mlrMBO | Bayesian Optimization and Model-Based Optimization | Machine Learning library

by mlr-org R Version: v1.1.1 License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | mlrMBO Summary

mlrMBO is a R library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. mlrMBO has no bugs, it has no vulnerabilities and it has low support. However mlrMBO has a Non-SPDX License. You can download it from GitHub.

mlrMBO is a highly configurable R toolbox for model-based / Bayesian optimization of black-box functions.

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Quality

Security

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Support

mlrMBO has a low active ecosystem.

It has 172 star(s) with 45 fork(s). There are 31 watchers for this library.

It had no major release in the last 12 months.

There are 79 open issues and 279 have been closed. On average issues are closed in 93 days. There are 12 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of mlrMBO is v1.1.1

Quality

mlrMBO has 0 bugs and 0 code smells.

Security

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

mlrMBO code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

mlrMBO has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

mlrMBO releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 8137 lines of code, 0 functions and 49 files.

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

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mlrMBO Key Features

No Key Features are available at this moment for mlrMBO.

mlrMBO Examples and Code Snippets

No Code Snippets are available at this moment for mlrMBO.

Community Discussions

Trending Discussions on mlrMBO

R: Error in fn(unlist(x), ...) : unused argument (unlist(x))

(R) Error: unused arguments (forbidden = expression(x2 > x1))

Error with SVM hyperparameter tuning in mlrMBO Bayesian optimization

Error with mlrMBO rBayesianOptimization of R keras model through caret

QUESTION

R: Error in fn(unlist(x), ...) : unused argument (unlist(x))

Asked 2021-Jul-15 at 08:52

I am using the "mlrMBO" library in R (https://cran.r-project.org/web/packages/mlrMBO/index.html). I am trying to learn how to run an optimization algorithm on some arbitrary function that I defined:

...

ANSWER

Answered 2021-Jul-15 at 08:52

You need to add one setting and adjust another one:

Source https://stackoverflow.com/questions/68389229

QUESTION

(R) Error: unused arguments (forbidden = expression(x2 > x1))

Asked 2021-Jul-13 at 23:06

I am working with the R programming language. I am learning how to use the "mlrMBO" library for the purpose of optimizing multi-objective functions (using Bayesian Methods).

In my example, for the following constraints:

...

ANSWER

Answered 2021-Jul-13 at 22:50

It looks like you're passing two arguments of the same name to makeNumericParamSet(), which only takes 1 argument of the name 'forbidden'. I checked ?makeNumericParamSet in the R console, and saw that the function which takes 'par.set' and 'forbidden' as arguments is actually makeParamSet, so I tried this:

attempt

Source https://stackoverflow.com/questions/68369943

QUESTION

Error with SVM hyperparameter tuning in mlrMBO Bayesian optimization

Asked 2020-Jul-25 at 11:27

I am trying to optimize an SVM for a classification task, which has worked for many other models I've tried this process on. Yet, when I used an SVM in my model based optimization function it returns an error: "Error in checkStuff(fun, design, learner, control) : Provided learner does not support factor parameters."

Attached is the relevant code. In my training task, all independent variables are numeric, the only factor is my outcome of interest.

...

ANSWER

Answered 2020-Jul-25 at 04:29

The problem is that your parameter set has a categorical parameter (kernel) and the surrogate model you're using (regr.km) doesn't support that. You could try for example a random forest as surrogate model instead.

Source https://stackoverflow.com/questions/63083496

QUESTION

Error with mlrMBO rBayesianOptimization of R keras model through caret

Asked 2020-Jun-07 at 21:29

I am trying to implement a Multi-layer Perceptron through the Keras package (and tensorflow) to run a fast MLP. I want to use Bayesian Optimization to train the algorithm's hyperparameters. I get an error message though, saying "ValueError: rate is neither scalar nor scalar tensor" and then it prints the random value for the dropout parameter from keras. I then also get an error from caret that "There were missing values in resampled performance measures." I can get the process to work for non caret/keras algorithms.

Here is my code applied to the iris dataset that should reproduce the error:

...

ANSWER

Answered 2020-Jun-07 at 21:29

This is merely a syntax problem

The following works for me:

Source https://stackoverflow.com/questions/62240928

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

Vulnerabilities

No vulnerabilities reported

Install mlrMBO

We recommend to install the official release version:.

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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