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README.md

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 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- musk
+- tabular_classification
+- binary_classification
+- multiclass_classification
+pretty_name: Musk
+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:
+- musk1
+- musk2
 ---
+# Musk
+The [Musk dataset](https://archive.ics.uci.edu/ml/datasets/Musk) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
+Census dataset including personal characteristic of a person, and their income threshold.
+
+# Configurations and tasks
+| **Configuration** | **Task**                  | **Description**        |
+|-------------------|---------------------------|------------------------|
+| musk1             | Binary classification     | Is the molecule a musk?|
+| musk2             | Binary classification     | Is the molecule a musk?|
+
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/musk", "musk1")["train"]
+```

musk.py

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+"""Musk: A Census Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+_BASE_FEATURE_NAMES = [
+    "name",
+    "conformation_name",
+    "ray_0",
+    "ray_1",
+    "ray_2",
+    "ray_3",
+    "ray_4",
+    "ray_5",
+    "ray_6",
+    "ray_7",
+    "ray_8",
+    "ray_9",
+    "ray_10",
+    "ray_11",
+    "ray_12",
+    "ray_13",
+    "ray_14",
+    "ray_15",
+    "ray_16",
+    "ray_17",
+    "ray_18",
+    "ray_19",
+    "ray_20",
+    "ray_21",
+    "ray_22",
+    "ray_23",
+    "ray_24",
+    "ray_25",
+    "ray_26",
+    "ray_27",
+    "ray_28",
+    "ray_29",
+    "ray_30",
+    "ray_31",
+    "ray_32",
+    "ray_33",
+    "ray_34",
+    "ray_35",
+    "ray_36",
+    "ray_37",
+    "ray_38",
+    "ray_39",
+    "ray_40",
+    "ray_41",
+    "ray_42",
+    "ray_43",
+    "ray_44",
+    "ray_45",
+    "ray_46",
+    "ray_47",
+    "ray_48",
+    "ray_49",
+    "ray_50",
+    "ray_51",
+    "ray_52",
+    "ray_53",
+    "ray_54",
+    "ray_55",
+    "ray_56",
+    "ray_57",
+    "ray_58",
+    "ray_59",
+    "ray_60",
+    "ray_61",
+    "oxy_distance",
+    "displacement_1",
+    "displacement_2",
+    "displacement_3",
+    "is_musk"
+]
+
+DESCRIPTION = "Musk dataset from the UCI ML repository."
+_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Musk"
+_URLS = ("https://huggingface.co/datasets/mstz/musk/raw/musk.csv")
+_CITATION = """
+@misc{misc_musk_(version_1)_74,
+  author       = {Chapman,David & Jain,Ajay},
+  title        = {{Musk (Version 1)}},
+  year         = {1994},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5ZK5B}}
+}"""
+
+# Dataset info
+urls_per_split = {
+    "musk1": {
+        "train": "https://huggingface.co/datasets/mstz/musk/raw/main/clean1.data"
+    },
+    "musk2": {
+        "train": "https://huggingface.co/datasets/mstz/musk/raw/main/clean2.data"
+    }
+}
+features_types_per_config = {
+    "musk1": {
+        "ray_0": datasets.Value("float64"),
+        "ray_1": datasets.Value("float64"),
+        "ray_2": datasets.Value("float64"),
+        "ray_3": datasets.Value("float64"),
+        "ray_4": datasets.Value("float64"),
+        "ray_5": datasets.Value("float64"),
+        "ray_6": datasets.Value("float64"),
+        "ray_7": datasets.Value("float64"),
+        "ray_8": datasets.Value("float64"),
+        "ray_9": datasets.Value("float64"),
+        "ray_10": datasets.Value("float64"),
+        "ray_11": datasets.Value("float64"),
+        "ray_12": datasets.Value("float64"),
+        "ray_13": datasets.Value("float64"),
+        "ray_14": datasets.Value("float64"),
+        "ray_15": datasets.Value("float64"),
+        "ray_16": datasets.Value("float64"),
+        "ray_17": datasets.Value("float64"),
+        "ray_18": datasets.Value("float64"),
+        "ray_19": datasets.Value("float64"),
+        "ray_20": datasets.Value("float64"),
+        "ray_21": datasets.Value("float64"),
+        "ray_22": datasets.Value("float64"),
+        "ray_23": datasets.Value("float64"),
+        "ray_24": datasets.Value("float64"),
+        "ray_25": datasets.Value("float64"),
+        "ray_26": datasets.Value("float64"),
+        "ray_27": datasets.Value("float64"),
+        "ray_28": datasets.Value("float64"),
+        "ray_29": datasets.Value("float64"),
+        "ray_30": datasets.Value("float64"),
+        "ray_31": datasets.Value("float64"),
+        "ray_32": datasets.Value("float64"),
+        "ray_33": datasets.Value("float64"),
+        "ray_34": datasets.Value("float64"),
+        "ray_35": datasets.Value("float64"),
+        "ray_36": datasets.Value("float64"),
+        "ray_37": datasets.Value("float64"),
+        "ray_38": datasets.Value("float64"),
+        "ray_39": datasets.Value("float64"),
+        "ray_40": datasets.Value("float64"),
+        "ray_41": datasets.Value("float64"),
+        "ray_42": datasets.Value("float64"),
+        "ray_43": datasets.Value("float64"),
