{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d683865-b1cc-4826-973b-f31eb87d15d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import lightgbm as lgb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efc65ac4-fcbf-4fb7-8251-353017129972",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('..\\params.yaml', 'r') as fd:\n",
    "    params = yaml.safe_load(fd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a782515-f51f-4cbc-bb43-6ae00cb73a1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickledir = params['prepare-step2']['pickle-dir']\n",
    "with open(pickledir, 'rb') as fd:\n",
    "    each_faction_dataset = pickle.load(fd)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "828d229a-988f-46fa-a6c9-b1b4c79e0ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "feats = each_faction_dataset['auren']['features']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c48ee433-73ec-479c-a539-2bbd0ac28da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = np.array(each_faction_dataset['auren']['vp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e49647c2-3e2c-4813-a439-211a31ad5547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "58631132-ccb0-4b0d-91af-1c5ed98b15c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11610,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "8eb2d842-f641-4154-b685-5140547461c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "faction = 'swarmlings'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d1586415-2be1-4a00-acd8-7e29c6ac05db",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xdata = each_faction_dataset[faction]['features']\n",
    "Xdata.drop(['Unnamed: 0', 'game'], axis=1)\n",
    "traindata = lgb.Dataset(Xdata,\n",
    "                        label=np.array(each_faction_dataset[faction]['vp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5071ec0c-8728-43a4-93aa-76584c6a397a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27967, 71)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xdata = each_faction_dataset[faction]['features']\n",
    "Xdata = Xdata.drop(['Unnamed: 0', 'game'], axis=1)\n",
    "Xdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "de0efbdb-1e7b-4f45-b0c3-fab4f81a39cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['r1_SCORE1', 'r1_SCORE2', 'r1_SCORE3', 'r1_SCORE4', 'r1_SCORE5',\n",
       "       'r1_SCORE6', 'r1_SCORE7', 'r1_SCORE8', 'r1_SCORE9', 'r2_SCORE1',\n",
       "       'r2_SCORE2', 'r2_SCORE3', 'r2_SCORE4', 'r2_SCORE5', 'r2_SCORE6',\n",
       "       'r2_SCORE7', 'r2_SCORE8', 'r2_SCORE9', 'r3_SCORE1', 'r3_SCORE2',\n",
       "       'r3_SCORE3', 'r3_SCORE4', 'r3_SCORE5', 'r3_SCORE6', 'r3_SCORE7',\n",
       "       'r3_SCORE8', 'r3_SCORE9', 'r4_SCORE1', 'r4_SCORE2', 'r4_SCORE3',\n",
       "       'r4_SCORE4', 'r4_SCORE5', 'r4_SCORE6', 'r4_SCORE7', 'r4_SCORE8',\n",
       "       'r4_SCORE9', 'r5_SCORE1', 'r5_SCORE2', 'r5_SCORE3', 'r5_SCORE4',\n",
       "       'r5_SCORE5', 'r5_SCORE6', 'r5_SCORE7', 'r5_SCORE8', 'r5_SCORE9',\n",
       "       'r6_SCORE1', 'r6_SCORE2', 'r6_SCORE3', 'r6_SCORE4', 'r6_SCORE5',\n",
       "       'r6_SCORE6', 'r6_SCORE7', 'r6_SCORE8', 'r6_SCORE9', 'BON1', 'BON2',\n",
       "       'BON3', 'BON4', 'BON5', 'BON6', 'BON7', 'BON8', 'BON9', 'BON10',\n",
       "       '2players', '3players', '4players', '5players', 'map1', 'map2', 'map3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xdata.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "2be0344e-fed9-4948-ad08-09a149e2879c",
   "metadata": {},
   "outputs": [],
   "source": [
    "traindata = lgb.Dataset(Xdata,\n",
