skactiveml.pool.ExpectedModelOutputChange#

class skactiveml.pool.ExpectedModelOutputChange(integration_dict=None, loss=None, missing_label=nan, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Regression based Expected Model Output Change (EMOC)

This class implements an “Expected Model Output Change” (EMOC) based approach for regression [1], where samples are queried that change the output of the regression model the most.

Parameters
integration_dictdict, default=None

Dictionary for integration arguments, i.e. integration_method etc., used for calculating the expected y value for the candidate samples. For details see method skactiveml.pool.utils._conditional_expect. The default integration_method is assume_linear.

losscallable, default=None

The loss for predicting a target value instead of the true value. Takes in the predicted values of an evaluation set and the true values of the evaluation set and returns the error, a scalar value. The default loss is sklearn.metrics.mean_squared_error an alternative might be sklearn.metrics.mean_absolute_error.

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

Christoph Kaeding, Erik Rodner, Alexander Freytag, Oliver Mothes, Oliver, Bjoern Barz and Joachim Denzler. Active Learning for Regression Tasks with Expected Model Output Change, BMVC, page 1-15, 2018.

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, X_eval=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).

regProbabilisticRegressor

Predicts the output and the target distribution.

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).

X_evalarray-like of shape (n_eval_samples, n_features), default=None

Evaluation data set that is used for estimating the probability distribution of the feature space. In the referenced paper it is proposed to use the unlabeled data, i.e., X_eval=X[is_unlabeled(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) 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.ExpectedModelOutputChange#

Expected Model Output Change

Expected Model Output Change