Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part, which features interactive data analysis and component-based assembly of data mining procedures. In the selection and design of components, we focus on the flexibility of their reuse: our principal intention is to let the user write simple and clear scripts in Python, which build upon C++ implementations of computationally-intensive tasks. Orange is intended both for experienced users and programmers, as well as for students of data mining

Curk, Tomaž

Demšar, Janez

Erjavec, Aleš

Gorup, Črtomir

Hočevar, Tomaž

Milutinović, Mitar

Možina, Martin

Polajnar, Matija

Starič, Anže

Toplak, Marko

Umek, Lan

Zupan, Blaz

Štajdohar, Miha

Žagar, Lan

Žbontar, Jure

Žitnik, Marinka

English

ePrints.FRI

Journal of Machine Learning Research 14 (2013) 2349-2353 Submitted 3/13; Published 8/13Orange: Data Mining Toolbox in PythonJanez Demsˇar JANEZ.DEMSAR@FRI.UNI-LJ.SITomazˇ Curk TOMAZ.CURK@FRI.UNI-LJ.SIAlesˇ Erjavec ALES.ERJAVE@FRI.UNI-LJ.SICˇrt Gorup CRT.GORUP@FRI.UNI-LJ.SITomazˇ Hocˇevar TOMAZ.HOCEVAR@FRI.UNI-LJ.SIMitar Milutinovicˇ MITAR.MILUTINOVIC@FRI.UNI-LJ.SIMartin Mozˇina MARTIN.MOZINA@FRI.UNI-LJ.SIMatija Polajnar MATIJA.POLAJNAR@FRI.UNI-LJ.SIMarko Toplak MARKO.TOPLAK@FRI.UNI-LJ.SIAnzˇe Staricˇ ANZE.STARIC@FRI.UNI-LJ.SIMiha Sˇtajdohar MIHA.STAJDOHAR@FRI.UNI-LJ.SILan Umek LAN.UMEK@FRI.UNI-LJ.SILan Zˇagar LAN.ZAGAR@FRI.UNI-LJ.SIJure Zˇbontar JURE.ZBONTAR@FRI.UNI-LJ.SIMarinka Zˇitnik MARINKA.ZITNIK@FRI.UNI-LJ.SIBlazˇ Zupan BLAZ.ZUPAN@FRI.UNI-LJ.SIFaculty of Computer and Information ScienceUniversity of LjubljanaTrzˇasˇka 25, SI-1000 Ljubljana, SloveniaAbstractOrange is a machine learning and data mining suite for data analysis through Python scriptingand visual programming. Here we report on the scripting part, which features interactive dataanalysis and component-based assembly of data mining procedures. In the selection and design ofcomponents, we focus on the flexibility of their reuse: our principal intention is to let the user writesimple and clear scripts in Python, which build upon C++ implementations of computationally-intensive tasks. Orange is intended both for experienced users and programmers, as well as forstudents of data mining.Keywords: Python, data mining, machine learning, toolbox, scripting1. IntroductionScripting languages have recently risen in popularity in all fields of computer science. Within thecontext of explorative data analysis, they offer advantages like interactivity and fast prototyping bygluing together existing components or adapting them for new tasks. Python is a scripting languagewith clear and simple syntax, which also made it popular in education. Its relatively slow executioncan be circumvented by using libraries that implement the computationally intensive tasks in low-level languages.Python offers a huge number of extension libraries. Many are related to machine learning,including several general packages like scikit-learn (Pedregosa et al., 2011), PyBrain (Schaul et al.,2010) and mlpy (Albanese et al., 2012). Orange was conceived in late 1990s and is among the oldestof such tools. It focuses on simplicity, interactivity through scripting, and component-based design.c©2013 Janez Demsˇar, Tomazˇ Curk, Alesˇ Erjavec, Cˇrt Gorup, Tomazˇ Hocˇevar, Mitar Milutinovicˇ, Martin Mozˇina, Matija Polajnar,Marko Toplak, Anzˇe Staricˇ, Miha Sˇtajdohar, Lan Umek, Lan Zˇagar, Jure Zˇbontar, Marinka Zˇitnik and Blazˇ ZupanDEMSˇAR, CURK, ERJAVEC ET AL.2. Toolbox OverviewOrange library is a hierarchically-organized toolbox of data mining components. The low-levelprocedures at the bottom of the hierarchy, like data filtering, probability assessment and featurescoring, are assembled into higher-level algorithms, such as classification tree learning. This allowsdevelopers to easily add new functionality at any level and fuse it with the existing code. The mainbranches of the component hierarchy are:data management and preprocessing for data input and output, data filtering and sampling, im-putation, feature manipulation (discretization, continuization, normalization, scaling and scor-ing), and feature selection,classification with implementations of various supervised machine learning algorithms (trees, forests,instance-based and Bayesian approaches, rule induction), borrowing from some well-knownexternal libraries such as LIBSVM (Chang and Lin, 2011),regression