feat: Commit the configurations file

nucleotide_transformer_downstream_tasks.py

@@ -0,0 +1,149 @@
+# 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 all the downstream tasks from the Nucleotide
+Transformer paper. There are 18 downstream tasks."""
+
+from typing import List
+import datasets
+from Bio import SeqIO
+
+# 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 = """\
+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):
+    """Genomes from 850 species, filtered and split into chunks of consecutive
+    nucleotides. 50 genomes are taken for test, 50 for validation and 800
+    for training."""
+
+    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):
+        key = 0
+        with open(file, 'rt') as f:
+            fasta_sequences = SeqIO.parse(f, 'fasta')
+
+            for record in fasta_sequences:
+
+                # parse descriptions in the fasta file
+                sequence, name = str(record.seq), str(record.name)
+                label = int(name.split("|")[-1])
+
+                # yield example
+                yield key, {
+                    'sequence': sequence,
+                    'name': name,
+                    'label': label,
+                }
+                key += 1