chore: import upstream snapshot with attribution

2026-07-13 12:49:22 +08:00
commit a41b2ab474
303 changed files with 64772 additions and 0 deletions
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+"""
+Methods to find label issues in span classification datasets (text data), each token in a sentence receives one or more class labels.
+
+The underlying label error detection algorithms are in `cleanlab.token_classification`.
+"""
+
+import numpy as np
+from typing import List, Tuple, Optional
+
+from cleanlab.token_classification.filter import find_label_issues as find_label_issues_token
+from cleanlab.token_classification.summary import display_issues as display_issues_token
+from cleanlab.token_classification.rank import (
+    get_label_quality_scores as get_label_quality_scores_token,
+)
+
+
+def find_label_issues(
+    labels: list,
+    pred_probs: list,
+):
+    """Identifies tokens with label issues in a span classification dataset.
+
+    Tokens identified with issues will be ranked by their individual label quality score.
+
+    To rank the sentences based on their overall label quality, use :py:func:`experimental.span_classification.get_label_quality_scores <cleanlab.experimental.span_classification.get_label_quality_scores>`
+
+    Parameters
+    ----------
+    labels:
+        Nested list of given labels for all tokens.
+         Refer to documentation for this argument in :py:func:`token_classification.filter.find_label_issues <cleanlab.token_classification.filter.find_label_issues>` for further details.
+
+      Note:  Currently, only a single span class is supported.
+
+    pred_probs:
+        An array of shape ``(T, K)`` of model-predicted class probabilities.
+       Refer to documentation for this argument in :py:func:`token_classification.filter.find_label_issues <cleanlab.token_classification.filter.find_label_issues>` for further details.
+
+    Returns
+    -------
+    issues:
+        List of label issues identified by cleanlab, such that each element is a tuple ``(i, j)``, which
+        indicates that the `j`-th token of the `i`-th sentence has a label issue.
+
+        These tuples are ordered in `issues` list based on the likelihood that the corresponding token is mislabeled.
+
+        Use :py:func:`experimental.span_classification.get_label_quality_scores <cleanlab.experimental.span_classification.get_label_quality_scores>`
+        to view these issues within the original sentences.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from cleanlab.experimental.span_classification import find_label_issues
+    >>> labels = [[0, 0, 1, 1], [1, 1, 0]]
+    >>> pred_probs = [
+    ...     np.array([0.9, 0.9, 0.9, 0.1]),
+    ...     np.array([0.1, 0.1, 0.9]),
+    ... ]
+    >>> find_label_issues(labels, pred_probs)
+    """
+    pred_probs_token = _get_pred_prob_token(pred_probs)
+    return find_label_issues_token(labels, pred_probs_token)
+
+
+def display_issues(
+    issues: list,
+    tokens: List[List[str]],
+    *,
+    labels: Optional[list] = None,
+    pred_probs: Optional[list] = None,
+    exclude: List[Tuple[int, int]] = [],
+    class_names: Optional[List[str]] = None,
+    top: int = 20,
+) -> None:
+    """
+    See documentation of :py:meth:`token_classification.summary.display_issues<cleanlab.token_classification.summary.display_issues>` for description.
+    """
+    display_issues_token(
+        issues,
+        tokens,
+        labels=labels,
+        pred_probs=pred_probs,
+        exclude=exclude,
+        class_names=class_names,
+        top=top,
+    )
+
+
+def get_label_quality_scores(
+    labels: list,
+    pred_probs: list,
+    **kwargs,
+) -> Tuple[np.ndarray, list]:
+    """
+    See documentation of :py:meth:`token_classification.rank.get_label_quality_scores<cleanlab.token_classification.rank.get_label_quality_scores>` for description.
+    """
+    pred_probs_token = _get_pred_prob_token(pred_probs)
+    return get_label_quality_scores_token(labels, pred_probs_token, **kwargs)
+
+
+def _get_pred_prob_token(pred_probs: list) -> list:
+    """Converts pred_probs for span classification to pred_probs for token classification."""
+    pred_probs_token = []
+    for probs in pred_probs:
+        pred_probs_token.append(np.stack([1 - probs, probs], axis=1))
+    return pred_probs_token