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# cython: profile=False
# cython: linetrace=False
# distutils: define_macros=CYTHON_TRACE_NOGIL=1
# Code adapted from github.com/adrianveres/Polo, licensed:
#
# The MIT License (MIT)
# Copyright (c) 2016 Adrian Veres
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import numpy as np
cimport numpy as np
cimport cython
from libc.stdlib cimport malloc, free
from scipy.spatial.distance import squareform, is_valid_y, is_valid_dm
@cython.profile(False)
@cython.boundscheck(False)
@cython.wraparound(False)
cdef inline void dual_swap(float* darr, int* iarr,
int i1, int i2):
"""
[Taken from Scikit-learn.]
swap the values at inex i1 and i2 of both darr and iarr"""
cdef float dtmp = darr[i1]
darr[i1] = darr[i2]
darr[i2] = dtmp
cdef int itmp = iarr[i1]
iarr[i1] = iarr[i2]
iarr[i2] = itmp
@cython.profile(False)
@cython.boundscheck(False)
@cython.wraparound(False)
cdef int _simultaneous_sort(float* dist, int* idx,
int size) except -1:
"""
[Taken from Scikit-learn.]
Perform a recursive quicksort on the dist array, simultaneously
performing the same swaps on the idx array. The equivalent in
numpy (though quite a bit slower) is
def simultaneous_sort(dist, idx):
i = np.argsort(dist)
return dist[i], idx[i]
"""
cdef int pivot_idx, i, store_idx
cdef float pivot_val
# in the small-array case, do things efficiently
if size <= 1:
pass
elif size == 2:
if dist[0] > dist[1]:
dual_swap(dist, idx, 0, 1)
elif size == 3:
if dist[0] > dist[1]:
dual_swap(dist, idx, 0, 1)
if dist[1] > dist[2]:
dual_swap(dist, idx, 1, 2)
if dist[0] > dist[1]:
dual_swap(dist, idx, 0, 1)
else:
# Determine the pivot using the median-of-three rule.
# The smallest of the three is moved to the beginning of the array,
# the middle (the pivot value) is moved to the end, and the largest
# is moved to the pivot index.
pivot_idx = size / 2
if dist[0] > dist[size - 1]:
dual_swap(dist, idx, 0, size - 1)
if dist[size - 1] > dist[pivot_idx]:
dual_swap(dist, idx, size - 1, pivot_idx)
if dist[0] > dist[size - 1]:
dual_swap(dist, idx, 0, size - 1)
pivot_val = dist[size - 1]
# partition indices about pivot. At the end of this operation,
# pivot_idx will contain the pivot value, everything to the left
# will be smaller, and everything to the right will be larger.
store_idx = 0
for i in range(size - 1):
if dist[i] < pivot_val:
dual_swap(dist, idx, i, store_idx)
store_idx += 1
dual_swap(dist, idx, store_idx, size - 1)
pivot_idx = store_idx
# recursively sort each side of the pivot
if pivot_idx > 1:
_simultaneous_sort(dist, idx, pivot_idx)
if pivot_idx + 2 < size:
_simultaneous_sort(dist + pivot_idx + 1,
idx + pivot_idx + 1,
size - pivot_idx - 1)
return 0
cdef inline void _sort_M_slice(float[:, ::1] M,
float* vals, int* idx,
int dim1_min, int dim1_max, int dim2_val):
"""
Simultaneously sort indices and values of M[{m}, u] using
`_simultaneous_sort`
This is equivalent to :
m_sort = M[dim1_min:dim1_max, dim2_val].argsort()
m_iter = np.arange(dim1_min, dim1_max)[m_sort]
but much faster because we don't have to pay the numpy overhead. This
matters a lot for the sorting of M[{k}, w] which is executed many times.
"""
cdef int i
for i in range(0, dim1_max - dim1_min):
vals[i] = M[dim1_min + i, dim2_val]
idx[i] = dim1_min + i
_simultaneous_sort(vals, idx, dim1_max - dim1_min)
@cython.boundscheck(False)
@cython.wraparound(False)
cdef int[:] identify_swaps(int[:, ::1] sorted_Z,
double[:, ::1] sorted_D,
int[:, ::1] cluster_ranges):
"""
Implements the Optimal Leaf Ordering algorithm described in
"Fast Optimal leaf ordering for hierarchical clustering"
Ziv Bar-Joseph, David K. Gifford, Tommi S. Jaakkola
Bioinformatics, 2001, doi: 10.1093/bioinformatics/17.suppl_1.S22
https://doi.org/10.1093/bioinformatics/17.suppl_1.S22
`sorted_Z` : Linkage list, with 'height' column removed.
