.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/assisted/plot_b09post_processing.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_assisted_plot_b09post_processing.py: Re-estimation of best models ============================ After running the assisted specification algorithm for the 432 specifications in :ref:`everything_spec_section`, we use post processing to re-estimate all Pareto optimal models, and display some information about the algorithm. See `Bierlaire and Ortelli (2023) `_. :author: Michel Bierlaire, EPFL :date: Thu Jul 20 17:15:37 2023 .. GENERATED FROM PYTHON SOURCE LINES 17-34 .. code-block:: default try: import matplotlib.pyplot as plt can_plot = True except ModuleNotFoundError: can_plot = False import biogeme.biogeme_logging as blog import biogeme.biogeme as bio from biogeme.assisted import ParetoPostProcessing from everything_spec import model_catalog, database logger = blog.get_screen_logger(level=blog.INFO) logger.info('Example b08selected_specification') PARETO_FILE_NAME = 'saved_results/b07everything_assisted.pareto' .. rst-class:: sphx-glr-script-out .. code-block:: none Example b08selected_specification .. GENERATED FROM PYTHON SOURCE LINES 35-36 Create the biogeme object from the catalog. .. GENERATED FROM PYTHON SOURCE LINES 36-39 .. code-block:: default the_biogeme = bio.BIOGEME(database, model_catalog) the_biogeme.modelName = 'b09post_processing' .. rst-class:: sphx-glr-script-out .. code-block:: none File biogeme.toml has been parsed. .. GENERATED FROM PYTHON SOURCE LINES 40-41 Create the post processing object. .. GENERATED FROM PYTHON SOURCE LINES 41-45 .. code-block:: default post_processing = ParetoPostProcessing( biogeme_object=the_biogeme, pareto_file_name=PARETO_FILE_NAME ) .. rst-class:: sphx-glr-script-out .. code-block:: none Pareto set initialized from file with 192 elements [11 Pareto] and 29 invalid elements. .. GENERATED FROM PYTHON SOURCE LINES 46-47 Re-estimate the models. .. GENERATED FROM PYTHON SOURCE LINES 47-49 .. code-block:: default all_results = post_processing.reestimate(recycle=True) .. rst-class:: sphx-glr-script-out .. code-block:: none File biogeme.toml has been parsed. Recycling was requested, but no pickle file was found *** Initial values of the parameters are obtained from the file __b09post_processing_000000.iter Cannot read file __b09post_processing_000000.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter cube_tt_coef square_tt_coef Function Relgrad Radius Rho 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.5 -5.7 - 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.25 -1.9 - 2 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 2.5 1 ++ 3 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 1.2 -4.2 - 4 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.62 -3.5 - 5 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.31 -2.9 - 6 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.16 -2.5 - 7 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.078 -2.5 - 8 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.039 -2.6 - 9 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.02 -2.8 - 10 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0098 -3.1 - 11 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0049 -3.3 - 12 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0024 -2.1 - 13 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0012 -1.4 - 14 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.00061 -0.77 - 15 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.00031 -0.03 - 16 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 -0.00031 0.00031 5.8e+03 2.9 0.00031 0.71 + 17 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 -0.00026 0.00049 5.8e+03 0.98 0.00031 0.89 + 18 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 -0.00027 0.00067 5.8e+03 0.082 0.0031 1 ++ 19 0.099 -0.25 -0.043 -0.035 -0.25 0.083 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 -0.00026 0.0025 5.8e+03 1.9 0.031 1 ++ 20 0.1 -0.26 -0.042 -0.036 -0.27 0.09 -0.25 -0.12 -0.19 0.24 -0.28 -0.28 -0.26 -0.00036 0.021 5.7e+03 0.36 0.31 1 ++ 21 0.16 -0.31 -0.04 -0.043 -0.46 0.2 -0.23 -0.12 -0.14 0.11 -0.56 -0.59 -0.31 -0.001 0.18 5.5e+03 2.5 0.31 0.86 + 22 0.036 -0.4 -0.14 -0.06 -0.38 0.42 -0.00025 -0.11 -0.25 -0.2 -0.71 -0.72 -0.29 -0.00022 -0.0093 5.3e+03 1.6 0.31 0.83 + 23 0.047 -0.45 -0.11 -0.074 -0.55 0.57 0.046 -0.11 -0.25 -0.36 -1 -0.99 -0.34 -0.00027 0.0041 5.1e+03 0.87 3.1 1.1 ++ 24 0.047 -0.45 -0.11 -0.074 -0.55 0.57 0.046 -0.11 -0.25 -0.36 -1 -0.99 -0.34 -0.00027 0.0041 5.1e+03 0.87 1.5 -49 - 25 0.047 -0.45 -0.11 -0.074 -0.55 0.57 0.046 -0.11 -0.25 -0.36 -1 -0.99 -0.34 -0.00027 0.0041 