    "                        label=np.array(each_faction_dataset[faction]['vp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "fe7a3155-d267-46f8-a43b-3f3118ae9603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27967"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.array(each_faction_dataset[faction]['vp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f09074d1-9b69-4dd7-856c-5442216a60e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004396 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 138\n",
      "[LightGBM] [Info] Number of data points in the train set: 27967, number of used features: 69\n",
      "[LightGBM] [Info] Start training from score 101.014803\n"
     ]
    }
   ],
   "source": [
    "num_round=500\n",
    "param = {'num_leaves': 300, 'objective': 'regression'}\n",
    "bst = lgb.train(param, traindata, num_round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "86325e1c-1589-446a-a228-54b3a25f707f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27967, 71)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdata = Xdata.iloc[:, :]\n",
    "testdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "292895d5-cf79-453f-942f-37aea70e3f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = bst.predict(Xdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "2d81210a-c6a7-4c2e-b7b8-d482769f9675",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ffd227ba-1852-473c-8e9a-c781b0bf908d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.046741950738864"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE = metrics.mean_absolute_error(ypred, np.array(each_faction_dataset[faction]['vp']))\n",
    "MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "c4fd6197-784b-41f6-bd79-4ad30efb5df8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "line = list(range(250))\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_aspect('equal', adjustable='box')\n",
    "ax.set_title(f'{faction} residuals')\n",
    "plt.plot(ypred, np.array(each_faction_dataset[faction]['vp']), 'bo', line, line, 'r--')\n",
    "plt.xlabel('y_pred')\n",
    "plt.ylabel('y_real')\n",
    "plt.xlim([0, 250])\n",
    "plt.ylim([0, 250])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "466b46dc-fadd-4290-9eca-7c9d3a3d3b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals = np.array(ypred) - np.array(each_faction_dataset[faction]['vp'])\n",
    "avgres = np.mean(residuals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "c73d5a71-188e-4b18-bd3c-b47cb3650d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qwYIF6tevn2bMmGHenbRnz55StQkJCfrzn/+s0aNHa/DgwTpw4IBmzZqla6+91qPuwm3zo0eP1j333KODBw/qv/7rvxQWFqZ9+/Zd9nj79++vyMhIderUSddee60OHDigl19+Wc2aNVPLli2vagyv5Omnn9ahQ4fUs2dPNWnSRAUFBXrllVfk4+NjBtgbbrhBfn5+WrJkiVq3bq1rrrlG4eHhCg8P16xZs3T//fcrLi5ODz/8sNxut55//nkVFBRo5syZ5vs888wz6tevn/r06aPHHntM586d0/PPP69rrrlGJ06cKFdfa+PPF2oGQgwso379+urcubMWLVqk7OxslZSUqGnTppo8ebIef/xxj9pevXrp1Vdf1e233+4xfd2rVy+98cYbHqeSpJ8vRlywYIGee+459e/fX9ddd51GjhypkJAQjRgx4jc5vspWr149/c///I8mTpyomTNnqri4WN26ddPixYvVpUsXNWzYUFLFxrUi5s2bpy5dumjevHmaO3euzp8/r/DwcHXr1k233XabJKlly5Zq2LChZs2apSNHjsjX11etWrVSamqqhg0bdsl9O51ObdiwQY899pgeffRRNWjQQHfffbfmzJmju+66y6M2Pj5eR44c0euvv66FCxcqMjJSr732mqZPn+5R9+CDDyovL0+vv/66FixYoOuvv15TpkzRoUOHStVerEePHnr33XfNW6SdTqd69+6tp556Sj4+Plc5gpfXuXNnbd26VZMnT9axY8fUsGFDderUSevWrdPNN98sSWrQoIEWLFig6dOnKyYmRiUlJZo2bZqSk5MVHx8vf39/paSkaOjQofLy8lKXLl20fv16de3a1Xyfvn376t1339XTTz+toUOHyul0avTo0Tpy5Ei5Z1Jq488XagabYVz0SUMAarULnw/y2WefefyxAsqrpKTEvIPoww8/rO7uoA5jJgaoxd5++20dPnxYbdq0Ub169ZSZmannn39ed9xxBwEG5TZixAj17t1bYWFhys3N1euvv67du3eX+xOJgapCiAFqsYCAAC