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"""NBFI: A Census 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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"ID", |
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"Client_Income", |
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"Car_Owned", |
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"Bike_Owned", |
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"Active_Loan", |
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"House_Own", |
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"Child_Count", |
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"Credit_Amount", |
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"Loan_Annuity", |
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"Accompany_Client", |
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"Client_Income_Type", |
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"Client_Education", |
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"Client_Marital_Status", |
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"Client_Gender", |
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"Loan_Contract_Type", |
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"Client_Housing_Type", |
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"Population_Region_Relative", |
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"Age_Days", |
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"Employed_Days", |
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"Registration_Days", |
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"ID_Days", |
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"Own_House_Age", |
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"Mobile_Tag", |
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"Homephone_Tag", |
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"Workphone_Working", |
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"Client_Occupation", |
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"Client_Family_Members", |
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"Cleint_City_Rating", |
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"Application_Process_Day", |
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"Application_Process_Hour", |
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"Client_Permanent_Match_Tag", |
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"Client_Contact_Work_Tag", |
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"Type_Organization", |
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"Score_Source_1", |
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"Score_Source_2", |
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"Score_Source_3", |
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"Social_Circle_Default", |
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"Phone_Change", |
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"Credit_Bureau", |
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"Default" |
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] |
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_BASE_FEATURE_NAMES = [ |
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"income", |
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"owns_a_car", |
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"owns_a_bike", |
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"has_an_active_loan", |
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"owns_a_house", |
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"nr_children", |
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"credit", |
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"loan_annuity", |
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"accompanied_by", |
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"income_type", |
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"education_level", |
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"marital_status", |
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"is_male", |
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"type_of_contract", |
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"type_of_housing", |
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"residence_density", |
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"age_in_days", |
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"consecutive_days_of_employment", |
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"nr_days_since_last_registration_change", |
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"nr_days_since_last_document_change", |
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"has_provided_a_mobile_number", |
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"has_provided_a_home_number", |
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"was_reachable_at_work", |
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"job", |
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"nr_family_members", |
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"city_rating", |
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"weekday_of_application", |
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"hour_of_application", |
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"same_residence_and_home", |
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"same_work_and_home", |
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"score_1", |
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"score_2", |
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"score_3", |
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"nr_defaults_in_social_circle", |
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"inquiries_in_last_year", |
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"has_defaulted" |
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] |
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features_types_per_config = { |
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"default": { |
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"income": datasets.Value("float32"), |
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"owns_a_car": datasets.Value("bool"), |
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"owns_a_bike": datasets.Value("bool"), |
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"has_an_active_loan": datasets.Value("bool"), |
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"owns_a_house": datasets.Value("bool"), |
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"nr_children": datasets.Value("int8"), |
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"credit": datasets.Value("float32"), |
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"loan_annuity": datasets.Value("float32"), |
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"accompanied_by": datasets.Value("string"), |
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"income_type": datasets.Value("string"), |
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"education_level": datasets.Value("float32"), |
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"marital_status": datasets.Value("string"), |
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"is_male": datasets.Value("bool"), |
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"type_of_contract": datasets.Value("string"), |
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"type_of_housing": datasets.Value("string"), |
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"residence_density": datasets.Value("float32"), |
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"age_in_days": datasets.Value("int32"), |
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"consecutive_days_of_employment": datasets.Value("int16"), |
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"nr_days_since_last_registration_change": datasets.Value("int32"), |
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"nr_days_since_last_document_change": datasets.Value("int32"), |
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"has_provided_a_mobile_number": datasets.Value("bool"), |
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"has_provided_a_home_number": datasets.Value("bool"), |
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"was_reachable_at_work": datasets.Value("bool"), |
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"job": datasets.Value("string"), |
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"nr_family_members": datasets.Value("int8"), |
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"city_rating": datasets.Value("int8"), |
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"weekday_of_application": datasets.Value("int8"), |
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"hour_of_application": datasets.Value("float32"), |
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"same_residence_and_home": datasets.Value("bool"), |
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"same_work_and_home": datasets.Value("bool"), |
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"score_1": datasets.Value("float32"), |
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"score_2": datasets.Value("float32"), |
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"score_3": datasets.Value("float32"), |
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"nr_defaults_in_social_circle": datasets.Value("int8"), |
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"inquiries_in_last_year": datasets.Value("float32"), |
