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"""Fertility""" |
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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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"season_of_sampling", |
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"age_at_time_of_sampling", |
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"has_had_childhood_diseases", |
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"has_had_serious_trauma", |
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"has_had_surgical_interventions", |
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"has_had_high_fevers_in_the_past_year", |
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"frequency_of_alcohol_consumption", |
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"smoking_frequency", |
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"number_of_sitting_hours_per_day", |
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"has_fertility_issues" |
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] |
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_ENCODING_DICS = { |
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"season_of_sampling" : { |
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-1: "winter", |
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-0.33: "spring", |
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+0.33: "summer", |
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+1: "fall", |
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}, |
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"has_had_childhood_diseases" : { |
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1: True, |
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0: False |
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}, |
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"has_had_serious_trauma" : { |
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1: True, |
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0: False |
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}, |
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"has_had_surgical_interventions" : { |
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1: True, |
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0: False |
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}, |
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"has_had_high_fevers_in_the_past_year" : { |
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1: "no", |
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0: "more than three months ago", |
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-1: "less than three months ago" |
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}, |
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"smoking_frequency" : { |
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1: "daily", |
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0: "occasionally", |
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-1: "never" |
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}, |
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"has_fertility_issues": { |
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"N": 0, |
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"O": 1 |
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} |
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} |
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DESCRIPTION = "Fertility dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Fertility" |
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_URLS = ("https://archive.ics.uci.edu/ml/datasets/Fertility") |
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_CITATION = """ |
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@misc{misc_fertility_244, |
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author = {Gil,David & Girela,Jose}, |
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title = {{Fertility}}, |
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year = {2013}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5Z01Z}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/fertility/raw/main/fertility_Diagnosis.txt" |
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} |
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features_types_per_config = { |
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"encoding": { |
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"feature": datasets.Value("string"), |
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"original_value": datasets.Value("string"), |
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"encoded_value": datasets.Value("int64"), |
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}, |
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"fertility": { |
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"season_of_sampling": datasets.Value("string"), |
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"age_at_time_of_sampling": datasets.Value("int8"), |
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"has_had_childhood_diseases": datasets.Value("bool"), |
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"has_had_serious_trauma": datasets.Value("bool"), |
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"has_had_surgical_interventions": datasets.Value("bool"), |
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"has_had_high_fevers_in_the_past_year": datasets.Value("string"), |
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"frequency_of_alcohol_consumption": datasets.Value("float64"), |
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"smoking_frequency": datasets.Value("string"), |
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"number_of_sitting_hours_per_day": datasets.Value("float64"), |
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"has_fertility_issues": 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 FertilityConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(FertilityConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Fertility(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "fertility" |
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BUILDER_CONFIGS = [ |
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FertilityConfig(name="encoding", |
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description="Encoding dictionaries for discrete features."), |
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FertilityConfig(name="fertility", |
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description="Fertility 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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else: |
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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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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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return data |
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def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
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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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data.loc[:, "age_at_time_of_sampling"] = data["age_at_time_of_sampling"].apply(lambda x: 18 + x * 18) |
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data = data[list(features_types_per_config[config].keys())] |
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return data |
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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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