covid_delta7.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "covid_simple_delta7.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCs2Q5vvGiDM",
        "colab_type": "text"
      },
      "source": [
        "#Подключаем библиотеки и загружаем данные"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLz0ubq_yI_r",
        "colab_type": "code",
        "outputId": "cc91a001-6888-4c84-bedb-894ae17cb024",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "!pip install catboost"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting catboost\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b1/61/2b8106c8870601671d99ca94d8b8d180f2b740b7cdb95c930147508abcf9/catboost-0.23-cp36-none-manylinux1_x86_64.whl (64.7MB)\n",
            "\u001b[K     |████████████████████████████████| 64.8MB 54kB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.2.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.18.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.12.0)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.0.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.8.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-0.23\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JMyOSZ7bLoa",
        "colab_type": "code",
        "outputId": "1cf02e35-5040-4035-8f7d-206dc89494bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "from sklearn import ensemble, model_selection, datasets, metrics, tree, linear_model, preprocessing \n",
        "from sklearn.neural_network import MLPRegressor\n",
        "\n",
        "from catboost import CatBoostRegressor\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.autograd import Variable\n",
        "import torch.optim as optim\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "import lightgbm as lgb\n",
        "\n",
        "import xgboost as xgb\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import random\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GBQ5OY9sVBkK",
        "colab_type": "text"
      },
      "source": [
        "## Данные по заражениям"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjSHpNYZCUjY",
        "colab_type": "code",
        "outputId": "4d8b0f77-4f77-48df-fc4a-4be16a54c94e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "!git clone https://github.com/CSSEGISandData/COVID-19 -b web-data COVID-19-new"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'COVID-19-new'...\n",
            "remote: Enumerating objects: 14, done.\u001b[K\n",
            "remote: Counting objects: 100% (14/14), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 22646 (delta 5), reused 10 (delta 5), pack-reused 22632\u001b[K\n",
            "Receiving objects: 100% (22646/22646), 98.65 MiB | 31.69 MiB/s, done.\n",
            "Resolving deltas: 100% (12274/12274), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eEM4nwAcT-uj",
        "colab_type": "code",
        "outputId": "b51804b7-cf27-40ef-e4ab-a2ed0bcb98ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "!git clone https://github.com/CSSEGISandData/COVID-19.git\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'COVID-19'...\n",
            "remote: Enumerating objects: 14, done.\u001b[K\n",
            "remote: Counting objects:   7% (1/14)\u001b[K\rremote: Counting objects:  14% (2/14)\u001b[K\rremote: Counting objects:  21% (3/14)\u001b[K\rremote: Counting objects:  28% (4/14)\u001b[K\rremote: Counting objects:  35% (5/14)\u001b[K\rremote: Counting objects:  42% (6/14)\u001b[K\rremote: Counting objects:  50% (7/14)\u001b[K\rremote: Counting objects:  57% (8/14)\u001b[K\rremote: Counting objects:  64% (9/14)\u001b[K\rremote: Counting objects:  71% (10/14)\u001b[K\rremote: Counting objects:  78% (11/14)\u001b[K\rremote: Counting objects:  85% (12/14)\u001b[K\rremote: Counting objects:  92% (13/14)\u001b[K\rremote: Counting objects: 100% (14/14)\u001b[K\rremote: Counting objects: 100% (14/14), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 22646 (delta 5), reused 10 (delta 5), pack-reused 22632\u001b[K\n",
            "Receiving objects: 100% (22646/22646), 98.65 MiB | 30.94 MiB/s, done.\n",
            "Resolving deltas: 100% (12274/12274), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XI3hn-gjnTV5",
        "colab_type": "code",
        "outputId": "dcd730ed-4a4f-4c6b-ae5a-088572f0c96e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "!git clone https://github.com/grwlf/COVID-19_plus_Russia.git"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'COVID-19_plus_Russia'...\n",
            "remote: Enumerating objects: 17561, done.\u001b[K\n",
            "remote: Total 17561 (delta 0), reused 0 (delta 0), pack-reused 17561\u001b[K\n",
            "Receiving objects: 100% (17561/17561), 83.49 MiB | 31.13 MiB/s, done.\n",
            "Resolving deltas: 100% (8922/8922), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hcd2xqhUVNAB",
        "colab_type": "text"
      },
      "source": [
        "## Данные про страны"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNntNTQ1UsKO",
        "colab_type": "code",
        "outputId": "bae81066-a6e6-49f1-8a46-0a42e82b252a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "!git clone https://github.com/vlomme/sberbank-covid19-forecast-2020.git"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'sberbank-covid19-forecast-2020'...\n",
            "remote: Enumerating objects: 45, done.\u001b[K\n",
            "remote: Counting objects:   2% (1/45)\u001b[K\rremote: Counting objects:   4% (2/45)\u001b[K\rremote: Counting objects:   6% (3/45)\u001b[K\rremote: Counting objects:   8% (4/45)\u001b[K\rremote: Counting objects:  11% (5/45)\u001b[K\rremote: Counting objects:  13% (6/45)\u001b[K\rremote: Counting objects:  15% (7/45)\u001b[K\rremote: Counting objects:  17% (8/45)\u001b[K\rremote: Counting objects:  20% (9/45)\u001b[K\rremote: Counting objects:  22% (10/45)\u001b[K\rremote: Counting objects:  24% (11/45)\u001b[K\rremote: Counting objects:  26% (12/45)\u001b[K\rremote: Counting objects:  28% (13/45)\u001b[K\rremote: Counting objects:  31% (14/45)\u001b[K\rremote: Counting objects:  33% (15/45)\u001b[K\rremote: Counting objects:  35% (16/45)\u001b[K\rremote: Counting objects:  37% (17/45)\u001b[K\rremote: Counting objects:  40% (18/45)\u001b[K\rremote: Counting objects:  42% (19/45)\u001b[K\rremote: Counting objects:  44% (20/45)\u001b[K\rremote: Counting objects:  46% (21/45)\u001b[K\rremote: Counting objects:  48% (22/45)\u001b[K\rremote: Counting objects:  51% (23/45)\u001b[K\rremote: Counting objects:  53% (24/45)\u001b[K\rremote: Counting objects:  55% (25/45)\u001b[K\rremote: Counting objects:  57% (26/45)\u001b[K\rremote: Counting objects:  60% (27/45)\u001b[K\rremote: Counting objects:  62% (28/45)\u001b[K\rremote: Counting objects:  64% (29/45)\u001b[K\rremote: Counting objects:  66% (30/45)\u001b[K\rremote: Counting objects:  68% (31/45)\u001b[K\rremote: Counting objects:  71% (32/45)\u001b[K\rremote: Counting objects:  73% (33/45)\u001b[K\rremote: Counting objects:  75% (34/45)\u001b[K\rremote: Counting objects:  77% (35/45)\u001b[K\rremote: Counting objects:  80% (36/45)\u001b[K\rremote: Counting objects:  82% (37/45)\u001b[K\rremote: Counting objects:  84% (38/45)\u001b[K\rremote: Counting objects:  86% (39/45)\u001b[K\rremote: Counting objects:  88% (40/45)\u001b[K\rremote: Counting objects:  91% (41/45)\u001b[K\rremote: Counting objects:  93% (42/45)\u001b[K\rremote: Counting objects:  95% (43/45)\u001b[K\rremote: Counting objects:  97% (44/45)\u001b[K\rremote: Counting objects: 100% (45/45)\u001b[K\rremote: Counting objects: 100% (45/45), done.\u001b[K\n",
            "remote: Compressing objects: 100% (45/45), done.\u001b[K\n",
            "remote: Total 238 (delta 28), reused 0 (delta 0), pack-reused 193\u001b[K\n",
            "Receiving objects: 100% (238/238), 7.53 MiB | 10.61 MiB/s, done.\n",
            "Resolving deltas: 100% (122/122), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73CcvjEpY2OX",
        "colab_type": "text"
      },
      "source": [
        "## Данные по карантинам"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7qgyyfjYva5",
        "colab_type": "code",
        "outputId": "ec59c7de-eb70-4802-9141-bb9b3e47d438",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "!git clone https://github.com/tyz910/sberbank-covid19.git"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'sberbank-covid19'...\n",
            "remote: Enumerating objects: 160, done.\u001b[K\n",
            "remote: Counting objects: 100% (160/160), done.\u001b[K\n",
            "remote: Compressing objects: 100% (98/98), done.\u001b[K\n",
            "remote: Total 160 (delta 73), reused 143 (delta 56), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (160/160), 1.17 MiB | 2.72 MiB/s, done.\n",
            "Resolving deltas: 100% (73/73), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwR6lCCZev3a",
        "colab_type": "text"
      },
      "source": [
        "## Загружаем данные"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6z9w3muR2NA1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "today = pd.to_datetime('today').strftime(\"%Y-%m-%d\")\n",
        "#Загрузить список стран и удалить лишние атрибуты \n",
        "countries = pd.read_csv(\"/content/sberbank-covid19-forecast-2020/data/countries.csv\")\n",
        "countries.drop(['iso_alpha2','iso_numeric','official_name','name'], axis='columns',inplace=True)\n",
        "\n",
        "#Загрузить список регионов\n",
        "regions = pd.read_csv(\"/content/sberbank-covid19-forecast-2020/data/russia_regions.csv\")\n",
        "regions = regions[[\"iso_code\",\"csse_province_state\",\"csse_province_state2\",\"population\",\"population_urban\"]]\n",
        "regions.population_urban = regions.population_urban/regions.population\n",
        "regions.rename(columns={\"iso_code\": \"iso_alpha3\",\"csse_province_state\":\"ccse_name\",\"csse_province_state2\":\"ccse_name2\",\"population_urban\":\"urban_pop_rate\"},inplace=True)\n",
        "\n",
        "regions_info = pd.read_csv(\"/content/sberbank-covid19-forecast-2020/data/regions-info.csv\", decimal=\",\")\n",
        "regions_info = regions_info[[\"Region_eng\",\"Density_pop_sqkm\",\"Area\"]]\n",
        "regions_info.rename(columns={\"Region_eng\": \"ccse_name2\",\"Area\": \"land_area\",\"Density_pop_sqkm\": \"density\"},inplace=True)\n",
        "regions= regions.merge(regions_info, how = 'left', left_on='ccse_name2', right_on='ccse_name2')\n",
        "\n",
        "#Загрузить данные заражений по России\n",
        "data_Ru = pd.read_csv(\"/content/COVID-19_plus_Russia/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_RU.csv\").groupby(\"Province_State\").sum()\n",
        "data_Ru = data_Ru.iloc[:,15:].stack().reset_index()\n",
        "data_Ru.columns = [\"countries\",\"date\",\"confirmed\"]\n",
        "#Загрузить данные смертей по России\n",
        "data_deaths_Ru = pd.read_csv(\"/content/COVID-19_plus_Russia/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_RU.csv\").groupby(\"Province_State\").sum()\n",
        "data_deaths_Ru = data_deaths_Ru.iloc[:,15:].stack().reset_index()\n",
        "data_deaths_Ru.columns = [\"countries\",\"date\",\"deaths\"]\n",
        "data_Ru = data_Ru.merge(data_deaths_Ru, how = 'left', left_on=('countries','date'), right_on=('countries','date'))\n",
        "data_Ru[\"recovered\"] = 0\n",
        "\n",
        "data_Ru_today = pd.read_csv(\"/content/sberbank-covid19-forecast-2020/data/covid19-russia-cases.csv\")\n",
        "data_Ru_today = data_Ru_today[data_Ru_today['Region/City-Eng']!=\"Diamond Princess\"]\n",
        "data_Ru_today = data_Ru_today[[\"Region/City-Eng\",\"Date\",\"Confirmed\",\t\"Deaths\",\t\"Recovered\"]]\n",
        "data_Ru_today.columns = [\"countries\",\"date\",\"confirmed\",\"deaths\",\"recovered\"]\n",
        "data_Ru_today = data_Ru_today[data_Ru_today.date == today]\n",
        "data_Ru_today = data_Ru_today.replace(regions.ccse_name2.to_list(), regions.ccse_name.to_list())\n",
        "data_Ru = pd.concat([data_Ru, data_Ru_today])\n",
        "regions.drop(['ccse_name2'], axis='columns',inplace=True)\n",
        "countries = pd.concat([countries, regions])\n",
        "\n",
        "#Загрузить USA\n",
        "data_USA = pd.read_csv(\"/content/COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv\")\n",
        "data_USA = data_USA.groupby(\"Province_State\").sum()\n",
        "data_USA = data_USA.iloc[:,5:].stack().reset_index()\n",
        "data_USA.columns = [\"countries\",\"date\",\"confirmed\"]\n",
        "\n",
        "data_USA_deaths = pd.read_csv(\"/content/COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv\")\n",
        "data_USA_deaths = data_USA_deaths.groupby(\"Province_State\").sum()\n",
        "data_USA_deaths = data_USA_deaths.iloc[:,5:].stack().reset_index()\n",
        "data_USA_deaths.columns = [\"countries\",\"date\",\"deaths\"]\n",
        "data_USA = data_USA.merge(data_USA_deaths, how = 'left', left_on=('countries','date'), right_on=('countries','date'))\n",
        "data_USA[\"recovered\"] = 0\n",
        "data_USA.loc[data_USA.countries ==\"Georgia\",\"countries\"] = \"Georgia2\"\n",
        "USA = pd.read_csv(\"/content/sberbank-covid19-forecast-2020/data/USA.csv\")\n",
        "USA[\"iso_alpha3\"] = \"USA_\" + USA[\"ccse_name\"]\n",
        "USA[\"urban_pop_rate\"] = USA[\"urban_pop_rate\"]/100\n",
        "countries = pd.concat([countries, USA])\n",
        "\n",
        "#Загрузить список стран c датами\n",
        "data_ans = pd.read_csv(\"/content/sberbank-covid19-forecast-2020/sample_submission.csv\").iloc[:,:-1]\n",
        "data_ans[\"confirmed\"] = -1\n",
        "data_ans = data_ans.merge(countries, how = 'left', left_on='region', right_on='iso_alpha3')\n",
        "data_ans = data_ans[[\"date\",\"ccse_name\",\"confirmed\"]]\n",
        "data_ans = data_ans.rename(columns={\"ccse_name\": \"countries\"})\n",
        "data_ans[\"deaths\"] = 0\n",
        "data_ans[\"recovered\"] = 0\n",
        "data_ans = data_ans[data_ans.date >= today]\n",
        "\n",
        "#Загрузить данные заражений по миру\n",
        "data = pd.read_csv(\"/content/COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\")\n",
        "data = data.groupby(\"Country/Region\").sum()\n",
        "\n",
        "#Загрузить данные смертей по миру\n",
        "data_deaths = pd.read_csv(\"/content/COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\")\n",
        "data_deaths = data_deaths.groupby(\"Country/Region\").sum()\n",
        "\n",
        "\n",
        "#Каждый день на новой строке\n",
        "data_deaths = data_deaths.iloc[:,4:].stack().reset_index()\n",
        "data_deaths.columns = [\"countries\",\"date\",\"deaths\"]\n",
        "\n",
        "#Загрузить данные смертей по миру\n",
        "data_recovered = pd.read_csv(\"/content/COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv\")\n",
        "data_recovered = data_recovered.groupby(\"Country/Region\").sum()\n",
        "data_recovered = data_recovered.iloc[:,4:].stack().reset_index()\n",
        "data_recovered.columns = [\"countries\",\"date\",\"recovered\"]\n",
        "data_recovered[\"recovered\"] = np.log10(data_recovered[\"recovered\"]+1)\n",
        "\n",
        "#Карантин\n",
        "data_quarantine = pd.read_csv(\"/content/sberbank-covid19/data/quarantine.csv\")\n",
        "data_quarantine = data_quarantine.groupby(\"country\").max().loc[:,\"date\"]\n",
        "countries = countries.set_index(\"iso_alpha3\", drop=False)\n",
        "countries[\"Quarantine\"] = data_quarantine\n",
        "countries = countries.rename(columns={\"ccse_name\": \"countries\"})\n",
        "dic_countries = dict(zip(countries.iso_alpha3.to_list(), countries.countries.to_list()))\n",
        "\n",
        "# Сегоднящние данные\n",
        "countries_today = pd.read_csv(\"/content/COVID-19-new/data/cases_country.csv\")\n",
        "countries_today[\"Last_Update\"] = pd.to_datetime(countries_today[\"Last_Update\"]).apply(lambda x: pd.Series(x.strftime(\"%Y-%m-%d\")))\n",
        "countries_today = countries_today[[\"Country_Region\",\"Last_Update\",\"Confirmed\",\"Deaths\",\"Recovered\"]]\n",
        "countries_today.columns = ['countries', 'date', 'confirmed', 'deaths', 'recovered']\n",
        "countries_today.recovered =  np.log10(countries_today[\"recovered\"]+1)\n",
        "countries_today = countries_today[countries_today.countries.isin(countries.countries.to_list())]\n",
        "countries_today = countries_today.sort_values(by='countries')\n",
        "\n",
        "#Здравохранение\n",
        "Health_systems = pd.read_csv(\"/content/sberbank-covid19-forecast-2020/data/2.12_Health_systems.csv\").groupby(\"Country_Region\").mean()\n",
        "Health_systems = Health_systems.fillna(Health_systems.mean())\n",
        "Health_systems = Health_systems[[\"Health_exp_pct_GDP_2016\",\"Health_exp_per_capita_USD_2016\",\"Physicians_per_1000_2009-18\",\"Nurse_midwife_per_1000_2009-18\"]]\n",
        "Health_systems.columns = [\"Health_GDP\",\"Health_USD\",\"Physicians\",\"Nurse\"]\n",
        "countries = countries.merge(Health_systems, how = 'left', left_on='countries', right_on='Country_Region')\n",
        "\n",
        "#Возраст\n",
        "WorldPop = pd.read_csv(\"/content/sberbank-covid19-forecast-2020/data/WorldPopulationByAge2020.csv\")\n",
        "WorldPop = WorldPop.fillna(WorldPop.mean())\n",
        "WorldPop2 = (WorldPop[WorldPop[\"AgeGrp\"]==\"60+\"][\"PopTotal\"].to_numpy() + WorldPop[WorldPop[\"AgeGrp\"]==\"40-59\"][\"PopTotal\"].to_numpy())/(WorldPop[WorldPop[\"AgeGrp\"]==\"0-19\"][\"PopTotal\"].to_numpy()+WorldPop[WorldPop[\"AgeGrp\"]==\"20-39\"][\"PopTotal\"].to_numpy())\n",
        "WorldPop = WorldPop.groupby(\"Location\").mean()\n",
        "WorldPop[\"Age_old/new\"] = WorldPop2\n",
        "WorldPop = WorldPop[[\"Age_old/new\"]].reset_index()\n",
        "countries = countries.merge(WorldPop, how = 'left', left_on='countries', right_on='Location')\n",
        "\n",
        "#Курение\n",
        "smoke = pd.read_csv(\"/content/sberbank-covid19-forecast-2020/data/share-of-adults-who-smoke.csv\")\n",
        "smoke = smoke[smoke.Year > 2015].groupby(\"Entity\").mean()\n",
        "smoke = smoke[[\"Smoking prevalence, total (ages 15+) (% of adults)\"]]\n",
        "smoke.columns = [\"Smoking\"]\n",
        "smoke = smoke.reset_index()\n",
        "countries= countries.merge(smoke, how = 'left', left_on='countries', right_on='Entity')\n",
        "countries.drop(['Entity','Location'], axis='columns',inplace=True)\n",
        "\n",
        "#Еще инфа о странах\n",
        "countries2 = pd.read_csv(\"/content/sberbank-covid19-forecast-2020/data/covid19countryinfo.csv\")\n",
        "countries2 = countries2.fillna(0).groupby(\"country\").sum()\n",
        "countries2 = countries2[[\"tests\",\"testpop\",\"gatheringlimit\",\"hospibed\",\"healthperpop\"]]\n",
        "countries = countries.merge(countries2, how = 'left', left_on='countries', right_on='country')\n",
        "\n",
        "\n",
        "countries[\"region\"] = 0\n",
        "countries.loc[countries.iso_alpha3.str.contains('-'),\"region\"] = 1\n",
        "countries.loc[countries.iso_alpha3.str.contains('USA_'),\"region\"] = 2\n",
        "\n",
        "#mobility-google\n",
        "mobility = pd.read_csv(\"/content/sberbank-covid19/data/mobility-google.csv\")\n",
        "mobility.columns = [\"country\",\"date\",\"Parks\",\"Residential\",\"Retail & recreation\",\"Transit stations\",\"Workplaces\",\"Grocery & pharmacy\"]\n",
        "mobility[\"date\"] = pd.to_datetime(mobility.date)\n",
        "\n",
        "mobility_apple = pd.read_csv(\"/content/sberbank-covid19/data/mobility-apple.csv\")\n",
        "mobility_apple.drop(['apl-transit'], axis='columns',inplace=True)\n",
        "mobility_apple.columns = [\"date\", \"country\", \"driving\", \"walking\"]\n",
        "mobility_apple[\"date\"] = pd.to_datetime(mobility_apple.date)\n",
        "\n",
        "mobility_yandex = pd.read_csv(\"/content/sberbank-covid19/data/mobility-yandex.csv\")\n",
        "mobility_yandex[\"date\"] = pd.to_datetime(mobility_yandex.date)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwl1Gki0kcPO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Избавляемся от Nan\n",
        "countries_mean = countries.mean()\n",
        "countries[countries.region==2] = countries[countries.region==2].fillna(countries[countries.iso_alpha3 == \"USA\"].to_dict('records')[0])\n",
        "countries[countries.region==1] = countries[countries.region==1].fillna(countries[countries.iso_alpha3 == \"RUS\"].to_dict('records')[0])\n",
        "countries.loc[countries.tests == 0,\"tests\"] = countries.tests.mean()\n",
        "countries.loc[countries.testpop == 0,\"testpop\"] = countries.testpop.mean()\n",
        "countries.loc[countries.gatheringlimit == 0,\"gatheringlimit\"] = countries.gatheringlimit.mean()\n",
        "countries.loc[countries.hospibed == 0,\"hospibed\"] = countries.hospibed.mean()\n",
        "countries['Quarantine'] = countries['Quarantine'].fillna(\"2020-04-10\")\n",
        "countries = countries.fillna(countries_mean.to_dict())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v2gXwGZ-Js5u",
        "colab_type": "text"
      },
      "source": [
        "## Смотрим на первые страны"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMVUPrvb_JYP",
        "colab_type": "code",
        "outputId": "e55506e8-a700-4e01-ca8f-47047945d4c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "plt.rcParams['figure.figsize'] = [20, 10]\n",
        "data.iloc[:8].T.iloc[50:].plot()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3911f512e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZQjmbQDZz6NH",
        "colab_type": "text"
      },
      "source": [
        "# Обрабатываем данные"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aWqa7oXAHPmJ",
        "colab_type": "text"
      },
      "source": [
        "## Представляем данные в виде, где каждой дате соответствует свой столбец"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZyMgfcUSUIB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data2 = data.iloc[:,4:].stack().reset_index()\n",
        "data2.columns = [\"countries\",\"date\",\"confirmed\"]\n",
        "\n",
        "#Соединим смерти и заражения\n",
        "data2 = data2.merge(data_deaths, how = 'left', left_on=('countries','date'), right_on=('countries','date'))\n",
        "\n",
        "# Добавим выздоровивших\n",
        "data2 = data2.merge(data_recovered, how = 'left', left_on=('countries','date'), right_on=('countries','date'))\n",
        "data2 = data2[data2.countries.isin(countries.countries.to_list())]\n",
        "\n",
        "#Объединим с сегодня\n",
        "last_confirmed = data2.loc[pd.to_datetime(data2.date) == pd.to_datetime(today) - pd.offsets.Day(1)].sort_values(by='countries')\n",
        "countries_today_new = countries_today[countries_today.confirmed.values > last_confirmed.confirmed.values]\n",
        "data2 = pd.concat([data2, countries_today_new],ignore_index=True)\n",
        "\n",
        "#Добавим Россию\n",
        "data2 = pd.concat([data2, data_Ru],ignore_index=True)\n",
        "\n",
        "#Добавим USA\n",
        "data2 = pd.concat([data2, data_USA],ignore_index=True)\n",
        "\n",
        "#Объединим с будущим\n",
        "data_ans = data_ans[(data_ans.date != today)|(~(data_ans.countries.isin(countries_today_new.countries.values))&~(data_ans.countries.isin(data_Ru_today.countries.values)))]\n",
        "data2 = pd.concat([data2, data_ans],ignore_index=True)\n",
        "\n",
        "\n",
        "#Добавим информацию о стране(плотность, население и тд)\n",
        "data2 = data2.merge(countries, how = 'right', left_on='countries', right_on='countries')\n",
        "\n",
        "#Удалим дни без больных\n",
        "data2 = data2[data2.confirmed != 0]\n",
        "data2.loc[data2.confirmed == -1,\"confirmed\"] = 0\n",
        "data2[\"date\"] = pd.to_datetime(data2.date)\n",
        "\n",
        "\n",
        "data2 = data2.merge(mobility_yandex, how = 'left', left_on=('iso_alpha3','date'), right_on=('country','date'))\n",
        "mobility_min = mobility_yandex.groupby(\"country\").min().drop(['date'], axis='columns')\n",
        "mobility_min.columns = ['min_isolation']\n",
        "data2 = data2.merge(mobility_min, how = 'left', left_on='iso_alpha3', right_on='country')\n",
