diff --git a/Machine Learning/DS_mRNA_limma_dataset_xgb_final-F.ipynb b/Machine Learning/DS_mRNA_limma_dataset_xgb_final-F.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..98104f8b356b6f37a3f195d91e28cbba228b7da0
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+++ b/Machine Learning/DS_mRNA_limma_dataset_xgb_final-F.ipynb
@@ -0,0 +1,2273 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "id": "f097ad55",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "import pandas as pd\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "#from sklearn.model_selection import cross_val_score\n",
+ "#from sklearn.metrics import accuracy_score\n",
+ "#import sklearn.metrics as metrics\n",
+ "#from sklearn.metrics import auc\n",
+ "from sklearn.metrics import RocCurveDisplay\n",
+ "#from sklearn.model_selection import KFold\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from imblearn.over_sampling import SMOTE\n",
+ "from sklearn.linear_model import Lasso\n",
+ "import xgboost as xgb\n",
+ "from sklearn.model_selection import GridSearchCV\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "np.random.seed(7)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "73b6611a",
+ "metadata": {},
+ "source": [
+ "# Data Preprocessing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "id": "0eeb7a35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train = pd.read_csv(\"DS/mRNA_DS_preprocessed_training_data.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "id": "c04fc6bc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test = pd.read_csv(\"DS/mRNA_DS_test_data.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "id": "683b63ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train = df_train.T\n",
+ "df_test = df_test.T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "id": "6ecc5606",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df_test = df_test[:-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "id": "c7c9cdfc",
+ "metadata": {},
+ "outputs": [
+ {
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+ ],
+ "text/plain": [
+ " 0 1 2 3 4 5 \\\n",
+ "Gene_symbol ABAT ABHD5 ABLIM1 ABLIM3 ACAA1 ACADM \n",
+ "GSM1727130 186 2603 42653 220 2132 22869 \n",
+ "GSM1727131 93 1137 16493 69 1816 17788 \n",
+ "GSM1727132 198 5593 53918 263 3490 39276 \n",
+ "GSM1727133 104 1636 19203 127 1518 17951 \n",
+ "... ... ... ... ... ... ... \n",
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+ "GSM573951 4.461845 4.846861 7.099264 6.269944 8.190815 8.865402 \n",
+ "\n",
+ " 6 7 8 9 ... 568 569 \\\n",
+ "Gene_symbol ACADVL ACD ACLY ACOT11 ... XYLT1 YOD1 \n",
+ "GSM1727130 19775 4486 8835 2332 ... 392 222 \n",
+ "GSM1727131 16870 7993 21434 2211 ... 62 78 \n",
+ "GSM1727132 25847 4413 9212 7419 ... 481 355 \n",
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+ "GSM573951 10.212028 8.61563 7.266994 7.081327 ... 6.941117 5.060393 \n",
+ "\n",
+ " 570 571 572 573 574 575 \\\n",
+ "Gene_symbol YTHDC1 ZBTB16 ZDHHC13 ZFP64 ZNF185 ZNF365 \n",
+ "GSM1727130 295 4598 7009 568 65123 56 \n",
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+ "\n",
+ " 576 577 \n",
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+ "GSM1727133 361 8517 \n",
+ "... ... ... \n",
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+ "\n",
+ "[220 rows x 578 columns]"
+ ]
+ },
+ "execution_count": 109,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "id": "d9b0088e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Transform the input data\n",
+ "df_train.rename(columns=df_train.iloc[0], inplace = True)\n",
+ "df_train.drop(df_train.index[0], inplace = True)\n",
+ "df_train=df_train.reset_index()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "id": "2e78017d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Transform the input data\n",
+ "df_test.rename(columns=df_test.iloc[-1], inplace = True)\n",
+ "df_test.drop(df_test.index[-1], inplace = True)\n",
+ "df_test=df_test.reset_index()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "id": "7168825e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "metadata_test = pd.read_csv(\"DS/mRNA_DS_metadata_col_test_info.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "id": "26c98c3e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test= df_test.merge(metadata_test, left_on=\"index\", right_on= \"Unnamed: 0\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "id": "b42beaa4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test['title0'] = df_test['title0'].replace('(?i)mucosa|normal|healthy', 0, regex=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "id": "515cd9fc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test['title0'] = df_test['title0'].replace('(?i)Tumor|Cancer|carcinoma', 1, regex=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 116,
+ "id": "b4f38f66",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "title0\n",
+ "0 30\n",
+ "1 30\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 116,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_test['title0'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 117,
+ "id": "dcdefec2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test = df_test[pd.to_numeric(df_test['title0'], errors='coerce').notnull()]#remove all non-numeric data from the column."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 118,
+ "id": "ca48568a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test= df_test.drop(['index', 'Unnamed: 0'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "id": "d4b7e8b7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test= df_test.rename(columns={\"title0\": \"index\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "id": "299ca65d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_test=df_test.drop(\"index\",axis=1)\n",
+ "y_test=df_test['index']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "id": "4c50c510",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "metadata_train = pd.read_csv(\"DS/mRNA_DS_metadata_col_info.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "id": "6730cf89",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train= df_train.merge(metadata_train, left_on=\"index\", right_on= \"Unnamed: 0\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "id": "7a8ad8ad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train['title0'] = df_train['title0'].replace('(?i)mucosa|normal|healthy', 0, regex=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 124,
+ "id": "a8cf8643",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train['title0'] = df_train['title0'].replace('(?i)Tumor|Cancer|carcinoma', 1, regex=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "id": "c9e8772b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "title0\n",
+ "0 111\n",
+ "1 108\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 125,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train['title0'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 126,
+ "id": "f5d203aa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train = df_train[pd.to_numeric(df_train['title0'], errors='coerce').notnull()]#remove all non-numeric data from the column."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 127,
+ "id": "523bdaa6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train= df_train.drop(['index', 'Unnamed: 0'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "id": "46a6fb36",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train= df_train.rename(columns={\"title0\": \"index\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 129,
+ "id": "e26f88c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "index\n",
+ "0 111\n",
+ "1 108\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 129,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train['index'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 130,
+ "id": "fbaf2507",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train= df_train.apply(pd.to_numeric)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 131,
+ "id": "2e4ab71d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ABAT</th>\n",
+ " <th>ABHD5</th>\n",
+ " <th>ABLIM1</th>\n",
+ " <th>ABLIM3</th>\n",
+ " <th>ACAA1</th>\n",
+ " <th>ACADM</th>\n",
+ " <th>ACADVL</th>\n",
+ " <th>ACD</th>\n",
+ " <th>ACLY</th>\n",
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+ " <th>...</th>\n",
+ " <th>YOD1</th>\n",
+ " <th>YTHDC1</th>\n",
+ " <th>ZBTB16</th>\n",
+ " <th>ZDHHC13</th>\n",
+ " <th>ZFP64</th>\n",
+ " <th>ZNF185</th>\n",
+ " <th>ZNF365</th>\n",
+ " <th>ZNF426</th>\n",
+ " <th>ZNF710</th>\n",
+ " <th>index</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>186.000000</td>\n",
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+ " <td>42653.000000</td>\n",
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+ " <td>2132.000000</td>\n",
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+ " <td>2332.000000</td>\n",
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+ " <td>295.000000</td>\n",
+ " <td>4598.000000</td>\n",
+ " <td>7009.000000</td>\n",
+ " <td>568.000000</td>\n",
+ " <td>65123.000000</td>\n",
+ " <td>56.000000</td>\n",
+ " <td>308.000000</td>\n",
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+ " <td>0</td>\n",
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+ " <th>1</th>\n",
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+ " <td>69.000000</td>\n",
+ " <td>1816.000000</td>\n",
+ " <td>17788.000000</td>\n",
+ " <td>16870.000000</td>\n",
+ " <td>7993.000000</td>\n",
+ " <td>21434.000000</td>\n",
+ " <td>2211.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>78.000000</td>\n",
+ " <td>144.000000</td>\n",
+ " <td>2132.000000</td>\n",
+ " <td>2602.000000</td>\n",
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+ " <td>47.000000</td>\n",
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+ " <td>1</td>\n",
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+ " <td>53918.000000</td>\n",
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+ " <td>3490.000000</td>\n",
+ " <td>39276.000000</td>\n",
+ " <td>25847.000000</td>\n",
+ " <td>4413.000000</td>\n",
+ " <td>9212.000000</td>\n",
+ " <td>7419.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>355.000000</td>\n",
+ " <td>308.000000</td>\n",
+ " <td>1071.000000</td>\n",
+ " <td>10289.000000</td>\n",
+ " <td>379.000000</td>\n",
+ " <td>65131.000000</td>\n",
+ " <td>206.000000</td>\n",
+ " <td>1251.000000</td>\n",
+ " <td>11768.000000</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>104.000000</td>\n",
+ " <td>1636.000000</td>\n",
+ " <td>19203.000000</td>\n",
+ " <td>127.000000</td>\n",
+ " <td>1518.000000</td>\n",
+ " <td>17951.000000</td>\n",
+ " <td>16854.000000</td>\n",
+ " <td>12800.000000</td>\n",
+ " <td>11939.000000</td>\n",
+ " <td>5136.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>122.000000</td>\n",
+ " <td>244.000000</td>\n",
+ " <td>482.000000</td>\n",
+ " <td>3578.000000</td>\n",
+ " <td>1990.000000</td>\n",
+ " <td>37715.000000</td>\n",
+ " <td>66.000000</td>\n",
+ " <td>361.000000</td>\n",
+ " <td>8517.000000</td>\n",
+ " <td>1</td>\n",
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+ " <tr>\n",
+ " <th>4</th>\n",
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+ " <td>56984.000000</td>\n",
+ " <td>495.000000</td>\n",
+ " <td>3309.000000</td>\n",
+ " <td>24427.000000</td>\n",
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+ " <td>5718.000000</td>\n",
+ " <td>8192.000000</td>\n",
+ " <td>6748.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>275.000000</td>\n",
+ " <td>200.000000</td>\n",
+ " <td>3632.000000</td>\n",
+ " <td>7275.000000</td>\n",
+ " <td>509.000000</td>\n",
+ " <td>65138.000000</td>\n",
+ " <td>188.000000</td>\n",
+ " <td>587.000000</td>\n",
+ " <td>9390.000000</td>\n",
+ " <td>0</td>\n",
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+ " <td>5.246957</td>\n",
+ " <td>9.597787</td>\n",
+ " <td>6.158036</td>\n",
+ " <td>7.843278</td>\n",
+ " <td>7.540486</td>\n",
+ " <td>10.125865</td>\n",
+ " <td>8.390029</td>\n",
+ " <td>7.260406</td>\n",
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+ " <td>7.675928</td>\n",
+ " <td>7.964469</td>\n",
+ " <td>6.295932</td>\n",
+ " <td>5.095579</td>\n",
+ " <td>8.884501</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>215</th>\n",
+ " <td>5.587715</td>\n",
+ " <td>5.008800</td>\n",
+ " <td>8.983841</td>\n",
+ " <td>8.052282</td>\n",
+ " <td>8.080951</td>\n",
+ " <td>8.557505</td>\n",
+ " <td>9.603772</td>\n",
+ " <td>8.493015</td>\n",
+ " <td>6.808220</td>\n",
+ " <td>7.442001</td>\n",
+ " <td>...</td>\n",
+ " <td>4.748696</td>\n",
+ " <td>9.966924</td>\n",
+ " <td>6.370717</td>\n",
+ " <td>6.694095</td>\n",
+ " <td>7.215412</td>\n",
+ " <td>9.596515</td>\n",
+ " <td>6.052377</td>\n",
+ " <td>5.453787</td>\n",
+ " <td>8.364667</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>216</th>\n",
+ " <td>4.448848</td>\n",
+ " <td>5.210555</td>\n",
+ " <td>9.464238</td>\n",
+ " <td>6.475869</td>\n",
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+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>5.032263</td>\n",
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+ " <td>9.468484</td>\n",
+ " <td>7.966742</td>\n",
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+ " <td>7.389599</td>\n",
+ " <td>...</td>\n",
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+ " <td>6.113286</td>\n",
+ " <td>5.045287</td>\n",
