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<h1 class="title">Bilan semaine 28 2025 : 07 juillet - 11 juillet</h1>
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<div class="quarto-category">colBiSBM</div>
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<div class="quarto-category">inférence</div>
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<div class="quarto-title-meta-heading">Affiliation</div>
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<p class="author">Louis Lacoste <a href="mailto:louis.lacoste@agroparistech.fr" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> <a href="https://orcid.org/0009-0004-0178-9821" class="quarto-title-author-orcid"> <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAA2ZpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADw/eHBhY2tldCBiZWdpbj0i77u/IiBpZD0iVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkIj8+IDx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IkFkb2JlIFhNUCBDb3JlIDUuMC1jMDYwIDYxLjEzNDc3NywgMjAxMC8wMi8xMi0xNzozMjowMCAgICAgICAgIj4gPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4gPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIgeG1sbnM6eG1wTU09Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9tbS8iIHhtbG5zOnN0UmVmPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvc1R5cGUvUmVzb3VyY2VSZWYjIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iIHhtcE1NOk9yaWdpbmFsRG9jdW1lbnRJRD0ieG1wLmRpZDo1N0NEMjA4MDI1MjA2ODExOTk0QzkzNTEzRjZEQTg1NyIgeG1wTU06RG9jdW1lbnRJRD0ieG1wLmRpZDozM0NDOEJGNEZGNTcxMUUxODdBOEVCODg2RjdCQ0QwOSIgeG1wTU06SW5zdGFuY2VJRD0ieG1wLmlpZDozM0NDOEJGM0ZGNTcxMUUxODdBOEVCODg2RjdCQ0QwOSIgeG1wOkNyZWF0b3JUb29sPSJBZG9iZSBQaG90b3Nob3AgQ1M1IE1hY2ludG9zaCI+IDx4bXBNTTpEZXJpdmVkRnJvbSBzdFJlZjppbnN0YW5jZUlEPSJ4bXAuaWlkOkZDN0YxMTc0MDcyMDY4MTE5NUZFRDc5MUM2MUUwNEREIiBzdFJlZjpkb2N1bWVudElEPSJ4bXAuZGlkOjU3Q0QyMDgwMjUyMDY4MTE5OTRDOTM1MTNGNkRBODU3Ii8+IDwvcmRmOkRlc2NyaXB0aW9uPiA8L3JkZjpSREY+IDwveDp4bXBtZXRhPiA8P3hwYWNrZXQgZW5kPSJyIj8+84NovQAAAR1JREFUeNpiZEADy85ZJgCpeCB2QJM6AMQLo4yOL0AWZETSqACk1gOxAQN+cAGIA4EGPQBxmJA0nwdpjjQ8xqArmczw5tMHXAaALDgP1QMxAGqzAAPxQACqh4ER6uf5MBlkm0X4EGayMfMw/Pr7Bd2gRBZogMFBrv01hisv5jLsv9nLAPIOMnjy8RDDyYctyAbFM2EJbRQw+aAWw/LzVgx7b+cwCHKqMhjJFCBLOzAR6+lXX84xnHjYyqAo5IUizkRCwIENQQckGSDGY4TVgAPEaraQr2a4/24bSuoExcJCfAEJihXkWDj3ZAKy9EJGaEo8T0QSxkjSwORsCAuDQCD+QILmD1A9kECEZgxDaEZhICIzGcIyEyOl2RkgwAAhkmC+eAm0TAAAAABJRU5ErkJggg=="></a></p>
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</div>
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<div class="quarto-title-meta-contents">
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<p class="affiliation">
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MIA Paris-Saclay, INRAE, AgroParisTech, Université Paris-Saclay
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<div class="quarto-title-meta-heading">Date de publication</div>
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<p class="date">7 juillet 2025</p>
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<div class="quarto-title-meta-heading">Modifié</div>
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<div class="quarto-title-meta-contents">
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<p class="date-modified">11 juillet 2025</p>
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<h2 id="toc-title">Sur cette page</h2>
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<li><a href="#todo-list" id="toc-todo-list" class="nav-link active" data-scroll-target="#todo-list">TODO List</a>
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<li><a href="#inférence-et-microbes" id="toc-inférence-et-microbes" class="nav-link" data-scroll-target="#inférence-et-microbes">Inférence et microbes</a></li>
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<li><a href="#biblio-à-faire" id="toc-biblio-à-faire" class="nav-link" data-scroll-target="#biblio-à-faire">Biblio à faire</a></li>
