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Add cosine distance as valid metric #36

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Add cosine distance as valid metric #36

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7b60bb8
Add cosine distance as valid metric
olgabot Jan 28, 2021
7890bcc
Change default metric to "cosine"
olgabot Jan 28, 2021
2c00555
Update metric within bbknn_pca_matrix
olgabot Jan 30, 2021
73a487a
Update bbknn/__init__.py
olgabot Jan 30, 2021
8107010
Update bbknn/__init__.py
olgabot Jan 30, 2021
f54b4b8
set metric to "angular" for AnnoyIndex internally
olgabot Jan 30, 2021
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21 changes: 11 additions & 10 deletions bbknn/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,6 +72,7 @@ def compute_connectivities_umap(knn_indices, knn_dists,
# In umap-learn 0.4, this returns (result, sigmas, rhos)
connectivities = connectivities[0]
distances = get_sparse_matrix_from_indices_distances_umap(knn_indices, knn_dists, n_obs, n_neighbors)
, n_neighbors)
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return distances, connectivities.tocsr()

Expand Down Expand Up @@ -211,7 +212,7 @@ def trimming(cnts,trim):
cnts = cnts.T.tocsr()
return cnts

def bbknn(adata, batch_key='batch', use_rep='X_pca', approx=True, metric='angular', copy=False, **kwargs):
def bbknn(adata, batch_key='batch', use_rep='X_pca', approx=True, metric='cosine', copy=False, **kwargs):
'''
Batch balanced KNN, altering the KNN procedure to identify each cell's top neighbours in
each batch separately instead of the entire cell pool with no accounting for batch.
Expand Down Expand Up @@ -248,8 +249,8 @@ def bbknn(adata, batch_key='batch', use_rep='X_pca', approx=True, metric='angula
If ``approx=False`` and the metric is "euclidean", use the faiss package to compute
nearest neighbours if installed. This improves performance at a minor cost to numerical
precision as faiss operates on float32.
metric : ``str`` or ``sklearn.neighbors.DistanceMetric``, optional (default: "angular")
What distance metric to use. If using ``approx=True``, the options are "angular",
metric : ``str`` or ``sklearn.neighbors.DistanceMetric``, optional (default: "cosine")
What distance metric to use. If using ``approx=True``, the options are "cosine",
"euclidean", "manhattan" and "hamming". Otherwise, the options are "euclidean",
a member of the ``sklearn.neighbors.KDTree.valid_metrics`` list, or parameterised
``sklearn.neighbors.DistanceMetric`` `objects
Expand Down Expand Up @@ -280,9 +281,9 @@ def bbknn(adata, batch_key='batch', use_rep='X_pca', approx=True, metric='angula
if use_rep not in adata.obsm.keys():
raise ValueError("Did not find "+use_rep+" in `.obsm.keys()`. You need to compute it first.")
#metric sanity checks
if approx and metric not in ['angular', 'euclidean', 'manhattan', 'hamming']:
logg.warning('unrecognised metric for type of neighbor calculation, switching to angular')
metric = 'angular'
if approx and metric not in ['euclidean', 'manhattan', 'hamming', 'cosine']:
logg.warning('unrecognised metric for type of neighbor calculation, switching to cosine (')
metric = 'cosine'
elif not approx and not (metric=='euclidean' or isinstance(metric,DistanceMetric) or metric in KDTree.valid_metrics):
logg.warning('unrecognised metric for type of neighbor calculation, switching to euclidean')
metric = 'euclidean'
Expand Down Expand Up @@ -317,7 +318,7 @@ def bbknn(adata, batch_key='batch', use_rep='X_pca', approx=True, metric='angula
return adata if copy else None

def bbknn_pca_matrix(pca, batch_list, neighbors_within_batch=3, n_pcs=50, trim=None,
approx=True, n_trees=10, use_faiss=True, metric='angular',
approx=True, n_trees=10, use_faiss=True, metric='cosine',
set_op_mix_ratio=1, local_connectivity=1):
'''
Scanpy-independent BBKNN variant that runs on a PCA matrix and list of per-cell batch assignments instead of
Expand All @@ -343,9 +344,9 @@ def bbknn_pca_matrix(pca, batch_list, neighbors_within_batch=3, n_pcs=50, trim=N
if np.min(counts) < neighbors_within_batch:
raise ValueError("Not all batches have at least `neighbors_within_batch` cells in them.")
#metric sanity checks (duplicating the ones in bbknn(), but without scanpy logging)
if approx and metric not in ['angular', 'euclidean', 'manhattan', 'hamming']:
print('unrecognised metric for type of neighbor calculation, switching to angular')
metric = 'angular'
if approx and metric not in ['cosine', 'euclidean', 'manhattan', 'hamming']:
print('unrecognised metric for type of neighbor calculation, switching to cosine')
metric = 'cosine'
elif not approx and not (metric=='euclidean' or isinstance(metric,DistanceMetric) or metric in KDTree.valid_metrics):
print('unrecognised metric for type of neighbor calculation, switching to euclidean')
metric = 'euclidean'
Expand Down