slides_clustering_simple_exec.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playground for Clustering Slides\n",
    "\n",
    "- Stephen W. Thomas\n",
    "- Used for MMA 869 and MMAI 869\n",
    "- The Easy version - no hard dependencies or packages (but also, less pretty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-03-05 14:48:16.105624\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "print(datetime.datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "import sklearn.metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "import itertools\n",
    "\n",
    "import scipy\n",
    "\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 2)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 60.29215704, 152.88087142],\n",
       "       [ 56.20047163, 179.08547014],\n",
       "       [ 57.93621395, 165.2207545 ],\n",
       "       [ 61.7226796 , 152.43350033],\n",
       "       [ 60.60267397, 158.87726465],\n",
       "       [ 52.06816636, 156.67348655],\n",
       "       [ 57.85026525, 179.88158146],\n",
       "       [ 54.54592838, 145.89683472],\n",
       "       [ 54.69034344, 156.26135403],\n",
       "       [ 56.23179551, 132.91808521],\n",
       "       [ 34.89402037,   1.67747371],\n",
       "       [ 41.30723719,   1.78908126],\n",
       "       [ 41.7288724 ,   1.15610713],\n",
       "       [ 38.51566996,   0.60960177],\n",
       "       [ 44.53950925,   1.42604393],\n",
       "       [ 37.09126865,   1.67817448],\n",
       "       [ 40.09151703,   2.21514534],\n",
       "       [ 39.6256323 ,   2.20118992],\n",
       "       [ 43.06555843,   1.40633659],\n",
       "       [ 42.93871754,   1.44884862],\n",
       "       [ 31.8543411 ,  82.09066878],\n",
       "       [ 30.73994619, 107.73804996],\n",
       "       [ 29.88118943,  89.78389725],\n",
       "       [ 40.85232619,  76.38735632],\n",
       "       [ 33.47104345,  99.43635543],\n",
       "       [ 33.6857771 , 108.56663741],\n",
       "       [ 31.24161392, 101.33034445],\n",
       "       [ 37.33247107, 106.04943795],\n",
       "       [ 30.15830646,  87.31355813],\n",
       "       [ 34.36177916,  92.74517668],\n",
       "       [ 52.96140198,  11.45818112],\n",
       "       [ 51.18727719,  10.25796582],\n",
       "       [ 53.17983179,  12.27880137],\n",
       "       [ 54.14262995,   7.53034836],\n",
       "       [ 50.64147496,  10.80468328],\n",
       "       [ 56.92472119,   8.63037982],\n",
       "       [ 55.66601454,   8.2584057 ],\n",
       "       [ 52.65389491,   8.84230067],\n",
       "       [ 55.23248053,   9.37689494],\n",
       "       [ 50.93940511,  10.11233068]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After experiementation, this random state generates a \"good looking\" dataset\n",
    "np.random.seed(0)\n",
    "\n",
    "n = 10\n",
    "\n",
    "# Old with lots of savings\n",
    "X_1 = np.ones((n, 2))\n",
    "X_1[:,0] = np.random.normal(55, 3, n)\n",
    "X_1[:,1] = np.random.normal(150, 20, n)\n",
    "\n",
    "# Young with no savings\n",
    "X_2= np.ones((n, 2))\n",
    "X_2[:,0] = np.random.normal(40, 2, n)\n",
    "X_2[:,1] = np.random.normal(1.6, 0.5, n)\n",
    "\n",
    "# Young with some savings\n",
    "X_3= np.ones((n, 2))\n",
    "X_3[:,0] = np.random.normal(35, 3, n)\n",
    "X_3[:,1] = np.random.normal(100, 20, n)\n",
    "\n",
    "# Old with little savings\n",
    "X_4= np.ones((n, 2))\n",
    "X_4[:,0] = np.random.uniform(low=50, high=60, size=n)\n",
    "X_4[:,1] = np.random.normal(10, 2, n)\n",
    "\n",
    "X = np.concatenate((X_1, X_2, X_3, X_4), axis=0)\n",
    "X[X<0] = 0\n",
    "\n",
    "np.savetxt(\"Clustering.csv\", X, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 2)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 40.9,  76.4],\n",
       "       [ 40.1,   2.2],\n",
       "       [ 37.1,   1.7],\n",
       "       [ 44.5,   1.4],\n",
       "       [ 29.9,  89.8],\n",
       "       [ 31.2, 101.3],\n",
       "       [ 30.2,  87.3],\n",
       "       [ 51.2,  10.3],\n",
       "       [ 55.7,   8.3],\n",
       "       [ 38.5,   0.6],\n",
       "       [ 57.9, 165.2],\n",
       "       [ 56.2, 179.1],\n",
       "       [ 53. ,  11.5],\n",
       "       [ 30.7, 107.7],\n",
       "       [ 34.4,  92.7],\n",
       "       [ 50.9,  10.1],\n",
       "       [ 56.2, 132.9],\n",
       "       [ 43.1,   1.4],\n",
       "       [ 53.2,  12.3],\n",
       "       [ 54.1,   7.5],\n",
       "       [ 33.7, 108.6],\n",
       "       [ 41.7,   1.2],\n",
       "       [ 42.9,   1.4],\n",
       "       [ 60.3, 152.9],\n",
       "       [ 54.5, 145.9],\n",
       "       [ 37.3, 106. ],\n",
