'Sex': 1,
'Pclass': 1
})
q1 = bn.inference.fit(model, variables=['Survived'], evidence={'Sex': 0})
bn.print_CPD(model)
# %%
DAG = bn.import_DAG('sprinkler', CPD=False)
DAG = bn.import_DAG('asia')
bn.plot(DAG)
bn.print_CPD(DAG)
df = bn.sampling(DAG, n=1000)
vector = bn.adjmat2vec(DAG['adjmat'])
adjmat = bn.vec2adjmat(vector['source'], vector['target'])
# %%
from pgmpy.factors.discrete import TabularCPD
edges = [('A', 'E'), ('S', 'E'), ('E', 'O'), ('E', 'R'), ('O', 'T'),
('R', 'T')]
DAG = bn.make_DAG(edges)
bn.plot(DAG)
cpd_A = TabularCPD(variable='A', variable_card=3, values=[[0.3], [0.5], [0.2]])
print(cpd_A)
cpd_S = TabularCPD(variable='S', variable_card=2, values=[[0.6], [0.4]])
print(cpd_S)
cpd_E = TabularCPD(variable='E',
variable_card=2,
def test_vec2adjmat():
DAG = bn.import_DAG('Sprinkler', verbose=0)
out = bn.adjmat2vec(DAG['adjmat'])
# TEST: conversion
assert bn.vec2adjmat(out['source'],
out['target']).shape == DAG['adjmat'].shape
model_hc_bic = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic', verbose=0)
# %% Chow-Liu algorithm
DAG = bn.import_DAG('sprinkler', verbose=0)
df = bn.sampling(DAG, n=1000, verbose=0)
# Structure learning
model_hc_bic = bn.structure_learning.fit(df, methodtype='cl', root_node='Cloudy', verbose=0)
G = bn.plot(model)
# %% Load example dataframe from sprinkler
import bnlearn as bn
DAG = bn.import_DAG('alarm', verbose=0)
to_vector = bn.adjmat2vec(DAG['adjmat'])
to_adjmat = bn.vec2adjmat(to_vector['source'], to_vector['target'])
# %% Load example dataframe from sprinkler
df = bn.import_example('sprinkler', verbose=0)
# Structure learning
model = bn.structure_learning.fit(df, verbose=0)
# Plot
G = bn.plot(model, verbose=0)
model_hc_bic = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic', verbose=0)
# %% Try all methods vs score types
model_hc_bic = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic')
model_hc_k2 = bn.structure_learning.fit(df, methodtype='hc', scoretype='k2')
model_hc_bdeu = bn.structure_learning.fit(df, methodtype='hc', scoretype='bdeu')
model_ex_bic = bn.structure_learning.fit(df, methodtype='ex', scoretype='bic')