{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SIR Model for Viral Marketing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from decimal import Decimal\n",
    "from datetime import timedelta\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from tabulate import tabulate\n",
    "\n",
    "from __future__ import print_function\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import clear_output\n",
    "\n",
    "from SimCAD.configuration import Configuration\n",
    "from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \\\n",
    "    ep_time_step\n",
    "from SimCAD.engine import ExecutionMode, ExecutionContext, Executor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sim_config = {\n",
    "    'N': 1,\n",
    "    'T': range(50)\n",
    "}\n",
    "\n",
    "seed = {}\n",
    "env_processes = {}\n",
    "initial_condition = {\n",
    "    'Budget': float(50),\n",
    "    'Ps': float(1000),\n",
    "    'Pi': float(10),\n",
    "    'Pr': float(0),\n",
    "    'beta': float(0.05), # contact rate between S and I\n",
    "    'gamma': float(0.20), # recover rate from I to R\n",
    "    'timestamp': '2019-01-01 00:00:00'\n",
    "}\n",
    "\n",
    "# Parameters\n",
    "epsilon = 0.03\n",
    "subscription_fee = 1.0\n",
    "incentive_cost = 10.0\n",
    "stickiness_cost = 5.0\n",
    "delta_beta = 0.1\n",
    "delta_gamma = 0.1\n",
    "\n",
    "ts_format = '%Y-%m-%d %H:%M:%S'\n",
    "t_delta = timedelta(days=0, minutes=0, seconds=1)\n",
    "def time_model(step, sL, s, _input):\n",
    "    y = 'timestamp'\n",
    "    x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)\n",
    "    return (y, x)\n",
    "\n",
    "exogenous_states = exo_update_per_ts(\n",
    "    {\n",
    "    'timestamp': time_model\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Behaviors choose actions depending on states\n",
    "# 1) increase contact rate, beta (create incentive to spread)\n",
    "# 2) decrease recover rate, gamma (improve stickiness)\n",
    "\n",
    "def add_incentive(step, sL, s):\n",
    "    delta = 0.0\n",
    "    potential_delta = s['beta'] * delta_beta\n",
    "    if (s['Ps'] > 3 * s['Pi']) and s['Budget'] > \\\n",
    "        abs(potential_delta * incentive_cost * s['Ps']):\n",
    "        delta = potential_delta\n",
    "    return {'delta': delta}\n",
    "\n",
    "def add_stickiness(step, sL, s):\n",
    "    delta = 0.0\n",
    "    potential_delta = s['gamma'] * delta_gamma\n",
    "    if (s['Pr'] > 2 * s['Pi']) and s['Budget'] > \\\n",
    "        abs(potential_delta * stickiness_cost * s['Pi']):\n",
    "        delta = potential_delta\n",
    "    return {'delta': delta}\n",
    "\n",
    "def dummy_behavior(step, sL, s):\n",
    "    return {'delta': 0.0}\n",
    "\n",
    "# Mechanisms incur cost to modify beta or gamma\n",
    "# 1) incur cost to create incentive to spread\n",
    "# 2) incur cost to improve stickiness\n",
    "\n",
    "def incur_incentive_cost(step, sL, s, _input):\n",
    "    y = 'Budget'\n",
    "    x = s['Budget'] - abs(_input['delta'] * s['Ps'] * incentive_cost)\n",
    "    return (y, x)\n",
    "\n",
    "def incur_stickiness_cost(step, sL, s, _input):\n",
    "    y = 'Budget'\n",
    "    x = s['Budget'] - abs(_input['delta'] * s['Pi'] * stickiness_cost)\n",
    "    return (y, x)\n",
    "\n",
    "def update_beta(step, sL, s, _input):\n",
    "    y = 'beta'\n",
    "    x = s['beta'] + _input['delta']\n",
    "    return (y, x)\n",
    "\n",
    "def update_gamma(step, sL, s, _input):\n",
    "    y = 'gamma'\n",
    "    x = s['gamma'] - _input['delta']\n",
    "    return (y, x)\n",
    "\n",
    "def S_model(step, sL, s, _input):\n",
    "    y = 'Ps'\n",
    "    x = s['Ps'] - s['beta'] * s['Ps']\n",
    "    return (y, x)\n",
    "\n",
    "def I_model(step, sL, s, _input):\n",
    "    y = 'Pi'\n",
    "    x = s['Pi'] + s['beta'] * s['Ps'] - s['gamma'] * s['Pi']\n",
    "    return (y, x)\n",
    "    \n",
    "def R_model(step, sL, s, _input):\n",
    "    y = 'Pr'\n",
    "    x = s['Pr'] + s['gamma'] * s['Pi']\n",
    "    return (y, x)\n",
    "\n",
    "def collect_subscription(step, sL, s, _input):\n",
    "    y = 'Budget'\n",
    "    x = s['Budget'] + s['Pi'] * epsilon * subscription_fee\n",
    "    return (y, x)\n",
    "\n",
    "mechanisms = {\n",
    "    'spread': {\n",
    "        'behaviors': {\n",
    "            'dummy': dummy_behavior\n",
    "        },\n",
    "        'states': {\n",