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<li><a href="#lectures-en-cours" id="toc-lectures-en-cours" class="nav-link" data-scroll-target="#lectures-en-cours">Lectures en cours 📚</a>
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<li><a href="#inférence-de-graphes" id="toc-inférence-de-graphes" class="nav-link" data-scroll-target="#inférence-de-graphes">Inférence de graphes</a></li>
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<li><a href="#causalité-1" id="toc-causalité-1" class="nav-link" data-scroll-target="#causalité-1">Causalité</a></li>
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<li><a href="#congés-ps" id="toc-congés-ps" class="nav-link" data-scroll-target="#congés-ps">Congés P&S</a></li>
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<li><a href="#thèse" id="toc-thèse" class="nav-link" data-scroll-target="#thèse">Thèse</a></li>
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<li><a href="#interprétation-écologiques-résultats-de-baldock" id="toc-interprétation-écologiques-résultats-de-baldock" class="nav-link" data-scroll-target="#interprétation-écologiques-résultats-de-baldock">Interprétation écologiques résultats de Baldock</a></li>
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<li><a href="#inférence" id="toc-inférence" class="nav-link" data-scroll-target="#inférence">Inférence</a></li>
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<section id="todo-list" class="level2">
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<h2 class="anchored" data-anchor-id="todo-list">TODO List</h2>
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<ul>
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<li><p>Pour clustering de collections sur données <del>réelles</del> :<br>
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→ L’intuition de Pierre semble être confirmé, les dissimilarités semblent arrêter de varier sensiblement pour de grandes valeurs <span class="math inline">(Q_1,Q_2)</span>.</p>
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<ul>
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<li>❓Je n’arrive plus à reproduire le bug pour l’inférence…</li>
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<li>S’assurer que ça marche et relancer</li>
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</ul></li>
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<li><p>Creuser et explorer avec easy16s !</p></li>
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<li><p>⌛ <strong>Calcul du score F1</strong>Revérifier que j’entraîne correctement le VGAE car résultats de généralisation trop bons sur les autres réseaux Doré, ce qui est étonnant</p></li>
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<li><p>Ajouter au tableau comparatif sep BiSBM</p></li>
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<li><p>Regarder les codes Mangal database pour <span class="math inline">\delta</span></p></li>
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<li><p>✅ Formules ci-dessous. Voir <span class="math inline">\delta</span> mais additif</p></li>
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||
</ul>
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||
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||
<span class="math inline">\delta</span> additif Bernoulli
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</div>
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</div>
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<div class="callout-body-container callout-body">
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<p>En Bernoulli pas de forme analytique non plus : Pour <span class="math inline">\alpha_{qr}</span>: <span class="math display"> \sum_{m=1}^M \sum_{i=1}^{n_1^m} \sum_{j=1}^{n_2^m} \tau_{iq}^{1,m}\tau_{jr}^{2,m}(\frac{X_{ij}^m}{\alpha_{qr}} + \frac{(1-X_{ij}^m)}{\alpha_{qr} + \delta_m -1}) = 0</span> <span class="math display">\Leftrightarrow \sum_m \frac{e^m_{qr}}{\alpha_{qr}} + \frac{1}{\alpha_{qr}+\delta_m-1} (n^m_{qr}-e^m_{qr}) = 0</span></p>