       "       [ 52.1, 156.7],\n",
       "       [ 52.7,   8.8],\n",
       "       [ 57.9, 179.9],\n",
       "       [ 56.9,   8.6],\n",
       "       [ 55.2,   9.4],\n",
       "       [ 39.6,   2.2],\n",
       "       [ 33.5,  99.4],\n",
       "       [ 50.6,  10.8],\n",
       "       [ 41.3,   1.8],\n",
       "       [ 34.9,   1.7],\n",
       "       [ 61.7, 152.4],\n",
       "       [ 54.7, 156.3],\n",
       "       [ 31.9,  82.1],\n",
       "       [ 60.6, 158.9]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base = \"C:\\\\Users\\\\st50\\\\OneDrive - Queen's University\\\\Courses\\\\202003_ExecEdAI\\\\data\\\\\"\n",
    "file = \"clustering_exec.csv\"\n",
    "df = pd.read_csv(base + file)\n",
    "df.info()\n",
    "df.head()\n",
    "\n",
    "X = df.to_numpy()\n",
    "X.shape\n",
    "X\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default');\n",
    "\n",
    "plt.figure(figsize=(12, 10));\n",
    "plt.grid(True);\n",
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], c=\"black\", s=500);\n",
    "plt.xlabel('Age', fontsize=38);\n",
    "plt.ylabel('Current Savings (K)', fontsize=38);\n",
    "plt.xticks(fontsize=28);\n",
    "plt.yticks(fontsize=28);\n",
    "plt.savefig('out/exec_clusters.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=43, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "k_means = KMeans(init='k-means++', n_clusters=4, n_init=10, random_state=43)\n",
    "scaler = StandardScaler()\n",
    "X_scales = scaler.fit_transform(X)\n",
    "k_means.fit(X_scales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 3, 3, 0, 0, 0, 1, 1, 3, 2, 2, 1, 0, 0, 1, 2, 3, 1, 1, 0, 3,\n",
       "       3, 2, 2, 0, 2, 1, 2, 1, 1, 3, 0, 1, 3, 3, 2, 2, 0, 2])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 33.37,  95.13],\n",
       "       [ 53.35,   9.76],\n",
       "       [ 57.21, 158.02],\n",
       "       [ 40.37,   1.56]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at the centers\n",
    "k_means.cluster_centers_\n",
    "means = scaler.inverse_transform(k_means.cluster_centers_)\n",
    "means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default');\n",
    "\n",
    "plt.figure(figsize=(12, 10));\n",
    "plt.grid(True);\n",
    "\n",
    "sc = plt.scatter(X[:, 0], X[:, 1], s=500, c=k_means.labels_);\n",
    "plt.xlabel('Annual Income (K)', fontsize=38);\n",
    "plt.ylabel('Spending Score', fontsize=38);\n",
    "plt.xticks(fontsize=28);\n",
    "plt.yticks(fontsize=28);\n",
    "\n",
    "for label in k_means.labels_:\n",
    "    plt.text(x=means[label, 0], y=means[label, 1], s=label, fontsize=32, \n",
    "             horizontalalignment='center', verticalalignment='center', color='black',\n",
    "             bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.1', alpha=0.2));\n",
    "plt.savefig('out/exec_clusters2.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Internal Validation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3281.261960118365"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# WCSS == Inertia\n",
    "k_means.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7642633058904399"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(X, k_means.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default');\n",
    "\n",
    "sample_silhouette_values = silhouette_samples(X, k_means.labels_)\n",
    "sizes = 200*sample_silhouette_values\n",
    "\n",
    "plt.figure(figsize=(16, 10));\n",
    "plt.grid(True);\n",
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], s=sizes, c=k_means.labels_)\n",
    "plt.scatter(k_means.cluster_centers_[:, 0], k_means.cluster_centers_[:, 1], marker='x', s=500, c=\"black\")\n",
    "\n",
    "plt.title(\"K-Means (Dot Size = Silhouette Distance)\", fontsize=20);\n",
    "plt.xlabel('Annual Income (K)', fontsize=22);\n",
    "plt.ylabel('Spending Score', fontsize=22);\n",
    "plt.xticks(fontsize=18);\n",
    "plt.yticks(fontsize=18);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elbow Method (Manual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inertias = {}\n",
    "silhouettes = {}\n",
    "for k in range(2, 11):\n",
    "    kmeans = KMeans(init='k-means++', n_init=10, n_clusters=k, max_iter=1000, random_state=42).fit(X)\n",
    "    inertias[k] = kmeans.inertia_ # Inertia: Sum of distances of samples to their closest cluster center\n",
    "    silhouettes[k] = silhouette_score(X, kmeans.labels_, metric='euclidean')\n",
    "    \n",
    "\n",
    "plt.figure();\n",
    "plt.grid(True);\n",
    "plt.plot(list(inertias.keys()), list(inertias.values()));\n",
    "plt.title('K-Means, Elbow Method')\n",
    "plt.xlabel(\"Number of clusters, K\");\n",
    "plt.ylabel(\"Inertia\");\n",
