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from typing import Union, Optional, Any, Mapping, Callable
import numpy as np
import scipy
from anndata import AnnData
from numpy.random import RandomState
from scipy.sparse import issparse, coo_matrix
from sklearn.metrics import pairwise_distances
from .. import settings
from .. import logging as logg
from .. import utils
from ..utils import doc_params
from ..logging import _settings_verbosity_greater_or_equal_than
from import choose_representation, doc_use_rep, doc_n_pcs
N_DCS = 15 # default number of diffusion components
N_PCS = 50 # default number of PCs
@doc_params(n_pcs=doc_n_pcs, use_rep=doc_use_rep)
def neighbors(
adata: AnnData,
n_neighbors: int = 15,
n_pcs: Optional[int] = None,
use_rep: Optional[str] = None,
knn: bool = True,
random_state: Optional[Union[int, RandomState]] = 0,
method: str = 'umap',
metric: Union[str, Callable[[np.ndarray, np.ndarray], float]] = 'euclidean',
metric_kwds: Mapping[str, Any] = {},
copy: bool = False
) -> Optional[AnnData]:
Compute a neighborhood graph of observations [McInnes18]_.
The neighbor search efficiency of this heavily relies on UMAP [McInnes18]_,
which also provides a method for estimating connectivities of data points -
the connectivity of the manifold (`method=='umap'`). If `method=='gauss'`,
connectivities are computed according to [Coifman05]_, in the adaption of
Annotated data matrix.
The size of local neighborhood (in terms of number of neighboring data
points) used for manifold approximation. Larger values result in more
global views of the manifold, while smaller values result in more local
data being preserved. In general values should be in the range 2 to 100.
If `knn` is `True`, number of nearest neighbors to be searched. If `knn`
is `False`, a Gaussian kernel width is set to the distance of the
`n_neighbors` neighbor.
If `True`, use a hard threshold to restrict the number of neighbors to
`n_neighbors`, that is, consider a knn graph. Otherwise, use a Gaussian
Kernel to assign low weights to neighbors more distant than the
`n_neighbors` nearest neighbor.
A numpy random seed.
method : {{'umap', 'gauss', `None`}} (default: `'umap'`)
Use 'umap' [McInnes18]_ or 'gauss' (Gauss kernel following [Coifman05]_
with adaptive width [Haghverdi16]_) for computing connectivities.
A known metric’s name or a callable that returns a distance.
Options for the metric.
Return a copy instead of writing to adata.
Depending on `copy`, updates or returns `adata` with the following:
connectivities : sparse matrix (`.uns['neighbors']`, dtype `float32`)
Weighted adjacency matrix of the neighborhood graph of data
points. Weights should be interpreted as connectivities.
distances : sparse matrix (`.uns['neighbors']`, dtype `float32`)
Instead of decaying weights, this stores distances for each pair of
"""'computing neighbors', r=True)
adata = adata.copy() if copy else adata
if adata.isview: # we shouldn't need this here...
neighbors = Neighbors(adata)
n_neighbors=n_neighbors, knn=knn, n_pcs=n_pcs, use_rep=use_rep,
method=method, metric=metric, metric_kwds=metric_kwds,
adata.uns['neighbors'] = {}
adata.uns['neighbors']['params'] = {'n_neighbors': n_neighbors, 'method': method}
adata.uns['neighbors']['distances'] = neighbors.distances
adata.uns['neighbors']['connectivities'] = neighbors.connectivities' finished', time=True, end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
'added to `.uns[\'neighbors\']`\n'
' \'distances\', distances for each pair of neighbors\n'
' \'connectivities\', weighted adjacency matrix')
return adata if copy else None
def compute_neighbors_umap(
X, n_neighbors, random_state=None,
metric='euclidean', metric_kwds={}, angular=False,
"""This is from umap.fuzzy_simplicial_set [McInnes18]_.
Given a set of data X, a neighborhood size, and a measure of distance
compute the fuzzy simplicial set (here represented as a fuzzy graph in
the form of a sparse matrix) associated to the data. This is done by
locally approximating geodesic distance at each point, creating a fuzzy
simplicial set for each such point, and then combining all the local
fuzzy simplicial sets into a global one via a fuzzy union.
