def setup_titanic(): # Build a model of the titanic disaster global titanic_network, passenger, gender, tclass # Passengers on the Titanic either survive or perish passenger = DiscreteDistribution({'survive': 0.6, 'perish': 0.4}) # Gender, given survival data gender = ConditionalProbabilityTable( [['survive', 'male', 0.0], ['survive', 'female', 1.0], ['perish', 'male', 1.0], ['perish', 'female', 0.0]], [passenger]) # Class of travel, given survival data tclass = ConditionalProbabilityTable( [['survive', 'first', 0.0], ['survive', 'second', 1.0], ['survive', 'third', 0.0], ['perish', 'first', 1.0], ['perish', 'second', 0.0], ['perish', 'third', 0.0]], [passenger]) # State objects hold both the distribution, and a high level name. s1 = State(passenger, name="passenger") s2 = State(gender, name="gender") s3 = State(tclass, name="class") # Create the Bayesian network object with a useful name titanic_network = BayesianNetwork("Titanic Disaster") # Add the three nodes to the network titanic_network.add_nodes(s1, s2, s3) # Add transitions which represent conditional dependencies, where the # second node is conditionally dependent on the first node (Monty is # dependent on both guest and prize) titanic_network.add_edge(s1, s2) titanic_network.add_edge(s1, s3) titanic_network.bake()
def get_bayesnet(self): door_lock = DiscreteDistribution({'d1': 0.7, 'd2': 0.3}) clock_alarm = DiscreteDistribution( { 'a1' : 0.8, 'a2' : 0.2} ) light = ConditionalProbabilityTable( [[ 'd1', 'a1', 'l1', 0.96 ], ['d1', 'a1', 'l2', 0.04 ], [ 'd1', 'a2', 'l1', 0.89 ], [ 'd1', 'a2', 'l2', 0.11 ], [ 'd2', 'a1', 'l1', 0.96 ], [ 'd2', 'a1', 'l2', 0.04 ], [ 'd2', 'a2', 'l1', 0.89 ], [ 'd2', 'a2', 'l2', 0.11 ]], [door_lock, clock_alarm]) coffee_maker = ConditionalProbabilityTable( [[ 'a1', 'c1', 0.92 ], [ 'a1', 'c2', 0.08 ], [ 'a2', 'c1', 0.03 ], [ 'a2', 'c2', 0.97 ]], [clock_alarm] ) s_door_lock = State(door_lock, name="door_lock") s_clock_alarm = State(clock_alarm, name="clock_alarm") s_light = State(light, name="light") s_coffee_maker = State(coffee_maker, name="coffee_maker") network = BayesianNetwork("User_pref") network.add_nodes(s_door_lock, s_clock_alarm, s_light, s_coffee_maker) network.add_edge(s_door_lock,s_light) network.add_edge(s_clock_alarm,s_coffee_maker) network.add_edge(s_clock_alarm,s_light) network.bake() return network
def setup_monty(): # Build a model of the Monty Hall Problem global monty_network, monty_index, prize_index, guest_index random.seed(0) # Friends emissions are completely random guest = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3}) # The actual prize is independent of the other distributions prize = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3}) # Monty is dependent on both the guest and the prize. monty = ConditionalProbabilityTable( [['A', 'A', 'A', 0.0], ['A', 'A', 'B', 0.5], ['A', 'A', 'C', 0.5], ['A', 'B', 'A', 0.0], ['A', 'B', 'B', 0.0], ['A', 'B', 'C', 1.0], ['A', 'C', 'A', 0.0], ['A', 'C', 'B', 1.0], ['A', 'C', 'C', 0.0], ['B', 'A', 'A', 0.0], ['B', 'A', 'B', 0.0], ['B', 'A', 'C', 1.0], ['B', 'B', 'A', 0.5], ['B', 'B', 'B', 