{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "US_changing_point.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.10"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqXl71t9suiG"
      },
      "source": [
        "# Estimating the Date of COVID-19 Changes\n",
        "\n",
        "https://nbviewer.jupyter.org/github/jramkiss/jramkiss.github.io/blob/master/_posts/notebooks/covid19-changes.ipynb "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HomIs0U8a7OM"
      },
      "source": [
        "https://www.nytimes.com/interactive/2020/us/covid-19-vaccine-doses.html\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gFnvD8OysuiI",
        "outputId": "12b90a28-6743-4382-8ce0-2e61e75c9e90"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sns; sns.set()\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.dates as mdates\n",
        "\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "from scipy import stats\n",
        "import statsmodels.api as sm\n",
        "import pylab\n",
        "\n",
        "# from google.colab import files\n",
        "# from io import StringIO\n",
        "# uploaded = files.upload()\n",
        "\n",
        "from io import BytesIO\n",
        "from zipfile import ZipFile\n",
        "import pandas\n",
        "import requests\n",
        "\n",
        "\n",
        "url = 'https://raw.githubusercontent.com/assemzh/ProbProg-COVID-19/master/us_total.csv'\n",
        "\n",
        "data = pd.read_csv(url)\n",
        "\n",
        "data.Date = pd.to_datetime(data.Date)\n",
        "\n",
        "# for fancy python printing\n",
        "from IPython.display import Markdown, display\n",
        "def printmd(string):\n",
        "    display(Markdown(string))\n",
        "    \n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import matplotlib as mpl\n",
        "mpl.rcParams['figure.dpi'] = 250"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/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": "hzvPpvVvphTD"
      },
      "source": [
        "## Create country\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "Jcd9EqPddWNC",
        "outputId": "3dfc1fe3-6ae5-4753-cc32-642905d85cb0"
      },
      "source": [
        "data[\"Country/Region\"]='US'\n",
        "data"
      ],
      "execution_count": null,
      "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>Date</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "      <th>Active</th>\n",
              "      <th>New cases</th>\n",
              "      <th>New deaths</th>\n",
              "      <th>New recovered</th>\n",
              "      <th>Country/Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-22</td>\n",
              "      <td>33918.0</td>\n",
              "      <td>435.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9836.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>43754.0</td>\n",
              "      <td>577.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10122.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>348.0</td>\n",
              "      <td>US</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>53876.0</td>\n",
              "      <td>717.0</td>\n",
              "      <td>348.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12085.0</td>\n",
              "      <td>239.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>US</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-25</td>\n",
              "      <td>65961.0</td>\n",
              "      <td>956.0</td>\n",
              "      <td>361.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18012.0</td>\n",
              "      <td>269.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>US</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-26</td>\n",
              "      <td>83973.0</td>\n",
              "      <td>1225.0</td>\n",
              "      <td>681.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17894.0</td>\n",
              "      <td>378.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>US</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349</th>\n",
              "      <td>2021-03-06</td>\n",
              "      <td>28952970.0</td>\n",
              "      <td>524362.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28429067.0</td>\n",
              "      <td>40903.0</td>\n",
              "      <td>671.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>350</th>\n",
              "      <td>2021-03-07</td>\n",
              "      <td>28993873.0</td>\n",
              "      <td>525033.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44758.0</td>\n",
              "      <td>719.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>351</th>\n",
              "      <td>2021-03-08</td>\n",
              "      <td>29038631.0</td>\n",
              "      <td>525752.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57417.0</td>\n",
              "      <td>1947.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>2021-03-09</td>\n",
              "      <td>29096048.0</td>\n",
              "      <td>527699.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57667.0</td>\n",
