9_auto_regress_ALL_depts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression on Covid-19 incidence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Akin Kazakci, MINES ParisTech, PSL University_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Input:\n",
    "- Observed Covid-19 cases (per department)\n",
    "\n",
    "Output:\n",
    "- Regression baseline model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data can be downloaded from https://www.data.gouv.fr/en/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn import linear_model\n",
    "import xgboost as xgb\n",
    "from xgboost import plot_importance \n",
    "from xgboost.sklearn import XGBRegressor\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will to a simple regregression model per department as a baseline. Data starts at '2020-03-19'. We have 35 points (update with more) per department. This is not much and fitting powerful models is likely to fail. We will use a simple cross-validations scheme, where we shall train on date < '2020-04-08' and test on remaining data.\n",
    "\n",
    "In France, lockdown has started at '2020-03-16', if we assume that it has shown its effect in observed cases starting 14 days later, we would see that effect starting from '2020-04-01'. Adding one more week for the incubation period of new cases within the new dynamics, puts us at our 'split date' ('2020-04-08') for our cross-validation scheme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data.csv', index_col = 0)\n",
    "data = data[data.date >= '2020-03-25']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# update the split as time goes; make sure we are using same split with later notebooks and models\n",
    "dates =  data.date\n",
    "split = '2020-04-20'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['code_insee', 'date', 'incid_hosp', 'date_time', 'length_km',\n",
       "       'movement', 'movement_baseline', 'movement_difference',\n",
       "       'movement_percent_change', 'density_weighted_movement',\n",
       "       ...\n",
       "       'betweenness_centrality-8_16 (t-20)',\n",
       "       'closeness_centrality-8_16 (t-20)', 'degree_centrality-8_16 (t-20)',\n",
       "       'eigenvector_centrality-8_16 (t-20)', 'incid_hosp (t-1)',\n",
       "       'incid_hosp (t-2)', 'incid_hosp (t-3)', 'incid_hosp (t-4)',\n",
       "       'incid_hosp (t-5)', 'incid_hosp (t-6)'],\n",
       "      dtype='object', length=335)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression on aggregated data (all departments combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>code_insee</th>\n",
       "      <th>incid_hosp</th>\n",
       "      <th>incid_hosp (t-1)</th>\n",
       "      <th>incid_hosp (t-2)</th>\n",
       "      <th>incid_hosp (t-3)</th>\n",
       "      <th>incid_hosp (t-4)</th>\n",
       "      <th>incid_hosp (t-5)</th>\n",
       "      <th>incid_hosp (t-6)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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>2020-03-25</th>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>78</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>84</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>84</td>\n",
       "      <td>13.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>78</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           code_insee  incid_hosp  incid_hosp (t-1)  incid_hosp (t-2)  \\\n",
       "date                                                                    \n",
       "2020-03-25         48         1.0             146.0             146.0   \n",
       "2020-03-25         78        80.0              80.0              47.0   \n",
       "2020-03-25         84        13.0              13.0              80.0   \n",
       "2020-03-25         84        13.0              80.0              80.0   \n",
       "2020-03-25         78        80.0              80.0              80.0   \n",
       "\n",
       "            incid_hosp (t-3)  incid_hosp (t-4)  incid_hosp (t-5)  \\\n",
       "date                                                               \n",
       "2020-03-25             146.0               2.0               2.0   \n",
       "2020-03-25              47.0              47.0              18.0   \n",
       "2020-03-25              80.0              80.0              47.0   \n",
       "2020-03-25              80.0              47.0              47.0   \n",
       "2020-03-25              47.0              47.0              47.0   \n",
       "\n",
       "            incid_hosp (t-6)  \n",
       "date                          \n",
       "2020-03-25               3.0  \n",
       "2020-03-25              18.0  \n",
       "2020-03-25              47.0  \n",
       "2020-03-25              47.0  \n",
       "2020-03-25              18.0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[['code_insee','date','incid_hosp','incid_hosp (t-1)',\n",
    "       'incid_hosp (t-2)', 'incid_hosp (t-3)', 'incid_hosp (t-4)',\n",
    "       'incid_hosp (t-5)', 'incid_hosp (t-6)' ]]\n",
    "\n",
