Exemple #1
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    def setUp(self):
        nodedata = NodeData.load("unittestlgdict.txt")
        skel = GraphSkeleton()
        skel.load("unittestdict.txt")
        skel.toporder()

        self.lgb = LGBayesianNetwork(nodedata)
Exemple #2
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 def setUp(self):
     skel = GraphSkeleton()
     skel.load("unittestdict.txt")
     skel.toporder()
     nodedata = NodeData.load("unittestdict.txt")
     self.bn = DiscreteBayesianNetwork(nodedata)
     self.fn = TableCPDFactorization(self.bn)
Exemple #3
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 def setUp(self):
     skel = GraphSkeleton()
     skel.load("unittestdict.txt")
     skel.toporder()
     nodedata = NodeData()
     nodedata.load("unittestdict.txt")
     self.instance = DiscreteBayesianNetwork(skel, nodedata)
Exemple #4
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 def setUp(self):
     self.nd = NodeData()
     self.nd.load("unittestdyndict.txt")
     self.skel = GraphSkeleton()
     self.skel.load("unittestdyndict.txt")
     self.skel.toporder()
     self.d = DynDiscBayesianNetwork(self.skel, self.nd)
Exemple #5
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 def setUp(self):
     self.nd = HybridNodeData.load("unittesthdict.txt")
     self.nd.entriestoinstances()
     self.skel = GraphSkeleton()
     self.skel.load("unittestdict.txt")
     self.skel.toporder()
     self.hybn = HyBayesianNetwork(self.skel, self.nd)
Exemple #6
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def graph_skeleton_from_node_data(nd):
    skel = GraphSkeleton()
    skel.V = []
    skel.E = []
    for name, v in nd.Vdata.items():
        skel.V += [name]
        skel.E += [[name, c] for c in v["children"]]
    return skel
Exemple #7
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 def setUp(self):
     skel = GraphSkeleton()
     skel.load("unittestdict.txt")
     skel.toporder()
     nodedata = NodeData.load("unittestdict.txt")
     self.instance = DiscreteBayesianNetwork(nodedata)
     self.factor = TableCPDFactor("Grade", self.instance)
     self.factor2 = TableCPDFactor("Letter", self.instance)
Exemple #8
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def graph_skeleton_from_node_data(nd):
    skel = GraphSkeleton()
    skel.V = []
    skel.E = []
    for name, v in nd.Vdata.items():
        skel.V += [name]
        skel.E += [[name, c] for c in v["children"]]
    return skel
Exemple #9
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def getTableCPD():
    nd = NodeData()
    skel = GraphSkeleton()
    jsonpath = ""
    nd.load(jsonpath)
    skel.load(jsonpath)
    bn = DiscreteBayesianNetwork(skel, nd)
    tablecpd = TableCPDFactorization(bn)
    return tablecpd
Exemple #10
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    def test_hybn_mte_estimateparams(self):
        skel = GraphSkeleton()
        skel.load("../tests/bn_skeleton.txt")
        skel.toporder()
        
        with open('../tests/dataBR2.json', 'r') as f:
            samples = eval(f.read())

        result = self.l.hybn_mte_estimateparams(self.skel, self.samplelgseq)        
def q_without_ros():
    skel = GraphSkeleton()
    skel.V = ["prize_door", "guest_door", "monty_door"]
    skel.E = [["prize_door", "monty_door"],
              ["guest_door", "monty_door"]]
    skel.toporder()
    nd = NodeData()
    nd.Vdata = {
        "prize_door": {
            "numoutcomes": 3,
            "parents": None,
            "children": ["monty_door"],
            "vals": ["A", "B", "C"],
            "cprob": [1.0/3, 1.0/3, 1.0/3],
        },
        "guest_door": {
            "numoutcomes": 3,
            "parents": None,
            "children": ["monty_door"],
            "vals": ["A", "B", "C"],
            "cprob": [1.0/3, 1.0/3, 1.0/3],
        },
        "monty_door": {
            "numoutcomes": 3,
            "parents": ["prize_door", "guest_door"],
            "children": None,
            "vals": ["A", "B", "C"],
            "cprob": {
                "['A', 'A']": [0., 0.5, 0.5],
                "['B', 'B']": [0.5, 0., 0.5],
                "['C', 'C']": [0.5, 0.5, 0.],
                "['A', 'B']": [0., 0., 1.],
                "['A', 'C']": [0., 1., 0.],
                "['B', 'A']": [0., 0., 1.],
                "['B', 'C']": [1., 0., 0.],
                "['C', 'A']": [0., 1., 0.],
                "['C', 'B']": [1., 0., 0.],
            },
        },
    }
    bn = DiscreteBayesianNetwork(skel, nd)
    fn = TableCPDFactorization(bn)

