def is_monotonic(alpha, mgr, num_features, constraint_sdd):
counterexample = [[None, None] for _ in xrange(num_features)]
for i in xrange(num_features):
beta1 = sdd.sdd_condition(i + 1, alpha, mgr)
beta2 = sdd.sdd_condition(-(i + 1), alpha, mgr)
beta3 = sdd.sdd_conjoin(beta1, beta2, mgr)
# check if f|x does not entail f|!x
gamma = sdd.sdd_conjoin(
sdd.sdd_conjoin(sdd.sdd_negate(beta2, mgr), beta1, mgr),
constraint_sdd, mgr)
model = next(models.models(gamma, sdd.sdd_manager_vtree(mgr)))
counterexample[i][0] = [v for _, v in model.items()]
if counterexample[i][0]:
counterexample[i][0][i] = 1
# check if f|!x does not entail f|x
gamma = sdd.sdd_conjoin(
sdd.sdd_conjoin(sdd.sdd_negate(beta1, mgr), beta2, mgr),
constraint_sdd, mgr)
model = next(models.models(gamma, sdd.sdd_manager_vtree(mgr)))
counterexample[i][1] = [v for _, v in model.items()]
if counterexample[i][1]:
counterexample[i][1][i] = 0
for c in counterexample:
if c[0] and c[1]:
return False, counterexample
return True, counterexample
def test_admission():
var_count = 4
vtree = sdd.sdd_vtree_new(var_count, "balanced")
mgr = sdd.sdd_manager_new(vtree)
# WFEG
# ( w ^ g )
alpha = sdd.sdd_conjoin(sdd.sdd_manager_literal(1, mgr),
sdd.sdd_manager_literal(4, mgr), mgr)
# ( w ^ f ^ e )
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(1, mgr),
sdd.sdd_manager_literal(2, mgr), mgr)
beta = sdd.sdd_conjoin(beta, sdd.sdd_manager_literal(3, mgr), mgr)
# ( f ^ e ^ g )
gamma = sdd.sdd_conjoin(sdd.sdd_manager_literal(2, mgr),
sdd.sdd_manager_literal(3, mgr), mgr)
gamma = sdd.sdd_conjoin(gamma, sdd.sdd_manager_literal(4, mgr), mgr)
alpha = sdd.sdd_disjoin(alpha, beta, mgr)
alpha = sdd.sdd_disjoin(alpha, gamma, mgr)
alpha = sdd.sdd_negate(alpha, mgr)
beta, pmgr = primes(alpha, mgr)
_sanity_check(alpha, mgr, beta, pmgr)
vtree = sdd.sdd_manager_vtree(mgr)
pvtree = sdd.sdd_manager_vtree(pmgr)
import models
for model in models.models(alpha, vtree):
print models.str_model(model)
for model in models.models(beta, pvtree):
print models.str_model(model)
for model in models.models(alpha, vtree):
print "==", models.str_model(model)
model_list = [model[var] for var in sorted(model.keys())]
gamma, pmgr = compatible_primes(alpha,
model_list,
mgr,
primes_mgr=(beta, pmgr))
pvtree = sdd.sdd_manager_vtree(pmgr)
for prime_model in models.models(gamma, pvtree):
print models.str_model(prime_model)
term = prime_to_dict(prime_model, var_count)
print " ".join([
("*" if var not in term else "+" if term[var] == 1 else "-")
for var in xrange(1, var_count + 1)
])
print "dead-nodes:", sdd.sdd_manager_dead_count(mgr)
print "dead-nodes:", sdd.sdd_manager_dead_count(pmgr)
def primes_by_length(primes, pmgr, var_count):
by_length = defaultdict(list)
pvtree = sdd.sdd_manager_vtree(pmgr)
for model in models.models(primes, pvtree):
term = prime_to_dict(model, var_count)
by_length[len(term)].append(term)
return by_length
def run_prime_implicant_query(alpha, mgr, num_features, models_list):
#print num_features, models_list
for model_list in models_list:
gamma, pmgr, pvtree2 = primes_given_term(alpha, model_list, mgr,
_primes_one_given_term)
pvtree = sdd.sdd_manager_vtree(pmgr)
pi_str = []
gamma = sdd.sdd_global_minimize_cardinality(gamma, pmgr)
for prime_model in models.models(gamma, pvtree):
try:
term = prime_to_dict(prime_model, num_features)
term_str = " ".join([("*" if var not in term else
"1" if term[var] == 1 else "0")
for var in xrange(1, num_features + 1)])
pi_str.append(term_str)
except:
pi_str = [
"Key error. Make sure instance is is a model of the SDD."
