{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monte Carlo example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add PyTwoWay to system path (do not run this)\n",
    "# import sys\n",
    "# sys.path.append('../../..')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the PyTwoWay package\n",
    "\n",
    "Make sure to install it using `pip install pytwoway`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-22T21:42:40.756437Z",
     "start_time": "2020-12-22T21:42:39.576929Z"
    }
   },
   "outputs": [],
   "source": [
    "import pytwoway as tw\n",
    "import bipartitepandas as bpd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First, check out parameter options\n",
    "\n",
    "Do this by running:\n",
    "\n",
    "- FE - `tw.fe_params().describe_all()`\n",
    "\n",
    "- CRE - `tw.cre_params().describe_all()`\n",
    "\n",
    "- Clustering - `bpd.cluster_params().describe_all()`\n",
    "\n",
    "- Cleaning - `bpd.clean_params().describe_all()`\n",
    "\n",
    "- Simulating - `bpd.sim_params().describe_all()`\n",
    "\n",
    "Alternatively, run `x_params().keys()` to view all the keys for a parameter dictionary, then `x_params().describe(key)` to get a description for a single key."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second, set parameter choices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nl = 5 # Number of worker types\n",
    "nk = 10 # Number of firm types\n",
    "# FE\n",
    "fe_params = tw.fe_params(\n",
    "    {\n",
    "        'he': True\n",
    "    }\n",
    ")\n",
    "## Clustering ##\n",
    "# Group using k-means\n",
    "kmeans = bpd.grouping.KMeans(n_clusters=nk)\n",
    "cluster_params = bpd.cluster_params(\n",
    "    {\n",
    "        'grouping': kmeans\n",
    "    }\n",
    ")\n",
    "# Simulating\n",
    "sim_params = bpd.sim_params({\n",
    "    'n_workers': 1000,\n",
    "    'nl': nl, 'nk': nk,\n",
    "    'firm_size': 5,\n",
    "    'alpha_sig': 2, 'w_sig': 2,\n",
    "    'c_sort': 1.5, 'c_netw': 1.5,\n",
    "    'p_move': 0.1\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Third, run the Monte Carlo simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-22T21:43:16.646468Z",
     "start_time": "2020-12-22T21:42:40.780053Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the MonteCarlo object\n",
    "twmc = tw.MonteCarlo(\n",
    "    sim_params=sim_params,\n",
    "    fe_params=fe_params,\n",
    "    estimate_bs=True,\n",
    "    cluster_params=cluster_params,\n",
    "    collapse='spell',\n",
    "    move_to_worker=False\n",
    ")\n",
    "# Run the Monte Carlo estimation\n",
    "twmc.monte_carlo(\n",
    "    N=500,\n",
    "    ncore=8\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finally, plot histograms of parameter estimates (histograms show the difference from the truth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twmc.hist()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3.10.4 ('tw-env')",
   "language": "python",
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  "language_info": {
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   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
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   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
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   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
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  "nbsphinx-toctree": {
   "hidden": true,
   "maxdepth": 1,
   "titlesonly": true
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
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    "lenVar": 40
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
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    "r": {
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     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   },
   "types_to_exclude": [
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