              "      <td>1494.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>2021-03-10</td>\n",
              "      <td>29153715.0</td>\n",
              "      <td>529193.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>354 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date   Confirmed    Deaths  ...  New deaths  New recovered  Country/Region\n",
              "0   2020-03-22     33918.0     435.0  ...       142.0            0.0              US\n",
              "1   2020-03-23     43754.0     577.0  ...       140.0          348.0              US\n",
              "2   2020-03-24     53876.0     717.0  ...       239.0           13.0              US\n",
              "3   2020-03-25     65961.0     956.0  ...       269.0          320.0              US\n",
              "4   2020-03-26     83973.0    1225.0  ...       378.0          188.0              US\n",
              "..         ...         ...       ...  ...         ...            ...             ...\n",
              "349 2021-03-06  28952970.0  524362.0  ...       671.0            0.0              US\n",
              "350 2021-03-07  28993873.0  525033.0  ...       719.0            0.0              US\n",
              "351 2021-03-08  29038631.0  525752.0  ...      1947.0            0.0              US\n",
              "352 2021-03-09  29096048.0  527699.0  ...      1494.0            0.0              US\n",
              "353 2021-03-10  29153715.0  529193.0  ...         NaN            NaN              US\n",
              "\n",
              "[354 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "koX5yGHrsuib"
      },
      "source": [
        "# function to make the time series of confirmed and daily confirmed cases for a specific country\n",
        "def create_country (country, end_date, state = False) : \n",
        "    if state :\n",
        "        df = data.loc[data[\"Province/State\"] == country, [\"Province/State\", \"Date\", \"Confirmed\", \"Deaths\", \"Recovered\"]]\n",
        "    else : \n",
        "        df = data.loc[data[\"Country/Region\"] == country, [\"Country/Region\", \"Date\", \"Confirmed\", \"Deaths\", \"Recovered\"]]\n",
        "    df.columns = [\"country\", \"date\", \"confirmed\", \"deaths\", \"recovered\"]\n",
        "\n",
        "    # group by country and date, sum(confirmed, deaths, recovered). do this because countries have multiple cities \n",
        "    df = df.groupby(['country','date'])['confirmed', 'deaths', 'recovered'].sum().reset_index()\n",
        "\n",
        "    # # convert date string to datetime\n",
        "    # std_dateparser = lambda x: str(x)[5:10]\n",
        "    # df.date = pd.to_datetime(df.date)\n",
        "    # df['date_only'] = df.date.apply(std_dateparser)\n",
        "    df = df.sort_values(by = \"date\")\n",
        "    df = df[df.date <= end_date]\n",
        "\n",
        "    # make new confirmed cases every day:\n",
        "    cases_shifted = np.array([0] + list(df.confirmed[:-1]))\n",
        "    daily_confirmed = np.array(df.confirmed) - cases_shifted\n",
        "    df[\"daily_confirmed\"] = daily_confirmed \n",
        "\n",
        "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(7, 6))\n",
        "    ax = [ax]\n",
        "    sns.lineplot(x = df.date, \n",
        "                 y = df.daily_confirmed, \n",
        "                 ax = ax[0])\n",
        "\n",
        "    ax[0].set(ylabel='Daily Confirmed Cases')\n",
        "\n",
        "    ax[0].axvline(pd.to_datetime('2020-12-14'), \n",
        "              linestyle = '--', linewidth = 1.5,\n",
        "              label = \"Start of Vaccination: Dec 14, 2020\" ,\n",
        "              color = \"red\") \n",
        "\n",
        "    \n",
        "    ax[0].xaxis.get_label().set_fontsize(16)\n",
        "    ax[0].yaxis.get_label().set_fontsize(16)\n",
        "    ax[0].title.set_fontsize(20)\n",
        "    ax[0].tick_params(labelsize=16)\n",
        "    myFmt = mdates.DateFormatter('%b %-d')\n",
        "    ax[0].xaxis.set_major_formatter(myFmt)\n",
        "\n",
        "    ax[0].set(ylabel='Daily Confirmed Cases', xlabel='');\n",
        "    ax[0].legend(loc = \"bottom right\", fontsize=12.8)\n",
        "    sns.set_style(\"ticks\")\n",
        "    plt.tight_layout()\n",
        "    sns.despine()\n",
        "\n",
        "    plt.savefig('/content/sample_data/us_daily.pdf')\n",
        "    print(df.tail())\n",
        "    return df\n",
        "\n",
        "\n",
        "def summary(samples):\n",
        "    site_stats = {}\n",
        "    for k, v in samples.items():\n",
        "        site_stats[k] = {\n",
        "            \"mean\": torch.mean(v, 0),\n",
        "            \"std\": torch.std(v, 0),\n",
        "            \"5%\": v.kthvalue(int(len(v) * 0.05), dim=0)[0],\n",
        "            \"95%\": v.kthvalue(int(len(v) * 0.95), dim=0)[0],\n",
        "        }\n",
        "    return site_stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "w_A0fd4Zsuiw",
        "outputId": "1ad8a9f2-4689-4062-911f-cd657413cba8"
      },
      "source": [
