Unsupervised_Cancer_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f0c2ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ff0dcd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=\"C:/Users/TUF GAMING/Documents/HRC Laptop/Hackathon Notebook/Hackathon Notebook/Practice Notebook/Dataset/Cancer Dataset/data.csv\"\n",
    "label=\"C:/Users/TUF GAMING/Documents/HRC Laptop/Hackathon Notebook/Hackathon Notebook/Practice Notebook/Dataset/Cancer Dataset/labels.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f25eeee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>gene_0</th>\n",
       "      <th>gene_1</th>\n",
       "      <th>gene_2</th>\n",
       "      <th>gene_3</th>\n",
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       "      <th>gene_6</th>\n",
       "      <th>gene_7</th>\n",
       "      <th>gene_8</th>\n",
       "      <th>...</th>\n",
       "      <th>gene_20521</th>\n",
       "      <th>gene_20522</th>\n",
       "      <th>gene_20523</th>\n",
       "      <th>gene_20524</th>\n",
       "      <th>gene_20525</th>\n",
       "      <th>gene_20526</th>\n",
       "      <th>gene_20527</th>\n",
       "      <th>gene_20528</th>\n",
       "      <th>gene_20529</th>\n",
       "      <th>gene_20530</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>4.926711</td>\n",
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       "      <td>7.220030</td>\n",
       "      <td>9.119813</td>\n",
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       "      <td>9.650743</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sample_1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.592732</td>\n",
       "      <td>1.588421</td>\n",
       "      <td>7.586157</td>\n",
       "      <td>9.623011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.816049</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.593372</td>\n",
       "      <td>7.323865</td>\n",
       "      <td>9.740931</td>\n",
       "      <td>6.256586</td>\n",
       "      <td>8.381612</td>\n",
       "      <td>12.674552</td>\n",
       "      <td>10.517059</td>\n",
       "      <td>9.397854</td>\n",
       "      <td>2.094168</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sample_2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.511759</td>\n",
       "      <td>4.327199</td>\n",
       "      <td>6.881787</td>\n",
       "      <td>9.870730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.972130</td>\n",
       "      <td>0.452595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.125213</td>\n",
       "      <td>8.127123</td>\n",
       "      <td>10.908640</td>\n",
       "      <td>5.401607</td>\n",
       "      <td>9.911597</td>\n",
       "      <td>9.045255</td>\n",
       "      <td>9.788359</td>\n",
       "      <td>10.090470</td>\n",
       "      <td>1.683023</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sample_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.663618</td>\n",
       "      <td>4.507649</td>\n",
       "      <td>6.659068</td>\n",
       "      <td>10.196184</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.843375</td>\n",
       "      <td>0.434882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.076566</td>\n",
       "      <td>8.792959</td>\n",
       "      <td>10.141520</td>\n",
       "      <td>8.942805</td>\n",
       "      <td>9.601208</td>\n",
       "      <td>11.392682</td>\n",
       "      <td>9.694814</td>\n",
       "      <td>9.684365</td>\n",
       "      <td>3.292001</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sample_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.655741</td>\n",
       "      <td>2.821547</td>\n",
       "      <td>6.539454</td>\n",
       "      <td>9.738265</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.566967</td>\n",
       "      <td>0.360982</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.996032</td>\n",
       "      <td>8.891425</td>\n",
       "      <td>10.373790</td>\n",
       "      <td>7.181162</td>\n",
       "      <td>9.846910</td>\n",
       "      <td>11.922439</td>\n",
       "      <td>9.217749</td>\n",
       "      <td>9.461191</td>\n",
       "      <td>5.110372</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>sample_796</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.865642</td>\n",
       "      <td>2.718197</td>\n",
       "      <td>7.350099</td>\n",
       "      <td>10.006003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.764792</td>\n",
       "      <td>0.496922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.088133</td>\n",
       "      <td>9.118313</td>\n",
       "      <td>10.004852</td>\n",
       "      <td>4.484415</td>\n",
       "      <td>9.614701</td>\n",
       "      <td>12.031267</td>\n",
       "      <td>9.813063</td>\n",
       "      <td>10.092770</td>\n",
