xpectra.CalibratedModel

class xpectra.CalibratedModel(estimator, method='sigmoid', cv=5, random_state=42)[source]

Bases: ClassifierMixin, BaseEstimator

One estimator + one calibration method behind a uniform proba API.

method: ‘none’ (as-shipped probabilities), ‘sigmoid’/’isotonic’ (CalibratedClassifierCV over the scaler+clf pipeline), or ‘temperature’ (fit on 80%, scale logits on the held-out 20%).

Parameters:

  • estimator (BaseEstimator)

  • method (str)

  • cv (int)

  • random_state (int)

__init__(estimator, method='sigmoid', cv=5, random_state=42)[source]
Parameters:
Return type:

None

Methods

__init__(estimator[, method, cv, random_state])

fit(X, y)

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

predict_proba(X)

score(X, y[, sample_weight])

Return accuracy on provided data and labels.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Configure whether metadata should be requested to be passed to the score method.
fit(X, y)[source]
Parameters:
Return type:

CalibratedModel

predict_proba(X)[source]
Parameters:

X (ndarray)

Return type:

ndarray

predict(X)[source]
Parameters:

X (ndarray)

Return type:

ndarray

set_score_request(*, sample_weight='$UNCHANGED$')

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.

  • self (CalibratedModel)

Returns:

self – The updated object.

Return type:

object