+ " <td>8.657596</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>218</th>\n",
+ " <td>4.461845</td>\n",
+ " <td>4.846861</td>\n",
+ " <td>7.099264</td>\n",
+ " <td>6.269944</td>\n",
+ " <td>8.190815</td>\n",
+ " <td>8.865402</td>\n",
+ " <td>10.212028</td>\n",
+ " <td>8.615630</td>\n",
+ " <td>7.266994</td>\n",
+ " <td>7.081327</td>\n",
+ " <td>...</td>\n",
+ " <td>5.060393</td>\n",
+ " <td>10.228238</td>\n",
+ " <td>6.012290</td>\n",
+ " <td>6.759003</td>\n",
+ " <td>7.871117</td>\n",
+ " <td>6.893819</td>\n",
+ " <td>5.949150</td>\n",
+ " <td>5.135424</td>\n",
+ " <td>8.292854</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>219 rows × 579 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ABAT ABHD5 ABLIM1 ABLIM3 ACAA1 \\\n",
+ "0 186.000000 2603.000000 42653.000000 220.000000 2132.000000 \n",
+ "1 93.000000 1137.000000 16493.000000 69.000000 1816.000000 \n",
+ "2 198.000000 5593.000000 53918.000000 263.000000 3490.000000 \n",
+ "3 104.000000 1636.000000 19203.000000 127.000000 1518.000000 \n",
+ "4 205.000000 4720.000000 56984.000000 495.000000 3309.000000 \n",
+ ".. ... ... ... ... ... \n",
+ "214 4.273622 5.246957 9.597787 6.158036 7.843278 \n",
+ "215 5.587715 5.008800 8.983841 8.052282 8.080951 \n",
+ "216 4.448848 5.210555 9.464238 6.475869 7.987815 \n",
+ "217 5.032263 4.849188 9.468484 7.966742 8.143446 \n",
+ "218 4.461845 4.846861 7.099264 6.269944 8.190815 \n",
+ "\n",
+ " ACADM ACADVL ACD ACLY ACOT11 ... \\\n",
+ "0 22869.000000 19775.000000 4486.000000 8835.000000 2332.000000 ... \n",
+ "1 17788.000000 16870.000000 7993.000000 21434.000000 2211.000000 ... \n",
+ "2 39276.000000 25847.000000 4413.000000 9212.000000 7419.000000 ... \n",
+ "3 17951.000000 16854.000000 12800.000000 11939.000000 5136.000000 ... \n",
+ "4 24427.000000 28197.000000 5718.000000 8192.000000 6748.000000 ... \n",
+ ".. ... ... ... ... ... ... \n",
+ "214 7.540486 10.125865 8.390029 7.260406 7.029879 ... \n",
+ "215 8.557505 9.603772 8.493015 6.808220 7.442001 ... \n",
+ "216 8.141677 9.614827 8.336654 7.629702 7.163679 ... \n",
+ "217 8.049146 9.544212 8.524367 6.777896 7.389599 ... \n",
+ "218 8.865402 10.212028 8.615630 7.266994 7.081327 ... \n",
+ "\n",
+ " YOD1 YTHDC1 ZBTB16 ZDHHC13 ZFP64 \\\n",
+ "0 222.000000 295.000000 4598.000000 7009.000000 568.000000 \n",
+ "1 78.000000 144.000000 2132.000000 2602.000000 1720.000000 \n",
+ "2 355.000000 308.000000 1071.000000 10289.000000 379.000000 \n",
+ "3 122.000000 244.000000 482.000000 3578.000000 1990.000000 \n",
+ "4 275.000000 200.000000 3632.000000 7275.000000 509.000000 \n",
+ ".. ... ... ... ... ... \n",
+ "214 4.940705 9.863287 6.235713 7.042848 7.675928 \n",
+ "215 4.748696 9.966924 6.370717 6.694095 7.215412 \n",
+ "216 5.044658 9.909041 6.399272 6.468483 7.134219 \n",
+ "217 4.971315 10.181616 6.812211 8.065029 7.252720 \n",
+ "218 5.060393 10.228238 6.012290 6.759003 7.871117 \n",
+ "\n",
+ " ZNF185 ZNF365 ZNF426 ZNF710 index \n",
+ "0 65123.000000 56.000000 308.000000 10385.000000 0 \n",
+ "1 13531.000000 47.000000 140.000000 6441.000000 1 \n",
+ "2 65131.000000 206.000000 1251.000000 11768.000000 0 \n",
+ "3 37715.000000 66.000000 361.000000 8517.000000 1 \n",
+ "4 65138.000000 188.000000 587.000000 9390.000000 0 \n",
+ ".. ... ... ... ... ... \n",
+ "214 7.964469 6.295932 5.095579 8.884501 1 \n",
+ "215 9.596515 6.052377 5.453787 8.364667 1 \n",
+ "216 8.123325 6.016430 5.066430 8.760867 1 \n",
+ "217 8.542159 6.113286 5.045287 8.657596 1 \n",
+ "218 6.893819 5.949150 5.135424 8.292854 1 \n",
+ "\n",
+ "[219 rows x 579 columns]"
+ ]
+ },
+ "execution_count": 131,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 132,
+ "id": "38a993d9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.manifold import TSNE\n",
+ "# Assuming your data is stored in the variable 'data'\n",
+ "tsne = TSNE(n_components=3)\n",
+ "embedded_data = tsne.fit_transform(df_train)\n",
+ "\n",
+ "# Step 2: Separate data points by class\n",
+ "class_1_indices = np.where(df_train['index'] == 0)[0]\n",
+ "class_2_indices = np.where(df_train['index'] == 1)[0]\n",
+ "\n",
+ "class_1_data = embedded_data[class_1_indices]\n",
+ "class_2_data = embedded_data[class_2_indices]\n",
+ "\n",
+ "# Step 3: Plot the t-SNE plot with different colors for each class\n",
+ "plt.scatter(class_1_data[:, 0], class_1_data[:, 1], color='red', label='Non-Cancer')\n",
+ "plt.scatter(class_2_data[:, 0], class_2_data[:, 1], color='blue', label='Cancer')\n",
+ "\n",
+ "plt.title('t-SNE Plot')\n",
+ "plt.xlabel('Dimension 1')\n",
+ "plt.ylabel('Dimension 2')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 133,
+ "id": "776cfbee",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "index\n",
+ "0 111\n",
+ "1 108\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 133,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train['index'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 134,
+ "id": "8c0011ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X=df_train.drop(\"index\",axis=1)\n",
+ "y=df_train['index']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 135,
+ "id": "a10804ed",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X=X.astype('int')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "id": "93e28118",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y=y.astype('int')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9830b6c",
+ "metadata": {},
+ "source": [
+ "# Feature Selection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 137,
+ "id": "f0f1977f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ABAT</th>\n",
+ " <th>ABHD5</th>\n",
+ " <th>ABLIM1</th>\n",
+ " <th>ABLIM3</th>\n",
+ " <th>ACAA1</th>\n",
+ " <th>ACADM</th>\n",
+ " <th>ACADVL</th>\n",
+ " <th>ACD</th>\n",
+ " <th>ACLY</th>\n",
+ " <th>ACOT11</th>\n",
+ " <th>...</th>\n",
+ " <th>XYLT1</th>\n",
+ " <th>YOD1</th>\n",
+ " <th>YTHDC1</th>\n",
+ " <th>ZBTB16</th>\n",
+ " <th>ZDHHC13</th>\n",
+ " <th>ZFP64</th>\n",
+ " <th>ZNF185</th>\n",
+ " <th>ZNF365</th>\n",
+ " <th>ZNF426</th>\n",
+ " <th>ZNF710</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>186</td>\n",
+ " <td>2603</td>\n",
+ " <td>42653</td>\n",
+ " <td>220</td>\n",
+ " <td>2132</td>\n",
+ " <td>22869</td>\n",
+ " <td>19775</td>\n",
+ " <td>4486</td>\n",
+ " <td>8835</td>\n",
+ " <td>2332</td>\n",
+ " <td>...</td>\n",
+ " <td>392</td>\n",
+ " <td>222</td>\n",
+ " <td>295</td>\n",
+ " <td>4598</td>\n",
+ " <td>7009</td>\n",
+ " <td>568</td>\n",
+ " <td>65123</td>\n",
+ " <td>56</td>\n",
+ " <td>308</td>\n",
+ " <td>10385</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>93</td>\n",
+ " <td>1137</td>\n",
+ " <td>16493</td>\n",
+ " <td>69</td>\n",
+ " <td>1816</td>\n",
+ " <td>17788</td>\n",
+ " <td>16870</td>\n",
+ " <td>7993</td>\n",
+ " <td>21434</td>\n",
+ " <td>2211</td>\n",
+ " <td>...</td>\n",
+ " <td>62</td>\n",
+ " <td>78</td>\n",
+ " <td>144</td>\n",
+ " <td>2132</td>\n",
+ " <td>2602</td>\n",
+ " <td>1720</td>\n",
+ " <td>13531</td>\n",
+ " <td>47</td>\n",
+ " <td>140</td>\n",
+ " <td>6441</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>198</td>\n",
+ " <td>5593</td>\n",
+ " <td>53918</td>\n",
+ " <td>263</td>\n",
+ " <td>3490</td>\n",
+ " <td>39276</td>\n",
+ " <td>25847</td>\n",
+ " <td>4413</td>\n",
+ " <td>9212</td>\n",
+ " <td>7419</td>\n",
+ " <td>...</td>\n",
+ " <td>481</td>\n",
+ " <td>355</td>\n",
+ " <td>308</td>\n",
+ " <td>1071</td>\n",
+ " <td>10289</td>\n",
+ " <td>379</td>\n",
+ " <td>65131</td>\n",
+ " <td>206</td>\n",
+ " <td>1251</td>\n",
+ " <td>11768</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>104</td>\n",
+ " <td>1636</td>\n",
+ " <td>19203</td>\n",
+ " <td>127</td>\n",
+ " <td>1518</td>\n",
+ " <td>17951</td>\n",
+ " <td>16854</td>\n",
+ " <td>12800</td>\n",
+ " <td>11939</td>\n",
+ " <td>5136</td>\n",
+ " <td>...</td>\n",
+ " <td>213</td>\n",
+ " <td>122</td>\n",
+ " <td>244</td>\n",
+ " <td>482</td>\n",
+ " <td>3578</td>\n",
+ " <td>1990</td>\n",
+ " <td>37715</td>\n",
+ " <td>66</td>\n",
+ " <td>361</td>\n",
+ " <td>8517</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>205</td>\n",
+ " <td>4720</td>\n",
+ " <td>56984</td>\n",
+ " <td>495</td>\n",
+ " <td>3309</td>\n",
+ " <td>24427</td>\n",
+ " <td>28197</td>\n",
+ " <td>5718</td>\n",
+ " <td>8192</td>\n",
+ " <td>6748</td>\n",
+ " <td>...</td>\n",
+ " <td>169</td>\n",
+ " <td>275</td>\n",
+ " <td>200</td>\n",
+ " <td>3632</td>\n",
+ " <td>7275</td>\n",
+ " <td>509</td>\n",
+ " <td>65138</td>\n",
+ " <td>188</td>\n",
+ " <td>587</td>\n",
+ " <td>9390</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>214</th>\n",
+ " <td>4</td>\n",
+ " <td>5</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>10</td>\n",
+ " <td>8</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>...</td>\n",
+ " <td>7</td>\n",
+ " <td>4</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>215</th>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " <td>8</td>\n",
+ " <td>8</td>\n",
+ " <td>8</td>\n",
+ " <td>9</td>\n",
+ " <td>8</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>...</td>\n",
+ " <td>7</td>\n",
+ " <td>4</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>216</th>\n",
+ " <td>4</td>\n",
+ " <td>5</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>8</td>\n",
+ " <td>9</td>\n",
+ " <td>8</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>...</td>\n",
+ " <td>7</td>\n",
+ " <td>5</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>8</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>217</th>\n",
+ " <td>5</td>\n",
+ " <td>4</td>\n",
+ " <td>9</td>\n",
+ " <td>7</td>\n",
+ " <td>8</td>\n",
+ " <td>8</td>\n",
+ " <td>9</td>\n",
+ " <td>8</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>...</td>\n",
+ " <td>7</td>\n",
+ " <td>4</td>\n",
+ " <td>10</td>\n",
+ " <td>6</td>\n",
+ " <td>8</td>\n",
+ " <td>7</td>\n",
+ " <td>8</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>218</th>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>7</td>\n",
+ " <td>6</td>\n",
+ " <td>8</td>\n",
+ " <td>8</td>\n",
+ " <td>10</td>\n",
+ " <td>8</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>...</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>10</td>\n",
+ " <td>6</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>219 rows × 578 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ABAT ABHD5 ABLIM1 ABLIM3 ACAA1 ACADM ACADVL ACD ACLY ACOT11 \\\n",
+ "0 186 2603 42653 220 2132 22869 19775 4486 8835 2332 \n",
+ "1 93 1137 16493 69 1816 17788 16870 7993 21434 2211 \n",
+ "2 198 5593 53918 263 3490 39276 25847 4413 9212 7419 \n",
+ "3 104 1636 19203 127 1518 17951 16854 12800 11939 5136 \n",
+ "4 205 4720 56984 495 3309 24427 28197 5718 8192 6748 \n",
+ ".. ... ... ... ... ... ... ... ... ... ... \n",
+ "214 4 5 9 6 7 7 10 8 7 7 \n",
+ "215 5 5 8 8 8 8 9 8 6 7 \n",
+ "216 4 5 9 6 7 8 9 8 7 7 \n",
+ "217 5 4 9 7 8 8 9 8 6 7 \n",
+ "218 4 4 7 6 8 8 10 8 7 7 \n",
+ "\n",
+ " ... XYLT1 YOD1 YTHDC1 ZBTB16 ZDHHC13 ZFP64 ZNF185 ZNF365 ZNF426 \\\n",
+ "0 ... 392 222 295 4598 7009 568 65123 56 308 \n",
+ "1 ... 62 78 144 2132 2602 1720 13531 47 140 \n",
+ "2 ... 481 355 308 1071 10289 379 65131 206 1251 \n",
+ "3 ... 213 122 244 482 3578 1990 37715 66 361 \n",
+ "4 ... 169 275 200 3632 7275 509 65138 188 587 \n",
+ ".. ... ... ... ... ... ... ... ... ... ... \n",
+ "214 ... 7 4 9 6 7 7 7 6 5 \n",
+ "215 ... 7 4 9 6 6 7 9 6 5 \n",
+ "216 ... 7 5 9 6 6 7 8 6 5 \n",
+ "217 ... 7 4 10 6 8 7 8 6 5 \n",
+ "218 ... 6 5 10 6 6 7 6 5 5 \n",
+ "\n",
+ " ZNF710 \n",
+ "0 10385 \n",
+ "1 6441 \n",
+ "2 11768 \n",
+ "3 8517 \n",
+ "4 9390 \n",
+ ".. ... \n",
+ "214 8 \n",
+ "215 8 \n",
+ "216 8 \n",
+ "217 8 \n",
+ "218 8 \n",
+ "\n",
+ "[219 rows x 578 columns]"
+ ]
+ },
+ "execution_count": 137,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "id": "1cc528fb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# LASSO model:\n",
+ "lasso = Lasso(alpha=1)\n",
+ "# fitting the model:\n",
+ "lasso.fit(X, y)\n",
+ "# select all coefficients and the feature names\n",
+ "lasso_coefs = lasso.coef_\n",
+ "feature_names = X.columns\n",
+ "\n",
+ "# collect the selected features:\n",
+ "selected_feature_indices = np.nonzero(lasso_coefs)[0]\n",
+ "selected_features = [feature_names[i] for i in selected_feature_indices]\n",
+ "X_selected = X.iloc[:, selected_feature_indices]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "id": "8afa29ae",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "98"
+ ]
+ },
+ "execution_count": 139,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(selected_features)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6cee6462",
+ "metadata": {},
+ "source": [
+ "# Test train split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "id": "cff9bd67",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train = X_selected\n",
+ "y_train = y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "id": "129430e6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(index\n",
+ " 0 30\n",
+ " 1 30\n",
+ " Name: count, dtype: int64,\n",
+ " index\n",
+ " 0 111\n",
+ " 1 108\n",
+ " Name: count, dtype: int64)"
+ ]
+ },
+ "execution_count": 141,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_test.value_counts(),y_train.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1cfe2a06",
+ "metadata": {},
+ "source": [
+ "# Cross validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "id": "059bf577",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n",
