ft_eng_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kurma\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "import os\n",
    "import random\n",
    "from tqdm import tqdm\n",
    "import featuretools as ft\n",
    "from joblib import dump, load\n",
    "from imblearn.combine import SMOTETomek\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, minmax_scale, scale, power_transform, quantile_transform, normalize \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import make_scorer, log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score, train_test_split\n",
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "from  sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import make_pipeline,Pipeline\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.preprocessing import  QuantileTransformer, PowerTransformer\n",
    "\n",
    "from lightgbm import LGBMClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "RANDOM_SEED = 777\n",
    "np.random.seed(RANDOM_SEED)\n",
    "random.seed(RANDOM_SEED)\n",
    "os.environ['PYTHONHASHSEED'] = str(RANDOM_SEED)\n",
    "    \n",
    "import warnings\n",
    "warnings.filterwarnings(\n",
    "    action='ignore',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = pd.read_csv('data/metadata.csv')\n",
    "sup_metadata = pd.read_csv('data/supplemental_metadata.csv')\n",
    "train_labels = pd.read_csv('data/train_labels.csv', index_col=\"sample_id\")\n",
    "val_labels = pd.read_csv('data/val_labels.csv',index_col='sample_id')\n",
    "targets = pd.read_csv('targets.csv',index_col='sample_id')\n",
    "submission_format = pd.read_csv('data/submission_format.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_50 = pd.read_csv('train/train_50.csv',index_col=0,header=[0,1])\n",
    "val_50 = pd.read_csv('train/val_50.csv',index_col=0,header=[0,1])\n",
    "test_50 = pd.read_csv('test/test_df_50.csv',index_col=0,header=[0,1])\n",
    "\n",
    "train_50.sort_index(axis=1,inplace=True)\n",
    "val_50.sort_index(axis=1,inplace=True)\n",
    "test_50.sort_index(axis=1,inplace=True)\n",
    "\n",
    "val_50.index = metadata[metadata.split == 'val'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1059, 4200), (804, 4200))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_50 = pd.concat([train_50,val_50],axis=0)\n",
    "test_set_50 = pd.concat([val_50,test_50],axis=0)\n",
    "train_set_50.shape, test_set_50.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_temp_100 = pd.read_csv('train/train_temp_qt_v1_100.csv',index_col=0,header=[0,1])\n",
    "val_temp_100 = pd.read_csv('train/val_temp_qt_v1_100.csv',index_col=0,header=[0,1])\n",
    "test_temp_100 = pd.read_csv('test/test_temp_qt_v1_100.csv',index_col=0,header=[0,1])\n",
    "\n",
    "train_temp_100.sort_index(axis=1,inplace=True)\n",
    "val_temp_100.sort_index(axis=1,inplace=True)\n",
    "test_temp_100.sort_index(axis=1,inplace=True)\n",
    "\n",
    "val_temp_100.index = metadata[metadata.split == 'val'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "m/z               0                                                    \\\n",
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       "sample_id                                                               \n",
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       "\n",
       "m/z                                                                     ...  \\\n",
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       "sample_id                                                               ...   \n",
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       "\n",
       "m/z                 99                                                \\\n",
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       "sample_id                                                              \n",
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       "765                0.0          0.0   0.266767   0.226226   0.305806   \n",
       "\n",
       "m/z                                                                \n",
       "time_bin  (500, 600] (600, 700] (700, 800] (800, 900] (900, 1000]  \n",
       "sample_id                                                          \n",
       "0           0.000000   0.000000   0.000000   0.000000    0.000000  \n",
       "1           0.000000   0.000000   0.000000   0.000000    0.000000  \n",
       "2           0.505402   0.484424   0.573439   0.546809    0.549400  \n",
       "3           0.553812   0.540699   0.521665   0.528313    0.490145  \n",
       "4           0.503662   0.396654   0.475675   0.478454    0.423608  \n",
       "...              ...        ...        ...        ...         ...  \n",
       "761         0.277277   0.250250   0.313814   0.277277    0.250250  \n",
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       "763         0.164665   0.241742   0.164665   0.164665    0.229229  \n",
       "764         0.260260   0.260260   0.260260   0.312813    0.312813  \n",
       "765         0.266767   0.266767   0.226226   0.305806    0.266767  \n",
       "\n",
       "[766 rows x 1600 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_temp_100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 807 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "test_100 = pd.read_csv('test/test_100.csv',index_col=0,header=[0,1])\n",
    "train_100 = pd.read_csv('train/train_100.csv',index_col=0,header=[0,1])\n",
    "val_100 = pd.read_csv('train/val_100.csv',index_col=0,header=[0,1])\n",
    "\n",
    "val_100.index = metadata[metadata.split == 'val'].sample_id\n",
    "\n",
    "val_100.sort_index(inplace=True,axis=1)\n",
    "test_100.sort_index(inplace=True,axis=1)\n",
    "train_100.sort_index(inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1059, 1600), (804, 1600))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_100 = pd.concat([train_100,val_100],axis=0)\n",
    "test_set_100 = pd.concat([val_100,test_100],axis=0)\n",
    "train_set_100.shape, test_set_100.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_temp = pd.read_csv('train/train_temp_df_0.csv',index_col=0,header=[0,1])\n",
    "train_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_temp_1 = pd.read_csv('train/val_temp_df_1.csv',index_col=0,header=[0,1])\n",
    "val_temp_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>293 rows × 50000 columns</p>\n",
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      "text/plain": [
       "m/z            0                                                         \\\n",
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       "sample_id                                                                 \n",
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       "\n",
       "m/z                                      ...           99               \\\n",
       "time_bin   (35, 40]  (40, 45]  (45, 50]  ... (2450, 2455] (2455, 2460]   \n",
       "sample_id                                ...                             \n",
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       "\n",
       "m/z                                                                         \\\n",
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       "sample_id                                                                    \n",
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       "\n",
       "m/z                                               \n",
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       "sample_id                                         \n",
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       "768                0.0          0.0          0.0  \n",
       "769                0.0          0.0          0.0  \n",
       "770                0.0          0.0          0.0  \n",
       "...                ...          ...          ...  \n",
       "1054               0.0          0.0          0.0  \n",
       "1055               0.0          0.0          0.0  \n",
       "1056               0.0          0.0          0.0  \n",
       "1057               0.0          0.0          0.0  \n",
       "1058               0.0          0.0          0.0  \n",
       "\n",
       "[293 rows x 50000 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_temp_0 = pd.read_csv('train/val_temp_df_0.csv',index_col=0,header=[0,1])\n",
    "val_temp_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_100 = pd.read_csv('train/train_100.csv', index_col=0,header=[0,1])\n",
    "val_100 = pd.read_csv('train/val_100.csv', index_col=0,header=[0,1])\n",
    "test_100 = pd.read_csv('test/test_100.csv', index_col=0,header=[0,1])\n",
    "\n",
    "train_100.sort_index(axis=1,inplace=True)\n",
    "val_100.sort_index(axis=1,inplace=True)\n",
    "test_100.sort_index(axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1059, 1600), (804, 1600))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_100 = pd.concat([train_100,val_100],axis=0)\n",
    "test_set_100 = pd.concat([val_100,test_100],axis=0)\n",
    "train_set_100.shape, test_set_100.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_he = pd.read_csv('train_100_he.csv', index_col=0,header=[0,1])\n",
    "val_he = pd.read_csv('val_100_he.csv', index_col=0,header=[0,1])\n",
    "test_he = pd.read_csv('test_100_he.csv', index_col=0,header=[0,1])\n",
    "\n",
    "train_he.sort_index(axis=1,inplace=True)\n",
    "val_he.sort_index(axis=1,inplace=True)\n",
    "test_he.sort_index(axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "skf = StratifiedKFold(n_splits=5,random_state=RANDOM_SEED,shuffle=True)\n",
    "log_loss_scorer = make_scorer(log_loss,needs_proba=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=10, penalty='l1', random_state=777, solver='liblinear')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phy = 'phyllosilicate'\n",
    "bas = 'basalt'\n",
    "lr = LogisticRegression(solver='liblinear',C=10,random_state=RANDOM_SEED,penalty='l1')\n",
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_relhe_abund(df):\n",
    "    rel_df = pd.DataFrame(index = df.index,columns = df.columns)\n",
    "    df_4 = df[get_mz(4,df)]\n",
    "    for i in tqdm(range(100)):\n",
    "        df_i = df[get_mz(i,df)]\n",
    "        df_i_values = df_4.values - df_i.values\n",
    "        rel_df[get_mz(i,rel_df)] = df_i_values\n",
    "    return rel_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coefs(X,y,c=10):\n",
    "    lr = LogisticRegression(solver='liblinear',C=c,random_state=RANDOM_SEED,penalty='l1')\n",
    "    print(cross_val_score(lr,X,y,cv=skf,scoring=log_loss_scorer).mean())\n",
    "    lr.fit(X,y)\n",
    "    # print(log_loss(y,lr.predict_proba(X)))\n",
    "    coefs =  lr.coef_\n",
    "    coefs_list  = []\n",
    "    for id,wt in enumerate(tqdm(coefs[0])):             \n",
    "        if wt != 0:\n",
    "            coefs_list.append(id)\n",
    "    print(len(coefs_list))\n",
    "    return coefs_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mz(mz,df):\n",
    "    ''' returns list of columns for given mz'''\n",
    "    cols_list = []\n",
    "    for col in df.columns:\n",
    "        if col[0] == str(mz):\n",
    "            cols_list.append(col)\n",
    "    return cols_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_total_abundance(df):\n",
    "    ''' returns total abundance for each sample'''\n",
    "    tot_abund_df = pd.DataFrame(index=df.index)\n",
    "    tot_abund_df['tot_abund'] = df.sum(axis=1)\n",
    "    for mz in tqdm(range(0,100)):\n",
    "        tot_abund_df[f\"tot_abund_{mz}\"] = df[get_mz(mz,df)].sum(axis=1)\n",
    "    print(tot_abund_df.shape)\n",
    "    return tot_abund_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_grads(df,bin_size=5):\n",
    "    ''' returns gradients for each mz'''\n",
    "    grad_df = pd.DataFrame(index=df.index)\n",
    "    for mz in tqdm(range(0,100)):\n",
    "        df_mz = df.loc[:,get_mz(mz,df)]\n",
    "        # df_mz_col1 = df_mz.iloc[:,0].values\n",
    "        df_mz = df_mz.diff(axis=1)\n",
    "        # df_mz.iloc[:,0] = df_mz_col1\n",
    "        df_mz.dropna(axis='columns',inplace=True)\n",
    "        # df_mz = df_mz/bin_size\n",
    "        df_mz.columns = [col[0]+ '_' + col[1] + '_' + 'grad' for col in df_mz.columns]\n",
    "        grad_df = pd.concat([grad_df,df_mz],axis=1)\n",
    "    print(grad_df.shape)\n",
    "\n",
    "    return  grad_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mean_std(df):\n",
    "    ''' returns mean and std for each mz'''\n",
    "    mean_std_df = pd.DataFrame(index=df.index)\n",
    "    temp = pd.DataFrame(index=df.index)\n",
    "    for mz in tqdm(range(0,100)):\n",
    "        df_mz = df.loc[:,get_mz(mz,df)]\n",
    "        temp[f'{mz}_mean'] = df_mz.mean(axis=1)\n",
    "        temp[f'{mz}_std'] = df_mz.std(axis=1)\n",
    "        # mean_std_df = pd.concat([mean_std_df,temp],axis=1)\n",
    "    print(temp.shape)\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:03<00:00, 30.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1059, 200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sample_id'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = get_mean_std(train_set_100)\n",
    "m.plot(legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th colspan=\"10\" halign=\"left\">0</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_bin</th>\n",
       "      <th>(-100, 0]</th>\n",
       "      <th>(0, 100]</th>\n",
       "      <th>(100, 200]</th>\n",
       "      <th>(1000, 1100]</th>\n",
       "      <th>(1100, 1200]</th>\n",
       "      <th>(1200, 1300]</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>...</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>(400, 500]</th>\n",
       "      <th>(500, 600]</th>\n",
       "      <th>(600, 700]</th>\n",
       "      <th>(700, 800]</th>\n",
       "      <th>(800, 900]</th>\n",
       "      <th>(900, 1000]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S0000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004085</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.898509e-06</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002270</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001688</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.257968e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>9.397170e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001336</td>\n",
       "      <td>0.002464</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.000992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.379099e-06</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>4.693477e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0004</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005993</td>\n",
       "      <td>0.012429</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009380</td>\n",
       "      <td>0.005099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.904920e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>3.174421e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</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>1054</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129083</td>\n",
       "      <td>0.131998</td>\n",
       "      <td>0.096385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.157281</td>\n",
       "      <td>0.156331</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.437657e-04</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>2.672030e-04</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.037724</td>\n",
       "      <td>0.036757</td>\n",
       "      <td>0.020977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.042467</td>\n",
