distinct.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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 evaluate
import datasets



_CITATION = """\
@inproceedings{liu-etal-2022-rethinking,
    title = "Rethinking and Refining the Distinct Metric",
    author = "Liu, Siyang  and
      Sabour, Sahand  and
      Zheng, Yinhe  and
      Ke, Pei  and
      Zhu, Xiaoyan  and
      Huang, Minlie",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    year = "2022",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-short.86",
    doi = "10.18653/v1/2022.acl-short.86",
    }
    
@inproceedings{li-etal-2016-diversity,
    title = "A Diversity-Promoting Objective Function for Neural Conversation Models",
    author = "Li, Jiwei  and
      Galley, Michel  and
      Brockett, Chris  and
      Gao, Jianfeng  and
      Dolan, Bill",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    year = "2016",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N16-1014",
    doi = "10.18653/v1/N16-1014",
}
"""
# ![Comparison between original distinct and and EAD ](https://huggingface.co/spaces/lsy641/distinct/resolve/main/distinct_compare_pic.jpg)

_DESCRIPTION = """\
Distinct metric is to calculate corpus-level diversity of language. We provide two versions of distinct score. Expectation-Adjusted-Distinct (EAD) is the default one, which removes
the biases of the original distinct score on lengthier sentences (see Figure below). Distinct is the original version.


For the use of Expectation-Adjusted-Distinct, vocab_size is required.

Please follow ACL paper https://aclanthology.org/2022.acl-short.86 for motivation and follow the rules of thumb provided by https://github.com/lsy641/Expectation-Adjusted-Distinct/blob/main/EAD.ipynb to determine the vocab_size

<img src="https://huggingface.co/spaces/lsy641/distinct/resolve/main/distinct_compare_pic.jpg" alt="drawing" width="200"/>
img[alt=drawing]{}

"""



_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of sentecnes. Each prediction should be a string.
Returns:
    Expectation-Adjusted-Distinct
    Distinct-1
    Distinct-2
    Distinct-3
Examples:
    Examples should be written in doctest format, and should illustrate how
    to use the function.

    >>> my_new_module = evaluate.load("lsy641/distinct")
    >>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], vocab_size=50257)
    >>> print(results)
    
    
    >>> dataset = ["This is my friend jack", "I'm sorry to hear that", "But you know I am the one who always support you", "Welcome to our family"]
    >>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], dataForVocabCal = dataset)
    >>> print(results)
    
    
    >>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], mode="Distinct")
    >>> print(results)    
    
"""

# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class distinct(evaluate.Measurement):

    def _info(self):
        return evaluate.MeasurementInfo(
            # This is the description that will appear on the modules page.
            module_type="measurement",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            features=datasets.Features({
                'predictions': datasets.Value('string')
            }),
            # Homepage of the module for documentation
            homepage="https://huggingface.co/spaces/lsy641/distinct",
            # Additional links to the codebase or references
            codebase_urls=["https://github.com/lsy641/Expectation-Adjusted-Distinct/tree/main"],
            reference_urls=["https://aclanthology.org/2022.acl-short.86/"]
        )

    def _download_and_prepare(self, dl_manager):
        """Optional: download external resources useful to compute the scores"""
        pass

    def _compute(self, predictions, dataForVocabCal=None, vocab_size=None, tokenizer="white_space", mode="Expectation-Adjusted-Distinct"):
        
        from nltk.util import ngrams
        
        """Returns the scores"""
        if mode == "Expectation-Adjusted-Distinct" and vocab_size is None and dataForVocabCal is None:
            raise ValueError("Either vocab_size or dataForVocabCal needs to be specified when using mode 'Expectation-Adjusted-Distinct'. See https://github.com/lsy641/Expectation-Adjusted-Distinct/blob/main/EAD.ipynb for vocab_size specification. \n Or use mode='Distinct' to get original version of distinct score.")
        elif mode == "Expectation-Adjusted-Distinct" and vocab_size is not None and dataForVocabCal is not None:
            raise Warning("We've detected that both vocab_size and dataForVocabCal are specified. We will use dataForVocabCal.")
        elif mode == "Distinct":
            pass
        
        if mode == "Expectation-Adjusted-Distinct" and dataForVocabCal is not None:
            if isinstance(dataForVocabCal, list) and len(dataForVocabCal) > 0 and isinstance(dataForVocabCal[0], str):
                vocab = set()
                for sentence in dataForVocabCal:
                    if tokenizer == "white_space":
                        vocab = vocab | set(sentence.split(" "))
                    else:
                        vocab = vocab | set(tokenizer.tokenize(sentence))
                vocab_size = len(vocab)
            else:
                raise TypeError("Argument dataForVocabCal should be a list of strings")
        distinct_tokens = set()
        distinct_tokens_2grams = set()
        distinct_tokens_3grams = set()
        total_tokens = []
        total_tokens_2grams = []
        total_tokens_3grams = []

        for prediction in predictions:
            if tokenizer == "white_space":
                tokens = prediction.split(" ")
                tokens_2grams = list(ngrams(prediction.split(" "), 2, pad_left=True, left_pad_symbol='<s>'))
                tokens_3grams = list(ngrams(prediction.split(" "), 3, pad_left=True, left_pad_symbol='<s>'))           
            else:
                try:
                    tokens = list(tokenizer.tokenize(prediction))
                    tokens_2grams = list(ngrams(list(tokenizer.tokenize(prediction)), 2, pad_left=True, left_pad_symbol='<s>'))
                    tokens_3grams = list(ngrams(list(tokenizer.tokenize(prediction)), 3, pad_left=True, left_pad_symbol='<s>'))                   
                except Exception as e:
                    raise e

            distinct_tokens = distinct_tokens | set(tokens)
            distinct_tokens_2grams = distinct_tokens_2grams | set(tokens_2grams)
            distinct_tokens_3grams = distinct_tokens_3grams | set(tokens_3grams)
            total_tokens.extend(tokens)
            total_tokens_2grams.extend(list(tokens_2grams))
            total_tokens_3grams.extend(list(tokens_3grams))
    
        Distinct_1 = len(distinct_tokens)/len(total_tokens)
        Distinct_2 = len(distinct_tokens_2grams)/len(total_tokens_2grams)
        Distinct_3 = len(distinct_tokens_3grams)/len(total_tokens_3grams)
        if mode == "Expectation-Adjusted-Distinct":
            Expectation_Adjusted_Distinct = len(distinct_tokens)/(vocab_size*(1-((vocab_size-1)/vocab_size)**len(total_tokens)))
            return {
                "Expectation-Adjusted-Distinct": Expectation_Adjusted_Distinct,
                "Distinct-1": Distinct_1,
                "Distinct-2": Distinct_2,
                "Distinct-3": Distinct_3
            }

        if mode == "Distinct":
            return {
                "Distinct-1": Distinct_1,
                "Distinct-2": Distinct_2,
                "Distinct-3": Distinct_3
            }