* Docstring remove args test
* exclude members test
Please enter the commit message for your changes. Lines starting
* Adding params as class instance attribute
* Added autoattribute: params
* Adding autoattribute under class
* Removed no-exclude-members and addded params to members
* autoattribute with indent
* Added annotation params
* Revert to removing default params only
* Removed params as instance var listing
* Consist italics vs code syntax in docstring
* docstring formatting
* add tiebreak info
* add example link to tutorial
* add missing backticks
* minor docstring text edits
* clarify "task"
Co-authored-by: Jonas Mueller <1390638+jwmueller@users.noreply.github.com>
* header for multiannotator module
* header for outlier module
* header for token_classification.filter
* header for token_classification.rank
* header for token_classification.summary
* header for util
* header for latent_algebra
* header for label_quality_utils
* header for token_classification_utils
* header for huggingfacekerasclassifier
* add experimental modules' dependencies to readme
* Added base structure outline for get_ood_scores()
Added base test for get_ood_scores()
* Added warning for illogical param combo
* Addressed PR comments
* Added better unit tests
* TODO: test for correctly identifying OOD example
* Moved logic from get_ood_scores to _subtract_confident_thresholds
* TODO: Is this a cleaner way of doing this? Minimal code repeat but strange change to _subtract function
* Addressed type issue
* Switched logic for getting confident thresholds
* Fixing typecheck issues wiht labels parameter being None
* Simplified helper function. Testing type
* Fixed mypy static typing issue
* Mypy typecheck logic test
* removed uncessesary imports in util file
* typechecker debugging (add assert)
* Fixed type logic and removed confident_thresholds=None return
* Added extra arg in helper func to end of func
* Added zero-index checking for label param
* Added ood examples to outlier score notebook
* Added skeleton file structure for implementing outliers
* Make adjust_pred_probs=True by default not false
* Added base Outlier class functionality
TODO:
* test_outlier.py
* Added logic tests for function
* Updated get_ood_scores to always return confident_thresholds
* Even if none were calculated (then return is None)
* Added warning for fit that doesn't calculate confident_thresholds
* Moved get_outlier_scores and get_ood_scores to outlier.py
* Changed param dicts to dicts
* Added docstring to outlier.py
* Added proper return types
* Fixed mypy typing issues
* Switched outliers -> features; ood -> predictions naming conv
* Switched docstring to stem from fit and score functions
* Changed return of helper functions
* Fixed tutorials notebook to use OutOfDistribution class
* Moved imports to top of file
* Added option for different knn objects, addressed pr comments
* Switched params arg to init only
* Addressed PR comment for notebook, cleared notebook
* Fixed PR comments, wording.
* Testing relative links on build
* Changed ood_pred_probs scores to reflect 0 = most ood 1 = least
* Added MLP for detection outliers with pred_probs
* Fixed typos
* Added MLP Import
* Improved warning when fit call unnecessary
* Changed referenced to params dict in warnings/errors
* Added bagging+MLP classifier into notebook
* Reverted tutorial wording
* OOD tutorial improvements
* cleanup OOD documentation
* adopting->using
Co-authored-by: Jonas Mueller <1390638+jwmueller@users.noreply.github.com>
* 🎨 remove wildcard imports
* 🏷️ review type annotations in display_issues
Restrict nested lists with `List`. Keep unrestricted lists as `list`. Remove hard-coded tags from docstrings, should be auto-tagged in later PR.
* ♻️ use `isinstance` for type-check at runtime
* 🏷️ review type annotations in common_label_issues and filter_by_token
Remove hard-coded tags in docstrings. Should be auto-tagged in later PR.
* ✅ improve test coverage of display issues
No part of the function is easily testable except ensuring it completes exection. Some parts handle edge cases that were never reached during testing.
* ✅ parametrize tests for coverage on common_label_issues and filter_by_token
* ✨ search tokenized sentence for coloring
Searches through the list of tokens before trying to match substrings. Thanks for this code suggestion Eric!
