Coronavirus_in_Indonesia_Analysis_nightly_ver.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of Coronavirus in Indonesia Analysis-with-optimizer.ipynb",
      "provenance": [],
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      "toc_visible": true,
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/IqbalLx/Coronavirus-in-Indonesia-Analysis/blob/master/Coronavirus_in_Indonesia_Analysis_nightly_ver.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gTSQdnqLbXX",
        "colab_type": "text"
      },
      "source": [
        "# Preparing the Dataset\n",
        "\n",
        "To download a file, go to https://kawalcovid19.blob.core.windows.net/viz/statistik_harian.html. In the top right corner of \"Kasus Harian\" plotting, there is three horizontal dots that contains exporting data. Choose `.csv` and save in your local folder. Run the below cell, and upload that `.csv` file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eba9qJaXLNGE",
        "colab_type": "code",
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        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload() #Data from kawalcovid19.id"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-cdc9c09c-be5e-4e23-8868-5b82c0e676af\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-cdc9c09c-be5e-4e23-8868-5b82c0e676af\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Kasus Harian-2020-04-21.csv to Kasus Harian-2020-04-21.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMymKgZlR43H",
        "colab_type": "code",
        "outputId": "db3a1d06-3cac-4365-bdc6-9b85c069901d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# verify last date\n",
        "val_list = [val for val in uploaded.values()][0]\n",
        "last_date = val_list.decode().split('\\n')[-2]\n",
        "print(last_date)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-04-21 00:00:00,7135,375,95,26\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4LiUd0EEM2Gc",
        "colab_type": "code",
        "outputId": "2417de5a-f30b-41e9-e8c2-b3c01ebb899a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "filenames = [key for key in uploaded.keys()]\n",
        "filenames[0]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Kasus Harian-2020-04-21.csv'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSwklAqvLjX1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "\n",
        "import plotly.graph_objs as go\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.svm import SVR\n",
        "from scipy.integrate import odeint\n",
        "from scipy.optimize import minimize\n",
        "from sklearn.metrics import mean_squared_error as mse"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K57j_wwkLvKr",
        "colab_type": "code",
        "outputId": "28466ee8-3dc0-4d97-9417-248432502451",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "df = pd.read_csv(filenames[0])\n",
        "print(df.shape)\n",
        "df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(51, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "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>DT</th>\n",
              "      <th>Kasus (Kumulatif)</th>\n",
              "      <th>Kasus Baru</th>\n",
              "      <th>Sembuh (baru)</th>\n",
              "      <th>Meninggal (baru)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-02 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-03 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-04 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-05 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-06 00:00:00</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    DT  Kasus (Kumulatif)  ...  Sembuh (baru)  Meninggal (baru)\n",
              "0  2020-03-02 00:00:00                  2  ...            NaN               NaN\n",
              "1  2020-03-03 00:00:00                  2  ...            NaN               NaN\n",
              "2  2020-03-04 00:00:00                  2  ...            NaN               NaN\n",
              "3  2020-03-05 00:00:00                  2  ...            NaN               NaN\n",
