from typing import Optional, Union import warnings import numpy as np from packaging import version from anndata import AnnData from sklearn.utils import check_random_state, check_array from ._utils import get_init_pos_from_paga, _choose_representation from .. import logging as logg from .._settings import settings from .._compat import Literal from .._utils import AnyRandom, NeighborsView _InitPos = Literal['paga', 'spectral', 'random'] def umap( adata: AnnData, min_dist: float = 0.5, spread: float = 1.0, n_components: int = 2, maxiter: Optional[int] = None, alpha: float = 1.0, gamma: float = 1.0, negative_sample_rate: int = 5, init_pos: Union[_InitPos, np.ndarray, None] = 'spectral', random_state: AnyRandom = 0, a: Optional[float] = None, b: Optional[float] = None, copy: bool = False, method: Literal['umap', 'rapids'] = 'umap', neighbors_key: Optional[str] = None, ) -> Optional[AnnData]: """\ Embed the neighborhood graph using UMAP [McInnes18]_. UMAP (Uniform Manifold Approximation and Projection) is a manifold learning technique suitable for visualizing high-dimensional data. Besides tending to be faster than tSNE, it optimizes the embedding such that it best reflects the topology of the data, which we represent throughout Scanpy using a neighborhood graph. tSNE, by contrast, optimizes the distribution of nearest-neighbor distances in the embedding such that these best match the distribution of distances in the high-dimensional space. We use the implementation of `umap-learn `__ [McInnes18]_. For a few comparisons of UMAP with tSNE, see this `preprint `__. Parameters ---------- adata Annotated data matrix. min_dist The effective minimum distance between embedded points. Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. The value should be set relative to the ``spread`` value, which determines the scale at which embedded points will be spread out. The default of in the `umap-learn` package is 0.1. spread The effective scale of embedded points. In combination with `min_dist` this determines how clustered/clumped the embedded points are. n_components The number of dimensions of the embedding. maxiter The number of iterations (epochs) of the optimization. Called `n_epochs` in the original UMAP. alpha The initial learning rate for the embedding optimization. gamma Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples. negative_sample_rate The number of negative edge/1-simplex samples to use per positive edge/1-simplex sample in optimizing the low dimensional embedding. init_pos How to initialize the low dimensional embedding. Called `init` in the original UMAP. Options are: * Any key for `adata.obsm`. * 'paga': positions from :func:`~scanpy.pl.paga`. * 'spectral': use a spectral embedding of the graph. * 'random': assign initial embedding positions at random. * A numpy array of initial embedding positions. random_state If `int`, `random_state` is the seed used by the random number generator; If `RandomState` or `Generator`, `random_state` is the random number generator; If `None`, the random number generator is the `RandomState` instance used by `np.random`. a More specific parameters controlling the embedding. If `None` these values are set automatically as determined by `min_dist` and `spread`. b More specific parameters controlling the embedding. If `None` these values are set automatically as determined by `min_dist` and `spread`. copy Return a copy instead of writing to adata. method Use the original 'umap' implementation, or 'rapids' (experimental, GPU only) neighbors_key If not specified, umap looks .uns['neighbors'] for neighbors settings and .obsp['connectivities'] for connectivities (default storage places for pp.neighbors). If specified, umap looks .uns[neighbors_key] for neighbors settings and .obsp[.uns[neighbors_key]['connectivities_key']] for connectivities. Returns ------- Depending on `copy`, returns or updates `adata` with the following fields. **X_umap** : `adata.obsm` field UMAP coordinates of data. """ adata = adata.copy() if copy else adata if neighbors_key is None: neighbors_key = 'neighbors' if neighbors_key not in adata.uns: raise ValueError( f'Did not find .uns["{neighbors_key}"]. Run `sc.pp.neighbors` first.' ) start = logg.info('computing UMAP') neighbors = NeighborsView(adata, neighbors_key) if 'params' not in neighbors or neighbors['params']['method'] != 'umap': logg.warning( f'.obsp["{neighbors["connectivities_key"]}"] have not been computed using umap' ) # Compat for umap 0.4 -> 0.5 with warnings.catch_warnings(): # umap 0.5.0 warnings.filterwarnings("ignore", message=r"Tensorflow not installed") import umap if version.parse(umap.__version__) >= version.parse("0.5.0"): def simplicial_set_embedding(*args, **kwargs): from umap.umap_ import simplicial_set_embedding X_umap, _ = simplicial_set_embedding( *args, densmap=False, densmap_kwds={}, output_dens=False, **kwargs, ) return X_umap else: from umap.umap_ import simplicial_set_embedding from umap.umap_ import find_ab_params if a is None or b is None: a, b = find_ab_params(spread, min_dist) else: a = a b = b adata.uns['umap'] = {'params': {'a': a, 'b': b}} if isinstance(init_pos, str) and init_pos in adata.obsm.keys(): init_coords = adata.obsm[init_pos] elif isinstance(init_pos, str) and init_pos == 'paga': init_coords = get_init_pos_from_paga( adata, random_state=random_state, neighbors_key=neighbors_key ) else: init_coords = init_pos # Let umap handle it if hasattr(init_coords, "dtype"): init_coords = check_array(init_coords, dtype=np.float32, accept_sparse=False) if random_state != 0: adata.uns['umap']['params']['random_state'] = random_state random_state = check_random_state(random_state) neigh_params = neighbors['params'] X = _choose_representation( adata, neigh_params.get('use_rep', None), neigh_params.get('n_pcs', None), silent=True, ) if method == 'umap': # the data matrix X is really only used for determining the number of connected components # for the init condition in the UMAP embedding default_epochs = 500 if neighbors['connectivities'].shape[0] <= 10000 else 200 n_epochs = default_epochs if maxiter is None else maxiter X_umap = simplicial_set_embedding( X, neighbors['connectivities'].tocoo(), n_components, alpha, a, b, gamma, negative_sample_rate, n_epochs, init_coords, random_state, neigh_params.get('metric', 'euclidean'), neigh_params.get('metric_kwds', {}), verbose=settings.verbosity > 3, ) elif method == 'rapids': metric = neigh_params.get('metric', 'euclidean') if metric != 'euclidean': raise ValueError( f'`sc.pp.neighbors` was called with `metric` {metric!r}, ' "but umap `method` 'rapids' only supports the 'euclidean' metric." ) from cuml import UMAP n_neighbors = neighbors['params']['n_neighbors'] n_epochs = ( 500 if maxiter is None else maxiter ) # 0 is not a valid value for rapids, unlike original umap X_contiguous = np.ascontiguousarray(X, dtype=np.float32) umap = UMAP( n_neighbors=n_neighbors, n_components=n_components, n_epochs=n_epochs, learning_rate=alpha, init=init_pos, min_dist=min_dist, spread=spread, negative_sample_rate=negative_sample_rate, a=a, b=b, verbose=settings.verbosity > 3, random_state=random_state, ) X_umap = umap.fit_transform(X_contiguous) adata.obsm['X_umap'] = X_umap # annotate samples with UMAP coordinates logg.info( ' finished', time=start, deep=('added\n' " 'X_umap', UMAP coordinates (adata.obsm)"), ) return adata if copy else None