GMM_Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f7420df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ucimlrepo import fetch_ucirepo \n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import matplotlib.cm as cm\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from scipy.spatial.distance import cdist\n",
    "import time\n",
    "import seaborn as sns\n",
    "from matplotlib.patches import Ellipse\n",
    "from scipy import linalg\n",
    "from scipy import interpolate\n",
    "from scipy.signal import argrelextrema\n",
    "from kneed import KneeLocator\n",
    "import itertools\n",
    "import matplotlib as mpl\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from matplotlib import rcParams\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95411e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE = 101\n",
    "seeds = [1, 10, 25, 50, 101]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0bcb7bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wine Quality Data\n",
    "# fetch dataset \n",
    "wq_df = fetch_ucirepo(id=186) \n",
    "  \n",
    "# data (as pandas dataframes) \n",
    "wq_X = wq_df.data.features \n",
    "wq_y = wq_df.data.targets \n",
    "\n",
    "# Breast Cancer Data\n",
    "# fetch dataset \n",
    "bc_df = fetch_ucirepo(id=17) \n",
    "  \n",
    "# data (as pandas dataframes) \n",
    "bc_X = bc_df.data.features \n",
    "bc_y = bc_df.data.targets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ca917ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into training and testing sets. Don't use test set until the very end\n",
    "bc_X, X_test, bc_y, y_test = train_test_split(bc_X, bc_y, test_size=.2, random_state=RANDOM_STATE, stratify = bc_y)\n",
    "# Split into training and testing sets. Don't use test set until the very end\n",
    "wq_X, X_test, wq_y, y_test = train_test_split(wq_X, wq_y, test_size=.2, random_state=RANDOM_STATE, stratify = wq_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "021351f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardized data\n",
    "scaler = MinMaxScaler()\n",
    "wq_scaled = scaler.fit_transform(wq_X)\n",
    "bc_scaled = scaler.fit_transform(bc_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee96efc",
   "metadata": {},
   "source": [
    "## Choose Covariance and K"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9c5fa2",
   "metadata": {},
   "source": [
    "### Wine Quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e949f7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BIC and AIC\n",
    "bic_full = []\n",
    "bic_tied = []\n",
    "bic_diag = []\n",
    "bic_sphere = []\n",
    "\n",
    "aic_full = []\n",
    "aic_tied = []\n",
    "aic_diag = []\n",
    "aic_sphere = []\n",
    "\n",
    "for seed in seeds:\n",
    "    n_components = np.arange(1, 12)\n",
    "    bic_full_models = [GaussianMixture(n, covariance_type='full', random_state=seed).fit(wq_scaled)\n",
    "              for n in n_components]\n",
    "    bic_tied_models = [GaussianMixture(n, covariance_type='tied', random_state=seed).fit(wq_scaled)\n",
    "              for n in n_components]\n",
    "    bic_diag_models = [GaussianMixture(n, covariance_type='diag', random_state=seed).fit(wq_scaled)\n",
    "              for n in n_components]\n",
    "    bic_spherical_models = [GaussianMixture(n, covariance_type='spherical', random_state=seed).fit(wq_scaled)\n",
    "              for n in n_components]\n",
    "    \n",
    "    b1 = [m.bic(wq_scaled) for m in bic_full_models]\n",
    "    b2 = [m.bic(wq_scaled) for m in bic_tied_models]\n",
    "    b3 = [m.bic(wq_scaled) for m in bic_diag_models]\n",
    "    b4 = [m.bic(wq_scaled) for m in bic_spherical_models]\n",
    "    bic_full.append(b1)\n",
    "    bic_tied.append(b2)\n",
    "    bic_diag.append(b3)\n",
    "    bic_sphere.append(b4)\n",
    "\n",
    "\n",
    "    aic_full_models = [GaussianMixture(n, covariance_type='full', random_state=seed).fit(wq_scaled)\n",
