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