ppb_affinity.py

import datasets
import csv
import random

class ppb_affinity(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.2")
    
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="raw", description="Raw parsed PDBs dataset with critical filtrations only."),
        datasets.BuilderConfig(name="filtered", description="Raw dataset with additional cleaning and train/val/test splits."),
    ]
    
    def _info(self):
        return datasets.DatasetInfo()

    def _split_generators(self, dl_manager):
        """Downloads and defines dataset splits"""
        filepath = dl_manager.download_and_extract("filtered.csv")
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": filepath, "split": "train"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": filepath, "split": "val"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": filepath, "split": "test"},
            ),

            datasets.SplitGenerator(
                name=datasets.NamedSplit("train_rand"),
                gen_kwargs={"filepath": filepath, "split": "train_rand", "random_split": True},
            ),
            datasets.SplitGenerator(
                name=datasets.NamedSplit("validation_rand"),
                gen_kwargs={"filepath": filepath, "split": "val_rand", "random_split": True},
            ),
            datasets.SplitGenerator(
                name=datasets.NamedSplit("test_rand"),
                gen_kwargs={"filepath": filepath, "split": "test_rand", "random_split": True},
            ),
        ]

    def _generate_examples(self, filepath, split=None, random_split=False):
        """Generates examples, either using predefined splits or random splits"""
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f)
            data = list(reader)

        if random_split:
            return self._generate_examples_rand(data, split)
        
        for idx, row in enumerate(data):
            if row["split"] == split:
                del row["split"]  # Remove split column from examples
                yield idx, row

    def _generate_examples_rand(self, data, split):
        """Randomly splits the dataset into 80% train, 10% val, 10% test with a fixed seed"""
        random.seed(42)
        random.shuffle(data)

        total = len(data)
        train_end = int(0.8 * total)
        val_end = train_end + int(0.1 * total)

        split_map = {
            "train_rand": data[:train_end],
            "val_rand": data[train_end:val_end],
            "test_rand": data[val_end:]
        }

        for idx, row in enumerate(split_map[split]):
            yield idx, row