PartiallyCollapsedLDA | various fast parallelized samplers for LDA | Topic Modeling library

 by   lejon Java Version: v8.5.1 License: No License

kandi X-RAY | PartiallyCollapsedLDA Summary

kandi X-RAY | PartiallyCollapsedLDA Summary

PartiallyCollapsedLDA is a Java library typically used in Artificial Intelligence, Topic Modeling applications. PartiallyCollapsedLDA has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Implementations of various fast parallelized samplers for LDA, including Partially Collapsed LDA, Light LDA, Partially Collapsed Light LDA and a very efficient Polya-Urn LDA

            kandi-support Support

              PartiallyCollapsedLDA has a low active ecosystem.
              It has 27 star(s) with 13 fork(s). There are 7 watchers for this library.
              It had no major release in the last 12 months.
              There are 6 open issues and 6 have been closed. On average issues are closed in 224 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of PartiallyCollapsedLDA is v8.5.1

            kandi-Quality Quality

              PartiallyCollapsedLDA has 0 bugs and 0 code smells.

            kandi-Security Security

              PartiallyCollapsedLDA has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              PartiallyCollapsedLDA code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              PartiallyCollapsedLDA 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.

            kandi-Reuse Reuse

              PartiallyCollapsedLDA releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              PartiallyCollapsedLDA saves you 15403 person hours of effort in developing the same functionality from scratch.
              It has 30727 lines of code, 2286 functions and 218 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed PartiallyCollapsedLDA and discovered the below as its top functions. This is intended to give you an instant insight into PartiallyCollapsedLDA implemented functionality, and help decide if they suit your requirements.
            • Sampling on topic assignments
            • Calculate the score for a topic
            • Get the extra mapping table
            • Sample topic assignments
            • Increment the number of tokens for a topic
            • Sample a topic indicator
            • A small helper method to sample topic assignments
            • Helper method to remove a non - zero or more non - zero topic
            • Sample a topic indicator
            • Returns a string representation of the matrix
            • Returns the log - likelihood of the ADDA
            • Collects document statistics
            • Returns the log - likelihood of the model
            • This function calculates and returns the word vectors
            • Deserialize this object
            • Deserialization
            • Sample topics for one doc
            • Imports the instances
            • Calculate the variance
            • Sample the assignments of each topic in the corpus
            • Checks if the given object is equal to this one
            • Calculates the Euclidean distance of an instance
            • Performs a parallelism sampling on each topic
            • Continue sampling
            • A helper function to sample the number of topics in each topic
            • Sample an LDA model
            Get all kandi verified functions for this library.

            PartiallyCollapsedLDA Key Features

            No Key Features are available at this moment for PartiallyCollapsedLDA.

            PartiallyCollapsedLDA Examples and Code Snippets

            No Code Snippets are available at this moment for PartiallyCollapsedLDA.

            Community Discussions


            TensorFlow word embedding model + LDA Negative values in data passed to
            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:



            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:



            Display document to topic mapping after LSI using Gensim
            Asked 2022-Feb-22 at 19:27

            I am new to using LSI with Python and Gensim + Scikit-learn tools. I was able to achieve topic modeling on a corpus using LSI from both the Scikit-learn and Gensim libraries, however, when using the Gensim approach I was not able to display a list of documents to topic mapping.

            Here is my work using Scikit-learn LSI where I successfully displayed document to topic mapping:



            Answered 2022-Feb-22 at 19:27

            In order to get the representation of a document (represented as a bag-of-words) from a trained LsiModel as a vector of topics, you use Python dict-style bracket-accessing (model[bow]).

            For example, to get the topics for the 1st item in your training data, you can use:



            Normalizing Topic Vectors in Top2vec
            Asked 2022-Feb-16 at 16:13

            I am trying to understand how Top2Vec works. I have some questions about the code that I could not find an answer for in the paper. A summary of what the algorithm does is that it:

            • embeds words and vectors in the same semantic space and normalizes them. This usually has more than 300 dimensions.
            • projects them into 5-dimensional space using UMAP and cosine similarity.
            • creates topics as centroids of clusters using HDBSCAN with Euclidean metric on the projected data.

            what troubles me is that they normalize the topic vectors. However, the output from UMAP is not normalized, and normalizing the topic vectors will probably move them out of their clusters. This is inconsistent with what they described in their paper as the topic vectors are the arithmetic mean of all documents vectors that belong to the same topic.

            This leads to two questions:

            How are they going to calculate the nearest words to find the keywords of each topic given that they altered the topic vector by normalization?

            After creating the topics as clusters, they try to deduplicate the very similar topics. To do so, they use cosine similarity. This makes sense with the normalized topic vectors. In the same time, it is an extension of the inconsistency that normalizing topic vectors introduced. Am I missing something here?



            Answered 2022-Feb-16 at 16:13

            I got the answer to my questions from the source code. I was going to delete the question but I will leave the answer any way.

            It is the part I missed and is wrong in my question. Topic vectors are the arithmetic mean of all documents vectors that belong to the same topic. Topic vectors belong to the same semantic space where words and documents vector live.

            That is why it makes sense to normalize them since all words and documents vectors are normalized, and to use the cosine metric when looking for duplicated topics in the higher original semantic space.



            Extract Topic Scores for Documents LDA Gensim Python
            Asked 2021-Dec-10 at 10:33

            I am trying to extract topic scores for documents in my dataset after using and LDA model. Specifically, I have followed most of the code from here:

            I have completed the topic model and have the results I want, but the provided code only gives the most dominant topic for each document. Is there a simple way to modify the following code to give me the scores for say the 5 most dominant topics?



            Answered 2021-Dec-10 at 10:33

            Right this is a crusty example because you haven't provided data to reproduce but using some gensim testing corpus, texts and dictionary we can do:



            How to get list of words for each topic for a specific relevance metric value (lambda) in pyLDAvis?
            Asked 2021-Nov-24 at 10:43

            I am using pyLDAvis along with gensim.models.LdaMulticore for topic modeling. I have totally 10 topics. When I visualize the results using pyLDAvis, there is a bar called lambda with this explanation: "Slide to adjust relevance metric". I am interested to extract the list of words for each topic separately for lambda = 0.1. I cannot find a way to adjust lambda in the document for extracting keywords.

            I am using these lines:



            Answered 2021-Nov-24 at 10:43

            You may want to read this github page:

            According to this example, your code could go like this:



            Wait. BoW and Contextual Embeddings have different sizes
            Asked 2021-Oct-11 at 15:19

            Working with the OCTIS package, I am running a CTM topic model on the BBC (default) dataset.



            Answered 2021-Oct-11 at 15:19

            I'm one of the developers of OCTIS.

            Short answer: If I understood your problem, you can fix this issue by modifying the parameter "bert_path" of CTM and make it dataset-specific, e.g. CTM(bert_path="path/to/store/the/files/" + data)

            TL;DR: I think the problem is related to the fact that CTM generates and stores the document representations in some files with a default name. If these files already exist, it uses them without generating new representations, even if the dataset has changed in the meantime. Then CTM will raise that issue because it is using the BOW representation of a dataset, but the contextualized representations of another dataset, resulting in two representations with different dimensions. Changing the name of the files with respect to the name of the dataset will allow the model to retrieve the correct representations.

            If you have other issues, please open a GitHub issue in the repo. I've found out about this issue by chance.



            Can I input a pandas dataframe into "TfidfVectorizer"? If so, how do I find out how many documents are in my dataframe?
            Asked 2021-Sep-20 at 01:19

            Here's the raw data:

            Here's about the first half of the data after reading it into a pandas dataframe:

            I'm trying to run TfidfVectorizer but I keep getting the following error:



            Answered 2021-Sep-20 at 01:19

            You should pass a column of data to the fit_transform function. Here is the example



            Should bi-gram and tri-gram be used in LDA topic modeling?
            Asked 2021-Sep-13 at 21:11

            I read several posts(here and here) online about LDA topic modeling. All of them only use uni-grams. I would like to know why bi-grams and tri-grams are not used for LDA topic modeling?



            Answered 2021-Sep-13 at 08:30

            It's a matter of scale. If you have 1000 types (ie "dictionary words"), you might end up (in the worst case, which is not going to happen) with 1,000,000 bigrams, and 1,000,000,000 trigrams. These numbers are hard to manage, especially as you will have a lot more types in a realistic text.

            The gains in accuracy/performance don't outweigh the computational cost here.



            Determine the correct number of topics using latent semantic analysis
            Asked 2021-Sep-08 at 11:20

            Starting from the following example



            Answered 2021-Sep-08 at 11:20

            You can compute the explained variance with a range of the possible number of components. The maximum number of components is the size of your vocabulary.



            Pandas: LDA Top n keywords and topics with weights
            Asked 2021-Jun-23 at 08:01

            I am doing a topic modelling task with LDA, and I am getting 10 components with 15 top words each:



            Answered 2021-Jun-23 at 08:01

            If I understand correctly, you have a dataframe with all values and you want to keep the top 10 in each row, and have 0s on remaining values.

            Here we transform each row by:

            • getting the 10th highest values
            • reindexing to the original index of the row (thus the columns of the dataframe) and filling with 0s:


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


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            Install PartiallyCollapsedLDA

            Install the package using maven as follows:.
            Install Apache Maven
            Install the package using maven as follows:


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