tpot | Python Automated Machine Learning tool | Machine Learning library

 by   EpistasisLab Python Version: 0.12.1 License: LGPL-3.0

kandi X-RAY | tpot Summary

kandi X-RAY | tpot Summary

tpot is a Python library typically used in Artificial Intelligence, Machine Learning applications. tpot has no bugs, it has no vulnerabilities, it has build file available, it has a Weak Copyleft License and it has high support. You can install using 'pip install tpot' or download it from GitHub, PyPI.

TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.

            kandi-support Support

              tpot has a highly active ecosystem.
              It has 9085 star(s) with 1526 fork(s). There are 290 watchers for this library.
              There were 1 major release(s) in the last 6 months.
              There are 259 open issues and 620 have been closed. On average issues are closed in 235 days. There are 9 open pull requests and 0 closed requests.
              It has a negative sentiment in the developer community.
              The latest version of tpot is 0.12.1

            kandi-Quality Quality

              tpot has 0 bugs and 0 code smells.

            kandi-Security Security

              tpot has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              tpot code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              tpot is licensed under the LGPL-3.0 License. This license is Weak Copyleft.
              Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.

            kandi-Reuse Reuse

              tpot releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              tpot saves you 5516 person hours of effort in developing the same functionality from scratch.
              It has 11556 lines of code, 382 functions and 68 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed tpot and discovered the below as its top functions. This is intended to give you an instant insight into tpot implemented functionality, and help decide if they suit your requirements.
            • Create a TOTP operator class
            • Decode source code
            • Create an ARGType subclass
            • Check if estimator is a selector
            • Sets up the input tensor
            • Add terminal terminals
            • Import the given operator and add it to the graph
            • Add operators to the pipeline
            • Create TPOT classifier
            • Check if the features are consistent
            • Compute the score for the given features
            • Impute missing values in feature set
            • Decorator for pre test tests
            • Convert an expression into a tree
            • Generate the code for the pipeline
            • Update the progress bar
            • Setup the config dictionary
            • Reads the specified TPOT operator config file
            • Return whether or not the module is installed
            • Fit X to X
            • Replace all values in X
            • Compile the pipeline into a sklearn pipeline
            • Recursively sets a parameter
            • Return an argument parser
            • Replace the features in X
            • Calculate version number
            Get all kandi verified functions for this library.

            tpot Key Features

            No Key Features are available at this moment for tpot.

            tpot Examples and Code Snippets

            Pythondot img1Lines of Code : 39dot img1License : Weak Copyleft (LGPL-3.0)
            copy iconCopy
            from tpot import TPOTClassifier
            from sklearn.datasets import load_digits
            from sklearn.model_selection import train_test_split
            digits = load_digits()
            X_train, X_test, y_train, y_test = train_test_split(,,
            Citing TPOT
            Pythondot img2Lines of Code : 38dot img2License : Weak Copyleft (LGPL-3.0)
            copy iconCopy
              title={Scaling tree-based automated machine learning to biomedical big data with a feature set selector},
              author={Le, Trang T and Fu, Weixuan and Moore, Jason H},
            Citing TPOT
            pypidot img3Lines of Code : 38dot img3no licencesLicense : No License
            copy iconCopy
              title={Scaling tree-based automated machine learning to biomedical big data with a feature set selector},
              author={Le, Trang T and Fu, Weixuan and Moore, Jason H},
            tpot - worker
            JavaScriptdot img4Lines of Code : 119dot img4License : Non-SPDX (GNU Lesser General Public License v3.0)
            copy iconCopy
            var base_path = 'function' === typeof importScripts ? '.' : '/search/';
            var allowSearch = false;
            var index;
            var documents = {};
            var lang = ['en'];
            var data;
            function getScript(script, callback) {
              console.log('Loading script: ' + script);
            tpot - main
            JavaScriptdot img5Lines of Code : 86dot img5License : Non-SPDX (GNU Lesser General Public License v3.0)
            copy iconCopy
            function getSearchTermFromLocation() {
              var sPageURL =;
              var sURLVariables = sPageURL.split('&');
              for (var i = 0; i < sURLVariables.length; i++) {
                var sParameterName = sURLVariables[i].split('=');
            tpot - tpot iris pipeline
            Pythondot img6Lines of Code : 16dot img6License : Non-SPDX (GNU Lesser General Public License v3.0)
            copy iconCopy
            import numpy as np
            import pandas as pd
            from sklearn.kernel_approximation import RBFSampler
            from sklearn.model_selection import train_test_split
            from sklearn.pipeline import make_pipeline
            from sklearn.tree import DecisionTreeClassifier
            # NOTE: Make s  
            How to find which model is selected by TPOT
            Pythondot img7Lines of Code : 4dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            my_tpot = TPOTClassifier()
            TPOT error in python cannot set using a slice indexer with a different length
            Pythondot img8Lines of Code : 31dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from sklearn.ensemble import RandomForestClassifier
            from sklearn.model_selection import RandomizedSearchCV
            from sklearn.model_selection import train_test_split
            from tpot import TPOTClassifier
            from sklearn import datasets
            iris = datasets.lo
            Looking for some guidance on combinatorics in python
            Pythondot img9Lines of Code : 7dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            a = np.arange(0.5, -0.01, -0.01)
            for i in range(len(a)):
                first_element = round(a[i], 2) # this is the 1st element, rounded to 2 digit
                for j in range(i, len(a)):
                    second_element = round(a[j], 2) # same with 2nd element
            Can't solve the errorr message "Expected 2D array, got 1D array instead"?
            Pythondot img10Lines of Code : 2dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            results = exported_pipeline.predict(x_test)

