def add_edge(self, word, other_word, word_m, other_word_m, beta, simtype):
''' Add an edge between the two given words, if their similarity is
higher than a threshold'''
if word == other_word:
return False
#sim = self.calculate_similarity(word_features, other_word_features)
sim = evaluate.calculate_similarity(beta, word_m, other_word_m,
simtype)
# if the words are similar enough, connect them.
# TODO this can be done probabilistically -- that is connect based on similarity
if sim >= self._sim_threshold:
vert = self._words_vertex_map[word]
other_vert = self._words_vertex_map[other_word]
new_edge = self._graph.add_edge(vert, other_vert)
self._graph.edge_properties["distance"][new_edge] = max(0, 1 - sim)
self._graph.edge_properties["similarity"][new_edge] = sim
#update the list of nodes with most degree
self.update_most_list(vert, vert.out_degree(),
self._highest_degree_nodes, self._hubs_num)
self.update_most_list(other_vert, other_vert.out_degree(),
self._highest_degree_nodes, self._hubs_num)
return True
return False
def add_edge(self, word, other_word, word_m, other_word_m, beta, simtype):
''' Add an edge between the two given words, if their similarity is
higher than a threshold'''
if word == other_word:
return False
#sim = self.calculate_similarity(word_features, other_word_features)
sim = evaluate.calculate_similarity(beta, word_m, other_word_m, simtype)
# if the words are similar enough, connect them.
# TODO this can be done probabilistically -- that is connect based on similarity
if sim >= self._sim_threshold:
vert = self._words_vertex_map[word]
other_vert = self._words_vertex_map[other_word]
new_edge = self._graph.add_edge(vert, other_vert)
self._graph.edge_properties["distance"][new_edge] = max(0, 1 - sim)
self._graph.edge_properties["similarity"][new_edge] = sim
#update the list of nodes with most degree
self.update_most_list(vert, vert.out_degree(), self._highest_degree_nodes, self._hubs_num)
self.update_most_list(other_vert, other_vert.out_degree(), self._highest_degree_nodes ,self._hubs_num)
return True
return False
def calculate_acquisition_score(self, word):
"""
Calculate and return the acquisition score of word. If "forgetting" is
activated then the meaning probabilities need to be recalculated.
"""
if self._forget_flag:
self.update_meaning_prob(word)
true_m = self._gold_lexicon.meaning(word)
lrnd_m = self._learned_lexicon.meaning(word)
sim = evaluate.calculate_similarity(self._beta, lrnd_m, true_m, self._simtype)
self._acquisition_scores[word] = sim
return sim
def calculate_acquisition_score(self, word):
"""
Calculate and return the acquisition score of word. If "forgetting" is
activated then the meaning probabilities need to be recalculated.
"""
if self._forget_flag:
self.update_meaning_prob(word)
true_m = self._gold_lexicon.meaning(word)
lrnd_m = self._learned_lexicon.meaning(word)
sim = evaluate.calculate_similarity(self._beta, lrnd_m, true_m,
self._simtype)
self._acquisition_scores[word] = sim
return sim
def create_final_graph(self, words, lexicon, beta, simtype):
""" create a graph, given a set of words and their meanings """
graph = Graph(directed=False)
graph.vertex_properties["label"] = graph.new_vertex_property("string")
graph.edge_properties["distance"] = graph.new_edge_property("double")
graph.vertex_properties["acqscore"] = graph.new_vertex_property(
"double")
word_vertex_map = {}
for word in words:
word_vertex_map[word] = graph.add_vertex()
graph.vertex_properties["label"][word_vertex_map[word]] = word
for word in words:
for otherword in words:
if word == otherword:
continue
vert = word_vertex_map[word]
othervert = word_vertex_map[otherword]
if graph.edge(vert, othervert) != None or graph.edge(
othervert, vert) != None:
continue
word_m = lexicon.meaning(word)
# word_m_top_features = self.select_features(word_m._meaning_probs)
otherword_m = lexicon.meaning(otherword)
