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eval.py
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eval.py
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import numpy as np
import re
import string
import json
import argparse
from scipy.optimize import linear_sum_assignment # https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.optimize.linear_sum_assignment.html
from collections import OrderedDict
tag2role = OrderedDict({'perp_individual_id': "PerpInd", 'perp_organization_id': "PerpOrg", 'phys_tgt_id': "Target", 'hum_tgt_name': "Victim", 'incident_instrument_id': "Weapon"})
def f1(p_num, p_den, r_num, r_den, beta=1):
p = 0 if p_den == 0 else p_num / float(p_den)
r = 0 if r_den == 0 else r_num / float(r_den)
return 0 if p + r == 0 else (1 + beta * beta) * p * r / (beta * beta * p + r)
def phi_strict(c1, c2):
# similarity: if c2 (pred) is subset of c1 (gold) return 1
for m in c2:
if m not in c1:
return 0
return 1
def phi_prop(c1, c2):
# # similarity: len(overlap of c2 (pred) and c1 (gold)) / len(c2)
return len([m for m in c1 if m in c2]) / len(c2)
def ceaf(clusters, gold_clusters, phi_similarity):
# !!! need to comment the next line, the conll-2012 eval ignore singletons
# clusters = [c for c in clusters if len(c) != 1]
scores = np.zeros((len(gold_clusters), len(clusters)))
for i in range(len(gold_clusters)):
for j in range(len(clusters)):
scores[i, j] = phi_similarity(gold_clusters[i], clusters[j])
# matching = linear_assignment(-scores) # [deprecated] linear_assignment from sklearn
# similarity = sum(scores[matching[:, 0], matching[:, 1]])
row_ind, col_ind = linear_sum_assignment(-scores)
similarity = sum(scores[row_ind, col_ind])
return similarity, len(clusters), similarity, len(gold_clusters)
def eval_ceaf_base(preds, golds, phi_similarity, docids=[]):
result = OrderedDict()
all_keys = list(role for _, role in tag2role.items()) + ["micro_avg"]
for key in all_keys:
result[key] = {"p_num": 0, "p_den": 0, "r_num": 0, "r_den": 0, "p": 0, "r": 0, "f1": 0}
if not docids:
for docid in golds:
docids.append(docid)
for docid in docids:
pred = preds[docid]
gold = golds[docid]
for role in gold:
pred_clusters = []
gold_clusters = []
for entity in gold[role]:
gold_c = []
for mention in entity:
gold_c.append(mention)
gold_clusters.append(gold_c)
for entity in pred[role]:
pred_c = []
for mention in entity:
pred_c.append(mention)
pred_clusters.append(pred_c)
pn, pd, rn, rd = ceaf(pred_clusters, gold_clusters, phi_similarity)
result[role]["p_num"] += pn
result[role]["p_den"] += pd
result[role]["r_num"] += rn
result[role]["r_den"] += rd
result["micro_avg"]["p_num"] = sum(result[role]["p_num"] for _, role in tag2role.items())
result["micro_avg"]["p_den"] = sum(result[role]["p_den"] for _, role in tag2role.items())
result["micro_avg"]["r_num"] = sum(result[role]["r_num"] for _, role in tag2role.items())
result["micro_avg"]["r_den"] = sum(result[role]["r_den"] for _, role in tag2role.items())
for key in all_keys:
result[key]["p"] = 0 if result[key]["p_num"] == 0 else result[key]["p_num"] / float(result[key]["p_den"])
result[key]["r"] = 0 if result[key]["r_num"] == 0 else result[key]["r_num"] / float(result[key]["r_den"])
result[key]["f1"] = f1(result[key]["p_num"], result[key]["p_den"], result[key]["r_num"], result[key]["r_den"])
return result
def normalize_string(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
return re.sub(regex, ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def eval_ceaf(preds, golds, docids=[]):
# normalization mention strings
for docid in preds:
for role in preds[docid]:
for idx in range(len(preds[docid][role])):
for idy in range(len(preds[docid][role][idx])):
preds[docid][role][idx][idy] = normalize_string(preds[docid][role][idx][idy])
for docid in golds:
for role in golds[docid]:
for idx in range(len(golds[docid][role])):
for idy in range(len(golds[docid][role][idx])):
golds[docid][role][idx][idy] = normalize_string(golds[docid][role][idx][idy])
results_strict = eval_ceaf_base(preds, golds, phi_strict, docids)
results_prop = eval_ceaf_base(preds, golds, phi_prop, docids)
final_results = OrderedDict()
final_results["strict"] = results_strict
final_results["prop"] = results_prop
return final_results
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--pred_file", default=None, type=str, required=False, help="preds output file")
parser.add_argument("--gold_file", default="./data/muc/processed/test.json", type=str, required=False, help="gold file")
args = parser.parse_args()
## get pred and gold extracts
preds = OrderedDict()
golds = OrderedDict()
with open(args.pred_file, encoding="utf-8") as f:
out_dict = json.load(f)
for docid in out_dict:
preds[docid] = out_dict[docid]["pred_extracts"]
with open(args.gold_file, encoding="utf-8") as f:
for line in f:
line = json.loads(line)
docid = str(int(line["docid"].split("-")[0][-1])*10000 + int(line["docid"].split("-")[-1]))
extracts_raw = line["extracts"]
extracts = OrderedDict()
for role, entitys_raw in extracts_raw.items():
extracts[role] = []
for entity_raw in entitys_raw:
entity = []
for mention_offset_pair in entity_raw:
entity.append(mention_offset_pair[0])
if entity:
extracts[role].append(entity)
golds[docid] = extracts
# import ipdb; ipdb.set_trace()
docids = []
results = eval_ceaf(preds, golds, docids)
all_keys = list(role for _, role in tag2role.items()) + ["micro_avg"]
str_print = []
for key in all_keys:
if key == "micro_avg":
print("***************** {} *****************".format(key))
else:
print("================= {} =================".format(key))
str_print += [results["strict"][key]["p"] * 100, results["strict"][key]["r"] * 100, results["strict"][key]["f1"] * 100]
print("P: {:.2f}%, R: {:.2f}%, F1: {:.2f}%".format(results["strict"][key]["p"] * 100, results["strict"][key]["r"] * 100, results["strict"][key]["f1"] * 100)) # phi_strict
# print("phi_prop: P: {:.2f}%, R: {:.2f}%, F1: {:.2f}%".format(results["prop"][key]["p"] * 100, results["prop"][key]["r"] * 100, results["prop"][key]["f1"] * 100))
print()
str_print= ["{:.2f}".format(r) for r in str_print]
print("print: {}".format(" ".join(str_print)))