0.0], ['B', 'B', 'C', 0.5], ['B', 'C', 'A', 1.0], ['B', 'C', 'B', 0.0], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 1.0], ['C', 'B', 'B', 0.0], ['C', 'B', 'C', 0.0], ['C', 'C', 'A', 0.5], ['C', 'C', 'B', 0.5], ['C', 'C', 'C', 0.0]], [guest, prize]) # Make the states s1 = State(guest, name="guest") s2 = State(prize, name="prize") s3 = State(monty, name="monty") # Make the bayes net, add the states, and the conditional dependencies. monty_network = BayesianNetwork("test") monty_network.add_nodes(s1, s2, s3) monty_network.add_edge(s1, s3) monty_network.add_edge(s2, s3) monty_network.bake() monty_index = monty_network.states.index(s3) prize_index = monty_network.states.index(s2) guest_index = monty_network.states.index(s1)
def __init__(self): Pollution = DiscreteDistribution({'F': 0.9, 'T': 0.1}) Smoker = DiscreteDistribution({'T': 0.3, 'F': 0.7}) print(Smoker) Cancer = ConditionalProbabilityTable([ ['T', 'T', 'T', 0.05], ['T', 'T', 'F', 0.95], ['T', 'F', 'T', 0.02], ['T', 'F', 'F', 0.98], ['F', 'T', 'T', 0.03], ['F', 'T', 'F', 0.97], ['F', 'F', 'T', 0.001], ['F', 'F', 'F', 0.999], ], [Pollution, Smoker]) print(Cancer) XRay = ConditionalProbabilityTable([ ['T', 'T', 0.9], ['T', 'F', 0.1], ['F', 'T', 0.2], ['F', 'F', 0.8], ], [Cancer]) Dyspnoea = ConditionalProbabilityTable([ ['T', 'T', 0.65], ['T', 'F', 0.35], ['F', 'T', 0.3], ['F', 'F', 0.7], ], [Cancer]) s1 = Node(Pollution, name="Pollution") s2 = Node(Smoker, name="Smoker") s3 = Node(Cancer, name="Cancer") s4 = Node(XRay, name="XRay") s5 = Node(Dyspnoea, name="Dyspnoea") model = BayesianNetwork("Lung Cancer") model.add_states(s1, s2, s3, s4, s5) model.add_edge(s1, s3) model.add_edge(s2, s3) model.add_edge(s3, s4) model.add_edge(s3, s5) model.bake() self.model = model meta = [] name_mapper = ["Pollution", "Smoker", "Cancer", "XRay", "Dyspnoea"] for i in range(self.model.node_count()): meta.append({ "name": name_mapper[i], "type": "categorical", "size": 2, "i2s": ['T', 'F'] }) self.meta = meta
def __init__(self): A = DiscreteDistribution({'1': 1. / 3, '2': 1. / 3, '3': 1. / 3}) B = ConditionalProbabilityTable([ ['1', '1', 0.5], ['1', '2', 0.5], ['1', '3', 0], ['2', '1', 0], ['2', '2', 0.5], ['2', '3', 0.5], ['3', '1', 0.5], ['3', '2', 0], ['3', '3', 0.5], ], [A]) C = ConditionalProbabilityTable([ ['1', '4', 0.5], ['1', '5', 0.5], ['1', '6', 0], ['2', '4', 0], ['2', '5', 0.5], ['2', '6', 0.5], ['3', '4', 0.5], ['3', '5', 0], ['3', '6', 0.5], ], [A]) s1 = Node(A, name="A") s2 = Node(B, name="B") s3 = Node(C, name="C") model = BayesianNetwork("tree") model.add_states(s1, s2, s3) model.add_edge(s1, s2) model.add_edge(s1, s3) model.bake() self.model = model meta = [] for i in range(self.model.node_count() - 1): meta.append({ "name": chr(ord('A') + i), "type": "categorical", "size": 3, "i2s": ['1', '2', '3'] }) meta.append({ "name": "C", "type": "categorical", "size": 3, "i2s": ['4', '5', '6'] }) self.meta = meta
