Add scoring mechanism to sentiment task

spacy_llm/tasks/sentiment/registry.py

@@ -1,10 +1,10 @@
 from typing import Optional, Type
 
 from ...registry import registry
-from ...ty import ExamplesConfigType, FewshotExample, TaskResponseParser
+from ...ty import ExamplesConfigType, FewshotExample, Scorer, TaskResponseParser
 from .parser import parse_responses_v1
 from .task import DEFAULT_SENTIMENT_TEMPLATE_V1, SentimentTask
-from .util import SentimentExample
+from .util import SentimentExample, score
 
 
 @registry.llm_tasks("spacy.Sentiment.v1")
@@ -14,6 +14,7 @@ def make_sentiment_task(
     prompt_example_type: Optional[Type[FewshotExample]] = None,
     examples: ExamplesConfigType = None,
     field: str = "sentiment",
+    scorer: Optional[Scorer] = None,
 ):
     """Sentiment.v1 task factory.
 
@@ -24,6 +25,7 @@ def make_sentiment_task(
     examples (Optional[Callable[[], Iterable[Any]]]): Optional callable that reads a file containing task examples for
         few-shot learning. If None is passed, then zero-shot learning will be used.
     field (str): The name of the doc extension in which to store the summary.
+    scorer (Optional[Scorer]): Scorer function.
     """
     raw_examples = examples() if callable(examples) else examples
     example_type = prompt_example_type or SentimentExample
@@ -37,4 +39,5 @@ def make_sentiment_task(
         prompt_example_type=example_type,
         prompt_examples=sentiment_examples,
         field=field,
+        scorer=scorer or score,
     )

spacy_llm/tasks/sentiment/task.py

@@ -1,10 +1,10 @@
-from typing import Callable, Iterable, List, Optional, Type
+from typing import Any, Callable, Dict, Iterable, List, Optional, Type
 
 from spacy.language import Language
 from spacy.tokens import Doc
 from spacy.training import Example
 
-from ...ty import FewshotExample, Self, TaskResponseParser
+from ...ty import FewshotExample, Scorer, Self, TaskResponseParser
 from ..builtin_task import BuiltinTask
 from ..templates import read_template
 from .util import SentimentExample
@@ -20,6 +20,7 @@ def __init__(
         prompt_example_type: Type[FewshotExample],
         field: str,
         prompt_examples: Optional[List[SentimentExample]],
+        scorer: Scorer,
     ):
         """Sentiment analysis task.
 
@@ -36,6 +37,7 @@ def __init__(
             prompt_examples=prompt_examples,
         )
         self._field = field
+        self._scorer = scorer
         self._check_doc_extension()
 
     def _check_doc_extension(self):
@@ -79,6 +81,9 @@ def parse_responses(
 
             yield doc
 
+    def scorer(self, examples: Iterable[Example]) -> Dict[str, Any]:
+        return self._scorer(examples, field=self._field)
+
     @property
     def _cfg_keys(self) -> List[str]:
         return ["_template"]

spacy_llm/tasks/sentiment/util.py

@@ -1,3 +1,5 @@
+from typing import Any, Dict, Iterable
+
 from spacy.training import Example
 
 from ...compat import Self
@@ -14,3 +16,19 @@ def generate(cls, example: Example, **kwargs) -> Self:
             text=example.reference.text,
             score=getattr(example.reference._, kwargs["field"]),
         )
+
+
+def score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
+    """Score lemmatization accuracy in examples.
+    examples (Iterable[Example]): Examples to score.
+    RETURNS (Dict[str, Any]): Dict with metric name -> score.
+    """
+    score_diffs = [
+        abs(
+            getattr(example.predicted._, kwargs["field"])
+            - getattr(example.reference._, kwargs["field"])
+        )
+        for example in examples
+    ]
+
+    return {"acc_sentiment": 1 - (sum(score_diffs) / len(score_diffs))}

spacy_llm/tests/tasks/test_sentiment.py

@@ -1,8 +1,10 @@
 from pathlib import Path
 
+import numpy
 import pytest
 import spacy
 from confection import Config
+from spacy.training import Example
 from spacy.util import make_tempdir
 
 from spacy_llm.registry import fewshot_reader, file_reader
@@ -263,3 +265,20 @@ def test_external_template_actually_loads():
 Sentiment:
 """.strip()
     )
+
+
+@pytest.mark.external
+@pytest.mark.skipif(has_openai_key is False, reason="OpenAI API key not available")
+def test_sentiment_score(request):
+    """Test scoring mechanism."""
+    cfg = request.getfixturevalue("zeroshot_cfg_string")
+    orig_config = Config().from_str(cfg)
+    nlp = spacy.util.load_model_from_config(orig_config, auto_fill=True)
+
+    sent_diff = 0.2
+    doc1 = nlp("This works well.")
+    doc2 = doc1.copy()
+    doc2._.sentiment -= sent_diff
+    assert numpy.isclose(
+        nlp.get_pipe("llm").score([Example(doc1, doc2)])["acc_sentiment"], 1 - sent_diff
+    )