"""Landsat Dataset""" from typing import List from functools import partial import datasets import pandas VERSION = datasets.Version("1.0.0") _ENCODING_DICS = {} DESCRIPTION = "Landsat dataset." _HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/198/steel+plates+faults" _URLS = ("https://archive-beta.ics.uci.edu/dataset/198/steel+plates+faults") _CITATION = """ @misc{misc_steel_plates_faults_198, author = {Buscema,M, Terzi,S & Tastle,W}, title = {{Steel Plates Faults}}, year = {2010}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: \\url{10.24432/C5J88N}} } """ # Dataset info urls_per_split = { "train": "https://huggingface.co/datasets/mstz/steel_plates/raw/main/steel_plates.csv" } features_types_per_config = { "steel_plates": { "x_minimum": datasets.Value("int64"), "x_maximum": datasets.Value("int64"), "y_minimum": datasets.Value("int64"), "y_maximum": datasets.Value("int64"), "pixels_areas": datasets.Value("int64"), "x_perimeter": datasets.Value("int64"), "y_perimeter": datasets.Value("int64"), "sum_of_luminosity": datasets.Value("int64"), "minimum_of_luminosity": datasets.Value("int64"), "maximum_of_luminosity": datasets.Value("int64"), "length_of_conveyer": datasets.Value("int64"), "typeofsteel_a300": datasets.Value("int64"), "typeofsteel_a400": datasets.Value("int64"), "steel_plate_thickness": datasets.Value("int64"), "edges_index": datasets.Value("float64"), "empty_index": datasets.Value("float64"), "square_index": datasets.Value("float64"), "outside_x_index": datasets.Value("float64"), "edges_x_index": datasets.Value("float64"), "edges_y_index": datasets.Value("float64"), "outside_global_index": datasets.Value("float64"), "logofareas": datasets.Value("float64"), "log_x_index": datasets.Value("float64"), "log_y_index": datasets.Value("float64"), "orientation_index": datasets.Value("float64"), "luminosity_index": datasets.Value("float64"), "sigmoidofareas": datasets.Value("float64"), "class": datasets.ClassLabel(num_classes=7), }, "steel_plates_0": { "x_minimum": datasets.Value("int64"), "x_maximum": datasets.Value("int64"), "y_minimum": datasets.Value("int64"), "y_maximum": datasets.Value("int64"), "pixels_areas": datasets.Value("int64"), "x_perimeter": datasets.Value("int64"), "y_perimeter": datasets.Value("int64"), "sum_of_luminosity": datasets.Value("int64"), "minimum_of_luminosity": datasets.Value("int64"), "maximum_of_luminosity": datasets.Value("int64"), "length_of_conveyer": datasets.Value("int64"), "typeofsteel_a300": datasets.Value("int64"), "typeofsteel_a400": datasets.Value("int64"), "steel_plate_thickness": datasets.Value("int64"), "edges_index": datasets.Value("float64"), "empty_index": datasets.Value("float64"), "square_index": datasets.Value("float64"), "outside_x_index": datasets.Value("float64"), "edges_x_index": datasets.Value("float64"), "edges_y_index": datasets.Value("float64"), "outside_global_index": datasets.Value("float64"), "logofareas": datasets.Value("float64"), "log_x_index": datasets.Value("float64"), "log_y_index": datasets.Value("float64"), "orientation_index": datasets.Value("float64"), "luminosity_index": datasets.Value("float64"), "sigmoidofareas": datasets.Value("float64"), "class": datasets.ClassLabel(num_classes=2), }, "steel_plates_1": { "x_minimum": datasets.Value("int64"), "x_maximum": datasets.Value("int64"), "y_minimum": datasets.Value("int64"), "y_maximum": datasets.Value("int64"), "pixels_areas": datasets.Value("int64"), "x_perimeter": datasets.Value("int64"), "y_perimeter": datasets.Value("int64"), "sum_of_luminosity": datasets.Value("int64"), "minimum_of_luminosity": datasets.Value("int64"), "maximum_of_luminosity": datasets.Value("int64"), "length_of_conveyer": datasets.Value("int64"), "typeofsteel_a300": datasets.Value("int64"), "typeofsteel_a400": datasets.Value("int64"), "steel_plate_thickness": datasets.Value("int64"), "edges_index": datasets.Value("float64"), "empty_index": datasets.Value("float64"), "square_index": datasets.Value("float64"), "outside_x_index": datasets.Value("float64"), "edges_x_index": datasets.Value("float64"), "edges_y_index": datasets.Value("float64"), "outside_global_index": datasets.Value("float64"), "logofareas": datasets.Value("float64"), "log_x_index": datasets.Value("float64"), "log_y_index": datasets.Value("float64"), "orientation_index": datasets.Value