svd_mpsic/kMedoids.py

import random

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import MinMaxScaler


class TrainModel:
    def __init__(self, data, k_value):
        self.data = data
        scaler = MinMaxScaler()
        # self.data = scaler.fit_transform(self.data)
        self.k_value = k_value
        self.kmedoids(self.data)

    def get_random_medoids(self, data):
        points = random.sample(range(0, len(data)), self.k_value)
        medoids = []
        for i in range(self.k_value):
            medoids.append(data[i])
        return medoids

    def get_closest_medoids(self, sample_point, medoids):
        min_distance = float('inf')
        closest_medoid = None
        for i in range(len(medoids)):
            distance = self.calculateDistance(sample_point, medoids[i])
            if distance < min_distance:
                min_distance = distance
                closest_medoid = i
        return closest_medoid

    def get_clusters(self, data_points, medoids):
        clusters = [[] for _ in range(self.k_value)]
        for i in range(len(data_points)):
            x = self.get_closest_medoids(data_points[i], medoids)
            clusters[x].append(data_points[i])
        return clusters

    def calculate_cost(self, data_points, clusters, medoids):
        cost = 0
        for i in range(len(clusters)):
            for j in range(len(clusters[i])):
                cost += self.calculateDistance(medoids[i], clusters[i][j])
        return cost

    def get_non_medoids(self, data_points, medoids):
        non_medoids = []
        for sample in data_points:
            flag = False
            for m in medoids:
                if (sample == m).all():
                    flag = True
            if flag == False:
                non_medoids.append(sample)
        return non_medoids

    def get_clusters_label(self, data_points, clusters):
        labels = []
        for i in range(len(data_points)):
            labels.append(0)
        for i in range(len(clusters)):
            cluster = clusters[i]
            for j in range(len(cluster)):
                for k in range(len(data_points)):
                    if (cluster[j] == data_points[k]).all():
                        labels[k] = i
                        break
        return labels

    def kmedoids(self, data):
        medoids = self.get_random_medoids(data)
        clusters = self.get_clusters(data, medoids)
        initial_cost = self.calculate_cost(data, clusters, medoids)
        while True:
            best_medoids = medoids
            lowest_cost = initial_cost
            for i in range(len(medoids)):
                non_medoids = self.get_non_medoids(data, medoids)
                for j in range(len(non_medoids)):
                    new_medoids = medoids.copy()
                    for k in range(len(new_medoids)):
                        if (new_medoids[k] == medoids[i]).all():
                            new_medoids[k] = non_medoids[j]
                    new_clusters = self.get_clusters(data, new_medoids)
                    new_cost = self.calculate_cost(data, new_clusters, new_medoids)
                    if new_cost < lowest_cost:
                        lowest_cost = new_cost
                        best_medoids = new_medoids
            if lowest_cost < initial_cost:
                initial_cost = lowest_cost
                medoids = best_medoids
            else:
                break
        final_clusters = self.get_clusters(data, medoids)
        cluster_labels = self.get_clusters_label(data, final_clusters)
        silhouette_avg = silhouette_score(data, cluster_labels)

        # First cluster
        x0 = np.squeeze(final_clusters[0])[:, 0]
        y0 = np.squeeze(final_clusters[0])[:, 1]

        # Second cluster
        x1 = np.squeeze(final_clusters[1])[:, 0]
        y1 = np.squeeze(final_clusters[1])[:, 1]

        plt.scatter(x0, y0, c='red')
        plt.scatter(x1, y1, c='green')

        # Draw medoids
        mx = []
        my = []
        for m in medoids:
            mx.append(m[0])
            my.append(m[1])
        plt.scatter(mx, my, c='yellow', marker='*')

        plt.xlabel("X")
        plt.ylabel("Y")
        plt.title("K-medoids clusters")
        plt.show()


        print('Sylwetka (ang. Silhouette) dla algorytmu k-medoid dla k =', self.k_value, 10 * '-', silhouette_avg)

    def calculateDistance(self, x, y):
        return np.linalg.norm(x - y)


# Prepare dataset
dataset = np.array([
    [5, 6],
    [4, 7],
    [4, 8],
    [4, 6],
    [5, 7],
    [5, 8],
    [7, 6],
    [8, 8],
    [7, 7],
    [7, 8]]
)
column_values = ['x', 'y']
df = pd.DataFrame(data=dataset, columns=column_values, index=None)

# Draw data distribution
sns.set_theme(style='darkgrid')
sns.scatterplot(data=df, x='x', y='y')
plt.show()

# Run K-Medoids algorithm
model = TrainModel(dataset, 2)




# dataset = pd.read_csv('iris.csv')
# dataset = dataset.iloc[:,:-1]
# dataset = dataset.iloc[: , 1:]
# dataset = dataset.values