def load(mln, dbfiles, ignore_unknown_preds=False, db=None):
'''
Reads one or multiple database files containing literals and/or domains.
Returns one or multiple databases where domains is dictionary mapping
domain names to lists of constants defined in the database
and evidence is a dictionary mapping ground atom strings to truth values
:param dbfile: a single one or a list of paths to database file.
:param mln: the MLN object which should be used to load the database.
:returns: either one single or a list of database objects.
:Example:
>>> mln = MLN()
>>> db = Database.load(mln, './example.db')
'''
if type(dbfiles) is not list:
dbfiles = [dbfiles]
dbs = []
for dbpath in dbfiles:
if isinstance(dbpath, basestring):
dbpath = mlnpath(dbpath)
if isinstance(dbpath, mlnpath):
projectpath = None
if dbpath.project is not None:
projectpath = dbpath.projectloc
dirs = [os.path.dirname(fp) for fp in dbfiles]
dbs_ = parse_db(mln, content=dbpath.content, ignore_unknown_preds=ignore_unknown_preds, db=db, dirs=dirs, projectpath=projectpath)
dbs.extend(dbs_)
else:
raise Exception('Illegal db file specifier: %s' % dbpath)
if len(dbs) > 1 and db is not None:
raise Exception('Cannot attach multiple databases to a single database object. Use Database.load(..., db=None).')
else:
return dbs
def load(mln, dbfiles, ignore_unknown_preds=False, db=None):
'''
Reads one or multiple database files containing literals and/or domains.
Returns one or multiple databases where domains is dictionary mapping
domain names to lists of constants defined in the database
and evidence is a dictionary mapping ground atom strings to truth values
:param dbfile: a single one or a list of paths to database file.
:param mln: the MLN object which should be used to load the database.
:returns: either one single or a list of database objects.
:Example:
>>> mln = MLN()
>>> db = Database.load(mln, './example.db')
'''
if type(dbfiles) is not list:
dbfiles = [dbfiles]
dbs = []
for dbpath in dbfiles:
if isinstance(dbpath, basestring):
dbpath = mlnpath(dbpath)
if isinstance(dbpath, mlnpath):
projectpath = None
if dbpath.project is not None:
projectpath = dbpath.projectloc
dirs = [os.path.dirname(fp) for fp in dbfiles]
dbs_ = parse_db(mln, content=dbpath.content, ignore_unknown_preds=ignore_unknown_preds, db=db, dirs=dirs, projectpath=projectpath)
dbs.extend(dbs_)
else:
raise Exception('Illegal db file specifier: %s' % dbpath)
if len(dbs) > 1 and db is not None:
raise Exception('Cannot attach multiple databases to a single database object. Use Database.load(..., db=None).')
else:
return dbs
def load(files, logic='FirstOrderLogic', grammar='PRACGrammar', mln=None):
'''
Reads an MLN object from a file or a set of files.
:param files: one or more :class:`pracmln.mlnpath` strings. If multiple file names are given,
the contents of all files will be concatenated.
:param logic: (string) the type of logic to be used. Either `FirstOrderLogic` or `FuzzyLogic`.
:param grammar: (string) the syntax to be used for parsing the MLN file. Either `PRACGrammar` or `StandardGrammar`.
'''
# read MLN file
text = ''
if files is not None:
if not type(files) is list:
files = [files]
projectpath = None
for f in files:
if isinstance(f, basestring):
p = mlnpath(f)
if p.project is not None:
projectpath = p.projectloc
text += p.content
elif isinstance(f, mlnpath):
text += f.content
else:
raise Exception('Unexpected file specification: %s' %
str(f))
dirs = [os.path.dirname(fn) for fn in files]
return parse_mln(text,
searchpaths=dirs,
projectpath=projectpath,
logic=logic,
grammar=grammar,
mln=mln)
raise Exception('No mln files given.')
def load(files, logic='FirstOrderLogic', grammar='PRACGrammar', mln=None):
'''
Reads an MLN object from a file or a set of files.
:param files: one or more :class:`pracmln.mlnpath` strings. If multiple file names are given,
the contents of all files will be concatenated.
:param logic: (string) the type of logic to be used. Either `FirstOrderLogic` or `FuzzyLogic`.
:param grammar: (string) the syntax to be used for parsing the MLN file. Either `PRACGrammar` or `StandardGrammar`.
