skactiveml.pool.ExpectedModelChangeMaximization#

class skactiveml.pool.ExpectedModelChangeMaximization(bootstrap_size=3, n_train=0.5, ord=2, feature_map=None, missing_label=nan, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Expected Model Change (EMC)

This class implements “Expected Model Change” (EMC) [1], an active learning query strategy for linear regression.

Parameters
bootstrap_sizeint, default=3

The number of bootstraps used to estimate the true model.

n_trainint or float, default=0.5

The size of a bootstrap compared to the training data if of type float. Must lie in the range of (0, 1]. The total size of a bootstrap if of type int. Must be greater or equal to 1.

ordint or string, default=2

The norm to measure the gradient length. Argument will be passed to np.linalg.norm.

feature_mapcallable, default=None

The feature map of the linear regressor. Takes in the feature data. Must output a np.array of dimension 2. The default value is the identity function. An example feature map is sklearn.preprocessing.PolynomialFeatures().fit_transform.

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

Value to represent a missing label.

random_stateint or np.random.RandomState or None, default=None

Random state for candidate selection.

References

1

Cai, Wenbin, Ya Zhang, and Jun Zhou. Maximizing expected model change for active learning in regression, IEEE International Conference on Data Mining, pages 51–60, 2013.

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

Regressor to predict the data. Assumes a linear regressor with respect to the parameters.

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, *), candidates is considered as the candidate samples in (X,y).

batch_sizeint, default=1

The number of samples to be selected in one AL cycle.

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)

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, the indexing refers to the samples in X.

  • If candidates is of shape (n_candidates,) and of type int, utilities refers to the samples in X.

  • If candidates is of shape (n_candidates, *), utilities refers to the indexing 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.ExpectedModelChangeMaximization#

Expected Model Change Maximization

Expected Model Change Maximization