combo 0.0.8

A Python Toolbox for Machine Learning Model Combination

Deployment & Documentation & Stats

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Build Status & Coverage & Maintainability & License

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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 [3].

combo library supports the combination of models and score from key ML libraries such as scikit-learn and xgboost. See figure below for some representative combination approaches.

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:

• awesome-ensemble-learning (ensemble learning related books, papers, and more)

• View the latest codes on Github

• View the documentation & API

• View all examples

• Execute Interactive Jupyter Notebooks

Table of Contents:

• Installation

• API Cheatsheet & Reference

• Implemented Algorithms

• Example 1: Classifier Combination with Stacking/DCS/DES

• Example 2: Simple Classifier Combination

• Example 3: Clustering Combination

• Example 4: Outlier Detector Combination

• Development Status

• Inclusion Criteria

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Installation

It is recommended to use pip for installation. Please make sure the latest version is installed, as combo is updated frequently:

pip install combo            # normal install
pip install --upgrade combo  # or update if needed
pip install --pre combo      # or include pre-release version for new features

Alternatively, you could clone and run setup.py file:

git clone https://github.com/yzhao062/combo.git
cd combo
pip install .

Required Dependencies:

• Python 3.5, 3.6, or 3.7

• joblib

• matplotlib (optional for running examples)

• numpy>=1.13

• numba>=0.35

• pyod

• scipy>=0.19.1

• scikit_learn>=0.19.1

Note on Python 2: The maintenance of Python 2.7 will be stopped by January 1, 2020 (see official announcement). To be consistent with the Python change and combo’s dependent libraries, e.g., scikit-learn, combo only supports Python 3.5+ and we encourage you to use Python 3.5 or newer for the latest functions and bug fixes. More information can be found at Moving to require Python 3.

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API Cheatsheet & Reference

Full API Reference: (https://pycombo.readthedocs.io/en/latest/api.html). The following APIs are consistent for most of the models (API Cheatsheet: https://pycombo.readthedocs.io/en/latest/api_cc.html).

• fit(X, y): Fit estimator. y is optional for unsupervised methods.

• predict(X): Predict on a particular sample once the estimator is fitted.

• predict_proba(X): Predict the probability of a sample belonging to each class once the estimator is fitted.

• fit_predict(X, y): Fit estimator and predict on X. y is optional for unsupervised methods.

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).

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Implemented Algorithms

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

Task	Algorithm	Year	Ref
General Purpose	Average & Weighted Average: average across all scores/prediction results, maybe with weights	N/A	[11]
General Purpose	Maximization: simple combination by taking the maximum scores	N/A	[11]
General Purpose	Median: take the median value across all scores/prediction results	N/A	[11]
General Purpose	Majority Vote & Weighted Majority Vote	N/A	[11]
Classification	SimpleClassifierAggregator: combining classifiers by general purpose methods above	N/A	N/A
Classification	DCS: Dynamic Classifier Selection (Combination of multiple classifiers using local accuracy estimates)	1997	[7]
Classification	DES: Dynamic Ensemble Selection (From dynamic classifier selection to dynamic ensemble selection)	2008	[5]
Classification	Stacking (meta ensembling): use a meta learner to learn the base classifier results	N/A	[4]
Clustering	Clusterer Ensemble: combine the results of multiple clustering results by relabeling	2006	[10]
Clustering	Combining multiple clusterings using evidence accumulation (EAC)	2002	[6]
Anomaly Detection	SimpleDetectorCombination: combining outlier detectors by general purpose methods above	N/A	[2]
Anomaly Detection	Average of Maximum (AOM): divide base detectors into subgroups to take the maximum, and then average	2015	[1]
Anomaly Detection	Maximum of Average (MOA): divide base detectors into subgroups to take the average, and then maximize	2015	[1]
Anomaly Detection	XGBOD: a semi-supervised combination framework for outlier detection	2018	[9]
Anomaly Detection	Locally Selective Combination (LSCP)	2019	[8]

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”.

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All implemented modes are associated with examples, check “combo examples” for more information.

