lisphilar/scenario_japan_20201229_time-series.ipynb Secret

## scenario_japan_20201229_time-series.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Scenario_Japan_20201229_time-series.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP1AXCDFhtQwEnhvQbh/QYy",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/lisphilar/30cb8d615659948334fb3aa5faa20aca/scenario_japan_20201229_time-series.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMWsO6SLt9Yc"
      },
      "source": [
        "!pip install --upgrade \"git+https://github.com/lisphilar/covid19-sir.git#egg=covsirphy\" -q"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zQ4Qf3rhuaun",
        "outputId": "671586cd-1e97-4d51-b9ca-073f6ac168f6"
      },
      "source": [
        "import covsirphy as cs\r\n",
        "cs.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.13.3-theta-fu1.new.56.420.422.429.449.fix.438.441.450'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TSFfN0m0uee8",
        "outputId": "d4b0c131-033e-4d3c-9836-8d4551fbef3c"
      },
      "source": [
        "data_loader = cs.DataLoader()\r\n",
        "jhu_data = data_loader.jhu()\r\n",
        "population_data = data_loader.population()\r\n",
        "oxcgrt_data = data_loader.oxcgrt()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Retrieving datasets from COVID-19 Data Hub: https://covid19datahub.io/\n",
            "\n",
            "Please set verbose=2 to see the detailed citation list.\n",
            "\n",
            "\n",
            "Retrieving COVID-19 dataset in Japan from https://github.com/lisphilar/covid19-sir/data/japan\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "eSTwNQr7ugiK",
        "outputId": "ebc07c2f-ce59-4c25-b02e-6d18d8807372"
      },
      "source": [
        "snl = cs.Scenario(jhu_data, population_data, \"Japan\")\r\n",
        "_ = snl.trend()\r\n",
        "snl.estimate(cs.SIRF)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "<SIR-F model: parameter estimation>\n",
            "Running optimization with 2 CPUs...\n",
            "\t 9th phase (24Oct2020 - 10Nov2020): finished 2322 trials in 3 min  0 sec\n",
            "\t 2nd phase (03Sep2020 - 29Sep2020): finished  198 trials in 0 min 10 sec\n",
            "\t12th phase (16Jul2020 - 01Aug2020): finished  838 trials in 0 min 50 sec\n",
            "\t 7th phase (06Feb2020 - 16Apr2020): finished 2154 trials in 3 min  1 sec\n",
            "\t 8th phase (11Dec2020 - 19Dec2020): finished  204 trials in 0 min 10 sec\n",
            "\t 4th phase (17Apr2020 - 15Jul2020): finished 2134 trials in 3 min  1 sec\n",
            "\t 0th phase (11Nov2020 - 20Nov2020): finished 1096 trials in 1 min 10 sec\n",
            "\t 1st phase (30Sep2020 - 23Oct2020): finished 2109 trials in 3 min  1 sec\n",
            "\t 5th phase (20Dec2020 - 28Dec2020): finished  685 trials in 0 min 40 sec\n",
            "\t 6th phase (02Aug2020 - 15Aug2020): finished 2087 trials in 3 min  1 sec\n",
            "\t 3rd phase (16Aug2020 - 02Sep2020): finished 1201 trials in 1 min 20 sec\n",
            "\t10th phase (21Nov2020 - 01Dec2020): finished 2208 trials in 3 min  1 sec\n",
            "\t11th phase (02Dec2020 - 10Dec2020): finished  238 trials in 0 min 10 sec\n",
            "Completed optimization. Total: 13 min  7 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "0YBFy6z9usGv",
        "outputId": "d28ffd13-27a8-4a5f-ac95-dd93d9c6221a"
      },
      "source": [
        "param_df = snl._track_param(name=\"Main\")[cs.SIRF.PARAMETERS]\r\n",
        "param_df"
      ],
      "execution_count": 5,
      "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>theta</th>\n",
              "      <th>kappa</th>\n",
              "      <th>rho</th>\n",
              "      <th>sigma</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-02-06</th>\n",
              "      <td>0.016776</td>\n",
              "      <td>0.000335</td>\n",
              "      <td>0.051836</td>\n",
              "      <td>0.010442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-07</th>\n",
              "      <td>0.016776</td>\n",
              "      <td>0.000335</td>\n",
              "      <td>0.051836</td>\n",
