Module_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bd44aa5b-2c47-47ab-93b9-f3fc5b76dd4f",
   "metadata": {},
   "source": [
    "# Библиотеки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "15a91684-fdd1-4c7c-8272-69e3210903d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Для работы с данными\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.metrics import r2_score, mean_absolute_percentage_error\n",
    "from scipy.linalg import hankel\n",
    "# Визуализация\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# Процесс выполнения\n",
    "from tqdm.notebook import tqdm,trange\n",
    "# Tensorflow\n",
    "import tensorflow\n",
    "from tensorflow.keras import Input,Model\n",
    "from tensorflow.keras.layers import Dense, concatenate, Dropout, LSTM\n",
    "\n",
    "# Отдельные модули\n",
    "import preprocessing\n",
    "import micro_model_functions\n",
    "import micro_help_functions\n",
    "import macro_model_functions\n",
    "# !!!!!!!!!!!!!!!!убрать\n",
    "import importlib\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a0546f-5659-44d2-8323-263edc40b152",
   "metadata": {},
   "source": [
    "# Загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "36d26d28-67de-4b13-97db-635c9389ab08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df shape:  (12807982, 6)\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REGNUM</th>\n",
       "      <th>TRANS_DATE</th>\n",
       "      <th>mcc</th>\n",
       "      <th>PRC_AMT</th>\n",
       "      <th>date</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158892</td>\n",
       "      <td>2020-01-30 09:17:33</td>\n",
       "      <td>5411</td>\n",
       "      <td>163.00</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15403</td>\n",
       "      <td>2020-01-31 17:40:59</td>\n",
       "      <td>5411</td>\n",
       "      <td>984.50</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2680437</td>\n",
       "      <td>2020-01-31 15:08:59</td>\n",
       "      <td>5411</td>\n",
       "      <td>542.09</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1136161</td>\n",
       "      <td>2020-01-30 09:19:13</td>\n",
       "      <td>5411</td>\n",
       "      <td>215.93</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3054580</td>\n",
       "      <td>2020-01-31 14:27:07</td>\n",
       "      <td>5411</td>\n",
       "      <td>239.00</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    REGNUM           TRANS_DATE   mcc  PRC_AMT        date group\n",
       "0   158892  2020-01-30 09:17:33  5411   163.00  2020-01-30  food\n",
       "1    15403  2020-01-31 17:40:59  5411   984.50  2020-01-31  food\n",
       "2  2680437  2020-01-31 15:08:59  5411   542.09  2020-01-31  food\n",
       "3  1136161  2020-01-30 09:19:13  5411   215.93  2020-01-30  food\n",
       "4  3054580  2020-01-31 14:27:07  5411   239.00  2020-01-31  food"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_to_dataset = 'user_trans.csv'\n",
    "df = pd.read_csv(path_to_dataset)\n",
    "\n",
    "print('df shape: ', df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae235b93-2afa-437b-a9e0-7da4b03afb99",
   "metadata": {},
   "source": [
    "# Предобработка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0aef7b-db7c-4db3-804d-e12fc9498da5",
   "metadata": {},
   "source": [
    "## Переход к трем категориям ценностей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff1e6223-0bc9-4546-8aa8-1adb1136d242",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REGNUM</th>\n",
       "      <th>TRANS_DATE</th>\n",
       "      <th>mcc</th>\n",
       "      <th>PRC_AMT</th>\n",
       "      <th>date</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158892</td>\n",
       "      <td>2020-01-30 09:17:33</td>\n",
       "      <td>5411</td>\n",
       "      <td>163.00</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15403</td>\n",
       "      <td>2020-01-31 17:40:59</td>\n",
       "      <td>5411</td>\n",
       "      <td>984.50</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2680437</td>\n",
       "      <td>2020-01-31 15:08:59</td>\n",
       "      <td>5411</td>\n",
       "      <td>542.09</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1136161</td>\n",
       "      <td>2020-01-30 09:19:13</td>\n",
       "      <td>5411</td>\n",
       "      <td>215.93</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3054580</td>\n",
       "      <td>2020-01-31 14:27:07</td>\n",
       "      <td>5411</td>\n",
       "      <td>239.00</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    REGNUM           TRANS_DATE   mcc  PRC_AMT        date group\n",
       "0   158892  2020-01-30 09:17:33  5411   163.00  2020-01-30  food\n",
       "1    15403  2020-01-31 17:40:59  5411   984.50  2020-01-31  food\n",
       "2  2680437  2020-01-31 15:08:59  5411   542.09  2020-01-31  food\n",
       "3  1136161  2020-01-30 09:19:13  5411   215.93  2020-01-30  food\n",
       "4  3054580  2020-01-31 14:27:07  5411   239.00  2020-01-31  food"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# если есть только MCC-коды, то сначала переходим к 14-ти категориям\n",
    "df = preprocessing.cats_to_14(df=df, col='mcc', new_col='group')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1976c821-214c-4959-85a4-0f976d16d5bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survival            6952773\n",
       "socialization       2910513\n",
       "self_realization    1495481\n",
       "Name: group_3, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# не будем учитывать категорию деньги\n",
    "df = df[~df['group'].isin(['money'])]\n",
    "# Составляем словарь вида \"MCC-код\":\"категория\"\n",
    "cats_3_dict = {'food':'survival', 'outfit':'survival', 'dwelling':'survival', 'health':'survival',\n",
    "     'remote':'socialization','travel':'socialization','nonfood':'socialization','telecom':'socialization','misc':'socialization',\n",
    "     'beauty':'self_realization', 'kids':'self_realization','fun':'self_realization', 'charity':'self_realization'}\n",
    "\n",
    "# Создаем новую колонку с тремя категориями\n",
    "df['group_3'] = df['group'].apply(lambda x: cats_3_dict[x])\n",
    "df['group_3'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6674c384-670a-45df-a5b7-d704bf1e0f74",
   "metadata": {},
   "source": [
    "## Составление датафрейма: факт совершения транзакций, общая сумма за день"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dbc96b99-86c9-43fb-8ac9-56caa2f456fb",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bin_trans</th>\n",
       "      <th>money_trans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>[[0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,...</td>\n",
       "      <td>[[0, 0, 669, 0, 0, 0, 489, 0, 0, 596, 0, 265, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>[[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,...</td>\n",
       "      <td>[[0, 0, 0, 0, 0, 0, 0, 100, 2120, 2029, 910, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879</th>\n",
       "      <td>[[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0,...</td>\n",
       "      <td>[[0, 0, 0, 0, 0, 0, 0, 0, 54, 187, 54, 0, 5100...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>[[0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,...</td>\n",
       "      <td>[[0, 160, 915, 0, 0, 0, 0, 0, 0, 100, 932, 490...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2079</th>\n",
       "      <td>[[1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,...</td>\n",
       "      <td>[[22, 393, 927, 0, 0, 1901, 0, 0, 0, 0, 0, 370...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              bin_trans  \\\n",
       "913   [[0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,...   \n",
       "1490  [[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,...   \n",
       "1879  [[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0,...   \n",
       "2021  [[0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,...   \n",
       "2079  [[1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,...   \n",
       "\n",
       "                                            money_trans  \n",
       "913   [[0, 0, 669, 0, 0, 0, 489, 0, 0, 596, 0, 265, ...  \n",
       "1490  [[0, 0, 0, 0, 0, 0, 0, 100, 2120, 2029, 910, 0...  \n",
       "1879  [[0, 0, 0, 0, 0, 0, 0, 0, 54, 187, 54, 0, 5100...  \n",
       "2021  [[0, 160, 915, 0, 0, 0, 0, 0, 0, 100, 932, 490...  \n",
       "2079  [[22, 393, 927, 0, 0, 1901, 0, 0, 0, 0, 0, 370...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_compressed = preprocessing.create_trans_data(df=df, col_clientid='REGNUM', col_date='date', \n",
    "                                   col_group='group_3', col_amount='PRC_AMT')\n",
    "bin_compressed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e8a46521-ebb6-42d7-a3e2-e474b8ffcc99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Сохраним для того, чтобы получить реальные id клиентов и сумму позже\n",
    "bin_compressed.to_pickle('real_id_bin_money.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb16276-995e-464f-ac38-58e120e6bbe1",
   "metadata": {},
   "source": [
    "## Добавление признаков из даты"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14a23d57-c47e-49b5-b6ab-881d7fb24efa",
   "metadata": {},
   "source": [
    "К данным по транзакциям добавим информацию о дне неделе и месяце, которые были закодированы с помощью синуса и косинуса для обеспечения одинакового расстояния между всеми элементами. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1144eb6-9c64-4343-909d-4dcf90f9d0f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2020-01-01', '2021-06-29')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date.min(), df.date.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "35943cda-5ced-40a4-b527-32962ee7ff86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [[0.0, 0.0, 1.0, 0.9749279121818236, -0.222520...\n",
       "1       [[0.0, 0.0, 0.0, 0.9749279121818236, -0.222520...\n",
       "2       [[0.0, 1.0, 0.0, 0.9749279121818236, -0.222520...\n",
       "3       [[0.0, 0.0, 1.0, 0.9749279121818236, -0.222520...\n",
       "4       [[1.0, 0.0, 0.0, 0.9749279121818236, -0.222520...\n",
       "                              ...                        \n",
       "7280    [[0.0, 0.0, 0.0, 0.9749279121818236, -0.222520...\n",
       "7281    [[0.0, 0.0, 0.0, 0.9749279121818236, -0.222520...\n",
       "7282    [[0.0, 0.0, 0.0, 0.9749279121818236, -0.222520...\n",
       "7283    [[0.0, 0.0, 0.0, 0.9749279121818236, -0.222520...\n",
       "7284    [[0.0, 0.0, 0.0, 0.9749279121818236, -0.222520...\n",
       "Length: 7285, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_df = preprocessing.add_date_features(start_date=df.date.min(), end_date=df.date.max(),\n",
    "                                          main_feat_trans=bin_compressed['bin_trans'])\n",
    "feat_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc6f5c0d-3f56-481f-989c-b5facfdee802",
   "metadata": {},
   "source": [
    "В итоге, каждый день представляет собой вектор из семи признаков (факт совершения транзакции первой категории, второй, третьей, синус и косинус дня недели, синус и косинус месяца), что дает окончательную размерность признакового пространства для каждого пользователя, равную 546х7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "173152f9-2bd5-4487-a309-672a018167b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(546, 7)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_df.iloc[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "187a0b1e-e0cc-48b7-bd43-df66c6e151da",
   "metadata": {},
   "source": [
    "## Выбор клиентов"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67634c15-91af-4c6f-b132-18f4921d11d2",
   "metadata": {},
   "source": [
    "При большом наличии клиентов появилась необходимость отобрать тех, кто более-менее регулярно совершает покупки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ecba0b3-82f5-4da0-b8d3-3f5d577f53e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Посмотрим на кол-во дней с совершенными транзакциями (из 546 возможных) по первой категории у каждого клиента\n",
    "w = feat_df.apply(lambda x: x[x[:,0]==1].shape[0]).sort_values(ascending=False)\n",
    "plt.hist(w);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da84fb07-0cf4-4b37-9030-b0e25884b755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4054,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Оставим только пользователей с количеством дней с транзакциями не менее 100\n",
    "nonzero_clients = w[w>100].index\n",
    "feat_df_nonzero = feat_df.loc[nonzero_clients.sort_values()]\n",
    "feat_df_nonzero.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af38960-aa5f-4860-8730-ac6152aec9eb",
   "metadata": {},
   "source": [
    "## Меняем временной интервал"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d08a91-7e6b-41ed-a825-747045a33867",
   "metadata": {},
   "source": [
    "Так как одна из задач -- оценка динамики предсказуемости при наличии переходных финансовых процессов, то было необходимо оставить кризисный период конец марта — начало апреля 2020 года для тестовой выборки. \n",
    "\n",
    "Руководствуясь предположением, что поведение пользователей банковских карт слабо меняется в зависимости от года, был изменен порядок дней: промежуток 01.01.20 — 29.06.20 поменялся местами с полугодием 01.01.21 — 29.06.21, что дало итоговый искусственный интервал 01.01.2019 - 29.06.2020.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "773505d3-5426-4929-aac7-f45b621a6668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       [[0.0, 0.0, 1.0, -0.433883739117558, -0.900968...\n",
       "2       [[0.0, 0.0, 0.0, -0.433883739117558, -0.900968...\n",
       "3       [[0.0, 1.0, 1.0, -0.433883739117558, -0.900968...\n",
       "4       [[0.0, 0.0, 0.0, -0.433883739117558, -0.900968...\n",
       "5       [[0.0, 0.0, 0.0, -0.433883739117558, -0.900968...\n",
       "                              ...                        \n",
       "7262    [[0.0, 1.0, 1.0, -0.433883739117558, -0.900968...\n",
       "7264    [[0.0, 1.0, 1.0, -0.433883739117558, -0.900968...\n",
       "7268    [[1.0, 1.0, 1.0, -0.433883739117558, -0.900968...\n",
       "7271    [[0.0, 0.0, 0.0, -0.433883739117558, -0.900968...\n",
       "7283    [[0.0, 0.0, 1.0, -0.433883739117558, -0.900968...\n",
       "Length: 4054, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# заменим 01.21-06.21 на 01.20-06.20, т.е. поменяем их местами\n",
    "feat_df_nonzero = feat_df_nonzero.apply(lambda x: np.concatenate((x[366:], # 01.01.21 - ... теперь это 2019 год\n",
    "                                        x[181:366], # оставляем середину (теперь это 2019)\n",
    "                                        x[:181])))\n",
    "feat_df_nonzero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a64d651a-0ac8-424b-b542-bd52ba768e22",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32634ef9-3633-49f0-9c6d-5594032eb45f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feat_df_nonzero.to_pickle(\"4054_amnt_binclass_3c.pkl\", protocol=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780bf03f-eef6-4c35-bb4f-5385cbff10b5",
   "metadata": {},
   "source": [
    "# Предсказания на микро-уровне (каждый клиент)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b50c92-dd40-438c-b717-64b81d332c1e",
   "metadata": {},
   "source": [
    "## Обучающая и тестовая выборки"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98725bbc-d7f5-4446-b6d4-5914724ab155",
   "metadata": {},
   "source": [
    "Разделение данных на обучающую (с 1 января по 31 декабря 2019) и тестовую (25 недель с 1 января по 23 июня 2020) выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6afdeff8-5a0f-4f0d-88ac-f472fbc24129",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_train=365\n",
    "train_df = feat_df_nonzero.apply(lambda x: x[ : n_train])\n",
    "test_df = feat_df_nonzero.apply(lambda x: x[n_train : ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f102d8d5-86da-4fb6-925e-dac605764020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1682, 1396, 2712, 3644, 1955, 1301, 6497, 4369, 2096, 1638],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "# Зададим порядок клиентов \n",
    "clients = feat_df_nonzero.index\n",
    "random_clients = np.random.choice(clients,size=clients.shape[0],replace=False)\n",
    "random_clients[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d23e5ba-7a71-4f16-9724-f6201473c354",
   "metadata": {},
   "source": [
    "## Предсказания для базовой и инкрементальной моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c166e39a-040b-436e-ada9-149ccaab17b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71114f579ff1453fb311031824e6a9f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "clients:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca93dfb25c3d449580057bb6ac66eb0e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "epoch:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a14d42905bd940139b1ee9525f75bc19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "epoch:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcab1d53010d4ae1bc8c1a274bb499ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "epoch:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "client_dict = {}\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Для каждого клиента отдельная модель\n",
    "for client_id in tqdm(random_clients[:1], desc='clients'):\n",
    "    b_cats_r = []\n",
    "    i_cats_r = []\n",
    "    \n",
    "    # Получим данные для клиента \n",
    "    all_x,all_y,all_test_x,all_test_y = micro_help_functions.get_splits_by_client(client_id, train_df, test_df)\n",
    "    \n",
    "    # Для каждой категории отдельная модель\n",
    "    for cat in range(3):\n",
    "        \n",
    "        # Обучаем базовую модель\n",
    "        cl_model, cl_optimizer, train_losses, test_losses, cl_checkpoint, weights = micro_model_functions.train_model(all_x,all_y,\n",
    "                                                                                       all_test_x,all_test_y, \n",
    "                                                                                       e_n=1000, lr=0.0005,cat=cat)\n",
    "        \n",
    "        # Сохраняем базовую модель, чтобы потом на ее основе сделать инкрементальную модель\n",
    "        torch.save(cl_checkpoint, f'model_{cat}.pth')\n",
    "        model_after = micro_model_functions.LSTM_cat_model(input_size=5, hidden_size=64, to_pred=7).to(device)\n",
