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Fix fill_with_mode when using Dask #3054

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merged 2 commits into from
Feb 7, 2023
Merged

Fix fill_with_mode when using Dask #3054

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15dd742
Fix fill_with_mode when using Dask
arnavgarg1 Feb 7, 2023
e37e685
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Feb 7, 2023
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9 changes: 8 additions & 1 deletion ludwig/data/preprocessing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,6 +108,7 @@
STATA_FORMATS,
TSV_FORMATS,
)
from ludwig.utils.dataframe_utils import is_dask_series_or_df
from ludwig.utils.defaults import default_preprocessing_parameters, default_random_seed
from ludwig.utils.fs_utils import file_lock, path_exists
from ludwig.utils.misc_utils import get_from_registry, merge_dict
Expand Down Expand Up @@ -1487,7 +1488,13 @@ def precompute_fill_value(dataset_cols, feature, preprocessing_parameters: Prepr
if missing_value_strategy == FILL_WITH_CONST:
return preprocessing_parameters["fill_value"]
elif missing_value_strategy == FILL_WITH_MODE:
return dataset_cols[feature[COLUMN]].value_counts().index[0]
# Requires separate handling if Dask since Dask has lazy evaluation
# Otherwise, dask returns a Dask index structure instead of a value to use as a fill value
return (
dataset_cols[feature[COLUMN]].value_counts().index.compute()[0]
if is_dask_series_or_df(dataset_cols[feature[COLUMN]], backend)
else dataset_cols[feature[COLUMN]].value_counts().index[0]
)
elif missing_value_strategy == FILL_WITH_MEAN:
if feature[TYPE] != NUMBER:
raise ValueError(
Expand Down
31 changes: 31 additions & 0 deletions tests/integration_tests/test_preprocessing.py
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Expand Up @@ -540,3 +540,34 @@ def test_vit_encoder_different_dimension_image(tmpdir, csv_filename, use_pretrai
# Failure happens post preprocessing but before training during the ECD model creation phase
# so make sure the model can be created properly and training can proceed
model.train(dataset=data_csv)


@pytest.mark.parametrize(
"df_engine",
[
pytest.param("pandas", id="pandas"),
pytest.param("dask", id="dask", marks=pytest.mark.distributed),
],
)
def test_fill_with_mode_different_df_engine(tmpdir, csv_filename, df_engine, ray_cluster_2cpu):
config = {
"input_features": [category_feature(preprocessing={"missing_value_strategy": "fill_with_mode"})],
"output_features": [binary_feature()],
}

training_data_csv_path = generate_data(
config["input_features"], config["output_features"], os.path.join(tmpdir, csv_filename)
)

df = pd.read_csv(training_data_csv_path)

if df_engine == "dask":
import dask.dataframe as dd

df = dd.from_pandas(df, npartitions=1)

# Only support Dask on Ray backend
config["backend"] = {"type": "ray"}

ludwig_model = LudwigModel(config)
ludwig_model.preprocess(dataset=df)