-
- Read the Docs
- v: ${config.versions.current.slug}
-
-
-
- ${renderLanguages(config)}
- ${renderVersions(config)}
- ${renderDownloads(config)}
-
- On Read the Docs
-
- Project Home
-
-
- Builds
-
-
- Downloads
-
-
-
- Search
-
-
-
-
-
-
- Hosted by Read the Docs
-
-
-
- `;
-
- // Inject the generated flyout into the body HTML element.
- document.body.insertAdjacentHTML("beforeend", flyout);
-
- // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
- document
- .querySelector("#flyout-search-form")
- .addEventListener("focusin", () => {
- const event = new CustomEvent("readthedocs-search-show");
- document.dispatchEvent(event);
- });
- })
-}
-
-if (themeLanguageSelector || themeVersionSelector) {
- function onSelectorSwitch(event) {
- const option = event.target.selectedIndex;
- const item = event.target.options[option];
- window.location.href = item.dataset.url;
- }
-
- document.addEventListener("readthedocs-addons-data-ready", function (event) {
- const config = event.detail.data();
-
- const versionSwitch = document.querySelector(
- "div.switch-menus > div.version-switch",
- );
- if (themeVersionSelector) {
- let versions = config.versions.active;
- if (config.versions.current.hidden || config.versions.current.type === "external") {
- versions.unshift(config.versions.current);
- }
- const versionSelect = `
-
- ${versions
- .map(
- (version) => `
-
- ${version.slug}
- `,
- )
- .join("\n")}
-
- `;
-
- versionSwitch.innerHTML = versionSelect;
- versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
- }
-
- const languageSwitch = document.querySelector(
- "div.switch-menus > div.language-switch",
- );
-
- if (themeLanguageSelector) {
- if (config.projects.translations.length) {
- // Add the current language to the options on the selector
- let languages = config.projects.translations.concat(
- config.projects.current,
- );
- languages = languages.sort((a, b) =>
- a.language.name.localeCompare(b.language.name),
- );
-
- const languageSelect = `
-
- ${languages
- .map(
- (language) => `
-
- ${language.language.name}
- `,
- )
- .join("\n")}
-
- `;
-
- languageSwitch.innerHTML = languageSelect;
- languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
- }
- else {
- languageSwitch.remove();
- }
- }
- });
-}
-
-document.addEventListener("readthedocs-addons-data-ready", function (event) {
- // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
- document
- .querySelector("[role='search'] input")
- .addEventListener("focusin", () => {
- const event = new CustomEvent("readthedocs-search-show");
- document.dispatchEvent(event);
- });
-});
\ No newline at end of file
diff --git a/docs/_static/language_data.js b/docs/_static/language_data.js
deleted file mode 100644
index c7fe6c6..0000000
--- a/docs/_static/language_data.js
+++ /dev/null
@@ -1,192 +0,0 @@
-/*
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- */
-
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
-
-
-/* Non-minified version is copied as a separate JS file, if available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
- var step2list = {
- ational: 'ate',
- tional: 'tion',
- enci: 'ence',
- anci: 'ance',
- izer: 'ize',
- bli: 'ble',
- alli: 'al',
- entli: 'ent',
- eli: 'e',
- ousli: 'ous',
- ization: 'ize',
- ation: 'ate',
- ator: 'ate',
- alism: 'al',
- iveness: 'ive',
- fulness: 'ful',
- ousness: 'ous',
- aliti: 'al',
- iviti: 'ive',
- biliti: 'ble',
- logi: 'log'
- };
-
- var step3list = {
- icate: 'ic',
- ative: '',
- alize: 'al',
- iciti: 'ic',
- ical: 'ic',
- ful: '',
- ness: ''
- };
-
- var c = "[^aeiou]"; // consonant
- var v = "[aeiouy]"; // vowel
- var C = c + "[^aeiouy]*"; // consonant sequence
- var V = v + "[aeiou]*"; // vowel sequence
-
- var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
- var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
- var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
- var s_v = "^(" + C + ")?" + v; // vowel in stem
-
- this.stemWord = function (w) {
- var stem;
- var suffix;
- var firstch;
- var origword = w;
-
- if (w.length < 3)
- return w;
-
- var re;
- var re2;
- var re3;
- var re4;
-
- firstch = w.substr(0,1);
- if (firstch == "y")
- w = firstch.toUpperCase() + w.substr(1);
-
- // Step 1a
- re = /^(.+?)(ss|i)es$/;
- re2 = /^(.+?)([^s])s$/;
-
- if (re.test(w))
- w = w.replace(re,"$1$2");
- else if (re2.test(w))
- w = w.replace(re2,"$1$2");
-
- // Step 1b
- re = /^(.+?)eed$/;
- re2 = /^(.+?)(ed|ing)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- re = new RegExp(mgr0);
- if (re.test(fp[1])) {
- re = /.$/;
- w = w.replace(re,"");
- }
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1];
- re2 = new RegExp(s_v);
- if (re2.test(stem)) {
- w = stem;
- re2 = /(at|bl|iz)$/;
- re3 = new RegExp("([^aeiouylsz])\\1$");
- re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re2.test(w))
- w = w + "e";
- else if (re3.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
- else if (re4.test(w))
- w = w + "e";
- }
- }
-
- // Step 1c
- re = /^(.+?)y$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(s_v);
- if (re.test(stem))
- w = stem + "i";
- }
-
- // Step 2
- re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step2list[suffix];
- }
-
- // Step 3
- re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step3list[suffix];
- }
-
- // Step 4
- re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
- re2 = /^(.+?)(s|t)(ion)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- if (re.test(stem))
- w = stem;
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1] + fp[2];
- re2 = new RegExp(mgr1);
- if (re2.test(stem))
- w = stem;
- }
-
- // Step 5
- re = /^(.+?)e$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- re2 = new RegExp(meq1);
- re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
- w = stem;
- }
- re = /ll$/;
- re2 = new RegExp(mgr1);
- if (re.test(w) && re2.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
-
- // and turn initial Y back to y
- if (firstch == "y")
- w = firstch.toLowerCase() + w.substr(1);
- return w;
- }
-}
-
diff --git a/docs/_static/minus.png b/docs/_static/minus.png
deleted file mode 100644
index d96755f..0000000
Binary files a/docs/_static/minus.png and /dev/null differ
diff --git a/docs/_static/outline_carte.jpg b/docs/_static/outline_carte.jpg
deleted file mode 100644
index e7e8f7a..0000000
Binary files a/docs/_static/outline_carte.jpg and /dev/null differ
diff --git a/docs/_static/performance.png b/docs/_static/performance.png
deleted file mode 100644
index 2188c3b..0000000
Binary files a/docs/_static/performance.png and /dev/null differ
diff --git a/docs/_static/plus.png b/docs/_static/plus.png
deleted file mode 100644
index 7107cec..0000000
Binary files a/docs/_static/plus.png and /dev/null differ
diff --git a/docs/_static/pygments.css b/docs/_static/pygments.css
deleted file mode 100644
index 84ab303..0000000
--- a/docs/_static/pygments.css
+++ /dev/null
@@ -1,75 +0,0 @@
-pre { line-height: 125%; }
-td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-.highlight .hll { background-color: #ffffcc }
-.highlight { background: #f8f8f8; }
-.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
-.highlight .k { color: #008000; font-weight: bold } /* Keyword */
-.highlight .o { color: #666666 } /* Operator */
-.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #9C6500 } /* Comment.Preproc */
-.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
-.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
-.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 */
-.highlight .go { color: #717171 } /* Generic.Output */
-.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
-.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
-.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
-.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
-.highlight .kp { color: #008000 } /* Keyword.Pseudo */
-.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
-.highlight .kt { color: #B00040 } /* Keyword.Type */
-.highlight .m { color: #666666 } /* Literal.Number */
-.highlight .s { color: #BA2121 } /* Literal.String */
-.highlight .na { color: #687822 } /* Name.Attribute */
-.highlight .nb { color: #008000 } /* Name.Builtin */
-.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
-.highlight .no { color: #880000 } /* Name.Constant */
-.highlight .nd { color: #AA22FF } /* Name.Decorator */
-.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
-.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
-.highlight .nf { color: #0000FF } /* Name.Function */
-.highlight .nl { color: #767600 } /* Name.Label */
-.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
-.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
-.highlight .nv { color: #19177C } /* Name.Variable */
-.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
-.highlight .mb { color: #666666 } /* Literal.Number.Bin */
-.highlight .mf { color: #666666 } /* Literal.Number.Float */
-.highlight .mh { color: #666666 } /* Literal.Number.Hex */
-.highlight .mi { color: #666666 } /* Literal.Number.Integer */
-.highlight .mo { color: #666666 } /* Literal.Number.Oct */
-.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
-.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
-.highlight .sc { color: #BA2121 } /* Literal.String.Char */
-.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
-.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
-.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
-.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
-.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
-.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
-.highlight .sx { color: #008000 } /* Literal.String.Other */
-.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
-.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
-.highlight .ss { color: #19177C } /* Literal.String.Symbol */
-.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #0000FF } /* Name.Function.Magic */
-.highlight .vc { color: #19177C } /* Name.Variable.Class */
-.highlight .vg { color: #19177C } /* Name.Variable.Global */
-.highlight .vi { color: #19177C } /* Name.Variable.Instance */
-.highlight .vm { color: #19177C } /* Name.Variable.Magic */
-.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js
deleted file mode 100644
index 2c774d1..0000000
--- a/docs/_static/searchtools.js
+++ /dev/null
@@ -1,632 +0,0 @@
-/*
- * Sphinx JavaScript utilities for the full-text search.
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
- var Scorer = {
- // Implement the following function to further tweak the score for each result
- // The function takes a result array [docname, title, anchor, descr, score, filename]
- // and returns the new score.
