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

1637 lines
49 KiB
Python

# Author: Leland McInnes <leland.mcinnes@gmail.com>
#
# License: BSD 2 clause
from warnings import warn
import locale
import numpy as np
import numba
import scipy.sparse
from pynndescent.sparse import (
sparse_mul,
sparse_diff,
sparse_sum,
arr_intersect,
sparse_dot_product,
)
from pynndescent.utils import tau_rand_int, norm
import joblib
from collections import namedtuple
locale.setlocale(locale.LC_NUMERIC, "C")
# Used for a floating point "nearly zero" comparison
EPS = 1e-8
INT32_MIN = np.iinfo(np.int32).min + 1
INT32_MAX = np.iinfo(np.int32).max - 1
FlatTree = namedtuple(
"FlatTree", ["hyperplanes", "offsets", "children", "indices", "leaf_size"]
)
dense_hyperplane_type = numba.float32[::1]
sparse_hyperplane_type = numba.float64[:, ::1]
bit_hyperplane_type = numba.uint8[::1]
offset_type = numba.float64
children_type = numba.typeof((np.int32(-1), np.int32(-1)))
point_indices_type = numba.int32[::1]
popcnt = np.array(
[bin(i).count('1') for i in range(256)],
dtype=np.float32
)
@numba.njit(
numba.types.Tuple(
(numba.int32[::1], numba.int32[::1], dense_hyperplane_type, offset_type)
)(numba.float32[:, ::1], numba.int32[::1], numba.int64[::1]),
locals={
"n_left": numba.uint32,
"n_right": numba.uint32,
"hyperplane_vector": numba.float32[::1],
"hyperplane_offset": numba.float32,
"margin": numba.float32,
"d": numba.uint32,
"i": numba.uint32,
"left_index": numba.uint32,
"right_index": numba.uint32,
},
fastmath=True,
nogil=True,
cache=True,
)
def angular_random_projection_split(data, indices, rng_state):
"""Given a set of ``graph_indices`` for graph_data points from ``graph_data``, create
a random hyperplane to split the graph_data, returning two arrays graph_indices
that fall on either side of the hyperplane. This is the basis for a
random projection tree, which simply uses this splitting recursively.
This particular split uses cosine distance to determine the hyperplane
and which side each graph_data sample falls on.
Parameters
----------
data: array of shape (n_samples, n_features)
The original graph_data to be split
indices: array of shape (tree_node_size,)
The graph_indices of the elements in the ``graph_data`` array that are to
be split in the current operation.
rng_state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
indices_left: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
indices_right: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
"""
dim = data.shape[1]
# Select two random points, set the hyperplane between them
left_index = tau_rand_int(rng_state) % indices.shape[0]
right_index = tau_rand_int(rng_state) % indices.shape[0]
right_index += left_index == right_index
right_index = right_index % indices.shape[0]
left = indices[left_index]
right = indices[right_index]
left_norm = norm(data[left])
right_norm = norm(data[right])
if abs(left_norm) < EPS:
left_norm = 1.0
if abs(right_norm) < EPS:
right_norm = 1.0
# Compute the normal vector to the hyperplane (the vector between
# the two points)
hyperplane_vector = np.empty(dim, dtype=np.float32)
for d in range(dim):
hyperplane_vector[d] = (data[left, d] / left_norm) - (
data[right, d] / right_norm
)
hyperplane_norm = norm(hyperplane_vector)
if abs(hyperplane_norm) < EPS:
hyperplane_norm = 1.0
for d in range(dim):
hyperplane_vector[d] = hyperplane_vector[d] / hyperplane_norm
# For each point compute the margin (project into normal vector)
# If we are on lower side of the hyperplane put in one pile, otherwise
# put it in the other pile (if we hit hyperplane on the nose, flip a coin)
n_left = 0
n_right = 0
side = np.empty(indices.shape[0], np.int8)
for i in range(indices.shape[0]):
margin = 0.0
for d in range(dim):
margin += hyperplane_vector[d] * data[indices[i], d]
if abs(margin) < EPS:
side[i] = tau_rand_int(rng_state) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
elif margin > 0:
side[i] = 0
n_left += 1
else:
side[i] = 1
n_right += 1
# If all points end up on one side, something went wrong numerically
# In this case, assign points randomly; they are likely very close anyway
if n_left == 0 or n_right == 0:
n_left = 0
n_right = 0
for i in range(indices.shape[0]):
side[i] = tau_rand_int(rng_state) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
# Now that we have the counts allocate arrays
indices_left = np.empty(n_left, dtype=np.int32)
indices_right = np.empty(n_right, dtype=np.int32)
# Populate the arrays with graph_indices according to which side they fell on
n_left = 0
n_right = 0
for i in range(side.shape[0]):
if side[i] == 0:
indices_left[n_left] = indices[i]
n_left += 1
else:
indices_right[n_right] = indices[i]
n_right += 1
return indices_left, indices_right, hyperplane_vector, 0.0
@numba.njit(
numba.types.Tuple(
(numba.int32[::1], numba.int32[::1], bit_hyperplane_type, offset_type)
)(numba.uint8[:, ::1], numba.int32[::1], numba.int64[::1]),
locals={
"n_left": numba.uint32,
"n_right": numba.uint32,
"hyperplane_vector": numba.uint8[::1],
"hyperplane_offset": numba.float32,
"margin": numba.float32,
"d": numba.uint32,
"i": numba.uint32,
"left_index": numba.uint32,
"right_index": numba.uint32,
},
fastmath=True,
nogil=True,
cache=True,
)
def angular_bitpacked_random_projection_split(data, indices, rng_state):
"""Given a set of ``graph_indices`` for graph_data points from ``graph_data``, create
a random hyperplane to split the graph_data, returning two arrays graph_indices
that fall on either side of the hyperplane. This is the basis for a
random projection tree, which simply uses this splitting recursively.
