Metadata-Version: 2.1
Name: lcensemble
Version: 0.1.0
Summary: Local Cascade Ensemble package
Home-page: https://lce.readthedocs.io/en/latest/
Download-URL: https://github.com/LocalCascadeEnsemble/LCE
Maintainer: Kevin Fauvel
Maintainer-email: kfauvel.lce@gmail.com
License: new BSD
Project-URL: Documentation, https://lce.readthedocs.io/en/latest/
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Provides-Extra: tests
Provides-Extra: docs
License-File: LICENSE

LCE: Local Cascade Ensemble

===========================



|CircleCI|_ |ReadTheDocs|_



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**Local Cascade Ensemble (LCE)** proposes to **further enhance** the prediction performance of the state-of-the-art 

**Random Forest** and **XGBoost** by combining their strengths and adopting a complementary implicit diversification way. 

LCE is a hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by 

machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data.

LCE has been evaluated on a public benchmark and published in the journal *Data Mining and Knowledge Discovery*.



LCE package is **compatible with scikit-learn**; it passes the `check_estimator <https://scikit-learn.org/stable/modules/generated/sklearn.utils.estimator_checks.check_estimator.html#sklearn.utils.estimator_checks.check_estimator>`_.

Therefore, it can interact with scikit-learn pipelines and model selection tools.





Getting Started

===============



Installation

------------



You can install LCE from PyPI with the following command::



	pip install lce

	



First Example on Iris Dataset

-----------------------------



LCEClassifier prediction on an Iris test set::



	from lce import LCEClassifier

	from sklearn.datasets import load_iris

	from sklearn.metrics import classification_report

	from sklearn.model_selection import train_test_split





	# Load data and generate a train/test split

	data = load_iris()

	X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, random_state=0)



	# Train LCEClassifier with default parameters

	clf = LCEClassifier(random_state=0)

	clf.fit(X_train, y_train)



	# Make prediction and generate classification report

	y_pred = clf.predict(X_test)

	print(classification_report(y_test, y_pred))





Documentation

=============

LCE documentation can be found `here <https://lce.readthedocs.io/en/latest/>`_.





Reference

=========

The full information about LCE can be found in the associated journal paper.



.. [1] Fauvel, K., E. Fromont, V. Masson, P. Faverdin and A. Termier. "XEM: An explainable-by-design ensemble method for multivariate time series classification", Data Mining and Knowledge Discovery, 2022. `https://hal.inria.fr/hal-03599214/document <https://hal.inria.fr/hal-03599214/document>`_



If you use the package, please cite us with the following BibTex::



	@article{Fauvel22-LCE,

	  author = {Fauvel, K. and E. Fromont and V. Masson and P. Faverdin and A. Termier},

	  title = {{XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification}},

	  journal = {Data Mining and Knowledge Discovery},

	  year = {2022},

	  doi = {10.1007/s10618-022-00823-6}

	}



