.. 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() .. rst-class:: sphx-glr-script-out .. code-block:: none *** Initial values of the parameters are obtained from the file __b11a_cnl.iter Cannot read file __b11a_cnl.iter. Statement is ignored. Starting values for the algorithm: {} As the model is rather complex, we cancel the calculation of second derivatives. If you want to control the parameters, change the algorithm from "automatic" to "simple_bounds" in the TOML file. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: BFGS with trust region for simple bounds Iter. asc_train b_time_train b_cost b_headway_train ga_train alpha_existing existing_nest_p b_time_swissmet b_headway_swiss ga_swissmetro asc_car b_time_car public_nest_par Function Relgrad Radius Rho 0 0 0 0 0 0 0.5 1 0 0 0 0 0 1 7e+03 16 0.5 -0.93 - 1 0 0 0 0 0 0.5 1 0 0 0 0 0 1 7e+03 16 0.25 -0.79 - 2 0 0 0 0 0 0.5 1 0 0 0 0 0 1 7e+03 16 0.12 -0.66 - 3 0 0 0 0 0 0.5 1 0 0 0 0 0 1 7e+03 16 0.062 -0.48 - 4 0 0 0 0 0 0.5 1 0 0 0 0 0 1 7e+03 16 0.031 -0.22 - 5 -0.031 -0.031 -0.031 -0.031 0.031 0.5 1 0.031 0.031 0.031 -0.031 -0.031 1 6.5e+03 6.1 0.031 0.1 + 6 -0.032 -0.036 -0.039 -0.029 0.036 0.5 1 0.031 0 0.028 -0.031 -0.035 1 6.2e+03 4.4 0.031 0.72 + 7 -0.032 -0.036 -0.039 -0.029 0.036 0.5 1 0.031 0 0.028 -0.031 -0.035 1 6.2e+03 4.4 0.016 -0.49 - 8 -0.016 -0.02 -0.055 -0.013 0.051 0.52 1 0.047 0.016 0.013 -0.047 -0.051 1 5.9e+03 0.56 0.016 0.57 + 9 -0.016 -0.02 -0.055 -0.013 0.051 0.52 1 0.047 0.016 0.013 -0.047 -0.051 1 5.9e+03 0.56 0.0078 -2.2 - 10 -0.016 -0.02 -0.055 -0.013 0.051 0.52 1 0.047 0.016 0.013 -0.047 -0.051 1 5.9e+03 0.56 0.0039 -1.5 - 11 -0.016 -0.02 -0.055 -0.013 0.051 0.52 1 0.047 0.016 0.013 -0.047 -0.051 1 5.9e+03 0.56 0.002 -0.45 - 12 -0.018 -0.022 -0.057 -0.015 0.053 0.52 1 0.049 0.018 0.011 -0.049 -0.053 1 5.9e+03 0.72 0.002 0.37 + 13 -0.018 -0.023 -0.057 -0.013 0.053 0.52 1 0.049 0.019 0.011 -0.049 -0.053 1 5.9e+03 0.19 0.002 0.73 + 14 -0.019 -0.025 -0.059 -0.014 0.055 0.52 1 0.049 0.02 0.0098 -0.049 -0.054 1 5.9e+03 0.2 0.002 0.66 + 15 -0.019 -0.027 -0.061 -0.013 0.056 0.52 1 0.049 0.019 0.0091 -0.049 -0.055 1 5.9e+03 0.066 0.02 1 ++ 16 -0.025 -0.046 -0.08 -0.017 0.066 0.52 1 0.051 0.013 0.0019 -0.049 -0.064 1 5.9e+03 1.4 0.02 0.19 + 17 -0.031 -0.065 -0.1 -0.013 0.077 0.52 1 0.053 0.021 -0.0054 -0.049 -0.074 1 5.8e+03 0.38 0.2 0.91 ++ 18 -0.031 -0.065 -0.1 -0.013 0.077 0.52 1 0.053 0.021 -0.0054 -0.049 -0.074 1 5.8e+03 0.38 0.098 -15 - 19 -0.031 -0.065 -0.1 -0.013 0.077 0.52 1 0.053 0.021 -0.0054 -0.049 -0.074 1 5.8e+03 0.38 0.049 -23 - 20 -0.031 -0.065 -0.1 -0.013 0.077 0.52 1 0.053 0.021 -0.0054 -0.049 -0.074 1 5.8e+03 0.38 0.024 -37 - 21 -0.031 -0.065 -0.1 -0.013 0.077 0.52 1 0.053 0.021 -0.0054 -0.049 -0.074 1 5.8e+03 0.38 0.012 -17 - 22 -0.031 -0.065 -0.1 -0.013 0.077 0.52 1 0.053 0.021 -0.0054 -0.049 -0.074 1 5.8e+03 0.38 0.0061 -12 - 23 -0.031 -0.065 -0.1 -0.013 0.077 0.52 1 0.053 0.021 -0.0054 -0.049 -0.074 1 5.8e+03 0.38 0.0031 -2.3 - 24 -0.031 -0.065 -0.1 -0.013 0.077 0.52 1 0.053 0.021 -0.0054 -0.049 -0.074 1 5.8e+03 0.38 0.0015 -0.37 - 25 -0.032 -0.067 -0.1 -0.011 0.079 0.52 1 0.051 0.019 -0.0069 -0.047 -0.075 1 5.8e+03 0.62 0.0015 0.37 + 26 -0.032 -0.067 -0.1 -0.013 0.079 0.52 1 0.051 0.018 -0.0071 -0.047 -0.075 1 5.8e+03 0.14 0.0015 0.77 + 27 -0.033 -0.069 -0.1 -0.012 0.08 0.52 1 0.052 0.017 -0.0077 -0.047 -0.076 1 5.8e+03 0.11 0.015 0.97 ++ 28 -0.037 -0.084 -0.12 -0.012 0.089 0.52 1.1 0.053 0.021 -0.014 -0.047 -0.084 1 5.8e+03 0.3 0.15 0.92 ++ 29 -0.08 -0.23 -0.27 -0.011 0.18 0.52 1.1 0.063 0.01 -0.072 -0.048 -0.16 1 5.6e+03 0.42 0.15 0.89 + 30 -0.12 -0.37 -0.42 -0.0023 0.27 0.53 1.2 0.062 0.02 -0.14 -0.053 -0.24 1.1 5.5e+03 2.4 0.15 0.47 + 31 -0.15 -0.49 -0.58 -0.0073 0.38 0.55 1.3 0.045 -0.001 -0.21 -0.065 -0.33 1.1 5.3e+03 0.71 0.15 0.73 + 32 -0.16 -0.58 -0.73 -0.0042 0.53 0.59 1.4 -0.024 0.002 -0.3 -0.099 -0.44 1.2 5.2e+03 0.75 0.15 0.83 + 33 -0.17 -0.66 -0.86 -0.0063 0.68 0.65 1.5 -0.14 -0.0061 -0.39 -0.13 -0.54 1.3 5.1e+03 0.54 0.15 0.74 + 34 -0.18 -0.74 -0.94 -0.004 0.8 0.7 1.6 -0.3 0.0027 -0.45 -0.16 -0.59 1.3 5.1e+03 0.81 0.15 0.86 + 35 -0.2 -0.84 -1 -0.0067 0.91 0.74 1.7 -0.43 -0.0018 -0.49 -0.24 -0.74 1.3 5e+03 0.6 0.15 0.69 + 36 -0.2 -0.84 -1 -0.0067 0.91 0.74 1.7 -0.43 -0.0018 -0.49 -0.24 -0.74 1.3 5e+03 0.6 0.076 0.089 - 37 -0.22 -0.9 -0.99 -0.0046 0.93 0.75 1.7 -0.51 -0.0017 -0.48 -0.24 -0.7 1.3 5e+03 0.19 0.076 0.58 + 38 -0.21 -0.9 -0.98 -0.0052 0.95 0.78 1.8 -0.56 -0.0062 -0.47 -0.3 -0.78 1.4 5e+03 1 0.076 0.14 + 39 -0.23 -0.98 -1 -0.0045 0.98 0.78 1.8 -0.64 0.0014 -0.47 -0.3 -0.75 1.4 5e+03 0.49 0.076 0.54 + 40 -0.22 -0.97 -0.96 -0.0062 0.97 0.8 1.8 -0.71 -0.005 -0.42 -0.38 -0.81 1.4 5e+03 0.04 0.076 0.71 + 41 -0.25 -1 -0.94 -0.0041 0.97 0.79 1.8 -0.78 -0.0033 -0.39 -0.4 -0.77 1.4 5e+03 0.066 0.076 0.56 + 42 -0.26 -1.1 -0.97 -0.0049 1 0.79 1.9 -0.85 -0.0045 -0.37 -0.47 -0.84 1.4 5e+03 0.14 0.076 0.58 + 43 -0.28 -1.1 -0.97 -0.0043 1 0.76 1.9 -0.93 -0.0032 -0.33 -0.51 -0.81 1.4 5e+03 0.14 0.076 0.44 + 44 -0.31 -1.1 -0.92 -0.005 1 0.72 1.8 -0.95 -0.0086 -0.28 -0.58 -0.85 1.5 5e+03 0.039 0.076 0.26 + 45 -0.33 -1.1 -1 -0.0047 1.1 0.73 1.8 -0.97 -0.0068 -0.28 -0.59 -0.85 1.5 5e+03 0.044 0.076 0.51 + 46 -0.33 -1.1 -1 -0.0047 1.1 0.73 1.8 -0.97 -0.0068 -0.28 -0.59 -0.85 1.5 5e+03 0.044 0.038 -5.3 - 47 -0.33 -1.1 -1 -0.0047 1.1 0.73 1.8 -0.97 -0.0068 -0.28 -0.59 -0.85 1.5 5e+03 