Cancer_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b41346ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib inline\n",
    "import mpl_toolkits.mplot3d \n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, PrecisionRecallDisplay, precision_score\n",
    "from sklearn.metrics import recall_score, classification_report, RocCurveDisplay, auc, roc_curve, precision_recall_curve\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "03993b07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(72, 2)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>cancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>ALL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient cancer\n",
       "0        1    ALL\n",
       "1        2    ALL\n",
       "2        3    ALL\n",
       "3        4    ALL\n",
       "4        5    ALL"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the csv file\n",
    "y = pd.read_csv('./actual.csv')\n",
    "print(y.shape)\n",
    "y.head()\n",
    "#y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b3c7ce3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ALL    47\n",
       "AML    25\n",
       "Name: cancer, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To display the counts of ALL and AML\n",
    "y['cancer'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b7acd1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>cancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   patient  cancer\n",
       "0        1       0\n",
       "1        2       0\n",
       "2        3       0\n",
       "3        4       0\n",
       "4        5       0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coverting class labels to integers i.e., '0' and '1'\n",
    "y = y.replace({'ALL':0,'AML':1})\n",
    "labels = ['ALL', 'AML']\n",
    "y.head()\n",
    "#y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dfcbb350",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7129, 78)\n",
      "(7129, 70)\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene Description</th>\n",
       "      <th>Gene Accession Number</th>\n",
       "      <th>1</th>\n",
       "      <th>call</th>\n",
       "      <th>2</th>\n",
       "      <th>call.1</th>\n",
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       "      <th>call.2</th>\n",
       "      <th>4</th>\n",
       "      <th>call.3</th>\n",
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       "      <th>call.36</th>\n",
       "      <th>33</th>\n",
       "      <th>call.37</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFFX-BioB-5_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-5_at</td>\n",
       "      <td>-214</td>\n",
       "      <td>A</td>\n",
       "      <td>-139</td>\n",
       "      <td>A</td>\n",
       "      <td>-76</td>\n",
       "      <td>A</td>\n",
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       "      <td>15</td>\n",
       "      <td>A</td>\n",
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       "      <td>A</td>\n",
       "      <td>-32</td>\n",
       "      <td>A</td>\n",
       "      <td>-124</td>\n",
       "      <td>A</td>\n",
       "      <td>-135</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFFX-BioB-M_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-M_at</td>\n",
       "      <td>-153</td>\n",
       "      <td>A</td>\n",
       "      <td>-73</td>\n",
       "      <td>A</td>\n",
       "      <td>-49</td>\n",
       "      <td>A</td>\n",
       "      <td>-114</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>-114</td>\n",
       "      <td>A</td>\n",
       "      <td>-192</td>\n",
       "      <td>A</td>\n",
       "      <td>-49</td>\n",
       "      <td>A</td>\n",
       "      <td>-79</td>\n",
       "      <td>A</td>\n",
       "      <td>-186</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFFX-BioB-3_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-3_at</td>\n",
       "      <td>-58</td>\n",
       "      <td>A</td>\n",
       "      <td>-1</td>\n",
       "      <td>A</td>\n",
       "      <td>-307</td>\n",
       "      <td>A</td>\n",
       "      <td>265</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>-95</td>\n",
       "      <td>A</td>\n",
       "      <td>49</td>\n",
       "      <td>A</td>\n",
       "      <td>-37</td>\n",
       "      <td>A</td>\n",
       "      <td>-70</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFFX-BioC-5_at (endogenous control)</td>\n",
       "      <td>AFFX-BioC-5_at</td>\n",
       "      <td>88</td>\n",
       "      <td>A</td>\n",
       "      <td>283</td>\n",
       "      <td>A</td>\n",
       "      <td>309</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>193</td>\n",
       "      <td>A</td>\n",
       "      <td>312</td>\n",
       "      <td>A</td>\n",
       "      <td>230</td>\n",
       "      <td>P</td>\n",
       "      <td>330</td>\n",
       "      <td>A</td>\n",
       "      <td>337</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFFX-BioC-3_at (endogenous control)</td>\n",
       "      <td>AFFX-BioC-3_at</td>\n",
       "      <td>-295</td>\n",
       "      <td>A</td>\n",
       "      <td>-264</td>\n",
       "      <td>A</td>\n",
       "      <td>-376</td>\n",
       "      <td>A</td>\n",
       "      <td>-419</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>-51</td>\n",
       "      <td>A</td>\n",
       "      <td>-139</td>\n",
       "      <td>A</td>\n",
       "      <td>-367</td>\n",
       "      <td>A</td>\n",
       "      <td>-188</td>\n",
       "      <td>A</td>\n",
       "      <td>-407</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 78 columns</p>\n",
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      ],
      "text/plain": [
       "                      Gene Description Gene Accession Number    1 call    2  \\\n",
       "0  AFFX-BioB-5_at (endogenous control)        AFFX-BioB-5_at -214    A -139   \n",
       "1  AFFX-BioB-M_at (endogenous control)        AFFX-BioB-M_at -153    A  -73   \n",
       "2  AFFX-BioB-3_at (endogenous control)        AFFX-BioB-3_at  -58    A   -1   \n",
       "3  AFFX-BioC-5_at (endogenous control)        AFFX-BioC-5_at   88    A  283   \n",
       "4  AFFX-BioC-3_at (endogenous control)        AFFX-BioC-3_at -295    A -264   \n",
       "\n",
       "  call.1    3 call.2    4 call.3  ...   29 call.33   30 call.34   31 call.35  \\\n",
       "0      A  -76      A -135      A  ...   15       A -318       A  -32       A   \n",
       "1      A  -49      A -114      A  ... -114       A -192       A  -49       A   \n",
       "2      A -307      A  265      A  ...    2       A  -95       A   49       A   \n",
       "3      A  309      A   12      A  ...  193       A  312       A  230       P   \n",
       "4      A -376      A -419      A  ...  -51       A -139       A -367       A   \n",
       "\n",
       "    32 call.36   33 call.37  \n",
       "0 -124       A -135       A  \n",
       "1  -79       A -186       A  \n",
       "2  -37       A  -70       A  \n",
       "3  330       A  337       A  \n",
       "4 -188       A -407       A  \n",
       "\n",
       "[5 rows x 78 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading train data\n",
    "df_train = pd.read_csv('./data_set_ALL_AML_train.csv')\n",
    "print(df_train.shape)\n",
    "\n",
    "# Reading test data\n",
    "df_test = pd.read_csv('./data_set_ALL_AML_independent.csv')\n",
    "print(df_test.shape)\n",
    "\n",
    "df_train.head()\n",
    "#df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6aafe1c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gene Description', 'Gene Accession Number', '1', 'call', '2', 'call.1',\n",
       "       '3', 'call.2', '4', 'call.3', '5', 'call.4', '6', 'call.5', '7',\n",
       "       'call.6', '8', 'call.7', '9', 'call.8', '10', 'call.9', '11', 'call.10',\n",
       "       '12', 'call.11', '13', 'call.12', '14', 'call.13', '15', 'call.14',\n",
       "       '16', 'call.15', '17', 'call.16', '18', 'call.17', '19', 'call.18',\n",
       "       '20', 'call.19', '21', 'call.20', '22', 'call.21', '23', 'call.22',\n",
       "       '24', 'call.23', '25', 'call.24', '26', 'call.25', '27', 'call.26',\n",
       "       '34', 'call.27', '35', 'call.28', '36', 'call.29', '37', 'call.30',\n",
       "       '38', 'call.31', '28', 'call.32', '29', 'call.33', '30', 'call.34',\n",
       "       '31', 'call.35', '32', 'call.36', '33', 'call.37'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns\n",
    "#[col for col in df_train.columns if \"call\" not in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c4a2e4bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing \"call\" columns from training and testing data because they seem to provide not much information \n",
    "\n",
    "train_to_keep = [col for col in df_train.columns if \"call\" not in col]\n",
    "test_to_keep = [col for col in df_test.columns if \"call\" not in col]\n",
    "\n",
    "X_train_tr = df_train[train_to_keep]\n",
    "X_test_tr = df_test[test_to_keep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "107635ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reordering column names so that they would be in the same order\n",
    "\n",
    "train_columns_titles = ['Gene Description', 'Gene Accession Number', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10',\n",
    "       '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', \n",
    "       '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38']\n",
    "\n",
    "X_train_tr = X_train_tr.reindex(columns=train_columns_titles)\n",
    "\n",
    "#X_train_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49ae96bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_columns_titles = ['Gene Description', 'Gene Accession Number','39', '40', '41', '42', '43', '44', '45', '46',\n",
    "       '47', '48', '49', '50', '51', '52', '53',  '54', '55', '56', '57', '58', '59',\n",
    "       '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72']\n",
    "\n",
    "X_test_tr = X_test_tr.reindex(columns=test_columns_titles)\n",
    "\n",
    "#X_test_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1be71fb9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "output_type": "stream",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gene Description</th>\n",
       "      <td>AFFX-BioB-5_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-M_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-3_at (endogenous control)</td>\n",
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       "      <td>AFFX-BioDn-3_at (endogenous control)</td>\n",
       "      <td>AFFX-CreX-5_at (endogenous control)</td>\n",
       "      <td>AFFX-CreX-3_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-5_st (endogenous control)</td>\n",
       "      <td>...</td>\n",
       "      <td>Transcription factor Stat5b (stat5b) mRNA</td>\n",
       "      <td>Breast epithelial antigen BA46 mRNA</td>\n",
       "      <td>GB DEF = Calcium/calmodulin-dependent protein ...</td>\n",
       "      <td>TUBULIN ALPHA-4 CHAIN</td>\n",
       "      <td>CYP4B1 Cytochrome P450; subfamily IVB; polypep...</td>\n",
       "      <td>PTGER3 Prostaglandin E receptor 3 (subtype EP3...</td>\n",
       "      <td>HMG2 High-mobility group (nonhistone chromosom...</td>\n",
       "      <td>RB1 Retinoblastoma 1 (including osteosarcoma)</td>\n",
       "      <td>GB DEF = Glycophorin Sta (type A) exons 3 and ...</td>\n",
       "      <td>GB DEF = mRNA (clone 1A7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene Accession Number</th>\n",
       "      <td>AFFX-BioB-5_at</td>\n",
       "      <td>AFFX-BioB-M_at</td>\n",
       "      <td>AFFX-BioB-3_at</td>\n",
       "      <td>AFFX-BioC-5_at</td>\n",
       "      <td>AFFX-BioC-3_at</td>\n",
       "      <td>AFFX-BioDn-5_at</td>\n",
       "      <td>AFFX-BioDn-3_at</td>\n",
       "      <td>AFFX-CreX-5_at</td>\n",
       "      <td>AFFX-CreX-3_at</td>\n",
       "      <td>AFFX-BioB-5_st</td>\n",
       "      <td>...</td>\n",
       "      <td>U48730_at</td>\n",
       "      <td>U58516_at</td>\n",
       "      <td>U73738_at</td>\n",
       "      <td>X06956_at</td>\n",
       "      <td>X16699_at</td>\n",
       "      <td>X83863_at</td>\n",
       "      <td>Z17240_at</td>\n",
       "      <td>L49218_f_at</td>\n",
       "      <td>M71243_f_at</td>\n",
       "      <td>Z78285_f_at</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-214</td>\n",
       "      <td>-153</td>\n",
       "      <td>-58</td>\n",
       "      <td>88</td>\n",
       "      <td>-295</td>\n",
       "      <td>-558</td>\n",
       "      <td>199</td>\n",
       "      <td>-176</td>\n",
       "      <td>252</td>\n",
       "      <td>206</td>\n",
       "      <td>...</td>\n",
       "      <td>185</td>\n",
       "      <td>511</td>\n",
       "      <td>-125</td>\n",
       "      <td>389</td>\n",
       "      <td>-37</td>\n",
       "      <td>793</td>\n",
       "      <td>329</td>\n",
       "      <td>36</td>\n",
       "      <td>191</td>\n",
       "      <td>-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-139</td>\n",
       "      <td>-73</td>\n",
       "      <td>-1</td>\n",
       "      <td>283</td>\n",
       "      <td>-264</td>\n",
       "      <td>-400</td>\n",
       "      <td>-330</td>\n",
       "      <td>-168</td>\n",
       "      <td>101</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>169</td>\n",
       "      <td>837</td>\n",
