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mark tests
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parent
583de3d80c
commit
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2 changed files with 24 additions and 5 deletions
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@ -16,12 +16,15 @@ from ..datasets.test_datasets import data_url_from_file
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@pytest.mark.parametrize("scoring_fn_id", ["basic::equality"])
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@pytest.mark.skip(reason="TODO(xiyan): fix this")
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def test_evaluate_rows(llama_stack_client, text_model_id, scoring_fn_id):
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dataset = llama_stack_client.datasets.register(
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purpose="eval/messages-answer",
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source={
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"type": "uri",
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"uri": data_url_from_file(Path(__file__).parent.parent / "datasets" / "test_dataset.csv"),
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"uri": data_url_from_file(
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Path(__file__).parent.parent / "datasets" / "test_dataset.csv"
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),
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},
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)
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response = llama_stack_client.datasets.list()
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@ -65,12 +68,15 @@ def test_evaluate_rows(llama_stack_client, text_model_id, scoring_fn_id):
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@pytest.mark.parametrize("scoring_fn_id", ["basic::subset_of"])
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@pytest.mark.skip(reason="TODO(xiyan): fix this")
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def test_evaluate_benchmark(llama_stack_client, text_model_id, scoring_fn_id):
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dataset = llama_stack_client.datasets.register(
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purpose="eval/messages-answer",
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source={
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"type": "uri",
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"uri": data_url_from_file(Path(__file__).parent.parent / "datasets" / "test_dataset.csv"),
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"uri": data_url_from_file(
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Path(__file__).parent.parent / "datasets" / "test_dataset.csv"
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),
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},
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)
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benchmark_id = str(uuid.uuid4())
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@ -93,10 +99,14 @@ def test_evaluate_benchmark(llama_stack_client, text_model_id, scoring_fn_id):
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},
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)
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assert response.job_id == "0"
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job_status = llama_stack_client.eval.jobs.status(job_id=response.job_id, benchmark_id=benchmark_id)
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job_status = llama_stack_client.eval.jobs.status(
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job_id=response.job_id, benchmark_id=benchmark_id
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)
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assert job_status and job_status == "completed"
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eval_response = llama_stack_client.eval.jobs.retrieve(job_id=response.job_id, benchmark_id=benchmark_id)
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eval_response = llama_stack_client.eval.jobs.retrieve(
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job_id=response.job_id, benchmark_id=benchmark_id
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)
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assert eval_response is not None
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assert len(eval_response.generations) == 5
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assert scoring_fn_id in eval_response.scores
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@ -43,12 +43,14 @@ def register_scoring_function(
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)
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@pytest.mark.skip(reason="TODO(xiyan): fix this")
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def test_scoring_functions_list(llama_stack_client):
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response = llama_stack_client.scoring_functions.list()
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assert isinstance(response, list)
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assert len(response) > 0
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@pytest.mark.skip(reason="TODO(xiyan): fix this")
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def test_scoring_functions_register(
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llama_stack_client,
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sample_scoring_fn_id,
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@ -81,6 +83,7 @@ def test_scoring_functions_register(
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@pytest.mark.parametrize("scoring_fn_id", ["basic::equality"])
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@pytest.mark.skip(reason="TODO(xiyan): fix this")
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def test_scoring_score(llama_stack_client, scoring_fn_id):
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# scoring individual rows
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df = pd.read_csv(Path(__file__).parent.parent / "datasets" / "test_dataset.csv")
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@ -100,6 +103,7 @@ def test_scoring_score(llama_stack_client, scoring_fn_id):
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assert len(response.results[x].score_rows) == len(rows)
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@pytest.mark.skip(reason="TODO(xiyan): fix this")
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def test_scoring_score_with_params_llm_as_judge(
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llama_stack_client,
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sample_judge_prompt_template,
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@ -139,6 +143,7 @@ def test_scoring_score_with_params_llm_as_judge(
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"braintrust",
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],
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)
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@pytest.mark.skip(reason="TODO(xiyan): fix this")
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def test_scoring_score_with_aggregation_functions(
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llama_stack_client,
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sample_judge_prompt_template,
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@ -149,7 +154,11 @@ def test_scoring_score_with_aggregation_functions(
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df = pd.read_csv(Path(__file__).parent.parent / "datasets" / "test_dataset.csv")
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rows = df.to_dict(orient="records")
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scoring_fns_list = [x for x in llama_stack_client.scoring_functions.list() if x.provider_id == provider_id]
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scoring_fns_list = [
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x
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for x in llama_stack_client.scoring_functions.list()
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if x.provider_id == provider_id
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]
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if len(scoring_fns_list) == 0:
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pytest.skip(f"No scoring functions found for provider {provider_id}, skipping")
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