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rag_context_correctness.py
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rag_context_correctness.py
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from unitxt import add_to_catalog
from unitxt.collections_operators import Wrap
from unitxt.metrics import MetricPipeline
from unitxt.operators import Copy, Rename
from unitxt.test_utils.metrics import test_evaluate, test_metric
base = "metrics.rag.context_correctness"
default = "mrr"
for new_catalog_name, base_catalog_name, main_score in [
("mrr", "metrics.mrr", "score"),
("map", "metrics.map", "score"),
("retrieval_at_k", "metrics.retrieval_at_k", "score"),
]:
metric = MetricPipeline(
main_score=main_score,
preprocess_steps=[
Copy(field="context_ids", to_field="prediction"),
Wrap(
field="ground_truths_context_ids", inside="list", to_field="references"
),
],
metric=base_catalog_name,
)
add_to_catalog(metric, f"{base}.{new_catalog_name}", overwrite=True)
if new_catalog_name == default:
add_to_catalog(metric, base, overwrite=True)
def test_context_correctness():
task_data = [
{ # MRR is 1, MAP is (1 + 2/3)/2 = 0.833
"context_ids": ["A", "B", "C"],
"ground_truths_context_ids": ["A", "C"],
},
{ # MRR and MAP are both 0.5
"context_ids": ["A", "B"],
"ground_truths_context_ids": ["B"],
},
]
map_instance_targets = [
{"map": 0.83, "score": 0.83, "score_name": "map"},
{"map": 0.5, "score": 0.5, "score_name": "map"},
]
mrr_instance_targets = [
{"mrr": 1.0, "score": 1.0, "score_name": "mrr"},
{"mrr": 0.5, "score": 0.5, "score_name": "mrr"},
]
retrieval_at_k_instance_targets = [
{
"match_at_1": 1.0,
"match_at_3": 1.0,
"match_at_5": 1.0,
"match_at_10": 1.0,
"match_at_20": 1.0,
"match_at_40": 1.0,
"precision_at_1": 1.0,
"precision_at_3": 0.67,
"precision_at_5": 0.67,
"precision_at_10": 0.67,
"precision_at_20": 0.67,
"precision_at_40": 0.67,
"recall_at_1": 0.5,
"recall_at_3": 1.0,
"recall_at_5": 1.0,
"recall_at_10": 1.0,
"recall_at_20": 1.0,
"recall_at_40": 1.0,
"score": 1.0,
"score_name": "match_at_1",
},
{
"match_at_1": 0.0,
"match_at_10": 1.0,
"match_at_20": 1.0,
"match_at_3": 1.0,
"match_at_40": 1.0,
"match_at_5": 1.0,
"precision_at_1": 0.0,
"precision_at_10": 0.5,
"precision_at_20": 0.5,
"precision_at_3": 0.5,
"precision_at_40": 0.5,
"precision_at_5": 0.5,
"recall_at_1": 0.0,
"recall_at_10": 1.0,
"recall_at_20": 1.0,
"recall_at_3": 1.0,
"recall_at_40": 1.0,
"recall_at_5": 1.0,
"score": 0.0,
"score_name": "match_at_1",
},
]
map_global_target = {
"map": 0.67,
"map_ci_high": 0.83,
"map_ci_low": 0.5,
"score": 0.67,
"score_ci_high": 0.83,
"score_ci_low": 0.5,
"score_name": "map",
}
mrr_global_target = {
"mrr": 0.75,
"mrr_ci_high": 1.0,
"mrr_ci_low": 0.5,
"score": 0.75,
"score_ci_high": 1.0,
"score_ci_low": 0.5,
"score_name": "mrr",
}
retrieval_at_k_global_target = {
"match_at_1": 0.5,
"match_at_1_ci_high": 1.0,
"match_at_1_ci_low": 0.0,
"match_at_3": 1.0,
"match_at_5": 1.0,
"match_at_10": 1.0,
"match_at_20": 1.0,
"match_at_40": 1.0,
"precision_at_1": 0.5,
"precision_at_1_ci_high": 1.0,
"precision_at_1_ci_low": 0.0,
"precision_at_3": 0.58,
"precision_at_3_ci_high": 0.67,
"precision_at_3_ci_low": 0.5,
"precision_at_5": 0.58,
"precision_at_5_ci_high": 0.67,
"precision_at_5_ci_low": 0.5,
"precision_at_10": 0.58,
"precision_at_10_ci_high": 0.67,
"precision_at_10_ci_low": 0.5,
"precision_at_20": 0.58,
"precision_at_20_ci_high": 0.67,
"precision_at_20_ci_low": 0.5,
"precision_at_40": 0.58,
"precision_at_40_ci_high": 0.67,
"precision_at_40_ci_low": 0.5,
"recall_at_1": 0.25,
"recall_at_1_ci_high": 0.5,
"recall_at_1_ci_low": 0.0,
"recall_at_3": 1.0,
"recall_at_5": 1.0,
"recall_at_10": 1.0,
"recall_at_20": 1.0,
"recall_at_40": 1.0,
"score": 0.5,
"score_ci_high": 1.0,
"score_ci_low": 0.0,
"score_name": "match_at_1",
}
for catalog_name, global_target, instance_targets in [
(
"metrics.rag.context_correctness.map",
map_global_target,
map_instance_targets,
),
(
"metrics.rag.context_correctness.mrr",
mrr_global_target,
mrr_instance_targets,
),
(
"metrics.rag.context_correctness",
mrr_global_target,
mrr_instance_targets,
),
(
"metrics.rag.context_correctness.retrieval_at_k",
retrieval_at_k_global_target,
retrieval_at_k_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=[
Rename(field_to_field={"task_data/context_ids": "context_ids"}),
Rename(
field_to_field={
"task_data/ground_truths_context_ids": "ground_truths_context_ids"
}
),
],
metric=f"{catalog_name}",
)
test_metric(
metric=test_pipeline,
predictions=[None, None],
references=[[], []],
instance_targets=instance_targets,
global_target=global_target,
task_data=task_data,
)
test_context_correctness()