def test_bucketized(tmpdir, df, dataset, gpu_memory_frac, engine, use_dict):
cont_names = ["x", "y"]
boundaries = [[-1, 0, 1], [-4, 100]]
if use_dict:
bucketize_op = ops.Bucketize(
{name: boundary
for name, boundary in zip(cont_names, boundaries)})
else:
bucketize_op = ops.Bucketize(boundaries, cont_names)
columns_ctx = {}
columns_ctx["continuous"] = {}
columns_ctx["continuous"]["base"] = list(cont_names)
for gdf in dataset.to_iter():
new_gdf = bucketize_op.apply_op(gdf, columns_ctx, "continuous")
for col, bs in zip(cont_names, boundaries):
assert np.all(new_gdf[col].values >= 0)
assert np.all(new_gdf[col].values <= len(bs))
def test_bucketized(tmpdir, df, dataset, gpu_memory_frac, engine):
cont_names = ["x", "y"]
boundaries = [[-1, 0, 1], [-4, 100]]
bucketize_op = ops.Bucketize(dict(zip(cont_names, boundaries)))
bucket_features = cont_names >> bucketize_op
processor = nvtabular.Workflow(bucket_features)
processor.fit(dataset)
new_gdf = processor.transform(dataset).to_ddf().compute()
for col, bs in zip(cont_names, boundaries):
assert np.all(new_gdf[col].values >= 0)
assert np.all(new_gdf[col].values <= len(bs))
workflow1 = Workflow(cat_features)
workflow1.fit_schema(schema)
assert workflow1.output_schema.column_names == [
"TE_x_cost_renamed", "TE_y_cost_renamed"
]
# initial column selector works with tags
# filter within the workflow by tags
# test tags correct at output
@pytest.mark.parametrize(
"op",
[
ops.Bucketize([1]),
ops.Rename(postfix="_trim"),
ops.Categorify(),
ops.Categorify(encode_type="combo"),
ops.Clip(0),
ops.DifferenceLag("col1"),
ops.FillMissing(),
ops.Groupby("col1"),
ops.HashBucket(1),
ops.HashedCross(1),
ops.JoinGroupby("col1"),
ops.ListSlice(0),
ops.LogOp(),
ops.Normalize(),
ops.TargetEncoding("col1"),
],
