Developed Embeddings Analysis Pipeline with UMAP and Plotly
- Day: 2025-02-17
- Time: 21:20 to 22:20
- Project: Dev
- Workspace: WP 2: Operational
- Status: Completed
- Priority: MEDIUM
- Assignee: Matías Nehuen Iglesias
- Tags: Embeddings, UMAP, Plotly, Python, Clustering
Description
Session Goal
The goal of this session was to develop and document a comprehensive pipeline for analyzing datasets of embeddings, focusing on dimensionality reduction, clustering, and visualization.
Key Activities
- Guideline Creation: Developed a guide for analyzing embeddings, covering techniques like dimensionality reduction, clustering, and visualization.
- Data Conversion: Implemented Python code to convert CSV string embeddings to lists or NumPy arrays, preparing them for further processing.
- UMAP Integration: Converted CSV embeddings for UMAP application, reducing dimensions for visualization.
- Visualization with Plotly: Created interactive scatter plots using Plotly, with enhanced features like multiline hover text.
- Pipeline Development: Built a pipeline that integrates PCA, UMAP, and K-Means for clustering and visualization of embeddings.
- Cosine Distance Calculation: Developed a workflow for calculating cosine distances to identify duplicates or similar ideas in embeddings.
- Cluster Representation Strategies: Explored methods for representing clusters, including centroid calculation and content summarization.
Achievements
- Successfully created a detailed guide and Python scripts for processing and visualizing embeddings.
- Developed a robust pipeline for embedding analysis using PCA, UMAP, and K-Means.
- Enhanced visualization techniques with Plotly, including interactive features.
Pending Tasks
- Further testing and optimization of the pipeline for larger datasets.
- Exploration of additional clustering techniques and visualization tools.
Evidence
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- event_ids: []