nonconformist.nc.ClassifierNc

class nonconformist.nc.ClassifierNc(model, err_func=<nonconformist.nc.MarginErrFunc object>, normalizer=None, beta=0)

Nonconformity scorer using an underlying class probability estimating model.

Parameters:

model : ClassifierAdapter

Underlying classification model used for calculating nonconformity scores.

err_func : ClassificationErrFunc

Error function object.

See also

RegressorNc, NormalizedRegressorNc

Attributes

model	(ClassifierAdapter) Underlying model object.
err_func	(ClassificationErrFunc) Scorer function used to calculate nonconformity scores.

__init__(model, err_func=<nonconformist.nc.MarginErrFunc object>, normalizer=None, beta=0)

fit(x, y)

Fits the underlying model of the nonconformity scorer.

Parameters:

x : numpy array of shape [n_samples, n_features]

Inputs of examples for fitting the underlying model.

y : numpy array of shape [n_samples]

Outputs of examples for fitting the underlying model.

Returns:

None

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

score(x, y=None)

Calculates the nonconformity score of a set of samples.

Parameters:

x : numpy array of shape [n_samples, n_features]

Inputs of examples for which to calculate a nonconformity score.

y : numpy array of shape [n_samples]

Outputs of examples for which to calculate a nonconformity score.

Returns:

nc : numpy array of shape [n_samples]

Nonconformity scores of samples.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self