bug: 5123 metrics (#5245)

# Description
<!-- Please include a summary of the changes and the related issue.
Please also include relevant motivation and context. List any
dependencies that are required for this change. -->

Only added field_name to agreement metrics as only alpha is influenced
by the content field.

Closes #5123 

**Type of change**
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PR according to the type of change -->

- Bug fix (non-breaking change which fixes an issue)
- Documentation update

**How Has This Been Tested**
<!-- Please add some reference about how your feature has been tested.
-->

**Checklist**
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account -->

- I did a self-review of my code
- I made corresponding changes to the documentation

argilla/src/argilla/client/feedback/dataset/mixins.py

@@ -85,6 +85,7 @@ def compute_agreement_metrics(
         self,
         metric_names: Union[str, List[str]] = None,
         question_name: Union[str, LabelQuestion, MultiLabelQuestion, RatingQuestion, RankingQuestion] = None,
+        field_name: Union[str, List[str]] = None,
     ) -> Union["AgreementMetricResult", List["AgreementMetricResult"]]:
         """Compute agreement or reliability of annotation metrics.
 
@@ -94,6 +95,7 @@ def compute_agreement_metrics(
         Args:
             metric_names: Metric name or list of metric names of the metrics, dependent on the question type.
             question_name: Question for which we want to compute the metrics.
+            field_name: Name of the fields related to the question we want to analyse the agreement.
 
         Note:
             Currently, TextQuestion is not supported.
@@ -104,7 +106,7 @@ def compute_agreement_metrics(
         """
         from argilla.client.feedback.metrics.agreement_metrics import AgreementMetric
 
-        return AgreementMetric(self, question_name).compute(metric_names)
+        return AgreementMetric(self, question_name, field_name).compute(metric_names)
 
 
 class UnificationMixin:

argilla/src/argilla/client/feedback/metrics/agreement_metrics.py

@@ -12,7 +12,8 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-"""This module contains metrics to gather information related to inter-Annotator agreement. """
+"""This module contains metrics to gather information related to inter-Annotator agreement."""
+
 import warnings
 from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Union
 
@@ -52,6 +53,7 @@ def __init__(self, **kwargs):
 def prepare_dataset_for_annotation_task(
     dataset: Union["FeedbackDataset", "RemoteFeedbackDataset"],
     question_name: str,
+    field_name: Union[str, List[str]],
     filter_by: Optional[Dict[str, Union["ResponseStatusFilter", List["ResponseStatusFilter"]]]] = None,
     sort_by: Optional[List["SortBy"]] = None,
     max_records: Optional[int] = None,
@@ -74,6 +76,7 @@ def prepare_dataset_for_annotation_task(
     Args:
         dataset: FeedbackDataset to compute the metrics.
         question_name: Name of the question for which we want to analyse the agreement.
+        field_name: Name of the fields related to the question we want to analyse the agreement.
         filter_by: A dict with key the field to filter by, and values the filters to apply.
             Can be one of: draft, pending, submitted, and discarded. If set to None,
             no filter will be applied. Defaults to None (no filter is applied).
@@ -108,7 +111,9 @@ def prepare_dataset_for_annotation_task(
 
     for row in hf_dataset:
         responses_ = row[question_name]
-        question_text = row["text"]
+        question_text = (
+            " ".join([row[field] for field in field_name]) if isinstance(field_name, list) else row[field_name]
+        )
         for response in responses_:
             user_id = response["user_id"]
             if user_id is None:
@@ -181,7 +186,7 @@ class AgreementMetric(MetricBase):
     Example:
         >>> import argilla as rg
         >>> from argilla.client.feedback.metrics import AgreementMetric
-        >>> metric = AgreementMetric(dataset=dataset, question_name=question, filter_by={"response_status": "submitted"})
+        >>> metric = AgreementMetric(dataset=dataset, question_name=question, field_name=field, filter_by={"response_status": "submitted"})
         >>> metrics_report = metric.compute("alpha")
 
