Datasets:
Upload 3 files
Browse files- README.md +30 -1
- clean1V2.data +0 -0
- muskV2.py +410 -0
README.md
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---
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---
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---
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language:
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- en
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tags:
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- musk
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- tabular_classification
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- binary_classification
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- multiclass_classification
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pretty_name: Musk
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size_categories:
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- 100<n<1K
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task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
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- tabular-classification
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configs:
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- musk
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---
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# Musk
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The [Musk dataset](https://archive.ics.uci.edu/ml/datasets/Musk) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
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Census dataset including personal characteristic of a person, and their income threshold.
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# Configurations and tasks
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| **Configuration** | **Task** | **Description** |
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|-------------------|---------------------------|------------------------|
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| musk | Binary classification | Is the molecule a musk?|
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# Usage
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```python
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from datasets import load_dataset
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dataset = load_dataset("mstz/musk", "musk")["train"]
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```
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clean1V2.data
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muskV2.py
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"""Musk: A Census Dataset"""
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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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_BASE_FEATURE_NAMES = [
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"name",
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"conformation_name",
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"ray_0",
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"ray_1",
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"ray_2",
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"ray_3",
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"ray_4",
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"ray_5",
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"ray_6",
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"ray_7",
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"ray_8",
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"ray_9",
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"ray_10",
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"ray_11",
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"ray_12",
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"ray_13",
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"ray_14",
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"ray_15",
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"ray_16",
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"ray_17",
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"ray_18",
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"ray_19",
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"ray_20",
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"ray_21",
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"ray_22",
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"ray_23",
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"ray_24",
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"ray_25",
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"ray_26",
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"ray_27",
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"ray_28",
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"ray_29",
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"ray_30",
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"ray_31",
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"ray_32",
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"ray_33",
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"ray_34",
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"ray_35",
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"ray_36",
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"ray_37",
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"ray_38",
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"ray_39",
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"ray_40",
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"ray_41",
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"ray_42",
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"ray_43",
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"ray_44",
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"ray_45",
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"ray_46",
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"ray_47",
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"ray_48",
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"ray_49",
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"ray_50",
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"ray_51",
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"ray_52",
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"ray_53",
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"ray_54",
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"ray_55",
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"ray_56",
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"ray_57",
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"ray_58",
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"ray_59",
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"ray_60",
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"ray_61",
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"ray_62",
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"ray_63",
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"ray_64",
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"ray_65",
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"ray_66",
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"ray_67",
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"ray_68",
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"ray_69",
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"ray_70",
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"ray_71",
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"ray_72",
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"ray_73",
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"ray_74",
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"ray_75",
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"ray_76",
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"ray_77",
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"ray_78",
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"ray_79",
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"ray_80",
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"ray_81",
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"ray_82",
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"ray_83",
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"ray_84",
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"ray_85",
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"ray_86",
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"ray_87",
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"ray_88",
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"ray_89",
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"ray_90",
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"ray_91",
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"ray_92",
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"ray_93",
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"ray_94",
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"ray_95",
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"ray_96",
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"ray_97",
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"ray_98",
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"ray_99",
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"ray_100",
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"ray_101",
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"ray_102",
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"ray_103",
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"ray_104",
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"ray_105",
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"ray_106",
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"ray_107",
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"ray_108",
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"ray_109",
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"ray_110",
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"ray_111",
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"ray_112",
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"ray_113",
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"ray_114",
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"ray_115",
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"ray_116",
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"ray_117",
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"ray_118",
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"ray_119",
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"ray_120",
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"ray_121",
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"ray_122",
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"ray_123",
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"ray_124",
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"ray_125",
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"ray_126",
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"ray_127",
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"ray_128",
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"ray_129",
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"ray_130",
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"ray_131",
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"ray_132",
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"ray_133",
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"ray_134",
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"ray_135",
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"ray_136",
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"ray_137",
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"ray_138",
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"ray_139",
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"ray_140",
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"ray_141",
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"ray_142",
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"ray_143",
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"ray_144",
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"ray_145",
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"ray_146",
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"ray_147",
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"ray_148",
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"ray_149",
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"ray_150",
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"ray_151",
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"ray_152",
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"ray_153",
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"ray_154",
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"ray_155",
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"ray_156",
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"ray_157",
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"ray_158",
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"ray_159",
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"ray_160",
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"ray_161",
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"oxy_distance",
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"displacement_1",
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"displacement_2",
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"displacement_3",
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"is_muskV2"
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]
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DESCRIPTION = "Musk dataset from the UCI ML repository."
