def parse_output_popf(file_name):
from krrt.utils import match_value, read_file
popf_lines = read_file(file_name)
assert '; Plan found with metric' in popf_lines[0]
assert '; States evaluated' in popf_lines[1]
assert '; Time' in popf_lines[2]
# Get the plan lines
popf_actions = popf_lines[3:]
time_slots = [popf_actions[0].split(':')[0]]
for act in popf_actions:
if time_slots[-1] != act.split(':')[0]:
time_slots.append(act.split(':')[0])
layered_solution = []
for time in time_slots:
actions = filter(lambda x: x.split(':')[0] == time, popf_actions)
layered_solution.append([])
for act in actions:
action_line = act.split('(')[1].split(')')[0]
layered_solution[-1].append(Action(action_line))
return layered_solution
def convert_PRE(input, output):
def cpyrtn(old, new):
new.append(old[0])
return old.pop(0)
old_lines = read_file(input)
new_lines = []
#-- Pop the top MPT flag
old_lines.pop(0)
#-- Convert the operators back to normal form
#- Find the operator section
while 'end_goal' != old_lines[0]:
new_lines.append(old_lines.pop(0))
new_lines.append(old_lines.pop(0))
#- Get the number of operators
num_ops = int(old_lines.pop(0))
new_lines.append(str(num_ops))
for op_num in range(num_ops):
assert('begin_operator' == cpyrtn(old_lines, new_lines))
op_name = cpyrtn(old_lines, new_lines)
num_prevail = int(cpyrtn(old_lines, new_lines))
for prev_num in range(num_prevail):
cpyrtn(old_lines, new_lines)
num_effects = int(cpyrtn(old_lines, new_lines))
for eff_num in range(num_effects):
preconds = []
num_precond = old_lines.pop(0)
for precond_num in range(int(num_precond)):
var, val = old_lines.pop(0).split()
preconds.append(var + " " + val)
effect = old_lines.pop(0)
new_lines.append(num_precond + " " + " ".join(preconds) + " " + effect)
op_cost = int(cpyrtn(old_lines, new_lines))
assert('end_operator' == cpyrtn(old_lines, new_lines))
#-- Get rid of SG and everything after it.
while 'begin_SG' != old_lines[0]:
new_lines.append(old_lines.pop(0))
write_file(output, new_lines)
def doit_fip(domain, dom_probs, exp_name='fip'):
print "\n\nRunning FIP experiments on domain, %s" % domain
fip_args = [
"-o ../%s.fip -f ../%s" % (item[0], item[1]) for item in dom_probs
]
fip_results = run_experiment(
base_command='./../fip',
single_arguments={'domprob': fip_args},
time_limit=TIME_LIMIT,
memory_limit=MEM_LIMIT,
results_dir="RESULTS/%s-%s" % (exp_name, domain),
progress_file=None,
processors=CORES,
sandbox=exp_name,
clean_sandbox=True,
output_file_func=(
lambda res: res.single_args['domprob'].split('/')[-1] + '.out'),
error_file_func=(
lambda res: res.single_args['domprob'].split('/')[-1] + '.err'))
timeouts = 0
memouts = 0
errorouts = 0
fip_csv = ['domain,problem,runtime,size,status']
for res_id in fip_results.get_ids():
result = fip_results[res_id]
prob = result.single_args['domprob'].split(' ')[3].split('/')[-1]
if match_value(result.output_file, 'No plan will solve it'
) or 'No solutions are found!' in read_file(
result.output_file)[-1]:
fip_csv.append("%s,%s,-1,-1,N" % (domain, prob))
else:
if result.timed_out:
timeouts += 1
fip_csv.append("%s,%s,-1,-1,T" % (domain, prob))
elif result.mem_out:
memouts += 1
fip_csv.append("%s,%s,-1,-1,M" % (domain, prob))
elif not result.clean_run or check_segfault(result.output_file):
errorouts += 1
fip_csv.append("%s,%s,-1,-1,E" % (domain, prob))
else:
run, size = parse_fip(result.output_file)
fip_csv.append("%s,%s,%f,%d,-" % (domain, prob, run, size))
print "\nTimed out %d times." % timeouts
print "Ran out of memory %d times." % memouts
print "Unknown error %d times." % errorouts
append_file("RESULTS/%s-%s-results.csv" % (exp_name, domain), fip_csv)
def _parse_file(self, filename):
#--- First and last line are useless
lines = read_file(filename)[1:-1]
#--- Second line is number of groups
self.num_groups = int(lines.pop(0))
#--- Parse each group
for i in xrange(self.num_groups):
