{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Aus_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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gFnvD8OysuiI",
        "outputId": "7e034393-4436-4c24-d21a-82621993c364"
      },
      "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",
        "url = 'https://raw.githubusercontent.com/assemzh/ProbProg-COVID-19/master/full_grouped.csv'\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": 1,
      "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": {
        "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",
        "\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('2020-03-21'), \n",
        "              linestyle = '--', linewidth = 1.5,\n",
        "              label = \"Policy start: Mar 21, 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",
        "    ax[0].xaxis.set_major_locator(mdates.MonthLocator(interval=1))   #to get a tick every month\n",
        "\n",
        "    sns.set_style(\"ticks\")\n",
        "    plt.tight_layout()\n",
        "    sns.despine()\n",
        "    plt.savefig('/content/sample_data/aus_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": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "w_A0fd4Zsuiw",
        "outputId": "cae5ef33-a210-498b-9512-4e404abbda13"
      },
      "source": [
        "cad = create_country(\"Australia\", end_date = \"2020-05-31\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       country       date  confirmed  ...  recovered  date_only daily_confirmed\n",
            "126  Australia 2020-05-27       7150  ...       6579      05-27              11\n",
            "127  Australia 2020-05-28       7165  ...       6576      05-28              15\n",
            "128  Australia 2020-05-29       7184  ...       6605      05-29              19\n",
            "129  Australia 2020-05-30       7192  ...       6614      05-30               8\n",
            "130  Australia 2020-05-31       7202  ...       6618      05-31              10\n",
            "\n",
            "[5 rows x 7 columns]\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": "code",
      "metadata": {
        "id": "UR0BM7TysujG"
      },
      "source": [
        "cad_start = \"2020-03-01\" # 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": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaTHo6I2sujp",
        "outputId": "30a54a68-76d8-4d6a-d9f1-e0da7bde0f42"
      },
      "source": [
        "cad.shape\n",
        "cad_tmp = cad[cad.date < \"2020-05-01\"]\n",
        "cad_tmp.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "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_ = \"Australia (Before May 1st)\"\n",
        "reg_data = cad_tmp.copy()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "3RjDFEbA91X-",
        "outputId": "7e6d9b7d-c810-4557-cc4f-63c932ccb0f5"
      },
      "source": [
        "reg_data.head()"
      ],
      "execution_count": 13,
      "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>date_only</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>Australia</td>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>03-01</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Australia</td>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>03-02</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Australia</td>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>03-03</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Australia</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>52</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>03-04</td>\n",
              "      <td>13</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Australia</td>\n",
              "      <td>2020-03-05</td>\n",
              "      <td>55</td>\n",
              "      <td>2</td>\n",
              "      <td>21</td>\n",
              "      <td>03-05</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     country       date  confirmed  ...  date_only  daily_confirmed days_since_start\n",
              "0  Australia 2020-03-01         27  ...      03-01                2                1\n",
              "1  Australia 2020-03-02         30  ...      03-02                3                2\n",
              "2  Australia 2020-03-03         39  ...      03-03                9                3\n",
              "3  Australia 2020-03-04         52  ...      03-04               13                4\n",
              "4  Australia 2020-03-05         55  ...      03-05                3                5\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "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": "1ab61711-b660-4fad-de50-b2a5d94cb8f8"
      },
      "source": [
        "!pip install pyro-ppl\n",
        "!pip install numpyro"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pyro-ppl\n",
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            "\u001b[?25hRequirement already satisfied: tqdm>=4.36 in /usr/local/lib/python3.7/dist-packages (from pyro-ppl) (4.41.1)\n",
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            "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",
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            "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",
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            "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=7d0fef4762e0db39ed221ac892a147049a5b63230b808d9e1d7f4399db61851c\n",
            "  Stored in directory: /root/.cache/pip/wheels/44/ea/ac/3be3bc19ee3b62f6fe1561eb6df1199284bb6bab819c1befa4\n",
            "Successfully built jax\n",
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            "  Found existing installation: jax 0.2.12\n",
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          ],
          "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": 15,
      "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": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "if0toOMysupU",
        "outputId": "058628f5-8c10-4ec7-d4f5-97e248e72d2f"
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prior mean for Bias 1:  4.5138054\n",
            "Prior mean for Bias 2:  8.800875\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": {
