{"id":175010,"date":"2023-12-10T18:06:49","date_gmt":"2023-12-10T17:06:49","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=175010"},"modified":"2023-12-10T18:09:47","modified_gmt":"2023-12-10T17:09:47","slug":"tpot-all-about-this-machine-learning-python-library","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/tpot-all-about-this-machine-learning-python-library","title":{"rendered":"TPOT: All about this Machine Learning Python library"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"175010\" class=\"elementor elementor-175010\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cf91f76 e-flex e-con-boxed e-con\" data-id=\"cf91f76\" 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-bd7e09a elementor-widget elementor-widget-heading\" data-id=\"bd7e09a\" 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\">Before you can put a Machine Learning model into production, you first have to determine the most suitable model, select the optimal parameters, etc. These are redundant operations that will apply to different projects. Wouldn't it be simpler to automate these processes? That's where TPOT comes in.<\/h2>\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-0168e4f e-flex e-con-boxed e-con\" data-id=\"0168e4f\" 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-9205c1e elementor-widget elementor-widget-heading\" data-id=\"9205c1e\" 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 is TPOT?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ef635a1 elementor-widget elementor-widget-text-editor\" data-id=\"ef635a1\" 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><strong>TPOT<\/strong> is an extremely useful library for automating the process of selecting the best <a href=\"https:\/\/datascientest.com\/en\/cracking-the-code-on-underfitting-in-machine-learning\">Machine Learning model<\/a> and corresponding hyperparameters, saving you time and optimizing your results. Instead of manually testing different models and configurations for each new dataset, TPOT can explore a multitude of <a href=\"https:\/\/datascientest.com\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\">Machine Learning pipelines<\/a> and determine the one most suitable for your specific dataset using genetic programming.<\/p><p>In summary, TPOT simplifies the search for the optimal model and parameters by automating the process, which can significantly speed up the <a href=\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\">development of Machine Learning<\/a> models and help you achieve better performance in your data analysis tasks.<\/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-e7045b5 e-flex e-con-boxed e-con\" data-id=\"e7045b5\" 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-d3e2430 elementor-widget elementor-widget-heading\" data-id=\"d3e2430\" 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\">Why use TPOT?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9ce2d03 elementor-widget elementor-widget-text-editor\" data-id=\"9ce2d03\" 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><strong>Automatic Machine Learning (AutoML)<\/strong> tools address a simple problem: how to make the creation and training of models less time-consuming?<\/p><p><strong>AutoML<\/strong>, as the name suggests, automates a large part of the model creation process without sacrificing quality, allowing Data Scientists to focus on analysis. Its <a href=\"https:\/\/datascientest.com\/en\/all-about-data-pipeline\">pipeline<\/a> consists of several processes that help build a <a href=\"https:\/\/datascientest.com\/en\/machine-learning-python-where-to-start\">high-performing Machine Learning model<\/a> (feature engineering, model generation, hyperparameter optimization).<\/p><p>To clarify, in <strong>Machine Learning,<\/strong> a pipeline encodes and automates the workflow that transforms and correlates data into a model that can be analyzed. The data loading into the model is entirely automated.<\/p><p>A pipeline can also be used to separate the workflow of a model into different independent and reusable parts, simplifying its creation and avoiding task repetition.<\/p><p>A good pipeline enhances the efficiency and scalability of building and <a href=\"https:\/\/datascientest.com\/en\/adversarial-examples-definition-and-importance-in-machine-learning\">deploying Machine Learning models.<\/a><\/p><p>Additionally, TPOT offers great flexibility as it can be adapted for<a href=\"https:\/\/datascientest.com\/en\/pytorch-all-about-this-framework\"> neural network models with PyTorch<\/a>. TPOT also supports the use of Dask for parallel training, further enhancing its capabilities.<\/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-b6b3f28 e-flex e-con-boxed e-con\" data-id=\"b6b3f28\" 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-6d008b6 elementor-widget elementor-widget-heading\" data-id=\"6d008b6\" 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 TPOT work?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b4d9cb0 elementor-widget elementor-widget-text-editor\" data-id=\"b4d9cb0\" 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><strong>TPOT, or Tree-based Pipeline Optimization,<\/strong> uses a structure based on binary decision trees to represent a pipeline model. This includes data preparation, algorithm modeling, hyperparameter settings and model selection.<\/p><p>Below is an example of a pipeline showing the elements automated by TPOT:<\/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-915d7c9 elementor-widget elementor-widget-image\" data-id=\"915d7c9\" 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\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/image1-1.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\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<div class=\"elementor-element elementor-element-eecbc99 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"eecbc99\" 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-scientist-course\">\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\">Understanding how TPOT works<\/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<div class=\"elementor-element elementor-element-1bb4f2c elementor-widget elementor-widget-text-editor\" data-id=\"1bb4f2c\" 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>By <strong>combining stochastic search algorithms<\/strong> like genetic programming and a flexible expression tree representation, TPOT automatically designs and optimizes features, Machine Learning models, and hyperparameters. The goal is to maximize the accuracy of supervised classification on your dataset.<\/p><p>It&#8217;s essential to note that finding the most optimized pipeline may require letting TPOT work for a certain amount of time. Running TPOT for just a few minutes may not be sufficient to discover the <a href=\"https:\/\/datascientest.com\/en\/datasets-top-5-places-to-find-quality-datasets\">best model for your dataset.<\/a><\/p><p>Depending on the size of your dataset, TPOT can take several hours or even days to complete its search. For a comprehensive and effective search, it&#8217;s recommended to run multiple instances concurrently for several hours.<\/p><p>Since TPOT&#8217;s optimization algorithm is stochastic, meaning it involves partial randomization, it&#8217;s possible that two runs may recommend different <a href=\"https:\/\/datascientest.com\/en\/what-is-a-dataset-how-do-i-work-with-it\">pipelines for the same dataset.<\/a><\/p><p>In such cases, either the pipelines may not match due to limited runtime or they may have very similar performance scores.<\/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-88ac489 e-flex e-con-boxed e-con\" data-id=\"88ac489\" 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-067c828 elementor-widget elementor-widget-heading\" data-id=\"067c828\" 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\">Find the best pipeline with TPOT<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-43ff54c elementor-widget elementor-widget-text-editor\" data-id=\"43ff54c\" 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 we understand what TPOT is and its significance, let&#8217;s see how to set it up and use it. As a reminder, TPOT is <a href=\"https:\/\/datascientest.com\/en\/scikit-learn-discover-the-python-library-dedicated-to-machine-learning\">based on scikit-learn,<\/a> which makes its code familiar if you have already worked with this library.<\/p><ol><li>Start by importing the<strong> TPOT modules<\/strong> and any other modules you need to define your model: &#8216;pip install tpot&#8217;.<\/li><li>During the data transformation step, it&#8217;s crucial to rename the target variable and give it the name &#8216;class&#8217;.<br \/>TPOT only handles data in numeric format, so you will need to apply the necessary transformations to the explanatory variables.<\/li><li>After splitting your dataset into a training set and a test set, you can define your TPOT Classifier and its parameters.<\/li><li>To apply TPOT to your dataset, simply use the .fit() method.<\/li><li>Once the computation is complete, you will see the best pipeline for your dataset in the output. You can then use the .score() method to measure the performance of the model chosen by TPOT.<\/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-8699638 elementor-widget elementor-widget-text-editor\" data-id=\"8699638\" 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 <strong>how to create a pipeline using TPOT:<\/strong><\/p><p># For classification<br \/>from tpot import TPOTClassifier<\/p><p># For regression<br \/>from tpot import TPOTRegressor<br \/>from sklearn.model_selection import train_test_split<\/p><p># Setting up TPOTClassifier<br \/>tpot_classification = TPOTClassifier(verbosity=2, max_time_mins=2, max_eval_time_mins=0.04, population_size=40)<\/p><p># Setting up TPOTRegressor<br \/>tpot_regression = TPOTRegressor(generations=5, population_size=50, scoring=&#8217;neg_mean_absolute_error&#8217;, cv=cv, verbosity=2, random_state=1, n_jobs=-1)<\/p><p># Applying TPOT to our training dataset<br \/>tpot_classification.fit(X_train, y_train)<br \/>tpot_regression.fit(X_train, y_train)<\/p><p># Calculating the accuracy score<br \/>tpot.score(X_test, y_test)<\/p><p># Extracting the code generated by TPOT to modify the created pipeline<br \/>tpot.export(&#8216;tpot_titanic_pipeline.py&#8217;)<\/p><p>This code demonstrates how to use TPOT for both classification and regression tasks. You can customize the parameters according to your dataset and requirements. Once TPOT is applied to your data, it will generate the best pipeline, which you can further modify if needed.<\/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-729584f e-flex e-con-boxed e-con\" data-id=\"729584f\" 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-12e1113 elementor-widget elementor-widget-heading\" data-id=\"12e1113\" 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-aadcb81 elementor-widget elementor-widget-text-editor\" data-id=\"aadcb81\" 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>TPOT is a very useful tool for finding a first optimized model. It will certainly be necessary to rework the resulting pipeline before sending it into production. However, for initial results, this tool is more than sufficient and will save you a considerable amount of time.<\/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-a0d1ce3 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"a0d1ce3\" 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-scientist-course\">\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\">Learn to use TPOT<\/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-35da05f e-flex e-con-boxed e-con\" data-id=\"35da05f\" 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-1a713ce elementor-widget elementor-widget-html\" data-id=\"1a713ce\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"What is TPOT?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"TPOT is an open source library used for Machine Learning automation. 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Its pipeline is made up of several processes that will help build a high-performance Machine Learning model (feature engineering, model generation, hyperparameter optimization).\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How does TPOT work?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"TPOT, or Tree-based Pipeline Optimazation, uses a structure based on binary decision trees to represent a pipeline model. This includes data preparation, algorithm modeling, hyperparameter settings and model selection.\"\n    }\n  }]\n}\n<\/script>\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\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Before you can put a Machine Learning model into production, you first have to determine the most suitable model, select the optimal parameters, etc. These are redundant operations that will apply to different projects. Wouldn&#8217;t it be simpler to automate these processes? That&#8217;s where TPOT comes in. What is TPOT? TPOT is an extremely useful [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":175011,"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>TPOT: All about this Machine Learning Python library<\/title>\n<meta name=\"description\" content=\"TPOT simplifies the process of building and optimizing machine learning pipelines, saving time and effort while potentially improving model performance\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/tpot-all-about-this-machine-learning-python-library\" \/>\n<meta property=\"og:locale\" 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