{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Sing_changing_point.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.10"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqXl71t9suiG"
      },
      "source": [
        "# Estimating the Date of COVID-19 Changes\n",
        "\n",
        "https://nbviewer.jupyter.org/github/jramkiss/jramkiss.github.io/blob/master/_posts/notebooks/covid19-changes.ipynb "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gFnvD8OysuiI",
        "outputId": "9d079adb-1f38-431f-844b-4b1f0ca0a752"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hzvPpvVvphTD"
      },
      "source": [
        "## Create country\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "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=(8, 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('2020-04-07'), \n",
        "              linestyle = '--', linewidth = 1.5,\n",
        "              label = \"Policy start: Apr 7, 2020\" ,\n",
        "              color = \"red\") \n",
        "\n",
        "    \n",
        "    ax[0].xaxis.get_label().set_fontsize(22)\n",
        "    ax[0].yaxis.get_label().set_fontsize(22)\n",
        "    x = df.date\n",
        "    # ax[0].set_xticks(x[::30])\n",
        "    ax[0].xaxis.set_major_locator(mdates.MonthLocator(interval=2))   #to get a tick every month\n",
        "\n",
        "    ax[0].title.set_fontsize(20)\n",
        "    ax[0].tick_params(labelsize=22)\n",
        "    myFmt = mdates.DateFormatter('%b %-d, %Y')\n",
        "    ax[0].xaxis.set_major_formatter(myFmt)\n",
        "\n",
        "    ax[0].set(ylabel='', xlabel='');\n",
        "    ax[0].legend(loc = \"bottom right\", fontsize=22)\n",
        "\n",
        "    sns.set_style(\"ticks\")\n",
        "    plt.tight_layout()\n",
        "    sns.despine()\n",
        "    plt.savefig('/content/sample_data/sing_daily.pdf')\n",
        "    print(df.tail())\n",
        "    return df\n",
        "\n",
        "\n",
        "def summary(samples):\n",
        "    site_stats = {}\n",
        "    for k, v in samples.items():\n",
        "        site_stats[k] = {\n",
        "            \"mean\": torch.mean(v, 0),\n",
        "            \"std\": torch.std(v, 0),\n",
        "            \"5%\": v.kthvalue(int(len(v) * 0.05), dim=0)[0],\n",
        "            \"95%\": v.kthvalue(int(len(v) * 0.95), dim=0)[0],\n",
        "        }\n",
        "    return site_stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "w_A0fd4Zsuiw",
        "outputId": "4d7bda7c-ce27-4beb-ba50-2b56ac8ab98e"
      },
      "source": [
        "cad = create_country(\"Singapore\", end_date = \"2020-05-31\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       country       date  confirmed  ...  recovered  date_only daily_confirmed\n",
            "126  Singapore 2020-05-27      32876  ...      17276      05-27             533\n",
            "127  Singapore 2020-05-28      33249  ...      18294      05-28             373\n",
            "128  Singapore 2020-05-29      33860  ...      19631      05-29             611\n",
            "129  Singapore 2020-05-30      34366  ...      20727      05-30             506\n",
            "130  Singapore 2020-05-31      34884  ...      21699      05-31             518\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 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UR0BM7TysujG"
      },
      "source": [
        "cad_start = \"2020-04-01\" # 13 confirmed cases\n",
        "cad = cad[cad.date >= cad_start].reset_index(drop = True)\n",
        "cad[\"days_since_start\"] = np.arange(cad.shape[0]) + 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaTHo6I2sujp",
        "outputId": "7c987c2f-0231-4e66-c528-249e9833921f"
      },
      "source": [
        "cad.shape\n",
        "cad_tmp = cad[cad.date < \"2020-05-31\"]\n",
        "cad_tmp.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "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_ = \"Singapore\"\n",
        "reg_data = cad_tmp.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "3RjDFEbA91X-",
        "outputId": "96731b02-a2a6-45df-f744-a490bf38f002"
      },
      "source": [
        "reg_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>date</th>\n",
              "      <th>confirmed</th>\n",
              "      <th>deaths</th>\n",
              "      <th>recovered</th>\n",
              "      <th>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>Singapore</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>1000</td>\n",
              "      <td>3</td>\n",
              "      <td>245</td>\n",
