Source code for pgmpy.estimators.MLE

# coding:utf-8

from itertools import chain

import numpy as np
from joblib import Parallel, delayed

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


[docs]class MaximumLikelihoodEstimator(ParameterEstimator): def __init__(self, model, data, **kwargs): """ Class used to compute parameters for a model using Maximum Likelihood Estimation. Parameters ---------- model: A pgmpy.models.BayesianNetwork instance data: pandas DataFrame object DataFrame object with column names identical to the variable names of the network. (If some values in the data are missing the data cells should be set to `numpy.NaN`. Note that pandas converts each column containing `numpy.NaN`s to dtype `float`.) state_names: dict (optional) A dict indicating, for each variable, the discrete set of states that the variable can take. If unspecified, the observed values in the data set are taken to be the only possible states. complete_samples_only: bool (optional, default `True`) Specifies how to deal with missing data, if present. If set to `True` all rows that contain `np.NaN` somewhere are ignored. If `False` then, for each variable, every row where neither the variable nor its parents are `np.NaN` is used. Examples -------- >>> import numpy as np >>> import pandas as pd >>> from pgmpy.models import BayesianNetwork >>> from pgmpy.estimators import MaximumLikelihoodEstimator >>> data = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 5)), ... columns=['A', 'B', 'C', 'D', 'E']) >>> model = BayesianNetwork([('A', 'B'), ('C', 'B'), ('C', 'D'), ('B', 'E')]) >>> estimator = MaximumLikelihoodEstimator(model, data) """ if not isinstance(model, BayesianNetwork): raise NotImplementedError( "Maximum Likelihood Estimate is only implemented for BayesianNetwork" ) elif set(model.nodes()) > set(data.columns): raise ValueError( f"Maximum Likelihood Estimator works only for models with all observed variables. Found latent variables: {model.latents}." ) super(MaximumLikelihoodEstimator, self).__init__(model, data, **kwargs)
[docs] def get_parameters(self, n_jobs=-1, weighted=False): """ Method to estimate the model parameters (CPDs) using Maximum Likelihood Estimation. Parameters ---------- n_jobs: int (default: -1) Number of jobs to run in parallel. Default: -1 uses all the processors. weighted: bool If weighted=True, the data must contain a `_weight` column specifying the weight of each datapoint (row). If False, assigns an equal weight to each datapoint. Returns ------- parameters: list List of TabularCPDs, one for each variable of the model n_jobs: int Number of processes to spawn Examples -------- >>> import numpy as np >>> import pandas as pd >>> from pgmpy.models import BayesianNetwork >>> from pgmpy.estimators import MaximumLikelihoodEstimator >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 4)), ... columns=['A', 'B', 'C', 'D']) >>> model = BayesianNetwork([('A', 'B'), ('C', 'B'), ('C', 'D')]) >>> estimator = MaximumLikelihoodEstimator(model, values) >>> estimator.get_parameters() [<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 = Parallel(n_jobs=n_jobs, prefer="threads")( delayed(self.estimate_cpd)(node, weighted) for node in self.model.nodes() ) return parameters
[docs] def estimate_cpd(self, node, weighted=False): """ 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. weighted: bool If weighted=True, the data must contain a `_weight` column specifying the weight of each datapoint (row). If False, assigns an equal weight to each datapoint. Returns ------- CPD: TabularCPD Examples -------- >>> import pandas as pd >>> from pgmpy.models import BayesianNetwork >>> from pgmpy.estimators import MaximumLikelihoodEstimator >>> data = pd.DataFrame(data={'A': [0, 0, 1], 'B': [0, 1, 0], 'C': [1, 1, 0]}) >>> model = BayesianNetwork([('A', 'C'), ('B', 'C')]) >>> cpd_A = MaximumLikelihoodEstimator(model, data).estimate_cpd('A') >>> print(cpd_A) ╒══════╤══════════╕ │ A(0) │ 0.666667 │ ├──────┼──────────┤ │ A(1) │ 0.333333 │ ╘══════╧══════════╛ >>> cpd_C = MaximumLikelihoodEstimator(model, data).estimate_cpd('C') >>> print(cpd_C) ╒══════╤══════╤══════╤══════╤══════╕ │ A │ A(0) │ A(0) │ A(1) │ A(1) │ ├──────┼──────┼──────┼──────┼──────┤ │ B │ B(0) │ B(1) │ B(0) │ B(1) │ ├──────┼──────┼──────┼──────┼──────┤ │ C(0) │ 0.0 │ 0.0 │ 1.0 │ 0.5 │ ├──────┼──────┼──────┼──────┼──────┤ │ C(1) │ 1.0 │ 1.0 │ 0.0 │ 0.5 │ ╘══════╧══════╧══════╧══════╧══════╛ """ state_counts = self.state_counts(node, weighted=weighted) # if a column contains only `0`s (no states observed for some configuration # of parents' states) fill that column uniformly instead state_counts.loc[:, (state_counts == 0).all()] = 1 parents = sorted(self.model.get_parents(node)) parents_cardinalities = [len(self.state_names[parent]) for parent in parents] node_cardinality = len(self.state_names[node]) # Get the state names for the CPD state_names = {node: list(state_counts.index)} if parents: state_names.update( { state_counts.columns.names[i]: list(state_counts.columns.levels[i]) for i in range(len(parents)) } ) cpd = TabularCPD( node, node_cardinality, np.array(state_counts), evidence=parents, evidence_card=parents_cardinalities, state_names={var: self.state_names[var] for var in chain([node], parents)}, ) cpd.normalize() return cpd