+        "ray_44": datasets.Value("float64"),
+        "ray_45": datasets.Value("float64"),
+        "ray_46": datasets.Value("float64"),
+        "ray_47": datasets.Value("float64"),
+        "ray_48": datasets.Value("float64"),
+        "ray_49": datasets.Value("float64"),
+        "ray_50": datasets.Value("float64"),
+        "ray_51": datasets.Value("float64"),
+        "ray_52": datasets.Value("float64"),
+        "ray_53": datasets.Value("float64"),
+        "ray_54": datasets.Value("float64"),
+        "ray_55": datasets.Value("float64"),
+        "ray_56": datasets.Value("float64"),
+        "ray_57": datasets.Value("float64"),
+        "ray_58": datasets.Value("float64"),
+        "ray_59": datasets.Value("float64"),
+        "ray_60": datasets.Value("float64"),
+        "ray_61": datasets.Value("float64"),
+        "oxy_distance": datasets.Value("float64"),
+        "displacement_1": datasets.Value("float64"),
+        "displacement_2": datasets.Value("float64"),
+        "displacement_3": datasets.Value("float64"),
+        "is_musk": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
+    },
+    "musk2": {
+        "ray_0": datasets.Value("float64"),
+        "ray_1": datasets.Value("float64"),
+        "ray_2": datasets.Value("float64"),
+        "ray_3": datasets.Value("float64"),
+        "ray_4": datasets.Value("float64"),
+        "ray_5": datasets.Value("float64"),
+        "ray_6": datasets.Value("float64"),
+        "ray_7": datasets.Value("float64"),
+        "ray_8": datasets.Value("float64"),
+        "ray_9": datasets.Value("float64"),
+        "ray_10": datasets.Value("float64"),
+        "ray_11": datasets.Value("float64"),
+        "ray_12": datasets.Value("float64"),
+        "ray_13": datasets.Value("float64"),
+        "ray_14": datasets.Value("float64"),
+        "ray_15": datasets.Value("float64"),
+        "ray_16": datasets.Value("float64"),
+        "ray_17": datasets.Value("float64"),
+        "ray_18": datasets.Value("float64"),
+        "ray_19": datasets.Value("float64"),
+        "ray_20": datasets.Value("float64"),
+        "ray_21": datasets.Value("float64"),
+        "ray_22": datasets.Value("float64"),
+        "ray_23": datasets.Value("float64"),
+        "ray_24": datasets.Value("float64"),
+        "ray_25": datasets.Value("float64"),
+        "ray_26": datasets.Value("float64"),
+        "ray_27": datasets.Value("float64"),
+        "ray_28": datasets.Value("float64"),
+        "ray_29": datasets.Value("float64"),
+        "ray_30": datasets.Value("float64"),
+        "ray_31": datasets.Value("float64"),
+        "ray_32": datasets.Value("float64"),
+        "ray_33": datasets.Value("float64"),
+        "ray_34": datasets.Value("float64"),
+        "ray_35": datasets.Value("float64"),
+        "ray_36": datasets.Value("float64"),
+        "ray_37": datasets.Value("float64"),
+        "ray_38": datasets.Value("float64"),
+        "ray_39": datasets.Value("float64"),
+        "ray_40": datasets.Value("float64"),
+        "ray_41": datasets.Value("float64"),
+        "ray_42": datasets.Value("float64"),
+        "ray_43": datasets.Value("float64"),
+        "ray_44": datasets.Value("float64"),
+        "ray_45": datasets.Value("float64"),
+        "ray_46": datasets.Value("float64"),
+        "ray_47": datasets.Value("float64"),
+        "ray_48": datasets.Value("float64"),
+        "ray_49": datasets.Value("float64"),
+        "ray_50": datasets.Value("float64"),
+        "ray_51": datasets.Value("float64"),
+        "ray_52": datasets.Value("float64"),
+        "ray_53": datasets.Value("float64"),
+        "ray_54": datasets.Value("float64"),
+        "ray_55": datasets.Value("float64"),
+        "ray_56": datasets.Value("float64"),
+        "ray_57": datasets.Value("float64"),
+        "ray_58": datasets.Value("float64"),
+        "ray_59": datasets.Value("float64"),
+        "ray_60": datasets.Value("float64"),
+        "ray_61": datasets.Value("float64"),
+        "oxy_distance": datasets.Value("float64"),
+        "displacement_1": datasets.Value("float64"),
+        "displacement_2": datasets.Value("float64"),
+        "displacement_3": datasets.Value("float64"),
+        "is_musk": 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 MuskConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(MuskConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Musk(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "musk1"
+    BUILDER_CONFIGS = [
+        MuskConfig(name="musk1",
+                    description="Musk for binary classification."),
+        MuskConfig(name="musk2",
+                    description="Musk for 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):
+        data = pandas.read_csv(filepath)
+        data = data.drop("name", axis="columns", inplace=True)
+        data = data.drop("conformation_name", axis="columns", inplace=True)
+
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+
+            yield row_id, data_row