    "              for n in n_components]\n",
    "    aic_tied_models = [GaussianMixture(n, covariance_type='tied', random_state=seed).fit(wq_scaled)\n",
    "              for n in n_components]\n",
    "    aic_diag_models = [GaussianMixture(n, covariance_type='diag', random_state=seed).fit(wq_scaled)\n",
    "              for n in n_components]\n",
    "    aic_spherical_models = [GaussianMixture(n, covariance_type='spherical', random_state=seed).fit(wq_scaled)\n",
    "              for n in n_components]\n",
    "    \n",
    "    a1 = [m.aic(wq_scaled) for m in aic_full_models]\n",
    "    a2 = [m.aic(wq_scaled) for m in aic_tied_models]\n",
    "    a3 = [m.aic(wq_scaled) for m in aic_diag_models]\n",
    "    a4 = [m.aic(wq_scaled) for m in aic_spherical_models]\n",
    "    aic_full.append(a1)\n",
    "    aic_tied.append(a2)\n",
    "    aic_diag.append(a3)\n",
    "    aic_sphere.append(a4)\n",
    "\n",
    "bic_full_models = np.mean(bic_full, axis=0)\n",
    "bic_tied_models = np.mean(bic_tied, axis=0)\n",
    "bic_diag_models = np.mean(bic_diag, axis=0)\n",
    "bic_spherical_models = np.mean(bic_sphere, axis=0)\n",
    "\n",
    "aic_full_models = np.mean(aic_full, axis=0)\n",
    "aic_tied_models = np.mean(aic_tied, axis=0)\n",
    "aic_diag_models = np.mean(aic_diag, axis=0)\n",
    "aic_spherical_models = np.mean(aic_sphere, axis=0)\n",
    "    \n",
    "bic_full_kn = KneeLocator(n_components, bic_full_models, curve='convex', direction='decreasing')\n",
    "bic_tied_kn = KneeLocator(n_components, bic_tied_models, curve='convex', direction='decreasing')\n",
    "bic_diag_kn = KneeLocator(n_components, bic_diag_models, curve='convex', direction='decreasing')\n",
    "bic_spherical_kn = KneeLocator(n_components, bic_spherical_models, curve='convex', direction='decreasing')    \n",
    "    \n",
    "aic_full_kn = KneeLocator(n_components, aic_full_models, curve='convex', direction='decreasing')\n",
    "aic_tied_kn = KneeLocator(n_components, aic_tied_models, curve='convex', direction='decreasing')\n",
    "aic_diag_kn = KneeLocator(n_components, aic_diag_models, curve='convex', direction='decreasing')\n",
    "aic_spherical_kn = KneeLocator(n_components, aic_spherical_models, curve='convex', direction='decreasing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a84cf7f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Silhouette\n",
    "\n",
    "holder1 = []\n",
    "holder2 = []\n",
    "holder3 = []\n",
    "holder4 = []\n",
    "n_components = np.arange(2, 12)\n",
    "\n",
    "for seed in seeds:\n",
    "    full_S = []\n",
    "    tied_S = []\n",
    "    diag_S = []\n",
    "    sphere_S = []\n",
    "    for n in n_components:\n",
    "        full_model = GaussianMixture(n, covariance_type='full', random_state=seed)\n",
    "        tied_model = GaussianMixture(n, covariance_type='tied', random_state=seed)\n",
    "        diag_model = GaussianMixture(n, covariance_type='diag', random_state=seed)\n",
    "        sphere_model = GaussianMixture(n, covariance_type='spherical', random_state=seed)\n",
    "\n",
    "        full_labels = full_model.fit_predict(wq_scaled)\n",
    "        tied_labels = tied_model.fit_predict(wq_scaled)\n",
    "        diag_labels = diag_model.fit_predict(wq_scaled)\n",
    "        sphere_labels = sphere_model.fit_predict(wq_scaled)\n",
    "\n",
    "        full_S.append(metrics.silhouette_score(wq_scaled, full_labels))\n",
    "        tied_S.append(metrics.silhouette_score(wq_scaled, tied_labels))\n",
    "        diag_S.append(metrics.silhouette_score(wq_scaled, diag_labels))\n",
    "        sphere_S.append(metrics.silhouette_score(wq_scaled, sphere_labels))\n",
    "    holder1.append(full_S)\n",
    "    holder2.append(tied_S)\n",
    "    holder3.append(diag_S)\n",
    "    holder4.append(sphere_S)\n",
    "full_S = np.mean(holder1, axis=0)\n",
    "tied_S = np.mean(holder2, axis=0)\n",
    "diag_S = np.mean(holder3, axis=0)\n",
