auto-sklearn | Automated Machine | Machine Learning library

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

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
    Support
      Quality
        Security
          License
            Reuse
            Support
              Quality
                Security
                  License
                    Reuse

                      kandi-support Support

                        summary
                        auto-sklearn has a medium active ecosystem.
                        summary
                        It has 6797 star(s) with 1218 fork(s). There are 213 watchers for this library.
                        summary
                        There were 1 major release(s) in the last 6 months.
                        summary
                        There are 145 open issues and 816 have been closed. On average issues are closed in 99 days. There are 7 open pull requests and 0 closed requests.
                        summary
                        It has a neutral sentiment in the developer community.
                        summary
                        The latest version of auto-sklearn is v0.15.0
                        auto-sklearn Support
                          Best in #Machine Learning
                            Average in #Machine Learning
                            auto-sklearn Support
                              Best in #Machine Learning
                                Average in #Machine Learning

                                  kandi-Quality Quality

                                    summary
                                    auto-sklearn has 0 bugs and 0 code smells.
                                    auto-sklearn Quality
                                      Best in #Machine Learning
                                        Average in #Machine Learning
                                        auto-sklearn Quality
                                          Best in #Machine Learning
                                            Average in #Machine Learning

                                              kandi-Security Security

                                                summary
                                                auto-sklearn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
                                                summary
                                                auto-sklearn code analysis shows 0 unresolved vulnerabilities.
                                                summary
                                                There are 0 security hotspots that need review.
                                                auto-sklearn Security
                                                  Best in #Machine Learning
                                                    Average in #Machine Learning
                                                    auto-sklearn Security
                                                      Best in #Machine Learning
                                                        Average in #Machine Learning

                                                          kandi-License License

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

                                                                      kandi-Reuse Reuse

                                                                        summary
                                                                        auto-sklearn releases are available to install and integrate.
                                                                        summary
                                                                        Build file is available. You can build the component from source.
                                                                        summary
                                                                        Installation instructions are not available. Examples and code snippets are available.
                                                                        summary
                                                                        It has 34349 lines of code, 1704 functions and 322 files.
                                                                        summary
                                                                        It has medium code complexity. Code complexity directly impacts maintainability of the code.
                                                                        auto-sklearn Reuse
                                                                          Best in #Machine Learning
                                                                            Average in #Machine Learning
                                                                            auto-sklearn Reuse
                                                                              Best in #Machine Learning
                                                                                Average in #Machine Learning
                                                                                  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.
                                                                                  Get all kandi verified functions for this library.

                                                                                  auto-sklearn Key Features

                                                                                  Automated Machine Learning with scikit-learn

                                                                                  auto-sklearn Examples and Code Snippets

                                                                                  FLASH,How to Run?
                                                                                  Pythondot imgLines of Code : 10dot imgLicense : Strong Copyleft (GPL-3.0)
                                                                                  copy iconCopy
                                                                                  
                                                                                                                      cd /path/to/FLASH/benchmarks/sklearn python run_flash.py
                                                                                  cd /path/to/FLASH/benchmarks/sklearn python run_flash_star.py
                                                                                  cd /path/to/FLASH/benchmarks/sklearn python run_smac.py
                                                                                  cd /path/to/FLASH/benchmarks/sklearn python run_tpe.py
                                                                                  cd /path/to/FLASH/benchmarks/sklearn python run_random.py
                                                                                  Community Discussions

                                                                                  Trending Discussions on auto-sklearn

                                                                                  How can update trained IsolationForest model with new datasets/datafarmes in python?
                                                                                  chevron right
                                                                                  How to specify Search Space in Auto-Sklearn
                                                                                  chevron right
                                                                                  Python creates Folder inside docker image but remove when processing completes
                                                                                  chevron right
                                                                                  Is it possible to use azureml without any login things?
                                                                                  chevron right

                                                                                  QUESTION

                                                                                  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?

                                                                                  ANSWER

                                                                                  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:

                                                                                  # Model
                                                                                  from sklearn.ensemble import IsolationForest
                                                                                  
                                                                                  # Saving file
                                                                                  import joblib
                                                                                  
                                                                                  # Data
                                                                                  import numpy as np
                                                                                  
                                                                                  # Create a new model
                                                                                  model = IsolationForest()
                                                                                  
                                                                                  # Generate some old data
                                                                                  df1 = np.random.randint(1,100,(100,10))
                                                                                  # Train the model
                                                                                  model.fit(df1)
                                                                                  
                                                                                  # Save it off
                                                                                  joblib.dump(model, 'isf_model.joblib')
                                                                                  
                                                                                  # Load the model
                                                                                  model = joblib.load('isf_model.joblib')
                                                                                  
                                                                                  # Generate new data
                                                                                  df2 = np.random.randint(1,500,(1000,10))
                                                                                  
                                                                                  # If the original data is now not important, I can just call .fit() again.
                                                                                  # If you are using time-series based data, this is preferred, as older data may not be representative of the current state
                                                                                  model.fit(df2)
                                                                                  
                                                                                  # If the original data is important, I can simply join the old data to new data. There are multiple options for this:
                                                                                  # Pandas: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html
                                                                                  # Numpy: https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html
                                                                                  
                                                                                  combined_data = np.concatenate((df1, df2))
                                                                                  model.fit(combined_data)
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/71326545

                                                                                  QUESTION

                                                                                  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

                                                                                  mdl = autosklearn.classification.AutoSklearnClassifier(
                                                                                      include = {
                                                                                           'classifier': ["random_forest", "gaussian_nb", "libsvm_svc", "adaboost"],
                                                                                           'feature_preprocessor': ["no_preprocessing"]
                                                                                      },
                                                                                      exclude=None)
                                                                                  

                                                                                  I know that Auto-Sklearn use Bayesian Optimisation SMAC

                                                                                  but I would like to specify the HyperParameters in AutoSklearn

                                                                                  For example, I want to specify random_forest with Estimator = 1000 only or MLP with HiddenLayerSize = 100 only.

