Datasets:
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from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "Glass efficiency dataset from the UCI repository."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/242/glass+efficiency"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/30/glass+method+choice")
_CITATION = """
@misc{misc_glass_efficiency_242,
author = {Tsanas,Athanasios & Xifara,Angeliki},
title = {{Glass efficiency}},
year = {2012},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C51307}}
}"""
# Dataset info
_BASE_FEATURE_NAMES = [
"relative_compactness",
"surface_area",
"wall_area",
"roof_area",
"overall_height",
"orientation",
"glazing_area",
"glazing_area_distribution",
"heating_load",
"cooling_load"
]
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/glass/raw/main/glass.data"
}
features_types_per_config = {
"glass": {
"refractive_index": datasets.Value("float64"),
"sodium": datasets.Value("float64"),
"magnesium": datasets.Value("float64"),
"aluminum": datasets.Value("float64"),
"silicon": datasets.Value("float64"),
"potassium": datasets.Value("float64"),
"calcium": datasets.Value("float64"),
"barium": datasets.Value("int8"),
"iron": datasets.Value("float64"),
"glass_type": datasets.ClassLabel(num_classes=6, names=("windows_1", "windows_2",
"vehicle_windows_1", "vehicle_windows_2",
"containers", "tableware", "headlamps"))
},
"windows": {
"refractive_index": datasets.Value("float64"),
"sodium": datasets.Value("float64"),
"magnesium": datasets.Value("float64"),
"aluminum": datasets.Value("float64"),
"silicon": datasets.Value("float64"),
"potassium": datasets.Value("float64"),
"calcium": datasets.Value("float64"),
"barium": datasets.Value("int8"),
"iron": datasets.Value("float64"),
"is_windows_glass": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
"vehicles": {
"refractive_index": datasets.Value("float64"),
"sodium": datasets.Value("float64"),
"magnesium": datasets.Value("float64"),
"aluminum": datasets.Value("float64"),
"silicon": datasets.Value("float64"),
"potassium": datasets.Value("float64"),
"calcium": datasets.Value("float64"),
"barium": datasets.Value("int8"),
"iron": datasets.Value("float64"),
"is_vehicle_glass": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
"containers": {
"refractive_index": datasets.Value("float64"),
"sodium": datasets.Value("float64"),
"magnesium": datasets.Value("float64"),
"aluminum": datasets.Value("float64"),
"silicon": datasets.Value("float64"),
"potassium": datasets.Value("float64"),
"calcium": datasets.Value("float64"),
"barium": datasets.Value("int8"),
"iron": datasets.Value("float64"),
"is_container_glass": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
"tableware": {
"refractive_index": datasets.Value("float64"),
"sodium": datasets.Value("float64"),
"magnesium": datasets.Value("float64"),
"aluminum": datasets.Value("float64"),
"silicon": datasets.Value("float64"),
"potassium": datasets.Value("float64"),
"calcium": datasets.Value("float64"),
"barium": datasets.Value("int8"),
"iron": datasets.Value("float64"),
"is_tableware_glass": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
"headlamps": {
"refractive_index": datasets.Value("float64"),
"sodium": datasets.Value("float64"),
"magnesium": datasets.Value("float64"),
"aluminum": datasets.Value("float64"),
"silicon": datasets.Value("float64"),
"potassium": datasets.Value("float64"),
"calcium": datasets.Value("float64"),
"barium": datasets.Value("int8"),
"iron": datasets.Value("float64"),
"is_headlamp_glass": 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 GlassConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(GlassConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Glass(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "glass"
BUILDER_CONFIGS = [
GlassConfig(name="glass", description="Glass dataset."),
GlassConfig(name="windows", description="Is this windows glass?"),
GlassConfig(name="vehicles", description="Is this vehicles glass?"),
GlassConfig(name="containers", description="Is this containers glass?"),
GlassConfig(name="tableware", description="Is this tableware glass?"),
GlassConfig(name="headlamps", description="Is this headlamps glass?")
]
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, header=None)
data = self.preprocess(data)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
data.columns = _BASE_FEATURE_NAMES
if self.config.name == "windows":
data = data.rename(columns={"glass_type", "is_windows_glass"})
data.loc[:, "is_windows_glass"] = data.is_windows_glass.apply(lambda x: 0 if data.is_windows_glass in {1, 2} else 0)
elif self.config.name == "vehicles":
data = data.rename(columns={"glass_type", "is_vehicles_glass"})
data.loc[:, "is_vehicles_glass"] = data.is_vehicles_glass.apply(lambda x: 0 if data.is_vehicles_glass in {3, 4} else 0)
elif self.config.name == "containers":
data = data.rename(columns={"glass_type", "is_containers_glass"})
data.loc[:, "is_containers_glass"] = data.is_containers_glass.apply(lambda x: 0 if data.is_containers_glass == 5 else 0)
elif self.config.name == "tableware":
data = data.rename(columns={"glass_type", "is_tableware_glass"})
data.loc[:, "is_tableware_glass"] = data.is_tableware_glass.apply(lambda x: 0 if data.is_tableware_glass == 6 else 0)
elif self.config.name == "headlamps":
data = data.rename(columns={"glass_type", "is_headlamps_glass"})
data.loc[:, "is_headlamps_glass"] = data.is_headlamps_glass.apply(lambda x: 0 if data.is_headlamps_glass == 7 else 0)
else:
data.loc[:, "glass_type"] = data.glass_type.apply(lambda x: x - 1)
return data
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