+ "[CV 1/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.886 total time= 0.6s\n",
+ "[CV 2/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.955 total time= 0.1s\n",
+ "[CV 3/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.977 total time= 0.1s\n",
+ "[CV 4/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=1.000 total time= 0.1s\n",
+ "[CV 5/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.953 total time= 0.1s\n",
+ "[CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.773 total time= 0.1s\n",
+ "[CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.636 total time= 0.1s\n",
+ "[CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.955 total time= 0.1s\n",
+ "[CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.909 total time= 0.1s\n",
+ "[CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.860 total time= 0.1s\n",
+ "[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.659 total time= 0.1s\n",
+ "[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.614 total time= 0.1s\n",
+ "[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.955 total time= 0.1s\n",
+ "[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.864 total time= 0.1s\n",
+ "[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.884 total time= 0.1s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
+ " callbacks=None, colsample_bylevel=None,\n",
+ " colsample_bynode=None,\n",
+ " colsample_bytree=None,\n",
+ " early_stopping_rounds=None,\n",
+ " enable_categorical=False, eval_metric=None,\n",
+ " feature_types=None, gamma=None,\n",
+ " gpu_id=None, grow_policy=None,\n",
+ " importance_type=None,\n",
+ " interaction_constraints=None,\n",
+ " learning_rate=None, max_bin=None,\n",
+ " max_cat_threshold=None,\n",
+ " max_cat_to_onehot=None,\n",
+ " max_delta_step=None, max_depth=None,\n",
+ " max_leaves=None, min_child_weight=None,\n",
+ " missing=nan, monotone_constraints=None,\n",
+ " n_estimators=100, n_jobs=None,\n",
+ " num_parallel_tree=None, predictor=None,\n",
+ " random_state=42, ...),\n",
+ " param_grid={'learning_rate': [0.1, 0.01, 0.001], 'max_depth': [3],\n",
+ " 'n_estimators': [100]},\n",
+ " verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
+ " callbacks=None, colsample_bylevel=None,\n",
+ " colsample_bynode=None,\n",
+ " colsample_bytree=None,\n",
+ " early_stopping_rounds=None,\n",
+ " enable_categorical=False, eval_metric=None,\n",
+ " feature_types=None, gamma=None,\n",
+ " gpu_id=None, grow_policy=None,\n",
+ " importance_type=None,\n",
+ " interaction_constraints=None,\n",
+ " learning_rate=None, max_bin=None,\n",
+ " max_cat_threshold=None,\n",
+ " max_cat_to_onehot=None,\n",
+ " max_delta_step=None, max_depth=None,\n",
+ " max_leaves=None, min_child_weight=None,\n",
+ " missing=nan, monotone_constraints=None,\n",
+ " n_estimators=100, n_jobs=None,\n",
+ " num_parallel_tree=None, predictor=None,\n",
+ " random_state=42, ...),\n",
+ " param_grid={'learning_rate': [0.1, 0.01, 0.001], 'max_depth': [3],\n",
+ " 'n_estimators': [100]},\n",
+ " verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+ " colsample_bylevel=None, colsample_bynode=None,\n",
+ " colsample_bytree=None, early_stopping_rounds=None,\n",
+ " enable_categorical=False, eval_metric=None, feature_types=None,\n",
+ " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
+ " interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+ " max_cat_threshold=None, max_cat_to_onehot=None,\n",
+ " max_delta_step=None, max_depth=None, max_leaves=None,\n",
+ " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+ " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
+ " predictor=None, random_state=42, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+ " colsample_bylevel=None, colsample_bynode=None,\n",
+ " colsample_bytree=None, early_stopping_rounds=None,\n",
+ " enable_categorical=False, eval_metric=None, feature_types=None,\n",
+ " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
+ " interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+ " max_cat_threshold=None, max_cat_to_onehot=None,\n",
+ " max_delta_step=None, max_depth=None, max_leaves=None,\n",
+ " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+ " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
+ " predictor=None, random_state=42, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
+ " callbacks=None, colsample_bylevel=None,\n",
+ " colsample_bynode=None,\n",
+ " colsample_bytree=None,\n",
+ " early_stopping_rounds=None,\n",
+ " enable_categorical=False, eval_metric=None,\n",
+ " feature_types=None, gamma=None,\n",
+ " gpu_id=None, grow_policy=None,\n",
+ " importance_type=None,\n",
+ " interaction_constraints=None,\n",
+ " learning_rate=None, max_bin=None,\n",
+ " max_cat_threshold=None,\n",
+ " max_cat_to_onehot=None,\n",
+ " max_delta_step=None, max_depth=None,\n",
+ " max_leaves=None, min_child_weight=None,\n",
+ " missing=nan, monotone_constraints=None,\n",
+ " n_estimators=100, n_jobs=None,\n",
+ " num_parallel_tree=None, predictor=None,\n",
+ " random_state=42, ...),\n",
+ " param_grid={'learning_rate': [0.1, 0.01, 0.001], 'max_depth': [3],\n",
+ " 'n_estimators': [100]},\n",
+ " verbose=3)"
+ ]
+ },
+ "execution_count": 142,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model = xgb.XGBClassifier(random_state=42)\n",
+ "\n",
+ "# Defining parameter range\n",
+ "param_grid = {\n",
+ " 'max_depth': [3],\n",
+ " 'learning_rate': [0.1 ,0.01, 0.001],\n",
+ " 'n_estimators': [100]\n",
+ "}\n",
+ "\n",
+ "grid = GridSearchCV(model, param_grid, refit=True, verbose=3)\n",
+ "\n",
+ "# Fitting the model for grid search\n",
+ "grid.fit(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "id": "5d327876",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100}\n",
+ "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+ " colsample_bylevel=None, colsample_bynode=None,\n",
+ " colsample_bytree=None, early_stopping_rounds=None,\n",
+ " enable_categorical=False, eval_metric=None, feature_types=None,\n",
+ " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
+ " interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
+ " max_cat_threshold=None, max_cat_to_onehot=None,\n",
+ " max_delta_step=None, max_depth=3, max_leaves=None,\n",
+ " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+ " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
+ " predictor=None, random_state=42, ...)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# print best parameter after tuning\n",
+ "print(grid.best_params_)\n",
+ " \n",
+ "# print how our model looks after hyper-parameter tuning\n",
+ "print(grid.best_estimator_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
+ "id": "29da15ed",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# Select columns in df1 based on columns in df2\n",
+ "X_test = X_test.loc[:, X_train.columns]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "id": "d77bf449",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_test = X_test.astype('int')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "id": "3b2776c0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+ " colsample_bylevel=None, colsample_bynode=None,\n",
+ " colsample_bytree=None, early_stopping_rounds=None,\n",
+ " enable_categorical=False, eval_metric=None, feature_types=None,\n",
+ " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
+ " interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
+ " max_cat_threshold=None, max_cat_to_onehot=None,\n",
+ " max_delta_step=None, max_depth=3, max_leaves=None,\n",
+ " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+ " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
+ " predictor=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+ " colsample_bylevel=None, colsample_bynode=None,\n",
+ " colsample_bytree=None, early_stopping_rounds=None,\n",
+ " enable_categorical=False, eval_metric=None, feature_types=None,\n",
+ " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
+ " interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
+ " max_cat_threshold=None, max_cat_to_onehot=None,\n",
+ " max_delta_step=None, max_depth=3, max_leaves=None,\n",
+ " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+ " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
+ " predictor=None, random_state=42, ...)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+ " colsample_bylevel=None, colsample_bynode=None,\n",
+ " colsample_bytree=None, early_stopping_rounds=None,\n",
+ " enable_categorical=False, eval_metric=None, feature_types=None,\n",
+ " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
+ " interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
+ " max_cat_threshold=None, max_cat_to_onehot=None,\n",
+ " max_delta_step=None, max_depth=3, max_leaves=None,\n",
+ " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+ " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
+ " predictor=None, random_state=42, ...)"
+ ]
+ },
+ "execution_count": 146,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model_xgb = grid.best_estimator_\n",
+ "model_xgb.fit(X_train,y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "id": "94871ada",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_proba = model_xgb.fit(X_train, y_train).predict_proba(X_test)[:,1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "id": "e785a04b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 0.87 0.93 30\n",
+ " 1 0.88 1.00 0.94 30\n",
+ "\n",
+ " accuracy 0.93 60\n",
+ " macro avg 0.94 0.93 0.93 60\n",
+ "weighted avg 0.94 0.93 0.93 60\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import classification_report, confusion_matrix\n",
+ "grid_predictions = grid.predict(X_test)\n",
+ "print(classification_report(y_test, grid_predictions))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "id": "daf93766",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#######CONFUSION MATRIX ###########\n",
+ "from sklearn import metrics\n",
+ "y_test_pred_xgb = model_xgb.predict(X_test)\n",
+ "confusion_matrix_test = metrics.confusion_matrix(y_test, y_test_pred_xgb)\n",
+ "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix_test, display_labels = [False, True])\n",
+ "cm_display.plot()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "id": "d478f11a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy : 0.9333333333333333\n",
+ "Sensitivity : 0.8666666666666667\n",
+ "Specificity : 1.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "total1=sum(sum(confusion_matrix_test))\n",
+ "#####from confusion matrix calculate accuracy\n",
+ "accuracy1=(confusion_matrix_test[0,0]+confusion_matrix_test[1,1])/total1\n",
+ "print ('Accuracy : ', accuracy1)\n",
+ "\n",
+ "sensitivity1 = confusion_matrix_test[0,0]/(confusion_matrix_test[0,0]+confusion_matrix_test[0,1])\n",
+ "print('Sensitivity : ', sensitivity1 )\n",
+ "\n",
+ "specificity1 = confusion_matrix_test[1,1]/(confusion_matrix_test[1,0]+confusion_matrix_test[1,1])\n",
+ "print('Specificity : ', specificity1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "id": "fddf8856",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import StratifiedKFold\n",
+ "from sklearn.feature_selection import SelectKBest, f_classif\n",
+ "from sklearn.metrics import auc\n",
+ "def roc(X_train,y_train,model,label):\n",
+ " cv = StratifiedKFold(n_splits=6)\n",
+ " classifier = model\n",
+ " tprs = []\n",
+ " aucs = []\n",
+ " mean_fpr = np.linspace(0, 1, 100)\n",
+ "\n",
+ " fig, ax = plt.subplots(figsize=(6, 6))\n",
+ " for fold, (train, test) in enumerate(cv.split(X_train, y_train)):\n",
+ " classifier.fit(X_train.iloc[train], y_train.iloc[train])\n",
+ " viz = RocCurveDisplay.from_estimator(\n",
+ " classifier,\n",
+ " X_train.iloc[test],\n",
+ " y_train.iloc[test],\n",
+ " name=f\"ROC fold {fold}\",\n",
+ " alpha=0.3,\n",
+ " lw=1,\n",
+ " ax=ax,\n",
+ " )\n",
+ " interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)\n",
+ " interp_tpr[0] = 0.0\n",
+ " tprs.append(interp_tpr)\n",
+ " aucs.append(viz.roc_auc)\n",
+ " ax.plot([0, 1], [0, 1], \"k--\", label=\"chance level (AUC = 0.5)\")\n",
+ "\n",
+ " mean_tpr = np.mean(tprs, axis=0)\n",
+ " mean_tpr[-1] = 1.0\n",
+ " mean_auc = auc(mean_fpr, mean_tpr)\n",
+ " std_auc = np.std(aucs)\n",
+ " ax.plot(\n",
+ " mean_fpr,\n",
+ " mean_tpr,\n",
+ " color=\"b\",\n",
+ " label=r\"Mean ROC (AUC = %0.2f $\\pm$ %0.2f)\" % (mean_auc, std_auc),\n",
+ " lw=2,\n",
+ " alpha=0.8,\n",
+ " )\n",
+ "\n",
+ " std_tpr = np.std(tprs, axis=0)\n",
+ " tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
+ " tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
+ " ax.fill_between(\n",
+ " mean_fpr,\n",
+ " tprs_lower,\n",
+ " tprs_upper,\n",
+ " color=\"grey\",\n",
+ " alpha=0.2,\n",
+ " label=r\"$\\pm$ 1 std. dev.\",\n",
+ " )\n",
+ "\n",
+ " ax.set(\n",
+ " xlim=[-0.05, 1.05],\n",
+ " ylim=[-0.05, 1.05],\n",
+ " xlabel=\"False Positive Rate\",\n",
+ " ylabel=\"True Positive Rate\",\n",
+ " title=label,\n",
+ " )\n",
+ " ax.axis(\"square\")\n",
+ " ax.legend(loc=\"lower right\")\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 152,
+ "id": "a7a0f6a7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 600x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = model_xgb\n",
+ "label=\"ROC curve of training data\"\n",
