Add: BERTScore metric to func and class factories.

src/aac_metrics/classes/evaluate.py

@@ -13,6 +13,7 @@
 from torch import Tensor
 
 from aac_metrics.classes.base import AACMetric
+from aac_metrics.classes.bert_score_mrefs import BERTScoreMRefs
 from aac_metrics.classes.bleu import BLEU, BLEU1, BLEU2, BLEU3, BLEU4
 from aac_metrics.classes.cider_d import CIDErD
 from aac_metrics.classes.fense import FENSE
@@ -251,6 +252,11 @@ def _get_metric_factory_classes(
             **init_kwds,
         ),
         "vocab": lambda: Vocab(
+            verbose=verbose,
+            **init_kwds,
+        ),
+        "bert_score": lambda: BERTScoreMRefs(
+            verbose=verbose,
             **init_kwds,
         ),
     }

src/aac_metrics/classes/vocab.py

@@ -37,13 +37,15 @@ def __init__(
         tokenizer: Callable[[str], list[str]] = str.split,
         dtype: torch.dtype = torch.float64,
         pop_strategy: str = "max",
+        verbose: int = 0,
     ) -> None:
         super().__init__()
         self._return_all_scores = return_all_scores
         self._seed = seed
         self._tokenizer = tokenizer
         self._dtype = dtype
         self._pop_strategy = pop_strategy
+        self._verbose = verbose
 
         self._candidates = []
         self._mult_references = []
@@ -57,6 +59,7 @@ def compute(self) -> Union[tuple[dict[str, Tensor], dict[str, Tensor]], Tensor]:
             tokenizer=self._tokenizer,
             dtype=self._dtype,
             pop_strategy=self._pop_strategy,
+            verbose=self._verbose,
         )
 
     def get_output_names(self) -> tuple[str, ...]:

src/aac_metrics/functional/bert_score_mrefs.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-from typing import Callable, Optional, TypeVar, Union
+from typing import Callable, Optional, Union
 
 import torch
 
@@ -14,9 +14,6 @@
 from aac_metrics.utils.collections import flat_list, unflat_list, duplicate_list
 
 
-T = TypeVar("T")
-
-
 def bert_score_mrefs(
     candidates: list[str],
     mult_references: list[list[str]],

src/aac_metrics/functional/evaluate.py

@@ -12,6 +12,7 @@
 
 from torch import Tensor
 
+from aac_metrics.functional.bert_score_mrefs import bert_score_mrefs
 from aac_metrics.functional.bleu import bleu, bleu_1, bleu_2, bleu_3, bleu_4
 from aac_metrics.functional.cider_d import cider_d
 from aac_metrics.functional.fense import fense
@@ -67,6 +68,7 @@
         "fense",  # includes sbert, fer
         "spider_fl",  # includes cider_d, spice, spider, fer
         "vocab",
+        "bert_score",
     ),
 }
 DEFAULT_METRICS_SET_NAME = "default"
@@ -332,6 +334,11 @@ def _get_metric_factory_functions(
         ),
         "vocab": partial(
             vocab,
+            verbose=verbose,
+            **init_kwds,
+        ),
+        "bert_score": partial(
+            bert_score_mrefs,
             **init_kwds,
         ),
     }

src/aac_metrics/functional/vocab.py

@@ -21,6 +21,7 @@ def vocab(
     tokenizer: Callable[[str], list[str]] = str.split,
     dtype: torch.dtype = torch.float64,
     pop_strategy: str = "max",
+    verbose: int = 0,
 ) -> Union[tuple[dict[str, Tensor], dict[str, Tensor]], Tensor]:
     """Compute vocabulary statistics.
 
@@ -35,6 +36,7 @@ def vocab(
     :param tokenizer: The function used to split a sentence into tokens. defaults to str.split.
     :param dtype: Torch floating point dtype for numerical precision. defaults to torch.float64.
     :param pop_strategy: Strategy to compute average reference vocab. defaults to "max".
+    :param verbose: The verbose level. defaults to 0.
     :returns: A tuple of globals and locals scores or a scalar tensor with the main global score.
     """
     tok_cands = list(map(tokenizer, candidates))
@@ -79,6 +81,9 @@ def vocab(
                 f"Invalid argument {pop_strategy=}. (expected one of {POP_STRATEGIES} or an integer value)"
             )
 
+        if verbose >= 2:
+            pylog.debug(f"Found {n_samples=} with {pop_strategy=}.")
+
         vocab_mrefs_lens = torch.empty((n_samples,), dtype=dtype)
 
         for i in range(n_samples):