# Author: Leland McInnes <leland.mcinnes@gmail.com>
#
# License: BSD 3 clause
from __future__ import print_function
import numpy as np
import numba
from umap.utils import (
tau_rand,
make_heap,
heap_push,
unchecked_heap_push,
smallest_flagged,
rejection_sample,
new_build_candidates,
deheap_sort,
)
import umap.distances as dist
from umap.rp_tree import search_flat_tree
@numba.njit(fastmath=True)
def init_current_graph(data, dist, n_neighbors, rng_state):
current_graph = make_heap(data.shape[0], n_neighbors)
for i in range(data.shape[0]):
indices = rejection_sample(n_neighbors, data.shape[0], rng_state)
for j in range(indices.shape[0]):
d = dist(data[i], data[indices[j]])
heap_push(current_graph, i, d, indices[j], 1)
heap_push(current_graph, indices[j], d, i, 1)
return current_graph
@numba.njit(fastmath=True)
def init_rp_tree(data, dist, current_graph, leaf_array, tried=None):
if tried is None:
tried = set([(-1, -1)])
for n in range(leaf_array.shape[0]):
for i in range(leaf_array.shape[1]):
p = leaf_array[n, i]
if p < 0:
break
for j in range(i + 1, leaf_array.shape[1]):
q = leaf_array[n, j]
if q < 0:
break
if (p, q) in tried:
continue
d = dist(data[p], data[q])
heap_push(current_graph, p, d, q, 1)
tried.add((p, q))
if p != q:
heap_push(current_graph, q, d, p, 1)
tried.add((q, p))
@numba.njit(fastmath=True)
def nn_descent_internal_low_memory(
current_graph,
data,
n_neighbors,
rng_state,
max_candidates=50,
dist=dist.euclidean,
n_iters=10,
delta=0.001,
rho=0.5,
verbose=False,
):
n_vertices = data.shape[0]
for n in range(n_iters):
if verbose:
print("\t", n, " / ", n_iters)
(new_candidate_neighbors, old_candidate_neighbors) = new_build_candidates(
current_graph, n_vertices, n_neighbors, max_candidates, rng_state, rho
)
c = 0
for i in range(n_vertices):
for j in range(max_candidates):
p = int(new_candidate_neighbors[0, i, j])
if p < 0:
continue
for k in range(j, max_candidates):
q = int(new_candidate_neighbors[0, i, k])
if q < 0:
continue
d = dist(data[p], data[q])
c += heap_push(current_graph, p, d, q, 1)
if p != q:
c += heap_push(current_graph, q, d, p, 1)
for k in range(max_candidates):
q = int(old_candidate_neighbors[0, i, k])
if q < 0:
continue
d = dist(data[p], data[q])
c += heap_push(current_graph, p, d, q, 1)
if p != q:
c += heap_push(current_graph, q, d, p, 1)
if c <= delta * n_neighbors * data.shape[0]:
return
@numba.njit(fastmath=True)
def nn_descent_internal_high_memory(
current_graph,
data,
n_neighbors,
rng_state,
tried,
max_candidates=50,
dist=dist.euclidean,
n_iters=10,
delta=0.001,
rho=0.5,
verbose=False,
):
n_vertices = data.shape[0]
for n in range(n_iters):
if verbose:
print("\t", n, " / ", n_iters)
(new_candidate_neighbors, old_candidate_neighbors) = new_build_candidates(
current_graph, n_vertices, n_neighbors, max_candidates, rng_state, rho
)
c = 0
for i in range(n_vertices):
for j in range(max_candidates):
p = int(new_candidate_neighbors[0, i, j])
if p < 0:
continue
for k in range(j, max_candidates):
q = int(new_candidate_neighbors[0, i, k])
if q < 0 or (p, q) in tried:
continue
d = dist(data[p], data[q])
c += unchecked_heap_push(current_graph, p, d, q, 1)
tried.add((p, q))
if p != q:
c += unchecked_heap_push(current_graph, q, d, p, 1)
