Aepsych

Latest version: v0.4.0

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0.4.0

New features:
- [Ax](https://ax.dev/) can now be used as a backend. This is opt-in for now, but will become the default in a future version. Documentation [here](https://aepsych.org/docs/ax_backend).
- Added `aepsych_database` as a command-line executable for performing database operations.
- Added [MultitaskGPRModel](https://github.com/facebookresearch/aepsych/blob/main/aepsych/models/multitask_regression.py#L12) and [IndependentMultitaskGPRModel](https://github.com/facebookresearch/aepsych/blob/main/aepsych/models/multitask_regression.py#L88) for offline analysis of multi-subject data.
- Added the [semi-parametric models](https://github.com/facebookresearch/aepsych/blob/main/aepsych/models/semi_p.py) from [Keeley et al., 2023](https://arxiv.org/abs/2302.01187). Tutorial [here](https://aepsych.org/tutorials/Semi_P_tutorial).
- 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](https://aepsych.org/docs/gp_intro) on Gaussian process active learning.
- Implemented an [info message](https://github.com/facebookresearch/aepsych/blob/main/aepsych/server/message_handlers/handle_info.py) 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`.

0.3.0

New features:
- Added an [example psychophysics experiment](https://github.com/facebookresearch/aepsych/tree/main/examples/contrast_discrimination_psychopy)
- 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](https://github.com/facebookresearch/aepsych/tree/main/visualizer)
- 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` for`MonotonicProjectionGP`
- Fixed a casting error in `MonotonicThompsonSamplerGenerator`

0.2.0

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

0.1.0

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

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