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"""Letter Dataset""" |
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from typing import List |
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from functools import partial |
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import string |
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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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_ENCODING_DICS = { |
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"letter": {letter: i for i, letter in enumerate(string.ascii_uppercase)} |
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} |
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DESCRIPTION = "Letter dataset." |
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_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/170/letter" |
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_URLS = ("https://archive-beta.ics.uci.edu/dataset/170/letter") |
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_CITATION = """ |
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@misc{misc_letter_recognition_59, |
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author = {Slate,David}, |
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title = {{Letter Recognition}}, |
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year = {1991}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5ZP40}} |
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} |
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""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/letter/resolve/main/letter.data" |
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} |
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features_types_per_config = { |
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"letter": { |
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"x-box": datasets.Value("int64"), |
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"y-box": datasets.Value("int64"), |
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"width": datasets.Value("int64"), |
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"high": datasets.Value("int64"), |
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"onpix": datasets.Value("int64"), |
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"x-bar": datasets.Value("int64"), |
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"y-bar": datasets.Value("int64"), |
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"x2bar": datasets.Value("int64"), |
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"y2bar": datasets.Value("int64"), |
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"xybar": datasets.Value("int64"), |
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"x2ybr": datasets.Value("int64"), |
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"xy2br": datasets.Value("int64"), |
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"x-ege": datasets.Value("int64"), |
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"xegvy": datasets.Value("int64"), |
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"y-ege": datasets.Value("int64"), |
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"yegvx": datasets.Value("int64"), |
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"letter": datasets.ClassLabel(num_classes=26) |
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} |
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} |
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for i, letter in enumerate(string.ascii_uppercase): |
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features_types_per_config[letter] = { |
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"x-box": datasets.Value("int64"), |
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"y-box": datasets.Value("int64"), |
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"width": datasets.Value("int64"), |
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"high": datasets.Value("int64"), |
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"onpix": datasets.Value("int64"), |
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"x-bar": datasets.Value("int64"), |
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"y-bar": datasets.Value("int64"), |
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"x2bar": datasets.Value("int64"), |
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"y2bar": datasets.Value("int64"), |
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"xybar": datasets.Value("int64"), |
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"x2ybr": datasets.Value("int64"), |
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"xy2br": datasets.Value("int64"), |
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"x-ege": datasets.Value("int64"), |
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"xegvy": datasets.Value("int64"), |
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"y-ege": datasets.Value("int64"), |
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"yegvx": datasets.Value("int64"), |
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"letter": datasets.ClassLabel(num_classes=2) |
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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 LetterConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(LetterConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Letter(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "letter" |
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BUILDER_CONFIGS = [ |
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LetterConfig(name="letter", description="Letter for multiclass classification."), |
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LetterConfig(name="A", description="Letter for binary letter A classification."), |
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LetterConfig(name="B", description="Letter for binary letter B classification."), |
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LetterConfig(name="C", description="Letter for binary letter C classification."), |
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LetterConfig(name="D", description="Letter for binary letter D classification."), |
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LetterConfig(name="E", description="Letter for binary letter E classification."), |
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LetterConfig(name="F", description="Letter for binary letter F classification."), |
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LetterConfig(name="G", description="Letter for binary letter G classification."), |
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LetterConfig(name="H", description="Letter for binary letter H classification."), |
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LetterConfig(name="I", description="Letter for binary letter I classification."), |
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LetterConfig(name="J", description="Letter for binary letter J classification."), |
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LetterConfig(name="K", description="Letter for binary letter K classification."), |
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LetterConfig(name="L", description="Letter for binary letter L classification."), |
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LetterConfig(name="M", description="Letter for binary letter M classification."), |
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LetterConfig(name="N", description="Letter for binary letter N classification."), |