     """
@@ -190,6 +195,7 @@ def __init__(
         self,
         dataset: FeedbackDataset,
         question_name: str,
+        field_name: Union[str, List[str]],
         filter_by: Optional[Dict[str, Union["ResponseStatusFilter", List["ResponseStatusFilter"]]]] = None,
         sort_by: Optional[List["SortBy"]] = None,
         max_records: Optional[int] = None,
@@ -199,6 +205,7 @@ def __init__(
         Args:
             dataset: FeedbackDataset to compute the metrics.
             question_name: Name of the question for which we want to analyse the agreement.
+            field_name: Name of the fields related to the question we want to analyse the agreement.
             filter_by: A dict with key the field to filter by, and values the filters to apply.
                 Can be one of: draft, pending, submitted, and discarded. If set to None,
                 no filter will be applied. Defaults to None (no filter is applied).
@@ -207,6 +214,7 @@ def __init__(
             max_records: The maximum number of records to use for training. Defaults to None.
         """
         self._metrics_per_question = METRICS_PER_QUESTION
+        self._field_name = field_name
         super().__init__(dataset, question_name)
         self._filter_by = filter_by
         self._sort_by = sort_by
@@ -231,6 +239,7 @@ def compute(self, metric_names: Union[str, List[str]]) -> List[AgreementMetricRe
         dataset = prepare_dataset_for_annotation_task(
             self._dataset,
             self._question_name,
+            self._field_name,
             filter_by=self._filter_by,
             sort_by=self._sort_by,
             max_records=self._max_records,

argilla/tests/integration/client/feedback/metrics/test_agreement_metrics.py

@@ -65,18 +65,20 @@ def test_allowed_metrics(
     )
     dataset.add_records(records=feedback_dataset_records_with_paired_suggestions)
 
-    metric = AgreementMetric(dataset=dataset, question_name=question)
+    metric = AgreementMetric(
+        dataset=dataset, question_name=question, field_name=[field.name for field in feedback_dataset_fields]
+    )
     assert set(metric.allowed_metrics) == metric_names
 
 
 @pytest.mark.parametrize(
-    "question, num_items, type_of_data",
+    "field, question, num_items, type_of_data",
     [
-        ("question-1", None, None),
-        ("question-2", 12, int),
-        ("question-3", 12, str),
-        ("question-4", 12, frozenset),
-        ("question-5", 12, tuple),
+        (["text"], "question-1", None, None),
+        (["text", "label"], "question-2", 12, int),
+        (["text", "label"], "question-3", 12, str),
+        (["text"], "question-4", 12, FrozenSet),
+        (["label"], "question-5", 12, Tuple),
     ],
 )
 @pytest.mark.usefixtures(
@@ -91,6 +93,7 @@ def test_prepare_dataset_for_annotation_task(
     feedback_dataset_questions: List["AllowedQuestionTypes"],
     feedback_dataset_records_with_paired_suggestions: List[FeedbackRecord],
     question: str,
+    field: Union[str, List[str]],
     num_items: int,
     type_of_data: Union[str, int, FrozenSet, Tuple[str]],
 ):
@@ -101,19 +104,24 @@ def test_prepare_dataset_for_annotation_task(
     )
     dataset.add_records(records=feedback_dataset_records_with_paired_suggestions)
 
-    if question in ("question-1",):
+    if question == "question-1":
         with pytest.raises(NotImplementedError, match=r"^Question '"):
-            prepare_dataset_for_annotation_task(dataset, question)
+            prepare_dataset_for_annotation_task(dataset, question, field)
     else:
-        formatted_dataset = prepare_dataset_for_annotation_task(dataset, question)
+        formatted_dataset = prepare_dataset_for_annotation_task(dataset, question, field)
         assert isinstance(formatted_dataset, list)
         assert len(formatted_dataset) == num_items
         item = formatted_dataset[0]
         assert isinstance(item, tuple)
         assert isinstance(item[0], str)
         assert item[0].startswith("00000000-")  # beginning of our uuid for tests
         assert isinstance(item[1], str)
-        assert item[1] == feedback_dataset_records_with_paired_suggestions[0].fields["text"]
+        expected_field_value = (
+            " ".join([feedback_dataset_records_with_paired_suggestions[0].fields[f] for f in field])
+            if isinstance(field, list)
+            else feedback_dataset_records_with_paired_suggestions[0].fields[field]
+        )
+        assert item[1] == expected_field_value
         assert isinstance(item[2], type_of_data)
 
