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rouge.py
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rouge.py
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import nltk
import os
import re
import itertools
import collections
import pkg_resources
from io import open
class Rouge:
DEFAULT_METRICS = {"rouge-n"}
DEFAULT_N = 1
STATS = ["f", "p", "r"]
AVAILABLE_METRICS = {"rouge-n", "rouge-l", "rouge-w"}
AVAILABLE_LENGTH_LIMIT_TYPES = {'words', 'bytes'}
REMOVE_CHAR_PATTERN = re.compile('[^A-Za-z0-9]')
# Hack to not tokenize "cannot" to "can not" and consider them different as in the official ROUGE script
KEEP_CANNOT_IN_ONE_WORD = re.compile('cannot')
KEEP_CANNOT_IN_ONE_WORD_REVERSED = re.compile('_cannot_')
WORDNET_KEY_VALUE = {}
WORDNET_DB_FILEPATH = 'wordnet_key_value.txt'
WORDNET_DB_FILEPATH_SPECIAL_CASE = 'wordnet_key_value_special_cases.txt'
WORDNET_DB_DELIMITER = '|'
STEMMER = None
def __init__(self, metrics=None, max_n=None, limit_length=True, length_limit=665, length_limit_type='bytes', apply_avg=True, apply_best=False, stemming=True, alpha=0.5, weight_factor=1.0, ensure_compatibility=True):
"""
Handle the ROUGE score computation as in the official perl script.
Note 1: Small differences might happen if the resampling of the perl script is not high enough (as the average depends on this).
Note 2: Stemming of the official Porter Stemmer of the ROUGE perl script is slightly different and the Porter one implemented in NLTK. However, special cases of DUC 2004 have been traited.
The solution would be to rewrite the whole perl stemming in python from the original script
Args:
metrics: What ROUGE score to compute. Available: ROUGE-N, ROUGE-L, ROUGE-W. Default: ROUGE-N
max_n: N-grams for ROUGE-N if specify. Default:1
limit_length: If the summaries must be truncated. Defaut:True
length_limit: Number of the truncation where the unit is express int length_limit_Type. Default:665 (bytes)
length_limit_type: Unit of length_limit. Available: words, bytes. Default: 'bytes'
apply_avg: If we should average the score of multiple samples. Default: True. If apply_Avg & apply_best = False, then each ROUGE scores are independant
apply_best: Take the best instead of the average. Default: False, then each ROUGE scores are independant
stemming: Apply stemming to summaries. Default: True
alpha: Alpha use to compute f1 score: P*R/((1-a)*P + a*R). Default:0.5
weight_factor: Weight factor to be used for ROUGE-W. Official rouge score defines it at 1.2. Default: 1.0
ensure_compatibility: Use same stemmer and special "hacks" to product same results as in the official perl script (besides the number of sampling if not high enough). Default:True
Raises:
ValueError: raises exception if metric is not among AVAILABLE_METRICS
ValueError: raises exception if length_limit_type is not among AVAILABLE_LENGTH_LIMIT_TYPES
ValueError: raises exception if weight_factor < 0
"""
self.metrics = metrics[:] if metrics is not None else Rouge.DEFAULT_METRICS
for m in self.metrics:
if m not in Rouge.AVAILABLE_METRICS:
raise ValueError("Unknown metric '{}'".format(m))
self.max_n = max_n if "rouge-n" in self.metrics else None
# Add all rouge-n metrics
if self.max_n is not None:
index_rouge_n = self.metrics.index('rouge-n')
del self.metrics[index_rouge_n]
self.metrics += ['rouge-{}'.format(n) for n in range(1, self.max_n + 1)]
self.metrics = set(self.metrics)
self.limit_length = limit_length
if self.limit_length:
if length_limit_type not in Rouge.AVAILABLE_LENGTH_LIMIT_TYPES:
raise ValueError("Unknown length_limit_type '{}'".format(length_limit_type))
self.length_limit = length_limit
if self.length_limit == 0:
self.limit_length = False
self.length_limit_type = length_limit_type
self.stemming = stemming
self.apply_avg = apply_avg
self.apply_best = apply_best
self.alpha = alpha
self.weight_factor = weight_factor
if self.weight_factor <= 0:
raise ValueError("ROUGE-W weight factor must greater than 0.")