This particular split uses cosine distance to determine the hyperplane
and which side each graph_data sample falls on.
Parameters
----------
data: array of shape (n_samples, n_features)
The original graph_data to be split
indices: array of shape (tree_node_size,)
The graph_indices of the elements in the ``graph_data`` array that are to
be split in the current operation.
rng_state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
indices_left: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
indices_right: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
"""
dim = data.shape[1]
# Select two random points, set the hyperplane between them
left_index = tau_rand_int(rng_state) % indices.shape[0]
right_index = tau_rand_int(rng_state) % indices.shape[0]
right_index += left_index == right_index
right_index = right_index % indices.shape[0]
left = indices[left_index]
right = indices[right_index]
left_norm = 0.0
right_norm = 0.0
# Compute the normal vector to the hyperplane (the vector between
# the two points)
hyperplane_vector = np.empty(dim * 2, dtype=np.uint8)
positive_hyperplane_component = hyperplane_vector[:dim]
negative_hyperplane_component = hyperplane_vector[dim:]
for d in range(dim):
xor_vector = (data[left, d]) ^ (data[right, d])
positive_hyperplane_component[d] = xor_vector & (data[left, d])
negative_hyperplane_component[d] = xor_vector & (data[right, d])
hyperplane_norm = 0.0
for d in range(dim):
hyperplane_norm += popcnt[hyperplane_vector[d]]
left_norm += popcnt[data[left, d]]
right_norm += popcnt[data[right, d]]
# For each point compute the margin (project into normal vector)
# If we are on lower side of the hyperplane put in one pile, otherwise
# put it in the other pile (if we hit hyperplane on the nose, flip a coin)
n_left = 0
n_right = 0
side = np.empty(indices.shape[0], np.int8)
for i in range(indices.shape[0]):
margin = 0.0
for d in range(dim):
margin += popcnt[positive_hyperplane_component[d] & data[indices[i], d]]
margin -= popcnt[negative_hyperplane_component[d] & data[indices[i], d]]
if abs(margin) < EPS:
side[i] = tau_rand_int(rng_state) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
elif margin > 0:
side[i] = 0
n_left += 1
else:
side[i] = 1
n_right += 1
# If all points end up on one side, something went wrong numerically
# In this case, assign points randomly; they are likely very close anyway
if n_left == 0 or n_right == 0:
n_left = 0
n_right = 0
for i in range(indices.shape[0]):
side[i] = tau_rand_int(rng_state) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
# Now that we have the counts allocate arrays
indices_left = np.empty(n_left, dtype=np.int32)
indices_right = np.empty(n_right, dtype=np.int32)
# Populate the arrays with graph_indices according to which side they fell on
n_left = 0
n_right = 0
for i in range(side.shape[0]):
if side[i] == 0:
indices_left[n_left] = indices[i]
n_left += 1
else:
indices_right[n_right] = indices[i]
n_right += 1
return indices_left, indices_right, hyperplane_vector, 0.0
@numba.njit(
numba.types.Tuple(
(numba.int32[::1], numba.int32[::1], dense_hyperplane_type, offset_type)
)(numba.float32[:, ::1], numba.int32[::1], numba.int64[::1]),
locals={
"n_left": numba.uint32,
"n_right": numba.uint32,
"hyperplane_vector": numba.float32[::1],
"hyperplane_offset": numba.float32,
"margin": numba.float32,
"d": numba.uint32,
"i": numba.uint32,
"left_index": numba.uint32,
"right_index": numba.uint32,
},
fastmath=True,
nogil=True,
cache=True,
)
def euclidean_random_projection_split(data, indices, rng_state):
"""Given a set of ``graph_indices`` for graph_data points from ``graph_data``, create
a random hyperplane to split the graph_data, returning two arrays graph_indices
that fall on either side of the hyperplane. This is the basis for a
random projection tree, which simply uses this splitting recursively.
This particular split uses euclidean distance to determine the hyperplane
and which side each graph_data sample falls on.