       "      <td>8.819269</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>sample_797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.942955</td>\n",
       "      <td>4.453807</td>\n",
       "      <td>6.346597</td>\n",
       "      <td>10.056868</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.320331</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.371876</td>\n",
       "      <td>9.623335</td>\n",
       "      <td>9.823921</td>\n",
       "      <td>6.555327</td>\n",
       "      <td>9.064002</td>\n",
       "      <td>11.633422</td>\n",
       "      <td>10.317266</td>\n",
       "      <td>8.745983</td>\n",
       "      <td>9.659081</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>sample_798</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.249582</td>\n",
       "      <td>3.707492</td>\n",
       "      <td>8.185901</td>\n",
       "      <td>9.504082</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.536589</td>\n",
       "      <td>1.811101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.719386</td>\n",
       "      <td>8.610704</td>\n",
       "      <td>10.485517</td>\n",
       "      <td>3.589763</td>\n",
       "      <td>9.350636</td>\n",
       "      <td>12.180944</td>\n",
       "      <td>10.681194</td>\n",
       "      <td>9.466711</td>\n",
       "      <td>4.677458</td>\n",
       "      <td>0.586693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>sample_799</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.590339</td>\n",
       "      <td>2.787976</td>\n",
       "      <td>7.318624</td>\n",
       "      <td>9.987136</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.213464</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.785237</td>\n",
       "      <td>8.605387</td>\n",
       "      <td>11.004677</td>\n",
       "      <td>4.745888</td>\n",
       "      <td>9.626383</td>\n",
       "      <td>11.198279</td>\n",
       "      <td>10.335513</td>\n",
       "      <td>10.400581</td>\n",
       "      <td>5.718751</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>sample_800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.325242</td>\n",
       "      <td>3.805932</td>\n",
       "      <td>6.530246</td>\n",
       "      <td>9.560367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.957027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.403075</td>\n",
       "      <td>8.594354</td>\n",
       "      <td>10.243079</td>\n",
       "      <td>9.139459</td>\n",
       "      <td>10.102934</td>\n",
       "      <td>11.641081</td>\n",
       "      <td>10.607358</td>\n",
       "      <td>9.844794</td>\n",
       "      <td>4.550716</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>801 rows × 20532 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  gene_0    gene_1    gene_2    gene_3     gene_4  gene_5  \\\n",
       "0      sample_0     0.0  2.017209  3.265527  5.478487  10.431999     0.0   \n",
       "1      sample_1     0.0  0.592732  1.588421  7.586157   9.623011     0.0   \n",
       "2      sample_2     0.0  3.511759  4.327199  6.881787   9.870730     0.0   \n",
       "3      sample_3     0.0  3.663618  4.507649  6.659068  10.196184     0.0   \n",
       "4      sample_4     0.0  2.655741  2.821547  6.539454   9.738265     0.0   \n",
       "..          ...     ...       ...       ...       ...        ...     ...   \n",
       "796  sample_796     0.0  1.865642  2.718197  7.350099  10.006003     0.0   \n",
       "797  sample_797     0.0  3.942955  4.453807  6.346597  10.056868     0.0   \n",
       "798  sample_798     0.0  3.249582  3.707492  8.185901   9.504082     0.0   \n",
       "799  sample_799     0.0  2.590339  2.787976  7.318624   9.987136     0.0   \n",
       "800  sample_800     0.0  2.325242  3.805932  6.530246   9.560367     0.0   \n",
       "\n",
       "       gene_6    gene_7  gene_8  ...  gene_20521  gene_20522  gene_20523  \\\n",
       "0    7.175175  0.591871     0.0  ...    4.926711    8.210257    9.723516   \n",
       "1    6.816049  0.000000     0.0  ...    4.593372    7.323865    9.740931   \n",
       "2    6.972130  0.452595     0.0  ...    5.125213    8.127123   10.908640   \n",
       "3    7.843375  0.434882     0.0  ...    6.076566    8.792959   10.141520   \n",
       "4    6.566967  0.360982     0.0  ...    5.996032    8.891425   10.373790   \n",
       "..        ...       ...     ...  ...         ...         ...         ...   \n",
       "796  6.764792  0.496922     0.0  ...    6.088133    9.118313   10.004852   \n",
       "797  7.320331  0.000000     0.0  ...    6.371876    9.623335    9.823921   \n",
       "798  7.536589  1.811101     0.0  ...    5.719386    8.610704   10.485517   \n",
       "799  9.213464  0.000000     0.0  ...    5.785237    8.605387   11.004677   \n",
       "800  7.957027  0.000000     0.0  ...    6.403075    8.594354   10.243079   \n",
       "\n",
       "     gene_20524  gene_20525  gene_20526  gene_20527  gene_20528  gene_20529  \\\n",
