semf1.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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# TODO: Add test cases, Remove tokenize_sentences flag since it can be determined from the input itself.
"""Sem-F1 metric"""

from typing import List, Optional, Tuple

import datasets
import evaluate
import nltk
import numpy as np
from numpy.typing import NDArray
from sklearn.metrics.pairwise import cosine_similarity

from encoder_models import get_encoder
from type_aliases import DEVICE_TYPE, PREDICTION_TYPE, REFERENCE_TYPE
from utils import is_nested_list_of_type, Scores, slice_embeddings, flatten_list, get_gpu

_CITATION = """\
@inproceedings{bansal-etal-2022-sem,
    title = "{SEM}-F1: an Automatic Way for Semantic Evaluation of Multi-Narrative Overlap Summaries at Scale",
    author = "Bansal, Naman  and
      Akter, Mousumi  and
      Karmaker Santu, Shubhra Kanti",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.49",
    doi = "10.18653/v1/2022.emnlp-main.49",
    pages = "780--792",
    abstract = "Recent work has introduced an important yet relatively under-explored NLP task called Semantic Overlap Summarization (SOS) that entails generating a summary from multiple alternative narratives which conveys the common information provided by those narratives. Previous work also published a benchmark dataset for this task by collecting 2,925 alternative narrative pairs from the web and manually annotating 411 different reference summaries by engaging human annotators. In this paper, we exclusively focus on the automated evaluation of the SOS task using the benchmark dataset. More specifically, we first use the popular ROUGE metric from text-summarization literature and conduct a systematic study to evaluate the SOS task. Our experiments discover that ROUGE is not suitable for this novel task and therefore, we propose a new sentence-level precision-recall style automated evaluation metric, called SEM-F1 (Semantic F1). It is inspired by the benefits of the sentence-wise annotation technique using overlap labels reported by the previous work. Our experiments show that the proposed SEM-F1 metric yields a higher correlation with human judgment and higher inter-rater agreement compared to the ROUGE metric.",
}
"""

_DESCRIPTION = """\
Sem-F1 metric leverages the pre-trained contextual embeddings and evaluates the model generated 
semantic overlap summary with the reference overlap summary. It evaluates the semantic overlap summary at the 
sentence level and computes precision, recall and F1 scores.
"""

_KWARGS_DESCRIPTION = """
Sem-F1 compares the system-generated summaries (predictions) with ground truth reference summaries (references) 
using precision, recall, and F1 score based on sentence embeddings.

Args:
    predictions (list): List of predictions. Format varies based on `tokenize_sentences` and `multi_references` flags.
    references (list): List of references. Format varies based on `tokenize_sentences` and `multi_references` flags.
    model_type (str): Model to use for encoding sentences. Options: ['pv1', 'stsb', 'use']
        pv1 - paraphrase-distilroberta-base-v1 (Default)
        stsb - stsb-roberta-large
        use - Universal Sentence Encoder
    tokenize_sentences (bool): Flag to indicate whether to tokenize the sentences in the input documents. Default: True.
    multi_references (bool): Flag to indicate whether multiple references are provided. Default is False.
    gpu (Union[bool, str, int, List[Union[str, int]]]): Whether to use GPU or CPU for computation.
        bool - 
            False - CPU (Default)
            True - GPU (device 0) if gpu is available else CPU
        int - 
            n - GPU, device index n
        str - 
            'cuda', 'gpu', 'cpu' 
        List[Union[str, int]] - Multiple GPUs/cpus i.e. use multiple processes when computing embeddings
    batch_size (int): Batch size for encoding. Default is 32.
    verbose (bool): Flag to indicate verbose output. Default is False.

Returns:
    List of Scores dataclass with attributes as follows - 
        precision: float - precision score
        recall: List[float] - List of recall scores corresponding to single/multiple references
        f1: float - F1 score (between precision and average recall) 

Examples of input formats:

Case 1: multi_references = False, tokenize_sentences = False
    predictions: List[List[str]] - List of predictions where each prediction is a list of sentences.
    references: List[List[str]] - List of references where each reference is a list of sentences.
    Example:
        predictions = [["This is a prediction sentence 1.", "This is a prediction sentence 2."]]
        references = [["This is a reference sentence 1.", "This is a reference sentence 2."]]

