init

kMedoids.py

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+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+from numpy.random import choice, seed
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import MinMaxScaler
+
+seed(42)
+
+
+def initialize_medoids(num_medoids, data):
+    return [data.iloc[idx] for idx in choice(len(data), size=num_medoids, replace=False)]
+
+
+def assign_points_to_medoids(data, medoids):
+    return [np.argmin([distance_vec2vec(point[1], medoid) for medoid in medoids]) for point in data.iterrows()]
+
+
+def distance_vec2vec(a, b) -> np.float64:
+    return sum([(abs(a[i] - b[i]) ** 2) for i in range(len(a))])
+
+
+def reassign_medoids(data, assignments, initial_medoids):
+    new_medoids = []
+    for idm, medoid in enumerate(initial_medoids):
+        new_medoid = medoid
+        medoid_score = sum([distance_vec2vec(medoid, x[1]) if assignments[idx] == idm else 0
+                            for idx, x in enumerate(data.iterrows())])
+        for point in data.iterrows():
+            point_score = sum(sum([distance_vec2vec(point, x[1]) if assignments[idx] == idm else 0
+                                   for idx, x in enumerate(data.iterrows())]))
+            if medoid_score > point_score:
+                new_medoid = point
+        new_medoids.append(new_medoid)
+    return new_medoids
+
+
+def is_finished(old_medoids, new_medoids):
+    return set([tuple(om) for om in old_medoids]) == set([tuple(nm) for nm in new_medoids])
+
+
+def kmedoids(num_samples, num_clusters):
+    df = pd.read_csv('CC GENERAL.csv', index_col='CUST_ID')
+    df = df[:num_samples]
+    df_scaled = pd.DataFrame(MinMaxScaler().fit_transform(df))
+    df_scaled = df_scaled.fillna(0)
+
+    # initialize medoids (at random)
+    medoids = initialize_medoids(num_medoids=num_clusters, data=df_scaled)
+
+    # assign data points to the medoids
+    assignments = assign_points_to_medoids(data=df_scaled, medoids=medoids)
+
+    # fit
+    new_medoids = reassign_medoids(data=df_scaled, assignments=assignments, initial_medoids=medoids)
+    while not is_finished(old_medoids=medoids, new_medoids=new_medoids):
+        medoids = new_medoids
+        new_medoids = reassign_medoids(data=df_scaled, assignments=assignments, initial_medoids=medoids)
+
+    new_assignments = assign_points_to_medoids(data=df_scaled, medoids=new_medoids)
+    data = pd.DataFrame(PCA(n_components=2).fit_transform(df_scaled), columns=['0', '1'])
+    data['cluster'] = new_assignments
+
+    sns.relplot(x='0', y='1', hue='cluster', data=data, palette=sns.color_palette("husl", num_clusters))
+    plt.show()
+
+
+kmedoids(num_samples=500, num_clusters=3)

readme.md

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+# K-medoids (Partitioning Around Medoids)
+
+PAM k-medoids implementation for classes of Mathematical Foundations of Artificial Intelligence
+and Cyber-Security at AMU Poznań.