              "      <td>04-01</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>2020-04-02</td>\n",
              "      <td>1049</td>\n",
              "      <td>4</td>\n",
              "      <td>266</td>\n",
              "      <td>04-02</td>\n",
              "      <td>49</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>2020-04-03</td>\n",
              "      <td>1114</td>\n",
              "      <td>5</td>\n",
              "      <td>282</td>\n",
              "      <td>04-03</td>\n",
              "      <td>65</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>2020-04-04</td>\n",
              "      <td>1189</td>\n",
              "      <td>6</td>\n",
              "      <td>297</td>\n",
              "      <td>04-04</td>\n",
              "      <td>75</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>2020-04-05</td>\n",
              "      <td>1309</td>\n",
              "      <td>6</td>\n",
              "      <td>320</td>\n",
              "      <td>04-05</td>\n",
              "      <td>120</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  Singapore 2020-04-01       1000  ...      04-01               74                1\n",
              "1  Singapore 2020-04-02       1049  ...      04-02               49                2\n",
              "2  Singapore 2020-04-03       1114  ...      04-03               65                3\n",
              "3  Singapore 2020-04-04       1189  ...      04-04               75                4\n",
              "4  Singapore 2020-04-05       1309  ...      04-05              120                5\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkO0Z8M0supC"
      },
      "source": [
        "## Change Point Estimation in Pyro"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIUed4Ny3-oq"
      },
      "source": [
        "!pip install pyro-ppl\n",
        "!pip install numpyro"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZS9fTPxsupD"
      },
      "source": [
        "import torch\n",
        "\n",
        "import pyro\n",
        "import pyro.distributions as dist\n",
        "from torch import nn\n",
        "from pyro.nn import PyroModule, PyroSample\n",
        "\n",
        "from pyro.infer import MCMC, NUTS, HMC\n",
        "from pyro.infer.autoguide import AutoGuide, AutoDiagonalNormal\n",
        "\n",
        "from pyro.infer import SVI, Trace_ELBO\n",
        "from pyro.infer import Predictive"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gPrEaEJsupP"
      },
      "source": [
        "# we should be able to have an empirical estimate for the mean of the prior for the 2nd regression bias term\n",
        "# this will be something like b = log(max(daily_confirmed))\n",
        "\n",
        "# might be able to have 1 regression model but change the data so that we have new terms for (tau < t) \n",
        "# like an interaction term\n",
        "\n",
        "class COVID_change(PyroModule):\n",
        "    def __init__(self, in_features, out_features, b1_mu, b2_mu):\n",
        "        super().__init__()\n",
        "        self.linear1 = PyroModule[nn.Linear](in_features, out_features, bias = False)\n",
        "        self.linear1.weight = PyroSample(dist.Normal(0.5, 0.25).expand([1, 1]).to_event(1))\n",
        "        self.linear1.bias = PyroSample(dist.Normal(b1_mu, 1.))\n",
        "        \n",
        "        # could possibly have stronger priors for the 2nd regression line, because we wont have as much data\n",
        "        self.linear2 = PyroModule[nn.Linear](in_features, out_features, bias = False)\n",
        "        self.linear2.weight = PyroSample(dist.Normal(0., 0.25).expand([1, 1])) #.to_event(1))\n",
        "        self.linear2.bias = PyroSample(dist.Normal(b2_mu, b2_mu/4))\n",
        "\n",
        "    def forward(self, x, y=None):\n",
        "        tau = pyro.sample(\"tau\", dist.Beta(4, 3))\n",
        "        sigma = pyro.sample(\"sigma\", dist.Uniform(0., 3.))\n",
        "        # fit lm's to data based on tau\n",
        "        sep = int(np.ceil(tau.detach().numpy() * len(x)))\n",
        "        mean1 = self.linear1(x[:sep]).squeeze(-1)\n",
        "        mean2 = self.linear2(x[sep:]).squeeze(-1)\n",
        "        mean = torch.cat((mean1, mean2))\n",
        "        obs = pyro.sample(\"obs\", dist.StudentT(2, mean, sigma), obs=y)\n",
        "        return mean"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "if0toOMysupU",
        "outputId": "7d5834af-c2ea-4157-a36f-78cad6dc62e8"
      },
      "source": [
        "tensor_data = torch.tensor(reg_data[[\"confirmed\", \"days_since_start\", \"daily_confirmed\"]].values, dtype=torch.float)\n",