Case 2: multi_references = False, tokenize_sentences = True
    predictions: List[str] - List of predictions where each prediction is a document.
    references: List[str] - List of references where each reference is a document.
    Example:
        predictions = ["This is a prediction sentence 1. This is a prediction sentence 2."]
        references = ["This is a reference sentence 1. This is a reference sentence 2."]

Case 3: multi_references = True, tokenize_sentences = False
    predictions: List[List[str]] - List of predictions where each prediction is a list of sentences.
    references: List[List[List[str]]] - List of references where each example has multi-references (List[r1, r2, ...]) 
        and each ri is a List of sentences.
    Example:
        predictions = [["Prediction sentence 1.", "Prediction sentence 2."]]
        references = [
            [
                ["Reference sentence 1.", "Reference sentence 2."],  # Reference 1 
                ["Alternative reference 1.", "Alternative reference 2."],  # Reference 2
            ]
        ]

Case 4: multi_references = True, tokenize_sentences = True
    predictions: List[str] - List of predictions where each prediction is a document.
    references: List[List[str]] - List of references where each example has multi-references (List[r1, r2, ...]) where 
    each r1 is a document.
    Example:
        predictions = ["Prediction sentence 1. Prediction sentence 2."]
        references = [
            [
                "Reference sentence 1. Reference sentence 2.",  # Reference 1 
                "Alternative reference 1. Alternative reference 2.",  # Reference 2
            ]
        ]

Examples:

    >>> import evaluate
    >>> predictions = [
        ["I go to School. You are stupid."],
        ["I love adventure sports."],
    ]
    >>> references = [
        ["I go to School. You are stupid."],
        ["I love outdoor sports."],
    ]
    >>> metric = evaluate.load("semf1")
    >>> results = metric.compute(predictions=predictions, references=references)
    >>> for score in results:
    >>>     print(f"Precision: {score.precision}, Recall: {score.recall}, F1: {score.f1}")
"""


def _compute_cosine_similarity(pred_embeds: NDArray, ref_embeds: NDArray) -> Tuple[float, float]:
    """
        Compute precision and recall based on cosine similarity between predicted and reference embeddings.

        Args:
            pred_embeds (NDArray): Predicted embeddings (shape: [num_pred, embedding_dim]).
            ref_embeds (NDArray): Reference embeddings (shape: [num_ref, embedding_dim]).

        Returns:
            Tuple[float, float]: Precision and recall based on cosine similarity scores.
                Precision: Average maximum cosine similarity score per predicted embedding.
                Recall: Average maximum cosine similarity score per reference embedding.
        """
    # Compute cosine similarity between predicted and reference embeddings
    cosine_scores = cosine_similarity(pred_embeds, ref_embeds)

    # Compute precision per predicted embedding
    precision_per_sentence_sim = np.max(cosine_scores, axis=-1)

    # Compute recall per reference embedding
    recall_per_sentence_sim = np.max(cosine_scores, axis=0)

    # Calculate mean precision and recall scores
    precision = np.mean(precision_per_sentence_sim).item()
    recall = np.mean(recall_per_sentence_sim).item()

    return precision, recall


def _validate_input_format(
        tokenize_sentences: bool,
        multi_references: bool,
        predictions: PREDICTION_TYPE,
        references: REFERENCE_TYPE,
):
    """
        Validate the format of predictions and references based on specified criteria.

        Args:
        - tokenize_sentences (bool): Flag indicating whether sentences should be tokenized.
        - multi_references (bool): Flag indicating whether multiple references are provided.
        - predictions (PREDICTION_TYPE): Predictions to validate.
        - references (REFERENCE_TYPE): References to validate.

        Raises:
        - ValueError: If the format of predictions or references does not meet the specified criteria.

        Validation Criteria:
        The function validates predictions and references based on the following conditions:
        1. If `tokenize_sentences` is True and `multi_references` is True:
           - Predictions must be a list of strings (`is_list_of_strings_at_depth(predictions, 1)`).
           - References must be a list of list of strings (`is_list_of_strings_at_depth(references, 2)`).