0.044 0.019 -1.7 - 48 -0.33 -1.1 -1 -0.0047 1.1 0.73 1.8 -0.97 -0.0068 -0.28 -0.59 -0.85 1.5 5e+03 0.044 0.0095 -0.17 - 49 -0.33 -1.1 -0.99 -0.0045 1.1 0.72 1.8 -0.97 -0.0068 -0.28 -0.59 -0.84 1.5 5e+03 0.0089 0.0095 0.49 + 50 -0.33 -1.1 -0.99 -0.0045 1.1 0.72 1.8 -0.97 -0.0068 -0.28 -0.59 -0.84 1.5 5e+03 0.0089 0.0048 -0.78 - 51 -0.33 -1.1 -0.98 -0.0047 1.1 0.72 1.8 -0.97 -0.0075 -0.27 -0.59 -0.85 1.5 5e+03 0.034 0.0048 0.39 + 52 -0.33 -1.1 -0.98 -0.0047 1.1 0.72 1.8 -0.97 -0.007 -0.27 -0.59 -0.85 1.5 5e+03 0.038 0.0048 0.3 + 53 -0.34 -1.1 -0.98 -0.0046 1.1 0.72 1.8 -0.98 -0.007 -0.27 -0.59 -0.85 1.5 5e+03 0.0093 0.048 0.94 ++ 54 -0.37 -1.1 -0.96 -0.0047 1.1 0.71 1.8 -1 -0.0078 -0.23 -0.61 -0.86 1.6 5e+03 0.051 0.048 0.64 + 55 -0.37 -1.1 -0.96 -0.0047 1.1 0.71 1.8 -1 -0.0078 -0.23 -0.61 -0.86 1.6 5e+03 0.051 0.024 -0.36 - 56 -0.37 -1.1 -0.99 -0.0044 1.1 0.71 1.8 -0.99 -0.0078 -0.23 -0.61 -0.86 1.6 5e+03 0.031 0.024 0.31 + 57 -0.36 -1.1 -0.97 -0.0046 1.2 0.69 1.8 -1 -0.0074 -0.22 -0.61 -0.86 1.6 5e+03 0.015 0.024 0.54 + 58 -0.36 -1.1 -0.97 -0.0046 1.2 0.69 1.8 -1 -0.0074 -0.22 -0.61 -0.86 1.6 5e+03 0.015 0.012 -0.21 - 59 -0.37 -1.1 -0.98 -0.0045 1.1 0.7 1.8 -0.99 -0.0081 -0.2 -0.61 -0.86 1.6 5e+03 0.01 0.012 0.6 + 60 -0.37 -1.1 -0.98 -0.0045 1.1 0.7 1.8 -0.99 -0.0081 -0.2 -0.61 -0.86 1.6 5e+03 0.01 0.006 -0.22 - 61 -0.37 -1.1 -0.98 -0.0045 1.1 0.7 1.8 -1 -0.0076 -0.2 -0.61 -0.86 1.6 5e+03 0.0093 0.006 0.47 + 62 -0.37 -1.1 -0.98 -0.0045 1.2 0.7 1.8 -1 -0.008 -0.2 -0.61 -0.86 1.6 5e+03 0.00044 0.006 0.68 + 63 -0.36 -1.1 -0.98 -0.0046 1.2 0.7 1.8 -1 -0.0078 -0.19 -0.61 -0.86 1.6 5e+03 0.0083 0.006 0.73 + 64 -0.36 -1.1 -0.98 -0.0045 1.2 0.69 1.8 -1 -0.0079 -0.19 -0.61 -0.86 1.6 5e+03 0.0029 0.006 0.81 + 65 -0.36 -1.1 -0.98 -0.0045 1.2 0.69 1.8 -1 -0.0077 -0.19 -0.61 -0.86 1.6 5e+03 0.00042 0.006 0.89 + 66 -0.36 -1.1 -0.98 -0.0045 1.2 0.69 1.8 -1 -0.008 -0.18 -0.61 -0.87 1.6 5e+03 0.0036 0.006 0.78 + 67 -0.36 -1.1 -0.98 -0.0045 1.2 0.69 1.8 -1 -0.0078 -0.18 -0.61 -0.86 1.6 5e+03 0.0063 0.006 0.87 + 68 -0.36 -1.1 -0.98 -0.0045 1.2 0.69 1.8 -1 -0.008 -0.18 -0.61 -0.87 1.7 5e+03 0.005 0.06 0.9 ++ 69 -0.34 -1.1 -0.97 -0.0045 1.2 0.67 1.7 -1 -0.0076 -0.14 -0.6 -0.87 1.7 5e+03 0.028 0.06 0.61 + 70 -0.34 -1.1 -0.97 -0.0045 1.2 0.67 1.7 -1 -0.0076 -0.14 -0.6 -0.87 1.7 5e+03 0.028 0.03 -14 - 71 -0.34 -1.1 -0.97 -0.0045 1.2 0.67 1.7 -1 -0.0076 -0.14 -0.6 -0.87 1.7 5e+03 0.028 0.015 -5.4 - 72 -0.34 -1.1 -0.97 -0.0045 1.2 0.67 1.7 -1 -0.0076 -0.14 -0.6 -0.87 1.7 5e+03 0.028 0.0075 -1.5 - 73 -0.34 -1.1 -0.97 -0.0045 1.2 0.67 1.7 -1 -0.0076 -0.14 -0.6 -0.87 1.7 5e+03 0.028 0.0037 -0.1 - 74 -0.34 -1.1 -0.98 -0.0044 1.2 0.67 1.7 -1 -0.0079 -0.15 -0.6 -0.87 1.7 5e+03 0.013 