def test_io_fit(): d1 = DiscreteDistribution({True: 0.6, False: 0.4}) d2 = ConditionalProbabilityTable([ [True, 'A', 0.2], [True, 'B', 0.8], [False, 'A', 0.3], [False, 'B', 0.7]], [d1]) d3 = ConditionalProbabilityTable([ ['A', 0, 0.3], ['A', 1, 0.7], ['B', 0, 0.8], ['B', 1, 0.2]], [d2]) n1 = Node(d1) n2 = Node(d2) n3 = Node(d3) model1 = BayesianNetwork() model1.add_nodes(n1, n2, n3) model1.add_edge(n1, n2) model1.add_edge(n2, n3) model1.bake() model1.fit(X, weights=weights) d1 = DiscreteDistribution({True: 0.2, False: 0.8}) d2 = ConditionalProbabilityTable([ [True, 'A', 0.7], [True, 'B', 0.2], [False, 'A', 0.4], [False, 'B', 0.6]], [d1]) d3 = ConditionalProbabilityTable([ ['A', 0, 0.9], ['A', 1, 0.1], ['B', 0, 0.0], ['B', 1, 1.0]], [d2]) n1 = Node(d1) n2 = Node(d2) n3 = Node(d3) model2 = BayesianNetwork() model2.add_nodes(n1, n2, n3) model2.add_edge(n1, n2) model2.add_edge(n2, n3) model2.bake() model2.fit(data_generator) logp1 = model1.log_probability(X) logp2 = model2.log_probability(X) assert_array_almost_equal(logp1, logp2)
def build_net(cpts): states = dict() for name, cpt in cpts.items(): states[name] = State(cpt, name=name) model = BayesianNetwork('Poker Game') model.add_states(*list(states.values())) for name, parents, _ in sheets: for parent in parents: print(states[parent]) model.add_transition(states[parent], states[name]) model.bake() return model
def __init__(self): Rain = DiscreteDistribution({'T': 0.2, 'F': 0.8}) Sprinkler = ConditionalProbabilityTable([ ['F', 'T', 0.4], ['F', 'F', 0.6], ['T', 'T', 0.1], ['T', 'F', 0.9], ], [Rain]) Wet = ConditionalProbabilityTable([ ['F', 'F', 'T', 0.01], ['F', 'F', 'F', 0.99], ['F', 'T', 'T', 0.8], ['F', 'T', 'F', 0.2], ['T', 'F', 'T', 0.9], ['T', 'F', 'F', 0.1], ['T', 'T', 'T', 0.99], ['T', 'T', 'F', 0.01], ], [Sprinkler, Rain]) s1 = Node(Rain, name="Rain") s2 = Node(Sprinkler, name="Sprinkler") s3 = Node(Wet, name="Wet") model = BayesianNetwork("Simple fully connected") model.add_states(s1, s2, s3) model.add_edge(s1, s2) model.add_edge(s1, s3) model.add_edge(s2, s3) model.bake() self.model = model meta = [] for i in range(self.model.node_count()): meta.append({ "name": None, "type": "categorical", "size": 2, "i2s": ['T', 'F'] }) meta[0]['name'] = 'Rain' meta[1]['name'] = 'Sprinkler' meta[2]['name'] = 'Wet' self.meta = meta
def pomegranate_User_pref(): door_lock = DiscreteDistribution({'True': 0.7, 'False': 0.3}) thermostate = ConditionalProbabilityTable( [['True', 'True', 0.2], ['True', 'False', 0.8], ['False', 'True', 0.01], ['False', 'False', 0.99]], [door_lock]) clock_alarm = DiscreteDistribution({'True': 0.8, 'False': 0.2}) light = ConditionalProbabilityTable( [['True', 'True', 'True', 0.96], ['True', 'True', 'False', 0.04], ['True', 'False', 'True', 0.89], ['True', 'False', 'False', 0.11], ['False', 'True', 'True', 0.96], ['False', 'True', 'False', 0.04], ['False', 'False', 'True', 0.89], ['False', 'False', 'False', 0.11]], [door_lock, clock_alarm]) coffee_maker = ConditionalProbabilityTable( [['True', 'True', 0.92], ['True', 'False', 0.08], ['False', 'True', 0.03], ['False', 'False', 0.97]], [clock_alarm]) window = ConditionalProbabilityTable( [['True', 'True', 0.885], ['True', 'False', 0.115], ['False', 'True', 0.04], ['False', 'False', 0.96]], [thermostate]) s0_door_lock = State(door_lock, name="door_lock") s1_clock_alarm = State(clock_alarm, name="clock_alarm") s2_light = State(light, name="light") s3_coffee_maker = State(coffee_maker, name="coffee_maker") s4_thermostate = State(thermostate, name="thermostate") s5_window = State(window, name="Window") network = BayesianNetwork("User_pref") network.add_nodes(s0_door_lock, s1_clock_alarm, s2_light, s3_coffee_maker, s4_thermostate, s5_window) network.add_edge(s0_door_lock, s2_light) network.add_edge(s0_door_lock, s4_thermostate) network.add_edge(s1_clock_alarm, s3_coffee_maker) network.add_edge(s1_clock_alarm, s2_light) network.add_edge(s4_thermostate, s5_window) network.bake() return network
def __get_bayesian_network_model( self, symptom_distributions: List, symptom_states: List, file_name: str, disease_name: str, ): disease_conditional_distribution = list() for (s1, s2, s3, s4, s5, d, p) in get_from_csv(file_name): disease_conditional_distribution.append( [s1, s2, s3, s4, s5, d, float(p)]) disease_distribution = ConditionalProbabilityTable( disease_conditional_distribution, symptom_distributions, ) disease = Node(disease_distribution, name=disease_name) model = BayesianNetwork(disease_name) model.add_state(disease) for symptom_state in symptom_states: model.add_state(symptom_state) model.add_edge(symptom_state, disease) model.bake() return model
from pomegranate import DiscreteDistribution from pomegranate import ConditionalProbabilityTable from pomegranate import BayesianNetwork from pomegranate import Node guest = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3}) prize = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3}) monty = ConditionalProbabilityTable( [['A', 'A', 'A', 0.0], ['A', 'A', 'B', 0.5], ['A', 'A', 'C', 0.5], ['A', 'B', 'A', 0.0], ['A', 'B', 'B', 0.0], ['A', 'B', 'C', 1.0], ['A', 'C', 'A', 0.0], ['A', 'C', 'B', 1.0], ['A', 'C', 'C', 0.0], ['B', 'A', 'A', 0.0], ['B', 'A', 'B', 0.0], ['B', 'A', 'C', 1.0], ['B', 'B', 'A', 0.5], ['B', 'B', 'B', 0.0], ['B', 'B', 'C', 0.5], ['B', 'C', 'A', 1.0], ['B', 'C', 'B', 0.0], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 1.0], ['C', 'B', 'B', 0.0], ['C', 'B', 'C', 0.0], ['C', 'C', 'A', 0.5], ['C', 'C', 'B', 0.5], ['C', 'C', 'C', 0.0]], [guest, prize]) s1 = Node(guest, name="guest") s2 = Node(prize, name="prize") s3 = Node(monty, name="monty") model = BayesianNetwork("Monty Hall Problem") model.add_states(s1, s2, s3) model.add_edge(s1, s3) model.add_edge(s2, s3) model.bake()
def __init__(self, filename): with open(filename) as f: bif = f.read() vars = re.findall(r"variable[^\{]+{[^\}]+}", bif) probs = re.findall(r"probability[^\{]+{[^\}]+}", bif) var_nodes = {} var_index_to_name = [] edges = [] self.meta = [] todo = set() for v, p in zip(vars, probs): m = re.search(r"variable\s+([^\{\s]+)\s+", v) v_name = m.group(1) m = re.search(r"type\s+discrete\s+\[\s*(\d+)\s*\]\s*\{([^\}]+)\}", v) v_opts_n = int(m.group(1)) v_opts = m.group(2).replace(',', ' ').split() assert v_opts_n == len(v_opts) # print(v_name, v_opts_n, v_opts) m = re.search(r"probability\s*\(([^)]+)\)", p) cond = m.group(1).replace('|', ' ').replace(',', ' ').split() assert cond[0] == v_name # print(cond) self.meta.append({ "name": v_name, "type": "categorical", "size": v_opts_n, "i2s": v_opts }) if len(cond) == 1: m = re.search(r"table([e\-\d\.