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"""Soybean Dataset""" |
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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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_ENCODING_DICS = { |
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"class": { |
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value: i for i, value in enumerate(["diaporthe_stem_canker", |
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"charcoal_rot", "rhizoctonia_root_rot", |
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"phytophthora_rot", "brown_stem_rot", "powdery_mildew", |
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"downy_mildew", "brown_spot", "bacterial_blight", |
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"bacterial_pustule", "purple_seed_stain", "anthracnose", |
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"phyllosticta_leaf_spot", "alternarialeaf_spot", |
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"frog_eye_leaf_spot", "diaporthe_pod_&_stem_blight", |
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"cyst_nematode", "2_4_d_injury", "herbicide_injury"]) |
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} |
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} |
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_BASE_FEATURE_NAMES = [ |
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"date", |
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"plant_stand", |
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"precip", |
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"temp", |
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"hail", |
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"crop_hist", |
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"area_damaged", |
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"severity", |
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"seed_tmt", |
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"germination", |
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"plant_growth", |
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"leaves", |
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"leafspots_halo", |
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"leafspots_marg", |
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"leafspot_size", |
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"leaf_shread", |
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"leaf_malf", |
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"leaf_mild", |
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"stem", |
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"lodging", |
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"stem_cankers", |
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"canker_lesion", |
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"fruiting_bodies", |
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"external decay", |
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"mycelium", |
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"int_discolor", |
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"sclerotia", |
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"fruit_pods", |
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"fruit spots", |
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"seed", |
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"mold_growth", |
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"seed_discolor", |
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"seed_size", |
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"shriveling", |
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"roots", |
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"class", |
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] |
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DESCRIPTION = "Soybean dataset." |
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_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990" |
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_URLS = ("https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990") |
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_CITATION = """ |
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@misc{misc_us_census_data_(1990)_116, |
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author = {Meek,Meek, Thiesson,Thiesson & Heckerman,Heckerman}, |
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title = {{US Census Data (1990)}}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5VP42}} |
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} |
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""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/soybean/resolve/main/soybean.csv" |
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} |
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features_types_per_config = { |
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"soybean": { |
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"date": datasets.Value("string"), |
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"plant_stand": datasets.Value("string"), |
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"precip": datasets.Value("string"), |
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"temp": datasets.Value("string"), |
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"hail": datasets.Value("string"), |
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"crop_hist": datasets.Value("string"), |
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"area_damaged": datasets.Value("string"), |
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"severity": datasets.Value("string"), |
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"seed_tmt": datasets.Value("string"), |
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"germination": datasets.Value("string"), |
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"plant_growth": datasets.Value("string"), |
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"leaves": datasets.Value("string"), |
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"leafspots_halo": datasets.Value("string"), |
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"leafspots_marg": datasets.Value("string"), |
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"leafspot_size": datasets.Value("string"), |
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"leaf_shread": datasets.Value("string"), |
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"leaf_malf": datasets.Value("string"), |
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"leaf_mild": datasets.Value("string"), |
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"stem": datasets.Value("string"), |
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"lodging": datasets.Value("string"), |
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"stem_cankers": datasets.Value("string"), |
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"canker_lesion": datasets.Value("string"), |
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"fruiting_bodies": datasets.Value("string"), |
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"external decay": datasets.Value("string"), |
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"mycelium": datasets.Value("string"), |
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"int_discolor": datasets.Value("string"), |
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"sclerotia": datasets.Value("string"), |
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"fruit_pods": datasets.Value("string"), |
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"fruit spots": datasets.Value("string"), |
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"seed": datasets.Value("string"), |
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"mold_growth": datasets.Value("string"), |
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"seed_discolor": datasets.Value("string"), |
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"seed_size": datasets.Value("string"), |
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"shriveling": datasets.Value("string"), |
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"roots": datasets.Value("string"), |
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"class": datasets.ClassLabel(num_classes=19) |
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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 SoybeanConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(SoybeanConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Soybean(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "soybean" |
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binary_configurations = [SoybeanConfig(name=c, description=f"Is this instance of class {c}?") |
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for c in _ENCODING_DICS["class"].keys()] |
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BUILDER_CONFIGS = [SoybeanConfig(name="soybean", description="Soybean for binary classification.")] |
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BUILDER_CONFIGS += binary_configurations |
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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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for c in _ENCODING_DICS["class"].keys(): |
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if self.config.name == c: |
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data["class"] = data["class"].apply(lambda x: 1 if x == c else 0) |
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break |
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for feature in _ENCODING_DICS: |
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encoding_function = partial(self.encode, feature) |
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data[feature] = data[feature].apply(encoding_function) |
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data = data.rename(columns={"instance migration_code_change_in_msa": "migration_code_change_in_msa"}) |
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return data[list(features_types_per_config[self.config.name].keys())] |
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def encode(self, feature, value): |
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if feature in _ENCODING_DICS: |
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return _ENCODING_DICS[feature][value] |
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raise ValueError(f"Unknown feature: {feature}") |
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