{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code used to train the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing, model_selection, metrics, ensemble\n",
    "from sklearn.metrics import *\n",
    "import pickle\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier as RandomForestClassifier\n",
    "import pyarrow\n",
    "from scipy.stats import randint as sp_randint\n",
    "from sklearn.utils import class_weight\n",
    "import random, os\n",
    "\n",
    "# pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_file = 'data\\\\LEAVES_full_HBRF.csv'\n",
    "X_test_file = 'data\\\\LEAVES_test_HBRF.csv'\n",
    "model_name = 'final'\n",
    "\n",
    "model_dir = os.path.join('F:\\\\Light Curves\\\\models', model_name)\n",
    "plot_dir = os.path.join('F:\\\\Light Curves\\\\plots', model_name)\n",
    "os.makedirs(model_dir, exist_ok=True)\n",
    "os.makedirs(plot_dir, exist_ok=True)\n",
    "\n",
    "# Paths to feature list and model layers\n",
    "features_4model = os.path.join(model_dir, 'optimal_hierarchical_features.pkl')\n",
    "model_init_layer = os.path.join(model_dir, 'hierarchical_model_init_level.pkl')\n",
    "model_variable_layer = os.path.join(model_dir, 'hierarchical_model_var_level.pkl')\n",
    "model_extrinsic_layer = os.path.join(model_dir, 'hierarchical_model_extrinsic_level.pkl')\n",
    "model_intrinsic_layer = os.path.join(model_dir, 'hierarchical_model_intrinsic_level.pkl')\n",
    "model_ecl_layer = os.path.join(model_dir, 'hierarchical_model_ecl_level.pkl')\n",
    "model_rrl_layer = os.path.join(model_dir, 'hierarchical_model_rrl_level.pkl')\n",
    "model_lpv_layer = os.path.join(model_dir, 'hierarchical_model_lpv_level.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5714025946899135\n"
     ]
    }
   ],
   "source": [
    "random.seed(10)\n",
    "print(random.random())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_order = ['EA','EW','ROT', # extrinsic\n",
    "               'RRAB','RRC','CEP','M','SR','DSCT', # intrinsic\n",
    "               'Non-var']\n",
    "\n",
    "cm_classes_init = ['Variable','Non-var']\n",
    "cm_classes_variable = ['Extrinsic','Intrinsic']\n",
    "cm_classes_original = label_order\n",
    "\n",
    "cm_classes_extrinsic = ['EB', 'ROT']\n",
    "cm_classes_intrinsic = ['RR', 'CEP','LPV', 'DSCT']\n",
    "cm_classes_variable2 = ['EB', 'ROT', 'RR', 'CEP','LPV', 'DSCT', 'Non-var']\n",
    "\n",
    "cm_classes_ecl = ['EA','EW']\n",
    "cm_classes_rrl = ['RRAB','RRC']\n",
    "cm_classes_lpv = ['M','SR']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  ### Defining functions to plot the confusion matrix and the feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes, plot_name,\n",
    "                          normalize=True,\n",
    "                          title=None,\n",
    "                          cmap=plt.cm.Blues,\n",
    "                          fig_size=(12, 10), \n",
    "                          rotation=45, \n",
    "                          font_size=20, \n",
    "                          text_font_size=23):\n",
    "    \n",
    "    if normalize:\n",
    "        cm = np.round(cm.astype('float') / (cm.sum(axis=1)[:, np.newaxis] * 1.0 ), 2)\n",
    "        cm[np.isnan(cm)] = 0\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "    print(cm)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=fig_size)\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    # plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=rotation, fontsize = font_size)\n",
    "    plt.yticks(tick_marks, classes, fontsize = font_size)\n",
    "\n",
    "    #fmt = '.2f' if normalize else 'd'\n",
    "    fmt =  '.2f'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\",fontsize = text_font_size)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label',fontsize = font_size+2)\n",
    "    plt.xlabel('Predicted label',fontsize = font_size+2)\n",
    "    plt.savefig(plot_name, bbox_inches='tight')\n",
    "    #plt.close()\n",
    "    \n",
    "def plot_confusion_matrix_all(cm, classes, plot_name,\n",
    "                          normalize=True,\n",
    "                          title=None,\n",
    "                          cmap=plt.cm.Blues):\n",
    "    plot_confusion_matrix(cm, classes, plot_name, normalize, title, \n",
    "                          fig_size=(12, 12), rotation=90, font_size=17, text_font_size=16)\n",
    "\n",
    "def plot_feature_importances(model, feature_names,feature_importances_name):\n",
    "    I = np.argsort(model.feature_importances_)[::-1]\n",
    "    I = I[0:60]\n",
    "    for i in I[0:30]:\n",
    "        print(feature_names[i], \"& %.3f\" % (model.feature_importances_[i]))\n",
    "    fig, ax = plt.subplots(figsize=(16, 5), tight_layout=True)\n",
    "    x_plot = np.arange(len(I))\n",
    "    plt.xticks(x_plot, [feature_names[i] for i in I], rotation='vertical')\n",
    "    ax.bar(x_plot, height=model.feature_importances_[I]);\n",
    "    plt.savefig(feature_importances_name, bbox_inches='tight')\n",
    "    #plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing training data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train = pd.read_csv(X_train_file)\n",
    "X_test = pd.read_csv(X_test_file)\n",
    "\n",
    "rm_nd_cols = ['ID', 'class_original', 'class_variable2', 'class_variable', 'class_init']\n",
    "X_train_init = X_train.drop(rm_nd_cols, axis=1)\n",
    "X_test_init = X_test.drop(rm_nd_cols, axis=1)\n",
    "y_train_bottom = X_train['class_original']\n",
    "y_test_bottom = X_test['class_original']\n",
    "y_train_variable2 = X_train['class_variable2']\n",
    "y_test_variable2 = X_test['class_variable2']\n",
    "y_train_variable = X_train['class_variable']\n",
    "y_test_variable = X_test['class_variable']\n",
    "y_train_init = X_train['class_init']\n",
    "y_test_init = X_test['class_init']\n",
    "\n",
    "# the second level (variable) has only variable classes\n",
    "X_train_variable = X_train_init.loc[y_train_init=='Variable', :]\n",
    "y_train_variable = y_train_variable.loc[y_train_init=='Variable']\n",
    "\n",
    "y_train_variable2 = y_train_variable2.loc[y_train_init=='Variable']\n",
    "y_train_bottom = y_train_bottom.loc[y_train_init=='Variable']\n",
    "\n",
    "# the third level has only variable classes\n",
    "X_train_extrinsic = X_train_variable.loc[y_train_variable=='Extrinsic', :]\n",
    "y_train_extrinsic = y_train_variable2.loc[y_train_variable=='Extrinsic']\n",
    "\n",
    "X_train_intrinsic = X_train_variable.loc[y_train_variable=='Intrinsic', :]\n",
    "y_train_intrinsic = y_train_variable2.loc[y_train_variable=='Intrinsic']\n",
    "\n",
    "# the forth level (bottom) has only Extrinsic or 'Intrinsic' classes\n",
    "X_train_ecl = X_train_extrinsic.loc[y_train_variable2=='EB', :]\n",
    "y_train_ecl = y_train_bottom.loc[y_train_variable2=='EB']\n",
    "\n",
    "X_train_rrl = X_train_intrinsic.loc[y_train_variable2=='RR', :]\n",
    "y_train_rrl = y_train_bottom.loc[y_train_variable2=='RR']\n",
    "\n",
    "X_train_lpv = X_train_intrinsic.loc[y_train_variable2=='LPV', :]\n",
    "y_train_lpv = y_train_bottom.loc[y_train_variable2=='LPV']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0          Variable\n",
      "1          Variable\n",
      "2          Variable\n",
      "3          Variable\n",
      "4          Variable\n",
      "             ...   \n",
      "1096797     Non-var\n",
      "1096798     Non-var\n",
      "1096799     Non-var\n",
      "1096800     Non-var\n",
      "1096801     Non-var\n",
      "Name: class_init, Length: 1096802, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(y_train_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:546: FutureWarning: The default of `sampling_strategy` will change from `'auto'` to `'all'` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `'all'` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n",
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:558: FutureWarning: The default of `replacement` will change from `False` to `True` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `True` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n",
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\sklearn\\base.py:432: UserWarning: X has feature names, but BalancedRandomForestClassifier was fitted without feature names\n",
      "  warnings.warn(\n",
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\sklearn\\base.py:432: UserWarning: X has feature names, but BalancedRandomForestClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Non-var' 'Variable']\n",
      "Accuracy: 0.9637036665587776\n",
      "Balanced accuracy: 0.9793524094042229\n",
      " 'weighted' precision accuracy:0.972076\n",
      " 'weighted' recall accuracy:0.963704\n",
      " 'weighted' f1 accuracy:0.965687\n"
     ]
    }
   ],
   "source": [
    "#Training first layer of the RF model\n",
    "\n",
    "rf_model_init = RandomForestClassifier(\n",
    "            n_estimators=700,\n",
    "            max_features=0.4,\n",
    "            max_depth=50,\n",
    "            n_jobs=-1,\n",
    "            bootstrap=True,\n",
    "            class_weight='balanced_subsample',\n",
    "            criterion='entropy',\n",
    "            min_samples_split=2,\n",
    "            min_samples_leaf=1\n",
    "            )\n",
    "\n",
    "rf_model_init.fit(X_train_init.values, y_train_init)\n",
    "\n",
    "# with open(model_init_layer, 'rb') as file:\n",
    "#     # 使用 pickle.load() 方法读取模型\n",
    "#     rf_model_init = pickle.load(file)\n",
    "\n",
    "# testing the first layer performance\n",
    "\n",
    "y_true, y_pred = y_test_init, rf_model_init.predict(X_test_init)\n",
    "y_pred_proba_init = rf_model_init.predict_proba(X_test_init)\n",
    "\n",
    "classes_order_proba_init = rf_model_init.classes_\n",
    "print(classes_order_proba_init)\n",
    "\n",
    "\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_true, y_pred))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_true, y_pred))\n",
    "\n",
    "print(\" 'weighted' precision accuracy:%f\"%(precision_score(y_true,y_pred,average=\"weighted\")))\n",
    "print(\" 'weighted' recall accuracy:%f\"%(recall_score(y_true,y_pred,average=\"weighted\")))\n",
    "print(\" 'weighted' f1 accuracy:%f\"%(f1_score(y_true,y_pred,average=\"weighted\")))\n",
    "\n",
    "features_init = list(X_train_init)\n",
    "\n",
    "# Save model        \n",
    "with open(model_init_layer, 'wb') as f:\n",
    "            pickle.dump(rf_model_init, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(features_4model, 'wb') as f:\n",
    "            pickle.dump(features_init, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[184845   7962]\n",
      " [     0  26554]]\n",
      "Normalized confusion matrix\n",
      "[[0.96 0.04]\n",
