scanpy.tl.umap after bbknn

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Hi,
I got an error when running tl.umap after bbknn normalisation... new in version 1.7.2

Minimal code sample (that we can copy&paste without having any data)

adata_bbknn = bbknn.bbknn(adata, batch_key = metacol, n_pcs = number_of_pcs_for_reduction,copy=True)
scanpy.tl.umap(adata_bbknn, min_dist=0.2, spread=2, n_components=3)
  

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-73-a5a2e6833485> in <module>()
      1 adata_bbknn = bbknn.bbknn(adata, batch_key = metacol, n_pcs = number_of_pcs_for_reduction,copy=True)
----> 2 scanpy.tl.umap(adata_bbknn, min_dist=0.2, spread=2, n_components=3)

/home/sguenth/.conda/envs/scRNAseq_analysis_1.6/lib/python3.7/site-packages/scanpy/tools/_umap.py in umap(adata, min_dist, spread, n_components, maxiter, alpha, gamma, negative_sample_rate, init_pos, random_state, a, b, copy, method, neighbors_key)
    205             neigh_params.get('metric', 'euclidean'),
    206             neigh_params.get('metric_kwds', {}),
--> 207             verbose=settings.verbosity > 3,
    208         )
    209     elif method == 'rapids':

/home/sguenth/.conda/envs/scRNAseq_analysis_1.6/lib/python3.7/site-packages/umap/umap_.py in simplicial_set_embedding(data, graph, n_components, initial_alpha, a, b, gamma, negative_sample_rate, n_epochs, init, random_state, metric, metric_kwds, output_metric, output_metric_kwds, euclidean_output, parallel, verbose)
   1037             random_state,
   1038             metric=metric,
-> 1039             metric_kwds=metric_kwds,
   1040         )
   1041         expansion = 10.0 / np.abs(initialisation).max()

/home/sguenth/.conda/envs/scRNAseq_analysis_1.6/lib/python3.7/site-packages/umap/spectral.py in spectral_layout(data, graph, dim, random_state, metric, metric_kwds)
    304             random_state,
    305             metric=metric,
--> 306             metric_kwds=metric_kwds,
    307         )
    308 

/home/sguenth/.conda/envs/scRNAseq_analysis_1.6/lib/python3.7/site-packages/umap/spectral.py in multi_component_layout(data, graph, n_components, component_labels, dim, random_state, metric, metric_kwds)
    191             random_state,
    192             metric=metric,
--> 193             metric_kwds=metric_kwds,
    194         )
    195     else:

/home/sguenth/.conda/envs/scRNAseq_analysis_1.6/lib/python3.7/site-packages/umap/spectral.py in component_layout(data, n_components, component_labels, dim, random_state, metric, metric_kwds)
    120             else:
    121                 distance_matrix = pairwise_distances(
--> 122                     component_centroids, metric=metric, **metric_kwds
    123                 )
    124 

/home/sguenth/.conda/envs/scRNAseq_analysis_1.6/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     70                           FutureWarning)
     71         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72         return f(**kwargs)
     73     return inner_f
     74 

/home/sguenth/.conda/envs/scRNAseq_analysis_1.6/lib/python3.7/site-packages/sklearn/metrics/pairwise.py in pairwise_distances(X, Y, metric, n_jobs, force_all_finite, **kwds)
   1738         raise ValueError("Unknown metric %s. "
   1739                          "Valid metrics are %s, or 'precomputed', or a "
-> 1740                          "callable" % (metric, _VALID_METRICS))
   1741 
   1742     if metric == "precomputed":

ValueError: Unknown metric angular. Valid metrics are ['euclidean', 'l2', 'l1', 'manhattan', 'cityblock', 'braycurtis', 'canberra', 'chebyshev', 'correlation', 'cosine', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule', 'wminkowski', 'nan_euclidean', 'haversine'], or 'precomputed', or a callable

Versions

Start with scanpy/tools/_umap.py and the reported call to scanpy.tl.umap after bbknn, then trace how the angular metric reaches umap's spectral layout and sklearn's pairwise_distances. Re-run the minimal sample with the listed versions. Done means the reported workflow completes without the Unknown metric angular error and has regression coverage for this case.