Questions tagged [runumap]
UMAP (Uniform Manifold Approximation and Projection) is a novel manifold manifold learning technique for dimension reduction. The UMAP algorithm is competitive with t-SNE for visualization quality and arguably preserves more of the global structure and superior run time performance. UMAP has not computational restrictions on embedding dimensions making it viable as a general purpose dimension reduction technique for machine learning
runumap
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Cluster Analysis after a process
I have a dataset with 535 samples, 63 dimensions and 7 clusters (labels). I am going to run some processes on my dataset and then track the impact of the process on the dataset.
What approach would ...
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bokeh HoverTool and tooltips not working correctly
Hi there I just got the plot I wanted with some help and tweaks here and there using bokeh; however, I can't figure out how to add information of interest to my tooltips navigation/hover tool.
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legend in bokeh UMAP shows only one entry
Hi there I just managed to get this plot done in bokeh, so I imagine there are many things that could be improved. Nonetheless, what bothers me the most is that I cannot figure out how to have all ...
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how to plot in bokeh
Hi there I'm quite new to Python plotting in general but somehow I can get the gist of things from examples; however, I couldn't find anything useful/helpful to understand how to use bokeh to plot a ...
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Can the results of UMAP for HDBScan clustering be made more consistent?
I have a set of ~40K phrases which I'm clustering with HDBScan after using UMAP for dimensionality reduction. The steps are:
Generate embeddings using a fine-tuned BERT model
Reduce dimensions with ...
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Applying UMAP to a distance matrix
I am working on a clustering analysis and computed a distance matrix with a custom metric (it is the fusion of three differently weighted distance matrices) and I am trying to get components out of it ...
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UMAP "ValueError: cannot assign slice from input of different size"
I am using umap-learn 0.5.3 for dimension reduction of a Numpy array.
The array, say arrival_tfidf, is shaped (7898, 2969) and is a TF-IDF transformation of 7898 messages, containing float64 elements. ...
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How do I compute LSC scores ("stemness") onto a UMAP in Seurat?
In Seurat, I am working with a UMAP of blast cells and would like to use the LSC17 coefficients to produce a feature plot with "stemness" scores generated from the LSC17 onto the UMAP. I'm ...
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scRNAseq UMAP error with FindVariableFeatures
I am new to scRNAseq analysis and am self-taught so apologies if this is really basic!
I have downloaded metadata and TPM counts for a scRNAseq dataset (https://github.com/Martin-Villalba-lab/Data/...
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Latest version of RAPIDS cuML in Kaggle notebooks
First of all, I am fairly new to running models on GPU, so sorry in advance for stupid questions.
I use RAPIDS cuML to GPU-accelerate some algorithms, but I noticed I cannot use the latest version (23....
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Clustering text. Chatintets library Python. HBDSCAN, UMAP
I'm using chatintents (https://github.com/dborrelli/chat-intents) for automatically clustering. To embed sentences I use sentence transformers. The problem is when I set the maximum and minimum number ...
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How to Systematically Tune UMAP Hyperparameters for Supervised Learning
I have a question about using Uniform Manifold Approximation and Projection (UMAP) for feature extraction.
In my project I am using two tabular datasets both containing around 10000 samples. One has ...
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UMAP validation to calculate trustworthiness_vector problem
I have a dataset with over 200.000 data samples with 256 features, then, I used UMAP with n_components = 8, 16, 32, 64, to reduce data dimension fron 256 to 64, 32, 16, 8, respectively. I do not have ...
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Best parameters for UMAP + HistGradientBoostingClassifier
I'm trying to find the best parameters for the UMAP (dimensionality reduction) model together with HistGradientBoostingClassifier.
The loop I have created is:
vectorizer = TfidfVectorizer(use_idf=True,...
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TypeError: 'module' object is not callable - while using UMAP
import umap as UMAP
import umap
retarget = {df_train['target'].value_counts().reset_index()['index'][i]: i for i in range(len(df_train['target'].value_counts()))}
retarget2 = {i: k for k, i in ...
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