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kalmanson.py
613 lines (525 loc) · 18.5 KB
/
kalmanson.py
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import sys, os, re
# import multiprocessing
# import multiprocessing.dummy as multiprocessing
from multiprocessing_jth import pyprocessing
from matrify import matrify
import numpy as np
from itertools import izip_longest, combinations, ifilter, imap, product
import itertools as it
from functools import partial
from sage.all import matrix, binomial, zero_matrix, \
combinations_iterator, vector, permutation_action, db, Set, Fan
from memoized import memoized
import utility_matrices as um
np.set_printoptions(linewidth=100)
# Utility functions
def collapse_list(lst):
"Turn a list of numbers into a dict counting how many times each number occurs."
ret = {}
for num in lst:
ret[num] = ret.get(num,0) + 1
return ret
@matrify
def conjugate(D,M):
"Return matrix conjugation (D^T)*M*D."
return np.dot(np.array(D).T, np.dot(M, D))
def textual_vector(n):
return textual_symmetric_matrix(n)[triu_indices(n,1)]
@matrify
def symmetric_matrix(n, triu):
"Make a symmetric nxn matrix from upper triangular entries triu."
A = np.zeros([n,n], dtype=int)
A[triu_indices(n,1)] = triu
return A + A.T
def rowsums(M):
return map(sum, M)
def number_of_nonzero(vec):
return np.nonzero(vec)[0].shape[0]
def invert_dict(d):
return dict([v,k] for k,v in d.items())
def prettify_cone_descs(f):
"Prettify the cone descriptions of a fan."
rays_h = {}
for i,r in enumerate(f.rays()):
rays_h[r] = "r%i" % i
for c in flatten(f.cones()):
c.rename(",".join(map(lambda r: rays_h[r], c.rays())))
def distance_matrix(n):
"Make a stock distance matrix."
M = np.zeros([n,n], dtype=int)
M[triu_indices(n, 1)] = np.arange(binomial(n,2))
M += M.T
return matrix(M)
def textual_symmetric_matrix(n):
"""
Return a symmetric matrix whose (i,j) entry is "d_ij".
"""
return np.frompyfunc(lambda x,y: "d_%i%i" % (x+1,y+1), 2, 1).outer(range(n), range(n))
def grouper(n, iterable, fillvalue=None):
"grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return izip_longest(fillvalue=fillvalue, *args)
def triu_indices(n, d=0):
"""
Return the indices for the upper triangular part of nxn
square matrix with diagonal offset d. (Backport of a
Numpy 1.5 function.)
"""
r = []
c = []
for i in range(n):
for j in range(i+d, n):
r.append(i)
c.append(j)
return list(map(np.array, (r,c)))
def upper_triangle(M):
"Return the upper triangle of M as a vector."
M = np.array(M)
return vector(list(M[triu_indices(M.shape[0], 1)]))
# Real code
class KalmansonSystem:
def __init__(self, n, ieqs=None):
self._n = n
if ieqs:
assert ieqs.ncols() == binomial(n,2)
self._ieqs = ieqs
else:
self._ieqs = kalmanson_matrix(n)
def __eq__(self, other):
try:
return self.to_Set() == other.to_Set()
except:
return False
def __ne__(self, other):
return not self.__eq__(other)
def to_Set(self):
"""
Return a set consisting of the rows of self. Needed to get
around immutability issues.
"""
rows = self._ieqs.rows()
[row.set_immutable() for row in rows]
return Set(rows)
def permute(self, g):
"""
Returns a new KalmansonSystem obtained by permuting the leaves
of self according to permutation p.
"""
ind = permutation_vector(g)
ieqs = matrix(np.vstack([np.array(row)[ind] for row in self._ieqs.rows()]))
return KalmansonSystem(self._n, ieqs)
def stabilizer(self):
"""
Return the set of permutations in S_n under whose action
the set of inequalities defining this system is unchanged.
