| \n", + " | altitude | \n", + "cadence | \n", + "distance | \n", + "hr | \n", + "latitude | \n", + "longitude | \n", + "power | \n", + "speed | \n", + "time | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "185.800003 | \n", + "51 | \n", + "3.46 | \n", + "81 | \n", + "30.313309 | \n", + "-97.732711 | \n", + "45 | \n", + "3.459 | \n", + "2016-10-20T22:01:26.000Z | \n", + "
| 1 | \n", + "185.800003 | \n", + "68 | \n", + "7.17 | \n", + "82 | \n", + "30.313277 | \n", + "-97.732715 | \n", + "0 | \n", + "3.710 | \n", + "2016-10-20T22:01:27.000Z | \n", + "
| 2 | \n", + "186.399994 | \n", + "38 | \n", + "11.04 | \n", + "82 | \n", + "30.313243 | \n", + "-97.732717 | \n", + "42 | \n", + "3.874 | \n", + "2016-10-20T22:01:28.000Z | \n", + "
| 3 | \n", + "186.800003 | \n", + "38 | \n", + "15.18 | \n", + "83 | \n", + "30.313212 | \n", + "-97.732720 | \n", + "5 | \n", + "4.135 | \n", + "2016-10-20T22:01:29.000Z | \n", + "
| 4 | \n", + "186.600006 | \n", + "38 | \n", + "19.43 | \n", + "83 | \n", + "30.313172 | \n", + "-97.732723 | \n", + "1 | \n", + "4.250 | \n", + "2016-10-20T22:01:30.000Z | \n", + "
Unfortunately, we could not find the page you were looking for. Try:
+using search to browse documentation
checking the table of contents on the left
filing an issue or +starting a discussion on GitHub
| \n", + " | altitude | \n", + "cadence | \n", + "distance | \n", + "hr | \n", + "latitude | \n", + "longitude | \n", + "power | \n", + "speed | \n", + "time | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "185.800003 | \n", + "51 | \n", + "3.46 | \n", + "81 | \n", + "30.313309 | \n", + "-97.732711 | \n", + "45 | \n", + "3.459 | \n", + "2016-10-20T22:01:26.000Z | \n", + "
| 1 | \n", + "185.800003 | \n", + "68 | \n", + "7.17 | \n", + "82 | \n", + "30.313277 | \n", + "-97.732715 | \n", + "0 | \n", + "3.710 | \n", + "2016-10-20T22:01:27.000Z | \n", + "
| 2 | \n", + "186.399994 | \n", + "38 | \n", + "11.04 | \n", + "82 | \n", + "30.313243 | \n", + "-97.732717 | \n", + "42 | \n", + "3.874 | \n", + "2016-10-20T22:01:28.000Z | \n", + "
| 3 | \n", + "186.800003 | \n", + "38 | \n", + "15.18 | \n", + "83 | \n", + "30.313212 | \n", + "-97.732720 | \n", + "5 | \n", + "4.135 | \n", + "2016-10-20T22:01:29.000Z | \n", + "
| 4 | \n", + "186.600006 | \n", + "38 | \n", + "19.43 | \n", + "83 | \n", + "30.313172 | \n", + "-97.732723 | \n", + "1 | \n", + "4.250 | \n", + "2016-10-20T22:01:30.000Z | \n", + "
Short
+ */ + .o-tooltip--left { + position: relative; + } + + .o-tooltip--left:after { + opacity: 0; + visibility: hidden; + position: absolute; + content: attr(data-tooltip); + padding: .2em; + font-size: .8em; + left: -.2em; + background: grey; + color: white; + white-space: nowrap; + z-index: 2; + border-radius: 2px; + transform: translateX(-102%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); +} + +.o-tooltip--left:hover:after { + display: block; + opacity: 1; + visibility: visible; + transform: translateX(-100%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); + transition-delay: .5s; +} + +/* By default the copy button shouldn't show up when printing a page */ +@media print { + button.copybtn { + display: none; + } +} diff --git a/2024.3/_static/copybutton.js b/2024.3/_static/copybutton.js new file mode 100644 index 0000000000..2ea7ff3e21 --- /dev/null +++ b/2024.3/_static/copybutton.js @@ -0,0 +1,248 @@ +// Localization support +const messages = { + 'en': { + 'copy': 'Copy', + 'copy_to_clipboard': 'Copy to clipboard', + 'copy_success': 'Copied!', + 'copy_failure': 'Failed to copy', + }, + 'es' : { + 'copy': 'Copiar', + 'copy_to_clipboard': 'Copiar al portapapeles', + 'copy_success': '¡Copiado!', + 'copy_failure': 'Error al copiar', + }, + 'de' : { + 'copy': 'Kopieren', + 'copy_to_clipboard': 'In die Zwischenablage kopieren', + 'copy_success': 'Kopiert!', + 'copy_failure': 'Fehler beim Kopieren', + }, + 'fr' : { + 'copy': 'Copier', + 'copy_to_clipboard': 'Copier dans le presse-papier', + 'copy_success': 'Copié !', + 'copy_failure': 'Échec de la copie', + }, + 'ru': { + 'copy': 'Скопировать', + 'copy_to_clipboard': 'Скопировать в буфер', + 'copy_success': 'Скопировано!', + 'copy_failure': 'Не удалось скопировать', + }, + 'zh-CN': { + 'copy': '复制', + 'copy_to_clipboard': '复制到剪贴板', + 'copy_success': '复制成功!', + 'copy_failure': '复制失败', + }, + 'it' : { + 'copy': 'Copiare', + 'copy_to_clipboard': 'Copiato negli appunti', + 'copy_success': 'Copiato!', + 'copy_failure': 'Errore durante la copia', + } +} + +let locale = 'en' +if( document.documentElement.lang !== undefined + && messages[document.documentElement.lang] !== undefined ) { + locale = document.documentElement.lang +} + +let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; +if (doc_url_root == '#') { + doc_url_root = ''; +} + +/** + * SVG files for our copy buttons + */ +let iconCheck = `` + +// If the user specified their own SVG use that, otherwise use the default +let iconCopy = ``; +if (!iconCopy) { + iconCopy = `` +} + +/** + * Set up copy/paste for code blocks + */ + +const runWhenDOMLoaded = cb => { + if (document.readyState != 'loading') { + cb() + } else if (document.addEventListener) { + document.addEventListener('DOMContentLoaded', cb) + } else { + document.attachEvent('onreadystatechange', function() { + if (document.readyState == 'complete') cb() + }) + } +} + +const codeCellId = index => `codecell${index}` + +// Clears selected text since ClipboardJS will select the text when copying +const clearSelection = () => { + if (window.getSelection) { + window.getSelection().removeAllRanges() + } else if (document.selection) { + document.selection.empty() + } +} + +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + +const temporarilyChangeTooltip = (el, oldText, newText) => { + el.setAttribute('data-tooltip', newText) + el.classList.add('success') + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) +} + +// Changes the copy button icon for two seconds, then changes it back +const temporarilyChangeIcon = (el) => { + el.innerHTML = iconCheck; + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) +} + +const addCopyButtonToCodeCells = () => { + // If ClipboardJS hasn't loaded, wait a bit and try again. This + // happens because we load ClipboardJS asynchronously. + if (window.ClipboardJS === undefined) { + setTimeout(addCopyButtonToCodeCells, 250) + return + } + + // Add copybuttons to all of our code cells + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) + codeCells.forEach((codeCell, index) => { + const id = codeCellId(index) + codeCell.setAttribute('id', id) + + const clipboardButton = id => + `` + codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) + }) + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} + + +var copyTargetText = (trigger) => { + var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') +} + + // Initialize with a callback so we can modify the text before copy + const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) + + // Update UI with error/success messages + clipboard.on('success', event => { + clearSelection() + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) + temporarilyChangeIcon(event.trigger) + }) + + clipboard.on('error', event => { + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) + }) +} + +runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/2024.3/_static/copybutton_funcs.js b/2024.3/_static/copybutton_funcs.js new file mode 100644 index 0000000000..dbe1aaad79 --- /dev/null +++ b/2024.3/_static/copybutton_funcs.js @@ -0,0 +1,73 @@ +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} diff --git a/2024.3/_static/css/badge_only.css b/2024.3/_static/css/badge_only.css new file mode 100644 index 0000000000..c718cee441 --- /dev/null +++ b/2024.3/_static/css/badge_only.css @@ -0,0 +1 @@ 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If a group tab + * is selected, also select tab in other tabLists. + * @param {Node} e the element that was clicked + */ +function changeTabs(e) { + // Use this instead of the element that was clicked, in case it's a child + const notSelected = this.getAttribute("aria-selected") === "false"; + const positionBefore = this.parentNode.getBoundingClientRect().top; + const notClosable = !this.parentNode.classList.contains("closeable"); + + deselectTabList(this); + + if (notSelected || notClosable) { + selectTab(this); + const name = this.getAttribute("name"); + selectNamedTabs(name, this.id); + + if (this.classList.contains("group-tab")) { + // Persist during session + session.setItem('sphinx-tabs-last-selected', name); + } + } + + const positionAfter = this.parentNode.getBoundingClientRect().top; + const positionDelta = positionAfter - positionBefore; + // Scroll to offset content resizing + window.scrollTo(0, window.scrollY + positionDelta); +} + +/** + * Select tab and show associated panel. + * @param {Node} tab tab to select + */ +function selectTab(tab) { + tab.setAttribute("aria-selected", true); + + // Show the associated panel + document + .getElementById(tab.getAttribute("aria-controls")) + .removeAttribute("hidden"); +} + +/** + * Hide the panels associated with all tabs within the + * tablist containing this tab. + * @param {Node} tab a tab within the tablist to deselect + */ +function deselectTabList(tab) { + const parent = tab.parentNode; + const grandparent = parent.parentNode; + + Array.from(parent.children) + .forEach(t => t.setAttribute("aria-selected", false)); + + Array.from(grandparent.children) + .slice(1) // Skip tablist + .forEach(panel => panel.setAttribute("hidden", true)); +} + +/** + * Select grouped tabs with the same name, but no the tab + * with the given id. + * @param {Node} name name of grouped tab to be selected + * @param {Node} clickedId id of clicked tab + */ +function selectNamedTabs(name, clickedId=null) { + const groupedTabs = document.querySelectorAll(`.sphinx-tabs-tab[name="${name}"]`); + const tabLists = Array.from(groupedTabs).map(tab => tab.parentNode); + + tabLists + .forEach(tabList => { + // Don't want to change the tabList containing the clicked tab + const clickedTab = tabList.querySelector(`[id="${clickedId}"]`); + if (clickedTab === null ) { + // Select first tab with matching name + const tab = tabList.querySelector(`.sphinx-tabs-tab[name="${name}"]`); + deselectTabList(tab); + selectTab(tab); + } + }) +} + +if (typeof exports === 'undefined') { + exports = {}; +} + +exports.keyTabs = keyTabs; +exports.changeTabs = changeTabs; +exports.selectTab = selectTab; +exports.deselectTabList = deselectTabList; +exports.selectNamedTabs = selectNamedTabs; diff --git a/2024.3/_static/version_switcher.js b/2024.3/_static/version_switcher.js new file mode 100644 index 0000000000..a2543fdb9c --- /dev/null +++ b/2024.3/_static/version_switcher.js @@ -0,0 +1,36 @@ +fetch("https://aepanchi.github.io/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> + load_versions(respText)); + + +function load_versions(json){ + var button = document.getElementById('version-switcher-button') + var container = document.getElementById('version-switcher-dropdown') + var loc = window.location.href; + var s = document.createElement('select'); + s.style = "border-radius:5px;" + const versions = JSON.parse(json); + for (entry of versions){ + var o = document.createElement('option'); + var optionText = ''; + if ('name' in entry){ + optionText = entry.name; + }else{ + optionText = entry.version; + } + o.value = entry.url; + if (current_version == entry.version){ + o.selected = true; + optionText = optionText; + } + o.innerHTML = optionText; + s.append(o); + } + s.addEventListener("change", (event)=> { + var current_url = new URL(window.location.href); + var path = current_url.pathname; + //strip version from path + var page_path = path.substring(project_name.length+current_version.length+3); + window.location.href = s.value + page_path; + }); + container.append(s); +} \ No newline at end of file diff --git a/2024.3/algorithms.html b/2024.3/algorithms.html new file mode 100644 index 0000000000..703d7b701e --- /dev/null +++ b/2024.3/algorithms.html @@ -0,0 +1,893 @@ + + + + + + +Applying Intel® Extension for Scikit-learn* impacts the following scikit-learn algorithms:
