8. Structure Learning in Bayesian Networks¶

In this notebook, we show examples for using the Structure Learning Algorithms in pgmpy. Currently, pgmpy has implementation of 3 main algorithms: 1. PC with stable and parallel variants. 2. Hill-Climb Search 3. Exhaustive Search

For PC the following conditional independence test can be used: 1. Chi-Square test (https://en.wikipedia.org/wiki/Chi-squared_test) 2. Pearsonr (https://en.wikipedia.org/wiki/Partial_correlation#Using_linear_regression) 3. G-squared (https://en.wikipedia.org/wiki/G-test) 4. Log-likelihood (https://en.wikipedia.org/wiki/G-test) 5. Freeman-Tuckey (Read, Campbell B. “Freeman—Tukey chi-squared goodness-of-fit statistics.” Statistics & probability letters 18.4 (1993): 271-278.) 6. Modified Log-likelihood 7. Neymann (https://en.wikipedia.org/wiki/Neyman%E2%80%93Pearson_lemma) 8. Cressie Read (Cressie, Noel, and Timothy RC Read. “Multinomial goodness‐of‐fit tests.” Journal of the Royal Statistical Society: Series B (Methodological) 46.3 (1984): 440-464) 9. Power Divergence (Cressie, Noel, and Timothy RC Read. “Multinomial goodness‐of‐fit tests.” Journal of the Royal Statistical Society: Series B (Methodological) 46.3 (1984): 440-464.)

For Hill-Climb and Exhausitive Search the following scoring methods can be used: 1. K2 Score 2. BDeu Score 3. Bic Score

8.1. Generate some data¶

[1]:

from itertools import combinations

import networkx as nx
from sklearn.metrics import f1_score

from pgmpy.estimators import PC, HillClimbSearch, ExhaustiveSearch
from pgmpy.estimators import K2Score
from pgmpy.utils import get_example_model
from pgmpy.sampling import BayesianModelSampling

[2]:

model = get_example_model("alarm")
samples = BayesianModelSampling(model).forward_sample(size=int(1e3))

Generating for node: CVP: 100%|██████████| 37/37 [00:00<00:00, 544.13it/s]

[2]:

HISTORY CVP PCWP HYPOVOLEMIA LVEDVOLUME LVFAILURE STROKEVOLUME ERRLOWOUTPUT HRBP HREKG ... MINVOLSET VENTMACH VENTTUBE VENTLUNG VENTALV ARTCO2 CATECHOL HR CO BP
0 TRUE LOW LOW FALSE LOW TRUE LOW FALSE HIGH NORMAL ... NORMAL NORMAL LOW ZERO ZERO HIGH HIGH HIGH LOW LOW
1 FALSE NORMAL NORMAL FALSE NORMAL FALSE NORMAL FALSE HIGH HIGH ... NORMAL NORMAL LOW ZERO LOW HIGH HIGH HIGH HIGH HIGH
2 FALSE NORMAL NORMAL FALSE NORMAL FALSE NORMAL FALSE HIGH HIGH ... LOW LOW ZERO ZERO ZERO HIGH HIGH HIGH HIGH HIGH
3 FALSE NORMAL NORMAL FALSE NORMAL FALSE HIGH FALSE HIGH HIGH ... HIGH HIGH HIGH LOW HIGH LOW HIGH HIGH HIGH HIGH
4 FALSE NORMAL NORMAL FALSE NORMAL FALSE NORMAL FALSE HIGH HIGH ... NORMAL NORMAL ZERO HIGH LOW HIGH HIGH HIGH HIGH LOW

5 rows × 37 columns

[3]:

# Funtion to evaluate the learned model structures.
def get_f1_score(estimated_model, true_model):
nodes = estimated_model.nodes()
estimated_model.to_undirected(), nodelist=nodes, weight=None
)
true_model.to_undirected(), nodelist=nodes, weight=None
)

print("F1-score for the model skeleton: ", f1)


8.2. Learn the model structure using PC¶

[4]:

est = PC(data=samples)
estimated_model = est.estimate(variant="stable", max_cond_vars=4)
get_f1_score(estimated_model, model)

Working for n conditional variables: 4: 100%|██████████| 4/4 [00:11<00:00,  2.89s/it]

F1-score for the model skeleton:  0.7777777777777779



[5]:

est = PC(data=samples)
estimated_model = est.estimate(variant="orig", max_cond_vars=4)
get_f1_score(estimated_model, model)

Working for n conditional variables: 4: 100%|██████████| 4/4 [00:11<00:00,  2.88s/it]

F1-score for the model skeleton:  0.7777777777777779