SOM with Minisom to implement Credit card fraud detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![somimage](https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcStCGq9GejI7MAnHYvG0mfetdvBTN5v3DEQLg&usqp=CAU)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6AX0Y23PnyOI"
   },
   "source": [
    "### Importing the libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gaSkOFKKBB3b"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SCl7ERn_n539"
   },
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4hgeaN3CoRfl"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Credit_Card_Applications.csv')\n",
    "X = dataset.iloc[:, :-1].values \n",
    "y = dataset.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>CustomerID</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15776156</td>\n",
       "      <td>1</td>\n",
       "      <td>22.08</td>\n",
       "      <td>11.46</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.585</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>1213</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15739548</td>\n",
       "      <td>0</td>\n",
       "      <td>22.67</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15662854</td>\n",
       "      <td>0</td>\n",
       "      <td>29.58</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15687688</td>\n",
       "      <td>0</td>\n",
       "      <td>21.67</td>\n",
       "      <td>11.50</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15715750</td>\n",
       "      <td>1</td>\n",
       "      <td>20.17</td>\n",
       "      <td>8.17</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1.960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  A1     A2     A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
       "0    15776156   1  22.08  11.46   2   4   4  1.585   0   0    0    1    2   \n",
       "1    15739548   0  22.67   7.00   2   8   4  0.165   0   0    0    0    2   \n",
       "2    15662854   0  29.58   1.75   1   4   4  1.250   0   0    0    1    2   \n",
       "3    15687688   0  21.67  11.50   1   5   3  0.000   1   1   11    1    2   \n",
       "4    15715750   1  20.17   8.17   2   6   4  1.960   1   1   14    0    2   \n",
       "\n",
       "   A13   A14  Class  \n",
       "0  100  1213      0  \n",
       "1  160     1      0  \n",
       "2  280     1      0  \n",
       "3    0     1      1  \n",
       "4   60   159      1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(690, 16)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fYHJ0dd6n-d4"
   },
   "source": [
    "## Feature Scaling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sY7JiUxwoSOd"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range = (0,1))\n",
    "X = sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##To install minisom\n",
    "!pip install minisom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aPp-7wfNoAhR"
   },
   "source": [
    "##Training the SOM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from minisom import MiniSom\n",
    "som=MiniSom(12,12,15,sigma=1.0,learning_rate=0.5)\n",
    "som.random_weights_init(X)\n",
    "som.train_random(X,num_iteration=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YiU27C-ToCnH"
   },
   "source": [
    "##Visualizing the results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To visualize result we will use MID(mean internode distance).Higher be the MID far is the node far from his neighbours more likely to be an outlier we use color to specify MID.The larger be the MID the color gets close to white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import pcolor,colorbar,plot,show,bone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "bone()\n",
    "pcolor(som.distance_map().T)## To calcuate MID\n",
    "colorbar()\n",
    "# we create marker\n",
    "# red circle who didn't get approval and blue square who  got approval\n",
    "markers=[\"*\",\"s\"]\n",
    "colors=[\"r\",\"b\"]\n",
    "#we will create a loop which iterates through the customers for each customer we will mark the winning node depending the customer get approval or not this winning node will be colored\n",
    "for i,x in enumerate(X):\n",
    "    win_node=som.winner(x)\n",
    "    plot(win_node[0]+0.5,win_node[1]+0.5,markers[y[i]],markeredgecolor=colors[y[i]],markerfacecolor=None,markeredgewidth=3,markersize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here the node with color close to white are potential frauds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "mappings=som.win_map(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "frauds=np.concatenate((mappings[(10,0)],mappings[(10,2)],mappings[(8,4)],mappings[(10,10)],mappings[(6,10)],mappings[(5,1)],mappings[(1,4)],mappings[(9,7)],mappings[(8,8)],mappings[(9,8)],mappings[(9,2)]),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    CustomerID  A1    A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "64    15766183   0  24.5  0.5   2  11   8  1.5   1   0    0    0    2  280   \n",
