frame.py
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#! /usr/bin/python3
# -*- coding: utf-8 -*-
from collections import defaultdict
import numpy as np
from sqlalchemy import func
from sqlalchemy.orm import aliased
from local_db import Subtree
_general_selprefs_translations = {
u'LUDZIE': [6047, 7702],
u'PODMIOTY': [6047, 7702, 228023],
u'ISTOTY': [6047, 6045, 103330],
u'CECHA': [323],
u'KONCEPCJA': [1088, 8137],
u'KOMUNIKAT': [1088, 3998],
u'JADŁO': [10738, 5268],
u'DOBRA': [1648, 2646, 2903, 10738, 5268],
u'OBIEKTY': [6047, 234224, 1282],
u'WYTWÓR': [2646, 2903],
u'MIEJSCE': [4750, 4897, 234224],
u'OTOCZENIE': [4750, 4897, 234224],
u'POŁOŻENIE': [4750, 4897, 234224],
u'CZYNNOŚĆ': [10765, 23278, 146069, 79514],
u'SYTUACJA': [10765, 6526, 247969, 47401],
u'CZAS': [366, 50721, 6096, 33638],
u'KIEDY': [366, 50721, 6096, 33638, 10765, 6526, 247969, 47401],
u'CZEMU': [6047, 7702, 323, 10765, 6526, 247969, 47401],
u'ILOŚĆ': [1078, 3960, 102887, 1161, 102892],
}
class LexicalUnit():
_session = None
def __init__(self, luid):
self._id = luid
@classmethod
def similarity(cls, l1, l2): # (2*ic(lcs(l1, l2)))/(ic(l1) + ic(l2))
lu1 = l1._id
lu2 = l2._id
if lu1 is None or lu2 is None:
return 1.0
if lu1 == lu2:
return 1.0
if cls._session is None:
raise LexicalUnitSessionError()
else:
session = cls._session
ST1 = aliased(Subtree)
ST2 = aliased(Subtree)
ST3 = aliased(Subtree)
lcs = session.query(ST1.ancestor, func.count(ST1.ancestor)).join(ST2, ST1.ancestor == ST2.ancestor).filter(ST1.descendant==lu1).filter(ST2.descendant==lu2).join(ST3, ST1.ancestor == ST3.ancestor).group_by(ST1.ancestor).order_by(func.count(ST1.ancestor)).first()
if lcs is not None:
lcs_sid, lcs_count = lcs
ic_lcs = cls.count_ic(lcs_count)
ic_lu1 = cls.ic(lu1)
ic_lu2 = cls.ic(lu2)
return (2 * ic_lcs)/(ic_lu1 + ic_lu2)
else:
return 0.0
# 235383 synsets
@classmethod
def ic(cls, lu):
if cls._session is None:
raise LexicalUnitSessionError()
else:
session = cls._session
query = session.query(Subtree).filter(Subtree.ancestor==lu).distinct().count()
return cls.count_ic(query)
_SYNSET_COUNT = 235383
@classmethod
def count_ic(cls, count):
return 1.0 - (np.log(count)/np.log(cls._SYNSET_COUNT))
class SelectionalPreference():
_session = None
def __init__(self, id, content=(True, [])):
self._id = id
self._content = content
def __str__(self):
return str(self._id) + "[" + str(self._content) + "]"
def not_all(self):
return not self._content[0]
@classmethod
def from_slowal(cls, selprefs, argument_id):
if selprefs == None:
return cls(argument_id, (True, []))
else:
synsets = []
all_synsets = False
for general in selprefs.generals.all():
if general.name == u'ALL':
all_synsets = True
else:
synsets += _general_selprefs_translations[general.name]
for synset in selprefs.synsets.all():
if SelectionalPreference._humanoid(synset.id):
synsets += _general_selprefs_translations[u'LUDZIE']
else:
synsets.append(synset.id)
for relation in selprefs.relations.all():
all_synsets = True
return cls(argument_id, (all_synsets, synsets))
@classmethod
def _humanoid(cls, sid):
if cls._session is None:
raise SelectionalPreferenceSessionError()
else:
session = cls._session
test = session.query(Subtree).filter(Subtree.ancestor==6047).filter(Subtree.descendant==sid).count()
