Expected improvement acquisition function for Bayesian optimization. Args: optimizer: The :class:`~modAL.models.BayesianOptimizer` o

Probability of improvement acquisition function for Bayesian optimization. Args: optimizer: The :class:`~modAL.models.BayesianOptimi

Upper confidence bound acquisition function for Bayesian optimization. Args: optimizer: The :class:`~modAL.models.BayesianOptimizer`

Query our top :n_instances: to request for labeling. Refer to Cardoso et al.'s "Ranked batch-mode active learning": https://www.scie

Define what to do if our batch-mode sampling doesn't have any labeled data -- a cold start. If our ranked batch sampling algorithm doesn't h

Core iteration strategy for selecting another record from our unlabeled records. Given a set of labeled records (X_training) and unlabeled r

Trains the predictor with the passed data (warm_start decides if params are resetted or not). Args: X: The new samples

Calculates the vote entropy for the Committee. First it computes the predictions of X for each learner in the Committee, then calculates the

SVM binary minimum multilabel active learning strategy. For details see the paper Klaus Brinker, On Active Learning in Multi-label Classifica

Calculates the margin of the prediction probabilities. Args: proba: Prediction probabilities. Returns: Margin of the pr

AvgConfidence query strategy for multilabel classification. For more details on this query strategy, see Esuli and Sebastiani., Active L

AvgScore query strategy for multilabel classification. For more details on this query strategy, see Esuli and Sebastiani., Active Learni

Returns the batch size of a single forward pass.

Consensus entropy sampling strategy. Args: committee: The committee for which the labels are to be queried. X: The pool of s

This function compiles and returns a Keras model. Should be passed to KerasClassifier in the Keras scikit-learn API.

Entropy sampling query strategy. Selects the instances where the class probabilities have the largest entropy. Args: classifier:

Expected error reduction query strategy. References: Roy and McCallum, 2001 (http://groups.csail.mit.edu/rrg/papers/icml01.pdf)

Fits every learner to a subset sampled with replacement from X. Calling this method makes the learner forget the data it has seen up

Interface for the fit method of the predictor. Fits the predictor to the supplied data. Args: X: The samples to be fitte

Return list of HTML theme paths.

Takes the given utility measure and selector functions and makes a query strategy by combining them. Args: utility_measure: Utility

Margin sampling query strategy. Selects the instances where the difference between the first most likely and second most likely classes are t

Maximum EI query strategy. Selects the instance with highest expected improvement. Args: optimizer: The :class:`~modAL.models.Bayesi

Maximum PI query strategy. Selects the instance with highest probability of improvement. Args: optimizer: The :class:`~modAL.models.

Maximum UCB query strategy. Selects the instance with highest upper confidence bound. Args: optimizer: The :class:`~modAL.models.Bay

Maximum disagreement sampling strategy. Args: committee: The committee for which the labels are to be queried. X: The pool o

Max Loss query strategy for SVM multilabel classification. For more details on this query strategy, see Li et al., Multilabel SVM active

MaxScore query strategy for multilabel classification. For more details on this query strategy, see Esuli and Sebastiani., Active Learni

Regressor standard deviation sampling strategy. Args: regressor: The regressor for which the labels are to be queried. X: Th

Mc-Dropout bald query strategy. Returns the indexes of the instances with the largest BALD (Bayesian Active Learning by Disagreement

Mc-Dropout maximum entropy query strategy. Returns the indexes of the instances with the largest entropy of the per class calculated

Mc-Dropout maximum variation ratios query strategy. Returns the indexes of the instances with the largest variation ratios over mult

Mc-Dropout mean standard deviation query strategy. Returns the indexes of the instances with the largest mean of the per class calcu

Mean Max Loss query strategy for SVM multilabel classification. For more details on this query strategy, see Li et al., Multilabel SVM a

MinConfidence query strategy for multilabel classification. For more details on this query strategy, see Esuli and Sebastiani., Active L

Returns the number of epochs of a single fit cycle.

Finds the n_instances most informative point in the data provided by calling the query_strategy function. Args: X_pool:

Interface for the score method of the predictor. Args: X: The samples for which prediction accuracy is to be calculated.

Returns the mean accuracy on the given test data and labels. Todo: Why accuracy? Args: X: The samples