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evaluation.py
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460 lines (419 loc) · 20.9 KB
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from __future__ import absolute_import
import os
import re
import glob
import copy
import itertools
from typing import List
import munkres
import argparse
'''
python evaluation.py -p_file <Prediction Label File> -g_file <Gold Label File> -s_file <Output Label File> (optional)
'''
def _is_divider(line: str) -> bool:
empty_line = line.strip() == ""
return empty_line
def get_data_from_file(fname: str) -> List[List[str]]:
data_points: List[List[str]] = []
with open(fname) as datafile:
for is_divider, lines in itertools.groupby(datafile, _is_divider):
if not is_divider:
data_point: List[str] = [line.strip() for line in lines]
data_points.append(data_point)
return data_points
class Post(object):
def __init__(self):
self.label2idx = {'type': 0, 'attr': 1, 'location': 2, 'temporal': 3}
self.raw_post = []
self.post_with_features = []
self.gold_instances = None
self.prediction_instances = None
self.precision_list = [None for idx in range(len(self.label2idx))]
self.recall_list = [None for idx in range(len(self.label2idx))]
# TODO : Remove this
self.precision_list_mm = [None for idx in range(len(self.label2idx))]
self.recall_list_mm = [None for idx in range(len(self.label2idx))]
self.mode = 'multi_match' # set to max_match for maximal_match
self.temporal = False
def longest_common_subsequence(self, post1, post2):
post1 = post1.split(' ')
post2 = post2.split(' ')
n = len(post1)
m = len(post2)
lcs = [[0 for idx in range(m + 1)] for idx in range(n + 1)]
for idx in range(1, n + 1):
for jdx in range(1, m + 1):
if post1[idx - 1] == post2[jdx - 1]:
lcs[idx][jdx] = 1 + lcs[idx - 1][jdx - 1]
else:
lcs[idx][jdx] = max(lcs[idx - 1][jdx], lcs[idx][jdx - 1])
return lcs[n][m]
def removeDuplicates(self, instance_list):
new_instance_list = []
# pdb.set_trace()
add_to_list = [True for idx in range(len(instance_list))]
for idx in range(len(instance_list) - 1):
for jdx in range(idx + 1, len(instance_list)):
lcs = self.longest_common_subsequence(
instance_list[idx], instance_list[jdx])
if lcs == len(instance_list[idx].split(' ')):
add_to_list[idx] = False
elif lcs == len(instance_list[jdx].split(' ')):
add_to_list[jdx] = False
for idx in range(len(instance_list)):
if add_to_list[idx]:
new_instance_list.append(instance_list[idx])
return new_instance_list
def set_values(self, post: List[str], prediction: List[str]):
# post = post.split('\n')
self.post_with_features = copy.deepcopy(post)
# prediction = prediction.split('\n')
assert(len(post) == len(prediction))
self.gold_instances = [[] for idx in range(len(self.label2idx))]
self.prediction_instances = [[] for idx in range(len(self.label2idx))]
curr_gold_label, curr_gold_instance, curr_predicted_label, curr_predicted_instance = "", "", "", ""
for idx in range(len(post)):
word = post[idx].split(' ')[0]
gold_label = post[idx].split(' ')[-1]
predicted_label = prediction[idx]
self.raw_post.append((word, predicted_label, gold_label))
if gold_label != curr_gold_label:
if curr_gold_instance != "":
if curr_gold_label in self.label2idx:
self.gold_instances[self.label2idx[curr_gold_label]].append(curr_gold_instance)
curr_gold_instance = word
curr_gold_label = gold_label
else:
curr_gold_instance += ' ' + word
