dengue-benchmark-statsmodels.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DengAI: Predicting Disease Spread - Benchmark\n",
    "\n",
    "Dengue fever is bad. It's real bad. Dengue is a mosquito-borne disease that occurs in tropical and sub-tropical parts of the world. In mild cases, symptoms are similar to the flu: fever, rash and muscle and joint pain. But severe cases are dangerous, and dengue fever can cause severe bleeding, low blood pressure and even death.\n",
    "\n",
    "Because it is carried by mosquitoes, the transmission dynamics of dengue are related to climate variables such as temperature and precipitation. Although the relationship to climate is complex, a growing number of scientists argue that climate change is likely to produce distributional shifts that will have significant public health implications worldwide.\n",
    "\n",
    "This notebook provides a look at the data and how to get started for the [competition](https://www.drivendata.org/competitions/44/)! This notebook is adapted from [benchmark solution](https://www.drivendata.co/blog/dengue-benchmark/) released. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As always, we begin with the sacred `import`'s of data science:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# just for the sake of this blog post!\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Tale of Two Cities\n",
    "\n",
    "![](https://community.drivendata.org/uploads/default/original/1X/4c3a8204d1715b5e2ee24da78abbad1515eccd5f.png)\n",
    "\n",
    "This dataset has two cities in it: San Juan, Puerto Rico (right) and Iquitos, Peru (left). Since we hypothesize that the spread of dengue may follow different patterns between the two, we will divide the dataset, train seperate models for each city, and then join our predictions before making our final submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the provided data\n",
    "train_features = pd.read_csv('./data-processed/dengue_features_train.csv',\n",
    "                             index_col=[0,1,2])\n",
    "\n",
    "train_labels = pd.read_csv('./data-processed/dengue_labels_train.csv',\n",
    "                           index_col=[0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperate data for San Juan\n",
    "sj_train_features = train_features.loc['sj']\n",
    "sj_train_labels = train_labels.loc['sj']\n",
    "\n",
    "# Separate data for Iquitos\n",
    "iq_train_features = train_features.loc['iq']\n",
    "iq_train_labels = train_labels.loc['iq']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "San Juan\n",
      "features:  (936, 21)\n",
      "labels  :  (936, 1)\n",
      "\n",
      "Iquitos\n",
      "features:  (520, 21)\n",
      "labels  :  (520, 1)\n"
     ]
    }
   ],
   "source": [
    "print('San Juan')\n",
    "print('features: ', sj_train_features.shape)\n",
    "print('labels  : ', sj_train_labels.shape)\n",
    "\n",
    "print('\\nIquitos')\n",
    "print('features: ', iq_train_features.shape)\n",
    "print('labels  : ', iq_train_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding the labels\n",
    "\n",
    "The target to be predicted is the `total_cases` for all the given features. As we have already noted that the data is of two distinct cities. However, this data is available for many years for each city. Let us understand it better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000,\n",
       "            2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008],\n",
       "           dtype='int64', name='year')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sj_years = sj_train_features.index.get_level_values(0)\n",
    "sj_years.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010], dtype='int64', name='year')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iq_years = iq_train_features.index.get_level_values(0)\n",
    "iq_years.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is having a temporal aspect to it, such that each sample of our data has the `year` and `weakofyear` as index. Let us see the labels as well and plot and visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year,weekofyear'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "sj_train_labels.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the data can be visualized as a time-series. The predictions have to be on the test-data which would follow the similar suite of having predicted total cases for each combination of `(year, weekofyear)`.\n",
    "\n",
    "We can further drill down and see labels for one year at a time too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_train_labels['year'] = sj_train_labels.index.get_level_values(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_train_labels_1991 = sj_train_labels[sj_train_labels.year==1991]\n",
    "sj_train_labels_1991.drop('year', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year,weekofyear'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sj_train_labels_1991.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_train_labels_1992 = sj_train_labels[sj_train_labels.year==1992]\n",