- /*
- score: result => {
- const [docname, title, anchor, descr, score, filename, kind] = result
- return score
- },
- */
-
- // query matches the full name of an object
- objNameMatch: 11,
- // or matches in the last dotted part of the object name
- objPartialMatch: 6,
- // Additive scores depending on the priority of the object
- objPrio: {
- 0: 15, // used to be importantResults
- 1: 5, // used to be objectResults
- 2: -5, // used to be unimportantResults
- },
- // Used when the priority is not in the mapping.
- objPrioDefault: 0,
-
- // query found in title
- title: 15,
- partialTitle: 7,
- // query found in terms
- term: 5,
- partialTerm: 2,
- };
-}
-
-// Global search result kind enum, used by themes to style search results.
-class SearchResultKind {
- static get index() { return "index"; }
- static get object() { return "object"; }
- static get text() { return "text"; }
- static get title() { return "title"; }
-}
-
-const _removeChildren = (element) => {
- while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
- string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, searchTerms, highlightTerms) => {
- const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
- const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
- const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
- const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
- const contentRoot = document.documentElement.dataset.content_root;
-
- const [docName, title, anchor, descr, score, _filename, kind] = item;
-
- let listItem = document.createElement("li");
- // Add a class representing the item's type:
- // can be used by a theme's CSS selector for styling
- // See SearchResultKind for the class names.
- listItem.classList.add(`kind-${kind}`);
- let requestUrl;
- let linkUrl;
- if (docBuilder === "dirhtml") {
- // dirhtml builder
- let dirname = docName + "/";
- if (dirname.match(/\/index\/$/))
- dirname = dirname.substring(0, dirname.length - 6);
- else if (dirname === "index/") dirname = "";
- requestUrl = contentRoot + dirname;
- linkUrl = requestUrl;
- } else {
- // normal html builders
- 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) {
- 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())
- .then((data) => {
- if (data)
- listItem.appendChild(
- Search.makeSearchSummary(data, searchTerms, anchor)
- );
- // 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);
-};
-const _finishSearch = (resultCount) => {
- Search.stopPulse();
- Search.title.innerText = _("Search Results");
- if (!resultCount)
- Search.status.innerText = Documentation.gettext(
- "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
- );
- else
- Search.status.innerText = Documentation.ngettext(
- "Search finished, found one page matching the search query.",
- "Search finished, found ${resultCount} pages matching the search query.",
- resultCount,
- ).replace('${resultCount}', resultCount);
-};
-const _displayNextItem = (
- results,
- resultCount,
- 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, highlightTerms);
- setTimeout(
- () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
- 5
- );
- }
- // search finished, update title and status message
- else _finishSearch(resultCount);
-};
-// Helper function used by query() to order search results.
-// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
-// Order the results by score (in opposite order of appearance, since the
-// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
-const _orderResultsByScoreThenName = (a, b) => {
- const leftScore = a[4];
- const rightScore = b[4];
- if (leftScore === rightScore) {
- // same score: sort alphabetically
- const leftTitle = a[1].toLowerCase();
- const rightTitle = b[1].toLowerCase();
- if (leftTitle === rightTitle) return 0;
- return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
- }
- return leftScore > rightScore ? 1 : -1;
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
- var splitQuery = (query) => query
- .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
- .filter(term => term) // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
- _index: null,
- _queued_query: null,
- _pulse_status: -1,
-
- htmlToText: (htmlString, anchor) => {
- const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
- for (const removalQuery of [".headerlink", "script", "style"]) {
- htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
- }
- if (anchor) {
- const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
- if (anchorContent) return anchorContent.textContent;
-
- console.warn(
- `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
- );
- }
-
- // if anchor not specified or not found, fall back to main content
- const docContent = htmlElement.querySelector('[role="main"]');
- if (docContent) return docContent.textContent;
-
- console.warn(
- "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
- );
- return "";
- },
-
- init: () => {
- const query = new URLSearchParams(window.location.search).get("q");
- document
- .querySelectorAll('input[name="q"]')
- .forEach((el) => (el.value = query));
- if (query) Search.performSearch(query);
- },
-
- loadIndex: (url) =>
- (document.body.appendChild(document.createElement("script")).src = url),
-
- setIndex: (index) => {
- Search._index = index;
- if (Search._queued_query !== null) {
- const query = Search._queued_query;
- Search._queued_query = null;
- Search.query(query);
- }
- },
-
- hasIndex: () => Search._index !== null,
-
- deferQuery: (query) => (Search._queued_query = query),
-
- stopPulse: () => (Search._pulse_status = -1),
-
- startPulse: () => {
- if (Search._pulse_status >= 0) return;
-
- const pulse = () => {
- Search._pulse_status = (Search._pulse_status + 1) % 4;
- Search.dots.innerText = ".".repeat(Search._pulse_status);
- if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
- };
- pulse();
- },
-
- /**
- * perform a search for something (or wait until index is loaded)
- */
- performSearch: (query) => {
- // create the required interface elements
- const searchText = document.createElement("h2");
- searchText.textContent = _("Searching");
- const searchSummary = document.createElement("p");
- searchSummary.classList.add("search-summary");
- searchSummary.innerText = "";
- const searchList = document.createElement("ul");
- searchList.setAttribute("role", "list");
- searchList.classList.add("search");
-
- const out = document.getElementById("search-results");
- Search.title = out.appendChild(searchText);
- Search.dots = Search.title.appendChild(document.createElement("span"));
- Search.status = out.appendChild(searchSummary);
- Search.output = out.appendChild(searchList);
-
- const searchProgress = document.getElementById("search-progress");
- // Some themes don't use the search progress node
- if (searchProgress) {
- searchProgress.innerText = _("Preparing search...");
- }
- Search.startPulse();
-
- // index already loaded, the browser was quick!
- if (Search.hasIndex()) Search.query(query);
- else Search.deferQuery(query);
- },
-
- _parseQuery: (query) => {
- // stem the search terms and add them to the correct list
- const stemmer = new Stemmer();
- const searchTerms = new Set();
- const excludedTerms = new Set();
- const highlightTerms = new Set();
- const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
- splitQuery(query.trim()).forEach((queryTerm) => {
- const queryTermLower = queryTerm.toLowerCase();
-
- // maybe skip this "word"
- // stopwords array is from language_data.js
- if (
- stopwords.indexOf(queryTermLower) !== -1 ||
- queryTerm.match(/^\d+$/)
- )
- return;
-
- // stem the word
- let word = stemmer.stemWord(queryTermLower);
- // select the correct list
- if (word[0] === "-") excludedTerms.add(word.substr(1));
- else {
- searchTerms.add(word);
- highlightTerms.add(queryTermLower);
- }
- });
-
- if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
- localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
- }
-
- // console.debug("SEARCH: searching for:");
- // console.info("required: ", [...searchTerms]);
- // console.info("excluded: ", [...excludedTerms]);
-
- return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
- },
-
- /**
- * execute search (requires search index to be loaded)
- */
- _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
- const allTitles = Search._index.alltitles;
- const indexEntries = Search._index.indexentries;
-
- // Collect multiple result groups to be sorted separately and then ordered.
- // Each is an array of [docname, title, anchor, descr, score, filename, kind].
- const normalResults = [];
- const nonMainIndexResults = [];
-
- _removeChildren(document.getElementById("search-progress"));
-
- const queryLower = query.toLowerCase().trim();
- for (const [title, foundTitles] of Object.entries(allTitles)) {
- if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
- for (const [file, id] of foundTitles) {
- const score = Math.round(Scorer.title * queryLower.length / title.length);
- const boost = titles[file] === title ? 1 : 0; // add a boost for document titles
- normalResults.push([
- docNames[file],
- titles[file] !== title ? `${titles[file]} > ${title}` : title,
- id !== null ? "#" + id : "",
- null,
- score + boost,
- filenames[file],
- SearchResultKind.title,
- ]);
- }
- }
- }
-
- // search for explicit entries in index directives
- for (const [entry, foundEntries] of Object.entries(indexEntries)) {
- if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
- for (const [file, id, isMain] of foundEntries) {
- const score = Math.round(100 * queryLower.length / entry.length);
- const result = [
- docNames[file],
- titles[file],
- id ? "#" + id : "",
- null,
- score,
- filenames[file],
- SearchResultKind.index,
- ];
- if (isMain) {
- normalResults.push(result);
- } else {
- nonMainIndexResults.push(result);
- }
- }
- }
- }
-
- // lookup as object
- objectTerms.forEach((term) =>
- normalResults.push(...Search.performObjectSearch(term, objectTerms))
- );
-
- // lookup as search terms in fulltext
- normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
-
- // let the scorer override scores with a custom scoring function
- if (Scorer.score) {
- normalResults.forEach((item) => (item[4] = Scorer.score(item)));
- nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
- }
-
- // Sort each group of results by score and then alphabetically by name.
- normalResults.sort(_orderResultsByScoreThenName);
- nonMainIndexResults.sort(_orderResultsByScoreThenName);
-
- // Combine the result groups in (reverse) order.
- // Non-main index entries are typically arbitrary cross-references,
- // so display them after other results.
