{
 "cells": [
  {
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   "source": [
    "\n",
    "File: 05-solution.py\n",
    "\n",
    "\n",
    "Michel Bierlaire\n",
    "\n",
    "Tue Aug 05 2025, 16:06:33\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e38c9d4a",
   "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",
    "    compile_estimation_results,\n",
    "    get_pandas_estimated_parameters,\n",
    ")\n",
    "\n",
    "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": "e0d0d93a",
   "metadata": {},
   "source": [
    "Variables used for the specification of the Swissmetro model are defined in the file `swissmetro_variables.py`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6621535c",
   "metadata": {},
   "source": [
    "The objective of this series of exercises is perform a similar modeling exercise as seen during the lecture."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc26fcf",
   "metadata": {},
   "source": [
    "The starting point is the logit model presented below. Then,\n",
    "\n",
    "1. estimate a model where the travel time coefficient is normally distributed within the population,\n",
    "2. compare the results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b541f03b",
   "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": "36fe0744",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_draws = 10\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f79ed252",
   "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": "48a417ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = blog.get_screen_logger(level=blog.INFO)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f8c2091",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e21c92a",
   "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": "4532baa3",
   "metadata": {},
   "source": [
    "# Availability conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2d29e98",
   "metadata": {},
   "outputs": [],
   "source": [
    "av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90b15fa",
   "metadata": {},
   "source": [
    "# Logit model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a7af05",
   "metadata": {},
   "source": [
    "## Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd1f965d",
   "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": "922adae6",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba47f64f",
   "metadata": {},
   "outputs": [],
   "source": [
    "logprob = loglogit(v, av, CHOICE)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce96fcd",
   "metadata": {},
   "source": [
    "## Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b06922ee",
   "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": "44ed3729",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c15bb8",
   "metadata": {},
   "source": [
    "General statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4af4ce81",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(results_logit.short_summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca4739da",
   "metadata": {},
   "source": [
    "Estimated parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66ebe193",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_logit = get_pandas_estimated_parameters(estimation_results=results_logit)\n",
    "display(param_logit)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec089287",
   "metadata": {},
   "source": [
    "Now, specify and estimate a model where the travel time coefficient is normally distributed within the population.\n",
    "1. Introduce the additional parameters.\n",
    "2. Specify the utility functions.\n",
    "3. Specify the choice model to obtain the log likelihood function.\n",
    "4. Estimate the values of the parameters.\n",
    "5. Compare the results."
   ]
  }
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