skactiveml.pool.RegressionTreeBasedAL#

class skactiveml.pool.RegressionTreeBasedAL(method='random', missing_label=nan, random_state=None, max_iter_representativity=5)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Regression Tree-based Active Learning (RT-AL)

This class implements the query strategy Regression Tree-based Active Learning (RT-AL) [1], which is based on a regression tree and selects the number n_k of samples to be selected from each leaf k given a certain batch size. It than uses one of the three methods ‘random’, ‘diversity’, or ‘representativity’ to select n_k samples from each leaf k.

Parameters
methodstr, default=’random’

Possible values are ‘random’, ‘diversity’, and ‘representativity’.

missing_labelscalar or string or np.nan or None,
default=skactiveml.utils.MISSING_LABEL

Value to represent a missing label.

random_stateint or np.random.RandomState, default=None

The random state to use.

max_iter_representativityint, default=5

Maximum number of optimisation iterations. Only used if method=’representativity’.

References

1

A. Jose, J. P. A. de Mendonça, E. Devijver, N. Jakse, V. Monbet, and R. Poloni. Regression Tree-based Active Learning. Data Min. Knowl. Discov., pages 420–460, 2023.

Methods

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

query(X, y, reg[, fit_reg, sample_weight, ...])

Determines for which candidate samples labels are to be queried.

set_params(**params)

Set the parameters of this estimator.
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.

query(X, y, reg, fit_reg=True, sample_weight=None, candidates=None, batch_size=1, return_utilities=False)[source]#

Determines for which candidate samples labels are to be queried.

Parameters
Xarray-like of shape (n_samples, n_features)

Training data set, usually complete, i.e., including the labeled and unlabeled samples.

yarray-like of shape (n_samples,)

Labels of the training data set (possibly including unlabeled ones indicated by self.MISSING_LABEL).

regSkactivemlRegressor

The regressor must be sklearn.tree.DecisionTreeRegressor to predict the data. Ensure that the number of samples in the leaf is greater than 1. For example, by setting min_samples_leaf >= 2 or by restricting the tree’s depth.

fit_regbool, default=True

Defines whether the regressor should be fitted on X, y, and sample_weight.

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

Weights of training samples in X.

candidatesNone or array-like of shape (n_candidates), dtype=int or array-like of shape (n_candidates, n_features), default=None

  • If candidates is None, the unlabeled samples from (X,y) are considered as candidates.

  • If candidates is of shape (n_candidates,) and of type int, candidates is considered as the indices of the samples in (X,y).

  • If candidates is of shape (n_candidates, *), the candidate samples are directly given in candidates (not necessarily contained in X). This is not supported by all query strategies.

batch_sizeint, default=1

The number of samples to be selected in one AL cycle. Originally, this query strategy is developed for batch_sizes > 1.

return_utilitiesbool, default=False

If true, also return the utilities based on the query strategy.

Returns
query_indicesnumpy.ndarray of shape (batch_size,)

The query indices indicate for which candidate sample a label is to be queried, e.g., query_indices[0] indicates the first selected sample.

  • If candidates is None or of shape (n_candidates,), the indexing refers to the samples in X.

  • If candidates is of shape (n_candidates, n_features), the indexing refers to the samples in candidates.

utilitiesnumpy.ndarray of shape (batch_size, n_samples) or numpy.ndarray of shape (batch_size, n_candidates)

The utilities of samples after each selected sample of the batch, e.g., utilities[0] indicates the utilities used for selecting the first sample (with index query_indices[0]) of the batch. Utilities for labeled samples will be set to np.nan.

  • If candidates is None or of shape (n_candidates,), the indexing refers to the samples in X.

  • If candidates is of shape (n_candidates, n_features), the indexing refers to the samples in candidates.

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.

Examples using skactiveml.pool.RegressionTreeBasedAL#

Regression Tree Based Active Learning (RT-AL) with Diversity Selection

Regression Tree Based Active Learning (RT-AL) with Diversity Selection

Regression Tree Based Active Learning (RT-AL) with Random Selection

Regression Tree Based Active Learning (RT-AL) with Random Selection

Regression Tree Based Active Learning (RT-AL) with Representativity Selection

Regression Tree Based Active Learning (RT-AL) with Representativity Selection