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"""Isolet dataset."""
from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "Isolet dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Isolet"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/54/isolet")
_CITATION = """
@misc{misc_isolet_54,
author = {Cole,Ron & Fanty,Mark},
title = {{ISOLET}},
year = {1994},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C51G69}}
}"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co./datasets/mstz/isolet/resolve/main/isolet.zip"
}
features_types_per_config = {
"isolet": {
str(i): datasets.Value("float64") for i in range(617)
}
}
features_types_per_config["isolet"]["617"] = datasets.ClassLabel(num_classes=26)
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class IsoletConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(IsoletConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Isolet(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "isolet"
BUILDER_CONFIGS = [
IsoletConfig(name="isolet",
description="Isolet for letter 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 + "/isolet1+2+3+4.data", header=None).infer_objects()
data = self.preprocess(data, config=self.config.name)
for row_id, row in data.iterrows():
data_row = dict(row)
data_row["617"] = int(data_row["617"])
yield row_id, data_row
def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
data.columns = [str(i) for i in range(618)]
data = data.astype({"617": "int8"})
data.loc[:, "617"] = data["617"].apply(lambda x: int(x) - 1)
data = data.astype({str(i): "float64" for i in range(617)})
return data
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