Source code for pgmpy.estimators.BayesianEstimator

# -*- coding: utf-8 -*-

import numpy as np

from pgmpy.estimators import ParameterEstimator
from pgmpy.factors.discrete import TabularCPD
from pgmpy.models import BayesianModel


[docs]class BayesianEstimator(ParameterEstimator): def __init__(self, model, data, **kwargs): """ Class used to compute parameters for a model using Bayesian Parameter Estimation. See `MaximumLikelihoodEstimator` for constructor parameters. """ if not isinstance(model, BayesianModel): raise NotImplementedError( "Bayesian Parameter Estimation is only implemented for BayesianModel" ) super(BayesianEstimator, self).__init__(model, data, **kwargs)
[docs] def get_parameters( self, prior_type="BDeu", equivalent_sample_size=5, pseudo_counts=None ): """ Method to estimate the model parameters (CPDs). Parameters ---------- prior_type: 'dirichlet', 'BDeu', or 'K2' string indicting which type of prior to use for the model parameters. - If 'prior_type' is 'dirichlet', the following must be provided: 'pseudo_counts' = dirichlet hyperparameters; a dict containing, for each variable, a 2-D array of the shape (node_card, product of parents_card) with a "virtual" count for each variable state in the CPD, that is added to the state counts. (lexicographic ordering of states assumed) - If 'prior_type' is 'BDeu', then an 'equivalent_sample_size' must be specified instead of 'pseudo_counts'. This is equivalent to 'prior_type=dirichlet' and using uniform 'pseudo_counts' of `equivalent_sample_size/(node_cardinality*np.prod(parents_cardinalities))` for each node. 'equivalent_sample_size' can either be a numerical value or a dict that specifies the size for each variable seperately. - A prior_type of 'K2' is a shorthand for 'dirichlet' + setting every pseudo_count to 1, regardless of the cardinality of the variable. Returns ------- parameters: list List of TabularCPDs, one for each variable of the model Examples -------- >>> import numpy as np >>> import pandas as pd >>> from pgmpy.models import BayesianModel >>> from pgmpy.estimators import BayesianEstimator >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 4)), ... columns=['A', 'B', 'C', 'D']) >>> model = BayesianModel([('A', 'B'), ('C', 'B'), ('C', 'D')]) >>> estimator = BayesianEstimator(model, values) >>> estimator.get_parameters(prior_type='BDeu', equivalent_sample_size=5) [<TabularCPD representing P(C:2) at 0x7f7b534251d0>, <TabularCPD representing P(B:2 | C:2, A:2) at 0x7f7b4dfd4da0>, <TabularCPD representing P(A:2) at 0x7f7b4dfd4fd0>, <TabularCPD representing P(D:2 | C:2) at 0x7f7b4df822b0>] """ parameters = [] for node in self.model.nodes(): _equivalent_sample_size = ( equivalent_sample_size[node] if isinstance(equivalent_sample_size, dict) else equivalent_sample_size ) _pseudo_counts = pseudo_counts[node] if pseudo_counts else None cpd = self.estimate_cpd( node, prior_type=prior_type, equivalent_sample_size=_equivalent_sample_size, pseudo_counts=_pseudo_counts, ) parameters.append(cpd) return parameters
[docs] def estimate_cpd( self, node, prior_type="BDeu", pseudo_counts=[], equivalent_sample_size=5 ): """ Method to estimate the CPD for a given variable. Parameters ---------- node: int, string (any hashable python object) The name of the variable for which the CPD is to be estimated. prior_type: 'dirichlet', 'BDeu', 'K2', string indicting which type of prior to use for the model parameters. - If 'prior_type' is 'dirichlet', the following must be provided: 'pseudo_counts' = dirichlet hyperparameters; 2-D array of shape (node_card, product of parents_card) with a "virtual" count for each variable state in the CPD. The virtual counts are added to the actual state counts found in the data. (if a list is provided, a lexicographic ordering of states is assumed) - If 'prior_type' is 'BDeu', then an 'equivalent_sample_size' must be specified instead of 'pseudo_counts'. This is equivalent to 'prior_type=dirichlet' and using uniform 'pseudo_counts' of `equivalent_sample_size/(node_cardinality*np.prod(parents_cardinalities))`. - A prior_type of 'K2' is a shorthand for 'dirichlet' + setting every pseudo_count to 1, regardless of the cardinality of the variable. Returns ------- CPD: TabularCPD Examples -------- >>> import pandas as pd >>> from pgmpy.models import BayesianModel >>> from pgmpy.estimators import BayesianEstimator >>> data = pd.DataFrame(data={'A': [0, 0, 1], 'B': [0, 1, 0], 'C': [1, 1, 0]}) >>> model = BayesianModel([('A', 'C'), ('B', 'C')]) >>> estimator = BayesianEstimator(model, data) >>> cpd_C = estimator.estimate_cpd('C', prior_type="dirichlet", pseudo_counts=[1, 2]) >>> print(cpd_C) ╒══════╤══════╤══════╤══════╤════════════════════╕ │ A │ A(0) │ A(0) │ A(1) │ A(1) │ ├──────┼──────┼──────┼──────┼────────────────────┤ │ B │ B(0) │ B(1) │ B(0) │ B(1) │ ├──────┼──────┼──────┼──────┼────────────────────┤ │ C(0) │ 0.25 │ 0.25 │ 0.5 │ 0.3333333333333333 │ ├──────┼──────┼──────┼──────┼────────────────────┤ │ C(1) │ 0.75 │ 0.75 │ 0.5 │ 0.6666666666666666 │ ╘══════╧══════╧══════╧══════╧════════════════════╛ """ node_cardinality = len(self.state_names[node]) parents = sorted(self.model.get_parents(node)) parents_cardinalities = [len(self.state_names[parent]) for parent in parents] cpd_shape = (node_cardinality, np.prod(parents_cardinalities, dtype=int)) if prior_type == "K2": pseudo_counts = np.ones(cpd_shape, dtype=int) elif prior_type == "BDeu": alpha = float(equivalent_sample_size) / ( node_cardinality * np.prod(parents_cardinalities) ) pseudo_counts = np.ones(cpd_shape, dtype=float) * alpha elif prior_type == "dirichlet": pseudo_counts = np.array(pseudo_counts) if pseudo_counts.shape != cpd_shape: raise ValueError( "The shape of pseudo_counts for the node: {node} must be of shape: {shape}".format( node=node, shape=str(cpd_shape) ) ) else: raise ValueError("'prior_type' not specified") state_counts = self.state_counts(node) bayesian_counts = state_counts + pseudo_counts cpd = TabularCPD( node, node_cardinality, np.array(bayesian_counts), evidence=parents, evidence_card=parents_cardinalities, state_names=self.state_names, ) cpd.normalize() return cpd