class TestSampleAggregator(unittest.TestCase): def setUp(self): skel = GraphSkeleton() skel.load("unittestdict.txt") skel.toporder() nodedata = NodeData() nodedata.load("unittestdict.txt") self.bn = DiscreteBayesianNetwork(skel, nodedata) agg = SampleAggregator() agg.aggregate(self.bn.randomsample(50)) self.rseq = agg.seq self.ravg = agg.avg self.fn = TableCPDFactorization(self.bn) evidence = dict(Letter='weak') agg.aggregate(self.fn.gibbssample(evidence, 51)) self.gseq = agg.seq self.gavg = agg.avg def test_rseq(self): self.assertTrue(len(self.rseq) == 50) for key in self.ravg.keys(): summ = 0 for entry in self.ravg[key].keys(): summ += self.ravg[key][entry] self.assertTrue(summ > .99 and summ < 1.01) def test_gseq(self): self.assertTrue(len(self.gseq) == 51) for key in self.gavg.keys(): summ = 0 for entry in self.gavg[key].keys(): summ += self.gavg[key][entry] self.assertTrue(summ > .99 and summ < 1.01)
class TestSampleAggregator(unittest.TestCase): def setUp(self): skel = GraphSkeleton() skel.load("unittestdict.txt") skel.toporder() nodedata = NodeData() nodedata.load("unittestdict.txt") self.bn = DiscreteBayesianNetwork(skel, nodedata) agg = SampleAggregator() agg.aggregate(self.bn.randomsample(50)) self.rseq = agg.seq self.ravg = agg.avg self.fn = TableCPDFactorization(self.bn) evidence = dict(Letter='weak') agg.aggregate(self.fn.gibbssample(evidence, 51)) self.gseq = agg.seq self.gavg = agg.avg def test_rseq(self): self.assertTrue(len(self.rseq) == 50) for key in self.ravg.keys(): summ = 0 for entry in self.ravg[key].keys(): summ += self.ravg[key][entry] self.assertTrue(summ > .99 and summ < 1.01) def test_gseq(self): self.assertTrue(len(self.gseq) == 51) for key in self.gavg.keys(): summ = 0 for entry in self.gavg[key].keys(): summ += self.gavg[key][entry] self.assertTrue(summ > .99 and summ < 1.01)
class TestTableCPDFactorization(unittest.TestCase): def setUp(self): skel = GraphSkeleton() skel.load("unittestdict.txt") skel.toporder() nodedata = NodeData() nodedata.load("unittestdict.txt") self.bn = DiscreteBayesianNetwork(skel, nodedata) self.fn = TableCPDFactorization(self.bn) def test_constructor(self): self.assertTrue(len(self.fn.originalfactorlist) == 5) for x in range(5): self.assertTrue(isinstance(self.fn.originalfactorlist[x], TableCPDFactor)) def test_refresh(self): evidence = dict(Letter='weak') query = dict(Intelligence=['high']) result1 = self.fn.specificquery(query, evidence) self.fn.refresh() result2 = self.fn.specificquery(query, evidence) self.assertEqual(result1, result2) def test_sumproducteliminatevar(self): self.fn.refresh() self.fn.sumproducteliminatevar("Difficulty") yes = 0 for x in range(len(self.fn.factorlist)): if (self.fn.factorlist[x].scope == ['Grade', 'Intelligence']): yes += 1 index = x self.assertTrue(yes == 1) exp = [0.2, 0.33999999999999997, 0.45999999999999996, 0.74, 0.16799999999999998, 0.09200000000000001] for x in range(6): self.assertTrue(abs(self.fn.factorlist[index].vals[x] - exp[x]) < .01) def test_sumproductve(self): input = ["Difficulty", "Grade", "Intelligence", "SAT"] self.fn.refresh() self.fn.sumproductve(input) exp = [.498, .502] for x in range(2): self.assertTrue(abs(self.fn.factorlist.vals[x] - exp[x]) < .01) def test_condprobve(self): evidence = dict(Grade='C', SAT='highscore') query = dict(Intelligence='high') self.fn.refresh() self.fn.condprobve(query, evidence) exp = [.422, .578] for x in range(2): self.assertTrue(abs(self.fn.factorlist.vals[x] - exp[x]) < .01) def test_specificquery(self): evidence = dict(Difficulty='easy') query = dict(Grade=['A', 'B']) self.fn.refresh() answer = self.fn.specificquery(query, evidence) self.assertTrue(abs(answer - .784) < .01) def test_gibbssample(self): evidence = dict(Letter='weak') gs = self.fn.gibbssample(evidence, 5) self.assertTrue(gs[0]["Difficulty"] == 'easy' or gs[0]["Difficulty"] == 'hard') self.assertTrue(len(gs) == 5) for entry in gs: self.assertTrue(entry["Letter"] == 'weak')