Parameters
----------
data: array of shape (n_samples, n_features)
The original graph_data to be split
indices: array of shape (tree_node_size,)
The graph_indices of the elements in the ``graph_data`` array that are to
be split in the current operation.
rng_state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
indices_left: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
indices_right: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
"""
dim = data.shape[1]
# Select two random points, set the hyperplane between them
left_index = tau_rand_int(rng_state) % indices.shape[0]
right_index = tau_rand_int(rng_state) % indices.shape[0]
right_index += left_index == right_index
right_index = right_index % indices.shape[0]
left = indices[left_index]
right = indices[right_index]
# Compute the normal vector to the hyperplane (the vector between
# the two points) and the offset from the origin
hyperplane_offset = 0.0
hyperplane_vector = np.empty(dim, dtype=np.float32)
for d in range(dim):
hyperplane_vector[d] = data[left, d] - data[right, d]
hyperplane_offset -= (
hyperplane_vector[d] * (data[left, d] + data[right, d]) / 2.0
)
# For each point compute the margin (project into normal vector, add offset)
# If we are on lower side of the hyperplane put in one pile, otherwise
# put it in the other pile (if we hit hyperplane on the nose, flip a coin)
n_left = 0
n_right = 0
side = np.empty(indices.shape[0], np.int8)
for i in range(indices.shape[0]):
margin = hyperplane_offset
for d in range(dim):
margin += hyperplane_vector[d] * data[indices[i], d]
if abs(margin) < EPS:
side[i] = abs(tau_rand_int(rng_state)) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
elif margin > 0:
side[i] = 0
n_left += 1
else:
side[i] = 1
n_right += 1
# If all points end up on one side, something went wrong numerically
# In this case, assign points randomly; they are likely very close anyway
if n_left == 0 or n_right == 0:
n_left = 0
n_right = 0
for i in range(indices.shape[0]):
side[i] = tau_rand_int(rng_state) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
# Now that we have the counts allocate arrays
indices_left = np.empty(n_left, dtype=np.int32)
indices_right = np.empty(n_right, dtype=np.int32)
# Populate the arrays with graph_indices according to which side they fell on
n_left = 0
n_right = 0
for i in range(side.shape[0]):
if side[i] == 0:
indices_left[n_left] = indices[i]
n_left += 1
else:
indices_right[n_right] = indices[i]
n_right += 1
return indices_left, indices_right, hyperplane_vector, hyperplane_offset
@numba.njit(
fastmath=True,
nogil=True,
cache=True,
locals={
"normalized_left_data": numba.types.float32[::1],
"normalized_right_data": numba.types.float32[::1],
"hyperplane_norm": numba.types.float32,
"i": numba.types.uint32,
},
)
def sparse_angular_random_projection_split(inds, indptr, data, indices, rng_state):
"""Given a set of ``graph_indices`` for graph_data points from a sparse graph_data set
presented in csr sparse format as inds, graph_indptr and graph_data, create
a random hyperplane to split the graph_data, returning two arrays graph_indices
that fall on either side of the hyperplane. This is the basis for a
random projection tree, which simply uses this splitting recursively.
This particular split uses cosine distance to determine the hyperplane
and which side each graph_data sample falls on.
Parameters
----------
inds: array
CSR format index array of the matrix
indptr: array
CSR format index pointer array of the matrix
data: array
CSR format graph_data array of the matrix
indices: array of shape (tree_node_size,)
The graph_indices of the elements in the ``graph_data`` array that are to
be split in the current operation.
rng_state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
indices_left: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
indices_right: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
"""
# Select two random points, set the hyperplane between them
left_index = tau_rand_int(rng_state) % indices.shape[0]
right_index = tau_rand_int(rng_state) % indices.shape[0]
right_index += left_index == right_index
right_index = right_index % indices.shape[0]
left = indices[left_index]
right = indices[right_index]
left_inds = inds[indptr[left] : indptr[left + 1]]
left_data = data[indptr[left] : indptr[left + 1]]
right_inds = inds[indptr[right] : indptr[right + 1]]
right_data = data[indptr[right] : indptr[right + 1]]
left_norm = norm(left_data)
right_norm = norm(right_data)
if abs(left_norm) < EPS:
left_norm = 1.0
if abs(right_norm) < EPS:
right_norm = 1.0
# Compute the normal vector to the hyperplane (the vector between
# the two points)
normalized_left_data = (left_data / left_norm).astype(np.float32)
normalized_right_data = (right_data / right_norm).astype(np.float32)
hyperplane_inds, hyperplane_data = sparse_diff(
left_inds, normalized_left_data, right_inds, normalized_right_data
)
hyperplane_norm = norm(hyperplane_data)
if abs(hyperplane_norm) < EPS:
hyperplane_norm = 1.0
for d in range(hyperplane_data.shape[0]):
hyperplane_data[d] = hyperplane_data[d] / hyperplane_norm
# For each point compute the margin (project into normal vector)
# If we are on lower side of the hyperplane put in one pile, otherwise
# put it in the other pile (if we hit hyperplane on the nose, flip a coin)
n_left = 0
n_right = 0
side = np.empty(indices.shape[0], np.int8)
for i in range(indices.shape[0]):
margin = 0.0
i_inds = inds[indptr[indices[i]] : indptr[indices[i] + 1]]
i_data = data[indptr[indices[i]] : indptr[indices[i] + 1]]
_, mul_data = sparse_mul(hyperplane_inds, hyperplane_data, i_inds, i_data)
for val in mul_data:
margin += val
if abs(margin) < EPS:
side[i] = tau_rand_int(rng_state) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
elif margin > 0:
side[i] = 0
n_left += 1
else:
side[i] = 1
n_right += 1
# If all points end up on one side, something went wrong numerically
# In this case, assign points randomly; they are likely very close anyway
if n_left == 0 or n_right == 0:
n_left = 0
n_right = 0
for i in range(indices.shape[0]):
side[i] = tau_rand_int(rng_state) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
# Now that we have the counts allocate arrays
indices_left = np.empty(n_left, dtype=np.int32)
indices_right = np.empty(n_right, dtype=np.int32)
# Populate the arrays with graph_indices according to which side they fell on
n_left = 0
n_right = 0
for i in range(side.shape[0]):
if side[i] == 0:
indices_left[n_left] = indices[i]
n_left += 1
else:
indices_right[n_right] = indices[i]
n_right += 1
hyperplane = np.vstack((hyperplane_inds, hyperplane_data))
return indices_left, indices_right, hyperplane, 0.0
@numba.njit(fastmath=True, nogil=True, cache=True)
def sparse_euclidean_random_projection_split(inds, indptr, data, indices, rng_state):
"""Given a set of ``graph_indices`` for graph_data points from a sparse graph_data set
presented in csr sparse format as inds, graph_indptr and graph_data, create
a random hyperplane to split the graph_data, returning two arrays graph_indices
that fall on either side of the hyperplane. This is the basis for a
random projection tree, which simply uses this splitting recursively.