- let results = [...nonMainIndexResults, ...normalResults];
-
- // remove duplicate search results
- // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
- let seen = new Set();
- results = results.reverse().reduce((acc, result) => {
- let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
- if (!seen.has(resultStr)) {
- acc.push(result);
- seen.add(resultStr);
- }
- return acc;
- }, []);
-
- return results.reverse();
- },
-
- query: (query) => {
- const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
- const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
-
- // for debugging
- //Search.lastresults = results.slice(); // a copy
- // console.info("search results:", Search.lastresults);
-
- // print the results
- _displayNextItem(results, results.length, searchTerms, highlightTerms);
- },
-
- /**
- * search for object names
- */
- performObjectSearch: (object, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const objects = Search._index.objects;
- const objNames = Search._index.objnames;
- const titles = Search._index.titles;
-
- const results = [];
-
- const objectSearchCallback = (prefix, match) => {
- const name = match[4]
- const fullname = (prefix ? prefix + "." : "") + name;
- const fullnameLower = fullname.toLowerCase();
- if (fullnameLower.indexOf(object) < 0) return;
-
- let score = 0;
- const parts = fullnameLower.split(".");
-
- // check for different match types: exact matches of full name or
- // "last name" (i.e. last dotted part)
- if (fullnameLower === object || parts.slice(-1)[0] === object)
- score += Scorer.objNameMatch;
- else if (parts.slice(-1)[0].indexOf(object) > -1)
- score += Scorer.objPartialMatch; // matches in last name
-
- const objName = objNames[match[1]][2];
- const title = titles[match[0]];
-
- // If more than one term searched for, we require other words to be
- // found in the name/title/description
- const otherTerms = new Set(objectTerms);
- otherTerms.delete(object);
- if (otherTerms.size > 0) {
- const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
- if (
- [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
- )
- return;
- }
-
- let anchor = match[3];
- if (anchor === "") anchor = fullname;
- else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
- const descr = objName + _(", in ") + title;
-
- // add custom score for some objects according to scorer
- if (Scorer.objPrio.hasOwnProperty(match[2]))
- score += Scorer.objPrio[match[2]];
- else score += Scorer.objPrioDefault;
-
- results.push([
- docNames[match[0]],
- fullname,
- "#" + anchor,
- descr,
- score,
- filenames[match[0]],
- SearchResultKind.object,
- ]);
- };
- Object.keys(objects).forEach((prefix) =>
- objects[prefix].forEach((array) =>
- objectSearchCallback(prefix, array)
- )
- );
- return results;
- },
-
- /**
- * search for full-text terms in the index
- */
- performTermsSearch: (searchTerms, excludedTerms) => {
- // prepare search
- const terms = Search._index.terms;
- const titleTerms = Search._index.titleterms;
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
-
- const scoreMap = new Map();
- const fileMap = new Map();
-
- // perform the search on the required terms
- searchTerms.forEach((word) => {
- const files = [];
- const arr = [
- { files: terms[word], score: Scorer.term },
- { files: titleTerms[word], score: Scorer.title },
- ];
- // add support for partial matches
- if (word.length > 2) {
- const escapedWord = _escapeRegExp(word);
- if (!terms.hasOwnProperty(word)) {
- Object.keys(terms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: terms[term], score: Scorer.partialTerm });
- });
- }
- if (!titleTerms.hasOwnProperty(word)) {
- Object.keys(titleTerms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
- });
- }
- }
-
- // no match but word was a required one
- if (arr.every((record) => record.files === undefined)) return;
-
- // found search word in contents
- arr.forEach((record) => {
- if (record.files === undefined) return;
-
- let recordFiles = record.files;
- if (recordFiles.length === undefined) recordFiles = [recordFiles];
- files.push(...recordFiles);
-
- // set score for the word in each file
- recordFiles.forEach((file) => {
- if (!scoreMap.has(file)) scoreMap.set(file, {});
- scoreMap.get(file)[word] = record.score;
- });
- });
-
- // create the mapping
- files.forEach((file) => {
- if (!fileMap.has(file)) fileMap.set(file, [word]);
- else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
- });
- });
-
- // now check if the files don't contain excluded terms
- const results = [];
- for (const [file, wordList] of fileMap) {
- // check if all requirements are matched
-
- // as search terms with length < 3 are discarded
- const filteredTermCount = [...searchTerms].filter(
- (term) => term.length > 2
- ).length;
- if (
- wordList.length !== searchTerms.size &&
- wordList.length !== filteredTermCount
- )
- continue;
-
- // ensure that none of the excluded terms is in the search result
- if (
- [...excludedTerms].some(
- (term) =>
- terms[term] === file ||
- titleTerms[term] === file ||
- (terms[term] || []).includes(file) ||
- (titleTerms[term] || []).includes(file)
- )
- )
- break;
-
- // select one (max) score for the file.
- const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
- // add result to the result list
- results.push([
- docNames[file],
- titles[file],
- "",
- null,
- score,
- filenames[file],
- SearchResultKind.text,
- ]);
- }
- return results;
- },
-
- /**
- * helper function to return a node containing the
- * search summary for a given text. keywords is a list
- * of stemmed words.
- */
- makeSearchSummary: (htmlText, keywords, anchor) => {
- const text = Search.htmlToText(htmlText, anchor);
- if (text === "") return null;
-
- const textLower = text.toLowerCase();
- const actualStartPosition = [...keywords]
- .map((k) => textLower.indexOf(k.toLowerCase()))
- .filter((i) => i > -1)
- .slice(-1)[0];
- const startWithContext = Math.max(actualStartPosition - 120, 0);
-
- const top = startWithContext === 0 ? "" : "...";
- const tail = startWithContext + 240 < text.length ? "..." : "";
-
- let summary = document.createElement("p");
- summary.classList.add("context");
- summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
-
- return summary;
- },
-};
-
-_ready(Search.init);
diff --git a/docs/_static/sphinx_highlight.js b/docs/_static/sphinx_highlight.js
deleted file mode 100644
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--- a/docs/_static/sphinx_highlight.js
+++ /dev/null
@@ -1,154 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
-
-const SPHINX_HIGHLIGHT_ENABLED = true
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
- if (node.nodeType === Node.TEXT_NODE) {
- const val = node.nodeValue;
- const parent = node.parentNode;
- const pos = val.toLowerCase().indexOf(text);
- if (
- pos >= 0 &&
- !parent.classList.contains(className) &&
- !parent.classList.contains("nohighlight")
- ) {
- let span;
-
- const closestNode = parent.closest("body, svg, foreignObject");
- const isInSVG = closestNode && closestNode.matches("svg");
- if (isInSVG) {
- span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
- } else {
- span = document.createElement("span");
- span.classList.add(className);
- }
-
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
- const rest = document.createTextNode(val.substr(pos + text.length));
- parent.insertBefore(
- span,
- parent.insertBefore(
- 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(
- "http://www.w3.org/2000/svg",
- "rect"
- );
- const bbox = parent.getBBox();
- rect.x.baseVal.value = bbox.x;
- rect.y.baseVal.value = bbox.y;
- rect.width.baseVal.value = bbox.width;
- rect.height.baseVal.value = bbox.height;
- rect.setAttribute("class", className);
- addItems.push({ parent: parent, target: rect });
- }
- }
- } else if (node.matches && !node.matches("button, select, textarea")) {
- node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
- }
-};
-const _highlightText = (thisNode, text, className) => {
- let addItems = [];
- _highlight(thisNode, addItems, text, className);
- addItems.forEach((obj) =>
- obj.parent.insertAdjacentElement("beforebegin", obj.target)
- );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
-
- /**
- * highlight the search words provided in localstorage in the text
- */
- highlightSearchWords: () => {
- if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
-
- // get and clear terms from localstorage
- const url = new URL(window.location);
- const highlight =
- localStorage.getItem("sphinx_highlight_terms")
- || url.searchParams.get("highlight")
- || "";
- localStorage.removeItem("sphinx_highlight_terms")
- url.searchParams.delete("highlight");
- window.history.replaceState({}, "", url);
-
- // get individual terms from highlight string
- const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
- if (terms.length === 0) return; // nothing to do
-
- // There should never be more than one element matching "div.body"
- const divBody = document.querySelectorAll("div.body");
- const body = divBody.length ? divBody[0] : document.querySelector("body");
- window.setTimeout(() => {
- terms.forEach((term) => _highlightText(body, term, "highlighted"));
- }, 10);
-
- const searchBox = document.getElementById("searchbox");
- if (searchBox === null) return;
- searchBox.appendChild(
- document
- .createRange()
- .createContextualFragment(
- '
' +
- '' +
- _("Hide Search Matches") +
- "
"
- )
- );
- },
-
- /**
- * helper function to hide the search marks again
- */
- hideSearchWords: () => {
- document
- .querySelectorAll("#searchbox .highlight-link")
- .forEach((el) => el.remove());
- document
- .querySelectorAll("span.highlighted")
- .forEach((el) => el.classList.remove("highlighted"));
- localStorage.removeItem("sphinx_highlight_terms")
- },
-
- initEscapeListener: () => {
- // only install a listener if it is really needed
- if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
-
- document.addEventListener("keydown", (event) => {
- // bail for input elements
- if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
- // bail with special keys
- if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
- if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
- SphinxHighlight.hideSearchWords();
- event.preventDefault();
- }
- });
- },
-};
-
-_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();
-});
diff --git a/docs/_static/t2g.png b/docs/_static/t2g.png
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diff --git a/docs/carte_ai.configs.html b/docs/carte_ai.configs.html
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carte_ai.configs package — CARTE-AI 1.0.0 documentation
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-carte_ai.configs package
-
-
-carte_ai.configs.carte_configs module
-Specific configurations for the CARTE paper.
-
-
-carte_ai.configs.directory module
-
-
-carte_ai.configs.model_parameters module
-Parameter distributions for hyperparameter optimization
-
-
-class carte_ai.configs.model_parameters. loguniform_int ( a b )
-Bases: object
-Integer valued version of the log-uniform distribution
-
-
-rvs ( * args ** kwargs )
-Random variable sample
-
-
-
-
-
-
-class carte_ai.configs.model_parameters. norm_int ( a b )
-Bases: object
-Integer valued version of the normal distribution
-
-
-rvs ( * args ** kwargs )
-Random variable sample
-
-
-
-
-
-
-carte_ai.configs.visuailization module
-Visualization configurations
-
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diff --git a/docs/carte_ai.data.html b/docs/carte_ai.data.html
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carte_ai.data package — CARTE-AI Documentation
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- CARTE-AI
-
-
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-
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-
- carte_ai.data package
-
- carte_ai.data.load_data module
-
-
-
-
-
- carte_ai.data.load_data. spotify ()
-
-
-
- Load and explore the Spotify dataset, which contains detailed information about over 600,000 Spotify tracks, including audio features, popularity metrics, and genres.