        "data2.loc[(data2.isolation.isnull())&(data2.date>pd.to_datetime(\"2020-04-01\")),\n",
        "          'isolation'] = data2.loc[(data2.isolation.isnull())&(data2.date>pd.to_datetime(\"2020-04-01\")),'min_isolation'].to_numpy()\n",
        "data2.drop(['country', 'min_isolation'], axis='columns',inplace=True)\n",
        "#data2['isolation'] = data2['isolation'].fillna(data2['isolation'].mean())\n",
        "data2['isolation'] = (data2['isolation'])/5\n",
        "\n",
        "\n",
        "data2 = data2.merge(mobility_apple, how = 'left', left_on=('iso_alpha3','date'), right_on=('country','date'))\n",
        "mobility_min = mobility_apple.groupby(\"country\").min().drop(['date'], axis='columns')\n",
        "mobility_min.columns = ['min_driving', 'min_walking']\n",
        "data2 = data2.merge(mobility_min, how = 'left', left_on='iso_alpha3', right_on='country')\n",
        "data2.loc[(data2.driving.isnull())&(data2.date>pd.to_datetime(\"2020-04-01\")),\n",
        "          ['driving', 'walking']] = data2.loc[(data2.driving.isnull())&(data2.date>pd.to_datetime(\"2020-04-01\")),['min_driving', 'min_walking']].to_numpy()\n",
        "data2.drop(['country', 'min_driving', 'min_walking'], axis='columns',inplace=True)\n",
        "#data2.loc[data2.region==2,['driving', 'walking']] = data2.loc[data2.region==2,['driving', 'walking']].fillna(data2.loc[data2.iso_alpha3 == \"USA\",['driving', 'walking']].to_dict('records')[0])\n",
        "#data2.loc[data2.region==1,['driving', 'walking']] = data2.loc[data2.region==1,['driving', 'walking']].fillna(data2.loc[data2.iso_alpha3 == \"RUS\",['driving', 'walking']].to_dict('records')[0])\n",
        "#data2[['driving', 'walking']] = data2[['driving', 'walking']].fillna(data2[['driving', 'walking']].mean())\n",
        "data2.loc[data2.region != 1,'isolation'] = (data2['driving'] +  data2['walking'])/100\n",
        "\n",
        "\n",
        "data2 = data2.merge(mobility, how = 'left', left_on=('iso_alpha3','date'), right_on=('country','date'))\n",
        "mobility_min = mobility.groupby(\"country\").min().drop(['date'], axis='columns')\n",
        "mobility_min.columns = ['min_Retail & recreation', 'min_Grocery & pharmacy', 'min_Parks',\n",
        "       'min_Transit stations', 'min_Workplaces', 'min_Residential']\n",
        "data2 = data2.merge(mobility_min, how = 'left', left_on='iso_alpha3', right_on='country')\n",
        "data2.loc[(data2.Parks.isnull())&(data2.date>pd.to_datetime(\"2020-04-01\")),\n",
        "          ['Retail & recreation', 'Grocery & pharmacy', 'Parks', 'Transit stations', 'Workplaces', \n",
        "           'Residential']] = data2.loc[(data2.Parks.isnull())&(data2.date>pd.to_datetime(\"2020-04-01\")),\n",
        "                                       ['min_Retail & recreation', 'min_Grocery & pharmacy', 'min_Parks',\n",
        "       'min_Transit stations', 'min_Workplaces', 'min_Residential']].to_numpy()\n",
        "data2.drop(['country', 'min_Retail & recreation', 'min_Grocery & pharmacy', 'min_Parks',\n",
        "       'min_Transit stations', 'min_Workplaces', 'min_Residential'], axis='columns',inplace=True)\n",
        "\"\"\"data2[['Retail & recreation', 'Grocery & pharmacy', 'Parks',\n",
        "       'Transit stations', 'Workplaces', 'Residential']] = data2[['Retail & recreation', 'Grocery & pharmacy', 'Parks',\n",
        "       'Transit stations', 'Workplaces', 'Residential']].fillna(data2[['Retail & recreation', 'Grocery & pharmacy', 'Parks',\n",
        "       'Transit stations', 'Workplaces', 'Residential']].mean())\"\"\"\n",
        "data2.loc[data2.isolation.isnull(),'isolation'] = (data2['Retail & recreation'] +data2['Grocery & pharmacy'] +data2[ 'Parks'] \n",
        "                                                   +data2['Transit stations'] +data2[ 'Workplaces'] +data2[ 'Residential']+500)/1000     \n",
        "\n",
        "data2['isolation'] = data2['isolation'].fillna(data2['isolation'].mean())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_bWNQR45Jsx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "log_labels = True\n",
        "\n",
        "if log_labels:\n",
        "  data2.confirmed = np.log10(data2.confirmed+1)\n",
        "  data2.deaths = np.log10(data2.deaths+1)\n",
        "else:\n",
        "  data2.confirmed = data2.confirmed\n",
        "  data2.deaths = data2.deaths"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rgQAinJNj4HU",
        "colab_type": "text"
      },
      "source": [
        "## Добавить данные за прошлый день"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okBJhPd2jyGB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "old_con = data2[\"confirmed\"].iloc[:-1]\n",
        "old_con2 = data2[\"deaths\"].iloc[:-1]\n",
        "old_recovered = data2[\"recovered\"].iloc[:-1]\n",
        "data2 = data2.iloc[1:]\n",
        "data2[\"pred_conf\"] = old_con.values\n",
        "data2[\"pred_deaths\"] = old_con2.values\n",
        "data2[\"pred_recovered\"] = old_recovered.values\n",
        "\n",
        "if log_labels:\n",
        "  data2[\"delta_conf\"] = data2[\"confirmed\"] - data2[\"pred_conf\"]\n",
        "  data2[\"delta_deaths\"] = data2[\"deaths\"] - data2[\"pred_deaths\"]\n",
        "else:\n",
        "  data2[\"delta_conf\"] = data2[\"confirmed\"] - data2[\"pred_conf\"]\n",
        "  data2[\"delta_deaths\"] = data2[\"deaths\"] - data2[\"pred_deaths\"]  \n",
        "\n",
        "data2 = data2.iloc[1:]\n",
        "data2[\"delta_conf1\"] = old_con.values[1:] - old_con.values[:-1]\n",
        "data2[\"delta_deaths1\"] = old_con2.values[1:] - old_con2.values[:-1]\n",
        "\n",
        "data2 = data2.iloc[1:]\n",
        "data2[\"delta_conf2\"] = old_con.values[1:-1] - old_con.values[:-2]\n",
        "data2[\"delta_deaths2\"] = old_con2.values[1:-1] - old_con2.values[:-2]\n",
        "data2[\"delta_delta_conf1\"] = data2[\"delta_conf2\"] - data2[\"delta_conf1\"]\n",
        "data2 = data2.iloc[1:]\n",
        "data2[\"delta_conf3\"] = old_con.values[1:-2] - old_con.values[:-3]\n",
        "data2[\"delta_deaths3\"] = old_con2.values[1:-2] - old_con2.values[:-3]\n",
        "data2[\"delta_delta_conf2\"] = data2[\"delta_conf3\"] - data2[\"delta_conf2\"]\n",
        "data2 = data2.iloc[1:]\n",
        "data2[\"delta_conf4\"] = old_con.values[1:-3] - old_con.values[:-4]\n",
        "data2[\"delta_deaths4\"] = old_con2.values[1:-3] - old_con2.values[:-4]\n",
        "data2[\"delta_delta_conf3\"] = data2[\"delta_conf4\"] - data2[\"delta_conf3\"]\n",
        "data2 = data2.iloc[1:]\n",
        "data2[\"delta_conf5\"] = old_con.values[1:-4] - old_con.values[:-5]\n",
        "data2[\"delta_deaths5\"] = old_con2.values[1:-4] - old_con2.values[:-5]\n",
        "data2[\"delta_delta_conf4\"] = data2[\"delta_conf5\"] - data2[\"delta_conf4\"]\n",
        "data2 = data2.iloc[1:]\n",
        "data2[\"delta_conf6\"] = old_con.values[1:-5] - old_con.values[:-6]\n",
        "data2[\"delta_deaths6\"] = old_con2.values[1:-5] - old_con2.values[:-6]\n",
        "data2[\"delta_delta_conf5\"] = data2[\"delta_conf6\"] - data2[\"delta_conf5\"]\n",
        "data2[\"delta_conf_sum\"] = (data2[\"delta_conf1\"] + data2[\"delta_conf2\"] + data2[\"delta_conf3\"] + data2[\"delta_conf4\"] + data2[\"delta_conf5\"] + data2[\"delta_conf6\"]) /6\n",
        "data2[\"delta_deaths_sum\"] = (data2[\"delta_deaths1\"] + data2[\"delta_deaths2\"] + data2[\"delta_deaths3\"] + data2[\"delta_deaths4\"] + data2[\"delta_deaths5\"] + data2[\"delta_deaths6\"]) /6\n",
        "data2[\"delta_delta_sum\"] = (data2[\"delta_delta_conf1\"] + data2[\"delta_delta_conf2\"] + data2[\"delta_delta_conf3\"] + data2[\"delta_delta_conf4\"] + data2[\"delta_delta_conf5\"]) /5\n",
        "data2[\"delta_loss\"] = 0\n",
        "data2.loc[data2[\"delta_conf1\"] < data2[\"delta_conf_sum\"],\"delta_loss\"] = 1\n",
        "          \n",
        "delta_data = data2[\"confirmed\"].iloc[1:]\n",
        "\n",
        "data2 = data2.iloc[:-1]\n",
        "data2[\"delta2\"] =  delta_data.values - data2[\"confirmed\"] + data2[\"delta_conf\"]\n",
        "data2 = data2.iloc[:-1]\n",
        "data2[\"delta3\"] =  delta_data.values[1:]  - data2[\"confirmed\"] + data2[\"delta_conf\"]\n",
        "data2 = data2.iloc[:-1]\n",
        "data2[\"delta4\"] = delta_data.values[2:] - data2[\"confirmed\"] + data2[\"delta_conf\"]\n",
        "data2 = data2.iloc[:-1]\n",
        "data2[\"delta5\"] =  delta_data.values[3:] - data2[\"confirmed\"] + data2[\"delta_conf\"]\n",
        "data2 = data2.iloc[:-1]\n",
        "data2[\"delta6\"] =  delta_data.values[4:] - data2[\"confirmed\"] + data2[\"delta_conf\"]\n",
        "data2 = data2.iloc[:-1]\n",
        "data2[\"delta7\"] =  delta_data.values[5:] - data2[\"confirmed\"] + data2[\"delta_conf\"]\n",
        "\n",
        "data2[\"delta_conf_count\"] = 0\n",
        "data2.loc[(data2[\"delta_conf1\"] < data2[\"delta_conf2\"]) | ((data2[\"delta_conf1\"] <0.02) & (data2[\"delta_conf1\"]>0)),\"delta_conf_count\"] +=0.2\n",
        "data2.loc[(data2[\"delta_conf2\"] < data2[\"delta_conf3\"]) | ((data2[\"delta_conf2\"] <0.02) & (data2[\"delta_conf2\"]>0)),\"delta_conf_count\"] +=0.2\n",
        "data2.loc[(data2[\"delta_conf3\"] < data2[\"delta_conf4\"]) | ((data2[\"delta_conf3\"] <0.02) & (data2[\"delta_conf3\"]>0)),\"delta_conf_count\"] +=0.2\n",
        "data2.loc[(data2[\"delta_conf4\"] < data2[\"delta_conf5\"]) | ((data2[\"delta_conf4\"] <0.02) & (data2[\"delta_conf4\"]>0)),\"delta_conf_count\"] +=0.2\n",
        "data2.loc[(data2[\"delta_conf5\"] < data2[\"delta_conf6\"]) | ((data2[\"delta_conf5\"] <0.02) & (data2[\"delta_conf5\"]>0)),\"delta_conf_count\"] +=0.2\n",
        "\n",
        "data2['population_log'] = np.log10(data2.population+1)\n",
        "data2['pred_conf-population_log'] = data2['pred_conf'] - data2['population_log'] + 3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cP_K0n4zHt6z",
        "colab_type": "text"
      },
      "source": [
        "## Добавляем признак, количество дней от первого(пятого и тд) зараженного"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gF73ZobwHs-Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Считаем дни от первого больного\n",
        "end_date = \"2020-05-01\"\n",
        "#data2 = data2[(data2[\"date\"]<pd.to_datetime(\"2020-05-1\")) & ((data2[\"date\"]>pd.to_datetime(\"2020-04-1\")) | (data2.confirmed !=-1))]\n",
        "data4 = data2[[\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_min\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2[\"days\"] = (data2.date - data2.Date_min).dt.days\n",
        "\n",
        "data4 = data2.loc[data2.confirmed > 0.5, [\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_5\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2['Date_5'] = data2['Date_5'].fillna(pd.to_datetime(end_date))\n",
        "data2[\"days_5\"] = (data2.date - data2.Date_5).dt.days\n",
        "#data2.loc[data2[\"days_5\"]<0,\"days_5\"] = 0\n",
        "\n",
        "data4 = data2.loc[data2.confirmed > 0.8, [\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_8\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2['Date_8'] = data2['Date_8'].fillna(pd.to_datetime(end_date))\n",
        "data2[\"days_8\"] = (data2.date - data2.Date_8).dt.days\n",
        "#data2.loc[data2[\"days_10\"]<0,\"days_10\"] = 0\n",
        "\n",
        "data4 = data2.loc[data2.confirmed > 1, [\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_10\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2['Date_10'] = data2['Date_10'].fillna(pd.to_datetime(end_date))\n",
        "data2[\"days_10\"] = (data2.date - data2.Date_10).dt.days\n",
        "#data2.loc[data2[\"days_10\"]<0,\"days_10\"] = 0\n",
        "\n",
        "data4 = data2.loc[data2.confirmed > 1.5, [\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_50\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2['Date_50'] = data2['Date_50'].fillna(pd.to_datetime(end_date))\n",
        "data2[\"days_50\"] = (data2.date - data2.Date_50).dt.days\n",
        "#data2.loc[data2[\"days_50\"]<0,\"days_50\"] = 0\n",
        "\n",
        "data4 = data2.loc[data2.confirmed > 1.8, [\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_80\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2['Date_80'] = data2['Date_80'].fillna(pd.to_datetime(end_date))\n",