]
pi_str.sort(key=lambda x: x.count('*'), reverse=True)
print "Model: " + str(model_list) + ""
print "PI explanations:"
for pi in pi_str[:3]:
print str(pi)
sdd.sdd_vtree_free(pvtree2)
sdd.sdd_manager_free(pmgr)
def __getstate__(self):
tempfile = mktempfile()
vtree = sdd.sdd_manager_vtree(self.get_manager())
sdd.sdd_vtree_save(tempfile, vtree)
with open(tempfile) as f:
vtree_data = f.read()
nodes = []
for n in self.nodes:
if n is not None:
sdd.sdd_save(tempfile, n)
with open(tempfile) as f:
nodes.append(f.read())
else:
nodes.append(None)
sdd.sdd_save(tempfile, self.constraint_dd)
with open(tempfile) as f:
constraint_dd = f.read()
os.remove(tempfile)
return {
'varcount': self.varcount,
'nodes': nodes,
'vtree': vtree_data,
'constraint_dd': constraint_dd
}
def parse_bdd(filename):
var_count,node_count = pre_parse_bdd(filename)
print " zdd var count:", var_count
print " zdd node count:", node_count
manager = start_manager(var_count,range(1,var_count+1))
root = sdd.sdd_manager_vtree(manager)
nodes = [None] * (node_count+1)
index,id2index = 1,{}
f = open(filename)
for line in f.readlines():
if line.startswith("."): break
line = line.strip().split()
nid = int(line[0])
dvar = int(line[1])
lo,hi = line[2],line[3]
hi_lit = sdd.sdd_manager_literal( dvar,manager)
lo_lit = sdd.sdd_manager_literal(-dvar,manager)
if lo == 'T':
lo_sdd,lo_vtree = sdd.sdd_manager_true(manager),None
elif lo == 'B':
lo_sdd,lo_vtree = sdd.sdd_manager_false(manager),None
else:
lo_id = int(lo)
lo_sdd,lo_vtree = nodes[id2index[lo_id]]
if hi == 'T':
hi_sdd,hi_vtree = sdd.sdd_manager_true(manager),None
elif hi == 'B':
hi_sdd,hi_vtree = sdd.sdd_manager_false(manager),None
else:
hi_id = int(hi)
hi_sdd,hi_vtree = nodes[id2index[hi_id]]
#v1,v2 = sdd.sdd_vtree_of(hi_lit),sdd.sdd_vtree_of(hi_sdd)
#vt = sdd.sdd_vtree_lca(v1,v2,root)
vt = sdd.sdd_manager_vtree_of_var(dvar,manager)
vt = sdd.sdd_vtree_parent(vt)
vt = sdd.sdd_vtree_right(vt)
if dvar < var_count:
hi_sdd = zero_normalize_sdd(hi_sdd,hi_vtree,vt,manager)
lo_sdd = zero_normalize_sdd(lo_sdd,lo_vtree,vt,manager)
vt = sdd.sdd_vtree_parent(vt)
hi_sdd = sdd.sdd_conjoin(hi_lit,hi_sdd,manager)
lo_sdd = sdd.sdd_conjoin(lo_lit,lo_sdd,manager)
alpha = sdd.sdd_disjoin(hi_sdd,lo_sdd,manager)
nodes[index] = (alpha,vt)
id2index[nid] = index
index += 1
f.close()
return manager,nodes[-1][0]
def GetLocalConstraintsForRoot(self, file_prefix):
then_vtree_filename = "%s/%s_then_vtree.vtree" % (file_prefix,
self.name)
then_sdd_filename = "%s/%s_then_sdd.sdd" % (file_prefix, self.name)
constraint = {}
constraint["then_vtree"] = then_vtree_filename
constraint["then"] = [then_sdd_filename]
universe = []
# internal edges
for sub_region_edge_tup in self.sub_region_edges:
universe.append(sub_region_edge_tup)
GraphSet.set_universe(universe)
universe = GraphSet.universe()
paths = GraphSet()
child_names = self.children.keys()
for (i, j) in itertools.combinations(child_names, 2):
paths = paths.union(GraphSet.paths(i, j))
name_to_sdd_index = {}
zdd_to_sdd_index = [None] # for generating sdd from graphset
sdd_index = 0
for child in child_names:
sdd_index += 1
name_to_sdd_index["c%s" % child] = sdd_index
for sub_region_edge in universe:
corresponding_network_edges = self.sub_region_edges[
sub_region_edge]
coresponding_network_edges_sdd_index = []
for single_edge in corresponding_network_edges:
sdd_index += 1
name_to_sdd_index[str(single_edge)] = sdd_index
coresponding_network_edges_sdd_index.append(sdd_index)
zdd_to_sdd_index.append(coresponding_network_edges_sdd_index)
constraint["then_variable_mapping"] = name_to_sdd_index
rl_vtree = sdd.sdd_vtree_new(sdd_index, "right")
sdd_manager = sdd.sdd_manager_new(rl_vtree)
sdd.sdd_vtree_free(rl_vtree)
sdd.sdd_manager_auto_gc_and_minimize_off(sdd_manager)
# Construct simple path constraint
simple_path_constraint = generate_sdd_from_graphset(
paths, sdd_manager, zdd_to_sdd_index)
# non empty path in this region map
none_of_child = sdd.util.sdd_negative_term(
sdd_manager,
[name_to_sdd_index["c%s" % child] for child in self.children])
case_one = sdd.sdd_conjoin(none_of_child, simple_path_constraint,
sdd_manager)
# empty path in this region map
exactly_one_child = sdd.util.sdd_exactly_one(
sdd_manager,
[name_to_sdd_index["c%s" % child] for child in self.children])
empty_path_constraint = sdd.util.sdd_negative_term(
sdd_manager, sum(zdd_to_sdd_index[1:], []))
case_two = sdd.sdd_conjoin(exactly_one_child, empty_path_constraint,
sdd_manager)
total_constraint = sdd.sdd_disjoin(case_one, case_two, sdd_manager)
sdd.sdd_save(then_sdd_filename, total_constraint)
sdd.sdd_vtree_save(then_vtree_filename,
sdd.sdd_manager_vtree(sdd_manager))
sdd.sdd_manager_free(sdd_manager)
return constraint
def run_mincard_query(alpha, mgr, num_features, models_list):
for model in models_list:
beta = condition_and_minimize(alpha, mgr, num_features, model)
vtree = sdd.sdd_manager_vtree(mgr)
print "Model: ", model
print "MC Explanations: "
for model in sdd.models.models(beta, vtree):
print sdd.models.str_model(model)
def test():
var_count = 4
vtree = sdd.sdd_vtree_new(var_count, "balanced")
mgr = sdd.sdd_manager_new(vtree)
# A v B
alpha = sdd.sdd_disjoin(sdd.sdd_manager_literal(1, mgr),
sdd.sdd_manager_literal(2, mgr), mgr)