This particular split uses cosine distance to determine the hyperplane
and which side each graph_data sample falls on.
Parameters
----------
inds: array
CSR format index array of the matrix
indptr: array
CSR format index pointer array of the matrix
data: array
CSR format graph_data array of the matrix
indices: array of shape (tree_node_size,)
The graph_indices of the elements in the ``graph_data`` array that are to
be split in the current operation.
rng_state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
indices_left: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
indices_right: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
"""
# Select two random points, set the hyperplane between them
left_index = np.abs(tau_rand_int(rng_state)) % indices.shape[0]
right_index = np.abs(tau_rand_int(rng_state)) % indices.shape[0]
right_index += left_index == right_index
right_index = right_index % indices.shape[0]
left = indices[left_index]
right = indices[right_index]
left_inds = inds[indptr[left] : indptr[left + 1]]
left_data = data[indptr[left] : indptr[left + 1]]
right_inds = inds[indptr[right] : indptr[right + 1]]
right_data = data[indptr[right] : indptr[right + 1]]
# Compute the normal vector to the hyperplane (the vector between
# the two points) and the offset from the origin
hyperplane_offset = 0.0
hyperplane_inds, hyperplane_data = sparse_diff(
left_inds, left_data, right_inds, right_data
)
offset_inds, offset_data = sparse_sum(left_inds, left_data, right_inds, right_data)
offset_data = offset_data / 2.0
offset_inds, offset_data = sparse_mul(
hyperplane_inds, hyperplane_data, offset_inds, offset_data.astype(np.float32)
)
for val in offset_data:
hyperplane_offset -= val
# For each point compute the margin (project into normal vector, add offset)
# If we are on lower side of the hyperplane put in one pile, otherwise
# put it in the other pile (if we hit hyperplane on the nose, flip a coin)
n_left = 0
n_right = 0
side = np.empty(indices.shape[0], np.int8)
for i in range(indices.shape[0]):
margin = hyperplane_offset
i_inds = inds[indptr[indices[i]] : indptr[indices[i] + 1]]
i_data = data[indptr[indices[i]] : indptr[indices[i] + 1]]
_, mul_data = sparse_mul(hyperplane_inds, hyperplane_data, i_inds, i_data)
for val in mul_data:
margin += val
if abs(margin) < EPS:
side[i] = abs(tau_rand_int(rng_state)) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
elif margin > 0:
side[i] = 0
n_left += 1
else:
side[i] = 1
n_right += 1
# If all points end up on one side, something went wrong numerically
# In this case, assign points randomly; they are likely very close anyway
if n_left == 0 or n_right == 0:
n_left = 0
n_right = 0
for i in range(indices.shape[0]):
side[i] = abs(tau_rand_int(rng_state)) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
# Now that we have the counts allocate arrays
indices_left = np.empty(n_left, dtype=np.int32)
indices_right = np.empty(n_right, dtype=np.int32)
# Populate the arrays with graph_indices according to which side they fell on
n_left = 0
n_right = 0
for i in range(side.shape[0]):
if side[i] == 0:
indices_left[n_left] = indices[i]
n_left += 1
else:
indices_right[n_right] = indices[i]
n_right += 1
hyperplane = np.vstack((hyperplane_inds, hyperplane_data))
return indices_left, indices_right, hyperplane, hyperplane_offset
@numba.njit(
nogil=True,
locals={"left_node_num": numba.types.int32, "right_node_num": numba.types.int32},
)
def make_euclidean_tree(
data,
indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size=30,
max_depth=200,
):
if indices.shape[0] > leaf_size and max_depth > 0:
(
left_indices,
right_indices,
hyperplane,
offset,
) = euclidean_random_projection_split(data, indices, rng_state)
make_euclidean_tree(
data,
left_indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth - 1,
)
left_node_num = len(point_indices) - 1
make_euclidean_tree(
data,
right_indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth - 1,
)
right_node_num = len(point_indices) - 1
hyperplanes.append(hyperplane)
offsets.append(offset)
children.append((np.int32(left_node_num), np.int32(right_node_num)))
point_indices.append(np.array([-1], dtype=np.int32))
else:
hyperplanes.append(np.array([-1.0], dtype=np.float32))
offsets.append(-np.inf)
children.append((np.int32(-1), np.int32(-1)))
point_indices.append(indices)
return
@numba.njit(
nogil=True,
locals={
"children": numba.types.ListType(children_type),
"left_node_num": numba.types.int32,
"right_node_num": numba.types.int32,
},
)