       "      <td>0.046966</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.277169e-04</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>2.020603e-04</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.138303e-06</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>2.037079e-06</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000496</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.002152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.382588e-06</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.763123e-06</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003384</td>\n",
       "      <td>0.004575</td>\n",
       "      <td>0.006704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005057</td>\n",
       "      <td>0.005892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.827355e-05</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>1.037153e-05</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1059 rows × 1600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               0                                                 \\\n",
       "temp_bin  (-100, 0]  (0, 100] (100, 200] (1000, 1100] (1100, 1200]   \n",
       "sample_id                                                            \n",
       "S0000           0.0  0.004085   0.004641     0.000000          0.0   \n",
       "S0001           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S0002           0.0  0.002270   0.002495     0.000000          0.0   \n",
       "S0003           0.0  0.001336   0.002464     0.000306          0.0   \n",
       "S0004           0.0  0.005993   0.012429     0.000593          0.0   \n",
       "...             ...       ...        ...          ...          ...   \n",
       "1054            0.0  0.129083   0.131998     0.096385          0.0   \n",
       "1055            0.0  0.037724   0.036757     0.020977          0.0   \n",
       "1056            0.0  0.000371   0.001633     0.000016          0.0   \n",
       "1057            0.0  0.000235   0.000496     0.000063          0.0   \n",
       "1058            0.0  0.003384   0.004575     0.006704          0.0   \n",
       "\n",
       "m/z                                                                     ...  \\\n",
       "temp_bin  (1200, 1300] (1300, 1400] (1400, 1500] (200, 300] (300, 400]  ...   \n",
       "sample_id                                                               ...   \n",
       "S0000              0.0          0.0          0.0   0.001394   0.000188  ...   \n",
       "S0001              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S0002              0.0          0.0          0.0   0.001688   0.000636  ...   \n",
       "S0003              0.0          0.0          0.0   0.001099   0.000992  ...   \n",
       "S0004              0.0          0.0          0.0   0.009380   0.005099  ...   \n",
       "...                ...          ...          ...        ...        ...  ...   \n",
       "1054               0.0          0.0          0.0   0.157281   0.156331  ...   \n",
       "1055               0.0          0.0          0.0   0.042467   0.046966  ...   \n",
       "1056               0.0          0.0          0.0   0.000520   0.000234  ...   \n",
       "1057               0.0          0.0          0.0   0.000999   0.002152  ...   \n",
       "1058               0.0          0.0          0.0   0.005057   0.005892  ...   \n",
       "\n",
       "m/z                 99                                                   \\\n",
       "temp_bin  (1300, 1400] (1400, 1500]    (200, 300] (300, 400] (400, 500]   \n",
       "sample_id                                                                 \n",
       "S0000              0.0          0.0  2.898509e-06   0.000003   0.000000   \n",
       "S0001              0.0          0.0  0.000000e+00   0.000000   0.000000   \n",
       "S0002              0.0          0.0  9.257968e-07   0.000001   0.000001   \n",
       "S0003              0.0          0.0  4.379099e-06   0.000006   0.000005   \n",
       "S0004              0.0          0.0  3.904920e-06   0.000004   0.000004   \n",
       "...                ...          ...           ...        ...        ...   \n",
       "1054               0.0          0.0  3.437657e-04   0.000236   0.000353   \n",
       "1055               0.0          0.0  1.277169e-04   0.000127   0.000225   \n",
       "1056               0.0          0.0  2.138303e-06   0.000003   0.000003   \n",
       "1057               0.0          0.0  1.382588e-06   0.000002   0.000001   \n",
       "1058               0.0          0.0  1.827355e-05   0.000018   0.000010   \n",
       "\n",
       "m/z                                                                   \n",
       "temp_bin  (500, 600]    (600, 700] (700, 800] (800, 900] (900, 1000]  \n",
       "sample_id                                                             \n",
       "S0000       0.000000  0.000000e+00   0.000000   0.000000    0.000000  \n",
       "S0001       0.000000  0.000000e+00   0.000000   0.000000    0.000000  \n",
       "S0002       0.000001  9.397170e-07   0.000001   0.000001    0.000001  \n",
       "S0003       0.000005  4.693477e-06   0.000004   0.000004    0.000004  \n",
       "S0004       0.000005  3.174421e-06   0.000004   0.000004    0.000003  \n",
       "...              ...           ...        ...        ...         ...  \n",
       "1054        0.000350  2.672030e-04   0.000363   0.000355    0.000282  \n",
       "1055        0.000165  2.020603e-04   0.000148   0.000145    0.000142  \n",
       "1056        0.000003  2.037079e-06   0.000003   0.000002    0.000002  \n",
       "1057        0.000002  1.763123e-06   0.000002   0.000002    0.000002  \n",
       "1058        0.000012  1.037153e-05   0.000011   0.000012    0.000015  \n",
       "\n",
       "[1059 rows x 1600 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "If using all scalar values, you must pass an index",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12464\\644779495.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_set_100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     44\u001b[0m             )\n\u001b[0;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[1;34m(x, y, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, weights, hue, palette, hue_order, hue_norm, multiple, common_norm, common_grid, levels, thresh, bw_method, bw_adjust, log_scale, color, fill, data, data2, **kwargs)\u001b[0m\n\u001b[0;32m   1694\u001b[0m     \u001b[1;31m# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1695\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1696\u001b[1;33m     p = _DistributionPlotter(\n\u001b[0m\u001b[0;32m   1697\u001b[0m         \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1698\u001b[0m         \u001b[0mvariables\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_DistributionPlotter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_semantics\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlocals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, variables)\u001b[0m\n\u001b[0;32m    107\u001b[0m     ):\n\u001b[0;32m    108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 109\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    111\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_core.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, variables)\u001b[0m\n\u001b[0;32m    602\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    603\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 604\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massign_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    605\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    606\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mvar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcls\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_semantic_mappings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_core.py\u001b[0m in \u001b[0;36massign_variables\u001b[1;34m(self, data, variables)\u001b[0m\n\u001b[0;32m    665\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    666\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_format\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"long\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 667\u001b[1;33m             plot_data, variables = self._assign_variables_longform(\n\u001b[0m\u001b[0;32m    668\u001b[0m                 \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    669\u001b[0m             )\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_core.py\u001b[0m in \u001b[0;36m_assign_variables_longform\u001b[1;34m(self, data, **kwargs)\u001b[0m\n\u001b[0;32m    924\u001b[0m         \u001b[1;31m# Construct a tidy plot DataFrame. This will convert a number of\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    925\u001b[0m         \u001b[1;31m# types automatically, aligning on index in case of pandas objects\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 926\u001b[1;33m         \u001b[0mplot_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    927\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    928\u001b[0m         \u001b[1;31m# Reduce the variables dictionary to fields with valid data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    635\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    636\u001b[0m             \u001b[1;31m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 637\u001b[1;33m             \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    638\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    639\u001b[0m             \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36mdict_to_mgr\u001b[1;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[0;32m    500\u001b[0m         \u001b[1;31m# TODO: can we get rid of the dt64tz special case above?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    501\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 502\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtyp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconsolidate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    503\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    504\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[0;32m    118\u001b[0m         \u001b[1;31m# figure out the index, if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m             \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extract_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    121\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    122\u001b[0m             \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36m_extract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    662\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    663\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mindexes\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mraw_lengths\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 664\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"If using all scalar values, you must pass an index\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    665\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    666\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mhave_series\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: If using all scalar values, you must pass an index"
     ]
    }
   ],
   "source": [
    "sns.kdeplot(train_set_100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:03<00:00, 27.56it/s]\n",
      " 29%|██▉       | 29/100 [00:00<00:00, 287.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1059, 1600)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 34.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1059, 200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sample_id'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qt_100 = qt(train_set_100,'uniform')\n",
    "m_qt = get_mean_std(qt_100)\n",
    "m_qt.plot(legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_qt.iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:04<00:00, 21.71it/s]\n",
      " 30%|███       | 30/100 [00:00<00:00, 292.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1059, 1600)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 42.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1059, 200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sample_id'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qt_100 = qt(train_set_100,'normal')\n",
    "m_qt = get_mean_std(qt_100)\n",
    "m_qt.plot(legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_qt.iloc[0,0:4].plot(legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in qt_100.columns:\n",
    "    qt_100[col] = rank_gauss(qt_100[col].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 45.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1059, 200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sample_id'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rg = get_mean_std(qt_100)\n",
    "rg.plot(legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train_set_100.copy(deep=True)\n",
    "for col in x.columns:\n",
    "    x[col] = rank_gauss(x[col].values) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 42.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1059, 200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sample_id'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mx = get_mean_std(x)\n",
    "mx.plot(legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.41274493, 0.35726547, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.31691505, 0.25782433, ..., 0.15993683, 0.1575287 ,\n",
       "        0.2353135 ],\n",
       "       ...,\n",
       "       [0.        , 0.15022637, 0.20320851, ..., 0.23072551, 0.207178  ,\n",
       "        0.2810072 ],\n",
       "       [0.        , 0.11911443, 0.12707576, ..., 0.18815384, 0.18811941,\n",
       "        0.26024045],\n",
       "       [0.        , 0.3801473 , 0.35536575, ..., 0.43035369, 0.44241459,\n",
       "        0.55675364]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x  = quantile_transform(train_set_100)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th colspan=\"10\" halign=\"left\">0</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_bin</th>\n",
       "      <th>(-100, 0]</th>\n",
       "      <th>(0, 100]</th>\n",
       "      <th>(100, 200]</th>\n",
       "      <th>(1000, 1100]</th>\n",
       "      <th>(1100, 1200]</th>\n",
       "      <th>(1200, 1300]</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>...</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>(400, 500]</th>\n",
       "      <th>(500, 600]</th>\n",
       "      <th>(600, 700]</th>\n",
       "      <th>(700, 800]</th>\n",
       "      <th>(800, 900]</th>\n",
       "      <th>(900, 1000]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S0000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.412745</td>\n",
       "      <td>0.357265</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.198987</td>\n",
       "      <td>0.093172</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.220220</td>\n",
       "      <td>0.220222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.316915</td>\n",
       "      <td>0.257824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.217532</td>\n",
       "      <td>0.148498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.117714</td>\n",
       "      <td>0.125361</td>\n",
       "      <td>0.138066</td>\n",
       "      <td>0.143213</td>\n",
       "      <td>0.145424</td>\n",
       "      <td>0.159937</td>\n",
       "      <td>0.157529</td>\n",
       "      <td>0.235313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.257205</td>\n",
       "      <td>0.253290</td>\n",
       "      <td>0.469745</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181854</td>\n",
       "      <td>0.178221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.270278</td>\n",
       "      <td>0.298407</td>\n",
       "      <td>0.283838</td>\n",
       "      <td>0.303532</td>\n",
       "      <td>0.294744</td>\n",
       "      <td>0.282304</td>\n",
       "      <td>0.287418</td>\n",
