Mailman 3 April 2024 - Scikit-learn-commits

[scikit-learn/scikit-learn] 70829b: MNT Small check sample weight refactor (#16324)
by Thomas J Fan 15 Aug '24

15 Aug '24

Branch: refs/heads/master Home: https://github.com/scikit-learn/scikit-learn Commit: 70829b34df8a1d98a15250f041102c6c0b10e2d1 https://github.com/scikit-learn/scikit-learn/commit/70829b34df8a1d98a15250f… Author: Thomas J Fan <thomasjpfan(a)gmail.com> Date: 2020-01-30 (Thu, 30 Jan 2020) Changed paths: M sklearn/utils/validation.py Log Message: ----------- MNT Small check sample weight refactor (#16324)

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[scikit-learn/scikit-learn] 91badf: DOC Format docstrings in _coordinate_descent.py (#...
by Hirofumi Suzuki 09 Aug '24

09 Aug '24

Branch: refs/heads/master Home: https://github.com/scikit-learn/scikit-learn Commit: 91badfab662ad1eafda4b60d078f563f010265cc https://github.com/scikit-learn/scikit-learn/commit/91badfab662ad1eafda4b60… Author: Hirofumi Suzuki <hs-nazuna(a)users.noreply.github.com> Date: 2020-01-30 (Thu, 30 Jan 2020) Changed paths: M sklearn/linear_model/_coordinate_descent.py Log Message: ----------- DOC Format docstrings in _coordinate_descent.py (#16285)

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[scikit-learn/scikit-learn] 4b6273: ENH Native support for missing values in GBDTs (#1...
by Nicolas Hug 26 Jul '24

26 Jul '24

Branch: refs/heads/master Home: https://github.com/scikit-learn/scikit-learn Commit: 4b6273b87442a4437d8b3873ea3022ae163f4fdf https://github.com/scikit-learn/scikit-learn/commit/4b6273b87442a4437d8b387… Author: Nicolas Hug <contact(a)nicolas-hug.com> Date: 2019-08-21 (Wed, 21 Aug 2019) Changed paths: M benchmarks/bench_hist_gradient_boosting.py M doc/modules/ensemble.rst M doc/whats_new/v0.22.rst M sklearn/ensemble/_hist_gradient_boosting/_binning.pyx M sklearn/ensemble/_hist_gradient_boosting/_gradient_boosting.pyx M sklearn/ensemble/_hist_gradient_boosting/_loss.pyx M sklearn/ensemble/_hist_gradient_boosting/_predictor.pyx M sklearn/ensemble/_hist_gradient_boosting/binning.py A sklearn/ensemble/_hist_gradient_boosting/common.pxd A sklearn/ensemble/_hist_gradient_boosting/common.pyx M sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py M sklearn/ensemble/_hist_gradient_boosting/grower.py M sklearn/ensemble/_hist_gradient_boosting/histogram.pyx M sklearn/ensemble/_hist_gradient_boosting/loss.py M sklearn/ensemble/_hist_gradient_boosting/predictor.py M sklearn/ensemble/_hist_gradient_boosting/splitting.pyx M sklearn/ensemble/_hist_gradient_boosting/tests/test_binning.py M sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py M sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py M sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py M sklearn/ensemble/_hist_gradient_boosting/tests/test_histogram.py M sklearn/ensemble/_hist_gradient_boosting/tests/test_loss.py M sklearn/ensemble/_hist_gradient_boosting/tests/test_predictor.py M sklearn/ensemble/_hist_gradient_boosting/tests/test_splitting.py R sklearn/ensemble/_hist_gradient_boosting/types.pxd R sklearn/ensemble/_hist_gradient_boosting/types.pyx M sklearn/ensemble/_hist_gradient_boosting/utils.pyx M sklearn/ensemble/setup.py Log Message: ----------- ENH Native support for missing values in GBDTs (#13911) * Added NaN support in mapper * pep * WIP * some more * WIP * WIP * bug fix * basic tests * some doc * avoid some interactions * Added tag * better test * decent test + fix bug * add missing_fraction param to benchmark * bin training and validation data separately * shorter test * Map missing values to first bin instead of last * pep8 * Added whats new entry * avoid some python interactions * make predict_binned work * fixed bug due to offset in bin_thresholds_ attribute * more sensible binning strat * typo * user name * Add small test * convert to fortran array in tests * some doc * Added function test * pep8 * Bin validation data using binmaper of training data * Allocate first bin for missing entries based on the whole data, not just training data. * Addressed Thomas' comments * Update sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py * Addressed Guillaume's comments * always allocate first bin for missing values * reduce diff * minor more consistent test * typo * WIP * some doc * reduce diff * pep8 * minor * remove prints * towards nan only splits * don't check right to left on split_on_nan * cleaups * format and comment * Fixed bug + added more tests * refactor tests * put back n_threads to max value * minor changes * minor cleaning * Add (failing) test that checks equivalence with min max imputation * Decrease the likelihood of ties when training the trees * More robust test * Fix pytest parametrization * Check bin thresholds in test * Try to make the test even easier to see if the Linux 32bit build would pass in this case * Don't check last non-missing bin if there's no nan * Improve min-max imputation test * FIX: _find_best_bin_to_split_right_to_left is still required even when left to right wants to split on nans * comments * remove split_on_nan * ooops deleted useless files * Got rid of individual checks in predictor code +inf thresholds are only allowed in a split on nan situation. Thresholds that are computed as +inf are capped to a very high constant value * can also remove special case in binning code * minor typos + more consistent test * renamed types -> common * 1e300 -> almost inf * added user guide section on missing values * Addressed Olivier's comment + updated whatsnew * addressed comments * Fix doctest formatting * Fix nan predictive doctest