+Algorithm |
+Parameters |
+Data formats |
+
|---|---|---|
SVC |
+All parameters are supported |
+No limitations |
+
NuSVC |
+All parameters are supported |
+No limitations |
+
RandomForestClassifier |
+All parameters are supported except: +
|
+Multi-output and sparse data are not supported |
+
KNeighborsClassifier |
+
|
+Multi-output and sparse data are not supported |
+
LogisticRegression |
+All parameters are supported except: +
|
+Only dense data is supported |
+
Algorithm |
+Parameters |
+Data formats |
+
|---|---|---|
SVR |
+All parameters are supported |
+No limitations |
+
NuSVR |
+All parameters are supported |
+No limitations |
+
RandomForestRegressor |
+All parameters are supported except: +
|
+Multi-output and sparse data are not supported |
+
KNeighborsRegressor |
+All parameters are supported except: +
|
+Multi-output and sparse data are not supported |
+
LinearRegression |
+All parameters are supported except: +
|
+Only dense data is supported, #observations should be >= #features. |
+
Ridge |
+All parameters are supported except: +
|
+Only dense data is supported, #observations should be >= #features. |
+
ElasticNet |
+All parameters are supported except: +
|
+Multi-output and sparse data are not supported, #observations should be >= #features. |
+
Lasso |
+All parameters are supported except: +
|
+Multi-output and sparse data are not supported, #observations should be >= #features. |
+
Algorithm |
+Parameters |
+Data formats |
+
|---|---|---|
KMeans |
+All parameters are supported except: +
|
+No limitations |
+
DBSCAN |
+All parameters are supported except: +
|
+Only dense data is supported |
+
Algorithm |
+Parameters |
+Data formats |
+
|---|---|---|
PCA |
+All parameters are supported except: +
|
+Sparse data is not supported |
+
TSNE |
+All parameters are supported except: +
Refer to TSNE acceleration details to learn more. + |
+Sparse data is not supported |
+
Algorithm |
+Parameters |
+Data formats |
+
|---|---|---|
NearestNeighbors |
+
|
+Sparse data is not supported |
+
Algorithm |
+Parameters |
+Data formats |
+
|---|---|---|
train_test_split |
+All parameters are supported |
+Only dense data is supported |
+
assert_all_finite |
+All parameters are supported |
+Only dense data is supported |
+
pairwise_distance |
+All parameters are supported except: +
|
+Only dense data is supported |
+
roc_auc_score |
+All parameters are supported except: +
|
+No limitations |
+
Algorithm |
+Parameters |
+Data formats |
+
|---|---|---|
SVC |
+All parameters are supported except: +
|
+Only binary dense data is supported |
+
RandomForestClassifier |
+All parameters are supported except: +
|
+Multi-output and sparse data are not supported |
+
KNeighborsClassifier |
+All parameters are supported except: +
|
+Only dense data is supported |
+
LogisticRegression |
+All parameters are supported except: +
|
+Only dense data is supported |
+
Algorithm |
+Parameters |
+Data formats |
+
|---|---|---|
RandomForestRegressor |
+All parameters are supported except: +
|
+Multi-output and sparse data are not supported |
+
KNeighborsRegressor |
+All parameters are supported except: +
|
+Only dense data is supported |
+
LinearRegression |
+All parameters are supported except: +
|
+Only dense data is supported, #observations should be >= #features. |
+
Algorithm |
+Parameters |
+Data formats |
+
|---|---|---|
KMeans |
+All parameters are supported except: +
|
+Sparse data is not supported |
+
DBSCAN |
+All parameters are supported except: +
|
+Only dense data is supported |
+
Algorithm |
+Parameters |
+Data formats |
+
|---|---|---|
PCA |
+All parameters are supported except: +
|
+Sparse data is not supported |
+
Algorithm |
+Parameters |
+Data formats |
+
|---|---|---|
NearestNeighbors |
+All parameters are supported except: +
|
+Only dense data is supported |
+
Monkey-patched scikit-learn classes and functions passes scikit-learn’s own test +suite, with few exceptions, specified in deselected_tests.yaml.
+The results of the entire latest scikit-learn test suite with Intel® Extension for Scikit-learn*: CircleCI.
+We publish blogs on Medium, so follow us +to learn tips and tricks for more efficient data analysis the help of Intel® Extension for Scikit-learn*. +Here are our latest blogs:
+ +As an open source project, we welcome community contributions to Intel(R) Extension for Scikit-learn. +This document explains how to participate in project conversations, log bugs and enhancement requests, and submit code patches.
+Intel(R) Extension for Scikit-learn uses the Apache 2.0 License. By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms.
+No anonymous contributions are accepted. The name in the commit message Signed-off-by line and your email must match the change authorship information.
+Make sure your .gitconfig is set up correctly so you can use git commit -s for signing your patches:
+git config --global user.name "Kate Developer"
+git config --global user.email kate.developer@company.com
+Make sure you can build the product and run all the tests with your patch. +For a larger feature, provide a relevant test. +Document your code. Our project uses reStructuredText for documentation. +For new file(s), specify the appropriate copyright year in the first line. +Submit a pull request into the master branch. +Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository.
+We use black and isort formatters for Python* code. +The line length is 90 characters; use default options otherwise. You can find the linter configuration in .pyproject.toml.
+A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools.
+Optionally, you can install pre-commit hooks that do the formatting for you. For this, run from the top level of the repository:
+pip install pre-commit
+pre-commit install
+This page provides information about the deprecations of a specific Intel® Extension for Scikit-learn* functionality.
+Deprecation: macOS* support is deprecated for Intel® Extension for Scikit-learn*. The 2023.x releases are the last to provide it.
+Reasons for deprecation: No modern X86 macOS*-based systems are to be released.
+Alternatives: The 2023.x version on macOS*.
+Note
+Intel® Extension for Scikit-learn* contains scikit-learn patching functionality that was originally available in +daal4py package. +We recommend you to use scikit-learn-intelex package instead of daal4py. +You can learn more about daal4py in daal4py documentation.
+While daal4py is available in distribued mode, +Intel® Extension for Scikit-learn* does not currently offer this functionality.
+The performance of some algorithms changes based on the parameters that are used. +This section denotes the details of such cases.
+Refer to Supported Algorithms to see the full list of algorithms, parameters, and data formats supported in Intel® Extension for Scikit-learn*.
+TSNE algorithm consists of two components: KNN and Gradient Descent. +The overall acceleration of TSNE depends on the acceleration of each of these algorithms.
+The KNN part of the algorithm supports all parameters except:
+metric != ‘euclidean’ or ‘minkowski’ with p != 2
The Gradient Descent part of the algorithm supports all parameters except:
+n_components = 3
method = ‘exact’
verbose != 0
To get better performance, use parameters supported by both components.
+Random Forest models accelerated with Intel® Extension for Scikit-learn* and using the hist splitting +method discretize training data by creating a histogram with a configurable +number of bins. The following keyword arguments can be used to influence the +created histogram.
+Keyword argument |
+Possible values |
+Default value |
+Description |
+
|---|---|---|---|
|
+[0, inf) |
+
|
+Number of bins in the histogram with the discretized training data. The
+value |
+
|
+[1, inf) |
+
|
+Minimum number of training data points in each bin after discretization. |
+
|
+
|
+
|
+Selects the algorithm used to calculate bin edges. |
+
Note that using discretized training data can greatly accelerate model training
+times, especially for larger data sets. However, due to the reduced fidelity of
+the data, the resulting model can present worse performance metrics compared to
+a model trained on the original data. In such cases, the number of bins can be
+increased with the maxBins parameter, or binning can be disabled entirely by
+setting maxBins=0.
Intel(R) Extension for Scikit-learn is a free software AI accelerator designed to deliver up to 100X faster performance for your existing scikit-learn code. +The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel(R) platforms at launch time.
+Designed for Data Scientists and Framework Designers
+Use Intel(R) Extension for Scikit-learn, to:
+Speed up training and inference by up to 100x with the equivalent mathematical accuracy
Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs
Integrate the extension into your existing Scikit-learn applications without code modifications
Enable and disable the extension with a couple of lines of code or at the command line
Intel(R) Extension for Scikit-learn is also a part of Intel(R) AI Tools.