        "cad = create_country(\"US\", end_date = \"2021-03-10\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    country       date   confirmed    deaths  recovered  daily_confirmed\n",
            "349      US 2021-03-06  28952970.0  524362.0        0.0          58062.0\n",
            "350      US 2021-03-07  28993873.0  525033.0        0.0          40903.0\n",
            "351      US 2021-03-08  29038631.0  525752.0        0.0          44758.0\n",
            "352      US 2021-03-09  29096048.0  527699.0        0.0          57417.0\n",
            "353      US 2021-03-10  29153715.0  529193.0        0.0          57667.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DnUbQCQXP_eZ"
      },
      "source": [
        "https://www.dw.com/en/israels-clever-coronavirus-vaccination-strategy/a-56586888#:~:text=To%20date%2C%20Israel%20has%20administered,40%25%20of%20the%20country's%20population."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UR0BM7TysujG"
      },
      "source": [
        "cad_start = \"2020-11-01\" # 13 confirmed cases\n",
        "cad = cad[cad.date >= cad_start].reset_index(drop = True)\n",
        "cad[\"days_since_start\"] = np.arange(cad.shape[0]) + 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "bG8_D398bqPR",
        "outputId": "ea94aecf-bede-4bf5-d2b9-2c4b4d0ef47d"
      },
      "source": [
        "cad.head()"
      ],
      "execution_count": null,
      "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>country</th>\n",
              "      <th>date</th>\n",
              "      <th>confirmed</th>\n",
              "      <th>deaths</th>\n",
              "      <th>recovered</th>\n",
              "      <th>daily_confirmed</th>\n",
              "      <th>days_since_start</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>US</td>\n",
              "      <td>2020-11-01</td>\n",
              "      <td>9291068.0</td>\n",
              "      <td>231776.0</td>\n",
              "      <td>3630579.0</td>\n",
              "      <td>104899.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>US</td>\n",
              "      <td>2020-11-02</td>\n",
              "      <td>9376816.0</td>\n",
              "      <td>232312.0</td>\n",
              "      <td>3674981.0</td>\n",
              "      <td>85748.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>US</td>\n",
              "      <td>2020-11-03</td>\n",
              "      <td>9504162.0</td>\n",
              "      <td>233905.0</td>\n",
              "      <td>3705130.0</td>\n",
              "      <td>127346.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>US</td>\n",
              "      <td>2020-11-04</td>\n",
              "      <td>9607933.0</td>\n",
              "      <td>235055.0</td>\n",
              "      <td>3743527.0</td>\n",
              "      <td>103771.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>US</td>\n",
              "      <td>2020-11-05</td>\n",
              "      <td>9737135.0</td>\n",
              "      <td>236217.0</td>\n",
              "      <td>3781751.0</td>\n",
              "      <td>129202.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  country       date  confirmed  ...  recovered  daily_confirmed  days_since_start\n",
              "0      US 2020-11-01  9291068.0  ...  3630579.0         104899.0                 1\n",
              "1      US 2020-11-02  9376816.0  ...  3674981.0          85748.0                 2\n",
              "2      US 2020-11-03  9504162.0  ...  3705130.0         127346.0                 3\n",
              "3      US 2020-11-04  9607933.0  ...  3743527.0         103771.0                 4\n",
              "4      US 2020-11-05  9737135.0  ...  3781751.0         129202.0                 5\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaTHo6I2sujp",
        "outputId": "4280c88b-751b-4ff2-c087-7d6700608a19"
      },
      "source": [
        "cad.shape\n",
        "cad_tmp = cad\n",
        "cad_tmp.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(130, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loi3CtjSsuoz"
      },
      "source": [
        "## Data for Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Os_M7r4Tsuo4"
      },
      "source": [
        "# variable for data to easily swap it out:\n",
        "country_ = \"US\"\n",
        "reg_data = cad_tmp.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "3RjDFEbA91X-",
        "outputId": "8dae4943-24fa-4f62-f75c-f7d7ff7bf86a"
      },
      "source": [
        "reg_data.head()"
      ],
      "execution_count": null,
      "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>country</th>\n",
              "      <th>date</th>\n",
              "      <th>confirmed</th>\n",
              "      <th>deaths</th>\n",
              "      <th>recovered</th>\n",
              "      <th>daily_confirmed</th>\n",
              "      <th>days_since_start</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>US</td>\n",
              "      <td>2020-11-01</td>\n",
              "      <td>9291068.0</td>\n",
              "      <td>231776.0</td>\n",
              "      <td>3630579.0</td>\n",
