model_hc_bic = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic')
# %% 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')
model_ex_k2 = bn.structure_learning.fit(df, methodtype='ex', scoretype='k2')
model_ex_bdeu = bn.structure_learning.fit(df,
methodtype='ex',
scoretype='bdeu')
bn.compare_networks(model, model_hc_bic, pos=G['pos'])
# %% Example with dataset
import bnlearn as bn
DAG = bn.import_DAG('sprinkler')
# Print cpds
bn.print_CPD(DAG)
# plot ground truth
G = bn.plot(DAG)
df = bn.sampling(DAG, n=100)
# %% Inference using custom DAG
import bnlearn as bn
# Load asia DAG
df = bn.import_example('asia')
# from tabulate import tabulate
def test_compare_networks():
DAG = bn.import_DAG('Sprinkler', verbose=0)
G = bn.compare_networks(DAG, DAG, showfig=False)
assert np.all(G[0] == [[12, 0], [0, 4]])
# 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')
model_ex_k2 = bn.structure_learning.fit(df, methodtype='ex', scoretype='k2')
model_ex_bdeu = bn.structure_learning.fit(df, methodtype='ex', scoretype='bdeu')
bn.compare_networks(model, model_hc_bic, pos=G['pos'], verbose=0)
# %% Example with dataset
import bnlearn as bn
DAG = bn.import_DAG('sprinkler', verbose=3)
# Print cpds
bn.print_CPD(DAG)
# plot ground truth
G = bn.plot(DAG, verbose=0)
df = bn.sampling(DAG, n=100, verbose=3)
# %% Inference using custom DAG
import bnlearn as bn
# Load asia DAG
df = bn.import_example('asia')