def model_config(self,predicate,formula,database,mln_path,db_path):
"""
Returns the database and mln objects in MLN format
--Inputs--
predicate: predicate object with parsed predicates
formula: formula object with parsed predicates
database:.txt file containing the database(s)
mln_path: .mln file name to save the learned weights per formula
db_path: .db file to save the progress of the database learning
"""
base_path = os.getcwd()
mln = MLN(grammar='PRACGrammar',logic='FirstOrderLogic') #Parsing with PRACGrammar since we are using clusters
for i in predicate:
mln << i
print('input predicate successful:'+i)
for i in formula:
mln << i
print('input formula successful :'+i)
mln.write()
mln.tofile(base_path + '/'+ mln_path)
db = Database.load(mln,database)
#db.write()
#db.tofile(base_path + '/'+ db_path)
return (db,mln)
def model_config(predicate, formula, database, mln_path,
db_path): # mln_path,db_path 為string
base_path = os.getcwd()
mln = MLN(grammar='StandardGrammar', logic='FirstOrderLogic')
for i in predicate:
mln << i
print('input predicate successful:' + i)
for i in formula:
mln << i
print('input formula successful :' + i)
mln.write()
mln.tofile(base_path + '\\' + mln_path) #把谓语数据储存成 mln_path.mln 档案
db = Database(mln)
try:
for i in enumerate(database):
db << i[1][1]
print('input database successful : ' + i[1][0] + ' : ' +
i[1][1])
except:
for j in database[i[0]::]:
db << j[1]
db.write()
db.tofile(base_path + '\\' + db_path) #把证据数据储存成 db_path.db 档案
return (db, mln)
def test_learning_taxonomies():
p = '$PRACMLN_HOME/examples/taxonomies/taxonomies.pracmln'
mln = MLN(mlnfile=('%s:senses_and_roles.mln' % p), grammar='PRACGrammar')
mln.write()
dbs = Database.load(mln, dbfiles='%s:training.db' % p)
for method in ('DPLL', 'DBPLL_CG', 'DCLL'):
for multicore in (True, False):
print '=== LEARNING TEST:', method, '==='
learn(method=method, mln=mln, db=dbs, verbose=True, multicore=multicore, epreds='is_a', discr_preds=EVIDENCE_PREDS).run()
def test_learning_smokers():
p = '$PRACMLN_HOME/examples/smokers/smokers.pracmln'
mln = MLN(mlnfile=('%s:smoking.mln' % p), grammar='StandardGrammar')
mln.write()
db = Database(mln, dbfile='%s:smoking-train.db' % p)
for method in ('BPLL', 'BPLL_CG', 'CLL'):
for multicore in (True, False):
print '=== LEARNING TEST:', method, '==='
learn(method=method, mln=mln, db=db, verbose=True, multicore=multicore).run()
def test_learning_smokers():
p = os.path.join(locs.examples, 'smokers', 'smokers.pracmln')
mln = MLN(mlnfile=('%s:smoking.mln' % p), grammar='StandardGrammar')
mln.write()
db = Database(mln, dbfile='%s:smoking-train.db' % p)
for method in ('BPLL', 'BPLL_CG', 'CLL'):
for multicore in (True, False):
print('=== LEARNING TEST:', method, '===')
learn(method=method,
mln=mln,
db=db,
verbose=True,
multicore=multicore).run()
def test_learning_taxonomies():
p = os.path.join(locs.examples, 'taxonomies', 'taxonomies.pracmln')
mln = MLN(mlnfile=('%s:senses_and_roles.mln' % p), grammar='PRACGrammar')
mln.write()
dbs = Database.load(mln, dbfiles='%s:training.db' % p)
for method in ('DPLL', 'DBPLL_CG', 'DCLL'):
for multicore in (True, False):
print('=== LEARNING TEST:', method, '===')
learn(method=method,
mln=mln,
db=dbs,
verbose=True,
multicore=multicore,
epreds='is_a',
discr_preds=EVIDENCE_PREDS).run()
def test_mln():
mln = MLN()
mln << 'foo(x)' # predicate declaration
mln << 'bar(y)' # another pred declaration
mln << 'bar(?x) => bar(?y).' # hard logical constraint
mln << 'logx(.75)/log(.25) foo(?x)' # weighted formula
print('mln write:')
mln.write()
print('mln predicates:')
for pred in mln.predicates:
print(repr(pred))
print('mln formulas:')
for f in mln.formulas:
print(f)
f.print_structure()
return mln
def main():
path = os.path.join('Vessel Traffic Data sub-areas April 2020',
'cts_sub-areas_04_2020_pt',
'cts_bass_st_04_2020_pt.shp')
df = gpd.read_file(path)
mln = MLN()
populatePredicates(mln)
populateFormulas(mln)
mln.write()
dbs = []
for i in range(100):
collection = df.sample(n=3)
lons = collection['LON'].tolist()
lats = collection['LAT'].tolist()
points = list(zip(lons, lats))
evidence = ThreePointEvidence(*points)
db = Database(mln)
evidence.generateEvidence(db)
dbs.append(db)
mln.learn(dbs)
from pracmln import MLN
from pracmln import Database
from pracmln import MLNQuery
mln = MLN(mlnfile='./data/smokers/mlns/smoking_trained.mln',grammar='PRACGrammar', logic='FirstOrderLogic')
mln.write()
db = Database.load(mln,'./data/smokers/dbs/smoking-test.db')[0]
db.write()
print("Running Query...")
result = MLNQuery(mln=mln, db=db).run()
print(result)