              "      <td>104899.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>US</td>\n",
              "      <td>2020-11-02</td>\n",
              "      <td>9376816.0</td>\n",
              "      <td>232312.0</td>\n",
              "      <td>3674981.0</td>\n",
              "      <td>85748.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>US</td>\n",
              "      <td>2020-11-03</td>\n",
              "      <td>9504162.0</td>\n",
              "      <td>233905.0</td>\n",
              "      <td>3705130.0</td>\n",
              "      <td>127346.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>US</td>\n",
              "      <td>2020-11-04</td>\n",
              "      <td>9607933.0</td>\n",
              "      <td>235055.0</td>\n",
              "      <td>3743527.0</td>\n",
              "      <td>103771.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>US</td>\n",
              "      <td>2020-11-05</td>\n",
              "      <td>9737135.0</td>\n",
              "      <td>236217.0</td>\n",
              "      <td>3781751.0</td>\n",
              "      <td>129202.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  country       date  confirmed  ...  recovered  daily_confirmed  days_since_start\n",
              "0      US 2020-11-01  9291068.0  ...  3630579.0         104899.0                 1\n",
              "1      US 2020-11-02  9376816.0  ...  3674981.0          85748.0                 2\n",
              "2      US 2020-11-03  9504162.0  ...  3705130.0         127346.0                 3\n",
              "3      US 2020-11-04  9607933.0  ...  3743527.0         103771.0                 4\n",
              "4      US 2020-11-05  9737135.0  ...  3781751.0         129202.0                 5\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_hdR3EG12jx",
        "outputId": "f3f620fd-9d80-47dc-e5c2-c33599e71f72"
      },
      "source": [
        "reg_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(130, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkO0Z8M0supC"
      },
      "source": [
        "## Change Point Estimation in Pyro"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIUed4Ny3-oq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b13b8a8d-7689-4263-f806-eb05f75a316c"
      },
      "source": [
        "!pip install pyro-ppl\n",
        "!pip install numpyro\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pyro-ppl\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/aa/7a/fbab572fd385154a0c07b0fa138683aa52e14603bb83d37b198e5f9269b1/pyro_ppl-1.6.0-py3-none-any.whl (634kB)\n",
            "\u001b[K     |████████████████████████████████| 634kB 4.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from pyro-ppl) (3.3.0)\n",
            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.7/dist-packages (from pyro-ppl) (1.8.1+cu101)\n",
            "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.7/dist-packages (from pyro-ppl) (1.19.5)\n",
            "Collecting pyro-api>=0.1.1\n",
            "  Downloading https://files.pythonhosted.org/packages/fc/81/957ae78e6398460a7230b0eb9b8f1cb954c5e913e868e48d89324c68cec7/pyro_api-0.1.2-py3-none-any.whl\n",
            "Requirement already satisfied: tqdm>=4.36 in /usr/local/lib/python3.7/dist-packages (from pyro-ppl) (4.41.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.8.0->pyro-ppl) (3.7.4.3)\n",
            "Installing collected packages: pyro-api, pyro-ppl\n",
            "Successfully installed pyro-api-0.1.2 pyro-ppl-1.6.0\n",
            "Collecting numpyro\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/00/a6/064eedcec968207259acf06cf156c0ea9a6534328bdf7da0e768cfdb3239/numpyro-0.6.0-py3-none-any.whl (218kB)\n",
            "\u001b[K     |████████████████████████████████| 225kB 5.4MB/s \n",
            "\u001b[?25hCollecting jax==0.2.10\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/88/9d/2862825b5eddd0df64c78b22cc0b897f0128b1c6494bf39e4849e9e0fade/jax-0.2.10.tar.gz (589kB)\n",
            "\u001b[K     |████████████████████████████████| 593kB 7.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from numpyro) (4.41.1)\n",
            "Collecting jaxlib==0.1.62\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7e/75/30f1c643b7edb1309b6d748809042241737fe43127cb41754266eca79250/jaxlib-0.1.62-cp37-none-manylinux2010_x86_64.whl (35.7MB)\n",
            "\u001b[K     |████████████████████████████████| 35.7MB 98kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.12 in /usr/local/lib/python3.7/dist-packages (from jax==0.2.10->numpyro) (1.19.5)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax==0.2.10->numpyro) (0.12.0)\n",
            "Requirement already satisfied: opt_einsum in /usr/local/lib/python3.7/dist-packages (from jax==0.2.10->numpyro) (3.3.0)\n",
            "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from jaxlib==0.1.62->numpyro) (1.12)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from jaxlib==0.1.62->numpyro) (1.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py->jax==0.2.10->numpyro) (1.15.0)\n",
            "Building wheels for collected packages: jax\n",
