death_predictions_gam.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# death_predictions_gam\n",
    "\n",
    "\n",
    "## Usando modelos de Séries Temporais para prever mortes no Brasil\n",
    "\n",
    "Vamos tentar modelar o número de mortes no Brasil usando alguns modelos de séries temporais modernos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Começamos importando as bibliotecas necessárias. Condensamos boa parte do código no script `core.py`, para facilitar a utilização desse notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"axes.titlelocation\" on line 298 in\n",
      "/Users/guilherme.marmerola/Documents/Repositories/realtime_r0_brazil/matplotlibrc.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "# importing core \n",
    "# aqui empacotamos as funções que dá pra achar no código do Kevin Systrom\n",
    "from core import load_data, load_johns_hopkins_data, estimate_gam, fit_gam\n",
    "\n",
    "# imports básicos\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import timedelta\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import dates as mdates\n",
    "\n",
    "# modelos\n",
    "from pygam import GammaGAM, PoissonGAM, s, l\n",
    "from sklearn.utils import resample\n",
    "from scipy.stats import dirichlet\n",
    "from sklearn.metrics import mean_squared_log_error, mean_absolute_error\n",
    "\n",
    "# para plots com uma melhor resolução\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "# style of plot\n",
    "plt.style.use('bmh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Leitura dos dados\n",
    "\n",
    "Usamos o [repositório do Wesley Cota](https://github.com/wcota/covid19br) para pegar os dados mais atualizados por estado. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### reading data ###\n",
    "\n",
    "# lendo do repositório do Wesley Cota\n",
    "city_df, state_df = load_data()\n",
    "\n",
    "# lendo do repositório da johns hopkins\n",
    "country_df = load_johns_hopkins_data()\n",
    "\n",
    "# tratamento de números inválidos\n",
    "country_df = country_df.clip(lower=0)\n",
    "#country_df[country_df < 0] = np.nan\n",
    "#country_df = country_df.groupby('country').fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generalized Additive Models\n",
    "\n",
    "Usamos a biblioteca `pygam`, que implementa Modelos Generalizados Aditivos (GAMs), modelos bastante expressivos que usam B-splines penalizadas como *feature functions*, permitindo-os capturar relações não lineares. No nosso caso, como estamos modelando séries temporais, podemos enxergar a expressividade do modelo como memória: quanto mais expressivo o modelo, menos memória ele terá, e usará informação mais recente. GAMs estão em linha com [melhores práticas de previsão de séries temporais](https://peerj.com/preprints/3190/) por conta da sua flexibilidade e expressividade.\n",
    "\n",
    "Outro benefício de GAMs é permitir a modelagem de distribuições de erro não gaussianas. No nosso caso, isto é importante: como estamos modelando número de mortes, uma distribuição normal para o erro não deve funcionar já que não podemos admitir valores menores que 0. Em geral, a literatura (inclusive a epidemiológica) sugere o uso de uma distribuição *Poisson*. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ajuste\n",
    "\n",
    "Como GAMs são modelos expressivos, estabilizamos o modelo usando um procedimento de Bagging (Bootstrap Aggregating) modificado. Ao invés de utilizar reamostragem com reposição comum, mantemos todos os datapoints mas ponderamos seu peso na otimização (`weights`) com amostras de uma distribuição Dirichlet com parametrização uniforme. \n",
    "\n",
    "Tal procedimento é chamado de [*bayesian (smooth) bootstrap*](https://projecteuclid.org/euclid.aos/1176345338) e cria estimativas de boostrap mais suaves, o que é importante quando temos poucas observações (como é o caso). Assim, ao mesmo tempo, temos uma aproximação da incerteza do modelo e reduzimos *overfitting*. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abaixo mostramos uma função para ajustar a GAM, implementando o smooth boostrap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_gam(series, n_splines=25, algo=PoissonGAM, n_bootstrap=100):\n",
    "    \n",
    "    X = np.arange(series.shape[0])\n",
    "    y = series.values\n",
    "\n",
    "    # running GAM in bootstrap\n",
    "    bootstrap = []\n",
    "    for _ in range(n_bootstrap):\n",
    "\n",
    "        weights = dirichlet([1] * series.shape[0]).rvs(1)\n",
    "\n",
    "        gam = algo(s(0, n_splines) + l(0))\n",
    "        gam.fit(X, y, weights=weights[0])\n",
    "\n",
    "        bootstrap.append(gam)\n",
    "\n",
    "    return bootstrap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Encontrando `n_splines`\n",
    "\n",
    "Um hiperparâmetro importante para GAMs é o `n_splines`, o número de splines que vão ser usadas. Quanto maior esse número, mais expressivo será o modelo, podendo ajustar padrões mais não-lineares, mas ao mesmo tempo aumentando o risco de *overfitting*. Dessa forma, antes de ajustar o modelo aos dados do Brasil, vamos este hiperparâmetro `n_splines`, usando um esquema de validação com outros países.\n",
    "\n",
