docred.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 os

import datasets
import evaluate

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

    dataset_feat = {
        "title": datasets.Value("string"),
        # "sents": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
        "vertexSet": datasets.Sequence(
            datasets.Sequence(
                {
                    "name": datasets.Value("string"),
                    "sent_id": datasets.Value("int32"),
                    "pos": datasets.Sequence(datasets.Value("int32"), length=2),
                    "type": datasets.Value("string"),
                }
            )
        ),
        "labels": {
            "head": datasets.Sequence(datasets.Value("int32")),
            "tail": datasets.Sequence(datasets.Value("int32")),
            "relation_id": datasets.Sequence(datasets.Value("string")),
            "evidence": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))),
        },
    }
    eps = 1e-12

    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": self.dataset_feat, "references": self.dataset_feat}),
            # 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 _generate_fact(self, dataset):
        if dataset is None:
            return set()
        facts = set()
        for data in dataset:
            vertexSet = data["vertexSet"]
            labels = self._convert_labels_to_list(data["labels"])
            for label in labels:
                rel = label["relation_id"]
                for n1 in vertexSet[label["head"]]:
                    for n2 in vertexSet[label["tail"]]:
                        facts.add((n1["name"], n2["name"], rel))
        return facts

    def _convert_to_relation_set(self, data):
        relation_set = set()
        for d in data:
            labels = d["labels"]
            labels = self._convert_labels_to_list(labels)
            for label in labels:
                relation_set.add((d["title"], label["head"], label["tail"], label["relation_id"]))
        return relation_set

    def _convert_labels_to_list(self, labels):
        keys = list(labels.keys())
        labels = [{key: labels[key][i] for key in keys} for i in range(len(labels[keys[0]]))]
        return labels

    def _compute(self, predictions, references, train_data=None):
        """Returns the scores"""

        fact_in_train_annotated = self._generate_fact(train_data)

        std = {}
        tot_evidences = 0
        ref_titleset = set([])

        title2vectexSet = {}

        for x in references:
            title = x["title"]
            ref_titleset.add(title)

            vertexSet = x["vertexSet"]
            title2vectexSet[title] = vertexSet
            labels = self._convert_labels_to_list(x["labels"])
            for label in labels:
                r = label["relation_id"]
                h_idx = label["head"]
                t_idx = label["tail"]
                std[(title, r, h_idx, t_idx)] = set(label["evidence"])
                tot_evidences += len(label["evidence"])

        tot_relations = len(std)
        pred_rel = self._convert_to_relation_set(predictions)
        submission_answer = sorted(pred_rel, key=lambda x: (x[0], x[1], x[2], x[3]))

        correct_re = 0
        correct_evidence = 0
        pred_evi = 0

        correct_in_train_annotated = 0
        titleset2 = set([])
        for x in submission_answer:
            title, h_idx, t_idx, r = x
            titleset2.add(title)
            if title not in title2vectexSet:
                continue
            vertexSet = title2vectexSet[title]

            if "evidence" in x:
                evi = set(x["evidence"])
            else:
                evi = set([])
            pred_evi += len(evi)

            if (title, r, h_idx, t_idx) in std:
                correct_re += 1
                stdevi = std[(title, r, h_idx, t_idx)]
                correct_evidence += len(stdevi & evi)
                in_train_annotated = in_train_distant = False
                for n1 in vertexSet[h_idx]["name"]:
                    for n2 in vertexSet[t_idx]["name"]:
                        if (n1, n2, r) in fact_in_train_annotated:
                            in_train_annotated = True

                if in_train_annotated:
                    correct_in_train_annotated += 1
                # if in_train_distant:
                #     correct_in_train_distant += 1

        re_p = 1.0 * correct_re / (len(submission_answer) + self.eps)
        re_r = 1.0 * correct_re / (tot_relations + self.eps)
        if re_p + re_r == 0:
            re_f1 = 0
        else:
            re_f1 = 2.0 * re_p * re_r / (re_p + re_r)

        evi_p = 1.0 * correct_evidence / pred_evi if pred_evi > 0 else 0
        evi_r = 1.0 * correct_evidence / (tot_evidences + self.eps)
        if evi_p + evi_r == 0:
            evi_f1 = 0
        else:
            evi_f1 = 2.0 * evi_p * evi_r / (evi_p + evi_r)

        re_p_ignore_train_annotated = (
            1.0
            * (correct_re - correct_in_train_annotated)
            / (len(submission_answer) - correct_in_train_annotated + self.eps)
        )
        # re_p_ignore_train = (
        #     1.0 * (correct_re - correct_in_train_distant) / (len(submission_answer) - correct_in_train_distant + self.eps)
        # )

        if re_p_ignore_train_annotated + re_r == 0:
            re_f1_ignore_train_annotated = 0
        else:
            re_f1_ignore_train_annotated = (
                2.0 * re_p_ignore_train_annotated * re_r / (re_p_ignore_train_annotated + re_r)
            )

        # if re_p_ignore_train + re_r == 0:
        #     re_f1_ignore_train = 0
        # else:
        #     re_f1_ignore_train = 2.0 * re_p_ignore_train * re_r / (re_p_ignore_train + re_r)

        # return re_f1, evi_f1, re_f1_ignore_train_annotated, re_f1_ignore_train, re_p, re_r
        return {"f1": re_f1, "precision": re_p, "recall": re_r, "ign_f1": re_f1_ignore_train_annotated}