    "sj_train_labels_1992.drop('year', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year,weekofyear'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sj_train_labels_1992.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_train_labels.drop('year', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use these visualizations to decide on the number of past weeks data to consider in your model for Approach 2. You can also take a look at the distribution of the labels too for deciding the category range for Approach 3. In general, it is always a good idea to understand the data before beginning the predictions and doing some exploratory analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>936.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.180556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>51.381372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>461.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_cases\n",
       "count   936.000000\n",
       "mean     34.180556\n",
       "std      51.381372\n",
       "min       0.000000\n",
       "25%       9.000000\n",
       "50%      19.000000\n",
       "75%      37.000000\n",
       "max     461.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sj_train_labels.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding the features\n",
    "\n",
    "Now that we have understood what is to be predicted, let us move to the features we have.\n",
    "\n",
    "The [problem description](https://www.drivendata.org/competitions/44/page/82/) gives a good overview of the available variables, but we'll look at the head of the data here as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>week_start_date</th>\n",
       "      <th>ndvi_ne</th>\n",
       "      <th>ndvi_nw</th>\n",
       "      <th>ndvi_se</th>\n",
       "      <th>ndvi_sw</th>\n",
       "      <th>precipitation_amt_mm</th>\n",
       "      <th>reanalysis_air_temp_k</th>\n",
       "      <th>reanalysis_avg_temp_k</th>\n",
       "      <th>reanalysis_dew_point_temp_k</th>\n",
       "      <th>reanalysis_max_air_temp_k</th>\n",
       "      <th>...</th>\n",
       "      <th>reanalysis_precip_amt_kg_per_m2</th>\n",
       "      <th>reanalysis_relative_humidity_percent</th>\n",
       "      <th>reanalysis_sat_precip_amt_mm</th>\n",
       "      <th>reanalysis_specific_humidity_g_per_kg</th>\n",
       "      <th>reanalysis_tdtr_k</th>\n",
       "      <th>station_avg_temp_c</th>\n",
       "      <th>station_diur_temp_rng_c</th>\n",
       "      <th>station_max_temp_c</th>\n",
       "      <th>station_min_temp_c</th>\n",
       "      <th>station_precip_mm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1990</th>\n",
       "      <th>18</th>\n",
       "      <td>1990-04-30</td>\n",
       "      <td>0.122600</td>\n",
       "      <td>0.103725</td>\n",
       "      <td>0.198483</td>\n",
       "      <td>0.177617</td>\n",
       "      <td>12.42</td>\n",
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       "      <td>297.742857</td>\n",
       "      <td>292.414286</td>\n",
       "      <td>299.8</td>\n",
       "      <td>...</td>\n",
       "      <td>32.00</td>\n",
       "      <td>73.365714</td>\n",
       "      <td>12.42</td>\n",
       "      <td>14.012857</td>\n",
       "      <td>2.628571</td>\n",
       "      <td>25.442857</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>29.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1990-05-07</td>\n",
       "      <td>0.169900</td>\n",
       "      <td>0.142175</td>\n",
       "      <td>0.162357</td>\n",
       "      <td>0.155486</td>\n",
       "      <td>22.82</td>\n",
       "      <td>298.211429</td>\n",
       "      <td>298.442857</td>\n",
       "      <td>293.951429</td>\n",
       "      <td>300.9</td>\n",
       "      <td>...</td>\n",
       "      <td>17.94</td>\n",
       "      <td>77.368571</td>\n",
       "      <td>22.82</td>\n",
       "      <td>15.372857</td>\n",
       "      <td>2.371429</td>\n",
       "      <td>26.714286</td>\n",
       "      <td>6.371429</td>\n",
       "      <td>31.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1990-05-14</td>\n",
       "      <td>0.032250</td>\n",
       "      <td>0.172967</td>\n",
       "      <td>0.157200</td>\n",
       "      <td>0.170843</td>\n",
       "      <td>34.54</td>\n",
       "      <td>298.781429</td>\n",
       "      <td>298.878571</td>\n",
       "      <td>295.434286</td>\n",
       "      <td>300.5</td>\n",
       "      <td>...</td>\n",
       "      <td>26.10</td>\n",
       "      <td>82.052857</td>\n",
       "      <td>34.54</td>\n",
       "      <td>16.848571</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>26.714286</td>\n",
       "      <td>6.485714</td>\n",
       "      <td>32.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>41.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1990-05-21</td>\n",
       "      <td>0.128633</td>\n",
       "      <td>0.245067</td>\n",
       "      <td>0.227557</td>\n",
       "      <td>0.235886</td>\n",
       "      <td>15.36</td>\n",
       "      <td>298.987143</td>\n",
       "      <td>299.228571</td>\n",
       "      <td>295.310000</td>\n",
       "      <td>301.4</td>\n",
       "      <td>...</td>\n",
       "      <td>13.90</td>\n",
       "      <td>80.337143</td>\n",
       "      <td>15.36</td>\n",
       "      <td>16.672857</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>27.471429</td>\n",
       "      <td>6.771429</td>\n",
