Source code for cleanlab.internal.label_quality_utils

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Helper methods used internally for computing label quality scores

import numpy as np
from typing import Optional

from cleanlab.count import get_confident_thresholds

def _subtract_confident_thresholds(
    labels: Optional[np.ndarray],
    pred_probs: np.ndarray,
    multi_label: bool = False,
    confident_thresholds: Optional[np.ndarray] = None,
) -> np.ndarray:
    """Returns adjusted predicted probabilities by subtracting the class confident thresholds and renormalizing.
    The confident class threshold for a class j is the expected (average) "self-confidence" for class j.
    The purpose of this adjustment is to handle class imbalance.
    labels : np.ndarray
      Labels in the same format expected by the `cleanlab.count.get_confident_thresholds()` method.
      If labels is None, confident_thresholds needs to be passed in as it will not be calculated.
    pred_probs : np.ndarray (shape (N, K))
      Predicted-probabilities in the same format expected by the `cleanlab.count.get_confident_thresholds()` method.
    confident_thresholds : np.ndarray (shape (K,))
      Pre-calculated confident thresholds. If passed in, function will subtract these thresholds instead of calculating
      confident_thresholds from the given labels and pred_probs.
    multi_label : bool, optional
      If ``True``, labels should be an iterable (e.g. list) of iterables, containing a
      list of labels for each example, instead of just a single label.
      The multi-label setting supports classification tasks where an example has 1 or more labels.
      Example of a multi-labeled `labels` input: ``[[0,1], [1], [0,2], [0,1,2], [0], [1], ...]``.
      The major difference in how this is calibrated versus single-label is that
      the total number of errors considered is based on the number of labels,
      not the number of examples. So, the calibrated `confident_joint` will sum
      to the number of total labels.
    pred_probs_adj : np.ndarray (float)
      Adjusted pred_probs.

    # Get expected (average) self-confidence for each class
    # TODO: Test this for multi-label
    if confident_thresholds is None:
        if labels is None:
            raise ValueError(
                f"Cannot calculate confident_thresholds without labels. Pass in either labels or already calculated "
                f"confident_thresholds parameter. "
            confident_thresholds = get_confident_thresholds(
                labels, pred_probs, multi_label=multi_label

    # Subtract the class confident thresholds
    pred_probs_adj = pred_probs - confident_thresholds

    # Re-normalize by shifting data to take care of negative values from the subtraction
    pred_probs_adj += confident_thresholds.max()
    pred_probs_adj /= pred_probs_adj.sum(axis=1)[
        :, None
    ]  # The [:, None] adds a dimension to make the /= operator work for broadcasting.

    return pred_probs_adj

[docs]def get_normalized_entropy(pred_probs: np.ndarray, min_allowed_prob: float = 1e-6) -> np.ndarray: """Returns the normalized entropy of pred_probs. Normalized entropy is between 0 and 1. Higher values of entropy indicate higher uncertainty in the model's prediction of the correct label. Read more about normalized entropy `on Wikipedia <>`_. Normalized entropy is used in active learning for uncertainty sampling: Unlike label-quality scores, entropy only depends on the model's predictions, not the given label. Parameters ---------- pred_probs: Each row of this matrix corresponds to an example x and contains the model-predicted probabilities that x belongs to each possible class: P(label=k|x) min_allowed_prob: Minimum allowed probability value. Entries of `pred_probs` below this value will be clipped to this value. Ensures entropy remains well-behaved even when `pred_probs` contains zeros. Returns ------- entropy: Each element is the normalized entropy of the corresponding row of ``pred_probs``. """ num_classes = pred_probs.shape[1] # Note that dividing by log(num_classes) changes the base of the log which rescales entropy to 0-1 range clipped_pred_probs = np.clip(pred_probs, a_min=min_allowed_prob, a_max=None) return -np.sum(pred_probs * np.log(clipped_pred_probs), axis=1) / np.log(num_classes)