      " [0.   1.  ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ht3Wzaof2qWzaJOcdvXuaNGlUr7tAALBgfTHs8/LjjiutPMfxS7Vvn1atWmXs2LFJki+/GDbX0WCmOTvOec4kWWnl6eOGDRtW67gnH380//z71UmSE04+NeusO2/XC4IliawGsABNe/Of1NxNoC7GzXC6QJuWc38L0tfeG1K+dkKzyib50wl7p3Hj4n/+N167Y47uvfVM32vbatY5l2rTovx4WjC47Ob/ZcODLskJf7wvf7juiRx3yb3Z4MBLcuXtz5fH9v3Rztmg84pz/RoAWDhGjx5TftyyZcs6bdOixfRxY0bXHqlrM2bM9DlbtarjnDOsbcyY4jnHjBmTY372k5RKpXRZb4P88vhZ7wIBzEo0AFiAWsxwfYFJk6vrtM3EidPvE92yedN5mve0yx8oP+7ZvUse+cuP0mOrtdOudfM0adwoq6+0dE44tHse+suP0qpF5UzXNWjRvHKW/X0zXjz03Hs56U/3Z0r11Jm+P3lKdX550d158v/f4rFJk8b51cHbztNrAGDBmzBh+ilulZWz/jwo0qz59Avojhs/bq7nHD9++pxN6zhn82bT5xw/rnjOvqf/Op98PCiNGzfOZX+9Kk2bztvPVFjSiAYLWEVFRSoqKvKb3/ymQfZ/7bXXlucYNGjQPO/niCOOSEVFRVZbbbX5tjYgGT9DAKhs2rhO2zSbITSMmzDnCxkWueept3P65Q+Ub1+15Qar5s4LD8/nD56R0U+enbduOT5nH9UjbVo2yx2PvZEHnnm3vO3osRMKXsfM67jguidmO/8f/jX9+R5brT1PrwGABa958+lHlk2aVLcj5CZOmP5zo2WLuh0pMKMWLabPObmOc06YOH3OFgVHRDz95OP5xzVXJkl++rNjsul3N5/rdcGS6lsZDY488sjyG+NHH310rrZ96KGHytv+4he/aKAVAhQbM8P1CFq1qNunJy2bTf8kZG6vZzCji254Knv96tq88OYnhc9/XTUup/3lgRxyxk1pOcPRBSPHzBoNZnwd4ydOznNvFO9zmqdfG5TJU2qOrFi6bcus1rH9vLwEABawNm2mX9dmXC2f4H/T+BmOLmjdps1cz9m69fQ5x46t45wzrK1165nnHDt2bPm0hNVWXyOnnnnWXK8JlmTfygshfv/7389VV12VJLn++uuz44471nnbf/3rXzPtB2BBGvbV9PM0V1puzheGate6+UzXQfji6zGzGT1nj730YR576cN0WqFdNu/SKcu1b5XJU6bm489H5KlXPyqfMrHGSkuXt/lw8Fez7GfGCyl+8fWYVH/jtIRvmjBpSr6uGl++qOKy7Vpl0BC3YARY1C2/wvTr0Az57NM5jh81cmT5IohJstzyK8xmdB3mHDLnOWvWNv2iv8svv/xMz1160fkZ9FHNaXKXXvbXOl+bAajxrYwG3bp1y5prrpkPP/wwt912W/7yl7/MdBhTbcaOHZs77rgjSbLeeuvlu9/9bkMvdRalUmmBzwksOt4e9EX23q7m7gSdO835FlJrrjz9LgWfDhtZryMNZjR42KgMHjaq8LlWLSpnigZFRxG89dEX6bV9zeO6/rvm3z+Ab5911v1O7rvnriTJhx9+MMfxAwdOH9NxpZXn+s4JSbLW2uukUaNGmTp1akaNHJmvhg+f420XZ5x33S7rzfTcZ59ODw+99upR53W0bzX9rdJfrvhbDj7s8DpvC4uTb+XpCUly2GGHJUmqqqpy11131Wmb22+/vVw+p20PsCC99t6Q8uMtN1hljuO7bbTq9G3fH9oga/qmvbZZt3x3hXc//jKffD5yljEz3sZxhWXa1Ho3hmmaVTbJMu2mf7LzxYj6HTEBwIKx0cablh8//+wzcxz/7P+eLj/ecKON52nO5s2bZ511p9/+9/nnZj/v1KlT88Lzz06fd8N5mxco9q080iCpedP/29/+NqVSKddff30OPPDAOW4z7dSERo0a5dBDD23oJQLM4uHn38/4iZPTolnTbNh5xXTutEw+KDj8f5p9t1+//PjuJ95aEEvMzw7Yqvz4qhlulzijfi98kNHjJqZNy2Zp0axptlx/lfzv9UG17nObjVdL0yY1F34c9vWYwhABwKJnx513TfPmzTNhwoS8MeD1fPD+e+m8Vu0XtL3rztvKj/fq2Wue591jr555+603kiR33v6f7LHX3rWOfeKxfhk5ouaUt06rrJqNNtl0pue33LpbneYcO3ZM7r7z9vLXBx0y/VTm1ddcs85rh8XNt/ZIgzXWWCPdutX8A/Dggw/miy++mO34IUOGpF+/fkmSHXfcMSuttFKS5Lnnnsvpp5+e7bffPiuuuGIqKyvTtm3bdOnSJUcddVTeemv2v6R/8y4DQ4cOzcknn5z11lsvbdq0SUVFRR5//PHy+DndPWHgwIG56KKL0rNnz6y22mpp0aJFWrRokVVXXTV9+vTJAw88ULhdbSZOnJgLL7wwm266adq1a5e2bdtmiy22yOWXX57q6rrd7m12Ro0alfPOOy/dunXLcsstl8rKynTo0CE9e/bMf/7zH4cjwzeMHT8p9zxZ8+9Ko0aNcsoRO9Q6dofN1sxWG9YcaVA1dkLuearho8GvDt4mXderOQLi46Ej8s/7Xi4cN2HSlNz6SP/y1yd9f7vZ7vfEw6Y/f/eTCyZ+AFB/rVu3zh577ZOk5jSzP/z+nFrHPv7oI3nhuZpP/Nu0aZM9e+4zz/Me0OfgNGpU81blrjv+k3ffebtw3NSpU/OH86evqc9Bh8wy5vtH/F8uv+rvc/xz9rkXzLTdjM9ttfU28/xa4NvuWxsNkukXMpwyZUpuuumm2Y698cYby7cam7bdtddem6222irnnHNOnnjiiQwbNiyTJ0/O6NGj8/bbb+eKK67IhhtumMsvv7xO63nuueey4YYb5oILLshbb72VMWPm7vDbjz76KGuuuWZOOOGE3Hvvvfn4448zYcKETJgwIZ988kluueWW7L777jnssMMyZcqUOe5vxIgR6datW0488cS8+uqrqaqqyujRo/PCCy/k5z//eXbccce5XuOM+vXrlzXXXDOnnnpqnnnmmQwfPjyTJ0/O559/nnvvvTcHHHBA9tprr3rNAYujs67pl0mTa/4fPni3TfKrg2f9RWSDzivmb2fsX/76ouufysjRs97FYNtNVs/4/51T/lObioqKHHfwtjOdIjCj5pVN8tsjd8m5P989SVJdPTVHnnt7xo6v/VZXv/tbv/JdFHbdcu2cf8zuafKN0xSaNmmcS47rme02XSNJMm7CpFx0/ZO17hOARc+pZ/wmTZvW3MnnlptuyJ8uuXCWMQP6v56jfvyD8tfHHndilmo/651ynn7y8bRv1aT8pzbrrPudHHhwzenEkyZNyuGH9M4nn3w805jq6ur8+sRflU+JWGbZZXP0L46f+xcIzNa39vSEJOndu3eOPfbYTJgwIf/6179y7LHH1jp22qkJrVu3zve+970kNbGhffv22WeffdK9e/estdZaadWqVYYMGZJXXnklf/rTnzJ8+PAcffTRWXfddWd7l4YxY8Zkv/32y4QJE3Laaadll112ScuWLTNgwIB06NChTq+nuro6lZWV6dGjR3bZZZd06dIlSy+9dL7++uu89957+ctf/pI333wz119/fdZYY4389re/ne3+jjzyyLz88svp06dPDj/88Cy//PJ57733cskll+TFF1/Mk08+mcMOO6x8cci58b///S+77757Jk+enBVWWCHHHHNMNtpoo3Ts2DFDhgzJzTffnOuvvz73339/Dj/88Nx2221z3iksIT789Kuc9Kf7c+nxNYdanvvz3dNn143z8HPvZdzEydmw84rZo9u6qWxa80/0k68MzKX/fqpeczZqVJFzfr5bfnPkLnn+jU/y2ntD8sWIsWle2SRrrrRMdtlyrSzdtiYoTJlSnR+fc1ueeGXgbPc5dPjoHPX7O3Jt395p3LhRjj1wm/Tctkvu+9/bGfb1mHRYpk323OY7WbVDzS+NU6dOzdEX3JWPh7prAsC3yZqd18o551+Uk46r+V277+m/zq03/zs77bJrWrZsmTcG9M8D99+byZMnJ0m6bds9x8yHN+9nn3tBXnzhubz/3rt59523s9V3N8hee++btdZeOyNHjMj9992TjwZ+mCRp0qRJ/nLl39KuXbt6zwvMrKL0LT9+vE+fPrnllluSJO+8807WWWedWcb0798/G220UZKaowz++c9/Jkk+++yztG/fvtbbrowaNSrdu3dP//79s8022+Spp2b9pf2II44o769169Z5+umny3MVqaioSJL07dt3llMUxo4dm6qqqlojQ6lUyg9/+MNce+21adWqVT777LNZ/mG89tpr84MfTK+85557bk455ZSZxkyZMiV77bVXHnzwwSTJfffdlz322KPwda266qoZNGjQTM9Nnjw5a6+9dgYNGpTddtstt912W+Hf4dVXX52f/OQnSZKHHnoou+yySy1/K7OqqqpKu3bt0qzr8alo0mzOG8C30FH7b5lzfrZbWjRrWuuYe59+O/931q2pGlt814RtN1k9D/35R+WvW3Q7rXBc48aNMubJs+e4pvc++TK/vOiePPbSh3McO83+O22QS4/fu9ajGJJk1JgJ+fn5d+S2R9+o837h22bow+79zuLtqr/+OX1P/3UmTJj1yLdpdttjr1xxzT9rffP+9JOPp+fuO5e/HjF29kfPDh78Sf7v8IPz4vPP1Tpmqfbt88c/X5m9e31vDq9g9j75eFA26tK5zmuDb7uqqqqs2mHpjBo1arZ3OvlWn56QTD/VIJl+NME3zfj9GcevtNJKs71Pa7t27XLWWTW/ADz99NP56qvaL1aWJCeddNJsg8GctGrVarZHJVRUVOSiiy5K48aNM3bs2DzyyCOz3d+GG26YX//617N8v0mTJrnmmmvKh5nV9fSLaW666aYMGjQozZs3z3XXXVfr3+GPf/zjdO3aNUlNzJidiRMnpqqqaqY/sLj763+ey3cP+1Mu/NcT6f/+0IyoGp/xEydn0JCvc+sj/bPvCdflgJOvrzUYzI3q6qnZ61f/yAXXPZ6nX/sog4Z8nXETJmX0uIn5YPDw/Kdf/xze9+ZsdthlcxUMkuQ//QZkk4MvzRl/fTDPDfg4n381OpMmT8mXI8bmmdcH5cwrH8p39r9QMAD4lvvJUUfnmRdfzy+OOzHrrb9h2i21VJo3b55VVl0t++7fOzfddlf+feud8/XT/k6dVskDjzyZq/9xfXbbY6+stHKnVFZWZulllskmm26WU8/4TZ57aUC9gwFQu2/16QlJ0qNHj6ywwgoZNmxYbrjhhpx99tnlT/OTmsNhb7zxxiTJyiuvnB12qP2iY2PHjs2XX36ZsWPHli/gN+2NdZK8/vrrsz1F4ZBDZr3wSn1Mnjw5w4YNy+jRo2e6aOEyyyyTL774Iq+//nr222+/Wrc//PDDZ/q7mNHKK6+cXXfdNffdd18ef/zxVFdXp3HjxnVa1913350k2W677bLccsvNdmz37t3zwgsv5Nlnn53tuPPOO2+Op1vA4uijz77OGVc8lDOueGietn/q1Y9qPbrgm/q98EH6vTDne2zPiy9Hjs2F1z+ZC12vAGCxtvoaa+Y3Z5+X35x93jxtv0337ef6E/xGjRpl/94HZv/ec75bWn2ssupqji6AAt/6aNCkSZMcfPDBueSSSzJo0KA8/fTT2XbbbcvP9+vXL0OG1NwX/ZBDDilfhXWa4cOH5+KLL85tt92W999/f7ZX+x8+fHitz7Vu3TprrLFGPV9NTSi46qqr8q9//SuvvvpqJk2q/SJks1tPkmy++eazfb5r16657777Mnbs2AwcODBrrbVWndb40ksvJam5a0VtUeKbPv/889k+f8opp+S4444rf11VVZVOnTrVad8AAAA0jG99NEhqTjm45JJLktScijBjNKjt1IQkefnll9OjR485nnYwzfjx42t9bqmllpqLFRf7+uuvs+uuu+bll4tvcTY360mS5ZdffrbPr7DCCjPNXVdzur1lkTmttVmzZmnWzLULAAAAFiWLRTTYeOONs8EGG2TAgAG59dZbc9lll6VZs2YZO3Zsbr/99iTJd7/73XTp0qW8zaRJk9K7d+989dVXadq0aY455pjss88+WXvttdO+ffvyG9iBAwdmzTXXTJLZHoVQ10P7Z+cXv/hFORj06tUrP/zhD7Phhhtm+eWXT/Pmzcuf6q+yyioZPHjwbNeTpM5HAcytaadK7L777rngggvmMBoAAIBvq8UiGiQ1RxGceOKJGTlyZO65557sv//+ueOOOzJ27Njy8zN69NFHM3Bgze3ELr/88vzoRz+aZZ/J3H0CXx9VVVW5+eabk9ScRnH99dfXOnbEiLrdrmzYsGFZe+21Z/v8NEsvvXQdV1pzTYUhQ4Zk0qRJWX/99eu8HQAAAN8u3/q7J0xzyCGHlD/tn/aGe9qpCU2bNs1BBx000/g333yz/LhPnz617nfa+fsN7f333y/f23Z263nnnXcyZsyYOu3zxRdfrNPzLVu2nKvrMWyyySZJav5uZnfNBQAAAL7dFpto0KFDh+y8c809X++///688cYb6devX5Jkt912m+Uq/1OmTL8y6rSjEb5p6tSpufrqqxtoxTOry3qS5IorrqjzPv/1r3/VegrDZ599loceqrla+/bbbz9Xp1fsvffeSZJRo0blH//4R523AwAA4NtlsYkGyfRTECZPnpwDDzywfO79N09NSDLTnQKuvfbawv2dcsopeeWVV+b/Qgt07ty5fA2Cf/7zn4Vv9u+55578+c9/rvM+X3vttfzhD3+Y5ftTpkzJj3/84/JRAkcdddRcrfXwww8v39nghBNOyJNPzv4Wa08//XSeeOKJuZoDAACAhW+xuaZBkuy7775p06ZNRo8eXT79oH379unZs+csY3v06JHll18+X3zxRU4//fQMGjQo++67b5Zddtl88MEHufrqq9OvX79069Yt//vf/xp87csss0z22GOP3HfffXnggQey66675qijjsqqq66aL774IrfddluuvfbarLHGGhk5cmS+/PLLOe5zs802y8knn5zXXnst3//+97P88svn/fffz8UXX5wXXnghSdKzZ8/stddec7XWZs2a5ZZbbsn222+fMWPGZMcdd8yBBx6YXr16ZfXVV8/UqVMzdOjQvPzyy7njjjsyYMCAXHbZZdluu+3m6e8GAACAhWOxigYtWrTI/vvvP9Mh87179y68lV+rVq1y3XXXpVevXpkwYUKuvPLKXHnllTON2X777fPnP/95gV3s769//Wu22WabfPLJJ3nkkUfyyCOPzPT8KquskjvvvDN77LFHnfZ31VVX5f/+7//y73//O//+979neb5bt2654YYb5mmtW265ZR5//PH07t07gwcPzg033DDbfbVt23ae5gEAAGDhWaxOT0hqDp2fUdGpCdP06NEjL730Ug499NB07NgxTZs2zXLLLZftttsuV111Vfr165dWrVo19JLLOnXqlFdeeSUnnnhi1l577TRr1izt2rXLRhttlL59++a1116b6baRc9K+ffs888wzOe+887LxxhunTZs2ad26dTbffPNcdtlleeKJJ9KmTZt5Xu+WW26Z999/P1dccUX23HPPdOzYMZWVlWnevHk6deqUXXfdNeecc07eeeed2f53AAAAYNFUUartSnmwEFVVVaVdu3Zp1vX4VDSZ9UgRAFjUDH34rIW9BACos6qqqqzaYemMGjVqtkeGL3ZHGgAAAADzh2gAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAo1qcugs846q8EWcOaZZzbYvgEAAIB5V1EqlUpzGtSoUaNUVFQ0yAKqq6sbZL98u1VVVaVdu3Zp1vX4VDRptrCXAwBzNPThhvuQBQDmt6qqqqzaYemMGjUqbdu2rXVcnY40SJI6tIW51lAhAgAAAKi/OkWDxx57rKHXAQAAACxi6hQNtttuu4ZeBwAAALCIcfcEAAAAoJBoAAAAABSq84UQ62ry5Mn56KOP8vXXX2fSpEnp3r37/J4CAAAAWADmWzR49NFHc9FFF+Xxxx/PhAkTktTcHWHKlCkzjbv88svz2muvZeWVV86ZZ545v6YHAAAA5rN6R4OpU6fm5z//ea666qokc74143LLLZdrrrkmjRo1yuGHH55VV121vksAAAAAGkC9r2nwy1/+MldeeWVKpVLatGmTgw46KN/73vdqHd+rV6+0bds2pVIp99xzT32nBwAAABpIvaLByy+/nD//+c+pqKjIjjvumIEDB+aGG27IYYcdVus2TZs2zc4775xSqZQnnniiPtMDAAAADahe0eDKK69Mkqywwgq54447svTSS9dpu0033TRJ8tZbb9VnegAAAKAB1SsaPPnkk6moqMgRRxyRNm3a1Hm7Tp06JUk+++yz+kwPAAAANKB6RYNpb/o33HDDudquZcuWSZJx48bVZ3oAAACgAdUrGky7U0KjRnO3m6qqqiSZq6MTAAAAgAWrXtFgueWWS5J8/PHHc7Xd66+/niTp2LFjfaYHAAAAGlC9osHmm2+eUqmU++67r87bTJkyJf/5z39SUVGRrbfeuj7TAwAAAA2oXtGgV69eSZKnnnoq999/f522OeOMMzJkyJAkyQEHHFCf6QEAAIAGVK9ocOCBB2adddZJqVRKnz598u9//7vWsV9++WWOOuqoXHDBBamoqMgWW2yRnXfeuT7TAwAAAA2oSX02btSoUW699dZ069Yto0ePzqGHHpqTTz45HTp0KI854IADMnjw4Lzyyiuprq5OqVRK+/btc8MNN9R78QAAAEDDqdeRBkmy/vrr5/HHH8/qq6+eUqmUTz/9NC+99FIqKiqSJLfffntefPHFTJkyJaVSKauttlqeeOKJrL766vVePAAAANBw6h0NkmSTTTbJG2+8kUsvvTSbbrppKioqUiqVZvqz3nrr5YILLsibb76Z9ddff35MCwAAADSgep2eMKMWLVrk2GOPzbHHHpvRo0dn8ODBGTlyZFq3bp2VVlopyyyzzPyaCgAAAFgA5ls0mFGbNm3SpUuXhtg1AAAAsIDMl9MTAAAAgMXPfD/SYNCgQXnhhRcyZMiQjB49Om3atEnHjh2zxRZbZNVVV53f0wEAAAANZL5Fg1tuuSUXXHBBXn311VrHbLrppjn55JOz//77z69pAQAAgAZS79MTJk+enAMOOCAHHXRQXn311VnumjDjn1deeSV9+vRJ7969M2nSpPmxfgAAAKCB1PtIgz59+uTOO+8sf73OOutk5513zlprrZVWrVpl7Nix+eCDD/LII4/knXfeSZLcdtttqa6uzm233Vbf6QEAAIAGUq9ocPvtt+fOO+9MRUVFll122VxzzTXp2bNnrePvu+++/OhHP8qwYcNy55135o477si+++5bnyUAAAAADaRepyf8/e9/T5I0a9Ysjz322GyDQZLsueee6devX5o3b54kueaaa+ozPQAAANCA6hUNXnrppVRUVOTwww9Ply5d6rRNly5dcsQRR6RUKuXll1+uz/QAAABAA6pXNBg1alSSpFu3bnO13dZbbz3T9gAAAMCip17RYIUVVkiSNG7ceK62mzZ+2vYAAADAoqde0aBr165JkldeeWWutps2fsstt6zP9AAAAEADqlc0+OlPf5pSqZS//e1vGTp0aJ22GTp0aP72t7+loqIiRx55ZH2mBwAAABpQvaLBjjvumF/96lcZOXJkdtxxxwwYMGC24994443stNNOGTlyZI4//vjssMMO9ZkeAAAAaEBN6jLoySefrPW5ffbZJx9//HFuv/32bLrpptl1112z8847Z6211kqrVq0yduzYfPDBB3n44Yfz8MMPp7q6Ovvvv3/22muvPPnkk+nevft8ezEAAADA/FNRKpVKcxrUqFGjVFRUzHFnpVJptuO++XxFRUWmTJlSx6WyJKmqqkq7du3SrOvxqWjSbGEvBwDmaOjDZy3sJQBAnVVVVWXVDktn1KhRadu2ba3j6nSkQVLzhn9+jKvrfgAAAICFq07RoG/fvg29DgAAAGARIxoAAAAAhep19wQAAABg8SUaAAAAAIVEAwAAAKCQaAAAAAAUqvMtF+fkmWeeyT//+c8899xz+fTTT1NVVZWpU6fOdpuKiopMmTJlfi0BAAAAmI/qHQ3GjRuXH/7wh7n11luTJKVSqd6LAgAAABa+ekeDQw45JHfffXdKpVJatWqVDTbYIM8991wqKirSpUuXtGjRIoMGDcrw4cOT1Bxd8N3vfjetWrWq9+IBAACAhlOvaxo88sgjueuuu5IkvXr1ypAhQ/LMM8+Unz/nnHPywgsv5Isvvsjzzz+f3XbbLaVSKRMnTsy1116bxx57rH6rBwAAABpMvaLBddddlyTp0KFDbrzxxrRp06bWsZtvvnnuv//+/OIXv8iAAQPSq1evTJo0qT7TAwAAAA2oXtFg2mkIffr0SfPmzWd5vuj6BhdddFHWXXfd9O/fP3//+9/rMz0AAADQgOoVDT7//PMkyYYbbjjT9ysqKpIkEydOnHXCRo1y6KGHplQq5ZZbbqnP9AAAAEADqlc0mDBhQpKkbdu2M31/2kUOR4wYUbhd586dkyTvvvtufaYHAAAAGlC9osFSSy2VpOa2izNaZpllkiQffPBB4XbTYsJXX31Vn+kBAACABlSvaLDWWmslST7++OOZvr/++uunVCrlkUceKdzuiSeeSDLrEQoAAADAoqNe0WCzzTZLqVTKq6++OtP3d9tttyRJ//79c+WVV8703O23356bb745FRUV2WyzzeozPQAAANCA6hUNdtpppyTJo48+murq6vL3DznkkPIpCj/72c/StWvXHHzwwenatWsOOOCA8l0VfvKTn9RnegAAAKAB1Ssa9OjRI6uttloqKytnOhVhqaWWyjXXXJPGjRunVCrl5Zdfzs0335yXX365HAx++MMfplevXvVaPAAAANBw6hUNmjVrloEDB2bo0KHp0aPHTM/ts88+eeKJJ7LTTjuV40GpVMraa6+dyy+/PFdffXW9Fg4AAAA0rCYNufOtttoqDz/8cKZMmZLhw4enVatWadOmTUNOCQAAAMwnDRoNypM0aZIVV1xxQUwFAAAAzCf1Oj0BAAAAWHyJBgAAAEChOp2e8MknnzTYAlZZZZUG2zcAAAAw7+oUDVZbbbVUVFTM98krKioyZcqU+b5fAAAAoP7qfCHEUqnUkOsAAAAAFjF1igaHH354Q68DAAAAWMRUlBxCwCKoqqoq7dq1y7CvRqVt27YLezkAMEftNz96YS8BAOqsVD0pEwdcnVGjZv+ey90TAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEChBokGkyZNyueff55PPvmkIXYPAAAALABN5teO3nvvvfzxj3/Mgw8+mI8++ihJUlFRkSlTpsw07qabbsrAgQOz4oor5oc//OH8mh4AAACYz+ZLNDj//PNzxhlnpLq6OqVSabZjx44dm9NPPz1NmjTJXnvtleWXX35+LAEAAACYz+p9esLvf//7nHrqqZkyZUoaNWqUrbbaKttss02t4w866KA0b9481dXVufvuu+s7PQAAANBA6hUN3n///ZxxxhlJkvXXXz9vvPFG/ve//+X444+vdZuWLVtmxx13TJI8/vjj9ZkeAAAAaED1igZ//vOfU11dnXbt2uXBBx/MOuusU6ftNttss5RKpQwYMKA+0wMAAAANqF7R4NFHH01FRUW+//3vp0OHDnXebvXVV0+SDB48uD7TAwAAAA2oXtFg2pv+zTbbbK62a9OmTZJkzJgx9ZkeAAAAaED1igYTJ05MkjRv3nyutpsWC1q1alWf6QEAAIAGVK9osNxyyyVJPvvss7na7q233kqSrLDCCvWZHgAAAGhA9YoGG220UUqlUh555JE6b1MqlXLHHXekoqIiW2yxRX2mBwAAABpQvaJBz549kyQPPPBAXnzxxTptc9lll+X9999Pkuyzzz71mR4AAABoQPWKBocffng6duyYqVOnZu+9984zzzxT69jJkyfn/PPPz/HHH5+Kioqss846+d73vlef6QEAAIAG1KQ+Gzdr1iw33HBDdt1113zxxRfZdttts9VWW6V9+/blMSeeeGIGDx6cxx57LMOHD0+pVErz5s1z/fXX13vxAAAAQMOpKJVKpfru5L///W8OO+ywfP3116moqCgcM22apZZaKrfcckt23nnn+k7LYqyqqirt2rXLsK9GpW3btgt7OQAwR+03P3phLwEA6qxUPSkTB1ydUaNm/56rXqcnTLP77rvnjTfeyC9/+cssvfTSKZVKs/xp165dfvazn+WNN94QDAAAAOBbYL4cafBNb731VgYNGpSRI0emdevWWXnllbPxxhunUaP50ihYAjjSAIBvG0caAPBtUtcjDep1TYPadOnSJV26dGmIXQMAAAALiI/+AQAAgEKiAQAAAFCoXqcnPPnkk/VeQPfu3eu9DwAAAGD+q1c02H777Wu9xWJdVFRUZMqUKfVZAgAAANBA6n0hxAa4+QIAAACwCKhXNOjbt+8cx1RXV2f48OF59tln8/rrr6eioiL77LNPNtpoo/pMDQAAADSwBo8GM+rXr18OP/zwPPLII/nZz36WnXfeuT7TAwAAAA1ogd49YaeddsqDDz6YSZMm5eCDD87QoUMX5PQAAADAXFjgt1xcb731ctBBB2X48OG57LLLFvT0AAAAQB0t8GiQTL/N4l133bUwpgcAAADqYKFEgzZt2iRJPvnkk4UxPQAAAFAHCyUavPvuu0mSioqKhTE9AAAAUAcLPBoMHz48V1xxRSoqKtK5c+cFPT0AAABQRwskGkyZMiUff/xx/va3v6Vr16757LPPkiTf+973FsT0AAAAwDxoUp+NGzduPM/bdu7cOb/61a/qMz0AAADQgOp1pEGpVJqnPzvuuGP69euXVq1aza/XAQAAAMxn9TrSoHv37nW6mGGzZs3Svn37rLfeetl9993z3e9+tz7TAgAAAAtAvaLB448/Pp+WAQAAACxqFsotFwEAAIBFX72ONDjrrLOSJGussUYOPfTQ+bIgAAAAYNFQr2jwm9/8JhUVFTn77LPn13oAAACARUS9Tk9o165dkprbJwIAAACLl3pFg5VWWilJMnbs2PmyGAAAAGDRUa9o0KNHj5RKpTz99NPzaz0AAADAIqJe0eCoo45K8+bNc8MNN+TNN9+cX2sCAAAAFgH1igadO3fO1VdfnalTp2bnnXfOPffcM7/WBQAAACxkdb57wg9/+MMkybHHHpuNN944yfRbLu6www55+OGH06tXr6y66qrp1q1bVl555bRo0WKO+z3zzDPnYdkAAABAQ6solUqlugxs1KhRKioqcscdd2Tvvfee6XvTlEqlmb6ui+rq6rkaz5Khqqoq7dq1y7CvRqVt27YLezkAMEftNz96YS8BAOqsVD0pEwdcnVGjZv+eq85HGtQ60TeaQx0bRJLMdWAAAAAAFpx6RYPHHntsfq0DAAAAWMTUKxpst91282sdAAAAwCKmXndPAAAAABZfogEAAABQSDQAAAAACs31NQ1OP/30XHrppfNl8oqKivTr12++7AsAAACYv+Y6Grz55pvzZeJSqeSWiwAAALAIm+toUCqVGmIdAAAAwCJmrqPB