    "\n",
    "\n",
    "plt.figure();\n",
    "plt.grid(True);\n",
    "plt.plot(list(silhouettes.keys()), list(silhouettes.values()));\n",
    "plt.title('K-Means, Elbow Method')\n",
    "plt.xlabel(\"Number of clusters, K\");\n",
    "plt.ylabel(\"Silhouette\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intepretting the Clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 53.35291322,   9.75502918],\n",
       "       [ 33.35787941,  95.14414824],\n",
       "       [ 57.21406951, 158.0129203 ],\n",
       "       [ 40.37980031,   1.56080028]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 0:\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-fbe9ae407c48>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\nCluster {}:'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mX_tmp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk_means\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mX_tmp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mean'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX_tmp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mX_tmp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
     ]
    }
   ],
   "source": [
    "for label in set(k_means.labels_):\n",
    "    print('\\nCluster {}:'.format(label))\n",
    "    X_tmp = X[k_means.labels_==label].copy()\n",
    "    X_tmp.loc['mean'] = X_tmp.mean()\n",
    "    X_tmp.tail(13)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find Examplars\n",
    "\n",
    "Let's find the observation that is closest to the cluster center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial import distance\n",
    "\n",
    "for i, label in enumerate(set(k_means.labels_)):    \n",
    "    X_tmp = X[k_means.labels_==label].copy()\n",
    "    \n",
    "    exemplar_idx = distance.cdist([k_means.cluster_centers_[i]], X_tmp).argmin()\n",
    "    exemplar = pd.DataFrame(X_tmp.iloc[exemplar_idx])\n",
    "   \n",
    "    print('\\nCluster {}:'.format(label))\n",
    "    exemplar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Snake Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "dat = X.copy()\n",
    "\n",
    "dat['Cluster'] = k_means.labels_\n",
    "\n",
    "datamart_melt = pd.melt(dat.reset_index(),\n",
    "id_vars=['Cluster'],\n",
    "value_vars=['AnnualIncome','SpendingScore'],\n",
    "var_name='Feature',\n",
    "value_name='Value')\n",
    "\n",
    "plt.title('Snake Plot, K-Means, K=5')\n",
    "sns.lineplot(x=\"Feature\", y=\"Value\", hue='Cluster', data=datamart_melt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relative Importance Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_avg = dat.groupby(['Cluster']).mean()\n",
    "population_avg = dat.drop(['Cluster'], axis=1).mean()\n",
    "\n",
    "relative_imp = cluster_avg - population_avg\n",
    "\n",
    "plt.figure(figsize=(8, 2));\n",
    "plt.title('Relative importance of features');\n",
    "sns.heatmap(data=relative_imp, annot=True, fmt='.2f', cmap='RdYlGn');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment with Lots of Ks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_means = KMeans(init='k-means++', n_clusters=5, n_init=10, random_state=42)\n",
    "k_means.fit(X)\n",
    "\n",
    "distances = k_means.fit_transform(X)\n",
    "variance = 0\n",
    "i = 0\n",
    "\n",
    "for label in k_means.labels_:\n",
    "    variance = variance + distances[i][label]\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_kmeans(X, k):\n",
    "    k_means = KMeans(init='k-means++', n_clusters=k, n_init=10, random_state=42)\n",
    "    k_means.fit(X)\n",
    "    wcss = k_means.inertia_\n",
    "    sil = silhouette_score(X, k_means.labels_)\n",
    "    \n",
    "    plt.style.use('default');\n",
    "\n",
    "    sample_silhouette_values = silhouette_samples(X, k_means.labels_)\n",
    "    sizes = 200*sample_silhouette_values\n",
    "\n",
    "    plt.figure(figsize=(16, 10));\n",
    "    plt.grid(True);\n",
    "\n",
    "    plt.scatter(X.iloc[:, 0], X.iloc[:, 1], s=sizes, c=k_means.labels_)\n",
    "    plt.scatter(k_means.cluster_centers_[:, 0], k_means.cluster_centers_[:, 1], marker='x', s=500, c=\"black\")\n",
    "\n",
    "    plt.title(\"K-Means (K={}, WCSS={:.2f}, Sil={:.2f})\".format(k, wcss, sil), fontsize=20);\n",
    "    plt.xlabel('Annual Income (K)', fontsize=22);\n",
    "    plt.ylabel('Spending Score', fontsize=22);\n",
    "    plt.xticks(fontsize=18);\n",
    "    plt.yticks(fontsize=18);\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"K={}, WCSS={:.2f}, Sil={:.2f}\".format(k, wcss, sil))\n",
    "\n",
    "for k in range(2, 21):\n",