* 🚧 fix signature in all calls to color_sentence
* ✅ update color_sentence test after changing its api
(sentence, word) -> (word, tokens)
* ⏪ use regex for coloring tokens in sentence
Find word boundaries with regex, use builtin replace() as fallback w/o boundaries.
Closes#288
* 🚑 invert fallback condition
Use replace if NO substitutions were made with regex.
* ✅ add test fixture for get_label_quality_scores
* ✅ test softmin_sentence_score
include test cases for temperature limits
* ♻️ cleanup softmin_sentence_score
Remove unused keyword-only args, Fix tag in docstring, Change format of nested functions.
* ⚡ specialize edge-case temperature=inf in softmin_sentence_score
* ♻️ simplify temperature lookup
* ♻️ cleanup get_label_quality_scores
Remove unused args/variables. Update parameter list in docstring. Rename parameter of inner function. Function always returns a tuple.
* 🏷️ tag token_scores as optional
* ✅ test raised error
* 🩹 skip untestable elif statement
the elif statement only gets partial coverage because it can't evaluate to False due to the `assert sentence_score_method` at the start of the function
* 🏷️ restrict parameters for list types
* 🐛 only process characters in input token
Example: process_token("Cleanlab", [("C", "a"), ("a", "C")]) should return "aleCnlCb", not "CleCnlCb".
* ✅ use all sentences in test_get_sentence
* ✅ add test cases to test_filter_sentence
* ✅ extent test cases in test_mapping
* 📝 clean up docstrings
Restrict arg types based on docstrings. Fix punctuation and typos. Add examples to docstring.
* ✅ split tests for filter_sentence
* ✅ extend test_merge_probs
* ✅ test merge_probs with ignored/normalized columns in probs
* ✅ extend test cases for get_sentences
* ⚰️ remove unused pandas import
* 🏷️ pass strict mypy check
We ignore np.max as it is untyped.
No issues found in token_classification_utils.py by running
```
mypy --install-types --non-interactive --strict cleanlab/internal/token_classification_utils.py
```
* 👷 add strict type-checking in CI
* 💚 use strict type-check for single file
* ⏪ remove strict type check in CI
* refactor: 🏷️ use np.ndarray type instead of npt.NDArray
* ⏪ go back to generic np.ndarray type
* ♻️ always return tuple in filter_sentence
Remove unused argument+docstring. Simplify relevant unit tests.
* 🔥 resolve comments on typing
Remove ignore-comments. Remove duplicate tag in docstring. Remove unused imports.
* 🔥 remove duplicate tag in docstring
* calculate most likely class error from subset
* use verbose to control warning prints
* clip minimum to 1e-6 to prevent division by zero
* add docstring
* 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
* findlabelissues doc clarifications + arg order
* clarify return_indices_ranked_by specifies return
* moved multi_label up higher. kept ranked_by at top
Co-authored-by: Curtis G. Northcutt <curtis.northcutt@gmail.com>
* 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
* 🏷️ annotate function args and return values
Starting with the validation module:
- I think (X, y) might need some custom Union type to handle both numpy arrays and pandas dataframe, etc.
- All of the "assert" functions return None.
Ref #307
* refactor: 🏷️ swap npt.NDArray -> np.ndarray
np.ndarray seems more consistent with the rest of the repo.
Maybe it's necessary to go back to npt.NDArray when disallowing generics?
See numpy docs: https://numpy.org/devdocs/reference/typing.html#numpy.typing.NDArray
* refactor: 🔥 remove unused import
* 🏷️ unconstrain X input types
* 🐛 handle label type issues
- Have to restrict the output of labels_to_array to pass mypy checks.
- Returning the values of pd.Series isn't type-stable.