- This dataset can be used for:
-
- Building a recommendation system based on user input or preferences.
- Classification tasks using audio features and genres.
- Any other applications involving music analysis and prediction.
-
-
- Variables:
-
- track_id : Unique identifier for the track.artists : Names of the artists who performed the track (separated by ";").album_name : Name of the album.track_name : Name of the track.popularity : Popularity score (0–100).duration_ms : Length of the track in milliseconds.explicit : Whether the track contains explicit lyrics (true/false).danceability : Danceability score (0.0–1.0).energy : Energy score (0.0–1.0).key : Musical key of the track.loudness : Loudness in decibels (dB).mode : Modality of the track (major=1, minor=0).speechiness : Presence of spoken words (0.0–1.0).acousticness : Confidence measure for acoustic content (0.0–1.0).instrumentalness : Likelihood of being instrumental (0.0–1.0).liveness : Presence of audience (0.0–1.0).valence : Musical positiveness (0.0–1.0).tempo : Tempo in beats per minute (BPM).time_signature : Time signature (3–7).track_genre : Genre of the track.
-
- Example Usage:
-
-
-
- Copy
-
-
-
- from carte_ai.data.load_data import *
- num_train = 128 # Example: set the number of training groups/entities
- random_state = 1 # Set a random seed for reproducibility
- X_train , X_test , y_train , y_test = spotify (num_train, random_state)
- # Print dataset shapes
- print ( "Spotify dataset:" , X_train.shape, X_test.shape)
-
-
-
-
-
-
-
-
-
-
- carte_ai.data.load_data. wina_pl ()
-
-
-
- Load and explore the Wina_PL dataset, which contains detailed information about wine prices and attributes in the Polish market.
- This dataset is ideal for analysis and machine learning tasks related to wine classification, pricing, and preferences.
-
- Variables:
-
- name : Name of the wine.country : Country of origin.region : Region where the wine is produced.appellation : Controlled origin label.vineyard : Vineyard producing the wine.vintage : Year of production.volume : Bottle volume in milliliters.ABV : Alcohol by volume (percentage).serving_temperature : Recommended serving temperature.wine_type : Type of wine (e.g., red, white).taste : Wine taste profile (e.g., dry, sweet).style : Style of the wine (e.g., full-bodied).vegan : Whether the wine is vegan-friendly.natural : Indicates if the wine is natural.grapes : Main grape varieties used.
-
- Example Usage:
-
-
-
- Copy
-
-
-
- from carte_ai.data.load_data import *
- num_train = 128 # Example: set the number of training groups/entities
- random_state = 1 # Set a random seed for reproducibility
- X_train , X_test , y_train , y_test = wina_pl (num_train, random_state)
- # Print dataset shapes
- print ( "Wina Poland dataset:" , X_train.shape, X_test.shape)
-
-
-
-
-
-
-
-
- For more details, visit the Kaggle dataset page .
-
-
-
-
-
-
-
-
- carte_ai.data.load_data. wine_vivino_price ()
-
-
-
- Load and explore the Wine Vivino Price dataset, which contains detailed information about wine ratings, prices, and attributes from the Vivino platform.
- This dataset is ideal for analysis and machine learning tasks related to wine recommendation, pricing, and consumer preferences.
-
- Variables:
-
- Winery : Name of the winery.Year : Vintage year of the wine.Wine ID : Unique identifier for the wine.Wine : Name of the wine.Rating : Average rating of the wine.num_review : Number of reviews for the wine.price : Price of the wine.Country : Country where the wine is produced.Region : Region of the winery.
-
- Example Usage:
-
-
-
- Copy
-
-
-
- from carte_ai.data.load_data import *
- num_train = 128 # Example: set the number of training groups/entities
- random_state = 1 # Set a random seed for reproducibility
- X_train , X_test , y_train , y_test = wine_vivino_price (num_train, random_state)
- # Print dataset shapes
- print ( "Wine Vivino Price dataset:" , X_train.shape, X_test.shape)
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-carte_ai.scripts.compile_results_singletable module
-Script for compling results
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-carte_ai.scripts.download_data module
-Script for downloading required data.
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-carte_ai.scripts.download_data. main ( option = 'carte' include_raw = False include_ken = False )
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-carte_ai.scripts.evaluate_singletable module
-Script for evaluating a model of choice for single tables.
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-carte_ai.scripts.evaluate_singletable. main ( data_name num_train method random_state bagging device )
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-carte_ai.scripts.evaluate_singletable. run_model ( data_name num_train method random_state bagging device )
-Run model for specific experiment setting.
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-carte_ai.scripts.preprocess_lm module
-Python script for preparing datasets for evaluation
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-carte_ai.scripts.preprocess_lm. data_preprocess ( data_name : str device : str = 'cuda:0' )
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-carte_ai.scripts.preprocess_lm. main ( datalist device : str = 'cuda:0' )
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-Script for preprocessing raw data.
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-carte_ai.scripts.preprocess_raw. main ( data_name_list )
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-carte_ai.scripts.preprocess_raw. preprocess_data ( data_name )
-Preprocess the raw data with the given name of the dataset.
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-carte_ai.src package
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-carte_ai.src.carte_table_to_graph module
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-class carte_ai.src.carte_table_to_graph. Table2GraphTransformer ( * include_edge_attr : bool = True lm_model : str = 'fasttext' n_components : int = 300 n_jobs : int = 1 fasttext_model_path : str | None = None )
-Bases: TransformerMixin BaseEstimator
-Transformer from tables to a list of graphs.
-
-Parameters
-
-include_edge_attrbool, optional Whether to include edge attributes, by default True.
-lm_modelstr, optional Language model to use, by default “fasttext”.
-n_componentsint, optional Number of components for the encoder, by default 300.
-n_jobsint, optional Number of jobs for parallel processing, by default 1.
-fasttext_model_pathstr, optional Path to the FastText model file, required if lm_model is ‘fasttext’.
-
-
-
-fit ( X y = None )
-Fit function used for the Table2GraphTransformer.
-
-Parameters
-
-Xpandas.DataFrame Input data to fit.
-yarray-like, optional Target values, by default None.
-
-
-
-Returns
-
-selfTable2GraphTransformer Fitted transformer.
- Example Usage:
-
-
-
- Copy
-
-
-
- import fasttext
- from huggingface_hub import hf_hub_download
- # Download the FastText model from HuggingFace Hub
- model_path = hf_hub_download ("hi-paris/fastText" , "cc.en.300.bin" )
- # Initialize the Table2GraphTransformer
- preprocessor = Table2GraphTransformer (fasttext_model_path = model_path )
- # View the transformer details
- help (Table2GraphTransformer )
- # Fit and transform the training data
- X_train = preprocessor.fit_transform (X_train , y = y_train )
- # Transform the test data
- X_test = preprocessor.transform (X_test )
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-carte_ai.src.carte_estimator module
-CARTE estimators for regression and classification.
-
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-class carte_ai.src.carte_estimator. BaseCARTEEstimator ( * num_layers load_pretrain freeze_pretrain learning_rate batch_size max_epoch dropout val_size cross_validate early_stopping_patience num_model random_state n_jobs device disable_pbar pretrained_model_path = '/home/infres/gbrison/fcclip_v2/miniconda3/envs/carte/lib/python3.10/site-packages/carte_ai/data/etc/kg_pretrained.pt' )
-Bases: BaseEstimator
-Base class for CARTE Estimator.
-
-
-fit ( X y )
-Fit the CARTE model.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-yarray-like of shape (n_samples,) Target values.
-
-
-
-Returns
-
-selfobject Fitted estimator.
-
-
-
-
-Example Usage:
-
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- Copy
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-
- # Define some parameters
- fixed_params = dict ()
- fixed_params ["num_model"] = 10 # 10 models for the bagging strategy
- fixed_params ["disable_pbar"] = False # True if you want cleanness
- fixed_params ["random_state"] = 0
- fixed_params ["device"] = "cpu"
- fixed_params ["n_jobs"] = 10
- fixed_params ["pretrained_model_path"] = config_directory ["pretrained_model"]
- # Define the estimator and run fit/predict
- estimator = CARTERegressor (**fixed_params ) # CARTERegressor for Regression
- estimator.fit (X = X_train , y = y_train )
- y_pred = estimator.predict (X_test )
- # Obtain the r2 score on predictions
- score = r2_score (y_test , y_pred )
- print ("\nThe R2 score for CARTE:" , "{:.4f}" .format(score ))
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-class carte_ai.src.carte_estimator. BaseCARTEMultitableEstimator ( * source_data num_layers load_pretrain freeze_pretrain learning_rate batch_size max_epoch dropout val_size target_fraction early_stopping_patience num_model random_state n_jobs device disable_pbar pretrained_model_path )
-Bases: BaseCARTEEstimator
-Base class for CARTE Multitable Estimator.
-
-
-fit ( X y )
-Fit the CARTE Multitable model.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples of the target data.
-yarray-like of shape (n_samples,) Target values.
-
-
-
-Returns
-
-selfobject Fitted estimator.
