1_feature_enginnering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Feature Engineering for different cutoffs\n",
    "\n",
    "Generate the village level measures (20% cutoffs, avearge, binary_safe, binary_bad, etc.) and generate village level dataset for later use\n",
    "\n",
    "summary stats and pie plots generated as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up Notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# We do this to ignore several specific Pandas warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "# Standard imports\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "from matplotlib import cm\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV\n",
    "from sklearn.calibration import calibration_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "    \n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix,explained_variance_score\n",
    "from sklearn.metrics import mean_absolute_error,mean_squared_error,median_absolute_error,r2_score\n",
    "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import accuracy_score,classification_report\n",
    "from sklearn.metrics import roc_curve,auc,precision_recall_curve\n",
    "\n",
    "from sklearn.utils import resample\n",
    "from imblearn.over_sampling import RandomOverSampler \n",
    "from imblearn.combine import SMOTETomek\n",
    "from imblearn.over_sampling import ADASYN\n",
    "\n",
    "from numpy import mean\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# Read from local \n",
    "mw_hh= pd.read_csv('data/clean/dataset/mw_dataset_hh.csv')\n",
    "tz_hh= pd.read_csv('data/clean/dataset/tz_dataset_hh_nodup.csv')\n",
    "ug_hh= pd.read_csv('data/clean/dataset/ug_dataset_hh_nodup.csv')\n",
    "\n",
    "\n",
    "# Alternatively, read directly from github  \n",
    "\n",
    "# Malawi Data\n",
    "#url = 'https://raw.githubusercontent.com/zhou100/FoodSecurityPrediction/master/data/clean/dataset/mw_dataset_hh.csv'\n",
    "#mw_hh = pd.read_csv(url)\n",
    "\n",
    "# Tanzania Data \n",
    "#url_tz = 'https://raw.githubusercontent.com/zhou100/FoodSecurityPrediction/master/data/clean/dataset/tz_dataset_hh_nodup.csv'\n",
    "#tz_hh = pd.read_csv(url_tz)\n",
    "\n",
    "# Uganda Data \n",
    "#url_ug = 'https://raw.githubusercontent.com/zhou100/FoodSecurityPrediction/master/data/clean/dataset/ug_dataset_hh_nodup.csv'\n",
    "#ug_hh = pd.read_csv(url_ug)\n",
    "\n",
    "\n",
    "\n",
    "mw_hh = mw_hh.drop(['lat_modified','lon_modified','HHID'],axis=1)\n",
    "tz_hh = tz_hh.drop(['yearmon','lat_modified','lon_modified'],axis=1)\n",
    "ug_hh = ug_hh.drop(['lat_modified','lon_modified','HHID'],axis=1)\n",
    "\n",
    "# check for any missing values (should return false)\n",
    "\n",
    "print(ug_hh.isnull().values.any())\n",
    "print(mw_hh.isnull().values.any())\n",
    "print(tz_hh.isnull().values.any())\n",
    "\n",
    "mw_village = mw_hh.groupby(['FS_year','ea_id']).mean()\n",
    "tz_village = tz_hh.groupby(['FS_year','ea_id']).mean()\n",
    "ug_village = ug_hh.groupby(['FS_year','ea_id']).mean()\n",
    "\n",
    "# check for any missing values (should return false)\n",
    "print(ug_village.isnull().values.any())\n",
    "print(tz_village.isnull().values.any())\n",
    "print(mw_village.isnull().values.any())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>FCS</th>\n",
       "      <th>HDDS</th>\n",
       "      <th>rCSI</th>\n",
       "      <th>region_north</th>\n",
       "      <th>region_central</th>\n",
       "      <th>rural</th>\n",
       "      <th>dist_road</th>\n",
       "      <th>dist_admarc</th>\n",
       "      <th>dist_popcenter</th>\n",
       "      <th>percent_ag</th>\n",
       "      <th>...</th>\n",
       "      <th>cropyear</th>\n",
       "      <th>raincytot</th>\n",
       "      <th>day1rain</th>\n",
       "      <th>maxdaysnorain</th>\n",
       "      <th>lhz_day1rain</th>\n",
       "      <th>gdd</th>\n",
       "      <th>tmean</th>\n",
       "      <th>lhz_raincytot</th>\n",
       "      <th>lhz_maxdaysnorain</th>\n",
       "      <th>heatdays</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FS_year</th>\n",
       "      <th>ea_id</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",
