ai-content-maker/.venv/Lib/site-packages/pynndescent/utils.py

727 lines
19 KiB
Python

# Author: Leland McInnes <leland.mcinnes@gmail.com>
#
# License: BSD 2 clause
import time
import numba
from numba.core import types
import numba.experimental.structref as structref
import numpy as np
@numba.njit("void(i8[:], i8)", cache=True)
def seed(rng_state, seed):
"""Seed the random number generator with a given seed."""
rng_state.fill(seed + 0xFFFF)
@numba.njit("i4(i8[:])", cache=True)
def tau_rand_int(state):
"""A fast (pseudo)-random number generator.
Parameters
----------
state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
A (pseudo)-random int32 value
"""
state[0] = (((state[0] & 4294967294) << 12) & 0xFFFFFFFF) ^ (
(((state[0] << 13) & 0xFFFFFFFF) ^ state[0]) >> 19
)
state[1] = (((state[1] & 4294967288) << 4) & 0xFFFFFFFF) ^ (
(((state[1] << 2) & 0xFFFFFFFF) ^ state[1]) >> 25
)
state[2] = (((state[2] & 4294967280) << 17) & 0xFFFFFFFF) ^ (
(((state[2] << 3) & 0xFFFFFFFF) ^ state[2]) >> 11
)
return state[0] ^ state[1] ^ state[2]
@numba.njit("f4(i8[:])", cache=True)
def tau_rand(state):
"""A fast (pseudo)-random number generator for floats in the range [0,1]
Parameters
----------
state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
A (pseudo)-random float32 in the interval [0, 1]
"""
integer = tau_rand_int(state)
return abs(float(integer) / 0x7FFFFFFF)
@numba.njit(
[
"f4(f4[::1])",
numba.types.float32(
numba.types.Array(numba.types.float32, 1, "C", readonly=True)
),
],
locals={
"dim": numba.types.intp,
"i": numba.types.uint32,
# "result": numba.types.float32, # This provides speed, but causes errors in corner cases
},
fastmath=True,
cache=True,
)
def norm(vec):
"""Compute the (standard l2) norm of a vector.
Parameters
----------
vec: array of shape (dim,)
Returns
-------
The l2 norm of vec.
"""
result = 0.0
dim = vec.shape[0]
for i in range(dim):
result += vec[i] * vec[i]
return np.sqrt(result)
@numba.njit(cache=True)
def rejection_sample(n_samples, pool_size, rng_state):
"""Generate n_samples many integers from 0 to pool_size such that no
integer is selected twice. The duplication constraint is achieved via
rejection sampling.
Parameters
----------
n_samples: int
The number of random samples to select from the pool
pool_size: int
The size of the total pool of candidates to sample from
rng_state: array of int64, shape (3,)
Internal state of the random number generator
Returns
-------
sample: array of shape(n_samples,)
The ``n_samples`` randomly selected elements from the pool.
"""
result = np.empty(n_samples, dtype=np.int64)
for i in range(n_samples):
reject_sample = True
j = 0
while reject_sample:
j = tau_rand_int(rng_state) % pool_size
for k in range(i):
if j == result[k]:
break
else:
reject_sample = False
result[i] = j
return result
@structref.register
class HeapType(types.StructRef):
pass
class Heap(structref.StructRefProxy):
@property
def indices(self):
return Heap_get_indices(self)
@property
def distances(self):
return Heap_get_distances(self)
@property
def flags(self):
return Heap_get_flags(self)
@numba.njit(cache=True)
def Heap_get_flags(self):
return self.flags
@numba.njit(cache=True)
def Heap_get_distances(self):
return self.distances
@numba.njit(cache=True)
def Heap_get_indices(self):
return self.indices
structref.define_proxy(Heap, HeapType, ["indices", "distances", "flags"])
# Heap = namedtuple("Heap", ("indices", "distances", "flags"))
@numba.njit(cache=True)
def make_heap(n_points, size):
"""Constructor for the numba enabled heap objects. The heaps are used
for approximate nearest neighbor search, maintaining a list of potential
neighbors sorted by their distance. We also flag if potential neighbors
are newly added to the list or not. Internally this is stored as
a single ndarray; the first axis determines whether we are looking at the
array of candidate graph_indices, the array of distances, or the flag array for
whether elements are new or not. Each of these arrays are of shape
(``n_points``, ``size``)
Parameters
----------
n_points: int
The number of graph_data points to track in the heap.
size: int
The number of items to keep on the heap for each graph_data point.