       "      <td>0.347238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0004</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.502499</td>\n",
       "      <td>0.542519</td>\n",
       "      <td>0.519347</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.501386</td>\n",
       "      <td>0.397702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.251254</td>\n",
       "      <td>0.246425</td>\n",
       "      <td>0.268669</td>\n",
       "      <td>0.299633</td>\n",
       "      <td>0.248405</td>\n",
       "      <td>0.278263</td>\n",
       "      <td>0.281364</td>\n",
       "      <td>0.331212</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>1054</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.950534</td>\n",
       "      <td>0.939949</td>\n",
       "      <td>0.948970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.948090</td>\n",
       "      <td>0.949900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.876107</td>\n",
       "      <td>0.840891</td>\n",
       "      <td>0.872437</td>\n",
       "      <td>0.875508</td>\n",
       "      <td>0.851837</td>\n",
       "      <td>0.881860</td>\n",
       "      <td>0.878890</td>\n",
       "      <td>0.871365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.820046</td>\n",
       "      <td>0.788773</td>\n",
       "      <td>0.846908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.806945</td>\n",
       "      <td>0.823753</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.769821</td>\n",
       "      <td>0.777642</td>\n",
       "      <td>0.833803</td>\n",
       "      <td>0.798700</td>\n",
       "      <td>0.820993</td>\n",
       "      <td>0.785602</td>\n",
       "      <td>0.784253</td>\n",
       "      <td>0.799716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150226</td>\n",
       "      <td>0.203209</td>\n",
       "      <td>0.419818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.126418</td>\n",
       "      <td>0.101132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.175426</td>\n",
       "      <td>0.217492</td>\n",
       "      <td>0.211114</td>\n",
       "      <td>0.214826</td>\n",
       "      <td>0.196001</td>\n",
       "      <td>0.230726</td>\n",
       "      <td>0.207178</td>\n",
       "      <td>0.281007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.119114</td>\n",
       "      <td>0.127076</td>\n",
       "      <td>0.428219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.173453</td>\n",
       "      <td>0.251923</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142389</td>\n",
       "      <td>0.152153</td>\n",
       "      <td>0.149455</td>\n",
       "      <td>0.177475</td>\n",
       "      <td>0.178782</td>\n",
       "      <td>0.188154</td>\n",
       "      <td>0.188119</td>\n",
       "      <td>0.260240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.380147</td>\n",
       "      <td>0.355366</td>\n",
       "      <td>0.769731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.386528</td>\n",
       "      <td>0.424712</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.523628</td>\n",
       "      <td>0.513701</td>\n",
       "      <td>0.402619</td>\n",
       "      <td>0.442284</td>\n",
       "      <td>0.419349</td>\n",
       "      <td>0.430354</td>\n",
       "      <td>0.442415</td>\n",
       "      <td>0.556754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1059 rows × 1600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               0                                                 \\\n",
       "temp_bin  (-100, 0]  (0, 100] (100, 200] (1000, 1100] (1100, 1200]   \n",
       "sample_id                                                            \n",
       "S0000           0.0  0.412745   0.357265     0.000000          0.0   \n",
       "S0001           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S0002           0.0  0.316915   0.257824     0.000000          0.0   \n",
       "S0003           0.0  0.257205   0.253290     0.469745          0.0   \n",
       "S0004           0.0  0.502499   0.542519     0.519347          0.0   \n",
       "...             ...       ...        ...          ...          ...   \n",
       "1054            0.0  0.950534   0.939949     0.948970          0.0   \n",
       "1055            0.0  0.820046   0.788773     0.846908          0.0   \n",
       "1056            0.0  0.150226   0.203209     0.419818          0.0   \n",
       "1057            0.0  0.119114   0.127076     0.428219          0.0   \n",
       "1058            0.0  0.380147   0.355366     0.769731          0.0   \n",
       "\n",
       "m/z                                                                     ...  \\\n",
       "temp_bin  (1200, 1300] (1300, 1400] (1400, 1500] (200, 300] (300, 400]  ...   \n",
       "sample_id                                                               ...   \n",
       "S0000              0.0          0.0          0.0   0.198987   0.093172  ...   \n",
       "S0001              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S0002              0.0          0.0          0.0   0.217532   0.148498  ...   \n",
       "S0003              0.0          0.0          0.0   0.181854   0.178221  ...   \n",
       "S0004              0.0          0.0          0.0   0.501386   0.397702  ...   \n",
       "...                ...          ...          ...        ...        ...  ...   \n",
       "1054               0.0          0.0          0.0   0.948090   0.949900  ...   \n",
       "1055               0.0          0.0          0.0   0.806945   0.823753  ...   \n",
       "1056               0.0          0.0          0.0   0.126418   0.101132  ...   \n",
       "1057               0.0          0.0          0.0   0.173453   0.251923  ...   \n",
       "1058               0.0          0.0          0.0   0.386528   0.424712  ...   \n",
       "\n",
       "m/z                 99                                                \\\n",
       "temp_bin  (1300, 1400] (1400, 1500] (200, 300] (300, 400] (400, 500]   \n",
       "sample_id                                                              \n",
       "S0000              0.0          0.0   0.220220   0.220222   0.000000   \n",
       "S0001              0.0          0.0   0.000000   0.000000   0.000000   \n",
       "S0002              0.0          0.0   0.117714   0.125361   0.138066   \n",
       "S0003              0.0          0.0   0.270278   0.298407   0.283838   \n",
       "S0004              0.0          0.0   0.251254   0.246425   0.268669   \n",
       "...                ...          ...        ...        ...        ...   \n",
       "1054               0.0          0.0   0.876107   0.840891   0.872437   \n",
       "1055               0.0          0.0   0.769821   0.777642   0.833803   \n",
       "1056               0.0          0.0   0.175426   0.217492   0.211114   \n",
       "1057               0.0          0.0   0.142389   0.152153   0.149455   \n",
       "1058               0.0          0.0   0.523628   0.513701   0.402619   \n",
       "\n",
       "m/z                                                                \n",
       "temp_bin  (500, 600] (600, 700] (700, 800] (800, 900] (900, 1000]  \n",
       "sample_id                                                          \n",
       "S0000       0.000000   0.000000   0.000000   0.000000    0.000000  \n",
       "S0001       0.000000   0.000000   0.000000   0.000000    0.000000  \n",
       "S0002       0.143213   0.145424   0.159937   0.157529    0.235313  \n",
       "S0003       0.303532   0.294744   0.282304   0.287418    0.347238  \n",
       "S0004       0.299633   0.248405   0.278263   0.281364    0.331212  \n",
       "...              ...        ...        ...        ...         ...  \n",
       "1054        0.875508   0.851837   0.881860   0.878890    0.871365  \n",
       "1055        0.798700   0.820993   0.785602   0.784253    0.799716  \n",
       "1056        0.214826   0.196001   0.230726   0.207178    0.281007  \n",
       "1057        0.177475   0.178782   0.188154   0.188119    0.260240  \n",
       "1058        0.442284   0.419349   0.430354   0.442415    0.556754  \n",
       "\n",
       "[1059 rows x 1600 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.DataFrame(x,index=train_set_100.index,columns=train_set_100.columns)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sample_id'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.plot(legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 47.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1059, 200)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "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>0_mean</th>\n",
       "      <th>0_std</th>\n",
       "      <th>1_mean</th>\n",
       "      <th>1_std</th>\n",
       "      <th>2_mean</th>\n",
       "      <th>2_std</th>\n",
       "      <th>3_mean</th>\n",
       "      <th>3_std</th>\n",
       "      <th>4_mean</th>\n",
       "      <th>4_std</th>\n",
       "      <th>...</th>\n",
       "      <th>95_mean</th>\n",
       "      <th>95_std</th>\n",
       "      <th>96_mean</th>\n",
       "      <th>96_std</th>\n",
       "      <th>97_mean</th>\n",
       "      <th>97_std</th>\n",
       "      <th>98_mean</th>\n",
       "      <th>98_std</th>\n",
       "      <th>99_mean</th>\n",
       "      <th>99_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S0000</th>\n",
       "      <td>0.066386</td>\n",
       "      <td>0.135588</td>\n",
       "      <td>0.048662</td>\n",
       "      <td>0.103198</td>\n",
       "      <td>0.015588</td>\n",
       "      <td>0.036134</td>\n",
       "      <td>0.014010</td>\n",
       "      <td>0.025345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055336</td>\n",
       "      <td>0.099135</td>\n",
       "      <td>0.064400</td>\n",
       "      <td>0.115346</td>\n",
       "      <td>0.057679</td>\n",
       "      <td>0.103813</td>\n",
       "      <td>0.056431</td>\n",
       "      <td>0.101339</td>\n",
       "      <td>0.058548</td>\n",
       "      <td>0.105013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.119902</td>\n",
       "      <td>0.247418</td>\n",
       "      <td>0.243410</td>\n",
       "      <td>0.342241</td>\n",
       "      <td>0.130644</td>\n",
       "      <td>0.247866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0002</th>\n",
       "      <td>0.120566</td>\n",
       "      <td>0.106606</td>\n",
       "      <td>0.120561</td>\n",
       "      <td>0.109196</td>\n",
       "      <td>0.067853</td>\n",
       "      <td>0.061502</td>\n",
       "      <td>0.082467</td>\n",
       "      <td>0.069050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090631</td>\n",
       "      <td>0.076222</td>\n",
       "      <td>0.092751</td>\n",
       "      <td>0.078707</td>\n",
       "      <td>0.093286</td>\n",
       "      <td>0.078520</td>\n",
       "      <td>0.093759</td>\n",
       "      <td>0.078673</td>\n",
       "      <td>0.091781</td>\n",
       "      <td>0.078112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>0.163792</td>\n",
       "      <td>0.134041</td>\n",
       "      <td>0.159535</td>\n",
       "      <td>0.132279</td>\n",
       "      <td>0.142216</td>\n",
       "      <td>0.124667</td>\n",
       "      <td>0.156588</td>\n",
       "      <td>0.129083</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.222456</td>\n",
       "      <td>0.164739</td>\n",
       "      <td>0.203556</td>\n",
       "      <td>0.154117</td>\n",
       "      <td>0.213064</td>\n",
       "      <td>0.158589</td>\n",
       "      <td>0.203419</td>\n",
       "      <td>0.151842</td>\n",
       "      <td>0.214124</td>\n",
       "      <td>0.159382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0004</th>\n",
       "      <td>0.266244</td>\n",
       "      <td>0.212526</td>\n",
       "      <td>0.271886</td>\n",
       "      <td>0.215963</td>\n",
       "      <td>0.224724</td>\n",
       "      <td>0.175156</td>\n",
       "      <td>0.238630</td>\n",
       "      <td>0.184895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.196321</td>\n",
       "      <td>0.147407</td>\n",
       "      <td>0.207550</td>\n",
       "      <td>0.152393</td>\n",
       "      <td>0.208921</td>\n",
       "      <td>0.159576</td>\n",
       "      <td>0.209272</td>\n",
       "      <td>0.154778</td>\n",
       "      <td>0.205403</td>\n",
       "      <td>0.158020</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>1054</th>\n",
       "      <td>0.646646</td>\n",
       "      <td>0.450337</td>\n",
       "      <td>0.642445</td>\n",
       "      <td>0.447421</td>\n",
       "      <td>0.628729</td>\n",
       "      <td>0.438089</td>\n",
       "      <td>0.638965</td>\n",
       "      <td>0.444988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.599023</td>\n",
       "      <td>0.417589</td>\n",
       "      <td>0.602995</td>\n",
       "      <td>0.419988</td>\n",
       "      <td>0.604867</td>\n",
       "      <td>0.421338</td>\n",
       "      <td>0.600577</td>\n",
       "      <td>0.418296</td>\n",
       "      <td>0.601214</td>\n",
       "      <td>0.418881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>0.558399</td>\n",
       "      <td>0.389295</td>\n",
       "      <td>0.559652</td>\n",
       "      <td>0.390201</td>\n",
       "      <td>0.530923</td>\n",
       "      <td>0.370835</td>\n",
       "      <td>0.569252</td>\n",
       "      <td>0.396932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.546917</td>\n",
       "      <td>0.381261</td>\n",
       "      <td>0.539349</td>\n",
       "      <td>0.376402</td>\n",
       "      <td>0.543932</td>\n",
       "      <td>0.379082</td>\n",
       "      <td>0.550514</td>\n",
       "      <td>0.383709</td>\n",
       "      <td>0.548859</td>\n",
       "      <td>0.382581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>0.146015</td>\n",
       "      <td>0.126773</td>\n",
       "      <td>0.139968</td>\n",
       "      <td>0.132025</td>\n",
       "      <td>0.108627</td>\n",
       "      <td>0.117351</td>\n",
       "      <td>0.110945</td>\n",
       "      <td>0.118594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153166</td>\n",
       "      <td>0.122008</td>\n",
       "      <td>0.161105</td>\n",
       "      <td>0.126403</td>\n",
       "      <td>0.167825</td>\n",
       "      <td>0.129286</td>\n",
       "      <td>0.159347</td>\n",
       "      <td>0.127272</td>\n",
       "      <td>0.159379</td>\n",
       "      <td>0.124270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>0.123122</td>\n",
       "      <td>0.114728</td>\n",
       "      <td>0.118981</td>\n",
       "      <td>0.115125</td>\n",
       "      <td>0.127458</td>\n",
       "      <td>0.125968</td>\n",
       "      <td>0.096264</td>\n",
       "      <td>0.107657</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147737</td>\n",
       "      <td>0.122921</td>\n",
       "      <td>0.143147</td>\n",
       "      <td>0.118292</td>\n",
       "      <td>0.141852</td>\n",
       "      <td>0.120480</td>\n",
       "      <td>0.137779</td>\n",
       "      <td>0.118737</td>\n",
       "      <td>0.137997</td>\n",
       "      <td>0.120559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>0.375565</td>\n",
       "      <td>0.289112</td>\n",
       "      <td>0.390083</td>\n",
       "      <td>0.297678</td>\n",
       "      <td>0.363216</td>\n",
       "      <td>0.288945</td>\n",
       "      <td>0.350542</td>\n",
       "      <td>0.262503</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.336353</td>\n",
       "      <td>0.239680</td>\n",
       "      <td>0.308440</td>\n",
       "      <td>0.217528</td>\n",
       "      <td>0.335217</td>\n",
       "      <td>0.238723</td>\n",
       "      <td>0.336493</td>\n",
       "      <td>0.240854</td>\n",
       "      <td>0.323536</td>\n",