    "    do_kmeans(X, k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look at Individual Silhouette Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_means = KMeans(init='k-means++', n_clusters=7, n_init=10, random_state=42)\n",
    "k_means.fit(X)\n",
    "sample_silhouette_values = silhouette_samples(X, k_means.labels_)\n",
    "\n",
    "X_tmp = X.copy()\n",
    "X_tmp['Cluster ID'] = k_means.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tmp['Silhouette'] = sample_silhouette_values\n",
    "X_tmp = X_tmp.sort_values(['Silhouette'])\n",
    "X_tmp.head()\n",
    "X_tmp.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16, 10));\n",
    "plt.grid(True);\n",
    "\n",
    "plt.scatter(X_tmp['AnnualIncome'], X_tmp['SpendingScore'], sizes=200*(X_tmp['Silhouette']+0.05), c=X_tmp['Cluster ID'])\n",
    "\n",
    "plt.title(\"K-Means (K = {}, Sil={:.2f})\".format(k_means.n_clusters, silhouette_score(X, kmeans.labels_, metric='euclidean')), fontsize=20);\n",
    "plt.xlabel('Annual Income (K)', fontsize=22);\n",
    "plt.ylabel('Spending Score', fontsize=22);\n",
    "plt.xticks(fontsize=18);\n",
    "plt.yticks(fontsize=18);\n",
    "\n",
    "for label in k_means.labels_:\n",
    "    plt.text(x=k_means.cluster_centers_[label, 0], y=k_means.cluster_centers_[label, 1], s=label, fontsize=32, \n",
    "             horizontalalignment='center', verticalalignment='center', color='black',\n",
    "             bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.1', alpha=0.02));\n",
    "    \n",
    "for i, txt in enumerate(X_tmp.index.values):\n",
    "    sil = X_tmp['Silhouette'].iloc[i]\n",
    "    if sil < 0.05:\n",
    "        plt.text(x=X_tmp['AnnualIncome'].iloc[i], y=X_tmp['SpendingScore'].iloc[i], s=txt, fontsize=22)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = DBSCAN(eps=0.3, min_samples=2)\n",
    "db.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette_score(X, db.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16, 10));\n",
    "plt.grid(True);\n",
    "\n",
    "unique_labels = set(db.labels_)\n",
    "colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))];\n",
    "\n",
    "for k in unique_labels:\n",
    "    if k == -1:        # Black used for noise.\n",
    "        col = [0, 0, 0, 1]\n",
    "    else:\n",
    "        col = colors[k]\n",
    "\n",
    "    xy = X[db.labels_ == k]\n",
    "    plt.plot(xy.iloc[:, 0], xy.iloc[:, 1], 'o', markerfacecolor=tuple(col), markeredgecolor='k', markersize=14);\n",
    "\n",
    "    \n",
    "plt.title('');\n",
    "plt.title(\"DBSCAN (n_clusters = {:d}, black = outliers)\".format(len(unique_labels)), fontsize=20);\n",
    "plt.xlabel('Annual Income (K)', fontsize=22);\n",
    "plt.ylabel('Spending Score', fontsize=22);\n",
    "plt.xticks(fontsize=18);\n",
    "plt.yticks(fontsize=18);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouettes = {}\n",
    "for eps in np.arange(0.1, 0.6, 0.1):\n",
    "    db = DBSCAN(eps=eps, min_samples=3).fit(X)\n",
    "    silhouettes[eps] = silhouette_score(X, db.labels_, metric='euclidean')\n",
    "    \n",
    "\n",
    "plt.figure();\n",
    "plt.plot(list(silhouettes.keys()), list(silhouettes.values()));\n",
    "plt.title('DBSCAN, Elbow Method')\n",
    "plt.xlabel(\"Eps\");\n",
    "plt.ylabel(\"Silhouette\");\n",
    "plt.grid(True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_dbscan(X, eps, min_samples):\n",
    "    db = DBSCAN(eps=eps, min_samples=min_samples).fit(X)\n",
    "\n",
    "    unique_labels = set(db.labels_)\n",
    "    n_clusters = len(unique_labels)\n",
    "    \n",
    "    if n_clusters <= 1:\n",
    "        print('eps={}, min_samples={}, n_clusters <= 1. Returning.'.format(eps, min_samples))\n",
    "        return\n",
    "    \n",
    "    sil = silhouette_score(X, db.labels_)\n",
    "    print(\"eps={}, min_samples={}, n_clusters={}, sil={}\".format(eps, min_samples, n_clusters, sil))\n",
    "    \n",
    "    plt.figure(figsize=(16, 10));\n",
    "    plt.grid(True);   \n",
    "    \n",
    "    colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))];\n",
    "\n",
    "    for k in unique_labels:\n",
    "        if k == -1:        # Black used for noise.\n",
    "            col = [0, 0, 0, 1]\n",
    "        else:\n",
    "            col = colors[k]\n",
    "\n",
    "        xy = X[db.labels_ == k]\n",
    "        plt.plot(xy.iloc[:, 0], xy.iloc[:, 1], 'o', markerfacecolor=tuple(col), markeredgecolor='k', markersize=14);\n",
    "\n",
    "\n",
    "    plt.title('');\n",
    "    plt.title(\"DBSCAN (eps={}, min_samples={}, n_clusters = {:d}, sil={:.3f})\".format(eps, min_samples, n_clusters, sil), fontsize=20);\n",
    "    plt.xlabel('Annual Income (K)', fontsize=22);\n",