      "\n",
      "    A14  Class  \n",
      "64  825      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "170    15684722   0  27.67  1.5   2   7   4  2.0   1   0    0    0    1  368   \n",
      "\n",
      "     A14  Class  \n",
      "170    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "240    15772329   0  28.08  15.0   1  10   9  0.0   1   0    0    0    2    0   \n",
      "\n",
      "       A14  Class  \n",
      "240  13213      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "254    15734649   0  40.83  10.0   2  11   8  1.75   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "254   29  838      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  \\\n",
      "338    15646082   0  18.83  4.415   1   8   8  3.0   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "338  240    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "350    15717629   0  25.17  2.875   2  14   8  0.875   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "350  360    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "370    15757188   0  20.83  3.0   2   6   4  0.04   1   0    0    0    2  100   \n",
      "\n",
      "     A14  Class  \n",
      "370    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2      A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  \\\n",
      "414    15604536   0  20.5  11.835   2   8   8  6.0   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "414  340    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "416    15635598   0  24.58  0.67   2   6   8  1.75   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "416  400    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "419    15647191   0  32.33  0.54   2  13   4  0.04   1   0    0    0    2   \n",
      "\n",
      "     A13    A14  Class  \n",
      "419  440  11178      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "566    15623369   0  44.33  0.0   2   8   4  2.5   1   0    0    0    2    0   \n",
      "\n",
      "     A14  Class  \n",
      "566    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "632    15793896   0  28.42  3.5   2   9   4  0.835   1   0    0    0    1   \n",
      "\n",
      "     A13  A14  Class  \n",
      "632  280    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "663    15650591   0  26.08  8.665   2   6   4  1.415   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "663  160  151      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2      A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "682    15633608   0  22.92  11.585   2  13   4  0.04   1   0    0    0    2   \n",
      "\n",
      "     A13   A14  Class  \n",
      "682   80  1350      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "687    15675450   0  18.83  9.54   2   6   4  0.085   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "687  100    1      1  \n",
      "-----------------------------------\n",
      "    CustomerID  A1    A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "36    15686461   1  25.0  12.5   2   6   4  3.0   1   0    0    1    1   20   \n",
      "\n",
      "    A14  Class  \n",
      "36    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "186    15686885   1  37.17  4.0   2   8   5  5.0   1   0    0    1    1  280   \n",
      "\n",
      "     A14  Class  \n",
      "186    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "290    15680895   1  32.33  7.5   2  10   5  1.585   1   0    0    1    1   \n",
      "\n",
      "     A13  A14  Class  \n",
      "290  420    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "356    15623072   1  31.57  0.5   2   8   5  0.835   1   0    0    1    1   \n",
      "\n",
      "     A13  A14  Class  \n",
      "356  320    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "654    15788215   1  31.57  0.375   2   2   4  0.875   1   0    0    1    1   \n",
      "\n",
      "     A13  A14  Class  \n",
      "654  928    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "677    15722535   1  23.0  0.75   2   7   4  0.5   1   0    0    1    1  320   \n",
      "\n",
      "     A14  Class  \n",
      "677    1      0  \n",
      "-----------------------------------\n",
      "    CustomerID  A1     A2      A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  \\\n",
      "70    15692137   1  48.75  26.335   1   1   1  0.0   1   0    0    1    2   \n",
      "\n",
      "    A13  A14  Class  \n",
      "70    0    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  \\\n",