+
+These performance charts use benchmarks that you can find in the scikit-learn bench repository.
+See all of the Supported Algorithms.
+import numpy as np
+from sklearnex import patch_sklearn
+patch_sklearn()
+
+from sklearn.cluster import DBSCAN
+
+X = np.array([[1., 2.], [2., 2.], [2., 3.],
+ [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+import numpy as np
+import dpctl
+from sklearnex import patch_sklearn, config_context
+patch_sklearn()
+
+from sklearn.cluster import DBSCAN
+
+X = np.array([[1., 2.], [2., 2.], [2., 3.],
+ [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+with config_context(target_offload="gpu:0"):
+ clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+See Kaggle notebooks created by Intel® Extension for Scikit-learn* developers.
+Introduction to scikit-learn-intelex +provides a summary of the speedup you can achieve with Intel® Extension for Scikit-learn*.
+Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.
+The following Kaggle kernels show how to patch autoML frameworks with Intel® Extension for Scikit-learn*.
+TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.
+Kernel |
+Goal |
+
|---|---|
|
+ Data: [TPS Jun 2021] Synthetic eCommerce data + |
+Predict the category of an eCommerce product |
+
|
+ Data: Titanic datset + |
+Predict whether a passenger survivies |
+
|
+ Data: [TPS Oct 2021] Synthetic molecular response data + |
+Predict the biological response of molecules given various chemical properties |
+
|
+ Data: [TPS Nov 2021] Synthetic spam emails data + |
+Identify spam emails via features extracted from the email |
+
|
+ Data: [TPS Jan 2022] Fictional Sales data + |
+Predict the corresponding item sales for each date-country-store-item combination |
+
The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various classification tasks. +These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with Intel® Extension for Scikit-learn*.
+TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.
+Kernel |
+Goal |
+Content |
+
|---|---|---|
Logistic Regression for Binary Classification +Data: [TPS Nov 2021] Synthetic spam emails data + |
+Identify spam emails via features extracted from the email |
+
|
+
Feature Importance in Random Forest for Binary Classification +Data: [TPS Nov 2021] Synthetic spam emails data + |
+Identify spam emails via features extracted from the email |
+
|
+
Random Forest for Binary Classification +Data: [TPS Apr 2021] Synthetic data based on Titanic dataset + |
+Predict whether a passenger survivies |
+
|
+
Support Vector Classification (SVC) for Binary Classification +Data: [TPS Apr 2021] Synthetic data based on Titanic dataset + |
+Predict whether a passenger survivies |
+
|
+
Support Vector Classification (SVC) with Feature Preprocessing for Binary Classification +Data: [TPS Apr 2021] Synthetic data based on Titanic dataset + |
+Predict whether a passenger survivies |
+
|
+
Kernel |
+Goal |
+Content |
+
|---|---|---|
Logistic Regression for MultiClass Classification with Quantile Transformer +Data: [TPS Jun 2021] Synthetic eCommerce data + |
+Predict the category of an eCommerce product |
+
|
+
Support Vector Classification (SVC) for MultiClass Classification +Data: [TPS May 2021] Synthetic eCommerce data + |
+Predict the category of an eCommerce product |
+
|
+
Stacking Classifer with Logistic Regression, kNN, Random Forest, and Quantile Transformer +Data: [TPS Jun 2021] Synthetic eCommerce data + |
+Predict the category of an eCommerce product |
+
|
+
Support Vector Classification (SVC) for MultiClass Classification +Data: [TPS Dec 2021] Synthetic Forest Cover Type data + |
+Predict the forest cover type |
+
|
+
Feature Importance in Random Forest for MultiClass Classification +Data: [TPS Dec 2021] Synthetic Forest Cover Type data + |
+Predict the forest cover type |
+
|
+
k-Nearest Neighbors (kNN) for MultiClass Classification +Data: [TPS Feb 2022] Bacteria DNA + |
+Predict bacteria species based on repeated lossy measurements of DNA snippets |
+
|
+
Kernel |
+Goal |
+Content |
+
|---|---|---|
Support Vector Classification (SVC) for MultiClass Classification (CV task) +Data: Digit Recognizer (MNIST) + |
+Recognize hand-written digits |
+
|
+
k-Nearest Neighbors (kNN) for MultiClass Classification (CV task) +Data: Digit Recognizer (MNIST) + |
+Recognize hand-written digits |
+
|
+
Kernel |
+Goal |
+Content |
+
|---|---|---|
Support Vector Classification (SVC) for a Binary Classification (NLP task) +Data: Natural Language Processing with Disaster Tweets + |
+Predict which tweets are about real disasters and which ones are not |
+
|
+
One-vs-Rest Support Vector Machine (SVM) with Text Data for MultiClass Classification +Data: What’s Cooking + |
+Use recipe ingredients to predict the cuisine |
+
|
+
Support Vector Classification (SVC) for Binary Classification with Sparse Data (NLP task) +Data: Stack Overflow questions + |
+Predict the binary quality rating for Stack Overflow questions |
+
|
+
The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various regression tasks. +These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with Intel® Extension for Scikit-learn*.
+TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.
+Kernel |
+Goal |
+Content |
+
|---|---|---|
Baseline Nu Support Vector Regression (nuSVR) with RBF Kernel +Data: [TPS Jul 2021] Synthetic pollution data + |
+Predict air pollution measurements over time based on weather and input values from multiple sensors |
+
|
+
Nu Support Vector Regression (nuSVR) +Data: [TPS Aug 2021] Synthetic loan data + |
+Calculate loss associated with a loan defaults |
+
|
+
Nu Support Vector Regression (nuSVR) +Data: House Prices dataset + |
+Predict sale prices for a property based on its characteristics |
+
|
+
|
+ Data: [TPS Jul 2021] Synthetic pollution data + |
+Predict air pollution measurements over time based on weather and input values from multiple sensors |
+
|
+
Random Forest Regression with Feature Engineering +Data: [TPS Jul 2021] Synthetic pollution data + |
+Predict air pollution measurements over time based on weather and input values from multiple sensors |
+
|
+
Random Forest Regression with Feature Importance Computation +Data: [TPS Mar 2022] Spatio-temporal traffic data + |
+Forecast twelve-hours of traffic flow in a major U.S. metropolitan area |
+
|
+
|
+ Data: [TPS Sep 2021] Synthetic insurance data + |
+Predict the probability of a customer making a claim upon an insurance policy |
+
|
+
Kernel |
+Goal |
+Content |
+
|---|---|---|
Stacking Regressor with Random Fores, SVR, and LASSO +Data: [TPS Jul 2021] Synthetic pollution data + |
+Predict air pollution measurements over time based on weather and input values from multiple sensors |
+
|
+
Stacking Regressor with ElasticNet, LASSO, and Ridge Regression for Time-series data +Data: Predict Future Sales dataset + |
+Predict total sales for every product and store in the next month based on daily sales data |
+
|
+
License: https://github.com/intel/scikit-learn-intelex/blob/main/LICENSE
+Intel® Extension for Scikit-learn* supports oneAPI concepts, which +means that algorithms can be executed on different devices: CPUs and GPUs. +This is done via integration with +dpctl package that +implements core oneAPI concepts like queues and devices.
+For execution on GPU, DPC++ compiler runtime and driver are required. Refer to DPC++ system +requirements for details.
+DPC++ compiler runtime can be installed either from PyPI or Anaconda:
+Install from PyPI:
+pip install dpcpp-cpp-rt
+Install from Anaconda:
+conda install dpcpp_cpp_rt -c intel
+Intel® Extension for Scikit-learn* offers two options for running an algorithm on a +specific device with the help of dpctl:
+Pass input data as dpctl.tensor.usm_ndarray to the algorithm.
+The computation will run on the device where the input data is
+located, and the result will be returned as usm_ndarray to the same
+device.
Note
+All the input data for an algorithm must reside on the same device.
+Warning
+The usm_ndarray can only be consumed by the base methods
+like fit, predict, and transform.
+Note that only the algorithms in Intel® Extension for Scikit-learn* support
+usm_ndarray. The algorithms from the stock version of scikit-learn
+do not support this feature.
Use global configurations of Intel® Extension for Scikit-learn**:
+The target_offload option can be used to set the device primarily
+used to perform computations. Accepted data types are str and
+dpctl.SyclQueue. If you pass a string to target_offload,
+it should either be "auto", which means that the execution
+context is deduced from the location of input data, or a string
+with SYCL* filter selector. The default value is "auto".
The allow_fallback_to_host option
+is a Boolean flag. If set to True, the computation is allowed
+to fallback to the host device when a particular estimator does not support
+the selected device. The default value is False.
These options can be set using sklearnex.set_config() function or
+sklearnex.config_context. To obtain the current values of these options,
+call sklearnex.get_config().
Note
+Functions set_config, get_config and config_context
+are always patched after the sklearnex.patch_sklearn() call.
Compatibility considerations
+For compatibility reasons, algorithms in Intel® Extension for Scikit-learn* may be offloaded to the device using
+daal4py.oneapi.sycl_context. However, it is recommended to use one of the options
+described above for device offloading instead of using sycl_context.
An example on how to patch your code with Intel CPU/GPU optimizations:
+from sklearnex import patch_sklearn, config_context
+patch_sklearn()
+
+from sklearn.cluster import DBSCAN
+
+X = np.array([[1., 2.], [2., 2.], [2., 3.],
+ [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+with config_context(target_offload="gpu:0"):
+ clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+Note
+Current offloading behavior restricts fitting and inference of any models to be +in the same context or absence of context. For example, a model trained in the GPU context with +target_offload=”gpu:0” throws an error if the inference is made outside the same GPU context.
+Get ready to elevate your scikit-learn code with Intel® Extension for Scikit-learn* and experience the benefits of accelerated performance in just a few simple steps.
+Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn.
+Once you install Intel*(R) Extension for Scikit-learn*, you replace algorithms that exist in the scikit-learn package with their optimized versions from the extension.
+This action is called patching. This is not a permanent change so you can always undo the patching if necessary.
