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"""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]