# Copyright 2021 cstsunfu. 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.
from dlk.utils.vocab import Vocabulary
from dlk.utils.config import BaseConfig, ConfigTool
from typing import Dict, Callable, Set, List
from dlk.data.subprocessors import subprocessor_register, subprocessor_config_register, ISubProcessor
from functools import partial
from dlk.utils.logger import Logger
import os
logger = Logger.get_logger()
[docs]@subprocessor_config_register('token2id')
class Token2IDConfig(BaseConfig):
"""Config for Token2ID
Config Example:
>>> {
>>> "_name": "token2id",
>>> "config": {
>>> "train":{
>>> "data_pair": {
>>> "labels": "label_ids"
>>> },
>>> "data_set": { // for different stage, this processor will process different part of data
>>> "train": ['train', 'valid', 'test', 'predict'],
>>> "predict": ['predict'],
>>> "online": ['online']
>>> },
>>> "vocab": "label_vocab", // usually provided by the "token_gather" module
>>> }, //3
>>> "predict": "train",
>>> "online": "train",
>>> }
>>> }
"""
def __init__(self, stage, config: Dict):
super(Token2IDConfig, self).__init__(config)
self.config = ConfigTool.get_config_by_stage(stage, config)
self.data_set = self.config.get('data_set', {}).get(stage, [])
if not self.data_set:
return
self.data_pair = self.config.pop('data_pair', {})
if self.data_set and (not self.data_pair):
raise ValueError("You must provide 'data_pair' for token2id.")
self.vocab = self.config.get('vocab', "")
if self.data_set and (not self.vocab):
raise ValueError("You must provide 'vocab' for token2id.")
self.post_check(self.config, used=[
"data_pair",
"data_set",
"vocab",
"max_token_len",
])
[docs]@subprocessor_register('token2id')
class Token2ID(ISubProcessor):
"""Use 'Vocabulary' map the tokens to id
"""
def __init__(self, stage: str, config: Token2IDConfig):
super().__init__()
self.stage = stage
self.config = config
self.data_set = config.data_set
if not self.data_set:
logger.info(f"Skip 'token2id' at stage {self.stage}")
return
self.data_pair = config.data_pair
[docs] def process(self, data: Dict)->Dict:
"""Token2ID Entry
one_token like ['apple'] will generate [1] if the vocab.word2idx = {'apple': 1}
Args:
data: will process data
Returns:
updated data(tokens -> token_ids)
"""
if not self.data_set:
return data
vocab = Vocabulary.load(data[self.config.vocab])
def get_index_wrap(key, x):
return vocab.auto_get_index(x[key])
for data_set_name in self.data_set:
if data_set_name not in data['data']:
logger.info(f'The {data_set_name} not in data. We will skip do token2id on it.')
continue
data_set = data['data'][data_set_name]
for key, value in self.data_pair.items():
get_index = partial(get_index_wrap, key)
if os.environ.get('DISABLE_PANDAS_PARALLEL', 'false') != 'false':
data_set[value] = data_set.parallel_apply(get_index, axis=1)
else:
data_set[value] = data_set.apply(get_index, axis=1)
return data