Returns
-------
heap: An ndarray suitable for passing to other numba enabled heap functions.
"""
indices = np.full((int(n_points), int(size)), -1, dtype=np.int32)
distances = np.full((int(n_points), int(size)), np.infty, dtype=np.float32)
flags = np.zeros((int(n_points), int(size)), dtype=np.uint8)
result = (indices, distances, flags)
return result
@numba.njit(cache=True)
def siftdown(heap1, heap2, elt):
"""Restore the heap property for a heap with an out of place element
at position ``elt``. This works with a heap pair where heap1 carries
the weights and heap2 holds the corresponding elements."""
while elt * 2 + 1 < heap1.shape[0]:
left_child = elt * 2 + 1
right_child = left_child + 1
swap = elt
if heap1[swap] < heap1[left_child]:
swap = left_child
if right_child < heap1.shape[0] and heap1[swap] < heap1[right_child]:
swap = right_child
if swap == elt:
break
else:
heap1[elt], heap1[swap] = heap1[swap], heap1[elt]
heap2[elt], heap2[swap] = heap2[swap], heap2[elt]
elt = swap
@numba.njit(parallel=True, cache=False)
def deheap_sort(indices, distances):
"""Given two arrays representing a heap (indices and distances), reorder the
arrays by increasing distance. This is effectively just the second half of
heap sort (the first half not being required since we already have the
graph_data in a heap).
Note that this is done in-place.
Parameters
----------
indices : array of shape (n_samples, n_neighbors)
The graph indices to sort by distance.
distances : array of shape (n_samples, n_neighbors)
The corresponding edge distance.
Returns
-------
indices, distances: arrays of shape (n_samples, n_neighbors)
The indices and distances sorted by increasing distance.
"""
for i in numba.prange(indices.shape[0]):
# starting from the end of the array and moving back
for j in range(indices.shape[1] - 1, 0, -1):
indices[i, 0], indices[i, j] = indices[i, j], indices[i, 0]
distances[i, 0], distances[i, j] = distances[i, j], distances[i, 0]
siftdown(distances[i, :j], indices[i, :j], 0)
return indices, distances
# @numba.njit()
# def smallest_flagged(heap, row):
# """Search the heap for the smallest element that is
# still flagged.
#
# Parameters
# ----------
# heap: array of shape (3, n_samples, n_neighbors)
# The heaps to search
#
# row: int
# Which of the heaps to search
#
# Returns
# -------
# index: int
# The index of the smallest flagged element
# of the ``row``th heap, or -1 if no flagged
# elements remain in the heap.
# """
# ind = heap[0][row]
# dist = heap[1][row]
# flag = heap[2][row]
#
# min_dist = np.inf
# result_index = -1
#
# for i in range(ind.shape[0]):
# if flag[i] == 1 and dist[i] < min_dist:
# min_dist = dist[i]
# result_index = i
#
# if result_index >= 0:
# flag[result_index] = 0.0
# return int(ind[result_index])
# else:
# return -1
@numba.njit(parallel=True, locals={"idx": numba.types.int64}, cache=False)
def new_build_candidates(current_graph, max_candidates, rng_state, n_threads):
"""Build a heap of candidate neighbors for nearest neighbor descent. For
each vertex the candidate neighbors are any current neighbors, and any
vertices that have the vertex as one of their nearest neighbors.
Parameters
----------
current_graph: heap
The current state of the graph for nearest neighbor descent.
max_candidates: int
The maximum number of new candidate neighbors.
rng_state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
candidate_neighbors: A heap with an array of (randomly sorted) candidate
neighbors for each vertex in the graph.
