chemicalchecker.tool.targetmate.nonconformist.acp.AggregatedCp

class AggregatedCp(predictor, sampler=<chemicalchecker.tool.targetmate.nonconformist.acp.BootstrapSampler object>, aggregation_func=None, n_models=10)

Bases: BaseEstimator

Aggregated conformal predictor.

Combines multiple IcpClassifier or IcpRegressor predictors into an aggregated model.

Parameters

predictorobject

Prototype conformal predictor (e.g. IcpClassifier or IcpRegressor) used for defining conformal predictors included in the aggregate model.

samplerobject

Sampler object used to generate training and calibration examples for the underlying conformal predictors.

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

[1]

Vovk, V. (2013). Cross-conformal predictors. Annals of Mathematics and Artificial Intelligence, 1-20.

[2]

Carlsson, L., Eklund, M., & Norinder, U. (2014). Aggregated Conformal Prediction. In Artificial Intelligence Applications and Innovations (pp. 231-240). Springer Berlin Heidelberg.

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)

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.