'''
# read MLN file
text = ''
if files is not None:
if not type(files) is list:
files = [files]
projectpath = None
for f in files:
if isinstance(f, basestring):
p = mlnpath(f)
if p.project is not None:
projectpath = p.projectloc
text += p.content
elif isinstance(f, mlnpath):
text += f.content
else: raise Exception('Unexpected file specification: %s' % str(f))
dirs = [os.path.dirname(fn) for fn in files]
return parse_mln(text, searchpaths=dirs, projectpath=projectpath, logic=logic, grammar=grammar, mln=mln)
raise Exception('No mln files given.')
def import_file(self):
filename = askopenfilename(initialdir=self.dir, filetypes=self.fsettings.get('ftypes'), defaultextension=self.fsettings.get('extension', '.mln'))
if filename:
fpath, fname = ntpath.split(filename)
self.dir = os.path.abspath(fpath)
content = mlnpath(filename).content
if self.import_hook is not None:
self.import_hook(fname, content)
self.update_file_choices()
self.selected_file.set(fname)
self.dirty = True
def import_file(self):
filename = askopenfilename(initialdir=self.dir,
filetypes=self.fsettings.get('ftypes'),
defaultextension=self.fsettings.get(
'extension', '.mln'))
if filename:
fpath, fname = ntpath.split(filename)
self.dir = os.path.abspath(fpath)
content = mlnpath(filename).content
if self.import_hook is not None:
self.import_hook(fname, content)
self.update_file_choices()
self.selected_file.set(fname)
self.dirty = True
def parse_db(mln, content, ignore_unknown_preds=False, db=None, dirs=['.'], projectpath=None):
'''
Reads one or more databases in a string representation and returns
the respective Database objects.
:param mln: the MLN object which should be used to load the database.
:param content: the string representation of one or multiple ('---'-separated)
databases
:param ignore_unknown_preds: by default this function raises an Exception when it encounters
a predicate in the DB that has not been declared in the associated
MLN. ignore_unknown_preds=True simply ignores such predicates.
:param db: the Database object that shall receive the facts stored in the new DB.
If None, a new `Database` object will be created.
'''
log = logging.getLogger('db')
content = stripComments(content)
allow_multiple = True
if db is None:
allow_multiple = True
db = Database(mln, ignore_unknown_preds=ignore_unknown_preds)
dbs = []
# expand domains with dbtext constants and save evidence
for line, l in enumerate(content.split("\n")):
l = l.strip()
if l == '':
continue
# separator between independent databases
elif l == '---' and not db.isempty():
dbs.append(db)
db = Database(mln)
continue
# domain declaration
elif "{" in l:
domname, constants = db.mln.logic.parse_domain(l)
domnames = [domname for _ in constants]
# include
elif l.startswith('#include'):
filename = l[len("#include "):].strip()
m = re.match(r'"(?P<filename>.+)"', filename)
if m is not None:
filename = m.group('filename')
# if the path is relative, look for the respective file
# relatively to all paths specified. Take the first file matching.
if not mlnpath(filename).exists:
includefilename = None
for d in dirs:
mlnp = '/'.join([d, filename])
if mlnpath(mlnp).exists:
includefilename = mlnp
break
if includefilename is None:
raise Exception('File not found: %s' % filename)
else:
includefilename = filename
else:
m = re.match(r'<(?P<filename>.+)>', filename)
if m is not None:
filename = m.group('filename')
else:
raise MLNParsingError('Malformed #include statement: %s' % line)
if projectpath is None:
raise MLNParsingError('No project specified: Cannot locate import from project: %s' % filename)
includefilename = ':'.join([projectpath, filename])
logger.debug('Including file: "%s"' % includefilename)
p = mlnpath(includefilename)
dbs.extend(parse_db(content=mlnpath(includefilename).content, ignore_unknown_preds=ignore_unknown_preds, dirs=[p.resolve_path()]+dirs,