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(-3, mgr),
sdd.sdd_manager_literal(-4, mgr), mgr)
# A v B v ( ~C ^ ~D )
alpha = sdd.sdd_disjoin(alpha, beta, mgr)
beta, pmgr = primes(alpha, mgr)
_sanity_check(alpha, mgr, beta, pmgr)
pvtree = sdd.sdd_manager_vtree(pmgr)
import models
#beta2 = sdd.sdd_global_minimize_cardinality(beta,pmgr)
beta2 = beta
for model in models.models(beta2, pvtree):
print models.str_model(model)
global cache_hits
print "cache-hits:", cache_hits
print "all-ones"
beta, pmgr = compatible_primes(alpha, [1, 1, 1, 1], mgr)
pvtree = sdd.sdd_manager_vtree(pmgr)
for model in models.models(beta, pvtree):
print models.str_model(model)
print "all-zeros"
beta, pmgr = compatible_primes(alpha, [0, 0, 0, 0], mgr)
pvtree = sdd.sdd_manager_vtree(pmgr)
for model in models.models(beta, pvtree):
print models.str_model(model)
print "blah"
beta, pmgr = compatible_primes(alpha, [1, 0, 1, 0], mgr)
pvtree = sdd.sdd_manager_vtree(pmgr)
for model in models.models(beta, pvtree):
print models.str_model(model)
print "dead-nodes:", sdd.sdd_manager_dead_count(mgr)
print "dead-nodes:", sdd.sdd_manager_dead_count(pmgr)
def print_grids(alpha,m,n,g,manager):
from inf import models
var_count = m*(n-1) + (m-1)*n
#print "COUNT:", sdd.sdd_model_count(alpha,manager)
print "COUNT:", global_model_count(alpha,manager)
for model in models.models(alpha,sdd.sdd_manager_vtree(manager)):
print models.str_model(model,var_count=var_count)
draw_grid(model,m,n,g,True)
def enumerate_primes(primes, pmgr, var_count):
pvtree = sdd.sdd_manager_vtree(pmgr)
while not sdd.sdd_node_is_false(primes):
mincard = sdd.sdd_global_minimize_cardinality(primes, pmgr)
for model in models.models(mincard, pvtree):
term = prime_to_dict(model, var_count)
yield term
primes = sdd.sdd_conjoin(primes, sdd.sdd_negate(mincard, pmgr), pmgr)
def test_andy():
var_count = 3
vtree = sdd.sdd_vtree_new(var_count, "balanced")
mgr = sdd.sdd_manager_new(vtree)
# 100, 101, 111, 001, 011
alpha = sdd.sdd_manager_false(mgr)
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(1, mgr),
sdd.sdd_manager_literal(-2, mgr), mgr)
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(-3, mgr), beta, mgr)
alpha = sdd.sdd_disjoin(alpha, beta, mgr)
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(1, mgr),
sdd.sdd_manager_literal(-2, mgr), mgr)
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(3, mgr), beta, mgr)
alpha = sdd.sdd_disjoin(alpha, beta, mgr)
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(1, mgr),
sdd.sdd_manager_literal(2, mgr), mgr)
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(3, mgr), beta, mgr)
alpha = sdd.sdd_disjoin(alpha, beta, mgr)
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(-1, mgr),
sdd.sdd_manager_literal(-2, mgr), mgr)
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(3, mgr), beta, mgr)
alpha = sdd.sdd_disjoin(alpha, beta, mgr)
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(-1, mgr),
sdd.sdd_manager_literal(2, mgr), mgr)
beta = sdd.sdd_conjoin(sdd.sdd_manager_literal(3, mgr), beta, mgr)
alpha = sdd.sdd_disjoin(alpha, beta, mgr)
beta, pmgr = primes(alpha, mgr)
_sanity_check(alpha, mgr, beta, pmgr)
vtree = sdd.sdd_manager_vtree(mgr)
pvtree = sdd.sdd_manager_vtree(pmgr)
import models
for model in models.models(alpha, vtree):
print models.str_model(model)
for model in models.models(beta, pvtree):
print models.str_model(model)
print "dead-nodes:", sdd.sdd_manager_dead_count(mgr)
print "dead-nodes:", sdd.sdd_manager_dead_count(pmgr)
def PI(sdd_filename, vtree_filename, num_features, model_list):
vtree = sdd.sdd_vtree_new(num_features, "right")
mgr = sdd.sdd_manager_new(vtree)
vtree = sdd.sdd_manager_vtree(mgr)
alpha = sdd.sdd_read(sdd_filename, mgr)
sdd.sdd_vtree_save(vtree_filename, vtree)
print "-----Begin PI query-----"
explqs.run_prime_implicant_query(alpha, mgr, num_features, model_list)
print "-----End PI query-----\n"
def print_grids(alpha,dimension,manager):
#import pdb; pdb.set_trace()
from inf import models
var_count = 2*dimension*(dimension-1)
#var_count = 2*dimension*(dimension-1) + dimension*dimension
#var_count = 2*dimension[0]*dimension[1] - dimension[0] - dimension[1]
print "COUNT:", sdd.sdd_model_count(alpha,manager)
for model in models.models(alpha,sdd.sdd_manager_vtree(manager)):
print models.str_model(model,var_count=var_count)
draw_grid(model,dimension)
def _sanity_check(f, mgr, g, pmgr):
"""f is original function and g is its prime implicants"""
alpha = sdd.sdd_manager_false(mgr)
pvtree = sdd.sdd_manager_vtree(pmgr)
for prime in models.models(g, pvtree):
term = prime_to_term(prime, mgr)
beta = sdd.sdd_conjoin(term, f, mgr)
assert term == beta
assert _is_prime(prime, f, mgr)
alpha = sdd.sdd_disjoin(alpha, term, mgr)
mc1 = sdd.sdd_global_model_count(f, mgr)
mc2 = sdd.sdd_global_model_count(alpha, mgr)
print "mc-check:", mc1, mc2, ("ok" if mc1 == mc2 else "NOT OK")
assert mc1 == mc2
assert alpha == f
def run():
vtree = sdd.sdd_vtree_read(vtree_filename)
mgr = sdd.sdd_manager_new(vtree)
vtree = sdd.sdd_manager_vtree(mgr)
alpha = sdd.sdd_read(sdd_filename, mgr)
with open(variable_description_filename) as f:
variable_description = f.readlines()
num_features = int(variable_description[0].strip().split(" ")[1])
