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"""German Dataset""" |
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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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_ORIGINAL_FEATURE_NAMES = [ |
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"checking_account_status", |
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"account_life_in_months", |
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"credit_status", |
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"loan_purpose", |
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"current_credit", |
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"current_savings", |
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"employed_since", |
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"installment_rate_percentage", |
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"personal_status_and_sex", |
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"guarantors", |
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"years_living_in_current_residence", |
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"property", |
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"age", |
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"installment_plans", |
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"housing_status", |
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"nr_credit_accounts_in_bank", |
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"job_status", |
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"number_of_people_in_support", |
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"has_registered_phone_number", |
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"is_foreign", |
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"loan_granted", |
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] |
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_BASE_FEATURE_NAMES = [ |
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"checking_account_status", |
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"account_life_in_months", |
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"credit_status", |
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"loan_purpose", |
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"current_credit", |
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"current_savings", |
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"employed_since", |
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"installment_rate_percentage", |
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"sex", |
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"marital_status", |
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"guarantors", |
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"years_living_in_current_residence", |
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"age", |
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"installment_plans", |
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"housing_status", |
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"nr_credit_accounts_in_bank", |
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"job_status", |
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"number_of_people_in_support", |
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"has_registered_phone_number", |
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"is_foreign", |
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"loan_granted" |
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] |
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_ENCODING_DICS = { |
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"is_foreign": { |
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"A201": 0, |
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"A202": 1 |
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}, |
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"has_registered_phone_number": { |
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"A191": 0, |
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"A192": 1 |
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}, |
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"job_status": { |
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"A171": 0, |
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"A172": 1, |
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"A173": 2, |
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"A174": 3 |
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}, |
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"housing_status": { |
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"A153": 0, |
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"A151": 1, |
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"A152": 2 |
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}, |
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"installment_plans": { |
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"A141": 0, |
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"A142": 1, |
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"A143": 2 |
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}, |
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"guarantors": { |
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"A101": 0, |
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"A102": 1, |
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"A103": 2 |
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}, |
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"marital_status": { |
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"A91": 0, |
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"A92": 0, |
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"A93": 1, |
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"A94": 2, |
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"A95": 1, |
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}, |
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"sex": { |
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"A91": 0, |
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"A93": 0, |
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"A94": 0, |
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"A92": 1, |
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"A95": 1, |
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}, |
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"employed_since": { |
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"A71": 0, |
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"A72": 1, |
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"A73": 2, |
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"A74": 3, |
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"A75": 4, |
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}, |
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"current_savings": { |
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"A65": 0, |
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"A61": 1, |
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"A62": 2, |
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"A63": 3, |
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"A64": 4, |
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}, |
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"credit_status": { |
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"A30": 0, |
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"A31": 1, |
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"A32": 2, |
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"A33": 3, |
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"A34": 4, |
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}, |
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"checking_account_status": { |
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"A14": 0, |
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"A11": 1, |
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"A12": 2, |
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"A13": 3, |
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} |
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} |
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DESCRIPTION = "German dataset for cancer prediction." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29" |
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_URLS = ("https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/german/raw/main/german.data", |
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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("int8"), |
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}, |
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"loan": { |
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"checking_account_status": datasets.Value("int8"), |
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"account_life_in_months": datasets.Value("int8"), |
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"credit_status": datasets.Value("int8"), |
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"loan_purpose": datasets.Value("string"), |
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"current_credit": datasets.Value("int32"), |
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"current_savings": datasets.Value("int8"), |
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"employed_since": datasets.Value("int8"), |
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"installment_rate_percentage": datasets.Value("int8"), |
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"is_male": datasets.Value("bool"), |
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"marital_status": datasets.Value("string"), |
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"guarantors": datasets.Value("int8"), |
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"years_living_in_current_residence": datasets.Value("int8"), |
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"age": datasets.Value("int8"), |
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"installment_plans": datasets.Value("string"), |
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"housing_status": datasets.Value("int8"), |
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"nr_credit_accounts_in_bank": datasets.Value("int8"), |
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"job_status": datasets.Value("int8"), |
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"number_of_people_in_support": datasets.Value("int8"), |
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"has_registered_phone_number": datasets.Value("int8"), |
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"is_foreign": datasets.Value("bool"), |
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"loan_granted": 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 GermanConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(GermanConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class German(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "loan" |
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BUILDER_CONFIGS = [ |
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GermanConfig(name="encoding", |
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description="Encoding dictionaries for discrete features."), |
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GermanConfig(name="loan", |
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description="Binary classification of loan approval."), |
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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.encoding_dics() |
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else: |
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data = pandas.read_csv(filepath, sep=" ", header=None) |
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data.columns=_ORIGINAL_FEATURE_NAMES |
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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 preprocess(self, data: pandas.DataFrame, config: str = "loan") -> pandas.DataFrame: |
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for feature in _ENCODING_DICS: |
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if feature not in ["marital_status", "sex"]: |
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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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encode_marital_status = partial(self.encode, "marital_status") |
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encode_sex = partial(self.encode, "sex") |
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data.loc[:, "marital_status"] = data.personal_status_and_sex.apply(encode_marital_status) |
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data.loc[:, "sex"] = data.personal_status_and_sex.apply(encode_sex) |
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data.loc[:, "loan_purpose"] = data.loan_purpose.apply(self.encode_loan_purpose) |
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data.loc[:, "loan_granted"] = data.loan_granted.apply(lambda x: x - 1) |
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data.drop("personal_status_and_sex", axis="columns", inplace=True) |
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data = data[_BASE_FEATURE_NAMES] |
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data = data.rename(columns={"sex": "is_male"}) |
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data = data.astype({"is_foreign": "bool"}) |
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data.loc[:, "is_foreign"] = data.is_foreign.apply(bool) |
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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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def encoding_dics(self): |
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data = [pandas.DataFrame([(feature, original, encoded) for original, encoded in d.items()]) |
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for feature, d in _ENCODING_DICS.items()] |
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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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data.columns = ["feature", "original_value", "encoded_value"] |
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return data |
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def encode_loan_purpose(self, code): |
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return { |
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"A40": "new car", |
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"A41": "used car", |
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"A42": "furniture/equipment", |
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"A43": "radio/television", |
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"A44": "domestic appliances", |
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"A45": "repairs", |
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"A46": "education", |
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"A47": "vacation ", |
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"A48": "retraining", |
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"A49": "business", |
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"A410": "others" |
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}[code] |
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