projectpath=ifNone(p.project, projectpath, lambda x: '/'.join(p.path+[x])), mln=mln))
continue
# valued evidence
elif l[0] in "0123456789":
s = l.find(" ")
gndatom = l[s + 1:].replace(" ", "")
value = float(l[:s])
if value < 0 or value > 1:
raise Exception('Valued evidence must be in [0,1]')
if gndatom in db.evidence:
raise Exception("Duplicate soft evidence for '%s'" % gndatom)
try:
positive, predname, constants = mln.logic.parse_literal(gndatom) # TODO Should we allow soft evidence on non-atoms here? (This assumes atoms)
except NoSuchPredicateError, e:
if ignore_unknown_preds: continue
else: raise e
domnames = mln.predicate(predname).argdoms
db << (gndatom, value)
# literal
else:
if l[0] == "?":
raise Exception("Unknown literals not supported (%s)" % l) # this is an Alchemy feature
try:
true, predname, constants = mln.logic.parse_literal(l)
except NoSuchPredicateError, e:
if ignore_unknown_preds: continue
else: raise e
except Exception, e:
traceback.print_exc()
raise MLNParsingError('Error parsing line %d: %s (%s)' % (line+1, l, e.message))
def infer(self, savegeometry=True, options={}, *args):
mln_content = self.mln_container.editor.get("1.0", END).strip()
db_content = self.db_container.editor.get("1.0", END).strip()
# create conf from current gui settings
self.update_config()
# write gui settings
self.write_gconfig(savegeometry=savegeometry)
# hide gui
self.master.withdraw()
try:
print((headline('PRACMLN QUERY TOOL')))
print()
if options.get('mlnarg') is not None:
mlnobj = MLN(mlnfile=os.path.abspath(options.get('mlnarg')),
logic=self.config.get('logic', 'FirstOrderLogic'),
grammar=self.config.get('grammar', 'PRACGrammar'))
else:
mlnobj = parse_mln(
mln_content,
searchpaths=[self.dir],
projectpath=os.path.join(self.dir, self.project.name),
logic=self.config.get('logic', 'FirstOrderLogic'),
grammar=self.config.get('grammar', 'PRACGrammar'))
if options.get('emlnarg') is not None:
emln_content = mlnpath(options.get('emlnarg')).content
else:
emln_content = self.emln_container.editor.get("1.0",
END).strip()
if options.get('dbarg') is not None:
dbobj = Database.load(mlnobj,
dbfiles=[options.get('dbarg')],
ignore_unknown_preds=self.config.get(
'ignore_unknown_preds', True))
else:
out(self.config.get('ignore_unknown_preds', True))
dbobj = parse_db(mlnobj,
db_content,
ignore_unknown_preds=self.config.get(
'ignore_unknown_preds', True))
if options.get('queryarg') is not None:
self.config["queries"] = options.get('queryarg')
infer = MLNQuery(config=self.config,
mln=mlnobj,
db=dbobj,
emln=emln_content)
result = infer.run()
# write to file if run from commandline, otherwise save result to project results
if options.get('outputfile') is not None:
output = io.StringIO()
result.write(output)
with open(os.path.abspath(options.get('outputfile')),
'w') as f:
f.write(output.getvalue())
logger.info('saved result to {}'.format(
os.path.abspath(options.get('outputfile'))))
elif self.save.get():
output = io.StringIO()
result.write(output)
fname = self.output_filename.get()
self.project.add_result(fname, output.getvalue())
self.project.save(dirpath=self.dir)
logger.info(
'saved result to file results/{} in project {}'.format(
fname, self.project.name))
else:
logger.debug(
'No output file given - results have not been saved.')
except:
traceback.print_exc()
# restore main window
sys.stdout.flush()
self.master.deiconify()
def parse_mln(text,
searchpaths=['.'],
projectpath=None,
logic='FirstOrderLogic',
grammar='PRACGrammar',
mln=None):
'''
Reads an MLN from a stream providing a 'read' method.
'''
dirs = [
os.path.abspath(os.path.expandvars(os.path.expanduser(p)))
for p in searchpaths
]
formulatemplates = []
text = str(text)
if text == "":
raise MLNParsingError(
"No MLN content to construct model from was given; must specify either file/list of files or content string!"
