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balltree_numba.py
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balltree_numba.py
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import warnings
import numpy as np
import numba
from numba.experimental import jitclass
from numba import typed, types
#----------------------------------------------------------------------
# Distance computations
@numba.jit(nopython=True, cache=True)
def rdist(X1, i1, X2, i2):
d = 0
for k in range(X1.shape[1]):
tmp = (X1[i1, k] - X2[i2, k])
d += tmp * tmp
return d
@numba.jit(nopython=True, cache=True)
def min_rdist(node_centroids, node_radius, i_node, X, j):
d = rdist(node_centroids, i_node, X, j)
return np.square(max(0, np.sqrt(d) - node_radius[i_node]))
#----------------------------------------------------------------------
# Heap for distances and neighbors
@numba.jit(nopython=True)
def heap_create(N, k):
distances = np.full((N, k), np.finfo(np.float64).max)
indices = np.zeros((N, k), dtype=np.int64)
return distances, indices
@numba.jit(nopython=True)
def heap_sort(distances, indices):
distance_sorted = np.empty_like(distances)
index_sorted = np.empty_like(indices)
for i in range(distances.shape[0]):
k = np.argsort(distances[i])
for j in range(k.shape[0]):
distance_sorted[i, j] = distances[i, k[j]]
index_sorted[i, j] = indices[i, k[j]]
return distance_sorted, index_sorted
@numba.jit(nopython=True)
def heap_push(row, val, i_val, distances, indices):
size = distances.shape[1]
# check if val should be in heap
if val > distances[row, 0]:
return
# insert val at position zero
distances[row, 0] = val
indices[row, 0] = i_val
#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 distances[row, ic1] > val:
i_swap = ic1
else:
break
elif distances[row, ic1] >= distances[row, ic2]:
if val < distances[row, ic1]:
i_swap = ic1
else:
break
else:
if val < distances[row, ic2]:
i_swap = ic2
else:
break
distances[row, i] = distances[row, i_swap]
indices[row, i] = indices[row, i_swap]
i = i_swap
distances[row, i] = val
indices[row, i] = i_val
#----------------------------------------------------------------------
# Tools for building the tree
@numba.jit(nopython=True)
def _partition_indices(data, idx_array, idx_start, idx_end, split_index):
# Find the split dimension
n_features = data.shape[1]
split_dim = 0
max_spread = 0
for j in range(n_features):
max_val = -np.inf
min_val = np.inf
for i in range(idx_start, idx_end):
val = data[idx_array[i], j]
max_val = max(max_val, val)
min_val = min(min_val, val)
if max_val - min_val > max_spread:
max_spread = max_val - min_val
split_dim = j
# Partition using the split dimension
left = idx_start
right = idx_end - 1
while True:
midindex = left
for i in range(left, right):
d1 = data[idx_array[i], split_dim]
d2 = data[idx_array[right], split_dim]
if d1 < d2:
tmp = idx_array[i]
idx_array[i] = idx_array[midindex]
idx_array[midindex] = tmp
midindex += 1
tmp = idx_array[midindex]
idx_array[midindex] = idx_array[right]
idx_array[right] = tmp
if midindex == split_index:
break
elif midindex < split_index:
left = midindex + 1
else:
right = midindex - 1
@numba.jit(nopython=True)
def _recursive_build(i_node, idx_start, idx_end,
data, node_centroids, node_radius, idx_array,
node_idx_start, node_idx_end, node_is_leaf,
n_nodes, leaf_size):
# determine Node centroid
for j in range(data.shape[1]):
node_centroids[i_node, j] = 0
for i in range(idx_start, idx_end):
node_centroids[i_node, j] += data[idx_array[i], j]
node_centroids[i_node, j] /= (idx_end - idx_start)
# determine Node radius
sq_radius = 0.0
for i in range(idx_start, idx_end):
sq_dist = rdist(node_centroids, i_node, data, idx_array[i])
if sq_dist > sq_radius:
sq_radius = sq_dist
# set node properties
node_radius[i_node] = np.sqrt(sq_radius)
node_idx_start[i_node] = idx_start
node_idx_end[i_node] = idx_end
i_child = 2 * i_node + 1
# recursively create subnodes
if i_child >= n_nodes:
node_is_leaf[i_node] = True
if idx_end - idx_start > 2 * leaf_size:
# this shouldn't happen if our memory allocation is correct.
# We'll proactively prevent memory errors, but raise a
# warning saying we're doing so.
#warnings.warn("Internal: memory layout is flawed: "
# "not enough nodes allocated")
pass
elif idx_end - idx_start < 2:
# again, this shouldn't happen if our memory allocation is correct.
#warnings.warn("Internal: memory layout is flawed: "
# "too many nodes allocated")
node_is_leaf[i_node] = True
else:
# split node and recursively construct child nodes.
node_is_leaf[i_node] = False
n_mid = int((idx_end + idx_start) // 2)
_partition_indices(data, idx_array, idx_start, idx_end, n_mid)
_recursive_build(i_child, idx_start, n_mid,
data, node_centroids, node_radius, idx_array,
node_idx_start, node_idx_end, node_is_leaf,
n_nodes, leaf_size)
_recursive_build(i_child + 1, n_mid, idx_end,
data, node_centroids, node_radius, idx_array,
node_idx_start, node_idx_end, node_is_leaf,
n_nodes, leaf_size)
#----------------------------------------------------------------------
# Tools for querying the tree
@numba.jit(nopython=True)
def _query_recursive(i_node, X, i_pt, heap_distances, heap_indices, sq_dist_LB,
data, idx_array, node_centroids, node_radius,
node_is_leaf, node_idx_start, node_idx_end):
#------------------------------------------------------------
# Case 1: query point is outside node radius:
# trim it from the query
if sq_dist_LB > heap_distances[i_pt, 0]:
pass
#------------------------------------------------------------
# Case 2: this is a leaf node. Update set of nearby points
elif node_is_leaf[i_node]:
for i in range(node_idx_start[i_node],
node_idx_end[i_node]):
dist_pt = rdist(data, idx_array[i], X, i_pt)
if dist_pt < heap_distances[i_pt, 0]:
heap_push(i_pt, dist_pt, idx_array[i],
heap_distances, heap_indices)
#------------------------------------------------------------
# Case 3: Node is not a leaf. Recursively query subnodes
# starting with the closest
else:
i1 = 2 * i_node + 1
i2 = i1 + 1
sq_dist_LB_1 = min_rdist(node_centroids,
node_radius,
i1, X, i_pt)
sq_dist_LB_2 = min_rdist(node_centroids,
node_radius,
i2, X, i_pt)
# recursively query subnodes
if sq_dist_LB_1 <= sq_dist_LB_2:
_query_recursive(i1, X, i_pt, heap_distances,
heap_indices, sq_dist_LB_1,
data, idx_array, node_centroids, node_radius,
node_is_leaf, node_idx_start, node_idx_end)
_query_recursive(i2, X, i_pt, heap_distances,
heap_indices, sq_dist_LB_2,
data, idx_array, node_centroids, node_radius,
node_is_leaf, node_idx_start, node_idx_end)
else:
_query_recursive(i2, X, i_pt, heap_distances,
heap_indices, sq_dist_LB_2,
data, idx_array, node_centroids, node_radius,
node_is_leaf, node_idx_start, node_idx_end)
_query_recursive(i1, X, i_pt, heap_distances,
heap_indices, sq_dist_LB_1,
data, idx_array, node_centroids, node_radius,
node_is_leaf, node_idx_start, node_idx_end)
@numba.jit(nopython=True, parallel=True)
def _query_parallel(i_node, X, heap_distances, heap_indices,
data, idx_array, node_centroids, node_radius,
node_is_leaf, node_idx_start, node_idx_end):
for i_pt in numba.prange(X.shape[0]):
sq_dist_LB = min_rdist(node_centroids, node_radius, i_node, X, i_pt)
_query_recursive(i_node, X, i_pt, heap_distances, heap_indices, sq_dist_LB,
data, idx_array, node_centroids, node_radius, node_is_leaf,
node_idx_start, node_idx_end)
#----------------------------------------------------------------------
# The Ball Tree object
spec = [
('data', types.float64[:,:]),
('leaf_size', types.int64),
('n_samples', types.int64),
('n_features', types.int64),
('n_levels', types.int64),
('n_nodes', types.int64),
('idx_array', types.int64[:]),
('node_radius', types.float64[:]),
('node_idx_start', types.int64[:]),
('node_idx_end', types.int64[:]),
('node_is_leaf', types.boolean[:]),