def make_angular_tree(
data,
indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size=30,
max_depth=200,
):
if indices.shape[0] > leaf_size and max_depth > 0:
(
left_indices,
right_indices,
hyperplane,
offset,
) = angular_random_projection_split(data, indices, rng_state)
make_angular_tree(
data,
left_indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth - 1,
)
left_node_num = len(point_indices) - 1
make_angular_tree(
data,
right_indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth - 1,
)
right_node_num = len(point_indices) - 1
hyperplanes.append(hyperplane)
offsets.append(offset)
children.append((np.int32(left_node_num), np.int32(right_node_num)))
point_indices.append(np.array([-1], dtype=np.int32))
else:
hyperplanes.append(np.array([-1.0], dtype=np.float32))
offsets.append(-np.inf)
children.append((np.int32(-1), np.int32(-1)))
point_indices.append(indices)
return
@numba.njit(
nogil=True,
locals={
"children": numba.types.ListType(children_type),
"left_node_num": numba.types.int32,
"right_node_num": numba.types.int32,
},
)
def make_bit_tree(
data,
indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size=30,
max_depth=200,
):
if indices.shape[0] > leaf_size and max_depth > 0:
(
left_indices,
right_indices,
hyperplane,
offset,
) = angular_bitpacked_random_projection_split(data, indices, rng_state)
make_bit_tree(
data,
left_indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth - 1,
)
left_node_num = len(point_indices) - 1
make_bit_tree(
data,
right_indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth - 1,
)
right_node_num = len(point_indices) - 1
hyperplanes.append(hyperplane)
offsets.append(offset)
children.append((np.int32(left_node_num), np.int32(right_node_num)))
point_indices.append(np.array([-1], dtype=np.int32))
else:
hyperplanes.append(np.array([255], dtype=np.uint8))
offsets.append(-np.inf)
children.append((np.int32(-1), np.int32(-1)))
point_indices.append(indices)
return
@numba.njit(
nogil=True,
locals={"left_node_num": numba.types.int32, "right_node_num": numba.types.int32},
)
def make_sparse_euclidean_tree(
inds,
indptr,
data,
indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size=30,
max_depth=200,
):
if indices.shape[0] > leaf_size and max_depth > 0:
(
left_indices,
right_indices,
hyperplane,
offset,
) = sparse_euclidean_random_projection_split(
inds, indptr, data, indices, rng_state
)
make_sparse_euclidean_tree(
inds,
indptr,
data,
left_indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth - 1,
)
left_node_num = len(point_indices) - 1
make_sparse_euclidean_tree(
inds,
indptr,
data,
right_indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth - 1,
)
right_node_num = len(point_indices) - 1
hyperplanes.append(hyperplane)
offsets.append(offset)
children.append((np.int32(left_node_num), np.int32(right_node_num)))
point_indices.append(np.array([-1], dtype=np.int32))
else:
hyperplanes.append(np.array([[-1.0], [-1.0]], dtype=np.float64))
offsets.append(-np.inf)
children.append((np.int32(-1), np.int32(-1)))
point_indices.append(indices)
return
@numba.njit(
nogil=True,
locals={"left_node_num": numba.types.int32, "right_node_num": numba.types.int32},
)
def make_sparse_angular_tree(
inds,
indptr,
data,
indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size=30,
max_depth=200,
):
if indices.shape[0] > leaf_size and max_depth > 0:
(
left_indices,
right_indices,
hyperplane,
offset,
) = sparse_angular_random_projection_split(
inds, indptr, data, indices, rng_state
)
make_sparse_angular_tree(
inds,
indptr,
data,
left_indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth - 1,
)
left_node_num = len(point_indices) - 1
make_sparse_angular_tree(
inds,
indptr,
data,
right_indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth - 1,
)
right_node_num = len(point_indices) - 1
hyperplanes.append(hyperplane)
offsets.append(offset)
children.append((np.int32(left_node_num), np.int32(right_node_num)))
point_indices.append(np.array([-1], dtype=np.int32))
else:
hyperplanes.append(np.array([[-1.0], [-1.0]], dtype=np.float64))
offsets.append(-np.inf)
children.append((np.int32(-1), np.int32(-1)))
point_indices.append(indices)
@numba.njit(nogil=True)
def make_dense_tree(data, rng_state, leaf_size=30, angular=False, max_depth=200):
indices = np.arange(data.shape[0]).astype(np.int32)
hyperplanes = numba.typed.List.empty_list(dense_hyperplane_type)
offsets = numba.typed.List.empty_list(offset_type)
children = numba.typed.List.empty_list(children_type)
point_indices = numba.typed.List.empty_list(point_indices_type)