            "  Building wheel for jax (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for jax: filename=jax-0.2.10-cp37-none-any.whl size=679776 sha256=122fccd699209761ec50b95c9b10d0797db69830a250ca111d80d085f5371673\n",
            "  Stored in directory: /root/.cache/pip/wheels/44/ea/ac/3be3bc19ee3b62f6fe1561eb6df1199284bb6bab819c1befa4\n",
            "Successfully built jax\n",
            "Installing collected packages: jax, jaxlib, numpyro\n",
            "  Found existing installation: jax 0.2.12\n",
            "    Uninstalling jax-0.2.12:\n",
            "      Successfully uninstalled jax-0.2.12\n",
            "  Found existing installation: jaxlib 0.1.65+cuda110\n",
            "    Uninstalling jaxlib-0.1.65+cuda110:\n",
            "      Successfully uninstalled jaxlib-0.1.65+cuda110\n",
            "Successfully installed jax-0.2.10 jaxlib-0.1.62 numpyro-0.6.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZS9fTPxsupD"
      },
      "source": [
        "import torch\n",
        "\n",
        "import pyro\n",
        "import pyro.distributions as dist\n",
        "from torch import nn\n",
        "from pyro.nn import PyroModule, PyroSample\n",
        "\n",
        "from pyro.infer import MCMC, NUTS, HMC\n",
        "from pyro.infer.autoguide import AutoGuide, AutoDiagonalNormal\n",
        "\n",
        "from pyro.infer import SVI, Trace_ELBO\n",
        "from pyro.infer import Predictive"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gPrEaEJsupP"
      },
      "source": [
        "# we should be able to have an empirical estimate for the mean of the prior for the 2nd regression bias term\n",
        "# this will be something like b = log(max(daily_confirmed))\n",
        "\n",
        "# might be able to have 1 regression model but change the data so that we have new terms for (tau < t) \n",
        "# like an interaction term\n",
        "\n",
        "class COVID_change(PyroModule):\n",
        "    def __init__(self, in_features, out_features, b1_mu, b2_mu):\n",
        "        super().__init__()\n",
        "        self.linear1 = PyroModule[nn.Linear](in_features, out_features, bias = False)\n",
        "        self.linear1.weight = PyroSample(dist.Normal(0.5, 0.25).expand([1, 1]).to_event(1))\n",
        "        self.linear1.bias = PyroSample(dist.Normal(b1_mu, 1.))\n",
        "        \n",
        "        # could possibly have stronger priors for the 2nd regression line, because we wont have as much data\n",
        "        self.linear2 = PyroModule[nn.Linear](in_features, out_features, bias = False)\n",
        "        self.linear2.weight = PyroSample(dist.Normal(0., 0.25).expand([1, 1])) #.to_event(1))\n",
        "        self.linear2.bias = PyroSample(dist.Normal(b2_mu, b2_mu/4))\n",
        "\n",
        "    def forward(self, x, y=None):\n",
        "        tau = pyro.sample(\"tau\", dist.Beta(4, 3))\n",
        "        sigma = pyro.sample(\"sigma\", dist.Uniform(0., 3.))\n",
        "        # fit lm's to data based on tau\n",
        "        sep = int(np.ceil(tau.detach().numpy() * len(x)))\n",
        "        mean1 = self.linear1(x[:sep]).squeeze(-1)\n",
        "        mean2 = self.linear2(x[sep:]).squeeze(-1)\n",
        "        mean = torch.cat((mean1, mean2))\n",
        "        obs = pyro.sample(\"obs\", dist.StudentT(2, mean, sigma), obs=y)\n",
        "        return mean"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "if0toOMysupU",
        "outputId": "e7cbd010-8daa-4e9d-e87f-499a454084db"
      },
      "source": [
        "tensor_data = torch.tensor(reg_data[[\"confirmed\", \"days_since_start\", \"daily_confirmed\"]].values, dtype=torch.float)\n",
        "x_data = tensor_data[:, 1].unsqueeze_(1)\n",
        "y_data = np.log(tensor_data[:, 0])\n",
        "y_data_daily = np.log(tensor_data[:, 2])\n",
        "# prior hyper params\n",
        "# take log of the average of the 1st quartile to get the prior mean for the bias of the 2nd regression line\n",
        "q1 = np.quantile(y_data, q = 0.25)\n",
        "bias_1_mean = np.mean(y_data.numpy()[y_data <= q1])\n",
        "print(\"Prior mean for Bias 1: \", bias_1_mean)\n",
        "\n",
        "# take log of the average of the 4th quartile to get the prior mean for the bias of the 2nd regression line\n",
        "q4 = np.quantile(y_data, q = 0.75)\n",
        "bias_2_mean = np.mean(y_data.numpy()[y_data >= q4])\n",
        "print(\"Prior mean for Bias 2: \", bias_2_mean)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prior mean for Bias 1:  16.255508\n",
            "Prior mean for Bias 2:  17.152462\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nrm8RrFasupc"
      },
      "source": [
        "## Approximate Inference with Stochastic Variational Inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0nDLSPisupm"
      },
      "source": [
        "# HMC with NUTS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1rSXXtKsupm",
        "outputId": "24f82d77-a4c8-429a-820c-7863a7e056ff"
      },
      "source": [
        "model = COVID_change(1, 1, \n",
        "                     b1_mu = bias_1_mean,\n",
        "                     b2_mu = bias_2_mean)\n",
