"""Mushroom""" from typing import List from functools import partial import datasets import pandas import numpy VERSION = datasets.Version("1.0.0") _ORIGINAL_FEATURE_NAMES = [ "is_poisonous", "cap_shape", "cap_surface", "cap_color", "has_bruises", "odor", "gill_attachment", "gill_spacing", "gill_size", "gill_color", "stalk_shape", "stalk_root", "stalk_surface_above_ring", "stalk_surface_belows_ring", "stalk_color_above_ring", "stalk_color_below_ring", "veil_type", "veil_color", "number_of_rings", "ring_type", "spore_print_color", "population", "habitat" ] _BASE_FEATURE_NAMES = [ "cap_shape", "cap_surface", "cap_color", "has_bruises", "odor", "gill_attachment", "gill_spacing", "gill_size", "gill_color", "stalk_shape", "stalk_surface_above_ring", "stalk_surface_belows_ring", "stalk_color_above_ring", "stalk_color_below_ring", "veil_type", "veil_color", "number_of_rings", "ring_type", "spore_print_color", "population", "habitat" ] _ENCODING_DICS = { "is_poisonous": { "p": 1, "e": 0 }, "cap_shape": { "b": "bell", "c": "conical", "x": "convex", "f": "flat", "k": "knobbed", "s": "sunken", }, "cap_surface": { "f": "fibrous", "g": "grooves", "y": "scaly", "s": "smooth" }, "cap_color": { "n": "brown", "b": "buff", "c": "cinnamon", "g": "gray", "r": "green", "p": "pink", "u": "purple", "e": "red", "w": "white", "y": "yellow" }, "has_bruises": { "f": False, "t": True }, "odor": { "a": "almond", "l": "anise", "c": "creosote", "y": "fishy", "f": "foul", "m": "musty", "n": "none", "p": "pungent", "s": "spicy" }, "gill_attachment": { "a": "attached", "d": "descending", "f": "free", "n": "notched", }, "gill_spacing": { "c": "close", "w": "crowded", "d": "distant", }, "gill_size": { "b": "broad", "n": "narrow" }, "gill_color": { "k": "black", "n": "brown", "b": "buff", "h": "chocolate", "g": "gray", "r": "green", "o": "orange", "p": "pink", "u": "purple", "e": "red", "w": "white", "y": "yellow", }, "stalk_shape": { "e": "enlarging", "t": "tapering", }, "stalk_surface_above_ring": { "f": "fibrous", "y": "scaly", "k": "silky", "s": "smooth", }, "stalk_surface_above_ring": { "f": "fibrous", "y": "scaly", "k": "silky", "s": "smooth", }, "stalk_color_above_ring": { "n": "brown", "b": "buff", "c": "cinnamon", "g": "gray", "o": "orange", "p": "pink", "e": "red", "w": "white", "y": "yellow", }, "stalk_color_below_ring": { "n": "brown", "b": "buff", "c": "cinnamon", "g": "gray", "o": "orange", "p": "pink", "e": "red", "w": "white", "y": "yellow", }, "veil_type": { "p": "partial", "u": "universal", }, "veil_color": { "n": "brown", "o": "orange", "w": "white", "y": "yellow", }, # "ring_number": { # "n": 0, # "o": 1, # "t": 2, # }, "ring_type": { "c": "cobwebby", "e": "evanescent", "f": "flaring", "l": "large", "n": "none", "p": "pendant", "s": "sheathing", "z": "zone", }, "spore_print_color": { "k": "black", "n": "brown", "b": "buff", "h": "chocolate", "r": "green", "o": "orange", "u": "purple", "w": "white", "y": "yellow", }, "population": { "a": "abundant", "c": "clustered", "n": "numerous", "s": "scattered", "v": "several", "y": "solitary", }, "habitat": { "g": "grasses", "l": "leaves", "m": "meadows", "p": "paths", "u": "urban", "w": "waste", "d": "woods", } } DESCRIPTION = "Mushroom dataset from the UCI ML repository." _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Mushroom" _URLS = ("https://huggingface.co/datasets/mstz/mushroom/raw/mushroom.csv") _CITATION = """ @misc{misc_mushroom_73, title = {{Mushroom}}, year = {1987}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: \\url{10.24432/C5959T}} }""" # Dataset info urls_per_split = { "train": "https://huggingface.co/datasets/mstz/mushroom/raw/main/agaricus-lepiota.data" } features_types_per_config = { "mushroom": { "cap_shape": datasets.Value("string"), "cap_surface": datasets.Value("string"), "cap_color": datasets.Value("string"), "has_bruises": datasets.Value("bool"), "odor": datasets.Value("string"), "gill_attachment": datasets.Value("string"), "gill_spacing": datasets.Value("string"), "gill_size": datasets.Value("string"), "gill_color": datasets.Value("string"), "stalk_shape": datasets.Value("string"), # "stalk_root": datasets.Value("string"), "stalk_surface_above_ring": datasets.Value("string"), "stalk_surface_belows_ring": datasets.Value("string"), "stalk_color_above_ring": datasets.Value("string"), "stalk_color_below_ring": datasets.Value("string"), "veil_type": datasets.Value("string"), "veil_color": datasets.Value("string"), "number_of_rings": datasets.Value("string"), "ring_type": datasets.Value("string"), "spore_print_color": datasets.Value("string"), "population": datasets.Value("string"), "habitat": datasets.Value("string"), "is_poisonous": 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 MushroomConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(MushroomConfig, self).__init__(version=VERSION, **kwargs) self.features = features_per_config[kwargs["name"]] class Mushroom(datasets.GeneratorBasedBuilder): # dataset versions DEFAULT_CONFIG = "mushroom" BUILDER_CONFIGS = [ MushroomConfig(name="mushroom", description="Mushroom 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, header=None) 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 = _ORIGINAL_FEATURE_NAMES if "stalk_root" in data.columns: data.drop("stalk_root", axis="columns", inplace=True) data = data[features_types_per_config[config].keys()] for feature in _ENCODING_DICS: encoding_function = partial(self.encode, feature) data.loc[:, feature] = data[feature].apply(encoding_function) data = data.infer_objects() return data def encode(self, feature, value): if feature in _ENCODING_DICS: return _ENCODING_DICS[feature][value] raise ValueError(f"Unknown feature: {feature}") def encode_race(self, race): return _RACE_ENCODING[race]