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rename temporary column to something less likely to be used #1846

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rename temporary column to something less likely to be used #1846

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11f74de
rename temporary column to something less likely to be used
nv-alaiacano Jun 21, 2023
84ae407
fix syntax
nv-alaiacano Jun 21, 2023
5d8d460
catch renaming error
nv-alaiacano Jun 21, 2023
485081e
update col name in transform path
nv-alaiacano Jun 21, 2023
5d1f538
fix name
nv-alaiacano Jun 21, 2023
b5a45f6
undo
nv-alaiacano Jun 21, 2023
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35 changes: 20 additions & 15 deletions nvtabular/ops/categorify.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1615,6 +1615,8 @@ def _encode(
selection_r = ColumnSelector(name if isinstance(name, list) else [storage_name])
list_col = is_list_col(selection_l, df)

tmp_label_column = "__labels_tmp"

# Find number of oov buckets
if buckets and storage_name in buckets:
num_oov_buckets = buckets[storage_name]
Expand Down Expand Up @@ -1642,17 +1644,20 @@ def _encode(
cats_only=True,
reader=read_pq_func,
)
if len(value) and value["labels"].iloc[0] < OOV_OFFSET + num_oov_buckets:
if (
len(value)
and value[tmp_label_column].iloc[0] < OOV_OFFSET + num_oov_buckets
):
# See: https://github.com/rapidsai/cudf/issues/12837
value["labels"] += OOV_OFFSET + num_oov_buckets
value[tmp_label_column] += OOV_OFFSET + num_oov_buckets
else:
value = read_pq_func( # pylint: disable=unexpected-keyword-arg
path,
columns=selection_r.names,
**({"split_row_groups": False} if split_out > 1 else {}),
)

value.index = value.index.rename("labels")
value.index = value.index.rename(tmp_label_column)
if split_out > 1:
value = value.reset_index(drop=False)
if type(df).__module__.split(".")[0] == "cudf":
Expand All @@ -1665,7 +1670,7 @@ def _encode(
part_size = file_frag.metadata.num_rows
ranges.append((size, size + part_size))
size += part_size
value["labels"] = dd.from_map(lambda r: pd.RangeIndex(*r), ranges)
value[tmp_label_column] = dd.from_map(lambda r: pd.RangeIndex(*r), ranges)
else:
value.reset_index(drop=False, inplace=True)

Expand All @@ -1674,7 +1679,7 @@ def _encode(
for c in selection_r.names:
typ = df[selection_l.names[0]].dtype if len(selection_l.names) == 1 else df[c].dtype
value[c] = nullable_series([None], df, typ)
value.index = value.index.rename("labels")
value.index = value.index.rename(tmp_label_column)
value.reset_index(drop=False, inplace=True)

use_collection = isinstance(value, DaskDataFrame)
Expand All @@ -1684,7 +1689,7 @@ def _encode(
use_collection = False

# Determine encoding offsets
null_encoding_offset = value["labels"].head(1).iloc[0] if single_table else NULL_OFFSET
null_encoding_offset = value[tmp_label_column].head(1).iloc[0] if single_table else NULL_OFFSET
bucket_encoding_offset = null_encoding_offset + 1 # 2 (if not single_table)
distinct_encoding_offset = bucket_encoding_offset + num_oov_buckets

Expand Down Expand Up @@ -1727,7 +1732,7 @@ def _encode(
left_on=selection_l.names,
right_on=selection_r.names,
how="left",
).dropna(subset=["labels"])
).dropna(subset=[tmp_label_column])
for part in value.partitions
],
ignore_index=False,
Expand All @@ -1741,11 +1746,11 @@ def _encode(
if len(merged_df) < len(codes):
# Missing nulls
labels = df._constructor_sliced(indistinct)
labels.iloc[merged_df["order"]] = merged_df["labels"]
labels.iloc[merged_df["order"]] = merged_df[tmp_label_column]
labels = labels.values
else:
merged_df["labels"].fillna(df._constructor_sliced(indistinct), inplace=True)
labels = merged_df["labels"].values
merged_df[tmp_label_column].fillna(df._constructor_sliced(indistinct), inplace=True)
labels = merged_df[tmp_label_column].values
else:
# no hashing
if use_collection:
Expand All @@ -1757,7 +1762,7 @@ def _encode(
left_on=selection_l.names,
right_on=selection_r.names,
how="left",
).dropna(subset=["labels"])
).dropna(subset=[tmp_label_column])
for part in value.partitions
],
ignore_index=True,
Expand All @@ -1768,16 +1773,16 @@ def _encode(
np.full(
len(codes),
indistinct,
like=merged_df["labels"].values,
like=merged_df[tmp_label_column].values,
),
)
labels.iloc[merged_df["order"]] = merged_df["labels"]
labels.iloc[merged_df["order"]] = merged_df[tmp_label_column]
else:
labels = merged_df.sort_values("order")["labels"].reset_index(drop=True)
labels = merged_df.sort_values("order")[tmp_label_column].reset_index(drop=True)
else:
labels = codes.merge(
value, left_on=selection_l.names, right_on=selection_r.names, how="left"
).sort_values("order")["labels"]
).sort_values("order")[tmp_label_column]
labels.fillna(indistinct, inplace=True)
labels = labels.values
else:
Expand Down