0.0037 0.53 + 75 -0.34 -1.1 -0.98 -0.0045 1.2 0.67 1.7 -1 -0.0077 -0.14 -0.6 -0.87 1.7 5e+03 0.00096 0.0037 0.23 + 76 -0.34 -1.1 -0.98 -0.0045 1.2 0.67 1.7 -1 -0.0078 -0.15 -0.6 -0.87 1.7 5e+03 0.0015 0.0037 0.89 + 77 -0.34 -1.1 -0.98 -0.0045 1.2 0.67 1.7 -1 -0.0077 -0.15 -0.6 -0.87 1.7 5e+03 0.0081 0.0037 0.62 + 78 -0.34 -1.1 -0.98 -0.0044 1.2 0.67 1.7 -1 -0.0079 -0.15 -0.6 -0.87 1.7 5e+03 0.0091 0.0037 0.8 + 79 -0.34 -1.1 -0.97 -0.0045 1.2 0.67 1.7 -1 -0.0077 -0.15 -0.6 -0.87 1.7 5e+03 0.0054 0.0037 0.6 + 80 -0.34 -1.1 -0.98 -0.0045 1.2 0.67 1.8 -1 -0.0078 -0.15 -0.6 -0.87 1.7 5e+03 0.0024 0.0037 0.78 + 81 -0.34 -1.1 -0.97 -0.0045 1.2 0.67 1.8 -1 -0.0078 -0.15 -0.6 -0.87 1.7 5e+03 0.0075 0.0037 0.75 + 82 -0.34 -1.1 -0.98 -0.0045 1.2 0.67 1.8 -1 -0.0077 -0.15 -0.6 -0.86 1.7 5e+03 0.0012 0.0037 0.77 + 83 -0.33 -1.1 -0.98 -0.0044 1.2 0.67 1.8 -1 -0.0078 -0.15 -0.6 -0.86 1.7 5e+03 0.0049 0.0037 0.75 + 84 -0.33 -1.1 -0.97 -0.0044 1.2 0.67 1.8 -1 -0.0077 -0.15 -0.6 -0.86 1.7 5e+03 0.0018 0.0037 0.9 + 85 -0.33 -1.1 -0.98 -0.0044 1.2 0.66 1.8 -1 -0.0078 -0.15 -0.6 -0.86 1.8 5e+03 0.0025 0.037 0.99 ++ 86 -0.33 -1.1 -0.98 -0.0044 1.2 0.66 1.8 -1 -0.0078 -0.15 -0.6 -0.86 1.8 5e+03 0.0025 0.019 -0.31 - 87 -0.33 -1.1 -0.97 -0.0044 1.2 0.66 1.8 -1 -0.0076 -0.15 -0.6 -0.86 1.8 5e+03 0.02 0.019 0.38 + 88 -0.33 -1.1 -0.97 -0.0044 1.2 0.66 1.8 -1 -0.0076 -0.15 -0.6 -0.86 1.8 5e+03 0.02 0.0093 -1 - 89 -0.32 -1.1 -0.97 -0.0044 1.2 0.66 1.8 -1 -0.0078 -0.14 -0.61 -0.86 1.8 5e+03 0.0038 0.0093 0.19 + 90 -0.32 -1.1 -0.98 -0.0044 1.2 0.66 1.8 -1 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0028 0.0093 0.24 + 91 -0.32 -1.1 -0.97 -0.0044 1.2 0.65 1.8 -0.99 -0.0076 -0.15 -0.61 -0.86 1.8 5e+03 0.0044 0.0093 0.35 + 92 -0.32 -1.1 -0.97 -0.0044 1.2 0.65 1.8 -0.99 -0.0076 -0.15 -0.61 -0.86 1.8 5e+03 0.0044 0.0047 -0.36 - 93 -0.32 -1.1 -0.97 -0.0044 1.2 0.66 1.8 -0.99 -0.0079 -0.14 -0.6 -0.86 1.8 5e+03 0.0017 0.0047 0.12 + 94 -0.32 -1.1 -0.98 -0.0044 1.2 0.65 1.8 -1 -0.0077 -0.14 -0.6 -0.86 1.8 5e+03 0.012 0.0047 0.82 + 95 -0.32 -1.1 -0.98 -0.0044 1.2 0.65 1.8 -1 -0.0077 -0.14 -0.6 -0.86 1.8 5e+03 0.012 0.0023 -0.35 - 96 -0.32 -1.1 -0.97 -0.0044 1.2 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.001 0.0023 0.7 + 97 -0.32 -1.1 -0.98 -0.0044 1.2 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0035 0.0023 0.74 + 98 -0.32 -1.1 -0.98 -0.0044 1.2 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0035 0.0012 -0.11 - 99 -0.32 -1.1 -0.97 -0.0044 1.2 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0024 0.0012 0.46 + 100 -0.32 -1.1 -0.97 -0.0044 1.2 0.65 1.8 -0.99 -0.0078 -0.14 -0.61 -0.86 1.8 5e+03 0.00023 0.012 0.94 ++ 101 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0089 