Merge branch 'develop' into feature/RKHS_inner_product

.github/workflows/tests.yml

@@ -11,7 +11,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ['3.8', '3.9']
+        python-version: ['3.9', '3.10']
 
     steps:
     - uses: actions/checkout@v2

binder/requirements.txt

@@ -1,4 +1,4 @@
--r ../readthedocs-requirements.txt
+scikit-fda[docs]
 jupytext
 sphinx-gallery<=0.7.0
 .

docs/_static/switcher.json

@@ -0,0 +1,13 @@
+[
+    {
+        "name": "dev",
+        "version": "dev",
+        "url": "https://fda.readthedocs.io/en/latest/"
+    },
+    {
+    	"name": "0.9 (stable)",
+        "version": "stable",
+        "url": "https://fda.readthedocs.io/en/stable/",
+        "preferred": true
+    }
+]

docs/_templates/autosummary/class.rst

@@ -10,12 +10,16 @@
 
    .. autosummary::
    {% for item in methods %}
+      {% if item != "__init__" %}
       ~{{ name }}.{{ item }}
+      {% endif %}
    {%- endfor %}
    {% endif %}
 
    {% for item in methods %}
+      {% if item != "__init__" %}
    .. automethod:: {{ item }}
+      {% endif %}
    {%- endfor %}
    {% endblock %}
 

docs/conf.py

@@ -46,8 +46,16 @@
     "Universidad Autónoma de Madrid"
 )
 github_url = "https://github.com/GAA-UAM/scikit-fda"
-rtd_version = os.environ.get("READTHEDOCS_VERSION", "latest")
-branch = "master" if rtd_version == "stable" else "develop"
+rtd_version = os.environ.get("READTHEDOCS_VERSION")
+rtd_version_type = os.environ.get("READTHEDOCS_VERSION_TYPE")
+
+switcher_version = rtd_version
+if switcher_version == "latest":
+    switcher_version = "dev"
+elif rtd_version_type not in {"branch", "tag"}:
+    switcher_version = skfda.__version__
+
+rtd_branch = os.environ.get(" READTHEDOCS_GIT_IDENTIFIER", "develop")
 language = "en"
 
 try:
@@ -112,6 +120,14 @@
 html_theme_options = {
     "use_edit_page_button": True,
     "github_url": github_url,
+    "switcher": {
+        "json_url": (
+            "https://fda.readthedocs.io/en/latest/_static/switcher.json"
+        ),
+        "version_match": switcher_version,
+    },
+    "show_version_warning_banner": True,
+    "navbar_start": ["navbar-logo", "version-switcher"],
     "icon_links": [
         {
             "name": "PyPI",
@@ -289,7 +305,7 @@ def linkcode_resolve(domain: str, info: Mapping[str, str]) -> str | None:
     else:
         linespec = ""
 
-    return f"{github_url}/tree/{branch}/skfda/{fn}{linespec}"
+    return f"{github_url}/tree/{rtd_branch}/skfda/{fn}{linespec}"
 
 # -- Options for "sphinx.ext.mathjax" --
 
@@ -391,7 +407,7 @@ def __repr__(self) -> str:
     "binder": {
         "org": "GAA-UAM",
         "repo": "scikit-fda",
-        "branch": branch,
+        "branch": rtd_branch,
         "binderhub_url": "https://mybinder.org",
         "dependencies": ["../binder/requirements.txt"],
         "notebooks_dir": "../examples",

docs/index.rst

@@ -34,7 +34,7 @@ Github you can find more information related to the development of the package.
    :caption: More documentation
 
    apilist
-   glossary
+   Glossary <glossary>
    contributors
 
 An exhaustive list of all the contents of the package can be found in the

docs/modules/exploratory/stats.rst

@@ -31,4 +31,5 @@ statistics can be used.
 
    skfda.exploratory.stats.cov
    skfda.exploratory.stats.var
+   skfda.exploratory.stats.std
 

docs/modules/ml/regression.rst

@@ -57,4 +57,17 @@ regression is fitted using the coefficients of the functions in said basis.
 .. autosummary::
    :toctree: autosummary
 
-    skfda.ml.regression.FPCARegression
+    skfda.ml.regression.FPCARegression
+
+FPLS regression
+-----------------
+This module includes the implementation of FPLS (Functional Partial Least Squares) 
+regression. This implementation accepts either functional or multivariate data as the regressor and the response.
+FPLS regression consists on performing the FPLS dimensionality reduction algorithm 
+but using a regression deflation strategy.
+
+
+.. autosummary::
+   :toctree: autosummary
+
+    skfda.ml.regression.FPLSRegression

docs/modules/preprocessing/dim_reduction.rst

@@ -36,9 +36,12 @@ Other dimensionality reduction methods construct new features from
 existing ones. For example, in functional principal component
 analysis, we project the data samples into a smaller sample of
 functions that preserve most of the original
-variance.
+variance. Similarly, in functional partial least squares, we project
+the data samples into a smaller sample of functions that preserve most
+of the covariance between the two data blocks.
 