"""
cdef int n_points = len(sorted_Z) + 1
cdef:
# (n x n) floats
float[:, ::1] M = np.zeros((n_points, n_points), dtype=np.float32)
# (n x n x 2) booleans
int[:, :, :] swap_status = np.zeros((n_points, n_points, 2),
dtype=np.int32)
int[:] must_swap = np.zeros((len(sorted_Z),), dtype=np.int32)
int i, v_l, v_r, v_size,
int v_l_min, v_l_max, v_r_min, v_r_max
int* u_clusters
int* m_clusters
int* w_clusters
int* k_clusters
int total_u_clusters, total_w_clusters
int u, w, m, k
int u_min, u_max, m_min, m_max, w_min, w_max, k_min, k_max
int swap_L, swap_R
float* m_vals
int* m_idx
float* k_vals
int* k_idx
int mi, ki
float min_km_dist
float cur_min_M, current_M
int best_m, best_k
int best_u, best_w
for i in range(len(sorted_Z)):
# Iterate over the linkage list instead of recursion.
# v_l = sorted_Z[i, 0]
# v_r = sorted_Z[i, 1]
# are indices of the left and right children for node i.
#
# If the v_l or v_r are < n_points, then v_l or v_r are singleton
# clusters. Otherwise, it is the node defined in the (i - n_points)
#
# V
# / \
# -- --
# / \
# V_l V_r
# / \ / \
# V_l1 V_l2 V_r1 V_r2
# (u) (m) (k) (w)
#
# Briefly, for every node V, the algorithm finds left-most and
# right-most nodes u, w that minimizes U[u, w] the sum of distances of
# every neighboring singleton node in the linear ordering.
#
# This is done recursively, by finding the optimizing the ordering of
# v_l (bounded by nodes u, m) and v_r (bounded by k, w)
# such that
# U[u, w] = U[u, m] + U[k, w] + D[m, k]
# is then minimized.
#
# Part of the optimization is that at every search step,
# if (u) ~ V_l1, then (m) ~ V_l2 (and vice-versa)
# likewise for (w) ~ V_r1, then (w) ~ V_r2.
#
# This means we need to search 4 pairs of (V_li, V_rj) combinations.
# If V_l or V_r are singletons, for example, V_l = u = m.
v_l = sorted_Z[i, 0]
v_r = sorted_Z[i, 1]
v_size = sorted_Z[i, 2]
v_l_min = cluster_ranges[v_l, 0]; v_l_max = cluster_ranges[v_l, 1]
v_r_min = cluster_ranges[v_r, 0]; v_r_max = cluster_ranges[v_r, 1]
# Store the index of the clusters used to search for u, m, w, k.
u_clusters = <int*>malloc(sizeof(int) * 2)
m_clusters = <int*>malloc(sizeof(int) * 2)
w_clusters = <int*>malloc(sizeof(int) * 2)
k_clusters = <int*>malloc(sizeof(int) * 2)
if v_l < n_points:
# V_l is a singleton, so U = M = V_L.
total_u_clusters = 1
# This could be handled more efficiently, but in practice the code
# would get longer for no speed gain.
u_clusters[0] = v_l
m_clusters[0] = v_l
else:
total_u_clusters = 2
# First look for U from V_LL and M from V_LR
u_clusters[0] = sorted_Z[v_l - n_points, 0]
m_clusters[0] = sorted_Z[v_l - n_points, 1]
# Then look for U from V_LR and M from V_LL
u_clusters[1] = sorted_Z[v_l - n_points, 1]
m_clusters[1] = sorted_Z[v_l - n_points, 0]
if v_r < n_points:
total_w_clusters = 1
# V_r is a singleton, so W = K = V_R.
w_clusters[0] = v_r
k_clusters[0] = v_r
else:
total_w_clusters = 2
# First look for W from V_RR and L from V_RL
w_clusters = <int*>malloc(sizeof(int) * 2)
w_clusters[0] = sorted_Z[v_r - n_points, 1]
w_clusters[1] = sorted_Z[v_r - n_points, 0]
# Next look for W from V_RL and L from V_RR
k_clusters = <int*>malloc(sizeof(int) * 2)
k_clusters[0] = sorted_Z[v_r - n_points, 0]
k_clusters[1] = sorted_Z[v_r - n_points, 1]
for swap_L in range(total_u_clusters):
for swap_R in range(total_w_clusters):