    "sphere_S = np.mean(holder4, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "715546a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ((ax1, ax2, ax3, ax4)) = plt.subplots(1, 4, figsize=(26, 6))\n",
    "rcParams['font.weight'] = 'bold'\n",
    "\n",
    "n_components = np.arange(1, 12)\n",
    "ax1.plot(n_components, bic_full_models, label='full')\n",
    "ax1.plot(n_components, bic_tied_models, label='tied')\n",
    "ax1.plot(n_components, bic_diag_models, label='diag')\n",
    "ax1.plot(n_components, bic_spherical_models, label='spherical')\n",
    "ax1.axvline(x = bic_full_kn.knee, color='k', label = 'full elbow', linestyle='--')\n",
    "ax1.axvline(x = bic_tied_kn.knee, color='m', label = 'tied elbow', linestyle=':')\n",
    "ax1.axvline(x = bic_diag_kn.knee, color='c', label = 'diag elbow', linestyle='-.')\n",
    "ax1.axvline(x = bic_spherical_kn.knee, color='tab:orange', label = 'spherical elbow', linestyle='-.')\n",
    "ax1.set_xlabel('components', weight='bold')\n",
    "ax1.set_ylabel('BIC score', weight='bold')\n",
    "ax1.legend(loc='best', prop = { \"size\": 7.5, 'weight':'bold'})\n",
    "ax1.set_title('BIC Scores for GMM: Covariance Types', weight='bold')\n",
    "ax1.grid(True)\n",
    "\n",
    "ax2.plot(n_components, aic_full_models, label='full')\n",
    "ax2.plot(n_components, aic_tied_models, label='tied')\n",
    "ax2.plot(n_components, aic_diag_models, label='diag')\n",
    "ax2.plot(n_components, aic_spherical_models, label='spherical')\n",
    "ax2.axvline(x = aic_full_kn.knee, color='k', label = 'full elbow', linestyle='--')\n",
    "ax2.axvline(x = aic_tied_kn.knee, color='m', label = 'tied elbow', linestyle=':')\n",
    "ax2.axvline(x = aic_diag_kn.knee, color='c', label = 'diag elbow', linestyle='-.')\n",
    "ax2.axvline(x = aic_spherical_kn.knee, color='tab:orange', label = 'spherical elbow', linestyle='-.')\n",
    "ax2.set_xlabel('components', weight='bold')\n",
    "ax2.set_ylabel('AIC score', weight='bold')\n",
    "ax2.legend(loc='best', prop = { \"size\": 7.5, 'weight':'bold'})\n",
    "ax2.set_title('AIC Scores for GMM: Covariance Types', weight='bold')\n",
    "ax2.grid(True)\n",
    "\n",
    "n_components = np.arange(2, 12)\n",
    "ax3.plot(n_components, full_S, label='full')\n",
    "ax3.plot(n_components, tied_S, label='tied')\n",
    "ax3.plot(n_components, diag_S, label='diag')\n",
    "ax3.plot(n_components, sphere_S, label='spherical')\n",
    "ax3.set_xlabel('components', weight='bold')\n",
    "ax3.set_ylabel('silhouette score', weight='bold')\n",
    "ax3.legend(loc='best', prop = { \"size\": 7.5, 'weight':'bold'})\n",
    "ax3.set_title('Silhouette Scores for GMM: Covariance Types', weight='bold')\n",
    "ax3.grid(True)\n",
    "\n",
    "\n",
    "# ys = []\n",
    "# cs = []\n",
    "# for seed in seeds:\n",
    "gmm = GaussianMixture(4, random_state=seed).fit(wq_scaled)\n",
    "y_kmeans = gmm.predict(wq_scaled)\n",
    "# ys.append(y_kmeans)\n",
    "centers = gmm.means_\n",
    "# cs.append(centers)\n",
    "    \n",
    "# y_kmeans = np.mean(ys, axis=0)\n",
    "# print(set(y_kmeans))\n",
    "# centers = np.mean(cs, axis=0)\n",
    "ax4.scatter(wq_scaled[:, 0], wq_scaled[:, 1], c=y_kmeans, cmap='viridis', s=5)\n",
    "ax4.scatter(centers[:, 0], centers[:, 1], c='red', s=15)\n",
    "ax4.set_title('GMM with 4 Components and Full Covariance', weight='bold')\n",
    "ax4.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'plots/gmm_combined/gmm_wq_combined.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a72f16e6",
   "metadata": {},
   "source": [
    "#### Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "324d6f30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rand Index:  0.5784189907631616\n",
      "Adjusted Rand Index:  0.03666132555871111\n",
      "Adjusted Mutual Info Score:  0.05409246506960461\n",
      "Normalized Mutual Info Score:  0.05544420322898652\n",
      "Homogeneity Score:  0.054798904182998355\n",
      "Completeness Score:  0.05610488115354425\n",
