chemicalchecker.tool.targetmate.nonconformist.icp.OobCpClassifier

class OobCpClassifier(nc_function, condition=None, smoothing=True)[source]

Bases: IcpClassifier

Methods

`calibrate`	Calibrate conformal predictor based on underlying nonconformity scorer.
`fit`	Fit underlying nonconformity scorer.
`get_params`	Get parameters for this estimator.
`get_problem_type`
`predict`	Predict the output values for a set of input patterns.
`predict_conf`	Predict the output values for a set of input patterns, using the confidence-and-credibility output scheme.
`set_params`	Set the parameters of this estimator.

calibrate(x, y, increment=False)[source]

Calibrate conformal predictor based on underlying nonconformity scorer.

Parameters

xnumpy array of shape [n_samples, n_features]: Inputs of examples for calibrating the conformal predictor.
ynumpy array of shape [n_samples, n_features]: Outputs of examples for calibrating the conformal predictor.
incrementboolean: If True, performs an incremental recalibration of the conformal predictor. The supplied x and y are added to the set of previously existing calibration examples, and the conformal predictor is then calibrated on both the old and new calibration examples.

Returns

None

fit(x, y)[source]

Fit underlying nonconformity scorer.

Parameters

xnumpy array of shape [n_samples, n_features]: Inputs of examples for fitting the nonconformity scorer.
ynumpy array of shape [n_samples]: Outputs of examples for fitting the nonconformity scorer.

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, significance=None)

Predict the output values for a set of input patterns.

Parameters

xnumpy array of shape [n_samples, n_features]: Inputs of patters for which to predict output values.
significancefloat or None: Significance level (maximum allowed error rate) of predictions. Should be a float between 0 and 1. If None, then the p-values are output rather than the predictions.

Returns

pnumpy array of shape [n_samples, n_classes]: If significance is None, then p contains the p-values for each sample-class pair; if significance is a float between 0 and 1, then p is a boolean array denoting which labels are included in the prediction sets.

predict_conf(x)

Predict the output values for a set of input patterns, using the confidence-and-credibility output scheme.

Parameters

xnumpy array of shape [n_samples, n_features]: Inputs of patters for which to predict output values.

Returns

pnumpy array of shape [n_samples, 3]: p contains three columns: the first column contains the most likely class for each test pattern; the second column contains the confidence in the predicted class label, and the third column contains the credibility of the prediction.

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.