* 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>
* 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
Inspired by [1] and similar examples. See also: the pull request that
switches from `copy.deepcopy` to `sklearn.base.clone` [2]. Even the
latter calls `sklearn.base.clone(..., safe=False)` on the values
returned by `get_params()`, so the user has to be sure that their model
is correctly clonable, otherwise they may get silently incorrect
behavior.
[1]: https://github.com/cleanlab/cleanlab/issues/87
[2]: https://github.com/cleanlab/cleanlab/pull/144
* utilities -> internals
* docbuilding instructions improvement
* fixup formatting of contributing.md
* change contributor guidelines language to be optional
* line formatting
* 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.
This patch updates metadata in `setup.py` and comments in other files to
clarify that the current license is AGPLv3, as is specified in the
current `LICENSE` file.
This patch also removes the "this agreement applies to this version and
all previous versions" text from the README and code comments, because
it is redundant; the previous code was already released under a _more
liberal_ license, so giving the option of AGPLv3 is not useful (and
perhaps confusing).
Jonas Mueller