updated to datasets 4.*

README.md

@@ -1,18 +1,20 @@
 ---
-language:
-- en
+configs:
+- config_name: default
+  data_files:
+  - path: default/train.csv
+    split: train
+  default: true
+language: en
+license: cc
+pretty_name: Nbfi
+size_categories: 1M<n<10M
 tags:
-- nbfi
 - tabular_classification
 - binary_classification
-pretty_name: NBFI
-size_categories:
-- 1K<n<10K
+- multiclass_classification
 task_categories:
 - tabular-classification
-configs:
-- default
-license: cc
 ---
 # NBFI
 The [NBFI dataset](https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset) from the [Kaggle](https://www.kaggle.com/datasets).

Test_Dataset.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fce61c89339cc849186d4e9964c8d154c933359ea51e5c8d401c4312040aa137
-size 15229326

Train_Dataset.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bfaa92b0e2bf3cb523c2c287d00502328bdc19366cdb2a49486c58ce7cae5e33
-size 23193688

nbfi.py

@@ -1,264 +0,0 @@
-"""NBFI"""
-
-from typing import List
-from functools import partial
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-_ORIGINAL_FEATURE_NAMES = [
-    "ID",
-    "Client_Income",
-    "Car_Owned",
-    "Bike_Owned",
-    "Active_Loan",
-    "House_Own",
-    "Child_Count",
-    "Credit_Amount",
-    "Loan_Annuity",
-    "Accompany_Client",
-    "Client_Income_Type",
-    "Client_Education",
-    "Client_Marital_Status",
-    "Client_Gender",
-    "Loan_Contract_Type",
-    "Client_Housing_Type",
-    "Population_Region_Relative",
-    "Age_Days",
-    "Employed_Days",
-    "Registration_Days",
-    "ID_Days",
-    "Own_House_Age",
-    "Mobile_Tag",
-    "Homephone_Tag",
-    "Workphone_Working",
-    "Client_Occupation",
-    "Client_Family_Members",
-    "Cleint_City_Rating",
-    "Application_Process_Day",
-    "Application_Process_Hour",
-    "Client_Permanent_Match_Tag",
-    "Client_Contact_Work_Tag",
-    "Type_Organization",
-    "Score_Source_1",
-    "Score_Source_2",
-    "Score_Source_3",
-    "Social_Circle_Default",
-    "Phone_Change",
-    "Credit_Bureau",
-    "Default"
-]
-_BASE_FEATURE_NAMES = [
-    "income",
-    "owns_a_car",
-    "owns_a_bike",
-    "has_an_active_loan",
-    "owns_a_house",
-    "nr_children",
-    "credit",
-    "loan_annuity",
-    "accompanied_by",
-    "income_type",
-    "education_level",
-    "marital_status",
-    "is_male",
-    "type_of_contract",
-    "type_of_housing",
-    "residence_density",
-    "age_in_days",
-    "consecutive_days_of_employment",
-    "nr_days_since_last_registration_change",
-    "nr_days_since_last_document_change",
-    "has_provided_a_mobile_number",
-    "has_provided_a_home_number",
-    "was_reachable_at_work",
-    "job",
-    "nr_family_members",
-    "city_rating",
-    "weekday_of_application",
-    "hour_of_application",
-    "same_residence_and_home",
-    "same_work_and_home",
-    "score_1",
-    "score_2",
-    "score_3",
-    "nr_defaults_in_social_circle",
-    "inquiries_in_last_year",
-    "has_defaulted"
-]
-features_types_per_config = {
-    "default": {
-        "income": datasets.Value("float32"),
-        "owns_a_car": datasets.Value("bool"),
-        "owns_a_bike": datasets.Value("bool"),
-        "has_an_active_loan": datasets.Value("bool"),
-        "owns_a_house": datasets.Value("bool"),
-        "nr_children": datasets.Value("int8"),
-        "credit": datasets.Value("float32"),
-        "loan_annuity": datasets.Value("float32"),
-        "accompanied_by": datasets.Value("string"),
-        "income_type": datasets.Value("string"),
-        "education_level": datasets.Value("float32"),
-        "marital_status": datasets.Value("string"),
-        "is_male": datasets.Value("bool"),
-        "type_of_contract": datasets.Value("string"),
-        "type_of_housing": datasets.Value("string"),
-        "residence_density": datasets.Value("float32"),
-        "age_in_days": datasets.Value("int32"),
-        "consecutive_days_of_employment": datasets.Value("int16"),
-        "nr_days_since_last_registration_change": datasets.Value("int32"),
-        "nr_days_since_last_document_change": datasets.Value("int32"),
-        "has_provided_a_mobile_number": datasets.Value("bool"),
-        "has_provided_a_home_number": datasets.Value("bool"),
-        "was_reachable_at_work": datasets.Value("bool"),
-        "job": datasets.Value("string"),
-        "nr_family_members": datasets.Value("int8"),
-        "city_rating": datasets.Value("int8"),
-        "weekday_of_application": datasets.Value("int8"),
-        "hour_of_application": datasets.Value("float32"),
-        "same_residence_and_home": datasets.Value("bool"),
-        "same_work_and_home": datasets.Value("bool"),
-        "score_1": datasets.Value("float32"),
-        "score_2": datasets.Value("float32"),
-        "score_3": datasets.Value("float32"),
-        "nr_defaults_in_social_circle": datasets.Value("bool"),
-        "inquiries_in_last_year": datasets.Value("float32"),
-        "has_defaulted": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-    }
-}
-_ENCODING_DICS = {}
-_EDUCATION_ENCODING = {
-    "Junior secondary": 0,
-    "Secondary": 1,
-    "Graduation dropout": 2,
-    "Graduation": 2,
-    "Post Grad": 4