"""
S_n = SymmetricGroup(self._n)
return filter(lambda g: self.permute(g)==self, S_n)
def __repr__(self):
strs = textual_symmetric_matrix(self._n)[triu_indices(self._n, 1)]
vars = vector(var(",".join(strs)))
descs = [str(vars * row) + " >= 0" for row in self._ieqs]
return "A Kalmanson system of inequalities:\n%s" % "\n".join(descs)
def alt_kalmanson_matrix(n):
r,c = triu_indices(n, 1)
ncols = len(r)
pos = np.arange(ncols)
row_ind = lambda (i,j): pos[np.logical_and(r==i-1, c==j-1)]
upright = lambda (i,j): (i,j) if i<j else (j,i)
get_ind = lambda ind_lst: np.array(map(row_ind, map(upright, ind_lst)))
rows = []
kal_range = lambda m: range(m+1, n+1)
for i in range(1, n+1):
for j in kal_range(i):
for k in kal_range(j):
for l in kal_range(k):
for inds in [((i,j), (k,l), (i,k), (j,l)),
((i,l), (j,k), (i,k), (j,l))]:
indices = get_ind(inds)
row = np.zeros(ncols, dtype=np.int)
row[indices] = [-1, -1, 1, 1]
rows.append(row)
return matrix(np.vstack(rows))
def kalmanson_matrix(n, aug=False):
r,c = triu_indices(n, 1)
k = len(r)
inds = np.arange(k)
row_ind = lambda (i,j): inds[np.logical_and(r==i-1, c==j-1)]
upright = lambda (i,j): (i,j) if i<j else (j,i)
get_ind = lambda ind_lst: np.array(map(row_ind, map(upright, grouper(2, ind_lst))))
rows = []
for i in range(1,n-2):
for j in range(i+2, n):
ind_lst = (i, j+1, i+1, j, i, j, i+1, j+1)
indices = get_ind(ind_lst)
row = np.zeros(k, dtype=np.int)
row[indices] = [-1, -1, 1, 1]
rows.append(row)
sub = len(rows)
for i in range(2, n-1):
ind_lst = (i, 1, i+1, n, i, n, i+1, 1)
indices = get_ind(ind_lst)
row = np.zeros(k, dtype=np.int)
row[indices] = [-1, -1, 1, 1]
rows.append(row)
mat = matrix(np.vstack(rows))
mat.subdivide(sub, None)
if aug:
return zero_matrix(len(rows),1).augment(mat)
else:
return mat
def _check_ind(n):
M = []
for i in range(1, n-2):
for j in range(i+2, n):
M.append(A(n,i,j))
return(M)
def permute_matrix(g, M):
mat = permutation_action(g, permutation_action(g, M).transpose()).transpose()
mat.set_immutable()
return mat
def orbit(n, M):
"Return the orbit of the nxn symmetric matrix M."
Sn = SymmetricGroup(n)
return Set([permute_action(g, M) for g in Sn])
def standardize_elt(v):
v = vector(v)
return v if v[0]==1 else v*-1
def orbit_by_element(g,v):
v = tuple(v)
initial = copy(v)
v = tuple(standardize_elt(g(v)))
orb = []
while initial != v:
orb.append(copy(v))
v = tuple(standardize_elt(g(v)))
orb.append(copy(v))
return Set(orb)
def stabilizer(M):
"Return the stabilizer subgroup of the nxn symmetric matrix M."
n = M.ncols()
Sn = SymmetricGroup(n)
return filter(lambda g: permute_matrix(g, M)==M, Sn)
def permutation_vector(g):
"""
The distance matrix obtained by permuting taxa according
to the permutation g \in SymmetricGroup(n).
"""
n = g.parent().degree()
M = distance_matrix(n)
M = permutation_action(g,permutation_action(g,M).transpose()).transpose()
perm_vec = list(np.array(M)[triu_indices(n, 1)])
return perm_vec
def permuted_kalmanson_matrix(A, perm):
pv = permutation_vector(perm)
M = matrix(np.vstack([np.array(r)[pv] for r in A.rows()]))
M.set_immutable()
return M
def set_of_kalmanson_matrices(n):
"Return the set of all distinct Kalmanson nxn matrices."
A = kalmanson_matrix(n)
Sn = SymmetricGroup(n)
mats = [permuted_kalmanson_matrix(A,p) for p in Sn]
Kn = Set(map(set_of_rows, mats))
return Kn
def kalmanson_polyhedra(n):
"""
Return a list of all Kalmanson polyhedra for n taxa (including
reorderings.)