)
# replace some meta-directives in comments
text = re.compile(r'//\s*<group>\s*$', re.MULTILINE).sub("#group", text)
text = re.compile(r'//\s*</group>\s*$', re.MULTILINE).sub("#group.", text)
# remove comments
text = stripComments(text)
if mln is None:
mln = MLN(logic, grammar)
# read lines
mln.hard_formulas = []
templateIdx2GroupIdx = {}
inGroup = False
idxGroup = -1
fixWeightOfNextFormula = False
fuzzy = False
pseudofuzzy = False
uniquevars = None
fixedWeightTemplateIndices = []
lines = text.split("\n")
iLine = 0
while iLine < len(lines):
line = lines[iLine]
iLine += 1
line = line.strip()
try:
if len(line) == 0: continue
# meta directives
if line == "#group":
idxGroup += 1
inGroup = True
continue
elif line == "#group.":
inGroup = False
continue
elif line.startswith("#fixweight"):
fixWeightOfNextFormula = True
continue
elif line.startswith('#fuzzy'):
if not isinstance(mln.logic, FuzzyLogic):
logger.warning(
'Fuzzy declarations are not supported in %s. Treated as a binary predicate.'
% mln.logic.__class__.__name__)
pseudofuzzy = True
else:
fuzzy = True
continue
elif line.startswith("#include"):
filename = line[len("#include "):].strip()
m = re.match(r'"(?P<filename>.+)"', filename)
if m is not None:
filename = m.group('filename')
# if the path is relative, look for the respective file
# relatively to all paths specified. Take the first file matching.
if not mlnpath(filename).exists:
includefilename = None
for d in dirs:
mlnp = '/'.join([d, filename])
if mlnpath(mlnp).exists:
includefilename = mlnp
break
if includefilename is None:
raise Exception('File not found: %s' % filename)
else:
includefilename = filename
else:
m = re.match(r'<(?P<filename>.+)>', filename)
if m is not None:
filename = m.group('filename')
else:
raise MLNParsingError(
'Malformed #include statement: %s' % line)
if projectpath is None:
raise MLNParsingError(
'No project specified: Cannot locate import from project: %s'
% filename)
includefilename = ':'.join([projectpath, filename])
logger.debug('Including file: "%s"' % includefilename)
p = mlnpath(includefilename)
parse_mln(text=mlnpath(includefilename).content,
searchpaths=[p.resolve_path()] + dirs,
projectpath=ifnone(p.project, projectpath,
lambda x: '/'.join(p.path + [x])),
logic=logic,
grammar=grammar,
mln=mln)
continue
elif line.startswith('#unique'):
try:
uniVars = re.search('#unique{(.+)}', line)
uniVars = uniVars.groups()[0]
uniVars = map(str.strip, uniVars.split(','))
uniquevars = uniVars
except:
raise MLNParsingError(
'Malformed #unique expression: "%s"' % line)
continue
elif line.startswith(
"#AdaptiveMLNDependency"
): # declared as "#AdaptiveMLNDependency:pred:domain"; seems to be deprecated
depPredicate, domain = line.split(":")[1:3]
if hasattr(mln, 'AdaptiveDependencyMap'):
if depPredicate in mln.AdaptiveDependencyMap:
mln.AdaptiveDependencyMap[depPredicate].add(domain)
else:
mln.AdaptiveDependencyMap[depPredicate] = set([domain])
else:
mln.AdaptiveDependencyMap = {depPredicate: set([domain])}
continue
# domain decl
if '=' in line:
# try normal domain definition
parse = mln.logic.parse_domain(line)
if parse is not None:
domName, constants = parse
domName = str(domName)
constants = map(str, constants)
if domName in mln.domains:
logger.debug(
"Domain redefinition: Domain '%s' is being updated with values %s."
% (domName, str(constants)))
if domName not in mln.domains:
mln.domains[domName] = []
mln.constant(domName, *constants)
mln.domain_decls.append(line)
continue
# prior probability requirement
if line.startswith("P("):
m = re.match(r"P\((.*?)\)\s*=\s*([\.\de]+)", line)
if m is None:
raise MLNParsingError(
"Prior probability constraint formatted incorrectly: %s"
% line)
mln.prior(f=mln.logic.parse_formula(m.group(1)),
p=float(m.group(2)))
continue
# posterior probability requirement/soft evidence
if line.startswith("R(") or line.startswith("SE("):
m = re.match(r"(?:R|SE)\((.*?)\)\s*=\s*([\.\de]+)", line)
if m is None:
raise MLNParsingError(
"Posterior probability constraint formatted incorrectly: %s"
% line)
mln.posterior(f=mln.logic.parse_formula(m.group(1)),
p=float(m.group(2)))
continue
# variable definition
if re.match(r'(\$\w+)\s*=(.+)', line):
m = re.match(r'(\$\w+)\s*=(.+)', line)
if m is None:
raise MLNParsingError("Variable assigment malformed: %s" %
line)
mln.vars[m.group(1)] = "%s" % m.group(2).strip()
continue
# predicate decl or formula with weight
else:
isHard = False
isPredDecl = False
if line[-1] == '.': # hard (without explicit weight -> determine later)
isHard = True
formula = line[:-1]
else: # with weight
# try predicate declaration
isPredDecl = True
try:
pred = mln.logic.parse_predicate(line)
except Exception, e:
isPredDecl = False
if isPredDecl:
predname = str(pred[0])
argdoms = map(str, pred[1])
softmutex = False
mutex = None
for i, dom in enumerate(argdoms):
if dom[-1] in ('!', '?'):
if mutex is not None:
raise Exception(
'More than one arguments are specified as (soft-)functional'
)
if fuzzy:
raise Exception(
'(Soft-)functional predicates must not be fuzzy.'
)
mutex = i
if dom[-1] == '?': softmutex = True
argdoms = map(lambda x: x.strip('!?'), argdoms)
pred = None
if mutex is not None:
if softmutex:
pred = SoftFunctionalPredicate(
predname, argdoms, mutex)
else:
pred = FunctionalPredicate(predname, argdoms,
mutex)
elif fuzzy:
pred = FuzzyPredicate(predname, argdoms)
fuzzy = False
else:
pred = Predicate(predname, argdoms)
if pseudofuzzy:
mln.fuzzypreds.append(predname)
pseudofuzzy = False
mln.predicate(pred)
continue
else:
# formula (template) with weight or terminated by '.'
if not isHard:
spacepos = line.find(' ')
weight = line[:spacepos]
formula = line[spacepos:].strip()
try:
formula = mln.logic.parse_formula(formula)
if isHard:
weight = HARD # not set until instantiation when other weights are known
idxTemplate = len(formulatemplates)
formulatemplates.append(formula)
fixweight = False
if inGroup:
templateIdx2GroupIdx[idxTemplate] = idxGroup
if fixWeightOfNextFormula == True:
fixWeightOfNextFormula = False
fixweight = True
fixedWeightTemplateIndices.append(idxTemplate)
# expand predicate groups
for variant in formula.expandgrouplits():
mln.formula(variant,
weight=weight,
fixweight=fixweight,
unique_templvars=uniquevars)
if uniquevars:
uniquevars = None
except ParseException, e:
raise MLNParsingError("Error parsing formula '%s'\n" %
formula)
if fuzzy and not isPredDecl:
raise Exception(
'"#fuzzy" decorator not allowed at this place: %s' %
line)
from pracmln.utils.project import mlnpath
import pandas as pd
import numpy as np
pd.options.mode.chained_assignment = None
result_file_name = "test5_query_out_2.txt" #raw_input()
df_clean = pd.read_csv("hospital_clean_datatable.csv")
df_dirty = pd.read_csv("holo_hosp_data_dirty_from_mysql.csv")
df_mln_clean = df_dirty.copy()
path = '/home/seismic/PUD-Research-Project/examples/test_mln5.pracmln:' + result_file_name
p = mlnpath(path)
content = p.content
print content
"""
#to make above code work
#added code to line 489 of /home/seismic/.local/lib/python2.7/site-packages/pracmln/utils/project.py
elif fileext == 'txt':
res = proj.results.get(self.file)
if res is None: raise Exception('Project %s does not contain results named %s' % (self.project, self.file))
return res
"""
line1 = content.split("\n")
print line1[0]
entry1 = line1[0].split("%")
def parse_db(mln, content, ignore_unknown_preds=False, db=None, dirs=['.'], projectpath=None):
'''
Reads one or more databases in a string representation and returns
the respective Database objects.
:param mln: the MLN object which should be used to load the database.
:param content: the string representation of one or multiple ('---'-separated)
databases
:param ignore_unknown_preds: by default this function raises an Exception when it encounters
a predicate in the DB that has not been declared in the associated
MLN. ignore_unknown_preds=True simply ignores such predicates.
:param db: the Database object that shall receive the facts stored in the new DB.
If None, a new `Database` object will be created.
