activitysim.core.configuration.Settings#
- settings activitysim.core.configuration.Settings#
The overall settings for the ActivitySim model system.
The input for these settings is typically stored in one main YAML file, usually called
settings.yaml.Note that this implementation is presently used only for generating documentation, but future work may migrate the settings implementation to actually use this pydantic code to validate the settings before running the model.
- field check_for_variability: bool = False#
Debugging feature to find broken model specifications.
Enabling this check does not alter valid results but slows down model runs.
- field checkpoints: Union[bool, list] = True#
When to write checkpoint (intermediate table states) to disk.
If True, checkpoints are written at each step. If False, no intermediate checkpoints will be written before the end of run. Or, provide an explicit list of models to checkpoint.
- field chunk_size: int = None#
Approximate amount of RAM to allocate to ActivitySim for batch processing.
See Chunk for more details.
- field chunk_training_mode: str = None#
The method to use for chunk training.
Valid values include {disabled, training, production, adaptive}. See Chunk for more details.
- field cleanup_pipeline_after_run: bool = False#
Cleans up pipeline after successful run.
This will clean up pipeline only after successful runs, by creating a single-checkpoint pipeline file, and deleting any subprocess pipelines.
- field create_input_store: bool = False#
Write the inputs as read in back to an HDF5 store.
If enabled, this writes the store to the outputs folder to use for subsequent model runs, as reading HDF5 can be faster than reading CSV files.
- field households_sample_size: int = None#
Number of households to sample and simulate
If omitted or set to 0, ActivitySim will simulate all households.
- field input_store: str = None#
HDF5 inputs file
- field input_table_list: list[activitysim.core.configuration.InputTable] [Required]#
list of table names, indices, and column re-maps for each table in input_store
- field log_alt_losers: bool = False#
Write out expressions when all alternatives are unavailable.
This can be useful for model development to catch errors in specifications. Enabling this check does not alter valid results but slows down model runs.
- field models: list[str] [Required]#
list of model steps to run - auto ownership, tour frequency, etc.
See Pipeline for more details about each step.
- field output_tables: list[str] = None#
list of output tables to write to CSV or HDF5
- field resume_after: str = None#
to resume running the data pipeline after the last successful checkpoint
- field sharrow: Union[bool, str] = False#
Set the sharrow operating mode.
New in version 1.2.
false - Do not use sharrow. This is the default if no value is given.
true - Use sharrow optimizations when possible, but fall back to legacy pandas.eval systems when any error is encountered. This is the preferred mode for running with sharrow if reliability is more important than performance.
require - Use sharrow optimizations, and raise an error if they fail unexpectedly. This is the preferred mode for running with sharrow if performance is a concern.
test - Run every relevant calculation using both sharrow and legacy systems, and compare them to ensure the results match. This is the slowest mode of operation, but useful for development and debugging.
- field trace_hh_id: Union[int, list] = None#
Trace household id(s)
If omitted, no tracing is written out
- field trace_od: list[int] = None#
Trace origin, destination pair in accessibility calculation
If omitted, no tracing is written out.
- field use_shadow_pricing: bool = False#
turn shadow_pricing on and off for work and school location
- field want_dest_choice_sample_tables: bool = False#
turn writing of sample_tables on and off for all models