# can specify custom instances by doing
# model_list = [[0,0,0,0],[0,0,0,1],[0,0,1,0],...]
# enumerate a few positive instances from alpha
model_list = get_model_list(alpha, vtree, 10)
PI(alpha, mgr, num_features, model_list)
def sdd_exactly_one_among(manager, active_variables, background_variables):
if not all(x in background_variables for x in active_variables):
raise Exception(
"Invalid argument active variables %s, background_variables %s " %
(active_variables, background_variables))
result = sdd.sdd_manager_false(manager)
for positive_variable in active_variables:
cur_term = sdd.sdd_manager_true(manager)
for variable in background_variables:
if variable != positive_variable:
cur_lit = sdd.sdd_manager_literal(-variable, manager)
else:
cur_lit = sdd.sdd_manager_literal(variable, manager)
cur_term = sdd.sdd_conjoin(cur_term, cur_lit, manager)
sdd.sdd_save("t1.sdd", result)
sdd.sdd_save("t2.sdd", cur_term)
sdd.sdd_vtree_save("manager.vtree", sdd.sdd_manager_vtree(manager))
result = sdd.sdd_disjoin(result, cur_term, manager)
return result
def convert_obdd_to_sdd(output_filename, documentation_filename):
with open(output_filename, 'r') as f:
nodes = f.readlines()[1:]
with open(documentation_filename, 'r') as f:
num_variables = int(f.readline().split(' ')[1])
nodes = [x.strip().split(' ') for x in nodes]
nodes = [[int(x) if x.isdigit() else x for x in node] for node in nodes]
node_dict = {}
for l in nodes:
node_dict[l[0]] = l[1:]
#print node_dict
vtree = sdd.sdd_vtree_new(num_variables, "right")
mgr = sdd.sdd_manager_new(vtree)
vtree = sdd.sdd_manager_vtree(mgr)
root = 0
return convert_helper(root, mgr, node_dict, {}, 0), vtree, mgr
def convert(filename):
start = time.time()
manager,alpha = orig.parse_bdd(filename+".zdd")
end = time.time()
print " sdd node count: %d" % sdd.sdd_count(alpha)
print " sdd size: %d" % sdd.sdd_size(alpha)
print " sdd model count: %d" % sdd.sdd_model_count(alpha,manager)
print " global model count: %d" % orig.global_model_count(alpha,manager)
print " read bdd time: %.3fs" % (end-start)
sdd.sdd_save(filename + ".sdd",alpha)
#sdd.sdd_save_as_dot(filename +".sdd.dot",alpha)
vtree = sdd.sdd_manager_vtree(manager)
sdd.sdd_vtree_save(filename + ".vtree",vtree)
#sdd.sdd_vtree_save_as_dot(filename +".vtree.dot",vtree)
print "===================="
print "before garbage collecting..."
print "live size:", sdd.sdd_manager_live_count(manager)
print "dead size:", sdd.sdd_manager_dead_count(manager)
print "garbage collecting..."
sdd.sdd_manager_garbage_collect(manager)
print "live size:", sdd.sdd_manager_live_count(manager)
print "dead size:", sdd.sdd_manager_dead_count(manager)
print " global model count: %d" % global_model_count(alphaNoMP,managerNoMP)
print " read bdd time: %.3fs" % (end-start)
"""
sdd.sdd_ref(alpha,manager)
start = time.time()
sdd.sdd_manager_minimize(manager)
end = time.time()
print " min sdd node count: %d" % sdd.sdd_count(alpha)
print " min sdd time: %.3fs" % (end-start)
sdd.sdd_deref(alpha,manager)
"""
sdd.sdd_save(filename + ".sdd",alpha)
#sdd.sdd_save_as_dot(filename +".sdd.dot",alpha)
vtree = sdd.sdd_manager_vtree(manager)
sdd.sdd_vtree_save(filename + ".vtree",vtree)
#sdd.sdd_vtree_save_as_dot(filename +".vtree.dot",vtree)
sdd.sdd_save(filenameNoMP + ".sdd",alphaNoMP)
vtreeNoMP = sdd.sdd_manager_vtree(managerNoMP)
sdd.sdd_vtree_save(filenameNoMP + ".vtree",vtreeNoMP)
print "===================="
print "before garbage collecting..."
print "live size:", sdd.sdd_manager_live_count(manager)
print "dead size:", sdd.sdd_manager_dead_count(manager)
print "garbage collecting..."