              "4  2020-03-06 00:00:00                  4  ...            NaN               NaN\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "8ef48ec3-6d24-4a2c-8744-1bac5654c8f9",
        "id": "dKTY1Q1drEM1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df = df.fillna(0)\n",
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>DT</th>\n",
              "      <th>Kasus (Kumulatif)</th>\n",
              "      <th>Kasus Baru</th>\n",
              "      <th>Sembuh (baru)</th>\n",
              "      <th>Meninggal (baru)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-02 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-03 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-04 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-05 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-06 00:00:00</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    DT  Kasus (Kumulatif)  ...  Sembuh (baru)  Meninggal (baru)\n",
              "0  2020-03-02 00:00:00                  2  ...            0.0               0.0\n",
              "1  2020-03-03 00:00:00                  2  ...            0.0               0.0\n",
              "2  2020-03-04 00:00:00                  2  ...            0.0               0.0\n",
              "3  2020-03-05 00:00:00                  2  ...            0.0               0.0\n",
              "4  2020-03-06 00:00:00                  4  ...            0.0               0.0\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CE2zlJopMWDc",
        "colab_type": "code",
        "outputId": "5567b935-0bc1-49e0-9419-1859379edc30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 51 entries, 0 to 50\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   DT                 51 non-null     object \n",
            " 1   Kasus (Kumulatif)  51 non-null     int64  \n",
            " 2   Kasus Baru         51 non-null     float64\n",
            " 3   Sembuh (baru)      51 non-null     float64\n",
            " 4   Meninggal (baru)   51 non-null     float64\n",
            "dtypes: float64(3), int64(1), object(1)\n",
            "memory usage: 2.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A1xK_r13MBWm",
        "colab_type": "code",
        "outputId": "2faa76fd-ebaf-4580-a322-403d64e4fa4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df.DT = df.DT.str[:10]    # select only year-month-date in datetime column\n",
        "df.head()"
      ],
      "execution_count": 12,
      "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>DT</th>\n",
              "      <th>Kasus (Kumulatif)</th>\n",
              "      <th>Kasus Baru</th>\n",
              "      <th>Sembuh (baru)</th>\n",
              "      <th>Meninggal (baru)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-05</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           DT  Kasus (Kumulatif)  Kasus Baru  Sembuh (baru)  Meninggal (baru)\n",
              "0  2020-03-02                  2         2.0            0.0               0.0\n",
              "1  2020-03-03                  2         0.0            0.0               0.0\n",
              "2  2020-03-04                  2         0.0            0.0               0.0\n",
              "3  2020-03-05                  2         0.0            0.0               0.0\n",
              "4  2020-03-06                  4         2.0            0.0               0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NykKc_43MJz1",
        "colab_type": "code",
        "outputId": "88a0fb5c-d8fc-4ddc-8ee8-81be9f526f86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "df.DT = pd.to_datetime(df.DT, format=\"%Y-%m-%d\")\n",
        "df.DT.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   2020-03-02\n",
              "1   2020-03-03\n",
              "2   2020-03-04\n",
              "3   2020-03-05\n",
              "4   2020-03-06\n",
              "Name: DT, dtype: datetime64[ns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKDZyoflMRRB",
        "colab_type": "code",