'''
log = logs.getlogger('db')
content = stripComments(content)
allow_multiple = True
if db is None:
allow_multiple = True
db = Database(mln, ignore_unknown_preds=ignore_unknown_preds)
dbs = []
# expand domains with dbtext constants and save evidence
for line, l in enumerate(content.split("\n")):
l = l.strip()
if l == '':
continue
# separator between independent databases
elif l == '---' and not db.isempty():
dbs.append(db)
db = Database(mln)
continue
# domain declaration
elif "{" in l:
domname, constants = db.mln.logic.parse_domain(l)
domnames = [domname for _ in constants]
# include
elif l.startswith('#include'):
filename = l[len("#include "):].strip()
m = re.match(r'"(?P<filename>.+)"', filename)
if m is not None:
filename = m.group('filename')
# if the path is relative, look for the respective file
# relatively to all paths specified. Take the first file matching.
if not mlnpath(filename).exists:
includefilename = None
for d in dirs:
mlnp = '/'.join([d, filename])
if mlnpath(mlnp).exists:
includefilename = mlnp
break
if includefilename is None:
raise Exception('File not found: %s' % filename)
else:
includefilename = filename
else:
m = re.match(r'<(?P<filename>.+)>', filename)
if m is not None:
filename = m.group('filename')
else:
raise MLNParsingError('Malformed #include statement: %s' % line)
if projectpath is None:
raise MLNParsingError('No project specified: Cannot locate import from project: %s' % filename)
includefilename = ':'.join([projectpath, filename])
logger.debug('Including file: "%s"' % includefilename)
p = mlnpath(includefilename)
dbs.extend(parse_db(content=mlnpath(includefilename).content, ignore_unknown_preds=ignore_unknown_preds, dirs=[p.resolve_path()]+dirs,
projectpath=ifnone(p.project, projectpath, lambda x: '/'.join(p.path+[x])), mln=mln))
continue
# valued evidence
elif l[0] in "0123456789":
s = l.find(" ")
gndatom = l[s + 1:].replace(" ", "")
value = float(l[:s])
if value < 0 or value > 1:
raise Exception('Valued evidence must be in [0,1]')
if gndatom in db.evidence:
raise Exception("Duplicate soft evidence for '%s'" % gndatom)
try:
_, predname, constants = mln.logic.parse_literal(gndatom) # TODO Should we allow soft evidence on non-atoms here? (This assumes atoms)
except NoSuchPredicateError, e:
if ignore_unknown_preds: continue
else: raise e
domnames = mln.predicate(predname).argdoms
db << (gndatom, value)
# literal
else:
if l[0] == "?":
raise Exception("Unknown literals not supported (%s)" % l) # this is an Alchemy feature
try:
true, predname, constants = mln.logic.parse_literal(l)
except NoSuchPredicateError, e:
if ignore_unknown_preds: continue
else: raise e
except Exception, e:
traceback.print_exc()
raise MLNParsingError('Error parsing line %d: %s (%s)' % (line+1, l, e.message))
def parse_mln(text, searchpaths=['.'], projectpath=None, logic='FirstOrderLogic', grammar='PRACGrammar', mln=None):
'''
Reads an MLN from a stream providing a 'read' method.
'''
dirs = [os.path.abspath(os.path.expandvars(os.path.expanduser(p))) for p in searchpaths]
formulatemplates = []
text = str(text)
if text == "":
raise MLNParsingError("No MLN content to construct model from was given; must specify either file/list of files or content string!")
# replace some meta-directives in comments
text = re.compile(r'//\s*<group>\s*$', re.MULTILINE).sub("#group", text)
text = re.compile(r'//\s*</group>\s*$', re.MULTILINE).sub("#group.", text)
# remove comments
text = stripComments(text)
if mln is None:
mln = MLN(logic, grammar)
# read lines
mln.hard_formulas = []
templateIdx2GroupIdx = {}
inGroup = False
idxGroup = -1
fixWeightOfNextFormula = False
fuzzy = False
pseudofuzzy = False
uniquevars = None
fixedWeightTemplateIndices = []
lines = text.split("\n")
iLine = 0
while iLine < len(lines):
line = lines[iLine]
iLine += 1
line = line.strip()
try:
if len(line) == 0: continue
# meta directives
if line == "#group":
idxGroup += 1
inGroup = True
continue
elif line == "#group.":
inGroup = False
continue
elif line.startswith("#fixweight"):
fixWeightOfNextFormula = True
continue
elif line.startswith('#fuzzy'):
if not isinstance(mln.logic, FuzzyLogic):
logger.warning('Fuzzy declarations are not supported in %s. Treated as a binary predicate.' % mln.logic.__class__.__name__)
pseudofuzzy = True
else:
fuzzy = True
continue
elif line.startswith("#include"):
filename = line[len("#include "):].strip()
m = re.match(r'"(?P<filename>.+)"', filename)
if m is not None:
filename = m.group('filename')