("float64"), "luminosity_index": datasets.Value("float64"), "sigmoidofareas": datasets.Value("float64"), "class": datasets.ClassLabel(num_classes=2), }, "steel_plates_2": { "x_minimum": datasets.Value("int64"), "x_maximum": datasets.Value("int64"), "y_minimum": datasets.Value("int64"), "y_maximum": datasets.Value("int64"), "pixels_areas": datasets.Value("int64"), "x_perimeter": datasets.Value("int64"), "y_perimeter": datasets.Value("int64"), "sum_of_luminosity": datasets.Value("int64"), "minimum_of_luminosity": datasets.Value("int64"), "maximum_of_luminosity": datasets.Value("int64"), "length_of_conveyer": datasets.Value("int64"), "typeofsteel_a300": datasets.Value("int64"), "typeofsteel_a400": datasets.Value("int64"), "steel_plate_thickness": datasets.Value("int64"), "edges_index": datasets.Value("float64"), "empty_index": datasets.Value("float64"), "square_index": datasets.Value("float64"), "outside_x_index": datasets.Value("float64"), "edges_x_index": datasets.Value("float64"), "edges_y_index": datasets.Value("float64"), "outside_global_index": datasets.Value("float64"), "logofareas": datasets.Value("float64"), "log_x_index": datasets.Value("float64"), "log_y_index": datasets.Value("float64"), "orientation_index": datasets.Value("float64"), "luminosity_index": datasets.Value("float64"), "sigmoidofareas": datasets.Value("float64"), "class": datasets.ClassLabel(num_classes=2), }, "steel_plates_3": { "x_minimum": datasets.Value("int64"), "x_maximum": datasets.Value("int64"), "y_minimum": datasets.Value("int64"), "y_maximum": datasets.Value("int64"), "pixels_areas": datasets.Value("int64"), "x_perimeter": datasets.Value("int64"), "y_perimeter": datasets.Value("int64"), "sum_of_luminosity": datasets.Value("int64"), "minimum_of_luminosity": datasets.Value("int64"), "maximum_of_luminosity": datasets.Value("int64"), "length_of_conveyer": datasets.Value("int64"), "typeofsteel_a300": datasets.Value("int64"), "typeofsteel_a400": datasets.Value("int64"), "steel_plate_thickness": datasets.Value("int64"), "edges_index": datasets.Value("float64"), "empty_index": datasets.Value("float64"), "square_index": datasets.Value("float64"), "outside_x_index": datasets.Value("float64"), "edges_x_index": datasets.Value("float64"), "edges_y_index": datasets.Value("float64"), "outside_global_index": datasets.Value("float64"), "logofareas": datasets.Value("float64"), "log_x_index": datasets.Value("float64"), "log_y_index": datasets.Value("float64"), "orientation_index": datasets.Value("float64"), "luminosity_index": datasets.Value("float64"), "sigmoidofareas": datasets.Value("float64"), "class": datasets.ClassLabel(num_classes=2), }, "steel_plates_4": { "x_minimum": datasets.Value("int64"), "x_maximum": datasets.Value("int64"), "y_minimum": datasets.Value("int64"), "y_maximum": datasets.Value("int64"), "pixels_areas": datasets.Value("int64"), "x_perimeter": datasets.Value("int64"), "y_perimeter": datasets.Value("int64"), "sum_of_luminosity": datasets.Value("int64"), "minimum_of_luminosity": datasets.Value("int64"), "maximum_of_luminosity": datasets.Value("int64"), "length_of_conveyer": datasets.Value("int64"), "typeofsteel_a300": datasets.Value("int64"), "typeofsteel_a400": datasets.Value("int64"), "steel_plate_thickness": datasets.Value("int64"), "edges_index": datasets.Value("float64"), "empty_index": datasets.Value("float64"), "square_index": datasets.Value("float64"), "outside_x_index": datasets.Value("float64"), "edges_x_index": datasets.Value("float64"), "edges_y_index": datasets.Value("float64"), "outside_global_index": datasets.Value("float64"), "logofareas": datasets.Value("float64"), "log_x_index": datasets.Value("float64"), "log_y_index": datasets.Value("float64"), "orientation_index": datasets.Value("float64"), "luminosity_index": datasets.Value("float64"), "sigmoidofareas": datasets.Value("float64"), "class": datasets.ClassLabel(num_classes=2), }, "steel_plates_5": { "x_minimum": datasets.Value("int64"), "x_maximum": datasets.Value("int64"), "y_minimum": datasets.Value("int64"), "y_maximum": datasets.Value("int64"), "pixels_areas": datasets.Value("int64"), "x_perimeter": datasets.Value("int64"), "y_perimeter": datasets.Value("int64"), "sum_of_luminosity": datasets.Value("int64"), "minimum_of_luminosity": datasets.Value("int64"), "maximum_of_luminosity": datasets