       "      <td>33.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1990-05-28</td>\n",
       "      <td>0.196200</td>\n",
       "      <td>0.262200</td>\n",
       "      <td>0.251200</td>\n",
       "      <td>0.247340</td>\n",
       "      <td>7.52</td>\n",
       "      <td>299.518571</td>\n",
       "      <td>299.664286</td>\n",
       "      <td>295.821429</td>\n",
       "      <td>301.9</td>\n",
       "      <td>...</td>\n",
       "      <td>12.20</td>\n",
       "      <td>80.460000</td>\n",
       "      <td>7.52</td>\n",
       "      <td>17.210000</td>\n",
       "      <td>3.014286</td>\n",
       "      <td>28.942857</td>\n",
       "      <td>9.371429</td>\n",
       "      <td>35.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                week_start_date   ndvi_ne   ndvi_nw   ndvi_se   ndvi_sw  \\\n",
       "year weekofyear                                                           \n",
       "1990 18              1990-04-30  0.122600  0.103725  0.198483  0.177617   \n",
       "     19              1990-05-07  0.169900  0.142175  0.162357  0.155486   \n",
       "     20              1990-05-14  0.032250  0.172967  0.157200  0.170843   \n",
       "     21              1990-05-21  0.128633  0.245067  0.227557  0.235886   \n",
       "     22              1990-05-28  0.196200  0.262200  0.251200  0.247340   \n",
       "\n",
       "                 precipitation_amt_mm  reanalysis_air_temp_k  \\\n",
       "year weekofyear                                                \n",
       "1990 18                         12.42             297.572857   \n",
       "     19                         22.82             298.211429   \n",
       "     20                         34.54             298.781429   \n",
       "     21                         15.36             298.987143   \n",
       "     22                          7.52             299.518571   \n",
       "\n",
       "                 reanalysis_avg_temp_k  reanalysis_dew_point_temp_k  \\\n",
       "year weekofyear                                                       \n",
       "1990 18                     297.742857                   292.414286   \n",
       "     19                     298.442857                   293.951429   \n",
       "     20                     298.878571                   295.434286   \n",
       "     21                     299.228571                   295.310000   \n",
       "     22                     299.664286                   295.821429   \n",
       "\n",
       "                 reanalysis_max_air_temp_k  ...  \\\n",
       "year weekofyear                             ...   \n",
       "1990 18                              299.8  ...   \n",
       "     19                              300.9  ...   \n",
       "     20                              300.5  ...   \n",
       "     21                              301.4  ...   \n",
       "     22                              301.9  ...   \n",
       "\n",
       "                 reanalysis_precip_amt_kg_per_m2  \\\n",
       "year weekofyear                                    \n",
       "1990 18                                    32.00   \n",
       "     19                                    17.94   \n",
       "     20                                    26.10   \n",
       "     21                                    13.90   \n",
       "     22                                    12.20   \n",
       "\n",
       "                 reanalysis_relative_humidity_percent  \\\n",
       "year weekofyear                                         \n",
       "1990 18                                     73.365714   \n",
       "     19                                     77.368571   \n",
       "     20                                     82.052857   \n",
       "     21                                     80.337143   \n",
       "     22                                     80.460000   \n",
       "\n",
       "                 reanalysis_sat_precip_amt_mm  \\\n",
       "year weekofyear                                 \n",
       "1990 18                                 12.42   \n",
       "     19                                 22.82   \n",
       "     20                                 34.54   \n",
       "     21                                 15.36   \n",
       "     22                                  7.52   \n",
       "\n",
       "                 reanalysis_specific_humidity_g_per_kg  reanalysis_tdtr_k  \\\n",
       "year weekofyear                                                             \n",
       "1990 18                                      14.012857           2.628571   \n",
       "     19                                      15.372857           2.371429   \n",
       "     20                                      16.848571           2.300000   \n",
       "     21                                      16.672857           2.428571   \n",
       "     22                                      17.210000           3.014286   \n",
       "\n",
       "                 station_avg_temp_c  station_diur_temp_rng_c  \\\n",
       "year weekofyear                                                \n",
       "1990 18                   25.442857                 6.900000   \n",
       "     19                   26.714286                 6.371429   \n",
       "     20                   26.714286                 6.485714   \n",