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"has_defaulted": datasets.ClassLabel(num_classes=2, names=("no", "yes")) |
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} |
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} |
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_ENCODING_DICS = {} |
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_EDUCATION_ENCODING = { |
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"Junior secondary": 0, |
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"Secondary": 1, |
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"Graduation dropout": 2, |
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"Graduation": 2, |
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"Post Grad": 4 |
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} |
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DESCRIPTION = "NBFI dataset from default prediction." |
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_HOMEPAGE = "https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset" |
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_URLS = ("https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://gist.githubusercontent.com/msetzu/6c83dc3b7092d428ae2f08dc91e1020c/raw/9fc3171b293d0dc29963357450308eb4c7e3a15b/Train_Dataset.csv", |
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"test": "https://gist.githubusercontent.com/msetzu/f0032b855008f579299d7ad78d9dd9c2/raw/ba42badeb10b505cb283bdb16d3de581ffe7a332/Test_Dataset.csv" |
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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 NBFIConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(NBFIConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class NBFI(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "default" |
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BUILDER_CONFIGS = [ |
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NBFIConfig(name="default", |
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description="NBFI for default binary classification.") |
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] |
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def _info(self): |
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if self.config.name not in features_per_config: |
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raise ValueError(f"Unknown configuration: {self.config.name}") |
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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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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"]}), |
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] |
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def _generate_examples(self, filepath: str): |
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if self.config.name == "default": |
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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 preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
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data.drop("ID", axis="columns", inplace=True) |
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data.drop("Own_House_Age", axis="columns", inplace=True) |
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data.drop("Type_Organization", axis="columns", inplace=True) |
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data.drop("Phone_Change", axis="columns", inplace=True) |
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data = data[~data.Client_Income.isna()] |
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data = data[~data.Client_Education.isna()] |
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data = data[~data.Child_Count.isna()] |
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data = data[~data.Client_Marital_Status.isna()] |
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data = data[~data.Client_Gender.isna()] |
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data = data[~data.Loan_Contract_Type.isna()] |
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data = data[~data.Client_Housing_Type.isna()] |
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data = data[~data.Age_Days.isna()] |
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data = data[~data.Employed_Days.isna()] |
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data = data[~data.Registration_Days.isna()] |
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data = data[~data.ID_Days.isna()] |
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data = data[~data.Cleint_City_Rating.isna()] |
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data = data[~data.Application_Process_Day.isna()] |
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data = data[~data.Application_Process_Hour.isna()] |
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data = data[~data.Client_Permanent_Match_Tag.isna()] |
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data = data[~data.Client_Contact_Work_Tag.isna()] |
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data = data[~data.Score_Source_1.isna()] |
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data = data[~data.Score_Source_2.isna()] |
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data = data[~data.Score_Source_3.isna()] |
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data = data[~data.Credit_Bureau.isna()] |
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data = data[~data.Credit_Amount.isna()] |
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data = data[~data.Loan_Annuity.isna()] |
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data = data[~data.Accompany_Client.isna()] |
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data = data[~data.Client_Occupation.isna()] |
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data = data[~data.Client_Family_Members.isna()] |
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data = data[~data.Social_Circle_Default.isna()] |
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data = data[~data.Population_Region_Relative.isin(("@", "#"))] |
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data = data[data.Loan_Annuity != "#VALUE!"] |
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data = data[data.Age_Days != "x"] |
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data = data[data.Employed_Days != "x"] |
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data = data[data.Registration_Days != "x"] |
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data = data[data.ID_Days != "x"] |
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print("len(data.columns)") |
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print(len(data.columns)) |
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print("len(_BASE_FEATURE_NAMES)") |
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print(len(_BASE_FEATURE_NAMES)) |
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print("len(features_types_per_config[config].keys())") |
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print(len(features_types_per_config[config].keys())) |
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print("data.columns, features_types_per_config, _BASE_FEATURE_NAMES") |
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for f, ft, fb in zip(data.columns, features_types_per_config[config].keys(), _BASE_FEATURE_NAMES): |
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print(f, ft, fb) |
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data.columns = _BASE_FEATURE_NAMES |
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data["education_level"] = data["education_level"].apply(lambda x: _EDUCATION_ENCODING[x]) |
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data["is_male"] = data["is_male"].apply(lambda x: x == "M") |
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data["owns_a_car"] = data["owns_a_car"].apply(bool) |
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data["owns_a_bike"] = data["owns_a_bike"].apply(bool) |
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data["has_an_active_loan"] = data["has_an_active_loan"].apply(bool) |
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data["owns_a_house"] = data["owns_a_house"].apply(bool) |
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data["is_male"] = data["is_male"].apply(bool) |
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data["has_provided_a_mobile_number"] = data["has_provided_a_mobile_number"].apply(bool) |
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data["has_provided_a_home_number"] = data["has_provided_a_home_number"].apply(bool) |
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data["was_reachable_at_work"] = data["was_reachable_at_work"].apply(bool) |
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data["same_residence_and_home"] = data["same_residence_and_home"].apply(bool) |
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data["same_work_and_home"] = data["same_work_and_home"].apply(bool) |
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data["residence_density"] = data["residence_density"].apply(float) |
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data = data.astype({ |
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"is_male": "bool", |
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"nr_children": "int8" |
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}) |
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return data[list(features_types_per_config[config].keys())] |
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