wehub-resource-sync
636 lines
25 KiB
Python
636 lines
25 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 json
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import math
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import os
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import random
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from typing import Iterable
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import numpy as np
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import paddle
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from ..transformers import AutoModelForMaskedLM, AutoTokenizer
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from .base_augment import BaseAugment
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__all__ = ["WordSubstitute", "WordInsert", "WordSwap", "WordDelete"]
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class WordSubstitute(BaseAugment):
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"""
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WordSubstitute is a word-level substitution data augmentation strategy
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that supports replacing words in the input sequence based on existing
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dictionaries or custom dictionaries.
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Args:
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aug_type (str or list(str)):
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Substitution dictionary type
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custom_file_path (str, optional):
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Custom substitution dictionary file path
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delete_file_path (str, optional):
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Dictionary file path for deleting words in substitution dictionary
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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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tf_idf (bool):
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Use tf-idf to select the most unimportant word for substitution.
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tf_idf (str):
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File for calculating TF-IDF score.
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model_name (str):
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Model parameter name for MLM prediction task.
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"""
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def __init__(
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self,
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aug_type,
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custom_file_path=None,
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delete_file_path=None,
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create_n=1,
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aug_n=None,
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aug_percent=0.1,
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aug_min=1,
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aug_max=10,
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tf_idf=False,
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tf_idf_file=None,
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model_name="ernie-1.0-large-zh-cw",
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vocab="vocab",
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):
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super().__init__(
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create_n=create_n, aug_n=aug_n, aug_percent=aug_percent, aug_min=aug_min, aug_max=aug_max, vocab=vocab
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)
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self.custom_file_path = custom_file_path
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self.delete_file_path = delete_file_path
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self.tf_idf = tf_idf
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self.model_name = model_name
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if self.tf_idf:
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self._count_idf(tf_idf_file)
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if isinstance(aug_type, str):
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self.type = aug_type
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if aug_type in ["antonym", "embedding", "synonym", "homonym", "custom"]:
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self.dict = self._load_substitute_dict(aug_type)
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elif aug_type in ["mlm"]:
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self.mlm_model = AutoModelForMaskedLM.from_pretrained(self.model_name)
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self.mlm_tokenizer = AutoTokenizer.from_pretrained(self.model_name)
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elif isinstance(aug_type, Iterable):
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if len(aug_type) == 1:
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self.type = aug_type[0]
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else:
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self.type = "combination"
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if self.type in ["mlm"]:
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self.mlm_model = AutoModelForMaskedLM.from_pretrained(self.model_name)
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self.mlm_tokenizer = AutoTokenizer.from_pretrained(self.model_name)
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self.dict = {}
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# Merge dictionaries from different sources
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for t in aug_type:
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if t in ["antonym", "embedding", "synonym", "homonym", "custom"]:
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t_dict = self._load_substitute_dict(t)
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for k in t_dict:
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if k in self.dict:
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self.dict[k] = list(set(self.dict[k] + t_dict[k]))
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else:
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self.dict[k] = t_dict[k]
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# Todo: delete some words in the dictionary
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else:
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self.type = aug_type
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def _count_idf(self, tf_idf_file):
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if os.path.exists(tf_idf_file):
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with open(tf_idf_file, "r", encoding="utf-8") as f:
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self.word_count_dict = {}
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self.text_tf_idf = []
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self.num = 0
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for line in f:
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self.num += 1
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self.text_tf_idf.append(line.strip())
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for word in set(self.tokenizer.cut(line.strip())):
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if word not in self.word_count_dict:
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self.word_count_dict[word] = 0
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self.word_count_dict[word] += 1
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f.close()
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else:
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raise ValueError("The tf_idf_file should exist.")