# otherword_m_top_features = self.select_features(otherword_m._meaning_probs)
#sim = self.calculate_similarity(word_m_top_features, otherword_m_top_features)
sim = evaluate.calculate_similarity(beta, word_m, otherword_m,
simtype)
if sim >= self._sim_threshold:
new_edge = graph.add_edge(vert, othervert)
graph.edge_properties["distance"][new_edge] = max(
0, 1 - sim) #distance #TODO
return graph
def create_final_graph(self, words, lexicon, beta, simtype):
""" create a graph, given a set of words and their meanings """
graph = Graph(directed=False)
graph.vertex_properties["label"] = graph.new_vertex_property("string")
graph.edge_properties["distance"] = graph.new_edge_property("double")
graph.vertex_properties["acqscore"] = graph.new_vertex_property("double")
word_vertex_map = {}
for word in words:
word_vertex_map[word] = graph.add_vertex()
graph.vertex_properties["label"][word_vertex_map[word]] = word
for word in words:
for otherword in words:
if word == otherword:
continue
vert = word_vertex_map[word]
othervert = word_vertex_map[otherword]
if graph.edge(vert, othervert) != None or graph.edge(othervert, vert)!= None:
continue
word_m = lexicon.meaning(word)
# word_m_top_features = self.select_features(word_m._meaning_probs)
otherword_m = lexicon.meaning(otherword)
# otherword_m_top_features = self.select_features(otherword_m._meaning_probs)
#sim = self.calculate_similarity(word_m_top_features, otherword_m_top_features)
sim = evaluate.calculate_similarity(beta, word_m, otherword_m, simtype)
if sim >= self._sim_threshold:
new_edge = graph.add_edge(vert, othervert)
graph.edge_properties["distance"][new_edge] = max(0, 1 - sim ) #distance #TODO
return graph
def create_final_graph(self, words, lexicon, beta, simtype,words_to_compare=None):
""" create a graph, given a set of words and their meanings """
graph = Graph(directed=False)
graph.vertex_properties["label"] = graph.new_vertex_property("string")
graph.edge_properties["distance"] = graph.new_edge_property("double")
graph.vertex_properties["acqscore"] = graph.new_vertex_property("double")
word_vertex_map = {}
for word in words:
word_vertex_map[word] = graph.add_vertex()
graph.vertex_properties["label"][word_vertex_map[word]] = word
all_word_pairs = itertools.combinations(words,2)
for word,otherword in all_word_pairs:
vert = word_vertex_map[word]
othervert = word_vertex_map[otherword]
if graph.edge(vert, othervert) != None or graph.edge(othervert, vert)!= None:
continue
word_m = lexicon.meaning(word)
#word_m_top_features = self.select_features(word_m._meaning_probs)
otherword_m = lexicon.meaning(otherword)
#otherword_m_top_features = self.select_features(otherword_m._meaning_probs)
#sim = self.calculate_similarity(word_m_top_features, otherword_m_top_features)
sim = evaluate.calculate_similarity(beta, word_m, otherword_m, simtype)
if sim >= self._sim_threshold:
new_edge = graph.add_edge(vert, othervert)
graph.edge_properties["distance"][new_edge] = max(0, 1 - sim ) #distance #TODO
if words_to_compare:
words_to_compare = list(set(words_to_compare)-set(words))
for _word in words_to_compare:
for _otherword in words:
if _word == _otherword:
continue
_word_m = lexicon.meaning(_word)
#word_m_top_features = self.select_features(word_m._meaning_probs)
_otherword_m = lexicon.meaning(_otherword)
#otherword_m_top_features = self.select_features(otherword_m._meaning_probs)
#sim = self.calculate_similarity(word_m_top_features, otherword_m_top_features)
_sim = evaluate.calculate_similarity(beta, _word_m, _otherword_m, simtype)
if _sim >= self._sim_threshold:
#print _sim
_vert = word_vertex_map[_word] = graph.add_vertex()
graph.vertex_properties["label"][_vert] = _word
_othervert = word_vertex_map[_otherword]
#print _word
#print _otherword
_new_edge = graph.add_edge(_vert, _othervert)
graph.edge_properties["distance"][_new_edge] = max(0, 1 - _sim ) #distance #TODO
return graph