To patch Intel® Extension for Scikit-learn, use one of these methods:
+Method |
+Action |
+
|---|---|
Use a flag in the command line |
+Run this command: +python -m sklearnex my_application.py
+ |
+
Modify your script |
+Add the following lines: +from sklearnex import patch_sklearn
+patch_sklearn()
+ |
+
Import an estimator from the |
+Run this command: +from sklearnex.neighbors import NearestNeighbors
+ |
+
These patching methods are interchangeable. +They support different enabling scenarios while producing the same result.
+Example
+This example shows how to patch Intel(R) extension for Scikit-Learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import scikit-learn after these lines.
+ import numpy as np
+ from sklearnex import patch_sklearn
+ patch_sklearn()
+
+ # You need to re-import scikit-learn algorithms after the patch
+ from sklearn.cluster import KMeans
+
+ # The use of the original Scikit-learn is not changed
+ X = np.array([[1, 2], [1, 4], [1, 0],
+ [10, 2], [10, 4], [10, 0]])
+ kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
+ print(f"kmeans.labels_ = {kmeans.labels_}")
+You can also use global patching to patch all your scikit-learn applications without any additional actions.
+Before you begin, make sure that you have read and write permissions for Scikit-learn files.
+With global patching, you can:
+Task |
+Action |
+Note |
+
|---|---|---|
Patch all supported algorithms |
+Run this command: +python -m sklearnex.glob patch_sklearn
+ |
+If you run the global patching command several times with different parameters, then only the last configuration is applied. |
+
Patch selected algorithms |
+Use python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier
+ |
++ |
Enable global patching via code |
+Use the from sklearnex import patch_sklearn
+patch_sklearn(global_patch=True)
+import sklearn
+ |
+After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment. |
+
Disable patching notifications |
+Use python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier -nv
+ |
++ |
Disable global patching |
+Run this command: +python -m sklearnex.glob unpatch_sklearn
+ |
++ |
Disable global patching via code |
+Use the from sklearnex import unpatch_sklearn
+unpatch_sklearn(global_patch=True)
+ |
++ |
Tip
+If you clone an environment with enabled global patching, it will already be applied in the new environment.
+To undo the patch (also called unpatching) is to return scikit-learn to original implementation and +replace patched algorithms with the stock scikit-learn algorithms.
+To unpatch successfully, you must reimport the scikit-learn package:
+sklearnex.unpatch_sklearn()
+# Re-import scikit-learn algorithms after the unpatch
+from sklearn.cluster import KMeans
+Tip
+To prevent version conflicts, we recommend creating and activating a new environment for Intel® Extension for Scikit-learn*.
+Recommended by default.
+To install Intel® Extension for Scikit-learn*, run:
+pip install scikit-learn-intelex
+Supported Configurations
+OS / Python version |
+Python 3.8 |
+Python 3.9 |
+Python 3.10 |
+Python 3.11 |
+Python 3.12 |
+
|---|---|---|---|---|---|
Linux* OS |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+
Windows* OS |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+
To prevent version conflicts, we recommend installing scikit-learn-intelex into a new conda environment.
+Recommended by default.
+To install, run:
+conda install scikit-learn-intelex -c conda-forge
+OS / Python version |
+Python 3.8 |
+Python 3.9 |
+Python 3.10 |
+Python 3.11 |
+Python 3.12 |
+
|---|---|---|---|---|---|
Linux* OS |
+[CPU] |
+[CPU] |
+[CPU] |
+[CPU] |
+[CPU] |
+
Windows* OS |
+[CPU] |
+[CPU] |
+[CPU] |
+[CPU] |
+[CPU] |
+
Recommended for the Intel® Distribution for Python users.
+To install, run:
+conda install scikit-learn-intelex -c intel
+OS / Python version |
+Python 3.8 |
+Python 3.9 |
+Python 3.10 |
+Python 3.11 |
+Python 3.12 |
+
|---|---|---|---|---|---|
Linux* OS |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+
Windows* OS |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+[CPU, GPU] |
+
To install, run:
+conda install scikit-learn-intelex
+OS / Python version |
+Python 3.8 |
+Python 3.9 |
+Python 3.10 |
+Python 3.11 |
+Python 3.12 |
+
|---|---|---|---|---|---|
Linux* OS |
+[CPU] |
+[CPU] |
+[CPU] |
+[CPU] |
+[CPU] |
+
Windows* OS |
+[CPU] |
+[CPU] |
+[CPU] |
+[CPU] |
+[CPU] |
+
See Installation instructions to build Intel® Extension for Scikit-learn* from the sources.
+Download the Intel AI Tools here. The extension is already included.
+See the Release Notes for each version of Intel® Extension for Scikit-learn*.
+All processors with x86 architecture with at least one of the following instruction sets:
+++
+- +
SSE2
- +
SSE4.2
- +
AVX2
- +
AVX512
Note
+ARM* architecture is not supported.
+All Intel® integrated and discrete GPUs
Intel® GPU drivers
Tip
+Intel(R) processors provide better performance than other CPUs. Read more about hardware comparison in our blogs.
+Linux* OS: Ubuntu* 18.04 or newer
Windows* OS 10 or newer
Windows* Server 2019 or newer
Linux* OS: Ubuntu* 18.04 or newer
Windows* OS 10 or newer
Windows* Server 2019 or newer
Important
+If you use accelerators, refer to oneAPI DPC++/C++ Compiler System Requirements.
+Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn:
+1.0.X
1.1.X
1.2.X
1.3.X
By default, algorithms in Intel® Extension for Scikit-learn* run in the multi-thread mode. This mode uses all available threads. +Optimized scikit-learn algorithms can consume more RAM than their corresponding unoptimized versions.
+Algorithm |
+Single-thread mode |
+Multi-thread mode |
+
|---|---|---|
SVM |
+Both Scikit-learn and Intel® Extension for Scikit-learn* consume approximately the same amount of RAM. |
+In Intel® Extension for Scikit-learn*, an algorithm with |
+
In all Intel® Extension for Scikit-learn* algorithms with GPU support, computations run on device memory. +The device memory must be large enough to store a copy of the entire dataset. +You may also require additional device memory for internal arrays that are used in computation.
+See also
+ +The following samples are also provided as Jupyter notebooks in Intel® Extension for Scikit-learn* repository. +If you want to run them locally, refer to these instructions.
+Classification Tasks
+Regression Tasks
+Clustering Tasks
+ +Jupyter Notebook Samples
+See also
+There are also Kaggle kernels that use Intel® Extension for Scikit-learn* for a variety of machine learning scenarios.
+Note
+Explore the complete list of oneAPI code samples in the oneAPI Samples Catalog. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.
+[1]:
+from timeit import default_timer as timer
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+import warnings
+from sklearn.datasets import fetch_openml
+from sklearn.preprocessing import LabelEncoder
+from IPython.display import HTML
+
+warnings.filterwarnings("ignore")
+[2]:
+x, y = fetch_openml(name="Airlines_DepDelay_10M", return_X_y=True)
+Let’s encode categorical features with LabelEncoder
+[3]:
+for col in ["UniqueCarrier", "Origin", "Dest"]:
+ le = LabelEncoder().fit(x[col])
+ x[col] = le.transform(x[col])
+Split the data into train and test sets
+[4]:
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)
+x_train.shape, x_test.shape, y_train.shape, y_test.shape
+[4]:
+
+((9000000, 9), (1000000, 9), (9000000,), (1000000,))
+Normalize the data
+[5]:
+from sklearn.preprocessing import StandardScaler
+
+scaler_y = StandardScaler()
+[6]:
+y_train = y_train.to_numpy().reshape(-1, 1)
+y_test = y_test.to_numpy().reshape(-1, 1)
+
+scaler_y.fit(y_train)
+y_train = scaler_y.transform(y_train).ravel()
+y_test = scaler_y.transform(y_test).ravel()
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[7]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset
+[8]:
+from sklearn.linear_model import ElasticNet
+
+params = {
+ "alpha": 0.3,
+ "fit_intercept": False,
+ "l1_ratio": 0.7,
+ "random_state": 0,
+ "copy_X": False,
+}
+start = timer()
+model = ElasticNet(**params).fit(x_train, y_train)
+train_patched = timer() - start
+f"Intel® extension for Scikit-learn time: {train_patched:.2f} s"
+[8]:
+
+'Intel® extension for Scikit-learn time: 0.28 s'
+Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn
+[9]:
+y_predict = model.predict(x_test)
+mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)
+f"Patched Scikit-learn MSE: {mse_metric_opt}"
+[9]:
+
+'Patched Scikit-learn MSE: 1.0109113399224974'
+In order to cancel optimizations, we use unpatch_sklearn and reimport the class ElasticNet
+[10]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset
+[11]:
+from sklearn.linear_model import ElasticNet
+
+start = timer()
+model = ElasticNet(**params).fit(x_train, y_train)
+train_unpatched = timer() - start
+f"Original Scikit-learn time: {train_unpatched:.2f} s"
+[11]:
+
+'Original Scikit-learn time: 3.96 s'
+Predict and get a result of the ElasticNet algorithm with original Scikit-learn
+[12]:
+y_predict = model.predict(x_test)
+mse_metric_original = metrics.mean_squared_error(y_test, y_predict)
+f"Original Scikit-learn MSE: {mse_metric_original}"
+[12]:
+
+'Original Scikit-learn MSE: 1.0109113399545733'
+[13]:
+HTML(
+ f"<h3>Compare MSE metric of patched Scikit-learn and original</h3>"
+ f"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>"
+ f"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>"
+ f"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>"
+ f"<h3>With Scikit-learn-intelex patching you can:</h3>"
+ f"<ul>"
+ f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
+ f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
+ f"<li>Get the similar quality</li>"
+ f"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>"
+ f"</ul>"
+)
+[13]:
+The notebook below has been made to demonstrate daal4py in a data science context. It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user’s Power output in the absence of a power meter.
