scoring fix

tests/integration/scoring/test_scoring.py

@@ -5,23 +5,13 @@
 # the root directory of this source tree.
 
 
+import os
+from pathlib import Path
+
+import pandas as pd
+
 import pytest
 
-from ..datasetio.test_datasetio import register_dataset
-
-
-@pytest.fixture
-def rag_dataset_for_test(llama_stack_client):
-    dataset_id = "test_dataset"
-    register_dataset(llama_stack_client, for_rag=True, dataset_id=dataset_id)
-    yield  # This is where the test function will run
-
-    # Teardown - this always runs, even if the test fails
-    try:
-        llama_stack_client.datasets.unregister(dataset_id)
-    except Exception as e:
-        print(f"Warning: Failed to unregister test_dataset: {e}")
-
 
 @pytest.fixture
 def sample_judge_prompt_template():
@@ -92,49 +82,34 @@ def test_scoring_functions_register(
     # TODO: add unregister api for scoring functions
 
 
-def test_scoring_score(llama_stack_client, rag_dataset_for_test):
+@pytest.mark.parametrize("scoring_fn_id", ["basic::equality"])
+def test_scoring_score(llama_stack_client, scoring_fn_id):
     # scoring individual rows
-    rows = llama_stack_client.datasetio.get_rows_paginated(
-        dataset_id="test_dataset",
-        rows_in_page=3,
-    )
-    assert len(rows.rows) == 3
+    df = pd.read_csv(Path(__file__).parent.parent / "datasets" / "test_dataset.csv")
+    rows = df.to_dict(orient="records")
 
-    scoring_fns_list = llama_stack_client.scoring_functions.list()
     scoring_functions = {
-        scoring_fns_list[0].identifier: None,
+        scoring_fn_id: None,
     }
 
     response = llama_stack_client.scoring.score(
-        input_rows=rows.rows,
+        input_rows=rows,
         scoring_functions=scoring_functions,
     )
     assert len(response.results) == len(scoring_functions)
     for x in scoring_functions:
         assert x in response.results
-        assert len(response.results[x].score_rows) == len(rows.rows)
-
-    # score batch
-    response = llama_stack_client.scoring.score_batch(
-        dataset_id="test_dataset",
-        scoring_functions=scoring_functions,
-        save_results_dataset=False,
-    )
-    assert len(response.results) == len(scoring_functions)
-    for x in scoring_functions:
-        assert x in response.results
-        assert len(response.results[x].score_rows) == 5
+        assert len(response.results[x].score_rows) == len(rows)
 
 
 def test_scoring_score_with_params_llm_as_judge(
-    llama_stack_client, sample_judge_prompt_template, judge_model_id, rag_dataset_for_test
+    llama_stack_client,
+    sample_judge_prompt_template,
+    judge_model_id,
 ):
     # scoring individual rows
-    rows = llama_stack_client.datasetio.get_rows_paginated(
-        dataset_id="test_dataset",
-        rows_in_page=3,
-    )
-    assert len(rows.rows) == 3
+    df = pd.read_csv(Path(__file__).parent.parent / "datasets" / "test_dataset.csv")
+    rows = df.to_dict(orient="records")
 
     scoring_functions = {
         "llm-as-judge::base": dict(
@@ -149,24 +124,13 @@ def test_scoring_score_with_params_llm_as_judge(
     }
 
     response = llama_stack_client.scoring.score(
-        input_rows=rows.rows,
+        input_rows=rows,
         scoring_functions=scoring_functions,
     )
     assert len(response.results) == len(scoring_functions)
     for x in scoring_functions:
         assert x in response.results
-        assert len(response.results[x].score_rows) == len(rows.rows)
-
-    # score batch
-    response = llama_stack_client.scoring.score_batch(
-        dataset_id="test_dataset",
-        scoring_functions=scoring_functions,
-        save_results_dataset=False,
-    )
-    assert len(response.results) == len(scoring_functions)
-    for x in scoring_functions:
-        assert x in response.results
-        assert len(response.results[x].score_rows) == 5
+        assert len(response.results[x].score_rows) == len(rows)
 
 
 @pytest.mark.parametrize(
@@ -178,15 +142,20 @@ def test_scoring_score_with_params_llm_as_judge(
     ],
 )
 def test_scoring_score_with_aggregation_functions(
-    llama_stack_client, sample_judge_prompt_template, judge_model_id, provider_id, rag_dataset_for_test
+    llama_stack_client,
+    sample_judge_prompt_template,
+    judge_model_id,
+    provider_id,
+    rag_dataset_for_test,
 ):
-    rows = llama_stack_client.datasetio.get_rows_paginated(
-        dataset_id="test_dataset",
-        rows_in_page=3,
-    )
-    assert len(rows.rows) == 3
+    df = pd.read_csv(Path(__file__).parent.parent / "datasets" / "test_dataset.csv")
+    rows = df.to_dict(orient="records")
 
-    scoring_fns_list = [x for x in llama_stack_client.scoring_functions.list() if x.provider_id == provider_id]
+    scoring_fns_list = [
+        x
+        for x in llama_stack_client.scoring_functions.list()
+        if x.provider_id == provider_id
+    ]
     if len(scoring_fns_list) == 0:
         pytest.skip(f"No scoring functions found for provider {provider_id}, skipping")
 
@@ -224,12 +193,12 @@ def test_scoring_score_with_aggregation_functions(
         scoring_functions[scoring_fn.identifier] = None
 
     response = llama_stack_client.scoring.score(
-        input_rows=rows.rows,
+        input_rows=rows,
         scoring_functions=scoring_functions,
     )
 
     assert len(response.results) == len(scoring_functions)
     for x in scoring_functions:
         assert x in response.results
-        assert len(response.results[x].score_rows) == len(rows.rows)
+        assert len(response.results[x].score_rows) == len(rows)
         assert len(response.results[x].aggregated_results) == len(aggr_fns)