      "V Measure Score:  0.05544420322898652\n"
     ]
    }
   ],
   "source": [
    "quality = list(wq_y['quality'])\n",
    "        \n",
    "        \n",
    "ri = metrics.rand_score(quality, y_kmeans)\n",
    "print('Rand Index: ', ri)        \n",
    "ari = metrics.adjusted_rand_score(quality, y_kmeans)\n",
    "print('Adjusted Rand Index: ', ari)\n",
    "ami = metrics.adjusted_mutual_info_score(quality, y_kmeans) \n",
    "print('Adjusted Mutual Info Score: ', ami)\n",
    "nmi = metrics.normalized_mutual_info_score(quality, y_kmeans)  \n",
    "print('Normalized Mutual Info Score: ', nmi)\n",
    "homo = metrics.homogeneity_score(quality, y_kmeans)\n",
    "print('Homogeneity Score: ', homo)\n",
    "cs = metrics.completeness_score(quality, y_kmeans)\n",
    "print('Completeness Score: ', cs)\n",
    "v = metrics.v_measure_score(quality, y_kmeans)\n",
    "print('V Measure Score: ', v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9deadf36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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5, 5, 7, 6, 6, 6, 7, 6, 6, 4, 5, 6, 5, 5, 7, 6, 7, 6, 6, 6, 5, 7, 6, 7, 7, 7, 6, 5, 7, 5, 6, 8, 6, 5, 4, 6, 6, 6, 6, 6, 6, 5, 7, 5, 5, 6, 6, 7, 6, 7, 7, 6, 5, 5, 5, 6, 6, 5, 6, 5, 7, 4, 5, 7, 6, 6, 6, 6, 5, 5, 7, 4, 7, 5, 7, 5, 6, 5, 7, 8, 8, 5, 8, 6, 5, 5, 6, 6, 6, 4, 6, 5, 5, 6, 6, 6, 6, 7, 6, 6, 6, 5, 5, 5, 6, 6, 5, 6, 5, 6, 5, 6, 6, 6, 7, 7, 5, 6, 6, 5, 5, 5, 6, 5, 6, 7, 6, 5, 7, 4, 5, 6, 5, 6, 5, 7, 7, 6, 5, 8, 5, 6, 6, 6, 5, 6, 6, 7, 6, 5, 7, 7, 6, 6, 4, 7, 6, 8, 5, 5, 5, 8, 7, 7, 5, 5, 6, 6, 5, 7, 6, 4, 7, 7, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 5, 6, 5, 7, 5, 5, 5, 5, 6, 4, 6, 6, 5, 6, 6, 6, 6, 5, 5, 5, 5, 7, 5, 5, 5, 7, 6, 6, 6, 6, 5, 5, 6, 6, 6, 5, 6, 4, 5, 7, 6, 5, 5, 6, 5, 7, 6, 7, 8, 7, 6, 5, 7, 5, 6, 5, 6, 6, 6, 5, 3, 5, 7, 6, 7, 7, 7, 5, 6, 6, 5, 5, 6, 6, 6, 5, 7, 6, 5, 7, 6, 6, 4, 6, 5, 4, 6, 4, 5, 6, 5, 5, 5, 6, 5, 5, 6, 4, 6, 7, 6, 7, 5, 8, 5, 6, 5, 5, 5, 5, 6, 5, 5, 5, 6, 7, 4, 6, 6, 8, 5, 5, 6, 4, 5, 6, 5, 7, 6, 5, 6, 5, 5, 5, 5, 8, 6, 6, 6, 6, 4, 5, 4, 6, 5, 6, 5, 6, 6, 5, 5, 8, 6, 6, 5, 8, 5, 6, 6, 5, 6, 5, 7, 6, 7, 5, 7, 6, 6, 6, 5, 6, 3, 5, 6, 6, 5, 6, 5, 7, 6, 6, 6, 6, 6, 6, 5, 7, 5, 6, 8, 5, 7, 6, 5, 7, 7, 5, 5, 5, 6, 5, 6, 7, 6, 7, 7, 5, 5, 7, 6, 5, 5, 5, 6, 7, 5, 7, 7, 7, 5, 7, 6, 6, 5, 5, 6, 6, 7, 5, 7, 5, 7, 6, 6, 6, 5, 7, 5, 6, 7, 6, 6, 7, 6, 6, 4, 7, 6, 5, 6, 6, 6, 5, 6, 8, 5, 7, 5, 6, 6, 6, 6, 5, 5, 5, 5, 4, 6, 6, 6, 5, 6, 5, 6, 5, 4, 5, 6, 7, 6, 6, 6, 5, 6, 8, 6, 7, 6, 6, 6, 6, 7, 3, 6, 6, 7, 6, 6, 6, 5, 6, 5, 3, 6, 7, 6, 6, 6, 5, 5, 6, 5, 6, 6, 5, 7, 6, 5, 4, 5, 6, 5, 6, 6, 6, 5, 5, 6, 6, 6, 5, 6, 5, 6, 5, 6, 6, 6, 5, 6, 5, 6, 6, 6, 5, 7, 5, 5, 6, 5, 6, 5, 6, 7, 7, 6, 7, 7, 5, 6, 6, 4, 5, 6, 6, 7, 5, 6, 7, 6, 7, 6, 6, 5, 6, 6, 7, 6, 7, 6, 5, 6, 8, 7, 5, 7, 6, 6, 6, 5, 7, 8, 6, 6, 6, 7, 5, 5, 8, 6, 5, 6, 5, 7, 7, 7, 6, 6, 4, 6, 5, 5, 6, 7, 7, 6, 5, 5, 7, 6, 6, 7, 7, 5, 6, 5, 5, 5, 5, 5, 6, 6, 5, 4, 6, 6, 7, 4, 5, 5, 5, 6, 7, 6, 6, 5, 6, 5, 6, 7, 6, 6, 6, 6, 6, 4, 5, 6, 5, 6, 5, 5, 7, 5, 6, 6, 6, 6, 7, 5, 7, 5, 5, 6, 5, 6, 5, 8, 4, 6, 7, 7, 5, 7, 5, 5, 6, 5, 3, 5, 5, 7, 5, 7, 6, 6, 4, 5, 4, 5, 7, 6, 6, 5, 6, 8, 6, 6, 6, 6, 7, 6, 5, 5, 7, 6, 5, 5, 6, 6, 7, 6, 5, 6, 7, 7, 8, 7, 7, 6, 6, 6, 6, 6, 7, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 7, 6, 7, 6, 5, 6, 6, 5, 5, 5, 5, 6, 5, 6, 6, 4, 6, 6, 5, 6, 7, 7, 5, 6, 5, 6, 6, 6, 6, 5, 6, 7, 5, 6, 6, 6, 7, 4, 6, 6, 5, 7, 5, 8, 5, 5, 6, 6, 7, 6, 7, 5, 5, 6, 8, 6, 6, 8, 6, 6, 6, 6, 7, 6, 7, 5, 5, 6, 6, 8, 4, 6, 6, 6, 5, 7, 6, 6, 6, 6, 5, 5, 6, 6, 5, 6, 5, 6, 7, 7, 6, 4, 6, 6, 5, 7, 7, 5, 5, 6, 7, 6, 6, 6, 5, 6, 6, 6, 7, 7, 5, 7, 5, 8, 5, 6, 6, 5, 6, 6, 7, 7, 6, 5, 5, 6, 6, 5, 6, 5, 6, 6, 6, 6, 5, 6, 7, 7, 4, 5, 5, 6, 5, 5, 6, 6, 5, 6, 5, 5, 7, 5, 7, 6, 6, 7, 6, 5, 8, 6, 8, 7, 7, 6, 6, 8, 5, 7, 6, 6, 6, 6, 5, 7, 6, 6, 5, 4, 6, 6, 6, 6, 5, 6, 6, 7, 6, 3, 3, 6, 5, 5, 5, 5, 5, 6, 6, 6, 5, 5, 5, 6, 6, 6, 5, 6, 6, 7, 6, 7, 5, 5, 7, 5, 6, 4, 5, 5, 5, 7, 4, 6, 