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How does Scikit-learn enhance machine learning tasks, and why is it a go-to for data scientists?
by surya.resume2023＠gmail.com 23 Jul '24

23 Jul '24

Scikit-learn, a prominent machine learning library, plays a pivotal role in advancing the efficiency of machine learning tasks, particularly in the realm of digital certificate verification. Here's a detailed exploration of its contributions: Versatility in Algorithms: Scikit-learn offers a comprehensive suite of machine learning algorithms, from traditional models to cutting-edge techniques. For digital certificate tasks, this diversity ensures that data scientists can choose and implement the most suitable algorithm for verification purposes. User-Friendly Interface: Its user-friendly API design simplifies the implementation of machine learning models, making it accessible for both beginners and seasoned data scientists. The straightforward syntax allows for rapid development and experimentation. Effective Data Preprocessing: Scikit-learn provides robust tools for data preprocessing, a critical step in handling digital certificates. With features for scaling, normalization, and handling missing data, data scientists can ensure the integrity of the certificate data before model training. Model Selection and Evaluation: The library facilitates model selection and hyperparameter tuning, crucial for optimizing <a href="https://www.truscholar.io">digital certificate</a> verification models. Cross-validation methods and various scoring metrics enable thorough evaluation, ensuring the reliability of the implemented algorithms. Integration Capabilities: Scikit-learn seamlessly integrates with other Python libraries, creating a cohesive data science ecosystem. This integration is particularly valuable when working with digital certificates, as it allows for efficient collaboration with tools that handle cryptographic operations or data manipulation. Open Source Community Support: Being an open-source project, Scikit-learn benefits from a vibrant community of contributors and users. The active community ensures regular updates, bug fixes, and the incorporation of the latest advancements, making it a dependable choice for evolving digital certificate verification needs. Documentation and Resources: Scikit-learn offers extensive documentation and resources, including tutorials and examples. This wealth of information aids data scientists in mastering its functionalities and applying them effectively to tasks like digital certificate verification. Optimized Performance: Through integration with optimized numerical libraries like NumPy and SciPy, Scikit-learn achieves high-performance execution of mathematical operations. This optimization is crucial for handling complex cryptographic operations often associated with digital certificates. In essence, Scikit-learn's versatility, user-friendly design, effective preprocessing capabilities, model optimization tools, integration support,

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icmmeteo
by rebelssasha＠gmail.com 16 Jul '24

16 Jul '24

Living in Warsaw, where the weather can change on a dime, I rely on the https://icmmeteo.pl/1947/warszawa to plan my day before I leave my apartment. Checking the widget has become as routine as my morning coffee. It's never let me down, helping me decide whether to grab an umbrella or sunglasses on my way out.

