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import operator | |
from collections import Counter | |
import random | |
import numpy as np | |
np.random.seed(1) | |
random.seed(1) | |
def split(dataset, labels, column): | |
data_subsets = [] | |
label_subsets = [] | |
counts = list(set([data[column] for data in dataset])) | |
counts.sort() | |
for k in counts: | |
new_data_subset = [] | |
new_label_subset = [] | |
for i in range(len(dataset)): | |
if dataset[i][column] == k: | |
new_data_subset.append(dataset[i]) | |
new_label_subset.append(labels[i]) | |
data_subsets.append(new_data_subset) | |
label_subsets.append(new_label_subset) | |
return data_subsets, label_subsets | |
def gini(dataset): | |
impurity = 1 | |
label_counts = Counter(dataset) | |
for label in label_counts: | |
prob_of_label = label_counts[label] / len(dataset) | |
impurity -= prob_of_label ** 2 | |
return impurity | |
def information_gain(starting_labels, split_labels): | |
info_gain = gini(starting_labels) | |
for subset in split_labels: | |
info_gain -= gini(subset) * len(subset)/len(starting_labels) | |
return info_gain | |
class Leaf: | |
def __init__(self, labels, value): | |
self.labels = Counter(labels) | |
self.value = value | |
class Internal_Node: | |
def __init__(self, | |
feature, | |
branches, | |
value): | |
self.feature = feature | |
self.branches = branches | |
self.value = value | |
def find_best_split_subset(dataset, labels, num_features): | |
features = np.random.choice(6, 3, replace=False) | |
best_gain = 0 | |
best_feature = 0 | |
for feature in features: | |
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 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, value = ""): | |
best_feature, best_gain = find_best_split(data, labels) | |
if best_gain < 0.00000001: | |
return Leaf(Counter(labels), value) | |
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], data_subsets[i][0][best_feature]) | |
branches.append(branch) | |
return Internal_Node(best_feature, branches, value) | |
def build_tree_forest(data,labels, n_features, value=""): | |
best_feature, best_gain = find_best_split_subset(data, labels, n_features) | |
if best_gain < 0.00000001: | |
return Leaf(Counter(labels), value) | |
data_subsets, label_subsets = split(data, labels, best_feature) | |
branches = [] | |
for i in range(len(data_subsets)): | |
branch = build_tree_forest(data_subsets[i], label_subsets[i], n_features, data_subsets[i][0][best_feature]) | |
branches.append(branch) | |
return Internal_Node(best_feature, branches, value) | |
def print_tree(node, spacing=""): | |
"""World's most elegant tree printing function.""" | |
question_dict = {0: "Buying Price", 1:"Price of maintenance", 2:"Number of doors", 3:"Person Capacity", 4:"Size of luggage boot", 5:"Estimated Saftey"} | |
# Base case: we've reached a leaf | |
if isinstance(node, Leaf): | |
print (spacing + str(node.labels)) | |
return | |
# Print the question at this node | |
print (spacing + "Splitting on " + question_dict[node.feature]) | |
# Call this function recursively on the true branch | |
for i in range(len(node.branches)): | |
print (spacing + '--> Branch ' + node.branches[i].value+':') | |
print_tree(node.branches[i], spacing + " ") | |
def make_cars(): | |
f = open("car.data", "r") | |
cars = [] | |
for line in f: | |
cars.append(line.rstrip().split(",")) | |
return cars | |
def change_data(data): | |
dicts = [{'vhigh' : 1.0, 'high' : 2.0, 'med' : 3.0, 'low' : 4.0}, | |
{'vhigh' : 1.0, 'high' : 2.0, 'med' : 3.0, 'low' : 4.0}, | |
{'2' : 1.0, '3' : 2.0, '4' : 3.0, '5more' : 4.0}, | |
{'2' : 1.0, '4' : 2.0, 'more' : 3.0}, | |
{'small' : 1.0, 'med' : 2.0, 'big' : 3.0}, | |
{'low' : 1.0, 'med' : 2.0, 'high' : 3.0}] | |
for row in data: | |
for i in range(len(dicts)): | |
row[i] = dicts[i][row[i]] | |
return data | |
def classify(datapoint, tree): | |
if isinstance(tree, Leaf): | |
return max(tree.labels.items(), key=operator.itemgetter(1))[0] | |
value = datapoint[tree.feature] | |
for branch in tree.branches: | |
if branch.value == value: | |
return classify(datapoint, branch) | |
#return classify(datapoint, tree.branches[random.randint(0, len(tree.branches)-1)]) | |
cars = make_cars() | |
random.shuffle(cars) | |
car_data = [x[:-1] for x in cars] | |
car_labels = [x[-1] for x in cars] |
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