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"""Magic""" |
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from typing import List |
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from functools import partial |
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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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"major_axis_length" |
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"minor_axis_length" |
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"log_of_sum_of_content" |
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"ratio_of_sum_of_highest_pixels_and_size" |
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"ratio_of_highest_pixel_and_size" |
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"projected_distance_highest_to_center_pixel" |
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"third_root_of_third_moment_along_major_axis" |
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"third_root_of_third_moment_along_minor_axis" |
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"angle_major_axis_to_origin" |
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"distance_origin_to_center" |
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"class" |
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] |
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DESCRIPTION = "Magic dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Magic" |
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_URLS = ("https://archive.ics.uci.edu/ml/datasets/Magic") |
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_CITATION = """ |
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@misc{misc_magic_gamma_telescope_159, |
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author = {Bock,R.}, |
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title = {{MAGIC Gamma Telescope}}, |
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year = {2007}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C52C8B}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/magic/raw/main/magic04.data" |
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} |
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features_types_per_config = { |
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"magic": { |
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"major_axis_length": datasets.Value("float64"), |
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"minor_axis_length": datasets.Value("float64"), |
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"log_of_sum_of_content": datasets.Value("float64"), |
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"ratio_of_sum_of_highest_pixels_and_size": datasets.Value("float64"), |
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"ratio_of_highest_pixel_and_size": datasets.Value("float64"), |
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"projected_distance_highest_to_center_pixel": datasets.Value("float64"), |
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"third_root_of_third_moment_along_major_axis": datasets.Value("float64"), |
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"third_root_of_third_moment_along_minor_axis": datasets.Value("float64"), |
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"angle_major_axis_to_origin": datasets.Value("float64"), |
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"distance_origin_to_center": datasets.Value("float64"), |
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"class": datasets.ClassLabel(num_classes=2, names=("no", "yes")) |
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} |
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} |
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
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class MagicConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(MagicConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Magic(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "magic" |
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BUILDER_CONFIGS = [ |
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MagicConfig(name="magic", |
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description="Magic for binary classification.") |
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] |
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def _info(self): |
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, |
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features=features_per_config[self.config.name]) |
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return info |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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downloads = dl_manager.download_and_extract(urls_per_split) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}) |
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] |
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def _generate_examples(self, filepath: str): |
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if self.config.name == "encoding": |
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data = self.encodings() |
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for row_id, row in data.iterrows(): |
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data_row = dict(row) |
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yield row_id, data_row |
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elif self.config.name in ["magic", "magic-no race", "race"]: |
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data = pandas.read_csv(filepath) |
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data = self.preprocess(data, config=self.config.name) |
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for row_id, row in data.iterrows(): |
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data_row = dict(row) |
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yield row_id, data_row |
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else: |
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raise ValueError(f"Unknown config: {self.config.name}") |
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def encodings(self): |
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data = [pandas.DataFrame([(feature, original_value, encoded_value) |
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for original_value, encoded_value in d.items()], |
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columns=["feature", "original_value", "encoded_value"]) |
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for feature, d in _ENCODING_DICS.items()] |
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data.append(pandas.DataFrame([("race", original_value, encoded_value) |
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for original_value, encoded_value in _RACE_ENCODING.items()], |
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columns=["feature", "original_value", "encoded_value"])) |
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data.append(pandas.DataFrame([("education", original_value, encoded_value) |
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for original_value, encoded_value in _EDUCATION_ENCODING.items()], |
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columns=["feature", "original_value", "encoded_value"])) |
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data = pandas.concat(data, axis="rows").reset_index() |
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data.drop("index", axis="columns", inplace=True) |
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return data |
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def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
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data.drop("education", axis="columns", inplace=True) |
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data = data.rename(columns={"threshold": "over_threshold", "sex": "is_male"}) |
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data = data[["age", "capital_gain", "capital_loss", "education-num", "final_weight", |
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"hours_per_week", "marital_status", "native_country", "occupation", |
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"race", "relationship", "is_male", "workclass", "over_threshold"]] |
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data.columns = _BASE_FEATURE_NAMES |
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for feature in _ENCODING_DICS: |
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encoding_function = partial(self.encode, feature) |
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data.loc[:, feature] = data[feature].apply(encoding_function) |
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if config == "magic": |
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return data[list(features_types_per_config["magic"].keys())] |
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elif config == "magic-no race": |
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return data[list(features_types_per_config["magic-no race"].keys())] |
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elif config =="race": |
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data.loc[:, "race"] = data.race.apply(self.encode_race) |
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data = data[list(features_types_per_config["race"].keys())] |
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return data |
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else: |
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raise ValueError(f"Unknown config: {config}") |
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def encode(self, feature, value): |
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if feature in _ENCODING_DICS: |
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return _ENCODING_DICS[feature][value] |
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raise ValueError(f"Unknown feature: {feature}") |
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def encode_race(self, race): |
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return _RACE_ENCODING[race] |
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