demo_final_contact_matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), 'src')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>ID</th>\n",
       "      <th>date</th>\n",
       "      <th>merch_category</th>\n",
       "      <th>merch_postal_code</th>\n",
       "      <th>transaction_type</th>\n",
       "      <th>spendamt</th>\n",
       "      <th>nb_transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>Grocery Stores/Supermarkets</td>\n",
       "      <td>8700000</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>11238.128450</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>Grocery Stores/Supermarkets</td>\n",
       "      <td>500034</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>12848.165221</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>Grocery Stores/Supermarkets</td>\n",
       "      <td>110621</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>12116.165569</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>Hotels/Motels</td>\n",
       "      <td>8900000</td>\n",
       "      <td>OFFLINE</td>\n",
       "      <td>7745.998879</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>Restaurants</td>\n",
       "      <td>111941</td>\n",
       "      <td>OFFLINE</td>\n",
       "      <td>6927.424754</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID        date               merch_category  merch_postal_code  \\\n",
       "0   1  2019-01-01  Grocery Stores/Supermarkets            8700000   \n",
       "1   1  2019-01-01  Grocery Stores/Supermarkets             500034   \n",
       "2   2  2019-01-01  Grocery Stores/Supermarkets             110621   \n",
       "3   3  2019-01-01                Hotels/Motels            8900000   \n",
       "4   4  2019-01-01                  Restaurants             111941   \n",
       "\n",
       "  transaction_type      spendamt  nb_transactions  \n",
       "0           ONLINE  11238.128450              160  \n",
       "1           ONLINE  12848.165221              183  \n",
       "2           ONLINE  12116.165569              173  \n",
       "3          OFFLINE   7745.998879               38  \n",
       "4          OFFLINE   6927.424754              173  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'D:\\workspace\\PET\\technical_phase_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Public info about the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "txn_channel_col = \"transaction_type\"\n",
    "category_col = \"merch_category\"\n",
    "time_col = \"date\"\n",
    "postal_code_col = \"merch_postal_code\"\n",
    "num_txns_col = \"nb_transactions\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorising cities in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_city(code):\n",
    "    if code.startswith(\"5\"):\n",
    "        return \"Medellian\"\n",
    "    elif code.startswith(\"11\"):\n",
    "        return \"Bogota\"\n",
    "    elif code.startswith(\"70\"):\n",
    "        return \"Brasilia\"\n",
    "    else:\n",
    "        return \"Santiago\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_groups = ['0-4', '5-9', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75+']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumption_distribution_raw = pd.read_csv('consumption_distribution.csv')\n",
    "categories = consumption_distribution_raw['categories'].values\n",
    "consumption_distribution = {}\n",
    "for category in categories:\n",
    "    consumption_distribution[category] = consumption_distribution_raw[consumption_distribution_raw['categories'] == category].values[0][:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaling_factor = pd.read_csv('fractions_offline.csv')['0'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from DP_epidemiology.contact_matrix import get_age_group_count_map\n",
    "week =\"2021-01-05\"\n",
    "start_date = datetime.strptime(week, '%Y-%m-%d')\n",
    "end_date = datetime.strptime(week, '%Y-%m-%d')\n",
    "cities = data[postal_code_col].astype(str).apply(categorize_city).unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts_per_city = []\n",
    "for city in cities:\n",
    "    counts = get_age_group_count_map(data, age_groups, consumption_distribution, start_date, end_date, city)\n",
    "    counts_per_city.append(list(counts.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_groups = ['0-4', '5-9', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75+']\n",
    "consumption_distribution_raw = pd.read_csv('consumption_distribution.csv')\n",
    "categories = consumption_distribution_raw['categories'].values\n",
    "consumption_distribution = {}\n",
    "for category in categories:\n",
    "    consumption_distribution[category] = consumption_distribution_raw[consumption_distribution_raw['categories'] == category].values[0][:-1]\n",
    "fraction_offline_raw = pd.read_csv('fractions_offline.csv')\n",
    "fraction_offline = fraction_offline_raw['0'].values\n",
    "from DP_epidemiology.contact_matrix import get_age_group_count_map\n",
    "week =\"2021-01-05\"\n",
    "start_date = datetime.strptime(week, '%Y-%m-%d')\n",
    "end_date = datetime.strptime(week, '%Y-%m-%d')\n",
    "cities = data[\"merch_postal_code\"].astype(str).apply(categorize_city).unique()\n",
    "counts_per_city = []\n",
    "for city in cities:\n",
    "    counts = get_age_group_count_map(data, age_groups, consumption_distribution, start_date, end_date, city)\n",
    "    counts_per_city.append(list(counts.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "P = np.array([4136344, 4100716, 3991988, 3934088, 4090149, 4141051, 3895117, 3439202,\n",
    "              3075077, 3025100, 3031855, 2683253, 2187561, 1612948, 1088448, 1394217])  \n",
    "from DP_epidemiology.contact_matrix import get_contact_matrix_country\n",
    "estimated_contact_matrix = get_contact_matrix_country(counts_per_city, P, scaling_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        0.02991427],\n",
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       "        0.28212779, 0.2171456 , 0.19671234, 0.19364945, 0.1775585 ,\n",
       "        0.09782913, 0.14995707, 0.0807196 , 0.0716148 , 0.05012645,\n",
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       "       [0.07388504, 0.09771405, 0.18072971, 0.31930881, 0.2778716 ,\n",
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       "        0.00819294],\n",
       "       [0.01226062, 0.01619337, 0.02985305, 0.05269095, 0.04587575,\n",
       "        0.03397617, 0.02694784, 0.02474051, 0.02350629, 0.01737293,\n",
       "        0.01799559, 0.0142102 , 0.01174022, 0.00800189, 0.00591531,\n",
       "        0.00388865],\n",
       "       [0.01008305, 0.0132933 , 0.02439507, 0.0429973 , 0.03746194,\n",
       "        0.02808803, 0.02168008, 0.01966516, 0.01929376, 0.01736716,\n",
       "        0.01021783, 0.01463007, 0.0081922 , 0.0070819 , 0.00498106,\n",
       "        0.00230539]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimated_contact_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "diff = estimated_C \n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.imshow(diff, cmap = 'coolwarm', interpolation='none')\n",
    "plt.colorbar(label = 'Diff')"
   ]
  }
 ],
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