Scikeras

Fixes:
- Fixes a bug in inverse-transforms of predictions for multiclass labels that are not one-hot encoded (229). Credit to RKCZ on SO for reporting the issue: https://stackoverflow.com/q/67019157/6582418

Thank you to all of the collaborators that found bugs and submitted PRs to SciKeras!

v0.3.0 is a minor release that consists mainly of bug fixes, although we have made huge improvements under the hood as well.

Features:

* Implement batch_size=-1 (194)
* Lots of documentation improvements (200, 197, 178, 176, 174)

Fixes:
* Fix a bug in meta parameter collection (171)
* Allow `epochs` to be passed as a keyword argument to fit (154)
* Fix the signature of `BaseWrapper.scorer` (169)
* Fix inverse transforms for one-hot encoded targets (189)

Contributors to this release:

* stsievert
* data-hound

Thank you to stsievert for your continued support and contributions!

Release notes:

* Support autoencoders and more general use cases via BaseWrapper (123)
* Fix slowdown caused by sample_weight processing
(reported in [DaskML764](https://github.com/dask/dask-ml/issues/764), fixed in #123)
* Documentation improvements (134, 135, 145 and 138)
* Fix the `initialize` method in KerasClassifier (140)

* Move data transformations to a Scikit-Learn Transformer based interface (88)
* Add Keras parameters to BaseWrapper.__init__ (loss, optimizer, etc) (47, 55)
* Remove needless checks/array creation (63, 59)
* Make pre/post processing functions public (42)
* Some stability around `BaseWrapper.__call__` (35)
* Cleanup around loss names (38, 35)
* Parameter routing (67)
* Rename build_fn to model with deprecation cycle (98)
* Add ability for SciKeras to compile models (66)
* `class_weights` parameter (52, 103)
* `classes` param for `partial_fit` (69, 104)
* Provide an epochs parameter (51, 114)
* Updated docs, now hosted on RTD (58, 73)
* Checks to make sure models are correctly compiled (86, 100, 88)

* Add support for partial fitting and warm starts (17, thank you stsievert!).
* Add support for random states via the `random_state` parameter (27).
* Scikit-Learn SLEP10 compliance (26).
* Fix unnecessary data reshaping warnings (23).