How to determine initial cluster in kmeans

centroid_initialization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "contrary-neighbor",
   "metadata": {},
   "source": [
    "# How to determine initial cluster in kmeans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daily-cause",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "threaded-catholic",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "horizontal-yahoo",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data manipulation\n",
    "import pandas as pd\n",
    "\n",
    "# Linear algebra calculation\n",
    "import numpy as np\n",
    "\n",
    "# Generate data\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "# kmeans clustering\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Data viz\n",
    "import plotnine\n",
    "from plotnine import *\n",
    "\n",
    "# Ignore warning\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Random numbers\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aquatic-founder",
   "metadata": {},
   "source": [
    "## Generate dummy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "induced-speaker",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate dummy data\n",
    "features, clusters = make_blobs(\n",
    "    n_samples = 2000,\n",
    "    n_features = 2,\n",
    "    centers = 3,\n",
    "    cluster_std = 0.8,\n",
    "    shuffle = True,\n",
    "    random_state = 9\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "funky-adaptation",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1.12165821,  -6.93867365,   1.        ],\n",
       "       [-11.1996175 ,   0.1390546 ,   0.        ],\n",
       "       [ -9.82051884,   0.17331046,   0.        ],\n",
       "       ...,\n",
       "       [  1.87317622,  -8.03479076,   1.        ],\n",
       "       [ -6.33051068,  -6.01479745,   2.        ],\n",
       "       [ -9.36028648,   0.28474266,   0.        ]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concate these arrays\n",
    "array = np.column_stack([features, clusters])\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "changed-shuttle",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Data frame\n",
    "df = pd.DataFrame(data = array,\n",
    "                  columns = [\n",
    "                      'Feature 1',\n",
    "                      'Feature 2',\n",
    "                      'Cluster'\n",
    "                  ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "specialized-education",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension: 2000 rows and 3 columns\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature 1</th>\n",
       "      <th>Feature 2</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.121658</td>\n",
       "      <td>-6.938674</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-11.199618</td>\n",
       "      <td>0.139055</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-9.820519</td>\n",
       "      <td>0.173310</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-10.481695</td>\n",
       "      <td>-0.809478</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-7.200004</td>\n",
       "      <td>-7.886425</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Feature 1  Feature 2 Cluster\n",
       "0  -1.121658  -6.938674       1\n",
       "1 -11.199618   0.139055       0\n",
       "2  -9.820519   0.173310       0\n",
       "3 -10.481695  -0.809478       0\n",
       "4  -7.200004  -7.886425       2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show data frame\n",
    "print('Dimension: {} rows and {} columns'.format(\n",
    "    len(df),\n",
    "    len(df.columns))\n",
    ")\n",
    "df = df.astype({'Cluster': object})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "heard-might",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Centroid Feature 1</th>\n",
       "      <th>Centroid Feature 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-9.761820</td>\n",
       "      <td>0.059333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.099508</td>\n",
       "      <td>-7.355097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-7.193134</td>\n",
       "      <td>-5.640867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cluster  Centroid Feature 1  Centroid Feature 2\n",
       "0       0           -9.761820            0.059333\n",
       "1       1           -0.099508           -7.355097\n",
       "2       2           -7.193134           -5.640867"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Centroids\n",
    "df_centroids = df.groupby(['Cluster']).mean().reset_index().rename(columns = {\n",
    "    'Feature 1': 'Centroid Feature 1',\n",
    "    'Feature 2': 'Centroid Feature 2'\n",
    "    }\n",
    ").astype({'Cluster': object})\n",
    "df_centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "circular-ontario",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (158664034521)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotnine.options.figure_size = (8, 4.8)\n",
    "(\n",
    "    ggplot()+\n",
    "    geom_point(aes(x = 'Feature 1',\n",
    "                   y = 'Feature 2',\n",
    "                   color = 'Cluster'),\n",
