{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "<span style=\"color:#008385\">\n",
    "\n",
    "**15-448: Machine Learning in a Nutshell**, *CMU-Qatar* Spring'20\n",
    "\n",
    "**Gianni A. Di Caro**, www.giannidicaro.com\n",
    "\n",
    "<u>Disclaimer:</u> This notebook was prepared for teaching purposes. It can include material from different web sources. I'll happy to explicitly acknowledge a source if required. \n",
    "</span>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Clustering-examples-with-sklearn\" data-toc-modified-id=\"Clustering-examples-with-sklearn-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Clustering examples with sklearn</a></span><ul class=\"toc-item\"><li><span><a href=\"#Shopping-mall-customers\" data-toc-modified-id=\"Shopping-mall-customers-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Shopping mall customers</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering examples with sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shopping mall customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     CustomerID        Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
      "0             1          Male   19                  15                      39\n",
      "1             2          Male   21                  15                      81\n",
      "2             3     Female      20                  16                       6\n",
      "3             4        Female   23                  16                      77\n",
      "4             5        Female   31                  17                      40\n",
      "..          ...           ...  ...                 ...                     ...\n",
      "195         196        Female   35                 120                      79\n",
      "196         197        Female   45                 126                      28\n",
      "197         198          Male   32                 126                      74\n",
      "198         199          Male   32                 137                      18\n",
      "199         200          Male   30                 137                      83\n",
      "\n",
      "[200 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Read the CSV file\n",
    "customer_data = pd.read_csv('../datasets/mall_customers.csv')\n",
    "\n",
    "print(customer_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's cluster for the `Annual Income` and `Spending Score` to check for correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Annual Income (k$)  Spending Score (1-100)\n",
       "0                    15                      39\n",
       "1                    15                      81\n",
       "2                    16                       6\n",
       "3                    16                      77\n",
       "4                    17                      40\n",
       "..                  ...                     ...\n",
       "195                 120                      79\n",
       "196                 126                      28\n",
       "197                 126                      74\n",
       "198                 137                      18\n",
       "199                 137                      83\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = customer_data.iloc[:, [3, 4]]\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 2 columns):\n",
      "Annual Income (k$)        200 non-null int64\n",
      "Spending Score (1-100)    200 non-null int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 3.2 KB\n"
     ]
    }
   ],
   "source": [
    "features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Annual Income (k$)  Spending Score (1-100)\n",
       "count          200.000000              200.000000\n",
       "mean            60.560000               50.200000\n",
       "std             26.264721               25.823522\n",
       "min             15.000000                1.000000\n",
       "25%             41.500000               34.750000\n",
       "50%             61.500000               50.000000\n",
       "75%             78.000000               73.000000\n",
       "max            137.000000               99.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a9527b8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.plot.scatter(x=0, y=1, c='r', s=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "n_clusters = 6\n",
    "kmeans = KMeans(n_clusters=n_clusters)\n",
    "\n",
    "kmeans.fit(features)\n",
    "\n",
    "labels = kmeans.labels_\n",
    "\n",
    "centers = kmeans.cluster_centers_\n",
    "\n",
    "centers_labels = kmeans.predict(centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0\n",
      " 4 0 4 0 4 0 2 0 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 1 3 1 2 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 2 1 3 1 3 1\n",
      " 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 5 3 5 3\n",
      " 5 3 5 3 5 3 5 3 5 3 5 3 5 3 5]\n"
     ]
    }
   ],
   "source": [
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "print(centers_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams['figure.dpi']= 180 # set the resolution to x dpi\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "cmap_bold = ListedColormap(['darkorange', 'c', 'blue', 'darkred', 'lime', 'cyan'])\n",
    "\n",
    "plt.figsize=(10, 3)\n",
    "plt.xlabel(features.columns[0])\n",
    "plt.ylabel(features.columns[1])\n",
    "\n",
    "plt.scatter(features.iloc[:, 0], features.iloc[:, 1], \n",
    "            c=labels, marker='.', s=20, cmap=cmap_bold)\n",
    "\n",
    "scatter = plt.scatter(centers[:,0], centers[:,1], \n",
    "                      c=centers_labels, marker='.', s=150, \n",
    "                      edgecolors='k',\n",
    "                      cmap=cmap_bold)\n",
    "\n",
    "# produce a legend with the unique colors from the scatter\n",
    "legend = plt.legend(*scatter.legend_elements(), loc=\"center right\", title=\"Clusters\", fontsize=6)\n",
    "legend.get_title().set_fontsize('8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try out with different cluster numbers!!  \n",
    "\n",
    "Predict what it will happen based on the objective function K-Means is trying to minimize."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}