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import re
import numpy as np
from weakly_supervised_parser.tree.helpers import Tree
def CKY(sent_all, prob_s, label_s, verbose=False):
r"""
choose tree with maximum expected number of constituents,
or max \sum_{(i,j) \in tree} p((i,j) is constituent)
"""
def backpt_to_tree(sent, backpt, label_table):
def to_tree(i, j):
if j - i == 1:
return Tree(sent[i], None, sent[i])
else:
k = backpt[i][j]
return Tree(label_table[i][j], [to_tree(i, k), to_tree(k, j)], None)
return to_tree(0, len(sent))
def to_table(value_s, i_s, j_s):
table = [[None for _ in range(np.max(j_s) + 1)] for _ in range(np.max(i_s) + 1)]
for value, i, j in zip(value_s, i_s, j_s):
table[i][j] = value
return table
# produce list of spans to pass to is_constituent, while keeping track of which sentence
sent_s, i_s, j_s = [], [], []
idx_all = []
for sent in sent_all:
start = len(sent_s)
for i in range(len(sent)):
for j in range(i + 1, len(sent) + 1):
sent_s.append(sent)
i_s.append(i)
j_s.append(j)
idx_all.append((start, len(sent_s)))
# feed spans to is_constituent
# prob_s, label_s = self.is_constituent(sent_s, i_s, j_s, verbose = verbose)
# given span probs, perform CKY to get best tree for each sentence.
tree_all, prob_all = [], []
for sent, idx in zip(sent_all, idx_all):
# first, use tables to keep track of things
k, l = idx
prob, label = prob_s[k:l], label_s[k:l]
i, j = i_s[k:l], j_s[k:l]
prob_table = to_table(prob, i, j)
label_table = to_table(label, i, j)
# perform cky using scores and backpointers
score_table = [[None for _ in range(len(sent) + 1)] for _ in range(len(sent))]
backpt_table = [[None for _ in range(len(sent) + 1)] for _ in range(len(sent))]
for i in range(len(sent)): # base case: single words
score_table[i][i + 1] = 1
for j in range(2, len(sent) + 1):
for i in range(j - 2, -1, -1):
best, argmax = -np.inf, None
for k in range(i + 1, j): # find splitpoint
score = score_table[i][k] + score_table[k][j]
if score > best:
best, argmax = score, k
score_table[i][j] = best + prob_table[i][j]
backpt_table[i][j] = argmax
tree = backpt_to_tree(sent, backpt_table, label_table)
tree_all.append(tree)
prob_all.append(prob_table)
return tree_all, prob_all
def get_best_parse(sentence, spans):
flattened_scores = []
for i in range(spans.shape[0]):
for j in range(spans.shape[1]):
if i > j:
continue
else:
flattened_scores.append(spans[i, j])
prob_s, label_s = flattened_scores, ["S"] * len(flattened_scores)
# print(prob_s, label_s)
trees, _ = CKY(sent_all=sentence, prob_s=prob_s, label_s=label_s)
s = str(trees[0])
# Replace previous occurrence of string
out = re.sub(r"(?<![^\s()])([^\s()]+)(?=\s+\1(?![^\s()]))", "S", s)
# best_parse = "(ROOT " + out + ")"
return out # best_parse
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