"""
R = rays(n)
return [Polyhedron(rays=map(upper_triangle, map(partial(permute_matrix, g), R))) \
for g in SymmetricGroup(n)]
def make_cone(p):
from kalmanson import upper_triangle
return Cone(map(upper_triangle, p), ZZ**binomial(p[0].ncols(), 2))
def kalmanson_cones(n):
desc = "kalmanson_cones_%i" % n
try:
return db(desc)
except IOError:
R = rays(n)
ray_sets = Set([Set([permute_matrix(g,M) for M in R]) for g in SymmetricGroup(n)])
p_iter = sage.parallel.use_fork.p_iter_fork(sage.parallel.ncpus.ncpus() * 2,30)
P = parallel(p_iter=p_iter)
cones = [ret for ((poly,kwd),ret) in P(make_cone)(ray_sets.list())]
db_save(Set(cones), desc)
return cones
def kalmanson_fan(n):
fn = "kalmanson_fan_%i" % n
try:
return db(fn)
except IOError:
f = Fan(Set(kalmanson_cones(n)))
f.db(fn)
return f
# @parallel(ncpus=2, p_iter="multiprocessing")
def std_kalmanson_polyhedron(n, lineality=False):
return kalmanson_polyhedron(kalmanson_matrix(n), lineality)
def rays_lines_M2(M):
macaulay2.eval('load "Polyhedra.m2"')
P = macaulay2.matrix(M).intersection()
rays,lines = [m.to_sage().transpose() for m in [P.rays(), P.linSpace()]]
return rays,lines
def kalmanson_polyhedron(M, lineality=False):
rays,lines = [M.rows() for M in rays_lines_M2(M)]
if lineality:
return Polyhedron(rays=rays, lines=lines)# , eqns=ortho)
else:
return Polyhedron(rays=rays)
def projected_distances(M, v):
"""
Return the distance vector after modding out by the lineality space
of Kalmanson polyhedron given by the Kalmanson inequality matrix M.
"""
L = M.right_kernel().basis_matrix()
B = M.stack(L).transpose()
P = matrix(np.diag([1]*M.nrows() + [0]*L.nrows()))
return B*P*B**(-1)*v
def positive_lineality_space(n):
A = kalmanson_matrix(n)
n = A.nrows()
c = A.ncols()
eq = zero_matrix(n,1).augment(A).rows()
ieq = zero_matrix(c,1).augment(identity_matrix(c)).rows()
return Polyhedron(eqns=eq, ieqs=ieq)
def rays(n):
"The rays associated with the standard Kalmanson inequalities on n taxa."
ret = []
for i in range(2, n-1):
print i
ret.append(um.V(n, i))
for i in range(1, n-2):
for j in range(i+2, n):
print (i,j)
ret.append(um.V(n,i,j))
return ret
def all_rays(n, Cn=None):
if not Cn:
Cn = kalmanson_cones(n)
return Set([Set([tuple(ray_sign_vector(symmetric_matrix(n, r))) for r in cone.rays()]) \
for cone in Cn])
def textual_rays(n):
tsm = textual_symmetric_matrix(5)
return map(list, [tsm[np.nonzero(np.triu(r,1))] for r in rays(n)])
def block_structure(M):
"Diagonal block structure of nxn symmetric matrix M."
