ml18.py

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

def k_nearest_neighbors(data, predict, k=3):
    if len(data) >= k:
        warnings.warn('K is set to a value less than total voting groups!')

    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_result = Counter(votes).most_common(1)[0][0]
    confidence = Counter(votes).most_common(1)[0][1] / k
    return vote_result, confidence


accuracies = []

for i in range(25):

    df = pd.read_csv('breast-cancer-wisconsin.data')
    df.replace('?',-99999, inplace=True)
    df.drop(['id'], 1, inplace=True)
    full_data = df.astype(float).values.tolist()
    random.shuffle(full_data)

    test_size = 0.4
    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)):]

    # filling the set by selecting the class i[-1] and copying the features (without the label)
    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_neighbors(train_set, data, k=5)
            if group == vote:
                correct += 1
            # else:
            #     print(confidence)
            total +=1
    print('Accuracy: ', correct/total)
    accuracies.append(correct/total)

print(sum(accuracies)/len(accuracies))