-
Notifications
You must be signed in to change notification settings - Fork 91
/
exact_deduplication.py
86 lines (67 loc) · 2.83 KB
/
exact_deduplication.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import time
from nemo_curator.datasets import DocumentDataset
from nemo_curator.modules import ExactDuplicates
from nemo_curator.utils.distributed_utils import get_client, read_data, write_to_disk
from nemo_curator.utils.file_utils import get_all_files_paths_under
from nemo_curator.utils.script_utils import ArgumentHelper
def pre_imports():
import cudf # noqa: F401
def main(args):
dataset_dir = "/path/to/data"
log_dir = "./"
output_dir = "./"
dataset_id_field = "id"
dataset_text_field = "text"
client = get_client(**ArgumentHelper.parse_client_args(args))
backend = "cudf" if args.device == "gpu" else "pandas"
if args.device == "gpu":
client.run(pre_imports)
t0 = time.time()
input_dataset = DocumentDataset.read_json(dataset_dir, backend=backend)
exact_dup = ExactDuplicates(
logger=log_dir,
id_field=dataset_id_field,
text_field=dataset_text_field,
# cache_dir=output_dir # Optionally write the output to disk
)
duplicates = exact_dup(dataset=input_dataset)
# If caching, result is a path to the output dataset.
if isinstance(duplicates, str):
duplicates = DocumentDataset.read_parquet(duplicates, backend=backend)
# It's easy to apply dataframe operations to the dataset by using the underlying df.
# By default all duplicate id's are included in the result
# keep 1 document from each group of duplcates and mark the others to remove
# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.duplicated.html
docs_to_remove = duplicates.df.map_partitions(
lambda x: x[x._hashes.duplicated(keep="first")]
)
# When there are few duplicates we can compute the results to a list and use `isin`.
result = input_dataset.df[
~input_dataset.df[dataset_id_field].isin(
docs_to_remove[dataset_id_field].compute()
)
]
write_to_disk(result, output_dir, output_type="parquet")
print(time.time() - t0)
def attach_args(
parser=argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
),
):
return ArgumentHelper(parser).add_distributed_args()
if __name__ == "__main__":
main(attach_args().parse_args())