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[WIP] Required test #346

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[WIP] Required test #346

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cbcd935
bugfix when existing idx is empty
bobokvsky Nov 2, 2024
11b645f
add test_applying_prediction_on_best_model_only
bobokvsky Feb 14, 2025
ad27fb5
Merge remote-tracking branch 'origin/master' into required-test
elephantum Feb 18, 2025
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2 changes: 1 addition & 1 deletion datapipe/meta/sql_meta.py
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Original file line number Diff line number Diff line change
Expand Up @@ -234,7 +234,7 @@ def get_existing_idx(self, idx: Optional[IndexDF] = None) -> IndexDF:
# Empty index -> empty result
return cast(
IndexDF,
pd.DataFrame(columns=[column.name for column in self.sql_schema]), # type: ignore
pd.DataFrame(columns=self.primary_keys), # type: ignore
)
idx_cols = list(set(idx.columns.tolist()) & set(self.primary_keys))
else:
Expand Down
126 changes: 124 additions & 2 deletions tests/test_complex_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
from sqlalchemy.sql.sqltypes import Integer, String

from datapipe.compute import Catalog, Pipeline, Table, build_compute, run_steps
from datapipe.datatable import DataStore
from datapipe.datatable import DataStore, DataTable
from datapipe.step.batch_generate import BatchGenerate
from datapipe.step.batch_transform import BatchTransform
from datapipe.store.database import TableStoreDB
Expand Down Expand Up @@ -324,7 +324,6 @@ def complex_transform_with_many_recordings(dbconn, N: int):
[
Column("image_id", Integer, primary_key=True),
Column("model_id", Integer, primary_key=True),
Column("prediction__attribite", Integer),
],
True,
)
Expand Down Expand Up @@ -445,3 +444,126 @@ def test_complex_transform_with_many_recordings_N1000(dbconn):
@pytest.mark.skip(reason="fails on sqlite")
def test_complex_transform_with_many_recordings_N10000(dbconn):
complex_transform_with_many_recordings(dbconn, N=10000)


def test_applying_prediction_on_best_model_only(dbconn):
N = 100
ds = DataStore(dbconn, create_meta_table=True)
catalog = Catalog(
{
"tbl_image": Table(
store=TableStoreDB(
dbconn,
"tbl_image",
[
Column("image_id", Integer, primary_key=True),
],
True,
)
),
"tbl_model": Table(
store=TableStoreDB(
dbconn,
"tbl_model",
[
Column("model_id", Integer, primary_key=True),
],
True,
)
),
"tbl_best_model": Table(
store=TableStoreDB(
dbconn,
"tbl_best_model",
[
Column("model_id", Integer, primary_key=True),
],
True,
)
),
"tbl_prediction": Table(
store=TableStoreDB(
dbconn,
"tbl_prediction",
[
Column("image_id", Integer, primary_key=True),
Column("model_id", Integer, primary_key=True),
],
True,
)
),
}
)

def gen_tbls(df1, df2, df3):
yield df1, df2, df3

test_df__image = pd.DataFrame({"image_id": range(N)})
test_df__model = pd.DataFrame(
{
"model_id": [0, 1, 2, 3, 4]
}
)
test_df__best_model = pd.DataFrame({"model_id": [4]})

def inference_only_on_best_model(
df__image: pd.DataFrame,
df__model: pd.DataFrame,
df__best_model: pd.DataFrame,
idx: IndexDF,
):
assert all([model_id == 4 for model_id in idx["model_id"]])
df__prediction = pd.merge(df__image, df__model, how="cross")
return df__prediction[["image_id", "model_id"]]

pipeline = Pipeline(
[
BatchGenerate(
func=gen_tbls,
outputs=[
"tbl_image",
"tbl_model",
"tbl_best_model",
],
kwargs=dict(
df1=test_df__image,
df2=test_df__model,
df3=test_df__best_model,
),
),
BatchTransform(
func=inference_only_on_best_model,
inputs=[
"tbl_image", # image_id
"tbl_model", # model_id
Required("tbl_best_model"), # model_id
],
outputs=["tbl_prediction"],
transform_keys=["image_id", "model_id"],
),
]
)
steps = build_compute(ds, catalog, pipeline)
run_steps(ds, steps)
test__df_prediction = pd.DataFrame(
{"image_id": range(N), "model_id": [4] * N}
)
assert_df_equal(
ds.get_table("tbl_prediction").get_data(),
test__df_prediction,
index_cols=["image_id", "model_id"],
)

test_df__new_best_model = pd.DataFrame({"model_id": [3]})
dt__tbl_best_model: DataTable = ds.get_table("tbl_best_model")
dt__tbl_best_model.delete_by_idx(dt__tbl_best_model.get_data())
dt__tbl_best_model.store_chunk(test_df__new_best_model)
run_steps(ds, steps)
test__new_df_prediction = pd.DataFrame(
{"image_id": range(N), "model_id": [3] * N}
)
assert_df_equal(
ds.get_table("tbl_prediction").get_data(),
test__new_df_prediction,
index_cols=["image_id", "model_id"],
)
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