Example of Stacking/DCS/DES

“examples/classifier_stacking_example.py” demonstrates the basic API of stacking (meta ensembling). “examples/classifier_dcs_la_example.py” demonstrates the basic API of Dynamic Classifier Selection by Local Accuracy. “examples/classifier_des_la_example.py” demonstrates the basic API of Dynamic Ensemble Selection by Local Accuracy.

It is noted the basic API is consistent across all these models.

1. Initialize a group of classifiers as base estimators

   # initialize a group of classifiers
   classifiers = [DecisionTreeClassifier(random_state=random_state),
                  LogisticRegression(random_state=random_state),
                  KNeighborsClassifier(),
                  RandomForestClassifier(random_state=random_state),
                  GradientBoostingClassifier(random_state=random_state)]

2. Initialize, fit, predict, and evaluate with Stacking

   from combo.models.classifier_stacking import Stacking
   
   clf = Stacking(base_estimators=classifiers, n_folds=4, shuffle_data=False,
                keep_original=True, use_proba=False, random_state=random_state)
   
   clf.fit(X_train, y_train)
   y_test_predict = clf.predict(X_test)
   evaluate_print('Stacking | ', y_test, y_test_predict)

3. See a sample output of classifier_stacking_example.py

   Decision Tree        | Accuracy:0.9386, ROC:0.9383, F1:0.9521
   Logistic Regression  | Accuracy:0.9649, ROC:0.9615, F1:0.973
   K Neighbors          | Accuracy:0.9561, ROC:0.9519, F1:0.9662
   Gradient Boosting    | Accuracy:0.9605, ROC:0.9524, F1:0.9699
   Random Forest        | Accuracy:0.9605, ROC:0.961, F1:0.9693
   
   Stacking             | Accuracy:0.9868, ROC:0.9841, F1:0.9899

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Example of Classifier Combination

“examples/classifier_comb_example.py” demonstrates the basic API of predicting with multiple classifiers. It is noted that the API across all other algorithms are consistent/similar.

1. Initialize a group of classifiers as base estimators

   # initialize a group of classifiers
   classifiers = [DecisionTreeClassifier(random_state=random_state),
                  LogisticRegression(random_state=random_state),
                  KNeighborsClassifier(),
                  RandomForestClassifier(random_state=random_state),
                  GradientBoostingClassifier(random_state=random_state)]

2. Initialize, fit, predict, and evaluate with a simple aggregator (average)

   from combo.models.classifier_comb import SimpleClassifierAggregator
   
   clf = SimpleClassifierAggregator(classifiers, method='average')
   clf.fit(X_train, y_train)
   y_test_predicted = clf.predict(X_test)
   evaluate_print('Combination by avg   |', y_test, y_test_predicted)

3. See a sample output of classifier_comb_example.py

   Decision Tree        | Accuracy:0.9386, ROC:0.9383, F1:0.9521
   Logistic Regression  | Accuracy:0.9649, ROC:0.9615, F1:0.973
   K Neighbors          | Accuracy:0.9561, ROC:0.9519, F1:0.9662
   Gradient Boosting    | Accuracy:0.9605, ROC:0.9524, F1:0.9699
   Random Forest        | Accuracy:0.9605, ROC:0.961, F1:0.9693
   
   Combination by avg   | Accuracy:0.9693, ROC:0.9677, F1:0.9763
   Combination by w_avg | Accuracy:0.9781, ROC:0.9716, F1:0.9833
   Combination by max   | Accuracy:0.9518, ROC:0.9312, F1:0.9642
   Combination by w_vote| Accuracy:0.9649, ROC:0.9644, F1:0.9728
   Combination by median| Accuracy:0.9693, ROC:0.9677, F1:0.9763

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Example of Clustering Combination

“examples/cluster_comb_example.py” demonstrates the basic API of combining multiple base clustering estimators. “examples/cluster_eac_example.py” demonstrates the basic API of Combining multiple clusterings using evidence accumulation (EAC).