self.groups.append(self._parse_group(lines))
#--- Sanity check
assert 0 == len(lines)
def parse_output_mp(file_name):
from krrt.utils import read_file
mp_lines = read_file(file_name)
layered_solution = []
for line in mp_lines:
acts = line.split(': ')[1].split()
layered_solution.append(
map(Action, [("%s %s" % (a.split('(')[0], ' '.join(
a.split('(')[1][:-1].split(',')))).strip() for a in acts]))
return layered_solution
def parseNNF(fname):
lines = read_file(fname)
(nNodes, nEdges, nVars) = map(int, lines.pop(0).split()[1:])
assert nNodes == len(lines)
# TODO: Double check that this badlist isn't included in the final d-DNNF
badlist = set()
allvars = set()
nodes = []
for line in lines:
parts = line.split()
if 'A 0' == line:
nodes.append(True)
elif 'O 0 0' == line:
nodes.append(False)
elif 'L' == parts[0]:
num = int(parts[1])
lit = CNF.Variable(abs(num))
allvars.add(lit)
if num < 0:
lit = CNF.Not(lit)
nodes.append(Lit(lit))
elif 'A' == parts[0]:
nCh = int(parts[1])
children = list(map(int, parts[2:]))
assert nCh == len(children), "Bad line? %s" % line
assert max(children) < len(nodes)
nodes.append(And([nodes[i] for i in children]))
elif 'O' == parts[0]:
switch = int(parts[1])
nCh = int(parts[2])
children = list(map(int, parts[3:]))
assert nCh == len(children), "Bad line? %s" % line
assert max(children) < len(nodes)
nodes.append(Or([nodes[i] for i in children], switch))
if 0 == switch:
badlist.add(len(nodes) - 1)
else:
assert False, "Unrecognized line: %s" % line
return dDNNF(nodes[-1], allvars)
def parse_output_ipc(file_name):
from krrt.utils import read_file
# Check for the failed solution
#if match_value(file_name, '.* No plan will solve it.*'):
# print "No solution."
# return None
# Get the plan
action_list = read_file(file_name)
actions = [Action(line[1:-1].strip(' ').lower()) for line in action_list]
return Plan(actions)
def test_kripke(kb, rmls):
print
print kb
print "\nClosing...\n"
kb.logically_close()
print kb
print "Consistent: %s" % str(kb.is_consistent())
print "\nGenerating compressed kripke..."
M = kb.generate_kripke(True)
print
print "Generating dot export..."
M.generate_dot("graph.dot", True)
lines = read_file("graph.dot")
lines = [lines[0], " rankdir=LR;"] + lines[1:]
write_file("graph.dot", lines)
os.system("dot -Tpng graph.dot > graph.png")
print "\nGenerating full kripke..."
M = kb.generate_kripke(False)
print
print M.get_stats()
print
for rml in rmls:
print "Assessing %s: %s" % (str(rml), M.assess_rml(Belief(0, rml)))
print
all_match = all([M.assess_rml(rml) for rml in kb.perspectival_view()])
print "All rmls match: %s" % str(all_match)
if not all_match:
print "Non-matching:"
for rml in kb.construct_closed_world():
if not M.assess_rml(rml):
print " %s" % str(rml)
print
def parse_test_pdkb(fname):
lines = read_file(fname)
(depth, num_ag, props) = lines.pop(0).split(' ')
depth = int(depth)
num_ag = int(num_ag)
props = props.split(',')
assert 'B' not in props, "Error: Can't use 'B' as a proposition."
assert num_ag < 10, "Error: Can't use more than 10 agents."
def parse_rml(s):
if '!' == s[0]:
return neg(parse_rml(s[1:]))
elif 'B' == s[0]:
return Belief(int(s[1]), parse_rml(s[2:]))
elif 'P' == s[0]:
return Possible(int(s[1]), parse_rml(s[2:]))
else:
return Literal(s)
rmls = [parse_rml(l.replace(' ', '')) for l in lines[:lines.index('END')]]
desc = "\n".join(lines[lines.index('END') + 1:])
if desc:
print "Running test with the following description:"
print "\n--------\n"
print desc
print "\n-----\n"
kb = PDKB(depth, list(range(1, num_ag + 1)), map(Literal, props))
for rml in rmls:
kb.add_rml(rml)
return rmls
test_kripke(kb, rmls)
def read_pdkbddl_file(fname):
lines = read_file(fname)
found = True
count = 0
while found:
count += 1
if count > 100:
assert False, "Error: Already attempted at least 100 imports. Did you recursively import something?"