7, 7, 4, 6, 6, 7, 5, 7, 7, 5, 4, 5, 5, 8, 5, 5, 6, 6, 6, 6, 5, 5, 6, 5, 6, 5, 7, 7, 5, 6, 6, 6, 6, 5, 4, 5, 6, 6, 6, 8, 6, 5, 5, 5, 6, 6, 7, 5, 6, 5, 5, 4, 6, 4, 6, 6, 6, 6, 5, 5, 6, 7, 6, 6, 5, 6, 4, 6, 6, 5, 5, 5, 5, 5, 5, 7, 6, 7, 6, 6, 6, 5, 6, 6, 4, 6, 6, 7, 6, 6, 6, 7, 5, 6, 6, 5, 6, 5, 6, 5, 6, 7, 7, 5, 6, 4, 5, 5, 5, 6, 5, 5, 6, 4, 6, 6, 5, 6, 7, 7, 6, 5, 6, 6, 6, 5, 5, 7, 6, 6, 8, 5, 5, 6, 5, 6, 5, 6, 6, 5, 4, 5, 7, 5, 5, 6, 5, 5, 7, 7, 4, 5, 7, 5, 5, 5, 5, 5, 5, 6, 6, 5, 7, 6, 5, 5, 6, 6, 6, 5, 6, 5, 6, 5, 6, 5, 5, 6, 6, 5, 4, 6, 7, 7, 6, 6, 4, 6, 6, 6, 5, 6, 6, 6, 7, 6, 5, 7, 7, 6, 5, 7, 6, 6, 7, 7, 7, 5, 7, 5, 6, 6, 8, 7, 7, 6, 6, 7, 5, 5, 5, 5, 4, 7, 5, 5, 6, 6, 5, 6, 7, 6, 6, 6, 8, 6, 6, 5, 6, 5, 7, 7, 5, 5, 7, 8, 7, 6, 5, 5, 6, 5, 7, 6, 5, 5, 5, 6, 6, 5, 6, 7, 6, 5, 7, 6, 5, 6, 7, 5, 5, 6, 6, 4, 6, 7, 6, 6, 6, 5, 6, 5, 6, 5, 7, 5, 6, 7, 5, 6, 6, 4, 6, 7, 5, 6, 5, 6, 8, 7, 8, 6, 5, 6, 7, 5, 6, 5, 5, 7, 6, 6, 6, 6, 6, 5, 6, 8, 6, 6, 6, 6, 5, 7, 5, 6, 5, 6, 5, 6, 7, 6, 9, 6, 5, 6, 6, 5, 5, 6, 6, 6, 6, 5, 6, 7, 6, 6, 6, 5, 5, 5, 6, 6, 7, 6, 4, 7, 6, 5, 5, 6, 4, 6, 7, 6, 6, 7, 6, 8, 5, 7, 6, 6, 5, 7, 6, 6, 5, 6, 5, 6, 6, 7, 6, 5, 6, 6, 5, 6, 5, 5, 5, 6, 6, 5, 6, 3, 6, 7, 6, 5, 4, 5, 5, 5, 6, 6, 5, 6, 5, 7, 7, 5, 7, 5, 4, 6, 6, 6, 4, 5, 6, 6, 7, 5, 6, 6, 6, 6, 7, 5, 6, 5, 6, 7, 5, 4, 6, 6, 7, 5, 6, 8, 7, 6, 7, 7, 6, 6, 5, 6, 6, 6, 6, 6, 5, 5, 8, 5, 6, 5, 5, 6, 6, 6, 6, 6, 5, 5, 6, 5, 6, 5, 5, 7, 6, 5, 7, 7, 7, 6, 5, 6, 6, 6, 7, 6, 5, 6, 8, 6, 5, 6, 5, 6, 6, 6, 8, 7, 5, 6, 5, 6, 6, 6, 7, 3, 6, 5, 6, 5, 7, 5, 6, 6, 7, 5, 6, 6, 7, 4, 5, 6, 6, 6, 7, 6, 6, 6, 6, 5, 6, 6, 6, 5, 7, 6, 5, 7, 7, 6, 6, 6, 4, 6, 6, 6, 6, 6, 7, 5, 5, 6, 5, 6, 6, 7, 5, 7, 6, 5, 6, 5, 6, 5, 5, 7, 6, 6, 5, 5, 8, 6, 7, 6, 5, 6, 6]\n",
      "[1 2 1 ... 2 0 2]\n"
     ]
    }
   ],
   "source": [
    "print(quality)\n",
    "print(y_kmeans)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c30c4a7",
   "metadata": {},
   "source": [
    "### Breast Cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a74e2286",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BIC and AIC\n",
    "bic_full = []\n",
    "bic_tied = []\n",
    "bic_diag = []\n",
    "bic_sphere = []\n",
    "\n",
    "aic_full = []\n",
    "aic_tied = []\n",
    "aic_diag = []\n",
    "aic_sphere = []\n",
    "\n",
    "for seed in seeds:\n",
    "    n_components = np.arange(1, 31)\n",
    "    bic_full_models = [GaussianMixture(n, covariance_type='full', random_state=seed).fit(bc_scaled)\n",
    "              for n in n_components]\n",
    "    bic_tied_models = [GaussianMixture(n, covariance_type='tied', random_state=seed).fit(bc_scaled)\n",
    "              for n in n_components]\n",
    "    bic_diag_models = [GaussianMixture(n, covariance_type='diag', random_state=seed).fit(bc_scaled)\n",
    "              for n in n_components]\n",
    "    bic_spherical_models = [GaussianMixture(n, covariance_type='spherical', random_state=seed).fit(bc_scaled)\n",
    "              for n in n_components]\n",
    "    \n",
    "    b1 = [m.bic(bc_scaled) for m in bic_full_models]\n",
    "    b2 = [m.bic(bc_scaled) for m in bic_tied_models]\n",
    "    b3 = [m.bic(bc_scaled) for m in bic_diag_models]\n",
    "    b4 = [m.bic(bc_scaled) for m in bic_spherical_models]\n",
    "    bic_full.append(b1)\n",
    "    bic_tied.append(b2)\n",
    "    bic_diag.append(b3)\n",
    "    bic_sphere.append(b4)\n",
    "\n",
    "\n",
    "    aic_full_models = [GaussianMixture(n, covariance_type='full', random_state=seed).fit(bc_scaled)\n",
    "              for n in n_components]\n",
    "    aic_tied_models = [GaussianMixture(n, covariance_type='tied', random_state=seed).fit(bc_scaled)\n",
    "              for n in n_components]\n",
    "    aic_diag_models = [GaussianMixture(n, covariance_type='diag', random_state=seed).fit(bc_scaled)\n",
    "              for n in n_components]\n",
    "    aic_spherical_models = [GaussianMixture(n, covariance_type='spherical', random_state=seed).fit(bc_scaled)\n",
