check_deterministic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d86910ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from os import listdir\n",
    "from os.path import join\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from deterministic import (\n",
    "    DeterministicKMeans,\n",
    "    dot,\n",
    ")\n",
    "\n",
    "from kmeans_clustering import KMeansClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f5163f0",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_cluster</th>\n",
       "      <th>deterministic</th>\n",
       "      <th>det_iter</th>\n",
       "      <th>k</th>\n",
       "      <th>kmeans++</th>\n",
       "      <th>iter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>6.172807e+12</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>6.009280e+12</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6.172807e+12</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>5.724491e+12</td>\n",
       "      <td>51</td>\n",
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       "      <td>6.172807e+12</td>\n",
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       "      <td>23</td>\n",
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       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>6.172807e+12</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>5.724491e+12</td>\n",
       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>6.172807e+12</td>\n",
       "      <td>28</td>\n",
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       "      <th>265</th>\n",
       "      <td>20</td>\n",
       "      <td>8.016904e+04</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1.087636e+05</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>266</th>\n",
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       "      <td>8.016904e+04</td>\n",
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       "      <td>8.310727e+04</td>\n",
       "      <td>7</td>\n",
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       "      <td>8.016904e+04</td>\n",
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       "      <td>9.077171e+04</td>\n",
       "      <td>4</td>\n",
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       "      <td>20</td>\n",
       "      <td>8.016904e+04</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>7.768183e+04</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>20</td>\n",
       "      <td>8.016904e+04</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9.076808e+04</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     n_cluster  deterministic  det_iter   k      kmeans++  iter\n",
       "0            5   6.172807e+12        28   1  6.009280e+12     8\n",
       "1            5   6.172807e+12        28   2  5.724491e+12    51\n",
       "2            5   6.172807e+12        28   3  5.788610e+12    23\n",
       "3            5   6.172807e+12        28   4  5.724491e+12    29\n",
       "4            5   6.172807e+12        28   5  5.788501e+12    11\n",
       "..         ...            ...       ...  ..           ...   ...\n",
       "265         20   8.016904e+04         7   6  1.087636e+05     3\n",
       "266         20   8.016904e+04         7   7  8.310727e+04     7\n",
       "267         20   8.016904e+04         7   8  9.077171e+04     4\n",
       "268         20   8.016904e+04         7   9  7.768183e+04     5\n",
       "269         20   8.016904e+04         7  10  9.076808e+04     3\n",
       "\n",
       "[270 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "datadir = 'data/'\n",
    "outdir = 'out/'\n",
    "NUM_CLUSTERS = [5, 10, 20]\n",
    "data = []\n",
    "K = 10\n",
    "for file in listdir(datadir)[:]:\n",
    "    path = join(datadir, file)\n",
    "    df = pd.read_csv(path)\n",
    "    for num_cluster in NUM_CLUSTERS:\n",
    "        kmeans = DeterministicKMeans(df, num_cluster)\n",
    "        kmeans = kmeans.fit()\n",
    "        labels = kmeans.predict(df)\n",
    "        for i in range(K):\n",
    "            row = {}\n",
    "            row['n_cluster'] = num_cluster\n",
    "            row['deterministic'] = kmeans.inertia_\n",
    "            row['det_iter'] = kmeans.num_iters_\n",
    "            row['k'] = i+1\n",
    "            kmeans2 = KMeansClustering(init='k-means++', n_clusters=num_cluster)\n",
    "            kmeans2.fit_predict(df.to_numpy())\n",
    "            row['kmeans++'] = kmeans2.inertia_\n",
    "            row['iter'] = kmeans2.n_iter_\n",
    "            data.append(row)\n",
    "data = pd.DataFrame(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7cc152aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(270, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "743e34db",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(join(outdir, 'kmeans.csv'), index=False)"
   ]
  }
 ],
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