X: array of shape (n_samples, n_features)
The data to be modelled as a fuzzy simplicial set.
n_neighbors: int
The number of neighbors to use to approximate geodesic distance.
Larger numbers induce more global estimates of the manifold that can
miss finer detail, while smaller values will focus on fine manifold
structure to the detriment of the larger picture.
random_state: numpy RandomState or equivalent
A state capable being used as a numpy random state.
metric: string or function (optional, default 'euclidean')
The metric to use to compute distances in high dimensional space.
If a string is passed it must match a valid predefined metric. If
a general metric is required a function that takes two 1d arrays and
returns a float can be provided. For performance purposes it is
required that this be a numba jit'd function. Valid string metrics
* euclidean
* manhattan
* chebyshev
* minkowski
* canberra
* braycurtis
* mahalanobis
* wminkowski
* seuclidean
* cosine
* correlation
* haversine
* hamming
* jaccard
* dice
* russelrao
* kulsinski
* rogerstanimoto
* sokalmichener
* sokalsneath
* yule
Metrics that take arguments (such as minkowski, mahalanobis etc.)
can have arguments passed via the metric_kwds dictionary. At this
time care must be taken and dictionary elements must be ordered
appropriately; this will hopefully be fixed in the future.
metric_kwds: dict (optional, default {})
Arguments to pass on to the metric, such as the ``p`` value for
Minkowski distance.
angular: bool (optional, default False)
Whether to use angular/cosine distance for the random projection
forest for seeding NN-descent to determine approximate nearest
verbose: bool (optional, default False)
Whether to report information on the current progress of the algorithm.
knn_indices, knn_dists : np.arrays of shape (n_observations, n_neighbors)
from .umap import sparse
from .umap.umap_ import rptree_leaf_array, make_nn_descent
from .umap import distances as dist
from .umap import sparse
import scipy
from sklearn.utils import check_random_state
INT32_MIN = np.iinfo(np.int32).min + 1
INT32_MAX = np.iinfo(np.int32).max - 1
random_state = check_random_state(random_state)
if metric == 'precomputed':
# Note that this does not support sparse distance matrices yet ...
# Compute indices of n nearest neighbors
knn_indices = np.argsort(X)[:, :n_neighbors]
# Compute the nearest neighbor distances
# (equivalent to np.sort(X)[:, :n_neighbors])
knn_dists = X[np.arange(X.shape[0])[:, None], knn_indices].copy()
if callable(metric):
distance_func = metric
elif metric in dist.named_distances:
distance_func = dist.named_distances[metric]
raise ValueError('Metric is neither callable, ' +
'nor a recognised string')
if metric in ('cosine', 'correlation', 'dice', 'jaccard'):
angular = True
rng_state = random_state.randint(INT32_MIN, INT32_MAX, 3).astype(np.int64)
if scipy.sparse.isspmatrix_csr(X):
if metric in sparse.sparse_named_distances:
distance_func = sparse.sparse_named_distances[metric]
if metric in sparse.sparse_need_n_features:
metric_kwds['n_features'] = X.shape[1]
raise ValueError('Metric {} not supported for sparse ' +
metric_nn_descent = sparse.make_sparse_nn_descent(
distance_func, tuple(metric_kwds.values()))
leaf_array = rptree_leaf_array(X, n_neighbors,
rng_state, n_trees=10,
knn_indices, knn_dists = metric_nn_descent(X.indices,
metric_nn_descent = make_nn_descent(distance_func,
# TODO: Hacked values for now
n_trees = 5 + int(round((X.shape[0]) ** 0.5 / 20.0))
n_iters = max(5, int(round(np.log2(X.shape[0]))))
leaf_array = rptree_leaf_array(X, n_neighbors,
rng_state, n_trees=n_trees,
knn_indices, knn_dists = metric_nn_descent(X,
if np.any(knn_indices < 0):
logg.warn('Failed to correctly find n_neighbors for some samples. '
'Results may be less than ideal. Try re-running with '
'different parameters.')
return knn_indices, knn_dists
def compute_connectivities_umap(knn_indices, knn_dists,
n_obs, n_neighbors, set_op_mix_ratio=1.0,
local_connectivity=1.0, bandwidth=1.0):
"""This is from umap.fuzzy_simplicial_set [McInnes18]_.