       "      <td>0.230349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1059 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0_mean     0_std    1_mean     1_std    2_mean     2_std  \\\n",
       "sample_id                                                               \n",
       "S0000      0.066386  0.135588  0.048662  0.103198  0.015588  0.036134   \n",
       "S0001      0.000000  0.000000  0.119902  0.247418  0.243410  0.342241   \n",
       "S0002      0.120566  0.106606  0.120561  0.109196  0.067853  0.061502   \n",
       "S0003      0.163792  0.134041  0.159535  0.132279  0.142216  0.124667   \n",
       "S0004      0.266244  0.212526  0.271886  0.215963  0.224724  0.175156   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "1054       0.646646  0.450337  0.642445  0.447421  0.628729  0.438089   \n",
       "1055       0.558399  0.389295  0.559652  0.390201  0.530923  0.370835   \n",
       "1056       0.146015  0.126773  0.139968  0.132025  0.108627  0.117351   \n",
       "1057       0.123122  0.114728  0.118981  0.115125  0.127458  0.125968   \n",
       "1058       0.375565  0.289112  0.390083  0.297678  0.363216  0.288945   \n",
       "\n",
       "             3_mean     3_std  4_mean  4_std  ...   95_mean    95_std  \\\n",
       "sample_id                                     ...                       \n",
       "S0000      0.014010  0.025345     0.0    0.0  ...  0.055336  0.099135   \n",
       "S0001      0.130644  0.247866     0.0    0.0  ...  0.000000  0.000000   \n",
       "S0002      0.082467  0.069050     0.0    0.0  ...  0.090631  0.076222   \n",
       "S0003      0.156588  0.129083     0.0    0.0  ...  0.222456  0.164739   \n",
       "S0004      0.238630  0.184895     0.0    0.0  ...  0.196321  0.147407   \n",
       "...             ...       ...     ...    ...  ...       ...       ...   \n",
       "1054       0.638965  0.444988     0.0    0.0  ...  0.599023  0.417589   \n",
       "1055       0.569252  0.396932     0.0    0.0  ...  0.546917  0.381261   \n",
       "1056       0.110945  0.118594     0.0    0.0  ...  0.153166  0.122008   \n",
       "1057       0.096264  0.107657     0.0    0.0  ...  0.147737  0.122921   \n",
       "1058       0.350542  0.262503     0.0    0.0  ...  0.336353  0.239680   \n",
       "\n",
       "            96_mean    96_std   97_mean    97_std   98_mean    98_std  \\\n",
       "sample_id                                                               \n",
       "S0000      0.064400  0.115346  0.057679  0.103813  0.056431  0.101339   \n",
       "S0001      0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "S0002      0.092751  0.078707  0.093286  0.078520  0.093759  0.078673   \n",
       "S0003      0.203556  0.154117  0.213064  0.158589  0.203419  0.151842   \n",
       "S0004      0.207550  0.152393  0.208921  0.159576  0.209272  0.154778   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "1054       0.602995  0.419988  0.604867  0.421338  0.600577  0.418296   \n",
       "1055       0.539349  0.376402  0.543932  0.379082  0.550514  0.383709   \n",
       "1056       0.161105  0.126403  0.167825  0.129286  0.159347  0.127272   \n",
       "1057       0.143147  0.118292  0.141852  0.120480  0.137779  0.118737   \n",
       "1058       0.308440  0.217528  0.335217  0.238723  0.336493  0.240854   \n",
       "\n",
       "            99_mean    99_std  \n",
       "sample_id                      \n",
       "S0000      0.058548  0.105013  \n",
       "S0001      0.000000  0.000000  \n",
       "S0002      0.091781  0.078112  \n",
       "S0003      0.214124  0.159382  \n",
       "S0004      0.205403  0.158020  \n",
       "...             ...       ...  \n",
       "1054       0.601214  0.418881  \n",
       "1055       0.548859  0.382581  \n",
       "1056       0.159379  0.124270  \n",
       "1057       0.137997  0.120559  \n",
       "1058       0.323536  0.230349  \n",
       "\n",
       "[1059 rows x 200 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_mean = get_mean_std(x)\n",
    "x_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sample_id'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6Cbi7O7qcQshiu55PrO3goOXQjZT6mUS0pkiDZFURPf477Fs9bPJzaIkYTRB2w13jXkLlxKcRxr6N9ZLJ29+o+H0codJKZBngAvgw/3sIVtwwleWfXiqLZ2jnSAit0zVtipx5MEHTTJl7TkApSsQ5JhDzucYLqdtYXQQocBAIPJuTIA69+OfyUW/IMnRh/zvbZEUyUBZM0AzZFh0/eIj9wcehRx0+JFn6joZOFGV5kO0VF3rsABZd2PK3tL9AX2r0wkMozmHccxhwbt9xVfikMh1nB/fdHkq6BLy/9XFsGXSprfJCCgmJHVgxuR+WfI5f8pbixFrWSBtVKhPql+f8HV2BLEDxpToEP+amL0HA5F39Odvh7mLbLcuORQlsftFHjMhHw7ozwDkDwGHxV22Eo7PcSOc4+OOQoX+AzZhAygEAlLIJ+vjCDaj1dU9OAy3+ues2FERyDe8HIzxe/nEn/njEEHicmv7GRat7vgz9oCHo4XAbHA75uC1ZI8x5/EcMy/Lgu9vEJEqEiAd4qnEwoXHu0kEj9tLg+s/ReL4CbDhDpAjEF598RwjvqwmtVoo2RwQEqRhoX4kuhxi+IWLhgKQUyXAO9TtneNU6i9awB/2ELNTBmEvec8j9oO39MRAnGvRc/ZcSZ/9zESK8gE+uZkV2NDd5fKD6CKRRglMEHkhRWon6EEVlOILxIfsnEh4EDskT1oBDv3aypOP5fZI9tIYZMQeAN36pwOPfboXTweHPRw5V3bMndoxu0r0E/cDBTwsno6TktBjbxym+4/YOlsWL+kOnLNgvx55A3mAOxjdMAu9MzKElEZCognZPkEObQMSA9Sagwr4NIi+N1Fu5qyGkN2MajlD3R/cHGx0dG23Tv+HFFcgZ8xHqll/EvC/Jmkk2OyZPbMM2cP1fsTvxkBOPHXsjusLpECgzg2NCcJbPQ9HI/yHQpU1hYA5Jhs5RhyicPIBpXVc0PlIgbLzx22PXer7o5aCSodfVfYqYEshwAqq5w1TPVGKwM8zceAVm7j4D4bD9IaeUWjo8mSESEfALwni8JgevNpqZbYlgKc72BU6c+QxOnvi27nprdH36w+Z9D8QRJS+jUEwEnJfJdlayUsAmNW94JxrWnQEYmKQ6OR45no64bcbN0DVCtMuvbbLv0HbYzrOQJkSNBYgDp+S60KdtcOo6lWJ0dYkWXD6f3pKLhvZxppv9jIOKoANQ2INbwUKgafSUBYE1UiG5ospJlr21ETa8ch82vaY357OLbZWVuBl+BKrPsy4MIBzZt5NfYnZX7j4Ea3brTeAkIUHAImgSzysja5p/H2eUULnDbMsaq4kjMQqJyNBdlVPxeVcE7uoJpuWsNpW4EB3kdp/98Anj6o6AI2okIIXRPX3TtWaPdCu6eIqwyboLBsXQuJEwI0SugVcuCz2fPz9IRC5qIit5uImfZ3BOBRr9Bbr7Whm6/c9pMUEM7NDznALSCjbAQewrHuuGvQkAGI2bbT+jRFtbVG4fspfeVYhEUBkiKEzAsGHBjxPQp/RMjBhxt43S0TGKDuXkgUsMytnddOMngEbREnlLW/zEl/3OtAqsyVuJ3JapmGpSLhVWLluat2Bh1UJIfHV7h56gn7rhalRnG7jPc9HNPRYXff/FOXkhbyO2Eh7zIGche6XBg2ZeFCPGTuOMtUcMRKBs9HySflBw6JQRv5tGX+1lmokX+yriUMRcmqM/dSIYc1hZghgpVhYO+R8eG/gyInTfDXlLexdePe4vOLJkM/xbrTeFSCSCx+rS8Epj/Hkkeb4TlVX/TqSbhigMZ+OZ7bcjBLbXLwvW3K0kHGafRmwT9ATC5/LRZ6zMMGkKzEfP/excPL3yaUj9DQf0BL1f+3BMq9QrfgHExieYvRudhWvg4faPOA4A5gd5dPBqEdmGgAM1YSnEb1RvxnjWgL9igiq+6a7GLnyyuuclvDgoOHSVRpOo3df3HHovPO1y/HGdnQHR/LYAH+Hxe98MrHfvYt7nDKxcqtJEJZuVDXIq4RAqAABzBn+BFRlfA7jYtHyYF4UcO0P7ZtOxWmsXVp6N3XmbcWzbWPOCccTgoDTaroHIxMheW4tERC7N0Y2pkZhbOvEpEKJrN7YgI9zA6bku1IYjoJQHIZpjGSfOU1/+JvjyN2Fq551J9ylR3DPoF2QPXAKA3QdqwqHb4rpj80cue8JTPyEYEXD6xH7xdHW/4yDh0BkiF8XHCWbL+T8pSU7kEo6EMW7YjzgkPJRdwIAlaI86vwguHu2dwYQVkAIVbCtKiU/MkVjqDeD53HzL8nwojMJwLpxCYvv8mpXn4aNvPrJR0p4opXPkJ+h/+DPgc1LPKRGw9SER20podjlP6Yfw9nmf3WYoB3223A0hlI2O6vEId7G/SXc4eIUC7E3Ed9Tf8eNPk1TXyj0c0l3qE8ynQ5MPRZEosg1FciKk78jFwY5HwhEEgmpTTqJ4PhiH3f6BhIOCoKsXl8ShsxeF0XW76zgSCYNzRDCs/FvmfU7DuTX7GzBv+/uYWHcYrt/1RxDBjb/+4ws89dKHqnJ1v/yIrj3meSrnrVqJcx69Dhf+8wZbffUFlJvaHuyorsXH75yO2x9+QPNOHdi+/WEEwj78e/uDuLn6MgDAqFs+wUl32yHQwIaNMzFh8nvIrJuL9kAIR750HzbUaJOAqGE15AW5omUGl26e4/TN3RFw0lRmrOkPn1NG6hMLFPRbhX8+/QajT/JCfvaBR/HSw/9Q3Y9VTygW1yzWpetz5y2FK3cFs58NzcdibFc2GlqORNXP12DXN3ehtW0F6uu/VvfBYjIGwhFs3dtqWkaLCEOS9HHefFRvPBU8r47nMirNAUEjklrWng0A+NN/b8Nbi9RzFwCeevBxvP7EHXH1KVWQhosQAsEfAd8uE+p6XxibweumxY///hMW/3DIvuvkPsLBQdCV898yXDXV3FcXXFyzGC+uedGwqYABp6NtXsIDLzyBnY8V4KzBWzHgiCeRDoo85y7M86vtgtcHL8OKtfqUdkoMf6cTjzWeg6x6dpwRLXiNAvZ//34NWSXrMWfsx6rr23c8it17XkZX13J08hTT2scDAJyjbkdFqT3OzNclHk2dhVvx2MsP4o7hb2DBexYZbSwouuTcYhTBsqbNj8U7m3DKxG8QzqkAwHYkyRmtD6nrzK5Cn1x9fHFBwZmNnvEihk37F7tzhGLFY7tx74f2A2adE8jFX9O8OCUgKumFcDpWrDgP69ZfpSrHW+R0ff/hb/De88vx4acLbLctRNSivs5gACs9W/HfzF+Z5amGoNe7RQet80rnIq9aHy9/3KEvYMDE/5r2oaU9gC++2oaWziA2vrkK7dXtcntCMia6MhNX/fBS1Dwoc/R//GoP/gBGuOJBC8B72lWXCAR8tb4Wf/9wra74hoVVeOPmnxPs377DwUHQVQve4jgfk5mzy13xzRV4frVxggdfQJ4cJ37AUChp6ivdLYY8zcwSudVsPg+XFLfhqIbjdY+G08052i3Za7A5byk6MzeZlpPAEfWiFKIycu0pRRBE2a4Q5PF9RwS/dsnj6UizJ+6gipNJeTRB98hCtp7BLojkIWoQZ2fOC0/h0g/uRyBnp6IjFv1U/J0VdfISBIpAl/h3xMK5Sh47AYOHLkbZErbYRBAEVL68BiFFcu4+HIdQei36MAK4NXXKil+zV9i2rA67/R0oKVmMD5bsiV3fvPl2VFTIjMis9kl4rOIGSBOSKjxjA11h/P65Z3HR+F9x9RBZHKkE5dRjfnlmeuzv9PwKkx4aY8HzKzBhQS1ufPhTLPC/iu8+FTl63h9G1a0/o+rrDbbq0RJ+WR/GYX5oO35GdZw9kxVpZNFDOMqj935dtehfKJ11fZz17nscHASdIUPnKDHY8WUZOo0ICisXe/K3jk45uFZlZ6WOABiK8aIErySzEfWj/oOZ4/XHVgAY9+Y4w7a9M5+C55B/4vhydlowgybl31KMeG0fo+MUivAom/lPhArEFHRHtRyCo1rMj6X/WvcvXPP9NWpLI85K8Uti/+cV4SdDVRpLjCiHzhuYzF1aMxYXV2o2RqvPyFCg/vrhdrx6w0KE/BFLLlF6ms/fDs/IT5E5fL7q/lHbLsKcrZdg8zsLsb7xR6x6XA7D6+q3ELtm3QJ3n1/x4oxr8eMQOSTtlPu/i/0tsOQjUXzz6gb0GzwPQya8jnNyZW/Wqup3sGOnfCK6reoKjPEPleeAwpb/xnsXorN6OADA4Vba8EeLOoIIp6vDSQ8r2akrBwAtH2+3zVnnkq3YM/UhjC7+CUd3jgJ2i57BO6vETaVuoTXzEGkP4ov7XlVdi8nQAcxyZWNapv6Es96m2eW65j5Yu2ei7nqfaW/BldFs+q68wGPqG1Pw5tfxJyJJFQ4Kgq4i51JuTwJEFBYL0x74DlrsvV1xhLJ52mtvVrt2B/kgVuxm5wldVLUIWhkQcUTley521poMX7FlH8oMvBx1MCRu7Jdt6gohs2wlBhz+DADg57Rt+CljPUIRQTWWSjzx80f4dkOLSqFkPZjy6ejFaxbErq55VXP8l8zIKAchGIEQVG+efaa/gRmn/x+zamPI/ZSKbl8hnoyC/ggi4QiWdDnQaBRNU3M516PehEY0TsfwpimYV/cpXIc+jTWD3ovdc2aLBMudJeoGNpUYiDsMREwAkNl3NUrcItfPeVtj17/YeSwWMRNgRDl0hXXV58SPU4r0YgUJe6c8jECO/nTFImZdi2tAbcaEFkb8G/VCABPHfImqyU+gaOy7AIDaRpFJ4nlrc9lf1+3BfyLtcATkwGCysxfBziNuQMXM22P3hjn8ONzZjgfNYijFpiPBpGmvYNKIBZZlWdhdtwtBEsJT1fHHrU8VeixB94V98IWj3AWLQ4egsilukOK5KDxJKyAwzdeo4IQ/pN7RG3wFWFE3Hh0/VcSueTf+A76OTtzxsZxUWVndTc8266q3ch2v3f1X5vVEoD11SMOkt9WOcuiaxMlv8kfg+fbzUH77PJz49EJmG2ft/RNurb0ASkpndEoRBAELPjU2f1vQJnKFwV1tiLQGQaI6AJ4SVN/1K6rvUhPAvn1X6+qINwb8f2/+DmlcA8aNn4cwH8KzG57Bqyv/gnu3sLMDaWvXKsEl5HCipdDQdIUNvRRniOPh2nI3hu+8kvmsmZVL2azn4Ygu25DiJPTh9lPx6vrfGj4naHqe1mJgpQUgkMvmxtfXb9Vda+mvZ5TMUOPLif3NR08BkZC4IdghRp8t/wmzMjpxfJdsmWNmtXjdYffjd0ffjsP8doL9UB09kDaxnzuc+HtlGgQDDn1L8xac/s2ZYj80z0ciov4hEKjGL7/Mht9vbvyQDHosQb/45atxyStXiz8UYyxEs6YH+/0CXqBY3OlAdYjFPRK0Kjgh6SofKEHn1jsx6s6vVO1t+uUunLzmWnR2yjv9xek7sfvxX9HSIh9PlfbJldC3qzeXBFo/s84mlAi0hNtI1yaVC4RlDjgS6UT9yP8gMEnUJ2yrZ7uOnzXsawyd9qRBD9QrbOWvX6Et/COom13XVwWi12LDS2tR++iyGIcebyRMUyhdFjLqUXL8nxDp+y0e8xViUcU8zKv8HK8EyjGnyog4qsdUa4/OOf3gXL6YPwJROZLJZftyAeRG2KexiEUIBmmjs4wTL5YGADg0oqbRY9Xz++uKr1HbZZ54Y/FePVe/Zfh/wPP2IogSEPQN672Wq2tFApdhI+1iZlctRk57EydC9v42MlvcuOlmpEWjbU4yDMqh6B/DeUqi3+/Vl6K9ZarhItrbsReUAoG6E8EH+iFc70OouhMVG17EujdvQzDYiJqaD+CqGICajZ9Y9iVR9FiCflF2By7KlUQPAl7bNUh1f1ppFcIRHnNbPHikLg1pTr/qyOgMZGEuaqFdoL5d1wNUf/Q7e8AvaDj8BoQU8ba9GU3wcSHkhxV22zo2gaj+ifVBUeyZrfIRbXRK3Y81dcX2MmunmlBIHxfjq5fX6661DPpKfzw3oDNL1y9DZl4lhLSWWLFip9zHEK9w4uCpgqDHj/rNPxjcUZwkMkUC5u2zEWsbx6LZJ24d5TQNNyHLVjvaEALlZ/0F5WdeC84hnfAUvVckmL7+6Dtx2gS2rDUcNvaMfbW2ILaJxLXRKQjRtUEH/IXyt6QUuPHHG3HJvEtMqyjYqx+Te2rSEArZ8+Sl0Crko+IgGlSUMEeeS9w8HIPk76sUuUjgeR41Nf+L/Z6Wax3LppPBxEs9ClZcCWfjCYb+IzQswMOnIdx8BNr2XIG6J1ag/plVaP7EgxDPoXbjNvhCHPquuxKu/1k4yiWBHkvQc/utRHapOCkppZjUoT8i84q8kc8dfbO420YXw9BDXseonA2wSy4aRrwL3tuKiGIBE1D4uDD6pG+RrynWWH6RUvGpXoS8Isfn+4XfxP5+MKPVVn/sQLvgjWNciX0LmjCG4wvXo7FFb/7HhAHBEZrT1cUA3Dv+/tjvw52avK7RsY5XjAIA66qvsNFNsd7MoMjt/VixGgRAa78FCKWzuVXtfq0l6D9tPxYLth0PKJRwze9J80PtlpxVyD6ZdXa2M68DwKK1d8XapKBYV9lmWFbZljSGzV0hLPcQVhFLDr2pia33ifD2vZ9ZYpF2n/S+1mvRx0c3Abe+L8p50uVrVd3bMvlxs14BAKaNVqxXEgZxtkOgFAIVcNnoD/Dw7HsMlaLctgDe3fYIXj3uLzim/0+x65WjX0PnsM/w+a8/4sP1oriJdGP4jx5L0JUQqAAnw7JC0ERaE12q5WuT+u1QMGz2CHuLV15sxBHEUkRwQecouYDig3/acKkuL2Y7b57wuOkwvXPGoh2NWLxTrQht6QphW515wmlOp1zTi3toWACZPxHOQC7CCuWWdt5eO/ll9JkmZqX3hX24e9HdaA8ZEx6pmcpbFqJmsWiN4XKqLSpCnB99g5mx38fUa1L1REULiZxZdrf3MeiSsraoOCI6TrtrnCgM5aFuzBuoOPRuew1pCHrxyvNQuuocVSu+lWpzVCuHxmCXWoTRsnG15vlom4Ti1Ofs2UZLn3Pyfd/iF0SgnAPSG3AG+XAlNHTplfFHtE1FQ7O545eEAM+WYwd9Eodu/aU9LrEs75LnvkRklRx6c7P6hOlRKJAFhWXat19tY7aT1v9NZA5/EGsb1mHCWxOwvmk0/u+HxxCOsDcvT4cz5ox17nDZsikzOqSdXYFY3PbuxEFC0HmkMehxiFe79kY0yiY3HIhZn9ikGh8QWbtOCMVrwUKkh7MV1+SKXs/9CXvzq6FkiEZl6W3Nr9r5d9M2L3xlCc5/ebHq2rFP/ohjn/zJ4IlYb1S/YtYTCpGLf2MTsvcOQtGWCxDwy5N167tPGdb6/tb38cG2D/Dympdj1yJK2bSm/JZvfsbyimZdhvkJ47/ATWPko39ukSbyH5c4Qf9xsYHXInPfFi/mOxvw8k7xOepky4W1NvxaDp1zBsQ0coonPoo0R1ux9HoDALS0qzfKtRV/iv09CpxuEwGAt4UMPCSk4ecqNoFXLnSXgaLe6iTU0qCXod9SfRlCbfayIb2561jNFbG9cBh4pasTC2iL/iEN+Ch3K7hk82Gp/8rAiq2t6s3HG40aGQkHMH+B7JgX2P4xsx1nhjgXl1UvR6R5JoZSipdm3YtlVcuY5dsagLYoveEU4+iI/u1xBlDbZsEApQAHBUGnAo0NnBLKDCbz251ipESV/JhC9jKzh5Y2Wc4rPSPkb9NdA4CZ01/DqUc8hhNyXLIYYtCPujrHTTLwRjRBY6c4efybm42VqkYOGBC5Gq3bulIn1TzocxjB2u5YPZpeZ0BUqlrMttYxb2mqkTYgRds245zc7WWLB9SVqfuZTj26MTF9HpC/K6X4YucXKD/rGpSfdY3Kge3lcberH3GZJwr3aSIjUk6WUQ8ZPI8ZGGxD349QV/I5/vzdn9n9VXT74g51whC7Dppenh2GOeC3pxRtCOQyrzd2BjCo7zrkKTrpD0QQ7tBvFIThRyCJtZXMVF1Nq/ZBAIDPpz4lfu3rhNnqX7MyG/6603DkuP/Cmd6Ipz99AxubNurKtfh5vO0QTwWSQjyUVherm1IKLmw0J1OHHk/Q//vvh9Hw03KwuJ6gIo/iJ21uhHleU47Gzf6dFZAXI4nyXOEh8+QCCUThUwYPY+GGif/E9RPYLvhNb2xA5y9szzjjEzTFM6uewYS3JiAsKBaNRSIJ26DyPzfCh4awG26HjalGOQT36LkY5ZAKXVbyWrHwzsP/Ztmc8m0fm3UHnIIXIapXJCgVYSwO/eGvNmPhnp9wy8JbFN0Qa/fCgYldIkcoEc60/vJpqyWQg+rOEijnZYdfJuAbN/4NvFsWL5w1/AtZt6DoyrDxn2PspE8wNKDNzCpCFRY8Qy0ikfLvWkYEZnzCyklPoqbTpl+Edo1KY+SsBwXwc5kYDiMQ5vHG/fOw6/l/g2rSysXGX0HYaYwpk6+t+ZqdoamxXU1UlzSPMe3xvO1qvc8pbROwZ4veYGBHSwCj2otU/dg1Ww5ZTQQCErLOGpYsejxBL+n3MjZ7r2LS5a5gCPmhXKRHvLg4P4ir/6sOmuQoWxrjrqXN/bRRL+MP02/XVhXDhmmycoUQ4Ey4YrJeAOCM6HkC8bMljC7ehLEl9tyizRBbDIRi7mbRS7HVt1u6mZDyUa5baYcuH+IXI4J3/YXwVrfDUk9BOTS8sAZ1ziY0OlohkVwVRxqlijyDe0uk1+L/KYLNQ1DuDaNT0Nfr0yjY1FUI+OeCHahdqJHFRocjkr0Xd7adhB0NbCuLG3+6D3csug3KsfnP6mpsj5qJ1tTqPYqJSdLs53bdio6flR6XakXslOI1mDTyp5h5LyBvNCzuVwnKMOvrKlqDlhYLxawFXOEsHBUox2UNpwMA6juCGDvhJeyd/iDCAY2eiNFF555S8ZaCQ6dZFZrnxL431qk3szIH0Z3UAMATTkdR5wDd9fyJ/wT160/EnXwHjiltNe4kJQgJHB5EFxYhFXOXjR5P0AFjMtHp68B5K+7B+MrjMDWDx9JdDYaEVcoFenr/9ZiR245zi77Bfa/oU7ddVSZPiKzSNZgw9N2kiLWE6/am48MWvdLo210jmeWPHfgerp3yoKGjA8A6LEi2uPKNQDDq3CE47L9GmOK88FCEVPJHhhBd4vwA7Ni5OcYJGiJ6vyRSgEI+V2GhoW8mXJPY8VXDX4v/uAIoLVuFKeM+RRfRL7b6esUJiGg4RMn5qUrbH7FcJK0Jew55AHMe/xHMhR6FM2td7O90bg92NRq/n1CwRdV9Ldo+1zsGOTMb0Nbpw1UTX0XGYHV0R397ESZGxqAY6brnlJgANje7uUUvggBE8VjbNxXgO6UxZc9VB4CdR9yA0FBRzBcMRRCOnlrDGo6aeQAOiJyvMuJt7oDVumK/LtmMN75ZoLqWnbUTfG6FruzZ66/DRev1UU39QYK0Dv23GZbVgt0z74j2kfVhKMb1fx9lo57CTWZeq0ni4CDoBjStqq4KBaM/x4zQQADAOSXfsAsCusVxwqTPMXMoOwyqhLyiHSgbugBMYqbvpWldAPATwxD22MGbmWXPH/EzxhfU4rO2iCrfIqUUra3LQSk1FLkoe7I1qqPd015m1nnsbi/DxqZyAIBnpRczh6zDISXysVa1F0gNOAN48PBbkZW9FVlht/UIUK3zh8xBs/qeEBSLTVp4VKED6SrQBz5rapDN+XReBkRAIQhIhID3K/Ures2jmaz6LiIfx9NyK1TtROJg6AKg2JGtmDPRdxxQtBvLl7LjBAV8mVi47beo2/0n5n0J0wxs85v8bJFhx9YdWO6cg4ovxSiMvLMV6i8o9o3ziMrQQPEaAEC7vyU2foFKrSlr9F/Fd+zgRLn4ykp5/gSDeZreUFz++Urwgvo0MauYHQLheFchji7Wn6oCIdFKTaACBCpg885/Y0/lXLgylMYOBJ/ruHCKKcU1OKQ/26omVTg4CDrYVirvLH4dRWM/wcA5DwMRJ9IKN8GIJCQlPVawDYYi9BSJp1kIKqhpTdVHWLHyN6ip+lgv7439QTG4swx/qbkQ9TWizNgbzIBgIkO/d/FNeHyF6JkbDOuP/MpMTVK7JG8HSrydGDH6TXgpZz0ElMP1qlCnRPF/qYz2ZdQgACLNZhyQ3mxRCe90fRq9+uY63TW5CgEfIhNOdztmuZVERKMYRojZnoSj2qfH/nYLasc2X8BgmTIm26P95+KnIf/RXTc7HUUgMhKtwRzDMmJ7Bk41BonPu4LiSaIx4zMAQEfBV8xykdh7SMcv+b3CWiV4rA/y+8w+5B949bi/YGe9PHfCAc3mQ4BIOAMDM/aqLpcY5BztmvYU1s++Xnc9yDvR7mvGrLmzcOz/jkNVxd3YtvU2zfgSPKTjwruRAChgi6ATQk4ghGwhhGwnhNxiUOY8QshGQsgGQoh5YOQUw8hOZWbtabG/HXw0y3sSHLQhlARdElkq2DF/9s7k6rfAo5AVtW1bxKN721a9V6fy5e+quAInts7CyzmPAACcvAPUwHYzmye4b/oTeOXYa407wSAukly9X7AAbVqnIRYowTJlVDyJgybANwjjK4QsTTIooVj7xNemZSQolY1maG8TlX7BCK/aFMV/BdSAInvMB/jNoKWGdXAWaeeUcBCHikEJhdhBq1hf6+yByzGzeC+jjPFS7xIICtMa4eHMPT4bh37E7q8BQdfPefb84mKMhFg+pAglMOsTtXKTMN5jUJYoBuUjiiB5gnbMBLiJAE5zCvQY9OnN+mxs23YYMoY+irT+cnTHuRt+g/qAHx2hDtT75DkdUeoX4kiJmGpYEnQiJht8HsCJAEYDuIAQMlpTZjiAvwM4jFI6BsB1qe+qRT8ZEerCnGyi5AhJO7YBh27wDeoW/oRQnZX9qL7OMC9bS+w59F7DdpOFhwBdypg0UjOC3jFcHiO5L3w4F7PQjjWMwEQSzutyI83bbBiISmxX0Vr0T2ly5dM0hBwBWHIpBh50BBT3wo/7EbAxjAQLcti2wqrOxYGOqBlhRaNPP0ZEwG/QCWIQs11CBhe2PQMcgkzQl3c58GVTGrsgo0JHupHFifFS/76tPzJyVuPwgeYbYVv/Bczrbp2oTNtBgzGXNmzpd5T7DlFZXHG6S62AZK1zCeFsWXxCoRZfCi4/7hn1HqZl2RNKXDB6KSaM+AWcuwnOTFlMwsGBre36DbYpYq5/AMS16W8aAiftPgcjO283HcB2SulOSmkIwFwAp2vKXAHgeUpFzwBKqXmmhhTDiEPnHTIhDmVVMstIMEpNtz78e2xYeI+tHih7EdK4Q3cXf350lhOPOTJ11xv9jbp30ociA8J+0cztF6cfTgPO4l/ZQdz4k+yib/oylIC1kDMEL/RRHrXPsmXoTKMBk2qIzyx/avxfIhAIRp+kDKZTXJxOC3duLxe2zbkN9/VHg0/kOt9u9mBP0IhYGL9LBGrzSzORS1cwA81532ER0UdTtAOnYB74ilBiaN9f3xEAjTpAUAbDcNJRz6gvmI1hWKkfceqGJ7dsKTCUnTrSCI+2nokbq34X+53m4pnxuXhlvwzmQnXFFIR3HY6rSGtcfYgHdgh6PwBKwVNl9JoS5QDKCSG/EEIWE0JOSFUH7YCCTZApYeyECdiJdzq3mBcgejvlgNZFOIF27cCtkQHyUbfmQCSgO3VwJgwTyahTzcNWBT2YmV6PcZ5WZWn98ya/ACArYsMGV0vQo/3llGKW6N9dnexvQgC4ayYaN2HdCx3C0TDMgsB4M06W6frDRqIHIDNrDeyeDrKQiZou2bLGI1hzf1q0C5I8WW22yIIQ5nGxNxvD3QHLjE0sFPBs2buyyXe3vAvW+5/yzM86D1o+bNxZEtvj9XWVKjx0BXC2HabM0NcdwZx2OcmLm0Dnr9G54ncY6ytQXdMvd4I0PhPZU9/AwAFmJ8jkkCqlqBPAcABHArgAwCuEkFxtIULIHwkhywkhyxsa9Mb5icLowxWka7ZSEy9Dc9d/i5mh/HrRikJxmCZ0pPAEVl8nWgx0dflN9hD9DU+gSKGcAqo6M2J/Xz7rflx3hBzH3BFhcGRKPQKjfjvDIRSvw+/HKBV6ejt06VsL1H5AqGQRim7OlOWIptjMn1l8E4wwgIvEsZkIQFgWnWQKbJFLbmaj4dRshdoblZos9b6hXEwtqsX/9W3HhV9caLuXElw6ebUe9b56JhtQ3xHU6yW6eGwJAv+sZzABJozRKHer4hfFc6svt+yXFaPFRdRjn5G5CmGi1jU0BjKRPetJRZUEN7alIdCitmN3EXHdpHm6z2PUDkGvAtBf8bssek2JSgCfUkrDlNJdALZCJPAqUEpfppROpZROLSoq0t5OGAJlE+QIUS960UzNSAZncnwV7HknArKMzx+SuYXLv3nGtP6xS+/B8a3TddcXd1rHcNbCExafSQ97dKdTWaEk9+W62t/GrlFF0CffnhnGbbTZ9HhTdMDF23uXWf3EBL/qWN9yPSHJUzRRvVMCCispvZ64mWhl6DyuaPcAIDhVEatG28qRHZOZdQ8OcxgfVI9NBICzS9b/OAV2UCtnmTGn10wkcaM1hz7bGY3LTig2NdvLV6uEYEAUZRGbmA4yO2xgRRMrJv5BAxSvtQJbgvo5I3uKAjdVXaq616kkvgRY12juBWoHnEY27oIXXc42nN94As5uOgYAMECnDBfH3Nc4THU1FgTNxDEsWdgh6MsADCeEDCaEuAGcD+BTTZmPIXLnIIQUQhTBsNOepBhpzeWgIEyuUJulhQowNXczgllKMPFhJUEXawqE5U3gmBAHsxW1e8ZdGF2oV7wemhn/h5cSGbipU/dxGSa8AIAycPhLOF9lt+6P6LnCtmjo0g5GlEWll56PwbE543iViKsdmxGOySIJofBCwEhHCJGweUjd7hBsxXKaRrQzCqBEQK7AAZRg1KBfTGphp6M4p8uD4/3q8SLg0dUqt+Q2tCIxRhentqoxi52eXiIqE4mNBBMsCIbu0YoyENCn+gKmCaoy8TZgZchEYv8qTT0BIDOQoyhldyaYl3No1oFDICCU4HcNp+EP9Wex64guMKWjICUCBMnRjiUKThEsvyClNALgagBfA9gE4D1K6QZCyL2EEMku8GsATYSQjQDmA/gbpdRugIekMGD5rVGFhH7Ccpr4IYQ6YfwBjSe8x3JBKUUuURl6QOawzjCvHgCQk7fdvIBNSFIlMfa75mbst8w7UQC/hRulcMEjyJz3kOI90OKuanFyRwT9hFQ2NSbmcSjLRokjYps7XnzI3ajLlz0nCSgeLv8Sf5tzo+wKbliVeRuugfbCzSoRYyBbgtBz6AICUS2OOvKihpkg1Pb7hwjQ4pfnrpuYzD+DKjtJVOQS667xUk+Lcb3xnwgB+QQDAIu2Nxrmn+3SZarSHiEp87KqiCyf0d3zKEVTlIJLgQe3n2vDtqPkgGfT+OFwaJSe+mBpyr7JJhsxgs4IoZAq2NqSKaVfUkrLKaVDKaUPRK/dSSn9NPo3pZT+lVI6mlI6jlI617zG1KGl/3cIGXAWulM+JYYyM1YEOwmf1VlkGFFx6CJ8ishq+TOehxUnkOlOTWhNqX0HiFqZCP2JJfaMQySS/QU5ml5Z2Zr4GpbWIgE6R0iZYqLtuXxodNfbVgzfUzEYK8Jh+XkQZPURA1pFfG3qujUwM2tLFBLBEniGVpTw8GqUevq/EbWGsUfQKQjaVRYbxvkw+2WyA7P5nO1oHvC13KbJ0KdHqSShHA5vn2Krj2rI6+/Cfy3BU99FzfyU808Azhqrd9oCgFqaGe2iDYJu8iJuRegCyimtrczqM0dn4UZVqN6ccCEGh8TYMcZWWxpzzOi1GGOQgo3GCD3eU7R+1Nv4R70bzI+n2QkJMRN9GH/80WXs7Oxmz3Z2yQTdn7fNtH4AcCb4Ja5CF5TqNslj00UdOgIaYxCJoIo3kYcgWhztOC400VabLH2F5AfKuzpBHVENqMT45e1El7vB9NivhDNrE4irRbFJyMeNiLRR7kvfDUpRV/c5mqu2MEUuPBeMmloZE3Tq8tkXB3E8HIr8rh4Yc+jPFW6FwEicEMqpRcPId5QdNayj0BVdJ9SB69vn2O1lDFpmaGejnhMfurMInEbUQEHAeStxxozXosXEejhqTLZjRJRRwKWQGXKwOUUsmIwmqJ3PHkc20jtLAABtDuk9NS1Fx9qh6CSFUobeS9AtwSIyWtkeMTlSmn3XfpnmqbnUZosimluVmmyGdUSKsBY8qhRH3jAEfN3mRBiC3mxR4YUpWSZQUKRH0rHQs8M2B72YNzGjUyh8VGKYsR/AroQ71DQbjeHsWA1EYf8dCUe5JQOzJEIJTs+1k+HdPiiA9RuuRW3Vx4wGeTQP+wZ7tx7JPKnF6uBCzNff4eTxXWEVPvXIHpFtaR0IYHfsN2dCjIsqTsOEdybqb4RyIERk2XzfmiPhX8mO1dI64PtoJx3YPeNuw7aMYOerpvlc4HYeqbueMfg5xS8BlFJQ8IbLxSwiqGRFIvfKDuE0731QUIcQoASojIjxXHgK1IZZJwGxj3lOzQk5xqLvX6Voj4V2HQjU5BhGqCo1VZwtKSsCADQ3tWhKWE/7RBzI/jnrTlQ65A3ne24P5rW78T2phPZds7ui7gMKjpd3dYIASAtlonr8C7CDHzSTHLDer/iAfVvq4yPD0RDpy2yhtdWP1276Gc113cflaMFHnXQIdeoJtTOE4wZsgcfXHx6T5SRwfrBGyTVlLq4++m70P/TB2DUHdaiKukxELi0DvsGQ5uP07fkLsWbR72O/04d8i775u3XllLAKn2v8oNGNqNUKKNY1rse4BmPLKUDUMwhUAHh2qOGukD7OT8uO1tgVVZIbCrjzFll03BpuXm3R9ce2bGRHGUWeAg/Vpul5C0pQ4NQPiqQW369K0Z4DPcHUuqoP2vh7Yxk6gEgkQTm2xgY7GKyHN/iuad+YSOA7u9Nb4c/cjT3te8ALPELRyRIiepOeibzWHwwIptcBTh9I0Sb4ClnxX2yCsah5b1vsb6FpsG2Ry+lTn8PfZt8Ts3KJ2rrAEcpEcwMPf3sIKxazTUntthEfxO8nhNjerv78LehXtgpUEXGvzxi1IVgwZzdzFhw3cAEAIF0Rn7yGZqqSapiFNasf+V/8ZcLHuuvEwSPc3jfG1fgKN6BlEDs4loSYqCxFiESic5GPgAPBPG810obMVzSo64H4j4FS9eX//k8lQ/fn7EC4U+6zkph1ODvgKfzBso9WqScdmlP9sLOuQTAWl0cfSiNaK2ZlOjE8WKa4QuWIy/vZbLFHgPVdtIuvz8k3gzdRPhKLJLnGDyomIKHYtv1BOGmD6potBU04MWIUgICTPzoZ/1z1ghwSlgg6BVLwmBtjf/Ou9ljX0mY+BprAJPMpg0ZZdD3UOjju+uW6Kdwd/TBswXPg94jcckeX8cacalAQUApEIm7DBSPk7EYoU+ueISMrwE7fxoIvUIJdQggr61YCMCfoRkhE8SYY5FG1hFK0qfhzT4soHguEBYASjBn/unk9UcYoKDShKJQPp4aYrthbqSyM7/t+CArZyYc45HeuTVeWNYP5umRt4H3zdwEAeKcPo31DGA+J3ytL6eGr1N/1EnRrsHZaZtYzA4WEqNhJbDj4DHXoGt4XQiDoVVRObcmnreKBGEHSwv9csVAt/DGhA3uOvhbBjCqMDBZCSGs2LmiCdJeCS7agOX4jpxJTyDL0jl1zUF8+F5JhWjBi89STAhAQ3FCZhv841hvqFiNecyvdQ5vm2D49DCYudHLtePpfotesgyRgTsjJidnsP5OYyFFtWSR/Fz52yhBl4nqmQW+2SEFBIwKebLoYN9b+RnXbV/KLak7zv16n8hgePeXt2N+jGqci5NOK7VidN59HrI3RGQ3ERijF47tv1N2XoNwMCGiM8esVuSQIq+OUqiygco5JFBwI2re3IqwlYHYUjqHElHmE45EezEFbYwQRIi9KYuKhEYy4sdnZhv6OADJ3HWOre2NqzjbrhemzQQgJe/0QArR4WtAy6Ct0lr8PAKgLtyZWWUIQIIDASV2GZnPEwJtTLmC/tbOQi8PbhmH63pPhCadbZ3piNUeMFYtGoAk6FilDx3IQYorLto5WXLc3HZvDXSBMdab+CgVFW1Mb9k57GP2G/ai653F16Wox0k1NL1+GzppL4n4XLbxMCyOxzTaOLaIiUcbMQ12IvSOB4u9eKxdTpPMettXivjRtU4DneYR5r3VBDbxI0DqD41Ga1YwxxRshRAXxzs5i0w3qpbWX4uG2ImT4+iNzxxmm1gMScqtnmtw1p9a8TbGTukrZvltUKgKcI4hsDnBxbPFASdmq+NqwAcksj1DOmKDz5vFMXnJU2X59CmColwMH4Ox1N2J464Q4eiuCI2BGL2QiSshJ42iLgoYVKP6S26zsFC13fo6ammYwRJr37lBzuK2BVuxqEZ+jmepY6LWt42N5BgglAOGx9BN2rl1fwQYMLbMQ8YitmN4tosbr2MdI6aj8xSnIqxjzvfsJ0kFB0J/Ycx2TG4/HyYSA4oUFem/N2rjl2oJoMZMAQR/hT2xBOQjFX0qCODEnDA4cCnyl6LPhEmR3FRs+s6NtEEqpDzsO/yuEovWwM9km0BB8a+rBVEBbcJF8IhrfKAgALtomJ3A4KtuFqY5sZtmcwtR43Oo6AIgegkavacGhzyhaZ3s760tz4Qhm4NRcF4bltmPg8ffY7qoEBwGyTOzXlZBi/CTq8KJMOqEUJygVuwREH0+fEkwLqWXQf/ruT6gNs0WA3lAR0rtkMcrIc6+Ef+pdhv26r8iaQTJzKASApqGfGd4TaARrD70Z+kkhORYp7NA1erbuwkFB0LmCncyNNi5STIDXF+mDHT1Ua5BcwKiaaKMCbzOAlQLnT0nMzEopYeUoMLplAkpdRHQuMgBHBKS5mhDxNqN+wosoG/uRZTvlZ16L5ncUzjVxiAIEhamkfZDY/7P6rBabBLBi5k1oHfN+nHUlAUowLT2CgV62LTliPTNGxsgvbTe3/ci/IMyL9eUPjy9+twRCKI50Fsf1jUjCMnQZDkVYCCmBOQEwMNgHdo4ou1p2gjPwcxhaPwbDOXFdSXqj4jRjIwdffvyBxuxAOs0KXAi7vrpfp7yVDBMCChpAYC4CTRUOCoL+365cOBgBoeJ5OQKKyMBnddeHNMV/3N1Ju9AYL0dKCVwu8xRgRnAodvzdoVyUwIPcM/+AwmELTZ4R4DYQW1iBKXaw4O4EhWzVNmLBjCjSY5w3QWZmPfoMNA6EFRJSa37HgeKighAyXMSYo7PBddk2qeR4ePNEm3GSoCkhsREwS0KMk0yUoFPl34o6BJmgD81pYo5Re8Fq1W+BCsg1OO0cTrKQz7DvTg6JE9lm6ocnuwqU05rQRr2aGVm8uhsHBUEPthUiK8g4gqfgaHPc1sviKk8APMnvQYNFfkYdkshDqHSpbufdSHOJvwOFbPkiALgRxOwRPyXUHouoWdIPIiBkI2625iH9JRvj5KeJbYxGkLaqTm3iYWap1LaacNyPBKZ+ohw6p5CNewtfRHtIDLQqxJKJE9SNfVW3HjnqwAsteuLtMkjn53W1o270mwn10QgJrbroQ83Eh8En3A2+dLX6PgU606rhSmtUP6KQC9Nu4tYPCoKeKQjwTNErQOJSihqU7eeK/5OXuQQUZbbG+VQyBF09OSKCtfz+spLV6Ndnc0LtKcIMxa5Z01mCASPsJW+O1Sk9qUxwYeO5TpJags4RigZfPrz+PoZl+AzrhC3xOD1Rh/gOCZu4cTTaYjxmi4m1xQmyrN5Jfdjp/xgA0Bp1LGKlPpQwqrlcc4UYBBoG3OWfI5yeusQ4YnOJEFbxmS4He55REFTNvhUDD/lXrKz2VFu3KzXB+LQ4KAj64SPZckZHPGaLBjvmqAEb4+oLAXBjaQCTc+PNSpIEQVeYjY3Irkc6n2FSOvqMI0EnEoC5COysi5yMRutCrOaUP2xw6F1IbTYjAcAtP9+N1XyJYcRKOyBEzwG7v2CLF2KbWYKBnDhqJ9iEVDhKyBPcPJQGCe6gNyY7H99nI/6eY6yDihRtQG6hNgm0gUgPQDLike6AcUA9/RzhCNkn3T8oCLrhQT4egk4ovIJekVk55Ym4+hLjJuO16U3iYytf8+SyLciy8d5N4cQDWLEWnJUJv5U1AQtczNFKyaFbv9yzCcfkYUN6twjHA+7E04dlluhDK/xazw7NLNme8ybep/bQ/Y5Fypnujbhj4oTcgp0oyVY4XDHmQOlRD6t+m50oEplD1kikzqhS1CDxNZvp6J6gFFocFATdaKC4OD+WVludTF9onHWRJD4F0XiYzoRZ1nsRRUj8XdOz10oty32weCaRyRwpEBNBx7uQn/YYm2smBgEzc1bi1kOeQp6JMtYKYUFvRpg1+DBmWenASD0dzPvWiINDl57QKffsQWke7Aw7oA1KpE0CrXpWY79/ed1Z4EyCkaUcCUxMiaGRgrbZQWGzJiNnN1H3g4OgGwbcsj+lM0I5KAjmJt0XqcW4ve6SUIp2udR2uw4bHq+eJBbNVaO+0F3rHu5JgjJWiPW7bZvDDhObMAjwSNuRSVfjCumVqqeGpjLLGr2mAA5hG/blMSuXOD4LryHolS1DbT2nbMKNNAQ9tQjxsnVObSA32ik9R5tTNVv1+9SWw2HcaeOXqUUfvIirEYmbUYl/3kpPBA03W5ZTjNpsNxVe6SwcHATd4Ho8SlFCBPxj119jv9diIubi4oT7QuOeWIl/YLfOiSn5ONBmGJLZhDdxueoEbTXWyZB7ZdX75uCqRWo2K3fEWrcRa9Egrs/TuBGXkneZ95QgBBa5OWU0Ix8P43Z0cWqRY0fbILw4/2HwwUzLtiTk0FxE3M34fOfnsWuBqBki68zQxquvOeCIRdnUN2T8Qi/iaiwkR2EHhhmWYVaZxLf1cz72DcZuzLm7UIdSqUBcYUniwUFB0FOx4NxDFqBGYWr4MLkDn5Ez468o+qH2JYde4O+LSvSP/Y4lLDBrLon2OsJOnEf/jXg2oaTmr+LhZPqdamzAWPyAY+0/EMc0NSLGy8khtp7nINr+82ktlmU/wdlYSyZhEaeOV+6gDgTCmfDtPdS8LcUnkQKJMeXLDILMfM19+YmTaEsw3DH1lebOfA5hIp+KwxF26sBkcVAQdKPjvs7V2AQRbzM6nK0J9+H/8C/cgGdjc3ZfcpLuMf/DzeSp2O+O0qWWzyTTvxx3BGkkpHJnTobTaYRVaNn9a91ASAS8U68MfZDcg1fJlfbriWPMraxp7IwIn27PqoiPniYdGiscJ+VwrJ34QorOeKIEXf2uxjJ0DNEzHwpjWOu2TZ62h/jbKGgX45xzRsnjjZgORVPhSIrNL6M4KAi60UeJVynaSQ2OUDbQSvJQS/oqrFwS84pMBCQBy4ukRCB8dJGr0s2Z99+I4K/BRFxLXsIy2OM8KQiuxT/xMq4CALyCK7EBFkm8k4Sfd2DlYTcnXQ9L6d5VsJZR0vr7/APGMUwAwG4MfkCUywOAQ6PMzKNOnAlFxECjplTtRAm6QqYQ72bfXUmUG1HIOFGlShho57rcFummyIEHCUFnI94hCzgSJ+gyoh8t7mQZ+1aUkNQ0ZrhmE0IRUaXFtYcKDAYAU4I+mObIPyjQSIrxIxGTGS8gx+JBEn/wqnjgCQRw/Y8PWhe0QDpDrWJkFpudU40IMXb730DGm7bFAWh32nNeMeLQy8NZ4kZNgS6k4yLyAb7CSfoKFFPXZcah2z4xE82/USQpbnsQd+NVciW6IMeK6RZlvoEOYFJAuW56CXoM/ohf9dtY5BJfvUGSOocUKsRrttj9BF0Ah6dxA3ZgWFKyaFaoWIGP4HfkPbyPCwEAtSiNi7j/Qo5ALYw8MZU1sQ/kARgHQ/sKJ6EFeXJfweEDnAcfzPOcbsJoLMd0eNID+H3pKtOydsASAV5EPmCWpQBC8YaPUCAtsx67su1FnpQ4dE6jTM/bOyfWl7bo+P2bXI42qMNsEEKwA0OxEEewk8rECtrjvAWjuWlCfO2cAjogWhkF4MVT+Btuw6MJMTb5o+YBkCUAETjxC2bJdRkQ9IEqpXgvQY/huVXPqX57ctjppuKWpimOifmV18TbLbHNWBXGrdeD4XGomcSrMQm1Ma24iA5kxialDjZMGpqRj6VkJp7GjclJpRkEXeLy5mMOdmAobiDP42sWN2cC7fuyoHxNqpi+N+A5RmlxrP9NLseTuCl2bQkOxYfkN3gHvzVt635yH54kNyM3MgCzrNKnAWhFLl7E1QgZyJ3b+yZuwy7BqcjQYy5nJ4jk2/Nylr6dkqA7OktBYjJiorp3K7SnCoo7ySN4kfwFDof47h21skNUvDLxZPQxdnRDz+N6LCOHooIMSYhDD+XtwNKM9SDRcfsffoMXyPVYCdEE1chCKQYSZ0iGONAjCXpHSG3/Gc5ie9Pl++JzMBGoLEN08tbWAaYw4DJqUYrryQv4COdo7qjLP0puxw3kedW1K8mbuJK8gTdxeUJdkhaKkOxnZzjIKBdhDcRk1DswXFnAsFcSIgaEkKpiucjl/ZDNNVsJ25lKstn2K7jxcNS3OGTsY6yCp36c7tpO6G20/4tLsJAchSUQE4FUYFASY03gK1qtu+qCLIbZxehDDBTwBPOM7ysgc+hqGfou95toJB3Y6ylQvUcrMa6XgxPzNr2AfuEnY9diKlFCIYDDTzgSyzFd9f3UdSheQgVr4luPEuwCI8+nAjtVpo3qOhtQhJWYYtnOXQNeQGHXJABAS9SRz4coB26w9qsmPS3/6DVbjB/ucvsxqAEg7JS5HweDaNmBRNiMRBrSx98AjQw0DhHIN0TP+RJb7u4KwmijvWbkoxMMG2RWeFPF6SYmk4VSaWoNOw4zyprux72GpfaiP7qQYShOiAeDj7sfAHAV/oWPIabhYxENZVs7MRS3kcfxCc5KqE1CCapGvam77lLEqamObpzM52FfTyIY+Ey0eJpww6Q2PDjwHLyNS43bUmy45SV7IGhFRYrMU9/jWLxErsGT5Gb8gfzH9qYK2JtDL5FrcDt5NPZ7C0biJxxpu43ryIt4nNxqWe4pYTpmRcV8ulOBZW5gChqv0YRNHDQEvQOZuIh8gPUYjxbkYhNGwxGNKW0XVGE7+48SeWjWYiIWYVbsdwvyMBcXWxx5xeyKOzRclETk9Jxbsh/YevlKMj8KzpZj+DXkFVyNl/U3FJvdGkzEVzg5Jt8koKZE9BfMQsCAMwvbWNzKXu8hgw3L3UKewh14ONYX1jG+C5n4K57Dbgy0bBcA2kge3ieijsDB3CBkwtWAIgCy0jduECAS0W+cWksU4+eJaWCvH3EULiIfIAJnbIwEcOBj85Lg6/zpmL1pCQD2iUTV2ShGjlyB+nIDxydC0Y4c1aXPcLq+mMO43xE48Ar+HBtfK9xLHsBLRCs+TV4R+nPdYMMvYSlyAdArQ9fgbjyAWxSyvO0Qw3DOwym4A4/gfnKf6fNbMAICCHhw+CuewzJMV42GK60t9vfD5A48T66P/X4JV+Mzcia2YJSuXolRpQKHb3Ai7iSPYJ2CG5cWZCey1G7K+8BhRha52JdShola2ejL3aqasI+QO/Bvoo4ZL3PoylMDxU4MxQvkeryGPzLbMhK5RLzslGRWqCN9DMUJALAB41BH+uBjnfjLGg5GHI9YhEQoTQHNTwZck4Hik1IYxX6S2zOZMxSmoXfnRvUHXUiPEXEfMnAJeR9f4DSAAjN+Xo6MRp91W5rZ1OxoU/1WytC19YQYymzOSHlKgAoMwQJyDJ7FXzW3EiTSitPF1zjR9mOzpr+OGlctAHlsCCh2YTB8infazg3EA7gbEZvpAJNFjyToFBTbyEjsJTJnJXF9HgTQQgpMn1+LCbiXPIivcApakI860gdv4XLbSXWlj2MqHyUE1RAdEJTWGxLXWkP64UXInIOkJLkFT+ALnGqrH0oY2dy/g4tjlhTKOObaBRCCG0/ib6hHiWk7e6ezzfeUy1SIEXR1TknpGzWjIFqOqMbQSOTSVvaj/CPOjY8yTgupcPpSbhB34wHUo0SxsAXboh73crbzDwVhehsrv5vU3jqMV1nxSCWVX+UtXIb/KpTAQqyvcimJQZFOow5fGDwnMR3GY6btJa9558bq02I9tsL6LGLyfSjSIfpcNFk6o9mF3Ke3yB/iejKjeIvqNw8HbieP4Yn882LXXky7ABvJOFSjr6IksR2WIV70SILOQhBi3GUvrON8N0aPa1XoF5MRZ6ITiWadUYpeYha0AsH35HgAAB8lVDsxFPNxTKzsr0QZmEh8ci8ZiP+SS+PvhMEM+VwRvkDJoWvXzFpMwHJyKP5tIivVYq8i3ICyPpYMXdXVaOE78DD+Ry6IXdeKXLqQoTtaGyU/MEIqZOgsKDerbWQkPsbZCoIub2qsk4EdEEp0Ir0wnMxTzEPkLtyBh9UjQ9VT4mtyMr4gZyhbiP6fxsYmHK07DX4ABFXZ2TH/OLNR14rvOilBS0AhWgnlSS8Fs40BAC6dkWHCKMnPtyHXtB4zqHtgbz59gxPwZ7ymupY5bH60BrFGidHb4ZIZOLZok8IX6h6KftAQdCWHbgYBBJUYEPvdFSXoGegEb5Nxiyk+ox/yI5yruBu9p1ASSh/6DvJIjMgDgEuRKk1wGXt7diALHSzlpAKcYJ9gUZDEM+FAXhB34mHFNXmCMgk6obEym8kYAEAFUctltZzZjXgW15EXY793YyA6TL7vUuhjjpgR9GTM41iblR0OfT7m4KOoYhUABoaNAlFBpav4DsfjUvIu2olMKJXj1UIKcDtkZSAhorGhEeSzmkzQpU2IRrckEmhFi8Opa8uoLgkhALf9cnvs91EBsX5bJ+AwNVUqSjVQC8c921/WxoYFAG+SK1Rjz6qE9c2Nxm17Y2rz3krokQTdGdF3OxiVW1lx6J/gbHxNTgYgehk+FbVPzkQnOADbMBzf4vhoO+zPrE3BthmjFXejykFBT9C18EAm6NRp3O8ryRu4kugtHtQwn5JKR3AKgv4R8yOrmcJXkqGHFPJ1ouLQ9XJrO8RTW0a5gLZhOG4lT2BBzjTD558mf0OVxvLDfJHJ7X2H47Bea3lkAAGEyXlL9X6LE2NOS9tRrtKh/Itchf9FFasAcF6XkUMUp+KwXydsvYMSFURjecMQTy3BDLyJy9FFRH8GUcAhjg2vJOgU4L3pEGLE1Xg+aEPB5nFAUJHxfiwvzW1jVfx2DEcQbrhXNhmeaUTLnaiSm5ozJGYya9W8VRgK2IHZLJYs1ziLvgFAoD21aRIl9EiCPnGjXuMuEXQ3zHc+rSLTR0Tb0UyItu13k4fwRnTxHLeq07Suf5C7cCseVZleyZNbb8anhZKgx4sX8BeVl6G1oRRR/V3q9Uf/Zk9S9amDDWKgtZNETA7wWIZDoo421scfM6LfEJXtV7nMZfxBjQWNOYcuYgeG43XyJ/yD3IUOZGKP4gTHwhaMMhAniTVuImPxdlRRXEP64SFiHHel2CCLPaU01mPjGN9mSlH2aD5DbtSYvcocunKeBkgYvsGjITglDt0YnEbc59UQeN4jhdSgOsJJQdCGHNxFHsKLuAZcWwic9F7a1yNq2X8bsg29fcN2gooB2OAcgovIB7YtnVgct/T2y4h4QrQj3uuueDU9kqD72ljTS5YJSmCVMiKu6ejCiID6o56SPl/dblROr2xjNxmiJugxO3T5OWMOXc2Vx2Pi9gs5QvVbu6jYEMeIgkPDyHcQgRMXkw/wPi7QlZScY8wht6kcE+novh3leIrcZOmRyarP6I4Vp6+9yybo6kXZRGQ5/e14FH8nT0ILbb1agk4YFhx2MCOTPTcIgDXBQizFofgdeY9ZhrUZtyEbmzAaQpCHveVNdASdgiASFY+Es0UFtqlJqU6Uov4tePwG5URIm7DkKCUzCmamocBV5HVcixfxKw7DFjJaVcouQf/VIzJ4G20HeGMRdPU1B4PR0c7bgJB4KkMz9EiCbgaq+lv/ekYEnYOAiVTtWZpbuFL1+2X8H/NZlumV2gPSiV0MYq3l0G8jjzHrtwcrgq7k0EVIG9E3DHMtI8LJBzNiR3nVNFY5Fonj7o9ugI0oRtRuw7SHZtExY0dt0xr0kO3Q9YssQPQJjBsJ27tYuWhZHBgFwQoyPc7eAbyTHRDO6fbhEDiwHMZ10qgEXInncT3uJ/chEnbYFiSzCHoYTtRl5UHwihxwhJgRSO1XoVASY+IKiCGSiZHIRdZJKa1u9DJ3quDQxXs+koHnyF+hBUt5zJp/kpLdrlmhkRBWCbdlAnaKXaGtttqLFz2UoLM1x1qw5MBGH46CIJBTob6mCXfagOJYaSWCCoLuoBy2LLoMXEQ++vNw4nYGsbbtJGIDRuIPCSI3J3PoAgi+xQmx3+xFqce2T54y7wdYsUGofIxOEDJBje+oypahJ9O+xI2nZunsnH0j83phyUZklGyx4Pr1JEpS8tekF8LO9kcNOPT/ei7AR5OPQIeXLdLoQgaaouanLELpLXs79rfDGca15CXDN1AaGRTzBByRv/XTuAFP4GbVE1LPzcBr1vm/cGVMb6CEVMt7xF52MjunMGPrLmAlpoACcHRTysYeStCNoVQ4sSa0EYcugEPNxGdV17QRE6VJouUklcdRB+ExfMKnyFDkJ90XTgV2ZOiyUhRYhSl4j1wEgG1Prxy5ZZiOzSrdg168pXxAmvQyESWaHrDhQzraNZH8GC9iAXUr0rdRfrNERCNKAs4xjCelcL528S+IiTEEl3nIZrONgzLuS2K8iMNetE/REFB8G2l+UxBsd4jxTr4dwz4h/BXP4S9E9CLWjyaBK2sjInAiDCccipAaVmN/QYdH/oIEWEpmyicfIou1rL6g9vvMJ3FkljKFsQxdgpEM/WccgcfJrfjac4hi00otbBF0QsgJhJAthJDthJBbTMqdTQihhBB25tsUwW6kMhaHrt25JbC4VD1Bd8RKK6GU12UVbwWX0YiC8u9i14wIesqCZQFw8Facq1LkwqnGQbnI6lGKubhI9YZPkZtxH7mfUadChq4YOpmH0ot5zPAh+Q3+TF5n3ovVZeFYpBVDSGKfZO3Q1b4GQtIcuhWBccbycNrvlxIUxFb48c9xRuxvXvFOIVMRC9BJFBuvQUPX4QVcSt4F5zTWX4hXZA7doSpjfOq21qXY/D5xElbWeOtk6EovYsUtKexBLVfYbakULVlHQogDwPMAjgVQCWAZIeRTSulGTbksANcCWNIdHU0ELG7AiEOnKumdCIE6VdNCIszaWpV1EhpN8sBFmPe16EIG/kjewm/pa4Zl7ICzIXKRlaJEZQ2knKSVZAAqMQBuamKBw5Che8NKM0i1nb72uvove7BP0NV1B5gEPREOXSlDp3iU3BZ3HfGAi0r9F5NZhmUojL0qxXg91u/5OTkTDirOVUElQ7enVARgSNAlj21DV37p8ei3oRpiTR2s/AT2RC6pFGi0qJyYrMfUqTTXVby7FFgtTJzo8CUWzsIKdrax6QC2U0p3UkpDAOYCjIg6wH0AHgZsuGomCaVqxcwyJB6lqABONy+1rtd8jH/QaPGV7UTtdqmCoJuJXLqiITc/UTibJAKnYC6PVy5+CqIKw8oapxAxThjBrF8xz9V2+SL0pN0+h7sC8oHPjj27sm7J4YylFI0HWhl698Oe1ZKWaMcyyxP72xZPxPkpcehhuNHO5dp8Gkjn9cplJZwOeW4a26cpCTobRKUUNYddsZrxCUfEeozH1eRV3XVtz5Qw8g6W1lwILuytz2GWSRZ2VlU/AHsVvyuj12IghEwG0J9S+kUK+2YIXnFMkixD2Bps+0pRsax6OASNx5r8rIagE2WQLYmgy9yFuchF7KORF5pd+1irIFBqkQtRhWGNX6asP/ZKIVT9SMMOUq4qvZJMAyXqVqySICvxKq7UcW9mfVPL0F26q8krRVNjQ+zs7Gt804a8RKnoliDFhVeeyOxC4tCVMZLsYBpvlGlKhNXcfBh3RP+KClNNu233neyVowYiF2lctWGSzezQJbCUogQKDh0u8BH7YYPjQdLCW0IIB+AJADfYKPtHQshyQsjyhobEs17bXZBskYuJDF1RcSX6g4TVYgcjkYuqniirShUJlM04dGOnERG3EnbOSQm/QIwHwwnWR9CYUpRwaqsPwiEuMkd1f8T+ChJ2aNwWl/p7m20iXchQBeoiivJ2lqmybl4jlbVqW8IqTNbUKS+VVHHovKn4yLqNxTgs5nClf5rErZtJVJcTzGRnDJPg9soJabRjH4QXlWRA9F4UJsMic+h2RIzWMGYs4r0uQ0nQlYYIEoceJk7wSF26SyXsfMEqQBmFCWXRaxKyAIwFsIAQUgHgUACfshSjlNKXKaVTKaVTi4rsxTNmQeDsDoZaKReGU6X4UdUJDsrhuJk8hfcL1Bp+o81ACcktXkkAjJwytpJReBp/s6zTDC+Q69CAIjgtZOgCuFgAM7F/2okZDzenJ64eh/75bWRk7O8IcWm4ZOOp90fyliqrvaiElGToVhuXWgwhGCiyrfCYRkaulaGnAkp6TgF8ijNFe23AFoe+mYzB7XiEeU8gxCRHKxtmuh4zNIyYa3q/ZMTXil/qefItkX0gRDt0cftmgsjmoqkSuRjNQ5kQq+u5nPwHrbrAYFqlKDsshCuqLA3DhU5H91i+2SHoywAMJ4QMJoS4AZwP4NNYRylto5QWUkoHUUoHAVgM4DRK6fJu6TGAkhFv2iqn3H0XYA4uJe+iw0C0wZoAFV71kVjm0I0XmyDIyQIkBE0SGEvcSTLwI40Zn1uJV3El/kaeif1ORQhZ5TjkeM3bH1y+TvX79+Qd0/JKzz+laCp+Dp2V4CL+d1dzcqkh6Mo6m1GAd8nFeBTSRmKvDUn+rQMhiMSRDUjsz/62Ypa+Mfv7KE9qduuyQiJfssIixZ0XchJ7pX7AGVOKuhByNCXQsjUsvyClNALgagBfA9gE4D1K6QZCyL2EkNPMn+4eNJjE5VBCufsutnBlF828NGaLvJpjkb3ljKeBRECUiyOeNFuJIAwXnLz51FSH6k2coLsMhJydQ+eZPic6vCRGCIlCDW7H9V9N0EWCt5jMYkZjtIsFiM/O3A6U080Z3ZAl0za7sfmNkD1kPZzETlpCGYly6Cy4qTpI2oR6Udxg9lYUwOBzrgAP4CLyAb6H1rRTGV7BfHx+0D3LhpnZJ8Ceb9or2rWUgS5dWdG1TvwVhgvhaHKMVMPWlkwp/ZJSWk4pHUopfSB67U5K6aeMskd2J3cOAF/vPkr1eyXYZu/xEC2tyAUwPvWa1SpHobAWuaQKEbgszRa1SJSgn5TjgjJHJHR/sRGCO6kzQTyu/ywOHRCjMYr348dcconiV/xvwkqIzDMsZ4IpmysEBRm743oidRw6QVvJ7aorr64S9VHmYXgJbq5KBw2LnOxc6L037SrHpYiq1l01l5VbOroxkAa9s5iS0aAA+rWXxV2vHezvM1ZKILkga1ErmXABsFqEotmiuozRpDFTyEiWjsqdv4YYJ/NNBcJwwxEnmUqEoL+HCw3vWdWn9EhMBLJ5qlW/jWToyr4kL26KFzfgOd015SySlcpp+AHHQEgyGh8FQCxMWfX9SR2H7ncbmTKajb14TzpZaHVWRAjHvp0/GiU1WRjlaqYA9mAAviQsC219WSXYsX7Ujosenm08kCx6JEHP6syzVe5e8qBlSjUJLMciUAFtjB3ajm1CvJ6EVrFYzBCCixnhzQyJELVPiGQrz5JnWxPaZCB5VlqLXNTfMZViBLmN+NFG9HNWMOAOXyV/xkaiz1cbDyg4cDS+JApGBgPxtQuAEnhCbHcUs7ETQJDtL0Rmp6g/0VqH+bgQfLBPyO18JyNDIwrCjLoJiHNqG4YrrljPbWVfKBEt6rsDPY6gU0