      "390    15676909   1  73.42  17.75   2   1   1  0.0   1   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "390    0    1      1  \n",
      "-----------------------------------\n",
      "    CustomerID  A1     A2      A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  \\\n",
      "86    15575605   0  24.75  13.665   2  11   8  1.5   0   0    0    0    2   \n",
      "\n",
      "    A13  A14  Class  \n",
      "86  280    2      0  \n",
      "-----------------------------------\n",
      "    CustomerID  A1     A2    A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "94    15644453   0  18.17  10.0   1  11   8  0.165   0   0    0    0    2   \n",
      "\n",
      "    A13  A14  Class  \n",
      "94  340    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "167    15699238   0  22.5  8.46   1  14   4  2.46   0   0    0    0    2  164   \n",
      "\n",
      "     A14  Class  \n",
      "167    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2     A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "399    15577771   0  29.5  1.085   1  14   4  1.0   0   0    0    0    2  280   \n",
      "\n",
      "     A14  Class  \n",
      "399   14      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "495    15599272   0  33.67  0.375   2  13   4  0.375   0   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "495  300   45      1  \n",
      "-----------------------------------\n",
      "    CustomerID  A1     A2   A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  A13  \\\n",
      "40    15631685   1  23.17  0.0   2  13   4  0.085   1   0    0    0    2    0   \n",
      "\n",
      "    A14  Class  \n",
      "40    1      1  \n",
      "-----------------------------------\n",
      "    CustomerID  A1     A2     A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "42    15753591   1  26.75  1.125   2  14   8  1.25   1   0    0    0    2   \n",
      "\n",
      "    A13   A14  Class  \n",
      "42    0  5299      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2     A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "173    15682540   1  62.5  12.75   1   8   8  5.0   1   0    0    0    2  112   \n",
      "\n",
      "     A14  Class  \n",
      "173    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "194    15797246   1  23.42  0.585   2   8   8  0.085   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "194  180    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "227    15614813   1  20.25  9.96   2  10   2  0.0   1   0    0    0    2    0   \n",
      "\n",
      "     A14  Class  \n",
      "227    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "243    15701687   1  80.25  5.5   2   8   4  0.54   1   0    0    0    2    0   \n",
      "\n",
      "     A14  Class  \n",
      "243  341      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "261    15571081   1  22.67  10.5   2  11   8  1.335   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "261  100    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "287    15751167   1  41.33  0.0   2   8   5  15.0   1   0    0    0    2    0   \n",
      "\n",
      "     A14  Class  \n",
      "287    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "321    15778142   1  22.08  11.0   2  13   4  0.665   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "321  100    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "425    15625501   1  31.42  15.5   2   8   4  0.5   1   0    0    0    2  120   \n",
      "\n",
      "     A14  Class  \n",
      "425    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "440    15812918   1  41.75  0.96   2  14   4  2.5   1   0    0    0    2  510   \n",
      "\n",
      "     A14  Class  \n",
      "440  601      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "443    15715519   1  29.92  1.835   2   8   8  4.335   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "443  260  201      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "446    15630592   1  51.42  0.04   2  14   8  0.04   1   0    0    0    2   \n",
      "\n",
      "     A13   A14  Class  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "446    0  3001      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "481    15812766   1  21.5  9.75   2   8   4  0.25   1   0    0    0    2  140   \n",
      "\n",
      "     A14  Class  \n",
      "481    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "484    15784526   1  42.83  4.625   2  11   4  4.58   1   0    0    0    1   \n",
      "\n",
      "     A13  A14  Class  \n",
      "484    0    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "496    15589541   1  25.75  0.75   2   8   5  0.25   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "496  349   24      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "557    15757306   1  23.33  1.5   2   8   8  1.415   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "557  422  201      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "609    15598574   1  19.17  9.5   2   9   4  1.5   1   0    0    0    2  120   \n",
