Aepsych

Fixed a bug that prevented multi-stimuli experiments from returning points from an ask.

Quick patch to fix a bug that made loading old DBs without extra_data in tells to fail and require a double load.

Future Breaking Changes
In the next major version (0.8.0), we will be implementing multiple breaking changes to the internal API and begin removing functions/methods that have been deprecated to clean up our codebase. While we will maintain any features that assist in backwards compatibility that already exist, we will make no guarantees for backwards compatibility between server, client, dbs from version before 0.8.0. A full notice of what has changed will be available in that version.

Features
* Start new experiments by seeding a model with data from a previous experiment within the same DB. Documentation in "[Warm Starting a Strategy](https://aepsych.org/docs/finish_criteria)".
* Database queries now use the master_id instead of the experiment_id (which was default a generated UUID). The first experiment run in a db will have the master_id 1, then the next would be 2 and so on.
* The database now has additional helper methods to generate a dataframe or a csv. An additional command line command has been added to support summarizing an experiment or creating CSVs. [Code](https://github.com/facebookresearch/aepsych/blob/5a4c0d7f5ee82222ed2c6958cacdf063971003cc/aepsych/server/utils.py#L24 )
* Asks to the server can now have some parameters fixed to specific values using the `fixed_pars` key in the ask message.
* A new extension system to extend AEPsych on server runtime. Check out the example in our [documentation](https://aepsych.org/docs/extensions).
* New Plotting API to allow more easily composable plotting. The old plotting functions are deprecated and will be removed in the future. Take a look at this [demo](https://github.com/facebookresearch/aepsych/blob/main/examples/plotting_demo.ipynb).
* You can now ask for more than one point at a time ("batched ask"), note that this will not work with our lookahead acquisition functions (e.g., EAVC), but will work for other acquisition functions/generators where applicable. This can be used with `num_points` key in an ask message.
* Implemented the acqf grid search generator. AcqfGridSearchGenerator will evaluate a sobol grid of points on an acquisition function and return points based on the acquisition score without optimizing it like the OptimizeAcqfGenerator. This should allow much faster acquisition at the cost of the points being less optimal.
* Tells messages can now accept additional key-value pairs in the message outside of config, outcome, and model_data. These extra keys will be converted into a json string and stored in the raw table alongside the actual data. This is in addition to the previous method where extra metadata can be added to messages via the extra_info key outside of the message content. This is the intended method to store extra trial-level data, extra_info should not be used in this way to store trial-level data as it will not be directly tied to the data. Currently, the Python client has support for these extra keys, the other clients will be updated to follow.

Minor Changes
* We no longer warn when inducing size >= 100 in GPClassificationModel, this is due to the changed default inducing point algorithm.
* The visualizer and interactive notebooks have been removed, these will be replaced by a new standalone program soon.
* All generators know how the dimensionality of the search space (though they need not know the bounds).
* The server now logs the version on startup.

Important Bug fixes
* OptimizeAcqfGenerator should more reliably get the transformed bounds for the acquisition functions that need it. This is fixed in v.0.6.5, in case you would like a version without other latest changes.
* Server can remember multiple db master records to ensure data is correctly saved when resuming
* Configs are reliably tied to the master record when setup messages are sent

* Pinned SciPy to 1.14.1, latest SciPy (ver. 1.15.0) causes intermittent model fitting failure from BoTorch. We will remove this pin when the problem is solved.

The last minor release was also a bug fix patch, notes were missed. 0.6.1 was skipped.

* Initialize acqf method correct handles bounds again
* Plotting functions works again, no longer calls missing methods/attributes from models
* Query constraints works again, fixed by using dims to make dummies
* MyPy version pinned, copyright headers readded.

Major changes:
**Warning, the model API has changed, live experiments using configs should not break but custom code used in post-hoc analysis may not be compatible.**

* Models no longer possess bounds (lb/ub attributes in the initialization and the corresponding attributes are removed from the API).
* Models require the dim argument for initialization (i.e., dim is no longer an optional argument).
* The models can evaluate points outside of the bounds (which defines the search space, not the model's bounds). The only thing the models should know is the dimensionality of the space.
* Models no longer have multiple methods that should not be directly bound to the models (e.g., `dim_grid()` or `get_max()`). These are replaced by new functions in the `model.utils` submodule that accepts our models and the bounds to work on.
* Notice that it could be different bounds relative to the search space's bound, affording extra flexibility.
* While it is still possible access these functions with the Strategy class, it is recommended that post-hoc analysis simply load the model, the data, and use these separate functions.
* We are looking to improve the ergonomics of post-hoc analysis with a simplified API to load data and model from DBs without needing to replay, the next release will further bring more changes towards this goal.
* Approximate GP Models (like the GPClassificationModel) now accept a new inducing point allocator class to determine the inducing points instead of selecting the algorithm using a string argument.
* If inducing point methods were not modified before by the config, then nothing needs to change. To change the inducing point method, the `inducing_point_method` option in Configs need to be the exact InducingPointAllocator object (e.g., GreedyVarianceReduction or KMeansAllocator).
* The new default inducing point allocator for models is the GreedyVarianceReduction
* This should yield models that are at least as good as before while generally being more efficient to fit the model. To revert to the old default, use KMeansAllocator.
* Fixed parameters can now be defined a strings and the server will be able to handle this seamlessly.

Bug fixes:
* Query messages to the server can now handle models that would return values with gradients.
* Query responses will now correctly unpack dimensions.
* Query responses now respect transforms.
* Prediction queries now can actually predict in probability_space.
* Whitespaces are no longer meaningful in defining lists in config.
* The greedy variance allocator (previously the "pivoted_chol" option) now work with models that augment the dimensionality.
* MonotonicRejectionGP now respect the inducing point options from config.