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bike model initial commit
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‎resident/configs/bike_comfort.csv‎

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Label,Description,Expression,StrongAndFearless,EnthusedAndConfident,InterstedButConcerned,NoWayNoHow
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util_age,Age of persons in years,@df.age,coef_age_strong,coef_age_enthused,coef_age_concerned,0
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util_female,Female dummy,@df.female,coef_female_strong,coef_female_enthused,coef_female_concerned,0
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util_worker,Is worker dummy,@df.is_worker,coef_worker_strong,coef_worker_enthused,coef_worker_concerned,0
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util_holds_license,Person holds a license,@has_license,coef_has_license_strong,coef_has_license_enthused,coef_has_license_concerned,0
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#util_mobility_difficulty,Has mobility difficulty dummy,@df.has_mobility_difficulty,coef_mobility_difficulty_strong,coef_mobility_difficulty_enthused,coef_mobility_difficulty_concerned,0
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util_logsum_work_0auto,Work accessibility -- 0 autos,workplace_location_accessibility_0,coef_logsum_work_0auto_strong,coef_logsum_work_0auto_enthused,coef_logsum_work_0auto_concerned,0
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util_logsum_shop_0auto,Shopping accessibility -- 0 auto,shopping_accessibility_0,coef_logsum_shop_0auto_strong,coef_logsum_shop_0auto_enthused,coef_logsum_shop_0auto_concerned,0
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util_logsum_other_0auto,Other accessibility -- 0 autos,othdiscr_accessibility_0,coef_logsum_other_0auto_strong,coef_logsum_other_0auto_enthused,coef_logsum_other_0auto_concerned,0

‎resident/configs/bike_comfort.yaml‎

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SPEC: bike_comfort.csv
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COEFFICIENTS: bike_comfort_coefficients.csv
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LOGIT_TYPE: MNL
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COMFORT_LEVEL:

‎resident/configs/bike_comfort_coefficients.csv‎

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coefficient_name,value,constrain
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coef_age_strong,0,F
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coef_age_enthused,0,F
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coef_age_concerned,0,F
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coef_female_strong,0,F
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coef_female_enthused,0,F
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coef_female_concerned,0,F
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coef_worker_strong,0,F
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coef_worker_enthused,0,F
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coef_worker_concerned,0,F
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coef_has_license_strong,0,F
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coef_has_license_enthused,0,F
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coef_has_license_concerned,0,F
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coef_mobility_difficulty_strong,0,F
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coef_mobility_difficulty_enthused,0,F
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coef_mobility_difficulty_concerned,0,F
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coef_logsum_work_0auto_strong,0,F
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coef_logsum_work_0auto_enthused,0,F
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coef_logsum_work_0auto_concerned,0,F
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coef_logsum_shop_0auto_strong,0,F
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coef_logsum_shop_0auto_enthused,0,F
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coef_logsum_shop_0auto_concerned,0,F
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coef_logsum_other_0auto_strong,0,F
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coef_logsum_other_0auto_enthused,0,F
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coef_logsum_other_0auto_concerned,0,F

‎resident/extensions/__init__.py‎

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from . import external_identification
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from . import external_location_choice
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from . import adjust_auto_operating_cost
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from . import bike_comfort

‎resident/extensions/bike_comfort.py‎

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# ActivitySim
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# See full license in LICENSE.txt.
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from __future__ import annotations
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import logging
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import pandas as pd
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from activitysim.core import (
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config,
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estimation,
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expressions,
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simulate,
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tracing,
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workflow,
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)
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from activitysim.core.configuration.base import PreprocessorSettings
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from activitysim.core.configuration.logit import LogitComponentSettings
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logger = logging.getLogger("activitysim")
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class BikeComfortSettings(LogitComponentSettings, extra = "forbid"):
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"""
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Settings for the 'bike_comfort' model.
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"""
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CHOOSE_FILTER_COLUMN_NAME: str = "adult"
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"""Column name in the dataframe to filter persons eligible for license holding status model."""
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@workflow.steps
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def bike_comfort(
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state: workflow.State,
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persons_merged: pd.DataFrame,
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persons: pd.DataFrame,
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model_settings: BikeComfortSettings | None = None,
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model_settings_file_name: str = "bike_comfort.yaml",
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trace_label: str = "bike_comfort",
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) -> None:
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"""
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This model predicts the bike comfort level for each person.
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The alternatives of this model are NoWyNoHow, InterestedButConcerned, EnthsuedAndConfident, StrongAndFearless
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"""
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if model_settings is None:
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model_settings = BikeComfortSettings.read_settings_file(
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state.filesystem,
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model_settings_file_name,
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)
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choosers = persons_merged
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chooser_filter_columun_name = model_settings.CHOOSE_FILTER_COLUMN_NAME
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choosers = choosers[(choosers[chooser_filter_columun_name])]
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logger.info("Running %s with %d persons", trace_label, len(choosers))
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estimator = estimation.manager.begin_estimation(state, 'bike_comfort')
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constants = config.get_model_constants(model_settings)
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# - processor
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expressions.annotate_preprocesors(
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state,
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df=choosers,
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locals_dict=constants,
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skims=None,
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model_settings=model_settings,
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trace_label=trace_label,
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)
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model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC)
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coefficients_df = state.filesystem.read_model_coefficients(model_settings)
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model_spec = simulate.eval_coefficients(
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state, model_spec, coefficients_df, estimator
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)
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nest_spec = config.get_logit_model_settings(model_settings)
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if estimator:
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estimator.write_model_settings(model_settings, model_settings_file_name)
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estimator.write_spec(model_settings)
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estimator.write_coefficients(coefficients_df, model_settings)
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estimator.write_choosers(choosers)
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choices = simulate.simple_simulate(
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state,
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choosers=choosers,
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spec=model_spec,
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nest_spec=nest_spec,
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locals_d=constants,
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trace_label=trace_label,
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trace_choice_name="bike_comfort",
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estimator=estimator,
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compute_settings=model_settings.compute_settings,
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)
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choices = pd.Series(model_spec.columns[choices.values], index=choices.index)
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bike_comfort_cat = pd.api.types.CategoricalDtype(
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model_spec.columns.tolist() + [""],
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ordered=False,
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)
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choices = choices.astype(bike_comfort_cat)
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if estimator:
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estimator.write_choices(choices)
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choices = estimator.get_survey_values(
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choices, "persons", "bike_comfort")
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estimator.write_override_choices(choices)
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estimator.end_estimation()
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persons["bike_comfort"] = choices.reindex(persons.index).fillna("")
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state.add_table("persons", persons)
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tracing.print_summary(
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"bike_comfort", persons.telecommute_frequency, value_counts=True
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)
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if state.settings.trace_hh_id:
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state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True)
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expressions.annotate_tables(
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state,
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locals_dict=constants,
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skims=None,
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model_settings=model_settings,
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trace_label=trace_label,
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)

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