\s,]*);", p) margin_p = m.group(1).replace(',', ' ').split() margin_p = [float(x) for x in margin_p] assert abs(sum(margin_p) - 1) < 1e-6 assert len(margin_p) == v_opts_n margin_p = dict(zip(v_opts, margin_p)) var_index_to_name.append(v_name) tmp = DiscreteDistribution(margin_p) # print(tmp) var_nodes[v_name] = tmp else: m_iter = re.finditer(r"\(([^)]*)\)([\s\d\.,\-e]+);", p) cond_p_table = [] for m in m_iter: cond_values = m.group(1).replace(',', ' ').split() cond_p = m.group(2).replace(',', ' ').split() cond_p = [float(x) for x in cond_p] assert len(cond_values) == len(cond) - 1 assert len(cond_p) == v_opts_n assert abs(sum(cond_p) - 1) < 1e-6 for opt, opt_p in zip(v_opts, cond_p): cond_p_table.append(cond_values + [opt, opt_p]) var_index_to_name.append(v_name) tmp = (cond_p_table, cond) # print(tmp) var_nodes[v_name] = tmp for x in cond[1:]: edges.append((x, v_name)) todo.add(v_name) while len(todo) > 0: # print(todo) for v_name in todo: # print(v_name, type(var_nodes[v_name])) cond_p_table, cond = var_nodes[v_name] flag = True for y in cond[1:]: if y in todo: flag = False break if flag: cond_t = [var_nodes[x] for x in cond[1:]] var_nodes[v_name] = ConditionalProbabilityTable( cond_p_table, cond_t) todo.remove(v_name) break for x in var_index_to_name: var_nodes[x] = Node(var_nodes[x], name=x) var_nodes_list = [var_nodes[x] for x in var_index_to_name] # print(var_nodes_list) model = BayesianNetwork("tmp") model.add_states(*var_nodes_list) for edge in edges: model.add_edge(var_nodes[edge[0]], var_nodes[edge[1]]) model.bake() # print(model.to_json()) self.model = model
network = BayesianNetwork("Monty hall problem") network.add_states(*states.values()) network.add_edge(states["Peer_Pressure"],states["Smoking"]) network.add_edge(states["Anxiety"],states["Smoking"]) network.add_edge(states["Smoking"],states["Yellow_Fingers"]) network.add_edge(states["Genetics"],states["Lung_cancer"]) network.add_edge(states["Smoking"],states["Lung_cancer"]) network.add_edge(states["Genetics"],states["Attention_Disorder"]) network.add_edge(states['Lung_cancer'], states["Coughing"]) network.add_edge(states['Allergy'], states["Coughing"]) network.add_edge(states['Coughing'], states["Fatigue"]) network.add_edge(states['Lung_cancer'], states["Fatigue"]) network.add_edge(states["Fatigue"], states["Car_Accident"]) network.add_edge(states["Attention_Disorder"], states["Car_Accident"]) import ast network.bake() beliefs = network.predict_proba({"Genetics":"T"},max_iterations=100000) # print(beliefs) # beliefs = map(str, beliefs) # for state, belief in zip(network.states, beliefs): # if hasattr(belief,"parameters"): # print(state.name,belief.parameters) # exit() # network.add_edge(s1, s3) # network.add_edge(s2, s3) # # beliefs = network.predict_proba({"guest": "A", "monty": "B"}) # beliefs = map(str, beliefs) # for state, belief in zip(network.states, beliefs): # print(state.name, belief)