       "      <td>-36</td>\n",
       "      <td>442</td>\n",
       "      <td>-17</td>\n",
       "      <td>782</td>\n",
       "      <td>295</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-76</td>\n",
       "      <td>-49</td>\n",
       "      <td>-307</td>\n",
       "      <td>309</td>\n",
       "      <td>-376</td>\n",
       "      <td>-650</td>\n",
       "      <td>33</td>\n",
       "      <td>-367</td>\n",
       "      <td>206</td>\n",
       "      <td>-215</td>\n",
       "      <td>...</td>\n",
       "      <td>315</td>\n",
       "      <td>1199</td>\n",
       "      <td>33</td>\n",
       "      <td>168</td>\n",
       "      <td>52</td>\n",
       "      <td>1138</td>\n",
       "      <td>777</td>\n",
       "      <td>41</td>\n",
       "      <td>228</td>\n",
       "      <td>-41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 7129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      0     \\\n",
       "Gene Description       AFFX-BioB-5_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioB-5_at   \n",
       "1                                                     -214   \n",
       "2                                                     -139   \n",
       "3                                                      -76   \n",
       "\n",
       "                                                      1     \\\n",
       "Gene Description       AFFX-BioB-M_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioB-M_at   \n",
       "1                                                     -153   \n",
       "2                                                      -73   \n",
       "3                                                      -49   \n",
       "\n",
       "                                                      2     \\\n",
       "Gene Description       AFFX-BioB-3_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioB-3_at   \n",
       "1                                                      -58   \n",
       "2                                                       -1   \n",
       "3                                                     -307   \n",
       "\n",
       "                                                      3     \\\n",
       "Gene Description       AFFX-BioC-5_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioC-5_at   \n",
       "1                                                       88   \n",
       "2                                                      283   \n",
       "3                                                      309   \n",
       "\n",
       "                                                      4     \\\n",
       "Gene Description       AFFX-BioC-3_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioC-3_at   \n",
       "1                                                     -295   \n",
       "2                                                     -264   \n",
       "3                                                     -376   \n",
       "\n",
       "                                                       5     \\\n",
       "Gene Description       AFFX-BioDn-5_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioDn-5_at   \n",
       "1                                                      -558   \n",
       "2                                                      -400   \n",
       "3                                                      -650   \n",
       "\n",
       "                                                       6     \\\n",
       "Gene Description       AFFX-BioDn-3_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioDn-3_at   \n",
       "1                                                       199   \n",
       "2                                                      -330   \n",
       "3                                                        33   \n",
       "\n",
       "                                                      7     \\\n",
       "Gene Description       AFFX-CreX-5_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-CreX-5_at   \n",
       "1                                                     -176   \n",
       "2                                                     -168   \n",
       "3                                                     -367   \n",
       "\n",
       "                                                      8     \\\n",
       "Gene Description       AFFX-CreX-3_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-CreX-3_at   \n",
       "1                                                      252   \n",
       "2                                                      101   \n",
       "3                                                      206   \n",
       "\n",
       "                                                      9     ...  \\\n",
       "Gene Description       AFFX-BioB-5_st (endogenous control)  ...   \n",
       "Gene Accession Number                       AFFX-BioB-5_st  ...   \n",
       "1                                                      206  ...   \n",
       "2                                                       74  ...   \n",
       "3                                                     -215  ...   \n",
       "\n",
       "                                                            7119  \\\n",
       "Gene Description       Transcription factor Stat5b (stat5b) mRNA   \n",
       "Gene Accession Number                                  U48730_at   \n",
       "1                                                            185   \n",
       "2                                                            169   \n",
       "3                                                            315   \n",
       "\n",
       "                                                      7120  \\\n",
       "Gene Description       Breast epithelial antigen BA46 mRNA   \n",
       "Gene Accession Number                            U58516_at   \n",
       "1                                                      511   \n",
       "2                                                      837   \n",
       "3                                                     1199   \n",
       "\n",
       "                                                                    7121  \\\n",
       "Gene Description       GB DEF = Calcium/calmodulin-dependent protein ...   \n",
       "Gene Accession Number                                          U73738_at   \n",
       "1                                                                   -125   \n",
       "2                                                                    -36   \n",
       "3                                                                     33   \n",
       "\n",
       "                                        7122  \\\n",
       "Gene Description       TUBULIN ALPHA-4 CHAIN   \n",
       "Gene Accession Number              X06956_at   \n",
       "1                                        389   \n",
       "2                                        442   \n",
       "3                                        168   \n",
       "\n",
       "                                                                    7123  \\\n",
       "Gene Description       CYP4B1 Cytochrome P450; subfamily IVB; polypep...   \n",
       "Gene Accession Number                                          X16699_at   \n",
       "1                                                                    -37   \n",
       "2                                                                    -17   \n",
       "3                                                                     52   \n",
       "\n",
       "                                                                    7124  \\\n",
       "Gene Description       PTGER3 Prostaglandin E receptor 3 (subtype EP3...   \n",
       "Gene Accession Number                                          X83863_at   \n",
       "1                                                                    793   \n",
       "2                                                                    782   \n",
       "3                                                                   1138   \n",
       "\n",
       "                                                                    7125  \\\n",
       "Gene Description       HMG2 High-mobility group (nonhistone chromosom...   \n",
       "Gene Accession Number                                          Z17240_at   \n",
       "1                                                                    329   \n",
       "2                                                                    295   \n",
       "3                                                                    777   \n",
       "\n",
       "                                                                7126  \\\n",
       "Gene Description       RB1 Retinoblastoma 1 (including osteosarcoma)   \n",
       "Gene Accession Number                                    L49218_f_at   \n",
       "1                                                                 36   \n",
       "2                                                                 11   \n",
       "3                                                                 41   \n",
       "\n",
       "                                                                    7127  \\\n",
       "Gene Description       GB DEF = Glycophorin Sta (type A) exons 3 and ...   \n",
       "Gene Accession Number                                        M71243_f_at   \n",
       "1                                                                    191   \n",
       "2                                                                     76   \n",
       "3                                                                    228   \n",
       "\n",
       "                                            7128  \n",
       "Gene Description       GB DEF = mRNA (clone 1A7)  \n",
       "Gene Accession Number                Z78285_f_at  \n",
       "1                                            -37  \n",
       "2                                            -14  \n",
       "3                                            -41  \n",
       "\n",
       "[5 rows x 7129 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transposing so that the genes become features\n",
    "\n",
    "X_train = X_train_tr.T\n",
    "X_test = X_test_tr.T\n",
    "\n",
    "print(X_train.shape) \n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f214e80f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clearing the 2 header rows and making it one keeping only the 2nd column\n",
    "\n",
    "# For training data\n",
    "X_train.columns = X_train.iloc[1]\n",
    "X_train = X_train.drop([\"Gene Description\", \"Gene Accession Number\"]).apply(pd.to_numeric)\n",
    "\n",
    "# For testing data\n",
    "X_test.columns = X_test.iloc[1]\n",
    "X_test = X_test.drop([\"Gene Description\", \"Gene Accession Number\"]).apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b7b3ed33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38, 7129)\n",
      "(34, 7129)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Gene Accession Number</th>\n",
       "      <th>AFFX-BioB-5_at</th>\n",
       "      <th>AFFX-BioB-M_at</th>\n",
       "      <th>AFFX-BioB-3_at</th>\n",
       "      <th>AFFX-BioC-5_at</th>\n",
       "      <th>AFFX-BioC-3_at</th>\n",
       "      <th>AFFX-BioDn-5_at</th>\n",
       "      <th>AFFX-BioDn-3_at</th>\n",
       "      <th>AFFX-CreX-5_at</th>\n",
       "      <th>AFFX-CreX-3_at</th>\n",
       "      <th>AFFX-BioB-5_st</th>\n",
       "      <th>...</th>\n",
       "      <th>U48730_at</th>\n",
       "      <th>U58516_at</th>\n",
       "      <th>U73738_at</th>\n",
       "      <th>X06956_at</th>\n",
       "      <th>X16699_at</th>\n",
       "      <th>X83863_at</th>\n",
       "      <th>Z17240_at</th>\n",
       "      <th>L49218_f_at</th>\n",
       "      <th>M71243_f_at</th>\n",
       "      <th>Z78285_f_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-214</td>\n",
       "      <td>-153</td>\n",
       "      <td>-58</td>\n",
       "      <td>88</td>\n",
       "      <td>-295</td>\n",
       "      <td>-558</td>\n",
       "      <td>199</td>\n",
       "      <td>-176</td>\n",
       "      <td>252</td>\n",
       "      <td>206</td>\n",
       "      <td>...</td>\n",
       "      <td>185</td>\n",
       "      <td>511</td>\n",
       "      <td>-125</td>\n",
       "      <td>389</td>\n",
       "      <td>-37</td>\n",
       "      <td>793</td>\n",
       "      <td>329</td>\n",
       "      <td>36</td>\n",
       "      <td>191</td>\n",
       "      <td>-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-139</td>\n",
       "      <td>-73</td>\n",
       "      <td>-1</td>\n",
       "      <td>283</td>\n",
       "      <td>-264</td>\n",
       "      <td>-400</td>\n",
       "      <td>-330</td>\n",
       "      <td>-168</td>\n",
       "      <td>101</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>169</td>\n",
       "      <td>837</td>\n",
       "      <td>-36</td>\n",
       "      <td>442</td>\n",
       "      <td>-17</td>\n",
       "      <td>782</td>\n",
       "      <td>295</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-76</td>\n",
       "      <td>-49</td>\n",
       "      <td>-307</td>\n",
       "      <td>309</td>\n",
       "      <td>-376</td>\n",
       "      <td>-650</td>\n",
       "      <td>33</td>\n",
       "      <td>-367</td>\n",
       "      <td>206</td>\n",
       "      <td>-215</td>\n",
       "      <td>...</td>\n",
