def resultdb(self):
if '_resultdb' in self.__dict__:
return self._resultdb
db = Database(self.mrf.mln)
for atom in sorted(self.results, key=str):
db[str(atom)] = self.results[atom]
return db
def __init__(self, mln, db):
self.mln = mln.materialize(db)
self._evidence = []
# self.evidenceBackup = {}
self._variables = {}
self._variables_by_idx = {} # gnd atom idx -> variable
self._variables_by_gndatomidx = {} # gnd atom idx
self._gndatoms = {}
self._gndatoms_by_idx = {}
# get combined domain
self.domains = mergedom(self.mln.domains, db.domains)
# self.softEvidence = list(mln.posteriorProbReqs) # constraints on posterior
# probabilities are nothing but
# soft evidence and can be handled in exactly the same way
# ground members
self.formulas = list(self.mln.formulas)
# self.gndAtoms = {}
# self.gndBlockLookup = {}
# self.gndBlocks = {}
# self.gndAtomsByIdx = {}
# self.gndFormulas = []
# self.gndAtomOccurrencesInGFs = []
if isinstance(db, basestring):
db = Database.load(self.mln, dbfile=db)
elif isinstance(db, Database):
pass
elif db is None:
db = Database(self.mln)
else:
raise Exception("Not a valid database argument (type %s)" % (str(type(db))))
self.db = db
# materialize formula weights
self._materialize_weights()
return
def evalMLN(self, mln, dbs, module):
'''
Returns a confusion matrix for the given (learned) MLN evaluated on
the databases given in dbs.
'''
log = logs.getlogger(self.fold_id)
queryPred = self.params.queryPred
queryDom = self.params.queryDom
sig = [
'?arg%d' % i
for i, _ in enumerate(self.params.altMLN.predicates[queryPred])
]
querytempl = '%s(%s)' % (queryPred, ','.join(sig))
dbs = [db.duplicate() for db in dbs]
infer = PRACInference(module.prac, [])
inferenceStep = PRACInferenceStep(infer, self)
for db in dbs:
# save and remove the query predicates from the evidence
trueDB = Database(self.params.altMLN)
for bindings in db.query(querytempl):
atom = querytempl
for binding in bindings:
atom = atom.replace(binding, bindings[binding])
trueDB.addGroundAtom(atom)
db.retractGndAtom(atom)
try:
inferenceStep.output_dbs = [db]
infer.inference_steps = [inferenceStep]
module.prac.run(infer, module, mln=mln)
resultDB = infer.inference_steps[-1].output_dbs[-1]
sig2 = list(sig)
entityIdx = mln.predicates[queryPred].index(queryDom)
for entity in db.domains[queryDom]:
sig2[entityIdx] = entity
query = '%s(%s)' % (queryPred, ','.join(sig2))
for truth in trueDB.query(query):
truth = list(truth.values()).pop()
for pred in resultDB.query(query):
pred = list(pred.values()).pop()
self.confMatrix.addClassificationResult(pred, truth)
for e, v in trueDB.evidence.items():
if v is not None:
db.addGroundAtom('%s%s' %
('' if v is True else '!', e))
except:
log.critical(''.join(
traceback.format_exception(*sys.exc_info())))
def run(self):
watch = StopWatch()
watch.tag('inference', self.verbose)
# load the MLN
if isinstance(self.mln, MLN):
mln = self.mln
else:
raise Exception('No MLN specified')
if self.use_emln and self.emln is not None:
mlnstrio = io.StringIO()
mln.write(mlnstrio)
mlnstr = mlnstrio.getvalue()
mlnstrio.close()
emln = self.emln
mln = parse_mln(mlnstr + emln,
grammar=self.grammar,
logic=self.logic)
# load the database
if isinstance(self.db, Database):
db = self.db
elif isinstance(self.db, list) and len(self.db) == 1:
db = self.db[0]
elif isinstance(self.db, list) and len(self.db) == 0:
db = Database(mln)
elif isinstance(self.db, list):
raise Exception(
'Got {} dbs. Can only handle one for inference.'.format(
len(self.db)))
else:
raise Exception('DB of invalid format {}'.format(type(self.db)))
# expand the
# parameters
params = dict(self._config)
if 'params' in params:
params.update(eval("dict(%s)" % params['params']))
del params['params']
params['verbose'] = self.verbose
if self.verbose:
print((tabulate(sorted(list(params.items()),
key=lambda k_v: str(k_v[0])),
headers=('Parameter:', 'Value:'))))
if type(db) is list and len(db) > 1:
raise Exception('Inference can only handle one database at a time')
elif type(db) is list:
db = db[0]
params['cw_preds'] = [x for x in self.cw_preds if bool(x)]
# extract and remove all non-algorithm
for s in GUI_SETTINGS:
if s in params: del params[s]
if self.profile:
prof = Profile()
print('starting profiler...')