                                                                                  How to do that?

                                                                                  ANSWER

                                                                                  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:

                                                                                  cs = mdl.get_configuration_space(X, y)
                                                                                  config = cs.sample_configuration()
                                                                                  config._values['classifier:random_forest:n_estimators'] = 1000
                                                                                  pipeline, run_info, run_value = mdl.fit_pipeline(X=X_train, y=y_train,
                                                                                                                                   config=config,
                                                                                                                                   X_test=X_test, y_test=y_test)
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/70781470

                                                                                  QUESTION

                                                                                  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

                                                                                  FROM ubuntu:18.04
                                                                                  
                                                                                  # Upgrade installed packages
                                                                                  
                                                                                  RUN apt update
                                                                                  RUN apt upgrade -y
                                                                                  ENV TZ=Europe/London
                                                                                  RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
                                                                                  RUN apt-get install -y libreadline-gplv2-dev libncursesw5-dev libssl-dev libsqlite3-dev tk-dev libgdbm-dev libc6-dev libbz2-dev
                                                                                  
                                                                                  WORKDIR /code
                                                                                  RUN apt-get -y install python3-pip
                                                                                  RUN apt-get -y install python3-venv
                                                                                  RUN apt -y install python3-setuptools libffi-dev python3-dev
                                                                                  RUN apt install -y curl
                                                                                  RUN apt install -y unzip
                                                                                  
                                                                                  RUN apt-get install -y build-essential swig
                                                                                  
                                                                                  WORKDIR /code
                                                                                  RUN python3 -m venv .env
                                                                                  RUN . .env/bin/activate && pip install --upgrade pip && curl https://raw.githubusercontent.com/automl/auto-sklearn/master/requirements.txt | LC_ALL=C.UTF-8 xargs -n 1 -L 1 pip install
                                                                                  COPY requirements.txt requirements.txt
                                                                                  RUN . .env/bin/activate && pip install pyenchant && pip install -r requirements.txt
                                                                                  
                                                                                  RUN apt install -y libgl1-mesa-glx
                                                                                  RUN apt-get install -y libglib2.0-0
                                                                                  RUN apt-get install -y libenchant1c2a
                                                                                  
                                                                                  RUN mkdir embeddings
                                                                                  COPY . .
                                                                                  RUN curl -L http://nlp.stanford.edu/data/glove.6B.zip --output glove.zip
                                                                                  RUN unzip -o glove.zip -d embeddings/
                                                                                  
                                                                                  RUN . .env/bin/activate && python nltk_install.py
                                                                                  CMD . .env/bin/activate && python main.py
                                                                                   
                                                                                  

                                                                                  ANSWER

                                                                                  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.

                                                                                  Source https://stackoverflow.com/questions/66256913

                                                                                  QUESTION

                                                                                  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 azure.com.

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

                                                                                  from azureml.core import Workspace
                                                                                  
                                                                                  subscription_id = ''
                                                                                  resource_group  = ''
                                                                                  workspace_name  = ''
                                                                                  

                                                                                  ANSWER

                                                                                  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.

                                                                                  Source https://stackoverflow.com/questions/61299031

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

                                                                                  Vulnerabilities

                                                                                  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.

                                                                                  Support

                                                                                  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 .
                                                                                  Find more information at:
                                                                                  Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
                                                                                  Find more libraries
                                                                                  Explore Kits - Develop, implement, customize Projects, Custom Functions and Applications with kandi kits​
                                                                                  Save this library and start creating your kit
                                                                                  CLONE
                                                                                • HTTPS

                                                                                  https://github.com/automl/auto-sklearn.git

                                                                                • CLI

                                                                                  gh repo clone automl/auto-sklearn

                                                                                • sshUrl

                                                                                  git@github.com:automl/auto-sklearn.git

                                                                                • Share this Page

                                                                                  share link

                                                                                  Consider Popular Machine Learning Libraries

                                                                                  tensorflow

                                                                                  by tensorflow

                                                                                  youtube-dl

                                                                                  by ytdl-org

                                                                                  models

                                                                                  by tensorflow

                                                                                  pytorch

                                                                                  by pytorch

                                                                                  keras

                                                                                  by keras-team

                                                                                  Try Top Libraries by automl

                                                                                  Auto-PyTorch

                                                                                  by automlPython

                                                                                  SMAC3

                                                                                  by automlPython

                                                                                  TabPFN

                                                                                  by automlPython

                                                                                  HpBandSter

                                                                                  by automlPython

                                                                                  RoBO

                                                                                  by automlPython

                                                                                  Compare Machine Learning Libraries with Highest Support

                                                                                  youtube-dl

                                                                                  by ytdl-org

                                                                                  scikit-learn

                                                                                  by scikit-learn

                                                                                  models

                                                                                  by tensorflow

                                                                                  tensorflow

                                                                                  by tensorflow

                                                                                  keras

                                                                                  by keras-team

                                                                                  Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
                                                                                  Find more libraries
                                                                                  Explore Kits - Develop, implement, customize Projects, Custom Functions and Applications with kandi kits​
                                                                                  Save this library and start creating your kit