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December 16, 2020 03:54
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BIP Solver for Tracking
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import itertools | |
from collections import defaultdict | |
import numpy as np | |
from cvxopt import glpk, matrix, spmatrix | |
glpk.options = {"msg_lev": "GLP_MSG_ERR"} | |
FROZEN_POS_EDGE = -1 | |
FROZEN_NEG_EDGE = -2 | |
INVALID_EDGE = -100 | |
class _BIPSolver: | |
def __init__(self, min_affinity=-np.inf, max_affinity=np.inf, create_bip=None): | |
self.min_affinity = min_affinity | |
self.max_affinity = max_affinity | |
@staticmethod | |
def _create_bip(affinity_matrix, min_affinity, max_affinity): | |
n_nodes = affinity_matrix.shape[0] | |
# mask for selecting pairs of nodes | |
triu_mask = np.triu(np.ones_like(affinity_matrix, dtype=np.bool), 1) | |
affinities = affinity_matrix[triu_mask] | |
frozen_pos_mask = affinities >= max_affinity | |
frozen_neg_mask = affinities <= min_affinity | |
unfrozen_mask = np.logical_not(frozen_pos_mask | frozen_neg_mask) | |
# generate objective coefficients | |
objective_coefficients = affinities[unfrozen_mask] | |
if len(objective_coefficients) == 0: # nio unfrozen edges | |
objective_coefficients = np.asarray([affinity_matrix[0, -1]]) | |
unfrozen_mask = np.zeros_like(unfrozen_mask, dtype=np.bool) | |
unfrozen_mask[affinity_matrix.shape[1] - 1] = 1 | |
# create matrix whose rows are the indices of the three edges in a | |
# constraint x_ij + x_ik - x_jk <= 1 | |
constraints_edges_idx = [] | |
if n_nodes >= 3: | |
edges_idx = np.empty_like(affinities, dtype=int) | |
edges_idx[frozen_pos_mask] = FROZEN_POS_EDGE | |
edges_idx[frozen_neg_mask] = FROZEN_NEG_EDGE | |
edges_idx[unfrozen_mask] = np.arange(len(objective_coefficients)) | |
nodes_to_edge_matrix = np.empty_like(affinity_matrix, dtype=int) | |
nodes_to_edge_matrix.fill(INVALID_EDGE) | |
nodes_to_edge_matrix[triu_mask] = edges_idx | |
triplets = np.asarray( | |
tuple(itertools.combinations(range(n_nodes), 3)), dtype=int | |
) | |
constraints_edges_idx = np.zeros_like(triplets) | |
constraints_edges_idx[:, 0] = nodes_to_edge_matrix[ | |
(triplets[:, 0], triplets[:, 1]) | |
] | |
constraints_edges_idx[:, 1] = nodes_to_edge_matrix[ | |
(triplets[:, 0], triplets[:, 2]) | |
] | |
constraints_edges_idx[:, 2] = nodes_to_edge_matrix[ | |
(triplets[:, 1], triplets[:, 2]) | |
] | |
constraints_edges_idx = constraints_edges_idx[ | |
np.any(constraints_edges_idx >= 0, axis=1) | |
] | |
if len(constraints_edges_idx) == 0: # no constraints | |
constraints_edges_idx = np.asarray([0, 0, 0], dtype=int).reshape(-1, 3) | |
# add remaining constraints by permutation | |
constraints_edges_idx = np.vstack( | |
( | |
constraints_edges_idx, | |
np.roll(constraints_edges_idx, 1, axis=1), | |
np.roll(constraints_edges_idx, 2, axis=1), | |
) | |
) | |
# clean redundant constraints | |
# x1 + x2 <= 2 | |
constraints_edges_idx = constraints_edges_idx[ | |
constraints_edges_idx[:, 2] != FROZEN_POS_EDGE | |
] | |
# x1 - x2 <= 1 | |
constraints_edges_idx = constraints_edges_idx[ | |
np.all(constraints_edges_idx[:, 0:2] != FROZEN_NEG_EDGE, axis=1) | |
] | |
if len(constraints_edges_idx) == 0: # no constraints | |
constraints_edges_idx = np.asarray([0, 0, 0], dtype=int).reshape(-1, 3) | |
# generate constraint coefficients | |
constraints_coefficients = np.ones_like(constraints_edges_idx) | |
constraints_coefficients[:, 2] = -1 | |
# generate constraint upper bounds | |
upper_bounds = np.ones(len(constraints_coefficients), dtype=np.float) | |