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Musk"
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_URLS = ("https://archive.ics.uci.edu/ml/datasets/Musk")
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_CITATION = """
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@misc{misc_musk_(version_2)_75,
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author = {Chapman,David & Jain,Ajay},
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title = {{Musk (Version 2)}},
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year = {1994},
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howpublished = {UCI Machine Learning Repository},
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note = {{DOI}: \\url{10.24432/C51608}}
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}"""
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# Dataset info
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urls_per_split = {
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"train": "https://huggingface.co/datasets/mstz/muskV2/raw/main/clean1.data"
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}
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features_types_per_config = {
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"musk": {
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"ray_0": datasets.Value("float64"),
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"ray_1": datasets.Value("float64"),
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"ray_2": datasets.Value("float64"),
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"ray_3": datasets.Value("float64"),
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"ray_4": datasets.Value("float64"),
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"ray_5": datasets.Value("float64"),
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207 |
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"ray_6": datasets.Value("float64"),
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"ray_7": datasets.Value("float64"),
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"ray_8": datasets.Value("float64"),
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"ray_9": datasets.Value("float64"),
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"ray_10": datasets.Value("float64"),
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"ray_11": datasets.Value("float64"),
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"ray_12": datasets.Value("float64"),
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"ray_13": datasets.Value("float64"),
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"ray_14": datasets.Value("float64"),
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"ray_15": datasets.Value("float64"),
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"ray_16": datasets.Value("float64"),
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"ray_17": datasets.Value("float64"),
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"ray_18": datasets.Value("float64"),
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"ray_19": datasets.Value("float64"),
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"ray_20": datasets.Value("float64"),
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"ray_21": datasets.Value("float64"),
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"ray_22": datasets.Value("float64"),
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"ray_23": datasets.Value("float64"),
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"ray_24": datasets.Value("float64"),
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"ray_25": datasets.Value("float64"),
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"ray_26": datasets.Value("float64"),
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"ray_27": datasets.Value("float64"),
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"ray_28": datasets.Value("float64"),
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"ray_29": datasets.Value("float64"),
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"ray_30": datasets.Value("float64"),
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"ray_31": datasets.Value("float64"),
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"ray_32": datasets.Value("float64"),
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"ray_33": datasets.Value("float64"),
