BaseConformalPredictor¶

Reference¶

class conformity.BaseConformalPredictor(
    estimator,
    *,
    auto_calibrate=True,
    tts_kwargs=None,
)

Wrap a cloned estimator with held-out, unweighted split calibration.

Parameters¶

estimator BaseEstimator: Cloneable sklearn estimator; classifiers also require predict_proba.
auto_calibrate bool, default=True: Reserve held-out calibration data during fit; defaults to True.
tts_kwargs TrainTestSplitKwargs or None, default=None: train_test_split options used for automatic calibration. By default, test_size is 0.2, shuffle is True, and stratify is None. An explicit test_size here overrides the default.

Attributes¶

estimator_ BaseEstimator: Fitted clone; created by fit and removed by parameter changes.
n_features_in_ int: Number of validated fitting features.
feature_names_in_ ndarray: Input names, present when fitting named string columns.
classes_ ndarray: Classifier labels in probability and membership-column order.
calibration_scores_ ndarray: Immutable held-out scores; created by successful calibration.
sorted_calibration_scores_ ndarray: Immutable sorted scores cached for rank queries.
n_calibration_ int: Number of held-out calibration observations.
alpha_used_ float: Miscoverage probability from the most recent threshold query.
quantile_level_ float: Finite-sample rank divided by calibration count; may exceed one.
q_level_ float: Most recent threshold, including infinity when the rank exceeds n.
is_calibrated_ bool: Whether held-out scores exist. Refitting clears calibration state.

Examples¶

>>> from sklearn.linear_model import Ridge
>>> from conformity import BaseConformalPredictor, ConformalRegressor
>>> issubclass(ConformalRegressor, BaseConformalPredictor)
True
>>> model = ConformalRegressor(Ridge(), tts_kwargs={"test_size": 0.25})
>>> model.get_params()["tts_kwargs"]
{'test_size': 0.25}
>>> model.is_calibrated_
False

Methods¶

Method	Purpose
calibrate	Replace calibration scores using data independent of model fitting.
fit	Fit a fresh clone; optionally reserve an independent calibration split.
get_metadata_routing	Get metadata routing of this object.
get_params	Get parameters for this estimator.
set_fit_request	Request metadata passed to the `fit` method.
set_params	Set sklearn parameters and invalidate fitted and calibrated state.
### calibrate

[source]

calibrate(X, y)

Replace calibration scores using data independent of model fitting.

Parameters¶

X {array-like, sparse matrix, DataFrame} of shape (n_samples, n_features): Feature matrix of shape (n_samples, n_features); dense, sparse, or DataFrame.
y array-like of shape (n_samples,): One target per sample; classification labels follow the fitted classes.

Returns¶

self object: Predictor with replacement calibration scores.

Raises¶

ValueError: If input shapes, values, weights or configuration are invalid.
NotFittedError: If fit has not established a fitted estimator.

fit¶

[source]

fit(
    X,
    y,
    auto_calibrate=None,
    tts_kwargs=None,
    *,
    sample_weight=None,
    **fit_params,
)

Fit a fresh clone; optionally reserve an independent calibration split.

Parameters¶

X {array-like, sparse matrix, DataFrame} of shape (n_samples, n_features): Feature matrix of shape (n_samples, n_features); dense, sparse, or DataFrame.
y array-like of shape (n_samples,): One target per sample; classification labels follow the fitted classes.
auto_calibrate bool or None, default=None: Reserve an independent calibration split when fitting.
tts_kwargs TrainTestSplitKwargs or None, default=None: train_test_split options for this fit; requires automatic calibration. If provided, this mapping replaces the constructor mapping. Missing options use defaults: test_size=0.2, train_size=None, random_state=None, shuffle=True and stratify=None. Set random_state here for reproducible automatic splits.
sample_weight array-like of shape (n_samples,) or None, default=None: Optional finite, nonnegative weights; fitting or evaluation only, never calibration.
**fit_params dict: Estimator-specific fit metadata forwarded to the cloned estimator.

Returns¶

self object: Fitted predictor; calibrated when automatic calibration is enabled.

Raises¶

TypeError: If the estimator cannot be cloned or lacks the required prediction methods.
ValueError: If input shapes, values, weights or configuration are invalid.

get_metadata_routing¶

[inherited API]

get_metadata_routing()

Get metadata routing of this object.

Returns¶

routing MetadataRequest: A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating routing information.

get_params¶

[inherited API]

get_params(deep=True)

Get parameters for this estimator.

Parameters¶

deep bool, default=True, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns¶

params dict: Parameter names mapped to their values.

set_fit_request¶

[inherited API]

set_fit_request(
    *,
    auto_calibrate='$UNCHANGED$',
    sample_weight='$UNCHANGED$',
    tts_kwargs='$UNCHANGED$',
)

Request metadata passed to the fit method.

Parameters¶

auto_calibrate str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED, default='$UNCHANGED$': Metadata routing for auto_calibrate parameter in fit.
sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED, default='$UNCHANGED$': Metadata routing for sample_weight parameter in fit.
tts_kwargs str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED, default='$UNCHANGED$': Metadata routing for tts_kwargs parameter in fit.

Returns¶

self object: The updated object.

set_params¶

[source]

set_params(**params)

Set sklearn parameters and invalidate fitted and calibrated state.

Parameters¶

**params dict: Sklearn parameters, including nested estimator parameters.

Returns¶

self object: Predictor with updated parameters and invalidated fitted state.

Raises¶

ValueError: If input shapes, values, weights or configuration are invalid.

is_calibrated_¶

Report whether calibration scores are available.

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