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# Copyright (c) 2022, 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 os
import shutil
import pandas as pd
def convert_atis_multi_label(source_dir: str, target_dir: str, mode: str) -> None:
"""
Converts single label atis nemo data to multi-label data. Previous
labels in atis mapped multi-labels to a single index rather than two separate indicies.
Args:
source_dir: directory that stored original nemo files
target_dir: directory to store multi-label nemo files
mode: specifies the name of the dataset i.e, train, test, dev
Returns:
None
"""
data = pd.read_csv(f'{source_dir}/{mode}.tsv', sep='\t')
# Get the original intent dictionary
old_intents_file = f'{source_dir}/dict.intents.csv'
new_intents_file = f'{target_dir}/dict.intents.csv'
intent_labels = []
with open(old_intents_file, "r") as input_file:
old_intents = input_file.read().splitlines()
with open(new_intents_file, "r") as input_file:
new_intents = input_file.read().splitlines()
for index, intent in data.iterrows():
temp_dict = {}
temp_dict['sentence'] = intent['sentence']
old_label = old_intents[int(intent['label'])]
values = [old_label]
if '+' in old_label:
values = old_label.split('+')
for index, label in enumerate(new_intents):
if label in values:
if 'label' not in temp_dict:
temp_dict['label'] = f"{index}"
else:
temp_dict['label'] = f"{temp_dict['label']},{index}"
intent_labels.append(temp_dict)
multi_intent_df = pd.DataFrame(intent_labels)
multi_intent_df.to_csv(f'{target_dir}/{mode}.tsv', sep='\t', index=False)
def convert_intent_dictionary(source_dir: str, target_dir: str) -> None:
"""
Converts original intent dictionary containing labels that represented multiple labels into
dictionary with only single labels. Example: if index 5 was referring to label "a+b", it is no longer
a label in the new intent dictionary. Only labels "a" and "b" are included within the new dictionary
Args:
source_dir: directory that stored original nemo files
target_dir: directory to store multi-label nemo files
Returns:
None
"""
os.makedirs(target_dir, exist_ok=True)
source_file = os.path.join(source_dir, "dict.intents.csv")
target_file = os.path.join(target_dir, "dict.intents.csv")
with open(source_file, "r") as input_file:
orig_intents = input_file.read().splitlines()
with open(target_file, "w") as output_file:
for line in orig_intents:
if "+" not in line:
output_file.write(f"{line}\n")
if __name__ == "__main__":
# Parse the command-line arguments.
parser = argparse.ArgumentParser(description="Process and convert datasets into NeMo\'s format.")
parser.add_argument(
"--source_data_dir", required=True, type=str, help='path to the folder containing the dataset files'
)
parser.add_argument("--target_data_dir", required=True, type=str, help='path to save the processed dataset')
args = parser.parse_args()
source_dir = args.source_data_dir
target_dir = args.target_data_dir
shutil.copyfile(f'{source_dir}/test.tsv', f'{source_dir}/dev.tsv')
convert_intent_dictionary(f'{source_dir}', f'{target_dir}')
convert_atis_multi_label(f'{source_dir}', f'{target_dir}', 'train')
convert_atis_multi_label(f'{source_dir}', f'{target_dir}', 'dev')
shutil.copyfile(f'{source_dir}/dict.slots.csv', f'{target_dir}/dict.slots.csv')
shutil.copyfile(f'{source_dir}/train_slots.tsv', f'{target_dir}/train_slots.tsv')
shutil.copyfile(f'{source_dir}/test_slots.tsv', f'{target_dir}/dev_slots.tsv')
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