        "data2[\"days_80\"] = (data2.date - data2.Date_80).dt.days\n",
        "#data2.loc[data2[\"days_10\"]<0,\"days_10\"] = 0\n",
        "\n",
        "data4 = data2.loc[data2.confirmed > 2, [\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_100\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2['Date_100'] = data2['Date_100'].fillna(pd.to_datetime(end_date))\n",
        "data2[\"days_100\"] = (data2.date - data2.Date_100).dt.days\n",
        "#data2.loc[data2[\"days_100\"]<0,\"days_100\"] = 0\n",
        "\n",
        "data4 = data2.loc[data2.confirmed > 2.5, [\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_500\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2['Date_500'] = data2['Date_500'].fillna(pd.to_datetime(end_date))\n",
        "data2[\"days_500\"] = (data2.date - data2.Date_500).dt.days\n",
        "#data2.loc[data2[\"days_500\"]<0,\"days_500\"] = 0\n",
        "\n",
        "data4 = data2.loc[data2.confirmed > 3, [\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_1000\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2['Date_1000'] = data2['Date_1000'].fillna(pd.to_datetime(end_date))\n",
        "data2[\"days_1000\"] = (data2.date - data2.Date_1000).dt.days\n",
        "#data2.loc[data2[\"days_1000\"]<0,\"days_1000\"] = 0\n",
        "\n",
        "data2[\"days_mart\"] = (data2.date - pd.to_datetime(\"2020-03-1\")).dt.days\n",
        "data2[\"days_after_Quarantine\"] = (data2.date - pd.to_datetime(data2.Quarantine)).dt.days - 14\n",
        "data2.loc[data2[\"days_after_Quarantine\"]<0,\"days_after_Quarantine\"] = 0\n",
        "\n",
        "data4 = data2.loc[data2['pred_conf-population_log'] > 0, [\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_1_1000\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2['Date_1_1000'] = data2['Date_1_1000'].fillna(pd.to_datetime(end_date))\n",
        "data2[\"days_1_1000\"] = (data2.date - data2[\"Date_1_1000\"]).dt.days\n",
        "data2.loc[data2[\"days_1_1000\"]<0,\"days_1_1000\"] = 0\n",
        "\n",
        "data4 = data2.loc[data2['pred_conf-population_log'] > -1, [\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_1_100\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2['Date_1_100'] = data2['Date_1_100'].fillna(pd.to_datetime(end_date))\n",
        "data2[\"days_1_100\"] = (data2.date - data2[\"Date_1_100\"]).dt.days\n",
        "#data2.loc[data2[\"days_1_100\"]<0,\"days_1_100\"] = 0\n",
        "\n",
        "data4 = data2.loc[data2['pred_conf-population_log'] > -2, [\"countries\",\"date\"]].groupby(\"countries\").min()\n",
        "data4.columns = [\"Date_1_10000\"]\n",
        "data2 = data2.merge(data4, how = 'left', left_on='countries', right_on='countries')\n",
        "data2['Date_1_10000'] = data2['Date_1_10000'].fillna(pd.to_datetime(end_date))\n",
        "data2[\"days_1_10000\"] = (data2.date - data2[\"Date_1_10000\"]).dt.days\n",
        "#data2.loc[data2[\"days_1_10000\"]<0,\"days_1_10000\"] = 0\n",
        "\n",
        "data2.drop(['Date_min','Date_10','Date_100','Date_1000', 'Date_5','Date_50','Date_500', 'Date_1_1000', 'Date_1_100', 'Date_1_10000'], axis='columns',inplace=True)\n",
        "data2.date = data2[\"date\"].apply(lambda x: pd.Series(x.strftime(\"%m-%d\")))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DdugXNiT0_UW",
        "colab_type": "code",
        "outputId": "a7bd9f36-811b-4dbf-b191-24ce7b5c11c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        }
      },
      "source": [
        "Scale = True\n",
        "\n",
        "if Scale:\n",
        "  data2.density = data2.density/data2.density.max()\n",
        "  data2.fertility_rate = data2.fertility_rate/data2.fertility_rate.max()\n",
        "  data2.land_area = np.log10(data2.land_area+1)\n",
        "  data2.land_area = data2.land_area/data2.land_area.max()\n",
        "  data2.median_age = data2.median_age/data2.median_age.max()\n",
        "  data2.migrants = data2.migrants - data2.migrants.min()\n",
        "  data2.migrants = np.log10(data2.migrants+1)\n",
        "  data2.migrants = data2.migrants/data2.migrants.max()\n",
        "  data2.population = data2.population/data2.population.max()\n",
        "  data2.Health_GDP = data2.Health_GDP/data2.Health_GDP.max()\n",
        "  data2.Health_USD = data2.Health_USD/data2.Health_USD.max()\n",
        "  data2.Physicians = data2.Physicians/data2.Physicians.max()\n",
        "  data2.tests = data2.tests/data2.tests.max()\n",
        "  data2.testpop = data2.testpop/data2.testpop.max()\n",
        "  data2.gatheringlimit = data2.gatheringlimit/data2.gatheringlimit.max()\n",
        "  data2.hospibed = data2.hospibed/data2.hospibed.max()\n",
        "  data2.healthperpop = data2.healthperpop/data2.healthperpop.max()\n",
        "  data2.Nurse = data2.Nurse/data2.Nurse.max()\n",
        "  data2[\"Age_old/new\"] = data2[\"Age_old/new\"]/data2[\"Age_old/new\"].max()\n",
        "  data2.Smoking = data2.Smoking/50\n",
        "  data2.days = data2.days/100\n",
        "  data2.days_10 = data2.days_10/100\n",
        "  data2.days_100 = data2.days_100/100\n",
        "  data2.days_1000 = data2.days_1000/100\n",
        "  data2.days_5 = data2.days_5/100\n",
        "  data2.days_50 = data2.days_50/100\n",
        "  data2.days_500 = data2.days_500/100\n",
        "  data2.days_8 = data2.days_8/100\n",
        "  data2.days_80 = data2.days_80/100    \n",
        "  data2.days_1_100 = data2.days_1_100/100  \n",
        "  data2.days_1_1000 = data2.days_1_1000/100 \n",
        "  data2.days_1_10000 = data2.days_1_10000/100 \n",
        "  data2.days_mart = data2.days_mart/100\n",
        "\n",
        "  data2.days_after_Quarantine = data2.days_after_Quarantine/100\n",
        "  sc_days = 100\n",
        "else:\n",
        "  sc_days = 1\n",
        "data2[data2.iso_alpha3 == \"RUS\"].iloc[70:75]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "\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>countries</th>\n",
              "      <th>date</th>\n",
              "      <th>confirmed</th>\n",
              "      <th>deaths</th>\n",
              "      <th>recovered</th>\n",
              "      <th>iso_alpha3</th>\n",
              "      <th>density</th>\n",
              "      <th>fertility_rate</th>\n",
              "      <th>land_area</th>\n",
              "      <th>median_age</th>\n",
              "      <th>migrants</th>\n",
              "      <th>population</th>\n",
              "      <th>urban_pop_rate</th>\n",
              "      <th>world_share</th>\n",
              "      <th>Quarantine</th>\n",
              "      <th>Health_GDP</th>\n",
              "      <th>Health_USD</th>\n",
              "      <th>Physicians</th>\n",
              "      <th>Nurse</th>\n",
              "      <th>Age_old/new</th>\n",
              "      <th>Smoking</th>\n",
              "      <th>tests</th>\n",
              "      <th>testpop</th>\n",
              "      <th>gatheringlimit</th>\n",
              "      <th>hospibed</th>\n",
              "      <th>healthperpop</th>\n",
              "      <th>region</th>\n",
              "      <th>isolation</th>\n",
              "      <th>driving</th>\n",
              "      <th>walking</th>\n",
              "      <th>Parks</th>\n",
              "      <th>Residential</th>\n",
              "      <th>Retail &amp; recreation</th>\n",
              "      <th>Transit stations</th>\n",
              "      <th>Workplaces</th>\n",
              "      <th>Grocery &amp; pharmacy</th>\n",
              "      <th>pred_conf</th>\n",
              "      <th>pred_deaths</th>\n",
              "      <th>pred_recovered</th>\n",
              "      <th>delta_conf</th>\n",
              "      <th>...</th>\n",
              "      <th>delta_deaths3</th>\n",
              "      <th>delta_delta_conf2</th>\n",
              "      <th>delta_conf4</th>\n",
              "      <th>delta_deaths4</th>\n",
              "      <th>delta_delta_conf3</th>\n",
              "      <th>delta_conf5</th>\n",
              "      <th>delta_deaths5</th>\n",
              "      <th>delta_delta_conf4</th>\n",
              "      <th>delta_conf6</th>\n",
              "      <th>delta_deaths6</th>\n",
              "      <th>delta_delta_conf5</th>\n",
              "      <th>delta_conf_sum</th>\n",
              "      <th>delta_deaths_sum</th>\n",
              "      <th>delta_delta_sum</th>\n",
              "      <th>delta_loss</th>\n",
              "      <th>delta2</th>\n",
              "      <th>delta3</th>\n",
              "      <th>delta4</th>\n",
              "      <th>delta5</th>\n",
              "      <th>delta6</th>\n",
              "      <th>delta7</th>\n",
              "      <th>delta_conf_count</th>\n",
              "      <th>population_log</th>\n",
              "      <th>pred_conf-population_log</th>\n",
              "      <th>days</th>\n",
              "      <th>days_5</th>\n",
              "      <th>Date_8</th>\n",
              "      <th>days_8</th>\n",
              "      <th>days_10</th>\n",
              "      <th>days_50</th>\n",
              "      <th>Date_80</th>\n",
              "      <th>days_80</th>\n",
              "      <th>days_100</th>\n",
              "      <th>days_500</th>\n",
              "      <th>days_1000</th>\n",
              "      <th>days_mart</th>\n",
              "      <th>days_after_Quarantine</th>\n",
              "      <th>days_1_1000</th>\n",
              "      <th>days_1_100</th>\n",
              "      <th>days_1_10000</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17978</th>\n",
              "      <td>Russia</td>\n",
              "      <td>04-10</td>\n",
              "      <td>4.076203</td>\n",
              "      <td>1.977724</td>\n",
              "      <td>2.900913</td>\n",
              "      <td>RUS</td>\n",
              "      <td>0.000342</td>\n",
              "      <td>0.257143</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.9542</td>\n",
              "      <td>0.101391</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.0187</td>\n",
              "      <td>2020-03-30</td>\n",
              "      <td>0.309942</td>\n",
              "      <td>0.047529</td>\n",
              "      <td>0.487805</td>\n",
              "      <td>0.423645</td>\n",
              "      <td>0.391229</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.356813</td>\n",
              "      <td>0.003023</td>\n",
              "      <td>0.070348</td>\n",
              "      <td>0.288732</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0</td>\n",
              "      <td>1.1268</td>\n",
              "      <td>62.77</td>\n",
              "      <td>49.91</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.005695</td>\n",
              "      <td>1.886491</td>\n",
              "      <td>2.844477</td>\n",
              "      <td>0.070508</td>\n",
              "      <td>...</td>\n",
              "      <td>0.089611</td>\n",
              "      <td>0.009358</td>\n",
              "      <td>0.070774</td>\n",
              "      <td>0.018483</td>\n",
              "      <td>-0.001808</td>\n",
              "      <td>0.056544</td>\n",
              "      <td>0.019305</td>\n",
              "      <td>-0.014230</td>\n",
              "      <td>0.056997</td>\n",
              "      <td>0.099385</td>\n",
              "      <td>0.000453</td>\n",
              "      <td>0.064608</td>\n",
              "      <td>0.057070</td>\n",
              "      <td>-0.002106</td>\n",
              "      <td>0</td>\n",
              "      <td>0.127364</td>\n",
              "      <td>0.192164</td>\n",
              "      <td>0.257444</td>\n",
              "      <td>0.318649</td>\n",
              "      <td>0.383311</td>\n",
              "      <td>0.440516</td>\n",
              "      <td>0.4</td>\n",
              "      <td>8.164158</td>\n",
              "      <td>-1.158463</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.39</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17979</th>\n",
