v0.4.4

Minor bug fixes

• Revert tensor changes for LSE contour plotting
• Ensure manual generators don't hang strategies in replay
• Set default inducing size to 99, be aware that inducing size >= 100 can significantly slowdown the model on very specific hardware setups

v0.4.3

• Float64 are now the default data type for all tensors from AEPsych.
• Many functions are ported to only use PyTorch Tensors and not accept NumPy arrays
• Fixed ManualGenerators not knowing when it is finished.

v0.4.2

• BoTorch version bumped to latest at 0.12.0.
• Numpy pinned below v2.0 to ensure compatibility with Intel Macs
• Only Python 3.10+ is supported now (matching BoTorch requirements)

v0.4.1

• Updated point generation and model querying to be faster
• Bumped ax version to 0.3.7
• Miscellaneous bug fixes

v0.4.0

New features:

• Ax can now be used as a backend. This is opt-in for now, but will become the default in a future version. Documentation here.
• Added aepsych_database as a command-line executable for performing database operations.
• Added MultitaskGPRModel and IndependentMultitaskGPRModel for offline analysis of multi-subject data.
• Added the semi-parametric models from Keeley et al., 2023. Tutorial here.
• Added ability to pre-generate trials asynchronously on the server by specifying pregen_asks = True in the config file.
• default_mean_covar_factory can now take dim directly as an argument instead of having to read it from a Config.
• Expanded the tutorial on Gaussian process active learning.
• Implemented an info message that allows clients to query the server for info about its state.
• Added additional type hints and docstrings throughout the codebase.
• Updates to dependencies.

Bug fixes:

• Fixed bug that caused BinaryClassificationGP to calculate variance incorrectly in probability space.
• Removed redundant "model fitting" logs.
• Fixed a type error in MonotonicThompsonSamplerGenerator
• Fixed a shape error in EpsilonGreedyGenerator.
• Fixed a broken test in test_model_query.py.

Other changes:

• Removed versioned server messages since we now have versioned releases and refactored server messages to be helper functions instead of AEPsychServer methods.
• Updated example configs to suggest EAVC as the threshold-finding acquisition function instead of MCLSE.

v0.3.0

New features:

• Added an example psychophysics experiment
• Added an ordinal model and likelihood
• Added a new raw data table for easier analysis
• Can now choose which botorch optimizer to use to fit models
• Added a visualization dashboard
• Updated to botorch v0.8.0

Bug fixes

• Removed some hardcoded checks for stimuli_per_trial and outcome_types
• Fixed incorrect threshold estimation for non-probit links
• Implemented from_config forMonotonicProjectionGP
• Fixed a casting error in MonotonicThompsonSamplerGenerator

v0.2.0

Changes to pairwise experiments

• PairwiseProbitModel has been moved from prerelease to the main repo
• SobolGenerator and OptimizeAcqfGenerator now work with PairwiseProbitModel. The pairwise generators should still work for now but are being deprecated and will be removed in a future release.

Changes to configs

• Configs now have separate stimuli_per_trial and outcome_types settings instead of a single outcome_type parameter. The server should automatically reformat old-style configs.
• Experiment metadata such as the experiment's description or participant ID can now be included in config files

New server functionality

• Tell messages can now specify model_data=False to indicate that data should be recorded, but not modeled. This is useful, for example, when your experiment includes practice trials.
• The "get_config" message can be used to fetch config settings from the server.
• The "finish_strategy" message can be used to force the server to finish the current strategy and move to the next one.

Other new features

• New lookahead acquisition functions (MOCU, SMOCU, and BEMPS) were added.
• Added 3D plotting functionality
• Strategies can now be set to run indefinitely by including run_indefinitely=True in configs.

Bug fixes

• Experiments that used stopping criteria other than min_asks will now properly replay.
• An exception will now be raised if lb > ub.
• Changed LSE's default value of "beta" to 3.84 (1.96^2).
• Updates from GPytorch and Botorch should lead to more stable model fitting

v0.1.0

Initial stable release. AEPsych currently supports monotonic and non-monotonic versions of classification and regression GP models with single inputs and outcomes.