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"""
Enhanced evaluation module with multiple metrics
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
sys.path.insert(0, os.path.dirname(__file__))
from utils import util, metric
from models import evalu
def eval_metrics_multi(trans, target_file, indices=None, remove_bpe=False):
"""
Evaluate with multiple metrics: BLEU, OTEM, UTEM
Returns:
dict with keys: bleu4, bleu1, bleu2, bleu3, otem2, utem4
"""
target_valid_files = util.fetch_valid_ref_files(target_file)
if target_valid_files is None:
return {
'bleu4': 0.0,
'bleu1': 0.0,
'bleu2': 0.0,
'bleu3': 0.0,
'otem2': 0.0,
'utem4': 0.0
}
if indices is not None:
trans = [data[1] for data in sorted(zip(indices, trans), key=lambda x: x[0])]
# Load references
references = []
for ref_file in target_valid_files:
import tensorflow as tf
cur_refs = tf.gfile.Open(ref_file).readlines()
if remove_bpe:
cur_refs = [line.replace("@@ ", "") for line in cur_refs]
cur_refs = [line.strip().split() for line in cur_refs]
references.append(cur_refs)
references = list(zip(*references))
# Process translations
if remove_bpe:
new_trans = []
for line in trans:
line = (' '.join(line)).replace('@@ ', '').split()
new_trans.append(line)
trans = new_trans
# Calculate multiple metrics
results = {}
# BLEU-4 (default)
results['bleu4'] = metric.bleu(trans, references, n=4)
# BLEU-1, BLEU-2, BLEU-3
results['bleu1'] = metric.bleu(trans, references, n=1)
results['bleu2'] = metric.bleu(trans, references, n=2)
results['bleu3'] = metric.bleu(trans, references, n=3)
# OTEM-2 (Over-Translation Evaluation Metric)
results['otem2'] = metric.otem(trans, references, n=2)
# UTEM-4 (Under-Translation Evaluation Metric)
results['utem4'] = metric.utem(trans, references, n=4)
return results
def eval_metrics_full(trans, target_file, indices=None, remove_bpe=False):
"""
Full evaluation with all available metrics from eval/metrics.py
Requires sacrebleu and other dependencies
Returns:
dict with comprehensive metrics
"""
try:
# Import eval metrics (may fail if dependencies not installed)
from eval import metrics as eval_metrics
target_valid_files = util.fetch_valid_ref_files(target_file)
if target_valid_files is None:
return {}
if indices is not None:
trans = [data[1] for data in sorted(zip(indices, trans), key=lambda x: x[0])]
# Load references as strings
import tensorflow as tf
references = []
for ref_file in target_valid_files:
cur_refs = [line.strip() for line in tf.gfile.Open(ref_file).readlines()]
references.append(cur_refs)
# For single reference, take first one
references = references[0] if len(references) == 1 else references[0]
# Convert translations to strings
hypotheses = [' '.join(t) if isinstance(t, list) else t for t in trans]
if remove_bpe:
hypotheses = [h.replace('@@ ', '') for h in hypotheses]
references = [r.replace('@@ ', '') for r in references]
results = {}
# BLEU scores (BLEU-1 to BLEU-4)
bleu_scores = eval_metrics.bleu(references, hypotheses)
results.update(bleu_scores)
# chrF score
results['chrf'] = eval_metrics.chrf(references, hypotheses)
# ROUGE score
results['rouge'] = eval_metrics.rouge(references, hypotheses)
# Token accuracy
results['token_accuracy'] = eval_metrics.token_accuracy(references, hypotheses)
# Sequence accuracy
results['sequence_accuracy'] = eval_metrics.sequence_accuracy(references, hypotheses)
# WER and related metrics (for sign language recognition)
wer_scores = eval_metrics.wer_list(references, hypotheses)
results.update(wer_scores)
return results
except ImportError as e:
print(f"Warning: Could not import eval.metrics module: {e}")
print("Falling back to basic metrics...")
return eval_metrics_multi(trans, target_file, indices, remove_bpe)
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