class BayesNet:
"""
Simple Bayes net model for discrete variables and binary response, supports structure
learning and predicting of Naive Bayes and Tree Augmented Net.
"""
##
# train is a Data object representing a training dataset
# test is a Data object representing a testing dataset
def __init__(self):
self.train = None
self.test = None
self.graph = Graph()
##
# Load the training dataset.
# @param trainFilename A string of filename
def loadTrain(self, trainFilename):
self.train = Data(trainFilename)
self.train.parse()
##
# Load the testing dataset.
# @param testFilename A string of filename
def loadTest(self, testFilename):
self.test = Data(testFilename)
self.test.parse()
##
# Build the graph structure of Naive Bayes
def buildNaiveBayes(self):
for name in self.train.names:
self.graph.addNode(name)
nodes = self.graph.getNodes()
for node in nodes:
if node != 'class':
self.graph.addEdge('class', node.getId())
##
# Build the graph structure of TAN, using Prim's algo.
# Every element in priority queue is (score, to_node, frm_node), where
# score = (-CMI, colIndex(frm), colIndex(to))
# to_node is current node object
# frm_node is the parent node which has edge frm_node -> to_node, with score
def buildTAN(self):
self.buildNaiveBayes() # first make a Naive Bayes structure
pri_q = Q.PriorityQueue()
rootNode = self.graph.getNode(self.train.names[0]) # first attribute
pri_q.put(((0, 0, 0), rootNode, Node(None))) # frm is a dummy node
visited = set() # a set of visited nodes' names
while (not pri_q.empty()) and (len(visited) < self.train.names):
item = pri_q.get() # top element stored in priority queue
score = item[0]
currNode = item[1] # current node object
currName = currNode.getId()
if currName in visited: continue
frmNode = item[2] # frm node object
frmName = frmNode.getId()
self.graph.addEdge(frmName, currName, score)
visited.add(currName)
for attrName in self.train.names:
if attrName not in visited:
CMI = calcCondMI(self.train, currName, attrName, 'class')
indexes = self.train.getColIndex([currName, attrName])
score = (-CMI, indexes[0], indexes[1])
nextNode = self.graph.getNode(attrName)
pri_q.put((score, nextNode, currNode))
##
# Given the values of attributes of a testing instance, predict its class.
# @param instanceVals A list of instance variable values, including the 'class' at the end.
# @return [predicted class value, actual class value, posterior prob of predicted value]
def predictOneInstance(self, instanceVals):
names = self.train.names # including the 'class'
posteriors = []
for y in self.train.variables['class']:
# give the actual posterior porbability in the output
true_y = instanceVals[-1]
Py = calcProb(self.train, ['class'], [y])
Px = calcProb(self.train, names[:-1], instanceVals[:-1])
Px_pa = calcProbsCondParents(self.train, names[:-1],
instanceVals[:-1] + [y], self.graph)
#print Py, Px, Px_pa
pred_p = Py * Px_pa / Px
posteriors.append([y, true_y, pred_p])
# normalize the posteriors
total = sum(x[-1] for x in posteriors)
posteriors = [x[:2] + [round(x[-1] / total, 12)] for x in posteriors]
return sorted(posteriors, key=lambda x: -x[-1])[0]
##
# Predict the class in all instances in testing dataset.
# @return A list of predicted result for instances in testing set.
def predictTestData(self):
results = []
for instance in self.test.data:
results.append(self.predictOneInstance(instance))
return results
## output the result as homework requirement
def printResults(self):
results = self.predictTestData()
# display structures
for attr in self.train.names[:-1]:
node = self.graph.getNode(attr)
parentNodes = node.getParents()
parentIndexes = sorted([self.train.getColIndex([p.getId()])[0] \
for p in parentNodes])
parentNames = [self.train.names[i] for i in parentIndexes]
print attr + ' ' + ' '.join(parentNames) + ' '
print ''
# display predicts
corrects = 0
for r in results:
if r[0] == r[1]:
corrects += 1
print ' '.join([str(x) for x in r])
print ''
# display correct predicts
print str(corrects)
print ''
class BayesNet:
"""
Simple Bayes net model for discrete variables and binary response, supports structure
learning and predicting of Naive Bayes and Tree Augmented Net.
