tune-sklearn | RandomizedSearchCV -- but with cutting edge hyperparameter | Machine Learning library

by ray-project Python Version: 0.5.0 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | tune-sklearn Summary

tune-sklearn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. tune-sklearn has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install tune-sklearn' or download it from GitHub, PyPI.

A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.

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tune-sklearn has a low active ecosystem.

It has 369 star(s) with 39 fork(s). There are 16 watchers for this library.

It had no major release in the last 12 months.

There are 18 open issues and 89 have been closed. On average issues are closed in 22 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tune-sklearn is 0.5.0

Quality

tune-sklearn has 0 bugs and 0 code smells.

Security

tune-sklearn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tune-sklearn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tune-sklearn is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

tune-sklearn 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.

tune-sklearn saves you 1104 person hours of effort in developing the same functionality from scratch.

It has 3802 lines of code, 226 functions and 35 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed tune-sklearn and discovered the below as its top functions. This is intended to give you an instant insight into tune-sklearn implemented functionality, and help decide if they suit your requirements.

Train the model
Get bohb configuration space
Overrides run_args with default values
Get hyperopt parameters
Compute the model
Return True if clf is a LightGBM model
Check if lightgbm is installed
Fit the model
Run the optimizer
Return the number of samples in the parameter grid
The inverse transform
The number of splits
Reset the configuration
Setup the estimator
The refit time
Gets the best score
The best estimator
Check if early stopping is set
Returns the predicted log probabilities
Predict
Get the best parameters
Fit an early stopping ensemble using an ensemble
Gets the best index
Fill config in config dictionary
Checks the parameter grid for tuning
List of classes

Get all kandi verified functions for this library.

tune-sklearn Key Features

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

tune-sklearn Examples and Code Snippets

No Code Snippets are available at this moment for tune-sklearn.

Community Discussions

Trending Discussions on tune-sklearn

Unable to install ray[tune] tune-sklearn

QUESTION

Unable to install ray[tune] tune-sklearn

Asked 2022-Mar-14 at 20:10

I'm trying to install ray[tune] tune-sklearn on my machine but keeps failing. I'm using a MacBook Pro 2019 with Big Sur Version 11.6 and Python 3.9.7 (default, Sep 16 2021, 08:50:36) [Clang 10.0.0 ] :: Anaconda, Inc. on darwin. All other packages I've tried to installed fine either using conda install or pip install except for this one. I'm struggling to find an answer online myself. I was on Python 3.8 but I removed this and installed 3.9 as I thought this was the problem. Apologies in advance, I'm new to data mining and still don't know a great deal yet.

I tried

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ANSWER

Answered 2022-Mar-14 at 20:10

ray[tune] is a library within the Ray distributed compute project that supports scalable hyperparameter tuning -- not a stand-alone Python package. You should be able to install ray with the proper dependencies using:

Source https://stackoverflow.com/questions/71257435

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

Vulnerabilities

No vulnerabilities reported

Install tune-sklearn

You can install using 'pip install tune-sklearn' or download it from GitHub, PyPI.
You can use tune-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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