Datasets:

InstaDeepAI
/

nucleotide_transformer_downstream_tasks

@@ -1,181 +0,0 @@
-# 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.
-"""Script for the dataset containing the 18 downstream tasks from the Nucleotide
-Transformer paper."""
-from typing import List
-import datasets
-from datasets.utils import logging
-datasets.logging.set_verbosity(datasets.logging.WARNING)
-logger = logging.get_logger("datasets")
-# This function is a basic reimplementation of SeqIO's parse method. This allows the
-# dataset viewer to work as it does not require an external package.
-def parse_fasta(fp):
-    name, seq = None, []
-    for line in fp:
-        line = line.rstrip()
-        if line.startswith(">"):
-            if name:
-                # Slice to remove '>'
-                yield (name[1:], "".join(seq))
-            name, seq = line, []
-        else:
-            seq.append(line)
-    if name:
-        # Slice to remove '>'
-        yield (name[1:], "".join(seq))
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@article{dalla2023nucleotide,
-  title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics},
-  author={Dalla-Torre, Hugo and Gonzalez, Liam and Mendoza-Revilla, Javier and Carranza, Nicolas Lopez and Grzywaczewski, Adam Henryk and Oteri, Francesco and Dallago, Christian and Trop, Evan and Sirelkhatim, Hassan and Richard, Guillaume and others},
-  journal={bioRxiv},
-  pages={2023--01},
-  year={2023},
-  publisher={Cold Spring Harbor Laboratory}
-}
-"""
-# You can copy an official description
-_DESCRIPTION = """\
-The 18 classification downstream tasks from the Nucleotide Transformer paper. Each task
-corresponds to a dataset configuration.
-"""
-_HOMEPAGE = "https://github.com/instadeepai/nucleotide-transformer"
-_LICENSE = "https://github.com/instadeepai/nucleotide-transformer/LICENSE.md"
-_TASKS = [
-    "H4ac",
-    "H3K36me3",
-    "splice_sites_donors",
-    "splice_sites_acceptors",
-    "H3",
-    "H4",
-    "H3K4me3",
-    "splice_sites_all",
-    "H3K4me1",
-    "H3K14ac",
-    "enhancers_types",
-    "promoter_no_tata",
-    "H3K79me3",
-    "H3K4me2",
-    "promoter_tata",
-    "enhancers",
-    "H3K9ac",
-    "promoter_all",
-]
-class NucleotideTransformerDownstreamTasksConfig(datasets.BuilderConfig):
-    """BuilderConfig for The Nucleotide Transformer downstream taks dataset."""
-    def __init__(self, *args, task: str, **kwargs):
-        """BuilderConfig downstream tasks dataset.
-        Args:
-            task (:obj:`str`): Task name.
-            **kwargs: keyword arguments forwarded to super.
-        """
-        super().__init__(
-            *args,
-            name=f"{task}",
-            **kwargs,
-        )
-        self.task = task
-class NucleotideTransformerDownstreamTasks(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.1.0")
-    BUILDER_CONFIG_CLASS = NucleotideTransformerDownstreamTasksConfig
-    BUILDER_CONFIGS = [
-        NucleotideTransformerDownstreamTasksConfig(task=task) for task in _TASKS
-    ]
-    DEFAULT_CONFIG_NAME = "enhancers"
-    def _info(self):
-        features = datasets.Features(
-            {
-                "sequence": datasets.Value("string"),
-                "name": datasets.Value("string"),
-                "label": datasets.Value("int32"),
-            }
-        )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-    def _split_generators(
-        self, dl_manager: datasets.DownloadManager
-    ) -> List[datasets.SplitGenerator]:
-        train_file = dl_manager.download_and_extract(self.config.task + "/train.fna")
-        test_file = dl_manager.download_and_extract(self.config.task + "/test.fna")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"file": train_file}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"file": test_file}
-            ),
-        ]
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, file):
-        logger.warning("""
-        WARNING: Please note that the Nucleotide Transformer benchmark datasets have been revised
-        during the per-review process. This version is deprecated and the new datasets are available at
-        InstaDeepAI/nucleotide_transformer_downstream_tasks_revised.
-              """)
-        key = 0
-        with open(file, "rt") as f:
-            fasta_sequences = parse_fasta(f)
-            for name, seq in fasta_sequences:
-                # parse descriptions in the fasta file
-                sequence, name = str(seq), str(name)
-                label = int(name.split("|")[-1])
-                # yield example
-                yield key, {
-                    "sequence": sequence,
-                    "name": name,
-                    "label": label,
-                }
-                key += 1