    "        checkpoint = torch.load(f'./model_{cat}.pth')\n",
    "        model_after.load_state_dict(checkpoint['model'])\n",
    "        \n",
    "        # Разделим временной интервал тестовой выборки на недели\n",
    "        days = pd.date_range('2020-01-01','2020-06-23')\n",
    "        days = np.array(days).reshape(-1,7).astype('datetime64[D]')\n",
    "        # Получим данные для клиента с указанной датой для дней, которые будут предсказываться\n",
    "        q = micro_help_functions.get_df_full_y_new(feat_df.loc[client_id][337:], \n",
    "                                                   min_date='2019-12-04', max_date='2020-06-29')[::7]\n",
    "\n",
    "        # Результаты базовой модели для тестовых недель\n",
    "        b_r = micro_model_functions.base_model_data(q,model_after,days,cat=cat)\n",
    "        b_cats_r.append(b_r)\n",
    "\n",
    "        # Загружаем базовую модель\n",
    "        the_checkpoint = torch.load(f'./model_{cat}.pth')\n",
    "        imodel_after = micro_model_functions.LSTM_cat_model(input_size=5, hidden_size=64, to_pred=7).to(device)\n",
    "        imodel_after.load_state_dict(the_checkpoint['model'])\n",
    "        ioptimizer_after = torch.optim.Adam(params=filter(lambda p: p.requires_grad, imodel_after.parameters()),lr=0.0005)\n",
    "        ioptimizer_after.load_state_dict(the_checkpoint['optimizer'])\n",
    "        # Меняем шаг обучения у оптимизатора\n",
    "        for param_group in ioptimizer_after.param_groups:\n",
    "            param_group['lr'] = 0.05\n",
    "        \n",
    "        # Результаты инкрементальной модели для тестовых недель\n",
    "        i_r = micro_model_functions.incr_model_data(q, imodel_after,days,ioptimizer_after, weights,with_weights=True,cat=cat)\n",
    "        i_cats_r.append(i_r)\n",
    "    \n",
    "    # Сохраняем предсказания и реальные значения для двух моделей\n",
    "    client_dict[client_id] = [b_cats_r,i_cats_r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9b9281b8-a60c-400b-bb1e-4082b96f9867",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>[[[[0. 0. 0. 1. 0. 1. 0.], [1 1 1 1 1 1 1]], [...</td>\n",
       "      <td>[[[[0. 0. 0. 1. 0. 1. 0.], [[1 1 1 1 1 1 1]]],...</td>\n",
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       "                                                      0  \\\n",
       "1682  [[[[0. 0. 0. 1. 0. 1. 0.], [1 1 1 1 1 1 1]], [...   \n",
       "\n",
       "                                                      1  \n",
       "1682  [[[[0. 0. 0. 1. 0. 1. 0.], [[1 1 1 1 1 1 1]]],...  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.DataFrame.from_dict(client_dict).T\n",
    "#d.to_pickle('3300-4122_real_yt_yp.pkl')\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d7db87-9815-4013-8089-58488817fc9e",
   "metadata": {},
   "source": [
    "## Сравнение базовой модели с инкрементальной "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "a1b50616-4bfb-4ed1-9f79-48ebb18c4acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>base</th>\n",
       "      <th>inc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>[[[[0. 0. 1. 0. 0. 0. 0.], [1 1 1 1 1 1 1]], [...</td>\n",
       "      <td>[[[[0. 0. 1. 0. 0. 0. 0.], [[1 1 1 1 1 1 1]]],...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>[[[[0. 0. 1. 1. 0. 1. 0.], [1 1 1 1 1 1 1]], [...</td>\n",
       "      <td>[[[[0. 0. 1. 1. 0. 1. 0.], [[1 1 1 1 1 1 1]]],...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2712</th>\n",
       "      <td>[[[[0. 0. 1. 0. 1. 0. 0.], [1 1 1 1 1 1 1]], [...</td>\n",
       "      <td>[[[[0. 0. 1. 0. 1. 0. 0.], [[1 1 1 1 1 1 1]]],...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3644</th>\n",
       "      <td>[[[[0. 0. 1. 0. 0. 0. 1.], [0 0 0 0 0 0 0]], [...</td>\n",
       "      <td>[[[[0. 0. 1. 0. 0. 0. 1.], [[0 0 0 0 0 0 0]]],...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>[[[[0. 1. 0. 1. 1. 0. 1.], [1 1 1 1 1 1 1]], [...</td>\n",
       "      <td>[[[[0. 1. 0. 1. 1. 0. 1.], [[1 1 1 1 1 1 1]]],...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   base  \\\n",
       "1682  [[[[0. 0. 1. 0. 0. 0. 0.], [1 1 1 1 1 1 1]], [...   \n",
       "1396  [[[[0. 0. 1. 1. 0. 1. 0.], [1 1 1 1 1 1 1]], [...   \n",
       "2712  [[[[0. 0. 1. 0. 1. 0. 0.], [1 1 1 1 1 1 1]], [...   \n",
       "3644  [[[[0. 0. 1. 0. 0. 0. 1.], [0 0 0 0 0 0 0]], [...   \n",
       "1955  [[[[0. 1. 0. 1. 1. 0. 1.], [1 1 1 1 1 1 1]], [...   \n",
       "\n",
       "                                                    inc  \n",
       "1682  [[[[0. 0. 1. 0. 0. 0. 0.], [[1 1 1 1 1 1 1]]],...  \n",
       "1396  [[[[0. 0. 1. 1. 0. 1. 0.], [[1 1 1 1 1 1 1]]],...  \n",
       "2712  [[[[0. 0. 1. 0. 1. 0. 0.], [[1 1 1 1 1 1 1]]],...  \n",
       "3644  [[[[0. 0. 1. 0. 0. 0. 1.], [[0 0 0 0 0 0 0]]],...  \n",
       "1955  [[[[0. 1. 0. 1. 1. 0. 1.], [[1 1 1 1 1 1 1]]],...  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_to_2models_data_folder = 'sim_clients/'\n",
    "\n",
    "# Загрузим данные\n",
    "df_2models = pd.concat(map(pd.read_pickle, glob.glob(path_to_2models_data_folder+'*.pkl')))\n",
    "df_2models = df_2models[~df_2models.index.duplicated(keep='first')]\n",
    "df_2models.columns = [['base','inc']]\n",
    "\n",
    "df_2models.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "6c2f5a64-bceb-4311-8554-50fa233063b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'micro_help_functions' from 'C:\\\\Users\\\\user\\\\Documents\\\\mk\\\\micro_help_functions.py'>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(micro_help_functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "2ac43c77-d438-4a92-92dc-06ab33f581e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Получим значения для каждой модели и \"среднее\" значение метрики\n",
    "inc_with_metric, inc_with_metric_3in1, base_with_metric, \\\n",
    "base_with_metric_3in1 = micro_help_functions.get_base_inc_arrays(df_2models, micro_help_functions.F1metr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "990d1767-8483-4bd8-86f8-3c4bd3a7eb83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bts022/D0008zd+5c5syZw1NPPcXWW28NwGOPPbZcvxIZAwYM4O6772b27NkATJkyhWnTpi37TL9+/UqNo3nz5ixcuLDUZWoq1ZgQERERERGpLjKjb6zmUTkyfUyA96dwzz33UFBQwKBBg9hjjz3o06cPPXv2pHPnzgB8+eWXDBkyBDPDzLjpppsAuO+++zj++OOZO3cu6623Hvfcc09W6N7koV69ejz00EM0bdqUY489lo033pj27dvTt29fwGsuDB48mMsvv7zEWgrZdtttN3bbbbflpvXu3ZvBgwez6aabAnDMMcfQq1cvbrnlFoYNG8abb77JTTfdxOzZs5k+fTrPPvsse+65J2PHjmWLLbYAoGHDhjz44INcc801zJkzhxNPPDHv7x9zzDE0bNgQ8NoTsiIrrQpLVdOnT5+gHk9FRERERKQ6GTt2LF26dEk6jBrhoosuon///vTv33/ZtOeff54ZM2YwePDgxOKqavKVWTMbFULok2951ZgQERERERERAfbff/8VOqfs3bs3C1ax81ApnRITIiIiIiIiIkC3bt1WmFae5iKyatT5pYiIiIiIiIgkRokJEREREREREUmMEhMiIiIiIiIikhglJkRERERERKRUmeEuAX755Rfq16/PRRddlFxAUq0oMSEiIiIiIiLldu2117LWWmslHYZUI0pMiIiIiIiIVCPDh0P79lBQ4M/Dh6++7/7tt9/4v//7P4466qhl0wYPHszjjz++7H23bt0YP34848ePXzbKxdixY+nRoweTJk1i/PjxdO7cmb/85S90796d/fffn7lz5wIwatQott12WzbZZBMGDBjATz/9BED//v3ZYostlv3GWWedhZkte3/11VfTt29funfvzoUXXgiw3O8DPP744wwePJjvv/+enj170rNnTwoLC5e9njp1KnfccQd9+/alR48e7LfffsviyvydHTp0oGfPntSpU4cZM2bwxhtvsPvuu6+wnu69915OOumkZe9POukk7r33XgAuueQS+vbtS7du3RgyZAghBAA++ugjevToQc+ePWnTpk3eGimDBw+mbdu2LFmyBIBbb70VM2P8+PEAXHfddXTr1o1u3bpxww03LPvcjBkzqFOnDj179qRjx47LxZxv3WXWX7169ejZsydFRUXL/p7+/fszcuTIFWJbFUpMiIiIiIiIVBPDh8OQITBhAoTgz0OGrL7kxA033MDRRx+9XNOOskyZMoWDDz6Yhx56iHXXXReAr7/+miFDhvD555/TuHFjbrnlFhYtWsTJJ5/M448/zqhRozjqqKM477zzln3P0qVL+eqrr1iyZAmffPIJDRo0AGDEiBF8++23fPTRR4wePZpRo0bx1ltvlRjP+uuvz+jRoxk9ejT16tVb9rp169bsu+++fPzxx3z22Wd06dKFu+66a9nnlixZwrXXXrts2ZV10kkn8fHHHzNmzBjmzZvH888/D8CVV17JBRdcwOjRozn99NNL/HybNm14+eWXAXjmmWfo2LEj4Emde+65hw8//JAPPviAO+64g08//XRZ7G3btmX06NHceeedy76rtHW3ZMkSOnXqxOjRo7nkkktW+u8tj1qV+u0iIiIiIiKyWvXvv+K0Aw+EE06Ac86BrJv8gL8/9VQYNAhmzID9919+/htvlO93Z86cyf3337/CxS3AP/7xDy677DIAvv/++2XTZ8+ezcCBA9l+++3p2rXrsunrrrsu/fr1A+Cwww7j3//+NwMHDmTMmDHstNNOgF8Yr7POOss+c+SRR3LPPfew7bbbMnDgQN5//33AL65HjBhBr169lv3mt99+S1FR0bLaEQB//vkn2267bal/45gxYzj//PP5448/mD17NgMGDFg2b968edStW3eFz7z99tv07NkTM+Pkk09eVpvk0Ucf5Z133gE8OdOnTx8AXn/9da666irmzp3Lb7/9RteuXdljjz0oLCxk1qxZpcYHcPjhh/PAAw9QVFREp06dmDx5MgDvvPMO++yzz7KEzb777svbb79Nr169mD17Ns2aNVvhu0pad9tss02Jfy/AoEGDqFevHkVFRdx55520bNmyzLhLoxoTIiIiIiIi1UR0jbqCX39d9e+++eabGTRoEE2bNl1h3tVXX72s5sH666+/bPqkSZM455xzeP311xk7duyy6dnNMDLvQwh07dp12fd88cUXjBgxYtkyffr0YfTo0dx9990cdthhy6aHEDjnnHOWfe67777j6KOPBpavHXH11VeX+TcOHjyYm266iS+++IILL7yQ+fPnL5s3derUvDUltt56a0aPHs0rr7zCmWeeuaz5x0EHHbTstw866CAA5s+fzwknnMDjjz/OF198wbHHHrvsNy666CKuueYaOnbsyPXXX19ijK1atWLRokVcffXVHHnkkcuth5L8+OOPtG3bdoXppa27kv5egOHDhzN69Gi6d+++XJORlaXEhIiIiIiISBXyxhsrPk44wecVFeX/TLt2/rzWWit+tjwWL17MsGHDSm1ikE+XLl049NBD+c9//sNxxx237OJ54sSJy2o8PPzww2y11VZsuOGGTJ8+fdn0RYsW8eWXXy73fQcccADNmjVj7bXXXjZtwIAB3H333cyePRvw2gnTpk2rUJwZs2bNYp111mHRokUMz2r/8t133zF+/Hg22mijEj/bqFEjatWqtaz/h3wySYi11lqL2bNnL9c3R6tWrWjYsCFvvfVWmev5yCOPZNq0afTu3XvZtG222Yann36auXPnMmfOHJ566im23nprAB577LG8fWGUtu4ee+yxZbVaStK8eXMWLlxY6jLloaYcIiIiIiIi1cTQod6nRHZzjvr1ffqqWLBgAfvuu+9Kj8ax7bbb0rlzZ2699VZ23XVXunTpwn333cdxxx1Hp06d+Otf/0qdOnV4/PHHOeWUU/jzzz9ZvHgxp5122nJNQIYMGcKQIUOW++6dd96ZsWPHLuscs2HDhjz44IMUFhZWOM5LL72UzTbbjHbt2rHxxhsza9Yspk6dyl577cWwYcOoU6fOCp9577332GqrrZgzZw6nn346jRo1KvH7mzZtyrHHHsvGG29M+/bt6du3L+A1FwYPHszll19erv4rdtttN3bbbbflpvXu3ZvBgwez6aabAnDMMcfQq1cvbrnlFoYNG8abb77JTTfdxOzZs5k+fTrPPvsse+65Z951d8011zBnzhxOPPHEvL9/zDHHLOtnZPhq6MDESqvuscpfbjYQuBEoBO4MIVyRZ5n+wA1AbWBGCGHbaPp4YBawBFgcQuhT1u/16dMnrO7eQUVERERERJI0duxYunTpUu7lhw+H886DiRO9BsXQod6/RFqMHz+e3XffnTFjxiQdSo1w0UUX0b9/f/pndU7y/PPPM2PGDAYPHlwpv5mvzJrZqJKu6yutxoSZFQI3AzsBk4GPzezZEMJXWcs0BW4BBoYQJppZbo8Z24UQZlRWjCIiIiIiItXNoEHpSkRIsvbff/8VOqfs3bs3CxYsSCiiFVVmU45Nge9CCD8AmNkjwF7AV1nLHAo8GUKYCBBCWLmGQCIiIiIiIlIltG/fXrUlYtStW7cVpq3KcKeVoTI7v2wDTMp6Pzmalm0DYE0ze8PMRpnZEVnzAjAimj4EEREREREREal2KrPGhOWZltuhRS1gE2AHoB7wvpl9EEL4BugXQpgaNe94xczGhRDeWuFHPGkxBKCopC5oRUREREREqrAQwgpDbIqk0cr0Y1mZNSYmA+tmvW8LTM2zzEshhDlRXxJvAT0AQghTo+dpwFN405AVhBCGhRD6hBD6tGjRYjX/CSIiIiIiIsmqW7cuv/7660pd8InEKYTAr7/+St26dSv0ucqsMfEx0MnMOgBTgIPxPiWyPQPcZGa1gDrAZsD1ZtYAKAghzIpe7wxcUomxioiIiIiIpFLbtm2ZPHky06dPTzoUkTLVrVuXtm3bVugzlZaYCCEsNrOTgJfx4ULvDiF8aWbHR/NvCyGMNbOXgM+BpfiQomPMbD3gqaiqUi3goRDCS5UVq4iIiIiISFrVrl2bDh06JB2GSKWx6lQdqE+fPmHkyJFJhyEiIiIiIiIiWcxsVAihT755ldnHhIiIiIiIiIhIqZSYEBEREREREZHEKDEhIiIiIiIiIolRYkJEREREREREEqPEhIiIiIiIiIgkRokJEREREREREUmMEhMiIiIiIiIikhglJkREREREREQkMUpMiIiIiIiIiEhilJgQERERERERkcQoMSEiIiIiIiIiiVFiQkREREREREQSo8SEiIiIiIiIiCRGiQkRERERERERSYwSEyIiIiIiIiKSGCUmRERERERERCQxSkyIiIiIiIiISGKUmBARERERERGRxCgxISIiIiIiIiKJUWJCRERERERERBKjxISICDB8OLRvDwUF/jx8eNIRiYiIiIjUDLWSDkBEJGnDh8OQITB3rr+fMMHfAwwalFxcIiIiIiI1gWpMiEiNd9ZZxUmJjLlz4bzzkolHRERERKQmUWJCRGqcJUvg3Xc98dCzJ0yZkn+5CRPg5Zdh3rxYwxMRERERqVGUmBCRGmHRIn9essT7kNhqK7jySmja1B8lGTgQmjWDzz7z9/PnQwiVHGwVoD45RERERGR1UR8TIlItLV0Kn34KL7zgjyVL4KOPoLAQ/vY3aNsWdtrJkxK5fUwA1K8PN90E66wDr7wCXbr49H/+Ex55xBMWAwfCDjtAkyaJ/ImJUZ8cIiIiIrI6WahGt/769OkTRo4cmXQYIpKwm26CoUPh55/BDPr2hd12gwsu8Pf5DB/uTTsmToSiIv98vovsJ5+EBx+E116DmTM90bHrrvDss5X7N6XBr7/CF1/AvvvC77+vOL9tW5g0Kf64RERERCT9zGxUCKFPvnmqMSEiVVYI8NVXxbUiHnjAL47XXBO23dYTBgMHQsuWZX/XoEHlu9u/777+WLQIPvgAXnpp+aYd22zjiY2BA2Hnncv322mzcCGMGwfffAP77+/Tjj4a7r679M9Nngzrrgs9enjfHVtvDQMGVHq4IiIiIlLFVWpiwswGAjcChcCdIYQr8izTH7gBqA3MCCFsW97PikjNNHEiXHGFJyMmTPBp3bvDTz95YqK8SYZVUbu2X3hvvXXxtAULPCkxYkRxnwu9e8PZZ8MBB1RuPCsjk1Ax8+Yq994Ln3/uSYnFi33eb795omf33b05S/funqSYPHnF72vaFPr3h9GjPWHz9dfFiYm994Y2bYqTFt26eXMZEREREZFKS0yYWSFwM7ATMBn42MyeDSF8lbVMU+AWYGAIYaKZtSzvZ0Wk5vjhB09CrL8+7LIL1KrltSN22AHOPddrRrRtm3SUsMYa3swj07/FSy/5qB6Zi/zvvoMzz/TaFAMGQLt28cU2b54nHbIfX3wBb78NXbvC+PHwzjuw8cawxx7+3L07NG7sn99nn+LvuuKKkvvkyCSE5s/3pi6Z17/9Bq+/Drfc4tMKCuDii+H88732yf/+5wmLtdeu7DUhIiIiImlTmTUmNgW+CyH8AGBmjwB7AdnJhUOBJ0MIEwFCCNMq8FkRqcb+9z/47389ITFunE8bMsQTE61b+4Vu7drJxliSggLYZBN/nHde8fSJE2HkSHjqKX/fubMnKc480zvZXB2WLPFETibxsPfefsH/v/95rQeAhg098XDggVCnjk875hg49tjy/UYm+VBanxx16/oj8/qtt7yGxvjxXqPis89giy18/tixvh7AExM9e3rNiiOO8KSJiIiIiFRvlTlcaBsguxu0ydG0bBsAa5rZG2Y2ysyOqMBnRaSKKW2IySlT4MUXi9+fdRbcfLPXKrjxRvj2W7j99uL5aU1KlGb77b3pyVdfwXXX+QX9HXd4TQuAJ56A66/3C/VMM4vS1tmvv8Ivv/jryZNhs828hsMGG3jfEJde6okQ8CTA00970uLPP+G99+C226BTJ59fUqegJRk0yJMMS5f6c3mazphBhw5e++Kii7zGC0DHjvDGG3DDDZ54+uUXXw+ZjjT/9z//24YM8RoX770Hs2bl/420DmOa1rhERERE0qDSRuUwswOAASGEY6L3hwObhhBOzlrmJqAPsANQD3gf2A3oUdZns75jCDAEoKioaJMJmQbnIpIq+YbkXGMNb9IwYYLfQa9b1y+269f3jhfbtIEGDZKLOQ4LFxbXWjjqKLjnHn9dVORNV957z/uuyKhVy/t6+PVXmDrVhz699lpvLrH77sVNMDbeGDbaqOr247BwoScyatf2xMTQoV7T4rffipf58kv/Gz/7zJMYP/7o/XnkNjEZNizZYUxLGo426bhERERE4lTaqByVmZjYArgohDAgen8OQAjhX1nLnA3UDSFcFL2/C3gJryFR6mfz0XChIq68Q1+uioULYfp077tg3jy/6Jo3zzs1XGst/+0RI4qn/+tffqc+n2228eE8d93Vq+5X9O59dTJhgvdL8fLL3uQj3y66dm045BBPPvTvD33y7t6rnxC8Zsjo0f446yxP6px6Kvz73yV/rmlTr3GxdKl/Rwjeoedjj/n8c8+Fd99dfn7btvDooz7/hBO85kn2/A03hIcf9vkHHwxjxvj0zDJ9+nh/IwD16nniKFe7dl7bRERERKQmSGq40I+BTmbWAZgCHIz3KZHtGeAmM6sF1AE2A64HxpXjsyKSR+7d2QkTfBSF997zESL69PH2+zNmwDXXrJhYOPZYb+8/dqxfcGWmZx533+3T33sPtttuxd9/5hnYc0/v46A8fRaYwZtvrt51UJW1a+fbb8gQr/afz+LFcN998caVBmY+HOm663oHnRmXXeb9ZWy1Vf7P/fGHP8yKH7k1SQoKoLCweH69esXzGjf2ZFv257M76Swq8g48CwqK53fsWDw/X1ICPHknIiIiIpWYmAghLDazk4CX8SE/7w4hfGlmx0fzbwshjDWzl4DPgaX4sKBjAPJ9trJiFalKQvALn8WL4ZFH/A7ypEn+mDzZq7NnVxkHbwqQGQ3h8ss9MTF7trfjr1dv+ccff/hy9ep5fwD16vlFXGb+Bhv4/M6dvSp67uc33tjnb7+9x5T5fOfO+S/EiooqZTVVC0VFxcOh5k6XYo0aQb9+ntTJt77atYMPPij585dfXvr3X1HGYNVXXVX6/JLiKiqC77/3zk+POQZ23tmTIyIiIiI1TaU15UiCmnJIVbdwoScMmjXz9zfc4Bcu2cmHPff0DhND8Iv+BQu8Wnrbtn4n+YUX8n+3mVcbb9bMR2WIm9rZV5zWWcWkdX2VFlezZj76yIwZ/j88eLD3NdKhQ2LhioiIiFSK0ppyVOaoHCLVXkV62l+82GsMfJU16O3ll8N++8Gmm/pwkXXrejOJjP/8B+6/30ekaNEC9tqruPmEmXf+N2uWdwj4+ec+vGa7dvl/v6jIH0kkJcAvDIcN8/jM/DnpC8a00zqrmLSur9Li2mUXH5Hm8ce9ttHll3vnpjNnJhuziIiISJxUY0JkJeW7C1q3Lpx8cnHV7osv9iEwJ02Cn3/2jvE6d/b+G8DbyX//fXG7+bZtfUSFfff1+fPmLd/WfWXjSsNdYxEp26RJ8OGHPtwreA2pdu28n5iePRMNTURERGSVJDIqRxKUmJA4tW+fv924GSxZ4s9nnw2jRhU3s2jbFtZbD3bcsXJji2NUDhGpXIsWedOOJ57wJlu9e3uC4tBDfaQRERERkapEiQmRSlBQkH8ox+zEhIjIqvrtN3joIbjrLh8m9eabfQjTRYu8s8ySRm8RERERSRP1MSGyGv34I7z7bskjIxQVKSkhIqtPs2Zw0knw6adeAytT++nuu6FTJ68RNWVKsjGKiIiIrAolJkTKadEiHzawa1fvw+Gyy7zvhmz16/tFgohIZejdG5o08dft23si9Pzz/Xm33eDJJ/PX5BIRERFJMyUmRMrhnXegVy845xzvRf/ll+Gww9I5AoCI1AwDBsDrr8N333l/NqNHw9VXF9fY+vnnRMMTERERKTf1MSFShvffhy239DuSN98Mu++edEQiIitavBh++QXatPF+KVq3hk028Q4zDzwwuaGCRUREREB9TIhUWAjw9df+evPN4dZb4auvlJQQkfSqVcuTEpnXl13mCYqjj4Z11oFjjoEffkg2RhEREZF8lJgQyfH117DDDtCnD/z0k1eLPv54aNAg6chERMqncWM44wxPqL77rteYePRRHzEI4PvvYfp0H1q4fXsf2aN9e38vIiIiErdaSQcgkhbz58Pll8OVV3onltdeC2uvnXRUIiIrz8ybom25Jdx0E9Sr59PPPBOefrp4eGOACRO8Y19QXzkiIiISL9WYEAFmz4bu3eHSS+GAA2DcOD9BL9B/iIhUE5mkBPi+rkGD4qRExty5cN558cYlIiIiUq7LLjPbwMxeM7Mx0fvuZnZ+5YYmUvnmzvXnhg3hkEPglVfgwQdVU0JEqreNNvKEbD4TJ8Ybi4iIiEh57wffAZwDLAIIIXwOHFxZQYlUtqVL4fbbfaSNTz7xaRdfDDvumGxcIiJxKSqq2HQRERGRylLexET9EMJHOdMWr+5gROLw+efQr593aLnxxtCoUdIRiYjEb+hQ708nW716sNdeycQjIiIiNVd5ExMzzGx9IACY2f7AT5UWlUglueAC6N0bvvsO7r8f/vc/6NQp6ahEROI3aBAMGwbt2nknmO3a+WhEN98Mzz2XdHQiIiJSk5Q3MXEicDvQ2cymAKcBx1dWUCKVpU4dOPJIHxL08MP9ZFxEpKYaNAjGj/fmbePHw3//C716+fCi776bdHQiIiJSU5SZmDCzQuCvIYQdgRZA5xDCViGECZUencgqmjwZ9t0XnnnG359/PtxxBzRrlmxcIiJp1KgRvPCC9zOx++4wZkzSEYmIiEhNUGZiIoSwBNgkej0nhDCr0qMSWUWLF8MNN0CXLvDSSzBtmk9XDQkRkdK1aAEvv+z9T5x1VtLRiIiISE1Qq5zLfWpmzwKPAXMyE0MIT1ZKVCKrYORIGDIEPv0Udt0VbroJOnRIOioRkaqjfXvvg6dVq6QjERERkZqgvH1MNAN+BbYH9ogeu1dWUCKr4quv4Jdf4LHH4PnnlZQQEVkZG24ITZrA3Llw9tkwe3bSEYmIiEh1ZSGEpGNYbfr06RNGjhyZdBgSsxA8CTFnjndsGYKfQGsYUBGRVffGG7DDDrDjjj5aR506SUckIiIiVZGZjQoh9Mk3r1w1JsysrZk9ZWbTzOwXM3vCzNqu3jBFKu6HH7y5xkEH+fCfIXg/EkpKiIisHv37+7CiI0Z48nfp0qQjEhERkeqmvE057gGeBVoDbYDnomkisRg+3Ns8FxT48333weWXQ9euPqTdjTfCq6+qc0sRkcpw9NG+z33oIfj73z0JLCIiIrK6lKsph5mNDiH0LGta0tSUo3oaPtw7s5w7t3jaGmvAggWw336elGjTJrn4RERqghDg9NN9n/z557DOOklHJCIiIlXJKjflAGaY2WFmVhg9DsM7wxSpdOedt3xSAjwpsc468PjjSkqIiMTBDK67Dj75REkJERERWb3Km5g4CjgQ+Bn4Cdg/miZS6SZOzD/955/jjUNEpKYrKIB11/XaE//8Jzz7bNIRiYiISHVQrsRECGFiCGHPEEKLEELLEMLeIYQJZX3OzAaa2ddm9p2ZnZ1nfn8z+9PMRkePf2bNG29mX0TT1T6jBmvePP/0oqJ44xARETd/Prz0knc8/M47SUcjIiIiVV15R+W4z8yaZr1f08zuLuMzhcDNwC7ARsAhZrZRnkXfDiH0jB6X5MzbLpqetx2KVH8XXAAzZvhdumz168PQocnEJCJS09WrB//9ryeI99gDvvgi6YhERESkKitvU47uIYQ/Mm9CCL8Dvcr4zKbAdyGEH0IIC4FHgL1WKkqpsZo1g+OOg7vvhnbtvI1zu3Y+dN2gQUlHJyJSc7VoAS+/7InigQNhQpn1KEVERETyq1XO5QrMbM0oIYGZNSvHZ9sAk7LeTwY2y7PcFmb2GTAVOCOE8GU0PQAjzCwAt4cQhpUzVqniPv/c+4/YeWc47bTiIUD/8pdEwxIRkRzt23uTjh128E4x27VLOiIRERGpisqbmLgWeM/MHo/eHwCUVZHe8kzLHZv0E6BdCGG2me0KPA10iub1CyFMNbOWwCtmNi6E8NYKP2I2BBgCUKROB6q8Rx6Bo4/2k9svvoDCwqQjEhGR0my8MXz/PTRq5O9DKE4oi4iIiJRHeTu/vB/YD/gFmAbsG0J4oIyPTQbWzXrfFq8Vkf29M0MIs6PXLwC1zWyt6P3U6Hka8BTeNCRfbMNCCH1CCH1atGhRnj9HUmjxYvjHP+CQQ6BXL/jf/5SUEBGpKjJJiSefhL33hoULEw1HREREqpjydn65PvB9COEm4Atgx+zOMEvwMdDJzDqYWR3gYGC5gcXMrJWZ31cxs02jeH41swZm1iia3gDYGRhT/j9LqpJ582CXXeCaa+CEEzwp0apV0lFJjTN8uNdLLyjw5+HDk45IpMr54w8fQnTwYFi6NOloREREpKoob+eXTwBLzKwjcCfQAXiotA+EEBYDJwEvA2OB/wshfGlmx5vZ8dFi+wNjoj4m/g0cHEIIwNrAO9H0j4D/hhBequDfJlVE3brQsSPcdRfcfDPUqZN0RBWgi9nqYfhwGDLEe+8LwZ+HDNH2FKmgo46Cf/0LHn4Y/vY3/3cSERERKYuFcpw1mNknIYTeZnYmMC+E8B8z+zSEUNbIHLHq06dPGDlyZNJhSDk9/DD06AEb5RtEtirIXMzOnVs8rX59DRlS1SxdCm3bwk8/rTivXTsYPz72kESqshA8KXHDDXD55XDOOUlHJCIiImlgZqNCCH3yzStv55eLzOwQ4Ahgj2ha7dURnNQ8ixfDWWfBddd5dd977inHh4YPh/POg4kToagIhg6tnIv/ELxx9OzZ/pgzp/h17vuLL14+KQH+/pxzlJhIu4kT4ZVX/PHaazBjRv7lJkyAhx6CHXeEli3jjVGkijKDa6+FadNg+nR1hikiIiJlK29i4kjgeGBoCOFHM+sAPFh5YUl1NWMGHHSQ9yNx0kmenChTbs2ETDX7JUtgjz3yJw1KSyiUNm/2bP/eVTFpkvfguckmxY/u3b3NiiTjjz/gjTeKkxHffuvTW7WCgQPhxRfh119X/FxBQXGSqXdvH8N2wADYcssq1uZIJF4FBXD//f5sBosWQW3dzhAREZESlKspx3IfMOsdQvikkuJZJWrKkW4//ADbbw8//wy33ea1JcqlbVuYMmXVfrxWLWjY0B8NGhS/zn1f2rzc9z16+J33XI0bw+abw6hRxRe7hYXQtevyyYoePaBevVX7uyS/hQvhgw/g1Vc9EfHRR95ko0ED2HZb2GknrwXRtatfNZXULOe226BzZxgxwh/vvedVfho0gP79PUmx886wwQa6JSxSgrFjYffd4d57Yeutk45GREREklJaU46VSUx8EkLovVoiW82UmEi3BQvgsMPgzDOhb99SFlyyxC8kX3jBH5+Ukge7/vryJRvq1Fn9F45l9TERgicuRo3yxyef+PP06b5sYaF3sNG7d3GyomdP/w6pmBDgq688CfHqq147Ys4cv13bt68nInbayRNGJdV0KE9zoZkz/btHjICXX4bvvvPpRUXFSYoddoA116zMv1akSpkxA7baypPSb78NG2+cdEQiIiKShNWdmEhdp5cZSkykz6JFcOWVPgxos2alLDhjhl/ovfACvPQS/PabX1RuuSWMGeNV8XOloWPCivZ9EQJMnlycrMg8pk3z+QUF0KVLcaKid29PVjRsGMufU6X89FNxjYhXXy3uvLJTp+IaEdttB02bVl4MP/zgv//yy95XxcyZxcmQTKJis828xo5IDTZhgu/OQ/CKR+3bJx2RiIiIxG11Jyb2DiE8vToCW92UmEiXadO8P4k33oA77oBjjsmauXSp1yDI1Ir46CM/Y23ZEnbZBXbd1S8u11yz+o9+EQJMnbpisuLnn32+mTcnyG4G0rMnNGqUaNixmz0b3nyzOBnx5Zc+fa21vJZCJhnRrl0y8S1eDB9+WNzsI9N8pHFjjy/TP0WHDsnEJ5KwMWO8KUfLlvDOO9CiRdIRiYiISJxWa2Ii60s7hxDGrVJkq5kSE+kxciTsu6+3Whg2DA4/HPj9d79ge/FFf0yb5hfdm27qiYhdd/UaAgUFK35hXKNypMnUqcXNPzKPqVN9npn3a5CdrOjVyy+CoXqsr8WLvSBlakS8/75Xwalb169udtzRkxE9euQvM0n77Tfv5fXll/0xaZJP79ixOEnRv3/xNhOpAd5912vRDR9e83KrIiIiNV1lJSYmhhCKVimy1UyJiXR44QVPSqy9duCpK7+l949P+MT33/f+I5o184uyXXf1Z902K7+ff16+v4pRo7xpSMYGG0Dz5n5Bv2hR8fS01DApLWESgvfZkBk54/XX4c8/PQnTq1dxPxH9+lW9EU5CgG++8QTFiBH+t82d6008ttiiuNlH797e94hIDTBnjo/UoQFuREREaoaVTkyY2b9LmgX8JYSQqlt9SkykwMyZ/PzY2/z9X2txw+xjaPHLGJ/eu3dxrYhNN9XF1+o0bdryHWw++2z+IU8LCmCddbwz0Pr1y/9cnmXWWKPszkXzNcmpVw+OPhrmzfNkRGaUk/bti5tmbL+9N9eoThYs8Ib2mWYfmQ5emzXzv3vnnf3Rtm31qP0ikmPRIthmG2/Z9OCD6az0JCIikjZV/bRwVRITs4C/AwvyzL42hJCqqwUlJhIQAowdyy+PvsF1967J0ClHUmvJAq+evvPOnogYONAviCUeBQW+XfI56ihPDMyZU/LznDn5Extl/WZZCYz//te/O5+mTT0BkUlGrL9+zRp+c9o0b66SSVRkOvJs3drnLV5cvGzdunD55bD//p4Qyn7Euc6q+pFREnfllXD22XDKKXDDDTXrX15ERKSiqkO3e6uSmPgfcH4I4b08834MIaSqFzclJmIyZ45XRY86rvx4Qgv25UlmWAvePvwO+hzdw6un166ddKQ1U/v23gV+roqMYrJwYdkJjIo+f/11/t8y89unqkXjQvBeAkeMgPPPh/nzy//Z2rVXTFbUqbPitJIe5V32nXfgxhu95kdGVTsySuJCgL/9zZMSl18O55yTdEQiIiLptTpO8ZO2KomJZsD8EMLcEhdKESUmVoOS7oJ++23xCBpvvukXJA0acO8Gl3P8FyfQqhU89WwteqVyINkaJq3p1OqwN41babVf7rjD/w/L+1i4sPzLLl26cvE2aAAXXggbbugjyay3noZKlVItXQpHHOG7rfvu89ciIiLifd6/8453HP3OO8Utf3OZrfypW9xWJTFRFEKYWGmRrWZKTKyifBe0tWp5u/dp0/x9587LhvO85K3+XHhpLXbYAR55pPp1A1ClpbGafVoTJmmWVDJn8eLSkxmbblpywiRb7do+CknnzsWPTNKiSZPKi1+qlIUL4eST4cwzvRWXiIhITbN0qQ9g2Ly5VyTeaCPvEx78dHmzzeCLL2DGjBU/W5Xu8ZWWmCjrVtbTQO/oS54IIey3mmOTNDnvvOUvGsEvUGbOhJtu8oTEeustm7XbmjB3IVx2mW6Kps6gQem72M/Ek7aESZoNHZo/mTN0aOX+bq1a/mjQIP/8oqKSEyajR3uznXHjln8899zyfWW0arV8wiKTtCgqUk+INUydOnD77f46BPjxx+UONSIiItXO/Pk+iN477xTXiujb11vy1q4Ne+7pfaBvtRX07OnTSrrHV9mnhXEpq8bEpyGEXrmv00o1JlZRSdXGs+oHffih/8NccEHMsYnUVNWl9suiRX7FmZuwGDfObxFk1Kvnw97mJi022MB/ozyxpW19pTmulPnnP+Hf//ZOMW+7TatLRESqhxkzvMbDdtv5+wED/JoK/DRnq628//eDDir9e6r66cSqNOX4JITQO/d1WikxsQpeftlH0MjXQCmqH3TXXXDCCdCmjbdxato09ihFJC1W15ExBD9a50tY/Pjj8snSoqIVExadO3vtC7P0NhdKa1wpNGGC3xn644/lp2t1iYhIVTJlig+4lqkRMW6c3wP+4w9o1MiTEvPnw5Zb1qzm8KuSmFgCzAEMqAdkzqoMCCGExqs51lWixMRKWLTIb1FdcYXXF5oxY/lRAOrXZ+Etd3LqB4dw220+muPDD3v7JxGRSjV/vjewzJe0yB56tlEjT1B89VX+IWnXWguuu86HwV28uPhR2vuKLFvW+y++8H1trqrUKDRGrVsXj5ibTatLRETSaNEib8n6zjtw6KGw9to+eNlpp/mN3H79vEZEv36w+eY1e+DClU5MVDVKTFTQpElw8MHw3nt+N++GG+DJJ5e7CxouG8qA+wfxyiveMdnQoepPQkQSFoLfisjty+LVV1fP92f62KhVy4exLe/7kub9978l/9ZHH0GfPl7jQ4BytSoUERGpVGVVDJ0yxZscvvuuN3XPVIp8+mnYay/45ReYPt07sVTXWcWUmJAVPfccDB7s3aHfcYcnKMj/T5jJ6h14YHLhioiUqaRRTFq39mGOy5NQqIyzh5LiyujYEQ45xB9duqz+369iNLKwiIgkKV8LzDp1YNtt4fjjYd99vbVpx47e/HCrrYprRLRunVjYVUJpiQnlb2qahQvh73/3rl7btfPOIrKSEkOG+AlhCP48ZIhXT1JSQkRSb+jQFTvIrF8frrrKzx7at/cma61aQYsWsOaa3gykfn0/46isWxolxTVsGNx1l++Lhw712yq9enm8E6vMSN2rXb7VVbdu9el1XERE0mvBgvwDFS5c6BUzv/7a37dvD3/+CaNGebONAw5QUmJVqcZETfLjj97V68cfw0knwdVX+9leRHepRKTKS2t31WXF9dNP8H//5534fPihT9tqK69FccABnkipQbJXV4sWMG2aV5k97rikIxMRkerip588sfDJJ8WPrl19TAA1Kawcasoh8MQTcPTR/vruu70OUpbff4dmzfJ/VP+EIiIx+v57eOQReOgh79CzsNB7Hj70UNh7b6/lUYMsXuztdV9+GZ5/HgYOTDoiERGpSkLwRPeoUd43xMkn+/TttoM33vBrnQ03hN69vbnG5ZfrZm1lUWKiJps/H844A26+GTbd1E92O3RYYbH33vN2Ufnon1BEJAEh+IgeDz/sjwkTvJbb7rt7kmKXXZar9VadzZoF22zjg7S88w706JF0RCIikkZLl3qiwcwPnXff7TUhfvvN59etCzNneh96b7/trTh79ICGDYu/Q6N8Vx71MVFTffutD457883er8Tbby9LSoQAzz5b3GZ3yy19UI58zaDVrldEJAFm0L07/Otf3hTv3XfhmGO8I8999/XxyI46Cl55xasVVGONGnltiSZNYLfdfBx4ERGp2ZYs8YqFDz4Ip5/utR2aNi0ecnr6dK8Vvt9+cOut3lLyt9+KO/bfemu/MZudlABPPgwb5jdnzfxZSYnKpxoT1dXDD3uqr04duPde2GOPZbM+/dTzFK+/7u2oRo2CNdbweWltni0iIpHFi+F///P9/JNP+q2ftdf2XooPOcQHSa+mw49+9pnXmDjxxKQjERGR1aG81x4LF3oS4pNPYPvtvW+84cPhsMN8fr16XvOhd284+2xYd91Y/wwpJzXlqEnmzoVTT4U77/QU4MMPL/vP/OUXOOccz1M0awYXX+y5i0zWUEREqpj58+GFF3xf/9xz3p14hw4+2tKhh0K3bklHWGm++QbWW89HeRURkdKl8eZjWU0mpk7165VPPoHPP/fkBMAdd3gFwqlT4bXXPBmx4YY6HlQFSkzUFGPH+h2zMWM8A3HxxctlHcaP90zikCG+Y2raNLFIRURkdZs5E55+2jvNfPVVr+ParZvXojjkkLz9C1VVkydDly5wxBFw003VtoKIiMhqsTr6TAjBDyuLF/vrevV8+owZnhNfvLj4Ua+eJz/Akwrz5xfPW7IEWrb0a5KSRgRs2tSbYPz6K3Tq5ImH7EfHjpU3wrdUrsQSE2Y2ELgRKATuDCFckTO/P/AM8GM06ckQwiXl+Ww+NToxcd99cMIJ0KABPPAADBjA0qXe5uqNN7zjF/Dz1saNE41UREQq27Rp8PjjnqR4912ftvnmXoviwAO96Qek8xZaOZ15po96fd113rZYRERcCMUJ2xEj4KCD8vfN066dj0z9/vvLJxa6dfOaCOCHjpEjPaGQse22fn0BsMEG3q1dtt1390p8AOusAz//vPz8Qw7xw1NBQf5hOTN/Q+7fIlVfaYmJSqvwYmaFwM3ATsBk4GMzezaE8FXOom+HEHZfyc/K7Nne2Pb++6F/fz/JbN2aN9+Ev/3Ns5R9+8Kff3qnYUpKiIjUAC1berL6hBP8dtQjj3hzj1NOgdNO8wa6HTp49nrePP/MhAl+Sw2qRHLiiivghx+8z6T27WGffZKOSEQkHvPnFw/K9NprniSYONF34xMn+vypU33+nXeW3GHwxInQubNf+Neq5Y/CQk9YZBx2GOy4Y8nzL77YL0cy82vVgrZti+cPH+7JjsLC4vktW/q8oqKSh+XMUFKi5qi0GhNmtgVwUQhhQPT+HIAQwr+ylukPnJEnMVHmZ/OpcTUmPv/cU6Bffw3//CdccAE/Ty/khBPgqad8p3DFFZ6VVHUnERHhq6+Khx/9/vv8y1ShMaLnzfNx6D//HD76qFp3qSEiNUQIPnJE06Z+Mf/WW95KL5N0mDDBR5uYM8ebY/z97z6yXps2fqHfrp0/LrvMz/+nTYM+fWDSpBV/K+ndvYblrHmSGi60DZD9LzA5mpZrCzP7zMxeNLOuFfxszRSC/8dutpmnQF99lXDhRVBYSP363mv5ZZd5vmLQICUlREQkstFGcOmlXu+2pNtQEyfGG9MqqFfPh74+9VTv+ExEJGnDh3stroKC4pEjsi1e7LvZzMX4Rx/BccfBLrv4LrpRI1hrreLc8SefwG23eV65WTPYe2/fjWeaVlx8sdeQmDjRRy0aPhwuv7z4/L9lSx91un795eOoX99b7yVJw3JKtsrsuzTfGU9u9YxPgHYhhNlmtivwNNCpnJ/1HzEbAgwBKMr0slKdzZzpe69HHoGdd2bR3Q9w25MteewiHz2ucWNPSKhXWhERKZFZyXVoq9ixNHPSDX4XsXZtde4sIsnIrQEwYYKPHnHrrZ4omDABpkzxpMKrr8IOO3hNhqee8l1v584wYIC/zuzHTjzRk68l5ZIbNiw7rsyFfhq7FBo0KB1xSPIq8/J1MpA9gmxbYGr2AiGEmVmvXzCzW8xsrfJ8Nutzw4Bh4E05Vk/oKfXJJ95048cfCUMv5/muZ3HG9gV8843v2H77zU/QlJQQEZEyDR26Yh1aqLJniIsWwTbbQOvW8OKLUKdO0hGJSE0wfTrccosPjvfkk74vyjZ/PnzwAfTr551GZppbZGp57bsv7Ldfyd+fNcDeKlECQNKuMi9hPwY6mVkHYApwMHBo9gJm1gr4JYQQzGxTvGnJr8AfZX22RgkBbr7ZG5G1aMH0p97h4Bs253/n+U7t+edh113VOYyIiFRA7i20Nm38eHPddbD11jBwYLLxVVDt2j5S9l/+4hUL775bx0URWXVLlsCoUZ54yDy++gqOPhrOPhuWLvXmFO3br5iUyFi6FN58M/887adEXKUlJkIIi83sJOBlfMjPu0MIX5rZ8dH824D9gb+a2WJgHnBw8N448362smJNtd9/9z3fU0+xaJc9qX3/XazZdC2WXgf/+Y+ffK2uTKqIiNQwubfQZsyAnXeGPfeE//s/b8xchRxxhLfLvuQSWH99OP/8pCMSkaogBK/5kJ18WH99H8gIvDbWggV+zt2pE/ToAR07+ryWLX1Uivr1PTlRDVrIiSSi0kblSEK1G5Xjww/h4IOZO+lXrt3pRe75eks+/dRo0kRj+oqISCX54w/vhe3jj73B9EEHJR1RhYTgCYoHH4THHy+9irSI1CxLl3qfDmPHehOLTO51k028xXRGgwaes739dn//2ms+2t1665V+Q1CjTIiULqlROWRlhQDXXsvSflvzwKy92aDZDP75Uj9697ZlOzolJUREpFI0bQojRniD6EMPhXvvTTqiCjGDO++E007zu5wiUj2VNvrFokXw44/F74cO9eRDo0a+7C67wBlnFM8fPBiuvx5eeslbts2aVZyUAO/LbcMNy66lrFEmRFaeakykza+/wuDBzHz+TbZv+gmj/uhInz7FTX5FRERiMXeu30585RXv2e2vf006opWycCFMm+Z3O0WkeshXM6FWLejZE+bM8RGRa9f2JhYFBd6VzsiR0KWLPzbayJ/XWiuxP0GkRiqtxoTGb0iTd97hz4OG0GTG9zT+z7X0/mx9TtvWb1gVqG6LiIjEqX59ePZZOPBAOOEEr/d8+ulJR1Vhf/mLt0p5/31o0SLpaERkZfz5J3z+eXH/D7fd5rukbIsXw+jRsPvunlPt0sU7riwo8BoTIpJuSkwkZPgJ73DesPZMXNKaosKpnNfjeb7+dC7D+IjRT49nvT27+RioIiIiSalb1ztqOOww+Nvf/PbkeeclHVWFnHIKbLedX6i89pr/SSKSPkuWeMeR48Z58mHcOPjHP2CDDeCJJ7wvePCcaW5SIvs7nnoqvphFZPVRYiIBw094hyG39mIuDQCYsKQtQz45DggcffhiGmzWLdkARUREMurUgYce8iv688/35MRll1WZzo622AIeeMArfgwe7H+KaiGKJGfePG9qMXasN73YcEN47z3vxyE74dCihfe9u8EGMGAAvPii14JYd13vhFKjX4hUL0pMJOC8Ye2XJSWKGesUTOPO+9dOJCYREZES1arlnWDWqweXX+5XFtdeW2WSEwccAFdeCWedBZ07w0UXJR2RSPX322/eCeXaa/tQnEce6cmIH3/0ft4BrrrKa0Wsvz6ceKInHjp39kfz5sXf1aaNPzKGDs0/+oWabIhUXUpMJGDiktZ5p/+8VI1fRUQkpQoKvGF33breff28eXDzzVWm+sE//uE97e+7b9KRiFQtw4d7C66JE71GwtChK44yEQL8+9/FfUCMG+edzp5yCtx4IzRpAlOnQt++cPjhxQmIDTbwz6+9NlxzTfljyvx+WXGJSNWhUTkS0L7WZCYsWbF78HaFkxm/WN2Gi4hIioUA554LV1zhPUvedRcUFiYdVYVlLmZEpGT5Rr+oXRv69IEFC6BrV7j/fp/eurVPyyQdunTxEeU23TSZ2EUkfTQqR8oMHTKeIbeuuVxzjvrMYeiQ8YASEyIikmJm3pyjfn345z+9UfgDD/jVShVxzTVw6aXw7rvQTd06iSxnxgz47DN/XHLJ8kkJ8OYZH30EO+64/P/P2LHQuHGVaeElIimjGhMJyR2VY+iQ8Qy6ZaukwxIRESm/a67xNhJ77QWPPgprrJF0ROUyaRJstpnnUj74ANZZJ+mIROK3ZIn399Cxo7+/9FJvrTV1atmfNYOlSys3PhGpfkqrMaHEhIiIiKy8m2+Gk07ybvOffNJrUlQBn3wC22zjVc7ffBMa5PZJLVLNfPstjBhRXBviiy+8q5g//vA+IG67zUfH6NGj+NG3b/7RL9q1g/Hj4/4LRKSqU1MOERERqRwnnuijdRxzDOy2Gzz3HDRsmHRUZerdGx55xCt7HHaY51RUBV2quhA8YZBJPnz2GVx9tY968dprnkNs1syTDscd58+ZLmKOP94f2TT6hYjERYkJERERWTVHHeXJicMPh513hhdf9FuwKbf77l7ho2VLJSVqkvKMMlEVzJsHY8Z4p5Nt2sA773iZ/vNPn28GnTrBL794YuKgg3x+mzblL+8a/UJE4qKmHCIiIrJ6PPkkHHwwdO8OL78MzZsnHVGFTJ3qF3lSfeUbZaJ+fRg2LPmL7bISJjNnwi23FNeE+Ppr7+fh+uvhtNO8/F52GfTs6TUhunVTEyURSRf1MSEiIiLx+O9/Yb/9YIMN4JVXYO21k46oXF55BfbYAx57zJ+lapo1C376CaZP99Elpk/3PhTOOMPnr7mmv8+V6TPhggvgjTe8Y9Q6dfy5VSu44w5f7t//9tEnMvPq1PHOU08+2ec//jhMm7b8/JYtYYcdfP5HH/mQmtnzmzSBt95aMWFSq5b/Gw0a5CP0zpsHjRpB27bL9wOxxRZKqIlI1aA+JkRERCQeu+3myYk994Rtt/WG7W3aJB1VmbbcEjbe2Ct8vPUWbLJJ0hFVfavaZGLBguLkwkYb+UX8++97S6HM9MzzBx941yaXXOKDxeQ6+WQfNCZfUgI8RvBl6tSBhQs9SbBwoceR8cEH8OqrPmTmwoX+vMEGxYmJ667zGLNtvnlxYuKYY7zTyWw77ugdU+YOy7l4MXz3nbeSAn/+/XdPToiIVDeqMSEiIiKr39tve5KiRQtPTrRvn3REZfr5Z7+IXLAAPvzQL6Zl5ZTUZOLGG6Fr1xUTCyef7Ov78cfhrLN82qxZxZ8dNw423NCbLfz9796BY4sWsNZa/nzHHd5y6OOPvYlD9ry11ioeLKZ9+9U/ykQIxX02/PknzJ9fnLhYuNBrRqy/vs8fOdKXySQ1Fi70+Lbf3r8nl4blFJHqRE05REREJH4ffeTDiDZq5MmJTp2SjqhMX34J/frBuuv63fGq0EZ/dXfmuGQJzJmz/GOddbxJwm+/wQsvrDj/wAN9pJMxY+Ccc7xpTHZNg4wWLTzpkG2NNbwWwlZbeW2V229fMbGw447e5GHhQh9FIjOSREWltY+JykiYiIikjZpyiIiISPw23RRefx122gm22caTExttlHRUperaFZ54At59t/gue5rlXmhPmOCDpLz9tvc/kEkcbLGFD5gycyYcffSKiYVTT/WhIn/4ofjufrb//MeHmpwyxQdfyVa7tne02Lu3JzWmTs2flACvIfHf/3rCIZN0aNCguMbBNtv4oyR16lR8HWVL6ygTGpZTRGo61ZgQERGRyvXVV97IfvFiv5Xes2fSEZXbL7+kbzjRBx7wVTpuHDz/vK/WsvzjH3DVVTB7tueLGjRY/nHIIbDPPp64uOGGFef37AnrrecJhwkTlp9Xu/aKv6caABVXXYYxFREpiZpyiIiISLK+/daTE7Nm+VCim26adERlGj8e+vTxi/qzzorvd6dMKU48jB3rzx06wF13+fx27XzkiU6dfLl8zPx7MsmDlW36sLLS2mRCRESSU1pioiDuYERERKQG6tTJOxBo1sw7DHj77aQjKlNRkbdCOfts+L//W73fvWCB92fxxBN+Z/y884rnHXSQN7s45RR46CEfJrJVq+L5773nzS++/NKTFCXFvs460Lhx/EkJ8OTDsGEen5k/pyYpMXy4V+koKPDn4cOTjkhEpMZTjQkRERGJz5QpXnNi0iR45hlPUqTY/Pke4siR3kVGv34V+/zvv3uNh+++K+6b4ZRT4Oablx9toUsXTzSYwRtv+HPnzmU3I1HNhArSChMRSYyacoiIiEh6/PKLV0X45huvMrDbbklHVKoZM7zzyJ9+8pEhfvpp+T4Ali71fgHatPH+Fp54wjuLHDsWpk1b/nuaN4fHHoMvvvDEQ+fOPgzmqoz+ob4JKqCoyJNiudT5hYhIpVNiQkRERNLl1199KNHPP4eHH4b99ks6olJde633M7FkSfG0wkJPRkyf7s0tPvsMunf3P+fmm4sTD126+HOHDt56YLVTZgJC8MzPlCkwebI/53v9558lf8eIEbD55j68rYiIrHZKTIiIiEj6/Pkn7LorfPgh3Hdfqi+mSxplom5d+OtfPfGw997e9CJWNaFpwsKFXk2ltKTDlCm+XLaCAu+co00baNvWnx98EP74o+TfKijw7FK/fsWPoqJK/fNEJIWU8K0USkyIiIhIOs2eDXvsAW++CXfcAUcfnXREeRUU+E35XGbL9xURqwULYP31/aI8V6tW8OKLsMYaxY+6dYtf16pV+WOglufEfubMsms5ZLeHyahXb/mEQ+aR/b5VK/87c2PKl8i58UZvzvHuu/744AMvm+DfmZ2o6N59xe+V+KX1wjGtcUn51YSEb0ISS0yY2UDgRqAQuDOEcEUJy/UFPgAOCiE8Hk0bD8wClgCLS/oDsikxISIiUgXNmwf77gsvveSdM5x0UtIRraCkGhOrtWuCELwWyS+/+MX4tGnFr3Ofp00rvVlCWQoKSk5aVPR9vnkffAC33OLJk4xatbypxBprFCcdMhf/2Zo3XzHJkPu6adOVT6yU58Jx8WLvCCSTqHj33eK+KRo08L8jk6jYfHMf/kTik9YLx7TGBUqYlGXhQt/J//ADHHoo/Pbbiss0bAgnn+ydDTVp4vuhfM/161de4reKb8dEEhNmVgh8A+wETAY+Bg4JIXyVZ7lXgPnA3TmJiT4hhBnl/U0lJkRERKqoBQt8nMxnnoGDD4b330/Vidfw4TDkqMXMXVh8p7x+ncUMu7tW6aEtWuSdUJQn0TBt2orNEcBPcJs3h7XX9rYiLVsWv77uuvwn0C1bwu23+7AiCxYUP0p7X5FlM+8rqrAQ+vYtOenQurXXhkijiROXT1R8/rlXlykogI03XrH5R2XXSIlLXBdCIXiiauZMT7rlPrKn3323j5mbq04d6N277N9ZmdjKY/To/P/D9erBPvt40q5evYo9lzSvIrV20pwwidPvv3vi4fvvV3yeNKl81d9q1fLEZVnL5CYvSktk5JtWu/aK31sNtmNSiYktgItCCAOi9+cAhBD+lbPcacAioC/wvBITIiIiNdSiRbDNNn63PVsaTryGD2f4ka9y3qILmUgRRUxkaOGFDDq5mfdumS/R8Msv+ZMG4LUG8iUacp9btoS11ir5IiTJE9UQfJvlS1x065bCti+r2axZXlbzNf9o02b5REWPHlWz+Ud5y9eSJcWJg3yJhfIkG2bOLLtsFBR47ZTS+gnZeeey/66VSRqV5zMvvVTyvPXX9/+RefOKn1flOqywsPxJjOeey5/IadkSnn/eE59rreUdv1blhNqSJV4TK1/i4YcfPDGRrWVLWG893zbZz4cckr+JXLt28OOPvu3+/NPLYfZzvmn55s2cWfbfUq/eismKt99e/n8xO64qMqpQUomJ/YGBIYRjoveHA5uFEE7KWqYN8BCwPXAXyycmfgR+BwJwewhhWFm/qcSEiIhIFdeund+ZzWXm1Whh+ZP5ir5e2c9nD8dRkqZNS08wZE9bnRcAaazaG0vbl5Qpq/nHZpsVJyq22GLF5h9JbMclSzzBMmuWXyxlHpn3Z5yx4sUceM2Ejh2LL7jyNcnJlX0XOffRuHH5pjds6P83aS1fFYkrk9jLTlTMn79i8mJlnnOnfftt+eKvXRuaNfMkRfPmxQmL0l6vueaqDTdU0XI/e3b+pMMPP/g6XrRo+b+nffvlkw6Z1x06lDwCT2UnfDP/d+VNZGSeP/oo//dVoYRvUomJA4ABOYmJTUMIJ2ct8xhwbQjhAzO7l+UTE61DCFPNrCXe1OPkEMJbeX5nCDAEoKioaJMJ+XYGIiIiUjWU1MskwOmnF7/OvqhfldflXe7yy/PHZOYn1C1b+sWauGpQ5Xi1mDRp+UTFZ5/5BYRZcfOPrbby5j7nnlu+9RWCX2zmJhOyEwqlTct+n+8uenntt1/5EwpNmvjd+9WZjEtj+UprXCUlTNZe2zsdnjHDh3D+9deSX5fUfKGgwJMT5UliZF43a+ZJg5LW15VXei2jfAmI3M5w11wzf62H9df3ZmKFhSu3zpTwrRSpbcoR1YrI7KHWAuYCQ0IIT+d810XA7BDCNaX9pmpMiIiIVHFpPfFKa1xplsYT+6TNmuXD42YSFe+/X3ptg7p1vc+E3IRCeWrw1KrlCYLGjf3OcOZ1Rd5vtZVXjc+VhnKf1vKVxrhWNWESgpe70pIX+aaV1g9N48YeT1n9NRQU+HrMru2Q/bzmmuVbB9VBWhNfFZBUYqIW3vnlDsAUvPPLQ0MIX5aw/L1ENSbMrAFQEEKYFb1+BbgkhFBKwy0lJkRERKq8tJ54pTUuqdqWLPHmH716lbzMjjuuXGJhddRQULmvPpJImMydW3ptjP/8p+TPvvSSJx+KilQbLVsaE18VkORwobsCN+DDhd4dQhhqZscDhBBuy1n2XooTE+sBT0WzagEPhRCGlvV7SkyIiIhUA2k98UprXFL1pblGjsq9VJY0l3upFIklJuKmxISIiIiIVDmqmSA1kcp9jVNaYmIVulAVEREREZFVNmiQX4y1a+fNL9q108WZVH8q95JFNSZEREREREREpFKpxoSIiIiIiIiIpJISEyIiIiIiIiKSGCUmRERERERERCQxSkyIiIiIiIiISGKUmBARERERERGRxCgxISIiIiIiIiKJUWJCRERERERERBKjxISIiIiIiIiIJEaJCRERERERERFJjBITIiIiIiIiIpIYJSZEREREREREJDFKTIiIiIiIiIhIYpSYEBEREREREZHEKDEhIiIiIiIiIolRYkJEREREREREEqPEhIiIiIiIiIgkRokJEREREREREUmMEhMiIiIiIiIikhglJkREREREREQkMUpMiIiIiIiIiEhilJgQERERERERkcQoMSEiIiIiIiIiiVFiQkREREREREQSo8SEiIiIiIiIiCRGiQkRERERERERSYwSEyIiIiIiIiKSmEpNTJjZQDP72sy+M7OzS1mur5ktMbP9K/pZEREREREREam6Ki0xYWaFwM3ALsBGwCFmtlEJy10JvFzRz4qIiIiIiIhI1VaZNSY2Bb4LIfwQQlgIPALslWe5k4EngGkr8VkRERERERERqcIqMzHRBpiU9X5yNG0ZM2sD7APcVtHPioiIiIiIiEjVV6sSv9vyTAs5728AzgohLDFbbvHyfNYXNBsCDInezjazrysYZ9LWAmYkHUQeiqtiFFfFpDUuSG9siqtiFFfFKK6KUVwVo7gqRnFVjOKqGMVVMYpr9WpX0ozKTExMBtbNet8WmJqzTB/gkSgpsRawq5ktLudnAQghDAOGraaYY2dmI0MIfZKOI5fiqhjFVTFpjQvSG5viqhjFVTGKq2IUV8UoropRXBWjuCpGcVWM4opPZSYmPgY6mVkHYApwMHBo9gIhhA6Z12Z2L/B8COFpM6tV1mdFREREREREpOqrtMRECGGxmZ2Ej7ZRCNwdQvjSzI6P5uf2K1HmZysrVhERERERERFJRmXWmCCE8ALwQs60vAmJEMLgsj5bTaW1GYriqhjFVTFpjQvSG5viqhjFVTGKq2IUV8UoropRXBWjuCpGcVWM4oqJhZC3T0kRERERERERkUpXmcOFioiIiIiIiIiUSokJEREREREREUmMEhOVzKKxUNMmrXGlVdrXl5ml7n85jTHJyklr+U9rXFIxad2Oiqt6SNv6SuuxMa1xZWg7lo+Z1Uk6hnzSGldG2spXRlrjqiyp/KeqDsysKUAIIaSpUKU1roy0xZTm9WVmG5jZxmbWKISwNA0HyTTGlE/atmVGmuIys65mtqWZNUtT+U9rXNlSXO5TE1dat6OZbWtm+5lZbcVVNjOrn3QM+aR4fe0AnJWWeDLSGheAmbUCPw9LOpaMtK4vM9sTeMzM6qcptrTGBWBmnSBd5QvSG1dlS81JSnViZrsDD0b/iKm5qE1rXOD/gGbWImUxpXl97QI8A5wDjDOz1lEiILH40hhTRnSSepSZ/RXSsy1THNdA4AlgCDDRzNZKQ2wpjmsHMzvdzE4HCCEsTTKejBTHlbrtaBHgduBI4CAzW0NxlRrbLsDNZtYnyTiypXx9DQCuAt7LvthQXCWLYrvVzFokHUtGWtdXlCy5HLgrhDA3LRe0aY0LwMx2Bf5nfkOtIJqWhnKfyrjioMTEamZmXYA7gZ+AndJyUZvWuKLY9gJeAf5pZm1SElOa11df4AZgSAjhUOBBYJiZFSa1w09jTFmx7QrcBtQHzjazf0PyWegUx7UZ8B/guGgY50eBLpmTe8W1Qly7AjcCM4Bzzey8pGLJluK4UrkdYdn/3v+AqUBn4OCs6YprRV2BjYGBZrZl9owkj5NpXF9R8uZF4MQQwptm1szM1jazxoqrxNh2Ay4C/h1CmJ5kLBlpXl9AR+DqEMKzZraOmQ0ws25m1lxxrcjMugLXAoNDCF8AAYr3E0ntw9IaV1yUmFhNsgrKJOBo4DLgS2CP6MI7kYvatMaVFV8T4DjgcWAycLqZtU0qpjSvr+hGUAFQDzgvhPB2NOtWYF4IYYliWp6ZtQMuAE4KIdwE9AJ6mzc5STJRmLq4om1pwDzg8Oikqwg/qT8aeC268xHrgTGtcUW/ty5+4nxaCOEB4C/R9B3ijKMqxJXW7Zj5rayLihHAj8APeMLkYjM718xqKa4VjAd+xc8l9zCzDc2siZnVivsiLa3rK+ec4i1gF/O7/4/hyfwPkiz3aYsrK74WwMPAayGE182stZntbGYHxx1LFE8q11fWfhWgFbClma2J10g7DLgeOMHM2sQVU5rjyrEUeCmE8Fp0LDrfzM4ys60s2UR5WuOKhRITq08hQAhhNvBiCGEC8DQwEtjdootaIO7qaGmNiyiuP4GTgEuAN4GFwGlmVpTQP1+a11dhVBX7HeDNrJ3+DKDIzBrBsmRPTY4p+8RgAXBZtIOvDcwG5gNNkyhfaY0rUhjc5yGED8xsDWBX4MLozvYDeJXaljHHmNa4AP4EDg0hvGreDvoBoB1eQ+HfZlY35njSHFdat2PuedAcoCiEcDdgwNn4/+VixeWy9mMj8OP2o8BM4MLodbM444mkdX1lzil+AfYHtgB+AZ4MIRwC/Bu4yaLmTIrLRTUkhgDbmdnxeC3MnfF92H8s/o4U07q+CrJ+72b8/+B64IEQwuH4uXVPYN0YY0pzXNmWAP2jhNJdwCJgfXz7bgeJ1U5Ia1yxUGJiNTBvZ/mQmZ1tZodGF2qEEH4Gnscvarcxs/uBJ8ysQU2OK4ptXTOrY2b1Qwg/hBBmhhA+AJ7F/wlPjZbbxMxiOclJ+fpaFhtwQAhhelRzYw2gDtAEmG1mg4F74zhopzGmLG3NrBbwawjhvwAhhEUhhIX4XbRMlbi+Fm+HgKmMK6fsHxLFtQB4KIRwTfT+dmAU0FJx2bpRQmlRCOG7aJu2BI4NIQwBdsdPXI+OK6aUx5XW7bgL8HB0N+rQKI5XgJlmtjdwAHAHUNvMDo7rZDDFcWXKV71o0hrALsC3wAQ80TSbmBP3KV5fuecUM/C7xUeGEG6O4rwN+Izk9qupiSuKLfvc8BG82de/8RtFZwCbA5sS4z4sresrt9xHcX0IbIA3syJ4LdZfgS41Pa4otkz5ahRC+AZPeB0IjA0hXAGciNfq2zKKM5YkU1rjSkQIQY9VeACb4Qflw4BBwBfAJXmW+z9gItCzJscV/eZuwBi8c6pHgI458zcHzgTeA/4A1q3h66vM2PC7jf8A3gW618SY8pSvYVH56hRNrxM9PwZsCxwS/Q2tanhc+bblpVnzC6LnQ/ETr5oeV+7+K3c7ZuI6Bz9xjbvcpy2utG7HfHFdHs17GL8bOiB6P1hxrVC+Noim/zUqU9/htR8vxpup1avh66u85X5QCsp94nHllLHcY2T3nNjOA46oyesrT1xjgLOjecfi51/X4c34vgY61OS4cspXZh/WDmiNJy2/BDaPlhsC3ALUBqymxpXUI/EAqvoD2AYYlvW+Dd7u8uKsaVvjdxG6KS7Wjf4B++Ptzs4CpgCdc5a7Eb+DHEtsaV1fFYjta7ztY+eaGlN5y1d0UHwZr4LctSbHVZ5tCTQEDgfGAhvV5LjK2o4Un6QeDowGNqzJcaV1O5YS1wTgFKAxsGnWvNhOAtMYVwnlaypevXhgFN8e0bKdgZY1eX2VEld2ua+F968yjnTt75OKq7z7sCOifdgGNXx95YtrIsVJgDbApXiTiaSvPdIQV275OjuKqz3QHDgfv4l2DX7tkdQ5RSriSvKReABV/QH0xYdIbJI1rU2049wva1o7xeV37/CsYCuikwTgb3jHl+tH75vhWemeNX19lSO2g6L3p5FT86SmxVSO8tUxep/Z8ceZMEllXOXYlvvgJ177JrAtUxdXOfdfe+HNvuI86UplXGndjqXE1RY/9uyZNS3WO1NpjKuE8vV3/ER5LaBBEusqreurlLgy5X7f6P3AzP+n4iqxjE3K2oftkMC+Na3rq6Ry/xnLn7MWKK4Sy9cZeBJz3ej9xtG2bF/T40ryoT4mVlEI4WO8quBLWdOm4J29rJM1bYLiAvwktCnwlxD9x4UQroviOt/M6oUQfgP6hhBGxxVUitdXWbFl2jPeFEL4ribHFCmrfNXGh5HrF0IYp7jK3JZtQwiLgafj3pYpjau07XiemRUCXwG7hxDGKK7UbseS4pqMt2dfN2taUFx5y9e1wN3AlcDSpDpjS+n6Kqvct44mvRxC+F5xASWXsVvwfVgtvLnQrnHuw9K6vkop9zex/DnrUsUF5C9f1+DNhi6J+jT5IoTwUghhvOJKjhITFRTtHDOvCwCCdyo20czeM7PMRdlaQJ8kDtZpjSuKaS7ePvBUM/t71qxH8ZEK5kfvF8QVU3TSnsr1VY5tuUm0TGzDcqYxpiguK6N8LQze0eRHIYRJccaW1rgqsC1jO6k3l8q4ytiOi0MIS0II3wbvMDc2aY0rTduxgsfuvlE5jHv42VTGVUr5Go53tDovROKMqxzH7rRuxz4Wb6fLqY4riqm0fdii4KOpTAghTIsrprSur7Se46c1riimksrXw/g52FzFlQ4W83GkSot6mu0bQrgka1phCGFJ9PoWYG18VIkewP4hhC9jiKsjMCeE8FOa4sqKxTInLJm4zKwz8CqedX4Ub5d2ErBTCOH3So5nXaB2COGHrGmpWF9p3pZmVpDJcqclpjwxllm+gD8q+wQ6u8ynLK4meBXK37OmJb4tzawhfjyalTWtdghhUcJxNQ8h/Jop+2nYf+XEZyGEkMK4UrWv0LF7pWJL1XE7iiOVx24z2wyYF0L4PGVx1cf3q3PSFFdOjJmylXgZi/YThSGE53Pji15rO64YV+biOjMt8eN2bhzR+8TLV058qTynSAMlJsrJzHbEx5NtDewcQng9a16t4NVRMbMuQCNgWqjkajdR5nED4BO847z7QlZ12JwdRGxxZf1+kxDCn5lY85zkrI937LIQ2ATvKf6LSo5pLzw7WQA8m3Oimtj6Suu2NLNt8bGmbw0hLMy54Eik3GfFtgne0doHIYTv81ykxV6+ori2j37vmhKSE0nFtRtwOt7Z4KMhhOuz5iVZ9vcHTsCrND4GPBFCmBrNqxN8KNUk4todH6puqxDClBSVr62BNYH/RnGkJa5U7it07K5wbKk7bke/n9Zj9wBgKHBMyGl+mnBcu+H71UbAwyGEW7PmJX3s7gQUAj9k9u/ZcSV0brgr3un6WSGEJ3PmJXkcSuV2jP4fjwcWA8/hzfGmRfMSW1/Rb+6Jd5J6GLAgRfuwLniHvF+FEGblSU4kElfqhBR0dJH2Bz5O9xd4py6HAf/ED44FOcs1gNg7WKoDvAVchnee1ynPMknEtRvwNLBL1rRMIszwrDQUD2W3Zgwx9cRPBLvgbU/fJ8/QTkmsrzRuS2A7fNzkx/Dh4JYbdjDh8rUrPiTVfXjnWK2y5hlQK+7yFf3OAGAUsE3O9IKkyn30O7sAn+NjYPeKXjdLsnxFv9cu+p/cJHo8BlwE9Eg4rp3x4YrfA+7MlKes8pXE/svwk/nvgWfxYWVr55SvpMp9KvcV6Nhd0ZhSd9yOfqcnKTx248eh74CNo/e1cstWQnENiMr99kA/fASVvZKOK2udfQvchverNICszlMT2rfWBp7CE5fgyfsWmVi0HVf4vR74ec7G+M2hV/D+ZjZKMq7oN3fER7nIt56SPEYOxEeeehD4HWgcTU/03DCND/UxUQYza4afpJ4YvFOXWcCewNrBM10WLbcF3oNw7ZhDXIz3jD0XrzK1s5kNMLMto7j6xR2XmW0E3APMAHaLqscRQgiZOzDBs4MbAvWieXFUVWoEfBdCGAv8BjQBrjezC82sexT7liSzHSF927IxfofqP/jdxjOiTPhSi9oSJlHuzawNfiJ/eAjhL3i2vlf0v0pUvhbHXb7MrC/wIvDXEMJbZtbMzFqYd160NKlyb2ZrAN2A00MI7wG/4m36zzezY82bdySyr8AvzhYB34QQRgFn4qPy7GVmLZKIy8y2wcv8GcD+eDIgE0uS+y+CV919DT9B7QocFE1fmlXuN4g7LlK4r9Cxu2JSfNyG9B67u+MX1V+YWV3gVuAhMzstqf1qFMdmwD9DCP8LIbyLJ3vb5SwX+/oys3XwffyhIYTj8WP33cB+ZtY4q4zFtg/L7AfwY9HXZtYIT7A+CFwd1VhIYv9Vj5RuR3x//0PwThnHAVcBffD9Rt0oriTOJ8ATJVeGEJ4xs1ZmtqOZdTazNRM8N9wCPzYeH0I4DC/33aIaL4mdG6ZW0pmRqvAA6ua8vw3faWXfRasNtI45rkyG7ST8jtA6+A71T2DvBONqig9N1yGK7Q6WvwNTiJ+I3U6MWUF8yKIHonX0DX5x2y3ankOz1lebBMtaarYlUXYZz+jugvdyfh6wRtYyiZR7/AT6L/hF0Ey8Td7HwOFA/Wj9JVG+3ovKVTP8AvJRvHbCNtEybeKOK/rdTBa+AX5H9Cpgc+BliscXj31bRr97DXAM0Ch63wEfbuyEuOOKyvtBeH8E4HfOXgeuyFqmAB/aK5btSM4dJ+BAvFbCccC/gAuAf0TrqW0S5SuKq24K9xU6dpc/pqak8Lgd/XZqj93AWcA4fNSb06LyPwL4R4Lla6Noe2ZquxyHN5HLXiaJuGpF/3/bZE17Odq2m0bvkzpGno2PHPEwXrtqXTyJckMUd524yxewPtA8hduxI3AvsB+eJDwXT8q9DhyWVFzR716GN8lpgp+PPRT9P56Lny+2jKt84efQBvQGtoimFQGzo/X3BsXnhq2TKPdpfKiPiRKYt+NtC0wD/hdCCGZWN4Qw38x64QfuC0MIky2nk5VKjqsF8GvIGmrHzA7B/9lG4TvVUcA7wDMhhG/jiCuKI1971Db4yU4v4KkQwgtm1iKEMD26k1ypPc6a2eb4P/yfIYTXzDvP2hA4GTgo2p7t8YPlwcGHNYpFVMZa43ewX4vK2BF4+/FEtmVu+cpsU/PhLHfGqxZ+iR90LITwn