spamosaic.MNN

spamosaic.MNN.mnn(ds1, ds2, names1, names2, knn1=20, knn2=20, approx=True, metric='euclidean', way='hnsw', norm=False)

Compute Mutual Nearest Neighbors (MNN) between two datasets.

Parameters:

• ds1 (np.ndarray) – First dataset (queries).

• ds2 (np.ndarray) – Second dataset (references).

• names1 (list of str) – Identifiers for ds1.

• names2 (list of str) – Identifiers for ds2.

• knn1 (int) – Number of neighbors for ds1 → ds2.

• knn2 (int) – Number of neighbors for ds2 → ds1.

• approx (bool, default=True) – Whether to use approximate neighbor search.

• metric (str, default='euclidean') – Distance metric to use.

• way (str, default='hnsw') – Method for approximate search: ‘hnsw’ or ‘annoy’.

• norm (bool, default=False) – Whether to normalize data before Annoy search.

Returns:

Set of mutual nearest neighbor pairs.

Return type:

Set[Tuple[str, str]]

spamosaic.MNN.nn(ds1, ds2, names1, names2, knn=50, metric_p=2)

Exact nearest neighbor search using scikit-learn.

Parameters:

• ds1 (np.ndarray) – Query dataset.

• ds2 (np.ndarray) – Reference dataset.

• names1 (list of str) – Identifiers for ds1.

• names2 (list of str) – Identifiers for ds2.

• knn (int) – Number of nearest neighbors.

• metric_p (int) – Minkowski distance parameter (e.g., 2 for Euclidean).

Returns:

Set of matched nearest neighbor pairs.

Return type:

Set[Tuple[str, str]]

spamosaic.MNN.nn_annoy(ds1, ds2, names1, names2, norm=True, knn=20, metric='euclidean', n_trees=10, save_on_disk=False)

Approximate nearest neighbor search using Annoy index.

Parameters:

• ds1 (np.ndarray) – Query dataset.

• ds2 (np.ndarray) – Reference dataset.

• names1 (list of str) – Identifiers for ds1.

• names2 (list of str) – Identifiers for ds2.

• norm (bool, default=True) – Whether to L2 normalize datasets before indexing.

• knn (int) – Number of nearest neighbors to retrieve.

• metric (str, default='euclidean') – Distance metric (‘euclidean’, ‘manhattan’, etc.).

• n_trees (int, default=10) – Number of trees to build in Annoy index.

• save_on_disk (bool, default=False) – Whether to write index to disk.

Returns:

Set of nearest neighbor pairs.

Return type:

Set[Tuple[str, str]]

spamosaic.MNN.nn_approx(ds1, ds2, names1, names2, knn=50)

Approximate nearest neighbor search using HNSW (hnswlib).

Parameters:

• ds1 (np.ndarray) – Query dataset (N1, D).

• ds2 (np.ndarray) – Reference dataset (N2, D).

• names1 (list of str) – Identifiers for rows in ds1.

• names2 (list of str) – Identifiers for rows in ds2.

• knn (int, default=50) – Number of nearest neighbors to find.

Returns:

Set of matched (query_name, reference_name) pairs.

Return type:

Set[Tuple[str, str]]

Functions

spamosaic.MNN.mnn	Compute Mutual Nearest Neighbors (MNN) between two datasets.
spamosaic.MNN.nn	Exact nearest neighbor search using scikit-learn.
spamosaic.MNN.nn_annoy	Approximate nearest neighbor search using Annoy index.
spamosaic.MNN.nn_approx	Approximate nearest neighbor search using HNSW (hnswlib).