Set my folder as the main source of truth

Long-Term-Care-Aggregated-data.py

@@ -0,0 +1,180 @@
+# -*- 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
+from PIL import Image
+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}")