ValueError: Precomputed metric requires shape (n_queries, n_indexed)

I just wanted to bring to your attention this error message. I believe this error is a little misleading because the algorithm works for n_neighbors=15 but not n_neighbors=3. Do you know what it could be in the backend that is preventing it from working for n_neighbors=3 and throwing the shape message?

umap.__version__
0.3.7

# Shape?
print(X.shape)
​(5843, 5843)

# Symmetric?
def check_symmetric(a, tol=1e-8):
    return np.allclose(a, a.T, atol=tol)
print(check_symmetric(X))
​True

# Nulls?
print(np.any(X.isnull()))
​False

# Diagonal? 
print(np.unique(np.diagonal(X.values)))
​[0.]

# UMAP Precomputed
model = UMAP(n_neighbors=3, metric="precomputed")
embeddings = model.fit_transform(X)

Error

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-40-44805956fe15> in <module>
     18 # UMAP Precomputed
     19 model = UMAP(n_neighbors=3, metric="precomputed")
---> 20 embeddings = model.fit_transform(X)

~/anaconda/envs/µ_env/lib/python3.6/site-packages/umap/umap_.py in fit_transform(self, X, y)
   1564             Embedding of the training data in low-dimensional space.
   1565         """
-> 1566         self.fit(X, y)
   1567         return self.embedding_
   1568 

~/anaconda/envs/µ_env/lib/python3.6/site-packages/umap/umap_.py in fit(self, X, y)
   1536             self.metric,
   1537             self._metric_kwds,
-> 1538             self.verbose,
   1539         )
   1540 

~/anaconda/envs/µ_env/lib/python3.6/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, verbose)
    941             random_state,
    942             metric=metric,
--> 943             metric_kwds=metric_kwds,
    944         )
    945         expansion = 10.0 / initialisation.max()

~/anaconda/envs/µ_env/lib/python3.6/site-packages/umap/spectral.py in spectral_layout(data, graph, dim, random_state, metric, metric_kwds)
    238             random_state,
    239             metric=metric,
--> 240             metric_kwds=metric_kwds,
    241         )
    242 

~/anaconda/envs/µ_env/lib/python3.6/site-packages/umap/spectral.py in multi_component_layout(data, graph, n_components, component_labels, dim, random_state, metric, metric_kwds)
    120             dim,
    121             metric=metric,
--> 122             metric_kwds=metric_kwds,
    123         )
    124     else:

~/anaconda/envs/µ_env/lib/python3.6/site-packages/umap/spectral.py in component_layout(data, n_components, component_labels, dim, metric, metric_kwds)
     51 
     52     distance_matrix = pairwise_distances(
---> 53         component_centroids, metric=metric, **metric_kwds
     54     )
     55     affinity_matrix = np.exp(-distance_matrix ** 2)

~/anaconda/envs/µ_env/lib/python3.6/site-packages/sklearn/metrics/pairwise.py in pairwise_distances(X, Y, metric, n_jobs, **kwds)
   1381 
   1382     if metric == "precomputed":
-> 1383         X, _ = check_pairwise_arrays(X, Y, precomputed=True)
   1384         return X
   1385     elif metric in PAIRWISE_DISTANCE_FUNCTIONS:

~/anaconda/envs/µ_env/lib/python3.6/site-packages/sklearn/metrics/pairwise.py in check_pairwise_arrays(X, Y, precomputed, dtype)
    118                              "(n_queries, n_indexed). Got (%d, %d) "
    119                              "for %d indexed." %
--> 120                              (X.shape[0], X.shape[1], Y.shape[0]))
    121     elif X.shape[1] != Y.shape[1]:
    122         raise ValueError("Incompatible dimension for X and Y matrices: "

ValueError: Precomputed metric requires shape (n_queries, n_indexed). Got (291, 5843) for 291 indexed.

Start at umap/umap_.py fit and spectral.py component_layout, following the traceback into sklearn's pairwise_distances handling for precomputed matrices. Reproduce the provided 5843x5843 case with n_neighbors=3 and 15; done means the cause of the shape error is established and the n_neighbors=3 behavior or its error message is made actionable.