       "     21                   27.471429                 6.771429   \n",
       "     22                   28.942857                 9.371429   \n",
       "\n",
       "                 station_max_temp_c  station_min_temp_c  station_precip_mm  \n",
       "year weekofyear                                                             \n",
       "1990 18                        29.4                20.0               16.0  \n",
       "     19                        31.7                22.2                8.6  \n",
       "     20                        32.2                22.8               41.4  \n",
       "     21                        33.3                23.3                4.0  \n",
       "     22                        35.0                23.9                5.8  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sj_train_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are _a lot_ of climate variables here, but the first thing that we'll note is that the `week_start_date` is included in the feature set. This makes it easier for competitors to create time based features, but for this first-pass model, let us drop that column since we don't want to use it as a feature in our model, and find weather based features which provide us good predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove `week_start_date` string.\n",
    "sj_train_features.drop('week_start_date', axis=1, inplace=True)\n",
    "iq_train_features.drop('week_start_date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's check to see if we are missing any values in this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ndvi_ne                                  True\n",
       "ndvi_nw                                  True\n",
       "ndvi_se                                  True\n",
       "ndvi_sw                                  True\n",
       "precipitation_amt_mm                     True\n",
       "reanalysis_air_temp_k                    True\n",
       "reanalysis_avg_temp_k                    True\n",
       "reanalysis_dew_point_temp_k              True\n",
       "reanalysis_max_air_temp_k                True\n",
       "reanalysis_min_air_temp_k                True\n",
       "reanalysis_precip_amt_kg_per_m2          True\n",
       "reanalysis_relative_humidity_percent     True\n",
       "reanalysis_sat_precip_amt_mm             True\n",
       "reanalysis_specific_humidity_g_per_kg    True\n",
       "reanalysis_tdtr_k                        True\n",
       "station_avg_temp_c                       True\n",
       "station_diur_temp_rng_c                  True\n",
       "station_max_temp_c                       True\n",
       "station_min_temp_c                       True\n",
       "station_precip_mm                        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null check\n",
    "pd.isnull(sj_train_features).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(sj_train_features\n",
    "     .ndvi_ne\n",
    "     .plot\n",
    "     .line(lw=0.8))\n",
    "\n",
    "plt.title('Vegetation Index over Time')\n",
    "plt.xlabel('Time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since these are time-series, we can see the gaps where there are `NaN`s by plotting the data. Since we can't build a model without those values, we'll take a simple approach and just fill those values with the most recent value that we saw up to that point. This is probably a good part of the problem to improve your score by getting smarter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_train_features.fillna(method='ffill', inplace=True)\n",
    "iq_train_features.fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of labels\n",
    "\n",
    "Our target variable, `total_cases` is a non-negative integer, which means we're looking to make some **count predictions**. To model count data, we generally consider two distributions:\n",
    "\n",
    "1. Poisson regression\n",
    "2. Negative binomial regression\n",
    "\n",
    "Which techniqe will perform better depends on many things, but the choice between Poisson regression and negative binomial regression is pretty straightforward. \n",
    "+ Poisson regression fits according to the assumption that the mean and variance of the population distributions are equal. \n",
    "+ When the variance is much larger than the mean, the negative binomial approach is better. The negative binomial regression simply lifts the assumption that the population mean and variance are equal, allowing for a larger class of possible models. \n",
    "\n",
    "In fact, from this perspective, the Poisson distribution is but a special case of the negative binomial distribution.\n",
    "\n",
    "Let's see how our labels are distributed to be able to make a choice between these two distributions for our labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "San Juan\n",
      "mean:  34.18055555555556\n",
      "var : 2640.045439691045\n",
      "\n",
      "Iquitos\n",
      "mean:  7.565384615384615\n",
      "var : 115.8955239365642\n"
     ]
    }
   ],
   "source": [
    "print('San Juan')\n",
    "print('mean: ', sj_train_labels.mean()[0])\n",
    "print('var :', sj_train_labels.var()[0])\n",
    "\n",
    "print('\\nIquitos')\n",
    "print('mean: ', iq_train_labels.mean()[0])\n",