    "Abaixo criamos uma função para avaliar uma configuração de `n_splines` em países selecionados. Usamos como métrica de validação o [MASE (Mean Absolute Scaled Error)](https://en.wikipedia.org/wiki/Mean_absolute_scaled_error), que representa a razão entre o erro do nosso modelo e o erro de um modelo simples (baseline), que neste caso vamos escolher como um modelo que prevê a repetição do valor do dia anterior. \n",
    "\n",
    "Dessa forma, se o MASE for menor que 1, quer dizer que nosso modelo ganha de um que repete o valor do dia anterior como previsão. A validação cria um dataset de teste consistindo dos últimos `N_DAYS_TEST` dias mais recentes e um dataset de treino excluindo esses dias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# número de dias para testar\n",
    "N_DAYS_TEST = 3.0\n",
    "\n",
    "# lista de países\n",
    "country_list = ['Argentina','Australia','Korea, South',\n",
    "                'Italy','Spain','France','United Kingdom',\n",
    "                'Portugal','US','Germany']\n",
    "\n",
    "# data do test\n",
    "test_date = country_df.index.get_level_values(1).max() - timedelta(N_DAYS_TEST)\n",
    "\n",
    "train = country_df.loc[country_list].loc[lambda x: x.index.get_level_values(1) < test_date]['deaths_new'].dropna()\n",
    "test = country_df.loc[country_list].loc[lambda x: x.index.get_level_values(1) >= test_date]['deaths_new'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# função para avaliar resultados nos países\n",
    "def evaluate_hypeparameter(n_splines, n_bootstrap=10):\n",
    "    \n",
    "    # for numerical reasons \n",
    "    epsilon = 10\n",
    "    \n",
    "    # lista de países\n",
    "    countries = train.index.get_level_values(0).unique()\n",
    "\n",
    "    # inicializando lista de erros\n",
    "    error_list = []\n",
    "\n",
    "    # loop para cada país\n",
    "    for country in countries:\n",
    "        \n",
    "        # separando treino e teste para o país específico\n",
    "        temp_train = train.loc[country] + epsilon\n",
    "        temp_test = test.loc[country] \n",
    "\n",
    "        # ajustando\n",
    "        bootstrap = fit_gam(temp_train, n_splines=n_splines, n_bootstrap=20)\n",
    "\n",
    "        # grid para o teste\n",
    "        test_grid = (temp_test.index - temp_train.index.min()).days.values\n",
    "\n",
    "        # prevendo\n",
    "        preds = pd.DataFrame([m.predict(test_grid) - epsilon for m in bootstrap]).T\n",
    "        mean_preds = preds.mean(axis=1)\n",
    "\n",
    "        # comparando e salvando\n",
    "        baseline_pred = temp_test.shift(1).fillna(temp_train.iloc[-1])\n",
    "        error_baseline = mean_absolute_error(temp_test, baseline_pred)\n",
    "        error = mean_absolute_error(temp_test, mean_preds)\n",
    "        error_list.append(error/error_baseline)\n",
    "        \n",
    "        #error = mean_squared_log_error(temp_test, mean_preds)\n",
    "        #error_list.append(error)\n",
    "\n",
    "    return pd.DataFrame(error_list, index=countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Executamos a função para avaliar um intervalo grande `grid_nsplines` para o nosso hiperparâmetro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# criando grid para número de splines\n",
    "grid_nsplines = np.arange(4, 40, 2)\n",
    "\n",
    "# lista de resultados\n",
    "results_list = []\n",
    "\n",
    "# loop para cada elemento do grid\n",
    "for n_splines in grid_nsplines:\n",
    "    result = evaluate_hypeparameter(n_splines)\n",
    "    results_list.append(result)\n",
    "    \n",
    "# df with results\n",
    "results_df = pd.concat(results_list, axis=1)\n",
    "results_df.columns = grid_nsplines\n",
    "\n",
    "# series with avg result\n",
    "avg_result = results_df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A melhor escolha de `n_splines` fica guardada na variável `best_hp`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# melhor hiperparâmetro\n",
    "best_hp = avg_result.idxmin()\n",
    "best_hp_result = avg_result.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os resultados mostram que nosso modelo é razoavelmente melhor do que repetir o valor do dia anterior. Também vemos o melhor valor de `n_splines` abaixo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 551,
       "width": 1169
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizando resultados #\n",
    "\n",
    "# opening figure\n",
    "fig, ax = plt.subplots(figsize=(12, 5), dpi=120)\n",
    "\n",
    "# plotting mean predictions\n",
    "plt.scatter([best_hp],[best_hp_result], color='tomato', s=500, zorder=10, marker='*',\n",
    "            label= f'Melhor resultado: MASE = {np.round(best_hp_result,3)}, com n_splines = {best_hp}')\n",
    "plt.plot(avg_result.index, avg_result, 'k')\n",
    "plt.plot([grid_nsplines.min(), grid_nsplines.max()], [1,1], 'g--', linewidth=1, label='MASE baseline (prever igual ao dia anterior)')\n",
    "\n",
    "# more formatting\n",
    "plt.title('Otimização de hiperparâmetros do modelo: n_splines');\n",
    "plt.ylim(best_hp_result - 0.1, 3)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Série temporal\n",
    "\n",