if predicted_label != curr_predicted_label:
if curr_predicted_instance != "":
if curr_predicted_label in self.label2idx:
self.prediction_instances[self.label2idx[curr_predicted_label]].append(curr_predicted_instance)
curr_predicted_instance = word
curr_predicted_label = predicted_label
else:
curr_predicted_instance += ' ' + word
if curr_predicted_instance != '' and curr_predicted_label in self.label2idx:
self.prediction_instances[self.label2idx[curr_predicted_label]].append(curr_predicted_instance)
if curr_gold_instance != '' and curr_gold_label in self.label2idx:
self.gold_instances[self.label2idx[curr_gold_label]].append(curr_gold_instance)
# Removal by max sequence match
# self.prediction_instances = [self.removeDuplicates(elem) for elem in self.prediction_instances]
# self.gold_instances = [self.removeDuplicates(elem) for elem in self.gold_instances]
self.prediction_instances = [list(set(elem)) for elem in self.prediction_instances]
self.gold_instances = [list(set(elem)) for elem in self.gold_instances]
# Now computing the fractional counts for precision and recall
for idx in range(len(self.prediction_instances)):
if self.mode == 'multi_match':
# Multi match
self.precision_list[idx] = self.multi_match(
self.prediction_instances[idx], self.gold_instances[idx], True)
self.recall_list[idx] = self.multi_match(
self.prediction_instances[idx], self.gold_instances[idx], False)
self.precision_list_mm[idx] = self.maximal_match(
self.prediction_instances[idx], self.gold_instances[idx], True)
self.recall_list_mm[idx] = self.maximal_match(
self.prediction_instances[idx], self.gold_instances[idx], False)
elif self.mode == 'max_match':
# Maximal match
self.precision_list[idx] = self.maximal_match(
self.prediction_instances[idx], self.gold_instances[idx], True)
self.recall_list[idx] = self.maximal_match(
self.prediction_instances[idx], self.gold_instances[idx], False)
else:
raise ValueError
def multi_match(self, pred_list, gold_list, precision=True):
# pdb.set_trace()
n = len(pred_list)
m = len(gold_list)
# Base Cases:
# 1.1 no gold:
if m == 0:
if precision:
return [(0., pred_list[idx], '') for idx in range(n)]
else:
return []
# 1.2 no pred:
if n == 0:
if precision:
return []
else:
return [(0., '', gold_list[idx]) for idx in range(m)]
if precision:
ret = []
for idx in range(n):
max_match = -1
match_idx = -1
for jdx in range(m):
lcs = self.longest_common_subsequence(pred_list[idx], gold_list[jdx])
if max_match < lcs:
max_match = lcs
match_idx = jdx
ret.append((float(max_match) / len(pred_list[idx].split(' ')), pred_list[idx], gold_list[match_idx]))
return ret
else:
ret = []
for idx in range(m):
max_match = 0
match_idx = -1
for jdx in range(n):
lcs = self.longest_common_subsequence(gold_list[idx], pred_list[jdx])
if max_match < lcs:
max_match = lcs
match_idx = jdx
ret.append((float(max_match) / len(gold_list[idx].split(' ')), pred_list[match_idx], gold_list[idx]))
return ret
def maximal_match(self, pred_list, gold_list, precision=True):
n = len(pred_list)
m = len(gold_list)
# Base Cases:
# 1.1 no gold:
if m == 0:
if precision:
return [(0., pred_list[idx], '') for idx in range(n)]
else:
return []
# 1.2 no pred:
if n == 0:
if precision:
return []
else:
return [(0., '', gold_list[idx]) for idx in range(m)]
edges = [[0. for idx in range(m)] for jdx in range(n)]
for idx in range(n):
for jdx in range(m):
edges[idx][jdx] = float(self.longest_common_subsequence(pred_list[idx], gold_list[jdx]))