qR06ZWJArgsBJTmOVbbKaqEsDpvi4HilvBMhs0nipSFfEeX5PhXsNwwRFHxiIgcS41mLmXed3qfSkItmJEQm0qYYegqzL5aHJtCiD4H85Pqg+pCsxVwZXhIvIBdmAYtEQhLk9NBigALx+fJUWiIheiCEQnjf3t/3rWqLhZTZhceRwQzUakVfiu8I6Mic3swNYcNyxi/OxHOBd3k4c0RF397JP4Gx7A3bF6qMo2yixvanLocQT926b2WJJhCV/hFOzWZmuJQRpQK5EAOyFvK4OzMrVyiRH0+D5YMs4qYbjBgeJFXI21mBi7bq6ASmxCVcy8QxGHQm7B6n0pEEv6kAzilaGvJWp78l0Ywoy6Fw8S5fpDGiK93DkOALAMh+i+R6K5SGUQeKkDHcjEJobnLguJilwIr3wv8T0mbd0Qu6Keh2YydCCdeuEt3cC8v9kzLN6eWZYwWiNma2dv1NmvFZITl8aYAsBycig2knGqa8py2Z5Wy74lgu4PA5hidDGCUNWTUkZJEXa5DgEE2lyGxMDm2VQpGj1Gx8vt8BbBkMxAQeCgAhaSo7AQR+E/VBSNmHF5qTZbtH7fxNtTeWnaSORh9m7VSF4ZlWj0zF+hjstiFrws2dDKFARpcOBh3IKtNsU3hiaQVlBYD8Q8VBWfQACHHbMt/Q5BQZCXvxfZo78HGElRuiPggpWnqMFTqjK2RDsaGTopYG9ayaLHEfTd/qCtKHIS7BJWCg68t1V1LRExiBDt3L606RXAITeg7ys78YaIZAg6gdIyXO6DGVJ1xPRmm3sYP4/rTAluKr5LouKQdK31g1PpIKdxTonT5FAL0UlHQC1MwgtYwMsHEXDYiemjJOhRIsep9RiRtCbVfTYI0tLamKtODIER3xyyR2jNrVzs1a/l0OXfkg5PJUMH0EH2n6foAYX6UGIDYUVQ9jLySBp5BC4hxjbtEoeeKjmrHVAQHLdJPi53RpMcmBGebxlZiuyihWRiNSbp+mDVx1TA4TRX9LWQAkPnsVQhUdFEusI+WaxHniP6EKzJc+ggFB1IXLz0x6r3sXPhcZblBLfyvYjmX7WIysoOPSy4wSKmt5In4Exvt+yLuj7rOWckKnwNxkm5ZYGj0RqX65R8V1bgENUGUCr0WrkAACLB+DT30qBbfdw60gcBDUfL4tDDFocaiaAvxmHxdDMpyHGkRbwUTZVnxqmuJNMSbu/+ggvxKLldNTrdeSJRB8ZKXV2J4g38ISVty+nx9POMS5pDBzod3micnsTACxz2CPGJqFjxWOxugBQcCuCJW/9kDDtcNnt8tDl7zetQt7MB43RlviCnq+dxN2Us6nEil9aNa8DZtFwB4lNOtmnqZS00yZPPsL1oc++oHFG6FxQcpmXJfe2McmV2Ys8kggZnLgA5NC2w70QuSh6viNahgdgzS5WRfD8S9StYghmq32oOXQ3r6JlWIPBx8YVA1mITHYa5MA+Nq4X0nUMTleGjlQTd3LEojWU+nCCSE7kYQ07IIf63RqN430MGGT4pIeQz1vslgx7HodOudpTx5tHd1LD/wZ5U5S5kE3R9YB41gsSJF/AX222mAgIIaiY/rbveXanvPELU648oj9L7hqArxWBOi/g1BzrUsfxTa8bGiu8fL8pqmgAQnLZnQRxPEYACm06WCZZ9Dp1AiEQM50q8cygZkYsZwsQdrZ/DCtg/6SoDfo2vSVVKPDV6HEE/btsn6MPbD70bzwdrjpohSWARdDNFIwD87JkQ13EtFaAgCKXLYyL1u7sIupeRzWjfKYH1CrjEnrYPrblhoviBHK/6XUGGxv7Wm74lT+D1mU/tI59vwu4CkSjH0xOpxXuW/zV2LWKQ7pD1rJOG4QM7MXV3IFldl5SU2w4kCYDoxXuAxkPf10hrDSASarNdXpmlxwpaAs6Kj2ItWuge2Zh5m+x0Y/HYS8fT60QIejIESkplBsRrKqlHkxSeNg78npiHh00FUk3QKYiNPLPm+LF8Il46/HRTU9HlGg6V1W8z0ZIaBPWeLPyNJOKUxK7PCsmMM0V8a+xV8mcAAMdFwKXXJ9yuGXocQQ9zToR8NbbLN6PAdsAjLUFnferXTbTfhg91M7SnkD0YiI0YExeHHg+n4Rb0imnrhZGagYnHVJKFj8h5KelHdyNZtkD0mE28FgIKEAJKzJNNP0lu0ZxgCBwg+EOHTOjUuhxzGfre9GKT+/Ghu2ToMkjCFk/evB1JtGuMHkfQI3DE9WVfI1fiVjyOzWSMZVk9Qdc3ZK0Q2x8culqR5CcZeIDcGxdB/y/sK3FZCjtrDj312P/xu7sTyW2APBz4KueQlLRu5Y/hVyhOBRBE+i3FoWfKeqQqRcZKK5GL/V5Zw54MPfE51IXMhOLfUBDA0T36nx63Itb0G4V4yUMtsedcoRO5JGJpsB84dHWcCBnxWLlY6QaUYI3L/rByOVgIegeyUy5yWYA5mJ/Fjm9kD4oZZeGd61fJvMV+E0UKxqfIzbHcuFYiF8ql0ru5e0Uub5I/JGhJRkCSjKZphB63IkzTMCYJFofuoPE5Mu0Heg5tQCoJ8XDo8cQO8bg7GX3oPtd/IxwsBF2MJKmPB5IMWjVByeKFsjecJUFXpjZkQ8qNa0YAKeEALnVnue4XuSQeoTJI2NEok0WPs0N3pvNwdM/mxuDQKZzgwcdh5bA/lKIbMRYl0GdAiY+g2x9UFvHfVyKXZJWiByq0FlbJWl8EGQk14oEyWJyVyCWgslU3J5BWc9LTTbk2jZDsHEpUhp465yk1ehxBLxRau61uLVEjoPFHvdsPLPpqMgWrGeGD49HAx5MwYH+KXJJVih6ouI/cr/pNYT/0MwtmeWztgECM0S96mpp/O6XIxew7f4MTkjLpjV8p2r0iFyCxyJuSQ1J3oMcR9Fx0oiPFdqqZkU50OvVWHolELXa4uucolQi6yw6dpRTdV1YuShxMBF2L73F8UsRPcn5JHIrIIykQuQDAW7jcRrOpI3Vv4/fWzSU5LxPh0GnU+ao70ONWRCTuZGvWGOqrAMDm0LvDVGpf4ZskAnCZYX+KXJRH1f2RSm5fYQPG79f2VVYuFkR2h83sPbbiyqTwk/5IrBN7Jyva+oicm9BzvQkuoogYJQFMAtKEZcnQ92XUxFRjJzHKqJIcEhG5zMOpKWlb2c7BzKF3l4zVPmjsO3MWMQSUCZmTJ1RmbXXHKW//jHMKDyIq9LgVwVMOzjS9p2IykAg5y8olfg69xw1p3GCFdrVaGBtIajjO/38I+v59N6KQ9BLB/ipIlk4Fc3ea1J164rvf1ms37SM9bkU4gxEUTTb+6InAHREJFJugx5l5qBuSEh9oYBP0ffPeXsib+f4melYY1Fid8LP7+90cXDghmnMVeT2p3LEej8/wXnfohIw2iXSqN81NHYgijWNqcWCvCAbi4RbswhGN2aI3z6JxcwUHEkHPp40pr3MzRoGV/zRZqwq7+FURZ14Z7fFARFmL/SByWuxvgh6PDF2LL3FaajsTRXcEvTM6WbrQPRmFgO7V/fQ4gq7NV5gKSL4MWkKVKfh7NEHvjolzH7mfHeOG/CnlbbFg1+tXCS/1YzRd1w29MQehFKdVf5XQs/tb4Uui7mri3/EhGZYrkbSPyaCa9Gde7+7QzL0cehTd8cE5yp64ohSxewl6vxZ21LU5e5arfrtoCAU0Po6vuzaXA8mSxw5KUIMCpP60YoV+9TuREUxs4e5/XYzCWjpODj2Zvs/HMQk/m0qkiqB7BbYZc3etof09axJA6s3yJYJepdmtCeLXgsdLRE9duwjjK7frrjuoWk7dD5XIQnw5FbvLDn3/BDhIHEpuM5UYHtJ/NyXGtaxMuN3u5tDH05Wm95U+GPuSQ99GRibxdOrgSBFBz+H1a7Y7HYt6IEEHlmRNT2l9rLjnAKIzOV6CHh8RTduzFZTXKxmJhqALCbg5dVcKuv0tDogXXHT0Ug2rOr8qOjzhrEHdbU5nlABdghi/M3pyjdvGrmfNDxYSCszHBGvsSLcFpeqBBJ1ga3p5Sms0Dj5E4k6yG2+wnqqiPnAG/fo+CeoJJUZUjK/uSLeJXHrWgu02gm5B6Jryi5Pg0Lt3aVr1S9QnJUbQ978NffJIHUE3qJ/vHh69BxL01ENLPCUksonGyxX3pRQeQc+haycUBYlf/ENSkzpNi55G0Ek3HXKtuNxNw8YnzqF3swUPy1JJfV/BZ8f5EkbhnHsSWKa5iaAo0sS83ity6UYYcehWBLR/WJ+sOl4Z+sKy4czp79D0iUZDhR0I6GkEnes2ntF8WZ44/wPLMvsL1iRXYeWiKKrV7Zg93ZORKg49l2/DaN8m1TWROUvNhqHFgUEh4oRgJPNOEIYEnZhPygKhWXctXoJ+9C9fAIz30S4cwSBv6P7AgdIPu+gukYuVJcSYbWsO2JGyFrkoPEUV66N/QM/EaOEA3+PmiBapIugEgJOq5wkFwEW6UlK/Fj2PoKuzraUERh+PWhB0FhIxFWRtKNprRkksemENTiEPTiXcmuQn5y+apyuzr+2q7cIOwZI480Teweo0edTmFXHXuS+RKoLOUcIev25wkAR6IkFH6jlEYpAd3aqdYGe+7loiBJ2ldNJOKJ53dwtBP/O7j+N+Zn97McaL+LUP9uDUnKLG7mUl/j0wCbq1DF1h6qmYn6VBtd8ExxDBCBZCrv7Cbkxa/HUcvd33SJlSlABOTVXVpD9WFHSPeWbPWpkAAArOYncb5osv1gtnZLXo0VufKBEO6/uREEFnLHothx4MeRGOJJeFhoUzFs6P+5lfyeyU96M7IZKX1FstuDRH6fXDBuvKkO7MmZgE7IlcpL9F5AlNSOPVayID+pgnq8kU/AeXmtQNvHnaH2K/0wXj+C37C6m0cmHNgJBJ7tRk0OMIusMTgXuFfuEo0TcQn1eg0eQmHvNkFQLjo8dL0A8rqkBmmNGOZhZQ0j2y66ox8bvS9zR0l/ZBO2/Sw2q56CWDVyRgw71vYEWwVJZB0jsQvfggk0HQAeA7coJp/U35xbG/+/I15p3dD0gdA0BtiVRThZ5H0F0RLGc44igRr9GUsZVL/MOziwyLq/yhhXtxyLIlGNCujsy3r2Tozx9zccrrPNAghNzdJMtWf4/0YEj1u8jrO0AFLvYIeuztonORAvBG1GsiEx1J9+VA1DOk0vCS9X6ucK+VCwBxbt087nXTMvHufqyUasC+s+YIEQ/OXrtQ27j6J+keSfDdLz6e8joPBFzpey72tyj26H6iQTUZ628rv7bb20wUdogokbSiseWh59AzkIi1BsVpX8+Nqy/7Gqm0cmG9npXYOFHYIuiEkBMIIVsIIdsJIbcw7v+VELKRELKWEPI9IWRg6rsq4ru8wzCsdK15f+Pm0I0I+r5JfFwxcCC0X12bJYYIQkJWN1bw5h04OVBTiRJBDmTG0e7ZmrXfg2oYiYDDe8ByTHZk6JLYgSg4dO045kJvumsHPx2iCMK1n+i5hxrP/biTwwNwUUbiHcIe6+7axCznGyHEAeB5ACcCGA3gAkLIaE2xVQCmUkrHA/gfgEdS3VEJIeICIuYJcOMdLKPFbilyiT6YTDD8xdnjUFNags4sdTxxrXOeg+dh3NPE8fDhv015nQcC8nlZjyKEUm8hNGPHet2JSdsC161hmJKDlYx4G8oVIhepLImJX6Z3rsYVwvOYiFUJtd6aW6j41f1jNLJyK6MXxu0mwqEbzTCW+MbIOz1Z2GEgpgPYTindSSkNAZgL4HRlAUrpfEqppKpeDKAstd2UwQHgBHOCzhK5EFNnJPaH1RIBfR3i/dPwoWl/zOCJhJHrb9VNhpxQm+p3YWtLt4hcavL6pbzOAwGbNh6KKfxSAAAfJGgKDkhZ3Xm0GRMqt0O7hLXTLkD2TdKPRGAlI+aJS2e2qJx/HhrC1Miv6NyReHYiCfuCoE+o2BhXu4mGbGCCRY/2F4cOoB+AvYrfldFrRrgcgN7DAgAh5I+EkOWEkOUNDYllcyE2uswi6JnoxCQ/O8kBMSCUWg5dH19Fup7Mx6Hw8mFdDYE2J06q+h4A4KZB1ObnM7lMlh1wPGjLzkvq+f2N2ZXLcPYKveklP2gZuKjJIAEFTTFH9OXYQ6ETk2l+t7izuy2qXrKIi6DwEkHnFDOQwvvuYSB8Au+naTrZETqtwdr0lnKsVozHgAaykuiRDMGAYnVH5jUgxUpRQsjFAKYCeJR1n1L6MqV0KqV0alFRUWJtUApQ826zTMUIBKQZBJs3Ol5pCbrxIkj84xAAIQfV7eJhhGPvkUeb4YnwmNym1x0ks9M7BB4Rb+pt2/clCv0tKOps010flVePmOt6qsNFUYo9BaX6DVbzDfO7Djz7agn2zPIk138++ku9HvaWHJpQ9npWqsdkwBHrd3Ez3td0IxHij4fOWouUEDY96qZDiR2CXgVAmfmhLHpNBULIMQBuA3AapSztQGpgZzdnyqyo8aJ2MaIdAvrgXN1xTFrgnYJjtv6gey8HJ8cfoeAwsHY35jT8hMl0mapcMrJhh8CjuLku4ecBYBr9Nannk4UnyGNVid4voaRmBuo5kWmoyko8jK05NCIXzV1e6LZlkDTsbHAxohM93QjgVIQo2DIwYWuQ6at+TOg5FgzzGSgwtmaL7pqpDL2b/Qf2px36MgDDCSGDCSFuAOcD+FRZgBAyCcBLEIk5O6daykBtHGPZHDrr+nEblsLLsxPCajkS7QSQrByS4f5OOfrPcAthXd8cfBjOaEgCCoKJO7eCoxxcUNs6J0PQB/pq8PJCndFSXDgf/0nqecBKv2GOtFAIDz39kO567sZTsZsTCX1TWn7KHXx+98uXuq+ufQ8KxJ2+bd/Bul8xkaKgFLlIXLtUKrH5t2HE5NjfyW62omrPGKc0fo9sX3zZvlLFAAhgc+jdNS8sCTqlNALgagBfA9gE4D1K6QZCyL2EECm996MAMgG8TwhZTQj51KC6pCEOtJXIxeg5PU5d8I3hPbsil47t40z7Y4ZhY8cjwLl1feYJh9yQuNFQEFz07SfwBFObSq3aW4BDC/daFzRBqlJ1XfBTYlPGaDP9ae8bqt+p5tDzdm3QK80PTHE5E3a6GiPeUUc+7XrY6uIT5jS70lMjowaAsN+cHjgFwlRQm4mdEnsvA5ELS2LQTQk0bMnQKaVfUkrLKaVDKaUPRK/dSSn9NPr3MZTSEkrpxOh/p5nXmARsWILZCXYlwd1QzbwOAAvJUZp6NV2JKloCNLlQmEVpAd07hXknItHPQwlBuzMTWV0RxkJMnIoIQvJJFKwSAVyw7UvLOggoHoo8mVgHqICcwXpZtQABLgvOLVEQAIH+w3UEnWoUXSTEPvkdCNASGYeJzJhGT7BKDh0AlpQ/r7O93x/wd2RalCCoieg3ELOVkyqRCDXg0HuEUnRfQHR4MCdiLE7JKJ7H9tJ82zRRF8eCFwmJK8n47FWRPrprYbfMtUux0Ffn5UJL+ZNxNpqwcUnCz0qw4tA5hnXBkLY9qt8E1NBb1wqEUrx3yBm661cMWwIvutdpymxJjqnaCcKzNuADBereayNHAgoOXZKha7IodWXvALf/6Tl4zpwxETgglKnPJGZqtpiiEx0lxjq97kAPJOiwYeXCeo4afiL7MVvUNdxS+Sr+uP19jGjeZfN5NoLwyG7WUksel8JDTzy27XJMSaodLWYv/Rab5iYXnMuKELOW2iE1a1S/CSiYVmU2QChFK/Sml9mukGqzSbU45Nx339NXGv2EDhrB7O1rbce8zowk7piWKCIBNVfbJ8hQfUlEJ6wYx5jlkLrI/kBJu2j6bJWIhoKgoyCDeccIdhStdqBVJEvorqBtPY+gU4pKoudoVYh7sOyV1+7an6QfgSsb3kUXSY+zPV3FOuR3tiusXMQCg7eEwQeTbEsBpw1zLytYmb+xTMqK61o0dVBwCc5EI04q1MVBGthZe1bZrs8rmIdMlsBRPYOgJCzBxmqgOQCjuSUpUDNoJ6a37/tkDzSolinnB/VKwxjRNjAa+GPFxd1uDWIGiehaJXKnBJi4e6Xuutk2kIhZIas+yaxB36legg5AXMBveM6zKKMHH/IafkC7Ygst8ViaPx6TZ/wPYS6+xNBaCJwj9s3787tx2c+fw1EfhEQLRTkcUFj3S0qP8Np4MYlgybfTTO+zCK4vXa+gurn0rwm1bySLXLVYlnEPbK22f4C2UVC70cauU9nu/a3TrwDfCUOLLKVOxxvZ/3ILN4NoS+/piLD1JIXh/euU5ogqa3kLgg4QpDOS2JhxyYlw6Gfgfd01AWxDBpKkQ6ARehxB50Cxl5iLCVgDKPBsots5dGzCBN0dlpRuyS3IMHHHSAMBwWuzTkHE6Yy1J4DAywdRNaZfSm01CAEqpvS3LmiC4kj8ilWBqh2BCCjSahN7M6NFmd3QkdBY2eU4fXn6OaN8kne6MK5ig2EfZIJO0R0xeqyg3cs9TC5cMlGUdRGyyEW6t/82I2fUf0SwON5RiHJ0Lczk5I4EZs+p+ITRNmEn0ElxXmQJPY6gg1JLkx+WEoIa7IjU7YVtkYum2dkrFwMAPOHkTPcqB/fRSfh5OFQy9B3HjEP++h0pjbiYWZ6G3/8huThqnWHzeCUsBtWvy7xEUbRjW0LtmyuvSLT21MeS33xNmZ5DVxC7x5+6H8Nqdhi2qpRFHwiKUzZBF0EVmbliGwGV9FmJtffcpvsTe1CBGEG3FLkQEMJa/2ZKUXmxO2ji61vSf+nq76aQED2OoOc1t+jkthd/8LLqd7xcg93Fri03dct6fH3NJUl9cACoHNxPZ7kTcMpXKDhwua0Y3lif0gBdd8y+Nek6Moj5u7M2oDTNEZ4DxYw1a3Tl7MDIKWnrJcNjyygjZO6CP7Ep/s1k29aZrN7E/pq8ZUP0L6O5uH/FLLrTZoQlchHBGXhSA4m7sJ9T/21iDyrgiPbLismRLJ2Lg02q61pDBCWU1jvJWLwIBjZ5jm4yD+pxBH341godh/722X9U/bYbf9gb9OOUzz4DZe7eDGiqKG/bA3cknPQgUupQbUL/ePYf6OOrj5mLURCAChjV0BTjRqSgXAXBFn2FNhFOQV5DzoI7YlkgEJ2pI4UzQnHsrp/jb1/ze0DlDpz0/fvoIn+DRJKsQpVOapJDq9rb3MVvFQo7NFflZ98+cpZ4OjGojlNw8/uDQ9fSMi+DoEvvubbvKMWDqlr2q5nL2PrtAIDhzbstShK4Qy7MX34pBod3Ka7GL0Mv7Ihvvfnac5gMZjrXK0MHAFCiXwCzt6628aD+0sPP3IOMLp+lvKyoS9zZvbzaUSVjbAeWjJ0I3oYYZMLO9arfF//6FS5YInEpcvsUwKqBJShrrIotegoCTzCICHHGTCylAPyHNa/GBR+/ggIhvjyqAPCHd57EeZ+9FvdzSljlUGVyT5rhJgD63nIzBtYn4LWqIda/+fx1jNm2BpMaFQQqHppjg7pKRfydGZrrMpHOb6mBWaKkAy1Lj5vXn7Sk90zLOzDJxISODVj3w6kY7dPHaVGCAuBpBsbP/Egljm0ixgECwxE9Qc/vbEVjlrEiuGz5TbprvOBgEnS/v3t8JA7ML2UGhqH+mJoKy8fyu/QR+dYPEcO2+/zmYoOzNs3Ho2/chzyNadd2Wohb/u9mSxpwwvrFOGaD2olnePVO9K8XvVQlSa+EgdU7ERBk2T4FQTWfjxWDBsU0+tIphacRfHTibxMiD3ltTRhYtTOBJ2V0WdhaRxinHyGsVVBTFF5yCdJ9CUQnZCyWDcMnAKAx3jeeDPfpEXtmi9KTql9auSgx5vi7i6BP3r0OBe3WeT61yl8nywokOseErLTYNW2vE1WKnjbx2YSeU6IzkIYiRzv4dguRCwEaBhaAJ/at0Xwtes9SCgJPyDjgWkazNu8P4IiE2OaMJmKsZNDjCDohRCc3dV38B3UZxmI5dc1Pumt7isWsKRGf+Yd2RnhMXbJRp+Fb31Yq/mExpwc11SKvU+08ctfZV+GRU38XfV5fQSArHZwgtieAQ9MhLvjdHTEZeuzILvC49+3nkzi2J2dy6eTNp1Bns16skx5WE26p742FCSTboBS5LfIx+PEr7kZuyIm2UCj2WQjDZtwIRzcst9OoWK/mKqfQpczcZn7aIAorl1Raihy5+Vc4InY2RnWbDkbi9ZinskPsa1pIae0C8JQmaK9NsTRnfPwPatFFsWluX0Qs9y+CYGF8YaKVm7MjGg/ezUfw1bWXxlcPH2Gub4eB1V2y6IEEXW/l8vchajNGFlfUr7FCdy3ksuekw/kdqC8GMoPqmC2/Gyw6hBgR08LmOnj9fkz9dTF+6a8O4HXesu9x5koxhKig9GgE0DF8IjwZjtiKoiAQsgjOKtsly9AVYzBtuR0ipEegbDp8gycl9KwEV9h8KxEY9mKFYfZxMxFDLgLg5FWrY78v/34uDqsZjNW+NihcY2zX54iDQGWG1Ny80hTRTR0AMebEu4tDD7sc6NNYa1lOvxmpRz/f3yrfA3D66oU4Z8UChQiNotG/c/+6ikb7zDHERapiADykExm+DktHOAkORdiIXL8fAxpr4fH5EHLGR4hFazX9daHXykVER4jqtNPEjgx70SbdtZArDUXj2xGm5srBANeJoTNfRm67WuRS+FCN1AHmc8ct+QmBtDT84ZaHUNFHveksGDEJywaJyibliYMCgNOJUJZHZbbYvOVYZGbyMQ5dcqjhfU7wp3kSIhDhLAG8l+KSFZ8blpnYrh83FXgrs0V5im3/8QRs+ewE1EVyVWWkRdanLX49ACjF4A8/iP389ujDUJsxQKXwNeIiH3vyPjz6lMZ8zibLecpnn+GYxepTH42GpCAAvjrxBPCcG0abSTwhl+e021cWEypg7JZVtsvH+qMRnZ20cxGUJ5EBuzaicPta1UYwhN+asJXL+DpzubcdBDxONKd7dfl4dSDAoLRadKVnJdRfCoKihgZc+eF/cPyz/zYsd8OkP8X+lgLDeUMB3bfOCPrg7KYNvccR9LGBWhXxOqpxCQghOHHdr0jnjY+a9dl6ot3izUHh6E7wXvMcpR1pooLN3R6fjHfi9rV4+rG7AADpnTKxGrdnKybs2ICcrlYAgEuAyrgpkgVktPljtICCIL1iEJaGzpIDJEU5X8HP4ZgT34irX1qUNBqHsF+dPcrwHgBQi/yurrCsSM6EH6ECLxwaJkl6cyEBpoVCgLOwUHGFgNIIyqfny7PEYO3sLS1GZYlaMWbX5iSjy4cR27bCSRXKV05+19FrVpuGHOKUiZctEI/vAQWQ39GMMRa6Ea2Yh2iUy24+HJuTxNkXg7bvgi+jUN1bgSbMoc/cLDMK2rfLoJ04dcFHlnUIhMDliSDgMhenUBC0ZJbhuB0LYtfSIuZKSUGg+N3ir2K/T1g0H/867XyMqN2NGTvWM5/5Y5EsXy/jqwBKEXB7dczWcUsXIJDVPZE4exxBpzxRLaLciCgGqc4txASfOkXbkModsb83DtbXdfmudwGYe5qdVfEVCqOyUZZ7tBneOe4kXHvjPXjk5Xtx09eyNclFH7+KW19+Ac89KBL70c1qUU7e0uUoC3TFAlZREJS08djEz5Jl6BJBsLGgBrXqEkypUGeSV3TYnl2G9wCgjcoKszfvlt33j9q8AlM3rYZLcXQHgOxwJzIC2mBU0kkkfuLAMucNtj6D4v5pkBOUsOt1t+2Fu20vgPjC7EoEaECnOtuTi0r3KSrSAd7EC1QSzxjZKSsRj+8B5SkckZAYHMwEuho1BN1BlT1zYFO/YmR17lVveDRxgp6lOe2qQZHbZa2cFkCxdkw5fG6LUyKA4oGHI5NEYuN+yVZm2mMAwNitqzG0eg08QZEJo5THZzNn4qpVL+GoLatw0jcfwEv1/XuyTu4HoRQgBKN2V+g2z3xfDSL+HMv3SwQ9jqB/OPloVDiGxH5LE2zOwgVwKViia167H2d8+Wbs939mF6jqyaCdyIxu7OEQZ+igkhamcHGt4jNhmRANqLWyfQVCTvFU8OpxF+HtGRfKdbYG8O0pE1B67z0AgOE1mZCJDsFJa3ZA4LJii4USDoPGFQCExswEHdEF6OKkU4Pxwpq9lx386cjQGJzb7xjk+o1taydvW2j6jjlUjtr38pnyO47duAxFET/y29TcP20GQhlqU0dtBL94oP1qMW5IObMFgcnlzhp8BbaecwoIL/fHHzKWsWbT1tjf/Y9owqKz1XoRyWGYF5wY0ikgC27Dz+KM6k20WbFYsAo+pQShBEtnHgXia1Ncs45jopWhK+8TAnx71Ol446w/gUoyCwrketg21kqcW/Wdvm0AJ30pE9SCUKPu/oqp5jGCxD4D/5t4vI0Nj8Cb7UKGi1NdM8IJP/wPgt+Bfq3VGFa3F8eu+RmvfnQ/+q0S1/ystZuZz+0OyfOIgOKIZYswISTo5kC70416RwG6Az2OoHd52JlOXJGhUI6cNxRAUbvM+R7eNFX3TCYRCXRWexvSgwGcvvBzHLdXTcA4UPh5Md5JRtR2tE9zPbxdStU6e1JP7GjC6HAXZgz8HrtmyWKByuIsNE8Zi7zzxCBjG8oCUE6w1dMnoiJ/kKrek64aj9zy9JgM0BGLNOfEd/93keH0PGvNlwiDrSM48sFzMeaKWbo410oc13644T0AoLysWP5lwlQM3yMe9b31lahqDYNTBLJ40H0h/jzwNnjcGo89uTbTtlggBgk2OEIUWyQb27N2IeAV0NUmK9XCbeYiJAmZfYI487KrVNd4yeKHEqw65FC0FbgNOVgHlbMAWek/HDQeRZyAxuJi0PTsOJ5hEH2qVtwO9HfguG2LZf2VQDAsZ5j16cJGEpXSVUGc0vyF6tqZ875Aee2emAMdE1TANK4RYRvRFj3uMcgQwnJ/LTrelt0H/nAajtm8Avmdbejcm4afp48AADx8y591ojl3QC3iIgBuaR6NwzvH6b1yfZ2Y7LBWXCeCHkfQjaLrHT88R2HTQNDQZwz+c/rlsfunfP2rZmAp0jhxIXN8BFe+/SgOmfUD+vjVR0EiUGTzfeADUBiqQf/mOlzy60cYNmQHtCigDXiU/iX2+8QCL3447jAci6/xd9wXu75+cCuGZuXKfeePUS38LUNGwNVXLa8khKB44CA4ohr9sS3rAACZLX6U3XuPIVHo597DDqiihAmXNbd0Ph6gN+JC+gZm0/nIF5pV9zmShUG7t2JApei199yjd+GDv12B5393C26e9bTKk7R4w1YMWfsrBmlETLG+8/ET9IiRHTwnu2mKNEg/Bi8Xv4wmRweUG4nRBqHqZxSlhbNV1yKx/lN8NnYGWrMJApStd5EyPdmJxR+MJ7QE5SE4OdxOnzEtph0NnUwdgBS8iIJi+pbvManBCypt0FSMUaLN0qRFR7N+M9I+MWz8u8ir7FKV8GdmYmvpAOSg1bBugUZEpyGLMaQg4LgcuCqbFdeM8dif7oM/PRttmSIjJHBOzH1hJsbNroYzFMJXA9VMzm+3fIlpa79XXSOgKIIDDhCdzX9RWy3co0aa9jlR9DyCbhRdr71c9ZXeOP0CrB8uG/oXNTaqFh8BIDjEaZ3H1WHVyCkYiN26lFqEUhBXEANunIIIFfD9t5egZEg9LsJbAIDStkaolkflgNifuVlsOVluv6FIzyyN/XYTPVc4cPRoXYKEOXPGgfpEjqewQeRyHT6K3HPOYbYDAFmkFUUmIhUAICZx0df0+wGDsAsn4zNciedQIqjlxm5nOioGlmNP2TAAwP/mnIi2rFz40jIx/LuXQQVZYUUEID3gA69N1SaJXBIwQSA6Bw2xDodTzlFllCD8x6oq5EVyVbHY7ShFjTbPoEP+jtd6QygOOBFsZyvslBy6srrrqD5YGqHGziyMzoGj1DrWvWaeC4K6DeWYhd1tAA2DczhVlqCUWicm4QzSAHLp8snO5exAKSengiQAKvKMvTjlV+AQ4toQtkHF8vPzcfLg3yjmmklhQiAQoDGtBACwq6QMh2MBOmoG4+wP9cpal8CjtOpkXLrsQbmK6PgGXILOsqYqgwADe0UuAIxjLNzS/2nLZ7ULccnIIXh0yCUocXdhynoxcqKOU6FAZ1YjvIXpaKEZOOr0t/DP/HPQtvk4AMBJ6xaDQuZa+pb9Gns2r0A0VSzYfibcXTIB38bz6Fcm6wFYQofBY8bBpZHnEkJAAmLZSEgkHkELIsgHXdbCaRMO/bZSP2q3nARuz2zwETe8zeoF6lCYB17z9VyEnW5cduejAIDdXAvaqCyaEnzidGuJaO3/Jbls/ARd0HiiUt4Fpycdw6fJ1jmCwmtU9zwIgjkyIR7gq4i7DxJc1IMcXydmb1uDS045FX2DHngENjF2xJSinOa6nht3MuOvskEpsDutD/Z6Sk3LaUeDanUHVFGGil+oaNA0CFEKzgkUGYfOSFgpOmKlWq/DEdnggEBAR0YeTvxlPqOnMhygaOaaECHmZscUQGlpKTLdmbH1bSV3H1KzCX04ii35fVHY2oD1646G91txjE5c/bOGklCUeZx4K00tQweAXZl10DITAgljUFb3cOjd467UjTBSXv5Sexqm9xM1z5ICbPyW1Vg7YqLiWfXAjnDuRVrRCuyoEBUwzXvK9HFGqIAMiFzW4PpO8J0Ev3//Q4QuFT0B3XwExMmeHIV9xB2+YsCfsGvDWcBk8fqGpmORli3HAfGOKQAWyw1fcMEFKCsrgyukP/57wiKBcPEh/PaDlxCwMLnsas4DSTMtYprCa9qy+1Ax83YIENPJTVu9EcV9/fh67KEAgHNumY7iqy5BxMFhwk5R7HLGgm8QIcCfr/HjsFVjY3UN6gog2NyJPSPVHqExs8UEnC2CmuTGfJcb1771HgCFmtlAlHPPkD+DgsLbWY3y5gqMbViPpgx7MnQWcjiKC5Z9h2anOOChKf0AA2ONGIdOHCrS4gykA5rvFRLsezlylCLIeTDt0HdNy+mUorqJr2CACACSg4y8fghzYuYfF88j+/jjQJd+Y9pOaWud7pr2Kzf5M5Cp2LQIgIb8PDz55IM4a+bzhnU7BAeKWgeimjNP4cfazM2m2r0v3YQBNIQNpZMxorkaPpcLp9ZcCKHiCoThwmnz5uG78dMVL0IwMHMnvvCPwdTYFYpXc3/A1vbByNcMbVegDEXeBLyibaAHcujaxRm1BAkXKDhwir61e7B9wDD1syqRC0Xjkt9h1Mh/ID1HJK6RDWk6GT2hFAVOUcHEuYB37rgWY2p2oSijgdE79bNp0UA+Z5wwHFdfMx0AMJRuRXr/N1Q2yoNn90NM3gtgxAhR+UIY8TX6BioBAC5XJ1whH0Y1mZl/ATQiWJoDOkzkoJ7OMtXvdWMHoLhJVugUDcjG/ee34KFzRUuFLaV5WFNWgDeOFWWiHCdz4wOnNWDqxBp05mjMzCSuKRHvOY2x93k/faL4Jdl9suv9Z//zEQZFWlU6tuYPwocjToGnziyDvHn/cjwOzPzhR/x+7ttisx4HsqJmmxM1zj4OFWMij3/HHn3CkXBNiWm7qh5G7Mrb1e+ijTjCOvj1H5WHUDQhsxTMy2hE0mgXnqq7Ew5nq+6etuo1W+egvVl9arv2v//C+feb6wGcHgdycooR5CwciySYeO4qMXl3FdIcBDtcyxByONFemgY3nBhyQgPKz67ByD07Y05k0hvdJVCcU/wfRVMUr7dORX7eUF2b/bOzMa5fr9kiAOOMMqvuOFbxi6C6dAD6NsjxNPo8cL9OKdpv4iT07XseaFEp/jpyIUJjcnT1cxRANEOSN8ptUQePMM/S3qunN+eVWS2Hg8Nb9Fzcjdvga7sIXu8A7cP62hiEtjXPC4cQASEh/PanpSir22NaB8eITqmFYHJs/qlDTSBaC3Lw7yPOUDTAYVyoEdOD4gY3orYFa4ePxJKRHLJrn0TfLNnaIg8dKC1oQbEmamWMQ0/Ee04QFzPHR8DxEUzYvDR2S5ah87qay2vFceNAQRWyb7N0ZsqR/F/xP3T3ucI89K+vRU2pKO5IdwG8P4iLPn8Lh1SqRQxOlfJVIXJjDMH4HfYDqFGbClTlWvAG/XAzwrkqyzg9AgaMLoilW3RKsV8M5g4FQTEIOvzWsvBjSxtB/QobblAMqqvHZV+ZnzKcLgcOHVWKiMUpNWaymq30qDb+zoVDO+GdWorhvi6ckTEPf278LwDg3X4nojKrFBuPHKN+gBCEgwNQW3+q6h1eOW4MHr3mEN1paFhJHrhEs6JboMcRdJ3IJcpK5GXIadwogMffvA93v/1KrFju2WfrdsqZpwwFALT0mYQTpryE94qOgt7IgcIRjd+wrawvdhbnYfmomXA55IK80B4tSXAnFIoRhcMD4QgcEMBBgH/CZDh0Yg6q+L8IgfHRPVUUPOeEq4FDxcABaM0WdxkjzkPgI5ZWLmYpUS98/igsapM3pkk7Vmse5vDvmjq8XNuAnLE+7O6XjSF7tuKTymr8cvMxyM2OEnRK4WwQx8zrUssUJNd/ysmb5Jyti037LMETtRYSHE4IDqetFHKHdi1HcfUezJtSDoHyaM2T++PLNR4rZc3nXHWV7n7B4CH4YsZhaMgWRSSjT/wzOOThP6dcgmX9DlGVdSjM8ZQLXnuaSqddscw8SvShBs5iNhl0qc0JgfUIeNIQCenlclqhDIAYQXfFTgLGBL1i1yw4BGup7qCMepXuIBLNgsW7m023+DRXBgoQxhmL51u2AQBD5vSXaYTJkugzogMjp5+I/o40TG9fD+oX8GlrGKuPfAS/P/oj1JT2wWkrZFGT1MebW+UxjAQ8OPboQfB6nTqC7nIln4fACD2OoEe0M9ZA/jt58UaktdSoi2qmh8MjHvP6jzoKa7JGor0+XT8/qYDsKFEaeNEx+Pq08/HVYVPUZYJin8IhD6ogH5mJS0HQFf288tP/YhgjUbIWlJGYgUQtYrrSitBB92DlNIujmw3LEa1kZ/aOZarfkzICaPB7EGkajHPenI8PbrpS0R+CT/kZeC9yBPqObcUxh23BsesrkBsSiVp+iSgrHFa5HY2bs7Hr60LkOtR9lkaGpzKn1eaxd4xOd6tjkqvPYGLNWhpPKMHPk2dhUnY6hMYgNpUOwqmLvkS6rwuTa/XZ4U9s/ClanxSrhT2mA4eMQ55nIPaOnQ0AcPafAk/Uo3DcdnVMHKXIJWBh+17a3Ky7NgGiCEcb7lcAwaVfvIcjVv2qe8YMgocRD13xmtI36vDniuXbBV0ZJSgInhg+COkW8m0AaHb4VSarVAD6zWzG+nFjTJ4CsjIzAQq4AuZWQBLx5hz2yJ3jz78iZ9YlGNBnHLYtKwW/IIArnj4CD48cgB+mj8L0wEIsHD8V44LqEADKZRT2G8cSKulvrrBOBj2OoHNu9cfjWUcXAjx0PsGWMwzsncUi4KIxIEZkioR9tz8XLm2GcwHIHNAHADCqtC/em3I4MCQH06Z+LPchSjFy2ttUMl3Owd6Jf/PdF7qAYqyFwUqkNG6PqHgs7grgvouc4Fxu3bspwVOO6R9fSmUzMS27MkGTki3NSVGUFoSzYBdajsrC3gK1nPkv4WtwU+RP2Li8CFsWFeOOw8/DlOCLAICS/FIUNzWgsqgMrkPdyOwXAOmn1m3EzOMU78ubODspkZWZDwC498XHccPbr6g2eFU8dAOOjHZx2Dh4HKZu+xF3zr0dIUXmnpFhMYCU+Z4oV5ybm4mHcsfj7YhswcQXEtz90hNoHpGO/oooiEoOXYjIIijK6+dMrl/vbSnhhIZF6t7wAfzu849w98vm8mftSSY9R70xiFb8SoouvmdXKCriikYe1Zr5SqBw4LquZzGYViK/s011T/spPu0cDKII53HCqp+QPSCAGXnmgeGycgtAqQBn0CraotyirOg1/qhc/iAAQP/iEkR2cMiMcHB55Pm4ZccQtHPZKr+XqzM68VqWHB9m7N6NijbVSM/MNe1vMuhxBL2oQL3QjRwz3kQVLou0qdgzTse/iUM9OjMNa2eOwYuzpuqOt4RSFA0SCdj04nG4sXA3Xp50GLKzRbfvSfUEggMYu3U9OFcIXFCxT1tkI7eC3sYayPSU4sbPPoLTHcEDDU0Y12XuEehmyISvow/jPsjZVbTK10pvsbqORtkEcO/wEfj8D2cz21p88XAsuWII1uROjXGz+Xk5qC8owmWfvY/PM3+LJWOnI7N8gvo9o/8Kivykc1YvxJH0W1xFnzJ9v9Ji8QQQco5H3/ZiFPzhD7oylGEZdcuLYpTFvk17AMLhkbOuQRslWD1OH1ZCKcrTQmlBQwhBGE44FBYbfdv8qD0yC7+p+AqED2B2qyhKkswW02kXvC7zWDuu8v44ft18HLZlmWk5ACB2E5YrXuboX7/EoOJq1W0xNV7MTijGgBxevRCD6/Yii4h9NqKLXEQAn9eEhSUZ6PCam1m9dMFzCCnCMGdERAX7VNcSo0cAAPnForLYoWXCosijoq9GAa/YUGJmi8Zrk0QTYRSUirbiWaXqTXbUsEtRVicHCSQEOPqwGXj/JtnhqHKofArViokzM+Pz4o0HPc5s0anZ74wi0X3Cz8Tn/KEYWF+BDL8PwES9LEvB3Rd7XDhpfF+s+kJj+y1QDM4ViQbHcbhx3Omq++U/vIt158zG+vKxKOQbMGvpEnwz4yjTd/jbZQ4YBaxVqTAZBH3FiHVoam/DWMJhRe056FOSoSujhAABRBPGMB9NSFfY0zk04+LVxPkeN+VZ7PhlMVqr6gA+iCU5ywHM0LU10kXRhRCOGL4oStBPRnZuHuaf+RvwHEHl36djo5vH+WUlQJ180pIWRqbi0Bp0O3AFRC7/BVynaue4tfPxzXhxjAcOGAgAqMydiB0EuOjcY6EFgd50zT1ZNKecvX4B+jVVYOqeRjx69WWYuem9WJkYOaNUUZMaszcvwvdjjwQAlBRm49QhX2FayUYAYliHtOwc3F37OpaSEdhTMghjG/ZE6xTrGirsAtwcRoW2YJN7hK5+gKI9PAVfjzOfU7HSAVayZ72dkzJd3rkT3kVp2aOAUrJDY/+LlhP7u2rwSOzK64/grihBZFD0mduWwe91YSk/BNm+NISdGpGS5pEsrxeOsNzvI+YvxC+n/x07a3cDecbvmp2bDxpsgCvAtg2dgqWYRFdgpMK5y54qUnw39/jDMei8LHh++6jqbuGp/dFZW4tMdMReZ9JR/VWnbl71zupWvV7zNZsMehyHriXKLJELBcG14avR2UfA7pLB2DhoTPRZRT1GDWiPkJQi021syvbSiQThkHi/tLoRV7/3qvU7uPTZWlj94fz6iTp83UCc3ipgRLAAhx01D0dOOTL6PJtVclGiW3Suyllo3SBz6IJGJFPUoTbJzB06GKOOOAEj2qZgJlcOV9ZvVPfLMqswNGcXrl/2Fzy47G+4ZPBc/G6waBmQmV+EyJSpCB59DXIyb0Sf9Scjt2yo5t3F9guzFOIqjsDdPByhTafp3kki5gAwYJC42fbnHRgWYSdtJhH92DyYK9bR0X8ADl26BV8MaUbnjsdRXjxa/zxDYS3hpzHyxuZ0uXHGsC9RruDock45Hg00B+t3lWDijvWxD13It+Iq+hSuD7wCp0XGrPSu3bprwXYxNpCWn8nIsJZZa3Fr2hPIylKbp+pcsaLr4uK33saRW37GuI110XJ6LBo+DevLRqGfoxxp2IqLv/mEUUoNtyCLndIC7SgtOwvuveeaPpNXWILRs09HdXkh8z4BMBGr4GCtLhPfixhhdnmRdu9ScMOPUN0fNHMS/nPEMPYzUUxYLMd719IsZ7qZaWxy6HkE3YRDVw7cq8f9BX8Y87ZJPWwCqHXKc4XMj7Du005BJKcVh635GSdu+RQvnm596Pno/4yD5CtBw126a+uKM/Fu25+wLn0Ypo7/ESMPO960DgIHtNvF3jY/csbILubaXJg85XEc/RLTqSyfdTrE98r15OJZ959V5e+Z+TBuPeRJPPH7wXjgskGqe5kZOViXexmWuEpx6OljcMbNv0d6NlsJmF0kO1tEeIJHnIfj+yHmBCovT2ThykZlIavQwHqAsXjTh4vfv+rsCXjrWIJwydG4NWcQZoy5Wvmg+H8Nh66cO5lU7p/TmYExo5/EpIlvxq4dMWo2fjd+HPrt3Y6TV34ok0lCcRgWIl2IoH9lBmJWTpquEgCZnXtw8df/ETcEDbSzOEsRO2Zs63JkR1p0z8hvIuJkfIrBuVnGBRQ6iP5DT8Bl7/0bw4+PirYMHP36hKoxKnse/J7D8XuHeYA3AEgT5G+3Y2Z/RIozMNEiTVt2YQlcLg/m3PA48770nQRFGARJd5CAgawKYwuGK/2KdOhLZYMMLZEdWF6eZOvG6HEEXWuabeblmNE4DY/XXYn79l4PwJbBh07MoXW/1+LBIx7AgLwS/GnUU5g8Yzs2lBtbZ/RdfQ0GLL4TTrde4cfqGgf98XlqVgS7XQImFxciv1iRBcno3Zwc4x7Bgg55sVDNKYfjBfwOr+JayAvFkSu+V94h5TjyiK3Mpg4bMQozy0dh6MCbMGTADQAAl8eFtzICeM7lBuEI0jL1xDwmchkpT/QgCeC67FdxoucHXfnLV8qx5R1OcSxPv3YaLrl/tqqc9NoCI+DW87gCAPDbcZdi78Q+uOfau3DIrHcwePxg3fNmx/QLFr+m+l1aeho8HtkRKNOdiS/P/gKZZ5yO0eesUXROnFcdwSyEMi1iolOAo6U4Y7GJ/EFqj8gbzJO3PYH3r7uaXVAxJ/7Sbyoy8tUOTUQjcpGCrC2eEUTpjDsQPCS6ATCyeFy++CX8seoRlFSlYWbG4dgx4iV13YzJGs6WHYseSr8MQ2eUYu8YNuctISdfVIiXlbNEVUC4Kw9pLcOxo02ZLUxSippWnTTGTpDlV1oZOpekbs0MPU6Grv0SLEcQSggOHfQT3FNz0bVcFm/YCYmhLUMtsnM7OSeG9p+IQN1OVFePwqMXH4XztrHLZtVPMXXi0SIwaJDu2jEDz0X+lvU4svwUW3W4nF5ENAuIp058v/23kJKvUW1ORoaSyZHpRr8HDgM4ojtelq24AaAO4Gjx96ChciouB+FQSynOyDc54kb7Vz64H7BR5lTfbnLD788G1LQGR0/6Aq/iMsP6lDUDgJPjtAIEpHWIp4EhuUPw3blRKxKtrxeROHTxJ8tFa9b0FXjBRk9m/vUfWPZMOhClLXzIi3V1Y1FI9iJ3ZAOAoQZPUrSUZWPqqJ+A8ApAocxmIZO249lTs1HSAkzZWYSh1XrXeyUIgAEjfsu8Q1SlRBw+81hc1PUnzC+bH+2dfj733X0utncOxSjuMxx7xWSs2a5W5rJWQNY0mSg/c+EIOF0OHHPpaOAHk/5Hv487XRsbKNqOQODP24aM9koAj6reJFkOXVkJS4+3M6Qvty/Q4wg61ZxJBdW0k0aOImOIuGD9TYORVrBLelhZk1EDqp+EkQ1di6NPPgOvvvIlph0+HBPLDge2rWaWWzb5Qbi87RiAX3T3WORu2HR9DPepJwxGcf8cDBynjtZmJELKSE+DT9DK4gmqJh4T++XQHNSMnNiIgR1v/2PPAQzuOZwO/HLbHHgzrJ0pirPLAIjevVnuDOylz6I+T79YvfP+CZxkWV0sMQTrEDfzxO/1F7XPa/5ij7A9Vo8QgkEzzgB2/xi7Nq5kPdb7HPjz8f/Axz8Yy5kPvf5BbFvbhU27d5n0UcSLjvPRmp2Gk/48HX/aWQoHI3yEHVBozXzF95zZdybW/W6dfJ0xKJVD5uH04BpsdhTisAFZmJ79I7DKPF/smYddiLs2i8lQTh41KaE+G6E6S38qtJtq0BzGlLq9WRYfGsWf6g70OIKuHRozkQsAHHb4Jwj5o3EnKIvn0NSnWQB23NE5zo0r/vSGZTk+dxt2+D2wx1sDs4cfDmxXLwTOwWHQePOjqBJ5ucVoaNYo1QjBE+RqAMsB6MfQa5GjUYv0icWm9zNyzZ2EJBl1QVoBJILOUSemFH6EgajAvXhAVX5d//hyuxLqgs7vkSH2YjwZfV7zW1FXxdqLYkHXrFA8fbxM0CnweqMbnR2FuNZlbNZHAAwvKMfwo4CTMAmPzl9t2sacE87BgLJ+OGVIGT5562d0OAkumsl413ijJBqsM5bH5TvNR2IpGY68Ugd+DyArtwyAOUHPLygCYJzbNlF82OJC/V5Z0U1SKHIxq+LKK560WTK16HEydO3gCBbZSvJKMlAySLQJtUqXJdauMVu0a9drAwN+eAZHLH7Idvm0LHZ2pnhQNmQQtKGxBQLwkdbYb2089P7Dx8C37RgEtporXFMF1umCOJw4Dl9hBPTpvq78yxlx1e/2uJDIotLmb2VxdZfdcHt8lSqqCDUciZBbbdMf/+lc3af/O2ESzposWqzkB8IY3ikYnt7MWiRU0KwXo2Ob/vmFNx2JHbQfLp59BOMBNpzu7nGH/7ktDWOy9J6k8STetgSjqqwM+WRph+6kCj2PQ9eKXJgfxuBjJaAU5bQ2tElgtWs7/AhiGk7V3WN1zZth317VaNH2HzISWPqj6holwH2/XoffHCc9q94Up06bjZ21+kQL+xIutxtVvkzsbBkMlFmXN0OahWPL/sJho2oxsn0LgOugsvmmgiwuMiS4Bmaqbnm+/nXMc+gINAJgW4GYQlO94YmWcad/fgYqHjo5/jaZ3UiO8P4jvQxuZeAvadxSSGStatp3/HkPJOjawWNz6EaLQFnE4DNouNnBGq/GZFDt0MfkkMCSRTpdyX+efoMGM64SeGh+7JfWUGFAeTle/LUcPAHmJN0Da7CIltPtwp0/RwOdJUnQc3ILkyIMirzISUNZx1isBRdiOxOlAu9e+F9EhAjKF+pd6K1Gg4CovotRoupERnVfEThCKXI8flRvOUHRtqR0T74XdkLxSv3YV7AlciGEnEAI2UII2U4IuYVx30MIeTd6fwkhZFDKeyrBBodutHhVmcwNqhc0SWlPPu83BiXjx6Hh4Tg2pHcq6k4QhokU5Qi+uUHWKmozzzucTkxI5zA5L34nlUTA+haejAwM9bhwebn9WOBGKCm2DlXMgsR9Wmte7IMo/m3YfAI+qdL3TRuiIlF4HB5kuMxPeYYnAJvt7kMDjoSwacklmK/Qi8WsXFIpcrGYFwcUQSeEOAA8D+BEAKMBXEAIGa0pdjmAFkrpMABPAng41R2VoCXgygSxVnbmWhd4FpwaE48MA5OoRDAmUobSOrajh1USilSjQJGkmoXVNQOxYpf2M3cX9O+em5mL7+85Dndcprf0iRcjJk1OcHSjc6GbPs32nBxcf+51jDv7YC7Y0ifprVwYFaWoQ92DDaXAg3/Vm7im0mzRap+3uzmmAnbO9NMBbKeU7gQAQshcAKcD2KgoczqAu6N//w/Ac4QQQo1CsSWD6GZLKA9KHBAIh+MXiiZfUih9CsSuKTFM0x1WmcGaj8MqY42B7Gf7PYHwyP5wLdRbqShdYtTPGdSlgctgqI9f+Am0qimBEFV9UzUJJ45f+AluaTwSYcKbtGuvX8YYGPuLqOoRrz9VWYl/aa4p+2evfbHM7/esVi06AmrruVjYVYZSNJFvBMTM0EFAsXDAIVi4awuwa4uiBAEHGnOFUo+N3JYE7Vdn9SEN+eiEVsHOehdF3ZqKN+e5mHUfwSBW7HGw8w1Z45jDqIvVzkBmmY1TcvCbdXK0Sm/MysWYCtud007Fu5u9w3TNEB2/8BP84f+1d+bBXVVXHP98WWSnRnYCYWnBStUKBIEKBTewjlXbaktbF6zW1uWf2s7Ujk6n2z+2nXamY/fWmVZbq1arjNpaQKkVQUjKEogQYqAQtkSQTQ0k5PSPe0Me4ZflR8kvv/xyPjNvuO+++17u+Z13D/edd985+XncOL71L2jTpS0GPZ+GtWSBSmB6c23MrE7SQWAQTdYqSboTuBOgoOD0HoN7b93BjOHFTFuziaLJH2bamg0smRO+aPlI0WYOXdKHgvUVlI9sGqIVhpRs5eKz13Bc3ZhZ8iYvz7zklDb/7j+Syw+8TnWfgQx5/zC7B6XvwP1y6SL2DB7IWyOafCxy0TcAqE1he4vfPcqsw6uZ/GY5S2fOOlF/TfW/GFl1gNcubHlt3LxNxayY8C6jD+9jyIHD9D1SQ/mY4ewcXMAS68Xsw6s4d8ceNozJp3bbbmonXXTi3OcGFnDZwRX0r6mhrnt3tg0bx8MT36ZnbR21lsoHD1/a/Dz7P9CPslNC4baNz+xaSr/3j1IxdAgXlW7h5Y+F/9I+vXspVQMGsome9IrO/U9WLWPo/kPsHJJHfbduVA4u4OaKFznWswclY89r9m/cveZpysYOZ9fgAuavWEvP6XWAuHhVKf+88tJmz7u54u/U9OpJ/rZqij5aRV1JBbPyVjN5YzlrJ32QKRveYvGsRh3Ne2c53err2T50bKtyT9yyix39S/ngll1UDGuM7DhvZTEDp77HwZ1VfOXd5yidOIp6iQs3bWfJzMZ4MdfvfYW8A0cYWLmPIzN7k7+ugluO1LAxP59B7x+mcsiphu2G8ldZNWY8vY8fY+rWbWwZNYx+m8qZPmgQFxeXsHRuGD9fXf8sRRPG0qfuGGsOHGL+2jIGnH+Ey97Yw9OzL6Q2xVehf+49jLmHVnI4Lr2cWL2XdeNO1cmndy+l/7tHKR8+lGlrylg8dzYLt7zAwQF92Jw/AYAr31lOz/rjbBvaeM/dUvQSr5//YarPyiO/poq+dcfoV3OUYz16nPR7z9//GuN3VLFt1BBGVr1DyZjRXFBcypIrTx6/c4rXwVTov76Mm+rrWTxmMsL41LrlHBnYl5reZ7GxIPWXp02ZvXotPQrr6V7xX2onhuirX9z2D+q6dzvpvuxdvY8Ljm7EEIOOHqI6L5/j7bQ2Xa1NoiXdAFxlZnfE/ZuB6WZ2b6LNhtimMu6/Fds0u/i0sLDQioqKzoAIjuM4XQdJxWaW0hfZlpeiOzn54+tRsS5lG4Vgwh8A9qXfVcdxHOd0aYtBXw1MkDROIf/ZAmBRkzaLgFtj+Qbg5XbxnzuO4zjN0qoPPfrE7wVeAroDj5jZRknfA4rMbBHwe+BRSeWEMPkL2rPTjuM4zqm06csVM3sReLFJ3bcT5Rqg5Wj0juM4TrvSCWO5OI7jOKlwg+44jpMjuEF3HMfJEdygO47j5AitfljUbn9Yqgb+22rD1AymtYj5uUFXkLMryAhdQ86uICN0vJxjzGxIqgMdZtD/HyQVNfelVC7RFeTsCjJC15CzK8gI2S2nu1wcx3FyBDfojuM4OUJnNei/6egOZIiuIGdXkBG6hpxdQUbIYjk7pQ/dcRzHOZXOOkN3HMdxmuAG3XEcJ0foUIMu6QFJGyWtl7RW0vQYpveNmHD6iRiyt8VE1JK+Fes3S5qfqG8xuXWmSFPO+ySVxrZLJY1JXOdWSVvidmuifqqkknitn0lnNAPuGZcxcc5nJJmkwkRdzugytv9s1OdGSX9O1GetLltC0teiLBskPS6p9+mM2WxD0iOSqhSS9TTUnSNpcdTRYkl5sV5RN+XxPpiSOCelXjOGmXXIBswEVgC94v5gQsrFJ4EFse5XwF2xfDfwq1heADwRy5OAdUAvYBzwFiHMb/dYHg+cFdtM6gRyXgr0jeW7EnKeA1TEf/NiOS8eWwXMICSK/DvwiWyWMe4PAF4FVgKFOarLCcCahJ6GZrsuW5E/H9gK9In7TwIL0x2z2bgBHwemABsSdT8E7o/l+4GHYvnqqBtFXb3Rml4ztXXkDH0E8LaZHQWwkK5uN3AZIdE0wB+A62P5urhPPH55nL1cB/zFzI6a2VagnJDY+kRyazM7BjQkt840aclpZq+Y2XuxfiUhQxTAfGCxme03s3eAxcBVkkYAA81spYW76o80/maZIl1dAnwfeAioSdTllC6BLwM/j/rCzKpifTbrsjV6AH0UMpP15fTGbNZhZq8ScjkkSfa/qVx/tMBK4Oyou5R6bffOJ+hIg/5PYLSkMkm/kDSHkFj6gJnVxTaVhFkBNElEDTQkok6VxDq/hfpMk66cSW4nzASgZTkrU9RnkrRkjI+oo83shSbXyTVdTgQmSlouaaWkhsGdzbpsFjPbCfwY2E4w5AeBYtIfs52FYWa2O5b3AMNiOWvv0w4z6GZ2BJgK3AlUA08QHt9yitOVU9JNQCHwo/bs35kgHRkldQN+Anw9U/07U5yGLnsQ3C5zgc8Dv5V0drt2sh2JPuTrCO6wkUA/MjwD7SjiE1PWr/FuU8ai9sLMjgPLgGWSSgh5Sc+W1CP+j55MSN2QiLpSJyeibimJdWvJrTNCmnIi6QrgAWBOw+N9PD43cdlR8Zo7aXTLNNRnXM40ZBwAnB/bAQwHFkm6ltzTZSXBv1oLbJVURjDwWa3LFrgC2Gpm1QCSngEuIf0x21nYK2mEme2OLpUGl1lz92lzes0YHTZDl3SupAmJqosI0RdfISSahjBYnovl5hJRLwIWxDfq4wgDZhVtS27d7qQrp6TJwK+BaxM+Vwg5XedJyoszpXnAS/GR8JCkGdE/eQuNv1lGSEdGMztoZoPNbKyZjSW8J7jWzIrIMV0CzxIHuKTBBBdMBVmsy1bYDsyQ1Df273KglPTHbGch2f+mct0SV7vMAA5G3aXUa0Z7nMk3sMmN8Oj6OuGGWA88Q1g1MJ4wiMuBp2hcUdA77pfH4+MT13qAsApiM4lVAYS30WXx2AOdRM4lwF5gbdwWJa71pdi+HLgtUV8IbIhyPkz8AjhbZWxy7jLiKpcc1KUI7qVSoIS4EiSbddmG3+C7wKbYx0cJK5LSHrPZtgGPE94L1BKerG4n+PuXAlviuDwnodefRx2VNLl/U+o1U5t/+u84jpMj+JeijuM4OYIbdMdxnBzBDbrjOE6O4AbdcRwnR3CD7jiOkyO4QXccx8kR3KA7TgtIWqZEeN80zvudpEkp6hdKevjM9M5xTqZDP/13nFzFzO7o6D44XQ+foTudDkn9JL0gaZ1CooXPSfq2pNVx/zcNYVrjDPunkookvSlpmqRnYgKCH8Q2YyVtkvSn2Oavkvqm+LvzJK2Q9B9JT0nq30IfT8zsJd0WIzSuIsQ+cZx2wQ260xm5CthlZh81s/OBfwAPm9m0uN8HuCbR/piZFRKSLzwH3EMIELZQUkM413OBX5jZecAhQnKGE8RYLA8CV5jZFKAIuK+1jsagTt8lGPJZhCQejtMuuEF3OiMlwJWSHpI028wOApcqpDkrISRc+Eii/aLEeRvNbLeFKJYVNEbN22Fmy2P5MYLxTTKDYIyXS1pLCNY0htaZDiwzs2oLyTmeSEtSx0kD96E7nQ4zK4tJMq4GfiBpKWHWXWhmOyR9hxAYqoGGEMT1iXLDfsMYaBrUqOm+CNloPn8GRHCcdsFn6E6nQ9JI4D0ze4yQAKQhSe/b0a99Q7MnN0+BpJmx/AXgtSbHVwKXSPpQ7EM/SRPbcN03gDmSBknqCdx4Gn1znDbhM3SnM3IB8CNJ9YRwp3cR8j1uIKQKW30a19wM3CPpEUK4218mD5pZtaSFwOOSesXqBwkhfZvFQnKE7xCSSx8ghER2nHbBw+c6XR5JY4Hn4wtVx+m0uMvFcRwnR/AZuuP8H0j6GyFpcpJvmllmU485Dm7QHcdxcgZ3uTiO4+QIbtAdx3FyBDfojuM4OYIbdMdxnBzhfzeTq32jdqOqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_mean.plot(legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def qt(df,dist):\n",
    "    ''' returns quantile transformed dataframe'''\n",
    "    qt_df = pd.DataFrame(index=df.index)\n",
    "    for mz in tqdm(range(0,100)):\n",
    "        df_mz = df.loc[:,get_mz(mz,df)]\n",
    "        df_mz_trans = quantile_transform(df_mz,random_state=RANDOM_SEED,output_distribution=dist)\n",
    "        qt_df = pd.concat([qt_df,pd.DataFrame(df_mz_trans,index=df.index,columns=df_mz.columns)],axis=1)\n",
    "    print(qt_df.shape)\n",
    "    return qt_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rank_gauss(x):\n",
    "    from scipy.special import erfinv\n",
    "    N = x.shape[0]\n",
    "    temp = x.argsort()\n",
    "    rank_x = temp.argsort() / N\n",
    "    rank_x -= rank_x.mean()\n",
    "    rank_x *= 2\n",
    "    efi_x = erfinv(rank_x)\n",
    "    efi_x -= efi_x.mean()\n",
    "    return efi_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for fold, (train_id, test_id) in enumerate(tqdm(skf.split(train_set_100,targets[phy]))):\n",
    "    print('fold {}'.format(fold))\n",
    "    X_train, X_test = train_set_100.iloc[train_id], train_set_100.iloc[test_id]\n",
    "    y_train, y_test = targets[phy].iloc[train_id], targets[phy].iloc[test_id]\n",
    "    X_train_trans = qt(X_train)\n",
    "    X_test_trans = qt(X_test)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_clf = CatBoostClassifier(verbose=999,random_seed=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert (train_set_100.columns == test_set_100.columns).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 257.11it/s]\n",
      " 32%|███▏      | 32/100 [00:00<00:00, 158.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1059, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 105.77it/s]\n",
      " 15%|█▌        | 15/100 [00:00<00:00, 139.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1059, 1500)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 70.02it/s]\n",
      "  0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1059, 1800)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:09<00:00, 10.17it/s]\n"
     ]
    }
   ],
   "source": [
    "t_100 = get_total_abundance(train_set_100)\n",
    "g_100 = get_grads(train_set_100)\n",
    "m_100 = get_mean_std(train_set_100)\n",
    "r_100 = get_relhe_abund(train_set_100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 265.97it/s]\n",
      " 31%|███       | 31/100 [00:00<00:00, 151.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(804, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 113.68it/s]\n",
      " 14%|█▍        | 14/100 [00:00<00:00, 133.69it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(804, 1500)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 92.75it/s]\n",
      "  1%|          | 1/100 [00:00<00:13,  7.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(804, 1800)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:08<00:00, 12.19it/s]\n"
     ]
    }
   ],
   "source": [
    "t_100_test = get_total_abundance(test_set_100)\n",
    "g_100_test = get_grads(test_set_100)\n",
    "m_100_test = get_mean_std(test_set_100)\n",
    "r_100_test = get_relhe_abund(test_set_100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_100_test.sort_index(inplace=True,axis=1)\n",
    "t_100.sort_index(inplace=True,axis=1)\n",
    "\n",
    "m_100_test.sort_index(inplace=True,axis=1)\n",
    "m_100.sort_index(inplace=True,axis=1)\n",
    "\n",
    "g_100_test.sort_index(inplace=True,axis=1)\n",
    "g_100.sort_index(inplace=True,axis=1)\n",
    "\n",
    "r_100_test.sort_index(inplace=True,axis=1)\n",
    "r_100.sort_index(inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert (t_100.columns == t_100_test.columns).all()\n",
    "assert (g_100.columns == g_100_test.columns).all()\n",
    "assert (m_100.columns == m_100_test.columns).all()\n",
    "assert (r_100.columns == r_100_test.columns).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntrain = 1059\n",
    "ntest = 804\n",
    "NFOLDS = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "oof_train_df = pd.DataFrame(index=metadata[metadata.split != 'test'].index, columns=['sample_id',*targets.columns])\n",
