.. 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 method in Biogeme ======================================================== This example estimates the same logit model as in Example 1a, but uses `quick_estimate`, a lightweight estimation procedure designed for cases where only the estimated parameter values are required. Unlike the standard estimation procedure, `quick_estimate` skips the calculation of second-order derivatives and several post-estimation statistics. As a result, the estimation is faster, but some indicators normally reported by Biogeme are not available. The script illustrates: - the specification of a logit model, - the use of `quick_estimate`, - the inspection of the estimation results, - the extraction of the estimated parameters into a pandas DataFrame, - the manual generation of a YAML output file. The `# %%` markers are used to separate the script into notebook cells when the example gallery is converted into Jupyter notebooks. Tested with Biogeme 3.3.3. Michel Bierlaire, EPFL Tue Jun 09 2026, 14:45:00 .. GENERATED FROM PYTHON SOURCE LINES 31-34 .. code-block:: Python from IPython.core.display_functions import display .. GENERATED FROM PYTHON SOURCE LINES 35-36 Import the variables and the database prepared in the Swissmetro data-processing example. .. GENERATED FROM PYTHON SOURCE LINES 36-59 .. 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, ) 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 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 60-61 Define the model parameters to be estimated. .. GENERATED FROM PYTHON SOURCE LINES 61-68 .. 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 69-70 Define the utility functions. .. GENERATED FROM PYTHON SOURCE LINES 70-74 .. 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 75-76 Associate each utility function with the corresponding alternative identifier. .. GENERATED FROM PYTHON SOURCE LINES 76-78 .. code-block:: Python v = {1: v_train, 2: v_swissmetro, 3: v_car} .. GENERATED FROM PYTHON SOURCE LINES 79-80 Associate the availability conditions with each alternative. .. GENERATED FROM PYTHON SOURCE LINES 80-82 .. code-block:: Python av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP} .. GENERATED FROM PYTHON SOURCE LINES 83-84 Define the log-likelihood contribution of each observation. .. GENERATED FROM PYTHON SOURCE LINES 84-86 .. code-block:: Python logprob = loglogit(v, av, CHOICE) .. GENERATED FROM PYTHON SOURCE LINES 87-88 Create the Biogeme object. .. GENERATED FROM PYTHON SOURCE LINES 88-91 .. 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 92-93 Calculate the null log likelihood used in the estimation report. .. GENERATED FROM PYTHON SOURCE LINES 93-95 .. 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 96-97 Estimate the parameters using the quick estimation procedure. .. GENERATED FROM PYTHON SOURCE LINES 97-99 .. 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. Analytical Hessian method: full 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 100-101 Display a short summary of the estimation results. .. GENERATED FROM PYTHON SOURCE LINES 101-103 .. 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 104-107 The quick estimation procedure does not calculate the initial log likelihood, second derivatives, or several post-estimation statistics. It is useful when only the estimated parameter values are required. .. GENERATED FROM PYTHON SOURCE LINES 109-110 Convert the estimated parameters into a pandas DataFrame. .. GENERATED FROM PYTHON SOURCE LINES 110-115 .. 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 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 116-117 Display the available general estimation statistics. .. GENERATED FROM PYTHON SOURCE LINES 117-123 .. 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 124-126 The YAML file is not generated automatically when quick_estimate is used. Generate it manually if needed. .. GENERATED FROM PYTHON SOURCE LINES 126-127 .. 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 0.509 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 `_