       "      <th></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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010</th>\n",
       "      <th>10101016</th>\n",
       "      <td>42.71875</td>\n",
       "      <td>5.3750</td>\n",
       "      <td>3.6250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.656875</td>\n",
       "      <td>12.016250</td>\n",
       "      <td>62.406875</td>\n",
       "      <td>20.875000</td>\n",
       "      <td>...</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1031.634971</td>\n",
       "      <td>38.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>21.395283</td>\n",
       "      <td>1127.998114</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10101204</th>\n",
       "      <td>35.21875</td>\n",
       "      <td>4.9375</td>\n",
       "      <td>2.6875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.513750</td>\n",
       "      <td>10.821875</td>\n",
       "      <td>58.466875</td>\n",
       "      <td>48.687500</td>\n",
       "      <td>...</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1190.633367</td>\n",
       "      <td>38.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>21.395283</td>\n",
       "      <td>1127.998114</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10101215</th>\n",
       "      <td>41.84375</td>\n",
       "      <td>5.0625</td>\n",
       "      <td>2.4375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.275333</td>\n",
       "      <td>4.241333</td>\n",
       "      <td>91.712667</td>\n",
       "      <td>34.933333</td>\n",
       "      <td>...</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1017.861933</td>\n",
       "      <td>37.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>21.395283</td>\n",
       "      <td>1127.998114</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10101374</th>\n",
       "      <td>35.71875</td>\n",
       "      <td>4.7500</td>\n",
       "      <td>1.5625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.185625</td>\n",
       "      <td>5.985625</td>\n",
       "      <td>88.648749</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1037.343927</td>\n",
       "      <td>37.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>21.395283</td>\n",
       "      <td>1127.998114</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10101470</th>\n",
       "      <td>39.78125</td>\n",
       "      <td>5.1250</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.776875</td>\n",
       "      <td>13.585000</td>\n",
       "      <td>82.332500</td>\n",
       "      <td>54.312500</td>\n",
       "      <td>...</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>981.030130</td>\n",
       "      <td>38.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>21.395283</td>\n",
       "      <td>1127.998114</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2017</th>\n",
       "      <th>31546870</th>\n",
       "      <td>63.50000</td>\n",
       "      <td>5.0625</td>\n",
       "      <td>5.2500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>...</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>315.202517</td>\n",
       "      <td>48.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>28.032912</td>\n",
       "      <td>300.729923</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31548872</th>\n",
       "      <td>62.34375</td>\n",
       "      <td>4.7500</td>\n",
       "      <td>6.1875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.625000</td>\n",
       "      <td>10.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>315.202517</td>\n",
       "      <td>48.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>28.032912</td>\n",
       "      <td>300.729923</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31551874</th>\n",
       "      <td>75.37500</td>\n",
       "      <td>5.5000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.062500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>303.914782</td>\n",
       "      <td>48.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>28.032912</td>\n",
       "      <td>300.729923</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31553876</th>\n",
