Add announcement CZI EOSS 6 (#190)

Co-authored-by: Reshama Shaikh <[email protected]>
Co-authored-by: Tim Head <[email protected]>
Co-authored-by: Lucy Liu <[email protected]>

_posts/2024-08-06-czi-eoss6-announcement.md

@@ -0,0 +1,110 @@
+---
+title: "Chan Zuckerberg Initiative considers scikit-learn an Essential Open Source Software"
+date: August 6, 2024
+categories:
+  - Funding
+tags:
+  - Open Source
+  - Funding
+  - Internship
+  - Diversity
+featured-image: sklearn_czi.png
+
+postauthors:
+  - name: Guillaume Lemaitre
+    website: https://github.com/glemaitre
+    image: guillaume-lemaitre.jpg
+  - name: Lucy Liu
+    website: https://github.com/lucyleeow
+    image: lucyliu.jpeg
+---
+<div>
+  <img src="/assets/images/posts_images/{{ page.featured-image }}" alt="">
+  {% include postauthor.html %}
+</div>
+
+We are delighted to announce that `scikit-learn` has been awarded a grant from
+the [Chan Zuckerberg Initiative (CZI)](https://chanzuckerberg.com/)'s [Essential Open
+Source Software for Science
+(EOSS)](https://chanzuckerberg.com/rfa/essential-open-source-software-for-science/)
+program. This grant is funded by [Wellcome Trust](https://wellcome.org/).
+As in previous rounds, this cycle supports open-source software projects that are
+essential to biomedical research. This is the third time that CZI EOSS supports
+`scikit-learn`.
+
+In this new grant, we will focus on improving the [evaluation and inspection of
+predictive
+models](https://chanzuckerberg.com/eoss/proposals/predictive-models-evaluation-inspection-in-scikit-learn/).
+
+## Predictive models evaluation & inspection
+
+When building a machine learning pipeline for a specific research problem, two key
+aspects are closely connected: (i) design of the pipeline and (ii) assessment, analysis, and
+inspection of it. Researchers strive to identify the optimal pipeline, maximizing specific
+evaluation metrics, while also seeking at explaining the validity and rationale behind
+the pipeline's predictions. This is the cornerstone of answering research
+questions. With this proposal we aim to improve and extend the available `scikit-learn`
+tools.
+
+`scikit-learn` provides building blocks for model evaluation and statistical analysis of
+results. Originally, this information was presented in a raw format and required
+expertise from scientists to create intuitive reports for outreach to peers and
+outsiders. Recently, the `scikit-learn` community developed displays to easily generate
+visual figures for communicating such results. However, these displays are still in
+their early development stages and do not leverage all available statistical analysis
+tools (i.e., cross-validation) from `scikit-learn`. Thus, we aim to expand these
+displays, using the right statistical tools and thus promote the adoption of best
+practices when reporting results. Additionally, we also intend to create new displays
+to support common analysis tasks that are not yet covered in `scikit-learn`.
+
+In the domain of model inspection, we aim to address several areas: (i) model inspection
+during training, (ii) enhancing user experience through interactive inspection, and
+(iii) model explainability. First, during the training of a pipeline, researchers are
+interested in monitoring the internal characteristics of the model, which is a not yet
+addressed long-standing issue in `scikit-learn`. We want to build upon some initial work
+by implementing a "callback" framework that allows users to track these internal
+parameters. Next, researchers commonly use interactive tools such as Jupyter Notebook to
+develop pipelines. `scikit-learn` started some efforts to visually and interactively
+display pipelines in these environments. However, there is room for improvement in terms
+of user interaction and accessibility. Finally, as `scikit-learn` is widely used as a
+reference package, it is crucial to improve the section of the library dedicated to
+model explainability. We aim to improve the documentation and user experience with the
+existing explainability tools, making sure that they use the appropriate tool for their
+use cases. In addition, we propose to work on a scikit-learn enhancement proposal (SLEP)
+to define a common API for model explainability within scikit-learn. Ultimately, the
+goal is to come to a consensus to provide scikit-learn end-users with a consistent
+experience when using model explainability tools.
+
+On top of all these items, we intend to continue working on the general maintenance of
+the project, addressing bug reports and performance regressions. As a community-driven
+project, we also want to dedicate time reviewing external contributions.
+
+## Involved people
+
+To execute this project, we plan the following hires:
+
+- [Lucy Liu](https://github.com/lucyleeow) (Quansight Labs) will work about half-time on
+  the project, on topic related to displays and feature importance.
+- We will hire full-time internships to work on the other part of the project. The
+  initial plan is to hire two interns for a period of 6 months each and repeat this
+  process for the next 2 years. We want to provide opportunities to underrepresented
+  groups in the field of machine learning and data science, similarly to previous
+  initiatives (cf. [NumFOCUS Small Development
+  Grant](https://blog.scikit-learn.org/diversity/mentoring/)).
+
+## Past CZI EOSS grants
+
+In the past `scikit-learn` has been awarded two grants from the CZI EOSS program:
+
+- [CZI EOSS Cycle 1](https://chanzuckerberg.com/eoss/proposals/scikit-learn-maintenance-and-enhancement-for-gradient-boosting/)
+  helped at creating to the
+  [`HistGradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html) and
+  [`HistGradientBoostingRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html) estimators.
+  These estimators are the equivalent of gradient boosting models implemented in
+  `LightGBM` and `XGBoost`.
+- [CZI EOSS Cycle 4](https://chanzuckerberg.com/eoss/proposals/maintenance-extension-of-scikit-learn-machine-learning-in-python/)
+  extended `scikit-learn` to work better with missing values and categorical data in
+  several estimators.
+
+Both grants allowed us to maintain and enhance `scikit-learn` to better serve the
+community.