lir.bounding module

class lir.bounding.LLRBounder(lower_llr_bound: float | None = None, upper_llr_bound: float | None = None)[source]

Bases: Transformer, ABC

Base class for LLR bounders.

A bounder updates any LLRs that are out of bounds. Any LLR values within bounds remain unchanged. LLR values that are out-of-bounds are updated to the nearest bound.

Parameters:

lower_llr_bound (float | None) – The lower bound for the LLRs. If None, no lower bound is applied.
upper_llr_bound (float | None) – The upper bound for the LLRs. If None, no upper bound is applied.

apply(instances: InstanceData) → LLRData[source]

Recalculate the LLR data using the first step calibrator and applying the bounds.

Parameters:: instances (InstanceData) – The data to apply the bounder to. This should include the LLRs and their corresponding labels.
Returns:: The LLR data with the LLRs bounded according to the calculated bounds.
Return type:: LLRData

abstractmethod calculate_bounds(llrdata: LLRData) → tuple[float | None, float | None][source]

Calculate and return appropriate bounds for a set of LLRs and their labels.

Parameters:: llrdata (LLRData) – The LLR data for which to calculate the bounds. This includes the LLRs, their labels, and any other relevant information.

fit(instances: InstanceData) → Self[source]

Configure this bounder by calculating bounds.

assuming that y=1 corresponds to Hp, y=0 to Hd

Parameters:: instances (InstanceData) – The data to fit the bounder on. This should include the LLRs and their corresponding labels.
Returns:: The fitted bounder instance.
Return type:: Self

class lir.bounding.NSourceBounder(lower_llr_bound: float | None = None, upper_llr_bound: float | None = None)[source]

Bases: LLRBounder

Bound LLRs based on the number of sources.

This bounder sets the lower LLR bound to -log(N) and the upper bound to log(N), where N is the number of sources.

In non-log space, this corresponds to bounding likelihood ratios to [1/N, N]. This is a logical consequence of having N sources: no source can provide more than N support for one hypothesis over the other.

calculate_bounds(llrdata: LLRData) → tuple[float | None, float | None][source]

Calculate and return the lower and upper LLR bounds.

Parameters:: llrdata (LLRData) – The LLR data for which to calculate the bounds. This should include the source IDs.
Returns:: The lower and upper LLR bounds, calculated based on the number of sources.
Return type:: tuple[float | None, float | None]

class lir.bounding.StaticBounder(lower_llr_bound: float | None, upper_llr_bound: float | None)[source]

Bases: LLRBounder

Bound LLRs to constant values.

This bounder takes arguments for a lower and upper bound, which may take None in which case no bounds are applied.

Parameters:

lower_llr_bound (float | None) – The lower bound for the LLRs. If None, no lower bound is applied.
upper_llr_bound (float | None) – The upper bound for the LLRs. If None, no upper bound is applied.

calculate_bounds(llrdata: LLRData) → tuple[float | None, float | None][source]

Calculate and return the lower and upper LLR bounds.

Parameters:: llrdata (LLRData) – Not used, but included for compatibility with the base class.
Returns:: The lower and upper LLR bounds, as specified in the constructor.
Return type:: tuple[float | None, float | None]