"""
current_indices = current_graph[0]
current_flags = current_graph[2]
n_vertices = current_indices.shape[0]
n_neighbors = current_indices.shape[1]
new_candidate_indices = np.full((n_vertices, max_candidates), -1, dtype=np.int32)
new_candidate_priority = np.full(
(n_vertices, max_candidates), np.inf, dtype=np.float32
)
old_candidate_indices = np.full((n_vertices, max_candidates), -1, dtype=np.int32)
old_candidate_priority = np.full(
(n_vertices, max_candidates), np.inf, dtype=np.float32
)
for n in numba.prange(n_threads):
local_rng_state = rng_state + n
for i in range(n_vertices):
for j in range(n_neighbors):
idx = current_indices[i, j]
isn = current_flags[i, j]
if idx < 0:
continue
d = tau_rand(local_rng_state)
if isn:
if i % n_threads == n:
checked_heap_push(
new_candidate_priority[i], new_candidate_indices[i], d, idx
)
if idx % n_threads == n:
checked_heap_push(
new_candidate_priority[idx],
new_candidate_indices[idx],
d,
i,
)
else:
if i % n_threads == n:
checked_heap_push(
old_candidate_priority[i], old_candidate_indices[i], d, idx
)
if idx % n_threads == n:
checked_heap_push(
old_candidate_priority[idx],
old_candidate_indices[idx],
d,
i,
)
indices = current_graph[0]
flags = current_graph[2]
for i in numba.prange(n_vertices):
for j in range(n_neighbors):
idx = indices[i, j]
for k in range(max_candidates):
if new_candidate_indices[i, k] == idx:
flags[i, j] = 0
break
return new_candidate_indices, old_candidate_indices
@numba.njit("b1(u1[::1],i4)", cache=True)
def has_been_visited(table, candidate):
loc = candidate >> 3
mask = 1 << (candidate & 7)
return table[loc] & mask
@numba.njit("void(u1[::1],i4)", cache=True)
def mark_visited(table, candidate):
loc = candidate >> 3
mask = 1 << (candidate & 7)
table[loc] |= mask
return
@numba.njit(
"i4(f4[::1],i4[::1],f4,i4)",
fastmath=True,
locals={
"size": numba.types.intp,
"i": numba.types.uint16,
"ic1": numba.types.uint16,
"ic2": numba.types.uint16,
"i_swap": numba.types.uint16,
},
cache=True,
)
def simple_heap_push(priorities, indices, p, n):
if p >= priorities[0]:
return 0
size = priorities.shape[0]
# insert val at position zero
priorities[0] = p
indices[0] = n
# descend the heap, swapping values until the max heap criterion is met
i = 0
while True:
ic1 = 2 * i + 1
ic2 = ic1 + 1
if ic1 >= size:
break
elif ic2 >= size:
if priorities[ic1] > p:
i_swap = ic1
else:
break
elif priorities[ic1] >= priorities[ic2]:
if p < priorities[ic1]:
i_swap = ic1
else:
break
else:
if p < priorities[ic2]:
i_swap = ic2
else:
break
priorities[i] = priorities[i_swap]
indices[i] = indices[i_swap]
i = i_swap
priorities[i] = p
indices[i] = n
return 1
@numba.njit(
"i4(f4[::1],i4[::1],f4,i4)",
fastmath=True,
locals={
"size": numba.types.intp,
"i": numba.types.uint16,
"ic1": numba.types.uint16,
"ic2": numba.types.uint16,
"i_swap": numba.types.uint16,
},
cache=True,
)
def checked_heap_push(priorities, indices, p, n):
if p >= priorities[0]:
return 0
size = priorities.shape[0]
# break if we already have this element.
for i in range(size):
if n == indices[i]:
return 0
# insert val at position zero
priorities[0] = p
indices[0] = n
# descend the heap, swapping values until the max heap criterion is met
i = 0
while True:
ic1 = 2 * i + 1
ic2 = ic1 + 1
if ic1 >= size:
break
elif ic2 >= size:
if priorities[ic1] > p:
i_swap = ic1
else:
break
elif priorities[ic1] >= priorities[ic2]:
if p < priorities[ic1]:
i_swap = ic1
else:
break
else:
if p < priorities[ic2]:
i_swap = ic2
else:
break
priorities[i] = priorities[i_swap]
indices[i] = indices[i_swap]
i = i_swap
priorities[i] = p
indices[i] = n
return 1
@numba.njit(
"i4(f4[::1],i4[::1],u1[::1],f4,i4,u1)",
fastmath=True,
locals={
"size": numba.types.intp,
"i": numba.types.uint16,
"ic1": numba.types.uint16,
"ic2": numba.types.uint16,
"i_swap": numba.types.uint16,
},
cache=True,
)
def checked_flagged_heap_push(priorities, indices, flags, p, n, f):
if p >= priorities[0]:
return 0
size = priorities.shape[0]
