wehub-resource-sync
185 lines
9.3 KiB
Python
185 lines
9.3 KiB
Python
__version__ = "2.9.0"
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# 2.9.1 - Not yet released, you are using bleeding-edge developer version. See its documentation at: https://docs.cleanlab.ai/master/
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# ------------------------------------------------
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# | PREVIOUS MAJOR VERSION RELEASE NOTES SUMMARY |
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# ------------------------------------------------
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# 2.9.0 - Streamlined dependencies and improved maintainability
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# - Removed TensorFlow/Keras dependencies and cleanlab.models.keras module
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# - Updated documentation with modern installation methods
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# 2.8.0 - Python 3.12-3.14 support and compatibility improvements
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# - Add support for Python 3.12, 3.13 and 3.14
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# - Fix scikit-learn >= 1.8 compatibility issues
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# - Remove numpy version restriction (works with numpy>=1.22)
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# - Fix DataLab compatibility with datasets 4.0.0+
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# - Fix null pointer bugs in DataLab issue managers
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# - Update Python requirements to 3.10+
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# 2.7.0 - Broadening Data Quality Checks and ML Workflows
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#
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# Major new functionalities include:
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# - Detection of spurious correlations in image datasets with Datalab
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# - New tutorial for improving ML performance with train and test set curation
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# 2.6.0 - Elevating Data Insights: Comprehensive Issue Checks & Expanded ML Task Compatibility
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#
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# Major new functionalities include:
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# - Detection for null values, class imbalance, underperforming groups and data valuation in Datalab
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# - Extend Datalab support for tasks like regression and multi-label classification
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# - Scores for near duplicates and outliers rescaled to be more interpretable
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# 2.5.0 - cleanlab detects label errors in most ML tasks
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#
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# Major new functionalities include:
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# - Support for: regression, object detection, image segmentation
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# - Detection of low-quality images
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# 2.4.0 - One line of code to detect all sorts of dataset issues
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#
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# Major new functionalities include:
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# - Datalab: A unified audit to detect different types of issues in your data and labels. This is the primary way most users should apply cleanlab to their dataset.
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# - Nicer APIs for label issues in multi-label classification datasets, including dataset-level issue summaries for multi-label classification.
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# - Updated tutorials with more interesting datasets and ML models.
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# 2.3.0 - Extending cleanlab beyond label errors into a complete library for data-centric AI
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#
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# Major new functionalities include:
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# - Active learning with data re-labeling (ActiveLab)
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# - KerasWrapperModel and KerasSequentialWrapper to make arbitrary Keras models compatible with scikit-learn
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# - Computational improvements for detecting label issues (better efficiency and mini-batch estimation that works with lower memory)
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# 2.2.0 - Re-invented algorithms for multi-label classification and support for datasets with missing classes
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#
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# For detecting label errors in multi-label classification datasets (e.g. image/document tagging):
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# - Added cleanlab.multilabel_classification module for label quality scoring.
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# - Re-invented better algorithms and published paper describing how the new algorithms work and benchmarking their effectiveness.
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# - Provided new quickstart tutorials and examples on how to use cleanlab for multi-label datasets and train effective models for them.
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#
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# Additional improvements:
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# - cleanlab now works much better for datasets in which some classes happen to not be present.
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# - Algorithmic improvements to ensure count.num_label_issues() returns more accurate estimates.
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# - For developers: introduction of flake8 code linter and more comprehensive mypy type annotations.
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# 2.1.0 - "Multiannotator, Outlier detection, and Token Classification" - Cleanlab supports several new features
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#
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# For users (+ sometimes developers):
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# - Improved CleanLearning. Added support for pd.DataFrame, tf.keras.dataset, and other types of data besides np.ndarray
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# - Added cleanlab.multiannotator module for working with data labeled by multiple annotators.
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# - Added cleanlab.token_classification for token classification with text data.
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# - Added cleanlab.outlier for out-of-distribution detection (includes outlier/anomaly detection)
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#
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# For developers:
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# - No significant API-breaking changers.
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# - Tutorials for all new modules added to https://docs.cleanlab.ai
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# - Contributing resources added to https://github.com/cleanlab/cleanlab/blob/master/CONTRIBUTING.md
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# - Contributing ideas added to https://github.com/cleanlab/cleanlab/wiki#ideas-for-contributing-to-cleanlab
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# 2.0.0 - "Data-centric AI Ready" - Complete re-architecture of cleanlab API.
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#
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# For users (+ sometimes developers):
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# - All aspects of API have changed (method names, parameters, defaults, variables, classes, etc)
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# - Added new dataset module for dealing with dataset-level issues
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# - CleanLearning now handles most cleanlab tasks in one line of code.