       "0      7.220030    9.119813   12.003135    9.650743    8.921326    5.286759   \n",
       "1      6.256586    8.381612   12.674552   10.517059    9.397854    2.094168   \n",
       "2      5.401607    9.911597    9.045255    9.788359   10.090470    1.683023   \n",
       "3      8.942805    9.601208   11.392682    9.694814    9.684365    3.292001   \n",
       "4      7.181162    9.846910   11.922439    9.217749    9.461191    5.110372   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "796    4.484415    9.614701   12.031267    9.813063   10.092770    8.819269   \n",
       "797    6.555327    9.064002   11.633422   10.317266    8.745983    9.659081   \n",
       "798    3.589763    9.350636   12.180944   10.681194    9.466711    4.677458   \n",
       "799    4.745888    9.626383   11.198279   10.335513   10.400581    5.718751   \n",
       "800    9.139459   10.102934   11.641081   10.607358    9.844794    4.550716   \n",
       "\n",
       "     gene_20530  \n",
       "0      0.000000  \n",
       "1      0.000000  \n",
       "2      0.000000  \n",
       "3      0.000000  \n",
       "4      0.000000  \n",
       "..          ...  \n",
       "796    0.000000  \n",
       "797    0.000000  \n",
       "798    0.586693  \n",
       "799    0.000000  \n",
       "800    0.000000  \n",
       "\n",
       "[801 rows x 20532 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f8a92bf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    801\n",
       "Class           5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#label.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d9580d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As only np.array formate data is feed into the algo hence converting dictionary data to np,array data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1daede66",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.genfromtxt(\n",
    "     df,\n",
    "     delimiter=\",\",\n",
    "     usecols=range(1, 20532),\n",
    "     skip_header=1\n",
    " )\n",
    "\n",
    "true_label = np.genfromtxt(\n",
    "     label,\n",
    "     delimiter=\",\",\n",
    "     usecols=(1,),\n",
    "     skip_header=1,\n",
    "     dtype=\"str\"\n",
    " )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a6509b82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  2.01720929,  3.26552691, ...,  8.92132623,\n",
       "         5.28675919,  0.        ],\n",
       "       [ 0.        ,  0.59273209,  1.58842082, ...,  9.39785429,\n",
       "         2.09416849,  0.        ],\n",
       "       [ 0.        ,  3.51175898,  4.32719872, ..., 10.09046974,\n",
       "         1.68302267,  0.        ],\n",
       "       ...,\n",
       "       [ 0.        ,  3.24958187,  3.70749166, ...,  9.46671072,\n",
       "         4.6774575 ,  0.5866927 ],\n",
       "       [ 0.        ,  2.59033853,  2.78797567, ..., 10.40058062,\n",
       "         5.71875068,  0.        ],\n",
       "       [ 0.        ,  2.32524248,  3.80593214, ...,  9.84479363,\n",
       "         4.55071601,  0.        ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "911f7f37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PRAD', 'LUAD', 'PRAD', 'PRAD', 'BRCA', 'PRAD', 'KIRC', 'PRAD',\n",
       "       'BRCA', 'PRAD', 'BRCA', 'KIRC', 'PRAD', 'BRCA', 'BRCA', 'BRCA',\n",
       "       'LUAD', 'KIRC', 'KIRC', 'PRAD', 'BRCA', 'KIRC', 'LUAD', 'BRCA',\n",
       "       'KIRC', 'LUAD', 'COAD', 'BRCA', 'BRCA', 'BRCA', 'BRCA', 'BRCA',\n",
       "       'KIRC', 'BRCA', 'PRAD', 'BRCA', 'KIRC', 'LUAD', 'BRCA', 'BRCA',\n",
       "       'KIRC', 'PRAD', 'PRAD', 'KIRC', 'KIRC', 'BRCA', 'PRAD', 'COAD',\n",
       "       'BRCA', 'LUAD', 'BRCA', 'LUAD', 'BRCA', 'PRAD', 'COAD', 'BRCA',\n",
       "       'BRCA', 'COAD', 'KIRC', 'BRCA', 'LUAD', 'KIRC', 'BRCA', 'LUAD',\n",
       "       'PRAD', 'COAD', 'BRCA', 'PRAD', 'KIRC', 'BRCA', 'KIRC', 'BRCA',\n",
       "       'BRCA', 'LUAD', 'BRCA', 'LUAD', 'BRCA', 'KIRC', 'PRAD', 'COAD',\n",
       "       'BRCA', 'PRAD', 'BRCA', 'BRCA', 'PRAD', 'PRAD', 'BRCA', 'BRCA',\n",
       "       'KIRC', 'BRCA', 'PRAD', 'PRAD', 'BRCA', 'BRCA', 'BRCA', 'PRAD',\n",
       "       'COAD', 'BRCA', 'PRAD', 'BRCA', 'BRCA', 'KIRC', 'BRCA', 'KIRC',\n",
       "       'LUAD', 'KIRC', 'LUAD', 'COAD', 'LUAD', 'LUAD', 'PRAD', 'BRCA',\n",
       "       'LUAD', 'PRAD', 'BRCA', 'KIRC', 'KIRC', 'KIRC', 'BRCA', 'BRCA',\n",
       "       'LUAD', 'KIRC', 'LUAD', 'BRCA', 'PRAD', 'PRAD', 'PRAD', 'BRCA',\n",
       "       'KIRC', 'LUAD', 'COAD', 'BRCA', 'COAD', 'BRCA', 'BRCA', 'KIRC',\n",
       "       'LUAD', 'BRCA', 'KIRC', 'COAD', 'BRCA', 'PRAD', 'BRCA', 'KIRC',\n",
       "       'LUAD', 'COAD', 'PRAD', 'BRCA', 'LUAD', 'LUAD', 'LUAD', 'LUAD',\n",
       "       'BRCA', 'BRCA', 'LUAD', 'BRCA', 'BRCA', 'PRAD', 'PRAD', 'LUAD',\n",
       "       'PRAD', 'LUAD', 'KIRC', 'BRCA', 'PRAD', 'LUAD', 'COAD', 'KIRC',\n",