# Get bounds for the clusters from which we'll sample u, m, w, k
# (see note above for details).
# If in the chosen ordering,
# U came from V_ll : Don't swap V_l.
# U came from V_lr : Swap V_l.
# W came from V_rl : Swap V_r.
# W came from V_ll : Don't swap V_r.
u_min = cluster_ranges[u_clusters[swap_L], 0]
u_max = cluster_ranges[u_clusters[swap_L], 1]
m_min = cluster_ranges[m_clusters[swap_L], 0]
m_max = cluster_ranges[m_clusters[swap_L], 1]
w_min = cluster_ranges[w_clusters[swap_R], 0]
w_max = cluster_ranges[w_clusters[swap_R], 1]
k_min = cluster_ranges[k_clusters[swap_R], 0]
k_max = cluster_ranges[k_clusters[swap_R], 1]
# Find the minimum of D[m, k] for the appropriate sets {m}, {k}.
# This is C[{m}, {k}] in the paper's notation.
min_km_dist = 1073741824 #2^30
for m in range(m_min, m_max):
for k in range(k_min, k_max):
if sorted_D[m, k] < min_km_dist:
min_km_dist = sorted_D[m, k]
for u in range(u_min, u_max):
# Sort the values of M[{m}, u]
m_vals = <float*>malloc(sizeof(float) * (m_max - m_min))
m_idx = <int*>malloc(sizeof(int) * (m_max - m_min))
_sort_M_slice(M, m_vals, m_idx, m_min, m_max, u)
for w in range(w_min, w_max):
# Sort the values of M[{k}, w]
k_vals = <float*>malloc(sizeof(float) * (k_max - k_min))
k_idx = <int*>malloc(sizeof(int) * (k_max - k_min))
_sort_M_slice(M, k_vals, k_idx, k_min, k_max, w)
# Set initial value for cur_min_M.
# I used a large number.
cur_min_M = 1073741824.0 #2^30
for mi in range(0, m_max - m_min):
m = m_idx[mi]
if (M[u, m] + M[w, k_idx[0]] + min_km_dist
>= cur_min_M):
# Terminate the outer loop early, there will not
# be a better 'k' in the current k list.
break
for ki in range(0, k_max - k_min):
k = k_idx[ki]
if M[u, m] + M[w, k] + min_km_dist >= cur_min_M:
# Terminate the inner loop early
break
current_M = M[u, m] + M[w, k] + sorted_D[m, k]
if current_M < cur_min_M:
# We found a better m, k than previously.
cur_min_M = current_M
best_m = m
best_k = k
# For the chosen (u, w), record the resulting minimal
# M[u, w] = M[u, m] + M[k, w] + D[m, k]
M[u, w] = cur_min_M
M[w, u] = cur_min_M
# whether we need to swap V_l and V_r given the current
# chosen (m, k) (see note above). This saves us from
# storing (m, k) and doing back-tracking later.
swap_status[u, w, 0] = swap_L
swap_status[w, u, 0] = swap_L
swap_status[u, w, 1] = swap_R
swap_status[w, u, 1] = swap_R
# We are getting a fresh `w` so need to resort M[{k}, w]
free(k_vals)
free(k_idx)
# We are getting a fresh `u` so need to resort M[{m}, u]
free(m_vals)
free(m_idx)
# We are about to get fresh clusters.
free(u_clusters)
free(m_clusters)
free(w_clusters)
free(k_clusters)
# We are now ready to find the best minimal value for M[{u}, {w}]
cur_min_M = 1073741824.0 #2^30
for u in range(v_l_min, v_l_max):
for w in range(v_r_min, v_r_max):
if M[u, w] < cur_min_M:
cur_min_M = M[u, w]
best_u = u
best_w = w
# If v_l, v_r are not singletons, record whether our choice of (u, w)
# for V requires a swap of its children.
if v_l >= n_points:
must_swap[v_l - n_points] = int(swap_status[best_u, best_w, 0])
if v_r >= n_points:
must_swap[v_r - n_points] = int(swap_status[best_u, best_w, 1])
return must_swap
def optimal_leaf_ordering(Z, D):
"""
Compute the optimal leaf order for Z (according to D) and return an
optimally sorted Z.
