chemicalchecker.tool.targetmate.nonconformist.nc.RegressorNc

class RegressorNc(model, err_func=<chemicalchecker.tool.targetmate.nonconformist.nc.AbsErrorErrFunc object>, normalizer=None, beta=0)[source]

Bases: BaseModelNc

Nonconformity scorer using an underlying regression model.

Parameters

modelRegressorAdapter: Underlying regression model used for calculating nonconformity scores.
err_funcRegressionErrFunc: Error function object.
normalizerBaseScorer: Normalization model.
betafloat: Normalization smoothing parameter. As the beta-value increases, the normalized nonconformity function approaches a non-normalized equivalent.

Attributes

modelRegressorAdapter: Underlying model object.
err_funcRegressionErrFunc: Scorer function used to calculate nonconformity scores.

See also

ProbEstClassifierNc, NormalizedRegressorNc

Methods

`fit`	Fits the underlying model of the nonconformity scorer.
`get_params`	Get parameters for this estimator.
`predict`	Constructs prediction intervals for a set of test examples.
`score`	Calculates the nonconformity score of a set of samples.
`set_params`	Set the parameters of this estimator.

fit(x, y)

Fits the underlying model of the nonconformity scorer.

Parameters

xnumpy array of shape [n_samples, n_features]: Inputs of examples for fitting the underlying model.
ynumpy 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

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

Returns

paramsdict: Parameter names mapped to their values.

predict(x, nc, significance=None)[source]

Constructs prediction intervals for a set of test examples.

Predicts the output of each test pattern using the underlying model, and applies the (partial) inverse nonconformity function to each prediction, resulting in a prediction interval for each test pattern.

Parameters

xnumpy array of shape [n_samples, n_features]: Inputs of patters for which to predict output values.
significancefloat: Significance level (maximum allowed error rate) of predictions. Should be a float between 0 and 1. If None, then intervals for all significance levels (0.01, 0.02, …, 0.99) are output in a 3d-matrix.

Returns

pnumpy array of shape [n_samples, 2] or [n_samples, 2, 99]: If significance is None, then p contains the interval (minimum and maximum boundaries) for each test pattern, and each significance level (0.01, 0.02, …, 0.99). If significance is a float between 0 and 1, then p contains the prediction intervals (minimum and maximum boundaries) for the set of test patterns at the chosen significance level.

score(x, y=None)

Calculates the nonconformity score of a set of samples.

Parameters

xnumpy array of shape [n_samples, n_features]: Inputs of examples for which to calculate a nonconformity score.
ynumpy array of shape [n_samples]: Outputs of examples for which to calculate a nonconformity score.

Returns

ncnumpy 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 Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict: Estimator parameters.

Returns

selfestimator instance: Estimator instance.