{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/desktop3/itu\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.9.1-CAPI-1.14.2). Conversions between both will be slow.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# load libraries\n",
    "import yaml\n",
    "import lightgbm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import geopandas\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import pickle\n",
    "os.chdir('/home/desktop3/itu')\n",
    "path = os.getcwd()\n",
    "print(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Thailand Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in (yaml) configs\n",
    "with open(path + '/conf/model_config.yaml', 'r') as conf:\n",
    "    model_config = yaml.safe_load(conf)\n",
    "\n",
    "# import data\n",
    "dataset = model_config['model']['loc'] + model_config['model']['file']\n",
    "dataset = pd.read_csv(dataset)\n",
    "\n",
    "# define predictors and target\n",
    "predictor   =  ['avg_d_kbps',\n",
    "       'avg_u_kbps',\n",
    "       'mean_ghm', 'avg_rad_mean',  'cf_cvg_mean',  'slope_avg_rad',\n",
    "       'slope_cf_cvg', 'slope_month_avg_rad',\n",
    "       'slope_monthcf_cvg', 'estimate_mau', 'value_norm']\n",
    "target = ['A4A_right']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Current issue is that predictors are named differently in Thailand than in Brazil dataset and also, \n",
    "it needs to be the same exact predictors used for both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Unnamed: 0.1', 'School', 'source_school_id', 'ENUM',\n",
       "       'H107', 'lon', 'lat', 'range', 'samples', 'avg_d_kbps', 'avg_u_kbps',\n",
       "       'avg_d_kbps3', 'avg_u_kbps3', 'avg_rad_mean', 'cf_cvg_mean',\n",
       "       'slope_avg_rad', 'change_avg_rad', 'slope_cf_cvg', 'change_cf_cvg',\n",
       "       'slope_monthcf_cvg', 'change_monthcf_cvg', 'slope_month_avg_rad',\n",
       "       'change_month_avg_rad', 'slope_NDVI', 'change_NDVI', 'mean_ghm',\n",
       "       'estimate_dau', 'estimate_mau', 'estimate_ready', 'value',\n",
       "       'value_norm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>School</th>\n",
       "      <th>source_school_id</th>\n",
       "      <th>ENUM</th>\n",
       "      <th>H107</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>range</th>\n",
       "      <th>samples</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_month_avg_rad</th>\n",
       "      <th>change_month_avg_rad</th>\n",
       "      <th>slope_NDVI</th>\n",
       "      <th>change_NDVI</th>\n",
       "      <th>mean_ghm</th>\n",
       "      <th>estimate_dau</th>\n",
       "      <th>estimate_mau</th>\n",
       "      <th>estimate_ready</th>\n",
       "      <th>value</th>\n",
       "      <th>value_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (98.2554367 8.6658159)</td>\n",
       "      <td>255542593</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.257309</td>\n",
       "      <td>8.666424</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008125</td>\n",
       "      <td>0.008125</td>\n",
       "      <td>-63.107123</td>\n",
       "      <td>-63.107123</td>\n",
       "      <td>0.556310</td>\n",
       "      <td>4835</td>\n",
       "      <td>5900</td>\n",
       "      <td>True</td>\n",
       "      <td>254.46342</td>\n",
       "      <td>0.804133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (100.5637277 13.7281689)</td>\n",
       "      <td>280389453</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.563824</td>\n",
       "      <td>13.728090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.154787</td>\n",
       "      <td>0.154787</td>\n",
       "      <td>-124.015608</td>\n",
       "      <td>-124.015608</td>\n",
       "      <td>0.891303</td>\n",
       "      <td>1040046</td>\n",
       "      <td>1600000</td>\n",
       "      <td>True</td>\n",
       "      <td>13295.45700</td>\n",
       "      <td>0.908735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>POINT (99.3090438 6.4918845)</td>\n",
       "      <td>321664554</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.309902</td>\n",
       "      <td>6.491847</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003737</td>\n",
       "      <td>0.003737</td>\n",
       "      <td>132.490023</td>\n",
       "      <td>132.490023</td>\n",
       "      <td>0.348923</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>109.93822</td>\n",
       "      <td>0.802974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>POINT (98.99020710000001 18.8422541)</td>\n",
       "      <td>336232077</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.990578</td>\n",
       "      <td>18.842300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029212</td>\n",
       "      <td>0.029212</td>\n",
