🎉 Tool for better documentation!! Release of sphinx-gallery, to automatically integrate narrative 🐍 examples in documentations
https://sphinx-gallery.github.io/stable/index.html

Highlight: a light recommender system to show related examples

An illustration of sphinx-gallery:
https://scikit-learn.org/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html
(from @sklearn 's gallery). Note the links to function docs.

Sphinx-gallery comes with awesome features such as
◼online execution with binder or jupyterlite
◼mini-galleries eg to link an object's docstring to its examples

🎓👨‍🦱👩 Post-doc: From missing values to deep learning on sets
https://team.inria.fr/soda/job-offers

with myself and Marine le Morvan
at @Soda_Inria

Come work with us on an exciting topic across statistics and deep learning

If you want to see the replay of my talk at EuroSciPy 2023 regarding classifier tuning and the misconception behind class imbalance, here you go: https://youtu.be/6YnhoCfArQo

Slides are available at: https://docs.google.com/presentation/d/1IPXbEZpfrynjJMTXjI36rNGuPpOtY6_yJIrQOH6JBpI/edit?usp=sharing

joblib 1.3.0 is out in the wild!

joblib is a library that provides an generic way to call into thread-based, process-based and distributed parallelism (via external backends) + a way to cache expensive computation in repeated function calls on disk.

https://joblib.readthedocs.io

This new release provides several major new features, inclusing a `return_as="generator"` argument to the `Parallel`class to make it possible to aggregate parallel results when ready (preserving the submission order).

1/4

dirty_cat's TableVectorizer automatically turns complex dataframes into numerical data matrices ready for learning.

Piped with sklearn's HistGradientBoosting, it gives a strong default for learning on tables. Together, they form my go-to learner
https://dirty-cat.github.io/stable/auto_examples/01_dirty_categories.html#example-table-vectorizer

Tabular data can benefit from merging external sources of information.

The FeatureAugmenter is a sklearn transformer to augment a given dataframe by joins on reference tables.
https://dirty-cat.github.io/stable/generated/dirty_cat.FeatureAugmenter.html

fuzzy_join makes it robust to mismatch in vocabulary. Hyperparameter optimization can tune matches for prediction

For such external information,
diry-cat can download embeddings of wikipedia data on millions of entities: companies, cities, geographic locations...
https://dirty-cat.github.io/stable/auto_examples/07_ken_embeddings_example.html

Wrangling categories/entities with typos?

dirty-cat's fuzzy_join function is similar to pandas' merge function, but caters for typos by matching with string similarities across the two tables
https://dirty-cat.github.io/stable/generated/dirty_cat.fuzzy_join.html

The deduplicate function enables merging multiple morphological variants of the same string, to recover a category from data with typo:
https://dirty-cat.github.io/stable/generated/dirty_cat.deduplicate.html