{
  "cells": [
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "#%%\n",
        "\"\"\"File 09nested.py\n",
        "\n",
        ":author: Michel Bierlaire, EPFL\n",
        ":date: Sun Sep  8 00:36:04 2019\n",
        "\n",
        " Example of a nested logit model.\n",
        " Three alternatives: Train, Car and Swissmetro\n",
        " Train and car are in the same nest.\n",
        " SP data\n",
        "\"\"\"\n",
        "\n",
        "import pandas as pd\n",
        "import biogeme.database as db\n",
        "import biogeme.biogeme as bio\n",
        "from biogeme import models\n",
        "import biogeme.messaging as msg\n",
        "from biogeme.expressions import Beta, DefineVariable\n",
        "\n",
        "# Read the data\n",
        "df = pd.read_csv('swissmetro.dat', sep='\\t')\n",
        "database = db.Database('swissmetro', df)\n",
        "\n",
        "# The Pandas data structure is available as database.data. Use all the\n",
        "# Pandas functions to invesigate the database\n",
        "# print(database.data.describe())\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 can be done directly using pandas.\n",
        "# remove = (((database.data.PURPOSE != 1) &\n",
        "#           (database.data.PURPOSE != 3)) |\n",
        "#          (database.data.CHOICE == 0))\n",
        "# database.data.drop(database.data[remove].index,inplace=True)\n",
        "\n",
        "# Here we use the \"biogeme\" way for backward compatibility\n",
        "exclude = ((PURPOSE != 1) * (PURPOSE != 3) + (CHOICE == 0)) > 0\n",
        "database.remove(exclude)\n",
        "\n",
        "# Parameters to be estimated\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_COST = Beta('B_COST', 0, None, None, 0)\n",
        "MU = Beta('MU', 1, 1, 10, 0)\n",
        "\n",
        "# Definition of new variables\n",
        "SM_COST = SM_CO * (GA == 0)\n",
        "TRAIN_COST = TRAIN_CO * (GA == 0)\n",
        "\n",
        "# Definition of new variables: adding columns to the database\n",
        "CAR_AV_SP = DefineVariable('CAR_AV_SP', CAR_AV * (SP != 0), database)\n",
        "TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP', TRAIN_AV * (SP != 0), database)\n",
        "TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED', TRAIN_TT / 100.0, database)\n",
        "TRAIN_COST_SCALED = DefineVariable(\n",
        "    'TRAIN_COST_SCALED', TRAIN_COST / 100, database\n",
        ")\n",
        "SM_TT_SCALED = DefineVariable('SM_TT_SCALED', SM_TT / 100.0, database)\n",
        "SM_COST_SCALED = DefineVariable('SM_COST_SCALED', SM_COST / 100, database)\n",
        "CAR_TT_SCALED = DefineVariable('CAR_TT_SCALED', CAR_TT / 100, database)\n",
        "CAR_CO_SCALED = DefineVariable('CAR_CO_SCALED', CAR_CO / 100, database)\n",
        "\n",
        "# Definition of the utility functions\n",
        "V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED\n",
        "V2 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED\n",
        "V3 = ASC_CAR + B_TIME * 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",
        "# Definition of nests:\n",
        "# 1: nests parameter\n",
        "# 2: list of alternatives\n",
        "existing = MU, [1, 3]\n",
        "future = 1.0, [2]\n",
        "nests = existing, future\n",
        "\n",
        "# Definition of the model. This is the contribution of each\n",
        "# observation to the log likelihood function.\n",
        "# The choice model is a nested logit, with availability conditions\n",
        "logprob = models.lognested(V, av, nests, CHOICE)\n",
        "\n",
        "# Define level of verbosity\n",
        "logger = msg.bioMessage()\n",
        "# logger.setSilent()\n",
        "# logger.setWarning()\n",
        "logger.setGeneral()\n",
        "# logger.setDetailed()\n",
        "\n",
        "# Create the Biogeme object\n",
        "biogeme = bio.BIOGEME(database, logprob)\n",
        "biogeme.modelName = \"09nested\"\n",
        "\n",
        "# Calculate the null log likelihood for reporting.\n",
        "biogeme.calculateNullLoglikelihood(av)\n",
        "\n",
        "# Estimate the parameters\n",
        "results = biogeme.estimate()\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
}