       "      <td>315</td>\n",
       "      <td>1199</td>\n",
       "      <td>33</td>\n",
       "      <td>168</td>\n",
       "      <td>52</td>\n",
       "      <td>1138</td>\n",
       "      <td>777</td>\n",
       "      <td>41</td>\n",
       "      <td>228</td>\n",
       "      <td>-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-135</td>\n",
       "      <td>-114</td>\n",
       "      <td>265</td>\n",
       "      <td>12</td>\n",
       "      <td>-419</td>\n",
       "      <td>-585</td>\n",
       "      <td>158</td>\n",
       "      <td>-253</td>\n",
       "      <td>49</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>835</td>\n",
       "      <td>218</td>\n",
       "      <td>174</td>\n",
       "      <td>-110</td>\n",
       "      <td>627</td>\n",
       "      <td>170</td>\n",
       "      <td>-50</td>\n",
       "      <td>126</td>\n",
       "      <td>-91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-106</td>\n",
       "      <td>-125</td>\n",
       "      <td>-76</td>\n",
       "      <td>168</td>\n",
       "      <td>-230</td>\n",
       "      <td>-284</td>\n",
       "      <td>4</td>\n",
       "      <td>-122</td>\n",
       "      <td>70</td>\n",
       "      <td>252</td>\n",
       "      <td>...</td>\n",
       "      <td>156</td>\n",
       "      <td>649</td>\n",
       "      <td>57</td>\n",
       "      <td>504</td>\n",
       "      <td>-26</td>\n",
       "      <td>250</td>\n",
       "      <td>314</td>\n",
       "      <td>14</td>\n",
       "      <td>56</td>\n",
       "      <td>-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 7129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Gene Accession Number  AFFX-BioB-5_at  AFFX-BioB-M_at  AFFX-BioB-3_at  \\\n",
       "1                                -214            -153             -58   \n",
       "2                                -139             -73              -1   \n",
       "3                                 -76             -49            -307   \n",
       "4                                -135            -114             265   \n",
       "5                                -106            -125             -76   \n",
       "\n",
       "Gene Accession Number  AFFX-BioC-5_at  AFFX-BioC-3_at  AFFX-BioDn-5_at  \\\n",
       "1                                  88            -295             -558   \n",
       "2                                 283            -264             -400   \n",
       "3                                 309            -376             -650   \n",
       "4                                  12            -419             -585   \n",
       "5                                 168            -230             -284   \n",
       "\n",
       "Gene Accession Number  AFFX-BioDn-3_at  AFFX-CreX-5_at  AFFX-CreX-3_at  \\\n",
       "1                                  199            -176             252   \n",
       "2                                 -330            -168             101   \n",
       "3                                   33            -367             206   \n",
       "4                                  158            -253              49   \n",
       "5                                    4            -122              70   \n",
       "\n",
       "Gene Accession Number  AFFX-BioB-5_st  ...  U48730_at  U58516_at  U73738_at  \\\n",
       "1                                 206  ...        185        511       -125   \n",
       "2                                  74  ...        169        837        -36   \n",
       "3                                -215  ...        315       1199         33   \n",
       "4                                  31  ...        240        835        218   \n",
       "5                                 252  ...        156        649         57   \n",
       "\n",
       "Gene Accession Number  X06956_at  X16699_at  X83863_at  Z17240_at  \\\n",
       "1                            389        -37        793        329   \n",
       "2                            442        -17        782        295   \n",
       "3                            168         52       1138        777   \n",
       "4                            174       -110        627        170   \n",
       "5                            504        -26        250        314   \n",
       "\n",
       "Gene Accession Number  L49218_f_at  M71243_f_at  Z78285_f_at  \n",
       "1                               36          191          -37  \n",
       "2                               11           76          -14  \n",
       "3                               41          228          -41  \n",
       "4                              -50          126          -91  \n",
       "5                               14           56          -25  \n",
       "\n",
       "[5 rows x 7129 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13c62b06",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Gene Accession Number</th>\n",
       "      <th>AFFX-BioB-5_at</th>\n",
       "      <th>AFFX-BioB-M_at</th>\n",
       "      <th>AFFX-BioB-3_at</th>\n",
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       "      <th>AFFX-BioDn-3_at</th>\n",
       "      <th>AFFX-CreX-5_at</th>\n",
       "      <th>AFFX-CreX-3_at</th>\n",
       "      <th>AFFX-BioB-5_st</th>\n",
       "      <th>...</th>\n",
       "      <th>U48730_at</th>\n",
       "      <th>U58516_at</th>\n",
       "      <th>U73738_at</th>\n",
       "      <th>X06956_at</th>\n",
       "      <th>X16699_at</th>\n",
       "      <th>X83863_at</th>\n",
       "      <th>Z17240_at</th>\n",
       "      <th>L49218_f_at</th>\n",
       "      <th>M71243_f_at</th>\n",
       "      <th>Z78285_f_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-214</td>\n",
       "      <td>-153</td>\n",
       "      <td>-58</td>\n",
       "      <td>88</td>\n",
       "      <td>-295</td>\n",
       "      <td>-558</td>\n",
       "      <td>199</td>\n",
       "      <td>-176</td>\n",
       "      <td>252</td>\n",
       "      <td>206</td>\n",
       "      <td>...</td>\n",
       "      <td>185</td>\n",
       "      <td>511</td>\n",
       "      <td>-125</td>\n",
       "      <td>389</td>\n",
       "      <td>-37</td>\n",
       "      <td>793</td>\n",
       "      <td>329</td>\n",
       "      <td>36</td>\n",
       "      <td>191</td>\n",
       "      <td>-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-139</td>\n",
       "      <td>-73</td>\n",
       "      <td>-1</td>\n",
       "      <td>283</td>\n",
       "      <td>-264</td>\n",
       "      <td>-400</td>\n",
       "      <td>-330</td>\n",
       "      <td>-168</td>\n",
       "      <td>101</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>169</td>\n",
       "      <td>837</td>\n",
       "      <td>-36</td>\n",
       "      <td>442</td>\n",
       "      <td>-17</td>\n",
       "      <td>782</td>\n",
       "      <td>295</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-76</td>\n",
       "      <td>-49</td>\n",
       "      <td>-307</td>\n",
       "      <td>309</td>\n",
       "      <td>-376</td>\n",
       "      <td>-650</td>\n",
       "      <td>33</td>\n",
       "      <td>-367</td>\n",
       "      <td>206</td>\n",
       "      <td>-215</td>\n",
       "      <td>...</td>\n",
       "      <td>315</td>\n",
       "      <td>1199</td>\n",
       "      <td>33</td>\n",
       "      <td>168</td>\n",
       "      <td>52</td>\n",
       "      <td>1138</td>\n",
       "      <td>777</td>\n",
       "      <td>41</td>\n",
       "      <td>228</td>\n",
       "      <td>-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-135</td>\n",
       "      <td>-114</td>\n",
       "      <td>265</td>\n",
       "      <td>12</td>\n",
       "      <td>-419</td>\n",
       "      <td>-585</td>\n",
       "      <td>158</td>\n",
       "      <td>-253</td>\n",
       "      <td>49</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>835</td>\n",
       "      <td>218</td>\n",
       "      <td>174</td>\n",
       "      <td>-110</td>\n",
       "      <td>627</td>\n",
       "      <td>170</td>\n",
       "      <td>-50</td>\n",
       "      <td>126</td>\n",
       "      <td>-91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-106</td>\n",
       "      <td>-125</td>\n",
       "      <td>-76</td>\n",
       "      <td>168</td>\n",
       "      <td>-230</td>\n",
       "      <td>-284</td>\n",
       "      <td>4</td>\n",
       "      <td>-122</td>\n",
       "      <td>70</td>\n",
       "      <td>252</td>\n",
       "      <td>...</td>\n",
       "      <td>156</td>\n",
       "      <td>649</td>\n",
       "      <td>57</td>\n",
       "      <td>504</td>\n",
       "      <td>-26</td>\n",
       "      <td>250</td>\n",
       "      <td>314</td>\n",
       "      <td>14</td>\n",
       "      <td>56</td>\n",
       "      <td>-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 7129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Gene Accession Number  AFFX-BioB-5_at  AFFX-BioB-M_at  AFFX-BioB-3_at  \\\n",
       "0                                -214            -153             -58   \n",
       "1                                -139             -73              -1   \n",
       "2                                 -76             -49            -307   \n",
       "3                                -135            -114             265   \n",
       "4                                -106            -125             -76   \n",
       "\n",
       "Gene Accession Number  AFFX-BioC-5_at  AFFX-BioC-3_at  AFFX-BioDn-5_at  \\\n",
       "0                                  88            -295             -558   \n",
       "1                                 283            -264             -400   \n",
       "2                                 309            -376             -650   \n",
       "3                                  12            -419             -585   \n",
       "4                                 168            -230             -284   \n",
       "\n",
       "Gene Accession Number  AFFX-BioDn-3_at  AFFX-CreX-5_at  AFFX-CreX-3_at  \\\n",
       "0                                  199            -176             252   \n",
       "1                                 -330            -168             101   \n",
       "2                                   33            -367             206   \n",
       "3                                  158            -253              49   \n",
       "4                                    4            -122              70   \n",
       "\n",
       "Gene Accession Number  AFFX-BioB-5_st  ...  U48730_at  U58516_at  U73738_at  \\\n",
       "0                                 206  ...        185        511       -125   \n",
       "1                                  74  ...        169        837        -36   \n",
       "2                                -215  ...        315       1199         33   \n",
       "3                                  31  ...        240        835        218   \n",
       "4                                 252  ...        156        649         57   \n",
       "\n",
       "Gene Accession Number  X06956_at  X16699_at  X83863_at  Z17240_at  \\\n",
       "0                            389        -37        793        329   \n",
       "1                            442        -17        782        295   \n",
       "2                            168         52       1138        777   \n",
       "3                            174       -110        627        170   \n",
       "4                            504        -26        250        314   \n",
       "\n",
       "Gene Accession Number  L49218_f_at  M71243_f_at  Z78285_f_at  \n",
       "0                               36          191          -37  \n",
       "1                               11           76          -14  \n",
       "2                               41          228          -41  \n",
       "3                              -50          126          -91  \n",
       "4                               14           56          -25  \n",
       "\n",
       "[5 rows x 7129 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Associating the labels from actual.csv into the training and testing files\n",
    "\n",
    "# Subset the first 38 patient's cancer types\n",
    "X_train = X_train.reset_index(drop=True)\n",
    "y_train = y[y.patient <= 38].reset_index(drop=True)\n",
    "\n",
    "# Subset the rest for testing\n",
    "X_test = X_test.reset_index(drop=True)\n",
    "y_test = y[y.patient > 38].reset_index(drop=True)\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ad8c3eb3",
   "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>Gene Accession Number</th>\n",
       "      <th>AFFX-BioB-5_at</th>\n",
       "      <th>AFFX-BioB-M_at</th>\n",
       "      <th>AFFX-BioB-3_at</th>\n",
       "      <th>AFFX-BioC-5_at</th>\n",
       "      <th>AFFX-BioC-3_at</th>\n",
       "      <th>AFFX-BioDn-5_at</th>\n",
       "      <th>AFFX-BioDn-3_at</th>\n",
       "      <th>AFFX-CreX-5_at</th>\n",
       "      <th>AFFX-CreX-3_at</th>\n",
       "      <th>AFFX-BioB-5_st</th>\n",
       "      <th>...</th>\n",
       "      <th>U48730_at</th>\n",
       "      <th>U58516_at</th>\n",
       "      <th>U73738_at</th>\n",
       "      <th>X06956_at</th>\n",
       "      <th>X16699_at</th>\n",
       "      <th>X83863_at</th>\n",
       "      <th>Z17240_at</th>\n",
       "      <th>L49218_f_at</th>\n",
       "      <th>M71243_f_at</th>\n",
