dopr | Minimal CLI tool for Digital Ocean | Content Management System library

by ayoubeddafali Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | dopr Summary

dopr is a Python library typically used in Web Site, Content Management System, Drupal applications. dopr has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Minimal CLI tool for Digital Ocean

Support

Quality

Security

License

Reuse

Support

dopr has a low active ecosystem.

It has 5 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of dopr is current.

Quality

dopr has no bugs reported.

Security

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

License

dopr does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

dopr 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.

Top functions reviewed by kandi - BETA

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

Provision machine
Install given packages
Argument parser
Clean up resources
Removes a resource

Get all kandi verified functions for this library.

dopr Key Features

No Key Features are available at this moment for dopr.

dopr Examples and Code Snippets

No Code Snippets are available at this moment for dopr.

Community Discussions

Trending Discussions on dopr

Run trained Machine Learning model on a different dataset

Scala: Calling function dynamically

QUESTION

Run trained Machine Learning model on a different dataset

Asked 2018-Dec-12 at 10:01

I am new to Machine Learning and am in the process of trying to run a simple classification model that I trained and saved using pickle, on another dataset of the same format. I have the following python code.

Code

...

ANSWER

Answered 2018-Dec-12 at 10:00

Your question is a bit unclear but as I understand, you want to run your model on testX_train and on testX_test (which is just testFeatures splitted into two sub datasets).

So, either you can run your model on testX_train the same way you did for testX_test, e.g. :

result_RFC_train = loaded_model_RFC.score(textX_train, testy_train)

or you can just remove the following line :

testX_train, textX_test, testy_train, testy_test = model_selection.train_test_split(testFeatures, testlabels, test_size= 0.25, random_state = 42)

So you just don't split you data and run it on the full dataset :

result_RFC_train = loaded_model_RFC.score(testFeatures, testlabels)

Source https://stackoverflow.com/questions/53740141

QUESTION

Scala: Calling function dynamically

Asked 2017-Aug-24 at 22:33

I am very new to scala.

I am using rscala to integrate with R functions. For example, I am using the following method to call an R function and retreive a String value.

Method Signature:

def invokeS0(function: Reference, args: Any*): String

Method Implementation

...

ANSWER

Answered 2017-Aug-24 at 22:33

Convert the Map[String, String] into a Seq[(String, String)] with args.toSeq, and then use the _* ascription to pass it as a sequence of arguments instead of just one.

Source https://stackoverflow.com/questions/45871700

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

Vulnerabilities

No vulnerabilities reported

Install dopr

You can download it from GitHub.
You can use dopr 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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