    "            'Ps': S_model,\n",
    "            'Pi': I_model,\n",
    "            'Pr': R_model,\n",
    "            'Budget': collect_subscription\n",
    "        }      \n",
    "    },\n",
    "    'create_incentive': {\n",
    "        'behaviors': {\n",
    "            'action': add_incentive,\n",
    "        },\n",
    "        'states': {\n",
    "            'beta': update_beta,\n",
    "            'Budget': incur_incentive_cost,\n",
    "        }\n",
    "    },\n",
    "    'improve_stickiness': {\n",
    "        'behaviors': {\n",
    "            'action': add_stickiness\n",
    "        },\n",
    "        'states': {\n",
    "            'gamma': update_gamma,\n",
    "            'Budget': incur_stickiness_cost,\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c288ce264eca4566ab9fdc7db7e801f3"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.widget_handler>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def widget_handler(beta=float(0.05), gamma=float(0.20),\n",
    "                   subscription_fee=float(1.0), \n",
    "                   incentive_cost=float(10.0), \n",
    "                   stickiness_cost=float(5.0)):\n",
    "    initial_condition['beta'] = beta\n",
    "    initial_condition['gamma'] = gamma\n",
    "    subscription_fee = subscription_fee\n",
    "    incentive_cost = incentive_cost\n",
    "    stickiness_cost = stickiness_cost\n",
    "    \n",
    "    config = Configuration(\n",
    "            sim_config=sim_config,\n",
    "            state_dict=initial_condition,\n",
    "            seed=seed,\n",
    "            exogenous_states=exogenous_states,\n",
    "            env_processes=env_processes,\n",
    "            mechanisms=mechanisms)\n",
    "\n",
    "    exec_mode = ExecutionMode()\n",
    "    exec_context = ExecutionContext(exec_mode.single_proc)\n",
    "    executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n",
    "    raw_result, tensor = executor.main()\n",
    "    df = pd.DataFrame(raw_result)\n",
    "    df['timestamp'] = pd.to_datetime(df['timestamp'], format=ts_format)\n",
    "    \n",
    "    fig = plt.figure(figsize=(7, 14))\n",
    "    \n",
    "    sir = fig.add_subplot(3, 1, 1)\n",
    "    sir.plot('timestamp', 'Ps', data=df, marker='', color='skyblue', linewidth=2)\n",
    "    sir.plot('timestamp', 'Pi', data=df, marker='', color='orange', linewidth=2)\n",
    "    sir.plot('timestamp', 'Pr', data=df, marker='', color='green', linewidth=2)\n",
    "    sir.legend()\n",
    "    \n",
    "    beta_gamma = fig.add_subplot(3, 1, 2)\n",
    "    beta_gamma.plot('timestamp', 'beta', data=df, marker='', color='skyblue', linewidth=2)\n",
    "    beta_gamma.plot('timestamp', 'gamma', data=df, marker='', color='orange', linewidth=2)\n",
    "    beta_gamma.legend()\n",
    "    \n",
    "    budget_pi = fig.add_subplot(3, 1, 3)\n",
    "    budget_pi.plot('timestamp', 'Budget', data=df, marker='', color='skyblue', linewidth=2)\n",
    "    budget_pi.plot('timestamp', 'Pi', data=df, marker='', color='orange', linewidth=2)\n",
    "    budget_pi.legend()\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "sliders = interact_manual(widget_handler, \n",
    "                beta=(0, 1, 0.01),\n",
    "                gamma=(0, 1, 0.01),\n",
    "                subscription_fee=(0, 10, 0.1),\n",
    "                incentive_cost=(0, 20, 0.5),\n",
    "                stickiness_cost=(0, 20, 0.5)\n",
    "               )\n",
    "sliders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# config = Configuration(\n",
    "#             sim_config=sim_config,\n",
    "#             state_dict=initial_condition,\n",
    "#             seed=seed,\n",
    "#             exogenous_states=exogenous_states,\n",
    "#             env_processes=env_processes,\n",
    "#             mechanisms=mechanisms)\n",
    "\n",
    "# from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
    "# exec_mode = ExecutionMode()\n",
    "# exec_context = ExecutionContext(exec_mode.single_proc)\n",
    "# executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n",
    "# raw_result, tensor = executor.main()\n",
    "# df = pd.DataFrame(raw_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# df.plot('timestamp', ['Ps','Pi', 'Pr'])\n",
    "# df.plot('timestamp', ['beta', 'gamma'])\n",
    "# df.plot('timestamp', ['Budget', 'Pi'])\n",
    "# df[['Ps','Pi', 'Pr']].describe()"
   ]
  }
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