self.ensure_compatibility = ensure_compatibility
# Load static objects
if len(Rouge.WORDNET_KEY_VALUE) == 0:
Rouge.load_wordnet_db(ensure_compatibility)
if Rouge.STEMMER is None:
Rouge.load_stemmer(ensure_compatibility)
@staticmethod
def load_stemmer(ensure_compatibility):
"""
Load the stemmer that is going to be used if stemming is enabled
Args
ensure_compatibility: Use same stemmer and special "hacks" to product same results as in the official perl script (besides the number of sampling if not high enough)
"""
Rouge.STEMMER = nltk.stem.porter.PorterStemmer('ORIGINAL_ALGORITHM') if ensure_compatibility else nltk.stem.porter.PorterStemmer()
@staticmethod
def load_wordnet_db(ensure_compatibility):
"""
Load WordNet database to apply specific rules instead of stemming + load file for special cases to ensure kind of compatibility (at list with DUC 2004) with the original stemmer used in the Perl script
Args
ensure_compatibility: Use same stemmer and special "hacks" to product same results as in the official perl script (besides the number of sampling if not high enough)
Raises:
FileNotFoundError: If one of both databases is not found
"""
files_to_load = [Rouge.WORDNET_DB_FILEPATH]
if ensure_compatibility:
files_to_load.append(Rouge.WORDNET_DB_FILEPATH_SPECIAL_CASE)
for wordnet_db in files_to_load:
filepath = pkg_resources.resource_filename(__name__, wordnet_db)
if not os.path.exists(filepath):
raise FileNotFoundError("The file '{}' does not exist".format(filepath))
with open(filepath, 'r', encoding='utf-8') as fp:
for line in fp:
k, v = line.strip().split(Rouge.WORDNET_DB_DELIMITER)
assert k not in Rouge.WORDNET_KEY_VALUE
Rouge.WORDNET_KEY_VALUE[k] = v
@staticmethod
def tokenize_text(text, language='english'):
"""
Tokenize text in the specific language
Args:
text: The string text to tokenize
language: Language of the text
Returns:
List of tokens of text
"""
return nltk.word_tokenize(text, language)
@staticmethod
def split_into_sentences(text, ensure_compatibility, language='english'):
"""
Split text into sentences, using specified language. Use PunktSentenceTokenizer
Args:
text: The string text to tokenize
ensure_compatibility: Split sentences by '\n' instead of NLTK sentence tokenizer model
language: Language of the text
Returns:
List of tokens of text
"""
if ensure_compatibility:
return text.split('\n')
else:
return nltk.sent_tokenize(text, language)
@staticmethod
def stem_tokens(tokens):
"""
Apply WordNetDB rules or Stem each token of tokens
Args:
tokens: List of tokens to apply WordNetDB rules or to stem
Returns:
List of final stems
"""
# Stemming & Wordnet apply only if token has at least 3 chars
for i, token in enumerate(tokens):
if len(token) > 0:
if len(token) > 3:
if token in Rouge.WORDNET_KEY_VALUE:
token = Rouge.WORDNET_KEY_VALUE[token]
else:
token = Rouge.STEMMER.stem(token)
tokens[i] = token
return tokens
@staticmethod
def _get_ngrams(n, text):
"""
Calcualtes n-grams.
Args:
n: which n-grams to calculate
text: An array of tokens
Returns:
A set of n-grams with their number of occurences
"""
# Modified from https://github.com/pltrdy/seq2seq/blob/master/seq2seq/metrics/rouge.py
ngram_set = collections.defaultdict(int)
max_index_ngram_start = len(text) - n
for i in range(max_index_ngram_start + 1):
ngram_set[tuple(text[i:i + n])] += 1
return ngram_set
@staticmethod
def _split_into_words(sentences):
"""
Splits multiple sentences into words and flattens the result
Args:
sentences: list of string
Returns:
A list of words (split by white space)
"""
# Modified from https://github.com/pltrdy/seq2seq/blob/master/seq2seq/metrics/rouge.py
return list(itertools.chain(*[_.split() for _ in sentences]))
@staticmethod
def _get_word_ngrams_and_length(n, sentences):
"""
Calculates word n-grams for multiple sentences.
