5. recursive.py

from tree import *

car_data = [['med', 'low', '3', '4', 'med', 'med'], ['med', 'vhigh', '4', 'more', 'small', 'high'], ['high', 'med', '3', '2', 'med', 'low'], ['med', 'low', '4', '4', 'med', 'low'], ['med', 'low', '5more', '2', 'big', 'med'], ['med', 'med', '2', 'more', 'big', 'high'], ['med', 'med', '2', 'more', 'med', 'med'], ['vhigh', 'vhigh', '2', '2', 'med', 'low'], ['high', 'med', '4', '2', 'big', 'low'], ['low', 'low', '2', '4', 'big', 'med']]

car_labels = ['acc', 'acc', 'unacc', 'unacc', 'unacc', 'vgood', 'acc', 'unacc', 'unacc', 'good']

def find_best_split(dataset, labels):
    best_gain = 0
    best_feature = 0
    for feature in range(len(dataset[0])):
        data_subsets, label_subsets = split(dataset, labels, feature)
        gain = information_gain(labels, label_subsets)
        if gain > best_gain:
            best_gain, best_feature = gain, feature
    return best_feature, best_gain

def build_tree(data, labels):
  best_feature, best_gain = find_best_split(data, labels)
  if best_gain == 0:
    return Counter(labels)
  data_subsets, label_subsets = split(data, labels, best_feature)
  branches = []
  for i in range(len(data_subsets)):
    branch = build_tree(data_subsets[i], label_subsets[i])
    branches.append(branch)
  return branches
  
tree = build_tree(car_data, car_labels)
print_tree(tree)