|
from typing import List |
|
|
|
import datasets |
|
|
|
import pandas |
|
|
|
|
|
VERSION = datasets.Version("1.0.0") |
|
_BASE_FEATURE_NAMES = [ |
|
"family", |
|
"product_type", |
|
"steel_type", |
|
"carbon", |
|
"hardness", |
|
"temper_rolling", |
|
"condition", |
|
"formability", |
|
"strength", |
|
"non_ageing", |
|
"surface_finish", |
|
"surface_quality", |
|
"enamelability", |
|
"bc", |
|
"bf", |
|
"bt", |
|
"bw_time", |
|
"bl", |
|
"m", |
|
"chrom", |
|
"phos", |
|
"cbond", |
|
"marvi", |
|
"exptl", |
|
"ferro", |
|
"corr", |
|
"blue", |
|
"lustre", |
|
"jurofm", |
|
"s", |
|
"p", |
|
"is_coil", |
|
"thick", |
|
"width", |
|
"len", |
|
"oil", |
|
"bore", |
|
"packing", |
|
"class" |
|
] |
|
|
|
|
|
DESCRIPTION = "Anneal dataset from the UCI ML repository." |
|
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/3/annealing" |
|
_URLS = ("https://archive-beta.ics.uci.edu/dataset/3/annealing") |
|
_CITATION = """""" |
|
|
|
|
|
urls_per_split = { |
|
"train": "https://huggingface.co/datasets/mstz/anneal/raw/main/anneal.data", |
|
"test": "https://huggingface.co/datasets/mstz/anneal/raw/main/anneal.test" |
|
} |
|
features_types_per_config = { |
|
"annealing": { |
|
"family": datasets.Value("string"), |
|
"product_type": datasets.Value("string"), |
|
"steel_type": datasets.Value("string"), |
|
"carbon": datasets.Value("float64"), |
|
"hardness": datasets.Value("float64"), |
|
"temper_rolling": datasets.Value("bool"), |
|
"condition": datasets.Value("string"), |
|
"formability": datasets.Value("int8"), |
|
"strength": datasets.Value("float64"), |
|
"non_ageing": datasets.Value("bool"), |
|
"surface_finish": datasets.Value("string"), |
|
"surface_quality": datasets.Value("string"), |
|
"enamelability": datasets.Value("int8"), |
|
"bc": datasets.Value("bool"), |
|
"bf": datasets.Value("bool"), |
|
"bt": datasets.Value("bool"), |
|
"bw_time": datasets.Value("string"), |
|
"bl": datasets.Value("bool"), |
|
"m": datasets.Value("bool"), |
|
"chrom": datasets.Value("bool"), |
|
"phos": datasets.Value("bool"), |
|
"cbond": datasets.Value("bool"), |
|
"marvi": datasets.Value("bool"), |
|
"exptl": datasets.Value("bool"), |
|
"ferro": datasets.Value("bool"), |
|
"corr": datasets.Value("bool"), |
|
"blue": datasets.Value("string"), |
|
"lustre": datasets.Value("bool"), |
|
"jurofm": datasets.Value("bool"), |
|
"s": datasets.Value("bool"), |
|
"p": datasets.Value("bool"), |
|
"is_coil": datasets.Value("bool"), |
|
"thick": datasets.Value("float64"), |
|
"width": datasets.Value("float64"), |
|
"len": datasets.Value("float64"), |
|
"oil": datasets.Value("string"), |
|
"bore": datasets.Value("string"), |
|
"packing": datasets.Value("string"), |
|
"class": datasets.ClassLabel(num_classes=6, names=["1", "2", "3", "4", "5", "U"]) |
|
} |
|
} |
|
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
|
|
|
|
|
class AnnealConfig(datasets.BuilderConfig): |
|
def __init__(self, **kwargs): |
|
super(AnnealConfig, self).__init__(version=VERSION, **kwargs) |
|
self.features = features_per_config[kwargs["name"]] |
|
|
|
|
|
class Anneal(datasets.GeneratorBasedBuilder): |
|
|
|
DEFAULT_CONFIG = "annealing" |
|
BUILDER_CONFIGS = [ |
|
AnnealConfig(name="annealing", |
|
description="Anneal for multiclass classification.") |
|
] |
|
|
|
|
|
def _info(self): |
|
if self.config.name not in features_per_config: |
|
raise ValueError(f"Unknown configuration: {self.config.name}") |
|
|
|
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) |
|
data = self.preprocess(data, config=self.config.name) |
|
|
|
for row_id, row in data.iterrows(): |
|
data_row = dict(row) |
|
|
|
yield row_id, data_row |
|
|
|
def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
|
data.columns = _BASE_FEATURE_NAMES |
|
data.loc[:, "formability"] = data.formability.apply(lambda x: int(x) if x != "-" else 0) |
|
data.loc[:, "enamelability"] = data.enamelability.apply(lambda x: int(x) if x != "-" else 0) |
|
data.loc[:, "non_ageing"] = data.formability.apply(lambda x: True if x == "-" else False) |
|
data.loc[:, "bc"] = data.bc.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "bf"] = data.bf.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "bt"] = data.bt.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "bl"] = data.bl.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "m"] = data.m.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "chrom"] = data.chrom.apply(lambda x: True if x == "C" else False) |
|
data.loc[:, "phos"] = data.phos.apply(lambda x: True if x == "P" else False) |
|
data.loc[:, "cbond"] = data.cbond.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "marvi"] = data.marvi.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "exptl"] = data.exptl.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "ferro"] = data.ferro.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "corr"] = data.corr.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "lustre"] = data.lustre.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "jurofm"] = data.jurofm.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "s"] = data.s.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "p"] = data.p.apply(lambda x: True if x == "Y" else False) |
|
data.loc[:, "class"] = data.p.apply(lambda x: int(x) if x != "U" else 0) |
|
|
|
return data |
|
|