"""
##
# train is a Data object representing a training dataset
# test is a Data object representing a testing dataset
def __init__(self):
self.train = None
self.test = None
self.graph = Graph()
##
# Load the training dataset.
# @param trainFilename A string of filename
def loadTrain(self, trainFilename):
self.train = Data(trainFilename)
self.train.parse()
##
# Load the testing dataset.
# @param testFilename A string of filename
def loadTest(self, testFilename):
self.test = Data(testFilename)
self.test.parse()
##
# Build the graph structure of Naive Bayes
def buildNaiveBayes(self):
for name in self.train.names:
self.graph.addNode(name)
nodes = self.graph.getNodes()
for node in nodes:
if node != 'class':
self.graph.addEdge('class', node.getId())
##
# Build the graph structure of TAN, using Prim's algo.
# Every element in priority queue is (score, to_node, frm_node), where
# score = (-CMI, colIndex(frm), colIndex(to))
# to_node is current node object
# frm_node is the parent node which has edge frm_node -> to_node, with score
def buildTAN(self):
self.buildNaiveBayes() # first make a Naive Bayes structure
pri_q = Q.PriorityQueue()
rootNode = self.graph.getNode(self.train.names[0]) # first attribute
pri_q.put( ((0,0,0), rootNode, Node(None)) ) # frm is a dummy node
visited = set() # a set of visited nodes' names
while (not pri_q.empty()) and (len(visited) < self.train.names):
item = pri_q.get() # top element stored in priority queue
score = item[0]
currNode = item[1] # current node object
currName = currNode.getId()
if currName in visited: continue
frmNode = item[2] # frm node object
frmName = frmNode.getId()
self.graph.addEdge(frmName, currName, score)
visited.add(currName)
for attrName in self.train.names:
if attrName not in visited:
CMI = calcCondMI(self.train, currName, attrName, 'class')
indexes = self.train.getColIndex([currName, attrName])
score = (-CMI, indexes[0], indexes[1])
nextNode = self.graph.getNode(attrName)
pri_q.put((score, nextNode, currNode))
##
# Given the values of attributes of a testing instance, predict its class.
# @param instanceVals A list of instance variable values, including the 'class' at the end.
# @return [predicted class value, actual class value, posterior prob of predicted value]
def predictOneInstance(self, instanceVals):
names = self.train.names # including the 'class'
posteriors = []
for y in self.train.variables['class']:
# give the actual posterior porbability in the output
true_y = instanceVals[-1]
Py = calcProb(self.train, ['class'], [y])
Px = calcProb(self.train, names[:-1], instanceVals[:-1])
Px_pa = calcProbsCondParents(self.train, names[:-1], instanceVals[:-1] + [y], self.graph)
#print Py, Px, Px_pa
pred_p = Py * Px_pa / Px
posteriors.append([y, true_y, pred_p])
# normalize the posteriors
total = sum(x[-1] for x in posteriors)
posteriors = [x[:2] + [round(x[-1] / total, 12)] for x in posteriors]
return sorted(posteriors, key = lambda x: -x[-1])[0]
##
# Predict the class in all instances in testing dataset.
# @return A list of predicted result for instances in testing set.
def predictTestData(self):
results = []
for instance in self.test.data:
results.append(self.predictOneInstance(instance))
return results
## output the result as homework requirement
def printResults(self):
results = self.predictTestData()
# display structures
for attr in self.train.names[:-1]:
node = self.graph.getNode(attr)
parentNodes = node.getParents()
parentIndexes = sorted([self.train.getColIndex([p.getId()])[0] \
for p in parentNodes])
parentNames = [self.train.names[i] for i in parentIndexes]
print attr + ' ' + ' '.join(parentNames) + ' '
print ''
# display predicts
corrects = 0
for r in results:
if r[0] == r[1]:
corrects += 1
print ' '.join([str(x) for x in r])
print ''
# display correct predicts
print str(corrects)
print ''
def __init__(self):
self.train = None
self.test = None
self.graph = Graph()
def __init__(self):
self.train = None
self.test = None
self.graph = Graph()