    "oof_test_df = pd.DataFrame(index=metadata[metadata.split != 'train'].index, columns=['sample_id',*targets.columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TARGET  basalt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 101/101 [00:00<00:00, 101759.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3122080659673273\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 1500/1500 [00:00<00:00, 1427605.17it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2875808538517409\n",
      "54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/1800 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.26469583059665797\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1800/1800 [00:00<00:00, 1817464.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1600/1600 [00:00<00:00, 1642007.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.30109850825215095\n",
      "24\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 1600/1600 [00:00<00:00, 1608168.32it/s]\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.26355293461316703\n",
      "61\n",
      "(1059, 61) (1059, 11) (1059, 54) (1059, 64) (1059, 24)\n",
      "(1059, 214)\n",
      "(804, 61) (804, 11) (804, 54) (804, 64) (804, 24)\n",
      "(804, 214)\n",
      "(1494, 214) (1494,)\n",
      "Learning rate set to 0.011117\n",
      "0:\tlearn: 0.6845493\ttotal: 32.2ms\tremaining: 32.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Custom logger is already specified. Specify more than one logger at same time is not thread safe."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "999:\tlearn: 0.0182894\ttotal: 30.6s\tremaining: 0us\n",
      "Learning rate set to 0.011117\n",
      "0:\tlearn: 0.6840316\ttotal: 30.2ms\tremaining: 30.2s\n",
      "999:\tlearn: 0.0196252\ttotal: 30.9s\tremaining: 0us\n",
      "Learning rate set to 0.011117\n",
      "0:\tlearn: 0.6845952\ttotal: 30.8ms\tremaining: 30.8s\n",
      "999:\tlearn: 0.0173875\ttotal: 30.7s\tremaining: 0us\n",
      "Learning rate set to 0.011117\n",
      "0:\tlearn: 0.6846873\ttotal: 29.8ms\tremaining: 29.8s\n",
      "999:\tlearn: 0.0178668\ttotal: 31.8s\tremaining: 0us\n",
      "Learning rate set to 0.011121\n",
      "0:\tlearn: 0.6841249\ttotal: 29.1ms\tremaining: 29s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1it [02:37, 157.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "999:\tlearn: 0.0172700\ttotal: 32.2s\tremaining: 0us\n",
      "fold Loss 0:  0.14698039431826815\n",
      "(1492, 214) (1492,)\n",
      "Learning rate set to 0.011109\n",
      "0:\tlearn: 0.6846164\ttotal: 45ms\tremaining: 45s\n",
      "999:\tlearn: 0.0184541\ttotal: 33.1s\tremaining: 0us\n",
      "Learning rate set to 0.011109\n",
      "0:\tlearn: 0.6849938\ttotal: 33.4ms\tremaining: 33.4s\n",
      "999:\tlearn: 0.0166942\ttotal: 32.7s\tremaining: 0us\n",
      "Learning rate set to 0.011113\n",
      "0:\tlearn: 0.6855678\ttotal: 32.3ms\tremaining: 32.3s\n",
      "999:\tlearn: 0.0159906\ttotal: 35.4s\tremaining: 0us\n",
      "Learning rate set to 0.011113\n",
      "0:\tlearn: 0.6856551\ttotal: 32.8ms\tremaining: 32.8s\n",
      "999:\tlearn: 0.0190296\ttotal: 35.3s\tremaining: 0us\n",
      "Learning rate set to 0.011113\n",
      "0:\tlearn: 0.6829337\ttotal: 33.2ms\tremaining: 33.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2it [05:31, 167.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "999:\tlearn: 0.0165949\ttotal: 35.4s\tremaining: 0us\n",
      "fold Loss 1:  0.13339905107090017\n",
      "(1498, 214) (1498,)\n",
      "Learning rate set to 0.011128\n",
      "0:\tlearn: 0.6857523\ttotal: 34.6ms\tremaining: 34.6s\n",
      "999:\tlearn: 0.0203356\ttotal: 34.9s\tremaining: 0us\n",
      "Learning rate set to 0.011128\n",
      "0:\tlearn: 0.6837860\ttotal: 32.5ms\tremaining: 32.5s\n",
      "999:\tlearn: 0.0180668\ttotal: 34.9s\tremaining: 0us\n",
      "Learning rate set to 0.011128\n",
      "0:\tlearn: 0.6841166\ttotal: 34ms\tremaining: 33.9s\n",
      "999:\tlearn: 0.0171093\ttotal: 34.7s\tremaining: 0us\n",
      "Learning rate set to 0.011132\n",
      "0:\tlearn: 0.6827994\ttotal: 31ms\tremaining: 31s\n",
      "999:\tlearn: 0.0194642\ttotal: 37.3s\tremaining: 0us\n",
      "Learning rate set to 0.011132\n",
      "0:\tlearn: 0.6862425\ttotal: 33.1ms\tremaining: 33.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3it [08:31, 172.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "999:\tlearn: 0.0193575\ttotal: 35.7s\tremaining: 0us\n",
      "fold Loss 2:  0.1505327063219645\n",
      "(1500, 214) (1500,)\n",
      "Learning rate set to 0.011136\n",
      "0:\tlearn: 0.6862143\ttotal: 35.4ms\tremaining: 35.4s\n",
      "999:\tlearn: 0.0171974\ttotal: 35.2s\tremaining: 0us\n",
      "Learning rate set to 0.011136\n",
      "0:\tlearn: 0.6846942\ttotal: 31.5ms\tremaining: 31.4s\n",
      "999:\tlearn: 0.0148030\ttotal: 35.1s\tremaining: 0us\n",
      "Learning rate set to 0.011136\n",
      "0:\tlearn: 0.6847405\ttotal: 33.6ms\tremaining: 33.5s\n",
      "999:\tlearn: 0.0166465\ttotal: 35s\tremaining: 0us\n",
      "Learning rate set to 0.011136\n",
      "0:\tlearn: 0.6857395\ttotal: 29.4ms\tremaining: 29.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3it [10:48, 216.03s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_19172\\149446241.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     68\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m         \u001b[0mcalib\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCalibratedClassifierCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcb_clf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mskf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensemble\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'sigmoid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m         \u001b[0mcalib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_res\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY_res\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     71\u001b[0m         \u001b[0my_preds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m         \u001b[0mfold_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlog_loss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_val\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_preds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\calibration.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[0mparallel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 self.calibrated_classifiers_ = parallel(\n\u001b[0m\u001b[0;32m    342\u001b[0m                     delayed(_fit_classifier_calibrator_pair)(\n\u001b[0;32m    343\u001b[0m                         \u001b[0mclone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbase_estimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1044\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1045\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    857\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    860\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 777\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    778\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    214\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\calibration.py\u001b[0m in \u001b[0;36m_fit_classifier_calibrator_pair\u001b[1;34m(estimator, X, y, train, test, supports_sw, method, classes, sample_weight)\u001b[0m\n\u001b[0;32m    507\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0msupports_sw\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 509\u001b[1;33m         \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msw_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    510\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    511\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\catboost\\core.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, cat_features, text_features, embedding_features, sample_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[0;32m   4714\u001b[0m             \u001b[0mCatBoostClassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_is_compatible_loss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'loss_function'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4715\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4716\u001b[1;33m         self._fit(X, y, cat_features, text_features, embedding_features, None, sample_weight, None, None, None, None, baseline, use_best_model,\n\u001b[0m\u001b[0;32m   4717\u001b[0m                   \u001b[0meval_set\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogging_level\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn_description\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose_eval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetric_period\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4718\u001b[0m                   silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\catboost\\core.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, X, y, cat_features, text_features, embedding_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[0;32m   2035\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mlog_fixup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlog_cout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog_cerr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2036\u001b[0m             \u001b[0mplot_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_get_train_dir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2037\u001b[1;33m             self._train(\n\u001b[0m\u001b[0;32m   2038\u001b[0m                 \u001b[0mtrain_pool\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2039\u001b[0m                 \u001b[0mtrain_params\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"eval_sets\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\catboost\\core.py\u001b[0m in \u001b[0;36m_train\u001b[1;34m(self, train_pool, test_pool, params, allow_clear_pool, init_model)\u001b[0m\n\u001b[0;32m   1462\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1463\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_train\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_clear_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minit_model\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1464\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_object\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_train\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_clear_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minit_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_object\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minit_model\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1465\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_trained_model_attributes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1466\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._CatBoost._train\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._CatBoost._train\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "agg_loss = []\n",
    "for target in targets.columns:\n",
    "    print('TARGET ',target)\n",
    "    imp_fts = {}\n",
    "    \n",
    "    #  get all imp coefs for the target\n",
    "    t_100_coefs = get_coefs(t_100,targets[target],1)\n",
    "    g_100_coefs = get_coefs(g_100,targets[target],5)\n",
    "    m_100_coefs = get_coefs(m_100,targets[target],10)\n",
    "    r_100_coefs = get_coefs(r_100,targets[target],1)\n",
    "    o_100_coefs = get_coefs(train_set_100,targets[target],10)\n",
    "    \n",
    "    imp_fts['o_100_coefs'] = o_100_coefs\n",
    "    imp_fts['t_100_coefs'] = t_100_coefs\n",
    "    imp_fts['g_100_coefs'] = g_100_coefs\n",
    "    imp_fts['m_100_coefs'] = m_100_coefs\n",
    "    imp_fts['r_100_coefs'] = r_100_coefs\n",
    "\n",
    "    # get all imp_dfs and concat them\n",
    "    train_imp_ = train_set_100.iloc[:,o_100_coefs]\n",
    "    t_100_imp_ = t_100.iloc[:,t_100_coefs]\n",
    "    g_100_imp_ = g_100.iloc[:,g_100_coefs]\n",
    "    m_100_imp_ = m_100.iloc[:,m_100_coefs]\n",
    "    r_100_imp_ = r_100.iloc[:,r_100_coefs]\n",
    "\n",
    "    print(train_imp_.shape,t_100_imp_.shape,g_100_imp_.shape,m_100_imp_.shape, r_100_imp_.shape)\n",
    "    X = pd.concat([train_imp_,t_100_imp_,g_100_imp_,m_100_imp_,r_100_imp_],axis=1)\n",
    "    print(X.shape)\n",
    "\n",
    "    # same goes for test set\n",
    "    test_imp_ = test_set_100.iloc[:,o_100_coefs]\n",
    "    t_100_imp_test = t_100_test.iloc[:,t_100_coefs]\n",
    "    g_100_imp_test = g_100_test.iloc[:,g_100_coefs]\n",
    "    m_100_imp_test = m_100_test.iloc[:,m_100_coefs]\n",
    "    r_100_imp_test = r_100_test.iloc[:,r_100_coefs]\n",
    "\n",
    "    print(test_imp_.shape,t_100_imp_test.shape,g_100_imp_test.shape,m_100_imp_test.shape,r_100_imp_test.shape)\n",
    "    \n",
    "    X_test = pd.concat([test_imp_, t_100_imp_test, g_100_imp_test, m_100_imp_test, r_100_imp_test],axis=1)\n",
    "    print(X_test.shape)\n",
    "    \n",
    "    # check for same cols in both train and test sets..\n",
    "    assert (X.columns == X_test.columns).all()\n",
    "\n",
    "    oof_train = np.zeros((ntrain,))\n",
    "    oof_test = np.zeros((ntest,))\n",
    "    oof_test_skf = np.empty((NFOLDS, ntest))\n",
    "    \n",
    "    Y = targets[target]\n",
    "\n",
    "    cb_clf = CatBoostClassifier(verbose=999,random_seed=RANDOM_SEED)\n",
    "    cv_loss = []\n",
    "    for fold, (train_id, test_id) in enumerate(tqdm(skf.split(X,Y))):\n",
    "\n",
    "        #  ohh man unwanted semi colon in iloc throws an error...\n",
    "        X_train, Y_train = X.iloc[train_id,], Y.iloc[train_id,]\n",
    "        X_val, Y_val = X.iloc[test_id,], Y.iloc[test_id,]\n",
    "\n",
    "        # upsampling works??\n",
    "        upsampler = SMOTETomek(random_state=RANDOM_SEED)\n",
    "        X_res, Y_res = upsampler.fit_resample(X_train, Y_train) \n",
    "        print(X_res.shape,Y_res.shape)\n",
    "        \n",
    "        assert (X_res.columns == X_val.columns).all\n",
    "        \n",
    "        # train model  and pred on oof folds\n",
    "        # cb_clf.fit(X_res.values,Y_res)\n",
    "        \n",
    "        calib = CalibratedClassifierCV(cb_clf, cv = skf, ensemble=True, method='sigmoid')\n",
    "        calib.fit(X_res.values,Y_res)\n",
    "        y_preds = calib.predict_proba(X_val.values)[:,1]\n",
    "        fold_loss = log_loss(Y_val,y_preds)\n",
    "        print(f'fold Loss {fold}: ',fold_loss)\n",
    "        cv_loss.append(fold_loss)\n",
    "\n",
    "        oof_train[test_id] = y_preds\n",
    "        oof_test_skf[fold,:] = calib.predict_proba(X_test.values)[:,1]\n",
    "\n",
    "    print('Training Loss:', log_loss(Y,calib.predict_proba(X.values)[:,1]))\n",
    "    \n",
    "    oof_test[:] = oof_test_skf.mean(axis=0)\n",
    "    oof_test_df[target] = oof_test\n",
    "    oof_train_df[target] = oof_train\n",
    "\n",
    "    print(f'Loss for {target}:')\n",
    "    print(np.array(cv_loss).mean(),np.array(cv_loss).std())\n",
    "    agg_loss.append(np.array(cv_loss).mean())\n",
    "\n",
    "    print(\"---------------------------------------------------------------------------------------------\")\n",
    "\n",
    "print('AGG Loss:',np.array(agg_loss))\n",
    "print('AGG Loss mean:',np.array(agg_loss).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds = oof_test_df.iloc[:293]\n",
    "train_preds = oof_train_df.iloc[766:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_loss_train = []\n",
    "for tar in targets.columns:\n",
    "    loss = log_loss(val_labels[tar],train_preds[tar])\n",
    "    print(tar, loss)\n",
    "    agg_loss_train.append(loss)\n",
    "print(np.array(agg_loss_train).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_loss_test = []\n",
    "for tar in targets.columns:\n",
    "    loss = log_loss(val_labels[tar],test_preds[tar])\n",
    "    print(tar, loss)\n",
    "    agg_loss_test.append(loss)\n",
    "print(np.array(agg_loss_test).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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