* Added outlier detection tutorial into docs
* Switched to pytorch model/dataset implementation
* Fixed image normalization
* Fixed outlier detection for test set
* Added outlier thresholding into tutorial.
* Cleaned output
* Removed unused imports and renamed notebook
* Added outliers notebook to PR
* Added quickstart into tutorial
* Best quickstart header
* Cleaned cell output
* Changed to use subset of original data for speed
* Fixed randomness
* Cleared outputs
* fixed metadata tags
* Fixed metadata
* Cleaned kernel and verified output
* Improved unit test
Changed labels references to classes where apropriate
* Create FAQ page in cleanlab docs
* Add FAQ notebook to store answers to frequently asked questions
* Improved formatting issues for faq page
- added faq to sidebar
* Improved formatting issues for faq page
- added faq to sidebar
- changed email to comply with CLA
* Removed faq numbering
- Removed notebook output metadata
- Added notebook to index for left table link
* Text and example rewritten for clarity
- How do I format labels for cleanlab-- example improved
- How do I format labels for cleanlab-- text amended
- Can't find an answer to your question-- added links
* add links to issues/slack
Co-authored-by: Ulyana Tkachenko <uly@ulyana.lan>
Co-authored-by: Jonas Mueller <1390638+jwmueller@users.noreply.github.com>
* dataset health tutorial pulldown format
* tutorial ordering in sidebar
* pulldown format, bullet format
* reformat pulldown indepth tutorial
* shorten data fetching header
* adjust spacing
* 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
* improved language in docs Quickstart
* LearningWithNoisyLabels refactor to improve API flexibility and UX
* fix LearningWithNoisy rare label handling
* additional arg-checking unit tests
* Refactor modules pruning to filter and latent_estimation to count
* Remove polyplex (research) algorithms from cleanlab
* Create new module rank and move scoring functions to rank.
* Rename test to match new module names
* Fixed error in normalized margin. added ranking for arbitrary psx and labels.
* Remove unused tests and methods. add multi-label support for baseline.
* Move baseline methods to filter and delete baseline module.
* change filter.get_noise_indices to filter.find_label_issues
* Rename baseline methods. fill out docstrings.
* Only require 1 example to be left in each class after removing errors. (instead of 5)
* Remove K as a parameter to count.compute_confident_joint
* Add C_argmax and C_ij methods from CL paper to find_label_issues
* Add warnings for new prune methods and frac_noise. Fix tests.
* Add baseline tests to test_rank_filter and delete baseline test
* Remove inverse_noise_matrix parameter in classification call to find_label_issues
* add todo to update docstring with new ranking functions
* 100% tests pass. add multi-label support for prune_method
* Major NOT-backwards-compatible name changes to most components
* More Major NOT-backwards-compatible name changes
* fixed s -> label mistakes
* Several nomenclature updates from PR feedback. models renamed to example models.
* Remove python2 support across all modules.
* major api changes. psx -> pred_probs. prob_given_label -> self_confidence. testing added.
* enable python version 3.9 for pytorch model.
* ran spellcheck
* ran grammar check
* Update count.py
* Update filter.py
* Update setup.py and ci.yml to no longer support Python 2 and py3.4/5
* Increase test coverage and documentation of rank module methods.
* create utils submodule and move util and latent_algebra
* Rename y everywhere to true_labels, and p(true_label=..)
* Enforce positional arguments in methods. Fully remove py2 support.
Jonas Mueller