    query = {
        "prize_door": ["A","B","C"],
    }
    evidence = {
        "guest_door": "A",
        "monty_door": "B",
    }

    res = fn.condprobve(query, evidence)
    print res.vals
    print res.scope
    print res.card
    print res.stride
def getTableCPD():
    nd = NodeData()
    skel = GraphSkeleton()
    jsonpath = "./graph/graph_example.txt"
    nd.load(jsonpath)
    skel.load(jsonpath)
    # load Bayesian network
    bn = DiscreteBayesianNetwork(skel, nd)
    tablecpd = TableCPDFactorization(bn)
    return tablecpd
Exemple #13
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def test_libpgm(df1):

    data = df1.T.to_dict().values()
    #pprint(data)
    skel = GraphSkeleton()
    skel.load("bn_struct.txt")
    
    learner = PGMLearner()
    result = learner.discrete_mle_estimateparams(skel, data)
    
    print json.dumps(result.Vdata, indent=2)
Exemple #14
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class TestOrderedSkeleton(unittest.TestCase):
    def setUp(self):
        self.os = OrderedSkeleton()
        self.os.load("unittestdict.txt")
        self.gs = GraphSkeleton()
        self.gs.load("unittestdict.txt")

    def test_constructor(self):
        self.assertNotEqual(self.os.V, self.gs.V)
        self.gs.toporder()
        self.assertEqual(self.os.V, self.gs.V)
def getTableCPD():
   nd = NodeData()
   skel = GraphSkeleton()
   jsonpath = "job_interview.txt"
   nd.load(jsonpath)
   skel.load(jsonpath)

   #load bayesian network
   bn = DiscreteBayesianNetwork(skel, nd)
   tablecpd = TableCPDFactorization(bn)
   return tablecpd
    def load(self, file_name):
        #### Load BN
        nd = NodeData()
        skel = GraphSkeleton()
        nd.load(file_name)  # any input file
        skel.load(file_name)

        # topologically order graphskeleton
        skel.toporder()

        super(DiscreteBayesianNetworkExt, self).__init__(skel, nd)
        ##TODO load evidence
Exemple #17
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class TestOrderedSkeleton(unittest.TestCase):

    def setUp(self):
        self.os = OrderedSkeleton()
        self.os.load("unittestdict.txt")
        self.gs = GraphSkeleton()
        self.gs.load("unittestdict.txt")

    def test_constructor(self):
        self.assertNotEqual(self.os.V, self.gs.V)
        self.gs.toporder()
        self.assertEqual(self.os.V, self.gs.V)
Exemple #18
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class TestDynDiscBayesianNetwork(unittest.TestCase):
    def setUp(self):
        self.nd = NodeData.load("unittestdyndict.txt")
        self.skel = GraphSkeleton()
        self.skel.load("unittestdyndict.txt")
        self.skel.toporder()
        self.d = DynDiscBayesianNetwork(self.skel, self.nd)

    def test_randomsample(self):
        sample = self.d.randomsample(10)
        for i in range(1, 10):
            self.assertEqual(sample[0]['Difficulty'], sample[i]['Difficulty'])
Exemple #19
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class TestDynDiscBayesianNetwork(unittest.TestCase):

    def setUp(self):
        self.nd = NodeData.load("unittestdyndict.txt")
        self.skel = GraphSkeleton()
        self.skel.load("unittestdyndict.txt")
        self.skel.toporder()
        self.d = DynDiscBayesianNetwork(self.skel, self.nd)

    def test_randomsample(self):
        sample = self.d.randomsample(10)
        for i in range(1, 10):
            self.assertEqual(sample[0]['Difficulty'], sample[i]['Difficulty'])
def getBNparams(graph, ddata, n):
    # Gets Disc. BN parameters given a graph skeleton
    #skeleton should include t-1 and t nodes for each variable
    nodes = range(1, (n * 2) + 1)
    nodes = map(str, nodes)
    edges = gk.edgelist(graph)
    for i in range(len(edges)):
        edges[i] = list([edges[i][0], str(n + int(edges[i][1]))])
    skel = GraphSkeleton()
    skel.V = nodes
    skel.E = edges
    learner = PGMLearner()
    result = learner.discrete_mle_estimateparams(skel, ddata)
    return result
Exemple #21
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class TestHyBayesianNetwork(unittest.TestCase):
    def setUp(self):
        self.nd = HybridNodeData.load("unittesthdict.txt")
        self.nd.entriestoinstances()
        self.skel = GraphSkeleton()
        self.skel.load("unittestdict.txt")
        self.skel.toporder()
        self.hybn = HyBayesianNetwork(self.skel, self.nd)

