add developer API post

Author: adrinjalali

_posts/2024-12-05-dev-api.md

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+---
+#### Blog Post Template ####
+
+#### Post Information ####
+title: "Changes and development of scikit-learn's developer API"
+date: December 12, 2024
+
+#### Post Category and Tags ####
+# Format in titlecase without dashes (Ex. "Open Source" instead of "open-source")
+categories:
+  - Updates
+tags:
+  - Open Source
+  - Machine Learning
+  - License
+
+#### Featured Image ####
+featured-image: BSD_watermark.svg
+
+#### Author Info ####
+# Can accomodate multiple authors
+# Add SQUARE Author Image to /assets/images/author_images/ folder
+postauthors:
+  - name: Adrin Jalali
+    website: https://adrin.info/
+    image: adrin-jalali.jpeg
+---
+<div>
+  <img src="/assets/images/posts_images/{{ page.featured-image }}" alt="">
+  {% include postauthor.html %}
+</div>
+
+Historically, scikit-learn's API has been divided into public and private. Public API is
+intended to be used by users, and private API is used internally in scikit-learn to
+develop new features and estimators. However, many of those functionalities have become
+essential to develop scikit-learn estimators by third parties who develop them outside
+the scikit-learn codebase.
+
+When it comes to our public API, we have very strict and high standards on backward
+compatibility. The rule of thumb is that no change should cause a change in users'
+code unless we warn about it for two release cycles, which means we give users a year
+time to update their code.
+
+On the other hand, we have no such guarantees or constraints on our private API. This
+brings an issue to third party developers who would like to use methods used by
+scikit-learn developers to develop their estimators. Constantly changing private API
+without prior warning brings certain challenges to third party developers which is not
+ideal.
+
+As a result, we've been working on creating a developer API which would sit somewhere
+between our public and private API in terms of backward compatibility. That means we
+intend to try to keep that API stable, and if needed, introduce changes with one release
+cycle warning.
+
+In the past few releases, we've slowly introduced more functionalities under this
+umbrella. `__sklearn_clone__` and `__sklearn_is_fitted__` are two examples.
+
+In the latest release, at the time of writing this post, we focused on the testing
+infrasutructure and estimator tag system. Estimator tags used to be private, and we
+were not sure about their design. In the 1.6 release, new tags are introduced and
+using them looks like the following:
+
+```python
+from sklearn.base import BaseEstimator, ClassifierMixin
+
+class MyEstimator(ClassifierMixin, BaseEstimator):
+
+  ...
+
+  def __sklearn_tags__(self):
+    tags = super().__sklearn_tags__()
+    # modify tags here
+    tags.non_deterministic = True
+    return tags
+```
+
+The new tags mostly follow the same structure as the old tags, but there are certain
+changes to them. The main change is that the old `_xfail_checks` is no more present
+in the new tags. That tag was used to tell the common testing tools about the tests
+which are known to fail and are to be skipped. That information is now directly passed
+to the test functionalities. The old way of skipping a test was the following:
+
+```python
+from sklearn.base import BaseEstimator, ClassifierMixin
+
+class MyEstimator(ClassifierMixin, BaseEstimator):
+
+  ...
+
+  def _more_tags(self):
+    return {
+      "_xfail_checks": {
+        "check_to_skip_name": "this check is known to fail",
+        ...
+      }
+    }
+```
+
+And then when calling `check_estimator` or using `parametrize_with_checks` with `pytest`
+would automatically ignore those tests for the estimator.
+
+Instead, in this release, you pass that information directly to those methods:
+
+```python
+from sklearn.utils.estimator_checks import check_estimator, parametrize_with_checks
+
+CHECKS_EXPECTED_TO_FAIL = {
+  "check_to_skip_name": "this check is known to fail",
+  ...
+}
+
+# Using check_estimator
+def test_with_check_estimator():
+  check_estimator(MyEstimator(), expected_failed_checks=CHECKS_EXPECTED_TO_FAIL)
+
+# Using parametrize_with_checks
+@parametrize_with_checks(
+  [MyEstimator()],
+  expected_failed_checks=lambda est: CHECKS_EXPECTED_TO_FAIL
+)
+def test_with_parametrize_with_checks(estimator, check):
+  check(estimator)
+```
+
+While working on the testing infrastructure, we have also been working on improving our
+tests and that means in this release we had a particularly higher number of changes in
+their names and what they do. The changes should have made it easier for developers to
+fix issues with their estimators. Note that you can now pass `legacy=False` to both
+`check_estimator` and `parametrize_with_checks` to include only strictly API related
+tests.
+
+The above changes means developers need to updated their estimators and depending on
+what they use, write scikit-learn version specific code to handle supporting multiple
+scikit-learn versions. To make that process easier, we've worked on a package called
+[`sklearn_compat`](https://github.com/sklearn-compat/sklearn-compat/). You can either
+depend on it as a package dependency, or vendor a single file inside your project. At
+the moment this project is in its infancy and might change in the future. But hopefully
+it helps developers out there.
+
+If you think there are missing functionalities in the developer API, please let us know
+and give us feedback on your [issue tracker](
+https://github.com/scikit-learn/scikit-learn/issues).