        "outputId": "e6de5072-6f70-45a4-9253-49b93aedc4ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "df.info()    # check that datetime column is still in datetime format"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 51 entries, 0 to 50\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype         \n",
            "---  ------             --------------  -----         \n",
            " 0   DT                 51 non-null     datetime64[ns]\n",
            " 1   Kasus (Kumulatif)  51 non-null     int64         \n",
            " 2   Kasus Baru         51 non-null     float64       \n",
            " 3   Sembuh (baru)      51 non-null     float64       \n",
            " 4   Meninggal (baru)   51 non-null     float64       \n",
            "dtypes: datetime64[ns](1), float64(3), int64(1)\n",
            "memory usage: 2.1 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urdz8nZEO0lj",
        "colab_type": "text"
      },
      "source": [
        "# Visualizing the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sXcnV9fGYgrA",
        "colab": {}
      },
      "source": [
        "x = np.array(range(len(df.DT)))\n",
        "y = df['Kasus Baru'].values\n",
        "\n",
        "x = x.reshape(-1, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "802e8dde-c3c2-4728-c43f-25266044101e",
        "id": "AdWJk1YVYfQZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "model = SVR(kernel='poly')\n",
        "model.fit(x, y)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ql1_8NRBYeZK",
        "colab": {}
      },
      "source": [
        "line = model.predict(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkj8TDx-M9v7",
        "colab_type": "code",
        "outputId": "6eaec7d7-7e1c-4434-e3c9-e0ab22c6dca5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "name = ['New Cases', 'Died', 'Recovered']\n",
        "bar = [go.Bar(\n",
        "        x=df.DT,\n",
        "        y=df[column],\n",
        "        name=name[i],\n",
        "    ) for i, column in enumerate(['Kasus Baru', 'Meninggal (baru)', \n",
        "                                  'Sembuh (baru)'])]\n",
        "\n",
        "reg = go.Scatter(\n",
        "    x=df.DT,\n",
        "    y=line,\n",
        "    line={\n",
        "        'color':'green',\n",
        "        'dash': 'dash',\n",
        "        'width': 3\n",
        "    },\n",
        "    name='Trendline'\n",
        ")\n",
        "\n",
        "bar.append(reg)\n",
        "\n",
        "layout = {\n",
        "    'title':{\n",
        "        'text': 'Coronavirus in Indonesia',\n",
        "        'x':0.5, #range x axis 0 - 1\n",
        "    },\n",
        "    'xaxis':{\n",
        "        'title': 'Date',\n",
        "    },\n",
        "    'yaxis':{\n",
        "        'title': 'Num of Cases',\n",
        "    },\n",
        "    'legend':{\n",
        "        'orientation': 'h',\n",
        "        'x': 0.5,\n",
        "        'y': 1, #y itu 1 diatas, 0 dibawah\n",
        "        'xanchor': 'center',\n",
        "    }\n",
        "}\n",
        "\n",
        "fig = go.Figure(data=bar, layout=layout)\n",
        "fig.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"1c90f6b5-4106-42c3-85da-098a5f1fa3f5\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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              "                    Plotly.newPlot(\n",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADHZrzh9u-6Q",
        "colab_type": "code",
        "outputId": "4602ea54-fe96-4f56-867d-08752dd14cb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "y_cum = df['Kasus (Kumulatif)']\n",
        "\n",
        "model_cum = SVR(kernel='poly')\n",
        "model_cum.fit(x, y_cum)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z131UOGEvY7k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dt = datetime.datetime(2020, 3, 2)\n",
        "end = datetime.datetime(2020, 12, 30)\n",
        "step = datetime.timedelta(days=1)\n",
        "\n",
        "date_series = []\n",
        "\n",
        "while dt < end:\n",
        "    date_series.append(dt.strftime('%Y-%m-%d'))\n",
        "    dt += step\n",
        "\n",
