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fix: llm palm score tests #643

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Merged
merged 2 commits into from
Apr 29, 2024
Merged

fix: llm palm score tests #643

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5b45e6b
fix: llm palm score tests
ashleyxuu Apr 29, 2024
4c80848
address comments
ashleyxuu Apr 29, 2024
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11 changes: 6 additions & 5 deletions 11 tests/system/load/test_llm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,12 +49,13 @@ def llm_remote_text_df(session, llm_remote_text_pandas_df):
return session.read_pandas(llm_remote_text_pandas_df)


@pytest.mark.flaky(retries=2)
def test_llm_palm_configure_fit(llm_fine_tune_df_default_index, llm_remote_text_df):
model = bigframes.ml.llm.PaLM2TextGenerator(
model_name="text-bison", max_iterations=1
)

df = llm_fine_tune_df_default_index.dropna()
df = llm_fine_tune_df_default_index.dropna().sample(n=100)
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Let's add retry for experimental features.

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Done.

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curious how much run time reduction with sampling.

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It didn't improve much. It still takes the same amount of time around 30-40 mins, and the durations has some variations.

X_train = df[["prompt"]]
y_train = df[["label"]]
model.fit(X_train, y_train)
Expand All @@ -70,6 +71,7 @@ def test_llm_palm_configure_fit(llm_fine_tune_df_default_index, llm_remote_text_
# TODO(ashleyxu b/335492787): After bqml rolled out version control: save, load, check parameters to ensure configuration was kept


@pytest.mark.flaky(retries=2)
def test_llm_palm_score(llm_fine_tune_df_default_index):
model = bigframes.ml.llm.PaLM2TextGenerator(model_name="text-bison")

Expand All @@ -89,6 +91,7 @@ def test_llm_palm_score(llm_fine_tune_df_default_index):
assert all(col in score_result_col for col in expected_col)


@pytest.mark.flaky(retries=2)
def test_llm_palm_score_params(llm_fine_tune_df_default_index):
model = bigframes.ml.llm.PaLM2TextGenerator(
model_name="text-bison", max_iterations=1
Expand All @@ -102,12 +105,10 @@ def test_llm_palm_score_params(llm_fine_tune_df_default_index):
).to_pandas()
score_result_col = score_result.columns.to_list()
expected_col = [
"trial_id",
"precision",
"recall",
"accuracy",
"f1_score",
"log_loss",
"roc_auc",
"label",
"evaluation_status",
]
assert all(col in score_result_col for col in expected_col)
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