def parse_bdd(filename,var_count=None):
if var_count is None:
var_count,node_count = pre_parse_bdd(filename)
else:
max_count,node_count = pre_parse_bdd(filename)
#print " zdd var count:", var_count
#print " zdd node count:", node_count
manager = start_manager(var_count,range(1,var_count+1))
root = sdd.sdd_manager_vtree(manager)
nodes = [None] * (node_count+1)
index,id2index = 1,{}
f = open(filename)
for line in f.readlines():
if line.startswith("."): break
line = line.strip().split()
nid = int(line[0])
dvar = int(line[1])
lo,hi = line[2],line[3]
hi_lit = sdd.sdd_manager_literal( dvar,manager)
lo_lit = sdd.sdd_manager_literal(-dvar,manager)
if lo == 'T':
lo_sdd,lo_vtree = sdd.sdd_manager_true(manager),None
elif lo == 'B':
lo_sdd,lo_vtree = sdd.sdd_manager_false(manager),None
else:
lo_id = int(lo)
lo_sdd,lo_vtree = nodes[id2index[lo_id]]
if hi == 'T':
hi_sdd,hi_vtree = sdd.sdd_manager_true(manager),None
elif hi == 'B':
hi_sdd,hi_vtree = sdd.sdd_manager_false(manager),None
else:
hi_id = int(hi)
hi_sdd,hi_vtree = nodes[id2index[hi_id]]
#v1,v2 = sdd.sdd_vtree_of(hi_lit),sdd.sdd_vtree_of(hi_sdd)
#vt = sdd.sdd_vtree_lca(v1,v2,root)
vt = sdd.sdd_manager_vtree_of_var(dvar,manager)
vt = sdd.sdd_vtree_parent(vt)
vt = sdd.sdd_vtree_right(vt)
if dvar < var_count:
hi_sdd = zero_normalize_sdd(hi_sdd,hi_vtree,vt,manager)
lo_sdd = zero_normalize_sdd(lo_sdd,lo_vtree,vt,manager)
vt = sdd.sdd_vtree_parent(vt)
hi_sdd = sdd.sdd_conjoin(hi_lit,hi_sdd,manager)
lo_sdd = sdd.sdd_conjoin(lo_lit,lo_sdd,manager)
alpha = sdd.sdd_disjoin(hi_sdd,lo_sdd,manager)
nodes[index] = (alpha,vt)
id2index[nid] = index
index += 1
f.close()
last_sdd,last_vtree = nodes[-1]
vt = sdd.sdd_manager_vtree(manager)
if vt != last_vtree:
last_sdd = zero_normalize_sdd(last_sdd,last_vtree,vt,manager)
return manager,last_sdd
def run():
with timer.Timer("reading dataset"):
dataset = util.read_binary_dataset(test_filename)
domain = util.read_header(test_filename)
'''
if OPTIONS.majority_circuit_opt:
l = len(domain)
for k in xrange(num_trees):
domain["Tree_%d" % k] = l+k
'''
with timer.Timer("initializing manager"):
# start sdd manager
var_count = len(domain) - 1
vtree = sdd.sdd_vtree_new(var_count, "balanced")
manager = sdd.sdd_manager_new(vtree)
#sdd.sdd_manager_auto_gc_and_minimize_on(manager)
#sdd.sdd_manager_auto_gc_and_minimize_off(manager)
sdd_state = SddState(vtree, manager)
with timer.Timer("reading constraints"):
constraint_sdd, constraint_info = encode_logical_constraints(
constraint_filename, manager, domain)
sdd.sdd_ref(constraint_sdd, manager)
with timer.Timer("reading trees"):
tree_states = []
for filename in sorted(glob.glob(tree_basename.replace('%d', '*'))):
tree = pygv.AGraph(filename)
tree_state = TreeState(tree, domain, constraint_info)
tree_states.append(tree_state)
#tree.layout(prog='dot')
#tree.draw(filename+".png")
#num_trees = len(tree_states)
with timer.Timer("compiling trees"):
forest_sdds, _ = izip(*forest_sdds_iter(tree_states, sdd_state))
#forest_sdds = list(forest_sdds_iter(tree_states,sdd_state))
forest_sdds = [
(tree_state, tree_sdd)
for tree_state, tree_sdd in zip(tree_states, forest_sdds)
]
cmpf = lambda x, y: cmp(sdd.sdd_size(x[1]), sdd.sdd_size(y[1]))
forest_sdds.sort(cmp=cmpf)
tree_states = [tree_state for tree_state, tree_sdd in forest_sdds]
#ACACAC
sdd.sdd_manager_auto_gc_and_minimize_off(manager)
sdd.sdd_manager_minimize_limited(manager)
stats = SddSizeStats()
for tree_state, tree_sdd in forest_sdds:
stats.update(tree_sdd)
sdd.sdd_deref(tree_sdd, manager)
sdd.sdd_manager_garbage_collect(manager)
forest_sdds, used_vars_list = izip(
*forest_sdds_iter(tree_states, sdd_state))
print stats
with timer.Timer("compiling all", prefix="| "):
alpha = compile_all(forest_sdds, used_vars_list, num_trees, domain,
manager, constraint_sdd)
with timer.Timer("evaluating"):
msg = util.evaluate_dataset_all_sdd(dataset, alpha, manager)
print "| trees : %d" % num_trees
print "--- evaluating majority vote on random forest (compiled):"
print msg
print "| all size :", sdd.sdd_size(alpha)
print "| all count:", sdd.sdd_count(alpha)
print " model count:", sdd.sdd_global_model_count(alpha, manager)
with timer.Timer("checking monotonicity"):
result = is_monotone(alpha, manager)
print "Is monotone?", result
#for tree_sdd in forest_sdds: sdd.sdd_deref(tree_sdd,manager)
print "===================="
print "before garbage collecting..."
print "live size:", sdd.sdd_manager_live_count(manager)
print "dead size:", sdd.sdd_manager_dead_count(manager)
print "garbage collecting..."
sdd.sdd_manager_garbage_collect(manager)
print "live size:", sdd.sdd_manager_live_count(manager)
print "dead size:", sdd.sdd_manager_dead_count(manager)
vtree = sdd.sdd_manager_vtree(manager)
print "Writing sdd file %s and vtree file %s" % (sdd_filename,
vtree_filename)
sdd.sdd_save(sdd_filename, alpha)
sdd.sdd_vtree_save(vtree_filename, vtree)
print "Writing constraint sdd file %s and constraint vtree file %s" % (
constraint_sdd_filename, constraint_vtree_filename)
sdd.sdd_save(constraint_sdd_filename, constraint_sdd)
sdd.sdd_vtree_save(constraint_vtree_filename, vtree)
def str_model(model, var_count=None):
"""Convert model to string."""
