updated gh-pages

docs/_sources/index.rst.txt

@@ -16,6 +16,7 @@ true outputs within a user-defined error :math:`\alpha`.
    getting_started
    regression
    classification
+   object_detection
    anomaly_detection
    api
    metrics

docs/_sources/object_detection.rst.txt

@@ -0,0 +1,12 @@
+.. _object_detection:
+
+🖼️ Object Detection
+====================
+
+
+Currently implemented conformal object detection methods are listed in this page.
+
+Each of these wrappers conformalize object localization models that are passed
+as argument in the object constructor.
+
+.. autoclass:: deel.puncc.object_detection.SplitBoxWise

docs/_sources/theory_overview.rst.txt

@@ -190,9 +190,9 @@ Ensemble Batch Prediction Intervals (EnbPI)
 *******************************************
 .. _theory enbpi:
 
-TBC
+Source: [Xu2021]_
 
-.. Source: [Xu2021]_
+TBC
 
 .. Introduced in [Xu2021]_, the EnbPI algorithms builds prediction intervals for time series data of the form :math:`Y_t = f(X_t) + \epsilon_t`, where :math:`\epsilon_t` are identically distributed.
 .. Unlike the proper conformal algorithms seen above, EnbPI requires some additional hypothesis to attain the coverage guarantee.
@@ -226,14 +226,15 @@ Adaptive Prediction Sets (APS)
 *******************************************
 .. _theory aps:
 
-.. Source: [Romano2020]_
+Source: [Romano2020]_
 
 TBC
 
 Regularized Adaptive Prediction Sets (RAPS)
 *******************************************
 .. _theory raps:
-.. Source: [Angelopoulos2021]_
+
+Source: [Angelopoulos2021]_
 
 TBC
 
@@ -242,15 +243,24 @@ Conformal Anomaly Detection
 
 TBC
 
+Conformal Object Detection
+--------------------------
+.. _theory splitboxwise:
+
+Source: [deGrancey2022]_
+
+TBC
+
 References
 ----------
 
-.. .. [Angelopoulos2021] Angelopoulos, A. N., Bates, S., Jordan, M., & Malik, J (2021). Uncertainty Sets for Image Classifiers using Conformal Prediction. In Proceedings of ICLR 2021. https://openreview.net/forum?id=eNdiU_DbM9
+.. [Angelopoulos2021] Angelopoulos, A. N., Bates, S., Jordan, M., & Malik, J (2021). Uncertainty Sets for Image Classifiers using Conformal Prediction. In Proceedings of ICLR 2021. https://openreview.net/forum?id=eNdiU_DbM9
 .. [Angelopoulos2022] Angelopoulos, A.N. and Bates, S., (2021). A gentle introduction to conformal prediction and distribution-free uncertainty quantification. arXiv preprint arXiv:2107.07511. https://arxiv.org/abs/2107.07511
 .. [Barber2021] Barber, R. F., Candes, E. J., Ramdas, A., & Tibshirani, R. J. (2021). Predictive inference with the jackknife+. Ann. Statist. 49 (1) 486 - 507, February 2021. https://arxiv.org/abs/1905.02928
 .. [Lei2018] Lei, J., G'Sell, M., Rinaldo, A., Tibshirani, R.J. and Wasserman, L., (2018). Distribution-free predictive inference for regression. Journal of the American Statistical Association, 113(523), pp.1094-1111. https://arxiv.org/abs/1604.04173
 .. [Papadopoulos2002] Papadopoulos, H., Proedrou, K., Vovk, V. and Gammerman, A., (2002). Inductive confidence machines for regression. In Proceedings of ECML 2002, Springer. https://link.springer.com/chapter/10.1007/3-540-36755-1_29
-.. [Papadopoulos2008] Papadopoulos, H., Gammerman, A. and Vovk, V., (2008), February. Normalized nonconformity measures for regression conformal prediction. In Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 2008) (pp. 64-69).
+.. [Papadopoulos2008] Papadopoulos, H., Gammerman, A. and Vovk, V., (2008). Normalized nonconformity measures for regression conformal prediction. In Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 2008) (pp. 64-69).
+.. [deGrancey2022] de Grancey, F., Adam, J.L., Alecu, L., Gerchinovitz, S., Mamalet, F. and Vigouroux, D., 2022, June. Object detection with probabilistic guarantees: A conformal prediction approach. In International Conference on Computer Safety, Reliability, and Security.
 .. [Romano2019] Romano, Y., Patterson, E. and Candes, E., (2019). Conformalized quantile regression. In Proceedings of NeurIPS, 32. https://arxiv.org/abs/1905.03222
-.. .. [Romano2020] Romano, Y., Sesia, M., & Candes, E. (2020). Classification with valid and adaptive coverage. In Proceedings of NeurIPS, 33. https://arxiv.org/abs/2006.02544
-.. .. [Xu2021] Xu, C. & Xie, Y.. (2021). Conformal prediction interval for dynamic time-series. Proceedings of ICML 2021. https://proceedings.mlr.press/v139/xu21h.html.
+.. [Romano2020] Romano, Y., Sesia, M., & Candes, E. (2020). Classification with valid and adaptive coverage. In Proceedings of NeurIPS, 33. https://arxiv.org/abs/2006.02544
+.. [Xu2021] Xu, C. & Xie, Y.. (2021). Conformal prediction interval for dynamic time-series. Proceedings of ICML 2021. https://proceedings.mlr.press/v139/xu21h.html.