       "      <th>Z78285_f_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-120.868421</td>\n",
       "      <td>-150.526316</td>\n",
       "      <td>-17.157895</td>\n",
       "      <td>181.394737</td>\n",
       "      <td>-276.552632</td>\n",
       "      <td>-439.210526</td>\n",
       "      <td>-43.578947</td>\n",
       "      <td>-201.184211</td>\n",
       "      <td>99.052632</td>\n",
       "      <td>112.131579</td>\n",
       "      <td>...</td>\n",
       "      <td>178.763158</td>\n",
       "      <td>750.842105</td>\n",
       "      <td>8.815789</td>\n",
       "      <td>399.131579</td>\n",
       "      <td>-20.052632</td>\n",
       "      <td>869.052632</td>\n",
       "      <td>335.842105</td>\n",
       "      <td>19.210526</td>\n",
       "      <td>504.394737</td>\n",
       "      <td>-29.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>109.555656</td>\n",
       "      <td>75.734507</td>\n",
       "      <td>117.686144</td>\n",
       "      <td>117.468004</td>\n",
       "      <td>111.004431</td>\n",
       "      <td>135.458412</td>\n",
       "      <td>219.482393</td>\n",
       "      <td>90.838989</td>\n",
       "      <td>83.178397</td>\n",
       "      <td>211.815597</td>\n",
       "      <td>...</td>\n",
       "      <td>84.826830</td>\n",
       "      <td>298.008392</td>\n",
       "      <td>77.108507</td>\n",
       "      <td>469.579868</td>\n",
       "      <td>42.346031</td>\n",
       "      <td>482.366461</td>\n",
       "      <td>209.826766</td>\n",
       "      <td>31.158841</td>\n",
       "      <td>728.744405</td>\n",
       "      <td>30.851132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-476.000000</td>\n",
       "      <td>-327.000000</td>\n",
       "      <td>-307.000000</td>\n",
       "      <td>-36.000000</td>\n",
       "      <td>-541.000000</td>\n",
       "      <td>-790.000000</td>\n",
       "      <td>-479.000000</td>\n",
       "      <td>-463.000000</td>\n",
       "      <td>-82.000000</td>\n",
       "      <td>-215.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>-178.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>-112.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>-50.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-138.750000</td>\n",
       "      <td>-205.000000</td>\n",
       "      <td>-83.250000</td>\n",
       "      <td>81.250000</td>\n",
       "      <td>-374.250000</td>\n",
       "      <td>-547.000000</td>\n",
       "      <td>-169.000000</td>\n",
       "      <td>-239.250000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>-47.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>575.500000</td>\n",
       "      <td>-42.750000</td>\n",
       "      <td>174.500000</td>\n",
       "      <td>-48.000000</td>\n",
       "      <td>595.250000</td>\n",
       "      <td>232.750000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>-42.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-106.500000</td>\n",
       "      <td>-141.500000</td>\n",
       "      <td>-43.500000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>-263.000000</td>\n",
       "      <td>-426.500000</td>\n",
       "      <td>-33.500000</td>\n",
       "      <td>-185.500000</td>\n",
       "      <td>99.500000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>174.500000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>-18.000000</td>\n",
       "      <td>744.500000</td>\n",
       "      <td>308.500000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>243.500000</td>\n",
       "      <td>-26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-68.250000</td>\n",
       "      <td>-94.750000</td>\n",
       "      <td>47.250000</td>\n",
       "      <td>279.250000</td>\n",
       "      <td>-188.750000</td>\n",
       "      <td>-344.750000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>-144.750000</td>\n",
       "      <td>152.250000</td>\n",
       "      <td>242.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>231.750000</td>\n",
       "      <td>969.500000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>451.750000</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>1112.000000</td>\n",
       "      <td>389.500000</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>487.250000</td>\n",
       "      <td>-11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>-20.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>-51.000000</td>\n",
       "      <td>-155.000000</td>\n",
       "      <td>419.000000</td>\n",
       "      <td>-24.000000</td>\n",
       "      <td>283.000000</td>\n",
       "      <td>561.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>1653.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>2527.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2315.000000</td>\n",
       "      <td>1109.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>3193.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 7129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Gene Accession Number  AFFX-BioB-5_at  AFFX-BioB-M_at  AFFX-BioB-3_at  \\\n",
       "count                       38.000000       38.000000       38.000000   \n",
       "mean                      -120.868421     -150.526316      -17.157895   \n",
       "std                        109.555656       75.734507      117.686144   \n",
       "min                       -476.000000     -327.000000     -307.000000   \n",
       "25%                       -138.750000     -205.000000      -83.250000   \n",
       "50%                       -106.500000     -141.500000      -43.500000   \n",
       "75%                        -68.250000      -94.750000       47.250000   \n",
       "max                         17.000000      -20.000000      265.000000   \n",
       "\n",
       "Gene Accession Number  AFFX-BioC-5_at  AFFX-BioC-3_at  AFFX-BioDn-5_at  \\\n",
       "count                       38.000000       38.000000        38.000000   \n",
       "mean                       181.394737     -276.552632      -439.210526   \n",
       "std                        117.468004      111.004431       135.458412   \n",
       "min                        -36.000000     -541.000000      -790.000000   \n",
       "25%                         81.250000     -374.250000      -547.000000   \n",
       "50%                        200.000000     -263.000000      -426.500000   \n",
       "75%                        279.250000     -188.750000      -344.750000   \n",
       "max                        392.000000      -51.000000      -155.000000   \n",
       "\n",
       "Gene Accession Number  AFFX-BioDn-3_at  AFFX-CreX-5_at  AFFX-CreX-3_at  \\\n",
       "count                        38.000000       38.000000       38.000000   \n",
       "mean                        -43.578947     -201.184211       99.052632   \n",
       "std                         219.482393       90.838989       83.178397   \n",
       "min                        -479.000000     -463.000000      -82.000000   \n",
       "25%                        -169.000000     -239.250000       36.000000   \n",
       "50%                         -33.500000     -185.500000       99.500000   \n",
       "75%                          79.000000     -144.750000      152.250000   \n",
       "max                         419.000000      -24.000000      283.000000   \n",
       "\n",
       "Gene Accession Number  AFFX-BioB-5_st  ...   U48730_at    U58516_at  \\\n",
       "count                       38.000000  ...   38.000000    38.000000   \n",
       "mean                       112.131579  ...  178.763158   750.842105   \n",
       "std                        211.815597  ...   84.826830   298.008392   \n",
       "min                       -215.000000  ...   30.000000   224.000000   \n",
       "25%                        -47.000000  ...  120.000000   575.500000   \n",
       "50%                         70.500000  ...  174.500000   700.000000   \n",
       "75%                        242.750000  ...  231.750000   969.500000   \n",
       "max                        561.000000  ...  356.000000  1653.000000   \n",
       "\n",
       "Gene Accession Number   U73738_at    X06956_at   X16699_at    X83863_at  \\\n",
       "count                   38.000000    38.000000   38.000000    38.000000   \n",
       "mean                     8.815789   399.131579  -20.052632   869.052632   \n",
       "std                     77.108507   469.579868   42.346031   482.366461   \n",
       "min                   -178.000000    36.000000 -112.000000   195.000000   \n",
       "25%                    -42.750000   174.500000  -48.000000   595.250000   \n",
       "50%                     10.500000   266.000000  -18.000000   744.500000   \n",
       "75%                     57.000000   451.750000    9.250000  1112.000000   \n",
       "max                    218.000000  2527.000000   52.000000  2315.000000   \n",
       "\n",
       "Gene Accession Number    Z17240_at  L49218_f_at  M71243_f_at  Z78285_f_at  \n",
       "count                    38.000000    38.000000    38.000000    38.000000  \n",
       "mean                    335.842105    19.210526   504.394737   -29.210526  \n",
       "std                     209.826766    31.158841   728.744405    30.851132  \n",
       "min                      41.000000   -50.000000    -2.000000   -94.000000  \n",
       "25%                     232.750000     8.000000   136.000000   -42.750000  \n",
       "50%                     308.500000    20.000000   243.500000   -26.000000  \n",
       "75%                     389.500000    30.250000   487.250000   -11.500000  \n",
       "max                    1109.000000   115.000000  3193.000000    36.000000  \n",
       "\n",
       "[8 rows x 7129 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5aa5d5f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.86149567, -0.03310102, -0.3517011 , ...,  0.54606799,\n",
       "        -0.43582025, -0.25587506],\n",
       "       [-0.16772267,  1.03740009,  0.13913948, ..., -0.26704265,\n",
       "        -0.59574421,  0.49964792],\n",
       "       [ 0.41504666,  1.35855042, -2.49589941, ...,  0.70869012,\n",
       "        -0.38436645, -0.38727036],\n",
       "       ...,\n",
       "       [-0.85224537, -1.35784615,  1.31887912, ...,  0.22082373,\n",
       "        -0.35933487,  1.71505444],\n",
       "       [ 0.8868123 ,  1.74660708,  1.21554426, ..., -0.23451823,\n",
       "         3.73889051, -0.12447976],\n",
       "       [ 0.45204788,  0.15423667,  0.13913948, ...,  0.0582016 ,\n",
       "         2.80298767,  0.95953146]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert from integer to float\n",
    "X_train_fl = X_train.astype(float, 64)\n",
    "X_test_fl = X_test.astype(float, 64)\n",
    "\n",
    "# Apply the same scaling to both datasets\n",
    "scaler = StandardScaler()\n",
    "X_train_scl = scaler.fit_transform(X_train_fl)\n",
    "X_test_scl = scaler.transform(X_test_fl)\n",
    "\n",
    "X_train_scl\n",
    "#print(X_train_scl.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ceefdb7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.12032149e+03,  8.43574289e+03, -1.39441668e+04, ...,\n",
       "         2.51106855e+03,  3.92187680e+03,  1.22642865e-11],\n",
       "       [ 1.86283598e+04,  1.44078238e+04,  1.66177453e+04, ...,\n",
       "        -2.30960132e+02, -1.04099055e+03,  1.22642865e-11],\n",
       "       [-1.58238732e+04,  1.40484268e+04,  4.73320627e+04, ...,\n",
       "         5.48675197e+02, -2.26227734e+03,  1.22642865e-11],\n",
       "       ...,\n",
       "       [ 6.50848905e+04, -5.49595793e+04,  1.67854688e+04, ...,\n",
       "         1.18708820e+01, -1.47894896e+03,  1.22642865e-11],\n",
       "       [ 4.97670530e+04, -3.81956823e+04,  2.93511865e+03, ...,\n",
       "         2.66462156e+03,  7.99461277e+02,  1.22642865e-11],\n",
       "       [ 1.08241948e+04, -1.68550421e+04, -9.46017931e+02, ...,\n",
       "        -2.04773331e+03, -1.96917341e+03,  1.22642865e-11]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA()\n",
    "pca.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51f29567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22 features explain around 90% of the variance. From 7129 features to 22, not too bad.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total = sum(pca.explained_variance_)\n",
    "k = 0\n",
    "current_variance = 0\n",
    "while current_variance/total < 0.90:\n",
    "    current_variance += pca.explained_variance_[k]\n",
    "    k = k + 1\n",
    "    \n",
    "print(k, \" features explain around 90% of the variance. From 7129 features to \", k, \", not too bad.\", sep='')\n",
    "\n",
    "pca = PCA(n_components=k)\n",
    "X_train.pca = pca.fit(X_train)\n",
    "X_train_pca = pca.transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)\n",
    "\n",
    "var_exp = pca.explained_variance_ratio_.cumsum()\n",
    "var_exp = var_exp*100\n",
    "plt.bar(range(k), var_exp);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "18398caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         9.49879802e+03, -5.43702066e+03,  2.87547682e+03,\n",
       "        -9.75569332e+03,  1.51143815e+04,  5.83522882e+03,\n",
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       "        -4.88954432e+03, -1.38751145e+03,  1.14374294e+04,\n",
       "        -3.15836515e+03],\n",
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       "         2.15690843e+02, -9.35057782e+03,  2.88601967e+03,\n",
