modAL.utils¶

modAL.utils.
make_linear_combination
(*functions, weights: Optional[Sequence] = None) → Callable[source]¶ Takes the given functions and makes a function which returns the linear combination of the output of original functions. It works well with functions returning numpy arrays of the same shape.
Parameters:  *functions – Base functions for the linear combination.The functions shall have the same argument and if they return numpy arrays, the returned arrays shall have the same shape.
 weights – Coefficients of the functions in the linear combination. The ith given function will be multiplied with weights[i].
Returns: A function which returns the linear combination of the given functions output.

modAL.utils.
make_product
(*functions, exponents: Optional[Sequence] = None) → Callable[source]¶ Takes the given functions and makes a function which returns the product of the output of original functions. It works well with functions returning numpy arrays of the same shape.
Parameters:  *functions – Base functions for the product. The functions shall have the same argument and if they return numpy arrays, the returned arrays shall have the same shape.
 exponents – Exponents of the functions in the product. The ith given function in the product will be raised to the power of exponents[i].
Returns: A function which returns the product function of the given functions output.

modAL.utils.
make_query_strategy
(utility_measure: Callable, selector: Callable) → Callable[source]¶ Takes the given utility measure and selector functions and makes a query strategy by combining them.
Parameters:  utility_measure – Utility measure, for instance
vote_entropy()
, but it can be a custom function as well. Should take a classifier and the unlabelled data and should return an array containing the utility scores.  selector – Function selecting instances for query. Should take an array of utility scores and should return an array containing the queried items.
Returns: A function which returns queried instances given a classifier and an unlabelled pool.
 utility_measure – Utility measure, for instance

modAL.utils.
data_vstack
(blocks: Container) → Union[list, numpy.ndarray, scipy.sparse.csr.csr_matrix][source]¶ Stack vertically both sparse and dense arrays.
Parameters: blocks – Sequence of modALinput objects. Returns: New sequence of vertically stacked elements.

modAL.utils.
multi_argmax
(values: numpy.ndarray, n_instances: int = 1) → numpy.ndarray[source]¶ Selects the indices of the n_instances highest values.
Parameters:  values – Contains the values to be selected from.
 n_instances – Specifies how many indices to return.
Returns: The indices of the n_instances largest values.

modAL.utils.
weighted_random
(weights: numpy.ndarray, n_instances: int = 1) → numpy.ndarray[source]¶ Returns n_instances indices based on the weights.
Parameters:  weights – Contains the weights of the sampling.
 n_instances – Specifies how many indices to return.
Returns: n_instances random indices based on the weights.

modAL.utils.
check_class_labels
(*args) → bool[source]¶ Checks the known class labels for each classifier.
Parameters: *args – Classifier objects to check the known class labels. Returns: True, if class labels match for all classifiers, False otherwise.

modAL.utils.
check_class_proba
(proba: numpy.ndarray, known_labels: Sequence, all_labels: Sequence) → numpy.ndarray[source]¶ Checks the class probabilities and reshapes it if not all labels are present in the classifier.
Parameters:  proba – The class probabilities of a classifier.
 known_labels – The class labels known by the classifier.
 all_labels – All class labels.
Returns: Class probabilities augmented such that the probability of all classes is present. If the classifier is unaware of a particular class, all probabilities are zero.