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"""Titanic""" |
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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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DESCRIPTION = "Titanic dataset from the UCI ML repository." |
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_HOMEPAGE = "https://www.kaggle.com/datasets/vinicius150987/titanic3" |
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_URLS = ("https://www.kaggle.com/datasets/vinicius150987/titanic3") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/titanic/raw/main/titanic.csv" |
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} |
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features_types_per_config = { |
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"survival": { |
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"passenger_class": datasets.Value("int8"), |
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"is_male": datasets.Value("bool"), |
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"age": datasets.Value("float64"), |
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"sibsp": datasets.Value("float64"), |
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"parch": datasets.Value("float64"), |
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"ticket": datasets.Value("string"), |
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"fare": datasets.Value("float64"), |
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"cabin": datasets.Value("string"), |
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"embarked": datasets.Value("string"), |
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"has_survived": 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 TitanicConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(TitanicConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Titanic(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "survival" |
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BUILDER_CONFIGS = [ |
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TitanicConfig(name="survival", |
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description="Titanic 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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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): |
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data = data.rename(columns={"sex": "is_male"}) |
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data = data[list(features_types_per_config[self.config.name].keys())] |
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data.loc[:, "is_male"] = data.is_male.apply(lambda x: x == "male") |
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data.loc[data.age == "?", "age"] = data.age.apply(lambda x: x if x != "?" else -1) |
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return data |
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