      "\n",
      "      A14  Class  \n",
      "609  2207      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "671    15711759   1  22.67  2.54   1   8   8  2.585   1   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "671    0    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "685    15808223   1  31.57  10.5   2  14   4  6.5   1   0    0    0    2    0   \n",
      "\n",
      "     A14  Class  \n",
      "685    1      1  \n",
      "-----------------------------------\n",
      "    CustomerID  A1     A2     A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "32    15580914   1  52.33  1.375   1   8   8  9.46   1   0    0    1    2   \n",
      "\n",
      "    A13  A14  Class  \n",
      "32  200  101      0  \n",
      "-----------------------------------\n",
      "    CustomerID  A1    A2     A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "58    15684512   1  42.5  4.915   1   9   4  3.165   1   0    0    1    2   \n",
      "\n",
      "    A13   A14  Class  \n",
      "58   52  1443      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "179    15678210   1  42.17  5.04   2  11   8  12.75   1   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "179   92    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "202    15664615   1  33.17  1.04   2  12   8  6.5   1   0    0    1    2  164   \n",
      "\n",
      "       A14  Class  \n",
      "202  31286      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "232    15607986   1  40.92  2.25   1  14   8  10.0   1   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "232  176    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "341    15578722   1  19.42  1.5   1  13   4  2.0   1   0    0    1    2  100   \n",
      "\n",
      "     A14  Class  \n",
      "341   21      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "465    15569682   1  59.67  1.54   2  11   4  0.125   1   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "465  260    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "505    15587038   1  19.33  9.5   2  11   4  1.0   1   0    0    1    2   60   \n",
      "\n",
      "     A14  Class  \n",
      "505  401      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "630    15591107   1  32.0  1.75   1  10   8  0.04   1   0    0    1    2  393   \n",
      "\n",
      "     A14  Class  \n",
      "630    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "648    15674583   1  28.75  1.5   1   8   4  1.5   1   0    0    1    2    0   \n",
      "\n",
      "     A14  Class  \n",
      "648  226      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2   A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "650    15588854   1  26.0  1.0   2  11   4  1.75   1   0    0    1    2  280   \n",
      "\n",
      "     A14  Class  \n",
      "650    1      1  \n",
      "-----------------------------------\n",
      "   CustomerID  A1     A2   A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  A13  \\\n",
      "1    15739548   0  22.67  7.0   2   8   4  0.165   0   0    0    0    2  160   \n",
      "\n",
      "   A14  Class  \n",
      "1    1      0  \n",
      "-----------------------------------\n",
      "    CustomerID  A1     A2     A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "20    15699839   0  28.17  0.585   2   6   4  0.04   0   0    0    0    2   \n",
      "\n",
      "    A13   A14  Class  \n",
      "20  260  1005      0  \n",
      "-----------------------------------\n",
      "    CustomerID  A1     A2     A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "96    15783883   0  38.92  1.665   2   6   4  0.25   0   0    0    0    2   \n",
      "\n",
      "    A13  A14  Class  \n",
      "96    0  391      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "100    15756538   0  39.08  4.0   2   8   4  3.0   0   0    0    0    2  480   \n",
      "\n",
      "     A14  Class  \n",
      "100    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "131    15801473   0  20.83  0.5   1  10   2  1.0   0   0    0    0    2  260   \n",
      "\n",
      "     A14  Class  \n",
      "131    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "135    15728523   0  38.33  4.415   2   8   4  0.125   0   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "135  160    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "306    15794204   0  19.58  0.665   2   9   4  1.665   0   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "306  220    6      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  \\\n",
