t5-literary-coreference / get_ent_clusters.py
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import pandas as pd
import os
import re
import csv
def extract_paren(annotation):
ents = []
for i in range(len(annotation)):
if annotation[i] == "[":
ent = "["
open_paren = 0
for j in range(i+1, len(annotation)):
if annotation[j] == "[":
open_paren += 1
elif annotation[j] == "]":
if open_paren > 0:
open_paren -= 1
ent = ent[:len(ent)-3]
else:
ent += "]"
digit = re.search(r": [0-9]{1,3}", ent)
if digit:
matches = re.findall(r": [0-9]{1,3}", annotation[:i])
str_index = annotation[:i].count(" ") - len(matches)
ent += "|" + str(str_index)
ents.append(ent)
break
else:
ent += annotation[j]
return ents
def create_clusters(ents):
clusters = {}
for e in ents:
digit_ann = re.search(r": [0-9]{1,3}", e)
if digit_ann:
clean_e = e.replace("[", "").replace("]", "").replace(digit_ann.group(), "")
digit = re.search(r"[0-9]{1,3}", digit_ann.group())
digit = int(digit.group())
if digit not in clusters:
clusters[digit] = []
clusters[digit].append(clean_e)
else:
print("OH NO:", e)
print()
return clusters
headers = ["input", "model_output", "model_output_clusters"]
df = pd.read_csv("results.csv")
rows = []
for index, row in df.iterrows():
annotation = row["model_output"]
if isinstance(annotation, str):
ann_ents = extract_paren(annotation)
ann_clusters = {}
if ann_ents:
ann_clusters = create_clusters(ann_ents)
else:
ann_clusters = {}
new_row = [row["input"], annotation, str(ann_clusters)]
rows.append(new_row)
f = open("cluster_results.csv", "w")
writer = csv.writer(f)
writer.writerow(headers)
writer.writerows(rows)
f.close()