wehub-resource-sync
242 lines
8.3 KiB
Python
242 lines
8.3 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import os
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import re
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from typing import Iterable
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from paddle.dataset.common import md5file
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from paddle.utils.download import get_path_from_url
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from ..data import JiebaTokenizer, Vocab
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from ..utils.env import DATA_HOME
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class BaseAugment(object):
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"""
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A base class for data augmentation
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Args:
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create_n (int):
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Number of augmented sequences.
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aug_n (int):
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Number of augmented words in sequences.
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aug_percent (int):
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Percentage of augmented words in sequences.
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aug_min (int):
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Minimum number of augmented words in sequences.
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aug_max (int):
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Maximum number of augmented words in sequences.
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"""
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def __init__(self, create_n=1, aug_n=None, aug_percent=0.1, aug_min=1, aug_max=10, vocab="vocab"):
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self._DATA = {
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"stop_words": (
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"stopwords.txt",
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"a4a76df756194777ca18cd788231b474",
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"https://bj.bcebos.com/paddlenlp/data/stopwords.txt",
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),
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"vocab": (
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"baidu_encyclopedia_w2v_vocab.json",
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"25c2d41aec5a6d328a65c1995d4e4c2e",
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"https://bj.bcebos.com/paddlenlp/data/baidu_encyclopedia_w2v_vocab.json",
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),
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"test_vocab": (
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"test_vocab.json",
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"1d2fce1c80a4a0ec2e90a136f339ab88",
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"https://bj.bcebos.com/paddlenlp/data/test_vocab.json",
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),
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"word_synonym": (
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"word_synonym.json",
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"aaa9f864b4af4123bce4bf138a5bfa0d",
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"https://bj.bcebos.com/paddlenlp/data/word_synonym.json",
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),
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"word_embedding": (
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"word_embedding.json",
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"534aa4ad274def4deff585cefd8ead32",
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"https://bj.bcebos.com/paddlenlp/data/word_embedding.json",
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),
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"word_homonym": (
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"word_homonym.json",
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"a578c04201a697e738f6a1ad555787d5",
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"https://bj.bcebos.com/paddlenlp/data/word_homonym.json",
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),
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"char_homonym": (
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"char_homonym.json",
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"dd98d5d5d32a3d3dd45c8f7ca503c7df",
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"https://bj.bcebos.com/paddlenlp/data/char_homonym.json",
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),
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"char_antonym": (
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"char_antonym.json",
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"f892f5dce06f17d19949ebcbe0ed52b7",
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"https://bj.bcebos.com/paddlenlp/data/char_antonym.json",
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),
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"word_antonym": (
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"word_antonym.json",
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"cbea11fa99fbe9d07e8185750b37e84a",
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"https://bj.bcebos.com/paddlenlp/data/word_antonym.json",
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),
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}
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self.stop_words = self._get_data("stop_words")
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self.aug_n = aug_n
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self.aug_percent = aug_percent
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self.aug_min = aug_min
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self.aug_max = aug_max
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self.create_n = create_n
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self.vocab = Vocab.from_json(self._load_file(vocab))
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self.tokenizer = JiebaTokenizer(self.vocab)
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self.loop = 5
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@classmethod
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def clean(cls, sequences):
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"""Clean input sequences"""
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if isinstance(sequences, str):
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return sequences.strip()
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if isinstance(sequences, Iterable):
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return [str(s).strip() if s else s for s in sequences]
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return str(sequences).strip()
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def _load_file(self, mode):
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"""Check and download data"""
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default_root = os.path.join(DATA_HOME, self.__class__.__name__)
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filename, data_hash, url = self._DATA[mode]
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fullname = os.path.join(default_root, filename)
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if not os.path.exists(fullname) or (data_hash and not md5file(fullname) == data_hash):
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get_path_from_url(url, default_root, data_hash)
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return fullname
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def _get_data(self, mode):
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"""Read data as list"""
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fullname = self._load_file(mode)
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data = []
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if os.path.exists(fullname):
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with open(fullname, "r", encoding="utf-8") as f:
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for line in f:
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data.append(line.strip())
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f.close()
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else:
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raise ValueError("The {} should exist.".format(fullname))
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return data
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def _get_aug_n(self, size, size_a=None):
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"""Calculate number of words for data augmentation"""
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if size == 0:
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return 0
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aug_n = self.aug_n or int(math.ceil(self.aug_percent * size))
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if self.aug_min and aug_n < self.aug_min:
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aug_n = self.aug_min
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elif self.aug_max and aug_n > self.aug_max:
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aug_n = self.aug_max
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if size_a is not None:
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aug_n = min(aug_n, int(math.floor(size_a * 0.3)))
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return aug_n
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def _skip_stop_word_tokens(self, seq_tokens):
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"""Skip words. We can rewrite function to skip specify words."""
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indexes = []
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for i, seq_token in enumerate(seq_tokens):
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if (
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seq_token not in self.stop_words
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and not seq_token.isdigit()
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and not bool(re.search(r"\d", seq_token))
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and not seq_token.encode("UTF-8").isalpha()
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):
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indexes.append(i)
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return indexes
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def augment(self, sequences, num_thread=1):
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"""
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Apply augmentation strategy on input sequences.
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Args:
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sequences (str or list(str)):
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Input sequence or list of input sequences.
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num_thread (int):
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Number of threads
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"""
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sequences = self.clean(sequences)
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# Single Thread
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if num_thread == 1:
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if isinstance(sequences, str):
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return [self._augment(sequences)]
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else:
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output = []
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for sequence in sequences:
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output.append(self._augment(sequence))
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return output
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else:
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raise NotImplementedError
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def _augment(self, sequence):
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raise NotImplementedError
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class FileAugment(object):
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"""
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File data augmentation
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Args:
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strategies (List):
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List of augmentation strategies.
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"""
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def __init__(self, strategies):
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self.strategies = strategies
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def augment(self, input_file, output_file="aug.txt", separator=None, separator_id=0):
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output_sequences = []
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sequences = []
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input_sequences = self.file_read(input_file)
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if separator:
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for input_sequence in input_sequences:
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sequences.append(input_sequence.split(separator)[separator_id])
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else:
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sequences = input_sequences
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for strategy in self.strategies:
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aug_sequences = strategy.augment(sequences)
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if separator:
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for aug_sequence, input_sequence in zip(aug_sequences, input_sequences):
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input_items = input_sequence.split(separator)
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for s in aug_sequence:
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input_items[separator_id] = s
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output_sequences.append(separator.join(input_items))
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else:
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for aug_sequence in aug_sequences:
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output_sequences += aug_sequence
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if output_file:
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self.file_write(output_sequences, output_file)
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return output_sequences
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def file_read(self, input_file):
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input_sequences = []
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with open(input_file, "r", encoding="utf-8") as f:
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for line in f:
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input_sequences.append(line.strip())
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f.close()
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return input_sequences
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def file_write(self, output_sequences, output_file):
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with open(output_file, "w", encoding="utf-8") as f:
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for output_sequence in output_sequences:
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f.write(output_sequence + "\n")
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f.close()
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