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"ray_34": datasets.Value("float64"),
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"ray_35": datasets.Value("float64"),
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"ray_36": datasets.Value("float64"),
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"ray_37": datasets.Value("float64"),
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"ray_38": datasets.Value("float64"),
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"ray_39": datasets.Value("float64"),
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"ray_40": datasets.Value("float64"),
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"ray_41": datasets.Value("float64"),
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"ray_42": datasets.Value("float64"),
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"ray_43": datasets.Value("float64"),
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"ray_44": datasets.Value("float64"),
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"ray_45": datasets.Value("float64"),
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"ray_46": datasets.Value("float64"),
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"ray_47": datasets.Value("float64"),
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"ray_48": datasets.Value("float64"),
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"ray_49": datasets.Value("float64"),
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"ray_50": datasets.Value("float64"),
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"ray_51": datasets.Value("float64"),
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"ray_52": datasets.Value("float64"),
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+
"ray_53": datasets.Value("float64"),
|
255 |
+
"ray_54": datasets.Value("float64"),
|
256 |
+
"ray_55": datasets.Value("float64"),
|
257 |
+
"ray_56": datasets.Value("float64"),
|
258 |
+
"ray_57": datasets.Value("float64"),
|
259 |
+
"ray_58": datasets.Value("float64"),
|
260 |
+
"ray_59": datasets.Value("float64"),
|
261 |
+
"ray_60": datasets.Value("float64"),
|
262 |
+
"ray_61": datasets.Value("float64"),
|
263 |
+
"ray_62": datasets.Value("float64"),
|
264 |
+
"ray_63": datasets.Value("float64"),
|
265 |
+
"ray_64": datasets.Value("float64"),
|
266 |
+
"ray_65": datasets.Value("float64"),
|
267 |
+
"ray_66": datasets.Value("float64"),
|
268 |
+
"ray_67": datasets.Value("float64"),
|
269 |
+
"ray_68": datasets.Value("float64"),
|
270 |
+
"ray_69": datasets.Value("float64"),
|
271 |
+
"ray_70": datasets.Value("float64"),
|
272 |
+
"ray_71": datasets.Value("float64"),
|
273 |
+
"ray_72": datasets.Value("float64"),
|
274 |
+
"ray_73": datasets.Value("float64"),
|
275 |
+
"ray_74": datasets.Value("float64"),
|
276 |
+
"ray_75": datasets.Value("float64"),
|
277 |
+
"ray_76": datasets.Value("float64"),
|
278 |
+
"ray_77": datasets.Value("float64"),
|
279 |
+
"ray_78": datasets.Value("float64"),
|
280 |
+
"ray_79": datasets.Value("float64"),
|
281 |
+
"ray_80": datasets.Value("float64"),
|
282 |
+
"ray_81": datasets.Value("float64"),
|
283 |
+
"ray_82": datasets.Value("float64"),
|
284 |
+
"ray_83": datasets.Value("float64"),
|
285 |
+
"ray_84": datasets.Value("float64"),
|
286 |
+
"ray_85": datasets.Value("float64"),
|
287 |
+
"ray_86": datasets.Value("float64"),
|
288 |
+
"ray_87": datasets.Value("float64"),
|
289 |
+
"ray_88": datasets.Value("float64"),
|
290 |
+
"ray_89": datasets.Value("float64"),
|
291 |
+
"ray_90": datasets.Value("float64"),
|
292 |
+
"ray_91": datasets.Value("float64"),
|
293 |
+
"ray_92": datasets.Value("float64"),
|
294 |
+
"ray_93": datasets.Value("float64"),
|
295 |
+
"ray_94": datasets.Value("float64"),
|
296 |
+
"ray_95": datasets.Value("float64"),
|
297 |
+
"ray_96": datasets.Value("float64"),
|
298 |
+
"ray_97": datasets.Value("float64"),
|
299 |
+
"ray_98": datasets.Value("float64"),
|
300 |
+
"ray_99": datasets.Value("float64"),
|
301 |
+
"ray_100": datasets.Value("float64"),
|
302 |
+
"ray_101": datasets.Value("float64"),
|
303 |
+
"ray_102": datasets.Value("float64"),
|
304 |
+
"ray_103": datasets.Value("float64"),
|
305 |
+
"ray_104": datasets.Value("float64"),
|
306 |
+
"ray_105": datasets.Value("float64"),
|
307 |
+
"ray_106": datasets.Value("float64"),
|
308 |
+
"ray_107": datasets.Value("float64"),