 
@@ -156,9 +164,17 @@ def test_agreement_metrics(
 
     if question in ("question-1",):
         with pytest.raises(NotImplementedError, match=r"^No metrics are defined currently for"):
-            AgreementMetric(dataset=dataset, question_name=question)
+            AgreementMetric(
+                dataset=dataset,
+                question_name=question,
+                field_name=[field.name for field in feedback_dataset_fields],
+            )
     else:
-        metric = AgreementMetric(dataset=dataset, question_name=question)
+        metric = AgreementMetric(
+            dataset=dataset,
+            question_name=question,
+            field_name=[field.name for field in feedback_dataset_fields],
+        )
         # Test for repr method
         assert repr(metric) == f"AgreementMetric(question_name={question})"
         metrics_report = metric.compute(metric_names)
@@ -173,19 +189,19 @@ def test_agreement_metrics(
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
-    "question, metric_names",
+    "field, question, metric_names",
     [
         # TextQuestion
-        ("question-1", None),
+        (["text"], "question-1", None),
         # RatingQuestion
-        ("question-2", "alpha"),
-        ("question-2", ["alpha"]),
+        (["text", "label"], "question-2", "alpha"),
+        (["text", "label"], "question-2", ["alpha"]),
         # LabelQuestion
-        ("question-3", "alpha"),
+        ("text", "question-3", "alpha"),
         # MultiLabelQuestion
-        ("question-4", "alpha"),
+        ("label", "question-4", "alpha"),
         # RankingQuestion
-        ("question-5", "alpha"),
+        (["text", "label"], "question-5", "alpha"),
     ],
 )
 @pytest.mark.usefixtures(
@@ -200,6 +216,7 @@ async def test_agreement_metrics_remote(
     feedback_dataset_questions: List["AllowedQuestionTypes"],
     feedback_dataset_records_with_paired_suggestions: List[FeedbackRecord],
     question: str,
+    field: Union[str, List[str]],
     metric_names: Union[str, List[str]],
     owner: User,
 ):
@@ -219,9 +236,17 @@ async def test_agreement_metrics_remote(
 
     if question in ("question-1",):
         with pytest.raises(NotImplementedError, match=r"^No metrics are defined currently for"):
-            AgreementMetric(dataset=remote, question_name=question)
+            AgreementMetric(
+                dataset=remote,
+                question_name=question,
+                field_name=field,
+            )
     else:
-        metric = AgreementMetric(dataset=remote, question_name=question)
+        metric = AgreementMetric(
+            dataset=remote,
+            question_name=question,
+            field_name=field,
+        )
         # Test for repr method
         assert repr(metric) == f"AgreementMetric(question_name={question})"
         metrics_report = metric.compute(metric_names)
@@ -235,19 +260,19 @@ async def test_agreement_metrics_remote(
 
 
 @pytest.mark.parametrize(
-    "question, metric_names",
+    "field, question, metric_names",
     [
         # TextQuestion
-        ("question-1", None),
+        (["text"], "question-1", None),
         # RatingQuestion
-        ("question-2", "alpha"),
-        ("question-2", ["alpha"]),
+        (["text", "label"], "question-2", "alpha"),
+        (["text", "label"], "question-2", ["alpha"]),
         # LabelQuestion
-        ("question-3", "alpha"),
+        ("text", "question-3", "alpha"),
         # MultiLabelQuestion
-        ("question-4", "alpha"),
+        ("label", "question-4", "alpha"),
         # RankingQuestion
-        ("question-5", "alpha"),
+        (["text", "label"], "question-5", "alpha"),
     ],
 )
 @pytest.mark.usefixtures(
@@ -262,6 +287,7 @@ def test_agreement_metrics_from_feedback_dataset(
     feedback_dataset_questions: List["AllowedQuestionTypes"],
     feedback_dataset_records_with_paired_suggestions: List[FeedbackRecord],
     question: str,
+    field: Union[str, List[str]],
     metric_names: Union[str, List[str]],
 ):
     dataset = FeedbackDataset(
@@ -273,9 +299,11 @@ def test_agreement_metrics_from_feedback_dataset(
 