0.012 0.49 + 102 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0089 0.0058 -2.1 - 103 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0089 0.0029 -0.82 - 104 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.6 -0.86 1.8 5e+03 0.00039 0.0029 0.31 + 105 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.6 -0.86 1.8 5e+03 0.00039 0.0015 -0.23 - 106 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0078 -0.14 -0.61 -0.86 1.8 5e+03 0.00041 0.0015 0.79 + 107 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00033 0.0015 0.68 + 108 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00069 0.0015 0.72 + 109 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00011 0.015 0.99 ++ 110 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00011 0.0073 -1.3 - 111 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00011 0.0036 -0.07 - 112 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0057 0.0036 0.2 + 113 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0057 0.0018 -0.83 - 114 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00087 0.0018 0.3 + 115 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00087 0.00091 -3.9 - 116 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00087 0.00045 -0.26 - 117 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0019 0.00045 0.58 + 118 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0012 0.00045 0.67 + 119 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00032 0.00045 0.6 + 120 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0002 0.00045 0.86 + 121 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00012 0.00045 0.77 + 122 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00023 0.0045 0.9 ++ 123 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00023 0.0023 -0.23 - 124 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00019 0.0023 0.47 + 125 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00019 0.0011 -0.35 - 126 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.0018 0.0011 0.2 + 127 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 6e-05 0.0011 0.57 + 128 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 6e-05 0.00057 -1.7 - 129 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 6e-05 0.00028 0.029 - 130 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00049 0.00028 0.38 + 131 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 5.6e-05 0.00028 0.61 + 132 -0.31 -1.1 -0.97 -0.0044 1.1 0.65 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 5.6e-05 0.00014 -1 - 133 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00028 0.00014 0.29 + 134 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 1.3e-05 0.0014 