+[1]:
+import pandas as pd
+import matplotlib.pyplot as plt
+import glob
+import sys
+
+%matplotlib inline
+sys.version
+[1]:
+
+'3.8.10 (default, May 19 2021, 18:05:58) \n[GCC 7.3.0]'
+This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage. Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()
+[2]:
+workout_data_dd = pd.read_csv("data/cycling_dataset.csv", index_col=0)
+workout_data_dd.head()
+[2]:
+| + | altitude | +cadence | +distance | +hr | +latitude | +longitude | +power | +speed | +time | +
|---|---|---|---|---|---|---|---|---|---|
| 0 | +185.800003 | +51 | +3.46 | +81 | +30.313309 | +-97.732711 | +45 | +3.459 | +2016-10-20T22:01:26.000Z | +
| 1 | +185.800003 | +68 | +7.17 | +82 | +30.313277 | +-97.732715 | +0 | +3.710 | +2016-10-20T22:01:27.000Z | +
| 2 | +186.399994 | +38 | +11.04 | +82 | +30.313243 | +-97.732717 | +42 | +3.874 | +2016-10-20T22:01:28.000Z | +
| 3 | +186.800003 | +38 | +15.18 | +83 | +30.313212 | +-97.732720 | +5 | +4.135 | +2016-10-20T22:01:29.000Z | +
| 4 | +186.600006 | +38 | +19.43 | +83 | +30.313172 | +-97.732723 | +1 | +4.250 | +2016-10-20T22:01:30.000Z | +
In general, we are trying to predict on the ‘power’ in Watts to see if we can generate a model that can predict one’s power output without the usage of a cycling power meter. Below are some basic scatterplots as we explore the data. Scatterplots are great for looking for patterns and correlation in the data itself. Below, we can see that cadence and speed are positively correlated.
+[3]:
+workout_data_dd.plot.scatter("cadence", "power")
+plt.show()
+workout_data_dd.plot.scatter("hr", "power")
+plt.show()
+workout_data_dd.plot.scatter("cadence", "speed")
+plt.show()
+workout_data_dd.plot.scatter("speed", "power")
+plt.show()
+workout_data_dd.plot.scatter("altitude", "power")
+plt.show()
+workout_data_dd.plot.scatter("distance", "power")
+plt.show()
+
+
+
+
+
+
+In the sections below, we will be using daal4py directly. After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py’s training and prediction classes to generate a workable model.
+[4]:
+import daal4py as d4p
+It is now the time to split the dataset into train and test sets. This is demonstrated below.
+[5]:
+print(workout_data_dd.shape)
+train_set = workout_data_dd[0:3000]
+test_set = workout_data_dd[3000:]
+print(train_set.shape, test_set.shape)
+
+(3902, 9)
+(3000, 9) (902, 9)
+[6]:
+# Reduce the dataset, create X. We drop the target, and other non-essential features.
+reduced_dataset = train_set.drop(["time", "power", "latitude", "longitude"], axis=1)
+# Get the target, create Y
+target = train_set.power.values.reshape((-1, 1))
+# This is essentially doing np.array(dataset.power.values, ndmin=2).T
+# as it needs to force a 2 dimensional array as we only have 1 target
+X is 5 features by 3k rows, Y is 3k rows by 1 column
+[7]:
+print(reduced_dataset.values.shape, target.shape)
+
+(3000, 5) (3000, 1)
+Create the Linear Regression Model, and train the model with the data. We utilize daal4py’s linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters.
+[8]:
+d4p_lm = d4p.linear_regression_training(interceptFlag=True)
+lm_trained = d4p_lm.compute(reduced_dataset.values, target)
+[9]:
+print("Model has this number of features: ", lm_trained.model.NumberOfFeatures)
+
+Model has this number of features: 5
+Now that the model is trained, we can test it with the test part of the dataset. We drop the same features to match that of the trained model, and put it into daal4py’s linear_regression_prediction class.
+[10]:
+subset = test_set.drop(["time", "power", "latitude", "longitude"], axis=1)
+Now we can create the Prediction object and use the reduced dataset for prediction. The class’s arguments use the independent data and the trained model from above as the parameters.
+[11]:
+lm_predictor_component = d4p.linear_regression_prediction()
+result = lm_predictor_component.compute(subset.values, lm_trained.model)
+[12]:
+plt.plot(result.prediction[0:300])
+plt.plot(test_set.power.values[0:300])
+plt.show()
+
+The graph above shows the Orange (predicted) result over the Blue (original data). This data is notoriously sparse in features leading to a difficult to predict target!
+Another aspect of the model is the trained model’s properties, which are explored below.
+[13]:
+print("Betas:", lm_trained.model.Beta)
+print("Number of betas:", lm_trained.model.NumberOfBetas)
+print("Number of Features:", lm_trained.model.NumberOfFeatures)
+
+Betas: [[ 1.51003501e+01 -1.25075548e-01 1.32249115e+00 1.64363922e-03
+ 8.53155955e-01 -1.09595022e+01]]
+Number of betas: 6
+Number of Features: 5
+We can generate metrics on the independent data with daal4py’s low_order_moments() class.
+[14]:
+metrics_processor = d4p.low_order_moments()
+data = metrics_processor.compute(reduced_dataset.values)
+data.standardDeviation
+[14]:
+
+array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,
+ 1.81623064e+00]])
+Occasionally one may need to migrate the trained model to another system for inference only–this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine.
+[15]:
+import pickle
+[16]:
+with open("trained_model2.pickle", "wb") as model_pi:
+ pickle.dump(lm_trained.model, model_pi)
+ model_pi.close
+The trained model file above can be moved to an inference-only or embedded system. This is useful if the training is extreamly heavy or computed-limited.
+[17]:
+with open("trained_model2.pickle", "rb") as model_import:
+ lm_import = pickle.load(model_import)
+The imported model from file is now usable again. We can check the betas from the model to ensure that the trained model is present.
+[18]:
+lm_import.Beta
+[18]:
+
+array([[ 1.51003501e+01, -1.25075548e-01, 1.32249115e+00,
+ 1.64363922e-03, 8.53155955e-01, -1.09595022e+01]])
+| \n", + " | altitude | \n", + "cadence | \n", + "distance | \n", + "hr | \n", + "latitude | \n", + "longitude | \n", + "power | \n", + "speed | \n", + "time | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "185.800003 | \n", + "51 | \n", + "3.46 | \n", + "81 | \n", + "30.313309 | \n", + "-97.732711 | \n", + "45 | \n", + "3.459 | \n", + "2016-10-20T22:01:26.000Z | \n", + "
| 1 | \n", + "185.800003 | \n", + "68 | \n", + "7.17 | \n", + "82 | \n", + "30.313277 | \n", + "-97.732715 | \n", + "0 | \n", + "3.710 | \n", + "2016-10-20T22:01:27.000Z | \n", + "
| 2 | \n", + "186.399994 | \n", + "38 | \n", + "11.04 | \n", + "82 | \n", + "30.313243 | \n", + "-97.732717 | \n", + "42 | \n", + "3.874 | \n", + "2016-10-20T22:01:28.000Z | \n", + "
| 3 | \n", + "186.800003 | \n", + "38 | \n", + "15.18 | \n", + "83 | \n", + "30.313212 | \n", + "-97.732720 | \n", + "5 | \n", + "4.135 | \n", + "2016-10-20T22:01:29.000Z | \n", + "
| 4 | \n", + "186.600006 | \n", + "38 | \n", + "19.43 | \n", + "83 | \n", + "30.313172 | \n", + "-97.732723 | \n", + "1 | \n", + "4.250 | \n", + "2016-10-20T22:01:30.000Z | \n", + "
[1]:
+from timeit import default_timer as timer
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import davies_bouldin_score
+from sklearn.datasets import fetch_openml
+from IPython.display import HTML
+import warnings
+
+warnings.filterwarnings("ignore")
+[2]:
+x, y = fetch_openml(name="spoken-arabic-digit", return_X_y=True)
+Split the data into train and test sets
+[3]:
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)
+Normalize the data
+[4]:
+from sklearn.preprocessing import MinMaxScaler
+
+scaler_x = MinMaxScaler()
+[5]:
+scaler_x.fit(x_train)
+x_train = scaler_x.transform(x_train)
+x_test = scaler_x.transform(x_test)
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[6]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset
+[7]:
+from sklearn.cluster import DBSCAN
+
+params = {
+ "n_jobs": -1,
+}
+start = timer()
+y_pred = DBSCAN(**params).fit_predict(x_train)
+train_patched = timer() - start
+f"Intel® extension for Scikit-learn time: {train_patched:.2f} s"
+[7]:
+
+'Intel® extension for Scikit-learn time: 6.37 s'
+Let’s take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn
+[8]:
+score_opt = davies_bouldin_score(x_train, y_pred)
+f"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}"
+[8]:
+
+'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'
+In order to cancel optimizations, we use unpatch_sklearn and reimport the class DBSCAN
+[9]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset
+[10]:
+from sklearn.cluster import DBSCAN
+
+start = timer()
+y_pred = DBSCAN(**params).fit_predict(x_train)
+train_unpatched = timer() - start
+f"Original Scikit-learn time: {train_unpatched:.2f} s"
+[10]:
+
+'Original Scikit-learn time: 469.21 s'
+Let’s take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn
+[11]:
+score_original = davies_bouldin_score(x_train, y_pred)
+f"Original Scikit-learn Davies-Bouldin score: {score_opt}"
+[11]:
+
+'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'
+[12]:
+HTML(
+ f"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>"
+ f"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>"
+ f"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>"
+ f"Metrics ratio: {score_opt/score_original} <br>"
+ f"<h3>With Scikit-learn-intelex patching you can:</h3>"
+ f"<ul>"
+ f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
+ f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
+ f"<li>Get the similar quality</li>"
+ f"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>"
+ f"</ul>"
+)
+[12]:
+[1]:
+from timeit import default_timer as timer
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import fetch_openml
+from IPython.display import HTML
+import warnings
+
+warnings.filterwarnings("ignore")