        "worst_case = date_series[0:100]\n",
        "x_pred = np.array(range((len(worst_case))))\n",
        "x_pred = x_pred.reshape(-1, 1)\n",
        "\n",
        "line_cum = model_cum.predict(x_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5puvW5qNgy_",
        "colab_type": "code",
        "outputId": "fcc05404-c7f7-45a9-e7f6-2427a907da87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "kum = [go.Scatter(\n",
        "    x=df.DT,\n",
        "    y=df['Kasus (Kumulatif)'],\n",
        "    line={\n",
        "        'color':'green',\n",
        "        'width': 3\n",
        "    },\n",
        "    #mode='lines+markers',\n",
        "    mode='markers',\n",
        "    name='Total Cases'\n",
        ")]\n",
        "\n",
        "reg_cum = go.Scatter(\n",
        "    x=worst_case,\n",
        "    y=line_cum,\n",
        "    line={\n",
        "        'color':'orange',\n",
        "        'dash': 'dash',\n",
        "        'width': 1\n",
        "    },\n",
        "    name='exp. regres.'\n",
        ")\n",
        "\n",
        "kum.append(reg_cum)\n",
        "\n",
        "layout2 = {\n",
        "    'title':{\n",
        "        'text': 'Total Cases Coronavirus in Indonesia',\n",
        "        'x':0.5,  # this position is in unit scale\n",
        "    },\n",
        "    'xaxis':{\n",
        "        'title': 'Date',\n",
        "    },\n",
        "    'yaxis':{\n",
        "        'title': 'Total Cases',\n",
        "    },\n",
        "    'legend':{\n",
        "        'orientation': 'h',\n",
        "        'x': 0.5,\n",
        "        'y': 1,  # this position is in unit scale\n",
        "        'xanchor': 'center',\n",
        "    }\n",
        "}\n",
        "\n",
        "fig2 = go.Figure(kum, layout=layout2)\n",
        "fig2.show()"
      ],
      "execution_count": 21,
      "outputs": [
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ci7sTEB3Zruq",
        "colab_type": "text"
      },
      "source": [
        "# SIR Model Analysis with optimizer\n",
        "\n",
        "An important rule to remember when using `odeint`, is to make sure that `dt` is small enough and it must not similar to the time interval in the dataset which is `dt` is equal to a day.\n",
        "That's why we should define a `multplier_or_skip` variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZM1-h478ZuFm",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "def seird(y, t, N, gamma, beta, delta, rho, alpha):\n",
        "  S, E, I, R, D = y\n",
        "  dS = -beta(t) * I * S / N\n",
        "  dE = beta(t) * I * S / N - delta * E\n",
        "  dR = gamma * (1- alpha) * I\n",
        "  dD = rho * alpha * I\n",
        "  dI = delta * E - (dD + dR)\n",
        "  return dS, dE, dI, dR, dD"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvQY9B8bQrUG",
        "colab_type": "text"
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      "source": [
        "Run one of the below cells each time you change which variables\n",
        "you want to optimize."
      ]
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      "cell_type": "markdown",
      "metadata": {
        "id": "GJexHRiSDJQO",
        "colab_type": "text"
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      "source": [
        "## optimize `gamma`, `R_0_start`, `delta`, `rho`, and `alpha` \n",
        "(need to choose parameters carefully)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
        "colab": {
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          "height": 199
        },
        "outputId": "44f8115b-b1e2-4e52-c647-e60e15ce629d"
      },
      "source": [
        "def loss_func(point, data, S0, E0, I0, R0, D0, N, multiplier_or_skip):\n",
        "\n",
        "  gamma, R_0_start, delta, rho, alpha = point\n",
        "\n",
        "  k, x0, R_0_end = 0.05, 30, 0.05\n",
        "  logistic_R_0 = lambda t: (R_0_start-R_0_end) / (1 + np.exp(-k*(-t+x0))) + R_0_end\n",
        "  beta = lambda t: logistic_R_0(t) * gamma\n",
        "\n",
        "  N_t = len(data)\n",