+ "roc(X_train,y_train,model,label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "id": "816f08dd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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WRwICAtC2bVvMnDkTDx48QMOGDfHbb7+V6N9ga2uLDh06YMmSJSgsLETVqlXx77//4v79+yrvparr7Nq1K8RiMQICAjBmzBhkZWVh/fr1cHFxQXJystb3gJgBQw07IeRdUjf5FWNFExLVrl2b1a5dW2niKplMxjZt2sTatm3LbG1tmaWlJfPy8mILFixgWVlZap8rOjqade3alTk4ODChUMiqVKnCgoKC2NGjR98ap1QqZUuXLmX169dnYrGYOTs7s+7du7MLFy5w++Tk5LCRI0cyOzs7VqlSJda/f3/27NkztUNKnz9/rvb51q9fzwCwSpUqsdzcXJX7XLp0ifXp04c5OjoyCwsLVrNmTda/f3926NCht17P06dP2YgRI5iTkxMTi8WsUaNGKofDajKk9Pbt26xDhw7MysqqxFDMp0+fsvHjx7Pq1aszkUjE3NzcWOfOndmPP/7I7fPDDz+wDh06cNdTu3ZtNn36dJaRkaH0PF9++SWrWrUq4/P5ZZr86s3fLcXEXkeOHOG2SaVS9sUXXzA3NzdmZWXFPvzwQ3br1i3m6OjIxo4d+9Zrf/HiBRs6dCg3+dXQoUNVTn716NEj1rt3b2Zvb8/s7OxYv3792JMnT0r8jpR2nX/++Sdr3Lgxs7S0ZB4eHmzx4sVs48aNpU5ORgiPMQP0IiKEEAKgqOmhcuXK+Oqrr1Q2txFiSqhPBSGEvCO5ubkltin6OnTq1OndBkOIHlCfCkIIeUd27NiBqKgo9OjRAxKJBDExMdi2bRu6du2Ktm3bGjo8QsqNkgpCCHlHGjduDKFQiCVLliAzM5PrvPnVV18ZOjRCdIL6VBBCCCFEJ6hPBSGEEEJ0gpIKQgghhOiE2fWpkMvlePLkCSpVqqSXqXgJIYSQioQxhlevXsHd3b3EAoFvMruk4smTJ6hevbqhwyCEEEJMSlJSEqpVq1bqPmaXVCim4U1KSuKWeCaEEEKIapmZmahevXqZptY3u6RC0eRha2tLSQUhhBBSRmXpMkAdNQkhhBCiE5RUEEIIIUQnKKkghBBCiE5QUkEIIYQQnaCkghBCCCE6QUkFIYQQQnSCkgpCCCGE6AQlFYQQQgjRCUoqCCGEEKITlFQQQgghRCcoqSCEEEKITlBSQQghhBCdoKSCEEIIITpBSQUhhBBCdMKgScXx48cREBAAd3d38Hg8/PHHH2895ujRo/jggw9gYWGBOnXqICoqSu9xEkIIIeTtDJpUZGdnw9vbG2vWrCnT/vfv38dHH30EX19fXL58GZMnT8aoUaPwzz//6DlSQgghhLyN0JBP3r17d3Tv3r3M+0dERKBWrVpYvnw5AKBBgwaIiYlBeHg4/P399RUmIYSQdyA5Gdi0CcjIMHQkpq9RI2DIkHf/vAZNKjR1+vRp+Pn5KW3z9/fH5MmT1R6Tn5+P/Px87vvMzEy9xJbxKg1SWaFezm2OClJTkfPqFSCXGzoUQoie5RbKIWcMS7+rhQuX7cAYM3RIJkkmk0Eg4APgIS9XhCFDrN55DCaVVKSkpMDV1VVpm6urKzIzM5Gbmwsrq5I38JtvvsGCBQv0GlfGqzQcOfm7Xp/DnLCsbOSfewiptJBeXAip4BgD8mQMBYUinDw2ETJ5GnjgGToskyOVyfD0ZTqsLMSoXKkSMtN4AKq/8zhMKqnQxqxZszB16lTu+8zMTFSvrtsbrahQeL/fFraSyjo9tznKefwID27lQVi/GqzdXAwdDiFEj/IK5Lj3IhfZD9xgYesAuVwO3/ZpCB7yytChmYynz55h3NTpkMqTUcm+Cn5Y8z0at/jAILGYVFLh5uaGp0+fKm17+vQpbG1tVVYpAMDCwgIWFhbvIjzYSirD0d717TuSUslS0iCGAM4edWBZtZqhwyGE6FFWvgwiiywknHSH2NIKMpkMvfo7o9WHtQ0dmklISkrC4E8D8SQ5AZ6enjh6dI/OPzhrwqTmqWjdujUOHTqktO3AgQNo3bq1gSIiuiaVSpGRngEenweBQGDocAgh74BcDlw4X/Thz9KSoVkzavYsi6SkJHTq1AkJCYqE4qhBEwrAwElFVlYWLl++jMuXLwMoGjJ6+fJlJCYmAihquhg2bBi3/9ixY5GQkIAZM2bg9u3bWLt2LX799VdMmTLFEOETPUhPT0d+fh4lFISYkcQEC2RmFr0deXvnwsqK/v7fxhgTCsDAScX58+fRtGlTNG3aFAAwdepUNG3aFPPmzQMAJCcncwkGANSqVQt///03Dhw4AG9vbyxfvhwbNmyg4aQVRF5eHtLT02FhYQlQRy1CzMaty9bc/5s3zwGfb1JFdIM4f/48Hjx4YFQJBWDgPhWdOnUqtXe/qtkyO3XqhEuXLukxKt2QyWQoLKQhpmXFGMPLly8hlUphJRYZOhxCyDt060pRUsHjMTRvnk+VyjLo3bs3oqOj0bx5c6NJKAAT66hpSjIzM/Hs2TNDh2FS5HI5JBIJ5DnZhg6FEPKOJD8R4HmyCFYioF69QlSuDKpUqJGUlASBQAB3d3cARYmFsaGkQk8YY5DL5ahUqZKhQzEZPB4PPB4PNN0VIebj/LnXo/NatswFn8+npEIFRR8KgUCAo0ePcomFsaGkQo94PB79cRBCSCnOnX2dVDRvTkmFKm92ypTJZIYOSS36yekJzQRJCCGle/WKh5s3ivpQVXGXwt29AEKhEDweddRWMNZRHupQUqEnlFQQQkjpzp8XQy4vSiBatMgHY3IIhVRAVzC1hAKg5g9CzIJMBmzbZo2HD6lXPTEe9++/fgtq3jIfcjklFQqmmFAAlFQQYhYOHLDE1q3Wb9+REAOwspGjfoNCQAZKKv4jEAggEAhMKqEAKKnQGzkt2U2MyMmTYkOHQIhKPB7Q3j8DQiEgkzLqpPkfd3d3HD16FDKZzGQSCoCSCr2izkbEGOTm8nD5clFS4eQkx4oV6aBfTWIspJDiXuYrABLweOa95k9iYiLOnz+PPn36AIDRDhstDSUVhFRwFy6IIJUW/b9VqwI4O1MVjRiPrHwGZBZVd815GH5iYiJ8fX3x4MED7Ny5k0ssTI15/vQIMSOxsa/nAfDxyTdgJISoxxgz2zkqFAlFQkICPDw80KJFC0OHpDXz++m9IzSklBgDuRw4e7ao6cPKiqFxY1qPhhgnuVwOPp9vds0fxRMKU+uUqQolFXpEfSqIod26JURmZtHv4QcfFEJM/TWJkTLH5o+KllAAlFToDVUqiDGgpg9iKuRMDoFAYDZJxYsXLypcQgFQUkFIhXbmTFFpgs8HWrQoMHA0hKjHzGziKwcHB3z88ccVKqEAKKnQG6pUEEN7/JiPpKSi9un69Qthb0+/k8R4yeVyiEQiQ4fxzvB4PKxcuRJnz56tMAkFQEkFIRXW2WKrP7ZqRVUKYtwYYxW+UpGUlITx48cjP7+oKZLH48HR0dHAUelWxf4JGhBVKoihnT79ulemjw8lFcT4VeSRH8XX8gCANWvWGDgi/aBKhZ4wxmj0BzGYV694uPHfktLu7jJUry4zcESElI4xVNhOmm8uDjZz5kxDh6Q3VKkgRunoDU/cvV0VAhsbQ4dikjIy+FAsP9OyZQFNy02MHo9XMZMKU11tVFuUVBCjk/hIjMgjLSC0lYBnRh239KV1a2r6IMZNzuQVct0Pc0soAGr+0Ctq/tDOs1RKJHSlUaNCeHnRLJrEuFXEKbrlcjkCAgLMKqEAqFKhN9RRU3sFBa9fWIKCcvDxx3kGjMZ08flA5cpyavogRo/JGXgVbIpuPp+P1atXIyQkBH/99ZdZJBQAJRV6Q0mF9gqkr98FK1eWw8mJVtUkpCKTMzn4vIpRqSjeSb9du3a4ePFihbiusjKfKyUmo7DwdVJBXSoIqfiYvGiOClNvMk5KSkKrVq1w5coVbps5JRQAJRXECBVPKsRiqvgQUtHJmelP0a3olHn27FmMHj3abKvVlFToiVxOJXttUaWCEPNSNJum6faneHOUR3R0tMlXXbRFSYUemesvVXnlF77+taRKBSEVH2MMAhOtVJjjsNHSUFJBjI5UWrxSQUkFIRUdj8cDn2d6b0eUUJRkej9FUuEVFLxOKiwsStmREFJhmOJw0i+++IISijeYZr3JBJhrJx1dKN6nQiik+0hIRaZ4rTTFURKKRcG+/PJLSij+Q0mFHlGfCu0UUJ8KQsyGXF40RbepJBWZmZmwtbUFANjY2CAqKsqwARkZ0/gpErNSKC0+pNSAgRBC9E4uL5r4SiAw/rejpKQkNG3aFAsXLjR0KEbL+H+KJoiaPsqneJ8K6qhJSMXGGAOPb/yViuKdMn/66SdkZmYaOiSjZNw/RRNGiYX2lCe/MmAghBC9U0zRzTPipELVKA9FEwhRZrw/RWK2Cov1qaBKBSEVm1wuB5/PN9o+aDRsVDOUVOgBVSnKp0BK03QTYi6YXA6Bkc6mSQmF5mj0h54UX6mOaIam6dYfmUxGSS8xKjKZHAKBcbZzHjlyhBIKDVFSQYyOoqOmQMBgxM2sJkcqlSI7O9vkF24iFYtQJISgwDgrFcOGDQMA+Pr6UkJRRvTqQoyOok+FmPpT6JRMJoOFhQWqVq1KVTRiNF7lFeL5o1eGDoOTlJQEGxsbODg4AHidWJCyoaRCDxTlZXrh1o6iTwX1p9AtuVwOgUAAC5r7nBgRkZwHHozjtTIxMRG+vr6ws7PDwYMHucSClB0Vl/WE2q21p+hTIaIpunVKLpdDRJ1UCFFJkVAkJCQgIyMD2dnZhg7JJFFSQYyOok+FSCQ3cCQVC2OM+lMQokLxhII6ZZYPJRXE6Cj6VNAcFboll8spqSDkDZRQ6BYlFXpATR/lo1j7w4L6VOgUY8wkl5cmRF8oodA9SiqIUZHJAPl/rR7Up0K3eDweJRWEFFNQUID8/HxKKHSIaqF6RKM/NKe8mBj1qdAlxpjRL9pEyLtUp04dHD16FBYWFpRQ6Ai9whCjUlDw+v/Up0J3FOsrUFJBzF1SUhIOHDjAfV+nTh1KKHSIXmH0gPpUaE9phVJKKnRGkVRQ8wcxZ4q1PD7++GOlxILoDiUVekKJhXaKVyooqdAdqlQQc1d8cbBq1aqhfv36hg6pQqJXGD2iPhWaK16pEFJHTZ2hSgUxZ7Ta6LtDSQUxKsU7aorF1FFTVxSdNCnRJeaGEop3i5IKPaCmD+1RR039oCm6iTl69uwZJRTvGA0p1RNKLLRTvPnDgpIKnaHZNIk5cnR0RJs2bQCAEop3hF5liFEpnlTQPBW6wxijSgUxOwKBAFFRUXjx4gVcXFwMHY5ZoOYPPaAqhfby81//n5o/dIcmviLmIikpCZ9//jlkMhmAosSCEop3hyoVesIYo05xWlCuVFBSoSs8Ho+SClLhFe+UCQCLFy82cETmh15liFEpLHz9f5qnQjcUlTMaTkoqsjdHeYSEhBg6JLNESQUxKkpDSqlPhU4oqmZUqSAVFQ0bNR70KqMHik+G1PyhOZr8SvfkcjkEAgFVKkiFRAmFcaGkQk+os6Z2lKbpFtM91AW5XE6VClIhSaVSdOvWjRIKI0KvMsSoUEdN3VNUKiipIBWNUCjEsmXL0LBhQ0oojASN/iBGpXifCgvqU6ETitk0qTmOVETdu3dHly5daHI3I0EfXfSAmj60V7z5g/pU6AbNpkkqEkUfinv37nHb6PfbeFBSQYyK8oJilFToAq37QSoKRUJx7NgxjBo1ytDhEBUoqdAjKjdrTip9/X/qU6E7NPKDmLo3R3ls2bLF0CERFSipIEYlP7/4PBWUVOgKJRXElNGwUdNBSYUeUJ8K7SmN/hBSR01doHU/iCmjhMK00CuNnlBioZ3i03RT80f5KRIKSiqIqZoyZQolFCaEuszqEfWp0Bx11NQtxcRX1PxBTNWPP/4IAAgPD6eEwgRQUkGMSlHzR1EyIaIhpeVGE18RU5STkwNra2sAgIODA6Kjow0cESkreqXRA2r60J5ingoBn4HeB8uPKhXE1CQmJqJx48ZYs2aNoUMhWqCXbT2hxEI7iuYPkUBm4EgqBrlcTn0qiMlITEyEr68v4uPjER4ejpycHEOHRDRk8FeaNWvWwMPDA5aWlvDx8cHZs2dL3X/lypWoV68erKysUL16dUyZMgV5eXnvKNqyo/4U2uGSCiElFbrAGKOJr4hJSPovoVB0yjxy5AjXBEJMh0GTih07dmDq1KkICwvDxYsX4e3tDX9/fzx79kzl/r/88gtmzpyJsLAw3Lp1C5GRkdixYwdmz579jiMvHVUptKeY/IoqFbpBU3QTU/DsySN83K0rjfKoAAyaVKxYsQKffvopRowYgYYNGyIiIgLW1tbYuHGjyv1PnTqFtm3bYtCgQfDw8EDXrl0xcODAt1Y3DIESC+0oJr8SCWiOCl2gpIIYu6TEREz7XyAe3KeEoiIw2KtNQUEBLly4gFmzZnHb+Hw+/Pz8cPr0aZXHtGnTBj///DPOnj2Lli1bIiEhAXv37sXQoUPVPk9+fj7y8/O57zMzM3V3EcXJCoC8V0CeJZCfDX5BFngFBm9dMjmFBfYAk1Pzh44wxiAQCJBTIIVUTokuMT5//LEbyUkP4VGLEoqKwGBJRWpqKmQyGVxdXZW2u7q64vbt2yqPGTRoEFJTU9GuXTswxiCVSjF27NhSmz+++eYbLFiwQKexl1CQA6QnAo/PAxl2EOTlwib1BcQ51B6oKWm2I3gyQGQlA3iUlJUXj8dDnpTh/L0Xhg6FEJV8Ph6M8Rm5mPi/QZRQVAAmVRc9evQoFi1ahLVr18LHxwf37t3DpEmT8OWXX+KLL75QecysWbMwdepU7vvMzEzd/+LK//tU7doQcKoJaVYWcnjJENhW0u3zVHAyGSATSsAgg9DSEkxInV114r/k7P2qdrC2oKGlxPCePH4MWzs7SCQSAECbsM9hLTaptyOihsF+ik5OThAIBHj69KnS9qdPn8LNzU3lMV988QWGDh3KLXnbqFEjZGdnY/To0ZgzZ47KYXMWFhawsLDQ/QWoIrQGLO2AQj5kokwwMSUVmsjP5RW9AfIYRPT6Um6KOSoUfxfWFgLYWtJIEGJYSUlJ+KhrZ1StWhV79+7lEgtSMRisviwWi9GsWTMcOnSI2yaXy3Ho0CG0bt1a5TE5OTklEgfFpD7UMdL0KSa+AgAhLSZWboo5KgQCakYixqH44mCPHz9GRkaGoUMiOmbQz4NTp07F8OHD0bx5c7Rs2RIrV65EdnY2RowYAQAYNmwYqlatim+++QYAEBAQgBUrVqBp06Zc88cXX3yBgIAAo5sxkOap0JzSCqU0+qPcaOIrYkxUrTZatWpVQ4dFdMygSUVQUBCeP3+OefPmISUlBU2aNMH+/fu5zpuJiYlKL4hz584Fj8fD3Llz8fjxYzg7OyMgIABff/21oS5BJaqaaKd4pUIkoqSivBQrlPIoqSAGRsuXmw+Dt1yHhIQgJCRE5WNHjx5V+l4oFCIsLAxhYWHvILLyocRCc8UrFUK+HK+ysiGkRcW0xhiDRCKhSgUxKEoozIvBk4qKipo/NFd82XORsKiToZubm9E1bZkSkUiEQkMHQczay5cvkZ6eTgmFmaCkghgNpeYPoRw88GBlZQVLS0vDBVUBFOZRWkEMp3Hjxjhy5AgqV65MCYUZoKRCD6jpQztKHTX/G/1BFR9CTE9SUhIePXrEjeRr3LixgSMi7wo1tuoJJRaaU+pTQaM/CDFJij4UXbp0UbvkAqm4KKnQE/qErbliS7QUDSnl0X0kxJQU75Tp6uqKatWqGTok8o5RUkGMBlUqCDFdNMqDAJRU6AU1fWinsFh/QsXoD6pUEGL8KKEgCpRU6AklFppTGlJKlQpCTEJycjIlFIRDoz/0hD5ha06p+YMqFYSYBEdHR3h5eQEAJRSEkgp9oCqFdorPUyEWygwXCCGkzMRiMaKjo/HixQtUqVLF0OEQA6PmDz2hxEJz1FGTENOQlJSEr7/+mnudE4vFlFAQAFSpIEZEVZ8Kav4gxLgU75QJAHPmzDFwRMSYUKVCT+jNUHMlpumme0iIUXlzlMewYcMMHRIxMpRU6AE1fWineKVCSJUKQowKDRslZUFJhZ5QYqE5pbU/BFSpIMRYUEJByoqSCmI0ijd/CP8b/UGJBSGGVVBQAD8/P0ooSJlQUqEHjDF6M9QCjf4gxPiIxWLMnz8fdevWpYSCvBUlFXpATR/aUZqmW0CTXxFiLAYOHIhr165RQkHeipIKYjTy84v1qRBSpYIQQ0lKSkKPHj3w5MkTbptYLDZgRMRUUFJBjIZUqtz8QVUKQt49RafMffv2YdSoUYYOh5gYSir0gJo/tKPoqCkQAHz6zSTknXtzlMcPP/xg6JCIiaGXbj2gjpraUcxTIRIxgAF8yiwIeWdo2CjRBXrVJkZDkVSIxVTpIeRdooSC6AolFcRoSKVF/4qERUkFn6o9hLwTY8aMoYSC6AQlFXpAfSq0oxj9IRYzMNA9JORd2bBhA7p3704JBSk3WqVUT6hPheYU81SIREUJBd1DQvQnPz8fFhYWAAB3d3fs3bvXwBGRioAqFXpAlQrtKGbUFIsYQJ1dCdGbpKQkNGrUCFu3bjV0KKSCoaSCGAWZDJD/N9+VolJBCNE9RafMu3fvYsGCBcjPzzd0SKQCoaRCD6hSobniy56LFc0fNKSUEJ16c5THoUOHuCYQQnSBXrWJUSi+QqlIRN00CdE1GjZK3gVKKvSAKhWaK75CKXXUJES3KKEg7wolFXpCb4iaebNSQQjRnc2bN1NCQd4JGlKqB1Sp0FzxSoVYMU03j3JeQnRh9uzZAIBhw4ZRQkH0ipIKYhSUOmrSNN2ElFtycjIcHBxgYWEBHo+HOXPmGDokYgbooyAxCqqaP3h8akIiRBuJiYlo164d+vbtS0NGyTtFlQo9oFVKNVe8UlGUVFC1ghBtJCYmwtfXFwkJCQCAtLQ0VKlSxcBREXNBlQo9oD4VmpNKiyUVQgaAR4kZIRoqnlAoOmVSQkHeJUoqiFEoXqEt6lNBiRkhmlCVUFCnTPKuUVJBjEKJ0R9UqSCkzCihIMaCkgo9oOYPzSlWKAWoTwUhmnr8+DGePXtGCQUxOOqoqQfUUVNzJTpqSun+EVJWrVu3xr///otq1apRQkEMipIKYhRKTNMtpcSMkNIkJSXh5cuXaNy4MYCixIIQQ6PmD2IUis9TUdRRkxIKQtRRrOXh6+uLq1evGjocQjiUVOgB9anQnFLzx3/1M6pUEFJS8cXB7O3tUblyZUOHRAiHkgo9oTdEzSiN/hDLDRgJIcaLVhslxo6SCmIUlKbppkoFISVQQkFMASUVOkZNH9pRXlCMKhWEFPf48WNKKIhJoNEfekCJheaURn9QpYIQJfb29qhWrRoAUEJBjBolFcQovDn6o4AmvyKEY2Njg7179yI9PR1Vq1Y1dDiEqEXNH8QolJinAjzwaFgpMWNJSUlYtWoV972NjQ0lFMToUaVCxxRNH1S61wxN003Ia8U7ZQLAhAkTDBwRIWVDlQo9oD4VmsvPV7WgmOHiIcRQ3hzl0atXL0OHREiZUVJBjIJU+mbzByHmh4aNElNHSYWOUZVCO4qOmgIBwOMxmqWbmB1KKEhFQEmFHtAqpZpTzFNRtO5HEbqHxFzk5OTA19eXEgpi8iipIEbhdVIBULGHmBtra2tMmTIFtWvXpoSCmDRKKohRkEqL/i3en4IqFcScjB8/HlevXqWEgpg0Sip0jPpUaEcx+kPR/EHpBKnokpKSEBgYiBcvXnDbrK2tDRgRIeVXrnkq8vLyYGlpqatYiBlTzFMhEgEMNNcHqdjenIdi165dBo6IEN3QuFIhl8vx5ZdfomrVqpBIJNwfxRdffIHIyEidB2iq6A2x7Bh7PaNm8Y6ahFREb47yWLlypaFDIkRnNE4qvvrqK0RFRWHJkiUQi8Xc9vfffx8bNmzQaXDEPMhkgPy/hUlFIlbUhMTjgWa/IhUNDRslFZ3GScXmzZvx448/YvDgwRAIBNx2b29v3L59W6fBmSLqU6G54ut+FMtTCalQKKEg5kDjpOLx48eoU6dOie1yuRyFxRdwMGOUWGim+AqlitEfPPCoCYlUKEOHDqWEglR4GicVDRs2xIkTJ0psj46ORtOmTXUSVEVAb4hlR5UKYg42bNiAjh07UkJBKjSNR3/MmzcPw4cPx+PHjyGXy/Hbb78hLi4Omzdvxp49e/QRI6ng8vNf//91nwrDxUOIrkilUgiFRS+zderUwdGjRw0bECF6pnGlomfPnvjrr79w8OBB2NjYYN68ebh16xb++usvdOnSRR8xmhRq+tBc8cXEaJpuUlEkJiaicePG+Pvvvw0dCiHvjFbzVLRv3x4HDhzQdSwVBiUWmlFM0Q0opumm+0dMW2JiIreWx/Tp0+Hv789VLAipyDSuVHh6eirNAKeQnp4OT09PnQRFzIuqjpoAVSqIaSqeUHh6euKff/6hhIKYDY2TigcPHkAmk5XYnp+fj8ePH+skKGJelCsVVKUgpuvNhII6ZRJzU+b0+c8//+T+/88//8DOzo77XiaT4dChQ/Dw8NBpcKZIUbqnT9llV7xPhUhU7B5Sb01iQiihIESDpKJXr14Ait4shw8frvSYSCSCh4cHli9frtPgTBX1CdBM8dEfVKkgpmrt2rWUUBCzV+akQv7fPMq1atXCuXPn4OTkpLegiHlRnqeiaEgpDzwaVkpMytdffw2gaAlzSiiIudK499D9+/f1EUeFQVUKzRWfiFUkMlwchGjq2bNncHR0hEAggEAgwLfffmvokAgxKI07agJAdnY29u7di4iICHz//fdKX5pas2YNPDw8YGlpCR8fH5w9e7bU/dPT0zF+/HhUqVIFFhYWqFu3Lvbu3avNZegNY4z6VGigeEdNbppuHvVLIcYtKSkJrVu3RnBwsMrO64SYI40rFZcuXUKPHj2Qk5OD7OxsODg4IDU1FdbW1nBxccHEiRPLfK4dO3Zg6tSpiIiIgI+PD1auXAl/f3/ExcXBxcWlxP4FBQXo0qULXFxcEB0djapVq+Lhw4ewt7fX9DKIEaFpuompKb44GAC8ePFC5WsWIeZG46RiypQpCAgIQEREBOzs7HDmzBmIRCIMGTIEkyZN0uhcK1aswKeffooRI0YAACIiIvD3339j48aNmDlzZon9N27ciLS0NJw6dQqi/+rkxjji5P59EQ4erITcXK0KQWYnKen1r6GiTwUte06MlarVRimhIKSIxknF5cuX8cMPP4DP50MgECA/Px+enp5YsmQJhg8fjj59+pTpPAUFBbhw4QJmzZrFbePz+fDz88Pp06dVHvPnn3+idevWGD9+PHbv3g1nZ2cMGjQIn3/+udIy7MXl5+cjv9jwgszMTA2utuxkcjleFUghyy1ExI+Vce+uJainoebkfBlyCuUokMqRlS+FNI9Wvi2vnHwqzesKLV9OSOk0TipEIhH4/KJP4C4uLkhMTESDBg1gZ2eHpKSkMp8nNTUVMpkMrq6uSttdXV1x+/ZtlcckJCTg8OHDGDx4MPbu3Yt79+5h3LhxKCwsRFhYmMpjvvnmGyxYsKDMcWkjVyrD86wCXHuUAYG1De4/tkVOoZxSCg05VylEgd1L3H6aC4tsKfIT08F/JTd0WBWGkE+/keVBCQUhb6dxUtG0aVOcO3cO7733Hjp27Ih58+YhNTUVW7Zswfvvv6+PGDlyuRwuLi748ccfIRAI0KxZMzx+/BhLly5Vm1TMmjULU6dO5b7PzMzU+QuBVF7UufA9FwkcHO1hI+RBJuLDwVGOxUvTdPpcFVllBzn4fBtkPslGbpII9WrYw9LZ3tBhVQhCPg/WYpoqujzi4uLw6NEjSigIKYXGrzKLFi3Cq1evABSNyx42bBg+++wzvPfee4iMjCzzeZycnCAQCPD06VOl7U+fPoWbm5vKY6pUqQKRSKTU1NGgQQOkpKSgoKAAYhW9/CwsLGBhYVHmuMrDUixAJUshwOQQ8HmwsgBqVqVPh2VX9HOViviQCQWQWAhhbUljTIlx8PPzw99//4169epRQkGIGhonFc2bN+f+7+Ligv3792v1xGKxGM2aNcOhQ4e42TrlcjkOHTqEkJAQlce0bdsWv/zyC+RyOdcEc+fOHVSpUkVlQmEIjDHIZEWJBK0hpB2a64MYi6SkJOTn56NOnToAihILQoh6OhuecPHiRXz88ccaHTN16lSsX78eP/30E27duoXPPvsM2dnZ3GiQYcOGKXXk/Oyzz5CWloZJkybhzp07+Pvvv7Fo0SKMHz9eV5ehE4oh63wa/EGIyVL0oejUqRPu3btn6HAIMQkafZb+559/cODAAYjFYowaNQqenp64ffs2Zs6cib/++gv+/v4aPXlQUBCeP3+OefPmISUlBU2aNMH+/fu5zpuJiYlcRQIAqlevjn/++QdTpkxB48aNUbVqVUyaNAmff/65Rs+rb4qkQiikT9xaYQCPOhUSA3qzU+a7akIlxNSVOamIjIzEp59+CgcHB7x8+RIbNmzAihUrMGHCBAQFBeH69eto0KCBxgGEhISobe44evRoiW2tW7fGmTNnNH6ed0km44HHA9SMciWEGDEa5UGI9spcoP/uu++wePFipKam4tdff0VqairWrl2La9euISIiQquEoiKSyxn+W3uNkgpCTAwlFISUT5mTivj4ePTr1w8A0KdPHwiFQixduhTVqlXTW3CmqPgSAAIBNX9oi0ezfJB37NGjR5RQEFJOZU4qcnNzYW1tDaBooScLCwtUqVJFb4GZKuWkwnBxmDIGRrN0k3fO2toadnZ2lFAQUg4addTcsGEDJBIJAEAqlSIqKgpOTk5K+2iyoFhFREkFIabJwcEBBw8eRE5ODlVgCdFSmZOKGjVqYP369dz3bm5u2LJli9I+PB7P7JOKwsLXTR7U/KEdWlCMvCtJSUk4dOgQgoODARQlFg4ODoYNihATVuak4sGDB3oMo+KQSl//nyoVhBivN5cvVyQWhBDt0fRMOla8+YNm1NQenyoVRI/eHOXRuXNnQ4dESIVASYWO0egPHaBpuoke0bBRQvSHkgodK978QdN0a4nHA2hIKdEDSigI0S9629Mxav7QAapUED3IzMykhIIQPaOkQseKJxV8Pr05aoXHo0IF0TlbW1sMHz6cEgpC9EirpCI+Ph5z587FwIED8ezZMwDAvn37cOPGDZ0GZ4po9IcOUKWC6Mm8efNw6dIlSigI0RONk4pjx46hUaNGiI2NxW+//YasrCwAwJUrVxAWFqbzAE1N8aSCmj+0RCM/iI4kJiZi2LBh3OsUUFSxIIToh8ZJxcyZM/HVV19xS6ArfPjhh0a/eui7QKM/dINHiQUpp8TERPj6+mLLli0YN26cocMhxCxonFRcu3YNvXv3LrHdxcUFqampOgnKlFHzByGGp0goFJ0yv/76a0OHRIhZ0DipsLe3R3Jycontly5dQtWqVXUSlClTHv