Program
import numpy as np
import math
from data_loader import read_data
class Node:
def init (self, attribute):
self.attribute = attribute
self.children = []
self.answer = ""
def str (self):
return self.attribute
def subtables(data, col, delete):
dict = {}
items = np.unique(data[:, col])
count = np.zeros((items.shape[0], 1), dtype=np.int32)
for x in range(items.shape[0]):
for y in range(data.shape[0]):
if data[y, col] == items[x]:
count[x] += 1
for x in range(items.shape[0]):
dict[items[x]] = np.empty((int(count[x]), data.shape[1]), dtype="|S32")
pos = 0
for y in range(data.shape[0]):
if data[y, col] == items[x]:
dict[items[x]][pos] = data[y]
pos += 1
if delete:
dict[items[x]] = np.delete(dict[items[x]], col, 1)
return items, dict
def entropy(S):
items = np.unique(S)
if items.size == 1:
return 0
counts = np.zeros((items.shape[0], 1))
sums = 0
for x in range(items.shape[0]):
counts[x] = sum(S == items[x]) / (S.size * 1.0)
for count in counts:
sums += -1 * count * math.log(count, 2)
return sums
def gain_ratio(data, col):
items, dict = subtables(data, col, delete=False)
total_size = data.shape[0]
entropies = np.zeros((items.shape[0], 1))
intrinsic = np.zeros((items.shape[0], 1))
for x in range(items.shape[0]):
ratio = dict[items[x]].shape[0]/(total_size * 1.0)
entropies[x] = ratio * entropy(dict[items[x]][:, -1])
intrinsic[x] = ratio * math.log(ratio, 2)
total_entropy = entropy(data[:, -1])
iv = -1 * sum(intrinsic)
for x in range(entropies.shape[0]):
total_entropy -= entropies[x]
return total_entropy / iv
def create_node(data, metadata):
if (np.unique(data[:, -1])).shape[0] == 1:
node = Node("")
node.answer = np.unique(data[:, -1])[0]
return node
gains = np.zeros((data.shape[1] - 1, 1))
for col in range(data.shape[1] - 1):
gains[col] = gain_ratio(data, col)
split = np.argmax(gains)
node = Node(metadata[split])
metadata = np.delete(metadata, split, 0)
items, dict = subtables(data, split, delete=True)
for x in range(items.shape[0]):
child = create_node(dict[items[x]], metadata)
node.children.append((items[x], child))
return node
def empty(size):
s = ""
for x in range(size):
s += " "
return s
def print_tree(node, level):
if node.answer != "":
print(empty(level), node.answer)
return
print(empty(level), node.attribute)
for value, n in node.children:
print(empty(level + 1), value)
print_tree(n, level + 2)
metadata, traindata = read_data("tennis.csv")
data = np.array(traindata)
node = create_node(data, metadata)
print_tree(node, 0)
Data_loader.py
import csv
def read_data(filename):
with open(filename, 'r') as csvfile:
datareader = csv.reader(csvfile, delimiter=',')
headers = next(datareader)
metadata = []
traindata = []
for name in headers:
metadata.append(name)
for row in datareader:
traindata.append(row)
return (metadata, traindata)
Tennis.csv
outlook,temperature,humidity,wind,
answer sunny,hot,high,weak,no
sunny,hot,high,strong,no
overcast,hot,high,weak,yes
rain,mild,high,weak,yes
rain,cool,normal,weak,yes
rain,cool,normal,strong,no
overcast,cool,normal,strong,yes
sunny,mild,high,weak,no
sunny,cool,normal,weak,yes
rain,mild,normal,weak,yes
sunny,mild,normal,strong,yes
overcast,mild,high,strong,yes
overcast,hot,normal,weak,yes
rain,mild,high,strong,no
Output
outlook
overcast
b'yes'
rain
wind
b'strong'
b'no'
b'weak'
b'yes'
sunny
humidity
b'high'
b'no'
b'normal'
b'yes