* fix None labels issue
* build: ➕ add hypothesis to dev dependencies. Used for property-based testing
* test: ✅ test that find_overlapping_classes can run by only providing a confident joint
Resolves#651
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Co-authored-by: Elías Snorrason <eliassno@gmail.com>
* fix bug in hard-coded test. generalize the test
* 🐛 cast rounded num_issues to int
np.rint outputs an array of the same shape and type as its input. num_label_issues is expected to return an integer.
Co-authored-by: Elías Snorrason <eliassno@gmail.com>
* Changed all instances of np.array in docstring to np.ndarray
* np.array is NOT a proper class name it is just a function to
create np.ndarrays and therefore should not be parameter class
* Black formatting compliance
* validation_func docstring
* torch,tf compatibility+tests
* keras test
* skip tests if python < 3.7
* pytorch numpy int bug on windows
* make tensorflow test work on windows
* move tf env variable setting
* pytorch test increase epochs
* install cpu-tensorflow on windows CI
* torch test optimizer to adam
* fix bugs in shuffled TF dataset
* dummy unit test for TF on windows
* dummy code for TF windows testing
* deal with np.int bug on windows
* remove windows debugging code
* docstrings for new functionality
* address merge conflicts
* reformat after merge
* addressed comments
* update N in classification.py
* minor docstring changes on K-1 classes
* use K in shape
* minor grammar fixes
* docs language improvements
Co-authored-by: Jonas Mueller <1390638+jwmueller@users.noreply.github.com>
Our package doesn't have type annotations everywhere, so we can't use
mypy in strict mode just yet. Still, adding type checking in CI is
valuable, so we don't have unchecked annotations in our code.
This patch includes basic fixes to make type checking pass, including
switching the incorrect `np.array` type annotation for `np.ndarray` and
adding some assertions for flow-sensitive typing.
* cleanlearning w dfs
* work for sparse matrix as well
* simplify logic of labels_to_array and extend types
* address pr feedback
* add unit test
* rare label dataframe
* modularize subsetting code
* series rarelabel test
* replace cal.com with slack/email
* Add general method to find num_classes from labels
* compute num_classes with pred_probs.shape[1]
* fix broken commits, address 2nd round of comments
Co-authored-by: Curtis G. Northcutt <curtis.northcutt@gmail.com>
Add clarification of the labels format requirements for all major API functions.
* Fix broken link
* Clarify reqs for labels format. rank.py does not yet support multi_label
* add double ticks to code in docstrings
* clarify docstring
* further clarify multi_label vs single label labels reqs
* also add new docstring to confident joint
* 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>
* improved language in docs Quickstart
* LearningWithNoisyLabels refactor to improve API flexibility and UX
* fix LearningWithNoisy rare label handling
* additional arg-checking unit tests
Gibson Han