kneighbour.py

from math import sqrt
import numpy as np
import warnings
import matplotlib.pyplot as plt
from matplotlib import style
from collections import Counter
import pandas as pd
import random

style.use('ggplot')

# dataset = {'k':[[1,2],[2,3],[3,1]], 'r':[[6,5],[7,7],[8,6]]}
# new_features = [5,7]

def k_nearest_neighbours(data, predict, k=3):
    if len(data) >= k:
        warnings.warn('K is less than total voting set')
    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]

    return vote_result

# result = k_nearest_neighbours(dataset, new_features, k=3)
# print(result)
#
# [[plt.scatter(ii[0],ii[1],s=110,color=i) for ii in dataset[i]] for i in dataset]
# plt.scatter(new_features[0],new_features[1], color=result)
# plt.show()

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 = 0.2
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 = k_nearest_neighbours(train_set, data, k=5)
        if group == vote:
            correct +=1
        total +=1
print('Accuracy: ', correct/total)