if test > 0:
return True
else:
return False
@classmethod
def similarity(cls, selprefs1, selprefs2, table):
if (selprefs1._id, selprefs2._id) in table:
return table[(selprefs1._id, selprefs2._id)]
a1, s1 = selprefs1._content
a2, s2 = selprefs2._content
if a1 or a2 or len(s1) == 0 or len(s2) == 0:
table[(selprefs1._id, selprefs2._id)] = 1.0
table[(selprefs2._id, selprefs1._id)] = 1.0
return 1.0
else:
if cls._session is None:
raise SelectionalPreferenceSessionError()
else:
session = cls._session
ST1 = aliased(Subtree)
ST2 = aliased(Subtree)
count_sp1 = session.query(ST1.descendant).filter(ST1.ancestor.in_(s1)).distinct().count()
count_sp2 = session.query(ST2.descendant).filter(ST2.ancestor.in_(s2)).distinct().count()
count_common = session.query(ST1.descendant).join(ST2, ST1.descendant == ST2.descendant).filter(ST1.ancestor.in_(tuple(s1))).filter(ST2.ancestor.in_(tuple(s2))).distinct().count()
x = count_common + 1.0
y = count_sp1 + 1.0
z = count_sp2 + 1.0
if y == 1.0:
# may only happen if someone added a non-wordnet selectional preference, e.g. kupić-A
y += 1.0
if z == 1.0:
# may only happen if someone added a non-wordnet selectional preference, e.g. kupić-A
z += 1.0
sim = (np.log(x)/np.log(y) + np.log(x)/np.log(z))/2
table[(selprefs1._id, selprefs2._id)] = sim
table[(selprefs2._id, selprefs1._id)] = sim
return sim
class Frame():
_WORDNET_MIN_SIMILARITY = 0.8
def __init__(self, fid = None, name=None):
self._arguments = {}
self._name = name
self._id = fid
def add_argument(self, label, preference):
if label not in self._arguments:
self._arguments[label] = []
self._arguments[label].append(preference)
def get_arguments(self, label=None):
if label == None:
return self._arguments
elif label in self._arguments:
return self._arguments[label]
else:
return []
def get_role_labels(self):
return list(self._arguments.keys())
def __str__(self):
parts = []
for label in sorted(self._arguments):
sps = []
for sp in self._arguments[label]:
sps.append(str(sp._content))
parts.append(label + ': ' + ','.join(sps))
return str(self._id) + ' --> ' + '; '.join(parts)
@classmethod
def _get_pseudo_role(cls, complement):
return complement.roles.exclude(role='Foreground').exclude(role='Background').exclude(role='Source').exclude(role='Goal')[0].role
@classmethod
def from_slowal(cls, sf):
synsets = [LexicalUnit(lu.synset.id) for lu in sf.lexical_units.all() if lu.luid != -1]
frame = cls(fid = sf.id, name = synsets)
complements = sf.complements.all()
for complement in complements:
role = cls._get_pseudo_role(complement)
i = complement.id
if role != 'Lemma':
sp = SelectionalPreference.from_slowal(complement.selective_preference, i)
frame.add_argument(role, sp)
return frame
def lexical_closeness(self, frame, table):
if (self._id, frame._id) not in table:
sim_set = False
sim = 0.0
for l1 in self._name:
for l2 in frame._name:
local_sim = LexicalUnit.similarity(l1, l2)
sim = max(sim, local_sim)
sim_set = True
if not sim_set:
sim = 1.0
table[(self._id, frame._id)] = sim
table[(frame._id, self._id)] = sim
return table[(self._id, frame._id)]
@classmethod
def far(cls, id1, id2, table):
return (table[(id1, id2)] < cls._WORDNET_MIN_SIMILARITY)
if __name__ == '__main__':
pass