|
309 |
+
"ray_108": datasets.Value("float64"),
|
310 |
+
"ray_109": datasets.Value("float64"),
|
311 |
+
"ray_110": datasets.Value("float64"),
|
312 |
+
"ray_111": datasets.Value("float64"),
|
313 |
+
"ray_112": datasets.Value("float64"),
|
314 |
+
"ray_113": datasets.Value("float64"),
|
315 |
+
"ray_114": datasets.Value("float64"),
|
316 |
+
"ray_115": datasets.Value("float64"),
|
317 |
+
"ray_116": datasets.Value("float64"),
|
318 |
+
"ray_117": datasets.Value("float64"),
|
319 |
+
"ray_118": datasets.Value("float64"),
|
320 |
+
"ray_119": datasets.Value("float64"),
|
321 |
+
"ray_120": datasets.Value("float64"),
|
322 |
+
"ray_121": datasets.Value("float64"),
|
323 |
+
"ray_122": datasets.Value("float64"),
|
324 |
+
"ray_123": datasets.Value("float64"),
|
325 |
+
"ray_124": datasets.Value("float64"),
|
326 |
+
"ray_125": datasets.Value("float64"),
|
327 |
+
"ray_126": datasets.Value("float64"),
|
328 |
+
"ray_127": datasets.Value("float64"),
|
329 |
+
"ray_128": datasets.Value("float64"),
|
330 |
+
"ray_129": datasets.Value("float64"),
|
331 |
+
"ray_130": datasets.Value("float64"),
|
332 |
+
"ray_131": datasets.Value("float64"),
|
333 |
+
"ray_132": datasets.Value("float64"),
|
334 |
+
"ray_133": datasets.Value("float64"),
|
335 |
+
"ray_134": datasets.Value("float64"),
|
336 |
+
"ray_135": datasets.Value("float64"),
|
337 |
+
"ray_136": datasets.Value("float64"),
|
338 |
+
"ray_137": datasets.Value("float64"),
|
339 |
+
"ray_138": datasets.Value("float64"),
|
340 |
+
"ray_139": datasets.Value("float64"),
|
341 |
+
"ray_140": datasets.Value("float64"),
|
342 |
+
"ray_141": datasets.Value("float64"),
|
343 |
+
"ray_142": datasets.Value("float64"),
|
344 |
+
"ray_143": datasets.Value("float64"),
|
345 |
+
"ray_144": datasets.Value("float64"),
|
346 |
+
"ray_145": datasets.Value("float64"),
|
347 |
+
"ray_146": datasets.Value("float64"),
|
348 |
+
"ray_147": datasets.Value("float64"),
|
349 |
+
"ray_148": datasets.Value("float64"),
|
350 |
+
"ray_149": datasets.Value("float64"),
|
351 |
+
"ray_150": datasets.Value("float64"),
|
352 |
+
"ray_151": datasets.Value("float64"),
|
353 |
+
"ray_152": datasets.Value("float64"),
|
354 |
+
"ray_153": datasets.Value("float64"),
|
355 |
+
"ray_154": datasets.Value("float64"),
|
356 |
+
"ray_155": datasets.Value("float64"),
|
357 |
+
"ray_156": datasets.Value("float64"),
|
358 |
+
"ray_157": datasets.Value("float64"),
|
359 |
+
"ray_158": datasets.Value("float64"),
|
360 |
+
"ray_159": datasets.Value("float64"),
|
361 |
+
"ray_160": datasets.Value("float64"),
|
362 |
+
"ray_161": datasets.Value("float64"),
|
363 |
+
"oxy_distance": datasets.Value("float64"),
|
364 |
+
"displacement_1": datasets.Value("float64"),
|
365 |
+
"displacement_2": datasets.Value("float64"),
|
366 |
+
"displacement_3": datasets.Value("float64"),
|
367 |
+
"is_musk": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
|
368 |
+
}
|
369 |
+
}
|
370 |
+
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
|
371 |
+
|
372 |
+
|
373 |
+
class MuskConfig(datasets.BuilderConfig):
|
374 |
+
def __init__(self, **kwargs):
|
375 |
+
super(MuskConfig, self).__init__(version=VERSION, **kwargs)
|
376 |
+
self.features = features_per_config[kwargs["name"]]
|
377 |
+
|
378 |
+
|
379 |
+
class Musk(datasets.GeneratorBasedBuilder):
|
380 |
+
# dataset versions
|
381 |
+
DEFAULT_CONFIG = "musk"
|
382 |
+
BUILDER_CONFIGS = [
|
383 |
+
MuskConfig(name="musk",
|
384 |
+
description="Musk for binary classification.")
|
385 |
+
]
|
386 |
+
|
387 |
+
|
388 |
+
def _info(self):
|
389 |
+
info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
|
390 |
+
features=features_per_config[self.config.name])
|
391 |
+
|
392 |
+
return info
|
393 |
+
|
394 |
+
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
|
395 |
+
downloads = dl_manager.download_and_extract(urls_per_split)
|
396 |
+
|
397 |
+
return [
|
398 |
+
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
|
399 |
+
]
|
400 |
+
|
401 |
+
def _generate_examples(self, filepath: str):
|
402 |
+
data = pandas.read_csv(filepath, header=None)
|
403 |
+
data.columns = _BASE_FEATURE_NAMES
|
404 |
+
data.drop("name", axis="columns", inplace=True)
|
405 |
+
data.drop("conformation_name", axis="columns", inplace=True)
|
406 |
+
|
407 |
+
for row_id, row in data.iterrows():
|
408 |
+
data_row = dict(row)
|
409 |
+
|
410 |
+
yield row_id, data_row
|