Datasets:
Upload 2 files
Browse files- .gitattributes +1 -0
- covertype.py +126 -0
- covtype.data +3 -0
.gitattributes
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@@ -52,3 +52,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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covtype.data filter=lfs diff=lfs merge=lfs -text
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covertype.py
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from typing import List
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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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DESCRIPTION = "Covertype dataset from the UCI ML repository."
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_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/31/covertype"
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_URLS = ("https://archive-beta.ics.uci.edu/dataset/31/covertype")
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_CITATION = """"""
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# Dataset info
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urls_per_split = {
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"train": "https://huggingface.co/datasets/mstz/covertype/raw/main/covtype.data"
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}
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features_types_per_config = {
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"covertype": {
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"elevation": datasets.Value("float32"),
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"aspect": datasets.Value("float32"),
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"slope": datasets.Value("float32"),
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"horizontal_distance_to_hydrology": datasets.Value("float32"),
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"vertical_distance_to_hydrology": datasets.Value("float32"),
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"horizontal_distance_to_roadways": datasets.Value("float32"),
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"hillshade_9am": datasets.Value("float32"),
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"hillshade_noon": datasets.Value("float32"),
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"hillshade_3pm": datasets.Value("float32"),
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"horizontal_distance_to_fire_points": datasets.Value("float32"),
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"is_a_wilderness_area": datasets.Value("bool"),
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"soil_type_id_0": datasets.Value("bool"),
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"soil_type_id_1": datasets.Value("bool"),
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"soil_type_id_2": datasets.Value("bool"),
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"soil_type_id_3": datasets.Value("bool"),
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"soil_type_id_4": datasets.Value("bool"),
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"soil_type_id_5": datasets.Value("bool"),
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"soil_type_id_6": datasets.Value("bool"),
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"soil_type_id_7": datasets.Value("bool"),
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"soil_type_id_8": datasets.Value("bool"),
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"soil_type_id_9": datasets.Value("bool"),
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"soil_type_id_10": datasets.Value("bool"),
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"soil_type_id_11": datasets.Value("bool"),
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"soil_type_id_12": datasets.Value("bool"),
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"soil_type_id_13": datasets.Value("bool"),
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"soil_type_id_14": datasets.Value("bool"),
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"soil_type_id_15": datasets.Value("bool"),
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"soil_type_id_16": datasets.Value("bool"),
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"soil_type_id_17": datasets.Value("bool"),
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"soil_type_id_18": datasets.Value("bool"),
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"soil_type_id_19": datasets.Value("bool"),
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"soil_type_id_20": datasets.Value("bool"),
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"soil_type_id_21": datasets.Value("bool"),
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"soil_type_id_22": datasets.Value("bool"),
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"soil_type_id_23": datasets.Value("bool"),
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"soil_type_id_24": datasets.Value("bool"),
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"soil_type_id_25": datasets.Value("bool"),
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"soil_type_id_26": datasets.Value("bool"),
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"soil_type_id_27": datasets.Value("bool"),
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"soil_type_id_28": datasets.Value("bool"),
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"soil_type_id_29": datasets.Value("bool"),
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"soil_type_id_30": datasets.Value("bool"),
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"soil_type_id_31": datasets.Value("bool"),
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"soil_type_id_32": datasets.Value("bool"),
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"soil_type_id_33": datasets.Value("bool"),
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"soil_type_id_34": datasets.Value("bool"),
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"soil_type_id_35": datasets.Value("bool"),
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"soil_type_id_36": datasets.Value("bool"),
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"soil_type_id_37": datasets.Value("bool"),
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"soil_type_id_38": datasets.Value("bool"),
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"soil_type_id_39": datasets.Value("bool"),
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"soil_type": datasets.Value("string"),
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"cover_type
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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 CovertypeConfig(datasets.BuilderConfig):
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def __init__(self, **kwargs):
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super(CovertypeConfig, self).__init__(version=VERSION, **kwargs)
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self.features = features_per_config[kwargs["name"]]
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class Covertype(datasets.GeneratorBasedBuilder):
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# dataset versions
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DEFAULT_CONFIG = "covertype"
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BUILDER_CONFIGS = [
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CovertypeConfig(name="covertype",
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description="Covertype for multiclass classification.")
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]
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def _info(self):
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if self.config.name not in features_per_config:
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raise ValueError(f"Unknown configuration: {self.config.name}")
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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)
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data = self.preprocess(data, config=self.config.name)
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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, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
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data["500"] = data["500"].apply(lambda x: max(0, x)).astype(int)
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if "0.1" in data:
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data.drop("0.1", axis="columns", inplace=True)
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return data
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covtype.data
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version https://git-lfs.github.com/spec/v1
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oid sha256:a1825eb1a33610ad72b10ea943f20aed7951f503553ab0b872348ceb116f62c1
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size 75170171
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