Given a set of data X, a neighborhood size, and a measure of distance
compute the fuzzy simplicial set (here represented as a fuzzy graph in
the form of a sparse matrix) associated to the data. This is done by
locally approximating geodesic distance at each point, creating a fuzzy
simplicial set for each such point, and then combining all the local
fuzzy simplicial sets into a global one via a fuzzy union.
from .umap.umap_ import smooth_knn_dist
rows = np.zeros((n_obs * n_neighbors), dtype=np.int64)
cols = np.zeros((n_obs * n_neighbors), dtype=np.int64)
sims = np.zeros((n_obs * n_neighbors), dtype=np.float64)
dists = np.zeros((n_obs * n_neighbors), dtype=np.float64)
sigmas, rhos = smooth_knn_dist(knn_dists, n_neighbors,
for i in range(knn_indices.shape[0]):
for j in range(n_neighbors):
if knn_indices[i, j] == -1:
continue # We didn't get the full knn for i
if knn_indices[i, j] == i:
sim = 0.0
dist = 0.0
elif knn_dists[i, j] - rhos[i] <= 0.0:
sim = 1.0
dist = knn_dists[i, j]
sim = np.exp(-((knn_dists[i, j] - rhos[i]) / (sigmas[i] *
dist = knn_dists[i, j]
rows[i * n_neighbors + j] = i
cols[i * n_neighbors + j] = knn_indices[i, j]
sims[i * n_neighbors + j] = sim
dists[i * n_neighbors + j] = dist
connectivities = coo_matrix((sims, (rows, cols)),
shape=(n_obs, n_obs))
distances = coo_matrix((dists, (rows, cols)),
shape=(n_obs, n_obs))
transpose = connectivities.transpose()
prod_matrix = connectivities.multiply(transpose)
connectivities = set_op_mix_ratio * (connectivities + transpose - prod_matrix) + \
(1.0 - set_op_mix_ratio) * prod_matrix
return distances.tocsr(), connectivities.tocsr()
def get_sparse_matrix_from_indices_distances_umap(knn_indices, knn_dists, n_obs, n_neighbors):
rows = np.zeros((n_obs * n_neighbors), dtype=np.int64)
cols = np.zeros((n_obs * n_neighbors), dtype=np.int64)
vals = np.zeros((n_obs * n_neighbors), dtype=np.float64)
for i in range(knn_indices.shape[0]):
for j in range(n_neighbors):
if knn_indices[i, j] == -1:
continue # We didn't get the full knn for i
if knn_indices[i, j] == i:
val = 0.0
val = knn_dists[i, j]
rows[i * n_neighbors + j] = i
cols[i * n_neighbors + j] = knn_indices[i, j]
vals[i * n_neighbors + j] = val
result = coo_matrix((vals, (rows, cols)),
shape=(n_obs, n_obs))
return result.tocsr()
def get_sparse_matrix_from_indices_distances_numpy(indices, distances, n_obs, n_neighbors):
n_nonzero = n_obs * n_neighbors
indptr = np.arange(0, n_nonzero + 1, n_neighbors)
D = scipy.sparse.csr_matrix((distances.copy().ravel(), # copy the data, otherwise strange behavior here
shape=(n_obs, n_obs))
return D
def get_indices_distances_from_sparse_matrix(D, n_neighbors):
indices = np.zeros((D.shape[0], n_neighbors), dtype=int)
distances = np.zeros((D.shape[0], n_neighbors), dtype=D.dtype)
n_neighbors_m1 = n_neighbors - 1
for i in range(indices.shape[0]):
neighbors = D[i].nonzero() # 'true' and 'spurious' zeros
indices[i, 0] = i
distances[i, 0] = 0
# account for the fact that there might be more than n_neighbors
# due to an approximate search
# [the point itself was not detected as its own neighbor during the search]
if len(neighbors[1]) > n_neighbors_m1:
sorted_indices = np.argsort(D[i][neighbors].A1)[:n_neighbors_m1]
indices[i, 1:] = neighbors[1][sorted_indices]
distances[i, 1:] = D[i][
neighbors[0][sorted_indices], neighbors[1][sorted_indices]]
indices[i, 1:] = neighbors[1]
distances[i, 1:] = D[i][neighbors]
return indices, distances
def get_indices_distances_from_dense_matrix(D, n_neighbors):
sample_range = np.arange(D.shape[0])[:, None]
indices = np.argpartition(D, n_neighbors-1, axis=1)[:, :n_neighbors]
indices = indices[sample_range, np.argsort(D[sample_range, indices])]
distances = D[sample_range, indices]
return indices, distances
def _backwards_compat_get_full_X_diffmap(adata):
if 'X_diffmap0' in adata.obs:
return np.c_[adata.obs['X_diffmap0'].values[:, None],
return adata.obsm['X_diffmap']
def _backwards_compat_get_full_eval(adata):
if 'X_diffmap0' in adata.obs:
return np.r_[1, adata.uns['diffmap_evals']]
return adata.uns['diffmap_evals']
class OnFlySymMatrix:
"""Emulate a matrix where elements are calculated on the fly.