if var_count is None:
var_count = len(model)
return " ".join(str(model[var]) for var in xrange(1, var_count + 1))
if __name__ == '__main__':
var_count = 10
vtree = sdd.sdd_vtree_new(var_count, "balanced")
manager = sdd.sdd_manager_new(vtree)
alpha = sdd.sdd_manager_false(manager)
for var in xrange(1, var_count + 1):
lit = sdd.sdd_manager_literal(-var, manager)
alpha = sdd.sdd_disjoin(alpha, lit, manager)
vt = sdd.sdd_manager_vtree(manager)
model_count = 0
for model in models(alpha, vt):
model_count += 1
print str_model(model, var_count=var_count)
#lib_mc = sdd.sdd_model_count(alpha,manager)
print "model count: %d" % model_count
sdd.sdd_manager_free(manager)
sdd.sdd_vtree_free(vtree)
def str_model(model, var_count=None):
"""Convert model to string."""
if var_count is None:
var_count = len(model)
return " ".join(str(model[var]) for var in xrange(1, var_count + 1))
if __name__ == "__main__":
var_count = 10
vtree = sdd.sdd_vtree_new(var_count, "balanced")
manager = sdd.sdd_manager_new(vtree)
alpha = sdd.sdd_manager_false(manager)
for var in xrange(1, var_count + 1):
lit = sdd.sdd_manager_literal(-var, manager)
alpha = sdd.sdd_disjoin(alpha, lit, manager)
vt = sdd.sdd_manager_vtree(manager)
model_count = 0
for model in models(alpha, vt):
model_count += 1
print str_model(model, var_count=var_count)
# lib_mc = sdd.sdd_model_count(alpha,manager)
print "model count: %d" % model_count
sdd.sdd_manager_free(manager)
sdd.sdd_vtree_free(vtree)
def GetLocalConstraintsForLeaveClusters(self, file_prefix):
if_vtree_filename = "%s/%s_if_vtree.vtree" % (file_prefix, self.name)
if_sdd_filename_prefix = "%s/%s_if_sdd" % (file_prefix, self.name)
then_vtree_filename = "%s/%s_then_vtree.vtree" % (file_prefix,
self.name)
then_sdd_filename_prefix = "%s/%s_then_sdd" % (file_prefix, self.name)
ifs = []
thens = []
if_variable_mapping = {}
if_sdd_index = 0
if_sdd_index += 1
if_variable_mapping[
"c%s" %
self.name] = if_sdd_index # cluster indicator for current cluster
for external_edge in self.external_edges:
if_sdd_index += 1
if_variable_mapping[str(external_edge)] = if_sdd_index
then_variable_mapping = {}
zdd_to_sdd_index = [None]
universe = []
node_pair_to_edges = {}
for internal_edge in self.internal_edges:
if (internal_edge.x, internal_edge.y) not in node_pair_to_edges:
universe.append((internal_edge.x, internal_edge.y))
node_pair_to_edges.setdefault((internal_edge.x, internal_edge.y),
[]).append(internal_edge)
GraphSet.set_universe(universe)
universe = GraphSet.universe()
then_sdd_index = 0
for node_pair in universe:
correponding_sdd_indexes = []
for internal_edge in node_pair_to_edges[node_pair]:
then_sdd_index += 1
then_variable_mapping[str(internal_edge)] = then_sdd_index
correponding_sdd_indexes.append(then_sdd_index)
zdd_to_sdd_index.append(correponding_sdd_indexes)
if_vtree, then_vtree = sdd.sdd_vtree_new(if_sdd_index,
"right"), sdd.sdd_vtree_new(
then_sdd_index, "right")
if_manager, then_manager = sdd.sdd_manager_new(
if_vtree), sdd.sdd_manager_new(then_vtree)
sdd.sdd_manager_auto_gc_and_minimize_off(if_manager)
sdd.sdd_manager_auto_gc_and_minimize_off(then_manager)
sdd.sdd_vtree_free(if_vtree)
sdd.sdd_vtree_free(then_vtree)
#none of the external edges are used and cluster indicator is off
case_index = 0
case_one_if = sdd.util.sdd_negative_term(if_manager,
range(1, if_sdd_index + 1))
case_one_then = sdd.util.sdd_negative_term(
then_manager, range(1, then_sdd_index + 1))
sdd.sdd_save("%s_%s" % (if_sdd_filename_prefix, case_index),
case_one_if)
sdd.sdd_save("%s_%s" % (then_sdd_filename_prefix, case_index),
case_one_then)
ifs.append("%s_%s" % (if_sdd_filename_prefix, case_index))
thens.append("%s_%s" % (then_sdd_filename_prefix, case_index))
#none of the external edges are used and cluster indicator is on
case_index += 1
case_two_if = sdd.util.sdd_exactly_one_among(
if_manager, [if_variable_mapping["c%s" % self.name]],
range(1, if_sdd_index + 1))
paths = GraphSet()
for (i, j) in itertools.combinations(self.nodes, 2):
paths = paths.union(GraphSet.paths(i, j))
case_two_then = generate_sdd_from_graphset(paths, then_manager,
zdd_to_sdd_index)
sdd.sdd_save("%s_%s" % (if_sdd_filename_prefix, case_index),
case_two_if)
sdd.sdd_save("%s_%s" % (then_sdd_filename_prefix, case_index),
case_two_then)
ifs.append("%s_%s" % (if_sdd_filename_prefix, case_index))
thens.append("%s_%s" % (then_sdd_filename_prefix, case_index))
#exactly one of the external edge is used and cluster indicator is off
aggregated_cases = {}
for external_edge in self.external_edges:
aggregated_cases.setdefault(self.external_edges[external_edge],
[]).append(external_edge)
for entering_node in aggregated_cases:
case_index += 1
cur_case_if = sdd.util.sdd_exactly_one_among(
if_manager, [
if_variable_mapping[str(e)]
for e in aggregated_cases[entering_node]
], range(1, if_sdd_index + 1))
paths = GraphSet()
for node in self.nodes:
if node == entering_node:
continue
paths = paths.union(GraphSet.paths(entering_node, node))
cur_case_then = generate_sdd_from_graphset(paths, then_manager,
zdd_to_sdd_index)
# disjoin the empty path
cur_case_then = sdd.sdd_disjoin(
cur_case_then,
sdd.util.sdd_negative_term(then_manager,
range(1, then_sdd_index + 1)),
then_manager)
sdd.sdd_save("%s_%s" % (if_sdd_filename_prefix, case_index),
cur_case_if)
sdd.sdd_save("%s_%s" % (then_sdd_filename_prefix, case_index),
cur_case_then)
ifs.append("%s_%s" % (if_sdd_filename_prefix, case_index))
thens.append("%s_%s" % (then_sdd_filename_prefix, case_index))
# exactly two of the external edge is used and cluster_indicator is off
aggregated_cases = {}
for (i, j) in itertools.combinations(self.external_edges.keys(), 2):
entering_points = (self.external_edges[i], self.external_edges[j])
entering_points = (max(entering_points), min(entering_points))
aggregated_cases.setdefault(entering_points, []).append((i, j))
for entering_points in aggregated_cases:
case_index += 1
entering_edges = aggregated_cases[entering_points]
cur_case_if = generate_exactly_two_from_tuples(
if_manager,
[(if_variable_mapping[str(e1)], if_variable_mapping[str(e2)])
for (e1, e2) in entering_edges], range(1, if_sdd_index + 1))
if entering_points[0] == entering_points[1]:
cur_case_then = sdd.util.sdd_negative_term(
then_manager, range(1, then_sdd_index + 1))
else:
paths = GraphSet.paths(entering_points[0], entering_points[1])
cur_case_then = generate_sdd_from_graphset(
paths, then_manager, zdd_to_sdd_index)
sdd.sdd_save("%s_%s" % (if_sdd_filename_prefix, case_index),
cur_case_if)
sdd.sdd_save("%s_%s" % (then_sdd_filename_prefix, case_index),
cur_case_then)
ifs.append("%s_%s" % (if_sdd_filename_prefix, case_index))
thens.append("%s_%s" % (then_sdd_filename_prefix, case_index))
sdd.sdd_vtree_save(if_vtree_filename,
sdd.sdd_manager_vtree(if_manager))
sdd.sdd_vtree_save(then_vtree_filename,
sdd.sdd_manager_vtree(then_manager))
sdd.sdd_manager_free(if_manager)
sdd.sdd_manager_free(then_manager)
constraint = {}
constraint["if_vtree"] = if_vtree_filename
constraint["if"] = ifs
constraint["if_variable_mapping"] = if_variable_mapping
constraint["then_vtree"] = then_vtree_filename
constraint["then"] = thens
constraint["then_variable_mapping"] = then_variable_mapping
return constraint
def GetLocalConstraintsForInternalClusters(self, file_prefix):
if_vtree_filename = "%s/%s_if_vtree.vtree" % (file_prefix, self.name)
if_sdd_filename_prefix = "%s/%s_if_sdd" % (file_prefix, self.name)
then_vtree_filename = "%s/%s_then_vtree.vtree" % (file_prefix,
self.name)
then_sdd_filename_prefix = "%s/%s_then_sdd" % (file_prefix, self.name)
ifs = []
thens = []
if_variable_mapping = {}
if_sdd_index = 0
if_sdd_index += 1
if_variable_mapping[
"c%s" %
self.name] = if_sdd_index # cluster indicator for current cluster
for external_edge in self.external_edges:
if_sdd_index += 1
if_variable_mapping[str(external_edge)] = if_sdd_index
then_variable_mapping = {}
# variables for the child clusters
then_sdd_index = 0
zdd_to_sdd_index = [None]
for child in self.children:
then_sdd_index += 1
then_variable_mapping["c%s" % child] = then_sdd_index
universe = self.sub_region_edges.keys()
GraphSet.set_universe(universe)
universe = GraphSet.universe()
for node_pair in universe:
correponding_sdd_indexes = []
for internal_edge in self.sub_region_edges[node_pair]:
then_sdd_index += 1
then_variable_mapping[str(internal_edge)] = then_sdd_index
correponding_sdd_indexes.append(then_sdd_index)
zdd_to_sdd_index.append(correponding_sdd_indexes)
if_vtree, then_vtree = sdd.sdd_vtree_new(if_sdd_index,
"right"), sdd.sdd_vtree_new(
then_sdd_index, "right")
if_manager, then_manager = sdd.sdd_manager_new(
if_vtree), sdd.sdd_manager_new(then_vtree)
sdd.sdd_manager_auto_gc_and_minimize_off(if_manager)
sdd.sdd_manager_auto_gc_and_minimize_off(then_manager)
sdd.sdd_vtree_free(if_vtree)
sdd.sdd_vtree_free(then_vtree)
#none of the external edges are used and cluster indicator is off
case_index = 0
case_one_if = sdd.util.sdd_negative_term(if_manager,
range(1, if_sdd_index + 1))
case_one_then = sdd.util.sdd_negative_term(
then_manager, range(1, then_sdd_index + 1))
sdd.sdd_save("%s_%s" % (if_sdd_filename_prefix, case_index),
case_one_if)
sdd.sdd_save("%s_%s" % (then_sdd_filename_prefix, case_index),
case_one_then)
ifs.append("%s_%s" % (if_sdd_filename_prefix, case_index))
