def test_lambdaop_misalign(cpu):
size = 12
df0 = pd.DataFrame({
"a":
np.arange(size),
"b":
np.random.choice(["apple", "banana", "orange"], size),
"c":
np.random.choice([0, 1], size),
})
ddf0 = dd.from_pandas(df0, npartitions=4)
cont_names = ColumnGroup(["a"])
cat_names = ColumnGroup(["b"])
label = ColumnGroup(["c"])
if cpu:
label_feature = label >> (lambda col: np.where(col == 4, 0, 1))
else:
label_feature = label >> (lambda col: cp.where(col == 4, 0, 1))
workflow = nvt.Workflow(cat_names + cont_names + label_feature)
dataset = nvt.Dataset(ddf0, cpu=cpu)
transformed = workflow.transform(dataset)
assert_eq_dd(
df0[["a", "b"]],
transformed.to_ddf().compute()[["a", "b"]],
check_index=False,
)
def test_lambdaop(tmpdir, df, dataset, gpu_memory_frac, engine):
df_copy = df.copy()
# Substring
# Replacement
substring = ColumnGroup(["name-cat", "name-string"
]) >> (lambda col: col.str.slice(1, 3))
processor = nvtabular.Workflow(substring)
processor.fit(dataset)
new_gdf = processor.transform(dataset).to_ddf().compute()
assert_eq_dd(new_gdf["name-cat"],
df_copy["name-cat"].str.slice(1, 3),
check_index=False)
assert_eq_dd(new_gdf["name-string"],
df_copy["name-string"].str.slice(1, 3),
check_index=False)
# No Replacement from old API (skipped for other examples)
substring = (
ColumnGroup(["name-cat", "name-string"]) >>
(lambda col: col.str.slice(1, 3)) >> ops.Rename(postfix="_slice"))
processor = nvtabular.Workflow(["name-cat", "name-string"] + substring)
processor.fit(dataset)
new_gdf = processor.transform(dataset).to_ddf().compute()
assert_eq_dd(
new_gdf["name-cat_slice"],
df_copy["name-cat"].str.slice(1, 3),
check_index=False,
check_names=False,
)
assert_eq_dd(
new_gdf["name-string_slice"],
df_copy["name-string"].str.slice(1, 3),
check_index=False,
check_names=False,
)
assert_eq_dd(new_gdf["name-cat"], df_copy["name-cat"], check_index=False)
assert_eq_dd(new_gdf["name-string"],
df_copy["name-string"],
check_index=False)
# Replace
# Replacement
oplambda = ColumnGroup(["name-cat", "name-string"
]) >> (lambda col: col.str.replace("e", "XX"))
processor = nvtabular.Workflow(oplambda)
processor.fit(dataset)
new_gdf = processor.transform(dataset).to_ddf().compute()
assert_eq_dd(new_gdf["name-cat"],
df_copy["name-cat"].str.replace("e", "XX"),
check_index=False)
assert_eq_dd(new_gdf["name-string"],
df_copy["name-string"].str.replace("e", "XX"),
check_index=False)
# astype
# Replacement
oplambda = ColumnGroup(["id"]) >> (lambda col: col.astype(float))
processor = nvtabular.Workflow(oplambda)
processor.fit(dataset)
new_gdf = processor.transform(dataset).to_ddf().compute()
assert new_gdf["id"].dtype == "float64"
# Workflow
# Replacement
oplambda = (
ColumnGroup(["name-cat"]) >>
(lambda col: col.astype(str).str.slice(0, 1)) >> ops.Categorify())
processor = nvtabular.Workflow(oplambda)
processor.fit(dataset)
new_gdf = processor.transform(dataset).to_ddf().compute()
assert is_integer_dtype(new_gdf["name-cat"].dtype)
oplambda = (ColumnGroup(["name-cat", "name-string"]) >> ops.Categorify() >>
(lambda col: col + 100))
processor = nvtabular.Workflow(oplambda)
processor.fit(dataset)
new_gdf = processor.transform(dataset).to_ddf().compute()
assert is_integer_dtype(new_gdf["name-cat"].dtype)
assert np.sum(new_gdf["name-cat"] < 100) == 0