-}    
-
-DESCRIPTION = "NBFI dataset from default prediction."
-_HOMEPAGE = "https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset"
-_URLS = ("https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset")
-_CITATION = """"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://gist.githubusercontent.com/msetzu/6c83dc3b7092d428ae2f08dc91e1020c/raw/9fc3171b293d0dc29963357450308eb4c7e3a15b/Train_Dataset.csv"
-}
-
-features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
-
-
-class NBFIConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(NBFIConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class NBFI(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "default"
-    BUILDER_CONFIGS = [
-        NBFIConfig(name="default",
-                    description="NBFI for default binary classification.")
-    ]
-
-
-    def _info(self):
-        if self.config.name not in features_per_config:
-            raise ValueError(f"Unknown configuration: {self.config.name}")
-        
-        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
-                                    features=features_per_config[self.config.name])
-
-        return info
-    
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        downloads = dl_manager.download_and_extract(urls_per_split)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
-        ]
-    
-    def _generate_examples(self, filepath: str):
-        if self.config.name == "default":
-            data = pandas.read_csv(filepath)
-            data = self.preprocess(data, config=self.config.name)
-
-            for row_id, row in data.iterrows():
-                data_row = dict(row)
-
-                yield row_id, data_row
-        else:
-            raise ValueError(f"Unknown config: {self.config.name}")
-
-
-
-    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
-        data.drop("ID", axis="columns", inplace=True)
-        data.drop("Own_House_Age", axis="columns", inplace=True)
-        data.drop("Type_Organization", axis="columns", inplace=True)
-        data.drop("Phone_Change", axis="columns", inplace=True)
-
-        data = data[~data.Client_Income.isna()]
-        data = data[~data.Client_Education.isna()]
-        data = data[~data.Child_Count.isna()]
-        data = data[~data.Client_Marital_Status.isna()]
-        data = data[~data.Client_Gender.isna()]
-        data = data[~data.Loan_Contract_Type.isna()]
-        data = data[~data.Client_Housing_Type.isna()]
-        data = data[~data.Age_Days.isna()]
-        data = data[~data.Employed_Days.isna()]
-        data = data[~data.Registration_Days.isna()]
-        data = data[~data.ID_Days.isna()]
-        data = data[~data.Cleint_City_Rating.isna()]
-        data = data[~data.Application_Process_Day.isna()]
-        data = data[~data.Application_Process_Hour.isna()]
-        data = data[~data.Client_Permanent_Match_Tag.isna()]
-        data = data[~data.Client_Contact_Work_Tag.isna()]
-        data = data[~data.Score_Source_1.isna()]
-        data = data[~data.Score_Source_2.isna()]
-        data = data[~data.Score_Source_3.isna()]
-        data = data[~data.Credit_Bureau.isna()]
-        data = data[~data.Credit_Amount.isna()]
-        data = data[~data.Loan_Annuity.isna()]
-        data = data[~data.Accompany_Client.isna()]
-        data = data[~data.Client_Occupation.isna()]
-        data = data[~data.Client_Family_Members.isna()]
-        data = data[~data.Social_Circle_Default.isna()]
-        data = data[~data.Population_Region_Relative.isin(("@", "#"))]
-        data = data[~data.Population_Region_Relative.isna()]
-        data = data[data.Loan_Annuity != "#VALUE!"]
-        data = data[data.Age_Days != "x"]
-        data = data[data.Employed_Days != "x"]
-        data = data[data.Registration_Days != "x"]
-        data = data[data.ID_Days != "x"]
-        
-        data.columns = _BASE_FEATURE_NAMES
-
-        data["education_level"] = data["education_level"].apply(lambda x: _EDUCATION_ENCODING[x])
-        data["is_male"] = data["is_male"].apply(lambda x: x == "M")
-        data["owns_a_car"] = data["owns_a_car"].apply(bool)
-        data["owns_a_bike"] = data["owns_a_bike"].apply(bool)
-        data["has_an_active_loan"] = data["has_an_active_loan"].apply(bool)
-        data["owns_a_house"] = data["owns_a_house"].apply(bool)
-        data["is_male"] = data["is_male"].apply(bool)
-        data["has_provided_a_mobile_number"] = data["has_provided_a_mobile_number"].apply(bool)
-        data["has_provided_a_home_number"] = data["has_provided_a_home_number"].apply(bool)
-        data["was_reachable_at_work"] = data["was_reachable_at_work"].apply(bool)
-        data["same_residence_and_home"] = data["same_residence_and_home"].apply(bool)
-        data["same_work_and_home"] = data["same_work_and_home"].apply(bool)
-        data["nr_defaults_in_social_circle"] = data["same_work_and_home"].apply(bool)
-        data["residence_density"] = data["residence_density"].apply(float)
-        
-        data = data.astype({
-            "is_male": "bool",
-            "nr_children": "int8"
-        })
-        
-
-
-        return data[list(features_types_per_config[config].keys())]
-