    "               data = df)+\n",
    "    labs(title = 'Dummy Data for kmeans Clustering')+\n",
    "    xlab('Feature 1')+\n",
    "    ylab('Feature 2')+\n",
    "    scale_color_manual(name = 'Clusters', \n",
    "                       values = [\n",
    "                           '#80797c',\n",
    "                           '#981220',\n",
    "                           '#D4AF37'\n",
    "                       ],\n",
    "                       labels = [\n",
    "                           'Cluster 1',\n",
    "                           'Cluster 2',\n",
    "                           'Cluster 3'\n",
    "                       ]\n",
    "                      )+\n",
    "    theme_minimal()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eleven-railway",
   "metadata": {},
   "source": [
    "## kmeans clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "virtual-taste",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature 1 has minimum -12.119432493216202 and maximum 2.468113171142167\n",
      "Feature 2 has minimum -9.76023376218459 and maximum 2.0749286732011214\n"
     ]
    }
   ],
   "source": [
    "# Feature 1\n",
    "feature1_min = df['Feature 1'].min()\n",
    "feature1_max = df['Feature 1'].max()\n",
    "print('Feature 1 has minimum {} and maximum {}'.format(\n",
    "    feature1_min, feature1_max\n",
    "    )\n",
    ")\n",
    "# Feature 2\n",
    "feature2_min = df['Feature 2'].min()\n",
    "feature2_max = df['Feature 2'].max()\n",
    "print('Feature 2 has minimum {} and maximum {}'.format(\n",
    "    feature2_min, feature2_max\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "reverse-version",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroid for feature 1: [-3.2080044407353316, 1.7769384342238261, -0.4733407127783842]\n",
      "Centroid for feature 2: [0.25851107319911115, -3.9847528813479185, -7.386853615278098]\n",
      "\n",
      "Initial centroid:\n",
      " [[-3.20800444  0.25851107]\n",
      " [ 1.77693843 -3.98475288]\n",
      " [-0.47334071 -7.38685362]]\n"
     ]
    }
   ],
   "source": [
    "# Initial centroids\n",
    "cent_feature1 = [random.uniform(feature1_min, feature1_max) for _ in range(3)]\n",
    "cent_feature2 = [random.uniform(feature2_min, feature2_max) for _ in range(3)]\n",
    "print('Centroid for feature 1: {}'.format(cent_feature1))\n",
    "print('Centroid for feature 2: {}'.format(cent_feature2))\n",
    "# Merge into array\n",
    "centroid_init = np.array((cent_feature1, cent_feature2)).transpose()\n",
    "print('\\nInitial centroid:\\n {}'.format(centroid_init))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "uniform-hanging",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(init=array([[-3.20800444,  0.25851107],\n",
       "       [ 1.77693843, -3.98475288],\n",
       "       [-0.47334071, -7.38685362]]),\n",
       "       n_clusters=3, n_init=50, random_state=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clustering model\n",
    "kmeans = KMeans(\n",
    "    n_clusters = 3,\n",
    "    random_state = 0,\n",
    "    init = centroid_init,\n",
    "    n_init = 50\n",
    ")\n",
    "# Fit the kmeans clustering model\n",
    "kmeans.fit(df[['Feature 1', 'Feature 2']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "processed-environment",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-9.76182007,  0.05933306],\n",
       "       [-0.0995083 , -7.35509742],\n",
       "       [-7.19313408, -5.64086716]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Centroids\n",
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "civil-terminology",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kmeans clustering algorithm needs 5 iteration to converge\n"
     ]
    }
   ],
   "source": [
    "# Number of iterations\n",
    "n_iter = kmeans.n_iter_\n",
    "print('kmeans clustering algorithm needs {} iteration to converge'.format(n_iter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "rough-pepper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Iteration 1': array([[-9.11040471, -1.37170667],\n",
       "        [ 1.02790376, -5.54160527],\n",
       "        [-2.70047282, -6.90147012]]),\n",
       " 'Iteration 2': array([[-9.10580335, -1.624617  ],\n",
       "        [ 0.33237012, -7.25954782],\n",
       "        [-4.45043228, -6.62483033]]),\n",
       " 'Iteration 3': array([[-9.53014667, -0.59886607],\n",
       "        [-0.08271597, -7.36108046],\n",
       "        [-6.98508314, -5.81793561]]),\n",
       " 'Iteration 4': array([[-9.76182007,  0.05933306],\n",
       "        [-0.0995083 , -7.35509742],\n",
       "        [-7.19313408, -5.64086716]]),\n",
       " 'Iteration 5': array([[-9.76182007,  0.05933306],\n",
       "        [-0.0995083 , -7.35509742],\n",
       "        [-7.19313408, -5.64086716]])}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get centroids each iterations - how kmeans works\n",
    "centroids = {}\n",
    "for i in range(n_iter):\n",
    "    # Apply kmeans\n",
    "    kmeans = KMeans(\n",
    "        n_clusters = 3,\n",
    "        random_state = 0,\n",
    "        init = centroid_init,\n",