('node_centroids', types.float64[:,:]),
]
@jitclass(spec)
class BallTree(object):
def __init__(self, data, leaf_size=40):
self.data = data
self.leaf_size = leaf_size
# validate data
if self.data.size == 0:
raise ValueError("X is an empty array")
if leaf_size < 1:
raise ValueError("leaf_size must be greater than or equal to 1")
self.n_samples = self.data.shape[0]
self.n_features = self.data.shape[1]
# determine number of levels in the tree, and from this
# the number of nodes in the tree. This results in leaf nodes
# with numbers of points betweeen leaf_size and 2 * leaf_size
self.n_levels = 1 + np.log2(max(1, ((self.n_samples - 1)
// self.leaf_size)))
self.n_nodes = int(2 ** self.n_levels) - 1
# allocate arrays for storage
self.idx_array = np.arange(self.n_samples)
self.node_radius = np.zeros(self.n_nodes, dtype=np.float64)
self.node_idx_start = np.zeros(self.n_nodes, dtype=np.int64)
self.node_idx_end = np.zeros(self.n_nodes, dtype=np.int64)
self.node_is_leaf = np.zeros(self.n_nodes, dtype=np.uint8)
self.node_centroids = np.zeros((self.n_nodes, self.n_features),
dtype=np.float64)
# Allocate tree-specific data from TreeBase
_recursive_build(0, 0, self.n_samples,
self.data, self.node_centroids,
self.node_radius, self.idx_array,
self.node_idx_start, self.node_idx_end,
self.node_is_leaf, self.n_nodes, self.leaf_size)
def query(self, X, k=1, sort_results=True):
X = np.asarray(X, dtype=np.float64)
if X.shape[-1] != self.n_features:
raise ValueError("query data dimension must "
"match training data dimension")
if self.data.shape[0] < k:
raise ValueError("k must be less than or equal "
"to the number of training points")
# flatten X, and save original shape information
Xshape = X.shape
X = X.reshape((-1, self.data.shape[1]))
# initialize heap for neighbors
heap_distances, heap_indices = heap_create(X.shape[0], k)
#for i in range(X.shape[0]):
# sq_dist_LB = min_rdist(self.node_centroids,
# self.node_radius,
# 0, X, i)
# _query_recursive(0, X, i, heap_distances, heap_indices, sq_dist_LB,
# self.data, self.idx_array, self.node_centroids,
# self.node_radius, self.node_is_leaf,
# self.node_idx_start, self.node_idx_end)
_query_parallel(0, X, heap_distances, heap_indices,
self.data, self.idx_array, self.node_centroids, self.node_radius,
self.node_is_leaf, self.node_idx_start, self.node_idx_end)
distances, indices = heap_sort(heap_distances, heap_indices)
distances = np.sqrt(distances)
# deflatten results
return (distances.reshape(Xshape[:-1] + (k,)),
indices.reshape(Xshape[:-1] + (k,)))
#----------------------------------------------------------------------
# Testing function
def test_tree(N=1000, D=3, K=5, LS=40):
from time import time
from sklearn.neighbors import BallTree as skBallTree
print("-------------------------------------------------------")
print("Numba version: " + numba.__version__)
rseed = np.random.randint(10000)
print("-------------------------------------------------------")
print("{0} neighbors of {1} points in {2} dimensions".format(K, N, D))
print("random seed = {0}".format(rseed))
np.random.seed(rseed)
X = np.random.random((N, D))
# pre-run to jit compile the code
BallTree(X, leaf_size=LS).query(X, K)
t0 = time()
bt1 = skBallTree(X, leaf_size=LS)
t1 = time()
dist1, ind1 = bt1.query(X, K)
t2 = time()
bt2 = BallTree(X, leaf_size=LS)
t3 = time()
dist2, ind2 = bt2.query(X, K)
t4 = time()
print("results match: {0} {1}".format(np.allclose(dist1, dist2),
np.allclose(ind1, ind2)))
print("")
print("sklearn build: {0:.3g} sec".format(t1 - t0))
print("numba build : {0:.3g} sec".format(t3 - t2))
print("")
print("sklearn query: {0:.3g} sec".format(t2 - t1))
print("numba query : {0:.3g} sec".format(t4 - t3))
if __name__ == '__main__':
test_tree(10000, K=20, LS=20)