5.1e+03 0.87 0.76 -9.8 - 26 0.047 -0.45 -0.11 -0.074 -0.55 0.57 0.046 -0.11 -0.25 -0.36 -1 -0.99 -0.34 -0.00027 0.0041 5.1e+03 0.87 0.38 -1.1 - 27 -0.17 -0.52 -0.16 -0.1 -0.57 0.84 0.26 -0.096 -0.51 -0.5 -1.3 -1.4 -0.47 0.00042 -0.17 5.1e+03 52 0.38 0.32 + 28 -0.17 -0.52 -0.16 -0.1 -0.57 0.84 0.26 -0.096 -0.51 -0.5 -1.3 -1.4 -0.47 0.00042 -0.17 5.1e+03 52 0.19 -0.099 - 29 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.19 0.28 + 30 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.095 -3.8 - 31 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.048 -3.7 - 32 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.024 -2.4 - 33 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.012 -2 - 34 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.006 -1.7 - 35 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.003 -1.6 - 36 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.0015 -1.4 - 37 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.00075 -1.1 - 38 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.00037 -0.64 - 39 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.00019 -0.14 - 40 -0.082 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.0003 -0.011 5e+03 15 0.00019 0.28 + 41 -0.082 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.0003 -0.011 5e+03 15 9.3e-05 -0.62 - 42 -0.082 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.0002 -0.011 5e+03 7.7 9.3e-05 0.82 + 43 -0.082 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00021 -0.011 5e+03 0.43 0.00093 1 ++ 44 -0.082 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.0002 -0.012 5e+03 0.13 0.0093 1 ++ 45 -0.083 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.42 -0.7 -1.3 -1.4 -0.51 -0.00017 -0.021 5e+03 0.4 0.093 1 ++ 46 -0.14 -0.53 -0.12 -0.11 -0.58 0.89 0.3 -0.09 -0.48 -0.64 -1.3 -1.5 -0.52 0.00021 -0.11 5e+03 31 0.093 0.6 + 47 -0.17 -0.54 -0.11 -0.11 -0.61 0.93 0.31 -0.085 -0.52 -0.64 -1.4 -1.6 -0.56 0.00017 -0.099 4.9e+03 9.8 0.93 0.96 ++ 48 -0.27 -0.26 0.0038 -0.3 -0.64 1.2 0.6 0.26 -0.8 -1.1 -1.9 -2.1 -1.3 0.00027 -0.12 4.8e+03 20 9.3 0.96 ++ 49 -0.29 -0.19 0.012 -0.36 -0.67 1.3 0.67 0.35 -0.83 -1.1 -2 -2.1 -1.5 0.0002 -0.11 4.8e+03 21 9.3 0.58 + 50 -0.28 -0.19 0.011 -0.36 -0.69 1.3 0.68 0.35 -0.84 -1.1 -2 -2.1 -1.4 0.00023 -0.11 4.8e+03 1.6 93 0.99 ++ 51 -0.28 -0.19 0.012 -0.36 -0.7 1.3 0.68 0.36 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.8e+03 0.18 9.3e+02 0.98 ++ 52 -0.28 -0.19 0.012 -0.36 -0.7 1.3 0.68 0.36 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.8e+03 0.00095 9.3e+03 1 ++ 53 -0.28 -0.19 0.012 -0.36 -0.7 1.3 0.68 0.36 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.8e+03 4e-08 9.3e+03 1 ++ File biogeme.toml has been parsed. Recycling was requested, but no pickle file was found *** Initial values of the parameters are obtained from the file __b09post_processing_000001.iter Cannot read file __b09post_processing_000001.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME Function Relgrad Radius Rho 0 -0.49 -0.92 -0.88 -0.67 5.4e+03 0.041 10 1.1 ++ 1 -0.18 -0.73 -1 -1.2 5.3e+03 0.0072 1e+02 1.1 ++ 2 -0.16 -0.7 -1.1 -1.3 5.3e+03 0.00018 1e+03 1 ++ 3 -0.16 -0.7 -1.1 -1.3 5.3e+03 1.1e-07 1e+03 1 ++ File biogeme.toml has been parsed. Recycling was requested, but no pickle file was found *** Initial values of the parameters are obtained from the file __b09post_processing_000002.iter Cannot read file __b09post_processing_000002.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho 0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -7.6 - 1 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.25 -1.9 - 2 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 2.5 1 ++ 3 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 1.2 -6.9 - 4 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.62 -4.9 - 5 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.31 -3.7 - 6 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.16 -3.1 - 7 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.078 -3 - 8 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.039 -3 - 9 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.02 -3.2 - 10 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.0098 -3.3 - 11 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.0049 -2.3 - 12 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.0024 -1.7 - 13 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.0012 -1.3 - 14 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.00061 -0.74 - 15 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.8e+03 9.7 0.00031 -0.032 - 16 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 -0.00031 1.3 0.00031 5.7e+03 4.1 0.00031 0.71 + 