lir.algorithms.isotonic_regression module

class lir.algorithms.isotonic_regression.IsotonicCalibrator(add_misleading: int = 0)[source]

Bases: Transformer

Calculate LR from a score belonging to one of two distributions using isotonic regression.

Calculates a likelihood ratio of a score value, provided it is from one of two distributions. Uses isotonic regression for interpolation.

In contrast to IsotonicRegression, this class:

  • has an initialization argument that provides the option of adding misleading data points

  • outputs logodds instead of probabilities

Parameters:

add_misleading (int, optional) – Number of synthetic misleading points to add to reduce extreme LRs.

apply(instances: InstanceData) → LLRData[source]

Transform instances using the fitted isotonic regression model.

Parameters:

instances (InstanceData) – Instances to transform.

Returns:

Calibrated log-likelihood-ratio data.

Return type:

LLRData

fit(instances: InstanceData) → Self[source]

Fit the estimator on the given data.

Parameters:

instances (InstanceData) – Training instances.

Returns:

Fitted calibrator.

Return type:

Self

fit_apply(instances: InstanceData) → LLRData[source]

Fit and apply the calibrator to the given data.

Parameters:

instances (InstanceData) – Instances to fit and transform.

Returns:

Calibrated log-likelihood-ratio data.

Return type:

LLRData

class lir.algorithms.isotonic_regression.IsotonicRegression(*, y_min=None, y_max=None, increasing=True, out_of_bounds='nan')[source]

Bases: IsotonicRegression

Wrap SKlearn implementation to support infinite values.

Sklearn implementation IsotonicRegression throws an error when values are Inf or -Inf when in fact IsotonicRegression can handle infinite values. This wrapper around the sklearn implementation of IsotonicRegression prevents the error being thrown when Inf or -Inf values are provided.

fit(X: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], y: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], sample_weight: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str] | tuple | None = None) → IsotonicRegression[source]

Fit the model using X, y as training data.

X is stored for future use, as transform() needs X to interpolate new input data.

Parameters:

  • X (ArrayLike) – Training data with shape (n_samples,).

  • y (ArrayLike) – Training target with shape (n_samples,).

  • sample_weight (ArrayLike | tuple | None, optional) – Sample weights. If None, equal weights are used.

Returns:

Fitted estimator.

Return type:

IsotonicRegression

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → IsotonicRegression

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → IsotonicRegression

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

transform(T: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str]) → ndarray[source]

Transform new data by linear interpolation.

Parameters:

T (ArrayLike) – Data to transform.

Returns:

The transformed data.

Return type:

np.ndarray