Add educational UMAP implementation#14690
Open
AyushGarg76 wants to merge 4 commits into
Open
Conversation
Contributor
There was a problem hiding this comment.
Pull request overview
Adds an educational UMAP dimensionality reduction implementation under machine_learning, aligning with existing examples like t-SNE and PCA.
Changes:
- Adds UMAP helper functions for distances, nearest neighbors, membership strengths, and embedding optimization.
- Adds Iris dataset demo output and doctests.
- Updates
DIRECTORY.mdandCONTRIBUTING.md.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 6 comments.
| File | Description |
|---|---|
machine_learning/uniform_manifold_approximation_and_projection.py |
New educational UMAP implementation and demo. |
DIRECTORY.md |
Adds a directory entry for the new UMAP file. |
CONTRIBUTING.md |
Adds guidance about skipping pre-commit hooks. |
Comments suppressed due to low confidence (1)
machine_learning/uniform_manifold_approximation_and_projection.py:140
- This assumes the first sorted index is always the point itself. With duplicate samples (zero distance to another row) or any tied zero distances, argsort can put another index first and this slice can include i as its own nearest neighbor, violating the function contract and producing self-edges in the fuzzy graph.
sorted_indices = np.argsort(distance_matrix[i])
neighbor_indices[i] = sorted_indices[1 : n_neighbors + 1]
💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.
Comment on lines
+77
to
+80
| When you're making local experimental changes and don't want pre-commit hooks to run, you can bypass them using: | ||
|
|
||
| ```bash | ||
| git commit --no-verify -m "Exploratory: Testing XYZ" |
| * [Similarity Search](machine_learning/similarity_search.py) | ||
| * [Support Vector Machines](machine_learning/support_vector_machines.py) | ||
| * [T Stochastic Neighbour Embedding](machine_learning/t_stochastic_neighbour_embedding.py) | ||
| * [Uniform Manifold Approximation And Projection](machine_learning/uniform_manifold_approximation_and_projection.py) |
| print() | ||
| print("Loading Iris dataset …") | ||
| features, labels = collect_dataset() | ||
| print(f" Input shape : {features.shape} ((150 flowers x 2 dimensions))") |
Comment on lines
+75
to
+77
| ### Skipping Pre-commit Hooks for Exploratory Changes | ||
|
|
||
| When you're making local experimental changes and don't want pre-commit hooks to run, you can bypass them using: |
Comment on lines
+127
to
+133
|
|
||
| >>> dist = np.array([[0., 1., 2.], [1., 0., 3.], [2., 3., 0.]]) | ||
| >>> nn = find_nearest_neighbors(dist, n_neighbors=1) | ||
| >>> nn.tolist() | ||
| [[1], [0], [0]] | ||
| """ | ||
| n_samples = distance_matrix.shape[0] |
Comment on lines
+320
to
+324
| if n_components < 1: | ||
| raise ValueError("n_components must be >= 1") | ||
| if n_neighbors < 1: | ||
| raise ValueError("n_neighbors must be >= 1") | ||
| if n_epochs < 1: |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Describe your change:
Adds an educational implementation of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction in the
machine_learningsection.Features:
The implementation is simplified for educational purposes and follows the repository style used in existing dimensionality reduction algorithms such as PCA and t-SNE.
Fixes #14689
Checklist: