GEORGIA INSTITUTE OF TECHNOLOGY CS 6601 ,DECISION TREES,2020/2021 BAYES NETS

import numpy as np
import math
from random import randint
from collections import Counter

class DecisionNode():
"""Class to represent a single node in
a decision tree."""

def __init__(self, left, right, decision_function, class_label=None):
"""Create a node with a left child, right child,
decision function and optional class label
for leaf nodes."""
self.left = left
self.right = right
self.decision_function = decision_function
self.class_label = class_label

def decide(self, feature):
"""Return on a label if node is leaf,
or pass the decision down to the node's
left/right child (depending on decision
function)."""
if self.class_label is not None:
return self.class_label
elif self.decision_function(feature):
return self.left.decide(feature)
else:
return self.right.decide(feature)

def build_decision_tree():
"""Create decision tree
capable of handling the provided
data."""
A3_decision_func = lambda feature : feature[2] == 1
A3_node = DecisionNode(DecisionNode(None, None, None, 0), DecisionNode(None,
None, None, 1), A3_decision_func, None)

A2_decision_func = lambda feature : feature[1] == 1
A2_node = DecisionNode(DecisionNode(None, None, None, 0), DecisionNode(None,
None, None, 1), A2_decision_func, None)

A4_decision_func = lambda feature : feature[3] == 1
A4_node = DecisionNode(A2_node, A3_node, A4_decision_func, None)

# Root should be A1
A1_decision_func = lambda feature : feature[0] == 1
decision_tree_root = DecisionNode(DecisionNode(None, None, None, 1),
A4_node, A1_decision_func, None)

return decision_tree_root

def confusion_matrix(classifier_output, true_labels):
true_positive = 0.0
false_negative = 0.0
false_positive = 0.0
true_negative = 0.0

for i in range(len(classifier_output)):
if true_labels[i] == 1:
if true_labels[i] == classifier_output[i]:
true_positive += 1.0
else:
false_negative += 1.0

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, else:
if true_labels[i] == classifier_output[i]:
true_negative += 1.0
else:
false_positive += 1.0

return [[true_positive, false_negative], [false_positive, true_negative]]

def precision(classifier_output, true_labels):
#true_positive/ (true_positive + false_positive)
matrix = confusion_matrix(classifier_output, true_labels)

return matrix[0][0] / (matrix[0][0] + matrix[1][0])

def recall(classifier_output, true_labels):
#true_positive/ (true_positive + false_negative)
matrix = confusion_matrix(classifier_output, true_labels)

return matrix[0][0] / (matrix[0][0] + matrix[0][1])

def accuracy(classifier_output, true_labels):
#correct_classifications / total_number_examples
matrix = confusion_matrix(classifier_output, true_labels)

return (matrix[0][0] + matrix[1][1]) / len(true_labels)

def entropy(class_vector):
"""Compute the entropy for a list
of classes (given as either 0 or 1)."""

# Because we have 0 and 1 as class we can reduce the entropy function to
just
# entry: -(qlog2(q) + (1-q)log2(1-q)) where q = p/(p+n)
positive_count = class_vector.count(1)
negative_count = class_vector.count(0)

q = float(positive_count) / (positive_count + negative_count)

if positive_count == 0 or negative_count == 0:
return 0.0
else:
return -(q * math.log(q, 2) + (1 - q) * math.log(1 - q, 2))

def information_gain(previous_classes, current_classes):
"""Compute the information gain between the
previous and current classes (each
a list of 0 and 1 values)."""
previous_entropy = entropy(previous_classes)
total_example_count = len(previous_classes)

remainder = 0
for split in current_classes:
remainder += (float(len(split))/total_example_count) * entropy(split)

return previous_entropy - remainder

class DecisionTree():
"""Class for automatic tree-building
and classification."""

def __init__(self, depth_limit=200):
"""Create a decision tree with an empty root
and the specified depth limit."""
self.root = None

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