including linear and lasso regression, partial least square regression, regression treesand forests, and multivariate regression splines,association for association rules and frequent itemsets mining,ensembles implemented as wrappers for bagging, boosting, forest trees, and stacking,clustering, which includes k-means and hierarchical clustering approaches,evaluation with cross-validation and other sampling-based procedures, functions for scoring thequality of prediction methods, and procedures for reliability estimation,projections with implementations of principal component analysis, multi-dimensional scaling andself-organizing maps.The library is designed to simplify the assembly of data analysis workflows and crafting of datamining approaches from a combination of existing components. Besides broader range of features,Orange differs from most other Python-based machine learning libraries by its maturity (over 15years of active development and use), a large user community supported through an active forum,and extensive documentation that includes tutorials, scripting examples, data set repository, and doc-umentation for developers. Orange scripting library is also a foundation for its visual programmingplatform with graphical user interface components for interactive data visualization.The two major packages that are similar to Orange and are still actively developed are scikit-learn (Pedregosa et al., 2011) and mlpy (Albanese et al., 2012). Both are more tightly integratedwith numpy and at present better blend into Python’s numerical computing habitat. Orange was onthe other hand inspired by classical machine learning that focuses on symbolic methods. Rather thansupporting only numerical arrays, Orange data structures combine symbolic, string and numericalattributes and meta data information. User can for instance refer to variables and values by theirnames. Variables store mapping functions, a mechanism which for instance allows classifiers todefine transformations on training data that are then automatically applied when making predictions.These features also make Orange more suitable for interactive, explorative data analysis.2350ORANGE: DATA MINING TOOLBOX IN PYTHON3. Scripting ExamplesLet us illustrate the utility of Orange through an example of data analysis in Python shell:>>> import Orange>>> data = Orange.data.Table("titanic")>>> len(data)2201>>> nbc = Orange.classification.bayes.NaiveLearner()>>> svm = Orange.classification.svm.SVMLearner()>>> stack = Orange.ensemble.stacking.StackedClassificationLearner([nbc,svm])>>> res = Orange.evaluation.testing.cross_validation([nbc, svm, stack], data)>>> Orange.evaluation.scoring.AUC(res)[0.7148500435874006, 0.731873352343742, 0.7635593576372478]We first read the data on survival of 2,201 passengers from HMS Titanic and construct a setof learning algorithms: a naive Bayesian and SVM learner, and a stacked combination of the two(Wolpert, 1992). We then cross-validate the learners and report the area under ROC curves.Running stacking on the subset of about 470 female passengers improves AUC score:>>> females = Orange.data.Table([d for d in data if d["sex"]=="female"])>>> len(females)470>>> res = Orange.evaluation.testing.cross_validation([stack], females)>>> Orange.evaluation.scoring.AUC(res)[0.8124014221073045]We can use existing machine learning components to craft new ones. For instance, learningalgorithms must implement a call operator that accepts the training data and, optionally, datainstance weights, and has to return a model. The following example defines a new learner thatencloses another learner into a feature selection wrapper: it sorts the features by their informationgain (as implemented in Orange.feature.scoring.InfoGain), constructs a new data set withonly the m best features and calls the base learner.class FSSLearner(Orange.classification.PyLearner):def __init__(self, base_learner, m=5):self.m = mself.base_learner = base_learnerdef __call__(self, data, weights=None):gain = Orange.feature.scoring.InfoGain()best = sorted(data.domain.features, key=lambda x: -gain(x, data))[:self.m]domain = Orange.data.Domain(best + [data.domain.class_var])new_data = Orange.data.Table(domain, data)model = self.base_learner(new_data, weights)return Orange.classification.PyClassifier(classifier=model)Below we compare the original and wrapped naive Bayesian classifier on a data set with 106instances and 57 features:>>> data = Orange.data.Table("promoters")>>> len(data), len(data.domain.features)(106, 57)2351DEMSˇAR, CURK, ERJAVEC ET AL.