              "      <td>0.010442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-08</th>\n",
              "      <td>0.016776</td>\n",
              "      <td>0.000335</td>\n",
              "      <td>0.051836</td>\n",
              "      <td>0.010442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-09</th>\n",
              "      <td>0.016776</td>\n",
              "      <td>0.000335</td>\n",
              "      <td>0.051836</td>\n",
              "      <td>0.010442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-10</th>\n",
              "      <td>0.016776</td>\n",
              "      <td>0.000335</td>\n",
              "      <td>0.051836</td>\n",
              "      <td>0.010442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>0.001503</td>\n",
              "      <td>0.000722</td>\n",
              "      <td>0.050033</td>\n",
              "      <td>0.040436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-25</th>\n",
              "      <td>0.001503</td>\n",
              "      <td>0.000722</td>\n",
              "      <td>0.050033</td>\n",
              "      <td>0.040436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-26</th>\n",
              "      <td>0.001503</td>\n",
              "      <td>0.000722</td>\n",
              "      <td>0.050033</td>\n",
              "      <td>0.040436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-27</th>\n",
              "      <td>0.001503</td>\n",
              "      <td>0.000722</td>\n",
              "      <td>0.050033</td>\n",
              "      <td>0.040436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>0.001503</td>\n",
              "      <td>0.000722</td>\n",
              "      <td>0.050033</td>\n",
              "      <td>0.040436</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>327 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               theta     kappa       rho     sigma\n",
              "Date                                              \n",
              "2020-02-06  0.016776  0.000335  0.051836  0.010442\n",
              "2020-02-07  0.016776  0.000335  0.051836  0.010442\n",
              "2020-02-08  0.016776  0.000335  0.051836  0.010442\n",
              "2020-02-09  0.016776  0.000335  0.051836  0.010442\n",
              "2020-02-10  0.016776  0.000335  0.051836  0.010442\n",
              "...              ...       ...       ...       ...\n",
              "2020-12-24  0.001503  0.000722  0.050033  0.040436\n",
              "2020-12-25  0.001503  0.000722  0.050033  0.040436\n",
              "2020-12-26  0.001503  0.000722  0.050033  0.040436\n",
              "2020-12-27  0.001503  0.000722  0.050033  0.040436\n",
              "2020-12-28  0.001503  0.000722  0.050033  0.040436\n",
              "\n",
              "[327 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tARkYqirzVVZ"
      },
      "source": [
        "!pip install 'u8darts[all]' -q"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JO1s8cf0uso2"
      },
      "source": [
        "from datetime import timedelta\r\n",
        "from darts import TimeSeries\r\n",
        "import darts.models\r\n",
        "from darts.metrics import rmsle\r\n",
        "from matplotlib import pyplot as plt\r\n",
        "import pandas as pd"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V9W-eozZu7Mb"
      },
      "source": [
        "import logging\r\n",
        "import warnings\r\n",
        "logging.disable(logging.CRITICAL)\r\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZpjEHWRu2ky"
      },
      "source": [
        "models = [\r\n",
        "    darts.models.NaiveSeasonal,\r\n",
        "    darts.models.NaiveDrift,\r\n",
        "    darts.models.Prophet,\r\n",
        "    darts.models.ExponentialSmoothing,\r\n",
        "    darts.models.ARIMA,\r\n",
        "    darts.models.AutoARIMA,\r\n",
        "    darts.models.Theta,\r\n",
        "    darts.models.FFT,\r\n",
        "]"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kk_MCgifu7ya"
      },
      "source": [
        "def forecast(param_df, target, window=14, test_size=0.2, days=60):\r\n",