       "      <td>59.75000</td>\n",
       "      <td>4.8750</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.312500</td>\n",
       "      <td>8.687500</td>\n",
       "      <td>3.812500</td>\n",
       "      <td>3.375000</td>\n",
       "      <td>...</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>297.951362</td>\n",
       "      <td>48.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>28.501462</td>\n",
       "      <td>297.058074</td>\n",
       "      <td>53.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31555878</th>\n",
       "      <td>60.03125</td>\n",
       "      <td>5.0625</td>\n",
       "      <td>5.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>16.937500</td>\n",
       "      <td>...</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>315.923125</td>\n",
       "      <td>48.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>28.501462</td>\n",
       "      <td>297.058074</td>\n",
       "      <td>53.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1754 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       FCS    HDDS    rCSI  region_north  region_central  \\\n",
       "FS_year ea_id                                                              \n",
       "2010    10101016  42.71875  5.3750  3.6250           1.0             0.0   \n",
       "        10101204  35.21875  4.9375  2.6875           1.0             0.0   \n",
       "        10101215  41.84375  5.0625  2.4375           1.0             0.0   \n",
       "        10101374  35.71875  4.7500  1.5625           1.0             0.0   \n",
       "        10101470  39.78125  5.1250  0.6250           1.0             0.0   \n",
       "...                    ...     ...     ...           ...             ...   \n",
       "2017    31546870  63.50000  5.0625  5.2500           0.0             0.0   \n",
       "        31548872  62.34375  4.7500  6.1875           0.0             0.0   \n",
       "        31551874  75.37500  5.5000  0.0000           0.0             0.0   \n",
       "        31553876  59.75000  4.8750  4.0000           0.0             0.0   \n",
       "        31555878  60.03125  5.0625  5.4375           0.0             0.0   \n",
       "\n",
       "                  rural  dist_road  dist_admarc  dist_popcenter  percent_ag  \\\n",
       "FS_year ea_id                                                                 \n",
       "2010    10101016    1.0   2.656875    12.016250       62.406875   20.875000   \n",
       "        10101204    1.0   0.513750    10.821875       58.466875   48.687500   \n",
       "        10101215    1.0   9.275333     4.241333       91.712667   34.933333   \n",
       "        10101374    1.0   1.185625     5.985625       88.648749   36.000000   \n",
       "        10101470    1.0   0.776875    13.585000       82.332500   54.312500   \n",
       "...                 ...        ...          ...             ...         ...   \n",
       "2017    31546870    0.0   1.062500     2.000000       11.000000    1.125000   \n",
       "        31548872    0.0   1.000000     1.625000       10.125000    0.000000   \n",
       "        31551874    0.0   1.000000     4.000000        4.062500    0.000000   \n",
       "        31553876    0.0   1.312500     8.687500        3.812500    3.375000   \n",
       "        31555878    0.0   0.000000     2.312500        9.500000   16.937500   \n",
       "\n",
       "                  ...  cropyear    raincytot  day1rain  maxdaysnorain  \\\n",
       "FS_year ea_id     ...                                                   \n",
       "2010    10101016  ...    2009.0  1031.634971      38.0           97.0   \n",
       "        10101204  ...    2009.0  1190.633367      38.0           95.0   \n",
       "        10101215  ...    2009.0  1017.861933      37.0           99.0   \n",
       "        10101374  ...    2009.0  1037.343927      37.0           93.0   \n",
       "        10101470  ...    2009.0   981.030130      38.0           97.0   \n",
       "...               ...       ...          ...       ...            ...   \n",
       "2017    31546870  ...    2016.0   315.202517      48.0           52.0   \n",
       "        31548872  ...    2016.0   315.202517      48.0           52.0   \n",