        "  t = np.linspace(0, N_t, N_t*multiplier_or_skip)\n",
        "  y0 = S0, E0, I0, R0, D0\n",
        "\n",
        "  seird_res = odeint(seird, y0, t, args=(N, gamma, beta, delta, rho, alpha))\n",
        "  S, E, I, R, D= seird_res.T\n",
        "\n",
        "\n",
        "  loss = mse(data, I[::multiplier_or_skip], squared=True)\n",
        "  #print(round(loss, 2))\n",
        "  return loss\n",
        "\n",
        "\n",
        "def train(data, S0, E0, I0, R0, D0, N, multiplier_or_skip):\n",
        "  optimal = minimize(loss_func, [0.1, 10, 1/14., 1/21., 0.1], \n",
        "                     args=(data, S0, E0, I0, R0, D0, N, multiplier_or_skip), \n",
        "                     method='L-BFGS-B', bounds=[(1e-8, 1), (1e-8, 1), \n",
        "                                                (1e-8, 1), (1e-8, 1),\n",
        "                                                (1e-8, 1)])\n",
        "  print(optimal)\n",
        "  gamma, R_0_start, delta, rho, alpha = optimal.x\n",
        "  return gamma, R_0_start, delta, rho, alpha\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------------------------\n",
        "\n",
        "N = 40_000_000\n",
        "E0, I0, R0, D0 = 2, 2, 0, 0\n",
        "S0 = N-(E0+I0+R0+D0) \n",
        "\n",
        "N_t = len(date_series)     # this is not the length of available data\n",
        "multiplier_or_skip = 3     # if N_t = 300 -> len(t) will be = 1000\n",
        "t = np.linspace(0, N_t, N_t*multiplier_or_skip)\n",
        "y0 = S0, E0, I0, R0, D0\n",
        "\n",
        "\n",
        "gamma, R_0_start, delta, rho, alpha = train(df['Kasus (Kumulatif)'], S0, E0, I0, R0, D0, N, multiplier_or_skip)\n",
        "\n",
        "k = 0.05    # how quickly R_0 declines\n",
        "x0 = 30     # the x-value of the inflection point i.e. the date of the steepest \n",
        "            # decline in R_0,  this could be thought of as the main “lockdown” date)\n",
        "R_0_end = 0.05\n",
        "\n",
        "logistic_R_0 = lambda t: (R_0_start-R_0_end) / (1 + np.exp(-k*(-t+x0))) + R_0_end\n",
        "beta = lambda t: logistic_R_0(t) * gamma\n",
        "\n",
        "seird_res = odeint(seird, y0, t, args=(N, gamma, beta, delta, rho, alpha))\n",
        "S, E, I, R, D= seird_res.T"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      fun: 37241.75069420016\n",
            " hess_inv: <5x5 LbfgsInvHessProduct with dtype=float64>\n",
            "      jac: array([   287.69572964,   -244.74284146, -13631.30868413,   -535.74476624,\n",
            "          332.48725231])\n",
            "  message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n",
            "     nfev: 630\n",
            "      nit: 57\n",
            "   status: 0\n",
            "  success: True\n",
            "        x: array([0.66390923, 1.        , 1.        , 0.17419302, 1.        ])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9Zx2AipDYU3",
        "colab_type": "text"
      },
      "source": [
        "## optimize `gamma`, `R_0_start`, and `alpha`\n",
        "(need to choose parameters carefully)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUCtfyZZ2NX6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def loss_func2(point, data, S0, E0, I0, R0, D0, N, delta, rho, multiplier_or_skip):\n",
        "\n",
        "  gamma, R_0_start, alpha = point\n",
        "\n",
        "  k, x0, R_0_end = 0.05, 30, 0.05\n",
        "  logistic_R_0 = lambda t: (R_0_start-R_0_end) / (1 + np.exp(-k*(-t+x0))) + R_0_end\n",
        "  beta = lambda t: logistic_R_0(t) * gamma\n",
        "\n",
        "  N_t = len(data)\n",
        "  t = np.linspace(0, N_t, N_t*multiplier_or_skip)\n",
        "  y0 = S0, E0, I0, R0, D0\n",
        "\n",
        "  seird_res = odeint(seird, y0, t, args=(N, gamma, beta, delta, rho, alpha))\n",
        "  S, E, I, R, D= seird_res.T\n",
        "\n",
        "  loss = mse(data, I[::multiplier_or_skip], squared=True)\n",
        "  #print(round(loss, 2))\n",
        "  return loss\n",
        "\n",
        "\n",
        "def train2(data, S0, E0, I0, R0, D0, N, delta, rho, multiplier_or_skip):\n",