1BlQpC3rU3EwrqlEnIu6NxUjFgwAB8/vnnSElJAY/Hg1wux8mTJzFt2jQMGzZMHzGaFOXRH4aLgxBzRAkFIYal8dveokWLUL9+fVSvXh1ZWVlo2LAhOnTogDZt2mDu3Ln6iNGkUPOHbvBoTCnREGMM/fr1o4SCEAPSeHyCWCzG+vXr8cUXX+D69evIyspC06ZN8d577+kjPpNDHTV1gIaUEi3weDz8+OOPGD16NKKjoymhIMQANE4qYmJi0K5dO9SoUQM1atTQR0wmjSoVOsDj0ahSUmZyuRz8/9oavb29cebMGRo9RIiBaNz88eGHH6JWrVqYPXs2bt68qY+YTBpN060DVKkgZZSUlIQPPvgAMTEx3DZKKAgxHI2TiidPniA0NBTHjh3D+++/jyZNmmDp0qV49OiRPuIzOcoLitGbo1Z4PJoAi7yVYnGwK1euICQkBHK53NAhEWL2NE4qnJycEBISgpMnTyI+Ph79+vXDTz/9BA8PD3z44Yf6iNGkUKVCB6hSQd7izdVG//rrL64JhBBiOOX6K6xVqxZmzpyJb7/9Fo0aNcKxY8d0FZfJUu6oabg4TBpVKUgpaPlyQoyX1knFyZMnMW7cOFSpUgWDBg3C+++/j7///luXsZkkav4gRH8ooSDEuGlcoJ81axa2b9+OJ0+eoEuXLvjuu+/Qs2dPWFtb6yM+k0PNH+XDGKOOdkStxYsXU0JBiBHT+G3v+PHjmD59Ovr37w8nJyd9xGTSqPlDNyitIKqsWLECAPD5559TQkGIEdI4qTh58qQ+4qgwCgtf/58mv9Ico06a5A0vXryAg4MDeDwexGIxVq9ebeiQCCFqlCmp+PPPP9G9e3eIRCL8+eefpe77ySef6CQwU0XNH7pBTSAEeN2H4uOPP8bKlSvp94IQI1emt71evXohJSUFLi4u6NWrl9r9eDweZMXfVc2QXP76RY+aPwjRXvFOmXv27MG8efPg6Oho6LAIIaUoU1JRfFIZmmCmdIWFr8v3NPpDc4wx8MADo14VZk3VKA9KKAgxfhoPKd28eTPy8/NLbC8oKMDmzZt1EpQpo46ahJQPDRslxHRpnFSMGDECGRkZJba/evUKI0aM0ElQpky5TwVVKjTFGCsa+kGFCrNECQUhpk3jpELdPAKPHj2CnZ2dToIyZcWTCpo1mBDNnD9/Hg8ePKCEghATVebxCU2bNgWPxwOPx0Pnzp0hLDa0QSaT4f79++jWrZtegjQlxYeU0ugP7VAPf/PVu3dvREdHo3nz5pRQEGKCyvy2pxj1cfnyZfj7+0MikXCPicVieHh4IDAwUOcBmpqiSkXRmyLNU6E9SivMR1JSEvh8PqpWrQqgKLEghJimMicVYWFhAAAPDw8EBQXB0tJSb0GZMuqoWT40Tbd5UfSh4PP5OHr0KJdYEEJMk8at/sOHD6eEohTFFxSjpKIcKLGo8Ip3ypTL5TRcnZAKoEyVCgcHB9y5cwdOTk6oXLlyqZ8k09LSdBacKSr+ukjNH5qjabrNA43yIKRiKlNSER4ejkqVKnH/p/K0ejIZzaipCzzqVVFhUUJBSMVVpqRi+PDh3P+Dg4P1FUuFIJW+/qRNSQUhyiihIKRi07hPxcWLF3Ht2jXu+927d6NXr16YPXs2CgoKdBqcKVLuU0GlfE1xHTWpUFEhCQQCCAQCSigIqaA0TirGjBmDO3fuAAASEhIQFBQEa2tr7Ny5EzNmzNB5gKamePMHzVNBiDJ3d3ccPXqUEgpCKiiNk4o7d+6gSZMmAICdO3eiY8eO+OWXXxAVFYVdu3bpOj6TQ6M/yud1pYJKFRVFYmIifvvtN+57d3d3SigIqaC0mqZbMfTr4MGD6NGjBwCgevXqSE1N1W10Jkgme/1+SKuUEnOXmJgIX19f9OvXTymxIIRUTBonFc2bN8dXX32FLVu24NixY/joo48AAPfv34erq6vOAzQ1yguKGS4OU0ajiyoGRUKRkJAADw8PtGjRwtAhEUL0TOOkYuXKlbh48SJCQkIwZ84c1KlTBwAQHR2NNm3a6DxAU0MLiukGpRWmrXhCQZ0yCTEfGn+Wbty4sdLoD4WlS5dCQJ0IuI6afD4lFdqgabpNHyUUhJgvrQv0Fy5cwK1btwAADRs2xAcffKCzoEyZoqMm5VfEHL148YISCkLMmMZJxbNnzxAUFIRjx47B3t4eAJCeng5fX19s374dzs7Ouo7RpCiaP2iOCu0wxsCnEo/JcnBwQEBAAP766y9KKAgxQxq/ek+YMAFZWVm4ceMG0tLSkJaWhuvXryMzMxMTJ07UR4wm5XVSYdg4CDEEHo+H8PBwnDt3jhIKQsyQxknF/v37sXbtWjRo0IDb1rBhQ6xZswb79u3TaXCmiJKK8qN1P0xLUlISxo8fj/z8fABFiYWDg4OBoyKEGILGzR9yuRwikajEdpFIREsX43VHTWr+0E5RR01DR0HKqvhaHgCwZs0aA0dECDEkjSsVH374ISZNmoQnT55w2x4/fowpU6agc+fOOg3OFFFHTWIu3lwcbObMmYYOiRBiYBonFatXr0ZmZiY8PDxQu3Zt1K5dG7Vq1UJmZiZWrVqljxhNiqJYQ0mFdmhIqWmg1UYJIapo3PxRvXp1XLx4EYcOHeKGlDZo0AB+fn46D84USaVFb4hCITV/aIuSCuNGCQUhRB2NkoodO3bgzz//REFBATp37owJEyboKy6TpWj+oFGR2qOkwnjJ5XJ88sknlFAQQlQq81vfunXrMHDgQJw/fx53797F+PHjMX36dH3GZpIUzR9UqSgHSiqMFp/Px+rVq9GkSRNKKAghJZQ5qVi9ejXCwsIQFxeHy5cv46effsLatWv1GZtJKhr9waM+FVqiPhXGibHXSXLbtm1x4cIFSigIISWUOalISEjA8OHDue8HDRoEqVSK5ORkvQRmiuSMcfNUUPMHqSiSkpLQqlUrXLlyhdtGs54SQlQp8ytDfn4+bGxsXh/I50MsFiM3N1cvgZkixoq+ABr9oS3GGPg8esMyFopOmWfPnsXo0aOVKhaEEPImjTpqfvHFF7C2tua+LygowNdffw07Oztu24oVK3QXnYlRdNIEqE8FMX1vjvKIjo6mpilCSKnKnFR06NABcXFxStvatGnDzaQHUK99ufz19VOlohzM+9dIp2QyGQoLCzU+Ljk5GcOGDYNMJkO7du2wefNmODs7Iy8vTw9REkIMTSwW66RZs8xJxdGjR8v9ZBVd8ddumqZbO4wx8PiUVZQXYwwpKSlIT0/X+FipVIqnT59izpw5EAqFcHV1RV5eHu7fv6/7QAkhRoHP56NWrVoQi8XlOo/Gk18R9eSy1/+nSoV2eDye2Ve8dEGRULi4uMDa2lqje/ro0SPY29tDLBbDw8Oj3C8yhBDjJpfL8eTJEyQnJ6NGjRrleg2mpEKHZNT8UW7UEbD8ZDIZl1A4OjpqfHytWrWQmJiIqlWrUkJBiJlwdnbGkydPIJVKVS4aWlbUzV6HZMU6alLzh3aoSlF+ij4UxTtVv420WC9jgUCgkzIoIcR0KP7eZTLZW/YsHSUVOqT4WfB4VKkoD0osdKOs97GgoAC3bt1SWnmYEGJedPW6S0mFDsmkPABFFQpKKogpKCgoQFxcHPLz8/HixQuligUhhGhKq6TixIkTGDJkCFq3bo3Hjx8DALZs2YKYmBidBmdqpMWqRjRPhfaoUvFuFE8oLCwsUK9ePQiF1M2KEKI9jZOKXbt2wd/fH1ZWVrh06RLy8/MBABkZGVi0aJHOAzQlRet+FKFKBTFmqhIK6kOh7Pbt22jVqhUsLS3RpEmTMh0THByMXr16lbpPp06dMHny5HLHp8rQoUPN/nVYl/bv348mTZpArlgpkryVxknFV199hYiICKxfv16ph2jbtm1x8eJFnQZnamSy19UJSiq0R5UK/TLmhCI4OJgbViwSiVCrVi3MmDFD5aRbe/bsQceOHVGpUiVYW1ujRYsWiIqKUnneXbt2oVOnTrCzs4NEIkHjxo2xcOFCpKWlqY0lLCwMNjY2iIuLw6FDh3R1iWVy9OhRfPDBB7CwsECdOnXUXldxV65cwd69ezFx4sQSj23btg0CgQDjx48v8VhUVBTs7e1VnpPH4+GPP/5Q2qbNvSyvtLQ0DB48GLa2trC3t8fIkSORlZWldv8HDx5wv0dvfu3cuVNp36ioKDRu3BiWlpZwcXFRukfdunWDSCTC1q1b9XZtFY3GSUVcXBw6dOhQYrudnZ1WE+1UJEV9KorQ6A/t0JBS/cvMzDTKhEKhW7duSE5ORkJCAsLDw/HDDz8gLCxMaZ9Vq1ahZ8+eaNu2LWJjY3H16lUMGDAAY8eOxbRp05T2nTNnDoKCgtCiRQvs27cP169fx/Lly3HlyhVs2bJFbRzx8fFo164datasqdXQXG3dv38fH330EXx9fXH58mVMnjwZo0aNwj///FPqcatWrUK/fv0gkUhKPBYZGYkZM2Zg27Zt5ZoVVdt7WV6DBw/GjRs3cODAAezZswfHjx/H6NGj1e5fvXp1JCcnK30tWLAAEokE3bt35/ZbsWIF5syZg5kzZ+LGjRs4ePAg/P39lc4VHByM77//Xm/XVtFonFS4ubnh3r17JbbHxMTA09NTqyDWrFkDDw8PWFpawsfHB2fPni3Tcdu3bwePx3trufFdKT5PBS3iqB2a/Er/nJyc4OHhYZQJBQBYWFjAzc0N1atXR69eveDn54cDBw5wjyclJSE0NBSTJ0/GokWL0LBhQ9SpUwehoaFYunQpli9fjtjYWADA2bNnsWjRIixfvhxLly5FmzZt4OHhgS5dumDXrl1KKy8Xx+PxcOHCBSxcuBA8Hg/z588HAFy7dg0ffvghrKys4OjoiNGjR5f6iTk7OxvDhg2DRCJBlSpVsHz58rdef0REBGrVqoXly5ejQYMGCAkJQd++fREeHq72GJlMhujoaAQEBJR47P79+zh16hRmzpyJunXr4rfffntrDKpoey/L69atW9i/fz82bNgAHx8ftGvXDqtWrcL27dvVjlgSCARwc3NT+vr999/Rv39/Lul6+fIl5s6di82bN2PQoEGoXbs2GjdujE8++UTpXAEBATh//jzi4+P1cn0VjcZvfZ9++ikmTZqE2NhY8Hg8PHnyBFu3bsW0adPw2WefaRzAjh07MHXqVISFheHixYvw9vaGv78/nj17VupxDx48wLRp09C+fXuNn1NfZEoLihkuDlNGlQr9KCgoQH5BITLzir7EEjvkyXnc9/r8ksm1/5lev34dp06dUkp+oqOjUVhYWKIiAQBjxoyBRCLBtm3bAABbt26FRCLBuHHjVJ5fXdk/OTkZXl5eCA0NRXJyMqZNm4bs7Gz4+/ujcuXKOHfuHHbu3ImDBw8iJCREbfzTp0/HsWPHsHv3bvz77784evToW5uJT58+DT8/P6Vt/v7+OH36tNpjrl69ioyMDDRv3rzEY5s2bcJHH30EOzs7DBkyBJGRkaU+vzra3ksA8PLygkQiUftVvHrwptOnT8Pe3l7p2vz8/MDn87nk8W0uXLiAy5cvY+TIkdy2AwcOQC6X4/Hjx2jQoAGqVauG/v37IykpSenYGjVqwNXVFSdOnCjTc5k7jd/6Zs6cCblcjs6dOyMnJwcdOnSAhYUFpk2bhgkTJmgcwIoVK/Dpp59ixIgRAIqy9L///hsbN27EzJkzVR4jk8kwePBgLFiwACdOnDCaZpfiHTX5fHpz1AZVKnQvPz8fd+7cQZ6MhzShAwT8d9vhp6WnA2wtyz5D3549eyCRSCCVSpGfnw8+n4/Vq1dzj9+5cwd2dnaoUqVKiWPFYjE8PT1x584dAMDdu3fh6emp8QyBbm5uEAqFkEgkcHNzAwCsX78eeXl52Lx5M2xsbAAAq1evRkBAABYvXgxXV1elc2RlZSEyMhI///wzOnfuDAD46aefUK1atVKfOyUlpcS5XF1dkZmZidzcXFhZWZU45uHDhxAIBHBxcVHaLpfLERUVhVWrVgEABgwYgNDQUNy/fx+1atXS4I5ofy8BYO/evaUubKfqmhRSUlJKXJdQKISDgwNSUlLK9PyRkZFo0KAB2rRpw21LSEiAXC7HokWL8N1338HOzg5z585Fly5dcPXqVaVE1t3dHQ8fPizTc5k7jZMKHo+HOXPmYPr06bh37x6ysrLQsGFDle14b1NQUIALFy5g1qxZ3DY+nw8/P79Ss/KFCxfCxcUFI0eOfGv2mJ+fz41QAYrak/WFKhXlR5UK3VIkFPn5+RCJxGheww6id9zkYSPW7I/B19cX69atQ3Z2NsLDwyEUChEYGKjVc+vy9+nWrVvw9vbmEgqgqIO6XC5HXFxciUQgPj4eBQUF8PHx4bY5ODigXr16OotJITc3FxYWFiUS8gMHDiA7Oxs9evQAUNT01aVLF2zcuBFffvmlRs9RnntZs2ZNrY8tr9zcXPzyyy/44osvlLbL5XIUFhbi+++/R9euXQEUdWh1c3PDkSNHlPpWWFlZIScn553Gbaq0fusTi8Vo2LBhuZ48NTUVMplMZVZ++/ZtlcfExMQgMjISly9fLtNzfPPNN1iwYEG54iyr4n0qaJ4K7VCVQncKCgrw8OFDo+6UqYqNjQ3q1KkDANi4cSO8vb0RGRnJla7r1q2LjIwMPHnyBO7u7krHFhQUID4+Hr6+vty+MTExKCwsLNd6Bu+Sm5sbnj59qrTt6dOnsLW1VfuJ3snJCTk5OSgoKFD6GUdGRiItLU3pOLlcjqtXr2LBggXg8/mwtbVFdnY25HK50tLXigqwnZ0dgPLdSy8vr1I/6bdv3x779u1T+Zibm1uJ5nCpVIq0tDSuilSa6Oho5OTkYNiwYUrbFZWu4u9jzs7OcHJyQmJiotK+aWlpcHZ2futzES36VPj6+uLDDz9U+6VPr169wtChQ7F+/Xo4OTmV6ZhZs2YhIyOD+3qzvUyXilcqqKOm9nigxKK8pFIpHjx4YHIJxZv4fD5mz56NuXPnIjc3FwAQGBgIkUikstNjREQEsrOzMXDgQADAoEGDkJWVhbVr16o8vyZNpw0aNMCVK1eQnZ3NbTt58iT4fL7K6kPt2rUhEomU2v1fvnzJNc2o07p16xJDWA8cOIDWrVurPUYxj8bNmze5bS9evMDu3buxfft2XL58mfu6dOkSXr58iX///RcAUK9ePUil0hIf1BR9P+rWrQugfPdy7969SjG8+bVhwwa1x7Zu3Rrp6em4cOECt+3w4cOQy+VKVSB1IiMj8cknn5RICtq2bQugaESjQlpaGlJTU5UqK3l5eYiPj0fTpk3f+lxEi0rFm5PAFBYW4vLly7h+/brGvX+dnJwgEAhUZuWqMtD4+Hg8ePBAqYezYlISoVCIuLg41K5dW+kYCwsLWFhYaBSXtmS09DkxAk+ePMHTp09hb29v0gmFQr9+/TB9+nSsWbMG06ZNQ40aNbBkyRKEhobC0tISQ4cOhUgkwu7duzF79myEhoZybzY+Pj6YMWMGQkND8fjxY/Tu3Rvu7u64d+8eIiIi0K5dO0yaNKlMcQwePBhhYWEYPnw45s+fj+fPn2PChAkYOnRoiWorAEgkEowcORLTp0+Ho6MjXFxcMGfOHKVqgCpjx47F6tWrMWPGDPzvf//D4cOH8euvv+Lvv/9We4yzszM++OADxMTEcK/RW7ZsgaOjI/r371+iAtijRw9ERkaiW7du8PLyQteuXfG///0Py5cvh6enJ+Li4jB58mQEBQWhatWq5b6X5Wn+aNCgAbp164ZPP/0UERERKCwsREhICAYMGMBVqh4/fozOnTtj8+bNaNmyJXfsvXv3cPz4cezdu7fEeevWrYuePXti0qRJ+PHHH2Fra4tZs2ahfv36XKULAM6cOQMLC4tSkzrymsZJhbphTfPnzy91aJUqYrEYzZo1w6FDh7hhoXK5HIcOHVLZo7p+/fq4du2a0ra5c+fi1atX+O6771C9enWNnl/XinfUpOYP7VELSPlIpVIwxiAWi00+oQCKPjCEhIRgyZIl+Oyzz2BjY4PJkyfD09MTy5Ytw3fffQeZTAYvLy+sW7eO6/StsHjxYjRr1gxr1qxBREQE5HI5ateujb59+2r0Qcja2hr//PMPJk2ahBYtWsDa2hqBgYFYsWKF2mOWLl2KrKwsBAQEoFKlSggNDUVGRkapz1OrVi38/fffmDJlCr777jtUq1YNGzZsKDF/wptGjRqFzZs3c6+dGzduRO/evVU2KQYGBmLo0KFITU2Fk5MTduzYgbCwMIwZMwZPnjxBtWrV0Lt37xL9EHR1LzW1detWhISEoHPnzuDz+QgMDFSaO6KwsBBxcXEl+j1s3LgR1apV4/pMvGnz5s2YMmUKPvroI/D5fHTs2BH79+9Xat7Ztm0bBg8erNGqv+aMx3TUk+nevXto2bKlxrOq7dixA8OHD8cPP/yAli1bYuXKlfj1119x+/ZtuLq6YtiwYahatSq++eYblccHBwcjPT29xKxv6mRmZsLOzg4ZGRmwtbXVKFZ1HiTexv6/f4BIPAkrV7qBx+Nh/PhsBARoP8mMOWKMISsrC86WlZBy4jY8ujaFtUtlQ4dlcvLy8nDnzh14eHjo7HecGL/c3FzUq1cPO3bsoE/VOpKamop69erh/PnzGo+WMTV5eXncqCBLS0ulxzR539TZGIXTp0+XCKQsgoKC8Pz5c8ybNw8pKSlo0qQJ9u/fz5UTExMT31ouNBavF3jkUfNHOVBnTc0lJSXh9u3b6NKlCwBAJBKZfIWCaMbKygqbN29GamqqoUOpMB48eIC1a9dW+IRClzROKvr06aP0PWMMycnJOH/+fIlSWVmFhISonUDm6NGjpR5bljnx3xWplAfGisr31PyhORpOqp2kpCR06tQJjx49wp49e4xqQjjybnXq1MnQIVQozZs3VzmhGFFP46RCMbxIQdHzeeHChWrbrcxF8YXsqFKhPapUlJ0ioUhISICnpyfq169v6JAIIWZMo6RCJpNhxIgRaNSoESpXprbuN0mVFhQzYCDELLyZUBw9ehTVq1cv14JRhBBSHhp1VhAIBOjatavRTIttbIoPKaVpujXHGKNpustIXUJBCCGGpHEPyPfffx8JCQn6iMXkKQ8pNWAgpEJ79uwZJRSEEKOkcVLx1VdfYdq0adizZw+Sk5ORmZmp9GXOaPKr8qMqxds5OjqiXbt2lFAQQoxOmT9PL1y4EKGhodzCNJ988onSG4CidC0r/s5qZmiV0vLhRn9QYlEqgUCAjRs34sWLFyVWbySEEEMqc1KxYMECjB07FkeOHNFnPCZNSquUlhtVKlRLSkrC6tWrsWjRIggEApXLXBNCiKGV+a1P8SmyY8eOegvG1Mnl1KdCFyixUFa8UyZQNFUy0b/bt28jODgYly9fRv369cu0MnJZZvjt1KkTmjRpgpUrV+osVoUvvvgCT58+xY8//qjzc5uj1NRUNGzYEBcvXkS1atUMHY5J0KhPBb3Yl07R8sPjUfOHNhRNaOS1N0d5qJskrqIIDg7mRgCJRCLUqlULM2bMUDlMds+ePejYsSMqVaoEa2trtGjRQu1keLt27UKnTp1gZ2cHiUSCxo0bY+HChaUuKxAWFgYbGxvExcWVWDVUn5KTkzFo0CDUrVsXfD4fkydPLtNxKSkp+O677zBnzpwSj50+fRoCgQAfffRRiceOHj0KHo+nclSfh4dHieTnyJEj6NGjBxwdHWFtbY2GDRtyi4zpS15eHsaPHw9HR0dIJBIEBgaWWIjyTU+fPkVwcDDc3d1hbW2Nbt264e7duyX2O336ND788EPY2NjA1tYWHTp04FbEdXJywrBhwxAWFqaX66qINEoq6tatCwcHh1K/zBk1f+gGJRZFzHXYaLdu3ZCcnIyEhASEh4fjhx9+KPGivmrVKvTs2RNt27ZFbGwsrl69igEDBmDs2LGYNm2a0r5z5sxBUFAQWrRogX379uH69etYvnw5rly5gi1btqiNIz4+Hu3atUPNmjXh6Oiol2tVJT8/H87Ozpg7dy68vb3LfNyGDRvQpk0blSuCRkZGYsKECTh+/DiePHmidWw//PAD/Pz84Obmhl27duHmzZuIiIhARkaGyqXodWXKlCn466+/sHPnThw7dgxPnjwpMbtzcYwx9OrVCwkJCdi9ezcuXbqEmjVrws/PT2np+tOnT6Nbt27o2rUrzp49i3PnziEkJERpaYgRI0Zg69atGq9rZa40eutbsGBBiRk1yWtFzR9FFQoa/aE5mqb7NXNNKADAwsICbm5uAIDq1avDz88PBw4c4Jp9kpKSEBoaismTJ2PRokXccaGhoRCLxZg4cSL69esHHx8fnD17FosWLcLKlSuVluX28PBAly5d1M65o0hsL1y4gIULFyIsLAzz58/HtWvXMGnSJJw+fVpplVKJRKLyPNnZ2fjss8/w22+/oVKlSiUSHlU8PDzw3XffAShaZbOstm/fjs8++6zE9qysLOzYsQPnz59HSkoKoqKiMHv27DKfV+HRo0eYOHEiJk6cqLRatYeHBzp06KC3+YsyMjIQGRmJX375BR9++CEAYNOmTWjQoAHOnDmDVq1alTjm7t27OHPmDK5fvw4vLy8AwLp16+Dm5oZt27Zh1KhRAIqSlYkTJ2LmzJncsfXq1VM6l5eXF9zd3fH7779j5MiRernGikSjpGLAgAHUOawUNPpDN8y9UiGVStGtWzfdJxRyGVCQVf7zaEosAfjaZdnXr1/HqVOnlD59R0dHo7CwUOUb9JgxYzB79mxs27YNPj4+2Lp1KyQSCcaNG6fy/Pb29iq3Jycnw8/PD926dcO0adMgkUiQnZ0Nf39/tG7dGufOncOzZ88watQohISEqG12mT59Oo4dO4bdu3fDxcUFs2fPxsWLF9GkSRNNb0Wp0tLScPPmTZXrVPz666+oX78+6tWrhyFDhmDy5MmYNWuWxn9nO3fuREFBAWbMmKHycXX3EgC6d++OEydOqH28Zs2auHHjhsrHLly4gMLCQvj5+XHb6tevjxo1auD06dMqk4r8/HwAUFrkks/nw8LCAjExMRg1ahSePXuG2NhYDB48GG3atEF8fDzq16+Pr7/+Gu3atVM6X8uWLXHixAlKKsqgzEmFub/Ql0Xx0bTU/EG0JRQKsWzZMkybNg379+/XXYWiIAt4eEo359JEzTaAZdkrnHv27IFEIoFUKkV+fj74fD5Wr17NPX7nzh3Y2dmhSpUqJY4Vi8Xw9PTEnTt3ABR9YvX09IRIJNIoZDc3NwiFQkgkEq5qsn79euTl5WHz5s2wsbEBAKxevRoBAQFYvHgxt7KyQlZWFiIjI/Hzzz+jc+fOAICffvpJLx3+EhMTwRiDu7t7icciIyMxZMgQAEVNSxkZGTh27JjGi4/dvXsXtra2Ku/722zYsIHrp6BKaT+flJQUiMXiEkmLq6srUlJSVB6jSDpmzZqFH374ATY2NggPD8ejR4+QnJwMAFzH5/nz52PZsmVo0qQJNm/ejM6dO+P69et47733uPO5u7vj0qVLZb1cs6bx6A+iXvE+FdT8oTlumm6YZwJbvKNq9+7d0aVLFwh1mZ2KJUVv8O+aWHXTgDq+vr5Yt24dsrOzER4eDqFQiMDAQK2eWpevW7du3YK3tzeXUABA27ZtIZfLERcXVyKpiI+PR0FBAXx8fLhtDg4OJcrruqB4wy7+yRwA4uLicPbsWfz+++8AihLWoKAgREZGapxUlKcjddWqVbU6TlsikQi//fYbRo4cCQcHBwgEAvj5+aF79+7c74T8vxUgx4wZgxEjRgAAmjZtikOHDmHjxo345ptvuPNZWVkhJyfnnV6DqSpzR025XE5NH29RvPlDIKAkjJRdYmIiOnXqpNQ7XacJBVDUBGFp9+6/NGz6sLGxQZ06deDt7Y2NGzciNjYWkZGR3ON169ZFRkaGyg6HBQUFiI+PR926dbl9ExISUFhYWL57Z+ScnJwAAC9fvlTaHhkZCalUCnd3dwiFQgiFQqxbtw67du1CRkYGAMDW1hYAuO+LS09P5/rRKe674pO+Jrp37w6JRKL2S9HvQRU3NzcUFBSU6LPx9OlTroqkSrNmzXD58mWkp6cjOTkZ+/fvx4sXL+Dp6QkAXMWlYcOGSsc1aNAAiYmJStvS0tLg7OysySWbLY2n6SbqKScVBgzEhJljM1tiYiJ8fX1x/PhxjBo1iqqCxfD5fMyePRtz587lPo0HBgZCJBKpHG0QERGB7OxsDBw4EAAwaNAgZGVlYe3atSrPr0nnwgYNGuDKlStKowdOnjwJPp+vsvpQu3ZtiEQixMbGcttevnzJNc3oUu3atWFra4ubN29y26RSKTZv3ozly5fj8uXL3NeVK1fg7u6Obdu2AQDee+898Pl8XLhwQemcCQkJyMjI4BK0vn37QiwWY8mSJSpjKO1ebtiwQSmGN7/27t2r9thmzZpBJBIpDeuNi4tDYmIiWrdu/dZ7Y2dnB2dnZ9y9exfnz59Hz549ARR1MHV3d0dcXJzS/nfu3Ckxgub69eto2rTpW5+LaNhRk5Tuv2oaAKpUaIMrr5pRXqFIKBSdMn/++WezTKxK069fP0yfPh1r1qzBtGnTUKNGDSxZsgShoaGwtLTE0KFDIRKJsHv3bsyePRuhoaFck4OPjw9mzJjBzaPQu3dvuLu74969e4iIiEC7du2URoWUZvDgwQgLC8Pw4cMxf/58PH/+HBMmTMDQoUNLNH0AgEQiwciRIzF9+nQ4OjrCxcUFc+bMURquqI5ioq2srCw8f/4cly9fhlgsLvGpWoHP58PPzw8xMTHo1asXgKK+KS9fvsTIkSNLjNoLDAxEZGQkxo4di0qVKmHUqFEIDQ2FUChEo0aNkJSUhM8//xytWrVCmzZFTWbVq1dHeHg4QkJCkJmZiWHDhsHDwwOPHj3C5s2bIZFI1A4rLU/zh52dHUaOHImpU6fCwcEBtra2mDBhAlq3bq3USbN+/fr45ptv0Lt3bwBFHUudnZ1Ro0YNbtROr1690LVrVwBFH2CmT5+OsLAweHt7o0mTJvjpp59w+/ZtREdHc+fNycnBhQsXlEYaEfUoqdAhqZQqFeVlTm+obyYU5jRsVBNCoRAhISFYsmQJPvvsM9jY2GDy5Mnw9PTEsmXL8N1330Emk8HLywvr1q3j2scVFi9ejGbNmmHNmjWIiIiAXC5H7dq10bdvXwwfPrzMcVhbW+Off/7BpEmT0KJFC6UhpeosXboUWVlZCAgIQKVKlRAaGqqymeFNxT8VX7hwAb/88gtq1qyJBw8eqD1m1KhR+PTTT7FkyRLw+XxERkbCz89P5TQAgYGBWLJkCa5evYrGjRvju+++w7fffovPP/8cDx8+hJubG7p06YKvv/5a6W9y3LhxqFu3LpYtW4bevXsjNzcXHh4e+PjjjzF16tS3Xpe2wsPDwefzERgYiPz8