-
-
-
-
-
-
-
-
-class carte_ai.src.carte_estimator. CARTEClassifier ( * loss : str = 'binary_crossentropy' scoring : str = 'auroc' num_layers : int = 1 load_pretrain : bool = True freeze_pretrain : bool = True learning_rate : float = 0.001 batch_size : int = 16 max_epoch : int = 500 dropout : float = 0 val_size : float = 0.2 cross_validate : bool = False early_stopping_patience : None | int = 40 num_model : int = 1 random_state : int = 0 n_jobs : int = 1 device : str = 'cpu' disable_pbar : bool = True pretrained_model_path = '/home/infres/gbrison/fcclip_v2/miniconda3/envs/carte/lib/python3.10/site-packages/carte_ai/data/etc/kg_pretrained.pt' )
-Bases: ClassifierMixin BaseCARTEEstimator
-CARTE Classifier for Classification tasks.
-This estimator is GNN-based model compatible with the CARTE pretrained model.
-
-Parameters
-
-loss{‘binary_crossentropy’, ‘categorical_crossentropy’}, default=’binary_crossentropy’ The loss function used for backpropagation.
-scoring{‘auroc’, ‘auprc’, ‘binary_entropy’}, default=’auroc’ The scoring function used for validation.
-num_layersint, default=1 The number of layers for the NN model
-load_pretrainbool, default=True Indicates whether to load pretrained weights or not
-freeze_pretrainbool, default=True Indicates whether to freeze the pretrained weights in the training or not
-learning_ratefloat, default=1e-3 The learning rate of the model. The model uses AdamW as the optimizer
-batch_sizeint, default=16 The batch size used for training
-max_epochint or None, default=500 The maximum number of epoch for training
-dropoutfloat, default=0 The dropout rate for training
-val_sizefloat, default=0.1 The size of the validation set used for early stopping
-cross_validatebool, default=False Indicates whether to use cross-validation strategy for train/validation split
-early_stopping_patienceint or None, default=40 The early stopping patience when early stopping is used.
-If set to None, no early stopping is employed
-num_modelint, default=1 The total number of models used for Bagging strategy
-random_stateint or None, default=0 Pseudo-random number generator to control the train/validation data split
-if early stoppingis enabled, the weight initialization, and the dropout.
-Pass an int for reproducible output across multiple function calls.
-n_jobsint, default=1 Number of jobs to run in parallel. Training the estimator the score are parallelized
-over the number of models.
-device{“cpu”, “gpu”}, default=”cpu”, The device used for the estimator.
-disable_pbarbool, default=True Indicates whether to show progress bars for the training process.
-
-
-
-decision_function ( X )
-Compute the decision function of X.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-
-
-
-Returns
-decision : ndarray, shape (n_samples,)
-
-
-
-
-
-predict ( X )
-Predict classes for X.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-
-
-
-Returns
-
-yndarray, shape (n_samples,) The predicted classes.
-
-
-
-
-
-
-predict_proba ( X )
-Predict class probabilities for X.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-
-
-
-Returns
-
-pndarray, shape (n_samples,) for binary classification or (n_samples, n_classes) The class probabilities of the input samples.
-
-
-
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → CARTEClassifier
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-
-class carte_ai.src.carte_estimator. CARTEMultitableClassifer ( * loss : str = 'binary_crossentropy' scoring : str = 'auroc' source_data : dict = {} num_layers : int = 1 load_pretrain : bool = True freeze_pretrain : bool = True learning_rate : float = 0.001 batch_size : int = 16 max_epoch : int = 500 dropout : float = 0 val_size : float = 0.2 target_fraction : float = 0.125 early_stopping_patience : None | int = 40 num_model : int = 1 random_state : int = 0 n_jobs : int = 1 device : str = 'cpu' disable_pbar : bool = True pretrained_model_path = '/home/infres/gbrison/fcclip_v2/miniconda3/envs/carte/lib/python3.10/site-packages/carte_ai/data/etc/kg_pretrained.pt' )
-Bases: ClassifierMixin BaseCARTEMultitableEstimator
-CARTE Multitable Classifier for Classification tasks.
-This estimator is GNN-based model compatible with the CARTE pretrained model.
-
-Parameters
-
-loss{‘binary_crossentropy’, ‘categorical_crossentropy’}, default=’binary_crossentropy’ The loss function used for backpropagation.
-scoring{‘auroc’, ‘auprc’, ‘binary_entropy’}, default=’auroc’ The scoring function used for validation.
-source_datedict, default={} The source data used in multitable estimator.
-num_layersint, default=1 The number of layers for the NN model
-load_pretrainbool, default=True Indicates whether to load pretrained weights or not
-freeze_pretrainbool, default=True Indicates whether to freeze the pretrained weights in the training or not
-learning_ratefloat, default=1e-3 The learning rate of the model. The model uses AdamW as the optimizer
-batch_sizeint, default=16 The batch size used for training
-max_epochint or None, default=500 The maximum number of epoch for training
-dropoutfloat, default=0 The dropout rate for training
-val_sizefloat, default=0.1 The size of the validation set used for early stopping
-target_fractionfloat, default=0.125 The fraction of target data inside of a batch when training
-early_stopping_patienceint or None, default=40 The early stopping patience when early stopping is used.
-If set to None, no early stopping is employed
-num_modelint, default=1 The total number of models used for Bagging strategy
-random_stateint or None, default=0 Pseudo-random number generator to control the train/validation data split
-if early stoppingis enabled, the weight initialization, and the dropout.
-Pass an int for reproducible output across multiple function calls.
-n_jobsint, default=1 Number of jobs to run in parallel. Training the estimator the score are parallelized
-over the number of models.
-device{“cpu”, “gpu”}, default=”cpu”, The device used for the estimator.
-disable_pbarbool, default=True Indicates whether to show progress bars for the training process.
-
-
-
-decision_function ( X )
-Compute the decision function of X
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-
-
-
-Returns
-decision : ndarray, shape (n_samples,)
-
-
-
-
-
-predict ( X )
-Predict classes for X.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-
-
-
-Returns
-
-yndarray, shape (n_samples,) The predicted classes.
-
-
-
-
-
-
-predict_proba ( X )
-Predict class probabilities for X.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-
-
-
-Returns
-
-pndarray, shape (n_samples,) for binary classification or (n_samples, n_classes) The class probabilities of the input samples.
-
-
-
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → CARTEMultitableClassifer
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-
-class carte_ai.src.carte_estimator. CARTEMultitableRegressor ( * loss : str = 'squared_error' scoring : str = 'r2_score' source_data : dict = {} num_layers : int = 1 load_pretrain : bool = True freeze_pretrain : bool = True learning_rate : float = 0.001 batch_size : int = 16 max_epoch : int = 500 dropout : float = 0 val_size : float = 0.2 target_fraction : float = 0.125 early_stopping_patience : None | int = 40 num_model : int = 1 random_state : int = 0 n_jobs : int = 1 device : str = 'cpu' disable_pbar : bool = True pretrained_model_path = '/home/infres/gbrison/fcclip_v2/miniconda3/envs/carte/lib/python3.10/site-packages/carte_ai/data/etc/kg_pretrained.pt' )
-Bases: RegressorMixin BaseCARTEMultitableEstimator
-CARTE Multitable Regressor for Regression tasks.
-This estimator is GNN-based model compatible with the CARTE pretrained model.
-
-Parameters
-
-loss{‘squared_error’, ‘absolute_error’}, default=’squared_error’ The loss function used for backpropagation.
-scoring{‘r2_score’, ‘squared_error’}, default=’r2_score’ The scoring function used for validation.
-source_datedict, default={} The source data used in multitable estimator.
-num_layersint, default=1 The number of layers for the NN model
-load_pretrainbool, default=True Indicates whether to load pretrained weights or not
-freeze_pretrainbool, default=True Indicates whether to freeze the pretrained weights in the training or not
-learning_ratefloat, default=1e-3 The learning rate of the model. The model uses AdamW as the optimizer
-batch_sizeint, default=16 The batch size used for training
-max_epochint or None, default=500 The maximum number of epoch for training
-dropoutfloat, default=0 The dropout rate for training
-val_sizefloat, default=0.1 The size of the validation set used for early stopping
-target_fractionfloat, default=0.125 The fraction of target data inside of a batch when training
-early_stopping_patienceint or None, default=40 The early stopping patience when early stopping is used.
-If set to None, no early stopping is employed
-num_modelint, default=1 The total number of models used for Bagging strategy
-random_stateint or None, default=0 Pseudo-random number generator to control the train/validation data split
-if early stoppingis enabled, the weight initialization, and the dropout.
-Pass an int for reproducible output across multiple function calls.
-n_jobsint, default=1 Number of jobs to run in parallel. Training the estimator the score are parallelized
-over the number of models.
-device{“cpu”, “gpu”}, default=”cpu”, The device used for the estimator.
-disable_pbarbool, default=True Indicates whether to show progress bars for the training process.
-
-
-
-predict ( X )
-Predict values for X.
-Returns the weighted average of the singletable model and all pairwise model with 1-source.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-
-
-
-Returns
-
-yndarray, shape (n_samples,) The predicted values.
-
-
-
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → CARTEMultitableRegressor
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-
-class carte_ai.src.carte_estimator. CARTERegressor ( * loss : str = 'squared_error' scoring : str = 'r2_score' num_layers : int = 1 load_pretrain : bool = True freeze_pretrain : bool = True learning_rate : float = 0.001 batch_size : int = 16 max_epoch : int = 500 dropout : float = 0 val_size : float = 0.2 cross_validate : bool = False early_stopping_patience : None | int = 40 num_model : int = 1 random_state : int = 0 n_jobs : int = 1 device : str = 'cpu' disable_pbar : bool = True pretrained_model_path = '/home/infres/gbrison/fcclip_v2/miniconda3/envs/carte/lib/python3.10/site-packages/carte_ai/data/etc/kg_pretrained.pt' )
-Bases: RegressorMixin BaseCARTEEstimator
-CARTE Regressor for Regression tasks.
-This estimator is GNN-based model compatible with the CARTE pretrained model.
-
-Parameters
-
-loss{‘squared_error’, ‘absolute_error’}, default=’squared_error’ The loss function used for backpropagation.
-scoring{‘r2_score’, ‘squared_error’}, default=’r2_score’ The scoring function used for validation.
-num_layersint, default=1 The number of layers for the NN model
-load_pretrainbool, default=True Indicates whether to load pretrained weights or not
-freeze_pretrainbool, default=True Indicates whether to freeze the pretrained weights in the training or not
-learning_ratefloat, default=1e-3 The learning rate of the model. The model uses AdamW as the optimizer
-batch_sizeint, default=16 The batch size used for training
-max_epochint or None, default=500 The maximum number of epoch for training
-dropoutfloat, default=0 The dropout rate for training
-val_sizefloat, default=0.1 The size of the validation set used for early stopping
-cross_validatebool, default=False Indicates whether to use cross-validation strategy for train/validation split
-early_stopping_patienceint or None, default=40 The early stopping patience when early stopping is used.
-If set to None, no early stopping is employed
-num_modelint, default=1 The total number of models used for Bagging strategy
-random_stateint or None, default=0 Pseudo-random number generator to control the train/validation data split
-if early stoppingis enabled, the weight initialization, and the dropout.
-Pass an int for reproducible output across multiple function calls.
-n_jobsint, default=1 Number of jobs to run in parallel. Training the estimator the score are parallelized
-over the number of models.
-device{“cpu”, “gpu”}, default=”cpu”, The device used for the estimator.
-disable_pbarbool, default=True Indicates whether to show progress bars for the training process.
-
-
-
-predict ( X )
-Predict values for X. Returns the average of predicted values over all the models.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-
-
-
-Returns
-
-yndarray, shape (n_samples,) The predicted values.
-
-
-
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → CARTERegressor
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-
-class carte_ai.src.carte_estimator. CARTE_AblationClassifier ( * ablation_method : str = 'exclude-edge' loss : str = 'binary_crossentropy' scoring : str = 'auroc' num_layers : int = 1 load_pretrain : bool = False freeze_pretrain : bool = False learning_rate : float = 0.001 batch_size : int = 16 max_epoch : int = 500 dropout : float = 0 val_size : float = 0.2 cross_validate : bool = False early_stopping_patience : None | int = 40 num_model : int = 1 random_state : int = 0 n_jobs : int = 1 device : str = 'cpu' disable_pbar : bool = True pretrained_model_path = '/home/infres/gbrison/fcclip_v2/miniconda3/envs/carte/lib/python3.10/site-packages/carte_ai/data/etc/kg_pretrained.pt' )
-Bases: CARTEClassifier
-CARTE Ablation Classifier for Classification tasks.
-This estimator is GNN-based model compatible with the CARTE pretrained model.
-Note that this is an implementation for the ablation study of CARTE
-
-Parameters
-
-ablation_method{‘exclude-edge’, ‘exclude-attention’, ‘exclude-attention-edge’}, default=’exclude-edge’ The ablation method for CARTE Estimators.
-loss{‘binary_crossentropy’, ‘categorical_crossentropy’}, default=’binary_crossentropy’ The loss function used for backpropagation.
-scoring{‘auroc’, ‘auprc’, ‘binary_entropy’}, default=’auroc’ The scoring function used for validation.
-num_layersint, default=1 The number of layers for the NN model
-load_pretrainbool, default=True Indicates whether to load pretrained weights or not
-freeze_pretrainbool, default=True Indicates whether to freeze the pretrained weights in the training or not
-learning_ratefloat, default=1e-3 The learning rate of the model. The model uses AdamW as the optimizer
-batch_sizeint, default=16 The batch size used for training
-max_epochint or None, default=500 The maximum number of epoch for training
-dropoutfloat, default=0 The dropout rate for training
-val_sizefloat, default=0.1 The size of the validation set used for early stopping
-cross_validatebool, default=False Indicates whether to use cross-validation strategy for train/validation split
-early_stopping_patienceint or None, default=40 The early stopping patience when early stopping is used.
-If set to None, no early stopping is employed
-num_modelint, default=1 The total number of models used for Bagging strategy
-random_stateint or None, default=0 Pseudo-random number generator to control the train/validation data split
-if early stoppingis enabled, the weight initialization, and the dropout.
-Pass an int for reproducible output across multiple function calls.
-n_jobsint, default=1 Number of jobs to run in parallel. Training the estimator the score are parallelized
-over the number of models.
-device{“cpu”, “gpu”}, default=”cpu”, The device used for the estimator.
-disable_pbarbool, default=True Indicates whether to show progress bars for the training process.
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → CARTE_AblationClassifier
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-
-class carte_ai.src.carte_estimator. CARTE_AblationRegressor ( * ablation_method : str = 'exclude-edge' loss : str = 'squared_error' scoring : str = 'r2_score' num_layers : int = 1 load_pretrain : bool = True freeze_pretrain : bool = True learning_rate : float = 0.001 batch_size : int = 16 max_epoch : int = 500 dropout : float = 0 val_size : float = 0.2 cross_validate : bool = False early_stopping_patience : None | int = 40 num_model : int = 1 random_state : int = 0 n_jobs : int = 1 device : str = 'cpu' disable_pbar : bool = True pretrained_model_path = '/home/infres/gbrison/fcclip_v2/miniconda3/envs/carte/lib/python3.10/site-packages/carte_ai/data/etc/kg_pretrained.pt' )
-Bases: CARTERegressor
-CARTE Ablation Regressor for Regression tasks.
-This estimator is GNN-based model compatible with the CARTE pretrained model.
-Note that this is an implementation for the ablation study of CARTE
-
-Parameters
-
-ablation_method{‘exclude-edge’, ‘exclude-attention’, ‘exclude-attention-edge’}, default=’exclude-edge’ The ablation method for CARTE Estimators.
-loss{‘squared_error’, ‘absolute_error’}, default=’squared_error’ The loss function used for backpropagation.
-scoring{‘r2_score’, ‘squared_error’}, default=’r2_score’ The scoring function used for validation.
-num_layersint, default=1 The number of layers for the NN model
-load_pretrainbool, default=True Indicates whether to load pretrained weights or not
-freeze_pretrainbool, default=True Indicates whether to freeze the pretrained weights in the training or not
-learning_ratefloat, default=1e-3 The learning rate of the model. The model uses AdamW as the optimizer
-batch_sizeint, default=16 The batch size used for training
-max_epochint or None, default=500 The maximum number of epoch for training
-dropoutfloat, default=0 The dropout rate for training
-val_sizefloat, default=0.1 The size of the validation set used for early stopping
-cross_validatebool, default=False Indicates whether to use cross-validation strategy for train/validation split
-early_stopping_patienceint or None, default=40 The early stopping patience when early stopping is used.
-If set to None, no early stopping is employed
-num_modelint, default=1 The total number of models used for Bagging strategy
-random_stateint or None, default=0 Pseudo-random number generator to control the train/validation data split
-if early stoppingis enabled, the weight initialization, and the dropout.
-Pass an int for reproducible output across multiple function calls.
-n_jobsint, default=1 Number of jobs to run in parallel. Training the estimator the score are parallelized
-over the number of models.
-device{“cpu”, “gpu”}, default=”cpu”, The device used for the estimator.
-disable_pbarbool, default=True Indicates whether to show progress bars for the training process.
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → CARTE_AblationRegressor
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-
-class carte_ai.src.carte_estimator. IdxIterator ( n_batch : int domain_indicator : Tensor target_fraction : float )
-Bases: object
-Class for iterating indices to set up the batch for CARTE Multitables
-
-
-sample ( )
-
-
-
-
-set_num_samples ( )
-
-
-
-
-carte_ai.src.baseline_multitable module
-Baselines for multitable problem.
-
-
-class carte_ai.src.baseline_multitable. CatBoostMultitableClassifier ( * source_data : dict = {} max_depth : int = 6 learning_rate : float = 0.03 bagging_temperature : float = 1 l2_leaf_reg : float = 3.0 one_hot_max_size : int = 2 iterations : int = 1000 thread_count : int = 1 source_fraction : float = 0.5 num_model : int = 1 val_size : float = 0.1 random_state : int = 0 n_jobs : int = 1 )
-Bases: GradientBoostingClassifierBase
-Base class for CatBoost Multitable Classifier.
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → CatBoostMultitableClassifier
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-class carte_ai.src.baseline_multitable. CatBoostMultitableRegressor ( * source_data : dict = {} max_depth : int = 6 learning_rate : float = 0.03 bagging_temperature : float = 1 l2_leaf_reg : float = 3.0 one_hot_max_size : int = 2 iterations : int = 1000 thread_count : int = 1 source_fraction : float = 0.5 num_model : int = 1 val_size : float = 0.1 random_state : int = 0 n_jobs : int = 1 )
-Bases: GradientBoostingRegressorBase
-Base class for CatBoost Multitable Regressor.
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → CatBoostMultitableRegressor
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-class carte_ai.src.baseline_multitable. GradientBoostingClassifierBase ( * source_data source_fraction num_model val_size random_state n_jobs )
-Bases: ClassifierMixin GradientBoostingMultitableBase
-Base class for Gradient Boosting Multitable Classifier.
-
-
-decision_function ( X )
-Compute the decision function of X.
-
-
-
-
-predict ( X )
-Predict classes for X.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-
-
-
-Returns
-
-yndarray, shape (n_samples,) The predicted classes.
-
-
-
-
-
-
-predict_proba ( X )
-Predict class probabilities for X.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-
-
-
-Returns
-
-pndarray, shape (n_samples,) for binary classification or (n_samples, n_classes) The class probabilities of the input samples.
-
-
-
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → GradientBoostingClassifierBase
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-class carte_ai.src.baseline_multitable. GradientBoostingMultitableBase ( * source_data source_fraction num_model val_size random_state n_jobs )
-Bases: BaseEstimator
-Base class for Gradient Boosting Multitable Estimator.