docs/_static/basic.css

@@ -237,6 +237,10 @@ a.headerlink {
     visibility: hidden;
 }
 
+a:visited {
+    color: #551A8B;
+}
+
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
 h3:hover > a.headerlink,
@@ -670,6 +674,16 @@ dd {
     margin-left: 30px;
 }
 
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
 dl > dd:last-child,
 dl > dd:last-child > :last-child {
     margin-bottom: 0;
@@ -738,6 +752,14 @@ abbr, acronym {
     cursor: help;
 }
 
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
 /* -- code displays --------------------------------------------------------- */
 
 pre {

docs/_static/documentation_options.js

@@ -1,5 +1,4 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+const DOCUMENTATION_OPTIONS = {
     VERSION: '0.7.4',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,

docs/_static/pygments.css

@@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
 .highlight .gd { color: #A00000 } /* Generic.Deleted */
 .highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #E40000 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #008400 } /* Generic.Inserted */

docs/_static/searchtools.js

@@ -57,12 +57,12 @@ const _removeChildren = (element) => {
 const _escapeRegExp = (string) =>
   string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
 
-const _displayItem = (item, searchTerms) => {
+const _displayItem = (item, searchTerms, highlightTerms) => {
   const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
   const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
   const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
   const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
 
   const [docName, title, anchor, descr, score, _filename] = item;
 
@@ -75,20 +75,24 @@ const _displayItem = (item, searchTerms) => {
     if (dirname.match(/\/index\/$/))
       dirname = dirname.substring(0, dirname.length - 6);
     else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
+    requestUrl = contentRoot + dirname;
     linkUrl = requestUrl;
   } else {
     // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
+    requestUrl = contentRoot + docName + docFileSuffix;
     linkUrl = docName + docLinkSuffix;
   }
   let linkEl = listItem.appendChild(document.createElement("a"));
   linkEl.href = linkUrl + anchor;
   linkEl.dataset.score = score;
   linkEl.innerHTML = title;
-  if (descr)
+  if (descr) {
     listItem.appendChild(document.createElement("span")).innerHTML =
       " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
   else if (showSearchSummary)
     fetch(requestUrl)
       .then((responseData) => responseData.text())
@@ -97,6 +101,9 @@ const _displayItem = (item, searchTerms) => {
           listItem.appendChild(
             Search.makeSearchSummary(data, searchTerms)
           );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
       });
   Search.output.appendChild(listItem);
 };
@@ -115,14 +122,15 @@ const _finishSearch = (resultCount) => {
 const _displayNextItem = (
   results,
   resultCount,
-  searchTerms
+  searchTerms,
+  highlightTerms,
 ) => {
   // results left, load the summary and display it
   // this is intended to be dynamic (don't sub resultsCount)
   if (results.length) {
-    _displayItem(results.pop(), searchTerms);
+    _displayItem(results.pop(), searchTerms, highlightTerms);
     setTimeout(
-      () => _displayNextItem(results, resultCount, searchTerms),
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
       5
     );
   }
@@ -360,7 +368,7 @@ const Search = {
     // console.info("search results:", Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, searchTerms);
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
   },
 
   /**

docs/_static/sphinx_highlight.js

@@ -29,14 +29,19 @@ const _highlight = (node, addItems, text, className) => {
       }
 
       span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
       parent.insertBefore(
         span,
         parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
+          rest,
           node.nextSibling
         )
       );
       node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
 
       if (isInSVG) {
         const rect = document.createElementNS(
@@ -140,5 +145,10 @@ const SphinxHighlight = {
   },
 };
 
-_ready(SphinxHighlight.highlightSearchWords);
-_ready(SphinxHighlight.initEscapeListener);
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});