        "x_data = tensor_data[:, 1].unsqueeze_(1)\n",
        "y_data = np.log(tensor_data[:, 0])\n",
        "y_data_daily = np.log(tensor_data[:, 2])\n",
        "# prior hyper params\n",
        "# take log of the average of the 1st quartile to get the prior mean for the bias of the 2nd regression line\n",
        "q1 = np.quantile(y_data, q = 0.25)\n",
        "bias_1_mean = np.mean(y_data.numpy()[y_data <= q1])\n",
        "print(\"Prior mean for Bias 1: \", bias_1_mean)\n",
        "\n",
        "# take log of the average of the 4th quartile to get the prior mean for the bias of the 2nd regression line\n",
        "q4 = np.quantile(y_data, q = 0.75)\n",
        "bias_2_mean = np.mean(y_data.numpy()[y_data >= q4])\n",
        "print(\"Prior mean for Bias 2: \", bias_2_mean)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prior mean for Bias 1:  7.4748335\n",
            "Prior mean for Bias 2:  10.33601\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('sing.pkl', 'wb') as f:\n",
        "# \tdill.dump(mcmc, f)\n",
        "with open('sing.pkl', 'rb') as f:\n",
        "\tmcmc = dill.load(f)\n",
        " \n",
        "samples = mcmc.get_samples()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Z968a5xsupv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "954d8675-130d-4dff-ef52-f8a25295326a"
      },
      "source": [
        "# extract individual posteriors\n",
        "weight_1_post = samples[\"linear1.weight\"].detach().numpy()\n",
        "weight_2_post = samples[\"linear2.weight\"].detach().numpy()\n",
        "bias_1_post = samples[\"linear1.bias\"].detach().numpy()\n",
        "bias_2_post = samples[\"linear2.bias\"].detach().numpy()\n",
        "tau_post = samples[\"tau\"].detach().numpy()\n",
        "sigma_post = samples[\"sigma\"].detach().numpy()\n",
        "\n",
        "# build likelihood distribution:\n",
        "tau_days = list(map(int, np.ceil(tau_post * len(x_data))))\n",
        "mean_ = torch.zeros(len(tau_days), len(x_data))\n",
        "obs_ = torch.zeros(len(tau_days), len(x_data))\n",
        "for i in range(len(tau_days)) : \n",
        "    mean_[i, :] = torch.cat((x_data[:tau_days[i]] * weight_1_post[i] + bias_1_post[i],\n",
        "                          x_data[tau_days[i]:] * weight_2_post[i] + bias_2_post[i])).reshape(len(x_data))\n",
        "    obs_[i, :] = dist.Normal(mean_[i, :], sigma_post[i]).sample()\n",
        "samples[\"_RETURN\"] = mean_\n",
        "samples[\"obs\"] = obs_\n",
        "pred_summary = summary(samples)\n",
        "mu = pred_summary[\"_RETURN\"] # mean\n",
        "y = pred_summary[\"obs\"] # samples from likelihood: mu + sigma\n",
        "y_shift = np.exp(y[\"mean\"]) - np.exp(torch.cat((y[\"mean\"][0:1], y[\"mean\"][:-1])))\n",
        "print(y_shift)\n",
        "predictions = pd.DataFrame({\n",
        "    \"days_since_start\": x_data[:, 0],\n",
        "    \"mu_mean\": mu[\"mean\"],    # mean of likelihood\n",
        "    \"mu_perc_5\": mu[\"5%\"],\n",
        "    \"mu_perc_95\": mu[\"95%\"],\n",
        "    \"y_mean\": y[\"mean\"],      # mean of likelihood + noise\n",
        "    \"y_perc_5\": y[\"5%\"],\n",
        "    \"y_perc_95\": y[\"95%\"],\n",
        "    \"true_confirmed\": y_data,\n",
        "    \"true_daily_confirmed\": y_data_daily,\n",
        "    \"y_daily_mean\": y_shift\n",
        "})\n",
        "\n",
        "w1_ = pred_summary[\"linear1.weight\"]\n",
        "w2_ = pred_summary[\"linear2.weight\"]\n",
        "\n",
        "b1_ = pred_summary[\"linear1.bias\"]\n",
        "b2_ = pred_summary[\"linear2.bias\"]\n",
        "\n",
        "tau_ = pred_summary[\"tau\"]\n",
        "sigma_ = pred_summary[\"sigma\"]\n",
        "\n",
        "ind = int(np.ceil(tau_[\"mean\"] * len(x_data)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([   0.0000,   91.0333,  117.2389,  111.3232,  132.6851,  154.1053,\n",
            "         176.6852,  189.5868,  219.5638,  252.1475,  283.0125,  303.7195,\n",
            "         357.8264,  395.5989,  447.8628,  528.4072,  594.4321,  641.3208,\n",
            "         742.0659,  807.9922,  958.1919, 1067.6152, 1184.2754, 1413.8848,\n",
            "        1539.2334, 1647.6934,  846.7627,  343.4326,  442.3613,  434.3516,\n",
            "         469.3164,  466.8262,  464.2715,  548.7402,  389.9570,  517.7715,\n",
            "         599.3203,  608.7676,  428.4219,  556.6855,  609.4785,  663.9512,\n",
            "         555.2676,  791.9492,  698.9551,  444.7305,  645.4590,  683.9355,\n",
            "         779.8301,  730.1484,  674.6992,  931.6797,  799.6855,  783.1582,\n",