thens.append("%s_%s" % (then_sdd_filename_prefix, case_index))
#none of the external edges are used and cluster indicator is on
case_index += 1
case_two_if = sdd.util.sdd_exactly_one_among(
if_manager, [if_variable_mapping["c%s" % self.name]],
range(1, if_sdd_index + 1))
#***Non empty path in this region map
none_of_child = sdd.util.sdd_negative_term(
then_manager,
[then_variable_mapping["c%s" % child] for child in self.children])
paths = GraphSet()
child_names = self.children.keys()
for c1, c2 in itertools.combinations(child_names, 2):
paths = paths.union(GraphSet.paths(c1, c2))
simple_path_constraint = generate_sdd_from_graphset(
paths, then_manager, zdd_to_sdd_index)
case_one = sdd.sdd_conjoin(simple_path_constraint, none_of_child,
then_manager)
#***Empty path in the region map
exactly_one_chlid = sdd.util.sdd_exactly_one(
then_manager,
[then_variable_mapping["c%s" % child] for child in self.children])
empty_path_constraint = sdd.util.sdd_negative_term(
then_manager, sum(zdd_to_sdd_index[1:], []))
case_two = sdd.sdd_conjoin(empty_path_constraint, exactly_one_chlid,
then_manager)
case_two_then = sdd.sdd_disjoin(case_one, case_two, then_manager)
sdd.sdd_save("%s_%s" % (if_sdd_filename_prefix, case_index),
case_two_if)
sdd.sdd_save("%s_%s" % (then_sdd_filename_prefix, case_index),
case_two_then)
ifs.append("%s_%s" % (if_sdd_filename_prefix, case_index))
thens.append("%s_%s" % (then_sdd_filename_prefix, case_index))
#Exactly one of the external edge is used and cluster_indicator is off
aggregated_cases = {}
for external_edge in self.external_edges:
aggregated_cases.setdefault(self.external_edges[external_edge],
[]).append(external_edge)
for entering_node in aggregated_cases:
case_index += 1
cur_case_if = sdd.util.sdd_exactly_one_among(
if_manager, [
if_variable_mapping[str(e)]
for e in aggregated_cases[entering_node]
], range(1, if_sdd_index + 1))
paths = GraphSet()
for child in self.children:
if child == entering_node:
continue
paths = paths.union(GraphSet.paths(entering_node, child))
cur_case_then = generate_sdd_from_graphset(paths, then_manager,
zdd_to_sdd_index)
cur_case_then = sdd.sdd_disjoin(
cur_case_then,
sdd.util.sdd_negative_term(then_manager, [
then_variable_mapping[str(e)] for e in self.internal_edges
]), then_manager)
#conjoin that all the child indicator is off
cur_case_then = sdd.sdd_conjoin(
cur_case_then,
sdd.util.sdd_negative_term(then_manager, [
then_variable_mapping["c%s" % child]
for child in self.children
]), then_manager)
sdd.sdd_save("%s_%s" % (if_sdd_filename_prefix, case_index),
cur_case_if)
sdd.sdd_save("%s_%s" % (then_sdd_filename_prefix, case_index),
cur_case_then)
ifs.append("%s_%s" % (if_sdd_filename_prefix, case_index))
thens.append("%s_%s" % (then_sdd_filename_prefix, case_index))
#Exactly two of the external edge is used and cluster_indicator is off
aggregated_cases = {}
for (i, j) in itertools.combinations(self.external_edges.keys(), 2):
entering_points = (self.external_edges[i], self.external_edges[j])
entering_points = (max(entering_points), min(entering_points))
aggregated_cases.setdefault(entering_points, []).append((i, j))
for entering_points in aggregated_cases:
case_index += 1
entering_edges = aggregated_cases[entering_points]
cur_case_if = generate_exactly_two_from_tuples(
if_manager,
[(if_variable_mapping[str(e1)], if_variable_mapping[str(e2)])
for (e1, e2) in entering_edges], range(1, if_sdd_index + 1))
if entering_points[0] == entering_points[1]:
cur_case_then = sdd.util.sdd_negative_term(
then_manager, range(1, then_sdd_index + 1))
else:
paths = GraphSet.paths(entering_points[0], entering_points[1])
cur_case_then = generate_sdd_from_graphset(
paths, then_manager, zdd_to_sdd_index)
cur_case_then = sdd.sdd_conjoin(
cur_case_then,
sdd.util.sdd_negative_term(then_manager, [
then_variable_mapping["c%s" % child]
for child in self.children
]), then_manager)
sdd.sdd_save("%s_%s" % (if_sdd_filename_prefix, case_index),
cur_case_if)
sdd.sdd_save("%s_%s" % (then_sdd_filename_prefix, case_index),
cur_case_then)
ifs.append("%s_%s" % (if_sdd_filename_prefix, case_index))
thens.append("%s_%s" % (then_sdd_filename_prefix, case_index))
sdd.sdd_vtree_save(if_vtree_filename,
sdd.sdd_manager_vtree(if_manager))
sdd.sdd_vtree_save(then_vtree_filename,
sdd.sdd_manager_vtree(then_manager))
sdd.sdd_manager_free(if_manager)
sdd.sdd_manager_free(then_manager)
constraint = {}
constraint["if_vtree"] = if_vtree_filename
constraint["if"] = ifs
constraint["if_variable_mapping"] = if_variable_mapping
constraint["then_vtree"] = then_vtree_filename
constraint["then"] = thens
constraint["then_variable_mapping"] = then_variable_mapping
return constraint