+[2]:
+x, y = fetch_openml(name="spoken-arabic-digit", return_X_y=True)
+Split the data into train and test sets
+[3]:
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)
+x_train.shape, x_test.shape, y_train.shape, y_test.shape
+[3]:
+
+((236930, 14), (26326, 14), (236930,), (26326,))
+Normalize the data
+[4]:
+from sklearn.preprocessing import MinMaxScaler
+
+scaler_x = MinMaxScaler()
+[5]:
+scaler_x.fit(x_train)
+x_train = scaler_x.transform(x_train)
+x_test = scaler_x.transform(x_test)
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[6]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset
+[7]:
+from sklearn.cluster import KMeans
+
+params = {
+ "n_clusters": 128,
+ "random_state": 123,
+ "copy_x": False,
+}
+start = timer()
+model = KMeans(**params).fit(x_train, y_train)
+train_patched = timer() - start
+f"Intel® extension for Scikit-learn time: {train_patched:.2f} s"
+[7]:
+
+'Intel® extension for Scikit-learn time: 7.36 s'
+Let’s take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn
+[8]:
+inertia_opt = model.inertia_
+n_iter_opt = model.n_iter_
+print(f"Intel® extension for Scikit-learn inertia: {inertia_opt}")
+print(f"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}")
+
+Intel® extension for Scikit-learn inertia: 13346.641333761074
+Intel® extension for Scikit-learn number of iterations: 274
+In order to cancel optimizations, we use unpatch_sklearn and reimport the class KMeans
+[9]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset
+[10]:
+from sklearn.cluster import KMeans
+
+start = timer()
+model = KMeans(**params).fit(x_train, y_train)
+train_unpatched = timer() - start
+f"Original Scikit-learn time: {train_unpatched:.2f} s"
+[10]:
+
+'Original Scikit-learn time: 192.14 s'
+Let’s take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn
+[11]:
+inertia_original = model.inertia_
+n_iter_original = model.n_iter_
+print(f"Original Scikit-learn inertia: {inertia_original}")
+print(f"Original Scikit-learn number of iterations: {n_iter_original}")
+
+Original Scikit-learn inertia: 13352.813785961785
+Original Scikit-learn number of iterations: 212
+[12]:
+HTML(
+ f"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>"
+ f"<strong>Inertia:</strong><br>"
+ f"Patched Scikit-learn: {inertia_opt} <br>"
+ f"Unpatched Scikit-learn: {inertia_original} <br>"
+ f"Ratio: {inertia_opt/inertia_original} <br><br>"
+ f"<strong>Number of iterations:</strong><br>"
+ f"Patched Scikit-learn: {n_iter_opt} <br>"
+ f"Unpatched Scikit-learn: {n_iter_original} <br>"
+ f"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>"
+ f"Number of iterations is bigger but algorithm is much faster and inertia is lower"
+ f"<h3>With Scikit-learn-intelex patching you can:</h3>"
+ f"<ul>"
+ f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
+ f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
+ f"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>"
+ f"</ul>"
+)
+[12]:
+[1]:
+from timeit import default_timer as timer
+from IPython.display import HTML
+from sklearn import metrics
+from sklearn.datasets import fetch_openml
+from sklearn.model_selection import train_test_split
+[2]:
+x, y = fetch_openml(name="mnist_784", return_X_y=True)
+Split the data into train and test sets
+[3]:
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)
+x_train.shape, x_test.shape, y_train.shape, y_test.shape
+[3]:
+
+((56000, 784), (14000, 784), (56000,), (14000,))
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[4]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset
+[5]:
+from sklearn.neighbors import KNeighborsClassifier
+
+params = {"n_neighbors": 40, "weights": "distance", "n_jobs": -1}
+start = timer()
+knn = KNeighborsClassifier(**params).fit(x_train, y_train)
+predicted = knn.predict(x_test)
+time_opt = timer() - start
+f"Intel® extension for Scikit-learn time: {time_opt:.2f} s"
+[5]:
+
+'Intel® extension for Scikit-learn time: 1.45 s'
+[6]:
+report = metrics.classification_report(y_test, predicted)
+print(f"Classification report for Intel® extension for Scikit-learn KNN:\n{report}\n")
+
+Classification report for Intel® extension for Scikit-learn KNN:
+ precision recall f1-score support
+
+ 0 0.97 0.99 0.98 1365
+ 1 0.93 0.99 0.96 1637
+ 2 0.99 0.94 0.96 1401
+ 3 0.96 0.95 0.96 1455
+ 4 0.98 0.96 0.97 1380
+ 5 0.95 0.95 0.95 1219
+ 6 0.96 0.99 0.97 1317
+ 7 0.94 0.95 0.95 1420
+ 8 0.99 0.90 0.94 1379
+ 9 0.92 0.94 0.93 1427
+
+ accuracy 0.96 14000
+ macro avg 0.96 0.96 0.96 14000
+weighted avg 0.96 0.96 0.96 14000
+
+
+The first column of the classification report above is the class labels.
+In order to cancel optimizations, we use unpatch_sklearn and reimport the class KNeighborsClassifier.
+[7]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset
+[8]:
+from sklearn.neighbors import KNeighborsClassifier
+
+
+start = timer()
+knn = KNeighborsClassifier(**params).fit(x_train, y_train)
+predicted = knn.predict(x_test)
+time_original = timer() - start
+f"Original Scikit-learn time: {time_original:.2f} s"
+[8]:
+
+'Original Scikit-learn time: 36.15 s'
+[9]:
+report = metrics.classification_report(y_test, predicted)
+print(f"Classification report for original Scikit-learn KNN:\n{report}\n")
+
+Classification report for original Scikit-learn KNN:
+ precision recall f1-score support
+
+ 0 0.97 0.99 0.98 1365
+ 1 0.93 0.99 0.96 1637
+ 2 0.99 0.94 0.96 1401
+ 3 0.96 0.95 0.96 1455
+ 4 0.98 0.96 0.97 1380
+ 5 0.95 0.95 0.95 1219
+ 6 0.96 0.99 0.97 1317
+ 7 0.94 0.95 0.95 1420
+ 8 0.99 0.90 0.94 1379
+ 9 0.92 0.94 0.93 1427
+
+ accuracy 0.96 14000
+ macro avg 0.96 0.96 0.96 14000
+weighted avg 0.96 0.96 0.96 14000
+
+
+[10]:
+HTML(
+ f"<h2>With scikit-learn-intelex patching you can:</h2>"
+ f"<ul>"
+ f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
+ f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
+ f"<li>Get the similar quality</li>"
+ f"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>"
+ f"</ul>"
+)
+[10]:
+[1]:
+from timeit import default_timer as timer
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+import pandas as pd
+import numpy as np
+import requests
+import warnings
+import os
+from IPython.display import HTML
+
+warnings.filterwarnings("ignore")
+[2]:
+dataset_dir = "data"
+dataset_name = "year_prediction_msd"
+url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip"
+
+os.makedirs(dataset_dir, exist_ok=True)
+local_url = os.path.join(dataset_dir, os.path.basename(url))
+
+if not os.path.isfile(local_url):
+ response = requests.get(url, stream=True)
+ with open(local_url, "wb+") as file:
+ for data in response.iter_content(8192):
+ file.write(data)
+
+year = pd.read_csv(local_url, header=None)
+x = year.iloc[:, 1:].to_numpy(dtype=np.float32)
+y = year.iloc[:, 0].to_numpy(dtype=np.float32)
+Split the data into train and test sets
+[3]:
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)
+x_train.shape, x_test.shape, y_train.shape, y_test.shape
+[3]:
+
+((463810, 90), (51535, 90), (463810,), (51535,))
+[4]:
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+
+scaler_x = MinMaxScaler()
+scaler_y = StandardScaler()
+[5]:
+scaler_x.fit(x_train)
+x_train = scaler_x.transform(x_train)
+x_test = scaler_x.transform(x_test)
+
+scaler_y.fit(y_train.reshape(-1, 1))
+y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()
+y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[6]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset
+[7]:
+from sklearn.linear_model import Lasso
+
+params = {
+ "alpha": 0.01,
+ "fit_intercept": False,
+ "random_state": 0,
+ "copy_X": False,
+}
+start = timer()
+model = Lasso(**params).fit(x_train, y_train)
+train_patched = timer() - start
+f"Intel® extension for Scikit-learn time: {train_patched:.2f} s"
+[7]:
+
+'Intel® extension for Scikit-learn time: 0.06 s'
+Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn
+[8]:
+y_predict = model.predict(x_test)
+mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)
+f"Patched Scikit-learn MSE: {mse_metric_opt}"
+[8]:
+
+'Patched Scikit-learn MSE: 0.9676607251167297'
+In order to cancel optimizations, we use unpatch_sklearn and reimport the class Lasso
+[9]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset
+[10]:
+from sklearn.linear_model import Lasso
+
+start = timer()
+model = Lasso(**params).fit(x_train, y_train)
+train_unpatched = timer() - start
+f"Original Scikit-learn time: {train_unpatched:.2f} s"
+[10]:
+
+'Original Scikit-learn time: 0.83 s'
+Predict and get a result of the Lasso algorithm with original Scikit-learn
+[11]:
+y_predict = model.predict(x_test)
+mse_metric_original = metrics.mean_squared_error(y_test, y_predict)
+f"Original Scikit-learn MSE: {mse_metric_original}"
+[11]:
+
+'Original Scikit-learn MSE: 0.9676599502563477'
+[12]:
+HTML(
+ f"<h3>Compare MSE metric of patched Scikit-learn and original</h3>"
+ f"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>"
+ f"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>"
+ f"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>"
+ f"<h3>With Scikit-learn-intelex patching you can:</h3>"
+ f"<ul>"
+ f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
+ f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
+ f"<li>Get the similar quality</li>"
+ f"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>"
+ f"</ul>"
+)
+[12]:
+[1]:
+from timeit import default_timer as timer
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+import pandas as pd
+import numpy as np
+import os
+import requests
+import warnings
+from IPython.display import HTML
+
+warnings.filterwarnings("ignore")
