pitcp.models.SCP

class SCP[source]

Calibrates scalar nonconformity scores by split conformal prediction.

Calibration settings:

  • s: Held-out scalar nonconformity scores used to compute the finite-sample corrected empirical quantiles.

Prediction settings:

  • X: Test features. Only the number of samples is used because split conformal thresholds do not depend on the feature values.

  • confidence_level: Target marginal coverage level or sequence of levels in the open interval from zero to one. Defaults to 0.9.

Variables:

scores (np.ndarray) – Held-out calibration scores with shape (n_samples,).

__init__()
conformalize(s)[source]

Stores held-out calibration scores.

Parameters:

s (np.typing.ArrayLike) – Scores with shape (n_samples,).

Returns:

The calibrated estimator.

Return type:

Self

fit(s)

Stores held-out calibration scores.

Parameters:

s (np.typing.ArrayLike) – Scores with shape (n_samples,).

Returns:

The calibrated estimator.

Return type:

Self

thresholds(confidence_level=0.9)[source]

Computes finite-sample corrected score thresholds.

Parameters:

confidence_level (float | Sequence[float], optional) – Target marginal coverage level or levels. Defaults to 0.9.

Returns:

One threshold for each requested confidence level.

Return type:

np.ndarray

predict(X, *, confidence_level=0.9)[source]

Returns score-space thresholds for test features.

Parameters:

  • X (np.typing.ArrayLike) – Test features with shape (n_samples, n_features).

  • confidence_level (float | Sequence[float], optional) – Target marginal coverage level or levels. Defaults to 0.9.

Returns:

Score thresholds with shape `(n_samples,) or ``(n_samples,

n_levels)``.

Return type:

np.ndarray

contains(s, *, confidence_level=0.9)[source]

Tests whether scores lie inside calibrated regions.

Parameters:

  • s (np.typing.ArrayLike) – Test scores with shape (n_samples,).

  • confidence_level (float | Sequence[float], optional) – Requested coverage levels. Defaults to 0.9.

Returns:

Coverage indicators with shape (n_samples,) or ``(n_samples,

n_levels)``.

Return type:

np.ndarray

set_fit_request(*, s='$UNCHANGED$')

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:

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

  • self (SCP)

Returns:

self – The updated object.

Return type:

object

set_predict_request(*, confidence_level='$UNCHANGED$')

Configure whether metadata should be requested to be passed to the predict 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 predict 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 predict.

  • 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:

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

  • self (SCP)

Returns:

self – The updated object.

Return type:

object