asreview.models.queriers.Max

class asreview.models.queriers.Max

Bases: QueryMixin, BaseEstimator

Maximum query strategy.

Choose the most likely samples to be included according to the model.

Methods

__init__()
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
query(p)	Rank the instances of the feature matrix.
set_params(**params)	Set the parameters of this estimator.

Attributes

label
name

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing (MetadataRequest) – A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params (dict) – Parameter names mapped to their values.

label = 'Maximum'

name = 'max'

query(p)

Rank the instances of the feature matrix.

Parameters:

p (numpy.ndarray) – The probability of inclusion for each record in the feature matrix.

Returns:

numpy.ndarray – The QueryStrategy ranks the row numbers of the feature matrix. It returns an array of shape (len(X),) containing the row indices in ranked order.

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:

**params (dict) – Estimator parameters.

Returns:

self (estimator instance) – Estimator instance.