Change Log#

This document describes significant changes to ActivitySim. This includes major new features that may require modifications to existing model configurations or code to utilize, as well as breaking changes that may cause existing model configurations or code to fail to run correctly.

v1.3#

New Canonical Examples#

Beginning with version 1.3, ActivitySim provides two supported “canonical” example implementations:

  • the SANDAG Model is a two-zone model based on the SANDAG ABM3 model, and

  • the MTC Model is a one-zone model based on the MTC’s Travel Model One.

Each example implementation includes a complete set of model components, input data, and configuration files, and is intended to serve as a reference for users to build their own models. They are provided as stand-alone repositories, to highlight the fact that model implementations are separate from the ActivitySim core codebase, and to make it easier for users to fork and modify the examples for their own use without needing to modify the ActivitySim core codebase. The examples are maintained by the ActivitySim Consortium and are kept up-to-date with the latest version of ActivitySim.

Note

The two example models are not identical to the original agency models from which they were created. They are generally similar to those models, and have been calibrated and validated to reproduce reasonable results. They are intended to demonstrate the capabilities of ActivitySim and to provide a starting point for users to build their own models. However, they are not intended to be used as-is for policy analysis or forecasting.

Logging#

The reading of YAML configuration files has been modified to use the “safe” reader, which prohibits the use of arbitrary Python code in configuration files. This is a security enhancement, but it requires some changes to the way logging is configured.

In previous versions, the logging configuration file could contain Python code to place log files in various subdirectories of the output directory, which might vary for different subprocesses of the model, like this:

logging:
  handlers:
    logfile:
      class: logging.FileHandler
      filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log']
      mode: w
      formatter: fileFormatter
      level: NOTSET

In the new version, the use of !!python/object/apply is prohibited. Instead of using this directive, the log_file_path function can be invoked in the configuration file by using the get_log_file_path key, like this:

logging:
  handlers:
    logfile:
      class: logging.FileHandler
      filename:
        get_log_file_path: activitysim.log
      mode: w
      formatter: fileFormatter
      level: NOTSET

Similarly, previous use of the if_sub_task directive in the logging level configuration like this:

logging:
  handlers:
    console:
      class: logging.StreamHandler
      stream: ext://sys.stdout
      level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET]
      formatter: elapsedFormatter

can be replaced with the if_sub_task and if_not_sub_task keys, like this:

logging:
  handlers:
    console:
      class: logging.StreamHandler
      stream: ext://sys.stdout
      level:
        if_sub_task: WARNING
        if_not_sub_task: NOTSET
      formatter: elapsedFormatter

For more details, see logging.

Chunking#

Version 1.3 introduces a new “explicit” chunking mechanism.

Explicit chunking is simpler to use and understand than dynamic chunking, and in practice has been found to be more robust and reliable. It requires no “training” and is activated in the top level model configuration file (typically settings.yaml):

chunk_training_mode: explicit

Then, for model components that may stress the memory limits of the machine, the user can specify the number of choosers in each chunk explicitly, either as an integer number of choosers per chunk, or as a fraction of the overall number of choosers. This is done by setting the explicit_chunk configuration setting in the model component’s settings. For this setting, integer values greater than or equal to 1 correspond to the number of chooser rows in each explicit chunk. Fractional values less than 1 correspond to the fraction of the total number of choosers. If the explicit_chunk value is 0 or missing, then no chunking is applied for that component. The explicit_chunk values in each component’s settings are ignored if the chunk_training_mode is not set to explicit. Refer to each model component’s configuration documentation for details.

Refer to code updates that implement explicit chunking for accessibility in PR #759, for vehicle type choice, non-mandatory tour frequency, school escorting, and joint tour frequency in PR #804, and all remaining interaction-simulate components in PR #870.

Automatic dropping of unused columns#

Variables that are not used in a model component are now automatically dropped from the chooser table before the component is run. Whether a variable is deemed as “used” is determined by a text search of the model component code and specification files for the variable name. Dropping unused columns can be disabled by setting drop_unused_columns to False in the compute_settings for any model component, but by default this setting is True, as it can result in a significant reduction in memory usage for large models.