17 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 -0.00025 1.3 0.00045 5.7e+03 2 0.00031 0.82 + 18 -0.059 -0.057 -0.25 0.021 -0.12 0.25 -0.25 -0.25 -0.25 -0.00027 1.3 0.00059 5.7e+03 0.096 0.0031 1 ++ 19 -0.057 -0.058 -0.25 0.022 -0.12 0.25 -0.25 -0.25 -0.25 -0.0002 1.3 0.002 5.7e+03 7.8 0.031 1 ++ 20 -0.041 -0.062 -0.28 0.028 -0.11 0.24 -0.28 -0.28 -0.26 -0.00041 1.3 0.016 5.7e+03 6.3 0.31 0.94 ++ 21 0.058 -0.16 -0.43 0.18 -0.039 -0.058 -0.58 -0.58 -0.31 -0.0004 1.5 0.11 5.4e+03 22 0.31 0.74 + 22 0.058 -0.16 -0.43 0.18 -0.039 -0.058 -0.58 -0.58 -0.31 -0.0004 1.5 0.11 5.4e+03 22 0.15 -3.3 - 23 0.058 -0.16 -0.43 0.18 -0.039 -0.058 -0.58 -0.58 -0.31 -0.0004 1.5 0.11 5.4e+03 22 0.076 -2.5 - 24 0.058 -0.16 -0.43 0.18 -0.039 -0.058 -0.58 -0.58 -0.31 -0.0004 1.5 0.11 5.4e+03 22 0.038 -2.1 - 25 0.058 -0.16 -0.43 0.18 -0.039 -0.058 -0.58 -0.58 -0.31 -0.0004 1.5 0.11 5.4e+03 22 0.019 -1.8 - 26 0.058 -0.16 -0.43 0.18 -0.039 -0.058 -0.58 -0.58 -0.31 -0.0004 1.5 0.11 5.4e+03 22 0.0095 -1.6 - 27 0.058 -0.16 -0.43 0.18 -0.039 -0.058 -0.58 -0.58 -0.31 -0.0004 1.5 0.11 5.4e+03 22 0.0048 -1.5 - 28 0.058 -0.16 -0.43 0.18 -0.039 -0.058 -0.58 -0.58 -0.31 -0.0004 1.5 0.11 5.4e+03 22 0.0024 -1.3 - 29 0.058 -0.16 -0.43 0.18 -0.039 -0.058 -0.58 -0.58 -0.31 -0.0004 1.5 0.11 5.4e+03 22 0.0012 -0.92 - 30 0.058 -0.16 -0.43 0.18 -0.039 -0.058 -0.58 -0.58 -0.31 -0.0004 1.5 0.11 5.4e+03 22 0.0006 -0.42 - 31 0.058 -0.16 -0.43 0.18 -0.039 -0.059 -0.58 -0.58 -0.31 -0.001 1.5 0.1 5.4e+03 13 0.0006 0.12 + 32 0.058 -0.16 -0.43 0.18 -0.039 -0.059 -0.58 -0.58 -0.31 -0.001 1.5 0.1 5.4e+03 13 0.0003 -0.18 - 33 0.057 -0.16 -0.43 0.18 -0.04 -0.059 -0.58 -0.58 -0.3 -0.0007 1.5 0.1 5.3e+03 8.6 0.003 0.99 ++ 34 0.054 -0.16 -0.43 0.18 -0.042 -0.061 -0.58 -0.58 -0.3 -0.00065 1.5 0.1 5.3e+03 7.9 0.003 0.85 + 35 0.051 -0.16 -0.42 0.19 -0.045 -0.062 -0.58 -0.59 -0.3 -0.00065 1.5 0.1 5.3e+03 0.22 0.03 1 ++ 36 0.022 -0.17 -0.4 0.21 -0.074 -0.076 -0.58 -0.59 -0.3 -0.00055 1.5 0.075 5.3e+03 0.039 0.3 1 ++ 37 -0.23 -0.32 -0.27 0.5 -0.34 -0.32 -0.77 -0.82 -0.3 0.00026 1.7 -0.12 5.1e+03 24 0.3 0.65 + 38 -0.18 -0.34 -0.34 0.6 -0.33 -0.48 -0.91 -1.1 -0.42 -2.7e-05 1.8 -0.052 5e+03 3.3 0.3 0.76 + 39 -0.18 -0.34 -0.34 0.6 -0.33 -0.48 -0.91 -1.1 -0.42 -2.7e-05 1.8 -0.052 5e+03 3.3 0.15 -1.6 - 40 -0.18 -0.34 -0.34 0.6 -0.33 -0.48 -0.91 -1.1 -0.42 -2.7e-05 1.8 -0.052 5e+03 3.3 0.075 -1 - 41 -0.18 -0.34 -0.34 0.6 -0.33 -0.48 -0.91 -1.1 -0.42 -2.7e-05 1.8 -0.052 5e+03 3.3 0.037 0.044 - 42 -0.19 -0.34 -0.33 0.61 -0.34 -0.48 -0.91 -1.1 -0.42 0.00012 1.8 -0.09 5e+03 9.6 0.037 0.87 + 43 -0.22 -0.34 -0.31 0.63 -0.37 -0.49 -0.92 -1.2 -0.43 0.0001 1.9 -0.084 5e+03 0.082 0.37 1 ++ 44 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.37 0.86 + 45 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.19 -1.1 - 46 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.093 -1.5 - 47 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.047 -1.9 - 48 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.023 -2.3 - 49 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.012 -1.7 - 50 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.0058 -1.3 - 51 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.0029 -1.2 - 52 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.0015 -1.2 - 53 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.00073 -1.4 - 54 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.00036 -1.6 - 55 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 0.00018 -1.7 - 56 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 9.1e-05 -1.8 - 57 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 4.5e-05 -1.8 - 58 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00026 2 -0.12 4.9e+03 19 2.3e-05 -1.9 - 59 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00024 2 -0.12 4.9e+03 14 2.3e-05 0.7 + 60 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00024 2 -0.12 4.9e+03 1.9 0.00023 1.1 ++ 61 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00024 2 -0.12 4.9e+03 0.15 0.0023 1 ++ 62 -0.26 -0.33 -0.28 0.88 -0.48 -0.75 -1.1 -1.5 -0.58 0.00023 2 -0.11 4.9e+03 0.58 0.023 1 ++ 63 -0.27 -0.31 -0.26 0.89 -0.51 -0.75 -1.1 -1.6 -0.59 0.00018 1.9 -0.1 4.9e+03 0.85 0.23 0.96 ++ 64 -0.24 -0.14 -0.24 1 -0.53 -0.82 -1.2 -1.8 -0.74 0.0002 1.7 -0.11 4.9e+03 3.9 2.3 1 ++ 65 -0.26 0.0077 -0.26 1.4 -0.76 -1 -1.7 -2 -1.2 0.00021 1.1 -0.11 4.9e+03 0.16 2.3 0.64 + 66 -0.26 -0.014 -0.21 1.3 -0.76 -1.1 -1.7 -2 -1.2 0.00022 1.2 -0.11 4.9e+03 0.36 23 1.1 ++ 67 -0.27 0.017 -0.21 1.3 -0.74 -1 -1.7 -2 -1.2 0.00021 1.3 -0.11 4.9e+03 0.019 2.3e+02 1.1 ++ 68 -0.27 0.023 -0.21 1.3 -0.74 -1 -1.7 -2 -1.2 0.00021 1.3 -0.11 4.9e+03 0.0079 2.3e+03 1 ++ 69 -0.27 0.023 -0.21 1.3 -0.74 -1 -1.7 -2 -1.2 0.00021 1.3 -0.11 4.9e+03 5.5e-05 2.3e+04 1 ++ 70 -0.27 0.023 -0.21 1.3 -0.74 -1 -1.7 -2 -1.2 0.00021 1.3 -0.11 4.9e+03 6.1e-05 2.3e+05 1 ++ 71 -0.27 0.023 -0.21 1.3 -0.74 -1 -1.7 -2 -1.2 0.00021 1.3 -0.11 4.9e+03 2.1e-06 2.3e+05 1 ++ File biogeme.toml has been parsed. Recycling was requested, but no pickle file was found *** Initial values of the parameters are obtained from the file __b09post_processing_000003.iter Cannot read file __b09post_processing_000003.