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return
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def _calculate_tfidf(self, sequence, seq_tokens, aug_indexes):
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if sequence not in self.text_tf_idf:
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self.num += 1
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self.text_tf_idf.append(sequence)
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for word in set(seq_tokens):
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if word not in self.word_count_dict:
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self.word_count_dict[word] = 0
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self.word_count_dict[word] += 1
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sequence_count = {}
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for index in aug_indexes:
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if seq_tokens[index] in sequence_count:
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sequence_count[seq_tokens[index]] += 1
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else:
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sequence_count[seq_tokens[index]] = 1
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tfidf = []
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for index in aug_indexes:
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tf = sequence_count[seq_tokens[index]] / len(aug_indexes)
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idf = math.log(self.num / self.word_count_dict[seq_tokens[index]])
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tfidf.append(tf * idf)
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return np.array(tfidf)
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def _load_substitute_dict(self, source_type):
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"""Load substitution dictionary"""
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if source_type in ["antonym", "embedding", "synonym", "homonym"]:
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fullname = self._load_file("word_" + source_type)
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elif source_type in ["custom"]:
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fullname = self.custom_file_path
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elif source_type in ["delete"]:
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fullname = self.delete_file_path
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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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substitute_dict = json.load(f)
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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 substitute_dict
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def _generate_sequence(self, output_seq_tokens, aug_tokens):
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"""Generate the sequences according to the mapping list"""
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for aug_token in aug_tokens:
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idx, token = aug_token
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output_seq_tokens[int(idx)] = token
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return "".join(output_seq_tokens)
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def _augment(self, sequence):
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seq_tokens = self.tokenizer.cut(sequence)
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aug_indexes = self._skip_stop_word_tokens(seq_tokens)
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aug_n = self._get_aug_n(len(seq_tokens), len(aug_indexes))
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if self.tf_idf:
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tfidf = self._calculate_tfidf(sequence, seq_tokens, aug_indexes)
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p = (max(tfidf) + 0.01 - tfidf) / sum(max(tfidf) + 0.01 - tfidf)
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else:
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p = None
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if aug_n == 0:
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return []
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elif self.type == "mlm":
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return self._augment_mlm(sequence, seq_tokens, aug_indexes, p)
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elif aug_n == 1:
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return self._augment_single(seq_tokens, aug_indexes, p)
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else:
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return self._augment_multi(seq_tokens, aug_n, aug_indexes, p)
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@paddle.no_grad()
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def _augment_mlm(self, sequence, seq_tokens, aug_indexes, p):
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t = 0
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sentences = []
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while t < self.create_n * self.loop * 2 and len(sentences) < self.create_n:
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skip = False
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t += 1
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idx = np.random.choice(aug_indexes, replace=False, p=p)
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aug_tokens = [[idx, "[MASK]" * len(seq_tokens[idx])]]
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sequence_mask = self._generate_sequence(seq_tokens.copy(), aug_tokens)
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tokenized = self.mlm_tokenizer(sequence_mask)
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masked_positions = [
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i for i, idx in enumerate(tokenized["input_ids"]) if idx == self.mlm_tokenizer.mask_token_id
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]
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output = self.mlm_model(
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paddle.to_tensor([tokenized["input_ids"]]), paddle.to_tensor([tokenized["token_type_ids"]])
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)
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predicted = "".join(
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self.mlm_tokenizer.convert_ids_to_tokens(paddle.argmax(output[0][masked_positions], axis=-1))
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)
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for ppp in predicted:
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if ppp in self.stop_words:
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skip = True
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break
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if skip:
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continue
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aug_tokens = [[idx, predicted]]
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sequence_generate = self._generate_sequence(seq_tokens.copy(), aug_tokens)
