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        "#%%\n",
        "\"\"\"File 01nestedEstimation.py\n",
        "\n",
        ":author: Michel Bierlaire, EPFL\n",
        ":date: Wed Sep 11 09:59:55 2019\n",
        "\n",
        " Estimation of a nested logit model, that will be used for simuation.\n",
        " Three alternatives: public transporation, car and slow modes.\n",
        " RP 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",
        "from biogeme.expressions import Beta\n",
        "\n",
        "# Read the data\n",
        "df = pd.read_csv('optima.dat', sep='\\t')\n",
        "database = db.Database('optima', df)\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",
        "# Exclude observations such that the chosen alternative is -1\n",
        "database.remove(Choice == -1.0)\n",
        "\n",
        "# List of parameters to be estimated\n",
        "ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)\n",
        "ASC_PT = Beta('ASC_PT', 0, None, None, 1)\n",
        "ASC_SM = Beta('ASC_SM', 0, None, None, 0)\n",
        "BETA_TIME_FULLTIME = Beta('BETA_TIME_FULLTIME', 0, None, None, 0)\n",
        "BETA_TIME_OTHER = Beta('BETA_TIME_OTHER', 0, None, None, 0)\n",
        "BETA_DIST_MALE = Beta('BETA_DIST_MALE', 0, None, None, 0)\n",
        "BETA_DIST_FEMALE = Beta('BETA_DIST_FEMALE', 0, None, None, 0)\n",
        "BETA_DIST_UNREPORTED = Beta('BETA_DIST_UNREPORTED', 0, None, None, 0)\n",
        "BETA_COST = Beta('BETA_COST', 0, None, None, 0)\n",
        "\n",
        "# Definition of variables:\n",
        "# For numerical reasons, it is good practice to scale the data to\n",
        "# that the values of the parameters are around 1.0.\n",
        "TimePT_scaled = TimePT / 200\n",
        "TimeCar_scaled = TimeCar / 200\n",
        "MarginalCostPT_scaled = MarginalCostPT / 10\n",
        "CostCarCHF_scaled = CostCarCHF / 10\n",
        "distance_km_scaled = distance_km / 5\n",
        "male = Gender == 1\n",
        "female = Gender == 2\n",
        "unreportedGender = Gender == -1\n",
        "\n",
        "fulltime = OccupStat == 1\n",
        "notfulltime = OccupStat != 1\n",
        "\n",
        "# Definition of utility functions:\n",
        "V_PT = (\n",
        "    ASC_PT\n",
        "    + BETA_TIME_FULLTIME * TimePT_scaled * fulltime\n",
        "    + BETA_TIME_OTHER * TimePT_scaled * notfulltime\n",
        "    + BETA_COST * MarginalCostPT_scaled\n",
        ")\n",
        "V_CAR = (\n",
        "    ASC_CAR\n",
        "    + BETA_TIME_FULLTIME * TimeCar_scaled * fulltime\n",
        "    + BETA_TIME_OTHER * TimeCar_scaled * notfulltime\n",
        "    + BETA_COST * CostCarCHF_scaled\n",
        ")\n",
        "V_SM = (\n",
        "    ASC_SM\n",
        "    + BETA_DIST_MALE * distance_km_scaled * male\n",
        "    + BETA_DIST_FEMALE * distance_km_scaled * female\n",
        "    + BETA_DIST_UNREPORTED * distance_km_scaled * unreportedGender\n",
        ")\n",
        "\n",
        "# Associate utility functions with the numbering of alternatives\n",
        "V = {0: V_PT, 1: V_CAR, 2: V_SM}\n",
        "\n",
        "# Definition of the nests:\n",
        "# 1: nests parameter\n",
        "# 2: list of alternatives\n",
        "MU_NOCAR = Beta('MU_NOCAR', 1.0, 1.0, None, 0)\n",
        "\n",
        "CAR_NEST = 1.0, [1]\n",
        "NO_CAR_NEST = MU_NOCAR, [0, 2]\n",
        "nests = CAR_NEST, NO_CAR_NEST\n",
        "\n",
        "# The choice model is a nested logit, with availability conditions\n",
        "logprob = models.lognested(V, None, nests, Choice)\n",
        "\n",
        "# Create the Biogeme object\n",
        "biogeme = bio.BIOGEME(database, logprob)\n",
        "biogeme.modelName = '01nestedEstimation'\n",
        "\n",
        "# Estimate the parameters. Calculate also the standard errors using\n",
        "# bootstrapping.\n",
        "results = biogeme.estimate(bootstrap=100)\n",
        "\n",
        "# Get the results in a pandas table\n",
        "pandasResults = results.getEstimatedParameters()\n",
        "print(pandasResults)\n"
      ],
      "outputs": [],
      "execution_count": null
    }
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