              "      <td>Russia</td>\n",
              "      <td>04-11</td>\n",
              "      <td>4.133060</td>\n",
              "      <td>2.029384</td>\n",
              "      <td>3.019532</td>\n",
              "      <td>RUS</td>\n",
              "      <td>0.000342</td>\n",
              "      <td>0.257143</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.9542</td>\n",
              "      <td>0.101391</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.0187</td>\n",
              "      <td>2020-03-30</td>\n",
              "      <td>0.309942</td>\n",
              "      <td>0.047529</td>\n",
              "      <td>0.487805</td>\n",
              "      <td>0.423645</td>\n",
              "      <td>0.391229</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.356813</td>\n",
              "      <td>0.003023</td>\n",
              "      <td>0.070348</td>\n",
              "      <td>0.288732</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0030</td>\n",
              "      <td>55.68</td>\n",
              "      <td>44.62</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.076203</td>\n",
              "      <td>1.977724</td>\n",
              "      <td>2.900913</td>\n",
              "      <td>0.056856</td>\n",
              "      <td>...</td>\n",
              "      <td>0.035328</td>\n",
              "      <td>-0.004302</td>\n",
              "      <td>0.072582</td>\n",
              "      <td>0.089611</td>\n",
              "      <td>0.009358</td>\n",
              "      <td>0.070774</td>\n",
              "      <td>0.018483</td>\n",
              "      <td>-0.001808</td>\n",
              "      <td>0.056544</td>\n",
              "      <td>0.019305</td>\n",
              "      <td>-0.014230</td>\n",
              "      <td>0.066860</td>\n",
              "      <td>0.055712</td>\n",
              "      <td>-0.002793</td>\n",
              "      <td>0</td>\n",
              "      <td>0.121656</td>\n",
              "      <td>0.186935</td>\n",
              "      <td>0.248141</td>\n",
              "      <td>0.312803</td>\n",
              "      <td>0.370007</td>\n",
              "      <td>0.429069</td>\n",
              "      <td>0.2</td>\n",
              "      <td>8.164158</td>\n",
              "      <td>-1.087954</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.40</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.29</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17980</th>\n",
              "      <td>Russia</td>\n",
              "      <td>04-12</td>\n",
              "      <td>4.197859</td>\n",
              "      <td>2.117271</td>\n",
              "      <td>3.111263</td>\n",
              "      <td>RUS</td>\n",
              "      <td>0.000342</td>\n",
              "      <td>0.257143</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.9542</td>\n",
              "      <td>0.101391</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.0187</td>\n",
              "      <td>2020-03-30</td>\n",
              "      <td>0.309942</td>\n",
              "      <td>0.047529</td>\n",
              "      <td>0.487805</td>\n",
              "      <td>0.423645</td>\n",
              "      <td>0.391229</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.356813</td>\n",
              "      <td>0.003023</td>\n",
              "      <td>0.070348</td>\n",
              "      <td>0.288732</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0</td>\n",
              "      <td>0.9898</td>\n",
              "      <td>55.92</td>\n",
              "      <td>43.06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.133060</td>\n",
              "      <td>2.029384</td>\n",
              "      <td>3.019532</td>\n",
              "      <td>0.064800</td>\n",
              "      <td>...</td>\n",
              "      <td>0.080311</td>\n",
              "      <td>-0.002982</td>\n",
              "      <td>0.063224</td>\n",
              "      <td>0.035328</td>\n",
              "      <td>-0.004302</td>\n",
              "      <td>0.072582</td>\n",
              "      <td>0.089611</td>\n",
              "      <td>0.009358</td>\n",
              "      <td>0.070774</td>\n",
              "      <td>0.018483</td>\n",
              "      <td>-0.001808</td>\n",
              "      <td>0.066912</td>\n",
              "      <td>0.061104</td>\n",
              "      <td>0.002784</td>\n",
              "      <td>1</td>\n",
              "      <td>0.130079</td>\n",
              "      <td>0.191285</td>\n",
              "      <td>0.255947</td>\n",
              "      <td>0.313151</td>\n",
              "      <td>0.372212</td>\n",
              "      <td>0.432717</td>\n",
              "      <td>0.4</td>\n",
              "      <td>8.164158</td>\n",
              "      <td>-1.031098</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.41</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17981</th>\n",
              "      <td>Russia</td>\n",
              "      <td>04-13</td>\n",
              "      <td>4.263139</td>\n",
              "      <td>2.173186</td>\n",
              "      <td>3.167613</td>\n",
              "      <td>RUS</td>\n",
              "      <td>0.000342</td>\n",
              "      <td>0.257143</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.9542</td>\n",
              "      <td>0.101391</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.0187</td>\n",
              "      <td>2020-03-30</td>\n",
              "      <td>0.309942</td>\n",
              "      <td>0.047529</td>\n",
              "      <td>0.487805</td>\n",
              "      <td>0.423645</td>\n",
              "      <td>0.391229</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.356813</td>\n",
              "      <td>0.003023</td>\n",
              "      <td>0.070348</td>\n",
              "      <td>0.288732</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0975</td>\n",
              "      <td>59.94</td>\n",
              "      <td>49.81</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.197859</td>\n",
              "      <td>2.117271</td>\n",
              "      <td>3.111263</td>\n",
              "      <td>0.065280</td>\n",
              "      <td>...</td>\n",
              "      <td>0.091233</td>\n",
              "      <td>0.013652</td>\n",
              "      <td>0.067526</td>\n",
              "      <td>0.080311</td>\n",
              "      <td>-0.002982</td>\n",
              "      <td>0.063224</td>\n",
              "      <td>0.035328</td>\n",
              "      <td>-0.004302</td>\n",
              "      <td>0.072582</td>\n",
              "      <td>0.089611</td>\n",
              "      <td>0.009358</td>\n",
              "      <td>0.065916</td>\n",
              "      <td>0.072672</td>\n",
              "      <td>0.001557</td>\n",
              "      <td>1</td>\n",
              "      <td>0.126485</td>\n",
              "      <td>0.191147</td>\n",
              "      <td>0.248352</td>\n",
              "      <td>0.307413</td>\n",
              "      <td>0.367918</td>\n",
              "      <td>0.434132</td>\n",
              "      <td>0.4</td>\n",
              "      <td>8.164158</td>\n",
              "      <td>-0.966299</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.42</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.31</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17982</th>\n",
              "      <td>Russia</td>\n",
              "      <td>04-14</td>\n",
              "      <td>4.324344</td>\n",
              "      <td>2.232996</td>\n",
              "      <td>3.229170</td>\n",
              "      <td>RUS</td>\n",
              "      <td>0.000342</td>\n",
              "      <td>0.257143</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.9542</td>\n",
              "      <td>0.101391</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.0187</td>\n",
              "      <td>2020-03-30</td>\n",
              "      <td>0.309942</td>\n",
              "      <td>0.047529</td>\n",
              "      <td>0.487805</td>\n",
              "      <td>0.423645</td>\n",
              "      <td>0.391229</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.356813</td>\n",
              "      <td>0.003023</td>\n",
              "      <td>0.070348</td>\n",
              "      <td>0.288732</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0840</td>\n",
              "      <td>61.43</td>\n",
              "      <td>46.97</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.263139</td>\n",
              "      <td>2.173186</td>\n",
              "      <td>3.167613</td>\n",
              "      <td>0.061205</td>\n",
              "      <td>...</td>\n",
              "      <td>0.051660</td>\n",
              "      <td>-0.007943</td>\n",
              "      <td>0.070508</td>\n",
              "      <td>0.091233</td>\n",
              "      <td>0.013652</td>\n",
              "      <td>0.067526</td>\n",
              "      <td>0.080311</td>\n",
              "      <td>-0.002982</td>\n",
              "      <td>0.063224</td>\n",
              "      <td>0.035328</td>\n",
              "      <td>-0.004302</td>\n",
              "      <td>0.064699</td>\n",
              "      <td>0.067056</td>\n",
              "      <td>-0.000411</td>\n",
              "      <td>0</td>\n",
              "      <td>0.125868</td>\n",
              "      <td>0.183072</td>\n",
              "      <td>0.242133</td>\n",
              "      <td>0.302638</td>\n",
              "      <td>0.368853</td>\n",
              "      <td>0.410085</td>\n",
              "      <td>0.2</td>\n",
              "      <td>8.164158</td>\n",
              "      <td>-0.901019</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 87 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      countries   date  confirmed  ...  days_1_1000  days_1_100 days_1_10000\n",
              "17978    Russia  04-10   4.076203  ...          0.0       -0.03         0.11\n",
              "17979    Russia  04-11   4.133060  ...          0.0       -0.02         0.12\n",
              "17980    Russia  04-12   4.197859  ...          0.0       -0.01         0.13\n",
              "17981    Russia  04-13   4.263139  ...          0.0        0.00         0.14\n",
              "17982    Russia  04-14   4.324344  ...          0.0        0.01         0.15\n",
              "\n",
              "[5 rows x 87 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uhjKmMzLBCP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def prog_delta_log(model):\n",
        "  prediction_confirmed = model.predict(new_data_list)\n",
        "  if log_labels:\n",
        "    prediction_confirmed = prediction_confirmed  + new_data_list.pred_conf.values\n",
        "    prediction_confirmed = 10**prediction_confirmed - 1\n",
        "  else:\n",
        "    prediction_confirmed = prediction_confirmed + np.log10(abs(new_data_list.delta_conf1.values)+1)\n",
        "    prediction_confirmed = 10**prediction_confirmed - 1 + new_data_list.pred_conf.values\n",
        "    prediction_confirmed = prediction_confirmed \n",
        "  return prediction_confirmed\n",
        "def plot_new(list_countries, Matrix, subplot_x = 2, days_before = 10, point = True):\n",
        "  dat2 = data2.copy()\n",
        "  plt.rcParams['figure.figsize'] = [20, len(list_countries)*20/subplot_x**2]\n",
        "  if log_labels:\n",
        "    dat2.confirmed = 10**dat2.confirmed - 1\n",
        "  else:\n",
        "    dat2.confirmed = dat2.confirmed\n",
        "  for i, Country in enumerate(list_countries):\n",
        "    plt.subplot(len(list_countries)// subplot_x + 1, subplot_x, i+1)\n",
        "    yyy = dat2.loc[(dat2.iso_alpha3 == Country)&(dat2.confirmed>0)&(dat2.days_mart>=(days_x - days_before)/sc_days)]\n",
        "\n",
        "    if point:\n",
        "      plt.plot(yyy.date, yyy.confirmed, label = \"Истинное значение\", marker='o')\n",
        "      ax = plt.gca()\n",
        "      for x,y,i in zip(yyy.date,yyy.confirmed,range(0,len(yyy))):\n",