    "print('var :', iq_train_labels.var()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's looking like a negative-binomial sort of in these parts, seeing that the `mean` << `variance`. Let us also plot these labels and see how the distribution looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'total_cases'}>]], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sj_train_labels.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'total_cases'}>]], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iq_train_labels.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `variance  >>  mean` suggests `total_cases` can be described by a negative binomial distribution, so we'll use a negative binomial regression below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So based on our previous criterion, the choice of Negative Binomial distribution for these labels seems to be suitable.\n",
    "\n",
    "Wikipedia [link](https://en.wikipedia.org/wiki/Negative_binomial_distribution) describes the negative binomial distribution as a discrete probability distribution that models the number of successes (denoted k) in a sequence of independent and identically distributed Bernoulli trials before a specified (non-random) number of failures (denoted r) occurs. In the visualization below from the same link, the orange is the mean, while green line shows the standard deviation.\n",
    "\n",
    "![NGB](https://upload.wikimedia.org/wikipedia/commons/8/83/Negbinomial.gif)\n",
    "\n",
    "Based on the visualization, we can also see how the distribution can model many different cases, including our target variables. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have already noted that Negative Binomial does away with the **mean=variance** assumption present in Poisson. It uses a parameter $\\alpha$ which is used to express the variance in terms of the mean:\n",
    "\n",
    "$$ \\textrm{var} = \\textrm{mean} + \\alpha * \\textrm{mean}$$\n",
    "\n",
    "So, in our modeling, we will have to find the right $\\alpha$ for our distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which inputs strongly correlate with `total_cases`?\n",
    "\n",
    "Our next step in this process will be to select a subset of features to include in our regression. Our primary purpose here is to get a better understanding of the problem domain rather than eke out the last possible bit of predictive accuracy. The first thing we will do is to add the `total_cases` to our dataframe, and then look at the correlation of that variable with the climate variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_train_features['total_cases'] = sj_train_labels.total_cases\n",
    "iq_train_features['total_cases'] = iq_train_labels.total_cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the data correlation matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the correlations\n",
    "sj_correlations = sj_train_features.corr()\n",
    "iq_correlations = iq_train_features.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'San Juan Variable Correlations')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot san juan\n",
    "sj_corr_heat = sns.heatmap(sj_correlations)\n",
    "plt.title('San Juan Variable Correlations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iquitos Variable Correlations')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot iquitos\n",
    "iq_corr_heat = sns.heatmap(iq_correlations)\n",
    "plt.title('Iquitos Variable Correlations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Many of the temperature data are strongly correlated, which is expected. But the `total_cases` variable doesn't have many obvious strong correlations.\n",
    "\n",
    "Interestingly, `total_cases` seems to only have weak correlations with other variables. Many of the climate variables are much more strongly correlated. Interestingly, the vegetation index also only has weak correlation with other variables. These correlations may give us some hints as to how to improve our model. For now, let's take a `sorted` look at `total_cases` correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# San Juan\n",
    "(sj_correlations\n",
    "     .total_cases\n",
    "     .drop('total_cases') # don't compare with myself\n",
    "     .sort_values(ascending=False)\n",
    "     .plot\n",
    "     .barh())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Iquitos\n",
    "(iq_correlations\n",
    "     .total_cases\n",
    "     .drop('total_cases') # don't compare with myself\n",
    "     .sort_values(ascending=False)\n",
    "     .plot\n",
    "     .barh())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A few observations\n",
    "\n",
    "#### The wetter the better\n",
    "* The correlation strengths differ for each city, but it looks like `reanalysis_specific_humidity_g_per_kg` and `reanalysis_dew_point_temp_k` are the most strongly correlated with `total_cases`. This makes sense: we know mosquitos thrive _wet_ climates, the wetter the better!\n",
    "\n",
    "#### Hot and heavy\n",
    "* As we all know, \"cold and humid\" is not a thing. So it's not surprising that as minimum temperatures, maximum temperatures, and average temperatures rise, the `total_cases` of dengue fever tend to rise as well.\n",
    "\n",
    "#### Sometimes it rains, so what\n",