       "       'LUAD', 'BRCA', 'KIRC', 'LUAD', 'BRCA', 'LUAD', 'BRCA', 'BRCA',\n",
       "       'BRCA', 'PRAD', 'BRCA', 'KIRC', 'COAD', 'KIRC', 'BRCA', 'PRAD',\n",
       "       'PRAD', 'PRAD', 'LUAD', 'LUAD', 'BRCA', 'LUAD', 'LUAD', 'KIRC',\n",
       "       'LUAD', 'PRAD', 'LUAD', 'BRCA', 'BRCA', 'BRCA', 'LUAD', 'LUAD',\n",
       "       'BRCA', 'KIRC', 'KIRC', 'KIRC', 'KIRC', 'PRAD', 'BRCA', 'PRAD',\n",
       "       'BRCA', 'LUAD', 'LUAD', 'BRCA', 'PRAD', 'BRCA', 'PRAD', 'BRCA',\n",
       "       'BRCA', 'BRCA', 'LUAD', 'BRCA', 'KIRC', 'LUAD', 'KIRC', 'KIRC',\n",
       "       'LUAD', 'BRCA', 'KIRC', 'PRAD', 'BRCA', 'LUAD', 'LUAD', 'PRAD',\n",
       "       'COAD', 'BRCA', 'KIRC', 'PRAD', 'KIRC', 'COAD', 'BRCA', 'KIRC',\n",
       "       'KIRC', 'LUAD', 'PRAD', 'PRAD', 'LUAD', 'LUAD', 'KIRC', 'BRCA',\n",
       "       'BRCA', 'COAD', 'BRCA', 'PRAD', 'COAD', 'BRCA', 'PRAD', 'KIRC',\n",
       "       'PRAD', 'PRAD', 'PRAD', 'BRCA', 'COAD', 'COAD', 'LUAD', 'COAD',\n",
       "       'COAD', 'PRAD', 'LUAD', 'BRCA', 'BRCA', 'KIRC', 'KIRC', 'BRCA',\n",
       "       'COAD', 'PRAD', 'KIRC', 'PRAD', 'BRCA', 'BRCA', 'KIRC', 'BRCA',\n",
       "       'BRCA', 'BRCA', 'BRCA', 'LUAD', 'LUAD', 'BRCA', 'BRCA', 'BRCA',\n",
       "       'KIRC', 'KIRC', 'KIRC', 'KIRC', 'BRCA', 'BRCA', 'BRCA', 'KIRC',\n",
       "       'BRCA', 'BRCA', 'LUAD', 'PRAD', 'BRCA', 'BRCA', 'COAD', 'LUAD',\n",
       "       'PRAD', 'BRCA', 'BRCA', 'BRCA', 'COAD', 'BRCA', 'PRAD', 'BRCA',\n",
       "       'COAD', 'LUAD', 'LUAD', 'PRAD', 'KIRC', 'BRCA', 'KIRC', 'KIRC',\n",
       "       'LUAD', 'COAD', 'KIRC', 'BRCA', 'BRCA', 'BRCA', 'BRCA', 'KIRC',\n",
       "       'BRCA', 'BRCA', 'PRAD', 'BRCA', 'KIRC', 'BRCA', 'LUAD', 'PRAD',\n",
       "       'KIRC', 'BRCA', 'PRAD', 'COAD', 'BRCA', 'BRCA', 'BRCA', 'LUAD',\n",
       "       'LUAD', 'LUAD', 'BRCA', 'BRCA', 'PRAD', 'LUAD', 'BRCA', 'KIRC',\n",
       "       'BRCA', 'COAD', 'COAD', 'LUAD', 'BRCA', 'KIRC', 'BRCA', 'BRCA',\n",
       "       'BRCA', 'COAD', 'LUAD', 'COAD', 'KIRC', 'PRAD', 'KIRC', 'BRCA',\n",
       "       'BRCA', 'KIRC', 'BRCA', 'COAD', 'PRAD', 'LUAD', 'PRAD', 'BRCA',\n",
       "       'KIRC', 'PRAD', 'BRCA', 'COAD', 'KIRC', 'KIRC', 'COAD', 'COAD',\n",
       "       'PRAD', 'BRCA', 'BRCA', 'COAD', 'KIRC', 'BRCA', 'PRAD', 'BRCA',\n",
       "       'BRCA', 'BRCA', 'LUAD', 'LUAD', 'KIRC', 'LUAD', 'BRCA', 'KIRC',\n",
       "       'COAD', 'PRAD', 'BRCA', 'LUAD', 'PRAD', 'BRCA', 'BRCA', 'BRCA',\n",
       "       'LUAD', 'BRCA', 'BRCA', 'PRAD', 'BRCA', 'PRAD', 'COAD', 'BRCA',\n",
       "       'LUAD', 'BRCA', 'BRCA', 'LUAD', 'BRCA', 'BRCA', 'BRCA', 'KIRC',\n",
       "       'LUAD', 'PRAD', 'BRCA', 'PRAD', 'KIRC', 'BRCA', 'KIRC', 'COAD',\n",
       "       'BRCA', 'PRAD', 'LUAD', 'KIRC', 'BRCA', 'BRCA', 'KIRC', 'BRCA',\n",
       "       'LUAD', 'BRCA', 'BRCA', 'PRAD', 'COAD', 'BRCA', 'KIRC', 'LUAD',\n",
       "       'PRAD', 'BRCA', 'PRAD', 'BRCA', 'BRCA', 'BRCA', 'BRCA', 'KIRC',\n",
       "       'LUAD', 'BRCA', 'KIRC', 'BRCA', 'BRCA', 'LUAD', 'LUAD', 'KIRC',\n",
       "       'COAD', 'PRAD', 'COAD', 'BRCA', 'KIRC', 'KIRC', 'BRCA', 'PRAD',\n",
       "       'KIRC', 'COAD', 'LUAD', 'LUAD', 'BRCA', 'PRAD', 'PRAD', 'BRCA',\n",
       "       'PRAD', 'LUAD', 'KIRC', 'PRAD', 'BRCA', 'LUAD', 'PRAD', 'LUAD',\n",
       "       'BRCA', 'BRCA', 'COAD', 'LUAD', 'KIRC', 'COAD', 'LUAD', 'BRCA',\n",
       "       'PRAD', 'BRCA', 'BRCA', 'PRAD', 'BRCA', 'COAD', 'BRCA', 'COAD',\n",
       "       'KIRC', 'BRCA', 'BRCA', 'LUAD', 'LUAD', 'LUAD', 'COAD', 'KIRC',\n",
       "       'LUAD', 'LUAD', 'BRCA', 'BRCA', 'KIRC', 'PRAD', 'LUAD', 'PRAD',\n",
       "       'BRCA', 'KIRC', 'BRCA', 'KIRC', 'KIRC', 'PRAD', 'PRAD', 'LUAD',\n",
       "       'BRCA', 'KIRC', 'COAD', 'COAD', 'BRCA', 'KIRC', 'KIRC', 'BRCA',\n",
       "       'BRCA', 'PRAD', 'KIRC', 'COAD', 'BRCA', 'BRCA', 'COAD', 'LUAD',\n",
       "       'BRCA', 'BRCA', 'BRCA', 'KIRC', 'PRAD', 'LUAD', 'LUAD', 'BRCA',\n",
       "       'KIRC', 'COAD', 'KIRC', 'KIRC', 'BRCA', 'PRAD', 'LUAD', 'KIRC',\n",
       "       'BRCA', 'COAD', 'LUAD', 'LUAD', 'LUAD', 'PRAD', 'LUAD', 'KIRC',\n",
       "       'BRCA', 'BRCA', 'COAD', 'PRAD', 'BRCA', 'BRCA', 'BRCA', 'KIRC',\n",
       "       'LUAD', 'LUAD', 'BRCA', 'PRAD', 'LUAD', 'LUAD', 'BRCA', 'KIRC',\n",
       "       'PRAD', 'COAD', 'LUAD', 'PRAD', 'COAD', 'LUAD', 'COAD', 'KIRC',\n",
       "       'KIRC', 'BRCA', 'BRCA', 'KIRC', 'KIRC', 'COAD', 'BRCA', 'LUAD',\n",
       "       'PRAD', 'PRAD', 'KIRC', 'BRCA', 'LUAD', 'BRCA', 'BRCA', 'COAD',\n",
       "       'BRCA', 'PRAD', 'PRAD', 'BRCA', 'BRCA', 'COAD', 'BRCA', 'KIRC',\n",
       "       'BRCA', 'BRCA', 'COAD', 'BRCA', 'PRAD', 'BRCA', 'BRCA', 'KIRC',\n",
       "       'PRAD', 'LUAD', 'BRCA', 'BRCA', 'KIRC', 'BRCA', 'BRCA', 'BRCA',\n",
       "       'BRCA', 'BRCA', 'COAD', 'LUAD', 'LUAD', 'BRCA', 'BRCA', 'BRCA',\n",