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<p>Et pour <span class="math inline">\delta_m</span>: <span class="math display"> \sum_{i=1}^{n_1^m} \sum_{j=1}^{n_2^m} \sum_{q=1}^{Q_1} \sum_{r=1}^{Q_2} \tau_{iq}^{1,m}\tau_{jr}^{2,m}(\frac{X_{ij}^m}{\delta_{m}} + \frac{(1-X_{ij}^m)}{\alpha_{qr} + \delta_m -1}) = 0</span></p>
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</div>
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</div>
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<div class="callout-title-container flex-fill">
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<span class="math inline">\delta</span> additif Poisson
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</div>
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</div>
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<div class="callout-body-container callout-body">
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<p>Forme analytique mais risque de confusion ? <span class="math display">\widehat{\delta_m} = \frac{\sum_{q,r} e^m_{qr}}{\sum_{q,r} n^m_{qr}},~\widehat{\alpha_{qr}} = \frac{\sum_{m} e^m_{qr}}{\sum_{m} n^m_{qr}} </span></p>
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</div>
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</div>
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<ul>
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<li><p>Attente retour Pierre pour faire d’autres clustering</p></li>
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<li><p>✅ Implémenter décodeur Generalized Random Dot Product.</p></li>
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<li><p>✅ Réimplémentation propre et évolutive du DeepBVGAE (suivi des guidelines PyTorch Geometric)</p></li>
|
||
<li><p>Vérifier si il n’y a pas de data leakage (ie je prends aussi les données de val et de test pour prédire ?)</p></li>
|
||
<li><p>Dé-bugger les simulations :</p>
|
||
<ul>
|
||
<li>✅ Inférence : Relancer simus d’inférence avec n = 240 pour voir si la qualité augmenter (se rassurer). En fait on est déjà à 240, j’ai relancé avec M = 4 au lieu de M = 2. En attente résultats MIGALE -> BUG, dois creuser mais juste des problèmes techniques -> Visiblement il y a d’autres problèmes que juste le plan de parallélisation.</li>
|
||
</ul></li>
|
||
<li><p>⌛ <strong>Plutôt regarder pour introduire un modèle <span class="math inline">\delta</span>-colBiSBM</strong>.</p>
|
||
<ul>
|
||
<li>Ajouter le produit par <span class="math inline">\delta</span> là où nécessaire</li>
|
||
<li>Ajouter les modèles <span class="math inline">\delta</span>, <span class="math inline">\delta\pi, \dots</span> et les blocs conditionnels</li>
|
||
<li>Ajouter les tests unitaires adéquats et les vérifier</li>
|
||
</ul></li>
|
||
<li><p>🛑<strong>D’abord je lis la biblio dessus</strong> Regarder <em>Largest gap</em> sur réseaux Doré</p></li>
|
||
<li><p>Essayer <em>clustering</em> sur <code>supinfo</code></p></li>
|
||
<li><p>Homogénéiser notations dans les supplementaries</p></li>
|
||
</ul>
|
||
<div id="ref-kmeans-vae" class="quarto-float quarto-figure quarto-figure-center anchored">
|
||
<figure class="quarto-float quarto-float-ref figure">
|
||
<div aria-describedby="ref-kmeans-vae-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
|
||
<ul>
|
||
<li>Faire GNN-VAE Doré et sub-Doré avec kmeans et clustering sur l’espace latent J’ai commencé à regarder un peu</li>
|
||
</ul>
|
||
</div>
|
||
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-ref quarto-uncaptioned" id="ref-kmeans-vae-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
|
||
Reference 1
|
||
</figcaption>
|
||
</figure>
|
||
</div>
|
||
<section id="inférence-et-microbes" class="level3">
|
||
<h3 class="anchored" data-anchor-id="inférence-et-microbes">Inférence et microbes</h3>
|
||
<ul>
|
||
<li>Lancer <em>colBiSBM</em> sur <span class="math inline">OTU\times Sample</span> → problème du chargement en mémoire des données à voir</li>
|
||
<li>Lancer <em>colSBM</em> sur <span class="math inline">OTU\times OTU</span></li>
|
||