    def test_randomsample(self):
        sample = self.hybn.randomsample(1)[0]
        self.assertTrue(isinstance(sample['Grade'], float))
        self.assertTrue(isinstance(sample['Intelligence'], str))
        self.assertEqual(sample["SAT"][-12:], 'blueberries!')
Exemple #22
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 def setUp(self):
     skel = GraphSkeleton()
     skel.load("unittestdict.txt")
     skel.toporder()
     nodedata = NodeData.load("unittestdict.txt")
     self.bn = DiscreteBayesianNetwork(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
Exemple #23
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class TestHyBayesianNetwork(unittest.TestCase):

    def setUp(self):
        self.nd = HybridNodeData.load("unittesthdict.txt")
        self.nd.entriestoinstances()
        self.skel = GraphSkeleton()
        self.skel.load("unittestdict.txt")
        self.skel.toporder()
        self.hybn = HyBayesianNetwork(self.skel, self.nd)

    def test_randomsample(self):
        sample = self.hybn.randomsample(1)[0]
        self.assertTrue(isinstance(sample['Grade'], float))
        self.assertTrue(isinstance(sample['Intelligence'], str))
        self.assertEqual(sample["SAT"][-12:], 'blueberries!')
Exemple #24
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 def setUp(self):
     self.nd = NodeData()
     self.nd.load("unittestdyndict.txt")
     self.skel = GraphSkeleton()
     self.skel.load("unittestdyndict.txt")
     self.skel.toporder()
     self.d = DynDiscBayesianNetwork(self.skel, self.nd)
Exemple #25
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 def setUp(self):
     self.nd = HybridNodeData.load("unittesthdict.txt")
     self.nd.entriestoinstances()
     self.skel = GraphSkeleton()
     self.skel.load("unittestdict.txt")
     self.skel.toporder()
     self.hybn = HyBayesianNetwork(self.skel, self.nd)
def createData():
   nd = NodeData()
   skel = GraphSkeleton()
   fpath = "job_interview.txt"
   nd.load(fpath)
   skel.load(fpath)
   skel.toporder()
   bn = DiscreteBayesianNetwork(skel, nd)

   learner = PGMLearner()
   data = bn.randomsample(1000)
   X, Y = 'Grades', 'Offer'
   c,p,w=learner.discrete_condind(data, X, Y, ['Interview'])
   print "independence between X and Y: ", c, " p-value ", p, " witness node: ", w
   result = learner.discrete_constraint_estimatestruct(data)
   print result.E
    def add_sensor(self, sensor_keys):
        for key in sensor_keys:
            network_file = open(self.dbn_file_name, 'r')
            network_file_data = eval(network_file.read())

            network_skeleton = GraphSkeleton()
            network_skeleton.V = network_file_data["V"]
            network_skeleton.E = network_file_data["E"]

            self.network = DynDiscBayesianNetwork()
            self.network.V = network_skeleton.V
            self.network.E = network_skeleton.E
            self.network.initial_Vdata = network_file_data["initial_Vdata"]
            self.network.twotbn_Vdata = network_file_data["twotbn_Vdata"]

            self.inference_engines[key] = SensorDbnInferenceEngine(self.network)
Exemple #28
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class TestGraphSkeleton(unittest.TestCase):
    def setUp(self):
        self.instance = GraphSkeleton()
        self.instance.V = [1, 2, 3, 4, 5]
        self.instance.E = [[5, 1], [1, 2]]

    def test_getparents(self):
        self.assertEqual(self.instance.getparents(1), [5])
        self.assertEqual(self.instance.getparents(4), [])

    def test_getchildren(self):
        self.assertEqual(self.instance.getchildren(5), [1])
        self.assertEqual(self.instance.getchildren(4), [])

    def test_toporder(self):
        self.instance.toporder()
        self.assertTrue(self.instance.V.index(5) < self.instance.V.index(1))
        self.assertTrue(self.instance.V.index(5) < self.instance.V.index(2))
def net2():
    nd = NodeData()
    skel = GraphSkeleton()
    nd.load("net.txt")  # an input file
    skel.load("net.txt")