        "        if not i % ((subplot_x * (days_before+days_prog))//20):\n",
        "          plt.annotate(int(y), (x,y), textcoords=\"offset points\", xytext=(-5,10), ha=\"right\", va = \"bottom\")\n",
        "    else:\n",
        "      plt.plot(yyy.date, yyy.confirmed, label = \"Истинное значение\")\n",
        "    plt.gca().set(xlabel='Дата', ylabel='Заражения')\n",
        "    yyy2 = Matrix.loc[:,[Country]]\n",
        "    if point:\n",
        "      plt.plot(yyy2, label = \"name\", marker='o')\n",
        "      ax = plt.gca()\n",
        "      for x,y,i in zip(yyy2.index,yyy2.values.T[0],range(0,len(yyy2))):\n",
        "        if not i % ((subplot_x * (days_before+days_prog))//20):\n",
        "          plt.annotate(int(y), (x,y), textcoords=\"offset points\", xytext=(5,-10), ha=\"left\", va = \"top\")\n",
        "    else:\n",
        "      plt.plot(yyy2, label = \"name\")\n",
        "    plt.title(dic_countries[Country])\n",
        "    plt.xticks(np.arange(0, (days_before+days_prog), ((days_before+days_prog)//10)*subplot_x))\n",
        "    plt.grid(True)\n",
        "    plt.legend()  \n",
        "def pred_score(predictions, day):\n",
        "  yyy = new_data[(new_data.days_mart==(days_x + day - 1)/sc_days) & (new_data.confirmed > 0)]\n",
        "  region_list_x = new_data[(new_data.days_mart==(days_x)/sc_days) & (new_data.confirmed > 0)].countries.to_list()\n",
        "  yyy = yyy[yyy.countries.isin(region_list_x)]\n",
        "  if len(yyy)==len(predictions):\n",
        "    if log_labels:\n",
        "      labels = 10**yyy.confirmed.values  - 1\n",
        "    else:\n",
        "      labels = yyy.confirmed.values\n",
        "    return np.mean(np.abs(np.log10((predictions+1)/(labels+1))))\n",
        "  else:\n",
        "    return 0         "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LDR-u7ggH5Gz",
        "colab_type": "text"
      },
      "source": [
        "# Отделяем данные на трейн и тест"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eu0gPjRaKzj5",
        "colab_type": "code",
        "outputId": "61a9d049-8c5d-482d-82ee-93b267bf4789",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "source": [
        "#Выберите день начала приватных данных начиная с 1 марта\n",
        "days_x = 50 #57 \n",
        "iterat = 1000\n",
        "days_prog = 8\n",
        "\n",
        "#type_ans_delta - Предсказывать общее число, или изменение за день\n",
        "type_ans_delta = True\n",
        "\n",
        "#ВЫберите данные(только регионы России, только страны, всё)\n",
        "type_reg = \"all\"\n",
        "#type_reg = \"country\"\n",
        "#type_reg = \"region\"\n",
        "#type_reg = \"no_USA\"\n",
        "\n",
        "param_list =['countries', 'confirmed', 'deaths', 'iso_alpha3', 'delta_conf', 'delta_deaths', 'region',\n",
        "              'pred_conf', 'pred_deaths', 'delta_conf1', 'delta_deaths1',\n",
        "             'delta2',  'delta3',  'delta4',  'delta5',  'delta6',  'delta7', \n",
        "              'delta_conf_count',\n",
        "             'delta_deaths2','delta_deaths3','delta_deaths4','delta_deaths5','delta_deaths6',\n",
        "             \"isolation\",\n",
        "             'urban_pop_rate',             \n",
        "              'pred_conf-population_log',              \n",
        "             'population_log',\n",
        "             'land_area',\n",
        "             'days',\n",
        "             'days_10',\n",
        "             'days_100',\n",
        "#             'days_1000',\n",
        "             'days_5',\n",
        "             'days_50',\n",
        "             'days_500',\n",
        "             'days_8',\n",
        "             'days_80',                        \n",
        "             'days_1_100', \n",
        "             'days_1_1000',              \n",
        "             'days_1_10000',\n",
        "             \n",
        "             'delta_loss',  \n",
        "             'delta_delta_conf1',\n",
        "             'delta_delta_conf2',\n",
        "             'delta_delta_conf3',\n",
        "             'delta_delta_conf4',\n",
        "             'delta_delta_conf5',\n",
        "#             \"delta_conf_sum\",\n",
        "#             \"delta_deaths_sum\",\n",
        "#             'delta_delta_sum',\n",
        "\n",
        "             'delta_conf2', \n",
        "             'delta_conf3', \n",
        "             'delta_conf4', \n",
        "             'delta_conf5', \n",
        "             'delta_conf6', \n",
        "\n",
        "             'days_mart']\n",
        "\n",
        "data3 = data2[param_list]\n",
        "\n",
        "\n",
        "if type_reg == \"region\":\n",
        "  data3 = data3[data3['region'] == 1]\n",
        "elif  type_reg == \"country\":\n",
        "  data3 = data3[data3['region'] == 0]\n",
        "elif  type_reg == \"no_USA\":\n",
        "  data3 = data3[(data3['region'] == 0)|(data3['region'] == 1)]  \n",
        "else:\n",
        "  data3 = data3\n",
        "\n",
        "model_Confirmed, model_Death = {}, {}\n",
        "\n",
        "#Приватные данные\n",
        "type_reg_mean = \"no_USA\"\n",
        "\n",
        "new_data = data3[data3.days_mart >= (days_x)/sc_days]\n",
        "if type_reg_mean == \"region\":\n",
        "  new_data = new_data[new_data['region'] == 1]\n",
        "elif  type_reg_mean == \"country\":\n",
        "  new_data = new_data[new_data['region'] == 0]\n",
        "elif  type_reg_mean == \"no_USA\":\n",
        "  new_data = new_data[(new_data['region'] == 0)|(new_data['region'] == 1)]  \n",
        "\n",
        "data_x = new_data[new_data.days_mart == (days_x)/sc_days]\n",
        "old_data = data3[(data3.days_mart <= (days_x-5)/sc_days) & (data3.confirmed > 0)]\n",
        "\n",
        "if log_labels:\n",
        "  train_labels = old_data.delta_conf\n",
        "  delta2 = old_data.delta2\n",
        "  delta3 = old_data.delta3\n",
        "  delta4 = old_data.delta4\n",
        "  delta5 = old_data.delta5\n",
        "  delta6 = old_data.delta6\n",
        "  delta7 = old_data.delta7\n",
        "else:  \n",
        "  train_labels = - np.log10(abs(old_data.delta_conf1)+1) + np.log10(abs(old_data.delta_conf)+1)\n",
        "  delta2 = - np.log10(abs(old_data.delta_conf1)+1) + np.log10(abs(old_data.delta2)+1)\n",
        "  delta3 = - np.log10(abs(old_data.delta_conf1)+1) + np.log10(abs(old_data.delta3)+1)\n",
        "  delta4 = - np.log10(abs(old_data.delta_conf1)+1) + np.log10(abs(old_data.delta4)+1)\n",
        "  delta5 = - np.log10(abs(old_data.delta_conf1)+1) + np.log10(abs(old_data.delta5)+1)\n",
        "  delta6 = - np.log10(abs(old_data.delta_conf1)+1) + np.log10(abs(old_data.delta6)+1)\n",
        "  delta7 = - np.log10(abs(old_data.delta_conf1)+1) + np.log10(abs(old_data.delta7)+1)\n",
        "\n",
        "#Убираем ответ из данных\n",
        "train_data = old_data.drop(['delta2',  'delta3',  'delta4',  'delta5',  'delta6',  'delta7', 'iso_alpha3','region','confirmed','countries','deaths',\"delta_conf\",\"delta_deaths\"], axis='columns')\n",
        "new_data_list = data_x.drop(['delta2',  'delta3',  'delta4',  'delta5',  'delta6',  'delta7','iso_alpha3','region','confirmed','countries','deaths',\"delta_conf\",\"delta_deaths\"], axis='columns')  \n",
        "old_data"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>countries</th>\n",
              "      <th>confirmed</th>\n",
              "      <th>deaths</th>\n",
              "      <th>iso_alpha3</th>\n",
              "      <th>delta_conf</th>\n",
              "      <th>delta_deaths</th>\n",
              "      <th>region</th>\n",
              "      <th>pred_conf</th>\n",
              "      <th>pred_deaths</th>\n",
              "      <th>delta_conf1</th>\n",
              "      <th>delta_deaths1</th>\n",
              "      <th>delta2</th>\n",
              "      <th>delta3</th>\n",
              "      <th>delta4</th>\n",
              "      <th>delta5</th>\n",
              "      <th>delta6</th>\n",
              "      <th>delta7</th>\n",
              "      <th>delta_conf_count</th>\n",
              "      <th>delta_deaths2</th>\n",
              "      <th>delta_deaths3</th>\n",
              "      <th>delta_deaths4</th>\n",
              "      <th>delta_deaths5</th>\n",
              "      <th>delta_deaths6</th>\n",
              "      <th>isolation</th>\n",
              "      <th>urban_pop_rate</th>\n",
              "      <th>pred_conf-population_log</th>\n",
              "      <th>population_log</th>\n",
              "      <th>land_area</th>\n",
              "      <th>days</th>\n",
              "      <th>days_10</th>\n",
              "      <th>days_100</th>\n",
              "      <th>days_5</th>\n",
              "      <th>days_50</th>\n",
              "      <th>days_500</th>\n",
              "      <th>days_8</th>\n",
              "      <th>days_80</th>\n",
              "      <th>days_1_100</th>\n",
              "      <th>days_1_1000</th>\n",
              "      <th>days_1_10000</th>\n",
              "      <th>delta_loss</th>\n",
              "      <th>delta_delta_conf1</th>\n",
              "      <th>delta_delta_conf2</th>\n",
              "      <th>delta_delta_conf3</th>\n",
              "      <th>delta_delta_conf4</th>\n",
              "      <th>delta_delta_conf5</th>\n",
              "      <th>delta_conf2</th>\n",
              "      <th>delta_conf3</th>\n",
              "      <th>delta_conf4</th>\n",
              "      <th>delta_conf5</th>\n",
              "      <th>delta_conf6</th>\n",
              "      <th>days_mart</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>0.301030</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>AFG</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.301030</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.397940</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.529000</td>\n",
              "      <td>0.250</td>\n",
              "      <td>-4.289236</td>\n",
              "      <td>7.590266</td>\n",
              "      <td>0.806021</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.12</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>-0.34</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>-0.22</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>0.301030</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>AFG</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.301030</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.397940</td>\n",
              "      <td>0.397940</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.517000</td>\n",
              "      <td>0.250</td>\n",
              "      <td>-4.289236</td>\n",
              "      <td>7.590266</td>\n",
              "      <td>0.806021</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.11</td>\n",
              "      <td>-0.24</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-0.59</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>0.301030</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>AFG</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.301030</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.397940</td>\n",
              "      <td>0.397940</td>\n",
              "      <td>0.477121</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.250</td>\n",
              "      <td>-4.289236</td>\n",
              "      <td>7.590266</td>\n",
              "      <td>0.806021</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.10</td>\n",
              "      <td>-0.23</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.18</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>-0.58</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>0.301030</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>AFG</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.301030</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.397940</td>\n",
              "      <td>0.397940</td>\n",
              "      <td>0.477121</td>\n",
              "      <td>0.602060</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.531000</td>\n",