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LetterConfig(name="O", description="Letter for binary letter O classification."), |
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LetterConfig(name="P", description="Letter for binary letter P classification."), |
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LetterConfig(name="Q", description="Letter for binary letter Q classification."), |
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LetterConfig(name="R", description="Letter for binary letter R classification."), |
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LetterConfig(name="S", description="Letter for binary letter S classification."), |
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LetterConfig(name="T", description="Letter for binary letter T classification."), |
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LetterConfig(name="U", description="Letter for binary letter U classification."), |
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LetterConfig(name="V", description="Letter for binary letter V classification."), |
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LetterConfig(name="W", description="Letter for binary letter W classification."), |
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LetterConfig(name="X", description="Letter for binary letter X classification."), |
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LetterConfig(name="Y", description="Letter for binary letter Y classification."), |
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LetterConfig(name="Z", description="Letter for binary letter Z 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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data = pandas.read_csv(filepath) |
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data = self.preprocess(data) |
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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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def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame: |
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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 self.config.name == "A": |
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data.letter = data.letter.apply(lambda x: 1 if x == 0 else 0) |
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elif self.config.name == "B": |
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data.letter = data.letter.apply(lambda x: 1 if x == 1 else 0) |
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elif self.config.name == "C": |
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data.letter = data.letter.apply(lambda x: 1 if x == 2 else 0) |
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elif self.config.name == "D": |
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data.letter = data.letter.apply(lambda x: 1 if x == 3 else 0) |
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elif self.config.name == "E": |
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data.letter = data.letter.apply(lambda x: 1 if x == 4 else 0) |
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elif self.config.name == "F": |
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data.letter = data.letter.apply(lambda x: 1 if x == 5 else 0) |
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elif self.config.name == "G": |
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data.letter = data.letter.apply(lambda x: 1 if x == 6 else 0) |
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elif self.config.name == "H": |
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data.letter = data.letter.apply(lambda x: 1 if x == 7 else 0) |
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elif self.config.name == "I": |
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data.letter = data.letter.apply(lambda x: 1 if x == 8 else 0) |
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elif self.config.name == "J": |
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data.letter = data.letter.apply(lambda x: 1 if x == 9 else 0) |
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elif self.config.name == "K": |
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data.letter = data.letter.apply(lambda x: 1 if x == 10 else 0) |
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elif self.config.name == "L": |
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data.letter = data.letter.apply(lambda x: 1 if x == 11 else 0) |
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elif self.config.name == "M": |
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data.letter = data.letter.apply(lambda x: 1 if x == 12 else 0) |
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elif self.config.name == "N": |
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data.letter = data.letter.apply(lambda x: 1 if x == 13 else 0) |
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elif self.config.name == "O": |
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data.letter = data.letter.apply(lambda x: 1 if x == 14 else 0) |
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elif self.config.name == "P": |
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data.letter = data.letter.apply(lambda x: 1 if x == 15 else 0) |
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elif self.config.name == "Q": |
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data.letter = data.letter.apply(lambda x: 1 if x == 16 else 0) |
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elif self.config.name == "R": |
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data.letter = data.letter.apply(lambda x: 1 if x == 17 else 0) |
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elif self.config.name == "S": |
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data.letter = data.letter.apply(lambda x: 1 if x == 18 else 0) |
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elif self.config.name == "T": |
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data.letter = data.letter.apply(lambda x: 1 if x == 19 else 0) |
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elif self.config.name == "U": |
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data.letter = data.letter.apply(lambda x: 1 if x == 20 else 0) |
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elif self.config.name == "V": |
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data.letter = data.letter.apply(lambda x: 1 if x == 21 else 0) |
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elif self.config.name == "W": |
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data.letter = data.letter.apply(lambda x: 1 if x == 22 else 0) |
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elif self.config.name == "X": |
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data.letter = data.letter.apply(lambda x: 1 if x == 23 else 0) |
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elif self.config.name == "Y": |
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data.letter = data.letter.apply(lambda x: 1 if x == 24 else 0) |
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elif self.config.name == "Z": |
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data.letter = data.letter.apply(lambda x: 1 if x == 25 else 0) |
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return data[list(features_types_per_config[self.config.name].keys())] |
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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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