Enhance numerical stability and scoring accuracy for datasets with identical features
- Adjust minimum median values to 100x machine epsilon to prevent unstable operations.
- Implement specific scoring logic for outlier and near-duplicate issues in datasets with identical features, ensuring correct scores are assigned in such edge cases.
- Introduce unit tests to validate the expected behavior for datasets comprising entirely identical examples. Check an additional case with an extra unique example.
Related to issue #1055.
* create and test strategy for generating knn_graph as test inputs
* test strategy for generating knn_graphs
- Each row has the distances sorted in ascending order in the csr-format.
- The indices of the neighbors are unique within a column and don't have the query point as a neighbor.
- If points a and b are mutual neighbors, they have the same distance between them.
* add property based tests for near-duplicate sets
* flag near-duplicate issues based on items in near duplicate sets.
Preleminary work to ensure that default task (classification) will work smoothly.
Minimal implementation of label issue manager for regression
- issue_finder.py list_possible_issue_types, list_default_issue_types no longer a static methods as they depend on task and modification of get_available_issue_types for regression
* DataIssues with strategy pattern
- moving list_possible_issue_types and list_default_issue_types from the IssueFinder class to cleanlab/datalab/internal/issue_manager_factory.py
* make different strategies for getting available issue types
add test class for the IssueFinder when the task is regression
* apply black formatter
* avoid mapping labels column for regression in Datalab
* Pass in features to LabelIssueManager for regression
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Co-authored-by: Elías Snorrason <eliassno@gmail.com>
ChG