    "        max_iter = i + 1\n",
    "    )\n",
    "    kmeans.fit(df[['Feature 1', 'Feature 2']])\n",
    "    centroid = kmeans.cluster_centers_\n",
    "    # Store the centroids\n",
    "    centroids.update({'Iteration {}'.format(i + 1): centroid})\n",
    "\n",
    "# Array of centroids\n",
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "photographic-legislature",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transpose the array\n",
    "feature_1 = []\n",
    "feature_2 = []\n",
    "iterations = []\n",
    "for key in list(centroids.keys()):\n",
    "    for i, j in centroids[key]:\n",
    "        feature_1.append(i)\n",
    "        feature_2.append(j)\n",
    "        iterations.append(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "asian-airplane",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Centroid Feature 1</th>\n",
       "      <th>Centroid Feature 2</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-9.110405</td>\n",
       "      <td>-1.371707</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Iteration 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.027904</td>\n",
       "      <td>-5.541605</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Iteration 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.700473</td>\n",
       "      <td>-6.901470</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Iteration 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-9.105803</td>\n",
       "      <td>-1.624617</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Iteration 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.332370</td>\n",
       "      <td>-7.259548</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Iteration 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Centroid Feature 1  Centroid Feature 2    Cluster    Iteration\n",
       "0           -9.110405           -1.371707  Cluster 1  Iteration 1\n",
       "1            1.027904           -5.541605  Cluster 2  Iteration 1\n",
       "2           -2.700473           -6.901470  Cluster 3  Iteration 1\n",
       "3           -9.105803           -1.624617  Cluster 1  Iteration 2\n",
       "4            0.332370           -7.259548  Cluster 2  Iteration 2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a data frame\n",
    "df_iteration = pd.DataFrame(\n",
    "    {\n",
    "        'Centroid Feature 1': feature_1,\n",
    "        'Centroid Feature 2': feature_2,\n",
    "        'Cluster': [\n",
    "            'Cluster 1',\n",
    "            'Cluster 2',\n",
    "            'Cluster 3'\n",
    "        ] * (len(feature_1) // 3),\n",
    "        'Iteration': iterations\n",
    "    }\n",
    ")\n",
    "# Show data frame\n",
    "df_iteration.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "diverse-player",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (158664912616)>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotnine.options.figure_size = (8, 4.8)\n",
    "(\n",
    "    ggplot()+\n",
    "    geom_point(aes(x = 'Feature 1',\n",
    "                   y = 'Feature 2',\n",
    "                   color = 'Cluster'),\n",
    "               data = df)+\n",
    "    geom_point(aes(x = 'Centroid Feature 1',\n",
    "                   y = 'Centroid Feature 2'),\n",
    "               color = '#000000',\n",
    "               size = 3,\n",
    "               show_legend = False,\n",
    "               data = df_iteration)+\n",
    "    geom_point(aes(x = 'Centroid Feature 1',\n",
    "                   y = 'Centroid Feature 2'),\n",
    "               shape = 'X',\n",
    "               color = '#0000FF',\n",
    "               size = 5,\n",
    "               show_legend = False,\n",
    "               data = df_centroids)+\n",
    "    labs(title = 'Dummy Data for kmeans Clustering')+\n",
    "    xlab('Feature 1')+\n",
    "    ylab('Feature 2')+\n",
    "    scale_color_manual(name = 'Clusters',\n",
    "                       values = [\n",
    "                           '#80797c',\n",
    "                           '#981220',\n",
    "                           '#D4AF37'\n",
    "                       ],\n",
    "                       labels = [\n",
    "                           'Cluster 1',\n",
    "                           'Cluster 2',\n",
    "                           'Cluster 3'\n",
    "                       ]\n",
    "                      )+\n",
    "    theme_minimal()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fourth-filename",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Random 1': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742]])},\n",
       " 'Random 2': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 3': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 4': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 5': {'Max Iteration': 5,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 6': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 7': {'Max Iteration': 2,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-9.76182007,  0.05933306],\n",
       "         [-0.0995083 , -7.35509742]])},\n",
       " 'Random 8': {'Max Iteration': 12,\n",