       "       'PRAD', 'BRCA', 'BRCA', 'KIRC', 'LUAD', 'LUAD', 'KIRC', 'KIRC',\n",
       "       'LUAD', 'KIRC', 'COAD', 'BRCA', 'COAD', 'KIRC', 'BRCA', 'BRCA',\n",
       "       'PRAD', 'PRAD', 'PRAD', 'LUAD', 'PRAD', 'PRAD', 'COAD', 'BRCA',\n",
       "       'BRCA', 'COAD', 'LUAD', 'KIRC', 'BRCA', 'KIRC', 'COAD', 'BRCA',\n",
       "       'BRCA', 'BRCA', 'PRAD', 'LUAD', 'KIRC', 'BRCA', 'PRAD', 'KIRC',\n",
       "       'PRAD', 'BRCA', 'LUAD', 'KIRC', 'PRAD', 'LUAD', 'PRAD', 'PRAD',\n",
       "       'BRCA', 'KIRC', 'PRAD', 'LUAD', 'COAD', 'COAD', 'BRCA', 'BRCA',\n",
       "       'BRCA', 'LUAD', 'KIRC', 'KIRC', 'KIRC', 'BRCA', 'LUAD', 'KIRC',\n",
       "       'PRAD', 'BRCA', 'LUAD', 'PRAD', 'BRCA', 'PRAD', 'BRCA', 'KIRC',\n",
       "       'BRCA', 'COAD', 'PRAD', 'PRAD', 'KIRC', 'PRAD', 'KIRC', 'BRCA',\n",
       "       'LUAD', 'LUAD', 'BRCA', 'BRCA', 'KIRC', 'BRCA', 'BRCA', 'BRCA',\n",
       "       'BRCA', 'KIRC', 'KIRC', 'PRAD', 'COAD', 'KIRC', 'BRCA', 'BRCA',\n",
       "       'KIRC', 'BRCA', 'LUAD', 'BRCA', 'BRCA', 'PRAD', 'BRCA', 'PRAD',\n",
       "       'BRCA', 'COAD', 'BRCA', 'BRCA', 'KIRC', 'LUAD', 'BRCA', 'BRCA',\n",
       "       'PRAD', 'BRCA', 'PRAD', 'COAD', 'BRCA', 'BRCA', 'BRCA', 'LUAD',\n",
       "       'BRCA', 'LUAD', 'BRCA', 'LUAD', 'KIRC', 'KIRC', 'COAD', 'COAD',\n",
       "       'BRCA', 'BRCA', 'BRCA', 'LUAD', 'BRCA', 'LUAD', 'KIRC', 'BRCA',\n",
       "       'LUAD', 'KIRC', 'LUAD', 'LUAD', 'LUAD', 'KIRC', 'BRCA', 'PRAD',\n",
       "       'BRCA', 'BRCA', 'KIRC', 'BRCA', 'PRAD', 'KIRC', 'BRCA', 'BRCA',\n",
       "       'BRCA', 'BRCA', 'PRAD', 'LUAD', 'BRCA', 'LUAD', 'COAD', 'PRAD',\n",
       "       'PRAD'], dtype='<U4')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a6d75cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1d98357",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "true_labels = label_encoder.fit_transform(true_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3285ea06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 4, 4, 0, 4, 2, 4, 0, 4, 0, 2, 4, 0, 0, 0, 3, 2, 2, 4, 0, 2,\n",
       "       3, 0, 2, 3, 1, 0, 0, 0, 0, 0, 2, 0, 4, 0, 2, 3, 0, 0, 2, 4, 4, 2,\n",
       "       2, 0, 4, 1, 0, 3, 0, 3, 0, 4, 1, 0, 0, 1, 2, 0, 3, 2, 0, 3, 4, 1,\n",
       "       0, 4, 2, 0, 2, 0, 0, 3, 0, 3, 0, 2, 4, 1, 0, 4, 0, 0, 4, 4, 0, 0,\n",
       "       2, 0, 4, 4, 0, 0, 0, 4, 1, 0, 4, 0, 0, 2, 0, 2, 3, 2, 3, 1, 3, 3,\n",
       "       4, 0, 3, 4, 0, 2, 2, 2, 0, 0, 3, 2, 3, 0, 4, 4, 4, 0, 2, 3, 1, 0,\n",
       "       1, 0, 0, 2, 3, 0, 2, 1, 0, 4, 0, 2, 3, 1, 4, 0, 3, 3, 3, 3, 0, 0,\n",
       "       3, 0, 0, 4, 4, 3, 4, 3, 2, 0, 4, 3, 1, 2, 3, 0, 2, 3, 0, 3, 0, 0,\n",
       "       0, 4, 0, 2, 1, 2, 0, 4, 4, 4, 3, 3, 0, 3, 3, 2, 3, 4, 3, 0, 0, 0,\n",
       "       3, 3, 0, 2, 2, 2, 2, 4, 0, 4, 0, 3, 3, 0, 4, 0, 4, 0, 0, 0, 3, 0,\n",
       "       2, 3, 2, 2, 3, 0, 2, 4, 0, 3, 3, 4, 1, 0, 2, 4, 2, 1, 0, 2, 2, 3,\n",
       "       4, 4, 3, 3, 2, 0, 0, 1, 0, 4, 1, 0, 4, 2, 4, 4, 4, 0, 1, 1, 3, 1,\n",
       "       1, 4, 3, 0, 0, 2, 2, 0, 1, 4, 2, 4, 0, 0, 2, 0, 0, 0, 0, 3, 3, 0,\n",
       "       0, 0, 2, 2, 2, 2, 0, 0, 0, 2, 0, 0, 3, 4, 0, 0, 1, 3, 4, 0, 0, 0,\n",
       "       1, 0, 4, 0, 1, 3, 3, 4, 2, 0, 2, 2, 3, 1, 2, 0, 0, 0, 0, 2, 0, 0,\n",
       "       4, 0, 2, 0, 3, 4, 2, 0, 4, 1, 0, 0, 0, 3, 3, 3, 0, 0, 4, 3, 0, 2,\n",
       "       0, 1, 1, 3, 0, 2, 0, 0, 0, 1, 3, 1, 2, 4, 2, 0, 0, 2, 0, 1, 4, 3,\n",
       "       4, 0, 2, 4, 0, 1, 2, 2, 1, 1, 4, 0, 0, 1, 2, 0, 4, 0, 0, 0, 3, 3,\n",
       "       2, 3, 0, 2, 1, 4, 0, 3, 4, 0, 0, 0, 3, 0, 0, 4, 0, 4, 1, 0, 3, 0,\n",
       "       0, 3, 0, 0, 0, 2, 3, 4, 0, 4, 2, 0, 2, 1, 0, 4, 3, 2, 0, 0, 2, 0,\n",
       "       3, 0, 0, 4, 1, 0, 2, 3, 4, 0, 4, 0, 0, 0, 0, 2, 3, 0, 2, 0, 0, 3,\n",
       "       3, 2, 1, 4, 1, 0, 2, 2, 0, 4, 2, 1, 3, 3, 0, 4, 4, 0, 4, 3, 2, 4,\n",
       "       0, 3, 4, 3, 0, 0, 1, 3, 2, 1, 3, 0, 4, 0, 0, 4, 0, 1, 0, 1, 2, 0,\n",
       "       0, 3, 3, 3, 1, 2, 3, 3, 0, 0, 2, 4, 3, 4, 0, 2, 0, 2, 2, 4, 4, 3,\n",
       "       0, 2, 1, 1, 0, 2, 2, 0, 0, 4, 2, 1, 0, 0, 1, 3, 0, 0, 0, 2, 4, 3,\n",
       "       3, 0, 2, 1, 2, 2, 0, 4, 3, 2, 0, 1, 3, 3, 3, 4, 3, 2, 0, 0, 1, 4,\n",
       "       0, 0, 0, 2, 3, 3, 0, 4, 3, 3, 0, 2, 4, 1, 3, 4, 1, 3, 1, 2, 2, 0,\n",
       "       0, 2, 2, 1, 0, 3, 4, 4, 2, 0, 3, 0, 0, 1, 0, 4, 4, 0, 0, 1, 0, 2,\n",
       "       0, 0, 1, 0, 4, 0, 0, 2, 4, 3, 0, 0, 2, 0, 0, 0, 0, 0, 1, 3, 3, 0,\n",
       "       0, 0, 4, 0, 0, 2, 3, 3, 2, 2, 3, 2, 1, 0, 1, 2, 0, 0, 4, 4, 4, 3,\n",
       "       4, 4, 1, 0, 0, 1, 3, 2, 0, 2, 1, 0, 0, 0, 4, 3, 2, 0, 4, 2, 4, 0,\n",
       "       3, 2, 4, 3, 4, 4, 0, 2, 4, 3, 1, 1, 0, 0, 0, 3, 2, 2, 2, 0, 3, 2,\n",
       "       4, 0, 3, 4, 0, 4, 0, 2, 0, 1, 4, 4, 2, 4, 2, 0, 3, 3, 0, 0, 2, 0,\n",
       "       0, 0, 0, 2, 2, 4, 1, 2, 0, 0, 2, 0, 3, 0, 0, 4, 0, 4, 0, 1, 0, 0,\n",
