{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a45387e",
   "metadata": {},
   "source": [
    "\n",
    "File: 08-lecture.py\n",
    "\n",
    "\n",
    "Michel Bierlaire\n",
    "\n",
    "Thu Aug 07 2025, 08:35:26\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71b82846",
   "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": "e05dc46f",
   "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": "e1eaed6d",
   "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": "93d11b0a",
   "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": "126356f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = blog.get_screen_logger(level=blog.INFO)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba761813",
   "metadata": {},
   "source": [
    "The objective of this series of exercises is to perform a similar modeling exercise as seen during the lecture."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68fb94cf",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "353a0e85",
   "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": "547421fb",
   "metadata": {},
   "source": [
    "# Availability conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af33c06e",
   "metadata": {},
   "outputs": [],
   "source": [
    "av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795d3442",
   "metadata": {},
   "source": [
    "# Logit model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "182530ff",
   "metadata": {},
   "source": [
    "## Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e776c5db",
   "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": "43c59256",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d830e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "logprob = loglogit(v, av, CHOICE)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ed52af",
   "metadata": {},
   "source": [
    "## Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2428eb2c",
   "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": "baafcade",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c604c5f",
   "metadata": {},
   "source": [
    "General statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db769a1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(results_logit.short_summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9196c09",
   "metadata": {},
   "source": [
    "Estimated parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af0f4764",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_logit = get_pandas_estimated_parameters(estimation_results=results_logit)\n",
    "display(param_logit)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a313ce",
   "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. Estimate the same latent class model, including a class membership model, that involves the following binary\n",
    "variables:\n",
    "\n",
    "- `MALE`: 1 if individual is male.\n",
    "\n",
    "- `GA`: 1 if individual owns a yearly subscription.\n",
    "\n",
    "- `PURPOSE == 3`: 1 if trip purpose is business.\n",
    "\n",
    "- `INCOME <= 1`: 1 if the income is less or equal to 50kCHF per year.\n",
    "\n",
    "- `FIRST`: 1 if the individual is a first class traveler.\n",
    "\n",
    "5. Calculate, for each segment of the population, the probability to be in each class.\n",
    "\n",
    "6. Compare the results"
   ]
  }
 ],
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}