 .. autosummary::
    :toctree: autosummary
 
-   skfda.preprocessing.dim_reduction.FPCA
+   skfda.preprocessing.dim_reduction.FPCA
+   skfda.preprocessing.dim_reduction.FPLS

docs/refs.bib

@@ -115,6 +115,21 @@ @article{berrendero++_2022_functional
   keywords = {62J12,62R10,Functional data,Kernel methods in statistics,Logistic regression,Mathematics - Statistics Theory,Reproducing kernel Hilbert spaces}
 }
 
+@article{borggaard+thodberg_1992_optimal,
+  title = {Optimal Minimal Neural Interpretation of Spectra},
+  author = {Borggaard, {\relax Claus}. and Thodberg, Hans Henrik.},
+  year = {1992},
+  month = mar,
+  journal = {Analytical Chemistry},
+  volume = {64},
+  number = {5},
+  pages = {545--551},
+  issn = {0003-2700},
+  doi = {10.1021/ac00029a018},
+  url = {https://doi.org/10.1021/ac00029a018},
+  urldate = {2018-12-09}
+}
+
 @article{cuesta-albertos++_2017_ddgclassifier,
   title = {The {{DDᴳ}}-Classifier in the Functional Setting},
   author = {{Cuesta-Albertos}, J. A. and {Febrero-Bande}, M. and {Oviedo~de~la~Fuente}, M.},
@@ -310,6 +325,25 @@ @article{gutierrez++_1992_numerical
   pages={67--77},
   year={1992},
   publisher={Wiley Online Library}
+
+@article{hastie++_1995_penalized,
+  title = {Penalized Discriminant Analysis},
+  author = {Hastie, Trevor and Buja, Andreas and Tibshirani, Robert},
+  year = {1995},
+  month = feb,
+  journal = {The Annals of Statistics},
+  volume = {23},
+  number = {1},
+  pages = {73--102},
+  issn = {0090-5364, 2168-8966},
+  doi = {10.1214/aos/1176324456},
+  url = {https://projecteuclid.org/euclid.aos/1176324456},
+  urldate = {2018-12-09},
+  abstract = {Fisher's linear discriminant analysis (LDA) is a popular data-analytic tool for studying the relationship between a set of predictors and a categorical response. In this paper we describe a penalized version of LDA. It is designed for situations in which there are many highly correlated predictors, such as those obtained by discretizing a function, or the grey-scale values of the pixels in a series of images. In cases such as these it is natural, efficient and sometimes essential to impose a spatial smoothness constraint on the coefficients, both for improved prediction performance and interpretability. We cast the classification problem into a regression framework via optimal scoring. Using this, our proposal facilitates the use of any penalized regression technique in the classification setting. The technique is illustrated with examples in speech recognition and handwritten character recognition.},
+  langid = {english},
+  mrnumber = {MR1331657},
+  zmnumber = {0821.62031},
+  keywords = {discrimination,regularization,Signal and image classification}
 }
 
 @article{li++_2012_ddclassifier,
@@ -366,6 +400,14 @@ @article{marron++_2015_functional
   keywords = {alignment,dynamic time warping,elastic metric,Fisher–Rao metric,Functional data analysis,registration,warping}
 }
 
+@misc{meteorologicalstateagencyofspainaemet_2009_meteorological,
+  title = {Meteorological Data of {{Spanish}} Weather Stations between Years 1980 and 2009.},
+  author = {{Meteorological State Agency of Spain (AEMET)}},
+  year = {2009},
+  url = {http://www.aemet.es/},
+  abstract = {The data were obtained from the FTP of AEMET in 2009.}
+}
+
 @article{pini++_2018_hotelling,
   title = {Hotelling's {{T2}} in Separable {{Hilbert}} Spaces},
   author = {Pini, Alessia and Stamm, Aymeric and Vantini, Simone},
@@ -383,6 +425,35 @@ @article{pini++_2018_hotelling
   keywords = {Functional data,High-dimensional data Hotelling’s,Hilbert space,Nonparametric inference,Permutation test}
 }
 
