.firstMove: inf.elapseTime(gameState)
self.firstMove = False
if self.observe:
inf.observe(gameState)
self.ghostBeliefs[index] = inf.getBeliefDistribution()
beliefCopy = deepcopy(self.ghostBeliefs[index])
distributionList.append(beliefCopy)
action = random.choice([a for a in gameState.getLegalPacmanActions() if a != 'STOP'])
self.answerList.append((self.numMoves, action, distributionList))
random.seed(self.seed + self.numMoves)
return action
class ZeroWeightAgent(bustersAgents.BustersAgent):
"Tracks ghosts and compares to reference inference modules, while moving randomly"
def __init__( self, inference, ghostAgents, grades, seed, disp, elapse=True, observe=True ):
self.inferenceModules = [inference(a) for a in ghostAgents]
self.elapse = elapse
self.observe = observe
self.grades = grades
self.numMoves = 0
self.seed = seed
self.display = disp
self.reset = False
def final(self, gameState):
pass
def registerInitialState(self, gameState):
"Initializes beliefs and inference modules"
for inference in self.inferenceModules: inference.initialize(gameState)
self.ghostBeliefs = [inf.getBeliefDistribution() for inf in self.inferenceModules]
self.firstMove = True
def getAction(self, gameState):
"Updates beliefs, then chooses an action based on updated beliefs."
newBeliefs = [None] * len(self.inferenceModules)
self.numMoves += 1
for index,inf in enumerate(self.inferenceModules):
if self.elapse:
if not self.firstMove: inf.elapseTime(gameState)
self.firstMove = False
if self.observe:
inf.observe(gameState)
newBeliefs[index] = inf.getBeliefDistribution()
self.checkReset(newBeliefs, self.ghostBeliefs)
self.ghostBeliefs = newBeliefs
self.display.updateDistributions(self.ghostBeliefs)
random.seed(self.seed + self.numMoves)
action = random.choice([a for a in gameState.getLegalPacmanActions() if a != 'STOP'])
return action
def checkReset(self, newBeliefs, oldBeliefs):
for i in range(len(newBeliefs)):
newKeys = list(filter(lambda x: newBeliefs[i][x] != 0, newBeliefs[i].keys()))
oldKeys = list(filter(lambda x: oldBeliefs[i][x] != 0, oldBeliefs[i].keys()))
if len(newKeys) > len(oldKeys):
self.reset = True
def getReset(self):
return self.reset
class DoubleInferenceAgent(bustersAgents.BustersAgent):
"Tracks ghosts and compares to reference inference modules, while moving randomly"
def __init__( self, inference, refSolution, ghostAgents, grades, seed, disp, func, elapse=True, observe=True, L2Tolerance=0.2, burnIn=0, checkUniform = False):
self.inferenceModules = [inference(a) for a in ghostAgents]
self.refSolution = refSolution
self.func = func
self.elapse = elapse
self.observe = observe
self.grades = grades
self.L2Tolerance = L2Tolerance
self.errors = 0
self.burnIn = burnIn
self.numMoves = 0
self.seed = seed
self.display = disp
self.checkUniform = checkUniform
def final(self, gameState):
self.numMoves += 1
moveNum,action,dists = self.refSolution[self.numMoves]
for index,inf in enumerate(self.inferenceModules):
if self.observe:
inf.observe(gameState)
self.ghostBeliefs[index] = inf.getBeliefDistribution()
if self.numMoves >= self.burnIn:
self.distCompare(self.ghostBeliefs[index], dists[index])
self.display.updateDistributions(self.ghostBeliefs)
random.seed(self.seed + self.numMoves)
if not self.display.checkNullDisplay():
time.sleep(3)
def registerInitialState(self, gameState):
"Initializes beliefs and inference modules"
for inference in self.inferenceModules:
inference.initialize(gameState)
if (isinstance(inference, ParticleFilter)):
if len(inference.particles) != inference.numParticles:
t = (self.grades.currentQuestion, len(inference.particles), inference.numParticles)
summary = '%s) Filters do not have the same number of particles.\n\tstudent count: %d\n\treference count: %d' % t
self.grades.fail('%s' % (summary))
self.errors += 1
moveNum,action,dists = self.refSolution[self.numMoves]
for index,inf in enumerate(self.inferenceModules):
self.distCompare(inf.getBeliefDistribution(), dists[index])
self.ghostBeliefs = [inf.getBeliefDistribution() for inf in self.inferenceModules]
self.firstMove = True
def getAction(self, gameState):
"Updates beliefs, then chooses an action based on updated beliefs."
self.numMoves += 1
moveNum,action,dists = self.refSolution[self.numMoves]
for index,inf in enumerate(self.inferenceModules):
if self.elapse:
if not self.firstMove: inf.elapseTime(gameState)
self.firstMove = False
if self.observe:
inf.observe(gameState)
self.ghostBeliefs[index] = inf.getBeliefDistribution()
if self.numMoves >= self.burnIn: self.distCompare(self.ghostBeliefs[index], dists[index])
self.display.updateDistributions(self.ghostBeliefs)
random.seed(self.seed + self.numMoves)
return action
def distCompare(self, dist, refDist):
"Compares two distributions"
# copy and prepare distributions
dist = dist.copy()
refDist = refDist.copy()
for key in set(list(refDist.keys()) + list(dist.keys())):
if not key in dist.keys():
dist[key] = 0.0
if not key in refDist.keys():
refDist[key] = 0.0
# calculate l2 difference
if sum(refDist.values()) == 0 and self.func != 'ExactInference':
for key in refDist:
if key[1] != 1:
refDist[key] = 1.0 / float(len(refDist))
l2 = 0
for k in refDist.keys():
l2 += (dist[k] - refDist[k]) ** 2
if l2 > self.L2Tolerance:
if self.errors == 0:
t = (self.grades.currentQuestion, self.numMoves, l2)
summary = "%s) Distribution deviated at move %d by %0.4f (squared norm) from the correct answer.\n" % t