Create multi_sp.py

multi_sp.py

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+# 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 multi-species genomes dataset. This dataset contains the genomes
+from 850 different species."""
+
+from typing import List
+import datasets
+import pandas as pd
+from Bio import SeqIO
+import random
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{o2016reference,
+  title={Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation},
+  author={O'Leary, Nuala A and Wright, Mathew W and Brister, J Rodney and Ciufo, Stacy and Haddad, Diana and McVeigh, Rich and Rajput, Bhanu and Robbertse, Barbara and Smith-White, Brian and Ako-Adjei, Danso and others},
+  journal={Nucleic acids research},
+  volume={44},
+  number={D1},
+  pages={D733--D745},
+  year={2016},
+  publisher={Oxford University Press}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+Dataset made of diverse genomes available on NCBI.
+"""
+
+_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/"
+
+_LICENSE = "https://www.ncbi.nlm.nih.gov/home/about/policies/"
+
+_CHUNK_LENGTHS = [6000, 12000]
+
+
+def filter_fn(char: str) -> str:
+    """
+    Transforms any letter different from a base nucleotide into an 'N'.
+    """
+    if char in {'A', 'T', 'C', 'G'}:
+        return char
+    else:
+        return 'N'
+
+
+def clean_sequence(seq: str) -> str:
+    """
+    Process a chunk of DNA to have all letters in upper and restricted to
+    A, T, C, G and N.
+    """
+    seq = seq.upper()
+    seq = map(filter_fn, seq)
+    seq = ''.join(list(seq))
+    return seq
+
+
+class MultiSpeciesGenomesConfig(datasets.BuilderConfig):
+    """BuilderConfig for Genome Reads."""
+
+    def __init__(self, *args, chunk_length: int, overlap: int = 100, **kwargs):
+        """BuilderConfig for the multi species genomes.
+        Args:
+            chunk_length (:obj:`int`): Chunk length.
+            overlap: (:obj:`int`): Overlap in base pairs for two consecutive chunks (defaults to 100).
+            **kwargs: keyword arguments forwarded to super.
+        """
+        num_kbp = int(chunk_length/1000)
+        super().__init__(
+            *args,
+            name=f'{num_kbp}kbp',
+            **kwargs,
+        )
+        self.chunk_length = chunk_length
+        self.overlap = overlap
+
+
+class MultiSpeciesGenomes(datasets.GeneratorBasedBuilder):
+    """Genomes from all species listed in the urls.txt, filtered and split into chunks of consecutive
+    nucleotides. first splits are train, then validation, then test. the url.txt is splitter by empty lines."""
+
+    VERSION = datasets.Version("0.0.1")
+    BUILDER_CONFIG_CLASS = MultiSpeciesGenomesConfig
+    BUILDER_CONFIGS = [MultiSpeciesGenomesConfig(chunk_length=chunk_length) for chunk_length in _CHUNK_LENGTHS]
+    DEFAULT_CONFIG_NAME = "6kbp"
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "sequence": datasets.Value("string"),
+                "description": datasets.Value("string"),
+                "start_pos": datasets.Value("int32"),
+                "end_pos": datasets.Value("int32"),
+                "fasta_url": datasets.Value("string")
+            }
+        )
+        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]:
+
+        urls_filepath = dl_manager.download_and_extract('urls.txt')
+        train_urls, test_urls, validation_urls = [], [], []
+        
+        # all the line befor the first empy line are the train urls
+        with open(urls_filepath) as urls_file:
+            urls = [line.rstrip() for line in urls_file]
+            split = 0
+            for url in urls:
+                if url == '':
+                    split += 1
+                    continue
+                if split == 0:
+                    train_urls.append(url)
+                elif split == 1:
+                    validation_urls.append(url)
+                else:
+                    test_urls.append(url)
+        random.seed(42)
+        random.shuffle(train_urls)
+
+        train_downloaded_files = dl_manager.download_and_extract(train_urls)
+        test_downloaded_files = dl_manager.download_and_extract(test_urls)
+        validation_downloaded_files = dl_manager.download_and_extract(validation_urls)
+
+        return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": train_downloaded_files, "chunk_length": self.config.chunk_length, "split": "train"}),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"files": validation_downloaded_files, "chunk_length": self.config.chunk_length, "split": "validation"}),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"files": test_downloaded_files, "chunk_length": self.config.chunk_length, "split": "test"}),
+            ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, files, chunk_length, split):
+        key = 0
+        for file in files:
+            with open(file, 'rt') as f:
+                fasta_sequences = SeqIO.parse(f, 'fasta')
+                try:
+                    for record in fasta_sequences:
+
+                        # parse descriptions in the fasta file
+                        sequence, description = str(record.seq), record.description
+
+                        # clean chromosome sequence
+                        sequence = clean_sequence(sequence)
+                        seq_length = len(sequence)
+
+                        # split into chunks
+                        num_chunks = (seq_length - 2 * self.config.overlap) // chunk_length
+
+                        if num_chunks < 1:
+                            continue
+
+                        sequence = sequence[:(chunk_length * num_chunks + 2 * self.config.overlap)]
+                        seq_length = len(sequence)
+                        num_chunks = list(range(num_chunks))
+                        if split == 'validation':
+                            random.seed(42)
+                            random.shuffle(num_chunks)
+                            n_samples = int(len(num_chunks)*0.2)
+                            num_chunks = num_chunks[:n_samples]
+                        for i in num_chunks:
+                            # get chunk
+                            start_pos = i * chunk_length
+                            end_pos = min(seq_length, (i+1) * chunk_length + 2 * self.config.overlap)
+                            chunk_sequence = sequence[start_pos:end_pos]
+
+                            # yield chunk
+                            yield key, {
+                                'sequence': chunk_sequence,
+                                'description': description,
+                                'start_pos': start_pos,
+                                'end_pos': end_pos,
+                                'fasta_url': file.split('::')[-1]
+                            }
+                            key += 1
+                except Exception as e:
+                    print(f"Error while processing {file}: {e}")
+                    continue