       "        31551874  ...    2016.0   303.914782      48.0           55.0   \n",
       "        31553876  ...    2016.0   297.951362      48.0           54.0   \n",
       "        31555878  ...    2016.0   315.923125      48.0           55.0   \n",
       "\n",
       "                  lhz_day1rain    gdd      tmean  lhz_raincytot  \\\n",
       "FS_year ea_id                                                     \n",
       "2010    10101016          36.0  213.0  21.395283    1127.998114   \n",
       "        10101204          36.0  213.0  21.395283    1127.998114   \n",
       "        10101215          36.0  213.0  21.395283    1127.998114   \n",
       "        10101374          36.0  213.0  21.395283    1127.998114   \n",
       "        10101470          36.0  213.0  21.395283    1127.998114   \n",
       "...                        ...    ...        ...            ...   \n",
       "2017    31546870          48.0  213.0  28.032912     300.729923   \n",
       "        31548872          48.0  213.0  28.032912     300.729923   \n",
       "        31551874          48.0  213.0  28.032912     300.729923   \n",
       "        31553876          48.0  193.0  28.501462     297.058074   \n",
       "        31555878          48.0  193.0  28.501462     297.058074   \n",
       "\n",
       "                  lhz_maxdaysnorain  heatdays  \n",
       "FS_year ea_id                                  \n",
       "2010    10101016               50.0       0.0  \n",
       "        10101204               50.0       0.0  \n",
       "        10101215               50.0       0.0  \n",
       "        10101374               50.0       0.0  \n",
       "        10101470               50.0       0.0  \n",
       "...                             ...       ...  \n",
       "2017    31546870               59.0       0.0  \n",
       "        31548872               59.0       0.0  \n",
       "        31551874               59.0       0.0  \n",
       "        31553876               53.0      20.0  \n",
       "        31555878               53.0      20.0  \n",
       "\n",
       "[1754 rows x 71 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mw_village"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Transform and training pipeline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Generate different outcome variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_20_bad(df, measure):\n",
    "    '''\n",
    "    helper function to categorize continous food measure based on binary cutoffs\n",
    "    This function first classifies household level using the cutoffs \n",
    "    and then classifies whether the village has more than 20% of households in the insecure category\n",
    "    '''\n",
    "    \n",
    "    # define cutoffs of the most food insecure category\n",
    "    \n",
    "    if ( measure == 'FCS'):\n",
    "        labels = [1,0]\n",
    "        bins= [-1,28,200]\n",
    "        \n",
    "    elif (measure == 'rCSI'):   \n",
    "        labels = [0,1]\n",
    "        bins= [-1,17,50]\n",
    "        \n",
    "    categorized = measure + 'binary_bad'\n",
    "    \n",
    "    # cut the continuous into category (household level)\n",
    "    df[categorized] = pd.cut( x= df[measure], bins = bins, labels = labels)\n",
    "    df[categorized] = pd.to_numeric(df[categorized])\n",
    "    \n",
    "    df = df[['ea_id','FS_year',categorized]]\n",
    "    \n",
    "    # count the number of minority and majority cases and calculate the percentage \n",
    "    df = df.groupby(['ea_id', 'FS_year',categorized])[categorized].size().unstack(fill_value=0)\n",
    "\n",
    "    df.columns=['majority','minority']\n",
    "    \n",
    "    df['sum'] = df['majority'] + df['minority']\n",
    "    df['portion'] = df['minority']/df['sum']\n",
    "    \n",
    "    # set the cutoff to be 20% of the total households in the village \n",
    "    \n",
    "    binary = measure + '_20_bad'\n",
    "    df[binary]= np.where(df['portion']>=0.2, 1, 0) \n",
    "\n",
    "    df_result= df[binary].reset_index()\n",
    "    \n",
    "    return df_result \n",
    "\n",
    "def categorize_fs_binary_safe(df, measure):\n",
