Set my folder as the main source of truth

Long-Term-Care-Aggregated-data.py

@@ -1,179 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Long-Term-Care-Aggregated-Data.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/14YdgB8b4TtNetbHpTstGH7W4DLa3Yyxq
-"""
-
-from datasets import GeneratorBasedBuilder, DownloadManager, DatasetInfo, Array3D, BuilderConfig, SplitGenerator, Version
-from datasets.features import Features, Value, Sequence
-import datasets
-import pandas as pd
-import json
-import zipfile
-import numpy as np
-import io
-import csv
-import json
-import os
-from typing import List
-import datasets
-import logging
-
-_CITATION = """\
-@misc{long_term_care_aggregated_dataset,
-title = {Long-Term Care Aggregated Dataset},
-author = {Kao, Hsuan-Chen (Justin)},
-year = {2024},
-publisher = {Hugging Face},
-url = {https://github.com/justinkao44/STA663_Project_1},
-}
-"""
-
-_DESCRIPTION = """\
-The Long-Term Care Aggregated Dataset is a collection of insurance data for 'incidence' and 'termination' categories.
-It is compiled from Long Term Care insurance policies data, providing insights into trends and patterns in insurance claims
-and terminations. This dataset can be used for actuarial analysis, risk assessment, and to inform insurance product development.
-"""
-
-_HOMEPAGE = "https://github.com/justinkao44/STA663_Project_1"
-
-_LICENSE = "Apache-2.0"
-
-_URLS = {
-    "train_incidence": "https://github.com/justinkao44/STA663_Project_1/raw/main/train_filtered_incidence_df.zip",
-    "train_termination": "https://github.com/justinkao44/STA663_Project_1/raw/main/train_filtered_termination_df.zip",
-    "validation_incidence": "https://github.com/justinkao44/STA663_Project_1/raw/main/validation_filtered_incidence_df.zip",
-    "validation_termination": "https://github.com/justinkao44/STA663_Project_1/raw/main/validation_filtered_termination_df.zip",
-}
-
-
-class LongTermCareAggregatedData(datasets.GeneratorBasedBuilder):
-    """Dataset for insurance 'incidence' and 'termination' data."""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="incidence", version=datasets.Version("1.0.0"), description="This part of the dataset includes incidence features"),
-        datasets.BuilderConfig(name="termination", version=datasets.Version("1.0.0"), description="This part of the dataset includes termination features"),
-    ]
-
-    def _info(self):
-        if self.config.name == "incidence":
-            features = datasets.Features({
-                "Group_Indicator": datasets.Value("string"),
-                "Gender": datasets.Value("string"),
-                "Issue_Age_Bucket": datasets.Value("string"),
-                "Incurred_Age_Bucket": datasets.Value("string"),
-                "Issue_Year_Bucket": datasets.Value("string"),
-                "Policy_Year": datasets.Value("string"),
-                "Marital_Status": datasets.Value("string"),
-                "Premium_Class": datasets.Value("string"),
-                "Underwriting_Type": datasets.Value("string"),
-                "Coverage_Type_Bucket": datasets.Value("string"),
-                "Tax_Qualification_Status": datasets.Value("string"),
-                "Inflation_Rider": datasets.Value("string"),
-                "Rate_Increase_Flag": datasets.Value("string"),
-                "Restoration_of_Benefits": datasets.Value("string"),
-                "NH_Orig_Daily_Ben_Bucket": datasets.Value("string"),
-                "ALF_Orig_Daily_Ben_Bucket": datasets.Value("string"),
-                "HHC_Orig_Daily_Ben_Bucket": datasets.Value("string"),
-                "NH_Ben_Period_Bucket": datasets.Value("string"),
-                "ALF_Ben_Period_Bucket": datasets.Value("string"),
-                "HHC_Ben_Period_Bucket": datasets.Value("string"),
-                "NH_EP_Bucket": datasets.Value("string"),
-                "ALF_EP_Bucket": datasets.Value("string"),
-                "HHC_EP_Bucket": datasets.Value("string"),
-                "Region": datasets.Value("string"),
-                "Active_Exposure": datasets.Value("float64"),