def __init__(self, get_row, shape, DC_start=0, DC_end=-1, rows=None, restrict_array=None):
self.get_row = get_row
self.shape = shape
self.DC_start = DC_start
self.DC_end = DC_end
self.rows = {} if rows is None else rows
self.restrict_array = restrict_array # restrict the array to a subset
def __getitem__(self, index):
if isinstance(index, int) or isinstance(index, np.integer):
if self.restrict_array is None:
glob_index = index
# map the index back to the global index
glob_index = self.restrict_array[index]
if glob_index not in self.rows:
self.rows[glob_index] = self.get_row(glob_index)
row = self.rows[glob_index]
if self.restrict_array is None:
return row
return row[self.restrict_array]
if self.restrict_array is None:
glob_index_0, glob_index_1 = index
glob_index_0 = self.restrict_array[index[0]]
glob_index_1 = self.restrict_array[index[1]]
if glob_index_0 not in self.rows:
self.rows[glob_index_0] = self.get_row(glob_index_0)
return self.rows[glob_index_0][glob_index_1]
def restrict(self, index_array):
"""Generate a view restricted to a subset of indices.
new_shape = index_array.shape[0], index_array.shape[0]
return OnFlySymMatrix(self.get_row, new_shape, DC_start=self.DC_start,
rows=self.rows, restrict_array=index_array)
class Neighbors:
"""Data represented as graph of nearest neighbors.
Represent a data matrix as a graph of nearest neighbor relations (edges)
among data points (nodes).
Annotated data object.
Number of diffusion components to use.
def __init__(self, adata: AnnData, n_dcs: Optional[int] = None):
self._adata = adata
# use the graph in adata
info_str = ''
self.knn = None
self._distances = None
self._connectivities = None
self._number_connected_components = None
if 'neighbors' in adata.uns:
if 'distances' in adata.uns['neighbors']:
self.knn = issparse(adata.uns['neighbors']['distances'])
self._distances = adata.uns['neighbors']['distances']
if 'connectivities' in adata.uns['neighbors']:
self.knn = issparse(adata.uns['neighbors']['connectivities'])
self._connectivities = adata.uns['neighbors']['connectivities']
if 'params' in adata.uns['neighbors']:
self.n_neighbors = adata.uns['neighbors']['params']['n_neighbors']
# estimating n_neighbors
if self._connectivities is None:
self.n_neighbors = int(self._distances.count_nonzero() / self._distances.shape[0])
self.n_neighbors = int(self._connectivities.count_nonzero() / self._connectivities.shape[0] / 2)
info_str += '`.distances` `.connectivities` '
self._number_connected_components = 1
if issparse(self._connectivities):
from scipy.sparse.csgraph import connected_components
self._connected_components = connected_components(self._connectivities)
self._number_connected_components = self._connected_components[0]
if 'X_diffmap' in adata.obsm_keys():
self._eigen_values = _backwards_compat_get_full_eval(adata)
self._eigen_basis = _backwards_compat_get_full_X_diffmap(adata)
if n_dcs is not None:
if n_dcs > len(self._eigen_values):
raise ValueError(
'Cannot instantiate using `n_dcs`={}. '
'Compute diffmap/spectrum with more components first.'
