lda2vec

by cemoody Python Version: Current License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | lda2vec Summary

lda2vec is a Python library. lda2vec has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

lda2vec

Support

Quality

Security

License

Reuse

Support

lda2vec has a medium active ecosystem.

It has 2962 star(s) with 615 fork(s). There are 120 watchers for this library.

It had no major release in the last 6 months.

There are 53 open issues and 22 have been closed. On average issues are closed in 34 days. There are 8 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of lda2vec is current.

Quality

lda2vec has 0 bugs and 33 code smells.

Security

lda2vec has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

lda2vec code analysis shows 0 unresolved vulnerabilities.

There are 3 security hotspots that need review.

License

lda2vec is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

lda2vec 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.

lda2vec saves you 666 person hours of effort in developing the same functionality from scratch.

It has 1545 lines of code, 86 functions and 30 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed lda2vec and discovered the below as its top functions. This is intended to give you an instant insight into lda2vec implemented functionality, and help decide if they suit your requirements.

Generate fake data
Generate orthogonal matrix
Calculate softmax
Draw a random sample from a list of probabilities
Observe the given bow
Compute the value of the Gaussian distribution
Return the loss function for a given sample
Forward computation
Make samples from t
Calculate the loss function
Computes the Dirichlet likelihood
Compute the Dirichlet likelihood
Calculates the proportions of a set of documents
Fit a partial embedding
Generator of Variable objects
Partial prior distribution
Clean a line

Get all kandi verified functions for this library.

lda2vec Key Features

No Key Features are available at this moment for lda2vec.

lda2vec Examples and Code Snippets

lda2vec,Training dataset description

Jupyter Notebook

Lines of Code : 32

License : No License

Copy

from sklearn.datasets import fetch_20newsgroups

dataset = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))

PUT l2v_analyzer_index
{
  "settings" : {
      "index" : {
        "analysis" : {
          "filter" : {

Project Outline:,Approach:,Conclusion:

Jupyter Notebook

Lines of Code : 8

License : No License

Copy

Classification Summary:
* Logistic Regression (using CountVectorizer) performance was the best with - F1 score: 94 %.
* Multinomial NB with Tfidf was a close second with - F1 score: 92 %.

Clustering Summary:
* Both NMF and Lda with term frequency we

Community Discussions

Trending Discussions on lda2vec

RuntimeError: Expected object of backend CUDA but got backend CPU for argument #3 'index'

QUESTION

RuntimeError: Expected object of backend CUDA but got backend CPU for argument #3 'index'

Asked 2019-Aug-18 at 05:08

I'm working with the project 'lda2vec-pytorch' on Google CoLab, runnin pytorch 1.1.0

https://github.com/TropComplique/lda2vec-pytorch

...

ANSWER

Answered 2019-Aug-18 at 05:08

Variable noise is available on CPU while self.embedding is on GPU. We can send noise to GPU as well:

Source https://stackoverflow.com/questions/57539872

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

Vulnerabilities

No vulnerabilities reported

Install lda2vec

You can download it from GitHub.
You can use lda2vec 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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