if angular:
make_angular_tree(
data,
indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth=max_depth,
)
else:
make_euclidean_tree(
data,
indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth=max_depth,
)
max_leaf_size = leaf_size
for points in point_indices:
if len(points) > max_leaf_size:
max_leaf_size = numba.int32(len(points))
result = FlatTree(hyperplanes, offsets, children, point_indices, max_leaf_size)
return result
@numba.njit(nogil=True)
def make_sparse_tree(
inds,
indptr,
spdata,
rng_state,
leaf_size=30,
angular=False,
max_depth=200,
):
indices = np.arange(indptr.shape[0] - 1).astype(np.int32)
hyperplanes = numba.typed.List.empty_list(sparse_hyperplane_type)
offsets = numba.typed.List.empty_list(offset_type)
children = numba.typed.List.empty_list(children_type)
point_indices = numba.typed.List.empty_list(point_indices_type)
if angular:
make_sparse_angular_tree(
inds,
indptr,
spdata,
indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth=max_depth,
)
else:
make_sparse_euclidean_tree(
inds,
indptr,
spdata,
indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth=max_depth,
)
max_leaf_size = leaf_size
for points in point_indices:
if len(points) > max_leaf_size:
max_leaf_size = numba.int32(len(points))
return FlatTree(hyperplanes, offsets, children, point_indices, max_leaf_size)
@numba.njit(nogil=True)
def make_dense_bit_tree(data, rng_state, leaf_size=30, angular=False, max_depth=200):
indices = np.arange(data.shape[0]).astype(np.int32)
hyperplanes = numba.typed.List.empty_list(bit_hyperplane_type)
offsets = numba.typed.List.empty_list(offset_type)
children = numba.typed.List.empty_list(children_type)
point_indices = numba.typed.List.empty_list(point_indices_type)
if angular:
make_bit_tree(
data,
indices,
hyperplanes,
offsets,
children,
point_indices,
rng_state,
leaf_size,
max_depth=max_depth,
)
else:
raise NotImplementedError("Euclidean bit trees are not implemented yet.")
max_leaf_size = leaf_size
for points in point_indices:
if len(points) > max_leaf_size:
max_leaf_size = numba.int32(len(points))
result = FlatTree(hyperplanes, offsets, children, point_indices, max_leaf_size)
return result
@numba.njit(
[
"b1(f4[::1],f4,f4[::1],i8[::1])",
numba.types.boolean(
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
numba.types.float32,
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
numba.types.Array(numba.types.int64, 1, "C", readonly=False),
),
],
fastmath=True,
locals={
"margin": numba.types.float32,
"dim": numba.types.intp,
"d": numba.types.uint16,
},
cache=True,
)
def select_side(hyperplane, offset, point, rng_state):
margin = offset
dim = point.shape[0]
for d in range(dim):
margin += hyperplane[d] * point[d]
if abs(margin) < EPS:
side = np.abs(tau_rand_int(rng_state)) % 2
if side == 0:
return 0
else:
return 1
elif margin > 0:
return 0
else:
return 1
@numba.njit(
[
"b1(u1[::1],f4,u1[::1],i8[::1])",
numba.types.boolean(
numba.types.Array(numba.types.uint8, 1, "C", readonly=True),
numba.types.float32,
numba.types.Array(numba.types.uint8, 1, "C", readonly=True),
numba.types.Array(numba.types.int64, 1, "C", readonly=False),
),
],
fastmath=True,
locals={
"margin": numba.types.float32,
"dim": numba.types.intp,
"d": numba.types.uint16,
},
cache=True,
)
def select_side_bit(hyperplane, offset, point, rng_state):
margin = offset
dim = point.shape[0]
for d in range(dim):
margin += popcnt[hyperplane[d] & point[d]]
margin -= popcnt[hyperplane[dim + d] & point[d]]
if abs(margin) < EPS:
side = np.abs(tau_rand_int(rng_state)) % 2
if side == 0:
return 0
else:
return 1
elif margin > 0:
return 0
else:
return 1
@numba.njit(
[
"i4[::1](f4[::1],f4[:,::1],f4[::1],i4[:,::1],i4[::1],i8[::1])",
numba.types.Array(numba.types.int32, 1, "C", readonly=True)(
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
numba.types.Array(numba.types.float32, 2, "C", readonly=True),
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
numba.types.Array(numba.types.int32, 2, "C", readonly=True),
numba.types.Array(numba.types.int32, 1, "C", readonly=True),
numba.types.Array(numba.types.int64, 1, "C", readonly=False),
),
],
locals={"node": numba.types.uint32, "side": numba.types.boolean},
cache=True,
)
def search_flat_tree(point, hyperplanes, offsets, children, indices, rng_state):
node = 0
while children[node, 0] > 0:
side = select_side(hyperplanes[node], offsets[node], point, rng_state)
if side == 0:
node = children[node, 0]
else:
node = children[node, 1]
return indices[-children[node, 0] : -children[node, 1]]
