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auto-sklearn | Automated Machine | Machine Learning library

 by   automl Python Version: v0.14.6 License: BSD-3-Clause

 by   automl Python Version: v0.14.6 License: BSD-3-Clause

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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
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kandi-support Support

  • auto-sklearn has a medium active ecosystem.
  • It has 6205 star(s) with 1145 fork(s). There are 213 watchers for this library.
  • There were 4 major release(s) in the last 12 months.
  • There are 108 open issues and 752 have been closed. On average issues are closed in 127 days. There are 9 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of auto-sklearn is v0.14.6
auto-sklearn Support
Best in #Machine Learning
Average in #Machine Learning
auto-sklearn Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • 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

securitySecurity

  • 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.
auto-sklearn Security
Best in #Machine Learning
Average in #Machine Learning
auto-sklearn Security
Best in #Machine Learning
Average in #Machine Learning

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.
auto-sklearn License
Best in #Machine Learning
Average in #Machine Learning
auto-sklearn License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • 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.
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 in four lines of code

                                copy iconCopydownload iconDownload
                                import autosklearn.classification
                                cls = autosklearn.classification.AutoSklearnClassifier()
                                cls.fit(X_train, y_train)
                                predictions = cls.predict(X_test)
                                

                                Relevant publications

                                copy iconCopydownload iconDownload
                                @inproceedings{feurer-neurips15a,
                                    title     = {Efficient and Robust Automated Machine Learning},
                                    author    = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina  Springenberg, Jost and Blum, Manuel and Hutter, Frank},
                                    booktitle = {Advances in Neural Information Processing Systems 28 (2015)},
                                    pages     = {2962--2970},
                                    year      = {2015}
                                }
                                

                                How can update trained IsolationForest model with new datasets/datafarmes in python?

                                copy iconCopydownload iconDownload
                                # 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)
                                

                                How to specify Search Space in Auto-Sklearn

                                copy iconCopydownload iconDownload
                                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)
                                

                                Community Discussions

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

                                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

                                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 .

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