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"""
Methods to flag which examples have label issues in multi-label classification datasets.
Here each example can belong to one or more classes, or none of the classes at all.
Unlike in standard multi-class classification, model-predicted class probabilities need not sum to 1 for each row in multi-label classification.
"""
import warnings
from typing import Optional, Union, Tuple, List, Any
import numpy as np
[docs]def find_label_issues(
labels: list,
pred_probs: np.ndarray,
return_indices_ranked_by: Optional[str] = None,
rank_by_kwargs={},
filter_by: str = "prune_by_noise_rate",
frac_noise: float = 1.0,
num_to_remove_per_class: Optional[List[int]] = None,
min_examples_per_class=1,
confident_joint: Optional[np.ndarray] = None,
n_jobs: Optional[int] = None,
verbose: bool = False,
) -> np.ndarray:
"""
Identifies potentially mislabeled examples in a multi-label classification dataset.
An example is flagged as with a label issue if *any* of the classes appear to be incorrectly annotated for this example.
Parameters
----------
labels : List[List[int]]
List of noisy labels for multi-label classification where each example can belong to multiple classes.
This is an iterable of iterables where the i-th element of `labels` corresponds to a list of classes that the i-th example belongs to,
according to the original data annotation (e.g. ``labels = [[1,2],[1],[0],..]``).
This method will return the indices i where the inner list ``labels[i]`` is estimated to have some error.
For a dataset with K classes, each class must be represented as an integer in 0, 1, ..., K-1 within the labels.
pred_probs : np.ndarray
An array of shape ``(N, K)`` of model-predicted class probabilities.
Each row of this matrix corresponds to an example `x`
and contains the predicted probability that `x` belongs to each possible class,
for each of the K classes (along its columns).
The columns need not sum to 1 but must be ordered such that
these probabilities correspond to class 0, 1, ..., K-1.
Note
----
Estimated label quality scores are most accurate when they are computed based on out-of-sample ``pred_probs`` from your model.
To obtain out-of-sample predicted probabilities for every example in your dataset, you can use :ref:`cross-validation <pred_probs_cross_val>`.
This is encouraged to get better results.
return_indices_ranked_by : {None, 'self_confidence', 'normalized_margin', 'confidence_weighted_entropy'}, default = None
This function can return a boolean mask (if None) or an array of the example-indices with issues sorted based on the specified ranking method.
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
rank_by_kwargs : dict, optional
Optional keyword arguments to pass into scoring functions for ranking by
label quality score (see :py:func:`rank.get_label_quality_scores
<cleanlab.rank.get_label_quality_scores>`).
filter_by : {'prune_by_class', 'prune_by_noise_rate', 'both', 'confident_learning', 'predicted_neq_given', 'low_normalized_margin', 'low_self_confidence'}, default='prune_by_noise_rate'
The specific Confident Learning method to determine precisely which examples have label issues in a dataset.
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
frac_noise : float, default = 1.0
This will return the "top" frac_noise * num_label_issues estimated label errors, dependent on the filtering method used,
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
num_to_remove_per_class : array_like
An iterable that specifies the number of mislabeled examples to return from each class.
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
min_examples_per_class : int, default = 1
The minimum number of examples required per class below which examples from this class will not be flagged as label issues.
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
confident_joint : np.ndarray, optional
An array of shape ``(K, 2, 2)`` representing a one-vs-rest formatted confident joint, as is appropriate for multi-label classification tasks.
Entry ``(c, i, j)`` in this array is the number of examples confidently counted into a ``(class c, noisy label=i, true label=j)`` bin,
where `i, j` are either 0 or 1 to denote whether this example belongs to class `c` or not
(recall examples can belong to multiple classes in multi-label classification).
The `confident_joint` can be computed using :py:func:`count.compute_confident_joint <cleanlab.count.compute_confident_joint>` with ``multi_label=True``.
