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spans.py
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spans.py
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import numpy as np
def find_inner_LCA(path_dict,aspect_range):
path_range = [ [x] + path_dict[x] for x in aspect_range]
path_range.sort(key=lambda l:len(l))
for idx in range(len(path_range[0])):
flag = True
for pid in range(1,len(path_range)):
if path_range[0][idx] not in path_range[pid]:
flag = False #其中一个不在
break
if flag: #都在
LCA_node = path_range[0][idx]
break #already find
return LCA_node
def get_path_and_children_dict(heads):
path_dict = {}
remain_nodes = list(range(len(heads)))
delete_nodes = []
while len(remain_nodes) > 0:
for idx in remain_nodes:
#初始状态
if idx not in path_dict:
path_dict[idx] = [heads[idx]] # no self
if heads[idx] == -1:
delete_nodes.append(idx) #need delete root
else:
last_node = path_dict[idx][-1]
if last_node not in remain_nodes:
path_dict[idx].extend(path_dict[last_node])
delete_nodes.append(idx)
else:
path_dict[idx].append(heads[last_node])
#remove nodes
for del_node in delete_nodes:
remain_nodes.remove(del_node)
delete_nodes = []
#children_dict
children_dict = {}
for x,l in path_dict.items():
if l[0] == -1:
continue
if l[0] not in children_dict:
children_dict[l[0]] = [x]
else:
children_dict[l[0]].append(x)
return path_dict, children_dict
def form_layers_and_influence_range(path_dict,mapback):
sorted_path_dict = sorted(path_dict.items(),key=lambda x: len(x[1]))
influence_range = { cid:[idx,idx+1] for idx,cid in enumerate(mapback) }
layers = {}
node2layerid = {}
for cid,path_dict in sorted_path_dict[::-1]:
length = len(path_dict)-1
if length not in layers:
layers[length] = [cid]
node2layerid[cid] = length
else:
layers[length].append(cid)
node2layerid[cid] = length
father_idx = path_dict[0]
assert(father_idx not in mapback)
if father_idx not in influence_range:
influence_range[father_idx] = influence_range[cid][:] #deep copy
else:
influence_range[father_idx][0] = min(influence_range[father_idx][0], influence_range[cid][0])
influence_range[father_idx][1] = max(influence_range[father_idx][1], influence_range[cid][1])
layers = sorted(layers.items(),key=lambda x:x[0])
layers = [(cid,sorted(l)) for cid,l in layers] # or [(cid,l.sort()) for cid,l in layers]
return layers, influence_range,node2layerid
def form_spans(layers, influence_range, token_len, con_mapnode, special_token = '[N]'):
spans = []
sub_len = len(special_token)
for _, nodes in layers:
pointer = 0
add_pre = 0
temp = [0] * token_len
temp_indi = ['-'] * token_len
for node_idx in nodes:
begin,end = influence_range[node_idx]
if con_mapnode[node_idx][-sub_len:] == special_token:
temp_indi[begin:end] = [con_mapnode[node_idx][:-sub_len]] * (end-begin)
if(begin != pointer):
sub_pre = spans[-1][pointer]
temp[pointer:begin] = [x + add_pre-sub_pre for x in spans[-1][pointer:begin]] #
add_pre = temp[begin-1] + 1
temp[begin:end] = [add_pre] * (end-begin)
add_pre += 1
pointer = end
if pointer != token_len:
sub_pre = spans[-1][pointer]
temp[pointer:token_len] = [x + add_pre-sub_pre for x in spans[-1][pointer:token_len]]
add_pre = temp[begin-1] + 1
spans.append(temp)
return spans
def head_to_adj_oneshot(heads, sent_len, aspect_dict,
leaf2root=True, root2leaf=True, self_loop=True):
"""
Convert a sequence of head indexes into a 0/1 matirx.
"""
adj_matrix = np.zeros((sent_len, sent_len), dtype=np.float32)
heads = heads[:sent_len]
# aspect <self-loop>
for asp in aspect_dict:
from_ = asp['from']
to_ = asp['to']
for i_idx in range(from_, to_):
for j_idx in range(from_, to_):
adj_matrix[i_idx][j_idx] = 1
for idx, head in enumerate(heads):
if head != -1:
if leaf2root:
adj_matrix[head, idx] = 1
if root2leaf:
adj_matrix[idx, head] = 1
if self_loop:
adj_matrix[idx, idx] = 1
return adj_matrix
def get_conditional_adj(father, length, cd_span,
con_children, con_mapnode):
s_slist = [idx for idx, node in enumerate(con_children[father]) if con_mapnode[node] == 'S[N]' ]
st_adj = np.ones((length,length))
for i in range(len(s_slist)-1):
idx = s_slist[i]
begin_idx = cd_span.index(idx)
end_idx = len(cd_span) - cd_span[::-1].index(idx)
for j in range(idx + 1, len(s_slist)):
jdx = s_slist[j]
begin_jdx = cd_span.index(jdx)
end_jdx = len(cd_span) - cd_span[::-1].index(jdx)
for w_i in range(begin_idx,end_idx):
for w_j in range(begin_jdx,end_jdx):
st_adj[w_i][w_j] = 0
st_adj[w_j][w_i] = 0
return st_adj
def form_aspect_related_spans(aspect_node_idx, spans, mapnode, node2layerid, path_dict,select_N = ['ROOT','TOP','S','NP','VP'], special_token = '[N]'):
aspect2root_path = path_dict[aspect_node_idx]
span_indications = []
spans_range = []
for idx,f in enumerate(aspect2root_path[:-1]):
if mapnode[f][:-len(special_token)] in select_N:
span_idx = node2layerid[f]
span_temp = spans[span_idx]
if len(spans_range) == 0 or span_temp != spans_range[-1]:
spans_range.append(span_temp)
span_indications.append(mapnode[f][:-len(special_token)])
return spans_range, span_indications
def select_func(spans, max_num_spans, length):
if len(spans) <= max_num_spans:
lacd_span = spans[-1] if len(spans) > 0 else [0] * length
select_spans = spans + [lacd_span] * (max_num_spans - len(spans))
else:
if max_num_spans == 1:
select_spans = spans[0] if len(spans) > 0 else [0] * length
else:
gap = len(spans) // (max_num_spans-1)
select_spans = [ spans[gap * i] for i in range(max_num_spans-1)] + [spans[-1]]
return select_spans