* df return type, need tests still
* Add pandas as a dependency
We already decided that pandas will be a dependency of cleanlab (also
used in the dataset module, see
https://github.com/cleanlab/cleanlab/pull/182).
* Tweak documentation
* addressed comments
* remove lazy import
* address 2nd round comments
* unit tests
* improve codecov
* Fix typo
* methods to save more space
* nocover statements for prints
* extra nocover
* nocover warnings
* test docstring formatting
* test docstring formatting2
* test docstring formatting2
* move compress to helper, find-label docs params
* readded stuff lost in merge conflict
* addressed remaining PR review comments
* docs formatting
* docs formatting2
* docs formatting3
* docs formatting4
* docs formatting5
* docs formatting5
* docs formatting6
* docs formatting7
* docs formatting8
* docs formatting9
* docs formatting19
* docs formatting20
* docs formatting20
* docs formatting21
* code formatting
* fix a bug where confident joint isnt computed
The confident joint wasn't getting computed if noise_matrix was passed in and pred_probs was not passed in. But that's bad because it stops workflows like:
```python
cl = CleanLearning()
cl.fit(data, labels, noise_matrix=noise_matrix)
cleanlab.dataset.health_summary(labels, confident_joint=cl.confident_joint)
```
* fixed bug from last commit. code in wrong place.
* print overwrite bugfix
Co-authored-by: Anish Athalye <me@anishathalye.com>
Co-authored-by: Curtis G. Northcutt <curtis.northcutt@gmail.com>
* initial readme updates
* code example
* example errors image
* example errors image formatting
* example errors image formatting 2
* restof pulldowns
* rest of pulldowns formatting
* deleted extra details - pulldowns formatting
* pulldowns spacing
* pulldowns spacing
* news bullets
* release note link update
* Fix logo URL
* Fix whitespace errors
* Replace `cleanlab` -> cleanlab
This follows what we do in our documentation (see style guide in
DEVELOPMENT.md).
* Make various fixes and changes
* Hide bibtex behind summary/details
* updated example code for @cgnorthcutt feedback
Updated example code bringing back comments from the old readme, and updated the code snippet to use new CleanLearning API which is far more streamlined.
* example code formatting
* skeleton of migration guide added
* add changelog link
* Make banner logo transparent amazing in dark mode
* add link to docs migration guide
link needs to be verified, total guess
* link formatting fix in img caption
* add dataset module summary
* Major revision of entire README.
Co-authored-by: Anish Athalye <me@anishathalye.com>
Co-authored-by: Curtis G. Northcutt <curtis.northcutt@gmail.com>
* Makes some methods private that are not intended to be user-facing.
* Adds experimental module with fasttext.py and coteaching.py
* Adds header descriptions to code files which will render in docs
* Many miscellaneous fixes
* CleanLearning = Machine Learning with cleaned data
* Replace lnl instance naming with cl everywhere (CleanLearning)
* Add support for multi-class as well
* Add test based on #158
Co-authored-by: Anish Athalye <me@anishathalye.com>
* added LearningWithNoisyLabels.find_label_issues instance method
* LNL.find_label_issues no longer memoizes
* verbose unit test coverage
* Fixed all issues in PR. Fixed confident joint usage in LNL.find_label_issues. Fixed other minor bugs. Added tests
Co-authored-by: Curtis G. Northcutt <curtis.northcutt@gmail.com>
Co-authored-by: Anish Athalye <me@anishathalye.com>
This was used to skip e.g. importing PyTorch on some tests. However,
PyTorch supports all versions of Python that Cleanlab supports, so
there's no need to issue this warning. Furthermore, having this kind of
code in our codebase increases maintenance burden and contributes to
user frustration. If we mark a version of some library as unsupported,
but the library adds support for it in the future, there's no way users
can use the two packages together without waiting for us to push a new
version of cleanlab that marks that it's compatible and then switching
to the new version, which may be painful for unrelated reasons. It's
better to not have these kinds of checks; if users are using a version
of Python that's unsupported by some other library that they want to
use, that's not our problem, and they'll have issues installing or
importing it and figure it out themselves.
This patch also removes mentions of Python 2.
* Move _process_label_issues_kwargs to fit method
* Add test for sklearn GridSearchCV with kwargs
* Add comment to test for sklearn GridSearchCV with kwargs
* Change cv=3 for GridSearchCV in test
* improved language in docs Quickstart
* LearningWithNoisyLabels refactor to improve API flexibility and UX
* fix LearningWithNoisy rare label handling
* additional arg-checking unit tests
LearningWithNoisyLabels used `sklearn.utils.check_X_y` to enforce that
`X` was 2D, to be in line with what sklearn's standard estimators
expect. However, LearningWithNoisyLabels is dataset-agnostic: it doesn't
look at the data points themselves. If the underlying classifier
supports data in a different shape, there's no reason
LearningWithNoisyLabels should prohibit it. Users have requested that we
relax this unnecessary restriction [1] so LearningWithNoisyLabels will
more naturally support e.g. image datasets and CNN models.
Thanks to @kothari1997narayan for suggesting this change.
[1]: https://github.com/cleanlab/cleanlab/issues/86
Curtis G. Northcutt