    "    plt.ylabel('Spending Score', fontsize=22);\n",
    "    plt.xticks(fontsize=18);\n",
    "    plt.yticks(fontsize=18);\n",
    "    \n",
    "\n",
    "epss = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]\n",
    "min_samples = range(1, 10)\n",
    "\n",
    "for prod in list(itertools.product(epss, min_samples)):\n",
    "    do_dbscan(X, prod[0], prod[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical (Agglomerative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')\n",
    "agg.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette_score(X, agg.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('default');\n",
    "\n",
    "plt.figure(figsize=(16, 10));\n",
    "plt.grid(True);\n",
    "\n",
    "plt.scatter(X.iloc[:, 0], X.iloc[:, 1], s=200, c=agg.labels_);\n",
    "plt.title(\"Agglomerative\", fontsize=20);\n",
    "plt.xlabel('Annual Income (K)', fontsize=22);\n",
    "plt.ylabel('Spending Score', fontsize=22);\n",
    "plt.xticks(fontsize=18);\n",
    "plt.yticks(fontsize=18);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dendograms, Linkages, and Distance Metrics\n",
    "\n",
    "Note that scikit learn does not itself currently support plotting dendograms. Instead, we'll use SciPy's `cluster.hierarchy` module to re-do the clustering, and plot the dendogram."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_agg(X, linkage, metric):\n",
    "    aggl = scipy.cluster.hierarchy.linkage(X, method=linkage, metric=metric)\n",
    "    \n",
    "    labels = scipy.cluster.hierarchy.fcluster(aggl, 5, criterion=\"maxclust\")\n",
    "    \n",
    "    sil = 0\n",
    "    n = len(set(labels))\n",
    "    if n > 1:\n",
    "        sil = silhouette_score(X , labels, metric=metric)\n",
    "    print(\"Linkage={}, Metric={}, Clusters={}, Silhouette={:.3}\".format(linkage, metric, n, sil))\n",
    "    \n",
    "    # Plot the dendogram\n",
    "    plt.figure(figsize=(12, 5))  \n",
    "    plt.title(\"Mall Dendogram (Linkage={}, Distance={}, N={}, Sil={:.3f})\".format(linkage, metric, n, sil))  \n",
    "    dend = scipy.cluster.hierarchy.dendrogram(aggl) \n",
    "    \n",
    "    \n",
    "    # Plot the points\n",
    "    plt.style.use('default');\n",
    "    plt.figure(figsize=(16, 10));\n",
    "    plt.grid(True);\n",
    "\n",
    "    plt.scatter(X.iloc[:, 0], X.iloc[:, 1], s=200, c=labels);\n",
    "    plt.title(\"Mall Agglomerative (Linkage={}, Distance={}, N={}, Sil={:.3f})\".format(linkage, metric, n, sil), fontsize=20);\n",
    "    plt.xlabel('Annual Income (K)', fontsize=22);\n",
    "    plt.ylabel('Spending Score', fontsize=22);\n",
    "    plt.xticks(fontsize=18);\n",
    "    plt.yticks(fontsize=18);\n",
    "\n",
    "    \n",
    "linkages = ['complete', 'ward', 'single', 'centroid', 'average']\n",
    "metrics = ['euclidean', 'minkowski', 'cityblock', 'cosine', 'correlation', 'chebyshev', 'canberra', 'mahalanobis']\n",
    "\n",
    "for prod in list(itertools.product(linkages, metrics)):\n",
    "    \n",
    "    # Some combos are not allowed\n",
    "    if (prod[0] in ['ward', 'centroid']) and prod[1] != 'euclidean':\n",
    "        continue\n",
    "        \n",
    "    plot_agg(X, prod[0], prod[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Mixture Models (GMM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmm = GaussianMixture(n_components=5, covariance_type='full').fit(X)\n",
    "\n",
    "labels = gmm.predict(X)\n",
    "silhouette_score(X, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From:\n",
    "# https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html\n",
    "def make_ellipses(gmm, ax):\n",
    "    for n, label in enumerate(set(labels)):\n",
    "        if gmm.covariance_type == 'full':\n",
    "            covariances = gmm.covariances_[n][:2, :2]\n",
    "        elif gmm.covariance_type == 'tied':\n",
    "            covariances = gmm.covariances_[:2, :2]\n",
    "        elif gmm.covariance_type == 'diag':\n",
    "            covariances = np.diag(gmm.covariances_[n][:2])\n",
    "        elif gmm.covariance_type == 'spherical':\n",
    "            covariances = np.eye(gmm.means_.shape[1]) * gmm.covariances_[n]\n",
    "        v, w = np.linalg.eigh(covariances)\n",
    "        u = w[0] / np.linalg.norm(w[0])\n",
    "        angle = np.arctan2(u[1], u[0])\n",
    "        angle = 180 * angle / np.pi  # convert to degrees\n",
    "        v = 2. * np.sqrt(2.) * np.sqrt(v)\n",
    "        ell = mpl.patches.Ellipse(gmm.means_[n, :2], v[0], v[1],\n",
    "                                  180 + angle, color='grey')\n",
    "        ell.set_clip_box(ax.bbox)\n",
    "        ell.set_alpha(0.2)\n",