       "       2, 3, 0, 0, 4, 0, 4, 1, 0, 0, 0, 3, 0, 3, 0, 3, 2, 2, 1, 1, 0, 0,\n",
       "       0, 3, 0, 3, 2, 0, 3, 2, 3, 3, 3, 2, 0, 4, 0, 0, 2, 0, 4, 2, 0, 0,\n",
       "       0, 0, 4, 3, 0, 3, 1, 4, 4], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6104725b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BRCA', 'COAD', 'KIRC', 'LUAD', 'PRAD'], dtype='<U4')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d8598378",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_clusters=len(label_encoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "14f2db1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[801.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [167., 505., 128., ...,   0.,   0.,   0.],\n",
       "        [135., 545., 121., ...,   0.,   0.,   0.],\n",
       "        ...,\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
       "        [ 22., 186., 348., ...,   0.,   0.,   0.],\n",
       "        [798.,   1.,   2., ...,   0.,   0.,   0.]]),\n",
       " array([ 0.        ,  2.07788287,  4.15576574,  6.23364861,  8.31153148,\n",
       "        10.38941436, 12.46729723, 14.5451801 , 16.62306297, 18.70094584,\n",
       "        20.77882871]),\n",
       " <a list of 20531 BarContainer objects>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "938bfb12",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Looking at the above graph its clear that the data is not a normal distribution curve hence we will use minmax scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "389cc6d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([300.,   0.,  78.,   0.,   0., 146.,   0., 141.,   0., 136.]),\n",
       " array([0. , 0.4, 0.8, 1.2, 1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(true_labels,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "996b84ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# trans = MinMaxScaler()\n",
    "# scaled_data = trans.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cd6992fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0000000000000004"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scaled_data.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c5f47296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scaled_data.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "53452c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[766.,   1.,  10., ...,   0.,   2.,   1.],\n",
       "        [ 34.,  37.,  57., ...,  69.,  16.,   5.],\n",
       "        [  8.,  22.,  58., ...,  71.,  28.,  12.],\n",
       "        ...,\n",
       "        [ 15.,  44., 193., ...,   2.,   1.,   1.],\n",
       "        [ 11.,  44., 104., ...,  78.,  45.,   8.],\n",
       "        [743.,  45.,   6., ...,   2.,   0.,   1.]]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <a list of 20531 BarContainer objects>)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.hist(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6581d034",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "preprocessor = Pipeline(\n",
    "    [\n",
    "        (\"scaler\", MinMaxScaler()),\n",
    "        (\"pca\", PCA(n_components=2, random_state=42)),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "25f619a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "clusterer = Pipeline(\n",
    "   [\n",
    "       (\n",
    "           \"kmeans\",\n",
    "           KMeans(\n",
    "               n_clusters=n_clusters,\n",
    "               init=\"k-means++\",\n",
    "               n_init=50,\n",
    "               max_iter=500,\n",
    "               random_state=42,\n",
    "           ),\n",
    "       ),\n",
    "   ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6d6fea62",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(\n",
    "    [\n",
    "        (\"preprocessor\", preprocessor),\n",
    "        (\"clusterer\", clusterer)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a5814227",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler()),\n",
       "                                 (&#x27;pca&#x27;,\n",
       "                                  PCA(n_components=2, random_state=42))])),\n",
       "                (&#x27;clusterer&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
       "                                  KMeans(max_iter=500, n_clusters=5, n_init=50,\n",
       "                                         random_state=42))]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler()),\n",
       "                                 (&#x27;pca&#x27;,\n",
       "                                  PCA(n_components=2, random_state=42))])),\n",
       "                (&#x27;clusterer&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
       "                                  KMeans(max_iter=500, n_clusters=5, n_init=50,\n",
       "                                         random_state=42))]))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler()),\n",