       "  'Centroid': array([[ 0.37467013, -7.52601305],\n",
       "         [-8.50199933, -2.76012215],\n",
       "         [-1.04601386, -7.05582263]])},\n",
       " 'Random 9': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742]])},\n",
       " 'Random 10': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 11': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 12': {'Max Iteration': 17,\n",
       "  'Centroid': array([[-0.8538442 , -6.91445043],\n",
       "         [-8.49833025, -2.76704077],\n",
       "         [ 0.36303657, -7.66642263]])},\n",
       " 'Random 13': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742]])},\n",
       " 'Random 14': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 15': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-9.76182007,  0.05933306],\n",
       "         [-0.0995083 , -7.35509742]])},\n",
       " 'Random 16': {'Max Iteration': 8,\n",
       "  'Centroid': array([[-1.08606653, -7.04488696],\n",
       "         [ 0.35150083, -7.5169951 ],\n",
       "         [-8.50199933, -2.76012215]])},\n",
       " 'Random 17': {'Max Iteration': 5,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 18': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 19': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742]])},\n",
       " 'Random 20': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742]])},\n",
       " 'Random 21': {'Max Iteration': 5,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 22': {'Max Iteration': 6,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 23': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-8.48121486, -2.78592448],\n",
       "         [ 0.02587755, -6.74815276],\n",
       "         [-0.23812641, -7.95554056]])},\n",
       " 'Random 24': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 25': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 26': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 27': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 28': {'Max Iteration': 12,\n",
       "  'Centroid': array([[-1.08994176, -7.05784258],\n",
       "         [-8.50199933, -2.76012215],\n",
       "         [ 0.35375534, -7.50945783]])},\n",
       " 'Random 29': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 30': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 31': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 32': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 33': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 34': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 35': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 36': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 37': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-9.76182007,  0.05933306],\n",
       "         [-0.0995083 , -7.35509742]])},\n",
       " 'Random 38': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 39': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-9.76182007,  0.05933306],\n",
       "         [-0.0995083 , -7.35509742]])},\n",
       " 'Random 40': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 41': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 42': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 43': {'Max Iteration': 8,\n",
       "  'Centroid': array([[-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 44': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742]])},\n",
       " 'Random 45': {'Max Iteration': 5,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 46': {'Max Iteration': 3,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 47': {'Max Iteration': 6,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-7.19313408, -5.64086716]])},\n",
       " 'Random 48': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742],\n",
       "         [-9.76182007,  0.05933306]])},\n",
       " 'Random 49': {'Max Iteration': 4,\n",
       "  'Centroid': array([[-9.76182007,  0.05933306],\n",
       "         [-7.19313408, -5.64086716],\n",
       "         [-0.0995083 , -7.35509742]])},\n",
       " 'Random 50': {'Max Iteration': 18,\n",
       "  'Centroid': array([[-8.50028551, -2.76374982],\n",
       "         [ 0.36410967, -7.60568577],\n",
       "         [-0.94937003, -6.95767121]])}}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get centroids each iterations - with randomization\n",
    "array = {}\n",
    "for n_random in range(50):\n",
    "    # Initial centroids\n",
    "    cent_feature1 = [random.uniform(feature1_min, feature1_max) for _ in range(3)]\n",
    "    cent_feature2 = [random.uniform(feature2_min, feature2_max) for _ in range(3)]\n",
    "    # Merge into array\n",
    "    centroid_init = np.array((cent_feature1, cent_feature2)).transpose()\n",
    "    centroids = {}\n",
    "    \n",
    "    # Apply kmeans\n",
    "    kmeans = KMeans(\n",
    "        n_clusters = 3,\n",
    "        random_state = 0,\n",
    "        init = centroid_init,\n",
    "        max_iter = 50\n",
    "    )\n",
    "    kmeans.fit(df[['Feature 1', 'Feature 2']])\n",
    "    centroid = kmeans.cluster_centers_\n",
    "    \n",
    "    # Store the centroids\n",
    "    centroids.update({'Max Iteration': kmeans.n_iter_})\n",