self._eigen_values = self._eigen_values[:n_dcs]
self._eigen_basis = self._eigen_basis[:, :n_dcs]
self.n_dcs = len(self._eigen_values)
info_str += '`.eigen_values` `.eigen_basis` `.distances_dpt`'
self._eigen_values = None
self._eigen_basis = None
self.n_dcs = None
if info_str != '':
logg.msg(' initialized {}'.format(info_str), v=4)
def distances(self):
"""Distances between data points (sparse matrix).
return self._distances
def connectivities(self):
"""Connectivities between data points (sparse matrix).
return self._connectivities
def transitions(self):
"""Transition matrix (sparse matrix).
Is conjugate to the symmetrized transition matrix via::
self.transitions = self.Z * self.transitions_sym / self.Z
where ``self.Z`` is the diagonal matrix storing the normalization of the
underlying kernel matrix.
This has not been tested, in contrast to `transitions_sym`.
if issparse(self.Z):
Zinv = self.Z.power(-1)
Zinv = np.diag(1./np.diag(self.Z))
def transitions_sym(self):
"""Symmetrized transition matrix (sparse matrix).
Is conjugate to the transition matrix via::
self.transitions_sym = self.Z / self.transitions * self.Z
where ``self.Z`` is the diagonal matrix storing the normalization of the
underlying kernel matrix.
return self._transitions_sym
def eigen_values(self):
"""Eigen values of transition matrix (numpy array).
return self._eigen_values
def eigen_basis(self):
"""Eigen basis of transition matrix (numpy array).
return self._eigen_basis
def laplacian(self):
"""Graph laplacian (sparse matrix).
return self._laplacian
def distances_dpt(self):
"""DPT distances (on-fly matrix).
This is yields [Haghverdi16]_, Eq. 15 from the supplement with the
extensions of [Wolf17i]_, supplement on random-walk based distance
return OnFlySymMatrix(self._get_dpt_row, shape=self._adata.shape)
def to_igraph(self):
"""Generate igraph from connectiviies.
return utils.get_igraph_from_adjacency(self.connectivities)
@doc_params(n_pcs=doc_n_pcs, use_rep=doc_use_rep)
def compute_neighbors(
n_neighbors: int = 30,
knn: bool = True,
n_pcs: Optional[int] = None,
use_rep: Optional[str] = None,
method: str = 'umap',
random_state: Optional[Union[RandomState, int]] = 0,
write_knn_indices: bool = False,
metric: str = 'euclidean',
metric_kwds: Mapping[str, Any] = {}
) -> None:
Compute distances and connectivities of neighbors.
Use this number of nearest neighbors.
Restrict result to `n_neighbors` nearest neighbors.
Writes sparse graph attributes `.distances` and `.connectivities`.
Also writes `.knn_indices` and `.knn_distances` if
if n_neighbors > self._adata.shape[0]: # very small datasets
n_neighbors = 1 + int(0.5*self._adata.shape[0])
logg.warn('n_obs too small: adjusting to `n_neighbors = {}`'
if method == 'umap' and not knn:
raise ValueError('`method = \'umap\' only with `knn = True`.')
if method not in {'umap', 'gauss'}:
raise ValueError('`method` needs to be \'umap\' or \'gauss\'.')
if self._adata.shape[0] >= 10000 and not knn:
'Using high n_obs without `knn=True` takes a lot of memory...')