      "318    15734008   0  15.75  0.375   2   8   4  1.0   0   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "318  120   19      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "394    15757434   0  25.58  0.0   2   8   4  0.0   0   0    0    0    3  184   \n",
      "\n",
      "     A14  Class  \n",
      "394    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2   A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "400    15769548   0  32.0  6.0   2   2   4  1.25   0   0    0    0    2  272   \n",
      "\n",
      "     A14  Class  \n",
      "400    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "452    15673907   0  71.58  0.0   2   8   4  0.0   0   0    0    0    3  184   \n",
      "\n",
      "     A14  Class  \n",
      "452    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "477    15737909   0  20.83  8.5   2   8   4  0.165   0   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "477    0  352      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  \\\n",
      "519    15779207   0  27.33  1.665   2   1   1  0.0   0   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "519  340    2      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "531    15711299   0  22.67  0.335   2  11   4  0.75   0   0    0    0    1   \n",
      "\n",
      "     A13  A14  Class  \n",
      "531  160    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "552    15752578   0  41.58  1.04   2   6   4  0.665   0   0    0    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "552  240  238      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "660    15598586   1  26.67  2.71   1  13   4  5.25   1   1    1    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "660  211    1      1  \n",
      "-----------------------------------\n",
      "    CustomerID  A1    A2     A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "66    15568162   0  19.5  0.165   2  11   4  0.04   0   0    0    1    2  380   \n",
      "\n",
      "    A14  Class  \n",
      "66    1      0  \n",
      "-----------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     CustomerID  A1     A2     A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "188    15662152   0  29.75  0.665   2   9   4  0.25   0   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "188  300    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "262    15734578   0  21.75  11.75   2   8   4  0.25   0   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "262  180    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "327    15570508   0  51.92  6.5   2   3   5  3.085   0   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "327   73    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  \\\n",
      "365    15641733   0  50.25  0.835   2   6   4  0.5   0   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "365  240  118      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "386    15660144   0  26.17  2.0   2   5   3  0.0   0   0    0    1    2  276   \n",
      "\n",
      "     A14  Class  \n",
      "386    2      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "397    15603565   0  22.92  1.25   2  11   4  0.25   0   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "397  120  810      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "428    15664720   0  24.83  4.5   2   9   4  1.0   0   0    0    1    2  360   \n",
      "\n",
      "     A14  Class  \n",
      "428    7      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "498    15645820   0  18.08  0.375   3  13   1  10.0   0   0    0    1    1   \n",
      "\n",
      "     A13  A14  Class  \n",
      "498  300    1      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2   A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  A13  \\\n",
      "573    15567919   0  23.5  1.5   2   9   4  0.875   0   0    0    1    2  160   \n",
      "\n",
      "     A14  Class  \n",
      "573    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "576    15687634   0  33.17  2.25   1  13   4  3.5   0   0    0    1    2  200   \n",
      "\n",
      "     A14  Class  \n",
      "576  142      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "608    15638751   0  27.67  2.04   2   9   4  0.25   0   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "608  180   51      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "668    15606554   0  32.25  1.5   2   8   4  0.25   0   0    0    1    2  372   \n",
      "\n",
      "     A14  Class  \n",
      "668  123      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "144    15761158   0  16.5  1.25   2  11   4  0.25   0   1    1    0    2  108   \n",
      "\n",
      "     A14  Class  \n",
      "144   99      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2     A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  \\\n",