    # topologically order graphskeleton
    skel.toporder()

    # load bayesian network
    lgbn = LGBayesianNetwork(skel, nd)

    in_data=read_data.getdata2()
    learner = PGMLearner()
    bn=learner.lg_mle_estimateparams(skel,in_data)

    p=cal_prob(in_data[300:500],bn)
    print p
    return 0
def ConstructDynBN(num_graph, numvalues, A, ss):
    graph = conv.ian2g(num_graph)
    print(graph)
    V, E, initVdata = INITdata(graph, numvalues)
    tfVdata = gettfVdata(num_graph, numvalues, A)
    d = DynDiscBayesianNetwork()
    skel = GraphSkeleton()
    skel.V = V
    skel.E = E
    d.V = skel.V
    d.E = skel.E
    d.initial_Vdata = initVdata
    d.twotbn_Vdata = tfVdata
    print(d.V)
    print(d.E)
    print(d.initial_Vdata)
    print(d.twotbn_Vdata)
    data = sampleBN(d, ss)
    return data
Exemple #31
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class TestGraphSkeleton(unittest.TestCase):

    def setUp(self):
        self.instance = GraphSkeleton()
        self.instance.V = [1,2,3,4,5]
        self.instance.E = [[5,1],[1,2]]

    def test_getparents(self):
        self.assertEqual(self.instance.getparents(1), [5])
        self.assertEqual(self.instance.getparents(4), [])

    def test_getchildren(self):
        self.assertEqual(self.instance.getchildren(5), [1])
        self.assertEqual(self.instance.getchildren(4), [])

    def test_toporder(self):
        self.instance.toporder()
        self.assertTrue(self.instance.V.index(5)<self.instance.V.index(1))
        self.assertTrue(self.instance.V.index(5)<self.instance.V.index(2))
Exemple #32
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def BNskelFromCSV(csvdata, targets):

    #TODO: must know how to swap direction of too many inputs into a node

    ######## EXTRACT HEADER STRINGS FROM CSV FILE ########
    skel = GraphSkeleton()
    BNstructure = {}
    inputVerts = []

    # if data is a filepath
    if isinstance(csvdata, basestring):

        dataset = []

        with open(csvdata, 'rb') as csvfile:
            lines = csv.reader(csvfile)

            for row in lines:
                dataset.append(row)

        allVertices = dataset[0]

    else:
        allVertices = csvdata[0]

    BNstructure['V'] = allVertices
    skel.V = allVertices

    for verts in allVertices:
        if verts not in targets:
            inputVerts.append(verts)

    #target, each input
    edges = []

    if len(inputVerts) > len(targets):
        for target in targets:

            for input in inputVerts:
                edge = [target, input]
                edges.append(edge)

        BNstructure['E'] = edges
        skel.E = edges

    else:
        for input in inputVerts:
            for target in targets:
                edge = [input, target]
                edges.append(edge)

        BNstructure['E'] = edges
        skel.E = edges

    skel.toporder()

    return skel
Exemple #33
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    def setUp(self):
        # instantiate learner
        self.l = PGMLearner()

        # generate graph skeleton
        skel = GraphSkeleton()
        skel.load("unittestdict.txt")
        skel.toporder()

        # generate sample sequence to try to learn from - discrete
        nd = NodeData.load("unittestdict.txt")
        self.samplediscbn = DiscreteBayesianNetwork(nd)
        self.samplediscseq = self.samplediscbn.randomsample(5000)

        # generate sample sequence to try to learn from - discrete
        nda = NodeData.load("unittestlgdict.txt")
        self.samplelgbn = LGBayesianNetwork(nda)
        self.samplelgseq = self.samplelgbn.randomsample(10000)

        self.skel = skel
    def test_structure_estimation(self):
        req = DiscreteStructureEstimationRequest()

        skel = GraphSkeleton()
        skel.load(self.data_path)
        skel.toporder()
        teacher_nd = NodeData()
        teacher_nd.load(self.teacher_data_path)
        bn = DiscreteBayesianNetwork(skel, teacher_nd)
        data = bn.randomsample(8000)
        for v in data:
            gs = DiscreteGraphState()
            for k_s, v_s in v.items():
                gs.node_states.append(DiscreteNodeState(node=k_s, state=v_s))
            req.states.append(gs)

        res = self.struct_estimate(req)
        self.assertIsNotNone(res.graph)
        self.assertEqual(len(res.graph.nodes), 5)
        self.assertGreater(len(res.graph.edges), 0)
Exemple #35
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    def setUp(self):
        nodedata = NodeData.load("unittestlgdict.txt")
        skel = GraphSkeleton()
        skel.load("unittestdict.txt")
        skel.toporder()

        self.lgb = LGBayesianNetwork(nodedata)
Exemple #36
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 def setUp(self):
     skel = GraphSkeleton()
     skel.load("unittestdict.txt")
     skel.toporder()
     nodedata = NodeData.load("unittestdict.txt")
     self.bn = DiscreteBayesianNetwork(nodedata)
     self.fn = TableCPDFactorization(self.bn)
Exemple #37
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 def set_bayesnet(self):
     nd = NodeData()
     skel = GraphSkeleton()
     nd.load(self.file)
     skel.load(self.file)
     skel.toporder()
     self.bn = DiscreteBayesianNetwork(skel, nd)
Exemple #38
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def buildBN(trainingData, binstyleDict, numbinsDict,
            **kwargs):  # need to modify to accept skel or skelfile

    discretized_training_data, bin_ranges = discretizeTrainingData(
        trainingData, binstyleDict, numbinsDict, True)
    print 'discret training ', discretized_training_data

    if 'skel' in kwargs:
        # load file into skeleton
        if isinstance(kwargs['skel'], basestring):
            skel = GraphSkeleton()
            skel.load(kwargs['skel'])
            skel.toporder()
        else:
            skel = kwargs['skel']

