k_nearest_neighbors.py

import numpy as np
import warnings
from collections import Counter
import pandas as pd
import random


def k_nearest_neighbor(data, predict, k=3):
    if len(data) > k:
        warnings.warn('k is to small')
    distances = []
    for group in data:
        for features in data[group]:
            euclidean_distance = np.linalg.norm(np.array(features)- np.array(predict))
            distances.append([euclidean_distance, group])
    votes = [i[1] for i in sorted(distances)[:k]]
    vote_results = Counter(votes).most_common(1)[0][0]
    confidence = Counter(votes).most_common(1)[0][1]  / k
    return vote_results, confidence

df = pd.read_csv('breast-cancer-wisconsin.data.txt')
df.replace('?', -99999, inplace=True)
df.drop(['id'], 1, inplace=True)

full_data = df.astype(float).values.tolist()
random.shuffle(full_data)

test_size = 0.2
train_set = {2:[], 4:[]}
test_set = {2:[], 4:[]}
train_data = full_data[:-int(test_size * len(full_data))]
test_data = full_data[-int(test_size * len(full_data)):]

for i in train_data:
    train_set[i[-1]].append(i[:-1])

for i in test_data:
    test_set[i[-1]].append(i[:-1])

correct = 0
total = 0

for group in test_set:
    for data in test_set[group]:
        vote, confidence = k_nearest_neighbor(train_set, data, k=5)
        if vote == group:
            correct += 1
        else:
            print(confidence)
        total += 1


accuracy = correct / total
print('Accuracy:' , accuracy)