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME B_TIME_1st_clas Function Relgrad Radius Rho 0 -0.28 -0.24 -0.76 0.57 -1 -0.55 -0.62 5.2e+03 0.041 10 1.1 ++ 1 -0.3 -0.2 -1.2 1.8 -1.2 -0.62 -0.83 5e+03 0.015 1e+02 1.1 ++ 2 -0.28 -0.23 -1.4 1.9 -1.3 -0.62 -0.91 5e+03 0.0009 1e+03 1 ++ 3 -0.28 -0.23 -1.4 1.9 -1.3 -0.62 -0.91 5e+03 3.9e-06 1e+03 1 ++ File biogeme.toml has been parsed. Recycling was requested, but no pickle file was found *** Initial values of the parameters are obtained from the file __b09post_processing_000004.iter Cannot read file __b09post_processing_000004.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME cube_tt_coef square_tt_coef Function Relgrad Radius Rho 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.5 -0.83 - 1 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 5 1 ++ 2 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 2.5 -10 - 3 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 1.2 -6.3 - 4 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.62 -3.8 - 5 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.31 -2.3 - 6 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.16 -1.7 - 7 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.078 -1.8 - 8 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.039 -2.1 - 9 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.02 -2.7 - 10 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.0098 -3.2 - 11 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.0049 -3.5 - 12 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.0024 -2.4 - 13 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.0012 -1.6 - 14 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.00061 -0.92 - 15 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 0 0 5.7e+03 7.4 0.00031 -0.11 - 16 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 -0.00031 0.00031 5.6e+03 3.6 0.00031 0.69 + 17 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 -0.00025 0.00061 5.6e+03 1.3 0.00031 0.86 + 18 -0.063 -0.5 -0.5 0.15 -0.5 -0.5 -0.00027 0.00092 5.6e+03 0.16 0.0031 1 ++ 19 -0.064 -0.5 -0.5 0.15 -0.5 -0.5 -0.00027 0.004 5.6e+03 0.93 0.031 1 ++ 20 -0.068 -0.5 -0.52 0.15 -0.5 -0.53 -0.00041 0.034 5.6e+03 0.28 0.31 1 ++ 21 -0.094 -0.52 -0.68 0.25 -0.58 -0.83 -0.0014 0.28 5.4e+03 3.2 0.31 0.6 + 22 -0.06 -0.56 -0.81 0.55 -0.83 -0.95 -0.00027 0.0075 5.2e+03 5.2 0.31 0.76 + 23 -0.19 -0.59 -0.89 0.86 -0.91 -1.2 -0.00019 -0.017 5.1e+03 3.4 3.1 1 ++ 24 -0.19 -0.59 -0.89 0.86 -0.91 -1.2 -0.00019 -0.017 5.1e+03 3.4 1.5 -55 - 25 -0.19 -0.59 -0.89 0.86 -0.91 -1.2 -0.00019 -0.017 5.1e+03 3.4 0.76 -5.5 - 26 -0.19 -0.59 -0.89 0.86 -0.91 -1.2 -0.00019 -0.017 5.1e+03 3.4 0.38 -0.15 - 27 -0.13 -0.61 -1.1 1.2 -1 -1.5 0.00022 -0.11 5e+03 15 0.38 0.67 + 28 -0.2 -0.61 -1 1.6 -1 -1.9 0.00014 -0.091 5e+03 14 3.8 0.91 ++ 29 0.0062 -0.46 -0.91 2 -1.1 -2.5 0.00032 -0.13 5e+03 19 3.8 0.28 + 30 0.0014 -0.42 -0.91 1.9 -1.1 -2.3 0.00019 -0.1 5e+03 21 3.8 0.66 + 31 -0.025 -0.39 -0.96 2 -1.1 -2.3 0.00022 -0.11 5e+03 6.6 3.8 0.85 + 32 -0.034 -0.37 -0.97 2 -1.1 -2.3 0.00022 -0.11 5e+03 0.33 38 1 ++ 33 -0.034 -0.36 -0.98 2 -1.1 -2.3 0.00022 -0.11 5e+03 0.0042 3.8e+02 1 ++ 34 -0.034 -0.35 -0.98 2 -1.1 -2.3 0.00022 -0.11 5e+03 7.8e-05 3.8e+03 1 ++ 35 -0.035 -0.35 -0.98 2 -1.1 -2.3 0.00022 -0.11 5e+03 1.1e-05 3.8e+04 1 ++ 36 -0.035 -0.35 -0.98 2 -1.1 -2.3 0.00022 -0.11 5e+03 4.5e-06 3.8e+04 1 ++ File biogeme.toml has been parsed. Recycling was requested, but no pickle file was found *** Initial values of the parameters are obtained from the file __b09post_processing_000005.iter Cannot read file __b09post_processing_000005.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME Function Relgrad Radius Rho 0 -0.34 -0.62 -0.61 -0.69 -1 -0.69 5.3e+03 0.054 10 1.1 ++ 1 -0.38 -0.044 -0.88 -0.99 -2.2 -1 5.1e+03 0.039 1e+02 1.2 ++ 2 -0.42 0.15 -0.94 -1.1 -2.8 -1.1 5.1e+03 0.0097 1e+03 1.1 ++ 3 -0.43 0.19 -0.94 -1.1 -2.9 -1.1 5.1e+03 0.00053 1e+04 1 ++ 4 -0.43 0.19 -0.94 -1.1 -2.9 -1.1 5.1e+03 1.5e-06 1e+04 1 ++ File biogeme.toml has been parsed. Recycling was requested, but no pickle file was found *** Initial values of the parameters are obtained from the file __b09post_processing_000006.iter Cannot read file __b09post_processing_000006.