Full Example

We have covered how to learn the structure and parameters of a Bayesian Belief Network BBN. Let’s see how we can combine the structure and parameters to create a BBN. Additionally, let’s see how we can use the BBN for exact inference.

Load data

Let’s read our data into a Spark DataFrame SDF.

from pyspark_bbn.discrete.data import DiscreteData

sdf = spark.read.csv("hdfs://localhost/data-1479668986461.csv", header=True)
data = DiscreteData(sdf)

Structure learning

Let’s pick the naive Bayes algorithm to learn the structure.

from pyspark_bbn.discrete.scblearn import Naive

naive = Naive(data, "n3")
g = naive.get_network()

Parameter learning

After we have a structure, we can learn the parameters.

from pyspark_bbn.discrete.plearn import ParamLearner

param_learner = ParamLearner(data, g)
p = param_learner.get_params()

BBN

Now that we have the structure and parameters, we can build a BBN. Use the get_bbn utility method to help bring together the structure and parameters.

from pyspark_bbn.discrete.bbn import get_pybbn_data

bbn = get_pybbn_data(g, p)

Inference

With a BBN defined, we can use py-bbn to proceed with exact inference.

from pybbn.factory import create_reasoning_model

model = create_reasoning_model(bbn["d"], bbn["p"])

q = model.pquery()

q["n1"]

	n1	__p__
0	f	0.75065
1	t	0.24935

q["n2"]

	n2	__p__
0	f	0.6509
1	t	0.3491

q["n3"]

	n3	__p__
0	f	0.47345
1	t	0.52655

q["n4"]

	n4	__p__
0	f	0.40255
1	t	0.59745

q["n5"]

	n5	__p__
0	maybe	0.29585
1	no	0.29825
2	yes	0.40590