self.n_neighbors = n_neighbors
self.knn = knn
X = choose_representation(self._adata, use_rep=use_rep, n_pcs=n_pcs)
# neighbor search
use_dense_distances = (metric == 'euclidean' and X.shape[0] < 8192) or knn == False
if use_dense_distances:
_distances = pairwise_distances(X, metric=metric, **metric_kwds)
knn_indices, knn_distances = get_indices_distances_from_dense_matrix(
_distances, n_neighbors)
if knn:
self._distances = get_sparse_matrix_from_indices_distances_numpy(
knn_indices, knn_distances, X.shape[0], n_neighbors)
self._distances = _distances
# non-euclidean case and approx nearest neighbors
if X.shape[0] < 4096:
X = pairwise_distances(X, metric=metric, **metric_kwds)
metric = 'precomputed'
knn_indices, knn_distances = compute_neighbors_umap(
X, n_neighbors, random_state, metric=metric, metric_kwds=metric_kwds)
# write indices as attributes
if write_knn_indices:
self.knn_indices = knn_indices
self.knn_distances = knn_distances
logg.msg('computed neighbors', t=True, v=4)
if not use_dense_distances or method == 'umap':
# we need self._distances also for method == 'gauss' if we didn't
# use dense distances
self._distances, self._connectivities = compute_connectivities_umap(
knn_indices, knn_distances, self._adata.shape[0], self.n_neighbors)
# overwrite the umap connectivities if method is 'gauss'
# self._distances is unaffected by this
if method == 'gauss':
logg.msg('computed connectivities', t=True, v=4)
self._number_connected_components = 1
if issparse(self._connectivities):
from scipy.sparse.csgraph import connected_components
self._connected_components = connected_components(self._connectivities)
self._number_connected_components = self._connected_components[0]
def _compute_connectivities_diffmap(self, density_normalize=True):
# init distances
if self.knn:
Dsq = self._distances.power(2)
indices, distances_sq = get_indices_distances_from_sparse_matrix(
Dsq, self.n_neighbors)
Dsq = np.power(self._distances, 2)
indices, distances_sq = get_indices_distances_from_dense_matrix(
Dsq, self.n_neighbors)
# exclude the first point, the 0th neighbor
indices = indices[:, 1:]
distances_sq = distances_sq[:, 1:]
# choose sigma, the heuristic here doesn't seem to make much of a difference,
# but is used to reproduce the figures of Haghverdi et al. (2016)
if self.knn:
# as the distances are not sorted
# we have decay within the n_neighbors first neighbors
sigmas_sq = np.median(distances_sq, axis=1)
# the last item is already in its sorted position through argpartition
# we have decay beyond the n_neighbors neighbors
sigmas_sq = distances_sq[:, -1]/4
sigmas = np.sqrt(sigmas_sq)
# compute the symmetric weight matrix
if not issparse(self._distances):
Num = 2 * np.multiply.outer(sigmas, sigmas)
Den = np.add.outer(sigmas_sq, sigmas_sq)
W = np.sqrt(Num/Den) * np.exp(-Dsq/Den)
# make the weight matrix sparse
if not self.knn:
mask = W > 1e-14
W[mask == False] = 0
# restrict number of neighbors to ~k
# build a symmetric mask
mask = np.zeros(Dsq.shape, dtype=bool)
for i, row in enumerate(indices):
mask[i, row] = True
for j in row:
if i not in set(indices[j]):
W[j, i] = W[i, j]
mask[j, i] = True
# set all entries that are not nearest neighbors to zero
W[mask == False] = 0
W = Dsq.copy() # need to copy the distance matrix here; what follows is inplace
for i in range(len(Dsq.indptr[:-1])):
row = Dsq.indices[Dsq.indptr[i]:Dsq.indptr[i+1]]
num = 2 * sigmas[i] * sigmas[row]
den = sigmas_sq[i] + sigmas_sq[row][Dsq.indptr[i]:Dsq.indptr[i+1]] = np.sqrt(num/den) * np.exp([Dsq.indptr[i]: Dsq.indptr[i+1]] / den)
W = W.tolil()
for i, row in enumerate(indices):
for j in row:
if i not in set(indices[j]):
W[j, i] = W[i, j]
W = W.tocsr()
self._connectivities = W
def compute_transitions(self, density_normalize=True):
"""Compute transition matrix.
density_normalize : `bool`
The density rescaling of Coifman and Lafon (2006): Then only the
geometry of the data matters, not the sampled density.
Makes attributes `.transitions_sym` and `.transitions` available.