    # learn bayesian network
    learner = PGMLearner()
    # baynet = learner.discrete_mle_estimateparams(skel, discretized_training_data)
    # baynet = discrete_estimatebn(learner, discretized_training_data, skel, 0.05, 1)
    baynet = discrete_mle_estimateparams2(
        skel, discretized_training_data
    )  # using discrete_mle_estimateparams2 written as function in this file, not calling from libpgm

    return baynet
Exemple #39
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 def setUp(self):
     skel = GraphSkeleton()
     skel.load("unittestdict.txt")
     skel.toporder()
     nodedata = NodeData()
     nodedata.load("unittestdict.txt")
     self.instance = DiscreteBayesianNetwork(skel, nodedata)
def main():

    in_data=read_data.getdata()
    f_data=format_data(in_data)
    nd = NodeData()
    nd.load("net4.txt")    # an input file
    skel = GraphSkeleton()
    skel.load("net4.txt")
    skel.toporder()
    bn=DiscreteBayesianNetwork(skel,nd)


#training dataset:70%
    bn2=em(f_data[1:6000],bn,skel)

    pr_training = precision(f_data[1:6000],bn2)

    print "Prediction accuracy for training data:" , pr_training[1]

#testing dataset:30%
    pr=precision(f_data[6700:6800],bn2)
    print "Prediction accuracy for test data:", pr[1]
    def test_param_estimation(self):
        req = DiscreteParameterEstimationRequest()

        # load graph structure
        skel = GraphSkeleton()
        skel.load(self.data_path)
        req.graph.nodes = skel.V
        req.graph.edges = [GraphEdge(k, v) for k,v in skel.E]
        skel.toporder()

        # generate trial data
        teacher_nd = NodeData()
        teacher_nd.load(self.teacher_data_path)
        bn = DiscreteBayesianNetwork(skel, teacher_nd)
        data = bn.randomsample(200)
        for v in data:
            gs = DiscreteGraphState()
            for k_s, v_s in v.items():
                gs.node_states.append(DiscreteNodeState(node=k_s, state=v_s))
            req.states.append(gs)

        self.assertEqual(len(self.param_estimate(req).nodes), 5)
Exemple #42
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 def setUp(self):
     skel = GraphSkeleton()
     skel.load("unittestdict.txt")
     skel.toporder()
     nodedata = NodeData.load("unittestdict.txt")
     self.instance = DiscreteBayesianNetwork(nodedata)
     self.factor = TableCPDFactor("Grade", self.instance)
     self.factor2 = TableCPDFactor("Letter", self.instance)
Exemple #43
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Fichier : pgm.py Projet : ml4ai/b3
 def setup(self):
     self.nd = NodeData()
     self.skel = GraphSkeleton()
     self.skel.V, self.skel.E = [], []
     self.nd.Vdata = {}
     for i, node in enumerate(self.node.values()):
         dNode = {}
         node.sId = str(i)
         dNode["numoutcomes"] = len(node.values)
         dNode["vals"] = node.values
         dNode["cprob"] = node.cpt
         #             dNode["parents"] = map(lambda x: if x=x.name, node.parents);
         self.skel.V.append(node.name)
         aParents = []
         for parent in node.parents:
             if parent == None: continue
             aParents.append(parent.name)
             self.skel.E.append([parent.name, node.name])
         dNode["parents"] = aParents if len(aParents) > 0 else None
         self.nd.Vdata[node.name] = dNode
     self.skel.toporder()
     self.bn = DiscreteBayesianNetwork(self.skel, self.nd)
     self.fn = TableCPDFactorization(self.bn)
def q_without_ros():
    skel = GraphSkeleton()
    skel.V = ["prize_door", "guest_door", "monty_door"]
    skel.E = [["prize_door", "monty_door"], ["guest_door", "monty_door"]]
    skel.toporder()
    nd = NodeData()
    nd.Vdata = {
        "prize_door": {
            "numoutcomes": 3,
            "parents": None,
            "children": ["monty_door"],
            "vals": ["A", "B", "C"],
            "cprob": [1.0 / 3, 1.0 / 3, 1.0 / 3],
        },
        "guest_door": {
            "numoutcomes": 3,
            "parents": None,
            "children": ["monty_door"],
            "vals": ["A", "B", "C"],
            "cprob": [1.0 / 3, 1.0 / 3, 1.0 / 3],
        },
        "monty_door": {
            "numoutcomes": 3,
            "parents": ["prize_door", "guest_door"],
            "children": None,
            "vals": ["A", "B", "C"],
            "cprob": {
                "['A', 'A']": [0., 0.5, 0.5],
                "['B', 'B']": [0.5, 0., 0.5],
                "['C', 'C']": [0.5, 0.5, 0.],
                "['A', 'B']": [0., 0., 1.],
                "['A', 'C']": [0., 1., 0.],
                "['B', 'A']": [0., 0., 1.],
                "['B', 'C']": [1., 0., 0.],
                "['C', 'A']": [0., 1., 0.],
                "['C', 'B']": [1., 0., 0.],
            },
        },
    }
    bn = DiscreteBayesianNetwork(skel, nd)
    fn = TableCPDFactorization(bn)

