{"id":173237,"date":"2023-11-11T16:31:06","date_gmt":"2023-11-11T15:31:06","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=173237"},"modified":"2023-11-11T16:43:57","modified_gmt":"2023-11-11T15:43:57","slug":"optuna-unveiled-harnessing-the-power-of-machine-learning-optimization","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/optuna-unveiled-harnessing-the-power-of-machine-learning-optimization","title":{"rendered":"Optuna Unveiled: Harnessing the Power of Machine Learning Optimization"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"173237\" class=\"elementor elementor-173237\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-13b11d48 e-flex e-con-boxed e-con\" data-id=\"13b11d48\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6817ae4c elementor-widget elementor-widget-heading\" data-id=\"6817ae4c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Optuna is an automated search tool for optimising the hyperparameters of your Machine Learning models. Using different search methods and their combination, this library helps you identify the optimal hyperparameters.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1bdc441a elementor-widget elementor-widget-text-editor\" data-id=\"1bdc441a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>As a reminder, <strong>hyperparameters<\/strong> correspond to data that must be entered manually by the developer. This is in contrast to learning via training data. They have a significant impact on how the model works and on its performance.<\/p><p>For example, if you wanted to <a href=\"https:\/\/datascientest.com\/en\/convolutional-neural-network-everything-you-need-to-know\">train a neural network<\/a> with linear layers only, potential hyperparameters would include:<\/p><ul><li>Number of layers ;<\/li><li>Units per layer ;<\/li><li>Learning Rate ;<\/li><li>Regularisation force ;<\/li><li>Activation function.<\/li><\/ul><p>\u00a0<\/p><p>Even if you only have <strong>two potential values per variable<\/strong>, you can quickly end up with an impressive (and time-consuming) number of experiments.<\/p><p>You can do this manually using techniques such as cross-validation or trial-and-error optimisation, or choose Optuna to automate the process.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1a2d3b61 e-flex e-con-boxed e-con\" data-id=\"1a2d3b61\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6903610a elementor-widget elementor-widget-heading\" data-id=\"6903610a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How does Optuna work?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-726fb8f1 elementor-widget elementor-widget-text-editor\" data-id=\"726fb8f1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The <strong>principle behind Optuna<\/strong> is that the user provides a space of hyperparameters to be tested. The tool will then determine the combination of hyperparameters that <a href=\"https:\/\/datascientest.com\/en\/boosting-business-with-3-essential-machine-learning-algorithms\">optimise the Machine Learning model.<\/a><\/p><p>To do this, you need to provide Optuna with a performance metric. The tool will do its utmost to optimise it.<\/p><p>The <a href=\"https:\/\/datascientest.com\/en\/boosting-business-with-3-essential-machine-learning-algorithms\">search tool has separated algorithms<\/a> into two different categories: the sampling strategy and the pruning strategy. The sampling strategy actually conceals three distinct methods:<\/p><ol><li><strong>Random search:<\/strong> this selects the hyperparameters randomly in the defined search space. This is useful when you don&#8217;t know what to try.<\/li><li><strong>Grid search:<\/strong> this tries all possible combinations in the defined search space. It is useful when you want to try out a specific idea. On the other hand, it is very time-consuming if your search space is too large.<\/li><li><strong>Bayesian optimisation:<\/strong> this uses random search first, then refines the hyperparameters according to the results. <strong>Bayesian optimisation<\/strong> is often more effective than random search, because it orientates its trials on the basis of previous results.<\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-24df072f elementor-widget elementor-widget-text-editor\" data-id=\"24df072f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In addition to the sampling strategy, the &#8220;<strong>pruning strategy<\/strong>&#8221; reduces the number of useless trials by early stopping those that seem least promising. This saves you computing time.<\/p><p>Bear in mind that if you don&#8217;t specify a strategy, Optuna will use the sampling strategy with random search by default. So you won&#8217;t have any way of reducing the number of trials.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-16531d4e e-flex e-con-boxed e-con\" data-id=\"16531d4e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3c0b2058 elementor-widget elementor-widget-heading\" data-id=\"3c0b2058\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What are the advantages of Optuna?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-34ae5a6f elementor-widget elementor-widget-text-editor\" data-id=\"34ae5a6f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Whenever you need to<strong> optimise the hyperparameters of<\/strong> your <a href=\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\">Machine Learning models,<\/a> Optuna is the best solution for automating the process. This solution has a number of advantages. The first is its ease of use. The search tool integrates easily into your existing machine learning pipelines. All you have to do is define your hyperparameter search space and the loss function.<\/p><p><strong>Optuna<\/strong> then offers various search methods (detailed above). These different methods allow you to adjust your search according to the use case. What&#8217;s more, the tool can be used on any Machine Learning model. Clustering, classification or regression, it takes care of everything.<\/p><p>With easy-to-view results, <strong>Optuna<\/strong> gives you a better understanding of how hyperparameters influence the performance of your model. So you have everything you need to determine the optimal hyperparameter configuration for your model. All thanks to a<a href=\"https:\/\/datascientest.com\/en\/open-source-definition-use-cases-and-more\"> free, open-source project.