            Community Discussions


            How to find which model is selected by TPOT
            Asked 2022-Feb-18 at 06:34

            Hi am using TPOT for machine learning I am getting 99% accuracy but I am not sure to which model did it predict can someone help me with this also does it do SMOTE?



            Answered 2022-Feb-18 at 06:34

            If you stored the TPOTClassifier in the variable my_tpot, then you can access the final trained pipeline by accessing the fitted_pipeline_ attribute:



            TPOT taking too long to train
            Asked 2021-Jun-07 at 23:24

            Ive been trying to use tpot for the first time on a dataset that has approximately 7000 rows, when trying to train tpot on the training dataset which is 25% of the dataset as a whole, tpot takes too long. ive been running the code for approximately 45 minutes on google colab and the optimization progress is still at 4%. Ive just been trying to use the example as seen on : Is it typical for tpot to take this long, because so far i dont think its worth even trying to use it



            Answered 2021-Jun-07 at 23:24

            TPOT can take quite a long time depending on the dataset you have. You have to consider what TPOT is doing: TPOT is evaluating thousands of analysis pipelines and fitting thousands of ML models on your dataset in the background, and if you have a large dataset, then all that fitting can take a long time--especially if you're running it on a less powerful computer.

            If you'd like faster results, you have a few options:

            1. Use the "TPOT light" configuration, which uses simpler models and will run faster.

            2. Set the n_jobs parameter to -1 or a number greater than 1, which will allow TPOT to evaluate pipelines in parallel. -1 will use all of the available cores and speed things up significantly if you have a multicore machine.

            3. Subsample the data using the subsample parameter. The default is 1.0, corresponding to using 100% of your training data. You can subsample to lower percentages of the data and TPOT will run faster.



            Explanation of pipeline generated by tpot
            Asked 2021-May-20 at 14:28

            I was using tpotClassifier() and got the following pipeline as my optimal pipeline. I am attaching my pipeline code which I got. Can someone explain the pipeline processes and order?



            Answered 2021-May-20 at 14:28

            make_union just unions multiple datasets, and FunctionTransformer(copy) duplicates all the columns. So the nested make_union and FunctionTransformer(copy) makes several copies of each feature. That seems very odd, except that with ExtraTreesClassifier it will have an effect of "bootstrapping" the feature selections. See also Issue 581 for an explanation for why these are generated in the first place; basically, adding copies is useful in stacking ensembles, and the genetic algorithm used by TPOT means it needs to generate those first before exploring such ensembles. There it is recommended that doing more iterations of the genetic algorithm may clean up such artifacts.

            After that things are straightforward, I guess: you perform a univariate feature selection, and fit an extra-random trees classifier.



            Dask aws cluster error when initializing: User data is limited to 16384 bytes
            Asked 2021-May-05 at 13:39

            I'm following the guide here:

            In particular I set up my instance with packer, and am now trying to run the final piece of code:



            Answered 2021-May-05 at 13:39

            The Dask Community is tracking this problem here: and a potential solution 4465 should resolve the issues.



            Does the "tpot" model object automatically apply any scaling or other transformations when .score or .predict is called on new out-of-sample data?
            Asked 2021-Apr-22 at 16:41

            Here is basic code for training a model in TPOT:



            Answered 2021-Apr-22 at 16:12

            Does the "tpot" model object automatically apply any scaling or other transformations when .score or .predict is called on new out-of-sample data?

