Welcome to combo’s documentation!

Deployment & Documentation & Stats

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combo is a comprehensive Python toolbox for combining machine learning (ML) models and scores. Model combination can be considered as a subtask of ensemble learning, and has been widely used in real-world tasks and data science competitions like Kaggle [ABK07].

combo library supports the combination of models and score from key ML libraries such as scikit-learn and xgboost, and crucial tasks including classification, clustering, anomaly detection. See figure below for some representative combination approaches.

Combination Framework Demo

combo is featured for:

  • Unified APIs, detailed documentation, and interactive examples across various algorithms.
  • Advanced and latest models, such as Stacking/DCS/DES/EAC/LSCP.
  • Comprehensive coverage for classification, clustering, anomaly detection, and raw score.
  • Optimized performance with JIT and parallelization when possible, using numba and joblib.

API Demo:

from combo.models.classifier_stacking import Stacking
# initialize a group of base classifiers
classifiers = [DecisionTreeClassifier(), LogisticRegression(),
               KNeighborsClassifier(), RandomForestClassifier(),
               GradientBoostingClassifier()]

clf = Stacking(base_estimators=classifiers) # initialize a Stacking model
clf.fit(X_train, y_train) # fit the model

# predict on unseen data
y_test_labels = clf.predict(X_test)  # label prediction
y_test_proba = clf.predict_proba(X_test)  # probability prediction

Key Links and Resources:


API Cheatsheet & Reference

Full API Reference: (https://pycombo.readthedocs.io/en/latest/api.html). The following APIs are applicable for most models for easy use.

For raw score combination (after the score matrix is generated), use individual methods from “score_comb.py” directly. Raw score combination API: (https://pycombo.readthedocs.io/en/latest/api.html#score-combination).


Implemented Algorithms

combo groups combination frameworks by tasks. General purpose methods are fundamental ones which can be applied to various tasks.

Class/Function Task Algorithm Year Ref
combo.models.score_comb.average General Purpose Average & Weighted Average: average across all scores/prediction results, maybe with weights N/A [AZho12]
combo.models.score_comb.maximization General Purpose Maximization: simple combination by taking the maximum scores N/A [AZho12]
combo.models.score_comb.median General Purpose Median: take the median value across all scores/prediction results N/A [AZho12]
combo.models.score_comb.majority_vote General Purpose Majority Vote & Weighted Majority Vote N/A [AZho12]
combo.models.classifier_comb.SimpleClassifierAggregator Classification SimpleClassifierAggregator: combining classifiers by general purpose methods above N/A N/A
combo.models.classifier_dcs.DCS_LA Classification DCS: Dynamic Classifier Selection (Combination of multiple classifiers using local accuracy estimates) 1997 [AWKB97]
combo.models.classifier_des.DES_LA Classification DES: Dynamic Ensemble Selection (From dynamic classifier selection to dynamic ensemble selection) 2008 [AKSBJ08]
combo.models.classifier_stacking.Stacking Classification Stacking (meta ensembling): use a meta learner to learn the base classifier results N/A [AGor16]
combo.models.cluster_comb.ClustererEnsemble Clustering Clusterer Ensemble: combine the results of multiple clustering results by relabeling 2006 [AZT06]
combo.models.cluster_eac.EAC Clustering Combining multiple clusterings using evidence accumulation (EAC) 2002 [AFJ05]
combo.models.detector_comb.SimpleDetectorAggregator Anomaly Detection SimpleDetectorCombination: combining outlier detectors by general purpose methods above N/A [AAS17]
combo.models.score_comb.aom Anomaly Detection Average of Maximum (AOM): divide base detectors into subgroups to take the maximum, and then average 2015 [AAS15]
combo.models.score_comb.moa Anomaly Detection Maximum of Average (MOA): divide base detectors into subgroups to take the average, and then maximize 2015 [AAS15]
combo.models.detector_xgbod.XGBOD Anomaly Detection XGBOD: a semi-supervised combination framework for outlier detection 2018 [AZH18]
combo.models.detector_lscp.LSCP Anomaly Detection Locally Selective Combination (LSCP) 2019 [AZNHL19]

The comparison among selected implemented models is made available below (Figure, compare_selected_classifiers.py, Interactive Jupyter Notebooks). For Jupyter Notebooks, please navigate to “/notebooks/compare_selected_classifiers.ipynb”.

Comparison of Selected Models

Development Status

combo is currently under development as of July 30, 2019. A concrete plan has been laid out and will be implemented in the next few months.

Similar to other libraries built by us, e.g., Python Outlier Detection Toolbox (pyod), combo is also targeted to be published in Journal of Machine Learning Research (JMLR), open-source software track. A demo paper to AAAI or IJCAI may be submitted soon for progress update.

Watch & Star to get the latest update! Also feel free to send me an email (zhaoy@cmu.edu) for suggestions and ideas.



References

[AAS15](1, 2) Charu C Aggarwal and Saket Sathe. Theoretical foundations and algorithms for outlier ensembles. ACM SIGKDD Explorations Newsletter, 17(1):24–47, 2015.
[AAS17]Charu C Aggarwal and Saket Sathe. Outlier ensembles: An introduction. Springer, 2017.
[ABK07]Robert M Bell and Yehuda Koren. Lessons from the netflix prize challenge. SIGKDD Explorations, 9(2):75–79, 2007.
[AFJ05]Ana LN Fred and Anil K Jain. Combining multiple clusterings using evidence accumulation. IEEE transactions on pattern analysis and machine intelligence, 27(6):835–850, 2005.
[AGor16]Ben Gorman. A kaggler’s guide to model stacking in practice. Available at http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice, 2016.
[AKSBJ08]Albert HR Ko, Robert Sabourin, and Alceu Souza Britto Jr. From dynamic classifier selection to dynamic ensemble selection. Pattern recognition, 41(5):1718–1731, 2008.
[AWKB97]Kevin Woods, W. Philip Kegelmeyer, and Kevin Bowyer. Combination of multiple classifiers using local accuracy estimates. IEEE transactions on pattern analysis and machine intelligence, 19(4):405–410, 1997.
[AZH18]Yue Zhao and Maciej K Hryniewicki. XGBOD: improving supervised outlier detection with unsupervised representation learning. In 2018 International Joint Conference on Neural Networks, IJCNN 2018, 1–8. IEEE, 2018. URL: https://doi.org/10.1109/IJCNN.2018.8489605, doi:10.1109/IJCNN.2018.8489605.
[AZNHL19]Yue Zhao, Zain Nasrullah, Maciej K Hryniewicki, and Zheng Li. LSCP: locally selective combination in parallel outlier ensembles. In Proceedings of the 2019 SIAM International Conference on Data Mining, SDM 2019, 585–593. Calgary, Canada, May 2019. SIAM. URL: https://doi.org/10.1137/1.9781611975673.66, doi:10.1137/1.9781611975673.66.
[AZho12](1, 2, 3, 4) Zhi-Hua Zhou. Ensemble methods: foundations and algorithms. Chapman and Hall/CRC, 2012.
[AZT06]Zhi-Hua Zhou and Wei Tang. Clusterer ensemble. Knowledge-Based Systems, 19(1):77–83, 2006.

Indices and tables