hypertune | Hyperparameter tuning using Particle Swarm Optimization | Machine Learning library

by brodderickrodriguez Python Version: Current License: Apache-2.0

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

kandi X-RAY | hypertune Summary

hypertune is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Numpy applications. hypertune has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

A package to tune ML hyperparameters efficiently using Particle Swarm Optimization. Please see ./examples for examples on how to use this package with your existing implementation.

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hypertune has a low active ecosystem.

It has 12 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

hypertune has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of hypertune is current.

Quality

hypertune has 0 bugs and 0 code smells.

Security

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

hypertune code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

hypertune 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

hypertune releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

hypertune saves you 361 person hours of effort in developing the same functionality from scratch.

It has 862 lines of code, 112 functions and 20 files.

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

Top functions reviewed by kandi - BETA

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

Tune training data
Train the optimizer
Get data
Check the arguments of the optimizer
Get the parameters of a callable
Get parameters from callable
Simple run
Build the model
Estimate the loss function
Optimise the population
Evaluate the objective function
Move the node by k
Evaluate evolution cycle
Evaluate the population
Check if a is better
Return a sorted list of particles
Calculate the housing house
Get dataset
Calculate the weights for training
Mean squared error

Get all kandi verified functions for this library.

hypertune Key Features

No Key Features are available at this moment for hypertune.

hypertune Examples and Code Snippets

No Code Snippets are available at this moment for hypertune.

Community Discussions

Trending Discussions on hypertune

Hypertuning a logistic regression pipeline model in pyspark

How to force parameter dependency when using AI Platform hyper parameter tuning capability?

How to use Google DataFlow Runner and Templates in tf.Transform?

QUESTION

Hypertuning a logistic regression pipeline model in pyspark

Asked 2021-Jan-20 at 04:53

I am trying to hypertune a logistic regression model. I keep getting an error as 'label does not exist'. This is an income classifier model where label is the income column. I have tried to resolve this problem using the solutions provided on the internet but I am unable to do so and stuck on this from last 2 days. If anyone could help me find out my mistake that would be of great help.

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ANSWER

Answered 2021-Jan-20 at 04:53

After lots of research and findings, I finally managed to get a working pipeline model.

Source https://stackoverflow.com/questions/65655906

QUESTION

How to force parameter dependency when using AI Platform hyper parameter tuning capability?

Asked 2019-Dec-18 at 14:13

I have a scikit-learn model that I can train on GCP using the AI Platform training. I want to do hyper parameter tuning using also the AI Platform training. This is possible and just need to pass a YAML with the parameters and their ranges:

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ANSWER

Answered 2019-Dec-18 at 14:13

At the end I implemeted the idea I described above to return a metric of 0.0 (accuray in my test) when the parameters given to sciki-learn are incorrect (like when we have df_min>df_max).

As you can see below there is no accruary reported when the value 0.0 was return in the case of invalid hyper parameters:

What also found is that the code only accept float or string as input for metric as below but I didn't find more documentation that explain this in details:

Source https://stackoverflow.com/questions/59343183

QUESTION

How to use Google DataFlow Runner and Templates in tf.Transform?

Asked 2017-Oct-04 at 21:35

We are in the process of establishing a Machine Learning pipeline on Google Cloud, leveraging GC ML-Engine for distributed TensorFlow training and model serving, and DataFlow for distributed pre-processing jobs.

We would like to run our Apache Beam apps as DataFlow jobs on Google Cloud. looking at the ML-Engine samples it appears possible to get tensorflow_transform.beam.impl AnalyzeAndTransformDataset to specify which PipelineRunner to use as follows:

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ANSWER

Answered 2017-Mar-21 at 22:17

Unfortunately, Python pipelines cannot be used as templates. It is only available for Java today. Since you need to use the python library, it will not be feasible to do this.

tensorflow_transform would also need to support ValueProvider so that you can pass in options as a value provider type through it.

Source https://stackoverflow.com/questions/42924077

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

Vulnerabilities

No vulnerabilities reported

Install hypertune

You can download it from GitHub.
You can use hypertune 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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