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       "        -2.63411158e+03, -2.90515094e+03,  1.17968639e+03,\n",
       "         3.74325370e+03],\n",
       "       [-1.54833789e+04,  1.43440054e+04,  3.29006711e+04,\n",
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       "        -1.08769463e+04, -5.72317632e+03, -2.01165423e+03,\n",
       "         1.43515552e+04],\n",
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       "         6.19750903e+03,  6.55817052e+02, -1.29000346e+04,\n",
       "         6.58567145e+03,  8.29142178e+03,  5.31337674e+03,\n",
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       "        -2.89512094e+03,  1.00865017e+03,  1.78809291e+03,\n",
       "        -4.86849414e+03],\n",
       "       [-1.46813895e+04,  2.04262486e+04, -7.04767712e+03,\n",
       "         1.52068856e+03, -6.40318764e+03, -9.88803837e+03,\n",
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       "         4.57789831e+03],\n",
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       "         5.34016011e+03],\n",
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       "        -5.39532353e+03,  2.33734710e+03, -1.38821147e+04,\n",
       "         3.31799249e+03,  2.33118611e+04,  8.31881109e+03,\n",
       "         5.86331174e+03, -1.43021225e+04, -1.31596128e+03,\n",
       "        -9.16432629e+03,  1.68419594e+04, -6.05006906e+02,\n",
       "         6.88899904e+03, -8.95842244e+03,  1.72983280e+02,\n",
       "        -1.38386146e+04],\n",
       "       [-1.30011838e+04,  5.74901997e+03, -2.87280755e+04,\n",
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       "        -8.88936925e+02],\n",
       "       [-2.59172043e+04,  3.53770204e+04, -4.33195781e+04,\n",
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       "         8.60052512e+03, -6.56040383e+02, -4.69503618e+02,\n",
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       "        -1.00075230e+04,  1.33062123e+04,  1.03689197e+04,\n",
       "        -4.74761636e+03, -8.33123780e+01,  2.82927240e+03,\n",
       "         1.13409401e+04],\n",
       "       [-5.26135563e+04, -8.50204246e+03,  3.69016695e+04,\n",
       "         8.56031044e+03, -1.01086519e+04,  4.72242160e+03,\n",
       "         1.38122850e+04,  1.29226675e+04, -1.84736330e+03,\n",
       "         2.24075334e+03, -1.72033070e+04,  1.04131851e+04,\n",
       "         3.35187244e+03, -2.59582259e+03,  4.73321942e+03,\n",
       "        -8.39118555e+03,  2.15718411e+03, -5.44912656e+02,\n",
       "        -1.08839888e+04, -6.02866649e+03, -2.47084173e+03,\n",
       "         1.83424612e+03],\n",
       "       [-3.96553919e+03,  2.21153906e+04, -8.74866436e+03,\n",
       "        -1.88418559e+03, -3.19720709e+03, -6.11421971e+03,\n",
       "        -6.30707944e+03,  1.13107123e+04, -7.09422789e+03,\n",
       "        -1.49174345e+04, -6.26487631e+03, -2.79525329e+03,\n",
       "        -7.38517858e+02,  4.81956972e+02, -1.11966352e+04,\n",
       "         6.39360379e+03, -5.18069263e+03,  1.14218509e+04,\n",
       "        -5.58919821e+03, -1.00368398e+03,  7.66332032e+03,\n",
       "        -1.10756195e+03],\n",
       "       [ 5.56657147e+03, -7.99564576e+03, -2.11236392e+04,\n",
       "         1.22373181e+04, -1.40991203e+04,  2.33487420e+04,\n",
       "         4.42248587e+03, -2.76788682e+04, -2.70608153e+03,\n",
       "         5.30583396e+03, -2.05550793e+04, -2.11684891e+03,\n",
       "         4.52501779e+03,  4.44756345e+03, -1.40005557e+04,\n",
       "        -2.94354715e+02, -5.01363637e+03, -1.51880232e+04,\n",
       "         3.32162899e+03,  2.57932986e+03,  6.77639719e+03,\n",
       "        -6.09175826e+03],\n",
       "       [-5.82035980e+03,  1.09447341e+04, -2.98200437e+04,\n",
       "        -9.72602102e+03, -8.12537074e+03, -9.79884755e+03,\n",
       "        -7.23538350e+02, -2.29094144e+04,  2.36684490e+03,\n",
       "         8.67352576e+03,  6.01374467e+03, -3.62772889e+03,\n",
       "        -6.10636294e+03, -6.07577242e+03, -1.65422733e+03,\n",
       "        -6.09012588e+03, -8.82020386e+03,  1.92316336e+02,\n",
       "        -7.80979856e+03,  1.64713577e+04, -1.29757412e+04,\n",
       "        -1.92298796e+03],\n",
       "       [-3.80273221e+04, -1.18476530e+04, -1.37256144e+04,\n",
       "        -1.21113364e+03,  2.06738419e+03,  1.26991775e+03,\n",
       "         2.86532639e+03,  1.12368982e+04, -1.24452494e+04,\n",
       "        -7.74654619e+03,  7.23725225e+03, -4.86694050e+03,\n",
       "         5.22813898e+02,  5.68765416e+03, -1.14189145e+04,\n",
       "        -4.57759292e+03,  1.71762754e+04, -1.21609411e+04,\n",
       "        -8.87099589e+03,  1.24276617e+04, -1.04857020e+04,\n",
       "         9.09539188e+03],\n",
       "       [ 2.58715803e+04, -8.08161309e+03, -7.81603448e+03,\n",
       "         6.24729983e+03,  5.31435422e+03,  1.38565482e+04,\n",
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       "        -3.11564001e+03,  2.84723231e+02, -1.54779881e+04,\n",
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       "        -8.12624959e+03,  1.10742148e+04,  1.01460836e+03,\n",
       "         2.62850089e+03],\n",
       "       [ 4.97670531e+04, -3.81956825e+04,  2.93511847e+03,\n",
       "         2.17368496e+04, -4.89236989e+03,  9.66318766e+03,\n",
       "         6.26502859e+03,  1.83921944e+04,  2.67437761e+02,\n",
       "        -1.65319142e+03,  6.78177187e+03,  2.23916496e+04,\n",
       "        -1.01114789e+04, -7.92840094e+03, -1.30826921e+03,\n",
       "        -1.63582776e+03, -1.58570343e+04, -1.25038356e+04,\n",
       "         8.87665543e+03, -1.90054243e+03, -1.01701023e+03,\n",
       "         1.18069080e+04],\n",
       "       [ 1.08241948e+04, -1.68550421e+04, -9.46018108e+02,\n",
       "         3.65192679e+04,  1.63536533e+03,  4.51417951e+04,\n",
       "        -7.03060530e+03,  1.24629087e+04,  9.27423983e+03,\n",
       "         6.68066770e+03,  7.52383931e+03,  1.61919058e+03,\n",
       "         7.79866432e+02,  4.46600141e+03, -1.15170404e+04,\n",
       "        -2.74740562e+03,  2.09222326e+03,  2.15279743e+04,\n",
       "         1.17945565e+04,  6.66637568e+03, -9.01814131e+03,\n",
       "        -8.10924249e+03]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "360148fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(-60000.0, 0, ''),\n",
       " Text(-40000.0, 0, ''),\n",
       " Text(-20000.0, 0, ''),\n",
       " Text(0.0, 0, ''),\n",
       " Text(20000.0, 0, ''),\n",
       " Text(40000.0, 0, ''),\n",
       " Text(60000.0, 0, ''),\n",
       " Text(80000.0, 0, '')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca3 = PCA(n_components=3).fit(X_train)\n",
    "X_train_reduced = pca3.transform(X_train)\n",
    "\n",
    "plt.clf()\n",
    "fig = plt.figure(1, figsize=(10,6 ))\n",
    "#ax = Axes3D(fig, elev=-150, azim=110,)\n",
    "ax = plt.subplot(111, projection=\"3d\")\n",
    "\n",
    "ax.scatter(X_train_reduced[:, 0], X_train_reduced[:, 1], X_train_reduced[:, 2], c = y_train.iloc[:,1], cmap = plt.cm.Paired, linewidths=10)\n",
    "\n",
    "ax.set_title(\"First three PCA directions\")\n",
    "\n",
    "ax.set_xlabel(\"1st eigenvector\")\n",
    "ax.w_xaxis.set_ticklabels([])\n",
    "\n",
    "ax.set_ylabel(\"2nd eigenvector\")\n",
    "ax.w_yaxis.set_ticklabels([])\n",
    "\n",
    "ax.set_zlabel(\"3rd eigenvector\")\n",
    "ax.w_zaxis.set_ticklabels([])\n",
    "\n",
    "#X_train_reduced[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3ab5db06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2D Transformation of the Above Graph ')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize = (10, 6))\n",
    "plt.scatter(X_train_reduced[:, 0],  X_train_reduced[:, 1], c = y_train.iloc[:,1], cmap = plt.cm.Paired, linewidths=10)\n",
    "plt.annotate('Note the Brown Cluster', xy = (30000,-2000))\n",
    "plt.title(\"2D Transformation of the Above Graph \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ce2b0166",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simply predicting everything as acute lymphoblastic leukemia (ALL) results in an accuracy of 0.588.\n"
     ]
    }
   ],
   "source": [
    "print(\"Simply predicting everything as acute lymphoblastic leukemia (ALL) results in an accuracy of \", \n",
    "      round(1 - np.mean(y_test.iloc[:,1]), 3), \".\", sep = '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "68d29d29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "5     0\n",
       "6     0\n",
       "7     0\n",
       "8     0\n",
       "9     0\n",
       "10    0\n",
       "11    1\n",
       "12    1\n",
       "13    1\n",
       "14    1\n",
       "15    1\n",
       "16    0\n",
       "17    0\n",
       "18    1\n",
       "19    1\n",
       "20    0\n",
       "21    1\n",
       "22    1\n",
       "23    1\n",
       "24    1\n",
       "25    1\n",
       "26    1\n",
       "27    1\n",
       "28    0\n",
       "29    0\n",
       "30    0\n",
       "31    0\n",
       "32    0\n",
       "33    0\n",
       "Name: cancer, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "97312c96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters:\n",
      " {'C': 0.1, 'decision_function_shape': 'ovo', 'gamma': 1, 'kernel': 'linear'}\n",
      "SVM accuracy: 0.941\n",
      "SVM precision: 0.875\n",
      "SVM recall: 1.0\n",
      "SVM classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.90      0.95        20\n",
      "           1       0.88      1.00      0.93        14\n",
      "\n",
      "    accuracy                           0.94        34\n",
      "   macro avg       0.94      0.95      0.94        34\n",
      "weighted avg       0.95      0.94      0.94        34\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Parameter grid\n",
    "svm_param_grid = {'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001, 0.00001, 10], \"kernel\": [\"linear\", \"rbf\", \"poly\"], \"decision_function_shape\" : [\"ovo\", \"ovr\"]} \n",
    "\n",
    "svm_grid = GridSearchCV(SVC(), svm_param_grid, cv=3)\n",
    "svm_grid.fit(X_train_pca, y_train.iloc[:,1])\n",
    "\n",
    "print(\"Best Parameters:\\n\", svm_grid.best_params_)\n",
    "\n",
    "best_svc = svm_grid.best_estimator_\n",
    "svm_pred = best_svc.predict(X_test_pca)\n",
    "\n",
    "print('SVM accuracy:', round(accuracy_score(y_test.iloc[:,1], svm_pred), 3))\n",
    "print('SVM precision:', round(precision_score(y_test.iloc[:,1], svm_pred), 3))\n",
    "print('SVM recall:', round(recall_score(y_test.iloc[:,1], svm_pred), 3))\n",
    "print('SVM classification report:')\n",
    "print(classification_report(y_test.iloc[:,1], svm_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "28b49421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'ALL'), Text(1.5, 0, 'AML')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_svm =  confusion_matrix(y_test.iloc[:,1], svm_pred)\n",
    "\n",
    "ax = plt.subplot()\n",
    "sns.heatmap(cm_svm, annot=True, ax = ax, fmt='g', cmap='YlOrBr') \n",
    "\n",
    "ax.set_xlabel('Predicted Class')\n",
    "ax.set_ylabel('Actual Class') \n",
    "ax.set_title('SVM Confusion Matrix') \n",
    "ax.xaxis.set_ticklabels(labels) \n",
    "#ax.yaxis.set_ticklabels(labels, rotation=360);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fde0c4c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n9cwzz+irr75SfHy8rr/+eh09etSMmTRpkhYuXKjs7GytWrVKZWVlGjp0qKqqqsyYUaNGaePGjVq2bJmWLVumjRs3KjMz09xfVVWlIUOG6NixY1q1apWys7O1YMECTZkyxZtvAQAAAIAWzGIYRm0zMtaLP/3pT/ryyy/1xRdf1LrfMAzZ7XZNmjRJ06ZNk1TTmxUXF6c5c+Zo7NixcjgcatOmjV5//XWNHDlSklRYWKjExEQtWbJEGRkZ2rZtm5KTk7VmzRr16dNHkrRmzRqlpaVp+/bt6tq1q5YuXaqhQ4eqoKBAdrtdkpSdna2srCyVlJQoIiLinOfjdDpls9nkcDjqFA8AAAA0NydPnlReXp6SkpIUHFz/62U1Jmc717rWBl7t4Vq0aJF69+6tW265RbGxserVq5defPFFc39eXp6Ki4uVnp5utlmtVvXv31+rV6+WJOXm5qqiosItxm63KyUlxYzJycmRzWYziy1Juuqqq2Sz2dxiUlJSzGJLkjIyMszZSWrjcrnkdDrdHgAAAABQV14tuHbt2qX58+erS5cu+uijjzRu3DhNnDhR//rXvyRJxcXFkqS4uDi358XFxZn7iouLFRQUpKioqLPGxMbGehw/NjbWLeb040RFRSkoKMiMOd2sWbPMe8JsNpsSExPP9y0AAAAA0IJ5teCqrq7W5ZdfrpkzZ6pXr14aO3asxowZo/nz57vFWSzu0z8ahuHRdrrTY2qLv5CYn5o+fbocDof5KCgoOGtOAAAAAPBTXi24EhISlJyc7NbWvXt35efnS5Li4+MlyaOHqaSkxOyNio+PV3l5uUpLS88as3//fo/jHzhwwC3m9OOUlpaqoqLCo+frFKvVqoiICLcHAAAAANSVVwuufv36aceOHW5t3333nTp06CBJSkpKUnx8vFasWGHuLy8v18qVK9W3b19JUmpqqgIDA91iioqKtHnzZjMmLS1NDodD69atM2PWrl0rh8PhFrN582YVFRWZMcuXL5fValVqamo9nzkAAAAAeLnguu+++7RmzRrNnDlTO3fu1FtvvaUXXnhBd999t6SaIX6TJk3SzJkztXDhQm3evFlZWVkKDQ3VqFGjJEk2m02jR4/WlClT9Mknn2jDhg2644471KNHD1133XWSanrNBg8erDFjxmjNmjVas2aNxowZo6FDh6pr166SpPT0dCUnJyszM1MbNmzQJ598oqlTp2rMmDH0XAEAAAAtwOeff65hw4bJbrfLYrHo3//+t9ePGeDNF7/iiiu0cOFCTZ8+XY8++qiSkpL09NNP6/bbbzdj7r//fp04cULjx49XaWmp+vTpo+XLlys8PNyMeeqppxQQEKARI0boxIkTGjRokF599VX5+/ubMW+++aYmTpxozmY4fPhwPfPMM+Z+f39/ffjhhxo/frz69eunkJAQjRo1SnPnzvXmWwAAAACgNtVV0p7VUtl+qVWc1KGv5Od/7uf9DMeOHdNll12m3/72t7r55pu9eqxTvLoOV3PDOlwAAABo6eplHa6ti6Rl0yRn4Y9tEXZp8BwpeXj9JHoOFotFCxcu1I033njGmEa/DhcAAAAAuNm6SPrf/+debEmSs6imfesi3+TlJRRcAAAAABpGdVVNz5ZqG2T337Zlf6qJayYouAAAAAA0jD2rPXu23BiSc19NXDNBwQUAAACgYZR5rp37s+KaAAouAAAAAA2jVVz9xjUBXp0WHgAAAABMHfrWzEboLFLt93FZavZ36OuVw5eVlWnnzp3mdl5enjZu3KjWrVurffv2XjkmPVwAAAAAGoaff83U75Iky2k7/7s9eLbX1uNav369evXqpV69ekmSJk+erF69eukvf/mLV44n0cMFAAAAoCElD5dG/OsM63DN9uo6XAMGDFBDL0NMwQUAAACgYSUPl7oNqZmNsGx/zT1bHfp6rWfLlyi4AAAAADQ8P38p6RpfZ+F13MMFAAAAAF5CwQUAAAAAXkLBBQAAAOC8NfTkE75QH+dIwQUAAACgzgIDAyVJx48f93Em3nfqHE+d84Vg0gwAAAAAdebv76/IyEiVlJRIkkJDQ2WxnL6mVtNmGIaOHz+ukpISRUZGyt//wmdPpOACAAAAcF7