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME B_TIME_commuter cube_tt_coef mu_public square_tt_coef Function Relgrad Radius Rho 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -1.5 - 1 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 5 1.1 ++ 2 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 2.5 -2.3e+302 - 3 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 1.2 -7.7e+302 - 4 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.62 -2.3e+303 - 5 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.31 -5.7e+303 - 6 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.16 -1.1e+304 - 7 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.078 -3.9 - 8 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.039 -2.8 - 9 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.02 -2.2 - 10 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.0098 -1.9 - 11 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.0049 -1.7 - 12 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.0024 -1.5 - 13 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.0012 -1.2 - 14 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.00061 -0.7 - 15 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.00031 0.016 - 16 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00031 1 0.00031 5.6e+03 8.1 0.00031 0.73 + 17 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00031 1 0.00031 5.6e+03 8.1 0.00015 -1.2 - 18 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00031 1 0.00031 5.6e+03 8.1 7.6e-05 -1.4 - 19 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00023 1 0.00038 5.6e+03 11 7.6e-05 0.16 + 20 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00027 1 0.00046 5.6e+03 3.5 7.6e-05 0.72 + 21 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00026 1 0.00053 5.6e+03 0.72 0.00076 0.93 ++ 22 -0.12 -0.11 -0.5 0.043 -0.27 -0.5 -0.5 -0.00026 1 0.0013 5.6e+03 0.1 0.0076 1 ++ 23 -0.12 -0.12 -0.51 0.045 -0.27 -0.51 -0.5 -0.00029 1 0.0089 5.6e+03 0.95 0.076 1 ++ 24 -0.12 -0.12 -0.56 0.066 -0.3 -0.58 -0.51 -0.00061 1.1 0.085 5.5e+03 0.27 0.76 0.96 ++ 25 -0.26 -0.3 -0.73 0.83 -1 -1.1 -0.38 0.0004 1.4 -0.14 5.1e+03 3 7.6 0.99 ++ 26 -0.26 -0.3 -0.73 0.83 -1 -1.1 -0.38 0.0004 1.4 -0.14 5.1e+03 3 1.2 -5.7 - 27 -0.26 -0.3 -0.73 0.83 -1 -1.1 -0.38 0.0004 1.4 -0.14 5.1e+03 3 0.58 -1.8 - 28 -0.26 -0.3 -0.73 0.83 -1 -1.1 -0.38 0.0004 1.4 -0.14 5.1e+03 3 0.29 0.04 - 29 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.29 0.3 + 30 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.14 -1.3 - 31 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.072 -0.82 - 32 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.036 -0.64 - 33 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.018 -0.61 - 34 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.009 -0.68 - 35 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.0045 -0.75 - 36 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.0023 -0.79 - 37 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.0011 -0.82 - 38 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.00057 -0.83 - 39 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.00028 -0.059 - 40 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.0002 1.4 -0.021 5e+03 13 0.00028 0.7 + 41 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.0002 1.4 -0.021 5e+03 13 0.00014 -2.8 - 42 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.0002 1.4 -0.021 5e+03 13 7.1e-05 -2.6 - 43 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.0002 1.4 -0.021 5e+03 13 3.5e-05 -0.62 - 44 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00016 1.4 -0.021 5e+03 5 3.5e-05 0.66 + 45 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00017 1.4 -0.021 5e+03 0.24 0.00035 1 ++ 46 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00017 1.4 -0.021 5e+03 0.16 0.0035 1 ++ 47 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00015 1.4 -0.025 5e+03 0.24 0.035 1 ++ 48 -0.37 -0.32 -0.5 1.1 -1 -1.3 -0.48 -7.2e-06 1.5 -0.06 5e+03 0.89 0.35 0.97 ++ 49 -0.37 -0.32 -0.5 1.1 -1 -1.3 -0.48 -7.2e-06 1.5 -0.06 5e+03 0.89 0.18 -0.63 - 50 -0.32 -0.34 -0.62 1.2 -1 -1.5 -0.56 0.00031 1.6 -0.14 5e+03 35 0.18 0.24 + 51 -0.14 -0.36 -0.45 1.4 -1.1 -1.6 -0.74 4.9e-05 1.6 -0.067 5e+03 34 0.18 0.26 + 52 -0.14 -0.36 -0.45 1.4 -1.1 -1.6 -0.74 4.9e-05 1.6 -0.067 5e+03 34 0.088 -0.96 - 53 -0.14 -0.36 -0.45 1.4 -1.1 -1.6 -0.74 4.9e-05 1.6 -0.067 5e+03 34 0.044 -0.27 - 54 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.044 0.12 + 55 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.022 -0.33 - 56 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.011 -0.36 - 57 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.0055 -0.4 - 58 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.0028 -0.45 - 59 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.0014 -0.48 - 60 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.00069 -0.5 - 61 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.00035 -0.51 - 62 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.00017 -0.52 - 63 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 8.6e-05 -0.17 - 64 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00027 1.6 -0.11 4.9e+03 22 8.6e-05 0.29 + 65 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00027 1.6 -0.11 4.9e+03 22 4.3e-05 0.03 - 66 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00022 1.6 -0.11 4.9e+03 3.8 4.3e-05 0.82 + 67 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00023 1.6 -0.11 4.9e+03 0.32 0.00043 1 ++ 68 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00022 1.6 -0.11 4.9e+03 0.15 0.0043 1 ++ 69 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.0002 1.6 -0.11 4.9e+03 1.8 0.043 1 ++ 70 -0.21 -0.36 -0.49 1.4 -1.1 -1.6 -0.77 0.00018 1.6 -0.1 4.9e+03 0.54 0.43 1 ++ 71 -0.088 -0.44 -0.57 1.6 -1.1 -1.9 -1.2 0.00022 1.3 -0.11 4.9e+03 3.9 4.3 0.91 ++ 72 -0.084 -0.44 -0.7 1.8 -1.1 -2 -1.2 0.00021 1.2 -0.11 4.9e+03 0.0075 43 1.1 ++ 73 -0.045 -0.4 -0.81 1.9 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 0.04 4.3e+02 1 ++ 74 -0.05 -0.37 -0.83 2 -1.1 -2 -1.3 0.00021 1.1 -0.11 4.9e+03 0.0017 4.3e+03 1 ++ 75 -0.045 -0.37 -0.84 2 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 0.00027 4.3e+04 1 ++ 76 -0.047 -0.37 -0.84 2 -1.1 -2 -1.3 0.00021 1.1 -0.11 4.9e+03 0.038 4.3e+05 1 ++ 77 -0.046 -0.36 -0.84 2 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 6.2e-05 4.3e+06 1 ++ 78 -0.046 -0.36 -0.84 2 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 7e-06 4.3e+07 1 ++ 79 -0.046 -0.36 -0.84 2 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 0.0011 4.3e+08 1 ++ 80 -0.046 -0.36 -0.84 2 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 1.4e-06 4.3e+08 1 ++ File biogeme.toml has been parsed. Recycling was requested, but no pickle file was found *** Initial values of the parameters are obtained from the file __b09post_processing_000007.iter Cannot read file __b09post_processing_000007.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. Function Relgrad Radius Rho 0 7e+03 0.27 0.5 -7.8 - 1 7e+03 0.27 0.25 -2.4 - 2 5.7e+03 7.4 2.5 1 ++ 3 5.7e+03 7.4 1.2 -7.4 - 4 5.7e+03 7.4 0.62 -4.9 - 5 5.7e+03 7.4 0.31 -3.4 - 6 5.7e+03 7.4 0.16 -2.7 - 7 5.7e+03 7.4 0.078 -2.5 - 8 5.7e+03 7.4 0.039 -2.5 - 9 5.7e+03 7.4 0.02 -2.7 - 10 5.7e+03 7.4 0.0098 -2.9 - 11 5.7e+03 7.4 0.0049 -3.1 - 12 5.7e+03 7.4 0.0024 -2.3 - 13 5.7e+03 7.4 0.0012 -1.5 - 14 5.7e+03 7.4 0.00061 -0.81 - 15 5.7e+03 7.4 0.00031 -0.082 - 16 5.7e+03 3.1 0.00031 0.67 + 17 5.7e+03 0.81 0.0031 0.91 ++ 18 5.7e+03 0.075 0.031 1 ++ 19 5.7e+03 0.5 0.31 1 ++ 20 5.4e+03 6.5 0.31 0.81 + 21 5.2e+03 9.7 0.31 0.58 + 22 5e+03 4.7 3.1 0.93 ++ 23 4.9e+03 5.9 31 1.1 ++ 24 4.8e+03 19 31 0.33 + 25 4.8e+03 39 31 0.45 + 26 4.8e+03 4.2 31 0.75 + 27 4.8e+03 0.19 3.1e+02 1 ++ 28 4.8e+03 0.0018 3.1e+03 1 ++ 29 4.8e+03 6.6e-07 3.1e+03 1 ++ File biogeme.toml has been parsed. Recycling was requested, but no pickle file was found *** Initial values of the parameters are obtained from the file __b09post_processing_000008.iter Cannot read file __b09post_processing_000008.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -1.9 - 1 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 5 1 ++ 2 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 2.5 -8.7 - 3 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 1.2 -6.1 - 4 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.62 -4.6 - 5 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.31 -3.8 - 6 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.16 -3.4 - 7 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.078 -3.3 - 8 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.039 -3.5 - 9 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.02 -3.6 - 10 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.0098 -2.9 - 11 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.0049 -2.1 - 12 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.0024 -1.7 - 13 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.0012 -1.4 - 14 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.00061 -0.85 - 15 0.12 -0.088 -0.5 0.044 -0.27 -0.5 0 1.5 0 5.5e+03 11 0.00031 -0.17 - 16 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.00031 1.5 0.00031 5.5e+03 6.9 0.00031 0.61 + 17 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.00019 