Args:
n: wich n-grams to calculate
sentences: list of string
Returns:
A set of n-grams, their frequency and #n-grams in sentences
"""
# Modified from https://github.com/pltrdy/seq2seq/blob/master/seq2seq/metrics/rouge.py
assert len(sentences) > 0
assert n > 0
tokens = Rouge._split_into_words(sentences)
return Rouge._get_ngrams(n, tokens), tokens, len(tokens) - (n - 1)
@staticmethod
def _get_unigrams(sentences):
"""
Calcualtes uni-grams.
Args:
sentences: list of string
Returns:
A set of n-grams and their freqneucy
"""
assert len(sentences) > 0
tokens = Rouge._split_into_words(sentences)
unigram_set = collections.defaultdict(int)
for token in tokens:
unigram_set[token] += 1
return unigram_set, len(tokens)
@staticmethod
def _compute_p_r_f_score(evaluated_count, reference_count, overlapping_count, alpha=0.5, weight_factor=1.0):
"""
Compute precision, recall and f1_score (with alpha: P*R / ((1-alpha)*P + alpha*R))
Args:
evaluated_count: #n-grams in the hypothesis
reference_count: #n-grams in the reference
overlapping_count: #n-grams in common between hypothesis and reference
alpha: Value to use for the F1 score (default: 0.5)
weight_factor: Weight factor if we have use ROUGE-W (default: 1.0, no impact)
Returns:
A dict with 'p', 'r' and 'f' as keys fore precision, recall, f1 score
"""
precision = 0.0 if evaluated_count == 0 else overlapping_count / float(evaluated_count)
if weight_factor != 1.0:
precision = precision ** (1.0 / weight_factor)
recall = 0.0 if reference_count == 0 else overlapping_count / float(reference_count)
if weight_factor != 1.0:
recall = recall ** (1.0 / weight_factor)
f1_score = Rouge._compute_f_score(precision, recall, alpha)
return {"f": f1_score, "p": precision, "r": recall}
@staticmethod
def _compute_f_score(precision, recall, alpha=0.5):
"""
Compute f1_score (with alpha: P*R / ((1-alpha)*P + alpha*R))
Args:
precision: precision
recall: recall
overlapping_count: #n-grams in common between hypothesis and reference
Returns:
f1 score
"""
return 0.0 if (recall == 0.0 or precision == 0.0) else precision * recall / ((1 - alpha) * precision + alpha * recall)
@staticmethod
def _compute_ngrams(evaluated_sentences, reference_sentences, n):
"""
Computes n-grams overlap of two text collections of sentences.
Source: http://research.microsoft.com/en-us/um/people/cyl/download/
papers/rouge-working-note-v1.3.1.pdf
Args:
evaluated_sentences: The sentences that have been picked by the
summarizer
reference_sentences: The sentences from the referene set
n: Size of ngram
Returns:
Number of n-grams for evaluated_sentences, reference_sentences and intersection of both.
intersection of both count multiple of occurences in n-grams match several times
Raises:
ValueError: raises exception if a param has len <= 0
"""
# Modified from https://github.com/pltrdy/seq2seq/blob/master/seq2seq/metrics/rouge.py
if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
raise ValueError("Collections must contain at least 1 sentence.")
evaluated_ngrams, _, evaluated_count = Rouge._get_word_ngrams_and_length(n, evaluated_sentences)
reference_ngrams, _, reference_count = Rouge._get_word_ngrams_and_length(n, reference_sentences)
# Gets the overlapping ngrams between evaluated and reference
overlapping_ngrams = set(evaluated_ngrams.keys()).intersection(set(reference_ngrams.keys()))
overlapping_count = 0
for ngram in overlapping_ngrams:
overlapping_count += min(evaluated_ngrams[ngram], reference_ngrams[ngram])
return evaluated_count, reference_count, overlapping_count
@staticmethod
def _compute_ngrams_lcs(evaluated_sentences, reference_sentences, weight_factor=1.0):
"""
Computes ROUGE-L (summary level) of two text collections of sentences.
http://research.microsoft.com/en-us/um/people/cyl/download/papers/
rouge-working-note-v1.3.1.pdf
Args:
evaluated_sentences: The sentences that have been picked by the summarizer
reference_sentence: One of the sentences in the reference summaries
weight_factor: Weight factor to be used for WLCS (1.0 by default if LCS)
Returns:
Number of LCS n-grams for evaluated_sentences, reference_sentences and intersection of both.