    "Começamos separando uma série temporal de mortes para o Brasil. Modificando `STATE_NAME` e a linha `[['deaths_new']]` é possível modelar outras séries (e.g. casos confirmados) e em outras quebras (e.g. estado de São Paulo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nome do estado\n",
    "STATE_NAME = 'Brazil'\n",
    "\n",
    "# filtrando\n",
    "series = (state_df\n",
    "          #country_df\n",
    "          .loc[STATE_NAME]\n",
    "          [['deaths_new']]\n",
    "          .dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ajustamos o modelo usando a parametrização ótima que descobrimos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ajuste\n",
    "bootstrap = fit_gam(series, n_splines=best_hp, n_bootstrap=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Previsão\n",
    "\n",
    "Fazemos a previsão para um horizonte determinado (`HORIZON`). Como fizemos o procidimento de bootstrap teremos `N_MODELS` previsões, onde podemos tirar o valor esperado (média das previsões dos modelos individuais) e intervalos de previsão (percentis das previsões dos modelos individuais)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction horizon in days\n",
    "HORIZON = 7\n",
    "\n",
    "# prediction grid\n",
    "grid = np.arange(series.shape[0] + HORIZON)\n",
    "\n",
    "# date grid\n",
    "ini_date = series.index.min()\n",
    "grid_dates = [ini_date + timedelta(float(i)) for i in grid]\n",
    "\n",
    "# predicting\n",
    "preds = pd.DataFrame([m.predict(grid) for m in bootstrap]).T\n",
    "preds.index = grid_dates\n",
    "\n",
    "# getting mean prediction\n",
    "mean_preds = preds.mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resultados\n",
    "\n",
    "Vamos visualizar os resultados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 551,
       "width": 1183
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# opening figure\n",
    "fig, ax = plt.subplots(figsize=(12, 5), dpi=120)\n",
    "\n",
    "# plotting cases\n",
    "plt.scatter(series.index, series, color='tomato', label='Observações')\n",
    "\n",
    "# plotting mean predictions\n",
    "plt.plot(mean_preds.index, mean_preds, 'k--', label='Valor esperado')\n",
    "\n",
    "# plotting posterior samples\n",
    "#plt.plot(preds.index, preds, 'k', alpha=0.03)\n",
    "\n",
    "# plotting confidence interval\n",
    "plt.fill_between(preds.index, \n",
    "                 preds.quantile(0.025, axis=1), \n",
    "                 preds.quantile(0.975, axis=1),\n",
    "                 alpha=0.15, color='black',\n",
    "                 label='Intervalo de previsão 95%')\n",
    "\n",
    "# setting locators\n",
    "ax.xaxis.set_major_locator(mdates.DayLocator(interval=7))\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%d/%b'))\n",
    "ax.xaxis.set_minor_locator(mdates.DayLocator())\n",
    "\n",
    "# more formatting\n",
    "plt.title('COVID-19: Previsão de mortes para o Brasil');\n",
    "plt.xlim(preds.index.min() - timedelta(1.0), \n",
    "         preds.index.max() + timedelta(1.0))\n",
    "plt.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gerando tabela com resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>deaths_new</th>\n",
       "      <th>expected</th>\n",
       "      <th>P10</th>\n",
       "      <th>P90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>583.61</td>\n",
       "      <td>479.77</td>\n",
       "      <td>669.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2020-05-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>626.38</td>\n",
       "      <td>503.44</td>\n",
       "      <td>736.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>672.65</td>\n",
       "      <td>525.49</td>\n",
       "      <td>809.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>722.72</td>\n",
       "      <td>549.41</td>\n",
       "      <td>890.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>776.93</td>\n",
       "      <td>567.79</td>\n",
       "      <td>980.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  deaths_new  expected     P10     P90\n",
       "0  2020-02-25         0.0      0.01    0.01    0.02\n",
       "1  2020-02-26         0.0      0.02    0.01    0.02\n",
       "2  2020-02-27         0.0      0.02    0.01    0.03\n",
       "3  2020-02-28         0.0      0.03    0.02    0.04\n",
       "4  2020-02-29         0.0      0.03    0.02    0.04\n",
       "..        ...         ...       ...     ...     ...\n",
       "69 2020-05-04         NaN    583.61  479.77  669.38\n",
       "70 2020-05-05         NaN    626.38  503.44  736.11\n",
       "71 2020-05-06         NaN    672.65  525.49  809.50\n",
       "72 2020-05-07         NaN    722.72  549.41  890.23\n",
       "73 2020-05-08         NaN    776.93  567.79  980.57\n",
       "\n",
       "[74 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table with results\n",
    "results = (pd.concat([series, \n",
    "                      mean_preds, \n",
    "                      preds.quantile(0.10, axis=1), \n",
    "                      preds.quantile(0.90, axis=1)],\n",
    "                     axis=1)\n",
    "           .reset_index()\n",
    "           .rename(columns={0:'expected',0.1:'P10',0.9:'P90','index':'date'})\n",
    "           .round(2)\n",
    "          )\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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