    "              for n in n_components]\n",
    "    \n",
    "    a1 = [m.aic(bc_scaled) for m in aic_full_models]\n",
    "    a2 = [m.aic(bc_scaled) for m in aic_tied_models]\n",
    "    a3 = [m.aic(bc_scaled) for m in aic_diag_models]\n",
    "    a4 = [m.aic(bc_scaled) for m in aic_spherical_models]\n",
    "    aic_full.append(a1)\n",
    "    aic_tied.append(a2)\n",
    "    aic_diag.append(a3)\n",
    "    aic_sphere.append(a4)\n",
    "\n",
    "bic_full_models = np.mean(bic_full, axis=0)\n",
    "bic_tied_models = np.mean(bic_tied, axis=0)\n",
    "bic_diag_models = np.mean(bic_diag, axis=0)\n",
    "bic_spherical_models = np.mean(bic_sphere, axis=0)\n",
    "\n",
    "aic_full_models = np.mean(aic_full, axis=0)\n",
    "aic_tied_models = np.mean(aic_tied, axis=0)\n",
    "aic_diag_models = np.mean(aic_diag, axis=0)\n",
    "aic_spherical_models = np.mean(aic_sphere, axis=0)\n",
    "    \n",
    "bic_full_kn = KneeLocator(n_components, bic_full_models, curve='convex', direction='decreasing')\n",
    "bic_tied_kn = KneeLocator(n_components, bic_tied_models, curve='convex', direction='decreasing')\n",
    "bic_diag_kn = KneeLocator(n_components, bic_diag_models, curve='convex', direction='decreasing')\n",
    "bic_spherical_kn = KneeLocator(n_components, bic_spherical_models, curve='convex', direction='decreasing')    \n",
    "    \n",
    "aic_full_kn = KneeLocator(n_components, aic_full_models, curve='convex', direction='decreasing')\n",
    "aic_tied_kn = KneeLocator(n_components, aic_tied_models, curve='convex', direction='decreasing')\n",
    "aic_diag_kn = KneeLocator(n_components, aic_diag_models, curve='convex', direction='decreasing')\n",
    "aic_spherical_kn = KneeLocator(n_components, aic_spherical_models, curve='convex', direction='decreasing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "88f6ad0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Silhouette\n",
    "\n",
    "holder1 = []\n",
    "holder2 = []\n",
    "holder3 = []\n",
    "holder4 = []\n",
    "n_components = np.arange(2, 31)\n",
    "\n",
    "for seed in seeds:\n",
    "    full_S = []\n",
    "    tied_S = []\n",
    "    diag_S = []\n",
    "    sphere_S = []\n",
    "    for n in n_components:\n",
    "        full_model = GaussianMixture(n, covariance_type='full', random_state=seed)\n",
    "        tied_model = GaussianMixture(n, covariance_type='tied', random_state=seed)\n",
    "        diag_model = GaussianMixture(n, covariance_type='diag', random_state=seed)\n",
    "        sphere_model = GaussianMixture(n, covariance_type='spherical', random_state=seed)\n",
    "\n",
    "        full_labels = full_model.fit_predict(bc_scaled)\n",
    "        tied_labels = tied_model.fit_predict(bc_scaled)\n",
    "        diag_labels = diag_model.fit_predict(bc_scaled)\n",
    "        sphere_labels = sphere_model.fit_predict(bc_scaled)\n",
    "\n",
    "        full_S.append(metrics.silhouette_score(bc_scaled, full_labels))\n",
    "        tied_S.append(metrics.silhouette_score(bc_scaled, tied_labels))\n",
    "        diag_S.append(metrics.silhouette_score(bc_scaled, diag_labels))\n",
    "        sphere_S.append(metrics.silhouette_score(bc_scaled, sphere_labels))\n",
    "    holder1.append(full_S)\n",
    "    holder2.append(tied_S)\n",
    "    holder3.append(diag_S)\n",
    "    holder4.append(sphere_S)\n",
    "full_S = np.mean(holder1, axis=0)\n",
    "tied_S = np.mean(holder2, axis=0)\n",
    "diag_S = np.mean(holder3, axis=0)\n",
    "sphere_S = np.mean(holder4, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d8f1bac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0, 1}\n",
      "{0, 1}\n",
      "{0, 1}\n",
      "{0, 1}\n",
      "{0, 1}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2, ax3, ax4)) = plt.subplots(1, 4, figsize=(26, 6))\n",
    "rcParams['font.weight'] = 'bold'\n",
    "\n",
    "n_components = np.arange(1, 31)\n",
    "ax1.plot(n_components, bic_full_models, label='full')\n",
