File size: 2,237 Bytes
0275184 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | #!/usr/bin/env python
#
# File Name : mscoco_rouge.py
#
# Description : Computes ROUGE-L metric as described by Lin and Hovey (2004)
#
# Creation Date : 2015-01-07 06:03
# Author : Ramakrishna Vedantam <vrama91@vt.edu>
def my_lcs(string, sub):
"""
Calculates longest common subsequence for a pair of tokenized strings
:param string : list of str : tokens from a string split using whitespace
:param sub : list of str : shorter string, also split using whitespace
:returns: length (list of int): length of the longest common subsequence between the two strings
Note: my_lcs only gives length of the longest common subsequence, not the actual LCS
"""
if len(string) < len(sub):
sub, string = string, sub
lengths = [[0 for i in range(0, len(sub) + 1)] for j in range(0, len(string) + 1)]
for j in range(1, len(sub) + 1):
for i in range(1, len(string) + 1):
if string[i - 1] == sub[j - 1]:
lengths[i][j] = lengths[i - 1][j - 1] + 1
else:
lengths[i][j] = max(lengths[i - 1][j], lengths[i][j - 1])
return lengths[len(string)][len(sub)]
def calc_score(hypotheses, references, beta=1.2):
"""
Compute ROUGE-L score given one candidate and references for an image
:param hypotheses: str : candidate sentence to be evaluated
:param references: list of str : COCO reference sentences for the particular image to be evaluated
:returns score: int (ROUGE-L score for the candidate evaluated against references)
"""
assert len(hypotheses) == 1
assert len(references) > 0
prec = []
rec = []
# split into tokens
token_c = hypotheses[0].split(" ")
for reference in references:
# split into tokens
token_r = reference.split(" ")
# compute the longest common subsequence
lcs = my_lcs(token_r, token_c)
prec.append(lcs / float(len(token_c)))
rec.append(lcs / float(len(token_r)))
prec_max = max(prec)
rec_max = max(rec)
if prec_max != 0 and rec_max != 0:
score = ((1 + beta ** 2) * prec_max * rec_max) / float(
rec_max + beta ** 2 * prec_max
)
else:
score = 0.0
return score
|