    query = {
        "prize_door": ["A", "B", "C"],
    }
    evidence = {
        "guest_door": "A",
        "monty_door": "B",
    }

    res = fn.condprobve(query, evidence)
    print res.vals
    print res.scope
    print res.card
    print res.stride
Exemple #45
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    def load(self, file_name):
        #### Load BN
        nd = NodeData()
        skel = GraphSkeleton()
        nd.load(file_name)  # any input file
        skel.load(file_name)

        # topologically order graphskeleton
        skel.toporder()

        super(DiscreteBayesianNetworkExt, self).__init__(skel, nd)
Exemple #46
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def learnBN(fdata_array, bn_file):

    bn_path = os.path.join(experiment_dir, 'parameters', bn_file + '.txt')

    skel = GraphSkeleton()
    skel.load(bn_path)
    skel.toporder()

    learner = PGMLearner()
    bn = learner.discrete_mle_estimateparams(skel, fdata_array)

    return bn
Exemple #47
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def loadbn(param_file):
    """
    This function loads the bn model into the workspace from its associated .txt file.
    """
    file_path = os.path.join(experiment_dir, 'parameters', param_file + '.txt')

    nd = NodeData()
    skel = GraphSkeleton()
    nd.load(file_path)
    skel.load(file_path)
    skel.toporder()
    bn = DiscreteBayesianNetwork(skel, nd)
    return bn
Exemple #48
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 def setUp(self):
     skel = GraphSkeleton()
     skel.load("unittestdict.txt")
     skel.toporder()
     nodedata = NodeData.load("unittestdict.txt")
     self.bn = DiscreteBayesianNetwork(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
Exemple #49
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 def construct(self):
     skel = GraphSkeleton()
     skel.V = self.nodes.keys()
     skel.E = []
     for node, ndata in self.nodes.iteritems():
         if ndata['parents']:
             for p in ndata['parents']:
                 skel.E.append([p, node])
                 self.nodes[p]['children'].append(node)
     for node, ndata in self.nodes.iteritems():
         if len(ndata['children']) == 0:
             ndata['children'] = None
     data = NodeData()
     data.Vdata = self.nodes
     skel.toporder()
     bn = DiscreteBayesianNetwork(skel, data)
     return bn
Exemple #50
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def net2():
    nd = NodeData()
    skel = GraphSkeleton()
    nd.load("net.txt")  # an input file
    skel.load("net.txt")

    # topologically order graphskeleton
    skel.toporder()

    # load bayesian network
    lgbn = LGBayesianNetwork(skel, nd)

    in_data = read_data.getdata2()
    learner = PGMLearner()
    bn = learner.lg_mle_estimateparams(skel, in_data)

    p = cal_prob(in_data[300:500], bn)
    print p
    return 0
    def test_structure_estimation(self):
        req = DiscreteStructureEstimationRequest()

        skel = GraphSkeleton()
        skel.load(self.data_path)
        skel.toporder()
        teacher_nd = NodeData()
        teacher_nd.load(self.teacher_data_path)
        bn = DiscreteBayesianNetwork(skel, teacher_nd)
        data = bn.randomsample(8000)
        for v in data:
            gs = DiscreteGraphState()
            for k_s, v_s in v.items():
                gs.node_states.append(DiscreteNodeState(node=k_s, state=v_s))
            req.states.append(gs)

        res = self.struct_estimate(req)
        self.assertIsNotNone(res.graph)
        self.assertEqual(len(res.graph.nodes), 5)
        self.assertGreater(len(res.graph.edges), 0)
Exemple #52
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    def setUp(self):
        # instantiate learner
        self.l = PGMLearner()