7373u3Tr1q0h1gIAAAAsQuY6Gqy//vrZbrvtGmItAAAAwCLE3RMAAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoNFfRoFQqNdQ6AAAAgEVMnW+5+NFHHyVJll9++QZbDAAAALDoqHM0WHXVVRtyHQAAAMAixjUNAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQKEmC3sBAEuyjwYOzD/+fk0e/O/9GTz4k0yYMCErduiQzbtukYMOPjS77b7HfJ9z6tSp+c+tt+TWm/+d119/LV8MG5a2bdtm1dVWz149984RP/xRVlhhhbna54D+/XPtP/6Wxx59JJ99+mmqq6vToWPHbLvtdjn0+0dk627d5vvrAGDBWWapVtnkO6tkk+90yqZdVsmmXVbJKh2WLj+/64/+mKdefr/B5u+xTZcctEfXbL7+qllx2XaZMGlyBn8+Ig889WauvfOZDPrsq7na32orLZMf7Lt1emyzXjqt2D7NK5vm8+Gj8uIbH+ff97+QB59+q4FeCXz7VJRKpdLCXgR8U1VVVdq1a5dhX41K27ZtF/ZyoEFccflfcuqvT8z48eNrHdNzn1655u//nG//H3z66af5/iEH5tln/lfrmKWXXjqXX3lN9um17xz3VyqVctZvzswfzj8v1dXVtY77vx/9JJf86c9p2rTpPK0bvg3ab370wl4CNIgf7b9NLjvtwNmOaaho0K51i1x99mHpuf2GtY4ZO35ifn3xHbnmP0/XaZ9H9u6ec3/ZKy1bVNY65u5HX8+PzvxXRo+dMNdrhm+LUvWkTBxwdUaNmv17LkcaACwEV195RX71i+lvMDbYYMPs0mO3tGzZMv37v577770nU6ZMyT133Zk++++bu+79byora//lpi5GjBiRnnvsmnfefjtJ0qJFi+zT63tZe511MmLEiNx7z135aODAfP311zn0oN657c57smuP3Wa7zzNOOyUX/eH88tdbbrV1tu2+XZo0aZIXX3g+/R55OKVSKX+75qqMGzcuf7v2ulRUVNTrdQCwYDWvnPUtQ9WY8WnerGkqmzbc24nKpk1yyyU/SffN1kqSTJ5cnfufHJD+732WVi0qs8vWXbLB2iulVYtmuey0AzOlujrX3vHsbPf5o/23yaWn9C5/3f+9T/Pw/97OuAmTsuHaK2WP7hukadPG2XvHjXJzq+bZ5+jLM3lK7VEclgSONGCR5EgDFmcDP/wwm2zYJZMmTUqSnHXOeTnxpF/PNOa1V19Nr567Z9iwYTVjfnduTjz5lHrN+9Mf/1/+ee3fkyTrrLtu7rrnv1l1tdXKz1dXV+f4Xx6bK6+4PEmy3HLL5Y13Pqj1/8Gnn34qu+zQPUnSuHHjXP33f+aggw+Zacyj/R5J7/16ZezYsUmSa/91Y/oceFC9XgcsqhxpwOLqiH23ykF7dM2rb3+SV98anFfe/iTvf/xF3rnvt1m14zJJGuZIg1//eLf0/dleSZKhX47Kvsf8Na+/++lMY074wS45+9h9kiQTJk7ORvuenU+Gfl24v9VXXjav3nZamlXWHPV2xp/uyoX/eHimMRuts3Lu/PPPsuKyNT/7zrjs7lz494fm6+uCRUVdjzRwIUSABeys35xZDgZ9Djx4lmCQJBtvskmu+cd15a8vvOD3GTly5DzP+e477+Rf112bJGnatGluvOk/MwWDpOaN/8V/vCxbbV1z/YEvv/wyl158Ya37POPU6es+/sSTZwkGSbLjTjvn/D9cXP667xmnZurUqfP8OgBY8K6949n0+PEf8+uL78jND7yU9z/+osHnbN+2ZY47fOfy1z8641+zBIMkufAfD+fm/76UJGnerGnO/Nmete7zzKP2LAeDm+5/cZZgkCSvv/tpfnTG9J+/JxyxS9q1bjHPrwMWB6IBwAI0ZsyY3HXn7UmSioqKnHr6mbWO3XmXXdN1iy2T1Bx9c89dd87zvDf9+4bym/V999s/XdZbr3Bco0aNcsppZ5S/vvGGfxWOG/jhh3nu2WeSJC1btswvjzuh1rkP/8EPs9LKKydJPh40KE8/9eQ8vQYAlhx777hR2rRqniR57vWBefT5d2ode+5V/y3/jOu108Zp0XzW6+e0alGZfXbcKEnNBYHPveq/te6v33Pv5Pn+HyVJ2rVpkZ471H49BVgSiAYAC9AjDz+UCRNqLqq0wQYbZu111pnt+O/td0D58d133THP8957913lx/vt33s2I5Oddt4lSy21VJKaN/mvvfrqLGPumWF/O+68S9q3b1/r/po0aZK995l+UcW77pz31wHAkmGvGS58eNtDr8x27HuDhuXND4YmSVq1aJZdtvrOLGN23uo7adG85tpAA94fMsejJW5/ePrPvr3/f2yAJZVoALAAvfbq9F98tuq2zRzHd9tm2+nbvjbrm/e6mDhxYt5+e/qto7aew7yNGjXKllttXf769YJ5Z3wdc9pfMvPrKNofAMxok3VXLj9+5rWBcxz/v1c/LD/eaN1Oszy/8Qzfe3aGsbXu75UPpm+7zsqzGQmLP9EAYAF66603y487d15rjuPX7Ny5/PjTwYNTVVU113O++8475dshLrXUUll22WXnuM0aa06fd8Y1T/P23L6OGfb3dsH+AGCatq2bZ6UVph/B9sEnc76GwsDBX5Yfd1mzwyzPz/i9Dz75cpbnv+nDGfbXqcPS5VMlYEkkGgAsQMM+/7z8eNp5/rPTvn37tGrVqvz1F///bgpzNeewGeZcqW6flqzcafonMjOueV73OeP+vv7660yePLlO6wBgybPCMtOv4j567IRUjZkwx20+HTZihu3bzLrPZafv87MvRs5xfyNHj8+YcRPLXy9fsE9YUogGAAvQmDGjy49njAGz07Jly/Lj0aNHz2ZksRm3aVnXOVtMn3PGNRftsy6vY8bX8M3tAWBGM36qP3b8pDptM27C9HGtW856VEDrls1m2OfEWZ6f0z7bzLA9LGlEA4AFaPz48eXHlZWVddqmWfPpv/yMGzduruecMA9zNp/DnHP7OmbcX237BIAkadFs+t0PJk+eUqdtJkycPq5l81l/Ls24z0mTq+u0z4kTpx8VV7RPWFKIBgALUIsW0+/1PGlS3T49mThh+mGZ3/zEvi6az8OcE+Yw59y+jhn3V9s+ASBJxs/wZr1p0yZ12qZ5s+njZjxCoGiflU0b12mfzWYIDUX7hCXFYhMNHn/88VRUVJT/9OnTZ47bHHHEEeXxAAtC69bTz4kcO3ZsnbaZ8VP5Nm3m/pzKGbcZV9c5x0+fc8Y1F+2zLq/jm0cWzMvrAGDJMHrs9NDcqkXdPuGf8UiAMeNmvQbCjNcnaNWibqcazLjP0ePqdkoDLI4Wm2jwTbfeemsGDBiwsJcBMJMVVlyx/PizTz+d4/iRI0fO9KZ8+RVWmPs5V5hhzs/mPOc311Y059zuc8b9tW/fPk2bNp3NaACWZF98Nf26N21aNU/b1nO+c8FKyy9VuP00w4ZXFY6tTbvWLWa6DkLRPmFJsdhGg1KplL59+y7sZQDM5Dvf6VJ+/MEH789x/IcfTL9P9Eorr5y2bdvOZnSxtddZJ40a1fxzP3LkyAwfPnyO2wz8cPq8XbqsN8vz687t65hhf98p2B8ATDNqzPgMmeEOB51XWX6O26zZabny47cGDp3l+bcHTr/rT+dVlpvl+Vn2N8OYTz8fMdPRD7CkWSyjwbR7kN9xxx159dVXF/JqAKbbeJNNy4+fe+Z/cxz/v6efmr7txpvM05zNmzefKVY8O4d5p06dmueefab89UYF82686fTXMaf9JTO/jqL9AcCMXn17cPnxVhutMcfxW2+6Zvnx6+/MegTca+9M39+WG895f902mb6/196t21F6sLhaLKPBsccem2bNag4nOvPMMxfyagCm22XXHuU7CfTv/3ref++92Y6/4/b/lB/vvc++8zzvXnvvU358+39une3Yxx7tlxEjau53vcqqq2aTGQLBND17Tt/fo488nJEjR9a6v+rq6tx91x3lr/fpNe+vA4Alw71P9C8//t4us4/NnVdZPut37pgkGTd+Uh5+5u1Zxjz8zFsZ//8vZrjh2ivN8eiFfXfeuPz47kdfr+uyYbG0WEaDTp065Sc/+UmS5N57780LL7wwz/v68ssvc/rpp2eTTTbJUkstlebNm2e11VbLYYcdlqeffnq226622mqpqKjIEUcckSR599138+Mf/zirrbZamjVrlhVWWCH77rtvnnvuuXle31lnnVW+mOP778/5EOEePXqkoqIiHTp0SHX1zLebee6553L66adn++23z4orrpjKysq0bds2Xbp0yVFHHZW33nprtvuedmHJ1VZbLUkydOjQnHzyyVlvvfXSpk2bVFRU5PHHH5/XlwqLhdatW6fnPr2S1JxGdd45Z9c69tF+j5Q/8W/Tpk15u3lx4EGHlE9RuP22W/PO27P+QpXUHGUw45oOPuSwwnFrdu6crltsmaTmQoh/vOSiWue+7tp/5NPBNZ/wrLLqqtlm2+7z9BoAWHLc/Wj/8sULt95kzWzfde1ax576k93KP+Pu7Pda4Z0Oxo6flHserwkRjRo1yik/3q3W/e2wxTrZauOaIw2qxozPPY+JBizZFstokCSnnHJK+ZZgZ5xxxjzt46GHHkrnzp1zzjnn5LXXXsuoUaMyceLEfPzxx7n++uuz7bbb5uijj87UqVPnuK877rgjm266aa655pp8/PHHmTRpUr744ovceeed2WabbXLzzTfP0xoPPvjg8uMbb7xxtmOHDRuWfv36JUkOPPDANG48/XYz1157bbbaaqucc845eeKJJzJs2LBMnjw5o0ePzttvv50rrrgiG264YS6//PI6reu5557LhhtumAsuuCBvvfVWxowZMw+vDhZPZ/Y9q3whwH/feH0uvugPs4zp//rr+b8ffL/89fEnnpz27dvPMu7JJx5Pi6YV5T+1Wfc738khh9bsb9KkSTmoz375+OOPZxpTXV2d43/1i/KpBMsuu2x+edwJte7zd+f+vvz4wgt+n5tv+vcsYx57tF9OOuFX5a9/c9Y5M/3bA8CSZZUOS2f8q38u/1mlw9KF474eNTaX/POR8td//93h2WDtlWYZd9zhO+egPbsmSSZOmpyz/npfrXOf9df7MmnylCTJwXt1za++v9MsYzZYe6X87ezpP38vuvaRjBw9vm4vDhZTdbvx6bdQhw4dctRRR+Xiiy/OQw89lKeffjrbbLNNnbd/7bXX0rNnz0yaNClNmzbN0Ucfnb333jutWrXKq6++mt///vf56KOP8pe//CWtWrXK+eefX+u+BgwYkJtvvjkdOnTI8ccfn8022yylUikPPvhgfv/732fChAn5yU9+kh133DHLLTfnC7PMqHPnztliiy3y/PPP58Ybb5ztxR9vvvnm8tEFhxxyyEzPTZkyJe3bt88+++yT7t27Z6211kqrVq0yZMiQvPLKK/nTn/6U4cOH5+ijj866666bHXfcsdZ5xowZk/322y8TJkzIaaedll122SUtW7bMgAED0qFDh7l6fbA46rzWWrngwkvyq18cnSQ57dcn5eYbb8guPXZLy5Yt07//67n/3nsyeXLNPaW37b7dbN+819V5F1yY559/Nu+9+27eefvtbLphl+zT63tZe511MmLEiNx7z10Z+OGHSZImTZrkqr9dm3bt2tW6v2nruvTiCzNlypQccdjBueqKy7Nt9+3SuHHjvPjC83nk4YdSKpWSJL0PPCgHHnRwrfsDYNF14x/+b5bvLdu+dfnxGUftkeEjZv6Q6LaHXsltD8/79cUuuvbh7LjFOum2aed0WK5dnr7+xNz3xIAMeP+ztGreLDtv/Z1stM7K5fHHX/CffDzkq1r39+EnX+akC2/Ppaf0TpKc+6t902ePzfPwM29l3IRJ2XDtlbNH9/VT2bTmLdKTL72fS6/rN8/rh8VFRWnab3Pfco8//nh22GGHJMk//vGPHHHEEfniiy+yxhprZOzYsdlhhx3y6KOPzrTNEUcckX/+859Jkm/+NXTt2jUvvvhiGjdunPvvvz+77rrrTM+PGDEi22yzTd566600atQo/fv3z3rrzXxF8NVWW638Sd53v/vdPProo7Nc+fyGG27IoYcemiS5+OKL86tf/Spz67LLLsuxxx6bJHnxxRez2WabFY7bcsst8/zzz2fttdfOu+++O9Nzn332Wdq3b5+WLVsWbjtq1Kh07949/fv3zzbbbJOnnnpqljEz/n22bt06Tz/9dDbaaKM6vYaJEydm4sTp97+tqqpKp06dMuyrUfN0tXj4Nrj8z5fltFNOyoQJtV+Rec+9euZv1/6r1jfvTz7xeHrsvEP56/GTZ/9P+ieffJLvH3Jgnn/u2VrHtG/fPn+54urs+7395vAKav7tPPP0U3PJRX+Y5ZSnGR3xg//LH/98eSor63a/bfg2ar/50Qt7CdBgxr/657ne5ndX3J9zrrx/pu+t0mHpvHv/WeWv19njzHwy9Ota97FUmxa55uzvZ8/tNqh1zLjxk3LqpXfmyluerNO6jjpwu5zzi33SonntP5PufWJA/u/0f6ZqjLsmsPgqVU/KxAFXZ9So2b/nWmxPT0iS5ZdfPkcfXfMD/LHHHstjjz1Wp+1eeOGFvPjii0mSH//4x7MEg6Tml+qrrroqSc05wHM6bP/vf/974X+Igw8+OB071ly4peiNeF306dOnfLjvDTfcUDjmww8/zPPPP59k1qMMkmSllVaqNRgkSbt27XLWWTX/wD/99NP56qvaK26SnHTSSXUOBkly3nnnpV27duU/nTp1qvO28G31s6OPycuvvZHjTzw5G2ywYfm6Kauutlr2790nt991b/5zx92z/bR/bq2yyip59Imnc+2/bsyee/XMyp06pbKyMssss0w2/e5mOfM3Z+WV/m/VKRgkSUVFRc4+57w88/zL+enPjs663/lO2rRpk5YtW2bNzp1z+BE/zCOPPZW/XnWNYADAXBs5enz2/+WV+d6xV+TWB1/OoM+GZ/yESRlRNS793/s0f/j7Q9ms97l1DgZJ8tebnsh3Dzg3F/7jofR/79OMqBqX8RMmZdBnw3Prgy9n32P+mgN+eaVgAP/fYn2kQZJ89dVXWX311TN69Oh069ZtposX1nakwbnnnpvTTjstyew/uU+SLl265O233y789H7akQYbbLBB+vfvX8sekr322iv33XdfNtpoo7z22mtz9bqn2X333fPAAw+kQ4cO+fTTT8sXg5nm7LPPLt9J4v3330/nzp1nu7+xY8fmyy+/zNixY8t/N5988kn23HPPJEm/fv1mOUVhxr/PDz/8MGusMefb2UzjSAMAvu0caQDAt0ldjzRYbK9pMM0yyyyTX/7ylzn77LPzv//9Lw8++GB69Ogx223eeOONJEllZWU23njj2Y7dYost8vbbb+f999/PpEmTCj9JW3fddWe7j6WXrrkAzOjRo2f6/siRI/Ppp8X3ha2srMzaa0+/iuwhhxySBx54IEOHDs2jjz6anXfeeabx0y6SuMUWW9QaDIYPH56LL744t912W95///1ZTtn45tjatG7deq6CQZI0a9asfJtMAAAAFg2LfTRIkuOOOy6XXXZZRo4cmb59+84xGnz9dc15VUsvvXSaNJn9X9GKK66YpOZIhREjRmSFFVaYZczsDvtPUj4q4JvnA9955535wQ9+ULjNqquumkGDBpW/7tWrV1q2bJlx48blhhtumCkavPLKK3nnnXeSFJ+akCQvv/xyevToMcfTDqYZP772q8gutdRSddoHAAAAi7bF+poG0yy11FI57rjjkiTPP/987r333jptV1FR++3LFjWtW7fOPvvskyS5/fbbZ7qw2rSjDBo3bpw+ffrMsu2kSZPSu3fvfPXVV2natGmOO+64PPHEExk6dGgmTJiQUqmUUqmUD///VdWTWS8cOSO3UwMAAFg8LBHRIEl++ctfZplllkmS2d6WMJl+usBXX32VKVOmzHbs559/nqQmMBTdQ70+jjjiiPIb9m/+mfEog2mmHUVQVVVVDiNTp07NTTfdlCTZZZddsvzyy8+y3aOPPpqBAwcmSS6//PJcdNFF6d69e1ZcccWZThmYdgQGAAAAS4YlJhq0adMmJ554YpKaw/XvuOOOWseuv/76SWo+gZ/ThQlfeOGFJMlaa6210K8M3qNHjyy77LJJph9d8MQTT+Szzz5LUvupCW+++Wb5cdGRCNO89NJL82upAAAAfAssMdEgSY4++ujyJ+19+/at9RD7Ga8H8Pe//73W/T377LN56623ZtlmYWnSpEl69+6dJLn//vszcuTIcjxo2bJlevXqVbjdjEdTjB07tnDM1KlTc/XVV8/fBQMAALBIW6KiQatWrXLyyScnSQYMGJD777+/cFzXrl3Lt1m8+uqr069fv1nGjBo1KkceeWSSmgsZHnXUUQ206rkz7WiCiRMn5sYbb8xtt92WJNlnn33SunXrwm3WWmut8uNrr722cMwpp5ySV155Zf4uFgAAgEXaEhUNkuSoo45Khw4dksz+toFXX311KisrM2XKlOyxxx454YQT8sQTT+Sll17K1VdfnU033TQDBgxIkpxwwgnlUxoWtq233jqrr756kuS0007LiBEjktR+akJSc1rDtCMwTj/99Pz0pz/Ngw8+mJdffjk333xzdt5551xwwQXp1q1bw78AAAAAFhlLXDRo0aJFTj311DmO23jjjXPPPfekbdu2mTRpUi666KJsv/322XzzzfOTn/ykfOHAn//85znvvPMaetlz5eCDD06SjBw5Mkmy7LLLzvY2k61atcp1112X5s2bp7q6OldeeWV22223bLbZZjnwwAPTr1+/bL/99rnyyisXxPIBAABYRCxx0SBJfvzjH6dTp05zHLfrrrvmgw8+yKmnnpqNN944bdu2TbNmzbLKKqvkkEMOyVNPPZU///nPadRo0fpr/OZRBb17906TJk1mu02PHj3y0ksv5dBDD03Hjh3TtGnTLLfcctluu+1y1VVXpV+/fmnVqlVDLhsAAIBFTEWptqsBwkJUVVWVdu3aZdhXo9K2bduFvRwAmKP2mx+9sJcAAHVWqp6UiQOuzqhRs3/PtWh9RA4AAAAsMkQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAAIVEAwAAAKCQaAAAAAAUEg0AAACAQqIBAAAAUEg0AAAAAAqJBgAAAEAh0QAAAAAoJBoAAAAAhUQDAAAAoJBoAAAAABQSDQAAAIBCogEAAABQSDQAAAAACokGAAAAQCHRAAAAACgkGgAAAACFmizsBUCRUqmUJBldVbWQVwIAdVOqnrSwlwAAdTbt59a09161EQ1YJI0ePTpJ0nn1Tgt5JQAAAIuv0aNHp127drU+X1GaU1aAhWDq1KkZMmRI2rRpk4qKioW9HFhsVFVVpVOnThk8eHDatm27sJcDALPl5xY0nFKplNGjR6djx45p1Kj2Kxc40oBFUqNGjbLyyisv7GXAYqtt27Z++QLgW8PPLWgYszvCYBoXQgQAAAAKiQYAAABAIdEAYAnSrFmz9O3bN82aNVvYSwGAOfJzCxY+F0IEAAAACjnSAAAAACgkGgAAAACFRAMAAACgkGgAAAAAFBINAAAAgEKiAQAAAFBINAAAAAAKiQYAAABAIdEAAAAAKCQaAAAAFCiVSgt7CbDQiQYAAADfUF1dnYqKiiRJVVXVQl4NLDyiAQANZtonNEWf1Pj0BoBF1dSpU9O4ceMkyUknnZQjjzwyn3766UJeFSwcTRb2AgBYPE2dOjWNGtW06QkTJmTo0KEZPXp0Jk2alE022SQVFRVp3LjxTOMAYFEw7efSxRdfnAsvvDBJsuOOO+bAAw9MmzZtFubSYIETDQCY76qrq8uf0Nxwww25//7788ADD2TSpEmZMGFCtt1222y//fb50Y9+lI4dOwoHACxynn766Zx77rlJkr59+2bLLbcUDFgiiQYAzFczHtJ59tlnp2/fvuXnOnTokIkTJ+bxxx/P448/nrvuuiu33HJL1lxzTeEAgIVqxuCdJK+//npGjBiR4447LieddFJatGixEFcHC4/fzgCYr6a98f/9739fDgY///nPc+utt+bpp5/Oww8/nJ/+9Kfp0KFDXn311ay77roZPHiwYADAQlMqlcrB4Nprr01VVVXef//9lEql7LbbboIBSzRHGgAw391+++0566yzkiTnnXdefvazn5UP6Vx99dXTpEmT3HDDDUmSzp07Z8KECQttrQAw7S4JZ555Zn73u9/llltuSZs2bbL22mtnhx12SFITFqaNgyWJaADAXJndaQTTfqHq169fJk6cmMMPPzwHH3xw2rRpUz7s8/3338/++++f0aNHp1u3brnmmmuy1lprLeBXAQAzGzp0aJ555pk0b948Dz/8cCorK1MqlfLmm29mww03FAxYYjkWFIA6GTVqVJKa0w+qq6sLx1RUVGTYsGG57rrrUiqVsuOOO6ZTp07lwz4/+OCDdO/ePcOGDUv37t1z9dVXZ5111plpH1OmTGnw1wIA39ShQ4dceOGF6dmzZyorKzN+/Pg0adIk77zzThI/n1hyiQYAzNFHH32UddZZJ7/+9a+TJI0bN641HIwbNy5Tp07Naqutlu9973tJamLCBx98kG233bYcDK644oqsu+66M207dOjQPPbYYxk/fnzDviAAKLDxxhvn1FNPzR577JHWrVtnzJgx+e1vf5tRo0alSZMmtf7sg8WZaADAbI0bNy5HH310vvjii1x00UU5++yzk9QeDioqKjJ16tRMmjSpfHTCe++9N9tgUCqVkiSPPfZYTjrppNx+++1+MQNgodhoo41y2mmnpUePHmnbtm3efvvt7Lfffhk9evRsozksrkQDAGarZcuW6dOnT7beeutUV1enb9++tYaDUqmUZs2apVmzZhkyZEiee+65DB06NNttt91sjzCoqKjIF198kT/96U95/fXX8/bbb8902ysAaGjTAnZSc8TBaaedll122SWtW7fOo48+mgMPPDBjxowRDljiiAYA1GraL1Df//73c+yxx6Zr165JUms4qKioSIcOHfLjH/84SXL66adn/fXXLweDyy+/fJZgkCSTJ0/Of//733zwwQdZa621sv/++y+IlwfAEmTq1KkzfT1mzJiMHj26/PW0I+Wm/ezbeOONc/rpp6dHjx5p1apV/vvf/6ZPnz7CAUsc0QCAWk37BSpJevfuneOOO26O4SBJtt1223To0CHvvvtuRowYkfXXXz//+te/0qVLl8Jfst5444385S9/yddff53tttsua6yxxgJ4dQAsKaqrq8t3/rnvvvvy29/+Nttss0223nrrHH744fnjH/+YpOZivzP+7Ntoo41y+umnZ7fddhMOWGJVlGY8DgcACsx4m8VbbrklF198cV544YUkyW9/+9ucccYZSWquLN2kSc3dfI855pj85S9/SePGjcuHefbq1Wum/U6cODFvv/12jjvuuDz++ONZb731ct9992WVVVZZcC8OgMXatFv+Jskf/vCHnHfeeRkzZkz5bggVFRUplUrp06dPjj/++Gy88cZp0qTJTD/7Xn/99fzud7/LAw88kLFjx2b33XfPzTffnNatW8/0sw8WR6IBAHVS13AwadKkVFZWJkn69OmTW2+9NU2aNEnbtm1zzDHHZK+99krHjh0zbNiw3H333bnrrrvy6quvZoUVVsgTTzyRtddee+G8QAAWOzP+7PrNb36Ts846K0myzTbbZOONN85KK62UgQMH5uqrr06SbL/99jn55JOzww47pLKycrbhoGfPnrn++uvTpk2bhfPiYAERDQCos7qGgwkTJqR58+ZJkiOOOCI33XRTJk2alCRZYYUVkiRff/11Jk+enCTp0qVLbrvttqyzzjoL9PUAsGS47LLL8otf/CJJzfV2+vTpk/XWWy9JzVFvp5xySi699NIkSbdu3XLKKadk5513rjUcPProoxkxYkQOOOCA3HTTTamoqFgorwsWBNEAgLkyL+Hgsssuy2OPPZY777xzpn117do12267bY499th06tRpwb0IAJYYjz32WI444ogMHjw45513Xo455pi0bNkySc3PtPvuuy8//OEP89VXX5W36d69e0466aTCcNC/f/+ceOKJeeutt3L//fdngw02WCivCxYU0QCAOZr2o2LaJyl1DQcTJ05Ms2bNktSctvDyyy9n5MiRGT16dFq1apWddtopScpxAQDmp6lTp+akk07KH//4xxx88MH5/e9/nw4dOpSfv+eee8