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wiki_table_utils.py
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wiki_table_utils.py
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import codecs
import csv
from builtins import range
from fonduer.parser.models import Document, Sentence
from fonduer.supervision.models import GoldLabel, GoldLabelKey
try:
from IPython import get_ipython
if "IPKernelApp" not in get_ipython().config:
raise ImportError("console")
except (AttributeError, ImportError):
from tqdm import tqdm
else:
from tqdm import tqdm_notebook as tqdm
# Define labels
ABSTAIN = -1
FALSE = 0
TRUE = 1
def get_gold_dict(
filename, doc_on=True, presidentname_on=True, placeofbirth_on=True, docs=None
):
with codecs.open(filename) as csvfile:
gold_reader = csv.reader(csvfile, delimiter=";")
# skip header row
next(gold_reader)
gold_dict = set()
for row in gold_reader:
(doc, presidentname, placeofbirth) = row
docname_without_spaces = doc.replace(" ", "_")
if docs is None or docname_without_spaces.upper() in docs:
if not (doc and placeofbirth and presidentname):
continue
else:
key = []
if doc_on:
key.append(docname_without_spaces.upper())
if presidentname_on:
key.append(presidentname.upper())
if placeofbirth_on:
key.append(placeofbirth.upper())
gold_dict.add(tuple(key))
return gold_dict
gold_file = "data/president_tutorial_gold.csv"
gold_dict = get_gold_dict(gold_file)
def gold(c) -> int:
doc = (c[0].context.sentence.document.name).upper()
president_name = (c[0].context.get_span()).upper()
birthplace = (c[1].context.get_span()).upper()
cand_tuple = (doc, president_name, birthplace)
# gold_matches = [x for x in gold_dict if x[0] == doc]
if cand_tuple in gold_dict:
return TRUE
else:
return FALSE
# TODO: Should gold data only contain ONE true candidate per article?
def entity_confusion_matrix(pred, gold):
if not isinstance(pred, set):
pred = set(pred)
if not isinstance(gold, set):
gold = set(gold)
TP = pred.intersection(gold)
FP = pred.difference(gold)
FN = gold.difference(pred)
return (TP, FP, FN)
def entity_level_f1(candidates, gold_file, corpus=None):
"""Checks entity-level recall of candidates compared to gold.
Turns a CandidateSet into a normal set of entity-level tuples
(doc, president_name, birthplace)
then compares this to the entity-level tuples found in the gold.
Example Usage:
from hardware_utils import entity_level_total_recall
candidates = # CandidateSet of all candidates you want to consider
gold_file = 'tutorials/tables/data/hardware/hardware_gold.csv'
entity_level_total_recall(candidates, gold_file, 'stg_temp_min')
"""
docs = [(doc.name).upper() for doc in corpus] if corpus else None
gold_set = get_gold_dict(gold_file, docs=docs)
if len(gold_set) == 0:
print("Gold File: {gold_file}")
print("Gold set is empty.")
return
# Turn CandidateSet into set of tuples
print("Preparing candidates...")
entities = set()
for i, c in enumerate(tqdm(candidates)):
doc = c[0].context.sentence.document.name.upper()
president_name = c[0].context.get_span().upper()
birthplace = c[1].context.get_span().upper()
entities.add((doc, president_name, birthplace))
(TP_set, FP_set, FN_set) = entity_confusion_matrix(entities, gold_set)
TP = len(TP_set)
FP = len(FP_set)
FN = len(FN_set)
prec = TP / (TP + FP) if TP + FP > 0 else float("nan")
rec = TP / (TP + FN) if TP + FN > 0 else float("nan")
f1 = 2 * (prec * rec) / (prec + rec) if prec + rec > 0 else float("nan")
print("========================================")
print("Scoring on Entity-Level Gold Data")
print("========================================")
print(f"Corpus Precision {prec:.3}")
print(f"Corpus Recall {rec:.3}")
print(f"Corpus F1 {f1:.3}")
print("----------------------------------------")
print(f"TP: {TP} | FP: {FP} | FN: {FN}")
print("========================================\n")
return [sorted(list(x)) for x in [TP_set, FP_set, FN_set]]