       "      <td>-52.833499</td>\n",
       "      <td>-52.833499</td>\n",
       "      <td>0.773788</td>\n",
       "      <td>237917</td>\n",
       "      <td>330000</td>\n",
       "      <td>True</td>\n",
       "      <td>1407.96750</td>\n",
       "      <td>0.813386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>POINT (102.773318 16.4357572)</td>\n",
       "      <td>373156854</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102.774895</td>\n",
       "      <td>16.435555</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023249</td>\n",
       "      <td>0.023249</td>\n",
       "      <td>19.408091</td>\n",
       "      <td>19.408091</td>\n",
       "      <td>0.819569</td>\n",
       "      <td>42591</td>\n",
       "      <td>58000</td>\n",
       "      <td>True</td>\n",
       "      <td>548.45380</td>\n",
       "      <td>0.806492</td>\n",
       "    </tr>\n",
       "    <tr>\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>4718</th>\n",
       "      <td>4718</td>\n",
       "      <td>4718</td>\n",
       "      <td>POINT (101.2758538 14.2842555)</td>\n",
       "      <td>8879142203</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.275375</td>\n",
       "      <td>14.284611</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012734</td>\n",
       "      <td>0.012734</td>\n",
       "      <td>43.746437</td>\n",
       "      <td>43.746437</td>\n",
       "      <td>0.599212</td>\n",
       "      <td>10540</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "      <td>145.64415</td>\n",
       "      <td>0.803261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4719</th>\n",
       "      <td>4719</td>\n",
       "      <td>4719</td>\n",
       "      <td>POINT (105.4027044 14.9497545)</td>\n",
       "      <td>8905212408</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>105.407876</td>\n",
       "      <td>14.950656</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001070</td>\n",
       "      <td>0.001070</td>\n",
       "      <td>22.923611</td>\n",
       "      <td>22.923611</td>\n",
       "      <td>0.477034</td>\n",
       "      <td>1486</td>\n",
       "      <td>1700</td>\n",
       "      <td>True</td>\n",
       "      <td>37.91742</td>\n",
       "      <td>0.802397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4720</th>\n",
       "      <td>4720</td>\n",
       "      <td>4720</td>\n",
       "      <td>POINT (100.2320763 16.8519458)</td>\n",
       "      <td>8908633917</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.231919</td>\n",
       "      <td>16.851753</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005698</td>\n",
       "      <td>0.005698</td>\n",
       "      <td>27.090730</td>\n",
       "      <td>27.090730</td>\n",
       "      <td>0.799692</td>\n",
       "      <td>89652</td>\n",
       "      <td>120000</td>\n",
       "      <td>True</td>\n",
       "      <td>1028.61450</td>\n",
       "      <td>0.810343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4721</th>\n",
       "      <td>4721</td>\n",
       "      <td>4721</td>\n",
       "      <td>POINT (99.82662910000001 19.904528)</td>\n",
       "      <td>8931699084</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.826927</td>\n",
       "      <td>19.904137</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048839</td>\n",
       "      <td>0.048839</td>\n",
       "      <td>-143.544324</td>\n",
       "      <td>-143.544324</td>\n",
       "      <td>0.772304</td>\n",
       "      <td>97046</td>\n",
       "      <td>130000</td>\n",
       "      <td>True</td>\n",
       "      <td>2506.85130</td>\n",
       "      <td>0.822200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4722</th>\n",
       "      <td>4722</td>\n",
       "      <td>4722</td>\n",
       "      <td>POINT (99.8260198 19.9058235)</td>\n",
       "      <td>8931701767</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.825790</td>\n",
       "      <td>19.905846</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050350</td>\n",
       "      <td>0.050350</td>\n",
       "      <td>-144.775981</td>\n",
       "      <td>-144.775981</td>\n",
       "      <td>0.771019</td>\n",
       "      <td>97944</td>\n",
       "      <td>130000</td>\n",
       "      <td>True</td>\n",
       "      <td>2071.77270</td>\n",
       "      <td>0.818710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4723 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  Unnamed: 0.1                                School  \\\n",
       "0              0             0          POINT (98.2554367 8.6658159)   \n",
       "1              1             1        POINT (100.5637277 13.7281689)   \n",
       "2              2             2          POINT (99.3090438 6.4918845)   \n",
       "3              3             3  POINT (98.99020710000001 18.8422541)   \n",