rBoGfPntl///1z+OGHJ6m5qO/JJ59cGA7eeOONLLXUUll55ZUXyuuCBckVOwCYxYy/GCWZ5bDLRo0aZfLkyWnatGl69+6dJOVw0Ldv3yTJGWeckWbNmpXHVVZWZquttpplLu0agIbyxRdf5JZbbkl1dXUOOOCAdOjQIaVSKRUVFXn00UfLwWCvvfbKXXfdlSQZNGhQ+vbtm6eeeqp8d4RvhoP1119/Yb4sWKAcaQDATGa8yvSLL76Yjz/+OM8++2yWXnrpdOnSJRtttFH5logzXvRwdkcczLhPAGgI3/xZUyqVMnz48Fx66aUZMWJELr/88vJz77zzTrbddtt89dVX2X333XPfffclqbkL0CuvvJLevXvnk08+SVJzjYNTTz01O+20U/lnHixJRAOAJdCgQYPyzjvvZLfddpvp+zMeYXDBBRfkiiuuyODBg2e6D/Vmm22W7t2758ILL0yS8ic2iXAAwMIx48+Yl156KZtttln5ueHDh6dUKmW55ZZLknz11VfZb7/98uSTT2bbbbfN7bffnmWWWaZ8ZFyS9OzZsxwSkmSjjTbK+eefn1133XUBvipYNDSa8xAAFidDhgzJGmuskT322CN33313+fulUqkcDM4999z8+te/zqBBg9KkSZOsuuqq5XNAX3755Vx88cXZa6+98s4778x06kLv3r1z3HHHpWvXrkmSvn375uyzz06SNG7cOFOnTl1QLxOAJci0YHD88cena9euefnll8vPLbvssuVgkCTvvfdePvjggyy33HI57rjjsswyy6RUKqVp06blu/osvfTS6dy5cy655JIkNXdNcEtgllSiAcAS5rXXXkvnzp2TJAceeGD5jgbT3vzffvvtOf3005Mkp512Wu6555689dZbefbZZ3PxxRdnq622SmVlZe6///78/Oc/zxtvvJGk5pDOZNZwcNZZZ+WUU05JkpmukwAA81O/fv1yySWXpGXLlunfv3+SzHSk3DQPPvhghgwZksrKymyyySZJpv8MbNq0acaNG5d33nknn3zySXr37p3rr78+AwYMyGqrrbbAXgssSvz2BrCE2X777XP++edn8803z4QJE3LwwQfPdCvEp59+OknNqQWnnHJKdtppp7Ro0SKrrLJKfvnLX+aiiy7KkUcemWbNmuWxxx7LCSeckLFjx6ZJkyaZNGlSkunhYOutt051dXX++te/Zvjw4Qvj5QKwhNh2222z5ZZbZty4cTn//PMzYsSIwtPiWrRokSTp2LFj2rZtm2TmuPD888/nww8/zGabbZYOHTrk4IMPznrrrbdgXgQsgkQDgCVMy5Yts9tuu+Xkk0+eKRz85z//SalUyn//+98st9xy6dmzZ1q2bFn+9GXaJXC22GKLHH300fn5z3+eysrKPPTQQ/nFL36RJKmsrCz/4tW7d+8ceeSR2XHHHfPMM89k2WWXXTgvGIDFXnV1dSorK3PiiSdmueWWy3vvvZcrr7yy8EiDaUcMvPjii7niiiuS1JzeMO0iiL/5zW/y9ddfZ+utt86UKVPc5YclngshAiyhxo8fn//+9785//zz8+KLL6Z58+a57bbb8oc//CFVVVV56aWXZrv966+/nr59++a+++7Lsssum+uvvz477bRTkpkvjlhVVVX+JAcAGtLAgQPTq1evvPHGG9lzzz1z5513pnHjxjP9XBo5cmSOOeaY3HDDDUmSww8/PGuttVYGDx6c5557Lq+//nq+853v5L///W9WWWWVhflyYJEgGgAswb4ZDpo2bZpWrVplnXXWybPPPjvH7a+//vp8//vfT5Kcf/75OfHEE8vPzfgLGgAsKDfccEMOO+ywJMmf/vSnHH300bOMefDBB3PZZZfl/vvvn+W5jh07pl+/fllnnXUafK38v/buO66q+vHj+Iuh7CEoCFpIOHHrD83cqTnIppaZo9SGDStHroZY5qg00+pr9RPLkVp93asylbDUHDhxYKggigNRliDc+/vD3z0xLogLHO/n48Gjwz2f87mfc8LHOfd9P0NuBxqeICJyF3NycqJz584MHz6cxo0bc+nSJZKTk9m+fTurVq0q9DhL3tyrVy+aN28OwKpVqzCZTEZXUAUGIiJyIxX3u842bdrQtm1bAFauXEliYmKBMh07dmTEiBG88cYblClTBltbW2rUqEH37t3ZsGGDAgORXBQaiIjc5SzBwciRI2nQoAEAJpOJ33//nfPnz1s9xsbGxggHXF1dAcjMzMTW1lYrJIiIyA2Xk5NjNYy2NmdBpUqVaNWqFQC//fYbO3fuBP4NHSz/bdGiBVOmTCE6Oprdu3fz119/ER4eTlBQ0M06DZHbkp7sRETECA7eeecdGjVqRE5ODtOmTWPZsmWFHmNnZ0d6ejqnT58GLj+kgXoYiIjIjWdZBeGNN97g5ZdfZunSpXletzCZTAAMHTqUFi1akJ2dzZgxYzh58qRxf8p/n7rvvvuoVasWnp6eODs73+xTEbntKDQQEbnLWB6o8nfztAQHo0aNIiQkhKysLF544QV+/PFHYylFgOzsbGN7x44dnD17lrJly9KsWTOr9YqIiNwIX375JdOmTePrr79mwIABhIaGEhkZyYkTJ4wytra25OTk4ODgYEzOGx8fb/Q2sNYzQUSKptBAROQOZwkJ8su/lKLZbM4zx0FISAiZmZn06dOHyZMn8/fffwNgb29PVlYW27ZtY/To0Rw9epTAwEC6deuWp14REZEbKSAggOHDh+Ph4UFqaiqrVq3i2WefpUePHqxcudIYUmdnZ4e9vT19+/bFx8eH+Ph4vvrqK2OfiFwdrZ4gInIHy8nJMR6QtmzZwq5du1i5ciV+fn5UqFCBV199FU9PT8qUKQP8u+JB/lUV7O3tqVy5Mi1btsTPz4+DBw8SFRXFkSNHCAgIYPXq1Zo0SkRESkRERAQRERF89dVXeXoZdO/enebNmzNo0CCys7Oxt7fnk08+4Z133sHW1pbvvvuO7t27l2LLRW5PCg1ERO5QuQODKVOm8MUXX/DPP//kKVO7dm369evHk08+aaxFnT84GD9+PNu2bTOO8ff3JyEhgeDgYBo3bsz777/PfffdV3InJiIid6X8S/kmJCTw2WefsWXLFiIiIozXH3roIbp27Uq3bt04d+4cbdu2JTExkVdffZVp06aVRtNFbmsKDURE7kAmk8lYxWDMmDGMHTsWuPyBv1GjRjg5OfHXX38RHx+Pj48Pjz32GIMHD6Z69erAvw9mFy9eZOXKlYwbN44dO3ZgZ2fH5MmTeeihh4yQwcnJqXROUkRE7lqW+9ylS5dIS0tj+vTprFy5kq1bt5KdnY2Liws+Pj5Mnz6d6dOnG8sI//rrr8ZcByJSPAoNRETuMLm/ifnkk094++23ARg+fDhPPPEEISEhAJw+fZoHH3yQvXv34u7uTteuXXnnnXeMYQaWBzJLcPDRRx+xfft2ABYvXswjjzxSCmcnIiLyr9wh+YEDB4iKimL06NFcuHCBM2fO4OrqioODAxkZGaSnp/Pqq68yefJkY1ieiFyZQgMRkTvUsmXL6NOnD+fPn2fSpEkMGDAAT09PAC5evMjmzZt58sknSUpKAsDDw4OHH3640OBg9erVjBgxgoMHD7J3715q1apVWqcmIiJ3ueTkZOOelpWVRdmyZY19x44dIzIykvDwcNauXYudnR05OTm4ubmxefNmatasWUqtFrk9KTQQEbnNJCcnk5WVhY+PT6FlMjIyGDhwILNnz2bgwIGMHTsWLy8vY//y5ct5/vnnOXv2LB06dCAjI4PIyEjKlStHly5d8gQHuec4+O2336hTpw6BgYE3/TxFRESsiY+PZ9CgQVy6dIlly5bl2Zd/3oOvvvqKDRs2sGTJEv7++2/q1KlT0s0Vue0pNBARuY0cOnSIZ599lnbt2jFo0CD8/PysltuxYweNGzemSpUqzJo1i1atWhkPUmvWrKFXr16cPXuW0NBQli1bxsWLF6lbty6HDx/G09OT0NBQqz0ORERESlN8fDxvvfUWP//8M3D5vhgUFFSgXO7JgE0mE+fPn6dcuXIl2laRO4WeAEVEbhM5OTk89dRTbN26lfDwcL7++msuXbpktay/vz/VqlWjWbNmtGrVCgAbGxv27t1L7969OXv2LF26dDG+oXF0dGTq1Km4u7uTnJzM0qVL+eCDDzh48CCAAgMRESl1uQMDe3t7IiMjCQoKwtp3oJbAAC7fwxQYiFw79TQQEbmNHD58mJCQEJKTk4mMjOSBBx4otOzp06cpW7YsHh4eACQmJtKxY0d27dpF27ZtWbJkCa6ursZa1ocOHaJt27bGmtflypWjefPmfPrpp1StWrVEzk9ERO4O+YcRWBTWsy0uLo6hQ4fy448/Ym9vz/r163nggQfUE06kBNiXdgNERKR4srOzCQoKYtu2bRw/fjxPYJCamoqrq2ue8hUqVAD+7aK5cuVKYmJiCAgIICwsDFdXV0wmE/b2l28F1apVo1q1ajg7O+Pj48Off/7Jzp07taSiiIjcULmHDiQnJ3Pq1ClMJhNVq1Y17km5XbhwgWHDhvHjjz/i4ODA2rVrFRiIlCD9KxMRuU3Y29uTk5NDYGAgLVq0MF6fOHEiY8eO5eTJk1aPszyYbdiwgfT0dO69914aN24M5B12EB8fz4EDBwgKCuLnn3+mc+fOLFu2jEqVKt3EsxIRkbtJ7sBg+vTpPPnkk9SqVYumTZvSpEkTPv74Y2JiYgCMYQcZGRm4uLjg6OjIL7/8osBApISpp4GIyG0k9xhNgI8++oh33nmHe+65Bzc3N1544QUqVqxo9VjL0oouLi5G7wFL91CTycRff/3FuXPnsLe3x9fXl6VLlxZ4PxERkWtlNpuN+8qYMWMYO3Ystra2mM1mLl26RFRUFMeOHWPFihVMnjyZRo0aAeDr68vo0aN57733CAgIUGAgUsL0r01E5DZiMpny/N69e3d8fX2Ji4tj5syZfP311wV6HFiO8fX1BS6vX71x40YuXryIjY0NWVlZ7Nu3j6lTp5KZmUmHDh0ArI41FRERuVaW+8q3337L2LFjAejduzfvvfceEydOpGbNmiQlJREREcHTTz/Nli1bjGPvu+8+AgICAE3OK1LSNBGiiMhtIneXzvj4eCpUqICDgwOxsbG0aNGCEydOEBAQwPPPP8+LL75YoMfB1q1badGiBVlZWTRv3pxOnTrRpEkTNm3axKpVq9i0aRMNGjTQkAQREbmhLL3azGYz6enpPProo/z+++9MmjSJAQMG4OnpCcDZs2d55513WLJkCSdPnqRKlSrMnz+fJk2aFDpxoojcfAoNRERuA7m7Yg4dOpRFixYxd+5cGjRogKOjY5HBgdlsxmw2Y2try8yZM3n99dfJyMjAycmJ7OxscnJyMJlMVKxYkfXr11O9evVSPlsREblT5A68MzMzOX/+PBUrVqRr16588803+Pj4AHDp0iXKlClDUlISEyZMYN68eSQkJCg4ELkFqG+PiMhtwBIYfP7550yePJnY2FhWr15NamqqMTliZGQkfn5+HD16lPDwcGOogo2NjXF8jx49+O677/D29sbW1pZLly5RuXJlHnzwQQUGIiJyw1kCg3HjxvHGG2+we/dunJ2dad68uREYAJQpUwaTyYSXlxcjR46kZ8+e+Pv7c+TIEXr06MGWLVuM3goiUrI0EaKIyG1iz549TJ06Fbi8YsKjjz5K+fLlAfIEBy1atDCCAyDPUAVnZ2e6detG48aNOXbsGEeOHCEkJARfX1+8vb1L58REROSO9vHHH/Puu+/i7+/PiRMnSE9Pp3LlygB5eg/Y2tpiMpkoV64cI0eOBGDevHlGcKAeByKlQ6GBiMgtKneXToDDhw8TGxvLSy+9xMsvv4ybm5uxz87OrljBgWWoQmBgIIGBgbRu3brEz0tERO4urVu3JjAwkNjYWC5evAjA6dOnAQoEAFcKDhYsWEBISIiCA5ESpOEJIiK3oNzLUo0fP56IiAguXLgAXH74yh0YWOQPDq40VEFERORmM5lMNGnShJ9++omgoCBj+d+vvvqKhIQEY8nF3PIHB7mHKnTo0IFt27YpMBApQXpyFBG5BVkehsaPH8/o0aPp2bMnS5YswcvLiwcffBAouPwiFC84EBERKSmWUKBhw4bMnz+foKAgAI4cOcKYMWM4ffq01bkK8gcHvXv3xtHRkYyMDNzd3UvjVETuWgoNRERuUZmZmSQkJFCxYkUSEhL45ZdfjPWrofB1qgsLDubMmcOUKVNITEwsydMQEZG7TP5Q28bGBpPJROPGjVmwYAFBQUFkZWWxfPlyJkyYwJkzZwoNDsxmM+XKlWPYsGEMGzaM3bt3U61atZI8HZG7nkIDEZFblIODAx9++CH9+vXDz8+P1NRUAHbv3g1Q5AzS+YODe++9l5iYGFavXq3hCSIickPlDwkKu8/k5OTQqFEjIzg4efIkP/zwA+PHjy80OLAEDl5eXrz//vta5UekFNiYtW6JiMgtyWQyYWtry/nz55kwYQJz584lPj4egF9//ZV27dpdcSIoy2SKhw8fpmvXrsybN48GDRqU0BmIiMidLvekvXv37iU6Opq1a9fi4uKCm5sb3bt3x8fHh/Lly5OTkwNcDra3b9/O008/zeHDh6lYsSLPPPMMI0eOpHz58prkUOQWo9BAROQWlj84mD9/PkePHqV8+fIsWrSI5s2bX/HhKjs7G3t7e+O/IiIiN0LuwGDq1Kl8++237N27N0+ZgIAAgoODGTduHA0aNDB6Jdja2io4ELlNKDQQEblFWR6Y8gcH8+bNIy4ujsqVK/PDDz8UKzjIXZ+IiMj1stybAMLCwggLCwPA3d2d6tWrc+LECZKSksjIyADAycmJRYsW8dBDD2EymYxVgqwFB6NGjcLb21v3LZFbhAa2ioiUkvxjQBMTE4mNjSUtLQ2TyZQnMDCZTHh4eDBixAieffZZKleuTHx8PM888wwbN260Og40Pz14iYjIjWIJDKZNm2YEBmPGjGHNmjVs2bKFLVu2sGHDBjp06ICfnx8ZGRk8+uijrFixwjjWMsfBwoULjTkOfvrpJ0aNGkVSUpLuWyK3CPU0EBEpBbm7dC5cuJANGzawcOFCTCYT1apVo0aNGkyYMAE/P78Cx5w/f56JEycyZ84c4uPjr7rHgYiIyI2we/duevbsyd69e3nnnXcYMWIEzs7OwL9D406dOsWMGTOYN28eBw4cwN7ensjISJo0aVJgqEKvXr3Yv38/NWrUYP369fj6+pbm6YnI/1NPAxGRm6CoPDZ3YDBhwgT69OnDN998w9mzZ0lJSeHvv/9m9uzZtGrVioULF5KcnAz8uyKCh4cHw4cPp1evXtfU40BERKS4LJMXWhMbG0t0dDReXl507NjRCAwA7O3tMZlM+Pj4MGjQIJ5//nkCAwPJzs7mlVde4ejRo9ja2hq96Ro1asR3331Hw4YNWbhwoQIDkVuIQgMRkRss97f9M2bMYP/+/Xn2WQKDsLAwRo0aRVZWFqGhoXzwwQesWLGCiRMnUrNmTQ4fPsxbb73F3LlzOX36NJA3OBgxYkSe4KB3796sX79ePQ1EROS6vfnmm2zZsgU7O7sCw+ks/vrrL0wmEwEBATRp0qTAfltbW8xmMx4eHvTr14/mzZsDEBcXx9atW4HL90VLcBASEsKff/5J3bp1b96JichVU2ggInKDWT60P/vsswwcOJBjx44Bl7tqWvb95z//McaAhoWF8fHHHzN69Gg6dOjAkCFDGDhwIGXLluXEiROMGzeO+fPnFwgO3N3djeAgMDCQI0eO8Nprr5GRkaHeBiIics1GjBjB559/Trt27YiKisLW1tZqj4OLFy8CcPLkSc6ePWu1LksPuPLly/P+++/j7e3N6dOnWbBggbEf/p0joWzZsjfjlETkOig0EBG5CQ4ePMiSJUsA+Oijj8jIyDCWO9y8eTOff/45AB988AFDhw6latWqwOXJEZcvX87YsWPJysrCw8ODkydPMnHixCKDg65duxIcHMz8+fNxcnJSbwMREbkmFy5c4Pjx4/j5+ZGWlkarVq3YsWOHcd/JLTg42Hj977//BqwPZ7CxsSEnJ4egoCBat24NwPHjx8nMzLRaVkRuLQoNRERuAh8fH7p06QJcDhB+/fVXY99vv/3GwYMH6d69O7169cLJycnYt2LFCvr168fZs2d55JFH+OOPP6hVqxYJCQlMmDCBH374wWpw8MEHH/D7779Tp06dkj1RERG5o7i7uzNp0iSefPJJ/Pz8SE1NpXXr1kRFRRUIDurXrw/AqVOn+OGHHwCshguW1wEqV64MQHp6OllZWeoZJ3IbUGggInITeHp60r9/f+Byt82lS5cCl7tyrlmzBpPJxCOPPEJAQIDxwLRhwwb69+/P2bNnefjhh1m8eDF16tRh7Nix+Pr6cuLECcaPH18gODCZTLi5ueHj41M6JysiIncMs9mMn58fI0eO5PHHHzeCg1atWuUJDkwmE02aNOHNN9/ExsaG+fPnM2LECKDw4CAlJYWdO3cCULVqVdzc3NSzQOQ2oNBAROQmeeihh+jduzcAs2fPZtmyZTg4OPDiiy/y9ttv8+yzzwKXu2LGxMTw1FNPcebMGTp27GgMbTCZTDRr1oyAgAAAEhMTmTBhAgsXLiQxMRH4dxyoiIjI9bLMQeDn58fo0aMLDQ4sgXenTp0ICQkBYNKkSbz77rvAvz0LcocH0dHRnDhxAldXVzp37gwUvdqQiNwa9KQpInKT2NjY8MADDwCXH4oiIyOxsbHhiSeeYMiQIcDlUODChQsMHjyY06dP88ADDxAeHo6NjQ3Z2dnY2tri7+/Pgw8+iI2NDZ6enpw8eZJhw4axZMmSQme0FhERuVZXCg4scxwAtGvXjgEDBhAYGAjAuHHjeOGFF9i7dy8ZGRnY2dmRkpLCn3/+yYgRIzh48CDVq1enU6dOxnuJyK3Nxqx4T0Tkhsu97GKHDh1Yu3YtLi4ubNq0idq1a+cpe/ToUTp27EhCQgKff/45ffv2Bf6dOMrOzo6wsDCmT5/OF198wfDhwzl69Cj79++nevXqJX5uIiJy58l938r/mmUln0WLFhk9BTZs2EDDhg2NsjNmzODLL79kz549mM1mateujbe3N/fffz9///03cXFxxMTE4Ofnx7p163T/ErmN2Jd2A0RE7kQ2NjaYTCZsbW3p0aMH27dv59y5c3z77beMHz8eR0dHo2xERAQHDx7E0dGRunXr5nlos7Oz4+LFi0RFRXH27FkCAwNZvnw5Dg4OxooLIiIiV2vKlCnY29vz9NNP4+HhgYODg7HPEhbk73EAGMFB69at8wQHL730Er6+vixevJjvv/+evXv3AhAZGYnJZMLV1ZWQkBBmz56twEDkNqPQQETkJrHMNdCxY0fGjx/PuXPniIyMJC0tDUdHRyNUcHFxAaBChQqUKVMGgEuXLhnbu3btYseOHdSqVYtq1arh6elZKucjIiJ3hoEDBzJjxgwAvvzySypVqsTLL79MQEAAISEhRnht6ZCck5NTaHAQERFBgwYNAHjsscdo1aoVTzzxBPPnz+fQoUM4ODjg4+NDaGgonTt3xt/fv+RPWESui4YniIgUk+VDfm7WunNaO2b27NkMGDCAS5cuMWzYMCZOnGiUiYmJoWnTppw7d46HHnqIH3/8ETc3NzIzM9m2bRujRo0iIiKC559/nmnTpuHs7HzTzlFERO5sb775Jp9//jkODg5kZ2cDl0MBSw+4p556iqpVq9K3b1/c3d3x8PDIc3xCQgLjxo1j8eLFxlAFS3BgMpmMHgqWek0mkxGCi8jtSaGBiEgx5A4HPv30U6pUqULTpk2N9abzl8lv165dPP300xw4cIAWLVowZ84c7r33XrKzs7GxsWH8+PF8/PHHpKSk0LBhQxo1akRiYiKHDh3iwIEDBAUF8euvv1KlSpWSOF0REbkD5eTkMG3aNN59913S0tKwt7enUqVK2Nracv78eZKSkoyyAQEB+Pn50a9fP6pXr06rVq2MeXbi4uKYOHEiP/30E6dOncoTHGRnZ2Nvb1/gnnilkF1Ebl0KDURErsLrr7/OF198ga+vL+XKlWPs2LHUr1+fatWqGWUKezAKCwsjLCwMgJkzZ/Lcc88Z+6Kjo/nyyy+ZPXs2Fy5cyHPcvffey+rVq6lZs+bNOSkREblrZGVl8f333/PWW2+RlpZG/fr1GTRoEK6urhw+fJgFCxYQGxub517k7OxM27ZtadCgAd27dyc4OBiTycTgwYNZsWIFR48excXFhYiICBo2bGi1Z56I3L4UGoiIFNOpU6d4/vnn+eOPP0hNTQWgTJkyhISE0KVLF1599VWcnZ0pU6ZMngcmy/Y///xDz5492bJlC3Xq1GHRokUEBQUZ9cfGxvLHH38wfvx4MjMzKV++PE2aNGHw4MHcd999pXLOIiJy58nKyuK7775jyJAhpKam8j//8z8MHjyYHj16cO7cOeLi4ggPD2f//v2sWbMGW1tbY4lfd3d3atasSa9evfDy8iIiIoIVK1Zw/PhxXFxc+OOPP4yhCgoORO4MCg1ERK7CpUuXiI6O5oMPPuDAgQPs2bPHmF26ZcuWNGzYkKFDh1odtpCVlcXAgQMJDw+nQoUKzJw5k9DQUKMrp0V6ejqZmZm4uLhgZ2dnrIUtIiJyo1h6HAwePJjU1FRq167N6NGj6dKlC+7u7sZQhDVr1hAbG8tXX31FamoqsbGxRh333nsvmZmZXLhwgZycHLKysgCIioqiXr16pXVqInKDKTQQEbkGKSkpnDlzhk8//ZR169YRHR1t7PP392fAgAG0adOGNm3a5Dnu2LFjNG/enOPHj/PAAw+wdu1aHBwcjGBBYz5FRKSk5A8O6tWrx/Dhw+natSuurq55yp4+fZq0tDS+/fZb/vnnH+bPn4+DgwOZmZnY2tpia2trTKy4b98+DakTuYMoNBARuU7btm1j8+bNfPjhh1y8eJHk5GQcHR3JycnhjTfeoF27dnTs2BG4/ID25ptv8s033+Dh4cH06dPp0aOHwgIRESkV1oKDESNG0LVrV1xcXIxlF/Pfo/766y/279/P//7v/5KYmMjhw4fx9PTkzz//VGAgcodRaCAico3yf9Dft28ff/75J19//TVbt241Xvfz86NRo0aMGDGC+vXrc+DAAUJCQgDo168f3377bYm3XURExOJKwYGFtYA7KyuL48ePs2LFCtq3b6/AQOQOpNBAROQGy8nJ4auvvmLTpk3MmzfPmECqcuXKVK1albCwML744gsWLlwIwOLFi3nkkUdKudUiInI3K25wkJsmOxS5Oyg0EBG5gSwTR1ksXLiQxYsXs3r1apKTk43Xvby8OH/+PPb29rzwwgtMnjw5z2SIIiIiJe1aggMRufMpNBARuclSUlKIjY1l7Nix7N+/n3379gGXl2u8dOkSAQEB7NmzRw9kIiJS6hQciEh+Cg1ERG6S/CsipKWlERsby9dff82yZcs4evQozs7ObNq0iTp16pR2c0VERAAFByKSl0IDEZESkH/yqL///putW7fSpk0batWqVYotExERKchacDBq1Ci6dOlSYDlGEbmzKTQQESlBmjRKRERuF/mDg3vuuYcpU6bw+OOPa5lgkbuIQgMREREREbEqKyuLOXPmMGDAAABiYmK47777SrlVIlKSFBqIiIiIiEihMjMzWbBgAU2bNqVGjRql3RwRKWEKDUREREREpEj55+YRkbuHBtaKiIiIiEiRFBiI3L0UGoiIiIiIiIiIVQoNRERERERERMQqhQYiIiIiIiIiYpVCAxERERERERGxSqGBiIiIiIiIiFil0EBERERERERErFJoICIiIiIiIiJWKTQQEREREREREasUGoiIiIiIiIiIVQoNRERERERERMQqhQYiIiJSap577jlsbGywsbHhyJEjBfavX7/e2D9mzJgSb9+tZNasWca1mDVr1jXXU5LX9MiRI8Z7Pffcczf1va5WlSpVsLGxoUqVKqXdFBGRW5p9aTdARERE8rKxsSl0n4uLC15eXtSrV4/OnTvTu3dv3N3dS7B1dybLh+cqVarcch9uRURESpN6GoiIiNxG0tLSiIuLY8WKFbz22mtUr16dNWvWlHazbnthYWGEhYVd1zf4IiIidyL1NBAREbmFLVq0KM/vKSkpREVF8f3333PmzBkSExN59NFHWb9+Pffff38ptfLmadOmDWazubSbISIictdSaCAiInILe+yxxwq81rt3b0aNGkWnTp3YunUrmZmZvPXWW/z1118l30ARERG5o2l4goiIyG3I29ub7777zvh906ZNxMXFlWKLRERE5E6k0EBEROQ2FRwcTNWqVY3fd+3aZWxbm2l/+/btvPzyy1SvXh03N7dCZ+E/f/48n376Ke3bt8ff3x8HBwe8vLxo3LgxI0eO5Pjx48VqX0ZGBpMmTSIkJAQPDw/c3NwIDg7m7bffLnbAcbUz/W/cuJFXXnmFunXr4uXlRZkyZfDy8qJp06a89dZbREZG5ilvqdtiw4YNxmu5fwqb6yAnJ4e5c+fSvXt3qlSpgouLC66urtSoUYMXXniBrVu3Fus8AebPn0/Hjh3x8fHB0dGRwMBA+vTpw+bNm4tdx420Y8cOPvroI0JDQwkMDMTZ2RkHBwf8/Px46KGHmDp1KqmpqVdd7/Hjxxk5ciR16tTB3d0dd3d3GjZsyNixY7lw4UKx64mKiuKNN96gfv36eHl54eDggL+/P6GhocycOZPs7OyrbpuIiFhhFhERkVsKYPxcyQMPPGCUnTt3rvF6eHi48Xp4eLh54sSJZjs7uzx1W/bltnDhQrOXl1eBcrl/HB0dzbNmzSqyXYcPHzZXrVq10Dq8vLzMa9euNfft29d4LTY2tkA969atM/a///77hb7f2bNnzQ8//HCR7bb8REVFWb3WRf3kv05ms9m8e/duc82aNa947GuvvWbOzs4utO3p6enm0NDQQo+3s7Mzf/zxxwX+n16r4lzTsLCwYl0Xf39/8+bNmwt9r9jYWKNs3759zevWrTN7e3sXWd/27duLbP/FixfN/fr1M9vY2BTZttq1a5sPHz5caD0BAQFmwBwQEFCcyyYictfSnAYiIiK3sVOnThnbHh4eVsssXLiQVatW4erqSp8+fWjSpAllypRh3759VKxY0Sj3zTff8NJLL2E2mylbtiyPPvoorVq1wtfXl9TUVCIjI5k3bx4XL17kueeeo2zZsjzzzDMF3i85OZkHH3yQo0ePAlC5cmX69etHrVq1SElJYeXKlSxevJhu3bpRv379674GSUlJNGvWjIMHDwLg7OzMU089RbNmzShXrhwpKSns2bOH1atXEx0dnWdiRctEk48//jgAtWvX5sMPPyzwHo0aNcrz+44dO2jdujUpKSkAtGzZktDQUAICAjCZTOzatYtZs2aRmJjI9OnTycrKYsaMGVbb/8wzz7BixQoAnJyc6NevH02bNgUuDzsJDw9n2LBhRhtLQnp6OnZ2djRp0oTmzZtTvXp1PD09ycnJ4ciRIyxfvpyNGzeSkJBA586diYqK4p577imyzmPHjvHkk0+SlJTEww8/zMMPP4ynpyeHDh3iu+++IyYmhoSEBNq3b8/27dsJCAgoUEd2djadOnVi/fr1APj7+9OjRw/q1auHs7Mz8fHx/Pe//yUyMpK9e/fSqlUrduzYQYUKFW7GZRIRuTuUdmohIiIieVHMngbR0dF5yh49etTYl/tbacBcvXr1PPvz27lzp7ls2bJmwFytWjVzdHS01XL79u0z+/v7mwGzm5ub+ezZswXKvPjii8b7tmzZ0nzhwoUCZX788ccCPR+utadB165djTL333+/OSEhodDz3Lhxo/nEiRMFXrcc37p160KPtUhLSzPfd999ZsDs7OxsXrp0qdVyycnJ5rZt2xp1//rrrwXKzJs3z9hfsWJFq9d93759Zl9f3yv2fCiu4lzTLVu2mI8fP15kPbNnzzbb2tqaAXP//v2tlsnd04D/7zUxb968AuUyMjLMjz/+uFGuY8eOVusbMWKEUeaFF14wZ2RkWC03depUo9yzzz5rtYx6GoiIFI9CAxERkVtMcUKDpKQkc9OmTY1yTZs2zbM/d2hgY2NzxS7flg9sjo6O5kOHDhVZ9pdffjHqnjBhQp59p06dMsIHNzc3qx/QLYYOHXrdocGmTZuM/ZUrVzYnJSUV2fbCXE1okPsD6ezZs4sse+bMGbO7u7sZMHfq1KnA/oYNGxp1LV++vNB6li9fXqKhQXH16tXLCE+ysrIK7M8fGgwePLjQulJTU8333HOPUXbnzp159icmJpodHR3NgLl9+/ZXbFvPnj2NoCI+Pr7AfoUGIiLFo4kQRUREbmGLFy/O8zNnzhyGDRtGzZo1jQnyypYty5QpUwqto0WLFjRs2LDQ/cnJySxZsgS43E0/9+SK1nTo0AE/Pz8A1qxZk2ffihUryMrKAqBnz555hj/kN3jwYGxtr+9RZPbs2cb222+/Tbly5a6rvuKwrFpRqVIlevbsWWRZb29vQkNDgcuTOmZmZhr7jhw5wo4dOwCoUaOGUc6a0NBQatWqdb1Nv+FatGgBXB7OkHsiTmtsbW0ZMmRIoftdXFx45ZVXjN9/+umnPPsXLFjAxYsXARg2bNgV29a3b1/g8mSVa9euvWJ5ERGxTnMaiIiI3MKuNI69QoUKzJo1i2bNmhVapmXLlkXWsXHjRkwmEwAODg4sXrz4iu1yc3PjxIkT7Nu3L8/rW7ZsMbbbtWtXZB1+fn4EBwezZ8+eK75fYf744w9j+9FHH73meorrwoULREVFAZfbv3Tp0iseYwkKLl68SGxsLDVr1gSu7lpZykRHR19Dq6+N2Wxm1apV/PTTT2zbto24uDhSUlIKXZUgPj6exo0bF1pfcHAw/v7+Rb5n+/btGTlyJJD3+gBEREQY24mJiVf8O829ykf+v1MRESk+hQYiIiK3EScnJ7y9valbty6dO3emd+/eeHp6FnlM5cqVi9x/5MgRY3vWrFmFLi9oTVJSUp7fExISjO0r9ViwlLme0CA+Ph64/C31vffee831FFdcXJwRsGzduvWqJyfMfb2u5VqVlJMnT9KtWzc2btxY7GOutFxitWrVrlhH7jK5rw/k/Tvt06dPsdsFBf9ORUSk+BQaiIiI3MLMuWb6v1ZOTk5F7k9OTr7mui9dupTn99TUVGPb2dn5ise7uLhc83vDvx9UXV1dr6ue4rqeawUYQzeg5K9VcVlWKNi5cycA5cqVo2vXrtStW5eKFSvi5OSEnZ0dAL///jvTpk0DLg8DKEpx2p+7jGVlCovrufa5r7uIiFwdhQYiIiJ3udwfuD///HNef/31G1JXenr6FcunpaVd83sBuLu7k5SUlOcD+M2U+/yeeOIJfv755xtSV0lcq+JasGCBERi0a9eORYsW4ebmZrVs7iEAV1Kc9ucuk/89c1+vCxcuFNomERG5sTQRooiIyF0u9/CFuLi466qrUqVKxnZMTMwVyxenTFEsbU9LS+PYsWPXVVdx5D6/2+1aFdcvv/xibH/22WdFfjiPjY0tdr1Xe4755z+4kX+nIiJSfAoNRERE7nItW7bExsYGgNWrV19XXU2aNDG2f//99yLLnjhx4ron9mvVqpWxbVkB4lpYzv9Kw0HKly9P7dq1Adi+fTuJiYnX/J5Xc62AElsB4OTJk8b2leZRuJq/l7179xaYpyC/3377zdhu2rRpnn2tW7c2tletWlXs9xURkeuj0EBEROQu5+PjQ+fOnQHYvXs3P/zwwzXX1aVLF8qWLQvAvHnzivxQ/dlnn11xHPyV9O7d29ieNGkS586du6Z6LF3fi9OFPvdSfu+99941vR9AQEAAjRo1AmD//v1FfhBetWpVia2ckHtegaJ6ByxYsIC9e/cWu16TyVTk0qDp6el8+eWXxu/dunXLs79Hjx44ODgAMHnyZM6cOVPs9xYRkWun0EBEREQYN26c8WF/wIABVwwOkpKSmDx5cp5vhuHyEpDPPfcccHnceY8ePazON7Bo0SImT5583e1u0qSJsdRifHw8Xbp04cSJE4WW37RpU55v0i0CAwOByx/eMzIyinzPV199lSpVqgDw9ddfM3z48AITQuaWlZXFwoUL+eKLLwrsGzp0qLHdv39/Dh48WKDMwYMH6d+/f5FtupFCQkKM7dGjR1sNdtatW8eLL7541XVPmTKFhQsXFng9MzOTvn37GkNMOnXqRN26dfOUqVy5MoMGDQIur6zQsWNH/vnnnyLfb+fOnbz00ktX3U4REfmXJkIUERERGjRowIwZM+jfvz/p6en07NmTSZMm0bVrV6pVq4aTkxPnz58nJiaGLVu2EBERQXZ2NrNnzy5Q18SJE1m9ejXHjh1j/fr1BAcH079/f2rWrElKSgqrVq3iv//9L+XKlaN+/fqsX7/+uto+c+ZM7r//fg4dOsSmTZuoWrUqTz/9NM2aNaNcuXKkpKQQHR3N6tWr2b17Nzt27KBixYp56mjfvj27du0iLS2Nrl270qdPHypUqGAMW6hbt64xB4GzszNLly6lVatWJCcnM2nSJObMmUO3bt2oX78+7u7upKenExcXx/bt2/ntt9+4cOGC1Q/+zzzzDPPnz2fp0qWcOHGChg0b0q9fP6Nr/qZNmwgPDyc9PZ3HH3+cRYsWXde1Ko7+/fszfvx4UlJSWLp0KfXr16dPnz4EBARw7tw51qxZw5IlS7C1taVXr17MmTOnWPW2adOGXbt28fTTTzN37lxCQ0Px9PQkJiaGWbNmcejQIQC8vLz4z3/+Y7WOjz76iJ07d/LLL7+wfft2atasySOPPELLli3x8/PDZDJx5swZ9uzZw7p16zh48CB2dnbMmDHjhl0fEZG7jllERERuKYDxc63Cw8ONOsLDw4t93OrVq83+/v552lDYj4ODg3nVqlVW64mJiTFXrVq10GO9vLzMa9euNfft29d4LTY2tkA969atM/a///77hbb7zJkz5o4dOxar3Tt37ixw/PHjx82+vr6FHmPtGsbExJibNm1arPe0sbExv/fee1bbnp6ebu7SpUuhx9rZ2Zk/+eSTa/5/ml9xrunKlSvNzs7OhbbJ2dnZ/P3331+xTbGxscb+vn37mtevX2/29vYutF4/Pz/ztm3bimx/VlaWeciQIWZ7e/tiXfuAgACr9QQEBBS5X0RELtPwBBERETFYunzPnDmTbt26ERgYiKurK/b29pQrV46GDRvy3HPP8f3333Py5Ek6depktZ6goCB27drFxIkTadSoEW5ubri4uFCzZk2GDh1KVFQUDz744A1rt7e3N6tXr2bt2rX069eP6tWr4+bmhr29Pd7e3jRt2pQhQ4awefNm6tWrV+B4f39/tm/fzuDBg6lXrx5ubm5GL4PCBAUFsWnTJtasWcOAAQMIDg7G09MTOzs73NzcqFmzJk888QRTp07l8OHDhIWFWa3HycmJFStWMG/ePNq3b4+3tzcODg4EBATQq1cvNm7cyJAhQ27IdSquzp07s3PnTl588UUCAwMpW7YsHh4eBAcHM3jwYKKiovLMJ1FcrVu3ZufOnQwfPpzg4GBcXV1xdXWlXr16jBkzhujoaGOeh8KUKVOGTz75hJiYGN577z1atmxJxYoVKVu2LI6OjlSqVIm2bdsyYsQI1q1bd8UhDCIiUjQbs/kK0wSLiIiIiIiIyF1JPQ1ERERERERExCqFBiIiIiIiIiJilUIDEREREREREbFKoYGIiIiIiIiIWKXQQERERERERESsUmggIiIiIiIiIlYpNBARERERERERqxQaiIiIiIiIiIhVCg1ERERERERExCqFBiIiIiIiIiJilUIDEREREREREbFKoYGIiIiIiIiIWKXQQERERERERESsUmggIiIiIiIiIlb9H9FOlJnWHaN0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting confusion matrix\n",
    "cnf_matrix = metrics.confusion_matrix(y_true, y_pred, labels=cm_classes_init)\n",
    "print(cnf_matrix)\n",
    "plot_confusion_matrix(cnf_matrix,cm_classes_init,'plots/' + model_name + '/training_conf_matrix_init_level.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Psi_eta_0 & 0.401\n",
      "Freq1_harmonics_amplitude_0 & 0.163\n",
      "PeriodLS_0 & 0.082\n",
      "StetsonK_AC & 0.080\n",
      "SlottedA_length & 0.067\n",
      "Q31 & 0.050\n",
      "Meanvariance & 0.049\n",
      "MedianAbsDev & 0.048\n",
      "Skew & 0.032\n",
      "Gskew & 0.028\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(rf_model_init, features_init, 'plots/' + model_name + '/training_feature_ranking_init_level.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'Extrinsic': 102970, 'Intrinsic': 81875})\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'sources classified as Variable')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# separating classes (according to the predicted classification):\n",
    "\n",
    "y_test_variable = y_test_variable.loc[y_pred=='Variable']\n",
    "X_test_variable = X_test_init.loc[y_pred=='Variable',:]\n",
    "\n",
    "y_test_variable2_init = y_test_variable2.loc[y_pred=='Variable']\n",
    "X_test_variable2_init =  X_test_init.loc[y_pred=='Variable',:]\n",
    "\n",
    "y_test_bottom_init = y_test_bottom.loc[y_pred=='Variable']\n",
    "X_test_bottom_init =  X_test_init.loc[y_pred=='Variable',:]\n",
    "\n",
    "letter_counts = Counter(y_test_variable)\n",
    "print(letter_counts)\n",
    "\n",
    "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "df.plot(kind='bar')\n",
    "plt.yscale('log')\n",
    "plt.title('sources classified as Variable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'Non-var': 26554, 'SR': 5390, 'EA': 660, 'ROT': 650, 'EW': 644, 'DSCT': 578, 'RRAB': 32, 'RRC': 7, 'M': 1})\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'sources classified as Non-var')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "y_test_galaxy = y_test_bottom.loc[y_pred=='Non-var']\n",
    "X_test_galaxy = X_test_init.loc[y_pred=='Non-var',:]\n",
    "\n",
    "letter_counts = Counter(y_test_galaxy)\n",
    "print(letter_counts)\n",
    "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "df.plot(kind='bar')\n",
    "plt.yscale('log')\n",
    "plt.title('sources classified as Non-var')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Variable level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:546: FutureWarning: The default of `sampling_strategy` will change from `'auto'` to `'all'` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `'all'` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n",
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:558: FutureWarning: The default of `replacement` will change from `False` to `True` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `True` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9999675403716628\n",
      "Balanced accuracy: 0.999970865300573\n",
      " 'weighted' precision accuracy:0.999968\n",
      " 'weighted' recall accuracy:0.999968\n",
      " 'weighted' f1 accuracy:0.999968\n",
      "['Extrinsic' 'Intrinsic']\n"
     ]
    }
   ],
   "source": [
    "#Training variable level\n",
    "\n",
    "rf_model_variable = RandomForestClassifier(\n",
    "            n_estimators=700,\n",
    "            max_features=0.4,\n",
    "            max_depth=50,\n",
    "            n_jobs=-1,\n",
    "            class_weight='balanced_subsample',\n",
    "            criterion='entropy',\n",
    "            min_samples_split=2,\n",
    "            min_samples_leaf=1)\n",
    "\n",
    "rf_model_variable.fit(X_train_variable, y_train_variable)\n",
    "\n",
    "\n",
    "# with open(model_variable_layer, 'rb') as file:\n",
    "#     # 使用 pickle.load() 方法读取模型\n",
    "#     rf_model_variable = pickle.load(file)\n",
    "\n",
    "#testing variable layer performance\n",
    "\n",
    "y_true_variable, y_pred_variable = y_test_variable, rf_model_variable.predict(X_test_variable)\n",
    "\n",
    "y_pred_proba_variable = rf_model_variable.predict_proba(X_test_variable)\n",
    "\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_true_variable, y_pred_variable))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_true_variable, y_pred_variable))\n",
    "print(\" 'weighted' precision accuracy:%f\"%(precision_score(y_true_variable, y_pred_variable,average=\"weighted\")))\n",
    "print(\" 'weighted' recall accuracy:%f\"%(recall_score(y_true_variable, y_pred_variable,average=\"weighted\")))\n",
    "print(\" 'weighted' f1 accuracy:%f\"%(f1_score(y_true_variable, y_pred_variable,average=\"weighted\")))\n",
    "\n",
    "classes_order_proba_variable = rf_model_variable.classes_\n",
    "print(classes_order_proba_variable)\n",
    "\n",
    "features_variable = list(X_train_variable)\n",
    "\n",
    "with open(model_variable_layer, 'wb') as f:\n",
    "            pickle.dump(rf_model_variable, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[102964      6]\n",
      " [     0  81875]]\n",
      "Normalized confusion matrix\n",
      "[[1. 0.]\n",
      " [0. 1.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting confusion matrix\n",
    "\n",
    "cnf_matrix = metrics.confusion_matrix(y_true_variable, y_pred_variable, labels=cm_classes_variable)\n",
    "print(cnf_matrix)\n",
    "plot_confusion_matrix(cnf_matrix,cm_classes_variable,'plots/' + model_name + '/training_conf_matrix_variable_level.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PeriodLS_0 & 0.287\n",
      "SlottedA_length & 0.279\n",
      "Psi_eta_0 & 0.089\n",
      "Gskew & 0.075\n",
      "Skew & 0.069\n",
      "Meanvariance & 0.061\n",
      "MedianAbsDev & 0.038\n",
      "Freq1_harmonics_amplitude_0 & 0.035\n",
      "Q31 & 0.034\n",
      "StetsonK_AC & 0.033\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(rf_model_variable, features_variable, 'plots/' + model_name + '/training_feature_ranking_variable_level.pdf')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## separating classes (according to the predicted classification):\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'EB': 79989, 'ROT': 22975})\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'sources classified as extrinsic')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extrinsicss\n",
    "\n",
    "y_test_extrinsic = y_test_variable2_init.loc[y_pred_variable=='Extrinsic']\n",
    "X_test_extrinsic =  X_test_variable2_init.loc[y_pred_variable=='Extrinsic',:]\n",
    "\n",
    "letter_counts = Counter(y_test_extrinsic)\n",
    "print(letter_counts)\n",
