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move rag answer correctness metrics tests to test_metrics.py #1131

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move rag answer correctness metrics tests to test_metrics.py #1131

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159 changes: 1 addition & 158 deletions prepare/metrics/rag_answer_correctness.py
View file
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Original file line number Diff line number Diff line change
@@ -1,37 +1,6 @@
from unitxt import add_to_catalog
from unitxt.metrics import MetricPipeline
from unitxt.operators import Copy, RenameFields
from unitxt.test_utils.metrics import test_evaluate, test_metric


def test_answer_correctness(task_data, catalog_name, global_target, instance_targets):
# test the evaluate call
test_evaluate(
global_target,
instance_targets=[
{"score": instance["score"]} for instance in instance_targets
],
task_data=task_data,
metric_name=catalog_name,
)
# test using the usual metric pipeline
test_pipeline = MetricPipeline(
main_score="score",
preprocess_steps=[
RenameFields(field_to_field={"task_data/ground_truths": "ground_truths"}),
RenameFields(field_to_field={"task_data/answer": "answer"}),
],
metric=f"{catalog_name}",
)
test_metric(
metric=test_pipeline,
predictions=[None] * len(instance_targets),
references=[[]] * len(instance_targets),
instance_targets=instance_targets,
global_target=global_target,
task_data=task_data,
)

from unitxt.operators import Copy

base = "metrics.rag.answer_correctness"
default = "token_recall"
Expand Down Expand Up @@ -59,129 +28,3 @@ def test_answer_correctness(task_data, catalog_name, global_target, instance_tar

if new_catalog_name == default:
add_to_catalog(metric, base, overwrite=True)

if __name__ == "__main__":
# don't use "A" as a token because it is considered an article and removed by the token overlap
# metric
task_data = [
{ # recall is 0.5 for the first ground_truth, 0 for the second ground_truth.
# so overall its max(0.5, 0) = 0.5
"ground_truths": ["B C", "C"],
"answer": "B",
},
{ # recall is 1/3
"ground_truths": ["D E F"],
"answer": "B C D",
},
]

recall_instance_targets = [
{"f1": 0.67, "precision": 1.0, "recall": 0.5, "score": 0.5, "score_name": "f1"},
{
"f1": 0.33,
"precision": 0.33,
"recall": 0.33,
"score": 0.33,
"score_name": "f1",
},
]

recall_global_target = {
"f1": 0.5,
"f1_ci_high": 0.67,
"f1_ci_low": 0.33,
"precision": 0.67,
"precision_ci_high": 1.0,
"precision_ci_low": 0.33,
"recall": 0.42,
"recall_ci_high": 0.5,
"recall_ci_low": 0.33,
"score": 0.42,
"score_ci_high": 0.67,
"score_ci_low": 0.33,
"score_name": "f1",
}

for catalog_name, global_target, instance_targets in [
(
"metrics.rag.answer_correctness",
recall_global_target,
recall_instance_targets,
),
("metrics.rag.recall", recall_global_target, recall_instance_targets),
]:
test_answer_correctness(
task_data, catalog_name, global_target, instance_targets
)

test_answer_correctness(
task_data,
catalog_name="metrics.rag.bert_recall",
global_target={
"f1": 0.71,
"f1_ci_high": 0.71,
"f1_ci_low": 0.71,
"precision": 0.74,
"precision_ci_high": 0.77,
"precision_ci_low": 0.71,
"recall": 0.71,
"recall_ci_high": 0.71,
"recall_ci_low": 0.71,
"score": 0.71,
"score_ci_high": 0.71,
"score_ci_low": 0.71,
"score_name": "f1",
},
instance_targets=[
{
"f1": 0.71,
"precision": 0.77,
"recall": 0.71,
"score": 0.71,
"score_name": "f1",
},
{
"f1": 0.71,
"precision": 0.71,
"recall": 0.71,
"score": 0.71,
"score_name": "f1",
},
],
)

test_answer_correctness(
task_data,
catalog_name="metrics.rag.bert_recall_ml",
global_target={
"f1": 0.86,
"f1_ci_high": 0.97,
"f1_ci_low": 0.74,
"precision": 0.86,
"precision_ci_high": 0.97,
"precision_ci_low": 0.74,
"recall": 0.86,
"recall_ci_high": 0.97,
"recall_ci_low": 0.74,
"score": 0.86,
"score_ci_high": 0.97,
"score_ci_low": 0.74,
"score_name": "f1",
},
instance_targets=[
{
"f1": 0.97,
"precision": 0.97,
"recall": 0.97,
"score": 0.97,
"score_name": "f1",
},
{
"f1": 0.74,
"precision": 0.74,
"recall": 0.74,
"score": 0.74,
"score_name": "f1",
},
],
)
165 changes: 165 additions & 0 deletions tests/library/test_metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1942,3 +1942,168 @@ def text_context_correctness(self):
global_target=global_target,
task_data=task_data,
)