        "# need more than 400 samples/chain if we want to use a flat prior on b_2 and w_2\n",
        "num_samples = 400 \n",
        "# mcmc \n",
        "nuts_kernel = NUTS(model)\n",
        "mcmc = MCMC(nuts_kernel, \n",
        "            num_samples=num_samples,\n",
        "            warmup_steps = 200, \n",
        "            num_chains = 1)\n",
        "mcmc.run(x_data, y_data)\n",
        "samples = mcmc.get_samples()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sample: 100%|██████████| 600/600 [20:17,  2.03s/it, step size=1.81e-04, acc. prob=0.898]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLx_GE-7jb63"
      },
      "source": [
        "# Save the model:\n",
        "import dill\n",
        "# with open('us.pkl', 'wb') as f:\n",
        "# \tdill.dump(mcmc, f)\n",
        "with open('us.pkl', 'rb') as f:\n",
        "\tmcmc = dill.load(f)\n",
        " \n",
        "samples = mcmc.get_samples()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Z968a5xsupv",
        "outputId": "f58a15ac-76c3-4667-aa83-d0fb0bbe1dfa"
      },
      "source": [
        "# extract individual posteriors\n",
        "weight_1_post = samples[\"linear1.weight\"].detach().numpy()\n",
        "weight_2_post = samples[\"linear2.weight\"].detach().numpy()\n",
        "bias_1_post = samples[\"linear1.bias\"].detach().numpy()\n",
        "bias_2_post = samples[\"linear2.bias\"].detach().numpy()\n",
        "tau_post = samples[\"tau\"].detach().numpy()\n",
        "sigma_post = samples[\"sigma\"].detach().numpy()\n",
        "\n",
        "# build likelihood distribution:\n",
        "tau_days = list(map(int, np.ceil(tau_post * len(x_data))))\n",
        "mean_ = torch.zeros(len(tau_days), len(x_data))\n",
        "obs_ = torch.zeros(len(tau_days), len(x_data))\n",
        "for i in range(len(tau_days)) : \n",
        "    mean_[i, :] = torch.cat((x_data[:tau_days[i]] * weight_1_post[i] + bias_1_post[i],\n",
        "                          x_data[tau_days[i]:] * weight_2_post[i] + bias_2_post[i])).reshape(len(x_data))\n",
        "    obs_[i, :] = dist.Normal(mean_[i, :], sigma_post[i]).sample()\n",
        "samples[\"_RETURN\"] = mean_\n",
        "samples[\"obs\"] = obs_\n",
        "pred_summary = summary(samples)\n",
        "mu = pred_summary[\"_RETURN\"] # mean\n",
        "y = pred_summary[\"obs\"] # samples from likelihood: mu + sigma\n",
        "y_shift = np.exp(y[\"mean\"]) - np.exp(torch.cat((y[\"mean\"][0:1], y[\"mean\"][:-1])))\n",
        "print(y_shift)\n",
        "predictions = pd.DataFrame({\n",
        "    \"days_since_start\": x_data[:, 0],\n",
        "    \"mu_mean\": mu[\"mean\"],    # mean of likelihood\n",
        "    \"mu_perc_5\": mu[\"5%\"],\n",
        "    \"mu_perc_95\": mu[\"95%\"],\n",
        "    \"y_mean\": y[\"mean\"],      # mean of likelihood + noise\n",
        "    \"y_perc_5\": y[\"5%\"],\n",
        "    \"y_perc_95\": y[\"95%\"],\n",
        "    \"true_confirmed\": y_data,\n",
        "    \"true_daily_confirmed\": y_data_daily,\n",
        "    \"y_daily_mean\": y_shift\n",
        "})\n",
        "\n",
        "w1_ = pred_summary[\"linear1.weight\"]\n",
        "w2_ = pred_summary[\"linear2.weight\"]\n",
        "\n",
        "b1_ = pred_summary[\"linear1.bias\"]\n",
        "b2_ = pred_summary[\"linear2.bias\"]\n",
        "\n",
        "tau_ = pred_summary[\"tau\"]\n",
        "sigma_ = pred_summary[\"sigma\"]\n",
        "\n",
        "ind = int(np.ceil(tau_[\"mean\"] * len(x_data)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([     0., 123758., 107474., 143517., 139824., 111102., 117757., 137822.,\n",
            "        119536., 136832., 141099., 125632., 175256., 113747., 119790., 164404.,\n",
            "        142176., 151333., 157725., 127663., 160600., 153363., 152496., 178360.,\n",
            "        128725., 172123., 178194., 148925., 201997., 154104., 196959., 157244.,\n",
            "        172678., 197221., 160619., 200865., 142596., 213519., 195834., 187626.,\n",
            "        185048., 212983., 186515., 217653., 201873., 221126., 184906., 212976.,\n",
            "        253842., 221686., 211416., 246752., 218192., 241416., 244118., 237014.,\n",
            "        196282., 281720., 252228., 231080., 246670., 251356., 250950., 276856.,\n",
            "        223894., 295550., 289356., 285614., 251826., 293064., 261672., 343500.,\n",
            "        267660., 278670., 377790., 217006., 275918., 182914., 169854., 108930.,\n",
            "         89022.,  93620., 105324., 105762.,  90956.,  95094.,  80826.,  53410.,\n",
            "        121902.,  12100., 101208.,  46104., 102032., 127176.,  65978., 119180.,\n",
            "         67914.,  65600., 108964.,  58170., 135956., 101606., 106796., 103274.,\n",
            "         36088., 135646.,  13788., 100968.,  68676., 110404., 152796.,  79136.,\n",
            "         36322., 170090.,  70698., 113806.,  81500.,  60522., 133360., 127798.,\n",
            "         83650., 146232.,  28538., 130194.,  33138., 123618., 120502., 108912.,\n",
            "        106778.,  96290.])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KegzMbLOsuqC"
