.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/swissmetro/plot_b22multiple_models_spec.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_b22multiple_models_spec.py: .. _plot_b22multiple_models_spec: Specification of a catalog of models ==================================== Specification of the Catalog of expressions for the assisted specification algorithm. Note that this script does not perform any estimation. It is imported by other scripts: :ref:`plot_b22multiple_models`, :ref:`plot_b22process_pareto`. :author: Michel Bierlaire, EPFL :date: Fri Jul 21 17:56:47 2023 .. GENERATED FROM PYTHON SOURCE LINES 15-21 .. code-block:: Python from biogeme import models import biogeme.biogeme as bio from biogeme.expressions import Beta, logzero from biogeme.catalog import Catalog, segmentation_catalogs .. GENERATED FROM PYTHON SOURCE LINES 22-23 See the data processing script: :ref:`swissmetro_data`. .. GENERATED FROM PYTHON SOURCE LINES 23-42 .. 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, MALE, TRAIN_HE, SM_HE, LUGGAGE, GA, ) .. 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) B_TIME = Beta('B_TIME', 0, None, None, 0) B_COST = Beta('B_COST', 0, None, None, 0) B_HEADWAY = Beta('B_HEADWAY', 0, None, None, 0) .. GENERATED FROM PYTHON SOURCE LINES 51-52 Define segmentations .. GENERATED FROM PYTHON SOURCE LINES 52-75 .. code-block:: Python gender_segmentation = database.generate_segmentation( variable=MALE, mapping={0: 'female', 1: 'male'} ) GA_segmentation = database.generate_segmentation( variable=GA, mapping={0: 'without_ga', 1: 'with_ga'} ) luggage_segmentation = database.generate_segmentation( variable=LUGGAGE, mapping={0: 'no_lugg', 1: 'one_lugg', 3: 'several_lugg'} ) ASC_CAR_catalog, ASC_TRAIN_catalog = segmentation_catalogs( generic_name='ASC', beta_parameters=[ASC_CAR, ASC_TRAIN], potential_segmentations=( gender_segmentation, luggage_segmentation, GA_segmentation, ), maximum_number=2, ) .. GENERATED FROM PYTHON SOURCE LINES 76-77 We define a catalog with two different specifications for headway. .. GENERATED FROM PYTHON SOURCE LINES 77-89 .. code-block:: Python TRAIN_HEADWAY_catalog = Catalog.from_dict( catalog_name='TRAIN_HEADWAY_catalog', dict_of_expressions={'without_headway': 0, 'with_headway': B_HEADWAY * TRAIN_HE}, ) SM_HEADWAY_catalog = Catalog.from_dict( catalog_name='SM_HEADWAY_catalog', dict_of_expressions={'without_headway': 0, 'with_headway': B_HEADWAY * SM_HE}, controlled_by=TRAIN_HEADWAY_catalog.controlled_by, ) .. GENERATED FROM PYTHON SOURCE LINES 90-91 Parameter for Box-Cox transforms. .. GENERATED FROM PYTHON SOURCE LINES 91-93 .. code-block:: Python ell_TT = Beta('lambda_TT', 1, None, 10, 0) .. GENERATED FROM PYTHON SOURCE LINES 94-95 Non-linear specification for travel time. .. GENERATED FROM PYTHON SOURCE LINES 95-133 .. code-block:: Python TRAIN_TT_catalog = Catalog.from_dict( catalog_name='TRAIN_TT_catalog', dict_of_expressions={ 'linear': TRAIN_TT_SCALED, 'log': logzero(TRAIN_TT_SCALED), 'sqrt': TRAIN_TT_SCALED**0.5, 'piecewise_1': models.piecewise_formula(TRAIN_TT_SCALED, [0, 0.1, None]), 'piecewise_2': models.piecewise_formula(TRAIN_TT_SCALED, [0, 0.25, None]), 'boxcox': models.boxcox(TRAIN_TT_SCALED, ell_TT), }, ) SM_TT_catalog = Catalog.from_dict( catalog_name='SM_TT_catalog', dict_of_expressions={ 'linear': SM_TT_SCALED, 'log': logzero(SM_TT_SCALED), 'sqrt': SM_TT_SCALED**0.5, 'piecewise_1': models.piecewise_formula(SM_TT_SCALED, [0, 0.1, None]), 'piecewise_2': models.piecewise_formula(SM_TT_SCALED, [0, 0.25, None]), 'boxcox': models.boxcox(SM_TT_SCALED, ell_TT), }, controlled_by=TRAIN_TT_catalog.controlled_by, ) CAR_TT_catalog = Catalog.from_dict( catalog_name='CAR_TT_catalog', dict_of_expressions={ 'linear': CAR_TT_SCALED, 