intersection of both count multiple of occurences in n-grams match several times
Raises:
ValueError: raises exception if a param has len <= 0
"""
def _lcs(x, y):
m = len(x)
n = len(y)
vals = collections.defaultdict(int)
dirs = collections.defaultdict(int)
for i in range(1, m + 1):
for j in range(1, n + 1):
if x[i - 1] == y[j - 1]:
vals[i, j] = vals[i - 1, j - 1] + 1
dirs[i, j] = '|'
elif vals[i - 1, j] >= vals[i, j - 1]:
vals[i, j] = vals[i - 1, j]
dirs[i, j] = '^'
else:
vals[i, j] = vals[i, j - 1]
dirs[i, j] = '<'
return vals, dirs
def _wlcs(x, y, weight_factor):
m = len(x)
n = len(y)
vals = collections.defaultdict(float)
dirs = collections.defaultdict(int)
lengths = collections.defaultdict(int)
for i in range(1, m + 1):
for j in range(1, n + 1):
if x[i - 1] == y[j - 1]:
length_tmp = lengths[i - 1, j - 1]
vals[i, j] = vals[i - 1, j - 1] + (length_tmp + 1) ** weight_factor - length_tmp ** weight_factor
dirs[i, j] = '|'
lengths[i, j] = length_tmp + 1
elif vals[i - 1, j] >= vals[i, j - 1]:
vals[i, j] = vals[i - 1, j]
dirs[i, j] = '^'
lengths[i, j] = 0
else:
vals[i, j] = vals[i, j - 1]
dirs[i, j] = '<'
lengths[i, j] = 0
return vals, dirs
def _mark_lcs(mask, dirs, m, n):
while m != 0 and n != 0:
if dirs[m, n] == '|':
m -= 1
n -= 1
mask[m] = 1
elif dirs[m, n] == '^':
m -= 1
elif dirs[m, n] == '<':
n -= 1
else:
raise UnboundLocalError('Illegal move')
return mask
if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
raise ValueError("Collections must contain at least 1 sentence.")
evaluated_unigrams_dict, evaluated_count = Rouge._get_unigrams(evaluated_sentences)
reference_unigrams_dict, reference_count = Rouge._get_unigrams(reference_sentences)
# Has to use weight factor for WLCS
use_WLCS = weight_factor != 1.0
if use_WLCS:
evaluated_count = evaluated_count ** weight_factor
reference_count = 0
overlapping_count = 0.0
for reference_sentence in reference_sentences:
reference_sentence_tokens = reference_sentence.split()
if use_WLCS:
reference_count += len(reference_sentence_tokens) ** weight_factor
hit_mask = [0 for _ in range(len(reference_sentence_tokens))]
for evaluated_sentence in evaluated_sentences:
evaluated_sentence_tokens = evaluated_sentence.split()
if use_WLCS:
_, lcs_dirs = _wlcs(reference_sentence_tokens, evaluated_sentence_tokens, weight_factor)
else:
_, lcs_dirs = _lcs(reference_sentence_tokens, evaluated_sentence_tokens)
_mark_lcs(hit_mask, lcs_dirs, len(reference_sentence_tokens), len(evaluated_sentence_tokens))
overlapping_count_length = 0
for ref_token_id, val in enumerate(hit_mask):
if val == 1:
token = reference_sentence_tokens[ref_token_id]
if evaluated_unigrams_dict[token] > 0 and reference_unigrams_dict[token] > 0:
evaluated_unigrams_dict[token] -= 1
reference_unigrams_dict[ref_token_id] -= 1
if use_WLCS:
overlapping_count_length += 1
if (ref_token_id + 1 < len(hit_mask) and hit_mask[ref_token_id + 1] == 0) or ref_token_id + 1 == len(hit_mask):
overlapping_count += overlapping_count_length ** weight_factor
overlapping_count_length = 0
else:
overlapping_count += 1
if use_WLCS:
reference_count = reference_count ** weight_factor
return evaluated_count, reference_count, overlapping_count
def get_scores(self, hypothesis, references):
"""
Compute precision, recall and f1 score between hypothesis and references
Args:
hypothesis: hypothesis summary, string
references: reference summary/ies, either string or list of strings (if multiple)
Returns:
Return precision, recall and f1 score between hypothesis and references
Raises:
ValueError: raises exception if a type of hypothesis is different than the one of reference
ValueError: raises exception if a len of hypothesis is different than the one of reference
"""
if isinstance(hypothesis, str):
hypothesis, references = [hypothesis], [references]
if type(hypothesis) != type(references):
