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.
[0.11.0] - 2022-10-17#
Added#
Support for Optuna >3.0
Example on how to use
attrs
anddataclass
with tpcpAdded versions for
Dataset
andCustomOptunaOptimize
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).
Changed#
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 ofcross_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 supportattrs
anddataclass
[0.10.0] - 2022-09-09#
Changed#
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#
Fixed#
Classes without init can now pass the tpcp checks
Added#
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!
Added#
Bunch new high-level documentation
Added submission version of JOSS paper
Changed#
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#
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)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)
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 orget_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 thetpcp
side. (#42)
[0.7.0] - 2022-06-23#
Added#
The
Dataset
class now has a new parametergroup
, 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
andassert_is_single_group
methods for theDataset
class are added. They are shortcuts for callingself.is_single(groupby_cols=self.groupby_cols)
andself.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#
[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 withtpcp
and can be nested within tpcp operations. (#27)A new example for the
CustomOptunaOptimize
wrapper that explains how to create complex custom optimizers usingOptuna
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 nowGeneric
. 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 notList[float]
instead ofnp.ndarray
. This also effects the output ofcross_validate
,GridSearch.gs_results_
andGridSearchCV.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 ofSomething_
(#34)
[0.5.0] - 2022-03-15#
Added#
The
make_optimize_safe
decorator (and hence, theOptimize
method) make use of the parameter annotations to check that only parameters marked asOptimizableParameter
are changed by theself_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 fromdefault
->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 oftpcp
. 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
, orPureParameter
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 isGridSearchCV
. It now allows to setpure_parameters
toTrue
, which will collect the list of all attributes annotated withPureParameter
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
andself_optimize
safe are now reimagened and renamed tomake_run_safe
andmake_optimize_safe