upper_bounds -= np.sum( | |
constraints_coefficients * (constraints_edges_idx == FROZEN_POS_EDGE), | |
axis=1, | |
) | |
# flatten constraints data into sparse matrix format | |
constraints_idx = np.repeat(np.arange(len(constraints_edges_idx)), 3) | |
constraints_edges_idx = constraints_edges_idx.reshape(-1) | |
constraints_coefficients = constraints_coefficients.reshape(-1) | |
unfrozen_edges = constraints_edges_idx >= 0 | |
constraints_idx = constraints_idx[unfrozen_edges] | |
constraints_edges_idx = constraints_edges_idx[unfrozen_edges] | |
constraints_coefficients = constraints_coefficients[unfrozen_edges] | |
return ( | |
objective_coefficients, | |
unfrozen_mask, | |
frozen_pos_mask, | |
frozen_neg_mask, | |
(constraints_coefficients, constraints_idx, constraints_edges_idx), | |
upper_bounds, | |
) | |
@staticmethod | |
def _solve_bip(objective_coefficients, sparse_constraints, upper_bounds): | |
raise NotImplementedError | |
@staticmethod | |
def solution_mat_clusters(solution_mat): | |
n = solution_mat.shape[0] | |
labels = np.arange(1, n + 1) | |
for i in range(n): | |
for j in range(i + 1, n): | |
if solution_mat[i, j] > 0: | |
labels[j] = labels[i] | |
clusters = defaultdict(list) | |
for i, label in enumerate(labels): | |
clusters[label].append(i) | |
return list(clusters.values()) | |
def solve(self, affinity_matrix, rtn_matrix=False): | |
n_nodes = affinity_matrix.shape[0] | |
if n_nodes <= 1: | |
solution_x, sol_matrix = ( | |
np.asarray([], dtype=int), | |
np.asarray([0] * n_nodes, dtype=int), | |
) | |
sol_matrix = sol_matrix[:, None] | |
elif n_nodes == 2: | |
solution_matrix = np.zeros_like(affinity_matrix, dtype=int) | |
solution_matrix[0, 1] = affinity_matrix[0, 1] > 0 | |
solution_matrix += solution_matrix.T | |
solution_x = ( | |
[solution_matrix[0, 1]] | |
if self.min_affinity < affinity_matrix[0, 1] < self.max_affinity | |
else [] | |
) | |
solution_x, sol_matrix = np.asarray(solution_x), solution_matrix | |
else: | |
# create BIP problem | |
( | |
objective_coefficients, | |
unfrozen_mask, | |
frozen_pos_mask, | |
frozen_neg_mask, | |
sparse_constraints, | |
upper_bounds, | |
) = self._create_bip(affinity_matrix, self.min_affinity, self.max_affinity) | |
# solve | |
solution_x = self._solve_bip( | |
objective_coefficients, sparse_constraints, upper_bounds | |
) | |
# solution to matrix | |
all_sols = np.zeros_like(unfrozen_mask, dtype=int) | |
all_sols[unfrozen_mask] = np.array(solution_x, dtype=int).reshape(-1) | |
all_sols[frozen_neg_mask] = 0 | |
all_sols[frozen_pos_mask] = 1 | |
sol_matrix = np.zeros_like(affinity_matrix, dtype=int) | |
sol_matrix[ | |
np.triu(np.ones([n_nodes, n_nodes], dtype=int), 1) > 0 | |
] = all_sols | |
sol_matrix += sol_matrix.T | |
clusters = self.solution_mat_clusters(sol_matrix) | |
if not rtn_matrix: | |
return clusters | |
return clusters, sol_matrix | |
class GLPKSolver(_BIPSolver): | |
def __init__(self, min_affinity=-np.inf, max_affinity=np.inf): | |
super(GLPKSolver, self).__init__(min_affinity, max_affinity) | |
@staticmethod | |
def _solve_bip(objective_coefficients, sparse_constraints, upper_bounds): | |
c = matrix(-objective_coefficients) # max -> min | |
G = spmatrix( | |
*sparse_constraints, size=(len(upper_bounds), len(objective_coefficients)) | |
) # G * x <= h | |
h = matrix(upper_bounds) | |
status, solution = glpk.ilp(c, G, h, B=set(range(len(c)))) | |
assert solution is not None, "Solver error: {}".format(status) | |
return np.asarray(solution, int).reshape(-1) |
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