      },
      "source": [
        "## Model Diagnostics\n",
        "\n",
        "- Residual plots: Should these be samples from the likelihood compared with the actual data? Or just the mean of the likelihood?\n",
        "- $\\hat{R}$: The factor that the scale of the current distribution will be reduced by if we were to run the simulations forever. As n tends to $\\inf$, $\\hat{R}$ tends to 1. So we want values close to 1.\n",
        "- Mixing and Stationarity: I sampled 4 chains. Do I then take these chains, split them in half and plot them. If they converge to the same stationary distribution, does that mean the MCMC converged? What do I do with more sampled chains?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUh6fBjtsuqV",
        "outputId": "a6254d33-4497-40bf-f1a8-1379025ee42b"
      },
      "source": [
        "mcmc.summary()\n",
        "diag = mcmc.diagnostics()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "                         mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
            "                tau      0.60      0.03      0.59      0.55      0.64      5.89      1.31\n",
            "              sigma      0.02      0.00      0.02      0.01      0.03      2.75      2.24\n",
            "linear1.weight[0,0]      0.01      0.00      0.01      0.01      0.01      5.15      1.57\n",
            "       linear1.bias     16.05      0.01     16.05     16.03     16.06      5.46      1.59\n",
            "linear2.weight[0,0]      0.00      0.00      0.00      0.00      0.00     16.96      1.05\n",
            "       linear2.bias     16.76      0.03     16.76     16.72     16.81     15.75      1.06\n",
            "\n",
            "Number of divergences: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJ2a6Epnsuqf"
      },
      "source": [
        "## Posterior Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "bPpET7b6suqg",
        "outputId": "9ab32058-e6a0-40a1-be7e-d5c60238821b"
      },
      "source": [
        "print(ind)\n",
        "print(reg_data.date[ind])\n",
        "\n",
        "sns.distplot(weight_1_post, \n",
        "             kde_kws = {\"label\": \"Weight posterior before CP\"}, \n",
        "             color = \"red\",\n",
        "             norm_hist = True,\n",
        "             kde = True)\n",
        "plt.axvline(x = w1_[\"mean\"], linestyle = '--',label = \"Mean weight before CP\" ,\n",
        "              color = \"red\")\n",
        "\n",
        "sns.distplot(weight_2_post, \n",
        "             kde_kws = {\"label\": \"Weight posterior after CP\"}, \n",
        "             color = \"teal\",\n",
        "             norm_hist = True,\n",
        "             kde = True)\n",
        "plt.axvline(x = w2_[\"mean\"], linestyle = '--',label = \"Mean weight after CP\" ,\n",
        "              color = \"teal\")\n",
        "\n",
        "legend = plt.legend(loc='upper right')\n",
        "legend.get_frame().set_alpha(0.7)\n",
        "sns.set_style(\"ticks\")\n",
        "plt.tight_layout()\n",
        "sns.despine()\n",
        "plt.savefig('/content/sample_data/us_weights.pdf')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "78\n",
            "2021-01-18 00:00:00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tY1WVPjiokbD",
        "outputId": "bc052d37-f835-44f6-a9d0-d64ea7777d4d"
      },
      "source": [
        " print(w1_[\"mean\"])\n",
        " print(w2_[\"mean\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[0.0126]])\n",
            "tensor([[0.0034]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "gejnd0Qxsuq1",
        "outputId": "3a2e64ac-b19a-4898-fd1f-ace07cabbdd0"
      },
      "source": [
        "start_date_ = str(reg_data.date[0]).split(' ')[0]\n",
        "change_date_ = str(reg_data.date[ind]).split(' ')[0]\n",
        "print(\"Date of change for {}: {}\".format(country_, change_date_))\n",
        "import seaborn as sns\n",
        "\n",
        "# plot data:\n",
        "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(7, 5))\n",
        "ax = [ax]\n",
        "# log regression model\n",
        "ax[0].scatter(y = np.exp(y_data[:ind]), x = x_data[:ind], s = 15);\n",
        "ax[0].scatter(y = np.exp(y_data[ind:]), x = x_data[ind:], s = 15, color = \"red\");\n",
        "\n",
        "ax[0].plot(predictions[\"days_since_start\"],\n",
        "           np.exp(predictions[\"y_mean\"]), \n",
        "           color = \"green\",\n",
        "           label = \"Fitted line by MCMC-NUTS model\") \n",
        "ax[0].axvline(43, \n",
        "              linestyle = '--', linewidth = 1.5,\n",
        "              label = \"Date of Vaccination: Dec 14, 2020\" ,\n",
        "              color = \"red\")\n",
        "\n",
        "ax[0].axvline(ind, \n",
        "              linestyle = '--', linewidth = 1.5,\n",
        "              label = \"Date of Change: Jan 18, 2021\",\n",
        "              color = \"black\")\n",
        "\n",
        "ax[0].fill_between(predictions[\"days_since_start\"], \n",
        "                   np.exp(predictions[\"y_perc_5\"]), \n",
