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.

[0.18.0] - 2023-04-13#


  • When super().__init__() is called before all parameters of the child class are initialized, we don’t get an error anymore. Now all classes remember their parameters when they are defined and don’t try to access parameters that are not defined in their own init. (#69)


  • Validation is now performed recursively on all subclasses. Note like before validation is still only performed once per class. But with this change, we can also validate base classes that are not used directly. (#70)


  • We validate now, if a child class implements all the parameters of its parent class. While not strictly necessary, this is a sign of bad design, if not done. It could also lead to issues with tpcps validation logic. (#70)

  • It is now possible to hook into the validation and perform custom validation of classes. (#70)

  • The dataset class now activly triggers validation and checks if the dataset subclass implements groupby_cols and subset_index.

[0.17.0] - 2023-03-24#


  • We now have a workaround for global configuration that should be passed to worker processes when using multiprocessing. This is a workaround to a joblib issue and is quite hacky. If you want to use this feature with your own configs you can use tpcp.parallel.register_global_parallel_callback. If you need to write your own parallel loop using joblib, you need to use tpcp.parallel.delayed instead of joblib.delayed. (#65)

[0.16.0] - 2023-03-21#


  • We are now raising an explicit ValidationError, if any of the parameters of a class have a trailing underscore, as this syntax is reserved for result objects. (#63)


  • The Optuna search methods have new parameter called eval_str_paras that allows to automatically turn categorical string parameters into python objects. This can be usefull, if you want to select between complex objects and not just strings in your parameter search. To use this in your subclasses, you need to wrap the use of trial.params with self.sanitize_params(trial.params). (#64)

[0.15.0] - 2023-02-07#


  • GridSearch and GridSearchCV now have the option to pick the parameters with the lowest score if desired. This is useful, if your metric represents an error and you want to pick the parameters that minimize the error. To do that, you can set the return_optimized parameter of these classes to the name of metric prefixed with a -. (e.g. return_optimized="-rmse"). (#61)

  • A new Optimization Algorithm called OptunaSearch. This is a (nearly) drop-in replacement for GridSearch using Optuna under the hood. It can be used to quickly implement parameter searches with different samplers for non-optimizable algorithms. (#57)


  • In this release we added multiple safe guards against edge cases related to non-deterministic dataset indices. Most of these changes are internal and should not require any changes to your code. Still, they don’t solve all edge cases. Make sure your index is deterministic ;) (#62)

    • The index of datasets objects are now cached The first time create_index is called, the index is stored in subset_index and used for subsequent calls. This should avoid the overhead of creating the index every time (in particular if the index creation requires IO). It should also help to avoid edge cases, where create_index is called multiple times and returns different results.

    • When create_index of a dataset is called, we actually call it twice now, to check if the index is deterministic. Having a non-deterministic index can lead to hard to debug issues, so we want to make sure that this is not the case. It could still be that the index changes when using a different machine/OS (which is not ideal for reproducibility), but this should prevent most cases leading to strange issues.

    • Internally, the _optimize_and_score method now directly gets the subset of the dataset, instead of the indices of the train and test set. This should again help to avoid issues, where the index of the dataset changes between calculating the splits and actually retrieving the data.

[0.14.0] - 2023-02-01#


  • Custom Aggregators can now use the RETURN_RAW_SCORES class variable to specify, if their raw input scores should be returned. (#58)


  • GridSearch and GridSearchCV now correctly handle custom aggregators that return scores with new names. (#58)

  • When using the create_group_labels method on dataset with multiple groupby columns, the method returned a list of tuples. This caused issues with GroupKFold, as the method internally flattens the list of tuples. To avoid this, the method now return a list of strings. The respective string is simply the string representation of the tuple that was returned before. (#59)

  • The fix provided in 0.12.1 to fix hashing of objects defined in the __main__ module was only partially working. When the object in question was nested in another object, the hashing would still fail. This is hopefully now fixed for good. (#60)

[0.13.0] - 2023-01-11#


  • Some improvements to the documentation


  • Added an option to the optuna search to use multiprocessing using the suggestions made in . This has not been extensively tested in real projects. Therefore, use with care and please report any issues you encounter.


  • Fully deprecated the _skip_validation parameter for base classes, which was briefly used for some old versions.

[0.12.2] - 2022-12-14#


  • The previous for fixing hashing of objects defined in the __main__ module was not working This should now be fixed.

[0.12.1] - 2022-12-14#


  • The safe_run method did unintentionally double-wrap the run method, if it already had a make_action_safe decorator. This is now fixed.


  • Under certain conditions hashing of an object defined in the __main__ module failed. This release implements a workaround for this issue, that should hopefully resolve most cases.

[0.12.0] - 2022-11-15#


  • Added the concept of the self_optimize_with_info method that can be implemented instead or in addition to the self_optimize method. This method should be used when an optimize method requires to return/output additional information besides the main result and is supported by the Optimize wrapper. (#49)

  • Added a new method called __clone_param__ that gives a class control over how params are cloned. This can be helpful, if for some reason objects don’t behave well with deepcopy.

  • Added a new method called __repr_parameters__ that gives a class control over how params are represented. This can be used to customize the representation of individual parameters in the __repr__ method.

  • Add proper repr for CloneFactory

[0.11.0] - 2022-10-17#


  • Support for Optuna >3.0

  • Example on how to use attrs and dataclass with tpcp

  • Added versions for Dataset and CustomOptunaOptimize that work with dataclasses and attrs.

  • Added first class support for composite objects (e.g. objects that need a list of other objects as parameters). This is basically sklearn pipelines with fewer restrictions (#48).


  • CustomOptunaOptimize now expects a callable to define the study, instead of taking a study object itself. This ensures that the study objects can be independent when the class is called as part of cross_validate.

  • Parameters are only validated when get_params is called. This reduces the reliance on __init_subclass__ and that we correctly wrap the init. This makes it possible to easier support attrs and dataclass

[0.10.0] - 2022-09-09#


  • Reworked once again when and how annotations for tpcp classes are processed. Processing is now delayed until you are actually using the annotations (i.e. as part of the “safe wrappers”). The only user facing change is that the chance of running into edge cases is lower and that __field_annotations__ is now only available on class instances and not the class itself anymore.

[0.9.1] - 2022-09-08#


  • Classes without init can now pass the tpcp checks


  • You can nest parameter annotations into ClassVar and they will still be processed. This is helpful when using dataclasses and annotating nested parameter values.

[0.9.0] - 2022-08-11#

This release drops Python 3.7 support!


  • Bunch new high-level documentation

  • Added submission version of JOSS paper


  • The aggregate methods of custom aggregators now gets the list of datapoints in additions to the scores. Both parameters are now passed as keyword only arguments.

[0.8.0] - 2022-08-09#


  • 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)

  • All cross_validation based methods now have a new parameter called mock_labels. This can be used to provide a “y” value to the split method of a sklearn-cv splitter. This is required e.g. for Stratified KFold splitters. (#43)


  • 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#


  • 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).


  • 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#


  • 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#


  • 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)


  • 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#


  • 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