<li>TabNet pratiquer les <a href="https://github.com/cregouby/Tutoriel_torch">exercices</a></li>
|
||
<li>Regarder <strong>SPARTA</strong> Rennes</li>
|
||
<li>Lire Papiers compositional data (Aitchison et al. intro)</li>
|
||
<li>Lire article multi-niveaux Saint-Clair</li>
|
||
<li>Ecrire et étudier les modèles pour différents niveaux taxonomiques. <span class="math display">\begin{align*}
|
||
i \rightarrow &~N^1_i \subseteq N^2_i \subseteq N^3_i & \text{Taxonomie}\\
|
||
Z^0_i \overset{?}{=} & Z^1_i \overset{?}{=} Z^2_i \overset{?}{=} Z^3_i & \text{Groupes fonctionnels}
|
||
\end{align*}</span></li>
|
||
</ul>
|
||
<section id="causalité" class="level4">
|
||
<h4 class="anchored" data-anchor-id="causalité">Causalité</h4>
|
||
<p>Plus sur le temps long, à regarder</p>
|
||
<ul>
|
||
<li>GT causalité</li>
|
||
<li>Daria Bystrova lire présentation <span class="citation" data-cites="bystrovaCausalDiscovery">Bystrova (<a href="#ref-bystrovaCausalDiscovery" role="doc-biblioref">s. d.</a>)</span> (Meek rules, V-structure)</li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
</section>
|
||
<section id="biblio-à-faire" class="level2">
|
||
<h2 class="anchored" data-anchor-id="biblio-à-faire">Biblio à faire</h2>
|
||
<ul>
|
||
<li>Regarder Transport optimal graphes bipartite.</li>
|
||
<li>HDR VB, chapitre de modèle à blocs latents, bcp travaillé sur bipartite OT, comparaison clustering, adaption ARI, <em>Largest Gap</em></li>
|
||
</ul>
|
||
</section>
|
||
<section id="lectures-en-cours" class="level2">
|
||
<h2 class="anchored" data-anchor-id="lectures-en-cours">Lectures en cours 📚</h2>
|
||
<section id="ot" class="level3">
|
||
<h3 class="anchored" data-anchor-id="ot">OT</h3>
|
||
<ul>
|
||
<li>⌛ <span class="citation" data-cites="mazeletUnsupervisedLearningOptimal">Mazelet, Flamary, et Thirion (<a href="#ref-mazeletUnsupervisedLearningOptimal" role="doc-biblioref">s. d.</a>)</span> Intéressant pour le transport optimal entre graphes de tailles différentes | Regarder si regularization entropique ne marche pas bien pour le graphe.</li>
|
||
<li>⌛ <span class="citation" data-cites="nennaLecture2Entropic">Nenna (<a href="#ref-nennaLecture2Entropic" role="doc-biblioref">s. d.b</a>)</span> Pour comprendre le problème d’OT régularisé pour l’entropie.</li>
|
||
<li>⌛ <span class="citation" data-cites="nennaLecture1Monge">Nenna (<a href="#ref-nennaLecture1Monge" role="doc-biblioref">s. d.a</a>)</span></li>
|
||
</ul>
|
||
</section>
|
||
<section id="inférence-de-graphes" class="level3">
|
||
<h3 class="anchored" data-anchor-id="inférence-de-graphes">Inférence de graphes</h3>
|
||
<ul>
|
||
<li><p>⌛ <span class="citation" data-cites="aitchisonStatisticalAnalysisCompositional1982a">Aitchison (<a href="#ref-aitchisonStatisticalAnalysisCompositional1982a" role="doc-biblioref">1982</a>)</span>, en cours</p></li>
|
||
<li><p>❗📖 <span class="citation" data-cites="payneFiniteMixturesMultivariate2023">Payne et al. (<a href="#ref-payneFiniteMixturesMultivariate2023" role="doc-biblioref">2023</a>)</span> sur MixMPLN</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="causalité-1" class="level3">
|
||
<h3 class="anchored" data-anchor-id="causalité-1">Causalité</h3>
|
||
<ul>
|
||
<li>❗📖 <span class="citation" data-cites="bystrovaCausalDiscovery">Bystrova (<a href="#ref-bystrovaCausalDiscovery" role="doc-biblioref">s. d.</a>)</span></li>
|
||
</ul>
|
||
</section>
|
||
<section id="largest-gaps" class="level3">
|
||
<h3 class="anchored" data-anchor-id="largest-gaps">Largest Gaps</h3>
|
||
<ul>
|
||
<li>✅ <span class="citation" data-cites="braultGeneralisationLalgorithmeLargest">Brault, Channarond, et Robert (<a href="#ref-braultGeneralisationLalgorithmeLargest" role="doc-biblioref">s. d.</a>)</span> petit résumé de l’algo de <span class="citation" data-cites="braultFastConsistentAlgorithm2023">Brault et Channarond (<a href="#ref-braultFastConsistentAlgorithm2023" role="doc-biblioref">2023</a>)</span></li>
|
||
