cleanlab--cleanlab
Curtis G. Northcutt
115 行
5.0 KiB
Python
115 行
5.0 KiB
Python
# Copyright (c) 2017-2050 Curtis G. Northcutt
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# This file is part of cleanlab.
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#
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# cleanlab is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# cleanlab is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# This agreement applies to this version and all previous versions of cleanlab.
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__version__ = '1.0'
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# ----------------------------------------
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# | CURRENT STABLE VERSION RELEASE NOTES |
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# ----------------------------------------
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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: 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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# - cleanlab now natively supports Co-Teaching for learning with noisy labels (reqs python3, 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 seperate repo to reproduce state-of-the-art results.
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# ---------------------------
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# | FUTURE FEATURES PLANNED |
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# ---------------------------
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# - Extensions to regression
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# - Extensions for object detection and segmentation tasks.
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# - cleanlab pro (interface for businesses)
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# ----------------------------------
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# | PREVIOUS VERSION RELEASE NOTES |
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# ----------------------------------
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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 parallelization support in cleanlab/pruning.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 based on this version of cleanlab: 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 to be a true joint estimate.
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# - Confident joint algorithm changed! When an example is found confidently as 2+ labels, choose class with max probability.
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# 0.0.7 - Massive speed increases across the board. Estimating confident joint now nearly instant. NO major 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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