{
  "cells": [
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "#%%\n",
        "\"\"\" File: 07estimationMonteCarlo_anti_500.py\n",
        "\n",
        " Author: Michel Bierlaire, EPFL\n",
        " Date: Wed Dec 11 17:24:43 2019\n",
        "\n",
        "Estimation of a mixtures of logit models where the integral is\n",
        "approximated using MonteCarlo integration, with antithetic draws.\n",
        "\"\"\"\n",
        "# pylint: disable=invalid-name, undefined-variable\n",
        "\n",
        "import pandas as pd\n",
        "import biogeme.database as db\n",
        "import biogeme.biogeme as bio\n",
        "from biogeme import models\n",
        "from biogeme.expressions import Beta, bioDraws, MonteCarlo, log\n",
        "\n",
        "pandas = pd.read_csv('swissmetro.dat', sep='\\t')\n",
        "database = db.Database('swissmetro', pandas)\n",
        "\n",
        "# The following statement allows you to use the names of the variable\n",
        "# as Python variable.\n",
        "globals().update(database.variables)\n",
        "\n",
        "# Removing some observations\n",
        "exclude = ((PURPOSE != 1) * (PURPOSE != 3) + (CHOICE == 0)) > 0\n",
        "database.remove(exclude)\n",
        "\n",
        "ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)\n",
        "ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)\n",
        "ASC_SM = Beta('ASC_SM', 0, None, None, 1)\n",
        "B_TIME = Beta('B_TIME', 0, None, None, 0)\n",
        "B_TIME_S = Beta('B_TIME_S', 1, None, None, 0)\n",
        "B_COST = Beta('B_COST', 0, None, None, 0)\n",
        "\n",
        "# Define a random parameter, normally distributed, designed to be used\n",
        "# for Monte-Carlo simulation\n",
        "B_TIME_RND = B_TIME + B_TIME_S * bioDraws('B_TIME_RND', 'NORMAL_ANTI')\n",
        "\n",
        "# Definition of new variables\n",
        "SM_COST = SM_CO * (GA == 0)\n",
        "TRAIN_COST = TRAIN_CO * (GA == 0)\n",
        "CAR_AV_SP = CAR_AV * (SP != 0)\n",
        "TRAIN_AV_SP = TRAIN_AV * (SP != 0)\n",
        "TRAIN_TT_SCALED = TRAIN_TT / 100.0\n",
        "TRAIN_COST_SCALED = TRAIN_COST / 100\n",
        "SM_TT_SCALED = SM_TT / 100.0\n",
        "SM_COST_SCALED = SM_COST / 100\n",
        "CAR_TT_SCALED = CAR_TT / 100\n",
        "CAR_CO_SCALED = CAR_CO / 100\n",
        "\n",
        "# Definition of the utility functions\n",
        "V1 = ASC_TRAIN + B_TIME_RND * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED\n",
        "V2 = ASC_SM + B_TIME_RND * SM_TT_SCALED + B_COST * SM_COST_SCALED\n",
        "V3 = ASC_CAR + B_TIME_RND * CAR_TT_SCALED + B_COST * CAR_CO_SCALED\n",
        "\n",
        "# Associate utility functions with the numbering of alternatives\n",
        "V = {1: V1, 2: V2, 3: V3}\n",
        "\n",
        "# Associate the availability conditions with the alternatives\n",
        "av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}\n",
        "\n",
        "# The choice model is a logit, with availability conditions\n",
        "prob = models.logit(V, av, CHOICE)\n",
        "logprob = log(MonteCarlo(prob))\n",
        "\n",
        "\n",
        "R = 500\n",
        "biogeme = bio.BIOGEME(database, logprob, numberOfDraws=R)\n",
        "\n",
        "biogeme.modelName = '07estimationMonteCarlo_anti_500'\n",
        "results = biogeme.estimate()\n",
        "\n",
        "# Get the results in a pandas table\n",
        "pandasResults = results.getEstimatedParameters()\n",
        "print(pandasResults)\n"
      ],
      "outputs": [],
      "execution_count": null
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "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": "3.6.1"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}