outlier

Classes:

OutlierIssueManager(datalab[, threshold])

Manages issues related to out-of-distribution examples.

class cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager(datalab, threshold=None, **kwargs)

Bases: IssueManager

Manages issues related to out-of-distribution examples.

Attributes:

description	Short text that summarizes the type of issues handled by this IssueManager.
issue_name	Returns a key that is used to store issue summary results about the assigned Lab.
verbosity_levels	A dictionary of verbosity levels and their corresponding dictionaries of report items to print.
DEFAULT_THRESHOLDS	Default thresholds for outlier detection.
issue_score_key	Returns a key that is used to store issue score results about the assigned Lab.
info
issues
summary

Methods:

find_issues([features, pred_probs])	Finds occurrences of this particular issue in the dataset.
collect_info(*[, knn_graph])	Collects data for the info attribute of the Datalab.
make_summary(score)	Construct a summary dataframe.
report(issues, summary, info[, ...])	Compose a report of the issues found by this IssueManager.

description: ClassVar[str]

Short text that summarizes the type of issues handled by this IssueManager.

issue_name: ClassVar[str] = 'outlier'

Returns a key that is used to store issue summary results about the assigned Lab.

verbosity_levels: ClassVar[Dict[int, List[str]]]

A dictionary of verbosity levels and their corresponding dictionaries of report items to print.

Example

>>> verbosity_levels = {
...     0: [],
...     1: ["some_info_key"],
...     2: ["additional_info_key"],
... }

DEFAULT_THRESHOLDS = {'features': 0.37037, 'pred_probs': 0.13}

Default thresholds for outlier detection.

If outlier detection is performed on the features, an example whose average distance to their k nearest neighbors is greater than Q3_avg_dist + (1 / threshold - 1) * IQR_avg_dist is considered an outlier.

If outlier detection is performed on the predicted probabilities, an example whose average score is lower than threshold * median_outlier_score is considered an outlier.

ood: OutOfDistribution

find_issues(features=None, pred_probs=None, **kwargs)

Finds occurrences of this particular issue in the dataset.

Computes the issues and summary dataframes. Calls collect_info to compute the info dict.

Return type:

None

collect_info(*, knn_graph=None)

Collects data for the info attribute of the Datalab. :rtype: dict

Note

This method is called by find_issues() after find_issues() has set the issues and summary dataframes as instance attributes.

issue_score_key: ClassVar[str] = 'outlier_score'

Returns a key that is used to store issue score results about the assigned Lab.

classmethod make_summary(score)

Construct a summary dataframe.

Parameters:

score (float) – The overall score for this issue.

Return type:

DataFrame

Returns:

summary – A summary dataframe.

classmethod report(issues, summary, info, num_examples=5, verbosity=0, include_description=False, info_to_omit=None)

Compose a report of the issues found by this IssueManager.

Parameters:

• issues (DataFrame) –

  An issues dataframe.

  Example

  >>> import pandas as pd
  >>> issues = pd.DataFrame(
  ...     {
  ...         "is_X_issue": [True, False, True],
  ...         "X_score": [0.2, 0.9, 0.4],
  ...     },
  ... )
  

• summary (DataFrame) –

  The summary dataframe.

  Example

  >>> summary = pd.DataFrame(
  ...     {
  ...         "issue_type": ["X"],
  ...         "score": [0.5],
  ...     },
  ... )
  

• info (Dict[str, Any]) –

  The info dict.

  Example

  >>> info = {
  ...     "A": "val_A",
  ...     "B": ["val_B1", "val_B2"],
  ... }
  

• num_examples (int) – The number of examples to print.

• verbosity (int) – The verbosity level of the report.

• include_description (bool) – Whether to include a description of the issue in the report.

Return type:

str

Returns:

report_str – A string containing the report.

info: Dict[str, Any]

issues: pd.DataFrame

summary: pd.DataFrame