.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/latent/plot_b02_choice_only.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_latent_plot_b02_choice_only.py: Estimation of the choice model ============================== Choice model without any latent variable. Michel Bierlaire, EPFL Wed Sept 03 2025, 08:18:01 .. GENERATED FROM PYTHON SOURCE LINES 12-30 .. code-block:: Python from choice_model import v 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 ( EstimationResults, ) logger = blog.get_screen_logger(level=blog.INFO) database = read_data() .. GENERATED FROM PYTHON SOURCE LINES 31-32 We integrate over omega using numerical integration .. GENERATED FROM PYTHON SOURCE LINES 32-35 .. code-block:: Python log_likelihood = loglogit(v, None, Choice) .. GENERATED FROM PYTHON SOURCE LINES 36-37 Create the Biogeme object .. GENERATED FROM PYTHON SOURCE LINES 37-44 .. code-block:: Python print('Create the biogeme object') the_biogeme = BIOGEME(database, log_likelihood) the_biogeme.model_name = 'b02_choice_only' print('--- Estimate ---') results: EstimationResults = the_biogeme.estimate() .. rst-class:: sphx-glr-script-out .. code-block:: none Create the biogeme object Biogeme parameters read from biogeme.toml. --- Estimate --- *** Initial values of the parameters are obtained from the file __b02_choice_only.iter Cannot read file __b02_choice_only.iter. Statement is ignored. Starting values for the algorithm: {} As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. log_scale_choic log_scale_choic choice_asc_pt_s choice_asc_pt_l choice_beta_tim choice_beta_tim choice_beta_wai choice_asc_car_ choice_asc_car_ choice_beta_tim choice_beta_tim choice_beta_dis Function Relgrad Radius Rho 0 1 0.91 -0.014 -0.083 -0.034 -0.19 -0.022 0.13 0.058 0.076 0.064 -0.68 1.9e+03 0.36 1 0.16 + 1 1 0.91 -0.014 -0.083 -0.034 -0.19 -0.022 0.13 0.058 0.076 0.064 -0.68 1.9e+03 0.36 0.5 -8.5 - 2 0.5 0.41 0.33 0.0079 0.28 0.055 -0.021 -0.37 -0.043 -0.13 -0.081 -0.18 1.3e+03 1.7 0.5 0.76 + 3 -1.1e-16 -0.042 0.3 0.021 0.16 0.087 -0.042 -0.17 -0.076 -0.059 -0.13 -0.2 8.4e+02 0.75 5 1.2 ++ 4 -0.28 -0.9 2.1 0.36 -1.1 -0.14 -0.29 1 0.57 -2 -1 -0.25 5.6e+02 0.34 50 1.3 ++ 5 -0.66 -1.4 3.5 1.6 -2.4 -1.1 -0.97 2.3 1.1 -5.2 -3 -0.34 4.5e+02 0.16 5e+02 1.3 ++ 6 -0.92 -1.7 5 6.5 -3.9 -3.7 -2.6 4.1 4.4 -9.5 -8.5 -0.46 3.9e+02 0.074 5e+03 1.3 ++ 7 -1.2 -1.8 6.7 11 -5.4 -6.7 -5.2 6.2 10 -14 -17 -0.62 3.6e+02 0.033 5e+04 1.3 ++ 8 -1.4 -2 8.6 15 -7 -9.3 -8.1 8.7 15 -19 -24 -0.79 3.5e+02 0.014 5e+05 1.3 ++ 9 -1.6 -2.1 11 17 -8.5 -11 -11 11 18 -23 -30 -0.95 3.5e+02 0.0053 5e+06 1.3 ++ 10 -1.7 -2.1 12 18 -9.7 -13 -13 14 18 -27 -33 -1.1 3.4e+02 0.0018 5e+07 1.2 ++ 11 -1.8 -2.2 14 19 -11 -14 -14 15 20 -29 -36 -1.2 3.4e+02 0.00055 5e+08 1.1 ++ 12 -1.8 -2.2 14 22 -11 -15 -15 16 23 -30 -38 -1.2 3.4e+02 0.00013 5e+09 1.1 ++ 13 -1.8 -2.2 14 23 -10 -14 -14 15 24 -29 -38 -1.1 3.4e+02 7.2e-05 1e+10 0.95 ++ 14 -1.8 -2.2 14 25 -11 -15 -15 16 26 -29 -39 -1.2 3.4e+02 8.4e-05 1e+10 1 ++ 15 -1.8 -2.2 14 26 -10 -15 -14 16 27 -29 -39 -1.1 3.4e+02 2.7e-05 1e+10 0.98 ++ 16 -1.8 -2.2 14 26 -11 -15 -15 16 28 -29 -39 -1.1 3.4e+02 0.0001 1e+10 1 ++ 17 -1.8 -2.2 14 27 -10 -15 -15 16 28 -29 -39 -1.1 3.4e+02 1.4e-05 1e+10 0.99 ++ 18 -1.8 -2.2 14 27 -10 -15 -15 16 29 -29 -40 -1.1 3.4e+02 1.2e-05 1e+10 1 ++ 19 -1.8 -2.2 14 28 -10 -15 -15 16 29 -29 -39 -1.1 3.4e+02 1.1e-05 1e+10 0.99 ++ 20 -1.8 -2.2 14 28 -10 -15 -15 16 29 -29 -40 -1.1 3.4e+02 3.2e-05 1e+10 1 ++ 21 -1.8 -2.2 14 28 -10 -15 -15 16 30 -29 -40 -1.1 3.4e+02 7.1e-06 1e+10 1 ++ 22 -1.8 -2.3 14 28 -10 -15 -15 16 30 -29 -40 -1.1 3.4e+02 7.4e-06 1e+10 1 ++ 23 -1.8 -2.3 14 28 -10 -15 -15 16 30 -29 -40 -1.1 3.4e+02 3e-06 1e+10 1 ++ Optimization algorithm has converged. Relative gradient: 2.9917193448014054e-06 Cause of termination: Relative gradient = 3e-06 <= 6.1e-06 Number of function evaluations: 71 Number of gradient evaluations: 47 Number of hessian evaluations: 23 Algorithm: Newton with trust region for simple bound constraints Number of iterations: 24 Proportion of Hessian calculation: 23/23 = 100.0% Optimization time: 0:00:00.867998 Calculate second derivatives and BHHH File b02_choice_only.html has been generated. File b02_choice_only.yaml has been generated. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.254 seconds) .. _sphx_glr_download_auto_examples_latent_plot_b02_choice_only.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_b02_choice_only.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_b02_choice_only.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_b02_choice_only.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_