    "    centroids.update({'Centroid': centroid})\n",
    "    array_new = {'Random {}'.format(n_random + 1): centroids}\n",
    "    array.update(array_new)\n",
    "\n",
    "# Array of centroids\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "private-laundry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Max Iteration': 3,\n",
       " 'Centroid': array([[-9.76182007,  0.05933306],\n",
       "        [-7.19313408, -5.64086716],\n",
       "        [-0.0995083 , -7.35509742]])}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array['Random 1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "defensive-template",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transpose the array\n",
    "feature_1 = []\n",
    "feature_2 = []\n",
    "repetition = []\n",
    "randoms = []\n",
    "for key_1 in list(array.keys()):\n",
    "    for i, j in array[key_1]['Centroid']:\n",
    "        feature_1.append(i)\n",
    "        feature_2.append(j)\n",
    "        randoms.append(key_1)\n",
    "        repetition.append(array[key_1]['Max Iteration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "committed-connection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Centroid Feature 1</th>\n",
       "      <th>Centroid Feature 2</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Max Iteration</th>\n",
       "      <th>Random</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-9.761820</td>\n",
       "      <td>0.059333</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>3</td>\n",
       "      <td>Random 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-7.193134</td>\n",
       "      <td>-5.640867</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>3</td>\n",
       "      <td>Random 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.099508</td>\n",
       "      <td>-7.355097</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>3</td>\n",
       "      <td>Random 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-7.193134</td>\n",
       "      <td>-5.640867</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>3</td>\n",
       "      <td>Random 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.099508</td>\n",
       "      <td>-7.355097</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>3</td>\n",
       "      <td>Random 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Centroid Feature 1  Centroid Feature 2    Cluster  Max Iteration    Random\n",
       "0           -9.761820            0.059333  Cluster 1              3  Random 1\n",
       "1           -7.193134           -5.640867  Cluster 2              3  Random 1\n",
       "2           -0.099508           -7.355097  Cluster 3              3  Random 1\n",
       "3           -7.193134           -5.640867  Cluster 1              3  Random 2\n",
       "4           -0.099508           -7.355097  Cluster 2              3  Random 2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a data frame\n",
    "df_random = pd.DataFrame(\n",
    "    {\n",
    "        'Centroid Feature 1': feature_1,\n",
    "        'Centroid Feature 2': feature_2,\n",
    "        'Cluster': [\n",
    "            'Cluster 1',\n",
    "            'Cluster 2',\n",
    "            'Cluster 3'\n",
    "        ] * (len(feature_1) // 3),\n",
    "        'Max Iteration': repetition,\n",
    "        'Random': randoms\n",
    "    }\n",
    ")\n",
    "# Show data frame\n",
    "df_random.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "practical-fetish",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (158664641458)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotnine.options.figure_size = (8, 4.8)\n",
    "(\n",
    "    ggplot()+\n",
    "    geom_point(aes(x = 'Feature 1',\n",
    "                   y = 'Feature 2',\n",
    "                   color = 'Cluster'),\n",
    "               data = df)+\n",
    "    geom_point(aes(x = 'Centroid Feature 1',\n",
    "                   y = 'Centroid Feature 2'),\n",
    "               color = '#000000',\n",
    "               size = 3,\n",
    "               show_legend = False,\n",
    "               data = df_random)+\n",
    "    geom_point(aes(x = 'Centroid Feature 1',\n",
    "               y = 'Centroid Feature 2'),\n",
    "           shape = 'X',\n",
    "           color = '#0000FF',\n",
    "           size = 5,\n",
    "           show_legend = False,\n",
    "           data = df_centroids)+\n",
    "    labs(title = 'Dummy Data for kmeans Clustering')+\n",
    "    xlab('Feature 1')+\n",
    "    ylab('Feature 2')+\n",
    "    scale_color_manual(name = 'Clusters', \n",
    "                       values = [\n",
    "                           '#80797c',\n",
    "                           '#981220',\n",
    "                           '#D4AF37'\n",
    "                       ],\n",
    "                       labels = [\n",
    "                           'Cluster 1',\n",
    "                           'Cluster 2',\n",
    "                           'Cluster 3'\n",
    "                       ]\n",
    "                      )+\n",
    "    theme_minimal()\n",
    ")"
   ]
  }
 ],
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