    "        ax.add_artist(ell)\n",
    "        ax.set_aspect('equal', 'datalim')\n",
    "\n",
    "\n",
    "plt.style.use('default');\n",
    "\n",
    "plt.figure(figsize=(16, 10));\n",
    "plt.grid(True);\n",
    "\n",
    "plt.scatter(X.iloc[:, 0], X.iloc[:, 1], s=200, c=labels);\n",
    "make_ellipses(gmm, plt.gca())\n",
    "plt.title(\"GMM\", fontsize=20);\n",
    "plt.xlabel('Annual Income (K)', fontsize=22);\n",
    "plt.ylabel('Spending Score', fontsize=22);\n",
    "plt.xticks(fontsize=18);\n",
    "plt.yticks(fontsize=18);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical Data\n",
    "\n",
    "How do we deal with categorical data? Standard K-Means and Standard Euclidean distance don't work. \n",
    "\n",
    "We have a number of options:\n",
    "\n",
    "- Create dummy variables\n",
    "- Run PCA first\n",
    "- Use a categorical-aware distance metric, like Gower\n",
    "- Use a categorical-aware algorithm, like k-mediods/k-modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X1 = df.copy()\n",
    "\n",
    "# Create bins of Age\n",
    "X1['AgeBin'] = pd.cut(X1['Age'], [0, 30, 55, 100])\n",
    "X1 = X1.drop(['CustomerID', 'Age'], axis=1)\n",
    "\n",
    "features = ['AnnualIncome', 'SpendingScore']\n",
    "X1[features] = scaler.fit_transform(X1[features])\n",
    "\n",
    "X1.info()\n",
    "X1.describe().transpose()\n",
    "X1.head(n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running PCA First"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gower Distance\n",
    "\n",
    "Unfortunately, and strangely, sci-kit learn doesn't yet have an implementation of the Gower distance metric. And I coldn't find any package that did. However, I did find some random guy's code that implemented the metric, and it seems to work well. Kudos to him!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From: https://sourceforge.net/projects/gower-distance-4python/\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.spatial import distance \n",
    "from sklearn.utils import validation\n",
    "from sklearn.metrics import pairwise\n",
    "from scipy.sparse import issparse\n",
    "\n",
    "\n",
    "def check_pairwise_arrays(X, Y, precomputed=False, dtype=None):\n",
    "    X, Y, dtype_float = pairwise._return_float_dtype(X, Y)\n",
    "\n",
    "    warn_on_dtype = dtype is not None\n",
    "    estimator = 'check_pairwise_arrays'\n",
    "    if dtype is None:\n",
    "        dtype = dtype_float\n",
    "\n",
    "    if Y is X or Y is None:\n",
    "        X = Y = validation.check_array(X, accept_sparse='csr', dtype=dtype,\n",
    "                            warn_on_dtype=warn_on_dtype, estimator=estimator)\n",
    "    else:\n",
    "        X = validation.check_array(X, accept_sparse='csr', dtype=dtype,\n",
    "                        warn_on_dtype=warn_on_dtype, estimator=estimator)\n",
    "        Y = validation.check_array(Y, accept_sparse='csr', dtype=dtype,\n",
    "                        warn_on_dtype=warn_on_dtype, estimator=estimator)\n",
    "\n",
    "    if precomputed:\n",
    "        if X.shape[1] != Y.shape[0]:\n",
    "            raise ValueError(\"Precomputed metric requires shape \"\n",
    "                             \"(n_queries, n_indexed). Got (%d, %d) \"\n",
    "                             \"for %d indexed.\" %\n",
    "                             (X.shape[0], X.shape[1], Y.shape[0]))\n",
    "    elif X.shape[1] != Y.shape[1]:\n",
    "        raise ValueError(\"Incompatible dimension for X and Y matrices: \"\n",
    "                         \"X.shape[1] == %d while Y.shape[1] == %d\" % (\n",
    "                             X.shape[1], Y.shape[1]))\n",
    "\n",
    "    return X, Y\n",
    "\n",
    "\n",
    "# Vectorized Version\n",
    "def gower_distances(X, Y=None, feature_weight=None, categorical_features=None):\n",
    "    \"\"\"Computes the gower distances between X and Y\n",
    "\n",
    "    Gower is a similarity measure for categorical, boolean and numerical mixed\n",
    "    data.\n",
    "\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X : array-like, or pandas.DataFrame, shape (n_samples, n_features)\n",
    "\n",
    "    Y : array-like, or pandas.DataFrame, shape (n_samples, n_features)\n",
    "\n",
    "    feature_weight :  array-like, shape (n_features)\n",
    "        According the Gower formula, feature_weight is an attribute weight.\n",
    "\n",
    "    categorical_features: array-like, shape (n_features)\n",
    "        Indicates with True/False whether a column is a categorical attribute.\n",
    "        This is useful when categorical atributes are represented as integer\n",