0.92 ++ 135 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00011 0.0014 0.22 + 136 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00011 0.00071 -12 - 137 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00011 0.00036 -3.7 - 138 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 0.00011 0.00018 -0.6 - 139 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 2.2e-05 0.00018 0.46 + 140 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 2.2e-05 8.9e-05 -1.4 - 141 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 2.5e-05 8.9e-05 0.61 + 142 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 2.5e-05 4.4e-05 -4.7 - 143 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 2.5e-05 2.2e-05 -0.8 - 144 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 1.3e-05 2.2e-05 0.25 + 145 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 1.2e-05 0.00022 0.9 ++ 146 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 1.2e-05 0.00011 -1.8 - 147 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 1.2e-05 5.6e-05 -1.3 - 148 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 1.2e-05 2.8e-05 -0.22 - 149 -0.31 -1.1 -0.97 -0.0044 1.1 0.64 1.8 -0.99 -0.0077 -0.14 -0.61 -0.86 1.8 5e+03 1e-06 2.8e-05 0.66 - Optimization algorithm has converged. Relative gradient: 1.011991302601317e-06 Cause of termination: Relative gradient = 1e-06 <= 6.1e-06 Number of function evaluations: 341 Number of gradient evaluations: 191 Number of hessian evaluations: 0 Algorithm: BFGS with trust region for simple bound constraints Number of iterations: 150 Proportion of Hessian calculation: 0/95 = 0.0% Optimization time: 0:00:03.365394 Calculate second derivatives and BHHH File b11a_cnl.html has been generated. File b11a_cnl.yaml has been generated. .. 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 Name Value ... Robust t-stat. Robust p-value 0 asc_train -0.308465 ... -1.541500 1.231951e-01 1 b_time_train -1.073896 ... -7.579054 3.486100e-14 2 b_cost -0.973726 ... -14.711229 0.000000e+00 3 b_headway_train -0.004366 ... -4.491560 7.070336e-06 4 ga_train 1.142978 ... 4.934247 8.046038e-07 5 alpha_existing 0.644707 ... 3.744259 1.809270e-04 6 existing_nest_parameter 1.771215 ... 7.695616 1.398881e-14 7 b_time_swissmetro -0.991511 ... -5.574074 2.488505e-08 8 b_headway_swissmetro -0.007724 ... -2.601172 9.290596e-03 9 ga_swissmetro -0.138849 ... -0.861538 3.889421e-01 10 asc_car -0.606265 ... -4.886097 1.028545e-06 11 b_time_car -0.857011 ... -6.760465 1.375500e-11 12 public_nest_parameter 1.839948 ... 3.953511 7.701258e-05 [13 rows x 5 columns] .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 10.165 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 `_