8qOKU+M9fD2g9sB6+Hl6l9mdjBwIj7s0g9m1iBkdRZVifFkl/vWeE/12wInhxBuNrNT8QvvrUMIf8QYV2YfNp3i8tUQaBmizuPMrA9wBT4e+8zKjin6zdwyNgjYAj9BfTOEMDP6vz0f/x+t9HWVE1+mnXPmuSveIdVZIYQPs5ar9P1XCfENwIfMO9XMrsX3ZdeEEM6N5sdVvrbBL2LnA/+X9T+Q6L5Cx+4Kx5W643YUSyqP3VH5aoMft1+NytfF+Og3l0bL9MGThvuGrA59KzmuFsBvIeoQMWfeDvhx8YjoHGNhCOGROOLKiaMBnrDM1BbqhF9A/og38dgxeG2mOM8N5+LHnvWBIXing4eFEL6Lal09BhwXYhrOOGv/9TPwRlS+MseixLZjVrn/JYTwupkdijdPaI6X/b3MbCdgYAjh76V9VyXH2Rq4EFgDeD+EcLuZbYefG14RQhiZxLlh9L4OnlyqF0K4ysxOwfvp2CqE8FtccaWdmnLkYWY7451lbYbflX3FvJOSzFCW4/Ad2t8BYjyx2QdvO7VHdPKXMQa/SLsf37FehF/cxlYVyLzDv2FmdraZDcwcHIOP9/w8MBrYwczuBv7PvDffeZUc0+74HZWBwNFmdm4IYVII4VXgM2CXqBpfV/xAObsy48mJLVPGtsDL2KvmQ5+9gp/Ux74t85Wv6KBoURkfgWd5jwAuAd6u7JiyYutlZkVRTPPwk9Ej8VoJP0bTH8GruHeJPhbHyXN2ud8leKeNR+CdFt0cxXUjvs9oFWNcJe3DZgMTshbdEJhDTMPz5pSxNQBCCMPxdtoDgMPNrCl+wVuAN/OII67s8rU4+xnvw+QLvLNCov9TqOT9V/Rb25vZHWZ2lhVXrX8ZmGvewdc+eB8Y9czswGh/Fkf5ynQUtz5wADA4My/JfYWO3RWOK3XH7SiuVB67s8rX5njZzpSvC/EEb0YXfL+6cMVvqZS4MuVr9+jiJ9dU4GczOwivBTAmjrii2LL3rXPw2o6/4v2GNA0hnBhCuAY/Bli0XGUnJbLL12F4rcHv8D4vpgF/M7NW+PlZHbwmU6XL2X8dRXH5yhyLEtmOOeX+WDN7Du+I8wg80btvtGg3PCEQm+zyFZmN7yM6Eh2zg3d4PBOvSQTxnxsOjOJYCNwTQrgqev9vPHkf27lhlRBSUG0jbQ+8GurJWe/vw+9kFGRN6w28gGd7K71zF/xk6jn8rsqzeCdV2dVRr8IzlZn3DWJcX7viO8iT8SrFDwId8yz3OH6S3yuGmLpG26xn9H4n/AQ+01ncSfid7ReBkeR0tpdQGfswen0OsHuc27Ic5StTu+pE/MC9UWXHlPXbA4Cl0fZqnzNvPzw7vhNexX0csG5McWWX+zOjct8pe31Frw/FD5QtEy5fy/Zh+F3swXiHct1iiilfGcuu6n9AtB97F/gQ6J1A+WpXwjL7ReU+7/wYyldmv9oZb17yBDAFGBAtewx5OgOspLg2jrbRltH7TLOS9jnlPol9hY7dq1a+Ej1uR7+X2mN3nvJ1f57ydSTwacL71dzOevtE+7hPgc4xrq/ljt0Un0fkNk87Gk9eNk6ofN2dNb8Pfjz/L37jo2eC5Sv3uL15QtsxN64Ho/+9wqxpJwNfx7y/z3tuiNdCOylaf5fjyadx5Jw/VmJc+fat+To3PhRP/q4d1zqrCo/EA0jjA/grcEbOtPuiA2Fmx1oPaBJjTE3x6j7gmfrXogNQg5zl6sS8rlrivRlvF71vg1d92yJnuf74HYS4DtbrA8dmvW+E9+TdO2taR7zaXlFKytiDwONZ7wtjjKek8rVGznL7x3ygboC3oT8Gb/95R84BqBNwLd6Xw+sxlq8yyz1+8X9wdEDsmoLytWwfht9JuD6u9VVGGaufs9x65BkxJMby1S5rfvaF9sXEl/QqqXxlkgEb55S1FUYCqMTY2gObRK+bAePxu+t34P2XZI6R+8S5r4h+U8fuVStfiR63o99M7bG7HPvVNnjNiTTsV7OTvs3x5EWcF7OlHrujZWrhNxSmENMxspTytUnOci2AhikpXy9F5at59H8b23YsI67s/eqxQPeky1dWPE3wc4kr8OTExjHFVZ5zw8Z4f2hjiTGRU1UeiQeQxgc+pN73wA5Z0wrx7PjmCcaVfaA5Aq92tlv0vh8x3P0pIa4d8OqnmR3CdcDf8yzXIea4MkNQZTpwfJLiO9rdklpfZZSxB8k5OUxJ+doywfLVMWv93AsMiw44mbsIBdG8uDvLKrXcR3HtTp5MeYLl6wGgT/S+nspYyeUrz3JxJ33zla8z8iyX5H7sYLyHffATxafwdvVJxaNjd/ljSuVxO1OWoudUHbvL2K9mOsytm0BcpZWvzaPnWC+yo98sc9+K1/hI07lhLDWDKli+7qe4c9CmKYrrgYT3qyWdG+bWyIntJl/0e2WdG9bBa4jGfm5YFR7qYyKP4EPqnQ08bmY7RtMyHQq1TDCuBVG7SkII9+MZy+PM7FZ8yKXWccaTFctrYfmhbWbh2UrMbLeo0xxCCD/GGV8o7kRmSfAOx+YCC8w7SbyeaAjAJJRSxpYmFVcZ5esePPObRFzfRf1cLMGz43XwdVc70wFTCGFJiGl4pfKWe/wE5/kQYwe0GaWUr4B3rEXwvjrijqusMhbrPiyKI2/5MrM6ZnZIVt8OcbUXL618NY6W2TXqvI0Qnekk5NEQwkNRHHOAX/B9WCJ07C5b2o/b0W+m8thdxn61TfR+fsnfUGlxlVa+HjCz1sH7F4o7rrL2rQNDCFNTdm54lZklsq8oY/+VKV9/xBFLpjyVEVfAR+pJRBnnhtnH7hU6hK0MFTg37BZCeCyJc8OqQKNy5DAfc70geA+4e+PtPx/AhyDcC9gzxNQ7b57YluvhNZr2GLA1sFMI4Ysk4sqKJdNz8BF4BnMKcClwSIhGAYg5ntwece/CO5lZG+/Z+Mu4Y4riSFUZizpXWloFylftEMIiM6uFN9/YCq/auEvw8e2Tiis15T4qW5Ci8pWJK+1lTOWrXLGUth33xYdkOzCh/X2q9qs5saW53KemfGXFlLpjd1rKl+UZsSLN5SuKJbF9a3nWVxLlKzeuFJWv1sE78U5VXKXRsbt6qdE1JsysSZ7JISpImwGf4+3ffsDbd+0f045hhbgyO1Iz65u5k2FmHfG7QAMSPrHJlKPMnbJ5wH/w8YSPrux/wHKsr22z4tsIr3ocy4lNGsuYmfUxs/5mtmUUzFLzYeJSV77MZcpXpsf4xXjbvA54ddXYDzw5cSVS7qM4VtiWeMI56X3Ylma2e9Ydi6XRRW2qypjKV5lxlHc7Ho8P7zo4prh2MbNjc6enoNyvEFcayr1FIzWYFd8FTUn5Kmt9JXLsTnH5Ggicaj5kdmZaKs4Nzay1+XDU2dMS3beWY30lVb5WiIt0XHvsCvzbzDbInp50XFFsPcxs/ZxpiR+7y4grsX1rlRZS0J4kiQc+vM1XeGatIGfeZvgQLjunMK4v8Aw4eO/ssbWrx3fczXOmZWrd9MP/8QrxHux/IU8P3wmvr62Jt8fg1JUxvM+DT/H2eI8CJ6WofG2HZ+NPorgfhEw/Ev3wnqEL8Uz4RcTUG3s54oq93JdzWya1D9sV72n6CrzDtX2z5m2ZVBkDdgRuAc4j6sclZ/+VVPkqK66kyldFtuPG5OmXo5LiWgMfKWIhcEDOvEy5HxBHLBWMK6lyvzXRMKmZspWS8lWR9RXbsTvF5WsXfHSn/nnmJbZfjX5vN3wUi7WzpiW6b63g+oqzfJUVV1LH7b74UOLb5ZnXL6lyH/3+3sAHwNZ5ytdWSZSvcsSV2L61qj8SDyCRPxraAW/gPco+ho9VnLnQMLyn1yR2DOWOK1P4Y4xtVzz7dwM5wx4C3YGPgT2yprVO2fqKrdf6tJYxfNzwzzI7bbzX/H9H8RQkXL52w08UzsGrut0HrFNK+YqlI8KViKvSy31Ft2XM27EX8BHFF9iXRAflzDr7G8WdssVWxvCTwdF47+KXA//KWZejEipfFY0rrvJVke0Y6741+s3B+BBx3wPHZbYZ3r54YNzxVCSuBPatB+PH7ueANlnTe+BD/u0Zd/mq4Pqq8eUL6ILfpf5r9L45fqOod/Q+kf1q9Hu74HfS++WZl8i+tYLrK84RjcodVwJl7GDgyuh1G7yZxjHR+38kGNe6URnarITyNTLu8lWBuBLbt1blR43sY8LMWuO9ob5pZmfhw05dBowM3k6pMISwJNM+SHFZY7ya7s/4GPCTgetCCNOi+ZvgPfy/k2nrFVNc5V5feCdHsRX2NG5LM+uD3/G/LXpfhN8ZOjiE8IMVtx8vDDF1FhTF0RYfp/7SEMK7UTXUK4BzQwjf5JSvguDNFdIWV2zlPootrduyJ95D/Idm1gxP6nwC/AFMDyH8LVpuhTbRlRhTEZ60uS6E8JaZHQTsiQ8v9jt+J2ODaF6c5asiccVdvnpSzu0I8XXAmbXf3APvCPQz4Ak8AbwIOD9EHQHGvL9PZVxRbC3xofyaAdvid0br4cMmNklo/1Xu9QUqX2a2MXAcMAaYhF/AzsB7/h+TxH41+r218REjvgkhnGxmTYFD8M4kx+F34JPYt5Z7fUGs5SuV2zH6zX7AYcC/8NGVRuC1Rb8JIRyRYFwdgZtDCAPMbF08SVI/im800Cru8lXBuGLdt1YLSWdG4nwAjbJe18p6fRbwAsV3h2IdLiitceXE2DV6boGPVX8F0d2zrGUKiCFbn+b1lcbYcmKqHz3XBurid9GaR9NiHRs7J8a9cuK8lyhbnzVtWRXkmhpXWrdldlzR+0LgeOCI6H0rfAi7nRIqX5lhxdbCm788hNeC+QLYNomY0hhXWrcjOUMd4nerHolenwosAO5IYH2lMq6c7dcBT1quAVwIfA1MJebhldO8vlIcV/b+vhs+Isn3wPHRtHXwC6HtEyxjRwJD8Y5vPwVuBq6J9mFbaX2lOq6GWa/Xw5vjXMjyQ1u+BfwjwfLVELgVvyn6FJ7MOQT4Fr8RAzGfF6Y5rurwqEUNYWZ7AoOiBOl9eHupCQAhhCuj6aeZ9yy+i5ntEEL4pabGFcXWDr8jPC5EHQKFEKab2RD8rt6p+NBP+wHjQwijYogpzesrdbGVEtPi6AGw1MwOAw42syNCCL9VZkxZsbXDL67HhhCeiaZlsst/4FVnMR+eanIIYVwNjyuV2zJfXCGECWZ2b4iGzgsh/GxmP+DttmORsx0zHXS1Bi4IITwfLbMEbzf+puJK7XbMxBXwu7NjQwg/mtkMMzscGAL8EzjDzN4OPlxiTY6rHV7b5ZvgtaV+NLMv8eTl/wGnAL8Cf8YRT1ZcaV1faY/L8IT4+8B1wFshhKcAQgg/mdkUvDZHbHL2YfeY2YF4AvOuEMJN0TLT8H3YOzHFlMr1VQXiypT7N/AL7f8AI8xszeBDXj5L/PuK7H3YbDObiTd1/CqEcF20zG/AX83siRBfLe1UxlXd1IimHOY9zL6O9ybbB++tuCFwSwjh66zlRuCdeO0c4ukpO5VxRb+5P94Z2wLgPeCTEMKDWfNb4MPytIseA7JjrqSY0ry+UhdbKTHdHLwpQi3gQXwbdyXGYdhyytf7wKic8nUqftG9AD8pPCSEML4Gx5XKbZknrrWBBsCt2QkbM9sHX6/7J7AdV9h/ZS13CVAYQjivsmNKeVxp3Y754qqPVzfeHz/BHxRCeNy81/hfQzy916c1rtzyNTqEcL+ZnYT3Q9MWOAHvO6c7PpxepZ88p3h9VZW4WuHNb27I/r+LbnKcg3fSOX7Fb6qU2LLL2IfA29H62QD4NkQXFWZ2GUAI4fwYYkrl+qpCca2DJy4vAjYH/g68DDTB+xbaL8Q0OlVO+foAeD2E8IyZ3Y53Dnp4CGG0mR2KN308LMTQHDqtcVVLZVWpqA4P/ELw0az3vfE+E64D2kbTOuMdmfRQXDQAXsN3WPWBo4GbgNNyljsTv/PStSavr7TGVkZMRdG0J4Bv8HagSZevU7OWOR6YhnfA160mx5XybVmecn8yXsU36e14es5yh+D9JmxYk+NK63YsJa5/RnFtBLSPpteKK6a0xlVC+boZ71+iBX6hsV/W8q1q8vqqgnFdkPP/OBjvYC/O/8d8ZexWskaEipaLe9+a1vVVleK6MIprjej97sDFJH9ueBtwZDT/Wrxfpvvwjse71+S4qusjM9ZqdTcOWM/M/goQQvgE78xoMZAZr/dX/K7/Z4oLw9utF4YQ5uLVP1+OYj0QlnWo1RrYIcR0l530rq+0xlZaTB2jZf4P2CeE8E1MMUHJ5Wt98w4Awas0Bnxc8TE1PC5I77YsT7n/Er8TlPR27JC1/9oTb4p2eKjkml5VIC5I53YsKa4XorhahRDGm48ZH1sHrymOK1/5egkffWM7fNs9EdWuIoTwc4yxpXF9VdW4Mv+P3wMHxfz/mK+MvQBskLUP2wFP4Me5D0vr+qpKcT2Pj97TL4TwSQjh+RDChSk4N/wvsKmZ7RJC+DveP8fteJL18xoeV7VU7RMT5r0sL8Lv/myW2XkG7w/hN+Dw6P30EMKMmh5X9JuzgUeAf5jZ+iGEWXhb53F4m0GCj8hxdghhdBwxpXl9pTG2csR0RPT+0RgTS0S/WVr52jRa5i5g0xBDtdm0x5XWbVmBuP6Xou2Y2X89C+wd5/pKa1xp3Y6lxDUyiuuw6P3SEEJsbVLTGlcp5Wssvs+aGS0Xa/XitK6vKhhX7v/j2yGE7+OKK/rN8uzDXsOTYHE1C03N+jLzDnrSFldOjKXFNYPofDUJpZSvL4AB0TI/hhDeCyFMrOlxVVfVPjERiofK+wivirObmZ0STZuC70vWiDMmM7M0xpWJLXr5FD429anRP+JM4AFgczPrABCiDtHikKb1lX3wSVNs2XGVIyZSXL46AoQQJtTUuDJ3NaPfS9W2zKyrtMWVHRulb8f1Id47xmmLqyrsK8oRV0FKj92JxBW9zFe+7ge2MO+4LXZpWV8rcdyObTumeX+fUcF92LS44krZ+loj+r2ClMVF9HtV+dqjb9z7MDMrTGNc1V5IQXuS1fmAkodnwccK3h5v5/843oldj5ji6oK3WWwVvS9IQ1zR79crYXoPvL3ls0A/vN3gR0CzGGJqjY9+0CCF62tdoFZWbIVJx4bfrehXwrxE11dOLNnbMbHylea48GrX5+C9P6dmWwKbZK+ntMRVSrxpKl+FaYkrrfsKdOyuaFypO25Hv5/KYzcpPG5Hv53K/X2eWFJxjIy2Y3b5Sst2HIAPpbx2mrYj0B7vxLJJnu2o8pU/lk4lTE/NOUV1flSrUTnMbDdgQ2BY8Ko32fMsRH9slJ1uD8wMMWR2zWwA3jHKm3inM3sGH3bT8JOxpUnElRVbd+A/IU8NCPPRN/YF9sDbWV0QvD1aZcY0EO+IZxw+LN2FIbqLmOR2zIrtUjzTXARcFIpHRViaxLaMtuGNeO/ho7KmZ+5sJ7m+euOdBS0MIXwUTSvIWk+xl6/odzfF2wwuCF51FyseEjTJuHYBbgGOCyGMyJpeAF6tOHof9z6sFTAReBQ4KmT15J/wvrVxiKqolzA/qe24PZC5e3hHNC17PSUVVyr3FTp2r1RcqTpuR7+bymN3Go/b0e+lcn8f/eYW+AXtkhDCK9G0RPdh0X7iSuBd/KL6ghDC1ynYjpn96hTg0hDCG5nznBTsv/6FjzDWFO+k9PdoXtLn0pvhI4HMDSF8HE1LwznYTsBDwFkhhLvzzE8krhol6czI6noAfYE5wHf4uNMNS1huvZjj2hDvNGy76P0NwFoUZ3trJRFX9Ju7AJ8B/fPMq53zviFQJ4aYtsNHFtgK7wH3SnzYHVg+o5rE+uqEt9fdOlofFwKTiHotJsrcxxlbtJ6mZJWvhtFzvaRiyoptd7wX//vxzoKOy5pnOcvGUr6i39otKveXR7HdnjWvToJx1cHHEN81et8UHxZ0rex1ltC2XBPvSO+raFvWyZqXSFz4ycFneA2Agpx5ue/j3I67AGOAM/Cx4Q9JSflK5b4CHbtXpnyl6rgd/VYqj92k87htKd/f7xqVsavwTv72TLKMRetrXbxNf398WNe/A1OJRoVLcP81AB+BZGvgH8BzJSwXd1z9o+PQdviIcMOiMpZZT0meG+4CfBvF9DRwV9a8JI+RA4HR+HnhuZmyFz0ndk5R0x6JB7Da/hD/59sZH+bmdeBEsk5w8P40ioBngMZQcrXR1RxXO3wsePCM5Azg7qjwd8taJu64NgJ+BIZE75vjJ2Ib5yw3AKgb43b8B96bc+b9mSx/4WhJbMes7XRH1vtu+Dje3wHrR9PWjTM24K/4hWL3KL6H8GGMHiOqjpZQ+eqFt8frEb0/ALg+z3Jxl6/6+GgDO0Tvi/DhP+9OMq6s370W7xSrLT7s1H34SXS/aH77JMp+9NsnRGXpMeBe/ESsbzQv7nLfHngHeAXvlKpPvt9NoHw1wEfa2C16fxJe3bNP0uUr2n5p3Ffo2F3+mFJ53I5+M5XHblJ43M6K5bq07e+j/8ORwBbR+8uAPYGWSZYxoBC/kG1D8cXiKXiyNZNkirV8RfumG4Fto/e18dpVR+Us1zaB7XgSxUnV9ngS53q8hk7HaHoS+/tC/Jh9ePS+MX4sfzzh8tUfv5G2CT688s/ATnmWS+TcsCY9qnznl2a2oZltgu/URwevUnMusB8w2MwaRovWDd5b6mEhhJkhKmGVHNdmeHXGvmZ2Pd4W6SrgGPwA9JyZtQjemV4scWWph1+gLY2qOT4KXAJcZ2b/yVpuM6BVZQdjZh3NrDO+Xt7KmvUmXp0wY404t2NWbL3xYSIHmNlZZtYY77X7TuAu4OCoatykOGKLylcPvF3gB8CR0fMH+MnzJ8BNZtYowfJ1SygeGvVToJ+ZrZvVmRDAFsRQvrIYMAv4BSAqS/fhvVNfm7XcpnHFZWbrm9mmZlYXv8PRARiEJ0v+gldBftzM1gkhjCfefdgWZlYnmtQAv/t/AN7u/k2idRRXuc+yFDgvhLATXovjn8AmUZXU7I60tiTe8gXwUxRDT7zWxN7Av83siaxlYtmvRnF0NLONgCeB9/Bk5rskvK/QsXulpOq4Dek9dqfxuB3FtaGZ9Yv2VV8B6+DJiUT391lq4VX+3zezZsBRwLHAtQmeG/bF7/Y3wZuhBYAQwr/xxMC5Zhb3fqIT3lzvghDCm1HTjUX4/+R60TKZJnKTY4xrQzPrDtwTQng9mnwpnpS4E2+SeUfUDDL2c8PgnW9+mvV+ZghhK2BtM7s9a9HY9mGR+sDxIYRRIYTpeELuEDNbtg+Ltmds54Y1VtKZkVV54FV5x+EdztyLj//eNJrXL5p+MJ45fADf4caRRc3E9QZwNTAUv4N8IVA/a7n7gLYxr7MNsl73w3dW3+PjTmeqy70KbB1jTLvjd9ffjLZT16x5fYEPo9eHR+uyMI7tmBPbW/jd7H3x7O5d+LjPtYGdgGtjXF+Z8vU6nv3+J3AxPkRXZpm2+EVHrFXNcspXi+i5EN/pPwc0jqbl7VwoprguAibjtThuB27CTyTuyOw/YowrU77eiMrUzsAI/GJx16zl7gE6J1DGXgMejPYPW0X/g+2BH/B2q4+TU7U3xu3YJOv1BVH5ytTg2DiumPLEdRpeC+Ej4Kqs6R8BhyZUvt6Kync/4GSiO+7RMrHvK9Cxe1XKVyqO2znlK1XHblJ43M4pX6/htZWujPb1rwF7ZC0X6/4+TxkrxGsDnAj8JZrWFj/n6J9Q+boJr7kxHjgna5n2eE2KOGu6ZB+3HyWqQRXN647XThgY5/bLKV//w5sjnIL349Aya5nW0f4+7hpV2eXrMPwmTFHWtLXw84muMce1Yc77guh5U7yTy3bZ0/WIYZskHcBKB+4HlkcprvK2H8UnEmtG04rwu1eTgO4JxXVQdPC5Gm+jfXI0fRDehm+FHnwrMbbdgbnAI1nTNgX2yVnuXqJqfDHEtGW0I+0Vvb+FrGr1QEe8yvEBeM/BcV6c5cZ2B3BF9LpW1g7sGPxEYo3KPjjmKV8H4hfa12SXpah8vUGMF9ollK/MOioAXsCr7R2OX0SuGXNcj2ZNOxUfx/tKogtrvErjOgmWr9vxu7JN8RPVKyhOBoyLK7Y8ZWx/vAOtu/FmL78RnXThTQNiuUDL2o4PZ03L7uvigmhfcQXeFrllzHFll/v6+F3QHbOmXUVW8jCB8nUb3lkiZI0AEPe+Ik/50rG74uUr0eN2CeUrFcfuPHElftwupXydiSe5RkX7r22IeX+fU8Yezpm+Rs77uyhhRJ8YtuMw/C52a/yu//lRGRuMNz2J63yixHKfKUfA0XiirnmM2zDffvVavEZV46zlBuGJnljWV075yt6HXRrt37OTE48AmyUQV/Y5Ra2s13dRQp8helTeo6o35WiMd2wEPsZsJhN+SDStHX7QGRhC+DyhuB7Dq17OwKvtnW9mjwBn49Wif4kjIDNrgN99Og2Yb2YPAQQfKeGlrOX2A7riGd+4XBFC+DR6fSHQLKrWDjAdv7NxNnBECGFcjHHlxnYusFFUZXAxPs75oXiNhWtDCAtCtDerZNnl63H8omIJsI+ZFZjZELy974khhD9iiCdf+XoQvFdxM8vcfZmDt6k9Fe/x+PeY45pnZg9Hcd0YQrgshHBWCGGRmR2Gt9deVPK3VYrs8nU+sFG0zY4A/sQ7YzoE2C+E8FOMcWWXsSfxWhxj8ZPofUIILwGEEA4MXk21UuVsxwVZ5WuhRWOuhxAuxU9aj8BrJsTR63lJ+9W5+J3su6NmOkPw/dhnJX1XJckuXxcA7cysTghhQRT/0fjFUWz7ioiO3eWQ8uM2pPfYncbjNqx47B6F3zl+AW9ysguesIhtf1/SvjWyJGu5ffHaAJW+v8+SvR3Pw5MBU/H+ANYD/obHfmQc5xMlxJUp92vg+3zwjh0X483B4pS7X30WrzFxqJk1MLOT8eaFJ8S1vvLswzLnYBfgydTnzOw4MzsPH44zrlFBSjqnWJw5pwDOAhaZ2VZxxCSRpDMjq/LAD3rPElVfxKufHQoMx09ytiTq5CUFcf0Fv2PWEr8b1CqBuFrjPclmqkwNz5n/F7y9b7cYYyqkuHp/IV5d8FOKmwF0wrO7sVZrLGdsRXiVtPVTUL4OxavcN8LvbndJQfl6MGf+0/gJ/oYJx/VQ1rxa+MX/R0DPlJSvVpm4o+cGCWzLfGUs0w46s0ysVRvLUb42iNZfj4TjGp41L1OL4/k496tllK/M/ms9PFGYxL5Vx+7VUL6i+bEft8tZvhI5dpcjrkSO26WUryOAWymuudcogbhK3LdG/48n4kmUNJwbrhNNa4cfv5ukrHw1j55jq5FQRvk6FK+90QQfWWijFJSv7BoK++D9Ht2ZwD6srHOK+nitudiv12ryI/EAVil4zwSehFfx2iZr+uvE3E6pnHG9RUzVUssRY3Pgicw/It6Z3QkkMHRQVky1op3Ea9H7I/DqXk1TsL5yYzsMb+ebd2i7hMrXG0luvzLKVyd8eM7YD4plxNUNb7ea6IGnhPJ1O9FQjgnEU1IZ+x8xJ3DKuR174tWg10pJXI9E79eLYkt0WLESyteVZFXxTUn50rG7fOUrNcftEspXKo7daTpul1G+3gB6R69jHW2pHGWsM17LMfZEYRnbMbHjYylxDYq2bdqO22/gnWonWrZyytfD0fuuRP04pCSuTLnvg3dyqb4lYn7UogoLIcw3s+F4Fbhzop6hF+B3NmakMK7mRKMBJC2E8KuZHQdcbWZf41XQtg3xVhfPjWkxMNvMJpnZv/COAI8M8VYxzquU2GYnEEtJ5asF3lwicTnl65to8tYhpqZL5Yzra7yJybYhhJ8Tjitf+RocQpiXUDwllbG1iUadSFLOdhyH3xnaNoSQ2H4/T1zf4PvV7UIIcVfpzY2rpP3XzITi0bF7JaTxuB3Flcpjd5qO21E8pR27p0TLhCRiyyihjG2T5DEybcfHtMZVRvmam3TZgrzlqxBvmpOoPOcUtfCOXpcmHFqNU6UTEwAhhN/N7A68ivhxwHx8+JukL4BSGVe2EMIMM/scb9e4U9InN9FQPLWBraPnHUII3yYZU0baYqui5SsVseWJK+522StIW/mC9JexNG5HyBtXnG2y81L5qvpxZaTtuA3pLF+QzrjSXr4gbxlLNHGfxu0I6YyripavxI+RkN64appMD7LVQtTJXkhbhivFca2J96r/9xBvB2OlMrPBwMchhC+TjiVXGmNT+aqYtMYF6SxfkM4yltbtmNa4QOWrotIYl8pXxaU4rtSVL0hvGUvxdhxMOuNS+aqAtMZV01SrxIRUXNRT9fyk48hmZpaGKmf5pDm2NEpj+YJUx6XyVQEp3o5pjUvlqxpQ+aqYtMaVZmksY2ndjmmNK83SWL4gvXHVJEpMiIiIiIiIiEhiCpIOQERERERERERqLiUmRERERERERCQxSkyIiIiIiIiISGKUmBAREZHVxsxm57wfbGY3JRWPiIiIpJ8SEyIiIiIiIiKSGCUmREREJBZm1sLMnjCzj6NHv6x5Z5jZz2Y22sx+M7P9y/GZi8xsSvSZ2WbWJ5p+gZl9HU2fZ2btY/9jRUREpNxqJR2AiIiIVCv1zGx01vtmwLPR6xuB60MI75hZEfAy0CWaVwjcEkK4xMzuzfp8WZ+5NoRwnZm9AWBmjYGTgXYhhHlmNma1/4UiIiKyWikxISIiIqvTvBBCz8wbMxsM9Ine7ghsZGaZ2Y3NrFEIYRbQEPglz/eV9pl6wE95PmPRvHmr9qeIiIhIHJSYEBERkbgUAFuEEPIlDDoAoyr4mdbAO9kTQggzzeyfwPdmNhFYfxVjFhERkUqmPiZEREQkLiOAkzJvzKxn9NwU2Ap4rQKfWQvYGvgwz2emAc+HEHoA36+WyEVERKTSKDEhIiIicTkF6GNmn5vZV8Dx0fQRQEvg7ah/ij2Bq8v4zDvARSGE5ZpymFlH4Iys5URERCTlLISQdAwiIiJSg5nZGyGE/jnTHg8h7J9QSCIiIhIj1ZgQERGRpF2SZ9r1sUchIiIiiVCNCRERERERERFJjGpMiIiIiIiIiEhilJgQERERERERkcQoMSEiIiIiIiIiiVFiQkREREREREQSo8SEiIiIiIiIiCRGiQkRERERERERScz/A9fZlalZqxLhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Посмотрим на результаты двух моделей на тестовых неделях\n",
    "plt.rcParams['figure.figsize'] = 18, 4\n",
    "\n",
    "days = pd.date_range('2020-01-01','2020-06-23')\n",
    "days = np.array(days).reshape(-1,7).astype('datetime64[D]')\n",
    "a0 = pd.Series(days[:,0]).dt.strftime('%d.%m')\n",
    "a1 = pd.Series(days[:,-1]).dt.strftime('%d.%m')\n",
    "\n",
    "plt.plot(a0+'-'+a1, np.mean(base_with_metric_3in1,0),\n",
    "         'ro-',label='Базовая модель')\n",
    "plt.plot(a0+'-'+a1, np.mean(inc_with_metric_3in1,0),\n",
    "         'bo--',label='Инкрементальная модель')\n",
    "\n",
    "plt.ylim(0.4,0.7)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Неделя')\n",
    "plt.ylabel('F1-score')\n",
    "plt.title('Среднее для всех клиентов');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3def8e1f-5427-4618-9515-efd7a7549017",
   "metadata": {},
   "source": [
    "# Предсказания на мезо- (группы клиентов) и макро- (все клиенты) уровнях "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a23a27f-b80f-4657-8fdd-825f0c2c1229",
   "metadata": {},
   "source": [
    "## Разделение клиентов по группам предсказуемости"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "f25fdc31-90d4-4599-8bf9-8524cd7e2971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Посчитаем кол-во хорошо-предсказуемых клиентов для каждой тестовой недели\n",
    "\n",
    "threshold = 0.75 # порог метрики для определения хорошо и плохо предсказуемых \n",
    "\n",
    "# если метрика на опр. неделю выше порога -- ставим 1.\n",
    "bool_clients = np.array([[int(x_week>threshold) for x_week in user] for user in inc_with_metric_3in1])\n",
    "\n",
    "\n",
    "plt.plot(a0+'-'+a1, bool_clients.sum(0),'ro--')\n",
    "plt.fill_between(a0+'-'+a1,bool_clients.sum(0), 0, color='red', alpha=0.1)\n",
    "plt.fill_between(a0+'-'+a1, 4054, bool_clients.sum(0), color='gray', alpha=0.06)\n",
    "\n",
    "plt.ylim(0,4054)\n",
    "plt.margins(x=0)\n",
    "\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.title('Количество хороших клиентов')\n",
    "plt.ylabel('Кол-во')\n",
    "plt.xlabel('Неделя');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "26b88587-5159-4d61-a9f4-02dca15b2c3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Самое большое кол-во хороших клиентов:  804\n"
     ]
    }
   ],
   "source": [
    "# Перейдем к реальным id клиентов\n",
    "\n",
    "week_most_gclients = bool_clients.sum(0).argmax()\n",
    "good_clients_idx = np.where(bool_clients[:,week_most_gclients]==1)\n",
    "# Т.к. брали массивы из датасета, то индексы оттуда пропали; возвращаем соответствие!\n",
    "good_clients_id_upto8k = pd.Series(df_2models.index).iloc[good_clients_idx].values\n",
    "\n",
    "# Берем данные с реальными id\n",
    "df_real_id = pd.read_pickle('real_id_bin_money.pkl')\n",
    "good_clients_id = pd.Series(df_real_id.index).iloc[good_clients_id_upto8k].values\n",
    "print('Самое большое кол-во хороших клиентов: ', good_clients_id.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "a2c04e8f-00bd-4874-8fa8-2f86aa94d089",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Суммы хорошо-предсказуемых клиентов\n",
    "gclients_amount = df_real_id.loc[good_clients_id]['money_trans']\n",
    "# меняем полугодия местами, как и в начале\n",
    "gclients_amount = gclients_amount.apply(lambda x: np.concatenate([x[:,366:], x[:,181:366], x[:,:181]],1))\n",
    "\n",
    "# Суммы плохо-предсказуемых клиентов\n",
    "# их реальные id\n",
    "bad_clients_idx = np.where(bool_clients[:,week_most_gclients]==0)\n",
    "bad_clients_id_upto8k = pd.Series(w.index).iloc[bad_clients_idx].values\n",
    "bad_clients_id = pd.Series(df_real_id.index).iloc[bad_clients_id_upto8k].values\n",
    "# суммы\n",
    "bclients_amount = df_real_id.loc[bad_clients_id]['money_trans']\n",
    "bclients_amount = bclients_amount.apply(lambda x: np.concatenate([x[:,366:], x[:,181:366], x[:,:181]],1))\n",
    "\n",
    "# Суммы всех клиентов\n",
    "all_clients_amount = df_real_id.loc[pd.Series(df_real_id.index).iloc[w.index].values]['money_trans']\n",
    "all_clients_amount = all_clients_amount.apply(lambda x: np.concatenate([x[:,366:], x[:,181:366], x[:,:181]],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c01f6dcd-225b-4c99-ab8b-b16275e60f24",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "debf6056-e2d5-41e4-a8cb-68c9f60f8760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Посмотрим на суммы разных групп клиентов\n",
    "\n",
    "dates = pd.Series(pd.date_range('2019-01-01','2020-06-29')).dt.strftime('%d.%m.%y')\n",
    "\n",
    "ws = 14\n",
    "\n",
    "# Скользящая медиана, чтобы избавиться от провалов в конце кварталов и аномально богатых\n",
    "all_rw = pd.Series(np.array(all_clients_amount.to_list()).sum(0)[2,:]).rolling(window=ws).median()\n",
    "good_rw = pd.Series(np.array(gclients_amount.to_list()).sum(0)[2,:]).rolling(window=ws).median()\n",
    "bad_rw = pd.Series(np.array(bclients_amount.to_list()).sum(0)[2,:]).rolling(window=ws).median()\n",
    "\n",
    "fig = plt.figure(figsize=(20, 4))\n",
    "\n",
    "plt.plot(dates,all_rw, 'g-', label='Все клиенты')\n",
    "plt.plot(dates,good_rw, 'r-', label='Хорошо предсказумые')\n",
    "plt.plot(dates,bad_rw, '-', c='gray',label='Плохо предсказумые')\n",
    "c = all_rw.mean()/good_rw.mean()\n",
    "plt.plot(dates,good_rw*c, 'r--',label=f'Хорошо предсказумые * {c:.3f}')\n",
    "\n",