We start by sorting and relabelling Z and D according to the current leaf
order in Z.
This is because when everything is sorted each cluster (including
singletons) can be defined by its range over (0...n_points).
This is used extensively to loop efficiently over the various arrays in the
algorithm.
"""
# Import here to avoid import cycles
from scipy.cluster.hierarchy import leaves_list, is_valid_linkage
is_valid_linkage(Z, throw=True, name='Z')
if is_valid_y(D):
sorted_D = squareform(D)
elif is_valid_dm(D):
sorted_D = D
else:
raise("Not a valid distance matrix (neither condensed nor square form)")
n_points = Z.shape[0] + 1
n_clusters = 2*n_points - 1
# Get the current linear ordering
sorted_leaves = leaves_list(Z)
# Create map from original order to sorted order.
original_order_to_sorted_order = dict((orig_i, sorted_i) for sorted_i,orig_i
in enumerate(sorted_leaves))
# Re-write linkage map so it refers to sorted positions, rather than input
# positions. Remove the 'height' column so we can cast the whole thing as
# integer and simplify passing to C function above.
sorted_Z = []
for (v_l, v_r, _, v_size) in Z:
if v_l < n_points:
v_l = original_order_to_sorted_order[int(v_l)]
if v_r < n_points:
v_r = original_order_to_sorted_order[int(v_r)]
sorted_Z.append([v_l, v_r, v_size])
sorted_Z = np.array(sorted_Z).astype(np.int32).copy(order='C')
# Sort distance matrix D by the leaf order
sorted_D = sorted_D[sorted_leaves, :]
sorted_D = sorted_D[:, sorted_leaves].copy(order='C')
# Defines the range of each cluster over (0... n_points) as explained above.
cluster_ranges = np.zeros((n_clusters, 2))
cluster_ranges[np.arange(n_points), 0] = np.arange(n_points)
cluster_ranges[np.arange(n_points), 1] = np.arange(n_points) + 1
for link_i, (v_l, v_r, v_size) in enumerate(sorted_Z):
v = link_i + n_points
cluster_ranges[v, 0] = cluster_ranges[v_l, 0]
cluster_ranges[v, 1] = cluster_ranges[v_r, 1]
cluster_ranges = cluster_ranges.astype(np.int32).copy(order='C')
# Get Swaps
must_swap = identify_swaps(sorted_Z, sorted_D, cluster_ranges)
# To 'rotate' around the axis of a node, we need to consider the left-right
# children of every descendant of this target node.
#
# To do so efficiently, we record how many total times a given node must be
# swapped (once if it needs to be swapped itself, once for each parent that
# needs to be swapped) and take modulo 2 to find whether it needs to be
# swapped at all.
is_descendant = np.zeros((n_clusters - n_points, n_clusters - n_points),
dtype=int)
for i, (v_l, v_r, v_size) in enumerate(sorted_Z):
is_descendant[i, i] = 1
if v_l >= n_points:
is_descendant[i, v_l - n_points] = 1
is_descendant[i, :] += is_descendant[v_l - n_points, :]
if v_r >= n_points:
is_descendant[i, v_r - n_points] = 1
is_descendant[i, :] += is_descendant[v_r - n_points, :]
# To "rotate" a tree node, we need to 'swap' its left-right children,
# and do the same to all its children.
applied_swap = (np.array(is_descendant).astype(bool)
* np.array(must_swap).reshape(-1, 1))
final_swap = applied_swap.sum(axis=0) % 2
# Create a new linkage matrix by applying swaps where needed.
swapped_Z = []
for i, (in_l, in_r, h, v_size) in enumerate(Z):
if final_swap[i]:
out_l = in_r
out_r = in_l
else:
out_r = in_r
out_l = in_l
swapped_Z.append((out_l, out_r, h, v_size))
swapped_Z = np.array(swapped_Z)
return swapped_Z
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