<li>❗📖 <span class="citation" data-cites="braultFastConsistentAlgorithm2023">Brault et Channarond (<a href="#ref-braultFastConsistentAlgorithm2023" role="doc-biblioref">2023</a>)</span></li>
|
||
<li>❗📖 <span class="citation" data-cites="channarondClassificationEstimationStochastic2012">Channarond, Daudin, et Robin (<a href="#ref-channarondClassificationEstimationStochastic2012" role="doc-biblioref">2012</a>)</span> le papier qui introduit le <em>Largest Gaps</em></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<section id="a-discuter" class="level2">
|
||
<h2 class="anchored" data-anchor-id="a-discuter">A discuter</h2>
|
||
<section id="congés-ps" class="level3">
|
||
<h3 class="anchored" data-anchor-id="congés-ps">Congés P&S</h3>
|
||
</section>
|
||
<section id="thèse" class="level3">
|
||
<h3 class="anchored" data-anchor-id="thèse">Thèse</h3>
|
||
<ul>
|
||
<li>Faire préz CSI</li>
|
||
<li>Faire rapport CSI</li>
|
||
</ul>
|
||
</section>
|
||
<section id="interprétation-écologiques-résultats-de-baldock" class="level3">
|
||
<h3 class="anchored" data-anchor-id="interprétation-écologiques-résultats-de-baldock">Interprétation écologiques résultats de Baldock</h3>
|
||
<ul>
|
||
<li>⌛ Point avec Elisa, <strong>oui on relance</strong></li>
|
||
</ul>
|
||
</section>
|
||
<section id="inférence" class="level3">
|
||
<h3 class="anchored" data-anchor-id="inférence">Inférence</h3>
|
||
<ul>
|
||
<li>pbs : variance, bcp de zero, covariables, offset et taxonomie (Reseaux arretes differents niveaux : Genre, OTU …)</li>
|
||
</ul>
|
||
<blockquote class="blockquote">
|
||
<p>Combine networks at different taxonomic levels</p>
|
||
</blockquote>
|
||
<ul>
|
||
<li>Inférence + GREMLINS</li>
|
||
</ul>
|
||
|
||
|
||
|
||
</section>
|
||
</section>
|
||
|
||
<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">Les références</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
|
||
<div id="ref-aitchisonStatisticalAnalysisCompositional1982a" class="csl-entry" role="listitem">
|
||
Aitchison, J. 1982. <span>« The <span>Statistical Analysis</span> of <span>Compositional Data</span> »</span>. <em>Journal of the Royal Statistical Society. Series B (Methodological)</em> 44 (2): 139‑77. <a href="https://www.jstor.org/stable/2345821">https://www.jstor.org/stable/2345821</a>.
|
||
</div>
|
||
<div id="ref-braultFastConsistentAlgorithm2023" class="csl-entry" role="listitem">
|
||
Brault, Vincent, et Antoine Channarond. 2023. <span>« Fast and <span>Consistent Algorithm</span> for the <span>Latent Block Model</span> »</span>. 9 mars 2023. <a href="https://doi.org/10.48550/arXiv.1610.09005">https://doi.org/10.48550/arXiv.1610.09005</a>.
|
||
</div>
|
||
<div id="ref-braultGeneralisationLalgorithmeLargest" class="csl-entry" role="listitem">
|
||
Brault, Vincent, Antoine Channarond, et Valérie Robert. s. d. <span>« Généralisation de l’algorithme Largest Gaps pour le modèle des blocs latents non-paramétrique »</span>.
|
||
</div>
|
||
<div id="ref-bystrovaCausalDiscovery" class="csl-entry" role="listitem">
|
||
Bystrova, Daria. s. d. <span>« Causal Discovery »</span>.
|
||
</div>
|
||
<div id="ref-channarondClassificationEstimationStochastic2012" class="csl-entry" role="listitem">
|
||
Channarond, Antoine, Jean-Jacques Daudin, et Stéphane Robin. 2012. <span>« Classification and Estimation in the <span>Stochastic Blockmodel</span> Based on the Empirical Degrees »</span>. <em>Electronic Journal of Statistics</em> 6 (janvier). <a href="https://doi.org/10.1214/12-ejs753">https://doi.org/10.1214/12-ejs753</a>.
|
||
</div>
|
||
<div id="ref-mazeletUnsupervisedLearningOptimal" class="csl-entry" role="listitem">
|
||
Mazelet, Sonia, Rémi Flamary, et Bertrand Thirion. s. d. <span>« Unsupervised <span>Learning</span> for <span>Optimal Transport</span> Plan Prediction Between Unbalanced Graphs »</span>.
|
||
</div>
|
||
<div id="ref-nennaLecture1Monge" class="csl-entry" role="listitem">
|
||
Nenna, Luca. s. d.a. <span>« Lecture 1 <span>Monge</span> and <span>Kantorovich</span> Problems: From Primal to Dual »</span>.