class TestTableCPDFactorization(unittest.TestCase): def setUp(self): skel = GraphSkeleton() skel.load("unittestdict.txt") skel.toporder() nodedata = NodeData() nodedata.load("unittestdict.txt") self.bn = DiscreteBayesianNetwork(skel, nodedata) self.fn = TableCPDFactorization(self.bn) def test_constructor(self): self.assertTrue(len(self.fn.originalfactorlist) == 5) for x in range(5): self.assertTrue(isinstance(self.fn.originalfactorlist[x], TableCPDFactor)) def test_refresh(self): self.fn.refresh() for x in range(5): self.assertTrue(isinstance(self.fn.factorlist[x], TableCPDFactor)) def test_sumproducteliminatevar(self): self.fn.refresh() self.fn.sumproducteliminatevar("Difficulty") yes = 0 for x in range(len(self.fn.factorlist)): if (self.fn.factorlist[x].scope == ['Grade', 'Intelligence']): yes += 1 index = x self.assertTrue(yes == 1) exp = [0.2, 0.33999999999999997, 0.45999999999999996, 0.74, 0.16799999999999998, 0.09200000000000001] for x in range(6): self.assertTrue(abs(self.fn.factorlist[index].vals[x] - exp[x]) < .01) def test_sumproductve(self): input = ["Difficulty", "Grade", "Intelligence", "SAT"] self.fn.refresh() self.fn.sumproductve(input) exp = [.498, .502] for x in range(2): self.assertTrue(abs(self.fn.factorlist.vals[x] - exp[x]) < .01) def test_condprobve(self): evidence = dict(Grade='C', SAT='highscore') query = dict(Intelligence='high') self.fn.refresh() self.fn.condprobve(query, evidence) exp = [.422, .578] for x in range(2): self.assertTrue(abs(self.fn.factorlist.vals[x] - exp[x]) < .01) def test_specificquery(self): evidence = dict(Difficulty='easy') query = dict(Grade=['A', 'B']) self.fn.refresh() answer = self.fn.specificquery(query, evidence) self.assertTrue(abs(answer - .784) < .01) def test_gibbssample(self): evidence = dict(Letter='weak') gs = self.fn.gibbssample(evidence, 5) self.assertTrue(gs[0]["Difficulty"] == 'easy' or gs[0]["Difficulty"] == 'hard') self.assertTrue(len(gs) == 5) for entry in gs: self.assertTrue(entry["Letter"] == 'weak')
from libpgm.discretebayesiannetwork import DiscreteBayesianNetwork from libpgm.lgbayesiannetwork import LGBayesianNetwork from libpgm.hybayesiannetwork import HyBayesianNetwork from libpgm.dyndiscbayesiannetwork import DynDiscBayesianNetwork from libpgm.tablecpdfactorization import TableCPDFactorization from libpgm.sampleaggregator import SampleAggregator from libpgm.pgmlearner import PGMLearner lgbn = LGBayesianNetwork(skel, nd) text = open("../unifiedMLData2.json") data = text.read() printable = set(string.printable) asciiData = filter(lambda x: x in printable, data) listofDicts = json.loads(asciiData) skel = GraphSkeleton() skel.load("../skeleton.json") learner = PGMLearner() result = learner.discrete_mle_estimateparams(skel, listofDicts) tcf = TableCPDFactorization(result) myquery = dict(rating=[5]) myevidence = dict(occupation='student') res2 = tcf.gibbssample(evidence=myevidence, n=3) print json.dumps(res2, indent=2)
skel.load("../tests/unittestdict.txt") # toporder graph skeleton skel.toporder() # load evidence evidence = dict(Letter='weak') # load bayesian network bn = DiscreteBayesianNetwork(skel, nd) # load factorization fn = TableCPDFactorization(bn) # sample result = fn.gibbssample(evidence, 10) # output - toggle comment to see #print json.dumps(result, indent=2) # (5) -------------------------------------------------------------------------- # Compute the probability distribution over a specific node or nodes # load nodedata and graphskeleton nd = NodeData() skel = GraphSkeleton() nd.load("../tests/unittestdict.txt") skel.load("../tests/unittestdict.txt") # toporder graph skeleton skel.toporder()
def inference(bn, evidence): fn = TableCPDFactorization(bn) result = fn.gibbssample(evidence, GIBBS_ITERATIONS) agg = SampleAggregator() result = agg.aggregate(result) return json.dumps(result, indent=2)
skel.load("../tests/unittestdict.txt") # toporder graph skeleton skel.toporder() # load evidence evidence = dict(Letter='weak') # load bayesian network bn = DiscreteBayesianNetwork(skel, nd) # load factorization fn = TableCPDFactorization(bn) # sample result = fn.gibbssample(evidence, 10) # output - toggle comment to see #print json.dumps(result, indent=2) # (5) -------------------------------------------------------------------------- # Compute the probability distribution over a specific node or nodes # load nodedata and graphskeleton nd = NodeData() skel = GraphSkeleton() nd.load("../tests/unittestdict.txt") skel.load("../tests/unittestdict.txt") # toporder graph skeleton skel.toporder()
lgbn = LGBayesianNetwork(skel, nd) text = open("../unifiedMLData2.json") data=text.read() printable = set(string.printable) asciiData=filter(lambda x: x in printable, data) listofDicts=json.loads(asciiData) skel = GraphSkeleton() skel.load("../skeleton.json") learner = PGMLearner() result = learner.discrete_mle_estimateparams(skel, listofDicts) tcf=TableCPDFactorization(result) myquery = dict(rating=[5]) myevidence = dict(occupation='student') res2=tcf.gibbssample(evidence=myevidence,n=3) print json.dumps(res2, indent=2)