def setup_huge_monty(): # Build the huge monty hall huge_monty_network. This is an example I made # up with which may not exactly flow logically, but tests a varied type of # tables ensures heterogeneous types of data work together. global huge_monty_network, huge_monty_friend, huge_monty_guest, huge_monty global huge_monty_remaining, huge_monty_randomize, huge_monty_prize # Huge_Monty_Friend huge_monty_friend = DiscreteDistribution({True: 0.5, False: 0.5}) # Huge_Monty_Guest emisisons are completely random huge_monty_guest = ConditionalProbabilityTable( [[True, 'A', 0.50], [True, 'B', 0.25], [True, 'C', 0.25], [False, 'A', 0.0], [False, 'B', 0.7], [False, 'C', 0.3]], [huge_monty_friend]) # Number of huge_monty_remaining cars huge_monty_remaining = DiscreteDistribution({0: 0.1, 1: 0.7, 2: 0.2, }) # Whether they huge_monty_randomize is dependent on the numnber of # huge_monty_remaining cars huge_monty_randomize = ConditionalProbabilityTable( [[0, True, 0.05], [0, False, 0.95], [1, True, 0.8], [1, False, 0.2], [2, True, 0.5], [2, False, 0.5]], [huge_monty_remaining]) # Where the huge_monty_prize is depends on if they huge_monty_randomize or # not and also the huge_monty_guests huge_monty_friend huge_monty_prize = ConditionalProbabilityTable( [[True, True, 'A', 0.3], [True, True, 'B', 0.4], [True, True, 'C', 0.3], [True, False, 'A', 0.2], [True, False, 'B', 0.4], [True, False, 'C', 0.4], [False, True, 'A', 0.1], [False, True, 'B', 0.9], [False, True, 'C', 0.0], [False, False, 'A', 0.0], [False, False, 'B', 0.4], [False, False, 'C', 0.6]], [huge_monty_randomize, huge_monty_friend]) # Monty is dependent on both the huge_monty_guest and the huge_monty_prize. huge_monty = ConditionalProbabilityTable( [['A', 'A', 'A', 0.0], ['A', 'A', 'B', 0.5], ['A', 'A', 'C', 0.5], ['A', 'B', 'A', 0.0], ['A', 'B', 'B', 0.0], ['A', 'B', 'C', 1.0], ['A', 'C', 'A', 0.0], ['A', 'C', 'B', 1.0], ['A', 'C', 'C', 0.0], ['B', 'A', 'A', 0.0], ['B', 'A', 'B', 0.0], ['B', 'A', 'C', 1.0], ['B', 'B', 'A', 0.5], ['B', 'B', 'B', 0.0], ['B', 'B', 'C', 0.5], ['B', 'C', 'A', 1.0], ['B', 'C', 'B', 0.0], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 1.0], ['C', 'B', 'B', 0.0], ['C', 'B', 'C', 0.0], ['C', 'C', 'A', 0.5], ['C', 'C', 'B', 0.5], ['C', 'C', 'C', 0.0]], [huge_monty_guest, huge_monty_prize]) # Make the states s0 = State(huge_monty_friend, name="huge_monty_friend") s1 = State(huge_monty_guest, name="huge_monty_guest") s2 = State(huge_monty_prize, name="huge_monty_prize") s3 = State(huge_monty, name="huge_monty") s4 = State(huge_monty_remaining, name="huge_monty_remaining") s5 = State(huge_monty_randomize, name="huge_monty_randomize") # Make the bayes net, add the states, and the conditional dependencies. huge_monty_network = BayesianNetwork("test") huge_monty_network.add_nodes(s0, s1, s2, s3, s4, s5) huge_monty_network.add_transition(s0, s1) huge_monty_network.add_transition(s1, s3) huge_monty_network.add_transition(s2, s3) huge_monty_network.add_transition(s4, s5) huge_monty_network.add_transition(s5, s2) huge_monty_network.add_transition(s0, s2) huge_monty_network.bake()