-
-
-fit ( X y )
-Fit the model.
-
-Parameters
-
-XPandas dataframe of the target dataset (n_samples) The input samples.
-yarray-like of shape (n_samples,) Target values.
-
-
-
-Returns
-
-selfobject Fitted estimator.
-
-
-
-
-
-
-
-
-class carte_ai.src.baseline_multitable. GradientBoostingRegressorBase ( * source_data source_fraction num_model val_size random_state n_jobs )
-Bases: RegressorMixin GradientBoostingMultitableBase
-Base class for Gradient Boosting Multitable Regressor.
-
-
-predict ( X )
-Predict values for X. Returns the average of predicted values over all the models.
-
-Parameters
-
-Xlist of graph objects with size (n_samples) The input samples.
-
-
-
-Returns
-
-yndarray, shape (n_samples,) The predicted values.
-
-
-
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → GradientBoostingRegressorBase
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-class carte_ai.src.baseline_multitable. HistGBMultitableClassifier ( * source_data : dict = {} learning_rate : float = 0.1 max_depth : None | int = None max_leaf_nodes : int = 31 min_samples_leaf : int = 20 l2_regularization : float = 0 source_fraction : float = 0.5 num_model : int = 1 val_size : float = 0.1 random_state : int = 0 n_jobs : int = 1 )
-Bases: GradientBoostingClassifierBase
-Base class for Historgram Gradient Boosting Multitable Classifier.
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → HistGBMultitableClassifier
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-class carte_ai.src.baseline_multitable. HistGBMultitableRegressor ( * source_data : dict = {} learning_rate : float = 0.1 max_depth : None | int = None max_leaf_nodes : int = 31 min_samples_leaf : int = 20 l2_regularization : float = 0 source_fraction : float = 0.5 num_model : int = 1 val_size : float = 0.1 random_state : int = 0 n_jobs : int = 1 )
-Bases: GradientBoostingRegressorBase
-Base class for Historgram Gradient Boosting Multitable Regressor.
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → HistGBMultitableRegressor
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-class carte_ai.src.baseline_multitable. XGBoostMultitableClassifier ( * source_data : dict = {} n_estimators : int = 100 max_depth : int = 6 min_child_weight : float = 1 subsample : float = 1 learning_rate : float = 0.3 colsample_bylevel : float = 1 colsample_bytree : float = 1 reg_gamma : float = 0 reg_lambda : float = 1 reg_alpha : float = 0 source_fraction : float = 0.5 num_model : int = 1 val_size : float = 0.1 random_state : int = 0 n_jobs : int = 1 )
-Bases: GradientBoostingClassifierBase
-Base class for XGBoost Multitable Classifier.
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → XGBoostMultitableClassifier
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-class carte_ai.src.baseline_multitable. XGBoostMultitableRegressor ( * source_data : dict = {} n_estimators : int = 100 max_depth : int = 6 min_child_weight : float = 1 subsample : float = 1 learning_rate : float = 0.3 colsample_bylevel : float = 1 colsample_bytree : float = 1 reg_gamma : float = 0 reg_lambda : float = 1 reg_alpha : float = 0 source_fraction : float = 0.5 num_model : int = 1 val_size : float = 0.1 random_state : int = 0 n_jobs : int = 1 )
-Bases: GradientBoostingRegressorBase
-Base class for XGBoost Multitable Regressor.
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → XGBoostMultitableRegressor
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-carte_ai.src.baseline_singletable_nn module
-Neural network baseline for comparison.
-
-
-class carte_ai.src.baseline_singletable_nn. BaseMLPEstimator ( * hidden_dim : int = 256 num_layers : int = 2 dropout_prob : float = 0.2 learning_rate : float = 0.001 weight_decay : float = 0.01 batch_size : int = 128 val_size : float = 0.1 num_model : int = 1 max_epoch : int = 200 early_stopping_patience : None | int = 10 n_jobs : int = 1 device : str = 'cpu' random_state : int = 0 disable_pbar : bool = True )
-Bases: MLPBase
-Base class for MLP Estimator.
-
-
-
-
-class carte_ai.src.baseline_singletable_nn. BaseRESNETEstimator ( * normalization : str | None = 'layernorm' num_layers : int = 4 hidden_dim : int = 256 hidden_factor : int = 2 hidden_dropout_prob : float = 0.2 residual_dropout_prob : float = 0.2 learning_rate : float = 0.001 weight_decay : float = 0.01 batch_size : int = 128 val_size : float = 0.1 num_model : int = 1 max_epoch : int = 200 early_stopping_patience : None | int = 10 n_jobs : int = 1 device : str = 'cpu' random_state : int = 0 disable_pbar : bool = True )
-Bases: MLPBase
-Base class for RESNET Estimator.
-
-
-
-
-class carte_ai.src.baseline_singletable_nn. MLPBase ( * hidden_dim learning_rate weight_decay batch_size val_size num_model max_epoch early_stopping_patience n_jobs device random_state disable_pbar )
-Bases: BaseEstimator
-Base class for MLP.
-
-
-fit ( X y )
-
-
-
-
-
-
-class carte_ai.src.baseline_singletable_nn. MLPClassifier ( * loss : str = 'binary_crossentropy' hidden_dim : int = 256 num_layers : int = 2 dropout_prob : float = 0.2 learning_rate : float = 0.001 weight_decay : float = 0.01 batch_size : int = 128 val_size : float = 0.1 num_model : int = 1 max_epoch : int = 200 early_stopping_patience : None | int = 10 n_jobs : int = 1 device : str = 'cpu' random_state : int = 0 disable_pbar : bool = True )
-Bases: ClassifierMixin BaseMLPEstimator
-
-
-decision_function ( X )
-
-
-
-
-predict ( X )
-
-
-
-
-predict_proba ( X )
-
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → MLPClassifier
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-class carte_ai.src.baseline_singletable_nn. MLPRegressor ( * loss : str = 'squared_error' hidden_dim : int = 256 num_layers : int = 2 dropout_prob : float = 0.2 learning_rate : float = 0.001 weight_decay : float = 0.01 batch_size : int = 128 val_size : float = 0.1 num_model : int = 1 max_epoch : int = 200 early_stopping_patience : None | int = 10 n_jobs : int = 1 device : str = 'cpu' random_state : int = 0 disable_pbar : bool = True )
-Bases: RegressorMixin BaseMLPEstimator
-
-
-predict ( X )
-
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → MLPRegressor
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-class carte_ai.src.baseline_singletable_nn. MLP_Model ( input_dim : int hidden_dim : int output_dim : int dropout_prob : float num_layers : int )
-Bases: Module
-
-
-forward ( X )
-Define the computation performed at every call.
-Should be overridden by all subclasses.
-
-
Note
-
Although the recipe for forward pass needs to be defined within
-this function, one should call the Module
-
-
-
-
-
-
-class carte_ai.src.baseline_singletable_nn. RESNETClassifier ( * loss : str = 'binary_crossentropy' normalization : str | None = 'layernorm' num_layers : int = 4 hidden_dim : int = 256 hidden_factor : int = 2 hidden_dropout_prob : float = 0.2 residual_dropout_prob : float = 0.2 learning_rate : float = 0.001 weight_decay : float = 0.01 batch_size : int = 128 val_size : float = 0.1 num_model : int = 1 max_epoch : int = 200 early_stopping_patience : None | int = 10 n_jobs : int = 1 device : str = 'cpu' random_state : int = 0 disable_pbar : bool = True )
-Bases: ClassifierMixin BaseRESNETEstimator
-
-
-decision_function ( X )
-
-
-
-
-predict ( X )
-
-
-
-
-predict_proba ( X )
-
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → RESNETClassifier
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
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-class carte_ai.src.baseline_singletable_nn. RESNETRegressor ( * loss : str = 'squared_error' normalization : str | None = 'layernorm' num_layers : int = 4 hidden_dim : int = 256 hidden_factor : int = 2 hidden_dropout_prob : float = 0.2 residual_dropout_prob : float = 0.2 learning_rate : float = 0.001 weight_decay : float = 0.01 batch_size : int = 128 val_size : float = 0.1 num_model : int = 1 max_epoch : int = 200 early_stopping_patience : None | int = 10 n_jobs : int = 1 device : str = 'cpu' random_state : int = 0 disable_pbar : bool = True )
-Bases: RegressorMixin BaseRESNETEstimator
-
-
-predict ( X )
-
-
-
-
-set_score_request ( * sample_weight : bool | None | str = '$UNCHANGED$' ) → RESNETRegressor
-Request metadata passed to the score
-Note that this method is only relevant if
-enable_metadata_routing=True sklearn.set_config() User Guide on how the routing
-mechanism works.
-The options for each parameter are:
-
-True score
False score
None
str
-The default (sklearn.utils.metadata_routing.UNCHANGED
-
-
-
Note
-
This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-Pipeline
-
-Parameters
-
-sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED Metadata routing for sample_weight score
-
-
-
-Returns
-
-selfobject The updated object.
-
-
-
-
-
-
-
-
-class carte_ai.src.baseline_singletable_nn. RESNET_Model ( input_dim : int hidden_dim : int output_dim : int num_layers : int ** block_args )
-Bases: Module
-
-
-forward ( X )
-Define the computation performed at every call.
-Should be overridden by all subclasses.
-
-
Note
-
Although the recipe for forward pass needs to be defined within
-this function, one should call the Module
-
-
-
-
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-class carte_ai.src.baseline_singletable_nn. Residual_Block ( input_dim : int output_dim : int hidden_factor : int normalization : str | None = 'layernorm' hidden_dropout_prob : float = 0.2 residual_dropout_prob : float = 0.2 )
-Bases: Module
-
-
-forward ( x : Tensor )
-Define the computation performed at every call.