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# - Several new workflows possible with rank, count, and filter modules
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#
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# For developers:
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# - If you're coming from 1.0 (pre-1.0.1), you may need to re-clone.
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# - Extensive support available at https://docs.cleanlab.ai
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# 1.0.1 - Launch sphinx docs for Cleanlab 1.0 (in preparation for Cleanlab 2.0). Mostly superficial.
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#
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# For users (+ sometimes developers):
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# - This releases the new sphinx docs for cleanlab 1.0 documentation (in preparation for CL 2.0)
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# - Several superficial bug fixes (reduce error printing, fix broken urls, clarify links)
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# - Extensive docs/README updates
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# - Support was added for Conda Installation
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# - Moved to AGPL-3 license
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# - Added tutorials and a learning section for Cleanlab
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#
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# For developers:
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# - Moved to GitHub Actions CI
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# - Significantly shrunk the clone size to a few MB from 100MB+
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# 1.0 - cleanlab official 1.0 (beta) release!
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# - Added Amazon Reviews NLP to cleanlab/examples
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# - cleanlab now supports python 2, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8.
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# - Users have used cleanlab with python version 3.9 (use at your own risk!)
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# - Added more testing. All tests pass on windows/linux/macOS.
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# - Update to GNU GPL-3+ License.
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# - Added documentation: https://cleanlab.readthedocs.io/
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# - The cleanlab "confident learning" paper is published in the Journal of AI Research:
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# https://jair.org/index.php/jair/article/view/12125
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# - Added funding, community and contributing guidelines
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# - Fixed a number of errors in cleanlab/examples
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# - cleanlab now supports Windows, macOS, Linux, and unix systems
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# - Numerous examples added to the README and docs
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# - now natively supports Co-Teaching for learning with noisy labels, req: py3, PyTorch 1.4
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# - cleanlab built in support with handwritten datasets (besides MNIST)
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# - cleanlab built in support for CIFAR dataset
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# - Multiprocessing fixed for windows systems
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# - Adhered all core modules to PEP-8 styling
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# - Extensive benchmarking of cleanlab methods published.
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# - Future features planned are now supported in cleanlab/version.py
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# - Added confidentlearning-reproduce as a separate repo to reproduce state-of-the-art results.
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# 0.1.1 - Major update adding support for Windows and Python 3.7
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# - Added support for Python 3.7
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# - Added full support for Windows, including multiprocessing support in cleanlab/filter.py
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# - Improved PEP-8 adherence in core cleanlab/ code.
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# 0.1.0 - Release of confident learning paper: https://arxiv.org/pdf/1911.00068.pdf
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# - Documentation increase
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# - Add examples to find label errors in mnist, cifar, imagenet
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# - re-organized examples and added readme.
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# 0.0.14 - Major bug fix in classification. Unused param broke code.
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# 0.0.13 - Major bug fix in finding label errors.
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# - Fixed an important bug that broke finding label errors correctly.
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# - Added baseline methods for finding label errors and estimating joint
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# - Increased testing
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# - Simplified logic
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# 0.0.12 - Minor changes.
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# - Added support and testing for sparse matrices scipy.sparse.csr_matrix
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# - Dropped integrated dependency and support on fasttext. Use fasttext at your own risk.
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# - Added testing and dropping fasttext bumped testing code coverage up to 96%.
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# - Remove all ipynb artifacts of the form # In [ ].
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# 0.0.11 - New logo! Improved README.
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# 0.0.10 - Improved documentation, code formatting, README, and testing coverage.
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# 0.0.9 - Multiple major changes
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# - Important: refactored all confident joint methods and parameters
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# - Numerous important bug fixes
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# - Added multi_label support for labels (list of lists)
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# - Added automated ordering of label errors
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# - Added automatic calibration of the confident joint
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# - Version 0.0.8 is deprecated. Use this version going forward.
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# 0.0.8 - Multiple major changes
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# - Finding label errors is now fully parallelized.
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# - prune_count_method parameter has been removed.
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# - estimate_confident_joint_from_probabilities now automatically calibrates confident joint
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# to be a true joint estimate.
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# - Confident joint algorithm changed! When an example is found confidently as 2+ labels, choose
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# class with max probability.
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# 0.0.7 - Massive speed increases across the board. Estimate joint nearly instantly. NO API changes.
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# 0.0.6 - NO API changes. README updates. Examples added. Tutorials added.
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# 0.0.5 - Numerous small bug fixes, but not major API changes. 100% testing code coverage.
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# 0.0.4 - FIRST CROSS-PLATFORM WORKING VERSION OF CLEANLAB. Adding test support.
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# 0.0.3 - Adding working logo to README, pypi working
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# 0.0.2 - Added logo to README, but link does not load on pypi
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# 0.0.1 - initial commit
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