module: multi_types

class annotlib.multi_types.MultiAnnotTypes(annotator_types)

Bases: annotlib.base.BaseAnnot

This class enables to manage multiple types of annotators.

Parameters:

annotator_types: BaseAnnot | list, shape (n_annotators)

A single annotator or a list of annotators who are to be added.

Examples

>>> import numpy as np
>>> from sklearn.datasets import load_iris
>>> from annotlib import ClassifierBasedAnnot, ClusterBasedAnnot
>>> X, y_true = load_iris(return_X_y=True)
>>> # create two types of annotators
>>> classifier_annotators = ClassifierBasedAnnot(X=X, y_true=y_true, n_annotators=3)
>>> cluster_annotators = ClusterBasedAnnot(X=X, y_true=y_true, n_annotators=3)
>>> annotator_types = [classifier_annotators, cluster_annotators]
>>> # create instance of multiple annotator types
>>> multiple_annotators = MultiAnnotTypes(annotator_types=annotator_types)
>>> # there are 3+3=6 annotators
>>> multiple_annotators.n_annotators()
6
>>> # ask 6 annotators for class labels of 10 samples
>>> multiple_annotators.class_labels(X=X[0:10], query_value=10).shape
(10, 6)
>>> # check query values
>>> multiple_annotators.n_queries()
array([10, 10, 10, 10, 10, 10])

Attributes:

annotator_types_: list, shape (n_annotators)

List of added annotators.

add_annotators(self, annotator_types)

Method adds new annotators.

Parameters:

annotator_types: list, shape (n_annotators)

The annotator types to be added.

Returns:

self: sim_annotator_lib.multiple_annotator_types.MultiAnnotTypes

The instance itself.

class_labels(self, X, annotator_ids=None, query_value=1, **kwargs)

Method returning the class labels of the given samples.

Parameters:

X: array-like, shape (n_samples, n_features)

Samples whose class labels are queried.

annotator_ids: array-like, shape (n_queried_annotators)

The indices of the annotators whose class labels are queried.

query_value: int

The query value represents the increment of the query statistics of the queried annotators.

Returns:

Y: numpy.ndarray, shape (n_samples, n_annotators)

Class labels of the given samples which were provided by the queried annotators. The non queried annotators return np.nan values.

confidence_scores(self, X, annotator_ids=None, **kwargs)

Method returning the confidence scores for labelling the given samples.

Parameters:

X: array-like, shape (n_samples, n_features)

Samples whose class labels are queried.

annotator_ids: array-like, shape (n_queried_annotators)

The indices of the annotators whose confidence scores are queried.

Returns:

C: numpy.ndarray, shape (n_samples, n_annotators)

Confidence scores of the queried annotators for labelling the given samples. The non queried annotators should return np.nan values.

n_annotators(self)

Method for computing the number of annotators.

Returns:

n_annotators: int

Number of annotators.

n_queries(self)

Method for computing the number of queries posed to an annotator.

Returns:

n_queries: numpy.ndarray, shape (n_annotators)

An entry n_queries_[a] indicates how many queries annotator a has processed.

queried_samples(self)

Abstract method for returning the samples for which the annotators were queried to provide class labels.

Returns:

X_queried: numpy.ndarray, shape (n_annotators, n_queried_samples, n_features)

An entry X_queried_[a] represents the samples for which the annotator a was queried to provide class labels.