       "4              4             4         POINT (102.773318 16.4357572)   \n",
       "...          ...           ...                                   ...   \n",
       "4718        4718          4718        POINT (101.2758538 14.2842555)   \n",
       "4719        4719          4719        POINT (105.4027044 14.9497545)   \n",
       "4720        4720          4720        POINT (100.2320763 16.8519458)   \n",
       "4721        4721          4721   POINT (99.82662910000001 19.904528)   \n",
       "4722        4722          4722         POINT (99.8260198 19.9058235)   \n",
       "\n",
       "      source_school_id  ENUM  H107         lon        lat  range  samples  \\\n",
       "0            255542593  11.0   1.0   98.257309   8.666424    1.0      1.0   \n",
       "1            280389453  11.0   1.0  100.563824  13.728090    1.0     12.0   \n",
       "2            321664554  11.0   1.0   99.309902   6.491847    1.0      4.0   \n",
       "3            336232077  14.0   1.0   98.990578  18.842300    1.0      1.0   \n",
       "4            373156854  14.0   1.0  102.774895  16.435555    1.0     22.0   \n",
       "...                ...   ...   ...         ...        ...    ...      ...   \n",
       "4718        8879142203  11.0   1.0  101.275375  14.284611    1.0      3.0   \n",
       "4719        8905212408  11.0   1.0  105.407876  14.950656    1.0      7.0   \n",
       "4720        8908633917  14.0   1.0  100.231919  16.851753    1.0     11.0   \n",
       "4721        8931699084  14.0   1.0   99.826927  19.904137    1.0      4.0   \n",
       "4722        8931701767  14.0   1.0   99.825790  19.905846    1.0      1.0   \n",
       "\n",
       "      ...  slope_month_avg_rad  change_month_avg_rad  slope_NDVI  change_NDVI  \\\n",
       "0     ...             0.008125              0.008125  -63.107123   -63.107123   \n",
       "1     ...             0.154787              0.154787 -124.015608  -124.015608   \n",
       "2     ...             0.003737              0.003737  132.490023   132.490023   \n",
       "3     ...             0.029212              0.029212  -52.833499   -52.833499   \n",
       "4     ...             0.023249              0.023249   19.408091    19.408091   \n",
       "...   ...                  ...                   ...         ...          ...   \n",
       "4718  ...             0.012734              0.012734   43.746437    43.746437   \n",
       "4719  ...             0.001070              0.001070   22.923611    22.923611   \n",
       "4720  ...             0.005698              0.005698   27.090730    27.090730   \n",
       "4721  ...             0.048839              0.048839 -143.544324  -143.544324   \n",
       "4722  ...             0.050350              0.050350 -144.775981  -144.775981   \n",
       "\n",
       "      mean_ghm  estimate_dau  estimate_mau  estimate_ready        value  \\\n",
       "0     0.556310          4835          5900            True    254.46342   \n",
       "1     0.891303       1040046       1600000            True  13295.45700   \n",
       "2     0.348923             0             0           False    109.93822   \n",
       "3     0.773788        237917        330000            True   1407.96750   \n",
       "4     0.819569         42591         58000            True    548.45380   \n",
       "...        ...           ...           ...             ...          ...   \n",
       "4718  0.599212         10540         14000            True    145.64415   \n",
       "4719  0.477034          1486          1700            True     37.91742   \n",
       "4720  0.799692         89652        120000            True   1028.61450   \n",
       "4721  0.772304         97046        130000            True   2506.85130   \n",
       "4722  0.771019         97944        130000            True   2071.77270   \n",
       "\n",
       "      value_norm  \n",
       "0       0.804133  \n",
       "1       0.908735  \n",
       "2       0.802974  \n",
       "3       0.813386  \n",
       "4       0.806492  \n",
       "...          ...  \n",
       "4718    0.803261  \n",
       "4719    0.802397  \n",
       "4720    0.810343  \n",
       "4721    0.822200  \n",
       "4722    0.818710  \n",
       "\n",
       "[4723 rows x 32 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Shape: (4723, 11)\n"
     ]
    }
   ],
   "source": [
    "# prepare data\n",
    "X = dataset[predictor]\n",
    "# y = dataset[target]\n",
    "print('X Shape:', X.shape)\n",
    "# print('y Shape:', y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reloading saved pickel model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow\n",
    "logged_model = 'file:///files/mlruns/78/add3053875954bc494facaa833b20746/artifacts/model'\n",
    "\n",
    "# Load model as a PyFuncModel.\n",