        # generate graph skeleton
        skel = GraphSkeleton()
        skel.load("unittestdict.txt")
        skel.toporder()

        # generate sample sequence to try to learn from - discrete
        nd = NodeData.load("unittestdict.txt")
        self.samplediscbn = DiscreteBayesianNetwork(nd)
        self.samplediscseq = self.samplediscbn.randomsample(5000)

        # generate sample sequence to try to learn from - discrete
        nda = NodeData.load("unittestlgdict.txt")
        self.samplelgbn = LGBayesianNetwork(nda)
        self.samplelgseq = self.samplelgbn.randomsample(10000)

        self.skel = skel
Exemple #53
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def main():

    in_data = read_data.getdata()
    f_data = format_data(in_data)
    nd = NodeData()
    nd.load("net4.txt")  # an input file
    skel = GraphSkeleton()
    skel.load("net4.txt")
    skel.toporder()
    bn = DiscreteBayesianNetwork(skel, nd)

    #training dataset:70%
    bn2 = em(f_data[1:6000], bn, skel)

    pr_training = precision(f_data[1:6000], bn2)

    print "Prediction accuracy for training data:", pr_training[1]

    #testing dataset:30%
    pr = precision(f_data[6700:6800], bn2)
    print "Prediction accuracy for test data:", pr[1]
import sys
import string


from libpgm.graphskeleton import GraphSkeleton
from libpgm.tablecpdfactorization import TableCPDFactorization
from libpgm.pgmlearner import PGMLearner

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)

#Rating 1 Given Genre  is Drama
myquery = dict(rating=[1])
myevidence = dict(genre='Drama')
result=tcf.specificquery(query=myquery,evidence=myevidence)
print result

Exemple #55
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def Threshold(list):
    temp = []
    #temp.append(min(list)+float(max(list) - min(list))*1/3)
    #temp.append(min(list)+float(max(list) - min(list))*2/3)
    temp.append(float(max(list))/3)
    temp.append(float(max(list))/3*2)
    return temp
    
EachLikeThreshold = Threshold(EachLike) 
EachLikedThreshold = Threshold(EachLiked)
print EachLikeThreshold
print EachLikedThreshold

BulliedPro = []
nd = NodeData()
skel = GraphSkeleton()
nd.load('unittestdict.txt')
skel.load('unittestdict.txt')
bn = DiscreteBayesianNetwork(skel, nd)
fn = TableCPDFactorization(bn)

for i in range(len(EachLike)):
    evidence = {}
    if EachLike[i] <= EachLikeThreshold[0]:
        evidence['LikeN'] = 'Small'
    elif EachLikeThreshold[0] < EachLike[i] and EachLike[i] <= EachLikeThreshold[1]:
        evidence['LikeN'] = 'Mid'
    else:
        evidence['LikeN'] = 'Big'
    if EachLiked[i] <= EachLikedThreshold[0]:
        evidence['LikedN'] = 'Small'
Exemple #56
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def graph_skeleton_from_ros(graph_structure):
    skel = GraphSkeleton()
    skel.V = graph_structure.nodes
    skel.E = [[e.node_from, e.node_to] for e in graph_structure.edges]
    return skel
Exemple #57
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 def setUp(self):
     self.os = OrderedSkeleton()
     self.os.load("unittestdict.txt")
     self.gs = GraphSkeleton()
     self.gs.load("unittestdict.txt")
data_l = []
for line in data_r.readlines():
	data_l.append(map(int, line.split()))

truth_l = []
for row in truth_r:
	truth_l.append(row[0])

w = csv.writer(open("bayesian_outcome.txt", "wb"))

count = 0

for  i in range(104):
	nd = NodeData()
	skel = GraphSkeleton()
	nd.load('bayes_net/'+str(i)+".txt")    # any input file
	skel.load('bayes_net/'+str(i)+".txt")

	# topologically order graphskeleton
	skel.toporder()

	# load bayesian network
	# load bayesian network
	bn = DiscreteBayesianNetwork(skel, nd)
	dic1 = {}
	k = 1
	for c in data_l[i]:
		dic1[str(k)] = str(c)
		k += 2
	