       "                (&#x27;pca&#x27;, PCA(n_components=2, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=2, random_state=42)</pre></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">clusterer: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;kmeans&#x27;,\n",
       "                 KMeans(max_iter=500, n_clusters=5, n_init=50,\n",
       "                        random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(max_iter=500, n_clusters=5, n_init=50, random_state=42)</pre></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 Pipeline(steps=[('scaler', MinMaxScaler()),\n",
       "                                 ('pca',\n",
       "                                  PCA(n_components=2, random_state=42))])),\n",
       "                ('clusterer',\n",
       "                 Pipeline(steps=[('kmeans',\n",
       "                                  KMeans(max_iter=500, n_clusters=5, n_init=50,\n",
       "                                         random_state=42))]))])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "de3c6407",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessed_data = pipe[\"preprocessor\"].transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d5f8c9ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[  5.,  47., 219., 235., 122.,  29.,   6.,  10.,  39.,  89.],\n",
       "        [ 64.,  61.,  52., 166., 182., 115.,  86.,  38.,  33.,   4.]]),\n",
       " array([-14.06170881, -10.79418939,  -7.52666997,  -4.25915055,\n",
       "         -0.99163113,   2.27588829,   5.5434077 ,   8.81092712,\n",
       "         12.07844654,  15.34596596,  18.61348538]),\n",
       " <a list of 2 BarContainer objects>)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(preprocessed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "606dc23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_labels = pipe\n",
    "clusterer\"][\"kmeans\"].labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0b344ca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 0, 3, 0, 3, 1, 3, 4, 3, 0, 1, 3, 0, 0, 0, 4, 1, 1, 3, 0, 4,\n",
       "       4, 0, 1, 4, 2, 0, 4, 0, 0, 0, 1, 0, 3, 0, 1, 4, 0, 0, 1, 3, 3, 1,\n",
       "       1, 0, 3, 2, 0, 4, 0, 4, 0, 3, 2, 2, 0, 2, 1, 0, 4, 4, 0, 4, 3, 2,\n",
       "       0, 3, 1, 4, 1, 0, 0, 4, 4, 2, 0, 4, 3, 2, 0, 3, 0, 0, 3, 3, 0, 0,\n",
       "       1, 2, 3, 3, 0, 0, 0, 3, 2, 0, 3, 0, 0, 1, 0, 1, 4, 1, 4, 2, 4, 4,\n",
       "       3, 0, 4, 3, 0, 1, 1, 1, 0, 0, 4, 1, 4, 0, 3, 3, 3, 0, 1, 2, 2, 0,\n",
       "       2, 4, 0, 1, 4, 0, 1, 2, 0, 3, 3, 1, 4, 2, 3, 0, 4, 4, 4, 4, 0, 0,\n",
       "       4, 0, 0, 3, 3, 4, 3, 0, 1, 0, 3, 4, 2, 1, 4, 3, 1, 4, 0, 4, 0, 0,\n",
       "       0, 3, 0, 1, 2, 1, 0, 3, 3, 3, 4, 4, 0, 4, 4, 1, 2, 3, 4, 3, 4, 0,\n",
       "       2, 4, 0, 1, 1, 1, 1, 3, 2, 3, 0, 4, 4, 0, 3, 0, 3, 0, 0, 0, 4, 0,\n",
       "       1, 4, 1, 1, 4, 0, 1, 3, 0, 4, 4, 3, 2, 0, 1, 3, 1, 2, 0, 1, 1, 4,\n",
       "       3, 0, 4, 4, 1, 0, 4, 2, 0, 3, 4, 0, 3, 1, 3, 3, 3, 0, 2, 2, 4, 2,\n",
       "       2, 3, 4, 2, 0, 1, 1, 0, 2, 3, 1, 3, 0, 0, 1, 0, 4, 0, 0, 4, 4, 0,\n",
       "       0, 0, 1, 1, 1, 1, 0, 4, 0, 1, 3, 3, 4, 3, 0, 0, 2, 4, 3, 0, 0, 0,\n",
       "       2, 0, 3, 0, 2, 2, 4, 3, 1, 0, 1, 1, 4, 2, 4, 3, 0, 2, 4, 1, 0, 0,\n",
       "       3, 2, 1, 0, 4, 3, 1, 0, 0, 2, 0, 0, 0, 4, 2, 4, 0, 0, 3, 4, 3, 1,\n",
       "       0, 2, 4, 4, 0, 1, 0, 0, 0, 2, 4, 2, 1, 3, 1, 0, 0, 1, 4, 2, 3, 4,\n",
       "       3, 0, 1, 3, 0, 2, 1, 1, 2, 2, 3, 0, 0, 4, 1, 4, 3, 2, 0, 0, 4, 4,\n",
       "       1, 4, 4, 1, 2, 3, 0, 4, 3, 0, 2, 3, 4, 0, 2, 3, 0, 3, 2, 0, 2, 0,\n",
       "       2, 4, 0, 0, 3, 1, 4, 3, 0, 3, 1, 0, 1, 2, 0, 3, 4, 1, 0, 0, 1, 0,\n",
       "       4, 4, 4, 3, 2, 0, 1, 4, 3, 0, 3, 0, 0, 4, 0, 1, 4, 0, 1, 0, 0, 2,\n",
       "       4, 1, 2, 3, 2, 0, 1, 1, 0, 3, 1, 4, 4, 4, 0, 3, 3, 4, 3, 4, 1, 3,\n",
       "       0, 4, 3, 4, 0, 0, 2, 4, 1, 2, 4, 0, 3, 4, 0, 3, 0, 2, 0, 2, 1, 0,\n",
       "       0, 2, 4, 4, 2, 1, 4, 4, 0, 0, 0, 3, 4, 3, 0, 1, 0, 1, 1, 3, 3, 4,\n",
       "       0, 1, 2, 2, 4, 1, 1, 0, 0, 3, 1, 2, 0, 4, 2, 4, 0, 0, 0, 1, 3, 4,\n",
       "       4, 0, 1, 2, 1, 1, 0, 3, 2, 1, 0, 2, 4, 4, 4, 3, 4, 1, 0, 0, 2, 3,\n",
       "       4, 0, 2, 1, 4, 4, 4, 3, 4, 4, 0, 1, 3, 2, 4, 3, 2, 0, 2, 1, 1, 2,\n",
       "       0, 1, 1, 2, 0, 4, 3, 3, 1, 0, 4, 4, 0, 2, 0, 3, 3, 4, 0, 2, 0, 1,\n",
       "       2, 4, 4, 0, 3, 0, 2, 1, 3, 4, 0, 0, 1, 4, 0, 0, 3, 2, 2, 4, 4, 4,\n",
       "       0, 2, 3, 0, 0, 1, 4, 4, 1, 1, 4, 1, 2, 3, 2, 1, 0, 0, 3, 3, 3, 4,\n",
       "       3, 3, 2, 0, 0, 2, 4, 1, 0, 1, 2, 0, 0, 0, 3, 4, 1, 0, 0, 1, 3, 0,\n",
       "       4, 1, 3, 4, 3, 3, 2, 1, 3, 4, 2, 2, 0, 0, 0, 4, 1, 1, 1, 0, 4, 1,\n",
       "       3, 0, 4, 3, 0, 3, 0, 1, 4, 4, 3, 3, 1, 3, 1, 0, 4, 4, 0, 0, 1, 0,\n",
       "       0, 3, 0, 1, 1, 3, 2, 1, 2, 0, 1, 0, 4, 0, 0, 3, 0, 3, 0, 2, 0, 0,\n",
