feat: dimension reduction algorithm selection (UMAP / PCA / t-SNE)#24
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Add pydantic-settings fields for reduction_algorithm (tsne/umap/pca), tsne_perplexity, tsne_learning_rate, umap_n_neighbors, umap_min_dist, and umap_metric. Add umap-learn as optional dependency group. Include mypy override for umap stubs. Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Add reduce_dimensions() function supporting t-SNE, UMAP, and PCA with strategy dispatch. Refactor compute_plot_data() to use the new function. UMAP import is guarded with a friendly error message when not installed. Includes 8 new tests covering all algorithms, custom params, import guard, and invalid algorithm handling. Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Extend PlotRequest model with reduction_algorithm field (tsne/umap/pca) and algorithm-specific parameters with field_validator for mutual exclusivity. Update plot route to pass reduction params to Settings. Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Add ReductionAlgorithm type and algorithm params to PlotRequest interface. Extend plotStore with algorithm state and setters. Add radio selector for t-SNE/UMAP/PCA with conditional parameter panels: perplexity slider and learning rate dropdown for t-SNE, neighbors/min-dist sliders and metric dropdown for UMAP, and info text for PCA. Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Document t-SNE/UMAP/PCA algorithm selection in features, architecture diagram, and Plot parameters table. Add UMAP optional installation instructions and new env var reference for all 6 reduction parameters. Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Tests Report 📄Tests Succees ✅JUnit Details
Coverage Details (91% >= 90%) ✅Diff Cover Details
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Summary
Closes #10
Allow users to choose between t-SNE, UMAP, and PCA dimensionality reduction algorithms for the 3D scatter plot, with algorithm-specific parameter controls across backend and frontend.
Changes
settings.py,pyproject.toml): Add 6 new env-configurable fields (REDUCTION_ALGORITHM,TSNE_PERPLEXITY,TSNE_LEARNING_RATE,UMAP_N_NEIGHBORS,UMAP_MIN_DIST,UMAP_METRIC). UMAP is an optional dependency (uv sync --extra umap).scatter_plot.py): Addreduce_dimensions()with strategy dispatch for tsne/umap/pca. UMAP import is guarded with a friendly error when not installed.models.py,routes/plot.py): ExtendPlotRequestwith reduction algorithm and params, with field validation for algorithm-specific param constraints.PlotControls.tsx,plotStore.ts,types/index.ts): Radio selector for t-SNE/UMAP/PCA with conditional parameter panels (sliders, dropdowns) that only show for the selected algorithm.README.md): Updated features, architecture diagram, Plot parameter table, and UMAP install instructions.Testing
reduce_dimensions()(all algorithms, custom params, import guard, invalid algorithm)reduce_dimensionsfunction