'log': logzero(CAR_TT_SCALED), 'sqrt': CAR_TT_SCALED**0.5, 'piecewise_1': models.piecewise_formula(CAR_TT_SCALED, [0, 0.1, None]), 'piecewise_2': models.piecewise_formula(CAR_TT_SCALED, [0, 0.25, None]), 'boxcox': models.boxcox(CAR_TT_SCALED, ell_TT), }, controlled_by=TRAIN_TT_catalog.controlled_by, ) .. GENERATED FROM PYTHON SOURCE LINES 134-135 Parameter for Box-Cox transforms. .. GENERATED FROM PYTHON SOURCE LINES 135-137 .. code-block:: Python ell_COST = Beta('lambda_COST', 1, None, 10, 0) .. GENERATED FROM PYTHON SOURCE LINES 138-139 Nonlinear transformations for travel cost. .. GENERATED FROM PYTHON SOURCE LINES 139-177 .. code-block:: Python TRAIN_COST_catalog = Catalog.from_dict( catalog_name='TRAIN_COST_catalog', dict_of_expressions={ 'linear': TRAIN_COST_SCALED, 'log': logzero(TRAIN_COST_SCALED), 'sqrt': TRAIN_COST_SCALED**0.5, 'piecewise_1': models.piecewise_formula(TRAIN_COST_SCALED, [0, 0.1, None]), 'piecewise_2': models.piecewise_formula(TRAIN_COST_SCALED, [0, 0.25, None]), 'boxcox': models.boxcox(TRAIN_COST_SCALED, ell_COST), }, ) SM_COST_catalog = Catalog.from_dict( catalog_name='SM_COST_catalog', dict_of_expressions={ 'linear': SM_COST_SCALED, 'log': logzero(SM_COST_SCALED), 'sqrt': SM_COST_SCALED**0.5, 'piecewise_1': models.piecewise_formula(SM_COST_SCALED, [0, 0.1, None]), 'piecewise_2': models.piecewise_formula(SM_COST_SCALED, [0, 0.25, None]), 'boxcox': models.boxcox(SM_COST_SCALED, ell_COST), }, controlled_by=TRAIN_COST_catalog.controlled_by, ) CAR_COST_catalog = Catalog.from_dict( catalog_name='CAR_COST_catalog', dict_of_expressions={ 'linear': CAR_CO_SCALED, 'log': logzero(CAR_CO_SCALED), 'sqrt': CAR_CO_SCALED**0.5, 'piecewise_1': models.piecewise_formula(CAR_CO_SCALED, [0, 0.1, None]), 'piecewise_2': models.piecewise_formula(CAR_CO_SCALED, [0, 0.25, None]), 'boxcox': models.boxcox(CAR_CO_SCALED, ell_COST), }, controlled_by=TRAIN_COST_catalog.controlled_by, ) .. GENERATED FROM PYTHON SOURCE LINES 178-179 Definition of the utility functions .. GENERATED FROM PYTHON SOURCE LINES 179-189 .. code-block:: Python V1 = ( ASC_TRAIN_catalog + B_TIME * TRAIN_TT_catalog + B_COST * TRAIN_COST_catalog + TRAIN_HEADWAY_catalog ) V2 = B_TIME * SM_TT_catalog + B_COST * SM_COST_catalog + SM_HEADWAY_catalog V3 = ASC_CAR_catalog + B_TIME * CAR_TT_catalog + B_COST * CAR_COST_catalog .. GENERATED FROM PYTHON SOURCE LINES 190-191 Associate utility functions with the numbering of alternatives .. GENERATED FROM PYTHON SOURCE LINES 191-193 .. code-block:: Python V = {1: V1, 2: V2, 3: V3} .. GENERATED FROM PYTHON SOURCE LINES 194-195 Associate the availability conditions with the alternatives .. GENERATED FROM PYTHON SOURCE LINES 195-197 .. code-block:: Python av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP} .. GENERATED FROM PYTHON SOURCE LINES 198-200 Definition of the model. This is the contribution of each observation to the log likelihood function. .. GENERATED FROM PYTHON SOURCE LINES 200-202 .. code-block:: Python logprob = models.loglogit(V, av, CHOICE) .. GENERATED FROM PYTHON SOURCE LINES 203-206 .. code-block:: Python the_biogeme = bio.BIOGEME(database, logprob) the_biogeme.modelName = 'b22multiple_models' .. GENERATED FROM PYTHON SOURCE LINES 207-208 .. code-block:: Python PARETO_FILE_NAME = 'b22multiple_models.pareto' .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.103 seconds) .. _sphx_glr_download_auto_examples_swissmetro_plot_b22multiple_models_spec.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_b22multiple_models_spec.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_b22multiple_models_spec.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_b22multiple_models_spec.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_