space-boundary-tool | A fork of Berkeley Lab 's Space Boundary Tool | Topic Modeling library
kandi X-RAY | space-boundary-tool Summary
kandi X-RAY | space-boundary-tool Summary
Space Boundary Tool (version 1). The Space Boundary Tool (SBT) is a tool for automatically calculating whole-building energy performance simulation thermal space boundaries for Industry Foundation Classes (IFC) building models, and using these space boundaries to automatically generate EnergyPlus Input Definition Files (IDFs). The source code is hosted at A fork on Github exists at just to make it easier to find. If all you're looking for is the binary, it can be downloaded from Read on for build instructions.
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Lines of Code : 61License : Non-SPDX (BSD 2-Clause "Simplified" License)
Copydef plot_decision_boundaries(X, y, model_class, **model_params):
"""
Function to plot the decision boundaries of a classification model.
This uses just the first two columns of the data for fitting
the model as we need to find the pr def plot_partition_boundary(
model, train_data, ax, resolution=100, colors=("b", "k", "r")
):
"""
We can not get the optimum w of our kernel svm model which is different from linear
svm. For this reason, we generate randomly distribu 
License : No LicenseCopydef plot_decision_boundary(model, resolution=100, colors=('b', 'k', 'r')):
np.warnings.filterwarnings('ignore')
fig, ax = plt.subplots()
# Generate coordinate grid of shape [resolution x resolution]
# and evaluate the model over the entire s Community Discussions
Trending Discussions on Topic Modeling
QUESTION
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:
...ANSWER
Answered 2022-Feb-24 at 09:31As 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:
QUESTION
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:
...ANSWER
Answered 2022-Feb-22 at 19:27In 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:
QUESTION
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?
...ANSWER
Answered 2022-Feb-16 at 16:13I 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.
QUESTION
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?
...ANSWER
Answered 2021-Dec-10 at 10:33Right 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:
QUESTION
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:
...ANSWER
Answered 2021-Nov-24 at 10:43You may want to read this github page: https://nicharuc.github.io/topic_modeling/
According to this example, your code could go like this:
QUESTION
Working with the OCTIS package, I am running a CTM topic model on the BBC (default) dataset.
...ANSWER
Answered 2021-Oct-11 at 15:19I'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.
QUESTION
ANSWER
Answered 2021-Sep-20 at 01:19You should pass a column of data to the fit_transform function. Here is the example
QUESTION
ANSWER
Answered 2021-Sep-13 at 08:30It'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.
QUESTION
Starting from the following example
...ANSWER
Answered 2021-Sep-08 at 11:20You 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.
QUESTION
I am doing a topic modelling task with LDA, and I am getting 10 components with 15 top words each:
...ANSWER
Answered 2021-Jun-23 at 08:01If 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:
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