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README.md

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+---
+language:
+- en
+tags:
+- diamonds
+- tabular_classification
+- binary_classification
+pretty_name: Compas
+size_categories:
+- 10K<n<100K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- cut
+---
+# Diamonds
+The [Diamonds dataset](https://www.kaggle.com/datasets/ulrikthygepedersen/diamonds) is cool.

diamonds.py

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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 = {
+    "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"))
+    }
+    
+}
+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="cut",
+                   description="5-ary classification, predict the cut quality of the diamond."),
+    ]
+
+
+    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):
+        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.loc[:, "color"] = data.color.astype(str)
+        data.loc[:, "color"] = data.color.apply(lambda x: x[2])
+
+        encode_clarity = partial(encode, "clarity")
+        data.loc[:, "clarity"] = data.clarity.apply(encode_clarity)
+         
+        data.columns = _BASE_FEATURE_NAMES
+        data = data.drop_duplicates(subset=["carat", "color", "clarity", "depth", "table",
+                                            "price", "cut"])
+        
+        if config == "cut":
+            return data[list(features_types_per_config["cut"].keys())]
+        else:
+            raise ValueError(f"Unknown config: {config}")              
+
+    def encode(self, feature, value):
+        return _ENCODING_DICS[feature][value]