import csv import datasets class DNABarcodeDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( description="DNA barcode dataset with hierarchical taxonomy labels and multiple splits.", features=datasets.Features({ "processid": datasets.Value("string"), "sampleid": datasets.Value("string"), "dna_bin": datasets.Value("string"), "phylum": datasets.Value("string"), "class": datasets.Value("string"), "order": datasets.Value("string"), "family": datasets.Value("string"), "genus": datasets.Value("string"), "species": datasets.Value("string"), # label "dna_barcode": datasets.Value("string"), # input data "split": datasets.ClassLabel(names=["train", "val", "test", "test_unseen", "pretrain"]), }), supervised_keys=("dna_barcode", "species"), # For model training ) def _split_generators(self, dl_manager): data_path = dl_manager.download("https://huggingface.co/datasets/bioscan-ml/CanadianInvertebrates-ML/resolve/main/CanInv_metadata.csv") # Use a URL or relative path return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_path, "split": "train"}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_path, "split": "val"}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_path, "split": "test"}), datasets.SplitGenerator(name="test_unseen", gen_kwargs={"filepath": data_path, "split": "test_unseen"}), datasets.SplitGenerator(name="pretrain", gen_kwargs={"filepath": data_path, "split": "pretrain"}), ] def _generate_examples(self, filepath, split): with open(filepath, encoding="utf-8") as f: reader = csv.DictReader(f) idx = 0 for row in reader: if row["split"] == split: yield idx, { "processid": row["processid"], "sampleid": row["sampleid"], "dna_bin": row["dna_bin"], "phylum": row["phylum"], "class": row["class"], "order": row["order"], "family": row["family"], "genus": row["genus"], "species": row["species"], "dna_barcode": row["dna_barcode"], "split": row["split"], } idx += 1