.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/swissmetro/plot_b11a_cnl.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_swissmetro_plot_b11a_cnl.py: 11a. Cross-nested logit ======================= Example of a cross-nested logit model with two nests: - one with existing alternatives (car and train), - one with public transportation alternatives (train and Swissmetro) Michel Bierlaire, EPFL Sat Jun 21 2025, 16:33:38 .. GENERATED FROM PYTHON SOURCE LINES 15-28 .. code-block:: Python from IPython.core.display_functions import display import biogeme.biogeme_logging as blog from biogeme.biogeme import BIOGEME from biogeme.expressions import Beta from biogeme.models import logcnl from biogeme.nests import NestsForCrossNestedLogit, OneNestForCrossNestedLogit from biogeme.results_processing import ( EstimationResults, get_pandas_estimated_parameters, ) .. GENERATED FROM PYTHON SOURCE LINES 29-30 See the data processing script: :ref:`swissmetro_data`. .. GENERATED FROM PYTHON SOURCE LINES 30-50 .. code-block:: Python from swissmetro_data import ( CAR_AV_SP, CAR_CO_SCALED, CAR_TT_SCALED, CHOICE, GA, SM_AV, SM_COST_SCALED, SM_HE, SM_TT_SCALED, TRAIN_AV_SP, TRAIN_COST_SCALED, TRAIN_HE, TRAIN_TT_SCALED, database, ) logger = blog.get_screen_logger(level=blog.INFO) logger.info('Example b11a_cnl.py') .. rst-class:: sphx-glr-script-out .. code-block:: none Example b11a_cnl.py .. GENERATED FROM PYTHON SOURCE LINES 51-52 Parameters to be estimated. .. GENERATED FROM PYTHON SOURCE LINES 52-64 .. code-block:: Python asc_car = Beta('asc_car', 0, None, None, 0) asc_train = Beta('asc_train', 0, None, None, 0) asc_sm = Beta('asc_sm', 0, None, None, 1) b_time_swissmetro = Beta('b_time_swissmetro', 0, None, None, 0) b_time_train = Beta('b_time_train', 0, None, None, 0) b_time_car = Beta('b_time_car', 0, None, None, 0) b_cost = Beta('b_cost', 0, None, None, 0) b_headway_swissmetro = Beta('b_headway_swissmetro', 0, None, None, 0) b_headway_train = Beta('b_headway_train', 0, None, None, 0) ga_train = Beta('ga_train', 0, None, None, 0) ga_swissmetro = Beta('ga_swissmetro', 0, None, None, 0) .. GENERATED FROM PYTHON SOURCE LINES 65-68 .. code-block:: Python existing_nest_parameter = Beta('existing_nest_parameter', 1, 1, 5, 0) public_nest_parameter = Beta('public_nest_parameter', 1, 1, 5, 0) .. GENERATED FROM PYTHON SOURCE LINES 69-70 Nest membership parameters. .. GENERATED FROM PYTHON SOURCE LINES 70-73 .. code-block:: Python alpha_existing = Beta('alpha_existing', 0.5, 0, 1, 0) alpha_public = 1 - alpha_existing .. GENERATED FROM PYTHON SOURCE LINES 74-75 Definition of the utility functions .. GENERATED FROM PYTHON SOURCE LINES 75-91 .. code-block:: Python v_train = ( asc_train + b_time_train * TRAIN_TT_SCALED + b_cost * TRAIN_COST_SCALED + b_headway_train * TRAIN_HE + ga_train * GA ) v_swissmetro = ( asc_sm + b_time_swissmetro * SM_TT_SCALED + b_cost * SM_COST_SCALED + b_headway_swissmetro * SM_HE + ga_swissmetro * GA ) v_car = asc_car + b_time_car * CAR_TT_SCALED + b_cost * CAR_CO_SCALED .. GENERATED FROM PYTHON SOURCE LINES 92-93 Associate utility functions with the numbering of alternatives .. GENERATED FROM PYTHON SOURCE LINES 93-95 .. code-block:: Python v = {1: v_train, 2: v_swissmetro, 3: v_car} .. GENERATED FROM PYTHON SOURCE LINES 96-97 Associate the