-Should be overridden by all subclasses.
-
-
Note
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Although the recipe for forward pass needs to be defined within
-this function, one should call the Module
-
-
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-reset_parameters ( ) → None
-
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-Bases: Dataset
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-carte_ai.src.carte_gridsearch module
-Custom grid search used for CARTE-GNN model
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-carte_ai.src.carte_gridsearch. carte_gridsearch ( estimator X_train : list y_train : array param_distributions : dict refit : bool = True n_jobs : int = 1 )
-CARTE grid search.
-This function runs grid search for CARTE GNN models.
-
-Parameters
-
-estimatorCARTE estimator The CARTE estimator used for grid search
-X_trainlist The list of graph objects for the train data transformed using Table2GraphTransformer
-y_trainnumpy array of shape (n_samples,) The target variable of the train data.
-param_distributions: dict The dictionary of parameter grids to search for the optimial parameter.
-refit: bool, default=True Indicates whether to return a refitted estimator with the best parameter.
-n_jobs: int, default=1 Number of jobs to run in parallel. Training the estimator in the grid search is parallelized
-over the parameter grid.
-
-
-
-Returns
-
-ResultPandas DataFrame The result of each parameter grid.
-best_paramsdict The dictionary of best parameters obtained through grid search.
-best_estimatorCARTEGNN estimator The CARTE estimator trained using the best_params if refit is set to True.
-
-
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-carte_ai.src.carte_model module
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-class carte_ai.src.carte_model. CARTE_Attention ( input_dim : int output_dim : int num_heads : int = 1 concat : bool = True read_out : bool = False )
-Bases: Module
-
-
-forward ( x : Tensor edge_index : Tensor edge_attr : Tensor return_attention : bool = False )
-Define the computation performed at every call.
-Should be overridden by all subclasses.
-
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Note
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Although the recipe for forward pass needs to be defined within
-this function, one should call the Module
-
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-Bases: Module
-
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-forward ( x edge_index edge_attr return_attention = False )
-Define the computation performed at every call.
-Should be overridden by all subclasses.
-
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Note
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Although the recipe for forward pass needs to be defined within
-this function, one should call the Module
-
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-class carte_ai.src.carte_model. CARTE_Block ( input_dim : int ff_dim : int num_heads : int = 1 concat : bool = True dropout : float = 0.1 read_out : bool = False )
-Bases: Module
-
-
-forward ( x : Tensor edge_index : Tensor edge_attr : Tensor )
-Define the computation performed at every call.
-Should be overridden by all subclasses.
-
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Note
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Although the recipe for forward pass needs to be defined within
-this function, one should call the Module
-
-
-
-
-
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-class carte_ai.src.carte_model. CARTE_Contrast
-Bases: Module
-
-
-forward ( x : Tensor )
-Define the computation performed at every call.
-Should be overridden by all subclasses.
-
-
Note
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Although the recipe for forward pass needs to be defined within
-this function, one should call the Module
-
-
-
-
-
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-class carte_ai.src.carte_model. CARTE_NN_Model ( input_dim_x : int input_dim_e : int hidden_dim : int output_dim : int num_layers : int ** block_args )
-Bases: Module
-
-
-forward ( input )
-Define the computation performed at every call.
-Should be overridden by all subclasses.
-
-
Note
-
Although the recipe for forward pass needs to be defined within
-this function, one should call the Module
-
-
-
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-class carte_ai.src.carte_model. CARTE_NN_Model_Ablation ( ablation_method : str input_dim_x : int input_dim_e : int hidden_dim : int output_dim : int num_layers : int ** block_args )
-Bases: Module
-
-
-forward ( input )
-Define the computation performed at every call.
-Should be overridden by all subclasses.
-
-
Note
-
Although the recipe for forward pass needs to be defined within
-this function, one should call the Module
-
-
-
-
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-class carte_ai.src.carte_model. CARTE_Pretrain ( input_dim_x : int input_dim_e : int hidden_dim : int num_layers : int ** block_args )
-Bases: Module
-
-
-forward ( input )
-Define the computation performed at every call.
-Should be overridden by all subclasses.
-
-
Note
-
Although the recipe for forward pass needs to be defined within
-this function, one should call the Module
-
-
-
-
-
-
-
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-transform ( X y = None )
-Apply Table2GraphTransformer to each row of the data.
-
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-
-Xpandas.DataFrame Input data to transform.
-yarray-like, optional Target values, by default None.
-
-
-
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-
-data_graphlist List of transformed graph objects.
-
-
-
-
-
-
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-carte_ai.src.evaluate_utils module
-
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-carte_ai.src.evaluate_utils. check_pred_output ( y_train y_pred )
-Set the output as the mean of train data if it is nan.
-
-
-
-
-carte_ai.src.evaluate_utils. col_names_per_type ( data target_name )
-Extract column names per type.
-
-
-
-
-Extract the best parameters in the CARTE paper.
-
-
-
-
-carte_ai.src.evaluate_utils. reshape_pred_output ( y_pred )
-Reshape the predictive output accordingly.
-
-
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-carte_ai.src.evaluate_utils. return_score ( y_target y_pred task )
-Return score results for given task.
-
-
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-carte_ai.src.evaluate_utils. set_score_criterion ( task )
-Set scoring method for CV and score criterion in final result.
-
-
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-Set train/test split given the random state.
-
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-carte_ai.src.evaluate_utils. shorten_param ( param_name )
-Shorten the param_names for column names in search results.
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-carte_ai.src.visualization_utils module
-Functions that can be utilized for visualization.
-For Critical difference diagram, it modifies some of the codes from scikit-posthocs.
-
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-carte_ai.src.visualization_utils. critical_difference_diagram ( ranks : dict | Series sig_matrix : DataFrame * ax : SubplotBase | None = None label_fmt_left : str = '{label} ({rank:.2g})' label_fmt_right : str = '({rank:.2g}) {label}' label_props : dict | None = None marker_props : dict | None = None elbow_props : dict | None = None crossbar_props : dict | None = None color_palette : Dict [ str , str ] | List = {} line_style : Dict [ str , str ] | List = {} text_h_margin : float = 0.01 ) → Dict [ str , list ]
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-carte_ai.src.visualization_utils. generate_df_cdd ( df_normalized train_size = 'all' )
-
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-carte_ai.src.visualization_utils. prepare_result ( task models = 'all' rank_at = 2048 )
-
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-carte_ai.src.visualization_utils. sign_array ( p_values : List | ndarray alpha : float = 0.05 ) → ndarray
-
-
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-
-
-
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-carte_ai.src.visualization_utils. sign_table ( p_values : List | ndarray | DataFrame lower : bool = True upper : bool = True ) → DataFrame | ndarray
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- carte_ai
-
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- carte_ai.configs
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- carte_ai.configs.carte_configs
-
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- carte_ai.configs.directory
-
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- carte_ai.configs.model_parameters
-
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- carte_ai.configs.visuailization
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- carte_ai.data
-
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- carte_ai.data.load_data
-
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- carte_ai.scripts
-
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- carte_ai.scripts.compile_results_singletable
-
-
-
- carte_ai.scripts.download_data
-
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- carte_ai.scripts.evaluate_singletable
-
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-
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-Welcome to CARTE-AI documentation 📚!
-
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- CARTE is a pretrained model for tabular data by treating each table row as a star graph and training a graph transformer on top of this representation.
-
-
- Colab Examples (Give it a test):
-
-
-
- CARTERegressor on Wine Poland dataset
- CARTEClassifier on Spotify dataset
-
-
-
- 01 Install 🚀
- The library has been tested on Linux, MacOSX, and Windows.
- CARTE-AI can be installed from PyPI :
- Installation
-
-
-
- Example of use of the library
-
-
- 1️⃣ Load the Data 💽
- import pandas as pd
- from carte_ai.data.load_data import wina_pl
-
- num_train = 128 # Example: set the number of training groups/entities
- random_state = 1 # Set a random seed for reproducibility
- X_train , X_test , y_train , y_test = wina_pl ( num_train , random_state )
- print ( "Wina Poland dataset:" , X_train . shape , X_test . shape )
- 2️⃣ Convert Table 2 Graph 🪵
- import fasttext
- from huggingface_hub import hf_hub_download
- from carte_ai import Table2GraphTransformer
-
- model_path = hf_hub_download ( repo_id = "hi-paris/fastText" , filename = "cc.en.300.bin" )
-
- preprocessor = Table2GraphTransformer ( fasttext_model_path = model_path )
-
- # Fit and transform the training data
- X_train = preprocessor . fit_transform ( X_train , y = y_train )
-
- # Transform the test data
- X_test = preprocessor . transform ( X_test )
- 3️⃣ Make Predictions🔮
- from carte_ai import CARTERegressor , CARTEClassifier
-
- # Define some parameters
- fixed_params = dict ()
- fixed_params [ "num_model" ] = 10 # 10 models for the bagging strategy
- fixed_params [ "disable_pbar" ] = False # True if you want cleanness
- fixed_params [ "random_state" ] = 0
- fixed_params [ "device" ] = "cpu"
- fixed_params [ "n_jobs" ] = 10
- fixed_params [ "pretrained_model_path" ] = config_directory [ "pretrained_model" ]
-
- # Define the estimator and run fit/predict
- estimator = CARTERegressor (** fixed_params ) # CARTERegressor for Regression
- estimator . fit ( X_train , y_train )
- y_pred = estimator . predict ( X_test )
-
- # Obtain the r2 score on predictions
- score = r2_score ( y_test , y_pred )
- print ( "\nThe R2 score for CARTE:" , "{:.4f}" . format ( score ))
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- echo.
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- echo.https://www.sphinx-doc.org/
- exit /b 1
-)
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-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
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-:end
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