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[scikit-learn/scikit-learn] e1c764: DOC Cleaning parameter spec in docstrings of base ...
by lopusz 09 Jun '24

09 Jun '24

Branch: refs/heads/master Home: https://github.com/scikit-learn/scikit-learn Commit: e1c7649469eb91c4a23796717b58da266345ab8e https://github.com/scikit-learn/scikit-learn/commit/e1c7649469eb91c4a237967… Author: lopusz <lopusz(a)users.noreply.github.com> Date: 2020-01-31 (Fri, 31 Jan 2020) Changed paths: M sklearn/base.py Log Message: ----------- DOC Cleaning parameter spec in docstrings of base module (#16284)

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[scikit-learn/scikit-learn] c664df: DOC ensure all attributes are documented for RBFSa...
by judithabk6 09 Jun '24

09 Jun '24

Branch: refs/heads/master Home: https://github.com/scikit-learn/scikit-learn Commit: c664df47cc273ebc143a877825479a4f95f83db2 https://github.com/scikit-learn/scikit-learn/commit/c664df47cc273ebc143a877… Author: judithabk6 <judithabk6(a)users.noreply.github.com> Date: 2020-01-31 (Fri, 31 Jan 2020) Changed paths: M sklearn/kernel_approximation.py Log Message: ----------- DOC ensure all attributes are documented for RBFSampler (#16276)

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[scikit-learn/scikit-learn] 86f47d: Fix warning in approximate_nearest_neighbors.py (#...
by Hirofumi Suzuki 06 Jun '24

06 Jun '24

Branch: refs/heads/master Home: https://github.com/scikit-learn/scikit-learn Commit: 86f47da52954cb68b5eb17ffcc909194e31bbaee https://github.com/scikit-learn/scikit-learn/commit/86f47da52954cb68b5eb17f… Author: Hirofumi Suzuki <hs-nazuna(a)users.noreply.github.com> Date: 2020-01-31 (Fri, 31 Jan 2020) Changed paths: M examples/neighbors/approximate_nearest_neighbors.py Log Message: ----------- Fix warning in approximate_nearest_neighbors.py (#16339)

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[scikit-learn/scikit-learn] e05b9e: MNT Comment out instructions in bug report templat...
by Thomas J Fan 28 May '24

28 May '24

Branch: refs/heads/master Home: https://github.com/scikit-learn/scikit-learn Commit: e05b9e146006236aa41349105e8a01ac9535fba7 https://github.com/scikit-learn/scikit-learn/commit/e05b9e146006236aa413491… Author: Thomas J Fan <thomasjpfan(a)gmail.com> Date: 2020-01-30 (Thu, 30 Jan 2020) Changed paths: M .github/ISSUE_TEMPLATE/bug_report.md Log Message: ----------- MNT Comment out instructions in bug report template (#16325)

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[scikit-learn/scikit-learn] 3743a5: Fix: Possible unnecessary iterations in Newton-CG ...
by Rushabh Vasani 28 May '24

28 May '24

Branch: refs/heads/master Home: https://github.com/scikit-learn/scikit-learn Commit: 3743a55aed5c9ee18c8b94b95dfc4c41d0ae99f5 https://github.com/scikit-learn/scikit-learn/commit/3743a55aed5c9ee18c8b94b… Author: Rushabh Vasani <vasanirushabh24(a)gmail.com> Date: 2020-01-31 (Fri, 31 Jan 2020) Changed paths: M doc/whats_new/v0.23.rst M sklearn/utils/optimize.py Log Message: ----------- Fix: Possible unnecessary iterations in Newton-CG algorithm (#16266)

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