        2. If `tokenize_sentences` is False and `multi_references` is True:
           - Predictions must be a list of list of strings (`is_list_of_strings_at_depth(predictions, 2)`).
           - References must be a list of list of list of strings (`is_list_of_strings_at_depth(references, 3)`).

        3. If `tokenize_sentences` is True and `multi_references` is False:
           - Predictions must be a list of strings (`is_list_of_strings_at_depth(predictions, 1)`).
           - References must be a list of strings (`is_list_of_strings_at_depth(references, 1)`).

        4. If `tokenize_sentences` is False and `multi_references` is False:
           - Predictions must be a list of list of strings (`is_list_of_strings_at_depth(predictions, 2)`).
           - References must be a list of list of strings (`is_list_of_strings_at_depth(references, 2)`).

        The function checks these conditions and raises a ValueError if any condition is not met,
        indicating that predictions or references are not in the valid input format.

        Note:
        - `PREDICTION_TYPE` and `REFERENCE_TYPE` are defined at the top of the file
    """

    if len(predictions) != len(references):
        raise ValueError("Predictions and references must have the same length.")

    def is_list_of_strings_at_depth(lst_obj, depth: int):
        return is_nested_list_of_type(lst_obj, element_type=str, depth=depth)

    if tokenize_sentences and multi_references:
        condition = is_list_of_strings_at_depth(predictions, 1) and is_list_of_strings_at_depth(references, 2)
    elif not tokenize_sentences and multi_references:
        condition = is_list_of_strings_at_depth(predictions, 2) and is_list_of_strings_at_depth(references, 3)
    elif tokenize_sentences and not multi_references:
        condition = is_list_of_strings_at_depth(predictions, 1) and is_list_of_strings_at_depth(references, 1)
    else:
        condition = is_list_of_strings_at_depth(predictions, 2) and is_list_of_strings_at_depth(references, 2)

    if not condition:
        raise ValueError("Predictions are references are not valid input format. Refer to documentation.")


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class SemF1(evaluate.Metric):
    _MODEL_TYPE_TO_NAME = {
        "pv1": "paraphrase-distilroberta-base-v1",
        "stsb": "stsb-roberta-large",
        "use": "use",  # "sentence-transformers/use-cmlm-multilingual",  # TODO: check PyTorch USE VS TF USE
    }

    def _info(self):
        return evaluate.MetricInfo(
            # This is the description that will appear on the modules page.
            module_type="metric",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            features=[
                # F0: Multi References: False, Tokenize_Sentences = False
                datasets.Features(
                    {
                        # predictions: List[List[str]] - List of predictions where prediction is a list of sentences
                        "predictions": datasets.Sequence(datasets.Value("string", id="sequence"), id="predictions"),
                        # references: List[List[str]] - List of references where each reference is a list of sentences
                        "references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"),
                    }
                ),
                # F1: Multi References: False, Tokenize_Sentences = True
                datasets.Features(
                    {
                        # predictions: List[str] - List of predictions
                        "predictions": datasets.Value("string", id="sequence"),
                        # references: List[str] - List of documents
                        "references": datasets.Value("string", id="sequence"),
                    }
                ),
                # F2: Multi References: True, Tokenize_Sentences = False
                datasets.Features(
                    {
                        # predictions: List[List[str]] - List of predictions where prediction is a list of sentences
                        "predictions": datasets.Sequence(datasets.Value("string", id="sequence"), id="predictions"),
                        # references: List[List[List[str]]] - List of multi-references.
                        #                                     So each "reference" is also a list (r1, r2, ...).
                        #                                     Further, each ri's are also list of sentences.
                        "references": datasets.Sequence(
                            datasets.Sequence(datasets.Value("string", id="sequence"), id="ref"), id="references"),
                    }
                ),
                # F3: Multi References: True, Tokenize_Sentences = True
                datasets.Features(
                    {
                        # predictions: List[str] - List of predictions
                        "predictions": datasets.Value("string", id="sequence"),
                        # references: List[List[List[str]]] - List of multi-references.
                        #                                     So each "reference" is also a list (r1, r2, ...).
                        "references": datasets.Sequence(datasets.Value("string", id="ref"), id="references"),
                    }
                ),
            ],
            # # Homepage of the module for documentation
            # Additional links to the codebase or references
            reference_urls=["https://aclanthology.org/2022.emnlp-main.49/"]
        )

