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JGibbLabeledLDA | Labeled LDA in Java | Topic Modeling library

 by   myleott Java Version: Current License: GPL-2.0

 by   myleott Java Version: Current License: GPL-2.0

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kandi X-RAY | JGibbLabeledLDA Summary

JGibbLabeledLDA is a Java library typically used in Artificial Intelligence, Topic Modeling applications. JGibbLabeledLDA has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However JGibbLabeledLDA build file is not available. You can download it from GitHub.
This is a Java implementation of Labeled LDA based on the popular [JGibbLDA](http://jgibblda.sourceforge.net/) package. The code has been heavily refactored and a few additional options have been added. See sections below for more details.
Support
Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • JGibbLabeledLDA has a low active ecosystem.
  • It has 106 star(s) with 57 fork(s). There are 17 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 2 open issues and 5 have been closed. On average issues are closed in 20 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of JGibbLabeledLDA is current.
JGibbLabeledLDA Support
Best in #Topic Modeling
Average in #Topic Modeling
JGibbLabeledLDA Support
Best in #Topic Modeling
Average in #Topic Modeling

quality kandi Quality

  • JGibbLabeledLDA has 0 bugs and 0 code smells.
JGibbLabeledLDA Quality
Best in #Topic Modeling
Average in #Topic Modeling
JGibbLabeledLDA Quality
Best in #Topic Modeling
Average in #Topic Modeling

securitySecurity

  • JGibbLabeledLDA has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • JGibbLabeledLDA code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
JGibbLabeledLDA Security
Best in #Topic Modeling
Average in #Topic Modeling
JGibbLabeledLDA Security
Best in #Topic Modeling
Average in #Topic Modeling

license License

  • JGibbLabeledLDA is licensed under the GPL-2.0 License. This license is Strong Copyleft.
  • Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.
JGibbLabeledLDA License
Best in #Topic Modeling
Average in #Topic Modeling
JGibbLabeledLDA License
Best in #Topic Modeling
Average in #Topic Modeling

buildReuse

  • JGibbLabeledLDA releases are not available. You will need to build from source code and install.
  • JGibbLabeledLDA has no build file. You will be need to create the build yourself to build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • JGibbLabeledLDA saves you 398 person hours of effort in developing the same functionality from scratch.
  • It has 945 lines of code, 47 functions and 9 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
JGibbLabeledLDA Reuse
Best in #Topic Modeling
Average in #Topic Modeling
JGibbLabeledLDA Reuse
Best in #Topic Modeling
Average in #Topic Modeling
Top functions reviewed by kandi - BETA

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

  • Performs inf sampling .
    • Adds a document to this document .
      • Initialize the model
        • Performs a multinomial sampling using the specified number of documents .
          • Read tassign file .
            • Invoke the inference .
              • Main method for testing .
                • Starts sampling .
                  • Read word map .
                    • Load dataset .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      JGibbLabeledLDA Key Features

                      Labeled LDA in Java (based on JGibbLDA)

                      JGibbLabeledLDA Examples and Code Snippets

                      See all related Code Snippets

                      Unlabeled Documents

                      copy iconCopydownload iconDownload
                      document_1
                      document_2
                      ...
                      document_m

                      Labeled Documents

                      copy iconCopydownload iconDownload
                      [label_1,1 label_1,2 ... label_1,l_1] document_1
                      [label_2,1 label_2,2 ... label_2,l_2] document_2
                      ...
                      [label_m,1 label_m,2 ... label_m,l_m] document_m

                      Usage

                      copy iconCopydownload iconDownload
                      **-nburnin <int>**: Discard this many initial iterations when taking samples.

                      See all related Code Snippets

                      Community Discussions

                      Trending Discussions on Topic Modeling
                      • TensorFlow word embedding model + LDA Negative values in data passed to LatentDirichletAllocation.fit
                      • Display document to topic mapping after LSI using Gensim
                      • Normalizing Topic Vectors in Top2vec
                      • Extract Topic Scores for Documents LDA Gensim Python
                      • How to get list of words for each topic for a specific relevance metric value (lambda) in pyLDAvis?
                      • Wait. BoW and Contextual Embeddings have different sizes
                      • Can I input a pandas dataframe into "TfidfVectorizer"? If so, how do I find out how many documents are in my dataframe?
                      • Should bi-gram and tri-gram be used in LDA topic modeling?
                      • Determine the correct number of topics using latent semantic analysis
                      • Pandas: LDA Top n keywords and topics with weights
                      Trending Discussions on Topic Modeling

                      QUESTION

                      TensorFlow word embedding model + LDA Negative values in data passed to LatentDirichletAllocation.fit

                      Asked 2022-Feb-24 at 09:31

                      I am trying to use a pre-trained model from TensorFlow hub instead of frequency vectorization techniques for word embedding before passing the resultant feature vector to the LDA model.

                      I followed the steps for the TensorFlow model, but I got this error upon passing the resultant feature vector to the LDA model:

                      Negative values in data passed to LatentDirichletAllocation.fit
                      

                      Here's what I have implemented so far:

                      import pandas as pd
                      import matplotlib.pyplot as plt
                      import tensorflow_hub as hub
                      
                      from sklearn.decomposition import LatentDirichletAllocation
                      
                      embed = hub.load("https://tfhub.dev/google/tf2-preview/nnlm-en-dim50-with-normalization/1")
                      embeddings = embed(["cat is on the mat", "dog is in the fog"])
                      lda_model = LatentDirichletAllocation(n_components=2, max_iter=50)
                      lda = lda_model.fit_transform(embeddings)
                      

                      I realized that print(embeddings) prints some negative values as shown below:

                      tf.Tensor(
                      [[ 0.16589954  0.0254965   0.1574857   0.17688066  0.02911299 -0.03092718
                         0.19445257 -0.05709129 -0.08631689 -0.04391516  0.13032274  0.10905275
                        -0.08515751  0.01056632 -0.17220995 -0.17925954  0.19556305  0.0802278
                        -0.03247919 -0.49176937 -0.07767699 -0.03160921 -0.13952136  0.05959712
                         0.06858718  0.22386682 -0.16653948  0.19412343 -0.05491862  0.10997339
                        -0.15811177 -0.02576607 -0.07910853 -0.258499   -0.04206644 -0.20052543
                         0.1705603  -0.15314153  0.0039225  -0.28694248  0.02468278  0.11069503
                         0.03733957  0.01433943 -0.11048374  0.11931834 -0.11552787 -0.11110869
                         0.02384969 -0.07074881]
                      

                      But, is there a solution to this?

                      ANSWER

                      Answered 2022-Feb-24 at 09:31

                      As the fit function of LatentDirichletAllocation does not allow a negative array, I will recommend you to apply softplus on the embeddings.

                      Here is the code snippet:

                      import pandas as pd
                      import matplotlib.pyplot as plt
                      import tensorflow_hub as hub
                      from tensorflow.math import softplus
                      
                      from sklearn.decomposition import LatentDirichletAllocation
                      
                      embed = hub.load("https://tfhub.dev/google/tf2-preview/nnlm-en-dim50-with-normalization/1")
                      embeddings = softplus(embed(["cat is on the mat", "dog is in the fog"]))
                      
                      lda_model = LatentDirichletAllocation(n_components=2, max_iter=50)
                      lda = lda_model.fit_transform(embeddings)
                      

                      Source https://stackoverflow.com/questions/71249109

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

                      Vulnerabilities

                      No vulnerabilities reported

                      Install JGibbLabeledLDA

                      You can download it from GitHub.
                      You can use JGibbLabeledLDA like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the JGibbLabeledLDA component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      Support

                      Please direct questions to [Myle Ott](myleott@gmail.com).

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