            That depends on the final pipeline that TPOT chose. However, if the final pipeline that TPOT chose has any sort of data scaling or transformation, then it correctly applies those scaling and transformation operations in the predict and score functions as well.

            The reason for this is because, under the hood, TPOT is optimizing scikit-learn Pipeline objects.

            That said, if there are specific transformations to your data that you want to guarantee happen with your data, then you have a couple options:

            1. You can split your data into training and test, learn the transformation (e.g., StandardScaler) on the training set, then also apply it to your test set. You would do both of these operations before ever passing the data to TPOT.

            2. You can make use of TPOT's template functionality, which allows you to specify constraints on what the analysis pipeline should look like.



            TPOT error in python cannot set using a slice indexer with a different length
            Asked 2020-Dec-01 at 13:43

            I'm trying to run tpot to optimize hyperparameters of a random forest using genetic algorithms. I am receiving an error and am not quite sure how to fix it. Below is the essential code I'm using.



            Answered 2020-Dec-01 at 13:43

            I tried tpot with the iris dataset and I did get no error



            Looking for some guidance on combinatorics in python
            Asked 2020-Sep-27 at 09:16

            Still new to python and coding, only about 6 weeks into this adventure. I started a finance project to try and figure out what % of the portfolio should be in cash, and how much should be invested based on the current market performance. No idea if this research will have any relevance but it has been helpful getting stuck on every step and learning new things.

            For anyone interested, this is the google collab Jupiter notebook

            In Step 4, I am trying to run sorta a combinatorics simulation. I have been reading up on but it's a little overwhelming on where I need to get started. Just looking for some guidance on like what the terms or stuff I should be looking into, to solve this specific question.

            Also, looked and saw something called tpot was good for combinatorics?

            Combinatorics Question

            Currently, in Step 3, I did a predefined loop for the various drops in the market. It looked like this



            Answered 2020-Sep-27 at 09:16

            I didn't understand the question, I think you assume familiarity with some concepts here. If you can phrase your question more simply and shortly it would help me help you. Then again it might be only my inability.

            I will try to give you my 2 cents, according to what you I can understand from the post.

            First of all, I would like to point out that the way you initialized your lists is not optimal. Numpy has two very important functions:

            1. np.linspace:

            2. np.arange:

            So for example, you should replace your options_market_status initialization with the simple one-liner numpy call: np.arange(0.5, -0.02, -0.01).

            Now, if I understand your main question, it is how to iterate over options_market_status and options_cash_req, such that the element from options_market_status is bigger or equal to the options_cash_req option, right? Since they have the same values in your question, I will give a general solution. If we are given an array a, and want to iterate over it in a nested loop, one solution can be:



            Import rasterio failed. Reason: image not found
            Asked 2020-Sep-22 at 05:37

            I'm going to use rasterio in python. I downloaded rasterio via



            Answered 2020-Sep-22 at 05:37

            I've got some experience with rasterio, but I am not nearly a master with it. If I remember correctly, rasterio requires you to have installed the program GDAL(both binaries and python utilities), and some other dependencies listed on the PyPi page. I don't use conda at the moment, I like to use the regular python 3.8 installer with pip. Given what I'm seeing with your installation, I would uninstall rasterio and follow a different installation procedure.

            I follow the instructions listed here:
            This page also has separate instructions for those using Anaconda.

            The GDAL installation is by far the most annoying but once it's done, the hard part is over. The python utilities for both rasterio and gdal can be found here:
            The second link is also provided on the PyPi page but I like to keep it bookmarked because there's a lot of good resources there!



            Can't solve the errorr message "Expected 2D array, got 1D array instead"?
            Asked 2020-Sep-16 at 15:28

            I don't know what to do to get this model working. It says to reshape, but I've done that but then I get a inconsistent samples to data error. I'm lost on how this keeps on happening. I've ran other models without issues, but I'm confused as to why this is happening now.



            Answered 2020-Sep-16 at 15:28

            Predictions are typically based on x values rather than y values. So I think the correct line should be:



            Usage of LSTM/GRU and Flatten throws dimensional incompatibility error
            Asked 2020-Sep-15 at 20:26

            I want to make use of a promising NN I found at towardsdatascience for my case study.

            The data shapes I have are:



            Answered 2020-Aug-17 at 18:14

            I cannot reproduce your error, check if the following code works for you:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install tpot

            We maintain the TPOT installation instructions in the documentation. TPOT requires a working installation of Python.


            We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension to TPOT, please file a new issue so we can discuss it. Before submitting any contributions, please review our contribution guidelines.
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