Cannot transform sparse data with Jaccard metric

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UMAP can fit sparse data with the Jaccard metric but it cannot transform new sparse data. Minimum example below:

```
from scipy.sparse import random
from umap import UMAP

# create random training data
train = random(m = 100, n = 1000, density = 0.01)

# initialize model
mapper = UMAP(metric = 'jaccard')

# fit UMAP with Jaccard metric
embedding = mapper.fit_transform(train)

# transform training data
output = mapper.transform(train)
```

Interestingly, `mapper.fit_transform` works fine but `mapper.transform` fails with the following error:

```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/umap/umap_.py in transform(self, X)
2700 dmat = pairwise_distances(
-> 2701 X, self._raw_data, metric=_m, **self._metric_kwds
2702 )

/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64

/opt/conda/lib/python3.7/site-packages/sklearn/metrics/pairwise.py in pairwise_distances(X, Y, metric, n_jobs, force_all_finite, **kwds)
1767 if issparse(X) or issparse(Y):
-> 1768 raise TypeError("scipy distance metrics do not"
1769 " support sparse matrices.")

TypeError: scipy distance metrics do not support sparse matrices.

During handling of the above exception, another exception occurred:

TypingError Traceback (most recent call last)
in
----> 1 mapper.transform(train)

/opt/conda/lib/python3.7/site-packages/umap/umap_.py in transform(self, X)
2706 self._raw_data,
2707 metric=self._input_distance_func,
-> 2708 kwds=self._metric_kwds,
2709 )
2710 indices = np.argpartition(dmat, self._n_neighbors)[:, : self._n_neighbors]

/opt/conda/lib/python3.7/site-packages/umap/distances.py in pairwise_special_metric(X, Y, metric, kwds)
1260 return metric(_X, _Y, *kwd_vals)
1261
-> 1262 return pairwise_distances(X, Y, metric=_partial_metric)
1263 else:
1264 special_metric_func = named_distances[metric]

/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
61 extra_args = len(args) - len(all_args)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64
65 # extra_args > 0

/opt/conda/lib/python3.7/site-packages/sklearn/metrics/pairwise.py in pairwise_distances(X, Y, metric, n_jobs, force_all_finite, **kwds)
1788 func = partial(distance.cdist, metric=metric, **kwds)
1789
-> 1790 return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
1791
1792

/opt/conda/lib/python3.7/site-packages/sklearn/metrics/pairwise.py in _parallel_pairwise(X, Y, func, n_jobs, **kwds)
1357
1358 if effective_n_jobs(n_jobs) == 1:
-> 1359 return func(X, Y, **kwds)
1360
1361 # enforce a threading backend to prevent data communication overhead

/opt/conda/lib/python3.7/site-packages/sklearn/metrics/pairwise.py in _pairwise_callable(X, Y, metric, force_all_finite, **kwds)
1401 iterator = itertools.product(range(X.shape[0]), range(Y.shape[0]))
1402 for i, j in iterator:
-> 1403 out[i, j] = metric(X[i], Y[j], **kwds)
1404
1405 return out

/opt/conda/lib/python3.7/site-packages/numba/core/dispatcher.py in _compile_for_args(self, *args, **kws)
412 e.patch_message(msg)
413
--> 414 error_rewrite(e, 'typing')
415 except errors.UnsupportedError as e:
416 # Something unsupported is present in the user code, add help info

/opt/conda/lib/python3.7/site-packages/numba/core/dispatcher.py in error_rewrite(e, issue_type)
355 raise e
356 else:
--> 357 raise e.with_traceback(None)
358
359 argtypes = []

TypingError: Failed in nopython mode pipeline (step: nopython frontend)
non-precise type pyobject
During: typing of argument at /opt/conda/lib/python3.7/site-packages/umap/distances.py (1260)

File "../../opt/conda/lib/python3.7/site-packages/umap/distances.py", line 1260:
def _partial_metric(_X, _Y=None):
return metric(_X, _Y, *kwd_vals)
^

This error may have been caused by the following argument(s):
- argument 0: Cannot determine Numba type of
- argument 1: Cannot determine Numba type of
```

Start with transform in umap/umap_.py and follow its sparse-distance fallback into pairwise_special_metric in umap/distances.py. Reproduce the supplied sparse Jaccard example and verify that mapper.transform(train) completes without the reported TypeError or Numba TypingError.

Written by the indexing model from the issue text.