umap-learn

npx skills add majiayu000/claude-skill-registry --skill scientific-pkg-umap-learn --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# UMAP-Learn ## Overview UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique for visualization and general non-linear dimensionality reduction. Apply this skill for fast, scalable embeddings that preserve local and global structure, supervised learning, and clustering preprocessing. ## Quick Start ### Installation ```bash # Via conda conda install -c conda-forge umap-learn # Via pip pip install umap-learn ``` ### Basic Usage UMAP follows scikit-learn conventions and can be used as a drop-in replacement for t-SNE or PCA. ```python import umap from sklearn.preprocessing import StandardScaler # Prepare data (standardization is essential) scaled_data = StandardScaler().fit_transform(data) # Method 1: Single step (fit and transform) embedding = umap.UMAP().fit_transform(scaled_data) # Method 2: Separate steps (for reusing trained model) reducer = umap.UMAP(random_state=42) reducer.fit(scaled_data) embedding = reducer.embedding_ # Access the trained embedding ``` **Critical preprocessing requirement:** Always standardize features to comparable scales before applying UMAP to ensure equal weighting across dimensions. ### Typical Workflow ```python i

Via conda
conda install -c conda-forge umap-learn
Via pip
pip install umap-learn
pip install umap-learn[parametric_umap]
Requires TensorFlow 2.x