        "id": "X1rSXXtKsupm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1079fa45-006f-41e3-98c8-6c1239324981"
      },
      "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 [24:27,  2.45s/it, step size=1.31e-04, acc. prob=0.936]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-lXw8vBJsAT"
      },
      "source": [
        "# Save the model:\n",
        "import dill\n",
        "# with open('aus.pkl', 'wb') as f:\n",
        "# \tdill.dump(mcmc, f)\n",
        "with open('aus.pkl', 'rb') as f:\n",
        "\tmcmc = dill.load(f)\n",
        " \n",
        "samples = mcmc.get_samples()"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Z968a5xsupv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95c5079a-3771-482e-9419-072a288a5d16"
      },
      "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": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([   0.0000,    4.4640,    5.2986,    6.8328,    7.8025,    9.8307,\n",
            "          11.8612,   13.4422,   16.9518,   21.2977,   25.7285,   30.4241,\n",
            "          37.7013,   43.8305,   55.0542,   66.6997,   82.1629,   95.6561,\n",
            "         117.4060,  145.2230,  175.4886,  211.9290,  252.8590,  310.6555,\n",
            "         379.7236,  451.5439,  559.8037,  658.4072,  847.2842,  877.7178,\n",
            "         175.0576, -171.3018,   37.2290,   74.0874,   53.8413,    3.1011,\n",
            "          94.8687,   41.6509,   16.6680,   45.3633,   94.5469,   -3.4326,\n",
            "          76.7153,   62.1982,   78.2866,   68.9971,   30.5205,   19.0586,\n",
            "          94.0020,   30.3032,   79.4014,   12.0522,   31.2920,   95.9932,\n",
            "          42.5098,   67.3188,   84.3203,   12.1406,   83.1426,   40.8364,\n",
            "          60.2397])\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": {
        "id": "QUh6fBjtsuqV"
      },
      "source": [
        "mcmc.summary()\n",
        "diag = mcmc.diagnostics()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJ2a6Epnsuqf"
      },
      "source": [
        "## Posterior Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPpET7b6suqg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "7ec15eb6-55e2-4bbf-ed9d-a6772bd8f97f"
      },
      "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 = \"blue\",\n",
        "             norm_hist = True,\n",
        "             kde = True)\n",
        "plt.axvline(x = w1_[\"mean\"], linestyle = '--',label = \"Mean weight before CP\" ,)\n",
        "              # color = \"blue\")\n",
        "\n",
        "sns.distplot(weight_2_post, \n",
        "             kde_kws = {\"label\": \"Weight posterior after CP\"}, \n",
        "             color = \"red\",\n",
        "             norm_hist = True,\n",
        "             kde = True)\n",
        "plt.axvline(x = w2_[\"mean\"], linestyle = '--',label = \"Mean weight after CP\" ,\n",
        "              color = \"red\")\n",
        "\n",
        "legend = plt.legend(loc='upper right')\n",
        "legend.get_frame().set_alpha(1)\n",
        "sns.set_style(\"ticks\")\n",
        "plt.tight_layout()\n",
        "sns.despine()\n",
        "plt.savefig('/content/sample_data/aus_weights.pdf')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30\n",
            "2020-03-31 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": {
        "id": "byMfY8BxUxPg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "729d9fd1-81e0-40e3-f48c-33dd6fe88910"
      },
      "source": [
        "print(w1_[\"mean\"])\n",
        "print(w2_[\"mean\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[0.1928]])\n",
            "tensor([[0.0085]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vtfu-eSvR5fH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c134989-261a-4751-eeb1-a9d20b072d0b"
      },
      "source": [
        "1- w2_['mean']/w1_['mean']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.9561]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZCpLZ51R5gU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4c32b40-bfdd-445f-c649-410249dc54ca"
      },
      "source": [
        "reg_data.date[40]"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2020-04-10 00:00:00')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-eH-TrJKKa2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "2921c15c-2241-4188-e284-a677aa096efa"
      },
      "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(20, \n",
        "              linestyle = '--', linewidth = 1.5,\n",
        "              label = \"Policy start: Mar 21, 2020\" ,\n",
        "              color = \"red\")\n",
        "\n",
        "ax[0].axvline(ind, \n",
        "              linestyle = '--', linewidth = 1.5,\n",
        "              label = \"Date of Change: Mar 31, 2020\",\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",
        "\n",
        "ax[0].legend(loc = \"lower right\", fontsize=12.8)\n",
        "ax[0].set_ylim([100,10000])\n",
        "ax[0].set_xlim([5,60])\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=[10,20,30,50], labels=[\"Mar 10\",\n",
        "                                             \"Mar 21\",\n",
        "                                             \"Mar 31\",\n",
        "                                            \"Apr 20\"], 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",
        "myFmt = mdates.DateFormatter('%m-%d')\n",
        "sns.set_style(\"ticks\")\n",
        "sns.despine()\n",
        "plt.savefig('/content/sample_data/aus_cp.pdf')\n"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Date of change for Australia (Before May 1st): 2020-03-31\n",
            "Index(['country', 'date', 'confirmed', 'deaths', 'recovered', 'date_only',\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": {
        "id": "HrWADTLYsurS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "a7eb9863-6560-4fbb-eaa3-d7ec850ee514"
      },
      "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/sing_mean.pdf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "**Date of change for Australia (Before May 1st): 2020-03-31**",
            "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": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-RF4xjKoz9k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}