Exemple #59
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 def setUp(self):
     self.instance = GraphSkeleton()
     self.instance.V = [1,2,3,4,5]
     self.instance.E = [[5,1],[1,2]]
def learnDiscreteBN_with_structure(df, continous_columns, features_column_names, label_column='cat',
                                   draw_network=False):
    features_df = df.copy()
    features_df = features_df.drop(label_column, axis=1)

    labels_df = DataFrame()
    labels_df[label_column] = df[label_column].copy()

    for i in continous_columns:
        bins = np.arange((min(features_df[i])), (max(features_df[i])),
                         ((max(features_df[i]) - min(features_df[i])) / 5.0))
        features_df[i] = pandas.np.digitize(features_df[i], bins=bins)

    data = []
    for index, row in features_df.iterrows():
        dict = {}
        for i in features_column_names:
            dict[i] = row[i]
        dict[label_column] = labels_df[label_column][index]
        data.append(dict)

    print "Init done"
    learner = PGMLearner()

    graph = GraphSkeleton()

    graph.V = []
    graph.E = []

    graph.V.append(label_column)

    for vertice in features_column_names:
        graph.V.append(vertice)
        graph.E.append([vertice, label_column])

    test = learner.discrete_mle_estimateparams(graphskeleton=graph, data=data)

    print "done learning"

    edges = test.E
    vertices = test.V
    probas = test.Vdata

    # print probas

    dot_string = 'digraph BN{\n'
    dot_string += 'node[fontname="Arial"];\n'

    dataframes = {}

    print "save data"
    for vertice in vertices:
        print "New vertice: " + str(vertice)
        dataframe = DataFrame()

        pp = pprint.PrettyPrinter(indent=4)
        # pp.pprint(probas[vertice])
        dot_string += vertice.replace(" ", "_") + ' [label="' + vertice + '\n' + '" ]; \n'

        if len(probas[vertice]['parents']) == 0:
            dataframe['Outcome'] = None
            dataframe['Probability'] = None
            vertex_dict = {}
            for index_outcome, outcome in enumerate(probas[vertice]['vals']):
                vertex_dict[str(outcome)] = probas[vertice]["cprob"][index_outcome]

            od = collections.OrderedDict(sorted(vertex_dict.items()))
            # print "Vertice: " + str(vertice)
            # print "%-7s|%-11s" % ("Outcome", "Probability")
            # print "-------------------"
            for k, v in od.iteritems():
                # print "%-7s|%-11s" % (str(k), str(round(v, 3)))
                dataframe.loc[len(dataframe)] = [k, v]
            dataframes[vertice] = dataframe
        else:
            # pp.pprint(probas[vertice])
            dataframe['Outcome'] = None

            vertexen = {}
            for index_outcome, outcome in enumerate(probas[vertice]['vals']):
                temp = []
                for parent_index, parent in enumerate(probas[vertice]["parents"]):
                    # print str([str(float(index_outcome))])
                    temp = probas[vertice]["cprob"]
                    dataframe[parent] = None
                vertexen[str(outcome)] = temp

            dataframe['Probability'] = None
            od = collections.OrderedDict(sorted(vertexen.items()))

            # [str(float(i)) for i in ast.literal_eval(key)]


            # str(v[key][int(float(k))-1])

            # print "Vertice: " + str(vertice) + " with parents: " + str(probas[vertice]['parents'])
            # print "Outcome" + "\t\t" + '\t\t'.join(probas[vertice]['parents']) + "\t\tProbability"
            # print "------------" * len(probas[vertice]['parents']) *3
            # pp.pprint(od.values())

            counter = 0
            # print number_of_cols
            for outcome, cprobs in od.iteritems():
                for key in cprobs.keys():
                    array_frame = []
                    array_frame.append((outcome))
                    print_string = str(outcome) + "\t\t"
                    for parent_value, parent in enumerate([i for i in ast.literal_eval(key)]):
                        # print "parent-value:"+str(parent_value)
                        # print "parten:"+str(parent)
                        array_frame.append(int(float(parent)))
                        # print "lengte array_frame: "+str(len(array_frame))
                        print_string += parent + "\t\t"
                    array_frame.append(cprobs[key][counter])
                    # print "lengte array_frame (2): "+str(len(array_frame))
                    # print  cprobs[key][counter]
                    print_string += str(cprobs[key][counter]) + "\t"
                    # for stront in [str(round(float(i), 3)) for i in ast.literal_eval(key)]:
                    #     print_string += stront + "\t\t"
                    # print "print string: " + print_string
                    # print "array_frame:" + str(array_frame)
                    dataframe.loc[len(dataframe)] = array_frame
                counter += 1
        print "Vertice " + str(vertice) + " done"
        dataframes[vertice] = dataframe

    for edge in edges:
        dot_string += edge[0].replace(" ", "_") + ' -> ' + edge[1].replace(" ", "_") + ';\n'

    dot_string += '}'
    # src = Source(dot_string)
    # src.render('../data/BN', view=draw_network)
    # src.render('../data/BN', view=False)
    print "vizualisation done"
    return dataframes