.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/swissmetro/plot_b11cnl_old.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_b11cnl_old.py: Cross-nested logit (old syntax) =============================== Example of a cross-nested logit model, using the original syntax for nests. Since biogeme 3.13, a new syntax, more explicit, has been adopted. There are two nests: - one with existing alternatives (car and train), - one with public transportation alternatives (train and Swissmetro) :author: Michel Bierlaire, EPFL :date: Sun Apr 9 18:06:44 2023 .. GENERATED FROM PYTHON SOURCE LINES 17-23 .. code-block:: Python import biogeme.biogeme_logging as blog import biogeme.biogeme as bio from biogeme import models from biogeme.expressions import Beta .. GENERATED FROM PYTHON SOURCE LINES 24-25 See the data processing script: :ref:`swissmetro_data`. .. GENERATED FROM PYTHON SOURCE LINES 25-42 .. code-block:: Python from swissmetro_data import ( database, CHOICE, SM_AV, CAR_AV_SP, TRAIN_AV_SP, TRAIN_TT_SCALED, TRAIN_COST_SCALED, SM_TT_SCALED, SM_COST_SCALED, CAR_TT_SCALED, CAR_CO_SCALED, ) logger = blog.get_screen_logger(level=blog.INFO) logger.info('Example b11cnl.py') .. rst-class:: sphx-glr-script-out .. code-block:: none Example b11cnl.py .. GENERATED FROM PYTHON SOURCE LINES 43-44 Parameters to be estimated. .. GENERATED FROM PYTHON SOURCE LINES 44-50 .. 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 51-54 .. code-block:: Python MU_EXISTING = Beta('MU_EXISTING', 1, 1, 10, 0) MU_PUBLIC = Beta('MU_PUBLIC', 1, 1, 10, 0) .. GENERATED FROM PYTHON SOURCE LINES 55-56 Definition of the utility functions. .. GENERATED FROM PYTHON SOURCE LINES 56-60 .. code-block:: Python V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED V2 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED V3 = ASC_CAR + B_TIME * CAR_TT_SCALED + B_COST * CAR_CO_SCALED .. GENERATED FROM PYTHON SOURCE LINES 61-62 Associate utility functions with the numbering of alternatives. .. GENERATED FROM PYTHON SOURCE LINES 62-64 .. code-block:: Python V = {1: V1, 2: V2, 3: V3} .. GENERATED FROM PYTHON SOURCE LINES 65-66 Associate the availability conditions with the alternatives. .. GENERATED FROM PYTHON SOURCE LINES 66-68 .. code-block:: Python av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP} .. GENERATED FROM PYTHON SOURCE LINES 69-70 Definition of nests. .. GENERATED FROM PYTHON SOURCE LINES 70-81 .. code-block:: Python ALPHA_EXISTING = Beta('ALPHA_EXISTING', 0.5, 0, 1, 0) alpha_existing = {1: ALPHA_EXISTING, 2: 0.0, 3: 1.0} ALPHA_PUBLIC = 1 - ALPHA_EXISTING alpha_public = {1: ALPHA_PUBLIC, 2: 1.0, 3: 0.0} nest_existing = MU_EXISTING, alpha_existing nest_public = MU_PUBLIC, alpha_public nests = nest_existing, nest_public .. GENERATED FROM PYTHON SOURCE LINES 82-83 The choice model is a cross-nested logit, with availability conditions. .. GENERATED FROM PYTHON SOURCE LINES 83-85 .. code-block:: Python logprob = models.logcnl(V, av, nests, CHOICE) .. rst-class:: sphx-glr-script-out .. code-block:: none It is recommended to define the nests of the cross-nested logit model using the objects OneNestForNestedLogit and NestsForCrossNestedLogit defined in biogeme.nests. .. GENERATED FROM PYTHON SOURCE LINES 86-87 Create the Biogeme object. .. GENERATED FROM PYTHON SOURCE LINES 87-90 .. code-block:: Python the_biogeme = bio.BIOGEME(database, logprob) the_biogeme.modelName = 'b11cnl_old' .. rst-class:: sphx-glr-script-out .. code-block:: none Biogeme parameters read from biogeme.toml. .. GENERATED FROM PYTHON SOURCE LINES 91-92 Estimate the parameters .. GENERATED FROM PYTHON SOURCE LINES 92-94 .. code-block:: Python results = the_biogeme.estimate() .. rst-class:: sphx-glr-script-out .. code-block:: none As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" *** Initial values of the parameters are obtained from the file __b11cnl_old.iter Cannot read file __b11cnl_old.iter. Statement is ignored. As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ALPHA_EXISTING ASC_CAR ASC_TRAIN B_COST B_TIME MU_EXISTING MU_PUBLIC Function Relgrad Radius Rho 0 0.57 -0.088 -0.83 -0.27 -1 1.5 1.5 5.6e+03 0.081 1 0.61 + 1 0.86 -0.29 -0.32 -1.3 -0.93 1.8 1.7 5.3e+03 0.049 1 0.63 + 2 0.86 -0.29 -0.32 -1.3 -0.93 1.8 1.7 5.3e+03 0.049 0.5 0.013 - 3 0.79 -0.12 -0.34 -0.77 -0.9 2 1.8 5.2e+03 0.012 0.5 0.79 + 4 0.56 -0.25 -0.1 -0.88 -0.8 2.5 2.2 5.2e+03 0.013 0.5 0.69 + 5 0.55 -0.25 -0.057 -0.86 -0.8 2.5 2.6 5.2e+03 0.0039 5 1.3 ++ 6 0.51 -0.25 0.049 -0.84 -0.79 2.5 3.3 5.2e+03 0.0054 50 1.2 ++ 7 0.5 -0.24 0.077 -0.83 -0.78 2.5 3.7 5.2e+03 0.0017 5e+02 1.2 ++ 8 0.5 -0.24 0.095 -0.82 -0.78 2.5 4 5.2e+03 0.00053 5e+03 1.1 ++ 9 0.5 -0.24 0.095 -0.82 -0.78 2.5 4 5.2e+03 3.3e-05 5e+03 1 ++ Results saved in file b11cnl_old.html Results saved in file b11cnl_old.pickle .. GENERATED FROM PYTHON SOURCE LINES 95-97 .. code-block:: Python print(results.short_summary()) .. rst-class:: sphx-glr-script-out .. code-block:: none Results for model b11cnl_old Nbr of parameters: 7 Sample size: 6768 Excluded data: 3960 Final log likelihood: -5214.049 Akaike Information Criterion: 10442.1 Bayesian Information Criterion: 10489.84 .. GENERATED FROM PYTHON SOURCE LINES 98-100 .. code-block:: Python pandas_results = results.get_estimated_parameters() pandas_results .. raw:: html
Value Rob. Std err Rob. t-test Rob. p-value
ALPHA_EXISTING 0.495086 0.034723 14.258046 0.000000e+00
ASC_CAR -0.240519 0.053433 -4.501346 6.752446e-06
ASC_TRAIN 0.098094 0.069916 1.403026 1.606089e-01
B_COST -0.818963 0.058942 -13.894456 0.000000e+00
B_TIME -0.776852 0.102331 -7.591581 3.153033e-14
MU_EXISTING 2.514925 0.248163 10.134152 0.000000e+00
MU_PUBLIC 4.108825 0.494954 8.301433 0.000000e+00


.. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.373 seconds) .. _sphx_glr_download_auto_examples_swissmetro_plot_b11cnl_old.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_b11cnl_old.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_b11cnl_old.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_b11cnl_old.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_