seth.py

# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""

import re

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@Article{SETH2016,
  Title= {SETH detects and normalizes genetic variants in text.},
  Author= {Thomas, Philippe and Rockt{\"{a}}schel, Tim and Hakenberg, J{\"{o}}rg and Lichtblau, Yvonne and Leser, Ulf},
  Journal= {Bioinformatics},
  Year= {2016},
  Month= {Jun},
  Doi= {10.1093/bioinformatics/btw234},  
  Language = {eng},
  Medline-pst = {aheadofprint},
  Pmid = {27256315},
  Url = {http://dx.doi.org/10.1093/bioinformatics/btw234} Titel anhand dieser DOI in Citavi-Projekt übernehmen
}
"""

_DESCRIPTION = """\
This Dataset is used to for the Advanced Machine Learning and XAI course of the DHBW CAS in Heilbronn
"""



class SethConfig(datasets.BuilderConfig):
    """BuilderConfig for Seth Dataset"""

    def __init__(self, **kwargs):
        """BuilderConfig for Seth.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(SethConfig, self).__init__(**kwargs)


class Seth(datasets.GeneratorBasedBuilder):
    """Seth dataset."""

    BUILDER_CONFIGS = [
        SethConfig(name="Seth", version=datasets.Version("1.0.0"), description="Seth dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("int32"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "labels": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-Gene",
                                "B-SNP",
                                "I-SNP",
                                "I-Gene",
                                "B-RS",
                                "I-RS"
                            ]
                        )
                    )
                }
            ),
            supervised_keys=None,
            homepage="https://rockt.github.io/SETH/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_file_train = dl_manager.download("https://raw.githubusercontent.com/Erechtheus/mutationCorpora/master/corpora/IOB/SETH-train.iob")
        downloaded_file_test = dl_manager.download("https://raw.githubusercontent.com/Erechtheus/mutationCorpora/master/corpora/IOB/SETH-test.iob")
        data_files = {
            "train": "./SETH-train.iob",
            "test": "./SETH-test.iob",
        }

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file_train}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_file_test}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            document = {"id":None,
            "tokens":[],
            "labels":[]
            }
            documents = [] # Wird befüllt mit den Documented aus der Datei. Besteht aus einem Key "tokens" und "labels"
            pattern = r"#\d+" # Reg Experassion um eine Documented ID zu detektieren
            for idx, line in enumerate(f):
                match = re.match(pattern, line)
        #Überspringe erste Zeile weil das ein Header ist
                if idx == 0:
                    continue
                # Here ist die Dokumenten ID
                if match:
                    if document["id"] != None:
                        # Speichere altes Dokument bevor ein neues angelegt wird
                        documents.append(document)
                        yield guid,document
                        guid+=1
                        document = {"id":int(line[1:]), # Speichere nur die Nummer ohne die Raute
                                    "tokens":[],
                                    "labels":[]
                                    }
                    else:
                        #Initialisiere neues DOkument
                        document = {"id":int(line[1:]), # Speichere nur die Nummer ohne die Raute
                                    "tokens":[],
                                    "labels":[]
                                    }
                # Hier handeln wir die Sonderfälle ab
                elif len(line.split(",")) >2:
                    # Sonderfall 1: ,,Label
                    if(line.split(",")[0] == "" and line.split(",")[1]==""):
                        document["tokens"].append(",")
                        document["labels"].append(line.split(",")[2].split("\n")[0])
                    # Sonderfall 2:Text,Text,Test,Label -> Label steht immer am schluss
                    else:
                        document["tokens"].append(",".join(line.split(",")[0:-1])) # Bringe die Splits wieder zusammen ohne das Label
                        document["labels"].append(line.split(",")[-1].split("\n")[0])     
                # Sonst gehen wir einfach vom standard aus Word sowie Tag
                else:
                    word_tag = line.split(",")
                # Hier erkennen wir den Ende eines Satzes dieser besteht aus " , "
                    if word_tag[0] == " " and word_tag[1] == " \n":
                        continue
                    document["tokens"].append(word_tag[0])
                    document["labels"].append(word_tag[1].split("\n")[0])
            documents.append(document)
            yield guid,document