umap | Uniform Manifold Approximation and Projection | Data Visualization library

Consider the following dash app which is used inside a flask app:

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?

I have this code:

I am trying to use the Isolation Forest algorithm in the Solitude package to identify anomalous rows in my data.

I'm using the examples in the documentation to learn about the algorithm, this example uses the Pima Indians Diabetes dataset.

At the end of the example it provides a dataframe of ids, average_depth and anomaly_score sorted from highest score to lowest.

How can I tie back the results of the model to the original dataset to see the rows with the highest anomaly score?

Here's the example from the package documentation

Am using intelij and Ideavim. I remapped Esc to gh by typing inoremap gh in .ideavimrc and its been working fine. Recently, I found out that while typing a word like "Highlight", the gh inbetween will always be read as gh and then vim will move out of insert mode.

I've tried to revert it by deleting the content in .ideavim but the old gh is still working. I've also tried changing it to gg and then to jj in an attempt to may be erase the old gh, but they seem to be pilling up.

When I type in :imap, I see this

And all three of them, gg, gh, and jj are all working as Esc when in insert mode.

How do I get rid of the rest and leave only jj?

I've also tried a lot of suggestions, like using iumap or umap but it doesn't seem to be working. Am on windows.

I am trying to compare different regression stategies for a forecasting problem:

• Using algorithms that support multiple input output regression by default (i.e Linear Regression, Trees etc..).
• Using algorithms a wrapper to do multiple input output regression (i.e SVR, XGboost)
• Using the chained regressor to exploit correlations between my targets (as my forecast at t+1 is auto-correlated with the target at t+2).

The documentation of scikit for the multiple input output wrappers is actually not that good but it is mentioned that:

https://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputRegressor.html

Found a Solution, left it as an answer to this question down below :)

Info about the project: Classification task with 2 classes.

I am trying to get the output of the fully connected layer of my model for each image I put into the model during runtime. I plan to use them after the model is done training or testing all images to visualize with UMAP.

The model:

I have the following table:

So let's say I have an array, 1 2 1 2 3 4 2 1 and I want to store all the (arr[i], arr[i-1) such that arr[i] != arr[i-1] as a pair in unordered_map for counting these pairs.
For e.g.