skactiveml.pool.multiannotator.IntervalEstimationAnnotModel#

class skactiveml.pool.multiannotator.IntervalEstimationAnnotModel(classes=None, missing_label=nan, alpha=0.05, mode='upper', random_state=None)[source]#

Bases: BaseEstimator, AnnotatorModelMixin

IELearning

This annotator model relies on ‘Interval Estimation Learning’ (IELearning) for estimating the annotation performances, i.e., labeling accuracies, of multiple annotators [1]. Therefore, it computes the mean accuracy and the lower as well as the upper bound of the labeling accuracy per annotator. (Weighted) majority vote is used as estimated ground truth.

Parameters
classesarray-like of shape (n_classes,), default=None

Holds the label for each class.

missing_labelscalar or string or np.nan or None, default=np.nan

Value to represent a missing label.

alphafloat, interval=(0, 1), default=0.05

Half of the confidence level for student’s t-distribution.

mode‘lower’ or ‘mean’ or ‘upper’, default=’upper’

Mode of the estimated annotation performance.

random_stateNone or int or numpy.random.RandomState, default=None

The random state used for deciding on majority vote labels in case of ties.

References

1

P. Donmez, J. G. Carbonell, and J. Schneider. Efficiently Learning the Accuracy of Labeling Sources for Selective Sampling. In ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pages 259–268, 2009.

Attributes
n_annotators_: int

Number of annotators.

A_perf_ndarray, shape (n_annotators, 3)

Estimated annotation performances (i.e., labeling accuracies), where A_cand[i, 0] indicates the lower bound, A_cand[i, 1] indicates the mean, and A_cand[i, 2] indicates the upper bound of the estimation labeling accuracy.

Methods

fit(X, y[, sample_weight])

Fit annotator model for given samples.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict_annotator_perf(X)

Calculates the probability that an annotator provides the true label for a given sample.

set_fit_request(*[, sample_weight])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.
fit(X, y, sample_weight=None)[source]#

Fit annotator model for given samples.

Parameters
Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples, n_annotators)

Class labels of annotators.

sample_weightarray-like of shape (n_samples, n_annotators), default=None

Sample weight for each label and annotator.

Returns
selfIntervalEstimationAnnotModel object

The fitted annotator model.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsdict

Parameter names mapped to their values.

predict_annotator_perf(X)[source]#

Calculates the probability that an annotator provides the true label for a given sample.

Parameters
Xarray-like of shape (n_samples, n_features)

Test samples.

Returns
P_annotnumpy.ndarray of shape (n_samples, n_annotators)

P_annot[i,l] is the probability, that annotator l provides the correct class label for sample X[i].

set_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → IntervalEstimationAnnotModel#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns
selfobject

The updated object.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters
**paramsdict

Estimator parameters.

Returns
selfestimator instance

Estimator instance.