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if sequence_generate != sequence and sequence_generate not in sentences:
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sentences.append(sequence_generate)
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return sentences
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def _augment_multi(self, seq_tokens, aug_n, aug_indexes, p):
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sentences = []
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aug_n = min(aug_n, len(aug_indexes))
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if self.type in ["antonym", "embedding", "synonym", "homonym", "combination", "custom"]:
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candidate_tokens = []
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pp = []
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for i, aug_index in enumerate(aug_indexes):
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if seq_tokens[aug_index] in self.dict:
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candidate_tokens.append([aug_index, self.dict[seq_tokens[aug_index]]])
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if self.tf_idf:
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pp.append(p[i])
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pp = np.array(pp)
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pp /= sum(pp)
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aug_n = min(aug_n, len(candidate_tokens))
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if aug_n != 0:
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t = 0
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while t < self.create_n * self.loop and len(sentences) < self.create_n:
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t += 1
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if self.tf_idf:
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idxes = np.random.choice(list(range(len(candidate_tokens))), size=aug_n, replace=False, p=pp)
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else:
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idxes = random.sample(list(range(len(candidate_tokens))), aug_n)
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aug_tokens = []
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for idx in idxes:
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aug_index, aug_dict = candidate_tokens[idx]
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aug_tokens.append([aug_index, random.sample(aug_dict, 1)[0]])
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sentence = self._generate_sequence(seq_tokens.copy(), aug_tokens)
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if sentence not in sentences:
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sentences.append(sentence)
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elif self.type in ["random"]:
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t = 0
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while t < self.create_n * self.loop and len(sentences) < self.create_n:
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t += 1
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aug_tokens = []
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aug_choice_indexes = np.random.choice(aug_indexes, size=aug_n, replace=False, p=p)
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for aug_index in aug_choice_indexes:
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token = self.vocab.to_tokens(random.randint(0, len(self.vocab) - 2))
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aug_tokens.append([aug_index, token])
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sentence = self._generate_sequence(seq_tokens.copy(), aug_tokens)
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if sentence not in sentences:
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sentences.append(sentence)
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return sentences
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def _augment_single(self, seq_tokens, aug_indexes, p):
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sentences = []
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aug_tokens = []
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if self.type in ["antonym", "embedding", "synonym", "homonym", "combination", "custom"]:
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candidate_tokens = []
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pp = []
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for i, aug_index in enumerate(aug_indexes):
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if seq_tokens[aug_index] in self.dict:
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for token in self.dict[seq_tokens[aug_index]]:
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candidate_tokens.append([aug_index, token])
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if self.tf_idf:
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pp.append(p[i] / len(self.dict[seq_tokens[aug_index]]))
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create_n = min(self.create_n, len(candidate_tokens))
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pp = np.array(pp)
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pp /= sum(pp)
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if self.tf_idf:
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candidate_indexes = np.random.choice(range(len(candidate_tokens)), size=create_n, replace=False, p=pp)
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candidate_tokens = np.array(candidate_tokens)
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aug_tokens = candidate_tokens[candidate_indexes]
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else:
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aug_tokens = random.sample(candidate_tokens, create_n)
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elif self.type in ["random"]:
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t = 0
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while t < self.create_n * self.loop and len(aug_tokens) < self.create_n:
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t += 1
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aug_index = np.random.choice(aug_indexes, replace=False, p=p)
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token = self.vocab.to_tokens(random.randint(0, len(self.vocab) - 2))
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if [aug_index, token] not in aug_tokens:
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aug_tokens.append([aug_index, token])
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for aug_token in aug_tokens:
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sequence_generate = self._generate_sequence(seq_tokens.copy(), [aug_token])
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sentences.append(sequence_generate)
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return sentences
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class WordInsert(BaseAugment):
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"""
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WordInsert is a word-level insert data augmentation strategy.