    "data = data.sort_values('date', ascending = True).reset_index()\n",
    "#data.drop(data.date, inplace = True, axis = 1)\n",
    "data = data.set_index(dates).drop(['date','index'], axis = 1)\n",
    "\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data.drop('code_insee', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['incid_hosp', 'incid_hosp (t-1)', 'incid_hosp (t-2)',\n",
       "       'incid_hosp (t-3)', 'incid_hosp (t-4)', 'incid_hosp (t-5)',\n",
       "       'incid_hosp (t-6)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_all(target, dept, split, dates):\n",
    "    #we put aside everything after the split date, for ultimate validation\n",
    "    train = dept[dept.index < split].copy()\n",
    "    val = dept[dept.index >= split].copy()\n",
    "\n",
    "    xtr, xts = train.drop([target], axis=1), val.drop([target], axis=1)\n",
    "    print (xtr.shape, xts.shape)\n",
    "    ytr, yts = train[target].values, val[target].values\n",
    "    print (ytr.shape, yts.shape)\n",
    "\n",
    "    # Make sure we are using same time series logic in all pipelines\n",
    "    tscv = TimeSeriesSplit(30)\n",
    "    \n",
    "    # I experimented with various predictors, below I shall use xgboost\n",
    "    #clf = linear_model.Lasso(alpha=0.5)\n",
    "    #clf.fit(xtr, ytr)\n",
    "    \n",
    "    xgb1 = XGBRegressor()\n",
    "    # Various hyper-parameters to tune\n",
    "    parameters = {#'nthread':[4], #when use hyperthread, xgboost may become slower\n",
    "                  'learning_rate': [.03], #so called `eta` value\n",
    "                  'max_depth': [3, 5, 7, 10],\n",
    "                  'subsample': [0.7, 1.0],\n",
    "                  'colsample_bytree': [0.5,1.0],\n",
    "                  'n_estimators': [300]}\n",
    "\n",
    "    xgb_grid = GridSearchCV(xgb1,\n",
    "                            parameters,\n",
    "                            cv = tscv,\n",
    "                            verbose=True)\n",
    "\n",
    "    xgb_grid.fit(xtr, ytr)\n",
    " \n",
    "    y_preds = xgb_grid.predict(xts)\n",
    "    print(y_preds.shape)\n",
    "    error = mean_absolute_error(yts, y_preds)\n",
    "    print('Mean Error for','target',' is ', error )\n",
    "    \n",
    "\n",
    "    dept['preds']=''\n",
    "    dept['preds'][dept.index < split] = ytr\n",
    "    dept['preds'][dept.index >= split] =  y_preds\n",
    "    print(dept.shape)\n",
    "    return dept, xgb_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6063, 6) (4016, 6)\n",
      "(6063,) (4016,)\n",
      "Fitting 30 folds for each of 16 candidates, totalling 480 fits\n",
      "[18:25:17] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:25:17] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:25:17] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:25:17] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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      "[18:25:45] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:25:46] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:26:35] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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     ]
    },
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     "text": [
      "[18:27:46] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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      "[18:28:38] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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      "[18:28:39] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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      "[18:28:40] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:28:40] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:28:41] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:28:42] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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      "[18:28:51] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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      "[18:29:00] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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      "[18:29:03] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:29:03] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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      "[18:29:12] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[18:29:44] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[18:31:04] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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      "[18:31:58] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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      "[18:31:59] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:00] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:01] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:02] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:03] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:05] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:06] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:08] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:09] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:11] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:13] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:15] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:18] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:21] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:23] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:25] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:28] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:30] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:33] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:35] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:38] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:41] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:44] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:32:47] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 480 out of 480 | elapsed:  7.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:32:50] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "(4016,)\n",
      "Mean Error for target  is  9.901897794644968\n",
      "(10079, 8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:41: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "//anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:42: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "au, xgb_grid = fit_all('incid_hosp', data_train, split, dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "model = xgb_grid.best_estimator_\n",
    "filename = 'covid-19-auto_regression.sav'\n",
    "with open(filename, 'wb') as f:\n",
    "    pickle.dump(model, f)\n",
    "    f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>incid_hosp</th>\n",
       "      <th>incid_hosp (t-1)</th>\n",
       "      <th>incid_hosp (t-2)</th>\n",
       "      <th>incid_hosp (t-3)</th>\n",
       "      <th>incid_hosp (t-4)</th>\n",
       "      <th>incid_hosp (t-5)</th>\n",
       "      <th>incid_hosp (t-6)</th>\n",
       "      <th>preds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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>2020-05-06</th>\n",
       "      <td>6.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.6128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>14.7119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06</th>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.3025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06</th>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.17543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06</th>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            incid_hosp  incid_hosp (t-1)  incid_hosp (t-2)  incid_hosp (t-3)  \\\n",
       "date                                                                           \n",
       "2020-05-06         6.0              33.0               0.0              16.0   \n",
       "2020-05-06         1.0               4.0               5.0               9.0   \n",
       "2020-05-06        38.0               0.0              19.0               0.0   \n",
       "2020-05-06        27.0               1.0               6.0              38.0   \n",
       "2020-05-06        20.0               6.0              33.0               0.0   \n",
       "\n",
       "            incid_hosp (t-4)  incid_hosp (t-5)  incid_hosp (t-6)    preds  \n",
       "date                                                                       \n",
       "2020-05-06               0.0               1.0               1.0  30.6128  \n",
       "2020-05-06               0.0              10.0              70.0  14.7119  \n",
       "2020-05-06               1.0               4.0               2.0  12.3025  \n",
       "2020-05-06               0.0              19.0               0.0  5.17543  \n",
       "2020-05-06              16.0               0.0               1.0  24.0918  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "au.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following code is an artefact of an old version, keeping for the moment\n",
    "#single_data['dept'] = single_data['dept'].apply(lambda x: str(x).encode(\"utf-8\"))\n",
    "#single_data['dept'] = single_data['dept'].apply(lambda x: int.from_bytes(x, byteorder=\"big\"))\n",
    "\n",
    "#mBytes = m.encode(\"utf-8\")\n",
    "#mInt = int.from_bytes(mBytes, byteorder=\"big\")\n",
    "#mBytes = mInt.to_bytes(((mInt.bit_length() + 7) // 8), byteorder=\"big\")\n",
    "#m = mBytes.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing with indvidual models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO\n",
    "## Must revise & clean the old notebook (single models, 8_auto_regress_per_dept)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Raw score (MAE) is better than the mean of MAE for all departments. However, I would like to be able to compare department per department. This requires finding 'dept' indices for the rows of au (augmented single data matrix)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>incid_hosp</th>\n",
       "      <th>t-1</th>\n",
       "      <th>t-2</th>\n",
       "      <th>t-3</th>\n",
       "      <th>t-4</th>\n",
       "      <th>t-5</th>\n",
       "      <th>t-6</th>\n",
       "      <th>dept</th>\n",
       "      <th>preds</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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>2020-03-25</th>\n",
       "      <td>12</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12337</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>13</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12337</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>15</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12337</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-28</th>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12337</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-29</th>\n",
       "      <td>10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12337</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            incid_hosp   t-1   t-2   t-3   t-4   t-5   t-6   dept preds error\n",
       "date                                                                         \n",
       "2020-03-25          12  11.0  14.0   3.0   3.0   0.0   1.0  12337    12     0\n",
       "2020-03-26          13  12.0  11.0  14.0   3.0   3.0   0.0  12337    13     0\n",
       "2020-03-27          15  13.0  12.0  11.0  14.0   3.0   3.0  12337    15     0\n",
       "2020-03-28           7  15.0  13.0  12.0  11.0  14.0   3.0  12337     7     0\n",
       "2020-03-29          10   7.0  15.0  13.0  12.0  11.0  14.0  12337    10     0"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#au = au.loc[(au[['incid_hosp','t-1','t-2','t-3','t-4','t-5','t-6']].sum(axis=0) != 0)]\n",
    "single_data.head(5)\n",
    "#au[au.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1515,)\n",
      "(1515,)\n",
      "1515\n",
      "9.66\n",
      "30332\n"
     ]
    }
   ],
   "source": [
    "true_labels = single_data[single_data.index >= split]['incid_hosp']\n",
    "print(true_labels.shape)\n",
    "predicted = single_data[single_data.index >= split]['preds']\n",
    "\n",
    "\n",
    "print(predicted.shape)\n",
    "sum_error = np.sum(np.abs(true_labels-predicted))\n",
    "print(len(predicted))\n",
    "print(round(mean_absolute_error(true_labels,predicted),2))\n",
    "print(true_labels.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_data['error'] = np.abs( single_data['incid_hosp'] - single_data['preds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01 7.32\n",
      "02 18.88\n",
      "03 2.38\n",
      "04 3.66\n",
      "05 2.44\n",
      "06 7.58\n",
      "07 4.92\n",
      "08 6.04\n",
      "09 2.32\n",
      "10 23.21\n",
      "11 4.35\n",
      "12 2.82\n",
      "13 32.46\n",
      "14 3.8\n",
      "15 2.15\n",
      "16 2.3\n",
      "17 4.75\n",
      "18 5.11\n",
      "19 2.29\n",
      "21 14.25\n",
      "22 3.34\n",
      "23 3.97\n",
      "24 4.56\n",
      "25 11.74\n",
      "26 4.7\n",
      "27 8.67\n",
      "28 9.11\n",
      "29 3.12\n",
      "2A 2.17\n",
      "2B 4.26\n",
      "30 8.91\n",
      "31 7.18\n",
      "32 2.16\n",
      "33 6.61\n",
      "34 9.27\n",
      "35 5.17\n",
      "36 4.81\n",
      "37 5.88\n",
      "38 8.55\n",
      "39 4.3\n",
      "40 3.05\n",
      "41 4.3\n",
      "42 10.15\n",
      "43 3.75\n",
      "44 7.02\n",
      "45 6.03\n",
      "46 4.97\n",
      "47 2.01\n",
      "48 2.28\n",
      "49 5.69\n",
      "50 3.77\n",
      "51 12.03\n",
      "52 5.87\n",
      "53 4.61\n",
      "54 13.05\n",
      "55 4.73\n",
      "56 6.4\n",
      "57 30.48\n",
      "58 3.29\n",
      "59 20.8\n",
      "60 16.38\n",
      "61 3.1\n",
      "62 15.1\n",
      "63 2.29\n",
      "64 2.86\n",
      "65 3.42\n",
      "66 2.87\n",
      "67 24.64\n",
      "68 28.27\n",
      "69 24.24\n",
      "70 5.01\n",
      "71 9.8\n",
      "72 4.69\n",
      "73 4.38\n",
      "74 8.2\n",
      "75 53.24\n",
      "76 7.33\n",
      "77 25.82\n",
      "78 21.38\n",
      "79 2.25\n",
      "80 9.4\n",
      "81 2.57\n",
      "82 2.03\n",
      "83 11.05\n",
      "84 3.2\n",
      "85 4.83\n",
      "86 2.97\n",
      "87 2.8\n",
      "88 9.7\n",
      "89 5.77\n",
      "90 6.63\n",
      "91 41.17\n",
      "92 43.82\n",
      "93 49.61\n",
      "94 51.53\n",
      "95 25.04\n",
      "971 2.22\n",
      "972 2.64\n",
      "973 2.44\n",
      "974 2.9\n",
      "976 2.21\n"
     ]
    }
   ],
   "source": [
    "gr = single_data[single_data.index >= split].groupby('dept', as_index = False)\n",
    "for n,g in gr:\n",
    "    mBytes = n.to_bytes(((n.bit_length() + 7) // 8), byteorder=\"big\")\n",
    "    m = mBytes.decode(\"utf-8\")\n",
    "    print (m, round(g['error'].mean(),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: Put in the same format as the above scores table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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