Changelog#

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog (+ the Migration Guide), and this project adheres to Semantic Versioning.

[0.8.0] - 2022-08-09#

Added#

  • An example on how to use the dataclass decorator with tpcp classes. (#41)

  • In case you need complex aggregations of scores across data points, you can now wrap the return values of score functions in custom Aggregators. The best plac eto learn about this feature is the new “Custom Scorer” example. (#42)

Changed#

  • Most of the class proccesing and sanity checks now happens in the init (or rather a post init hook) instead of during class initialisation. This increases the chance for some edge cases, but allows to post-process classes, before tpcp checks are run. Most importantly, it allows the use of the dataclass decorator in combination with tpcp classes. For the “enduser”, this change will have minimal impact. Only, if you relied on accessing special tpcp class parameters before the class (e.g. __field_annotations__) was initialised, you will get an error now. Other than that, you will only notice a very slight overhead on class initialisation, as we know need to run some basic checks when you call the init or get_params. (#41)

  • The API of the Scorer class was modified. In case you used custom Scorer before, they will likely not work anymore. Further, we removed the error_score parameter from the Scorer and all related methods, that forwarded this parameter (e.g. GridSearch). Error that occur in the score function will now always be raised! If you need special handling of error cases, handle them in your error function yourself (i.e. using try-except). This gives more granular control and makes the implementation of the expected score function returns much easier on the tpcp side. (#42)

[0.7.0] - 2022-06-23#

Added#

  • The Dataset class now has a new parameter group, which will return the group/row information, if there is only a single group/row left in the dataset. This parameter returns either a string or a namedtuple to make it easy to access the group/row information.

  • The Dataset.groups parameter now returns a list of namedtuples when it previously returned a list of normal tuples.

  • New is_single_group and assert_is_single_group methods for the Dataset class are added. They are shortcuts for calling self.is_single(groupby_cols=self.groupby_cols) and self.assert_is_single(groupby_cols=self.groupby_cols).

Removed#

  • We removed the OptimizableAlgorithm base class, as it is not really useful. We recommend implementing your own base class or mixin if you are implementing a set of algorithms that need a normal and an optimizable version.

[0.6.3] - 2022-05-31#

  • It is now possible to use namedtuples as parameters and they are correctly cloned (#39)

[0.6.2] - 2022-04-21#

  • The poetry lockfiles are now committed to the repository.

  • Some dependencies are updated

[0.6.1] - 2022-04-05#

Changed#

  • Fixed bug with tensor hashing (#37)

  • Fixed an issue with memoization during hashing (#37)

  • Fixed an issue that the safe_optimize_wrapper could not correctly detect changes to mutable objects. This is now fixed by pre-calculating all the hashes. (#38)

[0.6.0] - 2022-04-04#

Added#

  • A new class to wrap the optimization framework Optuna. CustomOptunaOptimize can be used to create custom wrapper classes for various Optuna optimizations, that play nicely with tpcp and can be nested within tpcp operations. (#27)

  • A new example for the CustomOptunaOptimize wrapper that explains how to create complex custom optimizers using Optuna and the new Scorer callbacks (see below) (#27)

  • Scorer now supports an optional callback function, which will be called after each datapoint is scored. (#29)

  • Pipelines, Optimize objects, and Scorer are now Generic. This improves typing (in particular with VsCode), but means a little bit more typing (pun intended), when creating new Pipelines and Optimizers (#29)

  • Added option for scoring function to return arbitrary additional information using the NoAgg wrapper (#31)

  • (experimental) Torch compatibility for hash based comparisons (e.g. in the safe_run wrapper). Before the wrapper would fail, with torch module subclasses, as their pickle based hashes where not consistent. We implemented a custom hash function that should solve this. For now, we will consider this feature experimental, as we are not sure if it breaks in certain use-cases. (#33)

  • tpcp.types now exposes a bunch of internal types that might be helpful to type custom Pipelines and Optimizers. (#34)

Changed#

  • The return type for the individual values in the Scorer class is not List[float] instead of np.ndarray. This also effects the output of cross_validate, GridSearch.gs_results_ and GridSearchCV.cv_results_ (#29)

  • cf now has “faked” return type, so that type checkers in the user code, do not complain anymore. (#29)

  • All TypeVar Variables are now called SomethingT instead of Something_ (#34)

[0.5.0] - 2022-03-15#

Added#

  • The make_optimize_safe decorator (and hence, the Optimize method) make use of the parameter annotations to check that only parameters marked as OptimizableParameter are changed by the self_optimize method. This check also supports nested parameters, in case the optimization involves optimizing nested objects. (#9)

  • All tpcp objects now have a basic representation that is automatically generated based on their parameters (#13)

  • Added algo optimization and evaluation guide and improved docs overall (#26)

  • Added examples for all fundamental concepts (#23)

[0.4.0] - 2021-12-13#

In this release the entire core of tcpc was rewritten and a lot of the api was reimagened. This release also has no proper deprecation. This repo will the wild west until the 1.0 release

There are a multitude of changes, that I do not remember all. Here is the list of most important things:

  • A lot of the import paths have changed. It is likely that you need to update some imports

  • A new way to resolve the mutable default issue was introduced. It uses a explicit factory. To make that more clear, the name for mutable wrapper has changed from mdf -> cf or from default -> CloneFactory

  • The overall class API got a lot slimmer. A bunch of methods like get_attributes, are now functions that can be called with the class or instance as argument. Most methods were further renamed to represent there meaning in the context of tpcp. For example, get_attributes -> get_results

  • The new concept of Parameter Annotation was added. You can add type annotations with the types Parameter, HyperParameter, OptimizableParameter, or PureParameter to a class parameter to indicate its role for the algorithm. This is in particularly helpful for optimizable Pipelines and algorithms. Wrapper methods can then use these annotations to run implementation checks and optimizations. The only method that uses these at the momement is GridSearchCV. It now allows to set pure_parameters to True, which will collect the list of all attributes annotated with PureParameter and use this information for performance optimization. Before, you needed to specify the list of pure parameters manually.

  • Some core classes where renamed: BaseAlgorithm -> Algorithm, SimplePipeline -> Pipeline

  • The decorators to make run and self_optimize safe are now reimagened and renamed to make_run_safe and make_optimize_safe