<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-57656a8b elementor-widget elementor-widget-image\" data-id=\"57656a8b\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1025\" height=\"570\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/optuma.jpg\" class=\"attachment-full size-full wp-image-173241\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/optuma.jpg 1025w, https:\/\/datascientest.com\/en\/files\/2023\/11\/optuma-300x167.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/11\/optuma-768x427.jpg 768w\" sizes=\"(max-width: 1025px) 100vw, 1025px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-72558deb e-flex e-con-boxed e-con\" data-id=\"72558deb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-40ded4b5 elementor-widget elementor-widget-heading\" data-id=\"40ded4b5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How can I optimise my model's hyperparameters with Optuna?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-68c73539 elementor-widget elementor-widget-text-editor\" data-id=\"68c73539\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Now that you&#8217;re aware of the<strong> importance of Optuna<\/strong> and the time you&#8217;ll save by using it, find out how to set it up.<\/p><ul><li>Start by importing the Optuna modules and the other modules you need to define your model and loss function: &#8216;!pip install optuna&#8217;.<\/li><li>Define the hyperparameter search space. To do this, use the &#8216;create_study&#8217; function to create a &#8220;study&#8221; object. Specify the hyperparameter search space using the &#8216;suggest_uniform&#8217; and &#8216;suggest_loguniform&#8217; functions.<\/li><li>Define the cost function: it should take a hyperparameter configuration as input and return a performance score.<\/li><li>Start the search for hyperparameters by calling the &#8216;optimize&#8217; function of the &#8216;study&#8217; object with your cost function as an argument. Optuna will then start searching for the best hyperparameters using the sampling strategy you have chosen (by default, random search).<\/li><li>Access the results using the &#8216;get_best_params&#8217; and &#8216;get_best_value&#8217; functions.<\/li><li>You can also use the Optuna visualisation functions &#8216;plot_intermediate_values&#8217; and &#8216;plot_optimization_history&#8217;.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-57f5ca23 elementor-widget elementor-widget-text-editor\" data-id=\"57f5ca23\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Below is an example of how to optimise the hyperparameters of a linear regression model using Optuna :<\/p><p style=\"padding-left: 40px;\"><strong><em>import optuna<\/em><\/strong><\/p><p style=\"padding-left: 40px;\"><strong><em># Create the &#8220;study&#8221; object and define the hyperparameter search space<\/em><\/strong><\/p><p style=\"padding-left: 40px;\"><strong><em>study = optuna.create_study()<\/em><\/strong><\/p><p style=\"padding-left: 40px;\"><strong><em>study.suggest_uniform(&#8220;alpha&#8221;, 0.0, 1.0)<\/em><\/strong><\/p><p style=\"padding-left: 40px;\"><strong><em>study.suggest_uniform(&#8220;l1_ratio&#8221;, 0.0, 1.0)<\/em><\/strong><\/p><p style=\"padding-left: 40px;\"><strong><em># Define the cost function<\/em><\/strong><\/p><p style=\"padding-left: 40px;\"><strong><em>def objective(trial):<\/em><\/strong><\/p><p style=\"padding-left: 40px;\"><strong><em>alpha = trial.suggest_uniform(&#8220;alpha&#8221;, 0.0, 1.0)<\/em><\/strong><\/p><p style=\"padding-left: 40px;\"><strong><em>l1_ratio = trial.suggest_uniform(&#8220;l1_ratio&#8221;, 0.0, 1.0)<\/em><\/strong><\/p><p style=\"padding-left: 40px;\"><strong><em>model = Lasso(alpha=alpha, l1_ratio=l1_ratio)<\/em><\/strong><\/p><p style=\"padding-left: 40px;\"><strong><em>score = -1.0 * cross_val<\/em><\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-11a7c332 e-flex e-con-boxed e-con\" data-id=\"11a7c332\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-73ed3ec5 elementor-widget elementor-widget-heading\" data-id=\"73ed3ec5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-413a641c elementor-widget elementor-widget-text-editor\" data-id=\"413a641c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Optuna is an <strong>effective automated search tool<\/strong> for optimising the <a href=\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\">hyperparameters of its Machine Learning models.<\/a> Its ease of use, flexibility in the choice of optimisation algorithms and integration with existing pipelines make it a must-have.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6b8408c1 elementor-widget elementor-widget-text-editor\" data-id=\"6b8408c1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Now you know more about Optuna. If you want to master this tool to automate your processes by deploying Machine Learning models, find out more about DataScientest&#8217;s training courses.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f0a3c4c elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"3f0a3c4c\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover DataScientest training courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7ef94a00 e-flex e-con-boxed e-con\" data-id=\"7ef94a00\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Optuna is an automated search tool for optimising the hyperparameters of your Machine Learning models. Using different search methods and their combination, this library helps you identify the optimal hyperparameters. As a reminder, hyperparameters correspond to data that must be entered manually by the developer. This is in contrast to learning via training data. They [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":173240,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2409],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Optuna Unveiled: Harnessing the Power of Machine Learning Optimization<\/title>\n<meta name=\"description\" content=\"Unlock the potential of Optuna, a versatile machine learning tool. 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