# if the path is relative, look for the respective file
# relatively to all paths specified. Take the first file matching.
if not mlnpath(filename).exists:
includefilename = None
for d in dirs:
mlnp = '/'.join([d, filename])
if mlnpath(mlnp).exists:
includefilename = mlnp
break
if includefilename is None:
raise Exception('File not found: %s' % filename)
else:
includefilename = filename
else:
m = re.match(r'<(?P<filename>.+)>', filename)
if m is not None:
filename = m.group('filename')
else:
raise MLNParsingError('Malformed #include statement: %s' % line)
if projectpath is None:
raise MLNParsingError('No project specified: Cannot locate import from project: %s' % filename)
includefilename = ':'.join([projectpath, filename])
logger.debug('Including file: "%s"' % includefilename)
p = mlnpath(includefilename)
parse_mln(text=mlnpath(includefilename).content, searchpaths=[p.resolve_path()]+dirs,
projectpath=ifNone(p.project, projectpath, lambda x: '/'.join(p.path+[x])),
logic=logic, grammar=grammar, mln=mln)
continue
elif line.startswith('#unique'):
try:
uniVars = re.search('#unique{(.+)}', line)
uniVars = uniVars.groups()[0]
uniVars = map(str.strip, uniVars.split(','))
uniquevars = uniVars
except:
raise MLNParsingError('Malformed #unique expression: "%s"' % line)
continue
elif line.startswith("#AdaptiveMLNDependency"): # declared as "#AdaptiveMLNDependency:pred:domain"; seems to be deprecated
depPredicate, domain = line.split(":")[1:3]
if hasattr(mln, 'AdaptiveDependencyMap'):
if depPredicate in mln.AdaptiveDependencyMap:
mln.AdaptiveDependencyMap[depPredicate].add(domain)
else:
mln.AdaptiveDependencyMap[depPredicate] = set([domain])
else:
mln.AdaptiveDependencyMap = {depPredicate:set([domain])}
continue
# domain decl
if '=' in line:
# try normal domain definition
parse = mln.logic.parse_domain(line)
if parse is not None:
domName, constants = parse
domName = str(domName)
constants = map(str, constants)
if domName in mln.domains:
logger.debug("Domain redefinition: Domain '%s' is being updated with values %s." % (domName, str(constants)))
if domName not in mln.domains:
mln.domains[domName] = []
mln.constant(domName, *constants)
mln.domain_decls.append(line)
continue
# prior probability requirement
if line.startswith("P("):
m = re.match(r"P\((.*?)\)\s*=\s*([\.\de]+)", line)
if m is None:
raise MLNParsingError("Prior probability constraint formatted incorrectly: %s" % line)
mln.prior(f=mln.logic.parse_formula(m.group(1)), p=float(m.group(2)))
continue
# posterior probability requirement/soft evidence
if line.startswith("R(") or line.startswith("SE("):
m = re.match(r"(?:R|SE)\((.*?)\)\s*=\s*([\.\de]+)", line)
if m is None:
raise MLNParsingError("Posterior probability constraint formatted incorrectly: %s" % line)
mln.posterior(f=mln.logic.parse_formula(m.group(1)), p=float(m.group(2)))
continue
# variable definition
if re.match(r'(\$\w+)\s*=(.+)', line):
m = re.match(r'(\$\w+)\s*=(.+)', line)
if m is None:
raise MLNParsingError("Variable assigment malformed: %s" % line)
mln.vars[m.group(1)] = "%s" % m.group(2).strip()
continue
# predicate decl or formula with weight
else:
isHard = False
isPredDecl = False
if line[ -1] == '.': # hard (without explicit weight -> determine later)
isHard = True
formula = line[:-1]
else: # with weight
# try predicate declaration
isPredDecl = True
try:
pred = mln.logic.parse_predicate(line)
except Exception, e:
isPredDecl = False
if isPredDecl:
predname = str(pred[0])
argdoms = map(str, pred[1])
softmutex = False
mutex = None
for i, dom in enumerate(argdoms):
if dom[-1] in ('!', '?'):
if mutex is not None:
raise Exception('More than one arguments are specified as (soft-)functional')
if fuzzy: raise Exception('(Soft-)functional predicates must not be fuzzy.')