|
||
</div>
|
||
<div id="ref-nennaLecture2Entropic" class="csl-entry" role="listitem">
|
||
———. s. d.b. <span>« Lecture 2: <span>Entropic Optimal Transport</span> »</span>.
|
||
</div>
|
||
<div id="ref-payneFiniteMixturesMultivariate2023" class="csl-entry" role="listitem">
|
||
Payne, Andrea, Anjali Silva, Steven J. Rothstein, Paul D. McNicholas, et Sanjeena Subedi. 2023. <span>« Finite <span>Mixtures</span> of <span>Multivariate Poisson-Log Normal Factor Analyzers</span> for <span>Clustering Count Data</span> »</span>. 13 novembre 2023. <a href="https://doi.org/10.48550/arXiv.2311.07762">https://doi.org/10.48550/arXiv.2311.07762</a>.
|
||
</div>
|
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}
|
||
if (hash) {
|
||
const id = hash.replace(/^#\/?/, "");
|
||
const note = window.document.getElementById(id);
|
||
if (note !== null) {
|
||
try {
|
||
const html = processXRef(id, note.cloneNode(true));
|
||
instance.setContent(html);
|
||
} finally {
|
||
instance.enable();
|
||
instance.show();
|
||
}
|
||
} else {
|
||
// See if we can fetch this
|
||
fetch(url.split('#')[0])
|
||
.then(res => res.text())
|
||
.then(html => {
|
||
const parser = new DOMParser();
|
||
const htmlDoc = parser.parseFromString(html, "text/html");
|
||
const note = htmlDoc.getElementById(id);
|
||
if (note !== null) {
|
||
const html = processXRef(id, note);
|
||
instance.setContent(html);
|
||
}
|
||
}).finally(() => {
|
||
instance.enable();
|
||
instance.show();
|
||
});
|
||
}
|
||
} else {
|
||
// See if we can fetch a full url (with no hash to target)
|
||
// This is a special case and we should probably do some content thinning / targeting
|
||
fetch(url)
|
||
.then(res => res.text())
|
||
.then(html => {
|
||
const parser = new DOMParser();
|
||
const htmlDoc = parser.parseFromString(html, "text/html");
|
||
const note = htmlDoc.querySelector('main.content');
|
||
if (note !== null) {
|
||
// This should only happen for chapter cross references
|
||
// (since there is no id in the URL)
|
||
// remove the first header
|
||
if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
|
||
note.children[0].remove();
|
||
}
|
||
const html = processXRef(null, note);
|
||
instance.setContent(html);
|
||
}
|
||
}).finally(() => {
|
||
instance.enable();
|
||
instance.show();
|
||
});
|
||
}
|
||
}, function(instance) {
|
||
});
|
||
}
|
||
let selectedAnnoteEl;
|
||
const selectorForAnnotation = ( cell, annotation) => {
|
||
let cellAttr = 'data-code-cell="' + cell + '"';
|
||
let lineAttr = 'data-code-annotation="' + annotation + '"';
|
||
const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
|
||
return selector;
|
||
}
|
||
const selectCodeLines = (annoteEl) => {
|
||
const doc = window.document;
|
||
const targetCell = annoteEl.getAttribute("data-target-cell");
|
||
const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
|
||
const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
|
||
const lines = annoteSpan.getAttribute("data-code-lines").split(",");
|
||
const lineIds = lines.map((line) => {
|
||
return targetCell + "-" + line;
|
||
})
|
||
let top = null;
|
||
let height = null;
|
||
let parent = null;
|
||
if (lineIds.length > 0) {
|
||
//compute the position of the single el (top and bottom and make a div)
|
||
const el = window.document.getElementById(lineIds[0]);
|
||
top = el.offsetTop;
|
||
height = el.offsetHeight;
|
||
parent = el.parentElement.parentElement;
|
||
if (lineIds.length > 1) {
|
||
const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
|
||
const bottom = lastEl.offsetTop + lastEl.offsetHeight;
|
||
height = bottom - top;
|
||
}
|
||
if (top !== null && height !== null && parent !== null) {
|
||
// cook up a div (if necessary) and position it
|
||