1. Initialize a group of clustering methods as base estimators

   # Initialize a set of estimators
   estimators = [KMeans(n_clusters=n_clusters),
                 MiniBatchKMeans(n_clusters=n_clusters),
                 AgglomerativeClustering(n_clusters=n_clusters)]

2. Initialize a Clusterer Ensemble class and fit the model

   from combo.models.cluster_comb import ClustererEnsemble
   # combine by Clusterer Ensemble
   clf = ClustererEnsemble(estimators, n_clusters=n_clusters)
   clf.fit(X)

3. Get the aligned results

   # generate the labels on X
   aligned_labels = clf.aligned_labels_
   predicted_labels = clf.labels_

Example of Outlier Detector Combination

“examples/detector_comb_example.py” demonstrates the basic API of combining multiple base outlier detectors.

1. Initialize a group of outlier detection methods as base estimators

   # Initialize a set of estimators
   detectors = [KNN(), LOF(), OCSVM()]

2. Initialize a simple averaging aggregator, fit the model, and make the prediction.

   from combo.models.detector combination import SimpleDetectorAggregator
   clf = SimpleDetectorAggregator(base_estimators=detectors)
   clf_name = 'Aggregation by Averaging'
   clf.fit(X_train)
   
   y_train_pred = clf.labels_  # binary labels (0: inliers, 1: outliers)
   y_train_scores = clf.decision_scores_  # raw outlier scores
   
   # get the prediction on the test data
   y_test_pred = clf.predict(X_test)  # outlier labels (0 or 1)
   y_test_scores = clf.decision_function(X_test)  # outlier scores

3. Evaluate the prediction using ROC and Precision @ Rank n.

   # evaluate and print the results
   print("\nOn Training Data:")
   evaluate_print(clf_name, y_train, y_train_scores)
   print("\nOn Test Data:")
   evaluate_print(clf_name, y_test, y_test_scores)

4. See sample outputs on both training and test data.

   On Training Data:
   Aggregation by Averaging ROC:0.9994, precision @ rank n:0.95
   
   On Test Data:
   Aggregation by Averaging ROC:1.0, precision @ rank n:1.0

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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 ([e-mail]) for suggestions and ideas.

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Inclusion Criteria

Similarly to scikit-learn, We mainly consider well-established algorithms for inclusion. A rule of thumb is at least two years since publication, 50+ citations, and usefulness.

However, we encourage the author(s) of newly proposed models to share and add your implementation into combo for boosting ML accessibility and reproducibility. This exception only applies if you could commit to the maintenance of your model for at least two year period.

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Reference

[1] (1,2)

Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles. ACM SIGKDD Explorations Newsletter, 17(1), pp.24-47.

[2]

Aggarwal, C.C. and Sathe, S., 2017. Outlier ensembles: An introduction. Springer.

[3]

Bell, R.M. and Koren, Y., 2007. Lessons from the Netflix prize challenge. SIGKDD Explorations, 9(2), pp.75-79.

[4]

Gorman, B. (2016). A Kaggler’s Guide to Model Stacking in Practice. [online] The Official Blog of Kaggle.com. Available at: http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice [Accessed 26 Jul. 2019].

[5]

Ko, A.H., Sabourin, R. and Britto Jr, A.S., 2008. From dynamic classifier selection to dynamic ensemble selection. Pattern recognition, 41(5), pp.1718-1731.

[6]

Fred, A. L. N., & Jain, A. K. (2005). Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6), 835–850. https://doi.org/10.1109/TPAMI.2005.113

[7]

Woods, K., Kegelmeyer, W.P. and Bowyer, K., 1997. Combination of multiple classifiers using local accuracy estimates. IEEE transactions on pattern analysis and machine intelligence, 19(4), pp.405-410.

[8]

Zhao, Y., Nasrullah, Z., Hryniewicki, M.K. and Li, Z., 2019, May. LSCP: Locally selective combination in parallel outlier ensembles. In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), pp. 585-593. Society for Industrial and Applied Mathematics.

[9]

Zhao, Y. and Hryniewicki, M.K. XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning. IEEE International Joint Conference on Neural Networks, 2018.

[10]

Zhou, Z.H. and Tang, W., 2006. Clusterer ensemble. Knowledge-Based Systems, 19(1), pp.77-83.

[11] (1,2,3,4)

Zhou, Z.H., 2012. Ensemble methods: foundations and algorithms. Chapman and Hall/CRC.