📅 2025-02-17 — Session: Embeddings Analysis Pipeline Development
🕒 21:20–22:20
🏷️ Labels: Embeddings, UMAP, Data Visualization, Python, Clustering
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop a comprehensive pipeline for analyzing datasets of embeddings, focusing on dimensionality reduction, clustering, and visualization techniques.
Key Activities
- Developed a guide for analyzing embeddings, including techniques for dimensionality reduction, clustering, and visualization.
- Converted CSV embeddings into lists or NumPy arrays for processing.
- Implemented a Python script to convert string embeddings for UMAP dimensionality reduction.
- Created a visualization script using UMAP and Plotly for interactive scatter plots.
- Enhanced Plotly visualizations with multiline hover text.
- Developed a pipeline using PCA and UMAP for clustering and visualization.
- Calculated cosine distances to identify duplicate embeddings.
- Explored strategies for cluster representation using centroids and summaries.
Achievements
- Successfully created a detailed pipeline for embeddings analysis, integrating PCA, UMAP, K-Means, and Plotly.
- Implemented code snippets for each stage of the process, facilitating reproducibility.
Pending Tasks
- Further optimization of the pipeline for larger datasets.
- Exploration of additional clustering techniques beyond K-Means.