    "plt.title('Суммы по категории survival')\n",
    "plt.xlabel('День')\n",
    "plt.ylabel('Сумма')\n",
    "\n",
    "plt.xticks(dates[::10], rotation=45, ha='right')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "32b9ca0e-4413-4794-a777-9c3bb6ff4686",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_rw.to_csv('all_rw_n_sur.csv', index=False)\n",
    "good_rw.to_csv('good_rw_n.csv', index=False)\n",
    "bad_rw.to_csv('bad_rw_n.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6fecfcf-38dc-49e8-98c0-f9e2f393cc0a",
   "metadata": {},
   "source": [
    "## Предсказания"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b9b8e07-9b9b-4bc7-9c52-33b33c727921",
   "metadata": {},
   "source": [
    "Инкрементально предсказываются 15 дней от 29 марта до 29 июня 2020 года с 0-го по 14-й на основе предыдущих 28 дней, сдвигается начало на один день, предсказываются 15 дней с 1-го по 15-й на основе предыдущих 28 дней и так далее, пока не будет достигнут конец ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "a97bb6d5-da7d-4ce8-b0eb-1d568a2808a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Читаем данные по группам клиентов\n",
    "all_rw = pd.read_csv('all_rw_n_sur.csv')\n",
    "good_rw = pd.read_csv('good_rw_n.csv')\n",
    "bad_rw = pd.read_csv('bad_rw_n.csv')\n",
    "\n",
    "# Задаем гиперпараметры модели\n",
    "n_epochs_each_model = 65\n",
    "n_batch_each_model = 27 #10 from 03/02 \n",
    "lr_each_model = 0.00001\n",
    "n_neurons_each_model = 80\n",
    "\n",
    "# Параметры для \n",
    "dim=28 #размерность вложения\n",
    "mem=5 #глубина памяти для LSTM\n",
    "split=93 #Разбиение ряда для обучения\\прогноза (от конца)\n",
    "fwd = 15 # На сколько дней предсказываем на тестовой\n",
    "imp_num_max = split-fwd-1 # Кол-во предсказаний\n",
    "arr = hankel(np.arange(150))[:imp_num_max,:fwd] # Матрица для вычисления медианы предсказаний\n",
    "\n",
    "days = pd.date_range('2019-01-01','2020-06-29')\n",
    "days = pd.Series(days).dt.strftime('%d.%m.%Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d251c0-6f9c-4373-8e83-9a47f2bca4ee",
   "metadata": {},
   "source": [
    "### Хорошо предсказуемые"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9e4a61d2-1f2f-4e93-a432-72f2c224257e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((406, 5, 28), (406, 1))"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Готовим данные\n",
    "\n",
    "# ОТДЕЛЬНО нормализуем трейн и тест\n",
    "scaler = MinMaxScaler(clip=False, feature_range=(-1, 1))\n",
    "dat_train = scaler.fit_transform(good_rw[13:-split])\n",
    "dat_test = scaler.transform(good_rw[-split:])\n",
    "dat = np.concatenate((dat_train,dat_test))\n",
    "# Составляем сдвиги временного ряда\n",
    "ser=dat_train.reshape(-1)[:]\n",
    "X, y = macro_model_functions.MakeSet(ser, dim, mem)\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "a07be631-6f80-4c09-9ac6-65150d5cc8fc",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.1435 - mape: 125.8376 - val_loss: 0.2198 - val_mape: 112.9163\n",
      "Epoch 2/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1423 - mape: 124.2942 - val_loss: 0.2161 - val_mape: 111.9643\n",
      "Epoch 3/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1374 - mape: 122.2982 - val_loss: 0.2126 - val_mape: 111.0330\n",
      "Epoch 4/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1342 - mape: 117.4651 - val_loss: 0.2093 - val_mape: 110.1075\n",
      "Epoch 5/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1308 - mape: 117.5023 - val_loss: 0.2062 - val_mape: 109.2099\n",
      "Epoch 6/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1304 - mape: 115.3081 - val_loss: 0.2032 - val_mape: 108.3198\n",
      "Epoch 7/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1238 - mape: 111.1545 - val_loss: 0.2005 - val_mape: 107.4340\n",
      "Epoch 8/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1218 - mape: 110.1170 - val_loss: 0.1979 - val_mape: 106.5933\n",
      "Epoch 9/65\n",
      "12/12 [==============================] - 0s 39ms/step - loss: 0.1213 - mape: 106.8285 - val_loss: 0.1955 - val_mape: 105.7574\n",
      "Epoch 10/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1181 - mape: 103.2188 - val_loss: 0.1932 - val_mape: 104.9569\n",
      "Epoch 11/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1151 - mape: 102.7666 - val_loss: 0.1910 - val_mape: 104.1578\n",
      "Epoch 12/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1131 - mape: 100.1521 - val_loss: 0.1889 - val_mape: 103.3640\n",
      "Epoch 13/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.1108 - mape: 99.2109 - val_loss: 0.1869 - val_mape: 102.5868\n",
      "Epoch 14/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1097 - mape: 98.4543 - val_loss: 0.1850 - val_mape: 101.8200\n",
      "Epoch 15/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1093 - mape: 97.1085 - val_loss: 0.1832 - val_mape: 101.2709\n",
      "Epoch 16/65\n",
      "12/12 [==============================] - 0s 41ms/step - loss: 0.1062 - mape: 93.7853 - val_loss: 0.1814 - val_mape: 100.7829\n",
      "Epoch 17/65\n",
      "12/12 [==============================] - 0s 41ms/step - loss: 0.1034 - mape: 93.5176 - val_loss: 0.1798 - val_mape: 100.3050\n",
      "Epoch 18/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1032 - mape: 90.1780 - val_loss: 0.1783 - val_mape: 99.8364\n",
      "Epoch 19/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.1021 - mape: 91.3281 - val_loss: 0.1769 - val_mape: 99.3586\n",
      "Epoch 20/65\n",
      "12/12 [==============================] - 0s 41ms/step - loss: 0.1000 - mape: 88.4339 - val_loss: 0.1755 - val_mape: 98.8772\n",
      "Epoch 21/65\n",
      "12/12 [==============================] - 0s 41ms/step - loss: 0.0974 - mape: 87.8524 - val_loss: 0.1742 - val_mape: 98.4045\n",
      "Epoch 22/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0969 - mape: 87.8967 - val_loss: 0.1730 - val_mape: 97.9259\n",
      "Epoch 23/65\n",
      "12/12 [==============================] - 0s 41ms/step - loss: 0.0964 - mape: 86.6615 - val_loss: 0.1719 - val_mape: 97.4379\n",
      "Epoch 24/65\n",
      "12/12 [==============================] - 0s 39ms/step - loss: 0.0944 - mape: 85.4265 - val_loss: 0.1708 - val_mape: 96.9363\n",
      "Epoch 25/65\n",
      "12/12 [==============================] - 0s 39ms/step - loss: 0.0944 - mape: 87.7306 - val_loss: 0.1696 - val_mape: 96.4164\n",
      "Epoch 26/65\n",
      "12/12 [==============================] - 0s 41ms/step - loss: 0.0916 - mape: 84.9518 - val_loss: 0.1686 - val_mape: 95.8857\n",
      "Epoch 27/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0884 - mape: 84.4736 - val_loss: 0.1676 - val_mape: 95.4411\n",
      "Epoch 28/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0900 - mape: 85.7478 - val_loss: 0.1666 - val_mape: 95.3149\n",
      "Epoch 29/65\n",
      "12/12 [==============================] - 0s 41ms/step - loss: 0.0884 - mape: 84.4723 - val_loss: 0.1657 - val_mape: 95.2442\n",
      "Epoch 30/65\n",
      "12/12 [==============================] - 0s 39ms/step - loss: 0.0882 - mape: 84.6364 - val_loss: 0.1648 - val_mape: 95.1467\n",
      "Epoch 31/65\n",
      "12/12 [==============================] - 0s 39ms/step - loss: 0.0871 - mape: 82.8086 - val_loss: 0.1640 - val_mape: 95.0520\n",
      "Epoch 32/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0858 - mape: 82.6356 - val_loss: 0.1631 - val_mape: 94.9473\n",
      "Epoch 33/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0836 - mape: 84.7904 - val_loss: 0.1622 - val_mape: 94.8633\n",
      "Epoch 34/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0826 - mape: 84.9929 - val_loss: 0.1615 - val_mape: 94.9562\n",
      "Epoch 35/65\n",
      "12/12 [==============================] - 0s 41ms/step - loss: 0.0826 - mape: 84.0567 - val_loss: 0.1608 - val_mape: 95.0541\n",
      "Epoch 36/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0814 - mape: 82.0921 - val_loss: 0.1601 - val_mape: 95.1406\n",
      "Epoch 37/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0811 - mape: 83.2586 - val_loss: 0.1594 - val_mape: 95.2402\n",
      "Epoch 38/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0800 - mape: 83.9419 - val_loss: 0.1588 - val_mape: 95.3382\n",
      "Epoch 39/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0780 - mape: 84.2008 - val_loss: 0.1582 - val_mape: 95.3996\n",
      "Epoch 40/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0772 - mape: 82.0332 - val_loss: 0.1576 - val_mape: 95.4354\n",
      "Epoch 41/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0760 - mape: 83.0879 - val_loss: 0.1571 - val_mape: 95.4649\n",
      "Epoch 42/65\n",
      "12/12 [==============================] - 1s 42ms/step - loss: 0.0770 - mape: 87.2511 - val_loss: 0.1565 - val_mape: 95.4897\n",
      "Epoch 43/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0753 - mape: 84.8283 - val_loss: 0.1561 - val_mape: 95.5158\n",
      "Epoch 44/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0746 - mape: 84.9880 - val_loss: 0.1556 - val_mape: 95.5384\n",
      "Epoch 45/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0763 - mape: 85.2143 - val_loss: 0.1552 - val_mape: 95.5348\n",
      "Epoch 46/65\n",
      "12/12 [==============================] - 0s 41ms/step - loss: 0.0733 - mape: 83.1634 - val_loss: 0.1548 - val_mape: 95.5060\n",
      "Epoch 47/65\n",
      "12/12 [==============================] - 0s 41ms/step - loss: 0.0726 - mape: 86.5812 - val_loss: 0.1544 - val_mape: 95.4644\n",
      "Epoch 48/65\n",
      "12/12 [==============================] - 0s 39ms/step - loss: 0.0721 - mape: 86.3804 - val_loss: 0.1540 - val_mape: 95.3935\n",
      "Epoch 49/65\n",
      "12/12 [==============================] - 0s 39ms/step - loss: 0.0717 - mape: 84.7570 - val_loss: 0.1537 - val_mape: 95.3105\n",
      "Epoch 50/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0710 - mape: 85.2245 - val_loss: 0.1534 - val_mape: 95.1816\n",
      "Epoch 51/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0717 - mape: 84.8473 - val_loss: 0.1531 - val_mape: 95.0636\n",
      "Epoch 52/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0711 - mape: 87.4822 - val_loss: 0.1528 - val_mape: 94.9701\n",
      "Epoch 53/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0677 - mape: 84.5353 - val_loss: 0.1526 - val_mape: 94.8491\n",
      "Epoch 54/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0707 - mape: 89.9701 - val_loss: 0.1523 - val_mape: 94.7760\n",
      "Epoch 55/65\n",
      "12/12 [==============================] - 0s 41ms/step - loss: 0.0696 - mape: 87.2011 - val_loss: 0.1521 - val_mape: 94.7640\n",
      "Epoch 56/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0679 - mape: 85.4959 - val_loss: 0.1519 - val_mape: 94.7708\n",
      "Epoch 57/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0677 - mape: 85.8732 - val_loss: 0.1517 - val_mape: 94.7952\n",
      "Epoch 58/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0682 - mape: 87.5489 - val_loss: 0.1515 - val_mape: 94.8253\n",
      "Epoch 59/65\n",
      "12/12 [==============================] - 0s 39ms/step - loss: 0.0670 - mape: 87.9645 - val_loss: 0.1514 - val_mape: 94.8445\n",
      "Epoch 60/65\n",
      "12/12 [==============================] - 0s 39ms/step - loss: 0.0668 - mape: 85.8607 - val_loss: 0.1512 - val_mape: 94.8499\n",
      "Epoch 61/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0661 - mape: 87.5062 - val_loss: 0.1510 - val_mape: 94.8758\n",
      "Epoch 62/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0655 - mape: 86.7633 - val_loss: 0.1509 - val_mape: 94.8863\n",
      "Epoch 63/65\n",
      "12/12 [==============================] - 0s 39ms/step - loss: 0.0657 - mape: 86.7613 - val_loss: 0.1508 - val_mape: 94.9075\n",
      "Epoch 64/65\n",
      "12/12 [==============================] - 0s 40ms/step - loss: 0.0662 - mape: 86.3196 - val_loss: 0.1506 - val_mape: 94.9434\n",
      "Epoch 65/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0654 - mape: 86.8020 - val_loss: 0.1505 - val_mape: 94.9499\n"
     ]
    }
   ],
   "source": [
    "# Если до этого прогнозировали, то выкидываем из памяти лишнее\n",
    "try:\n",
    "    del model_good\n",
    "except:\n",
    "    pass\n",
    "\n",
    "model_good = macro_model_functions.fit_lstm(X[:-1], y[:], n_epochs_each_model, n_batch_each_model, \n",
    "                                            n_neurons_each_model, additional=[],learning_rate=lr_each_model) #only historical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "4dbbfb61-d4c6-4c14-ad86-b102df89ec7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Делаем новую модель, чтобы предсказывать с батчем == 1.\n",
    "# Тк у нас stateful LSTM, то размер батча фиксирован, \n",
    "# поэтому воспользуемся таким способом, чтобы предсказывать с др. батчем\n",
    "\n",
    "in1 = Input(batch_shape = (1,X.shape[1], X.shape[2]))\n",
    "out = LSTM(n_neurons_each_model, batch_input_shape=(1, X.shape[1], X.shape[2]), \n",
    "                stateful=True, return_sequences=False, activation='relu')(in1)\n",
    "out = Dense(32, activation='relu')(out)\n",
    "out = Dropout(0.1)(out)\n",
    "x = Dense(y.shape[1], activation='linear')(out)\n",
    "    \n",
    "model_for_pred = Model(inputs=[in1], outputs=x)\n",
    "fitdat=[X]\n",
    "optimizer = tensorflow.keras.optimizers.Adam(learning_rate=lr_each_model)\n",
    "model_for_pred.compile(loss='mean_squared_error',metrics=['mape'], optimizer=optimizer,  run_eagerly=True)\n",
    "model_for_pred.set_weights(model_good.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "dfd82f15-1644-4500-9fdf-81c5c1f25940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afbbc98fad0b488a9806d64b4af6b2e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/406 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "baaa9f660ac7407aa57488e83beb6c82",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/77 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Смотрим, как модель подстроилась под обучающую выборку\n",
    "\n",
    "y_pred = []\n",
    "for i in trange(len(X)):\n",
    "    y_pred.append(model_for_pred.predict([X[i:i+1]], verbose=0))\n",
    "# Трансформируем из нормализованных  \n",
    "y_pred_tr = scaler.inverse_transform(np.array(y_pred).reshape(-1,1)).reshape(-1)\n",
    "y_true = good_rw[13+mem+dim:-split].values.reshape(-1)\n",
    "\n",
    "# Прогнозируем на <fwd> шагов\n",
    "fwd_mapes = []\n",
    "full_fwd_pred_tr = []\n",
    "full_fwd_true = []\n",
    "for imp_num in trange(0,imp_num_max):\n",
    "    # Предсказания\n",
    "    fwd_pred = macro_model_functions.make_forecast(model_for_pred, dat.reshape(-1), [], dim, mem, split, imp_num, fwd)\n",
    "    # Трансформируем значения из нормализованных\n",
    "    fwd_pred_tr = scaler.inverse_transform(fwd_pred.reshape(-1,1)).reshape(-1)\n",
    "    # Реальные данные за тот же промежуток\n",
    "    fwd_true = good_rw[-split+imp_num:-split+imp_num+fwd].values.reshape(-1)\n",
    "    \n",
    "    full_fwd_pred_tr.append(fwd_pred_tr)\n",
    "    full_fwd_true.append(fwd_true)\n",
    "    # Считаем MAPE\n",
    "    fwd_mapes.append(mean_absolute_percentage_error(fwd_true, fwd_pred_tr))\n",
    "\n",
    "# Медиана предсказаний \n",
    "# (на первый день 1 значение, на второй -- 2, в середине -- 15)\n",
    "median_preds = []\n",
    "for i in range(split-2):\n",
    "    a0,a1 = np.where(np.array(arr)==i)\n",
    "    median_preds.append(np.median(np.array(full_fwd_pred_tr)[a0,a1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "4af1951b-2629-4ac4-b0ba-5334d994fdba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train MAPE: 6.562%\n",
      "test MAPE median: 7.675%\n",
      "test MAPE HP: 71.429%\n"
     ]
    }
   ],
   "source": [
    "# График\n",
    "\n",
    "plt.rcParams['figure.figsize'] = 18, 4\n",
    "# Реальные данные\n",
    "yt = good_rw[13+mem+dim:-split].values.reshape(-1)\n",
    "\n",
    "ticks=days.iloc[13+mem+dim:]\n",
    "xt=np.arange(0, len(ticks), 13)\n",
    "\n",
    "plt.plot(yt, c='darkblue', alpha=.5, label='Initial series')\n",
    "plt.plot(y_pred_tr, lw=2, label=f'Model on history. R2={r2_score(y_true[1:], y_pred_tr):.2f}')\n",
    "\n",
    "# Все предсказания по 15 дней\n",
    "for imp_num in range(0,imp_num_max):\n",
    "    t=np.arange(len(yt)+imp_num, len(yt)+imp_num+fwd)\n",
    "    plt.plot(t, full_fwd_true[imp_num], c='darkblue', alpha=.1, label=None)\n",
    "    plt.plot(t, full_fwd_pred_tr[imp_num], lw=2)\n",
    "\n",
    "plt.plot(np.arange(len(yt), len(yt)+split-2), median_preds, label='median', c='black')\n",
    "plt.plot(np.arange(len(yt), len(yt)+split-2), good_rw[-split:-2].values.reshape(-1),c='darkblue', alpha=.5)\n",
    "    \n",
    "plt.axvline(len(yt), ls=':', c='k')\n",
    "plt.text((len(yt))*0.92, plt.gca().get_ylim()[1]*0.95, \n",
    "         f'{days.iloc[-split]}', rotation=0)\n",
    "plt.xticks(xt, ticks.iloc[xt], rotation=45, ha='right')\n",
    "\n",
    "plt.title('Предсказания для хороших клиентов')\n",
    "plt.xlabel('Дата')\n",
    "plt.ylabel('Сумма')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "print(f'train MAPE: {mean_absolute_percentage_error( y_true[1:],y_pred_tr) * 100 :.3f}%')\n",
    "print(f'test MAPE median: {np.median(np.array(fwd_mapes)) *100 :.3f}%')\n",
    "print(f'test MAPE HP: {(np.where(np.array(fwd_mapes)<=0.15)[0].shape[0] / np.array(fwd_mapes).shape[0]) * 100 :.3f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7477f5-f4ec-4751-bdf7-7e8644706c03",
   "metadata": {},
   "source": [
    "HP(Hit Probability): процент ошибок меньше заданного интервала"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9ac3326-9160-4e38-b545-49ecd9577f65",
   "metadata": {},
   "source": [
    "### Все клиенты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "bfcca9c7-45bc-4376-8ebf-d12a44d99572",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/65\n",
      "12/12 [==============================] - 1s 49ms/step - loss: 0.0744 - mape: 132.2833 - val_loss: 0.3856 - val_mape: 128.2462\n",
      "Epoch 2/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0743 - mape: 134.7925 - val_loss: 0.3807 - val_mape: 126.3765\n",
      "Epoch 3/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0727 - mape: 135.0622 - val_loss: 0.3760 - val_mape: 124.5546\n",
      "Epoch 4/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0721 - mape: 134.7154 - val_loss: 0.3715 - val_mape: 122.7849\n",
