0.1.0

What's Changed

• feature(Package): Added license header to all files in src/. by @szemyd in #266
• feature(Package): added keywords, now directly referencing packages in README/ walkthrough by @almostintuitive in #267

Full Changelog: 0.0.6...0.1.0

0.0.6

What's Changed

• fix(Project): specify the name of the packages explicitly by @almostintuitive in #265

Full Changelog: 0.0.5...0.0.6

0.0.5

What's Changed

• fix(Tests): get ray to recognize package by @almostintuitive in #262
• fix(Tests): ability to load fold as a package, make ray detect if module can't be loaded by @almostintuitive in #264

Full Changelog: 0.0.4...0.0.5

0.0.4

Full Changelog: 0.0.3...0.0.4

0.0.3

Full Changelog: 0.0.2...0.0.3

0.0.2

What's Changed

• feature(Project): added loop, base classes and utils by @almostintuitive in #1
• refactor(Loop): fit_transformations now using single time pd.Series construction by @almostintuitive in #2
• fix(Transformation): remove unnecessary clone() method, use python 3.7 compatible List type by @almostintuitive in #5
• feature(Models): Ensemble model added, as well as the first test by @almostintuitive in #7
• feature(Splitters): using a better interface by @almostintuitive in #10
• fix(Transformations): inference wasn't working & added tests by @almostintuitive in #11
• refactor(API): don't differentiate between Transformations and Models by @almostintuitive in #13
• fix(Models): wrap model output into pd.Series by @almostintuitive in #15
• fix(Loop): introduced Composite base class, deepcopying composites is working now by @almostintuitive in #16
• feature(Composite): allow nested pipelines by @almostintuitive in #20
• fix(Loop): deepcopying done correctly, fixed Concat swapping column order if there are duplicates by @almostintuitive in #21
• chore(Project): remove unused imports by @almostintuitive in #22
• fix(Models): Ensemble was getting the mean on the wrong axis by @almostintuitive in #23
• feature(Splitters): added SingleWindowSplitter by @almostintuitive in #24
• feature(Models): added seasonal_mean and seasonal_naive Baseline strategies by @almostintuitive in #25
• feature(Transformations): added TransformTarget() Composite transformation by @almostintuitive in #26
• feature(Transformations): added PerColumnEnsemble & PerColumnTransformation by @almostintuitive in #29
• docs(Licence): Updated licence to include commons clause restrict… by @szemyd in #31
• docs(Readme): Added inital drift readme. by @szemyd in #30
• feature(CI): added code coverage to GH actions by @almostintuitive in #34
• fix(CI): environment specified by @almostintuitive in #35
• fix(CI): added pytest-cov by @almostintuitive in #36
• chore(Docs): updated README with codecov badge by @almostintuitive in #37
• fix(Transformations): added sklearn <1.2 compatibility by @almostintuitive in #39
• refactor(Loop): added Composite.clone() method to remove dependence between loop and models by @almostintuitive in #40
• fix(Transformations): TransformTarget should return a pd.Series, sklearn and FunctionTransformation inverse_transform is working now by @almostintuitive in #42
• feature(Transformations): make past training data available, introduced Properties, renamed infer to backtest by @almostintuitive in #43
• feature(Models): propagate probabilities along with preditions for classifers by @almostintuitive in #45
• feature(Tests): added tests for Ensembling probabilities by @almostintuitive in #46
• fix(Ensemble): match class columns exactly by @almostintuitive in #47
• feature(Composite): added MetaLabeling, Composite.Properties, ability to add secondary transformations in Composite by @almostintuitive in #49
• feature(Splitters): added optional embargo between train/test sets & tests for all Splitters by @almostintuitive in #50
• feature(Composite): added SkipNA class, and tests by @almostintuitive in #51
• feature(Loop): added Backend option utilizing ray by @almostintuitive in #52
• feature(Transformation): added Sampling class, wrapping any imblearn-learn sampler by @almostintuitive in #53
• feature(Models): added RandomClassifier by @almostintuitive in #56
• refactor(Composite): TransformTarget is now a Composite by @almostintuitive in #57
• feature(Transformations): added Breakpoint and TestIdentity transformations, fixed get_flat_list_of_transformations() by @almostintuitive in #58
• fix(Utils): flatten() should not unpack tuples, only flatten lists by @almostintuitive in #59
• fix(Transformations): made FunctionTransformation get all the past data, which is a better default by @almostintuitive in #60
• feature(Loop): support propagating sample_weights parameter to the models during training by @almostintuitive in #61
• feature(CI): run the tests on every combination of OSes and python versions by @almostintuitive in #64
• feature(CI): added github actions to deploy docs by @almostintuitive in #63
• feature(Loop): added ability to update models, deploy by @almostintuitive in #62
• fix(Loop): continuous updating should be done differently at training/inference time by @almostintuitive in #69
• fix(Project): eliminated some type errors, include README.md on index by @almostintuitive in #70
• feature(Docs): added passing tests badge by @almostintuitive in #72
• fix(Docs): tests badge by @almostintuitive in #73
• feature(Docs): added docs badge by @almostintuitive in #74
• feature(Models): added BaselineNaiveSeasonal, fixed slicing of y when updating continuous models by @almostintuitive in #75
• chore(Project): renamed project to fold by @almostintuitive in #76
• chore(Docs): updated project details by @almostintuitive in #77
• feature(Splitters): window_size can be either a float or an interger by @almostintuitive in #78
• feature(Models): support SKLearn Pipeline objects as well by @almostintuitive in #79
• feature(Docs): added a couple of new pages by @almostintuitive in #84
• feature(Loop): validate X, y types by @almostintuitive in #85
• feature(Docs): updated README by @almostintuitive in #86
• refactor(Project): removed unused imports, set up precommit hooks by @almostintuitive in #87
• refactor(Loop): remove tracking of in-sample predictions by @almostintuitive in #88
• fix(Docs): updated by @almostintuitive in #89
• feature(CI): build docs even when on a branch, but don't deploy by @almostintuitive in #90
• feature(Transformation): use .partial_fit if available in SKLearnTransformation, pass sample_weights parameter, if available by @almostintuitive in #91
• fix(Model): added ability to distinguish between in-sample and out-of-sample predictions by @almostintuitive in #96
• refactor(Loop): added .update() method, which we now call in the inner loop when requires_continuous_updates == True by @almostintuitive in #97
• fix(Splitters): allow window_size of 1 by @almostintuitive in #101
• feature(Utils): added generate_monotonous_data() by @almostintuitive in #102
• fix(Training): in-sample predictions can contain NaNs (at the start), then they're automatically trimmed by @almostintuitive in #105
• fix(Loop): condition when to use continuous updates vs in_sample inference by @almostintuitive in #108
• feature(Docs): unifying the different sections by @almostintuitive in #109
• refactor(Project): renamed Split class to Fold, requires_continuous_updates to Transformation.Properties.Mode.online by @almostintuitive in #110
• refactor(Composite): simplified API, made Sample a Composite by @almostintuitive in #116
• feature(Models): added Hybrid Composite by @almostintuitive in #93
• feature(Models): added SelectModel Composite by @almostintuitive in #117
• refactor(Loop): int...