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KaifAhmad1
409f3fdd27
- Added grid‑card layouts, icons, and admonitions to all reference pages - Removed all mkdocstrings ::: directives that caused alias‑resolution errors - Fixed export module alias issue (RDFExporter) by cleaning the docs - Updated README‑style sections for better readability - Added a PowerShell helper script (remove_mkdocstrings.ps1) for future clean‑ups
182 lines
5.0 KiB
Markdown
182 lines
5.0 KiB
Markdown
# Visualization Module
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Visualize knowledge graphs, embeddings, and analytics with interactive and static visualizations using multiple rendering engines.
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## Overview
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- **Graph Visualization**: Interactive and static knowledge graph rendering
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- **Embedding Visualization**: t-SNE, UMAP, PCA for high-dimensional data
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- **Analytics Visualization**: Charts, plots, and dashboards
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- **Temporal Visualization**: Time-series and evolution visualization
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- **Export Formats**: HTML, PNG, SVG, PDF
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---
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## Algorithms Used
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### Dimensionality Reduction
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- **t-SNE (t-Distributed Stochastic Neighbor Embedding)**: Non-linear dimensionality reduction, preserves local structure
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- **UMAP (Uniform Manifold Approximation and Projection)**: Faster than t-SNE, preserves global + local structure
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- **PCA (Principal Component Analysis)**: Linear dimensionality reduction, `X_reduced = X * eigenvectors`
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### Graph Layout Algorithms
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- **Force-Directed Layout**: Spring-electrical model with Fruchterman-Reingold algorithm
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- **Hierarchical Layout**: Tree-based layout with Sugiyama framework
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- **Circular Layout**: Nodes arranged in circle, edges minimize crossings
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- **Kamada-Kawai**: Energy-based layout minimizing edge length variance
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---
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## Main Classes
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### KGVisualizer
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**Methods:**
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| Method | Description | Algorithm |
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|--------|-------------|-----------|
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| `visualize(graph, output)` | Visualize knowledge graph | Force-directed layout with PyVis/Cytoscape |
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| `render_interactive(graph)` | Interactive HTML visualization | D3.js/PyVis rendering |
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| `render_static(graph, format)` | Static image rendering | Graphviz/Matplotlib rendering |
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| `export(graph, filename, format)` | Export visualization | Format-specific export (HTML/PNG/SVG) |
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| `customize_style(node_style, edge_style)` | Customize appearance | Style application |
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**Supported Engines:**
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- **PyVis**: Interactive HTML with physics simulation
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- **Cytoscape.js**: Web-based graph visualization
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- **Graphviz**: Static high-quality diagrams
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- **Matplotlib**: Python-native plotting
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- **Plotly**: Interactive web visualizations
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**Example:**
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```python
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from semantica.visualization import KGVisualizer
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visualizer = KGVisualizer(
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engine="pyvis", # pyvis, cytoscape, graphviz, matplotlib
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layout="force_directed", # force_directed, hierarchical, circular
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width="100%",
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height="800px"
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)
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# Interactive visualization
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visualizer.visualize(
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graph=kg,
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output="graph.html",
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show_physics=True,
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node_color_by="type",
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edge_width_by="weight"
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)
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# Static visualization
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visualizer.render_static(
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graph=kg,
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format="png",
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output="graph.png",
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dpi=300
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)
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```
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---
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### EmbeddingVisualizer
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**Methods:**
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| Method | Description | Algorithm |
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|--------|-------------|-----------|
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| `visualize(embeddings, method)` | Visualize embeddings | Dimensionality reduction + scatter plot |
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| `plot_tsne(embeddings, perplexity)` | t-SNE visualization | t-SNE with configurable perplexity |
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| `plot_umap(embeddings, n_neighbors)` | UMAP visualization | UMAP with neighbor parameter |
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| `plot_pca(embeddings, n_components)` | PCA visualization | PCA to 2D/3D |
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| `plot_clusters(embeddings, labels)` | Cluster visualization | Color-coded scatter plot |
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**t-SNE Parameters:**
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- `perplexity`: Balance between local and global structure (5-50)
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- `learning_rate`: Step size (10-1000)
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- `n_iter`: Number of iterations (250-1000)
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**UMAP Parameters:**
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- `n_neighbors`: Local neighborhood size (2-100)
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- `min_dist`: Minimum distance between points (0.0-0.99)
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- `metric`: Distance metric (euclidean, cosine, manhattan)
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**Example:**
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```python
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from semantica.visualization import EmbeddingVisualizer
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visualizer = EmbeddingVisualizer()
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# t-SNE visualization
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visualizer.plot_tsne(
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embeddings=embeddings,
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labels=labels,
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perplexity=30,
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output="tsne.html"
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)
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# UMAP visualization
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visualizer.plot_umap(
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embeddings=embeddings,
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n_neighbors=15,
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min_dist=0.1,
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output="umap.html"
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)
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```
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---
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### AnalyticsVisualizer
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**Methods:**
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| Method | Description | Algorithm |
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|--------|-------------|-----------|
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| `plot_metrics(metrics)` | Plot graph metrics | Bar/line charts |
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| `plot_distribution(data, bins)` | Plot distributions | Histogram generation |
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| `plot_timeline(events, timeline)` | Plot temporal data | Time-series visualization |
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| `create_dashboard(components)` | Create dashboard | Multi-panel layout |
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---
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## Configuration
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```yaml
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# config.yaml - Visualization Configuration
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visualization:
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kg:
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engine: pyvis # pyvis, cytoscape, graphviz, matplotlib
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layout: force_directed
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width: "100%"
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height: "800px"
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physics_enabled: true
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node_size_by: degree
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node_color_by: type
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edge_width_by: weight
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embeddings:
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method: umap # tsne, umap, pca
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n_components: 2
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perplexity: 30 # for t-SNE
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n_neighbors: 15 # for UMAP
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export:
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format: html # html, png, svg, pdf
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dpi: 300 # for raster formats
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transparent_background: false
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```
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---
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## See Also
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- [Knowledge Graph Module](kg.md)
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- [Embeddings Module](embeddings.md)
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- [Export Module](export.md)
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