    "\n",
    "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "df.plot(kind='bar')\n",
    "plt.yscale('log')\n",
    "plt.title('sources classified as extrinsic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'LPV': 66848, 'RR': 11457, 'DSCT': 3100, 'CEP': 470, 'ROT': 5, 'EB': 1})\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'sources classified as intrinsic')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Intrinsics\n",
    "\n",
    "y_test_intrinsic = y_test_variable2_init.loc[y_pred_variable=='Intrinsic']\n",
    "X_test_intrinsic = X_test_variable2_init.loc[y_pred_variable=='Intrinsic',:]\n",
    "\n",
    "letter_counts = Counter(y_test_intrinsic)\n",
    "print(letter_counts)\n",
    "\n",
    "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "df.plot(kind='bar')\n",
    "plt.yscale('log')\n",
    "plt.title('sources classified as intrinsic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ### Extrinsic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:546: FutureWarning: The default of `sampling_strategy` will change from `'auto'` to `'all'` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `'all'` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n",
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:558: FutureWarning: The default of `replacement` will change from `False` to `True` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `True` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9680956450798337\n",
      "Balanced accuracy: 0.9794659265649026\n",
      " 'weighted' precision accuracy:0.972087\n",
      " 'weighted' recall accuracy:0.968096\n",
      " 'weighted' f1 accuracy:0.968826\n",
      "['EB' 'ROT']\n"
     ]
    }
   ],
   "source": [
    "#Training extrinsic level\n",
    "\n",
    "rf_model_extrinsic = RandomForestClassifier(\n",
    "            n_estimators=400,\n",
    "            max_features=0.4,\n",
    "            max_depth=90,\n",
    "            n_jobs=-1,\n",
    "            class_weight='balanced_subsample',\n",
    "            criterion='entropy',\n",
    "            min_samples_split=2,\n",
    "            min_samples_leaf=1)\n",
    "\n",
    "rf_model_extrinsic.fit(X_train_extrinsic, y_train_extrinsic)\n",
    "\n",
    "# with open(model_extrinsic_layer, 'rb') as file:\n",
    "#     # 使用 pickle.load() 方法读取模型\n",
    "#     rf_model_extrinsic = pickle.load(file)\n",
    "\n",
    "#testing extrinsic layer performance\n",
    "\n",
    "y_true_extrinsic, y_pred_extrinsic = y_test_extrinsic, rf_model_extrinsic.predict(X_test_extrinsic)\n",
    "\n",
    "y_pred_proba_extrinsic = rf_model_extrinsic.predict_proba(X_test_extrinsic)\n",
    "\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_true_extrinsic, y_pred_extrinsic))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_true_extrinsic, y_pred_extrinsic))\n",
    "print(\" 'weighted' precision accuracy:%f\"%(precision_score(y_true_extrinsic, y_pred_extrinsic,average=\"weighted\")))\n",
    "print(\" 'weighted' recall accuracy:%f\"%(recall_score(y_true_extrinsic, y_pred_extrinsic,average=\"weighted\")))\n",
    "print(\" 'weighted' f1 accuracy:%f\"%(f1_score(y_true_extrinsic, y_pred_extrinsic,average=\"weighted\")))\n",
    "\n",
    "classes_order_proba_extrinsic = rf_model_extrinsic.classes_\n",
    "print(classes_order_proba_extrinsic)\n",
    "\n",
    "features_extrinsic = list(X_train_extrinsic)\n",
    "\n",
    "with open(model_extrinsic_layer, 'wb') as f:\n",
    "            pickle.dump(rf_model_extrinsic, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[76704  3285]\n",
      " [    0 22975]]\n",
      "Normalized confusion matrix\n",
      "[[0.96 0.04]\n",
      " [0.   1.  ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = metrics.confusion_matrix(y_true_extrinsic, y_pred_extrinsic, labels=cm_classes_extrinsic)\n",
    "print(cnf_matrix)\n",
    "plot_confusion_matrix(cnf_matrix,cm_classes_extrinsic,'plots/' + model_name + '/training_conf_matrix_extrinsic_level.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PeriodLS_0 & 0.311\n",
      "Gskew & 0.173\n",
      "Freq1_harmonics_amplitude_0 & 0.121\n",
      "StetsonK_AC & 0.096\n",
      "Skew & 0.092\n",
      "Meanvariance & 0.082\n",
      "Psi_eta_0 & 0.048\n",
      "Q31 & 0.042\n",
      "MedianAbsDev & 0.030\n",
      "SlottedA_length & 0.006\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(rf_model_extrinsic, features_extrinsic, 'plots/' + model_name + '/training_feature_ranking_extrinsic_level.pdf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intrinsic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:546: FutureWarning: The default of `sampling_strategy` will change from `'auto'` to `'all'` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `'all'` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n",
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:558: FutureWarning: The default of `replacement` will change from `False` to `True` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `True` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9897534226499433\n",
      "Balanced accuracy: 0.6609692697442439\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 'weighted' precision accuracy:0.991331\n",
      " 'weighted' recall accuracy:0.989753\n",
      " 'weighted' f1 accuracy:0.990203\n",
      "['CEP' 'DSCT' 'LPV' 'RR']\n"
     ]
    }
   ],
   "source": [
    "#Training intrinsic level\n",
    "\n",
    "rf_model_intrinsic = RandomForestClassifier(\n",
    "            n_estimators=200,\n",
    "            max_features=0.4,\n",
    "            max_depth=70,\n",
    "            n_jobs=-1,\n",
    "            class_weight='balanced_subsample',\n",
    "            criterion='entropy',\n",
    "            min_samples_split=2,\n",
    "            min_samples_leaf=1)\n",
    "\n",
    "rf_model_intrinsic.fit(X_train_intrinsic, y_train_intrinsic)\n",
    "\n",
    "# with open(model_intrinsic_layer, 'rb') as file:\n",
    "#     # 使用 pickle.load() 方法读取模型\n",
    "#     rf_model_intrinsic = pickle.load(file)\n",
    "\n",
    "#testing intrinsic layer performance\n",
    "\n",
    "y_true_intrinsic, y_pred_intrinsic = y_test_intrinsic, rf_model_intrinsic.predict(X_test_intrinsic)\n",
    "\n",
    "y_pred_proba_intrinsic = rf_model_intrinsic.predict_proba(X_test_intrinsic)\n",
    "\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_true_intrinsic, y_pred_intrinsic))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_true_intrinsic, y_pred_intrinsic))\n",
    "print(\" 'weighted' precision accuracy:%f\"%(precision_score(y_true_intrinsic, y_pred_intrinsic,average=\"weighted\")))\n",
    "print(\" 'weighted' recall accuracy:%f\"%(recall_score(y_true_intrinsic, y_pred_intrinsic,average=\"weighted\")))\n",
    "print(\" 'weighted' f1 accuracy:%f\"%(f1_score(y_true_intrinsic, y_pred_intrinsic,average=\"weighted\")))\n",
    "\n",
    "classes_order_proba_intrinsic = rf_model_intrinsic.classes_\n",
    "print(classes_order_proba_intrinsic)\n",
    "\n",
    "features_intrinsic = list(X_train_intrinsic)\n",
    "\n",
    "with open(model_intrinsic_layer, 'wb') as f:\n",
    "            pickle.dump(rf_model_intrinsic, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11297    67    24    69]\n",
      " [    0   470     0     0]\n",
      " [  175   219 66208   246]\n",
      " [   10     1    22  3067]]\n",
      "Normalized confusion matrix\n",
      "[[0.99 0.01 0.   0.01]\n",
      " [0.   1.   0.   0.  ]\n",
      " [0.   0.   0.99 0.  ]\n",
      " [0.   0.   0.01 0.99]]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = metrics.confusion_matrix(y_true_intrinsic, y_pred_intrinsic, labels=cm_classes_intrinsic)\n",
    "print(cnf_matrix)\n",
    "plot_confusion_matrix(cnf_matrix,cm_classes_intrinsic,'plots/' + model_name + '/training_conf_matrix_intrinsic_level.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PeriodLS_0 & 0.449\n",
      "SlottedA_length & 0.150\n",
      "StetsonK_AC & 0.131\n",
      "Freq1_harmonics_amplitude_0 & 0.081\n",
      "Psi_eta_0 & 0.068\n",
      "MedianAbsDev & 0.036\n",
      "Q31 & 0.029\n",
      "Meanvariance & 0.023\n",
      "Gskew & 0.018\n",
      "Skew & 0.015\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(rf_model_intrinsic, features_intrinsic, 'plots/' + model_name + '/training_feature_ranking_intrinsic_level.pdf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EB、RR、LPV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'EW': 62811, 'EA': 13893})\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'sources classified as EB')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1atTIatOmjTVkyBDr1VdfdY555ZVXrIEDB1otWrSwvL29rU6dOllPPvmkVVBQcNH3pqSkxPrLX/5idejQwbn+0aNHW4cPH3aO0Xmnun755RcrISHBatmypdW0aVNr2LBh1v79+63g4GCXU11///vfrb59+1r+/v5W48aNreuvv96aOXOm89Rafn6+lZiYaF1//fXWNddcY/n5+VmRkZHWv/71L5c51vRUV7msrCzrjjvucL4/wcHB1pgxY6zMzEyXcZs3b7YiIiIsLy8vq2PHjtbChQudp5guJjs724qNjbWaNm1qtWzZ0powYYL1xRdfuJzqutT9rcyFPs7+x32u7OPsnp6eVseOHa0nn3zSKioquui2gCvJZlkXOJ4MAADQgHCNDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMwQ0Mz+NwOPTTTz+pWbNmF/zp0AAAoP6wLEtFRUUKDAyUh0fVx3UIn/P89NNPat++vbunAQAAauDYsWNq165dlc8TPucpv73+sWPH5Ovr6+bZAACAS1FYWKj27dtf8MfkSIRPBeWnt3x9fQkfAACuMhe7TIWLmwEAgDEIHwAAYAzCBwAAGINrfAAAaCDKyspUWlrq7mlcEY0aNZLdbr/s9RA+AABc5SzLUk5Ojn799Vd3T+WK8vf3V5s2bS7rPnuEDwAAV7ny6GndurWaNGnS4G7Aa1mWSkpKlJeXJ0m67rrrarwuwgcAgKtYWVmZM3patGjh7ulcMY0bN5Yk5eXlqXXr1jU+7cXFzQAAXMXKr+lp0qSJm2dy5ZXv4+Vcx0T4AADQADS001uVqY19JHwAAIAxCB8AAGAMLm4GAKCBCpm6qs62dXTW8Bq9LjU1VbNnz1ZOTo7Cw8M1b9489e3bt5Zn9/9xxAcAALhFenq6kpKSNGPGDO3Zs0fh4eEaNmyY82PrVwLhAwAA3OLFF1/UhAkTlJCQoLCwMC1cuFBNmjTRokWLrtg2OdUFp7o8JAr3q+lhaQCoDefOndPu3bs1bdo05zIPDw/FxsZq+/btV2y7HPEBAAB1Lj8/X2VlZQoICHBZHhAQoJycnCu2XcIHAAAYg/ABAAB1rmXLlrLb7crNzXVZnpubqzZt2lyx7Tb48CkpKVFwcLCSk5PdPRUAAPAfXl5eioiIUGZmpnOZw+FQZmam+vfvf8W22+Avbp45c6b69evn7mkAAIDzJCUlKT4+Xr1791bfvn01d+5cFRcXKyEh4Ypts0GHz6FDh7R//36NGDFC+/btc/d0AADAH9x99906ceKEpk+frpycHPXs2VNr166tcMFzbapR+Bw/flxPP/201qxZo5KSEnXu3FlpaWnq3bt3rUxqy5Ytmj17tnbv3q2ff/5ZK1eu1KhRoyqMu9jdHpOTkzV79mx9+umntTIvAACuJlfDbSsmTZqkSZMm1dn2qn2Nzy+//KIBAwaoUaNGWrNmjbKzs/XCCy/o2muvrXT8tm3bKv3x8dnZ2RUuaCpXXFys8PBwpaamVjmPi93t8f3331doaKhCQ0Oru4sAAKCBqvYRn+eff17t27dXWlqac1mHDh0qHetwOJSYmKguXbpo2bJlstvtkqQDBw4oJiZGSUlJeuqppyq8Li4uTnFxcRecxx/v9ihJCxcu1KpVq7Ro0SJNnTpVn332mZYtW6bly5fr1KlTKi0tla+vr6ZPn17dXQYAAA1EtY/4fPDBB+rdu7fuuusutW7dWjfeeKNee+21ylfu4aHVq1crKytL48aNk8Ph0OHDhxUTE6NRo0ZVGj2Xovxuj7GxsS7b+uPdHlNSUnTs2DEdPXpUc+bM0YQJEy4YPampqQoLC1OfPn1qNCcAAFD/VTt8vvvuOy1YsEBdunTRunXr9Mgjj+jPf/6z3nzzzUrHBwYGasOGDdq6davGjh2rmJgYxcbGasGCBTWe9JW422NiYqKys7O1c+fOGs8LAADUb9U+1eVwONS7d28999xzkqQbb7xR+/bt08KFCxUfH1/pa4KCgrRkyRJFR0erY8eOeuONN2Sz2S5v5tUwfvz4OtsWAADuYFmWu6dwxdXGPlb7iM91112nsLAwl2XdunXTDz/8UOVrcnNzNXHiRI0YMUIlJSWaMmVK9Wf6B+662yMAAPVNo0aNJP1+w96Grnwfy/e5Jqp9xGfAgAE6cOCAy7KDBw8qODi40vH5+fkaMmSIunXrpuXLl+vgwYMaNGiQvL29NWfOnBpN+o93eyz/mHv53R7r8iNxAAC4m91ul7+/v/NTzU2aNKnTsyp1wbIslZSUKC8vT/7+/s4PS9VEtcNnypQp+tOf/qTnnntOY8aM0Y4dO/Tqq6/q1VdfrTDW4XAoLi5OwcHBSk9Pl6enp8LCwpSRkaGYmBi1bdu20qM/p06d0rfffut8fOTIEe3du1fNmzdXUFCQJPfc7REAgPqo/GxHefw0VP7+/pd9Zsdm1eCE2UcffaRp06bp0KFD6tChg5KSkjRhwoRKx2ZkZCgqKko+Pj4uy7OystSqVSu1a9euwms2bdqkwYMHV1geHx+vxYsXOx/Pnz/feQPDnj176uWXX1ZkZGR1d8dFYWGh/Pz8VFBQIF9f38ta19UmZOoqd08BdehquLEZgOopKyur9N55DUGjRo0ueKTnUr9/1yh8GjLCB6YgfAA0JJf6/bvB/3R2AACAcoQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEz3+kpqYqLCxMffr0cfdUAADAFUL4/EdiYqKys7O1c+dOd08FAABcIYQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADBGgw+fkpISBQcHKzk52d1TAQAAbtbgw2fmzJnq16+fu6cBAADqgQYdPocOHdL+/fsVFxfn7qkAAIB64LLCZ9asWbLZbHr88cdraTq/27Jli0aMGKHAwEDZbDa99957lY5LTU1VSEiIfHx8FBkZqR07drg8n5ycrJSUlFqdGwAAuHrVOHx27typV155RTfccMMFx23btk2lpaUVlmdnZys3N7fS1xQXFys8PFypqalVrjc9PV1JSUmaMWOG9uzZo/DwcA0bNkx5eXmSpPfff1+hoaEKDQ2txl4BAICGrEbhc+rUKd1333167bXXdO2111Y5zuFwKDExUWPHjlVZWZlz+YEDBxQTE6M333yz0tfFxcXp73//u26//fYq1/3iiy9qwoQJSkhIUFhYmBYuXKgmTZpo0aJFkqTPPvtMy5YtU0hIiJKTk/Xaa6/p2WefrcnuAgCABqJG4ZOYmKjhw4crNjb2wiv38NDq1auVlZWlcePGyeFw6PDhw4qJidGoUaP01FNP1WjS586d0+7du1227+HhodjYWG3fvl2SlJKSomPHjuno0aOaM2eOJkyYoOnTp1e5ztTUVIWFhalPnz41mhMAAKj/PKv7gmXLlmnPnj3auXPnJY0PDAzUhg0bFBUVpbFjx2r79u2KjY3VggULqj3Zcvn5+SorK1NAQIDL8oCAAO3fv79G60xMTFRiYqIKCwvl5+dX47kBAID6q1rhc+zYMT322GPKyMiQj4/PJb8uKChIS5YsUXR0tDp27Kg33nhDNput2pOtqfHjx9fZtgAAQP1VrVNdu3fvVl5ennr16iVPT095enpq8+bNevnll+Xp6elyHc8f5ebmauLEiRoxYoRKSko0ZcqUy5p0y5YtZbfbK1wcnZubqzZt2lzWugEAQMNVrfAZMmSIvvrqK+3du9f51bt3b913333au3ev7HZ7hdfk5+dryJAh6tatm959911lZmYqPT39su6k7OXlpYiICGVmZjqXORwOZWZmqn///jVeLwAAaNiqdaqrWbNm6tGjh8uya665Ri1atKiwXPo9RuLi4hQcHKz09HR5enoqLCxMGRkZiomJUdu2bSs9+nPq1Cl9++23zsdHjhzR3r171bx5cwUFBUmSkpKSFB8fr969e6tv376aO3euiouLlZCQUJ1dAgAABqn2xc3V4eHhoeeee05RUVHy8vJyLg8PD9f69evVqlWrSl+3a9cuDR482Pk4KSlJkhQfH6/FixdLku6++26dOHFC06dPV05Ojnr27Km1a9dWuOAZAACgnM2yLMvdk6hPyj/VVVBQIF9fX3dPp06FTF3l7imgDh2dNdzdUwCAWnOp378b9M/qAgAA+CPCBwAAGIPwAQAAxiB8AACAMQgfAABgDMIHAAAYg/ABAADGIHwAAIAxCB8AAGAMwgcAABiD8AEAAMYgfAAAgDEIHwAAYAzCBwAAGIPwAQAAxiB8AACAMQgfAABgDMIHAAAYg/ABAADGIHwAAIAxCB8AAGAMwgcAABiD8AEAAMYgfAAAgDEIHwAAYAzCBwAAGIPwAQAAxiB8AACAMQgfAABgDMIHAAAYg/ABAADGIHwAAIAxCB8AAGAMwgcAABiD8AEAAMYgfAAAgDEIHwAAYAzCBwAAGIPwAQAAxiB8AACAMQgfAABgDMIHAAAYg/ABAADGIHwAAIAxCB8AAGAMwgcAABiD8AEAAMYgfAAAgDEIHwAAYAzCBwAAGKPBh09JSYmCg4OVnJzs7qkAAAA3a/DhM3PmTPXr18/d0wAAAPVAgw6fQ4cOaf/+/YqLi3P3VAAAQD1Q7fBZsGCBbrjhBvn6+srX11f9+/fXmjVranVSW7Zs0YgRIxQYGCibzab33nuv0nGpqakKCQmRj4+PIiMjtWPHDpfnk5OTlZKSUqtzAwAAV69qh0+7du00a9Ys7d69W7t27VJMTIxGjhypr7/+utLx27ZtU2lpaYXl2dnZys3NrfQ1xcXFCg8PV2pqapXzSE9PV1JSkmbMmKE9e/YoPDxcw4YNU15eniTp/fffV2hoqEJDQ6u7iwAAoIGyWZZlXe5KmjdvrtmzZ+uhhx5yWe5wONSrVy916dJFy5Ytk91ulyQdOHBA0dHRSkpK0lNPPXXhCdpsWrlypUaNGuWyPDIyUn369NH8+fOd22rfvr0mT56sqVOnatq0aXr77bdlt9t16tQplZaW6oknntD06dMvuL3CwkL5+fmpoKBAvr6+1Xwnrm4hU1e5ewqoQ0dnDXf3FACg1lzq9+/LusanrKxMy5YtU3Fxsfr3719x5R4eWr16tbKysjRu3Dg5HA4dPnxYMTExGjVq1EWjpyrnzp3T7t27FRsb67Kt2NhYbd++XZKUkpKiY8eO6ejRo5ozZ44mTJhw0egBAAANm2dNXvTVV1+pf//+OnPmjJo2baqVK1cqLCys0rGBgYHasGGDoqKiNHbsWG3fvl2xsbFasGBBjSedn5+vsrIyBQQEuCwPCAjQ/v37a7TO1NRUpaamqqysrMbzAgAA9VuNwqdr167au3evCgoK9M477yg+Pl6bN2+uMn6CgoK0ZMkSRUdHq2PHjnrjjTdks9kua+LVMX78+IuOSUxMVGJiovNQGQAAaHhqdKrLy8tLnTt3VkREhFJSUhQeHq6XXnqpyvG5ubmaOHGiRowYoZKSEk2ZMqXGE5akli1bym63V7g4Ojc3V23atLmsdQMAgIarVu7j43A4dPbs2Uqfy8/P15AhQ9StWze9++67yszMVHp6+mXdSdnLy0sRERHKzMx0mUNmZmal1xoBAABINTjVNW3aNMXFxSkoKEhFRUVaunSpNm3apHXr1lUY63A4FBcXp+DgYKWnp8vT01NhYWHKyMhQTEyM2rZtW+nRn1OnTunbb791Pj5y5Ij27t2r5s2bKygoSJKUlJSk+Ph49e7dW3379tXcuXNVXFyshISE6u4SAAAwRLXDJy8vT+PGjdPPP/8sPz8/3XDDDVq3bp2GDh1aYayHh4eee+45RUVFycvLy7k8PDxc69evV6tWrSrdxq5duzR48GDn46SkJElSfHy8Fi9eLEm6++67deLECU2fPl05OTnq2bOn1q5dW+GCZwAAgHK1ch+fhoT7+MAU3McHQENSJ/fxAQAAuJoQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYzT48CkpKVFwcLCSk5PdPRUAAOBmDT58Zs6cqX79+rl7GgAAoB5o0OFz6NAh7d+/X3Fxce6eCgAAqAeqHT4pKSnq06ePmjVrptatW2vUqFE6cOBArU5qy5YtGjFihAIDA2Wz2fTee+9VOi41NVUhISHy8fFRZGSkduzY4fJ8cnKyUlJSanVuAADg6lXt8Nm8ebMSExP12WefKSMjQ6Wlpbr55ptVXFxc6fht27aptLS0wvLs7Gzl5uZW+pri4mKFh4crNTW1ynmkp6crKSlJM2bM0J49exQeHq5hw4YpLy9PkvT+++8rNDRUoaGh1d1FAADQQNksy7IuZwUnTpxQ69attXnzZg0cONDlOYfDoV69eqlLly5atmyZ7Ha7JOnAgQOKjo5WUlKSnnrqqQtP0GbTypUrNWrUKJflkZGR6tOnj+bPn+/cVvv27TV58mRNnTpV06ZN09tvvy273a5Tp06ptLRUTzzxhKZPn37B7RUWFsrPz08FBQXy9fWt5rtxdQuZusrdU0AdOjpruLunAAC15lK/f1/2NT4FBQWSpObNm1dcuYeHVq9eraysLI0bN04Oh0OHDx9WTEyMRo0addHoqcq5c+e0e/duxcbGumwrNjZW27dvl/T7Kbljx47p6NGjmjNnjiZMmHDB6ElNTVVYWJj69OlTozkBAID677LCx+Fw6PHHH9eAAQPUo0ePSscEBgZqw4YN2rp1q8aOHauYmBjFxsZqwYIFNd5ufn6+ysrKFBAQ4LI8ICBAOTk5NVpnYmKisrOztXPnzhrPCwAA1G+el/PixMRE7du3T1u3br3guKCgIC1ZskTR0dHq2LGj3njjDdlstsvZdLWMHz++zrYFAADqrxof8Zk0aZI++ugjbdy4Ue3atbvg2NzcXE2cOFEjRoxQSUmJpkyZUtPNSpJatmwpu91e4eLo3NxctWnT5rLWDQAAGq5qh49lWZo0aZJWrlypDRs2qEOHDhccn5+fryFDhqhbt2569913lZmZqfT09Mu6k7KXl5ciIiKUmZnpXOZwOJSZman+/fvXeL0AAKBhq/aprsTERC1dulTvv/++mjVr5rymxs/PT40bN3YZ63A4FBcXp+DgYKWnp8vT01NhYWHKyMhQTEyM2rZtW+nRn1OnTunbb791Pj5y5Ij27t2r5s2bKygoSJKUlJSk+Ph49e7dW3379tXcuXNVXFyshISE6u4SAAAwRLU/zl7VtTlpaWmVXkuTkZGhqKgo+fj4uCzPyspSq1atKj1NtmnTJg0ePLjC8vj4eC1evNj5eP78+Zo9e7ZycnLUs2dPvfzyy4qMjKzO7lTAx9lhCj7ODqAhudTv35d9H5+GhvCBKQgfAA1Jnd3HBwAA4GpB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIxB+AAAAGMQPgAAwBiEDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAYhA8AADAG4QMAAIzh6e4JAACuvJCpq9w9BdSho7OGu3sK9RZHfAAAgDEIHwAAYAzCBwAAGIPwAQAAxiB8AACAMQgfAABgDMIHAAAYg/ABAADGIHwAAIAxCB8AAGAMwgcAABiD8AEAAMYgfAAAgDEIHwAAYAzCBwAAGMPT3ROobyzLkiQVFha6eSZ1z3G2xN1TQB0y8c+4yfj7bRYT/36X73P59/Gq2KyLjTDMjz/+qPbt27t7GgAAoAaOHTumdu3aVfk84XMeh8Ohn376Sc2aNZPNZnP3dHCFFRYWqn379jp27Jh8fX3dPR0AtYi/32axLEtFRUUKDAyUh0fVV/Jwqus8Hh4eFyxFNEy+vr78wwg0UPz9Noefn99Fx3BxMwAAMAbhAwAAjEH4wGje3t6aMWOGvL293T0VALWMv9+oDBc3AwAAY3DEBwAAGIPwAQAAxiB8AACAMQgfAABgDMIHAAAYg/ABABihrKzM3VNAPUD4wBjBwcFKSEjQW2+9pWPHjrl7OgDqyMGDB/XUU0/x44ggifCBQRISEnTkyBH9z//8j0JCQtS5c2dNmDBB//znP5WTk+Pu6QGoRSUlJUpLS1NUVJTCwsK0ZcsWJSUluXtaqAe4gSGMc/bsWW3btk2bN2/Wpk2b9Pnnn6u0tFShoaGKiYlRamqqu6cIoIY+++wzvf7661q+fLmCgoL0zTffaOPGjYqKinL31FBPED4w3i+//KIXXnhB8+bN06lTp7gOALgKvfDCC1q0aJEKCgp077336v7771d4eLgaNWqkL774QmFhYe6eIuoJT3dPAKhr586d0/bt27Vp0ybnEZ+2bdtq9OjRio6Odvf0ANTA008/raefflrPPvus7Ha7u6eDeowjPjDGs88+6wyd4OBgDRw4UNHR0YqOjlZgYKC7pwfgMqSkpCgtLU1nzpzRvffeqwceeEA9evTgiA8qIHxgDA8PDwUFBWnq1Km666671KJFC3dPCUAt27x5sxYtWqR33nlHnTt31tdff63NmzdrwIAB7p4a6gnCB8ZYt26dNm7cqE2bNikrK0uhoaEaNGiQ86hPq1at3D1FALWkqKhIS5cu1aJFi7R792717dtXo0eP5pNdIHxgpqKiIn3yySfavHmzNm7cqC+++EKdO3fW4MGDNX/+fHdPD0At+uqrr/TGG29o6dKlysvLc/d04GaED4xWVlamHTt26IMPPtA//vEPPtUFXKVuvfVW/fOf/5Sfn58kadasWXr44Yfl7+8vSTp58qQiIyP17bffunGWqA8IHxjF4XBo165dzlNe27ZtU3Fxsdq1a6fBgwdr8ODBio+Pd/c0AVST3W7Xzz//rNatW0uSfH19tXfvXnXs2FGSlJubq8DAQP5jAz7ODnPExcXp008/VVFRkQIDAzV48GD93//9nwYPHuz8xxHA1en8/8Pzf3pUhfCBMfz9/TV79mwNHjxYXbp0cfd0AABuwM/qgjEKCgp09913O6Nn1qxZ+vXXX53Pnzx5knt9AFcpm80mm81WYRlwPq7xgTG4BgBouDw8PBQXFydvb29J0ocffqiYmBhdc801kn7/GX1r167l7zc41QVzcA0A0HCd/6GE+++/v8KYcePG1dV0UI8RPgCAq15aWpq7p4CrBNf4wBhcAwAA4IgPjGFZlsaPH++8BuDMmTN6+OGHXa4BAAA0bFzcDGMkJCRc0jgOmQNAw0X4AAAAY3CNDwAAMAbhAwAAjEH4AAAAYxA+AADAGIQPAAAwBuEDAACMQfgAAABjED4AAMAY/w9w9icXx4DIfwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EBs\n",
    "\n",
    "y_test_bottom_init = y_test_bottom_init.loc[y_pred_variable=='Extrinsic']\n",
    "X_test_bottom_init =  X_test_bottom_init.loc[y_pred_variable=='Extrinsic',:]\n",
    "\n",
    "y_test_ecl = y_test_bottom_init.loc[y_pred_extrinsic=='EB']\n",
    "X_test_ecl =  X_test_bottom_init.loc[y_pred_extrinsic=='EB',:]\n",
    "\n",
    "letter_counts = Counter(y_test_ecl)\n",
    "print(letter_counts)\n",
    "\n",
    "\n",
    "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "df.plot(kind='bar')\n",
    "plt.yscale('log')\n",
    "plt.title('sources classified as EB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'RRAB': 7780, 'RRC': 3517, 'SR': 172, 'DSCT': 10, 'M': 3})\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'sources classified as RR')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RRs\n",
    "y_test_bottom_init = y_test_bottom.loc[y_pred=='Variable']\n",
    "X_test_bottom_init =  X_test_init.loc[y_pred=='Variable',:]\n",
    "y_test_bottom_init = y_test_bottom_init.loc[y_pred_variable=='Intrinsic']\n",
    "X_test_bottom_init =  X_test_bottom_init.loc[y_pred_variable=='Intrinsic',:]\n",
    "\n",
    "y_test_rrl = y_test_bottom_init.loc[y_pred_intrinsic=='RR']\n",
    "X_test_rrl =  X_test_bottom_init.loc[y_pred_intrinsic=='RR',:]\n",
    "\n",
    "letter_counts = Counter(y_test_rrl)\n",
    "print(letter_counts)\n",
    "\n",
    "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "df.plot(kind='bar')\n",
    "plt.yscale('log')\n",
    "plt.title('sources classified as RR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'SR': 62536, 'M': 3672, 'DSCT': 22, 'RRAB': 21, 'ROT': 4, 'RRC': 3, 'EW': 1})\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'sources classified as LPV')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#LPVs\n",
    "y_test_bottom_init = y_test_bottom.loc[y_pred=='Variable']\n",
    "X_test_bottom_init =  X_test_init.loc[y_pred=='Variable',:]\n",
    "y_test_bottom_init = y_test_bottom_init.loc[y_pred_variable=='Intrinsic']\n",
    "X_test_bottom_init =  X_test_bottom_init.loc[y_pred_variable=='Intrinsic',:]\n",
    "\n",
    "y_test_lpv = y_test_bottom_init.loc[y_pred_intrinsic=='LPV']\n",
    "X_test_lpv =  X_test_bottom_init.loc[y_pred_intrinsic=='LPV',:]\n",
    "\n",
    "letter_counts = Counter(y_test_lpv)\n",
    "print(letter_counts)\n",
    "\n",
    "\n",
    "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "df.plot(kind='bar')\n",
    "plt.yscale('log')\n",
    "plt.title('sources classified as LPV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EB model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:546: FutureWarning: The default of `sampling_strategy` will change from `'auto'` to `'all'` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `'all'` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n",
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:558: FutureWarning: The default of `replacement` will change from `False` to `True` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `True` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9762333124739258\n",
      "Balanced accuracy: 0.9854882106637373\n",
      " 'weighted' precision accuracy:0.978990\n",
      " 'weighted' recall accuracy:0.976233\n",
      " 'weighted' f1 accuracy:0.976790\n",
      "['EA' 'EW']\n"
     ]
    }
   ],
   "source": [
    "#Training EB level\n",
    "\n",
    "rf_model_ecl = RandomForestClassifier(\n",
    "            n_estimators=200, # 400\n",
    "            max_features=0.4,\n",
    "            max_depth=90, # 50\n",
    "            n_jobs=-1,\n",
    "            class_weight='balanced_subsample',\n",
    "            criterion='entropy',\n",
    "            min_samples_split=2,\n",
    "            min_samples_leaf=1)\n",
    "\n",
    "rf_model_ecl.fit(X_train_ecl, y_train_ecl)\n",
    "\n",
    "# with open(model_ecl_layer, 'rb') as file:\n",
    "#     # 使用 pickle.load() 方法读取模型\n",
    "#     rf_model_ecl = pickle.load(file)\n",
    "\n",
    "#testing ecl layer performance\n",
    "\n",
    "y_true_ecl, y_pred_ecl = y_test_ecl, rf_model_ecl.predict(X_test_ecl)\n",
    "\n",
    "y_pred_proba_ecl = rf_model_ecl.predict_proba(X_test_ecl)\n",
    "\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_true_ecl, y_pred_ecl))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_true_ecl, y_pred_ecl))\n",
    "print(\" 'weighted' precision accuracy:%f\"%(precision_score(y_true_ecl, y_pred_ecl,average=\"weighted\")))\n",
    "print(\" 'weighted' recall accuracy:%f\"%(recall_score(y_true_ecl, y_pred_ecl,average=\"weighted\")))\n",
    "print(\" 'weighted' f1 accuracy:%f\"%(f1_score(y_true_ecl, y_pred_ecl,average=\"weighted\")))\n",
    "\n",
    "classes_order_proba_ecl = rf_model_ecl.classes_\n",
    "print(classes_order_proba_ecl)\n",
    "\n",
    "features_ecl = list(X_train_ecl)\n",
    "\n",
    "with open(model_ecl_layer, 'wb') as f:\n",
    "            pickle.dump(rf_model_ecl, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13893     0]\n",
      " [ 1823 60988]]\n",
      "Normalized confusion matrix\n",
      "[[1.   0.  ]\n",
      " [0.03 0.97]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = metrics.confusion_matrix(y_true_ecl, y_pred_ecl, labels=cm_classes_ecl)\n",
    "print(cnf_matrix)\n",
    "plot_confusion_matrix(cnf_matrix,cm_classes_ecl,'plots/' + model_name + '/training_conf_matrix_ecl_level.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skew & 0.384\n",
      "PeriodLS_0 & 0.246\n",
      "MedianAbsDev & 0.096\n",
      "Gskew & 0.064\n",
      "Psi_eta_0 & 0.053\n",
      "Q31 & 0.051\n",
      "Freq1_harmonics_amplitude_0 & 0.039\n",
      "Meanvariance & 0.035\n",
      "StetsonK_AC & 0.026\n",
      "SlottedA_length & 0.005\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(rf_model_ecl, features_ecl, 'plots/' + model_name + '/training_feature_ranking_ecl_level.pdf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:546: FutureWarning: The default of `sampling_strategy` will change from `'auto'` to `'all'` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `'all'` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n",
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:558: FutureWarning: The default of `replacement` will change from `False` to `True` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `True` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9757010973697962\n",
      "Balanced accuracy: 0.39568280544077866\n",
      " 'weighted' precision accuracy:0.959988\n",
      " 'weighted' recall accuracy:0.975701\n",