    def _get_model_name(self, model_type: Optional[str] = None) -> str:
        if model_type is None:
            model_type = "use"

        if model_type not in self._MODEL_TYPE_TO_NAME.keys():
            raise ValueError(f"Provide a correct model_type.\n"
                             f"Options: {self._MODEL_TYPE_TO_NAME.keys()}\n"
                             f"Currently provided: {model_type}")

        return self._MODEL_TYPE_TO_NAME[model_type]

    def _download_and_prepare(self, dl_manager):
        """Optional: download external resources useful to compute the scores"""
        import nltk
        nltk.download("punkt", quiet=True)
        # if not nltk.data.find("tokenizers/punkt"):  # TODO: check why it is not working
        #     pass

    def _compute(
            self,
            predictions,
            references,
            model_type: Optional[str] = None,
            tokenize_sentences: bool = True,
            multi_references: bool = False,
            gpu: DEVICE_TYPE = False,
            batch_size: int = 32,
            verbose: bool = False,
    ) -> List[Scores]:
        """
            Compute precision, recall, and F1 scores for given predictions and references.

            :param predictions
            :param references
            :param model_type: Type of model to use for encoding.
                Options: [pv1, stsb, use]
                    pv1 - paraphrase-distilroberta-base-v1 (Default)
                    stsb - stsb-roberta-large
                    use - Universal Sentence Encoder
            :param tokenize_sentences: Flag to sentence tokenize the document.
            :param multi_references: Flag to indicate multiple references.
            :param gpu: GPU device to use.
            :param batch_size: Batch size for encoding.
            :param verbose: Flag to indicate verbose output.

            :return: List of Scores dataclass with precision, recall, and F1 scores.
        """

        # Note: I have to specifically handle this case because the library considers the feature corresponding to
        #  this case (F2) as the feature for the other case (F0) i.e. it can't make any distinction between
        #  List[str] and List[List[str]]
        if not tokenize_sentences and multi_references:
            references = [[eval(ref) for ref in mul_ref_ex] for mul_ref_ex in references]

        # Validate inputs corresponding to flags
        _validate_input_format(tokenize_sentences, multi_references, predictions, references)

        # Get GPU
        device = get_gpu(gpu)
        if verbose:
            print(f"Using devices: {device}")

        # Get the encoder model
        model_name = self._get_model_name(model_type)
        encoder = get_encoder(model_name, device=device, batch_size=batch_size, verbose=verbose)

        # We'll handle the single reference and multi-reference case same way. So change the data format accordingly
        if not multi_references:
            references = [[ref] for ref in references]

        # Tokenize sentences if required
        if tokenize_sentences:
            predictions = [nltk.tokenize.sent_tokenize(pred) for pred in predictions]
            references = [[nltk.tokenize.sent_tokenize(ref) for ref in refs] for refs in references]

        # Flatten the data for batch processing
        all_sentences = flatten_list(predictions) + flatten_list(references)

        # Get num of sentences to get the corresponding embeddings
        prediction_sentences_count = [len(pred) for pred in predictions]
        reference_sentences_count = [[len(ref) for ref in refs] for refs in references]

        # Note: This is the most optimal way of doing it
        # Encode all sentences in one go
        embeddings = encoder.encode(all_sentences)

        # Get embeddings corresponding to predictions and references
        pred_embeddings = slice_embeddings(embeddings, prediction_sentences_count)
        ref_embeddings = slice_embeddings(embeddings[sum(prediction_sentences_count):], reference_sentences_count)

        # Init output scores
        results = []

        # Compute scores
        for preds, refs in zip(pred_embeddings, ref_embeddings):
            # Precision: Concatenate all the sentences in all the references
            concat_refs = np.concatenate(refs, axis=0)
            precision, _ = _compute_cosine_similarity(preds, concat_refs)
            precision = np.clip(precision, a_min=0.0, a_max=1.0).item()

            # Recall: Compute individually for each reference
            recall_scores = [_compute_cosine_similarity(r_embeds, preds) for r_embeds in refs]
            recall_scores = [np.clip(r_scores, 0.0, 1.0).item() for (r_scores, _) in recall_scores]

            results.append(Scores(precision, recall_scores))

        return results