learning.TreeAugNaiveBayesLner.TreeAugNaiveBayesLner Class Reference

Inheritance diagram for learning.TreeAugNaiveBayesLner.TreeAugNaiveBayesLner:

Collaboration diagram for learning.TreeAugNaiveBayesLner.TreeAugNaiveBayesLner:

Public Member Functions
def	__init__ (self, states_df, tar_vtx, vtx_to_states=None)
def	learn_net_struc (self)
Public Member Functions inherited from learning.NetStrucLner.NetStrucLner
def	__init__ (self, is_quantum, states_df, vtx_to_states=None)
def	fill_bnet_with_parents (self, vtx_to_parents)

Public Attributes
	tar_vtx
	bnet
	ord_nodes
Public Attributes inherited from learning.NetStrucLner.NetStrucLner
	is_quantum
	bnet
	states_df
	ord_nodes

Additional Inherited Members
Static Public Member Functions inherited from learning.NetStrucLner.NetStrucLner
def	learn_nd_state_names (bnet, states_df)
def	import_nd_state_names (bnet, vtx_to_states)
def	int_sts_detector (sub_states_df)

Detailed Description

TreeAugNaiveBayesLner (Tree Augmented Naive Bayes Learner) is a simple
improvement of the Naive Bayes algorithm that combines Naive Bayes with
Chow Liu Trees.

Whereas in Naive Bayes, the only arrows are those emanating from a
target node to all other nodes, TAN Bayes first builds a CL tree from
all nodes except the target one, and then it adds arrows from the target
node to all other nodes. The idea is that under Naive Bayes all
non-target nodes are independent if the target is held fixed. In the TAN
Bayes graph, the non-target nodes are given a chance to be correlated,
even if the target is held fixed.

Attributes
----------
is_quantum : bool
    True for quantum bnets amd False for classical bnets
bnet : BayesNet
    a BayesNet in which we store what is learned
states_df : pandas.DataFrame
    a Pandas DataFrame with training data. column = node and row =
    sample. Each row/sample gives the state of the col/node.
ord_nodes : list[DirectedNode]
    a list of DirectedNode's named and in the same order as the column
    labels of self.states_df.


tar_vtx : str
    target vertex. This node has arrows pointing to all other nodes.

Constructor & Destructor Documentation

def learning.TreeAugNaiveBayesLner.TreeAugNaiveBayesLner.__init__	(		self,
			states_df,
			tar_vtx,
			vtx_to_states = None
	)

Constructor

Parameters
----------
states_df : pandas.DataFrame
tar_vtx : str
vtx_to_states : dict[str, list[str]]
    A dictionary mapping each node name to a list of its state names.
    This information will be stored in self.bnet. If
    vtx_to_states=None, constructor will learn vtx_to_states
    from states_df

Returns
-------
None

Member Function Documentation

def learning.TreeAugNaiveBayesLner.TreeAugNaiveBayesLner.learn_net_struc

(

self

)

This function learns a graph structure (a hybrid of a Naive Bayes
tree and a Chow Liu tree) from the data and stores what it learns in
self.bnet.

Returns
-------
None

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The documentation for this class was generated from the following file:

• learning/TreeAugNaiveBayesLner.py