If not provided, it is computed from the given (noisy) `labels` and `pred_probs`.
n_jobs : optional
Number of processing threads used by multiprocessing.
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
verbose : optional
If ``True``, prints when multiprocessing happens.
Returns
-------
label_issues : np.ndarray
If `return_indices_ranked_by` left unspecified, returns a boolean **mask** for the entire dataset
where ``True`` represents an example suffering from some label issue and
``False`` represents an example that appears accurately labeled.
If `return_indices_ranked_by` is specified, this method instead returns a list of **indices** of examples identified with
label issues (i.e. those indices where the mask would be ``True``).
Indices are sorted by the likelihood that *all* classes are correctly annotated for the corresponding example.
Note
----
Obtain the *indices* of examples with label issues in your dataset by setting
`return_indices_ranked_by`.
"""
from cleanlab.filter import _find_label_issues_multilabel
return _find_label_issues_multilabel(
labels=labels,
pred_probs=pred_probs,
return_indices_ranked_by=return_indices_ranked_by,
rank_by_kwargs=rank_by_kwargs,
filter_by=filter_by,
frac_noise=frac_noise,
num_to_remove_per_class=num_to_remove_per_class,
min_examples_per_class=min_examples_per_class,
confident_joint=confident_joint,
n_jobs=n_jobs,
verbose=verbose,
)
[docs]def find_multilabel_issues_per_class(
labels: list,
pred_probs: np.ndarray,
return_indices_ranked_by: Optional[str] = None,
rank_by_kwargs={},
filter_by: str = "prune_by_noise_rate",
frac_noise: float = 1.0,
num_to_remove_per_class: Optional[List[int]] = None,
min_examples_per_class=1,
confident_joint: Optional[np.ndarray] = None,
n_jobs: Optional[int] = None,
verbose: bool = False,
) -> Union[np.ndarray, Tuple[List[np.ndarray], List[Any], List[np.ndarray]]]:
"""
Identifies potentially bad labels for each example and each class in a multi-label classification dataset.
Whereas :py:func:`find_label_issues <cleanlab.multilabel_classification.filter.find_label_issues>`
estimates which examples have an erroneous annotation for *any* class, this method estimates which specific classes are incorrectly annotated as well.
This method returns a list of size K, the number of classes in the dataset.
Parameters
----------
labels : List[List[int]]
List of noisy labels for multi-label classification where each example can belong to multiple classes.
Refer to documentation for this argument in :py:func:`find_label_issues <cleanlab.multilabel_classification.filter.find_label_issues>` for further details.
This method will identify whether ``labels[i][k]`` appears correct, for every example ``i`` and class ``k``.
pred_probs : np.ndarray
An array of shape ``(N, K)`` of model-predicted class probabilities.
Refer to documentation for this argument in :py:func:`find_label_issues <cleanlab.multilabel_classification.filter.find_label_issues>` for further details.
return_indices_ranked_by : {None, 'self_confidence', 'normalized_margin', 'confidence_weighted_entropy'}, default = None
This function can return a boolean mask (if this argument is ``None``) or a sorted array of indices based on the specified ranking method (if not ``None``).
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
rank_by_kwargs : dict, optional
Optional keyword arguments to pass into scoring functions for ranking by.
label quality score (see :py:func:`rank.get_label_quality_scores
<cleanlab.rank.get_label_quality_scores>`).
filter_by : {'prune_by_class', 'prune_by_noise_rate', 'both', 'confident_learning', 'predicted_neq_given', 'low_normalized_margin', 'low_self_confidence'}, default = 'prune_by_noise_rate'
The specific method that can be used to filter or prune examples with label issues from a dataset.
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
frac_noise : float, default = 1.0
This will return the "top" frac_noise * num_label_issues estimated label errors, dependent on the filtering method used,
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
num_to_remove_per_class : array_like
This parameter is an iterable that specifies the number of mislabeled examples to return from each class.
