quantificationlib.ordinal.trees module

Ordinal quantification trees

class OrdinalQuantificationTree(estimator_test=None, verbose=0)[source]

Bases: PCC

Ordinal Quantification Trees proposed by (Martino, Gao and Sebastiani, 2016)

This class is just a wrapper. It is a PCC method in which the estimator for the test distribution is a FrankAndHallTreeClassifier. Notice that the estimator object for the OrdinalQuantificationTree must be the binary base estimator used by the FrankAndHallTreeClassifier

Instead of using this class, our recommedation is to employ directly a PCC object to improve the efficiency because the FrankAndHallTreeClassifier estimator can be shared with other quantifiers and trained just once.

References

Giovanni Da San Martino, Wei Gao, and Fabrizio Sebastiani. 2016a. Ordinal text quantification. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 937940.

Giovanni Da San Martino,Wei Gao, and Fabrizio Sebastiani. 2016b. QCRI at SemEval-2016 Task 4: Probabilistic methods for binary and ordinal quantification. In Proceedings of the 10th InternationalWorkshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, A, 5863.

set_fit_request(*, predictions_train='$UNCHANGED$')

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • predictions_train (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for predictions_train parameter in fit.

  • self (OrdinalQuantificationTree) –

Returns:

self – The updated object.

Return type:

object

set_predict_request(*, predictions_test='$UNCHANGED$')

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • predictions_test (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for predictions_test parameter in predict.

  • self (OrdinalQuantificationTree) –

Returns:

self – The updated object.

Return type:

object