        "                   np.exp(predictions[\"y_perc_95\"]), \n",
        "                   alpha = 0.25,\n",
        "                   label = \"90% CI of predictions\",\n",
        "                   color = \"teal\");\n",
        "ax[0].fill_betweenx([0, 1], \n",
        "                    tau_[\"5%\"] * len(x_data), \n",
        "                    tau_[\"95%\"] * len(x_data), \n",
        "                    alpha = 0.25,\n",
        "                    label = \"90% CI of changing point\",\n",
        "                    color = \"lightcoral\",\n",
        "                    transform=ax[0].get_xaxis_transform());\n",
        "ax[0].set(ylabel = \"Total Cases\",)\n",
        "          # xlabel = \"Days since %s\" % start_date_, \n",
        "          # title = \"Confirmed Cases in China\") /\n",
        "ax[0].legend(loc = \"lower right\", fontsize=12.8)\n",
        "ax[0].set_ylim([8000000,40000000])\n",
        "ax[0].set_xlim([35,130])\n",
        "ax[0].xaxis.get_label().set_fontsize(16)\n",
        "ax[0].yaxis.get_label().set_fontsize(16)\n",
        "ax[0].title.set_fontsize(20)\n",
        "ax[0].tick_params(labelsize=16)\n",
        "\n",
        "plt.xticks(ticks=[43, 60,78,99, 120], labels=[\"Dec 14\",\n",
        "                                              \"Dec 31\",\n",
        "                                             \"Jan 18\",\n",
        "                                             \"Feb 8\", \"Mar 1\"], fontsize=15)\n",
        "ax[0].set_yscale('log')\n",
        "plt.setp(ax[0].get_xticklabels(), rotation=0, horizontalalignment='center')\n",
        "print(reg_data.columns)\n",
        "\n",
        "myFmt = mdates.DateFormatter('%m-%d')\n",
        "sns.set_style(\"ticks\")\n",
        "sns.despine()\n",
        "plt.tight_layout()\n",
        "\n",
        "ax[0].figure.savefig('/content/sample_data/us_cp.pdf')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Date of change for US: 2021-01-18\n",
            "Index(['country', 'date', 'confirmed', 'deaths', 'recovered',\n",
            "       'daily_confirmed', 'days_since_start'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "HrWADTLYsurS",
        "outputId": "2df0a987-9dc3-48ff-f5ba-d942560e4852"
      },
      "source": [
        "fig, ax = plt.subplots(1,3, figsize=(15, 6))\n",
        "\n",
        "#plt.figure(figsize=(11, 5))\n",
        "sns.lineplot(x=\"date\", \n",
        "             y=\"confirmed\", \n",
        "             data= reg_data,\n",
        "             ax = ax[0]\n",
        "            ).set_title(\"Confirmed COVID-19 Cases in %s\" % country_)\n",
        "ax[0].axvline(reg_data.date[ind], color=\"red\", linestyle=\"--\")\n",
        "ax[1].scatter(y = reg_data.confirmed[:ind], x = x_data[:ind], s = 15);\n",
        "ax[1].scatter(y = reg_data.confirmed[ind:], x = x_data[ind:], s = 15, color = \"red\");\n",
        "\n",
        "ax[1].plot(predictions[\"days_since_start\"],\n",
        "           np.exp(predictions[\"y_mean\"]), \n",
        "           color = \"green\",\n",
        "           label = \"Mean\") \n",
        "ax[1].axvline(ind, linestyle = '--', \n",
        "              linewidth = 1,\n",
        "              label = \"Day of Change\")\n",
        "ax[1].legend(loc = \"upper left\")\n",
        "ax[1].set(ylabel = \"Confirmed Cases\", \n",
        "          xlabel = \"Days since %s\" % start_date_,\n",
        "          title = \"Confirmed Cases: %s\" % country_);\n",
        "\n",
        "\n",
        "ax[2].scatter(y = reg_data.daily_confirmed[:ind], x = x_data[:ind], s = 15);\n",
        "ax[2].scatter(y = reg_data.daily_confirmed[ind:], x = x_data[ind:], s = 15, color = \"red\");\n",
        "\n",
        "ax[2].plot(predictions[\"days_since_start\"],\n",
        "           predictions[\"y_daily_mean\"], \n",
        "           color = \"green\",\n",
        "           label = \"Mean\") \n",
        "\n",
        "ax[2].axvline(ind, linestyle = '--', \n",
        "              linewidth = 1,\n",
        "              label = \"Day of Change\")\n",
        "ax[2].legend(loc = \"upper left\")\n",
        "ax[2].set(ylabel = \"Daily Confirmed Cases\", \n",
        "          xlabel = \"Days since %s\" % start_date_,\n",
        "          title = \"Daily Confirmed Cases: %s\" % country_);\n",
        "printmd(\"**Date of change for {}: {}**\".format(country_, change_date_));\n",
        "\n",
        "import matplotlib.dates as mdates\n",
        "myFmt = mdates.DateFormatter('%m-%d')\n",
        "ax[0].xaxis.set_major_formatter(myFmt)\n",
        "\n",
        "# ax[0].set_xticklabels(reg_data.date, rotation = 45,  fontsize=\"10\", va=\"center\")\n",
        "plt.setp(ax[0].get_xticklabels(), rotation=30, horizontalalignment='right')\n",
        "ax[0].set(ylabel='Confirmed Cases', xlabel='Date');\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('/content/sample_data/us_mean.pdf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "**Date of change for US: 2021-01-18**",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-RF4xjKoz9k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}