    "* Interestingly, the `precipitation` measurements bear little to no correlation to `total_cases`, despite strong correlations to the `humidity` measurements, as evident by the heatmaps above.\n",
    "\n",
    "### This is just a first pass\n",
    "\n",
    "Precisely _none_ of these correlations are very strong. Of course, that doesn't mean that some **feature engineering wizardry** can't put us in a better place **(`standing_water` estimate, anyone?)**. Also, it's always useful to keep in mind that **life isn't linear**, but out-of-the-box correlation measurement is – or at least, it measures linear dependence.\n",
    "\n",
    "Nevertheless, for this benchmark we'll focus on the linear __wetness__ trend we see above, and reduce our inputs to \n",
    "\n",
    "#### A few good variables\n",
    "\n",
    "* `reanalysis_specific_humidity_g_per_kg`\n",
    "* `reanalysis_dew_point_temp_k`\n",
    "* `station_avg_temp_c`\n",
    "* `station_min_temp_c`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A mosquito model\n",
    "\n",
    "Now that we've explored this data, it's time to start modeling. Our first step will be to build a function that does all of the preprocessing we've done above from start to finish. This will make our lives easier, since it needs to be applied to the test set and the traning set before we make our predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(data_path, labels_path=None):\n",
    "    # load data and set index to city, year, weekofyear\n",
    "    df = pd.read_csv(data_path, index_col=[0, 1, 2])\n",
    "    \n",
    "    # select features we want\n",
    "    features = ['reanalysis_specific_humidity_g_per_kg', \n",
    "                 'reanalysis_dew_point_temp_k', \n",
    "                 'station_avg_temp_c', \n",
    "                 'station_min_temp_c']\n",
    "    df = df[features]\n",
    "    \n",
    "    # fill missing values\n",
    "    df.fillna(method='ffill', inplace=True)\n",
    "\n",
    "    # add labels to dataframe\n",
    "    if labels_path:\n",
    "        labels = pd.read_csv(labels_path, index_col=[0, 1, 2])\n",
    "        df = df.join(labels)\n",
    "    \n",
    "    # separate san juan and iquitos\n",
    "    sj = df.loc['sj']\n",
    "    iq = df.loc['iq']\n",
    "    \n",
    "    return sj, iq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_train, iq_train = preprocess_data('./data-processed/dengue_features_train.csv',\n",
    "                                    labels_path=\"./data-processed/dengue_labels_train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can take a look at the smaller dataset and see that it's ready to start modelling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reanalysis_specific_humidity_g_per_kg</th>\n",
       "      <th>reanalysis_dew_point_temp_k</th>\n",
       "      <th>station_avg_temp_c</th>\n",
       "      <th>station_min_temp_c</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.547535</td>\n",
       "      <td>295.104736</td>\n",
       "      <td>26.999191</td>\n",
       "      <td>22.594017</td>\n",
       "      <td>34.180556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.560663</td>\n",
       "      <td>1.570075</td>\n",
       "      <td>1.415079</td>\n",
       "      <td>1.506281</td>\n",
       "      <td>51.381372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.715714</td>\n",
       "      <td>289.642857</td>\n",
       "      <td>22.842857</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.233571</td>\n",
       "      <td>293.843929</td>\n",
       "      <td>25.842857</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.835000</td>\n",
       "      <td>295.451429</td>\n",
       "      <td>27.214286</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.854286</td>\n",
       "      <td>296.415714</td>\n",
       "      <td>28.175000</td>\n",
       "      <td>23.900000</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.440000</td>\n",
       "      <td>297.795714</td>\n",
       "      <td>30.071429</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>461.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reanalysis_specific_humidity_g_per_kg  reanalysis_dew_point_temp_k  \\\n",
       "count                             936.000000                   936.000000   \n",
       "mean                               16.547535                   295.104736   \n",
       "std                                 1.560663                     1.570075   \n",
       "min                                11.715714                   289.642857   \n",
       "25%                                15.233571                   293.843929   \n",
       "50%                                16.835000                   295.451429   \n",
       "75%                                17.854286                   296.415714   \n",
       "max                                19.440000                   297.795714   \n",
       "\n",
       "       station_avg_temp_c  station_min_temp_c  total_cases  \n",
       "count          936.000000          936.000000   936.000000  \n",
       "mean            26.999191           22.594017    34.180556  \n",
       "std              1.415079            1.506281    51.381372  \n",
       "min             22.842857           17.800000     0.000000  \n",