    "        values. Categorical ordinal attributes are treated as numeric, and must\n",
    "        be marked as false.\n",
    "        \n",
    "        Alternatively, the categorical_features array can be represented only\n",
    "        with the numerical indexes of the categorical attribtes.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    similarities : ndarray, shape (n_samples, n_samples)\n",
    "\n",
    "    Notes\n",
    "    ------\n",
    "    The non-numeric features, and numeric feature ranges are determined from X and not Y.\n",
    "    No support for sparse matrices.\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    if issparse(X) or issparse(Y):\n",
    "        raise TypeError(\"Sparse matrices are not supported for gower distance\")\n",
    "        \n",
    "    y_none = Y is None\n",
    "    \n",
    "    \n",
    "    # It is necessary to convert to ndarray in advance to define the dtype\n",
    "    if not isinstance(X, np.ndarray):\n",
    "        X = np.asarray(X)\n",
    "\n",
    "    array_type = np.object\n",
    "    # this is necessary as strangelly the validator is rejecting numeric\n",
    "    # arrays with NaN\n",
    "    if  np.issubdtype(X.dtype, np.number) and (np.isfinite(X.sum()) or np.isfinite(X).all()):\n",
    "        array_type = type(np.zeros(1,X.dtype).flat[0])\n",
    "    \n",
    "    X, Y = check_pairwise_arrays(X, Y, precomputed=False, dtype=array_type)\n",
    "    \n",
    "    n_rows, n_cols = X.shape\n",
    "    \n",
    "    if categorical_features is None:\n",
    "        categorical_features = np.zeros(n_cols, dtype=bool)\n",
    "        for col in range(n_cols):\n",
    "            # In numerical columns, None is converted to NaN,\n",
    "            # and the type of NaN is recognized as a number subtype\n",
    "            if not np.issubdtype(type(X[0, col]), np.number):\n",
    "                categorical_features[col]=True\n",
    "    else:          \n",
    "        categorical_features = np.array(categorical_features)\n",
    "    \n",
    "    \n",
    "    #if categorical_features.dtype == np.int32:\n",
    "    if np.issubdtype(categorical_features.dtype, np.int):\n",
    "        new_categorical_features = np.zeros(n_cols, dtype=bool)\n",
    "        new_categorical_features[categorical_features] = True\n",
    "        categorical_features = new_categorical_features\n",
    "    \n",
    "    print(categorical_features)\n",
    "  \n",
    "    # Categorical columns\n",
    "    X_cat =  X[:,categorical_features]\n",
    "    \n",
    "    # Numerical columns\n",
    "    X_num = X[:,np.logical_not(categorical_features)]\n",
    "    ranges_of_numeric = None\n",
    "    max_of_numeric = None\n",
    "    \n",
    "        \n",
    "    # Calculates the normalized ranges and max values of numeric values\n",
    "    _ ,num_cols=X_num.shape\n",
    "    ranges_of_numeric = np.zeros(num_cols)\n",
    "    max_of_numeric = np.zeros(num_cols)\n",
    "    for col in range(num_cols):\n",
    "        col_array = X_num[:, col].astype(np.float32) \n",
    "        max = np.nanmax(col_array)\n",
    "        min = np.nanmin(col_array)\n",
    "     \n",
    "        if np.isnan(max):\n",
    "            max = 0.0\n",
    "        if np.isnan(min):\n",
    "            min = 0.0\n",
    "        max_of_numeric[col] = max\n",
    "        ranges_of_numeric[col] = (1 - min / max) if (max != 0) else 0.0\n",
    "\n",
    "\n",
    "    # This is to normalize the numeric values between 0 and 1.\n",
    "    X_num = np.divide(X_num ,max_of_numeric,out=np.zeros_like(X_num), where=max_of_numeric!=0)\n",
    "\n",
    "    \n",
    "    if feature_weight is None:\n",
    "        feature_weight = np.ones(n_cols)\n",
    "        \n",
    "    feature_weight_cat=feature_weight[categorical_features]\n",
    "    feature_weight_num=feature_weight[np.logical_not(categorical_features)]\n",
    "    \n",
    "    \n",
    "    y_n_rows, _ = Y.shape\n",
    "    \n",
    "    dm = np.zeros((n_rows, y_n_rows), dtype=np.float32)\n",
    "        \n",
    "    feature_weight_sum = feature_weight.sum()\n",
    "\n",
    "    Y_cat=None\n",
    "    Y_num=None\n",
    "    \n",
    "    if not y_none:\n",
    "        Y_cat = Y[:,categorical_features]\n",
    "        Y_num = Y[:,np.logical_not(categorical_features)]\n",
    "        # This is to normalize the numeric values between 0 and 1.\n",
    "        Y_num = np.divide(Y_num ,max_of_numeric,out=np.zeros_like(Y_num), where=max_of_numeric!=0)\n",
    "    else:\n",
    "        Y_cat=X_cat\n",
    "        Y_num = X_num\n",
    "        \n",
    "    for i in range(n_rows):\n",