              "      <td>0.250</td>\n",
              "      <td>-4.289236</td>\n",
              "      <td>7.590266</td>\n",
              "      <td>0.806021</td>\n",
              "      <td>0.03</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>-0.22</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.31</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>-0.57</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.34</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>0.301030</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>AFG</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.301030</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.397940</td>\n",
              "      <td>0.397940</td>\n",
              "      <td>0.477121</td>\n",
              "      <td>0.602060</td>\n",
              "      <td>0.602060</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.527000</td>\n",
              "      <td>0.250</td>\n",
              "      <td>-4.289236</td>\n",
              "      <td>7.590266</td>\n",
              "      <td>0.806021</td>\n",
              "      <td>0.04</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.16</td>\n",
              "      <td>-0.30</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.18</td>\n",
              "      <td>-0.56</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40967</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>2.418301</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>USA_Wyoming</td>\n",
              "      <td>0.038090</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2</td>\n",
              "      <td>2.380211</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.016599</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.052758</td>\n",
              "      <td>0.060698</td>\n",
              "      <td>0.071575</td>\n",
              "      <td>0.079181</td>\n",
              "      <td>0.092545</td>\n",
              "      <td>0.105510</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.433431</td>\n",
              "      <td>0.648</td>\n",
              "      <td>-0.374259</td>\n",
              "      <td>5.754470</td>\n",
              "      <td>0.749036</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.17</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000660</td>\n",
              "      <td>-0.007366</td>\n",
              "      <td>0.002284</td>\n",
              "      <td>0.015440</td>\n",
              "      <td>-0.005110</td>\n",
              "      <td>0.017259</td>\n",
              "      <td>0.009893</td>\n",
              "      <td>0.012177</td>\n",
              "      <td>0.027617</td>\n",
              "      <td>0.022507</td>\n",
              "      <td>0.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40968</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>2.432969</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>USA_Wyoming</td>\n",
              "      <td>0.014668</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2</td>\n",
              "      <td>2.418301</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.038090</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.022608</td>\n",
              "      <td>0.033485</td>\n",
              "      <td>0.041091</td>\n",
              "      <td>0.054455</td>\n",
              "      <td>0.067420</td>\n",
              "      <td>0.073060</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.433431</td>\n",
              "      <td>0.648</td>\n",
              "      <td>-0.336169</td>\n",
              "      <td>5.754470</td>\n",
              "      <td>0.749036</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.18</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.021491</td>\n",
              "      <td>0.000660</td>\n",
              "      <td>-0.007366</td>\n",
              "      <td>0.002284</td>\n",
              "      <td>0.015440</td>\n",
              "      <td>0.016599</td>\n",
              "      <td>0.017259</td>\n",
              "      <td>0.009893</td>\n",
              "      <td>0.012177</td>\n",
              "      <td>0.027617</td>\n",
              "      <td>0.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40969</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>2.440909</td>\n",
              "      <td>0.30103</td>\n",
              "      <td>USA_Wyoming</td>\n",
              "      <td>0.007940</td>\n",
              "      <td>0.30103</td>\n",
              "      <td>2</td>\n",
              "      <td>2.432969</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.014668</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.018817</td>\n",
              "      <td>0.026423</td>\n",
              "      <td>0.039787</td>\n",
              "      <td>0.052752</td>\n",
              "      <td>0.058392</td>\n",
              "      <td>0.063960</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.433431</td>\n",
              "      <td>0.648</td>\n",
              "      <td>-0.321501</td>\n",
              "      <td>5.754470</td>\n",
              "      <td>0.749036</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.19</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1</td>\n",
              "      <td>0.023422</td>\n",
              "      <td>-0.021491</td>\n",
              "      <td>0.000660</td>\n",
              "      <td>-0.007366</td>\n",
              "      <td>0.002284</td>\n",
              "      <td>0.038090</td>\n",
              "      <td>0.016599</td>\n",
              "      <td>0.017259</td>\n",
              "      <td>0.009893</td>\n",
              "      <td>0.012177</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40970</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>2.451786</td>\n",
              "      <td>0.30103</td>\n",
              "      <td>USA_Wyoming</td>\n",
              "      <td>0.010877</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2</td>\n",
              "      <td>2.440909</td>\n",
              "      <td>0.30103</td>\n",
              "      <td>0.007940</td>\n",
              "      <td>0.30103</td>\n",
              "      <td>0.018483</td>\n",
              "      <td>0.031847</td>\n",
              "      <td>0.044812</td>\n",
              "      <td>0.050453</td>\n",
              "      <td>0.056021</td>\n",
              "      <td>0.061518</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.433431</td>\n",
              "      <td>0.648</td>\n",
              "      <td>-0.313561</td>\n",
              "      <td>5.754470</td>\n",
              "      <td>0.749036</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.20</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1</td>\n",
              "      <td>0.006728</td>\n",
              "      <td>0.023422</td>\n",
              "      <td>-0.021491</td>\n",
              "      <td>0.000660</td>\n",
              "      <td>-0.007366</td>\n",
              "      <td>0.014668</td>\n",
              "      <td>0.038090</td>\n",
              "      <td>0.016599</td>\n",
              "      <td>0.017259</td>\n",
              "      <td>0.009893</td>\n",
              "      <td>0.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40971</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>2.459392</td>\n",
              "      <td>0.30103</td>\n",
              "      <td>USA_Wyoming</td>\n",
              "      <td>0.007606</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2</td>\n",
              "      <td>2.451786</td>\n",
              "      <td>0.30103</td>\n",
              "      <td>0.010877</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.020970</td>\n",
              "      <td>0.033935</td>\n",
              "      <td>0.039575</td>\n",
              "      <td>0.045143</td>\n",
              "      <td>0.050641</td>\n",
              "      <td>0.195597</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.30103</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.433431</td>\n",
              "      <td>0.648</td>\n",
              "      <td>-0.302683</td>\n",
              "      <td>5.754470</td>\n",
              "      <td>0.749036</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.21</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.002938</td>\n",
              "      <td>0.006728</td>\n",
              "      <td>0.023422</td>\n",
              "      <td>-0.021491</td>\n",
              "      <td>0.000660</td>\n",
              "      <td>0.007940</td>\n",
              "      <td>0.014668</td>\n",
              "      <td>0.038090</td>\n",
              "      <td>0.016599</td>\n",
              "      <td>0.017259</td>\n",
              "      <td>0.45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11492 rows × 51 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         countries  confirmed   deaths  ... delta_conf5  delta_conf6  days_mart\n",
              "0      Afghanistan   0.301030  0.00000  ...    0.000000     0.000000       0.01\n",
              "1      Afghanistan   0.301030  0.00000  ...    0.000000     0.000000       0.02\n",
              "2      Afghanistan   0.301030  0.00000  ...    0.000000     0.000000       0.03\n",
              "3      Afghanistan   0.301030  0.00000  ...    0.000000     0.000000       0.04\n",
              "4      Afghanistan   0.301030  0.00000  ...    0.000000     0.000000       0.05\n",
              "...            ...        ...      ...  ...         ...          ...        ...\n",
              "40967      Wyoming   2.418301  0.00000  ...    0.027617     0.022507       0.41\n",
              "40968      Wyoming   2.432969  0.00000  ...    0.012177     0.027617       0.42\n",
              "40969      Wyoming   2.440909  0.30103  ...    0.009893     0.012177       0.43\n",
              "40970      Wyoming   2.451786  0.30103  ...    0.017259     0.009893       0.44\n",
              "40971      Wyoming   2.459392  0.30103  ...    0.016599     0.017259       0.45\n",
              "\n",
              "[11492 rows x 51 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ye2UK5_7Yags",
        "colab_type": "text"
      },
      "source": [
        "# Модели"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJT8FNQJYo-j",
        "colab_type": "text"
      },
      "source": [
        "### Обучаем xgboost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wmq-xcV95_cC",
        "colab_type": "code",
        "outputId": "04934ad2-72a2-419c-a5bb-1faf6f5d4412",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "\"\"\"model_Confirmed[\"Xgboost\"] = []\n",
        "model_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective ='reg:squarederror',n_estimators=iterat, max_depth=30, \n",
        "                                              n_jobs = -1).fit(train_data, train_labels))\n",
        "\n",
        "model_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective ='reg:squarederror',n_estimators=iterat, max_depth=30, \n",
        "                                              n_jobs = -1).fit(train_data[delta2 >= 0], delta2[delta2 >= 0]))\n",
        "model_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective ='reg:squarederror',n_estimators=iterat, max_depth=30, \n",
        "                                              n_jobs = -1).fit(train_data[delta3 >= 0], delta3[delta3 >= 0]))\n",
        "model_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective ='reg:squarederror',n_estimators=iterat, max_depth=30, \n",
        "                                              n_jobs = -1).fit(train_data[delta4 >= 0], delta4[delta4 >= 0]))\n",
        "model_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective ='reg:squarederror',n_estimators=iterat, max_depth=30, \n",
        "                                              n_jobs = -1).fit(train_data[delta5 >= 0], delta5[delta5 >= 0]))\n",
        "model_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective ='reg:squarederror',n_estimators=iterat, max_depth=30, \n",
        "                                              n_jobs = -1).fit(train_data[delta6 >= 0], delta6[delta6 >= 0]))\n",