    "ax1.plot(n_components, bic_tied_models, label='tied')\n",
    "ax1.plot(n_components, bic_diag_models, label='diag')\n",
    "ax1.plot(n_components, bic_spherical_models, label='spherical')\n",
    "ax1.axvline(x = bic_full_kn.knee, color='k', label = 'full elbow', linestyle='--')\n",
    "ax1.axvline(x = bic_tied_kn.knee, color='m', label = 'tied elbow', linestyle=':')\n",
    "ax1.axvline(x = bic_diag_kn.knee, color='c', label = 'diag elbow', linestyle='-.')\n",
    "ax1.axvline(x = bic_spherical_kn.knee, color='tab:orange', label = 'spherical elbow', linestyle='-.')\n",
    "ax1.set_xlabel('components', weight='bold')\n",
    "ax1.set_ylabel('BIC Score', weight='bold')\n",
    "ax1.legend(loc='best', prop = { \"size\": 7.5, 'weight':'bold'})\n",
    "ax1.set_title('BIC Scores for GMM: Covariance Types', weight='bold')\n",
    "ax1.grid(True)\n",
    "\n",
    "ax2.plot(n_components, aic_full_models, label='full')\n",
    "ax2.plot(n_components, aic_tied_models, label='tied')\n",
    "ax2.plot(n_components, aic_diag_models, label='diag')\n",
    "ax2.plot(n_components, aic_spherical_models, label='spherical')\n",
    "ax2.axvline(x = aic_full_kn.knee, color='k', label = 'full elbow', linestyle='--')\n",
    "ax2.axvline(x = aic_tied_kn.knee, color='m', label = 'tied elbow', linestyle=':')\n",
    "ax2.axvline(x = aic_diag_kn.knee, color='c', label = 'diag elbow', linestyle='-.')\n",
    "ax2.axvline(x = aic_spherical_kn.knee, color='tab:orange', label = 'spherical elbow', linestyle='-.')\n",
    "ax2.set_xlabel('components', weight='bold')\n",
    "ax2.set_ylabel('AIC Score', weight='bold')\n",
    "ax2.legend(loc='best', prop = { \"size\": 7.5, 'weight':'bold'})\n",
    "ax2.set_title('AIC Scores for GMM: Covariance Types', weight='bold')\n",
    "ax2.grid(True)\n",
    "\n",
    "n_components = np.arange(2, 31)\n",
    "ax3.plot(n_components, full_S, label='full')\n",
    "ax3.plot(n_components, tied_S, label='tied')\n",
    "ax3.plot(n_components, diag_S, label='diag')\n",
    "ax3.plot(n_components, sphere_S, label='spherical')\n",
    "ax3.set_xlabel('components', weight='bold')\n",
    "ax3.set_ylabel('silhouette score', weight='bold')\n",
    "ax3.legend(loc='best', prop = { \"size\": 7.5, 'weight':'bold'})\n",
    "ax3.set_title('Silhouette Scores for GMM: Covariance Types', weight='bold')\n",
    "ax3.grid(True)\n",
    "\n",
    "ys = []\n",
    "cs = []\n",
    "for seed in seeds:\n",
    "    gmm = GaussianMixture(2, random_state=seed).fit(bc_scaled)\n",
    "    y_kmeans = gmm.predict(bc_scaled)\n",
    "    print(set(y_kmeans))\n",
    "    ys.append(y_kmeans)\n",
    "    centers = gmm.means_\n",
    "    cs.append(centers)\n",
    "    \n",
    "y_kmeans = np.mean(ys, axis=0)\n",
    "centers = np.mean(cs, axis=0)\n",
    "ax4.scatter(bc_scaled[:, 0], bc_scaled[:, 1], c=y_kmeans, cmap='viridis', s=5)\n",
    "ax4.scatter(centers[:, 0], centers[:, 1], c='red', s=15)\n",
    "ax4.set_title('GMM with 2 Components and Full Covariance', weight='bold')\n",
    "ax4.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'plots/gmm_combined/gmm_bc_combined.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1ae2a71e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rand Index:  0.899849929805877\n",
      "Adjusted Rand Index:  0.798931452392641\n",
      "Adjusted Mutual Info Score:  0.6897560414159097\n",
      "Normalized Mutual Info Score:  0.6902739645793833\n",
      "Homogeneity Score:  0.6902739645793833\n",
      "Completeness Score:  0.6902739645793833\n",