if precision:
edges[idx][jdx] /= len(pred_list[idx].split(' '))
else:
edges[idx][jdx] /= len(gold_list[jdx].split(' '))
return self.maximal_match_helper(edges, pred_list, gold_list, precision)
def maximal_match_helper(self, edges, pred_list, gold_list, row_bool):
act_row = len(edges)
act_col = len(edges[0])
n = max(act_row, act_col)
large_val = max([max(elem) for elem in edges])
large_val = (large_val + 1) * (large_val + 1)
m = munkres.Munkres()
cost = [[large_val for idx in range(n)] for idx in range(n)]
for idx in range(act_row):
for jdx in range(act_col):
cost[idx][jdx] = large_val - edges[idx][jdx]
indexes = m.compute(cost)
ret = []
for row, col in indexes:
if row < act_row and col < act_col:
ret.append((large_val - cost[row][col], pred_list[row], gold_list[col]))
else:
if row < act_row:
ret.append((large_val - cost[row][col], pred_list[row], ''))
elif col < act_col:
ret.append((large_val - cost[row][col], '', gold_list[col]))
ret = sorted(ret, reverse=True)
if row_bool:
ret = ret[:act_row]
else:
ret = ret[:act_col]
return ret
def __str__(self, idx=None):
write_buf = '--------------------------- POST ----------------------\n'
if idx is not None:
write_buf += 'IDX: ' + str(idx) + '\n'
write_buf += ' '.join(map(lambda x: x[0], self.raw_post)) + '\n'
write_buf += 'TYPE: \n'
write_buf += '\t GOLD: %s\n' % str(self.gold_instances[0])
write_buf += '\t PRED: %s\n' % str(self.prediction_instances[0])
write_buf += '\t PRECISION COUNTS: %s\n' % str(self.precision_list[0])
write_buf += '\t RECALL COUNTS: %s\n' % str(self.recall_list[0])
write_buf += 'ATTR: \n'
write_buf += '\t GOLD: %s\n' % str(self.gold_instances[1])
write_buf += '\t PRED: %s\n' % str(self.prediction_instances[1])
write_buf += '\t PRECISION COUNTS: %s\n' % str(self.precision_list[1])
write_buf += '\t RECALL COUNTS: %s\n' % str(self.recall_list[1])
write_buf += 'LOCATION: \n'
write_buf += '\t GOLD: %s\n' % str(self.gold_instances[2])
write_buf += '\t PRED: %s\n' % str(self.prediction_instances[2])
write_buf += '\t PRECISION COUNTS: %s\n' % str(self.precision_list[2])
write_buf += '\t RECALL COUNTS: %s\n' % str(self.recall_list[2])
if self.temporal:
write_buf += 'TEMPORAL: \n'
write_buf += '\t GOLD: %s\n' % str(self.gold_instances[3])
write_buf += '\t PRED: %s\n' % str(self.prediction_instances[3])
write_buf += '\t PRECISION COUNTS: %s\n' % str(self.precision_list[3])
write_buf += '\t RECALL COUNTS: %s\n' % str(self.recall_list[3])
write_buf += '-------------------------------------------------------\n'
return write_buf
def clean_predictions(predictions: List[List[str]]) -> List[List[str]]:
new_predictions: List[List[str]] = []
for tags in predictions:
new_predictions.append([re.sub(r"^.*-", "", tag) for tag in tags])
return new_predictions
class Evaluator(object):
def __init__(self, posts: List[Post], verbose: bool = True, temporal: bool = False) -> None:
self.posts = posts
self.verbose = verbose
self.temporal = temporal
@classmethod
def from_file(cls, pred_file_name: str, gold_file_name: str, verbose: bool = True) -> "Evaluator":
posts = get_data_from_file(gold_file_name)
predictions = get_data_from_file(pred_file_name)
predictions = clean_predictions(predictions)
assert len(posts) == len(predictions)
assert all([len(x) == len(y) for x, y in zip(posts, predictions)])
new_posts = [None for idx in range(len(posts))]
