Block Coordinate-based prediction methods (`xcolumns.block_coordinate`)

xcolumns.block_coordinate module implements the methods for finding the optimal prediction for given test set using the Block Coordinate Ascend/Desend algorithm with 0-th order approximation of expected utility. The method was first introduced and described in the paper:

Erik Schultheis, Marek Wydmuch, Wojciech Kotłowski, Rohit Babbar, Krzysztof Dembczyński. Generalized test utilities for long-tail performance in extreme multi-label classification. NeurIPS 2023.

Note: BCA/BCD with 0-approximationuses tp, fp, fn, tn matrices parametrization of the confussion matrix, as opposed to algorithms presented in the paper, which use :math:t, q, p parametrization. However both algorithms are equivalent.

The main function of the module is predict_using_bc_with_0approx():

Wrapper functions for specific metrics

The module provides the wrapper functions for specific metrics that can be used as arguments for the predict_using_bc_with_0approx() function as well as factory function for creating such wrapper functions.

Special function for optimization of coverage

The module provides the special function for optimization of coverage metric that use other way of estimating the expected value of the metric than predict_using_bc_with_0approx() function.