      "Epoch 5/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0708 - mape: 136.7706 - val_loss: 0.3672 - val_mape: 121.0585\n",
      "Epoch 6/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0710 - mape: 126.7221 - val_loss: 0.3630 - val_mape: 119.4022\n",
      "Epoch 7/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0694 - mape: 127.0522 - val_loss: 0.3591 - val_mape: 117.8957\n",
      "Epoch 8/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0671 - mape: 116.5058 - val_loss: 0.3555 - val_mape: 116.4781\n",
      "Epoch 9/65\n",
      "12/12 [==============================] - 1s 50ms/step - loss: 0.0683 - mape: 123.5621 - val_loss: 0.3519 - val_mape: 115.0944\n",
      "Epoch 10/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0679 - mape: 119.8873 - val_loss: 0.3486 - val_mape: 113.8499\n",
      "Epoch 11/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0674 - mape: 118.5400 - val_loss: 0.3454 - val_mape: 112.6062\n",
      "Epoch 12/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0668 - mape: 118.5683 - val_loss: 0.3423 - val_mape: 111.3920\n",
      "Epoch 13/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0658 - mape: 117.3694 - val_loss: 0.3392 - val_mape: 110.2135\n",
      "Epoch 14/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0653 - mape: 125.6320 - val_loss: 0.3362 - val_mape: 109.0547\n",
      "Epoch 15/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0653 - mape: 119.2417 - val_loss: 0.3333 - val_mape: 107.9388\n",
      "Epoch 16/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0650 - mape: 114.2013 - val_loss: 0.3305 - val_mape: 106.8704\n",
      "Epoch 17/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0633 - mape: 108.2734 - val_loss: 0.3277 - val_mape: 105.8278\n",
      "Epoch 18/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0645 - mape: 108.6339 - val_loss: 0.3251 - val_mape: 104.8239\n",
      "Epoch 19/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0638 - mape: 111.5208 - val_loss: 0.3224 - val_mape: 103.8126\n",
      "Epoch 20/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0627 - mape: 108.1610 - val_loss: 0.3198 - val_mape: 102.7966\n",
      "Epoch 21/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0626 - mape: 106.7807 - val_loss: 0.3174 - val_mape: 101.8855\n",
      "Epoch 22/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0634 - mape: 109.2504 - val_loss: 0.3151 - val_mape: 101.0033\n",
      "Epoch 23/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0626 - mape: 113.6635 - val_loss: 0.3128 - val_mape: 100.1142\n",
      "Epoch 24/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0616 - mape: 110.3089 - val_loss: 0.3106 - val_mape: 99.2093\n",
      "Epoch 25/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0604 - mape: 107.9280 - val_loss: 0.3085 - val_mape: 98.3155\n",
      "Epoch 26/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0602 - mape: 106.5489 - val_loss: 0.3065 - val_mape: 97.4315\n",
      "Epoch 27/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0595 - mape: 102.9224 - val_loss: 0.3045 - val_mape: 96.5790\n",
      "Epoch 28/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0608 - mape: 111.0901 - val_loss: 0.3026 - val_mape: 95.9303\n",
      "Epoch 29/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0605 - mape: 116.8935 - val_loss: 0.3007 - val_mape: 95.3178\n",
      "Epoch 30/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0601 - mape: 109.9625 - val_loss: 0.2989 - val_mape: 94.7214\n",
      "Epoch 31/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0591 - mape: 109.6300 - val_loss: 0.2972 - val_mape: 94.1591\n",
      "Epoch 32/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0594 - mape: 109.5696 - val_loss: 0.2956 - val_mape: 93.6106\n",
      "Epoch 33/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0583 - mape: 106.3972 - val_loss: 0.2939 - val_mape: 93.0752\n",
      "Epoch 34/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0586 - mape: 107.8966 - val_loss: 0.2925 - val_mape: 92.5837\n",
      "Epoch 35/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0580 - mape: 111.6408 - val_loss: 0.2910 - val_mape: 92.0288\n",
      "Epoch 36/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0580 - mape: 111.8469 - val_loss: 0.2896 - val_mape: 91.4969\n",
      "Epoch 37/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0581 - mape: 113.3231 - val_loss: 0.2881 - val_mape: 90.9655\n",
      "Epoch 38/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0580 - mape: 116.0978 - val_loss: 0.2867 - val_mape: 90.4646\n",
      "Epoch 39/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0565 - mape: 111.4973 - val_loss: 0.2854 - val_mape: 89.9840\n",
      "Epoch 40/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0568 - mape: 106.0251 - val_loss: 0.2841 - val_mape: 89.5223\n",
      "Epoch 41/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0557 - mape: 111.6137 - val_loss: 0.2829 - val_mape: 89.0493\n",
      "Epoch 42/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0564 - mape: 112.9616 - val_loss: 0.2817 - val_mape: 88.5858\n",
      "Epoch 43/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0562 - mape: 118.2775 - val_loss: 0.2806 - val_mape: 88.1408\n",
      "Epoch 44/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0556 - mape: 113.5486 - val_loss: 0.2794 - val_mape: 87.6921\n",
      "Epoch 45/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0552 - mape: 116.3724 - val_loss: 0.2783 - val_mape: 87.2349\n",
      "Epoch 46/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0546 - mape: 112.5478 - val_loss: 0.2773 - val_mape: 86.7790\n",
      "Epoch 47/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0539 - mape: 114.7711 - val_loss: 0.2764 - val_mape: 86.3463\n",
      "Epoch 48/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0544 - mape: 110.8013 - val_loss: 0.2754 - val_mape: 85.9010\n",
      "Epoch 49/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0532 - mape: 112.2707 - val_loss: 0.2744 - val_mape: 85.4450\n",
      "Epoch 50/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0534 - mape: 116.5222 - val_loss: 0.2735 - val_mape: 85.0283\n",
      "Epoch 51/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0541 - mape: 119.2186 - val_loss: 0.2726 - val_mape: 84.5891\n",
      "Epoch 52/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0528 - mape: 116.6003 - val_loss: 0.2717 - val_mape: 84.1475\n",
      "Epoch 53/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0527 - mape: 114.7084 - val_loss: 0.2709 - val_mape: 83.7421\n",
      "Epoch 54/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0531 - mape: 118.8800 - val_loss: 0.2701 - val_mape: 83.3539\n",
      "Epoch 55/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0537 - mape: 112.6532 - val_loss: 0.2694 - val_mape: 82.9715\n",
      "Epoch 56/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0523 - mape: 120.4576 - val_loss: 0.2686 - val_mape: 82.5633\n",
      "Epoch 57/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0520 - mape: 121.4546 - val_loss: 0.2678 - val_mape: 82.1389\n",
      "Epoch 58/65\n",
      "12/12 [==============================] - 1s 42ms/step - loss: 0.0525 - mape: 114.8957 - val_loss: 0.2670 - val_mape: 81.7272\n",
      "Epoch 59/65\n",
      "12/12 [==============================] - 1s 42ms/step - loss: 0.0521 - mape: 118.3357 - val_loss: 0.2662 - val_mape: 81.3145\n",
      "Epoch 60/65\n",
      "12/12 [==============================] - 1s 42ms/step - loss: 0.0512 - mape: 116.9402 - val_loss: 0.2654 - val_mape: 80.9518\n",
      "Epoch 61/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0505 - mape: 114.1277 - val_loss: 0.2647 - val_mape: 80.6478\n",
      "Epoch 62/65\n",
      "12/12 [==============================] - 1s 44ms/step - loss: 0.0515 - mape: 120.7114 - val_loss: 0.2640 - val_mape: 80.4035\n",
      "Epoch 63/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0503 - mape: 120.0795 - val_loss: 0.2633 - val_mape: 80.1618\n",
      "Epoch 64/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0509 - mape: 119.0448 - val_loss: 0.2627 - val_mape: 79.9578\n",
      "Epoch 65/65\n",
      "12/12 [==============================] - 1s 43ms/step - loss: 0.0511 - mape: 120.4952 - val_loss: 0.2620 - val_mape: 79.8069\n"
     ]
    }
   ],
   "source": [
    "# Готовим данные\n",
    "scaler_all = MinMaxScaler(clip=False, feature_range=(-1, 1))\n",
    "dat_train_all = scaler_all.fit_transform(all_rw[13:-split])\n",
    "dat_test_all = scaler_all.transform(all_rw[-split:])\n",
    "dat_all = np.concatenate((dat_train_all,dat_test_all))\n",
    "\n",
    "ser_all=dat_train_all.reshape(-1)[:]\n",
    "X_all, y_all = macro_model_functions.MakeSet(ser_all, dim, mem)\n",
    "\n",
    "# Обучаем модель\n",
    "try:\n",
    "    del model_all\n",
    "except:\n",
    "    pass\n",
    "\n",
    "model_all=macro_model_functions.fit_lstm(X_all[:-1], y_all[:], n_epochs_each_model, n_batch_each_model, n_neurons_each_model, additional=[],\n",
    "                  learning_rate=lr_each_model) #only historical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "f40517d4-4133-4817-8c43-33031be33a9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e487552bc0944a78a06ad6e6db9e117f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/406 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb8f2168b6f84492b26f5b156fe09b17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/77 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Модель для предсказаний\n",
    "in1 = Input(batch_shape = (1,X.shape[1], X.shape[2]))\n",
    "out = LSTM(n_neurons_each_model, batch_input_shape=(1, X.shape[1], X.shape[2]), \n",
    "                stateful=True, return_sequences=False, activation='relu')(in1)\n",
    "out = Dense(32, activation='relu')(out)\n",
    "out = Dropout(0.1)(out)\n",
    "x = Dense(y.shape[1], activation='linear')(out)\n",
    "    \n",
    "model_for_pred_all = Model(inputs=[in1], outputs=x)\n",
    "fitdat=[X]\n",
    "optimizer = tensorflow.keras.optimizers.Adam(learning_rate=lr_each_model)\n",
    "model_for_pred_all.compile(loss='mean_squared_error',metrics=['mape'], optimizer=optimizer,  run_eagerly=True)\n",
    "model_for_pred_all.set_weights(model_all.get_weights())\n",
    "\n",
    "# На обучающей\n",
    "y_pred_all = []\n",
    "for i in trange(len(X_all)):\n",
    "    y_pred_all.append(model_for_pred_all.predict([X_all[i:i+1]], verbose=0))\n",
    "y_pred_tr_all = scaler_all.inverse_transform(np.array(y_pred_all).reshape(-1,1)).reshape(-1)\n",
    "y_true_all = all_rw[13+mem+dim:-split].values.reshape(-1)\n",
    "\n",
    "# Прогнозируем на <fwd> шагов\n",
    "fwd_mapes_all = []\n",
    "full_fwd_pred_tr_all = []\n",
    "full_fwd_true_all = []\n",
    "for imp_num in trange(0,split-fwd-1):\n",
    "    fwd_pred = macro_model_functions.make_forecast(model_for_pred_all, dat_all.reshape(-1), [], dim, mem, split, imp_num, fwd)\n",
    "    fwd_pred_tr = scaler_all.inverse_transform(fwd_pred.reshape(-1,1)).reshape(-1)\n",
    "    fwd_true = all_rw[-split+imp_num:-split+imp_num+fwd].values.reshape(-1)\n",
    "    \n",
    "    full_fwd_pred_tr_all.append(fwd_pred_tr)\n",
    "    full_fwd_true_all.append(fwd_true)\n",
    "    fwd_mapes_all.append(mean_absolute_percentage_error(fwd_true, fwd_pred_tr))\n",
    "    \n",
    "# Медианы предсказаний\n",
    "median_preds_all = []\n",
    "for i in range(split-2):\n",
    "    a0,a1 = np.where(np.array(arr)==i)\n",
    "    median_preds_all.append(np.median(np.array(full_fwd_pred_tr_all)[a0,a1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "d513009f-e4e3-44a9-b161-2919dd615e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train MAPE: 6.101%\n",
      "test MAPE median: 27.302%\n",
      "test MAPE HP: 7.792%\n"
     ]
    }
   ],
   "source": [
    "# График\n",
    "yt_all = all_rw[13+mem+dim:-split].values.reshape(-1)\n",
    "\n",
    "ticks=days[13+mem+dim:]\n",
    "\n",
    "xt=np.arange(0, len(ticks), 13)\n",
    "\n",
    "plt.plot(yt_all, c='darkblue', alpha=.5, label='Initial series')\n",
    "plt.plot(y_pred_tr_all, lw=2, label=f'Model on history. R2={r2_score(y_true_all[1:], y_pred_tr_all):.2f}')\n",
    "\n",
    "for imp_num in range(0,split-fwd-1):\n",
    "\n",
    "    t=np.arange(len(yt_all)+imp_num, len(yt_all)+imp_num+fwd)\n",
    "    plt.plot(t, full_fwd_true_all[imp_num], c='darkblue', alpha=.1, label=None)\n",
    "    plt.plot(t, full_fwd_pred_tr_all[imp_num], lw=2)\n",
    "    \n",
    "\n",
    "plt.axvline(len(yt_all), ls=':', c='k')\n",
    "plt.text((len(yt_all))*0.92, plt.gca().get_ylim()[1]*0.95, \n",
    "         f'{days.iloc[-split]}', rotation=0)\n",
    "plt.xticks(xt, ticks.iloc[xt], rotation=45, ha='right')\n",
    "\n",
    "plt.plot(np.arange(len(yt_all), len(yt_all)+split-2), median_preds_all, label='median', c='black')\n",
    "plt.plot(np.arange(len(yt_all), len(yt_all)+split-2), all_rw[-split:-2].values.reshape(-1),c='darkblue', alpha=.5)\n",
    "\n",
    "plt.title('Предсказания для всех клиентов')\n",
    "plt.xlabel('Дата')\n",
    "plt.ylabel('Сумма')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "print(f'train MAPE: {mean_absolute_percentage_error( y_true_all[1:],y_pred_tr_all) * 100 :.3f}%')\n",
    "print(f'test MAPE median: {np.median(np.array(fwd_mapes_all)) * 100 :.3f}%')\n",
    "print(f'test MAPE HP: {(np.where(np.array(fwd_mapes_all)<=0.15)[0].shape[0] / np.array(fwd_mapes_all).shape[0]) * 100 :.3f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38707374-8ee5-4d7a-ad3c-997431a2b406",
   "metadata": {},
   "source": [
    "### Плохо предсказуемые"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "4582fe30-fbfe-486c-92e0-ba5f49934630",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/65\n",
      "12/12 [==============================] - 1s 52ms/step - loss: 0.0690 - mape: 130.8779 - val_loss: 0.3692 - val_mape: 126.5803\n",
      "Epoch 2/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0687 - mape: 129.4890 - val_loss: 0.3684 - val_mape: 126.0877\n",
      "Epoch 3/65\n",
      "12/12 [==============================] - 1s 49ms/step - loss: 0.0679 - mape: 129.5072 - val_loss: 0.3676 - val_mape: 125.6135\n",
      "Epoch 4/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0672 - mape: 128.4276 - val_loss: 0.3667 - val_mape: 125.1257\n",
      "Epoch 5/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0656 - mape: 138.4811 - val_loss: 0.3659 - val_mape: 124.6433\n",
      "Epoch 6/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0660 - mape: 135.1974 - val_loss: 0.3650 - val_mape: 124.1328\n",
      "Epoch 7/65\n",
      "12/12 [==============================] - 1s 50ms/step - loss: 0.0659 - mape: 126.7073 - val_loss: 0.3642 - val_mape: 123.6624\n",
      "Epoch 8/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0634 - mape: 127.4322 - val_loss: 0.3635 - val_mape: 123.2218\n",
      "Epoch 9/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0651 - mape: 134.5583 - val_loss: 0.3628 - val_mape: 122.7809\n",
      "Epoch 10/65\n",
      "12/12 [==============================] - 1s 49ms/step - loss: 0.0650 - mape: 127.0251 - val_loss: 0.3623 - val_mape: 122.4156\n",
      "Epoch 11/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0654 - mape: 136.3032 - val_loss: 0.3617 - val_mape: 122.0166\n",
      "Epoch 12/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0641 - mape: 129.8474 - val_loss: 0.3610 - val_mape: 121.6161\n",
      "Epoch 13/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0633 - mape: 126.5860 - val_loss: 0.3604 - val_mape: 121.2242\n",
      "Epoch 14/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0628 - mape: 131.0184 - val_loss: 0.3597 - val_mape: 120.8536\n",
      "Epoch 15/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0627 - mape: 136.8902 - val_loss: 0.3591 - val_mape: 120.5181\n",
      "Epoch 16/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0629 - mape: 134.9725 - val_loss: 0.3584 - val_mape: 120.1752\n",
      "Epoch 17/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0612 - mape: 128.7592 - val_loss: 0.3577 - val_mape: 119.8261\n",
      "Epoch 18/65\n",
      "12/12 [==============================] - 1s 49ms/step - loss: 0.0632 - mape: 130.4594 - val_loss: 0.3570 - val_mape: 119.5098\n",
      "Epoch 19/65\n",
      "12/12 [==============================] - 1s 49ms/step - loss: 0.0618 - mape: 130.6663 - val_loss: 0.3563 - val_mape: 119.1998\n",
      "Epoch 20/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0607 - mape: 135.3492 - val_loss: 0.3555 - val_mape: 118.8750\n",
      "Epoch 21/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0608 - mape: 133.3717 - val_loss: 0.3548 - val_mape: 118.6160\n",
      "Epoch 22/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0617 - mape: 131.8872 - val_loss: 0.3542 - val_mape: 118.3860\n",
      "Epoch 23/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0612 - mape: 130.3066 - val_loss: 0.3536 - val_mape: 118.1434\n",
      "Epoch 24/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0601 - mape: 134.3510 - val_loss: 0.3531 - val_mape: 117.9012\n",
      "Epoch 25/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0586 - mape: 131.7865 - val_loss: 0.3525 - val_mape: 117.6805\n",
      "Epoch 26/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0595 - mape: 138.4807 - val_loss: 0.3519 - val_mape: 117.4492\n",
      "Epoch 27/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0586 - mape: 124.0714 - val_loss: 0.3514 - val_mape: 117.2319\n",
      "Epoch 28/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0601 - mape: 134.3670 - val_loss: 0.3508 - val_mape: 117.0124\n",
      "Epoch 29/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0599 - mape: 138.7507 - val_loss: 0.3503 - val_mape: 116.8163\n",
      "Epoch 30/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0599 - mape: 133.3495 - val_loss: 0.3498 - val_mape: 116.6282\n",
      "Epoch 31/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0584 - mape: 138.2647 - val_loss: 0.3494 - val_mape: 116.4342\n",
      "Epoch 32/65\n",
      "12/12 [==============================] - 1s 45ms/step - loss: 0.0591 - mape: 137.9473 - val_loss: 0.3488 - val_mape: 116.2316\n",
      "Epoch 33/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0580 - mape: 131.6578 - val_loss: 0.3483 - val_mape: 116.0373\n",
      "Epoch 34/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0583 - mape: 132.6471 - val_loss: 0.3480 - val_mape: 115.8878\n",
      "Epoch 35/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0579 - mape: 136.1621 - val_loss: 0.3475 - val_mape: 115.6761\n",
