Source code for forte.processors.data_augment.algorithms.distribution_replacement_op

# Copyright 2020 The Forte Authors. 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import random
from typing import Tuple, Union, Dict, Any

from forte.common.configurable import Configurable
from forte.common.configuration import Config
from import Annotation
from forte.processors.data_augment.algorithms.single_annotation_op import (
from forte.utils.utils import create_class_with_kwargs
from forte.utils import create_import_error_msg

__all__ = [

[docs]class DistributionReplacementOp(SingleAnnotationAugmentOp, Configurable): r""" This class is a replacement op to replace the input word with a new word that is sampled by a sampler from a distribution. """ def __init__(self, configs: Union[Config, Dict[str, Any]]): super().__init__(configs) self.configs = self.make_configs(configs) self.cofigure_sampler()
[docs] def single_annotation_augment( self, input_anno: Annotation ) -> Tuple[bool, str]: r""" This function replaces a word by sampling from a distribution. Args: input_anno: the input annotation. Returns: A tuple of two values, where the first element is a boolean value indicating whether the replacement happens, and the second element is the replaced word. """ if random.random() > self.configs.prob: return False, input_anno.text word: str = self.sampler.sample() return True, word
[docs] def cofigure_sampler(self) -> None: r""" This function sets the sampler that will be used by the distribution replacement op. The sampler will be set according to the configuration values """ try: import requests # pylint: disable=import-outside-toplevel except ImportError as e: raise ImportError( create_import_error_msg( "requests", "data_aug", "data augment support" ) ) from e try: if "data_path" in self.configs["sampler_config"]["kwargs"]: distribution_path = self.configs["sampler_config"]["kwargs"][ "data_path" ] try: r = requests.get(distribution_path, timeout=30) data = r.json() except requests.exceptions.RequestException: with open(distribution_path, encoding="utf8") as json_file: data = json.load(json_file) else: data = self.configs["sampler_config"]["kwargs"]["sampler_data"] self.sampler = create_class_with_kwargs( self.configs["sampler_config"]["type"], { "configs": { "sampler_data": data, } }, ) except KeyError as error: raise Exception from error
[docs] @classmethod def default_configs(cls): r""" Returns: A dictionary with the default config for this processor. Following are the keys for this dictionary: - `prob`: The probability of whether to replace the input, it should fall in `[0, 1]`. Default value is 0.1 - `sampler_data`: A dictionary representing the configurations required to create the required sampler. - type: The type of sampler to be used (pass the path of the class which defines the required sampler) - kwargs: This dictionary contains the data that is to be fed to the required sampler. 2 possible values are `sampler_data` and `data_path`.If both parameters are passed, the data read from the file pointed to by `data_path` will be considered. - `sampler_data`: Raw input to the sampler, This will be passed as the `sampler_data` config to the required sampler. - `data_path`: The path to the file that contains the the input that will be given to the sampler. For example, when using `UniformSampler`, `data_path` will point to a file (or `URl`) containing a list of values to be used as `sampler_data` in `UniformSampler`. .. code-block:: python { "type": "forte.processors.data_augment.algorithms.sampler.UniformSampler", "kwargs":{ "sample": ["apple", "banana", "orange"] } } """ return { "prob": 0.1, "sampler_config": { "type": "forte.processors.data_augment.algorithms.sampler.UniformSampler", "kwargs": {"sampler_data": []} # "sampler_data": [], }, }