Merge pull request #223 from lvapeab/master

Add evaluation using SacreBleu

keras_wrapper/extra/evaluation.py

@@ -4,6 +4,7 @@
 from builtins import map, zip
 import json
 import logging
+
 logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(message)s', datefmt='%d/%m/%Y %H:%M:%S')
 logger = logging.getLogger(__name__)
 
@@ -12,6 +13,60 @@
 
 # EVALUATION FUNCTIONS SELECTOR
 
+def get_sacrebleu_score(pred_list, verbose, extra_vars, split):
+    """
+    SacreBLEU! metrics
+    :param pred_list: dictionary of hypothesis sentences (id, sentence)
+    :param verbose: if greater than 0 the metric measures are printed out
+    :param extra_vars: extra variables, here are:
+            extra_vars['references'] - dict mapping sample indices to list with all valid captions (id, [sentences])
+            extra_vars['tokenize_f'] - tokenization function used during model training (used again for validation)
+            extra_vars['detokenize_f'] - detokenization function used during model training (used again for validation)
+            extra_vars['tokenize_hypotheses'] - Whether tokenize or not the hypotheses during evaluation
+    :param split: split on which we are evaluating
+    :return: Dictionary with the coco scores
+    """
+    import sacrebleu
+    gts = extra_vars[split]['references']
+    if extra_vars.get('tokenize_hypotheses', False):
+        hypo = [list(map(
+            lambda x: extra_vars['tokenize_f'](x.strip()), line)) for line in pred_list]
+    else:
+        hypo = [line.strip() for line in pred_list]
+
+    initial_references = gts.get(0)
+    if initial_references is None:
+        raise ValueError('You need to provide at least one reference')
+
+    num_references = len(initial_references)
+    refs = [[] for _ in range(num_references)]
+    for references in gts.values():
+        assert len(references) == num_references, '"get_sacrebleu_score" does not support a different number of references per sample.'
+        for ref_idx, reference in enumerate(references):
+            # De/Tokenize refereces if needed
+            tokenized_ref = extra_vars['tokenize_f'](reference) if extra_vars.get('tokenize_references', False)\
+                else reference
+            detokenized_ref = extra_vars['detokenize_f'](tokenized_ref) if extra_vars.get('apply_detokenization', False) else tokenized_ref
+            refs[ref_idx].append(detokenized_ref)
+
+    scorers = [
+        (sacrebleu.corpus_bleu, "Bleu_4"),
+    ]
+
+    final_scores = {}
+    for scorer, method in scorers:
+        score = scorer(hypo, refs)
+        final_scores[method] = score.score
+
+    if verbose > 0:
+        logger.info('Computing SacreBleu scores on the %s split...' % split)
+    for metric in sorted(final_scores):
+        value = final_scores[metric]
+        logger.info(metric + ': ' + str(value))
+
+    return final_scores
+
+
 def get_coco_score(pred_list, verbose, extra_vars, split):
     """
     COCO challenge metrics
@@ -91,7 +146,7 @@ def eval_vqa(pred_list, verbose, extra_vars, split):
     import datetime
     import os
     from pycocoevalcap.vqa import vqaEval, visual_qa
-    from read_write import list2vqa
+    from keras_wrapper.extra.read_write import list2vqa
 
     quesFile = extra_vars[split]['quesFile']
     annFile = extra_vars[split]['annFile']
@@ -103,7 +158,8 @@ def eval_vqa(pred_list, verbose, extra_vars, split):
     # create vqa object and vqaRes object
     vqa_ = visual_qa.VQA(annFile, quesFile)
     vqaRes = vqa_.loadRes(resFile, quesFile)
-    vqaEval_ = vqaEval.VQAEval(vqa_, vqaRes, n=2)  # n is precision of accuracy (number of places after decimal), default is 2
+    vqaEval_ = vqaEval.VQAEval(vqa_, vqaRes,
+                               n=2)  # n is precision of accuracy (number of places after decimal), default is 2
     vqaEval_.evaluate()
     os.remove(resFile)  # remove temporal file
 
@@ -189,7 +245,8 @@ def multilabel_metrics(pred_list, verbose, extra_vars, split):
 
     if verbose > 0:
         logger.info(
-            '"coverage_error" (best: avg labels per sample = %f): %f' % (float(np.sum(y_gt)) / float(n_samples), coverr))
+            '"coverage_error" (best: avg labels per sample = %f): %f' % (
+            float(np.sum(y_gt)) / float(n_samples), coverr))
         logger.info('Label Ranking "average_precision" (best: 1.0): %f' % avgprec)
         logger.info('Label "ranking_loss" (best: 0.0): %f' % rankloss)
         logger.info('precision: %f' % precision)
@@ -204,9 +261,6 @@ def multilabel_metrics(pred_list, verbose, extra_vars, split):
             'f1': f1}
 
 
-import numpy as np
-
-
 def multiclass_metrics(pred_list, verbose, extra_vars, split):
     """
     Multiclass classification metrics. See multilabel ranking metrics in sklearn library for more info:

requirements.txt

@@ -4,6 +4,7 @@ toolz
 cloudpickle
 matplotlib
 sacremoses
+sacrebleu
 scipy
 future
 cython

setup.py

@@ -30,6 +30,7 @@
                         'cloudpickle',
                         'matplotlib',
                         'sacremoses',
+                        'sacrebleu',
                         'scipy',
                         'future',
                         'cython',

tests/extra/test_wrapper_evaluation.py

@@ -1,7 +1,25 @@
 import pytest
-from keras_wrapper.extra.evaluation import *
+import numpy as np
+from keras_wrapper.extra.evaluation import get_sacrebleu_score, get_coco_score,  multilabel_metrics, compute_perplexity
 
 
+def test_get_sacrebleu_score():
+    pred_list = ['Prediction 1 X W Z', 'Prediction 2 X W Z', 'Prediction 3 X W Z']
+    extra_vars = {'val': {'references': {0: ['Prediction 1 X W Z', 'Prediction 5'],
+                                         1: ['Prediction 2 X W Z', 'X Y Z'],
+                                         2: ['Prediction 3 X W Z', 'Prediction 5']}},
+
+                  'test': {'references': {0: ['Prediction 2 X W Z'],
+                                          1: ['Prediction 3 X W Z'],
+                                          2: ['Prediction 1 X W Z']}}
+                  }
+    val_scores = get_sacrebleu_score(pred_list, 0, extra_vars, 'val')
+    assert np.allclose(val_scores['Bleu_4'], 100.0, atol=1e6)
+
+
+    test_scores = get_sacrebleu_score(pred_list, 0, extra_vars, 'test')
+    assert np.allclose(test_scores['Bleu_4'], 0., atol=1e6)
+
 def test_get_coco_score():
     pred_list = ['Prediction 1', 'Prediction 2', 'Prediction 3']
     extra_vars = {'val': {'references': {0: ['Prediction 1'], 1: ['Prediction 2'],