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Args:
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aug_type (str or list(str)):
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Insert dictionary type
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custom_file_path (str, optional):
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Custom insert dictionary file path
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delete_file_path (str, optional):
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Dictionary file path for deleting words in insert dictionary
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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__(
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self,
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aug_type,
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custom_file_path=None,
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delete_file_path=None,
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create_n=1,
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aug_n=None,
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aug_percent=0.1,
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aug_min=1,
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aug_max=10,
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model_name="ernie-1.0-large-zh-cw",
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vocab="vocab",
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):
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super().__init__(
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create_n=create_n, aug_n=aug_n, aug_percent=aug_percent, aug_min=aug_min, aug_max=aug_max, vocab=vocab
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)
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self.custom_file_path = custom_file_path
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self.delete_file_path = delete_file_path
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self.model_name = model_name
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if isinstance(aug_type, str):
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self.type = aug_type
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if aug_type in ["antonym", "embedding", "synonym", "homonym", "custom"]:
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self.dict = self._load_insert_dict(aug_type)
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elif aug_type in ["mlm"]:
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self.mlm_model = AutoModelForMaskedLM.from_pretrained(self.model_name)
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self.mlm_tokenizer = AutoTokenizer.from_pretrained(self.model_name)
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elif isinstance(aug_type, Iterable):
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self.type = "combination"
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self.dict = {}
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# Merge dictionaries from different sources
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for t in aug_type:
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if t in ["antonym", "embedding", "synonym", "homonym", "custom"]:
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t_dict = self._load_insert_dict(t)
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for k in t_dict:
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if k in self.dict:
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self.dict[k] = list(set(self.dict[k] + t_dict[k]))
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else:
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self.dict[k] = t_dict[k]
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# Todo: delete some words in the dictionary
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else:
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self.type = aug_type
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def _load_insert_dict(self, source_type):
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"""Load insert dictionary"""
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if source_type in ["antonym", "embedding", "synonym", "homonym"]:
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fullname = self._load_file("word_" + source_type)
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elif source_type in ["custom"]:
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fullname = self.custom_file_path
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elif source_type in ["delete"]:
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fullname = self.delete_file_path
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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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insert_dict = json.load(f)
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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 insert_dict
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def _augment(self, sequence):
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seq_tokens = self.tokenizer.cut(sequence)
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aug_indexes = self._skip_stop_word_tokens(seq_tokens)
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aug_n = self._get_aug_n(len(seq_tokens), len(aug_indexes))
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if aug_n == 0:
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return []
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elif self.type == "mlm":
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return self._augment_mlm(sequence, seq_tokens, aug_indexes)
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elif aug_n == 1:
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return self._augment_single(seq_tokens, aug_indexes)
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else:
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return self._augment_multi(seq_tokens, aug_n, aug_indexes)
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@paddle.no_grad()
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def _augment_mlm(self, sequence, seq_tokens, aug_indexes):
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t = 0
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sentences = []
