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+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5873 +10.21105/joss.05873 + +pudu: A Python library for agnostic feature selection and +explainability of Machine Learning spectroscopic +problems + + + +https://orcid.org/0000-0002-8357-5824 + +Grau-Luque +Enric + + + + +https://orcid.org/0000-0002-7087-6097 + +Becerril-Romero +Ignacio + + + + +https://orcid.org/0000-0002-3634-1355 + +Perez-Rodriguez +Alejandro + + + + + +https://orcid.org/0000-0002-2072-9566 + +Guc +Maxim + + + + +https://orcid.org/0000-0002-5502-3133 + +Izquierdo-Roca +Victor + + + + + +Catalonia Institute for Energy Research (IREC), Jardins de +les Dones de Negre 1, 08930 Sant Adrià de Besòs, Spain. + + + + +Departament d’Enginyeria Electrònica i Biomèdica, IN2UB, +Universitat de Barcelona, C/ Martí i Franqués 1, 08028 Barcelona, +Spain. + + + + +30 +6 +2023 + +8 +92 +5873 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Spectroscopy +Machine Learning +Explainability and intepretability +Classification and regression + + + + + + Statement of need +

Spectroscopic techniques (e.g. Raman, photoluminescence, + reflectance, transmittance, X-ray fluorescence) are an important and + widely used resource in different fields of science, such as + photovoltaics + (Fonoll-Rubio + et al., 2022) + (Grau-Luque + et al., 2021), cancer + (Bellisola + & Sorio, 2012), superconductors + (Fischer + et al., 2007), polymers + (Easton + et al., 2020), corrosion + (Haruna + et al., 2023), forensics + (P. + V. Bhatt & Rawtani, 2023), and environmental sciences + (Estefany + et al., 2023), to name just a few. This is due to the + versatile, non-destructive and fast acquisition nature that provides a + wide range of material properties, such as composition, morphology, + molecular structure, optical and electronic properties. As such, + machine learning (ML) has been used to analyze spectral data for + several years, elucidating their vast complexity, and uncovering + further potential on the information contained within them + (Goodacre, + 2003) + (Luo et + al., 2022). Unfortunately, most of these ML analyses lack + further interpretation of the derived results due to the complex + nature of such algorithms. In this regard, interpreting the results of + ML algorithms has become an increasingly important topic, as concerns + about the lack of interpretability of these models have grown + (Burkart + & Huber, 2021). In natural sciences (like materials, + physical, chemistry, etc.), as ML becomes more common, this concern + has gained especial interest, since results obtained from ML analyses + may lack scientific value if they cannot be properly interpreted, + which can affect scientific consistency and strongly diminish the + significance and confidence in the results, particularly when tackling + scientific problems + (Roscher + et al., 2020).

+

Even though there are methods and libraries available for + explaining different types of algorithms such as SHAP + (Lundberg + et al., 2017), LIME + (Ribeiro + et al., 2016), or GradCAM + (Selvaraju + et al., 2017), they can be difficult to interpret or understand + even for data scientists, leading to problems such as + miss-interpretation, miss-use and over-trust + (Kaur + et al., n.d.). Adding this to other human-related issues + (Krishnå1 + et al., 2022), researchers with expertise in natural sciences + with little or no data science background may face further issues when + using such methodologies + (Zhong + et al., 2022). Furthermore, these types of libraries normally + aim for problems composed of image, text, or tabular data, which + cannot be associated in a straightforward way with spectroscopic data. + On the other hand, time series (TS) data shares similarities with + spectroscopy, and while still having specific needs and differences, + TS dedicated tools can be a better approach. Unfortunately, despite + the existence of several libraries that aim to explain models for TS + with the potential to be applied to spectroscopic data, they are + mostly designed for a specialized audience, and many are + model-specific + (Rojat + et al., 2021). Moreover, spectral data normally manifests as an + array of peaks that are typically overlapped and can be distinguished + by their shape, intensity, and position. Minor shifts in these + patterns can indicate significant alterations in the fundamental + properties of the subject material. Conversely, pronounced variations + in the other case might only indicate negligible differences. + Therefore, comprehending such alterations and their implications is + paramount. This is still true with ML spectroscopic analysis where the + spectral variations are still of primary concern. In this context, a + tool with an easy and understandable approach that offers + spectroscopy-aimed functionalities that allow to aim for specific + patterns, areas, and variations, and that is beginner and + non-specialist friendly is of high interest. This can help the + different stakeholders to better understand the ML models that they + employ and considerably increase the transparency, comprehensibility, + and scientific impact of ML results + (U. + Bhatt et al., 2020) + (Belle + & Papantonis, 2021).