prof.enable()
# set the debug level
olddebug = logger.level
logger.level = (eval('logs.%s' %
params.get('debug', 'WARNING').upper()))
result = None
try:
mln_ = mln.materialize(db)
mrf = mln_.ground(db)
inference = self.method(mrf, self.queries, **params)
if self.verbose:
print()
print((headline('EVIDENCE VARIABLES')))
print()
mrf.print_evidence_vars()
result = inference.run()
if self.verbose:
print()
print((headline('INFERENCE RESULTS')))
print()
inference.write()
if self.verbose:
print()
inference.write_elapsed_time()
except SystemExit:
traceback.print_exc()
print('Cancelled...')
finally:
if self.profile:
prof.disable()
print((headline('PROFILER STATISTICS')))
ps = pstats.Stats(prof,
stream=sys.stdout).sort_stats('cumulative')
ps.print_stats()
# reset the debug level
logger.level = olddebug
if self.verbose:
print()
watch.finish()
watch.printSteps()
return result
f = mln.logic.grammar.parse_formula(f)
mln.write()
# print f, '==================================================================================='
# f.print_structure()
# print 'repr of f', repr(f)
# print 'list f.literals', list(f.literals())
# print 'parse_formula', mln.logic.parse_formula('bar(x)') in f.literals()
# print 'f', f
cnf = f.cnf()
# print 'structure:'
cnf.print_structure()
# print 'cnf:',cnf
mln.formula(cnf)
db = Database(mln)
matmln = mln.materialize(db)
matmln.write()
# test = ['!a(k)',
# 'a(c) ^ b(g)',
# 'b(x) v !a(l) ^ b(x)',
# '!(a(g)) => ((!(f(x) v b(a))))',
# "f(h) v (g(?h) <=> !f(?k) ^ d(e))",
# 'f(t) ^ ?x = y'
# ]
# for t in test:
# print t
# mln.logic.grammar.tree.reset()
# mln.logic.grammar.parse_formula(t).print_structure()
# print t
def resultdb(self):
db = Database(self.mrf.mln)
for atom in sorted(self.results, key=str):
db[str(atom)] = self.results[atom]
return db
from pracmln.mln.base import MLN
from pracmln.mln.database import Database
from pracmln.mln.learning.bpll import BPLL
mln = MLN(logic='FirstOrderLogic', grammar='StandardGrammar', mlnfile='onenote.mln')
# mln.fixweights = [True, False]
db = Database(mln, dbfile='onenote-train.db')
mrf = mln.ground(db)
method = BPLL(mrf, prior_stdev=1, verbose=True)
result = method.run()
for w in result:
print('{:.3f}'.format(w))
from pracmln.mln.base import MLN from pracmln.mln.database import Database from pracmln.mln.inference.exact import EnumerationAsk from pracmln.mln.inference.gibbs import GibbsSampler from pracmln.mln.inference.mcsat import MCSAT from pracmln.mln.constants import ALL mln = MLN(logic='FirstOrderLogic', grammar='StandardGrammar', mlnfile='onenote.mln') db = Database(mln, dbfile='onenote-infer.db') mrf = mln.ground(db) # method = EnumerationAsk(mrf, queries=ALL) # method = GibbsSampler(mrf, chains=1, maxsteps=50, sample=True) method = MCSAT(mrf, chains=10, maxsteps=500, sample=True) result = method.run() result.write()
