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
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
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 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 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
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)
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
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
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
# plt.scatter(test_data[:,0], test_data[:,1]) # figure.add_subplot(1,2,2) # plt.scatter(test_data[cluster_1,0], test_data[cluster_1,1], c= 'r', marker='o') # plt.scatter(test_data[cluster_2,0], test_data[cluster_2,1], c='b', marker='o') # plt.scatter(test_data[cluster_3,0], test_data[cluster_3,1], c='k', marker='o') # plt.scatter(means[:,0], means[:,1], c='c', marker='o') # plt.show() means = numpy.array([[ 2.00755688e-04, 1.65181639e-01], [ 8.37884753e-01, 9.99778286e-01], [ 9.75317567e-01, 2.46051178e-02]]) # sensor network sensor_network_file = open('test_bayesian_networks/graph_sensor_dbn.txt', 'r') sensor_network_file_data = eval(sensor_network_file.read()) sensor_network_skeleton = GraphSkeleton() sensor_network_skeleton.V = sensor_network_file_data["V"] sensor_network_skeleton.E = sensor_network_file_data["E"] sensor_network = DynDiscBayesianNetwork() sensor_network.V = sensor_network_skeleton.V sensor_network.E = sensor_network_skeleton.E sensor_network.initial_Vdata = sensor_network_file_data["initial_Vdata"] sensor_network.twotbn_Vdata = sensor_network_file_data["twotbn_Vdata"] # observation_network observation_network_file = open('test_bayesian_networks/graph_transition_dbn.txt', 'r') observation_network_file_data = eval(observation_network_file.read()) observation_network_skeleton = GraphSkeleton() observation_network_skeleton.V = observation_network_file_data["V"] observation_network_skeleton.E = observation_network_file_data["E"]
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
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
result = learner.discrete_estimatebn(data) # output - toggle comment to see #print json.dumps(result.E, indent=2) #print json.dumps(result.Vdata, indent=2) # (13) ----------------------------------------------------------------------- # Forward sample on dynamic Bayesian networks # read input file path = "../tests/unittestdyndict.txt" f = open(path, 'r') g = eval(f.read()) # set up dynamic BN d = DynDiscBayesianNetwork() skel = GraphSkeleton() skel.V = g["V"] skel.E = g["E"] skel.toporder() d.V = skel.V d.E = skel.E d.initial_Vdata = g["initial_Vdata"] d.twotbn_Vdata = g["twotbn_Vdata"] # forward sample seq = d.randomsample(10) # output - toggle comment to see #print json.dumps(seq, indent=2)
import json from libpgm.nodedata import NodeData from libpgm.graphskeleton import GraphSkeleton from libpgm.dyndiscbayesiannetwork import DynDiscBayesianNetwork from inference.sensor_dbn_inference import SensorDbnInference network_file = open('test_bayesian_networks/sensor_dbn.txt', '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"] network = DynDiscBayesianNetwork() network.V = network_skeleton.V network.E = network_skeleton.E network.initial_Vdata = network_file_data["initial_Vdata"] network.twotbn_Vdata = network_file_data["twotbn_Vdata"] inference_engine = SensorDbnInference(network) print 'Initial belief: ', inference_engine.get_current_belief() inference_engine.filter('1') print 'Measurement = 1: ', inference_engine.get_current_belief() inference_engine.filter('0') print 'Measurement = 0: ', inference_engine.get_current_belief() inference_engine.filter('0') print 'Measurement = 0: ', inference_engine.get_current_belief()