+
+ + Overview +

pudu is a Python library that quantifies the effect of + changes in spectral features over the predictions of ML models and + their effect to the target instances. In other words, it perturbates + the features in a predictable and deliberate way and evaluates the + features based on how the final prediction changes. For this, four + main methods are included and defined. Importance + quantifies the relevance of the features according to the changes in + the prediction. Thus, this is measured in probability or target value + difference for classification or regression problems, respectively. + Speed quantifies how fast a prediction changes + according to perturbations in the features. For this, the + importance is calculated at different perturbation + levels, and a line is fitted to the obtained values and the slope, or + the rate of change of importance, is extracted as the + speed. Synergy indicates how + features complement each other in terms of prediction change after + perturbations. Finally, re-activations account for + the number of unit activations in a Convolutional Neural Network (CNN) + that after perturbation, the value goes above the original activation + criteria. The latter is only applicable for CNNs, but the rest can be + applied to any other ML problem, including CNNs. To read in more + detail how these techniques work, please refer to the + definitions + in the documentation.

+

pudu is versatile as it can analyze classification and + regression algorithms for both 1- and 2-dimensional problems, offering + plenty of flexibility with parameters, and the ability to provide + localized explanations by selecting specific areas of interest. To + illustrate this, + [fig:figure1] + shows two analysis instances using the same + importance method but with different parameters. + Additionally, its other functionalities are shown in examples using + scikit-learn + (Pedregosa + et al., 2011), keras + (Chollet + et al., 2018), and localreg + (Marholm, + 2022) are found in the documentation, along with XAI methods + including LIME and GradCAM.

+

pudu is built in Python 3 + (Van + Rossum & Drake, 2009) and uses third-party packages + including numpy + (Harris + et al., 2020), matplotlib + (Caswell + et al., 2021), and keras. It is available in both PyPI and + conda, and comes with complete documentation, including quick start, + examples, and contribution guidelines. Source code and documentation + are available in https://github.com/pudu-py/pudu.

+ +

Two ways of using the same method + importance by A) using a sequential change pattern + over all the spectral features and B) selecting peaks of interest. + These spectras are measured from thin-film photovoltaic samples and + are correlated to their performance using ML, as explained in + (Fonoll-Rubio + et al., 2022). In A), the vector was divided in window sizes + of 25 pixels were perturbed individually. The impact of the peak in + the range of 1200-1400 opaques the impact of the rest. In contrast, + in B) specific ranges are defined, so only the first four main peaks + are selected to be analyzed and better visualize their impact in the + prediction.

+ +
+
+ + Acknowledgements +

Co-funded by the European Union (GA Nº 101058459 Platform-ZERO). + Views and opinions expressed are however those of the authors only and + do not necessarily reflect those of the European Union (EU) or + European Health and Digital Executive Agency (HADEA). Neither the EU + nor the granting authority can be held responsible for them. This + project has received funding from the EU’s Horizon 2020 research and + innovation programme under Marie Skłodowska-Curie GA Nº 801342 + (Tecniospring INDUSTRY) and the Government of Catalonia’s Agency for + Business Competitiveness (ACCIÓ). This work has received funding from + the EU’s Horizon 2020 Research and Innovation Programme under GA Nº + 958243 (SUNRISE project). Authors from IREC belong to the MNT-Solar + Consolidated Research Group of the “Generalitat de Catalunya” (ref. + 2021 SGR 01286) and are grateful to European Regional Development + Funds (ERDF, FEDER Programa Competitivitat de Catalunya + 2007–2013).