>>> bayes = Orange.classification.bayes.NaiveLearner()>>> res = Orange.evaluation.testing.cross_validation([bayes, FSSLearner(bayes)], data)>>> Orange.evaluation.scoring.AUC(res)[0.9329999999999998, 0.945]4. Code DesignOrange’s core is a collection of nearly 200 C++ classes that cover the basic data structures andmajority of preprocessing and modeling algorithms. The C++ part is self-contained, without anycalls to Python that would induce unnecessary overhead. The core includes several open sourcelibraries, including LIBSVM (Chang and Lin, 2011), LIBLINEAR (Rong-En et al., 2008), Earth(see http://www.milbo.users.sonic.net/earth), QHull (Barber et al., 1996) and a subset ofBLAS (Blackford et al., 2002). The Python layer also uses popular Python libraries numpy forlinear algebra, networkx (Hagberg et al., 2008) for working with networks and matplotlib (Hunter,2007) for basic visualization.The upper layer of Orange is written in Python and includes procedures that are not time-critical.This is also the place at which users outside the core development group most easily contribute tothe project.Automated testing of the system relies on over 1,500 regression tests that are mostly based oncode snippets from extensive documentation. A part of the code is also covered with stricter unittests.5. Availability, Requirements and Plans for the FutureOrange is free software released under GPL. The code is hosted on Bitbucket repository (https://bitbucket.org/biolab/orange). Orange runs on Windows, Mac OS X and Linux, and can alsobe installed from the Python Package Index repository (pip install Orange). Binary installerfor Windows and application bundle for Mac OS X are available on project’s web site (http://orange.biolab.si).Orange currently runs on Python 2.6 and 2.7. A version for Python 3 and higher is underdevelopment. There, we will switch to numpy-based data structures and scrap the C++ core infavor of using routines from numpy and scipy (Jones et al., 2001–), scikit-learn (Pedregosa et al.,2011) and similar libraries that did not exist when Orange was first conceived. Despite plannedchanges in the core, we will maintain backward compatibility. For existing users, the changes of thePython interface will be minor.AcknowledgmentsWe would like to acknowledge support for this project from the Slovenian Research Agency (P2-0209, J2-9699, L2-1112), National Institute of Health (P01-HD39691), and Astra Zeneca. We thankthe anonymous reviewers for their constructive comments.2352ORANGE: DATA MINING TOOLBOX IN PYTHONReferencesD. Albanese, R. Visintainer, S. Merler, S. Riccadonna, G. Jurman, and C. Furlanello. mlpy: Machinelearning Python. CoRR, abs/1202.6548, 2012.C. B. Barber, D. P. Dobkin, and H. T. Huhdanpaa. The Quickhull algorithm for convex hulls. ACMTrans. on Mathematical Software, 22(4), 1996.L. S. Blackford, A. Petitet, R. Pozo, K. Remington, R. C. Whaley, J. Demmel, J. Dongarra, I. Duff,S. Hammarling, and G. Henry. An updated set of basic linear algebra subprograms (BLAS). ACMTransactions on Mathematical Software, 28(2):135–151, 2002.C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions onIntelligent Systems and Technology, 2:27:1–27:27, 2011.Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart. Exploring network structure, dynamics, andfunction using NetworkX. In Proceedings of the 7th Python in Science Conference (SciPy2008),pages 11–15, Pasadena, CA USA, 2008.J. D. Hunter. Matplotlib: A 2D graphics environment. Computing In Science & Engineering, 9(3):90–95, 2007.E. Jones, T. Oliphant, P. Peterson, et al. SciPy: Open source scientific tools for Python, 2001–. URLhttp://www.scipy.org/.F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Pret-tenhofer, R. Weiss, and V. Dubourg. scikit-learn: Machine learning in Python. The Journal ofMachine Learning Research, 12:2825–2830, 2011.F. Rong-En, C.Kai-Wei, H. Cho-Jui, W. Xiang-Rui, and L. Chih-Jen. LIBLINEAR: A library forlarge linear classification. Journal of Machine Learning Research, 9:1871–1874, 2008.T. Schaul, J. Bayer, D. Wierstra, Y. Sun, M. Felder, F. Sehnke, T. Ru¨ckstieß, and J. Schmidhuber.PyBrain. Journal of Machine Learning Research, 11:743–746, 2010.D. H. Wolpert. Stacked generalization. Neural Networks, 5(2):241–259, 1992.2353