      "273    15666166   0  20.08  0.125   2  11   4  1.0   0   1    1    0    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "273  240  769      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "521    15665087   0  18.25  10.0   2   9   4  1.0   0   1    1    0    2  120   \n",
      "\n",
      "     A14  Class  \n",
      "521    2      0  \n",
      "-----------------------------------\n",
      "   CustomerID  A1     A2     A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  \\\n",
      "0    15776156   1  22.08  11.46   2   4   4  1.585   0   0    0    1    2   \n",
      "\n",
      "   A13   A14  Class  \n",
      "0  100  1213      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  A13  \\\n",
      "250    15745804   1  24.08  9.0   2   6   4  0.25   0   0    0    1    2    0   \n",
      "\n",
      "     A14  Class  \n",
      "250    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2      A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  \\\n",
      "301    15788224   1  20.0  11.045   2   8   4  2.0   0   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "301  136    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "391    15764922   1  22.5  11.5   1   7   4  1.5   0   0    0    1    2    0   \n",
      "\n",
      "      A14  Class  \n",
      "391  4001      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2   A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "431    15754578   1  32.08  4.0   1  13   4  1.5   0   0    0    1    2  120   \n",
      "\n",
      "     A14  Class  \n",
      "431    1      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1    A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "500    15790113   1  17.5  22.0   3   1   7  0.0   0   0    0    1    3  450   \n",
      "\n",
      "        A14  Class  \n",
      "500  100001      1  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6    A7  A8  A9  A10  A11  A12  \\\n",
      "623    15796413   1  23.58  1.79   2   8   4  0.54   0   0    0    1    2   \n",
      "\n",
      "     A13  A14  Class  \n",
      "623  136    2      0  \n",
      "-----------------------------------\n",
      "     CustomerID  A1     A2    A3  A4  A5  A6   A7  A8  A9  A10  A11  A12  A13  \\\n",
      "652    15776921   1  26.33  13.0   2  10   2  0.0   0   0    0    1    2  140   \n",
      "\n",
      "      A14  Class  \n",
      "652  1111      0  \n",
      "-----------------------------------\n"
     ]
    }
   ],
   "source": [
    "ids=[]\n",
    "for i in sc.inverse_transform(frauds)[:,0]:\n",
    "    print(dataset.loc[dataset[\"CustomerID\"]==i])\n",
    "    print(\"-----------------------------------\")\n",
    "    ids.append(dataset.loc[dataset[\"CustomerID\"]==i,\"CustomerID\"].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4C-I_jBfoEkP"
   },
   "source": [
    "## Finding the frauds\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "frXqAlpo84b1"
   },
   "source": [
    "##Printing the Fraunch Clients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_fraud=dataset[dataset.CustomerID.isin(ids.flatten())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "20     0\n",
       "32     0\n",
       "36     1\n",
       "      ..\n",
       "671    1\n",
       "677    0\n",
       "682    1\n",
       "685    1\n",
       "687    1\n",
       "Name: Class, Length: 99, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_fraud.Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.3090394 , 0.61183181, 0.74063874],\n",
       "       [0.17135028, 0.3526552 , 0.78711018],\n",
       "       [0.77894028, 0.20029293, 0.53569189],\n",
       "       [0.79544392, 0.77852448, 0.69892505],\n",
       "       [0.7648682 , 0.83713145, 0.13360056],\n",
       "       [0.17698875, 0.29263051, 0.60729175],\n",
       "       [0.42839315, 0.28278238, 0.23001543],\n",
       "       [0.6732647 , 0.6168658 , 0.13364586],\n",
       "       [0.20895833, 0.18978631, 0.16773248],\n",
       "       [0.86855088, 0.31445983, 0.81124909],\n",
       "       [0.77894028, 0.20029293, 0.53569189],\n",
       "       [0.68154399, 0.81609936, 0.65359823],\n",
       "       [0.89402169, 0.51037567, 0.49113637],\n",
       "       [0.6732647 , 0.6168658 , 0.13364586],\n",
       "       [0.19001615, 0.59355147, 0.76097725],\n",
       "       [0.41792879, 0.77979459, 0.86166312],\n",
       "       [0.42925101, 0.80751215, 0.31005187],\n",
       "       [0.42839315, 0.28278238, 0.23001543],\n",
       "       [0.69946943, 0.23807352, 0.39975446],\n",
       "       [0.83194454, 0.87819045, 0.08155599],\n",
       "       [0.6732647 , 0.6168658 , 0.13364586],\n",
       "       [0.20895833, 0.18978631, 0.16773248],\n",
