Datasets:
Upload german.py
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german.py
CHANGED
@@ -1,4 +1,4 @@
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"""
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from typing import List
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@@ -54,13 +54,83 @@ _BASE_FEATURE_NAMES = [
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"is_foreign",
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"loan_granted"
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]
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DESCRIPTION = "
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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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"""
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# Dataset info
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urls_per_split = {
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@@ -101,19 +171,19 @@ features_types_per_config = {
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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
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def __init__(self, **kwargs):
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super(
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self.features = features_per_config[kwargs["name"]]
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class
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# dataset versions
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DEFAULT_CONFIG = "loan"
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BUILDER_CONFIGS = [
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description="Encoding dictionaries for discrete features."),
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description="Binary classification of loan approval."),
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]
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@@ -135,8 +205,12 @@ class Breast(datasets.GeneratorBasedBuilder):
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]
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def _generate_examples(self, filepath: str):
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if self.config.name
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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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@@ -147,54 +221,11 @@ class Breast(datasets.GeneratorBasedBuilder):
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yield row_id, data_row
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def
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self.employed_since_encode_dic(),
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self.guarantors_encode_dic(),
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self.has_registered_phone_number_encode_dic(),
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self.housing_status_encode_dic(),
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self.installment_plans_encode_dic(),
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self.is_foreign_encode_dic(),
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self.job_status_encode_dic(),
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self.marital_status_encode_dic(),
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self.sex_encode_dic(),
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])
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data = [[(feature, value, code) for value, code in d.items()]
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for feature, d in zip(["checking_account_status",
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"credit_status",
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"current_savings",
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"employed_since",
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"guarantors",
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"has_registered_phone_number",
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"housing_status",
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"installment_plans",
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"is_foreign",
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"job_status",
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"marital_status",
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"sex"],
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dictionaries)]
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full_data = list()
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for d in data:
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full_data += d
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data = pandas.DataFrame(full_data,
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columns=["feature", "original_value", "encoded_value"])
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return data
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def preprocess(self, data: pandas.DataFrame, config: str = "cancer") -> pandas.DataFrame:
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data.loc[:, "checking_account_status"] = data.checking_account_status.apply(self.encode_checking_account_status)
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data.loc[:, "credit_status"] = data.credit_status.apply(self.encode_credit_status)
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data.loc[:, "current_savings"] = data.current_savings.apply(self.encode_current_savings)
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data.loc[:, "employed_since"] = data.employed_since.apply(self.encode_employed_since)
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data.loc[:, "guarantors"] = data.guarantors.apply(self.encode_guarantors)
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data.loc[:, "has_registered_phone_number"] = data.has_registered_phone_number.apply(self.encode_has_registered_phone_number)
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data.loc[:, "housing_status"] = data.housing_status.apply(self.encode_housing_status)
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data.loc[:, "installment_plans"] = data.installment_plans.apply(self.encode_installment_plans)
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data.loc[:, "is_foreign"] = data.is_foreign.apply(self.encode_is_foreign)
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data.loc[:, "job_status"] = data.job_status.apply(self.encode_job_status)
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data.loc[:, "marital_status"] = data.personal_status_and_sex.apply(self.encode_marital_status)
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data.loc[:, "sex"] = data.personal_status_and_sex.apply(self.encode_sex)
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data.loc[:, "loan_purpose"] = data.loan_purpose.apply(self.encode_loan_purpose)
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@@ -202,274 +233,11 @@ class Breast(datasets.GeneratorBasedBuilder):
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data.drop("personal_status_and_sex", axis="columns", inplace=True)
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print(data.sex.unique())
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data = data[_BASE_FEATURE_NAMES]
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raise ValueError(f"Unknown config: {config}")
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def encode_checking_account_status(self, status):
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return self.checking_account_status_encode_dic()[status]
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def checking_account_status_encode_dic(self):
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return {
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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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def decode_checking_account_status(self, code):
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return self.checking_account_status_decode_dic()[code]
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def checking_account_status_decode_dic(self):
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return {
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0: "A14",
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1: "A11",
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2: "A12",
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3: "A13",
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}
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def encode_credit_status(self, status):
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return self.credit_status_encode_dic()[status]
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def credit_status_encode_dic(self):
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return {
