filter#
Methods to identify which examples have label issues.
Functions:

Identifies potential label issues in the dataset using confident learning. 
This is a baseline approach that uses the confusion matrix of 


A simple baseline approach that considers 
 cleanlab.filter.find_label_issues(labels, pred_probs, *, confident_joint=None, filter_by='prune_by_noise_rate', return_indices_ranked_by=None, rank_by_kwargs={}, multi_label=False, frac_noise=1.0, num_to_remove_per_class=None, min_examples_per_class=1, n_jobs=None, verbose=False)[source]#
Identifies potential label issues in the dataset using confident learning.
Returns a boolean mask for the entire dataset where
True
represents a label issue andFalse
represents an example that is confidently/accurately labeled.Instead of a mask, you can obtain indices of the label issues in your dataset by setting return_indices_ranked_by to specify the label quality score used to order the label issues.
The number of indices returned is controlled by frac_noise: reduce its value to identify fewer label issues. If you aren’t sure, leave this set to 1.0.
Tip: if you encounter the error “pred_probs is not defined”, try setting
n_jobs=1
. Parameters
labels (
np.array
) – A discrete vector of noisy labels, i.e. some labels may be erroneous. Format requirements: for dataset with K classes, labels must be in 0, 1, …, K1.pred_probs (
np.array
, optional) –An array of shape
(N, K)
of modelpredicted probabilities,P(label=kx)
. Each row of this matrix corresponds to an example x and contains the modelpredicted probabilities that x belongs to each possible class, for each of the K classes. The columns must be ordered such that these probabilities correspond to class 0, 1, …, K1.Caution: pred_probs from your model must be outofsample! You should never provide predictions on the same examples used to train the model, as these will be overfit and unsuitable for finding labelerrors. To obtain outofsample predicted probabilities for every datapoint in your dataset, you can use crossvalidation. Alternatively it is ok if your model was trained on a separate dataset and you are only evaluating data that was previously heldout.
confident_joint (
np.array
, optional) – An array of shape(K, K)
representing the confident joint, the matrix used for identifying label issues, which estimates a confident subset of the joint distribution of the noisy and true labels,P_{noisy label, true label}
. Entry(j, k)
in the matrix is the number of examples confidently counted into the pair of(noisy label=j, true label=k)
classes. The confident_joint can be computed usingcount.compute_confident_joint
. If not provided, it is computed from the given (noisy) labels and pred_probs.filter_by (
{'prune_by_class', 'prune_by_noise_rate', 'both', 'confident_learning', 'predicted_neq_given'}
, default'prune_by_noise_rate'
) –Method used for filtering/pruning out the label issues:
'prune_by_noise_rate'
: works by removing examples with high probability of being mislabeled for every nondiagonal in the confident joint (see prune_counts_matrix in filter.py). These are the examples where (with high confidence) the given label is unlikely to match the predicted label for the example.'prune_by_class'
: works by removing the examples with smallest probability of belonging to their given class label for every class.'both'
: Removes only the examples that would be filtered by both'prune_by_noise_rate'
and'prune_by_class'
.'confident_learning'
: Returns the examples in the offdiagonals of the confident joint. These are the examples that are confidently predicted to be a different label than their given label.'predicted_neq_given'
: Find examples where the predicted class (i.e. argmax of the predicted probabilities) does not match the given label.
return_indices_ranked_by (
{None, 'self_confidence', 'normalized_margin', 'confidence_weighted_entropy'}
, defaultNone
) –If
None
, returns a boolean mask (True
if example at index is label error). If notNone
, returns an array of the label error indices (instead of a boolean mask) where error indices are ordered:'normalized_margin'
:normalized margin (p(label = k)  max(p(label != k)))
'self_confidence'
:[pred_probs[i][labels[i]] for i in label_issues_idx]
'confidence_weighted_entropy'
:entropy(pred_probs) / self_confidence
rank_by_kwargs (
dict
, optional) – Optional keyword arguments to pass into scoring functions for ranking by label quality score (seerank.get_label_quality_scores
).multi_label (
bool
, optional) – IfTrue
, 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 multilabel setting supports classification tasks where an example has 1 or more labels. Example of a multilabeled labels input:[[0,1], [1], [0,2], [0,1,2], [0], [1], ...]