i4+MlySy6mqvIyEjzXC8UBRcAAACA82KxWJSQkKDY2FhVVFT4Oh2vCAwM/Fk9W6dQcAEAAAC4IP7+/vVSlDRnTJoBAAAAAF5CwQUAAAAAXkLBBQAAAABeQsEFAAAAAF5CwQUAAAAAXkLBBQAAAABeQsEFAAAAAF5CwQUAAAAAXkLBBQAAAABeQsEFAAAAAF5CwQUAAAAAXkLBBQAAAABeEuDrBAAAgHeVHD2pH0rKVFUtdWoTJntkiK9TAoAWo8F6uGbNmiWLxaJJkyaZbYZhaMaMGbLb7QoJCdGAAQO0ZcsWt+e5XC7dc889iomJUVhYmIYPH669e/e6xZSWliozM1M2m002m02ZmZk6cuSIW0x+fr6GDRumsLAwxcTEaOLEiSovL/fW6QIA0CjkHSjTnS+v020vrtUdL63Vb55brR3FTl+nBQAtRoMUXF999ZVeeOEFXXrppW7tjz/+uJ588kk988wz+uqrrxQfH6/rr79eR48eNWMmTZqkhQsXKjs7W6tWrVJZWZmGDh2qqqoqM2bUqFHauHGjli1bpmXLlmnjxo3KzMw091dVVWnIkCE6duyYVq1apezsbC1YsEBTpkzx/skDAOBDi78t1LaiH3+vFh45qedX7lJ5ZdVZngUAqC9eL7jKysp0++2368UXX1RUVJTZbhiGnn76aT344IO66aablJKSotdee03Hjx/XW2+9JUlyOBx66aWX9Pe//13XXXedevXqpTfeeEObNm3Sxx9/LEnatm2bli1bpv/5n/9RWlqa0tLS9OKLL+qDDz7Qjh07JEnLly/X1q1b9cYbb6hXr1667rrr9Pe//10vvviinE6+5QMANE/llVX6v+0HPNq//OGgnCcqfZARALQ8Xi+47r77bg0ZMkTXXXedW3teXp6Ki4uVnp5utlmtVvXv31+rV6+WJOXm5qqiosItxm63KyUlxYzJycmRzWZTnz59zJirrrpKNpvNLSYlJUV2u92MycjIkMvlUm5u7hlzd7lccjqdbg8AAJqKoAB/9b+4jUf7VZ2iFR7CbdwA0BC8WnBlZ2crNzdXs2bN8thXXFwsSYqLi3Nrj4uLM/cVFxcrKCjIrWestpjY2FiP14+NjXWLOf04UVFRCgoKMmNqM2vWLPO+MJvNpsTExHOdMgAAjcqwnnZ1iW1lbseGWzWu/0WyBvj7MCsAaDm89vVWQUGB7r33Xi1fvlzBwcFnjLNYLG7bhmF4tJ3u9Jja4i8k5nTTp0/X5MmTzW2n00nRBQBoUi5q00pv3NVHO0vKVFVtqFObMLWLCvV1WgDQYnit4MrNzVVJSYlSU1PNtqqqKn3++ed65plnzPuriouLlZCQYMaUlJSYvVHx8fEqLy9XaWmpWy9XSUmJ+vbta8bs37/f4/gHDhxwe521a9e67S8tLVVFRYVHz9dPWa1WWa3W8z11AAAalbiIYMVFnPnLTwCA93htSOGgQYO0adMmbdy40Xz07t1bt99+uzZu3KhOnTopPj5eK1asMJ9TXl6ulStXmsVUamqqAgMD3WKKioq0efNmMyYtLU0Oh0Pr1q0zY9auXSuHw+EWs3nzZhUVFZkxy5cvl9VqdSsIAQAAAKA+ea2HKzw8XCkpKW5tYWFhio6ONtsnTZqkmTNnqkuXLurSpYtmzpyp0NBQjRo1SpJks9k0evRoTZkyRdHR0WrdurWmTp2qHj16mJNwdO/eXYMHD9aYMWP0/PPPS5J+//vfa+jQoerataskKT09XcnJycrMzNQTTzyhw4cPa+rUqRozZowiIiK89RYAAAAAaOF8OkXR/fffrxMnTmj8+PEqLS1Vnz59tHz5coWHh5sxTz31lAICAjRixAidOHFCgwYN0quvvip//x9v9n3zzTc1ceJEczbD4cOH65lnnjH3+/v768MPP9T48ePVr18/hYSEaNSoUZo7d27DnSwAAACAFsdiGIbh6ySaCqfTKZvNJofDQc8YWizDMHSorFwhQX4Kswb6Oh0AAACfqGttwCIcAOps7+Hjemd9gd5dv1dto4I1Jb2rruzYWgH+Xl/SDwAAoEniryQAdeKqrNLTH3+neZ/uVLHzpHL3HFHmS+u0uZAFwYHGgkErAND40MMFoE72lZ7Q+xv2ubVVVRtal3dYPRMjfZMUAEnSMVelvtp9WG+s2aPgAD9lpnVUz8RIWQNZ3BgAfI2CC0Cd+FksCvDzU3lVtVu7NYCOcsDXVn1/UGPfyDW3P9xcrLfu6qO0i2J8mBUAQGJIIYA6ahcVoruuSXJrCwn015VJrX2UEQBJcp6s0P/3yXdubYYhvbQqT5WnfUECAGh49HABqJMAfz9l9e2oTm1a6X/X5+uiNq1025Xt1T2BGTsBX6quNnSiwrOwOlZepWpxTxcA+BoFF4A6i40I1m9S2+nXPe3yZ2ZCoFGIDA3SmGuS9MDCzW7tWWkdFeTPPVwA4GsUXADOG8UW0LhkXBKvqmpDz63cpUB/i+69rovSLmK4LwA0BhRcAAA0cdGtrMpM66hf9kiQn59FUaFBvk4JAPBfFFwAADQT0a2svk4BAHAaxgUBAAAAgJdQcAEAAACAl1BwAQAAAICXcA8XAABotiqqqrWt0Kkd+48qNMhfKW1t6hAd5uu0ALQgFFwAAKDZ+nLnQf3u1a9U/d81oO2RwfrX765U59hw3yYGoMVgSCEAAGiWDpW59PCiLWaxJUmFR05q5XcHfZcUgBaHggsAADRLx8urlH/4uEf7jmKnD7IB0FJRcAEAgGYpppVV13eP82gf2C3WB9kAaKkouAAAQLMUEuSvP2Z01WXtbJIkfz+Lxl7bSVd0aO3jzAC0JEyaAQAAmq0uceF67XdXam/pCVkD/NQhOlRBAf6+TgtAC0LBBQAAmrXI0CBFhgb5Og0ALRRDCgEAAADASyi4AAAAAMBLKLgAAAAAwEsouAAAAADASyi4AAAAAMBLKLgAAAAAwEsouAAAF+xQmUtFjhOqrjZ8nQoAAI0S63ABAM7bcVel/m9HiWYt3S7niQrdflUH3dGng9pGhfg6NQAAGhUKLgDAedtQcER3v7XB3J7/2Q+qrKrWn27oLn8/iw8zAwCgcWFIIQDgvC3ZVOTR9q+cPSp2nPRBNgAANF70cAHABSo4fFzf7z8qQ1KX2FZqHx3m65QaTFRooEdbK2uAAvybf+/WMVel9jtPKiTIXwk2hlACAM7Oqz1cs2bN0hVXXKHw8HDFxsbqxhtv1I4dO9xiDMPQjBkzZLfbFRISogEDBmjLli1uMS6XS/fcc49iYmIUFham4cOHa+/evW4xpaWlyszMlM1mk81mU2Zmpo4cOeIWk5+fr2HDhiksLEwxMTGaOHGiysvLvXLuAJq3HcVO3TR/tX732nqNfm29bpq/WtuKnL5Oq8FkXBIva4D7r5D7B3dVXESwjzKqfyXOkyo4fFyuyiqz7fv9RzX29fX6xd9Xati8VVr8TaFOVlSd5VUAAC2dVwuulStX6u6779aaNWu0YsUKVVZWKj09XceOHTNjHn/8cT355JN65pln9NVXXyk+Pl7XX3+9jh49asZMmjRJCxcuVHZ2tlatWqWysjINHTpUVVU//pIbNWqUNm7cqGXLlmnZsmXauHGjMjMzzf1VVVUaMmSIjh07plWrVik7O1sLFizQlClTvPkWAGiGqqoNvbFmjw4cdZltB8vK9eqXeaqsqvZhZg0npa1N745L0+irkzT00gS9dGdv3ZAS7+u06sWJ8kot/qZQQ+et0oC5n+mB9zdp98Fjcpwo17QF32rVzkOSaq75PW9v0OZ9Dh9nDABozCyGYTTYXL4HDhxQbGysVq5cqWuvvVaGYchut2vSpEmaNm2apJrerLi4OM2ZM0djx46Vw+FQmzZt9Prrr2vkyJGSpMLCQiUmJmrJkiXKyMjQtm3blJycrDVr1qhPnz6SpDVr1igtLU3bt29X165dtXTpUg0dOlQFBQWy2+2SpOzsbGVlZamkpEQREREe+bpcLrlcP/5B5XQ6lZiYKIfDUWs8gJah7GSFbp6fox37j7q1d4oJ08LxfWULDfJRZqgPa3cd0sgX1ri1Dbk0QZMGddH1T33uET9jWLKy+iU1VHoAgEbC6XTKZrOdszZo0EkzHI6abwFbt24tScrLy1NxcbHS09PNGKvVqv79+2v16tWSpNzcXFVUVLjF2O12paSkmDE5OTmy2WxmsSVJV111lWw2m1tMSkqKWWxJUkZGhlwul3Jzc2vNd9asWeYQRZvNpsTExPp4GwA0cWHWAF2XHOvRPqh7nMKDPe9tQtPy5c6DHm1LNhXpZGWVxzBKSYqiwAYAnEWDFVyGYWjy5Mm6+uqrlZKSIkkqLi6WJMXFxbnFxsXFmfuKi4sVFBSkqKios8bExnr+8RMbG+sWc/pxoqKiFBQUZMacbvr06XI4HOajoKDgfE8bQDNksVj0m9R2uqydzWxLTgjXrVcmyo8p0Zu81q2sHm2tggIUGRKkyddf7NbeNjJYlyVGNlBmAICmqMFmKZwwYYK+/fZbrVq1ymOfxeL+B4phGB5tpzs9prb4C4n5KavVKqvV8xcvACTFtNLLWVco7+AxGYahpJhWignn86I5SOvUWhEhAXKeqDTbpmZ0VWLrUN16RXt1iw9Xzq5Dat86VFd1ilbHmJYzOyUA4Pw1SMF1zz33aNGiRfr888/Vrl07sz0+vuYG6+LiYiUkJJjtJSUlZm9UfHy8ysvLVVpa6tbLVVJSor59+5ox+/fv9zjugQMH3F5n7dq1bvtLS0tVUVHh0fMFAHUR3cqq6Fp6Q9C0dY2P0P/+Pk2fbi/R3tITui45Tqkdan7/2EID1b9rrPp39RxVAQBAbbw6pNAwDE2YMEHvv/++Pv30UyUlud9UnJSUpPj4eK1YscJsKy8v18qVK81iKjU1VYGBgW4xRUVF2rx5sxmTlpYmh8OhdevWmTFr166Vw+Fwi9m8ebOKin5crHP58uWyWq1KTU2t/5MHADRZ3RIiNH5gZ828qYd+0S1WthDuzQMAXBivzlI4fvx4vfXWW/rPf/6jrl27mu02m00hITWLRc6ZM0ezZs3SK6+8oi5dumjmzJn67LPPtGPHDoWHh0uS/vCHP+iDDz7Qq6++qtatW2vq1Kk6dOiQcnNz5e/vL0m64YYbVFhYqOeff16S9Pvf/14dOnTQ4sWLJdVMC9+zZ0/FxcXpiSee0OHDh5WVlaUbb7xR8+bNq9P51HUmEgAAAADNW11rA68WXGe6N+qVV15RVlaWpJpesEceeUTPP/+8SktL1adPH/3zn/80J9aQpJMnT+qPf/yj3nrrLZ04cUKDBg3Ss88+6zZr4OHDhzVx4kQtWrRIkjR8+HA988wzioyMNGPy8/M1fvx4ffrppwoJCdGoUaM0d+7cOt+nRcEFAAAAQGokBVdzQ8EFAAAAQGqk63ABAAAAQEvSYNPCA0BTdfiYS0WOk9qyz6mDZS717thaKW0jFBrERyhQmx8OlGn5lmKt31Oq67vH6ZqLY9Q2MtTXaQGAT/DXAgCcwfHySn22o0SFR07q+c936cBRl7nvqZGX6de92p3l2UDLtK/0uH73yjrtOXxCkvTJthINTonXE7+5VOHBzPYIoOVhSCEAnMHGgiP604JNOljmciu2JOmvH2xTkeOEjzIDGq/txUfNYuuUZZuLlXfwmI8yAgDfouACgDNYvqVY4cGBOnSs3GPf4WPlOllR7YOsgMatvKr2nwtXJT8vAFomCi4AOIPI0CAVOk7oojatPPYN6haruIi6LSkBtCSd27RSSKC/W9tFbcLUMZp7uAC0TBRcAHAG13WPkzXATzk/HNK4/p0UFlTzR+TVnaP1wC+7t4hJMzbtc+jRxVt058vrtGjjPh08bWglcLouceF6864+6t0hSiGB/vplj3jNvz1VbcKDfZ0aAPgE63CdB9bhAloWwzC0eZ9Di78tkquiUjf0sCsiOEDto0PVytr8b/7fXuTUzfNX61h5ldl233VdNOEXXeTvV/vC9sApR09WqOxkpaLCghR8Wo8XADQHda0Nmv/XswBwgSwWi3q0i1SPdpG+TqXBFBw+ph3FZaoyDAX4WTwKq2c/+0E39mqrDtFhPsoQTUV4cCCzEgKAKLgAAP/1/f6jynx5rYodNcMGI4IDNCW9qx5etMWMqaiqVmU1AyMAAKgr7uECAMgwDL2Xu9cstiTJebJSn24vUVqnaLNt+GV2tYsM8UWKAAA0SRRcAABVVFVrXd5hj/YdxUfVu2OUokID9ftrO2lyeldZuR8HAIA6Y0ghAHhR6fFynSyvUptwqwL8G+93XEEB/spIideGgiNu7YO6x+p3V3fUHVd1UJtWVvkxWQYAAOeFggsAvMBVUaVVOw9q5pJt2u906ebL22r01Ulq34gnm/hlSoI+/+6AVv9wSJLUPSFcv+uXpKhQz/XGqqoN5R86puMVVWobGaLI0KCGThcAgCaBggsAvGDTPofu+td6nVp447WcPSo9Xq4nfnNZox2S1z46VM/efrnyDh5TVbWhpJgwRbfyLLYcxyv09ro9eurj7+WqrFaPthGae0tPdY0P90HWAAA0bo13fAsANGFrdh3S6ascfvBtkfYdOeGbhOooMjRIvdpHqXfH1rUWW5L0zd4jmr1sh1yV1ZKkTfuceug/m3X0ZEVDpgoAQJNAwQUAXlDb+kPWAP9msWDwV7s9J9dYl3dY+52uWqIBAGjZKLgAwAv6JLVWK6v7qO17ftFZiVGhPsqo/iS29jyH1mFBCgtqnEMlAQDwJe7hAgAv6JYQoXfGXqUPvy1S3oFj+lUvu65Mat0sZvm7smNrtW8dovzDPw6PnDEsWQmszwUAgAeLYZx+lwHOxOl0ymazyeFwKCIiwtfpAGgi8g6WaUfxUVVUGbo4LrxZTC6x59Axbd7n0OFj5UpOsOmSthEKbqSTgQAA4A11rQ3o4QIAL9pR7NTt/7NWB8vKJUkhgf56a0wf9Wof5ePMfp4O0WHq0IinuAcAoLHgHi4A8KIFX+8ziy1JOlFRpSdXfKfj5ZW1xh9zVerA0ZOqrmbwAQAAzQE9XADgJVVV1fp6T6lH+/aioyo7WanQoB8/gqurDeXuKdXfV+xQ3sFjurFXW91+ZftGvVAyAAA4N3q4AMBL/P39NOwyu0f74JQ4tQ4LcmvbVuzUqP9ZozW7aqZXf37lLv3tw21n7AkDAABNAwUXAHjRdd3j9Mse8eZ2avso/bZfkgL83T9+v93rUEWV+zDC5Vv3q+Dw8QbJEwAAeAdDCgHAi9pGheiJmy/T3QM6q8ow1KF1qGyhQR5xQf6e08X7WSR/S9OfRh4AgJaMggsAvCwsOECXtLWdNaZHu0iFWwN01PXjEMJRfdorMbrpL5QMAEBLRsEFAI3AxXHhevv3V+mdrwq0tcip36S208CubWQNYG0rAACaMgouAGgkUtralNLWpsqqao97vAAAQNPEb3QAaGQotgAAaD74rQ4AAAAAXtLiCq5nn31WSUlJCg4OVmpqqr744gtfpwQAAACgmWpRBdc777yjSZMm6cEHH9SGDRt0zTXX6IYbblB+fr6vUwMAAADQDFkMwzDOHdY89OnTR5dffrnmz59vtnXv3l033nijZs2a5RHvcrnkcrnMbafTqcTERDkcDkVERDRIzgAAAAAaH6fTKZvNds7aoMX0cJWXlys3N1fp6elu7enp6Vq9enWtz5k1a5ZsNpv5SExMbIhUAQAAADQTLabgOnjwoKqqqhQXF+fWHhcXp+Li4lqfM336dDkcDvNRUFDQEKkCAAAAaCZa3DpcFovFbdswDI+2U6xWq6xWa0OkBQAAAKAZajE9XDExMfL39/fozSopKfHo9QIAAACA+tBiCq6goCClpqZqxYoVbu0rVqxQ3759fZQVAAAAgOasRQ0pnDx5sjIzM9W7d2+lpaXphRdeUH5+vsaNG+fr1AAAAAA0Qy2q4Bo5cqQOHTqkRx99VEVFRUpJSdGSJUvUoUMHX6cGAAAAoBlqUetw/Vx1nWsfAAAAQPPGOlwAAAAA4GMUXADQwp0sr1LpsXJfpwEAQLPUou7hAgD8qLra0IaCUv3jk++Vd/C4fpPaVjdd3k7tokJ9nRoAAM0GBRcAtFBbi5y67YW1Kq+qliQ9uaKm8Jp1Uw8FB/r7ODsAAJoHhhQCQAu1saDULLZO+ffGfSo4fNxHGQEA0PzQwwUAXpR3sEyff3dQWwoduqZLG12Z1FpxEcG+TkuS5O9n8Wjzs1hksXi2AwCAC0PBBQBesq/0uO56bb1+OHBMkvS/6/dq5BXt9PCwSxQa5PuP356JUQoJ9NeJiiqz7bYrE5XYOsSHWTUuBYePaUuhU44TleoaH67khHAFBTDcstk5tFM6nCdZw6WYrlJolK8zAtCM+P43Ps7bzpIyfb//qPz8LOoWH64O0WG+TglALbYXHzWLrVPe+Wqv/l9aR11it/koqx91T4hQ9u+v0ms5u/X9/jL9JrWd0pPjZKWgkCTtPnhMv331K+UdrLmGFos0//bLNTglwceZoV7t/lJ6a4RUXlaz3X24dMPjUgTXGUD9oOBqYr7de0SjXlyrMlelJKlNK6veuOtKdY1nIWagsTleXlVr+8mK2tt94bLESD1uv1QV1YZCmCjDzZq8Q2axJUmGIT30ny3q1T6q0QwLxc9UViL95+4fiy1J2rZI6nGLlDzcd3kBaFaYNKMJOVlRpXmffG8WW5J0oMyld9fvlWEYPswMQG06x7ZSoL/7/VAdY0LVvnXjmnY9wN+PYqsWPy22Tjlw1KVjP/kMRhN34rBUmufZXvxtw+cCoNmi4GpCyk5WanOh06N9/Z5SVZw20xgA3+saF67XfnelOsfWDPu9tkuMnrsjVW3C6R1pCq5KivZo65PUWrHhVh9kA68IiZaiOnq2x1/a4KkAaL4ouJqQyNBApSfHebQP6RHPTdznoexkpcpO8g01vM/Pz6K+F8Xof8em6fP7B2j+HanqxvDfJqNnYqTuu66L2UvZPSFcj/zqErUKDvRxZqg3rdpIv/qnFPSTe6G7DZXa9fZdTgCaHYvBWLQ6czqdstlscjgciojwzR9Nuw6U6b53NuqbvQ5J0oCL2+ivN6YosZENUWqMnCfKtfK7g3r2s52SpPEDOqv/xTGKCAnycWYAGquKqmrtOXRcJ8qr1DYqWK3D6N1qlg7ulEp3SUHhUpuuUmhrX2cEoAmoa21AwXUeGkPBJUmlx8u1++Ax+Vss6hgTpogQvm2tiyWbijT+za/d2v45qpeGXGr3UUYAAABoqupaGzBLYRMUFRqkqPb0ypyPE+WVeuHzHzzaX/h8lwZ0jVWYteZH4VCZSzv2H5XzRIWSYsLUOTa81sVhAQAAgLqg4ELLYLEowM/zlkV/P4ss/62nio6c0LQF3+rz7w9KkgL8LHrx//XWwG6xDZkpAAAAmhEmzUCLEBLor3EDLvJo/8OAixQaVPO9wzd7HWaxJUmV1YbuX/CtihwnGixPAAAANC/0cKHFSOsUrVeyrtD/fLFL1YahMddepD5JP94Y/cOBMo/nHDjqkuNEhRJsIQ2ZKgA0L5UnpUO7JJdTsrWXbNw7C6DloOBCixFmDdDAbrHq17lmbZ3Tp9LvHh/u8ZwO0aGKacWsZABwwY4flnL+Ka16UjKqpfAEaeSbUrtUX2cGAA2CIYVocYIC/Gtdt+yyxEhlXtXe3LaFBGruLZdRcAGof85i6fAuqfy4rzPxvsIN0hdza4otSTpaJP17nHTs4NmfBwDNBD1cwH9Ft7Jq2g3dNaJ3oo6erFRi61DWNwNQvypOSDuWScvul44dkC4eIl0/Q4rp4uvMvKd4k2fbwe+ksv1SWEzD5wMADYyCC/iJVtYA9WgX6es0ADRXhRul97J+3N7xgVRxTBr5umT1HNbcLER19GwLbS1ZbQ2eCgD4AkMKAQBoKHu/8mzb9X+SY2/D59JQ2qZK7fr8uG2xSEOekiLb+S4nAGhA9HABANBQQlt7tgWGSgHBDZ9LQ4lMlEa8Ku3fIp10SNGdpdhkX2cFAA2GggtAg3GeqNChYy61sgaoTXgz/gMTOJPEPlKrWKms5Me2AQ9IkR3O/JwTDikwWApowhP4RNhrHgDQAlFwAWgQWwodevg/W7R+T6naRobosV+n6OrOMQrwZ2QzWpCYLtKdH0g//J9Umid1HiS1u1Lyq+XnoHSPtOld6dvsmh6hfvfWDM8DADQpFsMwDF8n0VQ4nU7ZbDY5HA5FRET4Oh2gyShxntRN81drb+kJs83fz6JFE/rpEjs3zgMeyo9J/75b2rrwx7bAUOmuj6W4S3yXFwDAVNfagK+WAXhdQelxt2JLkqqqDe06UOajjNAkHDokvfKKNGeOrzNpeKW73YstSao4LhV965N0AAAXjiGFALwuJNBfFot0en96aBAfQTiLTZuk3/1OCg2V7r1XCm5J9/1ZztB8hnYAQKNFDxcAr0uKCdOdaR3d2rrHh6t7AkNzcRb9+kn9+0tTp0oul6+zaVhRHaWU37i3BYVJ8Zf6JB0AwIXjHq7zwD1cwIU7eNSlDQWlWrvrsLrGh+vKpNbqEB3m67SAxutIgbRl4Y+TZlw1Xmp7ua+zAtBQDEM6dlAKDJGsrXydDWrh83u4du/erdGjRyspKUkhISG66KKL9PDDD6u8vNwtLj8/X8OGDVNYWJhiYmI0ceJEj5hNmzapf//+CgkJUdu2bfXoo4/q9Dpx5cqVSk1NVXBwsDp16qTnnnvOI6cFCxYoOTlZVqtVycnJWrhwoUcMAO+ICbfq+uR4/Xlosm7pnUixBZxLZKLUb6L0u4+kG5+j2AJakiP50so50osDpTdvkfK+kKoqfZ0VLpDXbqDYvn27qqur9fzzz6tz587avHmzxowZo2PHjmnu3LmSpKqqKg0ZMkRt2rTRqlWrdOjQId15550yDEPz5s2TVFM5Xn/99Ro4cKC++uorfffdd8rKylJYWJimTJkiScrLy9Mvf/lLjRkzRm+88Ya+/PJLjR8/Xm3atNHNN98sScrJydHIkSP117/+Vb/+9a+1cOFCjRgxQqtWrVKfPn289TYAQPNXukc6sKPm/qI23aXIdr7OqHmxhvs6AwANqdIlfTZH2vhGzbajQHr9V9Loj/nipYlq0CGFTzzxhObPn69du3ZJkpYuXaqhQ4eqoKBAdnvNgojZ2dnKyspSSUmJIiIiNH/+fE2fPl379++X1Vqz6OPs2bM1b9487d27VxaLRdOmTdOiRYu0bds281jjxo3TN998o5ycHEnSyJEj5XQ6tXTpUjNm8ODBioqK0ttvv12n/BlSCACn2b9Zev3XPy7kG9lBGvW/Umw33+YFAE3Vwe+lf14pGdXu7emPSX0n+CYn1MrnQwpr43A41Lp1a3M7JydHKSkpZrElSRkZGXK5XMrNzTVj+vfvbxZbp2IKCwu1e/duMyY9Pd3tWBkZGVq/fr0qKirOGrN69eoz5utyueR0Ot0eaLoKDh/X2+vyNfV/N+q93ALtO3Lc1ykBTVtlubT6mR+LLUk6skfa+KbnlJQAgLqx+El+/p7t/oENnwvqRYMVXD/88IPmzZuncePGmW3FxcWKi4tzi4uKilJQUJCKi4vPGHNq+1wxlZWVOnjw4FljTr1GbWbNmiWbzWY+EhMTz+eU0YgcOHpSE9/eoOnvb9J7X+/T1He/1UMLt+jI8fJzPxlA7cqPSntq+dIqb6VUccKzHQBwbpHtpT7j3dsCQ6QOfX2TD3628y64ZsyYIYvFctbH+vXr3Z5TWFiowYMH65ZbbtFdd93lts9Sy5oihmG4tZ8ec2oUZH3E1Hb8U6ZPny6Hw2E+CgoKzhiLxu27/WXaUHDEre3THSX6gYV3m6WqakOHj7nkqqzydSrNm9Umdb7es/3iwVJQaMPnAwDNgX+glDZeGv5PKfFKqecdUtYSKb6HrzPDBTrvSTMmTJigW2+99awxHTt2NP9dWFiogQMHKi0tTS+88IJbXHx8vNauXevWVlpaqoqKCrM3Kj4+3qMXqqSkZvjKuWICAgIUHR191pjTe71+ymq1ug1lRNN1sqL2P7xPlPMHeXOz60CZ3lizR8u37levxEiNG3CRLrHbfJ1W8+QfIPX5fU2P1qHva9oSeko9RvgmnyMFUt5n0s5Pa74J7nyd1DrJN7kAwM8RHi9dfod02QjJEiD5sXRuU3beBVdMTIxiYmLqFLtv3z4NHDhQqampeuWVV+R32n+WtLQ0PfbYYyoqKlJCQoIkafny5bJarUpNTTVjHnjgAZWXlysoKMiMsdvtZmGXlpamxYsXu7328uXL1bt3bwUGBpoxK1as0H333ecW07cv3bMtQVJMmMKC/HXsJwVWdFiQOsYwNXlzcqjMpYlvb9Dmwpr7LfeWntAXOw9q4fi+SophDROvaNNVyvpAOrSzZpbC6C5Sq9iGz+P4YemDydLO5TXbW96XEi6rmcAjPL7h8wGA+uAf5OsMUA+8Vi4XFhZqwIABSkxM1Ny5c3XgwAEVFxe79TKlp6crOTlZmZmZ2rBhgz755BNNnTpVY8aMMWf6GDVqlKxWq7KysrR582YtXLhQM2fO1OTJk83hgOPGjdOePXs0efJkbdu2TS+//LJeeuklTZ061TzWvffeq+XLl2vOnDnavn275syZo48//liTJk3y1luARqRTm1b61+g+urRtTU9H745RevW3V6hdFMOempO8g8fMYuuUI8crtHM/Q0e9Kjxe6ni11KGfb4otqWZWr1PF1ilF30gl22qPBwCggXhtHa7ly5dr586d2rlzp9q1c1+T5dT9Vf7+/vrwww81fvx49evXTyEhIRo1apS5Tpck2Ww2rVixQnfffbd69+6tqKgoTZ48WZMnTzZjkpKStGTJEt1333365z//Kbvdrn/84x/mGlyS1LdvX2VnZ+vPf/6zHnroIV100UV65513WIOrBUntEKXXR18p58lKRYYEKjyE2X6aGz+/2u/JPNu9mmgmKk+eoZ3JOwAAvtWg63A1dazDBTRupcfKNfb1XK3bfdhsaxNu1YJxfdU+mt7MZs2xV/qfQdLRn9yraw2XxnwmxXT2WVoAgOarUa7DBQDeFBUWpMdvuVRT0y/WJfYI/bZvR70x+kqKrZbA1k4a9a7UaaDkFyC1u1K6YyHFFgDA5+jhOg/0cAFNx/HySgUH+J9xmCGaKVeZdOKwFBwpBfM5DQDwnrrWBl67hwsAfCk0iI+3FsnaquYBAEAjwZBCAAAAAPASCi4AAAAA8BIKLgAAAADwEgouAAAAAPASCi4AAAAA8BIKLgAAAADwEgouAAAAAPASCi4AAAAA8BIKLgAAAADwEgouAAAAAPASCi4AAAAA8BIKLgAAAADwEgouAAAAAPASCi4AAAAA8JIAXycAAACAFu74YenANunYISmqo9SmmxQQ5OusgHpBwQUAAADfKTsgLf+z9G12zbbFIv36eanHiJp/A00cQwoBAADgO/s3/1hsSZJhSB/cJx3e5bucgHpEwQUAAADfKdvv2VZ+TDp+qOFzAbyAggsAAAC+Y0v0bAuJklrFN3wugBdQcAEAAMB34lOk