Development of a Python package for Functional Data Analysis. Depth measures, applications and clustering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, the problem of analyzing functional data is addressed. Each observation in functional \ndata is a function that varies over a continuum. This kind of complex data is increasingly becoming \nmore common in many research fields. However, Functional Data Analysis (FDA) is a relatively recent \nfield in which software implementations are basically limited to R. In addition, although they may follow \nan open-source scheme, the contribution to them may turn out to be complicated. The final goal of this \nproject is to provide a comprehensive Python package for Functional Data Analysis, scikit-fda. \nIn this undergraduate thesis, the functionality implemented in the package includes functional depth \nmeasures together with their applications and elementary notions of clustering. In a functional space, \nestablishing an order is complicated due to its nature. Depth measures allow to define robust statistics \nfor functional data. In the package you can find some of the most common, Fraiman and Muniz depth \nmeasure, the band depth measure or a modification of the latter, the modified band depth. Depth measures \nare used in the construction of graphic tools, both the functional boxplot and the magnitude-shape \nplot are introduced in the package along with their outlier detection procedures. Furthermore, contributions \nin the area of machine learning are made in which basic clustering algorithms are added to the \npackage: K-means and Fuzzy K-means. Finally, the results of applying these methods to the Canadian \nWeather dataset are shown. \nThe Python package is published in a GitHub repository. It is open-source wth the aim of growing \nand being kept up to date. In the long term it is expected to cover the fundamental techniques in FDA \nand become a widely-used toolbox for research in FDA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it