.frac_noise (
float
, default1.0
) –Used to only return the “top”
frac_noise * num_label_issues
. The choice of which “top” label issues to return is dependent on the filter_by method used. It works by reducing the size of the offdiagonals of the joint distribution of given labels and true labels proportionally by frac_noise prior to estimating label issues with each method. This parameter only applies for filter_by=both, filter_by=prune_by_class, and filter_by=prune_by_noise_rate methods and currently is unused by other methods. Whenfrac_noise=1.0
, return all “confident” estimated noise indices (recommended).frac_noise * number_of_mislabeled_examples_in_class_k.
num_to_remove_per_class (
array_like
) –An iterable of length K, the number of classes. E.g. if K = 3,
num_to_remove_per_class=[5, 0, 1]
would return the indices of the 5 most likely mislabeled examples in class 0, and the most likely mislabeled example in class 2.Note
Only set this parameter if
filter_by='prune_by_class'
. You may use withfilter_by='prune_by_noise_rate'
, but ifnum_to_remove_per_class=k
, then either k1, k, or k+1 examples may be removed for any class due to rounding error. If you need exactly ‘k’ examples removed from every class, you should usefilter_by='prune_by_class'
.min_examples_per_class (
int
, default1
) – Minimum number of examples per class to avoid flagging as label issues. This is useful to avoid deleting too much data from one class when pruning noisy examples in datasets with rare classes.n_jobs (optional) – Number of processing threads used by multiprocessing. Default
None
sets to the number of cores on your CPU. Set this to 1 to disable parallel processing (if its causing issues). Windows users may see a speedup withn_jobs=1
.verbose (optional) – If
True
, prints when multiprocessing happens.
 Returns
label_issues – A boolean mask for the entire dataset where
True
represents a label issue andFalse
represents an example that is accurately labeled with high confidence.Note
You can also return the indices of the label issues in your dataset by setting return_indices_ranked_by.
 Return type
np.array
 cleanlab.filter.find_label_issues_using_argmax_confusion_matrix(labels, pred_probs, *, calibrate=True, filter_by='prune_by_noise_rate')[source]#
This is a baseline approach that uses the confusion matrix of
argmax(pred_probs)
and labels as the confident joint and then uses cleanlab (confident learning) to find the label issues using this matrix.The only difference between this and
find_label_issues
is that it uses the confusion matrix based on the argmax and given label instead of using the confident joint fromcount.compute_confident_joint
. Parameters
labels (
np.array
) – An array of shape(N,)
of noisy labels, i.e. some labels may be erroneous. Elements must be in the set 0, 1, …, K1, where K is the number of classes.pred_probs (
np.array (shape (N
,K))
) – An array of shape(N, K)
of modelpredicted probabilities,P(label=kx)
. Each row of this matrix corresponds to an example x and contains the modelpredicted probabilities that x belongs to each possible class, for each of the K classes. The columns must be ordered such that these probabilities correspond to class 0, 1, …, K1. pred_probs should have been computed using 3 (or higher) fold crossvalidation.calibrate (
bool
, defaultTrue
) – Set toTrue
to calibrate the confusion matrix created bypred != given labels
. This calibration adjusts the confusion matrix / confident joint so that the prior (given noisy labels) is correct based on the original labels.filter_by (
str
, default'prune_by_noise_rate'
) – See filter_by argument offind_label_issues
.
 Returns
label_issues_mask – A boolean mask for the entire dataset where
True
represents a label issue andFalse
represents an example that is accurately labeled with high confidence. Return type
np.array
 cleanlab.filter.find_predicted_neq_given(labels, pred_probs, *, multi_label=False)[source]#
A simple baseline approach that considers
argmax(pred_probs) != labels
as a label error. Parameters
labels (
np.array
) – A discrete vector of noisy labels, i.e. some labels may be erroneous. Format requirements: for dataset with K classes, labels must be in 0, 1, …, K1.pred_probs (
np.array
, optional) – An array of shape(N, K)
of modelpredicted probabilities,P(label=kx)
. Each row of this matrix corresponds to an example x and contains the modelpredicted probabilities that x belongs to each possible class, for each of the K classes. The columns must be ordered such that these probabilities correspond to class 0, 1, …, K1.multi_label (
bool
, optional) – IfTrue
, 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 multilabel setting supports classification tasks where an example has 1 or more labels. Example of a multilabeled labels input:[[0,1], [1], [0,2], [0,1,2], [0], [1], ...]
.
 Returns
label_issues_mask – A boolean mask for the entire dataset where
True
represents a label issue andFalse
represents an example that is accurately labeled with high confidence. Return type
np.array