      "V Measure Score:  0.6902739645793833\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/VivianNTran/anaconda3/envs/ML_A3_env/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/VivianNTran/anaconda3/envs/ML_A3_env/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/VivianNTran/anaconda3/envs/ML_A3_env/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/VivianNTran/anaconda3/envs/ML_A3_env/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/VivianNTran/anaconda3/envs/ML_A3_env/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/VivianNTran/anaconda3/envs/ML_A3_env/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/VivianNTran/anaconda3/envs/ML_A3_env/lib/python3.11/site-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received binary values for label, and continuous values for target\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "diagnosis = list(bc_y['Diagnosis'])\n",
    "for i in range(len(diagnosis)):\n",
    "    if diagnosis[i] == 'M':\n",
    "        diagnosis[i] = 1\n",
    "    else:\n",
    "        diagnosis[i] = 0\n",
    "        \n",
    "        \n",
    "ri = metrics.rand_score(diagnosis, y_kmeans)\n",
    "print('Rand Index: ', ri)        \n",
    "ari = metrics.adjusted_rand_score(diagnosis, y_kmeans)\n",
    "print('Adjusted Rand Index: ', ari)\n",
    "ami = metrics.adjusted_mutual_info_score(diagnosis, y_kmeans) \n",
    "print('Adjusted Mutual Info Score: ', ami)\n",
    "nmi = metrics.normalized_mutual_info_score(diagnosis, y_kmeans)  \n",
    "print('Normalized Mutual Info Score: ', nmi)\n",
    "homo = metrics.homogeneity_score(diagnosis, y_kmeans)\n",
    "print('Homogeneity Score: ', homo)\n",
    "cs = metrics.completeness_score(diagnosis, y_kmeans)\n",
    "print('Completeness Score: ', cs)\n",
    "v = metrics.v_measure_score(diagnosis, y_kmeans)\n",
    "print('V Measure Score: ', v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aa9a14c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0]\n",
      "[0.2 0.8 0.2 0.8 0.2 0.2 0.8 0.8 0.8 0.8 0.8 0.8 0.2 0.2 0.2 0.8 0.8 0.2\n",
      " 0.2 0.2 0.8 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.8 0.2 0.2 0.8 0.8 0.8 0.2\n",
      " 0.2 0.8 0.2 0.2 0.2 0.8 0.8 0.2 0.8 0.2 0.8 0.2 0.2 0.2 0.2 0.2 0.8 0.2\n",
      " 0.8 0.2 0.8 0.2 0.2 0.2 0.8 0.2 0.8 0.2 0.2 0.2 0.8 0.8 0.8 0.2 0.2 0.8\n",
      " 0.2 0.2 0.8 0.8 0.2 0.8 0.2 0.8 0.2 0.8 0.8 0.8 0.2 0.2 0.2 0.2 0.8 0.8\n",
      " 0.2 0.2 0.2 0.2 0.8 0.2 0.2 0.2 0.2 0.8 0.2 0.8 0.8 0.2 0.8 0.8 0.8 0.2\n",
      " 0.8 0.2 0.2 0.8 0.8 0.8 0.2 0.2 0.8 0.2 0.2 0.8 0.2 0.2 0.2 0.8 0.2 0.8\n",
      " 0.2 0.2 0.2 0.2 0.2 0.2 0.8 0.8 0.2 0.2 0.2 0.8 0.2 0.8 0.8 0.2 0.2 0.8\n",
      " 0.2 0.2 0.2 0.8 0.2 0.2 0.2 0.8 0.2 0.8 0.8 0.2 0.2 0.2 0.2 0.2 0.2 0.2\n",
      " 0.2 0.2 0.2 0.2 0.2 0.8 0.2 0.2 0.2 0.8 0.2 0.8 0.2 0.2 0.2 0.2 0.8 0.2\n",
      " 0.2 0.2 0.2 0.8 0.2 0.8 0.8 0.8 0.2 0.2 0.8 0.2 0.8 0.2 0.8 0.8 0.8 0.8\n",
      " 0.2 0.8 0.8 0.2 0.2 0.2 0.2 0.8 0.8 0.2 0.8 0.2 0.8 0.8 0.2 0.8 0.8 0.2\n",
      " 0.2 0.8 0.8 0.2 0.8 0.2 0.8 0.8 0.2 0.2 0.8 0.2 0.2 0.2 0.8 0.8 0.8 0.8\n",
      " 0.2 0.2 0.2 0.2 0.2 0.2 0.8 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.8 0.2 0.2 0.8\n",
      " 0.2 0.2 0.2 0.2 0.8 0.2 0.2 0.2 0.8 0.2 0.2 0.2 0.2 0.2 0.2 0.8 0.8 0.8\n",
      " 0.2 0.8 0.8 0.8 0.8 0.2 0.8 0.2 0.8 0.2 0.8 0.2 0.2 0.2 0.2 0.2 0.8 0.2\n",
      " 0.2 0.2 0.2 0.8 0.2 0.2 0.2 0.2 0.8 0.8 0.2 0.2 0.2 0.8 0.2 0.8 0.8 0.8\n",
      " 0.2 0.2 0.2 0.2 0.8 0.8 0.2 0.2 0.8 0.8 0.8 0.2 0.2 0.8 0.2 0.8 0.2 0.2\n",
      " 0.2 0.8 0.2 0.8 0.2 0.2 0.8 0.2 0.2 0.8 0.2 0.8 0.2 0.8 0.8 0.2 0.8 0.2\n",
      " 0.2 0.2 0.2 0.2 0.2 0.8 0.2 0.2 0.8 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.8\n",
      " 0.8 0.8 0.2 0.8 0.8 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.8\n",
      " 0.2 0.8 0.8 0.8 0.2 0.8 0.2 0.8 0.2 0.2 0.2 0.8 0.2 0.2 0.8 0.8 0.2 0.2\n",
      " 0.8 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.8 0.8 0.2 0.8 0.2 0.2 0.2\n",
      " 0.2 0.2 0.8 0.2 0.8 0.2 0.2 0.8 0.2 0.2 0.2 0.2 0.8 0.2 0.2 0.8 0.2 0.8\n",
      " 0.8 0.2 0.2 0.2 0.2 0.8 0.8 0.2 0.2 0.2 0.8 0.2 0.2 0.2 0.8 0.8 0.8 0.2\n",
      " 0.2 0.8 0.2 0.8 0.8]\n"
     ]
    }
   ],
   "source": [
    "print(diagnosis)\n",
    "print(y_kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33577306",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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