@staticmethod
def test_answer_correctness(
task_data, catalog_name, global_target, instance_targets
):
# test the evaluate call
test_evaluate(
global_target,
instance_targets=[
{"score": instance["score"]} for instance in instance_targets
],
task_data=task_data,
metric_name=catalog_name,
)
# test using the usual metric pipeline
test_pipeline = MetricPipeline(
main_score="score",
preprocess_steps=[
RenameFields(
field_to_field={"task_data/ground_truths": "ground_truths"}
),
RenameFields(field_to_field={"task_data/answer": "answer"}),
],
metric=f"{catalog_name}",
)
test_metric(
metric=test_pipeline,
predictions=[None] * len(instance_targets),
references=[[]] * len(instance_targets),
instance_targets=instance_targets,
global_target=global_target,
task_data=task_data,
)

def test_answer_correctness_metrics(self):
# don't use "A" as a token because it is considered an article and removed by the token overlap
# metric
task_data = [
{ # recall is 0.5 for the first ground_truth, 0 for the second ground_truth.
# so overall its max(0.5, 0) = 0.5
"ground_truths": ["B C", "C"],
"answer": "B",
},
{ # recall is 1/3
"ground_truths": ["D E F"],
"answer": "B C D",
},
]

recall_instance_targets = [
{
"f1": 0.67,
"precision": 1.0,
"recall": 0.5,
"score": 0.5,
"score_name": "f1",
},
{
"f1": 0.33,
"precision": 0.33,
"recall": 0.33,
"score": 0.33,
"score_name": "f1",
},
]

recall_global_target = {
"f1": 0.5,
"f1_ci_high": 0.67,
"f1_ci_low": 0.33,
"precision": 0.67,
"precision_ci_high": 1.0,
"precision_ci_low": 0.33,
"recall": 0.42,
"recall_ci_high": 0.5,
"recall_ci_low": 0.33,
"score": 0.42,
"score_ci_high": 0.67,
"score_ci_low": 0.33,
"score_name": "f1",
}

for catalog_name, global_target, instance_targets in [
(
"metrics.rag.answer_correctness",
recall_global_target,
recall_instance_targets,
),
("metrics.rag.recall", recall_global_target, recall_instance_targets),
]:
self.test_answer_correctness(
task_data, catalog_name, global_target, instance_targets
)

self.test_answer_correctness(
task_data,
catalog_name="metrics.rag.bert_recall",
global_target={
"f1": 0.71,
"f1_ci_high": 0.71,
"f1_ci_low": 0.71,
"precision": 0.74,
"precision_ci_high": 0.77,
"precision_ci_low": 0.71,
"recall": 0.71,
"recall_ci_high": 0.71,
"recall_ci_low": 0.71,
"score": 0.71,
"score_ci_high": 0.71,
"score_ci_low": 0.71,
"score_name": "f1",
},
instance_targets=[
{
"f1": 0.71,
"precision": 0.77,
"recall": 0.71,
"score": 0.71,
"score_name": "f1",
},
{
"f1": 0.71,
"precision": 0.71,
"recall": 0.71,
"score": 0.71,
"score_name": "f1",
},
],
)

self.test_answer_correctness(
task_data,
catalog_name="metrics.rag.bert_recall_ml",
global_target={
"f1": 0.86,
"f1_ci_high": 0.97,
"f1_ci_low": 0.74,
"precision": 0.86,
"precision_ci_high": 0.97,
"precision_ci_low": 0.74,
"recall": 0.86,
"recall_ci_high": 0.97,
"recall_ci_low": 0.74,
"score": 0.86,
"score_ci_high": 0.97,
"score_ci_low": 0.74,
"score_name": "f1",
},
instance_targets=[
{
"f1": 0.97,
"precision": 0.97,
"recall": 0.97,
"score": 0.97,
"score_name": "f1",
},
{
"f1": 0.74,
"precision": 0.74,
"recall": 0.74,
"score": 0.74,
"score_name": "f1",
},
],
)
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