* 🏷️ include np.generic in arg-type union
* test: ✅ test labels_to_array
* 🏷️ add type aliases for X and y
* 🚨 ignore type-checks for pandas indexing assertions
CI typechecker runs on Python 3.10 which gives this error:
'cleanlab/internal/validation.py:125: error: No overload variant of "__getitem__" of "_iLocIndexerSeries" matches argument type "List[int]"'
It should be fine to let mypy ignore these expressions as they don't return anything.
* 🥅 specify errors to ignore
"type: ignore" doesn't pass strict mypy type-checks unless the specific errors are provided
* 🏷️ annotate label series to array
* 🗑️ change labels arg -> y in CleanLearning.fit()
Set `label` as an optional keyword-only argument.
Anyone still using it in this method should get a deprecation warning.
Fixes#281
* 📝 add note for y/labels in docstring
* 🥅 make y an optional positional arg.
Should now resolve deprecated signatures.
* 📝 labels -> y in module docstring
* ⏪ revert "label -> y deprecation"
This reverts commit ab319a0cca2cec715a84eb5f628bbab7706c5f9c.
This reverts commit 1b739002d848e1f0acb6390a666f6e695e25fcaa.
This reverts commit 88bb6c3bcca298dab414c3cb20101783d78d35d1.
This reverts commit d988e3c3932107e779598d02d8f16d7e6671e9e7.
* ✨ add y alias for labels
Resolves#281
* 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>
* Added support to build KNN graph with only training data
- Added default classifier for get_knn_distance_ood_scores()
* Added unit test for checking default KNN model is used when nbrs=None
* Changed default num neighbors from 10 to K.
* Changed unit test to check score sums
* Fixed k=15
* Added support to build KNN graph with only training data
- Added default classifier for get_knn_distance_ood_scores()
- Added unit test for checking default KNN model is used when nbrs=None
- Changed default num neighbors from 10 to K.
- Changed unit test to check score sums
* Improved code readibility, extended unit test to check for default k
* Changed naming convention from get_knn_distance_ood_scores to get_outlier_scores
- Changed nbrs to knn
- Improved unit test readability
* Made unit test more robust to check if user-set value is passed
* changed classifier -> estimator
* fixed test warning handle
* Updated function headers to better definition
* Improved header writing added functionality to avoid training/testing with identical features
* Updated docstring, updated handling of features=None and test for it
* Added ValueError for k>len(features) and test to catch ValueError
* Updated argument types to Optional, added runtime typecheck.
* Added unit test to check TypeError
* Changed Exception type thrown when knn=None, features=None
* TypeError to ValueError
* Reversed scoring for outlier severity
* Added test to check t parameter
* Added t parameter for global rescaler
* Improved function definition concerning 't' parameter
* Fixed tests
* Removed repeated calls
* Added assertion to make sure X_ood is always smallest outlier score
* Improved logic for checking X_ood has the smallest score
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
* Add KNN distance OOD scoring function and unit tests
* Update KNN distance OOD scoring function
* Change query_features to features in unit tests for KNN distance OOD scoring function
* Update KNN distance OOD scoring function
* Update tests for KNN distance OOD scoring function to use auto for algo
* Allow k=None for KNN Distance OOD score
* added class to use cleanlab with tensorflow and huggingface models
* - Documentation refactoring
- predict and predict_proba functions work on new test data too
* Add log_loss_search weighting method for ensemble label quality scoring function
* Update log_loss_search weighting method
* Add test for log_loss_search method
* Add parameter to Ensemble label quality scoring function for t values in log_loss_search method
* Update ensemble label quality scoring function docstring
* Update ensemble label quality scoring function comments
* Update docstring in ensemble label quality scoring function
* Update docstring in ensemble label quality scoring function
* Update docstring in ensemble label quality scoring function
* Modify verbose printout for log_loss_search
* Add clipping of pred_prob when calculating weights for log_loss_search
* Add clipping of pred_prob and renormalization when calculating weights for log_loss_search
* Add comments for log_loss_search weighting scheme
Ulyana