       "       [0.17135028, 0.3526552 , 0.78711018],\n",
       "       [0.59137491, 0.24192388, 0.19952257],\n",
       "       [0.32351465, 0.69035885, 0.36037116],\n",
       "       [0.524199  , 0.56885401, 0.41762855],\n",
       "       [0.64679117, 0.12788423, 0.17598665],\n",
       "       [0.76369582, 0.73298521, 0.83780531],\n",
       "       [0.20895833, 0.18978631, 0.16773248],\n",
       "       [0.41792879, 0.77979459, 0.86166312],\n",
       "       [0.68277226, 0.91144193, 0.53501698],\n",
       "       [0.24661556, 0.23642606, 0.284085  ],\n",
       "       [0.86855088, 0.31445983, 0.81124909],\n",
       "       [0.14390161, 0.86918258, 0.46630192],\n",
       "       [0.50996188, 0.18295428, 0.10245363],\n",
       "       [0.84576297, 0.67218371, 0.24699823],\n",
       "       [0.79544392, 0.77852448, 0.69892505],\n",
       "       [0.76369582, 0.73298521, 0.83780531],\n",
       "       [0.3718188 , 0.23858149, 0.13788917],\n",
       "       [0.41792879, 0.77979459, 0.86166312],\n",
       "       [0.73851198, 0.15778692, 0.23637208],\n",
       "       [0.68277226, 0.91144193, 0.53501698],\n",
       "       [0.64679117, 0.12788423, 0.17598665],\n",
       "       [0.59137491, 0.24192388, 0.19952257],\n",
       "       [0.82314354, 0.49612187, 0.32914701],\n",
       "       [0.86855088, 0.31445983, 0.81124909],\n",
       "       [0.84576297, 0.67218371, 0.24699823],\n",
       "       [0.7648682 , 0.83713145, 0.13360056],\n",
       "       [0.50996188, 0.18295428, 0.10245363],\n",
       "       [0.83194454, 0.87819045, 0.08155599],\n",
       "       [0.16526494, 0.18321191, 0.78281845],\n",
       "       [0.32351465, 0.69035885, 0.36037116],\n",
       "       [0.82412068, 0.8733021 , 0.56460164],\n",
       "       [0.24661556, 0.23642606, 0.284085  ],\n",
       "       [0.78742035, 0.77111638, 0.11344969],\n",
       "       [0.86855088, 0.31445983, 0.81124909],\n",
       "       [0.17135028, 0.3526552 , 0.78711018],\n",
       "       [0.82314354, 0.49612187, 0.32914701],\n",
       "       [0.17698875, 0.29263051, 0.60729175],\n",
       "       [0.79544392, 0.77852448, 0.69892505],\n",
       "       [0.619405  , 0.73323853, 0.82537976],\n",
       "       [0.93144517, 0.37371427, 0.63455489],\n",
       "       [0.34706964, 0.16684754, 0.84800998],\n",
       "       [0.83194454, 0.87819045, 0.08155599],\n",
       "       [0.6732647 , 0.6168658 , 0.13364586],\n",
       "       [0.34706964, 0.16684754, 0.84800998],\n",
       "       [0.59137491, 0.24192388, 0.19952257],\n",
       "       [0.77894028, 0.20029293, 0.53569189],\n",
       "       [0.78742035, 0.77111638, 0.11344969],\n",
       "       [0.20895833, 0.18978631, 0.16773248],\n",
       "       [0.73851198, 0.15778692, 0.23637208],\n",
       "       [0.524199  , 0.56885401, 0.41762855],\n",
       "       [0.3090394 , 0.61183181, 0.74063874],\n",
       "       [0.15003714, 0.87550876, 0.71766373],\n",
       "       [0.24661556, 0.23642606, 0.284085  ],\n",
       "       [0.86855088, 0.31445983, 0.81124909],\n",
       "       [0.82314354, 0.49612187, 0.32914701],\n",
       "       [0.3090394 , 0.61183181, 0.74063874],\n",
       "       [0.18314915, 0.19733609, 0.49652942],\n",
       "       [0.49763221, 0.37498002, 0.39551791],\n",
       "       [0.83194454, 0.87819045, 0.08155599],\n",
       "       [0.84576297, 0.67218371, 0.24699823],\n",
       "       [0.4161208 , 0.90132887, 0.48788457],\n",
       "       [0.35159702, 0.3814145 , 0.81555526],\n",
       "       [0.59137491, 0.24192388, 0.19952257],\n",
       "       [0.14390161, 0.86918258, 0.46630192],\n",
       "       [0.34706964, 0.16684754, 0.84800998],\n",
       "       [0.73851198, 0.15778692, 0.23637208],\n",
       "       [0.16526494, 0.18321191, 0.78281845],\n",
       "       [0.93144517, 0.37371427, 0.63455489],\n",
       "       [0.524199  , 0.56885401, 0.41762855],\n",
       "       [0.17135028, 0.3526552 , 0.78711018],\n",
       "       [0.32351465, 0.69035885, 0.36037116],\n",
       "       [0.15003714, 0.87550876, 0.71766373],\n",
       "       [0.35159702, 0.3814145 , 0.81555526],\n",
       "       [0.34706964, 0.16684754, 0.84800998],\n",
       "       [0.86855088, 0.31445983, 0.81124909],\n",
       "       [0.68277226, 0.91144193, 0.53501698],\n",
       "       [0.50996188, 0.18295428, 0.10245363],\n",
       "       [0.82412068, 0.8733021 , 0.56460164]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## This vector will assign a code book vector(i.e a weight of BMU to each data point)\n",
    "som.quantization(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11846434816033295"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is the average of distance between each input and its BMU \n",