# break if we already have this element.
for i in range(size):
if n == indices[i]:
return 0
# insert val at position zero
priorities[0] = p
indices[0] = n
flags[0] = f
# descend the heap, swapping values until the max heap criterion is met
i = 0
while True:
ic1 = 2 * i + 1
ic2 = ic1 + 1
if ic1 >= size:
break
elif ic2 >= size:
if priorities[ic1] > p:
i_swap = ic1
else:
break
elif priorities[ic1] >= priorities[ic2]:
if p < priorities[ic1]:
i_swap = ic1
else:
break
else:
if p < priorities[ic2]:
i_swap = ic2
else:
break
priorities[i] = priorities[i_swap]
indices[i] = indices[i_swap]
flags[i] = flags[i_swap]
i = i_swap
priorities[i] = p
indices[i] = n
flags[i] = f
return 1
@numba.njit(
parallel=True,
locals={
"p": numba.int32,
"q": numba.int32,
"d": numba.float32,
"added": numba.uint8,
"n": numba.uint32,
"i": numba.uint32,
"j": numba.uint32,
},
cache=False,
)
def apply_graph_updates_low_memory(current_graph, updates, n_threads):
n_changes = 0
priorities = current_graph[1]
indices = current_graph[0]
flags = current_graph[2]
# n_threads = numba.get_num_threads()
for n in numba.prange(n_threads):
for i in range(len(updates)):
for j in range(len(updates[i])):
p, q, d = updates[i][j]
if p == -1 or q == -1:
continue
if p % n_threads == n:
added = checked_flagged_heap_push(
priorities[p], indices[p], flags[p], d, q, 1
)
n_changes += added
if q % n_threads == n:
added = checked_flagged_heap_push(
priorities[q], indices[q], flags[q], d, p, 1
)
n_changes += added
return n_changes
@numba.njit(locals={"p": numba.types.int64, "q": numba.types.int64}, cache=True)
def apply_graph_updates_high_memory(current_graph, updates, in_graph):
n_changes = 0
for i in range(len(updates)):
for j in range(len(updates[i])):
p, q, d = updates[i][j]
if p == -1 or q == -1:
continue
if q in in_graph[p] and p in in_graph[q]:
continue
elif q in in_graph[p]:
pass
else:
added = checked_flagged_heap_push(
current_graph[1][p],
current_graph[0][p],
current_graph[2][p],
d,
q,
1,
)
if added > 0:
in_graph[p].add(q)
n_changes += added
if p == q or p in in_graph[q]:
pass
else:
added = checked_flagged_heap_push(
current_graph[1][p],
current_graph[0][p],
current_graph[2][p],
d,
q,
1,
)
if added > 0:
in_graph[q].add(p)
n_changes += added
return n_changes
@numba.njit(cache=False)
def initalize_heap_from_graph_indices(heap, graph_indices, data, metric):
for i in range(graph_indices.shape[0]):
for idx in range(graph_indices.shape[1]):
j = graph_indices[i, idx]
if j >= 0:
d = metric(data[i], data[j])
checked_flagged_heap_push(heap[1][i], heap[0][i], heap[2][i], d, j, 1)
return heap
@numba.njit(cache=True)
def initalize_heap_from_graph_indices_and_distances(
heap, graph_indices, graph_distances
):
for i in range(graph_indices.shape[0]):
for idx in range(graph_indices.shape[1]):
j = graph_indices[i, idx]
if j >= 0:
d = graph_distances[i, idx]
checked_flagged_heap_push(heap[1][i], heap[0][i], heap[2][i], d, j, 1)
return heap
@numba.njit(parallel=True, cache=False)
def sparse_initalize_heap_from_graph_indices(
heap, graph_indices, data_indptr, data_indices, data_vals, metric
):
for i in numba.prange(graph_indices.shape[0]):
for idx in range(graph_indices.shape[1]):
j = graph_indices[i, idx]
ind1 = data_indices[data_indptr[i] : data_indptr[i + 1]]
data1 = data_vals[data_indptr[i] : data_indptr[i + 1]]
ind2 = data_indices[data_indptr[j] : data_indptr[j + 1]]
data2 = data_vals[data_indptr[j] : data_indptr[j + 1]]
d = metric(ind1, data1, ind2, data2)
checked_flagged_heap_push(heap[1][i], heap[0][i], heap[2][i], d, j, 1)
return heap
# Generates a timestamp for use in logging messages when verbose=True
def ts():
return time.ctime(time.time())