Datasets:
File size: 6,568 Bytes
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from typing import List
import datasets
import pandas
import gzip
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "Covertype dataset from the UCI ML repository."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/31/covertype"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/31/covertype")
_CITATION = """"""
# Dataset info
urls_per_split = {
"train": "https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz"
}
_BASE_FEATURE_NAMES = [
"elevation",
"aspect",
"slope",
"horizontal_distance_to_hydrology",
"vertical_distance_to_hydrology",
"horizontal_distance_to_roadways",
"hillshade_9am",
"hillshade_noon",
"hillshade_3pm",
"horizontal_distance_to_fire_points",
"wilderness_area_id_0",
"wilderness_area_id_1",
"wilderness_area_id_2",
"wilderness_area_id_3",
"soil_type_id_0",
"soil_type_id_1",
"soil_type_id_2",
"soil_type_id_3",
"soil_type_id_4",
"soil_type_id_5",
"soil_type_id_6",
"soil_type_id_7",
"soil_type_id_8",
"soil_type_id_9",
"soil_type_id_10",
"soil_type_id_11",
"soil_type_id_12",
"soil_type_id_13",
"soil_type_id_14",
"soil_type_id_15",
"soil_type_id_16",
"soil_type_id_17",
"soil_type_id_18",
"soil_type_id_19",
"soil_type_id_20",
"soil_type_id_21",
"soil_type_id_22",
"soil_type_id_23",
"soil_type_id_24",
"soil_type_id_25",
"soil_type_id_26",
"soil_type_id_27",
"soil_type_id_28",
"soil_type_id_29",
"soil_type_id_30",
"soil_type_id_31",
"soil_type_id_32",
"soil_type_id_33",
"soil_type_id_34",
"soil_type_id_35",
"soil_type_id_36",
"soil_type_id_37",
"soil_type_id_38",
"soil_type_id_39",
"cover_type"
]
features_types_per_config = {
"covertype": {
"elevation": datasets.Value("float32"),
"aspect": datasets.Value("float32"),
"slope": datasets.Value("float32"),
"horizontal_distance_to_hydrology": datasets.Value("float32"),
"vertical_distance_to_hydrology": datasets.Value("float32"),
"horizontal_distance_to_roadways": datasets.Value("float32"),
"hillshade_9am": datasets.Value("float32"),
"hillshade_noon": datasets.Value("float32"),
"hillshade_3pm": datasets.Value("float32"),
"horizontal_distance_to_fire_points": datasets.Value("float32"),
"wilderness_area_id_0": datasets.Value("bool"),
"wilderness_area_id_1": datasets.Value("bool"),
"wilderness_area_id_2": datasets.Value("bool"),
"wilderness_area_id_3": datasets.Value("bool"),
"soil_type_id_0": datasets.Value("bool"),
"soil_type_id_1": datasets.Value("bool"),
"soil_type_id_2": datasets.Value("bool"),
"soil_type_id_3": datasets.Value("bool"),
"soil_type_id_4": datasets.Value("bool"),
"soil_type_id_5": datasets.Value("bool"),
"soil_type_id_6": datasets.Value("bool"),
"soil_type_id_7": datasets.Value("bool"),
"soil_type_id_8": datasets.Value("bool"),
"soil_type_id_9": datasets.Value("bool"),
"soil_type_id_10": datasets.Value("bool"),
"soil_type_id_11": datasets.Value("bool"),
"soil_type_id_12": datasets.Value("bool"),
"soil_type_id_13": datasets.Value("bool"),
"soil_type_id_14": datasets.Value("bool"),
"soil_type_id_15": datasets.Value("bool"),
"soil_type_id_16": datasets.Value("bool"),
"soil_type_id_17": datasets.Value("bool"),
"soil_type_id_18": datasets.Value("bool"),
"soil_type_id_19": datasets.Value("bool"),
"soil_type_id_20": datasets.Value("bool"),
"soil_type_id_21": datasets.Value("bool"),
"soil_type_id_22": datasets.Value("bool"),
"soil_type_id_23": datasets.Value("bool"),
"soil_type_id_24": datasets.Value("bool"),
"soil_type_id_25": datasets.Value("bool"),
"soil_type_id_26": datasets.Value("bool"),
"soil_type_id_27": datasets.Value("bool"),
"soil_type_id_28": datasets.Value("bool"),
"soil_type_id_29": datasets.Value("bool"),
"soil_type_id_30": datasets.Value("bool"),
"soil_type_id_31": datasets.Value("bool"),
"soil_type_id_32": datasets.Value("bool"),
"soil_type_id_33": datasets.Value("bool"),
"soil_type_id_34": datasets.Value("bool"),
"soil_type_id_35": datasets.Value("bool"),
"soil_type_id_36": datasets.Value("bool"),
"soil_type_id_37": datasets.Value("bool"),
"soil_type_id_38": datasets.Value("bool"),
"soil_type_id_39": datasets.Value("bool"),
"soil_type": datasets.Value("string"),
"cover_type": datasets.ClassLabel(num_classes=7)
}
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class CovertypeConfig(datasets.BuilderConfig):
def __init__(self",
" **kwargs):
super(CovertypeConfig",
" self).__init__(version=VERSION",
" **kwargs)
self.features = features_per_config[kwargs["name"]]
class Covertype(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "covertype"
BUILDER_CONFIGS = [
CovertypeConfig(name="covertype"",
description="Covertype for multiclass classification.")
]
def _info(self):
if self.config.name not in features_per_config:
raise ValueError(f"Unknown configuration: {self.config.name}")
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):
# try:
# with gzip.open(filepath) as log:
# data = pandas.read_csv(log",
" header=None)
# except gzip.BadGzipFile:
data = pandas.read_csv(filepath",
" header=None)
print(data.columns)
print(data.shape[1]",
" len(_BASE_FEATURE_NAMES))
data.columns = _BASE_FEATURE_NAMES
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.loc[:",
" "cover_type"] = data["cover_type"].apply(lambda x: x - 1)
return data
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