      "Epoch 36/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0586 - mape: 142.9071 - val_loss: 0.3470 - val_mape: 115.4838\n",
      "Epoch 37/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0583 - mape: 149.3838 - val_loss: 0.3464 - val_mape: 115.2492\n",
      "Epoch 38/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0577 - mape: 141.5217 - val_loss: 0.3458 - val_mape: 115.0182\n",
      "Epoch 39/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0569 - mape: 137.9225 - val_loss: 0.3453 - val_mape: 114.8194\n",
      "Epoch 40/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0568 - mape: 139.1278 - val_loss: 0.3449 - val_mape: 114.6508\n",
      "Epoch 41/65\n",
      "12/12 [==============================] - 1s 50ms/step - loss: 0.0555 - mape: 137.6886 - val_loss: 0.3445 - val_mape: 114.4718\n",
      "Epoch 42/65\n",
      "12/12 [==============================] - 1s 49ms/step - loss: 0.0566 - mape: 138.8286 - val_loss: 0.3440 - val_mape: 114.2625\n",
      "Epoch 43/65\n",
      "12/12 [==============================] - 1s 51ms/step - loss: 0.0560 - mape: 139.1770 - val_loss: 0.3435 - val_mape: 114.0614\n",
      "Epoch 44/65\n",
      "12/12 [==============================] - 1s 50ms/step - loss: 0.0559 - mape: 140.4958 - val_loss: 0.3430 - val_mape: 113.8718\n",
      "Epoch 45/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0558 - mape: 138.4099 - val_loss: 0.3425 - val_mape: 113.6669\n",
      "Epoch 46/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0551 - mape: 140.1881 - val_loss: 0.3420 - val_mape: 113.4708\n",
      "Epoch 47/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0539 - mape: 134.8914 - val_loss: 0.3415 - val_mape: 113.2629\n",
      "Epoch 48/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0550 - mape: 143.1602 - val_loss: 0.3410 - val_mape: 113.0419\n",
      "Epoch 49/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0536 - mape: 147.1452 - val_loss: 0.3404 - val_mape: 112.7809\n",
      "Epoch 50/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0540 - mape: 136.1247 - val_loss: 0.3399 - val_mape: 112.5798\n",
      "Epoch 51/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0545 - mape: 140.3018 - val_loss: 0.3394 - val_mape: 112.3591\n",
      "Epoch 52/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0536 - mape: 138.1373 - val_loss: 0.3389 - val_mape: 112.1193\n",
      "Epoch 53/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0534 - mape: 145.8857 - val_loss: 0.3384 - val_mape: 111.9229\n",
      "Epoch 54/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0540 - mape: 136.4582 - val_loss: 0.3380 - val_mape: 111.7433\n",
      "Epoch 55/65\n",
      "12/12 [==============================] - 1s 46ms/step - loss: 0.0548 - mape: 142.6496 - val_loss: 0.3376 - val_mape: 111.5578\n",
      "Epoch 56/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0531 - mape: 140.2510 - val_loss: 0.3372 - val_mape: 111.3469\n",
      "Epoch 57/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0532 - mape: 144.3764 - val_loss: 0.3367 - val_mape: 111.1115\n",
      "Epoch 58/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0533 - mape: 138.5536 - val_loss: 0.3362 - val_mape: 110.9111\n",
      "Epoch 59/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0531 - mape: 147.5208 - val_loss: 0.3357 - val_mape: 110.7037\n",
      "Epoch 60/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0519 - mape: 141.9217 - val_loss: 0.3351 - val_mape: 110.4661\n",
      "Epoch 61/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0513 - mape: 138.5932 - val_loss: 0.3346 - val_mape: 110.2559\n",
      "Epoch 62/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0531 - mape: 139.7370 - val_loss: 0.3342 - val_mape: 110.0700\n",
      "Epoch 63/65\n",
      "12/12 [==============================] - 1s 48ms/step - loss: 0.0514 - mape: 146.1772 - val_loss: 0.3337 - val_mape: 109.8662\n",
      "Epoch 64/65\n",
      "12/12 [==============================] - 1s 47ms/step - loss: 0.0521 - mape: 139.0465 - val_loss: 0.3332 - val_mape: 109.6677\n",
      "Epoch 65/65\n",
      "12/12 [==============================] - 1s 50ms/step - loss: 0.0525 - mape: 140.3076 - val_loss: 0.3328 - val_mape: 109.4906\n"
     ]
    }
   ],
   "source": [
    "# Готовим данные\n",
    "scaler_bad = MinMaxScaler(clip=False,feature_range=(-1, 1))\n",
    "dat_train_bad = scaler_bad.fit_transform(bad_rw[13:-split])\n",
    "dat_test_bad = scaler_bad.transform(bad_rw[-split:])\n",
    "dat_bad = np.concatenate((dat_train_bad,dat_test_bad))\n",
    "\n",
    "ser_bad=dat_train_bad.reshape(-1)[:]\n",
    "X_bad, y_bad = macro_model_functions.MakeSet(ser_bad, dim, mem)\n",
    "\n",
    "# Обучаем модель\n",
    "try:\n",
    "    del model_bad\n",
    "except:\n",
    "    pass\n",
    "model_bad=macro_model_functions.fit_lstm(X_bad[:-1], y_bad[:], n_epochs_each_model, n_batch_each_model, n_neurons_each_model, additional=[],\n",
    "                  learning_rate=lr_each_model) #only historical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "ed9e9219-fc66-4292-8ca3-0a9436e140a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a28778ff27e4b2abdb8c21c0f2860e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/406 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4d9c17f37e342c3bf8a417a88d48912",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/77 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Модель для предсказаний\n",
    "in1 = Input(batch_shape = (1,X.shape[1], X.shape[2]))\n",
    "out = LSTM(n_neurons_each_model, batch_input_shape=(1, X.shape[1], X.shape[2]), \n",
    "                stateful=True, return_sequences=False, activation='relu')(in1)\n",
    "out = Dense(32, activation='relu')(out)\n",
    "out = Dropout(0.1)(out)\n",
    "x = Dense(y.shape[1], activation='linear')(out)\n",
    "    \n",
    "model_for_pred_bad = Model(inputs=[in1], outputs=x)\n",
    "fitdat=[X]\n",
    "optimizer = tensorflow.keras.optimizers.Adam(learning_rate=lr_each_model)\n",
    "model_for_pred_bad.compile(loss='mean_squared_error',metrics=['mape'], optimizer=optimizer,  run_eagerly=True)\n",
    "model_for_pred_bad.set_weights(model_bad.get_weights())\n",
    "\n",
    "# На обучающей\n",
    "y_pred_bad = []\n",
    "for i in trange(len(X_bad)):\n",
    "    y_pred_bad.append(model_for_pred_bad.predict([X_bad[i:i+1]], verbose=0))\n",
    "y_pred_tr_bad = scaler_bad.inverse_transform(np.array(y_pred_bad).reshape(-1,1)).reshape(-1)\n",
    "y_true_bad = bad_rw[13+mem+dim:-split].values.reshape(-1)\n",
    "\n",
    "# Прогнозируем на <fwd> шагов\n",
    "fwd_mapes_bad = []\n",
    "full_fwd_pred_tr_bad = []\n",
    "full_fwd_true_bad = []\n",
    "for imp_num in trange(0,split-fwd-1):\n",
    "    fwd_pred = macro_model_functions.make_forecast(model_for_pred_bad, dat_bad.reshape(-1), [], dim, mem, split, imp_num, fwd)\n",
    "    fwd_pred_tr = scaler_bad.inverse_transform(fwd_pred.reshape(-1,1)).reshape(-1)\n",
    "    fwd_true = bad_rw[-split+imp_num:-split+imp_num+fwd].values.reshape(-1)\n",
    "    \n",
    "    full_fwd_pred_tr_bad.append(fwd_pred_tr)\n",
    "    full_fwd_true_bad.append(fwd_true)\n",
    "    fwd_mapes_bad.append(mean_absolute_percentage_error(fwd_true, fwd_pred_tr))\n",
    "\n",
    "# Медианы предсказаний\n",
    "median_preds_bad = []\n",
    "for i in range(split-2):\n",
    "    a0,a1 = np.where(np.array(arr)==i)\n",
    "    median_preds_bad.append(np.median(np.array(full_fwd_pred_tr_bad)[a0,a1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "94f101be-145c-4c72-8dfe-e83468cdb2e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train MAPE: 6.359%\n",
      "test MAPE median: 35.339%\n",
      "test MAPE HP: 1.299%\n"
     ]
    }
   ],
   "source": [
    "# График\n",
    "yt_bad = bad_rw[13+mem+dim:-split].values.reshape(-1)\n",
    "\n",
    "ticks=days[13+mem+dim:]\n",
    "xt=np.arange(0, len(ticks), 13)\n",
    "\n",
    "plt.plot(yt_bad, c='darkblue', alpha=.5, label='Initial series')\n",
    "plt.plot(y_pred_tr_bad, lw=2, label=f'Model on history. R2={r2_score(y_true_bad[1:], y_pred_tr_bad):.2f}')\n",
    "\n",
    "for imp_num in range(0,split-fwd-1):\n",
    "    t=np.arange(len(yt_bad)+imp_num, len(yt_bad)+imp_num+fwd)\n",
    "    plt.plot(t, full_fwd_true_bad[imp_num], c='darkblue', alpha=.1, label=None)\n",
    "    plt.plot(t, full_fwd_pred_tr_bad[imp_num], lw=2)\n",
    "\n",
    "plt.plot(np.arange(len(yt_bad), len(yt_bad)+split-2), median_preds_bad, label='median', c='black')\n",
    "plt.plot(np.arange(len(yt_bad), len(yt_bad)+split-2), bad_rw[-split:-2].values.reshape(-1),c='darkblue', alpha=.5)\n",
    "\n",
    "plt.axvline(len(yt_bad), ls=':', c='k')\n",
    "plt.text((len(yt_bad))*0.92, plt.gca().get_ylim()[1]*0.95, \n",
    "         f'{days.iloc[-split]}', rotation=0)\n",
    "plt.xticks(xt, ticks.iloc[xt], rotation=45, ha='right')\n",
    "\n",
    "plt.title('Предсказания для плохих клиентов')\n",
    "plt.xlabel('Дата')\n",
    "plt.ylabel('Сумма')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "print(f'train MAPE: {mean_absolute_percentage_error(y_true_bad[1:], y_pred_tr_bad) * 100 :.3f}%')\n",
    "print(f'test MAPE median: {np.median(np.array(fwd_mapes_bad)) * 100 :.3f}%')\n",
    "print(f'test MAPE HP: {(np.where(np.array(fwd_mapes_bad)<=0.15)[0].shape[0] / np.array(fwd_mapes_bad).shape[0]) * 100 :.3f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7250f7b3-6fde-46a2-ad97-dc626063e28e",
   "metadata": {},
   "source": [
    "## Предсказания для хороших/плохих/всех сравниваем с реальными для всех клиентов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "ba7f2e01-ddac-479b-9fa7-b9f48c75fbe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Считаем MAPE для предсказаний хороших клиентов, умноженных на коэфф.\n",
    "coeff = np.median(all_rw[13+mem+dim:-split].values.reshape(-1))/np.median(good_rw[13+mem+dim:-split].values.reshape(-1))\n",
    "mape_good_pred_vs_all = []\n",
    "\n",
    "for fwd_true, fwd_pred_tr in zip(full_fwd_true_all, full_fwd_pred_tr):\n",
    "    mape_good_pred_vs_all.append(mean_absolute_percentage_error(fwd_true, fwd_pred_tr*coeff))\n",
    "    \n",
    "# Считаем MAPE для предсказаний плохих клиентов, умноженных на коэфф.\n",
    "coeff_bad = np.median(all_rw[13+mem+dim:-split].values.reshape(-1))/np.median(bad_rw[13+mem+dim:-split].values.reshape(-1))    \n",
    "mape_bad_pred_vs_all = []\n",
    "\n",
    "for fwd_true, fwd_pred_tr in zip(full_fwd_true_all, full_fwd_pred_tr_bad):\n",
    "    mape_bad_pred_vs_all.append(mean_absolute_percentage_error(fwd_true, fwd_pred_tr*coeff_bad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "c95a648e-4966-48ed-8e0b-28335fa6f182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(yt_all, c='darkblue', alpha=.3, label='Реальные данные всех клиентов')\n",
    "\n",
    "plt.axvline(len(yt_all), ls=':', c='k')\n",
    "plt.text((len(yt_all))*0.92, plt.gca().get_ylim()[1]*0.95, \n",
    "         f'{days.iloc[-split]}', rotation=0)\n",
    "plt.xticks(xt, ticks.iloc[xt], rotation=45, ha='right')\n",
    "\n",
    "plt.plot(np.arange(len(yt), len(yt)+split-2), median_preds_all, label='Медиана предсказаний всех клиентов', c='black')\n",
    "plt.plot(np.arange(len(yt), len(yt)+split-2), all_rw[-split:-2].values.reshape(-1),c='darkblue', alpha=.3)\n",
    "\n",
    "plt.plot(np.arange(len(yt), len(yt)+split-2), median_preds, label='Медиана предсказаний \"хороших\" клиентов', c='blue')\n",
    "plt.plot(np.arange(len(yt), len(yt)+split-2), coeff * np.array(median_preds) , '--', label=f'Медиана предсказаний \"хороших\" клиентов * {coeff:.2f}', c='blue')\n",
    "\n",
    "plt.plot(np.arange(len(yt), len(yt)+91), median_preds_bad, label='median preds for bad ones', c='red')\n",
    "plt.plot(np.arange(len(yt), len(yt)+91), coeff_bad * np.array(median_preds_bad) , '--', label=f'median preds for bad * {coeff_bad:.2f}', c='red')\n",
    "\n",
    "plt.title('Предсказания клиентов относительно реальных данных всех клиентов')\n",
    "plt.ylabel('Сумма')\n",
    "plt.xlabel('Дата')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "7915354d-0d56-465d-891e-5e1502d57af7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>good</th>\n",
       "      <th>all</th>\n",
       "      <th>bad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.254412</td>\n",
       "      <td>0.246026</td>\n",
       "      <td>0.243317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.283367</td>\n",
       "      <td>0.275054</td>\n",
       "      <td>0.272670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.309913</td>\n",
       "      <td>0.301810</td>\n",
       "      <td>0.298974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.335559</td>\n",
       "      <td>0.327402</td>\n",
       "      <td>0.324431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.357620</td>\n",
       "      <td>0.348522</td>\n",
       "      <td>0.344844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.046350</td>\n",
       "      <td>0.140172</td>\n",
       "      <td>0.203068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.046126</td>\n",
       "      <td>0.129887</td>\n",
       "      <td>0.190820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.045879</td>\n",
       "      <td>0.119458</td>\n",
       "      <td>0.178632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.045389</td>\n",
       "      <td>0.108901</td>\n",
       "      <td>0.166336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.044653</td>\n",
       "      <td>0.098021</td>\n",
       "      <td>0.153460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        good       all       bad\n",
       "0   0.254412  0.246026  0.243317\n",
       "1   0.283367  0.275054  0.272670\n",
       "2   0.309913  0.301810  0.298974\n",
       "3   0.335559  0.327402  0.324431\n",
       "4   0.357620  0.348522  0.344844\n",
       "..       ...       ...       ...\n",
       "72  0.046350  0.140172  0.203068\n",
       "73  0.046126  0.129887  0.190820\n",
       "74  0.045879  0.119458  0.178632\n",
       "75  0.045389  0.108901  0.166336\n",
       "76  0.044653  0.098021  0.153460\n",
       "\n",
       "[77 rows x 3 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MAPE для каждого из 77 предсказаний\n",
    "temp_vs_all = pd.DataFrame({'good':mape_good_pred_vs_all,'all': fwd_mapes_all, 'bad':mape_bad_pred_vs_all})\n",
    "temp_vs_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "0aa88296-01af-4261-978f-d015c299d8c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xxnTBAqgxxhjTBQugxhhjTBcsgBpjjDFdsABqjDHGdCFRc+EaE3eVSgXXdQedjSMcxyGbtWrCmEbsyDAmQjZs2MCtt9466GwcsXbtWtatWzfobBgTSaEGUBG5EPg84ADXqeon51nvhcBG4HWq+v92sq0xSbJmzRpWr1497/KqKo/sB1fnT6NcKvLdr13Fq990Bbl8oeE6Y3lYvkBa5sdxnJbrGJNWoQVQEXGAq4FXAjuBzSJyk6r+rMF6nwJu6XRbY5Imm8027TKdKimZXHuDF3L5wrwBtCSQy0NGWgdRY0xjYbZAzwe2quo2ABG5AbgYqA+C7wH+F/DCLraNrE6uZakqB7XSdto5MoxlOmsZ2LWsZJgsB5OOKkyXvZaoMaY7YdaopwA7al7vBC6oXUFETgF+C/hVjg6gLbeNOruWZcIwHVAAnUvLAqgx3QszgDbqG6q/cnMV8Geq6srRXUntbOutKHIZcBnA0qVLWb9+fccZDUO1WuUlL3lJW+u6KNNaPfZ91+WBTZt59gUvPOpalABj0lkLtFqtRmbfmO7NttFR4Va8lXbetwGnSa/DToG8XeI0pmthBtCdwIqa18uBXXXrrAJu8IPnEuDXRaTS5rYAqOq1wLUAq1at0ji2sna6Re6vTB/zvlsswabNDL/4BTiFo5sKa/MLyYndxpsmMxXl0QOt1yuXimzZdDvLn7tm3mugABmBs5fYNVBjuhVmAN0MnCkiZwBPAJcAf1C7gqqeMfe3iHwF+I6qfltEsq22TZLZBq3P1tsoOav7UqXY/mXytlQVyq6Sc6wgxV2jMRdVV9GQbykWBzINyk9axlyE9g1VtSIi78YbXesA16vqAyJyub/8mk63DSuvg1Zs3DvdYpsq41j/W5qUQqgMSy7krBjFno25GIxQTxFU9Wbg5rr3GgZOVb201bZJVeyiBdrNNibeKiH85GGkafqv9v7h4oRSPNx5GqVyketuuIq3X3IF+dz8Xf/HEBheDLnhp1uiabl/OPlt7Bgoauct0FIXrVYTb64FUDOPbDaLk3GYOQg6A/lc92nlc4XOAijgTkA2A4VxkBTdW2wBNALKXQTDchdB18RbGLHO4mcyVF1lei+4pcHloXgIqmUYXqxIJh1B1AJoBJS7qMasBZo+YZwz2XlY/FVmlen9hD5gqB3laXDLMHK84qRglKPdBxEBlS5qsW62McYkS/GwMrU3GsFzTrUMk7uhPJ38OspaoANWVe2qLVm1FmjqhHE+n6LLVYmiVa/VWZkZdE7mUYXpfZAvKUMLk3td1ALogLldBsKKBdDUCaMOsi6o+HFLyvQ+qAZ8X3AYShPeddmR47Xh/aJxZ8fPgHU7iMPCZ/qEMS4jYzVArJQmlcnd8Qiec9wiTD4F5dnk1Vp2+AxYt0UqeUXRtOKEcLQmsFGQSF6XrTJzgFge/OrC9B6YPaxogsZvWBfugHUdQJNTBk2bwgh2FkCjr1pRpvZBdYC3qASleMjv0j0uGbe6WAvUmJjIhnC0hpGmCU5l1u+yTUDwnFOZ8UbpuuX4twLs8Bmwbs/BEjqozTSRswCaKqWp6N2iEpRqGaZ2Q6UY7yBqh8+AdR1AA82FiYOgg11GwElAN1oSzR5WZvYTy+ud7dIqTO2BUozvF7VroAPWbZ1oZz7pE/RTU6z1GU0zB5XSxKBz0ScKM/sAVfKj8TuZs0NowJwu25IZa4OmTtADfsLoEja9mT2UouBZY2a/12UdN3YIDVimy4uZ9sOlj4gEGvTsOaDRUprs7jFkSTFzIH7XRK0ejoBsF63JrI0iSqUgg5514UZHpajMHBx0LgZMven/qm58gqgdQhHgdBEMu+36NfEWZNCzLtxoUI3vBAlBUxdmDw46F+2zQygCugmG3bRaTfwFGUCtBRoNpUnvtg7jKU9797/GgR1CEZCzLlzTpiBbjVm7BhoJpclB5yB6ijHZJxZAIyDXRTDsJuia+LMu3GSplDRWE8P3S2XWm/836uwQioBuGgLWhZtOQQZQmwd38NzioHMQUQpuDLq1LYBGQE46/xm6abWa+AvqiSzZTHIfchwn1QRO0xeUOLTMLYBGQL6L1qR14aZTUC1Qa30a0zubyi8CuhkQZIOI0imowBfGs0VN54I6jCtuhWqPzdlSuXjU/73IZByyTm/hJQ5VnAXQCOimBdrNNib+RARHlF7vNbcAGg2ZgGrgzfdsYNPdtwWS1nU3XNVzGhesfBmrz1vXUxpB7ZswxSCLydfp9UzBBhGlmZMBt8drZ9aFGw1OPph0XnjuGl7w3NU9pVEqF7nuhqt4+yVXkM8Vekork+nxHimBTK63JPrBAmgEdHo9MydiA0BSLIjgZwE0GjJZEKf3Z35mnSz02GU6J58r9BxAe5UtxGOQm3XkRECuw58haz9bqgXxCM+MFaFIEBGyQ4PORfTEZZ/YYRQBnXbh5mNwZmbCE0TwsxZodORiEiz6yQKoaVunA4LsFpZ0C+KgDaIVa4KRHQI7pJ+WyYKTi8cOsQAaARmRjh6QbQOI0i2IFqgF0OiQjJAd7CXHSMkODzoH7bMAGhGddMtaF266WQs0eeLSZdkPcdoXoQZQEblQRB4Ska0i8sEGyy8WkXtF5G4R2SIia2qWbReR++aWhZnPKOikVWkt0HQL4vzJSlC0WAvUJ5AN6NaefgjtNhYRcYCrgVcCO4HNInKTqv6sZrUfAjepqorI84BvAWfXLH+5qu4NK49RkhNp+4G6Ng9uugXRerQiFC2ZHEgGtDronAyWk/e6tOMizBbo+cBWVd2mqiXgBuDi2hVUdVJV58LGKCl+JnsnA4NsEFG6BfHrx6iOSgURCWxShTiL2z4IM4CeAuyoeb3Tf+8oIvJbIvIg8F3grTWLFPi+iNwpIpeFmM9IcDrpwrXmQ6pZF24yxWHmnbA5MdsHYc5E1OgYPaaFqao3AjeKyMuAvwFe4S96iaruEpETgR+IyIOqesxkj35wvQxg6dKlrF+/Pqj899UsVUp69O5xK97zfEqb7sbJPv1T3S8Zfm5VYGq5CuUmM9fMlZud9204qtzUeiprQTRqtDrYx5tVXK/c3PXwhp4ngu+WkyVWBTPMvbQTWFHzejmwa76VVfU2EXmmiCxR1b2qust/f7eI3IjXJXxMAFXVa4FrAVatWqXr1q0L8Cv0zy8qMzzizh71nlsswR0byV+wEqfwdN/GC3PjLIjDTMsmFAdnlV0T8y8vl4ps2XQ7y5+7hly+8eiUM4+DnM2mECmVkjL11OA+v1QusnHT7Zx31prBTOUnsOBkuwY6ZzNwpoicISJ54BLgptoVRORZ4k94KCLnAXlgn4iMisi4//4o8Crg/hDzOnBOB/1ynaxrkieIX9+KUPQMqNEXGRknXsETQmyBqmpFRN4N3AI4wPWq+oCIXO4vvwb4HeBNIlIGZoDX+SNyl+J1687l8Ruq+r2w8hoFdhuLaZv9/IkkGSGTVaqVQedkMOJ4DTjUcx5VvRm4ue69a2r+/hTwqQbbbQPODTNvUWMB1LQrkBZoAGmY4Dl5UhtA4zYCF2wmosjo5IewLtx0s18/ueIYRIISx8kkLIBGRLu3pljr05jkysVoHtggSSaeJw8WQCOi3ftArfVpTHJlsoITw5ZYr7LD8XiAdj0LoBHRbsvSCTkfxpjByo8NOgf9V4jpd7YAGhHtnnx18tgzk0xBTJea2jkzYyA3HM8Rqd3KjYCTj2e9ZgE0ItruwrUAagKIfmoRNLJEhOFFg85Fn2RgaOGgM9E9C6AR0W7XrE0eY4KIfRY/oy07JKnoyh1a6F33jauUz30RHe12zVoXrqlaCzQVhhaCW/L+tVIsFimV21ixiXK5SLFYZHJyglyut7TyuTyFQvPRULlRKIzFuz6zABoR7bZArcvABBFAg0jDhEsywsgSZfIp0BaTzP/91Vfyqav+OpDPvfLKK3tO48+u+Cgf+tOPzbvcKcDw4p4/ZuAsgEaEiCC07lqzFqhxAwh+QaRhwpdxhNETlKk9zYPon77rQ7z7svf19FnlcpF/vuFzvOOSPyHX