            "         930.9648,  828.2617,  765.9258,  992.9492,  900.7305, 1058.3867])\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",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "813cc59e-7471-457a-f5b6-4553e573da62"
      },
      "source": [
        "mcmc.summary()\n",
        "diag = mcmc.diagnostics()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "                         mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
            "                tau      0.42      0.02      0.42      0.40      0.45     34.82      1.13\n",
            "              sigma      0.05      0.01      0.05      0.04      0.07     20.31      1.04\n",
            "linear1.weight[0,0]      0.12      0.00      0.12      0.12      0.13     16.22      1.00\n",
            "       linear1.bias      6.47      0.04      6.47      6.40      6.54     15.20      1.00\n",
            "linear2.weight[0,0]      0.03      0.00      0.03      0.02      0.03    105.41      1.03\n",
            "       linear2.bias      8.97      0.06      8.96      8.86      9.06     96.12      1.03\n",
            "\n",
            "Number of divergences: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJ2a6Epnsuqf"
      },
      "source": [
        "## Posterior Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPpET7b6suqg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "11aaf3a0-44b8-4c60-da03-b03d37cc01a9"
      },
      "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",
        "             norm_hist = True,\n",
        "             kde = True)\n",
        "plt.axvline(x = w1_[\"mean\"], linestyle = '--',label = \"Mean weight before CP\" ,)\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/sing_weights.pdf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "26\n",
            "2020-04-27 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": "79ba6fcb-428f-4f3f-ef43-858de37e9e36"
      },
      "source": [
        "print(w1_[\"mean\"])\n",
        "print(w2_[\"mean\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[0.1212]])\n",
            "tensor([[0.0261]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vtfu-eSvR5fH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe1b310b-bc70-48f3-e4da-2e9bdf461b6c"
      },
      "source": [
        "1- w2_['mean']/w1_['mean']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.7850]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZCpLZ51R5gU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "47aa6a8a-134f-4d34-e949-e309600364b5"
      },
      "source": [
        "reg_data[reg_data.date== '2020-05-25']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>date</th>\n",
              "      <th>confirmed</th>\n",
              "      <th>deaths</th>\n",
              "      <th>recovered</th>\n",
              "      <th>date_only</th>\n",
              "      <th>daily_confirmed</th>\n",
              "      <th>days_since_start</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>2020-05-25</td>\n",
              "      <td>31960</td>\n",
              "      <td>23</td>\n",
              "      <td>15738</td>\n",
              "      <td>05-25</td>\n",
              "      <td>344</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      country       date  ...  daily_confirmed  days_since_start\n",
              "54  Singapore 2020-05-25  ...              344                55\n",
              "\n",
              "[1 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-eH-TrJKKa2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "f4650791-ffd8-4253-f91f-b3fcd1118490"
      },
      "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(6, \n",
        "              linestyle = '--', linewidth = 1.5,\n",
        "              label = \"Policy start: Apr 7, 2020\" ,\n",
        "              color = \"red\")\n",
        "\n",
        "ax[0].axvline(ind, \n",
        "              linestyle = '--', linewidth = 1.5,\n",
        "              label = \"Date of Change: Apr 27, 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([100,100000])\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=[6,26,39,54], labels=[\"Apr 7\",\n",
        "                                             \"Apr 27\",\n",
        "                                             \"May 10\",\n",
        "                                            \"May 25\"], 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/sing_cp.pdf')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Date of change for Singapore: 2020-04-27\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": "P-RF4xjKoz9k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}