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

return the indices and values of the n_instances highest values. Args: values: Contains the values to be selected from. n_in

Shuffles the values and sorts them afterwards. This can be used to break the tie when the highest utility score is not unique. The shuffle ra

Class probabilities if the predictor is a classifier. Interface with the predict_proba method of the classifier. Args: X

Adds X and y to the known training data and retrains the predictor with the augmented dataset. Args: X: The new samples

Estimator predictions for X. Interface with the predict method of the estimator. Args: X: The samples to be predicted.

Returns the rows I from the data set X For a single index, the result is as follows: * 1xM matrix in case of scipy sparse NxM matrix X

Fits self.estimator to the given data and labels. Args: X: The new samples for which the labels are supplied by the expe

Classification uncertainty of the classifier for the provided samples. Args: classifier: The classifier for which the uncertainty is

Stack vertically sparse/dense arrays and pandas data frames. Args: blocks: Sequence of modALinput objects. Returns: New

Adds the new data and label to the known data, but does not retrain the model. Args: X: The new samples for which the la

Fits self.estimator to the training data and labels provided to it so far. Args: bootstrap: If True, the method trains t

Runs num_predictions times the prediction of the classifier on the input X and puts the predictions in a list. Args:

Calculates the information density metric of the given data using the given metric. Args: X: The data for which the information dens

Consensus probabilities of the Committee. Args: X: The samples for which the class probabilities are to be predicted.

Checks the known class labels by each learner, merges the labels and returns a mapping which maps the learner's classes to the comple

return the indices and values of the n_instances smallest values. Args: values: Contains the values to be selected from. n_i

Predicts the class of the samples by picking the consensus prediction. Args: X: The samples to be predicted.

Shuffles the values and sorts them afterwards. This can be used to break the tie when the highest utility score is not unique. The shuffle ra

Takes a distance measure and converts it into a information_density measure. Args: distance_measure: The distance measure to be conv

Adds X and y to the known training data and retrains the predictor with the augmented dataset. This method also keeps track of the ma

Adds X and y to the known training data for each learner and retrains learners with the augmented dataset. Args: X: The n

Transforms the data as supplied to the estimator. * In case the estimator is an skearn pipeline, it applies all pipeline components

Predicts the probabilities of the classes for each sample and each learner. Args: X: The samples for which class probabil

Utility function for max_loss and mean_max_loss strategies. Args: multiclass_classifier: sklearn.multiclass.OneVsRestClassifier inst

Fits all learners to the training data and labels provided to it so far. Args: bootstrap: If True, each estimator is trai

Classification margin uncertainty of the classifier for the provided samples. This uncertainty measure takes the first and second most likely

Stack horizontally sparse/dense arrays and pandas data frames. Args: blocks: Sequence of modALinput objects. Returns: N

Returns the shape of the data set X

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

Takes the given functions and makes a function which returns the linear combination of the output of original functions. It works well with f

Takes the given functions and makes a function which returns the product of the output of original functions. It works well with functions re

Predicts the values of the samples by averaging the prediction of each regressor. Args: X: The samples to be predicted.

Refits every learner with a dataset bootstrapped from its training instances. Contrary to .bag(), it bootstraps the training data for

Function to change the mode of the dropout layers (bool: train_mode -> train or evaluation) Args: model: Pytorch model

Predicts the labels for the supplied data for each learner in the Committee. Args: X: The samples to cast votes.

Predicts the values for the supplied data for each regressor in the CommitteeRegressor. Args: X: The samples to cast vote

Calculates the max disagreement for the Committee. First it computes the class probabilties of X for each learner in the Committee, then calc

Adds the new data and label to the known data for each learner, but does not retrain the model. Args: X: The new samples

Calculates the bald divergence for each instance As it is explicitly formulated in: Deep Bayesian Active Learning with

Calculates the entropy per class over dropout cycles As it is explicitly formulated in: Deep Bayesian Active Learning w

Fits all learners to the given data and labels. Args: X: The new samples for which the labels are supplied by the expert.

Calculates the mean of the per class calculated standard deviations. As it is explicitly formulated in: Deep Bayesian A

Calculates the entropy of the prediction probabilities. Args: proba: Prediction probabilities. Returns: Uncertainty of

Calculates the uncertainty of the prediction probabilities. Args: proba: Prediction probabilities. Returns: Uncertainty

Calculates the variation ratios over dropout cycles As it is explicitly formulated in: Deep Bayesian Active Learning wi

Returns X' = [X row]

Checks the known class labels for each classifier. Args: *args: Classifier objects to check the known class labels. Returns:

Checks the class probabilities and reshapes it if not all labels are present in the classifier. Args: proba: The class probabilities

Entropy of predictions of the for the provided samples. Args: classifier: The classifier for which the prediction entropy is to be m

Calculates the consensus entropy for the Committee. First it computes the class probabilties of X for each learner in the Committee, then cal

Returns X without the row(s) at index/indices I

for i, x in enumerate_data(X): Depending on the data type of X, returns: * A 1xM matrix in case of scipy sparse NxM matrix X * pand

Interface for the fit method of the predictor. Fits the predictor to the supplied data, then stores it internally for the active lear

Gives the highest value so far. Returns: The location of the currently best value and the value itself.