       "       1, 4, 0, 4, 3, 0, 3, 4, 0, 0, 0, 4, 0, 4, 0, 4, 1, 1, 2, 4, 0, 0,\n",
       "       0, 4, 0, 4, 4, 0, 4, 1, 2, 4, 4, 1, 0, 3, 2, 0, 1, 0, 3, 4, 0, 0,\n",
       "       0, 0, 3, 4, 0, 4, 4, 3, 0])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "981a2c00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.511877552845029"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score, adjusted_rand_score\n",
    "silhouette_score(preprocessed_data, predicted_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b4281836",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As True labels are present hence we are using adjusted rand score to compare the pred vs actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b2af0f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.722276752060253"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjusted_rand_score(true_labels, predicted_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "7a4b9ca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the scale for each of these clustering performance metrics ranges from -1 to 1. \n",
    "#A silhouette coefficient of 0 indicates that clusters are significantly overlapping one another, and a \n",
    "#silhouette coefficient of 1 indicates clusters are well-separated. \n",
    "#An ARI score of 0 indicates that cluster labels \n",
    "#are randomly assigned, and an ARI score of 1 means that the true labels and predicted labels form identical clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f2fb1e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pcadf = pd.DataFrame(\n",
    "#     pipe[\"preprocessor\"].transform(data),\n",
    "#     columns=[\"component_1\", \"component_2\"],\n",
    "# )\n",
    "# pcadf[\"predicted_cluster\"] = pipe[\"clusterer\"][\"kmeans\"].labels_\n",
    "# pcadf[\"true_label\"] = label_encoder.inverse_transform(true_labels)\n",
    "\n",
    "# plt.style.use(\"fivethirtyeight\")\n",
    "# plt.figure(figsize=(8, 8))\n",
    "# scat = sns.scatterplot(\n",
    "#     \"component_1\",\n",
    "#     \"component_2\",\n",
    "#     s=50,\n",
    "#     data=pcadf,\n",
    "#     hue=\"predicted_cluster\",\n",
    "#     style=\"true_label\",\n",
    "#     palette=\"Set2\",\n",
    "# )\n",
    "# scat.set_title(\n",
    "#     \"Clustering results from TCGA Pan-Cancer\\nGene Expression Data\"\n",
    "# )\n",
    "# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c7d439b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#Hyper parameter tunning by changing the value to PCA n_component and checking Silhouette and ARI score\n",
    "silhouette_scores = []\n",
    "ari_scores = []\n",
    "for n in range(2, 11):\n",
    "    # This set the number of components for pca,\n",
    "    # but leaves other steps unchanged\n",
    "    pipe[\"preprocessor\"][\"pca\"].n_components = n\n",
    "    pipe.fit(data)\n",
    "    silhouette_coef = silhouette_score(\n",
    "        pipe[\"preprocessor\"].transform(data),\n",
    "        pipe[\"clusterer\"][\"kmeans\"].labels_,\n",
    "    )\n",
    "    ari = adjusted_rand_score(\n",
    "        true_labels,\n",
    "        pipe[\"clusterer\"][\"kmeans\"].labels_,\n",
    "    )\n",
    "    # Add metrics to their lists\n",
    "    silhouette_scores.append(silhouette_coef)\n",
    "    ari_scores.append(ari)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "885733c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.511877552845029,\n",
       " 0.5047270495704069,\n",
       " 0.47133548695193794,\n",
       " 0.46939987883449735,\n",
       " 0.44275560175959683,\n",
       " 0.4266922277902736,\n",
       " 0.40380691884278175,\n",
       " 0.390178822247916,\n",
       " 0.3772752581109923]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ee448c37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.722276752060253,\n",
       " 0.7458314873965364,\n",
       " 0.7252516114763512,\n",
       " 0.8591960195401789,\n",
       " 0.825156557127003,\n",
       " 0.9548332937934558,\n",
       " 0.8086619712734584,\n",
       " 0.8086619712734584,\n",
       " 0.8116720216397996]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ari_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0fb3b091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph representation between ARI and Silhouette \n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(\n",
    "    range(2, 11),\n",
    "    silhouette_scores,\n",
    "    c=\"#008fd5\",\n",
    "    label=\"Silhouette Coefficient\",\n",
    ")\n",
    "plt.plot(range(2, 11), ari_scores, c=\"#fc4f30\", label=\"ARI\")\n",
    "plt.xlabel(\"n_components\")\n",
    "plt.legend()\n",
    "plt.title(\"Clustering Performance as a Function of n_components\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df73ade8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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