Datasets:
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"""Diamond Dataset"""
from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
"carat",
"cut",
"color",
"clarity",
"depth",
"table",
"price",
"observation_point_on_axis_x",
"observation_point_on_axis_y",
"observation_point_on_axis_z"
]
_ENCODING_DICS = {
"cut": {
"Fair": 0,
"Good": 1,
"Very Good": 2,
"Premium": 3,
"Ideal": 4
},
"clarity": {
"IF": 0,
"VVS1": 1,
"VVS2": 2,
"VS1": 3,
"VS2": 4,
"SI1": 5,
"SI2": 6,
"I1": 7
}
}
DESCRIPTION = "Diamond quality dataset."
_HOMEPAGE = "https://www.kaggle.com/datasets/ulrikthygepedersen/diamonds"
_URLS = ("https://www.kaggle.com/datasets/ulrikthygepedersen/diamonds")
_CITATION = """"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/diamonds/raw/main/diamonds.csv",
}
features_types_per_config = {
"encoding": {
"feature": datasets.Value("string"),
"original_value": datasets.Value("string"),
"encoded_value": datasets.Value("int8"),
},
"cut": {
"carat": datasets.Value("float32"),
"color": datasets.Value("string"),
"clarity": datasets.Value("float32"),
"depth": datasets.Value("float32"),
"table": datasets.Value("float32"),
"price": datasets.Value("float32"),
"observation_point_on_axis_x": datasets.Value("float32"),
"observation_point_on_axis_y": datasets.Value("float32"),
"observation_point_on_axis_z": datasets.Value("float32"),
"cut": datasets.ClassLabel(num_classes=5, names=("Fair", "Good", "Very Good", "Premium", "Ideal"))
},
"cut_binary": {
"carat": datasets.Value("float32"),
"color": datasets.Value("string"),
"clarity": datasets.Value("float32"),
"depth": datasets.Value("float32"),
"table": datasets.Value("float32"),
"price": datasets.Value("float32"),
"observation_point_on_axis_x": datasets.Value("float32"),
"observation_point_on_axis_y": datasets.Value("float32"),
"observation_point_on_axis_z": datasets.Value("float32"),
"cut": 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 DiamondConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(DiamondConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Diamond(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "cut"
BUILDER_CONFIGS = [
DiamondConfig(name="encoding", description="Encoding dictionaries for discrete features."),
DiamondConfig(name="cut", description="5-ary classification, predict the cut quality of the diamond."),
DiamondConfig(name="cut_binary", description="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):
if self.config.name == "encoding":
data = self.encoding_dics()
else:
data = pandas.read_csv(filepath)
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 = "cut") -> pandas.DataFrame:
data["clarity"] = data.clarity.apply(lambda x: x.replace("b", "").replace("'", ""))
data["cut"] = data.cut.apply(lambda x: x.replace("b", "").replace("'", ""))
data["color"] = data.color.astype(str)
data["color"] = data.color.apply(lambda x: x[2]).replace("\"", "")
for feature in _ENCODING_DICS:
encoding_function = partial(self.encode, feature)
data[feature] = data[feature].apply(encoding_function)
data.columns = _BASE_FEATURE_NAMES
data = data.drop_duplicates(subset=["carat", "color", "clarity", "depth", "table", "price", "cut"])
if self.config.name == "cut_binary":
data.cut = data.cut.apply(lambda x: 0 if x <= 2 else 1)
return data[list(features_types_per_config["cut"].keys())]
def encode(self, feature, value):
if feature in _ENCODING_DICS:
return _ENCODING_DICS[feature][value]
raise ValueError(f"Unknown feature: {feature}")
def encoding_dics(self):
data = [pandas.DataFrame([(feature, original, encoded) for original, encoded in d.items()])
for feature, d in _ENCODING_DICS.items()]
data = pandas.concat(data, axis="rows").reset_index()
data.drop("index", axis="columns", inplace=True)
data.columns = ["feature", "original_value", "encoded_value"]
return data
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