found = False
include_indices = []
for i in range(len(lines)):
if lines[i].find('{include') == 0:
include_indices.append(i)
found = True
for index in reversed(include_indices):
new_file = '/'.join(
fname.split('/')[:-1]) + '/' + lines[index].split(':')[1][:-1]
lines = lines[:index] + read_pdkbddl_file(new_file) + lines[index +
1:]
# Strip out the comments and empty lines
lines = filter(lambda x: x != '', lines)
lines = filter(lambda x: x[0] != ';', lines)
lines = map(lambda x: x.split(';')[0], lines)
# Convert the [] and <> notation to nested B's
def replace_modal(left, right, sym, line):
new_line = ''
bracket_count = 0
negate = ''
for c in line:
if negate != '' and c != left:
new_line += '!'
negate = ''
if c == left:
new_line += "(%s%s{" % (negate, sym)
bracket_count += 1
negate = ''
elif c == right:
new_line += '}'
elif c == '!':
negate = '!'
else:
new_line += c
if c == ')':
while bracket_count:
new_line += ')'
bracket_count -= 1
return new_line
def replace_belief(line):
return replace_modal('[', ']', 'B', line)
def replace_possible(line):
return replace_modal('<', '>', 'P', line)
def replace_alwaysknow(line):
assert '%' not in line, "Error: Custom character % must be avoided"
assert '&' not in line, "Error: Custom character & must be avoided"
return replace_modal('&', '%', 'AK', line.replace('{AK}', '&%'))
for func in [replace_belief, replace_possible, replace_alwaysknow]:
lines = map(func, lines)
return lines
import os
from krrt.utils import get_file_list, read_file, write_file, get_lines
domains = get_file_list('.', ['fixed', 'fip'], ['d_'])
print domains
for dom in domains:
# Fix the original domain
lines = read_file(dom)
fixed_dom = '.'+''.join(dom.split('.')[:-1])+'-fixed.pddl'
write_file(fixed_dom, [lines[0]] + ["(:requirements :typing :strips :non-deterministic)"] + lines[1:])
# Fix the fip version
preface = get_lines(dom, upper_bound = ':action')
fixed_dom_fip = fixed_dom + '.fip'
write_file(fixed_dom_fip, preface)
print "python ../../src/translate/determinizer.py %s p_1_1.pddl >> %s" % (fixed_dom, fixed_dom_fip)
os.system("python ../../src/translate/determinizer.py %s p_1_1.pddl >> %s" % (fixed_dom, fixed_dom_fip))
os.system('echo ")" >> ' + fixed_dom_fip)
SYMBOLS = ['s', 'x', 'o', '^', '>', 'v', '<', 'd', 'p', 'h', '8']
COLOURS = ['b','g','k','m']
LINES = ['-', '--', '-.', ':']
handle = ax1.plot(xs, data, c='b', ls='-')
ax1.set_title(title)
if logplot:
ax1.set_yscale('log')
pylab.show()
if '-csv' in get_opts()[0]:
data = [line.split(',') for line in read_file(get_opts()[0]['-csv'])[1:]]
times = [float(i[0]) for i in data]
sizes = [float(i[1]) for i in data]
print "Mean time: %f" % (sum(times) / len(times))
print "Mean size: %f" % (sum(sizes) / len(sizes))
elif '-size' in get_opts()[0]:
dom = get_opts()[0]['-size']
fname = "%s.csv" % dom
data = [line.split(',') for line in read_file(fname)[1:]]
times = sorted([float(i[0]) for i in data])
sizes = sorted([float(i[1]) for i in data])
xs = [i+1 for i in range(len(data))]
individual_plot(xs, sizes, False, title_lookup[dom])
def parse_fip(outfile):
runtime = get_value(outfile, '.* ([0-9]+\.?[0-9]+) seconds searching.*',
float)
policy_size = len(filter(lambda x: 'case S' in x, read_file(outfile)))
return runtime, policy_size