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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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from abc import abstractmethod
from asreview.models.base import BaseModel
[docs]class BaseQueryStrategy(BaseModel):
"""Abstract class for query strategies."""
name = "base-query"
[docs] @abstractmethod
def query(self,
X,
classifier=None,
n_instances=None,
**kwargs):
"""Query new instances.
Arguments
---------
X: numpy.ndarray
Feature matrix to choose samples from.
classifier: SKLearnModel
Trained classifier to compute probabilities if they are necessary.
n_instances: int
Number of instances to query.
Returns
-------
(numpy.ndarray, numpy.ndarray)
The first is an array of shape (n_instances,) containing the row
indices of the new instances in query order. The second is an array
of shape (n_instances, n_feature_matrix_columns), containing the
feature vectors of the new instances.
"""
raise NotImplementedError
[docs]class ProbaQueryStrategy(BaseQueryStrategy):
name = "proba"
[docs] def query(self, X, classifier, n_instances=None, **kwargs):
"""Query method for strategies which use class probabilities.
"""
if n_instances is None:
n_instances = X.shape[0]
predictions = classifier.predict_proba(X)
query_idx = self._query(predictions, n_instances, X)
return query_idx
@abstractmethod
def _query(self, predictions, n_instances, X=None):
raise NotImplementedError