W = self._connectivities
# density normalization as of Coifman et al. (2005)
# ensures that kernel matrix is independent of sampling density
if density_normalize:
# q[i] is an estimate for the sampling density at point i
# it's also the degree of the underlying graph
q = np.asarray(W.sum(axis=0))
if not issparse(W):
Q = np.diag(1.0/q)
Q = scipy.sparse.spdiags(1.0/q, 0, W.shape[0], W.shape[0])
K =
K = W
# z[i] is the square root of the row sum of K
z = np.sqrt(np.asarray(K.sum(axis=0)))
if not issparse(K):
self.Z = np.diag(1.0/z)
self.Z = scipy.sparse.spdiags(1.0/z, 0, K.shape[0], K.shape[0])
self._transitions_sym =
logg.msg('computed transitions', v=4, time=True)
def compute_eigen(self, n_comps=15, sym=None, sort='decrease'):
"""Compute eigen decomposition of transition matrix.
n_comps : `int`
Number of eigenvalues/vectors to be computed, set `n_comps = 0` if
you need all eigenvectors.
sym : `bool`
Instead of computing the eigendecomposition of the assymetric
transition matrix, computed the eigendecomposition of the symmetric
Ktilde matrix.
matrix : sparse matrix, np.ndarray, optional (default: `.connectivities`)
Matrix to diagonalize. Merely for testing and comparison purposes.
Writes the following attributes.
eigen_values : np.ndarray
Eigenvalues of transition matrix.
eigen_basis : np.ndarray
Matrix of eigenvectors (stored in columns). `.eigen_basis` is
projection of data matrix on right eigenvectors, that is, the
projection on the diffusion components. these are simply the
components of the right eigenvectors and can directly be used for
if self._transitions_sym is None:
raise ValueError('Run `.compute_transitions` first.')
matrix = self._transitions_sym
# compute the spectrum
if n_comps == 0:
evals, evecs = scipy.linalg.eigh(matrix)
n_comps = min(matrix.shape[0]-1, n_comps)
# ncv = max(2 * n_comps + 1, int(np.sqrt(matrix.shape[0])))
ncv = None
which = 'LM' if sort == 'decrease' else 'SM'
# it pays off to increase the stability with a bit more precision
matrix = matrix.astype(np.float64)
evals, evecs = scipy.sparse.linalg.eigsh(matrix, k=n_comps,
which=which, ncv=ncv)
evals, evecs = evals.astype(np.float32), evecs.astype(np.float32)
if sort == 'decrease':
evals = evals[::-1]
evecs = evecs[:, ::-1]' eigenvalues of transition matrix\n'
' {}'.format(str(evals).replace('\n', '\n ')))
if self._number_connected_components > len(evals)/2:
logg.warn('Transition matrix has many disconnected components!')
self._eigen_values = evals
self._eigen_basis = evecs
def _init_iroot(self):
self.iroot = None
# set iroot directly
if 'iroot' in self._adata.uns:
if self._adata.uns['iroot'] >= self._adata.n_obs:
logg.warn('Root cell index {} does not exist for {} samples. '
'Is ignored.'
.format(self._adata.uns['iroot'], self._adata.n_obs))
self.iroot = self._adata.uns['iroot']
# set iroot via xroot
xroot = None
if 'xroot' in self._adata.uns: xroot = self._adata.uns['xroot']
elif 'xroot' in self._adata.var: xroot = self._adata.var['xroot']
# see whether we can set self.iroot using the full data matrix
if xroot is not None and xroot.size == self._adata.shape[1]:
def _get_dpt_row(self, i):
use_mask = False
if self._number_connected_components > 1:
use_mask = True
label = self._connected_components[1][i]
mask = self._connected_components[1] == label
row = sum([(self.eigen_values[l]/(1-self.eigen_values[l])
* (self.eigen_basis[i, l] - self.eigen_basis[:, l]))**2
# account for float32 precision
for l in range(0, self.eigen_values.size) if self.eigen_values[l] < 0.9994])
# thanks to Marius Lange for pointing Alex to this:
# we will likely remove the contributions from the stationary state below when making
# backwards compat breaking changes, they originate from an early implementation in 2015
# they never seem to have deteriorated results, but also other distance measures (see e.g.
# PAGA paper) don't have it, which makes sense
row += sum([(self.eigen_basis[i, l] - self.eigen_basis[:, l])**2
for l in range(0, self.eigen_values.size) if self.eigen_values[l] >= 0.9994])
if not use_mask:
return np.sqrt(row)
row[~mask] = np.inf
return np.sqrt(row)
def _compute_Lp_matrix(self):
"""See Fouss et al. (2006) and von Luxburg et al. (2007).