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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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def decode_credit_status(self, code):
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return self.credit_status_decode_dic()[code]
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def credit_status_decode_dic(self):
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return {
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0: "A30",
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1: "A31",
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2: "A32",
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3: "A33",
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4: "A34",
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}
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def encode_current_savings(self, status):
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return self.current_savings_encode_dic()[status]
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def current_savings_encode_dic(self):
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return {
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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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def decode_current_savings(self, code):
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return self.current_savings_decode_dic()[code]
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def current_savings_decode_dic(self):
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return {
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0: "A65",
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1: "A61",
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2: "A62",
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3: "A63",
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4: "A64",
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}
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def encode_employed_since(self, status):
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return self.employed_since_encode_dic()[status]
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def employed_since_encode_dic(self):
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return {
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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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def decode_employed_since(self, code):
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return self.employed_since_decode_dic()[code]
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def employed_since_decode_dic(self):
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return {
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0: "A71",
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1: "A72",
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2: "A73",
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3: "A74",
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4: "A75",
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}
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def encode_sex(self, status):
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return self.sex_encode_dic()[status]
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def sex_encode_dic(self):
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return {
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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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def decode_sex(self, code):
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return self.sex_decode_dic()[code]
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def sex_decode_dic(self):
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return {
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0: "A91",
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1: "A92",
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}
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def encode_marital_status(self, status):
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return self.marital_status_encode_dic()[status]
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def marital_status_encode_dic(self):
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return {
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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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def decode_marital_status(self, code):
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return self.marital_status_decode_dic()[code]
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def marital_status_decode_dic(self):
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return {
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0: "A91",
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1: "A93",
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2: "A94",
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}
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def encode_guarantors(self, status):
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return self.guarantors_encode_dic()[status]
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def guarantors_encode_dic(self):
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return {
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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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def decode_guarantors(self, code):
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return self.guarantors_decode_dic()[code]
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def guarantors_decode_dic(self):
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return {
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0: "A101",
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1: "A102",
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2: "A103",
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}
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def encode_installment_plans(self, status):
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return self.installment_plans_encode_dic()[status]
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def installment_plans_encode_dic(self):
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return {
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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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def decode_installment_plans(self, code):
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return self.installment_plans_decode_dic()[code]
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def installment_plans_decode_dic(self):
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return {
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0: "A141",
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1: "A142",
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2: "A143",
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}
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def encode_housing_status(self, status):
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return self.housing_status_encode_dic()[status]
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def housing_status_encode_dic(self):
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return {
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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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def decode_housing_status(self, code):
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return self.housing_status_decode_dic()[code]
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def housing_status_decode_dic(self):
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return {
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0: "A153",
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1: "A151",
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2: "A152",
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}
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def encode_job_status(self, status):
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return self.job_status_encode_dic()[status]
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def job_status_encode_dic(self):
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return {
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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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def decode_job_status(self, code):
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return self.job_status_decode_dic()[code]
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def job_status_decode_dic(self):
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return {
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0: "A171",
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1: "A172",
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2: "A173",
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3: "A174",
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}
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def encode_has_registered_phone_number(self, status):
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return self.has_registered_phone_number_encode_dic()[status]
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def has_registered_phone_number_encode_dic(self):
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return {
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"A191": 0,
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"A192": 1,
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}
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def decode_has_registered_phone_number(self, code):
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return self.has_registered_phone_number_decode_dic()[code]
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def has_registered_phone_number_decode_dic(self):
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return {
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0: "A191",
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1: "A192",
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}
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def encode_is_foreign(self, status):
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return self.is_foreign_encode_dic()[status]
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def is_foreign_encode_dic(self):
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return {
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"A201": 0,
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"A202": 1,
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}
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def decode_is_foreign(self, code):
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return self.is_foreign_decode_dic()[code]
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def is_foreign_decode_dic(self):
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return {
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0: "A201",
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1: "A202",
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}
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def encode_loan_purpose(self, status):
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return self.loan_purpose_encode_dic()[status]
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def loan_purpose_encode_dic(self):
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return {
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"A40": "new car",
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"A41": "used car",
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"A48": "retraining",
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"A49": "business",
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"A410": "others"
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}
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def decode_loan_purpose(self, code):
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return self.loan_purpose_decode_dic()[code]
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def loan_purpose_decode_dic(self):
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return {
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"new car": "A40",
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"used car": "A41",
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"furniture/equipment": "A42",
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"radio/television": "A43",
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"domestic appliances": "A44",
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"repairs": "A45",
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"education": "A46",
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"vacation ": "A47",
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"retraining": "A48",
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"business": "A49",
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"others": "A410"
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}
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"""German Dataset"""
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2 |
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3 |
from typing import List
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4 |
|
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54 |
"is_foreign",
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55 |
"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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73 |
+
"A153": 0,
|
74 |
+
"A151": 1,
|
75 |
+
"A152": 2
|
76 |
+
},
|
77 |
+
"installment_plans": {
|
78 |
+
"A141": 0,
|
79 |
+
"A142": 1,
|
80 |
+
"A143": 2
|
81 |
+
},
|
82 |
+
"guarantors": {
|
83 |
+
"A101": 0,
|
84 |
+
"A102": 1,
|
85 |
+
"A103": 2
|
86 |
+
},
|
87 |
+
"marital_status": {
|
88 |
+
"A91": 0,
|
89 |
+
"A92": 0,
|
90 |
+
"A93": 1,
|
91 |
+
"A94": 2,
|
92 |
+
"A95": 1,
|
93 |
+
},
|
94 |
+
"sex": {
|
95 |
+
"A91": 0,
|
96 |
+
"A93": 0,
|
97 |
+
"A94": 0,
|
98 |
+
"A92": 1,
|
99 |
+
"A95": 1,
|
100 |
+
},
|
101 |
+
"employed_since": {
|
102 |
+
"A71": 0,
|
103 |
+
"A72": 1,
|
104 |
+
"A73": 2,
|
105 |
+
"A74": 3,
|
106 |
+
"A75": 4,
|
107 |
+
},
|
108 |
+
"current_savings": {
|
109 |
+
"A65": 0,
|
110 |
+
"A61": 1,
|
111 |
+
"A62": 2,
|
112 |
+
"A63": 3,
|
113 |
+
"A64": 4,
|
114 |
+
},
|
115 |
+
"credit_status": {
|
116 |
+
"A30": 0,
|
117 |
+
"A31": 1,
|
118 |
+
"A32": 2,
|
119 |
+
"A33": 3,
|
120 |
+
"A34": 4,
|
121 |
+
},
|
122 |
+
"checking_account_status": {
|
123 |
+
"A14": 0,
|
124 |
+
"A11": 1,
|
125 |
+
"A12": 2,
|
126 |
+
"A13": 3,
|
127 |
+
}
|
128 |
+
}
|
129 |
|
130 |
+
DESCRIPTION = "German dataset for cancer prediction."
|
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29"
|
132 |
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29")
|
133 |
+
_CITATION = """"""
|
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|
134 |
|
135 |
# Dataset info
|
136 |
urls_per_split = {
|
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|
171 |
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
|
172 |
|
173 |
|
174 |
+
class GermanConfig(datasets.BuilderConfig):
|
175 |
def __init__(self, **kwargs):
|
176 |
+
super(GermanConfig, self).__init__(version=VERSION, **kwargs)
|
177 |
self.features = features_per_config[kwargs["name"]]
|
178 |
|
179 |
|
180 |
+
class German(datasets.GeneratorBasedBuilder):
|
181 |
# dataset versions
|
182 |
DEFAULT_CONFIG = "loan"
|
183 |
BUILDER_CONFIGS = [
|
184 |
+
GermanConfig(name="encoding",
|
185 |
description="Encoding dictionaries for discrete features."),
|
186 |
+
GermanConfig(name="loan",
|
187 |
description="Binary classification of loan approval."),
|
188 |
]
|
189 |
|
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|
205 |
]
|
206 |
|
207 |
def _generate_examples(self, filepath: str):
|
208 |
+
if self.config.name not in self.features_per_config:
|
209 |
+
raise ValueError(f"Unknown config: {self.config.name}")
|
210 |
+
|
211 |
+
elif self.config.name == "encoding":
|
212 |
+
data = self.encoding_dics()
|
213 |
+
|
214 |
else:
|
215 |
data = pandas.read_csv(filepath, sep=" ", header=None)
|
216 |
data.columns=_ORIGINAL_FEATURE_NAMES
|
|
|
221 |
|
222 |
yield row_id, data_row
|
223 |
|
224 |
+
def preprocess(self, data: pandas.DataFrame, config: str = "loan") -> pandas.DataFrame:
|
225 |
+
for feature in _ENCODING_DICS:
|
226 |
+
if feature not in ["marital_status", "sex"]:
|
227 |
+
encoding_function = partial(self.encode, feature)
|
228 |
+
data.loc[:, feature] = data[feature].apply(encoding_function)
|
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|
229 |
data.loc[:, "marital_status"] = data.personal_status_and_sex.apply(self.encode_marital_status)
|
230 |
data.loc[:, "sex"] = data.personal_status_and_sex.apply(self.encode_sex)
|
231 |
data.loc[:, "loan_purpose"] = data.loan_purpose.apply(self.encode_loan_purpose)
|
|
|
233 |
|
234 |
data.drop("personal_status_and_sex", axis="columns", inplace=True)
|
235 |
|
|
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|
|
236 |
data = data[_BASE_FEATURE_NAMES]
|
237 |
|
238 |
+
return data
|
239 |
+
|
240 |
+
def encode_loan_purpose(self, code):
|
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|
241 |
return {
|
242 |
"A40": "new car",
|
243 |
"A41": "used car",
|
|
|
250 |
"A48": "retraining",
|
251 |
"A49": "business",
|
252 |
"A410": "others"
|
253 |
+
}[code]
|
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