-                "Total_Exposure": datasets.Value("float64"),
-                "Claim_Count": datasets.Value("int32"),
-                "Count_NH": datasets.Value("int32"),
-                "Count_ALF": datasets.Value("int32"),
-                "Count_HHC": datasets.Value("int32"),
-                "Count_Unk": datasets.Value("int32"),
-            })
-        elif self.config.name == "termination":
-            features = datasets.Features({
-                "Gender": datasets.Value("string"),
-                "Incurred_Age_Bucket": datasets.Value("string"),
-                "Incurred_Year_Bucket": datasets.Value("string"),
-                "Claim_Type": datasets.Value("string"),
-                "Region": datasets.Value("string"),
-                "Diagnosis_Category": datasets.Value("string"),
-                "Claim_Duration": datasets.Value("string"),
-                "Exposure": datasets.Value("string"),
-                "Deaths": datasets.Value("string"),
-                "Recovery": datasets.Value("string"),
-                "Terminations": datasets.Value("string"),
-                "Benefit_Expiry": datasets.Value("string"),
-                "Others_Terminations": datasets.Value("string"),
-            })
-        else:
-            raise ValueError(f"BuilderConfig name not recognized: {self.config.name}")
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage="https://www.soa.org/resources/experience-studies/2020/2000-2016-ltc-aggregate-database/",
-            citation="Please cite this dataset as: Society of Actuaries (SOA). (2020). Long Term Care Insurance Aggregate Experience Data, 2000-2016."
-        )
-
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download(_URLS)
-
-        train_file = downloaded_files[f"train_{self.config.name}"]
-        validation_file = downloaded_files[f"validation_{self.config.name}"]
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "data_file": train_file,
-                    "split": self.config.name,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "data_file": validation_file,
-                    "split": self.config.name,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, data_file, split):
-        with zipfile.ZipFile(data_file, 'r') as zip_ref:
-            # Assume each zip contains only one CSV file
-            csv_filename = zip_ref.namelist()[0]
-            with zip_ref.open(csv_filename) as csv_file:
-                dataframe = pd.read_csv(io.TextIOWrapper(csv_file))
-
-        feature_columns = self._get_feature_columns_by_config()
-
-        for idx, row in dataframe.iterrows():
-            feature_dict = {column: row[column] for column in feature_columns if column in dataframe.columns}
-            yield idx, feature_dict
-
-    def _get_feature_columns_by_config(self):
-        if self.config.name == "incidence":
-            return [
-                "Group_Indicator", "Gender", "Issue_Age_Bucket", "Incurred_Age_Bucket",
-                "Issue_Year_Bucket", "Policy_Year", "Marital_Status", "Premium_Class",
-                "Underwriting_Type", "Coverage_Type_Bucket", "Tax_Qualification_Status",
-                "Inflation_Rider", "Rate_Increase_Flag", "Restoration_of_Benefits",
-                "NH_Orig_Daily_Ben_Bucket", "ALF_Orig_Daily_Ben_Bucket", "HHC_Orig_Daily_Ben_Bucket",
-                "NH_Ben_Period_Bucket", "ALF_Ben_Period_Bucket", "HHC_Ben_Period_Bucket",
-                "NH_EP_Bucket", "ALF_EP_Bucket", "HHC_EP_Bucket", "Region",
-                "Active_Exposure", "Total_Exposure", "Claim_Count", "Count_NH", "Count_ALF", "Count_HHC", "Count_Unk",
-            ]
-        elif self.config.name == "termination":
-            return [
-                "Gender", "Incurred_Age_Bucket", "Incurred_Year_Bucket", "Claim_Type",
-                "Region", "Diagnosis_Category", "Claim_Duration", "Exposure", "Deaths",
-                "Recovery", "Terminations", "Benefit_Expiry", "Others_Terminations",
-            ]
-        else:
-            raise ValueError(f"Config name not recognized: {self.config.name}")