    "    '''\n",
    "    helper function to categorize continous food measure based on binary cutoffs at the village level\n",
    "    \n",
    "    categorize the most food secure villages vs the rest \n",
    "    \n",
    "    '''\n",
    "    if ( measure == 'FCS'):\n",
    "        labels = [1,0]\n",
    "        bins= [-1,42,200]\n",
    "        \n",
    "    elif (measure == 'rCSI'):   \n",
    "        labels = [0,1]\n",
    "        bins= [-1,4,50]\n",
    "        \n",
    "    categorized = measure + '_binary_safe'\n",
    "    \n",
    "    df[categorized] = pd.cut( x= df[measure], bins = bins, labels = labels)\n",
    "    df[categorized] = pd.to_numeric(df[categorized])\n",
    "\n",
    "    return df \n",
    "\n",
    "def categorize_fs_binary_bad(df, measure):\n",
    "    '''\n",
    "    helper function to categorize continous food measure based on binary cutoffs at the village level\n",
    "    \n",
    "    categorize the most food insecure villages vs the rest \n",
    "    \n",
    "    '''\n",
    "    if ( measure == 'FCS'):\n",
    "        labels = [1,0]\n",
    "        bins= [-1,28,200]\n",
    "        \n",
    "    elif (measure == 'rCSI'):   \n",
    "        labels = [0,1]\n",
    "        bins= [-1,17,50]\n",
    "        \n",
    "    categorized = measure + '_binary_bad'\n",
    "    \n",
    "    df[categorized] = pd.cut( x= df[measure], bins = bins, labels = labels)\n",
    "    df[categorized] = pd.to_numeric(df[categorized])\n",
    "\n",
    "    \n",
    "    return df \n",
    "\n",
    "\n",
    "def categorize_fs_three(df, measure):\n",
    "    '''\n",
    "    helper function \n",
    "    categorize continous food measure based on given cutoffs to cut into three categories \n",
    "    '''\n",
    "    \n",
    "    if ( measure == 'FCS'):\n",
    "        labels = [2,1,0]\n",
    "        bins= [-1,28,42,200]\n",
    "        \n",
    "    elif (measure == 'rCSI'):   \n",
    "        labels = [0,1,2]\n",
    "        bins= [-1,4,17,50]\n",
    "        \n",
    "    categorized = measure + '_3_category'\n",
    "    \n",
    "    df[categorized] = pd.cut( x= df[measure], bins = bins, labels = labels)\n",
    "    \n",
    "    df[categorized] = pd.to_numeric(df[categorized])\n",
    "    \n",
    "    return df \n",
    "\n",
    "def category_percent(df,measure):\n",
    "    '''\n",
    "    helper function to \n",
    "    calculate the percent of a certain food security category (three category cutoffs) in a given village  \n",
    "    \n",
    "    watch out for not counting the NAs    \n",
    "    '''\n",
    "    \n",
    "    categorized = measure + '_3_category'\n",
    "    \n",
    "    # count each category \n",
    "    df_value_counts = df.groupby(['ea_id','FS_year'])[categorized].value_counts()\n",
    "    \n",
    "    # flatten to columns \n",
    "    df_value_counts_name =pd.DataFrame(df_value_counts).rename(columns={categorized: measure+'_count'}) \n",
    "\n",
    "    df_count = df_value_counts_name.reset_index().set_index(['ea_id','FS_year',categorized]).unstack(level=-1).fillna(0).reset_index(level=['FS_year','ea_id'])\n",
    "    \n",
    "    # rename columns \n",
    "    df_count['total'] = df_count[measure+'_count',0]+df_count[measure+'_count',1]+df_count[measure+'_count',2]\n",
    "    df_count[(measure + '_secure_percent')]= df_count[measure+'_count',0]/df_count['total']\n",
    "    df_count[(measure + '_mid_percent')]= df_count[measure+'_count',1]/df_count['total']\n",
    "    df_count[(measure + '_low_percent')]= df_count[measure+'_count',2]/df_count['total']\n",
    "    df_count[(measure + '_mid+low')]= df_count[measure+'_mid_percent']+df_count[measure+'_low_percent']\n",
    "    \n",
    "     \n",
    "    df_percent = df_count[['ea_id','FS_year',(measure+'_mid+low'),(measure+'_low_percent')]]\n",
    "    df_percent.columns= ['ea_id','FS_year',(measure+'_mid+low'),(measure+'_low_percent')]\n",
    "    \n",
    "    return df_percent\n",
    "\n",
    "def village_percent(df_village,df_hh,measure): \n",
    "    '''\n",
    "    helper function to \n",
    "    calculate and merge the percent numbers into village level dfs\n",
    "    '''\n",