    "        j_start= i\n",
    "        \n",
    "        # for non square results\n",
    "        if n_rows != y_n_rows:\n",
    "            j_start = 0\n",
    "\n",
    "      \n",
    "        Y_cat[j_start:n_rows,:]\n",
    "        Y_num[j_start:n_rows,:]\n",
    "        result= _gower_distance_row(X_cat[i,:], X_num[i,:],Y_cat[j_start:n_rows,:],\n",
    "                                    Y_num[j_start:n_rows,:],feature_weight_cat,feature_weight_num,\n",
    "                                    feature_weight_sum,categorical_features,ranges_of_numeric,\n",
    "                                    max_of_numeric) \n",
    "        dm[i,j_start:]=result\n",
    "        dm[i:,j_start]=result\n",
    "        \n",
    "\n",
    "    return dm\n",
    "\n",
    "\n",
    "def _gower_distance_row(xi_cat,xi_num,xj_cat,xj_num,feature_weight_cat,feature_weight_num,\n",
    "                        feature_weight_sum,categorical_features,ranges_of_numeric,max_of_numeric ):\n",
    "    # categorical columns\n",
    "    sij_cat = np.where(xi_cat == xj_cat,np.zeros_like(xi_cat),np.ones_like(xi_cat))\n",
    "    sum_cat = np.multiply(feature_weight_cat,sij_cat).sum(axis=1) \n",
    "\n",
    "    # numerical columns\n",
    "    abs_delta=np.absolute( xi_num-xj_num)\n",
    "    sij_num=np.divide(abs_delta, ranges_of_numeric, out=np.zeros_like(abs_delta), where=ranges_of_numeric!=0)\n",
    "\n",
    "    sum_num = np.multiply(feature_weight_num,sij_num).sum(axis=1)\n",
    "    sums= np.add(sum_cat,sum_num)\n",
    "    sum_sij = np.divide(sums,feature_weight_sum)\n",
    "    return sum_sij\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = gower_distances(X1)\n",
    "aggl = scipy.cluster.hierarchy.linkage(D)\n",
    "labels = scipy.cluster.hierarchy.fcluster(aggl, 6, criterion=\"maxclust\")\n",
    "n = len(set(labels))\n",
    "\n",
    "# Plot the dendogram\n",
    "plt.figure(figsize=(12, 5));\n",
    "plt.title(\"Mall Dendogram (N={})\".format(n)); \n",
    "dend = scipy.cluster.hierarchy.dendrogram(aggl) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.spatial.distance as dist\n",
    "import numpy as np\n",
    "\n",
    "# Prepare 3 vectors (data points) of 2 dimensions\n",
    "points = pd.DataFrame(data={'id':['A', 'B', 'C'], 'dim1':[1, 2, 5], 'dim2':[1, 1, 5]})\n",
    "display(points.head())\n",
    "\n",
    "for combo in itertools.combinations(points['id'], 2):\n",
    "    v1 = points[points['id'] == combo[0]][['dim1', 'dim2']]\n",
    "    v2 = points[points['id'] == combo[1]][['dim1', 'dim2']]\n",
    "    \n",
    "    print('\\nPoints {} and {}:'.format(combo[0], combo[1]))\n",
    "    print('Euclidean distance: {:.2f}'.format(dist.euclidean(v1, v2)))\n",
    "    print('Manhattan distance: {:.2f}'.format(dist.cityblock(v1, v2)))\n",
    "    print('Chebyshev distance: {:.2f}'.format(dist.chebyshev(v1, v2)))\n",
    "    print('Canberra distance: {:.2f}'.format(dist.canberra(v1, v2)))\n",
    "    print('Cosine distance: {:.2f}'.format(dist.cosine(v1, v2)))\n",
    "    \n",
    "    \n",
    "plt.style.use('default');\n",
    "\n",
    "plt.figure(figsize=(10, 10));\n",
    "plt.grid(True);\n",
    "\n",
    "plt.scatter(points['dim1'], points['dim2'], s=6000, marker='o', edgecolors='black');\n",
    "#plt.title(\"Distances\", fontsize=20);\n",
    "plt.xlabel('Feature 1', fontsize=32);\n",
    "plt.ylabel('Feature 2', fontsize=32);\n",
    "plt.xticks(fontsize=26);\n",
    "plt.yticks(fontsize=26);\n",
    "plt.xlim(left=0, right=6);\n",
    "plt.ylim(bottom=0, top=6);\n",
    "\n",
    "for i, txt in enumerate(points['id']):\n",
    "    plt.text(x=points['dim1'].iloc[i], y=points['dim2'].iloc[i], s=txt, fontsize=32, \n",
    "             horizontalalignment='center', verticalalignment='center', color='white');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = pd.DataFrame(data={'id':['A', 'B', 'C'], 'dim1':['young', 'young', 'old'], \n",
    "                            'dim2':['male', 'male', 'female'], 'dim3':['rural', 'city', 'city'],\n",
    "                            'dim4':['student', 'student', 'prof'], 'dim5':['low', 'low', 'high'],})\n",
    "display(points.head())\n",
    "\n",
    "for combo in itertools.combinations(points['id'], 2):\n",
    "    v1 = points[points['id'] == combo[0]][['dim1', 'dim2', 'dim3', 'dim4', 'dim5']]\n",
    "    v2 = points[points['id'] == combo[1]][['dim1', 'dim2', 'dim3', 'dim4', 'dim5']]\n",
    "    \n",
    "    print('\\nPoints {} and {}:'.format(combo[0], combo[1]))\n",
    "    print('Hamming distance: {:.2f}'.format(dist.hamming(v1, v2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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