notebook_sample_clustering_categorical_data.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "#Install required packages\n",
    "!pip install lshkrepresentatives numpy scikit-learn pandas kmodes networkx termcolor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the package\n",
    "import numpy as np\n",
    "from LSHkRepresentatives.LSHkRepresentatives import LSHkRepresentatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "array([[0, 0, 0],\n",
       "       [0, 1, 1],\n",
       "       [0, 0, 0],\n",
       "       [1, 0, 1],\n",
       "       [2, 2, 2],\n",
       "       [2, 3, 2],\n",
       "       [2, 3, 2]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Init clustering algorithm instance and create sample dataset\n",
    "kreps = LSHkRepresentatives(n_clusters=2,n_init=5) \n",
    "X = np.array([['red',0,np.nan],['green',1,1],['blue',0,0],[1,5111,1],[2,2,2],[2,6513,'rectangle'],[2,3,6565]])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SKIP LOADING distMatrix because: False bd=None\n",
      "Generating disMatrix for DILCA\n",
      "Saving DILCA to: saved_dist_matrices/json/DILCA_None.json\n",
      "Generating LSH hash table:   hbits: 2(4)  k 1  d 3  n= 7\n",
      "LSH time: 0.008312699999805773 Score:  6.333333333333334  Time: 0.0003225799999199808\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0, 0, 0], dtype=uint16)"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Run the clustering algorithm\n",
    "labels = kreps.fit(X)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "1"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Predict the label for a random instance\n",
    "x = np.array([1,2,0])\n",
    "label = kreps.predict(x)\n",
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Purity: 1.00 NMI: 1.00 ARI: 1.00 Sil:  0.59 Acc: 1.00 Recall: 1.00 Precision: 1.00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
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       " 0.5873015873015872,\n",
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       " 0.008312699999805773,\n",
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       " 1.0)"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#User the built-in evaluation\n",
    "y = np.array([0,0,0,0,1,1,1]) # y is the ground labels\n",
    "kreps.CalcScore(y)"
   ]
  }
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