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
min_examples_per_class : int, default = 1
The minimum number of examples required per class to avoid flagging as label issues.
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
confident_joint : np.ndarray, optional
An array of shape ``(K, 2, 2)`` representing a one-vs-rest formatted confident joint.
Refer to documentation for this argument in :py:func:`cleanlab.multilabel_classification.filter.find_label_issues <cleanlab.multilabel_classification.filter.find_label_issues>` for details.
n_jobs : optional
Number of processing threads used by multiprocessing.
Refer to documentation for this argument in :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` for details.
verbose : optional
If ``True``, prints when multiprocessing happens.
Returns
-------
per_class_label_issues : list(np.ndarray)
By default, this is a list of length K containing the examples where each class appears incorrectly annotated.
``per_class_label_issues[k]`` is a Boolean mask of the same length as the dataset,
where ``True`` values indicate examples where class ``k`` appears incorrectly annotated.
For more details, refer to :py:func:`cleanlab.multilabel_classification.filter.find_label_issues <cleanlab.multilabel_classification.filter.find_label_issues>`.
Otherwise if `return_indices_ranked_by` is not ``None``, then this method returns 3 objects (each of length K, the number of classes): `label_issues_list`, `labels_list`, `pred_probs_list`.
- *label_issues_list*: an ordered list of indices of examples where class k appears incorrectly annotated, sorted by the likelihood that class k is correctly annotated.
- *labels_list*: a binary one-hot representation of the original labels, useful if you want to compute label quality scores.
- *pred_probs_list*: a one-vs-rest representation of the original predicted probabilities of shape ``(N, 2)``, useful if you want to compute label quality scores.
``pred_probs_list[k][i][0]`` is the estimated probability that example ``i`` belongs to class ``k``, and is equal to: ``1 - pred_probs_list[k][i][1]``.
"""
import cleanlab.filter
from cleanlab.internal.multilabel_utils import get_onehot_num_classes, stack_complement
y_one, num_classes = get_onehot_num_classes(labels, pred_probs)
if return_indices_ranked_by is None:
bissues = np.zeros(y_one.shape).astype(bool)
else:
label_issues_list = []
labels_list = []
pred_probs_list = []
if confident_joint is not None:
confident_joint_shape = confident_joint.shape
if confident_joint_shape == (num_classes, num_classes):
warnings.warn(
f"The new recommended format for `confident_joint` in multi_label settings is (num_classes,2,2) as output by compute_confident_joint(...,multi_label=True). Your K x K confident_joint in the old format is being ignored."
)
confident_joint = None
elif confident_joint_shape != (num_classes, 2, 2):
raise ValueError("confident_joint should be of shape (num_classes, 2, 2)")
for class_num, (label, pred_prob_for_class) in enumerate(zip(y_one.T, pred_probs.T)):
pred_probs_binary = stack_complement(pred_prob_for_class)
if confident_joint is None:
conf = None
else:
conf = confident_joint[class_num]
if num_to_remove_per_class is not None:
ml_num_to_remove_per_class = [num_to_remove_per_class[class_num], 0]
else:
ml_num_to_remove_per_class = None
binary_label_issues = cleanlab.filter.find_label_issues(
labels=label,
pred_probs=pred_probs_binary,
return_indices_ranked_by=return_indices_ranked_by,
frac_noise=frac_noise,
rank_by_kwargs=rank_by_kwargs,
filter_by=filter_by,
num_to_remove_per_class=ml_num_to_remove_per_class,
min_examples_per_class=min_examples_per_class,
confident_joint=conf,
n_jobs=n_jobs,
verbose=verbose,
)
if return_indices_ranked_by is None:
bissues[:, class_num] = binary_label_issues
else:
label_issues_list.append(binary_label_issues)
labels_list.append(label)
pred_probs_list.append(pred_probs_binary)
if return_indices_ranked_by is None:
return bissues
else:
return label_issues_list, labels_list, pred_probs_list