1btkwzZszQqVOnFBYWpuHDh2vGjBnV3TVYyMmTJzVp0iQdO3ZMPj4+6tChg9auXVvqtCzwW+N0EgAAsCRusQYAAJZEiAEAAJZEiAEAAJZUay/sPX/+vI4cOaKAgABLfEQ8AACQDMPQyZMnFR4e7vGA0rLU2hBz5MgRRUREVHc3AADAVTh48KCaNGly2ZpaG2ICAgIk/TwIgYGB1dwbAABQHoWFhYqIiDD/jl9OrQ0xF04hBQYGEmIAALCY8lwKwoW9AADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkryruwMAUFWaT1lTqi17Zr9q6AmAqsBMDAAAsCRCDAAAsCROJwGoNco6fXSlGk4vAdbFTAwAALAkQgwAALAkQgwAALAkQgwAALAkQgwAALAkQgwAALAkQgwAALCkCoWYlJQU3XrrrQoICFBISIgGDhyovXv3etQYhqHk5GSFh4fLz89P0dHR2rVrl0eN2+3WuHHjFBwcLH9/fw0YMECHDh3yqMnPz1dCQoIcDoccDocSEhJUUFBwlYcJAABqmwqFmA0bNmjMmDHKzMxUenq6zp49q5iYGJ06dcqsmTVrll566SXNmTNHW7ZskdPpVO/evXXy5EmzJjExUStXrtSyZcu0ceNGFRUVKS4uTufOnTNr4uPjlZWVpbS0NKWlpSkrK0sJCQmVcMgAAKA2sBmGYVzti48dO6aQkBBt2LBBd9xxhwzDUHh4uBITEzV58mRJP8+6hIaG6rnnntPDDz8sl8ula6+9VosWLdLQoUMlSUeOHFFERITWrl2rPn36aPfu3brpppuUmZmpzp07S5IyMzMVFRWlPXv2qFWrVlfsW2FhoRwOh1wulwIDA6/2EAFYSHk+sfdifGIvULNU5O/3r7omxuVySZKCgoIkSfv371dubq5iYmLMGrvdru7du2vTpk2SpG3btqmkpMSjJjw8XJGRkWbN5s2b5XA4zAAjSV26dJHD4TBrLuZ2u1VYWOixAACA2uuqQ4xhGJowYYJuv/12RUZGSpJyc3MlSaGhoR61oaGh5rbc3Fz5+vqqUaNGl60JCQkp9Z4hISFmzcVSUlLM62ccDociIiKu9tAAAIAFXHWIGTt2rL7++mu9/fbbpbbZbDaPdcMwSrVd7OKasuovt5+kpCS5XC5zOXjwYHkOAwAAWNRVhZhx48Zp9erVWr9+vZo0aWK2O51OSSo1W5KXl2fOzjidThUXFys/P/+yNUePHi31vseOHSs1y3OB3W5XYGCgxwIAAGqvCoUYwzA0duxYrVixQuvWrVOLFi08trdo0UJOp1Pp6elmW3FxsTZs2KCuXbtKkjp27CgfHx+PmpycHO3cudOsiYqKksvl0hdffGHWfP7553K5XGYNAACo27wrUjxmzBgtXbpU7733ngICAswZF4fDIT8/P9lsNiUmJurZZ59Vy5Yt1bJlSz377LNq0KCB4uPjzdoRI0Zo4sSJaty4sYKCgjRp0iS1adNGvXr1kiS1bt1affv21ciRIzVv3jxJ0qhRoxQXF1euO5MAoLzKuqOJO5YAa6hQiHnttdckSdHR0R7tCxcu1PDhwyVJjz/+uE6fPq3Ro0crPz9fnTt31ocffqiAgACzfvbs2fL29taQIUN0+vRp9ezZU6mpqfLy8jJrlixZovHjx5t3MQ0YMEBz5sy5mmMEAAC10K/6nJiajM+JAeqeq/mcmLIwEwNUn9/sc2IAAACqCyEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYknd1dwAArkbzKWuquwsAqhkzMQAAwJIIMQAAwJIIMQAAwJIIMQAAwJIIMQAAwJIIMQAAwJIIMQAAwJIIMQAAwJIIMQAAwJIIMQAAwJIqHGI++eQT9e/fX+Hh4bLZbFq1apXHdpvNVuby/PPPmzXR0dGltt97770e+8nPz1dCQoIcDoccDocSEhJUUFBwlYcJAABqmwqHmFOnTqlt27aaM2dOmdtzcnI8lgULFshms2nw4MEedSNHjvSomzdvnsf2+Ph4ZWVlKS0tTWlpacrKylJCQkJFuwsAAGqpCj8AMjY2VrGxsZfc7nQ6Pdbfe+899ejRQ9dff71He4MGDUrVXrB7926lpaUpMzNTnTt3liS98cYbioqK0t69e9WqVauKdhsAANQyVXpNzNGjR7VmzRqNGDGi1LYlS5YoODhYN998syZNmqSTJ0+a2zZv3iyHw2EGGEnq0qWLHA6HNm3aVOZ7ud1uFRYWeiwAAKD2qvBMTEW8+eabCggI0KBBgzza77//frVo0UJOp1M7d+5UUlKSvvrqK6Wnp0uScnNzFRISUmp/ISEhys3NLfO9UlJSNH369Mo/CAAAUCNVaYhZsGCB7r//ftWvX9+jfeTIkebXkZGRatmypTp16qTt27erQ4cOkn6+QPhihmGU2S5JSUlJmjBhgrleWFioiIiIyjgMAABQA1VZiPn000+1d+9eLV++/Iq1HTp0kI+Pj/bt26cOHTrI6XTq6NGjpeqOHTum0NDQMvdht9tlt9t/db8BoPmUNR7r2TP7VVNPAFxOlV0TM3/+fHXs2FFt27a9Yu2uXbtUUlKisLAwSVJUVJRcLpe++OILs+bzzz+Xy+VS165dq6rLAADAQio8E1NUVKRvv/3WXN+/f7+ysrIUFBSkpk2bSvr5VM4777yjF198sdTrv/vuOy1ZskR//OMfFRwcrG+++UYTJ05U+/bt1a1bN0lS69at1bdvX40cOdK89XrUqFGKi4vjziQAACDpKmZitm7dqvbt26t9+/aSpAkTJqh9+/Z6+umnzZply5bJMAzdd999pV7v6+urjz76SH369FGrVq00fvx4xcTEKCMjQ15eXmbdkiVL1KZNG8XExCgmJka33HKLFi1adDXHCAAAaiGbYRhGdXeiKhQWFsrhcMjlcikwMLC6uwOgkl183UpV4poY4LdTkb/fPDsJAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYUpU9xRoAKtNv+Qm9AKyBmRgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJhBgAAGBJ3tXdAQCo6ZpPWVOqLXtmv2roCYBfYiYGAABYEiEGAABYEiEGAABYEiEGAABYUoVDzCeffKL+/fsrPDxcNptNq1at8tg+fPhw2Ww2j6VLly4eNW63W+PGjVNwcLD8/f01YMAAHTp0yKMmPz9fCQkJcjgccjgcSkhIUEFBwVUcIgAAqI0qHGJOnTqltm3bas6cOZes6du3r3Jycsxl7dq1HtsTExO1cuVKLVu2TBs3blRRUZHi4uJ07tw5syY+Pl5ZWVlKS0tTWlqasrKylJCQUNHuAgCAWqrCt1jHxsYqNjb2sjV2u11Op7PMbS6XS/Pnz9eiRYvUq1cvSdLixYsVERGhjIwM9enTR7t371ZaWpoyMzPVuXNnSdIbb7yhqKgo7d27V61atapotwEAQC1TJdfEfPzxxwoJCdGNN96okSNHKi8vz9y2bds2lZSUKCYmxmwLDw9XZGSkNm3aJEnavHmzHA6HGWAkqUuXLnI4HGbNxdxutwoLCz0WAABQe1V6iImNjdWSJUu0bt06vfjii9qyZYvuvPNOud1uSVJubq58fX3VqFEjj9eFhoYqNzfXrAkJCSm175CQELPmYikpKeb1Mw6HQxEREZV8ZAAAoCap9E/sHTp0qPl1ZGSkOnXqpGbNmmnNmjUaNGjQJV9nGIZsNpu5/suvL1XzS0lJSZowYYK5XlhYSJABAKAWq/JbrMPCwtSsWTPt27dPkuR0OlVcXKz8/HyPury8PIWGhpo1R48eLbWvY8eOmTUXs9vtCgwM9FgAAEDtVeUh5vjx4zp48KDCwsIkSR07dpSPj4/S09PNmpycHO3cuVNdu3aVJEVFRcnlcumLL74waz7//HO5XC6zBgAA1G0VPp1UVFSkb7/91lzfv3+/srKyFBQUpKCgICUnJ2vw4MEKCwtTdna2pk6dquDgYN19992SJIfDoREjRmjixIlq3LixgoKCNGnSJLVp08a8W6l169bq27evRo4cqXnz5kmSRo0apbi4OO5MAgAAkq4ixGzdulU9evQw1y9chzJs2DC99tpr2rFjh9566y0VFBQoLCxMPXr00PLlyxUQEGC+Zvbs2fL29taQIUN0+vRp9ezZU6mpqfLy8jJrlixZovHjx5t3MQ0YMOCyn00DAADqFpthGEZ1d6IqFBYWyuFwyOVycX0MUAs0n7KmurvgIXtmv+ruAlArVeTvN89OAgAAlkSIAQAAlkSIAQAAlkSIAQAAlkSIAQAAllTpjx0AgF+rpt2JBKBmYiYGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYknd1dwAArKj5lDUe69kz+1VTT4C6q8IzMZ988on69++v8PBw2Ww2rVq1ytxWUlKiyZMnq02bNvL391d4eLgeeOABHTlyxGMf0dHRstlsHsu9997rUZOfn6+EhAQ5HA45HA4lJCSooKDgKg8TAADUNhUOMadOnVLbtm01Z86cUtt++uknbd++XU899ZS2b9+uFStW6D//+Y8GDBhQqnbkyJHKyckxl3nz5nlsj4+PV1ZWltLS0pSWlqasrCwlJGOEpnEAACAASURBVCRUtLsAAKCWqvDppNjYWMXGxpa5zeFwKD093aPt1Vdf1W233aYffvhBTZs2NdsbNGggp9NZ5n52796ttLQ0ZWZmqnPnzpKkN954Q1FRUdq7d69atWpV0W4DAIBapsov7HW5XLLZbGrYsKFH+5IlSxQcHKybb75ZkyZN0smTJ81tmzdvlsPhMAOMJHXp0kUOh0ObNm0q833cbrcKCws9FgAAUHtV6YW9Z86c0ZQpUxQfH6/AwECz/f7771eLFi3kdDq1c+dOJSUl6auvvjJncXJzcxUSElJqfyEhIcrNzS3zvVJSUjR9+vSqORAAAFDjVFmIKSkp0b333qvz589r7ty5HttGjhxpfh0ZGamWLVuqU6dO2r59uzp06CBJstlspfZpGEaZ7ZKUlJSkCRMmmOuFhYWKiIiojEMBAAA1UJWEmJKSEg0ZMkT79+/XunXrPGZhytKhQwf5+Pho37596tChg5xOp44ePVqq7tixYwoNDS1zH3a7XXa7vVL6DwAAar5KvybmQoDZt2+fMjIy1Lhx4yu+ZteuXSopKVFYWJgkKSoqSi6XS1988YVZ8/nnn8vlcqlr166V3WUAAGBBFZ6JKSoq0rfffmuu79+/X1lZWQoKClJ4eLjuuecebd++Xe+//77OnTtnXsMSFBQkX19ffffdd1qyZIn++Mc/Kjg4WN98840mTpyo9u3bq1u3bpKk1q1bq2/fvho5cqR56/WoUaMUFxfHnUkAAEDSVYSYrVu3qkePHub6hetQhg0bpuTkZK1evVqS1K5dO4/XrV+/XtHR0fL19dVHH32kV155RUVFRYqIiFC/fv00bdo0eXl5mfVLlizR+PHjFRMTI0kaMGBAmZ9NAwAA6qYKh5jo6GgZhnHJ7ZfbJkkRERHasGHDFd8nKChIixcvrmj3AABAHcEDIAEAgCURYgAAgCURYgAAgCURYgAAgCURYgAAgCURYgAAgCVV6QMgAaA8mk9ZU91dAGBBzMQAAABLIsQAAABLIsQAAABLIsQAAABLIsQAAABLIsQAAABLIsQAAABLIsQAAABL4sPuAKASlPWBfdkz+1VDT4C6g5kYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSRUOMZ988on69++v8PBw2Ww2rVq1ymO7YRhKTk5WeHi4/Pz8FB0drV27dnnUuN1ujRs3TsHBwfL399eAAQN06NAhj5r8/HwlJCTI4XDI4XAoISFBBQUFV3GIAACgNqpwiDl16pTatm2rOXPmlLl91qxZeumllzRnzhxt2bJFTqdTvXv31smTJ82axMRErVy5UsuWLdPGjRtVVFSkuLg4nTt3zqyJj49XVlaW0tLSlJaWpqysLCUkJFzFIQIAgNrIZhiGcdUvttm0cuVKDRw4UNLPszDh4eFKTEzU5MmTJf086xIaGqrnnntODz/8sFwul6699lotWrRIQ4cOlSQdOXJEERERWrt2rfr06aPdu3frpptuUmZmpjp37ixJyszMVFRUlPbs2aNWrVpdsW+FhYVyOBxyuVwKDAy82kME8BtoPmVNdXehSmTP7FfdXQAspyJ/vyv1mpj9+/crNzdXMTExZpvdblf37t21adMmSdK2bdtUUlLiURMeHq7IyEizZvPmzXI4HGaAkaQuXbrI4XCYNRdzu90qLCz0WAAAQO1VqSEmNzdXkhQaGurRHhoaam7Lzc2Vr6+vGjVqdNmakJCQUvsPCQkxay6WkpJiXj/jcDgUERHxq48HAADUXFVyd5LNZvNYNwyjVNvFLq4pq/5y+0lKSpLL5TKXgwcPXkXPAQCAVVRqiHE6nZJUarYkLy/PnJ1xOp0qLi5Wfn7+ZWuOHj1aav/Hjh0rNctzgd1uV2BgoMcCAABqr0oNMS1atJDT6VR6errZVlxcrA0bNqhr166SpI4dO8rHx8ejJicnRzt37jRroqKi5HK59MUXX5g1n3/+uVwul1kDAADqNu+KvqCoqEjffvutub5//35lZWUpKChITZs2VWJiop599lm1bNlSLVu21LPPPqsGDRooPj5ekuRwODRixAhNnDhRjRs3VlBQkCZNmqQ2bdqoV69ekqTWrVurb9++GjlypObNmydJGjVqlOLi4sp1ZxIAAKj9Khxitm7dqh49epjrEyZMkCQNGzZMqampevzxx3X69GmNHj1a+fn56ty5sz788EMFBASYr5k9e7a8vb01ZMgQnT59Wj179lRqaqq8vLzMmiVLlmj8+PHmXUwDBgy45GfTAACAuudXfU5MTcbnxADWwefEALig2j4nBgAA4LdCiAEAAJZEiAEAAJZEiAEAAJZEiAEAAJZU4VusAQDlc/FdV9ytBFQuQgyA31RtvZ0awG+P00kAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSCDEAAMCSKj3ENG/eXDabrdQyZswYSdLw4cNLbevSpYvHPtxut8aNG6