Embeddings Visualization

Embeddings Visualization

Visualizing and analyzing high-dimensional embeddings — from dimensionality reduction (UMAP, t-SNE) through interactive visualization, cluster analysis, and similarity search.

When to Use

• Understanding embedding space structure and clusters
• Debugging embedding quality and semantic relationships
• Presenting embedding analysis to stakeholders
• Finding patterns in high-dimensional vector spaces

Visualization Pipeline

import numpy as np
from typing import List, Dict

class EmbeddingVisualizer:
    """Reduce and visualize embedding spaces."""
    
    @staticmethod
    def reduce(embeddings: np.array, method: str = 'umap', 
               n_components: int = 2) -> np.array:
        if method == 'umap':
            import umap
            reducer = umap.UMAP(n_components=n_components)
        elif method == 'tsne':
            from sklearn.manifold import TSNE
            reducer = TSNE(n_components=n_components)
        elif method == 'pca':
            from sklearn.decomposition import PCA
            reducer = PCA(n_components=n_components)
        return reducer.fit_transform(embeddings)
    
    @staticmethod
    def find_clusters(reduced: np.array, 
                      min_clusters: int = 3, max_clusters: int = 10) -> Dict:
        from sklearn.cluster import HDBSCAN
        clusterer = HDBSCAN(min_cluster_size=5)
        labels = clusterer.fit_predict(reduced)
        return {
            'n_clusters': len(set(labels) - {-1}),
            'noise_points': int((labels == -1).sum()),
            'labels': labels.tolist(),
        }

Verification Checklist

• Embedding dimension reduced to 2D/3D for visualization
• Labels/colors applied for semantic interpretation
• Clusters identified (HDBSCAN or similar)
• Nearest neighbor search working on reduced space
• Visualizations explainable to stakeholders
• Outlier/novelty detection from embedding space