        "    # Calculate moving average\r\n",
        "    df = param_df.copy()\r\n",
        "    df = df.rolling(window).mean().dropna()\r\n",
        "    # Create time series object of Darts for Stringency_index\r\n",
        "    time_series = TimeSeries.from_dataframe(df, value_cols=target)\r\n",
        "    split_date = param_df.index.min() + timedelta(days=int(len(df) * (1 - test_size)))\r\n",
        "    train, test = time_series.split_after(split_date)\r\n",
        "    # Modelling\r\n",
        "    score_dict = {}\r\n",
        "    for model in models:\r\n",
        "        m = model()\r\n",
        "        try:\r\n",
        "            m.fit(train)\r\n",
        "        except (ValueError, FloatingPointError):\r\n",
        "            continue\r\n",
        "        forecast = m.predict(len(test))\r\n",
        "        score = rmsle(test, forecast)\r\n",
        "        score_dict[model] = score\r\n",
        "        print(f\"RMSLE: {score:.4f} ({model.__name__})\")\r\n",
        "    # Forcasting\r\n",
        "    best_model = min(score_dict, key=score_dict.get)\r\n",
        "    print(f\"Best model: {best_model.__name__}\")\r\n",
        "    model_instance = best_model()\r\n",
        "    model_instance.fit(train)\r\n",
        "    forecast = model_instance.predict(len(test) + days)\r\n",
        "    time_series.plot(label=\"Actual\")\r\n",
        "    forecast.plot(label=\"Forecast\")\r\n",
        "    plt.legend()\r\n",
        "    plt.show()"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "f-g6N02uv8Hu",
        "outputId": "6982f7a8-a8ac-4599-bbef-a0922f60fde3"
      },
      "source": [
        "forecast(param_df, target=\"theta\", window=14, test_size=0.2, days=60)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSLE: 0.0002 (NaiveSeasonal)\n",
            "RMSLE: 0.0029 (NaiveDrift)\n",
            "RMSLE: 0.0366 (Prophet)\n",
            "RMSLE: 0.0002 (ExponentialSmoothing)\n",
            "RMSLE: 0.0024 (ARIMA)\n",
            "RMSLE: 0.0002 (AutoARIMA)\n",
            "RMSLE: 0.0026 (Theta)\n",
            "RMSLE: 0.0231 (FFT)\n",
            "Best model: ExponentialSmoothing\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "oQJRMxJqwEi5",
        "outputId": "e7cf9380-7d05-442b-c35c-da9208d7e0da"
      },
      "source": [
        "forecast(param_df, target=\"kappa\", window=14, test_size=0.2, days=60)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSLE: 0.0001 (NaiveSeasonal)\n",
            "RMSLE: 0.0001 (NaiveDrift)\n",
            "RMSLE: 0.0001 (Prophet)\n",
            "RMSLE: 0.0001 (ExponentialSmoothing)\n",
            "RMSLE: 0.0001 (ARIMA)\n",
            "RMSLE: 0.0001 (Theta)\n",
            "RMSLE: 0.0002 (FFT)\n",
            "Best model: NaiveDrift\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "W44EU05DwF3y",
        "outputId": "15f04d53-a2fd-4004-8c72-18e2f9d52f34"
      },
      "source": [
        "forecast(param_df, target=\"rho\", window=14, test_size=0.2, days=60)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSLE: 0.0066 (NaiveSeasonal)\n",
            "RMSLE: 0.0069 (NaiveDrift)\n",
            "RMSLE: 0.0060 (Prophet)\n",
            "RMSLE: 0.0066 (ExponentialSmoothing)\n",
            "RMSLE: 0.0125 (ARIMA)\n",
            "RMSLE: 0.0069 (AutoARIMA)\n",
            "RMSLE: 0.0068 (Theta)\n",
            "RMSLE: 0.0108 (FFT)\n",
            "Best model: Prophet\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "xvEQKBXpwHBy",
        "outputId": "ba00dfe9-043e-4050-9f45-5683ac53027b"
      },
      "source": [
        "forecast(param_df, target=\"sigma\", window=14, test_size=0.2, days=60)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSLE: 0.0070 (NaiveSeasonal)\n",
            "RMSLE: 0.0135 (NaiveDrift)\n",
            "RMSLE: 0.0210 (Prophet)\n",
            "RMSLE: 0.0070 (ExponentialSmoothing)\n",
            "RMSLE: 0.0134 (ARIMA)\n",
            "RMSLE: 0.0119 (AutoARIMA)\n",
            "RMSLE: 0.0106 (Theta)\n",
            "RMSLE: 0.0284 (FFT)\n",
            "Best model: ExponentialSmoothing\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNQRm4nQwIZw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}