fg4GD5+/trwIABOnToUGV3FQAAWFilh5gtW7YoJyfHXNLT0yVJf/rTn8yavn37etSsXbvWYx+JiYlauXKlli1bpo0bN6qoqEhxcXE6d+5cZXcXAABYlHdl7/Daa6/1WJ85c6ZuuOEGde/e3Wyz2+1yOp1lvt7lcmn+/PlatGiRevXqJUlavHixIiIilJGRoT59+lR2lwEAgAVV6TUxxcXFWrx4sR566CHZbDaz/eOPP1ZISIhuvPFGjRw5Unl5eea2bdu2qaSkRDExMWZbeHi4IiMjtWnTpku+l9vtVmFhoccCAABqryoNMatWrVJBQYGGDx9utsXGxmrJkiVat26dXnzxRW3ZskV33nmn3G63JCk3N1e+vr5q1KiRx75CQ0OVm5t7yfdKSUmRw+Ewl4iIiCo5JgAAUDNU+umkX5o/f75iY2MVHh5utg0dOtT8OjIyUp06dVKzZs20Zs0aDRo06JL7MgzDYzbnYklJSZowYYK5XlhYSJABAKAWq7IQc+DAAWVkZGjFihWXrQsLC1OzZs20b98+SZLT6VRxcbHy8/M9ZmPy8vLUtWvXS+7HbrfLbrdXTucBoAo0n7KmVFv2zH7V0BOgdqiy00kLFy5USEiI+vW7/A/o8ePHdfDgQYWFhUmSOnbsKB8fH/OuJknKycnRzp07LxtiAABA3VIlMzHnz5/XwoULNWzYMHl7/99bFBUVKTk5WYMHD1ZYWJiys7M1depUBQcH6+6775YkORwOjRgxQhMnTlTjxo0VFBSkSZMmqU2bNubdSgAAAFUSYjIyMvTDDz/ooYce8mj38vLSjh079NZbb6mgoEBhYWHq0aOHli9froCAALNu9uzZ8vb21pAhQ3T69Gn17NlTqamp8vLyqoruAgAAC7IZhmFUdyeqQmFhoRwOh1wulwIDA6u7OwD+V1nXhdRlXBMDeKrI32+enQQAACyJEAMAACyJEAMAACyJEAMAACyJEAMAACyJEAMAACypSp+dBADcUg2gqjATAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQA6DO8Cs+o+zn4pT9XJz8is9Ud3cA/Eo8xRpAnXGunpf+HdnT/LomuPgp39kz+1VTTwDrIcQAqDOKvX00qd9fq7sbACoJp5MAAIAlMRMDoO4wDPmVuCVJp33sks1WzR0C8GswEwOgzvArcWv37Hu0e/Y9ZpgBYF2EGAAAYEmEGAAAYEmEGAAAYEmEGAAAYEncnQSg0lz8wW0AUJWYiQEAAJZU6SEmOTlZNpvNY3E6neZ2wzCUnJys8PBw+fn5KTo6Wrt27fLYh9vt1rhx4xQcHCx/f38NGDBAhw4dquyuAqhjzterpzWtumlNq246X4//wwFWVyU/xTfffLNycnLMZceOHea2WbNm6aWXXtKcOXO0ZcsWOZ1O9e7dWydPnjRrEhMTtXLlSi1btkwbN25UUVGR4uLidO7cuaroLoA6wu3tqzEDkzRmYJLc3r7V3R0Av1KVXBPj7e3tMftygWEYevnll/XEE09o0KBBkqQ333xToaGhWrp0qR5++GG5XC7Nnz9fixYtUq9evSRJixcvVkREhDIyMtSnT5+q6DIAALCYKpmJ2bdvn8LDw9WiRQvde++9+v777yVJ+/fvV25urmJiYsxau92u7t27a9OmTZKkbdu2qaSkxKMmPDxckZGRZk1Z3G63CgsLPRYAAFB7VXqI6dy5s9566y198MEHeuONN5Sbm6uuXbvq+PHjys3NlSSFhoZ6vCY0NNTclpubK19fXzVq1OiSNWVJSUmRw+Ewl4iIiEo+MgBW51d8RtnPxSn7uTj5FZ+p7u4A+JUqPcTExsZq8ODBatOmjXr16qU1a36+5fLNN980a2wXPXTNMIxSbRe7Uk1SUpJcLpe5HDx48FccBQAAqOmq/PJ8f39/tWnTRvv27TOvk7l4RiUvL8+cnXE6nSouLlZ+fv4la8pit9sVGBjosQAAgNqrykOM2+3W7t27FRYWphYtWsjpdCo9Pd3cXlxcrA0bNqhr166SpI4dO8rHx8ejJicnRzt37jRrAAAAKv3upEmTJql///5q2rSp8vLyNGPGDBUWFmrYsGGy2WxKTEzUs88+q5YtW6ply5Z69tln1aBBA8XHx0uSHA6HRowYoYkTJ6px48YKCgrSpEmTzNNTAAAAUhWEmEOHDum+++7Tjz/+qGuvvVZdunRRZmammjVrJkl6/PHHdfr0aY0ePVr5+fnq3LmzPvzwQwUEBJj7mD17try9vTVkyBCdPn1aPXv2VGpqqry8vCq7uwAAwKJshmEY1d2JqlBYWCiHwyGXy8X1McBvpKY/O8mv+Ix2z75HktT6r//Wad/61dyj0rJn9qvuLgDVqiJ/v3kAJIA643y9elp3fSfzawDWRogBUGe4vX310J+Sq7sbACoJ/xUBAACWRIgBAACWRIgBUGf4FZ/RNy8N1jcvDeaxA0AtwDUxAOqUBiXu6u4CgEpCiAGAGqSs29S57RooG6eTAACAJRFiAACAJRFiAACAJRFiAACAJXFhL4A647zNpsyISPNrANZGiAFw1Wr6Ax8v5vax6974mdXdDQCVhNNJAADAkggxAADAkggxAOoMv+Iz2vbf8dr23/E8dgCoBbgmBkCd0vh0YXV3AUAlIcQAQA138QXUPIYA+BmnkwAAgCURYgAAgCURYgAAgCURYgAAgCVxYS+AOuO8zaavnC3NrwFYGyEGQJ3h9rHrrmGzq7sbACoJIQZAuVjtOUkAaj+uiQEAAJZEiAFQZ9QvOaONrz2kja89pPolPHYAsDpOJwGoM2yG1KQwz/wagLUxEwMAACyJEAMAACyJEAMAACyp0kNMSkqKbr31VgUEBCgkJEQDBw7U3r17PWqGDx8um83msXTp0sWjxu12a9y4cQoODpa/v78GDBigQ4cOVXZ3AcBymk9ZU2oB6qJKDzEbNmzQmDFjlJmZqfT0dJ09e1YxMTE6deqUR13fvn2Vk5NjLmvXrvXYnpiYqJUrV2rZsmXauHGjioqKFBcXp3PnzlV2lwEAgAVV+t1JaWlpHusLFy5USEiItm3bpjvuuMNst9vtcjqdZe7D5XJp/vz5WrRokXr16iVJWrx4sSIiIpSRkaE+ffpUdrcB1AGGTfpP46bm1wCsrcqviXG5XJKkoKAgj/aPP/5YISEhuvHGGzVy5Ejl5eWZ27Zt26aSkhLFxMSYbeHh4YqMjNSmTZuqussAaqkzPvUV85e5ivnLXJ3xqV/d3QHwK1Xp58QYhqEJEybo9ttvV2RkpNkeGxurP/3pT2rWrJn279+vp556Snfeeae2bdsmu92u3Nxc+fr6qlGjRh77Cw0NVW5ubpnv5Xa75Xa7zfXCwsKqOSgAAFAjVGmIGTt2rL7++mtt3LjRo33o0KHm15GRkerUqZOaNWumNWvWaNCgQZfcn2EYsl3iybMpKSmaPn165XQcAADUeFV2OmncuHFavXq11q9fryZNmly2NiwsTM2aNdO+ffskSU6nU8XFxcrPz/eoy8vLU2hoaJn7SEpKksvlMpeDBw9WzoEAqDXql5zRh/8crQ//OZrHDgC1QKWHGMMwNHbsWK1YsULr1q1TixYtrvia48eP6+DBgwoLC5MkdezYUT4+PkpPTzdrcnJytHPnTnXt2rXMfdjtdgUGBnosAPBLNkO68fgPuvH4Dzx2AKgFKv100pgxY7R06VK99957CggIMK9hcTgc8vPzU1FRkZKTkzV48GCFhYUpOztbU6dOVXBwsO6++26zdsSIEZo4caIaN26soKAgTZo0SW3atDHvVgIAAHVbpYeY1157TZIUHR3t0b5w4UINHz5cXl5e2rFjh9566y0VFBQoLCxMPXr00PLlyxUQEGDWz549W97e3hoyZIhOnz6tnj17KjU1VV5eXpXdZQAAYEE2wzBq5aRqYWGhHA6HXC4Xp5aAq1AbPwXWr/iMds++R5LU+q//1mnf2nubdfbMftXdBeCqVOTvN89OAgAAlkSIAQAAllSlnxMDADWJYZMOBYaYX9dmZZ0O5BQTahtCDIA644xPfd3+6ILq7gaASsLpJAAAYEmEGAAAYEmEGAB1hr3Erffe/Kvee/Ovspe4r/wCADUa18QAqDPqGYba5u4zvwZgbczEAAAASyLEAAAAS+J0EgDUERd/dgyfGwOrYyYGAABYEiEGAABYEqeTANTKJ1ZfynE/nmoP1BaEGAB1xmnf+uo4fml1dwNAJSHEAHVQXZp5AVB7cU0MAACwJEIMgDrDXuLWsqVTtGzpFB47ANQCnE4CUGfUMwx1ObjT/LquK+u0Ip8dAythJgYAAFgSIQYAAFgSp5MAACYeTQArYSYGAABYEiEGAABYEqeTgFqOD7bz9JOPvbq7YCncwYSajBADoM447VtfN014t7q7AaCSEGKAWoaZFwB1BdfEAAAAS2ImBkCdYT9brNdWPitJevTuqXJ7+1Zzj6yJ27BRUxBiAAvj1FHF1Dt/Xnd+v9X8GpWDi39RXQgxAIBKx2wNfgs1PsTMnTtXzz//vHJycnTzzTfr5Zdf1h/+8Ifq7hYAoAKYrUFVqNEhZvny5UpMTNTcuXPVrVs3zZs3T7Gxsfrmm2/UtGnT6u4eUKU4VYTajtka/Fo1OsS89NJLGjFihP7yl79Ikl5++WV98MEHeu2115SSklLNvQMAVCZma1BRNTbEFBcXa9u2bZoyZYpHe0xMjDZt2lRNvQKqDjMvQGnlma1hRqfuqrEh5scff9S5c+cUGhrq0R4aGqrc3NxS9W63W26321x3uVySpMLCwirpX+S0DzzWd07vUyXvYwUXj4V0deNR1n4qY7+Vue/yHGt53gvV41zxGV34jXDO/ZPOG9yhZDVN//pOpdSUpazfAVfzu6Oyfk9U1t+VqvwdXRV/+y783TYM48rFRg11+PBhQ5KxadMmj/YZM2YYrVq1KlU/bdo0QxILCwsLCwtLLVgOHjx4xaxQY2digoOD5eXlVWrWJS8vr9TsjCQlJSVpwoQJ5vr58+d14sQJNW7cWDabrdzvW1hYqIiICB08eFCBgYFXfwBgLCsRY1l5GMvKxXhWHsbyZ4Zh6OTJkwoPD79ibY0NMb6+vurYsaPS09N19913m+3p6em66667StXb7XbZ7Z5Pp23YsOFVv39gYGCd/iaqTIxl5WEsKw9jWbkYz8rDWEoOh6NcdTU2xEjShAkTlJCQoE6dOikqKkr/+Mc/9MMPP+iRRx6p7q4BAIBqVqNDzNChQ3X8+HE988wzysnJUWRkpNauXatmzZpVd9cAAEA180pOTk6u7k5czq233qrExEQ99dRTevjhh3+TAOPl5aXo6Gh5e9fojGcJjGXlYSwrD2NZuRjPysNYVozNMMpzDxMAAEDNUq+6OwAAAHA1CDEAAMCSCDEAAMCSCDEAAMCSCDG/sGbNGnXu3Fl+fn4KDg7WoEGDPLb/8MMP6t+/v/z9/RUcHKzx48eruLi4mnprDW63W+3atZPNZlNWVpbHNsbzyrKzszVixAi1aNFCfn5+uuGGGzRt2rRS48RYlt/cuXPVokUL1a9fXx07dtSnn35a3V2q8VJSUnTrrbcqICBAISEhGjhwoPbu3etRYxiGkpOTFR4eLj8/P0VHR2vXrl3V1GPrSElJkc1mU2JiotnGWJYfIeZ/vfvuu0pISNCDDz6or776Sp999pni4+PN7efOnVO/fv106tQpbdy4UcuWLdO7776riRMnVmOva77HH3+8zI+OZjzLZ8+ePTp//rzmzZunXbt2afbs2Xr99dc1depUs4axLL/ly5crMTFRTzzxhL788kv94Q9/UGxsrH744Yfq7lqNtmHDBo0ZM0aZmZlKT0/X2bNnFRMTo1OnTpk1s2bN0ksvvaQ5c+Zoy5Ytcjqd6t27t06ePFmNPa/ZtmzZon/84x+65ZZbPNoZywqojIc1Wl1JSYlx3XXXGf/85z8vWbN27VqjXr16xuHDh822t99+27Db7YbL5fotumk5a9euNX7/+98bu3btMiQZX375pcc2xvPqzJo1y2jRooW5zliW32233WY88sgjHm2///3vjSlTplRTj6wpLy/PkGRs2LDBMAzDOH/+vOF0Oo2ZM2eaNWfOnDEcDofx+uuvV1c3a7STJ08aLVu2NNLT043u3bsbjz32mGEYjGVFMRMjafv27Tp8+LDq1aun9u3bKywsTLGxsR7Td5s3b1ZkZKTHrEKfPn3kdru1bdu26uh2jXb06FGNHDlSixYtUoMGDUptZzyvnsvlUlBQkLnOWJZPcXGxtm3bppiYGI/2mJgYbdq0qZp6ZU0ul0uSzO/D/fv3Kzc312Ns7Xa7unfvzthewpgxY9SvXz/16tXLo52xrBhCjKTvv/9ekpScnKwnn3xS77//vho1aqTu3bvrxIkTkqTc3NxST89u1KiRfH19Sz1pu64zDEPDhw/XI488ok6dOpVZw3hene+++06vvvqqx/PDGMvy+fHHH3Xu3LlSYxUaGso4VYBhGJowYYJuv/12RUZGSpI5foxt+Sxbtkzbtm1TSkpKqW2MZcXU6hCTnJwsm8122WXr1q06f/68JOmJJ57Q4MGD1bFjRy1cuFA2m03vvPOOuT+bzVbqPQzDKLO9NirveL766qsqLCxUUlLSZfdXl8ezvGP5S0eOHFHfvn31pz/9SX/5y188ttXlsayoi8eEcaqYsWPH6uuvv9bbb79dahtje2UHDx7UY489piVLlqh+/fqXrGMsy6dWP5xh7Nixuvfeey9b07x5c/NiqZtuuslst9vtuv76680L/pxOpz7//HOP1+bn56ukpKRUYq6tyjueM2bMUGZmpux2u8e2Tp066f7779ebb75Z58ezvGN5wZEjR9SjRw/zae6/VNfHsryCg4Pl5eVV6n+zeXl5jFM5jRs3TqtXr9Ynn3yiJk2amO1Op1PSz7MIYWFhZjtjW9q2bduUl5enjh07mm3nzp3TJ598ojlz5ph3fTGW5VR9l+PUHC6Xy7Db7R4X9hYXFxshISHGvHnzDMP4v4snjxw5YtYsW7aMiyfLcODAAWPHjh3m8sEHHxiSjH//+9/GwYMHDcNgPCvi0KFDRsuWLY17773XOHv2bKntjGX53Xbbbcajjz7q0da6dWsu7L2C8+fPG2PGjDHCw8ON//znP2VudzqdxnPPPWe2ud1uLkYtQ2Fhocfvxx07dhidOnUy/vznPxs7duxgLCuIEPO/HnvsMeO6664zPvjgA2PPnj3GiBEjjJCQEOPEiROGYRjG2bNnjcjISKNnz57G9u3bjYyMDKNJkybG2LFjq7nnNd/+/ftL3Z3EeJbP4cOHjd/97nfGnXfeaRw6dMjIyckxlwsYy/JbtmyZ4ePjY8yfP9/45ptvjMTERMPf39/Izs6u7q7VaI8++qjhcDiMjz/+2ON78KeffjJrZs6caTgcDmPFihXGjh07jPvuu88ICwszCgsLq7Hn1vDLu5MMg7GsCELM/youLjYmTpxohISEGAEBAUavXr2MnTt3etQcOHDA6Nevn+Hn52cEBQUZY8eONc6cOVNNPbaOskKMYTCe5bFw4UJDUpnLLzGW5ff3v//daNasmeHr62t06NDBvE0Yl3ap78GFCxeaNefPnzemTZtmOJ1Ow263G3fccYexY8eO6uu0hVwcYhjL8rMZhmFUw1ksAACAX6VW350EAABqL0IMAACwJEIMAACwJEIMAACwJEIMAACwJEIMAACwJEIMAACwJEIMLMNms2nVqlXm+p49e9SlSxfVr19f7dq1u2QbqsZnn32mNm3ayMfHRwMHDqzu7vxq0dHRSkxMrPT9JicnV8r3YmXtpy77+OOPZbPZVFBQUN1dQSUhxKBaDR8+3Hxqs4+Pj0JDQ9W7d28tWLDAfLr4BTk5OYqNjTXXp02bJn9/f+3du1cfffTRJdvw8x/AKz1wsqImTJigdu3aaf/+/UpNTa3Ufdd1Fwd2SZo0aVKVf08TlGA1hBhUu759+yonJ0fZ2dn6f//v/6lHjx567LHHFBcXp7Nnz5p1TqfT48nY3333nW6//XY1a9ZMjRs3vmRbRRUXF/+6A/oNpaamKjo6+op1q1ev1l133fX/27v3oCir/w/g71VgQR9AuQoKbAooIIJIFmUgyMUwBcVEIS6tIBiEpWJNk+GlC6XOODijWSPgKHIpJK0RUcQOsAAADmBJREFUkIEFESNuclEYE4QBaZG4ZIC43M7vD388w8Iuu6CJ+D2vv3zO85xzPuc8z/oczvOc3Wdad11dHZycnLBgwQLMmTPnmZY9Wc/z3PX39z+3ugCAYZhJX9PT3XT6TFLPFx3EUFOOy+Vi3rx5mD9/PmxsbPDZZ5/h0qVLSE9PF/sLf+RfpxwOB6WlpTh06BA4HA4OHDggMQ0Ampub4e3tjblz50JTUxMeHh5oaGhgyw0MDISnpye++eYb6Ovrw9TUdEL5jh49Cj09PWhqaiIsLEzs5iYSibBv3z4YGBiAy+XCxMQEZ86cYfdXV1fD3d0dDMNAV1cXfn5+aGtre6b929TUhFu3bonNYskiEokQEREBHR0dKCsrY9WqVSguLgYANDQ0gMPhoL29HXw+HxwOR+JMzKFDh2BpaTkmfcWKFfjiiy9kxjDcvwcPHoSOjg7U1NQQEhIidkNbvXo1wsPDsXv3bmhpacHFxQWA7H7t6emBv78/GIaBnp4ejh07JjOe4VmK2NhYLFy4EFwuF4QQPHz4EDt27GBjdHJyQkVFhdRyiouL4eLiAi0tLairq8PBwQFlZWXsfh6PBwDYuHEjOBwOuz1yliQzMxPKyspjHotERETAwcGB3b5x4wbs7e2hoqICAwMDREREoKenR2Jc8fHxOHjwICoqKtjZ0eHz2tjYCA8PDzAMAzU1NWzZsgUPHjyQ2kYnJyeEh4eLpbW3t4PL5SInJ0dqvpF98OWXXyIwMBDq6uoIDg6Wqz3nz5+Hra0tVFVVMW/ePPj4+KC1tVVmfdT0RQcx1AvJyckJVlZWuHjxosT9QqEQFhYW2LNnD4RCIfbu3Ssx7dGjR3B0dATDMLh27RquX78OhmGwdu1asZthdnY2ampqkJWVhd9++03ufAKBAHV1dRAIBDh79izi4+PFbuj+/v5ISkpCTEwMampq8P3334NhGLYNDg4OsLa2RklJCTIyMvDgwQNs2bLlmfbl5cuXYW9vP6HZkn379iE1NRVnz55FWVkZjI2N4ebmho6ODhgYGEAoFEJNTQ3Hjx+HUCiEt7f3mDL4fD6qq6vZwQ8AVFZW4ubNmwgMDJQrjuHzIhAIkJiYiLS0NBw8eFDsmLNnz0JBQQEFBQU4ffq0XP0aGRkJgUCAtLQ0XL16Fbm5uSgtLZUZT21tLVJSUpCamory8nIAwLp169DS0oIrV66gtLQUNjY2WLNmDTo6OiSW0dXVhYCAAOTn56OwsBAmJiZwd3dHV1cXALD9FRcXB6FQKNZ/w5ydnTFnzhykpqayaYODg0hJSYGvry8AoKqqCm5ubti0aRMqKyuRnJyM69evjxlcDPP29saePXtgYWEBoVDInldCCDw9PdHR0YG8vDxkZWWhrq5O4jkfFhQUhAsXLkAkErFpCQkJ0NfXh6Oj43hdzDpy5AiWLl2K0tJS7N+/X6729PX14fDhw6ioqMAvv/yC+vp6ua81apqa2t+fpP7XBQQEEA8PD4n7vL29iZmZGbsNgKSlpbHbVlZWJCoqSizP6LQzZ86QxYsXk6GhITZNJBIRFRUVkpmZycagq6tLRCLRhPMZGRmRgYEB9ph3332XeHt7E0IIuXPnDgFAsrKyJLZv//79xNXVVSytqamJACB37tyRmGe0uLg44uDgMO4xLi4uJCYmRq7yCCGku7ubKCoqkoSEBDatr6+P6Ovrk++++45NU1dXF/sVY0nefvttsnPnTnb7o48+IqtXr5YrjoCAAKKhoUF6enrYtFOnThGGYcjg4CAh5Mmv/1pbW4vlk9WvXV1dRElJiSQlJbH729vbiYqKitgvCY8WFRVFFBUVSWtrK5uWnZ1N1NTUxvxi+KJFi8jp06fZfFZWVlLLHRgYIKqqquTXX39l00Zf65LKiYiIIE5OTux2ZmYmUVJSIh0dHYQQQvz8/MiOHTvEysjPzyczZswgvb29Uts4OtarV6+SmTNnksbGRjbt9u3bBAApKiqSWM7jx4+JhoYGSU5OZtOsra3JgQMHJB4/mpGREfH09BRLm0x7ioqKCADS1dVFCCFEIBAQAKSzs1OuOKgXH52JoV5YhBBwOJynKqO0tBS1tbVQVVUFwzBgGAYaGhp4/Pgx6urq2OMsLS2hpKQ04XwWFhaYOXMmu62np8dOX5eXl2PmzJli0/ujYxMIBGz5DMNgyZIlACBWx0iNjY1ix4eGhiI/P39M2rB///0XeXl52LBhAwAgNDRU7FhJ6urq0N/fjzfffJNNU1RUxMqVK1FTUyO5o6UIDg5GYmIiHj9+jP7+fiQkJIDP58ud38rKCrNmzWK37ezs0N3djaamJjbN1tZWLI+sfq2rq0NfXx/s7OzYPBoaGli8eLHMeIyMjKCtrS1WV3d3NzQ1NcXqq6+vl3oOW1tbERoaClNTU6irq0NdXR3d3d1obGyUr1P+n6+vL3Jzc/HXX38BeDLT4e7ujrlz57KxxcfHi8Xl5uaGoaEh1NfXy11PTU0NDAwMYGBgwKaZm5tjzpw5Uq8HLpeL9957D7GxsQCefBYqKiomNCsi6bzKas/Nmzfh4eEBIyMjqKqqsu+LTbRvqelDYaoDoChpampq8MorrzxVGUNDQ1ixYgUSEhLG7Bt5M5o9e/ak8ikqKort43A47KoqFRUVmbGtX78e33777Zh9enp6EvPo6+uzjzEA4OLFi0hNTRWLU01Njf13eno6zMzMYGRkBODJeyp79+4dNy5CCNuW0ekTHVSuX78eXC4XaWlp4HK5EIlE8PLymlAZkoyMQ9K5G69f7969O+l6JdWlp6eH3NzcMcdKe3wXGBiIv//+G8ePH4eRkRG4XC7s7Owm/PLqypUrsWjRIiQlJWHnzp1IS0tDXFycWGwhISGIiIgYk9fQ0FDueqSdd1nXQ1BQEKytrXH//n3ExsZizZo17HUoD0l9PV57enp64OrqCldXV5w/fx7a2tpobGyEm5sbfTH4JUYHMdQLKScnB1VVVfj444+fqhwbGxskJyezL13+1/lGsrS0xNDQEPLy8uDs7CyxjtTUVPB4PCgoyPdRVFBQgLGxMbuto6MDFRUVsbSRLl26xM7CDB+vo6Mzbh3GxsZQUlLC9evX4ePjA+DJSpySkpIJf4+KgoICAgICEBcXBy6Xi61bt4rNrMhSUVGB3t5edkBYWFgIhmGwYMECqXlk9auxsTEUFRVRWFjI3sw7Ozvx559/Sp01G6+ulpYWKCgosC/gypKfn4+TJ0/C3d0dwJMXr0e/zK2oqIjBwUGZZfn4+CAhIQELFizAjBkzsG7dOrHYbt++LfXakERJSWlMvebm5mhsbERTUxM7G1NdXY2HDx/CzMxMalmWlpawtbXFjz/+iAsXLuDEiRNyxyGJrPZUVVWhra0N0dHRbJwlJSVPVSf14qOPk6gpJxKJ0NLSgubmZpSVleHrr7+Gh4cH3nnnHfj7+z9V2b6+vtDS0oKHhwfy8/NRX1+PvLw87Nq1C/fv33/m+Ubi8XgICAgAn89nXzLMzc1FSkoKACAsLAwdHR3Ytm0bioqKcO/ePVy9ehV8Pl+uG5gsAwMDSE9Pn/DS6tmzZ2Pnzp2IjIxERkYGqqurERwcjEePHmH79u0TjiMoKAg5OTlIT0+f0KMk4MmLmtu3b0d1dTXS09MRFRWF8PBwzJgh/b8uWf3KMAy2b9+OyMhIZGdn49atWwgMDBy3TGmcnZ1hZ2cHT09PZGZmoqGhATdu3MDnn38u9QZqbGyMc+fOoaamBn/88Qd8fX3HzNrxeDxkZ2ejpaUFnZ2dUuv39fVFWVkZvvrqK2zevBnKysrsvk8++QS///47wsLCUF5ejrt37+Ly5cv48MMPpZbH4/FQX1+P8vJytLW1QSQSwdnZGcuWLWPrKioqgr+/PxwcHMY88hktKCgI0dHRGBwcxMaNG8c9VhZZ7TE0NISSkhJOnDiBe/fu4fLlyzh8+PBT1Um9+OgghppyGRkZ0NPTA4/Hw9q1ayEQCBATE4NLly6JvW8yGbNmzcK1a9dgaGiITZs2wczMDHw+H729vePOsEw232inTp3C5s2b8cEHH2DJkiUIDg5ml4Tq6+ujoKAAg4ODcHNzw9KlS7Fr1y6oq6tP6oY6Wl5eHhiGwYoVKyacNzo6Gl5eXvDz84ONjQ1qa2uRmZnJvm8xESYmJnjjjTewePFivPbaaxPKu2bNGpiYmMDe3h5btmzB+vXr2aXz0sjTr0eOHIG9vT02bNgAZ2dnrFq1alL9xOFwcOXKFdjb24PP58PU1BRbt25FQ0MDdHV1JeaJjY1FZ2cnli9fDj8/P3Yp+0jHjh1DVlYWDAwMsHz5cqn1m5iY4NVXX0VlZSW7KmnYsmXLkJeXh7t37+Ktt97C8uXLsX//fqmPKgHAy8sLa9euhaOjI7S1tZGYmMh+tcHcuXNhb28PZ2dnLFy4EMnJyTL7Z9u2bVBQUICPj4/YAGsyZLVHW1sb8fHx+Omnn2Bubo7o6GgcPXr0qeqkXnwcMvwAnKKol0pERAQGBgZw8uTJKY2DEIIlS5YgJCQEu3fvljtfYGAg/vnnnzHfXEtNH01NTeDxeCguLoaNjc1Uh0O9hOg7MRT1klq6dKnYCpyp0NrainPnzqG5uRnvv//+lMZCPT/9/f0QCoX49NNP8frrr9MBDPWfoYMYinpJ7dixY6pDgK6uLrS0tPDDDz+MeRQlbYk38GRVFTV9FRQUwNHREaampvj555/F9uXn54/77dHd3d3/dXjUS4Q+TqIoakrU1tZK3Td//nyZS9Sp6am3txfNzc1S909kNRVF0UEMRVEURVHTEl2dRFEURVHUtEQHMRRFURRFTUt0EENRFEVR1LREBzEURVEURU1LdBBDURRFUdS0RAcxFEVRFEVNS3QQQ1EURVHUtEQHMRRFURRFTUv/B5xcDhLLWoX6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title(f'{faction} residuals histogram')\n",
    "plt.hist(residuals, bins=100)\n",
    "plt.xlabel('Difference +/- of y_pred relative to y_real')\n",
    "h = ax.plot([avgres, avgres], [0, 600], 'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "a19a9388-36da-4c6f-bf5c-6216431b9c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "ax1.set_aspect('equal', adjustable='box')\n",
    "ax1.set_title(f'{faction} residuals')\n",
    "ax1.plot(ypred, np.array(each_faction_dataset[faction]['vp']), 'bo', line, line, 'r--')\n",
    "ax1.set(xlabel='y_pred', ylabel='y_real')\n",
    "ax1.set(xlim=[0, 250], ylim=[0, 250])\n",
    "\n",
    "ax2.set_title(f'{faction} residuals histogram')\n",
    "ax2.hist(residuals, bins=100)\n",
    "ax2.set(xlabel='Difference +/- of y_pred relative to y_real')\n",
    "h = ax2.plot([avgres, avgres], [0, 600], 'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "dc99b122-0c67-4af8-9c7d-22230cf32166",
   "metadata": {},
   "outputs": [],
   "source": [
    "metricsdir = 'data/faction-picker-bot/metrics/'\n",
    "plt.savefig(metricsdir + f'{faction} charts.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e65f854-b42e-485c-9af9-179e951c1f42",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}