+[2]:
+dataset_dir = "data"
+dataset_name = "year_prediction_msd"
+url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip"
+
+os.makedirs(dataset_dir, exist_ok=True)
+local_url = os.path.join(dataset_dir, os.path.basename(url))
+
+if not os.path.isfile(local_url):
+ response = requests.get(url, stream=True)
+ with open(local_url, "wb+") as file:
+ for data in response.iter_content(8192):
+ file.write(data)
+
+year = pd.read_csv(local_url, header=None)
+x = year.iloc[:, 1:].to_numpy(dtype=np.float32)
+y = year.iloc[:, 0].to_numpy(dtype=np.float32)
+Split the data into train and test sets
+[3]:
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)
+x_train.shape, x_test.shape, y_train.shape, y_test.shape
+[3]:
+
+((463810, 90), (51535, 90), (463810,), (51535,))
+[4]:
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+
+scaler_x = MinMaxScaler()
+scaler_y = StandardScaler()
+[5]:
+scaler_x.fit(x_train)
+x_train = scaler_x.transform(x_train)
+x_test = scaler_x.transform(x_test)
+
+scaler_y.fit(y_train.reshape(-1, 1))
+y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()
+y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[6]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset
+[7]:
+from sklearn.linear_model import LinearRegression
+
+params = {"n_jobs": -1, "copy_X": False}
+start = timer()
+model = LinearRegression(**params).fit(x_train, y_train)
+train_patched = timer() - start
+f"Intel® extension for Scikit-learn time: {train_patched:.2f} s"
+[7]:
+
+'Intel® extension for Scikit-learn time: 0.03 s'
+Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn
+[8]:
+y_predict = model.predict(x_test)
+mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)
+f"Patched Scikit-learn MSE: {mse_metric_opt}"
+[8]:
+
+'Patched Scikit-learn MSE: 0.7716818451881409'
+In order to cancel optimizations, we use unpatch_sklearn and reimport the class LinearRegression
+[9]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset
+[10]:
+from sklearn.linear_model import LinearRegression
+
+start = timer()
+model = LinearRegression(**params).fit(x_train, y_train)
+train_unpatched = timer() - start
+f"Original Scikit-learn time: {train_unpatched:.2f} s"
+[10]:
+
+'Original Scikit-learn time: 0.53 s'
+Predict and get a result of the Linear Regression algorithm with original Scikit-learn
+[11]:
+y_predict = model.predict(x_test)
+mse_metric_original = metrics.mean_squared_error(y_test, y_predict)
+f"Original Scikit-learn MSE: {mse_metric_original}"
+[11]:
+
+'Original Scikit-learn MSE: 0.7716856598854065'
+[12]:
+HTML(
+ f"<h3>Compare MSE metric of patched Scikit-learn and original</h3>"
+ f"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>"
+ f"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>"
+ f"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>"
+ f"<h3>With Scikit-learn-intelex patching you can:</h3>"
+ f"<ul>"
+ f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
+ f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
+ f"<li>Get the similar quality</li>"
+ f"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>"
+ f"</ul>"
+)
+[12]:
+[1]:
+from timeit import default_timer as timer
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+import warnings
+from IPython.display import HTML
+
+warnings.filterwarnings("ignore")
+[2]:
+from sklearn.datasets import fetch_openml
+
+x, y = fetch_openml(name="CIFAR-100", return_X_y=True)
+Split the data into train and test sets
+[3]:
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)
+x_train.shape, x_test.shape, y_train.shape
+[3]:
+
+((54000, 3072), (6000, 3072), (54000,))
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[4]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset
+[5]:
+from sklearn.linear_model import LogisticRegression
+
+params = {
+ "C": 0.1,
+ "solver": "lbfgs",
+ "multi_class": "multinomial",
+ "n_jobs": -1,
+}
+start = timer()
+classifier = LogisticRegression(**params).fit(x_train, y_train)
+train_patched = timer() - start
+f"Intel® extension for Scikit-learn time: {train_patched:.2f} s"
+[5]:
+
+'Intel® extension for Scikit-learn time: 24.82 s'
+Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn
+[6]:
+y_predict = classifier.predict_proba(x_test)
+log_loss_opt = metrics.log_loss(y_test, y_predict)
+f"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s"
+[6]:
+
+'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'
+In order to cancel optimizations, we use unpatch_sklearn and reimport the class LogisticRegression
+[7]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset
+[8]:
+from sklearn.linear_model import LogisticRegression
+
+start = timer()
+classifier = LogisticRegression(**params).fit(x_train, y_train)
+train_unpatched = timer() - start
+f"Original Scikit-learn time: {train_unpatched:.2f} s"
+[8]:
+
+'Original Scikit-learn time: 395.03 s'
+Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn
+[9]:
+y_predict = classifier.predict_proba(x_test)
+log_loss_original = metrics.log_loss(y_test, y_predict)
+f"Original Scikit-learn Log Loss: {log_loss_original} s"
+[9]:
+
+'Original Scikit-learn Log Loss: 3.7140870590578428 s'
+[10]:
+HTML(
+ f"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>"
+ f"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>"
+ f"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>"
+ f"Metrics ratio: {log_loss_opt/log_loss_original} <br>"
+ f"<h3>With Scikit-learn-intelex patching you can:</h3>"
+ f"<ul>"
+ f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
+ f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
+ f"<li>Get the similar quality</li>"
+ f"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>"
+ f"</ul>"
+)
+[10]:
+[1]:
+from timeit import default_timer as timer
+from sklearn.datasets import fetch_openml
+from sklearn.model_selection import train_test_split
+from IPython.display import HTML
+import warnings
+
+warnings.filterwarnings("ignore")
+[2]:
+x, y = fetch_openml(name="medical_charges_nominal", return_X_y=True)
+Encode categorical features
+[3]:
+cat_columns = x.select_dtypes(["category"]).columns
+x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)
+Split the data into train and test sets
+[4]:
+x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)
+x_train.shape, x_test.shape, y_train.shape, y_test.shape
+[4]:
+
+((48919, 11), (114146, 11), (48919,), (114146,))
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[5]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset
+[6]:
+from sklearn.svm import NuSVR
+
+params = {
+ "nu": 0.4,
+ "C": y_train.mean(),
+ "degree": 2,
+ "kernel": "poly",
+}
+start = timer()
+nusvr = NuSVR(**params).fit(x_train, y_train)
+train_patched = timer() - start
+f"Intel® extension for Scikit-learn time: {train_patched:.2f} s"
+[6]:
+
+'Intel® extension for Scikit-learn time: 24.69 s'
+Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn
+[7]:
+score_opt = nusvr.score(x_test, y_test)
+f"Intel® extension for Scikit-learn R2 score: {score_opt}"
+[7]:
+
+'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'
+In order to cancel optimizations, we use unpatch_sklearn and reimport the class NuSVR
+[8]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset
+[9]:
+from sklearn.svm import NuSVR
+
+start = timer()
+nusvr = NuSVR(**params).fit(x_train, y_train)
+train_unpatched = timer() - start
+f"Original Scikit-learn time: {train_unpatched:.2f} s"
+[9]:
+
+'Original Scikit-learn time: 331.85 s'
+Predict and get a result of the NuSVR algorithm with original Scikit-learn
+[10]:
+score_original = nusvr.score(x_test, y_test)
+f"Original Scikit-learn R2 score: {score_original}"
+[10]:
+
+'Original Scikit-learn R2 score: 0.8636031741516902'
+[11]:
+HTML(
+ f"<h3>Compare R2 score of patched Scikit-learn and original</h3>"
+ f"R2 score of patched Scikit-learn: {score_opt} <br>"
+ f"R2 score of unpatched Scikit-learn: {score_original} <br>"
+ f"Metrics ratio: {score_opt/score_original} <br>"
+ f"<h3>With Scikit-learn-intelex patching you can:</h3>"
+ f"<ul>"
+ f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
+ f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
+ f"<li>Get the similar quality</li>"
+ f"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>"
+ f"</ul>"
+)
+[11]:
+[1]:
+from timeit import default_timer as timer
+from sklearn import metrics
+from IPython.display import HTML
+from sklearn.datasets import fetch_openml
+from sklearn.model_selection import train_test_split
+[2]:
+x, y = fetch_openml(name="Yolanda", return_X_y=True)
+Split the data into train and test sets
+[3]:
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)
+x_train.shape, x_test.shape, y_train.shape, y_test.shape
+[3]:
+
+((280000, 100), (120000, 100), (280000,), (120000,))
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[4]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset
+[5]:
+from sklearn.ensemble import RandomForestRegressor
+
+params = {"n_estimators": 150, "random_state": 44, "n_jobs": -1}
+start = timer()
+rf = RandomForestRegressor(**params).fit(x_train, y_train)
+train_patched = timer() - start
+f"Intel® extension for Scikit-learn time: {train_patched:.2f} s"
+[5]:
+
+'Intel® extension for Scikit-learn time: 42.56 s'
+Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn
+[6]:
+y_pred = rf.predict(x_test)
+mse_opt = metrics.mean_squared_error(y_test, y_pred)
+f"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}"
+[6]:
+
+'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'
+In order to cancel optimizations, we use unpatch_sklearn and reimport the class RandomForestRegressor.