/tN+vF8rKEy66X+kqPa+zQuoJ0yaAQAAAN8JtklX3yd1GyqdKJVs7aTWnXydFVBvKLgAAADgW4EhUsKlvs4C8AqGFAIAAACAl1BwAQAAAICXUHABAAAAF+r4IclV5uss0IhxDxfOrbJcCgjydRYAAACNh2OftOk9KfflminsB06X2vflbyZ4oIcLZ1a6R1r9jPTqDdLyh6SSbb7OCAAAwPeqKqU186WP/yKV7pYK1kiv/1oq2ujrzNAI0cOF2p1wSB9OkXauqNneu1769h3pdx9JrZN8mxsAAIAvOfdK6553bzOqpe+XS4lX+iYnNFoN0sPlcrnUs2dPWSwWbdy40W1ffn6+hg0bprCwMMXExGjixIkqLy93i9m0aZP69++vkJAQtW3bVo8++qgMw3CLWblypVJTUxUcHKxOnTrpueee88hjwYIFSk5OltVqVXJyshYuXFjv59psHNr5Y7F1Stl+qWSrb/IBAABoNPwkv1r6LfwZTghPDVJw3X///bLb7R7tVVVVGjJkiI4dO6ZVq1YpOztbCxYs0JQpU8wYp9Op66+/Xna7XV999ZXmzZunuXPn6sknnzRj8vLy9Mtf/lLXXHONNmzYoAceeEATJ07UggULzJicnByNHDlSmZmZ+uabb5SZmakRI0Zo7dq13j35psqoqr29+gztAAAALYWtXc1izT/lHyh1vs43+aBRsxindxXVs6VLl2ry5MlasGCBLrnkEm3YsEE9e/Y09w0dOlQFBQVmQZadna2srCyVlJQoIiJC8+fP1/Tp07V//35ZrVZJ0uzZszVv3jzt3btXFotF06ZN06JFi7Rt24/3GI0bN07ffPONcnJyJEkjR46U0+nU0qVLzZjBgwcrKipKb7/9dp3Oxel0ymazyeFwKCIioj7ensbr2EHpjd9IRRt+bLOGS2M+lWIu9l1eAAAAjUFZibTzY2n9K1Jke6nPWKltb8mPKRJairrWBl79H7F//36NGTNGr7/+ukJDQz325+TkKCUlxa33KyMjQy6XS7m5uWZM//79zWLrVExhYaF2795txqSnp7u9dkZGhtavX6+KioqzxqxevfqM+btcLjmdTrdHixEWI938otT7Lik8Qbr4Bun/LaLYAgAAkKRWsVLPUVLWB9JNL9Tcu0WxhVp47X+FYRjKysrSuHHj1Lt371pjiouLFRcX59YWFRWloKAgFRcXnzHm1Pa5YiorK3Xw4MGzxpx6jdrMmjVLNpvNfCQmJp7rtJuXmC7SDXOksZ9Lt7witb3c1xkBAAA0LgFWyc/f11mgETvvgmvGjBmyWCxnfaxfv17z5s2T0+nU9OnTz/p6FovFo80wDLf202NOjYKsj5jajn/K9OnT5XA4zEdBQcFZz6VZ8g+o+QYnMMTXmQAAAABNznlPCz9hwgTdeuutZ43p2LGj/va3v2nNmjVuQwElqXfv3rr99tv12muvKT4+3mPSitLSUlVUVJi9UfHx8R69UCUlJZJ0zpiAgABFR0efNeb0Xq+fslqtHvkDAAAAQF2dd8EVExOjmJiYc8b94x//0N/+9jdzu7CwUBkZGXrnnXfUp08fSVJaWpoee+wxFRUVKSEhQZK0fPlyWa1WpaammjEPPPCAysvLFRQUZMbY7XZ17NjRjFm8eLHb8ZcvX67evXsrMDDQjFmxYoXuu+8+t5i+ffue71sAAAAAAHXitXu42rdvr5SUFPNx8cU1ky1cdNFFateunSQpPT1dycnJyszM1IYNG/TJJ59o6tSpGjNmjDnTx6hRo2S1WpWVlaXNmzdr4cKFmjlzpiZPnmwOBxw3bpz27NmjyZMna9u2bXr55Zf10ksvaerUqWY+9957r5YvX645c+Zo+/btmjNnjj7++GNNmjTJW28BAAAAgBbOp1Op+Pv768MPP1RwcLD69eunESNG6MYbb9TcuXPNGJvNphUrVmjv3r3q3bu3xo8fr8mTJ2vy5MlmTFJSkpYsWaLPPvtMPXv21F//+lf94x//0M0332zG9O3bV9nZ2XrllVd06aWX6tVXX3XrbQMAAACA+ub1dbiakxa1DhcAAACAM2oU63ABAAAAQEtGwQUAAAAAXkLBBQAAAABeQsEFAAAAAF5CwQUAAAAAXkLBBQAAAABeQsEFAAAAAF5CwQUAAAAAXkLBBQAAAABeQsEFAAAAAF5CwQUAAAAAXkLBBQAAAABeQsEFAAAAAF5CwQUAAAAAXkLBBQAAAABeQsEFAAAAAF5CwQUAAAAAXkLBBQAAAABeQsEFAAAAAF5CwQUAAAAAXkLBBQAAAABeEuDrBAAAAADgrI7ulwo3SAd3SG26SQk9pfA4X2dVJxRcAAAAABqvE6XS8gelTe/+2HbpSOmGx6WQSJ+lVVcMKQQAAADQeB3Y4V5sSdK379S0NwEUXAAAAAAar5OO82tvZCi4AAAAADReUR2lgGD3tsAQqXWST9I5XxRcAAAAABqvmIul27Kl8ISa7Qh7zXZ0Z9/mVUdMmgEAAACg8bJYpIsGSmM+lY4flkKjpYgEX2dVZxRcAAAAABq/CHvNo4lhSCEAAAAAeAkFFwAAAAB4idcLrg8//FB9+vRRSEiIYmJidNNNN7ntz8/P17BhwxQWFqaYmBhNnDhR5eXlbjGbNm1S//79FRISorZt2+rRRx+VYRhuMStXrlRqaqqCg4PVqVMnPffccx65LFiwQMnJybJarUpOTtbChQvr/4QBAAAA4L+8WnAtWLBAmZmZ+u1vf6tvvvlGX375pUaNGmXur6qq0pAhQ3Ts2DGtWrVK2dnZWrBggaZMmWLGOJ1OXX/99bLb7frqq680b948zZ07V08++aQZk5eXp1/+8pe65pprtGHDBj3wwAOaOHGiFixYYMbk5ORo5MiRyszM1DfffKPMzEyNGDFCa9eu9eZbAAAAAKAFsxindxXVk8rKSnXs2FGPPPKIRo8eXWvM0qVLNXToUBUUFMhur7kBLjs7W1lZWSopKVFERITmz5+v6dOna//+/bJarZKk2bNna968edq7d68sFoumTZumRYsWadu2beZrjxs3Tt98841ycnIkSSNHjpTT6dTSpUvNmMGDBysqKkpvv/12nc7J6XTKZrPJ4XAoIiLigt4XAAAAAE1fXWsDr/Vwff3119q3b5/8/PzUq1cvJSQk6IYbbtCWLVvMmJycHKWkpJjFliRlZGTI5XIpNzfXjOnfv79ZbJ2KKSws1O7du82Y9PR0t+NnZGRo/fr1qqioOGvM6tWrz3gOLpdLTqfT7QEAAAAAdeW1gmvXrl2SpBkzZujPf/6zPvjgA0VFRal///46fPiwJKm4uFhxcXFuz4uKilJQUJCKi4vPGHNq+1wxlZWVOnjw4FljTr1GbWbNmiWbzWY+EhMTz+s9AAAAANCynXfBNWPGDFkslrM+1q9fr+rqaknSgw8+qJtvvlmpqal65ZVXZLFY9O6775qvZ7FYPI5hGIZb++kxp0ZB1kdMbcc/Zfr06XI4HOajoKDgjLEAAAAAcLrzXvh4woQJuvXWW88a07FjRx09elSSlJycbLZbrVZ16tRJ+fn5kqT4+HiPSStKS0tVUVFh9kbFx8d79EKVlJRI0jljAgICFB0dfdaY03u9fspqtboNZTxVxDG0EAAAAGjZTtUE55oS47wLrpiYGMXExJwzLjU1VVarVTt27NDVV18tSaqoqNDu3bvVoUMHSVJaWpoee+wxFRUVKSEhQZK0fPlyWa1WpaammjEPPPCAysvLFRQUZMbY7XZ17NjRjFm8eLHb8ZcvX67evXsrMDDQjFmxYoXuu+8+t5i+ffvW+dxPFZEMLQQAAAAg1dQINpvtjPu9NkuhJE2aNEnvvfeeXn75ZXXo0EFPPPGEFi9erO3btysqKkpVVVXq2bOn4uLi9MQTT+jw4cPKysrSjTfeqHnz5kmSHA6Hunbtql/84hd64IEH9P333ysrK0t/+ctfzOnj8/LylJKSorFjx2rMmDHKycnRuHHj9Pbbb+vmm2+WJK1evVrXXnutHnvsMf3qV7/Sf/7zH/35z3/WqlWr1KdPnzqdT3V1tQoLCxUeHn7WoYhoXJxOpxITE1VQUMDsks0Q17d54/o2b1zf5otr27xxfWsYhqGjR4/KbrfLz+8sd2oZXlReXm5MmTLFiI2NNcLDw43rrrvO2Lx5s1vMnj17jCFDhhghISFG69atjQkTJhgnT550i/n222+Na665xrBarUZ8fLwxY8YMo7q62i3ms88+M3r16mUEBQUZHTt2NObPn++Rz7vvvmt07drVCAwMNLp162YsWLCg/k8ajY7D4TAkGQ6Hw9epwAu4vs0b17d54/o2X1zb5o3re3682sMFNAasn9a8cX2bN65v88b1bb64ts0b1/f8eG1aeAAAAABo6Si40OxZrVY9/PDDbjNOovng+jZvXN/mjevbfHFtmzeu7/lhSCEAAAAAeAk9XAAAAADgJRRcAAAAAOAlFFwAAAAA4CUUXAAAAADgJRRcAAAAAOAlFFxoclwul3r27CmLxaKNGze67cvPz9ewYcMUFhammJgYTZw4UeXl5W4xmzZtUv/+/RUSEqK2bdvq0Ucf1emTda5cuVKpqakKDg5Wp06d9Nxzz3nksWDBAiUnJ8tqtSo5OVkLFy6s93NtKXbv3q3Ro0crKSlJISEhuuiii/Twww97XDuub8vy7LPPKikpScHBwUpNTdUXX3zh65RatFmzZumKK65QeHi4YmNjdeONN2rHjh1uMYZhaMaMGbLb7QoJCdGAAQO0ZcsWtxiXy6V77rlHMTExCgsL0/Dhw7V37163mNLSUmVmZspms8lmsykzM1NHjhxxi6nL5wEu3KxZs2SxWDRp0iSzjevbtO3bt0933HGHoqOjFRoaqp49eyo3N9fcz/X1IgNoYiZOnGjccMMNhiRjw4YNZntlZaWRkpJiDBw40Pj666+NFStWGHa73ZgwYYIZ43A4jLi4OOPWW281Nm3aZCxYsMAIDw835s6da8bs2rXLCA0NNe69915j69atxosvvmgEBgYa7733nhmzevVqw9/f35g5c6axbds2Y+bMmUZAQICxZs2aBnkPmpulS5caWVlZxkcffWT88MMPxn/+8x8jNjbWmDJlihnD9W1ZsrOzjcDAQOPFF180tm7datx7771GWFiYsWfPHl+n1mJlZGQYr7zyirF582Zj48aNxpAhQ4z27dsbZWVlZszs2bON8PBwY8GCBcamTZuMkSNHGgkJCYbT6TRjxo0bZ7Rt29ZYsWKF8fXXXxsDBw40LrvsMqOystKMGTx4sJGSkmKsXr3aWL16tZGSkmIMHTrU3F+XzwNcuHXr1hkdO3Y0Lr30UuPee+8127m+Tdfhw4eNDh06GFlZWcbatWuNvLw84+OPPzZ27txpxnB9vYeCC03KkiVLjG7duhlbtmzxKLiWLFli+Pn5Gfv27TPb3n77bcNqtRoOh8MwDMN49tlnDZvNZpw8edKMmTVrlmG3243q6mrDMAzj/vvvN7p16+Z23LFjxxpXXXWVuT1ixAhj8ODBbjEZGRnGrbfeWm/n2tI9/vjjRlJSkrnN9W1ZrrzySmPcuHFubd26dTP+9Kc/+SgjnK6kpMSQZKxcudIwDMOorq424uPjjdmzZ5sxJ0+eNGw2m/Hcc88ZhmEYR44cMQIDA43s7GwzZt++fYafn5+xbNkywzAMY+vWrYYkty84cnJyDEnG9u3bDcOo2+cBLszRo0eNLl26GCtWrDD69+9vFlxc36Zt2rRpxtVXX33G/Vxf72JIIZqM/fv3a8yYMXr99dcVGhrqsT8nJ0cpKSmy2+1mW0ZGhlwul9llnpOTo/79+7utjJ6RkaHCwkLt3r3bjElPT3d77YyMDK1fv14VFRVnjVm9enW9nCskh8Oh1q1bm9tc35ajvLxcubm5HtcgPT2da9CIOBwOSTJ/TvPy8lRcXOx23axWq/r3729et9zcXFVUVLjF2O12paSkmDE5OTmy2Wzq06ePGXPVVVfJZrO5xZzr8wAX5u6779aQIUN03XXXubVzfZu2RYsWqXfv3rrlllsUGxurXr166cUXXzT3c329i4ILTYJhGMrKytK4cePUu3fvWmOKi4sVFxfn1hYVFaWgoCAVFxefMebU9rliKisrdfDgwbPGnHoN/Dw//PCD5s2bp3HjxpltXN+W4+DBg6qqquIaNGKGYWjy5Mm6+uqrlZKSIunHn7GzXbfi4mIFBQUpKirqrDGxsbEex4yNjT3rz/Hpnwc4f9nZ2crNzdWsWbM89nF9m7Zdu3Zp/vz56tKliz766CONGzdOEydO1L/+9S9JXF9vo+CCT82YMUMWi+Wsj/Xr12vevHlyOp2aPn36WV/PYrF4tBmG4dZ+eozx3wkV6iOmtuO3ZHW9vj9VWFiowYMH65ZbbtFdd93lto/r27JwDRqvCRMm6Ntvv9Xbb7/tse9Crtu5fo4vNAZ1V1BQoHvvvVdvvvmmgoODzxjH9W2aqqurdfnll2vmzJnq1auXxo4dqzFjxmj+/PlucVxf76Dggk9NmDBB27ZtO+sjJSVFn376qdasWSOr1aqAgAB17txZktS7d2/deeedkqT4+HiPb0ZKS0tVUVFhfpNSW0xJSYkknTMmICBA0dHRZ405/Rublq6u1/eUwsJCDRw4UGlpaXrhhRfcXovr23LExMTI39+fa9BI3XPPPVq0aJH+7//+T+3atTPb4+PjJems1y0+Pl7l5eUqLS09a8z+/fs9jnvgwIGz/hyf/nmA85Obm6uSkhKlpqYqICBAAQEBWrlypf7xj38oICDAY7TAKVzfpiEhIUHJyclubd27d1d+fr4kfn69rgHvFwMu2J49e4xNmzaZj48++siQZLz33ntGQUGBYRg/3oRZWFhoPi87O9tjUoXIyEjD5XKZMbNnz/aYVKF79+5uxx83bpzHpAo33HCDW8zgwYOZVOFn2Lt3r9GlSxfj1ltvdZvt6BSub8ty5ZVXGn/4wx/c2rp3786kGT5UXV1t3H333Ybdbje+++67WvfHx8cbc+bMMdtcLletN92/8847ZkxhYWGtN92vXbvWjFmzZk2tN92f7fMA58fpdLr9nt20aZPRu3dv44477jA2bdrE9W3ibrvtNo9JMyZNmmSkpaUZhsHPr7dRcKFJysvLO+O08IMGDTK+/vpr4+OPPzbatWvnNs3okSNHjLi4OOO2224zNm3aZLz//vtGRERErdOG33fffcbWrVuNl156yWPa8C+//NLw9/c3Zs+ebWzbts2YPXs204b/DPv27TM6d+5s/OIXvzD27t1rFBUVmY9TuL4ty6lp4V966SVj69atxqRJk4ywsDBj9+7dvk6txfrDH/5g2Gw247PPPnP7GT1+/LgZM3v2bMNmsxnvv/++sWnTJuO2226rdVrpdu3aGR9//LHx9ddfG7/4xS9qnVb60ksvNXJycoycnByjR48etU4rfbbPA/x8P52l0DC4vk3ZunXrjICAAOOxxx4zvv/+e+PNN980QkNDjTfeeMOM4fp6DwUXmqTaCi7DqOkJGzJkiBESEmK0bt3amDBhgtsU4YZhGN9++61xzTXXGFar1YiPjzdmzJhh9n6c8tlnnxm9evUygoKCjI4dOxrz58/3yOHdd981unbtagQGBhrdunUzFixYUO/n2VK88sorhqRaHz/F9W1Z/vnPfxodOnQwgoKCjMsvv9ycfhy+caaf0VdeecWMqa6uNh5++GEjPj7esFqtxrXXXmts2rTJ7XVOnDhhTJgwwWjdurUREhJiDB061MjPz3eLOXTokHH77bcb4eHhRnh4uHH77bcbpaWlbjF1+TzAz3N6wcX1bdoWL15spKSkGFar1ejWrZvxwgsvuO3n+nqPxTD+e7c4AAAAAKBeMWkGAAAAAHgJBRcAAAAAeAkFFwAAAAB4CQUXAAAAAHgJBRcAAAAAeAkFFwAAAAB4CQUXAAAAAHgJBRcAAAAAeAkFFwAAAAB4CQUXAAAAAHgJBRcAAAAAeMn/D5A8sj6QljssAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc_model = SVC(kernel='linear', random_state=32)\n",
    "svc_model.fit(X_train_pca, y_train.iloc[:,1])\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "# Plotting our two-features-space\n",
    "sns.scatterplot(x=X_train_pca[:, 0],\n",
    "                y = X_train_pca[:, 1], \n",
    "                hue=y_train.iloc[:,1], \n",
    "                s=20);\n",
    "# Constructing a hyperplane using a formula.\n",
    "w = svc_model.coef_[0]           # w consists of 2 elements\n",
    "b = svc_model.intercept_[0]      # b consists of 1 element\n",
    "x_points = np.linspace(-5, 5)    # generating x-points from -1 to 1\n",
    "y_points = -(w[0] / w[1]) * x_points - b / w[1]  # getting corresponding y-points\n",
    "# Plotting a red hyperplane\n",
    "plt.plot(x_points, y_points, c='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "de05e4f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "# Plotting our two-features-space\n",
    "sns.scatterplot(x=X_test_pca[:, 0],\n",
    "                y = X_test_pca[:, 1], \n",
    "                hue=y_test.iloc[:,1], \n",
    "                s=20);\n",
    "# Constructing a hyperplane using a formula.\n",
    "w = svc_model.coef_[0]           \n",
    "b = svc_model.intercept_[0]     \n",
    "x_points = np.linspace(-5, 5)    \n",
    "y_points = -(w[0] / w[1]) * x_points - b / w[1] \n",
    "# Plotting a red hyperplane\n",
    "plt.plot(x_points, y_points, c='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5b100a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 1. 1.] [0.  0.1 1. ]\n"
     ]
    }
   ],
   "source": [
    "#ROC Curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test.iloc[:,1], svm_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,estimator_name='ROC Curve')\n",
    "display.plot()\n",
    "plt.show()\n",
    "print(tpr,fpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "984a8b51",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = SVC(random_state=0).fit(X_train_pca, y_train.iloc[:,1])\n",
    "#RocCurveDisplay.from_estimator(clf, X_test_pca, y_test.iloc[:,1])\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5299ff2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ROC Curve\n",
    "y_score = clf.decision_function(X_test_pca)\n",
    "#fpr, tpr, _ = roc_curve(y_test.iloc[:,1], y_score, pos_label=clf.classes_[1])\n",
    "#roc_auc = auc(fpr, tpr)\n",
    "#roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,estimator_name='ROC Curve').plot()\n",
    "#print(\"TPR : \", tpr)\n",
    "#print(\"FPR : \", fpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7b460ced",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision :  [0.93333333 0.92857143 1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.        ] \n",
      "\n",
      "Recall :  [1.         0.92857143 0.92857143 0.85714286 0.78571429 0.71428571\n",
      " 0.64285714 0.57142857 0.5        0.42857143 0.35714286 0.28571429\n",
      " 0.21428571 0.14285714 0.07142857 0.        ] \n",
      "\n",
      "---------------------------------------------------------------------------------------\n",
      "AUC-PR :  0.995\n"
     ]
    },
    {
     "data": {
      "image/png": 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N1bZt287bf9myZQoJCZHD4VCPHj20du3aau9ff/31slgsNbaRI0c25DAAAEAz4uHOD1+/fr2mT5+upKQkRUZGavny5Ro+fLhycnLUuXPnGv2Tk5MVHx+vlJQUDRw4UFlZWZo8ebLatm2r0aNHS5JeffVVlZWVOfc5fvy4+vbtq9tvv73RxgUAAJo2i2EYhrs+PDw8XAMGDFBycrKzLSQkRDExMUpISKjRPyIiQpGRkVq4cKGzbfr06dq5c6e2b99e62csXrxYs2fPVn5+vlq1anVBdRUXF8vX11dFRUXy8fGp46gAAGi6Ssoq1Gv2O5KknPnRaunp1rWQelWX87fbLoGVlZUpOztbUVFR1dqjoqKUmZlZ6z6lpaWy2+3V2hwOh7KyslReXl7rPqtWrdLYsWPPG35KS0tVXFxcbQMAABcvtwWgwsJCVVZWyt/fv1q7v7+/CgoKat0nOjpaK1euVHZ2tgzD0M6dO5Wamqry8nIVFhbW6J+VlaW9e/dq0qRJ560lISFBvr6+zi0oKMj1gQEAgCbPpXWvn376SU899ZQ2b96sY8eOqaqqqtr7Bw4cuOBjWSyWaq8Nw6jRdtasWbNUUFCgQYMGyTAM+fv7a8KECXrmmWdktVpr9F+1apV69+6tq6+++rw1xMfHKy4uzvm6uLiYEAQAwEXMpQA0adIkbd26VePHj1dAQMA5A8v5+Pn5yWq11ljtOXbsWI1VobMcDodSU1O1fPlyfffddwoICNCKFSvk7e0tPz+/an1LSkqUlpam+fPn/2otXl5e8vLyqvMYAABA8+RSAHrrrbf05ptvKjIy0uUP9vT0VGhoqDIyMnTrrbc62zMyMjRmzJjz7muz2RQYGChJSktL06hRo9SiRfWref/85z9VWlqqP/3pTy7XCAAALk4uBaC2bduqXbt2v/nD4+LiNH78eIWFhWnw4MFasWKF8vLyNGXKFElnLk0dOXLE+ayf3NxcZWVlKTw8XCdOnFBiYqL27t2rNWvW1Dj2qlWrFBMTo0suueQ31wkAAC4uLgWgxx9/XLNnz9aaNWvUsmVLlz88NjZWx48f1/z585Wfn6/evXsrPT1dXbp0kSTl5+crLy/P2b+yslKLFi3Svn37ZLPZNHToUGVmZio4OLjacXNzc7V9+3a9++67LtcGAAAuXi49B6h///7av3+/DMNQcHCwbDZbtfd37dpVbwW6A88BAgBcrHgO0BkujTomJsaV3QAAAJoElwLQnDlz6rsOAACARvOb1r2ys7P1xRdfyGKxqFevXurfv3991QUAANBgXApAx44d09ixY7Vlyxa1adNGhmGoqKhIQ4cOVVpamtq3b1/fdQIAANQbl34K4y9/+YuKi4v1+eef64cfftCJEye0d+9eFRcX64EHHqjvGgEAAOqVSytAb7/9tt577z2FhIQ423r16qVly5bV+HFTAACApsalFaCqqqoaX32Xzjyh+Ze/CwYAANDUuBSAbrjhBk2bNk1Hjx51th05ckQPPvigbrzxxnorDgAAoCG4FICef/55nTx5UsHBwerevbsuu+wyde3aVSdPntTSpUvru0YAAIB65dI9QEFBQdq1a5cyMjL05ZdfyjAM9erVS8OGDavv+gAAAOrdb3oO0E033aSbbrqpvmoBAABoFBccgJYsWaK7775bdrtdS5YsOW9fvgoPAACasgsOQM8++6zGjRsnu92uZ5999pz9LBYLAQgAADRpFxyADh48WOs/AwAANDcufQvslyorK7Vnzx6dOHGiPg4HAADQoFwKQNOnT9eqVasknQk/1113nQYMGKCgoCBt2bKlPusDAACody4FoA0bNqhv376SpNdff12HDh3Sl19+qenTp2vGjBn1WiAAAEB9cykAFRYWqmPHjpKk9PR03X777briiis0ceJEffbZZ/VaIAAAQH1zKQD5+/srJydHlZWVevvtt50PQCwpKZHVaq3XAgEAAOqbSw9CvOuuu3THHXcoICBAFovF+TDEjz76SD179qzXAgEAAOqbSwFo7ty56t27tw4fPqzbb79dXl5ekiSr1arHHnusXgsEAACoby7/FMZtt91Wo+3OO+/8TcUAAAA0Bn4KAwAAmA4/hQEAAEyHn8IAAACmUy8/hQEAANCcuBSAbrvtNj311FM12hcuXKjbb7/9NxcFAADQkFwKQFu3btXIkSNrtN9888364IMPfnNRAAAADcmlAHTq1Cl5enrWaLfZbCouLv7NRQEAADQklwJQ7969tX79+hrtaWlp6tWr128uCgAAoCG59CDEWbNm6fe//73279+vG264QZK0efNmvfzyy/rXv/5VrwUCAADUN5cC0C233KKNGzfqySef1IYNG+RwONSnTx+99957GjJkSH3XCAAAUK9c/imMkSNH1nojNAAAQFPn8nOAfvzxR61cuVL/93//px9++EGStGvXLh05cqTeigMAAGgILq0Affrppxo2bJh8fX116NAhTZo0Se3atdNrr72mb775RmvXrq3vOgEAAOqNSytAcXFxmjBhgr766ivZ7XZn+/Dhw3kOEAAAaPJcCkAff/yx7rnnnhrtl156qQoKCup0rKSkJHXt2lV2u12hoaHatm3befsvW7ZMISEhcjgc6tGjR62rTT/++KOmTp2qgIAA2e12hYSEKD09vU51AQCAi5dLl8DsdnutDzzct2+f2rdvf8HHWb9+vaZPn66kpCRFRkZq+fLlGj58uHJyctS5c+ca/ZOTkxUfH6+UlBQNHDhQWVlZmjx5stq2bavRo0dLksrKynTTTTepQ4cO2rBhgwIDA3X48GF5e3u7MlQAAHARshiGYdR1p7vvvlvff/+9/vnPf6pdu3b69NNPZbVaFRMTo+uuu06LFy++oOOEh4drwIABSk5OdraFhIQoJiZGCQkJNfpHREQoMjJSCxcudLZNnz5dO3fu1Pbt2yVJf//737Vw4UJ9+eWXstlsdR2aJKm4uFi+vr4qKiqSj4+PS8cAAKApKimrUK/Z70iScuZHq6Wny18Ib3Lqcv526RLY3/72N33//ffq0KGDfv75Zw0ZMkSXXXaZvL299cQTT1zQMcrKypSdna2oqKhq7VFRUcrMzKx1n9LS0mr3HEmSw+FQVlaWysvLJUmbNm3S4MGDNXXqVPn7+6t379568sknVVlZec5aSktLVVxcXG0DAAAXL5din4+Pj7Zv3673339fu3btUlVVlQYMGKBhw4Zd8DEKCwtVWVkpf3//au3+/v7nvI8oOjpaK1euVExMjAYMGKDs7GylpqaqvLxchYWFCggI0IEDB/T+++9r3LhxSk9P11dffaWpU6eqoqJCs2fPrvW4CQkJmjdv3oVPAAAAaNbqHIAqKipkt9u1Z88e3XDDDc6fwnCVxWKp9towjBptZ82aNUsFBQUaNGiQDMOQv7+/JkyYoGeeeUZWq1WSVFVVpQ4dOmjFihWyWq0KDQ3V0aNHtXDhwnMGoPj4eMXFxTlfFxcXKygo6DeNCwAANF11vgTm4eGhLl26nPeS0oXw8/OT1Wqtsdpz7NixGqtCZzkcDqWmpqqkpESHDh1SXl6egoOD5e3tLT8/P0lSQECArrjiCmcgks7cV1RQUKCysrJaj+vl5SUfH59qGwAAuHi5dA/QzJkzFR8f73wCtCs8PT0VGhqqjIyMau0ZGRmKiIg47742m02BgYGyWq1KS0vTqFGj1KLFmaFERkbq66+/VlVVlbN/bm6uAgIC5Onp6XK9AADg4uHSPUBLlizR119/rU6dOqlLly5q1apVtfd37dp1QceJi4vT+PHjFRYWpsGDB2vFihXKy8vTlClTJJ25NHXkyBHns35yc3OVlZWl8PBwnThxQomJidq7d6/WrFnjPOa9996rpUuXatq0afrLX/6ir776Sk8++aQeeOABV4YKAAAuQi4FoJiYGFksFrnwDfpqYmNjdfz4cc2fP1/5+fnq3bu30tPT1aVLF0lSfn6+8vLynP0rKyu1aNEi7du3TzabTUOHDlVmZqaCg4OdfYKCgvTuu+/qwQcfVJ8+fXTppZdq2rRpevTRR39TrQAA4OJRp+cAlZSU6OGHH9bGjRtVXl6uG2+8UUuXLnXef3Ox4DlAAICLFc8BOqNO9wDNmTNHq1ev1siRI/WHP/xB7733nu69997fVCwAAEBjq1Pse/XVV7Vq1SqNHTtWkjRu3DhFRkaqsrKy2reuAAAAmrI6rQAdPnxY1157rfP11VdfLQ8PDx09erTeCwMAAGgodQpAlZWVNb5K7uHhoYqKinotCgAAoCHV6RKYYRiaMGGCvLy8nG2nT5/WlClTqn0V/tVXX62/CgEAAOpZnQLQnXfeWaPtT3/6U70VAwAA0BjqFIBeeOGFhqoDAACg0bj0UxgAAADNGQEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYjtsDUFJSkrp27Sq73a7Q0FBt27btvP2XLVumkJAQORwO9ejRQ2vXrq32/urVq2WxWGpsp0+fbshhAACAZsTDnR++fv16TZ8+XUlJSYqMjNTy5cs1fPhw5eTkqHPnzjX6JycnKz4+XikpKRo4cKCysrI0efJktW3bVqNHj3b28/Hx0b59+6rta7fbG3w8AACgeXBrAEpMTNTEiRM1adIkSdLixYv1zjvvKDk5WQkJCTX6r1u3Tvfcc49iY2MlSd26ddN///tfPf3009UCkMViUceOHS+4jtLSUpWWljpfFxcXuzokAADQDLjtElhZWZmys7MVFRVVrT0qKkqZmZm17lNaWlpjJcfhcCgrK0vl5eXOtlOnTqlLly4KDAzUqFGjtHv37vPWkpCQIF9fX+cWFBTk4qgAAEBz4LYAVFhYqMrKSvn7+1dr9/f3V0FBQa37REdHa+XKlcrOzpZhGNq5c6dSU1NVXl6uwsJCSVLPnj21evVqbdq0SS+//LLsdrsiIyP11VdfnbOW+Ph4FRUVObfDhw/X30ABAECT49ZLYNKZy1X/yzCMGm1nzZo1SwUFBRo0aJAMw5C/v78mTJigZ555RlarVZI0aNAgDRo0yLlPZGSkBgwYoKVLl2rJkiW1HtfLy0teXl71NCIAANDUuW0FyM/PT1artcZqz7Fjx2qsCp3lcDiUmpqqkpISHTp0SHl5eQoODpa3t7f8/Pxq3adFixYaOHDgeVeAAACAubgtAHl6eio0NFQZGRnV2jMyMhQREXHefW02mwIDA2W1WpWWlqZRo0apRYvah2IYhvbs2aOAgIB6qx0AADRvbr0EFhcXp/HjxyssLEyDBw/WihUrlJeXpylTpkg6c2/OkSNHnM/6yc3NVVZWlsLDw3XixAklJiZq7969WrNmjfOY8+bN06BBg3T55ZeruLhYS5Ys0Z49e7Rs2TK3jBEAADQ9bg1AsbGxOn78uObPn6/8/Hz17t1b6enp6tKliyQpPz9feXl5zv6VlZVatGiR9u3bJ5vNpqFDhyozM1PBwcHOPj/++KPuvvtuFRQUyNfXV/3799cHH3ygq6++urGHBwAAmiiLYRiGu4toaoqLi+Xr66uioiL5+Pi4uxwAAOpNSVmFes1+R5KUMz9aLT3d/n2oelOX87fbfwoDAACgsRGAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6bg9ACUlJalr166y2+0KDQ3Vtm3bztt/2bJlCgkJkcPhUI8ePbR27dpz9k1LS5PFYlFMTEw9Vw0AAJozD3d++Pr16zV9+nQlJSUpMjJSy5cv1/Dhw5WTk6POnTvX6J+cnKz4+HilpKRo4MCBysrK0uTJk9W2bVuNHj26Wt9vvvlGDz30kK699trGGg4AAGgm3LoClJiYqIkTJ2rSpEkKCQnR4sWLFRQUpOTk5Fr7r1u3Tvfcc49iY2PVrVs3jR07VhMnTtTTTz9drV9lZaXGjRunefPmqVu3br9aR2lpqYqLi6ttAADg4uW2AFRWVqbs7GxFRUVVa4+KilJmZmat+5SWlsput1drczgcysrKUnl5ubNt/vz5at++vSZOnHhBtSQkJMjX19e5BQUF1XE0AACgOXFbACosLFRlZaX8/f2rtfv7+6ugoKDWfaKjo7Vy5UplZ2fLMAzt3LlTqampKi8vV2FhoSTpww8/1KpVq5SSknLBtcTHx6uoqMi5HT582PWBAQCAJs+t9wBJksViqfbaMIwabWfNmjVLBQUFGjRokAzDkL+/vyZMmKBnnnlGVqtVJ0+e1J/+9CelpKTIz8/vgmvw8vKSl5fXbxoHAABoPty2AuTn5yer1VpjtefYsWM1VoXOcjgcSk1NVUlJiQ4dOqS8vDwFBwfL29tbfn5+2r9/vw4dOqTRo0fLw8NDHh4eWrt2rTZt2iQPDw/t37+/MYYGAACaOLcFIE9PT4WGhiojI6Nae0ZGhiIiIs67r81mU2BgoKxWq9LS0jRq1Ci1aNFCPXv21GeffaY9e/Y4t1tuuUVDhw7Vnj17uLcHAABIcvMlsLi4OI0fP15hYWEaPHiwVqxYoby8PE2ZMkXSmXtzjhw54nzWT25urrKyshQeHq4TJ04oMTFRe/fu1Zo1ayRJdrtdvXv3rvYZbdq0kaQa7QAAwLzcGoBiY2N1/PhxzZ8/X/n5+erdu7fS09PVpUsXSVJ+fr7y8vKc/SsrK7Vo0SLt27dPNptNQ4cOVWZmpoKDg900AgAA0BxZDMMw3F1EU1NcXCxfX18VFRXJx8fH3eUAAFBvSsoq1Gv2O5KknPnRaunp9u9D1Zu6nL/d/lMYAAAAjY0ABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATMfD3QUAAAD3KCmrdOvnO2xWWSwWt3w2AQgAABMxjP//z2EL3nNfIZJy5kerpad7ogiXwAAAMJGfy9276tNUsAIEAIBJbXtkqC5p7em2z3fYrG77bAIQAAAm5fC0uu0SlLtxCQwAAJgOAQgAAJgOAQgAAJiOOS/81QPDMFRRUaHKSu6mR8OyWq3y8PBw27MyAOBiRAByQVlZmfLz81VSUuLuUmASLVu2VEBAgDw93fdtDQC4mBCA6qiqqkoHDx6U1WpVp06d5OnpyX+Zo8EYhqGysjJ9//33OnjwoC6//HK1aMGVawD4rQhAdVRWVqaqqioFBQWpZcuW7i4HJuBwOGSz2fTNN9+orKxMdrvd3SUBQLPHf0q6iP8KR2Pi7w0A6hf/VgUAAKbj9gCUlJSkrl27ym63KzQ0VNu2bTtv/2XLlikkJEQOh0M9evTQ2rVrq73/6quvKiwsTG3atFGrVq3Ur18/rVu3riGHAAAAmhm3BqD169dr+vTpmjFjhnbv3q1rr71Ww4cPV15eXq39k5OTFR8fr7lz5+rzzz/XvHnzNHXqVL3++uvOPu3atdOMGTO0Y8cOffrpp7rrrrt011136Z133mmsYeEXgoODtXjx4nrv29zNnTtX/fr1c76eMGGCYmJi3FYPAJiJWwNQYmKiJk6cqEmTJikkJESLFy9WUFCQkpOTa+2/bt063XPPPYqNjVW3bt00duxYTZw4UU8//bSzz/XXX69bb71VISEh6t69u6ZNm6Y+ffpo+/bt56yjtLRUxcXF1baL0YQJE2SxWGSxWGSz2dStWzc99NBD+umnnxr0cz/++GPdfffd9d7XVVu2bHHOg8Vi0SWXXKIbbrhBH374YYN+LgCg6XBbACorK1N2draioqKqtUdFRSkzM7PWfUpLS2t8A8bhcCgrK0vl5eU1+huGoc2bN2vfvn267rrrzllLQkKCfH19nVtQUJALI2oebr75ZuXn5+vAgQNasGCBkpKS9NBDD9Xat7Y5dUX79u0v+Btzden7W+3bt0/5+fnasmWL2rdvr5EjR+rYsWON8tkAAPdyWwAqLCxUZWWl/P39q7X7+/uroKCg1n2io6O1cuVKZWdnyzAM7dy5U6mpqSovL1dhYaGzX1FRkVq3bi1PT0+NHDlSS5cu1U033XTOWuLj41VUVOTcDh8+XKexGIahkrIKt2yGYdSpVi8vL3Xs2FFBQUH64x//qHHjxmnjxo2S/v8lmdTUVHXr1k1eXl4yDENFRUW6++671aFDB/n4+OiGG27QJ598Uu24mzZtUlhYmOx2u/z8/PS73/3O+d4vL2vNnTtXnTt3lpeXlzp16qQHHnjgnH3z8vI0ZswYtW7dWj4+Prrjjjv03XffVTvW2fu8goOD5evrq7Fjx+rkyZO/OhcdOnRQx44dddVVV2nmzJkqKirSRx995Hw/JydHI0aMUOvWreXv76/x48dX+zurqqrS008/rcsuu0xeXl7q3LmznnjiCef7jz76qK644gq1bNlS3bp106xZs+otVAIAfhu3Pwfolw8RNAzjnA8WnDVrlgoKCjRo0CAZhiF/f39NmDBBzzzzjKxWq7Oft7e39uzZo1OnTmnz5s2Ki4tTt27ddP3119d6XC8vL3l5ebk8hp/LK9VrtnvuMcqZH62Wnq7/z+hwOKqdlL/++mv985//1CuvvOKc05EjR6pdu3ZKT0+Xr6+vli9frhtvvFG5ublq166d3nzzTf3ud7/TjBkztG7dOpWVlenNN9+s9fM2bNigZ599VmlpabryyitVUFBQI0ydZRiGYmJi1KpVK23dulUVFRW67777FBsbqy1btjj77d+/Xxs3btQbb7yhEydO6I477tBTTz1VLYycT0lJiV544QVJks1mkyTl5+dryJAhmjx5shITE/Xzzz/r0Ucf1R133KH3339f0pngnJKSomeffVbXXHON8vPz9eWXXzqP6+3trdWrV6tTp0767LPPNHnyZHl7e+uRRx65oLoAAA3HbQHIz89PVqu1xmrPsWPHaqwKneVwOJSamqrly5fru+++U0BAgFasWCFvb2/5+fk5+7Vo0UKXXXaZJKlfv3764osvlJCQcM4AZFZZWVl66aWXdOONNzrbysrKtG7dOrVv316S9P777+uzzz7TsWPHnCHxb3/7mzZu3KgNGzbo7rvv1hNPPKGxY8dq3rx5zuP07du31s/My8tTx44dNWzYMNlsNnXu3FlXX311rX3fe+89ffrppzp48KDzsuS6det05ZVX6uOPP9bAgQMlnVmJWb16tby9vSVJ48eP1+bNm381AAUGBko6E4AMw1BoaKhzLpKTkzVgwAA9+eSTzv6pqakKCgpSbm6uAgIC9Nxzz+n555/XnXfeKUnq3r27rrnmGmf/mTNnOv85ODhYf/3rX7V+/XoCEAA0AW4LQJ6engoNDVVGRoZuvfVWZ3tGRobGjBlz3n1tNpvz5JWWlqZRo0ad90FxhmGotLS0fgqvhcNmVc786AY7/q99dl288cYbat26tSoqKlReXq4xY8Zo6dKlzve7dOniDD+SlJ2drVOnTumSSy6pdpyff/5Z+/fvlyTt2bNHkydPvqDPv/3227V48WJ169ZNN998s0aMGKHRo0fLw6Pmn+IXX3yhoKCgavdk9erVS23atNEXX3zhDEDBwcHO8CNJAQEBF3Qvz7Zt29SqVSvt3r1bjz76qFavXu1cAcrOztZ//vMftW7dusZ++/fv148//qjS0tJq4fGXNmzYoMWLF+vrr7/WqVOnVFFRIR8fn1+tCwDQ8Nx6CSwuLk7jx49XWFiYBg8erBUrVigvL09TpkyRdOYSw5EjR5zP+snNzVVWVpbCw8N14sQJJSYmau/evVqzZo3zmAkJCQoLC1P37t1VVlam9PR0rV279pzfLKsPFovlN12GakxDhw5VcnKybDabOnXq5Dzhn9WqVatqr6uqqhQQEFDtktNZbdq0kXRmZe5CBQUFad++fcrIyNB7772n++67TwsXLtTWrVtr1HKuy6G/bP/lfhaLRVVVVb9aS9euXdWmTRtdccUVOn36tG699Vbt3btXXl5eqqqq0ujRo6t9w/CsgIAAHThw4LzH/u9//+tcFYuOjpavr6/S0tK0aNGiX60LANDw3HrWjo2N1fHjxzV//nzl5+erd+/eSk9PV5cuXSSduQ/jf58JVFlZqUWLFmnfvn2y2WwaOnSoMjMzFRwc7Ozz008/6b777tO3334rh8Ohnj176h//+IdiY2Mbe3hNUqtWrZyXBy/EgAEDVFBQIA8Pj2rz/L/69OmjzZs366677rqgYzocDt1yyy265ZZbNHXqVPXs2VOfffaZBgwYUK1fr169lJeXp8OHDztXgXJyclRUVKSQkJALHsOFGD9+vObPn6+kpCQ9+OCDGjBggF555RUFBwfXujp1+eWXy+FwaPPmzZo0aVKN9z/88EN16dJFM2bMcLZ988039VozAMB1bl+2uO+++3TffffV+t7q1aurvQ4JCdHu3bvPe7wFCxZowYIF9VWe6Q0bNkyDBw9WTEyMnn76afXo0UNHjx5Venq6YmJiFBYWpjlz5ujGG29U9+7dNXbsWFVUVOitt96q9V6X1atXq7KyUuHh4WrZsqXWrVsnh8PhDL2//Ow+ffpo3LhxWrx4sfMm6CFDhigsLKxex9miRQtNnz5dCxYs0D333KOpU6cqJSVFf/jDH/Twww/Lz89PX3/9tdLS0pSSkiK73a5HH31UjzzyiDw9PRUZGanvv/9en3/+uSZOnKjLLrtMeXl5SktL08CBA/Xmm2/qtddeq9eaAcAV7Vp61vrPZuP2n8JA02axWJSenq7rrrtOf/7zn3XFFVdo7NixOnTokPNm9euvv17/+te/tGnTJvXr10833HBDta+T/682bdooJSVFkZGRzpWj119/vcY9Rmc/e+PGjWrbtq2uu+46DRs2TN26ddP69esbZKx//vOfVV5erueff16dOnXShx9+qMrKSkVHR6t3796aNm2afH19nfebzZo1S3/96181e/ZshYSEKDY21nnv0ZgxY/Tggw/q/vvvV79+/ZSZmalZs2Y1SN0AUBctWlh04MkROvDkCLVoUfu3rs3AYtT1QTImUFxcLF9fXxUVFdW4afX06dM6ePCg8/fLgMbA3x0A/Lrznb9/iRUgAABgOgQgAABgOgQgAABgOgQgAABgOgQgF3HvOBoTf28AUL8IQHV09qnDJSUlbq4EZnL27+2XT70GALjG7Q9CbG6sVqvatGnjfN5Ly5Ytz/nr9cBvZRiGSkpKdOzYMbVp00ZWa91++w0AUDsCkAs6duwoSRf0g5tAfWjTpo3z7w4A8NsRgFxgsVgUEBCgDh06qLy83N3l4CJns9lY+QGAekYA+g2sVisnJgAAmiFuggYAAKZDAAIAAKZDAAIAAKbDPUC1OPvQueLiYjdXAgAALtTZ8/aFPDyWAFSLkydPSpKCgoLcXAkAAKirkydPytfX97x9LAbP2K+hqqpKR48elbe3d70/5LC4uFhBQUE6fPiwfHx86vXY+P+Y58bBPDcO5rnxMNeNo6Hm2TAMnTx5Up06dVKLFue/y4cVoFq0aNFCgYGBDfoZPj4+/J+rETDPjYN5bhzMc+NhrhtHQ8zzr638nMVN0AAAwHQIQAAAwHQIQI3My8tLc+bMkZeXl7tLuagxz42DeW4czHPjYa4bR1OYZ26CBgAApsMKEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CUANISkpS165dZbfbFRoaqm3btp23/9atWxUaGiq73a5u3brp73//eyNV2rzVZZ5fffVV3XTTTWrfvr18fHw0ePBgvfPOO41YbfNV17/nsz788EN5eHioX79+DVvgRaKu81xaWqoZM2aoS5cu8vLyUvfu3ZWamtpI1TZfdZ3nF198UX379lXLli0VEBCgu+66S8ePH2+kapunDz74QKNHj1anTp1ksVi0cePGX93HLedBA/UqLS3NsNlsRkpKipGTk2NMmzbNaNWqlfHNN9/U2v/AgQNGy5YtjWnTphk5OTlGSkqKYbPZjA0bNjRy5c1LXed52rRpxtNPP21kZWUZubm5Rnx8vGGz2Yxdu3Y1cuXNS13n+awff/zR6NatmxEVFWX07du3cYptxlyZ51tuucUIDw83MjIyjIMHDxofffSR8eGHHzZi1c1PXed527ZtRosWLYznnnvOOHDggLFt2zbjyiuvNGJiYhq58uYlPT3dmDFjhvHKK68YkozXXnvtvP3ddR4kANWzq6++2pgyZUq1tp49exqPPfZYrf0feeQRo2fPntXa7rnnHmPQoEENVuPFoK7zXJtevXoZ8+bNq+/SLiquznNsbKwxc+ZMY86cOQSgC1DXeX7rrbcMX19f4/jx441R3kWjrvO8cOFCo1u3btXalixZYgQGBjZYjRebCwlA7joPcgmsHpWVlSk7O1tRUVHV2qOiopSZmVnrPjt27KjRPzo6Wjt37lR5eXmD1dqcuTLPv1RVVaWTJ0+qXbt2DVHiRcHVeX7hhRe0f/9+zZkzp6FLvCi4Ms+bNm1SWFiYnnnmGV166aW64oor9NBDD+nnn39ujJKbJVfmOSIiQt9++63S09NlGIa+++47bdiwQSNHjmyMkk3DXedBfgy1HhUWFqqyslL+/v7V2v39/VVQUFDrPgUFBbX2r6ioUGFhoQICAhqs3ubKlXn+pUWLFumnn37SHXfc0RAlXhRcmeevvvpKjz32mLZt2yYPD/71ciFcmecDBw5o+/btstvteu2111RYWKj77rtPP/zwA/cBnYMr8xwREaEXX3xRsbGxOn36tCoqKnTLLbdo6dKljVGyabjrPMgKUAOwWCzVXhuGUaPt1/rX1o7q6jrPZ7388suaO3eu1q9frw4dOjRUeReNC53nyspK/fGPf9S8efN0xRVXNFZ5F426/D1XVVXJYrHoxRdf1NVXX60RI0YoMTFRq1evZhXoV9RlnnNycvTAAw9o9uzZys7O1ttvv62DBw9qypQpjVGqqbjjPMh/otUjPz8/Wa3WGv81cezYsRrp9qyOHTvW2t/Dw0OXXHJJg9XanLkyz2etX79eEydO1L/+9S8NGzasIcts9uo6zydPntTOnTu1e/du3X///ZLOnKgNw5CHh4feffdd3XDDDY1Se3Piyt9zQECALr30Uvn6+jrbQkJCZBiGvv32W11++eUNWnNz5Mo8JyQkKDIyUg8//LAkqU+fPmrVqpWuvfZaLViwgBX6euKu8yArQPXI09NToaGhysjIqNaekZGhiIiIWvcZPHhwjf7vvvuuwsLCZLPZGqzW5syVeZbOrPxMmDBBL730EtfwL0Bd59nHx0efffaZ9uzZ49ymTJmiHj16aM+ePQoPD2+s0psVV/6eIyMjdfToUZ06dcrZlpubqxYtWigwMLBB622uXJnnkpIStWhR/TRptVol/f8VCvx2bjsPNugt1iZ09muWq1atMnJycozp06cbrVq1Mg4dOmQYhmE89thjxvjx4539z37978EHHzRycnKMVatW8TX4C1DXeX7ppZcMDw8PY9myZUZ+fr5z+/HHH901hGahrvP8S3wL7MLUdZ5PnjxpBAYGGrfddpvx+eefG1u3bjUuv/xyY9KkSe4aQrNQ13l+4YUXDA8PDyMpKcnYv3+/sX37diMsLMy4+uqr3TWEZuHkyZPG7t27jd27dxuSjMTERGP37t3Oxw00lfMgAagBLFu2zOjSpYvh6elpDBgwwNi6davzvTvvvNMYMmRItf5btmwx+vfvb3h6ehrBwcFGcnJyI1fcPNVlnocMGWJIqrHdeeedjV94M1PXv+f/RQC6cHWd5y+++MIYNmyY4XA4jMDAQCMuLs4oKSlp5Kqbn7rO85IlS4xevXoZDofDCAgIMMaNG2d8++23jVx18/Kf//znvP++bSrnQYthsI4HAADMhXuAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAOACBQcHa/Hixc7XFotFGzdudFs9AFxHAALQLEyYMEEWi0UWi0UeHh7q3Lmz7r33Xp04ccLdpQFohghAAJqNm2++Wfn5+Tp06JBWrlyp119/Xffdd5+7ywLQDBGAADQbXl5e6tixowIDAxUVFaXY2Fi9++67zvdfeOEFhYSEyG63q2fPnkpKSqq2/7fffquxY8eqXbt2atWqlcLCwvTRRx9Jkvbv368xY8bI399frVu31sCBA/Xee+816vgANB4PdxcAAK44cOCA3n77bdlsNklSSkqK5syZo+eff179+/fX7t27NXnyZLVq1Up33nmnTp06pSFDhujSSy/Vpk2b1LFjR+3atUtVVVWSpFOnTmnEiBFasGCB7Ha71qxZo9GjR2vfvn3q3LmzO4cKoAEQgAA0G2+88YZat26tyspKnT59WpKUmJgoSXr88ce1aNEi/e53v5Mkde3aVTk5OVq+fLnuvPNOvfTSS/r+++/18ccfq127dpKkyy67zHnsvn37qm/fvs7XCxYs0GuvvaZNmzbp/vvvb6whAmgkBCAAzcbQoUOVnJyskpISrVy5Urm5ufrLX/6i77//XocPH9bEiRM1efJkZ/+Kigr5+vpKkvbs2aP+/fs7w88v/fTTT5o3b57eeOMNHT16VBUVFfr555+Vl5fXKGMD0LgIQACajVatWjlXbZYsWaKhQ4dq3rx5zhWalJQUhYeHV9vHarVKkhwOx3mP/fDDD+udd97R3/72N1122WVyOBy67bbbVFZW1gAjAeBuBCAAzdacOXM0fPhw3Xvvvbr00kt14MABjRs3rta+ffr00cqVK/XDDz/Uugq0bds2TZgwQbfeequkM/cEHTp0qCHLB+BGfAsMQLN1/fXX68orr9STTz6puXPnKiEhQc8995xyc3P12Wef6YUXXnDeI/SHP/xBHTt2VExMjD788EMdOHBAr7zyinbs2CHpzP1Ar776qvbs2aNPPvlEf/zjH503SAO4+BCAADRrcXFxSklJUXR0tFauXKnVq1frqquu0pAhQ7R69Wp17dpVkuTp6al3331XHTp00IgRI3TVVVfpqaeecl4ie/bZZ9W2bVtFRERo9OjRio6O1oABA9w5NAANyGIYhuHuIgAAABoTK0AAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0/h/PvmcEX6dGAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Precision recall curve\n",
    "prec, recall, _ = precision_recall_curve(y_test.iloc[:,1], y_score, pos_label=clf.classes_[1])\n",
    "pr_auc = auc(recall, prec)\n",
    "pr_display = PrecisionRecallDisplay(precision=prec, recall=recall, estimator_name='Precision Recall').plot()\n",
    "print(\"Precision : \", prec, \"\\n\")\n",
    "print(\"Recall : \", recall, \"\\n\")\n",
    "print(\"---------------------------------------------------------------------------------------\")\n",
    "print(\"AUC-PR : \", round(pr_auc,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b059309",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}