{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f6ca7417",
   "metadata": {},
   "source": [
    "\n",
    "File: 06-lecture.py\n",
    "\n",
    "\n",
    "Michel Bierlaire\n",
    "\n",
    "Wed Aug 06 2025, 11:49:36\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "180d8b2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import biogeme.biogeme_logging as blog\n",
    "from IPython.core.display_functions import display\n",
    "from biogeme.biogeme import BIOGEME\n",
    "from biogeme.expressions import Beta\n",
    "from biogeme.models import loglogit\n",
    "from biogeme.results_processing import (\n",
    "    EstimationResults,\n",
    "    get_pandas_estimated_parameters,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4423fc34",
   "metadata": {},
   "source": [
    "Variables used for the specification of the Swissmetro model are defined in the file `swissmetro_variables.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4109872",
   "metadata": {},
   "outputs": [],
   "source": [
    "from swissmetro_variables import (\n",
    "    CAR_AV_SP,\n",
    "    CAR_CO_SCALED,\n",
    "    CAR_TT_SCALED,\n",
    "    CHOICE,\n",
    "    SM_AV,\n",
    "    SM_COST_SCALED,\n",
    "    SM_HE_SCALED,\n",
    "    SM_TT_SCALED,\n",
    "    TRAIN_AV_SP,\n",
    "    TRAIN_COST_SCALED,\n",
    "    TRAIN_HE_SCALED,\n",
    "    TRAIN_TT_SCALED,\n",
    "    database,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d77d09b5",
   "metadata": {},
   "source": [
    "The objective of this series of exercises is perform a similar modeling exercise as seen during the lecture."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1d2f009",
   "metadata": {},
   "source": [
    "As the estimation time may be long, we ask Biogeme to report the details of the iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64452a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = blog.get_screen_logger(level=blog.INFO)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d41c4035",
   "metadata": {},
   "source": [
    "**Tip:**<div class=\"alert alert-block alert-info\">It is advised to start working with a low number of draws, until\n",
    "the script is working well. Then, increase the number of draws to 10000, say.\n",
    "Then, execute the script overnight.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8f7eccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_draws = 10\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2352ee28",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "594e3e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "asc_car = Beta('asc_car', 0, None, None, 0)\n",
    "asc_train = Beta('asc_train', 0, None, None, 0)\n",
    "b_time = Beta('b_time', 0, None, None, 0)\n",
    "b_cost = Beta('b_cost', 0, None, None, 0)\n",
    "b_fr = Beta('b_fr', 0, None, None, 0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "258ea004",
   "metadata": {},
   "source": [
    "# Availability conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d756c69b",
   "metadata": {},
   "outputs": [],
   "source": [
    "av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fdf2b11",
   "metadata": {},
   "source": [
    "# Logit model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077a3995",
   "metadata": {},
   "source": [
    "## Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "128b7942",
   "metadata": {},
   "outputs": [],
   "source": [
    "v_train = (\n",
    "    asc_train\n",
    "    + b_time * TRAIN_TT_SCALED\n",
    "    + b_cost * TRAIN_COST_SCALED\n",
    "    + b_fr * TRAIN_HE_SCALED\n",
    ")\n",
    "v_swissmetro = b_time * SM_TT_SCALED + b_cost * SM_COST_SCALED + b_fr * SM_HE_SCALED\n",
    "v_car = asc_car + b_time * CAR_TT_SCALED + b_cost * CAR_CO_SCALED\n",
    "v = {1: v_train, 2: v_swissmetro, 3: v_car}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8065801",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "848c3b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "logprob = loglogit(v, av, CHOICE)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf432426",
   "metadata": {},
   "source": [
    "## Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e20d51c",
   "metadata": {},
   "outputs": [],
   "source": [
    "biogeme = BIOGEME(database, logprob)\n",
    "biogeme.model_name = '01logit'\n",
    "results_logit: EstimationResults = biogeme.estimate(recycle=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bab794f",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada460c4",
   "metadata": {},
   "source": [
    "General statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff0728d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(results_logit.short_summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252afb54",
   "metadata": {},
   "source": [
    "Estimated parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c7eedc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_logit = get_pandas_estimated_parameters(estimation_results=results_logit)\n",
    "display(param_logit)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f423cbd",
   "metadata": {},
   "source": [
    "The starting point is the logit model presented above.\n",
    "\n",
    "1. Load the results of the model where the travel time coefficient is normally distributed within the population.\n",
    "2. Estimate a model where the travel time coefficient is log normally distributed within the population.\n",
    "3. Compare the results."
   ]
  }
 ],
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 "nbformat": 4,
 "nbformat_minor": 5
}