auto-sklearn | Automated Machine | Machine Learning library

 by   automl Python Version: v0.15.0 License: BSD-3-Clause

kandi X-RAY | auto-sklearn Summary

kandi X-RAY | auto-sklearn Summary

auto-sklearn is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning applications. auto-sklearn has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub, GitLab.

Automated Machine Learning with scikit-learn

            kandi-support Support

              auto-sklearn has a medium active ecosystem.
              It has 6984 star(s) with 1221 fork(s). There are 214 watchers for this library.
              It had no major release in the last 12 months.
              There are 150 open issues and 823 have been closed. On average issues are closed in 59 days. There are 5 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of auto-sklearn is v0.15.0

            kandi-Quality Quality

              auto-sklearn has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              auto-sklearn is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              auto-sklearn releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              It has 34349 lines of code, 1704 functions and 322 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed auto-sklearn and discovered the below as its top functions. This is intended to give you an instant insight into auto-sklearn implemented functionality, and help decide if they suit your requirements.
            • Run the ensemble builder
            • Sanitize an array
            • Calculate scores for a given solution
            • Compute a single score
            • Load the prediction files
            • Retrieve a dictionary of configuration matrices
            • Return a dict of hyperparameters
            • Returns a pandas DataFrame of the leaderboard
            • Return the leaderboard columns
            • Create a markdown summary for comparisons
            • Return the intersection of two items
            • Get hyperparameter search space
            • Get base search space
            • Fit the model
            • Lists the models in the ensemble
            • Iterate through the indices of the classes
            • Get a hyperparameter search space
            • Return the cv results as a dictionary
            • Run the builder
            • Fit the neural network
            • Returns a dictionary of hyperparameters
            • Retrieve the configuration matrices
            • Predict for each strategy
            • Fit an MLPClassifier
            • Fit the optimizer
            • Returns a hyperparameter search space
            • Return list of models
            • Fit a pipeline
            Get all kandi verified functions for this library.

            auto-sklearn Key Features

            No Key Features are available at this moment for auto-sklearn.

            auto-sklearn Examples and Code Snippets

            FLASH,How to Run?
            Pythondot img1Lines of Code : 10dot img1License : Strong Copyleft (GPL-3.0)
            copy iconCopy
            cd /path/to/FLASH/benchmarks/sklearn
            cd /path/to/FLASH/benchmarks/sklearn
            cd /path/to/FLASH/benchmarks/sklearn
            cd /path/to/FLASH/benchmarks/sklearn
            cd /path/to/FLAS  

            Community Discussions


            How can update trained IsolationForest model with new datasets/datafarmes in python?
            Asked 2022-Mar-02 at 20:42

            Let's say I fit IsolationForest() algorithm from scikit-learn on time-series based Dataset1 or dataframe1 df1 and save the model using the methods mentioned here & here. Now I want to update my model for new dataset2 or df2.

            My findings:

            ...learn incrementally from a mini-batch of instances (sometimes called “online learning”) is key to out-of-core learning as it guarantees that at any given time, there will be only a small amount of instances in the main memory. Choosing a good size for the mini-batch that balances relevancy and memory footprint could involve tuning.

            but Sadly IF algorithm doesn't support estimator.partial_fit(newdf)

            • auto-sklearn offers refit() is also not suitable for my case based on this post.

            How I can update the trained on Dataset1 and saved IF model with a new Dataset2?



            Answered 2022-Mar-02 at 17:41

            You can simply reuse the .fit() call available to the estimator on the new data.

            This would be preferred, especially in a time series, as the signal changes and you do not want older, non-representative data to be understood as potentially normal (or anomalous).

            If old data is important, you can simply join the older training data and newer input signal data together, and then call .fit() again.

            Also sidenote, according to sklearn documentation, it is better to use joblib than pickle

            An MRE with resources below:



            How to specify Search Space in Auto-Sklearn
            Asked 2022-Jan-20 at 14:26

            I know how to specify Feature Selection methods and the list of the Algorithms used in Auto-Sklearn 2.0



            Answered 2022-Jan-20 at 10:20

            You need to edit the config as specified in the docs.

            In your case it would be something like:



            Python creates Folder inside docker image but remove when processing completes
            Asked 2021-Feb-18 at 12:27

            Python Program does create folder and put some files over there. But when i try to run the program inside docker via CMD It creates the folder and put files over there and upon completion, the folder somehow gets removed or doesnt show inside the docker image.

            I have tried the following things:

            1. Check Folder Exist after creating - It shows folder created over there.
            2. Check inside the docker image using bash - It doesnt show the folder and contents.

            The dockerfile is



            Answered 2021-Feb-18 at 12:27

            Changes to filesystem are not stored in docker image. They exist in container created from an image but if you use 'docker run' command a new container is created.



            Is it possible to use azureml without any login things?
            Asked 2020-Apr-19 at 03:31

            To run sklearn, auto-sklearn on my local machine, I just need to pip install them, no need for login to anything.

            To run azureml, it seems to need login somewhere and finish a bunch of things if I am a new user to

            Is it possible to use azureml as simple as sklearn, just pip install it without any login things?



            Answered 2020-Apr-19 at 03:31

            If you want to use any of the services/products in Azure you need to have the login credentials. As you see you need to provide the subscription id and the workspace name is needed in order to run your ML model or whatever. In order to run those command you must login with your credentials. sklearn is a python library whereas Azure ML is a complete product/service which needs to have security integrated in place.


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


            No vulnerabilities reported

            Install auto-sklearn

            You can download it from GitHub, GitLab.
            You can use auto-sklearn like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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