K-Prototype+clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Prototype Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**About Blood Transfusion dataset**<br><br>\n",
    "To demonstrate the RFMTC marketing model (a modified version of RFM), this study adopted the donor database of Blood Transfusion Service Center in Hsin-Chu City in Taiwan. The center passes their blood transfusion service bus to one university in Hsin-Chu City to gather blood donated about every three months. To build a FRMTC model, we selected 748 donors at random from the donor database. These 748 donor data, each one included R (Recency - months since last donation), F (Frequency - total number of donation), M (Monetary - total blood donated in c.c.), T (Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Attribute Information:**\n",
    "\n",
    "- R (Recency - months since last donation), \n",
    "- F (Frequency - total number of donation), \n",
    "- M (Monetary - total blood donated in c.c.), \n",
    "- T (Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood). \n",
    "\n",
    "- Variable\tData Type\tMeasurement\tDescription\tmin\tmax\tmean\tstd \n",
    "- Recency quantitative\tMonths\tInput\t0.03\t74.4\t9.74\t8.07 \n",
    "- Frequency quantitative\tTimes\tInput\t1\t50\t5.51\t5.84 \n",
    "- Monetary\tquantitative\tc.c. blood\tInput\t250\t12500\t1378.68\t1459.83 \n",
    "- Time quantitative\tMonths\tInput\t2.27\t98.3\t34.42\t24.32 \n",
    "- Whether he/she donated blood in March 2007\tbinary\t1=yes 0=no\tOutput\t0\t1\t1 (24%) 0 (76%) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from kmodes.kprototypes import KPrototypes\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class KPrototypes in module kmodes.kprototypes:\n",
      "\n",
      "class KPrototypes(kmodes.kmodes.KModes)\n",
      " |  k-protoypes clustering algorithm for mixed numerical/categorical data.\n",
      " |  \n",
      " |  Parameters\n",
      " |  -----------\n",
      " |  n_clusters : int, optional, default: 8\n",
      " |      The number of clusters to form as well as the number of\n",
      " |      centroids to generate.\n",
      " |  \n",
      " |  max_iter : int, default: 300\n",
      " |      Maximum number of iterations of the k-modes algorithm for a\n",
      " |      single run.\n",
      " |  \n",
      " |  num_dissim : func, default: euclidian_dissim\n",
      " |      Dissimilarity function used by the algorithm for numerical variables.\n",
      " |      Defaults to the Euclidian dissimilarity function.\n",
      " |  \n",
      " |  cat_dissim : func, default: matching_dissim\n",
      " |      Dissimilarity function used by the kmodes algorithm for categorical variables.\n",
      " |      Defaults to the matching dissimilarity function.\n",
      " |  \n",
      " |  n_init : int, default: 10\n",
      " |      Number of time the k-modes algorithm will be run with different\n",
      " |      centroid seeds. The final results will be the best output of\n",
      " |      n_init consecutive runs in terms of cost.\n",
      " |  \n",
      " |  init : {'Huang', 'Cao', 'random' or a list of ndarrays}, default: 'Cao'\n",
      " |      Method for initialization:\n",
      " |      'Huang': Method in Huang [1997, 1998]\n",
      " |      'Cao': Method in Cao et al. [2009]\n",
      " |      'random': choose 'n_clusters' observations (rows) at random from\n",
      " |      data for the initial centroids.\n",
      " |      If a list of ndarrays is passed, it should be of length 2, with\n",
      " |      shapes (n_clusters, n_features) for numerical and categorical\n",
      " |      data respectively. These are the initial centroids.\n",
      " |  \n",
      " |  gamma : float, default: None\n",
      " |      Weighing factor that determines relative importance of numerical vs.\n",
      " |      categorical attributes (see discussion in Huang [1997]). By default,\n",
      " |      automatically calculated from data.\n",
      " |  \n",
      " |  verbose : integer, optional\n",
      " |      Verbosity mode.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  cluster_centroids_ : array, [n_clusters, n_features]\n",
      " |      Categories of cluster centroids\n",
      " |  \n",
      " |  labels_ :\n",
      " |      Labels of each point\n",
      " |  \n",
      " |  cost_ : float\n",
      " |      Clustering cost, defined as the sum distance of all points to\n",
      " |      their respective cluster centroids.\n",
      " |  \n",
      " |  n_iter_ : int\n",
      " |      The number of iterations the algorithm ran for.\n",
      " |  \n",
      " |  gamma : float\n",
      " |      The (potentially calculated) weighing factor.\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  See:\n",
      " |  Huang, Z.: Extensions to the k-modes algorithm for clustering large\n",
      " |  data sets with categorical values, Data Mining and Knowledge\n",
      " |  Discovery 2(3), 1998.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      KPrototypes\n",