        "model_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective ='reg:squarederror',n_estimators=iterat, max_depth=30, \n",
        "                                              n_jobs = -1).fit(train_data[delta7 >= 0], delta7[delta7 >= 0]))\"\"\""
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'model_Confirmed[\"Xgboost\"] = []\\nmodel_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective =\\'reg:squarederror\\',n_estimators=iterat, max_depth=30, \\n                                              n_jobs = -1).fit(train_data, train_labels))\\n\\nmodel_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective =\\'reg:squarederror\\',n_estimators=iterat, max_depth=30, \\n                                              n_jobs = -1).fit(train_data[delta2 >= 0], delta2[delta2 >= 0]))\\nmodel_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective =\\'reg:squarederror\\',n_estimators=iterat, max_depth=30, \\n                                              n_jobs = -1).fit(train_data[delta3 >= 0], delta3[delta3 >= 0]))\\nmodel_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective =\\'reg:squarederror\\',n_estimators=iterat, max_depth=30, \\n                                              n_jobs = -1).fit(train_data[delta4 >= 0], delta4[delta4 >= 0]))\\nmodel_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective =\\'reg:squarederror\\',n_estimators=iterat, max_depth=30, \\n                                              n_jobs = -1).fit(train_data[delta5 >= 0], delta5[delta5 >= 0]))\\nmodel_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective =\\'reg:squarederror\\',n_estimators=iterat, max_depth=30, \\n                                              n_jobs = -1).fit(train_data[delta6 >= 0], delta6[delta6 >= 0]))\\nmodel_Confirmed[\"Xgboost\"].append(xgb.XGBRegressor(objective =\\'reg:squarederror\\',n_estimators=iterat, max_depth=30, \\n                                              n_jobs = -1).fit(train_data[delta7 >= 0], delta7[delta7 >= 0]))'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jwah1COMYgad",
        "colab_type": "text"
      },
      "source": [
        "### Обучаем Adaboost Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYyabu1A7JEd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "treeDepth = 30\n",
        "mdl = tree.DecisionTreeRegressor(max_depth=treeDepth)\n",
        "param_grid = {\n",
        "    'n_estimators': [iterat//10],\n",
        "    'learning_rate': [0.0001],\n",
        "    'loss' : [\"exponential\"]}\n",
        "regrMdl = ensemble.AdaBoostRegressor(base_estimator=mdl)\n",
        "\n",
        "model_Confirmed[\"Adaboost\"] = []\n",
        "model_Confirmed[\"Adaboost\"].append(model_selection.GridSearchCV(estimator = regrMdl, param_grid = param_grid, \n",
        "                                         cv = 2, n_jobs = -1).fit(train_data,train_labels))\n",
        "model_Confirmed[\"Adaboost\"].append(model_selection.GridSearchCV(estimator = regrMdl, param_grid = param_grid, \n",
        "                                         cv = 2, n_jobs = -1).fit(train_data[delta2 >= 0], delta2[delta2 >= 0]))\n",
        "model_Confirmed[\"Adaboost\"].append(model_selection.GridSearchCV(estimator = regrMdl, param_grid = param_grid, \n",
        "                                         cv = 2, n_jobs = -1).fit(train_data[delta3 >= 0], delta3[delta3 >= 0]))\n",
        "model_Confirmed[\"Adaboost\"].append(model_selection.GridSearchCV(estimator = regrMdl, param_grid = param_grid, \n",
        "                                         cv = 2, n_jobs = -1).fit(train_data[delta4 >= 0], delta4[delta4 >= 0]))\n",
        "model_Confirmed[\"Adaboost\"].append(model_selection.GridSearchCV(estimator = regrMdl, param_grid = param_grid, \n",
        "                                         cv = 2, n_jobs = -1).fit(train_data[delta5 >= 0], delta5[delta5 >= 0]))\n",
        "model_Confirmed[\"Adaboost\"].append(model_selection.GridSearchCV(estimator = regrMdl, param_grid = param_grid, \n",
        "                                         cv = 2, n_jobs = -1).fit(train_data[delta6 >= 0], delta6[delta6 >= 0]))\n",
        "model_Confirmed[\"Adaboost\"].append(model_selection.GridSearchCV(estimator = regrMdl, param_grid = param_grid, \n",
        "                                         cv = 2, n_jobs = -1).fit(train_data[delta7 >= 0], delta7[delta7 >= 0]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThUgPuBoyOfo",
        "colab_type": "text"
      },
      "source": [
        "### Catboost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oySO6ADPva1g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#iterat больше лучше\n",
        "model_Confirmed[\"catboost\"] = []\n",
        "model_Confirmed[\"catboost\"].append(CatBoostRegressor(iterations=iterat, depth=12,  l2_leaf_reg = 1, verbose=False).fit(train_data,train_labels))\n",
        "model_Confirmed[\"catboost\"].append(CatBoostRegressor(iterations=iterat, depth=12,  l2_leaf_reg = 1, verbose=False).fit(train_data[delta2 >= 0], delta2[delta2 >= 0]))\n",
        "model_Confirmed[\"catboost\"].append(CatBoostRegressor(iterations=iterat, depth=12,  l2_leaf_reg = 1, verbose=False).fit(train_data[delta3 >= 0], delta3[delta3 >= 0]))\n",
        "model_Confirmed[\"catboost\"].append(CatBoostRegressor(iterations=iterat, depth=12,  l2_leaf_reg = 1, verbose=False).fit(train_data[delta4 >= 0], delta4[delta4 >= 0]))\n",
        "model_Confirmed[\"catboost\"].append(CatBoostRegressor(iterations=iterat, depth=12,  l2_leaf_reg = 1, verbose=False).fit(train_data[delta5 >= 0], delta5[delta5 >= 0]))\n",
        "model_Confirmed[\"catboost\"].append(CatBoostRegressor(iterations=iterat, depth=12,  l2_leaf_reg = 1, verbose=False).fit(train_data[delta6 >= 0], delta6[delta6 >= 0]))\n",
        "model_Confirmed[\"catboost\"].append(CatBoostRegressor(iterations=iterat, depth=12,  l2_leaf_reg = 1, verbose=False).fit(train_data[delta7 >= 0], delta7[delta7 >= 0]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nobqZjAPONnR",
        "colab_type": "text"
      },
      "source": [
        "### Lightgbm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWFCYX9qAco6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "params = {} \n",
        "model_Confirmed[\"lightgbm\"] = []\n",
        "d_train = lgb.Dataset(train_data, label=train_labels)\n",
        "model_Confirmed[\"lightgbm\"].append(lgb.train(params, d_train, iterat))\n",
        "d_train = lgb.Dataset(train_data[delta2 >= 0], delta2[delta2 >= 0])\n",
        "model_Confirmed[\"lightgbm\"].append(lgb.train(params, d_train, iterat))\n",
        "d_train = lgb.Dataset(train_data[delta3 >= 0], delta3[delta3 >= 0])\n",
        "model_Confirmed[\"lightgbm\"].append(lgb.train(params, d_train, iterat))\n",
        "d_train = lgb.Dataset(train_data[delta4 >= 0], delta4[delta4 >= 0])\n",
        "model_Confirmed[\"lightgbm\"].append(lgb.train(params, d_train, iterat))\n",
        "d_train = lgb.Dataset(train_data[delta5 >= 0], delta5[delta5 >= 0])\n",
        "model_Confirmed[\"lightgbm\"].append(lgb.train(params, d_train, iterat))\n",
        "d_train = lgb.Dataset(train_data[delta6 >= 0], delta6[delta6 >= 0])\n",
        "model_Confirmed[\"lightgbm\"].append(lgb.train(params, d_train, iterat))\n",
        "d_train = lgb.Dataset(train_data[delta7 >= 0], delta7[delta7 >= 0])\n",
        "model_Confirmed[\"lightgbm\"].append(lgb.train(params, d_train, iterat))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHt8X6mKdutc",
        "colab_type": "text"
      },
      "source": [
        "## Обучаем свою модель"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wNgf_jHuLMJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model_Confirmed[\"new\"] = \n",
        "#model_Death[\"new\"] = "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mK52KxpHbSN",
        "colab_type": "text"
      },
      "source": [
        "#Предсказываем новые данные"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBOvOvtke27Q",
        "colab_type": "text"
      },
      "source": [
        "## Функция отображения данных"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvblEMdS5rT-",
        "colab_type": "text"
      },
      "source": [
        "## Расчёт значений"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3bQVsTWd3MWV",
        "colab_type": "code",
        "outputId": "332793de-2a35-4bbd-c3b2-5c4943dbaa0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "predictions_mean = np.zeros((8,len(new_data_list)))\n",
        "for model in model_Confirmed:\n",
        "  if log_labels:\n",
        "    predictions = [10**new_data_list.pred_conf.values-1]\n",
        "  else:\n",
        "    predictions = [new_data_list.pred_conf.values]\n",
        "  for mod in model_Confirmed[model]:\n",
        "    pred = prog_delta_log(mod)\n",
        "    pred[pred < predictions[len(predictions)-1]] = predictions[len(predictions)-1][pred < predictions[len(predictions)-1]] \n",
        "    predictions.append(pred)\n",
        "  predictions_mean += np.array(predictions)\n",
        "  scores = []\n",
        "  for i,pred in enumerate(predictions):\n",
        "      score = pred_score(pred,i)\n",
        "      if score:\n",
        "        scores.append(score)\n",
        "  print(model, np.sum(scores), scores)      \n",
        "\n",
        "predictions_mean = predictions_mean/len(model_Confirmed)\n",
        "scores = []\n",
        "for i,pred in enumerate(predictions_mean):\n",
        "    score = pred_score(pred,i)\n",
        "    if score:\n",
        "      scores.append(score)\n",
        "print(\"Среднее\", np.sum(scores), scores)\n",
        "\n",
        "labels = data_x.iso_alpha3\n",
        "data_list = pd.date_range('2020-04-'+str(days_x-31), periods = 7+1, freq ='d')\n",
        "data_list = data_list.strftime('%#m-%#d') \n",
        "Matrix = pd.DataFrame(predictions_mean,columns = labels,index = data_list)\n",
        "\n",
        "\n",
        "\n",
        "# Создаём список стран\n",
        "#list_countries = countries.iso_alpha3.to_list()[-100:-90]\n",
        "list_countries = []\n",
        "list_countries.extend([\"RUS\",\"FRA\",\"ITA\",\"USA\"])\n",
        "list_countries.extend([\"RU-SPE\",\"RU-MOW\",\"RU-AL\",\"RU-ROS\"])\n",
        "# Количество графиков в строке\n",
        "subplot_x = 2\n",
        "\n",
        "# Отмечать точки\n",
        "point = True\n",
        "\n",
        "#За сколько дней до начала прогноза\n",
        "days_before = 10\n",
        "\n",
        "plot_new(list_countries, Matrix, subplot_x, days_before, point)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Adaboost 0.24727510386696977 [0.018774241432098965, 0.02760575810271814, 0.03527155531592316, 0.048875706615581355, 0.05419289051903117, 0.06255495188161696]\n",
            "catboost 0.24863830658766034 [0.019337507562303787, 0.02856364583691804, 0.035203045542451156, 0.047607075754959975, 0.054037168735971836, 0.06388986315505553]\n",
            "lightgbm 0.28231752224234485 [0.01995057632132921, 0.03112380371615294, 0.04191582322696294, 0.05533443967046048, 0.05968074431851716, 0.07431213498892214]\n",
            "Среднее 0.2500044163710721 [0.01883969116362326, 0.0279646055037186, 0.035895798281485175, 0.04887593662392209, 0.053810001541439126, 0.06461838325688392]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x2880 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9dg3lMgJ8QG",
        "colab_type": "text"
      },
      "source": [
        "# Сохраняем прогноз"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uhEYxJJyEDZL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ans = Matrix.stack().reset_index()\n",
        "ans_Death = Matrix.stack().reset_index()\n",
        "ans[\"prediction_deaths\"] = ans_Death[0]\n",
        "ans.columns = ['date','region','prediction_confirmed', 'prediction_deaths']\n",
        "ans['date'] = pd.to_datetime('2020-'+ans['date'])\n",
        "ans['prediction_confirmed'] = ans['prediction_confirmed'].astype('int')\n",
        "ans['prediction_deaths'] = ans['prediction_deaths'].astype('int')\n",
        "ans.sort_values(by=['region', 'date']).to_csv('submission.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}