See Proposition 6 in von Luxburg (2007) and the inline equations
right in the text above.
self.Lp = sum([1/self.eigen_values[i]
* np.outer(self.eigen_basis[:, i], self.eigen_basis[:, i])
for i in range(1, self.eigen_values.size)])
def _compute_C_matrix(self):
"""See Fouss et al. (2006) and von Luxburg et al. (2007).
This is the commute-time matrix. It's a squared-euclidian distance
matrix in :math:`\\mathbb{R}^n`.
self.C = np.repeat(np.diag(self.Lp)[:, np.newaxis],
self.Lp.shape[0], axis=1)
self.C += np.repeat(np.diag(self.Lp)[np.newaxis, :],
self.Lp.shape[0], axis=0)
self.C -= 2*self.Lp
# the following is much slower
# self.C = np.zeros(self.Lp.shape)
# for i in range(self.Lp.shape[0]):
# for j in range(self.Lp.shape[1]):
# self.C[i, j] = self.Lp[i, i] + self.Lp[j, j] - 2*self.Lp[i, j]
volG = np.sum(self.z)
self.C *= volG, 'computed commute distance matrix')
self.distances_dpt = self.C
def _compute_MFP_matrix(self):
"""See Fouss et al. (2006).
This is the mean-first passage time matrix. It's not a distance.
Mfp[i, k] := m(k|i) in the notation of Fouss et al. (2006). This
corresponds to the standard notation for transition matrices (left index
initial state, right index final state, i.e. a right-stochastic
matrix, with each row summing to one).
self.MFP = np.zeros(self.Lp.shape)
for i in range(self.Lp.shape[0]):
for k in range(self.Lp.shape[1]):
for j in range(self.Lp.shape[1]):
self.MFP[i, k] += (self.Lp[i, j] - self.Lp[i, k]
- self.Lp[k, j] + self.Lp[k, k]) * self.z[j], 'computed mean first passage time matrix')
self.distances_dpt = self.MFP
def _set_pseudotime(self):
"""Return pseudotime with respect to root point.
self.pseudotime = self.distances_dpt[self.iroot].copy()
self.pseudotime /= np.max(self.pseudotime[self.pseudotime < np.inf])
def _set_iroot_via_xroot(self, xroot):
"""Determine the index of the root cell.
Given an expression vector, find the observation index that is closest
to this vector.
xroot : np.ndarray
Vector that marks the root cell, the vector storing the initial
condition, only relevant for computing pseudotime.
if self._adata.shape[1] != xroot.size:
raise ValueError(
'The root vector you provided does not have the '
'correct dimension.')
# this is the squared distance
dsqroot = 1e10
iroot = 0
for i in range(self._adata.shape[0]):
diff = self._adata.X[i, :] - xroot
dsq =
if dsq < dsqroot:
dsqroot = dsq
iroot = i
if np.sqrt(dsqroot) < 1e-10: break
logg.msg('setting root index to', iroot, v=4)
if self.iroot is not None and iroot != self.iroot:
logg.warn('Changing index of iroot from {} to {}.'.format(self.iroot, iroot))
self.iroot = iroot
def _test_embed(self):
Checks and tests for embed.
# pl.semilogy(w,'x',label=r'$ \widetilde K$')
if _settings_verbosity_greater_or_equal_than(3):
# output of spectrum of K for comparison
w, v = np.linalg.eigh(self.K)
logg.msg('spectrum of K (kernel)')
if _settings_verbosity_greater_or_equal_than(4):
# direct computation of spectrum of T
w, vl, vr = scipy.linalg.eig(self.T, left=True)
logg.msg('spectrum of transition matrix (should be same as of Ktilde)')