10. Developer guideline#
10.1. Developer utilities#
Imbalanced-learn contains a number of utilities to help with development. These are
imblearn.utils, and include tools in a number of categories.
All the following functions and classes are in the module
These utilities are meant to be used internally within the imbalanced-learn package. They are not guaranteed to be stable between versions of imbalanced-learn. Backports, in particular, will be removed as the imbalanced-learn dependencies evolve.
10.1.1. Validation Tools#
These are tools used to check and validate input. When you write a function which accepts arrays, matrices, or sparse matrices as arguments, the following should be used when applicable.
check_neighbors_object: Check the objects is consistent to be a NN.
check_target_type: Check the target types to be conform to the current samplers.
check_sampling_strategy: Checks that sampling target is onsistent with the type and return a dictionary containing each targeted class with its corresponding number of pixel.
deprecate_parameter the rest of this section is taken from
scikit-learn. Please refer to their original documentation.
If any publicly accessible method, function, attribute or parameter
is renamed, we still support the old one for two releases and issue
a deprecation warning when it is called/passed/accessed.
E.g., if the function
zero_one is renamed to
we add the decorator
zero_one and call
zero_one_loss from that function:
from ..utils import deprecated def zero_one_loss(y_true, y_pred, normalize=True): # actual implementation pass @deprecated("Function 'zero_one' was renamed to 'zero_one_loss' " "in version 0.13 and will be removed in release 0.15. " "Default behavior is changed from 'normalize=False' to " "'normalize=True'") def zero_one(y_true, y_pred, normalize=False): return zero_one_loss(y_true, y_pred, normalize)
If an attribute is to be deprecated,
use the decorator
deprecated on a property.
E.g., renaming an attribute
classes_ can be done as:
@property @deprecated("Attribute labels_ was deprecated in version 0.13 and " "will be removed in 0.15. Use 'classes_' instead") def labels_(self): return self.classes_
If a parameter has to be deprecated, use
In the following example, k is deprecated and renamed to n_clusters:
import warnings def example_function(n_clusters=8, k=None): if k is not None: warnings.warn("'k' was renamed to n_clusters in version 0.13 and " "will be removed in 0.15.", DeprecationWarning) n_clusters = k
As in these examples, the warning message should always give both the version in which the deprecation happened and the version in which the old behavior will be removed. If the deprecation happened in version 0.x-dev, the message should say deprecation occurred in version 0.x and the removal will be in 0.(x+2). For example, if the deprecation happened in version 0.18-dev, the message should say it happened in version 0.18 and the old behavior will be removed in version 0.20.
In addition, a deprecation note should be added in the docstring, recalling the
same information as the deprecation warning as explained above. Use the
.. deprecated:: directive:
.. deprecated:: 0.13 ``k`` was renamed to ``n_clusters`` in version 0.13 and will be removed in 0.15.
On the top of all the functionality provided by scikit-learn. imbalanced-learn
deprecate_parameter: which is used to deprecate a sampler’s
parameter (attribute) by another one.
10.2. Making a release#
This section document the different steps that are necessary to make a new imbalanced-learn release.
10.2.1. Major release#
Update the release note
whats_new/v0.<version number>.rstby giving a date and removing the status “Under development” from the title.
bumpversion release. It will remove the
Commit the change
git commit -am "bumpversion 0.<version number>.0"(e.g.,
git commit -am "bumpversion 0.5.0").
Create a branch for this version (e.g.,
git checkout -b 0.<version number>.X).
Push the new branch into the upstream remote imbalanced-learn repository.
symlinkin the imbalanced-learn website repository such that stable points to the latest release version, i.e,
0.<version number>. To do this, clone the repository,
run unlink stable, followed by
ln -s 0.<version number> stable. To check that this was performed correctly, ensure that stable has the new version number using
Return to your imbalanced-learn repository, in the branch
Create the source distribution and wheel:
python setup.py sdistand
python setup.py bdist_wheel.
Upload these file to PyPI using
twine upload dist/*
Switch to the
masterbranch and run
bumpversion minor, commit and push on upstream. We are officially at
0.<version number + 1>.0.dev0.
Create a GitHub release by clicking on “Draft a new release” here. “Tag version” should be the latest version number (e.g.,
0.<version>.0), “Target” should be the branch for that the release (e.g.,
0.<version number>.X) and “Release title” should be “Version <version number>”. Add the notes from the release notes there.
Add a new
v0.<version number + 1>.rstfile in
.. include::this new file in
doc/whats_new.rst. Mark the version as the version under development.
Finally, go to the conda-forge feedstock and a new PR will be created when the feedstock will synchronizing with the PyPI repository. Merge this PR such that we have the binary for
10.2.2. Bug fix release#
Find the commit(s) hash of the bug fix commit you wish to back port using
Checkout the branch for the lastest release, e.g.,
git checkout 0.<version number>.X.
Append the bug fix commit(s) to the branch using
git cherry-pick <hash>. Alternatively, you can use interactive rebasing from the
Bump the version number with bumpversion patch. This will bump the patch version, for example from
Mark the current version as a release version (as opposed to
bumpversion release --allow-dirty. It will bump the version, for example from
Commit the changes with
git commit -am 'bumpversion <new version>'.
Push the changes to the release branch in upstream, e.g.
git push <upstream remote> <release branch>.
Use the same process as in a major release to upload on PyPI and conda-forge.