tried.add((q, p))
for k in range(max_candidates):
q = int(old_candidate_neighbors[0, i, k])
if q < 0 or (p, q) in tried:
continue
d = dist(data[p], data[q])
c += unchecked_heap_push(current_graph, p, d, q, 1)
tried.add((p, q))
if p != q:
c += unchecked_heap_push(current_graph, q, d, p, 1)
tried.add((q, p))
if c <= delta * n_neighbors * data.shape[0]:
return
@numba.njit(fastmath=True)
def nn_descent(
data,
n_neighbors,
rng_state,
max_candidates=50,
dist=dist.euclidean,
n_iters=10,
delta=0.001,
rho=0.5,
rp_tree_init=True,
leaf_array=None,
low_memory=False,
verbose=False,
):
tried = set([(-1, -1)])
current_graph = make_heap(data.shape[0], n_neighbors)
for i in range(data.shape[0]):
indices = rejection_sample(n_neighbors, data.shape[0], rng_state)
for j in range(indices.shape[0]):
d = dist(data[i], data[indices[j]])
heap_push(current_graph, i, d, indices[j], 1)
heap_push(current_graph, indices[j], d, i, 1)
tried.add((i, indices[j]))
tried.add((indices[j], i))
if rp_tree_init:
init_rp_tree(data, dist, current_graph, leaf_array, tried=tried)
if low_memory:
nn_descent_internal_low_memory(
current_graph,
data,
n_neighbors,
rng_state,
max_candidates=max_candidates,
dist=dist,
n_iters=n_iters,
delta=delta,
rho=rho,
verbose=verbose,
)
else:
nn_descent_internal_high_memory(
current_graph,
data,
n_neighbors,
rng_state,
tried,
max_candidates=max_candidates,
dist=dist,
n_iters=n_iters,
delta=delta,
rho=rho,
verbose=verbose,
)
return deheap_sort(current_graph)
@numba.njit()
def init_from_random(n_neighbors, data, query_points, heap, rng_state, dist):
for i in range(query_points.shape[0]):
indices = rejection_sample(n_neighbors, data.shape[0], rng_state)
for j in range(indices.shape[0]):
if indices[j] < 0:
continue
d = dist(data[indices[j]], query_points[i])
heap_push(heap, i, d, indices[j], 1)
return
@numba.njit()
def init_from_tree(tree, data, query_points, heap, rng_state, dist):
for i in range(query_points.shape[0]):
indices = search_flat_tree(
query_points[i],
tree.hyperplanes,
tree.offsets,
tree.children,
tree.indices,
rng_state,
)
for j in range(indices.shape[0]):
if indices[j] < 0:
continue
d = dist(data[indices[j]], query_points[i])
heap_push(heap, i, d, indices[j], 1)
return
def initialise_search(forest, data, query_points, n_neighbors, rng_state, dist):
results = make_heap(query_points.shape[0], n_neighbors)
init_from_random(n_neighbors, data, query_points, results, rng_state, dist)
if forest is not None:
for tree in forest:
init_from_tree(tree, data, query_points, results, rng_state, dist)
return results
@numba.njit(parallel=True)
def initialized_nnd_search(data, indptr, indices, initialization, query_points, dist):
for i in numba.prange(query_points.shape[0]):
tried = set(initialization[0, i])
while True:
# Find smallest flagged vertex
vertex = smallest_flagged(initialization, i)
if vertex == -1:
break
candidates = indices[indptr[vertex] : indptr[vertex + 1]]
for j in range(candidates.shape[0]):
if (
candidates[j] == vertex
or candidates[j] == -1
or candidates[j] in tried
):
continue
d = dist(data[candidates[j]], query_points[i])
unchecked_heap_push(initialization, i, d, candidates[j], 1)
tried.add(candidates[j])
return initialization