      " 'weighted' f1 accuracy:0.967768\n",
      "['RRAB' 'RRC']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "#Training RR level\n",
    "\n",
    "rf_model_rrl = RandomForestClassifier(\n",
    "            n_estimators=400,\n",
    "            max_features=0.4,\n",
    "            max_depth=10,\n",
    "            n_jobs=-1,\n",
    "            class_weight='balanced_subsample',\n",
    "            criterion='entropy',\n",
    "            min_samples_split=2,\n",
    "            min_samples_leaf=1)\n",
    "\n",
    "rf_model_rrl.fit(X_train_rrl, y_train_rrl)\n",
    "\n",
    "# with open(model_rrl_layer, 'rb') as file:\n",
    "#     # 使用 pickle.load() 方法读取模型\n",
    "#     rf_model_rrl = pickle.load(file)\n",
    "\n",
    "#testing rrl layer performance\n",
    "\n",
    "y_true_rrl, y_pred_rrl = y_test_rrl, rf_model_rrl.predict(X_test_rrl)\n",
    "\n",
    "y_pred_proba_rrl = rf_model_rrl.predict_proba(X_test_rrl)\n",
    "\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_true_rrl, y_pred_rrl))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_true_rrl, y_pred_rrl))\n",
    "print(\" 'weighted' precision accuracy:%f\"%(precision_score(y_true_rrl, y_pred_rrl,average=\"weighted\")))\n",
    "print(\" 'weighted' recall accuracy:%f\"%(recall_score(y_true_rrl, y_pred_rrl,average=\"weighted\")))\n",
    "print(\" 'weighted' f1 accuracy:%f\"%(f1_score(y_true_rrl, y_pred_rrl,average=\"weighted\")))\n",
    "\n",
    "\n",
    "classes_order_proba_rrl = rf_model_rrl.classes_\n",
    "print(classes_order_proba_rrl)\n",
    "\n",
    "features_ecl = list(X_train_rrl)\n",
    "\n",
    "with open(model_rrl_layer, 'wb') as f:\n",
    "            pickle.dump(rf_model_rrl, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7747   33]\n",
      " [  61 3456]]\n",
      "Normalized confusion matrix\n",
      "[[1.   0.  ]\n",
      " [0.02 0.98]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = metrics.confusion_matrix(y_true_rrl, y_pred_rrl, labels=cm_classes_rrl)\n",
    "print(cnf_matrix)\n",
    "plot_confusion_matrix(cnf_matrix,cm_classes_rrl,'plots/' + model_name + '/training_conf_matrix_rrl_level.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skew & 0.384\n",
      "PeriodLS_0 & 0.246\n",
      "MedianAbsDev & 0.096\n",
      "Gskew & 0.064\n",
      "Psi_eta_0 & 0.053\n",
      "Q31 & 0.051\n",
      "Freq1_harmonics_amplitude_0 & 0.039\n",
      "Meanvariance & 0.035\n",
      "StetsonK_AC & 0.026\n",
      "SlottedA_length & 0.005\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(rf_model_ecl, features_ecl, 'plots/' + model_name + '/training_feature_ranking_ecl_level.pdf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LPV model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:546: FutureWarning: The default of `sampling_strategy` will change from `'auto'` to `'all'` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `'all'` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n",
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\imblearn\\ensemble\\_forest.py:558: FutureWarning: The default of `replacement` will change from `False` to `True` in version 0.13. This change will follow the implementation proposed in the original paper. Set to `True` to silence this warning and adopt the future behaviour.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9880469068352978\n",
      "Balanced accuracy: 0.2840215464463897\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\Anaconda3\\envs\\alercebroker\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 'weighted' precision accuracy:0.989151\n",
      " 'weighted' recall accuracy:0.988047\n",
      " 'weighted' f1 accuracy:0.988143\n",
      "['M' 'SR']\n"
     ]
    }
   ],
   "source": [
    "#Training LPV level\n",
    "\n",
    "rf_model_lpv = RandomForestClassifier(\n",
    "            n_estimators=400,\n",
    "            max_features=0.4,\n",
    "            max_depth=70,\n",
    "            n_jobs=-1,\n",
    "            class_weight='balanced_subsample',\n",
    "            criterion='entropy',\n",
    "            min_samples_split=2,\n",
    "            min_samples_leaf=1)\n",
    "\n",
    "rf_model_lpv.fit(X_train_lpv, y_train_lpv)\n",
    "\n",
    "# with open(model_lpv_layer, 'rb') as file:\n",
    "#     # 使用 pickle.load() 方法读取模型\n",
    "#     rf_model_lpv = pickle.load(file)\n",
    "\n",
    "#testing lpv layer performance\n",
    "\n",
    "y_true_lpv, y_pred_lpv = y_test_lpv, rf_model_lpv.predict(X_test_lpv)\n",
    "\n",
    "y_pred_proba_lpv = rf_model_lpv.predict_proba(X_test_lpv)\n",
    "\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_true_lpv, y_pred_lpv))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_true_lpv, y_pred_lpv))\n",
    "print(\" 'weighted' precision accuracy:%f\"%(precision_score(y_true_lpv, y_pred_lpv,average=\"weighted\")))\n",
    "print(\" 'weighted' recall accuracy:%f\"%(recall_score(y_true_lpv, y_pred_lpv,average=\"weighted\")))\n",
    "print(\" 'weighted' f1 accuracy:%f\"%(f1_score(y_true_lpv, y_pred_lpv,average=\"weighted\")))\n",
    "\n",
    "classes_order_proba_lpv = rf_model_lpv.classes_\n",
    "print(classes_order_proba_lpv)\n",
    "\n",
    "features_lpv = list(X_train_lpv)\n",
    "\n",
    "with open(model_lpv_layer, 'wb') as f:\n",
    "            pickle.dump(rf_model_lpv, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3672     0]\n",
      " [  741 61795]]\n",
      "Normalized confusion matrix\n",
      "[[1.   0.  ]\n",
      " [0.01 0.99]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = metrics.confusion_matrix(y_true_lpv, y_pred_lpv, labels=cm_classes_lpv)\n",
    "print(cnf_matrix)\n",
    "plot_confusion_matrix(cnf_matrix,cm_classes_lpv,'plots/' + model_name + '/training_conf_matrix_lpv_level.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meanvariance & 0.346\n",
      "Q31 & 0.270\n",
      "Freq1_harmonics_amplitude_0 & 0.211\n",
      "MedianAbsDev & 0.081\n",
      "Psi_eta_0 & 0.046\n",
      "Gskew & 0.014\n",
      "SlottedA_length & 0.012\n",
      "PeriodLS_0 & 0.007\n",
      "Skew & 0.007\n",
      "StetsonK_AC & 0.005\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(rf_model_lpv, features_lpv, 'plots/' + model_name + '/training_feature_ranking_lpv_level.pdf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[76704  3285     0     0     1     0  1304]\n",
      " [    0 22975     0     0     4     1   650]\n",
      " [    0     0 11297    67    24    69    39]\n",
      " [    0     0     0   470     0     0     0]\n",
      " [    0     0   175   219 66208   246  5391]\n",
      " [    0     0    10     1    22  3067   578]\n",
      " [    0     0     0     0     0     0 26554]]\n",
      "Normalized confusion matrix\n",
      "[[0.94 0.04 0.   0.   0.   0.   0.02]\n",
      " [0.   0.97 0.   0.   0.   0.   0.03]\n",
      " [0.   0.   0.98 0.01 0.   0.01 0.  ]\n",
      " [0.   0.   0.   1.   0.   0.   0.  ]\n",
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      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generating confusion matrix for 7 classes\n",
    "\n",
    "y_true_variable2 = np.concatenate((y_true_intrinsic,y_true_extrinsic,y_test_variable2.loc[y_pred!='Variable']))\n",
    "y_pred_variable2 = np.concatenate((y_pred_intrinsic,y_pred_extrinsic,y_pred[np.where(y_pred!='Variable')]))\n",
    "\n",
    "cnf_matrix = metrics.confusion_matrix(y_true_variable2, y_pred_variable2,labels=cm_classes_variable2)\n",
    "print(cnf_matrix)\n",
    "plot_confusion_matrix_all(cnf_matrix,cm_classes_variable2, 'plots/' + model_name + '/training_conf_matrix_variable2_level.pdf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9449036063839972\n",
      "Balanced accuracy: 0.9498427494724354\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          EB       0.62      1.00      0.77       470\n",
      "         ROT       0.91      0.83      0.87      3678\n",
      "          RR       1.00      0.94      0.97     81294\n",
      "         CEP       1.00      0.92      0.96     72239\n",
      "         LPV       0.77      1.00      0.87     26554\n",
      "        DSCT       0.87      0.97      0.92     23630\n",
      "     Non-var       0.98      0.98      0.98     11496\n",
      "\n",
      "    accuracy                           0.94    219361\n",
      "   macro avg       0.88      0.95      0.91    219361\n",
      "weighted avg       0.96      0.94      0.95    219361\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\", metrics.accuracy_score(y_true_variable2, y_pred_variable2))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_true_variable2, y_pred_variable2))\n",
    "print(metrics.classification_report(y_true_variable2, y_pred_variable2, target_names=cm_classes_variable2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13893     0     0     0     0     0     0     0     0   660]\n",
      " [ 1823 60988     0     0     0     0     0     1     0   644]\n",
      " [    0     0 22975     0     0     0     0     4     1  1300]\n",
      " [    0     0     0  7747    33     0     1    20     0    32]\n",
      " [    0     0     0    61  3456     0     0     3     0     7]\n",
      " [    0     0     0     0     0   470     0     0     0     0]\n",
      " [    0     0     0     3     0     0  3672     0     0     1]\n",
      " [    0     0     0   131    41     0   741 61795     0  5390]\n",
      " [    0     0     0     4     6     1     0    22  3067  1156]\n",
      " [    0     0     0     0     0     0     0     0     0 53108]]\n",
      "Normalized confusion matrix\n",
      "[[0.95 0.   0.   0.   0.   0.   0.   0.   0.   0.05]\n",
      " [0.03 0.96 0.   0.   0.   0.   0.   0.   0.   0.01]\n",
      " [0.   0.   0.95 0.   0.   0.   0.   0.   0.   0.05]\n",
      " [0.   0.   0.   0.99 0.   0.   0.   0.   0.   0.  ]\n",
      " [0.   0.   0.   0.02 0.98 0.   0.   0.   0.   0.  ]\n",
      " [0.   0.   0.   0.   0.   1.   0.   0.   0.   0.  ]\n",
      " [0.   0.   0.   0.   0.   0.   1.   0.   0.   0.  ]\n",
      " [0.   0.   0.   0.   0.   0.   0.01 0.91 0.   0.08]\n",
      " [0.   0.   0.   0.   0.   0.   0.   0.01 0.72 0.27]\n",
      " [0.   0.   0.   0.   0.   0.   0.   0.   0.   1.  ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generating confusion matrix for 10classes\n",
    "\n",
    "y_true_bottom = np.concatenate((y_true_variable2, y_true_ecl,y_true_rrl,y_true_lpv,y_test_bottom.loc[y_pred!='Variable']))\n",
    "y_pred_bottom = np.concatenate((y_pred_variable2, y_pred_ecl,y_pred_rrl,y_pred_lpv,y_pred[np.where(y_pred!='Variable')]))\n",
    "\n",
    "y_true_bottom =pd.Series(y_true_bottom)\n",
    "\n",
    "cnf_matrix = metrics.confusion_matrix(y_true_bottom, y_pred_bottom,labels=cm_classes_original)\n",
    "print(cnf_matrix)\n",
    "plot_confusion_matrix_all(cnf_matrix,cm_classes_original, 'plots/' + model_name + '/training_conf_matrix_bottom_level.pdf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9438139507545515\n",
      "Balanced accuracy: 0.9457760845569998\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          EA       0.88      0.95      0.92     14553\n",
      "          EW       1.00      0.96      0.98     63456\n",
      "         ROT       0.87      0.95      0.91     24284\n",
      "        RRAB       0.97      0.99      0.98      7833\n",
      "         RRC       0.98      0.98      0.98      3527\n",
      "         CEP       0.62      1.00      0.77       470\n",
      "           M       0.83      1.00      0.91      3676\n",
      "          SR       1.00      0.91      0.95     68098\n",
      "        DSCT       0.91      0.72      0.80      4288\n",
      "     Non-var       0.77      1.00      0.87     53108\n",
      "\n",
      "   micro avg       0.91      0.95      0.93    243293\n",
      "   macro avg       0.88      0.95      0.91    243293\n",
      "weighted avg       0.92      0.95      0.93    243293\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\", metrics.accuracy_score(y_true_bottom, y_pred_bottom))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_true_bottom, y_pred_bottom))\n",
    "\n",
    "print(metrics.classification_report(y_true_bottom, y_pred_bottom,labels=cm_classes_original))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}