fPj7+5eoPsXFxSnd2+TkZEydOhVPnz5FlSpVMGzYMHzxxRdKx0yePBl5eXmYMmUK0tLS4O3tjQMHDqB27drcPrt370aNGjXQvn17vV1fRcJjZlZrzczMhJ2dHTIyMri2xPJ6kHgb+//+ATEnZ+Hy5UoQCATYufMFJBKzurXllpeXBz6fDxdrOzz49xI8ujaFtUtlQ4elF/pMKPLy8nD//n3UqlWrRMc9UnExxuDj44MpU6ZwzT+k/Fq1aoWJEydi0KBBhg5Fr0p73dDkfZP6VOgQddQsH3Oapnvq1KlUoSA6xePx8OOPP0JafBgaKZfU1FT06dOHkjQNUPOHDlFHTVJWigWfwsPDKaEgOtOkSROdT6xlzpycnNRO9kVUo6RCh2jtj/Kp6JWKnJwcWFtbAyiar6B4ZzBCCKkIqPlDhxRLnxetUmrgYExURU0qEhMT0ahRI6WZIQkhpKKhtz4dUkzTTQmF9ipiUlG8U+bKlStpZj5CSIVFb386pOhTQf0ptFMRmz/eHOVx5MgRrgmEEEIqGkoqdEhRqRAKaeQHoXkoCCHmh5IKHVL0qaDmD+1VlEoFJRSEEHNEb386JP2v+YMqFdqpSM0ff/75JyUUhBCzQ0mFDsmkiqTCwIGYsIqSVISEhGDVqlWUUJiw27dvo1WrVrC0tCzz3A/BwcHc2hvqdOrUCZMnTy53fKoMHTqU1qjQof3796NJkybcMunk7Sip0CEa/VF+ppxUPH78GFlZWdz3ISEhlFBoKDg4GDweDzweDyKRCLVq1cKMGTOQl5dXYt89e/agY8eOqFSpEqytrdGiRQtERUWpPO+uXbvQqVMn2NnZQSKRoHHjxli4cCHS0tLUxhIWFgYbGxvExcUprZCpb7/99hu6dOkCZ2dn2NraonXr1vjnn3/eetyVK1ewd+9eTJw4scRj27Ztg0AgwPjx40s8FhUVBXt7e5Xn5PF4+OOPP5S2aXMvy+vrr79GmzZtYG1trTbWNzHGMG/ePFSpUgVWVlbw8/PD3bt3lfZJS0vD4MGDYWtrC3t7e4wcOVLpb7hbt24QiUTYunWrLi+nQqO3Px16PfqDmj+0YcrNH0lJSejQoQN69Oih9KJENNetWzckJycjISEB4eHh+OGHHxAWFqa0z6pVq9CzZ0+0bdsWsbGxuHr1KgYMGICxY8di2rRpSvvOmTMHQUFBaNGiBfbt24fr169j+fLluHLlCrZs2aI2jvj4eLRr1w41a9aEo6OjXq5VlePHj6NLly7Yu3cvLly4AF9fXwQEBODSpUulHrdq1Sr069cPEomkxGORkZGYMWMGtm3bpjJBKytt72V5FRQUoF+/fvjss8/KfMySJUvw/fffIyIiArGxsbCxsYG/v7/S9Q8ePBg3btzAgQMHsGfPHhw/fhyjR49WOk9wcDC+//57nV1LRUeFeh2SyWlIqTlKSkpCp06dkJCQAADIyMhQ+cJOysbCwgJubm4Aipbb9vPzw4EDB7B48WIARfc7NDQUkydPVir1h4aGQiwWY+LEiejXrx98fHxw9uxZLFq0CCtXrlRa4tzDwwNdunRBenq6yhgUye2FCxewcOFChIWFYf78+dwS2qdPn1ZapVTdzzs7OxufffYZfvvtN1SqVKlEwqPKypUrlb5ftGgRdu/ejb/++ktp9dLiZDIZoqOjVX6ivn//Pk6dOoVdu3bhyJEj+O2337RaHEvbe6kLCxYsAAC1lag3McawcuVKzJ07Fz179gQAbN68Ga6urvjjjz8wYMAA3Lp1C/v378e5c+fQvHlzAEWJWY8ePbBs2TK4u7sDAAICAhASEoL4+Hil1UuJalSp0CH5f80flFRohzEGvom1HRVPKBSdMqtWrWrosFSSyWV4VfDqnX/JFH8YWrh+/TpOnToFsVjMbYuOjkZhYaHKN+gxY8ZAIpFg27ZtAICtW7dCIpFg3LhxKs+vrpSenJwMLy8vhIaGIjk5GdOmTUN2djb8/f1RuXJlnDt3Djt37sTBgwcREhKiNv7p06fj2LFj2L17N/79918cPXoUFy9e1OAOAHK5HK9evYKDg4Pafa5evYqMjAzuzbG4TZs24aOPPoKdnR2GDBmCyMhIjZ5fQdt7CQBeXl6QSCRqv7p3765VTOrcv38fKSkp8PPz47bZ2dnBx8cHp0+fBgCcPn0a9vb2SvfMz88PfD4fsbGx3LYaNWrA1dUVJ06c0GmMFRVVKnSImj/Mi6qEwpj7UORIc3Dh6YV3/rzNXJuhkrhSmfffs2cPJBIJpFIp8vPzwefzlaY3v3PnDuzs7FClSpUSx4rFYnh6euLOnTsAgLt378LT0xMikUijmN3c3CAUCiGRSLiqyfr165GXl4fNmzfDxsYGALB69WoEBARg8eLFcHV1VTpHVlYWIiMj8fPPP6Nz584AgJ9++gnVqlXTKJZly5YhKysL/fv3V7vPw4cPIRAI4OLiorRdLpcjKioKq1atAgAMGDAAoaGh3BLXmtD2XgLA3r17UVhYqPZxKysrjc9ZmpSUFAAo8TNxdXXlHktJSSlxv4RCIRwcHLh9FNzd3fHw4UOdxlhRUVKhQzSjZvmZSp8KU0soAMBaaI1mrs0M8rya8PX1xbp165CdnY3w8HAIhUIEBgZq9dyM6S7Bv3XrFry9vbmEAgDatm0LuVyOuLi4Em9g8fHxKCgogI+PD7fNwcEB9erVK/Nz/vLLL1iwYAF2795d4g2wuNzcXFhYWJT4+zlw4ACys7PRo0cPAEWrbnbp0gUbN27El19+WeY4gPLdy5o1a2p9rDGwsrKi6fXLiJIKHZHLeZDLixYToyGl2jGljprp6enIyMgwmYQCAAR8gUYVA0OxsbFBnTp1AAAbN26Et7c3IiMjMXLkSABA3bp1kZGRgSdPnnDt3goFBQWIj4+Hr68vt29MTAwKCwu1+oRtSNu3b8eoUaOwc+dOpTK+Kk5OTsjJyUFBQYFSU1FkZCTS0tKUKgFyuRxXr17FggULwOfzYWtri+zsbMjlcqXmR0UfCTs7OwDlu5deXl6lftJv37499u3bp9E5S6OoLj19+lSpovX06VNueLCbmxuePXumdJxUKkVaWhp3vEJaWhqcnZ11Fl9FZloN2EaMsddvhtT8oR1TSSgAoFGjRjhy5IjJJBSmis/nY/bs2Zg7dy5yc3MBAIGBgRCJRFi+fHmJ/SMiIpCdnY2BAwcCAAYNGoSsrCysXbtW5fk16VzYoEEDXLlyBdnZ2dy2kydPgs/nq6w+1K5dGyKRSKl9/uXLl1zTTGm2bduGESNGYNu2bfjoo4/eur/ijfLmzZvcthcvXmD37t3Yvn07Ll++zH1dunQJL1++xL///gsAqFevHqRSKS5fvqx0TkXfj7p16wIo373cu3evUgxvfm3YsOGt16iJWrVqwc3NTWkocGZmJmJjY9G6dWsAQOvWrZGeno4LF143CR4+fBhyuVypupSXl4f4+Hi1nWSJMvpMrSNy+ev8zMT6GhoVY04skpKS8OjRI+5FqVGjRgaOyDz069cP06dPx5o1azBt2jTUqFEDS5YsQWhoKCwtLTF06FCIRCLs3r0bs2fPRmhoKPem4OPjgxkzZiA0NBSPHz9G79694e7ujnv37iEiIgLt2rVTGslQmsGDByMsLAzDhw/H/Pnz8fz5c0yYMAFDhw4t0fQBABKJBCNHjsT06dPh6OgIFxcXzJkz562dkX/55RcMHz4c3333HXx8fLj2fSsrK65q8CZnZ2d88MEHiImJ4RKMLVu2wNHREf379y/xd9WjRw9ERkaiW7du8PLyQteuXfG///0Py5cvh6enJ+Li4jB58mQEBQVxHY/Lcy/L2/yRmJiItLQ0JCYmQiaTcQlQnTp1uJE39evXxzfffIPevXuDx+Nh8uTJ+Oqrr/Dee++hVq1a+OKLL+Du7s5NTtagQQN069YNn376KSIiIlBYWIiQkBAMGDBAqQJ25swZWFhYcH/3pHT09qcjxZMKav6oeBR9KLp06cL1HifvhlAoREhICJYsWcJVCSZPnozff/8dJ06cQPPmzfH+++/jl19+wbp167Bs2TKl4xcvXoxffvkFsbGx8Pf3h5eXF6ZOnYrGjRtj+PDhZY7D2toa//zzD9LS0tCiRQv07dsXnTt3VupE+qalS5eiffv2CAgIgJ+fH9q1a4dmzUrv1/Ljjz9CKpVi/PjxqFKlCvf1tuRn1KhRSkNKN27cyL3BvikwMBB//vknUlNTAQA7duxAx44dMWbMGHh5eWHixIno2bNniQqCru6lpubNm4emTZsiLCwMWVlZaNq0KZo2bYrz589z+8TFxSEjI4P7fsaMGZgwYQJGjx6NFi1aICsrC/v374elpSW3z9atW1G/fn107twZPXr0QLt27fDjjz8qPfe2bdswePBgWl24jHhMlz2ZTEBmZibs7OyQkZEBW1tbnZzzQeJt/B4dhQ2RX4DH46NVKxkWLszUybnNSWZmJtzc3CAuYHjw7yV4dG0Ka5fKhg7L5Dpl5uXlcb37i7+AkootNzcX9erVw44dO+hTtY6kpqaiXr16OH/+vMajZUxNaa8bmrxvUqVCR+Ty10M+qPmj4jC1hIKYLysrK2zevJmrPpDye/DgAdauXVvhEwpdokK9jsio+UMnikq1xlE8o4SCmJpOnToZOoQKpXnz5ionFCPq0WdqHWHFkgoa/aEdY2qJS05OpoSCEEI0RJ+pdUQm40PxCZsmv9KOYnVKY+Do6AgvLy8AoISCEELKiJIKHVGep8KAgZgwY6pUiMViREdH48WLFyqngyaEEFISNX/oiJyaP8rN0JWKxMREfPXVV1xyIxaLKaEghBANUKVCR2RKSYUBAzFhhqxUJCYmwtfXl1u+fO7cuQaLhRBCTBVVKnSEyfncmAVKKrRjqCpF8YTC09NTr5P4EEJIRUZJhY4UddQEAB41f2jJEAuKvZlQUKdMQgjRHiUVOsIYzVNRXpRQEGNy+/ZttGrVCpaWltx6Gm8THBzMrS2hTqdOnTB58uRyx6fK0KFDsWjRIr2c2xzt378fTZo0gVwuN3QoJoOSCh15Xamgjprl8a4Si/z8fPj5+VFCYWSCg4O5DrsikQi1atXCjBkzkJeXV2LfPXv2oGPHjqhUqRKsra3RokULREVFqTzvrl270KlTJ9jZ2UEikaBx48ZYuHAh0tLS1MYSFhYGGxsbxMXFKa12qW8xMTFo27YtHB0dYWVlhfr16yM8PPytx125cgV79+7FxIkTSzy2bds2CAQCjB8/vsRjUVFRsLe3V3lOHo+HP/74Q2mbNveyvNLS0jB48GDY2trC3t4eI0eORFZWVqnHpKSkYOjQoXBzc4ONjQ0++OAD7Nq1S6PzduvWDSKRSGlNFVI6Sip0RM743ESQ1KfC+FlYWGDBggWoW7cuJRRGplu3bkhOTkZCQgLCw8Pxww8/ICwsTGmfVatWoWfPnmjbti1iY2Nx9epVDBgwAGPHjsW0adOU9p0zZw6CgoLQokUL7Nu3D9evX8fy5ctx5coVbNmyRW0c8fHxaNeuHWrWrAlHR0e9XKsqNjY2CAkJwfHjx3Hr1i3MnTsXc+fOLbHQ1ZtWrVqFfv36cat2FhcZGYkZM2Zg27ZtKhO0stL2XpbX4MGDcePGDRw4cAB79uzB8ePHMXr06FKPGTZsGOLi4vDnn3/i2rVr6NOnD/r3749Lly5pdN7g4GB8//33ermuioiSCh1hNPpDJ95lE8jAgQNx7do1SiiMjIWFBdzc3FC9enX06tULfn5+OHDgAPd4UlISQkNDMXnyZCxatAgNGzZEnTp1EBoaiqVLl2L58uWIjY0FAJw9exaLFi3C8uXLsXTpUrRp0wYeHh7o0qULdu3apbZTLo/Hw4ULF7Bw4ULweDzMnz8fAHDt2jV8+OGHsLKygqOjI0aPHl3qJ+bs7GwMGzYMEokEVapUwfLly996/U2bNsXAgQPh5eUFDw8PDBkyBP7+/jhx4oTaY2QyGaKjoxEQEFDisfv37+PUqVOYOXMm6tati99+++2tMaii7b0sr1u3bmH//v3YsGEDfHx80K5dO6xatQrbt2/HkydP1B536tQpTJgwAS1btoSnpyfmzp0Le3t7XLhwQaPzBgQE4Pz584iPj9fL9VU0lFToCDV/lM+7GE6alJSE7t274/Hjx9w2sVis9+c1Fkwmg+zVq3f+xWQyrWO+fv06Tp06pfRzio6ORmFhYYmKBACMGTMGEokE27ZtA1C0tLVEIsG4ceNUnl9d2T85ORleXl4IDQ1FcnIypk2bhuzsbPj7+6Ny5co4d+4cdu7ciYMHDyIkJERt/NOnT8exY8ewe/du/Pvvvzh69CguXryowR0ALl26hFOnTqFjx45q97l69SoyMjJUrlOxadMmfPTRR7Czs8OQIUMQGRmp0fMraHsvAcDLywsSiUTtV/fu3dUee/r0adjb2ytdm5+fH/h8Ppc8qtKmTRvs2LEDaWlpkMvl2L59O/Ly8rj1Ucp63ho1asDV1bXUpI68Rl0KdUROM2qWm6ItXR/pRfHFwUaNGoV9+/bp4VmMmzwnBznnzr/z57Vu0RyCSpXKvP+ePXsgkUgglUqRn58PPp+P1atXc4/fuXMHdnZ2KicmE4vF8PT0xJ07dwAAd+/ehaenJ0QikUYxu7m5QSgUQiKRwM3NDQCwfv165OXlYfPmzbCxsQEArF69GgEBAVi8eDFcXV2VzpGVlYXIyEj8/PPP6Ny5MwDgp59+QrVq1coUQ7Vq1fD8+XNIpVLMnz8fo0aNUrvvw4cPIRAI4OLiorRdLpcjKioKq1atAgAMGDAAoaGh3BLXmtD2XgLA3r17UVhYqPZxKysrtY+lpKSUuC6hUAgHBwekpKSoPe7XX39FUFAQHB0dIRQKYW1tjd9//x116tTR+Lzu7u54+PCh2ucir1FSoSNy2etMgpIKzemzUvHmaqNva5uuqPjW1rBu8e5XXORbW2u0v6+vL9atW4fs7GyEh4dDKBQiMDBQq+fW5e/VrVu34O3tzSUUANC2bVvI5XLExcWVSCri4+NRUFAAHx8fbpuDgwPq1atXpuc7ceIEsrKycObMGcycORN16tTBwIEDVe6bm5sLCwuLEs2HBw4cQHZ2Nnr06AEAcHJyQpcuXbBx40Z8+eWXZYpDoTz3smbNmlofq60vvvgC6enpOHjwIJycnPDHH3+gf//+OHHiBBo1aqTRuaysrJCTk6OnSCsWSip0hKbp1g1dVypo+fLXeAKBRhUDQ7GxseE+TW7cuBHe3t6IjIzEyJEjAQB169ZFRkYGnjx5And3d6VjCwoKEB8fD19fX27fmJgYFBYWavUJ25AUlYRGjRrh6dOnmD9/vtqkwsnJCTk5OSgoKFBqKoqMjERaWppSJUAul+Pq1atYsGAB+Hw+bG1tkZ2dDblcDj7/9etYeno6AMDOzg5A+e6ll5dXqZ/027dvr7Z66ObmhmfPniltk0qlSEtL46pIb4qPj8fq1atx/fp1bmFAb29vnDhxAmvWrEFERIRG501LS4Ozs/Nbr5NQnwqdKUoqaJVSY0IJhenj8/mYPXs25s6di9zcXABAYGAgRCKRyk6PERERyM7O5t58Bw0ahKysLKxdu1bl+RVvnGXRoEEDXLlyBdnZ2dy2kydPgs/nq6w+1K5dGyKRSKl9/uXLl1zTjCbkcjny8/PVPq6YR+PmzZvcthcvXmD37t3Yvn07Ll++zH1dunQJL1++xL///gsAqFevHqRSKS5fvqx0TkXfj7p16wIo373cu3evUgxvfm3YsEHtsa1bt0Z6ejrXwRIADh8+DLlcrlQFKk5RVSieJAGAQCDg5pwo63nz8vIQHx+Ppk2bqo2RvEaVCh2Ry4vKjjweTX6lLV2P/BgzZgwlFBVAv379MH36dKxZswbTpk1DjRo1sGTJEoSGhsLS0hJDhw6FSCTC7t27MXv2bISGhnJvCj4+PpgxYwZCQ0Px+PFj9O7dG+7u7rh37x4iIiLQrl07TJo0qUxxDB48GGFhYRg+fDjmz5+P58+fY8KECRg6dGiJpg8AkEgkGDlyJKZPnw5HR0e4uLhgzpw5Jd7o3rRmzRrUqFED9evXBwAcP34cy5YtUzn/hIKzszM++OADxMTEcAnGli1b4OjoiP79+5f42+rRowciIyPRrVs3eHl5oWvXrvjf//6H5cuXw9PTE3FxcZg8eTKCgoJQtWrVct/L8jR/NGjQAN26dcOnn36KiIgIFBYWIiQkBAMGDOAqVY8fP0bnzp2xefNmtGzZEvXr10edOnUwZswYLFu2DI6Ojvjjjz+4oaNlPS8AnDlzBhYWFmjdurXW12BOqFKhI3L56/IEn0/NH5pStNfqMrHYsGEDunfvTgmFiRMKhQgJCcGSJUu4KsHkyZPx+++/48SJE2jevDnef/99/PLLL1i3bh2WLVumdPzixYvxyy+/IDY2Fv7+/vDy8sLUqVPRuHFjjYZBWltb459//kFaWhpatGiBvn37onPnzkqdSN+0dOlStG/fHgEBAfDz80O7du3QrFmzUp9HLpdj1qxZaNKkCZo3b441a9Zg8eLFWLhwYanHjRo1SmmSpo0bN6J3794q/6YCAwPx559/IjU1FQCwY8cOdOzYEWPGjIGXlxcmTpyInj17lqgg6Opeamrr1q2oX78+OnfujB49eqBdu3ZKfaMKCwsRFxfHVShEIhH27t0LZ2dnBAQEoHHjxti8eTN++uknrn9JWc4LFE0cNnjwYFhr2DfIXPGYIZeGNIDMzEzY2dkhIyMDtra2Ojnng8Tb+GpBLE6d7gOBQITPP8/Ghx+qL1WSkmQyGfLy8lCjRg3IMnLw4N9L8OjaFNYulTU6T35+PiwsLPQUpWnIy8vjevdbWloaOhzyjuTm5qJevXrYsWMHfarWkdTUVNSrVw/nz5/XeLSMqSntdUOT902qVOiInNb+KBddVCqSkpLQqFEjmlKXmCUrKyts3ryZqz6Q8nvw4AHWrl1b4RMKXaK3Px2Ry/hQ1Hyo+UM75U0oFJ0yFyxYgL59+5p9xYKYH8XETkQ3mjdvrnJCMaIeVSp0pPiQUqpUaE+bxOLNUR6HDh2ihIIQQgyAkgodoXkqykfbrj00bJQQQowHJRU6IqcFxcpNMU13WVFCQQghxoWSCh2hya/KR5tKxZYtWyihIIQQI0Kt/zpCzR+6oUmlYtasWQCAoUOHUkJBCCFGgJIKHaFVSt+N5ORkODg4cIsnzZ4929AhEUII+Q8lFTry5iqlBQUFpc7VT5TJZDJYWlqWWqlQ9KFo2LAhoqOjaYQHIYQYGUoqdET2RvNHbm4u7OzsIKTxpWUmFArVJhXFO2UCRasGVqlS5V2GR8zM7du3ERwcjMuXL6N+/folFtxSJTg4GOnp6fjjjz/U7tOpUyc0adIEK1eu1FmsCkOHDkWDBg2ogqcj+/fvx8yZM3Hx4sW3rtlC/sPMTEZGBgPAMjIydHbO+w9vsYCP/2b162ew99/PY6dOpbCbN2+ytLQ0nT2HOcl+msZubDnEsp8W3b/ExETm6enJADBPT0+WmJho4AiNW25uLrt58ybLzc01dCgaGz58OENRj2cmFAqZh4cHmz59uspr+euvv1iHDh2YRCJhVlZWrHnz5mzTpk0qzxsdHc06duzIbG1tmY2NDWvUqBFbsGABe/HihdpY+vfvzz788EP24MEDlpqaWub4e/bsWeo+HTt2ZJMmTSrT+WJiYphAIGDe3t5v3ffy5cvMwcGBvXr1qsRjv/zyC+Pz+WzcuHElHtu0aROzs7NTeU4A7Pfff1faps29LK8XL16wQYMGsUqVKjE7Ozv2v//9T+V1FpecnMyGDBnCXF1dmbW1NWvatCmLjo5WuW9eXh7z9vZmANilS5eUHmvevDnbvHmzri7FaJX2uqHJ+yalXjoikys3f2g6PJKoRsNGzU+3bt2QnJyMhIQEhIeH44cffkBYWJjSPqtWrULPnj3Rtm1bxMbG4urVqxgwYADGjh2LadOmKe07Z84cBAUFoUWLFti3bx+uX7+O5cuX48qVK9iyZYvaOOLj49GuXTvUrFkTjo6OernW0qSnp2PYsGHo3LlzmfZftWoV+vXrB4lEUuKxyMhIzJgxA9u2bUNeXp7WMWl7L8tr8ODBuHHjBrfK6PHjxzF69OhSjxk2bBji4uLw559/4tq1a+jTpw/69++PS5culdh3xowZSiuTFhccHIzvv/9eJ9dhFvSR8Whq9erVrGbNmszCwoK1bNmSxcbGqt33xx9/ZO3atWP29vbM3t6ede7cudT936SvSkW3bv9wlYpz55LZrVu3dPoc5kRRqYi7eJUqFFow9UrFm5/0+/Tpw5o2bcp9n5iYyEQiEZs6dWqJ47///nsGgJ05c4YxxlhsbCwDwFauXKny+V6+fKlyO/6rlii+wsLCGGOMXb16lfn6+jJLS0vm4ODAPv30U6VPzG/Gn5WVxYYOHcpsbGyYm5sbW7ZsWZkrFUFBQWzu3LksLCzsrZUKqVTK7Ozs2J49e0o8lpCQwKysrFh6ejrz8fFhW7duVXq8rJUKbe9led28eZMBYOfOneO27du3j/F4PPb48WO1x9nY2JSoMDg4OLD169crbdu7dy+rX78+u3HjhspKxcOHDxkAdu/evfJfjBGrMJWKHTt2YOrUqQgLC8PFixfh7e0Nf39/PHv2TOX+R48excCBA3HkyBGcPn0a1atXR9euXfH48eN3HLkypmKabqpUlM+T5GQ8e/aMKhQ6Ipcz5OdK3/mXXK79EOvr16/j1KlTEIvF3Lbo6GgUFhaWqEgAwJgxYyCRSLBt2zYARUtbSyQSjBs3TuX57e3tVW5PTk6Gl5cXQkNDkZycjGnTpiE7Oxv+/v6oXLkyzp07h507d+LgwYMICQlRG//06dNx7Ngx7N69G//++y+OHj2KixcvvvW6N23ahISEhBIVGnWuXr2KjIwMletUbNq0CR999BHs7OwwZMgQREZGlumcb9L2XgKAl5cXJBKJ2q/u3burPfb06dOwt7dXujY/Pz/w+XzExsaqPa5NmzbYsWMH0tLSIJfLsX37duTl5Smtj/L06VN8+umn2LJli9qlzWvUqAFXV1ecOHFC7XOR1wzei3DFihX49NNPMWLECABAREQE/v77b2zcuBEzZ84ssf+bK1Bu2LABu3btwqFDhzBs2LB3ErMqsjdm1GSMUVJRTi2bt8CBAwdQtWpVSih0oDBfhid309/587q/Zw8Lq7K/1OzZswcSiQRSqRT5+fng8/lYvXo19/idO3dgZ2ensqOuWCyGp6cn7ty5AwC4e/cuPD09IRKJNIrZzc0NQqEQEokEbm5uAID169cjLy8Pmzdvho2NDQBg9erVCAgIwOLFi+Hq6qp0jqysLERGRuLnn3/mmjB++uknVKtWrdTnvnv3LmbOnIkTJ06UuaP3w4cPIRAI4OLiorRdLpcjKioKq1atAgAMGDAAoaGh3BLXmtD2XgLA3r17UVhYqPZxKysrtY+lpKSUuC6hUAgHBwekpKSoPe7XX39FUFAQHB0dIRQKYW1tjd9//x116tQBUPQaHRwcjLFjx6J58+Z48OCB2nO5u7vj4cOHah8nrxk0qSgoKMCFCxe4SYwAgM/nw8/PD6dPny7TOXJyclBYWAgHBweVj+fn5ysN7czMzCxf0GowGR9MXvSLKi+UQSqTozBPhnyBVC/PV1ElJiXi6YMkKH6arVq1Mmg8FYnIQgD39+wN8rya8PX1xbp165CdnY3w8HAIhUIEBgZq9dxMyzVlVLl16xa8vb25hAIA2rZtC7lcjri4uBJJRXx8PAoKCuDj48Ntc3BwQL169dQ+h0wmw6BBg7BgwQLUrVu3zLHl5uZyc7cUd+DAAWRnZ6NHjx4AACcnJ3Tp0gUbN27El19+WebzA+W7lzVr1tT6WG198cUXSE9Px8GDB+Hk5IQ//vgD/fv3x4kTJ9CoUSOsWrUKr169Unr/UcfKygo5OTnvIGrTZ9CkIjU1FTKZrMQfo6urK27fvl2mc3z++edwd3eHn5+fyse/+eYbLFiwoNyxlkZWIIc0zwIyKcBjDC+f5EDIL4SoMAsWFuqzc6LscfIjDB7VG5mZ6YicsQSegg8MHVKFwufzNKoYGIqNjQ33aXLjxo3w9vZGZGQkRo4cCQCoW7cuMjIy8OTJkxKd6woKChAfHw9fX19u35iYGBQWFmr1Cftde/XqFc6fP49Lly5xzSpyuRyMMQiFQvz777/48MMPSxzn5OSEnJwcFBQUKDUVRUZGIi0tTakSIJfLcfXqVSxYsAB8Ph+2trbIzs6GXC5XGjaZnp4OALCzswNQvnvp5eVV6if99u3bY9++fSofc3NzK9EcLpVKkZaWxlWR3hQfH4/Vq1fj+vXr8PLyAgB4e3vjxIkTWLNmDSIiInD48GGcPn26xHw3zZs3x+DBg/HTTz9x29LS0uDs7FymazV3xv8KU4pvv/0W27dvx9GjR2Fpaalyn1mzZmHq1Knc95mZmTovpTM5A5PzwBcCAhEPLjUtwJgU7tXtYKEmLqIsMSkRwz4LROKjB6hV0wPv1awEkZimJjV3fD4fs2fPxtSpUzFo0CBYWVkhMDAQn3/+OZYvX47ly5cr7R8REYHs7GwMHDgQADBo0CB8//33WLt2LSZNmlTi/Onp6aX2BSiuQYMGiIqKQnZ2NletOHnyJPh8vsrqQ+3atSESiRAbG4saNWoAAF6+fIk7d+6gY8eOKp/D1tYW165dU9q2du1aHD58GNHR0WqbLJo0aQIAuHnzJvf/Fy9eYPfu3di+fTv3xgoUVUPatWuHf//9F926dUO9evUglUpx+fJlfPDB60Re0fdDUTEpz70sT/NH69atkZ6ejgsXLqBZs2YAgMOHD0MulytVgYpTVBXenFtCIBBALpcDAL7//nt89dVX3GNPnjyBv78/duzYoXTevLw8xMfHo2nTpmpjJMXotv+oZvLz85lAICgxDnrYsGHsk08+KfXYpUuXMjs7O6UewWWhj9Efd+Ous7YtzrJ6dYtGf9y9m8ji4uJYXl6ezp6jInv48KHSKI/7N2+yzEOHmTQz09ChmaSKNvqjsLCQVa1alS1dupTbFh4ezvh8Pps9eza7desWu3fvHlu+fDmzsLBgoaGhSsfPmDGDCQQCNn36dHbq1Cn24MEDdvDgQda3b1+1IxkYY8zb25sb9cEYY9nZ2axKlSosMDCQXbt2jR0+fJh5enqy4cOHq41/7NixrGbNmuzQoUPs2rVr7JNPPmESiaTM81Qwxso0+oMxxj744AO2atUq7vvw8HBWpUoVJpfLS+zbv39/1rdvX+77rl27Mm9vb3bw4EGWkJDA9u3bx+rVq8eCgoKUjtP2XpZXt27dWNOmTVlsbCyLiYlh7733Hhs4cCD3+KNHj1i9evW4kYAFBQWsTp06rH379iw2Npbdu3ePLVu2jPF4PPb333+rfI779++rHP1x5MgRJpFIWHZ2tt6uzxjoavSHwYeUtmzZkoWEhHDfy2QyVrVqVfbNN9+oPWbx4sXM1taWnT59WuPn01dS0abZOVavbgZr1CiPJSQ8ZHfu3GH5+fk6e46K6s2EIjExkUkzMympKIeKllQwxtg333zDnJ2dWVZWFrdt9+7drH379szGxoZZWlqyZs2asY0bN6o8744dO1iHDh1YpUqVmI2NDWvcuDFbuHBhqcMg30wqGNN8SOmrV6/YkCFDmLW1NXN1dWVLlizRaPIrxsqeVKxdu5a1atWK+75Ro0YqJ7tirOh+iMVi9vz5c8ZY0XDQiRMnstq1azMrKyv23nvvsRkzZqicYEqbe1leL168YAMHDmQSiYTZ2tqyESNGKMWmSAiOHDnCbbtz5w7r06cPc3FxYdbW1qxx48alTmKlLqkYPXo0GzNmjK4vyehUmKRi+/btzMLCgkVFRbGbN2+y0aNHM3t7e5aSksIYY2zo0KFs5syZ3P7ffvstE4vFLDo6miUnJ3Nfb5tdTUFfSUWrDy6wuv8lFffvP2B3795lBQUFOnuOiujRo0cq56GgpKJ8TDmpINrLyclh1atXZ6dOnTJ0KBXG8+fPmYODA0tISDB0KHqnq6TC4H0qgoKC8Pz5c8ybNw8pKSlo0qQJ9u/fz3XeTExMVGoXW7duHQoKCtC3b1+l84SFhWH+/PnvMnQliiGlRZ2vGc2oWQb29vbc8Dqah4KQ8rGyssLmzZuRmppq6FAqjAcPHmDt2rUaD781ZwZPKgAgJCRE7QQyR48eVfq+tLHEhsTkRQmEUFg07IqSirezsbHB3r17kZ6ejqpVqxo6HEJMXvGJnUj5NW/eXOWEYkQ9g8+oWVEoKhUCQdGQLR6PR6vaqZCUlKQ0j76NjQ0lFIQQUkEYRaWiIpBzSQVVKtR5c/nyiRMnGjgiQgghukQfpXVErqJSQV57c7XR3r17GzokQgghOkZJhY4wVpRECP6br0kgoImbFGj5ckIIMQ+UVOiIrFjzB2OM+lP8hxIKQggxH/TOpyPFmz8YY1SpQNFUub6+vpRQEEKImaCkQkfYG0kF9akArK2tMWXKFNSuXZsSCmJybt++jVatWsHS0pJbT+NtgoOD0atXr1L36dSpEyZPnlzu+FQZOnQoFi1apJdzm6P9+/ejSZMm3Hoh5O0oqdARRfOHUEjNH8WNHz8eV69epYSClElwcDA3ckokEqFWrVqYMWMG8vLySuy7Z88edOzYEZUqVYK1tTVatGiBqKgolefdtWsXOnXqBDs7O0gkEjRu3BgLFy5EWlqa2ljCwsJgY2ODuLg4HDp0SFeX+FZHjx7l7kHxr5SUlFKPu3LlCvbu3atyVNW2bdsgEAgwfvz4Eo9FRUWpXQiMx+Phjz/+UNqmzb0sr6+//hpt2rSBtbV1mReAY4xh3rx5qFKlCqysrODn54e7d+8q7ZOWlobBgwfD1tYW9vb2GDlyJLKysrjHu3XrBpFIhK1bt+rycio0eufTkeLNH0X/mmfzR1JSEgIDA/HixQtum7W1tQEjIqamW7duSE5ORkJCAsLDw/HDDz8gLCxMaZ9Vq1ahZ8+eaNu2LWJjY3H16lUMGDAAY8eOxbRp05T2nTNnDoKCgtCiRQvs27cP169fx/Lly3HlyhVs2bJFbRzx8fFo164datasCUdHR71ca2ni4uKQnJzMfbm4uJS6/6pVq9CvXz9IJJISj0VGRmLGjBnYtm2bygStrLS9l+VVUFCAfv364bPPPivzMUuWLMH333+PiIgIxMbGwsbGBv7+/krXP3jwYNy4cQMHDhzAnj17cPz4cYwePVrpPMHBwUpz65C30PH04UZPX2t/1KyWyOrWTWe9e2exW7du6XVxHWOVmJjIreXRp08frc9Da3+Ujymv/aFqQbE+ffqwpk2bct8nJiYykUjEpk6dWuL477//ngFgZ86cYYwxFhsbywCoXUFT3d8pAKUvxcJimi4olpWVxYYOHcpsbGyYm5sbW7Zs2VsXFDty5AgDoNFriFQqZXZ2dmzPnj0lHktISGBWVlYsPT2d+fj4sK1btyo9vmnTJmZnZ6fyvAC4VaS1vZe6VFqsxcnlcubm5qa0sm16ejqzsLBg27ZtY4wxdvPmTQZAaaXrffv2MR6Pxx4/fsxte/jwIQPA7t27p7sLMUK6WvuDKhU6wJjy5FfMDPtUvDnKY+XKlYYOiVQA169fx6lTpyAWi7lt0dHRKCwsLFGRAIAxY8ZAIpFg27ZtAICtW7dCIpFg3LhxKs+vrpSenJwMLy8vhIaGIjk5GdOmTUN2djb8/f1RuXJlnDt3Djt37sTBgwfVLjEAANOnT8exY8ewe/du/Pvvvzh69CguXrxYpmtv0qQJqlSpgi5duuDkyZOl7nv16lVkZGSonFJ606ZN+Oijj2BnZ4chQ4YgMjKyTM//Jm3vJQB4eXlBIpGo/erevbtWMalz//59pKSkwM/Pj9tmZ2cHHx8fnD59GgBw+vRp2NvbK90zPz8/8Pl8xMbGcttq1KgBV1dXnDhxQqcxVlQ0o6YOFO/Do+hKYU5JBQ0bNQ1yuQyF5Sh9a0tkaQk+v+zNgXv27IFEIoFUKkV+fj74fD5Wr17NPX7nzh3Y2dmhSpUqJY4Vi8Xw9PTEnTt3AAB3796Fp6cnRCKRRjG7ublBKBRCIpHAzc0NALB+/Xrk5eVh8+bNsLGxAQCsXr0aAQEBWLx4MbcIokJWVhYiIyPx888/o3PnzgCAn376iVtET50qVaogIiICzZs3R35+PjZs2IBOnTohNjYWH3zwgcpjHj58CIFAUKKJRC6XIyoqCqtWrQIADBgwAKGhobh//77Gi2Rpey8BYO/evSgsLFT7uJWVlcbnLI2i/8mbPxNXV1fusZSUlBL3SygUwsHBoUT/FXd3dzx8+FCnMVZUlFTogEz2OoEQCs1rim5KKExHYV4eHt26/s6ft1qD92FhbVPm/X19fbFu3TpkZ2cjPDwcQqEQgYGBWj03Y0yr41S5desWvL29uYQCANq2bQu5XI64uLgSb2Dx8fEoKCiAj48Pt83BwQH16tUr9Xnq1auntE+bNm0QHx+P8PBwtf0WcnNzYWFhUeJ158CBA8jOzkaPHj0AAE5OTujSpQs2btyIL7/8smwX/p/y3MuaNWtqfawxsLKyQk5OjqHDMAmUVOiATPb6/4r+meYy+mPYsGGUUJgIkaUlqjV43yDPqwkbGxvUqVMHALBx40Z4e3sjMjISI0eOBADUrVsXGRkZePLkCdzd3ZWOLSgoQHx8PHx9fbl9Y2JiUFhYqNUnbGPRsmVLxMTEqH3cyckJOTk5KCgoUGoqioyMRFpamlIlQC6X4+rVq1iwYAH4fD5sbW2RnZ0NuVyu9LqVnp4OoKjZACjfvfTy8ir1k3779u2xb98+jc5ZGkV16enTp0oVradPn3LDg93c3PDs2TOl46RSKdLS0rjjFdLS0uDs7Kyz+Coy83jn0zNpsUpF8QXFzIGiNEsJhfHj8wWwsLZ551+aNH2UjJmP2bNnY+7cucjNzQUABAYGQiQSYfny5SX2j4iIQHZ2NgYOHAgAGDRoELKysrB27VqV51e8cZZFgwYNcOXKFWRnZ3PbTp48CT6fr7L6ULt2bYhEIqX2+ZcvX3JNM5q4fPmyyuYeBcUb5c2bN7ltL168wO7du7F9+3ZcvnyZ+7p06RJevnyJf//9F0BRZUQqleLy5ctK51T0/ahbty6A8t3LvXv3KsXw5teGDRvedgs0UqtWLbi5uSkNBc7MzERsbCxat24NAGjdujXS09Nx4cIFbp/Dhw9DLpcrVZfy8vIQHx+Ppk2b6jTGCkvnXUiNnD5Gf5w/e5NVd3/E6tZNZyNHZrDbt2+znJwcnZ3f2BQWFur1/DT6o3wq2uiPwsJCVrVqVaWe/OHh4YzP57PZs2ezW7dusXv37rHly5czCwsLFhoaqnT8jBkzmEAgYNOnT2enTp1iDx48YAcPHmR9+/ZVO5KBMca8vb25UR+MMZadnc2qVKnCAgMD2bVr19jhw4eZp6cnGz58uNr4x44dy2rWrMkOHTrErl27xj755BMmkUhKHf0RHh7O/vjjD3b37l127do1NmnSJMbn89nBgwdLvXcffPABW7VqldJ5qlSpwuRyeYl9+/fvz/r27ct937VrV+bt7c0OHjzIEhIS2L59+1i9evVYUFCQ0nHa3svyevjwIbt06RJbsGABk0gk7NKlS+zSpUtKI2/q1avHfvvtN+77b7/9ltnb27Pdu3ezq1evsp49e7JatWop/V1069aNNW3alMXGxrKYmBj23nvvsYEDByo995EjR5hEImHZ2dl6uz5joKvRH5RU6EDs6VtcUjF6dFFSYYov6GWRmJjIGjRooHLomq5QUlE+FS2pYIyxb775hjk7O7OsrCxu2+7du1n79u2ZjY0Ns7S0ZM2aNWMbN25Ued4dO3awDh06sEqVKjEbGxvWuHFjtnDhwlKHQb6ZVDCm+ZDSV69esSFDhjBra2vm6urKlixZ8tYhpYsXL2a1a9fmnqNTp07s8OHDavdXWLt2LWvVqhX3faNGjdi4ceNU7rtjxw4mFov/3959R0V1fXsA/84MzDBUReooCoiiRkDRSLD8sKCgxmCJGkWDJRp/sYbYGxoFLNFoLDEmKiaiWF5M8iwYNTaUaFTQRLAgIBYsWEA6zOz3h48bRgZkcGi6P2vNWs6955677xGYPeeecw89evSIiF5MB500aRI1btyY5HI5NWnShKZPn652bcWP1bYtX1dAQECJab4A6NixY0IZALRlyxbhvUqlonnz5pG1tTXJZDLq1q0bXbt2Ta3ex48f05AhQ8jY2JhMTU1p5MiRJa557Nix9Omnn1batdUUukoqREQ6HMlUC2RkZMDMzAzp6ekwNTXVSZ3Rp69h8CBjyI2N0bUrEBj4EA0bNoRMJtNJ/TVF8UGZzZs3x+XLl6Gnp/thOcrnz5H913kYvtsWEhMTndf/psvNzRVG9xtoOZ6B1V45OTlwdnbGzp07hS5+9nrS0tLg7OyM8+fPaz1bprYp6++GNp+bPKZCB5RqU0pfPKPiTRuo+fIsj0OHDlVKQsEYqxi5XI4ff/wRaWlp1R3KGyM5ORnr169/4xMKXeJPBR1QveFTSnnaKGO1Q+fOnas7hDdK27ZtNT5QjJXuzfo6XU0Ki00pFYvfrNkfnFAwxhgrL04qdEBZqD6l9E3qqVi/fj0nFIwxxsqFb3/oQPExFRLJi2XP35SkYvHixQCAzz77jBMKxhhjZeKkQgeKj6koGqhZm5OKBw8ewMLCAhKJBBKJBKGhodUdEmOMsVqAb3/oQOFLSUVtnvmRkpKC9u3bIyAgAMrizx9njDHGXoF7KnRApbb2R+1NKlJSUtClSxckJiYCeDFH++VFkhhjjLHS1M5PvxpGqar9PRXFE4qiQZmcUDDGGNNG7fv0q4GUhf/+uzYmFZoSCh6UyWqa5ORkiESiEgtf1QYLFiwQFv2qKmFhYahTp84ry23atAk9evSo/IDeAPn5+bC3t8f58+erO5Qaq3Z9+tVQwpgKenH7QyKp+KqMVY0TCsbeXrm5uZg3bx6CgoJK7Ltz5w6kUilatmxZYl9ZCV7nzp0xZcoUtW0xMTEYOHAgrK2tYWBggCZNmmDMmDEVWrG1vIgI8+fPh62tLeRyOby9vXHjxo0yj1mwYIEw0L7o1axZM2G/VCrF1KlTMWPGjEqLu7bjpEIHhDEVoto3puL69eu4c+cOJxSMvYX27NkDU1NTdOjQocS+sLAwDBo0SFgyvKL27duH9957D3l5eQgPD0d8fDy2bdsGMzMzzJs373XCL9OyZcvwzTffYMOGDTh79iyMjIzg4+OD3NzcMo975513kJqaKryioqLU9vv7+yMqKgpXrlyptNhrs9rz6VeDvTz7ozZNJ/X29sb+/fs5oXhLZGVllfp6+Y9tWWVzcnLKVVZbKpUKy5Ytg5OTE2QyGRo2bIjg4GC1MomJiejSpQsMDQ3h5uaG6OhoYd/jx48xZMgQ1K9fH4aGhnBxccGOHTvUju/cuTMmTZqE6dOnw9zcHDY2NliwYIFamWfPnuHTTz8Vvlm3bNkS+/btE/ZHRUWhU6dOkMvlsLOzw6RJk7S+3h9++AHNmzeHgYEBmjVrhvXr1wv72rdvX+Lb8KNHj6Cvr4+TJ08CAPLy8jB16lTUr18fRkZG8PDwwPHjx7WKISIiAn369CmxnYiwZcsWDB8+HEOHDsWmTZu0qrdIdnY2Ro4ciV69euG3336Dt7c3HBwc4OHhga+++grfffddhep9FSLCqlWrMHfuXPj5+cHV1RU//vgj7t27h19++aXMY/X09GBjYyO8LCws1PbXrVsXHTp0QERERKXEXttxUqEDKrXbHzX/Ed23b99W6wb09vbmhOItYWxsXOprwIABamWtrKxKLduzZ0+1svb29hrLaWvWrFlYsmQJ5s2bh7i4OGzfvr3EgOE5c+Zg6tSpiI2NRdOmTTFkyBAUFr4Y2JSbm4s2bdpg//79+OeffzB27FgMHz4c586dU6tj69atMDIywtmzZ7Fs2TJ8+eWXOHz4MIAXiU3Pnj1x+vRpbNu2DXFxcViyZIlwW/PmzZvw9fXFgAEDcPnyZezcuRNRUVGYMGFCua8zPDwc8+fPR3BwMOLj4xESEoJ58+Zh69atAF58G46IiEDxRaR37twJhUKBTp06AQAmTJiA6OhoRERE4PLlyxg4cCB8fX1f2cVfXFRUlMa1LY4dO4bs7Gx4e3tj2LBhiIiIqFCSeOjQIaSlpWH69Oka95c15mPcuHFl/ryW9fOVlJSE+/fvw9vbW9hmZmYGDw8PtSRUkxs3bkChUMDR0RH+/v5ISUkpUaZdu3Y4depUmfW8tXS7InvNp8268OW18btkslPcoaZNntGqVXd0WreupaSkkKOjIykUCrp+/Xp1h6NRYUYGZRz9gwozMqo7lFopJyeH4uLiKCcnp8Q+AKW+evXqpVbW0NCw1LJeXl5qZS0sLDSW00ZGRgbJZDL6/vvvNe5PSkoiAPTDDz8I265cuUIAKD4+vtR6e/fuTV988YXw3svLizp27KhW5t1336UZM2YQEdGhQ4dILBbTtWvXNNY3evRoGjt2rNq2U6dOkVgs1tjmRERBQUHk5uYmvG/cuDFt375drcyiRYvI09OTiIgePnxIenp6dPLkSWG/p6enEOOtW7dIIpHQ3bt31ero1q0bzZo1i4iItmzZQmZmZhrjISJ6+vQpAVA7R5GhQ4fSlClThPdubm60ZcsW4X3R/0VMTEyJY728vGjy5MlERLR06VICQE+ePCk1jtI8ePCAbty4UearNKdPnyYAdO/ePbXtAwcOpEGDBpV63IEDB2jXrl106dIlioyMJE9PT2rYsCFlvPS3aPXq1WRvb6/1NdVkZf3d0OZzk59ToQPC2h+imn374+XFwQwMDKo7JFbFMjMzS9338gDjhw8fllr25XFDycnJrxUXAMTHxyMvLw/dunUrs5yrq6vwb1tbWwAvYm3WrBmUSiVCQkKwa9cu3L17F/n5+cjLy4OhoWGpdRTVU3S9sbGxaNCgAZo2barx/JcuXcLly5cRHh4ubCMiqFQqJCUloXnz5mXGn5WVhZs3b2L06NEYM2aMsL2wsBBmZmYAAEtLS/To0QPh4eHo1KkTkpKSEB0dLdwu+Pvvv6FUKkvEmJeXh3r16pV5/iJFt7Be/jvw7Nkz/Pzzz2pjCYYNG4ZNmzZhxIgR5aq7CBXradGWlZUVrKysKnx8RRTvgXN1dYWHhwcaNWqEXbt2YfTo0cI+uVyO7OzsKo2ttuCkQgeKr/1RU5MKXm2UAYCRkVG1ly2NXC4vVzl9fX3h30W/ayrVi1/C5cuXY/Xq1Vi1ahVcXFxgZGSEKVOmID8/v9Q6iuopquNVcWRmZuLTTz/FpEmTSuxr2LDhK+MvSuy+//57eHh4qO0rntj5+/tj0qRJWLNmDbZv3w4XFxe4uLgIdUgkEly4cKFEMlje20716tWDSCTC06dP1bZv374dubm5arEVJU3Xr19H06ZNYWpqCgBIT08vUe+zZ8+E5Kgo6bl69So8PT3LFVeRcePGYdu2bWWWKS1JtrGxAfBiyYGixLPovTZTe+vUqYOmTZsiISFBbfuTJ09gaWlZ7nreJjymQgeEngr6d5XSmoQTClYbNGnSBHK5HEePHq1wHadPn4afnx+GDRsGNzc3ODo6aj1t0dXVFXfu3Cn1OHd3d8TFxcHJyanESyqVvrJ+a2trKBQKJCYmljjewcFBKOfn54fc3FxERkZi+/bt8Pf3F/a1bt0aSqUSDx8+LFFH0Qfqq0ilUrRo0QJxcXFq2zdt2oQvvvgCsbGxwuvSpUvo1KkTNm/eDAAwNzeHhYUFLly4oHZsRkYGEhIShGSiR48esLCwwLJlyzTG8OzZs1Lj+/LLL9Vi0PQqjYODA2xsbNR+lopmsWiT3GRmZuLmzZtqiQkA/PPPP2jdunW563mbcE+FDhTvqdDXr1mLid25c4cTClYrGBgYYMaMGZg+fTqkUik6dOiAR48e4cqVK2pdz2Vp0qQJ9uzZgzNnzqBu3bpYuXIlHjx4gBYtWpQ7Di8vL/znP//BgAEDsHLlSjg5OeHq1asQiUTw9fXFjBkz8N5772HChAn45JNPYGRkhLi4OBw+fBhr164t1zkWLlyISZMmwczMDL6+vsjLy8P58+fx9OlTBAYGAnjR+9O3b1/MmzcP8fHxGDJkiHB806ZN4e/vj48//hgrVqxA69at8ejRIxw9ehSurq7o3bt3ueLw8fFBVFSU8FyJ2NhYXLx4EeHh4WrPZwCAIUOG4Msvv8TixYuhp6eHwMBAhISEwNraGu+99x4eP36MRYsWwdLSEv379xeu4YcffsDAgQPxwQcfYNKkSXByckJaWhp27dqFlJSUUmdRvM7tD5FIhClTpmDx4sVo0qQJHBwcMG/ePCgUCvTt21co161bN/Tr108YZDt16lT06dMHjRo1wr179xAUFASJRKLW9gBw6tQpLFq0qEKxvek4qdABoacCL2Z/1KTnVBgaGsLMzIwTClYrzJs3D3p6epg/fz7u3bsHW1tbjBs3rtzHz507F4mJifDx8YGhoSHGjh2Lvn37auymL8v//M//YOrUqRgyZAiysrLg5OSEJUuWAHjRk3HixAnMmTMHnTp1AhGhcePGGDx4cLnr/+STT2BoaIjly5dj2rRpMDIygouLS4mHRvn7+6NXr174z3/+U+LWypYtW7B48WJ88cUXuHv3LiwsLPDee+/h/fffL3cco0ePRtu2bZGeng4zMzNs2rQJLVq0KJFQABA+fA8cOIAPPvgA06dPh7GxMZYuXYqbN2/C3NwcHTp0wLFjx9RuIfn5+eHMmTMIDQ3F0KFDkZGRATs7O3Tt2hWLFy8ud6zamj59OrKysjB27Fg8e/YMHTt2RGRkpNoYkps3byItLU14f+fOHQwZMgSPHz+GpaUlOnbsiD///FPtVkd0dDTS09Px4YcfVlrstZmIXmckTS2UkZEBMzMzpKenC/cFX9fSJXexbg0gNzLGvPkZGDjQCjKZTCd168KTJ0+QnZ2NBg0aVHco5aJ8/hzZf52H4bttITExqe5wap3c3FwkJSXBwcGBB+OyVxo4cCDc3d0xa9as6g6lVhg8eDDc3Nwwe/bs6g5Fp8r6u6HN52bN+Updi6mvUlr9z6m4ffs2wsLChPfm5ua1JqFgjFWt5cuXV+iZIm+j/Px8uLi44PPPP6/uUGosvv2hAy+eqPmiw0dPr3rHVBQflAlA6ylgjLG3i729PSZOnFjdYdQKUqkUc+fOre4wajTuqdCBmtJT8fIsj1fN92eMMcZ0iZMKHVDvqaiepIKnjTLGGKtunFTogPL/1/4gvEgqqnr2BycUjDHGagJOKnRAWez2R1WPqcjIyOCEgjHGWI3ASYUOqFT/JhF6VTz01dTUFCNHjuSEgjHGWLXjpEIH/n/VZQCAVCopvWAlmTt3LmJiYjihYIwxVq04qdCBf3sqCHp6lX/r4/bt2/j444/VFtPR1YO8GGOMsYri51ToQPExFVJp5eZpLz+H4scff6zU8zHGGGPlxT0VOlA0+wNApfZUvDzLIzg4uNLOxRhjjGmLkwodKCy29Lm+fuUkFTxtlDFWmsePH8PKygrJycnVHQqrgT766COsWLGiSs7FSYUOqIqWPhcBMpnuB2pyQsHeJiNGjIBIJNK4Oun48eMhEolqxOPni+IUiUTQ19eHg4MDpk+fjtzcXLVyt2/fxqhRo6BQKCCVStGoUSNMnjwZjx8/LlHn/fv3MXHiRDg6OkImk8HOzg59+vTB0aNHy4wlODgYfn5+sLe3L7EvOjoaEolE43LonTt3LrEyKgCEhYWhTp06OolNF9atWwd7e3sYGBjAw8MD586dK7WsUqnEvHnz4ODgALlcjsaNG2PRokUovnbm8+fPMWXKFDRq1AhyuRzt27fHX3/9VenXAWh3LQBw8uRJ9OnTBwqFAiKRCL/88ovW9c6dOxfBwcFar9ZbEZxU6MC/tz9EkOg4pyAiDBw4kBMK9laxs7NDREQEcnJyhG25ubnYvn17iSXAq5Ovry9SU1ORmJiIr7/+Gt999x2CgoKE/YmJiWjbti1u3LiBHTt2ICEhARs2bMDRo0fh6emJJ0+eCGWTk5PRpk0b/PHHH1i+fDn+/vtvREZGokuXLhg/fnypMWRnZ2PTpk0YPXq0xv2bNm3CxIkTcfLkSdy7d69C11nR2HRh586dCAwMRFBQEC5evAg3Nzf4+Pjg4cOHGssvXboU3377LdauXYv4+HgsXboUy5Ytw5o1a4Qyn3zyCQ4fPoyffvoJf//9N3r06AFvb2/cvXu33HF17txZbeHGyrgWAMjKyoKbmxvWrVtX4XpbtmyJxo0bY9u2bVrFWyH0lklPTycAlJ6errM6h/mnkZ3iNjVt8ozu3Hmms3qLXLp0iTw8PCglJUXndddEhRkZlHH0DyrMyKjuUGqlnJwciouLo5ycnOoOpUICAgLIz8+PWrZsSdu2bRO2h4eHk6urK/n5+VFAQAARESmVSgoJCSF7e3syMDAgV1dX2r17t1p9Bw8epA4dOpCZmRmZm5tT7969KSEhQa2Ml5cXTZw4kaZNm0Z169Yla2trCgoKKlecxfXv359at24tvPf19aUGDRpQdna2WrnU1FQyNDSkcePGCdt69uxJ9evXp8zMzBLnevr0aalx7N69mywtLTXue/78ORkbG9PVq1dp8ODBFBwcrLbfy8uLJk+eXOK4LVu2kJmZ2WvHpgvt2rWj8ePHC++VSiUpFAoKDQ3VWL537940atQotW39+/cnf39/IiLKzs4miURC+/btUyvj7u5Oc+bMKXdcXl5etGXLlnKXJ9L+Wl4GgPbu3VuhehcuXEgdO3Yste6y/m5o87nJsz90oPhATV2NqVCpVMLjvl1dXREdHV3tS6qz2m34cEBDj3ulq1cP+Okn7Y8bNWoUtmzZAn9/fwDA5s2bMXLkSBw/flwoExoaim3btmHDhg1o0qQJTp48iWHDhsHS0hJeXl4AXnzTCwwMhKurKzIzMzF//nz069cPsbGxao/U37p1KwIDA3H27FlER0djxIgR6NChA7p3716ueP/55x+cOXMGjRo1AgA8efIEhw4dQnBwMORyuVpZGxsb+Pv7Y+fOnVi/fj2ePn2KyMhIBAcHw8jIqETdL9+KKO7UqVNo06aNxn27du1Cs2bN4OzsjGHDhmHKlCmYNWuWVn9Lnjx5UuHYACAkJAQhISFllomLi9PYA5Wfn48LFy5g1qxZwjaxWAxvb29ER0drrKt9+/bYuHEjrl+/jqZNm+LSpUuIiorCypUrAQCFhYVQKpUwMDBQO04ulyMqKqrMOF9HRa5Fl/W2a9cOwcHByMvLg0wmq/D5XoWTCh0QxlRAN7M/UlJS0KdPH6xduxadOnUCUH0rn7I3x+PHQBm9rDXOsGHDMGvWLNy6dQsAcPr0aURERAhJRV5eHkJCQnDkyBF4enoCABwdHREVFYXvvvtOSCoGDBigVu/mzZthaWmJuLg4tGzZUtju6uoq3Lpo0qQJ1q5di6NHj5aZVOzbtw/GxsYoLCxEXl4exGIx1q5dCwC4ceMGiAjNmzfXeGzz5s3x9OlTPHr0CMnJySAiNGvWTOt2unXrFhQKhcZ9mzZtwrBhwwC8uFWTnp6OEydOoHPnzuWuPyEhocKxAcC4ceMwaNCgMsuUFn9aWhqUSiWsra3VtltbW+Pq1asaj5k5cyYyMjLQrFkzSCQSKJVKBAcHC8mpiYkJPD09sWjRIjRv3hzW1tbYsWMHoqOj4eTkVGqMLydHOTk5+PPPPzFhwgRhW2nJUUWvpTzKW69CoUB+fj7u378vJL6VgZMKHRBmfwDQ13+9YSopKSno0qULEhMTMXHiRFy8eLHKFyhjb6Z69WrXeS0tLdG7d2+EhYWBiNC7d29YWFgI+xMSEpCdnV3iQz8/Px+tW7cW3t+4cQPz58/H2bNnkZaWBtX/fwtISUkpkVQUZ2trW+a9bgDo0qULvv32W2RlZeHrr7+Gnp5eiSSGig0QLE15ypQmJyenxLduALh27RrOnTuHvXv3AgD09PQwePBgbNq0Sauk4nViAwBzc3OYm5u/Vh3a2LVrF8LDw7F9+3a88847iI2NxZQpU6BQKBAQEAAA+OmnnzBq1CjUr18fEokE7u7uGDJkCC5cuFBqvS8nR/7+/hgwYAD69+8vbCstOaoJinrLsrOzK/U8nFToQNHtD5GIIJFUvEeheELh6OiI//3f/+WEgulMRW5BVLdRo0YJ3wRfHqhW9ETZ/fv3o379+mr7infv9unTB40aNcL3338PhUIBlUqFli1bIj8/X+0YfX19tfcikUhIQEpjZGQkfLvdvHkz3NzchEGTTk5OEIlEiI+PR79+/UocGx8fj7p168LS0hJ6enoQiUQV+sZqYWGBp0+flti+adMmFBYWqn3QERFkMhnWrl0LMzMzmJqaapwR8OzZM5iZmQF40WtT0diA17v9YWFhAYlEggcPHqhtf/DgAWxsbDTWNW3aNMycORMfffQRAMDFxQW3bt1CaGiokFQ0btwYJ06cQFZWFjIyMmBra4vBgwfD0dGx1BhfTo7kcjmsrKzK7N143WvRZb1Fg4ItLS0rfK7y4E8sHSh6oqZEUvHbFC8nFDzLg7EXXfb5+fkoKCiAj4+P2r4WLVpAJpMhJSUFTk5Oaq+i353Hjx/j2rVrmDt3Lrp16ybccqgMYrEYs2fPxty5c5GTk4N69eqhe/fuWL9+vdosFuDF9Mzw8HAMHjwYIpEI5ubm8PHxwbp165CVlVWi7mfPnpV63tatWyMuLk5tW2FhIX788UesWLECsbGxwuvSpUtQKBTYsWMHAMDZ2RkXL14sUefFixfRtGlTAHit2IAX3/CLx6DpVdo3fKlUijZt2qhNW1WpVMLsGU2ys7NLfBmTSCQaE0QjIyPY2tri6dOnOHToEPz8/Mq8ltdRkWvRZb3//PMPGjRooNbbVyleOZTzDVMZsz969XxGdorb1LLlkwqNuL916xY5OjoSAHJ0dHxrZnmUhmd/vJ43ZfZHkfT0dLXf1+KzP+bMmUP16tWjsLAwSkhIoAsXLtA333xDYWFhRPRiFHy9evVo2LBhdOPGDTp69Ci9++67JUbRa5oFUfw85YmTiKigoIDq169Py5cvJyKi69evk4WFBXXq1IlOnDhBKSkpdPDgQWrZsiU1adKEHj9+LBx78+ZNsrGxoRYtWtCePXvo+vXrFBcXR6tXr6ZmzZqVGsfly5dJT0+Pnjx5Imzbu3cvSaVSevas5Gy06dOnU9u2bYVzGhgY0MSJE+nSpUt09epVWrFiBenp6dHBgwdfOzZdiIiIIJlMRmFhYRQXF0djx46lOnXq0P3794mIaM2aNdS1a1ehfEBAANWvX5/27dtHSUlJ9PPPP5OFhQVNnz5dKBMZGUkHDx6kxMRE+v3338nNzY08PDwoPz+/1DieP39OqampZb4KCwt1ei1F542JiaGYmBgCQCtXrqSYmBi6detWuestapeXZ8UUp6vZH5xU6ICPz4ukws31MeXm5mp9/Pjx4zmhKIaTitfzpiUVLyv+Ya9SqWjVqlXk7OxM+vr6ZGlpST4+PnTixAmh/OHDh6l58+Ykk8nI1dWVjh8/XmlJBRFRaGgoWVpaCtMvk5OTKSAggKytrUlfX5/s7Oxo4sSJlJaWVuLYe/fu0fjx46lRo0YklUqpfv369MEHH9CxY8dKjYPoxZTCDRs2CO/ff/996tWrl8ayZ8+eJQB06dIlIiI6d+4cde/enSwtLcnMzIw8PDw0TlusaGy6sGbNGmrYsCFJpVJq164d/fnnn8K+oKAgatSokfA+IyODJk+eTA0bNiQDAwNydHSkOXPmUF5enlBm586d5OjoSFKplGxsbGj8+PEaE7DigoKCCECZr6SkJJ1eCxHRsWPHNJ7r5Z/NsurNyckhMzMzio6OLjUuXSUVIqLXHIVTy2RkZMDMzAzp6ek6W9mzR/cMXI3LgKW1HH+eNS1xb/ZV8vPzERgYiBkzZvAtDwDK58+R/dd5GL7bFhITk+oOp9bJzc1FUlISHBwcNA7gY2+e/fv3Y9q0afjnn394HBYr4dtvv8XevXvx+++/l1qmrL8b2nxu8kBNHSgaUyHWYkzF48ePYW5uDpFIBKlUKkxDY4wxbfXu3Rs3btzA3bt3+YsJK0FfX1/tiaKViVNaHSia/SGRULmSitu3b6Ndu3aYNGnSa0/XYowxAJgyZQonFEyjTz75BM7OzlVyLk4qdKBQi6Si+OJgBw4cUHv2P2OMMVabcVKhA6qiKaXispMKTauN1quuJxIxxhhjOsZJhQ7821NR+pgKXr6cMcbYm46TCh1QFXv4lSacUDDGGHsbcFKhA0rVvz0Vmpw/fx7JycmcUDDGGHuj8ZRSHVAWlp1U9OvXD3v27EHbtm05oWCMMfbG4qTiNalUQNGsUD29f6eH3r59G2KxWFjoSNOCQowxxtibhG9/vKaiB18B/yYVRWMoOnfujLt371ZTZIwxxljV4p6K11RY+O+/xRJRiUGZr1o6mbGqpFQqq/RnUiwWQ1LafcE3TOfOndGqVSusWrWqRtTDWHWoEUnFunXrsHz5cty/fx9ubm5Ys2YN2rVrV2r53bt3Y968eUhOTkaTJk2wdOlS9OrVqwoj/lfxnoq8vGx07uzNszxYjaRUKnHnzh0UFBRU2Tn19fXRoEGDKk0sTp48ieXLl+PChQtITU3F3r170bdv3wrXxx/yjJVftd/+2LlzJwIDAxEUFISLFy/Czc0NPj4+ePjwocbyZ86cwZAhQzB69GjExMSgb9++6Nu3L/75558qjvyFoqSiUKnE8WNHOaFgNZZKpUJBQQHEYjGkUmmlv8RiMQoKCl67Z6Rz584ICwsrd/msrCy4ublh3bp1r3Vexpj2qj2pWLlyJcaMGYORI0eiRYsW2LBhAwwNDbF582aN5VevXg1fX19MmzYNzZs3x6JFi+Du7l5tC3IVFgIFBQV4mPYEmVnpnFCwGk9PT6/KXtWhZ8+eWLx4sVaDo/fs2QMXFxfI5XLUq1cP3t7eyMrKwogRI3DixAmsXr0aIpEIIpEIycnJyMrKwscffwxjY2PY2tpixYoVFYq1PPWoVCqEhobCwcEBcrkcbm5u2LNnDwBg48aNUCgUJRI3Pz8/jBo1qkIxMfY6qvX2R35+Pi5cuIBZs2YJ28RiMby9vREdHa3xmOjoaAQGBqpt8/HxwS+//KKxfF5eHvLy8oT3GRkZrx94MUolABEgAmBsJMfRffugqFMHyufPdXqet4kqO7u6Q2BvkdTUVAwZMgTLli1Dv3798Pz5c5w6dQpEhNWrV+P69eto2bIlvvzySwCApaUlJk6ciBMnTuDXX3+FlZUVZs+ejYsXL6JVq1ZanXvatGmvrCc0NBTbtm3Dhg0b0KRJE5w8eRLDhg2DpaUlBg4ciIkTJ+LYsWPo1q0bAODJkyeIjIzEgQMHdNVEjJVbtSYVaWlpUCqVsLa2VttubW2Nq1evajzm/v37Gsvfv39fY/nQ0FAsXLhQNwFrUFgI6IslsKlbFx3d2qBe6n1kp2qOhWlH9JYM8GPqQkJCEBISIrzPycnBn3/+iQkTJgjb4uLi0LBhQ52cLzU1FYWFhejfvz8aNWoEAHBxcRH2S6VSGBoawsbGBgCQmZmJTZs2Ydu2bcIH+datW9GgQQOtzlueevLy8hASEoIjR47A09MTAODo6IioqCh899132L59O3r27Int27cLdezZswcWFhbo0qVLBVuEsYqrEQM1K9OsWbPUejYyMjJ0emvCxgbYf0CFuzefoX4DBxgq5Dqr+20mkkggNjSs7jBYNRg3bhwGDRokvPf398eAAQPQv39/YZtCodDZ+dzc3NCtWze4uLjAx8cHPXr0wIcffoi6detqLH/z5k3k5+fDw8ND2GZubq710tLlqSchIQHZ2dno3r272rH5+flo3bo1gBftM2bMGKxfvx4ymQzh4eH46KOPIBZX+91t9haq1qTCwsICEokEDx48UNv+4MED4VvBy2xsbLQqL5PJIJPJdBOwBmIx0NDeBA3tW1XaORh7m5ibm8Pc3Fx4L5fLYWVlBScnp0o5n0QiweHDh3HmzBn8/vvvWLNmDebMmYOzZ8/CwcGhUs5ZXpmZmQCA/fv3Cw/SK1L0d61Pnz4gIuzfvx/vvvsuTp06ha+//rrKY2UMqOaBmlKpFG3atMHRo0eFbSqVCkePHhW6+l7m6empVh4ADh8+XGp5xhh7FZFIhA4dOmDhwoWIiYmBVCrF3r17Abz4O6UsNne8cePG0NfXx9mzZ4VtT58+xfXr17U6Z3nqadGiBWQyGVJSUuDk5KT2KupxNTAwQP/+/REeHo4dO3bA2dkZ7u7uFWoHxl5Xtd/+CAwMREBAANq2bYt27dph1apVyMrKwsiRIwEAH3/8MerXr4/Q0FAAwOTJk+Hl5YUVK1agd+/eiIiIwPnz57Fx48bqvAzGao3C4k9sq4HnyczMFL6hA0BERAQAqI2bsrS0LPXZF5mZmUhISBDeJyUlITY2Fubm5hrHYZw9exZHjx5Fjx49YGVlhbNnz+LRo0do3rw5AMDe3h5nz55FcnIyjI2NYW5ujtGjR2PatGmoV68erKysMGfOnBK3G9auXYu9e/eW+BJUxNjY+JX1mJiYYOrUqfj888+hUqnQsWNHpKen4/Tp0zA1NUVAQACAF7dA3n//fVy5cgXDhg0rca5XxcKYrlR7UjF48GA8evQI8+fPx/3799GqVStERkYKgzFTUlLUfsnat2+P7du3Y+7cuZg9ezaaNGmCX375BS1btqyuS2CsVhCLxdDX10dBQQHy8/Or5Jz6+vpa39v/6quvXjm4OikpCfb29hr3nT9/Xm2QYtGYqoCAAI3PuzA1NcXJkyexatUqZGRkoFGjRlixYgV69uwJAJg6dSoCAgLQokUL5OTkICkpCcuXL0dmZib69OkDExMTfPHFF0hPT1erNy0tDTdv3izzOspTz6JFi2BpaYnQ0FAkJiaiTp06cHd3x+zZs4UyXbt2hbm5Oa5du4ahQ4eWOE95YmFMF0RERK8u9ubIyMiAmZkZ0tPTYWpqWt3hMKZzubm5SEpKgoODAwwMDNT28WO6GWOalPV3Q5vPzWrvqWCMVR2JRMIf8oyxSsNzjhhjjDGmE5xUMMYYY0wnOKlgjDHGmE5wUsHYG+otG4PNGHsNuvp7wUkFY28YfX19AEA2L8zGGCunomnmrzuQm2d/MPaGkUgkqFOnDh4+fAgAMDQ0hEgkquaoGGM1lUqlwqNHj2BoaAg9vddLCzipYOwNVLQWTlFiwRhjZRGLxWjYsOFrfwHhpIKxN5BIJIKtrS2srKxQUFBQ3eEwxmo4qVSqk5VtOalg7A3GD7tijFUlHqjJGGOMMZ3gpIIxxhhjOsFJBWOMMcZ04q0bU1H0gI+MjIxqjoQxxhir+Yo+L8vzgKy3Lql4/vw5AMDOzq6aI2GMMcZqj+fPn8PMzKzMMiJ6y57lq1KpcO/ePZiYmOjsgUAZGRmws7PD7du3X7nWPHs1bk/d4vbULW5P3eL21K3KaE8iwvPnz6FQKF457fSt66kQi8Vo0KBBpdRtamrKvxQ6xO2pW9yeusXtqVvcnrql6/Z8VQ9FER6oyRhjjDGd4KSCMcYYYzrBSYUOyGQyBAUFQSaTVXcobwRuT93i9tQtbk/d4vbUrepuz7duoCZjjDHGKgf3VDDGGGNMJzipYIwxxphOcFLBGGOMMZ3gpIIxxhhjOsFJRTmtW7cO9vb2MDAwgIeHB86dO1dm+d27d6NZs2YwMDCAi4sLDhw4UEWR1g7atOf333+PTp06oW7duqhbty68vb1f2f5vG21/PotERERAJBKhb9++lRtgLaNtez579gzjx4+Hra0tZDIZmjZtyr/zxWjbnqtWrYKzszPkcjns7Ozw+eefIzc3t4qirdlOnjyJPn36QKFQQCQS4ZdffnnlMcePH4e7uztkMhmcnJwQFhZWeQESe6WIiAiSSqW0efNmunLlCo0ZM4bq1KlDDx480Fj+9OnTJJFIaNmyZRQXF0dz584lfX19+vvvv6s48ppJ2/YcOnQorVu3jmJiYig+Pp5GjBhBZmZmdOfOnSqOvGbStj2LJCUlUf369alTp07k5+dXNcHWAtq2Z15eHrVt25Z69epFUVFRlJSURMePH6fY2Ngqjrxm0rY9w8PDSSaTUXh4OCUlJdGhQ4fI1taWPv/88yqOvGY6cOAAzZkzh37++WcCQHv37i2zfGJiIhkaGlJgYCDFxcXRmjVrSCKRUGRkZKXEx0lFObRr147Gjx8vvFcqlaRQKCg0NFRj+UGDBlHv3r3Vtnl4eNCnn35aqXHWFtq258sKCwvJxMSEtm7dWlkh1ioVac/CwkJq3749/fDDDxQQEMBJRTHatue3335Ljo6OlJ+fX1Uh1iratuf48eOpa9euatsCAwOpQ4cOlRpnbVSepGL69On0zjvvqG0bPHgw+fj4VEpMfPvjFfLz83HhwgV4e3sL28RiMby9vREdHa3xmOjoaLXyAODj41Nq+bdJRdrzZdnZ2SgoKIC5uXllhVlrVLQ9v/zyS1hZWWH06NFVEWatUZH2/O233+Dp6Ynx48fD2toaLVu2REhICJRKZVWFXWNVpD3bt2+PCxcuCLdIEhMTceDAAfTq1atKYn7TVPXn0Vu3oJi20tLSoFQqYW1trbbd2toaV69e1XjM/fv3NZa/f/9+pcVZW1SkPV82Y8YMKBSKEr8ob6OKtGdUVBQ2bdqE2NjYKoiwdqlIeyYmJuKPP/6Av78/Dhw4gISEBHz22WcoKChAUFBQVYRdY1WkPYcOHYq0tDR07NgRRITCwkKMGzcOs2fProqQ3zilfR5lZGQgJycHcrlcp+fjngpWqyxZsgQRERHYu3cvDAwMqjucWuf58+cYPnw4vv/+e1hYWFR3OG8ElUoFKysrbNy4EW3atMHgwYMxZ84cbNiwobpDq5WOHz+OkJAQrF+/HhcvXsTPP/+M/fv3Y9GiRdUdGisH7ql4BQsLC0gkEjx48EBt+4MHD2BjY6PxGBsbG63Kv00q0p5FvvrqKyxZsgRHjhyBq6trZYZZa2jbnjdv3kRycjL69OkjbFOpVAAAPT09XLt2DY0bN67coGuwivx82traQl9fHxKJRNjWvHlz3L9/H/n5+ZBKpZUac01WkfacN28ehg8fjk8++QQA4OLigqysLIwdOxZz5syBWMzfhbVR2ueRqampznspAO6peCWpVIo2bdrg6NGjwjaVSoWjR4/C09NT4zGenp5q5QHg8OHDpZZ/m1SkPQFg2bJlWLRoESIjI9G2bduqCLVW0LY9mzVrhr///huxsbHC64MPPkCXLl0QGxsLOzu7qgy/xqnIz2eHDh2QkJAgJGcAcP36ddja2r7VCQVQsfbMzs4ukTgUJWzES1Vprco/jypl+OcbJiIigmQyGYWFhVFcXByNHTuW6tSpQ/fv3yciouHDh9PMmTOF8qdPnyY9PT366quvKD4+noKCgnhKaTHatueSJUtIKpXSnj17KDU1VXg9f/68ui6hRtG2PV/Gsz/UadueKSkpZGJiQhMmTKBr167Rvn37yMrKihYvXlxdl1CjaNueQUFBZGJiQjt27KDExET6/fffqXHjxjRo0KDquoQa5fnz5xQTE0MxMTEEgFauXEkxMTF069YtIiKaOXMmDR8+XChfNKV02rRpFB8fT+vWreMppTXBmjVrqGHDhiSVSqldu3b0559/Cvu8vLwoICBArfyuXbuoadOmJJVK6Z133qH9+/dXccQ1mzbt2ahRIwJQ4hUUFFT1gddQ2v58FsdJRUnatueZM2fIw8ODZDIZOTo6UnBwMBUWFlZx1DWXNu1ZUFBACxYsoMaNG5OBgQHZ2dnRZ599Rk+fPq36wGugY8eOafx7WNSGAQEB5OXlVeKYVq1akVQqJUdHR9qyZUulxcdLnzPGGGNMJ3hMBWOMMcZ0gpMKxhhjjOkEJxWMMcYY0wlOKhhjjDGmE5xUMMYYY0wnOKlgjDHGmE5wUsEYY4wxneCkgjHGGGM6wUkFY2+QsLAw1KlTp7rDqDCRSIRffvmlzDIjRoxA3759qyQexph2OKlgrIYZMWIERCJRiVdCQkJ1h4awsDAhHrFYjAYNGmDkyJF4+PChTupPTU1Fz549AQDJyckQiUSIjY1VK7N69WqEhYXp5HylWbBggXCdEokEdnZ2GDt2LJ48eaJVPZwAsbcNL33OWA3k6+uLLVu2qG2ztLSspmjUmZqa4tq1a1CpVLh06RJGjhyJe/fu4dChQ69dd2nLYRdnZmb22ucpj3feeQdHjhyBUqlEfHw8Ro0ahfT0dOzcubNKzs9YbcQ9FYzVQDKZDDY2NmoviUSClStXwsXFBUZGRrCzs8Nnn32GzMzMUuu5dOkSunTpAhMTE5iamqJNmzY4f/68sD8qKgqdOnWCXC6HnZ0dJk2ahKysrDJjE4lEsLGxgUKhQM+ePTFp0iQcOXIEOTk5UKlU+PLLL9GgQQPIZDK0atUKkZGRwrH5+fmYMGECbG1tYWBggEaNGiE0NFSt7qLbHw4ODgCA1q1bQyQSoXPnzgDUv/1v3LgRCoVCbdlxAPDz88OoUaOE97/++ivc3d1hYGAAR0dHLFy4EIWFhWVep56eHmxsbFC/fn14e3tj4MCBOHz4sLBfqVRi9OjRcHBwgFwuh7OzM1avXi3sX7BgAbZu3Ypff/1V6PU4fvw4AOD27dsYNGgQ6tSpA3Nzc/j5+SE5ObnMeBirDTipYKwWEYvF+Oabb3DlyhVs3boVf/zxB6ZPn15qeX9/fzRo0AB//fUXLly4gJkzZ0JfXx8AcPPmTfj6+mLAgAG4fPkydu7ciaioKEyYMEGrmORyOVQqFQoLC7F69WqsWLECX331FS5fvgwfHx988MEHuHHjBgDgm2++wW+//YZdu3bh2rVrCA8Ph729vcZ6z507BwA4cuQIUlNT8fPPP5coM3DgQDx+/BjHjh0Ttj158gSRkZHw9/cHAJw6dQoff/wxJk+ejLi4OHz33XcICwtDcHBwua8xOTkZhw4dglQqFbapVCo0aNAAu3fvRlxcHObPn4/Zs2dj165dAICpU6di0KBB8PX1RWpqKlJTU9G+fXsUFBTAx8cHJiYmOHXqFE6fPg1jY2P4+voiPz+/3DExViNV2vqnjLEKCQgIIIlEQkZGRsLrww8/1Fh29+7dVK9ePeH9li1byMzMTHhvYmJCYWFhGo8dPXo0jR07Vm3bqVOnSCwWU05OjsZjXq7/+vXr1LRpU2rbti0RESkUCgoODlY75t1336XPPvuMiIgmTpxIXbt2JZVKpbF+ALR3714iIkpKSiIAFBMTo1bm5aXa/fz8aNSoUcL77777jhQKBSmVSiIi6tatG4WEhKjV8dNPP5Gtra3GGIiIgoKCSCwWk5GRERkYGAjLS69cubLUY4iIxo8fTwMGDCg11qJzOzs7q7VBXl4eyeVyOnToUJn1M1bT8ZgKxmqgLl264NtvvxXeGxkZAXjxrT00NBRXr15FRkYGCgsLkZubi+zsbBgaGpaoJzAwEJ988gl++uknoQu/cePGAF7cGrl8+TLCw8OF8kQElUqFpKQkNG/eXGNs6enpMDY2hkqlQm5uLjp27IgffvgBGRkZuHfvHjp06KBWvkOHDrh06RKAF7cuunfvDmdnZ/j6+uL9999Hjx49Xqut/P39MWbMGKxfvx4ymQzh4eH46KOPIBaLhes8ffq0Ws+EUqkss90AwNnZGb/99htyc3Oxbds2xMbGYuLEiWpl1q1bh82bNyMlJQU5OTnIz89Hq1atyoz30qVLSEhIgImJidr23Nxc3Lx5swItwFjNwUkFYzWQkZERnJyc1LYlJyfj/fffx3//+18EBwfD3NwcUVFRGD16NPLz8zV+OC5YsABDhw7F/v37cfDgQQQFBSEiIgL9+vVDZmYmPv30U0yaNKnEcQ0bNiw1NhMTE1y8eBFisRi2traQy+UAgIyMjFdel7u7O5KSknDw4EEcOXIEgwYNgre3N/bs2fPKY0vTp08fEBH279+Pd999F6dOncLXX38t7M/MzMTChQvRv3//EscaGBiUWq9UKhX+D5YsWYLevXtj4cKFWLRoEQAgIiICU6dOxYoVK+Dp6QkTExMsX74cZ8+eLTPezMxMtGnTRi2ZK1JTBuMyVlGcVDBWS1y4cAEqlQorVqwQvoUX3b8vS9OmTdG0aVN8/vnnGDJkCLZs2YJ+/frB3d0dcXFxJZKXVxGLxRqPMTU1hUKhwOnTp+Hl5SVsP336NNq1a6dWbvDgwRg8eDA+/PBD+Pr64smTJzA3N1err2j8glKpLDMeAwMD9O/fH+Hh4UhISICzszPc3d2F/e7u7rh27ZrW1/myuXPnomvXrvjvf/8rXGf79u3x2WefCWVe7mmQSqUl4nd3d8fOnTthZWUFU1PT14qJsZqGB2oyVks4OTmhoKAAa9asQWJiIn766Sds2LCh1PI5OTmYMGECjh8/jlu3buH06dP466+/hNsaM2bMwJkzZzBhwgTExsbixo0b+PXXX7UeqFnctGnTsHTpUuzcuRPXrl3DzJkzERsbi8mTJwMAVq5ciR07duDq1au4fv06du/eDRsbG40P7LKysoJcLkdkZCQePHiA9PT0Us/r7++P/fv3Y/PmzcIAzSLz58/Hjz/+iIULF+LKlSuIj49HREQE5s6dq9W1eXp6wtXVFSEhIQCAJk2a4Pz58zh06BCuX7+OefPm4a+//lI7xt7eHpcvX8a1a9eQlpaGgoIC+Pv7w8LCAn5+fjh16hSSkpJw/PhxTJo0CXfu3NEqJsZqnOoe1MEYU6dpcF+RlStXkq2tLcnlcvLx8aEff/yRANDTp0+JSH0gZV5eHn300UdkZ2dHUqmUFAoFTZgwQW0Q5rlz56h79+5kbGxMRkZG5OrqWmKgZXEvD9R8mVKppAULFlD9+vVJX1+f3Nzc6ODBg8L+jRs3UqtWrcjIyIhMTU2pW7dudPHiRWE/ig3UJCL6/vvvyc7OjsRiMXl5eZXaPkqlkmxtbQkA3bx5s0RckZGR1L59e5LL5WRqakrt2rWjjRs3lnodQUFB5ObmVmL7jh07SCaTUUpKCuXm5tKIESPIzMyM6tSpQ//9739p5syZasc9fPhQaF8AdOzYMSIiSk1NpY8//pgsLCxIJpORo6MjjRkzhtLT00uNibHaQEREVL1pDWOMMcbeBHz7gzHGGGM6wUkFY4wxxnSCkwrGGGOM6QQnFYwxxhjTCU4qGGOMMaYTnFQwxhhjTCc4qWCMMcaYTnBSwRhjjDGd4KSCMcYYYzrBSQVjjDHGdIKTCsYYY4zpxP8BuVqVhF8WQWwAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 600x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "label=\"ROC curve of testing data\"\n",
+ "roc(X_test,y_test,model,label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "c1095af0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# for important features:\n",
+ "important_feat = model_xgb.feature_importances_\n",
+ "#get indices of those important features\n",
+ "idx = important_feat.argsort(kind= \"quicksort\")\n",
+ "idx= idx[::-1][:50]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "ae7e0162",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df1 = X_selected.T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "1d97f818",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "top_met = df1.iloc[idx]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "4cd4227b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['RUVBL1', 'TRIP13', 'COL1A1', 'NDRG2', 'MCM2', 'DUOX1', 'FSCN1',\n",
+ " 'TMPRSS11E', 'ANO1', 'IGFBP3', 'TIAM1', 'GPX3', 'HOPX', 'CFD',\n",
+ " 'PPP1R3C', 'TMPRSS11D', 'COL5A2', 'IL1RN', 'LYPD3', 'SLURP1', 'MYH10',\n",
+ " 'SERPINB2', 'TSPAN6', 'SIM2', 'ALOX12', 'TYMP', 'SPRR3', 'MMP10',\n",
+ " 'ERCC3', 'EMP1', 'FLG', 'GABRP', 'GALE', 'GALNT1', 'HSPB8', 'HSPBAP1',\n",
+ " 'HSPD1', 'ENTPD6', 'ACPP', 'AIM2', 'ALDH9A1', 'AQP3', 'ATP6V1D',\n",
+ " 'ZNF185', 'CES2', 'CH25H', 'CLIC3', 'CYP4B1', 'CRISP3', 'CRNN'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "top_met.index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "8f6d88bb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['ACLY', 'ACPP', 'AIM2', 'ALDH9A1', 'ALOX12', 'ANO1', 'AQP3', 'ATP6V1D',\n",
+ " 'CCNG2', 'CES2', 'CFD', 'CH25H', 'CLIC3', 'COL1A1', 'COL5A2', 'CRABP2',\n",
+ " 'CRISP3', 'CRNN', 'CYP4B1', 'DHRS1', 'DHRS2', 'DUOX1', 'DUSP5', 'ECM1',\n",
+ " 'ECT2', 'EFNA1', 'EMP1', 'ENTPD6', 'ERCC3', 'FLG', 'FSCN1', 'GABRP',\n",
+ " 'GALE', 'GALNT1', 'GPX3', 'HOPX', 'HSPB8', 'HSPBAP1', 'HSPD1', 'ID4',\n",
+ " 'IFI35', 'IGF2BP2', 'IGFBP3', 'IL1RN', 'INPP1', 'KANK1', 'KLK13',\n",
+ " 'KRT4', 'LAMC2', 'LCN2', 'LEPROTL1', 'LYPD3', 'MAL', 'MCM2', 'MMP10',\n",
+ " 'MUC1', 'MYH10', 'NDRG2', 'NT5C2', 'PCSK5', 'PHLDA1', 'PITX1',\n",
+ " 'PPP1R3C', 'PSMB9', 'PTN', 'RAB11FIP1', 'RANBP9', 'RHCG', 'RND3',\n",
+ " 'RPN1', 'RUVBL1', 'SCNN1A', 'SERPINB13', 'SERPINB2', 'SIM2', 'SLC2A1',\n",
+ " 'SLK', 'SLURP1', 'SPINK5', 'SPRR3', 'SSRP1', 'STK24', 'SYNPO2L',\n",
+ " 'TAPBP', 'TFAP2B', 'TGIF1', 'TIAM1', 'TJP1', 'TMF1', 'TMPRSS11D',\n",
+ " 'TMPRSS11E', 'TRIP13', 'TSPAN6', 'TST', 'TYMP', 'UCHL1', 'ZBTB16',\n",
+ " 'ZNF185'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_selected.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4218303a",
+ "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": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}