     if question in ("question-1",):
         with pytest.raises(NotImplementedError, match=r"^No metrics are defined currently for"):
-            dataset.compute_agreement_metrics(question_name=question, metric_names=metric_names)
+            dataset.compute_agreement_metrics(question_name=question, field_name=field, metric_names=metric_names)
     else:
-        metrics_report = dataset.compute_agreement_metrics(question_name=question, metric_names=metric_names)
+        metrics_report = dataset.compute_agreement_metrics(
+            question_name=question, field_name=field, metric_names=metric_names
+        )
 
         if isinstance(metric_names, str):
             metrics_report = [metrics_report]
@@ -288,19 +316,19 @@ def test_agreement_metrics_from_feedback_dataset(
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
-    "question, metric_names",
+    "field, question, metric_names",
     [
         # TextQuestion
-        ("question-1", None),
+        (["text"], "question-1", None),
         # RatingQuestion
-        ("question-2", "alpha"),
-        ("question-2", ["alpha"]),
+        (["text", "label"], "question-2", "alpha"),
+        (["text", "label"], "question-2", ["alpha"]),
         # LabelQuestion
-        ("question-3", "alpha"),
+        ("text", "question-3", "alpha"),
         # MultiLabelQuestion
-        ("question-4", "alpha"),
+        ("label", "question-4", "alpha"),
         # RankingQuestion
-        ("question-5", "alpha"),
+        (["text", "label"], "question-5", "alpha"),
     ],
 )
 @pytest.mark.usefixtures(
@@ -315,6 +343,7 @@ async def test_agreement_metrics_from_remote_feedback_dataset(
     feedback_dataset_questions: List["AllowedQuestionTypes"],
     feedback_dataset_records_with_paired_suggestions: List[FeedbackRecord],
     question: str,
+    field: Union[str, List[str]],
     metric_names: Union[str, List[str]],
     owner: User,
 ) -> None:
@@ -335,9 +364,11 @@ async def test_agreement_metrics_from_remote_feedback_dataset(
 
     if question in ("question-1",):
         with pytest.raises(NotImplementedError, match=r"^No metrics are defined currently for"):
-            remote.compute_agreement_metrics(question_name=question, metric_names=metric_names)
+            remote.compute_agreement_metrics(question_name=question, field_name=field, metric_names=metric_names)
     else:
-        metrics_report = remote.compute_agreement_metrics(question_name=question, metric_names=metric_names)
+        metrics_report = remote.compute_agreement_metrics(
+            question_name=question, field_name=field, metric_names=metric_names
+        )
 
         if isinstance(metric_names, str):
             metrics_report = [metrics_report]

docs/_source/practical_guides/collect_responses.md

@@ -141,7 +141,7 @@ import argilla as rg
 from argilla.client.feedback.metrics import AgreementMetric
 
 feedback_dataset = rg.FeedbackDataset.from_argilla("...", workspace="...")
-metric = AgreementMetric(dataset=feedback_dataset, question_name="question_name")
+metric = AgreementMetric(dataset=feedback_dataset, field_name="text", question_name="question_name")
 agreement_metrics = metric.compute("alpha")
 # >>> agreement_metrics
 # [AgreementMetricResult(metric_name='alpha', count=1000, result=0.467889)]
@@ -156,7 +156,7 @@ import argilla as rg
 
 #dataset = rg.FeedbackDataset.from_huggingface("argilla/go_emotions_raw")
 
-agreement_metrics = dataset.compute_agreement_metrics(question_name="label", metric_names="alpha")
+agreement_metrics = dataset.compute_agreement_metrics(question_name="label", field_name="text", metric_names="alpha")
 agreement_metrics
 
 # AgreementMetricResult(metric_name='alpha', count=191792, result=0.2703263452657748)