ultraopt | Distributed Asynchronous Hyperparameter Optimization | Machine Learning library

by auto-flow Python Version: 0.1.1 License: BSD-3-Clause

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | ultraopt Summary

ultraopt is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. ultraopt 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 ultraopt' or download it from GitHub, PyPI.

Let's learn what UltraOpt doing with several examples (you can try it on your Jupyter Notebook).

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Support

ultraopt has a low active ecosystem.

It has 69 star(s) with 7 fork(s). There are 3 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ultraopt is 0.1.1

Quality

ultraopt has 0 bugs and 0 code smells.

Security

ultraopt has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ultraopt code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ultraopt 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.

Reuse

ultraopt 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.

Top functions reviewed by kandi - BETA

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

Wrapper function for fmin
Called when a job is received
Ask the optimizer
Start the pyro service
Get configuration for given budget
Check if a configuration exists
Discover available workers
Register a job as finished
Check if the connection is alive
Returns a dictionary with the incumbent
Returns a list of all runs
Sample from the kernel space
Register a new result for a job
Start the worker thread
Returns a pandas dataframe containing all runs
Get the configuration for a given budget
Start the name server
Start the worker
Returns a dictionary of learning curves
Attempts to load the nameserver
Plots the convergence over time
Called when a job is finished
Recursive function to create a ConfigurationSpace
Plot the correlation between two configurations
Calculates the weight of the model
Fit the KDE model
Plot the convergence rate
Calculate FANOVA data

Get all kandi verified functions for this library.

ultraopt Key Features

No Key Features are available at this moment for ultraopt.

ultraopt Examples and Code Snippets

Quick Start,Using UltraOpt in AutoML

Python

Lines of Code : 78

License : Permissive (BSD-3-Clause)

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HDL = {
    'classifier(choice)':{
        "RandomForestClassifier": {
          "n_estimators": {"_type": "int_quniform","_value": [10, 200, 10], "_default": 100},
          "criterion": {"_type": "choice","_value": ["gini", "entropy"],"_default": "

Quick Start,Using UltraOpt in HPO

Python

Lines of Code : 40

License : Permissive (BSD-3-Clause)

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HDL = {
    "n_estimators": {"_type": "int_quniform","_value": [10, 200, 10], "_default": 100},
    "criterion": {"_type": "choice","_value": ["gini", "entropy"],"_default": "gini"},
    "max_features": {"_type": "choice","_value": ["sqrt","log2"],"_

Citation

Python

Lines of Code : 10

License : Permissive (BSD-3-Clause)

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@misc{Tang_UltraOpt,
    author       = {Qichun Tang},
    title        = {UltraOpt : Distributed Asynchronous Hyperparameter Optimization better than HyperOpt},
    month        = January,
    year         = 2021,
    doi          = {10.5281/zenodo.

Community Discussions

Trending Discussions on ultraopt

how to do hyperparameter optimization in large data?

QUESTION

how to do hyperparameter optimization in large data?

Asked 2021-Oct-03 at 08:58

I almost finished my time series model, collected enough data and now I am stuck at hyperparameter optimization.

And after lots of googling I found new & good library called ultraopt, but problem is that how much amount of fragment of data should I use from my total data (~150 GB) for hyperparameter tuning. And I want to try lots of algorithm and combinations, is there any faster and easy way?

Is there any math involved, something like, mydata = 100%size

hyperparameter optimization with 5% of mydatasize,

optimized hyperparameter *or+ or something with left 95% of datasize #something like this

To get a similar result as full data used for optimization at a time. Is there any shortcut for these?

I am using Python 3.7, CPU: AMD ryzen5 3400g, GPU: AMD Vega 11, RAM: 16 GB

...

ANSWER

Answered 2021-Oct-02 at 20:29

Hyperparameter tuning is typically done on the validation set of a train-val-test split, where each split will have something along the lines of 70%, 10%, and 20% of the entire dataset respectively. As a baseline, random search can be used while Bayesian optimization with Gaussian processes has been shown to be more compute efficient. scikit-optimize is a good package for this.

Source https://stackoverflow.com/questions/69419809

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

Vulnerabilities

No vulnerabilities reported

Install ultraopt

UltraOpt requires Python 3.6 or higher.

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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