feat: Add main file

nucleotide_transformer_downstream_tasks_v2.py

@@ -0,0 +1,166 @@
+# 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
+
+
+# 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):
+        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