let div = window.document.getElementById("code-annotation-line-highlight");
|
||
if (div === null) {
|
||
div = window.document.createElement("div");
|
||
div.setAttribute("id", "code-annotation-line-highlight");
|
||
div.style.position = 'absolute';
|
||
parent.appendChild(div);
|
||
}
|
||
div.style.top = top - 2 + "px";
|
||
div.style.height = height + 4 + "px";
|
||
div.style.left = 0;
|
||
let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
|
||
if (gutterDiv === null) {
|
||
gutterDiv = window.document.createElement("div");
|
||
gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
|
||
gutterDiv.style.position = 'absolute';
|
||
const codeCell = window.document.getElementById(targetCell);
|
||
const gutter = codeCell.querySelector('.code-annotation-gutter');
|
||
gutter.appendChild(gutterDiv);
|
||
}
|
||
gutterDiv.style.top = top - 2 + "px";
|
||
gutterDiv.style.height = height + 4 + "px";
|
||
}
|
||
selectedAnnoteEl = annoteEl;
|
||
}
|
||
};
|
||
const unselectCodeLines = () => {
|
||
const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
|
||
elementsIds.forEach((elId) => {
|
||
const div = window.document.getElementById(elId);
|
||
if (div) {
|
||
div.remove();
|
||
}
|
||
});
|
||
selectedAnnoteEl = undefined;
|
||
};
|
||
// Handle positioning of the toggle
|
||
window.addEventListener(
|
||
"resize",
|
||
throttle(() => {
|
||
elRect = undefined;
|
||
if (selectedAnnoteEl) {
|
||
selectCodeLines(selectedAnnoteEl);
|
||
}
|
||
}, 10)
|
||
);
|
||
function throttle(fn, ms) {
|
||
let throttle = false;
|
||
let timer;
|
||
return (...args) => {
|
||
if(!throttle) { // first call gets through
|
||
fn.apply(this, args);
|
||
throttle = true;
|
||
} else { // all the others get throttled
|
||
if(timer) clearTimeout(timer); // cancel #2
|
||
timer = setTimeout(() => {
|
||
fn.apply(this, args);
|
||
timer = throttle = false;
|
||
}, ms);
|
||
}
|
||
};
|
||
}
|
||
// Attach click handler to the DT
|
||
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
|
||
for (const annoteDlNode of annoteDls) {
|
||
annoteDlNode.addEventListener('click', (event) => {
|
||
const clickedEl = event.target;
|
||
if (clickedEl !== selectedAnnoteEl) {
|
||
unselectCodeLines();
|
||
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
|
||
if (activeEl) {
|
||
activeEl.classList.remove('code-annotation-active');
|
||
}
|
||
selectCodeLines(clickedEl);
|
||
clickedEl.classList.add('code-annotation-active');
|
||
} else {
|
||
// Unselect the line
|
||
unselectCodeLines();
|
||
clickedEl.classList.remove('code-annotation-active');
|
||
}
|
||
});
|
||
}
|
||
const findCites = (el) => {
|
||
const parentEl = el.parentElement;
|
||
if (parentEl) {
|
||
const cites = parentEl.dataset.cites;
|
||
if (cites) {
|
||
return {
|
||
el,
|
||
cites: cites.split(' ')
|
||
};
|
||
} else {
|
||
return findCites(el.parentElement)
|
||
}
|
||
} else {
|
||
return undefined;
|
||
}
|
||
};
|
||
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
|
||
for (var i=0; i<bibliorefs.length; i++) {
|
||
const ref = bibliorefs[i];
|
||
const citeInfo = findCites(ref);
|
||
if (citeInfo) {
|
||
tippyHover(citeInfo.el, function() {
|
||
var popup = window.document.createElement('div');
|
||
citeInfo.cites.forEach(function(cite) {
|
||
var citeDiv = window.document.createElement('div');
|
||
citeDiv.classList.add('hanging-indent');
|
||
citeDiv.classList.add('csl-entry');
|
||
var biblioDiv = window.document.getElementById('ref-' + cite);
|
||
if (biblioDiv) {
|
||
citeDiv.innerHTML = biblioDiv.innerHTML;
|
||
}
|
||
popup.appendChild(citeDiv);
|
||
});
|
||
return popup.innerHTML;
|
||
});
|
||
}
|
||
}
|
||
});
|
||
</script>
|
||
</div> <!-- /content -->
|
||
|
||
|
||
|
||
|
||
</body></html> |