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Datasets:
mstz
/
promoters

Tasks:
Tabular Classification
Modalities:
Text
Languages:
English
Size:
< 1K
Tags:
promoters
tabular_classification
binary_classification
UCI
Libraries:
Datasets
License:
Dataset card Data Studio Files Community
promoters
File size: 4,753 Bytes 
5280ebb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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"""Promoters"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

DESCRIPTION = "Promoters dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Promoters"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Promoters")
_CITATION = """
@misc{misc_molecular_biology_(promoter_gene_sequences)_67,
  author       = {Harley,C., Reynolds,R. & Noordewier,M.},
  title        = {{Molecular Biology (Promoter Gene Sequences)}},
  year         = {1990},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5S01D}}
}"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/promoters/raw/main/promoters.data"
}
features_types_per_config = {
    "promoters": {
        "seq_0": datasets.Value("string"),
        "seq_1": datasets.Value("string"),
        "seq_2": datasets.Value("string"),
        "seq_3": datasets.Value("string"),
        "seq_4": datasets.Value("string"),
        "seq_5": datasets.Value("string"),
        "seq_6": datasets.Value("string"),
        "seq_7": datasets.Value("string"),
        "seq_8": datasets.Value("string"),
        "seq_9": datasets.Value("string"),
        "seq_10": datasets.Value("string"),
        "seq_11": datasets.Value("string"),
        "seq_12": datasets.Value("string"),
        "seq_13": datasets.Value("string"),
        "seq_14": datasets.Value("string"),
        "seq_15": datasets.Value("string"),
        "seq_16": datasets.Value("string"),
        "seq_17": datasets.Value("string"),
        "seq_18": datasets.Value("string"),
        "seq_19": datasets.Value("string"),
        "seq_20": datasets.Value("string"),
        "seq_21": datasets.Value("string"),
        "seq_22": datasets.Value("string"),
        "seq_23": datasets.Value("string"),
        "seq_24": datasets.Value("string"),
        "seq_25": datasets.Value("string"),
        "seq_26": datasets.Value("string"),
        "seq_27": datasets.Value("string"),
        "seq_28": datasets.Value("string"),
        "seq_29": datasets.Value("string"),
        "seq_30": datasets.Value("string"),
        "seq_31": datasets.Value("string"),
        "seq_32": datasets.Value("string"),
        "seq_33": datasets.Value("string"),
        "seq_34": datasets.Value("string"),
        "seq_35": datasets.Value("string"),
        "seq_36": datasets.Value("string"),
        "seq_37": datasets.Value("string"),
        "seq_38": datasets.Value("string"),
        "seq_39": datasets.Value("string"),
        "seq_40": datasets.Value("string"),
        "seq_41": datasets.Value("string"),
        "seq_42": datasets.Value("string"),
        "seq_43": datasets.Value("string"),
        "seq_44": datasets.Value("string"),
        "seq_45": datasets.Value("string"),
        "seq_46": datasets.Value("string"),
        "seq_47": datasets.Value("string"),
        "seq_48": datasets.Value("string"),
        "seq_49": datasets.Value("string"),
        "seq_50": datasets.Value("string"),
        "seq_51": datasets.Value("string"),
        "seq_52": datasets.Value("string"),
        "seq_53": datasets.Value("string"),
        "seq_54": datasets.Value("string"),
        "seq_55": datasets.Value("string"),
        "seq_56": datasets.Value("string"),
        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
    }
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


class PromotersConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(PromotersConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Promoters(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "promoters"
    BUILDER_CONFIGS = [
        PromotersConfig(name="promoters",
                    description="Promoters for binary classification.")
    ]

    def _info(self):
        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
                                    features=features_per_config[self.config.name])

        return info
    
    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        downloads = dl_manager.download_and_extract(urls_per_split)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
        ]
    
    def _generate_examples(self, filepath: str):              
        data = pandas.read_csv(filepath)

        for row_id, row in data.iterrows():
            data_row = dict(row)

            yield row_id, data_row