epftoolbox | access benchmark and toolbox for electricity price | Predictive Analytics library

by jeslago Python Version: Current License: AGPL-3.0

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

kandi X-RAY | epftoolbox Summary

epftoolbox is a Python library typically used in Analytics, Predictive Analytics, Tensorflow, Neural Network applications. epftoolbox has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. However epftoolbox has 16 bugs. You can download it from GitHub.

The epftoolbox is the first open-access library for driving research in electricity price forecasting. Its main goal is to make available a set of tools that ensure reproducibility and establish research standards in electricity price forecasting research.

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Quality

Security

License

Reuse

Support

epftoolbox has a low active ecosystem.

It has 114 star(s) with 52 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 6 have been closed. On average issues are closed in 64 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of epftoolbox is current.

Quality

epftoolbox has 16 bugs (0 blocker, 0 critical, 16 major, 0 minor) and 162 code smells.

Security

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

epftoolbox code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

epftoolbox is licensed under the AGPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

epftoolbox 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.

Installation instructions, examples and code snippets are available.

It has 1717 lines of code, 62 functions and 37 files.

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

Top functions reviewed by kandi - BETA

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

Evaluate DNN in test dataset
Calibrates training and forecasted training data
Read data from Zenodo
Regularize data
Wrapper function for hyperparameters
Estimate the metric for the model
Fit the model
Displays information about training
Computes the MAE
Generate a naive forecast for a time series
Transforms input prices for a naive forecast
Plots a multivariate DM test
R Compute the DM between two time series
Evaluate a training dataset
Recalibrate training and forecast based on the next day of day
Fit to data
Return a function to resolve linkcode
Reads the best hyperparameters file
Recalibrate training and forecast to next day of day
Compute the loss between loss and loss
Calibrate training and forecast for next day
Connects the model
Compute the MAE
Compute the DM between two time series
Plots the multivariate GW test
Root Mean Square Error
Compute the MAPE
Optimizer for hyperparameter optimization

Get all kandi verified functions for this library.

epftoolbox Key Features

No Key Features are available at this moment for epftoolbox.

epftoolbox Examples and Code Snippets

No Code Snippets are available at this moment for epftoolbox.

Community Discussions

Trending Discussions on Predictive Analytics

will TensorFlow utilize GPU for predictive Analysis?

Restructuring Pandas Dataframe for large number of columns

Display data from two json files in react native

QUESTION

will TensorFlow utilize GPU for predictive Analysis?

Asked 2020-Nov-21 at 21:35

GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?

note: predictive Analysis requires no graphics processing.

...

ANSWER

Answered 2020-Nov-21 at 21:35

The short answer: yes, it will! The slightly longer answer:

The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.

The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.

If you want to know more about this, I'd recommend you start here or here.

some things to consider:

how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.

Source https://stackoverflow.com/questions/64948197

QUESTION

Restructuring Pandas Dataframe for large number of columns

Asked 2020-Nov-01 at 19:39

I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.

The current structure is as below.

What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):

Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.

...

ANSWER

Answered 2020-Nov-01 at 19:39

You might want try pivoting your table:

Source https://stackoverflow.com/questions/64630691

QUESTION

Display data from two json files in react native

Asked 2020-May-17 at 23:55

I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files

...

ANSWER

Answered 2020-May-17 at 23:55

The new object to get params in React Navigation 5 is:

Source https://stackoverflow.com/questions/61859411

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

Vulnerabilities

No vulnerabilities reported

Install epftoolbox

Download the repository and navigate into the folder. Navigate to the examples folder and check the existing examples to get you started. The examples include several applications of the two state-of-the art forecasting model: a deep neural net and the LEAR model.

Support

The documentation can be found here. It provides an introduction to the library features and explains all functionalities in detail. Note that the documentation is still being built and some functionalities are still undocumented.

Find more information at: