<filename>datasets/credit/Credit.py
from code.models.Feature import Feature
from code.models.FeatureTypeCategorical import FeatureTypeCategorical
from code.models.FeatureTypeContinuous import FeatureTypeContinuous
from code.models.FeatureTypeOrdinal import FeatureTypeOrdinal
from code.models.SensitiveClass import SensitiveClass
from code.models.TargetClass import TargetClass
from code.models.dataset import Dataset


class Credit(Dataset):
    # https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)

    def __init__(self, test_size, oversampling_factor, safe_weight, borderline_weight, rare_weight):
        name = "credit"
        target_class = TargetClass("credit", "Positive", "Negative")
        sensitive_class_gender = SensitiveClass("personal_status", ["male"], ["female"])
        sensitive_class_age = SensitiveClass("age", ["adult"], ["young"])
        sensitive_classes = [sensitive_class_age, sensitive_class_gender]
        features = [
            self.get_feature_status(),
            self.get_feature_month(),
            self.get_feature_credit_history(),
            self.get_feature_purpose(),
            self.get_feature_credit_amount(),
            self.get_feature_savings(),
            self.get_feature_employment(),
            self.get_feature_investment_as_income_percentage(),
            self.get_feature_personal_status(),
            self.get_feature_other_debtors(),
            self.get_feature_residence_since(),
            self.get_feature_property(),
            self.get_feature_age(),
            self.get_feature_installment_plans(),
            self.get_feature_housing(),
            self.get_feature_number_of_credits(),
            self.get_feature_skill_level(),
            self.get_feature_people_liable_for(),
            self.get_feature_telephone(),
            self.get_feature_foreign_worker(),
            self.get_feature_credit()
        ]

        super().__init__(name, target_class, sensitive_classes, features, test_size, oversampling_factor,
                         safe_weight, borderline_weight, rare_weight)

    def create_raw_transformed_dataset(self):
        raw_dataset = self.get_raw_dataset()

        old = raw_dataset['age'] >= 25  # http://ieeexplore.ieee.org/document/4909197/
        raw_dataset.loc[old, 'age'] = "adult"
        young = raw_dataset['age'] != "adult"
        raw_dataset.loc[young, 'age'] = "young"

        positive = raw_dataset['credit'] == 1
        raw_dataset.loc[positive, 'credit'] = "Positive"
        negative = raw_dataset['credit'] == 2
        raw_dataset.loc[negative, 'credit'] = "Negative"

        raw_transformed_dataset_filename = self.get_raw_transformed_dataset_filename()
        f = open(raw_transformed_dataset_filename, "w+")
        f.write(raw_dataset.to_csv(index=False))
        f.close()

    @staticmethod
    def get_feature_status():
        name = "status"
        order = ["A11", "A14", "A12", "A13"]
        feature_type = FeatureTypeOrdinal(order)
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_month():
        name = "month"
        feature_type = FeatureTypeContinuous()
        should_standardize = True

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_credit_history():
        name = "credit_history"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_purpose():
        name = "purpose"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_credit_amount():
        name = "credit_amount"
        feature_type = FeatureTypeContinuous()
        should_standardize = True

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_savings():
        name = "savings"
        order = ["A65", "A61", "A62", "A63", "A64"]
        feature_type = FeatureTypeOrdinal(order)
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_employment():
        name = "employment"
        order = ["A71", "A72", "A73", "A74", "A75"]
        feature_type = FeatureTypeOrdinal(order)
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_investment_as_income_percentage():
        name = "investment_as_income_percentage"
        feature_type = FeatureTypeContinuous()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_personal_status():
        name = "personal_status"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_other_debtors():
        name = "other_debtors"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_residence_since():
        name = "residence_since"
        feature_type = FeatureTypeContinuous()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_property():
        name = "property"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_age():
        name = "age"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_installment_plans():
        name = "installment_plans"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_housing():
        name = "housing"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_number_of_credits():
        name = "number_of_credits"
        feature_type = FeatureTypeContinuous()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_skill_level():
        name = "skill_level"
        order = ["A171", "A172", "A173", "A174"]
        feature_type = FeatureTypeOrdinal(order)
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_people_liable_for():
        name = "people_liable_for"
        feature_type = FeatureTypeContinuous()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_telephone():
        name = "telephone"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_foreign_worker():
        name = "foreign_worker"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_credit():
        name = "credit"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

<filename>datasets/credit/Credit.py
from code.models.Feature import Feature
from code.models.FeatureTypeCategorical import FeatureTypeCategorical
from code.models.FeatureTypeContinuous import FeatureTypeContinuous
from code.models.FeatureTypeOrdinal import FeatureTypeOrdinal
from code.models.SensitiveClass import SensitiveClass
from code.models.TargetClass import TargetClass
from code.models.dataset import Dataset


class Credit(Dataset):
    # https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)

    def __init__(self, test_size, oversampling_factor, safe_weight, borderline_weight, rare_weight):
        name = "credit"
        target_class = TargetClass("credit", "Positive", "Negative")
        sensitive_class_gender = SensitiveClass("personal_status", ["male"], ["female"])
        sensitive_class_age = SensitiveClass("age", ["adult"], ["young"])
        sensitive_classes = [sensitive_class_age, sensitive_class_gender]
        features = [
            self.get_feature_status(),
            self.get_feature_month(),
            self.get_feature_credit_history(),
            self.get_feature_purpose(),
            self.get_feature_credit_amount(),
            self.get_feature_savings(),
            self.get_feature_employment(),
            self.get_feature_investment_as_income_percentage(),
            self.get_feature_personal_status(),
            self.get_feature_other_debtors(),
            self.get_feature_residence_since(),
            self.get_feature_property(),
            self.get_feature_age(),
            self.get_feature_installment_plans(),
            self.get_feature_housing(),
            self.get_feature_number_of_credits(),
            self.get_feature_skill_level(),
            self.get_feature_people_liable_for(),
            self.get_feature_telephone(),
            self.get_feature_foreign_worker(),
            self.get_feature_credit()
        ]

        super().__init__(name, target_class, sensitive_classes, features, test_size, oversampling_factor,
                         safe_weight, borderline_weight, rare_weight)

    def create_raw_transformed_dataset(self):
        raw_dataset = self.get_raw_dataset()

        old = raw_dataset['age'] >= 25  # http://ieeexplore.ieee.org/document/4909197/
        raw_dataset.loc[old, 'age'] = "adult"
        young = raw_dataset['age'] != "adult"
        raw_dataset.loc[young, 'age'] = "young"

        positive = raw_dataset['credit'] == 1
        raw_dataset.loc[positive, 'credit'] = "Positive"
        negative = raw_dataset['credit'] == 2
        raw_dataset.loc[negative, 'credit'] = "Negative"

        raw_transformed_dataset_filename = self.get_raw_transformed_dataset_filename()
        f = open(raw_transformed_dataset_filename, "w+")
        f.write(raw_dataset.to_csv(index=False))
        f.close()

    @staticmethod
    def get_feature_status():
        name = "status"
        order = ["A11", "A14", "A12", "A13"]
        feature_type = FeatureTypeOrdinal(order)
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_month():
        name = "month"
        feature_type = FeatureTypeContinuous()
        should_standardize = True

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_credit_history():
        name = "credit_history"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_purpose():
        name = "purpose"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_credit_amount():
        name = "credit_amount"
        feature_type = FeatureTypeContinuous()
        should_standardize = True

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_savings():
        name = "savings"
        order = ["A65", "A61", "A62", "A63", "A64"]
        feature_type = FeatureTypeOrdinal(order)
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_employment():
        name = "employment"
        order = ["A71", "A72", "A73", "A74", "A75"]
        feature_type = FeatureTypeOrdinal(order)
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_investment_as_income_percentage():
        name = "investment_as_income_percentage"
        feature_type = FeatureTypeContinuous()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_personal_status():
        name = "personal_status"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_other_debtors():
        name = "other_debtors"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_residence_since():
        name = "residence_since"
        feature_type = FeatureTypeContinuous()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_property():
        name = "property"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_age():
        name = "age"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_installment_plans():
        name = "installment_plans"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_housing():
        name = "housing"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_number_of_credits():
        name = "number_of_credits"
        feature_type = FeatureTypeContinuous()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_skill_level():
        name = "skill_level"
        order = ["A171", "A172", "A173", "A174"]
        feature_type = FeatureTypeOrdinal(order)
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_people_liable_for():
        name = "people_liable_for"
        feature_type = FeatureTypeContinuous()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_telephone():
        name = "telephone"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_foreign_worker():
        name = "foreign_worker"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

    @staticmethod
    def get_feature_credit():
        name = "credit"
        feature_type = FeatureTypeCategorical()
        should_standardize = False

        return Feature(name, feature_type, should_standardize)

# https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data) # http://ieeexplore.ieee.org/document/4909197/