relation_extraction.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.
"""TODO: Add a description here."""

import evaluate
import datasets
import numpy as np


# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""

# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is designed to solve this great ML task and is crafted with a lot of care.
"""


# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of predictions to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Returns:
    accuracy: description of the first score,
    another_score: description of the second score,
Examples:
    Examples should be written in doctest format, and should illustrate how
    to use the function.

    >>> my_new_module = evaluate.load("my_new_module")
    >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
    >>> print(results)
    {'accuracy': 1.0}
"""

# 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 relation_extraction(evaluate.Metric):
    """TODO: Short description of my evaluation module."""

    def _info(self):
        # TODO: Specifies the evaluate.EvaluationModuleInfo object
        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=datasets.Features({
                'predictions': datasets.Sequence(datasets.Sequence(datasets.Features({
                    "head": datasets.Value("string"),
                    "head_type": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "tail": datasets.Value("string"),
                    "tail_type": datasets.Value("string"),
                }))),
                'references': datasets.Sequence(datasets.Sequence(datasets.Features({
                    "head": datasets.Value("string"),
                    "head_type": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "tail": datasets.Value("string"),
                    "tail_type": datasets.Value("string"),
                }))),
            }),
            # Homepage of the module for documentation
            homepage="http://module.homepage",
            # Additional links to the codebase or references
            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
            reference_urls=["http://path.to.reference.url/new_module"]
        )

    def _download_and_prepare(self, dl_manager):
        """Optional: download external resources useful to compute the scores"""
        # TODO: Download external resources if needed
        pass

    def _compute(self, predictions, references, mode="strict", relation_types=[]):
        """Returns the scores"""
        # TODO: Compute the different scores of the module

        assert mode in ["strict", "boundaries"]

        # construct relation_types from ground truth if not given
        if len(relation_types) == 0:
            for triplets in references:
                for triplet in triplets:
                    relation = triplet["type"]
                    if relation not in relation_types:
                        relation_types.append(relation)

        scores = {rel: {"tp": 0, "fp": 0, "fn": 0} for rel in relation_types + ["ALL"]}

        # Count GT relations and Predicted relations
        n_sents = len(references)
        n_rels = sum([len([rel for rel in sent]) for sent in references])
        n_found = sum([len([rel for rel in sent]) for sent in predictions])

        # Count TP, FP and FN per type
        for pred_sent, gt_sent in zip(predictions, references):
            for rel_type in relation_types:
                # strict mode takes argument types into account
                if mode == "strict":
                    pred_rels = {(rel["head"], rel["head_type"], rel["tail"], rel["tail_type"]) for rel in pred_sent if
                                rel["type"] == rel_type}
                    gt_rels = {(rel["head"], rel["head_type"], rel["tail"], rel["tail_type"]) for rel in gt_sent if
                            rel["type"] == rel_type}

                # boundaries mode only takes argument spans into account
                elif mode == "boundaries":
                    pred_rels = {(rel["head"], rel["tail"]) for rel in pred_sent if rel["type"] == rel_type}
                    gt_rels = {(rel["head"], rel["tail"]) for rel in gt_sent if rel["type"] == rel_type}

                scores[rel_type]["tp"] += len(pred_rels & gt_rels)
                scores[rel_type]["fp"] += len(pred_rels - gt_rels)
                scores[rel_type]["fn"] += len(gt_rels - pred_rels)

        # Compute per entity Precision / Recall / F1
        for rel_type in scores.keys():
            if scores[rel_type]["tp"]:
                scores[rel_type]["p"] = 100 * scores[rel_type]["tp"] / (scores[rel_type]["fp"] + scores[rel_type]["tp"])
                scores[rel_type]["r"] = 100 * scores[rel_type]["tp"] / (scores[rel_type]["fn"] + scores[rel_type]["tp"])
            else:
                scores[rel_type]["p"], scores[rel_type]["r"] = 0, 0

            if not scores[rel_type]["p"] + scores[rel_type]["r"] == 0:
                scores[rel_type]["f1"] = 2 * scores[rel_type]["p"] * scores[rel_type]["r"] / (
                        scores[rel_type]["p"] + scores[rel_type]["r"])
            else:
                scores[rel_type]["f1"] = 0

        # Compute micro F1 Scores
        tp = sum([scores[rel_type]["tp"] for rel_type in relation_types])
        fp = sum([scores[rel_type]["fp"] for rel_type in relation_types])
        fn = sum([scores[rel_type]["fn"] for rel_type in relation_types])

        if tp:
            precision = 100 * tp / (tp + fp)
            recall = 100 * tp / (tp + fn)
            f1 = 2 * precision * recall / (precision + recall)

        else:
            precision, recall, f1 = 0, 0, 0

        scores["ALL"]["p"] = precision
        scores["ALL"]["r"] = recall
        scores["ALL"]["f1"] = f1
        scores["ALL"]["tp"] = tp
        scores["ALL"]["fp"] = fp
        scores["ALL"]["fn"] = fn

        # Compute Macro F1 Scores
        scores["ALL"]["Macro_f1"] = np.mean([scores[ent_type]["f1"] for ent_type in relation_types])
        scores["ALL"]["Macro_p"] = np.mean([scores[ent_type]["p"] for ent_type in relation_types])
        scores["ALL"]["Macro_r"] = np.mean([scores[ent_type]["r"] for ent_type in relation_types])

        return scores