       "25%             25.842857           21.700000     9.000000  \n",
       "50%             27.214286           22.800000    19.000000  \n",
       "75%             28.175000           23.900000    37.000000  \n",
       "max             30.071429           25.600000   461.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sj_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>reanalysis_specific_humidity_g_per_kg</th>\n",
       "      <th>reanalysis_dew_point_temp_k</th>\n",
       "      <th>station_avg_temp_c</th>\n",
       "      <th>station_min_temp_c</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>520.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>520.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.102019</td>\n",
       "      <td>295.498723</td>\n",
       "      <td>27.506331</td>\n",
       "      <td>21.210385</td>\n",
       "      <td>7.565385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.443048</td>\n",
       "      <td>1.414360</td>\n",
       "      <td>0.908973</td>\n",
       "      <td>1.257734</td>\n",
       "      <td>10.765478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.111429</td>\n",
       "      <td>290.088571</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>14.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.121429</td>\n",
       "      <td>294.596429</td>\n",
       "      <td>26.957500</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.428571</td>\n",
       "      <td>295.852143</td>\n",
       "      <td>27.587500</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.180357</td>\n",
       "      <td>296.557143</td>\n",
       "      <td>28.075000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.461429</td>\n",
       "      <td>298.450000</td>\n",
       "      <td>30.800000</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>116.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reanalysis_specific_humidity_g_per_kg  reanalysis_dew_point_temp_k  \\\n",
       "count                             520.000000                   520.000000   \n",
       "mean                               17.102019                   295.498723   \n",
       "std                                 1.443048                     1.414360   \n",
       "min                                12.111429                   290.088571   \n",
       "25%                                16.121429                   294.596429   \n",
       "50%                                17.428571                   295.852143   \n",
       "75%                                18.180357                   296.557143   \n",
       "max                                20.461429                   298.450000   \n",
       "\n",
       "       station_avg_temp_c  station_min_temp_c  total_cases  \n",
       "count          520.000000          520.000000   520.000000  \n",
       "mean            27.506331           21.210385     7.565385  \n",
       "std              0.908973            1.257734    10.765478  \n",
       "min             21.400000           14.700000     0.000000  \n",
       "25%             26.957500           20.600000     1.000000  \n",
       "50%             27.587500           21.400000     5.000000  \n",
       "75%             28.075000           22.000000     9.000000  \n",
       "max             30.800000           24.200000   116.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iq_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split it up!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is a timeseries model, we'll use a strict-future holdout set when we are splitting our train set and our test set. We'll keep around three quarters of the original data for training and use the rest to test. We'll do this separately for our San Juan model and for our Iquitos model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_train_subtrain = sj_train.head(800)\n",
    "sj_train_subtest = sj_train.tail(sj_train.shape[0] - 800)\n",
    "\n",
    "iq_train_subtrain = iq_train.head(400)\n",
    "iq_train_subtest = iq_train.tail(iq_train.shape[0] - 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training time\n",
    "\n",
    "This is where we start getting down to business. Here we use the **Generalized Linear Model (GLM)**. \n",
    "\n",
    "Simply put, the GLM is a flexible generalization of the linear regression seen in Week 3. What is the \"flexible generalization\"? The error term is allowed to have some other distribution than normal. So, the Logistic Regression can be seen as an example to understand the GLM easily. \n",
    "\n",
    "Let us see the Logistic Regression as an example to understand the GLM easily. The logit is called the *link* function and the distribution of the response variable is the *distribution* of the GLM. You can see the slide from Lecture 3 for your reference.  \n",
    "![](GLM_logit.png)\n",
    "\n",
    "The Linear regression OLS is also just a special case of GLM. \n",
    "\n",
    "So, as we have noted here, the negative binomial distribution is used in this benchmark, because it is often used for count data where the mean and variance are very different. The `statsmodel.formula.api` provides us with `glm` which can be used for doing this modeling, the same way we have done for OLS. The NegativeBinomial family can be mentioned using `statsmodel.families`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best alpha =  1e-08\n",