lst = []
n = M.ncols()
i = 0
while i < n:
subs = [M.submatrix(i,i,j,j) for j in range(1, n-i+1)]
block_size = [S for S in subs if S.is_zero()][-1].ncols()
lst.append(block_size)
i += block_size
return tuple(lst)
def ray_sign_pattern(R):
d = {-1:"-", 1:"+"}
sv = ray_sign_vector(R)
return "".join(map(d.get, sv))
def ray_sign_vector(R):
sa = it.cycle([-1,1])
v = vector([s for s,i in zip(sa, block_structure(R)) for k in range(i) ])
v.set_immutable()
return v
def test_permutation(g,R):
Rp = permute_matrix(g,R)
pat = map(ray_sign_pattern, [R,Rp])
vec = np.array(permutation_action(g, ray_sign_vector(R)))
if vec[0]==-1:
vec *= -1
pred = vector(list(vec))
d = {-1:"-", 1:"+"}
pred = "".join(map(d.get, pred))
#print R,"\n\n",Rp
#print "p0: %s\tp': %s\tp_pred: %s" % (pat[0], pat[1], pred)
assert pred==pat[1]
def cone_sign_matrix(n, C):
rays = [symmetric_matrix(n, r) for r in C.rays()]
return matrix([ray_sign_vector(r) for r in rays])
def ray_graph(R):
G = Graph(R)
n = R.ncols()
# splits = np.split(np.arange(R.ncols(), dtype=int), np.cumsum(block_structure(R)))
# partitions = [list(sp) for sp in splits if list(sp)]
pluses = tuple(i for i,c in enumerate(ray_sign_vector(R)) if c==1)
partitions = [pluses, tuple(i for i in range(n) if i not in pluses)]
return G,partitions
def ray_split(R):
sv = ray_sign_vector(R)
pluses = tuple(i for i,c in enumerate(sv) if c==1)
partition = [pluses, tuple(i for i in range(len(sv)) if i not in pluses)]
return partition
def permutations_which_fix(n,m):
Rn = Set(rays(n))
return filter(lambda g: Set(permute_matrix(g,M) for M in Rn).intersection(Rn).cardinality() == m,
SymmetricGroup(n))
def pairwise_compatible(sp_A, sp_B):
"True if the two split sets sp_A and sp_B are pairwise compatible."
return any(Set(A).intersection(Set(B)).cardinality() == 0 \
for A,B in it.product(sp_A, sp_B))
def compatibility_degree(splits):
"Check that this list of splits is pairwise compatible."
return sum(1 for pair in it.combinations(splits,2) \
if not any(Set(A).intersection(Set(B)).cardinality() == 0
for A,B in it.product(*pair)))
def cone_splits(C,n):
return [G[1] for G in make_cone_graph(C,n)]
def make_cone_graph(C,n):
graphpairs = [ray_graph(symmetric_matrix(n,r)) for r in C.rays()]
# return [G.plot(partition=part, layout="circular") for G,part in graphpairs]
return graphpairs
def ray_magic(C,n):
ray_sets = [Set([ray_sign_vector(symmetric_matrix(n,r)) for r in cone]) for cone in C]
canonical = ray_sets[0]
d = {}
for g in SymmetricGroup(n):
permute = Set([tuple(permutation_action(g, v)) for v in canonical])
d[permute] = d.get(permute, []) + [g]
return d
def ray_matrices(cone,n):
return [symmetric_matrix(n, r) for r in cone.rays()]
def all_ray_matrices(n):
Cn = kalmanson_cones(n)
return uniq(M for cone in Cn for M in ray_matrices(cone, n))
def graph_rays_cones(C):
n = C[0].lattice_dim() / 2
Rn = Set([r for g in SymmetricGroup(n) for v in map(ray_sign_vector, rays(n))
for r in orbit_by_element(g,v)])
d = {-1: "-", 1: "+"}
signer = lambda v: "".join(map(d.get, v))
descs = Set(map(signer, Rn))
part = [descs, []]
G = Graph()
G.add_vertices(descs)
i = 0
for c in C:
i += 1
desc = "Cone %i" % i
part[1].append(desc)
G.add_vertex(desc)
for r in c.rays():
sv = ray_sign_pattern(symmetric_matrix(n, r))
G.add_edge(sv, desc)
return G,part
def ray_to_splits(n, ray):
mat = symmetric_matrix(n, ray)
vec = ray_sign_vector(mat)
ns = range(n)
pos = Set(ind for s,ind in zip(vec, ns) if s==1)
neg = Set(ns) - pos
A = pos if 0 in pos else neg