mutex = i
if dom[-1] == '?': softmutex = True
argdoms = map(lambda x: x.strip('!?'), argdoms)
pred = None
if mutex is not None:
if softmutex:
pred = SoftFunctionalPredicate(predname, argdoms, mutex)
else:
pred = FunctionalPredicate(predname, argdoms, mutex)
elif fuzzy:
pred = FuzzyPredicate(predname, argdoms)
fuzzy = False
else:
pred = Predicate(predname, argdoms)
if pseudofuzzy:
mln.fuzzypreds.append(predname)
pseudofuzzy = False
mln.predicate(pred)
continue
else:
# formula (template) with weight or terminated by '.'
if not isHard:
spacepos = line.find(' ')
weight = line[:spacepos]
formula = line[spacepos:].strip()
try:
formula = mln.logic.parse_formula(formula)
if isHard:
weight = HARD # not set until instantiation when other weights are known
idxTemplate = len(formulatemplates)
formulatemplates.append(formula)
fixweight = False
if inGroup:
templateIdx2GroupIdx[idxTemplate] = idxGroup
if fixWeightOfNextFormula == True:
fixWeightOfNextFormula = False
fixweight = True
fixedWeightTemplateIndices.append(idxTemplate)
# expand predicate groups
for variant in formula.expandgrouplits():
mln.formula(variant, weight=weight,
fixweight=fixweight,
unique_templvars=uniquevars)
if uniquevars:
uniquevars = None
except ParseException, e:
raise MLNParsingError("Error parsing formula '%s'\n" % formula)
if fuzzy and not isPredDecl: raise Exception('"#fuzzy" decorator not allowed at this place: %s' % line)
def infer(self, savegeometry=True, options={}, *args):
mln_content = self.mln_container.editor.get("1.0", END).encode('utf8').strip()
db_content = self.db_container.editor.get("1.0", END).encode('utf8').strip()
# create conf from current gui settings
self.update_config()
# write gui settings
self.write_gconfig(savegeometry=savegeometry)
# hide gui
self.master.withdraw()
try:
print headline('PRACMLN QUERY TOOL')
print
if options.get('mlnarg') is not None:
mlnobj = MLN(mlnfile=os.path.abspath(options.get('mlnarg')),
logic=self.config.get('logic', 'FirstOrderLogic'),
grammar=self.config.get('grammar', 'PRACGrammar'))
else:
mlnobj = parse_mln(mln_content, searchpaths=[self.dir],
projectpath=os.path.join(self.dir, self.project.name),
logic=self.config.get('logic', 'FirstOrderLogic'),
grammar=self.config.get('grammar', 'PRACGrammar'))
if options.get('emlnarg') is not None:
emln_content = mlnpath(options.get('emlnarg')).content
else:
emln_content = self.emln_container.editor.get("1.0", END).encode('utf8').strip()
if options.get('dbarg') is not None:
dbobj = Database.load(mlnobj, dbfiles=[options.get('dbarg')], ignore_unknown_preds=self.config.get('ignore_unknown_preds', True))
else:
out(self.config.get('ignore_unknown_preds', True))
dbobj = parse_db(mlnobj, db_content, ignore_unknown_preds=self.config.get('ignore_unknown_preds', True))
if options.get('queryarg') is not None:
self.config["queries"] = options.get('queryarg')
infer = MLNQuery(config=self.config, mln=mlnobj, db=dbobj, emln=emln_content)
result = infer.run()
# write to file if run from commandline, otherwise save result to project results
if options.get('outputfile') is not None:
output = StringIO.StringIO()
result.write(output)
with open(os.path.abspath(options.get('outputfile')), 'w') as f:
f.write(output.getvalue())
logger.info('saved result to {}'.format(os.path.abspath(options.get('outputfile'))))
elif self.save.get():
output = StringIO.StringIO()
result.write(output)
fname = self.output_filename.get()
self.project.add_result(fname, output.getvalue())
self.project.save(dirpath=self.dir)
logger.info('saved result to file results/{} in project {}'.format(fname, self.project.name))
else:
logger.debug('No output file given - results have not been saved.')
except:
traceback.print_exc()
# restore main window
sys.stdout.flush()
self.master.deiconify()