+@incollection{pintado+romo_2005_depthbased,
+  title = {Depth-Based Classification for Functional Data},
+  shorttitle = {Data {{Depth}}},
+  booktitle = {Data {{Depth}}: {{Robust Multivariate Analysis}}, {{Computational Geometry}} and {{Applications}}},
+  author = {Pintado, Sara and Romo, Juan},
+  year = {2005},
+  month = nov,
+  series = {{{DIMACS Series}} in {{Discrete Mathematics}} and {{Theoretical Computer Science}}},
+  volume = {72},
+  pages = {103--119},
+  publisher = {{American Mathematical Society}},
+  doi = {10.1090/dimacs/072/08},
+  isbn = {978-0-8218-3596-8}
+}
+
+@inproceedings{ramos-carreno++_2022_scikitfda,
+  title = {{{scikit-fda}}: {{Computational}} Tools for Machine Learning with Functional Data},
+  shorttitle = {Scikit-Fda},
+  booktitle = {2022 {{IEEE}} 34th {{International Conference}} on {{Tools}} with {{Artificial Intelligence}} ({{ICTAI}})},
+  author = {{Ramos-Carre{\~n}o}, Carlos and Torrecilla, Jos{\'e} Luis and Hong, Yujian and Su{\'a}rez, Alberto},
+  year = {2022},
+  month = oct,
+  pages = {213--218},
+  issn = {2375-0197},
+  doi = {10.1109/ICTAI56018.2022.00038},
+  abstract = {Machine learning from functional data poses particular challenges that require specific computational tools that take into account their structure. In this work, we present scikit-fda, a Python library for functional data analysis, visualization, preprocessing, and machine learning. The library is designed for smooth integration in the Python scientific ecosystem. In particular, it complements and can be used in combination with scikit-learn, the reference Python library for machine learning. The functionality of scikit-fda is illustrated in clustering, regression, and classification problems from different areas of application.},
+  keywords = {Data analysis,data visualization,Data visualization,Documentation,Ecosystems,Extrapolation,functional data analysis,Interpolation,machine learning,Machine learning,Python toolbox}
+}
+
 @inbook{ramsay+silverman_2005_functionala,
   title = {From Functional Data to Smooth Functions},
   booktitle = {Functional Data Analysis},
@@ -440,6 +511,24 @@ @inbook{ramsay+silverman_2005_registration
   keywords = {Multivariate analysis}
 }
 
+@incollection{romeo+marzoljaen_2014_analisis,
+  title = {{An\'alisis del viento y la niebla en el aeropuerto de Los Rodeos (Tenerife). Cambios y tendencias}},
+  booktitle = {{Cambio clim\'atico y cambio global.}},
+  author = {Romeo, V{\'i}ctor M. and Marzol Ja{\'e}n, M{\textordfeminine} Victoria},
+  editor = {Fern{\'a}ndez Montes, Sonia and S{\'a}nchez Rodrigo, Fernando},
+  year = {2014},
+  series = {{Publicaciones de la Asociaci\'on Espa\~nola de Climatolog\'ia. Serie A.}},
+  number = {9},
+  pages = {325--334},
+  publisher = {{Asociaci\'on Espa\~nola de Climatolog\'ia}},
+  url = {https://repositorio.aemet.es/handle/20.500.11765/8173},
+  urldate = {2022-07-28},
+  abstract = {[ES]La particular localizaci\'on del aeropuerto Tenerife Norte-Los Rodeos, en el nordeste de la isla  de Tenerife, es la causa de la modificaci\'on de la direcci\'on habitual de los vientos alisios del  NE a vientos del NO, acompa\~nados frecuentemente por nubosidad. Esto ocasiona que en un  n\'umero significativo de d\'ias al a\~no este aeropuerto est\'e cubierto por la niebla, lo que afecta de  una forma muy importante a su operatividad. El objetivo del trabajo es caracterizar su  r\'egimen de vientos y de la niebla durante los \'ultimos trece a\~nos (2000-2012) y comprobar si  se han producido cambios significativos en ambas variables clim\'aticas con respecto a los  periodos normales de 1961-1990 y 1971-2000. Los resultados indican que las dos direcciones  dominantes del viento, NO y SE, mantienen sus frecuencias no as\'i la niebla que ha aumentado  su presencia en los \'ultimos a\~nos.},
+  copyright = {Licencia CC: Reconocimiento CC BY},
+  isbn = {978-84-16027-69-9},
+  langid = {spanish}
+}
+
 @article{ruiz-meana++_2003_cariporide,
   title = {Cariporide Preserves Mitochondrial Proton Gradient and Delays {{ATP}} Depletion in Cardiomyocytes during Ischemic Conditions},
   author = {{Ruiz-Meana}, Marisol and {Garcia-Dorado}, David and Pina, Pilar and Inserte, Javier and Agull{\'o}, Luis and {Soler-Soler}, Jordi},