      " |      kmodes.kmodes.KModes\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      sklearn.base.ClusterMixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, n_clusters=8, max_iter=100, num_dissim=<function euclidean_dissim at 0x000002215DD8AB70>, cat_dissim=<function matching_dissim at 0x000002215DD8AA60>, init='Huang', n_init=10, gamma=None, verbose=0)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  fit(self, X, y=None, categorical=None)\n",
      " |      Compute k-prototypes clustering.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape=[n_samples, n_features]\n",
      " |      categorical : Index of columns that contain categorical data\n",
      " |  \n",
      " |  predict(self, X, categorical=None)\n",
      " |      Predict the closest cluster each sample in X belongs to.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape = [n_samples, n_features]\n",
      " |          New data to predict.\n",
      " |      categorical : Index of columns that contain categorical data\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      labels : array, shape [n_samples,]\n",
      " |          Index of the cluster each sample belongs to.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  cluster_centroids_\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from kmodes.kmodes.KModes:\n",
      " |  \n",
      " |  fit_predict(self, X, y=None, **kwargs)\n",
      " |      Compute cluster centroids and predict cluster index for each sample.\n",
      " |      \n",
      " |      Convenience method; equivalent to calling fit(X) followed by\n",
      " |      predict(X).\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : boolean, optional\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : mapping of string to any\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as pipelines). The latter have parameters of the form\n",
      " |      ``<component>__<parameter>`` so that it's possible to update each\n",
      " |      component of a nested object.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(KPrototypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading Dataset\n",
    "blood = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/blood-transfusion/transfusion.data', sep=\",\",engine = 'python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency (months)</th>\n",
       "      <th>Frequency (times)</th>\n",
       "      <th>Monetary (c.c. blood)</th>\n",
       "      <th>Time (months)</th>\n",
       "      <th>whether he/she donated blood in March 2007</th>\n",
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      "text/plain": [
       "   Recency (months)  Frequency (times)  Monetary (c.c. blood)  Time (months)  \\\n",
       "0                 2                 50                  12500             98   \n",
       "1                 0                 13                   3250             28   \n",
       "2                 1                 16                   4000             35   \n",
       "3                 2                 20                   5000             45   \n",
       "4                 1                 24                   6000             77   \n",
       "\n",
       "   whether he/she donated blood in March 2007  \n",
       "0                                           1  \n",
       "1                                           1  \n",
       "2                                           1  \n",
       "3                                           1  \n",
       "4                                           0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sanity Check\n",
    "blood.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardizing data\n",
    "columns_to_normalize     = ['Recency (months)','Frequency (times)','Monetary (c.c. blood)','Time (months)']\n",
    "blood[columns_to_normalize] = blood[columns_to_normalize].apply(lambda x: (x - x.mean()) / np.std(x))"
   ]
  },
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   "execution_count": 6,
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       "      <th>Recency (months)</th>\n",
       "      <th>Frequency (times)</th>\n",
       "      <th>Monetary (c.c. blood)</th>\n",
       "      <th>Time (months)</th>\n",
       "      <th>whether he/she donated blood in March 2007</th>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   Recency (months)  Frequency (times)  Monetary (c.c. blood)  Time (months)  \\\n",
       "0         -0.927899           7.623346               7.623346       2.615633   \n",
       "1         -1.175118           1.282738               1.282738      -0.257881   \n",
       "2         -1.051508           1.796842               1.796842       0.029471   \n",
       "3         -0.927899           2.482313               2.482313       0.439973   \n",
       "4         -1.051508           3.167784               3.167784       1.753579   \n",
       "\n",