for idx, (_post, _prediction) in enumerate(zip(posts, predictions)):
new_posts[idx] = Post()
new_posts[idx].set_values(_post, _prediction)
idx += 1
verbose = verbose
temporal = False
return cls(new_posts, verbose, temporal)
@classmethod
def from_folder(cls, pred_folder_name: str, gold_file_name: str, verbose: bool = False) -> None:
posts = get_data_from_file(gold_file_name)
assert os.path.exists(pred_folder_name), f"Folder {pred_folder_name} not found"
prediction_files = glob.glob(os.path.join(pred_folder_name, "prediction*"))
assert len(prediction_files) == len(posts)
predictions: List[List[str]] = [None] * len(posts)
for pred_file in prediction_files:
fno = int(re.match(r".*prediction_(?P<fno>\d+).txt", pred_file).group("fno"))
prediction: List[str] = []
with open(pred_file, "r") as f:
prediction = [x.strip() for x in f.readlines() if x.strip() != ""]
predictions[fno] = prediction
assert len(predictions[fno]) == len(posts[fno])
predictions = clean_predictions(predictions)
new_posts = [None for idx in range(len(posts))]
for idx, (_post, _prediction) in enumerate(zip(posts, predictions)):
new_posts[idx] = Post()
new_posts[idx].set_values(_post, _prediction)
idx += 1
verbose = verbose
temporal = False
return cls(new_posts, verbose, temporal)
def compute_averages(self):
label2idx = {'type': 0, 'attr': 1, 'location': 2, 'temporal': 3}
self._precision_list = [0. for idx in range(len(label2idx))]
self._recall_list = [0. for idx in range(len(label2idx))]
self.average_per_post_precision_list = [0. for idx in range(len(label2idx))]
self.average_per_post_recall_list = [0. for idx in range(len(label2idx))]
for idx in range(len(label2idx)):
macro_count_prec = 0
macro_count_recall = 0
micro_count_prec = 0
micro_count_recall = 0
for post in self.posts:
x = sum([x[0] for x in post.precision_list[idx]])
xl = len(post.precision_list[idx])
y = sum([x[0] for x in post.recall_list[idx]])
yl = len(post.recall_list[idx])
self._precision_list[idx] += x
self._recall_list[idx] += y
if xl != 0:
self.average_per_post_precision_list[idx] += (float(x) / xl)
else:
assert(x == 0)
if yl != 0:
self.average_per_post_recall_list[idx] += (float(y) / yl)
else:
assert(y == 0)
macro_count_prec += xl
macro_count_recall += yl
micro_count_prec += min(1, xl)
micro_count_recall += min(1, yl)
if macro_count_prec != 0:
self._precision_list[idx] /= macro_count_prec
if macro_count_recall != 0:
self._recall_list[idx] /= macro_count_recall
if micro_count_prec != 0:
self.average_per_post_precision_list[idx] /= micro_count_prec
if micro_count_recall != 0:
self.average_per_post_recall_list[idx] /= micro_count_recall
def __str__(self):
write_buf = ''
if self.verbose:
for post in self.posts:
write_buf += str(post)
write_buf += '---------------------- UNWEIGHTED --------------------\n'
write_buf += 'TYPE: \n'
write_buf += '\t PREC: %.4f\n' % self._precision_list[0]
write_buf += '\t REC: %.4f\n' % self._recall_list[0]
write_buf += '\t FSCORE: %.4f\n' % (
2 * self._precision_list[0] * self._recall_list[0] / (self._precision_list[0] + self._recall_list[0] + 1e-6))
write_buf += 'ATTR: \n'
write_buf += '\t PREC: %.4f\n' % self._precision_list[1]
write_buf += '\t REC: %.4f\n' % self._recall_list[1]
write_buf += '\t FSCORE: %.4f\n' % (
2 * self._precision_list[1] * self._recall_list[1] / (self._precision_list[1] + self._recall_list[1] + 1e-6))
write_buf += 'LOCATION: \n'
write_buf += '\t PREC: %.4f\n' % self._precision_list[2]