    "    \n",
    "    df_hh=categorize_fs_three(df_hh,measure=measure)\n",
    "    \n",
    "    df_percent = category_percent(df_hh,measure=measure)    \n",
    "    \n",
    "    \n",
    "    df_village=categorize_fs_three(df_village,measure=measure)\n",
    "    df_village=categorize_fs_binary_safe(df_village,measure=measure)\n",
    "    df_village=categorize_fs_binary_bad(df_village,measure=measure)\n",
    "        \n",
    "    return df_percent\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the above defined functions to generate the category and percentage features \n",
    "\n",
    "# loop over country and measures\n",
    "df_village_list = [mw_village,tz_village,ug_village]\n",
    "df_hh_list = [mw_hh,tz_hh,ug_hh]\n",
    "measure_list = ['FCS','rCSI']\n",
    " \n",
    "for index in range(len(df_village_list)):\n",
    "      \n",
    "    fcs_percent = village_percent(df_village_list[index],df_hh_list[index],measure='FCS') \n",
    "    fcs_20_bad = categorize_20_bad(df_hh_list[index], 'FCS')\n",
    "    df_village_list[index] = pd.merge(df_village_list[index].reset_index(),fcs_20_bad,on=['ea_id','FS_year'])\n",
    "    df_village_list[index] = pd.merge(df_village_list[index],fcs_percent,on=['ea_id','FS_year'])\n",
    "\n",
    "    # handle the Uganda case where there is no rCSI in the survey\n",
    "    if index<2:\n",
    "        rsci_percent = village_percent(df_village_list[index],df_hh_list[index],measure='rCSI') \n",
    "        rsci_20_bad = categorize_20_bad(df_hh_list[index], 'rCSI')\n",
    "        df_village_list[index] = pd.merge(df_village_list[index].reset_index(),rsci_20_bad,on=['ea_id','FS_year'])\n",
    "        df_village_list[index] = pd.merge(df_village_list[index],rsci_percent,on=['ea_id','FS_year'])\n",
    "                                  \n",
    "\n",
    "mw_village = df_village_list[0]   \n",
    "tz_village = df_village_list[1] \n",
    "ug_village = df_village_list[2] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "mw_village.drop(columns=['index']).to_csv(\"data/clean/dataset/mw_village.csv\",index=False)\n",
    "tz_village.drop(columns=['index']).to_csv(\"data/clean/dataset/tz_village.csv\",index=False)\n",
    "ug_village.to_csv(\"data/clean/dataset/ug_village.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure 2 Pie Charts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x218ced24688>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 20 % cut-off\n",
    "mw_village['FCS_20_bad'].value_counts().plot(kind='pie', title='',autopct = \"%.2f%%\", colors = ['chartreuse', 'aqua','red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x218ce3c3d88>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mw_village['rCSI_20_bad'].value_counts().plot(kind='pie', title='',autopct = \"%.2f%%\", colors = ['chartreuse', 'aqua','red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x218cec82588>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tz_village['FCS_20_bad'].value_counts().plot(kind='pie', title='',autopct = \"%.2f%%\", colors = ['chartreuse', 'aqua','red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x218ce652f08>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tz_village['rCSI_20_bad'].value_counts().plot(kind='pie', title='',autopct = \"%.2f%%\", colors = ['chartreuse', 'aqua','red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x218ce82fdc8>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ug_village['FCS_20_bad'].value_counts().plot(kind='pie', title='',autopct = \"%.2f%%\", colors = ['chartreuse', 'aqua','red'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Table 1 summary Stats by country and year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "mw_village[(mw_village['FS_year']==2010 )| (mw_village['FS_year']==2011)].describe(exclude=object).to_csv(\"mw_10_summarystats.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "mw_village[(mw_village['FS_year']==2013)].describe(exclude=object).to_csv(\"mw_13_summarystats.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "mw_village[(mw_village['FS_year']==2016 )| (mw_village['FS_year']==2017)].describe(exclude=object).to_csv(\"mw_16_summarystats.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}