       "   whether he/she donated blood in March 2007  \n",
       "0                                           1  \n",
       "1                                           1  \n",
       "2                                           1  \n",
       "3                                           1  \n",
       "4                                           0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-check after standardizing data\n",
    "blood.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the dataset into matrix\n",
    "blood_matrix = blood.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.92789873,  7.62334626,  7.62334626,  2.61563344,  1.        ],\n",
       "       [-1.17511806,  1.28273826,  1.28273826, -0.2578809 ,  1.        ],\n",
       "       [-1.0515084 ,  1.79684161,  1.79684161,  0.02947053,  1.        ],\n",
       "       ...,\n",
       "       [ 1.66790417, -0.43093957, -0.43093957,  1.13782607,  0.        ],\n",
       "       [ 3.64565877, -0.77367514, -0.77367514,  0.19367135,  0.        ],\n",
       "       [ 7.72477762, -0.77367514, -0.77367514,  1.54832812,  0.        ]])"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Martix for analysis\n",
    "blood_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Running K-Prototype clustering\n",
    "kproto = KPrototypes(n_clusters=5, init='Cao')\n",
    "clusters = kproto.fit_predict(blood_matrix, categorical=[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 1.20155863, -0.0434147 , -0.0434147 ,  1.16721428],\n",
       "        [-0.5217527 ,  4.8324995 ,  4.8324995 ,  2.12009883],\n",
       "        [ 0.77649881, -0.51171646, -0.51171646, -0.41737993],\n",
       "        [-0.77483521, -0.39947752, -0.39947752, -0.73541024],\n",
       "        [-0.46834379,  0.94841338,  0.94841338,  0.92401242]]), array([[0.],\n",
       "        [0.],\n",
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "kproto.cluster_centroids_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "915.5424569187537"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the cost of the clusters created.\n",
    "kproto.cost_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the predicted clusters to the main dataset\n",
    "blood['cluster_id'] = clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.051508</td>\n",
       "      <td>1.796842</td>\n",
       "      <td>1.796842</td>\n",
       "      <td>0.029471</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.927899</td>\n",
       "      <td>2.482313</td>\n",
       "      <td>2.482313</td>\n",
       "      <td>0.439973</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.051508</td>\n",
       "      <td>3.167784</td>\n",
       "      <td>3.167784</td>\n",
       "      <td>1.753579</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Recency (months)  Frequency (times)  Monetary (c.c. blood)  Time (months)  \\\n",
       "0         -0.927899           7.623346               7.623346       2.615633   \n",
       "1         -1.175118           1.282738               1.282738      -0.257881   \n",
       "2         -1.051508           1.796842               1.796842       0.029471   \n",
       "3         -0.927899           2.482313               2.482313       0.439973   \n",
       "4         -1.051508           3.167784               3.167784       1.753579   \n",
       "\n",
       "   whether he/she donated blood in March 2007  cluster_id  \n",
       "0                                           1           4  \n",
       "1                                           1           0  \n",
       "2                                           1           3  \n",
       "3                                           1           3  \n",
       "4                                           0           3  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-check\n",
    "blood.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>cluster_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster_id\n",
       "1         267\n",
       "0         213\n",
       "2         180\n",
       "3          80\n",
       "4           8"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the clusters created\n",
    "blooddf = pd.DataFrame(blood['cluster_id'].value_counts())\n",
    "blooddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16353c16240>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16352ba1b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=blooddf.index, y=blooddf['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23e612a6e10>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23e611d5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Choosing optimal K\n",
    "cost = []\n",
    "for num_clusters in list(range(1,14)):\n",
    "    kproto = KPrototypes(n_clusters=num_clusters, init='Cao')\n",
    "    kproto.fit_predict(blood_matrix, categorical=[4])\n",
    "    cost.append(kproto.cost_)\n",
    "    \n",
    "plt.plot(cost)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}