raise ValueError("'hyps' and 'refs' are not of the same type")
if len(hypothesis) != len(references):
raise ValueError("'hyps' and 'refs' do not have the same length")
scores = {}
has_rouge_n_metric = len([metric for metric in self.metrics if metric.split('-')[-1].isdigit()]) > 0
if has_rouge_n_metric:
scores.update(self._get_scores_rouge_n(hypothesis, references))
# scores = {**scores, **self._get_scores_rouge_n(hypothesis, references)}
has_rouge_l_metric = len([metric for metric in self.metrics if metric.split('-')[-1].lower() == 'l']) > 0
if has_rouge_l_metric:
scores.update(self._get_scores_rouge_l_or_w(hypothesis, references, False))
# scores = {**scores, **self._get_scores_rouge_l_or_w(hypothesis, references, False)}
has_rouge_w_metric = len([metric for metric in self.metrics if metric.split('-')[-1].lower() == 'w']) > 0
if has_rouge_w_metric:
scores.update(self._get_scores_rouge_l_or_w(hypothesis, references, True))
# scores = {**scores, **self._get_scores_rouge_l_or_w(hypothesis, references, True)}
return scores
def _get_scores_rouge_n(self, all_hypothesis, all_references):
"""
Computes precision, recall and f1 score between all hypothesis and references
Args:
hypothesis: hypothesis summary, string
references: reference summary/ies, either string or list of strings (if multiple)
Returns:
Return precision, recall and f1 score between all hypothesis and references
"""
metrics = [metric for metric in self.metrics if metric.split('-')[-1].isdigit()]
if self.apply_avg or self.apply_best:
scores = {metric: {stat:0.0 for stat in Rouge.STATS} for metric in metrics}
else:
scores = {metric: [{stat:[] for stat in Rouge.STATS} for _ in range(len(all_hypothesis))] for metric in metrics}
for sample_id, (hypothesis, references) in enumerate(zip(all_hypothesis, all_references)):
assert isinstance(hypothesis, str)
has_multiple_references = False
if isinstance(references, list):
has_multiple_references = len(references) > 1
if not has_multiple_references:
references = references[0]
# Prepare hypothesis and reference(s)
hypothesis = self._preprocess_summary_as_a_whole(hypothesis)
references = [self._preprocess_summary_as_a_whole(reference) for reference in references] if has_multiple_references else [self._preprocess_summary_as_a_whole(references)]
# Compute scores
for metric in metrics:
suffix = metric.split('-')[-1]
n = int(suffix)
# Aggregate
if self.apply_avg:
# average model
total_hypothesis_ngrams_count = 0
total_reference_ngrams_count = 0
total_ngrams_overlapping_count = 0
for reference in references:
hypothesis_count, reference_count, overlapping_ngrams = Rouge._compute_ngrams(hypothesis, reference, n)
total_hypothesis_ngrams_count += hypothesis_count
total_reference_ngrams_count += reference_count
total_ngrams_overlapping_count += overlapping_ngrams
score = Rouge._compute_p_r_f_score(total_hypothesis_ngrams_count, total_reference_ngrams_count, total_ngrams_overlapping_count, self.alpha)
for stat in Rouge.STATS:
scores[metric][stat] += score[stat]
else:
# Best model
if self.apply_best:
best_current_score = None
for reference in references:
hypothesis_count, reference_count, overlapping_ngrams = Rouge._compute_ngrams(hypothesis, reference, n)
score = Rouge._compute_p_r_f_score(hypothesis_count, reference_count, overlapping_ngrams, self.alpha)
if best_current_score is None or score['r'] > best_current_score['r']:
best_current_score = score
for stat in Rouge.STATS:
scores[metric][stat] += best_current_score[stat]
# Keep all
else:
for reference in references:
hypothesis_count, reference_count, overlapping_ngrams = Rouge._compute_ngrams(hypothesis, reference, n)
score = Rouge._compute_p_r_f_score(hypothesis_count, reference_count, overlapping_ngrams, self.alpha)
for stat in Rouge.STATS:
scores[metric][sample_id][stat].append(score[stat])