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while t < self.create_n * self.loop and len(sentences) < self.create_n:
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skip = False
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t += 1
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p = random.randint(0, 1)
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idx = random.sample(aug_indexes, 1)[0]
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aug_tokens = [[idx, "[MASK]" * len(seq_tokens[idx])]]
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sequence_mask = self._generate_sequence(seq_tokens.copy(), aug_tokens, p)
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tokenized = self.mlm_tokenizer(sequence_mask)
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masked_positions = [
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i for i, idx in enumerate(tokenized["input_ids"]) if idx == self.mlm_tokenizer.mask_token_id
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]
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output = self.mlm_model(
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paddle.to_tensor([tokenized["input_ids"]]), paddle.to_tensor([tokenized["token_type_ids"]])
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)
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predicted = "".join(
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self.mlm_tokenizer.convert_ids_to_tokens(paddle.argmax(output[0][masked_positions], axis=-1))
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)
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for p in predicted:
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if p in self.stop_words:
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skip = True
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break
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if skip:
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continue
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aug_tokens = [[idx, predicted]]
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sequence_generate = self._generate_sequence(seq_tokens.copy(), aug_tokens, p)
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if sequence_generate != sequence and sequence_generate not in sentences:
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sentences.append(sequence_generate)
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return sentences
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def _augment_multi(self, seq_tokens, aug_n, aug_indexes):
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sentences = []
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if self.type in ["antonym", "embedding", "synonym", "homonym", "combination", "custom"]:
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candidate_tokens = []
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for aug_index in aug_indexes:
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if seq_tokens[aug_index] in self.dict:
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candidate_tokens.append([aug_index, self.dict[seq_tokens[aug_index]]])
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aug_n = min(aug_n, len(candidate_tokens))
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if aug_n != 0:
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t = 0
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while t < self.create_n * self.loop and len(sentences) < self.create_n:
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t += 1
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idxes = random.sample(list(range(len(candidate_tokens))), aug_n)
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aug_tokens = []
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|
for idx in idxes:
|
|
aug_index, aug_dict = candidate_tokens[idx]
|
|
aug_tokens.append([aug_index, random.sample(aug_dict, 1)[0]])
|
|
p = random.randint(0, 1)
|
|
sentence = self._generate_sequence(seq_tokens.copy(), aug_tokens, p)
|
|
if sentence not in sentences:
|
|
sentences.append(sentence)
|
|
elif self.type in ["random"]:
|
|
t = 0
|
|
while t < self.create_n * self.loop and len(sentences) < self.create_n:
|
|
t += 1
|
|
aug_tokens = []
|
|
aug_indexes = random.sample(aug_indexes, aug_n)
|
|
for aug_index in aug_indexes:
|
|
token = self.vocab.to_tokens(random.randint(0, len(self.vocab) - 2))
|
|
aug_tokens.append([aug_index, token])
|
|
p = random.randint(0, 1)
|
|
sentence = self._generate_sequence(seq_tokens.copy(), aug_tokens, p)
|
|
if sentence not in sentences:
|
|
sentences.append(sentence)
|
|
return sentences
|
|
|
|
def _augment_single(self, seq_tokens, aug_indexes):
|
|
|
|
sentences = []
|
|
aug_tokens = []
|
|
if self.type in ["antonym", "embedding", "synonym", "homonym", "combination", "custom"]:
|
|
candidate_tokens = []
|
|
for aug_index in aug_indexes:
|
|
if seq_tokens[aug_index] in self.dict:
|
|
for token in self.dict[seq_tokens[aug_index]]:
|
|
candidate_tokens.append([aug_index, token])
|
|
create_n = min(self.create_n, len(candidate_tokens))
|
|
aug_tokens = random.sample(candidate_tokens, create_n)
|
|
elif self.type in ["random"]:
|
|
t = 0
|
|
while t < self.create_n * self.loop and len(aug_tokens) < self.create_n:
|
|
t += 1
|
|
aug_index = random.sample(aug_indexes, 1)[0]
|
|
token = self.vocab.to_tokens(random.randint(0, len(self.vocab) - 2))
|
|
if [aug_index, token] not in aug_tokens:
|
|
aug_tokens.append([aug_index, token])
|
|
for aug_token in aug_tokens:
|
|
p = random.randint(0, 1)
|
|
sentences.append(self._generate_sequence(seq_tokens.copy(), [aug_token], p))
|
|
return sentences
|
|
|
|
def _generate_sequence(self, output_seq_tokens, aug_tokens, p):
|
|
"""Generate the sequences according to the mapping list"""
|
|
for aug_token in aug_tokens:
|
|
idx, token = aug_token
|
|
if p == 0:
|
|
output_seq_tokens[idx] = token + output_seq_tokens[idx]
|
|
else:
|
|
output_seq_tokens[idx] += token
|
|
return "".join(output_seq_tokens)
|
|
|
|
|
|
class WordSwap(BaseAugment):
|
|
"""
|
|
WordSwap is a word-level swap data augmentation strategy.
|
|
|
|
Args:
|
|
create_n (int):
|
|
Number of augmented sequences.
|
|
aug_n (int):
|
|
Number of augmented words in sequences.
|
|
aug_percent (int):
|
|
Percentage of augmented words in sequences.
|
|
aug_min (int):
|
|
Minimum number of augmented words in sequences.
|
|
aug_max (int):
|
|
Maximum number of augmented words in sequences.
