v0.4.1

Fixes:

• Add missing dependency on packaging (#247). Thank you @sim-san for reporting and fixing!
• Fixed an issue causing routed parameters (fit__class_weight) to be overridden by non-routed parameters, including default values (class_weight=None) (#244). Thank you @8549 for reporting the issue.
• Fix handling of class_weight="balanced" (#245)

v0.4.0

Features:

• Support routed parameters to callbacks (#233)

Changes:

• Official support for extra keyword arguments (**kwargs) in fit(), predict() and predict_proba() that are routed directly to Keras (#238)
• Remove hard dependency on tensorflow, allowing users to choose tensorflow-cpu or other distributions in environments where they can't control pip arguments (#237)

v0.3.3

v0.3.3 is a patch release that fixes version specification for dependencies

v0.3.1

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

v0.3.0

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

v0.2.1

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 DaskML#764, fixed in #123)
• Documentation improvements (#134, #135, #145 and #138)
• Fix the initialize method in KerasClassifier (#140)

Release v0.2.0

• 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)

Release v0.1.8

• 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).

v0.1.7

Bump version: 0.1.6 → 0.1.7