        "  optimal = minimize(loss_func2, [0.1, 10, 0.2], \n",
        "                     args=(data, S0, E0, I0, R0, D0, N, delta, rho, multiplier_or_skip), \n",
        "                     method='L-BFGS-B', bounds=[(1e-3, 0.5), (10, 20), \n",
        "                                                (1e-3, 0.5)])\n",
        "  print(optimal)\n",
        "  gamma, R_0_start, alpha = optimal.x\n",
        "  return gamma, R_0_start, alpha\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------------------------\n",
        "\n",
        "N = 40_000_000\n",
        "E0, I0, R0, D0 = 2, 2, 0, 0\n",
        "S0 = N-(E0+I0+R0+D0) \n",
        "\n",
        "N_t = len(date_series)     # this is not the length of available data\n",
        "multiplier_or_skip = 3     # if N_t = 300 -> len(t) will be = 1000\n",
        "t = np.linspace(0, N_t, N_t*multiplier_or_skip)\n",
        "y0 = S0, E0, I0, R0, D0\n",
        "\n",
        "delta = 1/14.\n",
        "rho = 1/21.\n",
        "\n",
        "gamma, R_0_start, alpha = train2(df['Kasus (Kumulatif)'], S0, E0, I0, R0, D0, N, delta, rho, multiplier_or_skip)\n",
        "\n",
        "k = 0.05    # how quickly R_0 declines\n",
        "x0 = 30     # the x-value of the inflection point i.e. the date of the steepest \n",
        "            # decline in R_0,  this could be thought of as the main “lockdown” date)\n",
        "R_0_end = 0.05\n",
        "\n",
        "logistic_R_0 = lambda t: (R_0_start-R_0_end) / (1 + np.exp(-k*(-t+x0))) + R_0_end\n",
        "beta = lambda t: logistic_R_0(t) * gamma\n",
        "\n",
        "seird_res = odeint(seird, y0, t, args=(N, gamma, beta, delta, rho, alpha))\n",
        "S, E, I, R, D= seird_res.T"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XPbF26yVHl0",
        "colab_type": "text"
      },
      "source": [
        "## optimize `gamma`, `R_0_start`, `alpha`, `k`, `x0`, and `R_0_end`\n",
        "\n",
        "(This is not giving the best result. Depend heavily in the choice of the boundary.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhU5GMzXRNoL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "1b8f0cab-5167-4bdd-ddbc-64bef862998d"
      },
      "source": [
        "def loss_func3(point, data, S0, E0, I0, R0, D0, N, delta, rho, multiplier_or_skip):\n",
        "\n",
        "  gamma, R_0_start, alpha, k, x0, R_0_end = point\n",
        "\n",
        "  logistic_R_0 = lambda t: (R_0_start-R_0_end) / (1 + np.exp(-k*(-t+x0))) + R_0_end\n",
        "  beta = lambda t: logistic_R_0(t) * gamma\n",
        "\n",
        "  N_t = len(data)\n",
        "  t = np.linspace(0, N_t, N_t*multiplier_or_skip)\n",
        "  y0 = S0, E0, I0, R0, D0\n",
        "\n",
        "  seird_res = odeint(seird, y0, t, args=(N, gamma, beta, delta, rho, alpha))\n",
        "  S, E, I, R, D= seird_res.T\n",
        "\n",
        "  loss = mse(data, I[::multiplier_or_skip], squared=True)\n",
        "  #print(round(loss, 2))\n",
        "  return loss\n",
        "\n",
        "\n",
        "def train3(data, S0, E0, I0, R0, D0, N, delta, rho, multiplier_or_skip):\n",
        "  optimal = minimize(loss_func3, [0.1, 10, 0.2, 0.05, 30, 0.05], \n",
        "                     args=(data, S0, E0, I0, R0, D0, N, delta, rho, multiplier_or_skip), \n",
        "                     method='L-BFGS-B', bounds=[(0.09, 0.1), (10, 11), \n",
        "                                                (0.1, 0.2), (0.05, 0.06),\n",
        "                                                (25, 30), (0.05, 0.06)])\n",
        "  print(optimal)\n",
        "  return optimal.x\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------------------------\n",
        "\n",
        "N = 40_000_000\n",
        "E0, I0, R0, D0 = 2, 2, 0, 0\n",
        "S0 = N-(E0+I0+R0+D0) \n",
        "\n",
        "N_t = len(date_series)     # this is not the length of available data\n",
        "multiplier_or_skip = 3     # if N_t = 300 -> len(t) will be = 1000\n",
        "t = np.linspace(0, N_t, N_t*multiplier_or_skip)\n",
        "y0 = S0, E0, I0, R0, D0\n",
        "\n",