An updated set of basic linear algebra subprograms (BLAS).

Exploring network structure, dynamics, and function using NetworkX.

LIBLINEAR: A library for large linear classiﬁcation.

LIBSVM: A library for support vector machines.

Matplotlib: A 2D graphics environment.

S.Merler, S.Riccadonna, G.Jurman, andC.Furlanello. mlpy:

scikit-learn: Machine learning in Python.

SciPy: Open source scientiﬁc tools for Python,

Stacked generalization.

The Quickhull algorithm for convex hulls.

Orange: data mining toolbox in Python

University of Ljubljana Computer and Information Science ePrints.fri

Journal of Machine Learning Research 14 (2013) 2349-2353 Submitted 3/13; Published 8/13
Orange: Data Mining Toolbox in Python
Janez Demsˇar JANEZ.DEMSAR@FRI.UNI-LJ.SI
Tomazˇ Curk TOMAZ.CURK@FRI.UNI-LJ.SI
Alesˇ Erjavec ALES.ERJAVE@FRI.UNI-LJ.SI
Cˇrt Gorup CRT.GORUP@FRI.UNI-LJ.SI
Tomazˇ Hocˇevar TOMAZ.HOCEVAR@FRI.UNI-LJ.SI
Mitar Milutinovicˇ MITAR.MILUTINOVIC@FRI.UNI-LJ.SI
Martin Mozˇina MARTIN.MOZINA@FRI.UNI-LJ.SI
Matija Polajnar MATIJA.POLAJNAR@FRI.UNI-LJ.SI
Marko Toplak MARKO.TOPLAK@FRI.UNI-LJ.SI
Anzˇe Staricˇ ANZE.STARIC@FRI.UNI-LJ.SI
Miha Sˇtajdohar MIHA.STAJDOHAR@FRI.UNI-LJ.SI
Lan Umek LAN.UMEK@FRI.UNI-LJ.SI
Lan Zˇagar LAN.ZAGAR@FRI.UNI-LJ.SI
Jure Zˇbontar JURE.ZBONTAR@FRI.UNI-LJ.SI
Marinka Zˇitnik MARINKA.ZITNIK@FRI.UNI-LJ.SI
Blazˇ Zupan BLAZ.ZUPAN@FRI.UNI-LJ.SI
Faculty of Computer and Information Science
University of Ljubljana
Trzˇasˇka 25, SI-1000 Ljubljana, Slovenia
Abstract
Orange is a machine learning and data mining suite for data analysis through Python scripting
and visual programming. Here we report on the scripting part, which features interactive data
analysis and component-based assembly of data mining procedures. In the selection and design of
components, we focus on the flexibility of their reuse: our principal intention is to let the user write
simple and clear scripts in Python, which build upon C++ implementations of computationally-
intensive tasks. Orange is intended both for experienced users and programmers, as well as for
students of data mining.
Keywords: Python, data mining, machine learning, toolbox, scripting
1. Introduction
Scripting languages have recently risen in popularity in all fields of computer science. Within the
context of explorative data analysis, they offer advantages like interactivity and fast prototyping by
gluing together existing components or adapting them for new tasks. Python is a scripting language
with clear and simple syntax, which also made it popular in education. Its relatively slow execution
can be circumvented by using libraries that implement the computationally intensive tasks in low-
level languages.
Python offers a huge number of extension libraries. Many are related to machine learning,
including several general packages like scikit-learn (Pedregosa et al., 2011), PyBrain (Schaul et al.,
2010) and mlpy (Albanese et al., 2012). Orange was conceived in late 1990s and is among the oldest
of such tools. It focuses on simplicity, interactivity through scripting, and component-based design.
c©2013 Janez Demsˇar, Tomazˇ Curk, Alesˇ Erjavec, Cˇrt Gorup, Tomazˇ Hocˇevar, Mitar Milutinovicˇ, Martin Mozˇina, Matija Polajnar,
Marko Toplak, Anzˇe Staricˇ, Miha Sˇtajdohar, Lan Umek, Lan Zˇagar, Jure Zˇbontar, Marinka Zˇitnik and Blazˇ Zupan
DEMSˇAR, CURK, ERJAVEC ET AL.
2. Toolbox Overview
Orange library is a hierarchically-organized toolbox of data mining components. The low-level
procedures at the bottom of the hierarchy, like data filtering, probability assessment and feature
scoring, are assembled into higher-level algorithms, such as classification tree learning. This allows
developers to easily add new functionality at any level and fuse it with the existing code. The main
branches of the component hierarchy are:
data management and preprocessing for data input and output, data filtering and sampling, im-
putation, feature manipulation (discretization, continuization, normalization, scaling and scor-
ing), and feature selection,
classification with implementations of various supervised machine learning algorithms (trees, forests,
instance-based and Bayesian approaches, rule induction), borrowing from some well-known
external libraries such as LIBSVM (Chang and Lin, 2011),
regression including linear and lasso regression, partial least square regression, regression trees
and forests, and multivariate regression splines,
association for association rules and frequent itemsets mining,
ensembles implemented as wrappers for bagging, boosting, forest trees, and stacking,
clustering, which includes k-means and hierarchical clustering approaches,
evaluation with cross-validation and other sampling-based procedures, functions for scoring the
quality of prediction methods, and procedures for reliability estimation,
projections with implementations of principal component analysis, multi-dimensional scaling and
self-organizing maps.
The library is designed to simplify the assembly of data analysis workflows and crafting of data
mining approaches from a combination of existing components. Besides broader range of features,
Orange differs from most other Python-based machine learning libraries by its maturity (over 15
years of active development and use), a large user community supported through an active forum,
and extensive documentation that includes tutorials, scripting examples, data set repository, and doc-
umentation for developers. Orange scripting library is also a foundation for its visual programming
platform with graphical user interface components for interactive data visualization.
The two major packages that are similar to Orange and are still actively developed are scikit-
learn (Pedregosa et al., 2011) and mlpy (Albanese et al., 2012). Both are more tightly integrated
with numpy and at present better blend into Python’s numerical computing habitat. Orange was on
the other hand inspired by classical machine learning that focuses on symbolic methods. Rather than
supporting only numerical arrays, Orange data structures combine symbolic, string and numerical
attributes and meta data information. User can for instance refer to variables and values by their
names. Variables store mapping functions, a mechanism which for instance allows classifiers to
define transformations on training data that are then automatically applied when making predictions.
These features also make Orange more suitable for interactive, explorative data analysis.
2350
ORANGE: DATA MINING TOOLBOX IN PYTHON
3. Scripting Examples
Let us illustrate the utility of Orange through an example of data analysis in Python shell:
>>> import Orange
>>> data = Orange.data.Table("titanic")
>>> len(data)
2201
>>> nbc = Orange.classification.bayes.NaiveLearner()
>>> svm = Orange.classification.svm.SVMLearner()
>>> stack = Orange.ensemble.stacking.StackedClassificationLearner([nbc,svm])
>>> res = Orange.evaluation.testing.cross_validation([nbc, svm, stack], data)
>>> Orange.evaluation.scoring.AUC(res)
[0.7148500435874006, 0.731873352343742, 0.7635593576372478]
We first read the data on survival of 2,201 passengers from HMS Titanic and construct a set
of learning algorithms: a naive Bayesian and SVM learner, and a stacked combination of the two
(Wolpert, 1992). We then cross-validate the learners and report the area under ROC curves.
Running stacking on the subset of about 470 female passengers improves AUC score:
>>> females = Orange.data.Table([d for d in data if d["sex"]=="female"])
>>> len(females)
470
>>> res = Orange.evaluation.testing.cross_validation([stack], females)
>>> Orange.evaluation.scoring.AUC(res)
[0.8124014221073045]
We can use existing machine learning components to craft new ones. For instance, learning
algorithms must implement a call operator that accepts the training data and, optionally, data
instance weights, and has to return a model. The following example defines a new learner that
encloses another learner into a feature selection wrapper: it sorts the features by their information
gain (as implemented in Orange.feature.scoring.InfoGain), constructs a new data set with
only the m best features and calls the base learner.
class FSSLearner(Orange.classification.PyLearner):
def __init__(self, base_learner, m=5):
self.m = m
self.base_learner = base_learner
def __call__(self, data, weights=None):
gain = Orange.feature.scoring.InfoGain()
best = sorted(data.domain.features, key=lambda x: -gain(x, data))[:self.m]
domain = Orange.data.Domain(best + [data.domain.class_var])
new_data = Orange.data.Table(domain, data)
model = self.base_learner(new_data, weights)
return Orange.classification.PyClassifier(classifier=model)
Below we compare the original and wrapped naive Bayesian classifier on a data set with 106
instances and 57 features:
>>> data = Orange.data.Table("promoters")
>>> len(data), len(data.domain.features)