+[7]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset
+[8]:
+from sklearn.ensemble import RandomForestRegressor
+
+start = timer()
+rf = RandomForestRegressor(**params).fit(x_train, y_train)
+train_unpatched = timer() - start
+f"Original Scikit-learn time: {train_unpatched:.2f} s"
+[8]:
+
+'Original Scikit-learn time: 123.34 s'
+Predict and get a result of the Random Forest algorithm with original Scikit-learn
+[9]:
+y_pred = rf.predict(x_test)
+mse_original = metrics.mean_squared_error(y_test, y_pred)
+f"Original Scikit-learn Mean Squared Error: {mse_opt}"
+[9]:
+
+'Original Scikit-learn Mean Squared Error: 83.62232345666878'
+[10]:
+HTML(
+ f"<h3>Compare MSE metric of patched Scikit-learn and original</h3>"
+ f"MSE metric of patched Scikit-learn: {mse_opt} <br>"
+ f"MSE metric of unpatched Scikit-learn: {mse_original} <br>"
+ f"Metrics ratio: {mse_opt/mse_original} <br>"
+ f"<h3>With Scikit-learn-intelex patching you can:</h3>"
+ f"<ul>"
+ f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
+ f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
+ f"<li>Get the similar quality</li>"
+ f"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>"
+ f"</ul>"
+)
+[10]:
+[1]:
+from timeit import default_timer as timer
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+import warnings
+from sklearn.datasets import fetch_openml
+from sklearn.preprocessing import LabelEncoder
+from IPython.display import HTML
+
+warnings.filterwarnings("ignore")
+[2]:
+x, y = fetch_openml(name="Airlines_DepDelay_10M", return_X_y=True)
+Let’s encode categorical features with LabelEncoder
+[3]:
+for col in ["UniqueCarrier", "Origin", "Dest"]:
+ le = LabelEncoder().fit(x[col])
+ x[col] = le.transform(x[col])
+Split the data into train and test sets
+[4]:
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)
+x_train.shape, x_test.shape, y_train.shape, y_test.shape
+[4]:
+
+((9000000, 9), (1000000, 9), (9000000,), (1000000,))
+Normalize the data
+[5]:
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+
+scaler_x = MinMaxScaler()
+scaler_y = StandardScaler()
+[6]:
+y_train = y_train.to_numpy().reshape(-1, 1)
+y_test = y_test.to_numpy().reshape(-1, 1)
+
+scaler_x.fit(x_train)
+x_train = scaler_x.transform(x_train)
+x_test = scaler_x.transform(x_test)
+
+scaler_y.fit(y_train)
+y_train = scaler_y.transform(y_train).ravel()
+y_test = scaler_y.transform(y_test).ravel()
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[7]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset
+[8]:
+from sklearn.linear_model import Ridge
+
+params = {
+ "alpha": 0.3,
+ "fit_intercept": False,
+ "random_state": 0,
+ "copy_X": False,
+}
+start = timer()
+model = Ridge(random_state=0).fit(x_train, y_train)
+train_patched = timer() - start
+f"Intel® extension for Scikit-learn time: {train_patched:.2f} s"
+[8]:
+
+'Intel® extension for Scikit-learn time: 0.06 s'
+Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn
+[9]:
+y_predict = model.predict(x_test)
+mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)
+f"Patched Scikit-learn MSE: {mse_metric_opt}"
+[9]:
+
+'Patched Scikit-learn MSE: 1.0014288520708046'
+In order to cancel optimizations, we use unpatch_sklearn and reimport the class Ridge
+[10]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset
+[11]:
+from sklearn.linear_model import Ridge
+
+start = timer()
+model = Ridge(random_state=0).fit(x_train, y_train)
+train_unpatched = timer() - start
+f"Original Scikit-learn time: {train_unpatched:.2f} s"
+[11]:
+
+'Original Scikit-learn time: 0.70 s'
+Predict and get a result of the Ridge Regression algorithm with original Scikit-learn
+[12]:
+y_predict = model.predict(x_test)
+mse_metric_original = metrics.mean_squared_error(y_test, y_predict)
+f"Original Scikit-learn MSE: {mse_metric_original}"
+[12]:
+
+'Original Scikit-learn MSE: 1.0014288520708057'
+[13]:
+HTML(
+ f"<h3>Compare MSE metric of patched Scikit-learn and original</h3>"
+ f"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>"
+ f"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>"
+ f"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>"
+ f"<h3>With Scikit-learn-intelex patching you can:</h3>"
+ f"<ul>"
+ f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
+ f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
+ f"<li>Get the similar quality</li>"
+ f"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>"
+ f"</ul>"
+)
+[13]:
+[1]:
+from timeit import default_timer as timer
+from IPython.display import HTML
+from sklearn import metrics
+from sklearn.datasets import fetch_openml
+from sklearn.model_selection import train_test_split
+[2]:
+x, y = fetch_openml(name="a9a", return_X_y=True)
+Split the data into train and test sets
+[3]:
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[4]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset
+[5]:
+from sklearn.svm import SVC
+
+params = {"C": 100.0, "kernel": "rbf", "gamma": "scale"}
+start = timer()
+classifier = SVC(**params).fit(x_train, y_train)
+train_patched = timer() - start
+f"Intel® extension for Scikit-learn time: {train_patched:.2f} s"
+[5]:
+
+'Intel® extension for Scikit-learn time: 14.08 s'
+Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn
+[6]:
+predicted = classifier.predict(x_test)
+report = metrics.classification_report(y_test, predicted)
+print(f"Classification report for Intel® extension for Scikit-learn SVC:\n{report}\n")
+
+Classification report for Intel® extension for Scikit-learn SVC:
+ precision recall f1-score support
+
+ -1.0 0.87 0.90 0.88 7414
+ 1.0 0.64 0.58 0.61 2355
+
+ accuracy 0.82 9769
+ macro avg 0.76 0.74 0.75 9769
+weighted avg 0.82 0.82 0.82 9769
+
+
+The first column of the classification report above is the class labels.
+In order to cancel optimizations, we use unpatch_sklearn and reimport the class SVC.
+[7]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training of the SVC algorithm with original Scikit-learn library for Adult dataset
+[8]:
+from sklearn.svm import SVC
+
+start = timer()
+classifier = SVC(**params).fit(x_train, y_train)
+train_unpatched = timer() - start
+f"Original Scikit-learn time: {train_unpatched:.2f} s"
+[8]:
+
+'Original Scikit-learn time: 803.06 s'
+Predict and get a result of the SVC algorithm with original Scikit-learn
+[9]:
+predicted = classifier.predict(x_test)
+report = metrics.classification_report(y_test, predicted)
+print(f"Classification report for original Scikit-learn SVC:\n{report}\n")
+
+Classification report for original Scikit-learn SVC:
+ precision recall f1-score support
+
+ -1.0 0.87 0.90 0.88 7414
+ 1.0 0.64 0.58 0.61 2355
+
+ accuracy 0.82 9769
+ macro avg 0.76 0.74 0.75 9769
+weighted avg 0.82 0.82 0.82 9769
+
+
+[10]:
+HTML(
+ f"<h2>With scikit-learn-intelex patching you can:</h2>"
+ f"<ul>"
+ f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
+ f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
+ f"<li>Get the similar quality</li>"
+ f"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>"
+ f"</ul>"
+)
+[10]:
+[1]:
+from timeit import default_timer as timer
+from sklearn import metrics
+from sklearn.datasets import make_blobs
+import matplotlib.pyplot as plt
+
+%matplotlib inline
+
+import warnings
+
+warnings.filterwarnings("ignore")
+[2]:
+x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)
+Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:
+[3]:
+from sklearnex import patch_sklearn
+
+patch_sklearn()
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.
+Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset
+[4]:
+from sklearn.manifold import TSNE
+
+params = {"n_components": 2, "random_state": 42}
+start = timer()
+tsne = TSNE(**params)
+embedding_intelex = tsne.fit_transform(x)
+time_opt = timer() - start
+
+print(f"Intel® extension for Scikit-learn time: {time_opt:.2f} s")
+print(f"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}")
+
+Intel® extension for Scikit-learn time: 12.63 s
+Intel® Extension for scikit-learn. Divergence: 4.289110606110757
+### Train the same algorithm with original Scikit-learn In order to cancel optimizations, we use unpatch_sklearn and reimport the class TSNE.
+[5]:
+from sklearnex import unpatch_sklearn
+
+unpatch_sklearn()
+Training algorithm with original Scikit-learn library for generated dataset
+[6]:
+from sklearn.manifold import TSNE
+
+params = {"n_components": 2, "random_state": 42}
+start = timer()
+tsne = TSNE(**params)
+embedding_original = tsne.fit_transform(x)
+time_original = timer() - start
+
+print(f"Original Scikit-learn time: {time_original:.2f} s")
+print(f"Original Scikit-learn. Divergence: {tsne.kl_divergence_}")
+
+Original Scikit-learn time: 37.66 s
+Original Scikit-learn. Divergence: 4.2955403327941895
+[7]:
+colors = [int(m) for m in y]
+[8]:
+for emb, title in zip(
+ [embedding_intelex, embedding_original],
+ ["Intel® Extension for scikit-learn", "Original scikit-learn"],
+):
+ plt.scatter(emb[:, 0], emb[:, 1], c=colors)
+ plt.title(title)
+ plt.xlabel("x")
+ plt.ylabel("y")
+ plt.show()
+
+
+[9]:
+f"Speedup for this run: {(time_original/time_opt):.1f}"
+[9]:
+
+'Speedup for this run: 3.0'
+We are committed to providing support and assistance to help you make the most out of Intel(R) Extension for Scikit-learn.
+Use the following methods if you face any challenges.
+If you have a problem, check out the GitHub Issues to see if the issue you want to address is already reported.
+You may find users that have encountered the same bug or have similar ideas for changes or updates.
+You can use issues to report a problem, make a feature request, or add comments on an existing issue.
+Use Intel® Extension for Scikit-learn* in verbose mode to find out which implementation of the algorithm is currently used, +Intel® Extension for Scikit-learn* or original Scikit-learn.
+Note
+Verbose mode is only available for supported algorithms.
+To enable verbose mode, set the SKLEARNEX_VERBOSE environment variable as shown below:
On Linux* OS
+export SKLEARNEX_VERBOSE=INFO
+On Windows* OS
+set SKLEARNEX_VERBOSE=INFO
+Alternatively, get Intel® Extension for Scikit-learn* logger and set its logging level in the Python code:
+import logging
+logger = logging.getLogger('sklearnex')
+logger.setLevel(logging.INFO)
+During the calls that use Intel-optimized scikit-learn, you will receive additional print statements +that indicate which implementation is being called. +These print statements are only available for supported algorithms.
+For example, for DBSCAN you get one of these print statements depending on which implementation is used:
+INFO:sklearnex: sklearn.cluster.DBSCAN.fit: running accelerated version on CPU
+INFO:sklearnex: sklearn.cluster.DBSCAN.fit: fallback to original Scikit-learn
+| \n", + " | altitude | \n", + "cadence | \n", + "distance | \n", + "hr | \n", + "latitude | \n", + "longitude | \n", + "power | \n", + "speed | \n", + "time | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "185.800003 | \n", + "51 | \n", + "3.46 | \n", + "81 | \n", + "30.313309 | \n", + "-97.732711 | \n", + "45 | \n", + "3.459 | \n", + "2016-10-20T22:01:26.000Z | \n", + "
| 1 | \n", + "185.800003 | \n", + "68 | \n", + "7.17 | \n", + "82 | \n", + "30.313277 | \n", + "-97.732715 | \n", + "0 | \n", + "3.710 | \n", + "2016-10-20T22:01:27.000Z | \n", + "
| 2 | \n", + "186.399994 | \n", + "38 | \n", + "11.04 | \n", + "82 | \n", + "30.313243 | \n", + "-97.732717 | \n", + "42 | \n", + "3.874 | \n", + "2016-10-20T22:01:28.000Z | \n", + "
| 3 | \n", + "186.800003 | \n", + "38 | \n", + "15.18 | \n", + "83 | \n", + "30.313212 | \n", + "-97.732720 | \n", + "5 | \n", + "4.135 | \n", + "2016-10-20T22:01:29.000Z | \n", + "
| 4 | \n", + "186.600006 | \n", + "38 | \n", + "19.43 | \n", + "83 | \n", + "30.313172 | \n", + "-97.732723 | \n", + "1 | \n", + "4.250 | \n", + "2016-10-20T22:01:30.000Z | \n", + "
Unfortunately, we could not find the page you were looking for. Try:
+using search to browse documentation
checking the table of contents on the left
filing an issue or +starting a discussion on GitHub