# Compute final score with the average or the the max
if (self.apply_avg or self.apply_best) and len(all_hypothesis) > 1:
for metric in metrics:
for stat in Rouge.STATS:
scores[metric][stat] /= len(all_hypothesis)
return scores
def _get_scores_rouge_l_or_w(self, all_hypothesis, all_references, use_w=False):
"""
Computes precision, recall and f1 score between all hypothesis and references
Args:
hypothesis: hypothesis summary, string
references: reference summary/ies, either string or list of strings (if multiple)
Returns:
Return precision, recall and f1 score between all hypothesis and references
"""
metric = "rouge-w" if use_w else "rouge-l"
if self.apply_avg or self.apply_best:
scores = {metric: {stat:0.0 for stat in Rouge.STATS}}
else:
scores = {metric: [{stat:[] for stat in Rouge.STATS} for _ in range(len(all_hypothesis))]}
for sample_id, (hypothesis_sentences, references_sentences) in enumerate(zip(all_hypothesis, all_references)):
assert isinstance(hypothesis_sentences, str)
has_multiple_references = False
if isinstance(references_sentences, list):
has_multiple_references = len(references_sentences) > 1
if not has_multiple_references:
references_sentences = references_sentences[0]
# Prepare hypothesis and reference(s)
hypothesis_sentences = self._preprocess_summary_per_sentence(hypothesis_sentences)
references_sentences = [self._preprocess_summary_per_sentence(reference) for reference in references_sentences] if has_multiple_references else [self._preprocess_summary_per_sentence(references_sentences)]
# Compute scores
# Aggregate
if self.apply_avg:
# average model
total_hypothesis_ngrams_count = 0
total_reference_ngrams_count = 0
total_ngrams_overlapping_count = 0
for reference_sentences in references_sentences:
hypothesis_count, reference_count, overlapping_ngrams = Rouge._compute_ngrams_lcs(hypothesis_sentences, reference_sentences, self.weight_factor if use_w else 1.0)
total_hypothesis_ngrams_count += hypothesis_count
total_reference_ngrams_count += reference_count
total_ngrams_overlapping_count += overlapping_ngrams
score = Rouge._compute_p_r_f_score(total_hypothesis_ngrams_count, total_reference_ngrams_count, total_ngrams_overlapping_count, self.alpha, self.weight_factor if use_w else 1.0)
for stat in Rouge.STATS:
scores[metric][stat] += score[stat]
else:
# Best model
if self.apply_best:
best_current_score = None
best_current_score_wlcs = None
for reference_sentences in references_sentences:
hypothesis_count, reference_count, overlapping_ngrams = Rouge._compute_ngrams_lcs(hypothesis_sentences, reference_sentences, self.weight_factor if use_w else 1.0)
score = Rouge._compute_p_r_f_score(total_hypothesis_ngrams_count, total_reference_ngrams_count, total_ngrams_overlapping_count, self.alpha, self.weight_factor if use_w else 1.0)
if use_w:
reference_count_for_score = reference_count ** (1.0 / self.weight_factor)
overlapping_ngrams_for_score = overlapping_ngrams
score_wlcs = (overlapping_ngrams_for_score / reference_count_for_score) ** (1.0 / self.weight_factor)
if best_current_score_wlcs is None or score_wlcs > best_current_score_wlcs:
best_current_score = score
best_current_score_wlcs = score_wlcs
else:
if best_current_score is None or score['r'] > best_current_score['r']:
best_current_score = score
for stat in Rouge.STATS:
scores[metric][stat] += best_current_score[stat]
# Keep all
else:
for reference_sentences in references_sentences:
hypothesis_count, reference_count, overlapping_ngrams = Rouge._compute_ngrams_lcs(hypothesis_sentences, reference_sentences, self.weight_factor if use_w else 1.0)
score = Rouge._compute_p_r_f_score(hypothesis_count, reference_count, overlapping_ngrams, self.alpha, self.weight_factor)
for stat in Rouge.STATS:
scores[metric][sample_id][stat].append(score[stat])
