chemicalchecker.tool.targetmate.nonconformist.acp.BootstrapConformalClassifier

class BootstrapConformalClassifier(predictor, n_models=10)[source]

Bases: AggregatedCp

Bootstrap conformal classifier.

Combines multiple IcpClassifiers into a bootstrap conformal classifier.

Parameters

predictorobject: Prototype conformal predictor (e.g. IcpClassifier or IcpRegressor) used for defining conformal predictors included in the aggregate model.
aggregation_funccallable: Function used to aggregate the predictions of the underlying conformal predictors. Defaults to numpy.mean.
n_modelsint: Number of models to aggregate.

Attributes

predictorobject: Prototype conformal predictor.
predictorslist: List of underlying conformal predictors.
samplerobject: Sampler object used to generate training and calibration examples.
agg_funccallable: Function used to aggregate the predictions of the underlying conformal predictors

References

Examples

Methods

`fit`	Fit underlying conformal predictors.
`get_params`	Get parameters for this estimator.
`predict`	Predict the output values for a set of input patterns.
`set_params`	Set the parameters of this estimator.

fit(x, y)

Fit underlying conformal predictors.

Parameters

xnumpy array of shape [n_samples, n_features]: Inputs of examples for fitting the underlying conformal predictors.
ynumpy array of shape [n_samples]: Outputs of examples for fitting the underlying conformal predictors.

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)[source]

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. Note: significance=None is applicable to classification problems only.

Returns

pnumpy array of shape [n_samples, n_classes] or [n_samples, 2]

For classification problems: 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.

For regression problems: Prediction interval (minimum and maximum boundaries) for the set of test patterns.

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.