.Value("int64"), "length_of_conveyer": datasets.Value("int64"), "typeofsteel_a300": datasets.Value("int64"), "typeofsteel_a400": datasets.Value("int64"), "steel_plate_thickness": datasets.Value("int64"), "edges_index": datasets.Value("float64"), "empty_index": datasets.Value("float64"), "square_index": datasets.Value("float64"), "outside_x_index": datasets.Value("float64"), "edges_x_index": datasets.Value("float64"), "edges_y_index": datasets.Value("float64"), "outside_global_index": datasets.Value("float64"), "logofareas": datasets.Value("float64"), "log_x_index": datasets.Value("float64"), "log_y_index": datasets.Value("float64"), "orientation_index": datasets.Value("float64"), "luminosity_index": datasets.Value("float64"), "sigmoidofareas": datasets.Value("float64"), "class": datasets.ClassLabel(num_classes=2), }, "steel_plates_6": { "x_minimum": datasets.Value("int64"), "x_maximum": datasets.Value("int64"), "y_minimum": datasets.Value("int64"), "y_maximum": datasets.Value("int64"), "pixels_areas": datasets.Value("int64"), "x_perimeter": datasets.Value("int64"), "y_perimeter": datasets.Value("int64"), "sum_of_luminosity": datasets.Value("int64"), "minimum_of_luminosity": datasets.Value("int64"), "maximum_of_luminosity": datasets.Value("int64"), "length_of_conveyer": datasets.Value("int64"), "typeofsteel_a300": datasets.Value("int64"), "typeofsteel_a400": datasets.Value("int64"), "steel_plate_thickness": datasets.Value("int64"), "edges_index": datasets.Value("float64"), "empty_index": datasets.Value("float64"), "square_index": datasets.Value("float64"), "outside_x_index": datasets.Value("float64"), "edges_x_index": datasets.Value("float64"), "edges_y_index": datasets.Value("float64"), "outside_global_index": datasets.Value("float64"), "logofareas": datasets.Value("float64"), "log_x_index": datasets.Value("float64"), "log_y_index": datasets.Value("float64"), "orientation_index": datasets.Value("float64"), "luminosity_index": datasets.Value("float64"), "sigmoidofareas": datasets.Value("float64"), "class": datasets.ClassLabel(num_classes=2), }, } features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} class LandsatConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(LandsatConfig, self).__init__(version=VERSION, **kwargs) self.features = features_per_config[kwargs["name"]] class Landsat(datasets.GeneratorBasedBuilder): # dataset versions DEFAULT_CONFIG = "steel_plates" BUILDER_CONFIGS = [ LandsatConfig(name="steel_plates", description="Landsat for multiclass classification."), LandsatConfig(name="steel_plates_0", description="Landsat for binary classification."), LandsatConfig(name="steel_plates_1", description="Landsat for binary classification."), LandsatConfig(name="steel_plates_2", description="Landsat for binary classification."), LandsatConfig(name="steel_plates_3", description="Landsat for binary classification."), LandsatConfig(name="steel_plates_4", description="Landsat for binary classification."), LandsatConfig(name="steel_plates_5", description="Landsat for binary classification."), LandsatConfig(name="steel_plates_6", description="Landsat 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) 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: if self.config.name == "steel_plates_0": data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0) elif self.config.name == "steel_plates_1": data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0) elif self.config.name == "steel_plates_2": data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0) elif self.config.name == "steel_plates_3": data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0) elif self.config.name == "steel_plates_4": data["class"] = data["class"].apply(lambda x: 1 if x == 4 else 0) elif self.config.name == "steel_plates_5": data["class"] = data["class"].apply(lambda x: 1 if x == 5 else 0) elif self.config.name == "steel_plates_6": data["class"] = data["class"].apply(lambda x: 1 if x == 6 else 0) for feature in _ENCODING_DICS: encoding_function = partial(self.encode, feature) data.loc[:, feature] = data[feature].apply(encoding_function) return data[list(features_types_per_config[self.config.name].keys())] def encode(self, feature, value): if feature in _ENCODING_DICS: return _ENCODING_DICS[feature][value] raise ValueError(f"Unknown feature: {feature}")