Source code for pgmpy.models.NaiveBayes

from pgmpy.independencies import Independencies
from pgmpy.models import BayesianNetwork

[docs]class NaiveBayes(BayesianNetwork): """ Class to represent Naive Bayes. Naive Bayes is a special case of Bayesian Model where the only edges in the model are from the feature variables to the dependent variable. """ def __init__(self, feature_vars=None, dependent_var=None): """ Method to initialize the `NaiveBayes` class. Parameters ---------- feature_vars: list (array-like) A list of variable predictor variables (i.e. the features) in the model. dependent_var: hashable object The dependent variable (i.e. the variable to be predicted) in the model. Returns ------- pgmpy.models.BayesianNetwork instance: An instance of a Bayesian Model with the initialized model structure. """ self.dependent = dependent_var self.features = set(feature_vars) if feature_vars is not None else set() if (feature_vars is not None) and (dependent_var is not None): ebunch = [(self.dependent, feature) for feature in self.features] else: ebunch = [] super(NaiveBayes, self).__init__(ebunch=ebunch)
[docs] def add_edge(self, u, v, *kwargs): """ Add an edge between `u` and `v`. The nodes `u` and `v` will be automatically added if they are not already in the graph. `u` will be the dependent variable (i.e. variable to be predicted) and `v` will be one of the features (i.e. predictors) in the model. Parameters ---------- u, v : nodes Nodes can be any hashable python object. Returns ------- None Examples -------- >>> from pgmpy.models import NaiveBayes >>> G = NaiveBayes() >>> G.add_nodes_from(['a', 'b', 'c']) >>> G.add_edge('a', 'b') >>> G.add_edge('a', 'c') >>> G.edges() OutEdgeView([('a', 'b'), ('a', 'c')]) """ if self.dependent and u != self.dependent: raise ValueError( f"Model can only have edges outgoing from: {self.dependent}" ) self.dependent = u self.features.add(v) super(NaiveBayes, self).add_edge(u, v, *kwargs)
[docs] def add_edges_from(self, ebunch): """ Adds edges to the model. Each tuple of the form (u, v) in ebunch adds a new edge in the model. Since there can only be one dependent variable in a Naive Bayes model, `u` should be the same for each tuple in `ebunch`. Parameters ---------- ebunch: list (array-like) A list of tuples of the form (u, v) representing an edge from u to v. Returns ------- None Examples -------- >>> from pgmpy.models import NaiveBayes >>> G = NaiveBayes() >>> G.add_nodes_from(['a', 'b', 'c']) >>> G.add_edges_from([('a', 'b'), ('a', 'c')]) >>> G.edges() OutEdgeView([('a', 'b'), ('a', 'c')]) """ for (u, v) in ebunch: self.add_edge(u, v)
def _get_ancestors_of(self, obs_nodes_list): """ Returns a list of all ancestors of all the observed nodes. Parameters ---------- obs_nodes_list: string, list-type name of all the observed nodes """ if not obs_nodes_list: return set() return set(obs_nodes_list) | set(self.dependent)
[docs] def active_trail_nodes(self, start, observed=None): """ Returns all the nodes reachable from start via an active trail. Parameters ---------- start: Graph node observed : List of nodes (optional) If given the active trail would be computed assuming these nodes to be observed. Examples -------- >>> from pgmpy.models import NaiveBayes >>> model = NaiveBayes() >>> model.add_nodes_from(['a', 'b', 'c', 'd']) >>> model.add_edges_from([('a', 'b'), ('a', 'c'), ('a', 'd')]) >>> model.active_trail_nodes('a') {'a', 'd', 'c', 'b'} >>> model.active_trail_nodes('a', ['b', 'c']) {'a', 'd'} >>> model.active_trail_nodes('b', ['a']) {'b'} """ if observed and self.dependent in observed: return set(start) else: return set(self.nodes()) - set(observed if observed else [])
[docs] def local_independencies(self, variables): """ Returns an instance of Independencies containing the local independencies of each of the variables. Parameters ---------- variables: str or array like variables whose local independencies are to found. Examples -------- >>> from pgmpy.models import NaiveBayes >>> model = NaiveBayes() >>> model.add_edges_from([('a', 'b'), ('a', 'c'), ('a', 'd')]) >>> ind = model.local_independencies('b') >>> ind (b \u27C2 d, c | a) """ independencies = Independencies() for variable in [variables] if isinstance(variables, str) else variables: if variable != self.dependent: independencies.add_assertions( [variable, list(set(self.features) - set(variable)), self.dependent] ) return independencies
[docs] def fit(self, data, parent_node=None, estimator=None): """ Computes the CPD for each node from a given data in the form of a pandas dataframe. If a variable from the data is not present in the model, it adds that node into the model. Parameters ---------- data : pandas DataFrame object A DataFrame object with column names same as the variable names of network parent_node: any hashable python object (optional) Parent node of the model, if not specified it looks for a previously specified parent node. estimator: Estimator class Any pgmpy estimator. If nothing is specified, the default ``MaximumLikelihoodEstimator`` would be used. Examples -------- >>> import numpy as np >>> import pandas as pd >>> from pgmpy.models import NaiveBayes >>> model = NaiveBayes() >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 5)), ... columns=['A', 'B', 'C', 'D', 'E']) >>>, 'A') >>> model.get_cpds() [<TabularCPD representing P(D:2 | A:2) at 0x4b72870>, <TabularCPD representing P(E:2 | A:2) at 0x4bb2150>, <TabularCPD representing P(A:2) at 0x4bb23d0>, <TabularCPD representing P(B:2 | A:2) at 0x4bb24b0>, <TabularCPD representing P(C:2 | A:2) at 0x4bb2750>] >>> model.edges() [('A', 'D'), ('A', 'E'), ('A', 'B'), ('A', 'C')] """ if not parent_node: if not self.dependent: raise ValueError("parent node must be specified for the model") else: parent_node = self.dependent if parent_node not in data.columns: raise ValueError( f"Dependent variable: {parent_node} is not present in the data" ) for child_node in data.columns: if child_node != parent_node: self.add_edge(parent_node, child_node) super(NaiveBayes, self).fit(data, estimator)