.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/hybrid_choice_models/plot_h01_mode_logit.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_hybrid_choice_models_plot_h01_mode_logit.py: Baseline mode choice model: maximum likelihood estimation ========================================================= This example estimates a multinomial logit model for transportation mode choice using maximum likelihood and Biogeme. It is the baseline specification of the hybrid-choice tutorial. The model contains only observed choice variables and standard utility functions: there are no latent variables, structural equations, or measurement equations. The results therefore provide a reference against which the hybrid-choice specifications introduced later can be compared. The script performs the following steps: - load the Optima mode-choice data, - build the utility functions from the common tutorial specification, - construct the logit log-likelihood, - estimate the model, or reload previously saved estimation results, - display the estimated parameters as pandas and LaTeX tables. Michel Bierlaire Sat Jun 06 2026, 15:23:41 .. GENERATED FROM PYTHON SOURCE LINES 25-43 .. code-block:: Python from choice_latent_variables import generate_utility_functions from optima import ( Choice, read_data, ) import biogeme.biogeme_logging as blog from biogeme.biogeme import BIOGEME from biogeme.models import loglogit from biogeme.results_processing import ( get_latex_estimated_parameters, get_latex_general_statistics, get_pandas_estimated_parameters, ) logger = blog.get_screen_logger(level=blog.INFO) .. rst-class:: sphx-glr-script-out .. code-block:: none /Users/bierlair/MyFiles/github/biogeme/docs/source/examples/hybrid_choice_models/choice_latent_variables.py:83: SyntaxWarning: "\m" is an invalid escape sequence. Such sequences will not work in the future. Did you mean "\\m"? A raw string is also an option. V_{\mathrm{PT}} .. GENERATED FROM PYTHON SOURCE LINES 44-45 Load the Optima mode-choice data. .. GENERATED FROM PYTHON SOURCE LINES 45-73 .. code-block:: Python database = read_data() # Build the utility functions for the transportation alternatives. utilities = generate_utility_functions() # Construct the log-likelihood of the multinomial logit model. log_likelihood = loglogit(utilities, None, Choice) # Create the Biogeme object used for estimation. biogeme = BIOGEME( database, log_likelihood, ) biogeme.model_name = 'plot_h01_mode_logit' # Estimate the model, or reload the saved results if they are already available. yaml_file_name = f'saved_results/{biogeme.model_name}.yaml' results = biogeme.estimate_or_load(yaml_file_name=yaml_file_name) # Display a compact summary and the estimated parameters. print(results.short_summary()) print(get_pandas_estimated_parameters(estimation_results=results)) general_statistics = get_latex_general_statistics(estimation_results=results) print(general_statistics) estimated_parameters = get_latex_estimated_parameters(estimation_results=results) print(estimated_parameters['']) .. rst-class:: sphx-glr-script-out .. code-block:: none Default values of the Biogeme parameters are used. File biogeme.toml has been created Estimation results are read from saved_results/plot_h01_mode_logit.yaml. No estimation is performed. Results for model plot_h01_mode_logit Nbr of parameters: 7 Sample size: 889 Excluded data: 0 Final log likelihood: -508.6166 Akaike Information Criterion: 1031.233 Bayesian Information Criterion: 1064.764 {'Estimated parameters': Name Value ... Robust t-stat. Robust p-value 0 choice_scale_parameter 0.078161 ... 4.176004 0.000030 1 choice_asc_pt -8.705850 ... -1.475779 0.140003 2 choice_beta_time_pt -12.182632 ... -2.859609 0.004242 3 choice_asc_car -3.065828 ... -0.609093 0.542463 4 choice_beta_time_car -25.982708 ... -2.940295 0.003279 5 choice_beta_dist_work -2.612986 ... -3.112401 0.001856 6 choice_beta_dist_other_purposes -4.124008 ... -3.377163 0.000732 [7 rows x 5 columns]} %% General statistics \section{General statistics} \begin{tabular}{ll} Number of estimated parameters & 7 \\ Sample size & 889 \\ Excluded observations & 0 \\ Init log likelihood & -5937.274 \\ Final log likelihood & -508.6166 \\ Likelihood ratio test for the init. model & 10857.31 \\ Rho-square for the init. model & 0.914 \\ Rho-square-bar for the init. model & 0.913 \\ Akaike Information Criterion & 1031.233 \\ Bayesian Information Criterion & 1064.764 \\ Final gradient norm & 2.8171E-05 \\ Bootstrapping time & None \\ Algorithm & \verb$Newton with trust region for simple bound constraints$ \\ Cause of termination & \verb$Relative gradient = 7.8e-08 <= 6.1e-06$ \\ Number of function evaluations & \verb$45$ \\ Number of gradient evaluations & \verb$27$ \\ Number of hessian evaluations & \verb$13$ \\ Number of iterations & \verb$18$ \\ Optimization time & \verb$0:00:01.428014$ \\ Proportion of Hessian calculation & \verb$13/13 = 100.0%$ \\ Relative gradient & \verb$7.786e-08$ \\ \end{tabular} \begin{tabular}{rlr@{.}lr@{.}lr@{.}lr@{.}l} & & \multicolumn{2}{l}{} & \multicolumn{2}{l}{Robust} & \multicolumn{4}{l}{} \\ Parameter & & \multicolumn{2}{l}{Coeff.} & \multicolumn{2}{l}{Asympt.} & \multicolumn{4}{l}{} \\ number & Description & \multicolumn{2}{l}{estimate} & \multicolumn{2}{l}{std. error} & \multicolumn{2}{l}{$t$-stat} & \multicolumn{2}{l}{$p$-value} \\ \hline 0 & choice\_scale\_parameter & 0&0782 & 0&0187 & 4&18 & 2&97e-05 \\ 1 & choice\_asc\_pt & -8&71 & 5&90 & -1&48 & 0&140 \\ 2 & choice\_beta\_time\_pt & -12&2 & 4&26 & -2&86 & 0&00424 \\ 3 & choice\_asc\_car & -3&07 & 5&03 & -0&609 & 0&542 \\ 4 & choice\_beta\_time\_car & -26&0 & 8&84 & -2&94 & 0&00328 \\ 5 & choice\_beta\_dist\_work & -2&61 & 0&840 & -3&11 & 0&00186 \\ 6 & choice\_beta\_dist\_other\_purposes & -4&12 & 1&22 & -3&38 & 0&000732 \\ \end{tabular} .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.082 seconds) .. _sphx_glr_download_auto_examples_hybrid_choice_models_plot_h01_mode_logit.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_h01_mode_logit.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_h01_mode_logit.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_h01_mode_logit.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_