    "som.quantization_error(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 7, 3)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import patches\n",
    "fig=plt.figure(figsize=(8,8))\n",
    "ax=fig.add_subplot(111,aspect=\"equal\")\n",
    "ax.set_xlim((0,net.shape[0]+1))\n",
    "ax.set_ylim((0,net.shape[1]+1))\n",
    "ax.set_title(\"Self Organising map 1000 Iteration\")\n",
    "for x in range(1,net.shape[0]+1):\n",
    "    for y in range(1,net.shape[1]+1):\n",
    "        ax.add_patch(patches.Rectangle((x-0.5,y-0.5),1,1,facecolor=net[x-1,y-1,:],edgecolor=\"None\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As you can see similar color have been clustered together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.25618517, 0.4303379 , 0.50119997, 0.50656637, 0.3281523 ,\n",
       "        0.3441321 , 0.15497103],\n",
       "       [0.45397802, 0.75383017, 0.865077  , 0.80979328, 0.64714014,\n",
       "        0.65038554, 0.3971628 ],\n",
       "       [0.42311589, 0.76738481, 0.93358917, 0.84660759, 0.82238702,\n",
       "        0.86237078, 0.51913002],\n",
       "       [0.31533857, 0.8559958 , 1.        , 0.81905289, 0.88043907,\n",
       "        0.89764397, 0.57581481],\n",
       "       [0.454697  , 0.7992618 , 0.84786716, 0.84090392, 0.8795648 ,\n",
       "        0.96247456, 0.51935149],\n",
       "       [0.40960878, 0.70740028, 0.79046968, 0.74098407, 0.70953079,\n",
       "        0.79071473, 0.51141447],\n",
       "       [0.19487926, 0.34422079, 0.42524161, 0.42005621, 0.34813174,\n",
       "        0.34725802, 0.25892914]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "som.distance_map()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {(2, 0): Counter({'Mali': 1, 'Kali': 2}),\n",
       "             (1, 1): Counter({'Sali': 2, 'Mali': 2}),\n",
       "             (2, 6): Counter({'Mali': 1, 'Kali': 1, 'Sali': 1}),\n",
       "             (6, 1): Counter({'Kali': 2, 'Sali': 1}),\n",
       "             (5, 4): Counter({'Sali': 2}),\n",
       "             (1, 2): Counter({'Kali': 1, 'Sali': 1}),\n",
       "             (1, 4): Counter({'Sali': 1, 'Kali': 1}),\n",
       "             (6, 6): Counter({'Mali': 1, 'Kali': 3}),\n",
       "             (0, 3): Counter({'Mali': 2, 'Sali': 1, 'Kali': 1}),\n",
       "             (3, 6): Counter({'Kali': 3, 'Mali': 2, 'Sali': 1}),\n",
       "             (5, 1): Counter({'Kali': 1}),\n",
       "             (4, 5): Counter({'Sali': 1}),\n",
       "             (2, 1): Counter({'Kali': 1}),\n",
       "             (4, 0): Counter({'Sali': 2, 'Mali': 1}),\n",
       "             (4, 3): Counter({'Sali': 1}),\n",
       "             (2, 5): Counter({'Mali': 1}),\n",
       "             (6, 4): Counter({'Kali': 1, 'Sali': 2, 'Mali': 1}),\n",
       "             (1, 5): Counter({'Kali': 2, 'Mali': 1, 'Sali': 1}),\n",
       "             (3, 3): Counter({'Mali': 1, 'Kali': 2}),\n",
       "             (3, 4): Counter({'Kali': 1, 'Mali': 2}),\n",
       "             (0, 6): Counter({'Kali': 1, 'Mali': 1}),\n",
       "             (6, 0): Counter({'Mali': 2}),\n",
       "             (5, 2): Counter({'Sali': 2, 'Mali': 1}),\n",
       "             (1, 3): Counter({'Sali': 1, 'Kali': 1, 'Mali': 1}),\n",
       "             (3, 2): Counter({'Sali': 1, 'Mali': 1}),\n",
       "             (0, 5): Counter({'Mali': 1, 'Kali': 1, 'Sali': 1}),\n",
       "             (5, 5): Counter({'Kali': 1, 'Sali': 2}),\n",
       "             (0, 4): Counter({'Sali': 1}),\n",
       "             (1, 6): Counter({'Kali': 1, 'Sali': 2}),\n",
       "             (5, 6): Counter({'Sali': 2, 'Kali': 1}),\n",
       "             (0, 1): Counter({'Sali': 2}),\n",
       "             (6, 2): Counter({'Mali': 1, 'Sali': 1}),\n",
       "             (6, 5): Counter({'Kali': 1, 'Mali': 1}),\n",
       "             (5, 0): Counter({'Kali': 1}),\n",
       "             (4, 6): Counter({'Sali': 1, 'Mali': 1}),\n",
       "             (0, 0): Counter({'Mali': 2, 'Sali': 2}),\n",
       "             (3, 1): Counter({'Kali': 1, 'Mali': 1}),\n",
       "             (0, 2): Counter({'Mali': 1}),\n",
       "             (2, 4): Counter({'Mali': 1}),\n",
       "             (4, 2): Counter({'Sali': 1}),\n",
       "             (1, 0): Counter({'Kali': 1, 'Sali': 1})})"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "som.labels_map(data,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    " a=[]\n",
    "for i in np.random.randint(0,3,100):\n",
    "    if i==0:\n",
    "      a.append(\"Kali\")\n",
    "    elif i==1:\n",
    "        a.append(\"Mali\")\n",
    "    else:\n",
    "        a.append(\"Sali\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "SOM.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}