        "delta = 1/14.\n",
        "rho = 1/21.\n",
        "\n",
        "gamma, R_0_start, alpha, k, x0, R_0_end = train3(df['Kasus (Kumulatif)'], S0, E0, I0, R0, D0, N, delta, rho, multiplier_or_skip)\n",
        "\n",
        "#k: how quickly R_0 declines\n",
        "#x0: the x-value of the inflection point i.e. the date of the steepest \n",
        "#    decline in R_0,  this could be thought of as the main “lockdown” date)\n",
        "\n",
        "logistic_R_0 = lambda t: (R_0_start-R_0_end) / (1 + np.exp(-k*(-t+x0))) + R_0_end\n",
        "beta = lambda t: logistic_R_0(t) * gamma\n",
        "\n",
        "seird_res = odeint(seird, y0, t, args=(N, gamma, beta, delta, rho, alpha))\n",
        "S, E, I, R, D= seird_res.T"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      fun: 6418118.956626135\n",
            " hess_inv: <6x6 LbfgsInvHessProduct with dtype=float64>\n",
            "      jac: array([-50808375.04938245,   -757149.14128184,  -2146866.6382134 ,\n",
            "       -16019992.43721366,   -176459.17832851,   -517422.98528552])\n",
            "  message: b'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'\n",
            "     nfev: 14\n",
            "      nit: 1\n",
            "   status: 0\n",
            "  success: True\n",
            "        x: array([ 0.1 , 11.  ,  0.2 ,  0.06, 30.  ,  0.06])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FayvZZqHRIcU",
        "colab_type": "text"
      },
      "source": [
        "## Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YC85z78Vg6uc",
        "colab_type": "code",
        "outputId": "b7d0227d-93d9-4f27-8171-365ffa8ca165",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "name = ['Exposed', 'Infected', 'Recovered', 'Death']\n",
        "\n",
        "data = [go.Scatter(\n",
        "    x=date_series,\n",
        "    y=ode[::multiplier_or_skip],\n",
        "    name=name[i]\n",
        ") for i, ode in enumerate([E, I, R, D])]\n",
        "\n",
        "\n",
        "layout3 = {\n",
        "    'title':{\n",
        "        'text': 'SEIRD Model for Coronavirus in Indonesia',\n",
        "        'x':0.5, #range x axis 0 - 1\n",
        "    },\n",
        "    'xaxis':{\n",
        "        'title': 'Date',\n",
        "    },\n",
        "    'yaxis':{\n",
        "        'title': 'Total Cases',\n",
        "    },\n",
        "}\n",
        "\n",
        "\n",
        "fig = go.Figure(data=data, layout=layout3)\n",
        "fig.show()"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "display_data",
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              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIRIYJ8JmcNe",
        "colab_type": "code",
        "outputId": "60145e0e-3b3c-49fd-d29c-cb8ade8bd9f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "data = go.Scatter(\n",
        "    x=date_series[0:120],\n",
        "    y=I[::multiplier_or_skip],\n",
        "    name='Prediction of Total Infected',\n",
        "    line={\n",
        "        'dash':'dash'\n",
        "    }\n",
        ")\n",
        "\n",
        "real_data = go.Scatter(\n",
        "    x=date_series[0:120],\n",
        "    y=df['Kasus Baru'],\n",
        "    name='Total Infected from kawalcovid.id',\n",
        "    mode='markers',\n",
        "    marker={'symbol':'diamond'}\n",
        ")\n",
        "\n",
        "layout3 = {\n",
        "    'title':{\n",
        "        'text': 'SEIRD Model for Coronavirus in Indonesia',\n",
        "        'x':0.5, #range x axis 0 - 1\n",
        "    },\n",
        "    'xaxis':{\n",
        "        'title': 'Date',\n",
        "        \n",
        "    },\n",
        "    'yaxis':{\n",
        "        'title': 'Total Infected',\n",
        "        #'range': [-10, 3_000],\n",
        "    },\n",
        "}\n",
        "\n",
        "fig = go.Figure(data=[data, real_data], layout=layout3)\n",
        "fig.show()"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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      "execution_count": 0,
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}