|
|
"""
|
|
|
|
def __init__(self, create_n=1, aug_n=None, aug_percent=None, aug_min=1, aug_max=10, vocab="vocab"):
|
|
super().__init__(
|
|
create_n=create_n, aug_n=aug_n, aug_percent=0.1, aug_min=aug_min, aug_max=aug_max, vocab=vocab
|
|
)
|
|
|
|
def _augment(self, sequence):
|
|
|
|
seq_tokens = self.tokenizer.cut(sequence)
|
|
aug_indexes = self._skip_words(seq_tokens)
|
|
aug_n = self._get_aug_n(len(seq_tokens), len(aug_indexes))
|
|
|
|
t = 0
|
|
sentences = []
|
|
|
|
if aug_n == 0:
|
|
return []
|
|
while t < self.create_n * self.loop and len(sentences) < self.create_n:
|
|
t += 1
|
|
idxes = random.sample(aug_indexes, aug_n)
|
|
output_seq_tokens = seq_tokens.copy()
|
|
for idx in range(len(seq_tokens)):
|
|
if idx in idxes:
|
|
output_seq_tokens[idx], output_seq_tokens[idx + 1] = (
|
|
output_seq_tokens[idx + 1],
|
|
output_seq_tokens[idx],
|
|
)
|
|
sentence = "".join(output_seq_tokens)
|
|
if sentence not in sentences:
|
|
sentences.append(sentence)
|
|
return sentences
|
|
|
|
def _skip_words(self, seq_tokens):
|
|
"""Skip specific words."""
|
|
indexes = []
|
|
for i, seq_token in enumerate(seq_tokens[:-1]):
|
|
if (
|
|
seq_token not in self.stop_words
|
|
and not seq_token.isdigit()
|
|
and not seq_token.encode("UTF-8").isalpha()
|
|
):
|
|
if (
|
|
seq_tokens[i + 1] not in self.stop_words
|
|
and not seq_tokens[i + 1].isdigit()
|
|
and not seq_tokens[i + 1].encode("UTF-8").isalpha()
|
|
):
|
|
indexes.append(i)
|
|
return indexes
|
|
|
|
|
|
class WordDelete(BaseAugment):
|
|
"""
|
|
WordDelete is a word-level deletion data augmentation strategy.
|
|
|
|
Args:
|
|
create_n (int):
|
|
Number of augmented sequences.
|
|
aug_n (int):
|
|
Number of augmented words in sequences.
|
|
aug_percent (int):
|
|
Percentage of augmented words in sequences.
|
|
aug_min (int):
|
|
Minimum number of augmented words in sequences.
|
|
aug_max (int):
|
|
Maximum number of augmented words in sequences.
|
|
"""
|
|
|
|
def __init__(self, create_n=1, aug_n=None, aug_percent=0.1, aug_min=1, aug_max=10, vocab="vocab"):
|
|
super().__init__(
|
|
create_n=create_n, aug_n=aug_n, aug_percent=aug_percent, aug_min=aug_min, aug_max=aug_max, vocab=vocab
|
|
)
|
|
|
|
def _augment(self, sequence):
|
|
|
|
seq_tokens = self.tokenizer.cut(sequence)
|
|
aug_indexes = self._skip_words(seq_tokens)
|
|
aug_n = self._get_aug_n(len(seq_tokens), len(aug_indexes))
|
|
|
|
t = 0
|
|
sentences = []
|
|
if aug_n == 0:
|
|
return sentences
|
|
while t < self.create_n * self.loop and len(sentences) < self.create_n:
|
|
t += 1
|
|
idxes = random.sample(aug_indexes, aug_n)
|
|
sentence = ""
|
|
for idx in range(len(seq_tokens)):
|
|
if idx not in idxes:
|
|
sentence += seq_tokens[idx]
|
|
if sentence not in sentences:
|
|
sentences.append(sentence)
|
|
return sentences
|
|
|
|
def _skip_words(self, seq_tokens):
|
|
"""Skip specific words."""
|
|
indexes = []
|
|
for i, seq_token in enumerate(seq_tokens):
|
|
if (
|
|
seq_token not in self.stop_words
|
|
and not seq_token.isdigit()
|
|
and not seq_token.encode("UTF-8").isalpha()
|
|
):
|
|
indexes.append(i)
|
|
return indexes
|