return Set([A, Set(ns) - A])
if pos.cardinality() < neg.cardinality():
return pos
elif pos.cardinality() == neg.cardinality():
return pos if sorted(pos)[0] < sorted(neg)[0] else neg
else:
return neg
def non_trivial_splits(n):
rng = Set(range(1,n+1))
splits = Set([Set(tuple(s)) for s in powerset(rng) if len(s)>1 and len(s)<n-1])
splits = Set([x if x.cardinality() <= floor(n/2) else rng - x \
for x in splits])
splits = Set([rng - x if x.cardinality() == n/2 and min(x) > min(rng - x) else x
for x in splits])
return splits
def show_partition_types(n,k):
G = []
Cn = graphs.CycleGraph(n)
for edges in combinations_iterator(filter(lambda (x,y): abs(x-y) % (n-1) > 1, combinations_iterator(range(n), 2)), k):
g = Cn.copy()
g.add_edges(edges)
G.append(g)
H = []
for g in G:
match = False
for j in H:
if g.is_isomorphic(j):
match = True
break
if not match:
H.append(g)
return H
def f_vector(fan):
return map(len, fan.cone_lattice().level_sets())[:-1]
def h_vector(fan):
f = f_vector(fan)
d = len(f)
x = var('x')
poly = sum([f[i]*(x-1)**(d-i) for i in range(d)])
return [a for a,b in reversed(poly.expand().coefficients())]
def number_simplex(n, rays, sought=None):
# General recurrence relation for multinomial:
# multi(k1,...,kn) = multi(k1-1,k2,...,kn) + multi(k1,k2-1,...,kn) + ... + multi(k1,...,kn-1)
dim = len(rays)
def level_set(c):
if len(c) == dim:
n = _simplicial_number(c,rays)
if n==sought:
print Exception("Found %i: f(%s,%s)" % (sought, c, rays))
return n
else:
subsimplices = [(c[:-1] + (c[-1]-i,) + (i,)) for i in range(c[-1]+1)]
return map(level_set, subsimplices)
return [level_set((i,)) for i in range(n)]
@memoized
def _simplicial_number(coords,rays):
z = list(np.nonzero(coords)[0])
if len(z) == 0:
return 1
elif len(z) == 1:
return rays[z[0]]
elif len(z) < len(rays):
z = coords.index(0)
cp = coords[:z] + coords[z+1:]
rp = rays[:z] + rays[z+1:]
return f(cp, rp)
else:
dim = len(coords)
shift_vec = np.array([-1] + [0]*(dim-1))
return sum(f(tuple(np.roll(shift_vec, i) + coords),rays) for i in range(dim))
def enumerate_splits(n,k,invalid=False):
f = invalid_splits if invalid_splits else valid_splits
s = f(n,k)
tups = [tuple(sorted(map(len, x))) for x in s]
return dict((u,tups.count(u)) for u in uniq(tups))
def valid_splits(n,k):
faces = kalmanson_fan(n)(k)
sp = Set([Set([ray_to_splits(n, r) for r in f.rays()]) for f in faces])
return sp
def invalid_splits(n,k):
faces = kalmanson_fan(n)(k)
return Set(map(Set, combinations(non_trivial_splits(n),k))) - \
valid_splits(n,k)
def dimension_of_span(n, k):
dim_counts = {}
for rays in combinations_iterator(all_ray_matrices(n), k):
kers = [r.image() for r in rays]
dim = reduce(lambda x,y: x.intersection(y), kers).dimension()
dim_counts[dim] = dim_counts.get(dim,0) + 1
return dim_counts
def count_stuff(fan):
cones = fan.cones()
d = len(cones)
for i in range(1,d):
c_i = cones[i]
print "Dimension %i (%i cones):" % (i, len(c_i))
dims = {}
for c in c_i:
rays = c.rays()
ind = tuple(sum(1 for cone in cones[j] if all(ray in cone.rays() for ray in rays)) \
for j in range(i, d))
dims[ind] = dims.get(ind,0) + 1
print "\n".join("\t%s: %i" % (k,v) for k,v in dims.iteritems())
def cones_fun(n, k):
fan = kalmanson_fan(n)
rays = [c.rays()[0] for c in fan(1)]
cones = Set(fan(k))
for r in combinations(rays, k):
c = Cone(r)
if c in cones:
print prod(symmetric_matrix(n, ray) for ray in r).change_ring(GF(2))
print ""
def cone_to_split_system(n, cone):
return Set(ray_to_splits(n, r) for r in cone.rays())