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Simple library to write decision trees

Project description

This is a simple library to implement Binary decision Trees.

In computer science, a binary decision diagram (BDD) or branching program is a data structure that is used to represent a Boolean function. On a more abstract level, BDDs can be considered as a compressed representation of sets or relations. Unlike other compressed representations, operations are performed directly on the compressed representation, i.e. without decompression.

A simple example of this would be the following tree: alt text

Each node has a pair of childs, one child is the one associated to its father when the function inside the father returns a true value, and the other is associated when the fathers function returns false.

Use

You can create the nodes manually as python objects like is shown in this example:

from bdt.tree import BDT
from bdt.node import Node


true_node = Node(
    'True Node'
)

false_node = Node(
    'False Node'
)

head_node = Node(
   'Head Node',
   lambda var: var < 10,
   true_child=true_node,
   false_child=false_node
)

tree = BDT(
    head_node
)

Other option is to create the tree by passing a python dictionary:

from bdt.tools import tree_from_dict


tree_dict = {
    'head': 'Head Node',

    'variables': [
        'withd',
        'height'
    ],

    'nodes': [
        {
            'name': 'Head Node',
            'function': 'withd * height < 50',
            'true_child': 'True Node',
            'false_child': 'False Node'
        },
        {
            'name': 'True Node',
            'function': 'withd * height < 25',
            'true_child': 'True True Node',
            'false_child': 'True False Node'
        },
        {
            'name': 'False Node',
            'function': 'withd * height < 100',
            'true_child': 'False True Node',
            'false_child': 'False False Node'
        },
        {
            'name': 'True True Node',
            'function': 'None',
            'true_child': 'None',
            'false_child': 'None'
        },
        {
            'name': 'True False Node',
            'function': 'None',
            'true_child': 'None',
            'false_child': 'None',
        },
        {
            'name': 'False True Node',
            'function': 'None',
            'true_child': 'None',
            'false_child': 'None'
        },
        {
            'name': 'False False Node',
            'function': 'None',
            'true_child': 'None',
            'false_child': 'None'
        },
    ]
}

tree = tree_from_dict(tree_dict)

And the final form would be to load it from a JSON file the matches the previous dictionary and load it by using:

import json

from bdt.tools import tree_from_json

json_data = open('{PATH_TO_FILE/file.json}', 'r')
tree = tree_from_json(json_data)

And the final form would be to load it from a JSON file the matches the previous dictionary and load it by using:

import json

from bdt.tools import tree_from_json

json_data = open('{PATH_TO_FILE/file.json}', 'r')
tree = tree_from_json(json_data)

To traverse the created tree yo can makeit like this:

import json

from bdt.tools import tree_from_json

json_data = open('{PATH_TO_FILE/file.json}', 'r')
tree = tree_from_json(json_data)

tree.set_parameters({
    'withd': 25,
    'height': 25
})

for node in tree:
    print node.name

The set_parameters function let you initialize the values needed to run the boolean functions inside each node.

Project details


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