Class PGMLearner

libpgm/pgmlearner.py:48–973

This class is a machine with tools for learning Bayesian networks from data. It contains the *discrete_mle_estimateparams*, *lg_mle_estimateparams*, *discrete_constraint_estimatestruct*, *lg_constraint_estimatestruct*, *discrete_condind*, *discrete_estimatebn*, and *lg_estimatebn* methods.

from sampleaggregator import SampleAggregator

class PGMLearner():
    '''
    This class is a machine with tools for learning Bayesian networks from data. It contains the *discrete_mle_estimateparams*, *lg_mle_estimateparams*, *discrete_constraint_estimatestruct*, *lg_constraint_estimatestruct*, *discrete_condind*, *discrete_estimatebn*, and *lg_estimatebn* methods.

    '''
    def discrete_mle_estimateparams(self, graphskeleton, data):
        '''
        Estimate parameters for a discrete Bayesian network with a structure given by *graphskeleton* in order to maximize the probability of data given by *data*. This function takes the following arguments:

            1. *graphskeleton* -- An instance of the :doc:`GraphSkeleton <graphskeleton>` class containing vertex and edge data.
            2. *data* -- A list of dicts containing samples from the network in {vertex: value} format. Example::

                    [
                        {
                            'Grade': 'B',
                            'SAT': 'lowscore',
                            ...
                        },
                        ...
                    ]

        This function normalizes the distribution of a node's outcomes for each combination of its parents' outcomes. In doing so it creates an estimated tabular conditional probability distribution for each node. It then instantiates a :doc:`DiscreteBayesianNetwork <discretebayesiannetwork>` instance based on the *graphskeleton*, and modifies that instance&#x27;s *Vdata* attribute to reflect the estimated CPDs. It then returns the instance. 

        The Vdata attribute instantiated is in the format seen in :doc:`unittestdict`, as described in :doc:`discretebayesiannetwork`.

        Usage example: this would learn parameters from a set of 200 discrete samples::

            import json

            from libpgm.nodedata import NodeData
            from libpgm.graphskeleton import GraphSkeleton
            from libpgm.discretebayesiannetwork import DiscreteBayesianNetwork
            from libpgm.pgmlearner import PGMLearner
            
            # generate some data to use
            nd = NodeData()
            nd.load("../tests/unittestdict.txt")    # an input file
            skel = GraphSkeleton()
            skel.load("../tests/unittestdict.txt")
            skel.toporder()
            bn = DiscreteBayesianNetwork(skel, nd)
            data = bn.randomsample(200)

            # instantiate my learner 
            learner = PGMLearner()

            # estimate parameters from data and skeleton
            result = learner.discrete_mle_estimateparams(skel, data)

            # output
            print json.dumps(result.Vdata, indent=2)

        ''&#x27;
        assert (isinstance(graphskeleton, GraphSkeleton)), "First arg must be a loaded GraphSkeleton class."
        assert (isinstance(data, list) and data and isinstance(data[0], dict)), "Second arg must be a list of dicts."

        # instantiate Bayesian network, and add parent and children data
        bn = DiscreteBayesianNetwork()