# Compute final score with the average or the the max
if (self.apply_avg or self.apply_best) and len(all_hypothesis) > 1:
for stat in Rouge.STATS:
scores[metric][stat] /= len(all_hypothesis)
return scores
def _preprocess_summary_as_a_whole(self, summary):
"""
Preprocessing (truncate text if enable, tokenization, stemming if enable, lowering) of a summary as a whole
Args:
summary: string of the summary
Returns:
Return the preprocessed summary (string)
"""
sentences = Rouge.split_into_sentences(summary, self.ensure_compatibility)
# Truncate
if self.limit_length:
# By words
if self.length_limit_type == 'words':
summary = ' '.join(sentences)
all_tokens = summary.split() # Counting as in the perls script
summary = ' '.join(all_tokens[:self.length_limit])
# By bytes
elif self.length_limit_type == 'bytes':
summary = ''
current_len = 0
for sentence in sentences:
sentence = sentence.strip()
sentence_len = len(sentence)
if current_len + sentence_len < self.length_limit:
if current_len != 0:
summary += ' '
summary += sentence
current_len += sentence_len
else:
if current_len > 0:
summary += ' '
summary += sentence[:self.length_limit-current_len]
break
else:
summary = ' '.join(sentences)
summary = Rouge.REMOVE_CHAR_PATTERN.sub(' ', summary.lower()).strip()
# Preprocess. Hack: because official ROUGE script bring "cannot" as "cannot" and "can not" as "can not",
# we have to hack nltk tokenizer to not transform "cannot/can not" to "can not"
if self.ensure_compatibility:
tokens = self.tokenize_text(Rouge.KEEP_CANNOT_IN_ONE_WORD.sub('_cannot_', summary))
else:
tokens = self.tokenize_text(Rouge.REMOVE_CHAR_PATTERN.sub(' ', summary))
if self.stemming:
self.stem_tokens(tokens) # stemming in-place
if self.ensure_compatibility:
preprocessed_summary = [Rouge.KEEP_CANNOT_IN_ONE_WORD_REVERSED.sub('cannot', ' '.join(tokens))]
else:
preprocessed_summary = [' '.join(tokens)]
return preprocessed_summary
def _preprocess_summary_per_sentence(self, summary):
"""
Preprocessing (truncate text if enable, tokenization, stemming if enable, lowering) of a summary by sentences
Args:
summary: string of the summary
Returns:
Return the preprocessed summary (string)
"""
sentences = Rouge.split_into_sentences(summary, self.ensure_compatibility)
# Truncate
if self.limit_length:
final_sentences = []
current_len = 0
# By words
if self.length_limit_type == 'words':
for sentence in sentences:
tokens = sentence.strip().split()
tokens_len = len(tokens)
if current_len + tokens_len < self.length_limit:
sentence = ' '.join(tokens)
final_sentences.append(sentence)
current_len += tokens_len
else:
sentence = ' '.join(tokens[:self.length_limit - current_len])
final_sentences.append(sentence)
break
# By bytes
elif self.length_limit_type == 'bytes':
for sentence in sentences:
sentence = sentence.strip()
sentence_len = len(sentence)
if current_len + sentence_len < self.length_limit:
final_sentences.append(sentence)
current_len += sentence_len
else:
sentence = sentence[:self.length_limit - current_len]
final_sentences.append(sentence)
break
sentences = final_sentences
final_sentences = []
for sentence in sentences:
sentence = Rouge.REMOVE_CHAR_PATTERN.sub(' ', sentence.lower()).strip()
# Preprocess. Hack: because official ROUGE script bring "cannot" as "cannot" and "can not" as "can not",
# we have to hack nltk tokenizer to not transform "cannot/can not" to "can not"
if self.ensure_compatibility:
tokens = self.tokenize_text(Rouge.KEEP_CANNOT_IN_ONE_WORD.sub('_cannot_', sentence))
else:
tokens = self.tokenize_text(Rouge.REMOVE_CHAR_PATTERN.sub(' ', sentence))
if self.stemming:
self.stem_tokens(tokens) # stemming in-place
if self.ensure_compatibility:
sentence = Rouge.KEEP_CANNOT_IN_ONE_WORD_REVERSED.sub('cannot', ' '.join(tokens))
else:
sentence = ' '.join(tokens)
final_sentences.append(sentence)
return final_sentences