write_buf += '\t REC: %.4f\n' % self._recall_list[2]
write_buf += '\t FSCORE: %.4f\n' % (
2 * self._precision_list[2] * self._recall_list[2] / (self._precision_list[2] + self._recall_list[2] + 1e-6))
if self.temporal:
write_buf += 'TEMPORAL: \n'
write_buf += '\t PREC: %.4f\n' % self._precision_list[3]
write_buf += '\t REC: %.4f\n' % self._recall_list[3]
if (self._precision_list[3] + self._recall_list[3]) != 0.:
write_buf += '\t FSCORE: %.4f\n' % (
(2 * self._precision_list[3] * self._recall_list[3]) /
(self._precision_list[3] + self._recall_list[3])
)
else:
write_buf += '\t FSCORE: NAN\n'
write_buf += '---------------------- WEIGHTED ---------------------\n'
tol = 1e-6
write_buf += 'TYPE: \n'
write_buf += '\t PREC: %.4f\n' % self.average_per_post_precision_list[0]
write_buf += '\t REC: %.4f\n' % self.average_per_post_recall_list[0]
write_buf += '\t FSCORE: %.4f\n' % (
(2 * self.average_per_post_precision_list[0] * self.average_per_post_recall_list[0]) /
(self.average_per_post_precision_list[0] + self.average_per_post_recall_list[0] + 1e-6))
write_buf += 'ATTR: \n'
write_buf += '\t PREC: %.4f\n' % self.average_per_post_precision_list[1]
write_buf += '\t REC: %.4f\n' % self.average_per_post_recall_list[1]
write_buf += '\t FSCORE: %.4f\n' % (
(2 * self.average_per_post_precision_list[1] * self.average_per_post_recall_list[1]) /
(self.average_per_post_precision_list[1] + self.average_per_post_recall_list[1] + 1e-6))
write_buf += 'LOCATION: \n'
write_buf += '\t PREC: %.4f\n' % self.average_per_post_precision_list[2]
write_buf += '\t REC: %.4f\n' % self.average_per_post_recall_list[2]
write_buf += '\t FSCORE: %.4f\n' % (
(2 * self.average_per_post_precision_list[2] * self.average_per_post_recall_list[2]) /
(self.average_per_post_precision_list[2] + self.average_per_post_recall_list[2] + 1e-6))
if self.temporal:
write_buf += 'TEMPORAL: \n'
write_buf += '\t PREC: %.4f\n' % self.average_per_post_precision_list[3]
write_buf += '\t REC: %.4f\n' % self.average_per_post_recall_list[3]
if self.average_per_post_precision_list[3] + self.average_per_post_recall_list[3] != 0.:
write_buf += '\t FSCORE: %.4f\n' % (
(2 * self.average_per_post_precision_list[3] * self.average_per_post_recall_list[3]) /
(self.average_per_post_precision_list[3] + self.average_per_post_recall_list[3]))
else:
write_buf += '\t FSCORE: NAN\n'
write_buf += '-------------------------------------------------------\n'
return write_buf
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Evaluator Function')
parser.add_argument('-p_file', dest='p_file', type=str,
default='', help='The Prediction File')
parser.add_argument('-p_folder', dest='p_folder', type=str,
default='', help='The Prediction Folder')
parser.add_argument('-g_file', dest='g_file', type=str,
default='', help='The Gold File')
parser.add_argument('-s_file', dest='save_file', type=str,
default='', help='File to save in')
parser.add_argument('-verbose', dest='verbose', type=str,
default='True', help='Info to print')
args = parser.parse_args()
assert((args.p_file or args.p_folder) and args.g_file)
verbosity = False if args.verbose == "False" else True
if args.p_file:
evl = Evaluator.from_file(args.p_file, args.g_file, verbosity)
elif args.p_folder:
evl = Evaluator.from_folder(args.p_folder, args.g_file, verbosity)
evl.compute_averages()
if args.save_file == '':
print(evl)
else:
open(args.save_file, 'wb').write(str(evl))