1.5 0.00061 5.5e+03 8.3 0.00031 0.18 + 18 0.12 -0.088 -0.5 0.045 -0.27 -0.5 -0.00028 1.5 0.00092 5.5e+03 4.5 0.00031 0.57 + 19 0.12 -0.088 -0.5 0.045 -0.27 -0.5 -0.00024 1.5 0.0012 5.5e+03 1.5 0.00031 0.87 + 20 0.12 -0.088 -0.5 0.045 -0.27 -0.5 -0.00025 1.5 0.0015 5.5e+03 0.11 0.0031 1 ++ 21 0.12 -0.089 -0.5 0.046 -0.27 -0.5 -0.00029 1.5 0.0046 5.5e+03 4 0.031 1 ++ 22 0.1 -0.094 -0.51 0.056 -0.28 -0.53 -0.00038 1.5 0.035 5.4e+03 1.1 0.31 1 ++ 23 -0.085 -0.21 -0.52 0.31 -0.59 -0.81 -0.00083 1.7 0.14 5.2e+03 4.2 3.1 0.9 ++ 24 -0.085 -0.21 -0.52 0.31 -0.59 -0.81 -0.00083 1.7 0.14 5.2e+03 4.2 1.5 -54 - 25 -0.085 -0.21 -0.52 0.31 -0.59 -0.81 -0.00083 1.7 0.14 5.2e+03 4.2 0.76 -21 - 26 -0.085 -0.21 -0.52 0.31 -0.59 -0.81 -0.00083 1.7 0.14 5.2e+03 4.2 0.38 -2.8 - 27 -0.063 -0.32 -0.7 0.69 -0.87 -1 0.0004 1.9 -0.15 5.1e+03 14 0.38 0.21 + 28 -0.21 -0.33 -0.53 0.86 -0.82 -1.4 -0.00021 1.9 -0.0026 5.1e+03 31 0.38 0.24 + 29 -0.21 -0.33 -0.53 0.86 -0.82 -1.4 -0.00021 1.9 -0.0026 5.1e+03 31 0.19 -2.1 - 30 -0.21 -0.33 -0.53 0.86 -0.82 -1.4 -0.00021 1.9 -0.0026 5.1e+03 31 0.095 -0.33 - 31 -0.19 -0.32 -0.53 0.87 -0.83 -1.4 0.00015 1.9 -0.098 5e+03 28 0.095 0.82 + 32 -0.095 -0.31 -0.61 0.9 -0.83 -1.4 0.00016 1.9 -0.094 5e+03 10 0.95 1 ++ 33 -0.0019 0.056 -0.59 1.5 -0.97 -2 0.00023 1.5 -0.11 4.9e+03 8 9.5 1 ++ 34 -0.021 0.031 -0.67 1.6 -0.98 -2 0.00021 1.5 -0.11 4.9e+03 0.16 95 1 ++ 35 -0.025 0.029 -0.69 1.6 -0.99 -2 0.00021 1.5 -0.11 4.9e+03 0.015 9.5e+02 1 ++ 36 -0.024 0.029 -0.69 1.6 -0.99 -2 0.00021 1.5 -0.11 4.9e+03 0.0097 9.5e+03 1 ++ 37 -0.024 0.028 -0.69 1.6 -0.99 -2 0.00021 1.5 -0.11 4.9e+03 0.0002 9.5e+04 1 ++ 38 -0.024 0.028 -0.69 1.6 -0.99 -2 0.00021 1.5 -0.11 4.9e+03 0.0017 9.5e+05 1 ++ 39 -0.024 0.028 -0.69 1.6 -0.99 -2 0.00021 1.5 -0.11 4.9e+03 0.00035 9.5e+06 1 ++ 40 -0.024 0.028 -0.69 1.6 -0.99 -2 0.00021 1.5 -0.11 4.9e+03 3.9e-05 9.5e+06 1 ++ File biogeme.toml has been parsed. Recycling was requested, but no pickle file was found *** Initial values of the parameters are obtained from the file __b09post_processing_000009.iter Cannot read file __b09post_processing_000009.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter cube_tt_coef square_tt_coef Function Relgrad Radius Rho 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.5 -5.4 - 1 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.25 -1.4 - 2 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 2.5 1 ++ 3 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 1.2 -4.3 - 4 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.62 -3.6 - 5 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.31 -2.8 - 6 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.16 -2.4 - 7 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.078 -2.3 - 8 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.039 -2.5 - 9 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.02 -2.7 - 10 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0098 -3 - 11 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0049 -3.2 - 12 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0024 -2.1 - 13 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0012 -1.4 - 14 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.00061 -0.77 - 15 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.00031 -0.023 - 16 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 -0.00031 0.00031 5.8e+03 2.9 0.00031 0.72 + 17 0.083 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 -0.00026 0.0005 5.8e+03 0.88 0.0031 0.91 ++ 18 0.083 -0.25 -0.25 0.082 -0.2 0.25 -0.25 -0.25 -0.25 -0.00028 0.0024 5.8e+03 0.095 0.031 1 ++ 19 0.09 -0.25 -0.28 0.086 -0.2 0.25 -0.28 -0.28 -0.26 -0.00035 0.021 5.7e+03 0.73 0.31 1 ++ 20 0.16 -0.3 -0.49 0.17 -0.13 0.12 -0.56 -0.59 -0.31 -0.001 0.19 5.5e+03 3.9 0.31 0.88 + 21 0.037 -0.39 -0.45 0.38 -0.24 -0.19 -0.72 -0.72 -0.29 -0.00022 -0.0045 5.3e+03 5.8 0.31 0.85 + 22 -0.077 -0.48 -0.54 0.6 -0.35 -0.36 -1 -1 -0.34 -0.00011 -0.039 5.1e+03 5.2 3.1 1.1 ++ 23 -0.077 -0.48 -0.54 0.6 -0.35 -0.36 -1 -1 -0.34 -0.00011 -0.039 5.1e+03 5.2 1.5 -61 - 24 -0.077 -0.48 -0.54 0.6 -0.35 -0.36 -1 -1 -0.34 -0.00011 -0.039 5.1e+03 5.2 0.76 -8.5 - 25 -0.077 -0.48 -0.54 0.6 -0.35 -0.36 -1 -1 -0.34 -0.00011 -0.039 5.1e+03 5.2 0.38 -1.4 - 26 -0.077 -0.48 -0.54 0.6 -0.35 -0.36 -1 -1 -0.34 -0.00011 -0.039 5.1e+03 5.2 0.19 -0.24 - 27 -0.2 -0.53 -0.51 0.78 -0.48 -0.47 -1.2 -1.2 -0.41 0.00034 -0.14 5.1e+03 20 0.19 0.39 + 28 -0.14 -0.54 -0.5 0.86 -0.43 -0.66 -1.2 -1.3 -0.48 -0.00014 -0.024 5e+03 19 0.19 0.45 + 29 -0.14 -0.54 -0.5 0.86 -0.43 -0.66 -1.2 -1.3 -0.48 -0.00014 -0.024 5e+03 19 0.095 -0.73 - 30 -0.21 -0.55 -0.49 0.9 -0.5 -0.6 -1.3 -1.4 -0.49 0.00023 -0.12 5e+03 37 0.095 0.45 + 31 -0.2 -0.56 -0.5 0.96 -0.5 -0.68 -1.4 -1.5 -0.55 0.00011 -0.085 5e+03 21 0.095 0.83 + 32 -0.27 -0.56 -0.49 1 -0.59 -0.66 -1.4 -1.6 -0.59 0.00024 -0.12 5e+03 26 0.095 0.44 + 33 -0.23 -0.57 -0.48 1.1 -0.56 -0.76 -1.5 -1.6 -0.62 0.00014 -0.094 4.9e+03 9.8 0.095 0.89 + 34 -0.28 -0.57 -0.47 1.1 -0.63 -0.76 -1.5 -1.7 -0.67 0.00021 -0.11 4.9e+03 6.8 0.95 0.91 ++ 35 -0.28 -0.32 -0.26 1.4 -0.81 -1.1 -2 -2.1 -1.3 0.00021 -0.11 4.9e+03 5.7 9.5 1.1 ++ 36 -0.28 -0.24 -0.22 1.4 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.9e+03 0.26 95 1 ++ 37 -0.28 -0.23 -0.22 1.4 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.9e+03 0.0068 9.5e+02 1 ++ 38 -0.28 -0.23 -0.22 1.4 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.9e+03 5.2e-05 9.5e+03 1 ++ 39 -0.28 -0.23 -0.22 1.4 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.9e+03 1.7e-06 9.5e+03 1 ++ File biogeme.toml has been parsed. Recycling was requested, but no pickle file was found *** Initial values of the parameters are obtained from the file __b09post_processing_000010.iter Cannot read file __b09post_processing_000010.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_1st_clas Function Relgrad Radius Rho 0 -0.42 -0.84 -0.93 -0.36 -0.7 5.3e+03 0.035 10 1.1 ++ 1 -0.21 -0.83 -1.2 -0.6 -0.96 5.2e+03 0.0057 1e+02 1.1 ++ 2 -0.19 -0.81 -1.2 -0.65 -1 5.2e+03 0.00016 1e+03 1 ++ 3 -0.19 -0.81 -1.2 -0.65 -1 5.2e+03 1.6e-07 1e+03 1 ++ .. GENERATED FROM PYTHON SOURCE LINES 50-51 We retieve the first estimation results for illustration. .. GENERATED FROM PYTHON SOURCE LINES 51-53 .. code-block:: default spec, results = next(iter(all_results.items())) .. GENERATED FROM PYTHON SOURCE LINES 54-56 .. code-block:: default print(spec) .. rst-class:: sphx-glr-script-out .. code-block:: none ASC:GA-LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:power .. GENERATED FROM PYTHON SOURCE LINES 57-59 .. code-block:: default print(results.short_summary()) .. rst-class:: sphx-glr-script-out .. code-block:: none Results for model b09post_processing_000000 Nbr of parameters: 15 Sample size: 6768 Excluded data: 3960 Final log likelihood: -4839.755 Akaike Information Criterion: 9709.51 Bayesian Information Criterion: 9811.809 .. GENERATED FROM PYTHON SOURCE LINES 60-63 .. code-block:: default results.getEstimatedParameters() .. raw:: html
Value Rob. Std err Rob. t-test Rob. p-value
ASC_CAR -0.284756 0.091667 -3.106434 1.893585e-03
ASC_CAR_GA -0.186113 0.203479 -0.914656 3.603724e-01
ASC_CAR_one_lugg 0.011704 0.067371 0.173727 8.620801e-01
ASC_CAR_several_lugg -0.363385 0.238797 -1.521729 1.280770e-01
ASC_TRAIN -0.696886 0.154578 -4.508313 6.534527e-06
ASC_TRAIN_GA 1.260534 0.138588 9.095548 0.000000e+00
ASC_TRAIN_one_lugg 0.680281 0.100743 6.752633 1.451861e-11
ASC_TRAIN_several_lugg 0.358554 0.218931 1.637750 1.014738e-01
B_COST_CAR -0.845203 0.108532 -7.787607 6.883383e-15
B_COST_SM -1.121181 0.075991 -14.754059 0.000000e+00
B_COST_TRAIN -1.979248 0.185181 -10.688181 0.000000e+00
B_TIME -2.128801 0.107722 -19.761998 0.000000e+00
B_TIME_commuters -1.394410 0.171790 -8.116936 4.440892e-16
cube_tt_coef 0.000223 0.000026 8.466100 0.000000e+00
square_tt_coef -0.112041 0.004485 -24.981351 0.000000e+00


.. GENERATED FROM PYTHON SOURCE LINES 64-75 The following plot illustrates all models that have been estimated. Each dot corresponds to a model. The x-coordinate corresponds to the Akaike Information Criterion (AIC). The y-coordinate corresponds to the Bayesian Information Criterion (BIC). Note that there is a third objective that does not appear on this picture: the number of parameters. If the shape of the dot is a circle, it means that it corresponds to a Pareto optimal model. If the shape is a cross, it means that the model has been Pareto optimal at some point during the algorithm and later removed as a new model dominating it has been found. If the shape is a start, it means that the model has been deemed invalid. .. GENERATED FROM PYTHON SOURCE LINES 75-82 .. code-block:: default if can_plot: _ = post_processing.plot( label_x='Nbr of parameters', label_y='Negative log likelihood', objective_x=1, objective_y=0, ) .. image-sg:: /auto_examples/assisted/images/sphx_glr_plot_b09post_processing_001.png :alt: plot b09post processing :srcset: /auto_examples/assisted/images/sphx_glr_plot_b09post_processing_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 24.329 seconds) .. _sphx_glr_download_auto_examples_assisted_plot_b09post_processing.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_b09post_processing.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_b09post_processing.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_