DGFraud | A Deep Graph-based Toolbox for Fraud Detection | Predictive Analytics library

by safe-graph Python Version: v0.1.0 License: Apache-2.0

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

kandi X-RAY | DGFraud Summary

DGFraud is a Python library typically used in Analytics, Predictive Analytics, Deep Learning applications. DGFraud 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 Deep Graph-based Toolbox for Fraud Detection

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Quality

Security

License

Reuse

Support

DGFraud has a low active ecosystem.

It has 559 star(s) with 152 fork(s). There are 14 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 10 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DGFraud is v0.1.0

Quality

DGFraud has 0 bugs and 0 code smells.

Security

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

DGFraud code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DGFraud 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

DGFraud releases are available to install and integrate.

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

Installation instructions are not available. Examples and code snippets are available.

DGFraud saves you 2858 person hours of effort in developing the same functionality from scratch.

It has 6179 lines of code, 382 functions and 51 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Evaluate the function
Evaluate the model
Runs the test function
Get train data
Run the test function
Create embedding
Splits the A_hat into a tensor
Convert matrices into tf SparseTensor
Apply embedding
Compute dot product attention
Generate random walk
Get sparsity split
Builds the model
Run random walk
Return a dictionary of values from the val_edges
Generate a dictionary of values for incremental iteration
Convert a matrix into adjacency list
Generate a node - value pair
Compute the loss between two inputs
Load the adjacency matrix
Get the negative sampling for the given pairs
Parse command line arguments
Argument parser
Load data from YAML file
Build the graph
Forward the genotype propagation
Call the convolution function

Get all kandi verified functions for this library.

DGFraud Key Features

No Key Features are available at this moment for DGFraud.

DGFraud Examples and Code Snippets

No Code Snippets are available at this moment for DGFraud.

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 DGFraud

You can download it from GitHub.
You can use DGFraud 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

You are welcomed to contribute to this open-source toolbox. The detailed instructions will be released soon. Currently, you can create issues or email to bdscsafegraph@gmail.com for inquiry.

Find more information at: