kandi X-RAY | PartiallyCollapsedLDA Summary
kandi X-RAY | PartiallyCollapsedLDA Summary
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
Top functions reviewed by kandi - BETA
- 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
- 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
PartiallyCollapsedLDA Key Features
PartiallyCollapsedLDA Examples and Code Snippets
Trending Discussions on Topic Modeling
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:...
ANSWERAnswered 2022-Feb-24 at 09:31
fit function of
LatentDirichletAllocation does not allow a negative array, I will recommend you to apply softplus on the
Here is the code snippet:
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:...
ANSWERAnswered 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 (
For example, to get the topics for the 1st item in your training data, you can use:
- 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?...
ANSWERAnswered 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.
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: https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/
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?...
ANSWERAnswered 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:
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:...
ANSWERAnswered 2021-Nov-24 at 10:43
You may want to read this github page: https://nicharuc.github.io/topic_modeling/
According to this example, your code could go like this:
Working with the OCTIS package, I am running a CTM topic model on the BBC (default) dataset....
ANSWERAnswered 2021-Oct-11 at 15:19
I'm one of the developers of OCTIS.
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.
ANSWERAnswered 2021-Sep-20 at 01:19
You should pass a column of data to the
fit_transform function. Here is the example
ANSWERAnswered 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.
Starting from the following example...
ANSWERAnswered 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.
I am doing a topic modelling task with LDA, and I am getting 10 components with 15 top words each:...
ANSWERAnswered 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.
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:
No vulnerabilities reported
Install Apache Maven
Install the package using maven as follows:
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