(106, 57)
2351
DEMSˇAR, CURK, ERJAVEC ET AL.
>>> bayes = Orange.classification.bayes.NaiveLearner()
>>> res = Orange.evaluation.testing.cross_validation([bayes, FSSLearner(bayes)], data)
>>> Orange.evaluation.scoring.AUC(res)
[0.9329999999999998, 0.945]
4. Code Design
Orange’s core is a collection of nearly 200 C++ classes that cover the basic data structures and
majority of preprocessing and modeling algorithms. The C++ part is self-contained, without any
calls to Python that would induce unnecessary overhead. The core includes several open source
libraries, including LIBSVM (Chang and Lin, 2011), LIBLINEAR (Rong-En et al., 2008), Earth
(see http://www.milbo.users.sonic.net/earth), QHull (Barber et al., 1996) and a subset of
BLAS (Blackford et al., 2002). The Python layer also uses popular Python libraries numpy for
linear algebra, networkx (Hagberg et al., 2008) for working with networks and matplotlib (Hunter,
2007) for basic visualization.
The upper layer of Orange is written in Python and includes procedures that are not time-critical.
This is also the place at which users outside the core development group most easily contribute to
the project.
Automated testing of the system relies on over 1,500 regression tests that are mostly based on
code snippets from extensive documentation. A part of the code is also covered with stricter unit
tests.
5. Availability, Requirements and Plans for the Future
Orange is free software released under GPL. The code is hosted on Bitbucket repository (https://
bitbucket.org/biolab/orange). Orange runs on Windows, Mac OS X and Linux, and can also
be installed from the Python Package Index repository (pip install Orange). Binary installer
for Windows and application bundle for Mac OS X are available on project’s web site (http:
//orange.biolab.si).
Orange currently runs on Python 2.6 and 2.7. A version for Python 3 and higher is under
development. There, we will switch to numpy-based data structures and scrap the C++ core in
favor of using routines from numpy and scipy (Jones et al., 2001–), scikit-learn (Pedregosa et al.,
2011) and similar libraries that did not exist when Orange was first conceived. Despite planned
changes in the core, we will maintain backward compatibility. For existing users, the changes of the
Python interface will be minor.
Acknowledgments
We would like to acknowledge support for this project from the Slovenian Research Agency (P2-
0209, J2-9699, L2-1112), National Institute of Health (P01-HD39691), and Astra Zeneca. We thank
the anonymous reviewers for their constructive comments.
2352
ORANGE: DATA MINING TOOLBOX IN PYTHON
References
D. Albanese, R. Visintainer, S. Merler, S. Riccadonna, G. Jurman, and C. Furlanello. mlpy: Machine
learning Python. CoRR, abs/1202.6548, 2012.
C. B. Barber, D. P. Dobkin, and H. T. Huhdanpaa. The Quickhull algorithm for convex hulls. ACM
Trans. on Mathematical Software, 22(4), 1996.
L. S. Blackford, A. Petitet, R. Pozo, K. Remington, R. C. Whaley, J. Demmel, J. Dongarra, I. Duff,
S. Hammarling, and G. Henry. An updated set of basic linear algebra subprograms (BLAS). ACM
Transactions on Mathematical Software, 28(2):135–151, 2002.
C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on
Intelligent Systems and Technology, 2:27:1–27:27, 2011.
Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart. Exploring network structure, dynamics, and
function using NetworkX. In Proceedings of the 7th Python in Science Conference (SciPy2008),
pages 11–15, Pasadena, CA USA, 2008.
J. D. Hunter. Matplotlib: A 2D graphics environment. Computing In Science & Engineering, 9(3):
90–95, 2007.
E. Jones, T. Oliphant, P. Peterson, et al. SciPy: Open source scientific tools for Python, 2001–. URL
http://www.scipy.org/.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Pret-
tenhofer, R. Weiss, and V. Dubourg. scikit-learn: Machine learning in Python. The Journal of
Machine Learning Research, 12:2825–2830, 2011.
F. Rong-En, C.Kai-Wei, H. Cho-Jui, W. Xiang-Rui, and L. Chih-Jen. LIBLINEAR: A library for
large linear classification. Journal of Machine Learning Research, 9:1871–1874, 2008.
T. Schaul, J. Bayer, D. Wierstra, Y. Sun, M. Felder, F. Sehnke, T. Ru¨ckstieß, and J. Schmidhuber.
PyBrain. Journal of Machine Learning Research, 11:743–746, 2010.
D. H. Wolpert. Stacked generalization. Neural Networks, 5(2):241–259, 1992.
2353


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