label

Classes:

LabelIssueManager(datalab[, k, ...])

Manages label issues in a Datalab.

class cleanlab.datalab.internal.issue_manager.label.LabelIssueManager(datalab, k=10, clean_learning_kwargs=None, health_summary_parameters=None, **_)

Bases: IssueManager

Manages label issues in a Datalab.

Parameters:

• datalab (Datalab) – A Datalab instance.

• k (int) – The number of nearest neighbors to consider when computing pred_probs from features. Only applicable if features are provided and pred_probs are not.

• clean_learning_kwargs (Optional[Dict[str, Any]]) – Keyword arguments to pass to the CleanLearning constructor.

• health_summary_parameters (Optional[Dict[str, Any]]) – Keyword arguments to pass to the health_summary function.

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

Methods:

find_issues([pred_probs, features])	Find label issues in the datalab.
get_health_summary(pred_probs)	Returns a short summary of the health of this Lab.
collect_info(issues, summary_dict, ...)	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] = 'label'

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"],
... }

health_summary_parameters: Dict[str, Any]

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

Find label issues in the datalab.

Parameters:

• pred_probs (Optional[ndarray[Any, dtype[TypeVar(ScalarType, bound= generic, covariant=True)]]]) – The predicted probabilities for each example.

• features (Optional[ndarray[Any, dtype[TypeVar(ScalarType, bound= generic, covariant=True)]]]) – The features for each example.

Return type:

None

get_health_summary(pred_probs)

Returns a short summary of the health of this Lab.

Return type:

dict

collect_info(issues, summary_dict, confident_thresholds)

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] = 'label_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