Serialization/Deserialization

We all need a way to save (serialize) and load (deserialize) our Bayesian Belief Networks (BBNs) and join trees (JTs). Here’s how to do so. Note that serde (serialization/deserialization) features are just writing to JSON or CSV formats and loading back from the such files. The code takes care of the serde process.

Serializing a BBN

JSON Serialization Format

from pybbn.graph.dag import Bbn
from pybbn.graph.edge import Edge, EdgeType
from pybbn.graph.node import BbnNode
from pybbn.graph.variable import Variable

# create graph
a = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
bbn = Bbn().add_node(a).add_node(b) \
    .add_edge(Edge(a, b, EdgeType.DIRECTED))

# serialize
Bbn.to_json(bbn, 'simple-bbn.json')

You will get a file simple-bbn.json written out with the following content.

{
  "nodes": {
    "0": {
      "probs": [
        0.2,
        0.8
      ],
      "variable": {
        "id": 0,
        "name": "a",
        "values": [
          "t",
          "f"
        ]
      }
    },
    "1": {
      "probs": [
        0.1,
        0.9,
        0.9,
        0.1
      ],
      "variable": {
        "id": 1,
        "name": "b",
        "values": [
          "t",
          "f"
        ]
      }
    }
  },
  "edges": [
    {
      "pa": 0,
      "ch": 1
    }
  ]
}

CSV Serialization Format

from pybbn.graph.dag import Bbn
from pybbn.graph.edge import Edge, EdgeType
from pybbn.graph.node import BbnNode
from pybbn.graph.variable import Variable

# create graph
a = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
bbn = Bbn().add_node(a).add_node(b) \
    .add_edge(Edge(a, b, EdgeType.DIRECTED))

# serialize
Bbn.to_csv(bbn, 'simple-bbn.csv')

You will get a file simple-bbn.csv written out with the following content.

0,a,t,f,|,0.2,0.8
1,b,t,f,|,0.1,0.9,0.9,0.1
0,1,directed

Deserializing a BBN

JSON Deserialization Format

from pybbn.graph.dag import Bbn

# deserialize
bbn = Bbn.from_json('simple-bbn.json')

CSV Deserialization Format

from pybbn.graph.dag import Bbn

# deserialize
bbn = Bbn.from_csv('simple-bbn.csv')

Join Tree Serde

A join tree may also be serialized and deserialized. Only json format is supported for now.

Serializing a Join Tree

import json

from pybbn.graph.dag import Bbn
from pybbn.graph.edge import EdgeType, Edge
from pybbn.graph.jointree import JoinTree
from pybbn.graph.node import BbnNode
from pybbn.graph.variable import Variable
from pybbn.pptc.inferencecontroller import InferenceController

a = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
bbn = Bbn().add_node(a).add_node(b) \
    .add_edge(Edge(a, b, EdgeType.DIRECTED))
jt = InferenceController.apply(bbn)

with open('simple-join-tree.json', 'w') as f:
    d = JoinTree.to_dict(jt, bbn)
    j = json.dumps(d, sort_keys=True, indent=2)
    f.write(j)

You will get a file simple-join-tree.json written out with the following content.

{
  "bbn_nodes": {
    "0": {
      "probs": [
        0.2,
        0.8
      ],
      "variable": {
        "id": 0,
        "name": "a",
        "values": [
          "t",
          "f"
        ]
      }
    },
    "1": {
      "probs": [
        0.1,
        0.9,
        0.9,
        0.1
      ],
      "variable": {
        "id": 1,
        "name": "b",
        "values": [
          "t",
          "f"
        ]
      }
    }
  },
  "jt": {
    "edges": [],
    "nodes": {
      "0-1": {
        "node_ids": [
          0,
          1
        ],
        "type": "clique"
      }
    },
    "parent_info": {
      "0": [],
      "1": [
        0
      ]
    }
  }
}

Deserializing a Join Tree

import json

from pybbn.graph.jointree import JoinTree
from pybbn.pptc.inferencecontroller import InferenceController

with open('simple-join-tree.json', 'r') as f:
    j = f.read()
    d = json.loads(j)
    jt = JoinTree.from_dict(d)
    jt = InferenceController.apply_from_serde(jt)