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README.md

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 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- soybean
+- tabular_classification
+- binary_classification
+- multiclass_classification
+- UCI
+pretty_name: Isoybean
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- soybean
 ---
+# Soybean
+The [Soybean dataset](https://archive-beta.ics.uci.edu/dataset/90/soybean+large) from the [UCI repository](https://archive-beta.ics.uci.edu/).
+Classify the type of soybean.
+
+
+# Configurations and tasks
+| **Configuration**     | **Task**                  | **Description** |
+|-----------------------|---------------------------|-----------------|
+| soybean               | Binary classification.| Classify soybean type. |
+| diaporthe_stem_canker | Binary classification | Is this instance of class diaporthe_stem_canker? |
+| charcoal_rot | Binary classification | Is this instance of class charcoal_rot? |
+| rhizoctonia_root_rot | Binary classification | Is this instance of class rhizoctonia_root_rot? |
+| phytophthora_rot | Binary classification | Is this instance of class phytophthora_rot? |
+| brown_stem_rot | Binary classification | Is this instance of class brown_stem_rot? |
+| powdery_mildew | Binary classification | Is this instance of class powdery_mildew? |
+| downy_mildew | Binary classification | Is this instance of class downy_mildew? |
+| brown_spot | Binary classification | Is this instance of class brown_spot? |
+| bacterial_blight | Binary classification | Is this instance of class bacterial_blight? |
+| bacterial_pustule | Binary classification | Is this instance of class bacterial_pustule? |
+| purple_seed_stain | Binary classification | Is this instance of class purple_seed_stain? |
+| anthracnose | Binary classification | Is this instance of class anthracnose? |
+| phyllosticta_leaf_spot | Binary classification | Is this instance of class phyllosticta_leaf_spot? |
+| alternarialeaf_spot | Binary classification | Is this instance of class alternarialeaf_spot? |
+| frog_eye_leaf_spot | Binary classification | Is this instance of class frog_eye_leaf_spot? |
+| diaporthe_pod_&_stem_blight | Binary classification | Is this instance of class diaporthe_pod_? |
+| cyst_nematode | Binary classification | Is this instance of class cyst_nematode? |
+| 2_4_d_injury | Binary classification | Is this instance of class 2_4_d_injury? |
+| herbicide_injury | Binary classification | Is this instance of class herbicide_injury? |

soybean.py

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+"""Soybean Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {
+	"class": {
+		value: i for i, value in enumerate(["diaporthe_stem_canker",
+											"charcoal_rot", "rhizoctonia_root_rot",
+											"phytophthora_rot", "brown_stem_rot", "powdery_mildew",
+											"downy_mildew", "brown_spot", "bacterial_blight",
+       										"bacterial_pustule", "purple_seed_stain", "anthracnose",
+       										"phyllosticta_leaf_spot", "alternarialeaf_spot",
+       										"frog_eye_leaf_spot", "diaporthe_pod_&_stem_blight",
+       										"cyst_nematode", "2_4_d_injury", "herbicide_injury"])
+	}
+}
+_BASE_FEATURE_NAMES = [
+	"date",
+	"plant_stand",
+	"precip",
+	"temp",
+	"hail",
+	"crop_hist",
+	"area_damaged",
+	"severity",
+	"seed_tmt",
+	"germination",
+	"plant_growth",
+	"leaves",
+	"leafspots_halo",
+	"leafspots_marg",
+	"leafspot_size",
+	"leaf_shread",
+	"leaf_malf",
+	"leaf_mild",
+	"stem",
+	"lodging",
+	"stem_cankers",
+	"canker_lesion",
+	"fruiting_bodies",
+	"external decay",
+	"mycelium",
+	"int_discolor",
+	"sclerotia",
+	"fruit_pods",
+	"fruit spots",
+	"seed",
+	"mold_growth",
+	"seed_discolor",
+	"seed_size",
+	"shriveling",
+	"roots",
+	"class",
+]
+
+DESCRIPTION = "Soybean dataset."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990")
+_CITATION = """
+@misc{misc_us_census_data_(1990)_116,
+  author       = {Meek,Meek, Thiesson,Thiesson & Heckerman,Heckerman},
+  title        = {{US Census Data (1990)}},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5VP42}}
+}
+"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/soybean/resolve/main/soybean.csv"
+}
+features_types_per_config = {
+	"soybean": {
+		"date": datasets.Value("string"),
+		"plant_stand": datasets.Value("string"),
+		"precip": datasets.Value("string"),
+		"temp": datasets.Value("string"),
+		"hail": datasets.Value("string"),
+		"crop_hist": datasets.Value("string"),
+		"area_damaged": datasets.Value("string"),
+		"severity": datasets.Value("string"),
+		"seed_tmt": datasets.Value("string"),
+		"germination": datasets.Value("string"),
+		"plant_growth": datasets.Value("string"),
+		"leaves": datasets.Value("string"),
+		"leafspots_halo": datasets.Value("string"),
+		"leafspots_marg": datasets.Value("string"),
+		"leafspot_size": datasets.Value("string"),
+		"leaf_shread": datasets.Value("string"),
+		"leaf_malf": datasets.Value("string"),
+		"leaf_mild": datasets.Value("string"),
+		"stem": datasets.Value("string"),
+		"lodging": datasets.Value("string"),
+		"stem_cankers": datasets.Value("string"),
+		"canker_lesion": datasets.Value("string"),
+		"fruiting_bodies": datasets.Value("string"),
+		"external decay": datasets.Value("string"),
+		"mycelium": datasets.Value("string"),
+		"int_discolor": datasets.Value("string"),
+		"sclerotia": datasets.Value("string"),
+		"fruit_pods": datasets.Value("string"),
+		"fruit spots": datasets.Value("string"),
+		"seed": datasets.Value("string"),
+		"mold_growth": datasets.Value("string"),
+		"seed_discolor": datasets.Value("string"),
+		"seed_size": datasets.Value("string"),
+		"shriveling": datasets.Value("string"),
+		"roots": datasets.Value("string"),
+		"class": datasets.ClassLabel(num_classes=19)
+	}
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class SoybeanConfig(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(SoybeanConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class Soybean(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "soybean"
+	binary_configurations = [SoybeanConfig(name=c, description=f"Is this instance of class {c}?")
+							 for c in _ENCODING_DICS["class"].keys()]
+	BUILDER_CONFIGS = [SoybeanConfig(name="soybean", description="Soybean for binary classification.")]
+	BUILDER_CONFIGS += binary_configurations
+
+
+	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)
+
+		for row_id, row in data.iterrows():
+			data_row = dict(row)
+
+			yield row_id, data_row
+
+	def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
+		data.columns = _BASE_FEATURE_NAMES
+
+		for c in _ENCODING_DICS["class"].keys():
+			if self.config.name == c:
+				data["class"] = data["class"].apply(lambda x: 1 if x == c else 0)
+				break
+
+		for feature in _ENCODING_DICS:
+			encoding_function = partial(self.encode, feature)
+			data[feature] = data[feature].apply(encoding_function)
+		
+		data = data.rename(columns={"instance migration_code_change_in_msa": "migration_code_change_in_msa"})
+
+				
+		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}")