+
+ + Authors contribution with + <ext-link ext-link-type="uri" xlink:href="https://credit.niso.org/">CRediT</ext-link> + + +

Enric Grau-Luque: Conceptualization, Data curation, Software, + Writing – original draft

+
+ +

Ignacio Becerril-Romero: Investigation, Methodology, Writing – + review & edition

+
+ +

Alejandro Perez-Rodriguez: Funding acquisition, Project + administration, Resources, Supervision

+
+ +

Maxim Guc: Formal analysis, Validation, Methodology, Writing – + review & edition

+
+ +

Victor Izquierdo-Roca: Funding acquisition, Project + administration, Supervision

+
+
+
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insertions(+) create mode 100644 joss.05873/10.21105.joss.05873.crossref.xml diff --git a/joss.05873/10.21105.joss.05873.crossref.xml b/joss.05873/10.21105.joss.05873.crossref.xml new file mode 100644 index 0000000000..53f1e60a9c --- /dev/null +++ b/joss.05873/10.21105.joss.05873.crossref.xml @@ -0,0 +1,496 @@ + + + + 20231212T165950-7df13fb8321d887c97dd9dda83a52185cd313025 + 20231212165950 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 12 + 2023 + + + 8 + + 92 + + + + pudu: A Python library for agnostic feature selection +and explainability of Machine Learning spectroscopic problems + + + + Enric + Grau-Luque + https://orcid.org/0000-0002-8357-5824 + + + Ignacio + Becerril-Romero + https://orcid.org/0000-0002-7087-6097 + + + Alejandro + Perez-Rodriguez + https://orcid.org/0000-0002-3634-1355 + + + Maxim + Guc + https://orcid.org/0000-0002-2072-9566 + + + Victor + Izquierdo-Roca + https://orcid.org/0000-0002-5502-3133 + + + + 12 + 12 + 2023 + + + 5873 + + + 10.21105/joss.05873 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10161346 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5873 + + + + 10.21105/joss.05873 + https://joss.theoj.org/papers/10.21105/joss.05873 + + + https://joss.theoj.org/papers/10.21105/joss.05873.pdf + + + + + + Explanatory analysis of spectroscopic data +using machine learning of simple, interpretable rules + Goodacre + Vibrational Spectroscopy + 1 + 32 + 10.1016/S0924-2031(03)00045-6 + 0924-2031 + 2003 + Goodacre, R. 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Scotts Valley, CA, 242. +ISBN: 978-1-4414-1269-0 + + + matplotlib/matplotlib: REL: +v3.4.2 + Caswell + 10.5281/ZENODO.4743323 + 2021 + Caswell, T. A., Droettboom, M., Lee, +A., Andrade, E. S. de, Hunter, J., Hoffmann, T., Firing, E., Klymak, J., +Stansby, D., Varoquaux, N., Nielsen, J. H., Root, B., May, R., Elson, +P., Seppänen, J. K., Dale, D., Lee, J.-J., McDougall, D., Straw, A., … +Ivanov, P. (2021). matplotlib/matplotlib: REL: v3.4.2. +https://doi.org/10.5281/ZENODO.4743323 + + + + + + From ba2559542bb4f778cad81e26c2eff4aa7462fba6 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Tue, 12 Dec 2023 17:00:13 +0000 Subject: [PATCH 3/4] Creating 10.21105.joss.05873.jats --- joss.05873/10.21105.joss.05873.jats | 871 ++++++++++++++++++++++++++++ 1 file changed, 871 insertions(+) create mode 100644 joss.05873/10.21105.joss.05873.jats diff --git a/joss.05873/10.21105.joss.05873.jats b/joss.05873/10.21105.joss.05873.jats new file mode 100644 index 0000000000..a01992574e --- /dev/null +++ b/joss.05873/10.21105.joss.05873.jats @@ -0,0 +1,871 @@ + + +