    "loaded_model = mlflow.pyfunc.load_model(logged_model)\n",
    "\n",
    "# Predict on a Pandas DataFrame.\n",
    "import pandas as pd\n",
    "y_pred = loaded_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.6241376 , 0.67853665, 0.7255879 , ..., 0.62164384, 0.7443871 ,\n",
       "       0.74196595], dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the Model\n",
    "Can't do the below because don't have ground truth on the same level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-471cdd741ba6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Absolute error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mavg_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#Low tail error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'y' is not defined"
     ]
    }
   ],
   "source": [
    "# Absolute error\n",
    "errors = abs(y_pred - y.iloc[:,0].to_numpy())\n",
    "avg_error = np.mean(errors)\n",
    "\n",
    "#Low tail error\n",
    "errors_low = abs(y_pred[y_pred<0.3] - np.asarray(y[y_pred<0.3]).flatten())\n",
    "\n",
    "#Low tail error\n",
    "errors_low_ytest = abs(y_pred[np.asarray(y).flatten()<0.3] - np.asarray(y[np.asarray(y).flatten()<0.3]).flatten())\n",
    "\n",
    "#avg error\n",
    "avg_error_low = np.mean(errors_low)\n",
    "\n",
    "\n",
    "#avg error\n",
    "avg_error_low_ytest = np.mean(errors_low)\n",
    "\n",
    "#standard deviation\n",
    "stan_dev_low= np.std(errors_low)\n",
    "\n",
    "print('errors: ', errors)\n",
    "print('avg error: ', avg_error)\n",
    "# print('Just the lower errors: ', errors_low)\n",
    "print('Mean lower error: ', avg_error_low)\n",
    "print('Mean ytest lower error: ', avg_error_low_ytest)\n",
    "# print('y test error: ', errors_low_ytest)\n",
    "print('Standard Dev of Low Error: ', stan_dev_low)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Absolute error\n",
    "errors = abs(y_pred - y.iloc[:,0].to_numpy())\n",
    "avg_error = np.mean(errors)\n",
    "\n",
    "#Low tail error\n",
    "errors_low = abs(y_pred[y_pred<0.5] - np.asarray(y[y_pred<0.5]).flatten())\n",
    "\n",
    "#Low tail error\n",
    "errors_low_ytest = abs(pred[np.asarray(y).flatten()<0.5] - np.asarray(y[np.asarray(y).flatten()<0.5]).flatten())\n",
    "\n",
    "#avg error\n",
    "avg_error_low = np.mean(errors_low)\n",
    "\n",
    "\n",
    "#avg error\n",
    "avg_error_low_ytest = np.mean(errors_low)\n",
    "\n",
    "#standard deviation\n",
    "stan_dev_low= np.std(errors_low)\n",
    "\n",
    "print('errors: ', errors)\n",
    "print('avg error: ', avg_error)\n",
    "# print('Just the lower errors: ', errors_low)\n",
    "print('Mean lower error: ', avg_error_low)\n",
    "print('Mean ytest lower error: ', avg_error_low_ytest)\n",
    "# print('y test error: ', errors_low_ytest)\n",
    "print('Standard Dev of Low Error: ', stan_dev_low)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping out the Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding the predictions as a column to the dataset\n",
    "dataset['Predictions']= y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a geodataframe\n",
    "from shapely import wkt\n",
    "#Changing dataframe into a geodataframe focused on the school geometry column\n",
    "dataset['School'] = geopandas.GeoSeries.from_wkt(dataset['School'])\n",
    "gdf = geopandas.GeoDataFrame(dataset, geometry='School')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4723, 33)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Set the figure size\n",
    "fig,ax =plt.subplots(1, figsize=(15,10))\n",
    "\n",
    "#plotting with the new geodataframe, each school point by prediction value\n",
    "gdf.plot(column=\"Predictions\", cmap = 'viridis' ,legend=True, ax=ax)\n",
    "\n",
    "# add a title and annotation\n",
    "plt.suptitle('Predictions for all Thailand schools', fontsize=18, fontweight=3)\n",
    "plt.title('4723 schools', fontsize=13)\n",
    "\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Schools below 50% because there were no schools below 30%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97, 33)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Low_pred = gdf.loc[gdf['Predictions']<.5]\n",
    "Low_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax =plt.subplots(1, figsize=(15,10))\n",
    "\n",
    "# add a title and annotation\n",
    "# ax.set_title('Predictions for Schools below 50% internet connectivity', fontdict={'fontsize': '13', 'fontweight' : '3'})\n",
    "# ax.set_suptitle('69 schools')\n",
    "\n",
    "Low_pred.plot(column=\"Predictions\", cmap = 'viridis' ,legend=True, ax=ax)\n",
    "plt.suptitle('Predictions for Schools below 50% internet connectivity', fontsize=18, fontweight=3)\n",
    "plt.title('97 schools', fontsize=13)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}