Dropping columns may also cause problems if the model is not correctly configured. If it is desired to use this feature, but some required columns are being dropped incorrectly, the user can specify columns that should not be dropped by setting the protect_columns setting under compute_settings. This allows the user to specify columns that should not be dropped, even if they are not apparently used in the model component. For example:

compute_settings:
  protect_columns:
  - origin_destination

Code updates to drop unused columns are in PR #833 and to protect columns in PR #871.

Automatic conversion of string data to categorical#

Version 1.3 introduces a new feature that automatically converts string data to categorical data. This reduces memory usage and speeds up processing for large models. The conversion is done automatically for string columns in most chooser tables.

To further reduce memory usage, there is also an optional downcasting of numeric data available. For example, this allows storing integers that never exceed 255 as int8 instead of int64. This feature is controlled by the downcast_int and downcast_float settings in the top level model configuration file (typically settings.yaml). The default value for these settings is False, meaning that downcasting is not applied. It is recommended to leave these settings at their default values unless memory availability is severely constrained, as downcasting can cause numerical instability in some cases. First, changing the precision of numeric data could cause results to change slightly and impact a previous calibrated model result. Second, downcasting to lower byte data types, e.g., int8, can cause numeric overflow in downstream components if the numeric variable is used in mathematical calculations that would result in values beyond the lower bit width limit (e.g. squaring the value). If downcasting is desired, it is strongly recommended to review all model specifications for compatability, and to review model results to verify if the changes are acceptable.

See code updates in PR #782 and PR #863

Alternatives preprocessors for trip destination.#

Added alternatives preprocessor in PR #865, and converted to separate preprocessors for sample (at the TAZ level) and simulate (at the MAZ level for 2 zone systems) in PR #869.

Per-component sharrow controls#

This version adds a uniform interface for controlling sharrow optimizations at the component level. This allows users to disable sharrow entirely, or to disable the “fastmath” optimization for individual components. Controls for sharrow are set in each component’s settings under compute_settings. For example, to disable sharrow entirely for a component, use:

compute_settings:
  sharrow_skip: true

This overrides the global sharrow setting, and is useful if you want to skip sharrow for particular components, either because their specifications are not compatible with sharrow or if the sharrow performance is known to be poor on this component.

When a component has multiple subcomponents, the sharrow_skip setting can be a dictionary that maps the names of the subcomponents to boolean values. For example, in the school escorting component, to skip sharrow for an OUTBOUND and OUTBOUND_COND subcomponent but not the INBOUND subcomponent, use the following settings:

compute_settings:
  sharrow_skip:
    OUTBOUND: true
    INBOUND: false
    OUTBOUND_COND: true

The compute_settings can also be used to disable the “fastmath” optimization. This is useful if the component is known to have numerical stability issues with the fastmath optimization enabled, usually when the component potentially works with data that includes NaN or Inf values. To disable fastmath for a component, use:

compute_settings:
  fastmath: false

Code updates that apply these settings are in PR #824.

Configuration validation#

Version 1.3 adds a configuration validation system using the Pydantic library. Previously, the YAML-based configuration files were allowed to contain arbitrary keys and values, which could lead to errors if the configuration was not correctly specified. The new validation system checks the configuration files for correctness, and provides useful error messages if the configuration is invalid. Invalid conditions include missing required keys, incorrect data types, and the presence of unexpected keys. Existing models may need to be cleaned up (i.e. extraneous settings in config files removed) to conform to the new validation system.

See PR #758 for code updates.

Input checker#

Version 1.3 adds an input checker that verifies that the input data is consistent with expectations. This tool can help identify problems with the input data before the model is run, and can be used to ensure that the input data is correctly formatted and complete.

See PR #753 for code updates.

Removal of orca dependency#

This new version of ActivitySim does not use orca as a dependency, and thus does not rely on orca’s global state to manage data. Instead, a new State class is introduced, which encapsulates the current state of a simulation including all data tables. This is a significant change “under the hood”, which may be particularly consequential for model that use “extensions” to the ActivitySim framework. See PR #654 for code updates.

v1.2#

The v1.2 release includes all updates and enhancements complete in the ActivitySim Consortium’s Phase 7 development cycle, including:

  • Sharrow performance enhancement

  • Explicit school escorting

  • Disaggregate accessibility

  • Simulation-based shadow pricing

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