lir.metrics package

Submodules

lir.metrics.devpav.devpav(llrs: ndarray, y: ndarray) → float: Calculates devPAV for LR data under H1 and H2.

lir.metrics.overestimation.llr_overestimation(llrs: ndarray, y: ndarray, **kwargs: Any) → float: Calculates the mean absolute value of the LLR-overestimation.

lir.metrics.cllr(llr_data: LLRData, weights: tuple[float, float] = (1, 1)) → float

Calculate a log likelihood ratio cost (C_llr) for a series of log likelihood ratios.

Nico Brümmer and Johan du Preez, Application-independent evaluation of speaker detection, In: Computer Speech and Language 20(2-3), 2006.

Parameters:

Returns:

CLLR, the log likelihood ratio cost

lir.metrics.cllr_cal(llr_data: LLRData, weights: tuple[float, float] = (1, 1)) → float

Calculate the difference between the C_llr before and after isotonic calibration.

Parameters:

Returns:

CLLR_cal, the difference after isotonic calibration

lir.metrics.cllr_min(llr_data: LLRData, weights: tuple[float, float] = (1, 1)) → float

Estimate the discriminative power from a collection of log likelihood ratios.

Parameters:

Returns:

CLLR_min, a measure of discrimination

lir.metrics.llr_lower_bound(llrs: LLRData) → float | None

Provide corresponding lower bound for provided LLR data.

When an LLRData object contains a lower bound, return it. If not, return None.

Parameters:: llrs – LLRs and their metadata, wrapped in an LLRData object
Returns:: the LLR lower bound, or None

lir.metrics.llr_upper_bound(llrs: LLRData) → float | None

Provide corresponding upper bound for provided LLR data.

When an LLRData object contains an upper bound, return it. If not, return None.

Parameters:: llrs – LLRs and their metadata, wrapped in an LLRData object
Returns:: the LLR upper bound, or None