{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d6769381",
   "metadata": {},
   "source": [
    "\n",
    "File: 09-lecture.py\n",
    "\n",
    "\n",
    "Michel Bierlaire\n",
    "\n",
    "Thu Aug 07 2025, 08:46:33\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc490d60",
   "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": "de0dfa97",
   "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": "caafb51a",
   "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": "7d022fdc",
   "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": "4009f436",
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = blog.get_screen_logger(level=blog.INFO)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf4d9d2",
   "metadata": {},
   "source": [
    "The objective of this series of exercises is perform a similar modeling exercise as seen during the lecture."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b3ad155",
   "metadata": {},
   "source": [
    "**Tip:**<div class=\"alert alert-block alert-info\">It is advised to start working with a low number of draws,\n",
    "until the script is working well. Then, increase the number of draws to 10000, say. Then, execute the script\n",
    "overnight.  </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74fe0b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_draws = 10\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be949dfd",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b231b356",
   "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": "3f5288fb",
   "metadata": {},
   "source": [
    "# Availability conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd88e236",
   "metadata": {},
   "outputs": [],
   "source": [
    "av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25feddb3",
   "metadata": {},
   "source": [
    "# Logit model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e0a485",
   "metadata": {},
   "source": [
    "## Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1c23a7a",
   "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": "0a925ba4",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aee3af37",
   "metadata": {},
   "outputs": [],
   "source": [
    "logprob = loglogit(v, av, CHOICE)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d441043f",
   "metadata": {},
   "source": [
    "## Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b46137b",
   "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": "71ef0775",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2727f33",
   "metadata": {},
   "source": [
    "General statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4f23e8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(results_logit.short_summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2e511fd",
   "metadata": {},
   "source": [
    "Estimated parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28f45a3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_logit = get_pandas_estimated_parameters(estimation_results=results_logit)\n",
    "display(param_logit)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24456e4f",
   "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",
    "\n",
    "2. Load the results of the model where the travel time coefficient is log normally distributed within the population.\n",
    "\n",
    "3. Load the results of the model with two latent classes: one where the travel time coefficient is constrained to\n",
    "be zero, and one where the travel time coefficient is estimated.\n",
    "\n",
    "4. Load the results of the latent class model with the class membership model.\n",
    "\n",
    "5. Estimate the same latent class model as above, where the travel time coefficient is normally distributed within\n",
    "the class.\n",
    "\n",
    "6. Compare the results"
   ]
  }
 ],
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}