42Ty+dz8N3Vm8jC6JJ63rdSzABohGQS3RQi1FqhxAxiGG0Qapj+cXOsgWigUWnaZtlIqe92uY2PjoT2N5UjwjNmk8fOx+jhC2ilTmQSctZneVAIIfkGkYfrHC6IgMW7yOAUYO8FrVSeFBdAIaad71n4wUw4g+AWRhukvJyeMnRjPe0SzQ8lqec6x+jhC2vkx7AdLN1Wl1GJASTuCSMP0X8bxgmicpvvLjcJIAoMnWH0cKdJWCzR5hdC0r1wN5haU2ZQ+MisJJCOMLvHmj426wgIYOU4SMWCoEQugEdJOEUtmMTTtmgko8FWqULF7WWJLMsLI8dGeN3doEQwtTHaNZQE0QiyAmlame7u//ShT5eDSMv0nIgwvFgoLBp2TOgLDx0NhPPm1lQXQCGnnx2inm9ckV5BBbyrAYGwGZ2ihMLRo0LnwCV7LeCQd9ZQF0Ahp6xpoOsqlaWCmEswAojkTJajanH6JUBiPQBD1g2duOD2VlAXQCGknOKanaJp6B2eDTc+twqS1QhOjMC4UBvVkkxQGT7AAGintXQNNVwE1HreqHA44gAIcCCFNMzhDCwbzFJehRekLnmABNFJsEJGZz/6ZcOavnSrBdNm6cZNkaFF/7xNNwlNVumUBNELaaV2ms5imm1tV9s2El/7uqfDSNv0nIowcB9KH2j2TIz0P/27AAmiEWAvUNPLUVLiPH5suw8FZa4UmSSYrDPXheujw4mTOMNQuC6ARYgHU1JsoauCDhxp5ahLK9oyzRMmNek8/CS39EcgW0l0jWQCNGRtElB5lV3lysj+f5SrsmvDm2jXJICIUxsNLP3ITOAyABdAIsRaomVNVZcfh/j52bKoMv7TroYmSGwZxgk/XKXhPh0k7C6ARYq1LA14r8InDg5nw/cAM7Ju2VmhSiAi5ECadz40En2YcWQCNEGuBGlWv23ZigBMcPDVlg4qSJBvCLS1hpBlHFkBjJqFPBTI8HTz7MWiolV0TcMCCaCI4QQ8kylj37RwLoBFiRTK9qqrsmohG8Jzz5AQcmLEgGnfiEGjl4mSDSyvuLIBGSDutS7tOmjxu1RswdKg46Jwc68lJ2D1lQTTORCTQgURhDEqKq1ADqIhcKCIPichWEflgg+UXi8i9InK3iGwRkTXtbptENhNR+pRdZfuhaD9abO80PHFY7cktMRbkpR+7jPS00AKoiDjA1cBFwDnA60XknLrVfgicq6orgbcC13WwbSpZ2U2OmYry6EEoDmC0bacOFeHxQ1AJc0okE54gKw6rhI4IswV6PrBVVbepagm4Abi4dgVVndSn79weBbTdbZPIymV6HCoq2w/29z7PXk2X4dGDMFuxIBo7Qf5k9vMfEWYAPQXYUfN6p//eUUTkt0TkQeC7eK3QtrdNGruNJflUld1T3n2ecewRLbuw/aA3xaCJjyDLWhzLbVjCHE/VqK4/Zter6o3AjSLyMuBvgFe0uy2AiFwGXAawdOlS1q9f321+B26WKqWa0ulWvL690qa7cbLeT3W3ZHAsjMZW2Q3nsWS15srNzvs2HCk3QdsOZDPePxN9brn1OhXXKzd3PbyBbJOhtiKQsZG4QLgBdCewoub1cmDXfCur6m0i8kwRWdLJtqp6LXAtwKpVq3TdunU9ZntwHqxMs919eiimWyzBHRvJX7ASp+DdzHVebpzFVnpjp+R6I237cb2zXCqyZdPtLH/uGnL5cO94X1CAk8chYyNLIkuryuEnWq9XKhfZuOl2zjtrDfnc/OVGsrBgmf3eEG4X7mbgTBE5Q0TywCXATbUriMizRLwjT0TOA/LAvna2NSYuZsrxGSzUqcNFeOygDS6KsmrA5U5de+jAnNCaMqpaEZF3A7cADnC9qj4gIpf7y68Bfgd4k4iUgRngdf6goobbhpXXqLDbWJJnoqjsnEj2daOZindd9NSFSt6xEho1QQdQ1EvTyQWcbgyF2heoqjcDN9e9d03N358CPtXutklng4iS5cCs8uTEoHPRHyXXG6F76gJl2KZ5i5R2rn92qlq2AAo2E1GkWABNjoMpCp5z3Kp3r6jd5hItbgiTdFQiPPFHP1kAjRmbyi/6Dhe9eW3TyFUviJbCHmps2qKqVEKYIjKMNOPIhnMaE6CZsvJED8HTdStUXbenPJRLxaP+70XGcXA6nD28UoXHDsEzFilOxk74BqkySygTH1RLUHWVTMqveVsAjZB0F8X4q6oXPHsZMPTgnRv42ZbbAsnPd792Vc9pnLPqZTz7/HUdb1d2veeKnjzecxZMD8ozIaY9DYWU/74WQCOkrVG4FmUja8+UN5imF2e/YA1nrVzdUxrlUpHvfu0qXv2mK3q+DzTjdP/ojYOzMJ5XxgtWaAdBqxpqAC1ZALUAGiXtPc7MRFFVlf0BPMvTcbIdd5nOJ5cvhD6RQiv7Z2B8sFlIrdIUEOJcy9USVIpKNsUnSDaIKEJsFG58TRSTfa9nt6bK3iPbTH9pVSlOhv85xcPhf0aUWQCNkPYCqIXQKJoK4V67pLB903+lSdA+zHxVmYXyTHpPkCyAxoyFz2iy1uf8bNf0V7WizPbxNqrZg16LN40sgEaITeVnksjKbH/NHCDUa5/1qhWYTWlXrgXQCGnnx7DKKJpGbFqzeQ3bUMW+KR5W797PPitNpLMr1wJohNggoviykaaNDWWhkLVS2w/lWR1oS3BmP7jldAVRC6ARYoOI4iubERZaED3G4uFB5yAd3LIys4+BXnDWKkzvS9f1UAugEdJOcLQfLLqWjoFjP9ARozlYZCcVoau6ytReL4ANWrWMn5d0BFE73CPEunDjLZsRThobdC6iQQSWjYPY1FmhqrrK1J7+3LLSLrcI0/vT8dBtC6AR0lYAtQop0hYWhONHBp2LwVs+jj1cO2RaVab3eq2+qKnMeNdEkx5EbXxchLSKjVYdxcPSUaHiKoe6eBhKqVikXO7tYYvlUpFiscjU5AS5fG9p5XJ58oXO+mFPHsfmvw2ZVr1u2zCe9RmU8jQgMLxYE3vibwE0QlpdA7UBRPGxbByqChMdVnD//Pkr+eJn/zqQPFx55ZU9p/HH7/8o7/rAx9pef+koLBqychqmI8EzBs/kLE95/yc1iFoAjZBW/enW3x4fGRFOWeA9WPtwBxXdO/77h7j0j9/X02eXS0W+89XP8Zo3/0nPk8nncvm21z1pDI4bTl4lGSWqyvS+eATPOeUpr3dtePGgcxI8C6AR0qrqSeAJXKJlRDhlXMmI92ivduQLhY67TOuVS3kKhQKjY+N9exrLyePW8gzbXPAcxEQJvSpNgmSUoYXJKiPWqImQTIsQ2mq5iR4R4eRxYUlCBxaJwIoFFjz7YeaANzgnroqHoTiRrEFFFkAjxLpwk+vE0eTd4uIInLbQBgz1Q/GwHrmeGGezh5I15Z/VyRHSqoVpg4ji7bhhYfmCZHTFZzNw2iIYySXgy0RcpajMHhp0LgKi3j2i1UoygqgF0AhpVbHajxV/CwrCqQsgE+O4U3DgjEUwZHPchk6ryvT+QeciYNXkTLRgdXKEtBxE1JdcmLCN5oXTFnmtuLgZzsHpiyBnkyT0RXEiWrMMBcUtQjnG13PnxPAQTi7HBhGlxnBWOH1RvILoaM675unEufkcI1VXKU4OOhfhKR6OfyvUbmOJkJaDiKzeSpS8I5y+SHnsIJQjMBF4M6N5b7RtJgkXcGOiMkvbD8auuBWqVbenzyuVi0f934tMxiHrNA8v1bL3z2n/VuPIsQAaIa1amK1aqCZ+8o5w2iJl+0GoRDSIWvAcjEoHcWzzPRvYdPdtgXzudTdc1XMaF6x8GavPW9dyvUrRAqgJiN3Gkk55Rzh1odcSdSPWozWU9SaGt+DZf9pBg/KF567hBc9d3dPnlcpFrrvhKt5+yRXkc71NwJHJOG2t12OjeeAsgEZIq0rKqrDkGsoKKxYqjx2CqFwWyjlwql3zHJwOdnvWyUKLLtN25XOFngNou+J+XmaNmohp1o1rg4iSbSQnLIvIZAtzMwxlLXgOjJMbdA7CF/fvaAE0YprdHeDE/XTNtLRoSFg0NOhcwLIxu89z0HLDg85ByASyESjrvQg1gIrIhSLykIhsFZEPNlj+BhG51/93h4icW7Nsu4jcJyJ3i8iWMPMZJc1boCYNThrzJisYlEVDNrdtFDh5iX2AaSY/BhLzHo7QroGKiANcDbwS2AlsFpGbVPVnNas9CqxV1QMichFwLXBBzfKXq+resPIYRc2CpAXQdMiIsGzcG5nbb9mM90xPEw1DC2GyCETkunhQxIGhBYPORe/CrJPPB7aq6jZVLQE3ABfXrqCqd6jqAf/lRmB5iPmJhWYtULuNJT1GcoPpyl06aoOGosTJSyICTb3hxfFvfUK4o3BPAXbUvN7J0a3Lem8D/qvmtQLfFxEFvqSq1zbaSEQuAy4DWLp0KevXr+8lzwM3pVVc/3TTrXhzeJU23Y2TzbJNMjxhQTRVZruYxm2u3Oy8bwNOtv1DPCOwe4Bdx2Z+VRc05PuEK65Xbu56eEPLSRB6kXFAHgkt+b4KM4A2qukbdkSIyMvxAuiamrdfoqq7RORE4Aci8qCqHnOnsB9YrwVYtWqVrlu3rueMD9LG8gQHq15BdosluGMj+QtW4hTynJkd5hlOgi+KmGM8NaXsm+5sm3KpyJZNt7P8uWs6eqD2qQthLG8naFGk1fAfpl0qF9m46XbOO2tNaLex5MdgeHFyyliYXbg7gRU1r5cDu+pXEpHnAdcBF6vqvrn3VXWX//9u4Ea8LuHEa/aDWBdu+hw/3J975YazFjyjTDLCyPHg9Of2zFDkRmFo0aBzEawwA+hm4EwROUNE8sAlwE21K4jIqcB/AH+oqg/XvD8qIuNzfwOvAu4PMa+R0fwaqEmbbEZY2IdKc3HSb5lIAMkIo0vieeuH1/IESditeKF14apqRUTeDdyCV/dfr6oPiMjl/vJrgI8CxwNf9HdsRVVXAUuBG/33ssA3VPV7YeU1SpqPwk1W4TPtWTQEB0PsussILIhxyyZNJCOMLFFm9kO5w679QSksgKGFyay7Qp3KT1VvBm6ue++amr/fDry9wXbbgHPr308Duw/U1BvJCTlHKYc0b+iCgs11GyciwvBximSgFOXHnYnXZVsYS27Zsjo5YprNRGSVXHqNh/jEijDTNuEQEYYXS3SvKQqMHJ/s4AkWQCPHWqCmkbCCnIj3uDITT4VxYfg4IvWkCcnA6AmQG45QpkJidXLE2ExEppHhXDijcUey1rMRd/lRYWQJkaggxIHREyFbSEeZisAuN7XsaSymkYwIwyGMWBiJ+dMwjCc35I3QlQHW6JksjJ0ITi499VTT3S0i804i5d+CYgJmLVAznzCC3bAF0MTIFoTRE7xWYL9lcl7LM5OyJ/i0qpPXz/0hIj+sW/btoDNjml/KEGuBplohhBboUKjj8E2/OXmvJdrPs+1M1rvmmWk2AjKhWu3m2j1yXJNlJiDNrkclYO5l04OhgFsW2Yw9MDuJjgTRPvy04qQ3eELrAKrz/N3otQmATaRg5pMPOIAO8pmjJlzZgj86N0wCo0vS121bq1UHzoki8qd45zJzf+O/PiHUnKVUs27a9BZTA969f0FOqJCzAJpo+RHBLSmliXDSH17stXbTrFUA/WdgvMHf4E0AbwJmg4hMM/kMFkBN24YWglsEtxRsurlR7/aZtGsaQFX1rwBEZImq7u1PltKt+SAik3ZOgGdRKe55Sw1vxiJlcjeBXXQTB4YXBZNW3LW6jeU1IrIHuFdEdorIi/uUr9SyUbimmWyQAdS6NFLByQv5seDSG1roTWpvWvcK/i3wUlU9Gfgd4Mrws5Rudg3UNBNkvWUTEKVHYZxArgFlcpAb6T2dpGi1Syuq+iCAqm7i6GugJgTWhWuaCbIMWHlKj4wj5Ed7T6cwnrxnevai3VG4DV+r6t+Hky3TiBVbE2QhsHowXfKj9DQiVzKQswevH6WTUbj1r+0+UGP6TAM86oJMy0SfkxOcvHY9Ijc7bNc+67U1CrcREXlh8NkxTbtwrcmQetUAg16QaZl4yA53f0uLtT6P1dFMmCJyDnAJ8HrgELAqjEwZYxpzqwGmZQE0dbJDUDzUxYYC2ULg2Ym9lgFURE7DC5ivByrAacAqVd0ebtaMMfXKAQbQUkATMpj4cHJ4Q0c7LEdOzrpvG2l1H+gdwM1ADvhdVX0BMGHB05jBCGoWoqDTMvEgIl4Q7ZCTDz4vSdDqNpY9eIOGlvL03LfW8WPMAFSqGmgLdNYCaCpZAA1O0wCqqhcDzwXuAv5KRB4FFovI+f3IXBrZ2YmZz2wl2PSKFajaUNzUyXQRQDP23NiGWs5NoaqHVPV6VX0l8CLgL4GrRGRH6LkzR1Gr7FJtqhx8mtMhpGmirZtgaAG0sY4md1LVp1T1C6r6YmBNSHkyxjQwWQw+zYmAn9Jhoi/T6VN4Mul9YHYrTc8rROSmFtv/ZoB5MS0oNhtRWpVcpRjCNcuJIpw0qnaPcYp02prsOOCmSKtduRrYAXwT2ITV36Fr1klrHbjpdWAmnHQrVa9reMwGiaSGiCCOom2ekFkAnV+rAHoS8Eq8e0D/APgu8E1VfSDsjKWVNgmTFkDTqarKwdnw0t8/YwE0bcSh7QAqFkDn1WoUrquq31PVN+MNINoKrBeR9/QldynUvAVqITSNDs6GO2vQZAlmK1a20iTTwegXsefGzqudmYgKwKvxWqGnA18A/iPcbMVfpVLBdTu/aFVyi7gVb2SHWzr6/6IWqXZZmh3HIZu1oXRxU1Vl73T4n7NnClYsDP9zTDR00qq0Ltz5tRpE9FXgOcB/AX+lqvf3JVcJsGHDBm699dZA0vrp1dcDsKWHNNauXcu6desCyY/pn30z3nXKsE2UYLqsjORsmEMadHIebi3Q+bVqkvwhMAWcBby3ZqSeAKqqC0LMW6ytWbOG1atXd7zdDrfIQxVvxIhbKvHTq6/n+e96K04+z5r8AoZ6aIGaeCm5/Wl9znlyEs5YpGRsRG7iWRduMFo9zsx2XZey2WxXXaZZV3Gco7t+nXwep5Anny9QsNKcGk9O9veZncWK1+I9YaR/n2kGo5MuXBtENL9Qa2MRuVBEHhKRrSLywQbL3yAi9/r/7hCRc9vdNqlsEJEBODCrTA1gkoO90zagKA06OQ/vpLWaNqHtGhFxgKuBi4BzgNf7zxOt9SiwVlWfB/wNcG0H2yZSs4ccW7WWDrMV5ZeTg/lsVdh52ObITTq7BhqMMHfN+cBWVd2mqiXgBuDi2hVU9Q5VPeC/3Agsb3fbpLL7QNOtqsoTh/vbdVuv5DKwAG76o+2gKPYc0GbCvK/hFLxZjObsBC5osv7b8Eb7drStiFwGXAawdOlS1q9f32V2o6GIUlJv2KVb8R6/Udp0N042y0/ECbfP3QxcuQpuj6Nu58rNzvs24HR569LjwM8dsClQk8ute5BAxfXKzV0PbyDreOVGgMzWPmcsRsIMoI0OvYbn1SLycrwAOjdBfdvbquq1+F2/q1at0rjfqvGLygyPuN60M26xBHdsJH/BSpxCnlW5cRbYYxESa8+0smeq93TKpSJbNt3O8ueuIZcv9JTWaQthNG9RNGm0qhx+4uj3SuUiGzfdznlnrSGf88pNJgvjy+z3n0+YDZqdwIqa18uBXfUricjzgOuAi1V1XyfbJpHNhZtOE8VggmfQdk54t9OYZJGMtDezuXV5NRXm7tkMnCkiZ4hIHrgEOOrpLiJyKt6sRn+oqg93sm1SVe0aaOrMVJSdE4PORWNuFR4/BG6z0W0mltq53dduCW4utP5AVa2IyLuBWwAHuF5VHxCRy/3l1wAfBY4HvuhP0lBR1VXzbRtWXqOkWTXVLLiaeCq7yo5Dgx001ErJ9UbmrlhokywkigXQnoV6QU1VbwZurnvvmpq/3w68vd1t08C6cNPDrSqPH+7PVH29mip7I3NPHh90TkxQRFrXKXYLS3O2eyLGAmg6qCpPTHiz/8TFwVnYN22lMDHaqf2tBdqUBdCIsQCaDrunvMeIxc1TU96AJxN/dg20dxZAI8YmUki+Q0Vl38ygc9G9XTYy1xjAAmjkNBtMYnPhxt9sRdkV0RG37XIVdth0f7HXVuvSWqBNWQCNmKZduFZfxVpVveCZhN+xWPG6oU2MWRduzyyARoxdA02ufdMwG6NBQ63sn4GpkpXKuLIWaO8sgEaMddMmU8lV9sb4uud8npy0rlyTXhZAI8aqomTaM5WMrtt6JRcOFwedC9MV68LtmQXQGElg/ZsKJVc5lOAgs3fau6/VmLSxABoj1r0bT4dmB52DcJVcmEnQtV1j2mXPxooYG0SUPFPl1uvMcd0KVdft6fPKpeJR//ci4zg4TutqYrIEI7meP870kQ0i6p0FUGNCVFVluoMA+uCdG/jZltsC+ezvfu2qntM4Z9XLePb561qu18l3NPFh10CbswAaMdYCTZZOLw2e/YI1nLVydU+fWS4V+e7XruLVb7qi5wdqZxynrfVsYqIYsuDYMwugEWNT+SVLp4/RdJxsW12m7cjlCz0H0HbZGKL4sS7c3tkgImNC5KTkCMum5HumjcXP5qzYR4x14SZLRoShFPTzDKfgOyaOtUB7ZgE0YpoHUAuhcZSG0alp+I7G1LMAakzIFg8NOgfhymVgLD/oXJhO2fNAe2cBNGKaDTqx9mc8FbKS6ABz3DCI1bSxY4OIemcBNGJsFG4ynTg66ByEI5eBRQlvYSeWtUB7ZgE0Yux5oMk0lBWOHxl0LoK3bBycjNWycWQt0N5ZAI0YG0SUXCeMQL69eQliYdEQjOWtho2tNmp/sQjRlO2eiLHbWJIrI8LyBcnoFis4cNLYoHNhetHWICKLEE3Z7okYC6DJNpQVTh4fdC564wisWOidEJj4ahkcMzY4rBULoBHTrJu2aiE0ERYWhCUxvh56ygLIO1axxl2mxeUEi52tWQCNmGqXy0y8nDgqLOzPNLWBWjZu1z2TQjLSdJBQqwBrLIBGTrNWpgXQZFk2DqMxmsFnyQgsHrLgmSTNunHFAmhLFkAjptljoVy7jyVR5gYVFWIwj+yiIa/VbJIl06TsWQu0NQugEaKqTVugrl0DTRwnI5y6AHIRrqzG8rDMRtwmUrMgaS3Q1iyARojbYrl14SZTzhFOWxjNR58N5/BvvbHWZxI1bYHGoGdk0CJ4yKZXpUULs9VyE195R1gRsXtE8w6sWGC3qySZdeH2xs4xIqRlALVroIk2khOWjys7Dg86J15r+NSFkLVp+mKhUqnguq36sBps5yqlsvd3qVw86v9CFdxid7+/4zhks8kPL6F+QxG5EPg84ADXqeon65afDXwZOA/4c1X9bM2y7cAEXs9mRVVXhZnXKGgVIK0FmnzjBeHEUWX31GDzscLu9YyVDRs2cOuttwaS1nU3XNVzGmvXrmXdunU9pxN1oQVQEXGAq4FXAjuBzSJyk6r+rGa1/cB7gdfOk8zLVXVvWHmMmnKLAFm2FmgqLBkRZivK4eJgPn/ZuNcaNvGxZs0aVq9ePehsHOE46ej/DbMFej6wVVW3AYjIDcDFwJEAqqq7gd0i8uoQ8xEbrQKki+Kq4tg1qcQ7eRyKLhQr/f3cxUN2r2ccZbPZVHSZRk2Ye/wUYEfN653ABR1sr8D3RUSBL6nqtY1WEpHLgMsAli5dyvr167vLbQSUUEr69Fhbt+LVnqVNd+P4B8et4tjIr5RQugugc+Vm530bjpSbdojAbgce6vwjjUmlMANoo9PYTvogX6Kqu0TkROAHIvKgqt52TIJeYL0WYNWqVRrnfvcHK9Nsd5/ut3OLJbhjI/kLVuIU8gA8PzfOYhtfnhoHZ5VdE51tUy4V2bLpdpY/dw25fHvzBYrAGYu8ye6NMe0JszGzE1hR83o5sKvdjVV1l///buBGvC7hRJtt4xrnrNrdoGmyaEgYy4f/OSeMWPA0plNhBtDNwJkicoaI5IFLgJva2VBERkVkfO5v4FXA/aHlNCJm2giOMzadQuqcNBbu/aGFLBw3HF76xiRVaH2BqloRkXcDt+DdxnK9qj4gIpf7y68RkZOALcACoCoiVwDnAEuAG/3ZT7LAN1T1e2HlNSpmWs5F1F6QNcmSd4QlI8qekG5tWTZmkyUY041QL6ap6s3AzXXvXVPz9y/xunbrHQbODTNvUVPWKqU2unCntfObpU38HT8MB2agEvD503jeblkxpls2oDMiJttsWba7nkmWjITzEO4TRoNP05i0sAAaEZNttiyLWj3qVheTHouGIMjJgcbyNnDImF5YAI2IQx10zXayrkmOjAiLAhzsYwOHjOmNBdCIOFht/475TtY1ybJ4KJh0cg6M5oJJy5i0sgAaAWWttt2FC9YCTbO8IwwHMPRvQcGe8WlMryyARsB+7axFeaBaoWoTy6fWgvYmFwo9DWPSzgJoBOztsEvWRTsOuiY5ep2ZKJuBYRs8ZEzPLIBGwJ5queNt9naxjUmGQlbI9nDkjti1T2MCYQF0wA5WK13Nb/tUtYxaN25q9RIER/swt64xaWABdMCerJa62m5GqxywbtzU6mUgURCDkIwxFkAHqqradQAF2GXduKk13GULVATyTrB5MSatLIAO0FPVclvz387nSbdE2WYlSqVCl0Gw4NjE8cYExQLoAD1WLbZeqQkXZWcPLVgTX06mu4FE1vo0JjgWQAfkYLUSyIxCj7tFG0yUUt0Ew25brsaYY1kAHZBH3NlA0pnRKk9YKzSVugmgOQugxgTGAugAHKhWurr3cz6PuLM2M1EK5awL15iBsgA6AA+7M4GmN6NVHu/xeqqJn25ak90EXWNMY3Y49dmTbokDITxN5RF3lqKNyE2VboKhY0e8MYGxw6mPKqo8FHDrc05ZNfCWrYm2Tlug2YzdwmJMkCyA9tFWd6arafva9YRbYr9NrpAaToex0LpvjQmWHVJ9cqBaYbsb/nXK+yszVGxAUSo4GSHTQRC17ltjgmWHVB9UVLmvMtWXz5pWN7RuYhM9nUymYC1QY4Jlh1QfPOjOMN3HAT473GKgt8mY6OokKPbyCDRjzLHskArZk26JnX3ouq13X2Uq1OutJho66ZbN2j2gxgTKAmiIJtXl/sr0QD67pMo9lSmbYCHhOhmJa124xgTLDqmQVFS5uzyFy+AC2IFqxW5tSTjrwjVmcOyQCoH6g4Ym1R10VtjuFtnl2ly5SdVJALUWqDHBskMqBI+4szwVoUE891emORTC7Edm8NrtwnXEu+3FGBMcC6AB+6VbYmtAT1oJShXlpzaoKJHabVXaU1iMCZ4F0AAdrla4b0CDhlqZ1So/LU/h2qCiRHEy0tZIXHsKizHBswAakFmtcldlsIOGWjmkFe6tTNkDuBOmneBoAdSY4IUaQEXkQhF5SES2isgHGyw/W0R+LCJFEXl/J9tGSUWVO8uTsegifapa5hcR62I2vclbC9SYgQgtgIqIA1wNXAScA7xeRM6pW20/8F7gs11sGwmqyr2VKSYiMOK2Xdvc2YFM7mDCkc+2sY4FUGMCF2YL9Hxgq6puU9UScANwce0KqrpbVTcD9UNWW24bFQ+5M+yO0Ijbdj1QmWZfDPNtjjXURnAsWAA1JnBhBtBTgB01r3f674W9bd887hb78oSVMChwd0TuVTW9KbRogWYzdguLMWFoo/Ona42O2HZHr7S9rYhcBlwGsHTpUtavX9/mR/SmgoY+Qbxb8e7dLG26Gycb/E9VAtYjjEqm4Q43/VetVqlWOy9XxZrbfF3Xe/HY3etxnCwZgce7bIFmMhkyGRtraEwjYQbQncCKmtfLgV1Bb6uq1wLXAqxatUrXrVvXcUY7NaUuG8sT5EMezeoWS3DHRvIXrMQp5EP7nLFMlhdkx8iIhdFBW79+PT/60Y8CSeunWzb1nMbatWvpxzFlTByFGUA3A2eKyBnAE8AlwB/0YdtQlbXKneUpygm6FWRftcKD7gznZEcGnZXUW7NmDatXrx50No5wHLt4asx8QgugqloRkXcDtwAOcL2qPiAil/vLrxGRk4AtwAKgKiJXAOeo6uFG24aV107cV5lmOoHXDR93iyySLCc74bV0TWvZbJZsCN31xpjghXqkqurNwM11711T8/cv8bpn29p20La5s7EccduuByrTjIvDeMZaHcYY04qNDmjT/mqZX1SS/Wgw158zt5Kg7mljjAmLBdA2VFS5rzId4Un6gjOtLg/aM0SNMaYlC6Bt+Lk7zUwMpukLyk63mOiuamOMCYIF0BZ2V8s8kcIHUj9QmaacopMGY4zplAXQJqqqPJjw657zKWqVR2zSeWOMmZcF0Ca2V4uJvGWlXY+5RZvqzxhj5mE3nM2jpFW2VbpvgZWKRSql3rp+3WKZYrHI9MQkTinXU1rZfJ58odDRNgo8XJnhvNxYT59tjDFJZAF0HjvcEpUext1+69Of418/8elgMnPllT0n8YY//wBv/IvOH6u6u1pmUl3GxO4NNcaYWqIJuudv1apVumXLlp7Tqapya/kwxR4G0QTVAv3p1f/C89/1NpxC/1ugc5Y7BZ5j0/wZY2JIRO5U1VVhpG0t0AaeqpZ7Cp4A+UKh64A1x82XKBQKjIyPhTqZfCu73BK/4gyRE7tkbowxc6xGbOApuwfyKFWUvdVK6xWNMSZFLIDWqaqy1wLoMWxiBWOMOZoF0DqH1O1p8FBS7VMLoMYYU8sCaB2777Gxkiolm5nIGGOOsABaZ8oC6LymLYAaY8wRFkDrpGnS+E7ZvjHGmKdZADXGGGO6YAHUGGOM6YIF0DoOMugsRFbGdo0xxhxhAbTOiM22M69Rmw/XGGOOsGhRx4JEYwKMWHExxpgjrEasM56xANrIqDhkxPpwjTFmjgXQOmPiWDduAydkensajDHGJI09jaWBJZkcj7vFntKoViqo29t9k67/ODS3x8eiAYiTIZPt/udekrGiYowxtaxWbGBpAAF014+38MTtPwkkPz+9+vqe0zjlJeez/KUv6mrbvAiLxYqKMcbUslqxgeMzOUbEYbqHaf1OXr2KZeef11M+3FKJn159Pc9/11tx8r09D1Sc7rull2cKdv3TGGPqWACdx6lOngcrM11vn8lmA9u7Tj4/0AdqL3cG99nGGBNVNlpmHqdk8uSs1cVSvzVujDHmaBZA55GTDKdnhgadjYES4Mzs8KCzYYwxkWQBtInTnAKFFN/SsiyTZ8xan8YY01B6o0MbsiI8y0lnK9RBrPVpjDFNWABtYXkmz6IU3gP5zOwQwylufRtjTCuh1pAicqGIPCQiW0Xkgw2Wi4h8wV9+r4icV7Nsu4jcJyJ3i8iWMPPZjIjwbGckVc9oGROH0zOFQWfDGGMiLbSmlYg4wNXAK4GdwGYRuUlVf1az2kXAmf6/C4B/8v+f83JV3RtWHts1nnF4hjPEI+7soLMSOgGekx2x+z6NMaaFMFug5wNbVXWbqpaAG4CL69a5GPiaejYCi0RkWYh56toznSHGUzCg5hnOUCq7rI0xplNhBtBTgB01r3f677W7jgLfF5E7ReSy0HLZpowIz8uOkklwZ+4CcXhmSgdNGWNMp8JsajSKNNrBOi9R1V0iciLwAxF5UFVvO+ZDvOB6GcDSpUtZv359D1luh1LS3iaJb5dbqQBQ2nQ3Tg8TwbdDgFlxOGYHG2OMaSjMWnknsKLm9XJgV7vrqOrc/7tF5Ea8LuFj6ndVvRa4FmDVqlW6bt26gLI/v5+WJ3mqWg79c9xiCe7YSP6ClaFP5XdudpRlNmWfMca0Lcwu3M3AmSJyhojkgUuAm+rWuQl4kz8a90XAIVV9UkRGRWQcQERGgVcB94eY1448JzuSqGeGrnAKFjyNMaZDobVAVbUiIu8GbgEc4HpVfUBELveXXwPcDPw6sBWYBt7ib74UuFG8kaBZ4Buq+r2w8tqpnGR4fnaMjeUJ3GN6peNloWT5b45NmGCMMZ0K9cKaqt6MFyRr37um5m8F3tVgu23AuWHmrVfjGYfnZEe4pzI16Kx0LS/Cytyo3bJijDFdsPsVerDMyXNIKzzulo5ZVq1UULe3wUZaKh/5X3sc/StOxnvE2txrYGV21GYbMsaYLlkA7dHZ2RHOzo4c8/76H6/n1ltvDeQz7rz6X3pOY+3atfRjgJUxxqSFBdCQrFmzhtWrVw86G0c4TvIngTDGmH6yABqSbDZLNuR7N40xxgyOXQAzxhhjumAB1BhjjOmCBVBjjDGmCxZAjTHGmC5YADXGGGO6YAHUGGOM6YIFUGOMMaYLFkCNMcaYLlgANcYYY7pgAdQYY4zpggVQY4wxpgsWQI0xxpguiPdM62QQkT3AY4POR8CWAHsHnQkTO1ZuTDeSWG5OU9UTwkg4UQE0iURki6quGnQ+TLxYuTHdsHLTGevCNcYYY7pgAdQYY4zpggXQ6Lt20BkwsWTlxnTDyk0H7BqoMcYY0wVrgRpjjDFdsAAaABFZISKPishx/uvF/uvTBp03k0wi4orI3SJyj4jcJSIvHnSezODUlIe5f4+LyD8OOl9JZ124ARGRDwDPUtXLRORLwHZVvXLQ+TLJJCKTqjrm//1rwIdVde2As2UGpLY8+K8vBVap6rsHl6vksxZocD4HvEhErgDWAH8nns+IyP0icp+IvA5ARNaJyG0icqOI/ExErhGRjL/s9f6694vIp+YSF5HJmr/vF5HT/b+/LiKv8f/eLiJLat6/3//b8fOxWUTuFZF39mWPmH5ZAByYeyEiH/DL0D0i8kn/vWeKyPdE5E4R+ZGInD2w3Jq+E5Gv+L1id4tISUSWNKmffktE/o+/fJmIPCwiJ4nIpXOtWhH5FRGpiMjv+q9fICK3+uXrFhFZNsjv2y/ZQWcgKVS1LCL/A/ge8CpVLYnI7wArgXPxZvjYLCK3+ZucD5yDN3PS94DfFpE7gE8BL8CrEL8vIq9V1W93khcReS7wnJq33gYcUtUXikgBuF1Evq+qj3b5dc3gDYvI3cAQsAz4VQARuQh4LXCBqk7PXVbAG115uar+QkQuAL44t41JBQd4n6r+h4hs99/7bRrUT6p6o193vQu4EPhLVf2liNSm9zfAgwAikgP+AbhYVff4gfgTwFvD/1qDZQE0WBcBT+IFrx/gtUS/qaou8JSI3Aq8EDgM/ERVtwGIyDf9dcvAelXd47//r8DLgG93mI+PA3+JV4gBXgU8b+5sEVgInAlYAI2vGVVdCSAiq4GvichzgFcAX1bVaQBV3S8iY8CLgX+vqQQL/c+yGaBhYLbuvfnqp5uA9wD3AxtV9Zu1G4nIC/B6L7f4b/0Kfp3nly8Hrx5MPAugARGRlcArgRcBG0TkBkCabFJ/8VlbrN+uFwOTwD212QPeo6q3BJC+iRhV/bHfdX8C3m9dX7YywMG5gGtS6WRgV917zeqbU4AqsFREMqparVn2ceBPgT+rSecBVV0dVGbjwq6BBkC8065/Aq5Q1ceBzwCfBW4DXudfgzwBrzX5E3+z80XkDP/a5+uADcAmYK1/fcIBXg/c2mF2PgZ8tO69W4A/8rtaEJGzRGS00+9posm/nukA+4DvA28VkRF/2XGqehh4VER+z39PROTcgWXY9JWIPAs4HfhZ3aKG9ZOIZIEvA38A/BwvWM5ZCzypqj+vee8h4AS/JwQRyYnIs0P5MhFjLdBgvAN4XFV/4L/+InApXoV2L15rUIEP+NcSzgZ+DHwSeC5eQb5RVasi8iHg/+Kd1d2sqv/ppzksIhv8v8/A644rAmcBN9TkZZOqPjI3yMh3Hd4BdJcf7PfgXScz8TV3DRS8svJmvyvue35vyBYRKQE3Ax8G3gD8k4h8BMjhlZl7jknVJIqInAz8J3CZqpbqFt8IrObY+umjwI9U9Ud+GdssIt/1tzkTeHVtIv54j98FviAiC/HiylXAAyF9rciw21gGQETWAe9X1dcMOCvGGGO6ZF24xhhjTBesBWqMMcZ0wVqgxhhjTBcsgBpjjDFdsABqjDHGdMECqDHGGNMFC6DGGGNMFyyAGmOMMV34/wH8qaO+lfwASgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# График\n",
    "\n",
    "plt.rcParams['figure.figsize'] = 7, 6\n",
    "\n",
    "viols = plt.violinplot(dataset=temp_vs_all,showmedians=True,bw_method=0.2,\n",
    "                       quantiles=[[0.25,0.75],[0.25,0.75],[0.25,0.75]])\n",
    "plt.xticks([1,2,3],['Хорошие','Все','Плохие'])\n",
    "\n",
    "viols['bodies'][0].set_facecolor('xkcd:aqua')\n",
    "viols['bodies'][1].set_facecolor('xkcd:sky blue')\n",
    "viols['bodies'][2].set_facecolor('xkcd:lilac')\n",
    "\n",
    "for partname in ('cbars','cmins','cmaxes','cquantiles'):\n",
    "    vp = viols[partname]\n",
    "    vp.set_linewidth(1.1)\n",
    "    vp.set_edgecolor('gray')\n",
    "    \n",
    "viols['cmedians'].set_edgecolor('black')\n",
    "viols['cmedians'].set_linewidth(1.1)\n",
    "\n",
    "plt.grid(axis='y')\n",
    "plt.ylabel('MAPE')\n",
    "plt.title('Предсказания относительно реальных данных для всех клиентов');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "773e9d2f-e9ca-4475-9f72-094733e4e233",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALL vs ALL preds: test MAPE median: 27.302%\n",
      "ALL vs ALL preds: test MAPE HP (<=10.0%): 1.299%\n",
      "\n",
      "GOOD vs ALL preds: test MAPE median: 8.521%\n",
      "GOOD vs ALL preds: test MAPE HP (<=10.0%): 63.636%\n",
      "\n",
      "BAD vs ALL preds: test MAPE median: 33.318%\n",
      "BAD vs ALL preds: test MAPE HP (<=10.0%): 0.000%\n"
     ]
    }
   ],
   "source": [
    "mape_hp = 0.1\n",
    "\n",
    "print(f'ALL vs ALL preds: test MAPE median: {np.median(np.array(fwd_mapes_all)) * 100 :.3f}%')\n",
    "print(f'ALL vs ALL preds: test MAPE HP (<={mape_hp*100}%): {(np.where(np.array(fwd_mapes_all)<=mape_hp)[0].shape[0] / np.array(fwd_mapes_all).shape[0]) * 100 :.3f}%\\n')\n",
    "\n",
    "print(f'GOOD vs ALL preds: test MAPE median: {np.median(np.array(mape_good_pred_vs_all)) * 100 :.3f}%')\n",
    "print(f'GOOD vs ALL preds: test MAPE HP (<={mape_hp*100}%): {(np.where(np.array(mape_good_pred_vs_all)<=mape_hp)[0].shape[0] / np.array(mape_good_pred_vs_all).shape[0]) * 100 :.3f}%\\n')\n",
    "\n",
    "print(f'BAD vs ALL preds: test MAPE median: {np.median(np.array(mape_bad_pred_vs_all)) * 100 :.3f}%')\n",
    "print(f'BAD vs ALL preds: test MAPE HP (<={mape_hp*100}%): {(np.where(np.array(mape_bad_pred_vs_all)<=mape_hp)[0].shape[0] / np.array(mape_good_pred_vs_all).shape[0]) * 100 :.3f}%')"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "55a4fbb1-e664-468b-be26-c5ccf4fb85fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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