Test2- Bare algorithm implementation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3f28073bf74ea6b321623db445b0225a6e615041"
   },
   "source": [
    "# Customer Segmentation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "c2810a2fabfc04755596b12532ac7ff9462dc866"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from kmodes import kmodes\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>maritalstatus</th>\n",
       "      <th>occupation</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>hoursperweek</th>\n",
       "      <th>nativecountry</th>\n",
       "      <th>over50k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   education        maritalstatus  \\\n",
       "0   39          State-gov   Bachelors        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors   Married-civ-spouse   \n",
       "2   38            Private     HS-grad             Divorced   \n",
       "3   53            Private        11th   Married-civ-spouse   \n",
       "4   28            Private   Bachelors   Married-civ-spouse   \n",
       "\n",
       "           occupation    race      sex  hoursperweek   nativecountry  over50k  \n",
       "0        Adm-clerical   White     Male            40   United-States        0  \n",
       "1     Exec-managerial   White     Male            13   United-States        0  \n",
       "2   Handlers-cleaners   White     Male            40   United-States        0  \n",
       "3   Handlers-cleaners   Black     Male            40   United-States        0  \n",
       "4      Prof-specialty   Black   Female            40            Cuba        0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_csv('./data/census.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0,    39,    40, ...,     1,     0,     0],\n",
       "       [    1,    50,    13, ...,     1,     0,     0],\n",
       "       [    2,    38,    40, ...,     1,     0,     0],\n",
       "       ...,\n",
       "       [31975,    58,    40, ...,     1,     0,     0],\n",
       "       [31976,    22,    20, ...,     1,     0,     0],\n",
       "       [31977,    52,    40, ...,     1,     0,     0]], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=dataset\n",
    "\n",
    "df_dummy = pd.get_dummies(df)\n",
    "\n",
    "#transform into numpy array\n",
    "x = df_dummy.reset_index().values\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "km = kmodes.KModes(n_clusters=3, init='Huang', n_init=5, verbose=0)\n",
    "clusters = km.fit_predict(x)\n",
    "df_dummy['clusters'] = clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1, 37, 40,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  1,  0,  1,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         1,  0,  0],\n",
       "       [ 4, 23, 40,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  1,  1,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         1,  0,  0],\n",
       "       [ 0, 23, 40,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  1,  0,  1,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         1,  0,  0]], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centroids_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:6: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(2)\n",
    "\n",
    "# Turn the dummified df into two columns with PCA\n",
    "plot_columns = pca.fit_transform(df_dummy.ix[:,0:12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.282721  ,  -0.52527795],\n",
       "       [  2.92434804, -29.55839558],\n",
       "       [ -0.68017424,  -0.2312154 ],\n",
       "       ...,\n",
       "       [ 18.44739053,  -6.07034959],\n",
       "       [-21.82035298, -14.68808249],\n",
       "       [ 12.72599109,  -4.31384129]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot based on the two dimensions, and shade by cluster label\n",
    "plt.scatter(x=plot_columns[:,1], y=plot_columns[:,0], c=df_dummy[\"clusters\"], s=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.read_csv('./data/census.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to calculate the similarity score between two categorical objects\n",
    "def similarity_score(x,y):\n",
    "    count = 0\n",
    "    for i in range(len(x)):\n",
    "        if x[i]==y[i]:\n",
    "            count +=1\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kmodes(data,clusters,iterations):\n",
    "    index=np.random.permutation(data.index)[:clusters]#randomly selecting index values for initial k modes\n",
    "    modes=[]\n",
    "    labels=[]\n",
    "    dis=[]\n",
    "    J=[]\n",
    "    d=dict()\n",
    "#calculating the dissimilarity score between each categorical object from the modes and assigning label to minimum score\n",
    "    for i in index:\n",
    "        modes.append(np.array(data.ix[i]))#inital k modes added \n",
    "    for i in range(len(data)):\n",
    "        dis=[]\n",
    "        for j in range(len(modes)):\n",
    "            dis.append(similarity_score(np.array(data.ix[i]),modes[j]))\n",
    "        labels.append(np.argmin(dis))\n",
    "    data['label']=np.array(labels)\n",
    "#using frequency based method to update the weights\n",
    "    for i in range(iterations):\n",
    "        modes=[]\n",
    "        labels=[]\n",
    "        for i in np.unique(data['label']):\n",
    "            modes.append(np.array(data.ix[data['label']==i,:-1].describe().ix[2,:]))\n",
    "#calculating the diss score between each categorical object and updated modes and assigning new label to minimum score\n",
    "        for i in range(len(data)):\n",
    "            dis=[]\n",
    "            for j in range(len(modes)):\n",
    "                dis.append(similarity_score(np.array(data.ix[i,:-1]),modes[j]))\n",
    "            labels.append(np.argmin(dis))\n",
    "        data['label']=np.array(labels)\n",
    "#calculating the cost function of individual cluster\n",
    "    for i in range(len(modes)):\n",
    "        dis=[]\n",
    "        for j in range(len(data.ix[data['label']==i,:-1])):\n",
    "            dis.append(similarity_score(np.array(data.ix[data['label']==i,:-1].iloc[j]),modes[i]))\n",
    "        J.append(sum(dis))\n",
    "    d['modes']=modes\n",
    "    d['costfunction']=J\n",
    "    d['totalcost']=sum(J)\n",
    "    d['data']=data\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:10: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:22: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:27: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 3 is out of bounds for axis 0 with size 3",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-31-d800108d41bd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdic\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkmodes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdat\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#calling kmodes on the cars dataset with k equal to 4 as the original had 4 labels.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-21-270b0339fe60>\u001b[0m in \u001b[0;36mkmodes\u001b[1;34m(data, clusters, iterations)\u001b[0m\n\u001b[0;32m     25\u001b[0m             \u001b[0mdis\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     26\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodes\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---> 27\u001b[1;33m                 \u001b[0mdis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimilarity_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\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     28\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m         \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'label'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-20-b38c766d8443>\u001b[0m in \u001b[0;36msimilarity_score\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mcount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\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----> 5\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\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      6\u001b[0m             \u001b[0mcount\u001b[0m \u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcount\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 3 is out of bounds for axis 0 with size 3"
     ]
    }
   ],
   "source": [
    "dic = kmodes(dat,1,1)#calling kmodes on the cars dataset with k equal to 4 as the original had 4 labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic['costfunction']#individual cost function of clusters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic['totalcost']#total cost function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cldata = dic['data']#lets see how the clusterd data looks like\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cldata.ix[cldata['label']==0,:].head()"
   ]
  }
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