.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/swissmetro/plot_b01c_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_swissmetro_plot_b01c_logit.py: 1c. Illustration of the quick_estimate of Biogeme ================================================= Same model as b01logit, estimated using the quick_estimate, that skips the calculation of the second orders statistics. Michel Bierlaire, EPFL Wed Jun 18 2025, 11:19:12 .. GENERATED FROM PYTHON SOURCE LINES 11-20 .. 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 loglogit from biogeme.results_processing import get_pandas_estimated_parameters .. GENERATED FROM PYTHON SOURCE LINES 21-22 See the data processing script: :ref:`swissmetro_data`. .. GENERATED FROM PYTHON SOURCE LINES 22-39 .. code-block:: Python from swissmetro_data import ( CAR_AV_SP, CAR_CO_SCALED, CAR_TT_SCALED, CHOICE, SM_AV, SM_COST_SCALED, SM_TT_SCALED, TRAIN_AV_SP, TRAIN_COST_SCALED, TRAIN_TT_SCALED, database, ) logger = blog.get_screen_logger(level=blog.INFO) logger.info('Example b01logit_ter.py') .. rst-class:: sphx-glr-script-out .. code-block:: none Example b01logit_ter.py .. GENERATED FROM PYTHON SOURCE LINES 40-41 Parameters to be estimated. .. GENERATED FROM PYTHON SOURCE LINES 41-48 .. 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 = Beta('b_time', 0, None, None, 0) b_cost = Beta('b_cost', 0, None, None, 0) .. GENERATED FROM PYTHON SOURCE LINES 49-50 Definition of the utility functions. .. GENERATED FROM PYTHON SOURCE LINES 50-54 .. code-block:: Python v_train = asc_train + b_time * TRAIN_TT_SCALED + b_cost * TRAIN_COST_SCALED v_swissmetro = asc_sm + b_time * SM_TT_SCALED + b_cost * SM_COST_SCALED v_car = asc_car + b_time * CAR_TT_SCALED + b_cost * CAR_CO_SCALED .. GENERATED FROM PYTHON SOURCE LINES 55-56 Associate utility functions with the numbering of alternatives. .. GENERATED FROM PYTHON SOURCE LINES 56-58 .. code-block:: Python v = {1: v_train, 2: v_swissmetro, 3: v_car} .. GENERATED FROM PYTHON SOURCE LINES 59-60 Associate the availability conditions with the alternatives. .. GENERATED FROM PYTHON SOURCE LINES 60-62 .. code-block:: Python av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP} .. GENERATED FROM PYTHON SOURCE LINES 63-65 Definition of the model. This is the contribution of each observation to the log likelihood function. .. GENERATED FROM PYTHON SOURCE LINES 65-67 .. code-block:: Python logprob = loglogit(v, av, CHOICE) .. GENERATED FROM PYTHON SOURCE LINES 68-69 Create the Biogeme object. .. GENERATED FROM PYTHON SOURCE LINES 69-72 .. code-block:: Python the_biogeme = BIOGEME(database, logprob) the_biogeme.model_name = 'b01c_logit' .. rst-class:: sphx-glr-script-out .. code-block:: none Biogeme parameters read from biogeme.toml. .. GENERATED FROM PYTHON SOURCE LINES 73-74 Calculate the null log likelihood for reporting. .. GENERATED FROM PYTHON SOURCE LINES 74-76 .. code-block:: Python the_biogeme.calculate_null_loglikelihood(av) .. rst-class:: sphx-glr-script-out .. code-block:: none -6964.662979192191 .. GENERATED FROM PYTHON SOURCE LINES 77-78 Estimate the parameters. .. GENERATED FROM PYTHON SOURCE LINES 78-80 .. code-block:: Python results = the_biogeme.quick_estimate() .. rst-class:: sphx-glr-script-out .. code-block:: none *** Initial values of the parameters are obtained from the file __b01c_logit.iter Cannot read file __b01c_logit.iter. Statement is ignored. 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. asc_train b_time b_cost asc_car Function Relgrad Radius Rho 0 -0.92 -0.67 -0.88 -0.49 5.4e+03 0.041 10 1.1 ++ 1 -0.73 -1.2 -1 -0.18 5.3e+03 0.0072 1e+02 1.1 ++ 2 -0.7 -1.3 -1.1 -0.16 5.3e+03 0.00018 1e+03 1 ++ 3 -0.7 -1.3 -1.1 -0.16 5.3e+03 1.1e-07 1e+03 1 ++ .. GENERATED FROM PYTHON SOURCE LINES 81-83 .. code-block:: Python print(results.short_summary()) .. rst-class:: sphx-glr-script-out .. code-block:: none Results for model b01c_logit Nbr of parameters: 4 Sample size: 6768 Excluded data: 3960 Null log likelihood: -6964.663 Final log likelihood: -5331.252 Likelihood ratio test (null): 3266.822 Rho square (null): 0.235 Rho bar square (null): 0.234 Akaike Information Criterion: 10670.5 Bayesian Information Criterion: 10697.78 .. GENERATED FROM PYTHON SOURCE LINES 84-87 Where quick_estimate is called, the initial log likelihood is not calculated. The derivatives of the loglikelihood function are not calculated either. It means that several statistics are missing in the report. This function is convenient when only the estimated values of the parameters are needed. .. GENERATED FROM PYTHON SOURCE LINES 89-90 Get the results in a pandas table .. GENERATED FROM PYTHON SOURCE LINES 90-95 .. code-block:: Python pandas_results = get_pandas_estimated_parameters( estimation_results=results, ) display(pandas_results) .. rst-class:: sphx-glr-script-out .. code-block:: none Name Value Robust std err. Robust t-stat. Robust p-value 0 asc_train -0.701187 NaN NaN NaN 1 b_time -1.277859 NaN NaN NaN 2 b_cost -1.083790 NaN NaN NaN 3 asc_car -0.154633 NaN NaN NaN .. GENERATED FROM PYTHON SOURCE LINES 96-98 Get general statistics. .. GENERATED FROM PYTHON SOURCE LINES 98-104 .. code-block:: Python print('General statistics') print('------------------') stats = results.get_general_statistics() for description, value in stats.items(): print(f'{description}: {value}') .. rst-class:: sphx-glr-script-out .. code-block:: none General statistics ------------------ Number of estimated parameters: 4 Sample size: 6768 Excluded observations: 3960 Null log likelihood: -6964.663 Final log likelihood: -5331.252 Likelihood ratio test for the null model: 3266.822 Rho-square for the null model: 0.235 Rho-square-bar for the null model: 0.234 Likelihood ratio test for the init. model: Rho-square for the init. model: Rho-square-bar for the init. model: Akaike Information Criterion: 10670.5 Bayesian Information Criterion: 10697.78 Final gradient norm: Bootstrapping time: None .. GENERATED FROM PYTHON SOURCE LINES 105-106 The YAML file is not automatically generated when quick_estimate is used. It can be done manually if needed. .. GENERATED FROM PYTHON SOURCE LINES 106-107 .. code-block:: Python results.dump_yaml_file(filename=f'{the_biogeme.model_name}.yaml') .. rst-class:: sphx-glr-script-out .. code-block:: none File b01c_logit.yaml has been generated. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.394 seconds) .. _sphx_glr_download_auto_examples_swissmetro_plot_b01c_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_b01c_logit.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_b01c_logit.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_b01c_logit.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_