availability conditions with the alternatives .. GENERATED FROM PYTHON SOURCE LINES 97-99 .. code-block:: Python av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP} .. GENERATED FROM PYTHON SOURCE LINES 100-101 Definition of nests. .. GENERATED FROM PYTHON SOURCE LINES 101-118 .. code-block:: Python nest_existing = OneNestForCrossNestedLogit( nest_param=existing_nest_parameter, dict_of_alpha={1: alpha_existing, 2: 0.0, 3: 1.0}, name='existing', ) nest_public = OneNestForCrossNestedLogit( nest_param=public_nest_parameter, dict_of_alpha={1: alpha_public, 2: 1.0, 3: 0.0}, name='public', ) nests = NestsForCrossNestedLogit( choice_set=[1, 2, 3], tuple_of_nests=(nest_existing, nest_public) ) .. GENERATED FROM PYTHON SOURCE LINES 119-120 The choice model is a cross-nested logit, with availability conditions. .. GENERATED FROM PYTHON SOURCE LINES 120-122 .. code-block:: Python log_probability = logcnl(v, av, nests, CHOICE) .. GENERATED FROM PYTHON SOURCE LINES 123-124 Create the Biogeme object .. GENERATED FROM PYTHON SOURCE LINES 124-127 .. code-block:: Python the_biogeme = BIOGEME(database, log_probability) the_biogeme.model_name = 'b11a_cnl' .. rst-class:: sphx-glr-script-out .. code-block:: none Biogeme parameters read from biogeme.toml. .. GENERATED FROM PYTHON SOURCE LINES 128-129 Estimate the parameters. .. GENERATED FROM PYTHON SOURCE LINES 129-136 .. code-block:: Python try: results = EstimationResults.from_yaml_file( filename=f'saved_results/{the_biogeme.model_name}.yaml' ) except FileNotFoundError: results = the_biogeme.estimate() .. GENERATED FROM PYTHON SOURCE LINES 137-139 .. code-block:: Python print(results.short_summary()) .. rst-class:: sphx-glr-script-out .. code-block:: none Results for model b11a_cnl Nbr of parameters: 13 Sample size: 6768 Excluded data: 3960 Final log likelihood: -4997.865 Akaike Information Criterion: 10021.73 Bayesian Information Criterion: 10110.39 .. GENERATED FROM PYTHON SOURCE LINES 140-142 .. code-block:: Python pandas_results = get_pandas_estimated_parameters(estimation_results=results) display(pandas_results) .. rst-class:: sphx-glr-script-out .. code-block:: none {'Estimated parameters': Name Value ... Robust t-stat. Robust p-value 0 asc_train -0.308539 ... -1.541721 1.231413e-01 1 b_time_train -1.073929 ... -7.579251 3.486100e-14 2 b_cost -0.973731 ... -14.711484 0.000000e+00 3 b_headway_train -0.004366 ... -4.491585 7.069491e-06 4 ga_train 1.143049 ... 4.934230 8.046743e-07 5 b_time_swissmetro -0.991520 ... -5.574118 2.487864e-08 6 b_headway_swissmetro -0.007724 ... -2.601213 9.289469e-03 7 ga_swissmetro -0.138879 ... -0.861718 3.888428e-01 8 asc_car -0.606268 ... -4.886032 1.028884e-06 9 b_time_car -0.857023 ... -6.760749 1.372791e-11 10 existing_nest_parameter 1.771146 ... 7.695657 1.398881e-14 11 public_nest_parameter 1.839669 ... 3.952927 7.720108e-05 12 alpha_existing 0.644768 ... 3.743739 1.813018e-04 [13 rows x 5 columns]} .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.145 seconds) .. _sphx_glr_download_auto_examples_swissmetro_plot_b11a_cnl.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_b11a_cnl.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_b11a_cnl.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_b11a_cnl.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_