@numba.njit(
[
"i4[::1](u1[::1],u1[:,::1],f4[::1],i4[:,::1],i4[::1],i8[::1])",
numba.types.Array(numba.types.int32, 1, "C", readonly=True)(
numba.types.Array(numba.types.uint8, 1, "C", readonly=True),
numba.types.Array(numba.types.uint8, 2, "C", readonly=True),
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
numba.types.Array(numba.types.int32, 2, "C", readonly=True),
numba.types.Array(numba.types.int32, 1, "C", readonly=True),
numba.types.Array(numba.types.int64, 1, "C", readonly=False),
),
],
locals={"node": numba.types.uint32, "side": numba.types.boolean},
cache=True,
)
def search_flat_bit_tree(point, hyperplanes, offsets, children, indices, rng_state):
node = 0
while children[node, 0] > 0:
side = select_side_bit(hyperplanes[node], offsets[node], point, rng_state)
if side == 0:
node = children[node, 0]
else:
node = children[node, 1]
return indices[-children[node, 0] : -children[node, 1]]
@numba.njit(fastmath=True, cache=True)
def sparse_select_side(hyperplane, offset, point_inds, point_data, rng_state):
margin = offset
hyperplane_size = hyperplane.shape[1]
while hyperplane[0, hyperplane_size - 1] < 0.0:
hyperplane_size -= 1
hyperplane_inds = hyperplane[0, :hyperplane_size].astype(np.int32)
hyperplane_data = hyperplane[1, :hyperplane_size]
margin += sparse_dot_product(
hyperplane_inds, hyperplane_data, point_inds, point_data
)
if abs(margin) < EPS:
side = tau_rand_int(rng_state) % 2
if side == 0:
return 0
else:
return 1
elif margin > 0:
return 0
else:
return 1
@numba.njit(locals={"node": numba.types.uint32}, cache=True)
def search_sparse_flat_tree(
point_inds, point_data, hyperplanes, offsets, children, indices, rng_state
):
node = 0
while children[node, 0] > 0:
side = sparse_select_side(
hyperplanes[node], offsets[node], point_inds, point_data, rng_state
)
if side == 0:
node = children[node, 0]
else:
node = children[node, 1]
return indices[-children[node, 0] : -children[node, 1]]
def make_forest(
data,
n_neighbors,
n_trees,
leaf_size,
rng_state,
random_state,
n_jobs=None,
angular=False,
bit_tree=False,
max_depth=200,
):
"""Build a random projection forest with ``n_trees``.
Parameters
----------
data
n_neighbors
n_trees
leaf_size
rng_state
angular
Returns
-------
forest: list
A list of random projection trees.
"""
# print(ts(), "Started forest construction")
result = []
if leaf_size is None:
leaf_size = max(10, np.int32(n_neighbors))
if n_jobs is None:
n_jobs = -1
rng_states = random_state.randint(INT32_MIN, INT32_MAX, size=(n_trees, 3)).astype(
np.int64
)
try:
if scipy.sparse.isspmatrix_csr(data):
result = joblib.Parallel(n_jobs=n_jobs, require="sharedmem")(
joblib.delayed(make_sparse_tree)(
data.indices,
data.indptr,
data.data,
rng_states[i],
leaf_size,
angular,
max_depth=max_depth,
)
for i in range(n_trees)
)
elif bit_tree:
result = joblib.Parallel(n_jobs=n_jobs, require="sharedmem")(
joblib.delayed(make_dense_bit_tree)(
data,
rng_states[i],
leaf_size,
angular,
max_depth=max_depth
)
for i in range(n_trees)
)
else:
result = joblib.Parallel(n_jobs=n_jobs, require="sharedmem")(
joblib.delayed(make_dense_tree)(
data,
rng_states[i],
leaf_size,
angular,
max_depth=max_depth
)
for i in range(n_trees)
)
except (RuntimeError, RecursionError, SystemError):
warn(
"Random Projection forest initialisation failed due to recursion"
"limit being reached. Something is a little strange with your "
"graph_data, and this may take longer than normal to compute."
)
return tuple(result)
@numba.njit(nogil=True)
def get_leaves_from_tree(tree, max_leaf_size):
n_leaves = 0
for i in range(len(tree.children)):
if tree.children[i][0] == -1 and tree.children[i][1] == -1:
n_leaves += 1
result = np.full((n_leaves, max_leaf_size), -1, dtype=np.int32)
leaf_index = 0
for i in range(len(tree.indices)):
if tree.children[i][0] == -1 or tree.children[i][1] == -1:
leaf_size = tree.indices[i].shape[0]
result[leaf_index, :leaf_size] = tree.indices[i]
leaf_index += 1
return result
def rptree_leaf_array_parallel(rp_forest):
max_leaf_size = np.max([rp_tree.leaf_size for rp_tree in rp_forest])
result = joblib.Parallel(n_jobs=-1, require="sharedmem")(
joblib.delayed(get_leaves_from_tree)(rp_tree, max_leaf_size) for rp_tree in rp_forest
)
return result
def rptree_leaf_array(rp_forest):
if len(rp_forest) > 0:
return np.vstack(rptree_leaf_array_parallel(rp_forest))
else:
return np.array([[-1]])
#@numba.njit()
def recursive_convert(
tree, hyperplanes, offsets, children, indices, node_num, leaf_start, tree_node
):
if tree.children[tree_node][0] < 0:
leaf_end = leaf_start + len(tree.indices[tree_node])
children[node_num, 0] = -leaf_start
children[node_num, 1] = -leaf_end
indices[leaf_start:leaf_end] = tree.indices[tree_node]
return node_num, leaf_end
else:
hyperplanes[node_num] = tree.hyperplanes[tree_node]
offsets[node_num] = tree.offsets[tree_node]
children[node_num, 0] = node_num + 1
old_node_num = node_num
node_num, leaf_start = recursive_convert(
tree,
hyperplanes,
offsets,
children,
indices,
node_num + 1,
leaf_start,
tree.children[tree_node][0],
)
children[old_node_num, 1] = node_num + 1
node_num, leaf_start = recursive_convert(
tree,
hyperplanes,
offsets,
children,
indices,
node_num + 1,
leaf_start,
tree.children[tree_node][1],
)
return node_num, leaf_start
@numba.njit()
def recursive_convert_sparse(
tree, hyperplanes, offsets, children, indices, node_num, leaf_start, tree_node
):
if tree.children[tree_node][0] < 0:
leaf_end = leaf_start + len(tree.indices[tree_node])
children[node_num, 0] = -leaf_start
children[node_num, 1] = -leaf_end
indices[leaf_start:leaf_end] = tree.indices[tree_node]
return node_num, leaf_end
else:
hyperplanes[
node_num, :, : tree.hyperplanes[tree_node].shape[1]
] = tree.hyperplanes[tree_node]
offsets[node_num] = tree.offsets[tree_node]
children[node_num, 0] = node_num + 1
old_node_num = node_num
node_num, leaf_start = recursive_convert_sparse(
tree,
hyperplanes,
offsets,
children,
indices,
node_num + 1,
leaf_start,
tree.children[tree_node][0],
)
children[old_node_num, 1] = node_num + 1
node_num, leaf_start = recursive_convert_sparse(
tree,
hyperplanes,
offsets,
children,
indices,
node_num + 1,
leaf_start,
tree.children[tree_node][1],
)
return node_num, leaf_start
@numba.njit(cache=True)
def num_nodes_and_leaves(tree):
n_nodes = 0
n_leaves = 0
for i in range(len(tree.children)):
if tree.children[i][0] < 0:
n_leaves += 1
n_nodes += 1
else:
n_nodes += 1
return n_nodes, n_leaves
def convert_tree_format(tree, data_size, data_dim):
n_nodes, n_leaves = num_nodes_and_leaves(tree)
is_sparse = False
if tree.hyperplanes[0].ndim == 1:
# dense hyperplanes
if tree.hyperplanes[0].dtype == np.uint8:
hyperplane_dim = data_dim * 2
else:
hyperplane_dim = data_dim
hyperplanes = np.zeros((n_nodes, hyperplane_dim), dtype=tree.hyperplanes[0].dtype)
else:
# sparse hyperplanes
is_sparse = True
hyperplane_dim = data_dim
hyperplanes = np.zeros((n_nodes, 2, hyperplane_dim), dtype=np.float32)
hyperplanes[:, 0, :] = -1
offsets = np.zeros(n_nodes, dtype=np.float32)
children = np.int32(-1) * np.ones((n_nodes, 2), dtype=np.int32)
indices = np.int32(-1) * np.ones(data_size, dtype=np.int32)
if is_sparse:
recursive_convert_sparse(
tree, hyperplanes, offsets, children, indices, 0, 0, len(tree.children) - 1
)
else:
recursive_convert(
tree, hyperplanes, offsets, children, indices, 0, 0, len(tree.children) - 1
)
return FlatTree(hyperplanes, offsets, children, indices, tree.leaf_size)
# Indices for tuple version of flat tree for pickle serialization
FLAT_TREE_HYPERPLANES = 0
FLAT_TREE_OFFSETS = 1
FLAT_TREE_CHILDREN = 2
FLAT_TREE_INDICES = 3
FLAT_TREE_LEAF_SIZE = 4
def denumbaify_tree(tree):
result = (
tree.hyperplanes,
tree.offsets,
tree.children,
tree.indices,
tree.leaf_size,
)
return result
def renumbaify_tree(tree):
result = FlatTree(
tree[FLAT_TREE_HYPERPLANES],
tree[FLAT_TREE_OFFSETS],
tree[FLAT_TREE_CHILDREN],
tree[FLAT_TREE_INDICES],
tree[FLAT_TREE_LEAF_SIZE],
)
return result
@numba.njit(
parallel=True,
locals={
"intersection": numba.int64[::1],
"result": numba.float32,
"i": numba.uint32,
},
cache=False,
)
def score_tree(tree, neighbor_indices, data, rng_state):
result = 0.0
for i in numba.prange(neighbor_indices.shape[0]):
leaf_indices = search_flat_tree(
data[i],
tree.hyperplanes,
tree.offsets,
tree.children,
tree.indices,
rng_state,
)
intersection = arr_intersect(neighbor_indices[i], leaf_indices)
result += numba.float32(intersection.shape[0] > 1)
return result / numba.float32(neighbor_indices.shape[0])
@numba.njit(nogil=True, locals={"node": numba.int32}, cache=False)
def score_linked_tree(tree, neighbor_indices):
result = 0.0
n_nodes = len(tree.children)
for i in range(n_nodes):
node = numba.int32(i)
left_child = tree.children[node][0]
right_child = tree.children[node][1]
if left_child == -1 and right_child == -1:
for j in range(tree.indices[node].shape[0]):
idx = tree.indices[node][j]
intersection = arr_intersect(neighbor_indices[idx], tree.indices[node])
result += numba.float32(intersection.shape[0] > 1)
return result / numba.float32(neighbor_indices.shape[0])