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Autorank: A Python package for automated ranking of classifiers

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Autorank: A Python package for automated ranking of
classiers
Steen Herbold1
1Institute for Computer Science, University of Goettingen, Germany
DOI: 10.21105/joss.02173
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Submitted: 17 February 2020
Published: 14 April 2020
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Summary
Analyses to determine dierences in the central tendency, e.g., mean or median values, are an
important application of statistics. Often, such comparisons must be done with paired samples,
i.e., populations that are not dependent on each other. This is, for example, required if the
performance dierent machine learning algorithms should be compared on multiple data sets.
The performance measures on each data set are then the paired samples, the dierence in
the central tendency can be used to rank the dierent algorithms. This problem is not new
and how such tests could be done was already described in the well-known article by Demšar
(2006).
Regardless, the correct use of Demšar’s guidelines is hard for non-experts in statistics. The
distribution of the populations must be analyzed with the Shapiro-Wilk test for normality
and, depending on the normality with Levene’s test or Bartlett’s tests for homogeneity of the
data. Based on the results and the number of populations, researchers must decide whether the
paired t-test, Wilcoxon’s rank sum test, repeated measures ANOVA with Tukey’s HSD as post-
hoc test, or Friedman’s tests and Nemenyi’s post-hoc test is the suitable statistical framework.
All this is already quite complex. Additionally, researchers must adjust the signicance level
due to the number of tests to achieve the desired family-wise signicance and control the
false-positive rate of the test results.
Moreover, there are important aspects that go beyond Demšar’s guidelines regarding best
practice for the reporting of statistical result. Good reporting of the results goes beyond
simply stating the signicance of ndings. Additional aspects also matter, e.g., eect sizes,
condence intervals, and the decision whether it is appropriate to report the mean value and
standard deviation, or whether the median value and the median absolute deviation are better
suited.
The goal of Autorank is to simplify the statistical analysis for non-experts. Autorank takes
care of all of the above with a single function call. This is the dierence between Autorank
other packages, like Scipy (Virtanen et al., 2020), who expect users to know which tests to
use and how to interpret the results. The decision ow of Autorank is as follows.
Herbold, S., (2020). Autorank: A Python package for automated ranking of classiers. Journal of Open Source Software, 5(48), 2173.
https://doi.org/10.21105/joss.02173
1
Figure 1: Decision Flow
Additional functions allow the generation of appropriate plots, result tables, and even of a
complete latex document. All that is required is the data about the populations is in a
dataframe.
We believe that Autorank can help to avoid common statistical errors such as, such as the use
of inappropriate statistical tests, reporting of parametric measures instead of non-parametric
measures in case of non-normal data, and incomplete reporting in general.
Using Autorank
In our research, we recently used autorank to compare dierences between data generation
methods for defect prediction research (Herbold, Trautsch, & Trautsch, 2020). In general,
Autorank can be used anywhere, where dierent classiers are compared on multiple data
sets. The results must only be prepared as a dataframe. For example, the dataframe could
contain the accuracy of classiers trained on dierent data sets.1The following three lines
would then perform the statistical tests, generate the textual description of the results, as well
as the plot of the results.
>from autorank import autorank, create_report, plot_stats
>results =autorank(data)
>create_report(results)
The statistical analysis was conducted for 6populations with 20 paired
samples.
The family-wise significance level of the tests is alpha=0.050.
We rejected the null hypothesis that the population is normal for the
population Random Forest (p=0.000). Therefore, we assume that not all
populations are normal.
Because we have more than two populations and the populations and one of
1See also: https://github.com/sherbold/autorank/tree/master/examples/sklearn.py
Herbold, S., (2020). Autorank: A Python package for automated ranking of classiers. Journal of Open Source Software, 5(48), 2173.
https://doi.org/10.21105/joss.02173
2
them is not normal, we use the non-parametric Friedman test as omnibus
test to determine if there are any significant differences between the
median values of the populations. We use the post-hoc Nemenyi test to
infer which differences are significant. We report the median (MD), the
median absolute deviation (MAD) and the mean rank (MR) among all
populations over the samples. Differences between populations are
significant, if the difference of the mean rank is greater than the
critical distance CD=1.686 of the Nemenyi test.
We reject the null hypothesis (p=0.000) of the Friedman test that there
is no difference in the central tendency of the populations Naive Bayes
(MD=0.875+-0.065, MAD=0.053, MR=2.750), Random Forest (MD=0.850+-0.100,
MAD=0.062, MR=2.850), RBF SVM (MD=0.885+-0.217, MAD=0.059, MR=2.900),
Neural Net (MD=0.876+-0.070, MAD=0.045, MR=3.300), Decision Tree
(MD=0.810+-0.173, MAD=0.074, MR=4.525), and Linear SVM (MD=0.710+-0.245,
MAD=0.253, MR=4.675). Therefore, we assume that there is a statistically
significant difference between the median values of the populations.
Based the post-hoc Nemenyi test, we assume that there are no significant
differences within the following groups: Naive Bayes, Random Forest, RBF
SVM, and Neural Net;Random Forest, RBF SVM, Neural Net, and Decision
Tree;Neural Net, Decision Tree, and Linear SVM. All other differences
are significant.
>plot_stats(data)
Figure 2: CD Diagram
Acknowledgements
This work is partially funded by DFG Grant 402774445.
References
Demšar, J. (2006). Statistical comparisons of classiers over multiple data sets. J. Mach.
Learn. Res.,7, 1–30.
Herbold, S., Trautsch, A., & Trautsch, F. (2020). Issues with szz: An empirical assessment
of the state of practice of defect prediction data collection. Retrieved from http://arxiv.
org/abs/1911.08938
Herbold, S., (2020). Autorank: A Python package for automated ranking of classiers. Journal of Open Source Software, 5(48), 2173.
https://doi.org/10.21105/joss.02173
3
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D.,
Burovski, E., et al. (2020). SciPy 1.0: Fundamental Algorithms for Scientic Computing
in Python. Nature Methods,17, 261–272. doi:10.1038/s41592-019-0686-2
Herbold, S., (2020). Autorank: A Python package for automated ranking of classiers. Journal of Open Source Software, 5(48), 2173.
https://doi.org/10.21105/joss.02173
4
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Issues with SZZ: An empirical assessment of the state of practice of defect prediction data collection
  • Steffen Herbold
  • Alexander Trautsch
  • Fabian Trautsch