* 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
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
Arbitrary scikit-learn estimators are not safe to clone via
`copy.deepcopy()`, for example when they contain state that cannot be
pickled, such as a Keras model that contains a `_thread.RLock` [1]. The
correct way to clone an estimator is to use `sklearn.base.clone()` [2],
which constructs a new unfitted estimator with the same parameters as
the original by calling `get_params()` followed by creating a new object
of the class, passing the parameters to `__init__()`. This patch
switches from `copy.deepcopy` to `sklearn.base.clone`.
This patch also updates the MNIST PyTorch CNN example model to be
compatible with `sklearn.base.clone`. Writing a custom `get_params`
implementation usually wouldn't be necessary with a more standard
estimator that inherits from `BaseEstimator`, but it was necessary to
override the method in this case because of the nonstandard
implementation that supports multiple datasets and does data loading
within the estimator itself based on the `dataset` argument.
[1]: https://github.com/cleanlab/cleanlab/issues/87
[2]: https://scikit-learn.org/stable/modules/generated/sklearn.base.clone.html
* utilities -> internals
* docbuilding instructions improvement
* fixup formatting of contributing.md
* change contributor guidelines language to be optional
* line formatting
* Add label quality scoring function for users to choose the scoring method.
Add confidence_weighted_entropy as another label quality score that is available for scoring and ranking.
Add option in scoring function to adjust predicted probabilities by subtracting the class thresholds.
Refactor order_label_issues to use the new label quality scoring function and accept keyword args.
* Cleanup docstring in label quality scoring functions
* Change rank_by_kwargs default to empty dict. Dict is used as keyword args for label quality scoring function.
* Cleanup docstrings. Add **rank_by_kwargs to signature of order_label_issues function.
* Add exception handler for invalid rank_by methods
* Add **rank_by_kwargs to allow keyword args in find_label_issues()
* Add test for confidence_weighted_entropy rank scoring function
* Add test for scoring function that accepts scoring method
* Cleanup comments
* Update test for scoring functions
* Update test for scoring functions
* Modify order_label_issues() function to run score_label_quality with (labels, pred_probs) and then filter with label_issues_mask. This is more robust to allow us to adjust the pred_probs (e.g. subtract confident class thresholds)
* Update test for label quality scoring
* Update test for label quality scoring
* Update exception handler for label scoring function
* Add test for subtracting confident class thresholds
* Update test for subtracting confident class thresholds
* Update test for subtracting confident class thresholds
* Add ensemble label quality scoring function
* Update test for ensemble label quality scoring function
* Update test for ensemble label quality scoring function
* Update docstrings for functions that accept adj_pred_probs with description of the adjustment to pred_probs
* Update find_label_issues to pass rank_by_kwargs as a dict
* Cleanup comments
* Refactor tests for scoring functions to avoid redundancies
* Refactor tests for scoring functions to avoid redundancies. Use parameterize instead of for-loop
* Parameterize scoring method to test ensemble scoring function
* Print scoring method when scoring function test fails
* Cleanup docstrings to avoid redundancies
* Move class confident threshold functions to new label quality utils module. Add weight_ensemble_members_by parameter to label quality ensemble scoring function allows users to choose weighting scheme (uniform, accuracy). Enhance tests for CI.
* Add test for CI. Test bad arg for weight_ensemble_members_by parameter.
* Add explicit error checking of pred_probs_list arg.
* Cleanup docstring and print statements
* Cleanup docstrings. Move get_entropy to utils. Refactor score_label_quality_ensemble to print accuracy weights
* Change default label quality scoring method to normalized_margin. Add to docstring explaining when to use normalized_margin vs self_confidence.
* Cleanup docstring
* Cleanup docstrings. Rename get_entropy() to get_normalized_entropy()
* Rename score_label_quality() to get_label_quality_scores(). Rename score_label_quality_ensemble() to get_label_quality_ensemble_scores().
* Remove extra indent in docstring
* Rename adj_pred_probs to adjust_pred_probs. Move get_confident_thresholds() to count.py module.
* Add comment to explain why we add a dimension for numpy broadcasting.
* Change renormalization logic when adjusting pred_probs. Raise ValueError when adjust_pred_probs is used with unsupported scoring method. Add test for ValueError.
* Update tests to account for unsupported method when running adjust_pred_probs.
* Enhance docstring 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.
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).
Anish Athalye