      "best score =  22.080882352941178\n",
      "best alpha =  1e-08\n",
      "best score =  6.466666666666667\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tools import eval_measures\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "def get_best_model(train, test):\n",
    "    # Step 1: specify the form of the model\n",
    "    model_formula = \"total_cases ~ 1 + \" \\\n",
    "                    \"reanalysis_specific_humidity_g_per_kg + \" \\\n",
    "                    \"reanalysis_dew_point_temp_k + \" \\\n",
    "                    \"station_min_temp_c + \" \\\n",
    "                    \"station_avg_temp_c\"\n",
    "    \n",
    "    grid = 10 ** np.arange(-8, -3, dtype=np.float64)\n",
    "                    \n",
    "    best_alpha = []\n",
    "    best_score = 1000\n",
    "        \n",
    "    # Step 2: Find the best hyper parameter, alpha\n",
    "    for alpha in grid:\n",
    "        model = smf.glm(formula=model_formula,\n",
    "                        data=train,\n",
    "                        family=sm.families.NegativeBinomial(alpha=alpha))\n",
    "\n",
    "        results = model.fit()\n",
    "        predictions = results.predict(test).astype(int)\n",
    "        score = eval_measures.meanabs(predictions, test.total_cases)\n",
    "\n",
    "        if score < best_score:\n",
    "            best_alpha = alpha\n",
    "            best_score = score\n",
    "\n",
    "    print('best alpha = ', best_alpha)\n",
    "    print('best score = ', best_score)\n",
    "            \n",
    "    # Step 3: refit on entire dataset\n",
    "    full_dataset = pd.concat([train, test])\n",
    "    model = smf.glm(formula=model_formula,\n",
    "                    data=full_dataset,\n",
    "                    family=sm.families.NegativeBinomial(alpha=best_alpha))\n",
    "\n",
    "    fitted_model = model.fit()\n",
    "    return fitted_model\n",
    "    \n",
    "sj_best_model = get_best_model(sj_train_subtrain, sj_train_subtest)\n",
    "iq_best_model = get_best_model(iq_train_subtrain, iq_train_subtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26e630c9c10>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figs, axes = plt.subplots(nrows=2, ncols=1)\n",
    "\n",
    "# plot sj\n",
    "sj_train['fitted'] = sj_best_model.fittedvalues\n",
    "sj_train.fitted.plot(ax=axes[0], label=\"Predictions\")\n",
    "sj_train.total_cases.plot(ax=axes[0], label=\"Actual\")\n",
    "\n",
    "# plot iq\n",
    "iq_train['fitted'] = iq_best_model.fittedvalues\n",
    "iq_train.fitted.plot(ax=axes[1], label=\"Predictions\")\n",
    "iq_train.total_cases.plot(ax=axes[1], label=\"Actual\")\n",
    "\n",
    "plt.suptitle(\"Dengue Predicted Cases vs. Actual Cases\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reflecting on our performance\n",
    "\n",
    "These graphs can actually tell us a lot about where our model is going wrong and give us some good hints about where investments will improve the model performance. For example, we see that our model in blue does track the seasonality of Dengue cases. However, the timing of the seasonality of our predictions has a mismatch with the actual results. One potential reason for this is that our features don't look far enough into the past--that is to say, we are asking to predict cases at the same time as we are measuring percipitation. Because dengue is misquito born, and the misquito lifecycle depends on water, we need to take both the life of a misquito and the time between infection and symptoms into account when modeling dengue. This is a critical avenue to explore when improving this model.\n",
    "\n",
    "The other important error is that our predictions are relatively consistent--we miss the spikes that are large outbreaks. One reason is that we don't take into account the contagiousness of dengue. A possible way to account for this is to build a model that progressively predicts a new value while taking into account the previous prediction. By training on the dengue outbreaks and then using the predicted number of patients in the week before, we can start to model this time dependence that the current model misses.\n",
    "\n",
    "So, we know we're not going to win this thing, but let's submit the model anyway!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_test, iq_test = preprocess_data('./data-processed/dengue_features_test.csv')\n",
    "\n",
    "sj_predictions = sj_best_model.predict(sj_test).astype(int)\n",
    "iq_predictions = iq_best_model.predict(iq_test).astype(int)\n",
    "\n",
    "submission = pd.read_csv(\"./data-processed/submission_format.csv\",\n",
    "                         index_col=[0, 1, 2])\n",
    "\n",
    "submission.total_cases = np.concatenate([sj_predictions, iq_predictions])\n",
    "submission.to_csv(\"./data-processed/benchmark.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://community.drivendata.org/uploads/default/original/1X/7af03e4997e8487057a77f9022691b9e9cb525f7.png)\n",
    "\n",
    "Alright, it's a start!\n",
    "\n",
    "Good luck, and enjoy!"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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 },
 "nbformat": 4,
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}